diff --git a/explanation/explanation.md b/explanation/explanation.md index e0966aa..906065f 100644 --- a/explanation/explanation.md +++ b/explanation/explanation.md @@ -2,8 +2,10 @@ A lot more details and discussion about the problem context and approaches of GreatAI along with its evaluation can be found in my thesis. -![front page](/media/thesis-frontpage.png) +
+ front page +
-[::fontawesome-solid-graduation-cap: Download (available soon)](thesis/main.pdf){ .md-button .md-button--primary } +[::fontawesome-solid-graduation-cap: Download](thesis/main.pdf){ .md-button .md-button--primary download="greatai_schmelczer.pdf" }
diff --git a/explanation/index.html b/explanation/index.html index 8fe8fd2..9f5edce 100644 --- a/explanation/index.html +++ b/explanation/index.html @@ -832,9 +832,12 @@

Explanation#

A lot more details and discussion about the problem context and approaches of GreatAI along with its evaluation can be found in my thesis.

-

front page

+
+ front page +
+
-

: Download (available soon)

+

: Download

@@ -842,7 +845,7 @@ Last update: - July 12, 2022 + August 6, 2022 diff --git a/media/thesis-frontpage.png b/media/thesis-frontpage.png index 50be95c..577e713 100644 Binary files a/media/thesis-frontpage.png and b/media/thesis-frontpage.png differ diff --git a/search/search_index.json b/search/search_index.json index 6f86de8..14e3538 100644 --- a/search/search_index.json +++ b/search/search_index.json @@ -1 +1 @@ -{"config":{"indexing":"full","lang":["en"],"min_search_length":3,"prebuild_index":false,"separator":"[\\s\\-]+"},"docs":[{"location":"","text":"Overview of GreatAI Applying AI is becoming increasingly easier but many case studies have shown that these applications are often deployed poorly. This may lead to suboptimal performance and to introducing unintended biases . GreatAI helps fixing this by allowing you to easily transform your prototype AI code into production-ready software . Case studies \"There is a need to consider and adapt well established SE practices which have been ignored or had a very narrow focus in ML literature.\" \u2014 John et al. \"Finally, we have found that existing tools to aid Machine Learning development do not address the specificities of different projects, and thus, are seldom adopted by teams.\" \u2014 Haakman et al. \"Because a mature system might end up being (at most) 5% machine learning code and (at least) 95% glue code, it may be less costly to create a clean native solution rather than re-use a generic package.\" \u2014 Sculley et al. \"For example, practice 25 is very important for \u201cTraceability\", yet relatively weakly adopted. We expect that the results from this type of analysis can, in the future, provide useful guidance for practitioners in terms of aiding them to assess their rate of adoption for each practice and to create roadmaps for improving their processes. \u2014 Serban et al. Features # Save prediction traces of each prediction including arguments and model versions Save feedback and merge it into a ground-truth database Version and store models and data on shared infrastructure (MongoDB GridFS, S3-compatible storage, shared volume) Automatically scaffolded custom REST API (and OpenAPI schema) for easy integration Input validation Sensible cache-policy Graceful error handling Seamless support for both synchronous and asynchronous inference methods Easy integration with remote GreatAI instances Built-in parallelisation (with support for multiprocessing, async, and mixed modes) for batch processing Well-tested utilities for common NLP tasks (cleaning, language-tagging, sentence-segmentation, etc.) A simple, unified configuration interface Fully-typed API for Pylance and MyPy support Auto-reload for development Docker support for deployment Deployable Jupyter Notebooks Dashboard for online monitoring and analysing traces Active support for Python 3.7, 3.8, 3.9, and 3.10 Roadmap # Prometheus & Grafana integration Well-tested feature extraction code for non-NLP data Support for direct file input Support for PostgreSQL Hello world # pip install great-ai demo.py from great_ai import GreatAI @GreatAI . create #(1) def greeter ( name : str ) -> str : #(2) return f \"Hello { name } !\" @GreatAI.create wraps your greeter function with a GreatAI instance. The function will behave very similarly but: its return value becomes a Trace[str] , it gets a process_batch method for supporting parallel execution, and it can be deployed using the great-ai command-line tool. Typing functions is recommended in general, however, not required for GreatAI to work. Note In practice, greeter could be an inference function of some AI/ML application. But it could also just wrap a black-box solution of some SaaS. Either way, it is imperative to have continuous oversight of the services you provide and data you process especially in the context of AI/ML applications. terminal great-ai demo.py Navigate to localhost:6060 in your browser. Success Your GreatAI service is ready for production use. Many of the SE4ML best practices are configured and implemented automatically. To have full control over your service and to understand what else you might need to do in your use case, continue reading this documentation. Why is this GREAT? # GreatAI fits between the prototype and deployment phases of your (or your organisation's) AI development lifecycle. This is highlighted with blue in the diagram. Here, several best practices can be automatically implemented aiming to achieve the following attributes: G eneral: use any Python library without restriction R obust: have error-handling and well-tested utilities out-of-the-box E nd-to-end: utilise end-to-end feedback as a built-in, first-class concept A utomated: focus only on what actually requires your attention T rustworthy: deploy models that you and society can confidently trust Why GreatAI? # There are other, existing solutions aiming to facilitate this phase. Amazon SageMaker and Seldon Core provide the most comprehensive suite of features. If you have the opportunity to use them, do that because they're great. However, research indicates that professionals rarely use them. This may be due to their inherent setup and operating complexity. 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The subpages show great-ai in action by going over the life-cycle of finetuning and deploying a BERT-based software service. Propriety data The purpose of this example is to show you different ways in which great-ai can assist you. The exact NLP task being solved is not central. Stemming from this, and from the difficult nature of obtaining appropriate training data, the propriety dataset used for the experiments is not shared. Objectives # You will see how the great_ai.utilities can integrate into your Data Science workflow. You will see how great_ai.large_file can be used to version and store your trained model. You will see how GreatAI should be used prepare your model for a robust and responsible deployment. You will see multiple ways of customising your deployment. Overview # One of the core features of the ScoutinScience platform is summarising research papers from a tech-transfer perspective. In short, extractive summarisation is preferred using a binary classifier trained on clients' judgement of sentence interestingness. Thus, documents are sentences and the expected output is a binary label showing whether a sentence is \"worthy\" of being in the tech-transfer summary. Providing an explanation for each decision is imperative since ScoutinScience embraces applying only explainable AI (XAI) methods wherever feasible. Success You are ready to start the tutorial. Feel free to come back to the summary section once you're finished. Examine data Train model Deploy service Summary # Data notebook # We load and analyse the data by calculating inter-rater reliability and checking the feasibility of using an AI-based approach by testing the accuracy of a trivial baseline method. Training notebook # We simply fine-tune SciBERT. After training and evaluating a model, it is exported using great_ai.save_model . For more info, checkout the configuration how-to page . 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These particular numbers are taken from here: * * https://github.com/material-components/material-components-web * https://material-components-web.appspot.com/elevation.html */ --jp-shadow-base-lightness: 0; --jp-shadow-umbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.2 ); --jp-shadow-penumbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.14 ); --jp-shadow-ambient-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.12 ); --jp-elevation-z0: none; --jp-elevation-z1: 0px 2px 1px -1px var(--jp-shadow-umbra-color), 0px 1px 1px 0px var(--jp-shadow-penumbra-color), 0px 1px 3px 0px var(--jp-shadow-ambient-color); --jp-elevation-z2: 0px 3px 1px -2px var(--jp-shadow-umbra-color), 0px 2px 2px 0px var(--jp-shadow-penumbra-color), 0px 1px 5px 0px var(--jp-shadow-ambient-color); --jp-elevation-z4: 0px 2px 4px -1px var(--jp-shadow-umbra-color), 0px 4px 5px 0px var(--jp-shadow-penumbra-color), 0px 1px 10px 0px var(--jp-shadow-ambient-color); --jp-elevation-z6: 0px 3px 5px -1px var(--jp-shadow-umbra-color), 0px 6px 10px 0px var(--jp-shadow-penumbra-color), 0px 1px 18px 0px var(--jp-shadow-ambient-color); --jp-elevation-z8: 0px 5px 5px -3px var(--jp-shadow-umbra-color), 0px 8px 10px 1px var(--jp-shadow-penumbra-color), 0px 3px 14px 2px var(--jp-shadow-ambient-color); --jp-elevation-z12: 0px 7px 8px -4px var(--jp-shadow-umbra-color), 0px 12px 17px 2px var(--jp-shadow-penumbra-color), 0px 5px 22px 4px var(--jp-shadow-ambient-color); --jp-elevation-z16: 0px 8px 10px -5px var(--jp-shadow-umbra-color), 0px 16px 24px 2px var(--jp-shadow-penumbra-color), 0px 6px 30px 5px var(--jp-shadow-ambient-color); --jp-elevation-z20: 0px 10px 13px -6px var(--jp-shadow-umbra-color), 0px 20px 31px 3px var(--jp-shadow-penumbra-color), 0px 8px 38px 7px var(--jp-shadow-ambient-color); --jp-elevation-z24: 0px 11px 15px -7px var(--jp-shadow-umbra-color), 0px 24px 38px 3px var(--jp-shadow-penumbra-color), 0px 9px 46px 8px var(--jp-shadow-ambient-color); /* Borders * * The following variables, specify the visual styling of borders in JupyterLab. */ --jp-border-width: 1px; --jp-border-color0: var(--md-grey-400); --jp-border-color1: var(--md-grey-400); --jp-border-color2: var(--md-grey-300); --jp-border-color3: var(--md-grey-200); --jp-border-radius: 2px; /* UI Fonts * * The UI font CSS variables are used for the typography all of the JupyterLab * user interface elements that are not directly user generated content. * * The font sizing here is done assuming that the body font size of --jp-ui-font-size1 * is applied to a parent element. When children elements, such as headings, are sized * in em all things will be computed relative to that body size. */ --jp-ui-font-scale-factor: 1.2; --jp-ui-font-size0: 0.83333em; --jp-ui-font-size1: 13px; /* Base font size */ --jp-ui-font-size2: 1.2em; --jp-ui-font-size3: 1.44em; --jp-ui-font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Helvetica, Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\"; /* * Use these font colors against the corresponding main layout colors. * In a light theme, these go from dark to light. */ /* Defaults use Material Design specification */ --jp-ui-font-color0: rgba(0, 0, 0, 1); --jp-ui-font-color1: rgba(0, 0, 0, 0.87); --jp-ui-font-color2: rgba(0, 0, 0, 0.54); --jp-ui-font-color3: rgba(0, 0, 0, 0.38); /* * Use these against the brand/accent/warn/error colors. * These will typically go from light to darker, in both a dark and light theme. */ --jp-ui-inverse-font-color0: rgba(255, 255, 255, 1); --jp-ui-inverse-font-color1: rgba(255, 255, 255, 1); --jp-ui-inverse-font-color2: rgba(255, 255, 255, 0.7); --jp-ui-inverse-font-color3: rgba(255, 255, 255, 0.5); /* Content Fonts * * Content font variables are used for typography of user generated content. * * The font sizing here is done assuming that the body font size of --jp-content-font-size1 * is applied to a parent element. When children elements, such as headings, are sized * in em all things will be computed relative to that body size. */ --jp-content-line-height: 1.6; --jp-content-font-scale-factor: 1.2; --jp-content-font-size0: 0.83333em; --jp-content-font-size1: 14px; /* Base font size */ --jp-content-font-size2: 1.2em; --jp-content-font-size3: 1.44em; --jp-content-font-size4: 1.728em; --jp-content-font-size5: 2.0736em; /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-content-presentation-font-size1: 17px; --jp-content-heading-line-height: 1; --jp-content-heading-margin-top: 1.2em; --jp-content-heading-margin-bottom: 0.8em; --jp-content-heading-font-weight: 500; /* Defaults use Material Design specification */ --jp-content-font-color0: rgba(0, 0, 0, 1); --jp-content-font-color1: rgba(0, 0, 0, 0.87); --jp-content-font-color2: rgba(0, 0, 0, 0.54); --jp-content-font-color3: rgba(0, 0, 0, 0.38); --jp-content-link-color: var(--md-blue-700); --jp-content-font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Helvetica, Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\"; /* * Code Fonts * * Code font variables are used for typography of code and other monospaces content. */ --jp-code-font-size: 13px; --jp-code-line-height: 1.3077; /* 17px for 13px base */ --jp-code-padding: 5px; /* 5px for 13px base, codemirror highlighting needs integer px value */ --jp-code-font-family-default: Menlo, Consolas, \"DejaVu Sans Mono\", monospace; --jp-code-font-family: var(--jp-code-font-family-default); /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-code-presentation-font-size: 16px; /* may need to tweak cursor width if you change font size */ --jp-code-cursor-width0: 1.4px; --jp-code-cursor-width1: 2px; --jp-code-cursor-width2: 4px; /* Layout * * The following are the main layout colors use in JupyterLab. In a light * theme these would go from light to dark. */ --jp-layout-color0: white; --jp-layout-color1: white; --jp-layout-color2: var(--md-grey-200); --jp-layout-color3: var(--md-grey-400); --jp-layout-color4: var(--md-grey-600); /* Inverse Layout * * The following are the inverse layout colors use in JupyterLab. In a light * theme these would go from dark to light. */ --jp-inverse-layout-color0: #111111; --jp-inverse-layout-color1: var(--md-grey-900); --jp-inverse-layout-color2: var(--md-grey-800); --jp-inverse-layout-color3: var(--md-grey-700); --jp-inverse-layout-color4: var(--md-grey-600); /* Brand/accent */ --jp-brand-color0: var(--md-blue-900); --jp-brand-color1: var(--md-blue-700); --jp-brand-color2: var(--md-blue-300); --jp-brand-color3: var(--md-blue-100); --jp-brand-color4: var(--md-blue-50); --jp-accent-color0: var(--md-green-900); --jp-accent-color1: var(--md-green-700); --jp-accent-color2: var(--md-green-300); --jp-accent-color3: var(--md-green-100); /* State colors (warn, error, success, info) */ --jp-warn-color0: var(--md-orange-900); --jp-warn-color1: var(--md-orange-700); --jp-warn-color2: var(--md-orange-300); --jp-warn-color3: var(--md-orange-100); --jp-error-color0: var(--md-red-900); --jp-error-color1: var(--md-red-700); --jp-error-color2: var(--md-red-300); --jp-error-color3: var(--md-red-100); --jp-success-color0: var(--md-green-900); --jp-success-color1: var(--md-green-700); --jp-success-color2: var(--md-green-300); --jp-success-color3: var(--md-green-100); --jp-info-color0: var(--md-cyan-900); --jp-info-color1: var(--md-cyan-700); --jp-info-color2: var(--md-cyan-300); --jp-info-color3: var(--md-cyan-100); /* Cell specific styles */ --jp-cell-padding: 5px; --jp-cell-collapser-width: 8px; --jp-cell-collapser-min-height: 20px; --jp-cell-collapser-not-active-hover-opacity: 0.6; --jp-cell-editor-background: var(--md-grey-100); --jp-cell-editor-border-color: var(--md-grey-300); --jp-cell-editor-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-cell-editor-active-background: var(--jp-layout-color0); --jp-cell-editor-active-border-color: var(--jp-brand-color1); --jp-cell-prompt-width: 64px; --jp-cell-prompt-font-family: var(--jp-code-font-family-default); --jp-cell-prompt-letter-spacing: 0px; --jp-cell-prompt-opacity: 1; --jp-cell-prompt-not-active-opacity: 0.5; --jp-cell-prompt-not-active-font-color: var(--md-grey-700); /* A custom blend of MD grey and blue 600 * See https://meyerweb.com/eric/tools/color-blend/#546E7A:1E88E5:5:hex */ --jp-cell-inprompt-font-color: #307fc1; /* A custom blend of MD grey and orange 600 * https://meyerweb.com/eric/tools/color-blend/#546E7A:F4511E:5:hex */ --jp-cell-outprompt-font-color: #bf5b3d; /* Notebook specific styles */ --jp-notebook-padding: 10px; --jp-notebook-select-background: var(--jp-layout-color1); --jp-notebook-multiselected-color: var(--md-blue-50); /* The scroll padding is calculated to fill enough space at the bottom of the notebook to show one single-line cell (with appropriate padding) at the top when the notebook is scrolled all the way to the bottom. We also subtract one pixel so that no scrollbar appears if we have just one single-line cell in the notebook. This padding is to enable a 'scroll past end' feature in a notebook. */ --jp-notebook-scroll-padding: calc( 100% - var(--jp-code-font-size) * var(--jp-code-line-height) - var(--jp-code-padding) - var(--jp-cell-padding) - 1px ); /* Rendermime styles */ --jp-rendermime-error-background: #fdd; --jp-rendermime-table-row-background: var(--md-grey-100); --jp-rendermime-table-row-hover-background: var(--md-light-blue-50); /* Dialog specific styles */ --jp-dialog-background: rgba(0, 0, 0, 0.25); /* Console specific styles */ --jp-console-padding: 10px; /* Toolbar specific styles */ --jp-toolbar-border-color: var(--jp-border-color1); --jp-toolbar-micro-height: 8px; --jp-toolbar-background: var(--jp-layout-color1); --jp-toolbar-box-shadow: 0px 0px 2px 0px rgba(0, 0, 0, 0.24); --jp-toolbar-header-margin: 4px 4px 0px 4px; --jp-toolbar-active-background: var(--md-grey-300); /* Statusbar specific styles */ --jp-statusbar-height: 24px; /* Input field styles */ --jp-input-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-input-active-background: var(--jp-layout-color1); --jp-input-hover-background: var(--jp-layout-color1); --jp-input-background: var(--md-grey-100); --jp-input-border-color: var(--jp-border-color1); --jp-input-active-border-color: var(--jp-brand-color1); --jp-input-active-box-shadow-color: rgba(19, 124, 189, 0.3); /* General editor styles */ --jp-editor-selected-background: #d9d9d9; --jp-editor-selected-focused-background: #d7d4f0; --jp-editor-cursor-color: var(--jp-ui-font-color0); /* Code mirror specific styles */ --jp-mirror-editor-keyword-color: #008000; --jp-mirror-editor-atom-color: #88f; --jp-mirror-editor-number-color: #080; --jp-mirror-editor-def-color: #00f; --jp-mirror-editor-variable-color: var(--md-grey-900); --jp-mirror-editor-variable-2-color: #05a; --jp-mirror-editor-variable-3-color: #085; --jp-mirror-editor-punctuation-color: #05a; --jp-mirror-editor-property-color: #05a; --jp-mirror-editor-operator-color: #aa22ff; --jp-mirror-editor-comment-color: #408080; --jp-mirror-editor-string-color: #ba2121; --jp-mirror-editor-string-2-color: #708; --jp-mirror-editor-meta-color: #aa22ff; --jp-mirror-editor-qualifier-color: #555; --jp-mirror-editor-builtin-color: #008000; --jp-mirror-editor-bracket-color: #997; --jp-mirror-editor-tag-color: #170; --jp-mirror-editor-attribute-color: #00c; --jp-mirror-editor-header-color: blue; --jp-mirror-editor-quote-color: #090; --jp-mirror-editor-link-color: #00c; --jp-mirror-editor-error-color: #f00; --jp-mirror-editor-hr-color: #999; /* Vega extension styles */ --jp-vega-background: white; /* Sidebar-related styles */ --jp-sidebar-min-width: 250px; /* Search-related styles */ --jp-search-toggle-off-opacity: 0.5; --jp-search-toggle-hover-opacity: 0.8; --jp-search-toggle-on-opacity: 1; --jp-search-selected-match-background-color: rgb(245, 200, 0); --jp-search-selected-match-color: black; --jp-search-unselected-match-background-color: var( --jp-inverse-layout-color0 ); --jp-search-unselected-match-color: var(--jp-ui-inverse-font-color0); /* Icon colors that work well with light or dark backgrounds */ --jp-icon-contrast-color0: var(--md-purple-600); --jp-icon-contrast-color1: var(--md-green-600); --jp-icon-contrast-color2: var(--md-pink-600); --jp-icon-contrast-color3: var(--md-blue-600); } [data-md-color-scheme=\"slate\"] .jupyter-wrapper { /* Elevation * * We style box-shadows using Material Design's idea of elevation. These particular numbers are taken from here: * * https://github.com/material-components/material-components-web * https://material-components-web.appspot.com/elevation.html */ /* The dark theme shadows need a bit of work, but this will probably also require work on the core layout * colors used in the theme as well. */ --jp-shadow-base-lightness: 32; --jp-shadow-umbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.2 ); --jp-shadow-penumbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.14 ); --jp-shadow-ambient-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.12 ); --jp-elevation-z0: none; --jp-elevation-z1: 0px 2px 1px -1px var(--jp-shadow-umbra-color), 0px 1px 1px 0px var(--jp-shadow-penumbra-color), 0px 1px 3px 0px var(--jp-shadow-ambient-color); --jp-elevation-z2: 0px 3px 1px -2px var(--jp-shadow-umbra-color), 0px 2px 2px 0px var(--jp-shadow-penumbra-color), 0px 1px 5px 0px var(--jp-shadow-ambient-color); --jp-elevation-z4: 0px 2px 4px -1px var(--jp-shadow-umbra-color), 0px 4px 5px 0px var(--jp-shadow-penumbra-color), 0px 1px 10px 0px var(--jp-shadow-ambient-color); --jp-elevation-z6: 0px 3px 5px -1px var(--jp-shadow-umbra-color), 0px 6px 10px 0px var(--jp-shadow-penumbra-color), 0px 1px 18px 0px var(--jp-shadow-ambient-color); --jp-elevation-z8: 0px 5px 5px -3px var(--jp-shadow-umbra-color), 0px 8px 10px 1px var(--jp-shadow-penumbra-color), 0px 3px 14px 2px var(--jp-shadow-ambient-color); --jp-elevation-z12: 0px 7px 8px -4px var(--jp-shadow-umbra-color), 0px 12px 17px 2px var(--jp-shadow-penumbra-color), 0px 5px 22px 4px var(--jp-shadow-ambient-color); --jp-elevation-z16: 0px 8px 10px -5px var(--jp-shadow-umbra-color), 0px 16px 24px 2px var(--jp-shadow-penumbra-color), 0px 6px 30px 5px var(--jp-shadow-ambient-color); --jp-elevation-z20: 0px 10px 13px -6px var(--jp-shadow-umbra-color), 0px 20px 31px 3px var(--jp-shadow-penumbra-color), 0px 8px 38px 7px var(--jp-shadow-ambient-color); --jp-elevation-z24: 0px 11px 15px -7px var(--jp-shadow-umbra-color), 0px 24px 38px 3px var(--jp-shadow-penumbra-color), 0px 9px 46px 8px var(--jp-shadow-ambient-color); /* Borders * * The following variables, specify the visual styling of borders in JupyterLab. */ --jp-border-width: 1px; --jp-border-color0: var(--md-grey-700); --jp-border-color1: var(--md-grey-700); --jp-border-color2: var(--md-grey-800); --jp-border-color3: var(--md-grey-900); --jp-border-radius: 2px; /* UI Fonts * * The UI font CSS variables are used for the typography all of the JupyterLab * user interface elements that are not directly user generated content. * * The font sizing here is done assuming that the body font size of --jp-ui-font-size1 * is applied to a parent element. When children elements, such as headings, are sized * in em all things will be computed relative to that body size. */ --jp-ui-font-scale-factor: 1.2; --jp-ui-font-size0: 0.83333em; --jp-ui-font-size1: 13px; /* Base font size */ --jp-ui-font-size2: 1.2em; --jp-ui-font-size3: 1.44em; --jp-ui-font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Helvetica, Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\"; /* * Use these font colors against the corresponding main layout colors. * In a light theme, these go from dark to light. */ /* Defaults use Material Design specification */ --jp-ui-font-color0: rgba(255, 255, 255, 1); --jp-ui-font-color1: rgba(255, 255, 255, 0.87); --jp-ui-font-color2: rgba(255, 255, 255, 0.54); --jp-ui-font-color3: rgba(255, 255, 255, 0.38); /* * Use these against the brand/accent/warn/error colors. * These will typically go from light to darker, in both a dark and light theme. */ --jp-ui-inverse-font-color0: rgba(0, 0, 0, 1); --jp-ui-inverse-font-color1: rgba(0, 0, 0, 0.8); --jp-ui-inverse-font-color2: rgba(0, 0, 0, 0.5); --jp-ui-inverse-font-color3: rgba(0, 0, 0, 0.3); /* Content Fonts * * Content font variables are used for typography of user generated content. * * The font sizing here is done assuming that the body font size of --jp-content-font-size1 * is applied to a parent element. When children elements, such as headings, are sized * in em all things will be computed relative to that body size. */ --jp-content-line-height: 1.6; --jp-content-font-scale-factor: 1.2; --jp-content-font-size0: 0.83333em; --jp-content-font-size1: 14px; /* Base font size */ --jp-content-font-size2: 1.2em; --jp-content-font-size3: 1.44em; --jp-content-font-size4: 1.728em; --jp-content-font-size5: 2.0736em; /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-content-presentation-font-size1: 17px; --jp-content-heading-line-height: 1; --jp-content-heading-margin-top: 1.2em; --jp-content-heading-margin-bottom: 0.8em; --jp-content-heading-font-weight: 500; /* Defaults use Material Design specification */ --jp-content-font-color0: rgba(255, 255, 255, 1); --jp-content-font-color1: rgba(255, 255, 255, 1); --jp-content-font-color2: rgba(255, 255, 255, 0.7); --jp-content-font-color3: rgba(255, 255, 255, 0.5); --jp-content-link-color: var(--md-blue-300); --jp-content-font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Helvetica, Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\"; /* * Code Fonts * * Code font variables are used for typography of code and other monospaces content. */ --jp-code-font-size: 13px; --jp-code-line-height: 1.3077; /* 17px for 13px base */ --jp-code-padding: 5px; /* 5px for 13px base, codemirror highlighting needs integer px value */ --jp-code-font-family-default: Menlo, Consolas, \"DejaVu Sans Mono\", monospace; --jp-code-font-family: var(--jp-code-font-family-default); /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-code-presentation-font-size: 16px; /* may need to tweak cursor width if you change font size */ --jp-code-cursor-width0: 1.4px; --jp-code-cursor-width1: 2px; --jp-code-cursor-width2: 4px; /* Layout * * The following are the main layout colors use in JupyterLab. In a light * theme these would go from light to dark. */ --jp-layout-color0: #111111; --jp-layout-color1: var(--md-grey-900); --jp-layout-color2: var(--md-grey-800); --jp-layout-color3: var(--md-grey-700); --jp-layout-color4: var(--md-grey-600); /* Inverse Layout * * The following are the inverse layout colors use in JupyterLab. In a light * theme these would go from dark to light. */ --jp-inverse-layout-color0: white; --jp-inverse-layout-color1: white; --jp-inverse-layout-color2: var(--md-grey-200); --jp-inverse-layout-color3: var(--md-grey-400); --jp-inverse-layout-color4: var(--md-grey-600); /* Brand/accent */ --jp-brand-color0: var(--md-blue-700); --jp-brand-color1: var(--md-blue-500); --jp-brand-color2: var(--md-blue-300); --jp-brand-color3: var(--md-blue-100); --jp-brand-color4: var(--md-blue-50); --jp-accent-color0: var(--md-green-700); --jp-accent-color1: var(--md-green-500); --jp-accent-color2: var(--md-green-300); --jp-accent-color3: var(--md-green-100); /* State colors (warn, error, success, info) */ --jp-warn-color0: var(--md-orange-700); --jp-warn-color1: var(--md-orange-500); --jp-warn-color2: var(--md-orange-300); --jp-warn-color3: var(--md-orange-100); --jp-error-color0: var(--md-red-700); --jp-error-color1: var(--md-red-500); --jp-error-color2: var(--md-red-300); --jp-error-color3: var(--md-red-100); --jp-success-color0: var(--md-green-700); --jp-success-color1: var(--md-green-500); --jp-success-color2: var(--md-green-300); --jp-success-color3: var(--md-green-100); --jp-info-color0: var(--md-cyan-700); --jp-info-color1: var(--md-cyan-500); --jp-info-color2: var(--md-cyan-300); --jp-info-color3: var(--md-cyan-100); /* Cell specific styles */ --jp-cell-padding: 5px; --jp-cell-collapser-width: 8px; --jp-cell-collapser-min-height: 20px; --jp-cell-collapser-not-active-hover-opacity: 0.6; --jp-cell-editor-background: var(--jp-layout-color1); --jp-cell-editor-border-color: var(--md-grey-700); --jp-cell-editor-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-cell-editor-active-background: var(--jp-layout-color0); --jp-cell-editor-active-border-color: var(--jp-brand-color1); --jp-cell-prompt-width: 64px; --jp-cell-prompt-font-family: var(--jp-code-font-family-default); --jp-cell-prompt-letter-spacing: 0px; --jp-cell-prompt-opacity: 1; --jp-cell-prompt-not-active-opacity: 1; --jp-cell-prompt-not-active-font-color: var(--md-grey-300); /* A custom blend of MD grey and blue 600 * See https://meyerweb.com/eric/tools/color-blend/#546E7A:1E88E5:5:hex */ --jp-cell-inprompt-font-color: #307fc1; /* A custom blend of MD grey and orange 600 * https://meyerweb.com/eric/tools/color-blend/#546E7A:F4511E:5:hex */ --jp-cell-outprompt-font-color: #bf5b3d; /* Notebook specific styles */ --jp-notebook-padding: 10px; --jp-notebook-select-background: var(--jp-layout-color1); --jp-notebook-multiselected-color: rgba(33, 150, 243, 0.24); /* The scroll padding is calculated to fill enough space at the bottom of the notebook to show one single-line cell (with appropriate padding) at the top when the notebook is scrolled all the way to the bottom. We also subtract one pixel so that no scrollbar appears if we have just one single-line cell in the notebook. This padding is to enable a 'scroll past end' feature in a notebook. */ --jp-notebook-scroll-padding: calc( 100% - var(--jp-code-font-size) * var(--jp-code-line-height) - var(--jp-code-padding) - var(--jp-cell-padding) - 1px ); /* Rendermime styles */ --jp-rendermime-error-background: rgba(244, 67, 54, 0.28); --jp-rendermime-table-row-background: var(--md-grey-900); --jp-rendermime-table-row-hover-background: rgba(3, 169, 244, 0.2); /* Dialog specific styles */ --jp-dialog-background: rgba(0, 0, 0, 0.6); /* Console specific styles */ --jp-console-padding: 10px; /* Toolbar specific styles */ --jp-toolbar-border-color: var(--jp-border-color2); --jp-toolbar-micro-height: 8px; --jp-toolbar-background: var(--jp-layout-color1); --jp-toolbar-box-shadow: 0px 0px 2px 0px rgba(0, 0, 0, 0.8); --jp-toolbar-header-margin: 4px 4px 0px 4px; --jp-toolbar-active-background: var(--jp-layout-color0); /* Statusbar specific styles */ --jp-statusbar-height: 24px; /* Input field styles */ --jp-input-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-input-active-background: var(--jp-layout-color0); --jp-input-hover-background: var(--jp-layout-color2); --jp-input-background: var(--md-grey-800); --jp-input-border-color: var(--jp-border-color1); --jp-input-active-border-color: var(--jp-brand-color1); --jp-input-active-box-shadow-color: rgba(19, 124, 189, 0.3); /* General editor styles */ --jp-editor-selected-background: var(--jp-layout-color2); --jp-editor-selected-focused-background: rgba(33, 150, 243, 0.24); --jp-editor-cursor-color: var(--jp-ui-font-color0); /* Code mirror specific styles */ --jp-mirror-editor-keyword-color: var(--md-green-500); --jp-mirror-editor-atom-color: var(--md-blue-300); --jp-mirror-editor-number-color: var(--md-green-400); --jp-mirror-editor-def-color: var(--md-blue-600); --jp-mirror-editor-variable-color: var(--md-grey-300); --jp-mirror-editor-variable-2-color: var(--md-blue-400); --jp-mirror-editor-variable-3-color: var(--md-green-600); --jp-mirror-editor-punctuation-color: var(--md-blue-400); --jp-mirror-editor-property-color: var(--md-blue-400); --jp-mirror-editor-operator-color: #aa22ff; --jp-mirror-editor-comment-color: #408080; --jp-mirror-editor-string-color: #ff7070; --jp-mirror-editor-string-2-color: var(--md-purple-300); --jp-mirror-editor-meta-color: #aa22ff; --jp-mirror-editor-qualifier-color: #555; --jp-mirror-editor-builtin-color: var(--md-green-600); --jp-mirror-editor-bracket-color: #997; --jp-mirror-editor-tag-color: var(--md-green-700); --jp-mirror-editor-attribute-color: var(--md-blue-700); --jp-mirror-editor-header-color: var(--md-blue-500); --jp-mirror-editor-quote-color: var(--md-green-300); --jp-mirror-editor-link-color: var(--md-blue-700); --jp-mirror-editor-error-color: #f00; --jp-mirror-editor-hr-color: #999; /* Vega extension styles */ --jp-vega-background: var(--md-grey-400); /* Sidebar-related styles */ --jp-sidebar-min-width: 250px; /* Search-related styles */ --jp-search-toggle-off-opacity: 0.6; --jp-search-toggle-hover-opacity: 0.8; --jp-search-toggle-on-opacity: 1; --jp-search-selected-match-background-color: rgb(255, 225, 0); --jp-search-selected-match-color: black; --jp-search-unselected-match-background-color: var( --jp-inverse-layout-color0 ); --jp-search-unselected-match-color: var(--jp-ui-inverse-font-color0); /* scrollbar related styles. Supports every browser except Edge. */ /* colors based on JetBrain's Darcula theme */ --jp-scrollbar-background-color: #3f4244; --jp-scrollbar-thumb-color: 88, 96, 97; /* need to specify thumb color as an RGB triplet */ --jp-scrollbar-endpad: 3px; /* the minimum gap between the thumb and the ends of a scrollbar */ /* hacks for setting the thumb shape. These do nothing in Firefox */ --jp-scrollbar-thumb-margin: 3.5px; /* the space in between the sides of the thumb and the track */ --jp-scrollbar-thumb-radius: 9px; /* set to a large-ish value for rounded endcaps on the thumb */ /* Icon colors that work well with light or dark backgrounds */ --jp-icon-contrast-color0: var(--md-purple-600); --jp-icon-contrast-color1: var(--md-green-600); --jp-icon-contrast-color2: var(--md-pink-600); --jp-icon-contrast-color3: var(--md-blue-600); } :root{--md-red-50: #ffebee;--md-red-100: #ffcdd2;--md-red-200: #ef9a9a;--md-red-300: #e57373;--md-red-400: #ef5350;--md-red-500: #f44336;--md-red-600: #e53935;--md-red-700: #d32f2f;--md-red-800: #c62828;--md-red-900: #b71c1c;--md-red-A100: #ff8a80;--md-red-A200: #ff5252;--md-red-A400: #ff1744;--md-red-A700: #d50000;--md-pink-50: #fce4ec;--md-pink-100: #f8bbd0;--md-pink-200: #f48fb1;--md-pink-300: #f06292;--md-pink-400: #ec407a;--md-pink-500: #e91e63;--md-pink-600: #d81b60;--md-pink-700: #c2185b;--md-pink-800: 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#7986cb;--md-indigo-400: #5c6bc0;--md-indigo-500: #3f51b5;--md-indigo-600: #3949ab;--md-indigo-700: #303f9f;--md-indigo-800: #283593;--md-indigo-900: #1a237e;--md-indigo-A100: #8c9eff;--md-indigo-A200: #536dfe;--md-indigo-A400: #3d5afe;--md-indigo-A700: #304ffe;--md-blue-50: #e3f2fd;--md-blue-100: #bbdefb;--md-blue-200: #90caf9;--md-blue-300: #64b5f6;--md-blue-400: #42a5f5;--md-blue-500: #2196f3;--md-blue-600: #1e88e5;--md-blue-700: #1976d2;--md-blue-800: #1565c0;--md-blue-900: #0d47a1;--md-blue-A100: #82b1ff;--md-blue-A200: #448aff;--md-blue-A400: #2979ff;--md-blue-A700: #2962ff;--md-light-blue-50: #e1f5fe;--md-light-blue-100: #b3e5fc;--md-light-blue-200: #81d4fa;--md-light-blue-300: #4fc3f7;--md-light-blue-400: #29b6f6;--md-light-blue-500: #03a9f4;--md-light-blue-600: #039be5;--md-light-blue-700: #0288d1;--md-light-blue-800: #0277bd;--md-light-blue-900: #01579b;--md-light-blue-A100: #80d8ff;--md-light-blue-A200: #40c4ff;--md-light-blue-A400: #00b0ff;--md-light-blue-A700: #0091ea;--md-cyan-50: #e0f7fa;--md-cyan-100: #b2ebf2;--md-cyan-200: #80deea;--md-cyan-300: #4dd0e1;--md-cyan-400: #26c6da;--md-cyan-500: #00bcd4;--md-cyan-600: #00acc1;--md-cyan-700: #0097a7;--md-cyan-800: #00838f;--md-cyan-900: #006064;--md-cyan-A100: #84ffff;--md-cyan-A200: #18ffff;--md-cyan-A400: #00e5ff;--md-cyan-A700: #00b8d4;--md-teal-50: #e0f2f1;--md-teal-100: #b2dfdb;--md-teal-200: #80cbc4;--md-teal-300: #4db6ac;--md-teal-400: #26a69a;--md-teal-500: #009688;--md-teal-600: #00897b;--md-teal-700: #00796b;--md-teal-800: #00695c;--md-teal-900: #004d40;--md-teal-A100: #a7ffeb;--md-teal-A200: #64ffda;--md-teal-A400: #1de9b6;--md-teal-A700: #00bfa5;--md-green-50: #e8f5e9;--md-green-100: #c8e6c9;--md-green-200: #a5d6a7;--md-green-300: #81c784;--md-green-400: #66bb6a;--md-green-500: #4caf50;--md-green-600: #43a047;--md-green-700: #388e3c;--md-green-800: #2e7d32;--md-green-900: #1b5e20;--md-green-A100: #b9f6ca;--md-green-A200: #69f0ae;--md-green-A400: #00e676;--md-green-A700: #00c853;--md-light-green-50: #f1f8e9;--md-light-green-100: #dcedc8;--md-light-green-200: #c5e1a5;--md-light-green-300: #aed581;--md-light-green-400: #9ccc65;--md-light-green-500: #8bc34a;--md-light-green-600: #7cb342;--md-light-green-700: #689f38;--md-light-green-800: #558b2f;--md-light-green-900: #33691e;--md-light-green-A100: #ccff90;--md-light-green-A200: #b2ff59;--md-light-green-A400: #76ff03;--md-light-green-A700: #64dd17;--md-lime-50: #f9fbe7;--md-lime-100: #f0f4c3;--md-lime-200: #e6ee9c;--md-lime-300: #dce775;--md-lime-400: #d4e157;--md-lime-500: #cddc39;--md-lime-600: #c0ca33;--md-lime-700: #afb42b;--md-lime-800: #9e9d24;--md-lime-900: #827717;--md-lime-A100: #f4ff81;--md-lime-A200: #eeff41;--md-lime-A400: #c6ff00;--md-lime-A700: #aeea00;--md-yellow-50: #fffde7;--md-yellow-100: #fff9c4;--md-yellow-200: #fff59d;--md-yellow-300: #fff176;--md-yellow-400: #ffee58;--md-yellow-500: #ffeb3b;--md-yellow-600: #fdd835;--md-yellow-700: #fbc02d;--md-yellow-800: #f9a825;--md-yellow-900: #f57f17;--md-yellow-A100: #ffff8d;--md-yellow-A200: #ffff00;--md-yellow-A400: #ffea00;--md-yellow-A700: #ffd600;--md-amber-50: #fff8e1;--md-amber-100: #ffecb3;--md-amber-200: #ffe082;--md-amber-300: #ffd54f;--md-amber-400: #ffca28;--md-amber-500: #ffc107;--md-amber-600: #ffb300;--md-amber-700: #ffa000;--md-amber-800: #ff8f00;--md-amber-900: #ff6f00;--md-amber-A100: #ffe57f;--md-amber-A200: #ffd740;--md-amber-A400: #ffc400;--md-amber-A700: #ffab00;--md-orange-50: #fff3e0;--md-orange-100: #ffe0b2;--md-orange-200: #ffcc80;--md-orange-300: #ffb74d;--md-orange-400: #ffa726;--md-orange-500: #ff9800;--md-orange-600: #fb8c00;--md-orange-700: #f57c00;--md-orange-800: #ef6c00;--md-orange-900: #e65100;--md-orange-A100: #ffd180;--md-orange-A200: #ffab40;--md-orange-A400: #ff9100;--md-orange-A700: #ff6d00;--md-deep-orange-50: #fbe9e7;--md-deep-orange-100: #ffccbc;--md-deep-orange-200: #ffab91;--md-deep-orange-300: #ff8a65;--md-deep-orange-400: #ff7043;--md-deep-orange-500: 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sourceMappingURL=mkdocs-jupyter.css.map*/ init_mathjax = function() { if (window.MathJax) { // MathJax loaded MathJax.Hub.Config({ TeX: { equationNumbers: { autoNumber: \"AMS\", useLabelIds: true } }, tex2jax: { inlineMath: [ ['$','$'], [\"\\\\(\",\"\\\\)\"] ], displayMath: [ ['$$','$$'], [\"\\\\[\",\"\\\\]\"] ], processEscapes: true, processEnvironments: true }, displayAlign: 'center', CommonHTML: { linebreaks: { automatic: true } } }); MathJax.Hub.Queue([\"Typeset\", MathJax.Hub]); } } init_mathjax(); Explore data and feasibility of approach \u00b6 We had asked our clients and in-house experts to annotate sentences using a rigorous guideline. The aim is to decide on which sentences they would like to see in a summary for a paper. The results are in JSON format, each annotator has a separate file. Let's load them. In [1]: Copied! from pathlib import Path import json annotations = [] for p in Path ( \"data\" ) . glob ( \"*.json\" ): with open ( p , encoding = \"utf-8\" ) as f : print ( p ) annotations . append ( json . load ( f )) evaluations = { sentence : [ annotation [ sentence ] for annotation in annotations if sentence in annotation ] for sentence in { sentence for annotation in annotations for sentence in annotation . keys () } } X = [ s for s in evaluations . keys ()] y = [ int ( sum ( e ) > 0 ) for e in evaluations . values ()] from pathlib import Path import json annotations = [] for p in Path(\"data\").glob(\"*.json\"): with open(p, encoding=\"utf-8\") as f: print(p) annotations.append(json.load(f)) evaluations = { sentence: [ annotation[sentence] for annotation in annotations if sentence in annotation ] for sentence in { sentence for annotation in annotations for sentence in annotation.keys() } } X = [s for s in evaluations.keys()] y = [int(sum(e) > 0) for e in evaluations.values()] data/evaluation-experiment-2-stage #1-sa6a0y.json data/evaluation-experiment-2-stage #1-2m6dmb.json Save the compiled and processed data for later use using LargeFileS3. In [2]: Copied! from great_ai.large_file import LargeFileS3 import json LargeFileS3 . configure_credentials_from_file ( \"config.ini\" ) with LargeFileS3 ( \"summary-train-dataset-small\" , \"w\" , encoding = \"utf-8\" ) as f : json . dump (( X , y ), f ) from great_ai.large_file import LargeFileS3 import json LargeFileS3.configure_credentials_from_file(\"config.ini\") with LargeFileS3(\"summary-train-dataset-small\", \"w\", encoding=\"utf-8\") as f: json.dump((X, y), f) Copying file for summary-train-dataset-small-0 Compressing summary-train-dataset-small-0 Uploading summary-train-dataset-small-0 to S3 as summary-train-dataset-small/0 Uploading summary-train-dataset-small-0.tar.gz 0.04/0.04 MB (100.0%) Filter out sentences which don't have enough annotations. In [3]: Copied! y1 = [ e [ 0 ] for e in evaluations . values () if len ( e ) == 2 ] y2 = [ e [ 1 ] for e in evaluations . values () if len ( e ) == 2 ] y1 = [e[0] for e in evaluations.values() if len(e) == 2] y2 = [e[1] for e in evaluations.values() if len(e) == 2] Calculate Cohen's kappa . It's a bit low but the task itself is pretty subjective so it's not all that surprising. In [4]: Copied! import sklearn.metrics sklearn . metrics . cohen_kappa_score ( y1 , y2 ) import sklearn.metrics sklearn.metrics.cohen_kappa_score(y1, y2) Out[4]: 0.3546448712421808 Can we train anything on this data? Let's try with a trivial SVM. In [5]: Copied! X = [ s for s in evaluations . keys ()] y = [ int ( sum ( e ) > 0 ) for e in evaluations . values ()] X = [s for s in evaluations.keys()] y = [int(sum(e) > 0) for e in evaluations.values()] In [6]: Copied! from sklearn.model_selection import train_test_split from sklearn.svm import LinearSVC from sklearn.pipeline import Pipeline from sklearn.feature_extraction.text import TfidfVectorizer from sklearn.model_selection import cross_val_score model = Pipeline ( steps = [ ( \"vectorizer\" , TfidfVectorizer ( sublinear_tf = True , min_df = 3 , max_df = 0.3 )), ( \"classifier\" , LinearSVC ()), ] ) # baseline model cross_val_score ( model , X , y , cv = 5 ) from sklearn.model_selection import train_test_split from sklearn.svm import LinearSVC from sklearn.pipeline import Pipeline from sklearn.feature_extraction.text import TfidfVectorizer from sklearn.model_selection import cross_val_score model = Pipeline( steps=[ (\"vectorizer\", TfidfVectorizer(sublinear_tf=True, min_df=3, max_df=0.3)), (\"classifier\", LinearSVC()), ] ) # baseline model cross_val_score(model, X, y, cv=5) Out[6]: array([0.79, 0.75, 0.77, 0.69, 0.77]) The cross-validation shows promising accuracies. But accuracy isn't everything, therefore, we should investigate the accuracy metrics. In [7]: Copied! X_train , X_test , y_train , y_test = train_test_split ( X , y , test_size = 0.2 , random_state = 0 ) model . fit ( X_train , y_train ) X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0) model.fit(X_train, y_train) Out[7]: #sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"\u25b8\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"\u25be\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;} Pipeline(steps=[('vectorizer', TfidfVectorizer(max_df=0.3, min_df=3, sublinear_tf=True)), ('classifier', LinearSVC())]) In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org. Pipeline Pipeline(steps=[('vectorizer', TfidfVectorizer(max_df=0.3, min_df=3, sublinear_tf=True)), ('classifier', LinearSVC())]) TfidfVectorizer TfidfVectorizer(max_df=0.3, min_df=3, sublinear_tf=True) LinearSVC LinearSVC() In [8]: Copied! y_predicted = model . predict ( X_test ) print ( sklearn . metrics . classification_report ( [ y > 0 for y in y_test ], [ y > 0 for y in y_predicted ] ) ) sklearn . metrics . ConfusionMatrixDisplay . from_predictions ( [ y > 0 for y in y_test ], [ y > 0 for y in y_predicted ], xticks_rotation = \"vertical\" , values_format = \".2f\" , ) None y_predicted = model.predict(X_test) print( sklearn.metrics.classification_report( [y > 0 for y in y_test], [y > 0 for y in y_predicted] ) ) sklearn.metrics.ConfusionMatrixDisplay.from_predictions( [y > 0 for y in y_test], [y > 0 for y in y_predicted], xticks_rotation=\"vertical\", values_format=\".2f\", ) None precision recall f1-score support False 0.78 0.78 0.78 51 True 0.78 0.78 0.78 49 accuracy 0.78 100 macro avg 0.78 0.78 0.78 100 weighted avg 0.78 0.78 0.78 100 We get an F1-score of 0.78 without any hyperparameter-optimisation; this task may be feasible to solve with AI. Next: Part 2","title":"Explore data and feasibility of approach"},{"location":"examples/scibert/data/#explore-data-and-feasibility-of-approach","text":"We had asked our clients and in-house experts to annotate sentences using a rigorous guideline. The aim is to decide on which sentences they would like to see in a summary for a paper. The results are in JSON format, each annotator has a separate file. Let's load them. In [1]: Copied! from pathlib import Path import json annotations = [] for p in Path ( \"data\" ) . glob ( \"*.json\" ): with open ( p , encoding = \"utf-8\" ) as f : print ( p ) annotations . append ( json . load ( f )) evaluations = { sentence : [ annotation [ sentence ] for annotation in annotations if sentence in annotation ] for sentence in { sentence for annotation in annotations for sentence in annotation . keys () } } X = [ s for s in evaluations . keys ()] y = [ int ( sum ( e ) > 0 ) for e in evaluations . values ()] from pathlib import Path import json annotations = [] for p in Path(\"data\").glob(\"*.json\"): with open(p, encoding=\"utf-8\") as f: print(p) annotations.append(json.load(f)) evaluations = { sentence: [ annotation[sentence] for annotation in annotations if sentence in annotation ] for sentence in { sentence for annotation in annotations for sentence in annotation.keys() } } X = [s for s in evaluations.keys()] y = [int(sum(e) > 0) for e in evaluations.values()] data/evaluation-experiment-2-stage #1-sa6a0y.json data/evaluation-experiment-2-stage #1-2m6dmb.json Save the compiled and processed data for later use using LargeFileS3. In [2]: Copied! from great_ai.large_file import LargeFileS3 import json LargeFileS3 . configure_credentials_from_file ( \"config.ini\" ) with LargeFileS3 ( \"summary-train-dataset-small\" , \"w\" , encoding = \"utf-8\" ) as f : json . dump (( X , y ), f ) from great_ai.large_file import LargeFileS3 import json LargeFileS3.configure_credentials_from_file(\"config.ini\") with LargeFileS3(\"summary-train-dataset-small\", \"w\", encoding=\"utf-8\") as f: json.dump((X, y), f) Copying file for summary-train-dataset-small-0 Compressing summary-train-dataset-small-0 Uploading summary-train-dataset-small-0 to S3 as summary-train-dataset-small/0 Uploading summary-train-dataset-small-0.tar.gz 0.04/0.04 MB (100.0%) Filter out sentences which don't have enough annotations. In [3]: Copied! y1 = [ e [ 0 ] for e in evaluations . values () if len ( e ) == 2 ] y2 = [ e [ 1 ] for e in evaluations . values () if len ( e ) == 2 ] y1 = [e[0] for e in evaluations.values() if len(e) == 2] y2 = [e[1] for e in evaluations.values() if len(e) == 2] Calculate Cohen's kappa . It's a bit low but the task itself is pretty subjective so it's not all that surprising. In [4]: Copied! import sklearn.metrics sklearn . metrics . cohen_kappa_score ( y1 , y2 ) import sklearn.metrics sklearn.metrics.cohen_kappa_score(y1, y2) Out[4]: 0.3546448712421808 Can we train anything on this data? Let's try with a trivial SVM. In [5]: Copied! X = [ s for s in evaluations . keys ()] y = [ int ( sum ( e ) > 0 ) for e in evaluations . values ()] X = [s for s in evaluations.keys()] y = [int(sum(e) > 0) for e in evaluations.values()] In [6]: Copied! from sklearn.model_selection import train_test_split from sklearn.svm import LinearSVC from sklearn.pipeline import Pipeline from sklearn.feature_extraction.text import TfidfVectorizer from sklearn.model_selection import cross_val_score model = Pipeline ( steps = [ ( \"vectorizer\" , TfidfVectorizer ( sublinear_tf = True , min_df = 3 , max_df = 0.3 )), ( \"classifier\" , LinearSVC ()), ] ) # baseline model cross_val_score ( model , X , y , cv = 5 ) from sklearn.model_selection import train_test_split from sklearn.svm import LinearSVC from sklearn.pipeline import Pipeline from sklearn.feature_extraction.text import TfidfVectorizer from sklearn.model_selection import cross_val_score model = Pipeline( steps=[ (\"vectorizer\", TfidfVectorizer(sublinear_tf=True, min_df=3, max_df=0.3)), (\"classifier\", LinearSVC()), ] ) # baseline model cross_val_score(model, X, y, cv=5) Out[6]: array([0.79, 0.75, 0.77, 0.69, 0.77]) The cross-validation shows promising accuracies. But accuracy isn't everything, therefore, we should investigate the accuracy metrics. In [7]: Copied! X_train , X_test , y_train , y_test = train_test_split ( X , y , test_size = 0.2 , random_state = 0 ) model . fit ( X_train , y_train ) X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0) model.fit(X_train, y_train) Out[7]: #sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"\u25b8\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"\u25be\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;} Pipeline(steps=[('vectorizer', TfidfVectorizer(max_df=0.3, min_df=3, sublinear_tf=True)), ('classifier', LinearSVC())]) In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org. Pipeline Pipeline(steps=[('vectorizer', TfidfVectorizer(max_df=0.3, min_df=3, sublinear_tf=True)), ('classifier', LinearSVC())]) TfidfVectorizer TfidfVectorizer(max_df=0.3, min_df=3, sublinear_tf=True) LinearSVC LinearSVC() In [8]: Copied! y_predicted = model . predict ( X_test ) print ( sklearn . metrics . classification_report ( [ y > 0 for y in y_test ], [ y > 0 for y in y_predicted ] ) ) sklearn . metrics . ConfusionMatrixDisplay . from_predictions ( [ y > 0 for y in y_test ], [ y > 0 for y in y_predicted ], xticks_rotation = \"vertical\" , values_format = \".2f\" , ) None y_predicted = model.predict(X_test) print( sklearn.metrics.classification_report( [y > 0 for y in y_test], [y > 0 for y in y_predicted] ) ) sklearn.metrics.ConfusionMatrixDisplay.from_predictions( [y > 0 for y in y_test], [y > 0 for y in y_predicted], xticks_rotation=\"vertical\", values_format=\".2f\", ) None precision recall f1-score support False 0.78 0.78 0.78 51 True 0.78 0.78 0.78 49 accuracy 0.78 100 macro avg 0.78 0.78 0.78 100 weighted avg 0.78 0.78 0.78 100 We get an F1-score of 0.78 without any hyperparameter-optimisation; this task may be feasible to solve with AI. Next: Part 2","title":"Explore data and feasibility of approach"},{"location":"examples/scibert/deploy/","text":"(function (global, factory) { typeof exports === 'object' && typeof module !== 'undefined' ? module.exports = factory() : typeof define === 'function' && define.amd ? define(factory) : (global = global || self, global.ClipboardCopyElement = factory()); }(this, function () { 'use strict'; function createNode(text) { const node = document.createElement('pre'); node.style.width = '1px'; node.style.height = '1px'; node.style.position = 'fixed'; node.style.top = '5px'; node.textContent = text; return node; } function copyNode(node) { if ('clipboard' in navigator) { // eslint-disable-next-line flowtype/no-flow-fix-me-comments // $FlowFixMe Clipboard is not defined in Flow yet. return navigator.clipboard.writeText(node.textContent); } const selection = getSelection(); if (selection == null) { return Promise.reject(new Error()); } selection.removeAllRanges(); const range = document.createRange(); 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background-color: #ffffc0; padding-left: 5px; padding-right: 5px; } span.linenos.special { color: #000000; background-color: #ffffc0; padding-left: 5px; padding-right: 5px; } .highlight-ipynb .hll { background-color: var(--jp-cell-editor-active-background) } .highlight-ipynb { background: var(--jp-cell-editor-background); color: var(--jp-mirror-editor-variable-color) } .highlight-ipynb .c { color: var(--jp-mirror-editor-comment-color); font-style: italic } /* Comment */ .highlight-ipynb .err { color: var(--jp-mirror-editor-error-color) } /* Error */ .highlight-ipynb .k { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword */ .highlight-ipynb .o { color: var(--jp-mirror-editor-operator-color); font-weight: bold } /* Operator */ .highlight-ipynb .p { color: var(--jp-mirror-editor-punctuation-color) } /* Punctuation */ .highlight-ipynb .ch { color: var(--jp-mirror-editor-comment-color); font-style: italic } /* Comment.Hashbang */ .highlight-ipynb .cm { color: var(--jp-mirror-editor-comment-color); font-style: italic } /* Comment.Multiline */ .highlight-ipynb .cp { color: var(--jp-mirror-editor-comment-color); font-style: italic } /* Comment.Preproc */ .highlight-ipynb .cpf { color: var(--jp-mirror-editor-comment-color); font-style: italic } /* Comment.PreprocFile */ .highlight-ipynb .c1 { color: var(--jp-mirror-editor-comment-color); font-style: italic } /* Comment.Single */ .highlight-ipynb .cs { color: var(--jp-mirror-editor-comment-color); font-style: italic } /* Comment.Special */ .highlight-ipynb .kc { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword.Constant */ .highlight-ipynb .kd { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword.Declaration */ .highlight-ipynb .kn { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword.Namespace */ .highlight-ipynb .kp { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword.Pseudo */ .highlight-ipynb .kr { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword.Reserved */ .highlight-ipynb .kt { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword.Type */ .highlight-ipynb .m { color: var(--jp-mirror-editor-number-color) } /* Literal.Number */ .highlight-ipynb .s { color: var(--jp-mirror-editor-string-color) } /* Literal.String */ .highlight-ipynb .ow { color: var(--jp-mirror-editor-operator-color); font-weight: bold } /* Operator.Word */ .highlight-ipynb .w { color: var(--jp-mirror-editor-variable-color) } /* Text.Whitespace */ .highlight-ipynb .mb { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Bin */ .highlight-ipynb .mf { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Float */ .highlight-ipynb .mh { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Hex */ .highlight-ipynb .mi { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Integer */ .highlight-ipynb .mo { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Oct */ .highlight-ipynb .sa { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Affix */ .highlight-ipynb .sb { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Backtick */ .highlight-ipynb .sc { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Char */ .highlight-ipynb .dl { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Delimiter */ .highlight-ipynb .sd { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Doc */ .highlight-ipynb .s2 { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Double */ .highlight-ipynb .se { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Escape */ .highlight-ipynb .sh { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Heredoc */ .highlight-ipynb .si { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Interpol */ .highlight-ipynb .sx { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Other */ .highlight-ipynb .sr { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Regex */ .highlight-ipynb .s1 { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Single */ .highlight-ipynb .ss { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Symbol */ .highlight-ipynb .il { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Integer.Long */ /* This file is taken from the built JupyterLab theme.css Found on share/nbconvert/templates/lab/static Some changes have been made and marked with CHANGE */ .jupyter-wrapper { /* Elevation * * We style box-shadows using Material Design's idea of elevation. These particular numbers are taken from here: * * https://github.com/material-components/material-components-web * https://material-components-web.appspot.com/elevation.html */ --jp-shadow-base-lightness: 0; --jp-shadow-umbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.2 ); --jp-shadow-penumbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.14 ); --jp-shadow-ambient-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.12 ); --jp-elevation-z0: none; --jp-elevation-z1: 0px 2px 1px -1px var(--jp-shadow-umbra-color), 0px 1px 1px 0px var(--jp-shadow-penumbra-color), 0px 1px 3px 0px var(--jp-shadow-ambient-color); --jp-elevation-z2: 0px 3px 1px -2px var(--jp-shadow-umbra-color), 0px 2px 2px 0px var(--jp-shadow-penumbra-color), 0px 1px 5px 0px var(--jp-shadow-ambient-color); --jp-elevation-z4: 0px 2px 4px -1px var(--jp-shadow-umbra-color), 0px 4px 5px 0px var(--jp-shadow-penumbra-color), 0px 1px 10px 0px var(--jp-shadow-ambient-color); --jp-elevation-z6: 0px 3px 5px -1px var(--jp-shadow-umbra-color), 0px 6px 10px 0px var(--jp-shadow-penumbra-color), 0px 1px 18px 0px var(--jp-shadow-ambient-color); --jp-elevation-z8: 0px 5px 5px -3px var(--jp-shadow-umbra-color), 0px 8px 10px 1px var(--jp-shadow-penumbra-color), 0px 3px 14px 2px var(--jp-shadow-ambient-color); --jp-elevation-z12: 0px 7px 8px -4px var(--jp-shadow-umbra-color), 0px 12px 17px 2px var(--jp-shadow-penumbra-color), 0px 5px 22px 4px var(--jp-shadow-ambient-color); --jp-elevation-z16: 0px 8px 10px -5px var(--jp-shadow-umbra-color), 0px 16px 24px 2px var(--jp-shadow-penumbra-color), 0px 6px 30px 5px var(--jp-shadow-ambient-color); --jp-elevation-z20: 0px 10px 13px -6px var(--jp-shadow-umbra-color), 0px 20px 31px 3px var(--jp-shadow-penumbra-color), 0px 8px 38px 7px var(--jp-shadow-ambient-color); --jp-elevation-z24: 0px 11px 15px -7px var(--jp-shadow-umbra-color), 0px 24px 38px 3px var(--jp-shadow-penumbra-color), 0px 9px 46px 8px var(--jp-shadow-ambient-color); /* Borders * * The following variables, specify the visual styling of borders in JupyterLab. */ --jp-border-width: 1px; --jp-border-color0: var(--md-grey-400); --jp-border-color1: var(--md-grey-400); --jp-border-color2: var(--md-grey-300); --jp-border-color3: var(--md-grey-200); --jp-border-radius: 2px; /* UI Fonts * * The UI font CSS variables are used for the typography all of the JupyterLab * user interface elements that are not directly user generated content. * * The font sizing here is done assuming that the body font size of --jp-ui-font-size1 * is applied to a parent element. When children elements, such as headings, are sized * in em all things will be computed relative to that body size. */ --jp-ui-font-scale-factor: 1.2; --jp-ui-font-size0: 0.83333em; --jp-ui-font-size1: 13px; /* Base font size */ --jp-ui-font-size2: 1.2em; --jp-ui-font-size3: 1.44em; --jp-ui-font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Helvetica, Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\"; /* * Use these font colors against the corresponding main layout colors. * In a light theme, these go from dark to light. */ /* Defaults use Material Design specification */ --jp-ui-font-color0: rgba(0, 0, 0, 1); --jp-ui-font-color1: rgba(0, 0, 0, 0.87); --jp-ui-font-color2: rgba(0, 0, 0, 0.54); --jp-ui-font-color3: rgba(0, 0, 0, 0.38); /* * Use these against the brand/accent/warn/error colors. * These will typically go from light to darker, in both a dark and light theme. */ --jp-ui-inverse-font-color0: rgba(255, 255, 255, 1); --jp-ui-inverse-font-color1: rgba(255, 255, 255, 1); --jp-ui-inverse-font-color2: rgba(255, 255, 255, 0.7); --jp-ui-inverse-font-color3: rgba(255, 255, 255, 0.5); /* Content Fonts * * Content font variables are used for typography of user generated content. * * The font sizing here is done assuming that the body font size of --jp-content-font-size1 * is applied to a parent element. When children elements, such as headings, are sized * in em all things will be computed relative to that body size. */ --jp-content-line-height: 1.6; --jp-content-font-scale-factor: 1.2; --jp-content-font-size0: 0.83333em; --jp-content-font-size1: 14px; /* Base font size */ --jp-content-font-size2: 1.2em; --jp-content-font-size3: 1.44em; --jp-content-font-size4: 1.728em; --jp-content-font-size5: 2.0736em; /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-content-presentation-font-size1: 17px; --jp-content-heading-line-height: 1; --jp-content-heading-margin-top: 1.2em; --jp-content-heading-margin-bottom: 0.8em; --jp-content-heading-font-weight: 500; /* Defaults use Material Design specification */ --jp-content-font-color0: rgba(0, 0, 0, 1); --jp-content-font-color1: rgba(0, 0, 0, 0.87); --jp-content-font-color2: rgba(0, 0, 0, 0.54); --jp-content-font-color3: rgba(0, 0, 0, 0.38); --jp-content-link-color: var(--md-blue-700); --jp-content-font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Helvetica, Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\"; /* * Code Fonts * * Code font variables are used for typography of code and other monospaces content. */ --jp-code-font-size: 13px; --jp-code-line-height: 1.3077; /* 17px for 13px base */ --jp-code-padding: 5px; /* 5px for 13px base, codemirror highlighting needs integer px value */ --jp-code-font-family-default: Menlo, Consolas, \"DejaVu Sans Mono\", monospace; --jp-code-font-family: var(--jp-code-font-family-default); /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-code-presentation-font-size: 16px; /* may need to tweak cursor width if you change font size */ --jp-code-cursor-width0: 1.4px; --jp-code-cursor-width1: 2px; --jp-code-cursor-width2: 4px; /* Layout * * The following are the main layout colors use in JupyterLab. In a light * theme these would go from light to dark. */ --jp-layout-color0: white; --jp-layout-color1: white; --jp-layout-color2: var(--md-grey-200); --jp-layout-color3: var(--md-grey-400); --jp-layout-color4: var(--md-grey-600); /* Inverse Layout * * The following are the inverse layout colors use in JupyterLab. In a light * theme these would go from dark to light. */ --jp-inverse-layout-color0: #111111; --jp-inverse-layout-color1: var(--md-grey-900); --jp-inverse-layout-color2: var(--md-grey-800); --jp-inverse-layout-color3: var(--md-grey-700); --jp-inverse-layout-color4: var(--md-grey-600); /* Brand/accent */ --jp-brand-color0: var(--md-blue-900); --jp-brand-color1: var(--md-blue-700); --jp-brand-color2: var(--md-blue-300); --jp-brand-color3: var(--md-blue-100); --jp-brand-color4: var(--md-blue-50); --jp-accent-color0: var(--md-green-900); --jp-accent-color1: var(--md-green-700); --jp-accent-color2: var(--md-green-300); --jp-accent-color3: var(--md-green-100); /* State colors (warn, error, success, info) */ --jp-warn-color0: var(--md-orange-900); --jp-warn-color1: var(--md-orange-700); --jp-warn-color2: var(--md-orange-300); --jp-warn-color3: var(--md-orange-100); --jp-error-color0: var(--md-red-900); --jp-error-color1: var(--md-red-700); --jp-error-color2: var(--md-red-300); --jp-error-color3: var(--md-red-100); --jp-success-color0: var(--md-green-900); --jp-success-color1: var(--md-green-700); --jp-success-color2: var(--md-green-300); --jp-success-color3: var(--md-green-100); --jp-info-color0: var(--md-cyan-900); --jp-info-color1: var(--md-cyan-700); --jp-info-color2: var(--md-cyan-300); --jp-info-color3: var(--md-cyan-100); /* Cell specific styles */ --jp-cell-padding: 5px; --jp-cell-collapser-width: 8px; --jp-cell-collapser-min-height: 20px; --jp-cell-collapser-not-active-hover-opacity: 0.6; --jp-cell-editor-background: var(--md-grey-100); --jp-cell-editor-border-color: var(--md-grey-300); --jp-cell-editor-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-cell-editor-active-background: var(--jp-layout-color0); --jp-cell-editor-active-border-color: var(--jp-brand-color1); --jp-cell-prompt-width: 64px; --jp-cell-prompt-font-family: var(--jp-code-font-family-default); --jp-cell-prompt-letter-spacing: 0px; --jp-cell-prompt-opacity: 1; --jp-cell-prompt-not-active-opacity: 0.5; --jp-cell-prompt-not-active-font-color: var(--md-grey-700); /* A custom blend of MD grey and blue 600 * See https://meyerweb.com/eric/tools/color-blend/#546E7A:1E88E5:5:hex */ --jp-cell-inprompt-font-color: #307fc1; /* A custom blend of MD grey and orange 600 * https://meyerweb.com/eric/tools/color-blend/#546E7A:F4511E:5:hex */ --jp-cell-outprompt-font-color: #bf5b3d; /* Notebook specific styles */ --jp-notebook-padding: 10px; --jp-notebook-select-background: var(--jp-layout-color1); --jp-notebook-multiselected-color: var(--md-blue-50); /* The scroll padding is calculated to fill enough space at the bottom of the notebook to show one single-line cell (with appropriate padding) at the top when the notebook is scrolled all the way to the bottom. We also subtract one pixel so that no scrollbar appears if we have just one single-line cell in the notebook. This padding is to enable a 'scroll past end' feature in a notebook. */ --jp-notebook-scroll-padding: calc( 100% - var(--jp-code-font-size) * var(--jp-code-line-height) - var(--jp-code-padding) - var(--jp-cell-padding) - 1px ); /* Rendermime styles */ --jp-rendermime-error-background: #fdd; --jp-rendermime-table-row-background: var(--md-grey-100); --jp-rendermime-table-row-hover-background: var(--md-light-blue-50); /* Dialog specific styles */ --jp-dialog-background: rgba(0, 0, 0, 0.25); /* Console specific styles */ --jp-console-padding: 10px; /* Toolbar specific styles */ --jp-toolbar-border-color: var(--jp-border-color1); --jp-toolbar-micro-height: 8px; --jp-toolbar-background: var(--jp-layout-color1); --jp-toolbar-box-shadow: 0px 0px 2px 0px rgba(0, 0, 0, 0.24); --jp-toolbar-header-margin: 4px 4px 0px 4px; --jp-toolbar-active-background: var(--md-grey-300); /* Statusbar specific styles */ --jp-statusbar-height: 24px; /* Input field styles */ --jp-input-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-input-active-background: var(--jp-layout-color1); --jp-input-hover-background: var(--jp-layout-color1); --jp-input-background: var(--md-grey-100); --jp-input-border-color: var(--jp-border-color1); --jp-input-active-border-color: var(--jp-brand-color1); --jp-input-active-box-shadow-color: rgba(19, 124, 189, 0.3); /* General editor styles */ --jp-editor-selected-background: #d9d9d9; --jp-editor-selected-focused-background: #d7d4f0; --jp-editor-cursor-color: var(--jp-ui-font-color0); /* Code mirror specific styles */ --jp-mirror-editor-keyword-color: #008000; --jp-mirror-editor-atom-color: #88f; --jp-mirror-editor-number-color: #080; --jp-mirror-editor-def-color: #00f; --jp-mirror-editor-variable-color: var(--md-grey-900); --jp-mirror-editor-variable-2-color: #05a; --jp-mirror-editor-variable-3-color: #085; --jp-mirror-editor-punctuation-color: #05a; --jp-mirror-editor-property-color: #05a; --jp-mirror-editor-operator-color: #aa22ff; --jp-mirror-editor-comment-color: #408080; --jp-mirror-editor-string-color: #ba2121; --jp-mirror-editor-string-2-color: #708; --jp-mirror-editor-meta-color: #aa22ff; --jp-mirror-editor-qualifier-color: #555; --jp-mirror-editor-builtin-color: #008000; --jp-mirror-editor-bracket-color: #997; --jp-mirror-editor-tag-color: #170; --jp-mirror-editor-attribute-color: #00c; --jp-mirror-editor-header-color: blue; --jp-mirror-editor-quote-color: #090; --jp-mirror-editor-link-color: #00c; --jp-mirror-editor-error-color: #f00; --jp-mirror-editor-hr-color: #999; /* Vega extension styles */ --jp-vega-background: white; /* Sidebar-related styles */ --jp-sidebar-min-width: 250px; /* Search-related styles */ --jp-search-toggle-off-opacity: 0.5; --jp-search-toggle-hover-opacity: 0.8; --jp-search-toggle-on-opacity: 1; --jp-search-selected-match-background-color: rgb(245, 200, 0); --jp-search-selected-match-color: black; --jp-search-unselected-match-background-color: var( --jp-inverse-layout-color0 ); --jp-search-unselected-match-color: var(--jp-ui-inverse-font-color0); /* Icon colors that work well with light or dark backgrounds */ --jp-icon-contrast-color0: var(--md-purple-600); --jp-icon-contrast-color1: var(--md-green-600); --jp-icon-contrast-color2: var(--md-pink-600); --jp-icon-contrast-color3: var(--md-blue-600); } [data-md-color-scheme=\"slate\"] .jupyter-wrapper { /* Elevation * * We style box-shadows using Material Design's idea of elevation. These particular numbers are taken from here: * * https://github.com/material-components/material-components-web * https://material-components-web.appspot.com/elevation.html */ /* The dark theme shadows need a bit of work, but this will probably also require work on the core layout * colors used in the theme as well. */ --jp-shadow-base-lightness: 32; --jp-shadow-umbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.2 ); --jp-shadow-penumbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.14 ); --jp-shadow-ambient-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.12 ); --jp-elevation-z0: none; --jp-elevation-z1: 0px 2px 1px -1px var(--jp-shadow-umbra-color), 0px 1px 1px 0px var(--jp-shadow-penumbra-color), 0px 1px 3px 0px var(--jp-shadow-ambient-color); --jp-elevation-z2: 0px 3px 1px -2px var(--jp-shadow-umbra-color), 0px 2px 2px 0px var(--jp-shadow-penumbra-color), 0px 1px 5px 0px var(--jp-shadow-ambient-color); --jp-elevation-z4: 0px 2px 4px -1px var(--jp-shadow-umbra-color), 0px 4px 5px 0px var(--jp-shadow-penumbra-color), 0px 1px 10px 0px var(--jp-shadow-ambient-color); --jp-elevation-z6: 0px 3px 5px -1px var(--jp-shadow-umbra-color), 0px 6px 10px 0px var(--jp-shadow-penumbra-color), 0px 1px 18px 0px var(--jp-shadow-ambient-color); --jp-elevation-z8: 0px 5px 5px -3px var(--jp-shadow-umbra-color), 0px 8px 10px 1px var(--jp-shadow-penumbra-color), 0px 3px 14px 2px var(--jp-shadow-ambient-color); --jp-elevation-z12: 0px 7px 8px -4px var(--jp-shadow-umbra-color), 0px 12px 17px 2px var(--jp-shadow-penumbra-color), 0px 5px 22px 4px var(--jp-shadow-ambient-color); --jp-elevation-z16: 0px 8px 10px -5px var(--jp-shadow-umbra-color), 0px 16px 24px 2px var(--jp-shadow-penumbra-color), 0px 6px 30px 5px var(--jp-shadow-ambient-color); --jp-elevation-z20: 0px 10px 13px -6px var(--jp-shadow-umbra-color), 0px 20px 31px 3px var(--jp-shadow-penumbra-color), 0px 8px 38px 7px var(--jp-shadow-ambient-color); --jp-elevation-z24: 0px 11px 15px -7px var(--jp-shadow-umbra-color), 0px 24px 38px 3px var(--jp-shadow-penumbra-color), 0px 9px 46px 8px var(--jp-shadow-ambient-color); /* Borders * * The following variables, specify the visual styling of borders in JupyterLab. */ --jp-border-width: 1px; --jp-border-color0: var(--md-grey-700); --jp-border-color1: var(--md-grey-700); --jp-border-color2: var(--md-grey-800); --jp-border-color3: var(--md-grey-900); --jp-border-radius: 2px; /* UI Fonts * * The UI font CSS variables are used for the typography all of the JupyterLab * user interface elements that are not directly user generated content. * * The font sizing here is done assuming that the body font size of --jp-ui-font-size1 * is applied to a parent element. When children elements, such as headings, are sized * in em all things will be computed relative to that body size. */ --jp-ui-font-scale-factor: 1.2; --jp-ui-font-size0: 0.83333em; --jp-ui-font-size1: 13px; /* Base font size */ --jp-ui-font-size2: 1.2em; --jp-ui-font-size3: 1.44em; --jp-ui-font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Helvetica, Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\"; /* * Use these font colors against the corresponding main layout colors. * In a light theme, these go from dark to light. */ /* Defaults use Material Design specification */ --jp-ui-font-color0: rgba(255, 255, 255, 1); --jp-ui-font-color1: rgba(255, 255, 255, 0.87); --jp-ui-font-color2: rgba(255, 255, 255, 0.54); --jp-ui-font-color3: rgba(255, 255, 255, 0.38); /* * Use these against the brand/accent/warn/error colors. * These will typically go from light to darker, in both a dark and light theme. */ --jp-ui-inverse-font-color0: rgba(0, 0, 0, 1); --jp-ui-inverse-font-color1: rgba(0, 0, 0, 0.8); --jp-ui-inverse-font-color2: rgba(0, 0, 0, 0.5); --jp-ui-inverse-font-color3: rgba(0, 0, 0, 0.3); /* Content Fonts * * Content font variables are used for typography of user generated content. * * The font sizing here is done assuming that the body font size of --jp-content-font-size1 * is applied to a parent element. When children elements, such as headings, are sized * in em all things will be computed relative to that body size. */ --jp-content-line-height: 1.6; --jp-content-font-scale-factor: 1.2; --jp-content-font-size0: 0.83333em; --jp-content-font-size1: 14px; /* Base font size */ --jp-content-font-size2: 1.2em; --jp-content-font-size3: 1.44em; --jp-content-font-size4: 1.728em; --jp-content-font-size5: 2.0736em; /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-content-presentation-font-size1: 17px; --jp-content-heading-line-height: 1; --jp-content-heading-margin-top: 1.2em; --jp-content-heading-margin-bottom: 0.8em; --jp-content-heading-font-weight: 500; /* Defaults use Material Design specification */ --jp-content-font-color0: rgba(255, 255, 255, 1); --jp-content-font-color1: rgba(255, 255, 255, 1); --jp-content-font-color2: rgba(255, 255, 255, 0.7); --jp-content-font-color3: rgba(255, 255, 255, 0.5); --jp-content-link-color: var(--md-blue-300); --jp-content-font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Helvetica, Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\"; /* * Code Fonts * * Code font variables are used for typography of code and other monospaces content. */ --jp-code-font-size: 13px; --jp-code-line-height: 1.3077; /* 17px for 13px base */ --jp-code-padding: 5px; /* 5px for 13px base, codemirror highlighting needs integer px value */ --jp-code-font-family-default: Menlo, Consolas, \"DejaVu Sans Mono\", monospace; --jp-code-font-family: var(--jp-code-font-family-default); /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-code-presentation-font-size: 16px; /* may need to tweak cursor width if you change font size */ --jp-code-cursor-width0: 1.4px; --jp-code-cursor-width1: 2px; --jp-code-cursor-width2: 4px; /* Layout * * The following are the main layout colors use in JupyterLab. In a light * theme these would go from light to dark. */ --jp-layout-color0: #111111; --jp-layout-color1: var(--md-grey-900); --jp-layout-color2: var(--md-grey-800); --jp-layout-color3: var(--md-grey-700); --jp-layout-color4: var(--md-grey-600); /* Inverse Layout * * The following are the inverse layout colors use in JupyterLab. In a light * theme these would go from dark to light. */ --jp-inverse-layout-color0: white; --jp-inverse-layout-color1: white; --jp-inverse-layout-color2: var(--md-grey-200); --jp-inverse-layout-color3: var(--md-grey-400); --jp-inverse-layout-color4: var(--md-grey-600); /* Brand/accent */ --jp-brand-color0: var(--md-blue-700); --jp-brand-color1: var(--md-blue-500); --jp-brand-color2: var(--md-blue-300); --jp-brand-color3: var(--md-blue-100); --jp-brand-color4: var(--md-blue-50); --jp-accent-color0: var(--md-green-700); --jp-accent-color1: var(--md-green-500); --jp-accent-color2: var(--md-green-300); --jp-accent-color3: var(--md-green-100); /* State colors (warn, error, success, info) */ --jp-warn-color0: var(--md-orange-700); --jp-warn-color1: var(--md-orange-500); --jp-warn-color2: var(--md-orange-300); --jp-warn-color3: var(--md-orange-100); --jp-error-color0: var(--md-red-700); --jp-error-color1: var(--md-red-500); --jp-error-color2: var(--md-red-300); --jp-error-color3: var(--md-red-100); --jp-success-color0: var(--md-green-700); --jp-success-color1: var(--md-green-500); --jp-success-color2: var(--md-green-300); --jp-success-color3: var(--md-green-100); --jp-info-color0: var(--md-cyan-700); --jp-info-color1: var(--md-cyan-500); --jp-info-color2: var(--md-cyan-300); --jp-info-color3: var(--md-cyan-100); /* Cell specific styles */ --jp-cell-padding: 5px; --jp-cell-collapser-width: 8px; --jp-cell-collapser-min-height: 20px; --jp-cell-collapser-not-active-hover-opacity: 0.6; --jp-cell-editor-background: var(--jp-layout-color1); --jp-cell-editor-border-color: var(--md-grey-700); --jp-cell-editor-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-cell-editor-active-background: var(--jp-layout-color0); --jp-cell-editor-active-border-color: var(--jp-brand-color1); --jp-cell-prompt-width: 64px; --jp-cell-prompt-font-family: var(--jp-code-font-family-default); --jp-cell-prompt-letter-spacing: 0px; --jp-cell-prompt-opacity: 1; --jp-cell-prompt-not-active-opacity: 1; --jp-cell-prompt-not-active-font-color: var(--md-grey-300); /* A custom blend of MD grey and blue 600 * See https://meyerweb.com/eric/tools/color-blend/#546E7A:1E88E5:5:hex */ --jp-cell-inprompt-font-color: #307fc1; /* A custom blend of MD grey and orange 600 * https://meyerweb.com/eric/tools/color-blend/#546E7A:F4511E:5:hex */ --jp-cell-outprompt-font-color: #bf5b3d; /* Notebook specific styles */ --jp-notebook-padding: 10px; --jp-notebook-select-background: var(--jp-layout-color1); --jp-notebook-multiselected-color: rgba(33, 150, 243, 0.24); /* The scroll padding is calculated to fill enough space at the bottom of the notebook to show one single-line cell (with appropriate padding) at the top when the notebook is scrolled all the way to the bottom. We also subtract one pixel so that no scrollbar appears if we have just one single-line cell in the notebook. This padding is to enable a 'scroll past end' feature in a notebook. */ --jp-notebook-scroll-padding: calc( 100% - var(--jp-code-font-size) * var(--jp-code-line-height) - var(--jp-code-padding) - var(--jp-cell-padding) - 1px ); /* Rendermime styles */ --jp-rendermime-error-background: rgba(244, 67, 54, 0.28); --jp-rendermime-table-row-background: var(--md-grey-900); --jp-rendermime-table-row-hover-background: rgba(3, 169, 244, 0.2); /* Dialog specific styles */ --jp-dialog-background: rgba(0, 0, 0, 0.6); /* Console specific styles */ --jp-console-padding: 10px; /* Toolbar specific styles */ --jp-toolbar-border-color: var(--jp-border-color2); --jp-toolbar-micro-height: 8px; --jp-toolbar-background: var(--jp-layout-color1); --jp-toolbar-box-shadow: 0px 0px 2px 0px rgba(0, 0, 0, 0.8); --jp-toolbar-header-margin: 4px 4px 0px 4px; --jp-toolbar-active-background: var(--jp-layout-color0); /* Statusbar specific styles */ --jp-statusbar-height: 24px; /* Input field styles */ --jp-input-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-input-active-background: var(--jp-layout-color0); --jp-input-hover-background: var(--jp-layout-color2); --jp-input-background: var(--md-grey-800); --jp-input-border-color: var(--jp-border-color1); --jp-input-active-border-color: var(--jp-brand-color1); --jp-input-active-box-shadow-color: rgba(19, 124, 189, 0.3); /* General editor styles */ --jp-editor-selected-background: var(--jp-layout-color2); --jp-editor-selected-focused-background: rgba(33, 150, 243, 0.24); --jp-editor-cursor-color: var(--jp-ui-font-color0); /* Code mirror specific styles */ --jp-mirror-editor-keyword-color: var(--md-green-500); --jp-mirror-editor-atom-color: var(--md-blue-300); --jp-mirror-editor-number-color: var(--md-green-400); --jp-mirror-editor-def-color: var(--md-blue-600); --jp-mirror-editor-variable-color: var(--md-grey-300); --jp-mirror-editor-variable-2-color: var(--md-blue-400); --jp-mirror-editor-variable-3-color: var(--md-green-600); --jp-mirror-editor-punctuation-color: var(--md-blue-400); --jp-mirror-editor-property-color: var(--md-blue-400); --jp-mirror-editor-operator-color: #aa22ff; --jp-mirror-editor-comment-color: #408080; --jp-mirror-editor-string-color: #ff7070; --jp-mirror-editor-string-2-color: var(--md-purple-300); --jp-mirror-editor-meta-color: #aa22ff; --jp-mirror-editor-qualifier-color: #555; --jp-mirror-editor-builtin-color: var(--md-green-600); --jp-mirror-editor-bracket-color: #997; --jp-mirror-editor-tag-color: var(--md-green-700); --jp-mirror-editor-attribute-color: var(--md-blue-700); --jp-mirror-editor-header-color: var(--md-blue-500); --jp-mirror-editor-quote-color: var(--md-green-300); --jp-mirror-editor-link-color: var(--md-blue-700); --jp-mirror-editor-error-color: #f00; --jp-mirror-editor-hr-color: #999; /* Vega extension styles */ --jp-vega-background: var(--md-grey-400); /* Sidebar-related styles */ --jp-sidebar-min-width: 250px; /* Search-related styles */ --jp-search-toggle-off-opacity: 0.6; --jp-search-toggle-hover-opacity: 0.8; --jp-search-toggle-on-opacity: 1; --jp-search-selected-match-background-color: rgb(255, 225, 0); --jp-search-selected-match-color: black; --jp-search-unselected-match-background-color: var( --jp-inverse-layout-color0 ); --jp-search-unselected-match-color: var(--jp-ui-inverse-font-color0); /* scrollbar related styles. Supports every browser except Edge. */ /* colors based on JetBrain's Darcula theme */ --jp-scrollbar-background-color: #3f4244; --jp-scrollbar-thumb-color: 88, 96, 97; /* need to specify thumb color as an RGB triplet */ --jp-scrollbar-endpad: 3px; /* the minimum gap between the thumb and the ends of a scrollbar */ /* hacks for setting the thumb shape. These do nothing in Firefox */ --jp-scrollbar-thumb-margin: 3.5px; /* the space in between the sides of the thumb and the track */ --jp-scrollbar-thumb-radius: 9px; /* set to a large-ish value for rounded endcaps on the thumb */ /* Icon colors that work well with light or dark backgrounds */ --jp-icon-contrast-color0: var(--md-purple-600); --jp-icon-contrast-color1: var(--md-green-600); --jp-icon-contrast-color2: var(--md-pink-600); --jp-icon-contrast-color3: var(--md-blue-600); } :root{--md-red-50: #ffebee;--md-red-100: #ffcdd2;--md-red-200: #ef9a9a;--md-red-300: #e57373;--md-red-400: #ef5350;--md-red-500: #f44336;--md-red-600: #e53935;--md-red-700: #d32f2f;--md-red-800: #c62828;--md-red-900: #b71c1c;--md-red-A100: #ff8a80;--md-red-A200: #ff5252;--md-red-A400: #ff1744;--md-red-A700: #d50000;--md-pink-50: #fce4ec;--md-pink-100: #f8bbd0;--md-pink-200: #f48fb1;--md-pink-300: #f06292;--md-pink-400: #ec407a;--md-pink-500: #e91e63;--md-pink-600: #d81b60;--md-pink-700: #c2185b;--md-pink-800: #ad1457;--md-pink-900: #880e4f;--md-pink-A100: #ff80ab;--md-pink-A200: #ff4081;--md-pink-A400: #f50057;--md-pink-A700: #c51162;--md-purple-50: #f3e5f5;--md-purple-100: #e1bee7;--md-purple-200: #ce93d8;--md-purple-300: #ba68c8;--md-purple-400: #ab47bc;--md-purple-500: #9c27b0;--md-purple-600: #8e24aa;--md-purple-700: #7b1fa2;--md-purple-800: #6a1b9a;--md-purple-900: #4a148c;--md-purple-A100: #ea80fc;--md-purple-A200: #e040fb;--md-purple-A400: #d500f9;--md-purple-A700: #aa00ff;--md-deep-purple-50: #ede7f6;--md-deep-purple-100: #d1c4e9;--md-deep-purple-200: #b39ddb;--md-deep-purple-300: #9575cd;--md-deep-purple-400: #7e57c2;--md-deep-purple-500: #673ab7;--md-deep-purple-600: #5e35b1;--md-deep-purple-700: #512da8;--md-deep-purple-800: #4527a0;--md-deep-purple-900: #311b92;--md-deep-purple-A100: #b388ff;--md-deep-purple-A200: #7c4dff;--md-deep-purple-A400: #651fff;--md-deep-purple-A700: #6200ea;--md-indigo-50: #e8eaf6;--md-indigo-100: #c5cae9;--md-indigo-200: #9fa8da;--md-indigo-300: #7986cb;--md-indigo-400: #5c6bc0;--md-indigo-500: #3f51b5;--md-indigo-600: #3949ab;--md-indigo-700: #303f9f;--md-indigo-800: #283593;--md-indigo-900: #1a237e;--md-indigo-A100: #8c9eff;--md-indigo-A200: #536dfe;--md-indigo-A400: #3d5afe;--md-indigo-A700: #304ffe;--md-blue-50: #e3f2fd;--md-blue-100: #bbdefb;--md-blue-200: #90caf9;--md-blue-300: #64b5f6;--md-blue-400: #42a5f5;--md-blue-500: #2196f3;--md-blue-600: #1e88e5;--md-blue-700: #1976d2;--md-blue-800: #1565c0;--md-blue-900: #0d47a1;--md-blue-A100: #82b1ff;--md-blue-A200: #448aff;--md-blue-A400: #2979ff;--md-blue-A700: #2962ff;--md-light-blue-50: #e1f5fe;--md-light-blue-100: #b3e5fc;--md-light-blue-200: #81d4fa;--md-light-blue-300: #4fc3f7;--md-light-blue-400: #29b6f6;--md-light-blue-500: #03a9f4;--md-light-blue-600: #039be5;--md-light-blue-700: #0288d1;--md-light-blue-800: #0277bd;--md-light-blue-900: #01579b;--md-light-blue-A100: #80d8ff;--md-light-blue-A200: #40c4ff;--md-light-blue-A400: #00b0ff;--md-light-blue-A700: #0091ea;--md-cyan-50: #e0f7fa;--md-cyan-100: #b2ebf2;--md-cyan-200: #80deea;--md-cyan-300: #4dd0e1;--md-cyan-400: #26c6da;--md-cyan-500: #00bcd4;--md-cyan-600: #00acc1;--md-cyan-700: #0097a7;--md-cyan-800: #00838f;--md-cyan-900: #006064;--md-cyan-A100: #84ffff;--md-cyan-A200: #18ffff;--md-cyan-A400: #00e5ff;--md-cyan-A700: #00b8d4;--md-teal-50: #e0f2f1;--md-teal-100: #b2dfdb;--md-teal-200: #80cbc4;--md-teal-300: #4db6ac;--md-teal-400: #26a69a;--md-teal-500: #009688;--md-teal-600: #00897b;--md-teal-700: #00796b;--md-teal-800: #00695c;--md-teal-900: #004d40;--md-teal-A100: #a7ffeb;--md-teal-A200: #64ffda;--md-teal-A400: #1de9b6;--md-teal-A700: #00bfa5;--md-green-50: #e8f5e9;--md-green-100: #c8e6c9;--md-green-200: #a5d6a7;--md-green-300: #81c784;--md-green-400: #66bb6a;--md-green-500: #4caf50;--md-green-600: #43a047;--md-green-700: #388e3c;--md-green-800: #2e7d32;--md-green-900: #1b5e20;--md-green-A100: #b9f6ca;--md-green-A200: #69f0ae;--md-green-A400: #00e676;--md-green-A700: #00c853;--md-light-green-50: #f1f8e9;--md-light-green-100: #dcedc8;--md-light-green-200: #c5e1a5;--md-light-green-300: #aed581;--md-light-green-400: #9ccc65;--md-light-green-500: #8bc34a;--md-light-green-600: #7cb342;--md-light-green-700: #689f38;--md-light-green-800: #558b2f;--md-light-green-900: #33691e;--md-light-green-A100: #ccff90;--md-light-green-A200: #b2ff59;--md-light-green-A400: #76ff03;--md-light-green-A700: #64dd17;--md-lime-50: #f9fbe7;--md-lime-100: #f0f4c3;--md-lime-200: #e6ee9c;--md-lime-300: #dce775;--md-lime-400: #d4e157;--md-lime-500: #cddc39;--md-lime-600: #c0ca33;--md-lime-700: #afb42b;--md-lime-800: #9e9d24;--md-lime-900: #827717;--md-lime-A100: #f4ff81;--md-lime-A200: #eeff41;--md-lime-A400: #c6ff00;--md-lime-A700: #aeea00;--md-yellow-50: #fffde7;--md-yellow-100: #fff9c4;--md-yellow-200: #fff59d;--md-yellow-300: #fff176;--md-yellow-400: #ffee58;--md-yellow-500: #ffeb3b;--md-yellow-600: #fdd835;--md-yellow-700: #fbc02d;--md-yellow-800: #f9a825;--md-yellow-900: #f57f17;--md-yellow-A100: #ffff8d;--md-yellow-A200: #ffff00;--md-yellow-A400: #ffea00;--md-yellow-A700: #ffd600;--md-amber-50: #fff8e1;--md-amber-100: #ffecb3;--md-amber-200: #ffe082;--md-amber-300: #ffd54f;--md-amber-400: #ffca28;--md-amber-500: #ffc107;--md-amber-600: #ffb300;--md-amber-700: #ffa000;--md-amber-800: #ff8f00;--md-amber-900: #ff6f00;--md-amber-A100: #ffe57f;--md-amber-A200: #ffd740;--md-amber-A400: #ffc400;--md-amber-A700: #ffab00;--md-orange-50: #fff3e0;--md-orange-100: #ffe0b2;--md-orange-200: #ffcc80;--md-orange-300: #ffb74d;--md-orange-400: #ffa726;--md-orange-500: #ff9800;--md-orange-600: #fb8c00;--md-orange-700: #f57c00;--md-orange-800: #ef6c00;--md-orange-900: #e65100;--md-orange-A100: #ffd180;--md-orange-A200: #ffab40;--md-orange-A400: #ff9100;--md-orange-A700: #ff6d00;--md-deep-orange-50: #fbe9e7;--md-deep-orange-100: #ffccbc;--md-deep-orange-200: #ffab91;--md-deep-orange-300: #ff8a65;--md-deep-orange-400: #ff7043;--md-deep-orange-500: 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sourceMappingURL=mkdocs-jupyter.css.map*/ init_mathjax = function() { if (window.MathJax) { // MathJax loaded MathJax.Hub.Config({ TeX: { equationNumbers: { autoNumber: \"AMS\", useLabelIds: true } }, tex2jax: { inlineMath: [ ['$','$'], [\"\\\\(\",\"\\\\)\"] ], displayMath: [ ['$$','$$'], [\"\\\\[\",\"\\\\]\"] ], processEscapes: true, processEnvironments: true }, displayAlign: 'center', CommonHTML: { linebreaks: { automatic: true } } }); MathJax.Hub.Queue([\"Typeset\", MathJax.Hub]); } } init_mathjax(); Create an inference function \u00b6 Everything is ready to wrap the previously trained model and deploy it. First, we need to configure the LargeFileBackend, the TracingDatabase and GreatAI. In [1]: Copied! from great_ai.utilities import ConfigFile from great_ai.large_file import LargeFileS3 from great_ai import configure , MongoDbDriver configuration = ConfigFile ( \"config.ini\" ) LargeFileS3 . configure_credentials_from_file ( configuration ) MongoDbDriver . configure_credentials_from_file ( configuration ) configure ( dashboard_table_size = 100 , # traces are small, we can show many prediction_cache_size = 4096 , # predictions are expensive, cache them ) from great_ai.utilities import ConfigFile from great_ai.large_file import LargeFileS3 from great_ai import configure, MongoDbDriver configuration = ConfigFile(\"config.ini\") LargeFileS3.configure_credentials_from_file(configuration) MongoDbDriver.configure_credentials_from_file(configuration) configure( dashboard_table_size=100, # traces are small, we can show many prediction_cache_size=4096, # predictions are expensive, cache them ) The value of `ENVIRONMENT` contains the \"ENV` prefix but `ENVIRONMENT` is not defined as an environment variable, using the default value defined above (`DEVELOPMENT`) Environment variable ENVIRONMENT is not set, defaulting to development mode \u203c\ufe0f MongoDbDriver has been already configured: skipping initialisation LargeFileS3 has been already configured: skipping initialisation GreatAI (v0.1.6): configured \u2705 \ud83d\udd29 tracing_database: MongoDbDriver \ud83d\udd29 large_file_implementation: LargeFileS3 \ud83d\udd29 is_production: False \ud83d\udd29 should_log_exception_stack: True \ud83d\udd29 prediction_cache_size: 4096 \ud83d\udd29 dashboard_table_size: 100 You still need to check whether you follow all best practices before trusting your deployment. > Find out more at https://se-ml.github.io/practices For a pleasant developer experience, we create some typed models that will show up in the automatically generated OpenAPI schema specification and will also provide runtime type validation. In [2]: Copied! from typing import List from pydantic import BaseModel class Attention ( BaseModel ): weight : float token : str class EvaluatedSentence ( BaseModel ): score : float text : str explanation : List [ Attention ] from typing import List from pydantic import BaseModel class Attention(BaseModel): weight: float token: str class EvaluatedSentence(BaseModel): score: float text: str explanation: List[Attention] Even though @use_model caches the remote files locally and it also handles deserialising objects, we only use it to store a directory. In this case, it gives back a path, the path to that directory. So, we need to load the files from that folder ourselves. In order to only load it once per process, we create a small model loader helper function. This is usually not needed, however, when we can outsmart dill so for optimisation purposes, we do it. In [3]: Copied! from great_ai import use_model from pathlib import Path from typing import Tuple from transformers import ( PreTrainedModel , PreTrainedTokenizer , ) from transformers import ( AutoConfig , AutoModelForSequenceClassification , AutoTokenizer , ) _tokenizer : PreTrainedTokenizer = None _loaded_model : PreTrainedModel = None @use_model ( \"scibert-highlights\" , version = \"latest\" , model_kwarg_name = \"model_path\" ) def get_tokenizer_and_model ( model_path : Path , original_model : str = \"allenai/scibert_scivocab_uncased\" ) -> Tuple [ PreTrainedTokenizer , PreTrainedModel ]: global _tokenizer , _loaded_model if _tokenizer is None : _tokenizer = AutoTokenizer . from_pretrained ( original_model ) if _loaded_model is None : config = AutoConfig . from_pretrained ( model_path , output_hidden_states = True , output_attentions = True ) _loaded_model = AutoModelForSequenceClassification . from_pretrained ( model_path , config = config ) return _tokenizer , _loaded_model from great_ai import use_model from pathlib import Path from typing import Tuple from transformers import ( PreTrainedModel, PreTrainedTokenizer, ) from transformers import ( AutoConfig, AutoModelForSequenceClassification, AutoTokenizer, ) _tokenizer: PreTrainedTokenizer = None _loaded_model: PreTrainedModel = None @use_model(\"scibert-highlights\", version=\"latest\", model_kwarg_name=\"model_path\") def get_tokenizer_and_model( model_path: Path, original_model: str = \"allenai/scibert_scivocab_uncased\" ) -> Tuple[PreTrainedTokenizer, PreTrainedModel]: global _tokenizer, _loaded_model if _tokenizer is None: _tokenizer = AutoTokenizer.from_pretrained(original_model) if _loaded_model is None: config = AutoConfig.from_pretrained( model_path, output_hidden_states=True, output_attentions=True ) _loaded_model = AutoModelForSequenceClassification.from_pretrained( model_path, config=config ) return _tokenizer, _loaded_model Latest version of scibert-highlights is 0 (from versions: 0) File scibert-highlights-0 found in cache Finally, implement the inference function. In [4]: Copied! from great_ai import GreatAI from great_ai.utilities import clean import re import numpy as np import torch from transformers.modeling_outputs import SequenceClassifierOutput @GreatAI . create def find_highlights ( sentence : str ) -> EvaluatedSentence : \"\"\"Get the interestingness prediction of the input sentence using SciBERT. Run the SciBERT model in inference mode and evaluate the sentence. Additionally, provide explanation in the form of the last layer's sum attention between `[CLS]` and the other tokens. \"\"\" tokenizer , loaded_model = get_tokenizer_and_model () sentence = clean ( sentence , convert_to_ascii = True , remove_brackets = True ) tensors = tokenizer ( sentence , return_tensors = \"pt\" , truncation = True , max_length = 512 ) with torch . inference_mode (): result : SequenceClassifierOutput = loaded_model ( ** tensors ) positive_likelihood = torch . nn . Softmax ( dim = 1 )( result . logits )[ 0 ][ 1 ] tokens = tensors [ \"input_ids\" ][ 0 ] attentions = np . sum ( result . attentions [ - 1 ] . numpy ()[ 0 ], axis = 0 )[ 0 ][ 1 : - 1 ] # Tuple of `torch.FloatTensor` (one for each layer) of shape # `(batch_size, num_heads, sequence_length, sequence_length)`. explanation = [] token_attentions = list ( zip ( attentions , tokens [ 1 : - 1 ])) for token in re . split ( r \"([ .,])\" , sentence ): token = token . strip () if not token : continue bert_tokens = tokenizer ( token , return_tensors = \"pt\" , truncation = True , max_length = 512 )[ \"input_ids\" ][ 0 ][ 1 : - 1 ] # truncation=True needed to fix `RuntimeError: Already borrowed` weight = 0 for t1 in bert_tokens : if not token_attentions : break a , t2 = token_attentions . pop ( 0 ) assert t1 == t2 , sentence weight += a explanation . append ( Attention ( token = token if token in \".,\" else \" \" + token , weight = round ( weight , 4 ) ) ) if not token_attentions : break return EvaluatedSentence ( score = positive_likelihood , text = sentence , explanation = explanation ) from great_ai import GreatAI from great_ai.utilities import clean import re import numpy as np import torch from transformers.modeling_outputs import SequenceClassifierOutput @GreatAI.create def find_highlights(sentence: str) -> EvaluatedSentence: \"\"\"Get the interestingness prediction of the input sentence using SciBERT. Run the SciBERT model in inference mode and evaluate the sentence. Additionally, provide explanation in the form of the last layer's sum attention between `[CLS]` and the other tokens. \"\"\" tokenizer, loaded_model = get_tokenizer_and_model() sentence = clean(sentence, convert_to_ascii=True, remove_brackets=True) tensors = tokenizer(sentence, return_tensors=\"pt\", truncation=True, max_length=512) with torch.inference_mode(): result: SequenceClassifierOutput = loaded_model(**tensors) positive_likelihood = torch.nn.Softmax(dim=1)(result.logits)[0][1] tokens = tensors[\"input_ids\"][0] attentions = np.sum(result.attentions[-1].numpy()[0], axis=0)[0][1:-1] # Tuple of `torch.FloatTensor` (one for each layer) of shape # `(batch_size, num_heads, sequence_length, sequence_length)`. explanation = [] token_attentions = list(zip(attentions, tokens[1:-1])) for token in re.split(r\"([ .,])\", sentence): token = token.strip() if not token: continue bert_tokens = tokenizer( token, return_tensors=\"pt\", truncation=True, max_length=512 )[\"input_ids\"][0][ 1:-1 ] # truncation=True needed to fix `RuntimeError: Already borrowed` weight = 0 for t1 in bert_tokens: if not token_attentions: break a, t2 = token_attentions.pop(0) assert t1 == t2, sentence weight += a explanation.append( Attention( token=token if token in \".,\" else \" \" + token, weight=round(weight, 4) ) ) if not token_attentions: break return EvaluatedSentence( score=positive_likelihood, text=sentence, explanation=explanation ) A simple test to see everything works. Note that the models list is filled by the @use_model call even though it's not on the main inference function. In [5]: Copied! if __name__ == \"__main__\" : find_highlights ( \"Our solution has outperformed the state-of-the-art.\" ), find_highlights ( \"Their solution did not perform well.\" ) if __name__ == \"__main__\": find_highlights( \"Our solution has outperformed the state-of-the-art.\" ), find_highlights(\"Their solution did not perform well.\") Out[5]: (Trace[EvaluatedSentence]({'created': '2022-07-16T18:47:29.581701', 'exception': None, 'feedback': None, 'logged_values': { 'arg:sentence:length': 51, 'arg:sentence:value': 'Our solution has outperformed the ' 'state-of-the-art.'}, 'models': [{'key': 'scibert-highlights', 'version': 0}], 'original_execution_time_ms': 7127.2063, 'output': { 'explanation': [ {'token': ' Our', 'weight': 0.3993}, {'token': ' solution', 'weight': 0.3481}, {'token': ' has', 'weight': 0.2945}, {'token': ' outperformed', 'weight': 0.4011}, {'token': ' the', 'weight': 0.1484}, {'token': ' state-of-the-art', 'weight': 0.5727}, {'token': '.', 'weight': 7.775}], 'score': 0.9991180300712585, 'text': 'Our solution has outperformed the state-of-the-art.'}, 'tags': ['find_highlights', 'online', 'development'], 'trace_id': '56e20e94-79df-4793-ae61-d20820ebe2d3'}), Trace[EvaluatedSentence]({'created': '2022-07-16T18:47:37.020275', 'exception': None, 'feedback': None, 'logged_values': { 'arg:sentence:length': 36, 'arg:sentence:value': 'Their solution did not perform ' 'well.'}, 'models': [{'key': 'scibert-highlights', 'version': 0}], 'original_execution_time_ms': 170.7057, 'output': { 'explanation': [ {'token': ' Their', 'weight': 1.1475}, {'token': ' solution', 'weight': 0.8205}, {'token': ' did', 'weight': 0.3254}, {'token': ' not', 'weight': 0.2921}, {'token': ' perform', 'weight': 0.4293}, {'token': ' well', 'weight': 0.2772}, {'token': '.', 'weight': 4.4723}], 'score': 0.12305451184511185, 'text': 'Their solution did not perform well.'}, 'tags': ['find_highlights', 'online', 'development'], 'trace_id': '7fcf8271-1738-4025-8305-d5a1e5100aea'})) In this case, the service is built as a docker image, pushed to our image registry and subsequent rolling update is performed in the production cluster. To check out the Dockerimage, go to the additional files page .","title":"Create an inference function"},{"location":"examples/scibert/deploy/#create-an-inference-function","text":"Everything is ready to wrap the previously trained model and deploy it. First, we need to configure the LargeFileBackend, the TracingDatabase and GreatAI. In [1]: Copied! from great_ai.utilities import ConfigFile from great_ai.large_file import LargeFileS3 from great_ai import configure , MongoDbDriver configuration = ConfigFile ( \"config.ini\" ) LargeFileS3 . configure_credentials_from_file ( configuration ) MongoDbDriver . configure_credentials_from_file ( configuration ) configure ( dashboard_table_size = 100 , # traces are small, we can show many prediction_cache_size = 4096 , # predictions are expensive, cache them ) from great_ai.utilities import ConfigFile from great_ai.large_file import LargeFileS3 from great_ai import configure, MongoDbDriver configuration = ConfigFile(\"config.ini\") LargeFileS3.configure_credentials_from_file(configuration) MongoDbDriver.configure_credentials_from_file(configuration) configure( dashboard_table_size=100, # traces are small, we can show many prediction_cache_size=4096, # predictions are expensive, cache them ) The value of `ENVIRONMENT` contains the \"ENV` prefix but `ENVIRONMENT` is not defined as an environment variable, using the default value defined above (`DEVELOPMENT`) Environment variable ENVIRONMENT is not set, defaulting to development mode \u203c\ufe0f MongoDbDriver has been already configured: skipping initialisation LargeFileS3 has been already configured: skipping initialisation GreatAI (v0.1.6): configured \u2705 \ud83d\udd29 tracing_database: MongoDbDriver \ud83d\udd29 large_file_implementation: LargeFileS3 \ud83d\udd29 is_production: False \ud83d\udd29 should_log_exception_stack: True \ud83d\udd29 prediction_cache_size: 4096 \ud83d\udd29 dashboard_table_size: 100 You still need to check whether you follow all best practices before trusting your deployment. > Find out more at https://se-ml.github.io/practices For a pleasant developer experience, we create some typed models that will show up in the automatically generated OpenAPI schema specification and will also provide runtime type validation. In [2]: Copied! from typing import List from pydantic import BaseModel class Attention ( BaseModel ): weight : float token : str class EvaluatedSentence ( BaseModel ): score : float text : str explanation : List [ Attention ] from typing import List from pydantic import BaseModel class Attention(BaseModel): weight: float token: str class EvaluatedSentence(BaseModel): score: float text: str explanation: List[Attention] Even though @use_model caches the remote files locally and it also handles deserialising objects, we only use it to store a directory. In this case, it gives back a path, the path to that directory. So, we need to load the files from that folder ourselves. In order to only load it once per process, we create a small model loader helper function. This is usually not needed, however, when we can outsmart dill so for optimisation purposes, we do it. In [3]: Copied! from great_ai import use_model from pathlib import Path from typing import Tuple from transformers import ( PreTrainedModel , PreTrainedTokenizer , ) from transformers import ( AutoConfig , AutoModelForSequenceClassification , AutoTokenizer , ) _tokenizer : PreTrainedTokenizer = None _loaded_model : PreTrainedModel = None @use_model ( \"scibert-highlights\" , version = \"latest\" , model_kwarg_name = \"model_path\" ) def get_tokenizer_and_model ( model_path : Path , original_model : str = \"allenai/scibert_scivocab_uncased\" ) -> Tuple [ PreTrainedTokenizer , PreTrainedModel ]: global _tokenizer , _loaded_model if _tokenizer is None : _tokenizer = AutoTokenizer . from_pretrained ( original_model ) if _loaded_model is None : config = AutoConfig . from_pretrained ( model_path , output_hidden_states = True , output_attentions = True ) _loaded_model = AutoModelForSequenceClassification . from_pretrained ( model_path , config = config ) return _tokenizer , _loaded_model from great_ai import use_model from pathlib import Path from typing import Tuple from transformers import ( PreTrainedModel, PreTrainedTokenizer, ) from transformers import ( AutoConfig, AutoModelForSequenceClassification, AutoTokenizer, ) _tokenizer: PreTrainedTokenizer = None _loaded_model: PreTrainedModel = None @use_model(\"scibert-highlights\", version=\"latest\", model_kwarg_name=\"model_path\") def get_tokenizer_and_model( model_path: Path, original_model: str = \"allenai/scibert_scivocab_uncased\" ) -> Tuple[PreTrainedTokenizer, PreTrainedModel]: global _tokenizer, _loaded_model if _tokenizer is None: _tokenizer = AutoTokenizer.from_pretrained(original_model) if _loaded_model is None: config = AutoConfig.from_pretrained( model_path, output_hidden_states=True, output_attentions=True ) _loaded_model = AutoModelForSequenceClassification.from_pretrained( model_path, config=config ) return _tokenizer, _loaded_model Latest version of scibert-highlights is 0 (from versions: 0) File scibert-highlights-0 found in cache Finally, implement the inference function. In [4]: Copied! from great_ai import GreatAI from great_ai.utilities import clean import re import numpy as np import torch from transformers.modeling_outputs import SequenceClassifierOutput @GreatAI . create def find_highlights ( sentence : str ) -> EvaluatedSentence : \"\"\"Get the interestingness prediction of the input sentence using SciBERT. Run the SciBERT model in inference mode and evaluate the sentence. Additionally, provide explanation in the form of the last layer's sum attention between `[CLS]` and the other tokens. \"\"\" tokenizer , loaded_model = get_tokenizer_and_model () sentence = clean ( sentence , convert_to_ascii = True , remove_brackets = True ) tensors = tokenizer ( sentence , return_tensors = \"pt\" , truncation = True , max_length = 512 ) with torch . inference_mode (): result : SequenceClassifierOutput = loaded_model ( ** tensors ) positive_likelihood = torch . nn . Softmax ( dim = 1 )( result . logits )[ 0 ][ 1 ] tokens = tensors [ \"input_ids\" ][ 0 ] attentions = np . sum ( result . attentions [ - 1 ] . numpy ()[ 0 ], axis = 0 )[ 0 ][ 1 : - 1 ] # Tuple of `torch.FloatTensor` (one for each layer) of shape # `(batch_size, num_heads, sequence_length, sequence_length)`. explanation = [] token_attentions = list ( zip ( attentions , tokens [ 1 : - 1 ])) for token in re . split ( r \"([ .,])\" , sentence ): token = token . strip () if not token : continue bert_tokens = tokenizer ( token , return_tensors = \"pt\" , truncation = True , max_length = 512 )[ \"input_ids\" ][ 0 ][ 1 : - 1 ] # truncation=True needed to fix `RuntimeError: Already borrowed` weight = 0 for t1 in bert_tokens : if not token_attentions : break a , t2 = token_attentions . pop ( 0 ) assert t1 == t2 , sentence weight += a explanation . append ( Attention ( token = token if token in \".,\" else \" \" + token , weight = round ( weight , 4 ) ) ) if not token_attentions : break return EvaluatedSentence ( score = positive_likelihood , text = sentence , explanation = explanation ) from great_ai import GreatAI from great_ai.utilities import clean import re import numpy as np import torch from transformers.modeling_outputs import SequenceClassifierOutput @GreatAI.create def find_highlights(sentence: str) -> EvaluatedSentence: \"\"\"Get the interestingness prediction of the input sentence using SciBERT. Run the SciBERT model in inference mode and evaluate the sentence. Additionally, provide explanation in the form of the last layer's sum attention between `[CLS]` and the other tokens. \"\"\" tokenizer, loaded_model = get_tokenizer_and_model() sentence = clean(sentence, convert_to_ascii=True, remove_brackets=True) tensors = tokenizer(sentence, return_tensors=\"pt\", truncation=True, max_length=512) with torch.inference_mode(): result: SequenceClassifierOutput = loaded_model(**tensors) positive_likelihood = torch.nn.Softmax(dim=1)(result.logits)[0][1] tokens = tensors[\"input_ids\"][0] attentions = np.sum(result.attentions[-1].numpy()[0], axis=0)[0][1:-1] # Tuple of `torch.FloatTensor` (one for each layer) of shape # `(batch_size, num_heads, sequence_length, sequence_length)`. explanation = [] token_attentions = list(zip(attentions, tokens[1:-1])) for token in re.split(r\"([ .,])\", sentence): token = token.strip() if not token: continue bert_tokens = tokenizer( token, return_tensors=\"pt\", truncation=True, max_length=512 )[\"input_ids\"][0][ 1:-1 ] # truncation=True needed to fix `RuntimeError: Already borrowed` weight = 0 for t1 in bert_tokens: if not token_attentions: break a, t2 = token_attentions.pop(0) assert t1 == t2, sentence weight += a explanation.append( Attention( token=token if token in \".,\" else \" \" + token, weight=round(weight, 4) ) ) if not token_attentions: break return EvaluatedSentence( score=positive_likelihood, text=sentence, explanation=explanation ) A simple test to see everything works. Note that the models list is filled by the @use_model call even though it's not on the main inference function. In [5]: Copied! if __name__ == \"__main__\" : find_highlights ( \"Our solution has outperformed the state-of-the-art.\" ), find_highlights ( \"Their solution did not perform well.\" ) if __name__ == \"__main__\": find_highlights( \"Our solution has outperformed the state-of-the-art.\" ), find_highlights(\"Their solution did not perform well.\") Out[5]: (Trace[EvaluatedSentence]({'created': '2022-07-16T18:47:29.581701', 'exception': None, 'feedback': None, 'logged_values': { 'arg:sentence:length': 51, 'arg:sentence:value': 'Our solution has outperformed the ' 'state-of-the-art.'}, 'models': [{'key': 'scibert-highlights', 'version': 0}], 'original_execution_time_ms': 7127.2063, 'output': { 'explanation': [ {'token': ' Our', 'weight': 0.3993}, {'token': ' solution', 'weight': 0.3481}, {'token': ' has', 'weight': 0.2945}, {'token': ' outperformed', 'weight': 0.4011}, {'token': ' the', 'weight': 0.1484}, {'token': ' state-of-the-art', 'weight': 0.5727}, {'token': '.', 'weight': 7.775}], 'score': 0.9991180300712585, 'text': 'Our solution has outperformed the state-of-the-art.'}, 'tags': ['find_highlights', 'online', 'development'], 'trace_id': '56e20e94-79df-4793-ae61-d20820ebe2d3'}), Trace[EvaluatedSentence]({'created': '2022-07-16T18:47:37.020275', 'exception': None, 'feedback': None, 'logged_values': { 'arg:sentence:length': 36, 'arg:sentence:value': 'Their solution did not perform ' 'well.'}, 'models': [{'key': 'scibert-highlights', 'version': 0}], 'original_execution_time_ms': 170.7057, 'output': { 'explanation': [ {'token': ' Their', 'weight': 1.1475}, {'token': ' solution', 'weight': 0.8205}, {'token': ' did', 'weight': 0.3254}, {'token': ' not', 'weight': 0.2921}, {'token': ' perform', 'weight': 0.4293}, {'token': ' well', 'weight': 0.2772}, {'token': '.', 'weight': 4.4723}], 'score': 0.12305451184511185, 'text': 'Their solution did not perform well.'}, 'tags': ['find_highlights', 'online', 'development'], 'trace_id': '7fcf8271-1738-4025-8305-d5a1e5100aea'})) In this case, the service is built as a docker image, pushed to our image registry and subsequent rolling update is performed in the production cluster. To check out the Dockerimage, go to the additional files page .","title":"Create an inference function"},{"location":"examples/scibert/train/","text":"(function (global, factory) { typeof exports === 'object' && typeof module !== 'undefined' ? module.exports = factory() : typeof define === 'function' && define.amd ? define(factory) : (global = global || self, global.ClipboardCopyElement = factory()); }(this, function () { 'use strict'; function createNode(text) { const node = document.createElement('pre'); node.style.width = '1px'; node.style.height = '1px'; node.style.position = 'fixed'; node.style.top = '5px'; node.textContent = text; return node; } function copyNode(node) { if ('clipboard' in navigator) { // eslint-disable-next-line flowtype/no-flow-fix-me-comments // $FlowFixMe Clipboard is not defined in Flow yet. return navigator.clipboard.writeText(node.textContent); } const selection = getSelection(); if (selection == null) { return Promise.reject(new Error()); } selection.removeAllRanges(); 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font-weight: bold } /* Keyword.Reserved */ .highlight-ipynb .kt { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword.Type */ .highlight-ipynb .m { color: var(--jp-mirror-editor-number-color) } /* Literal.Number */ .highlight-ipynb .s { color: var(--jp-mirror-editor-string-color) } /* Literal.String */ .highlight-ipynb .ow { color: var(--jp-mirror-editor-operator-color); font-weight: bold } /* Operator.Word */ .highlight-ipynb .w { color: var(--jp-mirror-editor-variable-color) } /* Text.Whitespace */ .highlight-ipynb .mb { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Bin */ .highlight-ipynb .mf { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Float */ .highlight-ipynb .mh { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Hex */ .highlight-ipynb .mi { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Integer */ .highlight-ipynb .mo { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Oct */ .highlight-ipynb .sa { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Affix */ .highlight-ipynb .sb { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Backtick */ .highlight-ipynb .sc { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Char */ .highlight-ipynb .dl { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Delimiter */ .highlight-ipynb .sd { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Doc */ .highlight-ipynb .s2 { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Double */ .highlight-ipynb .se { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Escape */ .highlight-ipynb .sh { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Heredoc */ .highlight-ipynb .si { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Interpol */ .highlight-ipynb .sx { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Other */ .highlight-ipynb .sr { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Regex */ .highlight-ipynb .s1 { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Single */ .highlight-ipynb .ss { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Symbol */ .highlight-ipynb .il { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Integer.Long */ /* This file is taken from the built JupyterLab theme.css Found on share/nbconvert/templates/lab/static Some changes have been made and marked with CHANGE */ .jupyter-wrapper { /* Elevation * * We style box-shadows using Material Design's idea of elevation. These particular numbers are taken from here: * * https://github.com/material-components/material-components-web * https://material-components-web.appspot.com/elevation.html */ --jp-shadow-base-lightness: 0; --jp-shadow-umbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.2 ); --jp-shadow-penumbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.14 ); --jp-shadow-ambient-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.12 ); --jp-elevation-z0: none; --jp-elevation-z1: 0px 2px 1px -1px var(--jp-shadow-umbra-color), 0px 1px 1px 0px var(--jp-shadow-penumbra-color), 0px 1px 3px 0px var(--jp-shadow-ambient-color); --jp-elevation-z2: 0px 3px 1px -2px var(--jp-shadow-umbra-color), 0px 2px 2px 0px var(--jp-shadow-penumbra-color), 0px 1px 5px 0px var(--jp-shadow-ambient-color); --jp-elevation-z4: 0px 2px 4px -1px var(--jp-shadow-umbra-color), 0px 4px 5px 0px var(--jp-shadow-penumbra-color), 0px 1px 10px 0px var(--jp-shadow-ambient-color); --jp-elevation-z6: 0px 3px 5px -1px var(--jp-shadow-umbra-color), 0px 6px 10px 0px var(--jp-shadow-penumbra-color), 0px 1px 18px 0px var(--jp-shadow-ambient-color); --jp-elevation-z8: 0px 5px 5px -3px var(--jp-shadow-umbra-color), 0px 8px 10px 1px var(--jp-shadow-penumbra-color), 0px 3px 14px 2px var(--jp-shadow-ambient-color); --jp-elevation-z12: 0px 7px 8px -4px var(--jp-shadow-umbra-color), 0px 12px 17px 2px var(--jp-shadow-penumbra-color), 0px 5px 22px 4px var(--jp-shadow-ambient-color); --jp-elevation-z16: 0px 8px 10px -5px var(--jp-shadow-umbra-color), 0px 16px 24px 2px var(--jp-shadow-penumbra-color), 0px 6px 30px 5px var(--jp-shadow-ambient-color); --jp-elevation-z20: 0px 10px 13px -6px var(--jp-shadow-umbra-color), 0px 20px 31px 3px var(--jp-shadow-penumbra-color), 0px 8px 38px 7px var(--jp-shadow-ambient-color); --jp-elevation-z24: 0px 11px 15px -7px var(--jp-shadow-umbra-color), 0px 24px 38px 3px var(--jp-shadow-penumbra-color), 0px 9px 46px 8px var(--jp-shadow-ambient-color); /* Borders * * The following variables, specify the visual styling of borders in JupyterLab. */ --jp-border-width: 1px; --jp-border-color0: var(--md-grey-400); --jp-border-color1: var(--md-grey-400); --jp-border-color2: var(--md-grey-300); --jp-border-color3: var(--md-grey-200); --jp-border-radius: 2px; /* UI Fonts * * The UI font CSS variables are used for the typography all of the JupyterLab * user interface elements that are not directly user generated content. * * The font sizing here is done assuming that the body font size of --jp-ui-font-size1 * is applied to a parent element. When children elements, such as headings, are sized * in em all things will be computed relative to that body size. */ --jp-ui-font-scale-factor: 1.2; --jp-ui-font-size0: 0.83333em; --jp-ui-font-size1: 13px; /* Base font size */ --jp-ui-font-size2: 1.2em; --jp-ui-font-size3: 1.44em; --jp-ui-font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Helvetica, Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\"; /* * Use these font colors against the corresponding main layout colors. * In a light theme, these go from dark to light. */ /* Defaults use Material Design specification */ --jp-ui-font-color0: rgba(0, 0, 0, 1); --jp-ui-font-color1: rgba(0, 0, 0, 0.87); --jp-ui-font-color2: rgba(0, 0, 0, 0.54); --jp-ui-font-color3: rgba(0, 0, 0, 0.38); /* * Use these against the brand/accent/warn/error colors. * These will typically go from light to darker, in both a dark and light theme. */ --jp-ui-inverse-font-color0: rgba(255, 255, 255, 1); --jp-ui-inverse-font-color1: rgba(255, 255, 255, 1); --jp-ui-inverse-font-color2: rgba(255, 255, 255, 0.7); --jp-ui-inverse-font-color3: rgba(255, 255, 255, 0.5); /* Content Fonts * * Content font variables are used for typography of user generated content. * * The font sizing here is done assuming that the body font size of --jp-content-font-size1 * is applied to a parent element. When children elements, such as headings, are sized * in em all things will be computed relative to that body size. */ --jp-content-line-height: 1.6; --jp-content-font-scale-factor: 1.2; --jp-content-font-size0: 0.83333em; --jp-content-font-size1: 14px; /* Base font size */ --jp-content-font-size2: 1.2em; --jp-content-font-size3: 1.44em; --jp-content-font-size4: 1.728em; --jp-content-font-size5: 2.0736em; /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-content-presentation-font-size1: 17px; --jp-content-heading-line-height: 1; --jp-content-heading-margin-top: 1.2em; --jp-content-heading-margin-bottom: 0.8em; --jp-content-heading-font-weight: 500; /* Defaults use Material Design specification */ --jp-content-font-color0: rgba(0, 0, 0, 1); --jp-content-font-color1: rgba(0, 0, 0, 0.87); --jp-content-font-color2: rgba(0, 0, 0, 0.54); --jp-content-font-color3: rgba(0, 0, 0, 0.38); --jp-content-link-color: var(--md-blue-700); --jp-content-font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Helvetica, Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\"; /* * Code Fonts * * Code font variables are used for typography of code and other monospaces content. */ --jp-code-font-size: 13px; --jp-code-line-height: 1.3077; /* 17px for 13px base */ --jp-code-padding: 5px; /* 5px for 13px base, codemirror highlighting needs integer px value */ --jp-code-font-family-default: Menlo, Consolas, \"DejaVu Sans Mono\", monospace; --jp-code-font-family: var(--jp-code-font-family-default); /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-code-presentation-font-size: 16px; /* may need to tweak cursor width if you change font size */ --jp-code-cursor-width0: 1.4px; --jp-code-cursor-width1: 2px; --jp-code-cursor-width2: 4px; /* Layout * * The following are the main layout colors use in JupyterLab. In a light * theme these would go from light to dark. */ --jp-layout-color0: white; --jp-layout-color1: white; --jp-layout-color2: var(--md-grey-200); --jp-layout-color3: var(--md-grey-400); --jp-layout-color4: var(--md-grey-600); /* Inverse Layout * * The following are the inverse layout colors use in JupyterLab. In a light * theme these would go from dark to light. */ --jp-inverse-layout-color0: #111111; --jp-inverse-layout-color1: var(--md-grey-900); --jp-inverse-layout-color2: var(--md-grey-800); --jp-inverse-layout-color3: var(--md-grey-700); --jp-inverse-layout-color4: var(--md-grey-600); /* Brand/accent */ --jp-brand-color0: var(--md-blue-900); --jp-brand-color1: var(--md-blue-700); --jp-brand-color2: var(--md-blue-300); --jp-brand-color3: var(--md-blue-100); --jp-brand-color4: var(--md-blue-50); --jp-accent-color0: var(--md-green-900); --jp-accent-color1: var(--md-green-700); --jp-accent-color2: var(--md-green-300); --jp-accent-color3: var(--md-green-100); /* State colors (warn, error, success, info) */ --jp-warn-color0: var(--md-orange-900); --jp-warn-color1: var(--md-orange-700); --jp-warn-color2: var(--md-orange-300); --jp-warn-color3: var(--md-orange-100); --jp-error-color0: var(--md-red-900); --jp-error-color1: var(--md-red-700); --jp-error-color2: var(--md-red-300); --jp-error-color3: var(--md-red-100); --jp-success-color0: var(--md-green-900); --jp-success-color1: var(--md-green-700); --jp-success-color2: var(--md-green-300); --jp-success-color3: var(--md-green-100); --jp-info-color0: var(--md-cyan-900); --jp-info-color1: var(--md-cyan-700); --jp-info-color2: var(--md-cyan-300); --jp-info-color3: var(--md-cyan-100); /* Cell specific styles */ --jp-cell-padding: 5px; --jp-cell-collapser-width: 8px; --jp-cell-collapser-min-height: 20px; --jp-cell-collapser-not-active-hover-opacity: 0.6; --jp-cell-editor-background: var(--md-grey-100); --jp-cell-editor-border-color: var(--md-grey-300); --jp-cell-editor-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-cell-editor-active-background: var(--jp-layout-color0); --jp-cell-editor-active-border-color: var(--jp-brand-color1); --jp-cell-prompt-width: 64px; --jp-cell-prompt-font-family: var(--jp-code-font-family-default); --jp-cell-prompt-letter-spacing: 0px; --jp-cell-prompt-opacity: 1; --jp-cell-prompt-not-active-opacity: 0.5; --jp-cell-prompt-not-active-font-color: var(--md-grey-700); /* A custom blend of MD grey and blue 600 * See https://meyerweb.com/eric/tools/color-blend/#546E7A:1E88E5:5:hex */ --jp-cell-inprompt-font-color: #307fc1; /* A custom blend of MD grey and orange 600 * https://meyerweb.com/eric/tools/color-blend/#546E7A:F4511E:5:hex */ --jp-cell-outprompt-font-color: #bf5b3d; /* Notebook specific styles */ --jp-notebook-padding: 10px; --jp-notebook-select-background: var(--jp-layout-color1); --jp-notebook-multiselected-color: var(--md-blue-50); /* The scroll padding is calculated to fill enough space at the bottom of the notebook to show one single-line cell (with appropriate padding) at the top when the notebook is scrolled all the way to the bottom. We also subtract one pixel so that no scrollbar appears if we have just one single-line cell in the notebook. This padding is to enable a 'scroll past end' feature in a notebook. */ --jp-notebook-scroll-padding: calc( 100% - var(--jp-code-font-size) * var(--jp-code-line-height) - var(--jp-code-padding) - var(--jp-cell-padding) - 1px ); /* Rendermime styles */ --jp-rendermime-error-background: #fdd; --jp-rendermime-table-row-background: var(--md-grey-100); --jp-rendermime-table-row-hover-background: var(--md-light-blue-50); /* Dialog specific styles */ --jp-dialog-background: rgba(0, 0, 0, 0.25); /* Console specific styles */ --jp-console-padding: 10px; /* Toolbar specific styles */ --jp-toolbar-border-color: var(--jp-border-color1); --jp-toolbar-micro-height: 8px; --jp-toolbar-background: var(--jp-layout-color1); --jp-toolbar-box-shadow: 0px 0px 2px 0px rgba(0, 0, 0, 0.24); --jp-toolbar-header-margin: 4px 4px 0px 4px; --jp-toolbar-active-background: var(--md-grey-300); /* Statusbar specific styles */ --jp-statusbar-height: 24px; /* Input field styles */ --jp-input-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-input-active-background: var(--jp-layout-color1); --jp-input-hover-background: var(--jp-layout-color1); --jp-input-background: var(--md-grey-100); --jp-input-border-color: var(--jp-border-color1); --jp-input-active-border-color: var(--jp-brand-color1); --jp-input-active-box-shadow-color: rgba(19, 124, 189, 0.3); /* General editor styles */ --jp-editor-selected-background: #d9d9d9; --jp-editor-selected-focused-background: #d7d4f0; --jp-editor-cursor-color: var(--jp-ui-font-color0); /* Code mirror specific styles */ --jp-mirror-editor-keyword-color: #008000; --jp-mirror-editor-atom-color: #88f; --jp-mirror-editor-number-color: #080; --jp-mirror-editor-def-color: #00f; --jp-mirror-editor-variable-color: var(--md-grey-900); --jp-mirror-editor-variable-2-color: #05a; --jp-mirror-editor-variable-3-color: #085; --jp-mirror-editor-punctuation-color: #05a; --jp-mirror-editor-property-color: #05a; --jp-mirror-editor-operator-color: #aa22ff; --jp-mirror-editor-comment-color: #408080; --jp-mirror-editor-string-color: #ba2121; --jp-mirror-editor-string-2-color: #708; --jp-mirror-editor-meta-color: #aa22ff; --jp-mirror-editor-qualifier-color: #555; --jp-mirror-editor-builtin-color: #008000; --jp-mirror-editor-bracket-color: #997; --jp-mirror-editor-tag-color: #170; --jp-mirror-editor-attribute-color: #00c; --jp-mirror-editor-header-color: blue; --jp-mirror-editor-quote-color: #090; --jp-mirror-editor-link-color: #00c; --jp-mirror-editor-error-color: #f00; --jp-mirror-editor-hr-color: #999; /* Vega extension styles */ --jp-vega-background: white; /* Sidebar-related styles */ --jp-sidebar-min-width: 250px; /* Search-related styles */ --jp-search-toggle-off-opacity: 0.5; --jp-search-toggle-hover-opacity: 0.8; --jp-search-toggle-on-opacity: 1; --jp-search-selected-match-background-color: rgb(245, 200, 0); --jp-search-selected-match-color: black; --jp-search-unselected-match-background-color: var( --jp-inverse-layout-color0 ); --jp-search-unselected-match-color: var(--jp-ui-inverse-font-color0); /* Icon colors that work well with light or dark backgrounds */ --jp-icon-contrast-color0: var(--md-purple-600); --jp-icon-contrast-color1: var(--md-green-600); --jp-icon-contrast-color2: var(--md-pink-600); --jp-icon-contrast-color3: var(--md-blue-600); } [data-md-color-scheme=\"slate\"] .jupyter-wrapper { /* Elevation * * We style box-shadows using Material Design's idea of elevation. These particular numbers are taken from here: * * https://github.com/material-components/material-components-web * https://material-components-web.appspot.com/elevation.html */ /* The dark theme shadows need a bit of work, but this will probably also require work on the core layout * colors used in the theme as well. */ --jp-shadow-base-lightness: 32; --jp-shadow-umbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.2 ); --jp-shadow-penumbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.14 ); --jp-shadow-ambient-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.12 ); --jp-elevation-z0: none; --jp-elevation-z1: 0px 2px 1px -1px var(--jp-shadow-umbra-color), 0px 1px 1px 0px var(--jp-shadow-penumbra-color), 0px 1px 3px 0px var(--jp-shadow-ambient-color); --jp-elevation-z2: 0px 3px 1px -2px var(--jp-shadow-umbra-color), 0px 2px 2px 0px var(--jp-shadow-penumbra-color), 0px 1px 5px 0px var(--jp-shadow-ambient-color); --jp-elevation-z4: 0px 2px 4px -1px var(--jp-shadow-umbra-color), 0px 4px 5px 0px var(--jp-shadow-penumbra-color), 0px 1px 10px 0px var(--jp-shadow-ambient-color); --jp-elevation-z6: 0px 3px 5px -1px var(--jp-shadow-umbra-color), 0px 6px 10px 0px var(--jp-shadow-penumbra-color), 0px 1px 18px 0px var(--jp-shadow-ambient-color); --jp-elevation-z8: 0px 5px 5px -3px var(--jp-shadow-umbra-color), 0px 8px 10px 1px var(--jp-shadow-penumbra-color), 0px 3px 14px 2px var(--jp-shadow-ambient-color); --jp-elevation-z12: 0px 7px 8px -4px var(--jp-shadow-umbra-color), 0px 12px 17px 2px var(--jp-shadow-penumbra-color), 0px 5px 22px 4px var(--jp-shadow-ambient-color); --jp-elevation-z16: 0px 8px 10px -5px var(--jp-shadow-umbra-color), 0px 16px 24px 2px var(--jp-shadow-penumbra-color), 0px 6px 30px 5px var(--jp-shadow-ambient-color); --jp-elevation-z20: 0px 10px 13px -6px var(--jp-shadow-umbra-color), 0px 20px 31px 3px var(--jp-shadow-penumbra-color), 0px 8px 38px 7px var(--jp-shadow-ambient-color); --jp-elevation-z24: 0px 11px 15px -7px var(--jp-shadow-umbra-color), 0px 24px 38px 3px var(--jp-shadow-penumbra-color), 0px 9px 46px 8px var(--jp-shadow-ambient-color); /* Borders * * The following variables, specify the visual styling of borders in JupyterLab. */ --jp-border-width: 1px; --jp-border-color0: var(--md-grey-700); --jp-border-color1: var(--md-grey-700); --jp-border-color2: var(--md-grey-800); --jp-border-color3: var(--md-grey-900); --jp-border-radius: 2px; /* UI Fonts * * The UI font CSS variables are used for the typography all of the JupyterLab * user interface elements that are not directly user generated content. * * The font sizing here is done assuming that the body font size of --jp-ui-font-size1 * is applied to a parent element. When children elements, such as headings, are sized * in em all things will be computed relative to that body size. */ --jp-ui-font-scale-factor: 1.2; --jp-ui-font-size0: 0.83333em; --jp-ui-font-size1: 13px; /* Base font size */ --jp-ui-font-size2: 1.2em; --jp-ui-font-size3: 1.44em; --jp-ui-font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Helvetica, Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\"; /* * Use these font colors against the corresponding main layout colors. * In a light theme, these go from dark to light. */ /* Defaults use Material Design specification */ --jp-ui-font-color0: rgba(255, 255, 255, 1); --jp-ui-font-color1: rgba(255, 255, 255, 0.87); --jp-ui-font-color2: rgba(255, 255, 255, 0.54); --jp-ui-font-color3: rgba(255, 255, 255, 0.38); /* * Use these against the brand/accent/warn/error colors. * These will typically go from light to darker, in both a dark and light theme. */ --jp-ui-inverse-font-color0: rgba(0, 0, 0, 1); --jp-ui-inverse-font-color1: rgba(0, 0, 0, 0.8); --jp-ui-inverse-font-color2: rgba(0, 0, 0, 0.5); --jp-ui-inverse-font-color3: rgba(0, 0, 0, 0.3); /* Content Fonts * * Content font variables are used for typography of user generated content. * * The font sizing here is done assuming that the body font size of --jp-content-font-size1 * is applied to a parent element. When children elements, such as headings, are sized * in em all things will be computed relative to that body size. */ --jp-content-line-height: 1.6; --jp-content-font-scale-factor: 1.2; --jp-content-font-size0: 0.83333em; --jp-content-font-size1: 14px; /* Base font size */ --jp-content-font-size2: 1.2em; --jp-content-font-size3: 1.44em; --jp-content-font-size4: 1.728em; --jp-content-font-size5: 2.0736em; /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-content-presentation-font-size1: 17px; --jp-content-heading-line-height: 1; --jp-content-heading-margin-top: 1.2em; --jp-content-heading-margin-bottom: 0.8em; --jp-content-heading-font-weight: 500; /* Defaults use Material Design specification */ --jp-content-font-color0: rgba(255, 255, 255, 1); --jp-content-font-color1: rgba(255, 255, 255, 1); --jp-content-font-color2: rgba(255, 255, 255, 0.7); --jp-content-font-color3: rgba(255, 255, 255, 0.5); --jp-content-link-color: var(--md-blue-300); --jp-content-font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Helvetica, Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\"; /* * Code Fonts * * Code font variables are used for typography of code and other monospaces content. */ --jp-code-font-size: 13px; --jp-code-line-height: 1.3077; /* 17px for 13px base */ --jp-code-padding: 5px; /* 5px for 13px base, codemirror highlighting needs integer px value */ --jp-code-font-family-default: Menlo, Consolas, \"DejaVu Sans Mono\", monospace; --jp-code-font-family: var(--jp-code-font-family-default); /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-code-presentation-font-size: 16px; /* may need to tweak cursor width if you change font size */ --jp-code-cursor-width0: 1.4px; --jp-code-cursor-width1: 2px; --jp-code-cursor-width2: 4px; /* Layout * * The following are the main layout colors use in JupyterLab. In a light * theme these would go from light to dark. */ --jp-layout-color0: #111111; --jp-layout-color1: var(--md-grey-900); --jp-layout-color2: var(--md-grey-800); --jp-layout-color3: var(--md-grey-700); --jp-layout-color4: var(--md-grey-600); /* Inverse Layout * * The following are the inverse layout colors use in JupyterLab. In a light * theme these would go from dark to light. */ --jp-inverse-layout-color0: white; --jp-inverse-layout-color1: white; --jp-inverse-layout-color2: var(--md-grey-200); --jp-inverse-layout-color3: var(--md-grey-400); --jp-inverse-layout-color4: var(--md-grey-600); /* Brand/accent */ --jp-brand-color0: var(--md-blue-700); --jp-brand-color1: var(--md-blue-500); --jp-brand-color2: var(--md-blue-300); --jp-brand-color3: var(--md-blue-100); --jp-brand-color4: var(--md-blue-50); --jp-accent-color0: var(--md-green-700); --jp-accent-color1: var(--md-green-500); --jp-accent-color2: var(--md-green-300); --jp-accent-color3: var(--md-green-100); /* State colors (warn, error, success, info) */ --jp-warn-color0: var(--md-orange-700); --jp-warn-color1: var(--md-orange-500); --jp-warn-color2: var(--md-orange-300); --jp-warn-color3: var(--md-orange-100); --jp-error-color0: var(--md-red-700); --jp-error-color1: var(--md-red-500); --jp-error-color2: var(--md-red-300); --jp-error-color3: var(--md-red-100); --jp-success-color0: var(--md-green-700); --jp-success-color1: var(--md-green-500); --jp-success-color2: var(--md-green-300); --jp-success-color3: var(--md-green-100); --jp-info-color0: var(--md-cyan-700); --jp-info-color1: var(--md-cyan-500); --jp-info-color2: var(--md-cyan-300); --jp-info-color3: var(--md-cyan-100); /* Cell specific styles */ --jp-cell-padding: 5px; --jp-cell-collapser-width: 8px; --jp-cell-collapser-min-height: 20px; --jp-cell-collapser-not-active-hover-opacity: 0.6; --jp-cell-editor-background: var(--jp-layout-color1); --jp-cell-editor-border-color: var(--md-grey-700); --jp-cell-editor-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-cell-editor-active-background: var(--jp-layout-color0); --jp-cell-editor-active-border-color: var(--jp-brand-color1); --jp-cell-prompt-width: 64px; --jp-cell-prompt-font-family: var(--jp-code-font-family-default); --jp-cell-prompt-letter-spacing: 0px; --jp-cell-prompt-opacity: 1; --jp-cell-prompt-not-active-opacity: 1; --jp-cell-prompt-not-active-font-color: var(--md-grey-300); /* A custom blend of MD grey and blue 600 * See https://meyerweb.com/eric/tools/color-blend/#546E7A:1E88E5:5:hex */ --jp-cell-inprompt-font-color: #307fc1; /* A custom blend of MD grey and orange 600 * https://meyerweb.com/eric/tools/color-blend/#546E7A:F4511E:5:hex */ --jp-cell-outprompt-font-color: #bf5b3d; /* Notebook specific styles */ --jp-notebook-padding: 10px; --jp-notebook-select-background: var(--jp-layout-color1); --jp-notebook-multiselected-color: rgba(33, 150, 243, 0.24); /* The scroll padding is calculated to fill enough space at the bottom of the notebook to show one single-line cell (with appropriate padding) at the top when the notebook is scrolled all the way to the bottom. We also subtract one pixel so that no scrollbar appears if we have just one single-line cell in the notebook. This padding is to enable a 'scroll past end' feature in a notebook. */ --jp-notebook-scroll-padding: calc( 100% - var(--jp-code-font-size) * var(--jp-code-line-height) - var(--jp-code-padding) - var(--jp-cell-padding) - 1px ); /* Rendermime styles */ --jp-rendermime-error-background: rgba(244, 67, 54, 0.28); --jp-rendermime-table-row-background: var(--md-grey-900); --jp-rendermime-table-row-hover-background: rgba(3, 169, 244, 0.2); /* Dialog specific styles */ --jp-dialog-background: rgba(0, 0, 0, 0.6); /* Console specific styles */ --jp-console-padding: 10px; /* Toolbar specific styles */ --jp-toolbar-border-color: var(--jp-border-color2); --jp-toolbar-micro-height: 8px; --jp-toolbar-background: var(--jp-layout-color1); --jp-toolbar-box-shadow: 0px 0px 2px 0px rgba(0, 0, 0, 0.8); --jp-toolbar-header-margin: 4px 4px 0px 4px; --jp-toolbar-active-background: var(--jp-layout-color0); /* Statusbar specific styles */ --jp-statusbar-height: 24px; /* Input field styles */ --jp-input-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-input-active-background: var(--jp-layout-color0); --jp-input-hover-background: var(--jp-layout-color2); --jp-input-background: var(--md-grey-800); --jp-input-border-color: var(--jp-border-color1); --jp-input-active-border-color: var(--jp-brand-color1); --jp-input-active-box-shadow-color: rgba(19, 124, 189, 0.3); /* General editor styles */ --jp-editor-selected-background: var(--jp-layout-color2); --jp-editor-selected-focused-background: rgba(33, 150, 243, 0.24); --jp-editor-cursor-color: var(--jp-ui-font-color0); /* Code mirror specific styles */ --jp-mirror-editor-keyword-color: var(--md-green-500); --jp-mirror-editor-atom-color: var(--md-blue-300); --jp-mirror-editor-number-color: var(--md-green-400); --jp-mirror-editor-def-color: var(--md-blue-600); --jp-mirror-editor-variable-color: var(--md-grey-300); --jp-mirror-editor-variable-2-color: var(--md-blue-400); --jp-mirror-editor-variable-3-color: var(--md-green-600); --jp-mirror-editor-punctuation-color: var(--md-blue-400); --jp-mirror-editor-property-color: var(--md-blue-400); --jp-mirror-editor-operator-color: #aa22ff; --jp-mirror-editor-comment-color: #408080; --jp-mirror-editor-string-color: #ff7070; --jp-mirror-editor-string-2-color: var(--md-purple-300); --jp-mirror-editor-meta-color: #aa22ff; --jp-mirror-editor-qualifier-color: #555; --jp-mirror-editor-builtin-color: var(--md-green-600); --jp-mirror-editor-bracket-color: #997; --jp-mirror-editor-tag-color: var(--md-green-700); --jp-mirror-editor-attribute-color: var(--md-blue-700); --jp-mirror-editor-header-color: var(--md-blue-500); --jp-mirror-editor-quote-color: var(--md-green-300); --jp-mirror-editor-link-color: var(--md-blue-700); --jp-mirror-editor-error-color: #f00; --jp-mirror-editor-hr-color: #999; /* Vega extension styles */ --jp-vega-background: var(--md-grey-400); /* Sidebar-related styles */ --jp-sidebar-min-width: 250px; /* Search-related styles */ --jp-search-toggle-off-opacity: 0.6; --jp-search-toggle-hover-opacity: 0.8; --jp-search-toggle-on-opacity: 1; --jp-search-selected-match-background-color: rgb(255, 225, 0); --jp-search-selected-match-color: black; --jp-search-unselected-match-background-color: var( --jp-inverse-layout-color0 ); --jp-search-unselected-match-color: var(--jp-ui-inverse-font-color0); /* scrollbar related styles. Supports every browser except Edge. */ /* colors based on JetBrain's Darcula theme */ --jp-scrollbar-background-color: #3f4244; --jp-scrollbar-thumb-color: 88, 96, 97; /* need to specify thumb color as an RGB triplet */ --jp-scrollbar-endpad: 3px; /* the minimum gap between the thumb and the ends of a scrollbar */ /* hacks for setting the thumb shape. These do nothing in Firefox */ --jp-scrollbar-thumb-margin: 3.5px; /* the space in between the sides of the thumb and the track */ --jp-scrollbar-thumb-radius: 9px; /* set to a large-ish value for rounded endcaps on the thumb */ /* Icon colors that work well with light or dark backgrounds */ --jp-icon-contrast-color0: var(--md-purple-600); --jp-icon-contrast-color1: var(--md-green-600); --jp-icon-contrast-color2: var(--md-pink-600); --jp-icon-contrast-color3: var(--md-blue-600); } :root{--md-red-50: #ffebee;--md-red-100: #ffcdd2;--md-red-200: #ef9a9a;--md-red-300: #e57373;--md-red-400: #ef5350;--md-red-500: #f44336;--md-red-600: #e53935;--md-red-700: #d32f2f;--md-red-800: #c62828;--md-red-900: #b71c1c;--md-red-A100: #ff8a80;--md-red-A200: #ff5252;--md-red-A400: #ff1744;--md-red-A700: #d50000;--md-pink-50: #fce4ec;--md-pink-100: #f8bbd0;--md-pink-200: #f48fb1;--md-pink-300: #f06292;--md-pink-400: #ec407a;--md-pink-500: #e91e63;--md-pink-600: #d81b60;--md-pink-700: #c2185b;--md-pink-800: 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#7986cb;--md-indigo-400: #5c6bc0;--md-indigo-500: #3f51b5;--md-indigo-600: #3949ab;--md-indigo-700: #303f9f;--md-indigo-800: #283593;--md-indigo-900: #1a237e;--md-indigo-A100: #8c9eff;--md-indigo-A200: #536dfe;--md-indigo-A400: #3d5afe;--md-indigo-A700: #304ffe;--md-blue-50: #e3f2fd;--md-blue-100: #bbdefb;--md-blue-200: #90caf9;--md-blue-300: #64b5f6;--md-blue-400: #42a5f5;--md-blue-500: #2196f3;--md-blue-600: #1e88e5;--md-blue-700: #1976d2;--md-blue-800: #1565c0;--md-blue-900: #0d47a1;--md-blue-A100: #82b1ff;--md-blue-A200: #448aff;--md-blue-A400: #2979ff;--md-blue-A700: #2962ff;--md-light-blue-50: #e1f5fe;--md-light-blue-100: #b3e5fc;--md-light-blue-200: #81d4fa;--md-light-blue-300: #4fc3f7;--md-light-blue-400: #29b6f6;--md-light-blue-500: #03a9f4;--md-light-blue-600: #039be5;--md-light-blue-700: #0288d1;--md-light-blue-800: #0277bd;--md-light-blue-900: #01579b;--md-light-blue-A100: #80d8ff;--md-light-blue-A200: #40c4ff;--md-light-blue-A400: #00b0ff;--md-light-blue-A700: #0091ea;--md-cyan-50: #e0f7fa;--md-cyan-100: #b2ebf2;--md-cyan-200: #80deea;--md-cyan-300: #4dd0e1;--md-cyan-400: #26c6da;--md-cyan-500: #00bcd4;--md-cyan-600: #00acc1;--md-cyan-700: #0097a7;--md-cyan-800: #00838f;--md-cyan-900: #006064;--md-cyan-A100: #84ffff;--md-cyan-A200: #18ffff;--md-cyan-A400: #00e5ff;--md-cyan-A700: #00b8d4;--md-teal-50: #e0f2f1;--md-teal-100: #b2dfdb;--md-teal-200: #80cbc4;--md-teal-300: #4db6ac;--md-teal-400: #26a69a;--md-teal-500: #009688;--md-teal-600: #00897b;--md-teal-700: #00796b;--md-teal-800: #00695c;--md-teal-900: #004d40;--md-teal-A100: #a7ffeb;--md-teal-A200: #64ffda;--md-teal-A400: #1de9b6;--md-teal-A700: #00bfa5;--md-green-50: #e8f5e9;--md-green-100: #c8e6c9;--md-green-200: #a5d6a7;--md-green-300: #81c784;--md-green-400: #66bb6a;--md-green-500: #4caf50;--md-green-600: #43a047;--md-green-700: #388e3c;--md-green-800: #2e7d32;--md-green-900: #1b5e20;--md-green-A100: #b9f6ca;--md-green-A200: #69f0ae;--md-green-A400: #00e676;--md-green-A700: #00c853;--md-light-green-50: #f1f8e9;--md-light-green-100: #dcedc8;--md-light-green-200: #c5e1a5;--md-light-green-300: #aed581;--md-light-green-400: #9ccc65;--md-light-green-500: #8bc34a;--md-light-green-600: #7cb342;--md-light-green-700: #689f38;--md-light-green-800: #558b2f;--md-light-green-900: #33691e;--md-light-green-A100: #ccff90;--md-light-green-A200: #b2ff59;--md-light-green-A400: #76ff03;--md-light-green-A700: #64dd17;--md-lime-50: #f9fbe7;--md-lime-100: #f0f4c3;--md-lime-200: #e6ee9c;--md-lime-300: #dce775;--md-lime-400: #d4e157;--md-lime-500: #cddc39;--md-lime-600: #c0ca33;--md-lime-700: #afb42b;--md-lime-800: #9e9d24;--md-lime-900: #827717;--md-lime-A100: #f4ff81;--md-lime-A200: #eeff41;--md-lime-A400: #c6ff00;--md-lime-A700: #aeea00;--md-yellow-50: #fffde7;--md-yellow-100: #fff9c4;--md-yellow-200: #fff59d;--md-yellow-300: #fff176;--md-yellow-400: #ffee58;--md-yellow-500: #ffeb3b;--md-yellow-600: #fdd835;--md-yellow-700: #fbc02d;--md-yellow-800: #f9a825;--md-yellow-900: #f57f17;--md-yellow-A100: #ffff8d;--md-yellow-A200: #ffff00;--md-yellow-A400: #ffea00;--md-yellow-A700: #ffd600;--md-amber-50: #fff8e1;--md-amber-100: #ffecb3;--md-amber-200: #ffe082;--md-amber-300: #ffd54f;--md-amber-400: #ffca28;--md-amber-500: #ffc107;--md-amber-600: #ffb300;--md-amber-700: #ffa000;--md-amber-800: #ff8f00;--md-amber-900: #ff6f00;--md-amber-A100: #ffe57f;--md-amber-A200: #ffd740;--md-amber-A400: #ffc400;--md-amber-A700: #ffab00;--md-orange-50: #fff3e0;--md-orange-100: #ffe0b2;--md-orange-200: #ffcc80;--md-orange-300: #ffb74d;--md-orange-400: #ffa726;--md-orange-500: #ff9800;--md-orange-600: #fb8c00;--md-orange-700: #f57c00;--md-orange-800: #ef6c00;--md-orange-900: #e65100;--md-orange-A100: #ffd180;--md-orange-A200: #ffab40;--md-orange-A400: #ff9100;--md-orange-A700: #ff6d00;--md-deep-orange-50: #fbe9e7;--md-deep-orange-100: #ffccbc;--md-deep-orange-200: #ffab91;--md-deep-orange-300: #ff8a65;--md-deep-orange-400: #ff7043;--md-deep-orange-500: 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sourceMappingURL=mkdocs-jupyter.css.map*/ init_mathjax = function() { if (window.MathJax) { // MathJax loaded MathJax.Hub.Config({ TeX: { equationNumbers: { autoNumber: \"AMS\", useLabelIds: true } }, tex2jax: { inlineMath: [ ['$','$'], [\"\\\\(\",\"\\\\)\"] ], displayMath: [ ['$$','$$'], [\"\\\\[\",\"\\\\]\"] ], processEscapes: true, processEnvironments: true }, displayAlign: 'center', CommonHTML: { linebreaks: { automatic: true } } }); MathJax.Hub.Queue([\"Typeset\", MathJax.Hub]); } } init_mathjax(); Fine-tune SciBERT \u00b6 We are planning to do a simple classification task on scientific text. For that, SciBERT is an ideal model to fine-tune since it has been pretrained of academic publications. This notebook was updated so that it can run in Google Colab . First, we need to install the dependencies. In [1]: Copied! ! pip install transformers datasets great - ai > / dev / null !pip install transformers datasets great-ai > /dev/null Load the training data from S3. (We have uploaded this to S3 in the data notebook.) In [2]: Copied! from great_ai.large_file import LargeFileS3 import json LargeFileS3 . configure_credentials_from_file ( \"config.ini\" ) with LargeFileS3 ( \"summary-train-dataset-small\" , encoding = \"utf-8\" ) as f : # splitting training and test data is done later by `datasets` X , y = json . load ( f ) from great_ai.large_file import LargeFileS3 import json LargeFileS3.configure_credentials_from_file(\"config.ini\") with LargeFileS3(\"summary-train-dataset-small\", encoding=\"utf-8\") as f: # splitting training and test data is done later by `datasets` X, y = json.load(f) Latest version of summary-train-dataset-small is 0 (from versions: 0) File summary-train-dataset-small-0 found in cache Finetune SciBERT, for more info about this step, check out HuggingFace . If you're only here for great-ai , feel free to skip the next cell. In [22]: Copied! from transformers import ( AutoModelForSequenceClassification , AutoTokenizer , DataCollatorWithPadding , Trainer , TrainingArguments , EarlyStoppingCallback , ) from pathlib import Path import numpy as np from datasets import Dataset , load_metric MODEL = \"allenai/scibert_scivocab_uncased\" BATCH_SIZE = 32 tokenizer = AutoTokenizer . from_pretrained ( MODEL ) model = AutoModelForSequenceClassification . from_pretrained ( MODEL , num_labels = 2 ) data_collator = DataCollatorWithPadding ( tokenizer = tokenizer ) def tokenize_function ( v ): return tokenizer ( v [ \"text\" ]) dataset = ( Dataset . from_dict ({ \"text\" : X , \"label\" : y }) . map ( lambda v : tokenizer ( v [ \"text\" ], truncation = True ), batched = True ) . remove_columns ( \"text\" ) . train_test_split ( test_size = 0.2 , shuffle = True ) # test is actually validation ) f1_score = load_metric ( \"f1\" ) def compute_metrics ( p ): pred , labels = p pred = np . argmax ( pred , axis = 1 ) return f1_score . compute ( predictions = pred , references = labels ) training_args = TrainingArguments ( output_dir = Path ( \"models\" ), per_device_train_batch_size = BATCH_SIZE , per_device_eval_batch_size = BATCH_SIZE , save_total_limit = 5 , num_train_epochs = 50 , save_strategy = \"epoch\" , evaluation_strategy = \"epoch\" , logging_strategy = \"epoch\" , weight_decay = 0.01 , metric_for_best_model = \"f1\" , load_best_model_at_end = True , ) result = Trainer ( model = model , args = training_args , train_dataset = dataset [ \"train\" ], eval_dataset = dataset [ \"test\" ], data_collator = data_collator , compute_metrics = compute_metrics , callbacks = [ EarlyStoppingCallback ( early_stopping_patience = 5 )], ) . train () from transformers import ( AutoModelForSequenceClassification, AutoTokenizer, DataCollatorWithPadding, Trainer, TrainingArguments, EarlyStoppingCallback, ) from pathlib import Path import numpy as np from datasets import Dataset, load_metric MODEL = \"allenai/scibert_scivocab_uncased\" BATCH_SIZE = 32 tokenizer = AutoTokenizer.from_pretrained(MODEL) model = AutoModelForSequenceClassification.from_pretrained(MODEL, num_labels=2) data_collator = DataCollatorWithPadding(tokenizer=tokenizer) def tokenize_function(v): return tokenizer(v[\"text\"]) dataset = ( Dataset.from_dict({\"text\": X, \"label\": y}) .map(lambda v: tokenizer(v[\"text\"], truncation=True), batched=True) .remove_columns(\"text\") .train_test_split(test_size=0.2, shuffle=True) # test is actually validation ) f1_score = load_metric(\"f1\") def compute_metrics(p): pred, labels = p pred = np.argmax(pred, axis=1) return f1_score.compute(predictions=pred, references=labels) training_args = TrainingArguments( output_dir=Path(\"models\"), per_device_train_batch_size=BATCH_SIZE, per_device_eval_batch_size=BATCH_SIZE, save_total_limit=5, num_train_epochs=50, save_strategy=\"epoch\", evaluation_strategy=\"epoch\", logging_strategy=\"epoch\", weight_decay=0.01, metric_for_best_model=\"f1\", load_best_model_at_end=True, ) result = Trainer( model=model, args=training_args, train_dataset=dataset[\"train\"], eval_dataset=dataset[\"test\"], data_collator=data_collator, compute_metrics=compute_metrics, callbacks=[EarlyStoppingCallback(early_stopping_patience=5)], ).train() 0%| | 0/1 [00:00 / dev / null !pip install transformers datasets great-ai > /dev/null Load the training data from S3. (We have uploaded this to S3 in the data notebook.) In [2]: Copied! from great_ai.large_file import LargeFileS3 import json LargeFileS3 . configure_credentials_from_file ( \"config.ini\" ) with LargeFileS3 ( \"summary-train-dataset-small\" , encoding = \"utf-8\" ) as f : # splitting training and test data is done later by `datasets` X , y = json . load ( f ) from great_ai.large_file import LargeFileS3 import json LargeFileS3.configure_credentials_from_file(\"config.ini\") with LargeFileS3(\"summary-train-dataset-small\", encoding=\"utf-8\") as f: # splitting training and test data is done later by `datasets` X, y = json.load(f) Latest version of summary-train-dataset-small is 0 (from versions: 0) File summary-train-dataset-small-0 found in cache Finetune SciBERT, for more info about this step, check out HuggingFace . If you're only here for great-ai , feel free to skip the next cell. In [22]: Copied! from transformers import ( AutoModelForSequenceClassification , AutoTokenizer , DataCollatorWithPadding , Trainer , TrainingArguments , EarlyStoppingCallback , ) from pathlib import Path import numpy as np from datasets import Dataset , load_metric MODEL = \"allenai/scibert_scivocab_uncased\" BATCH_SIZE = 32 tokenizer = AutoTokenizer . from_pretrained ( MODEL ) model = AutoModelForSequenceClassification . from_pretrained ( MODEL , num_labels = 2 ) data_collator = DataCollatorWithPadding ( tokenizer = tokenizer ) def tokenize_function ( v ): return tokenizer ( v [ \"text\" ]) dataset = ( Dataset . from_dict ({ \"text\" : X , \"label\" : y }) . map ( lambda v : tokenizer ( v [ \"text\" ], truncation = True ), batched = True ) . remove_columns ( \"text\" ) . train_test_split ( test_size = 0.2 , shuffle = True ) # test is actually validation ) f1_score = load_metric ( \"f1\" ) def compute_metrics ( p ): pred , labels = p pred = np . argmax ( pred , axis = 1 ) return f1_score . compute ( predictions = pred , references = labels ) training_args = TrainingArguments ( output_dir = Path ( \"models\" ), per_device_train_batch_size = BATCH_SIZE , per_device_eval_batch_size = BATCH_SIZE , save_total_limit = 5 , num_train_epochs = 50 , save_strategy = \"epoch\" , evaluation_strategy = \"epoch\" , logging_strategy = \"epoch\" , weight_decay = 0.01 , metric_for_best_model = \"f1\" , load_best_model_at_end = True , ) result = Trainer ( model = model , args = training_args , train_dataset = dataset [ \"train\" ], eval_dataset = dataset [ \"test\" ], data_collator = data_collator , compute_metrics = compute_metrics , callbacks = [ EarlyStoppingCallback ( early_stopping_patience = 5 )], ) . train () from transformers import ( AutoModelForSequenceClassification, AutoTokenizer, DataCollatorWithPadding, Trainer, TrainingArguments, EarlyStoppingCallback, ) from pathlib import Path import numpy as np from datasets import Dataset, load_metric MODEL = \"allenai/scibert_scivocab_uncased\" BATCH_SIZE = 32 tokenizer = AutoTokenizer.from_pretrained(MODEL) model = AutoModelForSequenceClassification.from_pretrained(MODEL, num_labels=2) data_collator = DataCollatorWithPadding(tokenizer=tokenizer) def tokenize_function(v): return tokenizer(v[\"text\"]) dataset = ( Dataset.from_dict({\"text\": X, \"label\": y}) .map(lambda v: tokenizer(v[\"text\"], truncation=True), batched=True) .remove_columns(\"text\") .train_test_split(test_size=0.2, shuffle=True) # test is actually validation ) f1_score = load_metric(\"f1\") def compute_metrics(p): pred, labels = p pred = np.argmax(pred, axis=1) return f1_score.compute(predictions=pred, references=labels) training_args = TrainingArguments( output_dir=Path(\"models\"), per_device_train_batch_size=BATCH_SIZE, per_device_eval_batch_size=BATCH_SIZE, save_total_limit=5, num_train_epochs=50, save_strategy=\"epoch\", evaluation_strategy=\"epoch\", logging_strategy=\"epoch\", weight_decay=0.01, metric_for_best_model=\"f1\", load_best_model_at_end=True, ) result = Trainer( model=model, args=training_args, train_dataset=dataset[\"train\"], eval_dataset=dataset[\"test\"], data_collator=data_collator, compute_metrics=compute_metrics, callbacks=[EarlyStoppingCallback(early_stopping_patience=5)], ).train() 0%| | 0/1 [00:00.p-SplitPanel-handle,.jupyter-wrapper .lm-SplitPanel[data-orientation=horizontal]>.lm-SplitPanel-handle{cursor:ew-resize}.jupyter-wrapper .p-SplitPanel[data-orientation=vertical]>.p-SplitPanel-handle,.jupyter-wrapper .lm-SplitPanel[data-orientation=vertical]>.lm-SplitPanel-handle{cursor:ns-resize}.jupyter-wrapper .p-SplitPanel[data-orientation=horizontal]>.p-SplitPanel-handle:after,.jupyter-wrapper .lm-SplitPanel[data-orientation=horizontal]>.lm-SplitPanel-handle:after{left:50%;min-width:8px;transform:translateX(-50%)}.jupyter-wrapper .p-SplitPanel[data-orientation=vertical]>.p-SplitPanel-handle:after,.jupyter-wrapper 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sourceMappingURL=mkdocs-jupyter.css.map*/ init_mathjax = function() { if (window.MathJax) { // MathJax loaded MathJax.Hub.Config({ TeX: { equationNumbers: { autoNumber: \"AMS\", useLabelIds: true } }, tex2jax: { inlineMath: [ ['$','$'], [\"\\\\(\",\"\\\\)\"] ], displayMath: [ ['$$','$$'], [\"\\\\[\",\"\\\\]\"] ], processEscapes: true, processEnvironments: true }, displayAlign: 'center', CommonHTML: { linebreaks: { automatic: true } } }); MathJax.Hub.Queue([\"Typeset\", MathJax.Hub]); } } init_mathjax(); Simple example: data engineering \u00b6 Here, we solve a problem similar to the tutorial's but with an explainable Naive Bayes classifier and more best practices. In short, we train a domain classifier on the semantic scholar dataset by taking full advantage of great-ai . Subsequently, we create a production-ready deployment. The blue boxes show the steps of a typical AI-development lifecycle implemented in this notebook. Since the true scope of great-ai is the phase between proof-of-concept code and production-ready service, it is predominantly used in the deployment notebook . Feel free to skip there, or continue reading if you'd like to see the full picture. Extract \u00b6 This can be achieved by downloading a public dataset (such as in this case), or by having a Data Engineer setup and give us access to the organisation's data. In this example, we download the semantic scholar dataset from a public S3 bucket. In [1]: Copied! MAX_CHUNK_COUNT = 4 MAX_CHUNK_COUNT = 4 In [2]: Copied! import urllib.request from random import shuffle manifest = ( urllib . request . urlopen ( \"https://s3-us-west-2.amazonaws.com/ai2-s2-research-public/\" \"open-corpus/2022-02-01/manifest.txt\" ) . read () . decode () ) # a list of available chunks separated by '\\n' characters lines = manifest . split () shuffle ( lines ) chunks = lines [: MAX_CHUNK_COUNT ] f \"\"\"Processing { len ( chunks ) } out of the { len ( manifest . split ()) } available chunks\"\"\" import urllib.request from random import shuffle manifest = ( urllib.request.urlopen( \"https://s3-us-west-2.amazonaws.com/ai2-s2-research-public/\" \"open-corpus/2022-02-01/manifest.txt\" ) .read() .decode() ) # a list of available chunks separated by '\\n' characters lines = manifest.split() shuffle(lines) chunks = lines[:MAX_CHUNK_COUNT] f\"\"\"Processing {len(chunks)} out of the { len(manifest.split()) } available chunks\"\"\" Out[2]: 'Processing 4 out of the 6002 available chunks' Transform \u00b6 Filter out non-English abstracts using great_ai.utilities.predict_language Project it to only keep the necessary components (text and labels), clean the textual content using great_ai.utilities.clean We will speed up processing using great_ai.utilities.simple_parallel_map . In [3]: Copied! from typing import List , Tuple import json import gzip from great_ai.utilities import ( simple_parallel_map , clean , is_english , predict_language , unchunk , ) def preprocess_chunk ( chunk_key : str ) -> List [ Tuple [ str , List [ str ]]]: response = urllib . request . urlopen ( f \"https://s3-us-west-2.amazonaws.com/ai2-s2-research-public/\" f \"open-corpus/2022-02-01/ { chunk_key } \" ) # a gzipped JSON Lines file decompressed = gzip . decompress ( response . read ()) decoded = decompressed . decode () chunk = [ json . loads ( line ) for line in decoded . split ( \" \\n \" ) if line ] # Transform return [ ( clean ( f ' { c [ \"title\" ] } { c [ \"paperAbstract\" ] } ' f ' { c [ \"journalName\" ] } { c [ \"venue\" ] } ' , convert_to_ascii = True , ), # The text is cleaned to remove common artifacts c [ \"fieldsOfStudy\" ], ) # Create pairs of `(text, [...domains])` for c in chunk if ( c [ \"fieldsOfStudy\" ] and is_english ( predict_language ( c [ \"paperAbstract\" ]))) ] preprocessed_data = unchunk ( simple_parallel_map ( preprocess_chunk , chunks , concurrency = 4 ) ) from typing import List, Tuple import json import gzip from great_ai.utilities import ( simple_parallel_map, clean, is_english, predict_language, unchunk, ) def preprocess_chunk(chunk_key: str) -> List[Tuple[str, List[str]]]: response = urllib.request.urlopen( f\"https://s3-us-west-2.amazonaws.com/ai2-s2-research-public/\" f\"open-corpus/2022-02-01/{chunk_key}\" ) # a gzipped JSON Lines file decompressed = gzip.decompress(response.read()) decoded = decompressed.decode() chunk = [json.loads(line) for line in decoded.split(\"\\n\") if line] # Transform return [ ( clean( f'{c[\"title\"]} {c[\"paperAbstract\"]} ' f'{c[\"journalName\"]} {c[\"venue\"]}', convert_to_ascii=True, ), # The text is cleaned to remove common artifacts c[\"fieldsOfStudy\"], ) # Create pairs of `(text, [...domains])` for c in chunk if (c[\"fieldsOfStudy\"] and is_english(predict_language(c[\"paperAbstract\"]))) ] preprocessed_data = unchunk( simple_parallel_map(preprocess_chunk, chunks, concurrency=4) ) 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 4/4 [04:22<00:00, 65.51s/it] In [4]: Copied! X , y = zip ( * preprocessed_data ) # X is the input, y is the expected output X, y = zip(*preprocessed_data) # X is the input, y is the expected output Load \u00b6 Upload the dataset (or a part of it) to a central repository using great_ai.add_ground_truth . This step automatically tags each data-point with a split label according to the ratios we set. Additional tags can be also given. Production-ready backend \u00b6 The MongoDB driver is automatically configured if mongo.ini exists with the following scheme: mongo_connection_string = mongodb://localhost:27017/ mongo_database = my_great_ai_db You can install MongoDB from here or use it as a service Otherwise, TinyDB is used which is just a local JSON file. In [5]: Copied! from great_ai import add_ground_truth add_ground_truth ( X , y , train_split_ratio = 0.8 , test_split_ratio = 0.2 ) from great_ai import add_ground_truth add_ground_truth(X, y, train_split_ratio=0.8, test_split_ratio=0.2) Environment variable ENVIRONMENT is not set, defaulting to development mode \u203c\ufe0f Cannot find credentials files, defaulting to using ParallelTinyDbDriver The selected tracing database (ParallelTinyDbDriver) is not recommended for production Cannot find credentials files, defaulting to using LargeFileLocal GreatAI (v0.1.6): configured \u2705 \ud83d\udd29 tracing_database: ParallelTinyDbDriver \ud83d\udd29 large_file_implementation: LargeFileLocal \ud83d\udd29 is_production: False \ud83d\udd29 should_log_exception_stack: True \ud83d\udd29 prediction_cache_size: 512 \ud83d\udd29 dashboard_table_size: 50 You still need to check whether you follow all best practices before trusting your deployment. > Find out more at https://se-ml.github.io/practices Next: Part 2 \u00b6","title":"Simple example: data engineering"},{"location":"examples/simple/data/#simple-example-data-engineering","text":"Here, we solve a problem similar to the tutorial's but with an explainable Naive Bayes classifier and more best practices. In short, we train a domain classifier on the semantic scholar dataset by taking full advantage of great-ai . Subsequently, we create a production-ready deployment. The blue boxes show the steps of a typical AI-development lifecycle implemented in this notebook. Since the true scope of great-ai is the phase between proof-of-concept code and production-ready service, it is predominantly used in the deployment notebook . Feel free to skip there, or continue reading if you'd like to see the full picture.","title":"Simple example: data engineering"},{"location":"examples/simple/data/#extract","text":"This can be achieved by downloading a public dataset (such as in this case), or by having a Data Engineer setup and give us access to the organisation's data. In this example, we download the semantic scholar dataset from a public S3 bucket. In [1]: Copied! MAX_CHUNK_COUNT = 4 MAX_CHUNK_COUNT = 4 In [2]: Copied! import urllib.request from random import shuffle manifest = ( urllib . request . urlopen ( \"https://s3-us-west-2.amazonaws.com/ai2-s2-research-public/\" \"open-corpus/2022-02-01/manifest.txt\" ) . read () . decode () ) # a list of available chunks separated by '\\n' characters lines = manifest . split () shuffle ( lines ) chunks = lines [: MAX_CHUNK_COUNT ] f \"\"\"Processing { len ( chunks ) } out of the { len ( manifest . split ()) } available chunks\"\"\" import urllib.request from random import shuffle manifest = ( urllib.request.urlopen( \"https://s3-us-west-2.amazonaws.com/ai2-s2-research-public/\" \"open-corpus/2022-02-01/manifest.txt\" ) .read() .decode() ) # a list of available chunks separated by '\\n' characters lines = manifest.split() shuffle(lines) chunks = lines[:MAX_CHUNK_COUNT] f\"\"\"Processing {len(chunks)} out of the { len(manifest.split()) } available chunks\"\"\" Out[2]: 'Processing 4 out of the 6002 available chunks'","title":"Extract"},{"location":"examples/simple/data/#transform","text":"Filter out non-English abstracts using great_ai.utilities.predict_language Project it to only keep the necessary components (text and labels), clean the textual content using great_ai.utilities.clean We will speed up processing using great_ai.utilities.simple_parallel_map . In [3]: Copied! from typing import List , Tuple import json import gzip from great_ai.utilities import ( simple_parallel_map , clean , is_english , predict_language , unchunk , ) def preprocess_chunk ( chunk_key : str ) -> List [ Tuple [ str , List [ str ]]]: response = urllib . request . urlopen ( f \"https://s3-us-west-2.amazonaws.com/ai2-s2-research-public/\" f \"open-corpus/2022-02-01/ { chunk_key } \" ) # a gzipped JSON Lines file decompressed = gzip . decompress ( response . read ()) decoded = decompressed . decode () chunk = [ json . loads ( line ) for line in decoded . split ( \" \\n \" ) if line ] # Transform return [ ( clean ( f ' { c [ \"title\" ] } { c [ \"paperAbstract\" ] } ' f ' { c [ \"journalName\" ] } { c [ \"venue\" ] } ' , convert_to_ascii = True , ), # The text is cleaned to remove common artifacts c [ \"fieldsOfStudy\" ], ) # Create pairs of `(text, [...domains])` for c in chunk if ( c [ \"fieldsOfStudy\" ] and is_english ( predict_language ( c [ \"paperAbstract\" ]))) ] preprocessed_data = unchunk ( simple_parallel_map ( preprocess_chunk , chunks , concurrency = 4 ) ) from typing import List, Tuple import json import gzip from great_ai.utilities import ( simple_parallel_map, clean, is_english, predict_language, unchunk, ) def preprocess_chunk(chunk_key: str) -> List[Tuple[str, List[str]]]: response = urllib.request.urlopen( f\"https://s3-us-west-2.amazonaws.com/ai2-s2-research-public/\" f\"open-corpus/2022-02-01/{chunk_key}\" ) # a gzipped JSON Lines file decompressed = gzip.decompress(response.read()) decoded = decompressed.decode() chunk = [json.loads(line) for line in decoded.split(\"\\n\") if line] # Transform return [ ( clean( f'{c[\"title\"]} {c[\"paperAbstract\"]} ' f'{c[\"journalName\"]} {c[\"venue\"]}', convert_to_ascii=True, ), # The text is cleaned to remove common artifacts c[\"fieldsOfStudy\"], ) # Create pairs of `(text, [...domains])` for c in chunk if (c[\"fieldsOfStudy\"] and is_english(predict_language(c[\"paperAbstract\"]))) ] preprocessed_data = unchunk( simple_parallel_map(preprocess_chunk, chunks, concurrency=4) ) 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 4/4 [04:22<00:00, 65.51s/it] In [4]: Copied! X , y = zip ( * preprocessed_data ) # X is the input, y is the expected output X, y = zip(*preprocessed_data) # X is the input, y is the expected output","title":"Transform"},{"location":"examples/simple/data/#load","text":"Upload the dataset (or a part of it) to a central repository using great_ai.add_ground_truth . This step automatically tags each data-point with a split label according to the ratios we set. Additional tags can be also given.","title":"Load"},{"location":"examples/simple/data/#production-ready-backend","text":"The MongoDB driver is automatically configured if mongo.ini exists with the following scheme: mongo_connection_string = mongodb://localhost:27017/ mongo_database = my_great_ai_db You can install MongoDB from here or use it as a service Otherwise, TinyDB is used which is just a local JSON file. In [5]: Copied! from great_ai import add_ground_truth add_ground_truth ( X , y , train_split_ratio = 0.8 , test_split_ratio = 0.2 ) from great_ai import add_ground_truth add_ground_truth(X, y, train_split_ratio=0.8, test_split_ratio=0.2) Environment variable ENVIRONMENT is not set, defaulting to development mode \u203c\ufe0f Cannot find credentials files, defaulting to using ParallelTinyDbDriver The selected tracing database (ParallelTinyDbDriver) is not recommended for production Cannot find credentials files, defaulting to using LargeFileLocal GreatAI (v0.1.6): configured \u2705 \ud83d\udd29 tracing_database: ParallelTinyDbDriver \ud83d\udd29 large_file_implementation: LargeFileLocal \ud83d\udd29 is_production: False \ud83d\udd29 should_log_exception_stack: True \ud83d\udd29 prediction_cache_size: 512 \ud83d\udd29 dashboard_table_size: 50 You still need to check whether you follow all best practices before trusting your deployment. > Find out more at https://se-ml.github.io/practices","title":"Production-ready backend"},{"location":"examples/simple/data/#next-part-2","text":"","title":"Next: Part 2"},{"location":"examples/simple/deploy/","text":"(function (global, factory) { typeof exports === 'object' && typeof module !== 'undefined' ? module.exports = factory() : typeof define === 'function' && define.amd ? define(factory) : (global = global || self, global.ClipboardCopyElement = factory()); 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When children elements, such as headings, are sized * in em all things will be computed relative to that body size. */ --jp-content-line-height: 1.6; --jp-content-font-scale-factor: 1.2; --jp-content-font-size0: 0.83333em; --jp-content-font-size1: 14px; /* Base font size */ --jp-content-font-size2: 1.2em; --jp-content-font-size3: 1.44em; --jp-content-font-size4: 1.728em; --jp-content-font-size5: 2.0736em; /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-content-presentation-font-size1: 17px; --jp-content-heading-line-height: 1; --jp-content-heading-margin-top: 1.2em; --jp-content-heading-margin-bottom: 0.8em; --jp-content-heading-font-weight: 500; /* Defaults use Material Design specification */ --jp-content-font-color0: rgba(0, 0, 0, 1); --jp-content-font-color1: rgba(0, 0, 0, 0.87); --jp-content-font-color2: rgba(0, 0, 0, 0.54); --jp-content-font-color3: rgba(0, 0, 0, 0.38); --jp-content-link-color: var(--md-blue-700); --jp-content-font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Helvetica, Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\"; /* * Code Fonts * * Code font variables are used for typography of code and other monospaces content. */ --jp-code-font-size: 13px; --jp-code-line-height: 1.3077; /* 17px for 13px base */ --jp-code-padding: 5px; /* 5px for 13px base, codemirror highlighting needs integer px value */ --jp-code-font-family-default: Menlo, Consolas, \"DejaVu Sans Mono\", monospace; --jp-code-font-family: var(--jp-code-font-family-default); /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-code-presentation-font-size: 16px; /* may need to tweak cursor width if you change font size */ --jp-code-cursor-width0: 1.4px; --jp-code-cursor-width1: 2px; --jp-code-cursor-width2: 4px; /* Layout * * The following are the main layout colors use in JupyterLab. In a light * theme these would go from light to dark. */ --jp-layout-color0: white; --jp-layout-color1: white; --jp-layout-color2: var(--md-grey-200); --jp-layout-color3: var(--md-grey-400); --jp-layout-color4: var(--md-grey-600); /* Inverse Layout * * The following are the inverse layout colors use in JupyterLab. In a light * theme these would go from dark to light. */ --jp-inverse-layout-color0: #111111; --jp-inverse-layout-color1: var(--md-grey-900); --jp-inverse-layout-color2: var(--md-grey-800); --jp-inverse-layout-color3: var(--md-grey-700); --jp-inverse-layout-color4: var(--md-grey-600); /* Brand/accent */ --jp-brand-color0: var(--md-blue-900); --jp-brand-color1: var(--md-blue-700); --jp-brand-color2: var(--md-blue-300); --jp-brand-color3: var(--md-blue-100); --jp-brand-color4: var(--md-blue-50); --jp-accent-color0: var(--md-green-900); --jp-accent-color1: var(--md-green-700); --jp-accent-color2: var(--md-green-300); --jp-accent-color3: var(--md-green-100); /* State colors (warn, error, success, info) */ --jp-warn-color0: var(--md-orange-900); --jp-warn-color1: var(--md-orange-700); --jp-warn-color2: var(--md-orange-300); --jp-warn-color3: var(--md-orange-100); --jp-error-color0: var(--md-red-900); --jp-error-color1: var(--md-red-700); --jp-error-color2: var(--md-red-300); --jp-error-color3: var(--md-red-100); --jp-success-color0: var(--md-green-900); --jp-success-color1: var(--md-green-700); --jp-success-color2: var(--md-green-300); --jp-success-color3: var(--md-green-100); --jp-info-color0: var(--md-cyan-900); --jp-info-color1: var(--md-cyan-700); --jp-info-color2: var(--md-cyan-300); --jp-info-color3: var(--md-cyan-100); /* Cell specific styles */ --jp-cell-padding: 5px; --jp-cell-collapser-width: 8px; --jp-cell-collapser-min-height: 20px; --jp-cell-collapser-not-active-hover-opacity: 0.6; --jp-cell-editor-background: var(--md-grey-100); --jp-cell-editor-border-color: var(--md-grey-300); --jp-cell-editor-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-cell-editor-active-background: var(--jp-layout-color0); --jp-cell-editor-active-border-color: var(--jp-brand-color1); --jp-cell-prompt-width: 64px; --jp-cell-prompt-font-family: var(--jp-code-font-family-default); --jp-cell-prompt-letter-spacing: 0px; --jp-cell-prompt-opacity: 1; --jp-cell-prompt-not-active-opacity: 0.5; --jp-cell-prompt-not-active-font-color: var(--md-grey-700); /* A custom blend of MD grey and blue 600 * See https://meyerweb.com/eric/tools/color-blend/#546E7A:1E88E5:5:hex */ --jp-cell-inprompt-font-color: #307fc1; /* A custom blend of MD grey and orange 600 * https://meyerweb.com/eric/tools/color-blend/#546E7A:F4511E:5:hex */ --jp-cell-outprompt-font-color: #bf5b3d; /* Notebook specific styles */ --jp-notebook-padding: 10px; --jp-notebook-select-background: var(--jp-layout-color1); --jp-notebook-multiselected-color: var(--md-blue-50); /* The scroll padding is calculated to fill enough space at the bottom of the notebook to show one single-line cell (with appropriate padding) at the top when the notebook is scrolled all the way to the bottom. We also subtract one pixel so that no scrollbar appears if we have just one single-line cell in the notebook. This padding is to enable a 'scroll past end' feature in a notebook. */ --jp-notebook-scroll-padding: calc( 100% - var(--jp-code-font-size) * var(--jp-code-line-height) - var(--jp-code-padding) - var(--jp-cell-padding) - 1px ); /* Rendermime styles */ --jp-rendermime-error-background: #fdd; --jp-rendermime-table-row-background: var(--md-grey-100); --jp-rendermime-table-row-hover-background: var(--md-light-blue-50); /* Dialog specific styles */ --jp-dialog-background: rgba(0, 0, 0, 0.25); /* Console specific styles */ --jp-console-padding: 10px; /* Toolbar specific styles */ --jp-toolbar-border-color: var(--jp-border-color1); --jp-toolbar-micro-height: 8px; --jp-toolbar-background: var(--jp-layout-color1); --jp-toolbar-box-shadow: 0px 0px 2px 0px rgba(0, 0, 0, 0.24); --jp-toolbar-header-margin: 4px 4px 0px 4px; --jp-toolbar-active-background: var(--md-grey-300); /* Statusbar specific styles */ --jp-statusbar-height: 24px; /* Input field styles */ --jp-input-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-input-active-background: var(--jp-layout-color1); --jp-input-hover-background: var(--jp-layout-color1); --jp-input-background: var(--md-grey-100); --jp-input-border-color: var(--jp-border-color1); --jp-input-active-border-color: var(--jp-brand-color1); --jp-input-active-box-shadow-color: rgba(19, 124, 189, 0.3); /* General editor styles */ --jp-editor-selected-background: #d9d9d9; --jp-editor-selected-focused-background: #d7d4f0; --jp-editor-cursor-color: var(--jp-ui-font-color0); /* Code mirror specific styles */ --jp-mirror-editor-keyword-color: #008000; --jp-mirror-editor-atom-color: #88f; --jp-mirror-editor-number-color: #080; --jp-mirror-editor-def-color: #00f; --jp-mirror-editor-variable-color: var(--md-grey-900); --jp-mirror-editor-variable-2-color: #05a; --jp-mirror-editor-variable-3-color: #085; --jp-mirror-editor-punctuation-color: #05a; --jp-mirror-editor-property-color: #05a; --jp-mirror-editor-operator-color: #aa22ff; --jp-mirror-editor-comment-color: #408080; --jp-mirror-editor-string-color: #ba2121; --jp-mirror-editor-string-2-color: #708; --jp-mirror-editor-meta-color: #aa22ff; --jp-mirror-editor-qualifier-color: #555; --jp-mirror-editor-builtin-color: #008000; --jp-mirror-editor-bracket-color: #997; --jp-mirror-editor-tag-color: #170; --jp-mirror-editor-attribute-color: #00c; --jp-mirror-editor-header-color: blue; --jp-mirror-editor-quote-color: #090; --jp-mirror-editor-link-color: #00c; --jp-mirror-editor-error-color: #f00; --jp-mirror-editor-hr-color: #999; /* Vega extension styles */ --jp-vega-background: white; /* Sidebar-related styles */ --jp-sidebar-min-width: 250px; /* Search-related styles */ --jp-search-toggle-off-opacity: 0.5; --jp-search-toggle-hover-opacity: 0.8; --jp-search-toggle-on-opacity: 1; --jp-search-selected-match-background-color: rgb(245, 200, 0); --jp-search-selected-match-color: black; --jp-search-unselected-match-background-color: var( --jp-inverse-layout-color0 ); --jp-search-unselected-match-color: var(--jp-ui-inverse-font-color0); /* Icon colors that work well with light or dark backgrounds */ --jp-icon-contrast-color0: var(--md-purple-600); --jp-icon-contrast-color1: var(--md-green-600); --jp-icon-contrast-color2: var(--md-pink-600); --jp-icon-contrast-color3: var(--md-blue-600); } [data-md-color-scheme=\"slate\"] .jupyter-wrapper { /* Elevation * * We style box-shadows using Material Design's idea of elevation. These particular numbers are taken from here: * * https://github.com/material-components/material-components-web * https://material-components-web.appspot.com/elevation.html */ /* The dark theme shadows need a bit of work, but this will probably also require work on the core layout * colors used in the theme as well. */ --jp-shadow-base-lightness: 32; --jp-shadow-umbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.2 ); --jp-shadow-penumbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.14 ); --jp-shadow-ambient-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.12 ); --jp-elevation-z0: none; --jp-elevation-z1: 0px 2px 1px -1px var(--jp-shadow-umbra-color), 0px 1px 1px 0px var(--jp-shadow-penumbra-color), 0px 1px 3px 0px var(--jp-shadow-ambient-color); --jp-elevation-z2: 0px 3px 1px -2px var(--jp-shadow-umbra-color), 0px 2px 2px 0px var(--jp-shadow-penumbra-color), 0px 1px 5px 0px var(--jp-shadow-ambient-color); --jp-elevation-z4: 0px 2px 4px -1px var(--jp-shadow-umbra-color), 0px 4px 5px 0px var(--jp-shadow-penumbra-color), 0px 1px 10px 0px var(--jp-shadow-ambient-color); --jp-elevation-z6: 0px 3px 5px -1px var(--jp-shadow-umbra-color), 0px 6px 10px 0px var(--jp-shadow-penumbra-color), 0px 1px 18px 0px var(--jp-shadow-ambient-color); --jp-elevation-z8: 0px 5px 5px -3px var(--jp-shadow-umbra-color), 0px 8px 10px 1px var(--jp-shadow-penumbra-color), 0px 3px 14px 2px var(--jp-shadow-ambient-color); --jp-elevation-z12: 0px 7px 8px -4px var(--jp-shadow-umbra-color), 0px 12px 17px 2px var(--jp-shadow-penumbra-color), 0px 5px 22px 4px var(--jp-shadow-ambient-color); --jp-elevation-z16: 0px 8px 10px -5px var(--jp-shadow-umbra-color), 0px 16px 24px 2px var(--jp-shadow-penumbra-color), 0px 6px 30px 5px var(--jp-shadow-ambient-color); --jp-elevation-z20: 0px 10px 13px -6px var(--jp-shadow-umbra-color), 0px 20px 31px 3px var(--jp-shadow-penumbra-color), 0px 8px 38px 7px var(--jp-shadow-ambient-color); --jp-elevation-z24: 0px 11px 15px -7px var(--jp-shadow-umbra-color), 0px 24px 38px 3px var(--jp-shadow-penumbra-color), 0px 9px 46px 8px var(--jp-shadow-ambient-color); /* Borders * * The following variables, specify the visual styling of borders in JupyterLab. */ --jp-border-width: 1px; --jp-border-color0: var(--md-grey-700); --jp-border-color1: var(--md-grey-700); --jp-border-color2: var(--md-grey-800); --jp-border-color3: var(--md-grey-900); --jp-border-radius: 2px; /* UI Fonts * * The UI font CSS variables are used for the typography all of the JupyterLab * user interface elements that are not directly user generated content. * * The font sizing here is done assuming that the body font size of --jp-ui-font-size1 * is applied to a parent element. When children elements, such as headings, are sized * in em all things will be computed relative to that body size. */ --jp-ui-font-scale-factor: 1.2; --jp-ui-font-size0: 0.83333em; --jp-ui-font-size1: 13px; /* Base font size */ --jp-ui-font-size2: 1.2em; --jp-ui-font-size3: 1.44em; --jp-ui-font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Helvetica, Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\"; /* * Use these font colors against the corresponding main layout colors. * In a light theme, these go from dark to light. */ /* Defaults use Material Design specification */ --jp-ui-font-color0: rgba(255, 255, 255, 1); --jp-ui-font-color1: rgba(255, 255, 255, 0.87); --jp-ui-font-color2: rgba(255, 255, 255, 0.54); --jp-ui-font-color3: rgba(255, 255, 255, 0.38); /* * Use these against the brand/accent/warn/error colors. * These will typically go from light to darker, in both a dark and light theme. */ --jp-ui-inverse-font-color0: rgba(0, 0, 0, 1); --jp-ui-inverse-font-color1: rgba(0, 0, 0, 0.8); --jp-ui-inverse-font-color2: rgba(0, 0, 0, 0.5); --jp-ui-inverse-font-color3: rgba(0, 0, 0, 0.3); /* Content Fonts * * Content font variables are used for typography of user generated content. * * The font sizing here is done assuming that the body font size of --jp-content-font-size1 * is applied to a parent element. When children elements, such as headings, are sized * in em all things will be computed relative to that body size. */ --jp-content-line-height: 1.6; --jp-content-font-scale-factor: 1.2; --jp-content-font-size0: 0.83333em; --jp-content-font-size1: 14px; /* Base font size */ --jp-content-font-size2: 1.2em; --jp-content-font-size3: 1.44em; --jp-content-font-size4: 1.728em; --jp-content-font-size5: 2.0736em; /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-content-presentation-font-size1: 17px; --jp-content-heading-line-height: 1; --jp-content-heading-margin-top: 1.2em; --jp-content-heading-margin-bottom: 0.8em; --jp-content-heading-font-weight: 500; /* Defaults use Material Design specification */ --jp-content-font-color0: rgba(255, 255, 255, 1); --jp-content-font-color1: rgba(255, 255, 255, 1); --jp-content-font-color2: rgba(255, 255, 255, 0.7); --jp-content-font-color3: rgba(255, 255, 255, 0.5); --jp-content-link-color: var(--md-blue-300); --jp-content-font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Helvetica, Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\"; /* * Code Fonts * * Code font variables are used for typography of code and other monospaces content. */ --jp-code-font-size: 13px; --jp-code-line-height: 1.3077; /* 17px for 13px base */ --jp-code-padding: 5px; /* 5px for 13px base, codemirror highlighting needs integer px value */ --jp-code-font-family-default: Menlo, Consolas, \"DejaVu Sans Mono\", monospace; --jp-code-font-family: var(--jp-code-font-family-default); /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-code-presentation-font-size: 16px; /* may need to tweak cursor width if you change font size */ --jp-code-cursor-width0: 1.4px; --jp-code-cursor-width1: 2px; --jp-code-cursor-width2: 4px; /* Layout * * The following are the main layout colors use in JupyterLab. In a light * theme these would go from light to dark. */ --jp-layout-color0: #111111; --jp-layout-color1: var(--md-grey-900); --jp-layout-color2: var(--md-grey-800); --jp-layout-color3: var(--md-grey-700); --jp-layout-color4: var(--md-grey-600); /* Inverse Layout * * The following are the inverse layout colors use in JupyterLab. In a light * theme these would go from dark to light. */ --jp-inverse-layout-color0: white; --jp-inverse-layout-color1: white; --jp-inverse-layout-color2: var(--md-grey-200); --jp-inverse-layout-color3: var(--md-grey-400); --jp-inverse-layout-color4: var(--md-grey-600); /* Brand/accent */ --jp-brand-color0: var(--md-blue-700); --jp-brand-color1: var(--md-blue-500); --jp-brand-color2: var(--md-blue-300); --jp-brand-color3: var(--md-blue-100); --jp-brand-color4: var(--md-blue-50); --jp-accent-color0: var(--md-green-700); --jp-accent-color1: var(--md-green-500); --jp-accent-color2: var(--md-green-300); --jp-accent-color3: var(--md-green-100); /* State colors (warn, error, success, info) */ --jp-warn-color0: var(--md-orange-700); --jp-warn-color1: var(--md-orange-500); --jp-warn-color2: var(--md-orange-300); --jp-warn-color3: var(--md-orange-100); --jp-error-color0: var(--md-red-700); --jp-error-color1: var(--md-red-500); --jp-error-color2: var(--md-red-300); --jp-error-color3: var(--md-red-100); --jp-success-color0: var(--md-green-700); --jp-success-color1: var(--md-green-500); --jp-success-color2: var(--md-green-300); --jp-success-color3: var(--md-green-100); --jp-info-color0: var(--md-cyan-700); --jp-info-color1: var(--md-cyan-500); --jp-info-color2: var(--md-cyan-300); --jp-info-color3: var(--md-cyan-100); /* Cell specific styles */ --jp-cell-padding: 5px; --jp-cell-collapser-width: 8px; --jp-cell-collapser-min-height: 20px; --jp-cell-collapser-not-active-hover-opacity: 0.6; --jp-cell-editor-background: var(--jp-layout-color1); --jp-cell-editor-border-color: var(--md-grey-700); --jp-cell-editor-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-cell-editor-active-background: var(--jp-layout-color0); --jp-cell-editor-active-border-color: var(--jp-brand-color1); --jp-cell-prompt-width: 64px; --jp-cell-prompt-font-family: var(--jp-code-font-family-default); --jp-cell-prompt-letter-spacing: 0px; --jp-cell-prompt-opacity: 1; --jp-cell-prompt-not-active-opacity: 1; --jp-cell-prompt-not-active-font-color: var(--md-grey-300); /* A custom blend of MD grey and blue 600 * See https://meyerweb.com/eric/tools/color-blend/#546E7A:1E88E5:5:hex */ --jp-cell-inprompt-font-color: #307fc1; /* A custom blend of MD grey and orange 600 * https://meyerweb.com/eric/tools/color-blend/#546E7A:F4511E:5:hex */ --jp-cell-outprompt-font-color: #bf5b3d; /* Notebook specific styles */ --jp-notebook-padding: 10px; --jp-notebook-select-background: var(--jp-layout-color1); --jp-notebook-multiselected-color: rgba(33, 150, 243, 0.24); /* The scroll padding is calculated to fill enough space at the bottom of the notebook to show one single-line cell (with appropriate padding) at the top when the notebook is scrolled all the way to the bottom. We also subtract one pixel so that no scrollbar appears if we have just one single-line cell in the notebook. This padding is to enable a 'scroll past end' feature in a notebook. */ --jp-notebook-scroll-padding: calc( 100% - var(--jp-code-font-size) * var(--jp-code-line-height) - var(--jp-code-padding) - var(--jp-cell-padding) - 1px ); /* Rendermime styles */ --jp-rendermime-error-background: rgba(244, 67, 54, 0.28); --jp-rendermime-table-row-background: var(--md-grey-900); --jp-rendermime-table-row-hover-background: rgba(3, 169, 244, 0.2); /* Dialog specific styles */ --jp-dialog-background: rgba(0, 0, 0, 0.6); /* Console specific styles */ --jp-console-padding: 10px; /* Toolbar specific styles */ --jp-toolbar-border-color: var(--jp-border-color2); --jp-toolbar-micro-height: 8px; --jp-toolbar-background: var(--jp-layout-color1); --jp-toolbar-box-shadow: 0px 0px 2px 0px rgba(0, 0, 0, 0.8); --jp-toolbar-header-margin: 4px 4px 0px 4px; --jp-toolbar-active-background: var(--jp-layout-color0); /* Statusbar specific styles */ --jp-statusbar-height: 24px; /* Input field styles */ --jp-input-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-input-active-background: var(--jp-layout-color0); --jp-input-hover-background: var(--jp-layout-color2); --jp-input-background: var(--md-grey-800); --jp-input-border-color: var(--jp-border-color1); --jp-input-active-border-color: var(--jp-brand-color1); --jp-input-active-box-shadow-color: rgba(19, 124, 189, 0.3); /* General editor styles */ --jp-editor-selected-background: var(--jp-layout-color2); --jp-editor-selected-focused-background: rgba(33, 150, 243, 0.24); --jp-editor-cursor-color: var(--jp-ui-font-color0); /* Code mirror specific styles */ --jp-mirror-editor-keyword-color: var(--md-green-500); --jp-mirror-editor-atom-color: var(--md-blue-300); --jp-mirror-editor-number-color: var(--md-green-400); --jp-mirror-editor-def-color: var(--md-blue-600); --jp-mirror-editor-variable-color: var(--md-grey-300); --jp-mirror-editor-variable-2-color: var(--md-blue-400); --jp-mirror-editor-variable-3-color: var(--md-green-600); --jp-mirror-editor-punctuation-color: var(--md-blue-400); --jp-mirror-editor-property-color: var(--md-blue-400); --jp-mirror-editor-operator-color: #aa22ff; --jp-mirror-editor-comment-color: #408080; --jp-mirror-editor-string-color: #ff7070; --jp-mirror-editor-string-2-color: var(--md-purple-300); --jp-mirror-editor-meta-color: #aa22ff; --jp-mirror-editor-qualifier-color: #555; --jp-mirror-editor-builtin-color: var(--md-green-600); --jp-mirror-editor-bracket-color: #997; --jp-mirror-editor-tag-color: var(--md-green-700); --jp-mirror-editor-attribute-color: var(--md-blue-700); --jp-mirror-editor-header-color: var(--md-blue-500); --jp-mirror-editor-quote-color: var(--md-green-300); --jp-mirror-editor-link-color: var(--md-blue-700); --jp-mirror-editor-error-color: #f00; --jp-mirror-editor-hr-color: #999; /* Vega extension styles */ --jp-vega-background: var(--md-grey-400); /* Sidebar-related styles */ --jp-sidebar-min-width: 250px; /* Search-related styles */ --jp-search-toggle-off-opacity: 0.6; --jp-search-toggle-hover-opacity: 0.8; --jp-search-toggle-on-opacity: 1; --jp-search-selected-match-background-color: rgb(255, 225, 0); --jp-search-selected-match-color: black; --jp-search-unselected-match-background-color: var( --jp-inverse-layout-color0 ); --jp-search-unselected-match-color: var(--jp-ui-inverse-font-color0); /* scrollbar related styles. Supports every browser except Edge. */ /* colors based on JetBrain's Darcula theme */ --jp-scrollbar-background-color: #3f4244; --jp-scrollbar-thumb-color: 88, 96, 97; /* need to specify thumb color as an RGB triplet */ --jp-scrollbar-endpad: 3px; /* the minimum gap between the thumb and the ends of a scrollbar */ /* hacks for setting the thumb shape. 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sourceMappingURL=mkdocs-jupyter.css.map*/ init_mathjax = function() { if (window.MathJax) { // MathJax loaded MathJax.Hub.Config({ TeX: { equationNumbers: { autoNumber: \"AMS\", useLabelIds: true } }, tex2jax: { inlineMath: [ ['$','$'], [\"\\\\(\",\"\\\\)\"] ], displayMath: [ ['$$','$$'], [\"\\\\[\",\"\\\\]\"] ], processEscapes: true, processEnvironments: true }, displayAlign: 'center', CommonHTML: { linebreaks: { automatic: true } } }); MathJax.Hub.Queue([\"Typeset\", MathJax.Hub]); } } init_mathjax(); Hardening and deployment \u00b6 Part 3: Create production inference function The blue boxes show the steps implemented in this notebook. In Part 2 , we trained our AI model. Now, it's time to create G eneral R obust E nd-to-end A utomated T rustworthy deployment. Create the inference function \u00b6 Next to the prediction, we also return the top-n most influential words based on their weights. In [1]: Copied! import re import numpy as np from sklearn.pipeline import Pipeline from great_ai.utilities import clean from great_ai import ( MultiLabelClassificationOutput , ClassificationOutput , GreatAI , use_model , parameter , ) @GreatAI . create @use_model ( \"small-domain-prediction\" , version = \"latest\" ) @parameter ( \"target_confidence\" , validate = lambda c : 0 <= c <= 100 ) def predict_domain ( text : str , model : Pipeline , target_confidence : int = 50 ) -> MultiLabelClassificationOutput : \"\"\" Predict the scientific domain of the input text. Return labels until their sum likelihood is larger than `target_confidence`. \"\"\" preprocessed = re . sub ( r \"[^a-zA-Z\\s]\" , \"\" , clean ( text , convert_to_ascii = True )) features = model . named_steps [ \"vectorizer\" ] . transform ([ preprocessed ]) prediction = model . named_steps [ \"classifier\" ] . predict_proba ( features )[ 0 ] best_classes = sorted ( enumerate ( prediction ), key = lambda v : v [ 1 ], reverse = True ) results = MultiLabelClassificationOutput () for class_index , probability in best_classes : results . labels . append ( get_label ( model = model , features = features , class_index = class_index , probability = probability , ) ) if sum ( r . confidence for r in results . labels ) >= target_confidence : break return results def get_label ( model : Pipeline , features : np . ndarray , class_index : int , probability : float ) -> ClassificationOutput : return ClassificationOutput ( label = model . named_steps [ \"classifier\" ] . classes_ [ class_index ], confidence = round ( probability * 100 ), explanation = [ word for _ , word in sorted ( ( ( weight , word ) for weight , word , count in zip ( model . named_steps [ \"classifier\" ] . feature_log_prob_ [ class_index ], model . named_steps [ \"vectorizer\" ] . get_feature_names_out (), features . A [ 0 ], ) if count > 0 ), reverse = True , ) ][: 5 ], ) import re import numpy as np from sklearn.pipeline import Pipeline from great_ai.utilities import clean from great_ai import ( MultiLabelClassificationOutput, ClassificationOutput, GreatAI, use_model, parameter, ) @GreatAI.create @use_model(\"small-domain-prediction\", version=\"latest\") @parameter(\"target_confidence\", validate=lambda c: 0 < = c < = 100) def predict_domain( text: str, model: Pipeline, target_confidence: int = 50 ) -> MultiLabelClassificationOutput: \"\"\" Predict the scientific domain of the input text. Return labels until their sum likelihood is larger than `target_confidence`. \"\"\" preprocessed = re.sub(r\"[^a-zA-Z\\s]\", \"\", clean(text, convert_to_ascii=True)) features = model.named_steps[\"vectorizer\"].transform([preprocessed]) prediction = model.named_steps[\"classifier\"].predict_proba(features)[0] best_classes = sorted(enumerate(prediction), key=lambda v: v[1], reverse=True) results = MultiLabelClassificationOutput() for class_index, probability in best_classes: results.labels.append( get_label( model=model, features=features, class_index=class_index, probability=probability, ) ) if sum(r.confidence for r in results.labels) >= target_confidence: break return results def get_label( model: Pipeline, features: np.ndarray, class_index: int, probability: float ) -> ClassificationOutput: return ClassificationOutput( label=model.named_steps[\"classifier\"].classes_[class_index], confidence=round(probability * 100), explanation=[ word for _, word in sorted( ( (weight, word) for weight, word, count in zip( model.named_steps[\"classifier\"].feature_log_prob_[class_index], model.named_steps[\"vectorizer\"].get_feature_names_out(), features.A[0], ) if count > 0 ), reverse=True, ) ][:5], ) Environment variable ENVIRONMENT is not set, defaulting to development mode \u203c\ufe0f Cannot find credentials files, defaulting to using ParallelTinyDbDriver The selected tracing database (ParallelTinyDbDriver) is not recommended for production Cannot find credentials files, defaulting to using LargeFileLocal GreatAI (v0.1.6): configured \u2705 \ud83d\udd29 tracing_database: ParallelTinyDbDriver \ud83d\udd29 large_file_implementation: LargeFileLocal \ud83d\udd29 is_production: False \ud83d\udd29 should_log_exception_stack: True \ud83d\udd29 prediction_cache_size: 512 \ud83d\udd29 dashboard_table_size: 50 You still need to check whether you follow all best practices before trusting your deployment. > Find out more at https://se-ml.github.io/practices Fetching cached versions of small-domain-prediction Latest version of small-domain-prediction is 2 (from versions: 0, 1, 2) File small-domain-prediction-2 found in cache Check accuracy on the test split \u00b6 Anything under if __name__ == \"__main__\": will not be run when the script is executed by the great-ai CLI app. This, combined with query_ground_truth and the /traces/{trace_id}/feedback endpoint are ideal for creating a continuous-integration job for checking the quality of the model before deployment. In [2]: Copied! if __name__ == \"__main__\" : from great_ai import query_ground_truth from sklearn import metrics data = query_ground_truth ( \"test\" ) X = [ d . input for d in data ] y_actual = [ d . feedback for d in data ] y_predicted = [ d . output . labels [ 0 ] . label for d in predict_domain . process_batch ( X , do_not_persist_traces = True ) ] y_actual_aligned = [ p if p in a else a [ 0 ] for p , a in zip ( y_predicted , y_actual )] import matplotlib.pyplot as plt # Configure matplotlib to have nice, high-resolution charts % matplotlib inline plt . rcParams [ \"figure.figsize\" ] = ( 19 , 18 ) plt . rcParams [ \"figure.facecolor\" ] = \"white\" plt . rcParams [ \"font.size\" ] = 16 plt . rcParams [ \"axes.xmargin\" ] = 0 print ( metrics . classification_report ( y_actual_aligned , y_predicted )) metrics . ConfusionMatrixDisplay . from_predictions ( y_true = y_actual_aligned , y_pred = y_predicted , xticks_rotation = \"vertical\" , normalize = \"pred\" , values_format = \".2f\" , ) plt . tight_layout () plt . savefig ( \"ss-confusion.png\" , dpi = 600 ) if __name__ == \"__main__\": from great_ai import query_ground_truth from sklearn import metrics data = query_ground_truth(\"test\") X = [d.input for d in data] y_actual = [d.feedback for d in data] y_predicted = [ d.output.labels[0].label for d in predict_domain.process_batch(X, do_not_persist_traces=True) ] y_actual_aligned = [p if p in a else a[0] for p, a in zip(y_predicted, y_actual)] import matplotlib.pyplot as plt # Configure matplotlib to have nice, high-resolution charts %matplotlib inline plt.rcParams[\"figure.figsize\"] = (19, 18) plt.rcParams[\"figure.facecolor\"] = \"white\" plt.rcParams[\"font.size\"] = 16 plt.rcParams[\"axes.xmargin\"] = 0 print(metrics.classification_report(y_actual_aligned, y_predicted)) metrics.ConfusionMatrixDisplay.from_predictions( y_true=y_actual_aligned, y_pred=y_predicted, xticks_rotation=\"vertical\", normalize=\"pred\", values_format=\".2f\", ) plt.tight_layout() plt.savefig(\"ss-confusion.png\", dpi=600) 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 12272/12272 [01:10<00:00, 174.77it/s] precision recall f1-score support Art 0.54 0.38 0.45 126 Biology 0.77 0.84 0.80 1215 Business 0.47 0.73 0.57 311 Chemistry 0.82 0.67 0.74 1205 Computer Science 0.77 0.76 0.76 1277 Economics 0.69 0.55 0.61 270 Engineering 0.55 0.52 0.53 754 Environmental Science 0.56 0.55 0.55 227 Geography 0.54 0.39 0.45 276 Geology 0.74 0.67 0.70 265 History 0.35 0.18 0.24 140 Materials Science 0.72 0.81 0.76 1011 Mathematics 0.77 0.70 0.74 498 Medicine 0.96 0.77 0.86 2835 Philosophy 0.57 0.06 0.10 71 Physics 0.66 0.75 0.70 611 Political Science 0.44 0.61 0.51 291 Psychology 0.52 0.84 0.64 574 Sociology 0.33 0.59 0.42 315 accuracy 0.71 12272 macro avg 0.62 0.60 0.59 12272 weighted avg 0.74 0.71 0.71 12272","title":"Hardening and deployment"},{"location":"examples/simple/deploy/#hardening-and-deployment","text":"Part 3: Create production inference function The blue boxes show the steps implemented in this notebook. In Part 2 , we trained our AI model. Now, it's time to create G eneral R obust E nd-to-end A utomated T rustworthy deployment.","title":"Hardening and deployment"},{"location":"examples/simple/deploy/#create-the-inference-function","text":"Next to the prediction, we also return the top-n most influential words based on their weights. In [1]: Copied! import re import numpy as np from sklearn.pipeline import Pipeline from great_ai.utilities import clean from great_ai import ( MultiLabelClassificationOutput , ClassificationOutput , GreatAI , use_model , parameter , ) @GreatAI . create @use_model ( \"small-domain-prediction\" , version = \"latest\" ) @parameter ( \"target_confidence\" , validate = lambda c : 0 <= c <= 100 ) def predict_domain ( text : str , model : Pipeline , target_confidence : int = 50 ) -> MultiLabelClassificationOutput : \"\"\" Predict the scientific domain of the input text. Return labels until their sum likelihood is larger than `target_confidence`. \"\"\" preprocessed = re . sub ( r \"[^a-zA-Z\\s]\" , \"\" , clean ( text , convert_to_ascii = True )) features = model . named_steps [ \"vectorizer\" ] . transform ([ preprocessed ]) prediction = model . named_steps [ \"classifier\" ] . predict_proba ( features )[ 0 ] best_classes = sorted ( enumerate ( prediction ), key = lambda v : v [ 1 ], reverse = True ) results = MultiLabelClassificationOutput () for class_index , probability in best_classes : results . labels . append ( get_label ( model = model , features = features , class_index = class_index , probability = probability , ) ) if sum ( r . confidence for r in results . labels ) >= target_confidence : break return results def get_label ( model : Pipeline , features : np . ndarray , class_index : int , probability : float ) -> ClassificationOutput : return ClassificationOutput ( label = model . named_steps [ \"classifier\" ] . classes_ [ class_index ], confidence = round ( probability * 100 ), explanation = [ word for _ , word in sorted ( ( ( weight , word ) for weight , word , count in zip ( model . named_steps [ \"classifier\" ] . feature_log_prob_ [ class_index ], model . named_steps [ \"vectorizer\" ] . get_feature_names_out (), features . A [ 0 ], ) if count > 0 ), reverse = True , ) ][: 5 ], ) import re import numpy as np from sklearn.pipeline import Pipeline from great_ai.utilities import clean from great_ai import ( MultiLabelClassificationOutput, ClassificationOutput, GreatAI, use_model, parameter, ) @GreatAI.create @use_model(\"small-domain-prediction\", version=\"latest\") @parameter(\"target_confidence\", validate=lambda c: 0 < = c < = 100) def predict_domain( text: str, model: Pipeline, target_confidence: int = 50 ) -> MultiLabelClassificationOutput: \"\"\" Predict the scientific domain of the input text. Return labels until their sum likelihood is larger than `target_confidence`. \"\"\" preprocessed = re.sub(r\"[^a-zA-Z\\s]\", \"\", clean(text, convert_to_ascii=True)) features = model.named_steps[\"vectorizer\"].transform([preprocessed]) prediction = model.named_steps[\"classifier\"].predict_proba(features)[0] best_classes = sorted(enumerate(prediction), key=lambda v: v[1], reverse=True) results = MultiLabelClassificationOutput() for class_index, probability in best_classes: results.labels.append( get_label( model=model, features=features, class_index=class_index, probability=probability, ) ) if sum(r.confidence for r in results.labels) >= target_confidence: break return results def get_label( model: Pipeline, features: np.ndarray, class_index: int, probability: float ) -> ClassificationOutput: return ClassificationOutput( label=model.named_steps[\"classifier\"].classes_[class_index], confidence=round(probability * 100), explanation=[ word for _, word in sorted( ( (weight, word) for weight, word, count in zip( model.named_steps[\"classifier\"].feature_log_prob_[class_index], model.named_steps[\"vectorizer\"].get_feature_names_out(), features.A[0], ) if count > 0 ), reverse=True, ) ][:5], ) Environment variable ENVIRONMENT is not set, defaulting to development mode \u203c\ufe0f Cannot find credentials files, defaulting to using ParallelTinyDbDriver The selected tracing database (ParallelTinyDbDriver) is not recommended for production Cannot find credentials files, defaulting to using LargeFileLocal GreatAI (v0.1.6): configured \u2705 \ud83d\udd29 tracing_database: ParallelTinyDbDriver \ud83d\udd29 large_file_implementation: LargeFileLocal \ud83d\udd29 is_production: False \ud83d\udd29 should_log_exception_stack: True \ud83d\udd29 prediction_cache_size: 512 \ud83d\udd29 dashboard_table_size: 50 You still need to check whether you follow all best practices before trusting your deployment. > Find out more at https://se-ml.github.io/practices Fetching cached versions of small-domain-prediction Latest version of small-domain-prediction is 2 (from versions: 0, 1, 2) File small-domain-prediction-2 found in cache","title":"Create the inference function"},{"location":"examples/simple/deploy/#check-accuracy-on-the-test-split","text":"Anything under if __name__ == \"__main__\": will not be run when the script is executed by the great-ai CLI app. This, combined with query_ground_truth and the /traces/{trace_id}/feedback endpoint are ideal for creating a continuous-integration job for checking the quality of the model before deployment. In [2]: Copied! if __name__ == \"__main__\" : from great_ai import query_ground_truth from sklearn import metrics data = query_ground_truth ( \"test\" ) X = [ d . input for d in data ] y_actual = [ d . feedback for d in data ] y_predicted = [ d . output . labels [ 0 ] . label for d in predict_domain . process_batch ( X , do_not_persist_traces = True ) ] y_actual_aligned = [ p if p in a else a [ 0 ] for p , a in zip ( y_predicted , y_actual )] import matplotlib.pyplot as plt # Configure matplotlib to have nice, high-resolution charts % matplotlib inline plt . rcParams [ \"figure.figsize\" ] = ( 19 , 18 ) plt . rcParams [ \"figure.facecolor\" ] = \"white\" plt . rcParams [ \"font.size\" ] = 16 plt . rcParams [ \"axes.xmargin\" ] = 0 print ( metrics . classification_report ( y_actual_aligned , y_predicted )) metrics . ConfusionMatrixDisplay . from_predictions ( y_true = y_actual_aligned , y_pred = y_predicted , xticks_rotation = \"vertical\" , normalize = \"pred\" , values_format = \".2f\" , ) plt . tight_layout () plt . savefig ( \"ss-confusion.png\" , dpi = 600 ) if __name__ == \"__main__\": from great_ai import query_ground_truth from sklearn import metrics data = query_ground_truth(\"test\") X = [d.input for d in data] y_actual = [d.feedback for d in data] y_predicted = [ d.output.labels[0].label for d in predict_domain.process_batch(X, do_not_persist_traces=True) ] y_actual_aligned = [p if p in a else a[0] for p, a in zip(y_predicted, y_actual)] import matplotlib.pyplot as plt # Configure matplotlib to have nice, high-resolution charts %matplotlib inline plt.rcParams[\"figure.figsize\"] = (19, 18) plt.rcParams[\"figure.facecolor\"] = \"white\" plt.rcParams[\"font.size\"] = 16 plt.rcParams[\"axes.xmargin\"] = 0 print(metrics.classification_report(y_actual_aligned, y_predicted)) metrics.ConfusionMatrixDisplay.from_predictions( y_true=y_actual_aligned, y_pred=y_predicted, xticks_rotation=\"vertical\", normalize=\"pred\", values_format=\".2f\", ) plt.tight_layout() plt.savefig(\"ss-confusion.png\", dpi=600) 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 12272/12272 [01:10<00:00, 174.77it/s] precision recall f1-score support Art 0.54 0.38 0.45 126 Biology 0.77 0.84 0.80 1215 Business 0.47 0.73 0.57 311 Chemistry 0.82 0.67 0.74 1205 Computer Science 0.77 0.76 0.76 1277 Economics 0.69 0.55 0.61 270 Engineering 0.55 0.52 0.53 754 Environmental Science 0.56 0.55 0.55 227 Geography 0.54 0.39 0.45 276 Geology 0.74 0.67 0.70 265 History 0.35 0.18 0.24 140 Materials Science 0.72 0.81 0.76 1011 Mathematics 0.77 0.70 0.74 498 Medicine 0.96 0.77 0.86 2835 Philosophy 0.57 0.06 0.10 71 Physics 0.66 0.75 0.70 611 Political Science 0.44 0.61 0.51 291 Psychology 0.52 0.84 0.64 574 Sociology 0.33 0.59 0.42 315 accuracy 0.71 12272 macro avg 0.62 0.60 0.59 12272 weighted avg 0.74 0.71 0.71 12272","title":"Check accuracy on the test split"},{"location":"examples/simple/stacked-bars/","text":"(function (global, factory) { typeof exports === 'object' && typeof module !== 'undefined' ? module.exports = factory() : typeof define === 'function' && define.amd ? define(factory) : (global = global || self, global.ClipboardCopyElement = factory()); 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font-weight: bold } /* Operator.Word */ .highlight-ipynb .w { color: var(--jp-mirror-editor-variable-color) } /* Text.Whitespace */ .highlight-ipynb .mb { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Bin */ .highlight-ipynb .mf { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Float */ .highlight-ipynb .mh { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Hex */ .highlight-ipynb .mi { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Integer */ .highlight-ipynb .mo { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Oct */ .highlight-ipynb .sa { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Affix */ .highlight-ipynb .sb { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Backtick */ .highlight-ipynb .sc { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Char */ .highlight-ipynb .dl { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Delimiter */ .highlight-ipynb .sd { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Doc */ .highlight-ipynb .s2 { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Double */ .highlight-ipynb .se { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Escape */ .highlight-ipynb .sh { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Heredoc */ .highlight-ipynb .si { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Interpol */ .highlight-ipynb .sx { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Other */ .highlight-ipynb .sr { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Regex */ .highlight-ipynb .s1 { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Single */ .highlight-ipynb .ss { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Symbol */ .highlight-ipynb .il { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Integer.Long */ /* This file is taken from the built JupyterLab theme.css Found on share/nbconvert/templates/lab/static Some changes have been made and marked with CHANGE */ .jupyter-wrapper { /* Elevation * * We style box-shadows using Material Design's idea of elevation. These particular numbers are taken from here: * * https://github.com/material-components/material-components-web * https://material-components-web.appspot.com/elevation.html */ --jp-shadow-base-lightness: 0; --jp-shadow-umbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.2 ); --jp-shadow-penumbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.14 ); --jp-shadow-ambient-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.12 ); --jp-elevation-z0: none; --jp-elevation-z1: 0px 2px 1px -1px var(--jp-shadow-umbra-color), 0px 1px 1px 0px var(--jp-shadow-penumbra-color), 0px 1px 3px 0px var(--jp-shadow-ambient-color); --jp-elevation-z2: 0px 3px 1px -2px var(--jp-shadow-umbra-color), 0px 2px 2px 0px var(--jp-shadow-penumbra-color), 0px 1px 5px 0px var(--jp-shadow-ambient-color); --jp-elevation-z4: 0px 2px 4px -1px var(--jp-shadow-umbra-color), 0px 4px 5px 0px var(--jp-shadow-penumbra-color), 0px 1px 10px 0px var(--jp-shadow-ambient-color); --jp-elevation-z6: 0px 3px 5px -1px var(--jp-shadow-umbra-color), 0px 6px 10px 0px var(--jp-shadow-penumbra-color), 0px 1px 18px 0px var(--jp-shadow-ambient-color); --jp-elevation-z8: 0px 5px 5px -3px var(--jp-shadow-umbra-color), 0px 8px 10px 1px var(--jp-shadow-penumbra-color), 0px 3px 14px 2px var(--jp-shadow-ambient-color); --jp-elevation-z12: 0px 7px 8px -4px var(--jp-shadow-umbra-color), 0px 12px 17px 2px var(--jp-shadow-penumbra-color), 0px 5px 22px 4px var(--jp-shadow-ambient-color); --jp-elevation-z16: 0px 8px 10px -5px var(--jp-shadow-umbra-color), 0px 16px 24px 2px var(--jp-shadow-penumbra-color), 0px 6px 30px 5px var(--jp-shadow-ambient-color); --jp-elevation-z20: 0px 10px 13px -6px var(--jp-shadow-umbra-color), 0px 20px 31px 3px var(--jp-shadow-penumbra-color), 0px 8px 38px 7px var(--jp-shadow-ambient-color); --jp-elevation-z24: 0px 11px 15px -7px var(--jp-shadow-umbra-color), 0px 24px 38px 3px var(--jp-shadow-penumbra-color), 0px 9px 46px 8px var(--jp-shadow-ambient-color); /* Borders * * The following variables, specify the visual styling of borders in JupyterLab. */ --jp-border-width: 1px; --jp-border-color0: var(--md-grey-400); --jp-border-color1: var(--md-grey-400); --jp-border-color2: var(--md-grey-300); --jp-border-color3: var(--md-grey-200); --jp-border-radius: 2px; /* UI Fonts * * The UI font CSS variables are used for the typography all of the JupyterLab * user interface elements that are not directly user generated content. * * The font sizing here is done assuming that the body font size of --jp-ui-font-size1 * is applied to a parent element. When children elements, such as headings, are sized * in em all things will be computed relative to that body size. */ --jp-ui-font-scale-factor: 1.2; --jp-ui-font-size0: 0.83333em; --jp-ui-font-size1: 13px; /* Base font size */ --jp-ui-font-size2: 1.2em; --jp-ui-font-size3: 1.44em; --jp-ui-font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Helvetica, Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\"; /* * Use these font colors against the corresponding main layout colors. * In a light theme, these go from dark to light. */ /* Defaults use Material Design specification */ --jp-ui-font-color0: rgba(0, 0, 0, 1); --jp-ui-font-color1: rgba(0, 0, 0, 0.87); --jp-ui-font-color2: rgba(0, 0, 0, 0.54); --jp-ui-font-color3: rgba(0, 0, 0, 0.38); /* * Use these against the brand/accent/warn/error colors. * These will typically go from light to darker, in both a dark and light theme. */ --jp-ui-inverse-font-color0: rgba(255, 255, 255, 1); --jp-ui-inverse-font-color1: rgba(255, 255, 255, 1); --jp-ui-inverse-font-color2: rgba(255, 255, 255, 0.7); --jp-ui-inverse-font-color3: rgba(255, 255, 255, 0.5); /* Content Fonts * * Content font variables are used for typography of user generated content. * * The font sizing here is done assuming that the body font size of --jp-content-font-size1 * is applied to a parent element. When children elements, such as headings, are sized * in em all things will be computed relative to that body size. */ --jp-content-line-height: 1.6; --jp-content-font-scale-factor: 1.2; --jp-content-font-size0: 0.83333em; --jp-content-font-size1: 14px; /* Base font size */ --jp-content-font-size2: 1.2em; --jp-content-font-size3: 1.44em; --jp-content-font-size4: 1.728em; --jp-content-font-size5: 2.0736em; /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-content-presentation-font-size1: 17px; --jp-content-heading-line-height: 1; --jp-content-heading-margin-top: 1.2em; --jp-content-heading-margin-bottom: 0.8em; --jp-content-heading-font-weight: 500; /* Defaults use Material Design specification */ --jp-content-font-color0: rgba(0, 0, 0, 1); --jp-content-font-color1: rgba(0, 0, 0, 0.87); --jp-content-font-color2: rgba(0, 0, 0, 0.54); --jp-content-font-color3: rgba(0, 0, 0, 0.38); --jp-content-link-color: var(--md-blue-700); --jp-content-font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Helvetica, Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\"; /* * Code Fonts * * Code font variables are used for typography of code and other monospaces content. */ --jp-code-font-size: 13px; --jp-code-line-height: 1.3077; /* 17px for 13px base */ --jp-code-padding: 5px; /* 5px for 13px base, codemirror highlighting needs integer px value */ --jp-code-font-family-default: Menlo, Consolas, \"DejaVu Sans Mono\", monospace; --jp-code-font-family: var(--jp-code-font-family-default); /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-code-presentation-font-size: 16px; /* may need to tweak cursor width if you change font size */ --jp-code-cursor-width0: 1.4px; --jp-code-cursor-width1: 2px; --jp-code-cursor-width2: 4px; /* Layout * * The following are the main layout colors use in JupyterLab. In a light * theme these would go from light to dark. */ --jp-layout-color0: white; --jp-layout-color1: white; --jp-layout-color2: var(--md-grey-200); --jp-layout-color3: var(--md-grey-400); --jp-layout-color4: var(--md-grey-600); /* Inverse Layout * * The following are the inverse layout colors use in JupyterLab. In a light * theme these would go from dark to light. */ --jp-inverse-layout-color0: #111111; --jp-inverse-layout-color1: var(--md-grey-900); --jp-inverse-layout-color2: var(--md-grey-800); --jp-inverse-layout-color3: var(--md-grey-700); --jp-inverse-layout-color4: var(--md-grey-600); /* Brand/accent */ --jp-brand-color0: var(--md-blue-900); --jp-brand-color1: var(--md-blue-700); --jp-brand-color2: var(--md-blue-300); --jp-brand-color3: var(--md-blue-100); --jp-brand-color4: var(--md-blue-50); --jp-accent-color0: var(--md-green-900); --jp-accent-color1: var(--md-green-700); --jp-accent-color2: var(--md-green-300); --jp-accent-color3: var(--md-green-100); /* State colors (warn, error, success, info) */ --jp-warn-color0: var(--md-orange-900); --jp-warn-color1: var(--md-orange-700); --jp-warn-color2: var(--md-orange-300); --jp-warn-color3: var(--md-orange-100); --jp-error-color0: var(--md-red-900); --jp-error-color1: var(--md-red-700); --jp-error-color2: var(--md-red-300); --jp-error-color3: var(--md-red-100); --jp-success-color0: var(--md-green-900); --jp-success-color1: var(--md-green-700); --jp-success-color2: var(--md-green-300); --jp-success-color3: var(--md-green-100); --jp-info-color0: var(--md-cyan-900); --jp-info-color1: var(--md-cyan-700); --jp-info-color2: var(--md-cyan-300); --jp-info-color3: var(--md-cyan-100); /* Cell specific styles */ --jp-cell-padding: 5px; --jp-cell-collapser-width: 8px; --jp-cell-collapser-min-height: 20px; --jp-cell-collapser-not-active-hover-opacity: 0.6; --jp-cell-editor-background: var(--md-grey-100); --jp-cell-editor-border-color: var(--md-grey-300); --jp-cell-editor-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-cell-editor-active-background: var(--jp-layout-color0); --jp-cell-editor-active-border-color: var(--jp-brand-color1); --jp-cell-prompt-width: 64px; --jp-cell-prompt-font-family: var(--jp-code-font-family-default); --jp-cell-prompt-letter-spacing: 0px; --jp-cell-prompt-opacity: 1; --jp-cell-prompt-not-active-opacity: 0.5; --jp-cell-prompt-not-active-font-color: var(--md-grey-700); /* A custom blend of MD grey and blue 600 * See https://meyerweb.com/eric/tools/color-blend/#546E7A:1E88E5:5:hex */ --jp-cell-inprompt-font-color: #307fc1; /* A custom blend of MD grey and orange 600 * https://meyerweb.com/eric/tools/color-blend/#546E7A:F4511E:5:hex */ --jp-cell-outprompt-font-color: #bf5b3d; /* Notebook specific styles */ --jp-notebook-padding: 10px; --jp-notebook-select-background: var(--jp-layout-color1); --jp-notebook-multiselected-color: var(--md-blue-50); /* The scroll padding is calculated to fill enough space at the bottom of the notebook to show one single-line cell (with appropriate padding) at the top when the notebook is scrolled all the way to the bottom. We also subtract one pixel so that no scrollbar appears if we have just one single-line cell in the notebook. This padding is to enable a 'scroll past end' feature in a notebook. */ --jp-notebook-scroll-padding: calc( 100% - var(--jp-code-font-size) * var(--jp-code-line-height) - var(--jp-code-padding) - var(--jp-cell-padding) - 1px ); /* Rendermime styles */ --jp-rendermime-error-background: #fdd; --jp-rendermime-table-row-background: var(--md-grey-100); --jp-rendermime-table-row-hover-background: var(--md-light-blue-50); /* Dialog specific styles */ --jp-dialog-background: rgba(0, 0, 0, 0.25); /* Console specific styles */ --jp-console-padding: 10px; /* Toolbar specific styles */ --jp-toolbar-border-color: var(--jp-border-color1); --jp-toolbar-micro-height: 8px; --jp-toolbar-background: var(--jp-layout-color1); --jp-toolbar-box-shadow: 0px 0px 2px 0px rgba(0, 0, 0, 0.24); --jp-toolbar-header-margin: 4px 4px 0px 4px; --jp-toolbar-active-background: var(--md-grey-300); /* Statusbar specific styles */ --jp-statusbar-height: 24px; /* Input field styles */ --jp-input-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-input-active-background: var(--jp-layout-color1); --jp-input-hover-background: var(--jp-layout-color1); --jp-input-background: var(--md-grey-100); --jp-input-border-color: var(--jp-border-color1); --jp-input-active-border-color: var(--jp-brand-color1); --jp-input-active-box-shadow-color: rgba(19, 124, 189, 0.3); /* General editor styles */ --jp-editor-selected-background: #d9d9d9; --jp-editor-selected-focused-background: #d7d4f0; --jp-editor-cursor-color: var(--jp-ui-font-color0); /* Code mirror specific styles */ --jp-mirror-editor-keyword-color: #008000; --jp-mirror-editor-atom-color: #88f; --jp-mirror-editor-number-color: #080; --jp-mirror-editor-def-color: #00f; --jp-mirror-editor-variable-color: var(--md-grey-900); --jp-mirror-editor-variable-2-color: #05a; --jp-mirror-editor-variable-3-color: #085; --jp-mirror-editor-punctuation-color: #05a; --jp-mirror-editor-property-color: #05a; --jp-mirror-editor-operator-color: #aa22ff; --jp-mirror-editor-comment-color: #408080; --jp-mirror-editor-string-color: #ba2121; --jp-mirror-editor-string-2-color: #708; --jp-mirror-editor-meta-color: #aa22ff; --jp-mirror-editor-qualifier-color: #555; --jp-mirror-editor-builtin-color: #008000; --jp-mirror-editor-bracket-color: #997; --jp-mirror-editor-tag-color: #170; --jp-mirror-editor-attribute-color: #00c; --jp-mirror-editor-header-color: blue; --jp-mirror-editor-quote-color: #090; --jp-mirror-editor-link-color: #00c; --jp-mirror-editor-error-color: #f00; --jp-mirror-editor-hr-color: #999; /* Vega extension styles */ --jp-vega-background: white; /* Sidebar-related styles */ --jp-sidebar-min-width: 250px; /* Search-related styles */ --jp-search-toggle-off-opacity: 0.5; --jp-search-toggle-hover-opacity: 0.8; --jp-search-toggle-on-opacity: 1; --jp-search-selected-match-background-color: rgb(245, 200, 0); --jp-search-selected-match-color: black; --jp-search-unselected-match-background-color: var( --jp-inverse-layout-color0 ); --jp-search-unselected-match-color: var(--jp-ui-inverse-font-color0); /* Icon colors that work well with light or dark backgrounds */ --jp-icon-contrast-color0: var(--md-purple-600); --jp-icon-contrast-color1: var(--md-green-600); --jp-icon-contrast-color2: var(--md-pink-600); --jp-icon-contrast-color3: var(--md-blue-600); } [data-md-color-scheme=\"slate\"] .jupyter-wrapper { /* Elevation * * We style box-shadows using Material Design's idea of elevation. These particular numbers are taken from here: * * https://github.com/material-components/material-components-web * https://material-components-web.appspot.com/elevation.html */ /* The dark theme shadows need a bit of work, but this will probably also require work on the core layout * colors used in the theme as well. */ --jp-shadow-base-lightness: 32; --jp-shadow-umbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.2 ); --jp-shadow-penumbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.14 ); --jp-shadow-ambient-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.12 ); --jp-elevation-z0: none; --jp-elevation-z1: 0px 2px 1px -1px var(--jp-shadow-umbra-color), 0px 1px 1px 0px var(--jp-shadow-penumbra-color), 0px 1px 3px 0px var(--jp-shadow-ambient-color); --jp-elevation-z2: 0px 3px 1px -2px var(--jp-shadow-umbra-color), 0px 2px 2px 0px var(--jp-shadow-penumbra-color), 0px 1px 5px 0px var(--jp-shadow-ambient-color); --jp-elevation-z4: 0px 2px 4px -1px var(--jp-shadow-umbra-color), 0px 4px 5px 0px var(--jp-shadow-penumbra-color), 0px 1px 10px 0px var(--jp-shadow-ambient-color); --jp-elevation-z6: 0px 3px 5px -1px var(--jp-shadow-umbra-color), 0px 6px 10px 0px var(--jp-shadow-penumbra-color), 0px 1px 18px 0px var(--jp-shadow-ambient-color); --jp-elevation-z8: 0px 5px 5px -3px var(--jp-shadow-umbra-color), 0px 8px 10px 1px var(--jp-shadow-penumbra-color), 0px 3px 14px 2px var(--jp-shadow-ambient-color); --jp-elevation-z12: 0px 7px 8px -4px var(--jp-shadow-umbra-color), 0px 12px 17px 2px var(--jp-shadow-penumbra-color), 0px 5px 22px 4px var(--jp-shadow-ambient-color); --jp-elevation-z16: 0px 8px 10px -5px var(--jp-shadow-umbra-color), 0px 16px 24px 2px var(--jp-shadow-penumbra-color), 0px 6px 30px 5px var(--jp-shadow-ambient-color); --jp-elevation-z20: 0px 10px 13px -6px var(--jp-shadow-umbra-color), 0px 20px 31px 3px var(--jp-shadow-penumbra-color), 0px 8px 38px 7px var(--jp-shadow-ambient-color); --jp-elevation-z24: 0px 11px 15px -7px var(--jp-shadow-umbra-color), 0px 24px 38px 3px var(--jp-shadow-penumbra-color), 0px 9px 46px 8px var(--jp-shadow-ambient-color); /* Borders * * The following variables, specify the visual styling of borders in JupyterLab. */ --jp-border-width: 1px; --jp-border-color0: var(--md-grey-700); --jp-border-color1: var(--md-grey-700); --jp-border-color2: var(--md-grey-800); --jp-border-color3: var(--md-grey-900); --jp-border-radius: 2px; /* UI Fonts * * The UI font CSS variables are used for the typography all of the JupyterLab * user interface elements that are not directly user generated content. * * The font sizing here is done assuming that the body font size of --jp-ui-font-size1 * is applied to a parent element. When children elements, such as headings, are sized * in em all things will be computed relative to that body size. */ --jp-ui-font-scale-factor: 1.2; --jp-ui-font-size0: 0.83333em; --jp-ui-font-size1: 13px; /* Base font size */ --jp-ui-font-size2: 1.2em; --jp-ui-font-size3: 1.44em; --jp-ui-font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Helvetica, Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\"; /* * Use these font colors against the corresponding main layout colors. * In a light theme, these go from dark to light. */ /* Defaults use Material Design specification */ --jp-ui-font-color0: rgba(255, 255, 255, 1); --jp-ui-font-color1: rgba(255, 255, 255, 0.87); --jp-ui-font-color2: rgba(255, 255, 255, 0.54); --jp-ui-font-color3: rgba(255, 255, 255, 0.38); /* * Use these against the brand/accent/warn/error colors. * These will typically go from light to darker, in both a dark and light theme. */ --jp-ui-inverse-font-color0: rgba(0, 0, 0, 1); --jp-ui-inverse-font-color1: rgba(0, 0, 0, 0.8); --jp-ui-inverse-font-color2: rgba(0, 0, 0, 0.5); --jp-ui-inverse-font-color3: rgba(0, 0, 0, 0.3); /* Content Fonts * * Content font variables are used for typography of user generated content. * * The font sizing here is done assuming that the body font size of --jp-content-font-size1 * is applied to a parent element. When children elements, such as headings, are sized * in em all things will be computed relative to that body size. */ --jp-content-line-height: 1.6; --jp-content-font-scale-factor: 1.2; --jp-content-font-size0: 0.83333em; --jp-content-font-size1: 14px; /* Base font size */ --jp-content-font-size2: 1.2em; --jp-content-font-size3: 1.44em; --jp-content-font-size4: 1.728em; --jp-content-font-size5: 2.0736em; /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-content-presentation-font-size1: 17px; --jp-content-heading-line-height: 1; --jp-content-heading-margin-top: 1.2em; --jp-content-heading-margin-bottom: 0.8em; --jp-content-heading-font-weight: 500; /* Defaults use Material Design specification */ --jp-content-font-color0: rgba(255, 255, 255, 1); --jp-content-font-color1: rgba(255, 255, 255, 1); --jp-content-font-color2: rgba(255, 255, 255, 0.7); --jp-content-font-color3: rgba(255, 255, 255, 0.5); --jp-content-link-color: var(--md-blue-300); --jp-content-font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Helvetica, Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\"; /* * Code Fonts * * Code font variables are used for typography of code and other monospaces content. */ --jp-code-font-size: 13px; --jp-code-line-height: 1.3077; /* 17px for 13px base */ --jp-code-padding: 5px; /* 5px for 13px base, codemirror highlighting needs integer px value */ --jp-code-font-family-default: Menlo, Consolas, \"DejaVu Sans Mono\", monospace; --jp-code-font-family: var(--jp-code-font-family-default); /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-code-presentation-font-size: 16px; /* may need to tweak cursor width if you change font size */ --jp-code-cursor-width0: 1.4px; --jp-code-cursor-width1: 2px; --jp-code-cursor-width2: 4px; /* Layout * * The following are the main layout colors use in JupyterLab. In a light * theme these would go from light to dark. */ --jp-layout-color0: #111111; --jp-layout-color1: var(--md-grey-900); --jp-layout-color2: var(--md-grey-800); --jp-layout-color3: var(--md-grey-700); --jp-layout-color4: var(--md-grey-600); /* Inverse Layout * * The following are the inverse layout colors use in JupyterLab. In a light * theme these would go from dark to light. */ --jp-inverse-layout-color0: white; --jp-inverse-layout-color1: white; --jp-inverse-layout-color2: var(--md-grey-200); --jp-inverse-layout-color3: var(--md-grey-400); --jp-inverse-layout-color4: var(--md-grey-600); /* Brand/accent */ --jp-brand-color0: var(--md-blue-700); --jp-brand-color1: var(--md-blue-500); --jp-brand-color2: var(--md-blue-300); --jp-brand-color3: var(--md-blue-100); --jp-brand-color4: var(--md-blue-50); --jp-accent-color0: var(--md-green-700); --jp-accent-color1: var(--md-green-500); --jp-accent-color2: var(--md-green-300); --jp-accent-color3: var(--md-green-100); /* State colors (warn, error, success, info) */ --jp-warn-color0: var(--md-orange-700); --jp-warn-color1: var(--md-orange-500); --jp-warn-color2: var(--md-orange-300); --jp-warn-color3: var(--md-orange-100); --jp-error-color0: var(--md-red-700); --jp-error-color1: var(--md-red-500); --jp-error-color2: var(--md-red-300); --jp-error-color3: var(--md-red-100); --jp-success-color0: var(--md-green-700); --jp-success-color1: var(--md-green-500); --jp-success-color2: var(--md-green-300); --jp-success-color3: var(--md-green-100); --jp-info-color0: var(--md-cyan-700); --jp-info-color1: var(--md-cyan-500); --jp-info-color2: var(--md-cyan-300); --jp-info-color3: var(--md-cyan-100); /* Cell specific styles */ --jp-cell-padding: 5px; --jp-cell-collapser-width: 8px; --jp-cell-collapser-min-height: 20px; --jp-cell-collapser-not-active-hover-opacity: 0.6; --jp-cell-editor-background: var(--jp-layout-color1); --jp-cell-editor-border-color: var(--md-grey-700); --jp-cell-editor-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-cell-editor-active-background: var(--jp-layout-color0); --jp-cell-editor-active-border-color: var(--jp-brand-color1); --jp-cell-prompt-width: 64px; --jp-cell-prompt-font-family: var(--jp-code-font-family-default); --jp-cell-prompt-letter-spacing: 0px; --jp-cell-prompt-opacity: 1; --jp-cell-prompt-not-active-opacity: 1; --jp-cell-prompt-not-active-font-color: var(--md-grey-300); /* A custom blend of MD grey and blue 600 * See https://meyerweb.com/eric/tools/color-blend/#546E7A:1E88E5:5:hex */ --jp-cell-inprompt-font-color: #307fc1; /* A custom blend of MD grey and orange 600 * https://meyerweb.com/eric/tools/color-blend/#546E7A:F4511E:5:hex */ --jp-cell-outprompt-font-color: #bf5b3d; /* Notebook specific styles */ --jp-notebook-padding: 10px; --jp-notebook-select-background: var(--jp-layout-color1); --jp-notebook-multiselected-color: rgba(33, 150, 243, 0.24); /* The scroll padding is calculated to fill enough space at the bottom of the notebook to show one single-line cell (with appropriate padding) at the top when the notebook is scrolled all the way to the bottom. We also subtract one pixel so that no scrollbar appears if we have just one single-line cell in the notebook. This padding is to enable a 'scroll past end' feature in a notebook. */ --jp-notebook-scroll-padding: calc( 100% - var(--jp-code-font-size) * var(--jp-code-line-height) - var(--jp-code-padding) - var(--jp-cell-padding) - 1px ); /* Rendermime styles */ --jp-rendermime-error-background: rgba(244, 67, 54, 0.28); --jp-rendermime-table-row-background: var(--md-grey-900); --jp-rendermime-table-row-hover-background: rgba(3, 169, 244, 0.2); /* Dialog specific styles */ --jp-dialog-background: rgba(0, 0, 0, 0.6); /* Console specific styles */ --jp-console-padding: 10px; /* Toolbar specific styles */ --jp-toolbar-border-color: var(--jp-border-color2); --jp-toolbar-micro-height: 8px; --jp-toolbar-background: var(--jp-layout-color1); --jp-toolbar-box-shadow: 0px 0px 2px 0px rgba(0, 0, 0, 0.8); --jp-toolbar-header-margin: 4px 4px 0px 4px; --jp-toolbar-active-background: var(--jp-layout-color0); /* Statusbar specific styles */ --jp-statusbar-height: 24px; /* Input field styles */ --jp-input-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-input-active-background: var(--jp-layout-color0); --jp-input-hover-background: var(--jp-layout-color2); --jp-input-background: var(--md-grey-800); --jp-input-border-color: var(--jp-border-color1); --jp-input-active-border-color: var(--jp-brand-color1); --jp-input-active-box-shadow-color: rgba(19, 124, 189, 0.3); /* General editor styles */ --jp-editor-selected-background: var(--jp-layout-color2); --jp-editor-selected-focused-background: rgba(33, 150, 243, 0.24); --jp-editor-cursor-color: var(--jp-ui-font-color0); /* Code mirror specific styles */ --jp-mirror-editor-keyword-color: var(--md-green-500); --jp-mirror-editor-atom-color: var(--md-blue-300); --jp-mirror-editor-number-color: var(--md-green-400); --jp-mirror-editor-def-color: var(--md-blue-600); --jp-mirror-editor-variable-color: var(--md-grey-300); --jp-mirror-editor-variable-2-color: var(--md-blue-400); --jp-mirror-editor-variable-3-color: var(--md-green-600); --jp-mirror-editor-punctuation-color: var(--md-blue-400); --jp-mirror-editor-property-color: var(--md-blue-400); --jp-mirror-editor-operator-color: #aa22ff; --jp-mirror-editor-comment-color: #408080; --jp-mirror-editor-string-color: #ff7070; --jp-mirror-editor-string-2-color: var(--md-purple-300); --jp-mirror-editor-meta-color: #aa22ff; --jp-mirror-editor-qualifier-color: #555; --jp-mirror-editor-builtin-color: var(--md-green-600); --jp-mirror-editor-bracket-color: #997; --jp-mirror-editor-tag-color: var(--md-green-700); --jp-mirror-editor-attribute-color: var(--md-blue-700); --jp-mirror-editor-header-color: var(--md-blue-500); --jp-mirror-editor-quote-color: var(--md-green-300); --jp-mirror-editor-link-color: var(--md-blue-700); --jp-mirror-editor-error-color: #f00; --jp-mirror-editor-hr-color: #999; /* Vega extension styles */ --jp-vega-background: var(--md-grey-400); /* Sidebar-related styles */ --jp-sidebar-min-width: 250px; /* Search-related styles */ --jp-search-toggle-off-opacity: 0.6; --jp-search-toggle-hover-opacity: 0.8; --jp-search-toggle-on-opacity: 1; --jp-search-selected-match-background-color: rgb(255, 225, 0); --jp-search-selected-match-color: black; --jp-search-unselected-match-background-color: var( --jp-inverse-layout-color0 ); --jp-search-unselected-match-color: var(--jp-ui-inverse-font-color0); /* scrollbar related styles. Supports every browser except Edge. */ /* colors based on JetBrain's Darcula theme */ --jp-scrollbar-background-color: #3f4244; --jp-scrollbar-thumb-color: 88, 96, 97; /* need to specify thumb color as an RGB triplet */ --jp-scrollbar-endpad: 3px; /* the minimum gap between the thumb and the ends of a scrollbar */ /* hacks for setting the thumb shape. These do nothing in Firefox */ --jp-scrollbar-thumb-margin: 3.5px; /* the space in between the sides of the thumb and the track */ --jp-scrollbar-thumb-radius: 9px; /* set to a large-ish value for rounded endcaps on the thumb */ /* Icon colors that work well with light or dark backgrounds */ --jp-icon-contrast-color0: var(--md-purple-600); --jp-icon-contrast-color1: var(--md-green-600); --jp-icon-contrast-color2: var(--md-pink-600); --jp-icon-contrast-color3: var(--md-blue-600); } :root{--md-red-50: #ffebee;--md-red-100: #ffcdd2;--md-red-200: #ef9a9a;--md-red-300: #e57373;--md-red-400: #ef5350;--md-red-500: #f44336;--md-red-600: #e53935;--md-red-700: #d32f2f;--md-red-800: #c62828;--md-red-900: #b71c1c;--md-red-A100: #ff8a80;--md-red-A200: #ff5252;--md-red-A400: #ff1744;--md-red-A700: #d50000;--md-pink-50: #fce4ec;--md-pink-100: #f8bbd0;--md-pink-200: #f48fb1;--md-pink-300: #f06292;--md-pink-400: #ec407a;--md-pink-500: #e91e63;--md-pink-600: #d81b60;--md-pink-700: #c2185b;--md-pink-800: 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#7986cb;--md-indigo-400: #5c6bc0;--md-indigo-500: #3f51b5;--md-indigo-600: #3949ab;--md-indigo-700: #303f9f;--md-indigo-800: #283593;--md-indigo-900: #1a237e;--md-indigo-A100: #8c9eff;--md-indigo-A200: #536dfe;--md-indigo-A400: #3d5afe;--md-indigo-A700: #304ffe;--md-blue-50: #e3f2fd;--md-blue-100: #bbdefb;--md-blue-200: #90caf9;--md-blue-300: #64b5f6;--md-blue-400: #42a5f5;--md-blue-500: #2196f3;--md-blue-600: #1e88e5;--md-blue-700: #1976d2;--md-blue-800: #1565c0;--md-blue-900: #0d47a1;--md-blue-A100: #82b1ff;--md-blue-A200: #448aff;--md-blue-A400: #2979ff;--md-blue-A700: #2962ff;--md-light-blue-50: #e1f5fe;--md-light-blue-100: #b3e5fc;--md-light-blue-200: #81d4fa;--md-light-blue-300: #4fc3f7;--md-light-blue-400: #29b6f6;--md-light-blue-500: #03a9f4;--md-light-blue-600: #039be5;--md-light-blue-700: #0288d1;--md-light-blue-800: #0277bd;--md-light-blue-900: #01579b;--md-light-blue-A100: #80d8ff;--md-light-blue-A200: #40c4ff;--md-light-blue-A400: #00b0ff;--md-light-blue-A700: #0091ea;--md-cyan-50: #e0f7fa;--md-cyan-100: #b2ebf2;--md-cyan-200: #80deea;--md-cyan-300: #4dd0e1;--md-cyan-400: #26c6da;--md-cyan-500: #00bcd4;--md-cyan-600: #00acc1;--md-cyan-700: #0097a7;--md-cyan-800: #00838f;--md-cyan-900: #006064;--md-cyan-A100: #84ffff;--md-cyan-A200: #18ffff;--md-cyan-A400: #00e5ff;--md-cyan-A700: #00b8d4;--md-teal-50: #e0f2f1;--md-teal-100: #b2dfdb;--md-teal-200: #80cbc4;--md-teal-300: #4db6ac;--md-teal-400: #26a69a;--md-teal-500: #009688;--md-teal-600: #00897b;--md-teal-700: #00796b;--md-teal-800: #00695c;--md-teal-900: #004d40;--md-teal-A100: #a7ffeb;--md-teal-A200: #64ffda;--md-teal-A400: #1de9b6;--md-teal-A700: #00bfa5;--md-green-50: #e8f5e9;--md-green-100: #c8e6c9;--md-green-200: #a5d6a7;--md-green-300: #81c784;--md-green-400: #66bb6a;--md-green-500: #4caf50;--md-green-600: #43a047;--md-green-700: #388e3c;--md-green-800: #2e7d32;--md-green-900: #1b5e20;--md-green-A100: #b9f6ca;--md-green-A200: #69f0ae;--md-green-A400: #00e676;--md-green-A700: #00c853;--md-light-green-50: #f1f8e9;--md-light-green-100: #dcedc8;--md-light-green-200: #c5e1a5;--md-light-green-300: #aed581;--md-light-green-400: #9ccc65;--md-light-green-500: #8bc34a;--md-light-green-600: #7cb342;--md-light-green-700: #689f38;--md-light-green-800: #558b2f;--md-light-green-900: #33691e;--md-light-green-A100: #ccff90;--md-light-green-A200: #b2ff59;--md-light-green-A400: #76ff03;--md-light-green-A700: #64dd17;--md-lime-50: #f9fbe7;--md-lime-100: #f0f4c3;--md-lime-200: #e6ee9c;--md-lime-300: #dce775;--md-lime-400: #d4e157;--md-lime-500: #cddc39;--md-lime-600: #c0ca33;--md-lime-700: #afb42b;--md-lime-800: #9e9d24;--md-lime-900: #827717;--md-lime-A100: #f4ff81;--md-lime-A200: #eeff41;--md-lime-A400: #c6ff00;--md-lime-A700: #aeea00;--md-yellow-50: #fffde7;--md-yellow-100: #fff9c4;--md-yellow-200: #fff59d;--md-yellow-300: #fff176;--md-yellow-400: #ffee58;--md-yellow-500: #ffeb3b;--md-yellow-600: #fdd835;--md-yellow-700: #fbc02d;--md-yellow-800: #f9a825;--md-yellow-900: #f57f17;--md-yellow-A100: #ffff8d;--md-yellow-A200: #ffff00;--md-yellow-A400: #ffea00;--md-yellow-A700: #ffd600;--md-amber-50: #fff8e1;--md-amber-100: #ffecb3;--md-amber-200: #ffe082;--md-amber-300: #ffd54f;--md-amber-400: #ffca28;--md-amber-500: #ffc107;--md-amber-600: #ffb300;--md-amber-700: #ffa000;--md-amber-800: #ff8f00;--md-amber-900: #ff6f00;--md-amber-A100: #ffe57f;--md-amber-A200: #ffd740;--md-amber-A400: #ffc400;--md-amber-A700: #ffab00;--md-orange-50: #fff3e0;--md-orange-100: #ffe0b2;--md-orange-200: #ffcc80;--md-orange-300: #ffb74d;--md-orange-400: #ffa726;--md-orange-500: #ff9800;--md-orange-600: #fb8c00;--md-orange-700: #f57c00;--md-orange-800: #ef6c00;--md-orange-900: #e65100;--md-orange-A100: #ffd180;--md-orange-A200: #ffab40;--md-orange-A400: #ff9100;--md-orange-A700: #ff6d00;--md-deep-orange-50: #fbe9e7;--md-deep-orange-100: #ffccbc;--md-deep-orange-200: #ffab91;--md-deep-orange-300: #ff8a65;--md-deep-orange-400: #ff7043;--md-deep-orange-500: 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sourceMappingURL=mkdocs-jupyter.css.map*/ init_mathjax = function() { if (window.MathJax) { // MathJax loaded MathJax.Hub.Config({ TeX: { equationNumbers: { autoNumber: \"AMS\", useLabelIds: true } }, tex2jax: { inlineMath: [ ['$','$'], [\"\\\\(\",\"\\\\)\"] ], displayMath: [ ['$$','$$'], [\"\\\\[\",\"\\\\]\"] ], processEscapes: true, processEnvironments: true }, displayAlign: 'center', CommonHTML: { linebreaks: { automatic: true } } }); MathJax.Hub.Queue([\"Typeset\", MathJax.Hub]); } } init_mathjax(); In [1]: Copied! from great_ai import query_ground_truth data = query_ground_truth ( \"train\" ) from great_ai import query_ground_truth data = query_ground_truth(\"train\") Environment variable ENVIRONMENT is not set, defaulting to development mode \u203c\ufe0f Cannot find credentials files, defaulting to using ParallelTinyDbDriver The selected tracing database (ParallelTinyDbDriver) is not recommended for production Cannot find credentials files, defaulting to using LargeFileLocal GreatAI (v0.1.6): configured \u2705 \ud83d\udd29 tracing_database: ParallelTinyDbDriver \ud83d\udd29 large_file_implementation: LargeFileLocal \ud83d\udd29 is_production: False \ud83d\udd29 should_log_exception_stack: True \ud83d\udd29 prediction_cache_size: 512 \ud83d\udd29 dashboard_table_size: 50 You still need to check whether you follow all best practices before trusting your deployment. > Find out more at https://se-ml.github.io/practices In [9]: Copied! from collections import Counter import matplotlib.pyplot as plt from great_ai.utilities import unique import numpy as np import matplotlib.ticker as mtick first = Counter ( d . feedback [ 0 ] for d in data if len ( d . feedback ) > 0 ) second = Counter ( d . feedback [ 1 ] for d in data if len ( d . feedback ) > 1 ) third = Counter ( d . feedback [ 2 ] for d in data if len ( d . feedback ) > 2 ) domains = sorted ( unique ( domain for d in data for domain in d . feedback )) first = [ first [ d ] / len ( data ) for d in domains ] second = [ second [ d ] / len ( data ) for d in domains ] third = [ third [ d ] / len ( data ) for d in domains ] # Configure matplotlib to have nice, high-resolution charts % matplotlib inline plt . rcParams [ \"figure.facecolor\" ] = \"white\" plt . rcParams [ \"font.size\" ] = 20 plt . rcParams [ \"figure.figsize\" ] = ( 14 , 10 ) fig , ax = plt . subplots () plt . xticks ( rotation = 90 ) ax . bar ( domains , first , label = \"Primary domain\" ) ax . bar ( domains , second , label = \"Secondary domain\" , bottom = first ) ax . bar ( domains , third , label = \"Tertiary domain\" , bottom = np . add ( first , second )) ax . yaxis . set_major_formatter ( mtick . PercentFormatter ( 1 )) ax . legend () ax . set_ylabel ( \"Ratio of all publications\" ) fig . tight_layout () fig . savefig ( \"ss-distribution.png\" , dpi = 500 ) None from collections import Counter import matplotlib.pyplot as plt from great_ai.utilities import unique import numpy as np import matplotlib.ticker as mtick first = Counter(d.feedback[0] for d in data if len(d.feedback) > 0) second = Counter(d.feedback[1] for d in data if len(d.feedback) > 1) third = Counter(d.feedback[2] for d in data if len(d.feedback) > 2) domains = sorted(unique(domain for d in data for domain in d.feedback)) first = [first[d] / len(data) for d in domains] second = [second[d] / len(data) for d in domains] third = [third[d] / len(data) for d in domains] # Configure matplotlib to have nice, high-resolution charts %matplotlib inline plt.rcParams[\"figure.facecolor\"] = \"white\" plt.rcParams[\"font.size\"] = 20 plt.rcParams[\"figure.figsize\"] = (14, 10) fig, ax = plt.subplots() plt.xticks(rotation=90) ax.bar(domains, first, label=\"Primary domain\") ax.bar(domains, second, label=\"Secondary domain\", bottom=first) ax.bar(domains, third, label=\"Tertiary domain\", bottom=np.add(first, second)) ax.yaxis.set_major_formatter(mtick.PercentFormatter(1)) ax.legend() ax.set_ylabel(\"Ratio of all publications\") fig.tight_layout() fig.savefig(\"ss-distribution.png\", dpi=500) None","title":"Stacked bars"},{"location":"examples/simple/train/","text":"(function (global, factory) { typeof exports === 'object' && typeof module !== 'undefined' ? module.exports = factory() : typeof define === 'function' && define.amd ? define(factory) : (global = global || self, global.ClipboardCopyElement = factory()); }(this, function () { 'use strict'; function createNode(text) { const node = document.createElement('pre'); node.style.width = '1px'; node.style.height = '1px'; node.style.position = 'fixed'; node.style.top = '5px'; node.textContent = text; return node; } function copyNode(node) { if ('clipboard' in navigator) { // eslint-disable-next-line flowtype/no-flow-fix-me-comments // $FlowFixMe Clipboard is not defined in Flow yet. return navigator.clipboard.writeText(node.textContent); } const selection = getSelection(); if (selection == null) { return Promise.reject(new Error()); } selection.removeAllRanges(); const range = document.createRange(); range.selectNodeContents(node); selection.addRange(range); document.execCommand('copy'); selection.removeAllRanges(); return Promise.resolve(); } function copyText(text) { if ('clipboard' in navigator) { // eslint-disable-next-line flowtype/no-flow-fix-me-comments // $FlowFixMe Clipboard is not defined in Flow yet. return navigator.clipboard.writeText(text); } const body = document.body; if (!body) { return Promise.reject(new Error()); } const node = createNode(text); body.appendChild(node); copyNode(node); body.removeChild(node); return Promise.resolve(); } function copy(button) { const id = button.getAttribute('for'); const text = button.getAttribute('value'); function trigger() { button.dispatchEvent(new CustomEvent('clipboard-copy', { bubbles: true })); } if (text) { copyText(text).then(trigger); } else if (id) { const root = 'getRootNode' in Element.prototype ? button.getRootNode() : button.ownerDocument; if (!(root instanceof Document || 'ShadowRoot' in window && root instanceof ShadowRoot)) return; const node = root.getElementById(id); if (node) copyTarget(node).then(trigger); } } function copyTarget(content) { if (content instanceof HTMLInputElement || content instanceof HTMLTextAreaElement) { return copyText(content.value); } else if (content instanceof HTMLAnchorElement && content.hasAttribute('href')) { return copyText(content.href); } else { return copyNode(content); } } function clicked(event) { const button = event.currentTarget; if (button instanceof HTMLElement) { copy(button); } } function keydown(event) { if (event.key === ' ' || event.key === 'Enter') { const button = event.currentTarget; if (button instanceof HTMLElement) { event.preventDefault(); copy(button); } } } function focused(event) { event.currentTarget.addEventListener('keydown', keydown); } function blurred(event) { event.currentTarget.removeEventListener('keydown', keydown); } class ClipboardCopyElement extends HTMLElement { constructor() { super(); this.addEventListener('click', clicked); this.addEventListener('focus', focused); this.addEventListener('blur', blurred); } connectedCallback() { if (!this.hasAttribute('tabindex')) { this.setAttribute('tabindex', '0'); } if (!this.hasAttribute('role')) { this.setAttribute('role', 'button'); } } get value() { return this.getAttribute('value') || ''; } set value(text) { this.setAttribute('value', text); } } if (!window.customElements.get('clipboard-copy')) { window.ClipboardCopyElement = ClipboardCopyElement; window.customElements.define('clipboard-copy', ClipboardCopyElement); } return ClipboardCopyElement; })); document.addEventListener('clipboard-copy', function(event) { const notice = event.target.querySelector('.notice') notice.hidden = false setTimeout(function() { notice.hidden = true }, 1000) }) pre { line-height: 125%; } td.linenos .normal { color: inherit; background-color: transparent; padding-left: 5px; padding-right: 5px; } span.linenos { color: inherit; background-color: transparent; padding-left: 5px; padding-right: 5px; } td.linenos .special { color: #000000; background-color: #ffffc0; padding-left: 5px; padding-right: 5px; } span.linenos.special { color: #000000; background-color: #ffffc0; padding-left: 5px; padding-right: 5px; } .highlight-ipynb .hll { background-color: var(--jp-cell-editor-active-background) } .highlight-ipynb { background: var(--jp-cell-editor-background); color: var(--jp-mirror-editor-variable-color) } .highlight-ipynb .c { color: var(--jp-mirror-editor-comment-color); font-style: italic } /* Comment */ .highlight-ipynb .err { color: var(--jp-mirror-editor-error-color) } /* Error */ .highlight-ipynb .k { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword */ .highlight-ipynb .o { color: var(--jp-mirror-editor-operator-color); font-weight: bold } /* Operator */ .highlight-ipynb .p { color: var(--jp-mirror-editor-punctuation-color) } /* Punctuation */ .highlight-ipynb .ch { color: var(--jp-mirror-editor-comment-color); font-style: italic } /* Comment.Hashbang */ .highlight-ipynb .cm { color: var(--jp-mirror-editor-comment-color); font-style: italic } /* Comment.Multiline */ .highlight-ipynb .cp { color: var(--jp-mirror-editor-comment-color); font-style: italic } /* Comment.Preproc */ .highlight-ipynb .cpf { color: var(--jp-mirror-editor-comment-color); font-style: italic } /* Comment.PreprocFile */ .highlight-ipynb .c1 { color: var(--jp-mirror-editor-comment-color); font-style: italic } /* Comment.Single */ .highlight-ipynb .cs { color: var(--jp-mirror-editor-comment-color); font-style: italic } /* Comment.Special */ .highlight-ipynb .kc { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword.Constant */ .highlight-ipynb .kd { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword.Declaration */ .highlight-ipynb .kn { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword.Namespace */ .highlight-ipynb .kp { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword.Pseudo */ .highlight-ipynb .kr { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword.Reserved */ .highlight-ipynb .kt { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword.Type */ .highlight-ipynb .m { color: var(--jp-mirror-editor-number-color) } /* Literal.Number */ .highlight-ipynb .s { color: var(--jp-mirror-editor-string-color) } /* Literal.String */ .highlight-ipynb .ow { color: var(--jp-mirror-editor-operator-color); font-weight: bold } /* Operator.Word */ .highlight-ipynb .w { color: var(--jp-mirror-editor-variable-color) } /* Text.Whitespace */ .highlight-ipynb .mb { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Bin */ .highlight-ipynb .mf { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Float */ .highlight-ipynb .mh { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Hex */ .highlight-ipynb .mi { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Integer */ .highlight-ipynb .mo { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Oct */ .highlight-ipynb .sa { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Affix */ .highlight-ipynb .sb { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Backtick */ .highlight-ipynb .sc { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Char */ .highlight-ipynb .dl { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Delimiter */ .highlight-ipynb .sd { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Doc */ .highlight-ipynb .s2 { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Double */ .highlight-ipynb .se { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Escape */ .highlight-ipynb .sh { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Heredoc */ .highlight-ipynb .si { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Interpol */ .highlight-ipynb .sx { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Other */ .highlight-ipynb .sr { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Regex */ .highlight-ipynb .s1 { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Single */ .highlight-ipynb .ss { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Symbol */ .highlight-ipynb .il { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Integer.Long */ /* This file is taken from the built JupyterLab theme.css Found on share/nbconvert/templates/lab/static Some changes have been made and marked with CHANGE */ .jupyter-wrapper { /* Elevation * * We style box-shadows using Material Design's idea of elevation. These particular numbers are taken from here: * * https://github.com/material-components/material-components-web * https://material-components-web.appspot.com/elevation.html */ --jp-shadow-base-lightness: 0; --jp-shadow-umbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.2 ); --jp-shadow-penumbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.14 ); --jp-shadow-ambient-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.12 ); --jp-elevation-z0: none; --jp-elevation-z1: 0px 2px 1px -1px var(--jp-shadow-umbra-color), 0px 1px 1px 0px var(--jp-shadow-penumbra-color), 0px 1px 3px 0px var(--jp-shadow-ambient-color); --jp-elevation-z2: 0px 3px 1px -2px var(--jp-shadow-umbra-color), 0px 2px 2px 0px var(--jp-shadow-penumbra-color), 0px 1px 5px 0px var(--jp-shadow-ambient-color); --jp-elevation-z4: 0px 2px 4px -1px var(--jp-shadow-umbra-color), 0px 4px 5px 0px var(--jp-shadow-penumbra-color), 0px 1px 10px 0px var(--jp-shadow-ambient-color); --jp-elevation-z6: 0px 3px 5px -1px var(--jp-shadow-umbra-color), 0px 6px 10px 0px var(--jp-shadow-penumbra-color), 0px 1px 18px 0px var(--jp-shadow-ambient-color); --jp-elevation-z8: 0px 5px 5px -3px var(--jp-shadow-umbra-color), 0px 8px 10px 1px var(--jp-shadow-penumbra-color), 0px 3px 14px 2px var(--jp-shadow-ambient-color); --jp-elevation-z12: 0px 7px 8px -4px var(--jp-shadow-umbra-color), 0px 12px 17px 2px var(--jp-shadow-penumbra-color), 0px 5px 22px 4px var(--jp-shadow-ambient-color); --jp-elevation-z16: 0px 8px 10px -5px var(--jp-shadow-umbra-color), 0px 16px 24px 2px var(--jp-shadow-penumbra-color), 0px 6px 30px 5px var(--jp-shadow-ambient-color); --jp-elevation-z20: 0px 10px 13px -6px var(--jp-shadow-umbra-color), 0px 20px 31px 3px var(--jp-shadow-penumbra-color), 0px 8px 38px 7px var(--jp-shadow-ambient-color); --jp-elevation-z24: 0px 11px 15px -7px var(--jp-shadow-umbra-color), 0px 24px 38px 3px var(--jp-shadow-penumbra-color), 0px 9px 46px 8px var(--jp-shadow-ambient-color); /* Borders * * The following variables, specify the visual styling of borders in JupyterLab. */ --jp-border-width: 1px; --jp-border-color0: var(--md-grey-400); --jp-border-color1: var(--md-grey-400); --jp-border-color2: var(--md-grey-300); --jp-border-color3: var(--md-grey-200); --jp-border-radius: 2px; /* UI Fonts * * The UI font CSS variables are used for the typography all of the JupyterLab * user interface elements that are not directly user generated content. * * The font sizing here is done assuming that the body font size of --jp-ui-font-size1 * is applied to a parent element. When children elements, such as headings, are sized * in em all things will be computed relative to that body size. */ --jp-ui-font-scale-factor: 1.2; --jp-ui-font-size0: 0.83333em; --jp-ui-font-size1: 13px; /* Base font size */ --jp-ui-font-size2: 1.2em; --jp-ui-font-size3: 1.44em; --jp-ui-font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Helvetica, Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\"; /* * Use these font colors against the corresponding main layout colors. * In a light theme, these go from dark to light. */ /* Defaults use Material Design specification */ --jp-ui-font-color0: rgba(0, 0, 0, 1); --jp-ui-font-color1: rgba(0, 0, 0, 0.87); --jp-ui-font-color2: rgba(0, 0, 0, 0.54); --jp-ui-font-color3: rgba(0, 0, 0, 0.38); /* * Use these against the brand/accent/warn/error colors. * These will typically go from light to darker, in both a dark and light theme. */ --jp-ui-inverse-font-color0: rgba(255, 255, 255, 1); --jp-ui-inverse-font-color1: rgba(255, 255, 255, 1); --jp-ui-inverse-font-color2: rgba(255, 255, 255, 0.7); --jp-ui-inverse-font-color3: rgba(255, 255, 255, 0.5); /* Content Fonts * * Content font variables are used for typography of user generated content. * * The font sizing here is done assuming that the body font size of --jp-content-font-size1 * is applied to a parent element. When children elements, such as headings, are sized * in em all things will be computed relative to that body size. */ --jp-content-line-height: 1.6; --jp-content-font-scale-factor: 1.2; --jp-content-font-size0: 0.83333em; --jp-content-font-size1: 14px; /* Base font size */ --jp-content-font-size2: 1.2em; --jp-content-font-size3: 1.44em; --jp-content-font-size4: 1.728em; --jp-content-font-size5: 2.0736em; /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-content-presentation-font-size1: 17px; --jp-content-heading-line-height: 1; --jp-content-heading-margin-top: 1.2em; --jp-content-heading-margin-bottom: 0.8em; --jp-content-heading-font-weight: 500; /* Defaults use Material Design specification */ --jp-content-font-color0: rgba(0, 0, 0, 1); --jp-content-font-color1: rgba(0, 0, 0, 0.87); --jp-content-font-color2: rgba(0, 0, 0, 0.54); --jp-content-font-color3: rgba(0, 0, 0, 0.38); --jp-content-link-color: var(--md-blue-700); --jp-content-font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Helvetica, Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\"; /* * Code Fonts * * Code font variables are used for typography of code and other monospaces content. */ --jp-code-font-size: 13px; --jp-code-line-height: 1.3077; /* 17px for 13px base */ --jp-code-padding: 5px; /* 5px for 13px base, codemirror highlighting needs integer px value */ --jp-code-font-family-default: Menlo, Consolas, \"DejaVu Sans Mono\", monospace; --jp-code-font-family: var(--jp-code-font-family-default); /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-code-presentation-font-size: 16px; /* may need to tweak cursor width if you change font size */ --jp-code-cursor-width0: 1.4px; --jp-code-cursor-width1: 2px; --jp-code-cursor-width2: 4px; /* Layout * * The following are the main layout colors use in JupyterLab. In a light * theme these would go from light to dark. */ --jp-layout-color0: white; --jp-layout-color1: white; --jp-layout-color2: var(--md-grey-200); --jp-layout-color3: var(--md-grey-400); --jp-layout-color4: var(--md-grey-600); /* Inverse Layout * * The following are the inverse layout colors use in JupyterLab. In a light * theme these would go from dark to light. */ --jp-inverse-layout-color0: #111111; --jp-inverse-layout-color1: var(--md-grey-900); --jp-inverse-layout-color2: var(--md-grey-800); --jp-inverse-layout-color3: var(--md-grey-700); --jp-inverse-layout-color4: var(--md-grey-600); /* Brand/accent */ --jp-brand-color0: var(--md-blue-900); --jp-brand-color1: var(--md-blue-700); --jp-brand-color2: var(--md-blue-300); --jp-brand-color3: var(--md-blue-100); --jp-brand-color4: var(--md-blue-50); --jp-accent-color0: var(--md-green-900); --jp-accent-color1: var(--md-green-700); --jp-accent-color2: var(--md-green-300); --jp-accent-color3: var(--md-green-100); /* State colors (warn, error, success, info) */ --jp-warn-color0: var(--md-orange-900); --jp-warn-color1: var(--md-orange-700); --jp-warn-color2: var(--md-orange-300); --jp-warn-color3: var(--md-orange-100); --jp-error-color0: var(--md-red-900); --jp-error-color1: var(--md-red-700); --jp-error-color2: var(--md-red-300); --jp-error-color3: var(--md-red-100); --jp-success-color0: var(--md-green-900); --jp-success-color1: var(--md-green-700); --jp-success-color2: var(--md-green-300); --jp-success-color3: var(--md-green-100); --jp-info-color0: var(--md-cyan-900); --jp-info-color1: var(--md-cyan-700); --jp-info-color2: var(--md-cyan-300); --jp-info-color3: var(--md-cyan-100); /* Cell specific styles */ --jp-cell-padding: 5px; --jp-cell-collapser-width: 8px; --jp-cell-collapser-min-height: 20px; --jp-cell-collapser-not-active-hover-opacity: 0.6; --jp-cell-editor-background: var(--md-grey-100); --jp-cell-editor-border-color: var(--md-grey-300); --jp-cell-editor-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-cell-editor-active-background: var(--jp-layout-color0); --jp-cell-editor-active-border-color: var(--jp-brand-color1); --jp-cell-prompt-width: 64px; --jp-cell-prompt-font-family: var(--jp-code-font-family-default); --jp-cell-prompt-letter-spacing: 0px; --jp-cell-prompt-opacity: 1; --jp-cell-prompt-not-active-opacity: 0.5; --jp-cell-prompt-not-active-font-color: var(--md-grey-700); /* A custom blend of MD grey and blue 600 * See https://meyerweb.com/eric/tools/color-blend/#546E7A:1E88E5:5:hex */ --jp-cell-inprompt-font-color: #307fc1; /* A custom blend of MD grey and orange 600 * https://meyerweb.com/eric/tools/color-blend/#546E7A:F4511E:5:hex */ --jp-cell-outprompt-font-color: #bf5b3d; /* Notebook specific styles */ --jp-notebook-padding: 10px; --jp-notebook-select-background: var(--jp-layout-color1); --jp-notebook-multiselected-color: var(--md-blue-50); /* The scroll padding is calculated to fill enough space at the bottom of the notebook to show one single-line cell (with appropriate padding) at the top when the notebook is scrolled all the way to the bottom. We also subtract one pixel so that no scrollbar appears if we have just one single-line cell in the notebook. This padding is to enable a 'scroll past end' feature in a notebook. */ --jp-notebook-scroll-padding: calc( 100% - var(--jp-code-font-size) * var(--jp-code-line-height) - var(--jp-code-padding) - var(--jp-cell-padding) - 1px ); /* Rendermime styles */ --jp-rendermime-error-background: #fdd; --jp-rendermime-table-row-background: var(--md-grey-100); --jp-rendermime-table-row-hover-background: var(--md-light-blue-50); /* Dialog specific styles */ --jp-dialog-background: rgba(0, 0, 0, 0.25); /* Console specific styles */ --jp-console-padding: 10px; /* Toolbar specific styles */ --jp-toolbar-border-color: var(--jp-border-color1); --jp-toolbar-micro-height: 8px; --jp-toolbar-background: var(--jp-layout-color1); --jp-toolbar-box-shadow: 0px 0px 2px 0px rgba(0, 0, 0, 0.24); --jp-toolbar-header-margin: 4px 4px 0px 4px; --jp-toolbar-active-background: var(--md-grey-300); /* Statusbar specific styles */ --jp-statusbar-height: 24px; /* Input field styles */ --jp-input-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-input-active-background: var(--jp-layout-color1); --jp-input-hover-background: var(--jp-layout-color1); --jp-input-background: var(--md-grey-100); --jp-input-border-color: var(--jp-border-color1); --jp-input-active-border-color: var(--jp-brand-color1); --jp-input-active-box-shadow-color: rgba(19, 124, 189, 0.3); /* General editor styles */ --jp-editor-selected-background: #d9d9d9; --jp-editor-selected-focused-background: #d7d4f0; --jp-editor-cursor-color: var(--jp-ui-font-color0); /* Code mirror specific styles */ --jp-mirror-editor-keyword-color: #008000; --jp-mirror-editor-atom-color: #88f; --jp-mirror-editor-number-color: #080; --jp-mirror-editor-def-color: #00f; --jp-mirror-editor-variable-color: var(--md-grey-900); --jp-mirror-editor-variable-2-color: #05a; --jp-mirror-editor-variable-3-color: #085; --jp-mirror-editor-punctuation-color: #05a; --jp-mirror-editor-property-color: #05a; --jp-mirror-editor-operator-color: #aa22ff; --jp-mirror-editor-comment-color: #408080; --jp-mirror-editor-string-color: #ba2121; --jp-mirror-editor-string-2-color: #708; --jp-mirror-editor-meta-color: #aa22ff; --jp-mirror-editor-qualifier-color: #555; --jp-mirror-editor-builtin-color: #008000; --jp-mirror-editor-bracket-color: #997; --jp-mirror-editor-tag-color: #170; --jp-mirror-editor-attribute-color: #00c; --jp-mirror-editor-header-color: blue; --jp-mirror-editor-quote-color: #090; --jp-mirror-editor-link-color: #00c; --jp-mirror-editor-error-color: #f00; --jp-mirror-editor-hr-color: #999; /* Vega extension styles */ --jp-vega-background: white; /* Sidebar-related styles */ --jp-sidebar-min-width: 250px; /* Search-related styles */ --jp-search-toggle-off-opacity: 0.5; --jp-search-toggle-hover-opacity: 0.8; --jp-search-toggle-on-opacity: 1; --jp-search-selected-match-background-color: rgb(245, 200, 0); --jp-search-selected-match-color: black; --jp-search-unselected-match-background-color: var( --jp-inverse-layout-color0 ); --jp-search-unselected-match-color: var(--jp-ui-inverse-font-color0); /* Icon colors that work well with light or dark backgrounds */ --jp-icon-contrast-color0: var(--md-purple-600); --jp-icon-contrast-color1: var(--md-green-600); --jp-icon-contrast-color2: var(--md-pink-600); --jp-icon-contrast-color3: var(--md-blue-600); } [data-md-color-scheme=\"slate\"] .jupyter-wrapper { /* Elevation * * We style box-shadows using Material Design's idea of elevation. These particular numbers are taken from here: * * https://github.com/material-components/material-components-web * https://material-components-web.appspot.com/elevation.html */ /* The dark theme shadows need a bit of work, but this will probably also require work on the core layout * colors used in the theme as well. */ --jp-shadow-base-lightness: 32; --jp-shadow-umbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.2 ); --jp-shadow-penumbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.14 ); --jp-shadow-ambient-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.12 ); --jp-elevation-z0: none; --jp-elevation-z1: 0px 2px 1px -1px var(--jp-shadow-umbra-color), 0px 1px 1px 0px var(--jp-shadow-penumbra-color), 0px 1px 3px 0px var(--jp-shadow-ambient-color); --jp-elevation-z2: 0px 3px 1px -2px var(--jp-shadow-umbra-color), 0px 2px 2px 0px var(--jp-shadow-penumbra-color), 0px 1px 5px 0px var(--jp-shadow-ambient-color); --jp-elevation-z4: 0px 2px 4px -1px var(--jp-shadow-umbra-color), 0px 4px 5px 0px var(--jp-shadow-penumbra-color), 0px 1px 10px 0px var(--jp-shadow-ambient-color); --jp-elevation-z6: 0px 3px 5px -1px var(--jp-shadow-umbra-color), 0px 6px 10px 0px var(--jp-shadow-penumbra-color), 0px 1px 18px 0px var(--jp-shadow-ambient-color); --jp-elevation-z8: 0px 5px 5px -3px var(--jp-shadow-umbra-color), 0px 8px 10px 1px var(--jp-shadow-penumbra-color), 0px 3px 14px 2px var(--jp-shadow-ambient-color); --jp-elevation-z12: 0px 7px 8px -4px var(--jp-shadow-umbra-color), 0px 12px 17px 2px var(--jp-shadow-penumbra-color), 0px 5px 22px 4px var(--jp-shadow-ambient-color); --jp-elevation-z16: 0px 8px 10px -5px var(--jp-shadow-umbra-color), 0px 16px 24px 2px var(--jp-shadow-penumbra-color), 0px 6px 30px 5px var(--jp-shadow-ambient-color); --jp-elevation-z20: 0px 10px 13px -6px var(--jp-shadow-umbra-color), 0px 20px 31px 3px var(--jp-shadow-penumbra-color), 0px 8px 38px 7px var(--jp-shadow-ambient-color); --jp-elevation-z24: 0px 11px 15px -7px var(--jp-shadow-umbra-color), 0px 24px 38px 3px var(--jp-shadow-penumbra-color), 0px 9px 46px 8px var(--jp-shadow-ambient-color); /* Borders * * The following variables, specify the visual styling of borders in JupyterLab. */ --jp-border-width: 1px; --jp-border-color0: var(--md-grey-700); --jp-border-color1: var(--md-grey-700); --jp-border-color2: var(--md-grey-800); --jp-border-color3: var(--md-grey-900); --jp-border-radius: 2px; /* UI Fonts * * The UI font CSS variables are used for the typography all of the JupyterLab * user interface elements that are not directly user generated content. * * The font sizing here is done assuming that the body font size of --jp-ui-font-size1 * is applied to a parent element. When children elements, such as headings, are sized * in em all things will be computed relative to that body size. */ --jp-ui-font-scale-factor: 1.2; --jp-ui-font-size0: 0.83333em; --jp-ui-font-size1: 13px; /* Base font size */ --jp-ui-font-size2: 1.2em; --jp-ui-font-size3: 1.44em; --jp-ui-font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Helvetica, Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\"; /* * Use these font colors against the corresponding main layout colors. * In a light theme, these go from dark to light. */ /* Defaults use Material Design specification */ --jp-ui-font-color0: rgba(255, 255, 255, 1); --jp-ui-font-color1: rgba(255, 255, 255, 0.87); --jp-ui-font-color2: rgba(255, 255, 255, 0.54); --jp-ui-font-color3: rgba(255, 255, 255, 0.38); /* * Use these against the brand/accent/warn/error colors. * These will typically go from light to darker, in both a dark and light theme. */ --jp-ui-inverse-font-color0: rgba(0, 0, 0, 1); --jp-ui-inverse-font-color1: rgba(0, 0, 0, 0.8); --jp-ui-inverse-font-color2: rgba(0, 0, 0, 0.5); --jp-ui-inverse-font-color3: rgba(0, 0, 0, 0.3); /* Content Fonts * * Content font variables are used for typography of user generated content. * * The font sizing here is done assuming that the body font size of --jp-content-font-size1 * is applied to a parent element. When children elements, such as headings, are sized * in em all things will be computed relative to that body size. */ --jp-content-line-height: 1.6; --jp-content-font-scale-factor: 1.2; --jp-content-font-size0: 0.83333em; --jp-content-font-size1: 14px; /* Base font size */ --jp-content-font-size2: 1.2em; --jp-content-font-size3: 1.44em; --jp-content-font-size4: 1.728em; --jp-content-font-size5: 2.0736em; /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-content-presentation-font-size1: 17px; --jp-content-heading-line-height: 1; --jp-content-heading-margin-top: 1.2em; --jp-content-heading-margin-bottom: 0.8em; --jp-content-heading-font-weight: 500; /* Defaults use Material Design specification */ --jp-content-font-color0: rgba(255, 255, 255, 1); --jp-content-font-color1: rgba(255, 255, 255, 1); --jp-content-font-color2: rgba(255, 255, 255, 0.7); --jp-content-font-color3: rgba(255, 255, 255, 0.5); --jp-content-link-color: var(--md-blue-300); --jp-content-font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Helvetica, Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\"; /* * Code Fonts * * Code font variables are used for typography of code and other monospaces content. */ --jp-code-font-size: 13px; --jp-code-line-height: 1.3077; /* 17px for 13px base */ --jp-code-padding: 5px; /* 5px for 13px base, codemirror highlighting needs integer px value */ --jp-code-font-family-default: Menlo, Consolas, \"DejaVu Sans Mono\", monospace; --jp-code-font-family: var(--jp-code-font-family-default); /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-code-presentation-font-size: 16px; /* may need to tweak cursor width if you change font size */ --jp-code-cursor-width0: 1.4px; --jp-code-cursor-width1: 2px; --jp-code-cursor-width2: 4px; /* Layout * * The following are the main layout colors use in JupyterLab. In a light * theme these would go from light to dark. */ --jp-layout-color0: #111111; --jp-layout-color1: var(--md-grey-900); --jp-layout-color2: var(--md-grey-800); --jp-layout-color3: var(--md-grey-700); --jp-layout-color4: var(--md-grey-600); /* Inverse Layout * * The following are the inverse layout colors use in JupyterLab. In a light * theme these would go from dark to light. */ --jp-inverse-layout-color0: white; --jp-inverse-layout-color1: white; --jp-inverse-layout-color2: var(--md-grey-200); --jp-inverse-layout-color3: var(--md-grey-400); --jp-inverse-layout-color4: var(--md-grey-600); /* Brand/accent */ --jp-brand-color0: var(--md-blue-700); --jp-brand-color1: var(--md-blue-500); --jp-brand-color2: var(--md-blue-300); --jp-brand-color3: var(--md-blue-100); --jp-brand-color4: var(--md-blue-50); --jp-accent-color0: var(--md-green-700); --jp-accent-color1: var(--md-green-500); --jp-accent-color2: var(--md-green-300); --jp-accent-color3: var(--md-green-100); /* State colors (warn, error, success, info) */ --jp-warn-color0: var(--md-orange-700); --jp-warn-color1: var(--md-orange-500); --jp-warn-color2: var(--md-orange-300); --jp-warn-color3: var(--md-orange-100); --jp-error-color0: var(--md-red-700); --jp-error-color1: var(--md-red-500); --jp-error-color2: var(--md-red-300); --jp-error-color3: var(--md-red-100); --jp-success-color0: var(--md-green-700); --jp-success-color1: var(--md-green-500); --jp-success-color2: var(--md-green-300); --jp-success-color3: var(--md-green-100); --jp-info-color0: var(--md-cyan-700); --jp-info-color1: var(--md-cyan-500); --jp-info-color2: var(--md-cyan-300); --jp-info-color3: var(--md-cyan-100); /* Cell specific styles */ --jp-cell-padding: 5px; --jp-cell-collapser-width: 8px; --jp-cell-collapser-min-height: 20px; --jp-cell-collapser-not-active-hover-opacity: 0.6; --jp-cell-editor-background: var(--jp-layout-color1); --jp-cell-editor-border-color: var(--md-grey-700); --jp-cell-editor-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-cell-editor-active-background: var(--jp-layout-color0); --jp-cell-editor-active-border-color: var(--jp-brand-color1); --jp-cell-prompt-width: 64px; --jp-cell-prompt-font-family: var(--jp-code-font-family-default); --jp-cell-prompt-letter-spacing: 0px; --jp-cell-prompt-opacity: 1; --jp-cell-prompt-not-active-opacity: 1; --jp-cell-prompt-not-active-font-color: var(--md-grey-300); /* A custom blend of MD grey and blue 600 * See https://meyerweb.com/eric/tools/color-blend/#546E7A:1E88E5:5:hex */ --jp-cell-inprompt-font-color: #307fc1; /* A custom blend of MD grey and orange 600 * https://meyerweb.com/eric/tools/color-blend/#546E7A:F4511E:5:hex */ --jp-cell-outprompt-font-color: #bf5b3d; /* Notebook specific styles */ --jp-notebook-padding: 10px; --jp-notebook-select-background: var(--jp-layout-color1); --jp-notebook-multiselected-color: rgba(33, 150, 243, 0.24); /* The scroll padding is calculated to fill enough space at the bottom of the notebook to show one single-line cell (with appropriate padding) at the top when the notebook is scrolled all the way to the bottom. We also subtract one pixel so that no scrollbar appears if we have just one single-line cell in the notebook. This padding is to enable a 'scroll past end' feature in a notebook. */ --jp-notebook-scroll-padding: calc( 100% - var(--jp-code-font-size) * var(--jp-code-line-height) - var(--jp-code-padding) - var(--jp-cell-padding) - 1px ); /* Rendermime styles */ --jp-rendermime-error-background: rgba(244, 67, 54, 0.28); --jp-rendermime-table-row-background: var(--md-grey-900); --jp-rendermime-table-row-hover-background: rgba(3, 169, 244, 0.2); /* Dialog specific styles */ --jp-dialog-background: rgba(0, 0, 0, 0.6); /* Console specific styles */ --jp-console-padding: 10px; /* Toolbar specific styles */ --jp-toolbar-border-color: var(--jp-border-color2); --jp-toolbar-micro-height: 8px; --jp-toolbar-background: var(--jp-layout-color1); --jp-toolbar-box-shadow: 0px 0px 2px 0px rgba(0, 0, 0, 0.8); --jp-toolbar-header-margin: 4px 4px 0px 4px; --jp-toolbar-active-background: var(--jp-layout-color0); /* Statusbar specific styles */ --jp-statusbar-height: 24px; /* Input field styles */ --jp-input-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-input-active-background: var(--jp-layout-color0); --jp-input-hover-background: var(--jp-layout-color2); --jp-input-background: var(--md-grey-800); --jp-input-border-color: var(--jp-border-color1); --jp-input-active-border-color: var(--jp-brand-color1); --jp-input-active-box-shadow-color: rgba(19, 124, 189, 0.3); /* General editor styles */ --jp-editor-selected-background: var(--jp-layout-color2); --jp-editor-selected-focused-background: rgba(33, 150, 243, 0.24); --jp-editor-cursor-color: var(--jp-ui-font-color0); /* Code mirror specific styles */ --jp-mirror-editor-keyword-color: var(--md-green-500); --jp-mirror-editor-atom-color: var(--md-blue-300); --jp-mirror-editor-number-color: var(--md-green-400); --jp-mirror-editor-def-color: var(--md-blue-600); --jp-mirror-editor-variable-color: var(--md-grey-300); --jp-mirror-editor-variable-2-color: var(--md-blue-400); --jp-mirror-editor-variable-3-color: var(--md-green-600); --jp-mirror-editor-punctuation-color: var(--md-blue-400); --jp-mirror-editor-property-color: var(--md-blue-400); --jp-mirror-editor-operator-color: #aa22ff; --jp-mirror-editor-comment-color: #408080; --jp-mirror-editor-string-color: #ff7070; --jp-mirror-editor-string-2-color: var(--md-purple-300); --jp-mirror-editor-meta-color: #aa22ff; --jp-mirror-editor-qualifier-color: #555; --jp-mirror-editor-builtin-color: var(--md-green-600); --jp-mirror-editor-bracket-color: #997; --jp-mirror-editor-tag-color: var(--md-green-700); --jp-mirror-editor-attribute-color: var(--md-blue-700); --jp-mirror-editor-header-color: var(--md-blue-500); --jp-mirror-editor-quote-color: var(--md-green-300); --jp-mirror-editor-link-color: var(--md-blue-700); --jp-mirror-editor-error-color: #f00; --jp-mirror-editor-hr-color: #999; /* Vega extension styles */ --jp-vega-background: var(--md-grey-400); /* Sidebar-related styles */ --jp-sidebar-min-width: 250px; /* Search-related styles */ --jp-search-toggle-off-opacity: 0.6; --jp-search-toggle-hover-opacity: 0.8; --jp-search-toggle-on-opacity: 1; --jp-search-selected-match-background-color: rgb(255, 225, 0); --jp-search-selected-match-color: black; --jp-search-unselected-match-background-color: var( --jp-inverse-layout-color0 ); --jp-search-unselected-match-color: var(--jp-ui-inverse-font-color0); /* scrollbar related styles. Supports every browser except Edge. */ /* colors based on JetBrain's Darcula theme */ --jp-scrollbar-background-color: #3f4244; --jp-scrollbar-thumb-color: 88, 96, 97; /* need to specify thumb color as an RGB triplet */ --jp-scrollbar-endpad: 3px; /* the minimum gap between the thumb and the ends of a scrollbar */ /* hacks for setting the thumb shape. These do nothing in Firefox */ --jp-scrollbar-thumb-margin: 3.5px; /* the space in between the sides of the thumb and the track */ --jp-scrollbar-thumb-radius: 9px; /* set to a large-ish value for rounded endcaps on the thumb */ /* Icon colors that work well with light or dark backgrounds */ --jp-icon-contrast-color0: var(--md-purple-600); --jp-icon-contrast-color1: var(--md-green-600); --jp-icon-contrast-color2: var(--md-pink-600); --jp-icon-contrast-color3: var(--md-blue-600); } :root{--md-red-50: #ffebee;--md-red-100: #ffcdd2;--md-red-200: #ef9a9a;--md-red-300: #e57373;--md-red-400: #ef5350;--md-red-500: #f44336;--md-red-600: #e53935;--md-red-700: #d32f2f;--md-red-800: #c62828;--md-red-900: #b71c1c;--md-red-A100: #ff8a80;--md-red-A200: #ff5252;--md-red-A400: #ff1744;--md-red-A700: #d50000;--md-pink-50: #fce4ec;--md-pink-100: #f8bbd0;--md-pink-200: #f48fb1;--md-pink-300: #f06292;--md-pink-400: #ec407a;--md-pink-500: #e91e63;--md-pink-600: #d81b60;--md-pink-700: #c2185b;--md-pink-800: #ad1457;--md-pink-900: #880e4f;--md-pink-A100: #ff80ab;--md-pink-A200: #ff4081;--md-pink-A400: #f50057;--md-pink-A700: #c51162;--md-purple-50: #f3e5f5;--md-purple-100: #e1bee7;--md-purple-200: #ce93d8;--md-purple-300: #ba68c8;--md-purple-400: #ab47bc;--md-purple-500: #9c27b0;--md-purple-600: #8e24aa;--md-purple-700: #7b1fa2;--md-purple-800: #6a1b9a;--md-purple-900: #4a148c;--md-purple-A100: #ea80fc;--md-purple-A200: #e040fb;--md-purple-A400: #d500f9;--md-purple-A700: #aa00ff;--md-deep-purple-50: #ede7f6;--md-deep-purple-100: #d1c4e9;--md-deep-purple-200: #b39ddb;--md-deep-purple-300: #9575cd;--md-deep-purple-400: #7e57c2;--md-deep-purple-500: #673ab7;--md-deep-purple-600: #5e35b1;--md-deep-purple-700: #512da8;--md-deep-purple-800: #4527a0;--md-deep-purple-900: #311b92;--md-deep-purple-A100: #b388ff;--md-deep-purple-A200: #7c4dff;--md-deep-purple-A400: #651fff;--md-deep-purple-A700: #6200ea;--md-indigo-50: #e8eaf6;--md-indigo-100: #c5cae9;--md-indigo-200: #9fa8da;--md-indigo-300: #7986cb;--md-indigo-400: #5c6bc0;--md-indigo-500: #3f51b5;--md-indigo-600: #3949ab;--md-indigo-700: #303f9f;--md-indigo-800: #283593;--md-indigo-900: #1a237e;--md-indigo-A100: #8c9eff;--md-indigo-A200: #536dfe;--md-indigo-A400: #3d5afe;--md-indigo-A700: #304ffe;--md-blue-50: #e3f2fd;--md-blue-100: #bbdefb;--md-blue-200: #90caf9;--md-blue-300: #64b5f6;--md-blue-400: #42a5f5;--md-blue-500: #2196f3;--md-blue-600: #1e88e5;--md-blue-700: #1976d2;--md-blue-800: #1565c0;--md-blue-900: #0d47a1;--md-blue-A100: #82b1ff;--md-blue-A200: #448aff;--md-blue-A400: #2979ff;--md-blue-A700: #2962ff;--md-light-blue-50: #e1f5fe;--md-light-blue-100: #b3e5fc;--md-light-blue-200: #81d4fa;--md-light-blue-300: #4fc3f7;--md-light-blue-400: #29b6f6;--md-light-blue-500: #03a9f4;--md-light-blue-600: #039be5;--md-light-blue-700: #0288d1;--md-light-blue-800: #0277bd;--md-light-blue-900: #01579b;--md-light-blue-A100: #80d8ff;--md-light-blue-A200: #40c4ff;--md-light-blue-A400: #00b0ff;--md-light-blue-A700: #0091ea;--md-cyan-50: #e0f7fa;--md-cyan-100: #b2ebf2;--md-cyan-200: #80deea;--md-cyan-300: #4dd0e1;--md-cyan-400: #26c6da;--md-cyan-500: #00bcd4;--md-cyan-600: #00acc1;--md-cyan-700: #0097a7;--md-cyan-800: #00838f;--md-cyan-900: #006064;--md-cyan-A100: #84ffff;--md-cyan-A200: #18ffff;--md-cyan-A400: #00e5ff;--md-cyan-A700: #00b8d4;--md-teal-50: #e0f2f1;--md-teal-100: #b2dfdb;--md-teal-200: #80cbc4;--md-teal-300: #4db6ac;--md-teal-400: #26a69a;--md-teal-500: #009688;--md-teal-600: #00897b;--md-teal-700: #00796b;--md-teal-800: #00695c;--md-teal-900: #004d40;--md-teal-A100: #a7ffeb;--md-teal-A200: #64ffda;--md-teal-A400: #1de9b6;--md-teal-A700: #00bfa5;--md-green-50: #e8f5e9;--md-green-100: #c8e6c9;--md-green-200: #a5d6a7;--md-green-300: #81c784;--md-green-400: #66bb6a;--md-green-500: #4caf50;--md-green-600: #43a047;--md-green-700: #388e3c;--md-green-800: #2e7d32;--md-green-900: #1b5e20;--md-green-A100: #b9f6ca;--md-green-A200: #69f0ae;--md-green-A400: #00e676;--md-green-A700: 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sourceMappingURL=mkdocs-jupyter.css.map*/ init_mathjax = function() { if (window.MathJax) { // MathJax loaded MathJax.Hub.Config({ TeX: { equationNumbers: { autoNumber: \"AMS\", useLabelIds: true } }, tex2jax: { inlineMath: [ ['$','$'], [\"\\\\(\",\"\\\\)\"] ], displayMath: [ ['$$','$$'], [\"\\\\[\",\"\\\\]\"] ], processEscapes: true, processEnvironments: true }, displayAlign: 'center', CommonHTML: { linebreaks: { automatic: true } } }); MathJax.Hub.Queue([\"Typeset\", MathJax.Hub]); } } init_mathjax(); Optimise and train a model \u00b6 The blue boxes show the steps implemented in this notebook. In the first part, we have cleaned and transformed our training data. We can now access this data using great_ai.LargeFile . Locally, it will gives us the cached version, otherwise, the latest version is downloaded from S3 or GridFS. In this part, we hyperparameter-optimise and train a simple Naive Bayes classifier which we then export for deployment using great_ai.save_model . Load data that has been extracted in part 1 \u00b6 In [1]: Copied! from great_ai import query_ground_truth data = query_ground_truth ( \"train\" ) X = [ d . input for d in data for domain in d . feedback ] y = [ domain for d in data for domain in d . feedback ] from great_ai import query_ground_truth data = query_ground_truth(\"train\") X = [d.input for d in data for domain in d.feedback] y = [domain for d in data for domain in d.feedback] Environment variable ENVIRONMENT is not set, defaulting to development mode \u203c\ufe0f Cannot find credentials files, defaulting to using ParallelTinyDbDriver The selected tracing database (ParallelTinyDbDriver) is not recommended for production Cannot find credentials files, defaulting to using LargeFileLocal GreatAI (v0.1.6): configured \u2705 \ud83d\udd29 tracing_database: ParallelTinyDbDriver \ud83d\udd29 large_file_implementation: LargeFileLocal \ud83d\udd29 is_production: False \ud83d\udd29 should_log_exception_stack: True \ud83d\udd29 prediction_cache_size: 512 \ud83d\udd29 dashboard_table_size: 50 You still need to check whether you follow all best practices before trusting your deployment. > Find out more at https://se-ml.github.io/practices In [2]: Copied! from collections import Counter import matplotlib.pyplot as plt domains , counts = zip ( * Counter ( y ) . most_common ()) # Configure matplotlib to have nice, high-resolution charts % matplotlib inline plt . rcParams [ \"figure.figsize\" ] = ( 20 , 5 ) plt . rcParams [ \"figure.facecolor\" ] = \"white\" plt . rcParams [ \"font.size\" ] = 12 plt . xticks ( rotation = 90 ) plt . bar ( domains , counts ) None from collections import Counter import matplotlib.pyplot as plt domains, counts = zip(*Counter(y).most_common()) # Configure matplotlib to have nice, high-resolution charts %matplotlib inline plt.rcParams[\"figure.figsize\"] = (20, 5) plt.rcParams[\"figure.facecolor\"] = \"white\" plt.rcParams[\"font.size\"] = 12 plt.xticks(rotation=90) plt.bar(domains, counts) None Optimise and train Multinomial Naive Bayes classifier \u00b6 In [3]: Copied! from sklearn.naive_bayes import MultinomialNB from sklearn.pipeline import Pipeline from sklearn.feature_extraction.text import TfidfVectorizer def create_pipeline () -> Pipeline : return Pipeline ( steps = [ ( \"vectorizer\" , TfidfVectorizer ( sublinear_tf = True )), ( \"classifier\" , MultinomialNB ()), ] ) from sklearn.naive_bayes import MultinomialNB from sklearn.pipeline import Pipeline from sklearn.feature_extraction.text import TfidfVectorizer def create_pipeline() -> Pipeline: return Pipeline( steps=[ (\"vectorizer\", TfidfVectorizer(sublinear_tf=True)), (\"classifier\", MultinomialNB()), ] ) In [4]: Copied! from sklearn.model_selection import GridSearchCV import pandas as pd optimisation_pipeline = GridSearchCV ( create_pipeline (), { \"vectorizer__min_df\" : [ 5 , 20 , 100 ], \"vectorizer__max_df\" : [ 0.05 , 0.1 ], \"classifier__alpha\" : [ 0.5 , 1 ], \"classifier__fit_prior\" : [ True , False ], }, scoring = \"f1_macro\" , cv = 3 , n_jobs =- 1 , verbose = 1 , ) optimisation_pipeline . fit ( X , y ) results = pd . DataFrame ( optimisation_pipeline . cv_results_ ) results . sort_values ( \"rank_test_score\" ) from sklearn.model_selection import GridSearchCV import pandas as pd optimisation_pipeline = GridSearchCV( create_pipeline(), { \"vectorizer__min_df\": [5, 20, 100], \"vectorizer__max_df\": [0.05, 0.1], \"classifier__alpha\": [0.5, 1], \"classifier__fit_prior\": [True, False], }, scoring=\"f1_macro\", cv=3, n_jobs=-1, verbose=1, ) optimisation_pipeline.fit(X, y) results = pd.DataFrame(optimisation_pipeline.cv_results_) results.sort_values(\"rank_test_score\") Fitting 3 folds for each of 24 candidates, totalling 72 fits Out[4]: .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } mean_fit_time std_fit_time mean_score_time std_score_time param_classifier__alpha param_classifier__fit_prior param_vectorizer__max_df param_vectorizer__min_df params split0_test_score split1_test_score split2_test_score mean_test_score std_test_score rank_test_score 7 14.549476 0.361685 8.476837 0.222398 0.5 False 0.05 20 {'classifier__alpha': 0.5, 'classifier__fit_pr... 0.518061 0.514842 0.511599 0.514834 0.002638 1 10 11.235289 0.130426 4.092868 0.082518 0.5 False 0.1 20 {'classifier__alpha': 0.5, 'classifier__fit_pr... 0.513897 0.515661 0.507867 0.512475 0.003337 2 19 7.383645 0.138110 4.130709 0.250048 1 False 0.05 20 {'classifier__alpha': 1, 'classifier__fit_prio... 0.496825 0.501045 0.496854 0.498241 0.001983 3 11 10.435154 0.305144 3.882101 0.128886 0.5 False 0.1 100 {'classifier__alpha': 0.5, 'classifier__fit_pr... 0.493247 0.497814 0.502245 0.497769 0.003674 4 8 13.643193 0.310696 4.173707 0.142980 0.5 False 0.05 100 {'classifier__alpha': 0.5, 'classifier__fit_pr... 0.489609 0.495207 0.498154 0.494323 0.003544 5 22 7.048340 0.050070 3.172948 0.152418 1 False 0.1 20 {'classifier__alpha': 1, 'classifier__fit_prio... 0.487456 0.493865 0.491157 0.490826 0.002627 6 23 7.564685 0.146092 2.374111 0.285026 1 False 0.1 100 {'classifier__alpha': 1, 'classifier__fit_prio... 0.485160 0.494039 0.490127 0.489776 0.003633 7 20 7.172353 0.212599 3.747219 0.130217 1 False 0.05 100 {'classifier__alpha': 1, 'classifier__fit_prio... 0.481303 0.490002 0.488269 0.486524 0.003759 8 6 14.276345 0.456576 8.318859 0.268701 0.5 False 0.05 5 {'classifier__alpha': 0.5, 'classifier__fit_pr... 0.482429 0.487735 0.484888 0.485017 0.002168 9 2 14.902358 0.737693 5.975091 0.171150 0.5 True 0.05 100 {'classifier__alpha': 0.5, 'classifier__fit_pr... 0.469598 0.474490 0.473637 0.472575 0.002134 10 9 12.677349 0.145143 4.374204 0.175674 0.5 False 0.1 5 {'classifier__alpha': 0.5, 'classifier__fit_pr... 0.468872 0.476451 0.470921 0.472082 0.003201 11 5 13.423686 0.482872 8.008324 0.442975 0.5 True 0.1 100 {'classifier__alpha': 0.5, 'classifier__fit_pr... 0.465726 0.474548 0.471879 0.470718 0.003694 12 1 13.819117 0.838347 6.161175 0.336590 0.5 True 0.05 20 {'classifier__alpha': 0.5, 'classifier__fit_pr... 0.463395 0.473982 0.471262 0.469546 0.004489 13 4 13.281476 0.588822 8.335852 0.254627 0.5 True 0.1 20 {'classifier__alpha': 0.5, 'classifier__fit_pr... 0.458734 0.468053 0.464418 0.463735 0.003835 14 14 7.282247 0.444940 3.567094 0.044519 1 True 0.05 100 {'classifier__alpha': 1, 'classifier__fit_prio... 0.438189 0.450160 0.446180 0.444843 0.004978 15 17 7.098797 0.196241 3.838628 0.091128 1 True 0.1 100 {'classifier__alpha': 1, 'classifier__fit_prio... 0.436488 0.444503 0.445900 0.442297 0.004147 16 18 7.492791 0.288889 3.843224 0.073438 1 False 0.05 5 {'classifier__alpha': 1, 'classifier__fit_prio... 0.428196 0.431945 0.430160 0.430100 0.001531 17 21 7.229826 0.099823 3.656332 0.073780 1 False 0.1 5 {'classifier__alpha': 1, 'classifier__fit_prio... 0.403130 0.410170 0.409801 0.407700 0.003235 18 13 7.158370 0.169818 3.765632 0.082924 1 True 0.05 20 {'classifier__alpha': 1, 'classifier__fit_prio... 0.399237 0.412872 0.407982 0.406697 0.005640 19 16 7.064643 0.119529 3.810983 0.125897 1 True 0.1 20 {'classifier__alpha': 1, 'classifier__fit_prio... 0.388060 0.399247 0.396325 0.394544 0.004737 20 0 13.749660 0.465174 6.407841 0.549166 0.5 True 0.05 5 {'classifier__alpha': 0.5, 'classifier__fit_pr... 0.384852 0.386487 0.386796 0.386045 0.000853 21 3 15.954147 0.318013 6.337361 0.261697 0.5 True 0.1 5 {'classifier__alpha': 0.5, 'classifier__fit_pr... 0.369785 0.375645 0.375858 0.373763 0.002814 22 12 7.120198 0.050452 3.833905 0.069540 1 True 0.05 5 {'classifier__alpha': 1, 'classifier__fit_prio... 0.277741 0.280564 0.285337 0.281214 0.003135 23 15 7.497707 0.183054 3.870714 0.062888 1 True 0.1 5 {'classifier__alpha': 1, 'classifier__fit_prio... 0.255578 0.263381 0.266184 0.261714 0.004487 24 In [5]: Copied! from sklearn import set_config set_config ( display = \"diagram\" ) classifier = create_pipeline () classifier . set_params ( ** optimisation_pipeline . best_params_ ) classifier . fit ( X , y ) from sklearn import set_config set_config(display=\"diagram\") classifier = create_pipeline() classifier.set_params(**optimisation_pipeline.best_params_) classifier.fit(X, y) Out[5]: #sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"\u25b8\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"\u25be\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;} Pipeline(steps=[('vectorizer', TfidfVectorizer(max_df=0.05, min_df=20, sublinear_tf=True)), ('classifier', MultinomialNB(alpha=0.5, fit_prior=False))]) In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org. Pipeline Pipeline(steps=[('vectorizer', TfidfVectorizer(max_df=0.05, min_df=20, sublinear_tf=True)), ('classifier', MultinomialNB(alpha=0.5, fit_prior=False))]) TfidfVectorizer TfidfVectorizer(max_df=0.05, min_df=20, sublinear_tf=True) MultinomialNB MultinomialNB(alpha=0.5, fit_prior=False) Export the model using GreatAI \u00b6 In [6]: Copied! from great_ai import save_model save_model ( classifier , key = \"small-domain-prediction\" , keep_last_n = 5 ) from great_ai import save_model save_model(classifier, key=\"small-domain-prediction\", keep_last_n=5) Fetching cached versions of small-domain-prediction Copying file for small-domain-prediction-2 Compressing small-domain-prediction-2 Model small-domain-prediction uploaded with version 2 Out[6]: 'small-domain-prediction:2' Next: Part 3 \u00b6","title":"Optimise and train a model"},{"location":"examples/simple/train/#optimise-and-train-a-model","text":"The blue boxes show the steps implemented in this notebook. In the first part, we have cleaned and transformed our training data. We can now access this data using great_ai.LargeFile . Locally, it will gives us the cached version, otherwise, the latest version is downloaded from S3 or GridFS. In this part, we hyperparameter-optimise and train a simple Naive Bayes classifier which we then export for deployment using great_ai.save_model .","title":"Optimise and train a model"},{"location":"examples/simple/train/#load-data-that-has-been-extracted-in-part-1","text":"In [1]: Copied! from great_ai import query_ground_truth data = query_ground_truth ( \"train\" ) X = [ d . input for d in data for domain in d . feedback ] y = [ domain for d in data for domain in d . feedback ] from great_ai import query_ground_truth data = query_ground_truth(\"train\") X = [d.input for d in data for domain in d.feedback] y = [domain for d in data for domain in d.feedback] Environment variable ENVIRONMENT is not set, defaulting to development mode \u203c\ufe0f Cannot find credentials files, defaulting to using ParallelTinyDbDriver The selected tracing database (ParallelTinyDbDriver) is not recommended for production Cannot find credentials files, defaulting to using LargeFileLocal GreatAI (v0.1.6): configured \u2705 \ud83d\udd29 tracing_database: ParallelTinyDbDriver \ud83d\udd29 large_file_implementation: LargeFileLocal \ud83d\udd29 is_production: False \ud83d\udd29 should_log_exception_stack: True \ud83d\udd29 prediction_cache_size: 512 \ud83d\udd29 dashboard_table_size: 50 You still need to check whether you follow all best practices before trusting your deployment. > Find out more at https://se-ml.github.io/practices In [2]: Copied! from collections import Counter import matplotlib.pyplot as plt domains , counts = zip ( * Counter ( y ) . most_common ()) # Configure matplotlib to have nice, high-resolution charts % matplotlib inline plt . rcParams [ \"figure.figsize\" ] = ( 20 , 5 ) plt . rcParams [ \"figure.facecolor\" ] = \"white\" plt . rcParams [ \"font.size\" ] = 12 plt . xticks ( rotation = 90 ) plt . bar ( domains , counts ) None from collections import Counter import matplotlib.pyplot as plt domains, counts = zip(*Counter(y).most_common()) # Configure matplotlib to have nice, high-resolution charts %matplotlib inline plt.rcParams[\"figure.figsize\"] = (20, 5) plt.rcParams[\"figure.facecolor\"] = \"white\" plt.rcParams[\"font.size\"] = 12 plt.xticks(rotation=90) plt.bar(domains, counts) None","title":"Load data that has been extracted in part 1"},{"location":"examples/simple/train/#optimise-and-train-multinomial-naive-bayes-classifier","text":"In [3]: Copied! from sklearn.naive_bayes import MultinomialNB from sklearn.pipeline import Pipeline from sklearn.feature_extraction.text import TfidfVectorizer def create_pipeline () -> Pipeline : return Pipeline ( steps = [ ( \"vectorizer\" , TfidfVectorizer ( sublinear_tf = True )), ( \"classifier\" , MultinomialNB ()), ] ) from sklearn.naive_bayes import MultinomialNB from sklearn.pipeline import Pipeline from sklearn.feature_extraction.text import TfidfVectorizer def create_pipeline() -> Pipeline: return Pipeline( steps=[ (\"vectorizer\", TfidfVectorizer(sublinear_tf=True)), (\"classifier\", MultinomialNB()), ] ) In [4]: Copied! from sklearn.model_selection import GridSearchCV import pandas as pd optimisation_pipeline = GridSearchCV ( create_pipeline (), { \"vectorizer__min_df\" : [ 5 , 20 , 100 ], \"vectorizer__max_df\" : [ 0.05 , 0.1 ], \"classifier__alpha\" : [ 0.5 , 1 ], \"classifier__fit_prior\" : [ True , False ], }, scoring = \"f1_macro\" , cv = 3 , n_jobs =- 1 , verbose = 1 , ) optimisation_pipeline . fit ( X , y ) results = pd . DataFrame ( optimisation_pipeline . cv_results_ ) results . sort_values ( \"rank_test_score\" ) from sklearn.model_selection import GridSearchCV import pandas as pd optimisation_pipeline = GridSearchCV( create_pipeline(), { \"vectorizer__min_df\": [5, 20, 100], \"vectorizer__max_df\": [0.05, 0.1], \"classifier__alpha\": [0.5, 1], \"classifier__fit_prior\": [True, False], }, scoring=\"f1_macro\", cv=3, n_jobs=-1, verbose=1, ) optimisation_pipeline.fit(X, y) results = pd.DataFrame(optimisation_pipeline.cv_results_) results.sort_values(\"rank_test_score\") Fitting 3 folds for each of 24 candidates, totalling 72 fits Out[4]: .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } mean_fit_time std_fit_time mean_score_time std_score_time param_classifier__alpha param_classifier__fit_prior param_vectorizer__max_df param_vectorizer__min_df params split0_test_score split1_test_score split2_test_score mean_test_score std_test_score rank_test_score 7 14.549476 0.361685 8.476837 0.222398 0.5 False 0.05 20 {'classifier__alpha': 0.5, 'classifier__fit_pr... 0.518061 0.514842 0.511599 0.514834 0.002638 1 10 11.235289 0.130426 4.092868 0.082518 0.5 False 0.1 20 {'classifier__alpha': 0.5, 'classifier__fit_pr... 0.513897 0.515661 0.507867 0.512475 0.003337 2 19 7.383645 0.138110 4.130709 0.250048 1 False 0.05 20 {'classifier__alpha': 1, 'classifier__fit_prio... 0.496825 0.501045 0.496854 0.498241 0.001983 3 11 10.435154 0.305144 3.882101 0.128886 0.5 False 0.1 100 {'classifier__alpha': 0.5, 'classifier__fit_pr... 0.493247 0.497814 0.502245 0.497769 0.003674 4 8 13.643193 0.310696 4.173707 0.142980 0.5 False 0.05 100 {'classifier__alpha': 0.5, 'classifier__fit_pr... 0.489609 0.495207 0.498154 0.494323 0.003544 5 22 7.048340 0.050070 3.172948 0.152418 1 False 0.1 20 {'classifier__alpha': 1, 'classifier__fit_prio... 0.487456 0.493865 0.491157 0.490826 0.002627 6 23 7.564685 0.146092 2.374111 0.285026 1 False 0.1 100 {'classifier__alpha': 1, 'classifier__fit_prio... 0.485160 0.494039 0.490127 0.489776 0.003633 7 20 7.172353 0.212599 3.747219 0.130217 1 False 0.05 100 {'classifier__alpha': 1, 'classifier__fit_prio... 0.481303 0.490002 0.488269 0.486524 0.003759 8 6 14.276345 0.456576 8.318859 0.268701 0.5 False 0.05 5 {'classifier__alpha': 0.5, 'classifier__fit_pr... 0.482429 0.487735 0.484888 0.485017 0.002168 9 2 14.902358 0.737693 5.975091 0.171150 0.5 True 0.05 100 {'classifier__alpha': 0.5, 'classifier__fit_pr... 0.469598 0.474490 0.473637 0.472575 0.002134 10 9 12.677349 0.145143 4.374204 0.175674 0.5 False 0.1 5 {'classifier__alpha': 0.5, 'classifier__fit_pr... 0.468872 0.476451 0.470921 0.472082 0.003201 11 5 13.423686 0.482872 8.008324 0.442975 0.5 True 0.1 100 {'classifier__alpha': 0.5, 'classifier__fit_pr... 0.465726 0.474548 0.471879 0.470718 0.003694 12 1 13.819117 0.838347 6.161175 0.336590 0.5 True 0.05 20 {'classifier__alpha': 0.5, 'classifier__fit_pr... 0.463395 0.473982 0.471262 0.469546 0.004489 13 4 13.281476 0.588822 8.335852 0.254627 0.5 True 0.1 20 {'classifier__alpha': 0.5, 'classifier__fit_pr... 0.458734 0.468053 0.464418 0.463735 0.003835 14 14 7.282247 0.444940 3.567094 0.044519 1 True 0.05 100 {'classifier__alpha': 1, 'classifier__fit_prio... 0.438189 0.450160 0.446180 0.444843 0.004978 15 17 7.098797 0.196241 3.838628 0.091128 1 True 0.1 100 {'classifier__alpha': 1, 'classifier__fit_prio... 0.436488 0.444503 0.445900 0.442297 0.004147 16 18 7.492791 0.288889 3.843224 0.073438 1 False 0.05 5 {'classifier__alpha': 1, 'classifier__fit_prio... 0.428196 0.431945 0.430160 0.430100 0.001531 17 21 7.229826 0.099823 3.656332 0.073780 1 False 0.1 5 {'classifier__alpha': 1, 'classifier__fit_prio... 0.403130 0.410170 0.409801 0.407700 0.003235 18 13 7.158370 0.169818 3.765632 0.082924 1 True 0.05 20 {'classifier__alpha': 1, 'classifier__fit_prio... 0.399237 0.412872 0.407982 0.406697 0.005640 19 16 7.064643 0.119529 3.810983 0.125897 1 True 0.1 20 {'classifier__alpha': 1, 'classifier__fit_prio... 0.388060 0.399247 0.396325 0.394544 0.004737 20 0 13.749660 0.465174 6.407841 0.549166 0.5 True 0.05 5 {'classifier__alpha': 0.5, 'classifier__fit_pr... 0.384852 0.386487 0.386796 0.386045 0.000853 21 3 15.954147 0.318013 6.337361 0.261697 0.5 True 0.1 5 {'classifier__alpha': 0.5, 'classifier__fit_pr... 0.369785 0.375645 0.375858 0.373763 0.002814 22 12 7.120198 0.050452 3.833905 0.069540 1 True 0.05 5 {'classifier__alpha': 1, 'classifier__fit_prio... 0.277741 0.280564 0.285337 0.281214 0.003135 23 15 7.497707 0.183054 3.870714 0.062888 1 True 0.1 5 {'classifier__alpha': 1, 'classifier__fit_prio... 0.255578 0.263381 0.266184 0.261714 0.004487 24 In [5]: Copied! from sklearn import set_config set_config ( display = \"diagram\" ) classifier = create_pipeline () classifier . set_params ( ** optimisation_pipeline . best_params_ ) classifier . fit ( X , y ) from sklearn import set_config set_config(display=\"diagram\") classifier = create_pipeline() classifier.set_params(**optimisation_pipeline.best_params_) classifier.fit(X, y) Out[5]: #sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"\u25b8\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"\u25be\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. 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Pipeline Pipeline(steps=[('vectorizer', TfidfVectorizer(max_df=0.05, min_df=20, sublinear_tf=True)), ('classifier', MultinomialNB(alpha=0.5, fit_prior=False))]) TfidfVectorizer TfidfVectorizer(max_df=0.05, min_df=20, sublinear_tf=True) MultinomialNB MultinomialNB(alpha=0.5, fit_prior=False)","title":"Optimise and train Multinomial Naive Bayes classifier"},{"location":"examples/simple/train/#export-the-model-using-greatai","text":"In [6]: Copied! from great_ai import save_model save_model ( classifier , key = \"small-domain-prediction\" , keep_last_n = 5 ) from great_ai import save_model save_model(classifier, key=\"small-domain-prediction\", keep_last_n=5) Fetching cached versions of small-domain-prediction Copying file for small-domain-prediction-2 Compressing small-domain-prediction-2 Model small-domain-prediction uploaded with version 2 Out[6]: 'small-domain-prediction:2'","title":"Export the model using GreatAI"},{"location":"examples/simple/train/#next-part-3","text":"","title":"Next: Part 3"},{"location":"examples/simple-mag/train/","text":"(function (global, factory) { typeof exports === 'object' && typeof module !== 'undefined' ? module.exports = factory() : typeof define === 'function' && define.amd ? define(factory) : (global = global || self, global.ClipboardCopyElement = factory()); 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These particular numbers are taken from here: * * https://github.com/material-components/material-components-web * https://material-components-web.appspot.com/elevation.html */ --jp-shadow-base-lightness: 0; --jp-shadow-umbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.2 ); --jp-shadow-penumbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.14 ); --jp-shadow-ambient-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.12 ); --jp-elevation-z0: none; --jp-elevation-z1: 0px 2px 1px -1px var(--jp-shadow-umbra-color), 0px 1px 1px 0px var(--jp-shadow-penumbra-color), 0px 1px 3px 0px var(--jp-shadow-ambient-color); --jp-elevation-z2: 0px 3px 1px -2px var(--jp-shadow-umbra-color), 0px 2px 2px 0px var(--jp-shadow-penumbra-color), 0px 1px 5px 0px var(--jp-shadow-ambient-color); --jp-elevation-z4: 0px 2px 4px -1px var(--jp-shadow-umbra-color), 0px 4px 5px 0px var(--jp-shadow-penumbra-color), 0px 1px 10px 0px var(--jp-shadow-ambient-color); --jp-elevation-z6: 0px 3px 5px -1px var(--jp-shadow-umbra-color), 0px 6px 10px 0px var(--jp-shadow-penumbra-color), 0px 1px 18px 0px var(--jp-shadow-ambient-color); --jp-elevation-z8: 0px 5px 5px -3px var(--jp-shadow-umbra-color), 0px 8px 10px 1px var(--jp-shadow-penumbra-color), 0px 3px 14px 2px var(--jp-shadow-ambient-color); --jp-elevation-z12: 0px 7px 8px -4px var(--jp-shadow-umbra-color), 0px 12px 17px 2px var(--jp-shadow-penumbra-color), 0px 5px 22px 4px var(--jp-shadow-ambient-color); --jp-elevation-z16: 0px 8px 10px -5px var(--jp-shadow-umbra-color), 0px 16px 24px 2px var(--jp-shadow-penumbra-color), 0px 6px 30px 5px var(--jp-shadow-ambient-color); --jp-elevation-z20: 0px 10px 13px -6px var(--jp-shadow-umbra-color), 0px 20px 31px 3px var(--jp-shadow-penumbra-color), 0px 8px 38px 7px var(--jp-shadow-ambient-color); --jp-elevation-z24: 0px 11px 15px -7px var(--jp-shadow-umbra-color), 0px 24px 38px 3px var(--jp-shadow-penumbra-color), 0px 9px 46px 8px var(--jp-shadow-ambient-color); /* Borders * * The following variables, specify the visual styling of borders in JupyterLab. */ --jp-border-width: 1px; --jp-border-color0: var(--md-grey-400); --jp-border-color1: var(--md-grey-400); --jp-border-color2: var(--md-grey-300); --jp-border-color3: var(--md-grey-200); --jp-border-radius: 2px; /* UI Fonts * * The UI font CSS variables are used for the typography all of the JupyterLab * user interface elements that are not directly user generated content. * * The font sizing here is done assuming that the body font size of --jp-ui-font-size1 * is applied to a parent element. When children elements, such as headings, are sized * in em all things will be computed relative to that body size. */ --jp-ui-font-scale-factor: 1.2; --jp-ui-font-size0: 0.83333em; --jp-ui-font-size1: 13px; /* Base font size */ --jp-ui-font-size2: 1.2em; --jp-ui-font-size3: 1.44em; --jp-ui-font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Helvetica, Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\"; /* * Use these font colors against the corresponding main layout colors. * In a light theme, these go from dark to light. */ /* Defaults use Material Design specification */ --jp-ui-font-color0: rgba(0, 0, 0, 1); --jp-ui-font-color1: rgba(0, 0, 0, 0.87); --jp-ui-font-color2: rgba(0, 0, 0, 0.54); --jp-ui-font-color3: rgba(0, 0, 0, 0.38); /* * Use these against the brand/accent/warn/error colors. * These will typically go from light to darker, in both a dark and light theme. */ --jp-ui-inverse-font-color0: rgba(255, 255, 255, 1); --jp-ui-inverse-font-color1: rgba(255, 255, 255, 1); --jp-ui-inverse-font-color2: rgba(255, 255, 255, 0.7); --jp-ui-inverse-font-color3: rgba(255, 255, 255, 0.5); /* Content Fonts * * Content font variables are used for typography of user generated content. * * The font sizing here is done assuming that the body font size of --jp-content-font-size1 * is applied to a parent element. When children elements, such as headings, are sized * in em all things will be computed relative to that body size. */ --jp-content-line-height: 1.6; --jp-content-font-scale-factor: 1.2; --jp-content-font-size0: 0.83333em; --jp-content-font-size1: 14px; /* Base font size */ --jp-content-font-size2: 1.2em; --jp-content-font-size3: 1.44em; --jp-content-font-size4: 1.728em; --jp-content-font-size5: 2.0736em; /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-content-presentation-font-size1: 17px; --jp-content-heading-line-height: 1; --jp-content-heading-margin-top: 1.2em; --jp-content-heading-margin-bottom: 0.8em; --jp-content-heading-font-weight: 500; /* Defaults use Material Design specification */ --jp-content-font-color0: rgba(0, 0, 0, 1); --jp-content-font-color1: rgba(0, 0, 0, 0.87); --jp-content-font-color2: rgba(0, 0, 0, 0.54); --jp-content-font-color3: rgba(0, 0, 0, 0.38); --jp-content-link-color: var(--md-blue-700); --jp-content-font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Helvetica, Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\"; /* * Code Fonts * * Code font variables are used for typography of code and other monospaces content. */ --jp-code-font-size: 13px; --jp-code-line-height: 1.3077; /* 17px for 13px base */ --jp-code-padding: 5px; /* 5px for 13px base, codemirror highlighting needs integer px value */ --jp-code-font-family-default: Menlo, Consolas, \"DejaVu Sans Mono\", monospace; --jp-code-font-family: var(--jp-code-font-family-default); /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-code-presentation-font-size: 16px; /* may need to tweak cursor width if you change font size */ --jp-code-cursor-width0: 1.4px; --jp-code-cursor-width1: 2px; --jp-code-cursor-width2: 4px; /* Layout * * The following are the main layout colors use in JupyterLab. In a light * theme these would go from light to dark. */ --jp-layout-color0: white; --jp-layout-color1: white; --jp-layout-color2: var(--md-grey-200); --jp-layout-color3: var(--md-grey-400); --jp-layout-color4: var(--md-grey-600); /* Inverse Layout * * The following are the inverse layout colors use in JupyterLab. In a light * theme these would go from dark to light. */ --jp-inverse-layout-color0: #111111; --jp-inverse-layout-color1: var(--md-grey-900); --jp-inverse-layout-color2: var(--md-grey-800); --jp-inverse-layout-color3: var(--md-grey-700); --jp-inverse-layout-color4: var(--md-grey-600); /* Brand/accent */ --jp-brand-color0: var(--md-blue-900); --jp-brand-color1: var(--md-blue-700); --jp-brand-color2: var(--md-blue-300); --jp-brand-color3: var(--md-blue-100); --jp-brand-color4: var(--md-blue-50); --jp-accent-color0: var(--md-green-900); --jp-accent-color1: var(--md-green-700); --jp-accent-color2: var(--md-green-300); --jp-accent-color3: var(--md-green-100); /* State colors (warn, error, success, info) */ --jp-warn-color0: var(--md-orange-900); --jp-warn-color1: var(--md-orange-700); --jp-warn-color2: var(--md-orange-300); --jp-warn-color3: var(--md-orange-100); --jp-error-color0: var(--md-red-900); --jp-error-color1: var(--md-red-700); --jp-error-color2: var(--md-red-300); --jp-error-color3: var(--md-red-100); --jp-success-color0: var(--md-green-900); --jp-success-color1: var(--md-green-700); --jp-success-color2: var(--md-green-300); --jp-success-color3: var(--md-green-100); --jp-info-color0: var(--md-cyan-900); --jp-info-color1: var(--md-cyan-700); --jp-info-color2: var(--md-cyan-300); --jp-info-color3: var(--md-cyan-100); /* Cell specific styles */ --jp-cell-padding: 5px; --jp-cell-collapser-width: 8px; --jp-cell-collapser-min-height: 20px; --jp-cell-collapser-not-active-hover-opacity: 0.6; --jp-cell-editor-background: var(--md-grey-100); --jp-cell-editor-border-color: var(--md-grey-300); --jp-cell-editor-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-cell-editor-active-background: var(--jp-layout-color0); --jp-cell-editor-active-border-color: var(--jp-brand-color1); --jp-cell-prompt-width: 64px; --jp-cell-prompt-font-family: var(--jp-code-font-family-default); --jp-cell-prompt-letter-spacing: 0px; --jp-cell-prompt-opacity: 1; --jp-cell-prompt-not-active-opacity: 0.5; --jp-cell-prompt-not-active-font-color: var(--md-grey-700); /* A custom blend of MD grey and blue 600 * See https://meyerweb.com/eric/tools/color-blend/#546E7A:1E88E5:5:hex */ --jp-cell-inprompt-font-color: #307fc1; /* A custom blend of MD grey and orange 600 * https://meyerweb.com/eric/tools/color-blend/#546E7A:F4511E:5:hex */ --jp-cell-outprompt-font-color: #bf5b3d; /* Notebook specific styles */ --jp-notebook-padding: 10px; --jp-notebook-select-background: var(--jp-layout-color1); --jp-notebook-multiselected-color: var(--md-blue-50); /* The scroll padding is calculated to fill enough space at the bottom of the notebook to show one single-line cell (with appropriate padding) at the top when the notebook is scrolled all the way to the bottom. We also subtract one pixel so that no scrollbar appears if we have just one single-line cell in the notebook. This padding is to enable a 'scroll past end' feature in a notebook. */ --jp-notebook-scroll-padding: calc( 100% - var(--jp-code-font-size) * var(--jp-code-line-height) - var(--jp-code-padding) - var(--jp-cell-padding) - 1px ); /* Rendermime styles */ --jp-rendermime-error-background: #fdd; --jp-rendermime-table-row-background: var(--md-grey-100); --jp-rendermime-table-row-hover-background: var(--md-light-blue-50); /* Dialog specific styles */ --jp-dialog-background: rgba(0, 0, 0, 0.25); /* Console specific styles */ --jp-console-padding: 10px; /* Toolbar specific styles */ --jp-toolbar-border-color: var(--jp-border-color1); --jp-toolbar-micro-height: 8px; --jp-toolbar-background: var(--jp-layout-color1); --jp-toolbar-box-shadow: 0px 0px 2px 0px rgba(0, 0, 0, 0.24); --jp-toolbar-header-margin: 4px 4px 0px 4px; --jp-toolbar-active-background: var(--md-grey-300); /* Statusbar specific styles */ --jp-statusbar-height: 24px; /* Input field styles */ --jp-input-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-input-active-background: var(--jp-layout-color1); --jp-input-hover-background: var(--jp-layout-color1); --jp-input-background: var(--md-grey-100); --jp-input-border-color: var(--jp-border-color1); --jp-input-active-border-color: var(--jp-brand-color1); --jp-input-active-box-shadow-color: rgba(19, 124, 189, 0.3); /* General editor styles */ --jp-editor-selected-background: #d9d9d9; --jp-editor-selected-focused-background: #d7d4f0; --jp-editor-cursor-color: var(--jp-ui-font-color0); /* Code mirror specific styles */ --jp-mirror-editor-keyword-color: #008000; --jp-mirror-editor-atom-color: #88f; --jp-mirror-editor-number-color: #080; --jp-mirror-editor-def-color: #00f; --jp-mirror-editor-variable-color: var(--md-grey-900); --jp-mirror-editor-variable-2-color: #05a; --jp-mirror-editor-variable-3-color: #085; --jp-mirror-editor-punctuation-color: #05a; --jp-mirror-editor-property-color: #05a; --jp-mirror-editor-operator-color: #aa22ff; --jp-mirror-editor-comment-color: #408080; --jp-mirror-editor-string-color: #ba2121; --jp-mirror-editor-string-2-color: #708; --jp-mirror-editor-meta-color: #aa22ff; --jp-mirror-editor-qualifier-color: #555; --jp-mirror-editor-builtin-color: #008000; --jp-mirror-editor-bracket-color: #997; --jp-mirror-editor-tag-color: #170; --jp-mirror-editor-attribute-color: #00c; --jp-mirror-editor-header-color: blue; --jp-mirror-editor-quote-color: #090; --jp-mirror-editor-link-color: #00c; --jp-mirror-editor-error-color: #f00; --jp-mirror-editor-hr-color: #999; /* Vega extension styles */ --jp-vega-background: white; /* Sidebar-related styles */ --jp-sidebar-min-width: 250px; /* Search-related styles */ --jp-search-toggle-off-opacity: 0.5; --jp-search-toggle-hover-opacity: 0.8; --jp-search-toggle-on-opacity: 1; --jp-search-selected-match-background-color: rgb(245, 200, 0); --jp-search-selected-match-color: black; --jp-search-unselected-match-background-color: var( --jp-inverse-layout-color0 ); --jp-search-unselected-match-color: var(--jp-ui-inverse-font-color0); /* Icon colors that work well with light or dark backgrounds */ --jp-icon-contrast-color0: var(--md-purple-600); --jp-icon-contrast-color1: var(--md-green-600); --jp-icon-contrast-color2: var(--md-pink-600); --jp-icon-contrast-color3: var(--md-blue-600); } [data-md-color-scheme=\"slate\"] .jupyter-wrapper { /* Elevation * * We style box-shadows using Material Design's idea of elevation. These particular numbers are taken from here: * * https://github.com/material-components/material-components-web * https://material-components-web.appspot.com/elevation.html */ /* The dark theme shadows need a bit of work, but this will probably also require work on the core layout * colors used in the theme as well. */ --jp-shadow-base-lightness: 32; --jp-shadow-umbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.2 ); --jp-shadow-penumbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.14 ); --jp-shadow-ambient-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.12 ); --jp-elevation-z0: none; --jp-elevation-z1: 0px 2px 1px -1px var(--jp-shadow-umbra-color), 0px 1px 1px 0px var(--jp-shadow-penumbra-color), 0px 1px 3px 0px var(--jp-shadow-ambient-color); --jp-elevation-z2: 0px 3px 1px -2px var(--jp-shadow-umbra-color), 0px 2px 2px 0px var(--jp-shadow-penumbra-color), 0px 1px 5px 0px var(--jp-shadow-ambient-color); --jp-elevation-z4: 0px 2px 4px -1px var(--jp-shadow-umbra-color), 0px 4px 5px 0px var(--jp-shadow-penumbra-color), 0px 1px 10px 0px var(--jp-shadow-ambient-color); --jp-elevation-z6: 0px 3px 5px -1px var(--jp-shadow-umbra-color), 0px 6px 10px 0px var(--jp-shadow-penumbra-color), 0px 1px 18px 0px var(--jp-shadow-ambient-color); --jp-elevation-z8: 0px 5px 5px -3px var(--jp-shadow-umbra-color), 0px 8px 10px 1px var(--jp-shadow-penumbra-color), 0px 3px 14px 2px var(--jp-shadow-ambient-color); --jp-elevation-z12: 0px 7px 8px -4px var(--jp-shadow-umbra-color), 0px 12px 17px 2px var(--jp-shadow-penumbra-color), 0px 5px 22px 4px var(--jp-shadow-ambient-color); --jp-elevation-z16: 0px 8px 10px -5px var(--jp-shadow-umbra-color), 0px 16px 24px 2px var(--jp-shadow-penumbra-color), 0px 6px 30px 5px var(--jp-shadow-ambient-color); --jp-elevation-z20: 0px 10px 13px -6px var(--jp-shadow-umbra-color), 0px 20px 31px 3px var(--jp-shadow-penumbra-color), 0px 8px 38px 7px var(--jp-shadow-ambient-color); --jp-elevation-z24: 0px 11px 15px -7px var(--jp-shadow-umbra-color), 0px 24px 38px 3px var(--jp-shadow-penumbra-color), 0px 9px 46px 8px var(--jp-shadow-ambient-color); /* Borders * * The following variables, specify the visual styling of borders in JupyterLab. */ --jp-border-width: 1px; --jp-border-color0: var(--md-grey-700); --jp-border-color1: var(--md-grey-700); --jp-border-color2: var(--md-grey-800); --jp-border-color3: var(--md-grey-900); --jp-border-radius: 2px; /* UI Fonts * * The UI font CSS variables are used for the typography all of the JupyterLab * user interface elements that are not directly user generated content. * * The font sizing here is done assuming that the body font size of --jp-ui-font-size1 * is applied to a parent element. When children elements, such as headings, are sized * in em all things will be computed relative to that body size. */ --jp-ui-font-scale-factor: 1.2; --jp-ui-font-size0: 0.83333em; --jp-ui-font-size1: 13px; /* Base font size */ --jp-ui-font-size2: 1.2em; --jp-ui-font-size3: 1.44em; --jp-ui-font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Helvetica, Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\"; /* * Use these font colors against the corresponding main layout colors. * In a light theme, these go from dark to light. */ /* Defaults use Material Design specification */ --jp-ui-font-color0: rgba(255, 255, 255, 1); --jp-ui-font-color1: rgba(255, 255, 255, 0.87); --jp-ui-font-color2: rgba(255, 255, 255, 0.54); --jp-ui-font-color3: rgba(255, 255, 255, 0.38); /* * Use these against the brand/accent/warn/error colors. * These will typically go from light to darker, in both a dark and light theme. */ --jp-ui-inverse-font-color0: rgba(0, 0, 0, 1); --jp-ui-inverse-font-color1: rgba(0, 0, 0, 0.8); --jp-ui-inverse-font-color2: rgba(0, 0, 0, 0.5); --jp-ui-inverse-font-color3: rgba(0, 0, 0, 0.3); /* Content Fonts * * Content font variables are used for typography of user generated content. * * The font sizing here is done assuming that the body font size of --jp-content-font-size1 * is applied to a parent element. When children elements, such as headings, are sized * in em all things will be computed relative to that body size. */ --jp-content-line-height: 1.6; --jp-content-font-scale-factor: 1.2; --jp-content-font-size0: 0.83333em; --jp-content-font-size1: 14px; /* Base font size */ --jp-content-font-size2: 1.2em; --jp-content-font-size3: 1.44em; --jp-content-font-size4: 1.728em; --jp-content-font-size5: 2.0736em; /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-content-presentation-font-size1: 17px; --jp-content-heading-line-height: 1; --jp-content-heading-margin-top: 1.2em; --jp-content-heading-margin-bottom: 0.8em; --jp-content-heading-font-weight: 500; /* Defaults use Material Design specification */ --jp-content-font-color0: rgba(255, 255, 255, 1); --jp-content-font-color1: rgba(255, 255, 255, 1); --jp-content-font-color2: rgba(255, 255, 255, 0.7); --jp-content-font-color3: rgba(255, 255, 255, 0.5); --jp-content-link-color: var(--md-blue-300); --jp-content-font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Helvetica, Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\"; /* * Code Fonts * * Code font variables are used for typography of code and other monospaces content. */ --jp-code-font-size: 13px; --jp-code-line-height: 1.3077; /* 17px for 13px base */ --jp-code-padding: 5px; /* 5px for 13px base, codemirror highlighting needs integer px value */ --jp-code-font-family-default: Menlo, Consolas, \"DejaVu Sans Mono\", monospace; --jp-code-font-family: var(--jp-code-font-family-default); /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-code-presentation-font-size: 16px; /* may need to tweak cursor width if you change font size */ --jp-code-cursor-width0: 1.4px; --jp-code-cursor-width1: 2px; --jp-code-cursor-width2: 4px; /* Layout * * The following are the main layout colors use in JupyterLab. In a light * theme these would go from light to dark. */ --jp-layout-color0: #111111; --jp-layout-color1: var(--md-grey-900); --jp-layout-color2: var(--md-grey-800); --jp-layout-color3: var(--md-grey-700); --jp-layout-color4: var(--md-grey-600); /* Inverse Layout * * The following are the inverse layout colors use in JupyterLab. In a light * theme these would go from dark to light. */ --jp-inverse-layout-color0: white; --jp-inverse-layout-color1: white; --jp-inverse-layout-color2: var(--md-grey-200); --jp-inverse-layout-color3: var(--md-grey-400); --jp-inverse-layout-color4: var(--md-grey-600); /* Brand/accent */ --jp-brand-color0: var(--md-blue-700); --jp-brand-color1: var(--md-blue-500); --jp-brand-color2: var(--md-blue-300); --jp-brand-color3: var(--md-blue-100); --jp-brand-color4: var(--md-blue-50); --jp-accent-color0: var(--md-green-700); --jp-accent-color1: var(--md-green-500); --jp-accent-color2: var(--md-green-300); --jp-accent-color3: var(--md-green-100); /* State colors (warn, error, success, info) */ --jp-warn-color0: var(--md-orange-700); --jp-warn-color1: var(--md-orange-500); --jp-warn-color2: var(--md-orange-300); --jp-warn-color3: var(--md-orange-100); --jp-error-color0: var(--md-red-700); --jp-error-color1: var(--md-red-500); --jp-error-color2: var(--md-red-300); --jp-error-color3: var(--md-red-100); --jp-success-color0: var(--md-green-700); --jp-success-color1: var(--md-green-500); --jp-success-color2: var(--md-green-300); --jp-success-color3: var(--md-green-100); --jp-info-color0: var(--md-cyan-700); --jp-info-color1: var(--md-cyan-500); --jp-info-color2: var(--md-cyan-300); --jp-info-color3: var(--md-cyan-100); /* Cell specific styles */ --jp-cell-padding: 5px; --jp-cell-collapser-width: 8px; --jp-cell-collapser-min-height: 20px; --jp-cell-collapser-not-active-hover-opacity: 0.6; --jp-cell-editor-background: var(--jp-layout-color1); --jp-cell-editor-border-color: var(--md-grey-700); --jp-cell-editor-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-cell-editor-active-background: var(--jp-layout-color0); --jp-cell-editor-active-border-color: var(--jp-brand-color1); --jp-cell-prompt-width: 64px; --jp-cell-prompt-font-family: var(--jp-code-font-family-default); --jp-cell-prompt-letter-spacing: 0px; --jp-cell-prompt-opacity: 1; --jp-cell-prompt-not-active-opacity: 1; --jp-cell-prompt-not-active-font-color: var(--md-grey-300); /* A custom blend of MD grey and blue 600 * See https://meyerweb.com/eric/tools/color-blend/#546E7A:1E88E5:5:hex */ --jp-cell-inprompt-font-color: #307fc1; /* A custom blend of MD grey and orange 600 * https://meyerweb.com/eric/tools/color-blend/#546E7A:F4511E:5:hex */ --jp-cell-outprompt-font-color: #bf5b3d; /* Notebook specific styles */ --jp-notebook-padding: 10px; --jp-notebook-select-background: var(--jp-layout-color1); --jp-notebook-multiselected-color: rgba(33, 150, 243, 0.24); /* The scroll padding is calculated to fill enough space at the bottom of the notebook to show one single-line cell (with appropriate padding) at the top when the notebook is scrolled all the way to the bottom. We also subtract one pixel so that no scrollbar appears if we have just one single-line cell in the notebook. This padding is to enable a 'scroll past end' feature in a notebook. */ --jp-notebook-scroll-padding: calc( 100% - var(--jp-code-font-size) * var(--jp-code-line-height) - var(--jp-code-padding) - var(--jp-cell-padding) - 1px ); /* Rendermime styles */ --jp-rendermime-error-background: rgba(244, 67, 54, 0.28); --jp-rendermime-table-row-background: var(--md-grey-900); --jp-rendermime-table-row-hover-background: rgba(3, 169, 244, 0.2); /* Dialog specific styles */ --jp-dialog-background: rgba(0, 0, 0, 0.6); /* Console specific styles */ --jp-console-padding: 10px; /* Toolbar specific styles */ --jp-toolbar-border-color: var(--jp-border-color2); --jp-toolbar-micro-height: 8px; --jp-toolbar-background: var(--jp-layout-color1); --jp-toolbar-box-shadow: 0px 0px 2px 0px rgba(0, 0, 0, 0.8); --jp-toolbar-header-margin: 4px 4px 0px 4px; --jp-toolbar-active-background: var(--jp-layout-color0); /* Statusbar specific styles */ --jp-statusbar-height: 24px; /* Input field styles */ --jp-input-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-input-active-background: var(--jp-layout-color0); --jp-input-hover-background: var(--jp-layout-color2); --jp-input-background: var(--md-grey-800); --jp-input-border-color: var(--jp-border-color1); --jp-input-active-border-color: var(--jp-brand-color1); --jp-input-active-box-shadow-color: rgba(19, 124, 189, 0.3); /* General editor styles */ --jp-editor-selected-background: var(--jp-layout-color2); --jp-editor-selected-focused-background: rgba(33, 150, 243, 0.24); --jp-editor-cursor-color: var(--jp-ui-font-color0); /* Code mirror specific styles */ --jp-mirror-editor-keyword-color: var(--md-green-500); --jp-mirror-editor-atom-color: var(--md-blue-300); --jp-mirror-editor-number-color: var(--md-green-400); --jp-mirror-editor-def-color: var(--md-blue-600); --jp-mirror-editor-variable-color: var(--md-grey-300); --jp-mirror-editor-variable-2-color: var(--md-blue-400); --jp-mirror-editor-variable-3-color: var(--md-green-600); --jp-mirror-editor-punctuation-color: var(--md-blue-400); --jp-mirror-editor-property-color: var(--md-blue-400); --jp-mirror-editor-operator-color: #aa22ff; --jp-mirror-editor-comment-color: #408080; --jp-mirror-editor-string-color: #ff7070; --jp-mirror-editor-string-2-color: var(--md-purple-300); --jp-mirror-editor-meta-color: #aa22ff; --jp-mirror-editor-qualifier-color: #555; --jp-mirror-editor-builtin-color: var(--md-green-600); --jp-mirror-editor-bracket-color: #997; --jp-mirror-editor-tag-color: var(--md-green-700); --jp-mirror-editor-attribute-color: var(--md-blue-700); --jp-mirror-editor-header-color: var(--md-blue-500); --jp-mirror-editor-quote-color: var(--md-green-300); --jp-mirror-editor-link-color: var(--md-blue-700); --jp-mirror-editor-error-color: #f00; --jp-mirror-editor-hr-color: #999; /* Vega extension styles */ --jp-vega-background: var(--md-grey-400); /* Sidebar-related styles */ --jp-sidebar-min-width: 250px; /* Search-related styles */ --jp-search-toggle-off-opacity: 0.6; --jp-search-toggle-hover-opacity: 0.8; --jp-search-toggle-on-opacity: 1; --jp-search-selected-match-background-color: rgb(255, 225, 0); --jp-search-selected-match-color: black; --jp-search-unselected-match-background-color: var( --jp-inverse-layout-color0 ); --jp-search-unselected-match-color: var(--jp-ui-inverse-font-color0); /* scrollbar related styles. Supports every browser except Edge. */ /* colors based on JetBrain's Darcula theme */ --jp-scrollbar-background-color: #3f4244; --jp-scrollbar-thumb-color: 88, 96, 97; /* need to specify thumb color as an RGB triplet */ --jp-scrollbar-endpad: 3px; /* the minimum gap between the thumb and the ends of a scrollbar */ /* hacks for setting the thumb shape. These do nothing in Firefox */ --jp-scrollbar-thumb-margin: 3.5px; /* the space in between the sides of the thumb and the track */ --jp-scrollbar-thumb-radius: 9px; /* set to a large-ish value for rounded endcaps on the thumb */ /* Icon colors that work well with light or dark backgrounds */ --jp-icon-contrast-color0: var(--md-purple-600); --jp-icon-contrast-color1: var(--md-green-600); --jp-icon-contrast-color2: var(--md-pink-600); --jp-icon-contrast-color3: var(--md-blue-600); } :root{--md-red-50: #ffebee;--md-red-100: #ffcdd2;--md-red-200: #ef9a9a;--md-red-300: #e57373;--md-red-400: #ef5350;--md-red-500: #f44336;--md-red-600: #e53935;--md-red-700: #d32f2f;--md-red-800: #c62828;--md-red-900: #b71c1c;--md-red-A100: #ff8a80;--md-red-A200: #ff5252;--md-red-A400: #ff1744;--md-red-A700: #d50000;--md-pink-50: #fce4ec;--md-pink-100: #f8bbd0;--md-pink-200: #f48fb1;--md-pink-300: #f06292;--md-pink-400: #ec407a;--md-pink-500: #e91e63;--md-pink-600: #d81b60;--md-pink-700: #c2185b;--md-pink-800: 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#7986cb;--md-indigo-400: #5c6bc0;--md-indigo-500: #3f51b5;--md-indigo-600: #3949ab;--md-indigo-700: #303f9f;--md-indigo-800: #283593;--md-indigo-900: #1a237e;--md-indigo-A100: #8c9eff;--md-indigo-A200: #536dfe;--md-indigo-A400: #3d5afe;--md-indigo-A700: #304ffe;--md-blue-50: #e3f2fd;--md-blue-100: #bbdefb;--md-blue-200: #90caf9;--md-blue-300: #64b5f6;--md-blue-400: #42a5f5;--md-blue-500: #2196f3;--md-blue-600: #1e88e5;--md-blue-700: #1976d2;--md-blue-800: #1565c0;--md-blue-900: #0d47a1;--md-blue-A100: #82b1ff;--md-blue-A200: #448aff;--md-blue-A400: #2979ff;--md-blue-A700: #2962ff;--md-light-blue-50: #e1f5fe;--md-light-blue-100: #b3e5fc;--md-light-blue-200: #81d4fa;--md-light-blue-300: #4fc3f7;--md-light-blue-400: #29b6f6;--md-light-blue-500: #03a9f4;--md-light-blue-600: #039be5;--md-light-blue-700: #0288d1;--md-light-blue-800: #0277bd;--md-light-blue-900: #01579b;--md-light-blue-A100: #80d8ff;--md-light-blue-A200: #40c4ff;--md-light-blue-A400: #00b0ff;--md-light-blue-A700: #0091ea;--md-cyan-50: #e0f7fa;--md-cyan-100: #b2ebf2;--md-cyan-200: #80deea;--md-cyan-300: #4dd0e1;--md-cyan-400: #26c6da;--md-cyan-500: #00bcd4;--md-cyan-600: #00acc1;--md-cyan-700: #0097a7;--md-cyan-800: #00838f;--md-cyan-900: #006064;--md-cyan-A100: #84ffff;--md-cyan-A200: #18ffff;--md-cyan-A400: #00e5ff;--md-cyan-A700: #00b8d4;--md-teal-50: #e0f2f1;--md-teal-100: #b2dfdb;--md-teal-200: #80cbc4;--md-teal-300: #4db6ac;--md-teal-400: #26a69a;--md-teal-500: #009688;--md-teal-600: #00897b;--md-teal-700: #00796b;--md-teal-800: #00695c;--md-teal-900: #004d40;--md-teal-A100: #a7ffeb;--md-teal-A200: #64ffda;--md-teal-A400: #1de9b6;--md-teal-A700: #00bfa5;--md-green-50: #e8f5e9;--md-green-100: #c8e6c9;--md-green-200: #a5d6a7;--md-green-300: #81c784;--md-green-400: #66bb6a;--md-green-500: #4caf50;--md-green-600: #43a047;--md-green-700: #388e3c;--md-green-800: #2e7d32;--md-green-900: #1b5e20;--md-green-A100: #b9f6ca;--md-green-A200: #69f0ae;--md-green-A400: #00e676;--md-green-A700: #00c853;--md-light-green-50: #f1f8e9;--md-light-green-100: #dcedc8;--md-light-green-200: #c5e1a5;--md-light-green-300: #aed581;--md-light-green-400: #9ccc65;--md-light-green-500: #8bc34a;--md-light-green-600: #7cb342;--md-light-green-700: #689f38;--md-light-green-800: #558b2f;--md-light-green-900: #33691e;--md-light-green-A100: #ccff90;--md-light-green-A200: #b2ff59;--md-light-green-A400: #76ff03;--md-light-green-A700: #64dd17;--md-lime-50: #f9fbe7;--md-lime-100: #f0f4c3;--md-lime-200: #e6ee9c;--md-lime-300: #dce775;--md-lime-400: #d4e157;--md-lime-500: #cddc39;--md-lime-600: #c0ca33;--md-lime-700: #afb42b;--md-lime-800: #9e9d24;--md-lime-900: #827717;--md-lime-A100: #f4ff81;--md-lime-A200: #eeff41;--md-lime-A400: #c6ff00;--md-lime-A700: #aeea00;--md-yellow-50: #fffde7;--md-yellow-100: #fff9c4;--md-yellow-200: #fff59d;--md-yellow-300: #fff176;--md-yellow-400: #ffee58;--md-yellow-500: #ffeb3b;--md-yellow-600: #fdd835;--md-yellow-700: #fbc02d;--md-yellow-800: #f9a825;--md-yellow-900: #f57f17;--md-yellow-A100: #ffff8d;--md-yellow-A200: #ffff00;--md-yellow-A400: #ffea00;--md-yellow-A700: #ffd600;--md-amber-50: #fff8e1;--md-amber-100: #ffecb3;--md-amber-200: #ffe082;--md-amber-300: #ffd54f;--md-amber-400: #ffca28;--md-amber-500: #ffc107;--md-amber-600: #ffb300;--md-amber-700: #ffa000;--md-amber-800: #ff8f00;--md-amber-900: #ff6f00;--md-amber-A100: #ffe57f;--md-amber-A200: #ffd740;--md-amber-A400: #ffc400;--md-amber-A700: #ffab00;--md-orange-50: #fff3e0;--md-orange-100: #ffe0b2;--md-orange-200: #ffcc80;--md-orange-300: #ffb74d;--md-orange-400: #ffa726;--md-orange-500: #ff9800;--md-orange-600: #fb8c00;--md-orange-700: #f57c00;--md-orange-800: #ef6c00;--md-orange-900: #e65100;--md-orange-A100: #ffd180;--md-orange-A200: #ffab40;--md-orange-A400: #ff9100;--md-orange-A700: #ff6d00;--md-deep-orange-50: #fbe9e7;--md-deep-orange-100: #ffccbc;--md-deep-orange-200: #ffab91;--md-deep-orange-300: #ff8a65;--md-deep-orange-400: #ff7043;--md-deep-orange-500: 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sourceMappingURL=mkdocs-jupyter.css.map*/ init_mathjax = function() { if (window.MathJax) { // MathJax loaded MathJax.Hub.Config({ TeX: { equationNumbers: { autoNumber: \"AMS\", useLabelIds: true } }, tex2jax: { inlineMath: [ ['$','$'], [\"\\\\(\",\"\\\\)\"] ], displayMath: [ ['$$','$$'], [\"\\\\[\",\"\\\\]\"] ], processEscapes: true, processEnvironments: true }, displayAlign: 'center', CommonHTML: { linebreaks: { automatic: true } } }); MathJax.Hub.Queue([\"Typeset\", MathJax.Hub]); } } init_mathjax(); Train a field of study (domain) classification of sentences on the MAG dataset \u00b6 SciBERT achieves an F1-score of 0.6571 on this dataset. This notebook shows that better results can be achieved without even using transformers. In [1]: Copied! import json from typing import Tuple from great_ai.utilities import clean , simple_parallel_map from tqdm.cli import tqdm def preprocess ( line : str ) -> Tuple [ str , str ]: data_point = json . loads ( line ) return ( clean ( data_point [ \"text\" ], convert_to_ascii = True ), data_point [ \"label\" ]) with open ( \"../../tutorial/data/train.txt\" , encoding = \"utf-8\" ) as f : training_data = simple_parallel_map ( preprocess , f . readlines ()) X_train = [ d [ 0 ] for d in training_data ] y_train = [ d [ 1 ] for d in training_data ] with open ( \"../../tutorial/data/test.txt\" , encoding = \"utf-8\" ) as f : test_data = simple_parallel_map ( preprocess , f . readlines ()) X_test = [ d [ 0 ] for d in test_data ] y_test = [ d [ 1 ] for d in test_data ] import json from typing import Tuple from great_ai.utilities import clean, simple_parallel_map from tqdm.cli import tqdm def preprocess(line: str) -> Tuple[str, str]: data_point = json.loads(line) return (clean(data_point[\"text\"], convert_to_ascii=True), data_point[\"label\"]) with open(\"../../tutorial/data/train.txt\", encoding=\"utf-8\") as f: training_data = simple_parallel_map(preprocess, f.readlines()) X_train = [d[0] for d in training_data] y_train = [d[1] for d in training_data] with open(\"../../tutorial/data/test.txt\", encoding=\"utf-8\") as f: test_data = simple_parallel_map(preprocess, f.readlines()) X_test = [d[0] for d in test_data] y_test = [d[1] for d in test_data] 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 84000/84000 [00:16<00:00, 4964.08it/s] 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 22399/22399 [00:05<00:00, 3847.04it/s] In [2]: Copied! from collections import Counter import matplotlib.pyplot as plt domains , counts = zip ( * Counter ( y_train ) . most_common ()) # Configure matplotlib to have nice, high-resolution charts % matplotlib inline plt . rcParams [ \"figure.facecolor\" ] = \"white\" plt . rcParams [ \"font.size\" ] = 18 plt . rcParams [ \"figure.figsize\" ] = ( 8 , 5 ) fig , ax = plt . subplots () plt . xticks ( rotation = 90 ) ax . bar ( domains , counts ) ax . set_ylabel ( \"Count\" ) fig . tight_layout () fig . savefig ( \"mag-distribution.png\" , dpi = 500 ) None from collections import Counter import matplotlib.pyplot as plt domains, counts = zip(*Counter(y_train).most_common()) # Configure matplotlib to have nice, high-resolution charts %matplotlib inline plt.rcParams[\"figure.facecolor\"] = \"white\" plt.rcParams[\"font.size\"] = 18 plt.rcParams[\"figure.figsize\"] = (8, 5) fig, ax = plt.subplots() plt.xticks(rotation=90) ax.bar(domains, counts) ax.set_ylabel(\"Count\") fig.tight_layout() fig.savefig(\"mag-distribution.png\", dpi=500) None In [3]: Copied! from sklearn.naive_bayes import MultinomialNB from sklearn.pipeline import Pipeline from sklearn.feature_extraction.text import TfidfVectorizer def create_pipeline () -> Pipeline : return Pipeline ( steps = [ ( \"vectorizer\" , TfidfVectorizer ( sublinear_tf = True )), ( \"classifier\" , MultinomialNB ()), ] ) from sklearn.naive_bayes import MultinomialNB from sklearn.pipeline import Pipeline from sklearn.feature_extraction.text import TfidfVectorizer def create_pipeline() -> Pipeline: return Pipeline( steps=[ (\"vectorizer\", TfidfVectorizer(sublinear_tf=True)), (\"classifier\", MultinomialNB()), ] ) In [4]: Copied! from sklearn.model_selection import GridSearchCV import pandas as pd optimisation_pipeline = GridSearchCV ( create_pipeline (), { \"vectorizer__min_df\" : [ 5 , 20 , 100 ], \"vectorizer__max_df\" : [ 0.05 , 0.1 ], \"classifier__alpha\" : [ 0.5 , 1 ], \"classifier__fit_prior\" : [ True , False ], }, scoring = \"f1_macro\" , cv = 3 , n_jobs =- 1 , verbose = 1 , ) optimisation_pipeline . fit ( X_train , y_train ) results = pd . DataFrame ( optimisation_pipeline . cv_results_ ) results . sort_values ( \"rank_test_score\" ) from sklearn.model_selection import GridSearchCV import pandas as pd optimisation_pipeline = GridSearchCV( create_pipeline(), { \"vectorizer__min_df\": [5, 20, 100], \"vectorizer__max_df\": [0.05, 0.1], \"classifier__alpha\": [0.5, 1], \"classifier__fit_prior\": [True, False], }, scoring=\"f1_macro\", cv=3, n_jobs=-1, verbose=1, ) optimisation_pipeline.fit(X_train, y_train) results = pd.DataFrame(optimisation_pipeline.cv_results_) results.sort_values(\"rank_test_score\") Fitting 3 folds for each of 24 candidates, totalling 72 fits Out[4]: .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } mean_fit_time std_fit_time mean_score_time std_score_time param_classifier__alpha param_classifier__fit_prior param_vectorizer__max_df param_vectorizer__min_df params split0_test_score split1_test_score split2_test_score mean_test_score std_test_score rank_test_score 12 2.352721 0.257936 1.357399 0.094580 1 True 0.05 5 {'classifier__alpha': 1, 'classifier__fit_prio... 0.677283 0.670398 0.665671 0.671118 0.004768 1 15 2.544958 0.051976 1.245658 0.137108 1 True 0.1 5 {'classifier__alpha': 1, 'classifier__fit_prio... 0.676970 0.670244 0.666123 0.671112 0.004470 2 18 2.472888 0.053978 1.447181 0.051170 1 False 0.05 5 {'classifier__alpha': 1, 'classifier__fit_prio... 0.675678 0.669321 0.665305 0.670101 0.004271 3 21 2.467637 0.005358 1.216254 0.048319 1 False 0.1 5 {'classifier__alpha': 1, 'classifier__fit_prio... 0.675569 0.669482 0.665225 0.670092 0.004245 4 3 2.477612 0.134880 1.428521 0.017417 0.5 True 0.1 5 {'classifier__alpha': 0.5, 'classifier__fit_pr... 0.675225 0.668912 0.665652 0.669930 0.003974 5 0 2.484810 0.067836 1.249979 0.209959 0.5 True 0.05 5 {'classifier__alpha': 0.5, 'classifier__fit_pr... 0.675264 0.668781 0.665394 0.669813 0.004095 6 9 2.452453 0.049376 1.279694 0.089200 0.5 False 0.1 5 {'classifier__alpha': 0.5, 'classifier__fit_pr... 0.674199 0.668005 0.664456 0.668887 0.004026 7 6 2.748628 0.098077 1.255213 0.101334 0.5 False 0.05 5 {'classifier__alpha': 0.5, 'classifier__fit_pr... 0.673996 0.668048 0.664572 0.668872 0.003891 8 13 2.371907 0.204820 1.375143 0.193984 1 True 0.05 20 {'classifier__alpha': 1, 'classifier__fit_prio... 0.656459 0.647433 0.644864 0.649585 0.004972 9 16 2.557486 0.184332 1.272137 0.133980 1 True 0.1 20 {'classifier__alpha': 1, 'classifier__fit_prio... 0.656196 0.646960 0.645436 0.649530 0.004754 10 4 2.362093 0.109796 1.344960 0.083562 0.5 True 0.1 20 {'classifier__alpha': 0.5, 'classifier__fit_pr... 0.654424 0.644690 0.644991 0.648035 0.004519 11 1 2.668012 0.131751 1.383441 0.049157 0.5 True 0.05 20 {'classifier__alpha': 0.5, 'classifier__fit_pr... 0.654622 0.644901 0.644425 0.647983 0.004699 12 19 2.517576 0.154192 1.374269 0.026773 1 False 0.05 20 {'classifier__alpha': 1, 'classifier__fit_prio... 0.655125 0.645586 0.643166 0.647959 0.005163 13 22 2.386240 0.069718 0.967638 0.076766 1 False 0.1 20 {'classifier__alpha': 1, 'classifier__fit_prio... 0.654254 0.645409 0.643514 0.647726 0.004680 14 7 2.416211 0.138001 1.316785 0.036402 0.5 False 0.05 20 {'classifier__alpha': 0.5, 'classifier__fit_pr... 0.652727 0.643691 0.642749 0.646389 0.004498 15 10 2.397768 0.244951 1.330007 0.008983 0.5 False 0.1 20 {'classifier__alpha': 0.5, 'classifier__fit_pr... 0.652290 0.642975 0.643421 0.646229 0.004290 16 14 2.590127 0.054396 1.155976 0.187602 1 True 0.05 100 {'classifier__alpha': 1, 'classifier__fit_prio... 0.587097 0.577411 0.580170 0.581560 0.004075 17 17 2.497556 0.085952 1.169808 0.061336 1 True 0.1 100 {'classifier__alpha': 1, 'classifier__fit_prio... 0.585713 0.577197 0.580763 0.581224 0.003492 18 2 2.467611 0.168399 1.313936 0.109695 0.5 True 0.05 100 {'classifier__alpha': 0.5, 'classifier__fit_pr... 0.586346 0.576987 0.579852 0.581062 0.003915 19 5 2.339322 0.092595 1.209515 0.132825 0.5 True 0.1 100 {'classifier__alpha': 0.5, 'classifier__fit_pr... 0.585634 0.576217 0.580320 0.580723 0.003855 20 20 2.644488 0.119967 1.238554 0.048871 1 False 0.05 100 {'classifier__alpha': 1, 'classifier__fit_prio... 0.581096 0.572833 0.576280 0.576736 0.003389 21 8 2.456030 0.072136 1.240463 0.092383 0.5 False 0.05 100 {'classifier__alpha': 0.5, 'classifier__fit_pr... 0.580841 0.572391 0.575731 0.576321 0.003475 22 23 2.335796 0.082338 0.786187 0.073499 1 False 0.1 100 {'classifier__alpha': 1, 'classifier__fit_prio... 0.579769 0.572373 0.576699 0.576280 0.003034 23 11 2.530331 0.107199 1.247842 0.078229 0.5 False 0.1 100 {'classifier__alpha': 0.5, 'classifier__fit_pr... 0.579743 0.571607 0.576352 0.575901 0.003337 24 In [5]: Copied! from sklearn import set_config set_config ( display = \"diagram\" ) classifier = create_pipeline () classifier . set_params ( ** optimisation_pipeline . best_params_ ) classifier . fit ( X_train , y_train ) from sklearn import set_config set_config(display=\"diagram\") classifier = create_pipeline() classifier.set_params(**optimisation_pipeline.best_params_) classifier.fit(X_train, y_train) Out[5]: #sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"\u25b8\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"\u25be\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;} Pipeline(steps=[('vectorizer', TfidfVectorizer(max_df=0.05, min_df=5, sublinear_tf=True)), ('classifier', MultinomialNB(alpha=1))]) In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org. Pipeline Pipeline(steps=[('vectorizer', TfidfVectorizer(max_df=0.05, min_df=5, sublinear_tf=True)), ('classifier', MultinomialNB(alpha=1))]) TfidfVectorizer TfidfVectorizer(max_df=0.05, min_df=5, sublinear_tf=True) MultinomialNB MultinomialNB(alpha=1) In [12]: Copied! from sklearn import metrics import matplotlib.pyplot as plt y_predicted = classifier . predict ( X_test ) % matplotlib inline plt . rcParams [ \"figure.figsize\" ] = ( 20 , 18 ) plt . rcParams [ \"figure.facecolor\" ] = \"white\" plt . rcParams [ \"font.size\" ] = 22 plt . rcParams [ \"axes.xmargin\" ] = 0 print ( metrics . classification_report ( y_test , y_predicted , digits = 4 )) metrics . ConfusionMatrixDisplay . from_predictions ( y_true = y_test , y_pred = y_predicted , xticks_rotation = \"vertical\" , normalize = \"pred\" , values_format = \".2f\" , ) plt . tight_layout () plt . savefig ( \"mag-confusion.png\" , dpi = 600 ) from sklearn import metrics import matplotlib.pyplot as plt y_predicted = classifier.predict(X_test) %matplotlib inline plt.rcParams[\"figure.figsize\"] = (20, 18) plt.rcParams[\"figure.facecolor\"] = \"white\" plt.rcParams[\"font.size\"] = 22 plt.rcParams[\"axes.xmargin\"] = 0 print(metrics.classification_report(y_test, y_predicted, digits=4)) metrics.ConfusionMatrixDisplay.from_predictions( y_true=y_test, y_pred=y_predicted, xticks_rotation=\"vertical\", normalize=\"pred\", values_format=\".2f\", ) plt.tight_layout() plt.savefig(\"mag-confusion.png\", dpi=600) precision recall f1-score support business 0.6412 0.7258 0.6808 3198 economics 0.6635 0.7146 0.6881 3189 geography 0.7461 0.6791 0.7111 3207 medicine 0.8806 0.9021 0.8912 3187 politics 0.5563 0.5895 0.5724 3169 psychology 0.7654 0.6811 0.7208 3252 sociology 0.5165 0.4698 0.4921 3197 accuracy 0.6803 22399 macro avg 0.6814 0.6803 0.6795 22399 weighted avg 0.6817 0.6803 0.6796 22399","title":"Train a field of study (domain) classification of sentences on the MAG dataset"},{"location":"examples/simple-mag/train/#train-a-field-of-study-domain-classification-of-sentences-on-the-mag-dataset","text":"SciBERT achieves an F1-score of 0.6571 on this dataset. This notebook shows that better results can be achieved without even using transformers. In [1]: Copied! import json from typing import Tuple from great_ai.utilities import clean , simple_parallel_map from tqdm.cli import tqdm def preprocess ( line : str ) -> Tuple [ str , str ]: data_point = json . loads ( line ) return ( clean ( data_point [ \"text\" ], convert_to_ascii = True ), data_point [ \"label\" ]) with open ( \"../../tutorial/data/train.txt\" , encoding = \"utf-8\" ) as f : training_data = simple_parallel_map ( preprocess , f . readlines ()) X_train = [ d [ 0 ] for d in training_data ] y_train = [ d [ 1 ] for d in training_data ] with open ( \"../../tutorial/data/test.txt\" , encoding = \"utf-8\" ) as f : test_data = simple_parallel_map ( preprocess , f . readlines ()) X_test = [ d [ 0 ] for d in test_data ] y_test = [ d [ 1 ] for d in test_data ] import json from typing import Tuple from great_ai.utilities import clean, simple_parallel_map from tqdm.cli import tqdm def preprocess(line: str) -> Tuple[str, str]: data_point = json.loads(line) return (clean(data_point[\"text\"], convert_to_ascii=True), data_point[\"label\"]) with open(\"../../tutorial/data/train.txt\", encoding=\"utf-8\") as f: training_data = simple_parallel_map(preprocess, f.readlines()) X_train = [d[0] for d in training_data] y_train = [d[1] for d in training_data] with open(\"../../tutorial/data/test.txt\", encoding=\"utf-8\") as f: test_data = simple_parallel_map(preprocess, f.readlines()) X_test = [d[0] for d in test_data] y_test = [d[1] for d in test_data] 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 84000/84000 [00:16<00:00, 4964.08it/s] 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 22399/22399 [00:05<00:00, 3847.04it/s] In [2]: Copied! from collections import Counter import matplotlib.pyplot as plt domains , counts = zip ( * Counter ( y_train ) . most_common ()) # Configure matplotlib to have nice, high-resolution charts % matplotlib inline plt . rcParams [ \"figure.facecolor\" ] = \"white\" plt . rcParams [ \"font.size\" ] = 18 plt . rcParams [ \"figure.figsize\" ] = ( 8 , 5 ) fig , ax = plt . subplots () plt . xticks ( rotation = 90 ) ax . bar ( domains , counts ) ax . set_ylabel ( \"Count\" ) fig . tight_layout () fig . savefig ( \"mag-distribution.png\" , dpi = 500 ) None from collections import Counter import matplotlib.pyplot as plt domains, counts = zip(*Counter(y_train).most_common()) # Configure matplotlib to have nice, high-resolution charts %matplotlib inline plt.rcParams[\"figure.facecolor\"] = \"white\" plt.rcParams[\"font.size\"] = 18 plt.rcParams[\"figure.figsize\"] = (8, 5) fig, ax = plt.subplots() plt.xticks(rotation=90) ax.bar(domains, counts) ax.set_ylabel(\"Count\") fig.tight_layout() fig.savefig(\"mag-distribution.png\", dpi=500) None In [3]: Copied! from sklearn.naive_bayes import MultinomialNB from sklearn.pipeline import Pipeline from sklearn.feature_extraction.text import TfidfVectorizer def create_pipeline () -> Pipeline : return Pipeline ( steps = [ ( \"vectorizer\" , TfidfVectorizer ( sublinear_tf = True )), ( \"classifier\" , MultinomialNB ()), ] ) from sklearn.naive_bayes import MultinomialNB from sklearn.pipeline import Pipeline from sklearn.feature_extraction.text import TfidfVectorizer def create_pipeline() -> Pipeline: return Pipeline( steps=[ (\"vectorizer\", TfidfVectorizer(sublinear_tf=True)), (\"classifier\", MultinomialNB()), ] ) In [4]: Copied! from sklearn.model_selection import GridSearchCV import pandas as pd optimisation_pipeline = GridSearchCV ( create_pipeline (), { \"vectorizer__min_df\" : [ 5 , 20 , 100 ], \"vectorizer__max_df\" : [ 0.05 , 0.1 ], \"classifier__alpha\" : [ 0.5 , 1 ], \"classifier__fit_prior\" : [ True , False ], }, scoring = \"f1_macro\" , cv = 3 , n_jobs =- 1 , verbose = 1 , ) optimisation_pipeline . fit ( X_train , y_train ) results = pd . DataFrame ( optimisation_pipeline . cv_results_ ) results . sort_values ( \"rank_test_score\" ) from sklearn.model_selection import GridSearchCV import pandas as pd optimisation_pipeline = GridSearchCV( create_pipeline(), { \"vectorizer__min_df\": [5, 20, 100], \"vectorizer__max_df\": [0.05, 0.1], \"classifier__alpha\": [0.5, 1], \"classifier__fit_prior\": [True, False], }, scoring=\"f1_macro\", cv=3, n_jobs=-1, verbose=1, ) optimisation_pipeline.fit(X_train, y_train) results = pd.DataFrame(optimisation_pipeline.cv_results_) results.sort_values(\"rank_test_score\") Fitting 3 folds for each of 24 candidates, totalling 72 fits Out[4]: .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } mean_fit_time std_fit_time mean_score_time std_score_time param_classifier__alpha param_classifier__fit_prior param_vectorizer__max_df param_vectorizer__min_df params split0_test_score split1_test_score split2_test_score mean_test_score std_test_score rank_test_score 12 2.352721 0.257936 1.357399 0.094580 1 True 0.05 5 {'classifier__alpha': 1, 'classifier__fit_prio... 0.677283 0.670398 0.665671 0.671118 0.004768 1 15 2.544958 0.051976 1.245658 0.137108 1 True 0.1 5 {'classifier__alpha': 1, 'classifier__fit_prio... 0.676970 0.670244 0.666123 0.671112 0.004470 2 18 2.472888 0.053978 1.447181 0.051170 1 False 0.05 5 {'classifier__alpha': 1, 'classifier__fit_prio... 0.675678 0.669321 0.665305 0.670101 0.004271 3 21 2.467637 0.005358 1.216254 0.048319 1 False 0.1 5 {'classifier__alpha': 1, 'classifier__fit_prio... 0.675569 0.669482 0.665225 0.670092 0.004245 4 3 2.477612 0.134880 1.428521 0.017417 0.5 True 0.1 5 {'classifier__alpha': 0.5, 'classifier__fit_pr... 0.675225 0.668912 0.665652 0.669930 0.003974 5 0 2.484810 0.067836 1.249979 0.209959 0.5 True 0.05 5 {'classifier__alpha': 0.5, 'classifier__fit_pr... 0.675264 0.668781 0.665394 0.669813 0.004095 6 9 2.452453 0.049376 1.279694 0.089200 0.5 False 0.1 5 {'classifier__alpha': 0.5, 'classifier__fit_pr... 0.674199 0.668005 0.664456 0.668887 0.004026 7 6 2.748628 0.098077 1.255213 0.101334 0.5 False 0.05 5 {'classifier__alpha': 0.5, 'classifier__fit_pr... 0.673996 0.668048 0.664572 0.668872 0.003891 8 13 2.371907 0.204820 1.375143 0.193984 1 True 0.05 20 {'classifier__alpha': 1, 'classifier__fit_prio... 0.656459 0.647433 0.644864 0.649585 0.004972 9 16 2.557486 0.184332 1.272137 0.133980 1 True 0.1 20 {'classifier__alpha': 1, 'classifier__fit_prio... 0.656196 0.646960 0.645436 0.649530 0.004754 10 4 2.362093 0.109796 1.344960 0.083562 0.5 True 0.1 20 {'classifier__alpha': 0.5, 'classifier__fit_pr... 0.654424 0.644690 0.644991 0.648035 0.004519 11 1 2.668012 0.131751 1.383441 0.049157 0.5 True 0.05 20 {'classifier__alpha': 0.5, 'classifier__fit_pr... 0.654622 0.644901 0.644425 0.647983 0.004699 12 19 2.517576 0.154192 1.374269 0.026773 1 False 0.05 20 {'classifier__alpha': 1, 'classifier__fit_prio... 0.655125 0.645586 0.643166 0.647959 0.005163 13 22 2.386240 0.069718 0.967638 0.076766 1 False 0.1 20 {'classifier__alpha': 1, 'classifier__fit_prio... 0.654254 0.645409 0.643514 0.647726 0.004680 14 7 2.416211 0.138001 1.316785 0.036402 0.5 False 0.05 20 {'classifier__alpha': 0.5, 'classifier__fit_pr... 0.652727 0.643691 0.642749 0.646389 0.004498 15 10 2.397768 0.244951 1.330007 0.008983 0.5 False 0.1 20 {'classifier__alpha': 0.5, 'classifier__fit_pr... 0.652290 0.642975 0.643421 0.646229 0.004290 16 14 2.590127 0.054396 1.155976 0.187602 1 True 0.05 100 {'classifier__alpha': 1, 'classifier__fit_prio... 0.587097 0.577411 0.580170 0.581560 0.004075 17 17 2.497556 0.085952 1.169808 0.061336 1 True 0.1 100 {'classifier__alpha': 1, 'classifier__fit_prio... 0.585713 0.577197 0.580763 0.581224 0.003492 18 2 2.467611 0.168399 1.313936 0.109695 0.5 True 0.05 100 {'classifier__alpha': 0.5, 'classifier__fit_pr... 0.586346 0.576987 0.579852 0.581062 0.003915 19 5 2.339322 0.092595 1.209515 0.132825 0.5 True 0.1 100 {'classifier__alpha': 0.5, 'classifier__fit_pr... 0.585634 0.576217 0.580320 0.580723 0.003855 20 20 2.644488 0.119967 1.238554 0.048871 1 False 0.05 100 {'classifier__alpha': 1, 'classifier__fit_prio... 0.581096 0.572833 0.576280 0.576736 0.003389 21 8 2.456030 0.072136 1.240463 0.092383 0.5 False 0.05 100 {'classifier__alpha': 0.5, 'classifier__fit_pr... 0.580841 0.572391 0.575731 0.576321 0.003475 22 23 2.335796 0.082338 0.786187 0.073499 1 False 0.1 100 {'classifier__alpha': 1, 'classifier__fit_prio... 0.579769 0.572373 0.576699 0.576280 0.003034 23 11 2.530331 0.107199 1.247842 0.078229 0.5 False 0.1 100 {'classifier__alpha': 0.5, 'classifier__fit_pr... 0.579743 0.571607 0.576352 0.575901 0.003337 24 In [5]: Copied! from sklearn import set_config set_config ( display = \"diagram\" ) classifier = create_pipeline () classifier . set_params ( ** optimisation_pipeline . best_params_ ) classifier . fit ( X_train , y_train ) from sklearn import set_config set_config(display=\"diagram\") classifier = create_pipeline() classifier.set_params(**optimisation_pipeline.best_params_) classifier.fit(X_train, y_train) Out[5]: #sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"\u25b8\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"\u25be\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;} Pipeline(steps=[('vectorizer', TfidfVectorizer(max_df=0.05, min_df=5, sublinear_tf=True)), ('classifier', MultinomialNB(alpha=1))]) In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org. Pipeline Pipeline(steps=[('vectorizer', TfidfVectorizer(max_df=0.05, min_df=5, sublinear_tf=True)), ('classifier', MultinomialNB(alpha=1))]) TfidfVectorizer TfidfVectorizer(max_df=0.05, min_df=5, sublinear_tf=True) MultinomialNB MultinomialNB(alpha=1) In [12]: Copied! from sklearn import metrics import matplotlib.pyplot as plt y_predicted = classifier . predict ( X_test ) % matplotlib inline plt . rcParams [ \"figure.figsize\" ] = ( 20 , 18 ) plt . rcParams [ \"figure.facecolor\" ] = \"white\" plt . rcParams [ \"font.size\" ] = 22 plt . rcParams [ \"axes.xmargin\" ] = 0 print ( metrics . classification_report ( y_test , y_predicted , digits = 4 )) metrics . ConfusionMatrixDisplay . from_predictions ( y_true = y_test , y_pred = y_predicted , xticks_rotation = \"vertical\" , normalize = \"pred\" , values_format = \".2f\" , ) plt . tight_layout () plt . savefig ( \"mag-confusion.png\" , dpi = 600 ) from sklearn import metrics import matplotlib.pyplot as plt y_predicted = classifier.predict(X_test) %matplotlib inline plt.rcParams[\"figure.figsize\"] = (20, 18) plt.rcParams[\"figure.facecolor\"] = \"white\" plt.rcParams[\"font.size\"] = 22 plt.rcParams[\"axes.xmargin\"] = 0 print(metrics.classification_report(y_test, y_predicted, digits=4)) metrics.ConfusionMatrixDisplay.from_predictions( y_true=y_test, y_pred=y_predicted, xticks_rotation=\"vertical\", normalize=\"pred\", values_format=\".2f\", ) plt.tight_layout() plt.savefig(\"mag-confusion.png\", dpi=600) precision recall f1-score support business 0.6412 0.7258 0.6808 3198 economics 0.6635 0.7146 0.6881 3189 geography 0.7461 0.6791 0.7111 3207 medicine 0.8806 0.9021 0.8912 3187 politics 0.5563 0.5895 0.5724 3169 psychology 0.7654 0.6811 0.7208 3252 sociology 0.5165 0.4698 0.4921 3197 accuracy 0.6803 22399 macro avg 0.6814 0.6803 0.6795 22399 weighted avg 0.6817 0.6803 0.6796 22399","title":"Train a field of study (domain) classification of sentences on the MAG dataset"},{"location":"how-to-guides/call-remote/","text":"How to call remote GreatAI instances # Microservices architecture (or SOA ) work well with ML applications. This is because their interfaces are usually very narrow, while the functionality provided quite comprehensive. Hence, drawing the boundaries of responsibilities is more straightforward in the case of ML services than in the case of more traditional business applications. For this reason, it is common to have a tree of models (preferably wrapped in GreatAI instances) communicating with each other. Although regular HTTP POST requests could be sent to each service's /predict endpoint, great-ai comes with two convenience functions: call_remote_great_ai and call_remote_great_ai_async to wrap this request. These provide you with some level of robustness and deserialisation. Inside notebooks The async variant, call_remote_great_ai_async , requires a running event loop while the synchronous variant disallows other running event-loops. Therefore, when running inside a Jupyter Notebook, always call call_remote_great_ai_async . Simple example # Let's create two processes: a server and a client. Server # server.py from great_ai import GreatAI from asyncio import sleep @GreatAI . create async def slow_greeter ( your_name ): await sleep ( 2 ) return f 'Hi { your_name } !' Run this in development mode by executing great-ai server.py or python3 -m great_ai server.py if you're on Windows and great-ai is not in your PATH . Client # client.py from great_ai import call_remote_great_ai names = [ 'Oliv\u00e9r' , 'Bal\u00e1zs' , 'Andr\u00e1s' ] results = [ call_remote_great_ai ( 'http://localhost:6060' , { 'your_name' : name } ) . output #(1) for name in names ] print ( results ) Only return the outputs so we don't clutter up the terminal. Run this script as a regular Python script by executing python3 client.py . As you can see, everything worked as expected. There is one way to improve it though. An async example # Let's send multiple requests at the same time to speed up the overall execution time. To do this, we will use the call_remote_great_ai_async function. Why is this possible? Note, that in server.py , the inference function is declared async . This means that multiple \"copies\" of it can run at the same time in the same thread. Since, there is no CPU bottleneck, the server has a quite large throughput (requests responded to per second), but its latency will stay around 2 seconds due to the async sleep command. If your great-ai server is not async , higher throughput can be achieved by running multiple instances of it, either manually, or by running it with multiple uvicorn workers like this: ENVIRONMENT=production great-ai server.py --worker_count 4 Async client # async-client.py from great_ai import call_remote_great_ai_async import asyncio names = [ 'Oliv\u00e9r' , 'Bal\u00e1zs' , 'Andr\u00e1s' ] async def main (): futures = [ call_remote_great_ai_async ( 'http://localhost:6060' , { 'your_name' : name } ) for name in names ] results = await asyncio . gather ( * futures ) print ([ r . output for r in results ]) asyncio . run ( main ()) Replace client.py with this async client. Note that even though async support is significantly more streamlined in recent Python versions, it still requires a bit more boilerplate than its synchronous counterpart. This also works, and in some use-cases might be considerably quicker.","title":"How to call remote GreatAI instances"},{"location":"how-to-guides/call-remote/#how-to-call-remote-greatai-instances","text":"Microservices architecture (or SOA ) work well with ML applications. This is because their interfaces are usually very narrow, while the functionality provided quite comprehensive. Hence, drawing the boundaries of responsibilities is more straightforward in the case of ML services than in the case of more traditional business applications. For this reason, it is common to have a tree of models (preferably wrapped in GreatAI instances) communicating with each other. Although regular HTTP POST requests could be sent to each service's /predict endpoint, great-ai comes with two convenience functions: call_remote_great_ai and call_remote_great_ai_async to wrap this request. These provide you with some level of robustness and deserialisation. Inside notebooks The async variant, call_remote_great_ai_async , requires a running event loop while the synchronous variant disallows other running event-loops. Therefore, when running inside a Jupyter Notebook, always call call_remote_great_ai_async .","title":"How to call remote GreatAI instances"},{"location":"how-to-guides/call-remote/#simple-example","text":"Let's create two processes: a server and a client.","title":"Simple example"},{"location":"how-to-guides/call-remote/#server","text":"server.py from great_ai import GreatAI from asyncio import sleep @GreatAI . create async def slow_greeter ( your_name ): await sleep ( 2 ) return f 'Hi { your_name } !' Run this in development mode by executing great-ai server.py or python3 -m great_ai server.py if you're on Windows and great-ai is not in your PATH .","title":"Server"},{"location":"how-to-guides/call-remote/#client","text":"client.py from great_ai import call_remote_great_ai names = [ 'Oliv\u00e9r' , 'Bal\u00e1zs' , 'Andr\u00e1s' ] results = [ call_remote_great_ai ( 'http://localhost:6060' , { 'your_name' : name } ) . output #(1) for name in names ] print ( results ) Only return the outputs so we don't clutter up the terminal. Run this script as a regular Python script by executing python3 client.py . As you can see, everything worked as expected. There is one way to improve it though.","title":"Client"},{"location":"how-to-guides/call-remote/#an-async-example","text":"Let's send multiple requests at the same time to speed up the overall execution time. To do this, we will use the call_remote_great_ai_async function. Why is this possible? Note, that in server.py , the inference function is declared async . This means that multiple \"copies\" of it can run at the same time in the same thread. Since, there is no CPU bottleneck, the server has a quite large throughput (requests responded to per second), but its latency will stay around 2 seconds due to the async sleep command. If your great-ai server is not async , higher throughput can be achieved by running multiple instances of it, either manually, or by running it with multiple uvicorn workers like this: ENVIRONMENT=production great-ai server.py --worker_count 4","title":"An async example"},{"location":"how-to-guides/call-remote/#async-client","text":"async-client.py from great_ai import call_remote_great_ai_async import asyncio names = [ 'Oliv\u00e9r' , 'Bal\u00e1zs' , 'Andr\u00e1s' ] async def main (): futures = [ call_remote_great_ai_async ( 'http://localhost:6060' , { 'your_name' : name } ) for name in names ] results = await asyncio . gather ( * futures ) print ([ r . output for r in results ]) asyncio . run ( main ()) Replace client.py with this async client. Note that even though async support is significantly more streamlined in recent Python versions, it still requires a bit more boilerplate than its synchronous counterpart. This also works, and in some use-cases might be considerably quicker.","title":"Async client"},{"location":"how-to-guides/configure-service/","text":"How to configure GreatAI # GreatAI aims to provide reasonable defaults wherever possible. The current configuration is always prominently displayed (and updated) on the dashboard and in the command-line start-up banner. Using great_ai.configure # You can override any of the default settings by calling great_ai.configure . If you don't call configure , the default settings are applied on the first call to most great-ai functions. Warning You must call great_ai.configure before calling (or decorating with) any other great-ai function. However, importing other functions before calling great_ai.configure is permitted. configure-demo.py from great_ai import configure , RouteConfig import logging configure ( version = '1.0.0' , log_level = logging . INFO , seed = 2 , should_log_exception_stack = False , prediction_cache_size = 0 , #(1) disable_se4ml_banner = True , dashboard_table_size = 200 , route_config = RouteConfig ( #(2) feedback_endpoints_enabled = False , dashboard_enabled = False ) ) Completely disable caching. The unspecified routes are enabled by default. Using remote storage # The only aspect that cannot be automated is choosing the backing storage for the database and file storage. Right now, you have 3 options for storing the models and large datasets: LargeFileLocal , LargeFileMongo , and LargeFileS3 . Without explicit configuration, LargeFileLocal is selected by default. This one still version-controls your files but it only stores them in a local path (which of course can be a remote volume attached by NFS , HDFS , etc.). Important If your working directory contains a mongo.ini or s3.ini file, an attempt is made to auto-configure LargeFileMongo or LargeFileS3 respectively. To use LargeFileMongo or LargeFileS3 explicitly, configure them before calling any other great-ai function. S3-compatible # s3.ini aws_region_name = eu-west-2 aws_access_key_id = MY_AWS_ACCESS_KEY # ENV:MY_AWS_ACCESS_KEY would also work aws_secret_access_key = MY_AWS_SECRET_KEY large_files_bucket_name = bucket-for-models use-s3.py from great_ai.large_file import LargeFileS3 from great_ai import save_model LargeFileS3 . configure_credentials_from_file ( 's3.ini' ) #(1) model = [ 4 , 3 ] save_model ( model , 'my-model' ) This line isn't strictly necessary because if s3.ini (or mongo.ini ) is available in the current working directory, they are automatically used to configure their respective LargeFile implementations/databases. Departing from AWS With the aws_endpoint_url argument, it is possible to use any other S3-compatible service such as Backblaze . In that case, it would be aws_endpoint_url=https://s3.us-west-002.backblazeb2.com . GridFS # GridFS specifies how to store files in MongoDB. The official MongoDB server and many compatible implementations support it. mongo.ini MONGO_CONNECTION_STRING = mongodb : // localhost : 27017 # this is the default value # if `MONGO_CONNECTION_STRING` is specified, this default is overridden MONGO_CONNECTION_STRING = ENV : MONGO_CONNECTION_STRING MONGO_DATABASE = my-database # it is automatically created if doesn't exist use-mongo.py from great_ai.large_file import LargeFileMongo from great_ai import save_model LargeFileMongo . configure_credentials_from_file ( 'mongo.ini' ) model = [ 4 , 3 ] save_model ( model , 'my-model' ) Simplifying config files You can combine mongo.ini or s3.ini with your application's config file because the unneeded keys are ignored by the configure_credentials_from_file method. Using a database # By default, a thread-safe version of TinyDB is utilised for saving the prediction traces into a local file. Unfortunately, for most production needs, this method is not suitable. MongoDB # At the moment, only MongoDB is supported as a production-ready TracingDatabase . In order to use it, you have to either place a file named mongo.ini in your working directory, or explicitly call either MongoDbDriver.configure_credentials_from_file or MongoDbDriver.configure_credentials .","title":"How to configure GreatAI"},{"location":"how-to-guides/configure-service/#how-to-configure-greatai","text":"GreatAI aims to provide reasonable defaults wherever possible. The current configuration is always prominently displayed (and updated) on the dashboard and in the command-line start-up banner.","title":"How to configure GreatAI"},{"location":"how-to-guides/configure-service/#using-great_aiconfigure","text":"You can override any of the default settings by calling great_ai.configure . If you don't call configure , the default settings are applied on the first call to most great-ai functions. Warning You must call great_ai.configure before calling (or decorating with) any other great-ai function. However, importing other functions before calling great_ai.configure is permitted. configure-demo.py from great_ai import configure , RouteConfig import logging configure ( version = '1.0.0' , log_level = logging . INFO , seed = 2 , should_log_exception_stack = False , prediction_cache_size = 0 , #(1) disable_se4ml_banner = True , dashboard_table_size = 200 , route_config = RouteConfig ( #(2) feedback_endpoints_enabled = False , dashboard_enabled = False ) ) Completely disable caching. The unspecified routes are enabled by default.","title":"Using great_ai.configure"},{"location":"how-to-guides/configure-service/#using-remote-storage","text":"The only aspect that cannot be automated is choosing the backing storage for the database and file storage. Right now, you have 3 options for storing the models and large datasets: LargeFileLocal , LargeFileMongo , and LargeFileS3 . Without explicit configuration, LargeFileLocal is selected by default. This one still version-controls your files but it only stores them in a local path (which of course can be a remote volume attached by NFS , HDFS , etc.). Important If your working directory contains a mongo.ini or s3.ini file, an attempt is made to auto-configure LargeFileMongo or LargeFileS3 respectively. To use LargeFileMongo or LargeFileS3 explicitly, configure them before calling any other great-ai function.","title":"Using remote storage"},{"location":"how-to-guides/configure-service/#s3-compatible","text":"s3.ini aws_region_name = eu-west-2 aws_access_key_id = MY_AWS_ACCESS_KEY # ENV:MY_AWS_ACCESS_KEY would also work aws_secret_access_key = MY_AWS_SECRET_KEY large_files_bucket_name = bucket-for-models use-s3.py from great_ai.large_file import LargeFileS3 from great_ai import save_model LargeFileS3 . configure_credentials_from_file ( 's3.ini' ) #(1) model = [ 4 , 3 ] save_model ( model , 'my-model' ) This line isn't strictly necessary because if s3.ini (or mongo.ini ) is available in the current working directory, they are automatically used to configure their respective LargeFile implementations/databases. Departing from AWS With the aws_endpoint_url argument, it is possible to use any other S3-compatible service such as Backblaze . In that case, it would be aws_endpoint_url=https://s3.us-west-002.backblazeb2.com .","title":"S3-compatible"},{"location":"how-to-guides/configure-service/#gridfs","text":"GridFS specifies how to store files in MongoDB. The official MongoDB server and many compatible implementations support it. mongo.ini MONGO_CONNECTION_STRING = mongodb : // localhost : 27017 # this is the default value # if `MONGO_CONNECTION_STRING` is specified, this default is overridden MONGO_CONNECTION_STRING = ENV : MONGO_CONNECTION_STRING MONGO_DATABASE = my-database # it is automatically created if doesn't exist use-mongo.py from great_ai.large_file import LargeFileMongo from great_ai import save_model LargeFileMongo . configure_credentials_from_file ( 'mongo.ini' ) model = [ 4 , 3 ] save_model ( model , 'my-model' ) Simplifying config files You can combine mongo.ini or s3.ini with your application's config file because the unneeded keys are ignored by the configure_credentials_from_file method.","title":"GridFS"},{"location":"how-to-guides/configure-service/#using-a-database","text":"By default, a thread-safe version of TinyDB is utilised for saving the prediction traces into a local file. Unfortunately, for most production needs, this method is not suitable.","title":"Using a database"},{"location":"how-to-guides/configure-service/#mongodb","text":"At the moment, only MongoDB is supported as a production-ready TracingDatabase . In order to use it, you have to either place a file named mongo.ini in your working directory, or explicitly call either MongoDbDriver.configure_credentials_from_file or MongoDbDriver.configure_credentials .","title":"MongoDB"},{"location":"how-to-guides/create-service/","text":"How to create a GreatAI service # The core value of great-ai lies in its GreatAI class. In order to take advantage of it, you need to create an instance wrapping your code. Let's say that you have the following greeter function: greeter.py def my_greeter_function ( your_name ): return f 'Hi { your_name } !' You can simply decorate (wrap) this function using the @GreatAI.create factory. greeter.py from great_ai import GreatAI @GreatAI . create def greeter ( your_name ): return f 'Hi { your_name } !' Why not simply use @GreatAI? The purpose of @GreatAI.create is simply to provide you with type-checking through MyPy, Pylance, and similar libraries. However, the overloading support for __new__ is lacking in MyPy, thus, a static factory method is used instead. With types # Even though it's not required by GreatAI, type annotating your codebase can save you from lots of trivial mistakes, that's why it's highly advised. Simply add the expected types to your function's signature. type_safe_greeter.py from great_ai import GreatAI @GreatAI . create def type_safe_greeter ( your_name : str ) -> str : return f 'Hi { your_name } !' This not only allows you to statically typecheck your code, but by default, GreatAI will check it during runtime as well using typeguard . With async # Asynchronous code can result in immense performance gains in certain cases. For example, you might rely on a third-party service, do database access, or call a remote GreatAI instance . In these cases, you can simply make your function async without any other changes. async_greeter.py from great_ai import GreatAI from asyncio import sleep @GreatAI . create async def async_greeter ( your_name : str ) -> str : await sleep ( 2 ) # simulate IO-bound operation return f 'Hi { your_name } !' With decorators # GreatAI can decorate already decorated functions. The only restriction is that @GreatAI.create must come last. There are two built-in decorators that you can use to customise your function but you can you use any third-party decorator as well. Using @use_model # If you have previously saved a model with save_model , you can inject it into your function by calling @use_model . greeter_with_model.py from great_ai import GreatAI , use_model @GreatAI . create @use_model ( 'name_of_my_model' , version = 'latest' ) #(1) def type_safe_greeter ( your_name : str , model ) -> str : return f 'Hi { your_name } !' assert type_safe_greeter ( 'Andras' ) . output == 'Hi Andras' By default, the parameter named model will be replaced by the loaded model. This behaviour can be customised by setting the model_kwarg_name . This way, even multiple models can be injected into a single function. Important You must call @use_model before @GreatAI.create . Note, that decorators are applied starting from the bottom-most one. Feel free to use @use_model in other places of the codebase, it works equally well outside of GreatAI services. Using @parameter # If you wish to turn off logging or specify custom validation for your parameters, you can use the @parameter decorator. Note By default, all parameters that are not affected by an explicit @parameter or @use_model are automatically decorated with @parameter when @GreatAI.create is called. greeter_with_validation.py from great_ai import GreatAI , use_model @GreatAI . create @parameter ( 'your_name' , disable_logging = True ) def type_safe_greeter ( your_name : str , model ) -> str : return f 'Hi { your_name } !' assert type_safe_greeter ( 'Andras' ) . output == 'Hi Andras' Important You must call @parameter before @GreatAI.create . Note, that decorators are applied starting from the bottom-most one. Feel free to use @parameter in other places of the codebase, it works equally well outside of GreatAI services. Complex example # Refer to the following example summarising the options you have when instantiating a GreatAI service. complex.py from great_ai import save_model , GreatAI , parameter , use_model , log_metric save_model ( 4 , 'secret-number' ) #(1) @GreatAI . create @parameter ( 'positive_number' , validate = lambda n : n > 0 , disable_logging = True ) @use_model ( 'secret-number' , version = 'latest' , model_kwarg_name = 'secret' ) def add_number ( positive_number : int , secret : int ) -> int : log_metric ( 'log directly into the returned Trace' , positive_number * 2 ) return positive_number + secret assert add_number ( 1 ) . output == 5 Refer to the configuration page for specifying where to store your models.","title":"How to create a GreatAI service"},{"location":"how-to-guides/create-service/#how-to-create-a-greatai-service","text":"The core value of great-ai lies in its GreatAI class. In order to take advantage of it, you need to create an instance wrapping your code. Let's say that you have the following greeter function: greeter.py def my_greeter_function ( your_name ): return f 'Hi { your_name } !' You can simply decorate (wrap) this function using the @GreatAI.create factory. greeter.py from great_ai import GreatAI @GreatAI . create def greeter ( your_name ): return f 'Hi { your_name } !' Why not simply use @GreatAI? The purpose of @GreatAI.create is simply to provide you with type-checking through MyPy, Pylance, and similar libraries. However, the overloading support for __new__ is lacking in MyPy, thus, a static factory method is used instead.","title":"How to create a GreatAI service"},{"location":"how-to-guides/create-service/#with-types","text":"Even though it's not required by GreatAI, type annotating your codebase can save you from lots of trivial mistakes, that's why it's highly advised. Simply add the expected types to your function's signature. type_safe_greeter.py from great_ai import GreatAI @GreatAI . create def type_safe_greeter ( your_name : str ) -> str : return f 'Hi { your_name } !' This not only allows you to statically typecheck your code, but by default, GreatAI will check it during runtime as well using typeguard .","title":"With types"},{"location":"how-to-guides/create-service/#with-async","text":"Asynchronous code can result in immense performance gains in certain cases. For example, you might rely on a third-party service, do database access, or call a remote GreatAI instance . In these cases, you can simply make your function async without any other changes. async_greeter.py from great_ai import GreatAI from asyncio import sleep @GreatAI . create async def async_greeter ( your_name : str ) -> str : await sleep ( 2 ) # simulate IO-bound operation return f 'Hi { your_name } !'","title":"With async"},{"location":"how-to-guides/create-service/#with-decorators","text":"GreatAI can decorate already decorated functions. The only restriction is that @GreatAI.create must come last. There are two built-in decorators that you can use to customise your function but you can you use any third-party decorator as well.","title":"With decorators"},{"location":"how-to-guides/create-service/#using-use_model","text":"If you have previously saved a model with save_model , you can inject it into your function by calling @use_model . greeter_with_model.py from great_ai import GreatAI , use_model @GreatAI . create @use_model ( 'name_of_my_model' , version = 'latest' ) #(1) def type_safe_greeter ( your_name : str , model ) -> str : return f 'Hi { your_name } !' assert type_safe_greeter ( 'Andras' ) . output == 'Hi Andras' By default, the parameter named model will be replaced by the loaded model. This behaviour can be customised by setting the model_kwarg_name . This way, even multiple models can be injected into a single function. Important You must call @use_model before @GreatAI.create . Note, that decorators are applied starting from the bottom-most one. Feel free to use @use_model in other places of the codebase, it works equally well outside of GreatAI services.","title":"Using @use_model"},{"location":"how-to-guides/create-service/#using-parameter","text":"If you wish to turn off logging or specify custom validation for your parameters, you can use the @parameter decorator. Note By default, all parameters that are not affected by an explicit @parameter or @use_model are automatically decorated with @parameter when @GreatAI.create is called. greeter_with_validation.py from great_ai import GreatAI , use_model @GreatAI . create @parameter ( 'your_name' , disable_logging = True ) def type_safe_greeter ( your_name : str , model ) -> str : return f 'Hi { your_name } !' assert type_safe_greeter ( 'Andras' ) . output == 'Hi Andras' Important You must call @parameter before @GreatAI.create . Note, that decorators are applied starting from the bottom-most one. Feel free to use @parameter in other places of the codebase, it works equally well outside of GreatAI services.","title":"Using @parameter"},{"location":"how-to-guides/create-service/#complex-example","text":"Refer to the following example summarising the options you have when instantiating a GreatAI service. complex.py from great_ai import save_model , GreatAI , parameter , use_model , log_metric save_model ( 4 , 'secret-number' ) #(1) @GreatAI . create @parameter ( 'positive_number' , validate = lambda n : n > 0 , disable_logging = True ) @use_model ( 'secret-number' , version = 'latest' , model_kwarg_name = 'secret' ) def add_number ( positive_number : int , secret : int ) -> int : log_metric ( 'log directly into the returned Trace' , positive_number * 2 ) return positive_number + secret assert add_number ( 1 ) . output == 5 Refer to the configuration page for specifying where to store your models.","title":"Complex example"},{"location":"how-to-guides/handle-training-data/","text":"How to manage training data # In order to simplify your training data management, great-ai provide two complementing approaches for inputting new data-points. Upload data # At the start of your experiments' first iteration, after you've gathered suitable samples for training, you can call great_ai.add_ground_truth . This automatically stores a timestamp and also allows you to assign tags to the data. Using these attributes, great_ai.query_ground_truth can be called to get a filtered view of the training data. Train-test-validation splits It is a best practice to lock-away the test split of your data that is only used for the final quality assessment. This prevents you from accidentally training on it, or inadvertently tuning the model to have the highest accuracy metrics on the test split. This, of course, may lead to dubious results, hence, care must be taken to avoid it. With great_ai.add_ground_truth , there is an option to tag the samples with train , test , and validation randomly, following a predefined distribution. This happens as soon as they're written in the database. Later, these can be queried by providing the name of the appropriate tags. The nice thing about this is that the 'input-expected output' pairs are stored as traces. Thus, they behave exactly like regular prediction traces. from great_ai import add_ground_truth add_ground_truth ( [ 1 , 2 ], [ 'odd' , 'even' ], tags = 'my_tag' , train_split_ratio = 1 , #(1) test_split_ratio = 1 ) Note that the ratios don't have to add up to 1. They are just weights. There is also a validation_split_ratio which is 0 by default. >>> from great_ai import query_ground_truth >>> query_ground_truth ( 'my_tag' ) [ Trace [ str ]({ 'created' : '2022-07-12T18:36:12.825706' , 'exception' : None , 'feedback' : 'odd' , #(1) 'logged_values' : { 'input' : 1 }, #(2) 'models' : [], 'original_execution_time_ms' : 0.0 , 'output' : 'odd' , 'tags' : [ 'ground_truth' , 'test' , 'my_tag' ], #(3) 'trace_id' : '4fcf2ce6-a172-469d-94b2-874577655814' }), Trace [ str ]({ 'created' : '2022-07-12T18:36:12.825706' , 'exception' : None , 'feedback' : 'even' , 'logged_values' : { 'input' : 2 }, 'models' : [], 'original_execution_time_ms' : 0.0 , 'output' : 'even' , 'tags' : [ 'ground_truth' , 'train' , 'my_tag' ], 'trace_id' : 'abee0671-beb9-4284-8c3b-c65e5836ce38' })] Expected output. This can be also accessed through the .output property. The input value is stored here. Notice how ground_truth is always included as a tag when using great_ai.add_ground_truth . Get feedback # After the initial data gathering, end-to-end feedback can be also integrated into the dataset. The scaffolded REST API contains endpoints for managing traces and their feedbacks. When great_ai.query_ground_truth is executed, it implicitly filters for traces that have feedback. Therefore, both the ground_truth and the online traces that have received feedback are returned. No matter the origin of the data, it can be accessed using the same API. Remove clutter # Traces can be deleted either through the REST API or by calling great_ai.delete_ground_truth . The latter provides the same interface as great_ai.query_ground_truth except it deletes the matched points.","title":"How to manage training data"},{"location":"how-to-guides/handle-training-data/#how-to-manage-training-data","text":"In order to simplify your training data management, great-ai provide two complementing approaches for inputting new data-points.","title":"How to manage training data"},{"location":"how-to-guides/handle-training-data/#upload-data","text":"At the start of your experiments' first iteration, after you've gathered suitable samples for training, you can call great_ai.add_ground_truth . This automatically stores a timestamp and also allows you to assign tags to the data. Using these attributes, great_ai.query_ground_truth can be called to get a filtered view of the training data. Train-test-validation splits It is a best practice to lock-away the test split of your data that is only used for the final quality assessment. This prevents you from accidentally training on it, or inadvertently tuning the model to have the highest accuracy metrics on the test split. This, of course, may lead to dubious results, hence, care must be taken to avoid it. With great_ai.add_ground_truth , there is an option to tag the samples with train , test , and validation randomly, following a predefined distribution. This happens as soon as they're written in the database. Later, these can be queried by providing the name of the appropriate tags. The nice thing about this is that the 'input-expected output' pairs are stored as traces. Thus, they behave exactly like regular prediction traces. from great_ai import add_ground_truth add_ground_truth ( [ 1 , 2 ], [ 'odd' , 'even' ], tags = 'my_tag' , train_split_ratio = 1 , #(1) test_split_ratio = 1 ) Note that the ratios don't have to add up to 1. They are just weights. There is also a validation_split_ratio which is 0 by default. >>> from great_ai import query_ground_truth >>> query_ground_truth ( 'my_tag' ) [ Trace [ str ]({ 'created' : '2022-07-12T18:36:12.825706' , 'exception' : None , 'feedback' : 'odd' , #(1) 'logged_values' : { 'input' : 1 }, #(2) 'models' : [], 'original_execution_time_ms' : 0.0 , 'output' : 'odd' , 'tags' : [ 'ground_truth' , 'test' , 'my_tag' ], #(3) 'trace_id' : '4fcf2ce6-a172-469d-94b2-874577655814' }), Trace [ str ]({ 'created' : '2022-07-12T18:36:12.825706' , 'exception' : None , 'feedback' : 'even' , 'logged_values' : { 'input' : 2 }, 'models' : [], 'original_execution_time_ms' : 0.0 , 'output' : 'even' , 'tags' : [ 'ground_truth' , 'train' , 'my_tag' ], 'trace_id' : 'abee0671-beb9-4284-8c3b-c65e5836ce38' })] Expected output. This can be also accessed through the .output property. The input value is stored here. Notice how ground_truth is always included as a tag when using great_ai.add_ground_truth .","title":"Upload data"},{"location":"how-to-guides/handle-training-data/#get-feedback","text":"After the initial data gathering, end-to-end feedback can be also integrated into the dataset. The scaffolded REST API contains endpoints for managing traces and their feedbacks. When great_ai.query_ground_truth is executed, it implicitly filters for traces that have feedback. Therefore, both the ground_truth and the online traces that have received feedback are returned. No matter the origin of the data, it can be accessed using the same API.","title":"Get feedback"},{"location":"how-to-guides/handle-training-data/#remove-clutter","text":"Traces can be deleted either through the REST API or by calling great_ai.delete_ground_truth . The latter provides the same interface as great_ai.query_ground_truth except it deletes the matched points.","title":"Remove clutter"},{"location":"how-to-guides/install/","text":"Installation guide # Provided you already have Python3 (and pip) installed, simply execute: pip install great-ai Python 3.7 or later is required. This will work on all major operating systems. Google Colab # In order to use GreatAI in Google Colab , you need to downgrade pyyaml to a Colab compatible version. See related StackOverflow question . pip install great-ai pyyaml == 5 .4.1 This will make GreatAI work in Colab. Command-line tools # After installation, great-ai and large-file are available as commands. The former is required for deploying your application while the latter lets you manage models and datasets from your terminal. Snakes & kebabs The library is called great-ai , therefore, its command-line entrypoint is also called great-ai . However, Python module names cannot contain hyphens, that's why you have to import great_ai with an underscore. The great-ai CLI tool is also available as python3 -m great_ai . To help with the confusion, a CLI executable called great_ai (and large_file ) are also installed, thus, if you prefer, you can always refer to GreatAI using its underscored name variant ( great_ai ). Windows On Windows, you might encounter a similar warning from pip : WARNING: The scripts great-ai.exe, great_ai.exe, large-file.exe and large_file.exe are installed in 'C:\\Users\\...\\Scripts' which is not on PATH. Consider adding this directory to PATH or, if you prefer to suppress this warning, use --no-warn-script-location. This means that great-ai.exe and large-file.exe are not in your PATH . Either add their containing directory ('C:\\Users...\\Scripts' in this case) to your PATH or use python3 -m great_ai and python3 -m great_ai.large_file instead of the exe-s. Update # If you wish to update to the latest version execute: pip install --upgrade great-ai Bleeding edge # You can also install the latest (usually unreleased) version from GitHub. pip install --upgrade git+https://github.com/schmelczer/great-ai.git Python 3.7 or later is required.","title":"Installation guide"},{"location":"how-to-guides/install/#installation-guide","text":"Provided you already have Python3 (and pip) installed, simply execute: pip install great-ai Python 3.7 or later is required. This will work on all major operating systems.","title":"Installation guide"},{"location":"how-to-guides/install/#google-colab","text":"In order to use GreatAI in Google Colab , you need to downgrade pyyaml to a Colab compatible version. See related StackOverflow question . pip install great-ai pyyaml == 5 .4.1 This will make GreatAI work in Colab.","title":"Google Colab"},{"location":"how-to-guides/install/#command-line-tools","text":"After installation, great-ai and large-file are available as commands. The former is required for deploying your application while the latter lets you manage models and datasets from your terminal. Snakes & kebabs The library is called great-ai , therefore, its command-line entrypoint is also called great-ai . However, Python module names cannot contain hyphens, that's why you have to import great_ai with an underscore. The great-ai CLI tool is also available as python3 -m great_ai . To help with the confusion, a CLI executable called great_ai (and large_file ) are also installed, thus, if you prefer, you can always refer to GreatAI using its underscored name variant ( great_ai ). Windows On Windows, you might encounter a similar warning from pip : WARNING: The scripts great-ai.exe, great_ai.exe, large-file.exe and large_file.exe are installed in 'C:\\Users\\...\\Scripts' which is not on PATH. Consider adding this directory to PATH or, if you prefer to suppress this warning, use --no-warn-script-location. This means that great-ai.exe and large-file.exe are not in your PATH . Either add their containing directory ('C:\\Users...\\Scripts' in this case) to your PATH or use python3 -m great_ai and python3 -m great_ai.large_file instead of the exe-s.","title":"Command-line tools"},{"location":"how-to-guides/install/#update","text":"If you wish to update to the latest version execute: pip install --upgrade great-ai","title":"Update"},{"location":"how-to-guides/install/#bleeding-edge","text":"You can also install the latest (usually unreleased) version from GitHub. pip install --upgrade git+https://github.com/schmelczer/great-ai.git Python 3.7 or later is required.","title":"Bleeding edge"},{"location":"how-to-guides/large-file/","text":"How to use LargeFiles # The functions save_model and @use_model wrap LargeFile instances. Hence, besides configuring LargeFile , users have few reasons to use LargeFiles directly. Motivation # Oftentimes, especially when working with data-heavy applications, large files can proliferate in a repository. Version controlling them is an obvious next step, however, GitHub's git LFS implementation doesn't support deleting large files, making it easy for them to eat-up the LFS quota and explode the size of your repos. DVC is a viable alternative, however, it requires users to learn to use one more CLI tool. Using LargeFile-s directly (usually not needed) LargeFile doesn't require users to learn too much new. It is a nearly exact copy of the built-in open() function of Python with which users are certainly already familiar. Simple example # from great_ai.large_file import LargeFileS3 LargeFileS3 . configure_credentials ({ \"aws_region_name\" : \"your_region_like_eu-west-2\" , \"aws_access_key_id\" : \"YOUR_ACCESS_KEY_ID\" , \"aws_secret_access_key\" : \"YOUR_VERY_SECRET_ACCESS_KEY\" , \"large_files_bucket_name\" : \"create_a_bucket_and_put_its_name_here\" , }) # Creates a new version and deletes the older version # leaving the 3 most recently used intact with LargeFileS3 ( \"test.txt\" , \"w\" , keep_last_n = 3 ) as f : for i in range ( 100000 ): f . write ( 'test \\n ' ) # The latest version is returned by default # but an optional `version` keyword argument can be provided as well with LargeFileS3 ( \"test.txt\" , \"r\" ) as f : #(1) print ( f . readlines ()[ 0 ]) In this case, the latest version is already in the local cache, no download is required. More details # LargeFile behaves like an opened file (in the background it is a temp file after all). Binary reads and writes are supported along with the different keywords open() accepts . The local cache can be configured with these properties: LargeFileS3 . cache_path = Path ( '.cache' ) LargeFileS3 . max_cache_size = \"30 GB\" I only need a path # In case you only need a path to the (proxy of the) remote file, this pattern can be applied: path_to_model = LargeFileS3 ( \"folder-of-my-bert-model\" , version = 31 ) . get () This will first download the file/folder into your local cache folder. Then, it returns a Path object to the local version. Which can be turned into a string with str(path_to_model) . The same approach works for uploads: LargeFileS3 ( \"folder-of-my-bert-model\" ) . push ( 'path_to_local/folder_or_file' ) This way both regular files and folders can be handled. The uploaded file is called folder-of-my-bert-model , the local name is ignored. Lastly, all version of the remote object can be deleted by calling LargeFileS3(\"my-file\").delete() . It will still reside in your local cache afterwards; its deletion will happen next time your local cache has to be pruned. From the command-line # The main reason for using the large-file or python3 -m great_ai.large_file commands is to upload or download models from the terminal. For example, when building a docker image, it is best practice to cache the referred models. Setup # Create an .ini file (or use ~/.aws/credentials ). It may look like this: aws_region_name = your_region_like_eu-west-2 aws_access_key_id = YOUR_ACCESS_KEY_ID aws_secret_access_key = YOUR_VERY_SECRET_ACCESS_KEY large_files_bucket_name = my_large_files Upload some files # large-file --backend s3 --secrets secrets.ini \\ --push my_first_file.json folder/my_second_file my_folder Only the filename is used as the S3 name, the rest of the path is ignored. Using MongoDB The possible values for --backend are s3 , mongo , and local . The latter doesn't need credentials, it only versions and stores your files in a local folder. MongoDB on the other hand requires a mongo_connection_string and a mongo_database to be specified. For storing large files, it uses the GridFS specification. Download some files to the local cache # This can be useful when building a Docker image for example. This way, the files can already reside inside the container and need not be downloaded later. large-file --backend s3 --secrets ~/.aws/credentials \\ --cache my_first_file.json:3 my_second_file my_folder:0 Versions may be specified by using : -s. Delete remote files # large-file --backend s3 --secrets ~/.aws/credentials \\ --delete my_first_file.json","title":"How to use LargeFiles"},{"location":"how-to-guides/large-file/#how-to-use-largefiles","text":"The functions save_model and @use_model wrap LargeFile instances. Hence, besides configuring LargeFile , users have few reasons to use LargeFiles directly.","title":"How to use LargeFiles"},{"location":"how-to-guides/large-file/#motivation","text":"Oftentimes, especially when working with data-heavy applications, large files can proliferate in a repository. Version controlling them is an obvious next step, however, GitHub's git LFS implementation doesn't support deleting large files, making it easy for them to eat-up the LFS quota and explode the size of your repos. DVC is a viable alternative, however, it requires users to learn to use one more CLI tool. Using LargeFile-s directly (usually not needed) LargeFile doesn't require users to learn too much new. It is a nearly exact copy of the built-in open() function of Python with which users are certainly already familiar.","title":"Motivation"},{"location":"how-to-guides/large-file/#simple-example","text":"from great_ai.large_file import LargeFileS3 LargeFileS3 . configure_credentials ({ \"aws_region_name\" : \"your_region_like_eu-west-2\" , \"aws_access_key_id\" : \"YOUR_ACCESS_KEY_ID\" , \"aws_secret_access_key\" : \"YOUR_VERY_SECRET_ACCESS_KEY\" , \"large_files_bucket_name\" : \"create_a_bucket_and_put_its_name_here\" , }) # Creates a new version and deletes the older version # leaving the 3 most recently used intact with LargeFileS3 ( \"test.txt\" , \"w\" , keep_last_n = 3 ) as f : for i in range ( 100000 ): f . write ( 'test \\n ' ) # The latest version is returned by default # but an optional `version` keyword argument can be provided as well with LargeFileS3 ( \"test.txt\" , \"r\" ) as f : #(1) print ( f . readlines ()[ 0 ]) In this case, the latest version is already in the local cache, no download is required.","title":"Simple example"},{"location":"how-to-guides/large-file/#more-details","text":"LargeFile behaves like an opened file (in the background it is a temp file after all). Binary reads and writes are supported along with the different keywords open() accepts . The local cache can be configured with these properties: LargeFileS3 . cache_path = Path ( '.cache' ) LargeFileS3 . max_cache_size = \"30 GB\"","title":"More details"},{"location":"how-to-guides/large-file/#i-only-need-a-path","text":"In case you only need a path to the (proxy of the) remote file, this pattern can be applied: path_to_model = LargeFileS3 ( \"folder-of-my-bert-model\" , version = 31 ) . get () This will first download the file/folder into your local cache folder. Then, it returns a Path object to the local version. Which can be turned into a string with str(path_to_model) . The same approach works for uploads: LargeFileS3 ( \"folder-of-my-bert-model\" ) . push ( 'path_to_local/folder_or_file' ) This way both regular files and folders can be handled. The uploaded file is called folder-of-my-bert-model , the local name is ignored. Lastly, all version of the remote object can be deleted by calling LargeFileS3(\"my-file\").delete() . It will still reside in your local cache afterwards; its deletion will happen next time your local cache has to be pruned.","title":"I only need a path"},{"location":"how-to-guides/large-file/#from-the-command-line","text":"The main reason for using the large-file or python3 -m great_ai.large_file commands is to upload or download models from the terminal. For example, when building a docker image, it is best practice to cache the referred models.","title":"From the command-line"},{"location":"how-to-guides/large-file/#setup","text":"Create an .ini file (or use ~/.aws/credentials ). It may look like this: aws_region_name = your_region_like_eu-west-2 aws_access_key_id = YOUR_ACCESS_KEY_ID aws_secret_access_key = YOUR_VERY_SECRET_ACCESS_KEY large_files_bucket_name = my_large_files","title":"Setup"},{"location":"how-to-guides/large-file/#upload-some-files","text":"large-file --backend s3 --secrets secrets.ini \\ --push my_first_file.json folder/my_second_file my_folder Only the filename is used as the S3 name, the rest of the path is ignored. Using MongoDB The possible values for --backend are s3 , mongo , and local . The latter doesn't need credentials, it only versions and stores your files in a local folder. MongoDB on the other hand requires a mongo_connection_string and a mongo_database to be specified. For storing large files, it uses the GridFS specification.","title":"Upload some files"},{"location":"how-to-guides/large-file/#download-some-files-to-the-local-cache","text":"This can be useful when building a Docker image for example. This way, the files can already reside inside the container and need not be downloaded later. large-file --backend s3 --secrets ~/.aws/credentials \\ --cache my_first_file.json:3 my_second_file my_folder:0 Versions may be specified by using : -s.","title":"Download some files to the local cache"},{"location":"how-to-guides/large-file/#delete-remote-files","text":"large-file --backend s3 --secrets ~/.aws/credentials \\ --delete my_first_file.json","title":"Delete remote files"},{"location":"how-to-guides/use-service/","text":"How to perform prediction with GreatAI # After creating a GreatAI service by wrapping your prediction function, and optionally configuring it , it's time to do some prediction. Let's take the following example: greeter.py from great_ai import GreatAI @GreatAI . create def greeter ( your_name : str ) -> str : return f 'Hi { your_name } !' One-off prediction # Even though greeter is now an instance of GreatAI , you can continue using it as a regular function. >>> greeter ( 'Bob' ) Trace [ str ]({ 'created' : '2022-07-11T14:31:46.183764' , 'exception' : None , 'feedback' : None , 'logged_values' : { 'arg:your_name:length' : 3 , 'arg:your_name:value' : 'Bob' }, 'models' : [], 'original_execution_time_ms' : 0.0381 , 'output' : 'Hi Bob!' , 'tags' : [ 'greeter' , 'online' , 'development' ], 'trace_id' : '7c284fd7-7f0d-4464-b5f8-3ef126df34af' }) As you can see, the original return value is wrapped in a Trace object (which is also persisted in your database of choice). You can access the original value under the output property. Online prediction # Likely, the main way you would like to expose your model is through an HTTP API. @GreatAI.create scaffolds many REST API endpoints for your model and creates a FastAPI app available under GreatAI.app . This can be served using uvicorn or any other ASGI server . Since most ML code lives in Jupyter notebooks, therefore, deploying a notebook containing the inference function is supported. To achieve this, uvicorn is wrapped by the great-ai command-line utility which \u2014 among others \u2014 takes care of feeding a notebook into uvicorn . It also supports auto-reloading. In development # great-ai greeter.py Success Your model is accessible at localhost:6060 . Some configuration options are also supported. great-ai greeter.py --port 8000 --host 127 .0.0.1 --timeout_keep_alive 10 More options For more options (but no Notebook support), simply use uvicorn for starting your app (available at greeter.app ). In production # There are three main approaches for deploying a GreatAI service. Manual deployment # The app is run in production-mode if the value of the ENVIRONMENT environment variable is set to production . ENVIRONMENT = production great-ai greeter.py Simply run ENVIRONMENT=production great-ai deploy.ipynb in the command-line of a production machine. This is the crudest approach; however, it might be fitting for some contexts. Containerised deployment # Run the notebook directly in a container or create a service for it using your favourite container orchestrator. docker run -p 6060 :6060 --volume ` pwd ` :/app --rm \\ schmelczera/great-ai deploy.ipynb You can replace pwd with the path to your code's folder. Use a Platform-as-a-Service # Similar to the previous approach, your code will run in a container. However, instead of manually managing it, you can just choose from a plethora of PaaS providers (such as AWS ECS , DO App platform , MLEM , Streamlit ) that take a Docker image as a source and handle the rest of the deployment. To this end, you can also create a custom Docker image. It is especially useful if you have third-party dependencies, such as PyTorch or TensorFlow . FROM schmelczera/great-ai:latest # Remove this block if you don't have a requirements.txt COPY requirements.txt ./ RUN pip install --no-cache-dir --requirement requirements.txt # If you store your models in S3 or GridFS, it may be a # good idea to cache them in the image so that you don't # have to download it each time a container starts RUN large-file --backend s3 --secrets s3.ini --cache my-domain-predictor # Add you application code to the image COPY . . # The default ENTRYPOINT is great-ai, specify its argument using CMD CMD [ \"deploy.ipynb\" ] Batch prediction # Processing larger amounts of data on a single machine is made easy by the GreatAI 's process_batch method. This relies on multiprocessing ( parallel_map ) to take full advantage of all available CPU-cores. >>> greeter . process_batch ([ 'Alice' , 'Bob' ]) [ Trace [ str ]({ 'created' : '2022-07-11T14:36:37.119183' , 'exception' : None , 'feedback' : None , 'logged_values' : { 'arg:your_name:length' : 5 , 'arg:your_name:value' : 'Alice' }, 'models' : [], 'original_execution_time_ms' : 0.1251 , 'output' : 'Hi Alice!' , 'tags' : [ 'greeter' , 'online' , 'development' ], 'trace_id' : '90ffa15f-e839-41c4-8e7a-3211168bc138' }), Trace [ str ]({ 'created' : '2022-07-11T14:36:37.166659' , 'exception' : None , 'feedback' : None , 'logged_values' : { 'arg:your_name:length' : 3 , 'arg:your_name:value' : 'Bob' }, 'models' : [], 'original_execution_time_ms' : 0.0571 , 'output' : 'Hi Bob!' , 'tags' : [ 'greeter' , 'online' , 'development' ], 'trace_id' : 'f48e94c7-0815-48b3-a864-41349d3dae84' })]","title":"How to perform prediction with GreatAI"},{"location":"how-to-guides/use-service/#how-to-perform-prediction-with-greatai","text":"After creating a GreatAI service by wrapping your prediction function, and optionally configuring it , it's time to do some prediction. Let's take the following example: greeter.py from great_ai import GreatAI @GreatAI . create def greeter ( your_name : str ) -> str : return f 'Hi { your_name } !'","title":"How to perform prediction with GreatAI"},{"location":"how-to-guides/use-service/#one-off-prediction","text":"Even though greeter is now an instance of GreatAI , you can continue using it as a regular function. >>> greeter ( 'Bob' ) Trace [ str ]({ 'created' : '2022-07-11T14:31:46.183764' , 'exception' : None , 'feedback' : None , 'logged_values' : { 'arg:your_name:length' : 3 , 'arg:your_name:value' : 'Bob' }, 'models' : [], 'original_execution_time_ms' : 0.0381 , 'output' : 'Hi Bob!' , 'tags' : [ 'greeter' , 'online' , 'development' ], 'trace_id' : '7c284fd7-7f0d-4464-b5f8-3ef126df34af' }) As you can see, the original return value is wrapped in a Trace object (which is also persisted in your database of choice). You can access the original value under the output property.","title":"One-off prediction"},{"location":"how-to-guides/use-service/#online-prediction","text":"Likely, the main way you would like to expose your model is through an HTTP API. @GreatAI.create scaffolds many REST API endpoints for your model and creates a FastAPI app available under GreatAI.app . This can be served using uvicorn or any other ASGI server . Since most ML code lives in Jupyter notebooks, therefore, deploying a notebook containing the inference function is supported. To achieve this, uvicorn is wrapped by the great-ai command-line utility which \u2014 among others \u2014 takes care of feeding a notebook into uvicorn . It also supports auto-reloading.","title":"Online prediction"},{"location":"how-to-guides/use-service/#in-development","text":"great-ai greeter.py Success Your model is accessible at localhost:6060 . Some configuration options are also supported. great-ai greeter.py --port 8000 --host 127 .0.0.1 --timeout_keep_alive 10 More options For more options (but no Notebook support), simply use uvicorn for starting your app (available at greeter.app ).","title":"In development"},{"location":"how-to-guides/use-service/#in-production","text":"There are three main approaches for deploying a GreatAI service.","title":"In production"},{"location":"how-to-guides/use-service/#manual-deployment","text":"The app is run in production-mode if the value of the ENVIRONMENT environment variable is set to production . ENVIRONMENT = production great-ai greeter.py Simply run ENVIRONMENT=production great-ai deploy.ipynb in the command-line of a production machine. This is the crudest approach; however, it might be fitting for some contexts.","title":"Manual deployment"},{"location":"how-to-guides/use-service/#containerised-deployment","text":"Run the notebook directly in a container or create a service for it using your favourite container orchestrator. docker run -p 6060 :6060 --volume ` pwd ` :/app --rm \\ schmelczera/great-ai deploy.ipynb You can replace pwd with the path to your code's folder.","title":"Containerised deployment"},{"location":"how-to-guides/use-service/#use-a-platform-as-a-service","text":"Similar to the previous approach, your code will run in a container. However, instead of manually managing it, you can just choose from a plethora of PaaS providers (such as AWS ECS , DO App platform , MLEM , Streamlit ) that take a Docker image as a source and handle the rest of the deployment. To this end, you can also create a custom Docker image. It is especially useful if you have third-party dependencies, such as PyTorch or TensorFlow . FROM schmelczera/great-ai:latest # Remove this block if you don't have a requirements.txt COPY requirements.txt ./ RUN pip install --no-cache-dir --requirement requirements.txt # If you store your models in S3 or GridFS, it may be a # good idea to cache them in the image so that you don't # have to download it each time a container starts RUN large-file --backend s3 --secrets s3.ini --cache my-domain-predictor # Add you application code to the image COPY . . # The default ENTRYPOINT is great-ai, specify its argument using CMD CMD [ \"deploy.ipynb\" ]","title":"Use a Platform-as-a-Service"},{"location":"how-to-guides/use-service/#batch-prediction","text":"Processing larger amounts of data on a single machine is made easy by the GreatAI 's process_batch method. This relies on multiprocessing ( parallel_map ) to take full advantage of all available CPU-cores. >>> greeter . process_batch ([ 'Alice' , 'Bob' ]) [ Trace [ str ]({ 'created' : '2022-07-11T14:36:37.119183' , 'exception' : None , 'feedback' : None , 'logged_values' : { 'arg:your_name:length' : 5 , 'arg:your_name:value' : 'Alice' }, 'models' : [], 'original_execution_time_ms' : 0.1251 , 'output' : 'Hi Alice!' , 'tags' : [ 'greeter' , 'online' , 'development' ], 'trace_id' : '90ffa15f-e839-41c4-8e7a-3211168bc138' }), Trace [ str ]({ 'created' : '2022-07-11T14:36:37.166659' , 'exception' : None , 'feedback' : None , 'logged_values' : { 'arg:your_name:length' : 3 , 'arg:your_name:value' : 'Bob' }, 'models' : [], 'original_execution_time_ms' : 0.0571 , 'output' : 'Hi Bob!' , 'tags' : [ 'greeter' , 'online' , 'development' ], 'trace_id' : 'f48e94c7-0815-48b3-a864-41349d3dae84' })]","title":"Batch prediction"},{"location":"reference/","text":"GreatAI reference # from great_ai import * Core # GreatAI # Bases: Generic [ T , V ] Wrapper for a prediction function providing the implementation of SE4ML best practices. Provides caching (with argument freezing), a TracingContext during execution, the scaffolding of HTTP endpoints using FastAPI and a dashboard using Dash. IMPORTANT: when a request is served from cache, no new trace is created. Thus, the same trace can be returned multiple times. If this is undesirable turn off caching using configure(prediction_cache_size=0) . Supports wrapping async and synchronous functions while also maintaining correct typing. Attributes: Name Type Description app FastAPI instance wrapping the scaffolded endpoints and the Dash app. version SemVer derived from the app's version and the model names and versions registered through use_model. Source code in great_ai/deploy/great_ai.py 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 class GreatAI ( Generic [ T , V ]): \"\"\"Wrapper for a prediction function providing the implementation of SE4ML best practices. Provides caching (with argument freezing), a TracingContext during execution, the scaffolding of HTTP endpoints using FastAPI and a dashboard using Dash. IMPORTANT: when a request is served from cache, no new trace is created. Thus, the same trace can be returned multiple times. If this is undesirable turn off caching using `configure(prediction_cache_size=0)`. Supports wrapping async and synchronous functions while also maintaining correct typing. Attributes: app: FastAPI instance wrapping the scaffolded endpoints and the Dash app. version: SemVer derived from the app's version and the model names and versions registered through use_model. \"\"\" __name__ : str # help for MyPy __doc__ : str # help for MyPy def __init__ ( self , func : Callable [ ... , Union [ V , Awaitable [ V ]]], ): \"\"\"Do not call this function directly, use GreatAI.create instead.\"\"\" func = automatically_decorate_parameters ( func ) get_function_metadata_store ( func ) . is_finalised = True self . _cached_func = self . _get_cached_traced_function ( func ) self . _wrapped_func = wraps ( func )( freeze_arguments ( self . _cached_func )) wraps ( func )( self ) self . __doc__ = ( f \"GreatAI wrapper for interacting with the ` { self . __name__ } ` \" + f \"function. \\n\\n { dedent ( self . __doc__ or '' ) } \" ) self . version = str ( get_context () . version ) flat_model_versions = \".\" . join ( f \" { k } -v { v } \" for k , v in model_versions ) if flat_model_versions : self . version += f \"+ { flat_model_versions } \" self . app = FastAPI ( title = snake_case_to_text ( self . __name__ ), version = self . version , description = self . __doc__ + f \" \\n\\n Find out more in the [dashboard]( { DASHBOARD_PATH } ).\" , docs_url = None , redoc_url = None , ) self . _bootstrap_rest_api () @overload @staticmethod def create ( # type: ignore # Overloaded function signatures 1 and 2 overlap with incompatible return types # https://github.com/python/mypy/issues/12759 func : Callable [ ... , Awaitable [ V ]], ) -> \"GreatAI[Awaitable[Trace[V]], V]\" : ... @overload @staticmethod def create ( func : Callable [ ... , V ], ) -> \"GreatAI[Trace[V], V]\" : ... @staticmethod def create ( func : Union [ Callable [ ... , Awaitable [ V ]], Callable [ ... , V ]], ) -> Union [ \"GreatAI[Awaitable[Trace[V]], V]\" , \"GreatAI[Trace[V], V]\" ]: \"\"\"Decorate a function by wrapping it in a GreatAI instance. The function can be typed, synchronous or async. If it has unwrapped parameters (parameters not affected by a [@parameter][great_ai.parameter] or [@use_model][great_ai.use_model] decorator), those will be automatically wrapped. The return value is replaced by a Trace (or Awaitable[Trace]), while the original return value is available under the `.output` property. For configuration options, see [great_ai.configure][]. Examples: >>> @GreatAI.create ... def my_function(a): ... return a + 2 >>> my_function(3).output 5 >>> @GreatAI.create ... def my_function(a: int) -> int: ... return a + 2 >>> my_function(3) Trace[int]... >>> my_function('3').output Traceback (most recent call last): ... TypeError: type of a must be int; got str instead Args: func: The prediction function that needs to be decorated. Returns: A GreatAI instance wrapping `func`. \"\"\" return GreatAI [ Trace [ V ], V ]( func , ) def __call__ ( self , * args : Any , ** kwargs : Any ) -> T : return self . _wrapped_func ( * args , ** kwargs ) @overload def process_batch ( self , batch : Sequence [ Tuple ], * , concurrency : Optional [ int ] = None , unpack_arguments : Literal [ True ], do_not_persist_traces : bool = ... , ) -> List [ Trace [ V ]]: ... @overload def process_batch ( self , batch : Sequence , * , concurrency : Optional [ int ] = None , unpack_arguments : Literal [ False ] = ... , do_not_persist_traces : bool = ... , ) -> List [ Trace [ V ]]: ... def process_batch ( self , batch : Sequence , * , concurrency : Optional [ int ] = None , unpack_arguments : bool = False , do_not_persist_traces : bool = False , ) -> List [ Trace [ V ]]: \"\"\"Map the wrapped function over a list of input_values (`batch`). A wrapper over [parallel_map][great_ai.utilities.parallel_map.parallel_map.parallel_map] providing type-safety and a progressbar through tqdm. Args: batch: A list of arguments for the original (wrapped) function. If the function expects multiple arguments, provide a list of tuples and set `unpack_arguments=True`. concurrency: Number of processes to start. Don't set it too much higher than the number of available CPU cores. unpack_arguments: Expect a list of tuples and unpack the tuples before giving them to the wrapped function. do_not_persist_traces: Don't save the traces in the database. Useful for evaluations run part of the CI. \"\"\" wrapped_function = self . _wrapped_func def inner ( value : Any ) -> T : return ( wrapped_function ( * value , do_not_persist_traces = do_not_persist_traces ) if unpack_arguments else wrapped_function ( value , do_not_persist_traces = do_not_persist_traces ) ) async def inner_async ( value : Any ) -> T : return await cast ( Awaitable , ( wrapped_function ( * value , do_not_persist_traces = do_not_persist_traces ) if unpack_arguments else wrapped_function ( value , do_not_persist_traces = do_not_persist_traces ) ), ) return list ( tqdm ( parallel_map ( inner_async if get_function_metadata_store ( self ) . is_asynchronous else inner , batch , concurrency = concurrency , ), total = len ( batch ), ) ) @staticmethod def _get_cached_traced_function ( func : Callable [ ... , Union [ V , Awaitable [ V ]]] ) -> Callable [ ... , T ]: @lru_cache ( maxsize = get_context () . prediction_cache_size ) def func_in_tracing_context_sync ( * args : Any , do_not_persist_traces : bool = False , ** kwargs : Any , ) -> T : with TracingContext [ V ]( func . __name__ , do_not_persist_traces = do_not_persist_traces ) as t : result = func ( * args , ** kwargs ) return cast ( T , t . finalise ( output = result )) @alru_cache ( maxsize = get_context () . prediction_cache_size ) async def func_in_tracing_context_async ( * args : Any , do_not_persist_traces : bool = False , ** kwargs : Any , ) -> T : with TracingContext [ V ]( func . __name__ , do_not_persist_traces = do_not_persist_traces ) as t : result = await cast ( Callable [ ... , Awaitable ], func )( * args , ** kwargs ) return cast ( T , t . finalise ( output = result )) return cast ( Callable [ ... , T ], ( func_in_tracing_context_async if get_function_metadata_store ( func ) . is_asynchronous else func_in_tracing_context_sync ), ) def _bootstrap_rest_api ( self , ) -> None : route_config = get_context () . route_config if route_config . prediction_endpoint_enabled : bootstrap_prediction_endpoint ( self . app , self . _wrapped_func ) if route_config . docs_endpoints_enabled : bootstrap_docs_endpoints ( self . app ) if route_config . dashboard_enabled : bootstrap_dashboard ( self . app , function_name = self . __name__ , documentation = self . __doc__ , ) if route_config . trace_endpoints_enabled : bootstrap_trace_endpoints ( self . app ) if route_config . feedback_endpoints_enabled : bootstrap_feedback_endpoints ( self . app ) if route_config . meta_endpoints_enabled : bootstrap_meta_endpoints ( self . app , self . _cached_func , ApiMetadata ( name = self . __name__ , version = self . version , documentation = self . __doc__ , configuration = get_context () . to_flat_dict (), ), ) create ( func ) staticmethod # Decorate a function by wrapping it in a GreatAI instance. The function can be typed, synchronous or async. If it has unwrapped parameters (parameters not affected by a @parameter or @use_model decorator), those will be automatically wrapped. The return value is replaced by a Trace (or Awaitable[Trace]), while the original return value is available under the .output property. For configuration options, see great_ai.configure . Examples: >>> @GreatAI . create ... def my_function ( a ): ... return a + 2 >>> my_function ( 3 ) . output 5 >>> @GreatAI . create ... def my_function ( a : int ) -> int : ... return a + 2 >>> my_function ( 3 ) Trace[int]... >>> my_function ( '3' ) . output Traceback (most recent call last): ... TypeError : type of a must be int; got str instead Parameters: Name Type Description Default func Union [ Callable [..., Awaitable [ V ]], Callable [..., V ]] The prediction function that needs to be decorated. required Returns: Type Description Union [ GreatAI[Awaitable[Trace[V]], V] , GreatAI[Trace[V], V] ] A GreatAI instance wrapping func . Source code in great_ai/deploy/great_ai.py 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 @staticmethod def create ( func : Union [ Callable [ ... , Awaitable [ V ]], Callable [ ... , V ]], ) -> Union [ \"GreatAI[Awaitable[Trace[V]], V]\" , \"GreatAI[Trace[V], V]\" ]: \"\"\"Decorate a function by wrapping it in a GreatAI instance. The function can be typed, synchronous or async. If it has unwrapped parameters (parameters not affected by a [@parameter][great_ai.parameter] or [@use_model][great_ai.use_model] decorator), those will be automatically wrapped. The return value is replaced by a Trace (or Awaitable[Trace]), while the original return value is available under the `.output` property. For configuration options, see [great_ai.configure][]. Examples: >>> @GreatAI.create ... def my_function(a): ... return a + 2 >>> my_function(3).output 5 >>> @GreatAI.create ... def my_function(a: int) -> int: ... return a + 2 >>> my_function(3) Trace[int]... >>> my_function('3').output Traceback (most recent call last): ... TypeError: type of a must be int; got str instead Args: func: The prediction function that needs to be decorated. Returns: A GreatAI instance wrapping `func`. \"\"\" return GreatAI [ Trace [ V ], V ]( func , ) process_batch ( batch , * , concurrency = None , unpack_arguments = False , do_not_persist_traces = False ) # Map the wrapped function over a list of input_values ( batch ). A wrapper over parallel_map providing type-safety and a progressbar through tqdm. Parameters: Name Type Description Default batch Sequence A list of arguments for the original (wrapped) function. If the function expects multiple arguments, provide a list of tuples and set unpack_arguments=True . required concurrency Optional [ int ] Number of processes to start. Don't set it too much higher than the number of available CPU cores. None unpack_arguments bool Expect a list of tuples and unpack the tuples before giving them to the wrapped function. False do_not_persist_traces bool Don't save the traces in the database. Useful for evaluations run part of the CI. False Source code in great_ai/deploy/great_ai.py 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 def process_batch ( self , batch : Sequence , * , concurrency : Optional [ int ] = None , unpack_arguments : bool = False , do_not_persist_traces : bool = False , ) -> List [ Trace [ V ]]: \"\"\"Map the wrapped function over a list of input_values (`batch`). A wrapper over [parallel_map][great_ai.utilities.parallel_map.parallel_map.parallel_map] providing type-safety and a progressbar through tqdm. Args: batch: A list of arguments for the original (wrapped) function. If the function expects multiple arguments, provide a list of tuples and set `unpack_arguments=True`. concurrency: Number of processes to start. Don't set it too much higher than the number of available CPU cores. unpack_arguments: Expect a list of tuples and unpack the tuples before giving them to the wrapped function. do_not_persist_traces: Don't save the traces in the database. Useful for evaluations run part of the CI. \"\"\" wrapped_function = self . _wrapped_func def inner ( value : Any ) -> T : return ( wrapped_function ( * value , do_not_persist_traces = do_not_persist_traces ) if unpack_arguments else wrapped_function ( value , do_not_persist_traces = do_not_persist_traces ) ) async def inner_async ( value : Any ) -> T : return await cast ( Awaitable , ( wrapped_function ( * value , do_not_persist_traces = do_not_persist_traces ) if unpack_arguments else wrapped_function ( value , do_not_persist_traces = do_not_persist_traces ) ), ) return list ( tqdm ( parallel_map ( inner_async if get_function_metadata_store ( self ) . is_asynchronous else inner , batch , concurrency = concurrency , ), total = len ( batch ), ) ) configure ( * , version = '0.0.1' , log_level = DEBUG , seed = 42 , tracing_database_factory = None , large_file_implementation = None , should_log_exception_stack = None , prediction_cache_size = 512 , disable_se4ml_banner = False , dashboard_table_size = 50 , route_config = RouteConfig ()) # Set the global configuration used by the great-ai library. You must call configure before calling (or decorating with) any other great-ai function. If tracing_database_factory or large_file_implementation is not specified, their default value is determined based on which TracingDatabase and LargeFile has been configured (e.g.: LargeFileS3.configure_credentials_from_file('s3.ini')), or whether there is any file named s3.ini or mongo.ini in the working directory. Examples: >>> configure ( prediction_cache_size = 0 ) Parameters: Name Type Description Default version Union [ int , str ] The version of your application (using SemVer is recommended). '0.0.1' log_level int Set the default logging level of logging . DEBUG seed int Set seed of random (and numpy if installed) for reproducibility. 42 tracing_database_factory Optional [ Type [ TracingDatabaseDriver ]] Specify a different TracingDatabaseDriver than the one already configured. None large_file_implementation Optional [ Type [ LargeFileBase ]] Specify a different LargeFile than the one already configured. None should_log_exception_stack Optional [ bool ] Log the traces of unhandled exceptions. None prediction_cache_size int Size of the LRU cache applied over the prediction functions. 512 disable_se4ml_banner bool Turn off the warning about the importance of SE4ML best- practices. False dashboard_table_size int Number of rows to display in the dashboard's table. 50 route_config RouteConfig Enable or disable specific HTTP API endpoints. RouteConfig() Source code in great_ai/context.py 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 def configure ( * , version : Union [ int , str ] = \"0.0.1\" , log_level : int = DEBUG , seed : int = 42 , tracing_database_factory : Optional [ Type [ TracingDatabaseDriver ]] = None , large_file_implementation : Optional [ Type [ LargeFileBase ]] = None , should_log_exception_stack : Optional [ bool ] = None , prediction_cache_size : int = 512 , disable_se4ml_banner : bool = False , dashboard_table_size : int = 50 , route_config : RouteConfig = RouteConfig (), ) -> None : \"\"\"Set the global configuration used by the great-ai library. You must call `configure` before calling (or decorating with) any other great-ai function. If `tracing_database_factory` or `large_file_implementation` is not specified, their default value is determined based on which TracingDatabase and LargeFile has been configured (e.g.: LargeFileS3.configure_credentials_from_file('s3.ini')), or whether there is any file named s3.ini or mongo.ini in the working directory. Examples: >>> configure(prediction_cache_size=0) Arguments: version: The version of your application (using SemVer is recommended). log_level: Set the default logging level of `logging`. seed: Set seed of `random` (and `numpy` if installed) for reproducibility. tracing_database_factory: Specify a different TracingDatabaseDriver than the one already configured. large_file_implementation: Specify a different LargeFile than the one already configured. should_log_exception_stack: Log the traces of unhandled exceptions. prediction_cache_size: Size of the LRU cache applied over the prediction functions. disable_se4ml_banner: Turn off the warning about the importance of SE4ML best- practices. dashboard_table_size: Number of rows to display in the dashboard's table. route_config: Enable or disable specific HTTP API endpoints. \"\"\" global _context logger = get_logger ( \"great_ai\" , level = log_level ) if _context is not None : logger . error ( \"Configuration has been already initialised, overwriting. \\n \" + \"Make sure to call `configure()` before importing your application code.\" ) is_production = _is_in_production_mode ( logger = logger ) _set_seed ( seed ) tracing_database_factory = _initialize_tracing_database ( tracing_database_factory , logger = logger ) tracing_database = tracing_database_factory () if not tracing_database . is_production_ready : message = f \"\"\"The selected tracing database ( { tracing_database_factory . __name__ } ) is not recommended for production\"\"\" if is_production : logger . error ( message ) else : logger . warning ( message ) _context = Context ( version = version , tracing_database = tracing_database , large_file_implementation = _initialize_large_file ( large_file_implementation , logger = logger ), is_production = is_production , logger = logger , should_log_exception_stack = not is_production if should_log_exception_stack is None else should_log_exception_stack , prediction_cache_size = prediction_cache_size , dashboard_table_size = dashboard_table_size , route_config = route_config , ) logger . info ( f \"GreatAI (v { __version__ } ): configured \u2705\" ) for k , v in get_context () . to_flat_dict () . items (): logger . info ( f \" { LIST_ITEM_PREFIX }{ k } : { v } \" ) if not is_production and not disable_se4ml_banner : logger . warning ( \"You still need to check whether you follow all best practices before \" \"trusting your deployment.\" ) logger . warning ( f \"> Find out more at { SE4ML_WEBSITE } \" ) save_model ( model , key , * , keep_last_n = None ) # Save (and optionally serialise) a model in order to use by use_model . The model can be a Path or string representing a path in which case the local file/folder is read and saved using the current LargeFile implementation. In case model is an object, it is serialised using dill before uploading it. Examples: >>> from great_ai import use_model >>> save_model ( 3 , 'my_number' ) 'my_number:...' >>> @use_model ( 'my_number' ) ... def my_function ( a , model ): ... return a + model >>> my_function ( 4 ) 7 Parameters: Name Type Description Default model Union [ Path , str , object ] The object or path to be uploaded. required key str The model's name. required keep_last_n Optional [ int ] If specified, remove old models and only keep the latest n. Directly passed to LargeFile. None Returns: Type Description str The key and version of the saved model separated by a colon. Example: \"key:version\" Source code in great_ai/models/save_model.py 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 def save_model ( model : Union [ Path , str , object ], key : str , * , keep_last_n : Optional [ int ] = None ) -> str : \"\"\"Save (and optionally serialise) a model in order to use by `use_model`. The `model` can be a Path or string representing a path in which case the local file/folder is read and saved using the current LargeFile implementation. In case `model` is an object, it is serialised using `dill` before uploading it. Examples: >>> from great_ai import use_model >>> save_model(3, 'my_number') 'my_number:...' >>> @use_model('my_number') ... def my_function(a, model): ... return a + model >>> my_function(4) 7 Args: model: The object or path to be uploaded. key: The model's name. keep_last_n: If specified, remove old models and only keep the latest n. Directly passed to LargeFile. Returns: The key and version of the saved model separated by a colon. Example: \"key:version\" \"\"\" file = get_context () . large_file_implementation ( name = key , mode = \"wb\" , keep_last_n = keep_last_n ) if isinstance ( model , Path ) or isinstance ( model , str ): file . push ( model ) else : with file as f : dump ( model , f ) get_context () . logger . info ( f \"Model { key } uploaded with version { file . version } \" ) return f \" { key } : { file . version } \" use_model ( key , * , version = 'latest' , model_kwarg_name = 'model' ) # Inject a model into a function. Load a model specified by key and version using the currently active LargeFile implementation. If it's a single object, it is deserialised using dill . If it's a directory of files, a pathlib.Path instance is given. By default, the function's model parameter is replaced by the loaded model. This can be customised by changing model_kwarg_name . Multiple models can be loaded by decorating the same function with use_model multiple times. Examples: >>> from great_ai import save_model >>> save_model ( 3 , 'my_number' ) 'my_number:...' >>> @use_model ( 'my_number' ) ... def my_function ( a , model ): ... return a + model >>> my_function ( 4 ) 7 Parameters: Name Type Description Default key str The model's name as stored by the LargeFile implementation. required version Union [ int , Literal [ latest ]] The model's version as stored by the LargeFile implementation. 'latest' model_kwarg_name str the parameter to use for injecting the loaded model 'model' Returns: Type Description Callable [[ F ], F ] A decorator for model injection. Source code in great_ai/models/use_model.py 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 def use_model ( key : str , * , version : Union [ int , Literal [ \"latest\" ]] = \"latest\" , model_kwarg_name : str = \"model\" , ) -> Callable [[ F ], F ]: \"\"\"Inject a model into a function. Load a model specified by `key` and `version` using the currently active `LargeFile` implementation. If it's a single object, it is deserialised using `dill`. If it's a directory of files, a `pathlib.Path` instance is given. By default, the function's `model` parameter is replaced by the loaded model. This can be customised by changing `model_kwarg_name`. Multiple models can be loaded by decorating the same function with `use_model` multiple times. Examples: >>> from great_ai import save_model >>> save_model(3, 'my_number') 'my_number:...' >>> @use_model('my_number') ... def my_function(a, model): ... return a + model >>> my_function(4) 7 Args: key: The model's name as stored by the LargeFile implementation. version: The model's version as stored by the LargeFile implementation. model_kwarg_name: the parameter to use for injecting the loaded model Returns: A decorator for model injection. \"\"\" assert ( isinstance ( version , int ) or version == \"latest\" ), \"Only integers or the string literal `latest` is allowed as a version\" model , actual_version = _load_model ( key = key , version = None if version == \"latest\" else version , ) def decorator ( func : F ) -> F : assert_function_is_not_finalised ( func ) store = get_function_metadata_store ( func ) store . model_parameter_names . append ( model_kwarg_name ) @wraps ( func ) def wrapper ( * args : List [ Any ], ** kwargs : Dict [ str , Any ]) -> Any : tracing_context = TracingContext . get_current_tracing_context () if tracing_context : tracing_context . log_model ( Model ( key = key , version = actual_version )) return func ( * args , ** kwargs , ** { model_kwarg_name : model }) return cast ( F , wrapper ) return decorator parameter ( parameter_name , * , validate = lambda _ : True , disable_logging = False ) # Control the validation and logging of function parameters. Basically, a parameter decorator. Unfortunately, Python does not have that concept, thus, it's a method decorator that expects the name of the to-be-decorated parameter. Examples: >>> @parameter ( 'a' ) ... def my_function ( a : int ): ... return a + 2 >>> my_function ( 4 ) 6 >>> my_function ( '3' ) Traceback (most recent call last): ... TypeError : type of a must be int; got str instead >>> @parameter ( 'positive_a' , validate = lambda v : v > 0 ) ... def my_function ( positive_a : int ): ... return a + 2 >>> my_function ( - 1 ) Traceback (most recent call last): ... great_ai.errors.argument_validation_error.ArgumentValidationError : ... Parameters: Name Type Description Default parameter_name str Name of parameter to consider. required validate Callable [[ Any ], bool ] Optional validate to run against the concrete argument. ArgumentValidationError is thrown when the return value is False. lambda _: True disable_logging bool Do not save the value in any active TracingContext. False Returns: Type Description Callable [[ F ], F ] A decorator for argument validation. Source code in great_ai/parameters/parameter.py 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 def parameter ( parameter_name : str , * , validate : Callable [[ Any ], bool ] = lambda _ : True , disable_logging : bool = False , ) -> Callable [[ F ], F ]: \"\"\"Control the validation and logging of function parameters. Basically, a parameter decorator. Unfortunately, Python does not have that concept, thus, it's a method decorator that expects the name of the to-be-decorated parameter. Examples: >>> @parameter('a') ... def my_function(a: int): ... return a + 2 >>> my_function(4) 6 >>> my_function('3') Traceback (most recent call last): ... TypeError: type of a must be int; got str instead >>> @parameter('positive_a', validate=lambda v: v > 0) ... def my_function(positive_a: int): ... return a + 2 >>> my_function(-1) Traceback (most recent call last): ... great_ai.errors.argument_validation_error.ArgumentValidationError: ... Args: parameter_name: Name of parameter to consider. validate: Optional validate to run against the concrete argument. ArgumentValidationError is thrown when the return value is False. disable_logging: Do not save the value in any active TracingContext. Returns: A decorator for argument validation. \"\"\" def decorator ( func : F ) -> F : get_function_metadata_store ( func ) . input_parameter_names . append ( parameter_name ) assert_function_is_not_finalised ( func ) actual_name = f \"arg: { parameter_name } \" @wraps ( func ) def wrapper ( * args : Any , ** kwargs : Dict [ str , Any ]) -> Any : arguments = get_arguments ( func , args , kwargs ) argument = arguments . get ( parameter_name ) expected_type = func . __annotations__ . get ( parameter_name ) if expected_type is not None : check_type ( parameter_name , argument , expected_type ) if not validate ( argument ): raise ArgumentValidationError ( f \"\"\"Argument { parameter_name } in { func . __name__ } did not pass validation\"\"\" ) context = TracingContext . get_current_tracing_context () if context and not disable_logging : context . log_value ( name = f \" { actual_name } :value\" , value = argument ) if isinstance ( argument , str ): context . log_value ( name = f \" { actual_name } :length\" , value = len ( argument )) return func ( * args , ** kwargs ) return cast ( F , wrapper ) return decorator log_metric ( argument_name , value ) # Log a key (argument_name)-value pair that is persisted inside the trace. The name of the function from where this is called is also stored. Parameters: Name Type Description Default argument_name str The key for storing the value. required value Any Value to log. Must be JSON-serialisable. required Source code in great_ai/parameters/log_metric.py 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 def log_metric ( argument_name : str , value : Any ) -> None : \"\"\"Log a key (argument_name)-value pair that is persisted inside the trace. The name of the function from where this is called is also stored. Args: argument_name: The key for storing the value. value: Value to log. Must be JSON-serialisable. \"\"\" tracing_context = TracingContext . get_current_tracing_context () caller = inspect . stack ()[ 1 ] . function actual_name = f \"metric: { caller } : { argument_name } \" if tracing_context : tracing_context . log_value ( name = actual_name , value = value ) get_context () . logger . info ( f \" { actual_name } = { value } \" ) Remote calls # call_remote_great_ai ( base_uri , data , retry_count = 4 , timeout_in_seconds = 300 , model_class = None ) # Communicate with a GreatAI object through an HTTP request. Wrapper over call_remote_great_ai_async making it synchronous. For more info, see call_remote_great_ai_async . Source code in great_ai/remote/call_remote_great_ai.py 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 def call_remote_great_ai ( base_uri : str , data : Mapping [ str , Any ], retry_count : int = 4 , timeout_in_seconds : Optional [ int ] = 300 , model_class : Optional [ Type [ T ]] = None , ) -> Trace [ T ]: \"\"\"Communicate with a GreatAI object through an HTTP request. Wrapper over `call_remote_great_ai_async` making it synchronous. For more info, see `call_remote_great_ai_async`. \"\"\" try : asyncio . get_running_loop () raise Exception ( f \"Already running in an event loop, you have to call ` { call_remote_great_ai_async . __name__ } `\" ) except RuntimeError : pass future = call_remote_great_ai_async ( base_uri = base_uri , data = data , retry_count = retry_count , timeout_in_seconds = timeout_in_seconds , model_class = model_class , ) return asyncio . run ( future ) call_remote_great_ai_async ( base_uri , data , retry_count = 4 , timeout_in_seconds = 300 , model_class = None ) async # Communicate with a GreatAI object through an HTTP request. Send a POST request using httpx to implement a remote call. Error-handling and retries are provided by httpx. The return value is inflated into a Trace. If model_class is specified, the original output is deserialised. Parameters: Name Type Description Default base_uri str Address of the remote instance, example: 'http://localhost:6060' required data Mapping [ str , Any ] The input sent as a json to the remote instance. required retry_count int Retry on any HTTP communication failure. 4 timeout_in_seconds Optional [ int ] Overall permissible max length of the request. None means no timeout. 300 model_class Optional [ Type [ T ]] A subtype of BaseModel to be used for deserialising the .output of the trace. None Source code in great_ai/remote/call_remote_great_ai_async.py 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 async def call_remote_great_ai_async ( base_uri : str , data : Mapping [ str , Any ], retry_count : int = 4 , timeout_in_seconds : Optional [ int ] = 300 , model_class : Optional [ Type [ T ]] = None , ) -> Trace [ T ]: \"\"\"Communicate with a GreatAI object through an HTTP request. Send a POST request using [httpx](https://www.python-httpx.org/) to implement a remote call. Error-handling and retries are provided by httpx. The return value is inflated into a Trace. If `model_class` is specified, the original output is deserialised. Args: base_uri: Address of the remote instance, example: 'http://localhost:6060' data: The input sent as a json to the remote instance. retry_count: Retry on any HTTP communication failure. timeout_in_seconds: Overall permissible max length of the request. `None` means no timeout. model_class: A subtype of BaseModel to be used for deserialising the `.output` of the trace. \"\"\" if base_uri . endswith ( \"/\" ): base_uri = base_uri [: - 1 ] if not base_uri . endswith ( \"/predict\" ): base_uri = f \" { base_uri } /predict\" transport = httpx . AsyncHTTPTransport ( retries = retry_count ) try : async with httpx . AsyncClient ( transport = transport , timeout = timeout_in_seconds ) as client : response = await client . post ( base_uri , json = data ) try : response . raise_for_status () except Exception : raise RemoteCallError ( f \"Unexpected status code, reason: { response . text } \" ) except Exception as e : raise RemoteCallError from e try : trace = response . json () except Exception : raise RemoteCallError ( f \"JSON parsing failed { response . text } \" , ) try : if model_class is not None : trace [ \"output\" ] = model_class . parse_obj ( trace [ \"output\" ]) return Trace . parse_obj ( trace ) except Exception : raise RemoteCallError ( \"Could not parse Trace\" ) Ground-truth # add_ground_truth ( inputs , expected_outputs , * , tags = [], train_split_ratio = 1 , test_split_ratio = 0 , validation_split_ratio = 0 ) # Add training data (with optional train-test splitting). Add and tag data-points, wrap them into traces. The inputs are available via the .input property, while expected_outputs under both the .output and .feedback properties. All generated traces are tagged with ground_truth by default. Additional tags can be also provided. Using the split_ratio arguments, tags can be given randomly with a user-defined distribution. Only the ratio of the splits matter, they don't have to add up to 1. Examples: >>> add_ground_truth ( ... [ 1 , 2 , 3 ], ... [ 'odd' , 'even' , 'odd' ], ... tags = 'my_tag' , ... train_split_ratio = 1 , ... test_split_ratio = 1 , ... validation_split_ratio = 0.5 , ... ) >>> add_ground_truth ( ... [ 1 , 2 ], ... [ 'odd' , 'even' , 'odd' ], ... tags = 'my_tag' , ... train_split_ratio = 1 , ... test_split_ratio = 1 , ... validation_split_ratio = 0.5 , ... ) Traceback (most recent call last): ... AssertionError : The length of the inputs and expected_outputs must be equal Parameters: Name Type Description Default inputs Iterable [ Any ] The inputs. (X in scikit-learn) required expected_outputs Iterable [ T ] The ground-truth values corresponding to the inputs. (y in scikit-learn) required tags Union [ List [ str ], str ] A single tag or a list of tags to append to each generated trace's tags. [] train_split_ratio float The probability-weight of giving each trace the train tag. 1 test_split_ratio float The probability-weight of giving each trace the test tag. 0 validation_split_ratio float The probability-weight of giving each trace the validation tag. 0 Source code in great_ai/tracing/add_ground_truth.py 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 def add_ground_truth ( inputs : Iterable [ Any ], expected_outputs : Iterable [ T ], * , tags : Union [ List [ str ], str ] = [], train_split_ratio : float = 1 , test_split_ratio : float = 0 , validation_split_ratio : float = 0 ) -> None : \"\"\"Add training data (with optional train-test splitting). Add and tag data-points, wrap them into traces. The `inputs` are available via the `.input` property, while `expected_outputs` under both the `.output` and `.feedback` properties. All generated traces are tagged with `ground_truth` by default. Additional tags can be also provided. Using the `split_ratio` arguments, tags can be given randomly with a user-defined distribution. Only the ratio of the splits matter, they don't have to add up to 1. Examples: >>> add_ground_truth( ... [1, 2, 3], ... ['odd', 'even', 'odd'], ... tags='my_tag', ... train_split_ratio=1, ... test_split_ratio=1, ... validation_split_ratio=0.5, ... ) >>> add_ground_truth( ... [1, 2], ... ['odd', 'even', 'odd'], ... tags='my_tag', ... train_split_ratio=1, ... test_split_ratio=1, ... validation_split_ratio=0.5, ... ) Traceback (most recent call last): ... AssertionError: The length of the inputs and expected_outputs must be equal Args: inputs: The inputs. (X in scikit-learn) expected_outputs: The ground-truth values corresponding to the inputs. (y in scikit-learn) tags: A single tag or a list of tags to append to each generated trace's tags. train_split_ratio: The probability-weight of giving each trace the `train` tag. test_split_ratio: The probability-weight of giving each trace the `test` tag. validation_split_ratio: The probability-weight of giving each trace the `validation` tag. \"\"\" inputs = list ( inputs ) expected_outputs = list ( expected_outputs ) assert len ( inputs ) == len ( expected_outputs ), \"The length of the inputs and expected_outputs must be equal\" tags = tags if isinstance ( tags , list ) else [ tags ] sum_ratio = train_split_ratio + test_split_ratio + validation_split_ratio assert sum_ratio > 0 , \"The sum of the split ratios must be a positive number\" train_split_ratio /= sum_ratio test_split_ratio /= sum_ratio validation_split_ratio /= sum_ratio values = list ( zip ( inputs , expected_outputs )) shuffle ( values ) split_tags = ( [ TRAIN_SPLIT_TAG_NAME ] * ceil ( train_split_ratio * len ( inputs )) + [ TEST_SPLIT_TAG_NAME ] * ceil ( test_split_ratio * len ( inputs )) + [ VALIDATION_SPLIT_TAG_NAME ] * ceil ( validation_split_ratio * len ( inputs )) ) shuffle ( split_tags ) created = datetime . utcnow () . isoformat () traces = [ cast ( Trace [ T ], Trace ( # avoid ValueError: \"Trace\" object has no field \"__orig_class__\" trace_id = str ( uuid4 ()), created = created , original_execution_time_ms = 0 , logged_values = X if isinstance ( X , dict ) else { \"input\" : X }, models = [], output = y , feedback = y , exception = None , tags = [ GROUND_TRUTH_TAG_NAME , split_tag , * tags ], ), ) for (( X , y ), split_tag ) in zip ( values , split_tags ) ] get_context () . tracing_database . save_batch ( traces ) query_ground_truth ( conjunctive_tags = [], * , since = None , until = None , return_max_count = None ) # Return training samples. Combines, filters, and returns data-points that have been either added by add_ground_truth or were the result of a prediction after which their trace got feedback through the RESP API-s /traces/{trace_id}/feedback endpoint (end-to-end feedback). Filtering can be used to only return points matching all given tags (or the single given tag) and by time of creation. Examples: >>> query_ground_truth () [...] Parameters: Name Type Description Default conjunctive_tags Union [ List [ str ], str ] Single tag or a list of tags which the returned traces have to match. The relationship between the tags is conjunctive (AND). [] since Optional [ datetime ] Only return traces created after the given timestamp. None means no filtering. None until Optional [ datetime ] Only return traces created before the given timestamp. None means no filtering. None return_max_count Optional [ int ] Return at-most this many traces. (take, limit) None Source code in great_ai/tracing/query_ground_truth.py 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 def query_ground_truth ( conjunctive_tags : Union [ List [ str ], str ] = [], * , since : Optional [ datetime ] = None , until : Optional [ datetime ] = None , return_max_count : Optional [ int ] = None ) -> List [ Trace ]: \"\"\"Return training samples. Combines, filters, and returns data-points that have been either added by `add_ground_truth` or were the result of a prediction after which their trace got feedback through the RESP API-s `/traces/{trace_id}/feedback` endpoint (end-to-end feedback). Filtering can be used to only return points matching all given tags (or the single given tag) and by time of creation. Examples: >>> query_ground_truth() [...] Args: conjunctive_tags: Single tag or a list of tags which the returned traces have to match. The relationship between the tags is conjunctive (AND). since: Only return traces created after the given timestamp. `None` means no filtering. until: Only return traces created before the given timestamp. `None` means no filtering. return_max_count: Return at-most this many traces. (take, limit) \"\"\" tags = ( conjunctive_tags if isinstance ( conjunctive_tags , list ) else [ conjunctive_tags ] ) db = get_context () . tracing_database items , length = db . query ( conjunctive_tags = tags , since = since , until = until , take = return_max_count , has_feedback = True , ) return items delete_ground_truth ( conjunctive_tags = [], * , since = None , until = None ) # Delete traces matching the given criteria. Takes the same arguments as query_ground_truth but instead of returning them, it simply deletes them. You can rely on the efficiency of the delete's implementation. Examples: >>> delete_ground_truth ([ 'train' , 'test' , 'validation' ]) Parameters: Name Type Description Default conjunctive_tags Union [ List [ str ], str ] Single tag or a list of tags which the deleted traces have to match. The relationship between the tags is conjunctive (AND). [] since Optional [ datetime ] Only delete traces created after the given timestamp. None means no filtering. None until Optional [ datetime ] Only delete traces created before the given timestamp. None means no filtering. None Source code in great_ai/tracing/delete_ground_truth.py 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 def delete_ground_truth ( conjunctive_tags : Union [ List [ str ], str ] = [], * , since : Optional [ datetime ] = None , until : Optional [ datetime ] = None , ) -> None : \"\"\"Delete traces matching the given criteria. Takes the same arguments as `query_ground_truth` but instead of returning them, it simply deletes them. You can rely on the efficiency of the delete's implementation. Examples: >>> delete_ground_truth(['train', 'test', 'validation']) Args: conjunctive_tags: Single tag or a list of tags which the deleted traces have to match. The relationship between the tags is conjunctive (AND). since: Only delete traces created after the given timestamp. `None` means no filtering. until: Only delete traces created before the given timestamp. `None` means no filtering. \"\"\" tags = ( conjunctive_tags if isinstance ( conjunctive_tags , list ) else [ conjunctive_tags ] ) db = get_context () . tracing_database items , length = db . query ( conjunctive_tags = tags , until = until , since = since , has_feedback = True ) db . delete_batch ([ i . trace_id for i in items ]) Tracing databases # TracingDatabaseDriver # Bases: ABC Interface expected from a database to be used for storing traces. Source code in great_ai/persistence/tracing_database_driver.py 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 class TracingDatabaseDriver ( ABC ): \"\"\"Interface expected from a database to be used for storing traces.\"\"\" is_production_ready : bool initialized : bool = False @classmethod def configure_credentials_from_file ( cls , secrets : Union [ Path , str , ConfigFile ], ) -> None : if not isinstance ( secrets , ConfigFile ): secrets = ConfigFile ( secrets ) cls . configure_credentials ( ** { k . lower (): v for k , v in secrets . items ()}) @classmethod def configure_credentials ( cls , ) -> None : cls . initialized = True @abstractmethod def save ( self , document : Trace ) -> str : pass @abstractmethod def save_batch ( self , documents : List [ Trace ], ) -> List [ str ]: pass @abstractmethod def get ( self , id : str ) -> Optional [ Trace ]: pass @abstractmethod def query ( self , * , skip : int = 0 , take : Optional [ int ] = None , conjunctive_filters : Sequence [ Filter ] = [], conjunctive_tags : Sequence [ str ] = [], until : Optional [ datetime ] = None , since : Optional [ datetime ] = None , has_feedback : Optional [ bool ] = None , sort_by : Sequence [ SortBy ] = [] ) -> Tuple [ List [ Trace ], int ]: pass @abstractmethod def update ( self , id : str , new_version : Trace ) -> None : pass @abstractmethod def delete ( self , id : str ) -> None : pass @abstractmethod def delete_batch ( self , ids : List [ str ], ) -> None : pass MongoDbDriver # Bases: TracingDatabaseDriver TracingDatabaseDriver implementation using MongoDB as a backend. A production-ready database driver suitable for efficiently handling semi-structured data. Checkout MongoDB Atlas for a hosted MongoDB solution. Source code in great_ai/persistence/mongodb_driver.py 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 class MongoDbDriver ( TracingDatabaseDriver ): \"\"\"TracingDatabaseDriver implementation using MongoDB as a backend. A production-ready database driver suitable for efficiently handling semi-structured data. Checkout [MongoDB Atlas](https://www.mongodb.com/cloud/atlas/register) for a hosted MongoDB solution. \"\"\" is_production_ready = True mongo_connection_string : str mongo_database : str def __init__ ( self ) -> None : super () . __init__ () if self . mongo_connection_string is None or self . mongo_database is None : raise ValueError ( \"Please configure the MongoDB access options by calling \" \"MongoDbDriver.configure_credentials\" ) with MongoClient [ Any ]( self . mongo_connection_string ) as client : client [ self . mongo_database ] . traces . create_index ( [( \"tags\" , ASCENDING ), ( \"created\" , DESCENDING )], background = True ) @classmethod def configure_credentials ( # type: ignore cls , * , mongo_connection_string : str , mongo_database : str , ** _ : Any , ) -> None : \"\"\"Configure the connection details for MongoDB. Args: mongo_connection_string: For example: 'mongodb://my_user:my_pass@localhost:27017' mongo_database: Name of the database to use. If doesn't exist, it is created and initialised. \"\"\" cls . mongo_connection_string = mongo_connection_string cls . mongo_database = mongo_database super () . configure_credentials () def save ( self , trace : Trace ) -> str : serialized = trace . to_flat_dict () serialized [ \"_id\" ] = trace . trace_id with MongoClient [ Any ]( self . mongo_connection_string ) as client : return client [ self . mongo_database ] . traces . insert_one ( serialized ) . inserted_id def save_batch ( self , documents : List [ Trace ]) -> List [ str ]: serialized = [ d . to_flat_dict () for d in documents ] for s in serialized : s [ \"_id\" ] = s [ \"trace_id\" ] with MongoClient [ Any ]( self . mongo_connection_string ) as client : return ( client [ self . mongo_database ] . traces . insert_many ( serialized , ordered = False ) . inserted_ids ) def get ( self , id : str ) -> Optional [ Trace ]: with MongoClient [ Any ]( self . mongo_connection_string ) as client : value = client [ self . mongo_database ] . traces . find_one ( id ) if value : value = Trace . parse_obj ( value ) return value def _get_operator ( self , filter : Filter ) -> str : if filter . operator == \"contains\" and not isinstance ( filter . value , str ): return operator_mapping [ \"=\" ] return operator_mapping [ filter . operator ] def query ( self , * , skip : int = 0 , take : Optional [ int ] = None , conjunctive_filters : Sequence [ Filter ] = [], conjunctive_tags : Sequence [ str ] = [], since : Optional [ datetime ] = None , until : Optional [ datetime ] = None , has_feedback : Optional [ bool ] = None , sort_by : Sequence [ SortBy ] = [], ) -> Tuple [ List [ Trace ], int ]: query : Dict [ str , Any ] = { \"filter\" : {}, } and_query : List [ Dict [ str , Any ]] = [] and_query . extend ({ \"tags\" : tag } for tag in conjunctive_tags ) and_query . extend ( { f . property : { self . _get_operator ( f ): f . value }} for f in conjunctive_filters ) if not and_query : and_query . append ({}) if since : and_query . append ({ \"created\" : { \"$gte\" : since }}) if until : and_query . append ({ \"created\" : { \"$lte\" : until }}) if has_feedback is not None : and_query . append ( { \"feedback\" : { \"$ne\" : None }} if has_feedback else { \"feedback\" : None } ) query [ \"filter\" ][ \"$and\" ] = and_query with MongoClient [ Any ]( self . mongo_connection_string ) as client : count = client [ self . mongo_database ] . traces . count_documents ( ** query ) if skip : query [ \"skip\" ] = skip if take : query [ \"limit\" ] = take query [ \"sort\" ] = [ ( col . column_id , 1 if col . direction == \"asc\" else - 1 ) for col in sort_by ] with client [ self . mongo_database ] . traces . find ( ** query ) as cursor : documents = [ Trace [ Any ] . parse_obj ( t ) for t in cursor ] return documents , count def update ( self , id : str , new_version : Trace ) -> None : serialized = new_version . to_flat_dict () serialized [ \"_id\" ] = new_version . trace_id with MongoClient [ Any ]( self . mongo_connection_string ) as client : client [ self . mongo_database ] . traces . update_one ({ \"_id\" : id }, serialized ) def delete ( self , id : str ) -> None : with MongoClient [ Any ]( self . mongo_connection_string ) as client : client [ self . mongo_database ] . traces . delete_one ({ \"_id\" : id }) def delete_batch ( self , ids : List [ str ]) -> None : with MongoClient [ Any ]( self . mongo_connection_string ) as client : for c in chunk ( ids , chunk_size = 10000 ): # avoid: 'delete' command document too large delete_filter = { \"_id\" : { \"$in\" : c }} client [ self . mongo_database ] . traces . delete_many ( delete_filter ) configure_credentials ( * , mongo_connection_string , mongo_database , ** _ ) classmethod # Configure the connection details for MongoDB. Parameters: Name Type Description Default mongo_connection_string str For example: 'mongodb://my_user:my_pass@localhost:27017' required mongo_database str Name of the database to use. If doesn't exist, it is created and initialised. required Source code in great_ai/persistence/mongodb_driver.py 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 @classmethod def configure_credentials ( # type: ignore cls , * , mongo_connection_string : str , mongo_database : str , ** _ : Any , ) -> None : \"\"\"Configure the connection details for MongoDB. Args: mongo_connection_string: For example: 'mongodb://my_user:my_pass@localhost:27017' mongo_database: Name of the database to use. If doesn't exist, it is created and initialised. \"\"\" cls . mongo_connection_string = mongo_connection_string cls . mongo_database = mongo_database super () . configure_credentials () ParallelTinyDbDriver # Bases: TracingDatabaseDriver TracingDatabaseDriver with TinyDB as a backend. Saves the database as a JSON into a single file. Highly inefficient on inserting, not advised for production use. A multiprocessing lock protects the database file to avoid parallelisation issues. Source code in great_ai/persistence/parallel_tinydb_driver.py 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 class ParallelTinyDbDriver ( TracingDatabaseDriver ): \"\"\"TracingDatabaseDriver with TinyDB as a backend. Saves the database as a JSON into a single file. Highly inefficient on inserting, not advised for production use. A multiprocessing lock protects the database file to avoid parallelisation issues. \"\"\" is_production_ready = False path_to_db = Path ( DEFAULT_TRACING_DB_FILENAME ) def save ( self , trace : Trace ) -> str : return self . _safe_execute ( lambda db : db . insert ( trace . dict ())) def save_batch ( self , documents : List [ Trace ]) -> List [ str ]: traces = [ d . dict () for d in documents ] return self . _safe_execute ( lambda db : db . insert_multiple ( traces )) def get ( self , id : str ) -> Optional [ Trace ]: value = self . _safe_execute ( lambda db : db . get ( lambda d : d [ \"trace_id\" ] == id )) if value : value = Trace . parse_obj ( value ) return value def query ( self , * , skip : int = 0 , take : Optional [ int ] = None , conjunctive_filters : Sequence [ Filter ] = [], conjunctive_tags : Sequence [ str ] = [], since : Optional [ datetime ] = None , until : Optional [ datetime ] = None , has_feedback : Optional [ bool ] = None , sort_by : Sequence [ SortBy ] = [] ) -> Tuple [ List [ Trace ], int ]: def does_match ( d : Dict [ str , Any ]) -> bool : return ( not set ( conjunctive_tags ) - set ( d [ \"tags\" ]) and ( since is None or datetime . fromisoformat ( d [ \"created\" ]) >= since ) and ( until is None or datetime . fromisoformat ( d [ \"created\" ]) <= until ) and ( has_feedback is None or has_feedback == ( d [ \"feedback\" ] is not None ) ) ) documents : List [ Trace ] = [ Trace . parse_obj ( t ) for t in self . _safe_execute ( lambda db : db . search ( does_match )) ] if not documents : return [], 0 df = pd . DataFrame ([ d . to_flat_dict () for d in documents ]) for f in conjunctive_filters : operator = f . operator . lower () if operator in operator_mapping : df = df . loc [ getattr ( df [ f . property ], operator_mapping [ f . operator ])( f . value ) ] elif operator == \"contains\" : df = df . loc [ df [ f . property ] . str . contains ( f . value , case = False )] if sort_by : df . sort_values ( [ col . column_id for col in sort_by ], ascending = [ col . direction == \"asc\" for col in sort_by ], inplace = True , ) count = len ( df ) result = df . iloc [ skip :] if take is None else df . iloc [ skip : skip + take ] return [ next ( d for d in documents if d . trace_id == trace_id ) for trace_id in result [ \"trace_id\" ] ], count def update ( self , id : str , new_version : Trace ) -> None : self . _safe_execute ( lambda db : db . update ( new_version . dict (), lambda d : d [ \"trace_id\" ] == id ) ) def delete ( self , id : str ) -> None : self . _safe_execute ( lambda db : db . remove ( lambda d : d [ \"trace_id\" ] == id )) def delete_batch ( self , ids : List [ str ]) -> None : for i in ids : self . delete ( i ) def _safe_execute ( self , func : Callable [[ TinyDB ], Any ]) -> Any : with lock : with TinyDB ( self . path_to_db ) as db : return func ( db )","title":"GreatAI reference"},{"location":"reference/#greatai-reference","text":"from great_ai import *","title":"GreatAI reference"},{"location":"reference/#core","text":"","title":"Core"},{"location":"reference/#great_ai.GreatAI","text":"Bases: Generic [ T , V ] Wrapper for a prediction function providing the implementation of SE4ML best practices. Provides caching (with argument freezing), a TracingContext during execution, the scaffolding of HTTP endpoints using FastAPI and a dashboard using Dash. IMPORTANT: when a request is served from cache, no new trace is created. Thus, the same trace can be returned multiple times. If this is undesirable turn off caching using configure(prediction_cache_size=0) . Supports wrapping async and synchronous functions while also maintaining correct typing. Attributes: Name Type Description app FastAPI instance wrapping the scaffolded endpoints and the Dash app. version SemVer derived from the app's version and the model names and versions registered through use_model. Source code in great_ai/deploy/great_ai.py 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 class GreatAI ( Generic [ T , V ]): \"\"\"Wrapper for a prediction function providing the implementation of SE4ML best practices. Provides caching (with argument freezing), a TracingContext during execution, the scaffolding of HTTP endpoints using FastAPI and a dashboard using Dash. IMPORTANT: when a request is served from cache, no new trace is created. Thus, the same trace can be returned multiple times. If this is undesirable turn off caching using `configure(prediction_cache_size=0)`. Supports wrapping async and synchronous functions while also maintaining correct typing. Attributes: app: FastAPI instance wrapping the scaffolded endpoints and the Dash app. version: SemVer derived from the app's version and the model names and versions registered through use_model. \"\"\" __name__ : str # help for MyPy __doc__ : str # help for MyPy def __init__ ( self , func : Callable [ ... , Union [ V , Awaitable [ V ]]], ): \"\"\"Do not call this function directly, use GreatAI.create instead.\"\"\" func = automatically_decorate_parameters ( func ) get_function_metadata_store ( func ) . is_finalised = True self . _cached_func = self . _get_cached_traced_function ( func ) self . _wrapped_func = wraps ( func )( freeze_arguments ( self . _cached_func )) wraps ( func )( self ) self . __doc__ = ( f \"GreatAI wrapper for interacting with the ` { self . __name__ } ` \" + f \"function. \\n\\n { dedent ( self . __doc__ or '' ) } \" ) self . version = str ( get_context () . version ) flat_model_versions = \".\" . join ( f \" { k } -v { v } \" for k , v in model_versions ) if flat_model_versions : self . version += f \"+ { flat_model_versions } \" self . app = FastAPI ( title = snake_case_to_text ( self . __name__ ), version = self . version , description = self . __doc__ + f \" \\n\\n Find out more in the [dashboard]( { DASHBOARD_PATH } ).\" , docs_url = None , redoc_url = None , ) self . _bootstrap_rest_api () @overload @staticmethod def create ( # type: ignore # Overloaded function signatures 1 and 2 overlap with incompatible return types # https://github.com/python/mypy/issues/12759 func : Callable [ ... , Awaitable [ V ]], ) -> \"GreatAI[Awaitable[Trace[V]], V]\" : ... @overload @staticmethod def create ( func : Callable [ ... , V ], ) -> \"GreatAI[Trace[V], V]\" : ... @staticmethod def create ( func : Union [ Callable [ ... , Awaitable [ V ]], Callable [ ... , V ]], ) -> Union [ \"GreatAI[Awaitable[Trace[V]], V]\" , \"GreatAI[Trace[V], V]\" ]: \"\"\"Decorate a function by wrapping it in a GreatAI instance. The function can be typed, synchronous or async. If it has unwrapped parameters (parameters not affected by a [@parameter][great_ai.parameter] or [@use_model][great_ai.use_model] decorator), those will be automatically wrapped. The return value is replaced by a Trace (or Awaitable[Trace]), while the original return value is available under the `.output` property. For configuration options, see [great_ai.configure][]. Examples: >>> @GreatAI.create ... def my_function(a): ... return a + 2 >>> my_function(3).output 5 >>> @GreatAI.create ... def my_function(a: int) -> int: ... return a + 2 >>> my_function(3) Trace[int]... >>> my_function('3').output Traceback (most recent call last): ... TypeError: type of a must be int; got str instead Args: func: The prediction function that needs to be decorated. Returns: A GreatAI instance wrapping `func`. \"\"\" return GreatAI [ Trace [ V ], V ]( func , ) def __call__ ( self , * args : Any , ** kwargs : Any ) -> T : return self . _wrapped_func ( * args , ** kwargs ) @overload def process_batch ( self , batch : Sequence [ Tuple ], * , concurrency : Optional [ int ] = None , unpack_arguments : Literal [ True ], do_not_persist_traces : bool = ... , ) -> List [ Trace [ V ]]: ... @overload def process_batch ( self , batch : Sequence , * , concurrency : Optional [ int ] = None , unpack_arguments : Literal [ False ] = ... , do_not_persist_traces : bool = ... , ) -> List [ Trace [ V ]]: ... def process_batch ( self , batch : Sequence , * , concurrency : Optional [ int ] = None , unpack_arguments : bool = False , do_not_persist_traces : bool = False , ) -> List [ Trace [ V ]]: \"\"\"Map the wrapped function over a list of input_values (`batch`). A wrapper over [parallel_map][great_ai.utilities.parallel_map.parallel_map.parallel_map] providing type-safety and a progressbar through tqdm. Args: batch: A list of arguments for the original (wrapped) function. If the function expects multiple arguments, provide a list of tuples and set `unpack_arguments=True`. concurrency: Number of processes to start. Don't set it too much higher than the number of available CPU cores. unpack_arguments: Expect a list of tuples and unpack the tuples before giving them to the wrapped function. do_not_persist_traces: Don't save the traces in the database. Useful for evaluations run part of the CI. \"\"\" wrapped_function = self . _wrapped_func def inner ( value : Any ) -> T : return ( wrapped_function ( * value , do_not_persist_traces = do_not_persist_traces ) if unpack_arguments else wrapped_function ( value , do_not_persist_traces = do_not_persist_traces ) ) async def inner_async ( value : Any ) -> T : return await cast ( Awaitable , ( wrapped_function ( * value , do_not_persist_traces = do_not_persist_traces ) if unpack_arguments else wrapped_function ( value , do_not_persist_traces = do_not_persist_traces ) ), ) return list ( tqdm ( parallel_map ( inner_async if get_function_metadata_store ( self ) . is_asynchronous else inner , batch , concurrency = concurrency , ), total = len ( batch ), ) ) @staticmethod def _get_cached_traced_function ( func : Callable [ ... , Union [ V , Awaitable [ V ]]] ) -> Callable [ ... , T ]: @lru_cache ( maxsize = get_context () . prediction_cache_size ) def func_in_tracing_context_sync ( * args : Any , do_not_persist_traces : bool = False , ** kwargs : Any , ) -> T : with TracingContext [ V ]( func . __name__ , do_not_persist_traces = do_not_persist_traces ) as t : result = func ( * args , ** kwargs ) return cast ( T , t . finalise ( output = result )) @alru_cache ( maxsize = get_context () . prediction_cache_size ) async def func_in_tracing_context_async ( * args : Any , do_not_persist_traces : bool = False , ** kwargs : Any , ) -> T : with TracingContext [ V ]( func . __name__ , do_not_persist_traces = do_not_persist_traces ) as t : result = await cast ( Callable [ ... , Awaitable ], func )( * args , ** kwargs ) return cast ( T , t . finalise ( output = result )) return cast ( Callable [ ... , T ], ( func_in_tracing_context_async if get_function_metadata_store ( func ) . is_asynchronous else func_in_tracing_context_sync ), ) def _bootstrap_rest_api ( self , ) -> None : route_config = get_context () . route_config if route_config . prediction_endpoint_enabled : bootstrap_prediction_endpoint ( self . app , self . _wrapped_func ) if route_config . docs_endpoints_enabled : bootstrap_docs_endpoints ( self . app ) if route_config . dashboard_enabled : bootstrap_dashboard ( self . app , function_name = self . __name__ , documentation = self . __doc__ , ) if route_config . trace_endpoints_enabled : bootstrap_trace_endpoints ( self . app ) if route_config . feedback_endpoints_enabled : bootstrap_feedback_endpoints ( self . app ) if route_config . meta_endpoints_enabled : bootstrap_meta_endpoints ( self . app , self . _cached_func , ApiMetadata ( name = self . __name__ , version = self . version , documentation = self . __doc__ , configuration = get_context () . to_flat_dict (), ), )","title":"GreatAI"},{"location":"reference/#great_ai.deploy.great_ai.GreatAI.create","text":"Decorate a function by wrapping it in a GreatAI instance. The function can be typed, synchronous or async. If it has unwrapped parameters (parameters not affected by a @parameter or @use_model decorator), those will be automatically wrapped. The return value is replaced by a Trace (or Awaitable[Trace]), while the original return value is available under the .output property. For configuration options, see great_ai.configure . Examples: >>> @GreatAI . create ... def my_function ( a ): ... return a + 2 >>> my_function ( 3 ) . output 5 >>> @GreatAI . create ... def my_function ( a : int ) -> int : ... return a + 2 >>> my_function ( 3 ) Trace[int]... >>> my_function ( '3' ) . output Traceback (most recent call last): ... TypeError : type of a must be int; got str instead Parameters: Name Type Description Default func Union [ Callable [..., Awaitable [ V ]], Callable [..., V ]] The prediction function that needs to be decorated. required Returns: Type Description Union [ GreatAI[Awaitable[Trace[V]], V] , GreatAI[Trace[V], V] ] A GreatAI instance wrapping func . Source code in great_ai/deploy/great_ai.py 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 @staticmethod def create ( func : Union [ Callable [ ... , Awaitable [ V ]], Callable [ ... , V ]], ) -> Union [ \"GreatAI[Awaitable[Trace[V]], V]\" , \"GreatAI[Trace[V], V]\" ]: \"\"\"Decorate a function by wrapping it in a GreatAI instance. The function can be typed, synchronous or async. If it has unwrapped parameters (parameters not affected by a [@parameter][great_ai.parameter] or [@use_model][great_ai.use_model] decorator), those will be automatically wrapped. The return value is replaced by a Trace (or Awaitable[Trace]), while the original return value is available under the `.output` property. For configuration options, see [great_ai.configure][]. Examples: >>> @GreatAI.create ... def my_function(a): ... return a + 2 >>> my_function(3).output 5 >>> @GreatAI.create ... def my_function(a: int) -> int: ... return a + 2 >>> my_function(3) Trace[int]... >>> my_function('3').output Traceback (most recent call last): ... TypeError: type of a must be int; got str instead Args: func: The prediction function that needs to be decorated. Returns: A GreatAI instance wrapping `func`. \"\"\" return GreatAI [ Trace [ V ], V ]( func , )","title":"create()"},{"location":"reference/#great_ai.deploy.great_ai.GreatAI.process_batch","text":"Map the wrapped function over a list of input_values ( batch ). A wrapper over parallel_map providing type-safety and a progressbar through tqdm. Parameters: Name Type Description Default batch Sequence A list of arguments for the original (wrapped) function. If the function expects multiple arguments, provide a list of tuples and set unpack_arguments=True . required concurrency Optional [ int ] Number of processes to start. Don't set it too much higher than the number of available CPU cores. None unpack_arguments bool Expect a list of tuples and unpack the tuples before giving them to the wrapped function. False do_not_persist_traces bool Don't save the traces in the database. Useful for evaluations run part of the CI. False Source code in great_ai/deploy/great_ai.py 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 def process_batch ( self , batch : Sequence , * , concurrency : Optional [ int ] = None , unpack_arguments : bool = False , do_not_persist_traces : bool = False , ) -> List [ Trace [ V ]]: \"\"\"Map the wrapped function over a list of input_values (`batch`). A wrapper over [parallel_map][great_ai.utilities.parallel_map.parallel_map.parallel_map] providing type-safety and a progressbar through tqdm. Args: batch: A list of arguments for the original (wrapped) function. If the function expects multiple arguments, provide a list of tuples and set `unpack_arguments=True`. concurrency: Number of processes to start. Don't set it too much higher than the number of available CPU cores. unpack_arguments: Expect a list of tuples and unpack the tuples before giving them to the wrapped function. do_not_persist_traces: Don't save the traces in the database. Useful for evaluations run part of the CI. \"\"\" wrapped_function = self . _wrapped_func def inner ( value : Any ) -> T : return ( wrapped_function ( * value , do_not_persist_traces = do_not_persist_traces ) if unpack_arguments else wrapped_function ( value , do_not_persist_traces = do_not_persist_traces ) ) async def inner_async ( value : Any ) -> T : return await cast ( Awaitable , ( wrapped_function ( * value , do_not_persist_traces = do_not_persist_traces ) if unpack_arguments else wrapped_function ( value , do_not_persist_traces = do_not_persist_traces ) ), ) return list ( tqdm ( parallel_map ( inner_async if get_function_metadata_store ( self ) . is_asynchronous else inner , batch , concurrency = concurrency , ), total = len ( batch ), ) )","title":"process_batch()"},{"location":"reference/#great_ai.configure","text":"Set the global configuration used by the great-ai library. You must call configure before calling (or decorating with) any other great-ai function. If tracing_database_factory or large_file_implementation is not specified, their default value is determined based on which TracingDatabase and LargeFile has been configured (e.g.: LargeFileS3.configure_credentials_from_file('s3.ini')), or whether there is any file named s3.ini or mongo.ini in the working directory. Examples: >>> configure ( prediction_cache_size = 0 ) Parameters: Name Type Description Default version Union [ int , str ] The version of your application (using SemVer is recommended). '0.0.1' log_level int Set the default logging level of logging . DEBUG seed int Set seed of random (and numpy if installed) for reproducibility. 42 tracing_database_factory Optional [ Type [ TracingDatabaseDriver ]] Specify a different TracingDatabaseDriver than the one already configured. None large_file_implementation Optional [ Type [ LargeFileBase ]] Specify a different LargeFile than the one already configured. None should_log_exception_stack Optional [ bool ] Log the traces of unhandled exceptions. None prediction_cache_size int Size of the LRU cache applied over the prediction functions. 512 disable_se4ml_banner bool Turn off the warning about the importance of SE4ML best- practices. False dashboard_table_size int Number of rows to display in the dashboard's table. 50 route_config RouteConfig Enable or disable specific HTTP API endpoints. RouteConfig() Source code in great_ai/context.py 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 def configure ( * , version : Union [ int , str ] = \"0.0.1\" , log_level : int = DEBUG , seed : int = 42 , tracing_database_factory : Optional [ Type [ TracingDatabaseDriver ]] = None , large_file_implementation : Optional [ Type [ LargeFileBase ]] = None , should_log_exception_stack : Optional [ bool ] = None , prediction_cache_size : int = 512 , disable_se4ml_banner : bool = False , dashboard_table_size : int = 50 , route_config : RouteConfig = RouteConfig (), ) -> None : \"\"\"Set the global configuration used by the great-ai library. You must call `configure` before calling (or decorating with) any other great-ai function. If `tracing_database_factory` or `large_file_implementation` is not specified, their default value is determined based on which TracingDatabase and LargeFile has been configured (e.g.: LargeFileS3.configure_credentials_from_file('s3.ini')), or whether there is any file named s3.ini or mongo.ini in the working directory. Examples: >>> configure(prediction_cache_size=0) Arguments: version: The version of your application (using SemVer is recommended). log_level: Set the default logging level of `logging`. seed: Set seed of `random` (and `numpy` if installed) for reproducibility. tracing_database_factory: Specify a different TracingDatabaseDriver than the one already configured. large_file_implementation: Specify a different LargeFile than the one already configured. should_log_exception_stack: Log the traces of unhandled exceptions. prediction_cache_size: Size of the LRU cache applied over the prediction functions. disable_se4ml_banner: Turn off the warning about the importance of SE4ML best- practices. dashboard_table_size: Number of rows to display in the dashboard's table. route_config: Enable or disable specific HTTP API endpoints. \"\"\" global _context logger = get_logger ( \"great_ai\" , level = log_level ) if _context is not None : logger . error ( \"Configuration has been already initialised, overwriting. \\n \" + \"Make sure to call `configure()` before importing your application code.\" ) is_production = _is_in_production_mode ( logger = logger ) _set_seed ( seed ) tracing_database_factory = _initialize_tracing_database ( tracing_database_factory , logger = logger ) tracing_database = tracing_database_factory () if not tracing_database . is_production_ready : message = f \"\"\"The selected tracing database ( { tracing_database_factory . __name__ } ) is not recommended for production\"\"\" if is_production : logger . error ( message ) else : logger . warning ( message ) _context = Context ( version = version , tracing_database = tracing_database , large_file_implementation = _initialize_large_file ( large_file_implementation , logger = logger ), is_production = is_production , logger = logger , should_log_exception_stack = not is_production if should_log_exception_stack is None else should_log_exception_stack , prediction_cache_size = prediction_cache_size , dashboard_table_size = dashboard_table_size , route_config = route_config , ) logger . info ( f \"GreatAI (v { __version__ } ): configured \u2705\" ) for k , v in get_context () . to_flat_dict () . items (): logger . info ( f \" { LIST_ITEM_PREFIX }{ k } : { v } \" ) if not is_production and not disable_se4ml_banner : logger . warning ( \"You still need to check whether you follow all best practices before \" \"trusting your deployment.\" ) logger . warning ( f \"> Find out more at { SE4ML_WEBSITE } \" )","title":"configure()"},{"location":"reference/#great_ai.save_model","text":"Save (and optionally serialise) a model in order to use by use_model . The model can be a Path or string representing a path in which case the local file/folder is read and saved using the current LargeFile implementation. In case model is an object, it is serialised using dill before uploading it. Examples: >>> from great_ai import use_model >>> save_model ( 3 , 'my_number' ) 'my_number:...' >>> @use_model ( 'my_number' ) ... def my_function ( a , model ): ... return a + model >>> my_function ( 4 ) 7 Parameters: Name Type Description Default model Union [ Path , str , object ] The object or path to be uploaded. required key str The model's name. required keep_last_n Optional [ int ] If specified, remove old models and only keep the latest n. Directly passed to LargeFile. None Returns: Type Description str The key and version of the saved model separated by a colon. Example: \"key:version\" Source code in great_ai/models/save_model.py 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 def save_model ( model : Union [ Path , str , object ], key : str , * , keep_last_n : Optional [ int ] = None ) -> str : \"\"\"Save (and optionally serialise) a model in order to use by `use_model`. The `model` can be a Path or string representing a path in which case the local file/folder is read and saved using the current LargeFile implementation. In case `model` is an object, it is serialised using `dill` before uploading it. Examples: >>> from great_ai import use_model >>> save_model(3, 'my_number') 'my_number:...' >>> @use_model('my_number') ... def my_function(a, model): ... return a + model >>> my_function(4) 7 Args: model: The object or path to be uploaded. key: The model's name. keep_last_n: If specified, remove old models and only keep the latest n. Directly passed to LargeFile. Returns: The key and version of the saved model separated by a colon. Example: \"key:version\" \"\"\" file = get_context () . large_file_implementation ( name = key , mode = \"wb\" , keep_last_n = keep_last_n ) if isinstance ( model , Path ) or isinstance ( model , str ): file . push ( model ) else : with file as f : dump ( model , f ) get_context () . logger . info ( f \"Model { key } uploaded with version { file . version } \" ) return f \" { key } : { file . version } \"","title":"save_model()"},{"location":"reference/#great_ai.use_model","text":"Inject a model into a function. Load a model specified by key and version using the currently active LargeFile implementation. If it's a single object, it is deserialised using dill . If it's a directory of files, a pathlib.Path instance is given. By default, the function's model parameter is replaced by the loaded model. This can be customised by changing model_kwarg_name . Multiple models can be loaded by decorating the same function with use_model multiple times. Examples: >>> from great_ai import save_model >>> save_model ( 3 , 'my_number' ) 'my_number:...' >>> @use_model ( 'my_number' ) ... def my_function ( a , model ): ... return a + model >>> my_function ( 4 ) 7 Parameters: Name Type Description Default key str The model's name as stored by the LargeFile implementation. required version Union [ int , Literal [ latest ]] The model's version as stored by the LargeFile implementation. 'latest' model_kwarg_name str the parameter to use for injecting the loaded model 'model' Returns: Type Description Callable [[ F ], F ] A decorator for model injection. Source code in great_ai/models/use_model.py 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 def use_model ( key : str , * , version : Union [ int , Literal [ \"latest\" ]] = \"latest\" , model_kwarg_name : str = \"model\" , ) -> Callable [[ F ], F ]: \"\"\"Inject a model into a function. Load a model specified by `key` and `version` using the currently active `LargeFile` implementation. If it's a single object, it is deserialised using `dill`. If it's a directory of files, a `pathlib.Path` instance is given. By default, the function's `model` parameter is replaced by the loaded model. This can be customised by changing `model_kwarg_name`. Multiple models can be loaded by decorating the same function with `use_model` multiple times. Examples: >>> from great_ai import save_model >>> save_model(3, 'my_number') 'my_number:...' >>> @use_model('my_number') ... def my_function(a, model): ... return a + model >>> my_function(4) 7 Args: key: The model's name as stored by the LargeFile implementation. version: The model's version as stored by the LargeFile implementation. model_kwarg_name: the parameter to use for injecting the loaded model Returns: A decorator for model injection. \"\"\" assert ( isinstance ( version , int ) or version == \"latest\" ), \"Only integers or the string literal `latest` is allowed as a version\" model , actual_version = _load_model ( key = key , version = None if version == \"latest\" else version , ) def decorator ( func : F ) -> F : assert_function_is_not_finalised ( func ) store = get_function_metadata_store ( func ) store . model_parameter_names . append ( model_kwarg_name ) @wraps ( func ) def wrapper ( * args : List [ Any ], ** kwargs : Dict [ str , Any ]) -> Any : tracing_context = TracingContext . get_current_tracing_context () if tracing_context : tracing_context . log_model ( Model ( key = key , version = actual_version )) return func ( * args , ** kwargs , ** { model_kwarg_name : model }) return cast ( F , wrapper ) return decorator","title":"use_model()"},{"location":"reference/#great_ai.parameter","text":"Control the validation and logging of function parameters. Basically, a parameter decorator. Unfortunately, Python does not have that concept, thus, it's a method decorator that expects the name of the to-be-decorated parameter. Examples: >>> @parameter ( 'a' ) ... def my_function ( a : int ): ... return a + 2 >>> my_function ( 4 ) 6 >>> my_function ( '3' ) Traceback (most recent call last): ... TypeError : type of a must be int; got str instead >>> @parameter ( 'positive_a' , validate = lambda v : v > 0 ) ... def my_function ( positive_a : int ): ... return a + 2 >>> my_function ( - 1 ) Traceback (most recent call last): ... great_ai.errors.argument_validation_error.ArgumentValidationError : ... Parameters: Name Type Description Default parameter_name str Name of parameter to consider. required validate Callable [[ Any ], bool ] Optional validate to run against the concrete argument. ArgumentValidationError is thrown when the return value is False. lambda _: True disable_logging bool Do not save the value in any active TracingContext. False Returns: Type Description Callable [[ F ], F ] A decorator for argument validation. Source code in great_ai/parameters/parameter.py 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 def parameter ( parameter_name : str , * , validate : Callable [[ Any ], bool ] = lambda _ : True , disable_logging : bool = False , ) -> Callable [[ F ], F ]: \"\"\"Control the validation and logging of function parameters. Basically, a parameter decorator. Unfortunately, Python does not have that concept, thus, it's a method decorator that expects the name of the to-be-decorated parameter. Examples: >>> @parameter('a') ... def my_function(a: int): ... return a + 2 >>> my_function(4) 6 >>> my_function('3') Traceback (most recent call last): ... TypeError: type of a must be int; got str instead >>> @parameter('positive_a', validate=lambda v: v > 0) ... def my_function(positive_a: int): ... return a + 2 >>> my_function(-1) Traceback (most recent call last): ... great_ai.errors.argument_validation_error.ArgumentValidationError: ... Args: parameter_name: Name of parameter to consider. validate: Optional validate to run against the concrete argument. ArgumentValidationError is thrown when the return value is False. disable_logging: Do not save the value in any active TracingContext. Returns: A decorator for argument validation. \"\"\" def decorator ( func : F ) -> F : get_function_metadata_store ( func ) . input_parameter_names . append ( parameter_name ) assert_function_is_not_finalised ( func ) actual_name = f \"arg: { parameter_name } \" @wraps ( func ) def wrapper ( * args : Any , ** kwargs : Dict [ str , Any ]) -> Any : arguments = get_arguments ( func , args , kwargs ) argument = arguments . get ( parameter_name ) expected_type = func . __annotations__ . get ( parameter_name ) if expected_type is not None : check_type ( parameter_name , argument , expected_type ) if not validate ( argument ): raise ArgumentValidationError ( f \"\"\"Argument { parameter_name } in { func . __name__ } did not pass validation\"\"\" ) context = TracingContext . get_current_tracing_context () if context and not disable_logging : context . log_value ( name = f \" { actual_name } :value\" , value = argument ) if isinstance ( argument , str ): context . log_value ( name = f \" { actual_name } :length\" , value = len ( argument )) return func ( * args , ** kwargs ) return cast ( F , wrapper ) return decorator","title":"parameter()"},{"location":"reference/#great_ai.log_metric","text":"Log a key (argument_name)-value pair that is persisted inside the trace. The name of the function from where this is called is also stored. Parameters: Name Type Description Default argument_name str The key for storing the value. required value Any Value to log. Must be JSON-serialisable. required Source code in great_ai/parameters/log_metric.py 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 def log_metric ( argument_name : str , value : Any ) -> None : \"\"\"Log a key (argument_name)-value pair that is persisted inside the trace. The name of the function from where this is called is also stored. Args: argument_name: The key for storing the value. value: Value to log. Must be JSON-serialisable. \"\"\" tracing_context = TracingContext . get_current_tracing_context () caller = inspect . stack ()[ 1 ] . function actual_name = f \"metric: { caller } : { argument_name } \" if tracing_context : tracing_context . log_value ( name = actual_name , value = value ) get_context () . logger . info ( f \" { actual_name } = { value } \" )","title":"log_metric()"},{"location":"reference/#remote-calls","text":"","title":"Remote calls"},{"location":"reference/#great_ai.call_remote_great_ai","text":"Communicate with a GreatAI object through an HTTP request. Wrapper over call_remote_great_ai_async making it synchronous. For more info, see call_remote_great_ai_async . Source code in great_ai/remote/call_remote_great_ai.py 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 def call_remote_great_ai ( base_uri : str , data : Mapping [ str , Any ], retry_count : int = 4 , timeout_in_seconds : Optional [ int ] = 300 , model_class : Optional [ Type [ T ]] = None , ) -> Trace [ T ]: \"\"\"Communicate with a GreatAI object through an HTTP request. Wrapper over `call_remote_great_ai_async` making it synchronous. For more info, see `call_remote_great_ai_async`. \"\"\" try : asyncio . get_running_loop () raise Exception ( f \"Already running in an event loop, you have to call ` { call_remote_great_ai_async . __name__ } `\" ) except RuntimeError : pass future = call_remote_great_ai_async ( base_uri = base_uri , data = data , retry_count = retry_count , timeout_in_seconds = timeout_in_seconds , model_class = model_class , ) return asyncio . run ( future )","title":"call_remote_great_ai()"},{"location":"reference/#great_ai.call_remote_great_ai_async","text":"Communicate with a GreatAI object through an HTTP request. Send a POST request using httpx to implement a remote call. Error-handling and retries are provided by httpx. The return value is inflated into a Trace. If model_class is specified, the original output is deserialised. Parameters: Name Type Description Default base_uri str Address of the remote instance, example: 'http://localhost:6060' required data Mapping [ str , Any ] The input sent as a json to the remote instance. required retry_count int Retry on any HTTP communication failure. 4 timeout_in_seconds Optional [ int ] Overall permissible max length of the request. None means no timeout. 300 model_class Optional [ Type [ T ]] A subtype of BaseModel to be used for deserialising the .output of the trace. None Source code in great_ai/remote/call_remote_great_ai_async.py 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 async def call_remote_great_ai_async ( base_uri : str , data : Mapping [ str , Any ], retry_count : int = 4 , timeout_in_seconds : Optional [ int ] = 300 , model_class : Optional [ Type [ T ]] = None , ) -> Trace [ T ]: \"\"\"Communicate with a GreatAI object through an HTTP request. Send a POST request using [httpx](https://www.python-httpx.org/) to implement a remote call. Error-handling and retries are provided by httpx. The return value is inflated into a Trace. If `model_class` is specified, the original output is deserialised. Args: base_uri: Address of the remote instance, example: 'http://localhost:6060' data: The input sent as a json to the remote instance. retry_count: Retry on any HTTP communication failure. timeout_in_seconds: Overall permissible max length of the request. `None` means no timeout. model_class: A subtype of BaseModel to be used for deserialising the `.output` of the trace. \"\"\" if base_uri . endswith ( \"/\" ): base_uri = base_uri [: - 1 ] if not base_uri . endswith ( \"/predict\" ): base_uri = f \" { base_uri } /predict\" transport = httpx . AsyncHTTPTransport ( retries = retry_count ) try : async with httpx . AsyncClient ( transport = transport , timeout = timeout_in_seconds ) as client : response = await client . post ( base_uri , json = data ) try : response . raise_for_status () except Exception : raise RemoteCallError ( f \"Unexpected status code, reason: { response . text } \" ) except Exception as e : raise RemoteCallError from e try : trace = response . json () except Exception : raise RemoteCallError ( f \"JSON parsing failed { response . text } \" , ) try : if model_class is not None : trace [ \"output\" ] = model_class . parse_obj ( trace [ \"output\" ]) return Trace . parse_obj ( trace ) except Exception : raise RemoteCallError ( \"Could not parse Trace\" )","title":"call_remote_great_ai_async()"},{"location":"reference/#ground-truth","text":"","title":"Ground-truth"},{"location":"reference/#great_ai.add_ground_truth","text":"Add training data (with optional train-test splitting). Add and tag data-points, wrap them into traces. The inputs are available via the .input property, while expected_outputs under both the .output and .feedback properties. All generated traces are tagged with ground_truth by default. Additional tags can be also provided. Using the split_ratio arguments, tags can be given randomly with a user-defined distribution. Only the ratio of the splits matter, they don't have to add up to 1. Examples: >>> add_ground_truth ( ... [ 1 , 2 , 3 ], ... [ 'odd' , 'even' , 'odd' ], ... tags = 'my_tag' , ... train_split_ratio = 1 , ... test_split_ratio = 1 , ... validation_split_ratio = 0.5 , ... ) >>> add_ground_truth ( ... [ 1 , 2 ], ... [ 'odd' , 'even' , 'odd' ], ... tags = 'my_tag' , ... train_split_ratio = 1 , ... test_split_ratio = 1 , ... validation_split_ratio = 0.5 , ... ) Traceback (most recent call last): ... AssertionError : The length of the inputs and expected_outputs must be equal Parameters: Name Type Description Default inputs Iterable [ Any ] The inputs. (X in scikit-learn) required expected_outputs Iterable [ T ] The ground-truth values corresponding to the inputs. (y in scikit-learn) required tags Union [ List [ str ], str ] A single tag or a list of tags to append to each generated trace's tags. [] train_split_ratio float The probability-weight of giving each trace the train tag. 1 test_split_ratio float The probability-weight of giving each trace the test tag. 0 validation_split_ratio float The probability-weight of giving each trace the validation tag. 0 Source code in great_ai/tracing/add_ground_truth.py 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 def add_ground_truth ( inputs : Iterable [ Any ], expected_outputs : Iterable [ T ], * , tags : Union [ List [ str ], str ] = [], train_split_ratio : float = 1 , test_split_ratio : float = 0 , validation_split_ratio : float = 0 ) -> None : \"\"\"Add training data (with optional train-test splitting). Add and tag data-points, wrap them into traces. The `inputs` are available via the `.input` property, while `expected_outputs` under both the `.output` and `.feedback` properties. All generated traces are tagged with `ground_truth` by default. Additional tags can be also provided. Using the `split_ratio` arguments, tags can be given randomly with a user-defined distribution. Only the ratio of the splits matter, they don't have to add up to 1. Examples: >>> add_ground_truth( ... [1, 2, 3], ... ['odd', 'even', 'odd'], ... tags='my_tag', ... train_split_ratio=1, ... test_split_ratio=1, ... validation_split_ratio=0.5, ... ) >>> add_ground_truth( ... [1, 2], ... ['odd', 'even', 'odd'], ... tags='my_tag', ... train_split_ratio=1, ... test_split_ratio=1, ... validation_split_ratio=0.5, ... ) Traceback (most recent call last): ... AssertionError: The length of the inputs and expected_outputs must be equal Args: inputs: The inputs. (X in scikit-learn) expected_outputs: The ground-truth values corresponding to the inputs. (y in scikit-learn) tags: A single tag or a list of tags to append to each generated trace's tags. train_split_ratio: The probability-weight of giving each trace the `train` tag. test_split_ratio: The probability-weight of giving each trace the `test` tag. validation_split_ratio: The probability-weight of giving each trace the `validation` tag. \"\"\" inputs = list ( inputs ) expected_outputs = list ( expected_outputs ) assert len ( inputs ) == len ( expected_outputs ), \"The length of the inputs and expected_outputs must be equal\" tags = tags if isinstance ( tags , list ) else [ tags ] sum_ratio = train_split_ratio + test_split_ratio + validation_split_ratio assert sum_ratio > 0 , \"The sum of the split ratios must be a positive number\" train_split_ratio /= sum_ratio test_split_ratio /= sum_ratio validation_split_ratio /= sum_ratio values = list ( zip ( inputs , expected_outputs )) shuffle ( values ) split_tags = ( [ TRAIN_SPLIT_TAG_NAME ] * ceil ( train_split_ratio * len ( inputs )) + [ TEST_SPLIT_TAG_NAME ] * ceil ( test_split_ratio * len ( inputs )) + [ VALIDATION_SPLIT_TAG_NAME ] * ceil ( validation_split_ratio * len ( inputs )) ) shuffle ( split_tags ) created = datetime . utcnow () . isoformat () traces = [ cast ( Trace [ T ], Trace ( # avoid ValueError: \"Trace\" object has no field \"__orig_class__\" trace_id = str ( uuid4 ()), created = created , original_execution_time_ms = 0 , logged_values = X if isinstance ( X , dict ) else { \"input\" : X }, models = [], output = y , feedback = y , exception = None , tags = [ GROUND_TRUTH_TAG_NAME , split_tag , * tags ], ), ) for (( X , y ), split_tag ) in zip ( values , split_tags ) ] get_context () . tracing_database . save_batch ( traces )","title":"add_ground_truth()"},{"location":"reference/#great_ai.query_ground_truth","text":"Return training samples. Combines, filters, and returns data-points that have been either added by add_ground_truth or were the result of a prediction after which their trace got feedback through the RESP API-s /traces/{trace_id}/feedback endpoint (end-to-end feedback). Filtering can be used to only return points matching all given tags (or the single given tag) and by time of creation. Examples: >>> query_ground_truth () [...] Parameters: Name Type Description Default conjunctive_tags Union [ List [ str ], str ] Single tag or a list of tags which the returned traces have to match. The relationship between the tags is conjunctive (AND). [] since Optional [ datetime ] Only return traces created after the given timestamp. None means no filtering. None until Optional [ datetime ] Only return traces created before the given timestamp. None means no filtering. None return_max_count Optional [ int ] Return at-most this many traces. (take, limit) None Source code in great_ai/tracing/query_ground_truth.py 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 def query_ground_truth ( conjunctive_tags : Union [ List [ str ], str ] = [], * , since : Optional [ datetime ] = None , until : Optional [ datetime ] = None , return_max_count : Optional [ int ] = None ) -> List [ Trace ]: \"\"\"Return training samples. Combines, filters, and returns data-points that have been either added by `add_ground_truth` or were the result of a prediction after which their trace got feedback through the RESP API-s `/traces/{trace_id}/feedback` endpoint (end-to-end feedback). Filtering can be used to only return points matching all given tags (or the single given tag) and by time of creation. Examples: >>> query_ground_truth() [...] Args: conjunctive_tags: Single tag or a list of tags which the returned traces have to match. The relationship between the tags is conjunctive (AND). since: Only return traces created after the given timestamp. `None` means no filtering. until: Only return traces created before the given timestamp. `None` means no filtering. return_max_count: Return at-most this many traces. (take, limit) \"\"\" tags = ( conjunctive_tags if isinstance ( conjunctive_tags , list ) else [ conjunctive_tags ] ) db = get_context () . tracing_database items , length = db . query ( conjunctive_tags = tags , since = since , until = until , take = return_max_count , has_feedback = True , ) return items","title":"query_ground_truth()"},{"location":"reference/#great_ai.delete_ground_truth","text":"Delete traces matching the given criteria. Takes the same arguments as query_ground_truth but instead of returning them, it simply deletes them. You can rely on the efficiency of the delete's implementation. Examples: >>> delete_ground_truth ([ 'train' , 'test' , 'validation' ]) Parameters: Name Type Description Default conjunctive_tags Union [ List [ str ], str ] Single tag or a list of tags which the deleted traces have to match. The relationship between the tags is conjunctive (AND). [] since Optional [ datetime ] Only delete traces created after the given timestamp. None means no filtering. None until Optional [ datetime ] Only delete traces created before the given timestamp. None means no filtering. None Source code in great_ai/tracing/delete_ground_truth.py 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 def delete_ground_truth ( conjunctive_tags : Union [ List [ str ], str ] = [], * , since : Optional [ datetime ] = None , until : Optional [ datetime ] = None , ) -> None : \"\"\"Delete traces matching the given criteria. Takes the same arguments as `query_ground_truth` but instead of returning them, it simply deletes them. You can rely on the efficiency of the delete's implementation. Examples: >>> delete_ground_truth(['train', 'test', 'validation']) Args: conjunctive_tags: Single tag or a list of tags which the deleted traces have to match. The relationship between the tags is conjunctive (AND). since: Only delete traces created after the given timestamp. `None` means no filtering. until: Only delete traces created before the given timestamp. `None` means no filtering. \"\"\" tags = ( conjunctive_tags if isinstance ( conjunctive_tags , list ) else [ conjunctive_tags ] ) db = get_context () . tracing_database items , length = db . query ( conjunctive_tags = tags , until = until , since = since , has_feedback = True ) db . delete_batch ([ i . trace_id for i in items ])","title":"delete_ground_truth()"},{"location":"reference/#tracing-databases","text":"","title":"Tracing databases"},{"location":"reference/#great_ai.TracingDatabaseDriver","text":"Bases: ABC Interface expected from a database to be used for storing traces. Source code in great_ai/persistence/tracing_database_driver.py 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 class TracingDatabaseDriver ( ABC ): \"\"\"Interface expected from a database to be used for storing traces.\"\"\" is_production_ready : bool initialized : bool = False @classmethod def configure_credentials_from_file ( cls , secrets : Union [ Path , str , ConfigFile ], ) -> None : if not isinstance ( secrets , ConfigFile ): secrets = ConfigFile ( secrets ) cls . configure_credentials ( ** { k . lower (): v for k , v in secrets . items ()}) @classmethod def configure_credentials ( cls , ) -> None : cls . initialized = True @abstractmethod def save ( self , document : Trace ) -> str : pass @abstractmethod def save_batch ( self , documents : List [ Trace ], ) -> List [ str ]: pass @abstractmethod def get ( self , id : str ) -> Optional [ Trace ]: pass @abstractmethod def query ( self , * , skip : int = 0 , take : Optional [ int ] = None , conjunctive_filters : Sequence [ Filter ] = [], conjunctive_tags : Sequence [ str ] = [], until : Optional [ datetime ] = None , since : Optional [ datetime ] = None , has_feedback : Optional [ bool ] = None , sort_by : Sequence [ SortBy ] = [] ) -> Tuple [ List [ Trace ], int ]: pass @abstractmethod def update ( self , id : str , new_version : Trace ) -> None : pass @abstractmethod def delete ( self , id : str ) -> None : pass @abstractmethod def delete_batch ( self , ids : List [ str ], ) -> None : pass","title":"TracingDatabaseDriver"},{"location":"reference/#great_ai.MongoDbDriver","text":"Bases: TracingDatabaseDriver TracingDatabaseDriver implementation using MongoDB as a backend. A production-ready database driver suitable for efficiently handling semi-structured data. Checkout MongoDB Atlas for a hosted MongoDB solution. Source code in great_ai/persistence/mongodb_driver.py 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 class MongoDbDriver ( TracingDatabaseDriver ): \"\"\"TracingDatabaseDriver implementation using MongoDB as a backend. A production-ready database driver suitable for efficiently handling semi-structured data. Checkout [MongoDB Atlas](https://www.mongodb.com/cloud/atlas/register) for a hosted MongoDB solution. \"\"\" is_production_ready = True mongo_connection_string : str mongo_database : str def __init__ ( self ) -> None : super () . __init__ () if self . mongo_connection_string is None or self . mongo_database is None : raise ValueError ( \"Please configure the MongoDB access options by calling \" \"MongoDbDriver.configure_credentials\" ) with MongoClient [ Any ]( self . mongo_connection_string ) as client : client [ self . mongo_database ] . traces . create_index ( [( \"tags\" , ASCENDING ), ( \"created\" , DESCENDING )], background = True ) @classmethod def configure_credentials ( # type: ignore cls , * , mongo_connection_string : str , mongo_database : str , ** _ : Any , ) -> None : \"\"\"Configure the connection details for MongoDB. Args: mongo_connection_string: For example: 'mongodb://my_user:my_pass@localhost:27017' mongo_database: Name of the database to use. If doesn't exist, it is created and initialised. \"\"\" cls . mongo_connection_string = mongo_connection_string cls . mongo_database = mongo_database super () . configure_credentials () def save ( self , trace : Trace ) -> str : serialized = trace . to_flat_dict () serialized [ \"_id\" ] = trace . trace_id with MongoClient [ Any ]( self . mongo_connection_string ) as client : return client [ self . mongo_database ] . traces . insert_one ( serialized ) . inserted_id def save_batch ( self , documents : List [ Trace ]) -> List [ str ]: serialized = [ d . to_flat_dict () for d in documents ] for s in serialized : s [ \"_id\" ] = s [ \"trace_id\" ] with MongoClient [ Any ]( self . mongo_connection_string ) as client : return ( client [ self . mongo_database ] . traces . insert_many ( serialized , ordered = False ) . inserted_ids ) def get ( self , id : str ) -> Optional [ Trace ]: with MongoClient [ Any ]( self . mongo_connection_string ) as client : value = client [ self . mongo_database ] . traces . find_one ( id ) if value : value = Trace . parse_obj ( value ) return value def _get_operator ( self , filter : Filter ) -> str : if filter . operator == \"contains\" and not isinstance ( filter . value , str ): return operator_mapping [ \"=\" ] return operator_mapping [ filter . operator ] def query ( self , * , skip : int = 0 , take : Optional [ int ] = None , conjunctive_filters : Sequence [ Filter ] = [], conjunctive_tags : Sequence [ str ] = [], since : Optional [ datetime ] = None , until : Optional [ datetime ] = None , has_feedback : Optional [ bool ] = None , sort_by : Sequence [ SortBy ] = [], ) -> Tuple [ List [ Trace ], int ]: query : Dict [ str , Any ] = { \"filter\" : {}, } and_query : List [ Dict [ str , Any ]] = [] and_query . extend ({ \"tags\" : tag } for tag in conjunctive_tags ) and_query . extend ( { f . property : { self . _get_operator ( f ): f . value }} for f in conjunctive_filters ) if not and_query : and_query . append ({}) if since : and_query . append ({ \"created\" : { \"$gte\" : since }}) if until : and_query . append ({ \"created\" : { \"$lte\" : until }}) if has_feedback is not None : and_query . append ( { \"feedback\" : { \"$ne\" : None }} if has_feedback else { \"feedback\" : None } ) query [ \"filter\" ][ \"$and\" ] = and_query with MongoClient [ Any ]( self . mongo_connection_string ) as client : count = client [ self . mongo_database ] . traces . count_documents ( ** query ) if skip : query [ \"skip\" ] = skip if take : query [ \"limit\" ] = take query [ \"sort\" ] = [ ( col . column_id , 1 if col . direction == \"asc\" else - 1 ) for col in sort_by ] with client [ self . mongo_database ] . traces . find ( ** query ) as cursor : documents = [ Trace [ Any ] . parse_obj ( t ) for t in cursor ] return documents , count def update ( self , id : str , new_version : Trace ) -> None : serialized = new_version . to_flat_dict () serialized [ \"_id\" ] = new_version . trace_id with MongoClient [ Any ]( self . mongo_connection_string ) as client : client [ self . mongo_database ] . traces . update_one ({ \"_id\" : id }, serialized ) def delete ( self , id : str ) -> None : with MongoClient [ Any ]( self . mongo_connection_string ) as client : client [ self . mongo_database ] . traces . delete_one ({ \"_id\" : id }) def delete_batch ( self , ids : List [ str ]) -> None : with MongoClient [ Any ]( self . mongo_connection_string ) as client : for c in chunk ( ids , chunk_size = 10000 ): # avoid: 'delete' command document too large delete_filter = { \"_id\" : { \"$in\" : c }} client [ self . mongo_database ] . traces . delete_many ( delete_filter )","title":"MongoDbDriver"},{"location":"reference/#great_ai.persistence.mongodb_driver.MongoDbDriver.configure_credentials","text":"Configure the connection details for MongoDB. Parameters: Name Type Description Default mongo_connection_string str For example: 'mongodb://my_user:my_pass@localhost:27017' required mongo_database str Name of the database to use. If doesn't exist, it is created and initialised. required Source code in great_ai/persistence/mongodb_driver.py 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 @classmethod def configure_credentials ( # type: ignore cls , * , mongo_connection_string : str , mongo_database : str , ** _ : Any , ) -> None : \"\"\"Configure the connection details for MongoDB. Args: mongo_connection_string: For example: 'mongodb://my_user:my_pass@localhost:27017' mongo_database: Name of the database to use. If doesn't exist, it is created and initialised. \"\"\" cls . mongo_connection_string = mongo_connection_string cls . mongo_database = mongo_database super () . configure_credentials ()","title":"configure_credentials()"},{"location":"reference/#great_ai.ParallelTinyDbDriver","text":"Bases: TracingDatabaseDriver TracingDatabaseDriver with TinyDB as a backend. Saves the database as a JSON into a single file. Highly inefficient on inserting, not advised for production use. A multiprocessing lock protects the database file to avoid parallelisation issues. Source code in great_ai/persistence/parallel_tinydb_driver.py 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 class ParallelTinyDbDriver ( TracingDatabaseDriver ): \"\"\"TracingDatabaseDriver with TinyDB as a backend. Saves the database as a JSON into a single file. Highly inefficient on inserting, not advised for production use. A multiprocessing lock protects the database file to avoid parallelisation issues. \"\"\" is_production_ready = False path_to_db = Path ( DEFAULT_TRACING_DB_FILENAME ) def save ( self , trace : Trace ) -> str : return self . _safe_execute ( lambda db : db . insert ( trace . dict ())) def save_batch ( self , documents : List [ Trace ]) -> List [ str ]: traces = [ d . dict () for d in documents ] return self . _safe_execute ( lambda db : db . insert_multiple ( traces )) def get ( self , id : str ) -> Optional [ Trace ]: value = self . _safe_execute ( lambda db : db . get ( lambda d : d [ \"trace_id\" ] == id )) if value : value = Trace . parse_obj ( value ) return value def query ( self , * , skip : int = 0 , take : Optional [ int ] = None , conjunctive_filters : Sequence [ Filter ] = [], conjunctive_tags : Sequence [ str ] = [], since : Optional [ datetime ] = None , until : Optional [ datetime ] = None , has_feedback : Optional [ bool ] = None , sort_by : Sequence [ SortBy ] = [] ) -> Tuple [ List [ Trace ], int ]: def does_match ( d : Dict [ str , Any ]) -> bool : return ( not set ( conjunctive_tags ) - set ( d [ \"tags\" ]) and ( since is None or datetime . fromisoformat ( d [ \"created\" ]) >= since ) and ( until is None or datetime . fromisoformat ( d [ \"created\" ]) <= until ) and ( has_feedback is None or has_feedback == ( d [ \"feedback\" ] is not None ) ) ) documents : List [ Trace ] = [ Trace . parse_obj ( t ) for t in self . _safe_execute ( lambda db : db . search ( does_match )) ] if not documents : return [], 0 df = pd . DataFrame ([ d . to_flat_dict () for d in documents ]) for f in conjunctive_filters : operator = f . operator . lower () if operator in operator_mapping : df = df . loc [ getattr ( df [ f . property ], operator_mapping [ f . operator ])( f . value ) ] elif operator == \"contains\" : df = df . loc [ df [ f . property ] . str . contains ( f . value , case = False )] if sort_by : df . sort_values ( [ col . column_id for col in sort_by ], ascending = [ col . direction == \"asc\" for col in sort_by ], inplace = True , ) count = len ( df ) result = df . iloc [ skip :] if take is None else df . iloc [ skip : skip + take ] return [ next ( d for d in documents if d . trace_id == trace_id ) for trace_id in result [ \"trace_id\" ] ], count def update ( self , id : str , new_version : Trace ) -> None : self . _safe_execute ( lambda db : db . update ( new_version . dict (), lambda d : d [ \"trace_id\" ] == id ) ) def delete ( self , id : str ) -> None : self . _safe_execute ( lambda db : db . remove ( lambda d : d [ \"trace_id\" ] == id )) def delete_batch ( self , ids : List [ str ]) -> None : for i in ids : self . delete ( i ) def _safe_execute ( self , func : Callable [[ TinyDB ], Any ]) -> Any : with lock : with TinyDB ( self . path_to_db ) as db : return func ( db )","title":"ParallelTinyDbDriver"},{"location":"reference/large-file/","text":"LargeFile # from great_ai.large_file import * For more details about using LargeFiles, check out the how to guide . LargeFileLocal # Bases: LargeFileBase LargeFile implementation using local filesystem as a backend. Store large files remotely using the familiar API of open() . With built-in versioning, pruning and local cache. IMPORTANT: If LargeFileLocal.max_cache_size is too small, it won't be enough to store all your files and they can end up deleted. See parent for more details. Examples: >>> LargeFileLocal . cache_path = Path ( \".cache\" ) >>> LargeFileLocal . max_cache_size = \"30GB\" >>> with LargeFileLocal ( \"my_test.txt\" , \"w\" , keep_last_n = 2 ) as f : ... f . write ( 'test' ) 4 >>> with LargeFileLocal ( \"my_test.txt\" ) as f : ... print ( f . read ()) test Source code in great_ai/large_file/large_file/large_file_local.py 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 class LargeFileLocal ( LargeFileBase ): \"\"\"LargeFile implementation using local filesystem as a backend. Store large files remotely using the familiar API of `open()`. With built-in versioning, pruning and local cache. IMPORTANT: If LargeFileLocal.max_cache_size is too small, it won't be enough to store all your files and they can end up deleted. See parent for more details. Examples: >>> LargeFileLocal.cache_path = Path(\".cache\") >>> LargeFileLocal.max_cache_size = \"30GB\" >>> with LargeFileLocal(\"my_test.txt\", \"w\", keep_last_n=2) as f: ... f.write('test') 4 >>> with LargeFileLocal(\"my_test.txt\") as f: ... print(f.read()) test \"\"\" def __init__ ( self , name : str , mode : str = \"r\" , * , buffering : int = - 1 , encoding : Optional [ str ] = None , errors : Optional [ str ] = None , newline : Optional [ str ] = None , version : Optional [ int ] = None , keep_last_n : Optional [ int ] = None , ): super () . __init__ ( name , mode , buffering = buffering , encoding = encoding , errors = errors , newline = newline , version = version , keep_last_n = keep_last_n , cache_only_mode = True , ) super () . configure_credentials () def _find_remote_instances ( self ) -> List [ DataInstance ]: return [] def _download ( self , remote_path : Any , local_path : Path , hide_progress : bool ) -> None : # This will never be called because the file must be in the cache raise NotImplementedError () def _upload ( self , local_path : Path , hide_progress : bool ) -> None : pass # the \"upload\" is already done py the parent's caching mechanism def _delete_old_remote_versions ( self ) -> None : if self . _keep_last_n is not None : for i in ( self . _instances [: - self . _keep_last_n ] if self . _keep_last_n > 0 else self . _instances ): logger . info ( f \"Removing old version (keep_last_n= { self . _keep_last_n } ): { i . remote_path } \" ) i . remote_path . unlink () LargeFileS3 # Bases: LargeFileBase LargeFile implementation using S3-compatible storage as a backend. Store large files remotely using the familiar API of open() . With built-in versioning, pruning and local cache. See parent for more details. Source code in great_ai/large_file/large_file/large_file_s3.py 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 class LargeFileS3 ( LargeFileBase ): \"\"\"LargeFile implementation using S3-compatible storage as a backend. Store large files remotely using the familiar API of `open()`. With built-in versioning, pruning and local cache. See parent for more details. \"\"\" region_name = None access_key_id = None secret_access_key = None bucket_name = None endpoint_url = None @classmethod def configure_credentials ( # type: ignore cls , * , aws_region_name : str , aws_access_key_id : str , aws_secret_access_key : str , large_files_bucket_name : str , aws_endpoint_url : Optional [ str ] = None , ** _ : Any , ) -> None : cls . region_name = aws_region_name cls . access_key_id = aws_access_key_id cls . secret_access_key = aws_secret_access_key cls . bucket_name = large_files_bucket_name cls . endpoint_url = aws_endpoint_url super () . configure_credentials () @cached_property def _client ( self ) -> boto3 . client : if ( self . region_name is None or self . access_key_id is None or self . secret_access_key is None or self . bucket_name is None ): raise ValueError ( \"Please configure the S3 access options by calling LargeFileS3.configure_credentials or set offline_mode=True in the constructor.\" ) return boto3 . client ( \"s3\" , aws_access_key_id = self . access_key_id , aws_secret_access_key = self . secret_access_key , region_name = self . region_name , endpoint_url = self . endpoint_url , ) def _find_remote_instances ( self ) -> List [ DataInstance ]: logger . debug ( f \"Fetching S3 versions of { self . _name } \" ) found_objects = self . _client . list_objects_v2 ( Bucket = self . bucket_name , Prefix = self . _name ) return ( [ DataInstance ( name = o [ \"Key\" ] . split ( S3_NAME_VERSION_SEPARATOR )[ 0 ], version = int ( o [ \"Key\" ] . split ( S3_NAME_VERSION_SEPARATOR )[ - 1 ]), remote_path = o [ \"Key\" ], ) for o in found_objects [ \"Contents\" ] if o [ \"Key\" ] . split ( S3_NAME_VERSION_SEPARATOR )[ 0 ] == self . _name ] if \"Contents\" in found_objects else [] ) def _download ( self , remote_path : Any , local_path : Path , hide_progress : bool ) -> None : logger . info ( f \"Downloading { remote_path } from S3\" ) size = self . _client . head_object ( Bucket = self . bucket_name , Key = remote_path )[ \"ContentLength\" ] self . _client . download_file ( Bucket = self . bucket_name , Key = remote_path , Filename = str ( local_path ), Callback = None if hide_progress else DownloadProgressBar ( name = str ( remote_path ), size = size , logger = logger ), ) def _upload ( self , local_path : Path , hide_progress : bool ) -> None : key = f \" { self . _name } / { self . version } \" logger . info ( f \"Uploading { self . _local_name } to S3 as { key } \" ) self . _client . upload_file ( Filename = str ( local_path ), Bucket = self . bucket_name , Key = key , Callback = None if hide_progress else UploadProgressBar ( path = local_path , logger = logger ), ) def _delete_old_remote_versions ( self ) -> None : if self . _keep_last_n is not None : for i in ( self . _instances [: - self . _keep_last_n ] if self . _keep_last_n > 0 else self . _instances ): logger . info ( f \"Removing old version from S3 (keep_last_n= { self . _keep_last_n } ): { i . remote_path } \" ) self . _client . delete_object ( Bucket = self . bucket_name , Key = i . remote_path ) LargeFileMongo # Bases: LargeFileBase LargeFile implementation using GridFS (MongoDB) as a backend. Store large files remotely using the familiar API of open() . With built-in versioning, pruning and local cache. See parent for more details. Source code in great_ai/large_file/large_file/large_file_mongo.py 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 class LargeFileMongo ( LargeFileBase ): \"\"\"LargeFile implementation using GridFS (MongoDB) as a backend. Store large files remotely using the familiar API of `open()`. With built-in versioning, pruning and local cache. See parent for more details. \"\"\" mongo_connection_string = None mongo_database = None @classmethod def configure_credentials ( # type: ignore cls , * , mongo_connection_string : str , mongo_database : str , ** _ : Any , ) -> None : cls . mongo_connection_string = mongo_connection_string cls . mongo_database = mongo_database super () . configure_credentials () @cached_property def _client ( self ) -> GridFSBucket : if self . mongo_connection_string is None or self . mongo_database is None : raise ValueError ( \"Please configure the MongoDB access options by calling LargeFileMongo.configure_credentials or set offline_mode=True in the constructor.\" ) db : Database = MongoClient ( self . mongo_connection_string )[ self . mongo_database ] return GridFSBucket ( db ) def _find_remote_instances ( self ) -> List [ DataInstance ]: logger . debug ( f \"Fetching Mongo (GridFS) versions of { self . _name } \" ) return [ DataInstance ( name = MONGO_NAME_VERSION_SEPARATOR . join ( f . name . split ( MONGO_NAME_VERSION_SEPARATOR )[: - 1 ] ), version = int ( f . name . split ( MONGO_NAME_VERSION_SEPARATOR )[ - 1 ]), remote_path = ( f . _id , f . length ), origin = \"mongodb\" , ) for f in self . _client . find ( { \"filename\" : re . compile ( re . escape ( self . _name + MONGO_NAME_VERSION_SEPARATOR ) + \".*\" ) } ) ] def _download ( self , remote_path : Any , local_path : Path , hide_progress : bool ) -> None : logger . info ( f \"Downloading { remote_path [ 0 ] } from Mongo (GridFS)\" ) progress = ( DownloadProgressBar ( name = str ( remote_path [ 0 ]), size = remote_path [ 1 ], logger = logger ) if not hide_progress else None ) with self . _client . open_download_stream ( remote_path [ 0 ]) as stream : with open ( local_path , \"wb\" ) as f : while True : content = stream . read ( DEFAULT_CHUNK_SIZE ) f . write ( content ) if progress : progress ( len ( content )) if len ( content ) < DEFAULT_CHUNK_SIZE : break def _upload ( self , local_path : Path , hide_progress : bool ) -> None : logger . info ( f \"Uploading { local_path } to Mongo (GridFS)\" ) progress = ( UploadProgressBar ( path = local_path , logger = logger ) if not hide_progress else None ) with self . _client . open_upload_stream ( f \" { self . _name }{ MONGO_NAME_VERSION_SEPARATOR }{ self . version } \" ) as stream : with open ( local_path , \"rb\" ) as f : while True : content = f . read ( DEFAULT_CHUNK_SIZE ) stream . write ( content ) if progress : progress ( len ( content )) if len ( content ) < DEFAULT_CHUNK_SIZE : break def _delete_old_remote_versions ( self ) -> None : if self . _keep_last_n is not None : for i in ( self . _instances [: - self . _keep_last_n ] if self . _keep_last_n > 0 else self . _instances ): logger . info ( f \"Removing old version from MongoDB (GridFS) (keep_last_n= { self . _keep_last_n } ): { i . name }{ MONGO_NAME_VERSION_SEPARATOR }{ i . version } \" ) self . _client . delete ( i . remote_path [ 0 ]) LargeFileBase # Bases: ABC Base for LargeFile implementations with different backends. Store large files remotely using the familiar API of open() . With built-in versioning, pruning and local cache. By default, files are stored in the \".cache\" folder and the least recently used is deleted after the overall size reaches 30 GBs. Examples: >>> LargeFileBase . cache_path = Path ( \".cache\" ) >>> LargeFileBase . max_cache_size = \"30GB\" Attributes: Name Type Description initialized Tell whether configure_credentials or configure_credentials_from_file has been already called. cache_path Storage location for cached files. max_cache_size Optional [ str ] Delete files until the folder at cache_path is smaller than this value. Examples: \"5 GB\", \"10MB\", \"0.3 TB\". Set to None for no automatic cache-pruning. Source code in great_ai/large_file/large_file/large_file_base.py 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 class LargeFileBase ( ABC ): \"\"\"Base for LargeFile implementations with different backends. Store large files remotely using the familiar API of `open()`. With built-in versioning, pruning and local cache. By default, files are stored in the \".cache\" folder and the least recently used is deleted after the overall size reaches 30 GBs. Examples: >>> LargeFileBase.cache_path = Path(\".cache\") >>> LargeFileBase.max_cache_size = \"30GB\" Attributes: initialized: Tell whether `configure_credentials` or `configure_credentials_from_file` has been already called. cache_path: Storage location for cached files. max_cache_size: Delete files until the folder at `cache_path` is smaller than this value. Examples: \"5 GB\", \"10MB\", \"0.3 TB\". Set to `None` for no automatic cache-pruning. \"\"\" initialized = False cache_path = Path ( \".cache\" ) max_cache_size : Optional [ str ] = \"30GB\" def __init__ ( self , name : str , mode : str = \"r\" , * , buffering : int = - 1 , encoding : Optional [ str ] = None , errors : Optional [ str ] = None , newline : Optional [ str ] = None , version : Optional [ int ] = None , keep_last_n : Optional [ int ] = None , cache_only_mode : bool = False , ): self . _name = name self . _version = version self . _mode = mode self . _keep_last_n = keep_last_n self . _cache_only_mode = cache_only_mode self . _buffering = buffering self . _encoding = encoding if errors is not None and sys . version_info [ 1 ] < 8 : raise RuntimeError ( \"The `errors` kwarg is only supported in 3.8 <= Python versions.\" ) self . _errors = errors self . _newline = newline LargeFileBase . cache_path . mkdir ( parents = True , exist_ok = True ) self . _find_instances () self . _check_mode_and_set_version () @classmethod def configure_credentials_from_file ( cls , secrets : Union [ Path , str , ConfigFile ], ) -> None : \"\"\"Load file and feed its content to `configure_credentials`. Extra keys are ignored. \"\"\" if not isinstance ( secrets , ConfigFile ): secrets = ConfigFile ( secrets ) cls . configure_credentials ( ** { k . lower (): v for k , v in secrets . items ()}) @classmethod def configure_credentials ( cls , ** kwargs : str ) -> None : \"\"\"Configure required credentials for the LargeFile backend.\"\"\" cls . initialized = True def __enter__ ( self ) -> IO : params = dict ( mode = self . _mode , buffering = self . _buffering , encoding = self . _encoding , newline = self . _newline , delete = False , prefix = \"large_file-\" , ) if sys . version_info [ 1 ] >= 8 : params [ \"errors\" ] = self . _errors self . _file : IO [ Any ] = ( tempfile . NamedTemporaryFile ( ** params ) # type: ignore if \"w\" in self . _mode else open ( self . get (), mode = self . _mode , buffering = self . _buffering , encoding = self . _encoding , newline = self . _newline , errors = self . _errors , ) ) return self . _file def __exit__ ( self , type : Optional [ Type [ BaseException ]], exc : Optional [ BaseException ], traceback : Optional [ TracebackType ], ) -> Literal [ False ]: self . _file . close () if type is None : if \"w\" in self . _mode : self . push ( Path ( self . _file . name )) os . unlink ( self . _file . name ) else : logger . exception ( \"Could not finish operation.\" ) return False @property def version ( self ) -> int : \"\"\"Numeric version of the file proxied by this LargeFile instance.\"\"\" return cast ( int , self . _version ) @lru_cache ( 1 ) def get ( self , hide_progress : bool = False ) -> Path : \"\"\"Return path to the proxy of a file (or directory). If not available in the local cache, an attempt is made to download it. Args: hide_progress: Do not show a progress update after each 10% of progress. \"\"\" remote_path = next ( i . remote_path for i in self . _instances if i . version == self . _version ) destination = self . cache_path / self . _local_name if not destination . exists (): logger . info ( f \"File { self . _local_name } does not exist locally\" ) with tempfile . TemporaryDirectory () as tmp : local_root_path = Path ( tmp ) tmp_file_archive = ( local_root_path / f \" { self . _local_name }{ ARCHIVE_EXTENSION } \" ) self . _download ( remote_path , tmp_file_archive , hide_progress = hide_progress ) logger . info ( f \"Decompressing { self . _local_name } \" ) shutil . unpack_archive ( str ( tmp_file_archive ), tmp , COMPRESSION_ALGORITHM ) shutil . move ( str ( local_root_path / self . _local_name ), str ( destination )) else : logger . info ( f \"File { self . _local_name } found in cache\" ) return destination def push ( self , path : Union [ Path , str ], hide_progress : bool = False ) -> None : \"\"\"Upload a file (or directory) as a new version of `key`. The file/directory is compressed before upload. Args: hide_progress: Do not show a progress update after each 10% of progress. \"\"\" if isinstance ( path , str ): path = Path ( path ) with tempfile . TemporaryDirectory () as tmp : if path . is_file (): logger . info ( f \"Copying file for { self . _local_name } \" ) copy : Any = shutil . copy else : logger . info ( f \"Copying directory for { self . _local_name } \" ) copy = shutil . copytree try : # Make local copy in the cache shutil . rmtree ( self . cache_path / self . _local_name , ignore_errors = True ) copy ( str ( path ), str ( self . cache_path / self . _local_name )) except shutil . SameFileError : pass # No worries copy ( str ( path ), str ( Path ( tmp ) / self . _local_name )) with tempfile . TemporaryDirectory () as tmp2 : # A directory has to be zipped and it cannot contain the output of the zipping logger . info ( f \"Compressing { self . _local_name } \" ) shutil . make_archive ( str ( Path ( tmp2 ) / self . _local_name ), COMPRESSION_ALGORITHM , tmp , ) file_to_be_uploaded = ( Path ( tmp2 ) / f \" { self . _local_name }{ ARCHIVE_EXTENSION } \" ) self . _upload ( file_to_be_uploaded , hide_progress = hide_progress ) self . clean_up () def delete ( self ) -> None : \"\"\"Delete all versions of the files under this `key`.\"\"\" self . _keep_last_n = 0 self . _delete_old_remote_versions () @property def versions_pretty ( self ) -> str : \"\"\"Formatted string of all available versions.\"\"\" return \", \" . join (( str ( i . version ) for i in self . _instances )) def clean_up ( self ) -> None : \"\"\"Delete local and remote versions according to currently set cache and retention policy.\"\"\" self . _delete_old_remote_versions () self . _prune_cache () @property def _local_name ( self ) -> str : return f \" { self . _name }{ CACHE_NAME_VERSION_SEPARATOR }{ self . version } \" def _find_instances ( self ) -> None : if self . _cache_only_mode : self . _instances = self . _find_instances_from_cache () else : self . _instances = self . _find_remote_instances () self . _instances = sorted ( self . _instances , key = lambda i : i . version ) def _find_instances_from_cache ( self ) -> List [ DataInstance ]: logger . info ( f \"Fetching cached versions of { self . _name } \" ) candidates = [ DataInstance ( name = CACHE_NAME_VERSION_SEPARATOR . join ( f . name . split ( CACHE_NAME_VERSION_SEPARATOR )[: - 1 ] ), version = int ( f . name . split ( CACHE_NAME_VERSION_SEPARATOR )[ - 1 ]), remote_path = f , ) for f in self . cache_path . glob ( f \" { self . _name }{ CACHE_NAME_VERSION_SEPARATOR } *\" ) ] return [ c for c in candidates if c . name == self . _name ] def _check_mode_and_set_version ( self ) -> None : if \"+\" in self . _mode : raise ValueError ( f \"File mode ` { self . _mode } ` is not allowed3, remove the `+`.\" ) if \"w\" in self . _mode : if self . _version is not None : raise ValueError ( \"Providing a version is not allowed in write mode.\" ) self . _version = self . _instances [ - 1 ] . version + 1 if self . _instances else 0 elif \"r\" in self . _mode : if not self . _instances : raise FileNotFoundError ( f \"File { self . _name } not found. No versions are available.\" ) if self . _version is None : self . _version = self . _instances [ - 1 ] . version logger . info ( f \"Latest version of { self . _name } is { self . _version } \" + f \"(from versions: { self . versions_pretty } )\" ) elif self . _version not in [ i . version for i in self . _instances ]: raise FileNotFoundError ( f \"File { self . _name } not found with version { self . _version } . \" + f \"(from versions: { self . versions_pretty } )\" ) else : raise ValueError ( \"Unsupported file mode.\" ) def _prune_cache ( self ) -> None : self . cache_path . mkdir ( parents = True , exist_ok = True ) if self . max_cache_size is None : return allowed_size = human_readable_to_byte ( self . max_cache_size ) assert allowed_size >= 0 least_recently_read = sorted ( [ f for f in self . cache_path . glob ( \"*\" )], key = lambda f : f . stat () . st_atime ) while sum ( os . path . getsize ( f ) for f in least_recently_read ) > allowed_size : file = least_recently_read . pop ( 0 ) logger . info ( f \"Deleting file from cache to meet quota (max_cache_size= { self . max_cache_size } ): { file } \" ) os . unlink ( file ) @abstractmethod def _find_remote_instances ( self ) -> List [ DataInstance ]: pass @abstractmethod def _download ( self , remote_path : Any , local_path : Path , hide_progress : bool ) -> None : pass @abstractmethod def _upload ( self , local_path : Path , hide_progress : bool ) -> None : pass @abstractmethod def _delete_old_remote_versions ( self ) -> None : pass clean_up () # Delete local and remote versions according to currently set cache and retention policy. Source code in great_ai/large_file/large_file/large_file_base.py 250 251 252 253 254 def clean_up ( self ) -> None : \"\"\"Delete local and remote versions according to currently set cache and retention policy.\"\"\" self . _delete_old_remote_versions () self . _prune_cache () configure_credentials ( ** kwargs ) classmethod # Configure required credentials for the LargeFile backend. Source code in great_ai/large_file/large_file/large_file_base.py 102 103 104 105 106 @classmethod def configure_credentials ( cls , ** kwargs : str ) -> None : \"\"\"Configure required credentials for the LargeFile backend.\"\"\" cls . initialized = True configure_credentials_from_file ( secrets ) classmethod # Load file and feed its content to configure_credentials . Extra keys are ignored. Source code in great_ai/large_file/large_file/large_file_base.py 88 89 90 91 92 93 94 95 96 97 98 99 100 @classmethod def configure_credentials_from_file ( cls , secrets : Union [ Path , str , ConfigFile ], ) -> None : \"\"\"Load file and feed its content to `configure_credentials`. Extra keys are ignored. \"\"\" if not isinstance ( secrets , ConfigFile ): secrets = ConfigFile ( secrets ) cls . configure_credentials ( ** { k . lower (): v for k , v in secrets . items ()}) delete () # Delete all versions of the files under this key . Source code in great_ai/large_file/large_file/large_file_base.py 239 240 241 242 243 def delete ( self ) -> None : \"\"\"Delete all versions of the files under this `key`.\"\"\" self . _keep_last_n = 0 self . _delete_old_remote_versions () get ( hide_progress = False ) cached # Return path to the proxy of a file (or directory). If not available in the local cache, an attempt is made to download it. Parameters: Name Type Description Default hide_progress bool Do not show a progress update after each 10% of progress. False Source code in great_ai/large_file/large_file/large_file_base.py 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 @lru_cache ( 1 ) def get ( self , hide_progress : bool = False ) -> Path : \"\"\"Return path to the proxy of a file (or directory). If not available in the local cache, an attempt is made to download it. Args: hide_progress: Do not show a progress update after each 10% of progress. \"\"\" remote_path = next ( i . remote_path for i in self . _instances if i . version == self . _version ) destination = self . cache_path / self . _local_name if not destination . exists (): logger . info ( f \"File { self . _local_name } does not exist locally\" ) with tempfile . TemporaryDirectory () as tmp : local_root_path = Path ( tmp ) tmp_file_archive = ( local_root_path / f \" { self . _local_name }{ ARCHIVE_EXTENSION } \" ) self . _download ( remote_path , tmp_file_archive , hide_progress = hide_progress ) logger . info ( f \"Decompressing { self . _local_name } \" ) shutil . unpack_archive ( str ( tmp_file_archive ), tmp , COMPRESSION_ALGORITHM ) shutil . move ( str ( local_root_path / self . _local_name ), str ( destination )) else : logger . info ( f \"File { self . _local_name } found in cache\" ) return destination push ( path , hide_progress = False ) # Upload a file (or directory) as a new version of key . The file/directory is compressed before upload. Parameters: Name Type Description Default hide_progress bool Do not show a progress update after each 10% of progress. False Source code in great_ai/large_file/large_file/large_file_base.py 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 def push ( self , path : Union [ Path , str ], hide_progress : bool = False ) -> None : \"\"\"Upload a file (or directory) as a new version of `key`. The file/directory is compressed before upload. Args: hide_progress: Do not show a progress update after each 10% of progress. \"\"\" if isinstance ( path , str ): path = Path ( path ) with tempfile . TemporaryDirectory () as tmp : if path . is_file (): logger . info ( f \"Copying file for { self . _local_name } \" ) copy : Any = shutil . copy else : logger . info ( f \"Copying directory for { self . _local_name } \" ) copy = shutil . copytree try : # Make local copy in the cache shutil . rmtree ( self . cache_path / self . _local_name , ignore_errors = True ) copy ( str ( path ), str ( self . cache_path / self . _local_name )) except shutil . SameFileError : pass # No worries copy ( str ( path ), str ( Path ( tmp ) / self . _local_name )) with tempfile . TemporaryDirectory () as tmp2 : # A directory has to be zipped and it cannot contain the output of the zipping logger . info ( f \"Compressing { self . _local_name } \" ) shutil . make_archive ( str ( Path ( tmp2 ) / self . _local_name ), COMPRESSION_ALGORITHM , tmp , ) file_to_be_uploaded = ( Path ( tmp2 ) / f \" { self . _local_name }{ ARCHIVE_EXTENSION } \" ) self . _upload ( file_to_be_uploaded , hide_progress = hide_progress ) self . clean_up () version () property # Numeric version of the file proxied by this LargeFile instance. Source code in great_ai/large_file/large_file/large_file_base.py 153 154 155 156 157 @property def version ( self ) -> int : \"\"\"Numeric version of the file proxied by this LargeFile instance.\"\"\" return cast ( int , self . _version ) versions_pretty () property # Formatted string of all available versions. Source code in great_ai/large_file/large_file/large_file_base.py 245 246 247 248 @property def versions_pretty ( self ) -> str : \"\"\"Formatted string of all available versions.\"\"\" return \", \" . join (( str ( i . version ) for i in self . _instances ))","title":"LargeFile"},{"location":"reference/large-file/#largefile","text":"from great_ai.large_file import * For more details about using LargeFiles, check out the how to guide .","title":"LargeFile"},{"location":"reference/large-file/#great_ai.large_file.LargeFileLocal","text":"Bases: LargeFileBase LargeFile implementation using local filesystem as a backend. Store large files remotely using the familiar API of open() . With built-in versioning, pruning and local cache. IMPORTANT: If LargeFileLocal.max_cache_size is too small, it won't be enough to store all your files and they can end up deleted. See parent for more details. Examples: >>> LargeFileLocal . cache_path = Path ( \".cache\" ) >>> LargeFileLocal . max_cache_size = \"30GB\" >>> with LargeFileLocal ( \"my_test.txt\" , \"w\" , keep_last_n = 2 ) as f : ... f . write ( 'test' ) 4 >>> with LargeFileLocal ( \"my_test.txt\" ) as f : ... print ( f . read ()) test Source code in great_ai/large_file/large_file/large_file_local.py 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 class LargeFileLocal ( LargeFileBase ): \"\"\"LargeFile implementation using local filesystem as a backend. Store large files remotely using the familiar API of `open()`. With built-in versioning, pruning and local cache. IMPORTANT: If LargeFileLocal.max_cache_size is too small, it won't be enough to store all your files and they can end up deleted. See parent for more details. Examples: >>> LargeFileLocal.cache_path = Path(\".cache\") >>> LargeFileLocal.max_cache_size = \"30GB\" >>> with LargeFileLocal(\"my_test.txt\", \"w\", keep_last_n=2) as f: ... f.write('test') 4 >>> with LargeFileLocal(\"my_test.txt\") as f: ... print(f.read()) test \"\"\" def __init__ ( self , name : str , mode : str = \"r\" , * , buffering : int = - 1 , encoding : Optional [ str ] = None , errors : Optional [ str ] = None , newline : Optional [ str ] = None , version : Optional [ int ] = None , keep_last_n : Optional [ int ] = None , ): super () . __init__ ( name , mode , buffering = buffering , encoding = encoding , errors = errors , newline = newline , version = version , keep_last_n = keep_last_n , cache_only_mode = True , ) super () . configure_credentials () def _find_remote_instances ( self ) -> List [ DataInstance ]: return [] def _download ( self , remote_path : Any , local_path : Path , hide_progress : bool ) -> None : # This will never be called because the file must be in the cache raise NotImplementedError () def _upload ( self , local_path : Path , hide_progress : bool ) -> None : pass # the \"upload\" is already done py the parent's caching mechanism def _delete_old_remote_versions ( self ) -> None : if self . _keep_last_n is not None : for i in ( self . _instances [: - self . _keep_last_n ] if self . _keep_last_n > 0 else self . _instances ): logger . info ( f \"Removing old version (keep_last_n= { self . _keep_last_n } ): { i . remote_path } \" ) i . remote_path . unlink ()","title":"LargeFileLocal"},{"location":"reference/large-file/#great_ai.large_file.LargeFileS3","text":"Bases: LargeFileBase LargeFile implementation using S3-compatible storage as a backend. Store large files remotely using the familiar API of open() . With built-in versioning, pruning and local cache. See parent for more details. Source code in great_ai/large_file/large_file/large_file_s3.py 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 class LargeFileS3 ( LargeFileBase ): \"\"\"LargeFile implementation using S3-compatible storage as a backend. Store large files remotely using the familiar API of `open()`. With built-in versioning, pruning and local cache. See parent for more details. \"\"\" region_name = None access_key_id = None secret_access_key = None bucket_name = None endpoint_url = None @classmethod def configure_credentials ( # type: ignore cls , * , aws_region_name : str , aws_access_key_id : str , aws_secret_access_key : str , large_files_bucket_name : str , aws_endpoint_url : Optional [ str ] = None , ** _ : Any , ) -> None : cls . region_name = aws_region_name cls . access_key_id = aws_access_key_id cls . secret_access_key = aws_secret_access_key cls . bucket_name = large_files_bucket_name cls . endpoint_url = aws_endpoint_url super () . configure_credentials () @cached_property def _client ( self ) -> boto3 . client : if ( self . region_name is None or self . access_key_id is None or self . secret_access_key is None or self . bucket_name is None ): raise ValueError ( \"Please configure the S3 access options by calling LargeFileS3.configure_credentials or set offline_mode=True in the constructor.\" ) return boto3 . client ( \"s3\" , aws_access_key_id = self . access_key_id , aws_secret_access_key = self . secret_access_key , region_name = self . region_name , endpoint_url = self . endpoint_url , ) def _find_remote_instances ( self ) -> List [ DataInstance ]: logger . debug ( f \"Fetching S3 versions of { self . _name } \" ) found_objects = self . _client . list_objects_v2 ( Bucket = self . bucket_name , Prefix = self . _name ) return ( [ DataInstance ( name = o [ \"Key\" ] . split ( S3_NAME_VERSION_SEPARATOR )[ 0 ], version = int ( o [ \"Key\" ] . split ( S3_NAME_VERSION_SEPARATOR )[ - 1 ]), remote_path = o [ \"Key\" ], ) for o in found_objects [ \"Contents\" ] if o [ \"Key\" ] . split ( S3_NAME_VERSION_SEPARATOR )[ 0 ] == self . _name ] if \"Contents\" in found_objects else [] ) def _download ( self , remote_path : Any , local_path : Path , hide_progress : bool ) -> None : logger . info ( f \"Downloading { remote_path } from S3\" ) size = self . _client . head_object ( Bucket = self . bucket_name , Key = remote_path )[ \"ContentLength\" ] self . _client . download_file ( Bucket = self . bucket_name , Key = remote_path , Filename = str ( local_path ), Callback = None if hide_progress else DownloadProgressBar ( name = str ( remote_path ), size = size , logger = logger ), ) def _upload ( self , local_path : Path , hide_progress : bool ) -> None : key = f \" { self . _name } / { self . version } \" logger . info ( f \"Uploading { self . _local_name } to S3 as { key } \" ) self . _client . upload_file ( Filename = str ( local_path ), Bucket = self . bucket_name , Key = key , Callback = None if hide_progress else UploadProgressBar ( path = local_path , logger = logger ), ) def _delete_old_remote_versions ( self ) -> None : if self . _keep_last_n is not None : for i in ( self . _instances [: - self . _keep_last_n ] if self . _keep_last_n > 0 else self . _instances ): logger . info ( f \"Removing old version from S3 (keep_last_n= { self . _keep_last_n } ): { i . remote_path } \" ) self . _client . delete_object ( Bucket = self . bucket_name , Key = i . remote_path )","title":"LargeFileS3"},{"location":"reference/large-file/#great_ai.large_file.LargeFileMongo","text":"Bases: LargeFileBase LargeFile implementation using GridFS (MongoDB) as a backend. Store large files remotely using the familiar API of open() . With built-in versioning, pruning and local cache. See parent for more details. Source code in great_ai/large_file/large_file/large_file_mongo.py 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 class LargeFileMongo ( LargeFileBase ): \"\"\"LargeFile implementation using GridFS (MongoDB) as a backend. Store large files remotely using the familiar API of `open()`. With built-in versioning, pruning and local cache. See parent for more details. \"\"\" mongo_connection_string = None mongo_database = None @classmethod def configure_credentials ( # type: ignore cls , * , mongo_connection_string : str , mongo_database : str , ** _ : Any , ) -> None : cls . mongo_connection_string = mongo_connection_string cls . mongo_database = mongo_database super () . configure_credentials () @cached_property def _client ( self ) -> GridFSBucket : if self . mongo_connection_string is None or self . mongo_database is None : raise ValueError ( \"Please configure the MongoDB access options by calling LargeFileMongo.configure_credentials or set offline_mode=True in the constructor.\" ) db : Database = MongoClient ( self . mongo_connection_string )[ self . mongo_database ] return GridFSBucket ( db ) def _find_remote_instances ( self ) -> List [ DataInstance ]: logger . debug ( f \"Fetching Mongo (GridFS) versions of { self . _name } \" ) return [ DataInstance ( name = MONGO_NAME_VERSION_SEPARATOR . join ( f . name . split ( MONGO_NAME_VERSION_SEPARATOR )[: - 1 ] ), version = int ( f . name . split ( MONGO_NAME_VERSION_SEPARATOR )[ - 1 ]), remote_path = ( f . _id , f . length ), origin = \"mongodb\" , ) for f in self . _client . find ( { \"filename\" : re . compile ( re . escape ( self . _name + MONGO_NAME_VERSION_SEPARATOR ) + \".*\" ) } ) ] def _download ( self , remote_path : Any , local_path : Path , hide_progress : bool ) -> None : logger . info ( f \"Downloading { remote_path [ 0 ] } from Mongo (GridFS)\" ) progress = ( DownloadProgressBar ( name = str ( remote_path [ 0 ]), size = remote_path [ 1 ], logger = logger ) if not hide_progress else None ) with self . _client . open_download_stream ( remote_path [ 0 ]) as stream : with open ( local_path , \"wb\" ) as f : while True : content = stream . read ( DEFAULT_CHUNK_SIZE ) f . write ( content ) if progress : progress ( len ( content )) if len ( content ) < DEFAULT_CHUNK_SIZE : break def _upload ( self , local_path : Path , hide_progress : bool ) -> None : logger . info ( f \"Uploading { local_path } to Mongo (GridFS)\" ) progress = ( UploadProgressBar ( path = local_path , logger = logger ) if not hide_progress else None ) with self . _client . open_upload_stream ( f \" { self . _name }{ MONGO_NAME_VERSION_SEPARATOR }{ self . version } \" ) as stream : with open ( local_path , \"rb\" ) as f : while True : content = f . read ( DEFAULT_CHUNK_SIZE ) stream . write ( content ) if progress : progress ( len ( content )) if len ( content ) < DEFAULT_CHUNK_SIZE : break def _delete_old_remote_versions ( self ) -> None : if self . _keep_last_n is not None : for i in ( self . _instances [: - self . _keep_last_n ] if self . _keep_last_n > 0 else self . _instances ): logger . info ( f \"Removing old version from MongoDB (GridFS) (keep_last_n= { self . _keep_last_n } ): { i . name }{ MONGO_NAME_VERSION_SEPARATOR }{ i . version } \" ) self . _client . delete ( i . remote_path [ 0 ])","title":"LargeFileMongo"},{"location":"reference/large-file/#great_ai.large_file.LargeFileBase","text":"Bases: ABC Base for LargeFile implementations with different backends. Store large files remotely using the familiar API of open() . With built-in versioning, pruning and local cache. By default, files are stored in the \".cache\" folder and the least recently used is deleted after the overall size reaches 30 GBs. Examples: >>> LargeFileBase . cache_path = Path ( \".cache\" ) >>> LargeFileBase . max_cache_size = \"30GB\" Attributes: Name Type Description initialized Tell whether configure_credentials or configure_credentials_from_file has been already called. cache_path Storage location for cached files. max_cache_size Optional [ str ] Delete files until the folder at cache_path is smaller than this value. Examples: \"5 GB\", \"10MB\", \"0.3 TB\". Set to None for no automatic cache-pruning. Source code in great_ai/large_file/large_file/large_file_base.py 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 class LargeFileBase ( ABC ): \"\"\"Base for LargeFile implementations with different backends. Store large files remotely using the familiar API of `open()`. With built-in versioning, pruning and local cache. By default, files are stored in the \".cache\" folder and the least recently used is deleted after the overall size reaches 30 GBs. Examples: >>> LargeFileBase.cache_path = Path(\".cache\") >>> LargeFileBase.max_cache_size = \"30GB\" Attributes: initialized: Tell whether `configure_credentials` or `configure_credentials_from_file` has been already called. cache_path: Storage location for cached files. max_cache_size: Delete files until the folder at `cache_path` is smaller than this value. Examples: \"5 GB\", \"10MB\", \"0.3 TB\". Set to `None` for no automatic cache-pruning. \"\"\" initialized = False cache_path = Path ( \".cache\" ) max_cache_size : Optional [ str ] = \"30GB\" def __init__ ( self , name : str , mode : str = \"r\" , * , buffering : int = - 1 , encoding : Optional [ str ] = None , errors : Optional [ str ] = None , newline : Optional [ str ] = None , version : Optional [ int ] = None , keep_last_n : Optional [ int ] = None , cache_only_mode : bool = False , ): self . _name = name self . _version = version self . _mode = mode self . _keep_last_n = keep_last_n self . _cache_only_mode = cache_only_mode self . _buffering = buffering self . _encoding = encoding if errors is not None and sys . version_info [ 1 ] < 8 : raise RuntimeError ( \"The `errors` kwarg is only supported in 3.8 <= Python versions.\" ) self . _errors = errors self . _newline = newline LargeFileBase . cache_path . mkdir ( parents = True , exist_ok = True ) self . _find_instances () self . _check_mode_and_set_version () @classmethod def configure_credentials_from_file ( cls , secrets : Union [ Path , str , ConfigFile ], ) -> None : \"\"\"Load file and feed its content to `configure_credentials`. Extra keys are ignored. \"\"\" if not isinstance ( secrets , ConfigFile ): secrets = ConfigFile ( secrets ) cls . configure_credentials ( ** { k . lower (): v for k , v in secrets . items ()}) @classmethod def configure_credentials ( cls , ** kwargs : str ) -> None : \"\"\"Configure required credentials for the LargeFile backend.\"\"\" cls . initialized = True def __enter__ ( self ) -> IO : params = dict ( mode = self . _mode , buffering = self . _buffering , encoding = self . _encoding , newline = self . _newline , delete = False , prefix = \"large_file-\" , ) if sys . version_info [ 1 ] >= 8 : params [ \"errors\" ] = self . _errors self . _file : IO [ Any ] = ( tempfile . NamedTemporaryFile ( ** params ) # type: ignore if \"w\" in self . _mode else open ( self . get (), mode = self . _mode , buffering = self . _buffering , encoding = self . _encoding , newline = self . _newline , errors = self . _errors , ) ) return self . _file def __exit__ ( self , type : Optional [ Type [ BaseException ]], exc : Optional [ BaseException ], traceback : Optional [ TracebackType ], ) -> Literal [ False ]: self . _file . close () if type is None : if \"w\" in self . _mode : self . push ( Path ( self . _file . name )) os . unlink ( self . _file . name ) else : logger . exception ( \"Could not finish operation.\" ) return False @property def version ( self ) -> int : \"\"\"Numeric version of the file proxied by this LargeFile instance.\"\"\" return cast ( int , self . _version ) @lru_cache ( 1 ) def get ( self , hide_progress : bool = False ) -> Path : \"\"\"Return path to the proxy of a file (or directory). If not available in the local cache, an attempt is made to download it. Args: hide_progress: Do not show a progress update after each 10% of progress. \"\"\" remote_path = next ( i . remote_path for i in self . _instances if i . version == self . _version ) destination = self . cache_path / self . _local_name if not destination . exists (): logger . info ( f \"File { self . _local_name } does not exist locally\" ) with tempfile . TemporaryDirectory () as tmp : local_root_path = Path ( tmp ) tmp_file_archive = ( local_root_path / f \" { self . _local_name }{ ARCHIVE_EXTENSION } \" ) self . _download ( remote_path , tmp_file_archive , hide_progress = hide_progress ) logger . info ( f \"Decompressing { self . _local_name } \" ) shutil . unpack_archive ( str ( tmp_file_archive ), tmp , COMPRESSION_ALGORITHM ) shutil . move ( str ( local_root_path / self . _local_name ), str ( destination )) else : logger . info ( f \"File { self . _local_name } found in cache\" ) return destination def push ( self , path : Union [ Path , str ], hide_progress : bool = False ) -> None : \"\"\"Upload a file (or directory) as a new version of `key`. The file/directory is compressed before upload. Args: hide_progress: Do not show a progress update after each 10% of progress. \"\"\" if isinstance ( path , str ): path = Path ( path ) with tempfile . TemporaryDirectory () as tmp : if path . is_file (): logger . info ( f \"Copying file for { self . _local_name } \" ) copy : Any = shutil . copy else : logger . info ( f \"Copying directory for { self . _local_name } \" ) copy = shutil . copytree try : # Make local copy in the cache shutil . rmtree ( self . cache_path / self . _local_name , ignore_errors = True ) copy ( str ( path ), str ( self . cache_path / self . _local_name )) except shutil . SameFileError : pass # No worries copy ( str ( path ), str ( Path ( tmp ) / self . _local_name )) with tempfile . TemporaryDirectory () as tmp2 : # A directory has to be zipped and it cannot contain the output of the zipping logger . info ( f \"Compressing { self . _local_name } \" ) shutil . make_archive ( str ( Path ( tmp2 ) / self . _local_name ), COMPRESSION_ALGORITHM , tmp , ) file_to_be_uploaded = ( Path ( tmp2 ) / f \" { self . _local_name }{ ARCHIVE_EXTENSION } \" ) self . _upload ( file_to_be_uploaded , hide_progress = hide_progress ) self . clean_up () def delete ( self ) -> None : \"\"\"Delete all versions of the files under this `key`.\"\"\" self . _keep_last_n = 0 self . _delete_old_remote_versions () @property def versions_pretty ( self ) -> str : \"\"\"Formatted string of all available versions.\"\"\" return \", \" . join (( str ( i . version ) for i in self . _instances )) def clean_up ( self ) -> None : \"\"\"Delete local and remote versions according to currently set cache and retention policy.\"\"\" self . _delete_old_remote_versions () self . _prune_cache () @property def _local_name ( self ) -> str : return f \" { self . _name }{ CACHE_NAME_VERSION_SEPARATOR }{ self . version } \" def _find_instances ( self ) -> None : if self . _cache_only_mode : self . _instances = self . _find_instances_from_cache () else : self . _instances = self . _find_remote_instances () self . _instances = sorted ( self . _instances , key = lambda i : i . version ) def _find_instances_from_cache ( self ) -> List [ DataInstance ]: logger . info ( f \"Fetching cached versions of { self . _name } \" ) candidates = [ DataInstance ( name = CACHE_NAME_VERSION_SEPARATOR . join ( f . name . split ( CACHE_NAME_VERSION_SEPARATOR )[: - 1 ] ), version = int ( f . name . split ( CACHE_NAME_VERSION_SEPARATOR )[ - 1 ]), remote_path = f , ) for f in self . cache_path . glob ( f \" { self . _name }{ CACHE_NAME_VERSION_SEPARATOR } *\" ) ] return [ c for c in candidates if c . name == self . _name ] def _check_mode_and_set_version ( self ) -> None : if \"+\" in self . _mode : raise ValueError ( f \"File mode ` { self . _mode } ` is not allowed3, remove the `+`.\" ) if \"w\" in self . _mode : if self . _version is not None : raise ValueError ( \"Providing a version is not allowed in write mode.\" ) self . _version = self . _instances [ - 1 ] . version + 1 if self . _instances else 0 elif \"r\" in self . _mode : if not self . _instances : raise FileNotFoundError ( f \"File { self . _name } not found. No versions are available.\" ) if self . _version is None : self . _version = self . _instances [ - 1 ] . version logger . info ( f \"Latest version of { self . _name } is { self . _version } \" + f \"(from versions: { self . versions_pretty } )\" ) elif self . _version not in [ i . version for i in self . _instances ]: raise FileNotFoundError ( f \"File { self . _name } not found with version { self . _version } . \" + f \"(from versions: { self . versions_pretty } )\" ) else : raise ValueError ( \"Unsupported file mode.\" ) def _prune_cache ( self ) -> None : self . cache_path . mkdir ( parents = True , exist_ok = True ) if self . max_cache_size is None : return allowed_size = human_readable_to_byte ( self . max_cache_size ) assert allowed_size >= 0 least_recently_read = sorted ( [ f for f in self . cache_path . glob ( \"*\" )], key = lambda f : f . stat () . st_atime ) while sum ( os . path . getsize ( f ) for f in least_recently_read ) > allowed_size : file = least_recently_read . pop ( 0 ) logger . info ( f \"Deleting file from cache to meet quota (max_cache_size= { self . max_cache_size } ): { file } \" ) os . unlink ( file ) @abstractmethod def _find_remote_instances ( self ) -> List [ DataInstance ]: pass @abstractmethod def _download ( self , remote_path : Any , local_path : Path , hide_progress : bool ) -> None : pass @abstractmethod def _upload ( self , local_path : Path , hide_progress : bool ) -> None : pass @abstractmethod def _delete_old_remote_versions ( self ) -> None : pass","title":"LargeFileBase"},{"location":"reference/large-file/#great_ai.large_file.large_file.large_file_base.LargeFileBase.clean_up","text":"Delete local and remote versions according to currently set cache and retention policy. Source code in great_ai/large_file/large_file/large_file_base.py 250 251 252 253 254 def clean_up ( self ) -> None : \"\"\"Delete local and remote versions according to currently set cache and retention policy.\"\"\" self . _delete_old_remote_versions () self . _prune_cache ()","title":"clean_up()"},{"location":"reference/large-file/#great_ai.large_file.large_file.large_file_base.LargeFileBase.configure_credentials","text":"Configure required credentials for the LargeFile backend. Source code in great_ai/large_file/large_file/large_file_base.py 102 103 104 105 106 @classmethod def configure_credentials ( cls , ** kwargs : str ) -> None : \"\"\"Configure required credentials for the LargeFile backend.\"\"\" cls . initialized = True","title":"configure_credentials()"},{"location":"reference/large-file/#great_ai.large_file.large_file.large_file_base.LargeFileBase.configure_credentials_from_file","text":"Load file and feed its content to configure_credentials . Extra keys are ignored. Source code in great_ai/large_file/large_file/large_file_base.py 88 89 90 91 92 93 94 95 96 97 98 99 100 @classmethod def configure_credentials_from_file ( cls , secrets : Union [ Path , str , ConfigFile ], ) -> None : \"\"\"Load file and feed its content to `configure_credentials`. Extra keys are ignored. \"\"\" if not isinstance ( secrets , ConfigFile ): secrets = ConfigFile ( secrets ) cls . configure_credentials ( ** { k . lower (): v for k , v in secrets . items ()})","title":"configure_credentials_from_file()"},{"location":"reference/large-file/#great_ai.large_file.large_file.large_file_base.LargeFileBase.delete","text":"Delete all versions of the files under this key . Source code in great_ai/large_file/large_file/large_file_base.py 239 240 241 242 243 def delete ( self ) -> None : \"\"\"Delete all versions of the files under this `key`.\"\"\" self . _keep_last_n = 0 self . _delete_old_remote_versions ()","title":"delete()"},{"location":"reference/large-file/#great_ai.large_file.large_file.large_file_base.LargeFileBase.get","text":"Return path to the proxy of a file (or directory). If not available in the local cache, an attempt is made to download it. Parameters: Name Type Description Default hide_progress bool Do not show a progress update after each 10% of progress. False Source code in great_ai/large_file/large_file/large_file_base.py 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 @lru_cache ( 1 ) def get ( self , hide_progress : bool = False ) -> Path : \"\"\"Return path to the proxy of a file (or directory). If not available in the local cache, an attempt is made to download it. Args: hide_progress: Do not show a progress update after each 10% of progress. \"\"\" remote_path = next ( i . remote_path for i in self . _instances if i . version == self . _version ) destination = self . cache_path / self . _local_name if not destination . exists (): logger . info ( f \"File { self . _local_name } does not exist locally\" ) with tempfile . TemporaryDirectory () as tmp : local_root_path = Path ( tmp ) tmp_file_archive = ( local_root_path / f \" { self . _local_name }{ ARCHIVE_EXTENSION } \" ) self . _download ( remote_path , tmp_file_archive , hide_progress = hide_progress ) logger . info ( f \"Decompressing { self . _local_name } \" ) shutil . unpack_archive ( str ( tmp_file_archive ), tmp , COMPRESSION_ALGORITHM ) shutil . move ( str ( local_root_path / self . _local_name ), str ( destination )) else : logger . info ( f \"File { self . _local_name } found in cache\" ) return destination","title":"get()"},{"location":"reference/large-file/#great_ai.large_file.large_file.large_file_base.LargeFileBase.push","text":"Upload a file (or directory) as a new version of key . The file/directory is compressed before upload. Parameters: Name Type Description Default hide_progress bool Do not show a progress update after each 10% of progress. False Source code in great_ai/large_file/large_file/large_file_base.py 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 def push ( self , path : Union [ Path , str ], hide_progress : bool = False ) -> None : \"\"\"Upload a file (or directory) as a new version of `key`. The file/directory is compressed before upload. Args: hide_progress: Do not show a progress update after each 10% of progress. \"\"\" if isinstance ( path , str ): path = Path ( path ) with tempfile . TemporaryDirectory () as tmp : if path . is_file (): logger . info ( f \"Copying file for { self . _local_name } \" ) copy : Any = shutil . copy else : logger . info ( f \"Copying directory for { self . _local_name } \" ) copy = shutil . copytree try : # Make local copy in the cache shutil . rmtree ( self . cache_path / self . _local_name , ignore_errors = True ) copy ( str ( path ), str ( self . cache_path / self . _local_name )) except shutil . SameFileError : pass # No worries copy ( str ( path ), str ( Path ( tmp ) / self . _local_name )) with tempfile . TemporaryDirectory () as tmp2 : # A directory has to be zipped and it cannot contain the output of the zipping logger . info ( f \"Compressing { self . _local_name } \" ) shutil . make_archive ( str ( Path ( tmp2 ) / self . _local_name ), COMPRESSION_ALGORITHM , tmp , ) file_to_be_uploaded = ( Path ( tmp2 ) / f \" { self . _local_name }{ ARCHIVE_EXTENSION } \" ) self . _upload ( file_to_be_uploaded , hide_progress = hide_progress ) self . clean_up ()","title":"push()"},{"location":"reference/large-file/#great_ai.large_file.large_file.large_file_base.LargeFileBase.version","text":"Numeric version of the file proxied by this LargeFile instance. Source code in great_ai/large_file/large_file/large_file_base.py 153 154 155 156 157 @property def version ( self ) -> int : \"\"\"Numeric version of the file proxied by this LargeFile instance.\"\"\" return cast ( int , self . _version )","title":"version()"},{"location":"reference/large-file/#great_ai.large_file.large_file.large_file_base.LargeFileBase.versions_pretty","text":"Formatted string of all available versions. Source code in great_ai/large_file/large_file/large_file_base.py 245 246 247 248 @property def versions_pretty ( self ) -> str : \"\"\"Formatted string of all available versions.\"\"\" return \", \" . join (( str ( i . version ) for i in self . _instances ))","title":"versions_pretty()"},{"location":"reference/utilities/","text":"Utilities # from great_ai.utilities import * NLP tools # Well-tested tools that can be used in production with confidence. The toolbox of feature-extraction functions is expected to grow to cover other domains as well. clean ( text , ignore_xml = False , ignore_latex = False , remove_brackets = False , convert_to_ascii = False ) # Clean all XML, LaTeX, PDF-extraction, and Unicode artifacts from the text. The cleaning is quite heavy-weight and can be destructive. However, when working with text, this is usually required to achieve sufficient cleanliness before further processing. Optionally, the text can be turned into ASCII. Carefully consider whether this is absolutely needed for your use-case. Examples: >>> clean ( '

Bj \\\\ \" {o} rn is \\t \\\\ textit {happy} \ud83d\ude42 <3

' ) 'Bj\u00f6rn is happy \ud83d\ude42 <3' >>> clean ( ... '

Bj \\\\ \" {o} rn is \\t \\\\ textit {happy} \ud83d\ude42 <3

' , ... convert_to_ascii = True ... ) 'Bjorn is happy <3' >>> clean ( ... '

Bj \\\\ \" {o} rn is \\t \\\\ textit {happy} \ud83d\ude42 <3

' , ... ignore_xml = True ... ) '

Bj\u00f6rn is happy \ud83d\ude42 lt;3

' Parameters: Name Type Description Default text str Text to be cleaned. required ignore_xml bool Do not process/remove XML-tags. False ignore_latex bool Do not process/remove LaTeX-tags. False remove_brackets bool Do not remove brackets ([]) False convert_to_ascii bool Strip (or convert) non-ascii characters. False Returns: Type Description str The cleaned input text with sensibly collapsed whitespace and optionally no markup. Source code in great_ai/utilities/clean.py 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 def clean ( text : str , ignore_xml : bool = False , ignore_latex : bool = False , remove_brackets : bool = False , convert_to_ascii : bool = False , ) -> str : \"\"\"Clean all XML, LaTeX, PDF-extraction, and Unicode artifacts from the text. The cleaning is quite heavy-weight and can be destructive. However, when working with text, this is usually required to achieve sufficient cleanliness before further processing. Optionally, the text can be turned into ASCII. Carefully consider whether this is absolutely needed for your use-case. Examples: >>> clean('

Bj\\\\\\\\\"{o}rn is \\\\t \\\\\\\\textit{happy} \ud83d\ude42 <3

') 'Bj\u00f6rn is happy \ud83d\ude42 <3' >>> clean( ... '

Bj\\\\\\\\\"{o}rn is \\\\t \\\\\\\\textit{happy} \ud83d\ude42 <3

', ... convert_to_ascii=True ... ) 'Bjorn is happy <3' >>> clean( ... '

Bj\\\\\\\\\"{o}rn is \\\\t \\\\\\\\textit{happy} \ud83d\ude42 <3

', ... ignore_xml=True ... ) '

Bj\u00f6rn is happy \ud83d\ude42 lt;3

' Args: text: Text to be cleaned. ignore_xml: Do not process/remove XML-tags. ignore_latex: Do not process/remove LaTeX-tags. remove_brackets: Do not remove brackets ([]) convert_to_ascii: Strip (or convert) non-ascii characters. Returns: The cleaned input text with sensibly collapsed whitespace and optionally no markup. \"\"\" if not ignore_xml : text = re . sub ( r \"<[^>]*>\" , \" \" , text ) text = html . unescape ( text ) if not ignore_latex : text = text . replace ( \"%\" , \" \\\\ %\" ) # escape LaTeX comments before parsing as LaTeX try : text = latex . latex_to_text ( text , tolerant_parsing = True , strict_braces = False ) text = text . replace ( \" %s \" , \" \" ) except : logger . exception ( \"Latex parsing error\" ) if convert_to_ascii : text = unicodedata . normalize ( \"NFKD\" , text ) try : text . encode ( \"ASCII\" , errors = \"strict\" ) except UnicodeEncodeError : text = \"\" . join ([ c for c in text if not unicodedata . combining ( c )]) text = unidecode . unidecode ( text ) text = re . sub ( r \"\\b[a-zA-Z](?:[\\t ]+[a-zA-Z]\\b)+\" , lambda m : re . sub ( r \"[\\t ]\" , \"\" , m [ 0 ]), text ) # A R T I C L E => ARTICLE if remove_brackets : text = re . sub ( r \"\\[[^\\]]*\\]\" , \" \" , text ) # fix hypens: break- word => break-word text = re . sub ( r \"(\\S)-\\s+\" , r \"\\1-\" , text ) text = re . sub ( r \"\\s+-(\\S)\" , r \"-\\1\" , text ) # collapse whitespace text = re . sub ( r \"\\s+\" , \" \" , text ) # fix punctuation text = re . sub ( rf \" ([ { joined_left_punctuations } ])\" , r \"\\1\" , text ) text = re . sub ( rf \"([ { joined_right_punctuations } ]) \" , r \"\\1\" , text ) text = text . strip () return text get_sentences ( text , ignore_partial = False , true_case = False , remove_punctuation = False ) # Return the list of sentences found in the input text. Use syntok to segment the sentences. Further processing can be enabled with optional arguments. Examples: >>> get_sentences ( 'This is a sentence. This is a half' ) ['This is a sentence.', 'This is a half'] >>> get_sentences ( 'This is a sentence. This is a half' , ignore_partial = True ) ['This is a sentence.'] >>> get_sentences ( 'I like Apple.' , true_case = True , remove_punctuation = True ) ['i like Apple'] Parameters: Name Type Description Default text str Text to be segmented into sentences. required ignore_partial bool Filter out sentences that are not capitalised/don't end with a punctuation. False true_case bool Crude method: lowercase the first word of each sentence. False remove_punctuation bool Remove all kinds of punctuation. False Returns: Type Description List [ str ] The found sentences (with partial sentences optionally filtered out). Source code in great_ai/utilities/get_sentences.py 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 def get_sentences ( text : str , ignore_partial : bool = False , true_case : bool = False , remove_punctuation : bool = False , ) -> List [ str ]: \"\"\"Return the list of sentences found in the input text. Use [syntok](https://github.com/fnl/syntok) to segment the sentences. Further processing can be enabled with optional arguments. Examples: >>> get_sentences('This is a sentence. This is a half') ['This is a sentence.', 'This is a half'] >>> get_sentences('This is a sentence. This is a half', ignore_partial=True) ['This is a sentence.'] >>> get_sentences('I like Apple.', true_case=True, remove_punctuation=True) ['i like Apple'] Args: text: Text to be segmented into sentences. ignore_partial: Filter out sentences that are not capitalised/don't end with a punctuation. true_case: Crude method: lowercase the first word of each sentence. remove_punctuation: Remove all kinds of punctuation. Returns: The found sentences (with partial sentences optionally filtered out). \"\"\" tokenizer = Tokenizer ( emit_hyphen_or_underscore_sep = True , replace_not_contraction = False ) token_stream = tokenizer . tokenize ( text ) def process ( sentence : str ) -> str : if true_case : sentence = sentence [ 0 ] . lower () + sentence [ 1 :] # very crude method if remove_punctuation : sentence = re . sub ( punctuations_pattern , \" \" , sentence ) return sentence . strip () sentences = [ process ( tokenizer . to_text ( sentence )) for sentence in segment ( token_stream ) ] if ignore_partial : sentences = [ sentence for sentence in sentences if sentence [ 0 ] . isupper () and sentence [ - 1 ] in sentence_ending_punctuations ] return sentences predict_language ( text ) # Predict the language code from text. A thin wrapper over langcodes for convenient language tagging. Examples: >>> predict_language ( 'This is a sentence.' ) 'en' Parameters: Name Type Description Default text Optional [ str ] Text used for prediction. required Returns: Type Description str The predicted language code (en, en-US) or und if a prediction could not be made. Source code in great_ai/utilities/language/predict_language.py 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 def predict_language ( text : Optional [ str ]) -> str : \"\"\"Predict the language code from text. A thin wrapper over [langcodes](https://github.com/rspeer/langcodes) for convenient language tagging. Examples: >>> predict_language('This is a sentence.') 'en' Args: text: Text used for prediction. Returns: The predicted language code (en, en-US) or `und` if a prediction could not be made. \"\"\" if not text : return Language . make () . to_tag () try : language_code = detect ( text ) except LangDetectException : return Language . make () . to_tag () return Language . get ( language_code ) . to_tag () english_name_of_language ( language_code ) # Human-friendly English name of language from its language_code . A thin wrapper over langcodes for convenient language tagging. Examples: >>> english_name_of_language ( 'en-US' ) 'English (United States)' >>> english_name_of_language ( 'und' ) 'Unknown language' Parameters: Name Type Description Default language_code Optional [ str ] Language code, for example, returned by great_ai.utilities.language.predict_language.predict_language . required Returns: Type Description str English name of language. Source code in great_ai/utilities/language/english_name_of_language.py 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 def english_name_of_language ( language_code : Optional [ str ]) -> str : \"\"\"Human-friendly English name of language from its `language_code`. A thin wrapper over [langcodes](https://github.com/rspeer/langcodes) for convenient language tagging. Examples: >>> english_name_of_language('en-US') 'English (United States)' >>> english_name_of_language('und') 'Unknown language' Args: language_code: Language code, for example, returned by [great_ai.utilities.language.predict_language.predict_language][]. Returns: English name of language. \"\"\" if not language_code : language_code = \"und\" return Language . get ( language_code ) . display_name () is_english ( language_code ) # Decide whether the language_code is of an English language. A thin wrapper over langcodes for convenient language tagging. Examples: >>> is_english ( 'en-US' ) True >>> is_english ( None ) False >>> is_english ( 'und' ) False Parameters: Name Type Description Default language_code Optional [ str ] Language code, for example, returned by ` great_ai.utilities.language.predict_language.predict_language . required Returns: Type Description bool Boolean indicating whether it's English. Source code in great_ai/utilities/language/is_english.py 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 def is_english ( language_code : Optional [ str ]) -> bool : \"\"\"Decide whether the `language_code` is of an English language. A thin wrapper over [langcodes](https://github.com/rspeer/langcodes) for convenient language tagging. Examples: >>> is_english('en-US') True >>> is_english(None) False >>> is_english('und') False Args: language_code: Language code, for example, returned by `[great_ai.utilities.language.predict_language.predict_language][]. Returns: Boolean indicating whether it's English. \"\"\" if not language_code : language_code = \"und\" language_code = standardize_tag ( language_code ) return tag_distance ( language_code , \"en\" ) < 15 evaluate_ranking ( expected , actual_scores , target_recall , title = '' , disable_interpolation = False , axes = None , output_svg = None , reverse_order = False , plot = True ) # Render the Precision-Recall curve of a ranking. And improved version of scikit-learn's PR-curve Parameters: Name Type Description Default expected List [ T ] Expected ordering of the elements (rank if it's an integer, alphabetical if a string) required actual_scores List [ float ] Actual ranking scores (need not be on the same scale as expected ) required title Optional [ str ] Title of the plot. '' disable_interpolation bool Do not interpolate. False axes Optional [ plt . Axes ] Matplotlib axes for plotting inside a subplot. None output_svg Optional [ Path ] If specified, save the chart as an svg to the given Path. None reverse_order bool Reverse the ranking specified by expected . False plot bool Display a plot on the screen. True Returns: Type Description Dict [ T , float ] Precision values at given recall. Source code in great_ai/utilities/evaluate_ranking/evaluate_ranking.py 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 def evaluate_ranking ( expected : List [ T ], actual_scores : List [ float ], target_recall : float , title : Optional [ str ] = \"\" , disable_interpolation : bool = False , axes : Optional [ plt . Axes ] = None , output_svg : Optional [ Path ] = None , reverse_order : bool = False , plot : bool = True , ) -> Dict [ T , float ]: \"\"\"Render the Precision-Recall curve of a ranking. And improved version of scikit-learn's [PR-curve](https://scikit-learn.org/stable/auto_examples/model_selection/plot_precision_recall.html#sphx-glr-auto-examples-model-selection-plot-precision-recall-py) Args: expected: Expected ordering of the elements (rank if it's an integer, alphabetical if a string) actual_scores: Actual ranking scores (need not be on the same scale as `expected`) title: Title of the plot. disable_interpolation: Do not interpolate. axes: Matplotlib axes for plotting inside a subplot. output_svg: If specified, save the chart as an svg to the given Path. reverse_order: Reverse the ranking specified by `expected`. plot: Display a plot on the screen. Returns: Precision values at given recall. \"\"\" assert 0 <= target_recall <= 1 if plot and axes is None : fig = plt . figure ( figsize = ( 20 , 20 )) fig . patch . set_facecolor ( \"white\" ) ax = plt . axes () else : ax = axes classes = sorted ( unique ( expected ), reverse = reverse_order ) str_classes = [ str ( c ) for c in classes ] with matplotlib . rc_context ({ \"font.size\" : 20 }): if plot : ax . set_xmargin ( 0 ) draw_f1_iso_lines ( axes = ax ) results : Dict [ T , float ] = {} for i in range ( len ( classes ) - 1 ): binarized_expected = [ ( v < classes [ i ]) if reverse_order else ( v > classes [ i ]) for v in expected ] sorted_expected_actual = sorted ( zip ( binarized_expected , actual_scores ), key = lambda v : v [ 1 ], reverse = True ) precision = [] recall = [] correct = 0 for all , ( e , score ) in enumerate ( sorted_expected_actual , start = 1 ): correct += int ( e ) precision . append ( correct / all ) recall . append ( all / len ( sorted_expected_actual )) if not disable_interpolation : for j in range ( len ( precision ) - 2 , - 1 , - 1 ): precision [ j ] = max ( precision [ j ], precision [ j + 1 ]) closest_recall_index = np . argmin ( np . abs ( np . array ( recall ) - target_recall )) precision_at_closest_recall = precision [ closest_recall_index ] average_precision = average_precision_score ( binarized_expected , actual_scores ) results [ classes [ i ]] = precision_at_closest_recall if plot : ax . plot ( recall , precision , label = f \" { '|' . join ( str_classes [: i + 1 ]) } \u2194 { '|' . join ( str_classes [ i + 1 :]) } (P@ { target_recall : .2f } = { precision_at_closest_recall : .2f } , AP= { average_precision : .2f } )\" , ) if plot : ax . legend ( loc = \"upper right\" ) ax . axvline ( x = target_recall , linestyle = \"--\" , color = \"#55c6bb\" , linewidth = 2.0 ) if title is None : title = \"Ranking evaluation\" ax . set_title ( f ' { title } ( { \" < \" . join ( str_classes ) } )' , pad = 20 ) ax . set_xlabel ( \"Recall\" ) ax . set_ylabel ( \"Precision\" ) ax . set_xticks ([ target_recall ] + sorted ( ax . get_xticks ())) if plot and output_svg is None : if axes is None : plt . show () elif output_svg : plt . savefig ( output_svg , format = \"svg\" ) return results Parallel processing # Multiprocessing and multithreading-based parallelism with support for async functions. Its main purpose is to implement great_ai.GreatAI.process_batch , however, the parallel processing functions are also convenient for covering other types of mapping needs with a friendlier API than joblib or multiprocess . simple_parallel_map ( func , input_values , * , chunk_size = None , concurrency = None ) # Execute a map operation on an list mimicking the API of the built-in map() . A thin-wrapper over parallel_map . For more options, consult the documentation of parallel_map . Examples: >>> import math >>> list ( simple_parallel_map ( math . sqrt , [ 9 , 4 , 1 ])) [3.0, 2.0, 1.0] Parameters: Name Type Description Default func Callable [[ T ], Union [ V , Awaitable [ V ]]] The function that should be applied to each element of input_values . It can async , in that case, a new event loop is started for each chunk. required input_values Sequence [ T ] An iterable of items that func is applied to. required chunk_size Optional [ int ] Tune the number of items processed in each step. Larger numbers result in smaller communication overhead but less parallelism at the start and end. If chunk_size has a __len__ property, the chunk_size is calculated automatically if not given. None concurrency Optional [ int ] Number of new processes to start. Shouldn't be too much more than the number of physical cores. None Returns: Type Description List [ V ] An iterable of results obtained from applying func to each input value. Raises: Type Description WorkerException If there was an error in the func function in a background process. Source code in great_ai/utilities/parallel_map/simple_parallel_map.py 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 def simple_parallel_map ( func : Callable [[ T ], Union [ V , Awaitable [ V ]]], input_values : Sequence [ T ], * , chunk_size : Optional [ int ] = None , concurrency : Optional [ int ] = None , ) -> List [ V ]: \"\"\"Execute a map operation on an list mimicking the API of the built-in `map()`. A thin-wrapper over [parallel_map][great_ai.utilities.parallel_map.parallel_map.parallel_map]. For more options, consult the documentation of [parallel_map][great_ai.utilities.parallel_map.parallel_map.parallel_map]. Examples: >>> import math >>> list(simple_parallel_map(math.sqrt, [9, 4, 1])) [3.0, 2.0, 1.0] Args: func: The function that should be applied to each element of `input_values`. It can `async`, in that case, a new event loop is started for each chunk. input_values: An iterable of items that `func` is applied to. chunk_size: Tune the number of items processed in each step. Larger numbers result in smaller communication overhead but less parallelism at the start and end. If `chunk_size` has a `__len__` property, the `chunk_size` is calculated automatically if not given. concurrency: Number of new processes to start. Shouldn't be too much more than the number of physical cores. Returns: An iterable of results obtained from applying `func` to each input value. Raises: WorkerException: If there was an error in the `func` function in a background process. \"\"\" input_values = list ( input_values ) # in case the input is mistakenly not a sequence generator = parallel_map ( func = func , input_values = input_values , chunk_size = chunk_size , concurrency = concurrency , ) return list ( tqdm ( generator , total = len ( input_values ), ) ) parallel_map ( func , input_values , * , chunk_size = None , ignore_exceptions = False , concurrency = None , unordered = False ) # Execute a map operation on an iterable stream. A custom parallel map operation supporting both synchronous and async map functions. The func function is serialised with dill . Exceptions encountered in the map function are sent to the host process where they are either raised (default) or ignored. The new processes are forked if the OS allows it, otherwise, new Python processes are bootstrapped which can incur some start-up cost. Each process processes a single chunk at once. Examples: >>> import math >>> list ( parallel_map ( math . sqrt , [ 9 , 4 , 1 ], concurrency = 2 )) [3.0, 2.0, 1.0] Parameters: Name Type Description Default func Callable [[ T ], Union [ V , Awaitable [ V ]]] The function that should be applied to each element of input_values . It can async , in that case, a new event loop is started for each chunk. required input_values Union [ Iterable [ T ], Sequence [ T ]] An iterable of items that func is applied to. required chunk_size Optional [ int ] Tune the number of items processed in each step. Larger numbers result in smaller communication overhead but less parallelism at the start and end. If chunk_size has a __len__ property, the chunk_size is calculated automatically if not given. None ignore_exceptions bool Ignore chunks if next() raises an exception on input_values . And return None if func raised an exception in a worker process. False concurrency Optional [ int ] Number of new processes to start. Shouldn't be too much more than the number of physical cores. None unordered bool Do not preserve the order of the elements, yield them as soon as they have been processed. This decreases the latency caused by difficult-to-process items. False Yields: Type Description Iterable [ Optional [ V ]] The next result obtained from applying func to each input value. May contain None -s if ignore_exceptions=True . May have different order than the input if unordered=True . Raises: Type Description WorkerException If there was an error in the func function in a background process and ignore_exceptions=False . Source code in great_ai/utilities/parallel_map/parallel_map.py 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 def parallel_map ( func : Callable [[ T ], Union [ V , Awaitable [ V ]]], input_values : Union [ Iterable [ T ], Sequence [ T ]], * , chunk_size : Optional [ int ] = None , ignore_exceptions : bool = False , concurrency : Optional [ int ] = None , unordered : bool = False , ) -> Iterable [ Optional [ V ]]: \"\"\"Execute a map operation on an iterable stream. A custom parallel map operation supporting both synchronous and `async` map functions. The `func` function is serialised with `dill`. Exceptions encountered in the map function are sent to the host process where they are either raised (default) or ignored. The new processes are forked if the OS allows it, otherwise, new Python processes are bootstrapped which can incur some start-up cost. Each process processes a single chunk at once. Examples: >>> import math >>> list(parallel_map(math.sqrt, [9, 4, 1], concurrency=2)) [3.0, 2.0, 1.0] Args: func: The function that should be applied to each element of `input_values`. It can `async`, in that case, a new event loop is started for each chunk. input_values: An iterable of items that `func` is applied to. chunk_size: Tune the number of items processed in each step. Larger numbers result in smaller communication overhead but less parallelism at the start and end. If `chunk_size` has a `__len__` property, the `chunk_size` is calculated automatically if not given. ignore_exceptions: Ignore chunks if `next()` raises an exception on `input_values`. And return `None` if `func` raised an exception in a worker process. concurrency: Number of new processes to start. Shouldn't be too much more than the number of physical cores. unordered: Do not preserve the order of the elements, yield them as soon as they have been processed. This decreases the latency caused by difficult-to-process items. Yields: The next result obtained from applying `func` to each input value. May contain `None`-s if `ignore_exceptions=True`. May have different order than the input if `unordered=True`. Raises: WorkerException: If there was an error in the `func` function in a background process and `ignore_exceptions=False`. \"\"\" config = get_config ( function = func , input_values = input_values , chunk_size = chunk_size , concurrency = concurrency , ) ctx = ( mp . get_context ( \"fork\" ) if \"fork\" in mp . get_all_start_methods () else mp . get_context ( \"spawn\" ) ) ctx . freeze_support () manager = ctx . Manager () input_queue = manager . Queue ( config . concurrency * 2 ) output_queue = manager . Queue ( config . concurrency * 2 ) should_stop = ctx . Event () serialized_map_function = dill . dumps ( func , byref = True , recurse = False ) processes = [ ctx . Process ( name = f \"parallel_map_ { config . function_name } _ { i } \" , target = mapper_function , daemon = True , kwargs = dict ( input_queue = input_queue , output_queue = output_queue , should_stop = should_stop , func = serialized_map_function , ), ) for i in range ( config . concurrency ) ] for p in processes : p . start () try : yield from manage_communication ( input_values = input_values , chunk_size = config . chunk_size , input_queue = input_queue , output_queue = output_queue , unordered = unordered , ignore_exceptions = ignore_exceptions , ) should_stop . set () except WorkerException : should_stop . set () raise except Exception : for p in processes : p . terminate () p . kill () raise finally : for p in processes : p . join () # terminated processes have to be joined else they remain zombies p . close () manager . shutdown () threaded_parallel_map ( func , input_values , * , chunk_size = None , ignore_exceptions = False , concurrency = None , unordered = False ) # Execute a map operation on an iterable stream. Similar to parallel_map but uses threads instead of processes. Hence, it is not helpful in CPU-bound situations. A custom parallel map operation supporting both synchronous and async map functions. Exceptions encountered in the map function are sent to the host thread where they are either raised (default) or ignored. Each process processes a single chunk at once. Examples: >>> list ( threaded_parallel_map ( lambda x : x ** 2 , [ 1 , 2 , 3 ])) [1, 4, 9] Parameters: Name Type Description Default func Callable [[ T ], Union [ V , Awaitable [ V ]]] The function that should be applied to each element of input_values . It can async , in that case, a new event loop is started for each chunk. required input_values Union [ Iterable [ T ], Sequence [ T ]] An iterable of items that func is applied to. required chunk_size Optional [ int ] Tune the number of items processed in each step. Larger numbers result in smaller communication overhead but less parallelism at the start and end. If chunk_size has a __len__ property, the chunk_size is calculated automatically if not given. None ignore_exceptions bool Ignore chunks if next() raises an exception on input_values . And return None if func raised an exception in a worker process. False concurrency Optional [ int ] Number of new threads to start. None unordered bool Do not preserve the order of the elements, yield them as soon as they have been processed. This decreases the latency caused by difficult-to-process items. False Yields: Type Description Iterable [ Optional [ V ]] The next result obtained from applying func to each input value. May contain None -s if ignore_exceptions=True . May have different order than the input if unordered=True . Raises: Type Description WorkerException If there was an error in the func function in a background thread and ignore_exceptions=False . Source code in great_ai/utilities/parallel_map/threaded_parallel_map.py 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 def threaded_parallel_map ( func : Callable [[ T ], Union [ V , Awaitable [ V ]]], input_values : Union [ Iterable [ T ], Sequence [ T ]], * , chunk_size : Optional [ int ] = None , ignore_exceptions : bool = False , concurrency : Optional [ int ] = None , unordered : bool = False , ) -> Iterable [ Optional [ V ]]: \"\"\"Execute a map operation on an iterable stream. Similar to [parallel_map][great_ai.utilities.parallel_map.parallel_map.parallel_map] but uses threads instead of processes. Hence, it is not helpful in CPU-bound situations. A custom parallel map operation supporting both synchronous and `async` map functions. Exceptions encountered in the map function are sent to the host thread where they are either raised (default) or ignored. Each process processes a single chunk at once. Examples: >>> list(threaded_parallel_map(lambda x: x ** 2, [1, 2, 3])) [1, 4, 9] Args: func: The function that should be applied to each element of `input_values`. It can `async`, in that case, a new event loop is started for each chunk. input_values: An iterable of items that `func` is applied to. chunk_size: Tune the number of items processed in each step. Larger numbers result in smaller communication overhead but less parallelism at the start and end. If `chunk_size` has a `__len__` property, the `chunk_size` is calculated automatically if not given. ignore_exceptions: Ignore chunks if `next()` raises an exception on `input_values`. And return `None` if `func` raised an exception in a worker process. concurrency: Number of new threads to start. unordered: Do not preserve the order of the elements, yield them as soon as they have been processed. This decreases the latency caused by difficult-to-process items. Yields: The next result obtained from applying `func` to each input value. May contain `None`-s if `ignore_exceptions=True`. May have different order than the input if `unordered=True`. Raises: WorkerException: If there was an error in the `func` function in a background thread and `ignore_exceptions=False`. \"\"\" config = get_config ( function = func , input_values = input_values , chunk_size = chunk_size , concurrency = concurrency , ) input_queue : queue . Queue = queue . Queue ( config . concurrency * 2 ) output_queue : queue . Queue = queue . Queue ( config . concurrency * 2 ) should_stop = threading . Event () threads = [ threading . Thread ( name = f \"threaded_parallel_map_ { config . function_name } _ { i } \" , target = mapper_function , daemon = True , kwargs = dict ( input_queue = input_queue , output_queue = output_queue , should_stop = should_stop , func = func , ), ) for i in range ( config . concurrency ) ] for t in threads : t . start () yield from manage_communication ( input_values = input_values , chunk_size = config . chunk_size , input_queue = input_queue , output_queue = output_queue , unordered = unordered , ignore_exceptions = ignore_exceptions , ) should_stop . set () for t in threads : t . join ( 1 ) Composable parallel processing # Because both threaded_parallel_map and parallel_map have a streaming interface, it is easy to compose them and end up with, for example, a process for each CPU core with its own thread-pool or event-loop. Longer pipelines are also easy to imagine. The chunking methods help in these compositions. chunk ( values , chunk_size ) # Turn an iterable of items into an iterable of lists (chunks) of items. Each returned chunk is of length chunk_size except the last one the length of which is between 1 and chunk_size . Useful for parallel processing. Examples: >>> list ( chunk ( range ( 10 ), chunk_size = 3 )) [[0, 1, 2], [3, 4, 5], [6, 7, 8], [9]] Parameters: Name Type Description Default values Iterable [ T ] The stream of items to pack into chunks. required chunk_size int Desired length of each (but the last) chunk. required Yields: Type Description Iterable [ List [ T ]] The next chunk. Source code in great_ai/utilities/chunk.py 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 def chunk ( values : Iterable [ T ], chunk_size : int ) -> Iterable [ List [ T ]]: \"\"\"Turn an iterable of items into an iterable of lists (chunks) of items. Each returned chunk is of length `chunk_size` except the last one the length of which is between 1 and `chunk_size`. Useful for parallel processing. Examples: >>> list(chunk(range(10), chunk_size=3)) [[0, 1, 2], [3, 4, 5], [6, 7, 8], [9]] Args: values: The stream of items to pack into chunks. chunk_size: Desired length of each (but the last) chunk. Yields: The next chunk. \"\"\" assert chunk_size >= 1 result : List [ T ] = [] for v in values : result . append ( v ) if len ( result ) == chunk_size : yield result result = [] if len ( result ) > 0 : yield result unchunk ( chunks ) # Turn a stream of chunks of items into a stream of items (flatten operation). The inverse operation of chunk . Useful for parallel processing. Similar to itertools.chain but ignores None chunks. Examples: >>> list ( unchunk ([[ 0 , 1 , 2 ], [ 3 , 4 , 5 ], [ 6 , 7 , 8 ], [ 9 ]])) [0, 1, 2, 3, 4, 5, 6, 7, 8, 9] Parameters: Name Type Description Default chunks Iterable [ Optional [ Iterable [ T ]]] Stream of chunks to unpack. required Yields: Type Description Iterable [ T ] The next item in the flattened iterable. Source code in great_ai/utilities/unchunk.py 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 def unchunk ( chunks : Iterable [ Optional [ Iterable [ T ]]]) -> Iterable [ T ]: \"\"\"Turn a stream of chunks of items into a stream of items (flatten operation). The inverse operation of [chunk][great_ai.utilities.chunk.chunk]. Useful for parallel processing. Similar to itertools.chain but ignores `None` chunks. Examples: >>> list(unchunk([[0, 1, 2], [3, 4, 5], [6, 7, 8], [9]])) [0, 1, 2, 3, 4, 5, 6, 7, 8, 9] Args: chunks: Stream of chunks to unpack. Yields: The next item in the flattened iterable. \"\"\" for chunk in chunks : if chunk is not None : yield from chunk Operations # ConfigFile # Bases: Mapping [ str , str ] A small and safe INI -style configuration loader with dict and ENV support. The values can be accessed using both dot- and index-notation. It is compatible with the dict interface. File format example: # comments are allowed everywhere key = value # you can leave or omit whitespace around the equal-sign my_hashtag = \"#great_ai\" # the r-value can be quoted with \" or ' or `. my_var = my_default_value # Default values can be given to env-vars, # see next line. The default value must come first. my_var = ENV : MY_ENV_VAR # If the value starts with the `ENV:` prefix, # it is looked up from the environment variables. Examples: >>> ConfigFile ( 'tests/utilities/data/simple.conf' ) ConfigFile(path=tests/utilities/data/simple.conf) {'zeroth_key': 'test', 'first_key': 'Andr\u00e1s'} >>> ConfigFile ( 'tests/utilities/data/simple.conf' ) . zeroth_key 'test' >>> ConfigFile ( 'tests/utilities/data/simple.conf' ) . second_key Traceback (most recent call last): ... KeyError : 'Key `second_key` is not found in configuration file ... >>> a = ConfigFile ( 'tests/utilities/data/simple.conf' ) >>> { ** a } {'zeroth_key': 'test', 'first_key': 'Andr\u00e1s'} Source code in great_ai/utilities/config_file/config_file.py 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 class ConfigFile ( Mapping [ str , str ]): \"\"\"A small and safe `INI`-style configuration loader with `dict` and `ENV` support. The values can be accessed using both dot- and index-notation. It is compatible with the `dict` interface. File format example: ```toml # comments are allowed everywhere key = value # you can leave or omit whitespace around the equal-sign my_hashtag = \"#great_ai\" # the r-value can be quoted with \" or ' or `. my_var = my_default_value # Default values can be given to env-vars, # see next line. The default value must come first. my_var = ENV:MY_ENV_VAR # If the value starts with the `ENV:` prefix, # it is looked up from the environment variables. ``` Examples: >>> ConfigFile('tests/utilities/data/simple.conf') ConfigFile(path=tests/utilities/data/simple.conf) {'zeroth_key': 'test', 'first_key': 'Andr\u00e1s'} >>> ConfigFile('tests/utilities/data/simple.conf').zeroth_key 'test' >>> ConfigFile('tests/utilities/data/simple.conf').second_key Traceback (most recent call last): ... KeyError: 'Key `second_key` is not found in configuration file ... >>> a = ConfigFile('tests/utilities/data/simple.conf') >>> {**a} {'zeroth_key': 'test', 'first_key': 'Andr\u00e1s'} \"\"\" ENVIRONMENT_VARIABLE_KEY_PREFIX = \"ENV\" def __init__ ( self , path : Union [ Path , str ], * , ignore_missing : bool = False ) -> None : \"\"\"Load and parse a configuration file. Everything is eager-loaded, thus, exceptions may be thrown here. Args: path: Local path of the configuration file. ignore_missing: Don't raise an exception on missing environment variables. Raises: FileNotFoundError: If there is no file at the specified path. ParseError: If the provided file does not conform to the expected format. KeyError: If there is duplication in the keys. ValueError: If an environment variable is referenced but it is not set in the system and `ignore_missing=False`. \"\"\" if not isinstance ( path , Path ): path = Path ( path ) if not path . exists (): raise FileNotFoundError ( path . absolute ()) self . _ignore_missing = ignore_missing self . _path = path self . _key_values : Dict [ str , str ] = {} self . _parse () @property def path ( self ) -> Path : \"\"\"Original path from where the configuration was loaded.\"\"\" return self . _path def _parse ( self ) -> None : with open ( self . _path , encoding = \"utf-8\" ) as f : lines : str = f . read () matches = pattern . findall ( lines ) for key , * values in matches : try : value = next ( v for v in values if v ) except StopIteration : raise ParseError ( f \"\"\"Cannot parse config file ( { self . _path . absolute () } ), error at key ` { key } `\"\"\" ) already_exists = key in self . _key_values if already_exists and not value . startswith ( f \" { self . ENVIRONMENT_VARIABLE_KEY_PREFIX } :\" ): raise KeyError ( f \"Key ` { key } ` has been already defined and its value is ` { self . _key_values [ key ] } `\" ) if value . startswith ( f \" { self . ENVIRONMENT_VARIABLE_KEY_PREFIX } :\" ): _ , value = value . split ( \":\" ) if value not in os . environ : issue = f \"\"\"The value of ` { key } ` contains the \" { self . ENVIRONMENT_VARIABLE_KEY_PREFIX } ` prefix but ` { value } ` is not defined as an environment variable\"\"\" if already_exists : logger . warning ( f \"\"\" { issue } , using the default value defined above (` { self . _key_values [ key ] } `)\"\"\" ) continue elif self . _ignore_missing : logger . warning ( issue ) else : raise ValueError ( f \" { issue } and no default value has been provided\" ) else : value = os . environ [ value ] self . _key_values [ key ] = value def __getattr__ ( self , key : str ) -> str : if key in self . _key_values : return self . _key_values [ key ] raise KeyError ( f \"Key ` { key } ` is not found in configuration file ( { self . _path . absolute () } )\" ) __getitem__ = __getattr__ def __iter__ ( self ) -> Iterator [ str ]: return iter ( self . _key_values ) def __len__ ( self ) -> int : return len ( self . _key_values ) def keys ( self ) -> KeysView [ str ]: return self . _key_values . keys () def values ( self ) -> ValuesView [ str ]: return self . _key_values . values () def items ( self ) -> ItemsView [ str , str ]: return self . _key_values . items () def __repr__ ( self ) -> str : return f \" { type ( self ) . __name__ } (path= { self . _path . as_posix () } ) { self . _key_values } \" __init__ ( path , * , ignore_missing = False ) # Load and parse a configuration file. Everything is eager-loaded, thus, exceptions may be thrown here. Parameters: Name Type Description Default path Union [ Path , str ] Local path of the configuration file. required ignore_missing bool Don't raise an exception on missing environment variables. False Raises: Type Description FileNotFoundError If there is no file at the specified path. ParseError If the provided file does not conform to the expected format. KeyError If there is duplication in the keys. ValueError If an environment variable is referenced but it is not set in the system and ignore_missing=False . Source code in great_ai/utilities/config_file/config_file.py 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 def __init__ ( self , path : Union [ Path , str ], * , ignore_missing : bool = False ) -> None : \"\"\"Load and parse a configuration file. Everything is eager-loaded, thus, exceptions may be thrown here. Args: path: Local path of the configuration file. ignore_missing: Don't raise an exception on missing environment variables. Raises: FileNotFoundError: If there is no file at the specified path. ParseError: If the provided file does not conform to the expected format. KeyError: If there is duplication in the keys. ValueError: If an environment variable is referenced but it is not set in the system and `ignore_missing=False`. \"\"\" if not isinstance ( path , Path ): path = Path ( path ) if not path . exists (): raise FileNotFoundError ( path . absolute ()) self . _ignore_missing = ignore_missing self . _path = path self . _key_values : Dict [ str , str ] = {} self . _parse () path () property # Original path from where the configuration was loaded. Source code in great_ai/utilities/config_file/config_file.py 83 84 85 86 @property def path ( self ) -> Path : \"\"\"Original path from where the configuration was loaded.\"\"\" return self . _path get_logger ( name , level = logging . INFO , disable_colors = False ) # Return a customised logger used throughout the GreatAI codebase. Uses colors, and only prints timestamps when not running inside notebook. Source code in great_ai/utilities/logger/get_logger.py 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 def get_logger ( name : str , level : int = logging . INFO , disable_colors : bool = False ) -> logging . Logger : \"\"\"Return a customised logger used throughout the GreatAI codebase. Uses colors, and only prints timestamps when not running inside notebook. \"\"\" if name not in loggers : logger = logging . getLogger ( name ) logger . setLevel ( level ) try : get_ipython () # type: ignore log_format = \" %(message)s \" except NameError : # will fail outside of a notebook https://ipython.org/ log_format = \" %(asctime)s | %(levelname)8s | %(message)s \" stdout_handler = logging . StreamHandler () stdout_handler . setLevel ( level ) if not disable_colors : stdout_handler . setFormatter ( CustomFormatter ( log_format )) logger . addHandler ( stdout_handler ) loggers [ name ] = logger return loggers [ name ]","title":"Utilities"},{"location":"reference/utilities/#utilities","text":"from great_ai.utilities import *","title":"Utilities"},{"location":"reference/utilities/#nlp-tools","text":"Well-tested tools that can be used in production with confidence. The toolbox of feature-extraction functions is expected to grow to cover other domains as well.","title":"NLP tools"},{"location":"reference/utilities/#great_ai.utilities.clean.clean","text":"Clean all XML, LaTeX, PDF-extraction, and Unicode artifacts from the text. The cleaning is quite heavy-weight and can be destructive. However, when working with text, this is usually required to achieve sufficient cleanliness before further processing. Optionally, the text can be turned into ASCII. Carefully consider whether this is absolutely needed for your use-case. Examples: >>> clean ( '

Bj \\\\ \" {o} rn is \\t \\\\ textit {happy} \ud83d\ude42 <3

' ) 'Bj\u00f6rn is happy \ud83d\ude42 <3' >>> clean ( ... '

Bj \\\\ \" {o} rn is \\t \\\\ textit {happy} \ud83d\ude42 <3

' , ... convert_to_ascii = True ... ) 'Bjorn is happy <3' >>> clean ( ... '

Bj \\\\ \" {o} rn is \\t \\\\ textit {happy} \ud83d\ude42 <3

' , ... ignore_xml = True ... ) '

Bj\u00f6rn is happy \ud83d\ude42 lt;3

' Parameters: Name Type Description Default text str Text to be cleaned. required ignore_xml bool Do not process/remove XML-tags. False ignore_latex bool Do not process/remove LaTeX-tags. False remove_brackets bool Do not remove brackets ([]) False convert_to_ascii bool Strip (or convert) non-ascii characters. False Returns: Type Description str The cleaned input text with sensibly collapsed whitespace and optionally no markup. Source code in great_ai/utilities/clean.py 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 def clean ( text : str , ignore_xml : bool = False , ignore_latex : bool = False , remove_brackets : bool = False , convert_to_ascii : bool = False , ) -> str : \"\"\"Clean all XML, LaTeX, PDF-extraction, and Unicode artifacts from the text. The cleaning is quite heavy-weight and can be destructive. However, when working with text, this is usually required to achieve sufficient cleanliness before further processing. Optionally, the text can be turned into ASCII. Carefully consider whether this is absolutely needed for your use-case. Examples: >>> clean('

Bj\\\\\\\\\"{o}rn is \\\\t \\\\\\\\textit{happy} \ud83d\ude42 <3

') 'Bj\u00f6rn is happy \ud83d\ude42 <3' >>> clean( ... '

Bj\\\\\\\\\"{o}rn is \\\\t \\\\\\\\textit{happy} \ud83d\ude42 <3

', ... convert_to_ascii=True ... ) 'Bjorn is happy <3' >>> clean( ... '

Bj\\\\\\\\\"{o}rn is \\\\t \\\\\\\\textit{happy} \ud83d\ude42 <3

', ... ignore_xml=True ... ) '

Bj\u00f6rn is happy \ud83d\ude42 lt;3

' Args: text: Text to be cleaned. ignore_xml: Do not process/remove XML-tags. ignore_latex: Do not process/remove LaTeX-tags. remove_brackets: Do not remove brackets ([]) convert_to_ascii: Strip (or convert) non-ascii characters. Returns: The cleaned input text with sensibly collapsed whitespace and optionally no markup. \"\"\" if not ignore_xml : text = re . sub ( r \"<[^>]*>\" , \" \" , text ) text = html . unescape ( text ) if not ignore_latex : text = text . replace ( \"%\" , \" \\\\ %\" ) # escape LaTeX comments before parsing as LaTeX try : text = latex . latex_to_text ( text , tolerant_parsing = True , strict_braces = False ) text = text . replace ( \" %s \" , \" \" ) except : logger . exception ( \"Latex parsing error\" ) if convert_to_ascii : text = unicodedata . normalize ( \"NFKD\" , text ) try : text . encode ( \"ASCII\" , errors = \"strict\" ) except UnicodeEncodeError : text = \"\" . join ([ c for c in text if not unicodedata . combining ( c )]) text = unidecode . unidecode ( text ) text = re . sub ( r \"\\b[a-zA-Z](?:[\\t ]+[a-zA-Z]\\b)+\" , lambda m : re . sub ( r \"[\\t ]\" , \"\" , m [ 0 ]), text ) # A R T I C L E => ARTICLE if remove_brackets : text = re . sub ( r \"\\[[^\\]]*\\]\" , \" \" , text ) # fix hypens: break- word => break-word text = re . sub ( r \"(\\S)-\\s+\" , r \"\\1-\" , text ) text = re . sub ( r \"\\s+-(\\S)\" , r \"-\\1\" , text ) # collapse whitespace text = re . sub ( r \"\\s+\" , \" \" , text ) # fix punctuation text = re . sub ( rf \" ([ { joined_left_punctuations } ])\" , r \"\\1\" , text ) text = re . sub ( rf \"([ { joined_right_punctuations } ]) \" , r \"\\1\" , text ) text = text . strip () return text","title":"clean()"},{"location":"reference/utilities/#great_ai.utilities.get_sentences.get_sentences","text":"Return the list of sentences found in the input text. Use syntok to segment the sentences. Further processing can be enabled with optional arguments. Examples: >>> get_sentences ( 'This is a sentence. This is a half' ) ['This is a sentence.', 'This is a half'] >>> get_sentences ( 'This is a sentence. This is a half' , ignore_partial = True ) ['This is a sentence.'] >>> get_sentences ( 'I like Apple.' , true_case = True , remove_punctuation = True ) ['i like Apple'] Parameters: Name Type Description Default text str Text to be segmented into sentences. required ignore_partial bool Filter out sentences that are not capitalised/don't end with a punctuation. False true_case bool Crude method: lowercase the first word of each sentence. False remove_punctuation bool Remove all kinds of punctuation. False Returns: Type Description List [ str ] The found sentences (with partial sentences optionally filtered out). Source code in great_ai/utilities/get_sentences.py 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 def get_sentences ( text : str , ignore_partial : bool = False , true_case : bool = False , remove_punctuation : bool = False , ) -> List [ str ]: \"\"\"Return the list of sentences found in the input text. Use [syntok](https://github.com/fnl/syntok) to segment the sentences. Further processing can be enabled with optional arguments. Examples: >>> get_sentences('This is a sentence. This is a half') ['This is a sentence.', 'This is a half'] >>> get_sentences('This is a sentence. This is a half', ignore_partial=True) ['This is a sentence.'] >>> get_sentences('I like Apple.', true_case=True, remove_punctuation=True) ['i like Apple'] Args: text: Text to be segmented into sentences. ignore_partial: Filter out sentences that are not capitalised/don't end with a punctuation. true_case: Crude method: lowercase the first word of each sentence. remove_punctuation: Remove all kinds of punctuation. Returns: The found sentences (with partial sentences optionally filtered out). \"\"\" tokenizer = Tokenizer ( emit_hyphen_or_underscore_sep = True , replace_not_contraction = False ) token_stream = tokenizer . tokenize ( text ) def process ( sentence : str ) -> str : if true_case : sentence = sentence [ 0 ] . lower () + sentence [ 1 :] # very crude method if remove_punctuation : sentence = re . sub ( punctuations_pattern , \" \" , sentence ) return sentence . strip () sentences = [ process ( tokenizer . to_text ( sentence )) for sentence in segment ( token_stream ) ] if ignore_partial : sentences = [ sentence for sentence in sentences if sentence [ 0 ] . isupper () and sentence [ - 1 ] in sentence_ending_punctuations ] return sentences","title":"get_sentences()"},{"location":"reference/utilities/#great_ai.utilities.language.predict_language.predict_language","text":"Predict the language code from text. A thin wrapper over langcodes for convenient language tagging. Examples: >>> predict_language ( 'This is a sentence.' ) 'en' Parameters: Name Type Description Default text Optional [ str ] Text used for prediction. required Returns: Type Description str The predicted language code (en, en-US) or und if a prediction could not be made. Source code in great_ai/utilities/language/predict_language.py 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 def predict_language ( text : Optional [ str ]) -> str : \"\"\"Predict the language code from text. A thin wrapper over [langcodes](https://github.com/rspeer/langcodes) for convenient language tagging. Examples: >>> predict_language('This is a sentence.') 'en' Args: text: Text used for prediction. Returns: The predicted language code (en, en-US) or `und` if a prediction could not be made. \"\"\" if not text : return Language . make () . to_tag () try : language_code = detect ( text ) except LangDetectException : return Language . make () . to_tag () return Language . get ( language_code ) . to_tag ()","title":"predict_language()"},{"location":"reference/utilities/#great_ai.utilities.language.english_name_of_language.english_name_of_language","text":"Human-friendly English name of language from its language_code . A thin wrapper over langcodes for convenient language tagging. Examples: >>> english_name_of_language ( 'en-US' ) 'English (United States)' >>> english_name_of_language ( 'und' ) 'Unknown language' Parameters: Name Type Description Default language_code Optional [ str ] Language code, for example, returned by great_ai.utilities.language.predict_language.predict_language . required Returns: Type Description str English name of language. Source code in great_ai/utilities/language/english_name_of_language.py 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 def english_name_of_language ( language_code : Optional [ str ]) -> str : \"\"\"Human-friendly English name of language from its `language_code`. A thin wrapper over [langcodes](https://github.com/rspeer/langcodes) for convenient language tagging. Examples: >>> english_name_of_language('en-US') 'English (United States)' >>> english_name_of_language('und') 'Unknown language' Args: language_code: Language code, for example, returned by [great_ai.utilities.language.predict_language.predict_language][]. Returns: English name of language. \"\"\" if not language_code : language_code = \"und\" return Language . get ( language_code ) . display_name ()","title":"english_name_of_language()"},{"location":"reference/utilities/#great_ai.utilities.language.is_english.is_english","text":"Decide whether the language_code is of an English language. A thin wrapper over langcodes for convenient language tagging. Examples: >>> is_english ( 'en-US' ) True >>> is_english ( None ) False >>> is_english ( 'und' ) False Parameters: Name Type Description Default language_code Optional [ str ] Language code, for example, returned by ` great_ai.utilities.language.predict_language.predict_language . required Returns: Type Description bool Boolean indicating whether it's English. Source code in great_ai/utilities/language/is_english.py 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 def is_english ( language_code : Optional [ str ]) -> bool : \"\"\"Decide whether the `language_code` is of an English language. A thin wrapper over [langcodes](https://github.com/rspeer/langcodes) for convenient language tagging. Examples: >>> is_english('en-US') True >>> is_english(None) False >>> is_english('und') False Args: language_code: Language code, for example, returned by `[great_ai.utilities.language.predict_language.predict_language][]. Returns: Boolean indicating whether it's English. \"\"\" if not language_code : language_code = \"und\" language_code = standardize_tag ( language_code ) return tag_distance ( language_code , \"en\" ) < 15","title":"is_english()"},{"location":"reference/utilities/#great_ai.utilities.evaluate_ranking.evaluate_ranking.evaluate_ranking","text":"Render the Precision-Recall curve of a ranking. And improved version of scikit-learn's PR-curve Parameters: Name Type Description Default expected List [ T ] Expected ordering of the elements (rank if it's an integer, alphabetical if a string) required actual_scores List [ float ] Actual ranking scores (need not be on the same scale as expected ) required title Optional [ str ] Title of the plot. '' disable_interpolation bool Do not interpolate. False axes Optional [ plt . Axes ] Matplotlib axes for plotting inside a subplot. None output_svg Optional [ Path ] If specified, save the chart as an svg to the given Path. None reverse_order bool Reverse the ranking specified by expected . False plot bool Display a plot on the screen. True Returns: Type Description Dict [ T , float ] Precision values at given recall. Source code in great_ai/utilities/evaluate_ranking/evaluate_ranking.py 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 def evaluate_ranking ( expected : List [ T ], actual_scores : List [ float ], target_recall : float , title : Optional [ str ] = \"\" , disable_interpolation : bool = False , axes : Optional [ plt . Axes ] = None , output_svg : Optional [ Path ] = None , reverse_order : bool = False , plot : bool = True , ) -> Dict [ T , float ]: \"\"\"Render the Precision-Recall curve of a ranking. And improved version of scikit-learn's [PR-curve](https://scikit-learn.org/stable/auto_examples/model_selection/plot_precision_recall.html#sphx-glr-auto-examples-model-selection-plot-precision-recall-py) Args: expected: Expected ordering of the elements (rank if it's an integer, alphabetical if a string) actual_scores: Actual ranking scores (need not be on the same scale as `expected`) title: Title of the plot. disable_interpolation: Do not interpolate. axes: Matplotlib axes for plotting inside a subplot. output_svg: If specified, save the chart as an svg to the given Path. reverse_order: Reverse the ranking specified by `expected`. plot: Display a plot on the screen. Returns: Precision values at given recall. \"\"\" assert 0 <= target_recall <= 1 if plot and axes is None : fig = plt . figure ( figsize = ( 20 , 20 )) fig . patch . set_facecolor ( \"white\" ) ax = plt . axes () else : ax = axes classes = sorted ( unique ( expected ), reverse = reverse_order ) str_classes = [ str ( c ) for c in classes ] with matplotlib . rc_context ({ \"font.size\" : 20 }): if plot : ax . set_xmargin ( 0 ) draw_f1_iso_lines ( axes = ax ) results : Dict [ T , float ] = {} for i in range ( len ( classes ) - 1 ): binarized_expected = [ ( v < classes [ i ]) if reverse_order else ( v > classes [ i ]) for v in expected ] sorted_expected_actual = sorted ( zip ( binarized_expected , actual_scores ), key = lambda v : v [ 1 ], reverse = True ) precision = [] recall = [] correct = 0 for all , ( e , score ) in enumerate ( sorted_expected_actual , start = 1 ): correct += int ( e ) precision . append ( correct / all ) recall . append ( all / len ( sorted_expected_actual )) if not disable_interpolation : for j in range ( len ( precision ) - 2 , - 1 , - 1 ): precision [ j ] = max ( precision [ j ], precision [ j + 1 ]) closest_recall_index = np . argmin ( np . abs ( np . array ( recall ) - target_recall )) precision_at_closest_recall = precision [ closest_recall_index ] average_precision = average_precision_score ( binarized_expected , actual_scores ) results [ classes [ i ]] = precision_at_closest_recall if plot : ax . plot ( recall , precision , label = f \" { '|' . join ( str_classes [: i + 1 ]) } \u2194 { '|' . join ( str_classes [ i + 1 :]) } (P@ { target_recall : .2f } = { precision_at_closest_recall : .2f } , AP= { average_precision : .2f } )\" , ) if plot : ax . legend ( loc = \"upper right\" ) ax . axvline ( x = target_recall , linestyle = \"--\" , color = \"#55c6bb\" , linewidth = 2.0 ) if title is None : title = \"Ranking evaluation\" ax . set_title ( f ' { title } ( { \" < \" . join ( str_classes ) } )' , pad = 20 ) ax . set_xlabel ( \"Recall\" ) ax . set_ylabel ( \"Precision\" ) ax . set_xticks ([ target_recall ] + sorted ( ax . get_xticks ())) if plot and output_svg is None : if axes is None : plt . show () elif output_svg : plt . savefig ( output_svg , format = \"svg\" ) return results","title":"evaluate_ranking()"},{"location":"reference/utilities/#parallel-processing","text":"Multiprocessing and multithreading-based parallelism with support for async functions. Its main purpose is to implement great_ai.GreatAI.process_batch , however, the parallel processing functions are also convenient for covering other types of mapping needs with a friendlier API than joblib or multiprocess .","title":"Parallel processing"},{"location":"reference/utilities/#great_ai.utilities.simple_parallel_map","text":"Execute a map operation on an list mimicking the API of the built-in map() . A thin-wrapper over parallel_map . For more options, consult the documentation of parallel_map . Examples: >>> import math >>> list ( simple_parallel_map ( math . sqrt , [ 9 , 4 , 1 ])) [3.0, 2.0, 1.0] Parameters: Name Type Description Default func Callable [[ T ], Union [ V , Awaitable [ V ]]] The function that should be applied to each element of input_values . It can async , in that case, a new event loop is started for each chunk. required input_values Sequence [ T ] An iterable of items that func is applied to. required chunk_size Optional [ int ] Tune the number of items processed in each step. Larger numbers result in smaller communication overhead but less parallelism at the start and end. If chunk_size has a __len__ property, the chunk_size is calculated automatically if not given. None concurrency Optional [ int ] Number of new processes to start. Shouldn't be too much more than the number of physical cores. None Returns: Type Description List [ V ] An iterable of results obtained from applying func to each input value. Raises: Type Description WorkerException If there was an error in the func function in a background process. Source code in great_ai/utilities/parallel_map/simple_parallel_map.py 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 def simple_parallel_map ( func : Callable [[ T ], Union [ V , Awaitable [ V ]]], input_values : Sequence [ T ], * , chunk_size : Optional [ int ] = None , concurrency : Optional [ int ] = None , ) -> List [ V ]: \"\"\"Execute a map operation on an list mimicking the API of the built-in `map()`. A thin-wrapper over [parallel_map][great_ai.utilities.parallel_map.parallel_map.parallel_map]. For more options, consult the documentation of [parallel_map][great_ai.utilities.parallel_map.parallel_map.parallel_map]. Examples: >>> import math >>> list(simple_parallel_map(math.sqrt, [9, 4, 1])) [3.0, 2.0, 1.0] Args: func: The function that should be applied to each element of `input_values`. It can `async`, in that case, a new event loop is started for each chunk. input_values: An iterable of items that `func` is applied to. chunk_size: Tune the number of items processed in each step. Larger numbers result in smaller communication overhead but less parallelism at the start and end. If `chunk_size` has a `__len__` property, the `chunk_size` is calculated automatically if not given. concurrency: Number of new processes to start. Shouldn't be too much more than the number of physical cores. Returns: An iterable of results obtained from applying `func` to each input value. Raises: WorkerException: If there was an error in the `func` function in a background process. \"\"\" input_values = list ( input_values ) # in case the input is mistakenly not a sequence generator = parallel_map ( func = func , input_values = input_values , chunk_size = chunk_size , concurrency = concurrency , ) return list ( tqdm ( generator , total = len ( input_values ), ) )","title":"simple_parallel_map()"},{"location":"reference/utilities/#great_ai.utilities.parallel_map.parallel_map.parallel_map","text":"Execute a map operation on an iterable stream. A custom parallel map operation supporting both synchronous and async map functions. The func function is serialised with dill . Exceptions encountered in the map function are sent to the host process where they are either raised (default) or ignored. The new processes are forked if the OS allows it, otherwise, new Python processes are bootstrapped which can incur some start-up cost. Each process processes a single chunk at once. Examples: >>> import math >>> list ( parallel_map ( math . sqrt , [ 9 , 4 , 1 ], concurrency = 2 )) [3.0, 2.0, 1.0] Parameters: Name Type Description Default func Callable [[ T ], Union [ V , Awaitable [ V ]]] The function that should be applied to each element of input_values . It can async , in that case, a new event loop is started for each chunk. required input_values Union [ Iterable [ T ], Sequence [ T ]] An iterable of items that func is applied to. required chunk_size Optional [ int ] Tune the number of items processed in each step. Larger numbers result in smaller communication overhead but less parallelism at the start and end. If chunk_size has a __len__ property, the chunk_size is calculated automatically if not given. None ignore_exceptions bool Ignore chunks if next() raises an exception on input_values . And return None if func raised an exception in a worker process. False concurrency Optional [ int ] Number of new processes to start. Shouldn't be too much more than the number of physical cores. None unordered bool Do not preserve the order of the elements, yield them as soon as they have been processed. This decreases the latency caused by difficult-to-process items. False Yields: Type Description Iterable [ Optional [ V ]] The next result obtained from applying func to each input value. May contain None -s if ignore_exceptions=True . May have different order than the input if unordered=True . Raises: Type Description WorkerException If there was an error in the func function in a background process and ignore_exceptions=False . Source code in great_ai/utilities/parallel_map/parallel_map.py 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 def parallel_map ( func : Callable [[ T ], Union [ V , Awaitable [ V ]]], input_values : Union [ Iterable [ T ], Sequence [ T ]], * , chunk_size : Optional [ int ] = None , ignore_exceptions : bool = False , concurrency : Optional [ int ] = None , unordered : bool = False , ) -> Iterable [ Optional [ V ]]: \"\"\"Execute a map operation on an iterable stream. A custom parallel map operation supporting both synchronous and `async` map functions. The `func` function is serialised with `dill`. Exceptions encountered in the map function are sent to the host process where they are either raised (default) or ignored. The new processes are forked if the OS allows it, otherwise, new Python processes are bootstrapped which can incur some start-up cost. Each process processes a single chunk at once. Examples: >>> import math >>> list(parallel_map(math.sqrt, [9, 4, 1], concurrency=2)) [3.0, 2.0, 1.0] Args: func: The function that should be applied to each element of `input_values`. It can `async`, in that case, a new event loop is started for each chunk. input_values: An iterable of items that `func` is applied to. chunk_size: Tune the number of items processed in each step. Larger numbers result in smaller communication overhead but less parallelism at the start and end. If `chunk_size` has a `__len__` property, the `chunk_size` is calculated automatically if not given. ignore_exceptions: Ignore chunks if `next()` raises an exception on `input_values`. And return `None` if `func` raised an exception in a worker process. concurrency: Number of new processes to start. Shouldn't be too much more than the number of physical cores. unordered: Do not preserve the order of the elements, yield them as soon as they have been processed. This decreases the latency caused by difficult-to-process items. Yields: The next result obtained from applying `func` to each input value. May contain `None`-s if `ignore_exceptions=True`. May have different order than the input if `unordered=True`. Raises: WorkerException: If there was an error in the `func` function in a background process and `ignore_exceptions=False`. \"\"\" config = get_config ( function = func , input_values = input_values , chunk_size = chunk_size , concurrency = concurrency , ) ctx = ( mp . get_context ( \"fork\" ) if \"fork\" in mp . get_all_start_methods () else mp . get_context ( \"spawn\" ) ) ctx . freeze_support () manager = ctx . Manager () input_queue = manager . Queue ( config . concurrency * 2 ) output_queue = manager . Queue ( config . concurrency * 2 ) should_stop = ctx . Event () serialized_map_function = dill . dumps ( func , byref = True , recurse = False ) processes = [ ctx . Process ( name = f \"parallel_map_ { config . function_name } _ { i } \" , target = mapper_function , daemon = True , kwargs = dict ( input_queue = input_queue , output_queue = output_queue , should_stop = should_stop , func = serialized_map_function , ), ) for i in range ( config . concurrency ) ] for p in processes : p . start () try : yield from manage_communication ( input_values = input_values , chunk_size = config . chunk_size , input_queue = input_queue , output_queue = output_queue , unordered = unordered , ignore_exceptions = ignore_exceptions , ) should_stop . set () except WorkerException : should_stop . set () raise except Exception : for p in processes : p . terminate () p . kill () raise finally : for p in processes : p . join () # terminated processes have to be joined else they remain zombies p . close () manager . shutdown ()","title":"parallel_map()"},{"location":"reference/utilities/#great_ai.utilities.threaded_parallel_map","text":"Execute a map operation on an iterable stream. Similar to parallel_map but uses threads instead of processes. Hence, it is not helpful in CPU-bound situations. A custom parallel map operation supporting both synchronous and async map functions. Exceptions encountered in the map function are sent to the host thread where they are either raised (default) or ignored. Each process processes a single chunk at once. Examples: >>> list ( threaded_parallel_map ( lambda x : x ** 2 , [ 1 , 2 , 3 ])) [1, 4, 9] Parameters: Name Type Description Default func Callable [[ T ], Union [ V , Awaitable [ V ]]] The function that should be applied to each element of input_values . It can async , in that case, a new event loop is started for each chunk. required input_values Union [ Iterable [ T ], Sequence [ T ]] An iterable of items that func is applied to. required chunk_size Optional [ int ] Tune the number of items processed in each step. Larger numbers result in smaller communication overhead but less parallelism at the start and end. If chunk_size has a __len__ property, the chunk_size is calculated automatically if not given. None ignore_exceptions bool Ignore chunks if next() raises an exception on input_values . And return None if func raised an exception in a worker process. False concurrency Optional [ int ] Number of new threads to start. None unordered bool Do not preserve the order of the elements, yield them as soon as they have been processed. This decreases the latency caused by difficult-to-process items. False Yields: Type Description Iterable [ Optional [ V ]] The next result obtained from applying func to each input value. May contain None -s if ignore_exceptions=True . May have different order than the input if unordered=True . Raises: Type Description WorkerException If there was an error in the func function in a background thread and ignore_exceptions=False . Source code in great_ai/utilities/parallel_map/threaded_parallel_map.py 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 def threaded_parallel_map ( func : Callable [[ T ], Union [ V , Awaitable [ V ]]], input_values : Union [ Iterable [ T ], Sequence [ T ]], * , chunk_size : Optional [ int ] = None , ignore_exceptions : bool = False , concurrency : Optional [ int ] = None , unordered : bool = False , ) -> Iterable [ Optional [ V ]]: \"\"\"Execute a map operation on an iterable stream. Similar to [parallel_map][great_ai.utilities.parallel_map.parallel_map.parallel_map] but uses threads instead of processes. Hence, it is not helpful in CPU-bound situations. A custom parallel map operation supporting both synchronous and `async` map functions. Exceptions encountered in the map function are sent to the host thread where they are either raised (default) or ignored. Each process processes a single chunk at once. Examples: >>> list(threaded_parallel_map(lambda x: x ** 2, [1, 2, 3])) [1, 4, 9] Args: func: The function that should be applied to each element of `input_values`. It can `async`, in that case, a new event loop is started for each chunk. input_values: An iterable of items that `func` is applied to. chunk_size: Tune the number of items processed in each step. Larger numbers result in smaller communication overhead but less parallelism at the start and end. If `chunk_size` has a `__len__` property, the `chunk_size` is calculated automatically if not given. ignore_exceptions: Ignore chunks if `next()` raises an exception on `input_values`. And return `None` if `func` raised an exception in a worker process. concurrency: Number of new threads to start. unordered: Do not preserve the order of the elements, yield them as soon as they have been processed. This decreases the latency caused by difficult-to-process items. Yields: The next result obtained from applying `func` to each input value. May contain `None`-s if `ignore_exceptions=True`. May have different order than the input if `unordered=True`. Raises: WorkerException: If there was an error in the `func` function in a background thread and `ignore_exceptions=False`. \"\"\" config = get_config ( function = func , input_values = input_values , chunk_size = chunk_size , concurrency = concurrency , ) input_queue : queue . Queue = queue . Queue ( config . concurrency * 2 ) output_queue : queue . Queue = queue . Queue ( config . concurrency * 2 ) should_stop = threading . Event () threads = [ threading . Thread ( name = f \"threaded_parallel_map_ { config . function_name } _ { i } \" , target = mapper_function , daemon = True , kwargs = dict ( input_queue = input_queue , output_queue = output_queue , should_stop = should_stop , func = func , ), ) for i in range ( config . concurrency ) ] for t in threads : t . start () yield from manage_communication ( input_values = input_values , chunk_size = config . chunk_size , input_queue = input_queue , output_queue = output_queue , unordered = unordered , ignore_exceptions = ignore_exceptions , ) should_stop . set () for t in threads : t . join ( 1 )","title":"threaded_parallel_map()"},{"location":"reference/utilities/#composable-parallel-processing","text":"Because both threaded_parallel_map and parallel_map have a streaming interface, it is easy to compose them and end up with, for example, a process for each CPU core with its own thread-pool or event-loop. Longer pipelines are also easy to imagine. The chunking methods help in these compositions.","title":"Composable parallel processing"},{"location":"reference/utilities/#great_ai.utilities.chunk.chunk","text":"Turn an iterable of items into an iterable of lists (chunks) of items. Each returned chunk is of length chunk_size except the last one the length of which is between 1 and chunk_size . Useful for parallel processing. Examples: >>> list ( chunk ( range ( 10 ), chunk_size = 3 )) [[0, 1, 2], [3, 4, 5], [6, 7, 8], [9]] Parameters: Name Type Description Default values Iterable [ T ] The stream of items to pack into chunks. required chunk_size int Desired length of each (but the last) chunk. required Yields: Type Description Iterable [ List [ T ]] The next chunk. Source code in great_ai/utilities/chunk.py 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 def chunk ( values : Iterable [ T ], chunk_size : int ) -> Iterable [ List [ T ]]: \"\"\"Turn an iterable of items into an iterable of lists (chunks) of items. Each returned chunk is of length `chunk_size` except the last one the length of which is between 1 and `chunk_size`. Useful for parallel processing. Examples: >>> list(chunk(range(10), chunk_size=3)) [[0, 1, 2], [3, 4, 5], [6, 7, 8], [9]] Args: values: The stream of items to pack into chunks. chunk_size: Desired length of each (but the last) chunk. Yields: The next chunk. \"\"\" assert chunk_size >= 1 result : List [ T ] = [] for v in values : result . append ( v ) if len ( result ) == chunk_size : yield result result = [] if len ( result ) > 0 : yield result","title":"chunk()"},{"location":"reference/utilities/#great_ai.utilities.unchunk.unchunk","text":"Turn a stream of chunks of items into a stream of items (flatten operation). The inverse operation of chunk . Useful for parallel processing. Similar to itertools.chain but ignores None chunks. Examples: >>> list ( unchunk ([[ 0 , 1 , 2 ], [ 3 , 4 , 5 ], [ 6 , 7 , 8 ], [ 9 ]])) [0, 1, 2, 3, 4, 5, 6, 7, 8, 9] Parameters: Name Type Description Default chunks Iterable [ Optional [ Iterable [ T ]]] Stream of chunks to unpack. required Yields: Type Description Iterable [ T ] The next item in the flattened iterable. Source code in great_ai/utilities/unchunk.py 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 def unchunk ( chunks : Iterable [ Optional [ Iterable [ T ]]]) -> Iterable [ T ]: \"\"\"Turn a stream of chunks of items into a stream of items (flatten operation). The inverse operation of [chunk][great_ai.utilities.chunk.chunk]. Useful for parallel processing. Similar to itertools.chain but ignores `None` chunks. Examples: >>> list(unchunk([[0, 1, 2], [3, 4, 5], [6, 7, 8], [9]])) [0, 1, 2, 3, 4, 5, 6, 7, 8, 9] Args: chunks: Stream of chunks to unpack. Yields: The next item in the flattened iterable. \"\"\" for chunk in chunks : if chunk is not None : yield from chunk","title":"unchunk()"},{"location":"reference/utilities/#operations","text":"","title":"Operations"},{"location":"reference/utilities/#great_ai.utilities.ConfigFile","text":"Bases: Mapping [ str , str ] A small and safe INI -style configuration loader with dict and ENV support. The values can be accessed using both dot- and index-notation. It is compatible with the dict interface. File format example: # comments are allowed everywhere key = value # you can leave or omit whitespace around the equal-sign my_hashtag = \"#great_ai\" # the r-value can be quoted with \" or ' or `. my_var = my_default_value # Default values can be given to env-vars, # see next line. The default value must come first. my_var = ENV : MY_ENV_VAR # If the value starts with the `ENV:` prefix, # it is looked up from the environment variables. Examples: >>> ConfigFile ( 'tests/utilities/data/simple.conf' ) ConfigFile(path=tests/utilities/data/simple.conf) {'zeroth_key': 'test', 'first_key': 'Andr\u00e1s'} >>> ConfigFile ( 'tests/utilities/data/simple.conf' ) . zeroth_key 'test' >>> ConfigFile ( 'tests/utilities/data/simple.conf' ) . second_key Traceback (most recent call last): ... KeyError : 'Key `second_key` is not found in configuration file ... >>> a = ConfigFile ( 'tests/utilities/data/simple.conf' ) >>> { ** a } {'zeroth_key': 'test', 'first_key': 'Andr\u00e1s'} Source code in great_ai/utilities/config_file/config_file.py 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 class ConfigFile ( Mapping [ str , str ]): \"\"\"A small and safe `INI`-style configuration loader with `dict` and `ENV` support. The values can be accessed using both dot- and index-notation. It is compatible with the `dict` interface. File format example: ```toml # comments are allowed everywhere key = value # you can leave or omit whitespace around the equal-sign my_hashtag = \"#great_ai\" # the r-value can be quoted with \" or ' or `. my_var = my_default_value # Default values can be given to env-vars, # see next line. The default value must come first. my_var = ENV:MY_ENV_VAR # If the value starts with the `ENV:` prefix, # it is looked up from the environment variables. ``` Examples: >>> ConfigFile('tests/utilities/data/simple.conf') ConfigFile(path=tests/utilities/data/simple.conf) {'zeroth_key': 'test', 'first_key': 'Andr\u00e1s'} >>> ConfigFile('tests/utilities/data/simple.conf').zeroth_key 'test' >>> ConfigFile('tests/utilities/data/simple.conf').second_key Traceback (most recent call last): ... KeyError: 'Key `second_key` is not found in configuration file ... >>> a = ConfigFile('tests/utilities/data/simple.conf') >>> {**a} {'zeroth_key': 'test', 'first_key': 'Andr\u00e1s'} \"\"\" ENVIRONMENT_VARIABLE_KEY_PREFIX = \"ENV\" def __init__ ( self , path : Union [ Path , str ], * , ignore_missing : bool = False ) -> None : \"\"\"Load and parse a configuration file. Everything is eager-loaded, thus, exceptions may be thrown here. Args: path: Local path of the configuration file. ignore_missing: Don't raise an exception on missing environment variables. Raises: FileNotFoundError: If there is no file at the specified path. ParseError: If the provided file does not conform to the expected format. KeyError: If there is duplication in the keys. ValueError: If an environment variable is referenced but it is not set in the system and `ignore_missing=False`. \"\"\" if not isinstance ( path , Path ): path = Path ( path ) if not path . exists (): raise FileNotFoundError ( path . absolute ()) self . _ignore_missing = ignore_missing self . _path = path self . _key_values : Dict [ str , str ] = {} self . _parse () @property def path ( self ) -> Path : \"\"\"Original path from where the configuration was loaded.\"\"\" return self . _path def _parse ( self ) -> None : with open ( self . _path , encoding = \"utf-8\" ) as f : lines : str = f . read () matches = pattern . findall ( lines ) for key , * values in matches : try : value = next ( v for v in values if v ) except StopIteration : raise ParseError ( f \"\"\"Cannot parse config file ( { self . _path . absolute () } ), error at key ` { key } `\"\"\" ) already_exists = key in self . _key_values if already_exists and not value . startswith ( f \" { self . ENVIRONMENT_VARIABLE_KEY_PREFIX } :\" ): raise KeyError ( f \"Key ` { key } ` has been already defined and its value is ` { self . _key_values [ key ] } `\" ) if value . startswith ( f \" { self . ENVIRONMENT_VARIABLE_KEY_PREFIX } :\" ): _ , value = value . split ( \":\" ) if value not in os . environ : issue = f \"\"\"The value of ` { key } ` contains the \" { self . ENVIRONMENT_VARIABLE_KEY_PREFIX } ` prefix but ` { value } ` is not defined as an environment variable\"\"\" if already_exists : logger . warning ( f \"\"\" { issue } , using the default value defined above (` { self . _key_values [ key ] } `)\"\"\" ) continue elif self . _ignore_missing : logger . warning ( issue ) else : raise ValueError ( f \" { issue } and no default value has been provided\" ) else : value = os . environ [ value ] self . _key_values [ key ] = value def __getattr__ ( self , key : str ) -> str : if key in self . _key_values : return self . _key_values [ key ] raise KeyError ( f \"Key ` { key } ` is not found in configuration file ( { self . _path . absolute () } )\" ) __getitem__ = __getattr__ def __iter__ ( self ) -> Iterator [ str ]: return iter ( self . _key_values ) def __len__ ( self ) -> int : return len ( self . _key_values ) def keys ( self ) -> KeysView [ str ]: return self . _key_values . keys () def values ( self ) -> ValuesView [ str ]: return self . _key_values . values () def items ( self ) -> ItemsView [ str , str ]: return self . _key_values . items () def __repr__ ( self ) -> str : return f \" { type ( self ) . __name__ } (path= { self . _path . as_posix () } ) { self . _key_values } \"","title":"ConfigFile"},{"location":"reference/utilities/#great_ai.utilities.config_file.config_file.ConfigFile.__init__","text":"Load and parse a configuration file. Everything is eager-loaded, thus, exceptions may be thrown here. Parameters: Name Type Description Default path Union [ Path , str ] Local path of the configuration file. required ignore_missing bool Don't raise an exception on missing environment variables. False Raises: Type Description FileNotFoundError If there is no file at the specified path. ParseError If the provided file does not conform to the expected format. KeyError If there is duplication in the keys. ValueError If an environment variable is referenced but it is not set in the system and ignore_missing=False . Source code in great_ai/utilities/config_file/config_file.py 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 def __init__ ( self , path : Union [ Path , str ], * , ignore_missing : bool = False ) -> None : \"\"\"Load and parse a configuration file. Everything is eager-loaded, thus, exceptions may be thrown here. Args: path: Local path of the configuration file. ignore_missing: Don't raise an exception on missing environment variables. Raises: FileNotFoundError: If there is no file at the specified path. ParseError: If the provided file does not conform to the expected format. KeyError: If there is duplication in the keys. ValueError: If an environment variable is referenced but it is not set in the system and `ignore_missing=False`. \"\"\" if not isinstance ( path , Path ): path = Path ( path ) if not path . exists (): raise FileNotFoundError ( path . absolute ()) self . _ignore_missing = ignore_missing self . _path = path self . _key_values : Dict [ str , str ] = {} self . _parse ()","title":"__init__()"},{"location":"reference/utilities/#great_ai.utilities.config_file.config_file.ConfigFile.path","text":"Original path from where the configuration was loaded. Source code in great_ai/utilities/config_file/config_file.py 83 84 85 86 @property def path ( self ) -> Path : \"\"\"Original path from where the configuration was loaded.\"\"\" return self . _path","title":"path()"},{"location":"reference/utilities/#great_ai.utilities.get_logger","text":"Return a customised logger used throughout the GreatAI codebase. Uses colors, and only prints timestamps when not running inside notebook. Source code in great_ai/utilities/logger/get_logger.py 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 def get_logger ( name : str , level : int = logging . INFO , disable_colors : bool = False ) -> logging . Logger : \"\"\"Return a customised logger used throughout the GreatAI codebase. Uses colors, and only prints timestamps when not running inside notebook. \"\"\" if name not in loggers : logger = logging . getLogger ( name ) logger . setLevel ( level ) try : get_ipython () # type: ignore log_format = \" %(message)s \" except NameError : # will fail outside of a notebook https://ipython.org/ log_format = \" %(asctime)s | %(levelname)8s | %(message)s \" stdout_handler = logging . StreamHandler () stdout_handler . setLevel ( level ) if not disable_colors : stdout_handler . setFormatter ( CustomFormatter ( log_format )) logger . addHandler ( stdout_handler ) loggers [ name ] = logger return loggers [ name ]","title":"get_logger()"},{"location":"reference/views/","text":"View models # Trace # Bases: Generic [ T ] , HashableBaseModel Universal structure for storing prediction traces and training data. Attributes: Name Type Description trace_id str UUID4 identifier for uniquely referring to a trace. created str Timestamp of its (original) construction. original_execution_time_ms float Wall-time elapsed while its generating TracingContext was alive. logged_values Dict [ str , Any ] Values persisted through using @parameter or log_metric() . models List [ Model ] Marks left by each encountered @use_model decorated function. exception Optional [ str ] Exception description if any was encountered. output Optional [ T ] Return value of the function wrapped by GreatAI. feedback Any Feedback obtained using the REST API of add_ground_truth . tags List [ str ] Tags used for filtering traces. Contains the name of the original function, value of ENVIRONMENT , its split if has any, and either ground_truth or online depending on the origin of the Trace. Source code in great_ai/views/trace.py 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 class Trace ( Generic [ T ], HashableBaseModel ): \"\"\"Universal structure for storing prediction traces and training data. Attributes: trace_id: UUID4 identifier for uniquely referring to a trace. created: Timestamp of its (original) construction. original_execution_time_ms: Wall-time elapsed while its generating TracingContext was alive. logged_values: Values persisted through using `@parameter` or `log_metric()`. models: Marks left by each encountered `@use_model` decorated function. exception: Exception description if any was encountered. output: Return value of the function wrapped by GreatAI. feedback: Feedback obtained using the REST API of `add_ground_truth`. tags: Tags used for filtering traces. Contains the name of the original function, value of `ENVIRONMENT`, its split if has any, and either `ground_truth` or `online` depending on the origin of the Trace. \"\"\" trace_id : str created : str original_execution_time_ms : float logged_values : Dict [ str , Any ] models : List [ Model ] exception : Optional [ str ] output : Optional [ T ] feedback : Any = None tags : List [ str ] class Config : extra = Extra . ignore @property def input ( self ) -> Any : return ( self . logged_values [ \"input\" ] if list ( self . logged_values . keys ()) == [ \"input\" ] else self . logged_values ) @property def models_flat ( self ) -> str : return \", \" . join ( f \" { m . key } : { m . version } \" for m in self . models ) @property def output_flat ( self ) -> str : return pformat ( self . output , indent = 2 , compact = True ) @property def exception_flat ( self ) -> str : return ( \"null\" if self . exception is None else pformat ( self . exception , indent = 2 , compact = True ) ) @property def feedback_flat ( self ) -> str : return ( \"null\" if self . feedback is None else pformat ( self . feedback , indent = 2 , compact = True ) ) @property def tags_flat ( self ) -> str : return \", \\n \" . join ( self . tags ) def to_flat_dict ( self , include_original : bool = True ) -> Dict [ str , Any ]: return { ** ( self . dict () if include_original else { \"trace_id\" : self . trace_id , \"created\" : self . created , \"original_execution_time_ms\" : self . original_execution_time_ms , } ), ** { k : pformat ( v , indent = 2 , compact = True ) for k , v in self . logged_values . items () }, \"models_flat\" : self . models_flat , \"exception_flat\" : self . exception_flat , \"output_flat\" : self . output_flat , \"feedback_flat\" : self . feedback_flat , \"tags_flat\" : self . tags_flat , } def __repr__ ( self ) -> str : return f \"\"\"Trace[ { type ( self . output ) . __name__ } ]( { pformat ( self . dict (), indent = 2 , compact = True ) . replace ( '{ ' , '{' , 1 ) } )\"\"\" RouteConfig # Bases: BaseModel Source code in great_ai/views/route_config.py 4 5 6 7 8 9 10 class RouteConfig ( BaseModel ): prediction_endpoint_enabled : bool = True docs_endpoints_enabled : bool = True dashboard_enabled : bool = True feedback_endpoints_enabled : bool = True trace_endpoints_enabled : bool = True meta_endpoints_enabled : bool = True ClassificationOutput # Bases: HashableBaseModel Source code in great_ai/views/outputs/classification_output.py 6 7 8 9 class ClassificationOutput ( HashableBaseModel ): label : Union [ str , int ] confidence : float explanation : Optional [ Any ] MultiLabelClassificationOutput # Bases: HashableBaseModel Source code in great_ai/views/outputs/multi_label_classification_output.py 7 8 class MultiLabelClassificationOutput ( HashableBaseModel ): labels : List [ ClassificationOutput ] = [] RegressionOutput # Bases: HashableBaseModel Source code in great_ai/views/outputs/regression_output.py 6 7 8 class RegressionOutput ( HashableBaseModel ): value : Union [ int , float ] explanation : Optional [ Any ] SequenceLabelingOutput # Bases: HashableBaseModel Source code in great_ai/views/outputs/sequence_labeling_output.py 15 16 17 class SequenceLabelingOutput ( HashableBaseModel ): labeled_tokens : List [ LabeledToken ] explanation : Optional [ Any ]","title":"View models"},{"location":"reference/views/#view-models","text":"","title":"View models"},{"location":"reference/views/#great_ai.Trace","text":"Bases: Generic [ T ] , HashableBaseModel Universal structure for storing prediction traces and training data. Attributes: Name Type Description trace_id str UUID4 identifier for uniquely referring to a trace. created str Timestamp of its (original) construction. original_execution_time_ms float Wall-time elapsed while its generating TracingContext was alive. logged_values Dict [ str , Any ] Values persisted through using @parameter or log_metric() . models List [ Model ] Marks left by each encountered @use_model decorated function. exception Optional [ str ] Exception description if any was encountered. output Optional [ T ] Return value of the function wrapped by GreatAI. feedback Any Feedback obtained using the REST API of add_ground_truth . tags List [ str ] Tags used for filtering traces. Contains the name of the original function, value of ENVIRONMENT , its split if has any, and either ground_truth or online depending on the origin of the Trace. Source code in great_ai/views/trace.py 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 class Trace ( Generic [ T ], HashableBaseModel ): \"\"\"Universal structure for storing prediction traces and training data. Attributes: trace_id: UUID4 identifier for uniquely referring to a trace. created: Timestamp of its (original) construction. original_execution_time_ms: Wall-time elapsed while its generating TracingContext was alive. logged_values: Values persisted through using `@parameter` or `log_metric()`. models: Marks left by each encountered `@use_model` decorated function. exception: Exception description if any was encountered. output: Return value of the function wrapped by GreatAI. feedback: Feedback obtained using the REST API of `add_ground_truth`. tags: Tags used for filtering traces. Contains the name of the original function, value of `ENVIRONMENT`, its split if has any, and either `ground_truth` or `online` depending on the origin of the Trace. \"\"\" trace_id : str created : str original_execution_time_ms : float logged_values : Dict [ str , Any ] models : List [ Model ] exception : Optional [ str ] output : Optional [ T ] feedback : Any = None tags : List [ str ] class Config : extra = Extra . ignore @property def input ( self ) -> Any : return ( self . logged_values [ \"input\" ] if list ( self . logged_values . keys ()) == [ \"input\" ] else self . logged_values ) @property def models_flat ( self ) -> str : return \", \" . join ( f \" { m . key } : { m . version } \" for m in self . models ) @property def output_flat ( self ) -> str : return pformat ( self . output , indent = 2 , compact = True ) @property def exception_flat ( self ) -> str : return ( \"null\" if self . exception is None else pformat ( self . exception , indent = 2 , compact = True ) ) @property def feedback_flat ( self ) -> str : return ( \"null\" if self . feedback is None else pformat ( self . feedback , indent = 2 , compact = True ) ) @property def tags_flat ( self ) -> str : return \", \\n \" . join ( self . tags ) def to_flat_dict ( self , include_original : bool = True ) -> Dict [ str , Any ]: return { ** ( self . dict () if include_original else { \"trace_id\" : self . trace_id , \"created\" : self . created , \"original_execution_time_ms\" : self . original_execution_time_ms , } ), ** { k : pformat ( v , indent = 2 , compact = True ) for k , v in self . logged_values . items () }, \"models_flat\" : self . models_flat , \"exception_flat\" : self . exception_flat , \"output_flat\" : self . output_flat , \"feedback_flat\" : self . feedback_flat , \"tags_flat\" : self . tags_flat , } def __repr__ ( self ) -> str : return f \"\"\"Trace[ { type ( self . output ) . __name__ } ]( { pformat ( self . dict (), indent = 2 , compact = True ) . replace ( '{ ' , '{' , 1 ) } )\"\"\"","title":"Trace"},{"location":"reference/views/#great_ai.RouteConfig","text":"Bases: BaseModel Source code in great_ai/views/route_config.py 4 5 6 7 8 9 10 class RouteConfig ( BaseModel ): prediction_endpoint_enabled : bool = True docs_endpoints_enabled : bool = True dashboard_enabled : bool = True feedback_endpoints_enabled : bool = True trace_endpoints_enabled : bool = True meta_endpoints_enabled : bool = True","title":"RouteConfig"},{"location":"reference/views/#great_ai.ClassificationOutput","text":"Bases: HashableBaseModel Source code in great_ai/views/outputs/classification_output.py 6 7 8 9 class ClassificationOutput ( HashableBaseModel ): label : Union [ str , int ] confidence : float explanation : Optional [ Any ]","title":"ClassificationOutput"},{"location":"reference/views/#great_ai.MultiLabelClassificationOutput","text":"Bases: HashableBaseModel Source code in great_ai/views/outputs/multi_label_classification_output.py 7 8 class MultiLabelClassificationOutput ( HashableBaseModel ): labels : List [ ClassificationOutput ] = []","title":"MultiLabelClassificationOutput"},{"location":"reference/views/#great_ai.RegressionOutput","text":"Bases: HashableBaseModel Source code in great_ai/views/outputs/regression_output.py 6 7 8 class RegressionOutput ( HashableBaseModel ): value : Union [ int , float ] explanation : Optional [ Any ]","title":"RegressionOutput"},{"location":"reference/views/#great_ai.SequenceLabelingOutput","text":"Bases: HashableBaseModel Source code in great_ai/views/outputs/sequence_labeling_output.py 15 16 17 class SequenceLabelingOutput ( HashableBaseModel ): labeled_tokens : List [ LabeledToken ] explanation : Optional [ Any ]","title":"SequenceLabelingOutput"},{"location":"tutorial/","text":"Train and deploy a SOTA model # Let's see great-ai in action by going over the life-cycle of a simple service. Objectives # You will see how the great_ai.utilities can integrate into your Data Science workflow. You will use great_ai.large_file to version and store your trained model. You will use GreatAI to prepare your model for a robust and responsible deployment. Overview # You are going to train a field of study (domain) classifier for scientific sentences. The exact task was proposed by the SciBERT paper in which SciBERT achieved an F1-score of 0.6571 . We are going to outperform it using a trivial text classification model: a Linear SVM . We use the same synthetic dataset derived from the Microsoft Academic Graph . The dataset is available here . Success You are ready to start the tutorial. Feel free to come back to the summary section once you're finished. Train it Deploy it Summary # Training notebook # We load and preprocess the dataset while relying on great_ai.utilities.clean for doing the heavy-lifting. Additionally, the preprocessing is parallelised using great_ai.utilities.simple_parallel_map After training and evaluating a model, it is exported using great_ai.save_model . Remote storage To store your model remotely, you need to set your credentials before calling save_model . For example, to use AWS S3 : from great_ai.large_file import LargeFileS3 LargeFileS3 . configure ( aws_region_name = 'eu-west-2' , aws_access_key_id = 'MY_AWS_ACCESS_KEY' , aws_secret_access_key = 'MY_AWS_SECRET_KEY' , large_files_bucket_name = 'my_bucket_for_models' ) from great_ai import save_model save_model ( model , key = 'my-domain-predictor' ) For more info, checkout the configuration how-to page . Deployment notebook # We create an inference function that can be hardened by wrapping it in a GreatAI instance. from great_ai import GreatAI , use_model from great_ai.utilities import clean @GreatAI . create @use_model ( 'my-domain-predictor' ) #(1) def predict_domain ( sentence , model ): inputs = [ clean ( sentence )] return str ( model . predict ( inputs )[ 0 ]) @use_model loads and injects your model into the predict_domain function's model argument. You can freely reference it knowing that the function is always provided with it. Finally, we test the model's inference function through the GreatAI dashboard. The only thing left is to deploy the hardened service properly. Learn about all the features Look at more examples","title":"Tutorial overview"},{"location":"tutorial/#train-and-deploy-a-sota-model","text":"Let's see great-ai in action by going over the life-cycle of a simple service.","title":"Train and deploy a SOTA model"},{"location":"tutorial/#objectives","text":"You will see how the great_ai.utilities can integrate into your Data Science workflow. You will use great_ai.large_file to version and store your trained model. You will use GreatAI to prepare your model for a robust and responsible deployment.","title":"Objectives"},{"location":"tutorial/#overview","text":"You are going to train a field of study (domain) classifier for scientific sentences. The exact task was proposed by the SciBERT paper in which SciBERT achieved an F1-score of 0.6571 . We are going to outperform it using a trivial text classification model: a Linear SVM . We use the same synthetic dataset derived from the Microsoft Academic Graph . The dataset is available here . Success You are ready to start the tutorial. Feel free to come back to the summary section once you're finished. Train it Deploy it","title":"Overview"},{"location":"tutorial/#summary","text":"","title":"Summary"},{"location":"tutorial/#training-notebook","text":"We load and preprocess the dataset while relying on great_ai.utilities.clean for doing the heavy-lifting. Additionally, the preprocessing is parallelised using great_ai.utilities.simple_parallel_map After training and evaluating a model, it is exported using great_ai.save_model . Remote storage To store your model remotely, you need to set your credentials before calling save_model . For example, to use AWS S3 : from great_ai.large_file import LargeFileS3 LargeFileS3 . configure ( aws_region_name = 'eu-west-2' , aws_access_key_id = 'MY_AWS_ACCESS_KEY' , aws_secret_access_key = 'MY_AWS_SECRET_KEY' , large_files_bucket_name = 'my_bucket_for_models' ) from great_ai import save_model save_model ( model , key = 'my-domain-predictor' ) For more info, checkout the configuration how-to page .","title":"Training notebook"},{"location":"tutorial/#deployment-notebook","text":"We create an inference function that can be hardened by wrapping it in a GreatAI instance. from great_ai import GreatAI , use_model from great_ai.utilities import clean @GreatAI . create @use_model ( 'my-domain-predictor' ) #(1) def predict_domain ( sentence , model ): inputs = [ clean ( sentence )] return str ( model . predict ( inputs )[ 0 ]) @use_model loads and injects your model into the predict_domain function's model argument. You can freely reference it knowing that the function is always provided with it. Finally, we test the model's inference function through the GreatAI dashboard. The only thing left is to deploy the hardened service properly. Learn about all the features Look at more examples","title":"Deployment notebook"},{"location":"tutorial/deploy/","text":"(function (global, factory) { typeof exports === 'object' && typeof module !== 'undefined' ? module.exports = factory() : typeof define === 'function' && define.amd ? define(factory) : (global = global || self, global.ClipboardCopyElement = factory()); }(this, function () { 'use strict'; function createNode(text) { const node = document.createElement('pre'); node.style.width = '1px'; node.style.height = '1px'; node.style.position = 'fixed'; node.style.top = '5px'; node.textContent = text; return node; } function copyNode(node) { if ('clipboard' in navigator) { // eslint-disable-next-line flowtype/no-flow-fix-me-comments // $FlowFixMe Clipboard is not defined in Flow yet. return navigator.clipboard.writeText(node.textContent); } const selection = getSelection(); if (selection == null) { return Promise.reject(new Error()); } selection.removeAllRanges(); const range = document.createRange(); 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const node = root.getElementById(id); if (node) copyTarget(node).then(trigger); } } function copyTarget(content) { if (content instanceof HTMLInputElement || content instanceof HTMLTextAreaElement) { return copyText(content.value); } else if (content instanceof HTMLAnchorElement && content.hasAttribute('href')) { return copyText(content.href); } else { return copyNode(content); } } function clicked(event) { const button = event.currentTarget; if (button instanceof HTMLElement) { copy(button); } } function keydown(event) { if (event.key === ' ' || event.key === 'Enter') { const button = event.currentTarget; if (button instanceof HTMLElement) { event.preventDefault(); copy(button); } } } function focused(event) { event.currentTarget.addEventListener('keydown', keydown); } function blurred(event) { event.currentTarget.removeEventListener('keydown', keydown); } class ClipboardCopyElement extends HTMLElement { constructor() { super(); this.addEventListener('click', clicked); this.addEventListener('focus', focused); this.addEventListener('blur', blurred); } connectedCallback() { if (!this.hasAttribute('tabindex')) { this.setAttribute('tabindex', '0'); } if (!this.hasAttribute('role')) { this.setAttribute('role', 'button'); } } get value() { return this.getAttribute('value') || ''; } set value(text) { this.setAttribute('value', text); } } if (!window.customElements.get('clipboard-copy')) { window.ClipboardCopyElement = ClipboardCopyElement; window.customElements.define('clipboard-copy', ClipboardCopyElement); } return ClipboardCopyElement; })); document.addEventListener('clipboard-copy', function(event) { const notice = event.target.querySelector('.notice') notice.hidden = false setTimeout(function() { notice.hidden = true }, 1000) }) pre { line-height: 125%; } td.linenos .normal { color: inherit; background-color: transparent; padding-left: 5px; padding-right: 5px; } span.linenos { color: inherit; background-color: transparent; padding-left: 5px; padding-right: 5px; } td.linenos .special { color: #000000; background-color: #ffffc0; padding-left: 5px; padding-right: 5px; } span.linenos.special { color: #000000; background-color: #ffffc0; padding-left: 5px; padding-right: 5px; } .highlight-ipynb .hll { background-color: var(--jp-cell-editor-active-background) } .highlight-ipynb { background: var(--jp-cell-editor-background); color: var(--jp-mirror-editor-variable-color) } .highlight-ipynb .c { color: var(--jp-mirror-editor-comment-color); font-style: italic } /* Comment */ .highlight-ipynb .err { color: var(--jp-mirror-editor-error-color) } /* Error */ .highlight-ipynb .k { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword */ .highlight-ipynb .o { color: var(--jp-mirror-editor-operator-color); font-weight: bold } /* Operator */ .highlight-ipynb .p { color: var(--jp-mirror-editor-punctuation-color) } /* Punctuation */ .highlight-ipynb .ch { color: var(--jp-mirror-editor-comment-color); font-style: italic } /* Comment.Hashbang */ .highlight-ipynb .cm { color: var(--jp-mirror-editor-comment-color); font-style: italic } /* Comment.Multiline */ .highlight-ipynb .cp { color: var(--jp-mirror-editor-comment-color); font-style: italic } /* Comment.Preproc */ .highlight-ipynb .cpf { color: var(--jp-mirror-editor-comment-color); font-style: italic } /* Comment.PreprocFile */ .highlight-ipynb .c1 { color: var(--jp-mirror-editor-comment-color); font-style: italic } /* Comment.Single */ .highlight-ipynb .cs { color: var(--jp-mirror-editor-comment-color); font-style: italic } /* Comment.Special */ .highlight-ipynb .kc { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword.Constant */ .highlight-ipynb .kd { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword.Declaration */ .highlight-ipynb .kn { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword.Namespace */ .highlight-ipynb .kp { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword.Pseudo */ .highlight-ipynb .kr { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword.Reserved */ .highlight-ipynb .kt { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword.Type */ .highlight-ipynb .m { color: var(--jp-mirror-editor-number-color) } /* Literal.Number */ .highlight-ipynb .s { color: var(--jp-mirror-editor-string-color) } /* Literal.String */ .highlight-ipynb .ow { color: var(--jp-mirror-editor-operator-color); font-weight: bold } /* Operator.Word */ .highlight-ipynb .w { color: var(--jp-mirror-editor-variable-color) } /* Text.Whitespace */ .highlight-ipynb .mb { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Bin */ .highlight-ipynb .mf { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Float */ .highlight-ipynb .mh { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Hex */ .highlight-ipynb .mi { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Integer */ .highlight-ipynb .mo { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Oct */ .highlight-ipynb .sa { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Affix */ .highlight-ipynb .sb { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Backtick */ .highlight-ipynb .sc { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Char */ .highlight-ipynb .dl { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Delimiter */ .highlight-ipynb .sd { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Doc */ .highlight-ipynb .s2 { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Double */ .highlight-ipynb .se { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Escape */ .highlight-ipynb .sh { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Heredoc */ .highlight-ipynb .si { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Interpol */ .highlight-ipynb .sx { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Other */ .highlight-ipynb .sr { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Regex */ .highlight-ipynb .s1 { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Single */ .highlight-ipynb .ss { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Symbol */ .highlight-ipynb .il { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Integer.Long */ /* This file is taken from the built JupyterLab theme.css Found on share/nbconvert/templates/lab/static Some changes have been made and marked with CHANGE */ .jupyter-wrapper { /* Elevation * * We style box-shadows using Material Design's idea of elevation. These particular numbers are taken from here: * * https://github.com/material-components/material-components-web * https://material-components-web.appspot.com/elevation.html */ --jp-shadow-base-lightness: 0; --jp-shadow-umbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.2 ); --jp-shadow-penumbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.14 ); --jp-shadow-ambient-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.12 ); --jp-elevation-z0: none; --jp-elevation-z1: 0px 2px 1px -1px var(--jp-shadow-umbra-color), 0px 1px 1px 0px var(--jp-shadow-penumbra-color), 0px 1px 3px 0px var(--jp-shadow-ambient-color); --jp-elevation-z2: 0px 3px 1px -2px var(--jp-shadow-umbra-color), 0px 2px 2px 0px var(--jp-shadow-penumbra-color), 0px 1px 5px 0px var(--jp-shadow-ambient-color); --jp-elevation-z4: 0px 2px 4px -1px var(--jp-shadow-umbra-color), 0px 4px 5px 0px var(--jp-shadow-penumbra-color), 0px 1px 10px 0px var(--jp-shadow-ambient-color); --jp-elevation-z6: 0px 3px 5px -1px var(--jp-shadow-umbra-color), 0px 6px 10px 0px var(--jp-shadow-penumbra-color), 0px 1px 18px 0px var(--jp-shadow-ambient-color); --jp-elevation-z8: 0px 5px 5px -3px var(--jp-shadow-umbra-color), 0px 8px 10px 1px var(--jp-shadow-penumbra-color), 0px 3px 14px 2px var(--jp-shadow-ambient-color); --jp-elevation-z12: 0px 7px 8px -4px var(--jp-shadow-umbra-color), 0px 12px 17px 2px var(--jp-shadow-penumbra-color), 0px 5px 22px 4px var(--jp-shadow-ambient-color); --jp-elevation-z16: 0px 8px 10px -5px var(--jp-shadow-umbra-color), 0px 16px 24px 2px var(--jp-shadow-penumbra-color), 0px 6px 30px 5px var(--jp-shadow-ambient-color); --jp-elevation-z20: 0px 10px 13px -6px var(--jp-shadow-umbra-color), 0px 20px 31px 3px var(--jp-shadow-penumbra-color), 0px 8px 38px 7px var(--jp-shadow-ambient-color); --jp-elevation-z24: 0px 11px 15px -7px var(--jp-shadow-umbra-color), 0px 24px 38px 3px var(--jp-shadow-penumbra-color), 0px 9px 46px 8px var(--jp-shadow-ambient-color); /* Borders * * The following variables, specify the visual styling of borders in JupyterLab. */ --jp-border-width: 1px; --jp-border-color0: var(--md-grey-400); --jp-border-color1: var(--md-grey-400); --jp-border-color2: var(--md-grey-300); --jp-border-color3: var(--md-grey-200); --jp-border-radius: 2px; /* UI Fonts * * The UI font CSS variables are used for the typography all of the JupyterLab * user interface elements that are not directly user generated content. * * The font sizing here is done assuming that the body font size of --jp-ui-font-size1 * is applied to a parent element. When children elements, such as headings, are sized * in em all things will be computed relative to that body size. */ --jp-ui-font-scale-factor: 1.2; --jp-ui-font-size0: 0.83333em; --jp-ui-font-size1: 13px; /* Base font size */ --jp-ui-font-size2: 1.2em; --jp-ui-font-size3: 1.44em; --jp-ui-font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Helvetica, Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\"; /* * Use these font colors against the corresponding main layout colors. * In a light theme, these go from dark to light. */ /* Defaults use Material Design specification */ --jp-ui-font-color0: rgba(0, 0, 0, 1); --jp-ui-font-color1: rgba(0, 0, 0, 0.87); --jp-ui-font-color2: rgba(0, 0, 0, 0.54); --jp-ui-font-color3: rgba(0, 0, 0, 0.38); /* * Use these against the brand/accent/warn/error colors. * These will typically go from light to darker, in both a dark and light theme. */ --jp-ui-inverse-font-color0: rgba(255, 255, 255, 1); --jp-ui-inverse-font-color1: rgba(255, 255, 255, 1); --jp-ui-inverse-font-color2: rgba(255, 255, 255, 0.7); --jp-ui-inverse-font-color3: rgba(255, 255, 255, 0.5); /* Content Fonts * * Content font variables are used for typography of user generated content. * * The font sizing here is done assuming that the body font size of --jp-content-font-size1 * is applied to a parent element. When children elements, such as headings, are sized * in em all things will be computed relative to that body size. */ --jp-content-line-height: 1.6; --jp-content-font-scale-factor: 1.2; --jp-content-font-size0: 0.83333em; --jp-content-font-size1: 14px; /* Base font size */ --jp-content-font-size2: 1.2em; --jp-content-font-size3: 1.44em; --jp-content-font-size4: 1.728em; --jp-content-font-size5: 2.0736em; /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-content-presentation-font-size1: 17px; --jp-content-heading-line-height: 1; --jp-content-heading-margin-top: 1.2em; --jp-content-heading-margin-bottom: 0.8em; --jp-content-heading-font-weight: 500; /* Defaults use Material Design specification */ --jp-content-font-color0: rgba(0, 0, 0, 1); --jp-content-font-color1: rgba(0, 0, 0, 0.87); --jp-content-font-color2: rgba(0, 0, 0, 0.54); --jp-content-font-color3: rgba(0, 0, 0, 0.38); --jp-content-link-color: var(--md-blue-700); --jp-content-font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Helvetica, Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\"; /* * Code Fonts * * Code font variables are used for typography of code and other monospaces content. */ --jp-code-font-size: 13px; --jp-code-line-height: 1.3077; /* 17px for 13px base */ --jp-code-padding: 5px; /* 5px for 13px base, codemirror highlighting needs integer px value */ --jp-code-font-family-default: Menlo, Consolas, \"DejaVu Sans Mono\", monospace; --jp-code-font-family: var(--jp-code-font-family-default); /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-code-presentation-font-size: 16px; /* may need to tweak cursor width if you change font size */ --jp-code-cursor-width0: 1.4px; --jp-code-cursor-width1: 2px; --jp-code-cursor-width2: 4px; /* Layout * * The following are the main layout colors use in JupyterLab. In a light * theme these would go from light to dark. */ --jp-layout-color0: white; --jp-layout-color1: white; --jp-layout-color2: var(--md-grey-200); --jp-layout-color3: var(--md-grey-400); --jp-layout-color4: var(--md-grey-600); /* Inverse Layout * * The following are the inverse layout colors use in JupyterLab. In a light * theme these would go from dark to light. */ --jp-inverse-layout-color0: #111111; --jp-inverse-layout-color1: var(--md-grey-900); --jp-inverse-layout-color2: var(--md-grey-800); --jp-inverse-layout-color3: var(--md-grey-700); --jp-inverse-layout-color4: var(--md-grey-600); /* Brand/accent */ --jp-brand-color0: var(--md-blue-900); --jp-brand-color1: var(--md-blue-700); --jp-brand-color2: var(--md-blue-300); --jp-brand-color3: var(--md-blue-100); --jp-brand-color4: var(--md-blue-50); --jp-accent-color0: var(--md-green-900); --jp-accent-color1: var(--md-green-700); --jp-accent-color2: var(--md-green-300); --jp-accent-color3: var(--md-green-100); /* State colors (warn, error, success, info) */ --jp-warn-color0: var(--md-orange-900); --jp-warn-color1: var(--md-orange-700); --jp-warn-color2: var(--md-orange-300); --jp-warn-color3: var(--md-orange-100); --jp-error-color0: var(--md-red-900); --jp-error-color1: var(--md-red-700); --jp-error-color2: var(--md-red-300); --jp-error-color3: var(--md-red-100); --jp-success-color0: var(--md-green-900); --jp-success-color1: var(--md-green-700); --jp-success-color2: var(--md-green-300); --jp-success-color3: var(--md-green-100); --jp-info-color0: var(--md-cyan-900); --jp-info-color1: var(--md-cyan-700); --jp-info-color2: var(--md-cyan-300); --jp-info-color3: var(--md-cyan-100); /* Cell specific styles */ --jp-cell-padding: 5px; --jp-cell-collapser-width: 8px; --jp-cell-collapser-min-height: 20px; --jp-cell-collapser-not-active-hover-opacity: 0.6; --jp-cell-editor-background: var(--md-grey-100); --jp-cell-editor-border-color: var(--md-grey-300); --jp-cell-editor-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-cell-editor-active-background: var(--jp-layout-color0); --jp-cell-editor-active-border-color: var(--jp-brand-color1); --jp-cell-prompt-width: 64px; --jp-cell-prompt-font-family: var(--jp-code-font-family-default); --jp-cell-prompt-letter-spacing: 0px; --jp-cell-prompt-opacity: 1; --jp-cell-prompt-not-active-opacity: 0.5; --jp-cell-prompt-not-active-font-color: var(--md-grey-700); /* A custom blend of MD grey and blue 600 * See https://meyerweb.com/eric/tools/color-blend/#546E7A:1E88E5:5:hex */ --jp-cell-inprompt-font-color: #307fc1; /* A custom blend of MD grey and orange 600 * https://meyerweb.com/eric/tools/color-blend/#546E7A:F4511E:5:hex */ --jp-cell-outprompt-font-color: #bf5b3d; /* Notebook specific styles */ --jp-notebook-padding: 10px; --jp-notebook-select-background: var(--jp-layout-color1); --jp-notebook-multiselected-color: var(--md-blue-50); /* The scroll padding is calculated to fill enough space at the bottom of the notebook to show one single-line cell (with appropriate padding) at the top when the notebook is scrolled all the way to the bottom. We also subtract one pixel so that no scrollbar appears if we have just one single-line cell in the notebook. This padding is to enable a 'scroll past end' feature in a notebook. */ --jp-notebook-scroll-padding: calc( 100% - var(--jp-code-font-size) * var(--jp-code-line-height) - var(--jp-code-padding) - var(--jp-cell-padding) - 1px ); /* Rendermime styles */ --jp-rendermime-error-background: #fdd; --jp-rendermime-table-row-background: var(--md-grey-100); --jp-rendermime-table-row-hover-background: var(--md-light-blue-50); /* Dialog specific styles */ --jp-dialog-background: rgba(0, 0, 0, 0.25); /* Console specific styles */ --jp-console-padding: 10px; /* Toolbar specific styles */ --jp-toolbar-border-color: var(--jp-border-color1); --jp-toolbar-micro-height: 8px; --jp-toolbar-background: var(--jp-layout-color1); --jp-toolbar-box-shadow: 0px 0px 2px 0px rgba(0, 0, 0, 0.24); --jp-toolbar-header-margin: 4px 4px 0px 4px; --jp-toolbar-active-background: var(--md-grey-300); /* Statusbar specific styles */ --jp-statusbar-height: 24px; /* Input field styles */ --jp-input-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-input-active-background: var(--jp-layout-color1); --jp-input-hover-background: var(--jp-layout-color1); --jp-input-background: var(--md-grey-100); --jp-input-border-color: var(--jp-border-color1); --jp-input-active-border-color: var(--jp-brand-color1); --jp-input-active-box-shadow-color: rgba(19, 124, 189, 0.3); /* General editor styles */ --jp-editor-selected-background: #d9d9d9; --jp-editor-selected-focused-background: #d7d4f0; --jp-editor-cursor-color: var(--jp-ui-font-color0); /* Code mirror specific styles */ --jp-mirror-editor-keyword-color: #008000; --jp-mirror-editor-atom-color: #88f; --jp-mirror-editor-number-color: #080; --jp-mirror-editor-def-color: #00f; --jp-mirror-editor-variable-color: var(--md-grey-900); --jp-mirror-editor-variable-2-color: #05a; --jp-mirror-editor-variable-3-color: #085; --jp-mirror-editor-punctuation-color: #05a; --jp-mirror-editor-property-color: #05a; --jp-mirror-editor-operator-color: #aa22ff; --jp-mirror-editor-comment-color: #408080; --jp-mirror-editor-string-color: #ba2121; --jp-mirror-editor-string-2-color: #708; --jp-mirror-editor-meta-color: #aa22ff; --jp-mirror-editor-qualifier-color: #555; --jp-mirror-editor-builtin-color: #008000; --jp-mirror-editor-bracket-color: #997; --jp-mirror-editor-tag-color: #170; --jp-mirror-editor-attribute-color: #00c; --jp-mirror-editor-header-color: blue; --jp-mirror-editor-quote-color: #090; --jp-mirror-editor-link-color: #00c; --jp-mirror-editor-error-color: #f00; --jp-mirror-editor-hr-color: #999; /* Vega extension styles */ --jp-vega-background: white; /* Sidebar-related styles */ --jp-sidebar-min-width: 250px; /* Search-related styles */ --jp-search-toggle-off-opacity: 0.5; --jp-search-toggle-hover-opacity: 0.8; --jp-search-toggle-on-opacity: 1; --jp-search-selected-match-background-color: rgb(245, 200, 0); --jp-search-selected-match-color: black; --jp-search-unselected-match-background-color: var( --jp-inverse-layout-color0 ); --jp-search-unselected-match-color: var(--jp-ui-inverse-font-color0); /* Icon colors that work well with light or dark backgrounds */ --jp-icon-contrast-color0: var(--md-purple-600); --jp-icon-contrast-color1: var(--md-green-600); --jp-icon-contrast-color2: var(--md-pink-600); --jp-icon-contrast-color3: var(--md-blue-600); } [data-md-color-scheme=\"slate\"] .jupyter-wrapper { /* Elevation * * We style box-shadows using Material Design's idea of elevation. These particular numbers are taken from here: * * https://github.com/material-components/material-components-web * https://material-components-web.appspot.com/elevation.html */ /* The dark theme shadows need a bit of work, but this will probably also require work on the core layout * colors used in the theme as well. */ --jp-shadow-base-lightness: 32; --jp-shadow-umbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.2 ); --jp-shadow-penumbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.14 ); --jp-shadow-ambient-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.12 ); --jp-elevation-z0: none; --jp-elevation-z1: 0px 2px 1px -1px var(--jp-shadow-umbra-color), 0px 1px 1px 0px var(--jp-shadow-penumbra-color), 0px 1px 3px 0px var(--jp-shadow-ambient-color); --jp-elevation-z2: 0px 3px 1px -2px var(--jp-shadow-umbra-color), 0px 2px 2px 0px var(--jp-shadow-penumbra-color), 0px 1px 5px 0px var(--jp-shadow-ambient-color); --jp-elevation-z4: 0px 2px 4px -1px var(--jp-shadow-umbra-color), 0px 4px 5px 0px var(--jp-shadow-penumbra-color), 0px 1px 10px 0px var(--jp-shadow-ambient-color); --jp-elevation-z6: 0px 3px 5px -1px var(--jp-shadow-umbra-color), 0px 6px 10px 0px var(--jp-shadow-penumbra-color), 0px 1px 18px 0px var(--jp-shadow-ambient-color); --jp-elevation-z8: 0px 5px 5px -3px var(--jp-shadow-umbra-color), 0px 8px 10px 1px var(--jp-shadow-penumbra-color), 0px 3px 14px 2px var(--jp-shadow-ambient-color); --jp-elevation-z12: 0px 7px 8px -4px var(--jp-shadow-umbra-color), 0px 12px 17px 2px var(--jp-shadow-penumbra-color), 0px 5px 22px 4px var(--jp-shadow-ambient-color); --jp-elevation-z16: 0px 8px 10px -5px var(--jp-shadow-umbra-color), 0px 16px 24px 2px var(--jp-shadow-penumbra-color), 0px 6px 30px 5px var(--jp-shadow-ambient-color); --jp-elevation-z20: 0px 10px 13px -6px var(--jp-shadow-umbra-color), 0px 20px 31px 3px var(--jp-shadow-penumbra-color), 0px 8px 38px 7px var(--jp-shadow-ambient-color); --jp-elevation-z24: 0px 11px 15px -7px var(--jp-shadow-umbra-color), 0px 24px 38px 3px var(--jp-shadow-penumbra-color), 0px 9px 46px 8px var(--jp-shadow-ambient-color); /* Borders * * The following variables, specify the visual styling of borders in JupyterLab. */ --jp-border-width: 1px; --jp-border-color0: var(--md-grey-700); --jp-border-color1: var(--md-grey-700); --jp-border-color2: var(--md-grey-800); --jp-border-color3: var(--md-grey-900); --jp-border-radius: 2px; /* UI Fonts * * The UI font CSS variables are used for the typography all of the JupyterLab * user interface elements that are not directly user generated content. * * The font sizing here is done assuming that the body font size of --jp-ui-font-size1 * is applied to a parent element. When children elements, such as headings, are sized * in em all things will be computed relative to that body size. */ --jp-ui-font-scale-factor: 1.2; --jp-ui-font-size0: 0.83333em; --jp-ui-font-size1: 13px; /* Base font size */ --jp-ui-font-size2: 1.2em; --jp-ui-font-size3: 1.44em; --jp-ui-font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Helvetica, Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\"; /* * Use these font colors against the corresponding main layout colors. * In a light theme, these go from dark to light. */ /* Defaults use Material Design specification */ --jp-ui-font-color0: rgba(255, 255, 255, 1); --jp-ui-font-color1: rgba(255, 255, 255, 0.87); --jp-ui-font-color2: rgba(255, 255, 255, 0.54); --jp-ui-font-color3: rgba(255, 255, 255, 0.38); /* * Use these against the brand/accent/warn/error colors. * These will typically go from light to darker, in both a dark and light theme. */ --jp-ui-inverse-font-color0: rgba(0, 0, 0, 1); --jp-ui-inverse-font-color1: rgba(0, 0, 0, 0.8); --jp-ui-inverse-font-color2: rgba(0, 0, 0, 0.5); --jp-ui-inverse-font-color3: rgba(0, 0, 0, 0.3); /* Content Fonts * * Content font variables are used for typography of user generated content. * * The font sizing here is done assuming that the body font size of --jp-content-font-size1 * is applied to a parent element. When children elements, such as headings, are sized * in em all things will be computed relative to that body size. */ --jp-content-line-height: 1.6; --jp-content-font-scale-factor: 1.2; --jp-content-font-size0: 0.83333em; --jp-content-font-size1: 14px; /* Base font size */ --jp-content-font-size2: 1.2em; --jp-content-font-size3: 1.44em; --jp-content-font-size4: 1.728em; --jp-content-font-size5: 2.0736em; /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-content-presentation-font-size1: 17px; --jp-content-heading-line-height: 1; --jp-content-heading-margin-top: 1.2em; --jp-content-heading-margin-bottom: 0.8em; --jp-content-heading-font-weight: 500; /* Defaults use Material Design specification */ --jp-content-font-color0: rgba(255, 255, 255, 1); --jp-content-font-color1: rgba(255, 255, 255, 1); --jp-content-font-color2: rgba(255, 255, 255, 0.7); --jp-content-font-color3: rgba(255, 255, 255, 0.5); --jp-content-link-color: var(--md-blue-300); --jp-content-font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Helvetica, Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\"; /* * Code Fonts * * Code font variables are used for typography of code and other monospaces content. */ --jp-code-font-size: 13px; --jp-code-line-height: 1.3077; /* 17px for 13px base */ --jp-code-padding: 5px; /* 5px for 13px base, codemirror highlighting needs integer px value */ --jp-code-font-family-default: Menlo, Consolas, \"DejaVu Sans Mono\", monospace; --jp-code-font-family: var(--jp-code-font-family-default); /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-code-presentation-font-size: 16px; /* may need to tweak cursor width if you change font size */ --jp-code-cursor-width0: 1.4px; --jp-code-cursor-width1: 2px; --jp-code-cursor-width2: 4px; /* Layout * * The following are the main layout colors use in JupyterLab. In a light * theme these would go from light to dark. */ --jp-layout-color0: #111111; --jp-layout-color1: var(--md-grey-900); --jp-layout-color2: var(--md-grey-800); --jp-layout-color3: var(--md-grey-700); --jp-layout-color4: var(--md-grey-600); /* Inverse Layout * * The following are the inverse layout colors use in JupyterLab. In a light * theme these would go from dark to light. */ --jp-inverse-layout-color0: white; --jp-inverse-layout-color1: white; --jp-inverse-layout-color2: var(--md-grey-200); --jp-inverse-layout-color3: var(--md-grey-400); --jp-inverse-layout-color4: var(--md-grey-600); /* Brand/accent */ --jp-brand-color0: var(--md-blue-700); --jp-brand-color1: var(--md-blue-500); --jp-brand-color2: var(--md-blue-300); --jp-brand-color3: var(--md-blue-100); --jp-brand-color4: var(--md-blue-50); --jp-accent-color0: var(--md-green-700); --jp-accent-color1: var(--md-green-500); --jp-accent-color2: var(--md-green-300); --jp-accent-color3: var(--md-green-100); /* State colors (warn, error, success, info) */ --jp-warn-color0: var(--md-orange-700); --jp-warn-color1: var(--md-orange-500); --jp-warn-color2: var(--md-orange-300); --jp-warn-color3: var(--md-orange-100); --jp-error-color0: var(--md-red-700); --jp-error-color1: var(--md-red-500); --jp-error-color2: var(--md-red-300); --jp-error-color3: var(--md-red-100); --jp-success-color0: var(--md-green-700); --jp-success-color1: var(--md-green-500); --jp-success-color2: var(--md-green-300); --jp-success-color3: var(--md-green-100); --jp-info-color0: var(--md-cyan-700); --jp-info-color1: var(--md-cyan-500); --jp-info-color2: var(--md-cyan-300); --jp-info-color3: var(--md-cyan-100); /* Cell specific styles */ --jp-cell-padding: 5px; --jp-cell-collapser-width: 8px; --jp-cell-collapser-min-height: 20px; --jp-cell-collapser-not-active-hover-opacity: 0.6; --jp-cell-editor-background: var(--jp-layout-color1); --jp-cell-editor-border-color: var(--md-grey-700); --jp-cell-editor-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-cell-editor-active-background: var(--jp-layout-color0); --jp-cell-editor-active-border-color: var(--jp-brand-color1); --jp-cell-prompt-width: 64px; --jp-cell-prompt-font-family: var(--jp-code-font-family-default); --jp-cell-prompt-letter-spacing: 0px; --jp-cell-prompt-opacity: 1; --jp-cell-prompt-not-active-opacity: 1; --jp-cell-prompt-not-active-font-color: var(--md-grey-300); /* A custom blend of MD grey and blue 600 * See https://meyerweb.com/eric/tools/color-blend/#546E7A:1E88E5:5:hex */ --jp-cell-inprompt-font-color: #307fc1; /* A custom blend of MD grey and orange 600 * https://meyerweb.com/eric/tools/color-blend/#546E7A:F4511E:5:hex */ --jp-cell-outprompt-font-color: #bf5b3d; /* Notebook specific styles */ --jp-notebook-padding: 10px; --jp-notebook-select-background: var(--jp-layout-color1); --jp-notebook-multiselected-color: rgba(33, 150, 243, 0.24); /* The scroll padding is calculated to fill enough space at the bottom of the notebook to show one single-line cell (with appropriate padding) at the top when the notebook is scrolled all the way to the bottom. We also subtract one pixel so that no scrollbar appears if we have just one single-line cell in the notebook. This padding is to enable a 'scroll past end' feature in a notebook. */ --jp-notebook-scroll-padding: calc( 100% - var(--jp-code-font-size) * var(--jp-code-line-height) - var(--jp-code-padding) - var(--jp-cell-padding) - 1px ); /* Rendermime styles */ --jp-rendermime-error-background: rgba(244, 67, 54, 0.28); --jp-rendermime-table-row-background: var(--md-grey-900); --jp-rendermime-table-row-hover-background: rgba(3, 169, 244, 0.2); /* Dialog specific styles */ --jp-dialog-background: rgba(0, 0, 0, 0.6); /* Console specific styles */ --jp-console-padding: 10px; /* Toolbar specific styles */ --jp-toolbar-border-color: var(--jp-border-color2); --jp-toolbar-micro-height: 8px; --jp-toolbar-background: var(--jp-layout-color1); --jp-toolbar-box-shadow: 0px 0px 2px 0px rgba(0, 0, 0, 0.8); --jp-toolbar-header-margin: 4px 4px 0px 4px; --jp-toolbar-active-background: var(--jp-layout-color0); /* Statusbar specific styles */ --jp-statusbar-height: 24px; /* Input field styles */ --jp-input-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-input-active-background: var(--jp-layout-color0); --jp-input-hover-background: var(--jp-layout-color2); --jp-input-background: var(--md-grey-800); --jp-input-border-color: var(--jp-border-color1); --jp-input-active-border-color: var(--jp-brand-color1); --jp-input-active-box-shadow-color: rgba(19, 124, 189, 0.3); /* General editor styles */ --jp-editor-selected-background: var(--jp-layout-color2); --jp-editor-selected-focused-background: rgba(33, 150, 243, 0.24); --jp-editor-cursor-color: var(--jp-ui-font-color0); /* Code mirror specific styles */ --jp-mirror-editor-keyword-color: var(--md-green-500); --jp-mirror-editor-atom-color: var(--md-blue-300); --jp-mirror-editor-number-color: var(--md-green-400); --jp-mirror-editor-def-color: var(--md-blue-600); --jp-mirror-editor-variable-color: var(--md-grey-300); --jp-mirror-editor-variable-2-color: var(--md-blue-400); --jp-mirror-editor-variable-3-color: var(--md-green-600); --jp-mirror-editor-punctuation-color: var(--md-blue-400); --jp-mirror-editor-property-color: var(--md-blue-400); --jp-mirror-editor-operator-color: #aa22ff; --jp-mirror-editor-comment-color: #408080; --jp-mirror-editor-string-color: #ff7070; --jp-mirror-editor-string-2-color: var(--md-purple-300); --jp-mirror-editor-meta-color: #aa22ff; --jp-mirror-editor-qualifier-color: #555; --jp-mirror-editor-builtin-color: var(--md-green-600); --jp-mirror-editor-bracket-color: #997; --jp-mirror-editor-tag-color: var(--md-green-700); --jp-mirror-editor-attribute-color: var(--md-blue-700); --jp-mirror-editor-header-color: var(--md-blue-500); --jp-mirror-editor-quote-color: var(--md-green-300); --jp-mirror-editor-link-color: var(--md-blue-700); --jp-mirror-editor-error-color: #f00; --jp-mirror-editor-hr-color: #999; /* Vega extension styles */ --jp-vega-background: var(--md-grey-400); /* Sidebar-related styles */ --jp-sidebar-min-width: 250px; /* Search-related styles */ --jp-search-toggle-off-opacity: 0.6; --jp-search-toggle-hover-opacity: 0.8; --jp-search-toggle-on-opacity: 1; --jp-search-selected-match-background-color: rgb(255, 225, 0); --jp-search-selected-match-color: black; --jp-search-unselected-match-background-color: var( --jp-inverse-layout-color0 ); --jp-search-unselected-match-color: var(--jp-ui-inverse-font-color0); /* scrollbar related styles. Supports every browser except Edge. */ /* colors based on JetBrain's Darcula theme */ --jp-scrollbar-background-color: #3f4244; --jp-scrollbar-thumb-color: 88, 96, 97; /* need to specify thumb color as an RGB triplet */ --jp-scrollbar-endpad: 3px; /* the minimum gap between the thumb and the ends of a scrollbar */ /* hacks for setting the thumb shape. These do nothing in Firefox */ --jp-scrollbar-thumb-margin: 3.5px; /* the space in between the sides of the thumb and the track */ --jp-scrollbar-thumb-radius: 9px; /* set to a large-ish value for rounded endcaps on the thumb */ /* Icon colors that work well with light or dark backgrounds */ --jp-icon-contrast-color0: var(--md-purple-600); --jp-icon-contrast-color1: var(--md-green-600); --jp-icon-contrast-color2: var(--md-pink-600); --jp-icon-contrast-color3: var(--md-blue-600); } :root{--md-red-50: #ffebee;--md-red-100: #ffcdd2;--md-red-200: #ef9a9a;--md-red-300: #e57373;--md-red-400: #ef5350;--md-red-500: #f44336;--md-red-600: #e53935;--md-red-700: #d32f2f;--md-red-800: #c62828;--md-red-900: #b71c1c;--md-red-A100: #ff8a80;--md-red-A200: #ff5252;--md-red-A400: #ff1744;--md-red-A700: #d50000;--md-pink-50: #fce4ec;--md-pink-100: #f8bbd0;--md-pink-200: #f48fb1;--md-pink-300: #f06292;--md-pink-400: #ec407a;--md-pink-500: #e91e63;--md-pink-600: #d81b60;--md-pink-700: #c2185b;--md-pink-800: #ad1457;--md-pink-900: #880e4f;--md-pink-A100: #ff80ab;--md-pink-A200: #ff4081;--md-pink-A400: #f50057;--md-pink-A700: #c51162;--md-purple-50: #f3e5f5;--md-purple-100: #e1bee7;--md-purple-200: #ce93d8;--md-purple-300: #ba68c8;--md-purple-400: #ab47bc;--md-purple-500: #9c27b0;--md-purple-600: #8e24aa;--md-purple-700: #7b1fa2;--md-purple-800: #6a1b9a;--md-purple-900: #4a148c;--md-purple-A100: #ea80fc;--md-purple-A200: #e040fb;--md-purple-A400: #d500f9;--md-purple-A700: #aa00ff;--md-deep-purple-50: #ede7f6;--md-deep-purple-100: #d1c4e9;--md-deep-purple-200: #b39ddb;--md-deep-purple-300: #9575cd;--md-deep-purple-400: #7e57c2;--md-deep-purple-500: #673ab7;--md-deep-purple-600: #5e35b1;--md-deep-purple-700: #512da8;--md-deep-purple-800: #4527a0;--md-deep-purple-900: #311b92;--md-deep-purple-A100: #b388ff;--md-deep-purple-A200: #7c4dff;--md-deep-purple-A400: #651fff;--md-deep-purple-A700: #6200ea;--md-indigo-50: #e8eaf6;--md-indigo-100: #c5cae9;--md-indigo-200: #9fa8da;--md-indigo-300: #7986cb;--md-indigo-400: #5c6bc0;--md-indigo-500: #3f51b5;--md-indigo-600: #3949ab;--md-indigo-700: #303f9f;--md-indigo-800: #283593;--md-indigo-900: #1a237e;--md-indigo-A100: #8c9eff;--md-indigo-A200: #536dfe;--md-indigo-A400: #3d5afe;--md-indigo-A700: #304ffe;--md-blue-50: #e3f2fd;--md-blue-100: #bbdefb;--md-blue-200: #90caf9;--md-blue-300: #64b5f6;--md-blue-400: #42a5f5;--md-blue-500: #2196f3;--md-blue-600: #1e88e5;--md-blue-700: #1976d2;--md-blue-800: #1565c0;--md-blue-900: #0d47a1;--md-blue-A100: #82b1ff;--md-blue-A200: #448aff;--md-blue-A400: #2979ff;--md-blue-A700: #2962ff;--md-light-blue-50: #e1f5fe;--md-light-blue-100: #b3e5fc;--md-light-blue-200: #81d4fa;--md-light-blue-300: #4fc3f7;--md-light-blue-400: #29b6f6;--md-light-blue-500: #03a9f4;--md-light-blue-600: #039be5;--md-light-blue-700: #0288d1;--md-light-blue-800: #0277bd;--md-light-blue-900: #01579b;--md-light-blue-A100: #80d8ff;--md-light-blue-A200: #40c4ff;--md-light-blue-A400: #00b0ff;--md-light-blue-A700: #0091ea;--md-cyan-50: #e0f7fa;--md-cyan-100: #b2ebf2;--md-cyan-200: #80deea;--md-cyan-300: #4dd0e1;--md-cyan-400: #26c6da;--md-cyan-500: #00bcd4;--md-cyan-600: #00acc1;--md-cyan-700: #0097a7;--md-cyan-800: #00838f;--md-cyan-900: #006064;--md-cyan-A100: #84ffff;--md-cyan-A200: #18ffff;--md-cyan-A400: #00e5ff;--md-cyan-A700: #00b8d4;--md-teal-50: #e0f2f1;--md-teal-100: #b2dfdb;--md-teal-200: #80cbc4;--md-teal-300: #4db6ac;--md-teal-400: #26a69a;--md-teal-500: #009688;--md-teal-600: #00897b;--md-teal-700: #00796b;--md-teal-800: #00695c;--md-teal-900: #004d40;--md-teal-A100: #a7ffeb;--md-teal-A200: #64ffda;--md-teal-A400: #1de9b6;--md-teal-A700: #00bfa5;--md-green-50: #e8f5e9;--md-green-100: #c8e6c9;--md-green-200: #a5d6a7;--md-green-300: #81c784;--md-green-400: #66bb6a;--md-green-500: #4caf50;--md-green-600: #43a047;--md-green-700: #388e3c;--md-green-800: #2e7d32;--md-green-900: #1b5e20;--md-green-A100: #b9f6ca;--md-green-A200: #69f0ae;--md-green-A400: #00e676;--md-green-A700: #00c853;--md-light-green-50: #f1f8e9;--md-light-green-100: #dcedc8;--md-light-green-200: #c5e1a5;--md-light-green-300: #aed581;--md-light-green-400: #9ccc65;--md-light-green-500: #8bc34a;--md-light-green-600: #7cb342;--md-light-green-700: #689f38;--md-light-green-800: #558b2f;--md-light-green-900: #33691e;--md-light-green-A100: #ccff90;--md-light-green-A200: #b2ff59;--md-light-green-A400: #76ff03;--md-light-green-A700: #64dd17;--md-lime-50: #f9fbe7;--md-lime-100: #f0f4c3;--md-lime-200: #e6ee9c;--md-lime-300: #dce775;--md-lime-400: #d4e157;--md-lime-500: #cddc39;--md-lime-600: #c0ca33;--md-lime-700: #afb42b;--md-lime-800: #9e9d24;--md-lime-900: #827717;--md-lime-A100: #f4ff81;--md-lime-A200: #eeff41;--md-lime-A400: #c6ff00;--md-lime-A700: #aeea00;--md-yellow-50: #fffde7;--md-yellow-100: #fff9c4;--md-yellow-200: #fff59d;--md-yellow-300: #fff176;--md-yellow-400: #ffee58;--md-yellow-500: #ffeb3b;--md-yellow-600: #fdd835;--md-yellow-700: #fbc02d;--md-yellow-800: #f9a825;--md-yellow-900: #f57f17;--md-yellow-A100: #ffff8d;--md-yellow-A200: #ffff00;--md-yellow-A400: #ffea00;--md-yellow-A700: #ffd600;--md-amber-50: #fff8e1;--md-amber-100: #ffecb3;--md-amber-200: #ffe082;--md-amber-300: #ffd54f;--md-amber-400: #ffca28;--md-amber-500: #ffc107;--md-amber-600: #ffb300;--md-amber-700: #ffa000;--md-amber-800: #ff8f00;--md-amber-900: #ff6f00;--md-amber-A100: #ffe57f;--md-amber-A200: #ffd740;--md-amber-A400: #ffc400;--md-amber-A700: #ffab00;--md-orange-50: #fff3e0;--md-orange-100: #ffe0b2;--md-orange-200: #ffcc80;--md-orange-300: #ffb74d;--md-orange-400: #ffa726;--md-orange-500: #ff9800;--md-orange-600: #fb8c00;--md-orange-700: #f57c00;--md-orange-800: #ef6c00;--md-orange-900: #e65100;--md-orange-A100: #ffd180;--md-orange-A200: #ffab40;--md-orange-A400: #ff9100;--md-orange-A700: #ff6d00;--md-deep-orange-50: #fbe9e7;--md-deep-orange-100: #ffccbc;--md-deep-orange-200: #ffab91;--md-deep-orange-300: #ff8a65;--md-deep-orange-400: #ff7043;--md-deep-orange-500: 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sourceMappingURL=mkdocs-jupyter.css.map*/ init_mathjax = function() { if (window.MathJax) { // MathJax loaded MathJax.Hub.Config({ TeX: { equationNumbers: { autoNumber: \"AMS\", useLabelIds: true } }, tex2jax: { inlineMath: [ ['$','$'], [\"\\\\(\",\"\\\\)\"] ], displayMath: [ ['$$','$$'], [\"\\\\[\",\"\\\\]\"] ], processEscapes: true, processEnvironments: true }, displayAlign: 'center', CommonHTML: { linebreaks: { automatic: true } } }); MathJax.Hub.Queue([\"Typeset\", MathJax.Hub]); } } init_mathjax(); Harden and deploy your app \u00b6 Finally, it's time to deploy your model. But before that, you have to make sure you follow AI deployment best practices . In the past, this step was too often either the source of unexpected struggle, or worse, simply ignored. With GreatAI , it has become a matter of 4 lines of code. In [1]: Copied! from great_ai import GreatAI , use_model from great_ai.utilities import clean @GreatAI . create @use_model ( \"my-domain-predictor\" ) def predict_domain ( sentence , model ): inputs = [ clean ( sentence )] return str ( model . predict ( inputs )[ 0 ]) from great_ai import GreatAI, use_model from great_ai.utilities import clean @GreatAI.create @use_model(\"my-domain-predictor\") def predict_domain(sentence, model): inputs = [clean(sentence)] return str(model.predict(inputs)[0]) Environment variable ENVIRONMENT is not set, defaulting to development mode \u203c\ufe0f Cannot find credentials files, defaulting to using ParallelTinyDbDriver The selected tracing database (ParallelTinyDbDriver) is not recommended for production Cannot find credentials files, defaulting to using LargeFileLocal GreatAI (v0.1.4): configured \u2705 \ud83d\udd29 tracing_database: ParallelTinyDbDriver \ud83d\udd29 large_file_implementation: LargeFileLocal \ud83d\udd29 is_production: False \ud83d\udd29 should_log_exception_stack: True \ud83d\udd29 prediction_cache_size: 512 \ud83d\udd29 dashboard_table_size: 50 You still need to check whether you follow all best practices before trusting your deployment. > Find out more at https://se-ml.github.io/practices Fetching cached versions of my-domain-predictor Latest version of my-domain-predictor is 9 (from versions: 0, 1, 2, 3, 4, 5, 6, 7, 8, 9) File my-domain-predictor-9 found in cache In [2]: Copied! predict_domain ( \"Mountains are just big rocks.\" ) predict_domain(\"Mountains are just big rocks.\") Out[2]: Trace[str]({'created': '2022-07-12T13:34:26.743292', 'exception': None, 'feedback': None, 'logged_values': { 'arg:sentence:length': 29, 'arg:sentence:value': 'Mountains are just big rocks.'}, 'models': [{'key': 'my-domain-predictor', 'version': 9}], 'original_execution_time_ms': 6.9699, 'output': 'geography', 'tags': ['predict_domain', 'online', 'development'], 'trace_id': 'c80bdee3-602b-49dd-a84d-6eef80127e5a'}) Notice how the original return value is under the .output key. Additionally, a plethora of metadata has been added which will be useful later on. Running your app in development-mode is as easy as executing great-ai deploy.ipynb from your terminal. In [3]: Copied! ! great - ai deploy . ipynb # leave this running and open http://127.0.0.1:6060 !great-ai deploy.ipynb # leave this running and open http://127.0.0.1:6060 2022-07-12 15:34:28 | INFO | Converting notebook to Python script 2022-07-12 15:34:29 | INFO | Found `predict_domain` to be the GreatAI app 2022-07-12 15:34:29 | INFO | Uvicorn running on http://0.0.0.0:6060 (Press CTRL+C to quit) 2022-07-12 15:34:31 | WARNING | Environment variable ENVIRONMENT is not set, defaulting to development mode \u203c\ufe0f 2022-07-12 15:34:31 | WARNING | Cannot find credentials files, defaulting to using ParallelTinyDbDriver 2022-07-12 15:34:31 | WARNING | The selected tracing database (ParallelTinyDbDriver) is not recommended for production 2022-07-12 15:34:31 | WARNING | Cannot find credentials files, defaulting to using LargeFileLocal 2022-07-12 15:34:31 | INFO | GreatAI (v0.1.4): configured \u2705 2022-07-12 15:34:31 | INFO | \ud83d\udd29 tracing_database: ParallelTinyDbDriver 2022-07-12 15:34:31 | INFO | \ud83d\udd29 large_file_implementation: LargeFileLocal 2022-07-12 15:34:31 | INFO | \ud83d\udd29 is_production: False 2022-07-12 15:34:31 | INFO | \ud83d\udd29 should_log_exception_stack: True 2022-07-12 15:34:31 | INFO | \ud83d\udd29 prediction_cache_size: 512 2022-07-12 15:34:31 | INFO | \ud83d\udd29 dashboard_table_size: 50 2022-07-12 15:34:31 | WARNING | You still need to check whether you follow all best practices before trusting your deployment. 2022-07-12 15:34:31 | WARNING | > Find out more at https://se-ml.github.io/practices 2022-07-12 15:34:31 | INFO | Fetching cached versions of my-domain-predictor 2022-07-12 15:34:31 | INFO | Latest version of my-domain-predictor is 9 (from versions: 0, 1, 2, 3, 4, 5, 6, 7, 8, 9) 2022-07-12 15:34:31 | INFO | File my-domain-predictor-9 found in cache 2022-07-12 15:34:31 | INFO | Started server process [199794] 2022-07-12 15:34:31 | INFO | Waiting for application startup. 2022-07-12 15:34:31 | INFO | Application startup complete. ^C 2022-07-12 15:34:33 | INFO | Shutting down 2022-07-12 15:34:33 | INFO | Waiting for application shutdown. 2022-07-12 15:34:33 | INFO | Application shutdown complete. 2022-07-12 15:34:33 | INFO | Finished server process [199794] Congrats, you've just created your first GreatAI service! \ud83c\udf89 Now that you've made sure your application is hardened enough for the intended use case, it is time to deploy it. The responsibilities of GreatAI end when it wraps your inference function and model into a production-ready service. You're given the freedom and responsibility to deploy this service. Fortunately, you (or your organisation) probably already have an established routine for deploying services. There are three main approaches to deploy a GreatAI service: For more info about them, check out the deployment how-to . For more thorough examples, see the examples page . Go back to the summary \u00b6","title":"Harden and deploy your app"},{"location":"tutorial/deploy/#harden-and-deploy-your-app","text":"Finally, it's time to deploy your model. But before that, you have to make sure you follow AI deployment best practices . In the past, this step was too often either the source of unexpected struggle, or worse, simply ignored. With GreatAI , it has become a matter of 4 lines of code. In [1]: Copied! from great_ai import GreatAI , use_model from great_ai.utilities import clean @GreatAI . create @use_model ( \"my-domain-predictor\" ) def predict_domain ( sentence , model ): inputs = [ clean ( sentence )] return str ( model . predict ( inputs )[ 0 ]) from great_ai import GreatAI, use_model from great_ai.utilities import clean @GreatAI.create @use_model(\"my-domain-predictor\") def predict_domain(sentence, model): inputs = [clean(sentence)] return str(model.predict(inputs)[0]) Environment variable ENVIRONMENT is not set, defaulting to development mode \u203c\ufe0f Cannot find credentials files, defaulting to using ParallelTinyDbDriver The selected tracing database (ParallelTinyDbDriver) is not recommended for production Cannot find credentials files, defaulting to using LargeFileLocal GreatAI (v0.1.4): configured \u2705 \ud83d\udd29 tracing_database: ParallelTinyDbDriver \ud83d\udd29 large_file_implementation: LargeFileLocal \ud83d\udd29 is_production: False \ud83d\udd29 should_log_exception_stack: True \ud83d\udd29 prediction_cache_size: 512 \ud83d\udd29 dashboard_table_size: 50 You still need to check whether you follow all best practices before trusting your deployment. > Find out more at https://se-ml.github.io/practices Fetching cached versions of my-domain-predictor Latest version of my-domain-predictor is 9 (from versions: 0, 1, 2, 3, 4, 5, 6, 7, 8, 9) File my-domain-predictor-9 found in cache In [2]: Copied! predict_domain ( \"Mountains are just big rocks.\" ) predict_domain(\"Mountains are just big rocks.\") Out[2]: Trace[str]({'created': '2022-07-12T13:34:26.743292', 'exception': None, 'feedback': None, 'logged_values': { 'arg:sentence:length': 29, 'arg:sentence:value': 'Mountains are just big rocks.'}, 'models': [{'key': 'my-domain-predictor', 'version': 9}], 'original_execution_time_ms': 6.9699, 'output': 'geography', 'tags': ['predict_domain', 'online', 'development'], 'trace_id': 'c80bdee3-602b-49dd-a84d-6eef80127e5a'}) Notice how the original return value is under the .output key. Additionally, a plethora of metadata has been added which will be useful later on. Running your app in development-mode is as easy as executing great-ai deploy.ipynb from your terminal. In [3]: Copied! ! great - ai deploy . ipynb # leave this running and open http://127.0.0.1:6060 !great-ai deploy.ipynb # leave this running and open http://127.0.0.1:6060 2022-07-12 15:34:28 | INFO | Converting notebook to Python script 2022-07-12 15:34:29 | INFO | Found `predict_domain` to be the GreatAI app 2022-07-12 15:34:29 | INFO | Uvicorn running on http://0.0.0.0:6060 (Press CTRL+C to quit) 2022-07-12 15:34:31 | WARNING | Environment variable ENVIRONMENT is not set, defaulting to development mode \u203c\ufe0f 2022-07-12 15:34:31 | WARNING | Cannot find credentials files, defaulting to using ParallelTinyDbDriver 2022-07-12 15:34:31 | WARNING | The selected tracing database (ParallelTinyDbDriver) is not recommended for production 2022-07-12 15:34:31 | WARNING | Cannot find credentials files, defaulting to using LargeFileLocal 2022-07-12 15:34:31 | INFO | GreatAI (v0.1.4): configured \u2705 2022-07-12 15:34:31 | INFO | \ud83d\udd29 tracing_database: ParallelTinyDbDriver 2022-07-12 15:34:31 | INFO | \ud83d\udd29 large_file_implementation: LargeFileLocal 2022-07-12 15:34:31 | INFO | \ud83d\udd29 is_production: False 2022-07-12 15:34:31 | INFO | \ud83d\udd29 should_log_exception_stack: True 2022-07-12 15:34:31 | INFO | \ud83d\udd29 prediction_cache_size: 512 2022-07-12 15:34:31 | INFO | \ud83d\udd29 dashboard_table_size: 50 2022-07-12 15:34:31 | WARNING | You still need to check whether you follow all best practices before trusting your deployment. 2022-07-12 15:34:31 | WARNING | > Find out more at https://se-ml.github.io/practices 2022-07-12 15:34:31 | INFO | Fetching cached versions of my-domain-predictor 2022-07-12 15:34:31 | INFO | Latest version of my-domain-predictor is 9 (from versions: 0, 1, 2, 3, 4, 5, 6, 7, 8, 9) 2022-07-12 15:34:31 | INFO | File my-domain-predictor-9 found in cache 2022-07-12 15:34:31 | INFO | Started server process [199794] 2022-07-12 15:34:31 | INFO | Waiting for application startup. 2022-07-12 15:34:31 | INFO | Application startup complete. ^C 2022-07-12 15:34:33 | INFO | Shutting down 2022-07-12 15:34:33 | INFO | Waiting for application shutdown. 2022-07-12 15:34:33 | INFO | Application shutdown complete. 2022-07-12 15:34:33 | INFO | Finished server process [199794] Congrats, you've just created your first GreatAI service! \ud83c\udf89 Now that you've made sure your application is hardened enough for the intended use case, it is time to deploy it. The responsibilities of GreatAI end when it wraps your inference function and model into a production-ready service. You're given the freedom and responsibility to deploy this service. Fortunately, you (or your organisation) probably already have an established routine for deploying services. There are three main approaches to deploy a GreatAI service: For more info about them, check out the deployment how-to . For more thorough examples, see the examples page .","title":"Harden and deploy your app"},{"location":"tutorial/deploy/#go-back-to-the-summary","text":"","title":"Go back to the summary"},{"location":"tutorial/train/","text":"(function (global, factory) { typeof exports === 'object' && typeof module !== 'undefined' ? module.exports = factory() : typeof define === 'function' && define.amd ? define(factory) : (global = global || self, global.ClipboardCopyElement = factory()); }(this, function () { 'use strict'; function createNode(text) { const node = document.createElement('pre'); node.style.width = '1px'; node.style.height = '1px'; node.style.position = 'fixed'; node.style.top = '5px'; node.textContent = text; return node; } function copyNode(node) { if ('clipboard' in navigator) { // eslint-disable-next-line flowtype/no-flow-fix-me-comments // $FlowFixMe Clipboard is not defined in Flow yet. return navigator.clipboard.writeText(node.textContent); } const selection = getSelection(); if (selection == null) { return Promise.reject(new Error()); 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When children elements, such as headings, are sized * in em all things will be computed relative to that body size. */ --jp-content-line-height: 1.6; --jp-content-font-scale-factor: 1.2; --jp-content-font-size0: 0.83333em; --jp-content-font-size1: 14px; /* Base font size */ --jp-content-font-size2: 1.2em; --jp-content-font-size3: 1.44em; --jp-content-font-size4: 1.728em; --jp-content-font-size5: 2.0736em; /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-content-presentation-font-size1: 17px; --jp-content-heading-line-height: 1; --jp-content-heading-margin-top: 1.2em; --jp-content-heading-margin-bottom: 0.8em; --jp-content-heading-font-weight: 500; /* Defaults use Material Design specification */ --jp-content-font-color0: rgba(0, 0, 0, 1); --jp-content-font-color1: rgba(0, 0, 0, 0.87); --jp-content-font-color2: rgba(0, 0, 0, 0.54); --jp-content-font-color3: rgba(0, 0, 0, 0.38); --jp-content-link-color: var(--md-blue-700); --jp-content-font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Helvetica, Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\"; /* * Code Fonts * * Code font variables are used for typography of code and other monospaces content. */ --jp-code-font-size: 13px; --jp-code-line-height: 1.3077; /* 17px for 13px base */ --jp-code-padding: 5px; /* 5px for 13px base, codemirror highlighting needs integer px value */ --jp-code-font-family-default: Menlo, Consolas, \"DejaVu Sans Mono\", monospace; --jp-code-font-family: var(--jp-code-font-family-default); /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-code-presentation-font-size: 16px; /* may need to tweak cursor width if you change font size */ --jp-code-cursor-width0: 1.4px; --jp-code-cursor-width1: 2px; --jp-code-cursor-width2: 4px; /* Layout * * The following are the main layout colors use in JupyterLab. In a light * theme these would go from light to dark. */ --jp-layout-color0: white; --jp-layout-color1: white; --jp-layout-color2: var(--md-grey-200); --jp-layout-color3: var(--md-grey-400); --jp-layout-color4: var(--md-grey-600); /* Inverse Layout * * The following are the inverse layout colors use in JupyterLab. In a light * theme these would go from dark to light. */ --jp-inverse-layout-color0: #111111; --jp-inverse-layout-color1: var(--md-grey-900); --jp-inverse-layout-color2: var(--md-grey-800); --jp-inverse-layout-color3: var(--md-grey-700); --jp-inverse-layout-color4: var(--md-grey-600); /* Brand/accent */ --jp-brand-color0: var(--md-blue-900); --jp-brand-color1: var(--md-blue-700); --jp-brand-color2: var(--md-blue-300); --jp-brand-color3: var(--md-blue-100); --jp-brand-color4: var(--md-blue-50); --jp-accent-color0: var(--md-green-900); --jp-accent-color1: var(--md-green-700); --jp-accent-color2: var(--md-green-300); --jp-accent-color3: var(--md-green-100); /* State colors (warn, error, success, info) */ --jp-warn-color0: var(--md-orange-900); --jp-warn-color1: var(--md-orange-700); --jp-warn-color2: var(--md-orange-300); --jp-warn-color3: var(--md-orange-100); --jp-error-color0: var(--md-red-900); --jp-error-color1: var(--md-red-700); --jp-error-color2: var(--md-red-300); --jp-error-color3: var(--md-red-100); --jp-success-color0: var(--md-green-900); --jp-success-color1: var(--md-green-700); --jp-success-color2: var(--md-green-300); --jp-success-color3: var(--md-green-100); --jp-info-color0: var(--md-cyan-900); --jp-info-color1: var(--md-cyan-700); --jp-info-color2: var(--md-cyan-300); --jp-info-color3: var(--md-cyan-100); /* Cell specific styles */ --jp-cell-padding: 5px; --jp-cell-collapser-width: 8px; --jp-cell-collapser-min-height: 20px; --jp-cell-collapser-not-active-hover-opacity: 0.6; --jp-cell-editor-background: var(--md-grey-100); --jp-cell-editor-border-color: var(--md-grey-300); --jp-cell-editor-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-cell-editor-active-background: var(--jp-layout-color0); --jp-cell-editor-active-border-color: var(--jp-brand-color1); --jp-cell-prompt-width: 64px; --jp-cell-prompt-font-family: var(--jp-code-font-family-default); --jp-cell-prompt-letter-spacing: 0px; --jp-cell-prompt-opacity: 1; --jp-cell-prompt-not-active-opacity: 0.5; --jp-cell-prompt-not-active-font-color: var(--md-grey-700); /* A custom blend of MD grey and blue 600 * See https://meyerweb.com/eric/tools/color-blend/#546E7A:1E88E5:5:hex */ --jp-cell-inprompt-font-color: #307fc1; /* A custom blend of MD grey and orange 600 * https://meyerweb.com/eric/tools/color-blend/#546E7A:F4511E:5:hex */ --jp-cell-outprompt-font-color: #bf5b3d; /* Notebook specific styles */ --jp-notebook-padding: 10px; --jp-notebook-select-background: var(--jp-layout-color1); --jp-notebook-multiselected-color: var(--md-blue-50); /* The scroll padding is calculated to fill enough space at the bottom of the notebook to show one single-line cell (with appropriate padding) at the top when the notebook is scrolled all the way to the bottom. We also subtract one pixel so that no scrollbar appears if we have just one single-line cell in the notebook. This padding is to enable a 'scroll past end' feature in a notebook. */ --jp-notebook-scroll-padding: calc( 100% - var(--jp-code-font-size) * var(--jp-code-line-height) - var(--jp-code-padding) - var(--jp-cell-padding) - 1px ); /* Rendermime styles */ --jp-rendermime-error-background: #fdd; --jp-rendermime-table-row-background: var(--md-grey-100); --jp-rendermime-table-row-hover-background: var(--md-light-blue-50); /* Dialog specific styles */ --jp-dialog-background: rgba(0, 0, 0, 0.25); /* Console specific styles */ --jp-console-padding: 10px; /* Toolbar specific styles */ --jp-toolbar-border-color: var(--jp-border-color1); --jp-toolbar-micro-height: 8px; --jp-toolbar-background: var(--jp-layout-color1); --jp-toolbar-box-shadow: 0px 0px 2px 0px rgba(0, 0, 0, 0.24); --jp-toolbar-header-margin: 4px 4px 0px 4px; --jp-toolbar-active-background: var(--md-grey-300); /* Statusbar specific styles */ --jp-statusbar-height: 24px; /* Input field styles */ --jp-input-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-input-active-background: var(--jp-layout-color1); --jp-input-hover-background: var(--jp-layout-color1); --jp-input-background: var(--md-grey-100); --jp-input-border-color: var(--jp-border-color1); --jp-input-active-border-color: var(--jp-brand-color1); --jp-input-active-box-shadow-color: rgba(19, 124, 189, 0.3); /* General editor styles */ --jp-editor-selected-background: #d9d9d9; --jp-editor-selected-focused-background: #d7d4f0; --jp-editor-cursor-color: var(--jp-ui-font-color0); /* Code mirror specific styles */ --jp-mirror-editor-keyword-color: #008000; --jp-mirror-editor-atom-color: #88f; --jp-mirror-editor-number-color: #080; --jp-mirror-editor-def-color: #00f; --jp-mirror-editor-variable-color: var(--md-grey-900); --jp-mirror-editor-variable-2-color: #05a; --jp-mirror-editor-variable-3-color: #085; --jp-mirror-editor-punctuation-color: #05a; --jp-mirror-editor-property-color: #05a; --jp-mirror-editor-operator-color: #aa22ff; --jp-mirror-editor-comment-color: #408080; --jp-mirror-editor-string-color: #ba2121; --jp-mirror-editor-string-2-color: #708; --jp-mirror-editor-meta-color: #aa22ff; --jp-mirror-editor-qualifier-color: #555; --jp-mirror-editor-builtin-color: #008000; --jp-mirror-editor-bracket-color: #997; --jp-mirror-editor-tag-color: #170; --jp-mirror-editor-attribute-color: #00c; --jp-mirror-editor-header-color: blue; --jp-mirror-editor-quote-color: #090; --jp-mirror-editor-link-color: #00c; --jp-mirror-editor-error-color: #f00; --jp-mirror-editor-hr-color: #999; /* Vega extension styles */ --jp-vega-background: white; /* Sidebar-related styles */ --jp-sidebar-min-width: 250px; /* Search-related styles */ --jp-search-toggle-off-opacity: 0.5; --jp-search-toggle-hover-opacity: 0.8; --jp-search-toggle-on-opacity: 1; --jp-search-selected-match-background-color: rgb(245, 200, 0); --jp-search-selected-match-color: black; --jp-search-unselected-match-background-color: var( --jp-inverse-layout-color0 ); --jp-search-unselected-match-color: var(--jp-ui-inverse-font-color0); /* Icon colors that work well with light or dark backgrounds */ --jp-icon-contrast-color0: var(--md-purple-600); --jp-icon-contrast-color1: var(--md-green-600); --jp-icon-contrast-color2: var(--md-pink-600); --jp-icon-contrast-color3: var(--md-blue-600); } [data-md-color-scheme=\"slate\"] .jupyter-wrapper { /* Elevation * * We style box-shadows using Material Design's idea of elevation. These particular numbers are taken from here: * * https://github.com/material-components/material-components-web * https://material-components-web.appspot.com/elevation.html */ /* The dark theme shadows need a bit of work, but this will probably also require work on the core layout * colors used in the theme as well. */ --jp-shadow-base-lightness: 32; --jp-shadow-umbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.2 ); --jp-shadow-penumbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.14 ); --jp-shadow-ambient-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.12 ); --jp-elevation-z0: none; --jp-elevation-z1: 0px 2px 1px -1px var(--jp-shadow-umbra-color), 0px 1px 1px 0px var(--jp-shadow-penumbra-color), 0px 1px 3px 0px var(--jp-shadow-ambient-color); --jp-elevation-z2: 0px 3px 1px -2px var(--jp-shadow-umbra-color), 0px 2px 2px 0px var(--jp-shadow-penumbra-color), 0px 1px 5px 0px var(--jp-shadow-ambient-color); --jp-elevation-z4: 0px 2px 4px -1px var(--jp-shadow-umbra-color), 0px 4px 5px 0px var(--jp-shadow-penumbra-color), 0px 1px 10px 0px var(--jp-shadow-ambient-color); --jp-elevation-z6: 0px 3px 5px -1px var(--jp-shadow-umbra-color), 0px 6px 10px 0px var(--jp-shadow-penumbra-color), 0px 1px 18px 0px var(--jp-shadow-ambient-color); --jp-elevation-z8: 0px 5px 5px -3px var(--jp-shadow-umbra-color), 0px 8px 10px 1px var(--jp-shadow-penumbra-color), 0px 3px 14px 2px var(--jp-shadow-ambient-color); --jp-elevation-z12: 0px 7px 8px -4px var(--jp-shadow-umbra-color), 0px 12px 17px 2px var(--jp-shadow-penumbra-color), 0px 5px 22px 4px var(--jp-shadow-ambient-color); --jp-elevation-z16: 0px 8px 10px -5px var(--jp-shadow-umbra-color), 0px 16px 24px 2px var(--jp-shadow-penumbra-color), 0px 6px 30px 5px var(--jp-shadow-ambient-color); --jp-elevation-z20: 0px 10px 13px -6px var(--jp-shadow-umbra-color), 0px 20px 31px 3px var(--jp-shadow-penumbra-color), 0px 8px 38px 7px var(--jp-shadow-ambient-color); --jp-elevation-z24: 0px 11px 15px -7px var(--jp-shadow-umbra-color), 0px 24px 38px 3px var(--jp-shadow-penumbra-color), 0px 9px 46px 8px var(--jp-shadow-ambient-color); /* Borders * * The following variables, specify the visual styling of borders in JupyterLab. */ --jp-border-width: 1px; --jp-border-color0: var(--md-grey-700); --jp-border-color1: var(--md-grey-700); --jp-border-color2: var(--md-grey-800); --jp-border-color3: var(--md-grey-900); --jp-border-radius: 2px; /* UI Fonts * * The UI font CSS variables are used for the typography all of the JupyterLab * user interface elements that are not directly user generated content. * * The font sizing here is done assuming that the body font size of --jp-ui-font-size1 * is applied to a parent element. When children elements, such as headings, are sized * in em all things will be computed relative to that body size. */ --jp-ui-font-scale-factor: 1.2; --jp-ui-font-size0: 0.83333em; --jp-ui-font-size1: 13px; /* Base font size */ --jp-ui-font-size2: 1.2em; --jp-ui-font-size3: 1.44em; --jp-ui-font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Helvetica, Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\"; /* * Use these font colors against the corresponding main layout colors. * In a light theme, these go from dark to light. */ /* Defaults use Material Design specification */ --jp-ui-font-color0: rgba(255, 255, 255, 1); --jp-ui-font-color1: rgba(255, 255, 255, 0.87); --jp-ui-font-color2: rgba(255, 255, 255, 0.54); --jp-ui-font-color3: rgba(255, 255, 255, 0.38); /* * Use these against the brand/accent/warn/error colors. * These will typically go from light to darker, in both a dark and light theme. */ --jp-ui-inverse-font-color0: rgba(0, 0, 0, 1); --jp-ui-inverse-font-color1: rgba(0, 0, 0, 0.8); --jp-ui-inverse-font-color2: rgba(0, 0, 0, 0.5); --jp-ui-inverse-font-color3: rgba(0, 0, 0, 0.3); /* Content Fonts * * Content font variables are used for typography of user generated content. * * The font sizing here is done assuming that the body font size of --jp-content-font-size1 * is applied to a parent element. When children elements, such as headings, are sized * in em all things will be computed relative to that body size. */ --jp-content-line-height: 1.6; --jp-content-font-scale-factor: 1.2; --jp-content-font-size0: 0.83333em; --jp-content-font-size1: 14px; /* Base font size */ --jp-content-font-size2: 1.2em; --jp-content-font-size3: 1.44em; --jp-content-font-size4: 1.728em; --jp-content-font-size5: 2.0736em; /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-content-presentation-font-size1: 17px; --jp-content-heading-line-height: 1; --jp-content-heading-margin-top: 1.2em; --jp-content-heading-margin-bottom: 0.8em; --jp-content-heading-font-weight: 500; /* Defaults use Material Design specification */ --jp-content-font-color0: rgba(255, 255, 255, 1); --jp-content-font-color1: rgba(255, 255, 255, 1); --jp-content-font-color2: rgba(255, 255, 255, 0.7); --jp-content-font-color3: rgba(255, 255, 255, 0.5); --jp-content-link-color: var(--md-blue-300); --jp-content-font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Helvetica, Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\"; /* * Code Fonts * * Code font variables are used for typography of code and other monospaces content. */ --jp-code-font-size: 13px; --jp-code-line-height: 1.3077; /* 17px for 13px base */ --jp-code-padding: 5px; /* 5px for 13px base, codemirror highlighting needs integer px value */ --jp-code-font-family-default: Menlo, Consolas, \"DejaVu Sans Mono\", monospace; --jp-code-font-family: var(--jp-code-font-family-default); /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-code-presentation-font-size: 16px; /* may need to tweak cursor width if you change font size */ --jp-code-cursor-width0: 1.4px; --jp-code-cursor-width1: 2px; --jp-code-cursor-width2: 4px; /* Layout * * The following are the main layout colors use in JupyterLab. In a light * theme these would go from light to dark. */ --jp-layout-color0: #111111; --jp-layout-color1: var(--md-grey-900); --jp-layout-color2: var(--md-grey-800); --jp-layout-color3: var(--md-grey-700); --jp-layout-color4: var(--md-grey-600); /* Inverse Layout * * The following are the inverse layout colors use in JupyterLab. In a light * theme these would go from dark to light. */ --jp-inverse-layout-color0: white; --jp-inverse-layout-color1: white; --jp-inverse-layout-color2: var(--md-grey-200); --jp-inverse-layout-color3: var(--md-grey-400); --jp-inverse-layout-color4: var(--md-grey-600); /* Brand/accent */ --jp-brand-color0: var(--md-blue-700); --jp-brand-color1: var(--md-blue-500); --jp-brand-color2: var(--md-blue-300); --jp-brand-color3: var(--md-blue-100); --jp-brand-color4: var(--md-blue-50); --jp-accent-color0: var(--md-green-700); --jp-accent-color1: var(--md-green-500); --jp-accent-color2: var(--md-green-300); --jp-accent-color3: var(--md-green-100); /* State colors (warn, error, success, info) */ --jp-warn-color0: var(--md-orange-700); --jp-warn-color1: var(--md-orange-500); --jp-warn-color2: var(--md-orange-300); --jp-warn-color3: var(--md-orange-100); --jp-error-color0: var(--md-red-700); --jp-error-color1: var(--md-red-500); --jp-error-color2: var(--md-red-300); --jp-error-color3: var(--md-red-100); --jp-success-color0: var(--md-green-700); --jp-success-color1: var(--md-green-500); --jp-success-color2: var(--md-green-300); --jp-success-color3: var(--md-green-100); --jp-info-color0: var(--md-cyan-700); --jp-info-color1: var(--md-cyan-500); --jp-info-color2: var(--md-cyan-300); --jp-info-color3: var(--md-cyan-100); /* Cell specific styles */ --jp-cell-padding: 5px; --jp-cell-collapser-width: 8px; --jp-cell-collapser-min-height: 20px; --jp-cell-collapser-not-active-hover-opacity: 0.6; --jp-cell-editor-background: var(--jp-layout-color1); --jp-cell-editor-border-color: var(--md-grey-700); --jp-cell-editor-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-cell-editor-active-background: var(--jp-layout-color0); --jp-cell-editor-active-border-color: var(--jp-brand-color1); --jp-cell-prompt-width: 64px; --jp-cell-prompt-font-family: var(--jp-code-font-family-default); --jp-cell-prompt-letter-spacing: 0px; --jp-cell-prompt-opacity: 1; --jp-cell-prompt-not-active-opacity: 1; --jp-cell-prompt-not-active-font-color: var(--md-grey-300); /* A custom blend of MD grey and blue 600 * See https://meyerweb.com/eric/tools/color-blend/#546E7A:1E88E5:5:hex */ --jp-cell-inprompt-font-color: #307fc1; /* A custom blend of MD grey and orange 600 * https://meyerweb.com/eric/tools/color-blend/#546E7A:F4511E:5:hex */ --jp-cell-outprompt-font-color: #bf5b3d; /* Notebook specific styles */ --jp-notebook-padding: 10px; --jp-notebook-select-background: var(--jp-layout-color1); --jp-notebook-multiselected-color: rgba(33, 150, 243, 0.24); /* The scroll padding is calculated to fill enough space at the bottom of the notebook to show one single-line cell (with appropriate padding) at the top when the notebook is scrolled all the way to the bottom. We also subtract one pixel so that no scrollbar appears if we have just one single-line cell in the notebook. This padding is to enable a 'scroll past end' feature in a notebook. */ --jp-notebook-scroll-padding: calc( 100% - var(--jp-code-font-size) * var(--jp-code-line-height) - var(--jp-code-padding) - var(--jp-cell-padding) - 1px ); /* Rendermime styles */ --jp-rendermime-error-background: rgba(244, 67, 54, 0.28); --jp-rendermime-table-row-background: var(--md-grey-900); --jp-rendermime-table-row-hover-background: rgba(3, 169, 244, 0.2); /* Dialog specific styles */ --jp-dialog-background: rgba(0, 0, 0, 0.6); /* Console specific styles */ --jp-console-padding: 10px; /* Toolbar specific styles */ --jp-toolbar-border-color: var(--jp-border-color2); --jp-toolbar-micro-height: 8px; --jp-toolbar-background: var(--jp-layout-color1); --jp-toolbar-box-shadow: 0px 0px 2px 0px rgba(0, 0, 0, 0.8); --jp-toolbar-header-margin: 4px 4px 0px 4px; --jp-toolbar-active-background: var(--jp-layout-color0); /* Statusbar specific styles */ --jp-statusbar-height: 24px; /* Input field styles */ --jp-input-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-input-active-background: var(--jp-layout-color0); --jp-input-hover-background: var(--jp-layout-color2); --jp-input-background: var(--md-grey-800); --jp-input-border-color: var(--jp-border-color1); --jp-input-active-border-color: var(--jp-brand-color1); --jp-input-active-box-shadow-color: rgba(19, 124, 189, 0.3); /* General editor styles */ --jp-editor-selected-background: var(--jp-layout-color2); --jp-editor-selected-focused-background: rgba(33, 150, 243, 0.24); --jp-editor-cursor-color: var(--jp-ui-font-color0); /* Code mirror specific styles */ --jp-mirror-editor-keyword-color: var(--md-green-500); --jp-mirror-editor-atom-color: var(--md-blue-300); --jp-mirror-editor-number-color: var(--md-green-400); --jp-mirror-editor-def-color: var(--md-blue-600); --jp-mirror-editor-variable-color: var(--md-grey-300); --jp-mirror-editor-variable-2-color: var(--md-blue-400); --jp-mirror-editor-variable-3-color: var(--md-green-600); --jp-mirror-editor-punctuation-color: var(--md-blue-400); --jp-mirror-editor-property-color: var(--md-blue-400); --jp-mirror-editor-operator-color: #aa22ff; --jp-mirror-editor-comment-color: #408080; --jp-mirror-editor-string-color: #ff7070; --jp-mirror-editor-string-2-color: var(--md-purple-300); --jp-mirror-editor-meta-color: #aa22ff; --jp-mirror-editor-qualifier-color: #555; --jp-mirror-editor-builtin-color: var(--md-green-600); --jp-mirror-editor-bracket-color: #997; --jp-mirror-editor-tag-color: var(--md-green-700); --jp-mirror-editor-attribute-color: var(--md-blue-700); --jp-mirror-editor-header-color: var(--md-blue-500); --jp-mirror-editor-quote-color: var(--md-green-300); --jp-mirror-editor-link-color: var(--md-blue-700); --jp-mirror-editor-error-color: #f00; --jp-mirror-editor-hr-color: #999; /* Vega extension styles */ --jp-vega-background: var(--md-grey-400); /* Sidebar-related styles */ --jp-sidebar-min-width: 250px; /* Search-related styles */ --jp-search-toggle-off-opacity: 0.6; --jp-search-toggle-hover-opacity: 0.8; --jp-search-toggle-on-opacity: 1; --jp-search-selected-match-background-color: rgb(255, 225, 0); --jp-search-selected-match-color: black; --jp-search-unselected-match-background-color: var( --jp-inverse-layout-color0 ); --jp-search-unselected-match-color: var(--jp-ui-inverse-font-color0); /* scrollbar related styles. Supports every browser except Edge. */ /* colors based on JetBrain's Darcula theme */ --jp-scrollbar-background-color: #3f4244; --jp-scrollbar-thumb-color: 88, 96, 97; /* need to specify thumb color as an RGB triplet */ --jp-scrollbar-endpad: 3px; /* the minimum gap between the thumb and the ends of a scrollbar */ /* hacks for setting the thumb shape. 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sourceMappingURL=mkdocs-jupyter.css.map*/ init_mathjax = function() { if (window.MathJax) { // MathJax loaded MathJax.Hub.Config({ TeX: { equationNumbers: { autoNumber: \"AMS\", useLabelIds: true } }, tex2jax: { inlineMath: [ ['$','$'], [\"\\\\(\",\"\\\\)\"] ], displayMath: [ ['$$','$$'], [\"\\\\[\",\"\\\\]\"] ], processEscapes: true, processEnvironments: true }, displayAlign: 'center', CommonHTML: { linebreaks: { automatic: true } } }); MathJax.Hub.Queue([\"Typeset\", MathJax.Hub]); } } init_mathjax(); Train your model \u00b6 The first step is, of course, to install great-ai in your Python environment. In [1]: Copied! % pip install great - ai > / dev / null %pip install great-ai > /dev/null Note: you may need to restart the kernel to use updated packages. First, we have to get some data. After downloading it from here , we might notice that the dataset is in JSON Lines format (each line is a separate JSON document). Let's write a function which takes a single line and returns the sentence and the corresponding label from it. Before returning, the sentence is also cleaned to remove any LaTeX, XML, unicode, PDF-extraction artifacts. In [2]: Copied! import json from great_ai.utilities import clean def preprocess ( line ): data_point = json . loads ( line ) sentence = data_point [ \"text\" ] label = data_point [ \"label\" ] return clean ( sentence ), label import json from great_ai.utilities import clean def preprocess(line): data_point = json.loads(line) sentence = data_point[\"text\"] label = data_point[\"label\"] return clean(sentence), label Now, we can load the dataset and extract the training samples from it. Since we're impatient, we can do it in parallel using the simple_parallel_map function. Open files in Python are iterable: in text mode, each iteration returns the next line. In [3]: Copied! from great_ai.utilities import simple_parallel_map with open ( \"data/train.txt\" , encoding = \"utf-8\" ) as f : training_data = simple_parallel_map ( preprocess , f ) X_train = [ d [ 0 ] for d in training_data ] y_train = [ d [ 1 ] for d in training_data ] from great_ai.utilities import simple_parallel_map with open(\"data/train.txt\", encoding=\"utf-8\") as f: training_data = simple_parallel_map(preprocess, f) X_train = [d[0] for d in training_data] y_train = [d[1] for d in training_data] 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 84000/84000 [00:09<00:00, 8960.63it/s] Let's do the same for the test data. In [4]: Copied! with open ( \"data/test.txt\" , encoding = \"utf-8\" ) as f : test_data = simple_parallel_map ( preprocess , f ) X_test = [ d [ 0 ] for d in test_data ] y_test = [ d [ 1 ] for d in test_data ] with open(\"data/test.txt\", encoding=\"utf-8\") as f: test_data = simple_parallel_map(preprocess, f) X_test = [d[0] for d in test_data] y_test = [d[1] for d in test_data] 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 22399/22399 [00:03<00:00, 6078.02it/s] After obtaining some clean data, it's time to create and train a model on it. We are going to use scikit-learn for this purpose. In this case, we opted for a linear Support Vector Machine because it's known to be an adequate baseline for simple classification tasks. In [5]: Copied! from sklearn.pipeline import make_pipeline from sklearn.feature_extraction.text import TfidfVectorizer from sklearn.svm import LinearSVC model = make_pipeline ( TfidfVectorizer (), LinearSVC ()) # todo: hyperparameter-optimisation model . fit ( X_train , y_train ) from sklearn.pipeline import make_pipeline from sklearn.feature_extraction.text import TfidfVectorizer from sklearn.svm import LinearSVC model = make_pipeline(TfidfVectorizer(), LinearSVC()) # todo: hyperparameter-optimisation model.fit(X_train, y_train) Out[5]: #sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"\u25b8\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"\u25be\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;} Pipeline(steps=[('tfidfvectorizer', TfidfVectorizer()), ('linearsvc', LinearSVC())]) In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org. Pipeline Pipeline(steps=[('tfidfvectorizer', TfidfVectorizer()), ('linearsvc', LinearSVC())]) TfidfVectorizer TfidfVectorizer() LinearSVC LinearSVC() It's already finished. Let's see how well it performs. But first, we need to configure matplotlib to make the charts more legible. ConfusionMatrixDisplay relies on matplotlib for rendering. In [6]: Copied! import matplotlib.pyplot as plt % matplotlib inline plt . rcParams [ \"figure.figsize\" ] = ( 30 , 15 ) plt . rcParams [ \"figure.facecolor\" ] = \"white\" plt . rcParams [ \"font.size\" ] = 12 import matplotlib.pyplot as plt %matplotlib inline plt.rcParams[\"figure.figsize\"] = (30, 15) plt.rcParams[\"figure.facecolor\"] = \"white\" plt.rcParams[\"font.size\"] = 12 Next, we check the quality of the model on the test split. We can use the classification report to check the common metrics, such as the macro F1-score. We also draw the confusion matrix to get some insights into the types of mistakes being made by the model. In [7]: Copied! from sklearn import metrics y_predicted = model . predict ( X_test ) print ( metrics . classification_report ( y_test , y_predicted )) metrics . ConfusionMatrixDisplay . from_predictions ( y_test , y_predicted ) None from sklearn import metrics y_predicted = model.predict(X_test) print(metrics.classification_report(y_test, y_predicted)) metrics.ConfusionMatrixDisplay.from_predictions(y_test, y_predicted) None precision recall f1-score support business 0.67 0.69 0.68 3198 economics 0.69 0.71 0.70 3189 geography 0.70 0.73 0.72 3207 medicine 0.90 0.91 0.90 3187 politics 0.63 0.57 0.60 3169 psychology 0.73 0.73 0.73 3252 sociology 0.51 0.51 0.51 3197 accuracy 0.69 22399 macro avg 0.69 0.69 0.69 22399 weighted avg 0.69 0.69 0.69 22399 Great work, we can be rightfully satisfied with our model. Seeing the results, we achieved an F1-score of 0.69 which is about 5% better than SciBERT's 0.6571! You might wonder that \"this is great, but besides some utility functions ( clean , simple_parallel_map , ...) what more value does GreatAI add?\" . This would be a valid argument because the scope of GreatAI actually only starts here. Not coincidentally, this is the point where the scope of Data Science ends but it's still a grey zone for software engineering. In order to use this model in production, we have to make it available on some possibly shared infrastructure. In [8]: Copied! from great_ai import save_model save_model ( model , key = \"my-domain-predictor\" ) from great_ai import save_model save_model(model, key=\"my-domain-predictor\") Environment variable ENVIRONMENT is not set, defaulting to development mode \u203c\ufe0f Cannot find credentials files, defaulting to using ParallelTinyDbDriver The selected tracing database (ParallelTinyDbDriver) is not recommended for production Cannot find credentials files, defaulting to using LargeFileLocal GreatAI (v0.1.4): configured \u2705 \ud83d\udd29 tracing_database: ParallelTinyDbDriver \ud83d\udd29 large_file_implementation: LargeFileLocal \ud83d\udd29 is_production: False \ud83d\udd29 should_log_exception_stack: True \ud83d\udd29 prediction_cache_size: 512 \ud83d\udd29 dashboard_table_size: 50 You still need to check whether you follow all best practices before trusting your deployment. > Find out more at https://se-ml.github.io/practices Fetching cached versions of my-domain-predictor Copying file for my-domain-predictor-9 Compressing my-domain-predictor-9 Model my-domain-predictor uploaded with version 9 Out[8]: 'my-domain-predictor:9' Let's continue by finishing the deployment in the next notebook. \u00b6","title":"Train your model"},{"location":"tutorial/train/#train-your-model","text":"The first step is, of course, to install great-ai in your Python environment. In [1]: Copied! % pip install great - ai > / dev / null %pip install great-ai > /dev/null Note: you may need to restart the kernel to use updated packages. First, we have to get some data. After downloading it from here , we might notice that the dataset is in JSON Lines format (each line is a separate JSON document). Let's write a function which takes a single line and returns the sentence and the corresponding label from it. Before returning, the sentence is also cleaned to remove any LaTeX, XML, unicode, PDF-extraction artifacts. In [2]: Copied! import json from great_ai.utilities import clean def preprocess ( line ): data_point = json . loads ( line ) sentence = data_point [ \"text\" ] label = data_point [ \"label\" ] return clean ( sentence ), label import json from great_ai.utilities import clean def preprocess(line): data_point = json.loads(line) sentence = data_point[\"text\"] label = data_point[\"label\"] return clean(sentence), label Now, we can load the dataset and extract the training samples from it. Since we're impatient, we can do it in parallel using the simple_parallel_map function. Open files in Python are iterable: in text mode, each iteration returns the next line. In [3]: Copied! from great_ai.utilities import simple_parallel_map with open ( \"data/train.txt\" , encoding = \"utf-8\" ) as f : training_data = simple_parallel_map ( preprocess , f ) X_train = [ d [ 0 ] for d in training_data ] y_train = [ d [ 1 ] for d in training_data ] from great_ai.utilities import simple_parallel_map with open(\"data/train.txt\", encoding=\"utf-8\") as f: training_data = simple_parallel_map(preprocess, f) X_train = [d[0] for d in training_data] y_train = [d[1] for d in training_data] 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 84000/84000 [00:09<00:00, 8960.63it/s] Let's do the same for the test data. In [4]: Copied! with open ( \"data/test.txt\" , encoding = \"utf-8\" ) as f : test_data = simple_parallel_map ( preprocess , f ) X_test = [ d [ 0 ] for d in test_data ] y_test = [ d [ 1 ] for d in test_data ] with open(\"data/test.txt\", encoding=\"utf-8\") as f: test_data = simple_parallel_map(preprocess, f) X_test = [d[0] for d in test_data] y_test = [d[1] for d in test_data] 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 22399/22399 [00:03<00:00, 6078.02it/s] After obtaining some clean data, it's time to create and train a model on it. We are going to use scikit-learn for this purpose. In this case, we opted for a linear Support Vector Machine because it's known to be an adequate baseline for simple classification tasks. In [5]: Copied! from sklearn.pipeline import make_pipeline from sklearn.feature_extraction.text import TfidfVectorizer from sklearn.svm import LinearSVC model = make_pipeline ( TfidfVectorizer (), LinearSVC ()) # todo: hyperparameter-optimisation model . fit ( X_train , y_train ) from sklearn.pipeline import make_pipeline from sklearn.feature_extraction.text import TfidfVectorizer from sklearn.svm import LinearSVC model = make_pipeline(TfidfVectorizer(), LinearSVC()) # todo: hyperparameter-optimisation model.fit(X_train, y_train) Out[5]: #sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"\u25b8\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"\u25be\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;} Pipeline(steps=[('tfidfvectorizer', TfidfVectorizer()), ('linearsvc', LinearSVC())]) In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org. Pipeline Pipeline(steps=[('tfidfvectorizer', TfidfVectorizer()), ('linearsvc', LinearSVC())]) TfidfVectorizer TfidfVectorizer() LinearSVC LinearSVC() It's already finished. Let's see how well it performs. But first, we need to configure matplotlib to make the charts more legible. ConfusionMatrixDisplay relies on matplotlib for rendering. In [6]: Copied! import matplotlib.pyplot as plt % matplotlib inline plt . rcParams [ \"figure.figsize\" ] = ( 30 , 15 ) plt . rcParams [ \"figure.facecolor\" ] = \"white\" plt . rcParams [ \"font.size\" ] = 12 import matplotlib.pyplot as plt %matplotlib inline plt.rcParams[\"figure.figsize\"] = (30, 15) plt.rcParams[\"figure.facecolor\"] = \"white\" plt.rcParams[\"font.size\"] = 12 Next, we check the quality of the model on the test split. We can use the classification report to check the common metrics, such as the macro F1-score. We also draw the confusion matrix to get some insights into the types of mistakes being made by the model. In [7]: Copied! from sklearn import metrics y_predicted = model . predict ( X_test ) print ( metrics . classification_report ( y_test , y_predicted )) metrics . ConfusionMatrixDisplay . from_predictions ( y_test , y_predicted ) None from sklearn import metrics y_predicted = model.predict(X_test) print(metrics.classification_report(y_test, y_predicted)) metrics.ConfusionMatrixDisplay.from_predictions(y_test, y_predicted) None precision recall f1-score support business 0.67 0.69 0.68 3198 economics 0.69 0.71 0.70 3189 geography 0.70 0.73 0.72 3207 medicine 0.90 0.91 0.90 3187 politics 0.63 0.57 0.60 3169 psychology 0.73 0.73 0.73 3252 sociology 0.51 0.51 0.51 3197 accuracy 0.69 22399 macro avg 0.69 0.69 0.69 22399 weighted avg 0.69 0.69 0.69 22399 Great work, we can be rightfully satisfied with our model. Seeing the results, we achieved an F1-score of 0.69 which is about 5% better than SciBERT's 0.6571! You might wonder that \"this is great, but besides some utility functions ( clean , simple_parallel_map , ...) what more value does GreatAI add?\" . This would be a valid argument because the scope of GreatAI actually only starts here. Not coincidentally, this is the point where the scope of Data Science ends but it's still a grey zone for software engineering. In order to use this model in production, we have to make it available on some possibly shared infrastructure. In [8]: Copied! from great_ai import save_model save_model ( model , key = \"my-domain-predictor\" ) from great_ai import save_model save_model(model, key=\"my-domain-predictor\") Environment variable ENVIRONMENT is not set, defaulting to development mode \u203c\ufe0f Cannot find credentials files, defaulting to using ParallelTinyDbDriver The selected tracing database (ParallelTinyDbDriver) is not recommended for production Cannot find credentials files, defaulting to using LargeFileLocal GreatAI (v0.1.4): configured \u2705 \ud83d\udd29 tracing_database: ParallelTinyDbDriver \ud83d\udd29 large_file_implementation: LargeFileLocal \ud83d\udd29 is_production: False \ud83d\udd29 should_log_exception_stack: True \ud83d\udd29 prediction_cache_size: 512 \ud83d\udd29 dashboard_table_size: 50 You still need to check whether you follow all best practices before trusting your deployment. > Find out more at https://se-ml.github.io/practices Fetching cached versions of my-domain-predictor Copying file for my-domain-predictor-9 Compressing my-domain-predictor-9 Model my-domain-predictor uploaded with version 9 Out[8]: 'my-domain-predictor:9'","title":"Train your model"},{"location":"tutorial/train/#lets-continue-by-finishing-the-deployment-in-the-next-notebook","text":"","title":"Let's continue by finishing the deployment in the next notebook."}]} \ No newline at end of file +{"config":{"indexing":"full","lang":["en"],"min_search_length":3,"prebuild_index":false,"separator":"[\\s\\-]+"},"docs":[{"location":"","text":"Overview of GreatAI Applying AI is becoming increasingly easier but many case studies have shown that these applications are often deployed poorly. This may lead to suboptimal performance and to introducing unintended biases . GreatAI helps fixing this by allowing you to easily transform your prototype AI code into production-ready software . Case studies \"There is a need to consider and adapt well established SE practices which have been ignored or had a very narrow focus in ML literature.\" \u2014 John et al. \"Finally, we have found that existing tools to aid Machine Learning development do not address the specificities of different projects, and thus, are seldom adopted by teams.\" \u2014 Haakman et al. \"Because a mature system might end up being (at most) 5% machine learning code and (at least) 95% glue code, it may be less costly to create a clean native solution rather than re-use a generic package.\" \u2014 Sculley et al. \"For example, practice 25 is very important for \u201cTraceability\", yet relatively weakly adopted. We expect that the results from this type of analysis can, in the future, provide useful guidance for practitioners in terms of aiding them to assess their rate of adoption for each practice and to create roadmaps for improving their processes. \u2014 Serban et al. Features # Save prediction traces of each prediction including arguments and model versions Save feedback and merge it into a ground-truth database Version and store models and data on shared infrastructure (MongoDB GridFS, S3-compatible storage, shared volume) Automatically scaffolded custom REST API (and OpenAPI schema) for easy integration Input validation Sensible cache-policy Graceful error handling Seamless support for both synchronous and asynchronous inference methods Easy integration with remote GreatAI instances Built-in parallelisation (with support for multiprocessing, async, and mixed modes) for batch processing Well-tested utilities for common NLP tasks (cleaning, language-tagging, sentence-segmentation, etc.) A simple, unified configuration interface Fully-typed API for Pylance and MyPy support Auto-reload for development Docker support for deployment Deployable Jupyter Notebooks Dashboard for online monitoring and analysing traces Active support for Python 3.7, 3.8, 3.9, and 3.10 Roadmap # Prometheus & Grafana integration Well-tested feature extraction code for non-NLP data Support for direct file input Support for PostgreSQL Hello world # pip install great-ai demo.py from great_ai import GreatAI @GreatAI . create #(1) def greeter ( name : str ) -> str : #(2) return f \"Hello { name } !\" @GreatAI.create wraps your greeter function with a GreatAI instance. The function will behave very similarly but: its return value becomes a Trace[str] , it gets a process_batch method for supporting parallel execution, and it can be deployed using the great-ai command-line tool. Typing functions is recommended in general, however, not required for GreatAI to work. Note In practice, greeter could be an inference function of some AI/ML application. But it could also just wrap a black-box solution of some SaaS. Either way, it is imperative to have continuous oversight of the services you provide and data you process especially in the context of AI/ML applications. terminal great-ai demo.py Navigate to localhost:6060 in your browser. Success Your GreatAI service is ready for production use. Many of the SE4ML best practices are configured and implemented automatically. To have full control over your service and to understand what else you might need to do in your use case, continue reading this documentation. Why is this GREAT? # GreatAI fits between the prototype and deployment phases of your (or your organisation's) AI development lifecycle. This is highlighted with blue in the diagram. Here, several best practices can be automatically implemented aiming to achieve the following attributes: G eneral: use any Python library without restriction R obust: have error-handling and well-tested utilities out-of-the-box E nd-to-end: utilise end-to-end feedback as a built-in, first-class concept A utomated: focus only on what actually requires your attention T rustworthy: deploy models that you and society can confidently trust Why GreatAI? # There are other, existing solutions aiming to facilitate this phase. Amazon SageMaker and Seldon Core provide the most comprehensive suite of features. If you have the opportunity to use them, do that because they're great. However, research indicates that professionals rarely use them. This may be due to their inherent setup and operating complexity. GreatAI is designed to be as simple to use as possible. Its clear, high-level API and sensible default configuration makes it extremely easy to start using. Despite its relative simplicity over Seldon Core, it still implements many of the SE4ML best practices , and thus, can meaningfully improve your deployment without requiring prohibitively large effort. Find it on PyPI Find it on DockerHub Check out the tutorial Production use # GreatAI has been battle-tested on the core platform services of ScoutinScience .","title":"Overview"},{"location":"#features","text":"Save prediction traces of each prediction including arguments and model versions Save feedback and merge it into a ground-truth database Version and store models and data on shared infrastructure (MongoDB GridFS, S3-compatible storage, shared volume) Automatically scaffolded custom REST API (and OpenAPI schema) for easy integration Input validation Sensible cache-policy Graceful error handling Seamless support for both synchronous and asynchronous inference methods Easy integration with remote GreatAI instances Built-in parallelisation (with support for multiprocessing, async, and mixed modes) for batch processing Well-tested utilities for common NLP tasks (cleaning, language-tagging, sentence-segmentation, etc.) A simple, unified configuration interface Fully-typed API for Pylance and MyPy support Auto-reload for development Docker support for deployment Deployable Jupyter Notebooks Dashboard for online monitoring and analysing traces Active support for Python 3.7, 3.8, 3.9, and 3.10","title":"Features"},{"location":"#roadmap","text":"Prometheus & Grafana integration Well-tested feature extraction code for non-NLP data Support for direct file input Support for PostgreSQL","title":"Roadmap"},{"location":"#hello-world","text":"pip install great-ai demo.py from great_ai import GreatAI @GreatAI . create #(1) def greeter ( name : str ) -> str : #(2) return f \"Hello { name } !\" @GreatAI.create wraps your greeter function with a GreatAI instance. The function will behave very similarly but: its return value becomes a Trace[str] , it gets a process_batch method for supporting parallel execution, and it can be deployed using the great-ai command-line tool. Typing functions is recommended in general, however, not required for GreatAI to work. Note In practice, greeter could be an inference function of some AI/ML application. But it could also just wrap a black-box solution of some SaaS. Either way, it is imperative to have continuous oversight of the services you provide and data you process especially in the context of AI/ML applications. terminal great-ai demo.py Navigate to localhost:6060 in your browser. Success Your GreatAI service is ready for production use. Many of the SE4ML best practices are configured and implemented automatically. To have full control over your service and to understand what else you might need to do in your use case, continue reading this documentation.","title":"Hello world"},{"location":"#why-is-this-great","text":"GreatAI fits between the prototype and deployment phases of your (or your organisation's) AI development lifecycle. This is highlighted with blue in the diagram. Here, several best practices can be automatically implemented aiming to achieve the following attributes: G eneral: use any Python library without restriction R obust: have error-handling and well-tested utilities out-of-the-box E nd-to-end: utilise end-to-end feedback as a built-in, first-class concept A utomated: focus only on what actually requires your attention T rustworthy: deploy models that you and society can confidently trust","title":"Why is this GREAT?"},{"location":"#why-greatai","text":"There are other, existing solutions aiming to facilitate this phase. Amazon SageMaker and Seldon Core provide the most comprehensive suite of features. If you have the opportunity to use them, do that because they're great. However, research indicates that professionals rarely use them. This may be due to their inherent setup and operating complexity. GreatAI is designed to be as simple to use as possible. Its clear, high-level API and sensible default configuration makes it extremely easy to start using. Despite its relative simplicity over Seldon Core, it still implements many of the SE4ML best practices , and thus, can meaningfully improve your deployment without requiring prohibitively large effort. Find it on PyPI Find it on DockerHub Check out the tutorial","title":"Why GreatAI?"},{"location":"#production-use","text":"GreatAI has been battle-tested on the core platform services of ScoutinScience .","title":"Production use"},{"location":"explanation/","text":"Explanation # A lot more details and discussion about the problem context and approaches of GreatAI along with its evaluation can be found in my thesis. : Download","title":"Explanation"},{"location":"explanation/#explanation","text":"A lot more details and discussion about the problem context and approaches of GreatAI along with its evaluation can be found in my thesis. : Download","title":"Explanation"},{"location":"examples/scibert/","text":"Summarising scientific publications from a tech-transfer perspective # This is a simplified example illustrating how great-ai is used in practice at ScoutinScience . The subpages show great-ai in action by going over the life-cycle of finetuning and deploying a BERT-based software service. Propriety data The purpose of this example is to show you different ways in which great-ai can assist you. The exact NLP task being solved is not central. Stemming from this, and from the difficult nature of obtaining appropriate training data, the propriety dataset used for the experiments is not shared. Objectives # You will see how the great_ai.utilities can integrate into your Data Science workflow. You will see how great_ai.large_file can be used to version and store your trained model. You will see how GreatAI should be used prepare your model for a robust and responsible deployment. You will see multiple ways of customising your deployment. Overview # One of the core features of the ScoutinScience platform is summarising research papers from a tech-transfer perspective. In short, extractive summarisation is preferred using a binary classifier trained on clients' judgement of sentence interestingness. Thus, documents are sentences and the expected output is a binary label showing whether a sentence is \"worthy\" of being in the tech-transfer summary. Providing an explanation for each decision is imperative since ScoutinScience embraces applying only explainable AI (XAI) methods wherever feasible. Success You are ready to start the tutorial. Feel free to come back to the summary section once you're finished. Examine data Train model Deploy service Summary # Data notebook # We load and analyse the data by calculating inter-rater reliability and checking the feasibility of using an AI-based approach by testing the accuracy of a trivial baseline method. Training notebook # We simply fine-tune SciBERT. After training and evaluating a model, it is exported using great_ai.save_model . For more info, checkout the configuration how-to page . Deployment notebook # We customise the GreatAI configuration, create custom cahcing for the model and implement an inference function that can be hardened by wrapping it in a GreatAI instance. We also extract the attention weights as a quasi-explanation. Finally, we test the model's inference function through the GreatAI dashboard. The only thing left is to deploy the hardened service properly. Additional files # There are some other files required for deploying the notebook. For example, the config file for S3 and MongoDB or a Dockerfile for building a custom image. These are compiled and shown on a separate page.","title":"Summarising scientific publications from a tech-transfer perspective"},{"location":"examples/scibert/#summarising-scientific-publications-from-a-tech-transfer-perspective","text":"This is a simplified example illustrating how great-ai is used in practice at ScoutinScience . 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You will see multiple ways of customising your deployment.","title":"Objectives"},{"location":"examples/scibert/#overview","text":"One of the core features of the ScoutinScience platform is summarising research papers from a tech-transfer perspective. In short, extractive summarisation is preferred using a binary classifier trained on clients' judgement of sentence interestingness. Thus, documents are sentences and the expected output is a binary label showing whether a sentence is \"worthy\" of being in the tech-transfer summary. Providing an explanation for each decision is imperative since ScoutinScience embraces applying only explainable AI (XAI) methods wherever feasible. Success You are ready to start the tutorial. Feel free to come back to the summary section once you're finished. Examine data Train model Deploy service","title":"Overview"},{"location":"examples/scibert/#summary","text":"","title":"Summary"},{"location":"examples/scibert/#data-notebook","text":"We load and analyse the data by calculating inter-rater reliability and checking the feasibility of using an AI-based approach by testing the accuracy of a trivial baseline method.","title":"Data notebook"},{"location":"examples/scibert/#training-notebook","text":"We simply fine-tune SciBERT. After training and evaluating a model, it is exported using great_ai.save_model . For more info, checkout the configuration how-to page .","title":"Training notebook"},{"location":"examples/scibert/#deployment-notebook","text":"We customise the GreatAI configuration, create custom cahcing for the model and implement an inference function that can be hardened by wrapping it in a GreatAI instance. We also extract the attention weights as a quasi-explanation. Finally, we test the model's inference function through the GreatAI dashboard. The only thing left is to deploy the hardened service properly.","title":"Deployment notebook"},{"location":"examples/scibert/#additional-files","text":"There are some other files required for deploying the notebook. For example, the config file for S3 and MongoDB or a Dockerfile for building a custom image. These are compiled and shown on a separate page.","title":"Additional files"},{"location":"examples/scibert/additional-files/","text":"Additional files in the repository # In order to give you a smooth experience while comprehending this example, all non-notebook files are presented on this page in one place. In reality, these files should be in your project's top-level directory. config.ini # config.ini ENVIRONMENT = DEVELOPMENT ENVIRONMENT = ENV:ENVIRONMENT MONGO_CONNECTION_STRING = ENV:MONGO_CONNECTION_STRING MONGO_DATABASE = highlights AWS_REGION_NAME = eu-west-2 AWS_ACCESS_KEY_ID = MY_DEFAULT_AWS_ACCESS_KEY_ID_FOR_DEVELOPMENT AWS_SECRET_ACCESS_KEY = MY_DEFAULT_AWS_SECRET_ACCESS_KEY_FOR_DEVELOPMENT LARGE_FILES_BUCKET_NAME = my-orgs-large-files AWS_REGION_NAME = ENV:AWS_REGION_NAME AWS_ACCESS_KEY_ID = ENV:AWS_ACCESS_KEY_ID AWS_SECRET_ACCESS_KEY = ENV:AWS_SECRET_ACCESS_KEY LARGE_FILES_BUCKET_NAME = ENV:LARGE_FILES_BUCKET_NAME All necessary configuration which is read by great_ai.utilities.ConfigFile . This will resolve values starting with ENV: from your environment variables. requirements.txt # requirements.txt torch==1.12.0 transformers==4.20.1 numpy==1.23.0 Usually, it is recommended to pin (freeze) the library versions on which we depend. This file is referenced by the Dockerfile . Dockerfile # Dockerfile FROM schmelczera/great-ai:v0.1.6 COPY requirements.txt ./ RUN pip install --no-cache-dir --requirement requirements.txt COPY . ./ RUN large-file --backend s3 --secrets s3.ini --cache scibert-highlights CMD [ \"deploy.ipynb\" ] This is used by the CD pipeline to create the production deployment of the service. .dockerignore # .dockerignore .cache .git data .gitignore .env .vscode .dockerignore Dockerfile .mypy_cache .gitignore **/__pycache__ **/.DS_Store README.md It is useful not to send, for example, the .cache folder used by LargeFile to the docker daemon; this will speed up your local build times substantially. .gitignore A very similar looking .gitignore file should also be present.","title":"Additional files in the repository"},{"location":"examples/scibert/additional-files/#additional-files-in-the-repository","text":"In order to give you a smooth experience while comprehending this example, all non-notebook files are presented on this page in one place. In reality, these files should be in your project's top-level directory.","title":"Additional files in the repository"},{"location":"examples/scibert/additional-files/#configini","text":"config.ini ENVIRONMENT = DEVELOPMENT ENVIRONMENT = ENV:ENVIRONMENT MONGO_CONNECTION_STRING = ENV:MONGO_CONNECTION_STRING MONGO_DATABASE = highlights AWS_REGION_NAME = eu-west-2 AWS_ACCESS_KEY_ID = MY_DEFAULT_AWS_ACCESS_KEY_ID_FOR_DEVELOPMENT AWS_SECRET_ACCESS_KEY = MY_DEFAULT_AWS_SECRET_ACCESS_KEY_FOR_DEVELOPMENT LARGE_FILES_BUCKET_NAME = my-orgs-large-files AWS_REGION_NAME = ENV:AWS_REGION_NAME AWS_ACCESS_KEY_ID = ENV:AWS_ACCESS_KEY_ID AWS_SECRET_ACCESS_KEY = ENV:AWS_SECRET_ACCESS_KEY LARGE_FILES_BUCKET_NAME = ENV:LARGE_FILES_BUCKET_NAME All necessary configuration which is read by great_ai.utilities.ConfigFile . This will resolve values starting with ENV: from your environment variables.","title":"config.ini"},{"location":"examples/scibert/additional-files/#requirementstxt","text":"requirements.txt torch==1.12.0 transformers==4.20.1 numpy==1.23.0 Usually, it is recommended to pin (freeze) the library versions on which we depend. This file is referenced by the Dockerfile .","title":"requirements.txt"},{"location":"examples/scibert/additional-files/#dockerfile","text":"Dockerfile FROM schmelczera/great-ai:v0.1.6 COPY requirements.txt ./ RUN pip install --no-cache-dir --requirement requirements.txt COPY . ./ RUN large-file --backend s3 --secrets s3.ini --cache scibert-highlights CMD [ \"deploy.ipynb\" ] This is used by the CD pipeline to create the production deployment of the service.","title":"Dockerfile"},{"location":"examples/scibert/additional-files/#dockerignore","text":".dockerignore .cache .git data .gitignore .env .vscode .dockerignore Dockerfile .mypy_cache .gitignore **/__pycache__ **/.DS_Store README.md It is useful not to send, for example, the .cache folder used by LargeFile to the docker daemon; this will speed up your local build times substantially. .gitignore A very similar looking .gitignore file should also be present.","title":".dockerignore"},{"location":"examples/scibert/data/","text":"(function (global, factory) { typeof exports === 'object' && typeof module !== 'undefined' ? module.exports = factory() : typeof define === 'function' && define.amd ? define(factory) : (global = global || self, global.ClipboardCopyElement = factory()); 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These particular numbers are taken from here: * * https://github.com/material-components/material-components-web * https://material-components-web.appspot.com/elevation.html */ --jp-shadow-base-lightness: 0; --jp-shadow-umbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.2 ); --jp-shadow-penumbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.14 ); --jp-shadow-ambient-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.12 ); --jp-elevation-z0: none; --jp-elevation-z1: 0px 2px 1px -1px var(--jp-shadow-umbra-color), 0px 1px 1px 0px var(--jp-shadow-penumbra-color), 0px 1px 3px 0px var(--jp-shadow-ambient-color); --jp-elevation-z2: 0px 3px 1px -2px var(--jp-shadow-umbra-color), 0px 2px 2px 0px var(--jp-shadow-penumbra-color), 0px 1px 5px 0px var(--jp-shadow-ambient-color); --jp-elevation-z4: 0px 2px 4px -1px var(--jp-shadow-umbra-color), 0px 4px 5px 0px var(--jp-shadow-penumbra-color), 0px 1px 10px 0px var(--jp-shadow-ambient-color); --jp-elevation-z6: 0px 3px 5px -1px var(--jp-shadow-umbra-color), 0px 6px 10px 0px var(--jp-shadow-penumbra-color), 0px 1px 18px 0px var(--jp-shadow-ambient-color); --jp-elevation-z8: 0px 5px 5px -3px var(--jp-shadow-umbra-color), 0px 8px 10px 1px var(--jp-shadow-penumbra-color), 0px 3px 14px 2px var(--jp-shadow-ambient-color); --jp-elevation-z12: 0px 7px 8px -4px var(--jp-shadow-umbra-color), 0px 12px 17px 2px var(--jp-shadow-penumbra-color), 0px 5px 22px 4px var(--jp-shadow-ambient-color); --jp-elevation-z16: 0px 8px 10px -5px var(--jp-shadow-umbra-color), 0px 16px 24px 2px var(--jp-shadow-penumbra-color), 0px 6px 30px 5px var(--jp-shadow-ambient-color); --jp-elevation-z20: 0px 10px 13px -6px var(--jp-shadow-umbra-color), 0px 20px 31px 3px var(--jp-shadow-penumbra-color), 0px 8px 38px 7px var(--jp-shadow-ambient-color); --jp-elevation-z24: 0px 11px 15px -7px var(--jp-shadow-umbra-color), 0px 24px 38px 3px var(--jp-shadow-penumbra-color), 0px 9px 46px 8px var(--jp-shadow-ambient-color); /* Borders * * The following variables, specify the visual styling of borders in JupyterLab. */ --jp-border-width: 1px; --jp-border-color0: var(--md-grey-400); --jp-border-color1: var(--md-grey-400); --jp-border-color2: var(--md-grey-300); --jp-border-color3: var(--md-grey-200); --jp-border-radius: 2px; /* UI Fonts * * The UI font CSS variables are used for the typography all of the JupyterLab * user interface elements that are not directly user generated content. * * The font sizing here is done assuming that the body font size of --jp-ui-font-size1 * is applied to a parent element. When children elements, such as headings, are sized * in em all things will be computed relative to that body size. */ --jp-ui-font-scale-factor: 1.2; --jp-ui-font-size0: 0.83333em; --jp-ui-font-size1: 13px; /* Base font size */ --jp-ui-font-size2: 1.2em; --jp-ui-font-size3: 1.44em; --jp-ui-font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Helvetica, Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\"; /* * Use these font colors against the corresponding main layout colors. * In a light theme, these go from dark to light. */ /* Defaults use Material Design specification */ --jp-ui-font-color0: rgba(0, 0, 0, 1); --jp-ui-font-color1: rgba(0, 0, 0, 0.87); --jp-ui-font-color2: rgba(0, 0, 0, 0.54); --jp-ui-font-color3: rgba(0, 0, 0, 0.38); /* * Use these against the brand/accent/warn/error colors. * These will typically go from light to darker, in both a dark and light theme. */ --jp-ui-inverse-font-color0: rgba(255, 255, 255, 1); --jp-ui-inverse-font-color1: rgba(255, 255, 255, 1); --jp-ui-inverse-font-color2: rgba(255, 255, 255, 0.7); --jp-ui-inverse-font-color3: rgba(255, 255, 255, 0.5); /* Content Fonts * * Content font variables are used for typography of user generated content. * * The font sizing here is done assuming that the body font size of --jp-content-font-size1 * is applied to a parent element. When children elements, such as headings, are sized * in em all things will be computed relative to that body size. */ --jp-content-line-height: 1.6; --jp-content-font-scale-factor: 1.2; --jp-content-font-size0: 0.83333em; --jp-content-font-size1: 14px; /* Base font size */ --jp-content-font-size2: 1.2em; --jp-content-font-size3: 1.44em; --jp-content-font-size4: 1.728em; --jp-content-font-size5: 2.0736em; /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-content-presentation-font-size1: 17px; --jp-content-heading-line-height: 1; --jp-content-heading-margin-top: 1.2em; --jp-content-heading-margin-bottom: 0.8em; --jp-content-heading-font-weight: 500; /* Defaults use Material Design specification */ --jp-content-font-color0: rgba(0, 0, 0, 1); --jp-content-font-color1: rgba(0, 0, 0, 0.87); --jp-content-font-color2: rgba(0, 0, 0, 0.54); --jp-content-font-color3: rgba(0, 0, 0, 0.38); --jp-content-link-color: var(--md-blue-700); --jp-content-font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Helvetica, Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\"; /* * Code Fonts * * Code font variables are used for typography of code and other monospaces content. */ --jp-code-font-size: 13px; --jp-code-line-height: 1.3077; /* 17px for 13px base */ --jp-code-padding: 5px; /* 5px for 13px base, codemirror highlighting needs integer px value */ --jp-code-font-family-default: Menlo, Consolas, \"DejaVu Sans Mono\", monospace; --jp-code-font-family: var(--jp-code-font-family-default); /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-code-presentation-font-size: 16px; /* may need to tweak cursor width if you change font size */ --jp-code-cursor-width0: 1.4px; --jp-code-cursor-width1: 2px; --jp-code-cursor-width2: 4px; /* Layout * * The following are the main layout colors use in JupyterLab. In a light * theme these would go from light to dark. */ --jp-layout-color0: white; --jp-layout-color1: white; --jp-layout-color2: var(--md-grey-200); --jp-layout-color3: var(--md-grey-400); --jp-layout-color4: var(--md-grey-600); /* Inverse Layout * * The following are the inverse layout colors use in JupyterLab. In a light * theme these would go from dark to light. */ --jp-inverse-layout-color0: #111111; --jp-inverse-layout-color1: var(--md-grey-900); --jp-inverse-layout-color2: var(--md-grey-800); --jp-inverse-layout-color3: var(--md-grey-700); --jp-inverse-layout-color4: var(--md-grey-600); /* Brand/accent */ --jp-brand-color0: var(--md-blue-900); --jp-brand-color1: var(--md-blue-700); --jp-brand-color2: var(--md-blue-300); --jp-brand-color3: var(--md-blue-100); --jp-brand-color4: var(--md-blue-50); --jp-accent-color0: var(--md-green-900); --jp-accent-color1: var(--md-green-700); --jp-accent-color2: var(--md-green-300); --jp-accent-color3: var(--md-green-100); /* State colors (warn, error, success, info) */ --jp-warn-color0: var(--md-orange-900); --jp-warn-color1: var(--md-orange-700); --jp-warn-color2: var(--md-orange-300); --jp-warn-color3: var(--md-orange-100); --jp-error-color0: var(--md-red-900); --jp-error-color1: var(--md-red-700); --jp-error-color2: var(--md-red-300); --jp-error-color3: var(--md-red-100); --jp-success-color0: var(--md-green-900); --jp-success-color1: var(--md-green-700); --jp-success-color2: var(--md-green-300); --jp-success-color3: var(--md-green-100); --jp-info-color0: var(--md-cyan-900); --jp-info-color1: var(--md-cyan-700); --jp-info-color2: var(--md-cyan-300); --jp-info-color3: var(--md-cyan-100); /* Cell specific styles */ --jp-cell-padding: 5px; --jp-cell-collapser-width: 8px; --jp-cell-collapser-min-height: 20px; --jp-cell-collapser-not-active-hover-opacity: 0.6; --jp-cell-editor-background: var(--md-grey-100); --jp-cell-editor-border-color: var(--md-grey-300); --jp-cell-editor-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-cell-editor-active-background: var(--jp-layout-color0); --jp-cell-editor-active-border-color: var(--jp-brand-color1); --jp-cell-prompt-width: 64px; --jp-cell-prompt-font-family: var(--jp-code-font-family-default); --jp-cell-prompt-letter-spacing: 0px; --jp-cell-prompt-opacity: 1; --jp-cell-prompt-not-active-opacity: 0.5; --jp-cell-prompt-not-active-font-color: var(--md-grey-700); /* A custom blend of MD grey and blue 600 * See https://meyerweb.com/eric/tools/color-blend/#546E7A:1E88E5:5:hex */ --jp-cell-inprompt-font-color: #307fc1; /* A custom blend of MD grey and orange 600 * https://meyerweb.com/eric/tools/color-blend/#546E7A:F4511E:5:hex */ --jp-cell-outprompt-font-color: #bf5b3d; /* Notebook specific styles */ --jp-notebook-padding: 10px; --jp-notebook-select-background: var(--jp-layout-color1); --jp-notebook-multiselected-color: var(--md-blue-50); /* The scroll padding is calculated to fill enough space at the bottom of the notebook to show one single-line cell (with appropriate padding) at the top when the notebook is scrolled all the way to the bottom. We also subtract one pixel so that no scrollbar appears if we have just one single-line cell in the notebook. This padding is to enable a 'scroll past end' feature in a notebook. */ --jp-notebook-scroll-padding: calc( 100% - var(--jp-code-font-size) * var(--jp-code-line-height) - var(--jp-code-padding) - var(--jp-cell-padding) - 1px ); /* Rendermime styles */ --jp-rendermime-error-background: #fdd; --jp-rendermime-table-row-background: var(--md-grey-100); --jp-rendermime-table-row-hover-background: var(--md-light-blue-50); /* Dialog specific styles */ --jp-dialog-background: rgba(0, 0, 0, 0.25); /* Console specific styles */ --jp-console-padding: 10px; /* Toolbar specific styles */ --jp-toolbar-border-color: var(--jp-border-color1); --jp-toolbar-micro-height: 8px; --jp-toolbar-background: var(--jp-layout-color1); --jp-toolbar-box-shadow: 0px 0px 2px 0px rgba(0, 0, 0, 0.24); --jp-toolbar-header-margin: 4px 4px 0px 4px; --jp-toolbar-active-background: var(--md-grey-300); /* Statusbar specific styles */ --jp-statusbar-height: 24px; /* Input field styles */ --jp-input-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-input-active-background: var(--jp-layout-color1); --jp-input-hover-background: var(--jp-layout-color1); --jp-input-background: var(--md-grey-100); --jp-input-border-color: var(--jp-border-color1); --jp-input-active-border-color: var(--jp-brand-color1); --jp-input-active-box-shadow-color: rgba(19, 124, 189, 0.3); /* General editor styles */ --jp-editor-selected-background: #d9d9d9; --jp-editor-selected-focused-background: #d7d4f0; --jp-editor-cursor-color: var(--jp-ui-font-color0); /* Code mirror specific styles */ --jp-mirror-editor-keyword-color: #008000; --jp-mirror-editor-atom-color: #88f; --jp-mirror-editor-number-color: #080; --jp-mirror-editor-def-color: #00f; --jp-mirror-editor-variable-color: var(--md-grey-900); --jp-mirror-editor-variable-2-color: #05a; --jp-mirror-editor-variable-3-color: #085; --jp-mirror-editor-punctuation-color: #05a; --jp-mirror-editor-property-color: #05a; --jp-mirror-editor-operator-color: #aa22ff; --jp-mirror-editor-comment-color: #408080; --jp-mirror-editor-string-color: #ba2121; --jp-mirror-editor-string-2-color: #708; --jp-mirror-editor-meta-color: #aa22ff; --jp-mirror-editor-qualifier-color: #555; --jp-mirror-editor-builtin-color: #008000; --jp-mirror-editor-bracket-color: #997; --jp-mirror-editor-tag-color: #170; --jp-mirror-editor-attribute-color: #00c; --jp-mirror-editor-header-color: blue; --jp-mirror-editor-quote-color: #090; --jp-mirror-editor-link-color: #00c; --jp-mirror-editor-error-color: #f00; --jp-mirror-editor-hr-color: #999; /* Vega extension styles */ --jp-vega-background: white; /* Sidebar-related styles */ --jp-sidebar-min-width: 250px; /* Search-related styles */ --jp-search-toggle-off-opacity: 0.5; --jp-search-toggle-hover-opacity: 0.8; --jp-search-toggle-on-opacity: 1; --jp-search-selected-match-background-color: rgb(245, 200, 0); --jp-search-selected-match-color: black; --jp-search-unselected-match-background-color: var( --jp-inverse-layout-color0 ); --jp-search-unselected-match-color: var(--jp-ui-inverse-font-color0); /* Icon colors that work well with light or dark backgrounds */ --jp-icon-contrast-color0: var(--md-purple-600); --jp-icon-contrast-color1: var(--md-green-600); --jp-icon-contrast-color2: var(--md-pink-600); --jp-icon-contrast-color3: var(--md-blue-600); } [data-md-color-scheme=\"slate\"] .jupyter-wrapper { /* Elevation * * We style box-shadows using Material Design's idea of elevation. These particular numbers are taken from here: * * https://github.com/material-components/material-components-web * https://material-components-web.appspot.com/elevation.html */ /* The dark theme shadows need a bit of work, but this will probably also require work on the core layout * colors used in the theme as well. */ --jp-shadow-base-lightness: 32; --jp-shadow-umbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.2 ); --jp-shadow-penumbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.14 ); --jp-shadow-ambient-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.12 ); --jp-elevation-z0: none; --jp-elevation-z1: 0px 2px 1px -1px var(--jp-shadow-umbra-color), 0px 1px 1px 0px var(--jp-shadow-penumbra-color), 0px 1px 3px 0px var(--jp-shadow-ambient-color); --jp-elevation-z2: 0px 3px 1px -2px var(--jp-shadow-umbra-color), 0px 2px 2px 0px var(--jp-shadow-penumbra-color), 0px 1px 5px 0px var(--jp-shadow-ambient-color); --jp-elevation-z4: 0px 2px 4px -1px var(--jp-shadow-umbra-color), 0px 4px 5px 0px var(--jp-shadow-penumbra-color), 0px 1px 10px 0px var(--jp-shadow-ambient-color); --jp-elevation-z6: 0px 3px 5px -1px var(--jp-shadow-umbra-color), 0px 6px 10px 0px var(--jp-shadow-penumbra-color), 0px 1px 18px 0px var(--jp-shadow-ambient-color); --jp-elevation-z8: 0px 5px 5px -3px var(--jp-shadow-umbra-color), 0px 8px 10px 1px var(--jp-shadow-penumbra-color), 0px 3px 14px 2px var(--jp-shadow-ambient-color); --jp-elevation-z12: 0px 7px 8px -4px var(--jp-shadow-umbra-color), 0px 12px 17px 2px var(--jp-shadow-penumbra-color), 0px 5px 22px 4px var(--jp-shadow-ambient-color); --jp-elevation-z16: 0px 8px 10px -5px var(--jp-shadow-umbra-color), 0px 16px 24px 2px var(--jp-shadow-penumbra-color), 0px 6px 30px 5px var(--jp-shadow-ambient-color); --jp-elevation-z20: 0px 10px 13px -6px var(--jp-shadow-umbra-color), 0px 20px 31px 3px var(--jp-shadow-penumbra-color), 0px 8px 38px 7px var(--jp-shadow-ambient-color); --jp-elevation-z24: 0px 11px 15px -7px var(--jp-shadow-umbra-color), 0px 24px 38px 3px var(--jp-shadow-penumbra-color), 0px 9px 46px 8px var(--jp-shadow-ambient-color); /* Borders * * The following variables, specify the visual styling of borders in JupyterLab. */ --jp-border-width: 1px; --jp-border-color0: var(--md-grey-700); --jp-border-color1: var(--md-grey-700); --jp-border-color2: var(--md-grey-800); --jp-border-color3: var(--md-grey-900); --jp-border-radius: 2px; /* UI Fonts * * The UI font CSS variables are used for the typography all of the JupyterLab * user interface elements that are not directly user generated content. * * The font sizing here is done assuming that the body font size of --jp-ui-font-size1 * is applied to a parent element. When children elements, such as headings, are sized * in em all things will be computed relative to that body size. */ --jp-ui-font-scale-factor: 1.2; --jp-ui-font-size0: 0.83333em; --jp-ui-font-size1: 13px; /* Base font size */ --jp-ui-font-size2: 1.2em; --jp-ui-font-size3: 1.44em; --jp-ui-font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Helvetica, Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\"; /* * Use these font colors against the corresponding main layout colors. * In a light theme, these go from dark to light. */ /* Defaults use Material Design specification */ --jp-ui-font-color0: rgba(255, 255, 255, 1); --jp-ui-font-color1: rgba(255, 255, 255, 0.87); --jp-ui-font-color2: rgba(255, 255, 255, 0.54); --jp-ui-font-color3: rgba(255, 255, 255, 0.38); /* * Use these against the brand/accent/warn/error colors. * These will typically go from light to darker, in both a dark and light theme. */ --jp-ui-inverse-font-color0: rgba(0, 0, 0, 1); --jp-ui-inverse-font-color1: rgba(0, 0, 0, 0.8); --jp-ui-inverse-font-color2: rgba(0, 0, 0, 0.5); --jp-ui-inverse-font-color3: rgba(0, 0, 0, 0.3); /* Content Fonts * * Content font variables are used for typography of user generated content. * * The font sizing here is done assuming that the body font size of --jp-content-font-size1 * is applied to a parent element. When children elements, such as headings, are sized * in em all things will be computed relative to that body size. */ --jp-content-line-height: 1.6; --jp-content-font-scale-factor: 1.2; --jp-content-font-size0: 0.83333em; --jp-content-font-size1: 14px; /* Base font size */ --jp-content-font-size2: 1.2em; --jp-content-font-size3: 1.44em; --jp-content-font-size4: 1.728em; --jp-content-font-size5: 2.0736em; /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-content-presentation-font-size1: 17px; --jp-content-heading-line-height: 1; --jp-content-heading-margin-top: 1.2em; --jp-content-heading-margin-bottom: 0.8em; --jp-content-heading-font-weight: 500; /* Defaults use Material Design specification */ --jp-content-font-color0: rgba(255, 255, 255, 1); --jp-content-font-color1: rgba(255, 255, 255, 1); --jp-content-font-color2: rgba(255, 255, 255, 0.7); --jp-content-font-color3: rgba(255, 255, 255, 0.5); --jp-content-link-color: var(--md-blue-300); --jp-content-font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Helvetica, Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\"; /* * Code Fonts * * Code font variables are used for typography of code and other monospaces content. */ --jp-code-font-size: 13px; --jp-code-line-height: 1.3077; /* 17px for 13px base */ --jp-code-padding: 5px; /* 5px for 13px base, codemirror highlighting needs integer px value */ --jp-code-font-family-default: Menlo, Consolas, \"DejaVu Sans Mono\", monospace; --jp-code-font-family: var(--jp-code-font-family-default); /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-code-presentation-font-size: 16px; /* may need to tweak cursor width if you change font size */ --jp-code-cursor-width0: 1.4px; --jp-code-cursor-width1: 2px; --jp-code-cursor-width2: 4px; /* Layout * * The following are the main layout colors use in JupyterLab. In a light * theme these would go from light to dark. */ --jp-layout-color0: #111111; --jp-layout-color1: var(--md-grey-900); --jp-layout-color2: var(--md-grey-800); --jp-layout-color3: var(--md-grey-700); --jp-layout-color4: var(--md-grey-600); /* Inverse Layout * * The following are the inverse layout colors use in JupyterLab. In a light * theme these would go from dark to light. */ --jp-inverse-layout-color0: white; --jp-inverse-layout-color1: white; --jp-inverse-layout-color2: var(--md-grey-200); --jp-inverse-layout-color3: var(--md-grey-400); --jp-inverse-layout-color4: var(--md-grey-600); /* Brand/accent */ --jp-brand-color0: var(--md-blue-700); --jp-brand-color1: var(--md-blue-500); --jp-brand-color2: var(--md-blue-300); --jp-brand-color3: var(--md-blue-100); --jp-brand-color4: var(--md-blue-50); --jp-accent-color0: var(--md-green-700); --jp-accent-color1: var(--md-green-500); --jp-accent-color2: var(--md-green-300); --jp-accent-color3: var(--md-green-100); /* State colors (warn, error, success, info) */ --jp-warn-color0: var(--md-orange-700); --jp-warn-color1: var(--md-orange-500); --jp-warn-color2: var(--md-orange-300); --jp-warn-color3: var(--md-orange-100); --jp-error-color0: var(--md-red-700); --jp-error-color1: var(--md-red-500); --jp-error-color2: var(--md-red-300); --jp-error-color3: var(--md-red-100); --jp-success-color0: var(--md-green-700); --jp-success-color1: var(--md-green-500); --jp-success-color2: var(--md-green-300); --jp-success-color3: var(--md-green-100); --jp-info-color0: var(--md-cyan-700); --jp-info-color1: var(--md-cyan-500); --jp-info-color2: var(--md-cyan-300); --jp-info-color3: var(--md-cyan-100); /* Cell specific styles */ --jp-cell-padding: 5px; --jp-cell-collapser-width: 8px; --jp-cell-collapser-min-height: 20px; --jp-cell-collapser-not-active-hover-opacity: 0.6; --jp-cell-editor-background: var(--jp-layout-color1); --jp-cell-editor-border-color: var(--md-grey-700); --jp-cell-editor-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-cell-editor-active-background: var(--jp-layout-color0); --jp-cell-editor-active-border-color: var(--jp-brand-color1); --jp-cell-prompt-width: 64px; --jp-cell-prompt-font-family: var(--jp-code-font-family-default); --jp-cell-prompt-letter-spacing: 0px; --jp-cell-prompt-opacity: 1; --jp-cell-prompt-not-active-opacity: 1; --jp-cell-prompt-not-active-font-color: var(--md-grey-300); /* A custom blend of MD grey and blue 600 * See https://meyerweb.com/eric/tools/color-blend/#546E7A:1E88E5:5:hex */ --jp-cell-inprompt-font-color: #307fc1; /* A custom blend of MD grey and orange 600 * https://meyerweb.com/eric/tools/color-blend/#546E7A:F4511E:5:hex */ --jp-cell-outprompt-font-color: #bf5b3d; /* Notebook specific styles */ --jp-notebook-padding: 10px; --jp-notebook-select-background: var(--jp-layout-color1); --jp-notebook-multiselected-color: rgba(33, 150, 243, 0.24); /* The scroll padding is calculated to fill enough space at the bottom of the notebook to show one single-line cell (with appropriate padding) at the top when the notebook is scrolled all the way to the bottom. We also subtract one pixel so that no scrollbar appears if we have just one single-line cell in the notebook. This padding is to enable a 'scroll past end' feature in a notebook. */ --jp-notebook-scroll-padding: calc( 100% - var(--jp-code-font-size) * var(--jp-code-line-height) - var(--jp-code-padding) - var(--jp-cell-padding) - 1px ); /* Rendermime styles */ --jp-rendermime-error-background: rgba(244, 67, 54, 0.28); --jp-rendermime-table-row-background: var(--md-grey-900); --jp-rendermime-table-row-hover-background: rgba(3, 169, 244, 0.2); /* Dialog specific styles */ --jp-dialog-background: rgba(0, 0, 0, 0.6); /* Console specific styles */ --jp-console-padding: 10px; /* Toolbar specific styles */ --jp-toolbar-border-color: var(--jp-border-color2); --jp-toolbar-micro-height: 8px; --jp-toolbar-background: var(--jp-layout-color1); --jp-toolbar-box-shadow: 0px 0px 2px 0px rgba(0, 0, 0, 0.8); --jp-toolbar-header-margin: 4px 4px 0px 4px; --jp-toolbar-active-background: var(--jp-layout-color0); /* Statusbar specific styles */ --jp-statusbar-height: 24px; /* Input field styles */ --jp-input-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-input-active-background: var(--jp-layout-color0); --jp-input-hover-background: var(--jp-layout-color2); --jp-input-background: var(--md-grey-800); --jp-input-border-color: var(--jp-border-color1); --jp-input-active-border-color: var(--jp-brand-color1); --jp-input-active-box-shadow-color: rgba(19, 124, 189, 0.3); /* General editor styles */ --jp-editor-selected-background: var(--jp-layout-color2); --jp-editor-selected-focused-background: rgba(33, 150, 243, 0.24); --jp-editor-cursor-color: var(--jp-ui-font-color0); /* Code mirror specific styles */ --jp-mirror-editor-keyword-color: var(--md-green-500); --jp-mirror-editor-atom-color: var(--md-blue-300); --jp-mirror-editor-number-color: var(--md-green-400); --jp-mirror-editor-def-color: var(--md-blue-600); --jp-mirror-editor-variable-color: var(--md-grey-300); --jp-mirror-editor-variable-2-color: var(--md-blue-400); --jp-mirror-editor-variable-3-color: var(--md-green-600); --jp-mirror-editor-punctuation-color: var(--md-blue-400); --jp-mirror-editor-property-color: var(--md-blue-400); --jp-mirror-editor-operator-color: #aa22ff; --jp-mirror-editor-comment-color: #408080; --jp-mirror-editor-string-color: #ff7070; --jp-mirror-editor-string-2-color: var(--md-purple-300); --jp-mirror-editor-meta-color: #aa22ff; --jp-mirror-editor-qualifier-color: #555; --jp-mirror-editor-builtin-color: var(--md-green-600); --jp-mirror-editor-bracket-color: #997; --jp-mirror-editor-tag-color: var(--md-green-700); --jp-mirror-editor-attribute-color: var(--md-blue-700); --jp-mirror-editor-header-color: var(--md-blue-500); --jp-mirror-editor-quote-color: var(--md-green-300); --jp-mirror-editor-link-color: var(--md-blue-700); --jp-mirror-editor-error-color: #f00; --jp-mirror-editor-hr-color: #999; /* Vega extension styles */ --jp-vega-background: var(--md-grey-400); /* Sidebar-related styles */ --jp-sidebar-min-width: 250px; /* Search-related styles */ --jp-search-toggle-off-opacity: 0.6; --jp-search-toggle-hover-opacity: 0.8; --jp-search-toggle-on-opacity: 1; --jp-search-selected-match-background-color: rgb(255, 225, 0); --jp-search-selected-match-color: black; --jp-search-unselected-match-background-color: var( --jp-inverse-layout-color0 ); --jp-search-unselected-match-color: var(--jp-ui-inverse-font-color0); /* scrollbar related styles. Supports every browser except Edge. */ /* colors based on JetBrain's Darcula theme */ --jp-scrollbar-background-color: #3f4244; --jp-scrollbar-thumb-color: 88, 96, 97; /* need to specify thumb color as an RGB triplet */ --jp-scrollbar-endpad: 3px; /* the minimum gap between the thumb and the ends of a scrollbar */ /* hacks for setting the thumb shape. These do nothing in Firefox */ --jp-scrollbar-thumb-margin: 3.5px; /* the space in between the sides of the thumb and the track */ --jp-scrollbar-thumb-radius: 9px; /* set to a large-ish value for rounded endcaps on the thumb */ /* Icon colors that work well with light or dark backgrounds */ --jp-icon-contrast-color0: var(--md-purple-600); --jp-icon-contrast-color1: var(--md-green-600); --jp-icon-contrast-color2: var(--md-pink-600); --jp-icon-contrast-color3: var(--md-blue-600); } :root{--md-red-50: #ffebee;--md-red-100: #ffcdd2;--md-red-200: #ef9a9a;--md-red-300: #e57373;--md-red-400: #ef5350;--md-red-500: #f44336;--md-red-600: #e53935;--md-red-700: #d32f2f;--md-red-800: #c62828;--md-red-900: #b71c1c;--md-red-A100: #ff8a80;--md-red-A200: #ff5252;--md-red-A400: #ff1744;--md-red-A700: #d50000;--md-pink-50: #fce4ec;--md-pink-100: #f8bbd0;--md-pink-200: #f48fb1;--md-pink-300: #f06292;--md-pink-400: #ec407a;--md-pink-500: #e91e63;--md-pink-600: #d81b60;--md-pink-700: #c2185b;--md-pink-800: 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#7986cb;--md-indigo-400: #5c6bc0;--md-indigo-500: #3f51b5;--md-indigo-600: #3949ab;--md-indigo-700: #303f9f;--md-indigo-800: #283593;--md-indigo-900: #1a237e;--md-indigo-A100: #8c9eff;--md-indigo-A200: #536dfe;--md-indigo-A400: #3d5afe;--md-indigo-A700: #304ffe;--md-blue-50: #e3f2fd;--md-blue-100: #bbdefb;--md-blue-200: #90caf9;--md-blue-300: #64b5f6;--md-blue-400: #42a5f5;--md-blue-500: #2196f3;--md-blue-600: #1e88e5;--md-blue-700: #1976d2;--md-blue-800: #1565c0;--md-blue-900: #0d47a1;--md-blue-A100: #82b1ff;--md-blue-A200: #448aff;--md-blue-A400: #2979ff;--md-blue-A700: #2962ff;--md-light-blue-50: #e1f5fe;--md-light-blue-100: #b3e5fc;--md-light-blue-200: #81d4fa;--md-light-blue-300: #4fc3f7;--md-light-blue-400: #29b6f6;--md-light-blue-500: #03a9f4;--md-light-blue-600: #039be5;--md-light-blue-700: #0288d1;--md-light-blue-800: #0277bd;--md-light-blue-900: #01579b;--md-light-blue-A100: #80d8ff;--md-light-blue-A200: #40c4ff;--md-light-blue-A400: #00b0ff;--md-light-blue-A700: #0091ea;--md-cyan-50: #e0f7fa;--md-cyan-100: #b2ebf2;--md-cyan-200: #80deea;--md-cyan-300: #4dd0e1;--md-cyan-400: #26c6da;--md-cyan-500: #00bcd4;--md-cyan-600: #00acc1;--md-cyan-700: #0097a7;--md-cyan-800: #00838f;--md-cyan-900: #006064;--md-cyan-A100: #84ffff;--md-cyan-A200: #18ffff;--md-cyan-A400: #00e5ff;--md-cyan-A700: #00b8d4;--md-teal-50: #e0f2f1;--md-teal-100: #b2dfdb;--md-teal-200: #80cbc4;--md-teal-300: #4db6ac;--md-teal-400: #26a69a;--md-teal-500: #009688;--md-teal-600: #00897b;--md-teal-700: #00796b;--md-teal-800: #00695c;--md-teal-900: #004d40;--md-teal-A100: #a7ffeb;--md-teal-A200: #64ffda;--md-teal-A400: #1de9b6;--md-teal-A700: #00bfa5;--md-green-50: #e8f5e9;--md-green-100: #c8e6c9;--md-green-200: #a5d6a7;--md-green-300: #81c784;--md-green-400: #66bb6a;--md-green-500: #4caf50;--md-green-600: #43a047;--md-green-700: #388e3c;--md-green-800: #2e7d32;--md-green-900: #1b5e20;--md-green-A100: #b9f6ca;--md-green-A200: #69f0ae;--md-green-A400: #00e676;--md-green-A700: #00c853;--md-light-green-50: #f1f8e9;--md-light-green-100: #dcedc8;--md-light-green-200: #c5e1a5;--md-light-green-300: #aed581;--md-light-green-400: #9ccc65;--md-light-green-500: #8bc34a;--md-light-green-600: #7cb342;--md-light-green-700: #689f38;--md-light-green-800: #558b2f;--md-light-green-900: #33691e;--md-light-green-A100: #ccff90;--md-light-green-A200: #b2ff59;--md-light-green-A400: #76ff03;--md-light-green-A700: #64dd17;--md-lime-50: #f9fbe7;--md-lime-100: #f0f4c3;--md-lime-200: #e6ee9c;--md-lime-300: #dce775;--md-lime-400: #d4e157;--md-lime-500: #cddc39;--md-lime-600: #c0ca33;--md-lime-700: #afb42b;--md-lime-800: #9e9d24;--md-lime-900: #827717;--md-lime-A100: #f4ff81;--md-lime-A200: #eeff41;--md-lime-A400: #c6ff00;--md-lime-A700: #aeea00;--md-yellow-50: #fffde7;--md-yellow-100: #fff9c4;--md-yellow-200: #fff59d;--md-yellow-300: #fff176;--md-yellow-400: #ffee58;--md-yellow-500: #ffeb3b;--md-yellow-600: #fdd835;--md-yellow-700: #fbc02d;--md-yellow-800: #f9a825;--md-yellow-900: #f57f17;--md-yellow-A100: #ffff8d;--md-yellow-A200: #ffff00;--md-yellow-A400: #ffea00;--md-yellow-A700: #ffd600;--md-amber-50: #fff8e1;--md-amber-100: #ffecb3;--md-amber-200: #ffe082;--md-amber-300: #ffd54f;--md-amber-400: #ffca28;--md-amber-500: #ffc107;--md-amber-600: #ffb300;--md-amber-700: #ffa000;--md-amber-800: #ff8f00;--md-amber-900: #ff6f00;--md-amber-A100: #ffe57f;--md-amber-A200: #ffd740;--md-amber-A400: #ffc400;--md-amber-A700: #ffab00;--md-orange-50: #fff3e0;--md-orange-100: #ffe0b2;--md-orange-200: #ffcc80;--md-orange-300: #ffb74d;--md-orange-400: #ffa726;--md-orange-500: #ff9800;--md-orange-600: #fb8c00;--md-orange-700: #f57c00;--md-orange-800: #ef6c00;--md-orange-900: #e65100;--md-orange-A100: #ffd180;--md-orange-A200: #ffab40;--md-orange-A400: #ff9100;--md-orange-A700: #ff6d00;--md-deep-orange-50: #fbe9e7;--md-deep-orange-100: #ffccbc;--md-deep-orange-200: #ffab91;--md-deep-orange-300: #ff8a65;--md-deep-orange-400: #ff7043;--md-deep-orange-500: 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sourceMappingURL=mkdocs-jupyter.css.map*/ init_mathjax = function() { if (window.MathJax) { // MathJax loaded MathJax.Hub.Config({ TeX: { equationNumbers: { autoNumber: \"AMS\", useLabelIds: true } }, tex2jax: { inlineMath: [ ['$','$'], [\"\\\\(\",\"\\\\)\"] ], displayMath: [ ['$$','$$'], [\"\\\\[\",\"\\\\]\"] ], processEscapes: true, processEnvironments: true }, displayAlign: 'center', CommonHTML: { linebreaks: { automatic: true } } }); MathJax.Hub.Queue([\"Typeset\", MathJax.Hub]); } } init_mathjax(); Explore data and feasibility of approach \u00b6 We had asked our clients and in-house experts to annotate sentences using a rigorous guideline. The aim is to decide on which sentences they would like to see in a summary for a paper. The results are in JSON format, each annotator has a separate file. Let's load them. In [1]: Copied! from pathlib import Path import json annotations = [] for p in Path ( \"data\" ) . glob ( \"*.json\" ): with open ( p , encoding = \"utf-8\" ) as f : print ( p ) annotations . append ( json . load ( f )) evaluations = { sentence : [ annotation [ sentence ] for annotation in annotations if sentence in annotation ] for sentence in { sentence for annotation in annotations for sentence in annotation . keys () } } X = [ s for s in evaluations . keys ()] y = [ int ( sum ( e ) > 0 ) for e in evaluations . values ()] from pathlib import Path import json annotations = [] for p in Path(\"data\").glob(\"*.json\"): with open(p, encoding=\"utf-8\") as f: print(p) annotations.append(json.load(f)) evaluations = { sentence: [ annotation[sentence] for annotation in annotations if sentence in annotation ] for sentence in { sentence for annotation in annotations for sentence in annotation.keys() } } X = [s for s in evaluations.keys()] y = [int(sum(e) > 0) for e in evaluations.values()] data/evaluation-experiment-2-stage #1-sa6a0y.json data/evaluation-experiment-2-stage #1-2m6dmb.json Save the compiled and processed data for later use using LargeFileS3. In [2]: Copied! from great_ai.large_file import LargeFileS3 import json LargeFileS3 . configure_credentials_from_file ( \"config.ini\" ) with LargeFileS3 ( \"summary-train-dataset-small\" , \"w\" , encoding = \"utf-8\" ) as f : json . dump (( X , y ), f ) from great_ai.large_file import LargeFileS3 import json LargeFileS3.configure_credentials_from_file(\"config.ini\") with LargeFileS3(\"summary-train-dataset-small\", \"w\", encoding=\"utf-8\") as f: json.dump((X, y), f) Copying file for summary-train-dataset-small-0 Compressing summary-train-dataset-small-0 Uploading summary-train-dataset-small-0 to S3 as summary-train-dataset-small/0 Uploading summary-train-dataset-small-0.tar.gz 0.04/0.04 MB (100.0%) Filter out sentences which don't have enough annotations. In [3]: Copied! y1 = [ e [ 0 ] for e in evaluations . values () if len ( e ) == 2 ] y2 = [ e [ 1 ] for e in evaluations . values () if len ( e ) == 2 ] y1 = [e[0] for e in evaluations.values() if len(e) == 2] y2 = [e[1] for e in evaluations.values() if len(e) == 2] Calculate Cohen's kappa . It's a bit low but the task itself is pretty subjective so it's not all that surprising. In [4]: Copied! import sklearn.metrics sklearn . metrics . cohen_kappa_score ( y1 , y2 ) import sklearn.metrics sklearn.metrics.cohen_kappa_score(y1, y2) Out[4]: 0.3546448712421808 Can we train anything on this data? Let's try with a trivial SVM. In [5]: Copied! X = [ s for s in evaluations . keys ()] y = [ int ( sum ( e ) > 0 ) for e in evaluations . values ()] X = [s for s in evaluations.keys()] y = [int(sum(e) > 0) for e in evaluations.values()] In [6]: Copied! from sklearn.model_selection import train_test_split from sklearn.svm import LinearSVC from sklearn.pipeline import Pipeline from sklearn.feature_extraction.text import TfidfVectorizer from sklearn.model_selection import cross_val_score model = Pipeline ( steps = [ ( \"vectorizer\" , TfidfVectorizer ( sublinear_tf = True , min_df = 3 , max_df = 0.3 )), ( \"classifier\" , LinearSVC ()), ] ) # baseline model cross_val_score ( model , X , y , cv = 5 ) from sklearn.model_selection import train_test_split from sklearn.svm import LinearSVC from sklearn.pipeline import Pipeline from sklearn.feature_extraction.text import TfidfVectorizer from sklearn.model_selection import cross_val_score model = Pipeline( steps=[ (\"vectorizer\", TfidfVectorizer(sublinear_tf=True, min_df=3, max_df=0.3)), (\"classifier\", LinearSVC()), ] ) # baseline model cross_val_score(model, X, y, cv=5) Out[6]: array([0.79, 0.75, 0.77, 0.69, 0.77]) The cross-validation shows promising accuracies. But accuracy isn't everything, therefore, we should investigate the accuracy metrics. In [7]: Copied! X_train , X_test , y_train , y_test = train_test_split ( X , y , test_size = 0.2 , random_state = 0 ) model . fit ( X_train , y_train ) X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0) model.fit(X_train, y_train) Out[7]: #sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"\u25b8\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"\u25be\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;} Pipeline(steps=[('vectorizer', TfidfVectorizer(max_df=0.3, min_df=3, sublinear_tf=True)), ('classifier', LinearSVC())]) In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org. Pipeline Pipeline(steps=[('vectorizer', TfidfVectorizer(max_df=0.3, min_df=3, sublinear_tf=True)), ('classifier', LinearSVC())]) TfidfVectorizer TfidfVectorizer(max_df=0.3, min_df=3, sublinear_tf=True) LinearSVC LinearSVC() In [8]: Copied! y_predicted = model . predict ( X_test ) print ( sklearn . metrics . classification_report ( [ y > 0 for y in y_test ], [ y > 0 for y in y_predicted ] ) ) sklearn . metrics . ConfusionMatrixDisplay . from_predictions ( [ y > 0 for y in y_test ], [ y > 0 for y in y_predicted ], xticks_rotation = \"vertical\" , values_format = \".2f\" , ) None y_predicted = model.predict(X_test) print( sklearn.metrics.classification_report( [y > 0 for y in y_test], [y > 0 for y in y_predicted] ) ) sklearn.metrics.ConfusionMatrixDisplay.from_predictions( [y > 0 for y in y_test], [y > 0 for y in y_predicted], xticks_rotation=\"vertical\", values_format=\".2f\", ) None precision recall f1-score support False 0.78 0.78 0.78 51 True 0.78 0.78 0.78 49 accuracy 0.78 100 macro avg 0.78 0.78 0.78 100 weighted avg 0.78 0.78 0.78 100 We get an F1-score of 0.78 without any hyperparameter-optimisation; this task may be feasible to solve with AI. Next: Part 2","title":"Explore data and feasibility of approach"},{"location":"examples/scibert/data/#explore-data-and-feasibility-of-approach","text":"We had asked our clients and in-house experts to annotate sentences using a rigorous guideline. The aim is to decide on which sentences they would like to see in a summary for a paper. The results are in JSON format, each annotator has a separate file. Let's load them. In [1]: Copied! from pathlib import Path import json annotations = [] for p in Path ( \"data\" ) . glob ( \"*.json\" ): with open ( p , encoding = \"utf-8\" ) as f : print ( p ) annotations . append ( json . load ( f )) evaluations = { sentence : [ annotation [ sentence ] for annotation in annotations if sentence in annotation ] for sentence in { sentence for annotation in annotations for sentence in annotation . keys () } } X = [ s for s in evaluations . keys ()] y = [ int ( sum ( e ) > 0 ) for e in evaluations . values ()] from pathlib import Path import json annotations = [] for p in Path(\"data\").glob(\"*.json\"): with open(p, encoding=\"utf-8\") as f: print(p) annotations.append(json.load(f)) evaluations = { sentence: [ annotation[sentence] for annotation in annotations if sentence in annotation ] for sentence in { sentence for annotation in annotations for sentence in annotation.keys() } } X = [s for s in evaluations.keys()] y = [int(sum(e) > 0) for e in evaluations.values()] data/evaluation-experiment-2-stage #1-sa6a0y.json data/evaluation-experiment-2-stage #1-2m6dmb.json Save the compiled and processed data for later use using LargeFileS3. In [2]: Copied! from great_ai.large_file import LargeFileS3 import json LargeFileS3 . configure_credentials_from_file ( \"config.ini\" ) with LargeFileS3 ( \"summary-train-dataset-small\" , \"w\" , encoding = \"utf-8\" ) as f : json . dump (( X , y ), f ) from great_ai.large_file import LargeFileS3 import json LargeFileS3.configure_credentials_from_file(\"config.ini\") with LargeFileS3(\"summary-train-dataset-small\", \"w\", encoding=\"utf-8\") as f: json.dump((X, y), f) Copying file for summary-train-dataset-small-0 Compressing summary-train-dataset-small-0 Uploading summary-train-dataset-small-0 to S3 as summary-train-dataset-small/0 Uploading summary-train-dataset-small-0.tar.gz 0.04/0.04 MB (100.0%) Filter out sentences which don't have enough annotations. In [3]: Copied! y1 = [ e [ 0 ] for e in evaluations . values () if len ( e ) == 2 ] y2 = [ e [ 1 ] for e in evaluations . values () if len ( e ) == 2 ] y1 = [e[0] for e in evaluations.values() if len(e) == 2] y2 = [e[1] for e in evaluations.values() if len(e) == 2] Calculate Cohen's kappa . It's a bit low but the task itself is pretty subjective so it's not all that surprising. In [4]: Copied! import sklearn.metrics sklearn . metrics . cohen_kappa_score ( y1 , y2 ) import sklearn.metrics sklearn.metrics.cohen_kappa_score(y1, y2) Out[4]: 0.3546448712421808 Can we train anything on this data? Let's try with a trivial SVM. In [5]: Copied! X = [ s for s in evaluations . keys ()] y = [ int ( sum ( e ) > 0 ) for e in evaluations . values ()] X = [s for s in evaluations.keys()] y = [int(sum(e) > 0) for e in evaluations.values()] In [6]: Copied! from sklearn.model_selection import train_test_split from sklearn.svm import LinearSVC from sklearn.pipeline import Pipeline from sklearn.feature_extraction.text import TfidfVectorizer from sklearn.model_selection import cross_val_score model = Pipeline ( steps = [ ( \"vectorizer\" , TfidfVectorizer ( sublinear_tf = True , min_df = 3 , max_df = 0.3 )), ( \"classifier\" , LinearSVC ()), ] ) # baseline model cross_val_score ( model , X , y , cv = 5 ) from sklearn.model_selection import train_test_split from sklearn.svm import LinearSVC from sklearn.pipeline import Pipeline from sklearn.feature_extraction.text import TfidfVectorizer from sklearn.model_selection import cross_val_score model = Pipeline( steps=[ (\"vectorizer\", TfidfVectorizer(sublinear_tf=True, min_df=3, max_df=0.3)), (\"classifier\", LinearSVC()), ] ) # baseline model cross_val_score(model, X, y, cv=5) Out[6]: array([0.79, 0.75, 0.77, 0.69, 0.77]) The cross-validation shows promising accuracies. But accuracy isn't everything, therefore, we should investigate the accuracy metrics. In [7]: Copied! X_train , X_test , y_train , y_test = train_test_split ( X , y , test_size = 0.2 , random_state = 0 ) model . fit ( X_train , y_train ) X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0) model.fit(X_train, y_train) Out[7]: #sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"\u25b8\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"\u25be\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;} Pipeline(steps=[('vectorizer', TfidfVectorizer(max_df=0.3, min_df=3, sublinear_tf=True)), ('classifier', LinearSVC())]) In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org. Pipeline Pipeline(steps=[('vectorizer', TfidfVectorizer(max_df=0.3, min_df=3, sublinear_tf=True)), ('classifier', LinearSVC())]) TfidfVectorizer TfidfVectorizer(max_df=0.3, min_df=3, sublinear_tf=True) LinearSVC LinearSVC() In [8]: Copied! y_predicted = model . predict ( X_test ) print ( sklearn . metrics . classification_report ( [ y > 0 for y in y_test ], [ y > 0 for y in y_predicted ] ) ) sklearn . metrics . ConfusionMatrixDisplay . from_predictions ( [ y > 0 for y in y_test ], [ y > 0 for y in y_predicted ], xticks_rotation = \"vertical\" , values_format = \".2f\" , ) None y_predicted = model.predict(X_test) print( sklearn.metrics.classification_report( [y > 0 for y in y_test], [y > 0 for y in y_predicted] ) ) sklearn.metrics.ConfusionMatrixDisplay.from_predictions( [y > 0 for y in y_test], [y > 0 for y in y_predicted], xticks_rotation=\"vertical\", values_format=\".2f\", ) None precision recall f1-score support False 0.78 0.78 0.78 51 True 0.78 0.78 0.78 49 accuracy 0.78 100 macro avg 0.78 0.78 0.78 100 weighted avg 0.78 0.78 0.78 100 We get an F1-score of 0.78 without any hyperparameter-optimisation; this task may be feasible to solve with AI. Next: Part 2","title":"Explore data and feasibility of approach"},{"location":"examples/scibert/deploy/","text":"(function (global, factory) { typeof exports === 'object' && typeof module !== 'undefined' ? module.exports = factory() : typeof define === 'function' && define.amd ? define(factory) : (global = global || self, global.ClipboardCopyElement = factory()); }(this, function () { 'use strict'; function createNode(text) { const node = document.createElement('pre'); node.style.width = '1px'; node.style.height = '1px'; node.style.position = 'fixed'; node.style.top = '5px'; node.textContent = text; return node; } function copyNode(node) { if ('clipboard' in navigator) { // eslint-disable-next-line flowtype/no-flow-fix-me-comments // $FlowFixMe Clipboard is not defined in Flow yet. return navigator.clipboard.writeText(node.textContent); } const selection = getSelection(); if (selection == null) { return Promise.reject(new Error()); } selection.removeAllRanges(); const range = document.createRange(); 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background-color: #ffffc0; padding-left: 5px; padding-right: 5px; } span.linenos.special { color: #000000; background-color: #ffffc0; padding-left: 5px; padding-right: 5px; } .highlight-ipynb .hll { background-color: var(--jp-cell-editor-active-background) } .highlight-ipynb { background: var(--jp-cell-editor-background); color: var(--jp-mirror-editor-variable-color) } .highlight-ipynb .c { color: var(--jp-mirror-editor-comment-color); font-style: italic } /* Comment */ .highlight-ipynb .err { color: var(--jp-mirror-editor-error-color) } /* Error */ .highlight-ipynb .k { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword */ .highlight-ipynb .o { color: var(--jp-mirror-editor-operator-color); font-weight: bold } /* Operator */ .highlight-ipynb .p { color: var(--jp-mirror-editor-punctuation-color) } /* Punctuation */ .highlight-ipynb .ch { color: var(--jp-mirror-editor-comment-color); font-style: italic } /* Comment.Hashbang */ .highlight-ipynb .cm { color: var(--jp-mirror-editor-comment-color); font-style: italic } /* Comment.Multiline */ .highlight-ipynb .cp { color: var(--jp-mirror-editor-comment-color); font-style: italic } /* Comment.Preproc */ .highlight-ipynb .cpf { color: var(--jp-mirror-editor-comment-color); font-style: italic } /* Comment.PreprocFile */ .highlight-ipynb .c1 { color: var(--jp-mirror-editor-comment-color); font-style: italic } /* Comment.Single */ .highlight-ipynb .cs { color: var(--jp-mirror-editor-comment-color); font-style: italic } /* Comment.Special */ .highlight-ipynb .kc { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword.Constant */ .highlight-ipynb .kd { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword.Declaration */ .highlight-ipynb .kn { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword.Namespace */ .highlight-ipynb .kp { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword.Pseudo */ .highlight-ipynb .kr { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword.Reserved */ .highlight-ipynb .kt { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword.Type */ .highlight-ipynb .m { color: var(--jp-mirror-editor-number-color) } /* Literal.Number */ .highlight-ipynb .s { color: var(--jp-mirror-editor-string-color) } /* Literal.String */ .highlight-ipynb .ow { color: var(--jp-mirror-editor-operator-color); font-weight: bold } /* Operator.Word */ .highlight-ipynb .w { color: var(--jp-mirror-editor-variable-color) } /* Text.Whitespace */ .highlight-ipynb .mb { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Bin */ .highlight-ipynb .mf { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Float */ .highlight-ipynb .mh { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Hex */ .highlight-ipynb .mi { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Integer */ .highlight-ipynb .mo { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Oct */ .highlight-ipynb .sa { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Affix */ .highlight-ipynb .sb { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Backtick */ .highlight-ipynb .sc { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Char */ .highlight-ipynb .dl { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Delimiter */ .highlight-ipynb .sd { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Doc */ .highlight-ipynb .s2 { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Double */ .highlight-ipynb .se { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Escape */ .highlight-ipynb .sh { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Heredoc */ .highlight-ipynb .si { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Interpol */ .highlight-ipynb .sx { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Other */ .highlight-ipynb .sr { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Regex */ .highlight-ipynb .s1 { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Single */ .highlight-ipynb .ss { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Symbol */ .highlight-ipynb .il { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Integer.Long */ /* This file is taken from the built JupyterLab theme.css Found on share/nbconvert/templates/lab/static Some changes have been made and marked with CHANGE */ .jupyter-wrapper { /* Elevation * * We style box-shadows using Material Design's idea of elevation. These particular numbers are taken from here: * * https://github.com/material-components/material-components-web * https://material-components-web.appspot.com/elevation.html */ --jp-shadow-base-lightness: 0; --jp-shadow-umbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.2 ); --jp-shadow-penumbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.14 ); --jp-shadow-ambient-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.12 ); --jp-elevation-z0: none; --jp-elevation-z1: 0px 2px 1px -1px var(--jp-shadow-umbra-color), 0px 1px 1px 0px var(--jp-shadow-penumbra-color), 0px 1px 3px 0px var(--jp-shadow-ambient-color); --jp-elevation-z2: 0px 3px 1px -2px var(--jp-shadow-umbra-color), 0px 2px 2px 0px var(--jp-shadow-penumbra-color), 0px 1px 5px 0px var(--jp-shadow-ambient-color); --jp-elevation-z4: 0px 2px 4px -1px var(--jp-shadow-umbra-color), 0px 4px 5px 0px var(--jp-shadow-penumbra-color), 0px 1px 10px 0px var(--jp-shadow-ambient-color); --jp-elevation-z6: 0px 3px 5px -1px var(--jp-shadow-umbra-color), 0px 6px 10px 0px var(--jp-shadow-penumbra-color), 0px 1px 18px 0px var(--jp-shadow-ambient-color); --jp-elevation-z8: 0px 5px 5px -3px var(--jp-shadow-umbra-color), 0px 8px 10px 1px var(--jp-shadow-penumbra-color), 0px 3px 14px 2px var(--jp-shadow-ambient-color); --jp-elevation-z12: 0px 7px 8px -4px var(--jp-shadow-umbra-color), 0px 12px 17px 2px var(--jp-shadow-penumbra-color), 0px 5px 22px 4px var(--jp-shadow-ambient-color); --jp-elevation-z16: 0px 8px 10px -5px var(--jp-shadow-umbra-color), 0px 16px 24px 2px var(--jp-shadow-penumbra-color), 0px 6px 30px 5px var(--jp-shadow-ambient-color); --jp-elevation-z20: 0px 10px 13px -6px var(--jp-shadow-umbra-color), 0px 20px 31px 3px var(--jp-shadow-penumbra-color), 0px 8px 38px 7px var(--jp-shadow-ambient-color); --jp-elevation-z24: 0px 11px 15px -7px var(--jp-shadow-umbra-color), 0px 24px 38px 3px var(--jp-shadow-penumbra-color), 0px 9px 46px 8px var(--jp-shadow-ambient-color); /* Borders * * The following variables, specify the visual styling of borders in JupyterLab. */ --jp-border-width: 1px; --jp-border-color0: var(--md-grey-400); --jp-border-color1: var(--md-grey-400); --jp-border-color2: var(--md-grey-300); --jp-border-color3: var(--md-grey-200); --jp-border-radius: 2px; /* UI Fonts * * The UI font CSS variables are used for the typography all of the JupyterLab * user interface elements that are not directly user generated content. * * The font sizing here is done assuming that the body font size of --jp-ui-font-size1 * is applied to a parent element. When children elements, such as headings, are sized * in em all things will be computed relative to that body size. */ --jp-ui-font-scale-factor: 1.2; --jp-ui-font-size0: 0.83333em; --jp-ui-font-size1: 13px; /* Base font size */ --jp-ui-font-size2: 1.2em; --jp-ui-font-size3: 1.44em; --jp-ui-font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Helvetica, Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\"; /* * Use these font colors against the corresponding main layout colors. * In a light theme, these go from dark to light. */ /* Defaults use Material Design specification */ --jp-ui-font-color0: rgba(0, 0, 0, 1); --jp-ui-font-color1: rgba(0, 0, 0, 0.87); --jp-ui-font-color2: rgba(0, 0, 0, 0.54); --jp-ui-font-color3: rgba(0, 0, 0, 0.38); /* * Use these against the brand/accent/warn/error colors. * These will typically go from light to darker, in both a dark and light theme. */ --jp-ui-inverse-font-color0: rgba(255, 255, 255, 1); --jp-ui-inverse-font-color1: rgba(255, 255, 255, 1); --jp-ui-inverse-font-color2: rgba(255, 255, 255, 0.7); --jp-ui-inverse-font-color3: rgba(255, 255, 255, 0.5); /* Content Fonts * * Content font variables are used for typography of user generated content. * * The font sizing here is done assuming that the body font size of --jp-content-font-size1 * is applied to a parent element. When children elements, such as headings, are sized * in em all things will be computed relative to that body size. */ --jp-content-line-height: 1.6; --jp-content-font-scale-factor: 1.2; --jp-content-font-size0: 0.83333em; --jp-content-font-size1: 14px; /* Base font size */ --jp-content-font-size2: 1.2em; --jp-content-font-size3: 1.44em; --jp-content-font-size4: 1.728em; --jp-content-font-size5: 2.0736em; /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-content-presentation-font-size1: 17px; --jp-content-heading-line-height: 1; --jp-content-heading-margin-top: 1.2em; --jp-content-heading-margin-bottom: 0.8em; --jp-content-heading-font-weight: 500; /* Defaults use Material Design specification */ --jp-content-font-color0: rgba(0, 0, 0, 1); --jp-content-font-color1: rgba(0, 0, 0, 0.87); --jp-content-font-color2: rgba(0, 0, 0, 0.54); --jp-content-font-color3: rgba(0, 0, 0, 0.38); --jp-content-link-color: var(--md-blue-700); --jp-content-font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Helvetica, Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\"; /* * Code Fonts * * Code font variables are used for typography of code and other monospaces content. */ --jp-code-font-size: 13px; --jp-code-line-height: 1.3077; /* 17px for 13px base */ --jp-code-padding: 5px; /* 5px for 13px base, codemirror highlighting needs integer px value */ --jp-code-font-family-default: Menlo, Consolas, \"DejaVu Sans Mono\", monospace; --jp-code-font-family: var(--jp-code-font-family-default); /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-code-presentation-font-size: 16px; /* may need to tweak cursor width if you change font size */ --jp-code-cursor-width0: 1.4px; --jp-code-cursor-width1: 2px; --jp-code-cursor-width2: 4px; /* Layout * * The following are the main layout colors use in JupyterLab. In a light * theme these would go from light to dark. */ --jp-layout-color0: white; --jp-layout-color1: white; --jp-layout-color2: var(--md-grey-200); --jp-layout-color3: var(--md-grey-400); --jp-layout-color4: var(--md-grey-600); /* Inverse Layout * * The following are the inverse layout colors use in JupyterLab. In a light * theme these would go from dark to light. */ --jp-inverse-layout-color0: #111111; --jp-inverse-layout-color1: var(--md-grey-900); --jp-inverse-layout-color2: var(--md-grey-800); --jp-inverse-layout-color3: var(--md-grey-700); --jp-inverse-layout-color4: var(--md-grey-600); /* Brand/accent */ --jp-brand-color0: var(--md-blue-900); --jp-brand-color1: var(--md-blue-700); --jp-brand-color2: var(--md-blue-300); --jp-brand-color3: var(--md-blue-100); --jp-brand-color4: var(--md-blue-50); --jp-accent-color0: var(--md-green-900); --jp-accent-color1: var(--md-green-700); --jp-accent-color2: var(--md-green-300); --jp-accent-color3: var(--md-green-100); /* State colors (warn, error, success, info) */ --jp-warn-color0: var(--md-orange-900); --jp-warn-color1: var(--md-orange-700); --jp-warn-color2: var(--md-orange-300); --jp-warn-color3: var(--md-orange-100); --jp-error-color0: var(--md-red-900); --jp-error-color1: var(--md-red-700); --jp-error-color2: var(--md-red-300); --jp-error-color3: var(--md-red-100); --jp-success-color0: var(--md-green-900); --jp-success-color1: var(--md-green-700); --jp-success-color2: var(--md-green-300); --jp-success-color3: var(--md-green-100); --jp-info-color0: var(--md-cyan-900); --jp-info-color1: var(--md-cyan-700); --jp-info-color2: var(--md-cyan-300); --jp-info-color3: var(--md-cyan-100); /* Cell specific styles */ --jp-cell-padding: 5px; --jp-cell-collapser-width: 8px; --jp-cell-collapser-min-height: 20px; --jp-cell-collapser-not-active-hover-opacity: 0.6; --jp-cell-editor-background: var(--md-grey-100); --jp-cell-editor-border-color: var(--md-grey-300); --jp-cell-editor-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-cell-editor-active-background: var(--jp-layout-color0); --jp-cell-editor-active-border-color: var(--jp-brand-color1); --jp-cell-prompt-width: 64px; --jp-cell-prompt-font-family: var(--jp-code-font-family-default); --jp-cell-prompt-letter-spacing: 0px; --jp-cell-prompt-opacity: 1; --jp-cell-prompt-not-active-opacity: 0.5; --jp-cell-prompt-not-active-font-color: var(--md-grey-700); /* A custom blend of MD grey and blue 600 * See https://meyerweb.com/eric/tools/color-blend/#546E7A:1E88E5:5:hex */ --jp-cell-inprompt-font-color: #307fc1; /* A custom blend of MD grey and orange 600 * https://meyerweb.com/eric/tools/color-blend/#546E7A:F4511E:5:hex */ --jp-cell-outprompt-font-color: #bf5b3d; /* Notebook specific styles */ --jp-notebook-padding: 10px; --jp-notebook-select-background: var(--jp-layout-color1); --jp-notebook-multiselected-color: var(--md-blue-50); /* The scroll padding is calculated to fill enough space at the bottom of the notebook to show one single-line cell (with appropriate padding) at the top when the notebook is scrolled all the way to the bottom. We also subtract one pixel so that no scrollbar appears if we have just one single-line cell in the notebook. This padding is to enable a 'scroll past end' feature in a notebook. */ --jp-notebook-scroll-padding: calc( 100% - var(--jp-code-font-size) * var(--jp-code-line-height) - var(--jp-code-padding) - var(--jp-cell-padding) - 1px ); /* Rendermime styles */ --jp-rendermime-error-background: #fdd; --jp-rendermime-table-row-background: var(--md-grey-100); --jp-rendermime-table-row-hover-background: var(--md-light-blue-50); /* Dialog specific styles */ --jp-dialog-background: rgba(0, 0, 0, 0.25); /* Console specific styles */ --jp-console-padding: 10px; /* Toolbar specific styles */ --jp-toolbar-border-color: var(--jp-border-color1); --jp-toolbar-micro-height: 8px; --jp-toolbar-background: var(--jp-layout-color1); --jp-toolbar-box-shadow: 0px 0px 2px 0px rgba(0, 0, 0, 0.24); --jp-toolbar-header-margin: 4px 4px 0px 4px; --jp-toolbar-active-background: var(--md-grey-300); /* Statusbar specific styles */ --jp-statusbar-height: 24px; /* Input field styles */ --jp-input-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-input-active-background: var(--jp-layout-color1); --jp-input-hover-background: var(--jp-layout-color1); --jp-input-background: var(--md-grey-100); --jp-input-border-color: var(--jp-border-color1); --jp-input-active-border-color: var(--jp-brand-color1); --jp-input-active-box-shadow-color: rgba(19, 124, 189, 0.3); /* General editor styles */ --jp-editor-selected-background: #d9d9d9; --jp-editor-selected-focused-background: #d7d4f0; --jp-editor-cursor-color: var(--jp-ui-font-color0); /* Code mirror specific styles */ --jp-mirror-editor-keyword-color: #008000; --jp-mirror-editor-atom-color: #88f; --jp-mirror-editor-number-color: #080; --jp-mirror-editor-def-color: #00f; --jp-mirror-editor-variable-color: var(--md-grey-900); --jp-mirror-editor-variable-2-color: #05a; --jp-mirror-editor-variable-3-color: #085; --jp-mirror-editor-punctuation-color: #05a; --jp-mirror-editor-property-color: #05a; --jp-mirror-editor-operator-color: #aa22ff; --jp-mirror-editor-comment-color: #408080; --jp-mirror-editor-string-color: #ba2121; --jp-mirror-editor-string-2-color: #708; --jp-mirror-editor-meta-color: #aa22ff; --jp-mirror-editor-qualifier-color: #555; --jp-mirror-editor-builtin-color: #008000; --jp-mirror-editor-bracket-color: #997; --jp-mirror-editor-tag-color: #170; --jp-mirror-editor-attribute-color: #00c; --jp-mirror-editor-header-color: blue; --jp-mirror-editor-quote-color: #090; --jp-mirror-editor-link-color: #00c; --jp-mirror-editor-error-color: #f00; --jp-mirror-editor-hr-color: #999; /* Vega extension styles */ --jp-vega-background: white; /* Sidebar-related styles */ --jp-sidebar-min-width: 250px; /* Search-related styles */ --jp-search-toggle-off-opacity: 0.5; --jp-search-toggle-hover-opacity: 0.8; --jp-search-toggle-on-opacity: 1; --jp-search-selected-match-background-color: rgb(245, 200, 0); --jp-search-selected-match-color: black; --jp-search-unselected-match-background-color: var( --jp-inverse-layout-color0 ); --jp-search-unselected-match-color: var(--jp-ui-inverse-font-color0); /* Icon colors that work well with light or dark backgrounds */ --jp-icon-contrast-color0: var(--md-purple-600); --jp-icon-contrast-color1: var(--md-green-600); --jp-icon-contrast-color2: var(--md-pink-600); --jp-icon-contrast-color3: var(--md-blue-600); } [data-md-color-scheme=\"slate\"] .jupyter-wrapper { /* Elevation * * We style box-shadows using Material Design's idea of elevation. These particular numbers are taken from here: * * https://github.com/material-components/material-components-web * https://material-components-web.appspot.com/elevation.html */ /* The dark theme shadows need a bit of work, but this will probably also require work on the core layout * colors used in the theme as well. */ --jp-shadow-base-lightness: 32; --jp-shadow-umbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.2 ); --jp-shadow-penumbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.14 ); --jp-shadow-ambient-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.12 ); --jp-elevation-z0: none; --jp-elevation-z1: 0px 2px 1px -1px var(--jp-shadow-umbra-color), 0px 1px 1px 0px var(--jp-shadow-penumbra-color), 0px 1px 3px 0px var(--jp-shadow-ambient-color); --jp-elevation-z2: 0px 3px 1px -2px var(--jp-shadow-umbra-color), 0px 2px 2px 0px var(--jp-shadow-penumbra-color), 0px 1px 5px 0px var(--jp-shadow-ambient-color); --jp-elevation-z4: 0px 2px 4px -1px var(--jp-shadow-umbra-color), 0px 4px 5px 0px var(--jp-shadow-penumbra-color), 0px 1px 10px 0px var(--jp-shadow-ambient-color); --jp-elevation-z6: 0px 3px 5px -1px var(--jp-shadow-umbra-color), 0px 6px 10px 0px var(--jp-shadow-penumbra-color), 0px 1px 18px 0px var(--jp-shadow-ambient-color); --jp-elevation-z8: 0px 5px 5px -3px var(--jp-shadow-umbra-color), 0px 8px 10px 1px var(--jp-shadow-penumbra-color), 0px 3px 14px 2px var(--jp-shadow-ambient-color); --jp-elevation-z12: 0px 7px 8px -4px var(--jp-shadow-umbra-color), 0px 12px 17px 2px var(--jp-shadow-penumbra-color), 0px 5px 22px 4px var(--jp-shadow-ambient-color); --jp-elevation-z16: 0px 8px 10px -5px var(--jp-shadow-umbra-color), 0px 16px 24px 2px var(--jp-shadow-penumbra-color), 0px 6px 30px 5px var(--jp-shadow-ambient-color); --jp-elevation-z20: 0px 10px 13px -6px var(--jp-shadow-umbra-color), 0px 20px 31px 3px var(--jp-shadow-penumbra-color), 0px 8px 38px 7px var(--jp-shadow-ambient-color); --jp-elevation-z24: 0px 11px 15px -7px var(--jp-shadow-umbra-color), 0px 24px 38px 3px var(--jp-shadow-penumbra-color), 0px 9px 46px 8px var(--jp-shadow-ambient-color); /* Borders * * The following variables, specify the visual styling of borders in JupyterLab. */ --jp-border-width: 1px; --jp-border-color0: var(--md-grey-700); --jp-border-color1: var(--md-grey-700); --jp-border-color2: var(--md-grey-800); --jp-border-color3: var(--md-grey-900); --jp-border-radius: 2px; /* UI Fonts * * The UI font CSS variables are used for the typography all of the JupyterLab * user interface elements that are not directly user generated content. * * The font sizing here is done assuming that the body font size of --jp-ui-font-size1 * is applied to a parent element. When children elements, such as headings, are sized * in em all things will be computed relative to that body size. */ --jp-ui-font-scale-factor: 1.2; --jp-ui-font-size0: 0.83333em; --jp-ui-font-size1: 13px; /* Base font size */ --jp-ui-font-size2: 1.2em; --jp-ui-font-size3: 1.44em; --jp-ui-font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Helvetica, Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\"; /* * Use these font colors against the corresponding main layout colors. * In a light theme, these go from dark to light. */ /* Defaults use Material Design specification */ --jp-ui-font-color0: rgba(255, 255, 255, 1); --jp-ui-font-color1: rgba(255, 255, 255, 0.87); --jp-ui-font-color2: rgba(255, 255, 255, 0.54); --jp-ui-font-color3: rgba(255, 255, 255, 0.38); /* * Use these against the brand/accent/warn/error colors. * These will typically go from light to darker, in both a dark and light theme. */ --jp-ui-inverse-font-color0: rgba(0, 0, 0, 1); --jp-ui-inverse-font-color1: rgba(0, 0, 0, 0.8); --jp-ui-inverse-font-color2: rgba(0, 0, 0, 0.5); --jp-ui-inverse-font-color3: rgba(0, 0, 0, 0.3); /* Content Fonts * * Content font variables are used for typography of user generated content. * * The font sizing here is done assuming that the body font size of --jp-content-font-size1 * is applied to a parent element. When children elements, such as headings, are sized * in em all things will be computed relative to that body size. */ --jp-content-line-height: 1.6; --jp-content-font-scale-factor: 1.2; --jp-content-font-size0: 0.83333em; --jp-content-font-size1: 14px; /* Base font size */ --jp-content-font-size2: 1.2em; --jp-content-font-size3: 1.44em; --jp-content-font-size4: 1.728em; --jp-content-font-size5: 2.0736em; /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-content-presentation-font-size1: 17px; --jp-content-heading-line-height: 1; --jp-content-heading-margin-top: 1.2em; --jp-content-heading-margin-bottom: 0.8em; --jp-content-heading-font-weight: 500; /* Defaults use Material Design specification */ --jp-content-font-color0: rgba(255, 255, 255, 1); --jp-content-font-color1: rgba(255, 255, 255, 1); --jp-content-font-color2: rgba(255, 255, 255, 0.7); --jp-content-font-color3: rgba(255, 255, 255, 0.5); --jp-content-link-color: var(--md-blue-300); --jp-content-font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Helvetica, Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\"; /* * Code Fonts * * Code font variables are used for typography of code and other monospaces content. */ --jp-code-font-size: 13px; --jp-code-line-height: 1.3077; /* 17px for 13px base */ --jp-code-padding: 5px; /* 5px for 13px base, codemirror highlighting needs integer px value */ --jp-code-font-family-default: Menlo, Consolas, \"DejaVu Sans Mono\", monospace; --jp-code-font-family: var(--jp-code-font-family-default); /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-code-presentation-font-size: 16px; /* may need to tweak cursor width if you change font size */ --jp-code-cursor-width0: 1.4px; --jp-code-cursor-width1: 2px; --jp-code-cursor-width2: 4px; /* Layout * * The following are the main layout colors use in JupyterLab. In a light * theme these would go from light to dark. */ --jp-layout-color0: #111111; --jp-layout-color1: var(--md-grey-900); --jp-layout-color2: var(--md-grey-800); --jp-layout-color3: var(--md-grey-700); --jp-layout-color4: var(--md-grey-600); /* Inverse Layout * * The following are the inverse layout colors use in JupyterLab. In a light * theme these would go from dark to light. */ --jp-inverse-layout-color0: white; --jp-inverse-layout-color1: white; --jp-inverse-layout-color2: var(--md-grey-200); --jp-inverse-layout-color3: var(--md-grey-400); --jp-inverse-layout-color4: var(--md-grey-600); /* Brand/accent */ --jp-brand-color0: var(--md-blue-700); --jp-brand-color1: var(--md-blue-500); --jp-brand-color2: var(--md-blue-300); --jp-brand-color3: var(--md-blue-100); --jp-brand-color4: var(--md-blue-50); --jp-accent-color0: var(--md-green-700); --jp-accent-color1: var(--md-green-500); --jp-accent-color2: var(--md-green-300); --jp-accent-color3: var(--md-green-100); /* State colors (warn, error, success, info) */ --jp-warn-color0: var(--md-orange-700); --jp-warn-color1: var(--md-orange-500); --jp-warn-color2: var(--md-orange-300); --jp-warn-color3: var(--md-orange-100); --jp-error-color0: var(--md-red-700); --jp-error-color1: var(--md-red-500); --jp-error-color2: var(--md-red-300); --jp-error-color3: var(--md-red-100); --jp-success-color0: var(--md-green-700); --jp-success-color1: var(--md-green-500); --jp-success-color2: var(--md-green-300); --jp-success-color3: var(--md-green-100); --jp-info-color0: var(--md-cyan-700); --jp-info-color1: var(--md-cyan-500); --jp-info-color2: var(--md-cyan-300); --jp-info-color3: var(--md-cyan-100); /* Cell specific styles */ --jp-cell-padding: 5px; --jp-cell-collapser-width: 8px; --jp-cell-collapser-min-height: 20px; --jp-cell-collapser-not-active-hover-opacity: 0.6; --jp-cell-editor-background: var(--jp-layout-color1); --jp-cell-editor-border-color: var(--md-grey-700); --jp-cell-editor-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-cell-editor-active-background: var(--jp-layout-color0); --jp-cell-editor-active-border-color: var(--jp-brand-color1); --jp-cell-prompt-width: 64px; --jp-cell-prompt-font-family: var(--jp-code-font-family-default); --jp-cell-prompt-letter-spacing: 0px; --jp-cell-prompt-opacity: 1; --jp-cell-prompt-not-active-opacity: 1; --jp-cell-prompt-not-active-font-color: var(--md-grey-300); /* A custom blend of MD grey and blue 600 * See https://meyerweb.com/eric/tools/color-blend/#546E7A:1E88E5:5:hex */ --jp-cell-inprompt-font-color: #307fc1; /* A custom blend of MD grey and orange 600 * https://meyerweb.com/eric/tools/color-blend/#546E7A:F4511E:5:hex */ --jp-cell-outprompt-font-color: #bf5b3d; /* Notebook specific styles */ --jp-notebook-padding: 10px; --jp-notebook-select-background: var(--jp-layout-color1); --jp-notebook-multiselected-color: rgba(33, 150, 243, 0.24); /* The scroll padding is calculated to fill enough space at the bottom of the notebook to show one single-line cell (with appropriate padding) at the top when the notebook is scrolled all the way to the bottom. We also subtract one pixel so that no scrollbar appears if we have just one single-line cell in the notebook. This padding is to enable a 'scroll past end' feature in a notebook. */ --jp-notebook-scroll-padding: calc( 100% - var(--jp-code-font-size) * var(--jp-code-line-height) - var(--jp-code-padding) - var(--jp-cell-padding) - 1px ); /* Rendermime styles */ --jp-rendermime-error-background: rgba(244, 67, 54, 0.28); --jp-rendermime-table-row-background: var(--md-grey-900); --jp-rendermime-table-row-hover-background: rgba(3, 169, 244, 0.2); /* Dialog specific styles */ --jp-dialog-background: rgba(0, 0, 0, 0.6); /* Console specific styles */ --jp-console-padding: 10px; /* Toolbar specific styles */ --jp-toolbar-border-color: var(--jp-border-color2); --jp-toolbar-micro-height: 8px; --jp-toolbar-background: var(--jp-layout-color1); --jp-toolbar-box-shadow: 0px 0px 2px 0px rgba(0, 0, 0, 0.8); --jp-toolbar-header-margin: 4px 4px 0px 4px; --jp-toolbar-active-background: var(--jp-layout-color0); /* Statusbar specific styles */ --jp-statusbar-height: 24px; /* Input field styles */ --jp-input-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-input-active-background: var(--jp-layout-color0); --jp-input-hover-background: var(--jp-layout-color2); --jp-input-background: var(--md-grey-800); --jp-input-border-color: var(--jp-border-color1); --jp-input-active-border-color: var(--jp-brand-color1); --jp-input-active-box-shadow-color: rgba(19, 124, 189, 0.3); /* General editor styles */ --jp-editor-selected-background: var(--jp-layout-color2); --jp-editor-selected-focused-background: rgba(33, 150, 243, 0.24); --jp-editor-cursor-color: var(--jp-ui-font-color0); /* Code mirror specific styles */ --jp-mirror-editor-keyword-color: var(--md-green-500); --jp-mirror-editor-atom-color: var(--md-blue-300); --jp-mirror-editor-number-color: var(--md-green-400); --jp-mirror-editor-def-color: var(--md-blue-600); --jp-mirror-editor-variable-color: var(--md-grey-300); --jp-mirror-editor-variable-2-color: var(--md-blue-400); --jp-mirror-editor-variable-3-color: var(--md-green-600); --jp-mirror-editor-punctuation-color: var(--md-blue-400); --jp-mirror-editor-property-color: var(--md-blue-400); --jp-mirror-editor-operator-color: #aa22ff; --jp-mirror-editor-comment-color: #408080; --jp-mirror-editor-string-color: #ff7070; --jp-mirror-editor-string-2-color: var(--md-purple-300); --jp-mirror-editor-meta-color: #aa22ff; --jp-mirror-editor-qualifier-color: #555; --jp-mirror-editor-builtin-color: var(--md-green-600); --jp-mirror-editor-bracket-color: #997; --jp-mirror-editor-tag-color: var(--md-green-700); --jp-mirror-editor-attribute-color: var(--md-blue-700); --jp-mirror-editor-header-color: var(--md-blue-500); --jp-mirror-editor-quote-color: var(--md-green-300); --jp-mirror-editor-link-color: var(--md-blue-700); --jp-mirror-editor-error-color: #f00; --jp-mirror-editor-hr-color: #999; /* Vega extension styles */ --jp-vega-background: var(--md-grey-400); /* Sidebar-related styles */ --jp-sidebar-min-width: 250px; /* Search-related styles */ --jp-search-toggle-off-opacity: 0.6; --jp-search-toggle-hover-opacity: 0.8; --jp-search-toggle-on-opacity: 1; --jp-search-selected-match-background-color: rgb(255, 225, 0); --jp-search-selected-match-color: black; --jp-search-unselected-match-background-color: var( --jp-inverse-layout-color0 ); --jp-search-unselected-match-color: var(--jp-ui-inverse-font-color0); /* scrollbar related styles. Supports every browser except Edge. */ /* colors based on JetBrain's Darcula theme */ --jp-scrollbar-background-color: #3f4244; --jp-scrollbar-thumb-color: 88, 96, 97; /* need to specify thumb color as an RGB triplet */ --jp-scrollbar-endpad: 3px; /* the minimum gap between the thumb and the ends of a scrollbar */ /* hacks for setting the thumb shape. These do nothing in Firefox */ --jp-scrollbar-thumb-margin: 3.5px; /* the space in between the sides of the thumb and the track */ --jp-scrollbar-thumb-radius: 9px; /* set to a large-ish value for rounded endcaps on the thumb */ /* Icon colors that work well with light or dark backgrounds */ --jp-icon-contrast-color0: var(--md-purple-600); --jp-icon-contrast-color1: var(--md-green-600); --jp-icon-contrast-color2: var(--md-pink-600); --jp-icon-contrast-color3: var(--md-blue-600); } :root{--md-red-50: #ffebee;--md-red-100: #ffcdd2;--md-red-200: #ef9a9a;--md-red-300: #e57373;--md-red-400: #ef5350;--md-red-500: #f44336;--md-red-600: #e53935;--md-red-700: #d32f2f;--md-red-800: #c62828;--md-red-900: #b71c1c;--md-red-A100: #ff8a80;--md-red-A200: #ff5252;--md-red-A400: #ff1744;--md-red-A700: #d50000;--md-pink-50: #fce4ec;--md-pink-100: #f8bbd0;--md-pink-200: #f48fb1;--md-pink-300: #f06292;--md-pink-400: #ec407a;--md-pink-500: #e91e63;--md-pink-600: #d81b60;--md-pink-700: #c2185b;--md-pink-800: #ad1457;--md-pink-900: #880e4f;--md-pink-A100: #ff80ab;--md-pink-A200: #ff4081;--md-pink-A400: #f50057;--md-pink-A700: #c51162;--md-purple-50: #f3e5f5;--md-purple-100: #e1bee7;--md-purple-200: #ce93d8;--md-purple-300: #ba68c8;--md-purple-400: #ab47bc;--md-purple-500: #9c27b0;--md-purple-600: #8e24aa;--md-purple-700: #7b1fa2;--md-purple-800: #6a1b9a;--md-purple-900: #4a148c;--md-purple-A100: #ea80fc;--md-purple-A200: #e040fb;--md-purple-A400: #d500f9;--md-purple-A700: #aa00ff;--md-deep-purple-50: #ede7f6;--md-deep-purple-100: #d1c4e9;--md-deep-purple-200: #b39ddb;--md-deep-purple-300: #9575cd;--md-deep-purple-400: #7e57c2;--md-deep-purple-500: #673ab7;--md-deep-purple-600: #5e35b1;--md-deep-purple-700: #512da8;--md-deep-purple-800: #4527a0;--md-deep-purple-900: #311b92;--md-deep-purple-A100: #b388ff;--md-deep-purple-A200: #7c4dff;--md-deep-purple-A400: #651fff;--md-deep-purple-A700: #6200ea;--md-indigo-50: #e8eaf6;--md-indigo-100: #c5cae9;--md-indigo-200: #9fa8da;--md-indigo-300: #7986cb;--md-indigo-400: #5c6bc0;--md-indigo-500: #3f51b5;--md-indigo-600: #3949ab;--md-indigo-700: #303f9f;--md-indigo-800: #283593;--md-indigo-900: #1a237e;--md-indigo-A100: #8c9eff;--md-indigo-A200: #536dfe;--md-indigo-A400: #3d5afe;--md-indigo-A700: #304ffe;--md-blue-50: #e3f2fd;--md-blue-100: #bbdefb;--md-blue-200: #90caf9;--md-blue-300: #64b5f6;--md-blue-400: #42a5f5;--md-blue-500: #2196f3;--md-blue-600: #1e88e5;--md-blue-700: #1976d2;--md-blue-800: #1565c0;--md-blue-900: #0d47a1;--md-blue-A100: #82b1ff;--md-blue-A200: #448aff;--md-blue-A400: #2979ff;--md-blue-A700: #2962ff;--md-light-blue-50: #e1f5fe;--md-light-blue-100: #b3e5fc;--md-light-blue-200: #81d4fa;--md-light-blue-300: #4fc3f7;--md-light-blue-400: #29b6f6;--md-light-blue-500: #03a9f4;--md-light-blue-600: #039be5;--md-light-blue-700: #0288d1;--md-light-blue-800: #0277bd;--md-light-blue-900: #01579b;--md-light-blue-A100: #80d8ff;--md-light-blue-A200: #40c4ff;--md-light-blue-A400: #00b0ff;--md-light-blue-A700: #0091ea;--md-cyan-50: #e0f7fa;--md-cyan-100: #b2ebf2;--md-cyan-200: #80deea;--md-cyan-300: #4dd0e1;--md-cyan-400: #26c6da;--md-cyan-500: #00bcd4;--md-cyan-600: #00acc1;--md-cyan-700: #0097a7;--md-cyan-800: #00838f;--md-cyan-900: #006064;--md-cyan-A100: #84ffff;--md-cyan-A200: #18ffff;--md-cyan-A400: #00e5ff;--md-cyan-A700: #00b8d4;--md-teal-50: #e0f2f1;--md-teal-100: #b2dfdb;--md-teal-200: #80cbc4;--md-teal-300: #4db6ac;--md-teal-400: #26a69a;--md-teal-500: #009688;--md-teal-600: #00897b;--md-teal-700: #00796b;--md-teal-800: #00695c;--md-teal-900: #004d40;--md-teal-A100: #a7ffeb;--md-teal-A200: #64ffda;--md-teal-A400: #1de9b6;--md-teal-A700: #00bfa5;--md-green-50: #e8f5e9;--md-green-100: #c8e6c9;--md-green-200: #a5d6a7;--md-green-300: #81c784;--md-green-400: #66bb6a;--md-green-500: #4caf50;--md-green-600: #43a047;--md-green-700: #388e3c;--md-green-800: #2e7d32;--md-green-900: #1b5e20;--md-green-A100: #b9f6ca;--md-green-A200: #69f0ae;--md-green-A400: #00e676;--md-green-A700: #00c853;--md-light-green-50: #f1f8e9;--md-light-green-100: #dcedc8;--md-light-green-200: #c5e1a5;--md-light-green-300: #aed581;--md-light-green-400: #9ccc65;--md-light-green-500: #8bc34a;--md-light-green-600: #7cb342;--md-light-green-700: #689f38;--md-light-green-800: #558b2f;--md-light-green-900: #33691e;--md-light-green-A100: #ccff90;--md-light-green-A200: #b2ff59;--md-light-green-A400: #76ff03;--md-light-green-A700: #64dd17;--md-lime-50: #f9fbe7;--md-lime-100: #f0f4c3;--md-lime-200: #e6ee9c;--md-lime-300: #dce775;--md-lime-400: #d4e157;--md-lime-500: #cddc39;--md-lime-600: #c0ca33;--md-lime-700: #afb42b;--md-lime-800: #9e9d24;--md-lime-900: #827717;--md-lime-A100: #f4ff81;--md-lime-A200: #eeff41;--md-lime-A400: #c6ff00;--md-lime-A700: #aeea00;--md-yellow-50: #fffde7;--md-yellow-100: #fff9c4;--md-yellow-200: #fff59d;--md-yellow-300: #fff176;--md-yellow-400: #ffee58;--md-yellow-500: #ffeb3b;--md-yellow-600: #fdd835;--md-yellow-700: #fbc02d;--md-yellow-800: #f9a825;--md-yellow-900: #f57f17;--md-yellow-A100: #ffff8d;--md-yellow-A200: #ffff00;--md-yellow-A400: #ffea00;--md-yellow-A700: #ffd600;--md-amber-50: #fff8e1;--md-amber-100: #ffecb3;--md-amber-200: #ffe082;--md-amber-300: #ffd54f;--md-amber-400: #ffca28;--md-amber-500: #ffc107;--md-amber-600: #ffb300;--md-amber-700: #ffa000;--md-amber-800: #ff8f00;--md-amber-900: #ff6f00;--md-amber-A100: #ffe57f;--md-amber-A200: #ffd740;--md-amber-A400: #ffc400;--md-amber-A700: #ffab00;--md-orange-50: #fff3e0;--md-orange-100: #ffe0b2;--md-orange-200: #ffcc80;--md-orange-300: #ffb74d;--md-orange-400: #ffa726;--md-orange-500: #ff9800;--md-orange-600: #fb8c00;--md-orange-700: #f57c00;--md-orange-800: #ef6c00;--md-orange-900: #e65100;--md-orange-A100: #ffd180;--md-orange-A200: #ffab40;--md-orange-A400: #ff9100;--md-orange-A700: #ff6d00;--md-deep-orange-50: #fbe9e7;--md-deep-orange-100: #ffccbc;--md-deep-orange-200: #ffab91;--md-deep-orange-300: #ff8a65;--md-deep-orange-400: #ff7043;--md-deep-orange-500: 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sourceMappingURL=mkdocs-jupyter.css.map*/ init_mathjax = function() { if (window.MathJax) { // MathJax loaded MathJax.Hub.Config({ TeX: { equationNumbers: { autoNumber: \"AMS\", useLabelIds: true } }, tex2jax: { inlineMath: [ ['$','$'], [\"\\\\(\",\"\\\\)\"] ], displayMath: [ ['$$','$$'], [\"\\\\[\",\"\\\\]\"] ], processEscapes: true, processEnvironments: true }, displayAlign: 'center', CommonHTML: { linebreaks: { automatic: true } } }); MathJax.Hub.Queue([\"Typeset\", MathJax.Hub]); } } init_mathjax(); Create an inference function \u00b6 Everything is ready to wrap the previously trained model and deploy it. First, we need to configure the LargeFileBackend, the TracingDatabase and GreatAI. In [1]: Copied! from great_ai.utilities import ConfigFile from great_ai.large_file import LargeFileS3 from great_ai import configure , MongoDbDriver configuration = ConfigFile ( \"config.ini\" ) LargeFileS3 . configure_credentials_from_file ( configuration ) MongoDbDriver . configure_credentials_from_file ( configuration ) configure ( dashboard_table_size = 100 , # traces are small, we can show many prediction_cache_size = 4096 , # predictions are expensive, cache them ) from great_ai.utilities import ConfigFile from great_ai.large_file import LargeFileS3 from great_ai import configure, MongoDbDriver configuration = ConfigFile(\"config.ini\") LargeFileS3.configure_credentials_from_file(configuration) MongoDbDriver.configure_credentials_from_file(configuration) configure( dashboard_table_size=100, # traces are small, we can show many prediction_cache_size=4096, # predictions are expensive, cache them ) The value of `ENVIRONMENT` contains the \"ENV` prefix but `ENVIRONMENT` is not defined as an environment variable, using the default value defined above (`DEVELOPMENT`) Environment variable ENVIRONMENT is not set, defaulting to development mode \u203c\ufe0f MongoDbDriver has been already configured: skipping initialisation LargeFileS3 has been already configured: skipping initialisation GreatAI (v0.1.6): configured \u2705 \ud83d\udd29 tracing_database: MongoDbDriver \ud83d\udd29 large_file_implementation: LargeFileS3 \ud83d\udd29 is_production: False \ud83d\udd29 should_log_exception_stack: True \ud83d\udd29 prediction_cache_size: 4096 \ud83d\udd29 dashboard_table_size: 100 You still need to check whether you follow all best practices before trusting your deployment. > Find out more at https://se-ml.github.io/practices For a pleasant developer experience, we create some typed models that will show up in the automatically generated OpenAPI schema specification and will also provide runtime type validation. In [2]: Copied! from typing import List from pydantic import BaseModel class Attention ( BaseModel ): weight : float token : str class EvaluatedSentence ( BaseModel ): score : float text : str explanation : List [ Attention ] from typing import List from pydantic import BaseModel class Attention(BaseModel): weight: float token: str class EvaluatedSentence(BaseModel): score: float text: str explanation: List[Attention] Even though @use_model caches the remote files locally and it also handles deserialising objects, we only use it to store a directory. In this case, it gives back a path, the path to that directory. So, we need to load the files from that folder ourselves. In order to only load it once per process, we create a small model loader helper function. This is usually not needed, however, when we can outsmart dill so for optimisation purposes, we do it. In [3]: Copied! from great_ai import use_model from pathlib import Path from typing import Tuple from transformers import ( PreTrainedModel , PreTrainedTokenizer , ) from transformers import ( AutoConfig , AutoModelForSequenceClassification , AutoTokenizer , ) _tokenizer : PreTrainedTokenizer = None _loaded_model : PreTrainedModel = None @use_model ( \"scibert-highlights\" , version = \"latest\" , model_kwarg_name = \"model_path\" ) def get_tokenizer_and_model ( model_path : Path , original_model : str = \"allenai/scibert_scivocab_uncased\" ) -> Tuple [ PreTrainedTokenizer , PreTrainedModel ]: global _tokenizer , _loaded_model if _tokenizer is None : _tokenizer = AutoTokenizer . from_pretrained ( original_model ) if _loaded_model is None : config = AutoConfig . from_pretrained ( model_path , output_hidden_states = True , output_attentions = True ) _loaded_model = AutoModelForSequenceClassification . from_pretrained ( model_path , config = config ) return _tokenizer , _loaded_model from great_ai import use_model from pathlib import Path from typing import Tuple from transformers import ( PreTrainedModel, PreTrainedTokenizer, ) from transformers import ( AutoConfig, AutoModelForSequenceClassification, AutoTokenizer, ) _tokenizer: PreTrainedTokenizer = None _loaded_model: PreTrainedModel = None @use_model(\"scibert-highlights\", version=\"latest\", model_kwarg_name=\"model_path\") def get_tokenizer_and_model( model_path: Path, original_model: str = \"allenai/scibert_scivocab_uncased\" ) -> Tuple[PreTrainedTokenizer, PreTrainedModel]: global _tokenizer, _loaded_model if _tokenizer is None: _tokenizer = AutoTokenizer.from_pretrained(original_model) if _loaded_model is None: config = AutoConfig.from_pretrained( model_path, output_hidden_states=True, output_attentions=True ) _loaded_model = AutoModelForSequenceClassification.from_pretrained( model_path, config=config ) return _tokenizer, _loaded_model Latest version of scibert-highlights is 0 (from versions: 0) File scibert-highlights-0 found in cache Finally, implement the inference function. In [4]: Copied! from great_ai import GreatAI from great_ai.utilities import clean import re import numpy as np import torch from transformers.modeling_outputs import SequenceClassifierOutput @GreatAI . create def find_highlights ( sentence : str ) -> EvaluatedSentence : \"\"\"Get the interestingness prediction of the input sentence using SciBERT. Run the SciBERT model in inference mode and evaluate the sentence. Additionally, provide explanation in the form of the last layer's sum attention between `[CLS]` and the other tokens. \"\"\" tokenizer , loaded_model = get_tokenizer_and_model () sentence = clean ( sentence , convert_to_ascii = True , remove_brackets = True ) tensors = tokenizer ( sentence , return_tensors = \"pt\" , truncation = True , max_length = 512 ) with torch . inference_mode (): result : SequenceClassifierOutput = loaded_model ( ** tensors ) positive_likelihood = torch . nn . Softmax ( dim = 1 )( result . logits )[ 0 ][ 1 ] tokens = tensors [ \"input_ids\" ][ 0 ] attentions = np . sum ( result . attentions [ - 1 ] . numpy ()[ 0 ], axis = 0 )[ 0 ][ 1 : - 1 ] # Tuple of `torch.FloatTensor` (one for each layer) of shape # `(batch_size, num_heads, sequence_length, sequence_length)`. explanation = [] token_attentions = list ( zip ( attentions , tokens [ 1 : - 1 ])) for token in re . split ( r \"([ .,])\" , sentence ): token = token . strip () if not token : continue bert_tokens = tokenizer ( token , return_tensors = \"pt\" , truncation = True , max_length = 512 )[ \"input_ids\" ][ 0 ][ 1 : - 1 ] # truncation=True needed to fix `RuntimeError: Already borrowed` weight = 0 for t1 in bert_tokens : if not token_attentions : break a , t2 = token_attentions . pop ( 0 ) assert t1 == t2 , sentence weight += a explanation . append ( Attention ( token = token if token in \".,\" else \" \" + token , weight = round ( weight , 4 ) ) ) if not token_attentions : break return EvaluatedSentence ( score = positive_likelihood , text = sentence , explanation = explanation ) from great_ai import GreatAI from great_ai.utilities import clean import re import numpy as np import torch from transformers.modeling_outputs import SequenceClassifierOutput @GreatAI.create def find_highlights(sentence: str) -> EvaluatedSentence: \"\"\"Get the interestingness prediction of the input sentence using SciBERT. Run the SciBERT model in inference mode and evaluate the sentence. Additionally, provide explanation in the form of the last layer's sum attention between `[CLS]` and the other tokens. \"\"\" tokenizer, loaded_model = get_tokenizer_and_model() sentence = clean(sentence, convert_to_ascii=True, remove_brackets=True) tensors = tokenizer(sentence, return_tensors=\"pt\", truncation=True, max_length=512) with torch.inference_mode(): result: SequenceClassifierOutput = loaded_model(**tensors) positive_likelihood = torch.nn.Softmax(dim=1)(result.logits)[0][1] tokens = tensors[\"input_ids\"][0] attentions = np.sum(result.attentions[-1].numpy()[0], axis=0)[0][1:-1] # Tuple of `torch.FloatTensor` (one for each layer) of shape # `(batch_size, num_heads, sequence_length, sequence_length)`. explanation = [] token_attentions = list(zip(attentions, tokens[1:-1])) for token in re.split(r\"([ .,])\", sentence): token = token.strip() if not token: continue bert_tokens = tokenizer( token, return_tensors=\"pt\", truncation=True, max_length=512 )[\"input_ids\"][0][ 1:-1 ] # truncation=True needed to fix `RuntimeError: Already borrowed` weight = 0 for t1 in bert_tokens: if not token_attentions: break a, t2 = token_attentions.pop(0) assert t1 == t2, sentence weight += a explanation.append( Attention( token=token if token in \".,\" else \" \" + token, weight=round(weight, 4) ) ) if not token_attentions: break return EvaluatedSentence( score=positive_likelihood, text=sentence, explanation=explanation ) A simple test to see everything works. Note that the models list is filled by the @use_model call even though it's not on the main inference function. In [5]: Copied! if __name__ == \"__main__\" : find_highlights ( \"Our solution has outperformed the state-of-the-art.\" ), find_highlights ( \"Their solution did not perform well.\" ) if __name__ == \"__main__\": find_highlights( \"Our solution has outperformed the state-of-the-art.\" ), find_highlights(\"Their solution did not perform well.\") Out[5]: (Trace[EvaluatedSentence]({'created': '2022-07-16T18:47:29.581701', 'exception': None, 'feedback': None, 'logged_values': { 'arg:sentence:length': 51, 'arg:sentence:value': 'Our solution has outperformed the ' 'state-of-the-art.'}, 'models': [{'key': 'scibert-highlights', 'version': 0}], 'original_execution_time_ms': 7127.2063, 'output': { 'explanation': [ {'token': ' Our', 'weight': 0.3993}, {'token': ' solution', 'weight': 0.3481}, {'token': ' has', 'weight': 0.2945}, {'token': ' outperformed', 'weight': 0.4011}, {'token': ' the', 'weight': 0.1484}, {'token': ' state-of-the-art', 'weight': 0.5727}, {'token': '.', 'weight': 7.775}], 'score': 0.9991180300712585, 'text': 'Our solution has outperformed the state-of-the-art.'}, 'tags': ['find_highlights', 'online', 'development'], 'trace_id': '56e20e94-79df-4793-ae61-d20820ebe2d3'}), Trace[EvaluatedSentence]({'created': '2022-07-16T18:47:37.020275', 'exception': None, 'feedback': None, 'logged_values': { 'arg:sentence:length': 36, 'arg:sentence:value': 'Their solution did not perform ' 'well.'}, 'models': [{'key': 'scibert-highlights', 'version': 0}], 'original_execution_time_ms': 170.7057, 'output': { 'explanation': [ {'token': ' Their', 'weight': 1.1475}, {'token': ' solution', 'weight': 0.8205}, {'token': ' did', 'weight': 0.3254}, {'token': ' not', 'weight': 0.2921}, {'token': ' perform', 'weight': 0.4293}, {'token': ' well', 'weight': 0.2772}, {'token': '.', 'weight': 4.4723}], 'score': 0.12305451184511185, 'text': 'Their solution did not perform well.'}, 'tags': ['find_highlights', 'online', 'development'], 'trace_id': '7fcf8271-1738-4025-8305-d5a1e5100aea'})) In this case, the service is built as a docker image, pushed to our image registry and subsequent rolling update is performed in the production cluster. To check out the Dockerimage, go to the additional files page .","title":"Create an inference function"},{"location":"examples/scibert/deploy/#create-an-inference-function","text":"Everything is ready to wrap the previously trained model and deploy it. First, we need to configure the LargeFileBackend, the TracingDatabase and GreatAI. In [1]: Copied! from great_ai.utilities import ConfigFile from great_ai.large_file import LargeFileS3 from great_ai import configure , MongoDbDriver configuration = ConfigFile ( \"config.ini\" ) LargeFileS3 . configure_credentials_from_file ( configuration ) MongoDbDriver . configure_credentials_from_file ( configuration ) configure ( dashboard_table_size = 100 , # traces are small, we can show many prediction_cache_size = 4096 , # predictions are expensive, cache them ) from great_ai.utilities import ConfigFile from great_ai.large_file import LargeFileS3 from great_ai import configure, MongoDbDriver configuration = ConfigFile(\"config.ini\") LargeFileS3.configure_credentials_from_file(configuration) MongoDbDriver.configure_credentials_from_file(configuration) configure( dashboard_table_size=100, # traces are small, we can show many prediction_cache_size=4096, # predictions are expensive, cache them ) The value of `ENVIRONMENT` contains the \"ENV` prefix but `ENVIRONMENT` is not defined as an environment variable, using the default value defined above (`DEVELOPMENT`) Environment variable ENVIRONMENT is not set, defaulting to development mode \u203c\ufe0f MongoDbDriver has been already configured: skipping initialisation LargeFileS3 has been already configured: skipping initialisation GreatAI (v0.1.6): configured \u2705 \ud83d\udd29 tracing_database: MongoDbDriver \ud83d\udd29 large_file_implementation: LargeFileS3 \ud83d\udd29 is_production: False \ud83d\udd29 should_log_exception_stack: True \ud83d\udd29 prediction_cache_size: 4096 \ud83d\udd29 dashboard_table_size: 100 You still need to check whether you follow all best practices before trusting your deployment. > Find out more at https://se-ml.github.io/practices For a pleasant developer experience, we create some typed models that will show up in the automatically generated OpenAPI schema specification and will also provide runtime type validation. In [2]: Copied! from typing import List from pydantic import BaseModel class Attention ( BaseModel ): weight : float token : str class EvaluatedSentence ( BaseModel ): score : float text : str explanation : List [ Attention ] from typing import List from pydantic import BaseModel class Attention(BaseModel): weight: float token: str class EvaluatedSentence(BaseModel): score: float text: str explanation: List[Attention] Even though @use_model caches the remote files locally and it also handles deserialising objects, we only use it to store a directory. In this case, it gives back a path, the path to that directory. So, we need to load the files from that folder ourselves. In order to only load it once per process, we create a small model loader helper function. This is usually not needed, however, when we can outsmart dill so for optimisation purposes, we do it. In [3]: Copied! from great_ai import use_model from pathlib import Path from typing import Tuple from transformers import ( PreTrainedModel , PreTrainedTokenizer , ) from transformers import ( AutoConfig , AutoModelForSequenceClassification , AutoTokenizer , ) _tokenizer : PreTrainedTokenizer = None _loaded_model : PreTrainedModel = None @use_model ( \"scibert-highlights\" , version = \"latest\" , model_kwarg_name = \"model_path\" ) def get_tokenizer_and_model ( model_path : Path , original_model : str = \"allenai/scibert_scivocab_uncased\" ) -> Tuple [ PreTrainedTokenizer , PreTrainedModel ]: global _tokenizer , _loaded_model if _tokenizer is None : _tokenizer = AutoTokenizer . from_pretrained ( original_model ) if _loaded_model is None : config = AutoConfig . from_pretrained ( model_path , output_hidden_states = True , output_attentions = True ) _loaded_model = AutoModelForSequenceClassification . from_pretrained ( model_path , config = config ) return _tokenizer , _loaded_model from great_ai import use_model from pathlib import Path from typing import Tuple from transformers import ( PreTrainedModel, PreTrainedTokenizer, ) from transformers import ( AutoConfig, AutoModelForSequenceClassification, AutoTokenizer, ) _tokenizer: PreTrainedTokenizer = None _loaded_model: PreTrainedModel = None @use_model(\"scibert-highlights\", version=\"latest\", model_kwarg_name=\"model_path\") def get_tokenizer_and_model( model_path: Path, original_model: str = \"allenai/scibert_scivocab_uncased\" ) -> Tuple[PreTrainedTokenizer, PreTrainedModel]: global _tokenizer, _loaded_model if _tokenizer is None: _tokenizer = AutoTokenizer.from_pretrained(original_model) if _loaded_model is None: config = AutoConfig.from_pretrained( model_path, output_hidden_states=True, output_attentions=True ) _loaded_model = AutoModelForSequenceClassification.from_pretrained( model_path, config=config ) return _tokenizer, _loaded_model Latest version of scibert-highlights is 0 (from versions: 0) File scibert-highlights-0 found in cache Finally, implement the inference function. In [4]: Copied! from great_ai import GreatAI from great_ai.utilities import clean import re import numpy as np import torch from transformers.modeling_outputs import SequenceClassifierOutput @GreatAI . create def find_highlights ( sentence : str ) -> EvaluatedSentence : \"\"\"Get the interestingness prediction of the input sentence using SciBERT. Run the SciBERT model in inference mode and evaluate the sentence. Additionally, provide explanation in the form of the last layer's sum attention between `[CLS]` and the other tokens. \"\"\" tokenizer , loaded_model = get_tokenizer_and_model () sentence = clean ( sentence , convert_to_ascii = True , remove_brackets = True ) tensors = tokenizer ( sentence , return_tensors = \"pt\" , truncation = True , max_length = 512 ) with torch . inference_mode (): result : SequenceClassifierOutput = loaded_model ( ** tensors ) positive_likelihood = torch . nn . Softmax ( dim = 1 )( result . logits )[ 0 ][ 1 ] tokens = tensors [ \"input_ids\" ][ 0 ] attentions = np . sum ( result . attentions [ - 1 ] . numpy ()[ 0 ], axis = 0 )[ 0 ][ 1 : - 1 ] # Tuple of `torch.FloatTensor` (one for each layer) of shape # `(batch_size, num_heads, sequence_length, sequence_length)`. explanation = [] token_attentions = list ( zip ( attentions , tokens [ 1 : - 1 ])) for token in re . split ( r \"([ .,])\" , sentence ): token = token . strip () if not token : continue bert_tokens = tokenizer ( token , return_tensors = \"pt\" , truncation = True , max_length = 512 )[ \"input_ids\" ][ 0 ][ 1 : - 1 ] # truncation=True needed to fix `RuntimeError: Already borrowed` weight = 0 for t1 in bert_tokens : if not token_attentions : break a , t2 = token_attentions . pop ( 0 ) assert t1 == t2 , sentence weight += a explanation . append ( Attention ( token = token if token in \".,\" else \" \" + token , weight = round ( weight , 4 ) ) ) if not token_attentions : break return EvaluatedSentence ( score = positive_likelihood , text = sentence , explanation = explanation ) from great_ai import GreatAI from great_ai.utilities import clean import re import numpy as np import torch from transformers.modeling_outputs import SequenceClassifierOutput @GreatAI.create def find_highlights(sentence: str) -> EvaluatedSentence: \"\"\"Get the interestingness prediction of the input sentence using SciBERT. Run the SciBERT model in inference mode and evaluate the sentence. Additionally, provide explanation in the form of the last layer's sum attention between `[CLS]` and the other tokens. \"\"\" tokenizer, loaded_model = get_tokenizer_and_model() sentence = clean(sentence, convert_to_ascii=True, remove_brackets=True) tensors = tokenizer(sentence, return_tensors=\"pt\", truncation=True, max_length=512) with torch.inference_mode(): result: SequenceClassifierOutput = loaded_model(**tensors) positive_likelihood = torch.nn.Softmax(dim=1)(result.logits)[0][1] tokens = tensors[\"input_ids\"][0] attentions = np.sum(result.attentions[-1].numpy()[0], axis=0)[0][1:-1] # Tuple of `torch.FloatTensor` (one for each layer) of shape # `(batch_size, num_heads, sequence_length, sequence_length)`. explanation = [] token_attentions = list(zip(attentions, tokens[1:-1])) for token in re.split(r\"([ .,])\", sentence): token = token.strip() if not token: continue bert_tokens = tokenizer( token, return_tensors=\"pt\", truncation=True, max_length=512 )[\"input_ids\"][0][ 1:-1 ] # truncation=True needed to fix `RuntimeError: Already borrowed` weight = 0 for t1 in bert_tokens: if not token_attentions: break a, t2 = token_attentions.pop(0) assert t1 == t2, sentence weight += a explanation.append( Attention( token=token if token in \".,\" else \" \" + token, weight=round(weight, 4) ) ) if not token_attentions: break return EvaluatedSentence( score=positive_likelihood, text=sentence, explanation=explanation ) A simple test to see everything works. Note that the models list is filled by the @use_model call even though it's not on the main inference function. In [5]: Copied! if __name__ == \"__main__\" : find_highlights ( \"Our solution has outperformed the state-of-the-art.\" ), find_highlights ( \"Their solution did not perform well.\" ) if __name__ == \"__main__\": find_highlights( \"Our solution has outperformed the state-of-the-art.\" ), find_highlights(\"Their solution did not perform well.\") Out[5]: (Trace[EvaluatedSentence]({'created': '2022-07-16T18:47:29.581701', 'exception': None, 'feedback': None, 'logged_values': { 'arg:sentence:length': 51, 'arg:sentence:value': 'Our solution has outperformed the ' 'state-of-the-art.'}, 'models': [{'key': 'scibert-highlights', 'version': 0}], 'original_execution_time_ms': 7127.2063, 'output': { 'explanation': [ {'token': ' Our', 'weight': 0.3993}, {'token': ' solution', 'weight': 0.3481}, {'token': ' has', 'weight': 0.2945}, {'token': ' outperformed', 'weight': 0.4011}, {'token': ' the', 'weight': 0.1484}, {'token': ' state-of-the-art', 'weight': 0.5727}, {'token': '.', 'weight': 7.775}], 'score': 0.9991180300712585, 'text': 'Our solution has outperformed the state-of-the-art.'}, 'tags': ['find_highlights', 'online', 'development'], 'trace_id': '56e20e94-79df-4793-ae61-d20820ebe2d3'}), Trace[EvaluatedSentence]({'created': '2022-07-16T18:47:37.020275', 'exception': None, 'feedback': None, 'logged_values': { 'arg:sentence:length': 36, 'arg:sentence:value': 'Their solution did not perform ' 'well.'}, 'models': [{'key': 'scibert-highlights', 'version': 0}], 'original_execution_time_ms': 170.7057, 'output': { 'explanation': [ {'token': ' Their', 'weight': 1.1475}, {'token': ' solution', 'weight': 0.8205}, {'token': ' did', 'weight': 0.3254}, {'token': ' not', 'weight': 0.2921}, {'token': ' perform', 'weight': 0.4293}, {'token': ' well', 'weight': 0.2772}, {'token': '.', 'weight': 4.4723}], 'score': 0.12305451184511185, 'text': 'Their solution did not perform well.'}, 'tags': ['find_highlights', 'online', 'development'], 'trace_id': '7fcf8271-1738-4025-8305-d5a1e5100aea'})) In this case, the service is built as a docker image, pushed to our image registry and subsequent rolling update is performed in the production cluster. To check out the Dockerimage, go to the additional files page .","title":"Create an inference function"},{"location":"examples/scibert/train/","text":"(function (global, factory) { typeof exports === 'object' && typeof module !== 'undefined' ? module.exports = factory() : typeof define === 'function' && define.amd ? define(factory) : (global = global || self, global.ClipboardCopyElement = factory()); }(this, function () { 'use strict'; function createNode(text) { const node = document.createElement('pre'); node.style.width = '1px'; node.style.height = '1px'; node.style.position = 'fixed'; node.style.top = '5px'; node.textContent = text; return node; } function copyNode(node) { if ('clipboard' in navigator) { // eslint-disable-next-line flowtype/no-flow-fix-me-comments // $FlowFixMe Clipboard is not defined in Flow yet. return navigator.clipboard.writeText(node.textContent); } const selection = getSelection(); if (selection == null) { return Promise.reject(new Error()); } selection.removeAllRanges(); 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font-weight: bold } /* Keyword.Reserved */ .highlight-ipynb .kt { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword.Type */ .highlight-ipynb .m { color: var(--jp-mirror-editor-number-color) } /* Literal.Number */ .highlight-ipynb .s { color: var(--jp-mirror-editor-string-color) } /* Literal.String */ .highlight-ipynb .ow { color: var(--jp-mirror-editor-operator-color); font-weight: bold } /* Operator.Word */ .highlight-ipynb .w { color: var(--jp-mirror-editor-variable-color) } /* Text.Whitespace */ .highlight-ipynb .mb { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Bin */ .highlight-ipynb .mf { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Float */ .highlight-ipynb .mh { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Hex */ .highlight-ipynb .mi { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Integer */ .highlight-ipynb .mo { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Oct */ .highlight-ipynb .sa { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Affix */ .highlight-ipynb .sb { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Backtick */ .highlight-ipynb .sc { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Char */ .highlight-ipynb .dl { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Delimiter */ .highlight-ipynb .sd { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Doc */ .highlight-ipynb .s2 { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Double */ .highlight-ipynb .se { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Escape */ .highlight-ipynb .sh { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Heredoc */ .highlight-ipynb .si { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Interpol */ .highlight-ipynb .sx { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Other */ .highlight-ipynb .sr { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Regex */ .highlight-ipynb .s1 { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Single */ .highlight-ipynb .ss { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Symbol */ .highlight-ipynb .il { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Integer.Long */ /* This file is taken from the built JupyterLab theme.css Found on share/nbconvert/templates/lab/static Some changes have been made and marked with CHANGE */ .jupyter-wrapper { /* Elevation * * We style box-shadows using Material Design's idea of elevation. These particular numbers are taken from here: * * https://github.com/material-components/material-components-web * https://material-components-web.appspot.com/elevation.html */ --jp-shadow-base-lightness: 0; --jp-shadow-umbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.2 ); --jp-shadow-penumbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.14 ); --jp-shadow-ambient-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.12 ); --jp-elevation-z0: none; --jp-elevation-z1: 0px 2px 1px -1px var(--jp-shadow-umbra-color), 0px 1px 1px 0px var(--jp-shadow-penumbra-color), 0px 1px 3px 0px var(--jp-shadow-ambient-color); --jp-elevation-z2: 0px 3px 1px -2px var(--jp-shadow-umbra-color), 0px 2px 2px 0px var(--jp-shadow-penumbra-color), 0px 1px 5px 0px var(--jp-shadow-ambient-color); --jp-elevation-z4: 0px 2px 4px -1px var(--jp-shadow-umbra-color), 0px 4px 5px 0px var(--jp-shadow-penumbra-color), 0px 1px 10px 0px var(--jp-shadow-ambient-color); --jp-elevation-z6: 0px 3px 5px -1px var(--jp-shadow-umbra-color), 0px 6px 10px 0px var(--jp-shadow-penumbra-color), 0px 1px 18px 0px var(--jp-shadow-ambient-color); --jp-elevation-z8: 0px 5px 5px -3px var(--jp-shadow-umbra-color), 0px 8px 10px 1px var(--jp-shadow-penumbra-color), 0px 3px 14px 2px var(--jp-shadow-ambient-color); --jp-elevation-z12: 0px 7px 8px -4px var(--jp-shadow-umbra-color), 0px 12px 17px 2px var(--jp-shadow-penumbra-color), 0px 5px 22px 4px var(--jp-shadow-ambient-color); --jp-elevation-z16: 0px 8px 10px -5px var(--jp-shadow-umbra-color), 0px 16px 24px 2px var(--jp-shadow-penumbra-color), 0px 6px 30px 5px var(--jp-shadow-ambient-color); --jp-elevation-z20: 0px 10px 13px -6px var(--jp-shadow-umbra-color), 0px 20px 31px 3px var(--jp-shadow-penumbra-color), 0px 8px 38px 7px var(--jp-shadow-ambient-color); --jp-elevation-z24: 0px 11px 15px -7px var(--jp-shadow-umbra-color), 0px 24px 38px 3px var(--jp-shadow-penumbra-color), 0px 9px 46px 8px var(--jp-shadow-ambient-color); /* Borders * * The following variables, specify the visual styling of borders in JupyterLab. */ --jp-border-width: 1px; --jp-border-color0: var(--md-grey-400); --jp-border-color1: var(--md-grey-400); --jp-border-color2: var(--md-grey-300); --jp-border-color3: var(--md-grey-200); --jp-border-radius: 2px; /* UI Fonts * * The UI font CSS variables are used for the typography all of the JupyterLab * user interface elements that are not directly user generated content. * * The font sizing here is done assuming that the body font size of --jp-ui-font-size1 * is applied to a parent element. When children elements, such as headings, are sized * in em all things will be computed relative to that body size. */ --jp-ui-font-scale-factor: 1.2; --jp-ui-font-size0: 0.83333em; --jp-ui-font-size1: 13px; /* Base font size */ --jp-ui-font-size2: 1.2em; --jp-ui-font-size3: 1.44em; --jp-ui-font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Helvetica, Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\"; /* * Use these font colors against the corresponding main layout colors. * In a light theme, these go from dark to light. */ /* Defaults use Material Design specification */ --jp-ui-font-color0: rgba(0, 0, 0, 1); --jp-ui-font-color1: rgba(0, 0, 0, 0.87); --jp-ui-font-color2: rgba(0, 0, 0, 0.54); --jp-ui-font-color3: rgba(0, 0, 0, 0.38); /* * Use these against the brand/accent/warn/error colors. * These will typically go from light to darker, in both a dark and light theme. */ --jp-ui-inverse-font-color0: rgba(255, 255, 255, 1); --jp-ui-inverse-font-color1: rgba(255, 255, 255, 1); --jp-ui-inverse-font-color2: rgba(255, 255, 255, 0.7); --jp-ui-inverse-font-color3: rgba(255, 255, 255, 0.5); /* Content Fonts * * Content font variables are used for typography of user generated content. * * The font sizing here is done assuming that the body font size of --jp-content-font-size1 * is applied to a parent element. When children elements, such as headings, are sized * in em all things will be computed relative to that body size. */ --jp-content-line-height: 1.6; --jp-content-font-scale-factor: 1.2; --jp-content-font-size0: 0.83333em; --jp-content-font-size1: 14px; /* Base font size */ --jp-content-font-size2: 1.2em; --jp-content-font-size3: 1.44em; --jp-content-font-size4: 1.728em; --jp-content-font-size5: 2.0736em; /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-content-presentation-font-size1: 17px; --jp-content-heading-line-height: 1; --jp-content-heading-margin-top: 1.2em; --jp-content-heading-margin-bottom: 0.8em; --jp-content-heading-font-weight: 500; /* Defaults use Material Design specification */ --jp-content-font-color0: rgba(0, 0, 0, 1); --jp-content-font-color1: rgba(0, 0, 0, 0.87); --jp-content-font-color2: rgba(0, 0, 0, 0.54); --jp-content-font-color3: rgba(0, 0, 0, 0.38); --jp-content-link-color: var(--md-blue-700); --jp-content-font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Helvetica, Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\"; /* * Code Fonts * * Code font variables are used for typography of code and other monospaces content. */ --jp-code-font-size: 13px; --jp-code-line-height: 1.3077; /* 17px for 13px base */ --jp-code-padding: 5px; /* 5px for 13px base, codemirror highlighting needs integer px value */ --jp-code-font-family-default: Menlo, Consolas, \"DejaVu Sans Mono\", monospace; --jp-code-font-family: var(--jp-code-font-family-default); /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-code-presentation-font-size: 16px; /* may need to tweak cursor width if you change font size */ --jp-code-cursor-width0: 1.4px; --jp-code-cursor-width1: 2px; --jp-code-cursor-width2: 4px; /* Layout * * The following are the main layout colors use in JupyterLab. In a light * theme these would go from light to dark. */ --jp-layout-color0: white; --jp-layout-color1: white; --jp-layout-color2: var(--md-grey-200); --jp-layout-color3: var(--md-grey-400); --jp-layout-color4: var(--md-grey-600); /* Inverse Layout * * The following are the inverse layout colors use in JupyterLab. In a light * theme these would go from dark to light. */ --jp-inverse-layout-color0: #111111; --jp-inverse-layout-color1: var(--md-grey-900); --jp-inverse-layout-color2: var(--md-grey-800); --jp-inverse-layout-color3: var(--md-grey-700); --jp-inverse-layout-color4: var(--md-grey-600); /* Brand/accent */ --jp-brand-color0: var(--md-blue-900); --jp-brand-color1: var(--md-blue-700); --jp-brand-color2: var(--md-blue-300); --jp-brand-color3: var(--md-blue-100); --jp-brand-color4: var(--md-blue-50); --jp-accent-color0: var(--md-green-900); --jp-accent-color1: var(--md-green-700); --jp-accent-color2: var(--md-green-300); --jp-accent-color3: var(--md-green-100); /* State colors (warn, error, success, info) */ --jp-warn-color0: var(--md-orange-900); --jp-warn-color1: var(--md-orange-700); --jp-warn-color2: var(--md-orange-300); --jp-warn-color3: var(--md-orange-100); --jp-error-color0: var(--md-red-900); --jp-error-color1: var(--md-red-700); --jp-error-color2: var(--md-red-300); --jp-error-color3: var(--md-red-100); --jp-success-color0: var(--md-green-900); --jp-success-color1: var(--md-green-700); --jp-success-color2: var(--md-green-300); --jp-success-color3: var(--md-green-100); --jp-info-color0: var(--md-cyan-900); --jp-info-color1: var(--md-cyan-700); --jp-info-color2: var(--md-cyan-300); --jp-info-color3: var(--md-cyan-100); /* Cell specific styles */ --jp-cell-padding: 5px; --jp-cell-collapser-width: 8px; --jp-cell-collapser-min-height: 20px; --jp-cell-collapser-not-active-hover-opacity: 0.6; --jp-cell-editor-background: var(--md-grey-100); --jp-cell-editor-border-color: var(--md-grey-300); --jp-cell-editor-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-cell-editor-active-background: var(--jp-layout-color0); --jp-cell-editor-active-border-color: var(--jp-brand-color1); --jp-cell-prompt-width: 64px; --jp-cell-prompt-font-family: var(--jp-code-font-family-default); --jp-cell-prompt-letter-spacing: 0px; --jp-cell-prompt-opacity: 1; --jp-cell-prompt-not-active-opacity: 0.5; --jp-cell-prompt-not-active-font-color: var(--md-grey-700); /* A custom blend of MD grey and blue 600 * See https://meyerweb.com/eric/tools/color-blend/#546E7A:1E88E5:5:hex */ --jp-cell-inprompt-font-color: #307fc1; /* A custom blend of MD grey and orange 600 * https://meyerweb.com/eric/tools/color-blend/#546E7A:F4511E:5:hex */ --jp-cell-outprompt-font-color: #bf5b3d; /* Notebook specific styles */ --jp-notebook-padding: 10px; --jp-notebook-select-background: var(--jp-layout-color1); --jp-notebook-multiselected-color: var(--md-blue-50); /* The scroll padding is calculated to fill enough space at the bottom of the notebook to show one single-line cell (with appropriate padding) at the top when the notebook is scrolled all the way to the bottom. We also subtract one pixel so that no scrollbar appears if we have just one single-line cell in the notebook. This padding is to enable a 'scroll past end' feature in a notebook. */ --jp-notebook-scroll-padding: calc( 100% - var(--jp-code-font-size) * var(--jp-code-line-height) - var(--jp-code-padding) - var(--jp-cell-padding) - 1px ); /* Rendermime styles */ --jp-rendermime-error-background: #fdd; --jp-rendermime-table-row-background: var(--md-grey-100); --jp-rendermime-table-row-hover-background: var(--md-light-blue-50); /* Dialog specific styles */ --jp-dialog-background: rgba(0, 0, 0, 0.25); /* Console specific styles */ --jp-console-padding: 10px; /* Toolbar specific styles */ --jp-toolbar-border-color: var(--jp-border-color1); --jp-toolbar-micro-height: 8px; --jp-toolbar-background: var(--jp-layout-color1); --jp-toolbar-box-shadow: 0px 0px 2px 0px rgba(0, 0, 0, 0.24); --jp-toolbar-header-margin: 4px 4px 0px 4px; --jp-toolbar-active-background: var(--md-grey-300); /* Statusbar specific styles */ --jp-statusbar-height: 24px; /* Input field styles */ --jp-input-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-input-active-background: var(--jp-layout-color1); --jp-input-hover-background: var(--jp-layout-color1); --jp-input-background: var(--md-grey-100); --jp-input-border-color: var(--jp-border-color1); --jp-input-active-border-color: var(--jp-brand-color1); --jp-input-active-box-shadow-color: rgba(19, 124, 189, 0.3); /* General editor styles */ --jp-editor-selected-background: #d9d9d9; --jp-editor-selected-focused-background: #d7d4f0; --jp-editor-cursor-color: var(--jp-ui-font-color0); /* Code mirror specific styles */ --jp-mirror-editor-keyword-color: #008000; --jp-mirror-editor-atom-color: #88f; --jp-mirror-editor-number-color: #080; --jp-mirror-editor-def-color: #00f; --jp-mirror-editor-variable-color: var(--md-grey-900); --jp-mirror-editor-variable-2-color: #05a; --jp-mirror-editor-variable-3-color: #085; --jp-mirror-editor-punctuation-color: #05a; --jp-mirror-editor-property-color: #05a; --jp-mirror-editor-operator-color: #aa22ff; --jp-mirror-editor-comment-color: #408080; --jp-mirror-editor-string-color: #ba2121; --jp-mirror-editor-string-2-color: #708; --jp-mirror-editor-meta-color: #aa22ff; --jp-mirror-editor-qualifier-color: #555; --jp-mirror-editor-builtin-color: #008000; --jp-mirror-editor-bracket-color: #997; --jp-mirror-editor-tag-color: #170; --jp-mirror-editor-attribute-color: #00c; --jp-mirror-editor-header-color: blue; --jp-mirror-editor-quote-color: #090; --jp-mirror-editor-link-color: #00c; --jp-mirror-editor-error-color: #f00; --jp-mirror-editor-hr-color: #999; /* Vega extension styles */ --jp-vega-background: white; /* Sidebar-related styles */ --jp-sidebar-min-width: 250px; /* Search-related styles */ --jp-search-toggle-off-opacity: 0.5; --jp-search-toggle-hover-opacity: 0.8; --jp-search-toggle-on-opacity: 1; --jp-search-selected-match-background-color: rgb(245, 200, 0); --jp-search-selected-match-color: black; --jp-search-unselected-match-background-color: var( --jp-inverse-layout-color0 ); --jp-search-unselected-match-color: var(--jp-ui-inverse-font-color0); /* Icon colors that work well with light or dark backgrounds */ --jp-icon-contrast-color0: var(--md-purple-600); --jp-icon-contrast-color1: var(--md-green-600); --jp-icon-contrast-color2: var(--md-pink-600); --jp-icon-contrast-color3: var(--md-blue-600); } [data-md-color-scheme=\"slate\"] .jupyter-wrapper { /* Elevation * * We style box-shadows using Material Design's idea of elevation. These particular numbers are taken from here: * * https://github.com/material-components/material-components-web * https://material-components-web.appspot.com/elevation.html */ /* The dark theme shadows need a bit of work, but this will probably also require work on the core layout * colors used in the theme as well. */ --jp-shadow-base-lightness: 32; --jp-shadow-umbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.2 ); --jp-shadow-penumbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.14 ); --jp-shadow-ambient-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.12 ); --jp-elevation-z0: none; --jp-elevation-z1: 0px 2px 1px -1px var(--jp-shadow-umbra-color), 0px 1px 1px 0px var(--jp-shadow-penumbra-color), 0px 1px 3px 0px var(--jp-shadow-ambient-color); --jp-elevation-z2: 0px 3px 1px -2px var(--jp-shadow-umbra-color), 0px 2px 2px 0px var(--jp-shadow-penumbra-color), 0px 1px 5px 0px var(--jp-shadow-ambient-color); --jp-elevation-z4: 0px 2px 4px -1px var(--jp-shadow-umbra-color), 0px 4px 5px 0px var(--jp-shadow-penumbra-color), 0px 1px 10px 0px var(--jp-shadow-ambient-color); --jp-elevation-z6: 0px 3px 5px -1px var(--jp-shadow-umbra-color), 0px 6px 10px 0px var(--jp-shadow-penumbra-color), 0px 1px 18px 0px var(--jp-shadow-ambient-color); --jp-elevation-z8: 0px 5px 5px -3px var(--jp-shadow-umbra-color), 0px 8px 10px 1px var(--jp-shadow-penumbra-color), 0px 3px 14px 2px var(--jp-shadow-ambient-color); --jp-elevation-z12: 0px 7px 8px -4px var(--jp-shadow-umbra-color), 0px 12px 17px 2px var(--jp-shadow-penumbra-color), 0px 5px 22px 4px var(--jp-shadow-ambient-color); --jp-elevation-z16: 0px 8px 10px -5px var(--jp-shadow-umbra-color), 0px 16px 24px 2px var(--jp-shadow-penumbra-color), 0px 6px 30px 5px var(--jp-shadow-ambient-color); --jp-elevation-z20: 0px 10px 13px -6px var(--jp-shadow-umbra-color), 0px 20px 31px 3px var(--jp-shadow-penumbra-color), 0px 8px 38px 7px var(--jp-shadow-ambient-color); --jp-elevation-z24: 0px 11px 15px -7px var(--jp-shadow-umbra-color), 0px 24px 38px 3px var(--jp-shadow-penumbra-color), 0px 9px 46px 8px var(--jp-shadow-ambient-color); /* Borders * * The following variables, specify the visual styling of borders in JupyterLab. */ --jp-border-width: 1px; --jp-border-color0: var(--md-grey-700); --jp-border-color1: var(--md-grey-700); --jp-border-color2: var(--md-grey-800); --jp-border-color3: var(--md-grey-900); --jp-border-radius: 2px; /* UI Fonts * * The UI font CSS variables are used for the typography all of the JupyterLab * user interface elements that are not directly user generated content. * * The font sizing here is done assuming that the body font size of --jp-ui-font-size1 * is applied to a parent element. When children elements, such as headings, are sized * in em all things will be computed relative to that body size. */ --jp-ui-font-scale-factor: 1.2; --jp-ui-font-size0: 0.83333em; --jp-ui-font-size1: 13px; /* Base font size */ --jp-ui-font-size2: 1.2em; --jp-ui-font-size3: 1.44em; --jp-ui-font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Helvetica, Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\"; /* * Use these font colors against the corresponding main layout colors. * In a light theme, these go from dark to light. */ /* Defaults use Material Design specification */ --jp-ui-font-color0: rgba(255, 255, 255, 1); --jp-ui-font-color1: rgba(255, 255, 255, 0.87); --jp-ui-font-color2: rgba(255, 255, 255, 0.54); --jp-ui-font-color3: rgba(255, 255, 255, 0.38); /* * Use these against the brand/accent/warn/error colors. * These will typically go from light to darker, in both a dark and light theme. */ --jp-ui-inverse-font-color0: rgba(0, 0, 0, 1); --jp-ui-inverse-font-color1: rgba(0, 0, 0, 0.8); --jp-ui-inverse-font-color2: rgba(0, 0, 0, 0.5); --jp-ui-inverse-font-color3: rgba(0, 0, 0, 0.3); /* Content Fonts * * Content font variables are used for typography of user generated content. * * The font sizing here is done assuming that the body font size of --jp-content-font-size1 * is applied to a parent element. When children elements, such as headings, are sized * in em all things will be computed relative to that body size. */ --jp-content-line-height: 1.6; --jp-content-font-scale-factor: 1.2; --jp-content-font-size0: 0.83333em; --jp-content-font-size1: 14px; /* Base font size */ --jp-content-font-size2: 1.2em; --jp-content-font-size3: 1.44em; --jp-content-font-size4: 1.728em; --jp-content-font-size5: 2.0736em; /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-content-presentation-font-size1: 17px; --jp-content-heading-line-height: 1; --jp-content-heading-margin-top: 1.2em; --jp-content-heading-margin-bottom: 0.8em; --jp-content-heading-font-weight: 500; /* Defaults use Material Design specification */ --jp-content-font-color0: rgba(255, 255, 255, 1); --jp-content-font-color1: rgba(255, 255, 255, 1); --jp-content-font-color2: rgba(255, 255, 255, 0.7); --jp-content-font-color3: rgba(255, 255, 255, 0.5); --jp-content-link-color: var(--md-blue-300); --jp-content-font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Helvetica, Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\"; /* * Code Fonts * * Code font variables are used for typography of code and other monospaces content. */ --jp-code-font-size: 13px; --jp-code-line-height: 1.3077; /* 17px for 13px base */ --jp-code-padding: 5px; /* 5px for 13px base, codemirror highlighting needs integer px value */ --jp-code-font-family-default: Menlo, Consolas, \"DejaVu Sans Mono\", monospace; --jp-code-font-family: var(--jp-code-font-family-default); /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-code-presentation-font-size: 16px; /* may need to tweak cursor width if you change font size */ --jp-code-cursor-width0: 1.4px; --jp-code-cursor-width1: 2px; --jp-code-cursor-width2: 4px; /* Layout * * The following are the main layout colors use in JupyterLab. In a light * theme these would go from light to dark. */ --jp-layout-color0: #111111; --jp-layout-color1: var(--md-grey-900); --jp-layout-color2: var(--md-grey-800); --jp-layout-color3: var(--md-grey-700); --jp-layout-color4: var(--md-grey-600); /* Inverse Layout * * The following are the inverse layout colors use in JupyterLab. In a light * theme these would go from dark to light. */ --jp-inverse-layout-color0: white; --jp-inverse-layout-color1: white; --jp-inverse-layout-color2: var(--md-grey-200); --jp-inverse-layout-color3: var(--md-grey-400); --jp-inverse-layout-color4: var(--md-grey-600); /* Brand/accent */ --jp-brand-color0: var(--md-blue-700); --jp-brand-color1: var(--md-blue-500); --jp-brand-color2: var(--md-blue-300); --jp-brand-color3: var(--md-blue-100); --jp-brand-color4: var(--md-blue-50); --jp-accent-color0: var(--md-green-700); --jp-accent-color1: var(--md-green-500); --jp-accent-color2: var(--md-green-300); --jp-accent-color3: var(--md-green-100); /* State colors (warn, error, success, info) */ --jp-warn-color0: var(--md-orange-700); --jp-warn-color1: var(--md-orange-500); --jp-warn-color2: var(--md-orange-300); --jp-warn-color3: var(--md-orange-100); --jp-error-color0: var(--md-red-700); --jp-error-color1: var(--md-red-500); --jp-error-color2: var(--md-red-300); --jp-error-color3: var(--md-red-100); --jp-success-color0: var(--md-green-700); --jp-success-color1: var(--md-green-500); --jp-success-color2: var(--md-green-300); --jp-success-color3: var(--md-green-100); --jp-info-color0: var(--md-cyan-700); --jp-info-color1: var(--md-cyan-500); --jp-info-color2: var(--md-cyan-300); --jp-info-color3: var(--md-cyan-100); /* Cell specific styles */ --jp-cell-padding: 5px; --jp-cell-collapser-width: 8px; --jp-cell-collapser-min-height: 20px; --jp-cell-collapser-not-active-hover-opacity: 0.6; --jp-cell-editor-background: var(--jp-layout-color1); --jp-cell-editor-border-color: var(--md-grey-700); --jp-cell-editor-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-cell-editor-active-background: var(--jp-layout-color0); --jp-cell-editor-active-border-color: var(--jp-brand-color1); --jp-cell-prompt-width: 64px; --jp-cell-prompt-font-family: var(--jp-code-font-family-default); --jp-cell-prompt-letter-spacing: 0px; --jp-cell-prompt-opacity: 1; --jp-cell-prompt-not-active-opacity: 1; --jp-cell-prompt-not-active-font-color: var(--md-grey-300); /* A custom blend of MD grey and blue 600 * See https://meyerweb.com/eric/tools/color-blend/#546E7A:1E88E5:5:hex */ --jp-cell-inprompt-font-color: #307fc1; /* A custom blend of MD grey and orange 600 * https://meyerweb.com/eric/tools/color-blend/#546E7A:F4511E:5:hex */ --jp-cell-outprompt-font-color: #bf5b3d; /* Notebook specific styles */ --jp-notebook-padding: 10px; --jp-notebook-select-background: var(--jp-layout-color1); --jp-notebook-multiselected-color: rgba(33, 150, 243, 0.24); /* The scroll padding is calculated to fill enough space at the bottom of the notebook to show one single-line cell (with appropriate padding) at the top when the notebook is scrolled all the way to the bottom. We also subtract one pixel so that no scrollbar appears if we have just one single-line cell in the notebook. This padding is to enable a 'scroll past end' feature in a notebook. */ --jp-notebook-scroll-padding: calc( 100% - var(--jp-code-font-size) * var(--jp-code-line-height) - var(--jp-code-padding) - var(--jp-cell-padding) - 1px ); /* Rendermime styles */ --jp-rendermime-error-background: rgba(244, 67, 54, 0.28); --jp-rendermime-table-row-background: var(--md-grey-900); --jp-rendermime-table-row-hover-background: rgba(3, 169, 244, 0.2); /* Dialog specific styles */ --jp-dialog-background: rgba(0, 0, 0, 0.6); /* Console specific styles */ --jp-console-padding: 10px; /* Toolbar specific styles */ --jp-toolbar-border-color: var(--jp-border-color2); --jp-toolbar-micro-height: 8px; --jp-toolbar-background: var(--jp-layout-color1); --jp-toolbar-box-shadow: 0px 0px 2px 0px rgba(0, 0, 0, 0.8); --jp-toolbar-header-margin: 4px 4px 0px 4px; --jp-toolbar-active-background: var(--jp-layout-color0); /* Statusbar specific styles */ --jp-statusbar-height: 24px; /* Input field styles */ --jp-input-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-input-active-background: var(--jp-layout-color0); --jp-input-hover-background: var(--jp-layout-color2); --jp-input-background: var(--md-grey-800); --jp-input-border-color: var(--jp-border-color1); --jp-input-active-border-color: var(--jp-brand-color1); --jp-input-active-box-shadow-color: rgba(19, 124, 189, 0.3); /* General editor styles */ --jp-editor-selected-background: var(--jp-layout-color2); --jp-editor-selected-focused-background: rgba(33, 150, 243, 0.24); --jp-editor-cursor-color: var(--jp-ui-font-color0); /* Code mirror specific styles */ --jp-mirror-editor-keyword-color: var(--md-green-500); --jp-mirror-editor-atom-color: var(--md-blue-300); --jp-mirror-editor-number-color: var(--md-green-400); --jp-mirror-editor-def-color: var(--md-blue-600); --jp-mirror-editor-variable-color: var(--md-grey-300); --jp-mirror-editor-variable-2-color: var(--md-blue-400); --jp-mirror-editor-variable-3-color: var(--md-green-600); --jp-mirror-editor-punctuation-color: var(--md-blue-400); --jp-mirror-editor-property-color: var(--md-blue-400); --jp-mirror-editor-operator-color: #aa22ff; --jp-mirror-editor-comment-color: #408080; --jp-mirror-editor-string-color: #ff7070; --jp-mirror-editor-string-2-color: var(--md-purple-300); --jp-mirror-editor-meta-color: #aa22ff; --jp-mirror-editor-qualifier-color: #555; --jp-mirror-editor-builtin-color: var(--md-green-600); --jp-mirror-editor-bracket-color: #997; --jp-mirror-editor-tag-color: var(--md-green-700); --jp-mirror-editor-attribute-color: var(--md-blue-700); --jp-mirror-editor-header-color: var(--md-blue-500); --jp-mirror-editor-quote-color: var(--md-green-300); --jp-mirror-editor-link-color: var(--md-blue-700); --jp-mirror-editor-error-color: #f00; --jp-mirror-editor-hr-color: #999; /* Vega extension styles */ --jp-vega-background: var(--md-grey-400); /* Sidebar-related styles */ --jp-sidebar-min-width: 250px; /* Search-related styles */ --jp-search-toggle-off-opacity: 0.6; --jp-search-toggle-hover-opacity: 0.8; --jp-search-toggle-on-opacity: 1; --jp-search-selected-match-background-color: rgb(255, 225, 0); --jp-search-selected-match-color: black; --jp-search-unselected-match-background-color: var( --jp-inverse-layout-color0 ); --jp-search-unselected-match-color: var(--jp-ui-inverse-font-color0); /* scrollbar related styles. Supports every browser except Edge. */ /* colors based on JetBrain's Darcula theme */ --jp-scrollbar-background-color: #3f4244; --jp-scrollbar-thumb-color: 88, 96, 97; /* need to specify thumb color as an RGB triplet */ --jp-scrollbar-endpad: 3px; /* the minimum gap between the thumb and the ends of a scrollbar */ /* hacks for setting the thumb shape. These do nothing in Firefox */ --jp-scrollbar-thumb-margin: 3.5px; /* the space in between the sides of the thumb and the track */ --jp-scrollbar-thumb-radius: 9px; /* set to a large-ish value for rounded endcaps on the thumb */ /* Icon colors that work well with light or dark backgrounds */ --jp-icon-contrast-color0: var(--md-purple-600); --jp-icon-contrast-color1: var(--md-green-600); --jp-icon-contrast-color2: var(--md-pink-600); --jp-icon-contrast-color3: var(--md-blue-600); } :root{--md-red-50: #ffebee;--md-red-100: #ffcdd2;--md-red-200: #ef9a9a;--md-red-300: #e57373;--md-red-400: #ef5350;--md-red-500: #f44336;--md-red-600: #e53935;--md-red-700: #d32f2f;--md-red-800: #c62828;--md-red-900: #b71c1c;--md-red-A100: #ff8a80;--md-red-A200: #ff5252;--md-red-A400: #ff1744;--md-red-A700: #d50000;--md-pink-50: #fce4ec;--md-pink-100: #f8bbd0;--md-pink-200: #f48fb1;--md-pink-300: #f06292;--md-pink-400: #ec407a;--md-pink-500: #e91e63;--md-pink-600: #d81b60;--md-pink-700: #c2185b;--md-pink-800: 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#7986cb;--md-indigo-400: #5c6bc0;--md-indigo-500: #3f51b5;--md-indigo-600: #3949ab;--md-indigo-700: #303f9f;--md-indigo-800: #283593;--md-indigo-900: #1a237e;--md-indigo-A100: #8c9eff;--md-indigo-A200: #536dfe;--md-indigo-A400: #3d5afe;--md-indigo-A700: #304ffe;--md-blue-50: #e3f2fd;--md-blue-100: #bbdefb;--md-blue-200: #90caf9;--md-blue-300: #64b5f6;--md-blue-400: #42a5f5;--md-blue-500: #2196f3;--md-blue-600: #1e88e5;--md-blue-700: #1976d2;--md-blue-800: #1565c0;--md-blue-900: #0d47a1;--md-blue-A100: #82b1ff;--md-blue-A200: #448aff;--md-blue-A400: #2979ff;--md-blue-A700: #2962ff;--md-light-blue-50: #e1f5fe;--md-light-blue-100: #b3e5fc;--md-light-blue-200: #81d4fa;--md-light-blue-300: #4fc3f7;--md-light-blue-400: #29b6f6;--md-light-blue-500: #03a9f4;--md-light-blue-600: #039be5;--md-light-blue-700: #0288d1;--md-light-blue-800: #0277bd;--md-light-blue-900: #01579b;--md-light-blue-A100: #80d8ff;--md-light-blue-A200: #40c4ff;--md-light-blue-A400: #00b0ff;--md-light-blue-A700: #0091ea;--md-cyan-50: #e0f7fa;--md-cyan-100: #b2ebf2;--md-cyan-200: #80deea;--md-cyan-300: #4dd0e1;--md-cyan-400: #26c6da;--md-cyan-500: #00bcd4;--md-cyan-600: #00acc1;--md-cyan-700: #0097a7;--md-cyan-800: #00838f;--md-cyan-900: #006064;--md-cyan-A100: #84ffff;--md-cyan-A200: #18ffff;--md-cyan-A400: #00e5ff;--md-cyan-A700: #00b8d4;--md-teal-50: #e0f2f1;--md-teal-100: #b2dfdb;--md-teal-200: #80cbc4;--md-teal-300: #4db6ac;--md-teal-400: #26a69a;--md-teal-500: #009688;--md-teal-600: #00897b;--md-teal-700: #00796b;--md-teal-800: #00695c;--md-teal-900: #004d40;--md-teal-A100: #a7ffeb;--md-teal-A200: #64ffda;--md-teal-A400: #1de9b6;--md-teal-A700: #00bfa5;--md-green-50: #e8f5e9;--md-green-100: #c8e6c9;--md-green-200: #a5d6a7;--md-green-300: #81c784;--md-green-400: #66bb6a;--md-green-500: #4caf50;--md-green-600: #43a047;--md-green-700: #388e3c;--md-green-800: #2e7d32;--md-green-900: #1b5e20;--md-green-A100: #b9f6ca;--md-green-A200: #69f0ae;--md-green-A400: #00e676;--md-green-A700: #00c853;--md-light-green-50: #f1f8e9;--md-light-green-100: #dcedc8;--md-light-green-200: #c5e1a5;--md-light-green-300: #aed581;--md-light-green-400: #9ccc65;--md-light-green-500: #8bc34a;--md-light-green-600: #7cb342;--md-light-green-700: #689f38;--md-light-green-800: #558b2f;--md-light-green-900: #33691e;--md-light-green-A100: #ccff90;--md-light-green-A200: #b2ff59;--md-light-green-A400: #76ff03;--md-light-green-A700: #64dd17;--md-lime-50: #f9fbe7;--md-lime-100: #f0f4c3;--md-lime-200: #e6ee9c;--md-lime-300: #dce775;--md-lime-400: #d4e157;--md-lime-500: #cddc39;--md-lime-600: #c0ca33;--md-lime-700: #afb42b;--md-lime-800: #9e9d24;--md-lime-900: #827717;--md-lime-A100: #f4ff81;--md-lime-A200: #eeff41;--md-lime-A400: #c6ff00;--md-lime-A700: #aeea00;--md-yellow-50: #fffde7;--md-yellow-100: #fff9c4;--md-yellow-200: #fff59d;--md-yellow-300: #fff176;--md-yellow-400: #ffee58;--md-yellow-500: #ffeb3b;--md-yellow-600: #fdd835;--md-yellow-700: #fbc02d;--md-yellow-800: #f9a825;--md-yellow-900: #f57f17;--md-yellow-A100: #ffff8d;--md-yellow-A200: #ffff00;--md-yellow-A400: #ffea00;--md-yellow-A700: #ffd600;--md-amber-50: #fff8e1;--md-amber-100: #ffecb3;--md-amber-200: #ffe082;--md-amber-300: #ffd54f;--md-amber-400: #ffca28;--md-amber-500: #ffc107;--md-amber-600: #ffb300;--md-amber-700: #ffa000;--md-amber-800: #ff8f00;--md-amber-900: #ff6f00;--md-amber-A100: #ffe57f;--md-amber-A200: #ffd740;--md-amber-A400: #ffc400;--md-amber-A700: #ffab00;--md-orange-50: #fff3e0;--md-orange-100: #ffe0b2;--md-orange-200: #ffcc80;--md-orange-300: #ffb74d;--md-orange-400: #ffa726;--md-orange-500: #ff9800;--md-orange-600: #fb8c00;--md-orange-700: #f57c00;--md-orange-800: #ef6c00;--md-orange-900: #e65100;--md-orange-A100: #ffd180;--md-orange-A200: #ffab40;--md-orange-A400: #ff9100;--md-orange-A700: #ff6d00;--md-deep-orange-50: #fbe9e7;--md-deep-orange-100: #ffccbc;--md-deep-orange-200: #ffab91;--md-deep-orange-300: #ff8a65;--md-deep-orange-400: #ff7043;--md-deep-orange-500: 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sourceMappingURL=mkdocs-jupyter.css.map*/ init_mathjax = function() { if (window.MathJax) { // MathJax loaded MathJax.Hub.Config({ TeX: { equationNumbers: { autoNumber: \"AMS\", useLabelIds: true } }, tex2jax: { inlineMath: [ ['$','$'], [\"\\\\(\",\"\\\\)\"] ], displayMath: [ ['$$','$$'], [\"\\\\[\",\"\\\\]\"] ], processEscapes: true, processEnvironments: true }, displayAlign: 'center', CommonHTML: { linebreaks: { automatic: true } } }); MathJax.Hub.Queue([\"Typeset\", MathJax.Hub]); } } init_mathjax(); Fine-tune SciBERT \u00b6 We are planning to do a simple classification task on scientific text. For that, SciBERT is an ideal model to fine-tune since it has been pretrained of academic publications. This notebook was updated so that it can run in Google Colab . First, we need to install the dependencies. In [1]: Copied! ! pip install transformers datasets great - ai > / dev / null !pip install transformers datasets great-ai > /dev/null Load the training data from S3. (We have uploaded this to S3 in the data notebook.) In [2]: Copied! from great_ai.large_file import LargeFileS3 import json LargeFileS3 . configure_credentials_from_file ( \"config.ini\" ) with LargeFileS3 ( \"summary-train-dataset-small\" , encoding = \"utf-8\" ) as f : # splitting training and test data is done later by `datasets` X , y = json . load ( f ) from great_ai.large_file import LargeFileS3 import json LargeFileS3.configure_credentials_from_file(\"config.ini\") with LargeFileS3(\"summary-train-dataset-small\", encoding=\"utf-8\") as f: # splitting training and test data is done later by `datasets` X, y = json.load(f) Latest version of summary-train-dataset-small is 0 (from versions: 0) File summary-train-dataset-small-0 found in cache Finetune SciBERT, for more info about this step, check out HuggingFace . If you're only here for great-ai , feel free to skip the next cell. In [22]: Copied! from transformers import ( AutoModelForSequenceClassification , AutoTokenizer , DataCollatorWithPadding , Trainer , TrainingArguments , EarlyStoppingCallback , ) from pathlib import Path import numpy as np from datasets import Dataset , load_metric MODEL = \"allenai/scibert_scivocab_uncased\" BATCH_SIZE = 32 tokenizer = AutoTokenizer . from_pretrained ( MODEL ) model = AutoModelForSequenceClassification . from_pretrained ( MODEL , num_labels = 2 ) data_collator = DataCollatorWithPadding ( tokenizer = tokenizer ) def tokenize_function ( v ): return tokenizer ( v [ \"text\" ]) dataset = ( Dataset . from_dict ({ \"text\" : X , \"label\" : y }) . map ( lambda v : tokenizer ( v [ \"text\" ], truncation = True ), batched = True ) . remove_columns ( \"text\" ) . train_test_split ( test_size = 0.2 , shuffle = True ) # test is actually validation ) f1_score = load_metric ( \"f1\" ) def compute_metrics ( p ): pred , labels = p pred = np . argmax ( pred , axis = 1 ) return f1_score . compute ( predictions = pred , references = labels ) training_args = TrainingArguments ( output_dir = Path ( \"models\" ), per_device_train_batch_size = BATCH_SIZE , per_device_eval_batch_size = BATCH_SIZE , save_total_limit = 5 , num_train_epochs = 50 , save_strategy = \"epoch\" , evaluation_strategy = \"epoch\" , logging_strategy = \"epoch\" , weight_decay = 0.01 , metric_for_best_model = \"f1\" , load_best_model_at_end = True , ) result = Trainer ( model = model , args = training_args , train_dataset = dataset [ \"train\" ], eval_dataset = dataset [ \"test\" ], data_collator = data_collator , compute_metrics = compute_metrics , callbacks = [ EarlyStoppingCallback ( early_stopping_patience = 5 )], ) . train () from transformers import ( AutoModelForSequenceClassification, AutoTokenizer, DataCollatorWithPadding, Trainer, TrainingArguments, EarlyStoppingCallback, ) from pathlib import Path import numpy as np from datasets import Dataset, load_metric MODEL = \"allenai/scibert_scivocab_uncased\" BATCH_SIZE = 32 tokenizer = AutoTokenizer.from_pretrained(MODEL) model = AutoModelForSequenceClassification.from_pretrained(MODEL, num_labels=2) data_collator = DataCollatorWithPadding(tokenizer=tokenizer) def tokenize_function(v): return tokenizer(v[\"text\"]) dataset = ( Dataset.from_dict({\"text\": X, \"label\": y}) .map(lambda v: tokenizer(v[\"text\"], truncation=True), batched=True) .remove_columns(\"text\") .train_test_split(test_size=0.2, shuffle=True) # test is actually validation ) f1_score = load_metric(\"f1\") def compute_metrics(p): pred, labels = p pred = np.argmax(pred, axis=1) return f1_score.compute(predictions=pred, references=labels) training_args = TrainingArguments( output_dir=Path(\"models\"), per_device_train_batch_size=BATCH_SIZE, per_device_eval_batch_size=BATCH_SIZE, save_total_limit=5, num_train_epochs=50, save_strategy=\"epoch\", evaluation_strategy=\"epoch\", logging_strategy=\"epoch\", weight_decay=0.01, metric_for_best_model=\"f1\", load_best_model_at_end=True, ) result = Trainer( model=model, args=training_args, train_dataset=dataset[\"train\"], eval_dataset=dataset[\"test\"], data_collator=data_collator, compute_metrics=compute_metrics, callbacks=[EarlyStoppingCallback(early_stopping_patience=5)], ).train() 0%| | 0/1 [00:00 / dev / null !pip install transformers datasets great-ai > /dev/null Load the training data from S3. (We have uploaded this to S3 in the data notebook.) In [2]: Copied! from great_ai.large_file import LargeFileS3 import json LargeFileS3 . configure_credentials_from_file ( \"config.ini\" ) with LargeFileS3 ( \"summary-train-dataset-small\" , encoding = \"utf-8\" ) as f : # splitting training and test data is done later by `datasets` X , y = json . load ( f ) from great_ai.large_file import LargeFileS3 import json LargeFileS3.configure_credentials_from_file(\"config.ini\") with LargeFileS3(\"summary-train-dataset-small\", encoding=\"utf-8\") as f: # splitting training and test data is done later by `datasets` X, y = json.load(f) Latest version of summary-train-dataset-small is 0 (from versions: 0) File summary-train-dataset-small-0 found in cache Finetune SciBERT, for more info about this step, check out HuggingFace . If you're only here for great-ai , feel free to skip the next cell. In [22]: Copied! from transformers import ( AutoModelForSequenceClassification , AutoTokenizer , DataCollatorWithPadding , Trainer , TrainingArguments , EarlyStoppingCallback , ) from pathlib import Path import numpy as np from datasets import Dataset , load_metric MODEL = \"allenai/scibert_scivocab_uncased\" BATCH_SIZE = 32 tokenizer = AutoTokenizer . from_pretrained ( MODEL ) model = AutoModelForSequenceClassification . from_pretrained ( MODEL , num_labels = 2 ) data_collator = DataCollatorWithPadding ( tokenizer = tokenizer ) def tokenize_function ( v ): return tokenizer ( v [ \"text\" ]) dataset = ( Dataset . from_dict ({ \"text\" : X , \"label\" : y }) . map ( lambda v : tokenizer ( v [ \"text\" ], truncation = True ), batched = True ) . remove_columns ( \"text\" ) . train_test_split ( test_size = 0.2 , shuffle = True ) # test is actually validation ) f1_score = load_metric ( \"f1\" ) def compute_metrics ( p ): pred , labels = p pred = np . argmax ( pred , axis = 1 ) return f1_score . compute ( predictions = pred , references = labels ) training_args = TrainingArguments ( output_dir = Path ( \"models\" ), per_device_train_batch_size = BATCH_SIZE , per_device_eval_batch_size = BATCH_SIZE , save_total_limit = 5 , num_train_epochs = 50 , save_strategy = \"epoch\" , evaluation_strategy = \"epoch\" , logging_strategy = \"epoch\" , weight_decay = 0.01 , metric_for_best_model = \"f1\" , load_best_model_at_end = True , ) result = Trainer ( model = model , args = training_args , train_dataset = dataset [ \"train\" ], eval_dataset = dataset [ \"test\" ], data_collator = data_collator , compute_metrics = compute_metrics , callbacks = [ EarlyStoppingCallback ( early_stopping_patience = 5 )], ) . train () from transformers import ( AutoModelForSequenceClassification, AutoTokenizer, DataCollatorWithPadding, Trainer, TrainingArguments, EarlyStoppingCallback, ) from pathlib import Path import numpy as np from datasets import Dataset, load_metric MODEL = \"allenai/scibert_scivocab_uncased\" BATCH_SIZE = 32 tokenizer = AutoTokenizer.from_pretrained(MODEL) model = AutoModelForSequenceClassification.from_pretrained(MODEL, num_labels=2) data_collator = DataCollatorWithPadding(tokenizer=tokenizer) def tokenize_function(v): return tokenizer(v[\"text\"]) dataset = ( Dataset.from_dict({\"text\": X, \"label\": y}) .map(lambda v: tokenizer(v[\"text\"], truncation=True), batched=True) .remove_columns(\"text\") .train_test_split(test_size=0.2, shuffle=True) # test is actually validation ) f1_score = load_metric(\"f1\") def compute_metrics(p): pred, labels = p pred = np.argmax(pred, axis=1) return f1_score.compute(predictions=pred, references=labels) training_args = TrainingArguments( output_dir=Path(\"models\"), per_device_train_batch_size=BATCH_SIZE, per_device_eval_batch_size=BATCH_SIZE, save_total_limit=5, num_train_epochs=50, save_strategy=\"epoch\", evaluation_strategy=\"epoch\", logging_strategy=\"epoch\", weight_decay=0.01, metric_for_best_model=\"f1\", load_best_model_at_end=True, ) result = Trainer( model=model, args=training_args, train_dataset=dataset[\"train\"], eval_dataset=dataset[\"test\"], data_collator=data_collator, compute_metrics=compute_metrics, callbacks=[EarlyStoppingCallback(early_stopping_patience=5)], ).train() 0%| | 0/1 [00:00.p-SplitPanel-handle,.jupyter-wrapper .lm-SplitPanel[data-orientation=horizontal]>.lm-SplitPanel-handle{cursor:ew-resize}.jupyter-wrapper .p-SplitPanel[data-orientation=vertical]>.p-SplitPanel-handle,.jupyter-wrapper .lm-SplitPanel[data-orientation=vertical]>.lm-SplitPanel-handle{cursor:ns-resize}.jupyter-wrapper .p-SplitPanel[data-orientation=horizontal]>.p-SplitPanel-handle:after,.jupyter-wrapper .lm-SplitPanel[data-orientation=horizontal]>.lm-SplitPanel-handle:after{left:50%;min-width:8px;transform:translateX(-50%)}.jupyter-wrapper .p-SplitPanel[data-orientation=vertical]>.p-SplitPanel-handle:after,.jupyter-wrapper 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sourceMappingURL=mkdocs-jupyter.css.map*/ init_mathjax = function() { if (window.MathJax) { // MathJax loaded MathJax.Hub.Config({ TeX: { equationNumbers: { autoNumber: \"AMS\", useLabelIds: true } }, tex2jax: { inlineMath: [ ['$','$'], [\"\\\\(\",\"\\\\)\"] ], displayMath: [ ['$$','$$'], [\"\\\\[\",\"\\\\]\"] ], processEscapes: true, processEnvironments: true }, displayAlign: 'center', CommonHTML: { linebreaks: { automatic: true } } }); MathJax.Hub.Queue([\"Typeset\", MathJax.Hub]); } } init_mathjax(); Simple example: data engineering \u00b6 Here, we solve a problem similar to the tutorial's but with an explainable Naive Bayes classifier and more best practices. In short, we train a domain classifier on the semantic scholar dataset by taking full advantage of great-ai . Subsequently, we create a production-ready deployment. The blue boxes show the steps of a typical AI-development lifecycle implemented in this notebook. Since the true scope of great-ai is the phase between proof-of-concept code and production-ready service, it is predominantly used in the deployment notebook . Feel free to skip there, or continue reading if you'd like to see the full picture. Extract \u00b6 This can be achieved by downloading a public dataset (such as in this case), or by having a Data Engineer setup and give us access to the organisation's data. In this example, we download the semantic scholar dataset from a public S3 bucket. In [1]: Copied! MAX_CHUNK_COUNT = 4 MAX_CHUNK_COUNT = 4 In [2]: Copied! import urllib.request from random import shuffle manifest = ( urllib . request . urlopen ( \"https://s3-us-west-2.amazonaws.com/ai2-s2-research-public/\" \"open-corpus/2022-02-01/manifest.txt\" ) . read () . decode () ) # a list of available chunks separated by '\\n' characters lines = manifest . split () shuffle ( lines ) chunks = lines [: MAX_CHUNK_COUNT ] f \"\"\"Processing { len ( chunks ) } out of the { len ( manifest . split ()) } available chunks\"\"\" import urllib.request from random import shuffle manifest = ( urllib.request.urlopen( \"https://s3-us-west-2.amazonaws.com/ai2-s2-research-public/\" \"open-corpus/2022-02-01/manifest.txt\" ) .read() .decode() ) # a list of available chunks separated by '\\n' characters lines = manifest.split() shuffle(lines) chunks = lines[:MAX_CHUNK_COUNT] f\"\"\"Processing {len(chunks)} out of the { len(manifest.split()) } available chunks\"\"\" Out[2]: 'Processing 4 out of the 6002 available chunks' Transform \u00b6 Filter out non-English abstracts using great_ai.utilities.predict_language Project it to only keep the necessary components (text and labels), clean the textual content using great_ai.utilities.clean We will speed up processing using great_ai.utilities.simple_parallel_map . In [3]: Copied! from typing import List , Tuple import json import gzip from great_ai.utilities import ( simple_parallel_map , clean , is_english , predict_language , unchunk , ) def preprocess_chunk ( chunk_key : str ) -> List [ Tuple [ str , List [ str ]]]: response = urllib . request . urlopen ( f \"https://s3-us-west-2.amazonaws.com/ai2-s2-research-public/\" f \"open-corpus/2022-02-01/ { chunk_key } \" ) # a gzipped JSON Lines file decompressed = gzip . decompress ( response . read ()) decoded = decompressed . decode () chunk = [ json . loads ( line ) for line in decoded . split ( \" \\n \" ) if line ] # Transform return [ ( clean ( f ' { c [ \"title\" ] } { c [ \"paperAbstract\" ] } ' f ' { c [ \"journalName\" ] } { c [ \"venue\" ] } ' , convert_to_ascii = True , ), # The text is cleaned to remove common artifacts c [ \"fieldsOfStudy\" ], ) # Create pairs of `(text, [...domains])` for c in chunk if ( c [ \"fieldsOfStudy\" ] and is_english ( predict_language ( c [ \"paperAbstract\" ]))) ] preprocessed_data = unchunk ( simple_parallel_map ( preprocess_chunk , chunks , concurrency = 4 ) ) from typing import List, Tuple import json import gzip from great_ai.utilities import ( simple_parallel_map, clean, is_english, predict_language, unchunk, ) def preprocess_chunk(chunk_key: str) -> List[Tuple[str, List[str]]]: response = urllib.request.urlopen( f\"https://s3-us-west-2.amazonaws.com/ai2-s2-research-public/\" f\"open-corpus/2022-02-01/{chunk_key}\" ) # a gzipped JSON Lines file decompressed = gzip.decompress(response.read()) decoded = decompressed.decode() chunk = [json.loads(line) for line in decoded.split(\"\\n\") if line] # Transform return [ ( clean( f'{c[\"title\"]} {c[\"paperAbstract\"]} ' f'{c[\"journalName\"]} {c[\"venue\"]}', convert_to_ascii=True, ), # The text is cleaned to remove common artifacts c[\"fieldsOfStudy\"], ) # Create pairs of `(text, [...domains])` for c in chunk if (c[\"fieldsOfStudy\"] and is_english(predict_language(c[\"paperAbstract\"]))) ] preprocessed_data = unchunk( simple_parallel_map(preprocess_chunk, chunks, concurrency=4) ) 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 4/4 [04:22<00:00, 65.51s/it] In [4]: Copied! X , y = zip ( * preprocessed_data ) # X is the input, y is the expected output X, y = zip(*preprocessed_data) # X is the input, y is the expected output Load \u00b6 Upload the dataset (or a part of it) to a central repository using great_ai.add_ground_truth . This step automatically tags each data-point with a split label according to the ratios we set. Additional tags can be also given. Production-ready backend \u00b6 The MongoDB driver is automatically configured if mongo.ini exists with the following scheme: mongo_connection_string = mongodb://localhost:27017/ mongo_database = my_great_ai_db You can install MongoDB from here or use it as a service Otherwise, TinyDB is used which is just a local JSON file. In [5]: Copied! from great_ai import add_ground_truth add_ground_truth ( X , y , train_split_ratio = 0.8 , test_split_ratio = 0.2 ) from great_ai import add_ground_truth add_ground_truth(X, y, train_split_ratio=0.8, test_split_ratio=0.2) Environment variable ENVIRONMENT is not set, defaulting to development mode \u203c\ufe0f Cannot find credentials files, defaulting to using ParallelTinyDbDriver The selected tracing database (ParallelTinyDbDriver) is not recommended for production Cannot find credentials files, defaulting to using LargeFileLocal GreatAI (v0.1.6): configured \u2705 \ud83d\udd29 tracing_database: ParallelTinyDbDriver \ud83d\udd29 large_file_implementation: LargeFileLocal \ud83d\udd29 is_production: False \ud83d\udd29 should_log_exception_stack: True \ud83d\udd29 prediction_cache_size: 512 \ud83d\udd29 dashboard_table_size: 50 You still need to check whether you follow all best practices before trusting your deployment. > Find out more at https://se-ml.github.io/practices Next: Part 2 \u00b6","title":"Simple example: data engineering"},{"location":"examples/simple/data/#simple-example-data-engineering","text":"Here, we solve a problem similar to the tutorial's but with an explainable Naive Bayes classifier and more best practices. In short, we train a domain classifier on the semantic scholar dataset by taking full advantage of great-ai . Subsequently, we create a production-ready deployment. The blue boxes show the steps of a typical AI-development lifecycle implemented in this notebook. Since the true scope of great-ai is the phase between proof-of-concept code and production-ready service, it is predominantly used in the deployment notebook . Feel free to skip there, or continue reading if you'd like to see the full picture.","title":"Simple example: data engineering"},{"location":"examples/simple/data/#extract","text":"This can be achieved by downloading a public dataset (such as in this case), or by having a Data Engineer setup and give us access to the organisation's data. In this example, we download the semantic scholar dataset from a public S3 bucket. In [1]: Copied! MAX_CHUNK_COUNT = 4 MAX_CHUNK_COUNT = 4 In [2]: Copied! import urllib.request from random import shuffle manifest = ( urllib . request . urlopen ( \"https://s3-us-west-2.amazonaws.com/ai2-s2-research-public/\" \"open-corpus/2022-02-01/manifest.txt\" ) . read () . decode () ) # a list of available chunks separated by '\\n' characters lines = manifest . split () shuffle ( lines ) chunks = lines [: MAX_CHUNK_COUNT ] f \"\"\"Processing { len ( chunks ) } out of the { len ( manifest . split ()) } available chunks\"\"\" import urllib.request from random import shuffle manifest = ( urllib.request.urlopen( \"https://s3-us-west-2.amazonaws.com/ai2-s2-research-public/\" \"open-corpus/2022-02-01/manifest.txt\" ) .read() .decode() ) # a list of available chunks separated by '\\n' characters lines = manifest.split() shuffle(lines) chunks = lines[:MAX_CHUNK_COUNT] f\"\"\"Processing {len(chunks)} out of the { len(manifest.split()) } available chunks\"\"\" Out[2]: 'Processing 4 out of the 6002 available chunks'","title":"Extract"},{"location":"examples/simple/data/#transform","text":"Filter out non-English abstracts using great_ai.utilities.predict_language Project it to only keep the necessary components (text and labels), clean the textual content using great_ai.utilities.clean We will speed up processing using great_ai.utilities.simple_parallel_map . In [3]: Copied! from typing import List , Tuple import json import gzip from great_ai.utilities import ( simple_parallel_map , clean , is_english , predict_language , unchunk , ) def preprocess_chunk ( chunk_key : str ) -> List [ Tuple [ str , List [ str ]]]: response = urllib . request . urlopen ( f \"https://s3-us-west-2.amazonaws.com/ai2-s2-research-public/\" f \"open-corpus/2022-02-01/ { chunk_key } \" ) # a gzipped JSON Lines file decompressed = gzip . decompress ( response . read ()) decoded = decompressed . decode () chunk = [ json . loads ( line ) for line in decoded . split ( \" \\n \" ) if line ] # Transform return [ ( clean ( f ' { c [ \"title\" ] } { c [ \"paperAbstract\" ] } ' f ' { c [ \"journalName\" ] } { c [ \"venue\" ] } ' , convert_to_ascii = True , ), # The text is cleaned to remove common artifacts c [ \"fieldsOfStudy\" ], ) # Create pairs of `(text, [...domains])` for c in chunk if ( c [ \"fieldsOfStudy\" ] and is_english ( predict_language ( c [ \"paperAbstract\" ]))) ] preprocessed_data = unchunk ( simple_parallel_map ( preprocess_chunk , chunks , concurrency = 4 ) ) from typing import List, Tuple import json import gzip from great_ai.utilities import ( simple_parallel_map, clean, is_english, predict_language, unchunk, ) def preprocess_chunk(chunk_key: str) -> List[Tuple[str, List[str]]]: response = urllib.request.urlopen( f\"https://s3-us-west-2.amazonaws.com/ai2-s2-research-public/\" f\"open-corpus/2022-02-01/{chunk_key}\" ) # a gzipped JSON Lines file decompressed = gzip.decompress(response.read()) decoded = decompressed.decode() chunk = [json.loads(line) for line in decoded.split(\"\\n\") if line] # Transform return [ ( clean( f'{c[\"title\"]} {c[\"paperAbstract\"]} ' f'{c[\"journalName\"]} {c[\"venue\"]}', convert_to_ascii=True, ), # The text is cleaned to remove common artifacts c[\"fieldsOfStudy\"], ) # Create pairs of `(text, [...domains])` for c in chunk if (c[\"fieldsOfStudy\"] and is_english(predict_language(c[\"paperAbstract\"]))) ] preprocessed_data = unchunk( simple_parallel_map(preprocess_chunk, chunks, concurrency=4) ) 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 4/4 [04:22<00:00, 65.51s/it] In [4]: Copied! X , y = zip ( * preprocessed_data ) # X is the input, y is the expected output X, y = zip(*preprocessed_data) # X is the input, y is the expected output","title":"Transform"},{"location":"examples/simple/data/#load","text":"Upload the dataset (or a part of it) to a central repository using great_ai.add_ground_truth . This step automatically tags each data-point with a split label according to the ratios we set. Additional tags can be also given.","title":"Load"},{"location":"examples/simple/data/#production-ready-backend","text":"The MongoDB driver is automatically configured if mongo.ini exists with the following scheme: mongo_connection_string = mongodb://localhost:27017/ mongo_database = my_great_ai_db You can install MongoDB from here or use it as a service Otherwise, TinyDB is used which is just a local JSON file. In [5]: Copied! from great_ai import add_ground_truth add_ground_truth ( X , y , train_split_ratio = 0.8 , test_split_ratio = 0.2 ) from great_ai import add_ground_truth add_ground_truth(X, y, train_split_ratio=0.8, test_split_ratio=0.2) Environment variable ENVIRONMENT is not set, defaulting to development mode \u203c\ufe0f Cannot find credentials files, defaulting to using ParallelTinyDbDriver The selected tracing database (ParallelTinyDbDriver) is not recommended for production Cannot find credentials files, defaulting to using LargeFileLocal GreatAI (v0.1.6): configured \u2705 \ud83d\udd29 tracing_database: ParallelTinyDbDriver \ud83d\udd29 large_file_implementation: LargeFileLocal \ud83d\udd29 is_production: False \ud83d\udd29 should_log_exception_stack: True \ud83d\udd29 prediction_cache_size: 512 \ud83d\udd29 dashboard_table_size: 50 You still need to check whether you follow all best practices before trusting your deployment. > Find out more at https://se-ml.github.io/practices","title":"Production-ready backend"},{"location":"examples/simple/data/#next-part-2","text":"","title":"Next: Part 2"},{"location":"examples/simple/deploy/","text":"(function (global, factory) { typeof exports === 'object' && typeof module !== 'undefined' ? module.exports = factory() : typeof define === 'function' && define.amd ? define(factory) : (global = global || self, global.ClipboardCopyElement = factory()); 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When children elements, such as headings, are sized * in em all things will be computed relative to that body size. */ --jp-content-line-height: 1.6; --jp-content-font-scale-factor: 1.2; --jp-content-font-size0: 0.83333em; --jp-content-font-size1: 14px; /* Base font size */ --jp-content-font-size2: 1.2em; --jp-content-font-size3: 1.44em; --jp-content-font-size4: 1.728em; --jp-content-font-size5: 2.0736em; /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-content-presentation-font-size1: 17px; --jp-content-heading-line-height: 1; --jp-content-heading-margin-top: 1.2em; --jp-content-heading-margin-bottom: 0.8em; --jp-content-heading-font-weight: 500; /* Defaults use Material Design specification */ --jp-content-font-color0: rgba(0, 0, 0, 1); --jp-content-font-color1: rgba(0, 0, 0, 0.87); --jp-content-font-color2: rgba(0, 0, 0, 0.54); --jp-content-font-color3: rgba(0, 0, 0, 0.38); --jp-content-link-color: var(--md-blue-700); --jp-content-font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Helvetica, Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\"; /* * Code Fonts * * Code font variables are used for typography of code and other monospaces content. */ --jp-code-font-size: 13px; --jp-code-line-height: 1.3077; /* 17px for 13px base */ --jp-code-padding: 5px; /* 5px for 13px base, codemirror highlighting needs integer px value */ --jp-code-font-family-default: Menlo, Consolas, \"DejaVu Sans Mono\", monospace; --jp-code-font-family: var(--jp-code-font-family-default); /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-code-presentation-font-size: 16px; /* may need to tweak cursor width if you change font size */ --jp-code-cursor-width0: 1.4px; --jp-code-cursor-width1: 2px; --jp-code-cursor-width2: 4px; /* Layout * * The following are the main layout colors use in JupyterLab. In a light * theme these would go from light to dark. */ --jp-layout-color0: white; --jp-layout-color1: white; --jp-layout-color2: var(--md-grey-200); --jp-layout-color3: var(--md-grey-400); --jp-layout-color4: var(--md-grey-600); /* Inverse Layout * * The following are the inverse layout colors use in JupyterLab. In a light * theme these would go from dark to light. */ --jp-inverse-layout-color0: #111111; --jp-inverse-layout-color1: var(--md-grey-900); --jp-inverse-layout-color2: var(--md-grey-800); --jp-inverse-layout-color3: var(--md-grey-700); --jp-inverse-layout-color4: var(--md-grey-600); /* Brand/accent */ --jp-brand-color0: var(--md-blue-900); --jp-brand-color1: var(--md-blue-700); --jp-brand-color2: var(--md-blue-300); --jp-brand-color3: var(--md-blue-100); --jp-brand-color4: var(--md-blue-50); --jp-accent-color0: var(--md-green-900); --jp-accent-color1: var(--md-green-700); --jp-accent-color2: var(--md-green-300); --jp-accent-color3: var(--md-green-100); /* State colors (warn, error, success, info) */ --jp-warn-color0: var(--md-orange-900); --jp-warn-color1: var(--md-orange-700); --jp-warn-color2: var(--md-orange-300); --jp-warn-color3: var(--md-orange-100); --jp-error-color0: var(--md-red-900); --jp-error-color1: var(--md-red-700); --jp-error-color2: var(--md-red-300); --jp-error-color3: var(--md-red-100); --jp-success-color0: var(--md-green-900); --jp-success-color1: var(--md-green-700); --jp-success-color2: var(--md-green-300); --jp-success-color3: var(--md-green-100); --jp-info-color0: var(--md-cyan-900); --jp-info-color1: var(--md-cyan-700); --jp-info-color2: var(--md-cyan-300); --jp-info-color3: var(--md-cyan-100); /* Cell specific styles */ --jp-cell-padding: 5px; --jp-cell-collapser-width: 8px; --jp-cell-collapser-min-height: 20px; --jp-cell-collapser-not-active-hover-opacity: 0.6; --jp-cell-editor-background: var(--md-grey-100); --jp-cell-editor-border-color: var(--md-grey-300); --jp-cell-editor-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-cell-editor-active-background: var(--jp-layout-color0); --jp-cell-editor-active-border-color: var(--jp-brand-color1); --jp-cell-prompt-width: 64px; --jp-cell-prompt-font-family: var(--jp-code-font-family-default); --jp-cell-prompt-letter-spacing: 0px; --jp-cell-prompt-opacity: 1; --jp-cell-prompt-not-active-opacity: 0.5; --jp-cell-prompt-not-active-font-color: var(--md-grey-700); /* A custom blend of MD grey and blue 600 * See https://meyerweb.com/eric/tools/color-blend/#546E7A:1E88E5:5:hex */ --jp-cell-inprompt-font-color: #307fc1; /* A custom blend of MD grey and orange 600 * https://meyerweb.com/eric/tools/color-blend/#546E7A:F4511E:5:hex */ --jp-cell-outprompt-font-color: #bf5b3d; /* Notebook specific styles */ --jp-notebook-padding: 10px; --jp-notebook-select-background: var(--jp-layout-color1); --jp-notebook-multiselected-color: var(--md-blue-50); /* The scroll padding is calculated to fill enough space at the bottom of the notebook to show one single-line cell (with appropriate padding) at the top when the notebook is scrolled all the way to the bottom. We also subtract one pixel so that no scrollbar appears if we have just one single-line cell in the notebook. This padding is to enable a 'scroll past end' feature in a notebook. */ --jp-notebook-scroll-padding: calc( 100% - var(--jp-code-font-size) * var(--jp-code-line-height) - var(--jp-code-padding) - var(--jp-cell-padding) - 1px ); /* Rendermime styles */ --jp-rendermime-error-background: #fdd; --jp-rendermime-table-row-background: var(--md-grey-100); --jp-rendermime-table-row-hover-background: var(--md-light-blue-50); /* Dialog specific styles */ --jp-dialog-background: rgba(0, 0, 0, 0.25); /* Console specific styles */ --jp-console-padding: 10px; /* Toolbar specific styles */ --jp-toolbar-border-color: var(--jp-border-color1); --jp-toolbar-micro-height: 8px; --jp-toolbar-background: var(--jp-layout-color1); --jp-toolbar-box-shadow: 0px 0px 2px 0px rgba(0, 0, 0, 0.24); --jp-toolbar-header-margin: 4px 4px 0px 4px; --jp-toolbar-active-background: var(--md-grey-300); /* Statusbar specific styles */ --jp-statusbar-height: 24px; /* Input field styles */ --jp-input-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-input-active-background: var(--jp-layout-color1); --jp-input-hover-background: var(--jp-layout-color1); --jp-input-background: var(--md-grey-100); --jp-input-border-color: var(--jp-border-color1); --jp-input-active-border-color: var(--jp-brand-color1); --jp-input-active-box-shadow-color: rgba(19, 124, 189, 0.3); /* General editor styles */ --jp-editor-selected-background: #d9d9d9; --jp-editor-selected-focused-background: #d7d4f0; --jp-editor-cursor-color: var(--jp-ui-font-color0); /* Code mirror specific styles */ --jp-mirror-editor-keyword-color: #008000; --jp-mirror-editor-atom-color: #88f; --jp-mirror-editor-number-color: #080; --jp-mirror-editor-def-color: #00f; --jp-mirror-editor-variable-color: var(--md-grey-900); --jp-mirror-editor-variable-2-color: #05a; --jp-mirror-editor-variable-3-color: #085; --jp-mirror-editor-punctuation-color: #05a; --jp-mirror-editor-property-color: #05a; --jp-mirror-editor-operator-color: #aa22ff; --jp-mirror-editor-comment-color: #408080; --jp-mirror-editor-string-color: #ba2121; --jp-mirror-editor-string-2-color: #708; --jp-mirror-editor-meta-color: #aa22ff; --jp-mirror-editor-qualifier-color: #555; --jp-mirror-editor-builtin-color: #008000; --jp-mirror-editor-bracket-color: #997; --jp-mirror-editor-tag-color: #170; --jp-mirror-editor-attribute-color: #00c; --jp-mirror-editor-header-color: blue; --jp-mirror-editor-quote-color: #090; --jp-mirror-editor-link-color: #00c; --jp-mirror-editor-error-color: #f00; --jp-mirror-editor-hr-color: #999; /* Vega extension styles */ --jp-vega-background: white; /* Sidebar-related styles */ --jp-sidebar-min-width: 250px; /* Search-related styles */ --jp-search-toggle-off-opacity: 0.5; --jp-search-toggle-hover-opacity: 0.8; --jp-search-toggle-on-opacity: 1; --jp-search-selected-match-background-color: rgb(245, 200, 0); --jp-search-selected-match-color: black; --jp-search-unselected-match-background-color: var( --jp-inverse-layout-color0 ); --jp-search-unselected-match-color: var(--jp-ui-inverse-font-color0); /* Icon colors that work well with light or dark backgrounds */ --jp-icon-contrast-color0: var(--md-purple-600); --jp-icon-contrast-color1: var(--md-green-600); --jp-icon-contrast-color2: var(--md-pink-600); --jp-icon-contrast-color3: var(--md-blue-600); } [data-md-color-scheme=\"slate\"] .jupyter-wrapper { /* Elevation * * We style box-shadows using Material Design's idea of elevation. These particular numbers are taken from here: * * https://github.com/material-components/material-components-web * https://material-components-web.appspot.com/elevation.html */ /* The dark theme shadows need a bit of work, but this will probably also require work on the core layout * colors used in the theme as well. */ --jp-shadow-base-lightness: 32; --jp-shadow-umbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.2 ); --jp-shadow-penumbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.14 ); --jp-shadow-ambient-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.12 ); --jp-elevation-z0: none; --jp-elevation-z1: 0px 2px 1px -1px var(--jp-shadow-umbra-color), 0px 1px 1px 0px var(--jp-shadow-penumbra-color), 0px 1px 3px 0px var(--jp-shadow-ambient-color); --jp-elevation-z2: 0px 3px 1px -2px var(--jp-shadow-umbra-color), 0px 2px 2px 0px var(--jp-shadow-penumbra-color), 0px 1px 5px 0px var(--jp-shadow-ambient-color); --jp-elevation-z4: 0px 2px 4px -1px var(--jp-shadow-umbra-color), 0px 4px 5px 0px var(--jp-shadow-penumbra-color), 0px 1px 10px 0px var(--jp-shadow-ambient-color); --jp-elevation-z6: 0px 3px 5px -1px var(--jp-shadow-umbra-color), 0px 6px 10px 0px var(--jp-shadow-penumbra-color), 0px 1px 18px 0px var(--jp-shadow-ambient-color); --jp-elevation-z8: 0px 5px 5px -3px var(--jp-shadow-umbra-color), 0px 8px 10px 1px var(--jp-shadow-penumbra-color), 0px 3px 14px 2px var(--jp-shadow-ambient-color); --jp-elevation-z12: 0px 7px 8px -4px var(--jp-shadow-umbra-color), 0px 12px 17px 2px var(--jp-shadow-penumbra-color), 0px 5px 22px 4px var(--jp-shadow-ambient-color); --jp-elevation-z16: 0px 8px 10px -5px var(--jp-shadow-umbra-color), 0px 16px 24px 2px var(--jp-shadow-penumbra-color), 0px 6px 30px 5px var(--jp-shadow-ambient-color); --jp-elevation-z20: 0px 10px 13px -6px var(--jp-shadow-umbra-color), 0px 20px 31px 3px var(--jp-shadow-penumbra-color), 0px 8px 38px 7px var(--jp-shadow-ambient-color); --jp-elevation-z24: 0px 11px 15px -7px var(--jp-shadow-umbra-color), 0px 24px 38px 3px var(--jp-shadow-penumbra-color), 0px 9px 46px 8px var(--jp-shadow-ambient-color); /* Borders * * The following variables, specify the visual styling of borders in JupyterLab. */ --jp-border-width: 1px; --jp-border-color0: var(--md-grey-700); --jp-border-color1: var(--md-grey-700); --jp-border-color2: var(--md-grey-800); --jp-border-color3: var(--md-grey-900); --jp-border-radius: 2px; /* UI Fonts * * The UI font CSS variables are used for the typography all of the JupyterLab * user interface elements that are not directly user generated content. * * The font sizing here is done assuming that the body font size of --jp-ui-font-size1 * is applied to a parent element. When children elements, such as headings, are sized * in em all things will be computed relative to that body size. */ --jp-ui-font-scale-factor: 1.2; --jp-ui-font-size0: 0.83333em; --jp-ui-font-size1: 13px; /* Base font size */ --jp-ui-font-size2: 1.2em; --jp-ui-font-size3: 1.44em; --jp-ui-font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Helvetica, Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\"; /* * Use these font colors against the corresponding main layout colors. * In a light theme, these go from dark to light. */ /* Defaults use Material Design specification */ --jp-ui-font-color0: rgba(255, 255, 255, 1); --jp-ui-font-color1: rgba(255, 255, 255, 0.87); --jp-ui-font-color2: rgba(255, 255, 255, 0.54); --jp-ui-font-color3: rgba(255, 255, 255, 0.38); /* * Use these against the brand/accent/warn/error colors. * These will typically go from light to darker, in both a dark and light theme. */ --jp-ui-inverse-font-color0: rgba(0, 0, 0, 1); --jp-ui-inverse-font-color1: rgba(0, 0, 0, 0.8); --jp-ui-inverse-font-color2: rgba(0, 0, 0, 0.5); --jp-ui-inverse-font-color3: rgba(0, 0, 0, 0.3); /* Content Fonts * * Content font variables are used for typography of user generated content. * * The font sizing here is done assuming that the body font size of --jp-content-font-size1 * is applied to a parent element. When children elements, such as headings, are sized * in em all things will be computed relative to that body size. */ --jp-content-line-height: 1.6; --jp-content-font-scale-factor: 1.2; --jp-content-font-size0: 0.83333em; --jp-content-font-size1: 14px; /* Base font size */ --jp-content-font-size2: 1.2em; --jp-content-font-size3: 1.44em; --jp-content-font-size4: 1.728em; --jp-content-font-size5: 2.0736em; /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-content-presentation-font-size1: 17px; --jp-content-heading-line-height: 1; --jp-content-heading-margin-top: 1.2em; --jp-content-heading-margin-bottom: 0.8em; --jp-content-heading-font-weight: 500; /* Defaults use Material Design specification */ --jp-content-font-color0: rgba(255, 255, 255, 1); --jp-content-font-color1: rgba(255, 255, 255, 1); --jp-content-font-color2: rgba(255, 255, 255, 0.7); --jp-content-font-color3: rgba(255, 255, 255, 0.5); --jp-content-link-color: var(--md-blue-300); --jp-content-font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Helvetica, Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\"; /* * Code Fonts * * Code font variables are used for typography of code and other monospaces content. */ --jp-code-font-size: 13px; --jp-code-line-height: 1.3077; /* 17px for 13px base */ --jp-code-padding: 5px; /* 5px for 13px base, codemirror highlighting needs integer px value */ --jp-code-font-family-default: Menlo, Consolas, \"DejaVu Sans Mono\", monospace; --jp-code-font-family: var(--jp-code-font-family-default); /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-code-presentation-font-size: 16px; /* may need to tweak cursor width if you change font size */ --jp-code-cursor-width0: 1.4px; --jp-code-cursor-width1: 2px; --jp-code-cursor-width2: 4px; /* Layout * * The following are the main layout colors use in JupyterLab. In a light * theme these would go from light to dark. */ --jp-layout-color0: #111111; --jp-layout-color1: var(--md-grey-900); --jp-layout-color2: var(--md-grey-800); --jp-layout-color3: var(--md-grey-700); --jp-layout-color4: var(--md-grey-600); /* Inverse Layout * * The following are the inverse layout colors use in JupyterLab. In a light * theme these would go from dark to light. */ --jp-inverse-layout-color0: white; --jp-inverse-layout-color1: white; --jp-inverse-layout-color2: var(--md-grey-200); --jp-inverse-layout-color3: var(--md-grey-400); --jp-inverse-layout-color4: var(--md-grey-600); /* Brand/accent */ --jp-brand-color0: var(--md-blue-700); --jp-brand-color1: var(--md-blue-500); --jp-brand-color2: var(--md-blue-300); --jp-brand-color3: var(--md-blue-100); --jp-brand-color4: var(--md-blue-50); --jp-accent-color0: var(--md-green-700); --jp-accent-color1: var(--md-green-500); --jp-accent-color2: var(--md-green-300); --jp-accent-color3: var(--md-green-100); /* State colors (warn, error, success, info) */ --jp-warn-color0: var(--md-orange-700); --jp-warn-color1: var(--md-orange-500); --jp-warn-color2: var(--md-orange-300); --jp-warn-color3: var(--md-orange-100); --jp-error-color0: var(--md-red-700); --jp-error-color1: var(--md-red-500); --jp-error-color2: var(--md-red-300); --jp-error-color3: var(--md-red-100); --jp-success-color0: var(--md-green-700); --jp-success-color1: var(--md-green-500); --jp-success-color2: var(--md-green-300); --jp-success-color3: var(--md-green-100); --jp-info-color0: var(--md-cyan-700); --jp-info-color1: var(--md-cyan-500); --jp-info-color2: var(--md-cyan-300); --jp-info-color3: var(--md-cyan-100); /* Cell specific styles */ --jp-cell-padding: 5px; --jp-cell-collapser-width: 8px; --jp-cell-collapser-min-height: 20px; --jp-cell-collapser-not-active-hover-opacity: 0.6; --jp-cell-editor-background: var(--jp-layout-color1); --jp-cell-editor-border-color: var(--md-grey-700); --jp-cell-editor-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-cell-editor-active-background: var(--jp-layout-color0); --jp-cell-editor-active-border-color: var(--jp-brand-color1); --jp-cell-prompt-width: 64px; --jp-cell-prompt-font-family: var(--jp-code-font-family-default); --jp-cell-prompt-letter-spacing: 0px; --jp-cell-prompt-opacity: 1; --jp-cell-prompt-not-active-opacity: 1; --jp-cell-prompt-not-active-font-color: var(--md-grey-300); /* A custom blend of MD grey and blue 600 * See https://meyerweb.com/eric/tools/color-blend/#546E7A:1E88E5:5:hex */ --jp-cell-inprompt-font-color: #307fc1; /* A custom blend of MD grey and orange 600 * https://meyerweb.com/eric/tools/color-blend/#546E7A:F4511E:5:hex */ --jp-cell-outprompt-font-color: #bf5b3d; /* Notebook specific styles */ --jp-notebook-padding: 10px; --jp-notebook-select-background: var(--jp-layout-color1); --jp-notebook-multiselected-color: rgba(33, 150, 243, 0.24); /* The scroll padding is calculated to fill enough space at the bottom of the notebook to show one single-line cell (with appropriate padding) at the top when the notebook is scrolled all the way to the bottom. We also subtract one pixel so that no scrollbar appears if we have just one single-line cell in the notebook. This padding is to enable a 'scroll past end' feature in a notebook. */ --jp-notebook-scroll-padding: calc( 100% - var(--jp-code-font-size) * var(--jp-code-line-height) - var(--jp-code-padding) - var(--jp-cell-padding) - 1px ); /* Rendermime styles */ --jp-rendermime-error-background: rgba(244, 67, 54, 0.28); --jp-rendermime-table-row-background: var(--md-grey-900); --jp-rendermime-table-row-hover-background: rgba(3, 169, 244, 0.2); /* Dialog specific styles */ --jp-dialog-background: rgba(0, 0, 0, 0.6); /* Console specific styles */ --jp-console-padding: 10px; /* Toolbar specific styles */ --jp-toolbar-border-color: var(--jp-border-color2); --jp-toolbar-micro-height: 8px; --jp-toolbar-background: var(--jp-layout-color1); --jp-toolbar-box-shadow: 0px 0px 2px 0px rgba(0, 0, 0, 0.8); --jp-toolbar-header-margin: 4px 4px 0px 4px; --jp-toolbar-active-background: var(--jp-layout-color0); /* Statusbar specific styles */ --jp-statusbar-height: 24px; /* Input field styles */ --jp-input-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-input-active-background: var(--jp-layout-color0); --jp-input-hover-background: var(--jp-layout-color2); --jp-input-background: var(--md-grey-800); --jp-input-border-color: var(--jp-border-color1); --jp-input-active-border-color: var(--jp-brand-color1); --jp-input-active-box-shadow-color: rgba(19, 124, 189, 0.3); /* General editor styles */ --jp-editor-selected-background: var(--jp-layout-color2); --jp-editor-selected-focused-background: rgba(33, 150, 243, 0.24); --jp-editor-cursor-color: var(--jp-ui-font-color0); /* Code mirror specific styles */ --jp-mirror-editor-keyword-color: var(--md-green-500); --jp-mirror-editor-atom-color: var(--md-blue-300); --jp-mirror-editor-number-color: var(--md-green-400); --jp-mirror-editor-def-color: var(--md-blue-600); --jp-mirror-editor-variable-color: var(--md-grey-300); --jp-mirror-editor-variable-2-color: var(--md-blue-400); --jp-mirror-editor-variable-3-color: var(--md-green-600); --jp-mirror-editor-punctuation-color: var(--md-blue-400); --jp-mirror-editor-property-color: var(--md-blue-400); --jp-mirror-editor-operator-color: #aa22ff; --jp-mirror-editor-comment-color: #408080; --jp-mirror-editor-string-color: #ff7070; --jp-mirror-editor-string-2-color: var(--md-purple-300); --jp-mirror-editor-meta-color: #aa22ff; --jp-mirror-editor-qualifier-color: #555; --jp-mirror-editor-builtin-color: var(--md-green-600); --jp-mirror-editor-bracket-color: #997; --jp-mirror-editor-tag-color: var(--md-green-700); --jp-mirror-editor-attribute-color: var(--md-blue-700); --jp-mirror-editor-header-color: var(--md-blue-500); --jp-mirror-editor-quote-color: var(--md-green-300); --jp-mirror-editor-link-color: var(--md-blue-700); --jp-mirror-editor-error-color: #f00; --jp-mirror-editor-hr-color: #999; /* Vega extension styles */ --jp-vega-background: var(--md-grey-400); /* Sidebar-related styles */ --jp-sidebar-min-width: 250px; /* Search-related styles */ --jp-search-toggle-off-opacity: 0.6; --jp-search-toggle-hover-opacity: 0.8; --jp-search-toggle-on-opacity: 1; --jp-search-selected-match-background-color: rgb(255, 225, 0); --jp-search-selected-match-color: black; --jp-search-unselected-match-background-color: var( --jp-inverse-layout-color0 ); --jp-search-unselected-match-color: var(--jp-ui-inverse-font-color0); /* scrollbar related styles. Supports every browser except Edge. */ /* colors based on JetBrain's Darcula theme */ --jp-scrollbar-background-color: #3f4244; --jp-scrollbar-thumb-color: 88, 96, 97; /* need to specify thumb color as an RGB triplet */ --jp-scrollbar-endpad: 3px; /* the minimum gap between the thumb and the ends of a scrollbar */ /* hacks for setting the thumb shape. 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sourceMappingURL=mkdocs-jupyter.css.map*/ init_mathjax = function() { if (window.MathJax) { // MathJax loaded MathJax.Hub.Config({ TeX: { equationNumbers: { autoNumber: \"AMS\", useLabelIds: true } }, tex2jax: { inlineMath: [ ['$','$'], [\"\\\\(\",\"\\\\)\"] ], displayMath: [ ['$$','$$'], [\"\\\\[\",\"\\\\]\"] ], processEscapes: true, processEnvironments: true }, displayAlign: 'center', CommonHTML: { linebreaks: { automatic: true } } }); MathJax.Hub.Queue([\"Typeset\", MathJax.Hub]); } } init_mathjax(); Hardening and deployment \u00b6 Part 3: Create production inference function The blue boxes show the steps implemented in this notebook. In Part 2 , we trained our AI model. Now, it's time to create G eneral R obust E nd-to-end A utomated T rustworthy deployment. Create the inference function \u00b6 Next to the prediction, we also return the top-n most influential words based on their weights. In [1]: Copied! import re import numpy as np from sklearn.pipeline import Pipeline from great_ai.utilities import clean from great_ai import ( MultiLabelClassificationOutput , ClassificationOutput , GreatAI , use_model , parameter , ) @GreatAI . create @use_model ( \"small-domain-prediction\" , version = \"latest\" ) @parameter ( \"target_confidence\" , validate = lambda c : 0 <= c <= 100 ) def predict_domain ( text : str , model : Pipeline , target_confidence : int = 50 ) -> MultiLabelClassificationOutput : \"\"\" Predict the scientific domain of the input text. Return labels until their sum likelihood is larger than `target_confidence`. \"\"\" preprocessed = re . sub ( r \"[^a-zA-Z\\s]\" , \"\" , clean ( text , convert_to_ascii = True )) features = model . named_steps [ \"vectorizer\" ] . transform ([ preprocessed ]) prediction = model . named_steps [ \"classifier\" ] . predict_proba ( features )[ 0 ] best_classes = sorted ( enumerate ( prediction ), key = lambda v : v [ 1 ], reverse = True ) results = MultiLabelClassificationOutput () for class_index , probability in best_classes : results . labels . append ( get_label ( model = model , features = features , class_index = class_index , probability = probability , ) ) if sum ( r . confidence for r in results . labels ) >= target_confidence : break return results def get_label ( model : Pipeline , features : np . ndarray , class_index : int , probability : float ) -> ClassificationOutput : return ClassificationOutput ( label = model . named_steps [ \"classifier\" ] . classes_ [ class_index ], confidence = round ( probability * 100 ), explanation = [ word for _ , word in sorted ( ( ( weight , word ) for weight , word , count in zip ( model . named_steps [ \"classifier\" ] . feature_log_prob_ [ class_index ], model . named_steps [ \"vectorizer\" ] . get_feature_names_out (), features . A [ 0 ], ) if count > 0 ), reverse = True , ) ][: 5 ], ) import re import numpy as np from sklearn.pipeline import Pipeline from great_ai.utilities import clean from great_ai import ( MultiLabelClassificationOutput, ClassificationOutput, GreatAI, use_model, parameter, ) @GreatAI.create @use_model(\"small-domain-prediction\", version=\"latest\") @parameter(\"target_confidence\", validate=lambda c: 0 < = c < = 100) def predict_domain( text: str, model: Pipeline, target_confidence: int = 50 ) -> MultiLabelClassificationOutput: \"\"\" Predict the scientific domain of the input text. Return labels until their sum likelihood is larger than `target_confidence`. \"\"\" preprocessed = re.sub(r\"[^a-zA-Z\\s]\", \"\", clean(text, convert_to_ascii=True)) features = model.named_steps[\"vectorizer\"].transform([preprocessed]) prediction = model.named_steps[\"classifier\"].predict_proba(features)[0] best_classes = sorted(enumerate(prediction), key=lambda v: v[1], reverse=True) results = MultiLabelClassificationOutput() for class_index, probability in best_classes: results.labels.append( get_label( model=model, features=features, class_index=class_index, probability=probability, ) ) if sum(r.confidence for r in results.labels) >= target_confidence: break return results def get_label( model: Pipeline, features: np.ndarray, class_index: int, probability: float ) -> ClassificationOutput: return ClassificationOutput( label=model.named_steps[\"classifier\"].classes_[class_index], confidence=round(probability * 100), explanation=[ word for _, word in sorted( ( (weight, word) for weight, word, count in zip( model.named_steps[\"classifier\"].feature_log_prob_[class_index], model.named_steps[\"vectorizer\"].get_feature_names_out(), features.A[0], ) if count > 0 ), reverse=True, ) ][:5], ) Environment variable ENVIRONMENT is not set, defaulting to development mode \u203c\ufe0f Cannot find credentials files, defaulting to using ParallelTinyDbDriver The selected tracing database (ParallelTinyDbDriver) is not recommended for production Cannot find credentials files, defaulting to using LargeFileLocal GreatAI (v0.1.6): configured \u2705 \ud83d\udd29 tracing_database: ParallelTinyDbDriver \ud83d\udd29 large_file_implementation: LargeFileLocal \ud83d\udd29 is_production: False \ud83d\udd29 should_log_exception_stack: True \ud83d\udd29 prediction_cache_size: 512 \ud83d\udd29 dashboard_table_size: 50 You still need to check whether you follow all best practices before trusting your deployment. > Find out more at https://se-ml.github.io/practices Fetching cached versions of small-domain-prediction Latest version of small-domain-prediction is 2 (from versions: 0, 1, 2) File small-domain-prediction-2 found in cache Check accuracy on the test split \u00b6 Anything under if __name__ == \"__main__\": will not be run when the script is executed by the great-ai CLI app. This, combined with query_ground_truth and the /traces/{trace_id}/feedback endpoint are ideal for creating a continuous-integration job for checking the quality of the model before deployment. In [2]: Copied! if __name__ == \"__main__\" : from great_ai import query_ground_truth from sklearn import metrics data = query_ground_truth ( \"test\" ) X = [ d . input for d in data ] y_actual = [ d . feedback for d in data ] y_predicted = [ d . output . labels [ 0 ] . label for d in predict_domain . process_batch ( X , do_not_persist_traces = True ) ] y_actual_aligned = [ p if p in a else a [ 0 ] for p , a in zip ( y_predicted , y_actual )] import matplotlib.pyplot as plt # Configure matplotlib to have nice, high-resolution charts % matplotlib inline plt . rcParams [ \"figure.figsize\" ] = ( 19 , 18 ) plt . rcParams [ \"figure.facecolor\" ] = \"white\" plt . rcParams [ \"font.size\" ] = 16 plt . rcParams [ \"axes.xmargin\" ] = 0 print ( metrics . classification_report ( y_actual_aligned , y_predicted )) metrics . ConfusionMatrixDisplay . from_predictions ( y_true = y_actual_aligned , y_pred = y_predicted , xticks_rotation = \"vertical\" , normalize = \"pred\" , values_format = \".2f\" , ) plt . tight_layout () plt . savefig ( \"ss-confusion.png\" , dpi = 600 ) if __name__ == \"__main__\": from great_ai import query_ground_truth from sklearn import metrics data = query_ground_truth(\"test\") X = [d.input for d in data] y_actual = [d.feedback for d in data] y_predicted = [ d.output.labels[0].label for d in predict_domain.process_batch(X, do_not_persist_traces=True) ] y_actual_aligned = [p if p in a else a[0] for p, a in zip(y_predicted, y_actual)] import matplotlib.pyplot as plt # Configure matplotlib to have nice, high-resolution charts %matplotlib inline plt.rcParams[\"figure.figsize\"] = (19, 18) plt.rcParams[\"figure.facecolor\"] = \"white\" plt.rcParams[\"font.size\"] = 16 plt.rcParams[\"axes.xmargin\"] = 0 print(metrics.classification_report(y_actual_aligned, y_predicted)) metrics.ConfusionMatrixDisplay.from_predictions( y_true=y_actual_aligned, y_pred=y_predicted, xticks_rotation=\"vertical\", normalize=\"pred\", values_format=\".2f\", ) plt.tight_layout() plt.savefig(\"ss-confusion.png\", dpi=600) 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 12272/12272 [01:10<00:00, 174.77it/s] precision recall f1-score support Art 0.54 0.38 0.45 126 Biology 0.77 0.84 0.80 1215 Business 0.47 0.73 0.57 311 Chemistry 0.82 0.67 0.74 1205 Computer Science 0.77 0.76 0.76 1277 Economics 0.69 0.55 0.61 270 Engineering 0.55 0.52 0.53 754 Environmental Science 0.56 0.55 0.55 227 Geography 0.54 0.39 0.45 276 Geology 0.74 0.67 0.70 265 History 0.35 0.18 0.24 140 Materials Science 0.72 0.81 0.76 1011 Mathematics 0.77 0.70 0.74 498 Medicine 0.96 0.77 0.86 2835 Philosophy 0.57 0.06 0.10 71 Physics 0.66 0.75 0.70 611 Political Science 0.44 0.61 0.51 291 Psychology 0.52 0.84 0.64 574 Sociology 0.33 0.59 0.42 315 accuracy 0.71 12272 macro avg 0.62 0.60 0.59 12272 weighted avg 0.74 0.71 0.71 12272","title":"Hardening and deployment"},{"location":"examples/simple/deploy/#hardening-and-deployment","text":"Part 3: Create production inference function The blue boxes show the steps implemented in this notebook. In Part 2 , we trained our AI model. Now, it's time to create G eneral R obust E nd-to-end A utomated T rustworthy deployment.","title":"Hardening and deployment"},{"location":"examples/simple/deploy/#create-the-inference-function","text":"Next to the prediction, we also return the top-n most influential words based on their weights. In [1]: Copied! import re import numpy as np from sklearn.pipeline import Pipeline from great_ai.utilities import clean from great_ai import ( MultiLabelClassificationOutput , ClassificationOutput , GreatAI , use_model , parameter , ) @GreatAI . create @use_model ( \"small-domain-prediction\" , version = \"latest\" ) @parameter ( \"target_confidence\" , validate = lambda c : 0 <= c <= 100 ) def predict_domain ( text : str , model : Pipeline , target_confidence : int = 50 ) -> MultiLabelClassificationOutput : \"\"\" Predict the scientific domain of the input text. Return labels until their sum likelihood is larger than `target_confidence`. \"\"\" preprocessed = re . sub ( r \"[^a-zA-Z\\s]\" , \"\" , clean ( text , convert_to_ascii = True )) features = model . named_steps [ \"vectorizer\" ] . transform ([ preprocessed ]) prediction = model . named_steps [ \"classifier\" ] . predict_proba ( features )[ 0 ] best_classes = sorted ( enumerate ( prediction ), key = lambda v : v [ 1 ], reverse = True ) results = MultiLabelClassificationOutput () for class_index , probability in best_classes : results . labels . append ( get_label ( model = model , features = features , class_index = class_index , probability = probability , ) ) if sum ( r . confidence for r in results . labels ) >= target_confidence : break return results def get_label ( model : Pipeline , features : np . ndarray , class_index : int , probability : float ) -> ClassificationOutput : return ClassificationOutput ( label = model . named_steps [ \"classifier\" ] . classes_ [ class_index ], confidence = round ( probability * 100 ), explanation = [ word for _ , word in sorted ( ( ( weight , word ) for weight , word , count in zip ( model . named_steps [ \"classifier\" ] . feature_log_prob_ [ class_index ], model . named_steps [ \"vectorizer\" ] . get_feature_names_out (), features . A [ 0 ], ) if count > 0 ), reverse = True , ) ][: 5 ], ) import re import numpy as np from sklearn.pipeline import Pipeline from great_ai.utilities import clean from great_ai import ( MultiLabelClassificationOutput, ClassificationOutput, GreatAI, use_model, parameter, ) @GreatAI.create @use_model(\"small-domain-prediction\", version=\"latest\") @parameter(\"target_confidence\", validate=lambda c: 0 < = c < = 100) def predict_domain( text: str, model: Pipeline, target_confidence: int = 50 ) -> MultiLabelClassificationOutput: \"\"\" Predict the scientific domain of the input text. Return labels until their sum likelihood is larger than `target_confidence`. \"\"\" preprocessed = re.sub(r\"[^a-zA-Z\\s]\", \"\", clean(text, convert_to_ascii=True)) features = model.named_steps[\"vectorizer\"].transform([preprocessed]) prediction = model.named_steps[\"classifier\"].predict_proba(features)[0] best_classes = sorted(enumerate(prediction), key=lambda v: v[1], reverse=True) results = MultiLabelClassificationOutput() for class_index, probability in best_classes: results.labels.append( get_label( model=model, features=features, class_index=class_index, probability=probability, ) ) if sum(r.confidence for r in results.labels) >= target_confidence: break return results def get_label( model: Pipeline, features: np.ndarray, class_index: int, probability: float ) -> ClassificationOutput: return ClassificationOutput( label=model.named_steps[\"classifier\"].classes_[class_index], confidence=round(probability * 100), explanation=[ word for _, word in sorted( ( (weight, word) for weight, word, count in zip( model.named_steps[\"classifier\"].feature_log_prob_[class_index], model.named_steps[\"vectorizer\"].get_feature_names_out(), features.A[0], ) if count > 0 ), reverse=True, ) ][:5], ) Environment variable ENVIRONMENT is not set, defaulting to development mode \u203c\ufe0f Cannot find credentials files, defaulting to using ParallelTinyDbDriver The selected tracing database (ParallelTinyDbDriver) is not recommended for production Cannot find credentials files, defaulting to using LargeFileLocal GreatAI (v0.1.6): configured \u2705 \ud83d\udd29 tracing_database: ParallelTinyDbDriver \ud83d\udd29 large_file_implementation: LargeFileLocal \ud83d\udd29 is_production: False \ud83d\udd29 should_log_exception_stack: True \ud83d\udd29 prediction_cache_size: 512 \ud83d\udd29 dashboard_table_size: 50 You still need to check whether you follow all best practices before trusting your deployment. > Find out more at https://se-ml.github.io/practices Fetching cached versions of small-domain-prediction Latest version of small-domain-prediction is 2 (from versions: 0, 1, 2) File small-domain-prediction-2 found in cache","title":"Create the inference function"},{"location":"examples/simple/deploy/#check-accuracy-on-the-test-split","text":"Anything under if __name__ == \"__main__\": will not be run when the script is executed by the great-ai CLI app. This, combined with query_ground_truth and the /traces/{trace_id}/feedback endpoint are ideal for creating a continuous-integration job for checking the quality of the model before deployment. In [2]: Copied! if __name__ == \"__main__\" : from great_ai import query_ground_truth from sklearn import metrics data = query_ground_truth ( \"test\" ) X = [ d . input for d in data ] y_actual = [ d . feedback for d in data ] y_predicted = [ d . output . labels [ 0 ] . label for d in predict_domain . process_batch ( X , do_not_persist_traces = True ) ] y_actual_aligned = [ p if p in a else a [ 0 ] for p , a in zip ( y_predicted , y_actual )] import matplotlib.pyplot as plt # Configure matplotlib to have nice, high-resolution charts % matplotlib inline plt . rcParams [ \"figure.figsize\" ] = ( 19 , 18 ) plt . rcParams [ \"figure.facecolor\" ] = \"white\" plt . rcParams [ \"font.size\" ] = 16 plt . rcParams [ \"axes.xmargin\" ] = 0 print ( metrics . classification_report ( y_actual_aligned , y_predicted )) metrics . ConfusionMatrixDisplay . from_predictions ( y_true = y_actual_aligned , y_pred = y_predicted , xticks_rotation = \"vertical\" , normalize = \"pred\" , values_format = \".2f\" , ) plt . tight_layout () plt . savefig ( \"ss-confusion.png\" , dpi = 600 ) if __name__ == \"__main__\": from great_ai import query_ground_truth from sklearn import metrics data = query_ground_truth(\"test\") X = [d.input for d in data] y_actual = [d.feedback for d in data] y_predicted = [ d.output.labels[0].label for d in predict_domain.process_batch(X, do_not_persist_traces=True) ] y_actual_aligned = [p if p in a else a[0] for p, a in zip(y_predicted, y_actual)] import matplotlib.pyplot as plt # Configure matplotlib to have nice, high-resolution charts %matplotlib inline plt.rcParams[\"figure.figsize\"] = (19, 18) plt.rcParams[\"figure.facecolor\"] = \"white\" plt.rcParams[\"font.size\"] = 16 plt.rcParams[\"axes.xmargin\"] = 0 print(metrics.classification_report(y_actual_aligned, y_predicted)) metrics.ConfusionMatrixDisplay.from_predictions( y_true=y_actual_aligned, y_pred=y_predicted, xticks_rotation=\"vertical\", normalize=\"pred\", values_format=\".2f\", ) plt.tight_layout() plt.savefig(\"ss-confusion.png\", dpi=600) 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 12272/12272 [01:10<00:00, 174.77it/s] precision recall f1-score support Art 0.54 0.38 0.45 126 Biology 0.77 0.84 0.80 1215 Business 0.47 0.73 0.57 311 Chemistry 0.82 0.67 0.74 1205 Computer Science 0.77 0.76 0.76 1277 Economics 0.69 0.55 0.61 270 Engineering 0.55 0.52 0.53 754 Environmental Science 0.56 0.55 0.55 227 Geography 0.54 0.39 0.45 276 Geology 0.74 0.67 0.70 265 History 0.35 0.18 0.24 140 Materials Science 0.72 0.81 0.76 1011 Mathematics 0.77 0.70 0.74 498 Medicine 0.96 0.77 0.86 2835 Philosophy 0.57 0.06 0.10 71 Physics 0.66 0.75 0.70 611 Political Science 0.44 0.61 0.51 291 Psychology 0.52 0.84 0.64 574 Sociology 0.33 0.59 0.42 315 accuracy 0.71 12272 macro avg 0.62 0.60 0.59 12272 weighted avg 0.74 0.71 0.71 12272","title":"Check accuracy on the test split"},{"location":"examples/simple/stacked-bars/","text":"(function (global, factory) { typeof exports === 'object' && typeof module !== 'undefined' ? module.exports = factory() : typeof define === 'function' && define.amd ? define(factory) : (global = global || self, global.ClipboardCopyElement = factory()); 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font-weight: bold } /* Operator.Word */ .highlight-ipynb .w { color: var(--jp-mirror-editor-variable-color) } /* Text.Whitespace */ .highlight-ipynb .mb { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Bin */ .highlight-ipynb .mf { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Float */ .highlight-ipynb .mh { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Hex */ .highlight-ipynb .mi { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Integer */ .highlight-ipynb .mo { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Oct */ .highlight-ipynb .sa { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Affix */ .highlight-ipynb .sb { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Backtick */ .highlight-ipynb .sc { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Char */ .highlight-ipynb .dl { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Delimiter */ .highlight-ipynb .sd { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Doc */ .highlight-ipynb .s2 { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Double */ .highlight-ipynb .se { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Escape */ .highlight-ipynb .sh { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Heredoc */ .highlight-ipynb .si { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Interpol */ .highlight-ipynb .sx { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Other */ .highlight-ipynb .sr { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Regex */ .highlight-ipynb .s1 { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Single */ .highlight-ipynb .ss { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Symbol */ .highlight-ipynb .il { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Integer.Long */ /* This file is taken from the built JupyterLab theme.css Found on share/nbconvert/templates/lab/static Some changes have been made and marked with CHANGE */ .jupyter-wrapper { /* Elevation * * We style box-shadows using Material Design's idea of elevation. These particular numbers are taken from here: * * https://github.com/material-components/material-components-web * https://material-components-web.appspot.com/elevation.html */ --jp-shadow-base-lightness: 0; --jp-shadow-umbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.2 ); --jp-shadow-penumbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.14 ); --jp-shadow-ambient-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.12 ); --jp-elevation-z0: none; --jp-elevation-z1: 0px 2px 1px -1px var(--jp-shadow-umbra-color), 0px 1px 1px 0px var(--jp-shadow-penumbra-color), 0px 1px 3px 0px var(--jp-shadow-ambient-color); --jp-elevation-z2: 0px 3px 1px -2px var(--jp-shadow-umbra-color), 0px 2px 2px 0px var(--jp-shadow-penumbra-color), 0px 1px 5px 0px var(--jp-shadow-ambient-color); --jp-elevation-z4: 0px 2px 4px -1px var(--jp-shadow-umbra-color), 0px 4px 5px 0px var(--jp-shadow-penumbra-color), 0px 1px 10px 0px var(--jp-shadow-ambient-color); --jp-elevation-z6: 0px 3px 5px -1px var(--jp-shadow-umbra-color), 0px 6px 10px 0px var(--jp-shadow-penumbra-color), 0px 1px 18px 0px var(--jp-shadow-ambient-color); --jp-elevation-z8: 0px 5px 5px -3px var(--jp-shadow-umbra-color), 0px 8px 10px 1px var(--jp-shadow-penumbra-color), 0px 3px 14px 2px var(--jp-shadow-ambient-color); --jp-elevation-z12: 0px 7px 8px -4px var(--jp-shadow-umbra-color), 0px 12px 17px 2px var(--jp-shadow-penumbra-color), 0px 5px 22px 4px var(--jp-shadow-ambient-color); --jp-elevation-z16: 0px 8px 10px -5px var(--jp-shadow-umbra-color), 0px 16px 24px 2px var(--jp-shadow-penumbra-color), 0px 6px 30px 5px var(--jp-shadow-ambient-color); --jp-elevation-z20: 0px 10px 13px -6px var(--jp-shadow-umbra-color), 0px 20px 31px 3px var(--jp-shadow-penumbra-color), 0px 8px 38px 7px var(--jp-shadow-ambient-color); --jp-elevation-z24: 0px 11px 15px -7px var(--jp-shadow-umbra-color), 0px 24px 38px 3px var(--jp-shadow-penumbra-color), 0px 9px 46px 8px var(--jp-shadow-ambient-color); /* Borders * * The following variables, specify the visual styling of borders in JupyterLab. */ --jp-border-width: 1px; --jp-border-color0: var(--md-grey-400); --jp-border-color1: var(--md-grey-400); --jp-border-color2: var(--md-grey-300); --jp-border-color3: var(--md-grey-200); --jp-border-radius: 2px; /* UI Fonts * * The UI font CSS variables are used for the typography all of the JupyterLab * user interface elements that are not directly user generated content. * * The font sizing here is done assuming that the body font size of --jp-ui-font-size1 * is applied to a parent element. When children elements, such as headings, are sized * in em all things will be computed relative to that body size. */ --jp-ui-font-scale-factor: 1.2; --jp-ui-font-size0: 0.83333em; --jp-ui-font-size1: 13px; /* Base font size */ --jp-ui-font-size2: 1.2em; --jp-ui-font-size3: 1.44em; --jp-ui-font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Helvetica, Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\"; /* * Use these font colors against the corresponding main layout colors. * In a light theme, these go from dark to light. */ /* Defaults use Material Design specification */ --jp-ui-font-color0: rgba(0, 0, 0, 1); --jp-ui-font-color1: rgba(0, 0, 0, 0.87); --jp-ui-font-color2: rgba(0, 0, 0, 0.54); --jp-ui-font-color3: rgba(0, 0, 0, 0.38); /* * Use these against the brand/accent/warn/error colors. * These will typically go from light to darker, in both a dark and light theme. */ --jp-ui-inverse-font-color0: rgba(255, 255, 255, 1); --jp-ui-inverse-font-color1: rgba(255, 255, 255, 1); --jp-ui-inverse-font-color2: rgba(255, 255, 255, 0.7); --jp-ui-inverse-font-color3: rgba(255, 255, 255, 0.5); /* Content Fonts * * Content font variables are used for typography of user generated content. * * The font sizing here is done assuming that the body font size of --jp-content-font-size1 * is applied to a parent element. When children elements, such as headings, are sized * in em all things will be computed relative to that body size. */ --jp-content-line-height: 1.6; --jp-content-font-scale-factor: 1.2; --jp-content-font-size0: 0.83333em; --jp-content-font-size1: 14px; /* Base font size */ --jp-content-font-size2: 1.2em; --jp-content-font-size3: 1.44em; --jp-content-font-size4: 1.728em; --jp-content-font-size5: 2.0736em; /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-content-presentation-font-size1: 17px; --jp-content-heading-line-height: 1; --jp-content-heading-margin-top: 1.2em; --jp-content-heading-margin-bottom: 0.8em; --jp-content-heading-font-weight: 500; /* Defaults use Material Design specification */ --jp-content-font-color0: rgba(0, 0, 0, 1); --jp-content-font-color1: rgba(0, 0, 0, 0.87); --jp-content-font-color2: rgba(0, 0, 0, 0.54); --jp-content-font-color3: rgba(0, 0, 0, 0.38); --jp-content-link-color: var(--md-blue-700); --jp-content-font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Helvetica, Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\"; /* * Code Fonts * * Code font variables are used for typography of code and other monospaces content. */ --jp-code-font-size: 13px; --jp-code-line-height: 1.3077; /* 17px for 13px base */ --jp-code-padding: 5px; /* 5px for 13px base, codemirror highlighting needs integer px value */ --jp-code-font-family-default: Menlo, Consolas, \"DejaVu Sans Mono\", monospace; --jp-code-font-family: var(--jp-code-font-family-default); /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-code-presentation-font-size: 16px; /* may need to tweak cursor width if you change font size */ --jp-code-cursor-width0: 1.4px; --jp-code-cursor-width1: 2px; --jp-code-cursor-width2: 4px; /* Layout * * The following are the main layout colors use in JupyterLab. In a light * theme these would go from light to dark. */ --jp-layout-color0: white; --jp-layout-color1: white; --jp-layout-color2: var(--md-grey-200); --jp-layout-color3: var(--md-grey-400); --jp-layout-color4: var(--md-grey-600); /* Inverse Layout * * The following are the inverse layout colors use in JupyterLab. In a light * theme these would go from dark to light. */ --jp-inverse-layout-color0: #111111; --jp-inverse-layout-color1: var(--md-grey-900); --jp-inverse-layout-color2: var(--md-grey-800); --jp-inverse-layout-color3: var(--md-grey-700); --jp-inverse-layout-color4: var(--md-grey-600); /* Brand/accent */ --jp-brand-color0: var(--md-blue-900); --jp-brand-color1: var(--md-blue-700); --jp-brand-color2: var(--md-blue-300); --jp-brand-color3: var(--md-blue-100); --jp-brand-color4: var(--md-blue-50); --jp-accent-color0: var(--md-green-900); --jp-accent-color1: var(--md-green-700); --jp-accent-color2: var(--md-green-300); --jp-accent-color3: var(--md-green-100); /* State colors (warn, error, success, info) */ --jp-warn-color0: var(--md-orange-900); --jp-warn-color1: var(--md-orange-700); --jp-warn-color2: var(--md-orange-300); --jp-warn-color3: var(--md-orange-100); --jp-error-color0: var(--md-red-900); --jp-error-color1: var(--md-red-700); --jp-error-color2: var(--md-red-300); --jp-error-color3: var(--md-red-100); --jp-success-color0: var(--md-green-900); --jp-success-color1: var(--md-green-700); --jp-success-color2: var(--md-green-300); --jp-success-color3: var(--md-green-100); --jp-info-color0: var(--md-cyan-900); --jp-info-color1: var(--md-cyan-700); --jp-info-color2: var(--md-cyan-300); --jp-info-color3: var(--md-cyan-100); /* Cell specific styles */ --jp-cell-padding: 5px; --jp-cell-collapser-width: 8px; --jp-cell-collapser-min-height: 20px; --jp-cell-collapser-not-active-hover-opacity: 0.6; --jp-cell-editor-background: var(--md-grey-100); --jp-cell-editor-border-color: var(--md-grey-300); --jp-cell-editor-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-cell-editor-active-background: var(--jp-layout-color0); --jp-cell-editor-active-border-color: var(--jp-brand-color1); --jp-cell-prompt-width: 64px; --jp-cell-prompt-font-family: var(--jp-code-font-family-default); --jp-cell-prompt-letter-spacing: 0px; --jp-cell-prompt-opacity: 1; --jp-cell-prompt-not-active-opacity: 0.5; --jp-cell-prompt-not-active-font-color: var(--md-grey-700); /* A custom blend of MD grey and blue 600 * See https://meyerweb.com/eric/tools/color-blend/#546E7A:1E88E5:5:hex */ --jp-cell-inprompt-font-color: #307fc1; /* A custom blend of MD grey and orange 600 * https://meyerweb.com/eric/tools/color-blend/#546E7A:F4511E:5:hex */ --jp-cell-outprompt-font-color: #bf5b3d; /* Notebook specific styles */ --jp-notebook-padding: 10px; --jp-notebook-select-background: var(--jp-layout-color1); --jp-notebook-multiselected-color: var(--md-blue-50); /* The scroll padding is calculated to fill enough space at the bottom of the notebook to show one single-line cell (with appropriate padding) at the top when the notebook is scrolled all the way to the bottom. We also subtract one pixel so that no scrollbar appears if we have just one single-line cell in the notebook. This padding is to enable a 'scroll past end' feature in a notebook. */ --jp-notebook-scroll-padding: calc( 100% - var(--jp-code-font-size) * var(--jp-code-line-height) - var(--jp-code-padding) - var(--jp-cell-padding) - 1px ); /* Rendermime styles */ --jp-rendermime-error-background: #fdd; --jp-rendermime-table-row-background: var(--md-grey-100); --jp-rendermime-table-row-hover-background: var(--md-light-blue-50); /* Dialog specific styles */ --jp-dialog-background: rgba(0, 0, 0, 0.25); /* Console specific styles */ --jp-console-padding: 10px; /* Toolbar specific styles */ --jp-toolbar-border-color: var(--jp-border-color1); --jp-toolbar-micro-height: 8px; --jp-toolbar-background: var(--jp-layout-color1); --jp-toolbar-box-shadow: 0px 0px 2px 0px rgba(0, 0, 0, 0.24); --jp-toolbar-header-margin: 4px 4px 0px 4px; --jp-toolbar-active-background: var(--md-grey-300); /* Statusbar specific styles */ --jp-statusbar-height: 24px; /* Input field styles */ --jp-input-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-input-active-background: var(--jp-layout-color1); --jp-input-hover-background: var(--jp-layout-color1); --jp-input-background: var(--md-grey-100); --jp-input-border-color: var(--jp-border-color1); --jp-input-active-border-color: var(--jp-brand-color1); --jp-input-active-box-shadow-color: rgba(19, 124, 189, 0.3); /* General editor styles */ --jp-editor-selected-background: #d9d9d9; --jp-editor-selected-focused-background: #d7d4f0; --jp-editor-cursor-color: var(--jp-ui-font-color0); /* Code mirror specific styles */ --jp-mirror-editor-keyword-color: #008000; --jp-mirror-editor-atom-color: #88f; --jp-mirror-editor-number-color: #080; --jp-mirror-editor-def-color: #00f; --jp-mirror-editor-variable-color: var(--md-grey-900); --jp-mirror-editor-variable-2-color: #05a; --jp-mirror-editor-variable-3-color: #085; --jp-mirror-editor-punctuation-color: #05a; --jp-mirror-editor-property-color: #05a; --jp-mirror-editor-operator-color: #aa22ff; --jp-mirror-editor-comment-color: #408080; --jp-mirror-editor-string-color: #ba2121; --jp-mirror-editor-string-2-color: #708; --jp-mirror-editor-meta-color: #aa22ff; --jp-mirror-editor-qualifier-color: #555; --jp-mirror-editor-builtin-color: #008000; --jp-mirror-editor-bracket-color: #997; --jp-mirror-editor-tag-color: #170; --jp-mirror-editor-attribute-color: #00c; --jp-mirror-editor-header-color: blue; --jp-mirror-editor-quote-color: #090; --jp-mirror-editor-link-color: #00c; --jp-mirror-editor-error-color: #f00; --jp-mirror-editor-hr-color: #999; /* Vega extension styles */ --jp-vega-background: white; /* Sidebar-related styles */ --jp-sidebar-min-width: 250px; /* Search-related styles */ --jp-search-toggle-off-opacity: 0.5; --jp-search-toggle-hover-opacity: 0.8; --jp-search-toggle-on-opacity: 1; --jp-search-selected-match-background-color: rgb(245, 200, 0); --jp-search-selected-match-color: black; --jp-search-unselected-match-background-color: var( --jp-inverse-layout-color0 ); --jp-search-unselected-match-color: var(--jp-ui-inverse-font-color0); /* Icon colors that work well with light or dark backgrounds */ --jp-icon-contrast-color0: var(--md-purple-600); --jp-icon-contrast-color1: var(--md-green-600); --jp-icon-contrast-color2: var(--md-pink-600); --jp-icon-contrast-color3: var(--md-blue-600); } [data-md-color-scheme=\"slate\"] .jupyter-wrapper { /* Elevation * * We style box-shadows using Material Design's idea of elevation. These particular numbers are taken from here: * * https://github.com/material-components/material-components-web * https://material-components-web.appspot.com/elevation.html */ /* The dark theme shadows need a bit of work, but this will probably also require work on the core layout * colors used in the theme as well. */ --jp-shadow-base-lightness: 32; --jp-shadow-umbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.2 ); --jp-shadow-penumbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.14 ); --jp-shadow-ambient-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.12 ); --jp-elevation-z0: none; --jp-elevation-z1: 0px 2px 1px -1px var(--jp-shadow-umbra-color), 0px 1px 1px 0px var(--jp-shadow-penumbra-color), 0px 1px 3px 0px var(--jp-shadow-ambient-color); --jp-elevation-z2: 0px 3px 1px -2px var(--jp-shadow-umbra-color), 0px 2px 2px 0px var(--jp-shadow-penumbra-color), 0px 1px 5px 0px var(--jp-shadow-ambient-color); --jp-elevation-z4: 0px 2px 4px -1px var(--jp-shadow-umbra-color), 0px 4px 5px 0px var(--jp-shadow-penumbra-color), 0px 1px 10px 0px var(--jp-shadow-ambient-color); --jp-elevation-z6: 0px 3px 5px -1px var(--jp-shadow-umbra-color), 0px 6px 10px 0px var(--jp-shadow-penumbra-color), 0px 1px 18px 0px var(--jp-shadow-ambient-color); --jp-elevation-z8: 0px 5px 5px -3px var(--jp-shadow-umbra-color), 0px 8px 10px 1px var(--jp-shadow-penumbra-color), 0px 3px 14px 2px var(--jp-shadow-ambient-color); --jp-elevation-z12: 0px 7px 8px -4px var(--jp-shadow-umbra-color), 0px 12px 17px 2px var(--jp-shadow-penumbra-color), 0px 5px 22px 4px var(--jp-shadow-ambient-color); --jp-elevation-z16: 0px 8px 10px -5px var(--jp-shadow-umbra-color), 0px 16px 24px 2px var(--jp-shadow-penumbra-color), 0px 6px 30px 5px var(--jp-shadow-ambient-color); --jp-elevation-z20: 0px 10px 13px -6px var(--jp-shadow-umbra-color), 0px 20px 31px 3px var(--jp-shadow-penumbra-color), 0px 8px 38px 7px var(--jp-shadow-ambient-color); --jp-elevation-z24: 0px 11px 15px -7px var(--jp-shadow-umbra-color), 0px 24px 38px 3px var(--jp-shadow-penumbra-color), 0px 9px 46px 8px var(--jp-shadow-ambient-color); /* Borders * * The following variables, specify the visual styling of borders in JupyterLab. */ --jp-border-width: 1px; --jp-border-color0: var(--md-grey-700); --jp-border-color1: var(--md-grey-700); --jp-border-color2: var(--md-grey-800); --jp-border-color3: var(--md-grey-900); --jp-border-radius: 2px; /* UI Fonts * * The UI font CSS variables are used for the typography all of the JupyterLab * user interface elements that are not directly user generated content. * * The font sizing here is done assuming that the body font size of --jp-ui-font-size1 * is applied to a parent element. When children elements, such as headings, are sized * in em all things will be computed relative to that body size. */ --jp-ui-font-scale-factor: 1.2; --jp-ui-font-size0: 0.83333em; --jp-ui-font-size1: 13px; /* Base font size */ --jp-ui-font-size2: 1.2em; --jp-ui-font-size3: 1.44em; --jp-ui-font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Helvetica, Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\"; /* * Use these font colors against the corresponding main layout colors. * In a light theme, these go from dark to light. */ /* Defaults use Material Design specification */ --jp-ui-font-color0: rgba(255, 255, 255, 1); --jp-ui-font-color1: rgba(255, 255, 255, 0.87); --jp-ui-font-color2: rgba(255, 255, 255, 0.54); --jp-ui-font-color3: rgba(255, 255, 255, 0.38); /* * Use these against the brand/accent/warn/error colors. * These will typically go from light to darker, in both a dark and light theme. */ --jp-ui-inverse-font-color0: rgba(0, 0, 0, 1); --jp-ui-inverse-font-color1: rgba(0, 0, 0, 0.8); --jp-ui-inverse-font-color2: rgba(0, 0, 0, 0.5); --jp-ui-inverse-font-color3: rgba(0, 0, 0, 0.3); /* Content Fonts * * Content font variables are used for typography of user generated content. * * The font sizing here is done assuming that the body font size of --jp-content-font-size1 * is applied to a parent element. When children elements, such as headings, are sized * in em all things will be computed relative to that body size. */ --jp-content-line-height: 1.6; --jp-content-font-scale-factor: 1.2; --jp-content-font-size0: 0.83333em; --jp-content-font-size1: 14px; /* Base font size */ --jp-content-font-size2: 1.2em; --jp-content-font-size3: 1.44em; --jp-content-font-size4: 1.728em; --jp-content-font-size5: 2.0736em; /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-content-presentation-font-size1: 17px; --jp-content-heading-line-height: 1; --jp-content-heading-margin-top: 1.2em; --jp-content-heading-margin-bottom: 0.8em; --jp-content-heading-font-weight: 500; /* Defaults use Material Design specification */ --jp-content-font-color0: rgba(255, 255, 255, 1); --jp-content-font-color1: rgba(255, 255, 255, 1); --jp-content-font-color2: rgba(255, 255, 255, 0.7); --jp-content-font-color3: rgba(255, 255, 255, 0.5); --jp-content-link-color: var(--md-blue-300); --jp-content-font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Helvetica, Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\"; /* * Code Fonts * * Code font variables are used for typography of code and other monospaces content. */ --jp-code-font-size: 13px; --jp-code-line-height: 1.3077; /* 17px for 13px base */ --jp-code-padding: 5px; /* 5px for 13px base, codemirror highlighting needs integer px value */ --jp-code-font-family-default: Menlo, Consolas, \"DejaVu Sans Mono\", monospace; --jp-code-font-family: var(--jp-code-font-family-default); /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-code-presentation-font-size: 16px; /* may need to tweak cursor width if you change font size */ --jp-code-cursor-width0: 1.4px; --jp-code-cursor-width1: 2px; --jp-code-cursor-width2: 4px; /* Layout * * The following are the main layout colors use in JupyterLab. In a light * theme these would go from light to dark. */ --jp-layout-color0: #111111; --jp-layout-color1: var(--md-grey-900); --jp-layout-color2: var(--md-grey-800); --jp-layout-color3: var(--md-grey-700); --jp-layout-color4: var(--md-grey-600); /* Inverse Layout * * The following are the inverse layout colors use in JupyterLab. In a light * theme these would go from dark to light. */ --jp-inverse-layout-color0: white; --jp-inverse-layout-color1: white; --jp-inverse-layout-color2: var(--md-grey-200); --jp-inverse-layout-color3: var(--md-grey-400); --jp-inverse-layout-color4: var(--md-grey-600); /* Brand/accent */ --jp-brand-color0: var(--md-blue-700); --jp-brand-color1: var(--md-blue-500); --jp-brand-color2: var(--md-blue-300); --jp-brand-color3: var(--md-blue-100); --jp-brand-color4: var(--md-blue-50); --jp-accent-color0: var(--md-green-700); --jp-accent-color1: var(--md-green-500); --jp-accent-color2: var(--md-green-300); --jp-accent-color3: var(--md-green-100); /* State colors (warn, error, success, info) */ --jp-warn-color0: var(--md-orange-700); --jp-warn-color1: var(--md-orange-500); --jp-warn-color2: var(--md-orange-300); --jp-warn-color3: var(--md-orange-100); --jp-error-color0: var(--md-red-700); --jp-error-color1: var(--md-red-500); --jp-error-color2: var(--md-red-300); --jp-error-color3: var(--md-red-100); --jp-success-color0: var(--md-green-700); --jp-success-color1: var(--md-green-500); --jp-success-color2: var(--md-green-300); --jp-success-color3: var(--md-green-100); --jp-info-color0: var(--md-cyan-700); --jp-info-color1: var(--md-cyan-500); --jp-info-color2: var(--md-cyan-300); --jp-info-color3: var(--md-cyan-100); /* Cell specific styles */ --jp-cell-padding: 5px; --jp-cell-collapser-width: 8px; --jp-cell-collapser-min-height: 20px; --jp-cell-collapser-not-active-hover-opacity: 0.6; --jp-cell-editor-background: var(--jp-layout-color1); --jp-cell-editor-border-color: var(--md-grey-700); --jp-cell-editor-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-cell-editor-active-background: var(--jp-layout-color0); --jp-cell-editor-active-border-color: var(--jp-brand-color1); --jp-cell-prompt-width: 64px; --jp-cell-prompt-font-family: var(--jp-code-font-family-default); --jp-cell-prompt-letter-spacing: 0px; --jp-cell-prompt-opacity: 1; --jp-cell-prompt-not-active-opacity: 1; --jp-cell-prompt-not-active-font-color: var(--md-grey-300); /* A custom blend of MD grey and blue 600 * See https://meyerweb.com/eric/tools/color-blend/#546E7A:1E88E5:5:hex */ --jp-cell-inprompt-font-color: #307fc1; /* A custom blend of MD grey and orange 600 * https://meyerweb.com/eric/tools/color-blend/#546E7A:F4511E:5:hex */ --jp-cell-outprompt-font-color: #bf5b3d; /* Notebook specific styles */ --jp-notebook-padding: 10px; --jp-notebook-select-background: var(--jp-layout-color1); --jp-notebook-multiselected-color: rgba(33, 150, 243, 0.24); /* The scroll padding is calculated to fill enough space at the bottom of the notebook to show one single-line cell (with appropriate padding) at the top when the notebook is scrolled all the way to the bottom. We also subtract one pixel so that no scrollbar appears if we have just one single-line cell in the notebook. This padding is to enable a 'scroll past end' feature in a notebook. */ --jp-notebook-scroll-padding: calc( 100% - var(--jp-code-font-size) * var(--jp-code-line-height) - var(--jp-code-padding) - var(--jp-cell-padding) - 1px ); /* Rendermime styles */ --jp-rendermime-error-background: rgba(244, 67, 54, 0.28); --jp-rendermime-table-row-background: var(--md-grey-900); --jp-rendermime-table-row-hover-background: rgba(3, 169, 244, 0.2); /* Dialog specific styles */ --jp-dialog-background: rgba(0, 0, 0, 0.6); /* Console specific styles */ --jp-console-padding: 10px; /* Toolbar specific styles */ --jp-toolbar-border-color: var(--jp-border-color2); --jp-toolbar-micro-height: 8px; --jp-toolbar-background: var(--jp-layout-color1); --jp-toolbar-box-shadow: 0px 0px 2px 0px rgba(0, 0, 0, 0.8); --jp-toolbar-header-margin: 4px 4px 0px 4px; --jp-toolbar-active-background: var(--jp-layout-color0); /* Statusbar specific styles */ --jp-statusbar-height: 24px; /* Input field styles */ --jp-input-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-input-active-background: var(--jp-layout-color0); --jp-input-hover-background: var(--jp-layout-color2); --jp-input-background: var(--md-grey-800); --jp-input-border-color: var(--jp-border-color1); --jp-input-active-border-color: var(--jp-brand-color1); --jp-input-active-box-shadow-color: rgba(19, 124, 189, 0.3); /* General editor styles */ --jp-editor-selected-background: var(--jp-layout-color2); --jp-editor-selected-focused-background: rgba(33, 150, 243, 0.24); --jp-editor-cursor-color: var(--jp-ui-font-color0); /* Code mirror specific styles */ --jp-mirror-editor-keyword-color: var(--md-green-500); --jp-mirror-editor-atom-color: var(--md-blue-300); --jp-mirror-editor-number-color: var(--md-green-400); --jp-mirror-editor-def-color: var(--md-blue-600); --jp-mirror-editor-variable-color: var(--md-grey-300); --jp-mirror-editor-variable-2-color: var(--md-blue-400); --jp-mirror-editor-variable-3-color: var(--md-green-600); --jp-mirror-editor-punctuation-color: var(--md-blue-400); --jp-mirror-editor-property-color: var(--md-blue-400); --jp-mirror-editor-operator-color: #aa22ff; --jp-mirror-editor-comment-color: #408080; --jp-mirror-editor-string-color: #ff7070; --jp-mirror-editor-string-2-color: var(--md-purple-300); --jp-mirror-editor-meta-color: #aa22ff; --jp-mirror-editor-qualifier-color: #555; --jp-mirror-editor-builtin-color: var(--md-green-600); --jp-mirror-editor-bracket-color: #997; --jp-mirror-editor-tag-color: var(--md-green-700); --jp-mirror-editor-attribute-color: var(--md-blue-700); --jp-mirror-editor-header-color: var(--md-blue-500); --jp-mirror-editor-quote-color: var(--md-green-300); --jp-mirror-editor-link-color: var(--md-blue-700); --jp-mirror-editor-error-color: #f00; --jp-mirror-editor-hr-color: #999; /* Vega extension styles */ --jp-vega-background: var(--md-grey-400); /* Sidebar-related styles */ --jp-sidebar-min-width: 250px; /* Search-related styles */ --jp-search-toggle-off-opacity: 0.6; --jp-search-toggle-hover-opacity: 0.8; --jp-search-toggle-on-opacity: 1; --jp-search-selected-match-background-color: rgb(255, 225, 0); --jp-search-selected-match-color: black; --jp-search-unselected-match-background-color: var( --jp-inverse-layout-color0 ); --jp-search-unselected-match-color: var(--jp-ui-inverse-font-color0); /* scrollbar related styles. Supports every browser except Edge. */ /* colors based on JetBrain's Darcula theme */ --jp-scrollbar-background-color: #3f4244; --jp-scrollbar-thumb-color: 88, 96, 97; /* need to specify thumb color as an RGB triplet */ --jp-scrollbar-endpad: 3px; /* the minimum gap between the thumb and the ends of a scrollbar */ /* hacks for setting the thumb shape. These do nothing in Firefox */ --jp-scrollbar-thumb-margin: 3.5px; /* the space in between the sides of the thumb and the track */ --jp-scrollbar-thumb-radius: 9px; /* set to a large-ish value for rounded endcaps on the thumb */ /* Icon colors that work well with light or dark backgrounds */ --jp-icon-contrast-color0: var(--md-purple-600); --jp-icon-contrast-color1: var(--md-green-600); --jp-icon-contrast-color2: var(--md-pink-600); --jp-icon-contrast-color3: var(--md-blue-600); } :root{--md-red-50: #ffebee;--md-red-100: #ffcdd2;--md-red-200: #ef9a9a;--md-red-300: #e57373;--md-red-400: #ef5350;--md-red-500: #f44336;--md-red-600: #e53935;--md-red-700: #d32f2f;--md-red-800: #c62828;--md-red-900: #b71c1c;--md-red-A100: #ff8a80;--md-red-A200: #ff5252;--md-red-A400: #ff1744;--md-red-A700: #d50000;--md-pink-50: #fce4ec;--md-pink-100: #f8bbd0;--md-pink-200: #f48fb1;--md-pink-300: #f06292;--md-pink-400: #ec407a;--md-pink-500: #e91e63;--md-pink-600: #d81b60;--md-pink-700: #c2185b;--md-pink-800: 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#7986cb;--md-indigo-400: #5c6bc0;--md-indigo-500: #3f51b5;--md-indigo-600: #3949ab;--md-indigo-700: #303f9f;--md-indigo-800: #283593;--md-indigo-900: #1a237e;--md-indigo-A100: #8c9eff;--md-indigo-A200: #536dfe;--md-indigo-A400: #3d5afe;--md-indigo-A700: #304ffe;--md-blue-50: #e3f2fd;--md-blue-100: #bbdefb;--md-blue-200: #90caf9;--md-blue-300: #64b5f6;--md-blue-400: #42a5f5;--md-blue-500: #2196f3;--md-blue-600: #1e88e5;--md-blue-700: #1976d2;--md-blue-800: #1565c0;--md-blue-900: #0d47a1;--md-blue-A100: #82b1ff;--md-blue-A200: #448aff;--md-blue-A400: #2979ff;--md-blue-A700: #2962ff;--md-light-blue-50: #e1f5fe;--md-light-blue-100: #b3e5fc;--md-light-blue-200: #81d4fa;--md-light-blue-300: #4fc3f7;--md-light-blue-400: #29b6f6;--md-light-blue-500: #03a9f4;--md-light-blue-600: #039be5;--md-light-blue-700: #0288d1;--md-light-blue-800: #0277bd;--md-light-blue-900: #01579b;--md-light-blue-A100: #80d8ff;--md-light-blue-A200: #40c4ff;--md-light-blue-A400: #00b0ff;--md-light-blue-A700: #0091ea;--md-cyan-50: #e0f7fa;--md-cyan-100: #b2ebf2;--md-cyan-200: #80deea;--md-cyan-300: #4dd0e1;--md-cyan-400: #26c6da;--md-cyan-500: #00bcd4;--md-cyan-600: #00acc1;--md-cyan-700: #0097a7;--md-cyan-800: #00838f;--md-cyan-900: #006064;--md-cyan-A100: #84ffff;--md-cyan-A200: #18ffff;--md-cyan-A400: #00e5ff;--md-cyan-A700: #00b8d4;--md-teal-50: #e0f2f1;--md-teal-100: #b2dfdb;--md-teal-200: #80cbc4;--md-teal-300: #4db6ac;--md-teal-400: #26a69a;--md-teal-500: #009688;--md-teal-600: #00897b;--md-teal-700: #00796b;--md-teal-800: #00695c;--md-teal-900: #004d40;--md-teal-A100: #a7ffeb;--md-teal-A200: #64ffda;--md-teal-A400: #1de9b6;--md-teal-A700: #00bfa5;--md-green-50: #e8f5e9;--md-green-100: #c8e6c9;--md-green-200: #a5d6a7;--md-green-300: #81c784;--md-green-400: #66bb6a;--md-green-500: #4caf50;--md-green-600: #43a047;--md-green-700: #388e3c;--md-green-800: #2e7d32;--md-green-900: #1b5e20;--md-green-A100: #b9f6ca;--md-green-A200: #69f0ae;--md-green-A400: #00e676;--md-green-A700: #00c853;--md-light-green-50: #f1f8e9;--md-light-green-100: #dcedc8;--md-light-green-200: #c5e1a5;--md-light-green-300: #aed581;--md-light-green-400: #9ccc65;--md-light-green-500: #8bc34a;--md-light-green-600: #7cb342;--md-light-green-700: #689f38;--md-light-green-800: #558b2f;--md-light-green-900: #33691e;--md-light-green-A100: #ccff90;--md-light-green-A200: #b2ff59;--md-light-green-A400: #76ff03;--md-light-green-A700: #64dd17;--md-lime-50: #f9fbe7;--md-lime-100: #f0f4c3;--md-lime-200: #e6ee9c;--md-lime-300: #dce775;--md-lime-400: #d4e157;--md-lime-500: #cddc39;--md-lime-600: #c0ca33;--md-lime-700: #afb42b;--md-lime-800: #9e9d24;--md-lime-900: #827717;--md-lime-A100: #f4ff81;--md-lime-A200: #eeff41;--md-lime-A400: #c6ff00;--md-lime-A700: #aeea00;--md-yellow-50: #fffde7;--md-yellow-100: #fff9c4;--md-yellow-200: #fff59d;--md-yellow-300: #fff176;--md-yellow-400: #ffee58;--md-yellow-500: #ffeb3b;--md-yellow-600: #fdd835;--md-yellow-700: #fbc02d;--md-yellow-800: #f9a825;--md-yellow-900: #f57f17;--md-yellow-A100: #ffff8d;--md-yellow-A200: #ffff00;--md-yellow-A400: #ffea00;--md-yellow-A700: #ffd600;--md-amber-50: #fff8e1;--md-amber-100: #ffecb3;--md-amber-200: #ffe082;--md-amber-300: #ffd54f;--md-amber-400: #ffca28;--md-amber-500: #ffc107;--md-amber-600: #ffb300;--md-amber-700: #ffa000;--md-amber-800: #ff8f00;--md-amber-900: #ff6f00;--md-amber-A100: #ffe57f;--md-amber-A200: #ffd740;--md-amber-A400: #ffc400;--md-amber-A700: #ffab00;--md-orange-50: #fff3e0;--md-orange-100: #ffe0b2;--md-orange-200: #ffcc80;--md-orange-300: #ffb74d;--md-orange-400: #ffa726;--md-orange-500: #ff9800;--md-orange-600: #fb8c00;--md-orange-700: #f57c00;--md-orange-800: #ef6c00;--md-orange-900: #e65100;--md-orange-A100: #ffd180;--md-orange-A200: #ffab40;--md-orange-A400: #ff9100;--md-orange-A700: #ff6d00;--md-deep-orange-50: #fbe9e7;--md-deep-orange-100: #ffccbc;--md-deep-orange-200: #ffab91;--md-deep-orange-300: #ff8a65;--md-deep-orange-400: #ff7043;--md-deep-orange-500: 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sourceMappingURL=mkdocs-jupyter.css.map*/ init_mathjax = function() { if (window.MathJax) { // MathJax loaded MathJax.Hub.Config({ TeX: { equationNumbers: { autoNumber: \"AMS\", useLabelIds: true } }, tex2jax: { inlineMath: [ ['$','$'], [\"\\\\(\",\"\\\\)\"] ], displayMath: [ ['$$','$$'], [\"\\\\[\",\"\\\\]\"] ], processEscapes: true, processEnvironments: true }, displayAlign: 'center', CommonHTML: { linebreaks: { automatic: true } } }); MathJax.Hub.Queue([\"Typeset\", MathJax.Hub]); } } init_mathjax(); In [1]: Copied! from great_ai import query_ground_truth data = query_ground_truth ( \"train\" ) from great_ai import query_ground_truth data = query_ground_truth(\"train\") Environment variable ENVIRONMENT is not set, defaulting to development mode \u203c\ufe0f Cannot find credentials files, defaulting to using ParallelTinyDbDriver The selected tracing database (ParallelTinyDbDriver) is not recommended for production Cannot find credentials files, defaulting to using LargeFileLocal GreatAI (v0.1.6): configured \u2705 \ud83d\udd29 tracing_database: ParallelTinyDbDriver \ud83d\udd29 large_file_implementation: LargeFileLocal \ud83d\udd29 is_production: False \ud83d\udd29 should_log_exception_stack: True \ud83d\udd29 prediction_cache_size: 512 \ud83d\udd29 dashboard_table_size: 50 You still need to check whether you follow all best practices before trusting your deployment. > Find out more at https://se-ml.github.io/practices In [9]: Copied! from collections import Counter import matplotlib.pyplot as plt from great_ai.utilities import unique import numpy as np import matplotlib.ticker as mtick first = Counter ( d . feedback [ 0 ] for d in data if len ( d . feedback ) > 0 ) second = Counter ( d . feedback [ 1 ] for d in data if len ( d . feedback ) > 1 ) third = Counter ( d . feedback [ 2 ] for d in data if len ( d . feedback ) > 2 ) domains = sorted ( unique ( domain for d in data for domain in d . feedback )) first = [ first [ d ] / len ( data ) for d in domains ] second = [ second [ d ] / len ( data ) for d in domains ] third = [ third [ d ] / len ( data ) for d in domains ] # Configure matplotlib to have nice, high-resolution charts % matplotlib inline plt . rcParams [ \"figure.facecolor\" ] = \"white\" plt . rcParams [ \"font.size\" ] = 20 plt . rcParams [ \"figure.figsize\" ] = ( 14 , 10 ) fig , ax = plt . subplots () plt . xticks ( rotation = 90 ) ax . bar ( domains , first , label = \"Primary domain\" ) ax . bar ( domains , second , label = \"Secondary domain\" , bottom = first ) ax . bar ( domains , third , label = \"Tertiary domain\" , bottom = np . add ( first , second )) ax . yaxis . set_major_formatter ( mtick . PercentFormatter ( 1 )) ax . legend () ax . set_ylabel ( \"Ratio of all publications\" ) fig . tight_layout () fig . savefig ( \"ss-distribution.png\" , dpi = 500 ) None from collections import Counter import matplotlib.pyplot as plt from great_ai.utilities import unique import numpy as np import matplotlib.ticker as mtick first = Counter(d.feedback[0] for d in data if len(d.feedback) > 0) second = Counter(d.feedback[1] for d in data if len(d.feedback) > 1) third = Counter(d.feedback[2] for d in data if len(d.feedback) > 2) domains = sorted(unique(domain for d in data for domain in d.feedback)) first = [first[d] / len(data) for d in domains] second = [second[d] / len(data) for d in domains] third = [third[d] / len(data) for d in domains] # Configure matplotlib to have nice, high-resolution charts %matplotlib inline plt.rcParams[\"figure.facecolor\"] = \"white\" plt.rcParams[\"font.size\"] = 20 plt.rcParams[\"figure.figsize\"] = (14, 10) fig, ax = plt.subplots() plt.xticks(rotation=90) ax.bar(domains, first, label=\"Primary domain\") ax.bar(domains, second, label=\"Secondary domain\", bottom=first) ax.bar(domains, third, label=\"Tertiary domain\", bottom=np.add(first, second)) ax.yaxis.set_major_formatter(mtick.PercentFormatter(1)) ax.legend() ax.set_ylabel(\"Ratio of all publications\") fig.tight_layout() fig.savefig(\"ss-distribution.png\", dpi=500) None","title":"Stacked bars"},{"location":"examples/simple/train/","text":"(function (global, factory) { typeof exports === 'object' && typeof module !== 'undefined' ? module.exports = factory() : typeof define === 'function' && define.amd ? define(factory) : (global = global || self, global.ClipboardCopyElement = factory()); }(this, function () { 'use strict'; function createNode(text) { const node = document.createElement('pre'); node.style.width = '1px'; node.style.height = '1px'; node.style.position = 'fixed'; node.style.top = '5px'; node.textContent = text; return node; } function copyNode(node) { if ('clipboard' in navigator) { // eslint-disable-next-line flowtype/no-flow-fix-me-comments // $FlowFixMe Clipboard is not defined in Flow yet. return navigator.clipboard.writeText(node.textContent); } const selection = getSelection(); if (selection == null) { return Promise.reject(new Error()); } selection.removeAllRanges(); const range = document.createRange(); range.selectNodeContents(node); selection.addRange(range); document.execCommand('copy'); selection.removeAllRanges(); return Promise.resolve(); } function copyText(text) { if ('clipboard' in navigator) { // eslint-disable-next-line flowtype/no-flow-fix-me-comments // $FlowFixMe Clipboard is not defined in Flow yet. return navigator.clipboard.writeText(text); } const body = document.body; if (!body) { return Promise.reject(new Error()); } const node = createNode(text); body.appendChild(node); copyNode(node); body.removeChild(node); return Promise.resolve(); } function copy(button) { const id = button.getAttribute('for'); const text = button.getAttribute('value'); function trigger() { button.dispatchEvent(new CustomEvent('clipboard-copy', { bubbles: true })); } if (text) { copyText(text).then(trigger); } else if (id) { const root = 'getRootNode' in Element.prototype ? button.getRootNode() : button.ownerDocument; if (!(root instanceof Document || 'ShadowRoot' in window && root instanceof ShadowRoot)) return; const node = root.getElementById(id); if (node) copyTarget(node).then(trigger); } } function copyTarget(content) { if (content instanceof HTMLInputElement || content instanceof HTMLTextAreaElement) { return copyText(content.value); } else if (content instanceof HTMLAnchorElement && content.hasAttribute('href')) { return copyText(content.href); } else { return copyNode(content); } } function clicked(event) { const button = event.currentTarget; if (button instanceof HTMLElement) { copy(button); } } function keydown(event) { if (event.key === ' ' || event.key === 'Enter') { const button = event.currentTarget; if (button instanceof HTMLElement) { event.preventDefault(); copy(button); } } } function focused(event) { event.currentTarget.addEventListener('keydown', keydown); } function blurred(event) { event.currentTarget.removeEventListener('keydown', keydown); } class ClipboardCopyElement extends HTMLElement { constructor() { super(); this.addEventListener('click', clicked); this.addEventListener('focus', focused); this.addEventListener('blur', blurred); } connectedCallback() { if (!this.hasAttribute('tabindex')) { this.setAttribute('tabindex', '0'); } if (!this.hasAttribute('role')) { this.setAttribute('role', 'button'); } } get value() { return this.getAttribute('value') || ''; } set value(text) { this.setAttribute('value', text); } } if (!window.customElements.get('clipboard-copy')) { window.ClipboardCopyElement = ClipboardCopyElement; window.customElements.define('clipboard-copy', ClipboardCopyElement); } return ClipboardCopyElement; })); document.addEventListener('clipboard-copy', function(event) { const notice = event.target.querySelector('.notice') notice.hidden = false setTimeout(function() { notice.hidden = true }, 1000) }) pre { line-height: 125%; } td.linenos .normal { color: inherit; background-color: transparent; padding-left: 5px; padding-right: 5px; } span.linenos { color: inherit; background-color: transparent; padding-left: 5px; padding-right: 5px; } td.linenos .special { color: #000000; background-color: #ffffc0; padding-left: 5px; padding-right: 5px; } span.linenos.special { color: #000000; background-color: #ffffc0; padding-left: 5px; padding-right: 5px; } .highlight-ipynb .hll { background-color: var(--jp-cell-editor-active-background) } .highlight-ipynb { background: var(--jp-cell-editor-background); color: var(--jp-mirror-editor-variable-color) } .highlight-ipynb .c { color: var(--jp-mirror-editor-comment-color); font-style: italic } /* Comment */ .highlight-ipynb .err { color: var(--jp-mirror-editor-error-color) } /* Error */ .highlight-ipynb .k { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword */ .highlight-ipynb .o { color: var(--jp-mirror-editor-operator-color); font-weight: bold } /* Operator */ .highlight-ipynb .p { color: var(--jp-mirror-editor-punctuation-color) } /* Punctuation */ .highlight-ipynb .ch { color: var(--jp-mirror-editor-comment-color); font-style: italic } /* Comment.Hashbang */ .highlight-ipynb .cm { color: var(--jp-mirror-editor-comment-color); font-style: italic } /* Comment.Multiline */ .highlight-ipynb .cp { color: var(--jp-mirror-editor-comment-color); font-style: italic } /* Comment.Preproc */ .highlight-ipynb .cpf { color: var(--jp-mirror-editor-comment-color); font-style: italic } /* Comment.PreprocFile */ .highlight-ipynb .c1 { color: var(--jp-mirror-editor-comment-color); font-style: italic } /* Comment.Single */ .highlight-ipynb .cs { color: var(--jp-mirror-editor-comment-color); font-style: italic } /* Comment.Special */ .highlight-ipynb .kc { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword.Constant */ .highlight-ipynb .kd { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword.Declaration */ .highlight-ipynb .kn { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword.Namespace */ .highlight-ipynb .kp { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword.Pseudo */ .highlight-ipynb .kr { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword.Reserved */ .highlight-ipynb .kt { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword.Type */ .highlight-ipynb .m { color: var(--jp-mirror-editor-number-color) } /* Literal.Number */ .highlight-ipynb .s { color: var(--jp-mirror-editor-string-color) } /* Literal.String */ .highlight-ipynb .ow { color: var(--jp-mirror-editor-operator-color); font-weight: bold } /* Operator.Word */ .highlight-ipynb .w { color: var(--jp-mirror-editor-variable-color) } /* Text.Whitespace */ .highlight-ipynb .mb { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Bin */ .highlight-ipynb .mf { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Float */ .highlight-ipynb .mh { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Hex */ .highlight-ipynb .mi { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Integer */ .highlight-ipynb .mo { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Oct */ .highlight-ipynb .sa { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Affix */ .highlight-ipynb .sb { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Backtick */ .highlight-ipynb .sc { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Char */ .highlight-ipynb .dl { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Delimiter */ .highlight-ipynb .sd { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Doc */ .highlight-ipynb .s2 { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Double */ .highlight-ipynb .se { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Escape */ .highlight-ipynb .sh { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Heredoc */ .highlight-ipynb .si { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Interpol */ .highlight-ipynb .sx { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Other */ .highlight-ipynb .sr { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Regex */ .highlight-ipynb .s1 { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Single */ .highlight-ipynb .ss { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Symbol */ .highlight-ipynb .il { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Integer.Long */ /* This file is taken from the built JupyterLab theme.css Found on share/nbconvert/templates/lab/static Some changes have been made and marked with CHANGE */ .jupyter-wrapper { /* Elevation * * We style box-shadows using Material Design's idea of elevation. These particular numbers are taken from here: * * https://github.com/material-components/material-components-web * https://material-components-web.appspot.com/elevation.html */ --jp-shadow-base-lightness: 0; --jp-shadow-umbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.2 ); --jp-shadow-penumbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.14 ); --jp-shadow-ambient-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.12 ); --jp-elevation-z0: none; --jp-elevation-z1: 0px 2px 1px -1px var(--jp-shadow-umbra-color), 0px 1px 1px 0px var(--jp-shadow-penumbra-color), 0px 1px 3px 0px var(--jp-shadow-ambient-color); --jp-elevation-z2: 0px 3px 1px -2px var(--jp-shadow-umbra-color), 0px 2px 2px 0px var(--jp-shadow-penumbra-color), 0px 1px 5px 0px var(--jp-shadow-ambient-color); --jp-elevation-z4: 0px 2px 4px -1px var(--jp-shadow-umbra-color), 0px 4px 5px 0px var(--jp-shadow-penumbra-color), 0px 1px 10px 0px var(--jp-shadow-ambient-color); --jp-elevation-z6: 0px 3px 5px -1px var(--jp-shadow-umbra-color), 0px 6px 10px 0px var(--jp-shadow-penumbra-color), 0px 1px 18px 0px var(--jp-shadow-ambient-color); --jp-elevation-z8: 0px 5px 5px -3px var(--jp-shadow-umbra-color), 0px 8px 10px 1px var(--jp-shadow-penumbra-color), 0px 3px 14px 2px var(--jp-shadow-ambient-color); --jp-elevation-z12: 0px 7px 8px -4px var(--jp-shadow-umbra-color), 0px 12px 17px 2px var(--jp-shadow-penumbra-color), 0px 5px 22px 4px var(--jp-shadow-ambient-color); --jp-elevation-z16: 0px 8px 10px -5px var(--jp-shadow-umbra-color), 0px 16px 24px 2px var(--jp-shadow-penumbra-color), 0px 6px 30px 5px var(--jp-shadow-ambient-color); --jp-elevation-z20: 0px 10px 13px -6px var(--jp-shadow-umbra-color), 0px 20px 31px 3px var(--jp-shadow-penumbra-color), 0px 8px 38px 7px var(--jp-shadow-ambient-color); --jp-elevation-z24: 0px 11px 15px -7px var(--jp-shadow-umbra-color), 0px 24px 38px 3px var(--jp-shadow-penumbra-color), 0px 9px 46px 8px var(--jp-shadow-ambient-color); /* Borders * * The following variables, specify the visual styling of borders in JupyterLab. */ --jp-border-width: 1px; --jp-border-color0: var(--md-grey-400); --jp-border-color1: var(--md-grey-400); --jp-border-color2: var(--md-grey-300); --jp-border-color3: var(--md-grey-200); --jp-border-radius: 2px; /* UI Fonts * * The UI font CSS variables are used for the typography all of the JupyterLab * user interface elements that are not directly user generated content. * * The font sizing here is done assuming that the body font size of --jp-ui-font-size1 * is applied to a parent element. When children elements, such as headings, are sized * in em all things will be computed relative to that body size. */ --jp-ui-font-scale-factor: 1.2; --jp-ui-font-size0: 0.83333em; --jp-ui-font-size1: 13px; /* Base font size */ --jp-ui-font-size2: 1.2em; --jp-ui-font-size3: 1.44em; --jp-ui-font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Helvetica, Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\"; /* * Use these font colors against the corresponding main layout colors. * In a light theme, these go from dark to light. */ /* Defaults use Material Design specification */ --jp-ui-font-color0: rgba(0, 0, 0, 1); --jp-ui-font-color1: rgba(0, 0, 0, 0.87); --jp-ui-font-color2: rgba(0, 0, 0, 0.54); --jp-ui-font-color3: rgba(0, 0, 0, 0.38); /* * Use these against the brand/accent/warn/error colors. * These will typically go from light to darker, in both a dark and light theme. */ --jp-ui-inverse-font-color0: rgba(255, 255, 255, 1); --jp-ui-inverse-font-color1: rgba(255, 255, 255, 1); --jp-ui-inverse-font-color2: rgba(255, 255, 255, 0.7); --jp-ui-inverse-font-color3: rgba(255, 255, 255, 0.5); /* Content Fonts * * Content font variables are used for typography of user generated content. * * The font sizing here is done assuming that the body font size of --jp-content-font-size1 * is applied to a parent element. When children elements, such as headings, are sized * in em all things will be computed relative to that body size. */ --jp-content-line-height: 1.6; --jp-content-font-scale-factor: 1.2; --jp-content-font-size0: 0.83333em; --jp-content-font-size1: 14px; /* Base font size */ --jp-content-font-size2: 1.2em; --jp-content-font-size3: 1.44em; --jp-content-font-size4: 1.728em; --jp-content-font-size5: 2.0736em; /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-content-presentation-font-size1: 17px; --jp-content-heading-line-height: 1; --jp-content-heading-margin-top: 1.2em; --jp-content-heading-margin-bottom: 0.8em; --jp-content-heading-font-weight: 500; /* Defaults use Material Design specification */ --jp-content-font-color0: rgba(0, 0, 0, 1); --jp-content-font-color1: rgba(0, 0, 0, 0.87); --jp-content-font-color2: rgba(0, 0, 0, 0.54); --jp-content-font-color3: rgba(0, 0, 0, 0.38); --jp-content-link-color: var(--md-blue-700); --jp-content-font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Helvetica, Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\"; /* * Code Fonts * * Code font variables are used for typography of code and other monospaces content. */ --jp-code-font-size: 13px; --jp-code-line-height: 1.3077; /* 17px for 13px base */ --jp-code-padding: 5px; /* 5px for 13px base, codemirror highlighting needs integer px value */ --jp-code-font-family-default: Menlo, Consolas, \"DejaVu Sans Mono\", monospace; --jp-code-font-family: var(--jp-code-font-family-default); /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-code-presentation-font-size: 16px; /* may need to tweak cursor width if you change font size */ --jp-code-cursor-width0: 1.4px; --jp-code-cursor-width1: 2px; --jp-code-cursor-width2: 4px; /* Layout * * The following are the main layout colors use in JupyterLab. In a light * theme these would go from light to dark. */ --jp-layout-color0: white; --jp-layout-color1: white; --jp-layout-color2: var(--md-grey-200); --jp-layout-color3: var(--md-grey-400); --jp-layout-color4: var(--md-grey-600); /* Inverse Layout * * The following are the inverse layout colors use in JupyterLab. In a light * theme these would go from dark to light. */ --jp-inverse-layout-color0: #111111; --jp-inverse-layout-color1: var(--md-grey-900); --jp-inverse-layout-color2: var(--md-grey-800); --jp-inverse-layout-color3: var(--md-grey-700); --jp-inverse-layout-color4: var(--md-grey-600); /* Brand/accent */ --jp-brand-color0: var(--md-blue-900); --jp-brand-color1: var(--md-blue-700); --jp-brand-color2: var(--md-blue-300); --jp-brand-color3: var(--md-blue-100); --jp-brand-color4: var(--md-blue-50); --jp-accent-color0: var(--md-green-900); --jp-accent-color1: var(--md-green-700); --jp-accent-color2: var(--md-green-300); --jp-accent-color3: var(--md-green-100); /* State colors (warn, error, success, info) */ --jp-warn-color0: var(--md-orange-900); --jp-warn-color1: var(--md-orange-700); --jp-warn-color2: var(--md-orange-300); --jp-warn-color3: var(--md-orange-100); --jp-error-color0: var(--md-red-900); --jp-error-color1: var(--md-red-700); --jp-error-color2: var(--md-red-300); --jp-error-color3: var(--md-red-100); --jp-success-color0: var(--md-green-900); --jp-success-color1: var(--md-green-700); --jp-success-color2: var(--md-green-300); --jp-success-color3: var(--md-green-100); --jp-info-color0: var(--md-cyan-900); --jp-info-color1: var(--md-cyan-700); --jp-info-color2: var(--md-cyan-300); --jp-info-color3: var(--md-cyan-100); /* Cell specific styles */ --jp-cell-padding: 5px; --jp-cell-collapser-width: 8px; --jp-cell-collapser-min-height: 20px; --jp-cell-collapser-not-active-hover-opacity: 0.6; --jp-cell-editor-background: var(--md-grey-100); --jp-cell-editor-border-color: var(--md-grey-300); --jp-cell-editor-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-cell-editor-active-background: var(--jp-layout-color0); --jp-cell-editor-active-border-color: var(--jp-brand-color1); --jp-cell-prompt-width: 64px; --jp-cell-prompt-font-family: var(--jp-code-font-family-default); --jp-cell-prompt-letter-spacing: 0px; --jp-cell-prompt-opacity: 1; --jp-cell-prompt-not-active-opacity: 0.5; --jp-cell-prompt-not-active-font-color: var(--md-grey-700); /* A custom blend of MD grey and blue 600 * See https://meyerweb.com/eric/tools/color-blend/#546E7A:1E88E5:5:hex */ --jp-cell-inprompt-font-color: #307fc1; /* A custom blend of MD grey and orange 600 * https://meyerweb.com/eric/tools/color-blend/#546E7A:F4511E:5:hex */ --jp-cell-outprompt-font-color: #bf5b3d; /* Notebook specific styles */ --jp-notebook-padding: 10px; --jp-notebook-select-background: var(--jp-layout-color1); --jp-notebook-multiselected-color: var(--md-blue-50); /* The scroll padding is calculated to fill enough space at the bottom of the notebook to show one single-line cell (with appropriate padding) at the top when the notebook is scrolled all the way to the bottom. We also subtract one pixel so that no scrollbar appears if we have just one single-line cell in the notebook. This padding is to enable a 'scroll past end' feature in a notebook. */ --jp-notebook-scroll-padding: calc( 100% - var(--jp-code-font-size) * var(--jp-code-line-height) - var(--jp-code-padding) - var(--jp-cell-padding) - 1px ); /* Rendermime styles */ --jp-rendermime-error-background: #fdd; --jp-rendermime-table-row-background: var(--md-grey-100); --jp-rendermime-table-row-hover-background: var(--md-light-blue-50); /* Dialog specific styles */ --jp-dialog-background: rgba(0, 0, 0, 0.25); /* Console specific styles */ --jp-console-padding: 10px; /* Toolbar specific styles */ --jp-toolbar-border-color: var(--jp-border-color1); --jp-toolbar-micro-height: 8px; --jp-toolbar-background: var(--jp-layout-color1); --jp-toolbar-box-shadow: 0px 0px 2px 0px rgba(0, 0, 0, 0.24); --jp-toolbar-header-margin: 4px 4px 0px 4px; --jp-toolbar-active-background: var(--md-grey-300); /* Statusbar specific styles */ --jp-statusbar-height: 24px; /* Input field styles */ --jp-input-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-input-active-background: var(--jp-layout-color1); --jp-input-hover-background: var(--jp-layout-color1); --jp-input-background: var(--md-grey-100); --jp-input-border-color: var(--jp-border-color1); --jp-input-active-border-color: var(--jp-brand-color1); --jp-input-active-box-shadow-color: rgba(19, 124, 189, 0.3); /* General editor styles */ --jp-editor-selected-background: #d9d9d9; --jp-editor-selected-focused-background: #d7d4f0; --jp-editor-cursor-color: var(--jp-ui-font-color0); /* Code mirror specific styles */ --jp-mirror-editor-keyword-color: #008000; --jp-mirror-editor-atom-color: #88f; --jp-mirror-editor-number-color: #080; --jp-mirror-editor-def-color: #00f; --jp-mirror-editor-variable-color: var(--md-grey-900); --jp-mirror-editor-variable-2-color: #05a; --jp-mirror-editor-variable-3-color: #085; --jp-mirror-editor-punctuation-color: #05a; --jp-mirror-editor-property-color: #05a; --jp-mirror-editor-operator-color: #aa22ff; --jp-mirror-editor-comment-color: #408080; --jp-mirror-editor-string-color: #ba2121; --jp-mirror-editor-string-2-color: #708; --jp-mirror-editor-meta-color: #aa22ff; --jp-mirror-editor-qualifier-color: #555; --jp-mirror-editor-builtin-color: #008000; --jp-mirror-editor-bracket-color: #997; --jp-mirror-editor-tag-color: #170; --jp-mirror-editor-attribute-color: #00c; --jp-mirror-editor-header-color: blue; --jp-mirror-editor-quote-color: #090; --jp-mirror-editor-link-color: #00c; --jp-mirror-editor-error-color: #f00; --jp-mirror-editor-hr-color: #999; /* Vega extension styles */ --jp-vega-background: white; /* Sidebar-related styles */ --jp-sidebar-min-width: 250px; /* Search-related styles */ --jp-search-toggle-off-opacity: 0.5; --jp-search-toggle-hover-opacity: 0.8; --jp-search-toggle-on-opacity: 1; --jp-search-selected-match-background-color: rgb(245, 200, 0); --jp-search-selected-match-color: black; --jp-search-unselected-match-background-color: var( --jp-inverse-layout-color0 ); --jp-search-unselected-match-color: var(--jp-ui-inverse-font-color0); /* Icon colors that work well with light or dark backgrounds */ --jp-icon-contrast-color0: var(--md-purple-600); --jp-icon-contrast-color1: var(--md-green-600); --jp-icon-contrast-color2: var(--md-pink-600); --jp-icon-contrast-color3: var(--md-blue-600); } [data-md-color-scheme=\"slate\"] .jupyter-wrapper { /* Elevation * * We style box-shadows using Material Design's idea of elevation. These particular numbers are taken from here: * * https://github.com/material-components/material-components-web * https://material-components-web.appspot.com/elevation.html */ /* The dark theme shadows need a bit of work, but this will probably also require work on the core layout * colors used in the theme as well. */ --jp-shadow-base-lightness: 32; --jp-shadow-umbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.2 ); --jp-shadow-penumbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.14 ); --jp-shadow-ambient-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.12 ); --jp-elevation-z0: none; --jp-elevation-z1: 0px 2px 1px -1px var(--jp-shadow-umbra-color), 0px 1px 1px 0px var(--jp-shadow-penumbra-color), 0px 1px 3px 0px var(--jp-shadow-ambient-color); --jp-elevation-z2: 0px 3px 1px -2px var(--jp-shadow-umbra-color), 0px 2px 2px 0px var(--jp-shadow-penumbra-color), 0px 1px 5px 0px var(--jp-shadow-ambient-color); --jp-elevation-z4: 0px 2px 4px -1px var(--jp-shadow-umbra-color), 0px 4px 5px 0px var(--jp-shadow-penumbra-color), 0px 1px 10px 0px var(--jp-shadow-ambient-color); --jp-elevation-z6: 0px 3px 5px -1px var(--jp-shadow-umbra-color), 0px 6px 10px 0px var(--jp-shadow-penumbra-color), 0px 1px 18px 0px var(--jp-shadow-ambient-color); --jp-elevation-z8: 0px 5px 5px -3px var(--jp-shadow-umbra-color), 0px 8px 10px 1px var(--jp-shadow-penumbra-color), 0px 3px 14px 2px var(--jp-shadow-ambient-color); --jp-elevation-z12: 0px 7px 8px -4px var(--jp-shadow-umbra-color), 0px 12px 17px 2px var(--jp-shadow-penumbra-color), 0px 5px 22px 4px var(--jp-shadow-ambient-color); --jp-elevation-z16: 0px 8px 10px -5px var(--jp-shadow-umbra-color), 0px 16px 24px 2px var(--jp-shadow-penumbra-color), 0px 6px 30px 5px var(--jp-shadow-ambient-color); --jp-elevation-z20: 0px 10px 13px -6px var(--jp-shadow-umbra-color), 0px 20px 31px 3px var(--jp-shadow-penumbra-color), 0px 8px 38px 7px var(--jp-shadow-ambient-color); --jp-elevation-z24: 0px 11px 15px -7px var(--jp-shadow-umbra-color), 0px 24px 38px 3px var(--jp-shadow-penumbra-color), 0px 9px 46px 8px var(--jp-shadow-ambient-color); /* Borders * * The following variables, specify the visual styling of borders in JupyterLab. */ --jp-border-width: 1px; --jp-border-color0: var(--md-grey-700); --jp-border-color1: var(--md-grey-700); --jp-border-color2: var(--md-grey-800); --jp-border-color3: var(--md-grey-900); --jp-border-radius: 2px; /* UI Fonts * * The UI font CSS variables are used for the typography all of the JupyterLab * user interface elements that are not directly user generated content. * * The font sizing here is done assuming that the body font size of --jp-ui-font-size1 * is applied to a parent element. When children elements, such as headings, are sized * in em all things will be computed relative to that body size. */ --jp-ui-font-scale-factor: 1.2; --jp-ui-font-size0: 0.83333em; --jp-ui-font-size1: 13px; /* Base font size */ --jp-ui-font-size2: 1.2em; --jp-ui-font-size3: 1.44em; --jp-ui-font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Helvetica, Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\"; /* * Use these font colors against the corresponding main layout colors. * In a light theme, these go from dark to light. */ /* Defaults use Material Design specification */ --jp-ui-font-color0: rgba(255, 255, 255, 1); --jp-ui-font-color1: rgba(255, 255, 255, 0.87); --jp-ui-font-color2: rgba(255, 255, 255, 0.54); --jp-ui-font-color3: rgba(255, 255, 255, 0.38); /* * Use these against the brand/accent/warn/error colors. * These will typically go from light to darker, in both a dark and light theme. */ --jp-ui-inverse-font-color0: rgba(0, 0, 0, 1); --jp-ui-inverse-font-color1: rgba(0, 0, 0, 0.8); --jp-ui-inverse-font-color2: rgba(0, 0, 0, 0.5); --jp-ui-inverse-font-color3: rgba(0, 0, 0, 0.3); /* Content Fonts * * Content font variables are used for typography of user generated content. * * The font sizing here is done assuming that the body font size of --jp-content-font-size1 * is applied to a parent element. When children elements, such as headings, are sized * in em all things will be computed relative to that body size. */ --jp-content-line-height: 1.6; --jp-content-font-scale-factor: 1.2; --jp-content-font-size0: 0.83333em; --jp-content-font-size1: 14px; /* Base font size */ --jp-content-font-size2: 1.2em; --jp-content-font-size3: 1.44em; --jp-content-font-size4: 1.728em; --jp-content-font-size5: 2.0736em; /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-content-presentation-font-size1: 17px; --jp-content-heading-line-height: 1; --jp-content-heading-margin-top: 1.2em; --jp-content-heading-margin-bottom: 0.8em; --jp-content-heading-font-weight: 500; /* Defaults use Material Design specification */ --jp-content-font-color0: rgba(255, 255, 255, 1); --jp-content-font-color1: rgba(255, 255, 255, 1); --jp-content-font-color2: rgba(255, 255, 255, 0.7); --jp-content-font-color3: rgba(255, 255, 255, 0.5); --jp-content-link-color: var(--md-blue-300); --jp-content-font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Helvetica, Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\"; /* * Code Fonts * * Code font variables are used for typography of code and other monospaces content. */ --jp-code-font-size: 13px; --jp-code-line-height: 1.3077; /* 17px for 13px base */ --jp-code-padding: 5px; /* 5px for 13px base, codemirror highlighting needs integer px value */ --jp-code-font-family-default: Menlo, Consolas, \"DejaVu Sans Mono\", monospace; --jp-code-font-family: var(--jp-code-font-family-default); /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-code-presentation-font-size: 16px; /* may need to tweak cursor width if you change font size */ --jp-code-cursor-width0: 1.4px; --jp-code-cursor-width1: 2px; --jp-code-cursor-width2: 4px; /* Layout * * The following are the main layout colors use in JupyterLab. In a light * theme these would go from light to dark. */ --jp-layout-color0: #111111; --jp-layout-color1: var(--md-grey-900); --jp-layout-color2: var(--md-grey-800); --jp-layout-color3: var(--md-grey-700); --jp-layout-color4: var(--md-grey-600); /* Inverse Layout * * The following are the inverse layout colors use in JupyterLab. In a light * theme these would go from dark to light. */ --jp-inverse-layout-color0: white; --jp-inverse-layout-color1: white; --jp-inverse-layout-color2: var(--md-grey-200); --jp-inverse-layout-color3: var(--md-grey-400); --jp-inverse-layout-color4: var(--md-grey-600); /* Brand/accent */ --jp-brand-color0: var(--md-blue-700); --jp-brand-color1: var(--md-blue-500); --jp-brand-color2: var(--md-blue-300); --jp-brand-color3: var(--md-blue-100); --jp-brand-color4: var(--md-blue-50); --jp-accent-color0: var(--md-green-700); --jp-accent-color1: var(--md-green-500); --jp-accent-color2: var(--md-green-300); --jp-accent-color3: var(--md-green-100); /* State colors (warn, error, success, info) */ --jp-warn-color0: var(--md-orange-700); --jp-warn-color1: var(--md-orange-500); --jp-warn-color2: var(--md-orange-300); --jp-warn-color3: var(--md-orange-100); --jp-error-color0: var(--md-red-700); --jp-error-color1: var(--md-red-500); --jp-error-color2: var(--md-red-300); --jp-error-color3: var(--md-red-100); --jp-success-color0: var(--md-green-700); --jp-success-color1: var(--md-green-500); --jp-success-color2: var(--md-green-300); --jp-success-color3: var(--md-green-100); --jp-info-color0: var(--md-cyan-700); --jp-info-color1: var(--md-cyan-500); --jp-info-color2: var(--md-cyan-300); --jp-info-color3: var(--md-cyan-100); /* Cell specific styles */ --jp-cell-padding: 5px; --jp-cell-collapser-width: 8px; --jp-cell-collapser-min-height: 20px; --jp-cell-collapser-not-active-hover-opacity: 0.6; --jp-cell-editor-background: var(--jp-layout-color1); --jp-cell-editor-border-color: var(--md-grey-700); --jp-cell-editor-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-cell-editor-active-background: var(--jp-layout-color0); --jp-cell-editor-active-border-color: var(--jp-brand-color1); --jp-cell-prompt-width: 64px; --jp-cell-prompt-font-family: var(--jp-code-font-family-default); --jp-cell-prompt-letter-spacing: 0px; --jp-cell-prompt-opacity: 1; --jp-cell-prompt-not-active-opacity: 1; --jp-cell-prompt-not-active-font-color: var(--md-grey-300); /* A custom blend of MD grey and blue 600 * See https://meyerweb.com/eric/tools/color-blend/#546E7A:1E88E5:5:hex */ --jp-cell-inprompt-font-color: #307fc1; /* A custom blend of MD grey and orange 600 * https://meyerweb.com/eric/tools/color-blend/#546E7A:F4511E:5:hex */ --jp-cell-outprompt-font-color: #bf5b3d; /* Notebook specific styles */ --jp-notebook-padding: 10px; --jp-notebook-select-background: var(--jp-layout-color1); --jp-notebook-multiselected-color: rgba(33, 150, 243, 0.24); /* The scroll padding is calculated to fill enough space at the bottom of the notebook to show one single-line cell (with appropriate padding) at the top when the notebook is scrolled all the way to the bottom. We also subtract one pixel so that no scrollbar appears if we have just one single-line cell in the notebook. This padding is to enable a 'scroll past end' feature in a notebook. */ --jp-notebook-scroll-padding: calc( 100% - var(--jp-code-font-size) * var(--jp-code-line-height) - var(--jp-code-padding) - var(--jp-cell-padding) - 1px ); /* Rendermime styles */ --jp-rendermime-error-background: rgba(244, 67, 54, 0.28); --jp-rendermime-table-row-background: var(--md-grey-900); --jp-rendermime-table-row-hover-background: rgba(3, 169, 244, 0.2); /* Dialog specific styles */ --jp-dialog-background: rgba(0, 0, 0, 0.6); /* Console specific styles */ --jp-console-padding: 10px; /* Toolbar specific styles */ --jp-toolbar-border-color: var(--jp-border-color2); --jp-toolbar-micro-height: 8px; --jp-toolbar-background: var(--jp-layout-color1); --jp-toolbar-box-shadow: 0px 0px 2px 0px rgba(0, 0, 0, 0.8); --jp-toolbar-header-margin: 4px 4px 0px 4px; --jp-toolbar-active-background: var(--jp-layout-color0); /* Statusbar specific styles */ --jp-statusbar-height: 24px; /* Input field styles */ --jp-input-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-input-active-background: var(--jp-layout-color0); --jp-input-hover-background: var(--jp-layout-color2); --jp-input-background: var(--md-grey-800); --jp-input-border-color: var(--jp-border-color1); --jp-input-active-border-color: var(--jp-brand-color1); --jp-input-active-box-shadow-color: rgba(19, 124, 189, 0.3); /* General editor styles */ --jp-editor-selected-background: var(--jp-layout-color2); --jp-editor-selected-focused-background: rgba(33, 150, 243, 0.24); --jp-editor-cursor-color: var(--jp-ui-font-color0); /* Code mirror specific styles */ --jp-mirror-editor-keyword-color: var(--md-green-500); --jp-mirror-editor-atom-color: var(--md-blue-300); --jp-mirror-editor-number-color: var(--md-green-400); --jp-mirror-editor-def-color: var(--md-blue-600); --jp-mirror-editor-variable-color: var(--md-grey-300); --jp-mirror-editor-variable-2-color: var(--md-blue-400); --jp-mirror-editor-variable-3-color: var(--md-green-600); --jp-mirror-editor-punctuation-color: var(--md-blue-400); --jp-mirror-editor-property-color: var(--md-blue-400); --jp-mirror-editor-operator-color: #aa22ff; --jp-mirror-editor-comment-color: #408080; --jp-mirror-editor-string-color: #ff7070; --jp-mirror-editor-string-2-color: var(--md-purple-300); --jp-mirror-editor-meta-color: #aa22ff; --jp-mirror-editor-qualifier-color: #555; --jp-mirror-editor-builtin-color: var(--md-green-600); --jp-mirror-editor-bracket-color: #997; --jp-mirror-editor-tag-color: var(--md-green-700); --jp-mirror-editor-attribute-color: var(--md-blue-700); --jp-mirror-editor-header-color: var(--md-blue-500); --jp-mirror-editor-quote-color: var(--md-green-300); --jp-mirror-editor-link-color: var(--md-blue-700); --jp-mirror-editor-error-color: #f00; --jp-mirror-editor-hr-color: #999; /* Vega extension styles */ --jp-vega-background: var(--md-grey-400); /* Sidebar-related styles */ --jp-sidebar-min-width: 250px; /* Search-related styles */ --jp-search-toggle-off-opacity: 0.6; --jp-search-toggle-hover-opacity: 0.8; --jp-search-toggle-on-opacity: 1; --jp-search-selected-match-background-color: rgb(255, 225, 0); --jp-search-selected-match-color: black; --jp-search-unselected-match-background-color: var( --jp-inverse-layout-color0 ); --jp-search-unselected-match-color: var(--jp-ui-inverse-font-color0); /* scrollbar related styles. Supports every browser except Edge. */ /* colors based on JetBrain's Darcula theme */ --jp-scrollbar-background-color: #3f4244; --jp-scrollbar-thumb-color: 88, 96, 97; /* need to specify thumb color as an RGB triplet */ --jp-scrollbar-endpad: 3px; /* the minimum gap between the thumb and the ends of a scrollbar */ /* hacks for setting the thumb shape. These do nothing in Firefox */ --jp-scrollbar-thumb-margin: 3.5px; /* the space in between the sides of the thumb and the track */ --jp-scrollbar-thumb-radius: 9px; /* set to a large-ish value for rounded endcaps on the thumb */ /* Icon colors that work well with light or dark backgrounds */ --jp-icon-contrast-color0: var(--md-purple-600); --jp-icon-contrast-color1: var(--md-green-600); --jp-icon-contrast-color2: var(--md-pink-600); --jp-icon-contrast-color3: var(--md-blue-600); } :root{--md-red-50: #ffebee;--md-red-100: #ffcdd2;--md-red-200: #ef9a9a;--md-red-300: #e57373;--md-red-400: #ef5350;--md-red-500: #f44336;--md-red-600: #e53935;--md-red-700: #d32f2f;--md-red-800: #c62828;--md-red-900: #b71c1c;--md-red-A100: #ff8a80;--md-red-A200: #ff5252;--md-red-A400: #ff1744;--md-red-A700: #d50000;--md-pink-50: #fce4ec;--md-pink-100: #f8bbd0;--md-pink-200: #f48fb1;--md-pink-300: #f06292;--md-pink-400: #ec407a;--md-pink-500: #e91e63;--md-pink-600: #d81b60;--md-pink-700: #c2185b;--md-pink-800: #ad1457;--md-pink-900: #880e4f;--md-pink-A100: #ff80ab;--md-pink-A200: #ff4081;--md-pink-A400: #f50057;--md-pink-A700: #c51162;--md-purple-50: #f3e5f5;--md-purple-100: #e1bee7;--md-purple-200: #ce93d8;--md-purple-300: #ba68c8;--md-purple-400: #ab47bc;--md-purple-500: #9c27b0;--md-purple-600: #8e24aa;--md-purple-700: #7b1fa2;--md-purple-800: #6a1b9a;--md-purple-900: #4a148c;--md-purple-A100: #ea80fc;--md-purple-A200: #e040fb;--md-purple-A400: #d500f9;--md-purple-A700: #aa00ff;--md-deep-purple-50: #ede7f6;--md-deep-purple-100: #d1c4e9;--md-deep-purple-200: #b39ddb;--md-deep-purple-300: #9575cd;--md-deep-purple-400: #7e57c2;--md-deep-purple-500: #673ab7;--md-deep-purple-600: #5e35b1;--md-deep-purple-700: #512da8;--md-deep-purple-800: #4527a0;--md-deep-purple-900: #311b92;--md-deep-purple-A100: #b388ff;--md-deep-purple-A200: #7c4dff;--md-deep-purple-A400: #651fff;--md-deep-purple-A700: #6200ea;--md-indigo-50: #e8eaf6;--md-indigo-100: #c5cae9;--md-indigo-200: #9fa8da;--md-indigo-300: #7986cb;--md-indigo-400: #5c6bc0;--md-indigo-500: #3f51b5;--md-indigo-600: #3949ab;--md-indigo-700: #303f9f;--md-indigo-800: #283593;--md-indigo-900: #1a237e;--md-indigo-A100: #8c9eff;--md-indigo-A200: #536dfe;--md-indigo-A400: #3d5afe;--md-indigo-A700: #304ffe;--md-blue-50: #e3f2fd;--md-blue-100: #bbdefb;--md-blue-200: #90caf9;--md-blue-300: #64b5f6;--md-blue-400: #42a5f5;--md-blue-500: #2196f3;--md-blue-600: #1e88e5;--md-blue-700: #1976d2;--md-blue-800: #1565c0;--md-blue-900: #0d47a1;--md-blue-A100: #82b1ff;--md-blue-A200: #448aff;--md-blue-A400: #2979ff;--md-blue-A700: #2962ff;--md-light-blue-50: #e1f5fe;--md-light-blue-100: #b3e5fc;--md-light-blue-200: #81d4fa;--md-light-blue-300: #4fc3f7;--md-light-blue-400: #29b6f6;--md-light-blue-500: #03a9f4;--md-light-blue-600: #039be5;--md-light-blue-700: #0288d1;--md-light-blue-800: #0277bd;--md-light-blue-900: #01579b;--md-light-blue-A100: #80d8ff;--md-light-blue-A200: #40c4ff;--md-light-blue-A400: #00b0ff;--md-light-blue-A700: #0091ea;--md-cyan-50: #e0f7fa;--md-cyan-100: #b2ebf2;--md-cyan-200: #80deea;--md-cyan-300: #4dd0e1;--md-cyan-400: #26c6da;--md-cyan-500: #00bcd4;--md-cyan-600: #00acc1;--md-cyan-700: #0097a7;--md-cyan-800: #00838f;--md-cyan-900: #006064;--md-cyan-A100: #84ffff;--md-cyan-A200: #18ffff;--md-cyan-A400: #00e5ff;--md-cyan-A700: #00b8d4;--md-teal-50: #e0f2f1;--md-teal-100: #b2dfdb;--md-teal-200: #80cbc4;--md-teal-300: #4db6ac;--md-teal-400: #26a69a;--md-teal-500: #009688;--md-teal-600: #00897b;--md-teal-700: #00796b;--md-teal-800: #00695c;--md-teal-900: #004d40;--md-teal-A100: #a7ffeb;--md-teal-A200: #64ffda;--md-teal-A400: #1de9b6;--md-teal-A700: #00bfa5;--md-green-50: #e8f5e9;--md-green-100: #c8e6c9;--md-green-200: #a5d6a7;--md-green-300: #81c784;--md-green-400: #66bb6a;--md-green-500: #4caf50;--md-green-600: #43a047;--md-green-700: #388e3c;--md-green-800: #2e7d32;--md-green-900: #1b5e20;--md-green-A100: #b9f6ca;--md-green-A200: #69f0ae;--md-green-A400: #00e676;--md-green-A700: 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sourceMappingURL=mkdocs-jupyter.css.map*/ init_mathjax = function() { if (window.MathJax) { // MathJax loaded MathJax.Hub.Config({ TeX: { equationNumbers: { autoNumber: \"AMS\", useLabelIds: true } }, tex2jax: { inlineMath: [ ['$','$'], [\"\\\\(\",\"\\\\)\"] ], displayMath: [ ['$$','$$'], [\"\\\\[\",\"\\\\]\"] ], processEscapes: true, processEnvironments: true }, displayAlign: 'center', CommonHTML: { linebreaks: { automatic: true } } }); MathJax.Hub.Queue([\"Typeset\", MathJax.Hub]); } } init_mathjax(); Optimise and train a model \u00b6 The blue boxes show the steps implemented in this notebook. In the first part, we have cleaned and transformed our training data. We can now access this data using great_ai.LargeFile . Locally, it will gives us the cached version, otherwise, the latest version is downloaded from S3 or GridFS. In this part, we hyperparameter-optimise and train a simple Naive Bayes classifier which we then export for deployment using great_ai.save_model . Load data that has been extracted in part 1 \u00b6 In [1]: Copied! from great_ai import query_ground_truth data = query_ground_truth ( \"train\" ) X = [ d . input for d in data for domain in d . feedback ] y = [ domain for d in data for domain in d . feedback ] from great_ai import query_ground_truth data = query_ground_truth(\"train\") X = [d.input for d in data for domain in d.feedback] y = [domain for d in data for domain in d.feedback] Environment variable ENVIRONMENT is not set, defaulting to development mode \u203c\ufe0f Cannot find credentials files, defaulting to using ParallelTinyDbDriver The selected tracing database (ParallelTinyDbDriver) is not recommended for production Cannot find credentials files, defaulting to using LargeFileLocal GreatAI (v0.1.6): configured \u2705 \ud83d\udd29 tracing_database: ParallelTinyDbDriver \ud83d\udd29 large_file_implementation: LargeFileLocal \ud83d\udd29 is_production: False \ud83d\udd29 should_log_exception_stack: True \ud83d\udd29 prediction_cache_size: 512 \ud83d\udd29 dashboard_table_size: 50 You still need to check whether you follow all best practices before trusting your deployment. > Find out more at https://se-ml.github.io/practices In [2]: Copied! from collections import Counter import matplotlib.pyplot as plt domains , counts = zip ( * Counter ( y ) . most_common ()) # Configure matplotlib to have nice, high-resolution charts % matplotlib inline plt . rcParams [ \"figure.figsize\" ] = ( 20 , 5 ) plt . rcParams [ \"figure.facecolor\" ] = \"white\" plt . rcParams [ \"font.size\" ] = 12 plt . xticks ( rotation = 90 ) plt . bar ( domains , counts ) None from collections import Counter import matplotlib.pyplot as plt domains, counts = zip(*Counter(y).most_common()) # Configure matplotlib to have nice, high-resolution charts %matplotlib inline plt.rcParams[\"figure.figsize\"] = (20, 5) plt.rcParams[\"figure.facecolor\"] = \"white\" plt.rcParams[\"font.size\"] = 12 plt.xticks(rotation=90) plt.bar(domains, counts) None Optimise and train Multinomial Naive Bayes classifier \u00b6 In [3]: Copied! from sklearn.naive_bayes import MultinomialNB from sklearn.pipeline import Pipeline from sklearn.feature_extraction.text import TfidfVectorizer def create_pipeline () -> Pipeline : return Pipeline ( steps = [ ( \"vectorizer\" , TfidfVectorizer ( sublinear_tf = True )), ( \"classifier\" , MultinomialNB ()), ] ) from sklearn.naive_bayes import MultinomialNB from sklearn.pipeline import Pipeline from sklearn.feature_extraction.text import TfidfVectorizer def create_pipeline() -> Pipeline: return Pipeline( steps=[ (\"vectorizer\", TfidfVectorizer(sublinear_tf=True)), (\"classifier\", MultinomialNB()), ] ) In [4]: Copied! from sklearn.model_selection import GridSearchCV import pandas as pd optimisation_pipeline = GridSearchCV ( create_pipeline (), { \"vectorizer__min_df\" : [ 5 , 20 , 100 ], \"vectorizer__max_df\" : [ 0.05 , 0.1 ], \"classifier__alpha\" : [ 0.5 , 1 ], \"classifier__fit_prior\" : [ True , False ], }, scoring = \"f1_macro\" , cv = 3 , n_jobs =- 1 , verbose = 1 , ) optimisation_pipeline . fit ( X , y ) results = pd . DataFrame ( optimisation_pipeline . cv_results_ ) results . sort_values ( \"rank_test_score\" ) from sklearn.model_selection import GridSearchCV import pandas as pd optimisation_pipeline = GridSearchCV( create_pipeline(), { \"vectorizer__min_df\": [5, 20, 100], \"vectorizer__max_df\": [0.05, 0.1], \"classifier__alpha\": [0.5, 1], \"classifier__fit_prior\": [True, False], }, scoring=\"f1_macro\", cv=3, n_jobs=-1, verbose=1, ) optimisation_pipeline.fit(X, y) results = pd.DataFrame(optimisation_pipeline.cv_results_) results.sort_values(\"rank_test_score\") Fitting 3 folds for each of 24 candidates, totalling 72 fits Out[4]: .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } mean_fit_time std_fit_time mean_score_time std_score_time param_classifier__alpha param_classifier__fit_prior param_vectorizer__max_df param_vectorizer__min_df params split0_test_score split1_test_score split2_test_score mean_test_score std_test_score rank_test_score 7 14.549476 0.361685 8.476837 0.222398 0.5 False 0.05 20 {'classifier__alpha': 0.5, 'classifier__fit_pr... 0.518061 0.514842 0.511599 0.514834 0.002638 1 10 11.235289 0.130426 4.092868 0.082518 0.5 False 0.1 20 {'classifier__alpha': 0.5, 'classifier__fit_pr... 0.513897 0.515661 0.507867 0.512475 0.003337 2 19 7.383645 0.138110 4.130709 0.250048 1 False 0.05 20 {'classifier__alpha': 1, 'classifier__fit_prio... 0.496825 0.501045 0.496854 0.498241 0.001983 3 11 10.435154 0.305144 3.882101 0.128886 0.5 False 0.1 100 {'classifier__alpha': 0.5, 'classifier__fit_pr... 0.493247 0.497814 0.502245 0.497769 0.003674 4 8 13.643193 0.310696 4.173707 0.142980 0.5 False 0.05 100 {'classifier__alpha': 0.5, 'classifier__fit_pr... 0.489609 0.495207 0.498154 0.494323 0.003544 5 22 7.048340 0.050070 3.172948 0.152418 1 False 0.1 20 {'classifier__alpha': 1, 'classifier__fit_prio... 0.487456 0.493865 0.491157 0.490826 0.002627 6 23 7.564685 0.146092 2.374111 0.285026 1 False 0.1 100 {'classifier__alpha': 1, 'classifier__fit_prio... 0.485160 0.494039 0.490127 0.489776 0.003633 7 20 7.172353 0.212599 3.747219 0.130217 1 False 0.05 100 {'classifier__alpha': 1, 'classifier__fit_prio... 0.481303 0.490002 0.488269 0.486524 0.003759 8 6 14.276345 0.456576 8.318859 0.268701 0.5 False 0.05 5 {'classifier__alpha': 0.5, 'classifier__fit_pr... 0.482429 0.487735 0.484888 0.485017 0.002168 9 2 14.902358 0.737693 5.975091 0.171150 0.5 True 0.05 100 {'classifier__alpha': 0.5, 'classifier__fit_pr... 0.469598 0.474490 0.473637 0.472575 0.002134 10 9 12.677349 0.145143 4.374204 0.175674 0.5 False 0.1 5 {'classifier__alpha': 0.5, 'classifier__fit_pr... 0.468872 0.476451 0.470921 0.472082 0.003201 11 5 13.423686 0.482872 8.008324 0.442975 0.5 True 0.1 100 {'classifier__alpha': 0.5, 'classifier__fit_pr... 0.465726 0.474548 0.471879 0.470718 0.003694 12 1 13.819117 0.838347 6.161175 0.336590 0.5 True 0.05 20 {'classifier__alpha': 0.5, 'classifier__fit_pr... 0.463395 0.473982 0.471262 0.469546 0.004489 13 4 13.281476 0.588822 8.335852 0.254627 0.5 True 0.1 20 {'classifier__alpha': 0.5, 'classifier__fit_pr... 0.458734 0.468053 0.464418 0.463735 0.003835 14 14 7.282247 0.444940 3.567094 0.044519 1 True 0.05 100 {'classifier__alpha': 1, 'classifier__fit_prio... 0.438189 0.450160 0.446180 0.444843 0.004978 15 17 7.098797 0.196241 3.838628 0.091128 1 True 0.1 100 {'classifier__alpha': 1, 'classifier__fit_prio... 0.436488 0.444503 0.445900 0.442297 0.004147 16 18 7.492791 0.288889 3.843224 0.073438 1 False 0.05 5 {'classifier__alpha': 1, 'classifier__fit_prio... 0.428196 0.431945 0.430160 0.430100 0.001531 17 21 7.229826 0.099823 3.656332 0.073780 1 False 0.1 5 {'classifier__alpha': 1, 'classifier__fit_prio... 0.403130 0.410170 0.409801 0.407700 0.003235 18 13 7.158370 0.169818 3.765632 0.082924 1 True 0.05 20 {'classifier__alpha': 1, 'classifier__fit_prio... 0.399237 0.412872 0.407982 0.406697 0.005640 19 16 7.064643 0.119529 3.810983 0.125897 1 True 0.1 20 {'classifier__alpha': 1, 'classifier__fit_prio... 0.388060 0.399247 0.396325 0.394544 0.004737 20 0 13.749660 0.465174 6.407841 0.549166 0.5 True 0.05 5 {'classifier__alpha': 0.5, 'classifier__fit_pr... 0.384852 0.386487 0.386796 0.386045 0.000853 21 3 15.954147 0.318013 6.337361 0.261697 0.5 True 0.1 5 {'classifier__alpha': 0.5, 'classifier__fit_pr... 0.369785 0.375645 0.375858 0.373763 0.002814 22 12 7.120198 0.050452 3.833905 0.069540 1 True 0.05 5 {'classifier__alpha': 1, 'classifier__fit_prio... 0.277741 0.280564 0.285337 0.281214 0.003135 23 15 7.497707 0.183054 3.870714 0.062888 1 True 0.1 5 {'classifier__alpha': 1, 'classifier__fit_prio... 0.255578 0.263381 0.266184 0.261714 0.004487 24 In [5]: Copied! from sklearn import set_config set_config ( display = \"diagram\" ) classifier = create_pipeline () classifier . set_params ( ** optimisation_pipeline . best_params_ ) classifier . fit ( X , y ) from sklearn import set_config set_config(display=\"diagram\") classifier = create_pipeline() classifier.set_params(**optimisation_pipeline.best_params_) classifier.fit(X, y) Out[5]: #sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"\u25b8\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"\u25be\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;} Pipeline(steps=[('vectorizer', TfidfVectorizer(max_df=0.05, min_df=20, sublinear_tf=True)), ('classifier', MultinomialNB(alpha=0.5, fit_prior=False))]) In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org. Pipeline Pipeline(steps=[('vectorizer', TfidfVectorizer(max_df=0.05, min_df=20, sublinear_tf=True)), ('classifier', MultinomialNB(alpha=0.5, fit_prior=False))]) TfidfVectorizer TfidfVectorizer(max_df=0.05, min_df=20, sublinear_tf=True) MultinomialNB MultinomialNB(alpha=0.5, fit_prior=False) Export the model using GreatAI \u00b6 In [6]: Copied! from great_ai import save_model save_model ( classifier , key = \"small-domain-prediction\" , keep_last_n = 5 ) from great_ai import save_model save_model(classifier, key=\"small-domain-prediction\", keep_last_n=5) Fetching cached versions of small-domain-prediction Copying file for small-domain-prediction-2 Compressing small-domain-prediction-2 Model small-domain-prediction uploaded with version 2 Out[6]: 'small-domain-prediction:2' Next: Part 3 \u00b6","title":"Optimise and train a model"},{"location":"examples/simple/train/#optimise-and-train-a-model","text":"The blue boxes show the steps implemented in this notebook. In the first part, we have cleaned and transformed our training data. We can now access this data using great_ai.LargeFile . Locally, it will gives us the cached version, otherwise, the latest version is downloaded from S3 or GridFS. In this part, we hyperparameter-optimise and train a simple Naive Bayes classifier which we then export for deployment using great_ai.save_model .","title":"Optimise and train a model"},{"location":"examples/simple/train/#load-data-that-has-been-extracted-in-part-1","text":"In [1]: Copied! from great_ai import query_ground_truth data = query_ground_truth ( \"train\" ) X = [ d . input for d in data for domain in d . feedback ] y = [ domain for d in data for domain in d . feedback ] from great_ai import query_ground_truth data = query_ground_truth(\"train\") X = [d.input for d in data for domain in d.feedback] y = [domain for d in data for domain in d.feedback] Environment variable ENVIRONMENT is not set, defaulting to development mode \u203c\ufe0f Cannot find credentials files, defaulting to using ParallelTinyDbDriver The selected tracing database (ParallelTinyDbDriver) is not recommended for production Cannot find credentials files, defaulting to using LargeFileLocal GreatAI (v0.1.6): configured \u2705 \ud83d\udd29 tracing_database: ParallelTinyDbDriver \ud83d\udd29 large_file_implementation: LargeFileLocal \ud83d\udd29 is_production: False \ud83d\udd29 should_log_exception_stack: True \ud83d\udd29 prediction_cache_size: 512 \ud83d\udd29 dashboard_table_size: 50 You still need to check whether you follow all best practices before trusting your deployment. > Find out more at https://se-ml.github.io/practices In [2]: Copied! from collections import Counter import matplotlib.pyplot as plt domains , counts = zip ( * Counter ( y ) . most_common ()) # Configure matplotlib to have nice, high-resolution charts % matplotlib inline plt . rcParams [ \"figure.figsize\" ] = ( 20 , 5 ) plt . rcParams [ \"figure.facecolor\" ] = \"white\" plt . rcParams [ \"font.size\" ] = 12 plt . xticks ( rotation = 90 ) plt . bar ( domains , counts ) None from collections import Counter import matplotlib.pyplot as plt domains, counts = zip(*Counter(y).most_common()) # Configure matplotlib to have nice, high-resolution charts %matplotlib inline plt.rcParams[\"figure.figsize\"] = (20, 5) plt.rcParams[\"figure.facecolor\"] = \"white\" plt.rcParams[\"font.size\"] = 12 plt.xticks(rotation=90) plt.bar(domains, counts) None","title":"Load data that has been extracted in part 1"},{"location":"examples/simple/train/#optimise-and-train-multinomial-naive-bayes-classifier","text":"In [3]: Copied! from sklearn.naive_bayes import MultinomialNB from sklearn.pipeline import Pipeline from sklearn.feature_extraction.text import TfidfVectorizer def create_pipeline () -> Pipeline : return Pipeline ( steps = [ ( \"vectorizer\" , TfidfVectorizer ( sublinear_tf = True )), ( \"classifier\" , MultinomialNB ()), ] ) from sklearn.naive_bayes import MultinomialNB from sklearn.pipeline import Pipeline from sklearn.feature_extraction.text import TfidfVectorizer def create_pipeline() -> Pipeline: return Pipeline( steps=[ (\"vectorizer\", TfidfVectorizer(sublinear_tf=True)), (\"classifier\", MultinomialNB()), ] ) In [4]: Copied! from sklearn.model_selection import GridSearchCV import pandas as pd optimisation_pipeline = GridSearchCV ( create_pipeline (), { \"vectorizer__min_df\" : [ 5 , 20 , 100 ], \"vectorizer__max_df\" : [ 0.05 , 0.1 ], \"classifier__alpha\" : [ 0.5 , 1 ], \"classifier__fit_prior\" : [ True , False ], }, scoring = \"f1_macro\" , cv = 3 , n_jobs =- 1 , verbose = 1 , ) optimisation_pipeline . fit ( X , y ) results = pd . DataFrame ( optimisation_pipeline . cv_results_ ) results . sort_values ( \"rank_test_score\" ) from sklearn.model_selection import GridSearchCV import pandas as pd optimisation_pipeline = GridSearchCV( create_pipeline(), { \"vectorizer__min_df\": [5, 20, 100], \"vectorizer__max_df\": [0.05, 0.1], \"classifier__alpha\": [0.5, 1], \"classifier__fit_prior\": [True, False], }, scoring=\"f1_macro\", cv=3, n_jobs=-1, verbose=1, ) optimisation_pipeline.fit(X, y) results = pd.DataFrame(optimisation_pipeline.cv_results_) results.sort_values(\"rank_test_score\") Fitting 3 folds for each of 24 candidates, totalling 72 fits Out[4]: .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } mean_fit_time std_fit_time mean_score_time std_score_time param_classifier__alpha param_classifier__fit_prior param_vectorizer__max_df param_vectorizer__min_df params split0_test_score split1_test_score split2_test_score mean_test_score std_test_score rank_test_score 7 14.549476 0.361685 8.476837 0.222398 0.5 False 0.05 20 {'classifier__alpha': 0.5, 'classifier__fit_pr... 0.518061 0.514842 0.511599 0.514834 0.002638 1 10 11.235289 0.130426 4.092868 0.082518 0.5 False 0.1 20 {'classifier__alpha': 0.5, 'classifier__fit_pr... 0.513897 0.515661 0.507867 0.512475 0.003337 2 19 7.383645 0.138110 4.130709 0.250048 1 False 0.05 20 {'classifier__alpha': 1, 'classifier__fit_prio... 0.496825 0.501045 0.496854 0.498241 0.001983 3 11 10.435154 0.305144 3.882101 0.128886 0.5 False 0.1 100 {'classifier__alpha': 0.5, 'classifier__fit_pr... 0.493247 0.497814 0.502245 0.497769 0.003674 4 8 13.643193 0.310696 4.173707 0.142980 0.5 False 0.05 100 {'classifier__alpha': 0.5, 'classifier__fit_pr... 0.489609 0.495207 0.498154 0.494323 0.003544 5 22 7.048340 0.050070 3.172948 0.152418 1 False 0.1 20 {'classifier__alpha': 1, 'classifier__fit_prio... 0.487456 0.493865 0.491157 0.490826 0.002627 6 23 7.564685 0.146092 2.374111 0.285026 1 False 0.1 100 {'classifier__alpha': 1, 'classifier__fit_prio... 0.485160 0.494039 0.490127 0.489776 0.003633 7 20 7.172353 0.212599 3.747219 0.130217 1 False 0.05 100 {'classifier__alpha': 1, 'classifier__fit_prio... 0.481303 0.490002 0.488269 0.486524 0.003759 8 6 14.276345 0.456576 8.318859 0.268701 0.5 False 0.05 5 {'classifier__alpha': 0.5, 'classifier__fit_pr... 0.482429 0.487735 0.484888 0.485017 0.002168 9 2 14.902358 0.737693 5.975091 0.171150 0.5 True 0.05 100 {'classifier__alpha': 0.5, 'classifier__fit_pr... 0.469598 0.474490 0.473637 0.472575 0.002134 10 9 12.677349 0.145143 4.374204 0.175674 0.5 False 0.1 5 {'classifier__alpha': 0.5, 'classifier__fit_pr... 0.468872 0.476451 0.470921 0.472082 0.003201 11 5 13.423686 0.482872 8.008324 0.442975 0.5 True 0.1 100 {'classifier__alpha': 0.5, 'classifier__fit_pr... 0.465726 0.474548 0.471879 0.470718 0.003694 12 1 13.819117 0.838347 6.161175 0.336590 0.5 True 0.05 20 {'classifier__alpha': 0.5, 'classifier__fit_pr... 0.463395 0.473982 0.471262 0.469546 0.004489 13 4 13.281476 0.588822 8.335852 0.254627 0.5 True 0.1 20 {'classifier__alpha': 0.5, 'classifier__fit_pr... 0.458734 0.468053 0.464418 0.463735 0.003835 14 14 7.282247 0.444940 3.567094 0.044519 1 True 0.05 100 {'classifier__alpha': 1, 'classifier__fit_prio... 0.438189 0.450160 0.446180 0.444843 0.004978 15 17 7.098797 0.196241 3.838628 0.091128 1 True 0.1 100 {'classifier__alpha': 1, 'classifier__fit_prio... 0.436488 0.444503 0.445900 0.442297 0.004147 16 18 7.492791 0.288889 3.843224 0.073438 1 False 0.05 5 {'classifier__alpha': 1, 'classifier__fit_prio... 0.428196 0.431945 0.430160 0.430100 0.001531 17 21 7.229826 0.099823 3.656332 0.073780 1 False 0.1 5 {'classifier__alpha': 1, 'classifier__fit_prio... 0.403130 0.410170 0.409801 0.407700 0.003235 18 13 7.158370 0.169818 3.765632 0.082924 1 True 0.05 20 {'classifier__alpha': 1, 'classifier__fit_prio... 0.399237 0.412872 0.407982 0.406697 0.005640 19 16 7.064643 0.119529 3.810983 0.125897 1 True 0.1 20 {'classifier__alpha': 1, 'classifier__fit_prio... 0.388060 0.399247 0.396325 0.394544 0.004737 20 0 13.749660 0.465174 6.407841 0.549166 0.5 True 0.05 5 {'classifier__alpha': 0.5, 'classifier__fit_pr... 0.384852 0.386487 0.386796 0.386045 0.000853 21 3 15.954147 0.318013 6.337361 0.261697 0.5 True 0.1 5 {'classifier__alpha': 0.5, 'classifier__fit_pr... 0.369785 0.375645 0.375858 0.373763 0.002814 22 12 7.120198 0.050452 3.833905 0.069540 1 True 0.05 5 {'classifier__alpha': 1, 'classifier__fit_prio... 0.277741 0.280564 0.285337 0.281214 0.003135 23 15 7.497707 0.183054 3.870714 0.062888 1 True 0.1 5 {'classifier__alpha': 1, 'classifier__fit_prio... 0.255578 0.263381 0.266184 0.261714 0.004487 24 In [5]: Copied! from sklearn import set_config set_config ( display = \"diagram\" ) classifier = create_pipeline () classifier . set_params ( ** optimisation_pipeline . best_params_ ) classifier . fit ( X , y ) from sklearn import set_config set_config(display=\"diagram\") classifier = create_pipeline() classifier.set_params(**optimisation_pipeline.best_params_) classifier.fit(X, y) Out[5]: #sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"\u25b8\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"\u25be\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. 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Pipeline Pipeline(steps=[('vectorizer', TfidfVectorizer(max_df=0.05, min_df=20, sublinear_tf=True)), ('classifier', MultinomialNB(alpha=0.5, fit_prior=False))]) TfidfVectorizer TfidfVectorizer(max_df=0.05, min_df=20, sublinear_tf=True) MultinomialNB MultinomialNB(alpha=0.5, fit_prior=False)","title":"Optimise and train Multinomial Naive Bayes classifier"},{"location":"examples/simple/train/#export-the-model-using-greatai","text":"In [6]: Copied! from great_ai import save_model save_model ( classifier , key = \"small-domain-prediction\" , keep_last_n = 5 ) from great_ai import save_model save_model(classifier, key=\"small-domain-prediction\", keep_last_n=5) Fetching cached versions of small-domain-prediction Copying file for small-domain-prediction-2 Compressing small-domain-prediction-2 Model small-domain-prediction uploaded with version 2 Out[6]: 'small-domain-prediction:2'","title":"Export the model using GreatAI"},{"location":"examples/simple/train/#next-part-3","text":"","title":"Next: Part 3"},{"location":"examples/simple-mag/train/","text":"(function (global, factory) { typeof exports === 'object' && typeof module !== 'undefined' ? module.exports = factory() : typeof define === 'function' && define.amd ? define(factory) : (global = global || self, global.ClipboardCopyElement = factory()); 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These particular numbers are taken from here: * * https://github.com/material-components/material-components-web * https://material-components-web.appspot.com/elevation.html */ --jp-shadow-base-lightness: 0; --jp-shadow-umbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.2 ); --jp-shadow-penumbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.14 ); --jp-shadow-ambient-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.12 ); --jp-elevation-z0: none; --jp-elevation-z1: 0px 2px 1px -1px var(--jp-shadow-umbra-color), 0px 1px 1px 0px var(--jp-shadow-penumbra-color), 0px 1px 3px 0px var(--jp-shadow-ambient-color); --jp-elevation-z2: 0px 3px 1px -2px var(--jp-shadow-umbra-color), 0px 2px 2px 0px var(--jp-shadow-penumbra-color), 0px 1px 5px 0px var(--jp-shadow-ambient-color); --jp-elevation-z4: 0px 2px 4px -1px var(--jp-shadow-umbra-color), 0px 4px 5px 0px var(--jp-shadow-penumbra-color), 0px 1px 10px 0px var(--jp-shadow-ambient-color); --jp-elevation-z6: 0px 3px 5px -1px var(--jp-shadow-umbra-color), 0px 6px 10px 0px var(--jp-shadow-penumbra-color), 0px 1px 18px 0px var(--jp-shadow-ambient-color); --jp-elevation-z8: 0px 5px 5px -3px var(--jp-shadow-umbra-color), 0px 8px 10px 1px var(--jp-shadow-penumbra-color), 0px 3px 14px 2px var(--jp-shadow-ambient-color); --jp-elevation-z12: 0px 7px 8px -4px var(--jp-shadow-umbra-color), 0px 12px 17px 2px var(--jp-shadow-penumbra-color), 0px 5px 22px 4px var(--jp-shadow-ambient-color); --jp-elevation-z16: 0px 8px 10px -5px var(--jp-shadow-umbra-color), 0px 16px 24px 2px var(--jp-shadow-penumbra-color), 0px 6px 30px 5px var(--jp-shadow-ambient-color); --jp-elevation-z20: 0px 10px 13px -6px var(--jp-shadow-umbra-color), 0px 20px 31px 3px var(--jp-shadow-penumbra-color), 0px 8px 38px 7px var(--jp-shadow-ambient-color); --jp-elevation-z24: 0px 11px 15px -7px var(--jp-shadow-umbra-color), 0px 24px 38px 3px var(--jp-shadow-penumbra-color), 0px 9px 46px 8px var(--jp-shadow-ambient-color); /* Borders * * The following variables, specify the visual styling of borders in JupyterLab. */ --jp-border-width: 1px; --jp-border-color0: var(--md-grey-400); --jp-border-color1: var(--md-grey-400); --jp-border-color2: var(--md-grey-300); --jp-border-color3: var(--md-grey-200); --jp-border-radius: 2px; /* UI Fonts * * The UI font CSS variables are used for the typography all of the JupyterLab * user interface elements that are not directly user generated content. * * The font sizing here is done assuming that the body font size of --jp-ui-font-size1 * is applied to a parent element. When children elements, such as headings, are sized * in em all things will be computed relative to that body size. */ --jp-ui-font-scale-factor: 1.2; --jp-ui-font-size0: 0.83333em; --jp-ui-font-size1: 13px; /* Base font size */ --jp-ui-font-size2: 1.2em; --jp-ui-font-size3: 1.44em; --jp-ui-font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Helvetica, Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\"; /* * Use these font colors against the corresponding main layout colors. * In a light theme, these go from dark to light. */ /* Defaults use Material Design specification */ --jp-ui-font-color0: rgba(0, 0, 0, 1); --jp-ui-font-color1: rgba(0, 0, 0, 0.87); --jp-ui-font-color2: rgba(0, 0, 0, 0.54); --jp-ui-font-color3: rgba(0, 0, 0, 0.38); /* * Use these against the brand/accent/warn/error colors. * These will typically go from light to darker, in both a dark and light theme. */ --jp-ui-inverse-font-color0: rgba(255, 255, 255, 1); --jp-ui-inverse-font-color1: rgba(255, 255, 255, 1); --jp-ui-inverse-font-color2: rgba(255, 255, 255, 0.7); --jp-ui-inverse-font-color3: rgba(255, 255, 255, 0.5); /* Content Fonts * * Content font variables are used for typography of user generated content. * * The font sizing here is done assuming that the body font size of --jp-content-font-size1 * is applied to a parent element. When children elements, such as headings, are sized * in em all things will be computed relative to that body size. */ --jp-content-line-height: 1.6; --jp-content-font-scale-factor: 1.2; --jp-content-font-size0: 0.83333em; --jp-content-font-size1: 14px; /* Base font size */ --jp-content-font-size2: 1.2em; --jp-content-font-size3: 1.44em; --jp-content-font-size4: 1.728em; --jp-content-font-size5: 2.0736em; /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-content-presentation-font-size1: 17px; --jp-content-heading-line-height: 1; --jp-content-heading-margin-top: 1.2em; --jp-content-heading-margin-bottom: 0.8em; --jp-content-heading-font-weight: 500; /* Defaults use Material Design specification */ --jp-content-font-color0: rgba(0, 0, 0, 1); --jp-content-font-color1: rgba(0, 0, 0, 0.87); --jp-content-font-color2: rgba(0, 0, 0, 0.54); --jp-content-font-color3: rgba(0, 0, 0, 0.38); --jp-content-link-color: var(--md-blue-700); --jp-content-font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Helvetica, Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\"; /* * Code Fonts * * Code font variables are used for typography of code and other monospaces content. */ --jp-code-font-size: 13px; --jp-code-line-height: 1.3077; /* 17px for 13px base */ --jp-code-padding: 5px; /* 5px for 13px base, codemirror highlighting needs integer px value */ --jp-code-font-family-default: Menlo, Consolas, \"DejaVu Sans Mono\", monospace; --jp-code-font-family: var(--jp-code-font-family-default); /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-code-presentation-font-size: 16px; /* may need to tweak cursor width if you change font size */ --jp-code-cursor-width0: 1.4px; --jp-code-cursor-width1: 2px; --jp-code-cursor-width2: 4px; /* Layout * * The following are the main layout colors use in JupyterLab. In a light * theme these would go from light to dark. */ --jp-layout-color0: white; --jp-layout-color1: white; --jp-layout-color2: var(--md-grey-200); --jp-layout-color3: var(--md-grey-400); --jp-layout-color4: var(--md-grey-600); /* Inverse Layout * * The following are the inverse layout colors use in JupyterLab. In a light * theme these would go from dark to light. */ --jp-inverse-layout-color0: #111111; --jp-inverse-layout-color1: var(--md-grey-900); --jp-inverse-layout-color2: var(--md-grey-800); --jp-inverse-layout-color3: var(--md-grey-700); --jp-inverse-layout-color4: var(--md-grey-600); /* Brand/accent */ --jp-brand-color0: var(--md-blue-900); --jp-brand-color1: var(--md-blue-700); --jp-brand-color2: var(--md-blue-300); --jp-brand-color3: var(--md-blue-100); --jp-brand-color4: var(--md-blue-50); --jp-accent-color0: var(--md-green-900); --jp-accent-color1: var(--md-green-700); --jp-accent-color2: var(--md-green-300); --jp-accent-color3: var(--md-green-100); /* State colors (warn, error, success, info) */ --jp-warn-color0: var(--md-orange-900); --jp-warn-color1: var(--md-orange-700); --jp-warn-color2: var(--md-orange-300); --jp-warn-color3: var(--md-orange-100); --jp-error-color0: var(--md-red-900); --jp-error-color1: var(--md-red-700); --jp-error-color2: var(--md-red-300); --jp-error-color3: var(--md-red-100); --jp-success-color0: var(--md-green-900); --jp-success-color1: var(--md-green-700); --jp-success-color2: var(--md-green-300); --jp-success-color3: var(--md-green-100); --jp-info-color0: var(--md-cyan-900); --jp-info-color1: var(--md-cyan-700); --jp-info-color2: var(--md-cyan-300); --jp-info-color3: var(--md-cyan-100); /* Cell specific styles */ --jp-cell-padding: 5px; --jp-cell-collapser-width: 8px; --jp-cell-collapser-min-height: 20px; --jp-cell-collapser-not-active-hover-opacity: 0.6; --jp-cell-editor-background: var(--md-grey-100); --jp-cell-editor-border-color: var(--md-grey-300); --jp-cell-editor-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-cell-editor-active-background: var(--jp-layout-color0); --jp-cell-editor-active-border-color: var(--jp-brand-color1); --jp-cell-prompt-width: 64px; --jp-cell-prompt-font-family: var(--jp-code-font-family-default); --jp-cell-prompt-letter-spacing: 0px; --jp-cell-prompt-opacity: 1; --jp-cell-prompt-not-active-opacity: 0.5; --jp-cell-prompt-not-active-font-color: var(--md-grey-700); /* A custom blend of MD grey and blue 600 * See https://meyerweb.com/eric/tools/color-blend/#546E7A:1E88E5:5:hex */ --jp-cell-inprompt-font-color: #307fc1; /* A custom blend of MD grey and orange 600 * https://meyerweb.com/eric/tools/color-blend/#546E7A:F4511E:5:hex */ --jp-cell-outprompt-font-color: #bf5b3d; /* Notebook specific styles */ --jp-notebook-padding: 10px; --jp-notebook-select-background: var(--jp-layout-color1); --jp-notebook-multiselected-color: var(--md-blue-50); /* The scroll padding is calculated to fill enough space at the bottom of the notebook to show one single-line cell (with appropriate padding) at the top when the notebook is scrolled all the way to the bottom. We also subtract one pixel so that no scrollbar appears if we have just one single-line cell in the notebook. This padding is to enable a 'scroll past end' feature in a notebook. */ --jp-notebook-scroll-padding: calc( 100% - var(--jp-code-font-size) * var(--jp-code-line-height) - var(--jp-code-padding) - var(--jp-cell-padding) - 1px ); /* Rendermime styles */ --jp-rendermime-error-background: #fdd; --jp-rendermime-table-row-background: var(--md-grey-100); --jp-rendermime-table-row-hover-background: var(--md-light-blue-50); /* Dialog specific styles */ --jp-dialog-background: rgba(0, 0, 0, 0.25); /* Console specific styles */ --jp-console-padding: 10px; /* Toolbar specific styles */ --jp-toolbar-border-color: var(--jp-border-color1); --jp-toolbar-micro-height: 8px; --jp-toolbar-background: var(--jp-layout-color1); --jp-toolbar-box-shadow: 0px 0px 2px 0px rgba(0, 0, 0, 0.24); --jp-toolbar-header-margin: 4px 4px 0px 4px; --jp-toolbar-active-background: var(--md-grey-300); /* Statusbar specific styles */ --jp-statusbar-height: 24px; /* Input field styles */ --jp-input-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-input-active-background: var(--jp-layout-color1); --jp-input-hover-background: var(--jp-layout-color1); --jp-input-background: var(--md-grey-100); --jp-input-border-color: var(--jp-border-color1); --jp-input-active-border-color: var(--jp-brand-color1); --jp-input-active-box-shadow-color: rgba(19, 124, 189, 0.3); /* General editor styles */ --jp-editor-selected-background: #d9d9d9; --jp-editor-selected-focused-background: #d7d4f0; --jp-editor-cursor-color: var(--jp-ui-font-color0); /* Code mirror specific styles */ --jp-mirror-editor-keyword-color: #008000; --jp-mirror-editor-atom-color: #88f; --jp-mirror-editor-number-color: #080; --jp-mirror-editor-def-color: #00f; --jp-mirror-editor-variable-color: var(--md-grey-900); --jp-mirror-editor-variable-2-color: #05a; --jp-mirror-editor-variable-3-color: #085; --jp-mirror-editor-punctuation-color: #05a; --jp-mirror-editor-property-color: #05a; --jp-mirror-editor-operator-color: #aa22ff; --jp-mirror-editor-comment-color: #408080; --jp-mirror-editor-string-color: #ba2121; --jp-mirror-editor-string-2-color: #708; --jp-mirror-editor-meta-color: #aa22ff; --jp-mirror-editor-qualifier-color: #555; --jp-mirror-editor-builtin-color: #008000; --jp-mirror-editor-bracket-color: #997; --jp-mirror-editor-tag-color: #170; --jp-mirror-editor-attribute-color: #00c; --jp-mirror-editor-header-color: blue; --jp-mirror-editor-quote-color: #090; --jp-mirror-editor-link-color: #00c; --jp-mirror-editor-error-color: #f00; --jp-mirror-editor-hr-color: #999; /* Vega extension styles */ --jp-vega-background: white; /* Sidebar-related styles */ --jp-sidebar-min-width: 250px; /* Search-related styles */ --jp-search-toggle-off-opacity: 0.5; --jp-search-toggle-hover-opacity: 0.8; --jp-search-toggle-on-opacity: 1; --jp-search-selected-match-background-color: rgb(245, 200, 0); --jp-search-selected-match-color: black; --jp-search-unselected-match-background-color: var( --jp-inverse-layout-color0 ); --jp-search-unselected-match-color: var(--jp-ui-inverse-font-color0); /* Icon colors that work well with light or dark backgrounds */ --jp-icon-contrast-color0: var(--md-purple-600); --jp-icon-contrast-color1: var(--md-green-600); --jp-icon-contrast-color2: var(--md-pink-600); --jp-icon-contrast-color3: var(--md-blue-600); } [data-md-color-scheme=\"slate\"] .jupyter-wrapper { /* Elevation * * We style box-shadows using Material Design's idea of elevation. These particular numbers are taken from here: * * https://github.com/material-components/material-components-web * https://material-components-web.appspot.com/elevation.html */ /* The dark theme shadows need a bit of work, but this will probably also require work on the core layout * colors used in the theme as well. */ --jp-shadow-base-lightness: 32; --jp-shadow-umbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.2 ); --jp-shadow-penumbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.14 ); --jp-shadow-ambient-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.12 ); --jp-elevation-z0: none; --jp-elevation-z1: 0px 2px 1px -1px var(--jp-shadow-umbra-color), 0px 1px 1px 0px var(--jp-shadow-penumbra-color), 0px 1px 3px 0px var(--jp-shadow-ambient-color); --jp-elevation-z2: 0px 3px 1px -2px var(--jp-shadow-umbra-color), 0px 2px 2px 0px var(--jp-shadow-penumbra-color), 0px 1px 5px 0px var(--jp-shadow-ambient-color); --jp-elevation-z4: 0px 2px 4px -1px var(--jp-shadow-umbra-color), 0px 4px 5px 0px var(--jp-shadow-penumbra-color), 0px 1px 10px 0px var(--jp-shadow-ambient-color); --jp-elevation-z6: 0px 3px 5px -1px var(--jp-shadow-umbra-color), 0px 6px 10px 0px var(--jp-shadow-penumbra-color), 0px 1px 18px 0px var(--jp-shadow-ambient-color); --jp-elevation-z8: 0px 5px 5px -3px var(--jp-shadow-umbra-color), 0px 8px 10px 1px var(--jp-shadow-penumbra-color), 0px 3px 14px 2px var(--jp-shadow-ambient-color); --jp-elevation-z12: 0px 7px 8px -4px var(--jp-shadow-umbra-color), 0px 12px 17px 2px var(--jp-shadow-penumbra-color), 0px 5px 22px 4px var(--jp-shadow-ambient-color); --jp-elevation-z16: 0px 8px 10px -5px var(--jp-shadow-umbra-color), 0px 16px 24px 2px var(--jp-shadow-penumbra-color), 0px 6px 30px 5px var(--jp-shadow-ambient-color); --jp-elevation-z20: 0px 10px 13px -6px var(--jp-shadow-umbra-color), 0px 20px 31px 3px var(--jp-shadow-penumbra-color), 0px 8px 38px 7px var(--jp-shadow-ambient-color); --jp-elevation-z24: 0px 11px 15px -7px var(--jp-shadow-umbra-color), 0px 24px 38px 3px var(--jp-shadow-penumbra-color), 0px 9px 46px 8px var(--jp-shadow-ambient-color); /* Borders * * The following variables, specify the visual styling of borders in JupyterLab. */ --jp-border-width: 1px; --jp-border-color0: var(--md-grey-700); --jp-border-color1: var(--md-grey-700); --jp-border-color2: var(--md-grey-800); --jp-border-color3: var(--md-grey-900); --jp-border-radius: 2px; /* UI Fonts * * The UI font CSS variables are used for the typography all of the JupyterLab * user interface elements that are not directly user generated content. * * The font sizing here is done assuming that the body font size of --jp-ui-font-size1 * is applied to a parent element. When children elements, such as headings, are sized * in em all things will be computed relative to that body size. */ --jp-ui-font-scale-factor: 1.2; --jp-ui-font-size0: 0.83333em; --jp-ui-font-size1: 13px; /* Base font size */ --jp-ui-font-size2: 1.2em; --jp-ui-font-size3: 1.44em; --jp-ui-font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Helvetica, Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\"; /* * Use these font colors against the corresponding main layout colors. * In a light theme, these go from dark to light. */ /* Defaults use Material Design specification */ --jp-ui-font-color0: rgba(255, 255, 255, 1); --jp-ui-font-color1: rgba(255, 255, 255, 0.87); --jp-ui-font-color2: rgba(255, 255, 255, 0.54); --jp-ui-font-color3: rgba(255, 255, 255, 0.38); /* * Use these against the brand/accent/warn/error colors. * These will typically go from light to darker, in both a dark and light theme. */ --jp-ui-inverse-font-color0: rgba(0, 0, 0, 1); --jp-ui-inverse-font-color1: rgba(0, 0, 0, 0.8); --jp-ui-inverse-font-color2: rgba(0, 0, 0, 0.5); --jp-ui-inverse-font-color3: rgba(0, 0, 0, 0.3); /* Content Fonts * * Content font variables are used for typography of user generated content. * * The font sizing here is done assuming that the body font size of --jp-content-font-size1 * is applied to a parent element. When children elements, such as headings, are sized * in em all things will be computed relative to that body size. */ --jp-content-line-height: 1.6; --jp-content-font-scale-factor: 1.2; --jp-content-font-size0: 0.83333em; --jp-content-font-size1: 14px; /* Base font size */ --jp-content-font-size2: 1.2em; --jp-content-font-size3: 1.44em; --jp-content-font-size4: 1.728em; --jp-content-font-size5: 2.0736em; /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-content-presentation-font-size1: 17px; --jp-content-heading-line-height: 1; --jp-content-heading-margin-top: 1.2em; --jp-content-heading-margin-bottom: 0.8em; --jp-content-heading-font-weight: 500; /* Defaults use Material Design specification */ --jp-content-font-color0: rgba(255, 255, 255, 1); --jp-content-font-color1: rgba(255, 255, 255, 1); --jp-content-font-color2: rgba(255, 255, 255, 0.7); --jp-content-font-color3: rgba(255, 255, 255, 0.5); --jp-content-link-color: var(--md-blue-300); --jp-content-font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Helvetica, Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\"; /* * Code Fonts * * Code font variables are used for typography of code and other monospaces content. */ --jp-code-font-size: 13px; --jp-code-line-height: 1.3077; /* 17px for 13px base */ --jp-code-padding: 5px; /* 5px for 13px base, codemirror highlighting needs integer px value */ --jp-code-font-family-default: Menlo, Consolas, \"DejaVu Sans Mono\", monospace; --jp-code-font-family: var(--jp-code-font-family-default); /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-code-presentation-font-size: 16px; /* may need to tweak cursor width if you change font size */ --jp-code-cursor-width0: 1.4px; --jp-code-cursor-width1: 2px; --jp-code-cursor-width2: 4px; /* Layout * * The following are the main layout colors use in JupyterLab. In a light * theme these would go from light to dark. */ --jp-layout-color0: #111111; --jp-layout-color1: var(--md-grey-900); --jp-layout-color2: var(--md-grey-800); --jp-layout-color3: var(--md-grey-700); --jp-layout-color4: var(--md-grey-600); /* Inverse Layout * * The following are the inverse layout colors use in JupyterLab. In a light * theme these would go from dark to light. */ --jp-inverse-layout-color0: white; --jp-inverse-layout-color1: white; --jp-inverse-layout-color2: var(--md-grey-200); --jp-inverse-layout-color3: var(--md-grey-400); --jp-inverse-layout-color4: var(--md-grey-600); /* Brand/accent */ --jp-brand-color0: var(--md-blue-700); --jp-brand-color1: var(--md-blue-500); --jp-brand-color2: var(--md-blue-300); --jp-brand-color3: var(--md-blue-100); --jp-brand-color4: var(--md-blue-50); --jp-accent-color0: var(--md-green-700); --jp-accent-color1: var(--md-green-500); --jp-accent-color2: var(--md-green-300); --jp-accent-color3: var(--md-green-100); /* State colors (warn, error, success, info) */ --jp-warn-color0: var(--md-orange-700); --jp-warn-color1: var(--md-orange-500); --jp-warn-color2: var(--md-orange-300); --jp-warn-color3: var(--md-orange-100); --jp-error-color0: var(--md-red-700); --jp-error-color1: var(--md-red-500); --jp-error-color2: var(--md-red-300); --jp-error-color3: var(--md-red-100); --jp-success-color0: var(--md-green-700); --jp-success-color1: var(--md-green-500); --jp-success-color2: var(--md-green-300); --jp-success-color3: var(--md-green-100); --jp-info-color0: var(--md-cyan-700); --jp-info-color1: var(--md-cyan-500); --jp-info-color2: var(--md-cyan-300); --jp-info-color3: var(--md-cyan-100); /* Cell specific styles */ --jp-cell-padding: 5px; --jp-cell-collapser-width: 8px; --jp-cell-collapser-min-height: 20px; --jp-cell-collapser-not-active-hover-opacity: 0.6; --jp-cell-editor-background: var(--jp-layout-color1); --jp-cell-editor-border-color: var(--md-grey-700); --jp-cell-editor-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-cell-editor-active-background: var(--jp-layout-color0); --jp-cell-editor-active-border-color: var(--jp-brand-color1); --jp-cell-prompt-width: 64px; --jp-cell-prompt-font-family: var(--jp-code-font-family-default); --jp-cell-prompt-letter-spacing: 0px; --jp-cell-prompt-opacity: 1; --jp-cell-prompt-not-active-opacity: 1; --jp-cell-prompt-not-active-font-color: var(--md-grey-300); /* A custom blend of MD grey and blue 600 * See https://meyerweb.com/eric/tools/color-blend/#546E7A:1E88E5:5:hex */ --jp-cell-inprompt-font-color: #307fc1; /* A custom blend of MD grey and orange 600 * https://meyerweb.com/eric/tools/color-blend/#546E7A:F4511E:5:hex */ --jp-cell-outprompt-font-color: #bf5b3d; /* Notebook specific styles */ --jp-notebook-padding: 10px; --jp-notebook-select-background: var(--jp-layout-color1); --jp-notebook-multiselected-color: rgba(33, 150, 243, 0.24); /* The scroll padding is calculated to fill enough space at the bottom of the notebook to show one single-line cell (with appropriate padding) at the top when the notebook is scrolled all the way to the bottom. We also subtract one pixel so that no scrollbar appears if we have just one single-line cell in the notebook. This padding is to enable a 'scroll past end' feature in a notebook. */ --jp-notebook-scroll-padding: calc( 100% - var(--jp-code-font-size) * var(--jp-code-line-height) - var(--jp-code-padding) - var(--jp-cell-padding) - 1px ); /* Rendermime styles */ --jp-rendermime-error-background: rgba(244, 67, 54, 0.28); --jp-rendermime-table-row-background: var(--md-grey-900); --jp-rendermime-table-row-hover-background: rgba(3, 169, 244, 0.2); /* Dialog specific styles */ --jp-dialog-background: rgba(0, 0, 0, 0.6); /* Console specific styles */ --jp-console-padding: 10px; /* Toolbar specific styles */ --jp-toolbar-border-color: var(--jp-border-color2); --jp-toolbar-micro-height: 8px; --jp-toolbar-background: var(--jp-layout-color1); --jp-toolbar-box-shadow: 0px 0px 2px 0px rgba(0, 0, 0, 0.8); --jp-toolbar-header-margin: 4px 4px 0px 4px; --jp-toolbar-active-background: var(--jp-layout-color0); /* Statusbar specific styles */ --jp-statusbar-height: 24px; /* Input field styles */ --jp-input-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-input-active-background: var(--jp-layout-color0); --jp-input-hover-background: var(--jp-layout-color2); --jp-input-background: var(--md-grey-800); --jp-input-border-color: var(--jp-border-color1); --jp-input-active-border-color: var(--jp-brand-color1); --jp-input-active-box-shadow-color: rgba(19, 124, 189, 0.3); /* General editor styles */ --jp-editor-selected-background: var(--jp-layout-color2); --jp-editor-selected-focused-background: rgba(33, 150, 243, 0.24); --jp-editor-cursor-color: var(--jp-ui-font-color0); /* Code mirror specific styles */ --jp-mirror-editor-keyword-color: var(--md-green-500); --jp-mirror-editor-atom-color: var(--md-blue-300); --jp-mirror-editor-number-color: var(--md-green-400); --jp-mirror-editor-def-color: var(--md-blue-600); --jp-mirror-editor-variable-color: var(--md-grey-300); --jp-mirror-editor-variable-2-color: var(--md-blue-400); --jp-mirror-editor-variable-3-color: var(--md-green-600); --jp-mirror-editor-punctuation-color: var(--md-blue-400); --jp-mirror-editor-property-color: var(--md-blue-400); --jp-mirror-editor-operator-color: #aa22ff; --jp-mirror-editor-comment-color: #408080; --jp-mirror-editor-string-color: #ff7070; --jp-mirror-editor-string-2-color: var(--md-purple-300); --jp-mirror-editor-meta-color: #aa22ff; --jp-mirror-editor-qualifier-color: #555; --jp-mirror-editor-builtin-color: var(--md-green-600); --jp-mirror-editor-bracket-color: #997; --jp-mirror-editor-tag-color: var(--md-green-700); --jp-mirror-editor-attribute-color: var(--md-blue-700); --jp-mirror-editor-header-color: var(--md-blue-500); --jp-mirror-editor-quote-color: var(--md-green-300); --jp-mirror-editor-link-color: var(--md-blue-700); --jp-mirror-editor-error-color: #f00; --jp-mirror-editor-hr-color: #999; /* Vega extension styles */ --jp-vega-background: var(--md-grey-400); /* Sidebar-related styles */ --jp-sidebar-min-width: 250px; /* Search-related styles */ --jp-search-toggle-off-opacity: 0.6; --jp-search-toggle-hover-opacity: 0.8; --jp-search-toggle-on-opacity: 1; --jp-search-selected-match-background-color: rgb(255, 225, 0); --jp-search-selected-match-color: black; --jp-search-unselected-match-background-color: var( --jp-inverse-layout-color0 ); --jp-search-unselected-match-color: var(--jp-ui-inverse-font-color0); /* scrollbar related styles. Supports every browser except Edge. */ /* colors based on JetBrain's Darcula theme */ --jp-scrollbar-background-color: #3f4244; --jp-scrollbar-thumb-color: 88, 96, 97; /* need to specify thumb color as an RGB triplet */ --jp-scrollbar-endpad: 3px; /* the minimum gap between the thumb and the ends of a scrollbar */ /* hacks for setting the thumb shape. These do nothing in Firefox */ --jp-scrollbar-thumb-margin: 3.5px; /* the space in between the sides of the thumb and the track */ --jp-scrollbar-thumb-radius: 9px; /* set to a large-ish value for rounded endcaps on the thumb */ /* Icon colors that work well with light or dark backgrounds */ --jp-icon-contrast-color0: var(--md-purple-600); --jp-icon-contrast-color1: var(--md-green-600); --jp-icon-contrast-color2: var(--md-pink-600); --jp-icon-contrast-color3: var(--md-blue-600); } :root{--md-red-50: #ffebee;--md-red-100: #ffcdd2;--md-red-200: #ef9a9a;--md-red-300: #e57373;--md-red-400: #ef5350;--md-red-500: #f44336;--md-red-600: #e53935;--md-red-700: #d32f2f;--md-red-800: #c62828;--md-red-900: #b71c1c;--md-red-A100: #ff8a80;--md-red-A200: #ff5252;--md-red-A400: #ff1744;--md-red-A700: #d50000;--md-pink-50: #fce4ec;--md-pink-100: #f8bbd0;--md-pink-200: #f48fb1;--md-pink-300: #f06292;--md-pink-400: #ec407a;--md-pink-500: #e91e63;--md-pink-600: #d81b60;--md-pink-700: #c2185b;--md-pink-800: 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#7986cb;--md-indigo-400: #5c6bc0;--md-indigo-500: #3f51b5;--md-indigo-600: #3949ab;--md-indigo-700: #303f9f;--md-indigo-800: #283593;--md-indigo-900: #1a237e;--md-indigo-A100: #8c9eff;--md-indigo-A200: #536dfe;--md-indigo-A400: #3d5afe;--md-indigo-A700: #304ffe;--md-blue-50: #e3f2fd;--md-blue-100: #bbdefb;--md-blue-200: #90caf9;--md-blue-300: #64b5f6;--md-blue-400: #42a5f5;--md-blue-500: #2196f3;--md-blue-600: #1e88e5;--md-blue-700: #1976d2;--md-blue-800: #1565c0;--md-blue-900: #0d47a1;--md-blue-A100: #82b1ff;--md-blue-A200: #448aff;--md-blue-A400: #2979ff;--md-blue-A700: #2962ff;--md-light-blue-50: #e1f5fe;--md-light-blue-100: #b3e5fc;--md-light-blue-200: #81d4fa;--md-light-blue-300: #4fc3f7;--md-light-blue-400: #29b6f6;--md-light-blue-500: #03a9f4;--md-light-blue-600: #039be5;--md-light-blue-700: #0288d1;--md-light-blue-800: #0277bd;--md-light-blue-900: #01579b;--md-light-blue-A100: #80d8ff;--md-light-blue-A200: #40c4ff;--md-light-blue-A400: #00b0ff;--md-light-blue-A700: #0091ea;--md-cyan-50: #e0f7fa;--md-cyan-100: #b2ebf2;--md-cyan-200: #80deea;--md-cyan-300: #4dd0e1;--md-cyan-400: #26c6da;--md-cyan-500: #00bcd4;--md-cyan-600: #00acc1;--md-cyan-700: #0097a7;--md-cyan-800: #00838f;--md-cyan-900: #006064;--md-cyan-A100: #84ffff;--md-cyan-A200: #18ffff;--md-cyan-A400: #00e5ff;--md-cyan-A700: #00b8d4;--md-teal-50: #e0f2f1;--md-teal-100: #b2dfdb;--md-teal-200: #80cbc4;--md-teal-300: #4db6ac;--md-teal-400: #26a69a;--md-teal-500: #009688;--md-teal-600: #00897b;--md-teal-700: #00796b;--md-teal-800: #00695c;--md-teal-900: #004d40;--md-teal-A100: #a7ffeb;--md-teal-A200: #64ffda;--md-teal-A400: #1de9b6;--md-teal-A700: #00bfa5;--md-green-50: #e8f5e9;--md-green-100: #c8e6c9;--md-green-200: #a5d6a7;--md-green-300: #81c784;--md-green-400: #66bb6a;--md-green-500: #4caf50;--md-green-600: #43a047;--md-green-700: #388e3c;--md-green-800: #2e7d32;--md-green-900: #1b5e20;--md-green-A100: #b9f6ca;--md-green-A200: #69f0ae;--md-green-A400: #00e676;--md-green-A700: #00c853;--md-light-green-50: #f1f8e9;--md-light-green-100: #dcedc8;--md-light-green-200: #c5e1a5;--md-light-green-300: #aed581;--md-light-green-400: #9ccc65;--md-light-green-500: #8bc34a;--md-light-green-600: #7cb342;--md-light-green-700: #689f38;--md-light-green-800: #558b2f;--md-light-green-900: #33691e;--md-light-green-A100: #ccff90;--md-light-green-A200: #b2ff59;--md-light-green-A400: #76ff03;--md-light-green-A700: #64dd17;--md-lime-50: #f9fbe7;--md-lime-100: #f0f4c3;--md-lime-200: #e6ee9c;--md-lime-300: #dce775;--md-lime-400: #d4e157;--md-lime-500: #cddc39;--md-lime-600: #c0ca33;--md-lime-700: #afb42b;--md-lime-800: #9e9d24;--md-lime-900: #827717;--md-lime-A100: #f4ff81;--md-lime-A200: #eeff41;--md-lime-A400: #c6ff00;--md-lime-A700: #aeea00;--md-yellow-50: #fffde7;--md-yellow-100: #fff9c4;--md-yellow-200: #fff59d;--md-yellow-300: #fff176;--md-yellow-400: #ffee58;--md-yellow-500: #ffeb3b;--md-yellow-600: #fdd835;--md-yellow-700: #fbc02d;--md-yellow-800: #f9a825;--md-yellow-900: #f57f17;--md-yellow-A100: #ffff8d;--md-yellow-A200: #ffff00;--md-yellow-A400: #ffea00;--md-yellow-A700: #ffd600;--md-amber-50: #fff8e1;--md-amber-100: #ffecb3;--md-amber-200: #ffe082;--md-amber-300: #ffd54f;--md-amber-400: #ffca28;--md-amber-500: #ffc107;--md-amber-600: #ffb300;--md-amber-700: #ffa000;--md-amber-800: #ff8f00;--md-amber-900: #ff6f00;--md-amber-A100: #ffe57f;--md-amber-A200: #ffd740;--md-amber-A400: #ffc400;--md-amber-A700: #ffab00;--md-orange-50: #fff3e0;--md-orange-100: #ffe0b2;--md-orange-200: #ffcc80;--md-orange-300: #ffb74d;--md-orange-400: #ffa726;--md-orange-500: #ff9800;--md-orange-600: #fb8c00;--md-orange-700: #f57c00;--md-orange-800: #ef6c00;--md-orange-900: #e65100;--md-orange-A100: #ffd180;--md-orange-A200: #ffab40;--md-orange-A400: #ff9100;--md-orange-A700: #ff6d00;--md-deep-orange-50: #fbe9e7;--md-deep-orange-100: #ffccbc;--md-deep-orange-200: #ffab91;--md-deep-orange-300: #ff8a65;--md-deep-orange-400: #ff7043;--md-deep-orange-500: 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sourceMappingURL=mkdocs-jupyter.css.map*/ init_mathjax = function() { if (window.MathJax) { // MathJax loaded MathJax.Hub.Config({ TeX: { equationNumbers: { autoNumber: \"AMS\", useLabelIds: true } }, tex2jax: { inlineMath: [ ['$','$'], [\"\\\\(\",\"\\\\)\"] ], displayMath: [ ['$$','$$'], [\"\\\\[\",\"\\\\]\"] ], processEscapes: true, processEnvironments: true }, displayAlign: 'center', CommonHTML: { linebreaks: { automatic: true } } }); MathJax.Hub.Queue([\"Typeset\", MathJax.Hub]); } } init_mathjax(); Train a field of study (domain) classification of sentences on the MAG dataset \u00b6 SciBERT achieves an F1-score of 0.6571 on this dataset. This notebook shows that better results can be achieved without even using transformers. In [1]: Copied! import json from typing import Tuple from great_ai.utilities import clean , simple_parallel_map from tqdm.cli import tqdm def preprocess ( line : str ) -> Tuple [ str , str ]: data_point = json . loads ( line ) return ( clean ( data_point [ \"text\" ], convert_to_ascii = True ), data_point [ \"label\" ]) with open ( \"../../tutorial/data/train.txt\" , encoding = \"utf-8\" ) as f : training_data = simple_parallel_map ( preprocess , f . readlines ()) X_train = [ d [ 0 ] for d in training_data ] y_train = [ d [ 1 ] for d in training_data ] with open ( \"../../tutorial/data/test.txt\" , encoding = \"utf-8\" ) as f : test_data = simple_parallel_map ( preprocess , f . readlines ()) X_test = [ d [ 0 ] for d in test_data ] y_test = [ d [ 1 ] for d in test_data ] import json from typing import Tuple from great_ai.utilities import clean, simple_parallel_map from tqdm.cli import tqdm def preprocess(line: str) -> Tuple[str, str]: data_point = json.loads(line) return (clean(data_point[\"text\"], convert_to_ascii=True), data_point[\"label\"]) with open(\"../../tutorial/data/train.txt\", encoding=\"utf-8\") as f: training_data = simple_parallel_map(preprocess, f.readlines()) X_train = [d[0] for d in training_data] y_train = [d[1] for d in training_data] with open(\"../../tutorial/data/test.txt\", encoding=\"utf-8\") as f: test_data = simple_parallel_map(preprocess, f.readlines()) X_test = [d[0] for d in test_data] y_test = [d[1] for d in test_data] 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 84000/84000 [00:16<00:00, 4964.08it/s] 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 22399/22399 [00:05<00:00, 3847.04it/s] In [2]: Copied! from collections import Counter import matplotlib.pyplot as plt domains , counts = zip ( * Counter ( y_train ) . most_common ()) # Configure matplotlib to have nice, high-resolution charts % matplotlib inline plt . rcParams [ \"figure.facecolor\" ] = \"white\" plt . rcParams [ \"font.size\" ] = 18 plt . rcParams [ \"figure.figsize\" ] = ( 8 , 5 ) fig , ax = plt . subplots () plt . xticks ( rotation = 90 ) ax . bar ( domains , counts ) ax . set_ylabel ( \"Count\" ) fig . tight_layout () fig . savefig ( \"mag-distribution.png\" , dpi = 500 ) None from collections import Counter import matplotlib.pyplot as plt domains, counts = zip(*Counter(y_train).most_common()) # Configure matplotlib to have nice, high-resolution charts %matplotlib inline plt.rcParams[\"figure.facecolor\"] = \"white\" plt.rcParams[\"font.size\"] = 18 plt.rcParams[\"figure.figsize\"] = (8, 5) fig, ax = plt.subplots() plt.xticks(rotation=90) ax.bar(domains, counts) ax.set_ylabel(\"Count\") fig.tight_layout() fig.savefig(\"mag-distribution.png\", dpi=500) None In [3]: Copied! from sklearn.naive_bayes import MultinomialNB from sklearn.pipeline import Pipeline from sklearn.feature_extraction.text import TfidfVectorizer def create_pipeline () -> Pipeline : return Pipeline ( steps = [ ( \"vectorizer\" , TfidfVectorizer ( sublinear_tf = True )), ( \"classifier\" , MultinomialNB ()), ] ) from sklearn.naive_bayes import MultinomialNB from sklearn.pipeline import Pipeline from sklearn.feature_extraction.text import TfidfVectorizer def create_pipeline() -> Pipeline: return Pipeline( steps=[ (\"vectorizer\", TfidfVectorizer(sublinear_tf=True)), (\"classifier\", MultinomialNB()), ] ) In [4]: Copied! from sklearn.model_selection import GridSearchCV import pandas as pd optimisation_pipeline = GridSearchCV ( create_pipeline (), { \"vectorizer__min_df\" : [ 5 , 20 , 100 ], \"vectorizer__max_df\" : [ 0.05 , 0.1 ], \"classifier__alpha\" : [ 0.5 , 1 ], \"classifier__fit_prior\" : [ True , False ], }, scoring = \"f1_macro\" , cv = 3 , n_jobs =- 1 , verbose = 1 , ) optimisation_pipeline . fit ( X_train , y_train ) results = pd . DataFrame ( optimisation_pipeline . cv_results_ ) results . sort_values ( \"rank_test_score\" ) from sklearn.model_selection import GridSearchCV import pandas as pd optimisation_pipeline = GridSearchCV( create_pipeline(), { \"vectorizer__min_df\": [5, 20, 100], \"vectorizer__max_df\": [0.05, 0.1], \"classifier__alpha\": [0.5, 1], \"classifier__fit_prior\": [True, False], }, scoring=\"f1_macro\", cv=3, n_jobs=-1, verbose=1, ) optimisation_pipeline.fit(X_train, y_train) results = pd.DataFrame(optimisation_pipeline.cv_results_) results.sort_values(\"rank_test_score\") Fitting 3 folds for each of 24 candidates, totalling 72 fits Out[4]: .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } mean_fit_time std_fit_time mean_score_time std_score_time param_classifier__alpha param_classifier__fit_prior param_vectorizer__max_df param_vectorizer__min_df params split0_test_score split1_test_score split2_test_score mean_test_score std_test_score rank_test_score 12 2.352721 0.257936 1.357399 0.094580 1 True 0.05 5 {'classifier__alpha': 1, 'classifier__fit_prio... 0.677283 0.670398 0.665671 0.671118 0.004768 1 15 2.544958 0.051976 1.245658 0.137108 1 True 0.1 5 {'classifier__alpha': 1, 'classifier__fit_prio... 0.676970 0.670244 0.666123 0.671112 0.004470 2 18 2.472888 0.053978 1.447181 0.051170 1 False 0.05 5 {'classifier__alpha': 1, 'classifier__fit_prio... 0.675678 0.669321 0.665305 0.670101 0.004271 3 21 2.467637 0.005358 1.216254 0.048319 1 False 0.1 5 {'classifier__alpha': 1, 'classifier__fit_prio... 0.675569 0.669482 0.665225 0.670092 0.004245 4 3 2.477612 0.134880 1.428521 0.017417 0.5 True 0.1 5 {'classifier__alpha': 0.5, 'classifier__fit_pr... 0.675225 0.668912 0.665652 0.669930 0.003974 5 0 2.484810 0.067836 1.249979 0.209959 0.5 True 0.05 5 {'classifier__alpha': 0.5, 'classifier__fit_pr... 0.675264 0.668781 0.665394 0.669813 0.004095 6 9 2.452453 0.049376 1.279694 0.089200 0.5 False 0.1 5 {'classifier__alpha': 0.5, 'classifier__fit_pr... 0.674199 0.668005 0.664456 0.668887 0.004026 7 6 2.748628 0.098077 1.255213 0.101334 0.5 False 0.05 5 {'classifier__alpha': 0.5, 'classifier__fit_pr... 0.673996 0.668048 0.664572 0.668872 0.003891 8 13 2.371907 0.204820 1.375143 0.193984 1 True 0.05 20 {'classifier__alpha': 1, 'classifier__fit_prio... 0.656459 0.647433 0.644864 0.649585 0.004972 9 16 2.557486 0.184332 1.272137 0.133980 1 True 0.1 20 {'classifier__alpha': 1, 'classifier__fit_prio... 0.656196 0.646960 0.645436 0.649530 0.004754 10 4 2.362093 0.109796 1.344960 0.083562 0.5 True 0.1 20 {'classifier__alpha': 0.5, 'classifier__fit_pr... 0.654424 0.644690 0.644991 0.648035 0.004519 11 1 2.668012 0.131751 1.383441 0.049157 0.5 True 0.05 20 {'classifier__alpha': 0.5, 'classifier__fit_pr... 0.654622 0.644901 0.644425 0.647983 0.004699 12 19 2.517576 0.154192 1.374269 0.026773 1 False 0.05 20 {'classifier__alpha': 1, 'classifier__fit_prio... 0.655125 0.645586 0.643166 0.647959 0.005163 13 22 2.386240 0.069718 0.967638 0.076766 1 False 0.1 20 {'classifier__alpha': 1, 'classifier__fit_prio... 0.654254 0.645409 0.643514 0.647726 0.004680 14 7 2.416211 0.138001 1.316785 0.036402 0.5 False 0.05 20 {'classifier__alpha': 0.5, 'classifier__fit_pr... 0.652727 0.643691 0.642749 0.646389 0.004498 15 10 2.397768 0.244951 1.330007 0.008983 0.5 False 0.1 20 {'classifier__alpha': 0.5, 'classifier__fit_pr... 0.652290 0.642975 0.643421 0.646229 0.004290 16 14 2.590127 0.054396 1.155976 0.187602 1 True 0.05 100 {'classifier__alpha': 1, 'classifier__fit_prio... 0.587097 0.577411 0.580170 0.581560 0.004075 17 17 2.497556 0.085952 1.169808 0.061336 1 True 0.1 100 {'classifier__alpha': 1, 'classifier__fit_prio... 0.585713 0.577197 0.580763 0.581224 0.003492 18 2 2.467611 0.168399 1.313936 0.109695 0.5 True 0.05 100 {'classifier__alpha': 0.5, 'classifier__fit_pr... 0.586346 0.576987 0.579852 0.581062 0.003915 19 5 2.339322 0.092595 1.209515 0.132825 0.5 True 0.1 100 {'classifier__alpha': 0.5, 'classifier__fit_pr... 0.585634 0.576217 0.580320 0.580723 0.003855 20 20 2.644488 0.119967 1.238554 0.048871 1 False 0.05 100 {'classifier__alpha': 1, 'classifier__fit_prio... 0.581096 0.572833 0.576280 0.576736 0.003389 21 8 2.456030 0.072136 1.240463 0.092383 0.5 False 0.05 100 {'classifier__alpha': 0.5, 'classifier__fit_pr... 0.580841 0.572391 0.575731 0.576321 0.003475 22 23 2.335796 0.082338 0.786187 0.073499 1 False 0.1 100 {'classifier__alpha': 1, 'classifier__fit_prio... 0.579769 0.572373 0.576699 0.576280 0.003034 23 11 2.530331 0.107199 1.247842 0.078229 0.5 False 0.1 100 {'classifier__alpha': 0.5, 'classifier__fit_pr... 0.579743 0.571607 0.576352 0.575901 0.003337 24 In [5]: Copied! from sklearn import set_config set_config ( display = \"diagram\" ) classifier = create_pipeline () classifier . set_params ( ** optimisation_pipeline . best_params_ ) classifier . fit ( X_train , y_train ) from sklearn import set_config set_config(display=\"diagram\") classifier = create_pipeline() classifier.set_params(**optimisation_pipeline.best_params_) classifier.fit(X_train, y_train) Out[5]: #sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"\u25b8\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"\u25be\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;} Pipeline(steps=[('vectorizer', TfidfVectorizer(max_df=0.05, min_df=5, sublinear_tf=True)), ('classifier', MultinomialNB(alpha=1))]) In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org. Pipeline Pipeline(steps=[('vectorizer', TfidfVectorizer(max_df=0.05, min_df=5, sublinear_tf=True)), ('classifier', MultinomialNB(alpha=1))]) TfidfVectorizer TfidfVectorizer(max_df=0.05, min_df=5, sublinear_tf=True) MultinomialNB MultinomialNB(alpha=1) In [12]: Copied! from sklearn import metrics import matplotlib.pyplot as plt y_predicted = classifier . predict ( X_test ) % matplotlib inline plt . rcParams [ \"figure.figsize\" ] = ( 20 , 18 ) plt . rcParams [ \"figure.facecolor\" ] = \"white\" plt . rcParams [ \"font.size\" ] = 22 plt . rcParams [ \"axes.xmargin\" ] = 0 print ( metrics . classification_report ( y_test , y_predicted , digits = 4 )) metrics . ConfusionMatrixDisplay . from_predictions ( y_true = y_test , y_pred = y_predicted , xticks_rotation = \"vertical\" , normalize = \"pred\" , values_format = \".2f\" , ) plt . tight_layout () plt . savefig ( \"mag-confusion.png\" , dpi = 600 ) from sklearn import metrics import matplotlib.pyplot as plt y_predicted = classifier.predict(X_test) %matplotlib inline plt.rcParams[\"figure.figsize\"] = (20, 18) plt.rcParams[\"figure.facecolor\"] = \"white\" plt.rcParams[\"font.size\"] = 22 plt.rcParams[\"axes.xmargin\"] = 0 print(metrics.classification_report(y_test, y_predicted, digits=4)) metrics.ConfusionMatrixDisplay.from_predictions( y_true=y_test, y_pred=y_predicted, xticks_rotation=\"vertical\", normalize=\"pred\", values_format=\".2f\", ) plt.tight_layout() plt.savefig(\"mag-confusion.png\", dpi=600) precision recall f1-score support business 0.6412 0.7258 0.6808 3198 economics 0.6635 0.7146 0.6881 3189 geography 0.7461 0.6791 0.7111 3207 medicine 0.8806 0.9021 0.8912 3187 politics 0.5563 0.5895 0.5724 3169 psychology 0.7654 0.6811 0.7208 3252 sociology 0.5165 0.4698 0.4921 3197 accuracy 0.6803 22399 macro avg 0.6814 0.6803 0.6795 22399 weighted avg 0.6817 0.6803 0.6796 22399","title":"Train a field of study (domain) classification of sentences on the MAG dataset"},{"location":"examples/simple-mag/train/#train-a-field-of-study-domain-classification-of-sentences-on-the-mag-dataset","text":"SciBERT achieves an F1-score of 0.6571 on this dataset. This notebook shows that better results can be achieved without even using transformers. In [1]: Copied! import json from typing import Tuple from great_ai.utilities import clean , simple_parallel_map from tqdm.cli import tqdm def preprocess ( line : str ) -> Tuple [ str , str ]: data_point = json . loads ( line ) return ( clean ( data_point [ \"text\" ], convert_to_ascii = True ), data_point [ \"label\" ]) with open ( \"../../tutorial/data/train.txt\" , encoding = \"utf-8\" ) as f : training_data = simple_parallel_map ( preprocess , f . readlines ()) X_train = [ d [ 0 ] for d in training_data ] y_train = [ d [ 1 ] for d in training_data ] with open ( \"../../tutorial/data/test.txt\" , encoding = \"utf-8\" ) as f : test_data = simple_parallel_map ( preprocess , f . readlines ()) X_test = [ d [ 0 ] for d in test_data ] y_test = [ d [ 1 ] for d in test_data ] import json from typing import Tuple from great_ai.utilities import clean, simple_parallel_map from tqdm.cli import tqdm def preprocess(line: str) -> Tuple[str, str]: data_point = json.loads(line) return (clean(data_point[\"text\"], convert_to_ascii=True), data_point[\"label\"]) with open(\"../../tutorial/data/train.txt\", encoding=\"utf-8\") as f: training_data = simple_parallel_map(preprocess, f.readlines()) X_train = [d[0] for d in training_data] y_train = [d[1] for d in training_data] with open(\"../../tutorial/data/test.txt\", encoding=\"utf-8\") as f: test_data = simple_parallel_map(preprocess, f.readlines()) X_test = [d[0] for d in test_data] y_test = [d[1] for d in test_data] 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 84000/84000 [00:16<00:00, 4964.08it/s] 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 22399/22399 [00:05<00:00, 3847.04it/s] In [2]: Copied! from collections import Counter import matplotlib.pyplot as plt domains , counts = zip ( * Counter ( y_train ) . most_common ()) # Configure matplotlib to have nice, high-resolution charts % matplotlib inline plt . rcParams [ \"figure.facecolor\" ] = \"white\" plt . rcParams [ \"font.size\" ] = 18 plt . rcParams [ \"figure.figsize\" ] = ( 8 , 5 ) fig , ax = plt . subplots () plt . xticks ( rotation = 90 ) ax . bar ( domains , counts ) ax . set_ylabel ( \"Count\" ) fig . tight_layout () fig . savefig ( \"mag-distribution.png\" , dpi = 500 ) None from collections import Counter import matplotlib.pyplot as plt domains, counts = zip(*Counter(y_train).most_common()) # Configure matplotlib to have nice, high-resolution charts %matplotlib inline plt.rcParams[\"figure.facecolor\"] = \"white\" plt.rcParams[\"font.size\"] = 18 plt.rcParams[\"figure.figsize\"] = (8, 5) fig, ax = plt.subplots() plt.xticks(rotation=90) ax.bar(domains, counts) ax.set_ylabel(\"Count\") fig.tight_layout() fig.savefig(\"mag-distribution.png\", dpi=500) None In [3]: Copied! from sklearn.naive_bayes import MultinomialNB from sklearn.pipeline import Pipeline from sklearn.feature_extraction.text import TfidfVectorizer def create_pipeline () -> Pipeline : return Pipeline ( steps = [ ( \"vectorizer\" , TfidfVectorizer ( sublinear_tf = True )), ( \"classifier\" , MultinomialNB ()), ] ) from sklearn.naive_bayes import MultinomialNB from sklearn.pipeline import Pipeline from sklearn.feature_extraction.text import TfidfVectorizer def create_pipeline() -> Pipeline: return Pipeline( steps=[ (\"vectorizer\", TfidfVectorizer(sublinear_tf=True)), (\"classifier\", MultinomialNB()), ] ) In [4]: Copied! from sklearn.model_selection import GridSearchCV import pandas as pd optimisation_pipeline = GridSearchCV ( create_pipeline (), { \"vectorizer__min_df\" : [ 5 , 20 , 100 ], \"vectorizer__max_df\" : [ 0.05 , 0.1 ], \"classifier__alpha\" : [ 0.5 , 1 ], \"classifier__fit_prior\" : [ True , False ], }, scoring = \"f1_macro\" , cv = 3 , n_jobs =- 1 , verbose = 1 , ) optimisation_pipeline . fit ( X_train , y_train ) results = pd . DataFrame ( optimisation_pipeline . cv_results_ ) results . sort_values ( \"rank_test_score\" ) from sklearn.model_selection import GridSearchCV import pandas as pd optimisation_pipeline = GridSearchCV( create_pipeline(), { \"vectorizer__min_df\": [5, 20, 100], \"vectorizer__max_df\": [0.05, 0.1], \"classifier__alpha\": [0.5, 1], \"classifier__fit_prior\": [True, False], }, scoring=\"f1_macro\", cv=3, n_jobs=-1, verbose=1, ) optimisation_pipeline.fit(X_train, y_train) results = pd.DataFrame(optimisation_pipeline.cv_results_) results.sort_values(\"rank_test_score\") Fitting 3 folds for each of 24 candidates, totalling 72 fits Out[4]: .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } mean_fit_time std_fit_time mean_score_time std_score_time param_classifier__alpha param_classifier__fit_prior param_vectorizer__max_df param_vectorizer__min_df params split0_test_score split1_test_score split2_test_score mean_test_score std_test_score rank_test_score 12 2.352721 0.257936 1.357399 0.094580 1 True 0.05 5 {'classifier__alpha': 1, 'classifier__fit_prio... 0.677283 0.670398 0.665671 0.671118 0.004768 1 15 2.544958 0.051976 1.245658 0.137108 1 True 0.1 5 {'classifier__alpha': 1, 'classifier__fit_prio... 0.676970 0.670244 0.666123 0.671112 0.004470 2 18 2.472888 0.053978 1.447181 0.051170 1 False 0.05 5 {'classifier__alpha': 1, 'classifier__fit_prio... 0.675678 0.669321 0.665305 0.670101 0.004271 3 21 2.467637 0.005358 1.216254 0.048319 1 False 0.1 5 {'classifier__alpha': 1, 'classifier__fit_prio... 0.675569 0.669482 0.665225 0.670092 0.004245 4 3 2.477612 0.134880 1.428521 0.017417 0.5 True 0.1 5 {'classifier__alpha': 0.5, 'classifier__fit_pr... 0.675225 0.668912 0.665652 0.669930 0.003974 5 0 2.484810 0.067836 1.249979 0.209959 0.5 True 0.05 5 {'classifier__alpha': 0.5, 'classifier__fit_pr... 0.675264 0.668781 0.665394 0.669813 0.004095 6 9 2.452453 0.049376 1.279694 0.089200 0.5 False 0.1 5 {'classifier__alpha': 0.5, 'classifier__fit_pr... 0.674199 0.668005 0.664456 0.668887 0.004026 7 6 2.748628 0.098077 1.255213 0.101334 0.5 False 0.05 5 {'classifier__alpha': 0.5, 'classifier__fit_pr... 0.673996 0.668048 0.664572 0.668872 0.003891 8 13 2.371907 0.204820 1.375143 0.193984 1 True 0.05 20 {'classifier__alpha': 1, 'classifier__fit_prio... 0.656459 0.647433 0.644864 0.649585 0.004972 9 16 2.557486 0.184332 1.272137 0.133980 1 True 0.1 20 {'classifier__alpha': 1, 'classifier__fit_prio... 0.656196 0.646960 0.645436 0.649530 0.004754 10 4 2.362093 0.109796 1.344960 0.083562 0.5 True 0.1 20 {'classifier__alpha': 0.5, 'classifier__fit_pr... 0.654424 0.644690 0.644991 0.648035 0.004519 11 1 2.668012 0.131751 1.383441 0.049157 0.5 True 0.05 20 {'classifier__alpha': 0.5, 'classifier__fit_pr... 0.654622 0.644901 0.644425 0.647983 0.004699 12 19 2.517576 0.154192 1.374269 0.026773 1 False 0.05 20 {'classifier__alpha': 1, 'classifier__fit_prio... 0.655125 0.645586 0.643166 0.647959 0.005163 13 22 2.386240 0.069718 0.967638 0.076766 1 False 0.1 20 {'classifier__alpha': 1, 'classifier__fit_prio... 0.654254 0.645409 0.643514 0.647726 0.004680 14 7 2.416211 0.138001 1.316785 0.036402 0.5 False 0.05 20 {'classifier__alpha': 0.5, 'classifier__fit_pr... 0.652727 0.643691 0.642749 0.646389 0.004498 15 10 2.397768 0.244951 1.330007 0.008983 0.5 False 0.1 20 {'classifier__alpha': 0.5, 'classifier__fit_pr... 0.652290 0.642975 0.643421 0.646229 0.004290 16 14 2.590127 0.054396 1.155976 0.187602 1 True 0.05 100 {'classifier__alpha': 1, 'classifier__fit_prio... 0.587097 0.577411 0.580170 0.581560 0.004075 17 17 2.497556 0.085952 1.169808 0.061336 1 True 0.1 100 {'classifier__alpha': 1, 'classifier__fit_prio... 0.585713 0.577197 0.580763 0.581224 0.003492 18 2 2.467611 0.168399 1.313936 0.109695 0.5 True 0.05 100 {'classifier__alpha': 0.5, 'classifier__fit_pr... 0.586346 0.576987 0.579852 0.581062 0.003915 19 5 2.339322 0.092595 1.209515 0.132825 0.5 True 0.1 100 {'classifier__alpha': 0.5, 'classifier__fit_pr... 0.585634 0.576217 0.580320 0.580723 0.003855 20 20 2.644488 0.119967 1.238554 0.048871 1 False 0.05 100 {'classifier__alpha': 1, 'classifier__fit_prio... 0.581096 0.572833 0.576280 0.576736 0.003389 21 8 2.456030 0.072136 1.240463 0.092383 0.5 False 0.05 100 {'classifier__alpha': 0.5, 'classifier__fit_pr... 0.580841 0.572391 0.575731 0.576321 0.003475 22 23 2.335796 0.082338 0.786187 0.073499 1 False 0.1 100 {'classifier__alpha': 1, 'classifier__fit_prio... 0.579769 0.572373 0.576699 0.576280 0.003034 23 11 2.530331 0.107199 1.247842 0.078229 0.5 False 0.1 100 {'classifier__alpha': 0.5, 'classifier__fit_pr... 0.579743 0.571607 0.576352 0.575901 0.003337 24 In [5]: Copied! from sklearn import set_config set_config ( display = \"diagram\" ) classifier = create_pipeline () classifier . set_params ( ** optimisation_pipeline . best_params_ ) classifier . fit ( X_train , y_train ) from sklearn import set_config set_config(display=\"diagram\") classifier = create_pipeline() classifier.set_params(**optimisation_pipeline.best_params_) classifier.fit(X_train, y_train) Out[5]: #sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"\u25b8\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"\u25be\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;} Pipeline(steps=[('vectorizer', TfidfVectorizer(max_df=0.05, min_df=5, sublinear_tf=True)), ('classifier', MultinomialNB(alpha=1))]) In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org. Pipeline Pipeline(steps=[('vectorizer', TfidfVectorizer(max_df=0.05, min_df=5, sublinear_tf=True)), ('classifier', MultinomialNB(alpha=1))]) TfidfVectorizer TfidfVectorizer(max_df=0.05, min_df=5, sublinear_tf=True) MultinomialNB MultinomialNB(alpha=1) In [12]: Copied! from sklearn import metrics import matplotlib.pyplot as plt y_predicted = classifier . predict ( X_test ) % matplotlib inline plt . rcParams [ \"figure.figsize\" ] = ( 20 , 18 ) plt . rcParams [ \"figure.facecolor\" ] = \"white\" plt . rcParams [ \"font.size\" ] = 22 plt . rcParams [ \"axes.xmargin\" ] = 0 print ( metrics . classification_report ( y_test , y_predicted , digits = 4 )) metrics . ConfusionMatrixDisplay . from_predictions ( y_true = y_test , y_pred = y_predicted , xticks_rotation = \"vertical\" , normalize = \"pred\" , values_format = \".2f\" , ) plt . tight_layout () plt . savefig ( \"mag-confusion.png\" , dpi = 600 ) from sklearn import metrics import matplotlib.pyplot as plt y_predicted = classifier.predict(X_test) %matplotlib inline plt.rcParams[\"figure.figsize\"] = (20, 18) plt.rcParams[\"figure.facecolor\"] = \"white\" plt.rcParams[\"font.size\"] = 22 plt.rcParams[\"axes.xmargin\"] = 0 print(metrics.classification_report(y_test, y_predicted, digits=4)) metrics.ConfusionMatrixDisplay.from_predictions( y_true=y_test, y_pred=y_predicted, xticks_rotation=\"vertical\", normalize=\"pred\", values_format=\".2f\", ) plt.tight_layout() plt.savefig(\"mag-confusion.png\", dpi=600) precision recall f1-score support business 0.6412 0.7258 0.6808 3198 economics 0.6635 0.7146 0.6881 3189 geography 0.7461 0.6791 0.7111 3207 medicine 0.8806 0.9021 0.8912 3187 politics 0.5563 0.5895 0.5724 3169 psychology 0.7654 0.6811 0.7208 3252 sociology 0.5165 0.4698 0.4921 3197 accuracy 0.6803 22399 macro avg 0.6814 0.6803 0.6795 22399 weighted avg 0.6817 0.6803 0.6796 22399","title":"Train a field of study (domain) classification of sentences on the MAG dataset"},{"location":"how-to-guides/call-remote/","text":"How to call remote GreatAI instances # Microservices architecture (or SOA ) work well with ML applications. This is because their interfaces are usually very narrow, while the functionality provided quite comprehensive. Hence, drawing the boundaries of responsibilities is more straightforward in the case of ML services than in the case of more traditional business applications. For this reason, it is common to have a tree of models (preferably wrapped in GreatAI instances) communicating with each other. Although regular HTTP POST requests could be sent to each service's /predict endpoint, great-ai comes with two convenience functions: call_remote_great_ai and call_remote_great_ai_async to wrap this request. These provide you with some level of robustness and deserialisation. Inside notebooks The async variant, call_remote_great_ai_async , requires a running event loop while the synchronous variant disallows other running event-loops. Therefore, when running inside a Jupyter Notebook, always call call_remote_great_ai_async . Simple example # Let's create two processes: a server and a client. Server # server.py from great_ai import GreatAI from asyncio import sleep @GreatAI . create async def slow_greeter ( your_name ): await sleep ( 2 ) return f 'Hi { your_name } !' Run this in development mode by executing great-ai server.py or python3 -m great_ai server.py if you're on Windows and great-ai is not in your PATH . Client # client.py from great_ai import call_remote_great_ai names = [ 'Oliv\u00e9r' , 'Bal\u00e1zs' , 'Andr\u00e1s' ] results = [ call_remote_great_ai ( 'http://localhost:6060' , { 'your_name' : name } ) . output #(1) for name in names ] print ( results ) Only return the outputs so we don't clutter up the terminal. Run this script as a regular Python script by executing python3 client.py . As you can see, everything worked as expected. There is one way to improve it though. An async example # Let's send multiple requests at the same time to speed up the overall execution time. To do this, we will use the call_remote_great_ai_async function. Why is this possible? Note, that in server.py , the inference function is declared async . This means that multiple \"copies\" of it can run at the same time in the same thread. Since, there is no CPU bottleneck, the server has a quite large throughput (requests responded to per second), but its latency will stay around 2 seconds due to the async sleep command. If your great-ai server is not async , higher throughput can be achieved by running multiple instances of it, either manually, or by running it with multiple uvicorn workers like this: ENVIRONMENT=production great-ai server.py --worker_count 4 Async client # async-client.py from great_ai import call_remote_great_ai_async import asyncio names = [ 'Oliv\u00e9r' , 'Bal\u00e1zs' , 'Andr\u00e1s' ] async def main (): futures = [ call_remote_great_ai_async ( 'http://localhost:6060' , { 'your_name' : name } ) for name in names ] results = await asyncio . gather ( * futures ) print ([ r . output for r in results ]) asyncio . run ( main ()) Replace client.py with this async client. Note that even though async support is significantly more streamlined in recent Python versions, it still requires a bit more boilerplate than its synchronous counterpart. This also works, and in some use-cases might be considerably quicker.","title":"How to call remote GreatAI instances"},{"location":"how-to-guides/call-remote/#how-to-call-remote-greatai-instances","text":"Microservices architecture (or SOA ) work well with ML applications. This is because their interfaces are usually very narrow, while the functionality provided quite comprehensive. Hence, drawing the boundaries of responsibilities is more straightforward in the case of ML services than in the case of more traditional business applications. For this reason, it is common to have a tree of models (preferably wrapped in GreatAI instances) communicating with each other. Although regular HTTP POST requests could be sent to each service's /predict endpoint, great-ai comes with two convenience functions: call_remote_great_ai and call_remote_great_ai_async to wrap this request. These provide you with some level of robustness and deserialisation. Inside notebooks The async variant, call_remote_great_ai_async , requires a running event loop while the synchronous variant disallows other running event-loops. Therefore, when running inside a Jupyter Notebook, always call call_remote_great_ai_async .","title":"How to call remote GreatAI instances"},{"location":"how-to-guides/call-remote/#simple-example","text":"Let's create two processes: a server and a client.","title":"Simple example"},{"location":"how-to-guides/call-remote/#server","text":"server.py from great_ai import GreatAI from asyncio import sleep @GreatAI . create async def slow_greeter ( your_name ): await sleep ( 2 ) return f 'Hi { your_name } !' Run this in development mode by executing great-ai server.py or python3 -m great_ai server.py if you're on Windows and great-ai is not in your PATH .","title":"Server"},{"location":"how-to-guides/call-remote/#client","text":"client.py from great_ai import call_remote_great_ai names = [ 'Oliv\u00e9r' , 'Bal\u00e1zs' , 'Andr\u00e1s' ] results = [ call_remote_great_ai ( 'http://localhost:6060' , { 'your_name' : name } ) . output #(1) for name in names ] print ( results ) Only return the outputs so we don't clutter up the terminal. Run this script as a regular Python script by executing python3 client.py . As you can see, everything worked as expected. There is one way to improve it though.","title":"Client"},{"location":"how-to-guides/call-remote/#an-async-example","text":"Let's send multiple requests at the same time to speed up the overall execution time. To do this, we will use the call_remote_great_ai_async function. Why is this possible? Note, that in server.py , the inference function is declared async . This means that multiple \"copies\" of it can run at the same time in the same thread. Since, there is no CPU bottleneck, the server has a quite large throughput (requests responded to per second), but its latency will stay around 2 seconds due to the async sleep command. If your great-ai server is not async , higher throughput can be achieved by running multiple instances of it, either manually, or by running it with multiple uvicorn workers like this: ENVIRONMENT=production great-ai server.py --worker_count 4","title":"An async example"},{"location":"how-to-guides/call-remote/#async-client","text":"async-client.py from great_ai import call_remote_great_ai_async import asyncio names = [ 'Oliv\u00e9r' , 'Bal\u00e1zs' , 'Andr\u00e1s' ] async def main (): futures = [ call_remote_great_ai_async ( 'http://localhost:6060' , { 'your_name' : name } ) for name in names ] results = await asyncio . gather ( * futures ) print ([ r . output for r in results ]) asyncio . run ( main ()) Replace client.py with this async client. Note that even though async support is significantly more streamlined in recent Python versions, it still requires a bit more boilerplate than its synchronous counterpart. This also works, and in some use-cases might be considerably quicker.","title":"Async client"},{"location":"how-to-guides/configure-service/","text":"How to configure GreatAI # GreatAI aims to provide reasonable defaults wherever possible. The current configuration is always prominently displayed (and updated) on the dashboard and in the command-line start-up banner. Using great_ai.configure # You can override any of the default settings by calling great_ai.configure . If you don't call configure , the default settings are applied on the first call to most great-ai functions. Warning You must call great_ai.configure before calling (or decorating with) any other great-ai function. However, importing other functions before calling great_ai.configure is permitted. configure-demo.py from great_ai import configure , RouteConfig import logging configure ( version = '1.0.0' , log_level = logging . INFO , seed = 2 , should_log_exception_stack = False , prediction_cache_size = 0 , #(1) disable_se4ml_banner = True , dashboard_table_size = 200 , route_config = RouteConfig ( #(2) feedback_endpoints_enabled = False , dashboard_enabled = False ) ) Completely disable caching. The unspecified routes are enabled by default. Using remote storage # The only aspect that cannot be automated is choosing the backing storage for the database and file storage. Right now, you have 3 options for storing the models and large datasets: LargeFileLocal , LargeFileMongo , and LargeFileS3 . Without explicit configuration, LargeFileLocal is selected by default. This one still version-controls your files but it only stores them in a local path (which of course can be a remote volume attached by NFS , HDFS , etc.). Important If your working directory contains a mongo.ini or s3.ini file, an attempt is made to auto-configure LargeFileMongo or LargeFileS3 respectively. To use LargeFileMongo or LargeFileS3 explicitly, configure them before calling any other great-ai function. S3-compatible # s3.ini aws_region_name = eu-west-2 aws_access_key_id = MY_AWS_ACCESS_KEY # ENV:MY_AWS_ACCESS_KEY would also work aws_secret_access_key = MY_AWS_SECRET_KEY large_files_bucket_name = bucket-for-models use-s3.py from great_ai.large_file import LargeFileS3 from great_ai import save_model LargeFileS3 . configure_credentials_from_file ( 's3.ini' ) #(1) model = [ 4 , 3 ] save_model ( model , 'my-model' ) This line isn't strictly necessary because if s3.ini (or mongo.ini ) is available in the current working directory, they are automatically used to configure their respective LargeFile implementations/databases. Departing from AWS With the aws_endpoint_url argument, it is possible to use any other S3-compatible service such as Backblaze . In that case, it would be aws_endpoint_url=https://s3.us-west-002.backblazeb2.com . GridFS # GridFS specifies how to store files in MongoDB. The official MongoDB server and many compatible implementations support it. mongo.ini MONGO_CONNECTION_STRING = mongodb : // localhost : 27017 # this is the default value # if `MONGO_CONNECTION_STRING` is specified, this default is overridden MONGO_CONNECTION_STRING = ENV : MONGO_CONNECTION_STRING MONGO_DATABASE = my-database # it is automatically created if doesn't exist use-mongo.py from great_ai.large_file import LargeFileMongo from great_ai import save_model LargeFileMongo . configure_credentials_from_file ( 'mongo.ini' ) model = [ 4 , 3 ] save_model ( model , 'my-model' ) Simplifying config files You can combine mongo.ini or s3.ini with your application's config file because the unneeded keys are ignored by the configure_credentials_from_file method. Using a database # By default, a thread-safe version of TinyDB is utilised for saving the prediction traces into a local file. Unfortunately, for most production needs, this method is not suitable. MongoDB # At the moment, only MongoDB is supported as a production-ready TracingDatabase . In order to use it, you have to either place a file named mongo.ini in your working directory, or explicitly call either MongoDbDriver.configure_credentials_from_file or MongoDbDriver.configure_credentials .","title":"How to configure GreatAI"},{"location":"how-to-guides/configure-service/#how-to-configure-greatai","text":"GreatAI aims to provide reasonable defaults wherever possible. The current configuration is always prominently displayed (and updated) on the dashboard and in the command-line start-up banner.","title":"How to configure GreatAI"},{"location":"how-to-guides/configure-service/#using-great_aiconfigure","text":"You can override any of the default settings by calling great_ai.configure . If you don't call configure , the default settings are applied on the first call to most great-ai functions. Warning You must call great_ai.configure before calling (or decorating with) any other great-ai function. However, importing other functions before calling great_ai.configure is permitted. configure-demo.py from great_ai import configure , RouteConfig import logging configure ( version = '1.0.0' , log_level = logging . INFO , seed = 2 , should_log_exception_stack = False , prediction_cache_size = 0 , #(1) disable_se4ml_banner = True , dashboard_table_size = 200 , route_config = RouteConfig ( #(2) feedback_endpoints_enabled = False , dashboard_enabled = False ) ) Completely disable caching. The unspecified routes are enabled by default.","title":"Using great_ai.configure"},{"location":"how-to-guides/configure-service/#using-remote-storage","text":"The only aspect that cannot be automated is choosing the backing storage for the database and file storage. Right now, you have 3 options for storing the models and large datasets: LargeFileLocal , LargeFileMongo , and LargeFileS3 . Without explicit configuration, LargeFileLocal is selected by default. This one still version-controls your files but it only stores them in a local path (which of course can be a remote volume attached by NFS , HDFS , etc.). Important If your working directory contains a mongo.ini or s3.ini file, an attempt is made to auto-configure LargeFileMongo or LargeFileS3 respectively. To use LargeFileMongo or LargeFileS3 explicitly, configure them before calling any other great-ai function.","title":"Using remote storage"},{"location":"how-to-guides/configure-service/#s3-compatible","text":"s3.ini aws_region_name = eu-west-2 aws_access_key_id = MY_AWS_ACCESS_KEY # ENV:MY_AWS_ACCESS_KEY would also work aws_secret_access_key = MY_AWS_SECRET_KEY large_files_bucket_name = bucket-for-models use-s3.py from great_ai.large_file import LargeFileS3 from great_ai import save_model LargeFileS3 . configure_credentials_from_file ( 's3.ini' ) #(1) model = [ 4 , 3 ] save_model ( model , 'my-model' ) This line isn't strictly necessary because if s3.ini (or mongo.ini ) is available in the current working directory, they are automatically used to configure their respective LargeFile implementations/databases. Departing from AWS With the aws_endpoint_url argument, it is possible to use any other S3-compatible service such as Backblaze . In that case, it would be aws_endpoint_url=https://s3.us-west-002.backblazeb2.com .","title":"S3-compatible"},{"location":"how-to-guides/configure-service/#gridfs","text":"GridFS specifies how to store files in MongoDB. The official MongoDB server and many compatible implementations support it. mongo.ini MONGO_CONNECTION_STRING = mongodb : // localhost : 27017 # this is the default value # if `MONGO_CONNECTION_STRING` is specified, this default is overridden MONGO_CONNECTION_STRING = ENV : MONGO_CONNECTION_STRING MONGO_DATABASE = my-database # it is automatically created if doesn't exist use-mongo.py from great_ai.large_file import LargeFileMongo from great_ai import save_model LargeFileMongo . configure_credentials_from_file ( 'mongo.ini' ) model = [ 4 , 3 ] save_model ( model , 'my-model' ) Simplifying config files You can combine mongo.ini or s3.ini with your application's config file because the unneeded keys are ignored by the configure_credentials_from_file method.","title":"GridFS"},{"location":"how-to-guides/configure-service/#using-a-database","text":"By default, a thread-safe version of TinyDB is utilised for saving the prediction traces into a local file. Unfortunately, for most production needs, this method is not suitable.","title":"Using a database"},{"location":"how-to-guides/configure-service/#mongodb","text":"At the moment, only MongoDB is supported as a production-ready TracingDatabase . In order to use it, you have to either place a file named mongo.ini in your working directory, or explicitly call either MongoDbDriver.configure_credentials_from_file or MongoDbDriver.configure_credentials .","title":"MongoDB"},{"location":"how-to-guides/create-service/","text":"How to create a GreatAI service # The core value of great-ai lies in its GreatAI class. In order to take advantage of it, you need to create an instance wrapping your code. Let's say that you have the following greeter function: greeter.py def my_greeter_function ( your_name ): return f 'Hi { your_name } !' You can simply decorate (wrap) this function using the @GreatAI.create factory. greeter.py from great_ai import GreatAI @GreatAI . create def greeter ( your_name ): return f 'Hi { your_name } !' Why not simply use @GreatAI? The purpose of @GreatAI.create is simply to provide you with type-checking through MyPy, Pylance, and similar libraries. However, the overloading support for __new__ is lacking in MyPy, thus, a static factory method is used instead. With types # Even though it's not required by GreatAI, type annotating your codebase can save you from lots of trivial mistakes, that's why it's highly advised. Simply add the expected types to your function's signature. type_safe_greeter.py from great_ai import GreatAI @GreatAI . create def type_safe_greeter ( your_name : str ) -> str : return f 'Hi { your_name } !' This not only allows you to statically typecheck your code, but by default, GreatAI will check it during runtime as well using typeguard . With async # Asynchronous code can result in immense performance gains in certain cases. For example, you might rely on a third-party service, do database access, or call a remote GreatAI instance . In these cases, you can simply make your function async without any other changes. async_greeter.py from great_ai import GreatAI from asyncio import sleep @GreatAI . create async def async_greeter ( your_name : str ) -> str : await sleep ( 2 ) # simulate IO-bound operation return f 'Hi { your_name } !' With decorators # GreatAI can decorate already decorated functions. The only restriction is that @GreatAI.create must come last. There are two built-in decorators that you can use to customise your function but you can you use any third-party decorator as well. Using @use_model # If you have previously saved a model with save_model , you can inject it into your function by calling @use_model . greeter_with_model.py from great_ai import GreatAI , use_model @GreatAI . create @use_model ( 'name_of_my_model' , version = 'latest' ) #(1) def type_safe_greeter ( your_name : str , model ) -> str : return f 'Hi { your_name } !' assert type_safe_greeter ( 'Andras' ) . output == 'Hi Andras' By default, the parameter named model will be replaced by the loaded model. This behaviour can be customised by setting the model_kwarg_name . This way, even multiple models can be injected into a single function. Important You must call @use_model before @GreatAI.create . Note, that decorators are applied starting from the bottom-most one. Feel free to use @use_model in other places of the codebase, it works equally well outside of GreatAI services. Using @parameter # If you wish to turn off logging or specify custom validation for your parameters, you can use the @parameter decorator. Note By default, all parameters that are not affected by an explicit @parameter or @use_model are automatically decorated with @parameter when @GreatAI.create is called. greeter_with_validation.py from great_ai import GreatAI , use_model @GreatAI . create @parameter ( 'your_name' , disable_logging = True ) def type_safe_greeter ( your_name : str , model ) -> str : return f 'Hi { your_name } !' assert type_safe_greeter ( 'Andras' ) . output == 'Hi Andras' Important You must call @parameter before @GreatAI.create . Note, that decorators are applied starting from the bottom-most one. Feel free to use @parameter in other places of the codebase, it works equally well outside of GreatAI services. Complex example # Refer to the following example summarising the options you have when instantiating a GreatAI service. complex.py from great_ai import save_model , GreatAI , parameter , use_model , log_metric save_model ( 4 , 'secret-number' ) #(1) @GreatAI . create @parameter ( 'positive_number' , validate = lambda n : n > 0 , disable_logging = True ) @use_model ( 'secret-number' , version = 'latest' , model_kwarg_name = 'secret' ) def add_number ( positive_number : int , secret : int ) -> int : log_metric ( 'log directly into the returned Trace' , positive_number * 2 ) return positive_number + secret assert add_number ( 1 ) . output == 5 Refer to the configuration page for specifying where to store your models.","title":"How to create a GreatAI service"},{"location":"how-to-guides/create-service/#how-to-create-a-greatai-service","text":"The core value of great-ai lies in its GreatAI class. In order to take advantage of it, you need to create an instance wrapping your code. Let's say that you have the following greeter function: greeter.py def my_greeter_function ( your_name ): return f 'Hi { your_name } !' You can simply decorate (wrap) this function using the @GreatAI.create factory. greeter.py from great_ai import GreatAI @GreatAI . create def greeter ( your_name ): return f 'Hi { your_name } !' Why not simply use @GreatAI? The purpose of @GreatAI.create is simply to provide you with type-checking through MyPy, Pylance, and similar libraries. However, the overloading support for __new__ is lacking in MyPy, thus, a static factory method is used instead.","title":"How to create a GreatAI service"},{"location":"how-to-guides/create-service/#with-types","text":"Even though it's not required by GreatAI, type annotating your codebase can save you from lots of trivial mistakes, that's why it's highly advised. Simply add the expected types to your function's signature. type_safe_greeter.py from great_ai import GreatAI @GreatAI . create def type_safe_greeter ( your_name : str ) -> str : return f 'Hi { your_name } !' This not only allows you to statically typecheck your code, but by default, GreatAI will check it during runtime as well using typeguard .","title":"With types"},{"location":"how-to-guides/create-service/#with-async","text":"Asynchronous code can result in immense performance gains in certain cases. For example, you might rely on a third-party service, do database access, or call a remote GreatAI instance . In these cases, you can simply make your function async without any other changes. async_greeter.py from great_ai import GreatAI from asyncio import sleep @GreatAI . create async def async_greeter ( your_name : str ) -> str : await sleep ( 2 ) # simulate IO-bound operation return f 'Hi { your_name } !'","title":"With async"},{"location":"how-to-guides/create-service/#with-decorators","text":"GreatAI can decorate already decorated functions. The only restriction is that @GreatAI.create must come last. There are two built-in decorators that you can use to customise your function but you can you use any third-party decorator as well.","title":"With decorators"},{"location":"how-to-guides/create-service/#using-use_model","text":"If you have previously saved a model with save_model , you can inject it into your function by calling @use_model . greeter_with_model.py from great_ai import GreatAI , use_model @GreatAI . create @use_model ( 'name_of_my_model' , version = 'latest' ) #(1) def type_safe_greeter ( your_name : str , model ) -> str : return f 'Hi { your_name } !' assert type_safe_greeter ( 'Andras' ) . output == 'Hi Andras' By default, the parameter named model will be replaced by the loaded model. This behaviour can be customised by setting the model_kwarg_name . This way, even multiple models can be injected into a single function. Important You must call @use_model before @GreatAI.create . Note, that decorators are applied starting from the bottom-most one. Feel free to use @use_model in other places of the codebase, it works equally well outside of GreatAI services.","title":"Using @use_model"},{"location":"how-to-guides/create-service/#using-parameter","text":"If you wish to turn off logging or specify custom validation for your parameters, you can use the @parameter decorator. Note By default, all parameters that are not affected by an explicit @parameter or @use_model are automatically decorated with @parameter when @GreatAI.create is called. greeter_with_validation.py from great_ai import GreatAI , use_model @GreatAI . create @parameter ( 'your_name' , disable_logging = True ) def type_safe_greeter ( your_name : str , model ) -> str : return f 'Hi { your_name } !' assert type_safe_greeter ( 'Andras' ) . output == 'Hi Andras' Important You must call @parameter before @GreatAI.create . Note, that decorators are applied starting from the bottom-most one. Feel free to use @parameter in other places of the codebase, it works equally well outside of GreatAI services.","title":"Using @parameter"},{"location":"how-to-guides/create-service/#complex-example","text":"Refer to the following example summarising the options you have when instantiating a GreatAI service. complex.py from great_ai import save_model , GreatAI , parameter , use_model , log_metric save_model ( 4 , 'secret-number' ) #(1) @GreatAI . create @parameter ( 'positive_number' , validate = lambda n : n > 0 , disable_logging = True ) @use_model ( 'secret-number' , version = 'latest' , model_kwarg_name = 'secret' ) def add_number ( positive_number : int , secret : int ) -> int : log_metric ( 'log directly into the returned Trace' , positive_number * 2 ) return positive_number + secret assert add_number ( 1 ) . output == 5 Refer to the configuration page for specifying where to store your models.","title":"Complex example"},{"location":"how-to-guides/handle-training-data/","text":"How to manage training data # In order to simplify your training data management, great-ai provide two complementing approaches for inputting new data-points. Upload data # At the start of your experiments' first iteration, after you've gathered suitable samples for training, you can call great_ai.add_ground_truth . This automatically stores a timestamp and also allows you to assign tags to the data. Using these attributes, great_ai.query_ground_truth can be called to get a filtered view of the training data. Train-test-validation splits It is a best practice to lock-away the test split of your data that is only used for the final quality assessment. This prevents you from accidentally training on it, or inadvertently tuning the model to have the highest accuracy metrics on the test split. This, of course, may lead to dubious results, hence, care must be taken to avoid it. With great_ai.add_ground_truth , there is an option to tag the samples with train , test , and validation randomly, following a predefined distribution. This happens as soon as they're written in the database. Later, these can be queried by providing the name of the appropriate tags. The nice thing about this is that the 'input-expected output' pairs are stored as traces. Thus, they behave exactly like regular prediction traces. from great_ai import add_ground_truth add_ground_truth ( [ 1 , 2 ], [ 'odd' , 'even' ], tags = 'my_tag' , train_split_ratio = 1 , #(1) test_split_ratio = 1 ) Note that the ratios don't have to add up to 1. They are just weights. There is also a validation_split_ratio which is 0 by default. >>> from great_ai import query_ground_truth >>> query_ground_truth ( 'my_tag' ) [ Trace [ str ]({ 'created' : '2022-07-12T18:36:12.825706' , 'exception' : None , 'feedback' : 'odd' , #(1) 'logged_values' : { 'input' : 1 }, #(2) 'models' : [], 'original_execution_time_ms' : 0.0 , 'output' : 'odd' , 'tags' : [ 'ground_truth' , 'test' , 'my_tag' ], #(3) 'trace_id' : '4fcf2ce6-a172-469d-94b2-874577655814' }), Trace [ str ]({ 'created' : '2022-07-12T18:36:12.825706' , 'exception' : None , 'feedback' : 'even' , 'logged_values' : { 'input' : 2 }, 'models' : [], 'original_execution_time_ms' : 0.0 , 'output' : 'even' , 'tags' : [ 'ground_truth' , 'train' , 'my_tag' ], 'trace_id' : 'abee0671-beb9-4284-8c3b-c65e5836ce38' })] Expected output. This can be also accessed through the .output property. The input value is stored here. Notice how ground_truth is always included as a tag when using great_ai.add_ground_truth . Get feedback # After the initial data gathering, end-to-end feedback can be also integrated into the dataset. The scaffolded REST API contains endpoints for managing traces and their feedbacks. When great_ai.query_ground_truth is executed, it implicitly filters for traces that have feedback. Therefore, both the ground_truth and the online traces that have received feedback are returned. No matter the origin of the data, it can be accessed using the same API. Remove clutter # Traces can be deleted either through the REST API or by calling great_ai.delete_ground_truth . The latter provides the same interface as great_ai.query_ground_truth except it deletes the matched points.","title":"How to manage training data"},{"location":"how-to-guides/handle-training-data/#how-to-manage-training-data","text":"In order to simplify your training data management, great-ai provide two complementing approaches for inputting new data-points.","title":"How to manage training data"},{"location":"how-to-guides/handle-training-data/#upload-data","text":"At the start of your experiments' first iteration, after you've gathered suitable samples for training, you can call great_ai.add_ground_truth . This automatically stores a timestamp and also allows you to assign tags to the data. Using these attributes, great_ai.query_ground_truth can be called to get a filtered view of the training data. Train-test-validation splits It is a best practice to lock-away the test split of your data that is only used for the final quality assessment. This prevents you from accidentally training on it, or inadvertently tuning the model to have the highest accuracy metrics on the test split. This, of course, may lead to dubious results, hence, care must be taken to avoid it. With great_ai.add_ground_truth , there is an option to tag the samples with train , test , and validation randomly, following a predefined distribution. This happens as soon as they're written in the database. Later, these can be queried by providing the name of the appropriate tags. The nice thing about this is that the 'input-expected output' pairs are stored as traces. Thus, they behave exactly like regular prediction traces. from great_ai import add_ground_truth add_ground_truth ( [ 1 , 2 ], [ 'odd' , 'even' ], tags = 'my_tag' , train_split_ratio = 1 , #(1) test_split_ratio = 1 ) Note that the ratios don't have to add up to 1. They are just weights. There is also a validation_split_ratio which is 0 by default. >>> from great_ai import query_ground_truth >>> query_ground_truth ( 'my_tag' ) [ Trace [ str ]({ 'created' : '2022-07-12T18:36:12.825706' , 'exception' : None , 'feedback' : 'odd' , #(1) 'logged_values' : { 'input' : 1 }, #(2) 'models' : [], 'original_execution_time_ms' : 0.0 , 'output' : 'odd' , 'tags' : [ 'ground_truth' , 'test' , 'my_tag' ], #(3) 'trace_id' : '4fcf2ce6-a172-469d-94b2-874577655814' }), Trace [ str ]({ 'created' : '2022-07-12T18:36:12.825706' , 'exception' : None , 'feedback' : 'even' , 'logged_values' : { 'input' : 2 }, 'models' : [], 'original_execution_time_ms' : 0.0 , 'output' : 'even' , 'tags' : [ 'ground_truth' , 'train' , 'my_tag' ], 'trace_id' : 'abee0671-beb9-4284-8c3b-c65e5836ce38' })] Expected output. This can be also accessed through the .output property. The input value is stored here. Notice how ground_truth is always included as a tag when using great_ai.add_ground_truth .","title":"Upload data"},{"location":"how-to-guides/handle-training-data/#get-feedback","text":"After the initial data gathering, end-to-end feedback can be also integrated into the dataset. The scaffolded REST API contains endpoints for managing traces and their feedbacks. When great_ai.query_ground_truth is executed, it implicitly filters for traces that have feedback. Therefore, both the ground_truth and the online traces that have received feedback are returned. No matter the origin of the data, it can be accessed using the same API.","title":"Get feedback"},{"location":"how-to-guides/handle-training-data/#remove-clutter","text":"Traces can be deleted either through the REST API or by calling great_ai.delete_ground_truth . The latter provides the same interface as great_ai.query_ground_truth except it deletes the matched points.","title":"Remove clutter"},{"location":"how-to-guides/install/","text":"Installation guide # Provided you already have Python3 (and pip) installed, simply execute: pip install great-ai Python 3.7 or later is required. This will work on all major operating systems. Google Colab # In order to use GreatAI in Google Colab , you need to downgrade pyyaml to a Colab compatible version. See related StackOverflow question . pip install great-ai pyyaml == 5 .4.1 This will make GreatAI work in Colab. Command-line tools # After installation, great-ai and large-file are available as commands. The former is required for deploying your application while the latter lets you manage models and datasets from your terminal. Snakes & kebabs The library is called great-ai , therefore, its command-line entrypoint is also called great-ai . However, Python module names cannot contain hyphens, that's why you have to import great_ai with an underscore. The great-ai CLI tool is also available as python3 -m great_ai . To help with the confusion, a CLI executable called great_ai (and large_file ) are also installed, thus, if you prefer, you can always refer to GreatAI using its underscored name variant ( great_ai ). Windows On Windows, you might encounter a similar warning from pip : WARNING: The scripts great-ai.exe, great_ai.exe, large-file.exe and large_file.exe are installed in 'C:\\Users\\...\\Scripts' which is not on PATH. Consider adding this directory to PATH or, if you prefer to suppress this warning, use --no-warn-script-location. This means that great-ai.exe and large-file.exe are not in your PATH . Either add their containing directory ('C:\\Users...\\Scripts' in this case) to your PATH or use python3 -m great_ai and python3 -m great_ai.large_file instead of the exe-s. Update # If you wish to update to the latest version execute: pip install --upgrade great-ai Bleeding edge # You can also install the latest (usually unreleased) version from GitHub. pip install --upgrade git+https://github.com/schmelczer/great-ai.git Python 3.7 or later is required.","title":"Installation guide"},{"location":"how-to-guides/install/#installation-guide","text":"Provided you already have Python3 (and pip) installed, simply execute: pip install great-ai Python 3.7 or later is required. This will work on all major operating systems.","title":"Installation guide"},{"location":"how-to-guides/install/#google-colab","text":"In order to use GreatAI in Google Colab , you need to downgrade pyyaml to a Colab compatible version. See related StackOverflow question . pip install great-ai pyyaml == 5 .4.1 This will make GreatAI work in Colab.","title":"Google Colab"},{"location":"how-to-guides/install/#command-line-tools","text":"After installation, great-ai and large-file are available as commands. The former is required for deploying your application while the latter lets you manage models and datasets from your terminal. Snakes & kebabs The library is called great-ai , therefore, its command-line entrypoint is also called great-ai . However, Python module names cannot contain hyphens, that's why you have to import great_ai with an underscore. The great-ai CLI tool is also available as python3 -m great_ai . To help with the confusion, a CLI executable called great_ai (and large_file ) are also installed, thus, if you prefer, you can always refer to GreatAI using its underscored name variant ( great_ai ). Windows On Windows, you might encounter a similar warning from pip : WARNING: The scripts great-ai.exe, great_ai.exe, large-file.exe and large_file.exe are installed in 'C:\\Users\\...\\Scripts' which is not on PATH. Consider adding this directory to PATH or, if you prefer to suppress this warning, use --no-warn-script-location. This means that great-ai.exe and large-file.exe are not in your PATH . Either add their containing directory ('C:\\Users...\\Scripts' in this case) to your PATH or use python3 -m great_ai and python3 -m great_ai.large_file instead of the exe-s.","title":"Command-line tools"},{"location":"how-to-guides/install/#update","text":"If you wish to update to the latest version execute: pip install --upgrade great-ai","title":"Update"},{"location":"how-to-guides/install/#bleeding-edge","text":"You can also install the latest (usually unreleased) version from GitHub. pip install --upgrade git+https://github.com/schmelczer/great-ai.git Python 3.7 or later is required.","title":"Bleeding edge"},{"location":"how-to-guides/large-file/","text":"How to use LargeFiles # The functions save_model and @use_model wrap LargeFile instances. Hence, besides configuring LargeFile , users have few reasons to use LargeFiles directly. Motivation # Oftentimes, especially when working with data-heavy applications, large files can proliferate in a repository. Version controlling them is an obvious next step, however, GitHub's git LFS implementation doesn't support deleting large files, making it easy for them to eat-up the LFS quota and explode the size of your repos. DVC is a viable alternative, however, it requires users to learn to use one more CLI tool. Using LargeFile-s directly (usually not needed) LargeFile doesn't require users to learn too much new. It is a nearly exact copy of the built-in open() function of Python with which users are certainly already familiar. Simple example # from great_ai.large_file import LargeFileS3 LargeFileS3 . configure_credentials ({ \"aws_region_name\" : \"your_region_like_eu-west-2\" , \"aws_access_key_id\" : \"YOUR_ACCESS_KEY_ID\" , \"aws_secret_access_key\" : \"YOUR_VERY_SECRET_ACCESS_KEY\" , \"large_files_bucket_name\" : \"create_a_bucket_and_put_its_name_here\" , }) # Creates a new version and deletes the older version # leaving the 3 most recently used intact with LargeFileS3 ( \"test.txt\" , \"w\" , keep_last_n = 3 ) as f : for i in range ( 100000 ): f . write ( 'test \\n ' ) # The latest version is returned by default # but an optional `version` keyword argument can be provided as well with LargeFileS3 ( \"test.txt\" , \"r\" ) as f : #(1) print ( f . readlines ()[ 0 ]) In this case, the latest version is already in the local cache, no download is required. More details # LargeFile behaves like an opened file (in the background it is a temp file after all). Binary reads and writes are supported along with the different keywords open() accepts . The local cache can be configured with these properties: LargeFileS3 . cache_path = Path ( '.cache' ) LargeFileS3 . max_cache_size = \"30 GB\" I only need a path # In case you only need a path to the (proxy of the) remote file, this pattern can be applied: path_to_model = LargeFileS3 ( \"folder-of-my-bert-model\" , version = 31 ) . get () This will first download the file/folder into your local cache folder. Then, it returns a Path object to the local version. Which can be turned into a string with str(path_to_model) . The same approach works for uploads: LargeFileS3 ( \"folder-of-my-bert-model\" ) . push ( 'path_to_local/folder_or_file' ) This way both regular files and folders can be handled. The uploaded file is called folder-of-my-bert-model , the local name is ignored. Lastly, all version of the remote object can be deleted by calling LargeFileS3(\"my-file\").delete() . It will still reside in your local cache afterwards; its deletion will happen next time your local cache has to be pruned. From the command-line # The main reason for using the large-file or python3 -m great_ai.large_file commands is to upload or download models from the terminal. For example, when building a docker image, it is best practice to cache the referred models. Setup # Create an .ini file (or use ~/.aws/credentials ). It may look like this: aws_region_name = your_region_like_eu-west-2 aws_access_key_id = YOUR_ACCESS_KEY_ID aws_secret_access_key = YOUR_VERY_SECRET_ACCESS_KEY large_files_bucket_name = my_large_files Upload some files # large-file --backend s3 --secrets secrets.ini \\ --push my_first_file.json folder/my_second_file my_folder Only the filename is used as the S3 name, the rest of the path is ignored. Using MongoDB The possible values for --backend are s3 , mongo , and local . The latter doesn't need credentials, it only versions and stores your files in a local folder. MongoDB on the other hand requires a mongo_connection_string and a mongo_database to be specified. For storing large files, it uses the GridFS specification. Download some files to the local cache # This can be useful when building a Docker image for example. This way, the files can already reside inside the container and need not be downloaded later. large-file --backend s3 --secrets ~/.aws/credentials \\ --cache my_first_file.json:3 my_second_file my_folder:0 Versions may be specified by using : -s. Delete remote files # large-file --backend s3 --secrets ~/.aws/credentials \\ --delete my_first_file.json","title":"How to use LargeFiles"},{"location":"how-to-guides/large-file/#how-to-use-largefiles","text":"The functions save_model and @use_model wrap LargeFile instances. Hence, besides configuring LargeFile , users have few reasons to use LargeFiles directly.","title":"How to use LargeFiles"},{"location":"how-to-guides/large-file/#motivation","text":"Oftentimes, especially when working with data-heavy applications, large files can proliferate in a repository. Version controlling them is an obvious next step, however, GitHub's git LFS implementation doesn't support deleting large files, making it easy for them to eat-up the LFS quota and explode the size of your repos. DVC is a viable alternative, however, it requires users to learn to use one more CLI tool. Using LargeFile-s directly (usually not needed) LargeFile doesn't require users to learn too much new. It is a nearly exact copy of the built-in open() function of Python with which users are certainly already familiar.","title":"Motivation"},{"location":"how-to-guides/large-file/#simple-example","text":"from great_ai.large_file import LargeFileS3 LargeFileS3 . configure_credentials ({ \"aws_region_name\" : \"your_region_like_eu-west-2\" , \"aws_access_key_id\" : \"YOUR_ACCESS_KEY_ID\" , \"aws_secret_access_key\" : \"YOUR_VERY_SECRET_ACCESS_KEY\" , \"large_files_bucket_name\" : \"create_a_bucket_and_put_its_name_here\" , }) # Creates a new version and deletes the older version # leaving the 3 most recently used intact with LargeFileS3 ( \"test.txt\" , \"w\" , keep_last_n = 3 ) as f : for i in range ( 100000 ): f . write ( 'test \\n ' ) # The latest version is returned by default # but an optional `version` keyword argument can be provided as well with LargeFileS3 ( \"test.txt\" , \"r\" ) as f : #(1) print ( f . readlines ()[ 0 ]) In this case, the latest version is already in the local cache, no download is required.","title":"Simple example"},{"location":"how-to-guides/large-file/#more-details","text":"LargeFile behaves like an opened file (in the background it is a temp file after all). Binary reads and writes are supported along with the different keywords open() accepts . The local cache can be configured with these properties: LargeFileS3 . cache_path = Path ( '.cache' ) LargeFileS3 . max_cache_size = \"30 GB\"","title":"More details"},{"location":"how-to-guides/large-file/#i-only-need-a-path","text":"In case you only need a path to the (proxy of the) remote file, this pattern can be applied: path_to_model = LargeFileS3 ( \"folder-of-my-bert-model\" , version = 31 ) . get () This will first download the file/folder into your local cache folder. Then, it returns a Path object to the local version. Which can be turned into a string with str(path_to_model) . The same approach works for uploads: LargeFileS3 ( \"folder-of-my-bert-model\" ) . push ( 'path_to_local/folder_or_file' ) This way both regular files and folders can be handled. The uploaded file is called folder-of-my-bert-model , the local name is ignored. Lastly, all version of the remote object can be deleted by calling LargeFileS3(\"my-file\").delete() . It will still reside in your local cache afterwards; its deletion will happen next time your local cache has to be pruned.","title":"I only need a path"},{"location":"how-to-guides/large-file/#from-the-command-line","text":"The main reason for using the large-file or python3 -m great_ai.large_file commands is to upload or download models from the terminal. For example, when building a docker image, it is best practice to cache the referred models.","title":"From the command-line"},{"location":"how-to-guides/large-file/#setup","text":"Create an .ini file (or use ~/.aws/credentials ). It may look like this: aws_region_name = your_region_like_eu-west-2 aws_access_key_id = YOUR_ACCESS_KEY_ID aws_secret_access_key = YOUR_VERY_SECRET_ACCESS_KEY large_files_bucket_name = my_large_files","title":"Setup"},{"location":"how-to-guides/large-file/#upload-some-files","text":"large-file --backend s3 --secrets secrets.ini \\ --push my_first_file.json folder/my_second_file my_folder Only the filename is used as the S3 name, the rest of the path is ignored. Using MongoDB The possible values for --backend are s3 , mongo , and local . The latter doesn't need credentials, it only versions and stores your files in a local folder. MongoDB on the other hand requires a mongo_connection_string and a mongo_database to be specified. For storing large files, it uses the GridFS specification.","title":"Upload some files"},{"location":"how-to-guides/large-file/#download-some-files-to-the-local-cache","text":"This can be useful when building a Docker image for example. This way, the files can already reside inside the container and need not be downloaded later. large-file --backend s3 --secrets ~/.aws/credentials \\ --cache my_first_file.json:3 my_second_file my_folder:0 Versions may be specified by using : -s.","title":"Download some files to the local cache"},{"location":"how-to-guides/large-file/#delete-remote-files","text":"large-file --backend s3 --secrets ~/.aws/credentials \\ --delete my_first_file.json","title":"Delete remote files"},{"location":"how-to-guides/use-service/","text":"How to perform prediction with GreatAI # After creating a GreatAI service by wrapping your prediction function, and optionally configuring it , it's time to do some prediction. Let's take the following example: greeter.py from great_ai import GreatAI @GreatAI . create def greeter ( your_name : str ) -> str : return f 'Hi { your_name } !' One-off prediction # Even though greeter is now an instance of GreatAI , you can continue using it as a regular function. >>> greeter ( 'Bob' ) Trace [ str ]({ 'created' : '2022-07-11T14:31:46.183764' , 'exception' : None , 'feedback' : None , 'logged_values' : { 'arg:your_name:length' : 3 , 'arg:your_name:value' : 'Bob' }, 'models' : [], 'original_execution_time_ms' : 0.0381 , 'output' : 'Hi Bob!' , 'tags' : [ 'greeter' , 'online' , 'development' ], 'trace_id' : '7c284fd7-7f0d-4464-b5f8-3ef126df34af' }) As you can see, the original return value is wrapped in a Trace object (which is also persisted in your database of choice). You can access the original value under the output property. Online prediction # Likely, the main way you would like to expose your model is through an HTTP API. @GreatAI.create scaffolds many REST API endpoints for your model and creates a FastAPI app available under GreatAI.app . This can be served using uvicorn or any other ASGI server . Since most ML code lives in Jupyter notebooks, therefore, deploying a notebook containing the inference function is supported. To achieve this, uvicorn is wrapped by the great-ai command-line utility which \u2014 among others \u2014 takes care of feeding a notebook into uvicorn . It also supports auto-reloading. In development # great-ai greeter.py Success Your model is accessible at localhost:6060 . Some configuration options are also supported. great-ai greeter.py --port 8000 --host 127 .0.0.1 --timeout_keep_alive 10 More options For more options (but no Notebook support), simply use uvicorn for starting your app (available at greeter.app ). In production # There are three main approaches for deploying a GreatAI service. Manual deployment # The app is run in production-mode if the value of the ENVIRONMENT environment variable is set to production . ENVIRONMENT = production great-ai greeter.py Simply run ENVIRONMENT=production great-ai deploy.ipynb in the command-line of a production machine. This is the crudest approach; however, it might be fitting for some contexts. Containerised deployment # Run the notebook directly in a container or create a service for it using your favourite container orchestrator. docker run -p 6060 :6060 --volume ` pwd ` :/app --rm \\ schmelczera/great-ai deploy.ipynb You can replace pwd with the path to your code's folder. Use a Platform-as-a-Service # Similar to the previous approach, your code will run in a container. However, instead of manually managing it, you can just choose from a plethora of PaaS providers (such as AWS ECS , DO App platform , MLEM , Streamlit ) that take a Docker image as a source and handle the rest of the deployment. To this end, you can also create a custom Docker image. It is especially useful if you have third-party dependencies, such as PyTorch or TensorFlow . FROM schmelczera/great-ai:latest # Remove this block if you don't have a requirements.txt COPY requirements.txt ./ RUN pip install --no-cache-dir --requirement requirements.txt # If you store your models in S3 or GridFS, it may be a # good idea to cache them in the image so that you don't # have to download it each time a container starts RUN large-file --backend s3 --secrets s3.ini --cache my-domain-predictor # Add you application code to the image COPY . . # The default ENTRYPOINT is great-ai, specify its argument using CMD CMD [ \"deploy.ipynb\" ] Batch prediction # Processing larger amounts of data on a single machine is made easy by the GreatAI 's process_batch method. This relies on multiprocessing ( parallel_map ) to take full advantage of all available CPU-cores. >>> greeter . process_batch ([ 'Alice' , 'Bob' ]) [ Trace [ str ]({ 'created' : '2022-07-11T14:36:37.119183' , 'exception' : None , 'feedback' : None , 'logged_values' : { 'arg:your_name:length' : 5 , 'arg:your_name:value' : 'Alice' }, 'models' : [], 'original_execution_time_ms' : 0.1251 , 'output' : 'Hi Alice!' , 'tags' : [ 'greeter' , 'online' , 'development' ], 'trace_id' : '90ffa15f-e839-41c4-8e7a-3211168bc138' }), Trace [ str ]({ 'created' : '2022-07-11T14:36:37.166659' , 'exception' : None , 'feedback' : None , 'logged_values' : { 'arg:your_name:length' : 3 , 'arg:your_name:value' : 'Bob' }, 'models' : [], 'original_execution_time_ms' : 0.0571 , 'output' : 'Hi Bob!' , 'tags' : [ 'greeter' , 'online' , 'development' ], 'trace_id' : 'f48e94c7-0815-48b3-a864-41349d3dae84' })]","title":"How to perform prediction with GreatAI"},{"location":"how-to-guides/use-service/#how-to-perform-prediction-with-greatai","text":"After creating a GreatAI service by wrapping your prediction function, and optionally configuring it , it's time to do some prediction. Let's take the following example: greeter.py from great_ai import GreatAI @GreatAI . create def greeter ( your_name : str ) -> str : return f 'Hi { your_name } !'","title":"How to perform prediction with GreatAI"},{"location":"how-to-guides/use-service/#one-off-prediction","text":"Even though greeter is now an instance of GreatAI , you can continue using it as a regular function. >>> greeter ( 'Bob' ) Trace [ str ]({ 'created' : '2022-07-11T14:31:46.183764' , 'exception' : None , 'feedback' : None , 'logged_values' : { 'arg:your_name:length' : 3 , 'arg:your_name:value' : 'Bob' }, 'models' : [], 'original_execution_time_ms' : 0.0381 , 'output' : 'Hi Bob!' , 'tags' : [ 'greeter' , 'online' , 'development' ], 'trace_id' : '7c284fd7-7f0d-4464-b5f8-3ef126df34af' }) As you can see, the original return value is wrapped in a Trace object (which is also persisted in your database of choice). You can access the original value under the output property.","title":"One-off prediction"},{"location":"how-to-guides/use-service/#online-prediction","text":"Likely, the main way you would like to expose your model is through an HTTP API. @GreatAI.create scaffolds many REST API endpoints for your model and creates a FastAPI app available under GreatAI.app . This can be served using uvicorn or any other ASGI server . Since most ML code lives in Jupyter notebooks, therefore, deploying a notebook containing the inference function is supported. To achieve this, uvicorn is wrapped by the great-ai command-line utility which \u2014 among others \u2014 takes care of feeding a notebook into uvicorn . It also supports auto-reloading.","title":"Online prediction"},{"location":"how-to-guides/use-service/#in-development","text":"great-ai greeter.py Success Your model is accessible at localhost:6060 . Some configuration options are also supported. great-ai greeter.py --port 8000 --host 127 .0.0.1 --timeout_keep_alive 10 More options For more options (but no Notebook support), simply use uvicorn for starting your app (available at greeter.app ).","title":"In development"},{"location":"how-to-guides/use-service/#in-production","text":"There are three main approaches for deploying a GreatAI service.","title":"In production"},{"location":"how-to-guides/use-service/#manual-deployment","text":"The app is run in production-mode if the value of the ENVIRONMENT environment variable is set to production . ENVIRONMENT = production great-ai greeter.py Simply run ENVIRONMENT=production great-ai deploy.ipynb in the command-line of a production machine. This is the crudest approach; however, it might be fitting for some contexts.","title":"Manual deployment"},{"location":"how-to-guides/use-service/#containerised-deployment","text":"Run the notebook directly in a container or create a service for it using your favourite container orchestrator. docker run -p 6060 :6060 --volume ` pwd ` :/app --rm \\ schmelczera/great-ai deploy.ipynb You can replace pwd with the path to your code's folder.","title":"Containerised deployment"},{"location":"how-to-guides/use-service/#use-a-platform-as-a-service","text":"Similar to the previous approach, your code will run in a container. However, instead of manually managing it, you can just choose from a plethora of PaaS providers (such as AWS ECS , DO App platform , MLEM , Streamlit ) that take a Docker image as a source and handle the rest of the deployment. To this end, you can also create a custom Docker image. It is especially useful if you have third-party dependencies, such as PyTorch or TensorFlow . FROM schmelczera/great-ai:latest # Remove this block if you don't have a requirements.txt COPY requirements.txt ./ RUN pip install --no-cache-dir --requirement requirements.txt # If you store your models in S3 or GridFS, it may be a # good idea to cache them in the image so that you don't # have to download it each time a container starts RUN large-file --backend s3 --secrets s3.ini --cache my-domain-predictor # Add you application code to the image COPY . . # The default ENTRYPOINT is great-ai, specify its argument using CMD CMD [ \"deploy.ipynb\" ]","title":"Use a Platform-as-a-Service"},{"location":"how-to-guides/use-service/#batch-prediction","text":"Processing larger amounts of data on a single machine is made easy by the GreatAI 's process_batch method. This relies on multiprocessing ( parallel_map ) to take full advantage of all available CPU-cores. >>> greeter . process_batch ([ 'Alice' , 'Bob' ]) [ Trace [ str ]({ 'created' : '2022-07-11T14:36:37.119183' , 'exception' : None , 'feedback' : None , 'logged_values' : { 'arg:your_name:length' : 5 , 'arg:your_name:value' : 'Alice' }, 'models' : [], 'original_execution_time_ms' : 0.1251 , 'output' : 'Hi Alice!' , 'tags' : [ 'greeter' , 'online' , 'development' ], 'trace_id' : '90ffa15f-e839-41c4-8e7a-3211168bc138' }), Trace [ str ]({ 'created' : '2022-07-11T14:36:37.166659' , 'exception' : None , 'feedback' : None , 'logged_values' : { 'arg:your_name:length' : 3 , 'arg:your_name:value' : 'Bob' }, 'models' : [], 'original_execution_time_ms' : 0.0571 , 'output' : 'Hi Bob!' , 'tags' : [ 'greeter' , 'online' , 'development' ], 'trace_id' : 'f48e94c7-0815-48b3-a864-41349d3dae84' })]","title":"Batch prediction"},{"location":"reference/","text":"GreatAI reference # from great_ai import * Core # GreatAI # Bases: Generic [ T , V ] Wrapper for a prediction function providing the implementation of SE4ML best practices. Provides caching (with argument freezing), a TracingContext during execution, the scaffolding of HTTP endpoints using FastAPI and a dashboard using Dash. IMPORTANT: when a request is served from cache, no new trace is created. Thus, the same trace can be returned multiple times. If this is undesirable turn off caching using configure(prediction_cache_size=0) . Supports wrapping async and synchronous functions while also maintaining correct typing. Attributes: Name Type Description app FastAPI instance wrapping the scaffolded endpoints and the Dash app. version SemVer derived from the app's version and the model names and versions registered through use_model. Source code in great_ai/deploy/great_ai.py 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 class GreatAI ( Generic [ T , V ]): \"\"\"Wrapper for a prediction function providing the implementation of SE4ML best practices. Provides caching (with argument freezing), a TracingContext during execution, the scaffolding of HTTP endpoints using FastAPI and a dashboard using Dash. IMPORTANT: when a request is served from cache, no new trace is created. Thus, the same trace can be returned multiple times. If this is undesirable turn off caching using `configure(prediction_cache_size=0)`. Supports wrapping async and synchronous functions while also maintaining correct typing. Attributes: app: FastAPI instance wrapping the scaffolded endpoints and the Dash app. version: SemVer derived from the app's version and the model names and versions registered through use_model. \"\"\" __name__ : str # help for MyPy __doc__ : str # help for MyPy def __init__ ( self , func : Callable [ ... , Union [ V , Awaitable [ V ]]], ): \"\"\"Do not call this function directly, use GreatAI.create instead.\"\"\" func = automatically_decorate_parameters ( func ) get_function_metadata_store ( func ) . is_finalised = True self . _cached_func = self . _get_cached_traced_function ( func ) self . _wrapped_func = wraps ( func )( freeze_arguments ( self . _cached_func )) wraps ( func )( self ) self . __doc__ = ( f \"GreatAI wrapper for interacting with the ` { self . __name__ } ` \" + f \"function. \\n\\n { dedent ( self . __doc__ or '' ) } \" ) self . version = str ( get_context () . version ) flat_model_versions = \".\" . join ( f \" { k } -v { v } \" for k , v in model_versions ) if flat_model_versions : self . version += f \"+ { flat_model_versions } \" self . app = FastAPI ( title = snake_case_to_text ( self . __name__ ), version = self . version , description = self . __doc__ + f \" \\n\\n Find out more in the [dashboard]( { DASHBOARD_PATH } ).\" , docs_url = None , redoc_url = None , ) self . _bootstrap_rest_api () @overload @staticmethod def create ( # type: ignore # Overloaded function signatures 1 and 2 overlap with incompatible return types # https://github.com/python/mypy/issues/12759 func : Callable [ ... , Awaitable [ V ]], ) -> \"GreatAI[Awaitable[Trace[V]], V]\" : ... @overload @staticmethod def create ( func : Callable [ ... , V ], ) -> \"GreatAI[Trace[V], V]\" : ... @staticmethod def create ( func : Union [ Callable [ ... , Awaitable [ V ]], Callable [ ... , V ]], ) -> Union [ \"GreatAI[Awaitable[Trace[V]], V]\" , \"GreatAI[Trace[V], V]\" ]: \"\"\"Decorate a function by wrapping it in a GreatAI instance. The function can be typed, synchronous or async. If it has unwrapped parameters (parameters not affected by a [@parameter][great_ai.parameter] or [@use_model][great_ai.use_model] decorator), those will be automatically wrapped. The return value is replaced by a Trace (or Awaitable[Trace]), while the original return value is available under the `.output` property. For configuration options, see [great_ai.configure][]. Examples: >>> @GreatAI.create ... def my_function(a): ... return a + 2 >>> my_function(3).output 5 >>> @GreatAI.create ... def my_function(a: int) -> int: ... return a + 2 >>> my_function(3) Trace[int]... >>> my_function('3').output Traceback (most recent call last): ... TypeError: type of a must be int; got str instead Args: func: The prediction function that needs to be decorated. Returns: A GreatAI instance wrapping `func`. \"\"\" return GreatAI [ Trace [ V ], V ]( func , ) def __call__ ( self , * args : Any , ** kwargs : Any ) -> T : return self . _wrapped_func ( * args , ** kwargs ) @overload def process_batch ( self , batch : Sequence [ Tuple ], * , concurrency : Optional [ int ] = None , unpack_arguments : Literal [ True ], do_not_persist_traces : bool = ... , ) -> List [ Trace [ V ]]: ... @overload def process_batch ( self , batch : Sequence , * , concurrency : Optional [ int ] = None , unpack_arguments : Literal [ False ] = ... , do_not_persist_traces : bool = ... , ) -> List [ Trace [ V ]]: ... def process_batch ( self , batch : Sequence , * , concurrency : Optional [ int ] = None , unpack_arguments : bool = False , do_not_persist_traces : bool = False , ) -> List [ Trace [ V ]]: \"\"\"Map the wrapped function over a list of input_values (`batch`). A wrapper over [parallel_map][great_ai.utilities.parallel_map.parallel_map.parallel_map] providing type-safety and a progressbar through tqdm. Args: batch: A list of arguments for the original (wrapped) function. If the function expects multiple arguments, provide a list of tuples and set `unpack_arguments=True`. concurrency: Number of processes to start. Don't set it too much higher than the number of available CPU cores. unpack_arguments: Expect a list of tuples and unpack the tuples before giving them to the wrapped function. do_not_persist_traces: Don't save the traces in the database. Useful for evaluations run part of the CI. \"\"\" wrapped_function = self . _wrapped_func def inner ( value : Any ) -> T : return ( wrapped_function ( * value , do_not_persist_traces = do_not_persist_traces ) if unpack_arguments else wrapped_function ( value , do_not_persist_traces = do_not_persist_traces ) ) async def inner_async ( value : Any ) -> T : return await cast ( Awaitable , ( wrapped_function ( * value , do_not_persist_traces = do_not_persist_traces ) if unpack_arguments else wrapped_function ( value , do_not_persist_traces = do_not_persist_traces ) ), ) return list ( tqdm ( parallel_map ( inner_async if get_function_metadata_store ( self ) . is_asynchronous else inner , batch , concurrency = concurrency , ), total = len ( batch ), ) ) @staticmethod def _get_cached_traced_function ( func : Callable [ ... , Union [ V , Awaitable [ V ]]] ) -> Callable [ ... , T ]: @lru_cache ( maxsize = get_context () . prediction_cache_size ) def func_in_tracing_context_sync ( * args : Any , do_not_persist_traces : bool = False , ** kwargs : Any , ) -> T : with TracingContext [ V ]( func . __name__ , do_not_persist_traces = do_not_persist_traces ) as t : result = func ( * args , ** kwargs ) return cast ( T , t . finalise ( output = result )) @alru_cache ( maxsize = get_context () . prediction_cache_size ) async def func_in_tracing_context_async ( * args : Any , do_not_persist_traces : bool = False , ** kwargs : Any , ) -> T : with TracingContext [ V ]( func . __name__ , do_not_persist_traces = do_not_persist_traces ) as t : result = await cast ( Callable [ ... , Awaitable ], func )( * args , ** kwargs ) return cast ( T , t . finalise ( output = result )) return cast ( Callable [ ... , T ], ( func_in_tracing_context_async if get_function_metadata_store ( func ) . is_asynchronous else func_in_tracing_context_sync ), ) def _bootstrap_rest_api ( self , ) -> None : route_config = get_context () . route_config if route_config . prediction_endpoint_enabled : bootstrap_prediction_endpoint ( self . app , self . _wrapped_func ) if route_config . docs_endpoints_enabled : bootstrap_docs_endpoints ( self . app ) if route_config . dashboard_enabled : bootstrap_dashboard ( self . app , function_name = self . __name__ , documentation = self . __doc__ , ) if route_config . trace_endpoints_enabled : bootstrap_trace_endpoints ( self . app ) if route_config . feedback_endpoints_enabled : bootstrap_feedback_endpoints ( self . app ) if route_config . meta_endpoints_enabled : bootstrap_meta_endpoints ( self . app , self . _cached_func , ApiMetadata ( name = self . __name__ , version = self . version , documentation = self . __doc__ , configuration = get_context () . to_flat_dict (), ), ) create ( func ) staticmethod # Decorate a function by wrapping it in a GreatAI instance. The function can be typed, synchronous or async. If it has unwrapped parameters (parameters not affected by a @parameter or @use_model decorator), those will be automatically wrapped. The return value is replaced by a Trace (or Awaitable[Trace]), while the original return value is available under the .output property. For configuration options, see great_ai.configure . Examples: >>> @GreatAI . create ... def my_function ( a ): ... return a + 2 >>> my_function ( 3 ) . output 5 >>> @GreatAI . create ... def my_function ( a : int ) -> int : ... return a + 2 >>> my_function ( 3 ) Trace[int]... >>> my_function ( '3' ) . output Traceback (most recent call last): ... TypeError : type of a must be int; got str instead Parameters: Name Type Description Default func Union [ Callable [..., Awaitable [ V ]], Callable [..., V ]] The prediction function that needs to be decorated. required Returns: Type Description Union [ GreatAI[Awaitable[Trace[V]], V] , GreatAI[Trace[V], V] ] A GreatAI instance wrapping func . Source code in great_ai/deploy/great_ai.py 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 @staticmethod def create ( func : Union [ Callable [ ... , Awaitable [ V ]], Callable [ ... , V ]], ) -> Union [ \"GreatAI[Awaitable[Trace[V]], V]\" , \"GreatAI[Trace[V], V]\" ]: \"\"\"Decorate a function by wrapping it in a GreatAI instance. The function can be typed, synchronous or async. If it has unwrapped parameters (parameters not affected by a [@parameter][great_ai.parameter] or [@use_model][great_ai.use_model] decorator), those will be automatically wrapped. The return value is replaced by a Trace (or Awaitable[Trace]), while the original return value is available under the `.output` property. For configuration options, see [great_ai.configure][]. Examples: >>> @GreatAI.create ... def my_function(a): ... return a + 2 >>> my_function(3).output 5 >>> @GreatAI.create ... def my_function(a: int) -> int: ... return a + 2 >>> my_function(3) Trace[int]... >>> my_function('3').output Traceback (most recent call last): ... TypeError: type of a must be int; got str instead Args: func: The prediction function that needs to be decorated. Returns: A GreatAI instance wrapping `func`. \"\"\" return GreatAI [ Trace [ V ], V ]( func , ) process_batch ( batch , * , concurrency = None , unpack_arguments = False , do_not_persist_traces = False ) # Map the wrapped function over a list of input_values ( batch ). A wrapper over parallel_map providing type-safety and a progressbar through tqdm. Parameters: Name Type Description Default batch Sequence A list of arguments for the original (wrapped) function. If the function expects multiple arguments, provide a list of tuples and set unpack_arguments=True . required concurrency Optional [ int ] Number of processes to start. Don't set it too much higher than the number of available CPU cores. None unpack_arguments bool Expect a list of tuples and unpack the tuples before giving them to the wrapped function. False do_not_persist_traces bool Don't save the traces in the database. Useful for evaluations run part of the CI. False Source code in great_ai/deploy/great_ai.py 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 def process_batch ( self , batch : Sequence , * , concurrency : Optional [ int ] = None , unpack_arguments : bool = False , do_not_persist_traces : bool = False , ) -> List [ Trace [ V ]]: \"\"\"Map the wrapped function over a list of input_values (`batch`). A wrapper over [parallel_map][great_ai.utilities.parallel_map.parallel_map.parallel_map] providing type-safety and a progressbar through tqdm. Args: batch: A list of arguments for the original (wrapped) function. If the function expects multiple arguments, provide a list of tuples and set `unpack_arguments=True`. concurrency: Number of processes to start. Don't set it too much higher than the number of available CPU cores. unpack_arguments: Expect a list of tuples and unpack the tuples before giving them to the wrapped function. do_not_persist_traces: Don't save the traces in the database. Useful for evaluations run part of the CI. \"\"\" wrapped_function = self . _wrapped_func def inner ( value : Any ) -> T : return ( wrapped_function ( * value , do_not_persist_traces = do_not_persist_traces ) if unpack_arguments else wrapped_function ( value , do_not_persist_traces = do_not_persist_traces ) ) async def inner_async ( value : Any ) -> T : return await cast ( Awaitable , ( wrapped_function ( * value , do_not_persist_traces = do_not_persist_traces ) if unpack_arguments else wrapped_function ( value , do_not_persist_traces = do_not_persist_traces ) ), ) return list ( tqdm ( parallel_map ( inner_async if get_function_metadata_store ( self ) . is_asynchronous else inner , batch , concurrency = concurrency , ), total = len ( batch ), ) ) configure ( * , version = '0.0.1' , log_level = DEBUG , seed = 42 , tracing_database_factory = None , large_file_implementation = None , should_log_exception_stack = None , prediction_cache_size = 512 , disable_se4ml_banner = False , dashboard_table_size = 50 , route_config = RouteConfig ()) # Set the global configuration used by the great-ai library. You must call configure before calling (or decorating with) any other great-ai function. If tracing_database_factory or large_file_implementation is not specified, their default value is determined based on which TracingDatabase and LargeFile has been configured (e.g.: LargeFileS3.configure_credentials_from_file('s3.ini')), or whether there is any file named s3.ini or mongo.ini in the working directory. Examples: >>> configure ( prediction_cache_size = 0 ) Parameters: Name Type Description Default version Union [ int , str ] The version of your application (using SemVer is recommended). '0.0.1' log_level int Set the default logging level of logging . DEBUG seed int Set seed of random (and numpy if installed) for reproducibility. 42 tracing_database_factory Optional [ Type [ TracingDatabaseDriver ]] Specify a different TracingDatabaseDriver than the one already configured. None large_file_implementation Optional [ Type [ LargeFileBase ]] Specify a different LargeFile than the one already configured. None should_log_exception_stack Optional [ bool ] Log the traces of unhandled exceptions. None prediction_cache_size int Size of the LRU cache applied over the prediction functions. 512 disable_se4ml_banner bool Turn off the warning about the importance of SE4ML best- practices. False dashboard_table_size int Number of rows to display in the dashboard's table. 50 route_config RouteConfig Enable or disable specific HTTP API endpoints. RouteConfig() Source code in great_ai/context.py 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 def configure ( * , version : Union [ int , str ] = \"0.0.1\" , log_level : int = DEBUG , seed : int = 42 , tracing_database_factory : Optional [ Type [ TracingDatabaseDriver ]] = None , large_file_implementation : Optional [ Type [ LargeFileBase ]] = None , should_log_exception_stack : Optional [ bool ] = None , prediction_cache_size : int = 512 , disable_se4ml_banner : bool = False , dashboard_table_size : int = 50 , route_config : RouteConfig = RouteConfig (), ) -> None : \"\"\"Set the global configuration used by the great-ai library. You must call `configure` before calling (or decorating with) any other great-ai function. If `tracing_database_factory` or `large_file_implementation` is not specified, their default value is determined based on which TracingDatabase and LargeFile has been configured (e.g.: LargeFileS3.configure_credentials_from_file('s3.ini')), or whether there is any file named s3.ini or mongo.ini in the working directory. Examples: >>> configure(prediction_cache_size=0) Arguments: version: The version of your application (using SemVer is recommended). log_level: Set the default logging level of `logging`. seed: Set seed of `random` (and `numpy` if installed) for reproducibility. tracing_database_factory: Specify a different TracingDatabaseDriver than the one already configured. large_file_implementation: Specify a different LargeFile than the one already configured. should_log_exception_stack: Log the traces of unhandled exceptions. prediction_cache_size: Size of the LRU cache applied over the prediction functions. disable_se4ml_banner: Turn off the warning about the importance of SE4ML best- practices. dashboard_table_size: Number of rows to display in the dashboard's table. route_config: Enable or disable specific HTTP API endpoints. \"\"\" global _context logger = get_logger ( \"great_ai\" , level = log_level ) if _context is not None : logger . error ( \"Configuration has been already initialised, overwriting. \\n \" + \"Make sure to call `configure()` before importing your application code.\" ) is_production = _is_in_production_mode ( logger = logger ) _set_seed ( seed ) tracing_database_factory = _initialize_tracing_database ( tracing_database_factory , logger = logger ) tracing_database = tracing_database_factory () if not tracing_database . is_production_ready : message = f \"\"\"The selected tracing database ( { tracing_database_factory . __name__ } ) is not recommended for production\"\"\" if is_production : logger . error ( message ) else : logger . warning ( message ) _context = Context ( version = version , tracing_database = tracing_database , large_file_implementation = _initialize_large_file ( large_file_implementation , logger = logger ), is_production = is_production , logger = logger , should_log_exception_stack = not is_production if should_log_exception_stack is None else should_log_exception_stack , prediction_cache_size = prediction_cache_size , dashboard_table_size = dashboard_table_size , route_config = route_config , ) logger . info ( f \"GreatAI (v { __version__ } ): configured \u2705\" ) for k , v in get_context () . to_flat_dict () . items (): logger . info ( f \" { LIST_ITEM_PREFIX }{ k } : { v } \" ) if not is_production and not disable_se4ml_banner : logger . warning ( \"You still need to check whether you follow all best practices before \" \"trusting your deployment.\" ) logger . warning ( f \"> Find out more at { SE4ML_WEBSITE } \" ) save_model ( model , key , * , keep_last_n = None ) # Save (and optionally serialise) a model in order to use by use_model . The model can be a Path or string representing a path in which case the local file/folder is read and saved using the current LargeFile implementation. In case model is an object, it is serialised using dill before uploading it. Examples: >>> from great_ai import use_model >>> save_model ( 3 , 'my_number' ) 'my_number:...' >>> @use_model ( 'my_number' ) ... def my_function ( a , model ): ... return a + model >>> my_function ( 4 ) 7 Parameters: Name Type Description Default model Union [ Path , str , object ] The object or path to be uploaded. required key str The model's name. required keep_last_n Optional [ int ] If specified, remove old models and only keep the latest n. Directly passed to LargeFile. None Returns: Type Description str The key and version of the saved model separated by a colon. Example: \"key:version\" Source code in great_ai/models/save_model.py 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 def save_model ( model : Union [ Path , str , object ], key : str , * , keep_last_n : Optional [ int ] = None ) -> str : \"\"\"Save (and optionally serialise) a model in order to use by `use_model`. The `model` can be a Path or string representing a path in which case the local file/folder is read and saved using the current LargeFile implementation. In case `model` is an object, it is serialised using `dill` before uploading it. Examples: >>> from great_ai import use_model >>> save_model(3, 'my_number') 'my_number:...' >>> @use_model('my_number') ... def my_function(a, model): ... return a + model >>> my_function(4) 7 Args: model: The object or path to be uploaded. key: The model's name. keep_last_n: If specified, remove old models and only keep the latest n. Directly passed to LargeFile. Returns: The key and version of the saved model separated by a colon. Example: \"key:version\" \"\"\" file = get_context () . large_file_implementation ( name = key , mode = \"wb\" , keep_last_n = keep_last_n ) if isinstance ( model , Path ) or isinstance ( model , str ): file . push ( model ) else : with file as f : dump ( model , f ) get_context () . logger . info ( f \"Model { key } uploaded with version { file . version } \" ) return f \" { key } : { file . version } \" use_model ( key , * , version = 'latest' , model_kwarg_name = 'model' ) # Inject a model into a function. Load a model specified by key and version using the currently active LargeFile implementation. If it's a single object, it is deserialised using dill . If it's a directory of files, a pathlib.Path instance is given. By default, the function's model parameter is replaced by the loaded model. This can be customised by changing model_kwarg_name . Multiple models can be loaded by decorating the same function with use_model multiple times. Examples: >>> from great_ai import save_model >>> save_model ( 3 , 'my_number' ) 'my_number:...' >>> @use_model ( 'my_number' ) ... def my_function ( a , model ): ... return a + model >>> my_function ( 4 ) 7 Parameters: Name Type Description Default key str The model's name as stored by the LargeFile implementation. required version Union [ int , Literal [ latest ]] The model's version as stored by the LargeFile implementation. 'latest' model_kwarg_name str the parameter to use for injecting the loaded model 'model' Returns: Type Description Callable [[ F ], F ] A decorator for model injection. Source code in great_ai/models/use_model.py 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 def use_model ( key : str , * , version : Union [ int , Literal [ \"latest\" ]] = \"latest\" , model_kwarg_name : str = \"model\" , ) -> Callable [[ F ], F ]: \"\"\"Inject a model into a function. Load a model specified by `key` and `version` using the currently active `LargeFile` implementation. If it's a single object, it is deserialised using `dill`. If it's a directory of files, a `pathlib.Path` instance is given. By default, the function's `model` parameter is replaced by the loaded model. This can be customised by changing `model_kwarg_name`. Multiple models can be loaded by decorating the same function with `use_model` multiple times. Examples: >>> from great_ai import save_model >>> save_model(3, 'my_number') 'my_number:...' >>> @use_model('my_number') ... def my_function(a, model): ... return a + model >>> my_function(4) 7 Args: key: The model's name as stored by the LargeFile implementation. version: The model's version as stored by the LargeFile implementation. model_kwarg_name: the parameter to use for injecting the loaded model Returns: A decorator for model injection. \"\"\" assert ( isinstance ( version , int ) or version == \"latest\" ), \"Only integers or the string literal `latest` is allowed as a version\" model , actual_version = _load_model ( key = key , version = None if version == \"latest\" else version , ) def decorator ( func : F ) -> F : assert_function_is_not_finalised ( func ) store = get_function_metadata_store ( func ) store . model_parameter_names . append ( model_kwarg_name ) @wraps ( func ) def wrapper ( * args : List [ Any ], ** kwargs : Dict [ str , Any ]) -> Any : tracing_context = TracingContext . get_current_tracing_context () if tracing_context : tracing_context . log_model ( Model ( key = key , version = actual_version )) return func ( * args , ** kwargs , ** { model_kwarg_name : model }) return cast ( F , wrapper ) return decorator parameter ( parameter_name , * , validate = lambda _ : True , disable_logging = False ) # Control the validation and logging of function parameters. Basically, a parameter decorator. Unfortunately, Python does not have that concept, thus, it's a method decorator that expects the name of the to-be-decorated parameter. Examples: >>> @parameter ( 'a' ) ... def my_function ( a : int ): ... return a + 2 >>> my_function ( 4 ) 6 >>> my_function ( '3' ) Traceback (most recent call last): ... TypeError : type of a must be int; got str instead >>> @parameter ( 'positive_a' , validate = lambda v : v > 0 ) ... def my_function ( positive_a : int ): ... return a + 2 >>> my_function ( - 1 ) Traceback (most recent call last): ... great_ai.errors.argument_validation_error.ArgumentValidationError : ... Parameters: Name Type Description Default parameter_name str Name of parameter to consider. required validate Callable [[ Any ], bool ] Optional validate to run against the concrete argument. ArgumentValidationError is thrown when the return value is False. lambda _: True disable_logging bool Do not save the value in any active TracingContext. False Returns: Type Description Callable [[ F ], F ] A decorator for argument validation. Source code in great_ai/parameters/parameter.py 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 def parameter ( parameter_name : str , * , validate : Callable [[ Any ], bool ] = lambda _ : True , disable_logging : bool = False , ) -> Callable [[ F ], F ]: \"\"\"Control the validation and logging of function parameters. Basically, a parameter decorator. Unfortunately, Python does not have that concept, thus, it's a method decorator that expects the name of the to-be-decorated parameter. Examples: >>> @parameter('a') ... def my_function(a: int): ... return a + 2 >>> my_function(4) 6 >>> my_function('3') Traceback (most recent call last): ... TypeError: type of a must be int; got str instead >>> @parameter('positive_a', validate=lambda v: v > 0) ... def my_function(positive_a: int): ... return a + 2 >>> my_function(-1) Traceback (most recent call last): ... great_ai.errors.argument_validation_error.ArgumentValidationError: ... Args: parameter_name: Name of parameter to consider. validate: Optional validate to run against the concrete argument. ArgumentValidationError is thrown when the return value is False. disable_logging: Do not save the value in any active TracingContext. Returns: A decorator for argument validation. \"\"\" def decorator ( func : F ) -> F : get_function_metadata_store ( func ) . input_parameter_names . append ( parameter_name ) assert_function_is_not_finalised ( func ) actual_name = f \"arg: { parameter_name } \" @wraps ( func ) def wrapper ( * args : Any , ** kwargs : Dict [ str , Any ]) -> Any : arguments = get_arguments ( func , args , kwargs ) argument = arguments . get ( parameter_name ) expected_type = func . __annotations__ . get ( parameter_name ) if expected_type is not None : check_type ( parameter_name , argument , expected_type ) if not validate ( argument ): raise ArgumentValidationError ( f \"\"\"Argument { parameter_name } in { func . __name__ } did not pass validation\"\"\" ) context = TracingContext . get_current_tracing_context () if context and not disable_logging : context . log_value ( name = f \" { actual_name } :value\" , value = argument ) if isinstance ( argument , str ): context . log_value ( name = f \" { actual_name } :length\" , value = len ( argument )) return func ( * args , ** kwargs ) return cast ( F , wrapper ) return decorator log_metric ( argument_name , value ) # Log a key (argument_name)-value pair that is persisted inside the trace. The name of the function from where this is called is also stored. Parameters: Name Type Description Default argument_name str The key for storing the value. required value Any Value to log. Must be JSON-serialisable. required Source code in great_ai/parameters/log_metric.py 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 def log_metric ( argument_name : str , value : Any ) -> None : \"\"\"Log a key (argument_name)-value pair that is persisted inside the trace. The name of the function from where this is called is also stored. Args: argument_name: The key for storing the value. value: Value to log. Must be JSON-serialisable. \"\"\" tracing_context = TracingContext . get_current_tracing_context () caller = inspect . stack ()[ 1 ] . function actual_name = f \"metric: { caller } : { argument_name } \" if tracing_context : tracing_context . log_value ( name = actual_name , value = value ) get_context () . logger . info ( f \" { actual_name } = { value } \" ) Remote calls # call_remote_great_ai ( base_uri , data , retry_count = 4 , timeout_in_seconds = 300 , model_class = None ) # Communicate with a GreatAI object through an HTTP request. Wrapper over call_remote_great_ai_async making it synchronous. For more info, see call_remote_great_ai_async . Source code in great_ai/remote/call_remote_great_ai.py 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 def call_remote_great_ai ( base_uri : str , data : Mapping [ str , Any ], retry_count : int = 4 , timeout_in_seconds : Optional [ int ] = 300 , model_class : Optional [ Type [ T ]] = None , ) -> Trace [ T ]: \"\"\"Communicate with a GreatAI object through an HTTP request. Wrapper over `call_remote_great_ai_async` making it synchronous. For more info, see `call_remote_great_ai_async`. \"\"\" try : asyncio . get_running_loop () raise Exception ( f \"Already running in an event loop, you have to call ` { call_remote_great_ai_async . __name__ } `\" ) except RuntimeError : pass future = call_remote_great_ai_async ( base_uri = base_uri , data = data , retry_count = retry_count , timeout_in_seconds = timeout_in_seconds , model_class = model_class , ) return asyncio . run ( future ) call_remote_great_ai_async ( base_uri , data , retry_count = 4 , timeout_in_seconds = 300 , model_class = None ) async # Communicate with a GreatAI object through an HTTP request. Send a POST request using httpx to implement a remote call. Error-handling and retries are provided by httpx. The return value is inflated into a Trace. If model_class is specified, the original output is deserialised. Parameters: Name Type Description Default base_uri str Address of the remote instance, example: 'http://localhost:6060' required data Mapping [ str , Any ] The input sent as a json to the remote instance. required retry_count int Retry on any HTTP communication failure. 4 timeout_in_seconds Optional [ int ] Overall permissible max length of the request. None means no timeout. 300 model_class Optional [ Type [ T ]] A subtype of BaseModel to be used for deserialising the .output of the trace. None Source code in great_ai/remote/call_remote_great_ai_async.py 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 async def call_remote_great_ai_async ( base_uri : str , data : Mapping [ str , Any ], retry_count : int = 4 , timeout_in_seconds : Optional [ int ] = 300 , model_class : Optional [ Type [ T ]] = None , ) -> Trace [ T ]: \"\"\"Communicate with a GreatAI object through an HTTP request. Send a POST request using [httpx](https://www.python-httpx.org/) to implement a remote call. Error-handling and retries are provided by httpx. The return value is inflated into a Trace. If `model_class` is specified, the original output is deserialised. Args: base_uri: Address of the remote instance, example: 'http://localhost:6060' data: The input sent as a json to the remote instance. retry_count: Retry on any HTTP communication failure. timeout_in_seconds: Overall permissible max length of the request. `None` means no timeout. model_class: A subtype of BaseModel to be used for deserialising the `.output` of the trace. \"\"\" if base_uri . endswith ( \"/\" ): base_uri = base_uri [: - 1 ] if not base_uri . endswith ( \"/predict\" ): base_uri = f \" { base_uri } /predict\" transport = httpx . AsyncHTTPTransport ( retries = retry_count ) try : async with httpx . AsyncClient ( transport = transport , timeout = timeout_in_seconds ) as client : response = await client . post ( base_uri , json = data ) try : response . raise_for_status () except Exception : raise RemoteCallError ( f \"Unexpected status code, reason: { response . text } \" ) except Exception as e : raise RemoteCallError from e try : trace = response . json () except Exception : raise RemoteCallError ( f \"JSON parsing failed { response . text } \" , ) try : if model_class is not None : trace [ \"output\" ] = model_class . parse_obj ( trace [ \"output\" ]) return Trace . parse_obj ( trace ) except Exception : raise RemoteCallError ( \"Could not parse Trace\" ) Ground-truth # add_ground_truth ( inputs , expected_outputs , * , tags = [], train_split_ratio = 1 , test_split_ratio = 0 , validation_split_ratio = 0 ) # Add training data (with optional train-test splitting). Add and tag data-points, wrap them into traces. The inputs are available via the .input property, while expected_outputs under both the .output and .feedback properties. All generated traces are tagged with ground_truth by default. Additional tags can be also provided. Using the split_ratio arguments, tags can be given randomly with a user-defined distribution. Only the ratio of the splits matter, they don't have to add up to 1. Examples: >>> add_ground_truth ( ... [ 1 , 2 , 3 ], ... [ 'odd' , 'even' , 'odd' ], ... tags = 'my_tag' , ... train_split_ratio = 1 , ... test_split_ratio = 1 , ... validation_split_ratio = 0.5 , ... ) >>> add_ground_truth ( ... [ 1 , 2 ], ... [ 'odd' , 'even' , 'odd' ], ... tags = 'my_tag' , ... train_split_ratio = 1 , ... test_split_ratio = 1 , ... validation_split_ratio = 0.5 , ... ) Traceback (most recent call last): ... AssertionError : The length of the inputs and expected_outputs must be equal Parameters: Name Type Description Default inputs Iterable [ Any ] The inputs. (X in scikit-learn) required expected_outputs Iterable [ T ] The ground-truth values corresponding to the inputs. (y in scikit-learn) required tags Union [ List [ str ], str ] A single tag or a list of tags to append to each generated trace's tags. [] train_split_ratio float The probability-weight of giving each trace the train tag. 1 test_split_ratio float The probability-weight of giving each trace the test tag. 0 validation_split_ratio float The probability-weight of giving each trace the validation tag. 0 Source code in great_ai/tracing/add_ground_truth.py 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 def add_ground_truth ( inputs : Iterable [ Any ], expected_outputs : Iterable [ T ], * , tags : Union [ List [ str ], str ] = [], train_split_ratio : float = 1 , test_split_ratio : float = 0 , validation_split_ratio : float = 0 ) -> None : \"\"\"Add training data (with optional train-test splitting). Add and tag data-points, wrap them into traces. The `inputs` are available via the `.input` property, while `expected_outputs` under both the `.output` and `.feedback` properties. All generated traces are tagged with `ground_truth` by default. Additional tags can be also provided. Using the `split_ratio` arguments, tags can be given randomly with a user-defined distribution. Only the ratio of the splits matter, they don't have to add up to 1. Examples: >>> add_ground_truth( ... [1, 2, 3], ... ['odd', 'even', 'odd'], ... tags='my_tag', ... train_split_ratio=1, ... test_split_ratio=1, ... validation_split_ratio=0.5, ... ) >>> add_ground_truth( ... [1, 2], ... ['odd', 'even', 'odd'], ... tags='my_tag', ... train_split_ratio=1, ... test_split_ratio=1, ... validation_split_ratio=0.5, ... ) Traceback (most recent call last): ... AssertionError: The length of the inputs and expected_outputs must be equal Args: inputs: The inputs. (X in scikit-learn) expected_outputs: The ground-truth values corresponding to the inputs. (y in scikit-learn) tags: A single tag or a list of tags to append to each generated trace's tags. train_split_ratio: The probability-weight of giving each trace the `train` tag. test_split_ratio: The probability-weight of giving each trace the `test` tag. validation_split_ratio: The probability-weight of giving each trace the `validation` tag. \"\"\" inputs = list ( inputs ) expected_outputs = list ( expected_outputs ) assert len ( inputs ) == len ( expected_outputs ), \"The length of the inputs and expected_outputs must be equal\" tags = tags if isinstance ( tags , list ) else [ tags ] sum_ratio = train_split_ratio + test_split_ratio + validation_split_ratio assert sum_ratio > 0 , \"The sum of the split ratios must be a positive number\" train_split_ratio /= sum_ratio test_split_ratio /= sum_ratio validation_split_ratio /= sum_ratio values = list ( zip ( inputs , expected_outputs )) shuffle ( values ) split_tags = ( [ TRAIN_SPLIT_TAG_NAME ] * ceil ( train_split_ratio * len ( inputs )) + [ TEST_SPLIT_TAG_NAME ] * ceil ( test_split_ratio * len ( inputs )) + [ VALIDATION_SPLIT_TAG_NAME ] * ceil ( validation_split_ratio * len ( inputs )) ) shuffle ( split_tags ) created = datetime . utcnow () . isoformat () traces = [ cast ( Trace [ T ], Trace ( # avoid ValueError: \"Trace\" object has no field \"__orig_class__\" trace_id = str ( uuid4 ()), created = created , original_execution_time_ms = 0 , logged_values = X if isinstance ( X , dict ) else { \"input\" : X }, models = [], output = y , feedback = y , exception = None , tags = [ GROUND_TRUTH_TAG_NAME , split_tag , * tags ], ), ) for (( X , y ), split_tag ) in zip ( values , split_tags ) ] get_context () . tracing_database . save_batch ( traces ) query_ground_truth ( conjunctive_tags = [], * , since = None , until = None , return_max_count = None ) # Return training samples. Combines, filters, and returns data-points that have been either added by add_ground_truth or were the result of a prediction after which their trace got feedback through the RESP API-s /traces/{trace_id}/feedback endpoint (end-to-end feedback). Filtering can be used to only return points matching all given tags (or the single given tag) and by time of creation. Examples: >>> query_ground_truth () [...] Parameters: Name Type Description Default conjunctive_tags Union [ List [ str ], str ] Single tag or a list of tags which the returned traces have to match. The relationship between the tags is conjunctive (AND). [] since Optional [ datetime ] Only return traces created after the given timestamp. None means no filtering. None until Optional [ datetime ] Only return traces created before the given timestamp. None means no filtering. None return_max_count Optional [ int ] Return at-most this many traces. (take, limit) None Source code in great_ai/tracing/query_ground_truth.py 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 def query_ground_truth ( conjunctive_tags : Union [ List [ str ], str ] = [], * , since : Optional [ datetime ] = None , until : Optional [ datetime ] = None , return_max_count : Optional [ int ] = None ) -> List [ Trace ]: \"\"\"Return training samples. Combines, filters, and returns data-points that have been either added by `add_ground_truth` or were the result of a prediction after which their trace got feedback through the RESP API-s `/traces/{trace_id}/feedback` endpoint (end-to-end feedback). Filtering can be used to only return points matching all given tags (or the single given tag) and by time of creation. Examples: >>> query_ground_truth() [...] Args: conjunctive_tags: Single tag or a list of tags which the returned traces have to match. The relationship between the tags is conjunctive (AND). since: Only return traces created after the given timestamp. `None` means no filtering. until: Only return traces created before the given timestamp. `None` means no filtering. return_max_count: Return at-most this many traces. (take, limit) \"\"\" tags = ( conjunctive_tags if isinstance ( conjunctive_tags , list ) else [ conjunctive_tags ] ) db = get_context () . tracing_database items , length = db . query ( conjunctive_tags = tags , since = since , until = until , take = return_max_count , has_feedback = True , ) return items delete_ground_truth ( conjunctive_tags = [], * , since = None , until = None ) # Delete traces matching the given criteria. Takes the same arguments as query_ground_truth but instead of returning them, it simply deletes them. You can rely on the efficiency of the delete's implementation. Examples: >>> delete_ground_truth ([ 'train' , 'test' , 'validation' ]) Parameters: Name Type Description Default conjunctive_tags Union [ List [ str ], str ] Single tag or a list of tags which the deleted traces have to match. The relationship between the tags is conjunctive (AND). [] since Optional [ datetime ] Only delete traces created after the given timestamp. None means no filtering. None until Optional [ datetime ] Only delete traces created before the given timestamp. None means no filtering. None Source code in great_ai/tracing/delete_ground_truth.py 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 def delete_ground_truth ( conjunctive_tags : Union [ List [ str ], str ] = [], * , since : Optional [ datetime ] = None , until : Optional [ datetime ] = None , ) -> None : \"\"\"Delete traces matching the given criteria. Takes the same arguments as `query_ground_truth` but instead of returning them, it simply deletes them. You can rely on the efficiency of the delete's implementation. Examples: >>> delete_ground_truth(['train', 'test', 'validation']) Args: conjunctive_tags: Single tag or a list of tags which the deleted traces have to match. The relationship between the tags is conjunctive (AND). since: Only delete traces created after the given timestamp. `None` means no filtering. until: Only delete traces created before the given timestamp. `None` means no filtering. \"\"\" tags = ( conjunctive_tags if isinstance ( conjunctive_tags , list ) else [ conjunctive_tags ] ) db = get_context () . tracing_database items , length = db . query ( conjunctive_tags = tags , until = until , since = since , has_feedback = True ) db . delete_batch ([ i . trace_id for i in items ]) Tracing databases # TracingDatabaseDriver # Bases: ABC Interface expected from a database to be used for storing traces. Source code in great_ai/persistence/tracing_database_driver.py 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 class TracingDatabaseDriver ( ABC ): \"\"\"Interface expected from a database to be used for storing traces.\"\"\" is_production_ready : bool initialized : bool = False @classmethod def configure_credentials_from_file ( cls , secrets : Union [ Path , str , ConfigFile ], ) -> None : if not isinstance ( secrets , ConfigFile ): secrets = ConfigFile ( secrets ) cls . configure_credentials ( ** { k . lower (): v for k , v in secrets . items ()}) @classmethod def configure_credentials ( cls , ) -> None : cls . initialized = True @abstractmethod def save ( self , document : Trace ) -> str : pass @abstractmethod def save_batch ( self , documents : List [ Trace ], ) -> List [ str ]: pass @abstractmethod def get ( self , id : str ) -> Optional [ Trace ]: pass @abstractmethod def query ( self , * , skip : int = 0 , take : Optional [ int ] = None , conjunctive_filters : Sequence [ Filter ] = [], conjunctive_tags : Sequence [ str ] = [], until : Optional [ datetime ] = None , since : Optional [ datetime ] = None , has_feedback : Optional [ bool ] = None , sort_by : Sequence [ SortBy ] = [] ) -> Tuple [ List [ Trace ], int ]: pass @abstractmethod def update ( self , id : str , new_version : Trace ) -> None : pass @abstractmethod def delete ( self , id : str ) -> None : pass @abstractmethod def delete_batch ( self , ids : List [ str ], ) -> None : pass MongoDbDriver # Bases: TracingDatabaseDriver TracingDatabaseDriver implementation using MongoDB as a backend. A production-ready database driver suitable for efficiently handling semi-structured data. Checkout MongoDB Atlas for a hosted MongoDB solution. Source code in great_ai/persistence/mongodb_driver.py 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 class MongoDbDriver ( TracingDatabaseDriver ): \"\"\"TracingDatabaseDriver implementation using MongoDB as a backend. A production-ready database driver suitable for efficiently handling semi-structured data. Checkout [MongoDB Atlas](https://www.mongodb.com/cloud/atlas/register) for a hosted MongoDB solution. \"\"\" is_production_ready = True mongo_connection_string : str mongo_database : str def __init__ ( self ) -> None : super () . __init__ () if self . mongo_connection_string is None or self . mongo_database is None : raise ValueError ( \"Please configure the MongoDB access options by calling \" \"MongoDbDriver.configure_credentials\" ) with MongoClient [ Any ]( self . mongo_connection_string ) as client : client [ self . mongo_database ] . traces . create_index ( [( \"tags\" , ASCENDING ), ( \"created\" , DESCENDING )], background = True ) @classmethod def configure_credentials ( # type: ignore cls , * , mongo_connection_string : str , mongo_database : str , ** _ : Any , ) -> None : \"\"\"Configure the connection details for MongoDB. Args: mongo_connection_string: For example: 'mongodb://my_user:my_pass@localhost:27017' mongo_database: Name of the database to use. If doesn't exist, it is created and initialised. \"\"\" cls . mongo_connection_string = mongo_connection_string cls . mongo_database = mongo_database super () . configure_credentials () def save ( self , trace : Trace ) -> str : serialized = trace . to_flat_dict () serialized [ \"_id\" ] = trace . trace_id with MongoClient [ Any ]( self . mongo_connection_string ) as client : return client [ self . mongo_database ] . traces . insert_one ( serialized ) . inserted_id def save_batch ( self , documents : List [ Trace ]) -> List [ str ]: serialized = [ d . to_flat_dict () for d in documents ] for s in serialized : s [ \"_id\" ] = s [ \"trace_id\" ] with MongoClient [ Any ]( self . mongo_connection_string ) as client : return ( client [ self . mongo_database ] . traces . insert_many ( serialized , ordered = False ) . inserted_ids ) def get ( self , id : str ) -> Optional [ Trace ]: with MongoClient [ Any ]( self . mongo_connection_string ) as client : value = client [ self . mongo_database ] . traces . find_one ( id ) if value : value = Trace . parse_obj ( value ) return value def _get_operator ( self , filter : Filter ) -> str : if filter . operator == \"contains\" and not isinstance ( filter . value , str ): return operator_mapping [ \"=\" ] return operator_mapping [ filter . operator ] def query ( self , * , skip : int = 0 , take : Optional [ int ] = None , conjunctive_filters : Sequence [ Filter ] = [], conjunctive_tags : Sequence [ str ] = [], since : Optional [ datetime ] = None , until : Optional [ datetime ] = None , has_feedback : Optional [ bool ] = None , sort_by : Sequence [ SortBy ] = [], ) -> Tuple [ List [ Trace ], int ]: query : Dict [ str , Any ] = { \"filter\" : {}, } and_query : List [ Dict [ str , Any ]] = [] and_query . extend ({ \"tags\" : tag } for tag in conjunctive_tags ) and_query . extend ( { f . property : { self . _get_operator ( f ): f . value }} for f in conjunctive_filters ) if not and_query : and_query . append ({}) if since : and_query . append ({ \"created\" : { \"$gte\" : since }}) if until : and_query . append ({ \"created\" : { \"$lte\" : until }}) if has_feedback is not None : and_query . append ( { \"feedback\" : { \"$ne\" : None }} if has_feedback else { \"feedback\" : None } ) query [ \"filter\" ][ \"$and\" ] = and_query with MongoClient [ Any ]( self . mongo_connection_string ) as client : count = client [ self . mongo_database ] . traces . count_documents ( ** query ) if skip : query [ \"skip\" ] = skip if take : query [ \"limit\" ] = take query [ \"sort\" ] = [ ( col . column_id , 1 if col . direction == \"asc\" else - 1 ) for col in sort_by ] with client [ self . mongo_database ] . traces . find ( ** query ) as cursor : documents = [ Trace [ Any ] . parse_obj ( t ) for t in cursor ] return documents , count def update ( self , id : str , new_version : Trace ) -> None : serialized = new_version . to_flat_dict () serialized [ \"_id\" ] = new_version . trace_id with MongoClient [ Any ]( self . mongo_connection_string ) as client : client [ self . mongo_database ] . traces . update_one ({ \"_id\" : id }, serialized ) def delete ( self , id : str ) -> None : with MongoClient [ Any ]( self . mongo_connection_string ) as client : client [ self . mongo_database ] . traces . delete_one ({ \"_id\" : id }) def delete_batch ( self , ids : List [ str ]) -> None : with MongoClient [ Any ]( self . mongo_connection_string ) as client : for c in chunk ( ids , chunk_size = 10000 ): # avoid: 'delete' command document too large delete_filter = { \"_id\" : { \"$in\" : c }} client [ self . mongo_database ] . traces . delete_many ( delete_filter ) configure_credentials ( * , mongo_connection_string , mongo_database , ** _ ) classmethod # Configure the connection details for MongoDB. Parameters: Name Type Description Default mongo_connection_string str For example: 'mongodb://my_user:my_pass@localhost:27017' required mongo_database str Name of the database to use. If doesn't exist, it is created and initialised. required Source code in great_ai/persistence/mongodb_driver.py 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 @classmethod def configure_credentials ( # type: ignore cls , * , mongo_connection_string : str , mongo_database : str , ** _ : Any , ) -> None : \"\"\"Configure the connection details for MongoDB. Args: mongo_connection_string: For example: 'mongodb://my_user:my_pass@localhost:27017' mongo_database: Name of the database to use. If doesn't exist, it is created and initialised. \"\"\" cls . mongo_connection_string = mongo_connection_string cls . mongo_database = mongo_database super () . configure_credentials () ParallelTinyDbDriver # Bases: TracingDatabaseDriver TracingDatabaseDriver with TinyDB as a backend. Saves the database as a JSON into a single file. Highly inefficient on inserting, not advised for production use. A multiprocessing lock protects the database file to avoid parallelisation issues. Source code in great_ai/persistence/parallel_tinydb_driver.py 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 class ParallelTinyDbDriver ( TracingDatabaseDriver ): \"\"\"TracingDatabaseDriver with TinyDB as a backend. Saves the database as a JSON into a single file. Highly inefficient on inserting, not advised for production use. A multiprocessing lock protects the database file to avoid parallelisation issues. \"\"\" is_production_ready = False path_to_db = Path ( DEFAULT_TRACING_DB_FILENAME ) def save ( self , trace : Trace ) -> str : return self . _safe_execute ( lambda db : db . insert ( trace . dict ())) def save_batch ( self , documents : List [ Trace ]) -> List [ str ]: traces = [ d . dict () for d in documents ] return self . _safe_execute ( lambda db : db . insert_multiple ( traces )) def get ( self , id : str ) -> Optional [ Trace ]: value = self . _safe_execute ( lambda db : db . get ( lambda d : d [ \"trace_id\" ] == id )) if value : value = Trace . parse_obj ( value ) return value def query ( self , * , skip : int = 0 , take : Optional [ int ] = None , conjunctive_filters : Sequence [ Filter ] = [], conjunctive_tags : Sequence [ str ] = [], since : Optional [ datetime ] = None , until : Optional [ datetime ] = None , has_feedback : Optional [ bool ] = None , sort_by : Sequence [ SortBy ] = [] ) -> Tuple [ List [ Trace ], int ]: def does_match ( d : Dict [ str , Any ]) -> bool : return ( not set ( conjunctive_tags ) - set ( d [ \"tags\" ]) and ( since is None or datetime . fromisoformat ( d [ \"created\" ]) >= since ) and ( until is None or datetime . fromisoformat ( d [ \"created\" ]) <= until ) and ( has_feedback is None or has_feedback == ( d [ \"feedback\" ] is not None ) ) ) documents : List [ Trace ] = [ Trace . parse_obj ( t ) for t in self . _safe_execute ( lambda db : db . search ( does_match )) ] if not documents : return [], 0 df = pd . DataFrame ([ d . to_flat_dict () for d in documents ]) for f in conjunctive_filters : operator = f . operator . lower () if operator in operator_mapping : df = df . loc [ getattr ( df [ f . property ], operator_mapping [ f . operator ])( f . value ) ] elif operator == \"contains\" : df = df . loc [ df [ f . property ] . str . contains ( f . value , case = False )] if sort_by : df . sort_values ( [ col . column_id for col in sort_by ], ascending = [ col . direction == \"asc\" for col in sort_by ], inplace = True , ) count = len ( df ) result = df . iloc [ skip :] if take is None else df . iloc [ skip : skip + take ] return [ next ( d for d in documents if d . trace_id == trace_id ) for trace_id in result [ \"trace_id\" ] ], count def update ( self , id : str , new_version : Trace ) -> None : self . _safe_execute ( lambda db : db . update ( new_version . dict (), lambda d : d [ \"trace_id\" ] == id ) ) def delete ( self , id : str ) -> None : self . _safe_execute ( lambda db : db . remove ( lambda d : d [ \"trace_id\" ] == id )) def delete_batch ( self , ids : List [ str ]) -> None : for i in ids : self . delete ( i ) def _safe_execute ( self , func : Callable [[ TinyDB ], Any ]) -> Any : with lock : with TinyDB ( self . path_to_db ) as db : return func ( db )","title":"GreatAI reference"},{"location":"reference/#greatai-reference","text":"from great_ai import *","title":"GreatAI reference"},{"location":"reference/#core","text":"","title":"Core"},{"location":"reference/#great_ai.GreatAI","text":"Bases: Generic [ T , V ] Wrapper for a prediction function providing the implementation of SE4ML best practices. Provides caching (with argument freezing), a TracingContext during execution, the scaffolding of HTTP endpoints using FastAPI and a dashboard using Dash. IMPORTANT: when a request is served from cache, no new trace is created. Thus, the same trace can be returned multiple times. If this is undesirable turn off caching using configure(prediction_cache_size=0) . Supports wrapping async and synchronous functions while also maintaining correct typing. Attributes: Name Type Description app FastAPI instance wrapping the scaffolded endpoints and the Dash app. version SemVer derived from the app's version and the model names and versions registered through use_model. Source code in great_ai/deploy/great_ai.py 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 class GreatAI ( Generic [ T , V ]): \"\"\"Wrapper for a prediction function providing the implementation of SE4ML best practices. Provides caching (with argument freezing), a TracingContext during execution, the scaffolding of HTTP endpoints using FastAPI and a dashboard using Dash. IMPORTANT: when a request is served from cache, no new trace is created. Thus, the same trace can be returned multiple times. If this is undesirable turn off caching using `configure(prediction_cache_size=0)`. Supports wrapping async and synchronous functions while also maintaining correct typing. Attributes: app: FastAPI instance wrapping the scaffolded endpoints and the Dash app. version: SemVer derived from the app's version and the model names and versions registered through use_model. \"\"\" __name__ : str # help for MyPy __doc__ : str # help for MyPy def __init__ ( self , func : Callable [ ... , Union [ V , Awaitable [ V ]]], ): \"\"\"Do not call this function directly, use GreatAI.create instead.\"\"\" func = automatically_decorate_parameters ( func ) get_function_metadata_store ( func ) . is_finalised = True self . _cached_func = self . _get_cached_traced_function ( func ) self . _wrapped_func = wraps ( func )( freeze_arguments ( self . _cached_func )) wraps ( func )( self ) self . __doc__ = ( f \"GreatAI wrapper for interacting with the ` { self . __name__ } ` \" + f \"function. \\n\\n { dedent ( self . __doc__ or '' ) } \" ) self . version = str ( get_context () . version ) flat_model_versions = \".\" . join ( f \" { k } -v { v } \" for k , v in model_versions ) if flat_model_versions : self . version += f \"+ { flat_model_versions } \" self . app = FastAPI ( title = snake_case_to_text ( self . __name__ ), version = self . version , description = self . __doc__ + f \" \\n\\n Find out more in the [dashboard]( { DASHBOARD_PATH } ).\" , docs_url = None , redoc_url = None , ) self . _bootstrap_rest_api () @overload @staticmethod def create ( # type: ignore # Overloaded function signatures 1 and 2 overlap with incompatible return types # https://github.com/python/mypy/issues/12759 func : Callable [ ... , Awaitable [ V ]], ) -> \"GreatAI[Awaitable[Trace[V]], V]\" : ... @overload @staticmethod def create ( func : Callable [ ... , V ], ) -> \"GreatAI[Trace[V], V]\" : ... @staticmethod def create ( func : Union [ Callable [ ... , Awaitable [ V ]], Callable [ ... , V ]], ) -> Union [ \"GreatAI[Awaitable[Trace[V]], V]\" , \"GreatAI[Trace[V], V]\" ]: \"\"\"Decorate a function by wrapping it in a GreatAI instance. The function can be typed, synchronous or async. If it has unwrapped parameters (parameters not affected by a [@parameter][great_ai.parameter] or [@use_model][great_ai.use_model] decorator), those will be automatically wrapped. The return value is replaced by a Trace (or Awaitable[Trace]), while the original return value is available under the `.output` property. For configuration options, see [great_ai.configure][]. Examples: >>> @GreatAI.create ... def my_function(a): ... return a + 2 >>> my_function(3).output 5 >>> @GreatAI.create ... def my_function(a: int) -> int: ... return a + 2 >>> my_function(3) Trace[int]... >>> my_function('3').output Traceback (most recent call last): ... TypeError: type of a must be int; got str instead Args: func: The prediction function that needs to be decorated. Returns: A GreatAI instance wrapping `func`. \"\"\" return GreatAI [ Trace [ V ], V ]( func , ) def __call__ ( self , * args : Any , ** kwargs : Any ) -> T : return self . _wrapped_func ( * args , ** kwargs ) @overload def process_batch ( self , batch : Sequence [ Tuple ], * , concurrency : Optional [ int ] = None , unpack_arguments : Literal [ True ], do_not_persist_traces : bool = ... , ) -> List [ Trace [ V ]]: ... @overload def process_batch ( self , batch : Sequence , * , concurrency : Optional [ int ] = None , unpack_arguments : Literal [ False ] = ... , do_not_persist_traces : bool = ... , ) -> List [ Trace [ V ]]: ... def process_batch ( self , batch : Sequence , * , concurrency : Optional [ int ] = None , unpack_arguments : bool = False , do_not_persist_traces : bool = False , ) -> List [ Trace [ V ]]: \"\"\"Map the wrapped function over a list of input_values (`batch`). A wrapper over [parallel_map][great_ai.utilities.parallel_map.parallel_map.parallel_map] providing type-safety and a progressbar through tqdm. Args: batch: A list of arguments for the original (wrapped) function. If the function expects multiple arguments, provide a list of tuples and set `unpack_arguments=True`. concurrency: Number of processes to start. Don't set it too much higher than the number of available CPU cores. unpack_arguments: Expect a list of tuples and unpack the tuples before giving them to the wrapped function. do_not_persist_traces: Don't save the traces in the database. Useful for evaluations run part of the CI. \"\"\" wrapped_function = self . _wrapped_func def inner ( value : Any ) -> T : return ( wrapped_function ( * value , do_not_persist_traces = do_not_persist_traces ) if unpack_arguments else wrapped_function ( value , do_not_persist_traces = do_not_persist_traces ) ) async def inner_async ( value : Any ) -> T : return await cast ( Awaitable , ( wrapped_function ( * value , do_not_persist_traces = do_not_persist_traces ) if unpack_arguments else wrapped_function ( value , do_not_persist_traces = do_not_persist_traces ) ), ) return list ( tqdm ( parallel_map ( inner_async if get_function_metadata_store ( self ) . is_asynchronous else inner , batch , concurrency = concurrency , ), total = len ( batch ), ) ) @staticmethod def _get_cached_traced_function ( func : Callable [ ... , Union [ V , Awaitable [ V ]]] ) -> Callable [ ... , T ]: @lru_cache ( maxsize = get_context () . prediction_cache_size ) def func_in_tracing_context_sync ( * args : Any , do_not_persist_traces : bool = False , ** kwargs : Any , ) -> T : with TracingContext [ V ]( func . __name__ , do_not_persist_traces = do_not_persist_traces ) as t : result = func ( * args , ** kwargs ) return cast ( T , t . finalise ( output = result )) @alru_cache ( maxsize = get_context () . prediction_cache_size ) async def func_in_tracing_context_async ( * args : Any , do_not_persist_traces : bool = False , ** kwargs : Any , ) -> T : with TracingContext [ V ]( func . __name__ , do_not_persist_traces = do_not_persist_traces ) as t : result = await cast ( Callable [ ... , Awaitable ], func )( * args , ** kwargs ) return cast ( T , t . finalise ( output = result )) return cast ( Callable [ ... , T ], ( func_in_tracing_context_async if get_function_metadata_store ( func ) . is_asynchronous else func_in_tracing_context_sync ), ) def _bootstrap_rest_api ( self , ) -> None : route_config = get_context () . route_config if route_config . prediction_endpoint_enabled : bootstrap_prediction_endpoint ( self . app , self . _wrapped_func ) if route_config . docs_endpoints_enabled : bootstrap_docs_endpoints ( self . app ) if route_config . dashboard_enabled : bootstrap_dashboard ( self . app , function_name = self . __name__ , documentation = self . __doc__ , ) if route_config . trace_endpoints_enabled : bootstrap_trace_endpoints ( self . app ) if route_config . feedback_endpoints_enabled : bootstrap_feedback_endpoints ( self . app ) if route_config . meta_endpoints_enabled : bootstrap_meta_endpoints ( self . app , self . _cached_func , ApiMetadata ( name = self . __name__ , version = self . version , documentation = self . __doc__ , configuration = get_context () . to_flat_dict (), ), )","title":"GreatAI"},{"location":"reference/#great_ai.deploy.great_ai.GreatAI.create","text":"Decorate a function by wrapping it in a GreatAI instance. The function can be typed, synchronous or async. If it has unwrapped parameters (parameters not affected by a @parameter or @use_model decorator), those will be automatically wrapped. The return value is replaced by a Trace (or Awaitable[Trace]), while the original return value is available under the .output property. For configuration options, see great_ai.configure . Examples: >>> @GreatAI . create ... def my_function ( a ): ... return a + 2 >>> my_function ( 3 ) . output 5 >>> @GreatAI . create ... def my_function ( a : int ) -> int : ... return a + 2 >>> my_function ( 3 ) Trace[int]... >>> my_function ( '3' ) . output Traceback (most recent call last): ... TypeError : type of a must be int; got str instead Parameters: Name Type Description Default func Union [ Callable [..., Awaitable [ V ]], Callable [..., V ]] The prediction function that needs to be decorated. required Returns: Type Description Union [ GreatAI[Awaitable[Trace[V]], V] , GreatAI[Trace[V], V] ] A GreatAI instance wrapping func . Source code in great_ai/deploy/great_ai.py 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 @staticmethod def create ( func : Union [ Callable [ ... , Awaitable [ V ]], Callable [ ... , V ]], ) -> Union [ \"GreatAI[Awaitable[Trace[V]], V]\" , \"GreatAI[Trace[V], V]\" ]: \"\"\"Decorate a function by wrapping it in a GreatAI instance. The function can be typed, synchronous or async. If it has unwrapped parameters (parameters not affected by a [@parameter][great_ai.parameter] or [@use_model][great_ai.use_model] decorator), those will be automatically wrapped. The return value is replaced by a Trace (or Awaitable[Trace]), while the original return value is available under the `.output` property. For configuration options, see [great_ai.configure][]. Examples: >>> @GreatAI.create ... def my_function(a): ... return a + 2 >>> my_function(3).output 5 >>> @GreatAI.create ... def my_function(a: int) -> int: ... return a + 2 >>> my_function(3) Trace[int]... >>> my_function('3').output Traceback (most recent call last): ... TypeError: type of a must be int; got str instead Args: func: The prediction function that needs to be decorated. Returns: A GreatAI instance wrapping `func`. \"\"\" return GreatAI [ Trace [ V ], V ]( func , )","title":"create()"},{"location":"reference/#great_ai.deploy.great_ai.GreatAI.process_batch","text":"Map the wrapped function over a list of input_values ( batch ). A wrapper over parallel_map providing type-safety and a progressbar through tqdm. Parameters: Name Type Description Default batch Sequence A list of arguments for the original (wrapped) function. If the function expects multiple arguments, provide a list of tuples and set unpack_arguments=True . required concurrency Optional [ int ] Number of processes to start. Don't set it too much higher than the number of available CPU cores. None unpack_arguments bool Expect a list of tuples and unpack the tuples before giving them to the wrapped function. False do_not_persist_traces bool Don't save the traces in the database. Useful for evaluations run part of the CI. False Source code in great_ai/deploy/great_ai.py 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 def process_batch ( self , batch : Sequence , * , concurrency : Optional [ int ] = None , unpack_arguments : bool = False , do_not_persist_traces : bool = False , ) -> List [ Trace [ V ]]: \"\"\"Map the wrapped function over a list of input_values (`batch`). A wrapper over [parallel_map][great_ai.utilities.parallel_map.parallel_map.parallel_map] providing type-safety and a progressbar through tqdm. Args: batch: A list of arguments for the original (wrapped) function. If the function expects multiple arguments, provide a list of tuples and set `unpack_arguments=True`. concurrency: Number of processes to start. Don't set it too much higher than the number of available CPU cores. unpack_arguments: Expect a list of tuples and unpack the tuples before giving them to the wrapped function. do_not_persist_traces: Don't save the traces in the database. Useful for evaluations run part of the CI. \"\"\" wrapped_function = self . _wrapped_func def inner ( value : Any ) -> T : return ( wrapped_function ( * value , do_not_persist_traces = do_not_persist_traces ) if unpack_arguments else wrapped_function ( value , do_not_persist_traces = do_not_persist_traces ) ) async def inner_async ( value : Any ) -> T : return await cast ( Awaitable , ( wrapped_function ( * value , do_not_persist_traces = do_not_persist_traces ) if unpack_arguments else wrapped_function ( value , do_not_persist_traces = do_not_persist_traces ) ), ) return list ( tqdm ( parallel_map ( inner_async if get_function_metadata_store ( self ) . is_asynchronous else inner , batch , concurrency = concurrency , ), total = len ( batch ), ) )","title":"process_batch()"},{"location":"reference/#great_ai.configure","text":"Set the global configuration used by the great-ai library. You must call configure before calling (or decorating with) any other great-ai function. If tracing_database_factory or large_file_implementation is not specified, their default value is determined based on which TracingDatabase and LargeFile has been configured (e.g.: LargeFileS3.configure_credentials_from_file('s3.ini')), or whether there is any file named s3.ini or mongo.ini in the working directory. Examples: >>> configure ( prediction_cache_size = 0 ) Parameters: Name Type Description Default version Union [ int , str ] The version of your application (using SemVer is recommended). '0.0.1' log_level int Set the default logging level of logging . DEBUG seed int Set seed of random (and numpy if installed) for reproducibility. 42 tracing_database_factory Optional [ Type [ TracingDatabaseDriver ]] Specify a different TracingDatabaseDriver than the one already configured. None large_file_implementation Optional [ Type [ LargeFileBase ]] Specify a different LargeFile than the one already configured. None should_log_exception_stack Optional [ bool ] Log the traces of unhandled exceptions. None prediction_cache_size int Size of the LRU cache applied over the prediction functions. 512 disable_se4ml_banner bool Turn off the warning about the importance of SE4ML best- practices. False dashboard_table_size int Number of rows to display in the dashboard's table. 50 route_config RouteConfig Enable or disable specific HTTP API endpoints. RouteConfig() Source code in great_ai/context.py 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 def configure ( * , version : Union [ int , str ] = \"0.0.1\" , log_level : int = DEBUG , seed : int = 42 , tracing_database_factory : Optional [ Type [ TracingDatabaseDriver ]] = None , large_file_implementation : Optional [ Type [ LargeFileBase ]] = None , should_log_exception_stack : Optional [ bool ] = None , prediction_cache_size : int = 512 , disable_se4ml_banner : bool = False , dashboard_table_size : int = 50 , route_config : RouteConfig = RouteConfig (), ) -> None : \"\"\"Set the global configuration used by the great-ai library. You must call `configure` before calling (or decorating with) any other great-ai function. If `tracing_database_factory` or `large_file_implementation` is not specified, their default value is determined based on which TracingDatabase and LargeFile has been configured (e.g.: LargeFileS3.configure_credentials_from_file('s3.ini')), or whether there is any file named s3.ini or mongo.ini in the working directory. Examples: >>> configure(prediction_cache_size=0) Arguments: version: The version of your application (using SemVer is recommended). log_level: Set the default logging level of `logging`. seed: Set seed of `random` (and `numpy` if installed) for reproducibility. tracing_database_factory: Specify a different TracingDatabaseDriver than the one already configured. large_file_implementation: Specify a different LargeFile than the one already configured. should_log_exception_stack: Log the traces of unhandled exceptions. prediction_cache_size: Size of the LRU cache applied over the prediction functions. disable_se4ml_banner: Turn off the warning about the importance of SE4ML best- practices. dashboard_table_size: Number of rows to display in the dashboard's table. route_config: Enable or disable specific HTTP API endpoints. \"\"\" global _context logger = get_logger ( \"great_ai\" , level = log_level ) if _context is not None : logger . error ( \"Configuration has been already initialised, overwriting. \\n \" + \"Make sure to call `configure()` before importing your application code.\" ) is_production = _is_in_production_mode ( logger = logger ) _set_seed ( seed ) tracing_database_factory = _initialize_tracing_database ( tracing_database_factory , logger = logger ) tracing_database = tracing_database_factory () if not tracing_database . is_production_ready : message = f \"\"\"The selected tracing database ( { tracing_database_factory . __name__ } ) is not recommended for production\"\"\" if is_production : logger . error ( message ) else : logger . warning ( message ) _context = Context ( version = version , tracing_database = tracing_database , large_file_implementation = _initialize_large_file ( large_file_implementation , logger = logger ), is_production = is_production , logger = logger , should_log_exception_stack = not is_production if should_log_exception_stack is None else should_log_exception_stack , prediction_cache_size = prediction_cache_size , dashboard_table_size = dashboard_table_size , route_config = route_config , ) logger . info ( f \"GreatAI (v { __version__ } ): configured \u2705\" ) for k , v in get_context () . to_flat_dict () . items (): logger . info ( f \" { LIST_ITEM_PREFIX }{ k } : { v } \" ) if not is_production and not disable_se4ml_banner : logger . warning ( \"You still need to check whether you follow all best practices before \" \"trusting your deployment.\" ) logger . warning ( f \"> Find out more at { SE4ML_WEBSITE } \" )","title":"configure()"},{"location":"reference/#great_ai.save_model","text":"Save (and optionally serialise) a model in order to use by use_model . The model can be a Path or string representing a path in which case the local file/folder is read and saved using the current LargeFile implementation. In case model is an object, it is serialised using dill before uploading it. Examples: >>> from great_ai import use_model >>> save_model ( 3 , 'my_number' ) 'my_number:...' >>> @use_model ( 'my_number' ) ... def my_function ( a , model ): ... return a + model >>> my_function ( 4 ) 7 Parameters: Name Type Description Default model Union [ Path , str , object ] The object or path to be uploaded. required key str The model's name. required keep_last_n Optional [ int ] If specified, remove old models and only keep the latest n. Directly passed to LargeFile. None Returns: Type Description str The key and version of the saved model separated by a colon. Example: \"key:version\" Source code in great_ai/models/save_model.py 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 def save_model ( model : Union [ Path , str , object ], key : str , * , keep_last_n : Optional [ int ] = None ) -> str : \"\"\"Save (and optionally serialise) a model in order to use by `use_model`. The `model` can be a Path or string representing a path in which case the local file/folder is read and saved using the current LargeFile implementation. In case `model` is an object, it is serialised using `dill` before uploading it. Examples: >>> from great_ai import use_model >>> save_model(3, 'my_number') 'my_number:...' >>> @use_model('my_number') ... def my_function(a, model): ... return a + model >>> my_function(4) 7 Args: model: The object or path to be uploaded. key: The model's name. keep_last_n: If specified, remove old models and only keep the latest n. Directly passed to LargeFile. Returns: The key and version of the saved model separated by a colon. Example: \"key:version\" \"\"\" file = get_context () . large_file_implementation ( name = key , mode = \"wb\" , keep_last_n = keep_last_n ) if isinstance ( model , Path ) or isinstance ( model , str ): file . push ( model ) else : with file as f : dump ( model , f ) get_context () . logger . info ( f \"Model { key } uploaded with version { file . version } \" ) return f \" { key } : { file . version } \"","title":"save_model()"},{"location":"reference/#great_ai.use_model","text":"Inject a model into a function. Load a model specified by key and version using the currently active LargeFile implementation. If it's a single object, it is deserialised using dill . If it's a directory of files, a pathlib.Path instance is given. By default, the function's model parameter is replaced by the loaded model. This can be customised by changing model_kwarg_name . Multiple models can be loaded by decorating the same function with use_model multiple times. Examples: >>> from great_ai import save_model >>> save_model ( 3 , 'my_number' ) 'my_number:...' >>> @use_model ( 'my_number' ) ... def my_function ( a , model ): ... return a + model >>> my_function ( 4 ) 7 Parameters: Name Type Description Default key str The model's name as stored by the LargeFile implementation. required version Union [ int , Literal [ latest ]] The model's version as stored by the LargeFile implementation. 'latest' model_kwarg_name str the parameter to use for injecting the loaded model 'model' Returns: Type Description Callable [[ F ], F ] A decorator for model injection. Source code in great_ai/models/use_model.py 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 def use_model ( key : str , * , version : Union [ int , Literal [ \"latest\" ]] = \"latest\" , model_kwarg_name : str = \"model\" , ) -> Callable [[ F ], F ]: \"\"\"Inject a model into a function. Load a model specified by `key` and `version` using the currently active `LargeFile` implementation. If it's a single object, it is deserialised using `dill`. If it's a directory of files, a `pathlib.Path` instance is given. By default, the function's `model` parameter is replaced by the loaded model. This can be customised by changing `model_kwarg_name`. Multiple models can be loaded by decorating the same function with `use_model` multiple times. Examples: >>> from great_ai import save_model >>> save_model(3, 'my_number') 'my_number:...' >>> @use_model('my_number') ... def my_function(a, model): ... return a + model >>> my_function(4) 7 Args: key: The model's name as stored by the LargeFile implementation. version: The model's version as stored by the LargeFile implementation. model_kwarg_name: the parameter to use for injecting the loaded model Returns: A decorator for model injection. \"\"\" assert ( isinstance ( version , int ) or version == \"latest\" ), \"Only integers or the string literal `latest` is allowed as a version\" model , actual_version = _load_model ( key = key , version = None if version == \"latest\" else version , ) def decorator ( func : F ) -> F : assert_function_is_not_finalised ( func ) store = get_function_metadata_store ( func ) store . model_parameter_names . append ( model_kwarg_name ) @wraps ( func ) def wrapper ( * args : List [ Any ], ** kwargs : Dict [ str , Any ]) -> Any : tracing_context = TracingContext . get_current_tracing_context () if tracing_context : tracing_context . log_model ( Model ( key = key , version = actual_version )) return func ( * args , ** kwargs , ** { model_kwarg_name : model }) return cast ( F , wrapper ) return decorator","title":"use_model()"},{"location":"reference/#great_ai.parameter","text":"Control the validation and logging of function parameters. Basically, a parameter decorator. Unfortunately, Python does not have that concept, thus, it's a method decorator that expects the name of the to-be-decorated parameter. Examples: >>> @parameter ( 'a' ) ... def my_function ( a : int ): ... return a + 2 >>> my_function ( 4 ) 6 >>> my_function ( '3' ) Traceback (most recent call last): ... TypeError : type of a must be int; got str instead >>> @parameter ( 'positive_a' , validate = lambda v : v > 0 ) ... def my_function ( positive_a : int ): ... return a + 2 >>> my_function ( - 1 ) Traceback (most recent call last): ... great_ai.errors.argument_validation_error.ArgumentValidationError : ... Parameters: Name Type Description Default parameter_name str Name of parameter to consider. required validate Callable [[ Any ], bool ] Optional validate to run against the concrete argument. ArgumentValidationError is thrown when the return value is False. lambda _: True disable_logging bool Do not save the value in any active TracingContext. False Returns: Type Description Callable [[ F ], F ] A decorator for argument validation. Source code in great_ai/parameters/parameter.py 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 def parameter ( parameter_name : str , * , validate : Callable [[ Any ], bool ] = lambda _ : True , disable_logging : bool = False , ) -> Callable [[ F ], F ]: \"\"\"Control the validation and logging of function parameters. Basically, a parameter decorator. Unfortunately, Python does not have that concept, thus, it's a method decorator that expects the name of the to-be-decorated parameter. Examples: >>> @parameter('a') ... def my_function(a: int): ... return a + 2 >>> my_function(4) 6 >>> my_function('3') Traceback (most recent call last): ... TypeError: type of a must be int; got str instead >>> @parameter('positive_a', validate=lambda v: v > 0) ... def my_function(positive_a: int): ... return a + 2 >>> my_function(-1) Traceback (most recent call last): ... great_ai.errors.argument_validation_error.ArgumentValidationError: ... Args: parameter_name: Name of parameter to consider. validate: Optional validate to run against the concrete argument. ArgumentValidationError is thrown when the return value is False. disable_logging: Do not save the value in any active TracingContext. Returns: A decorator for argument validation. \"\"\" def decorator ( func : F ) -> F : get_function_metadata_store ( func ) . input_parameter_names . append ( parameter_name ) assert_function_is_not_finalised ( func ) actual_name = f \"arg: { parameter_name } \" @wraps ( func ) def wrapper ( * args : Any , ** kwargs : Dict [ str , Any ]) -> Any : arguments = get_arguments ( func , args , kwargs ) argument = arguments . get ( parameter_name ) expected_type = func . __annotations__ . get ( parameter_name ) if expected_type is not None : check_type ( parameter_name , argument , expected_type ) if not validate ( argument ): raise ArgumentValidationError ( f \"\"\"Argument { parameter_name } in { func . __name__ } did not pass validation\"\"\" ) context = TracingContext . get_current_tracing_context () if context and not disable_logging : context . log_value ( name = f \" { actual_name } :value\" , value = argument ) if isinstance ( argument , str ): context . log_value ( name = f \" { actual_name } :length\" , value = len ( argument )) return func ( * args , ** kwargs ) return cast ( F , wrapper ) return decorator","title":"parameter()"},{"location":"reference/#great_ai.log_metric","text":"Log a key (argument_name)-value pair that is persisted inside the trace. The name of the function from where this is called is also stored. Parameters: Name Type Description Default argument_name str The key for storing the value. required value Any Value to log. Must be JSON-serialisable. required Source code in great_ai/parameters/log_metric.py 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 def log_metric ( argument_name : str , value : Any ) -> None : \"\"\"Log a key (argument_name)-value pair that is persisted inside the trace. The name of the function from where this is called is also stored. Args: argument_name: The key for storing the value. value: Value to log. Must be JSON-serialisable. \"\"\" tracing_context = TracingContext . get_current_tracing_context () caller = inspect . stack ()[ 1 ] . function actual_name = f \"metric: { caller } : { argument_name } \" if tracing_context : tracing_context . log_value ( name = actual_name , value = value ) get_context () . logger . info ( f \" { actual_name } = { value } \" )","title":"log_metric()"},{"location":"reference/#remote-calls","text":"","title":"Remote calls"},{"location":"reference/#great_ai.call_remote_great_ai","text":"Communicate with a GreatAI object through an HTTP request. Wrapper over call_remote_great_ai_async making it synchronous. For more info, see call_remote_great_ai_async . Source code in great_ai/remote/call_remote_great_ai.py 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 def call_remote_great_ai ( base_uri : str , data : Mapping [ str , Any ], retry_count : int = 4 , timeout_in_seconds : Optional [ int ] = 300 , model_class : Optional [ Type [ T ]] = None , ) -> Trace [ T ]: \"\"\"Communicate with a GreatAI object through an HTTP request. Wrapper over `call_remote_great_ai_async` making it synchronous. For more info, see `call_remote_great_ai_async`. \"\"\" try : asyncio . get_running_loop () raise Exception ( f \"Already running in an event loop, you have to call ` { call_remote_great_ai_async . __name__ } `\" ) except RuntimeError : pass future = call_remote_great_ai_async ( base_uri = base_uri , data = data , retry_count = retry_count , timeout_in_seconds = timeout_in_seconds , model_class = model_class , ) return asyncio . run ( future )","title":"call_remote_great_ai()"},{"location":"reference/#great_ai.call_remote_great_ai_async","text":"Communicate with a GreatAI object through an HTTP request. Send a POST request using httpx to implement a remote call. Error-handling and retries are provided by httpx. The return value is inflated into a Trace. If model_class is specified, the original output is deserialised. Parameters: Name Type Description Default base_uri str Address of the remote instance, example: 'http://localhost:6060' required data Mapping [ str , Any ] The input sent as a json to the remote instance. required retry_count int Retry on any HTTP communication failure. 4 timeout_in_seconds Optional [ int ] Overall permissible max length of the request. None means no timeout. 300 model_class Optional [ Type [ T ]] A subtype of BaseModel to be used for deserialising the .output of the trace. None Source code in great_ai/remote/call_remote_great_ai_async.py 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 async def call_remote_great_ai_async ( base_uri : str , data : Mapping [ str , Any ], retry_count : int = 4 , timeout_in_seconds : Optional [ int ] = 300 , model_class : Optional [ Type [ T ]] = None , ) -> Trace [ T ]: \"\"\"Communicate with a GreatAI object through an HTTP request. Send a POST request using [httpx](https://www.python-httpx.org/) to implement a remote call. Error-handling and retries are provided by httpx. The return value is inflated into a Trace. If `model_class` is specified, the original output is deserialised. Args: base_uri: Address of the remote instance, example: 'http://localhost:6060' data: The input sent as a json to the remote instance. retry_count: Retry on any HTTP communication failure. timeout_in_seconds: Overall permissible max length of the request. `None` means no timeout. model_class: A subtype of BaseModel to be used for deserialising the `.output` of the trace. \"\"\" if base_uri . endswith ( \"/\" ): base_uri = base_uri [: - 1 ] if not base_uri . endswith ( \"/predict\" ): base_uri = f \" { base_uri } /predict\" transport = httpx . AsyncHTTPTransport ( retries = retry_count ) try : async with httpx . AsyncClient ( transport = transport , timeout = timeout_in_seconds ) as client : response = await client . post ( base_uri , json = data ) try : response . raise_for_status () except Exception : raise RemoteCallError ( f \"Unexpected status code, reason: { response . text } \" ) except Exception as e : raise RemoteCallError from e try : trace = response . json () except Exception : raise RemoteCallError ( f \"JSON parsing failed { response . text } \" , ) try : if model_class is not None : trace [ \"output\" ] = model_class . parse_obj ( trace [ \"output\" ]) return Trace . parse_obj ( trace ) except Exception : raise RemoteCallError ( \"Could not parse Trace\" )","title":"call_remote_great_ai_async()"},{"location":"reference/#ground-truth","text":"","title":"Ground-truth"},{"location":"reference/#great_ai.add_ground_truth","text":"Add training data (with optional train-test splitting). Add and tag data-points, wrap them into traces. The inputs are available via the .input property, while expected_outputs under both the .output and .feedback properties. All generated traces are tagged with ground_truth by default. Additional tags can be also provided. Using the split_ratio arguments, tags can be given randomly with a user-defined distribution. Only the ratio of the splits matter, they don't have to add up to 1. Examples: >>> add_ground_truth ( ... [ 1 , 2 , 3 ], ... [ 'odd' , 'even' , 'odd' ], ... tags = 'my_tag' , ... train_split_ratio = 1 , ... test_split_ratio = 1 , ... validation_split_ratio = 0.5 , ... ) >>> add_ground_truth ( ... [ 1 , 2 ], ... [ 'odd' , 'even' , 'odd' ], ... tags = 'my_tag' , ... train_split_ratio = 1 , ... test_split_ratio = 1 , ... validation_split_ratio = 0.5 , ... ) Traceback (most recent call last): ... AssertionError : The length of the inputs and expected_outputs must be equal Parameters: Name Type Description Default inputs Iterable [ Any ] The inputs. (X in scikit-learn) required expected_outputs Iterable [ T ] The ground-truth values corresponding to the inputs. (y in scikit-learn) required tags Union [ List [ str ], str ] A single tag or a list of tags to append to each generated trace's tags. [] train_split_ratio float The probability-weight of giving each trace the train tag. 1 test_split_ratio float The probability-weight of giving each trace the test tag. 0 validation_split_ratio float The probability-weight of giving each trace the validation tag. 0 Source code in great_ai/tracing/add_ground_truth.py 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 def add_ground_truth ( inputs : Iterable [ Any ], expected_outputs : Iterable [ T ], * , tags : Union [ List [ str ], str ] = [], train_split_ratio : float = 1 , test_split_ratio : float = 0 , validation_split_ratio : float = 0 ) -> None : \"\"\"Add training data (with optional train-test splitting). Add and tag data-points, wrap them into traces. The `inputs` are available via the `.input` property, while `expected_outputs` under both the `.output` and `.feedback` properties. All generated traces are tagged with `ground_truth` by default. Additional tags can be also provided. Using the `split_ratio` arguments, tags can be given randomly with a user-defined distribution. Only the ratio of the splits matter, they don't have to add up to 1. Examples: >>> add_ground_truth( ... [1, 2, 3], ... ['odd', 'even', 'odd'], ... tags='my_tag', ... train_split_ratio=1, ... test_split_ratio=1, ... validation_split_ratio=0.5, ... ) >>> add_ground_truth( ... [1, 2], ... ['odd', 'even', 'odd'], ... tags='my_tag', ... train_split_ratio=1, ... test_split_ratio=1, ... validation_split_ratio=0.5, ... ) Traceback (most recent call last): ... AssertionError: The length of the inputs and expected_outputs must be equal Args: inputs: The inputs. (X in scikit-learn) expected_outputs: The ground-truth values corresponding to the inputs. (y in scikit-learn) tags: A single tag or a list of tags to append to each generated trace's tags. train_split_ratio: The probability-weight of giving each trace the `train` tag. test_split_ratio: The probability-weight of giving each trace the `test` tag. validation_split_ratio: The probability-weight of giving each trace the `validation` tag. \"\"\" inputs = list ( inputs ) expected_outputs = list ( expected_outputs ) assert len ( inputs ) == len ( expected_outputs ), \"The length of the inputs and expected_outputs must be equal\" tags = tags if isinstance ( tags , list ) else [ tags ] sum_ratio = train_split_ratio + test_split_ratio + validation_split_ratio assert sum_ratio > 0 , \"The sum of the split ratios must be a positive number\" train_split_ratio /= sum_ratio test_split_ratio /= sum_ratio validation_split_ratio /= sum_ratio values = list ( zip ( inputs , expected_outputs )) shuffle ( values ) split_tags = ( [ TRAIN_SPLIT_TAG_NAME ] * ceil ( train_split_ratio * len ( inputs )) + [ TEST_SPLIT_TAG_NAME ] * ceil ( test_split_ratio * len ( inputs )) + [ VALIDATION_SPLIT_TAG_NAME ] * ceil ( validation_split_ratio * len ( inputs )) ) shuffle ( split_tags ) created = datetime . utcnow () . isoformat () traces = [ cast ( Trace [ T ], Trace ( # avoid ValueError: \"Trace\" object has no field \"__orig_class__\" trace_id = str ( uuid4 ()), created = created , original_execution_time_ms = 0 , logged_values = X if isinstance ( X , dict ) else { \"input\" : X }, models = [], output = y , feedback = y , exception = None , tags = [ GROUND_TRUTH_TAG_NAME , split_tag , * tags ], ), ) for (( X , y ), split_tag ) in zip ( values , split_tags ) ] get_context () . tracing_database . save_batch ( traces )","title":"add_ground_truth()"},{"location":"reference/#great_ai.query_ground_truth","text":"Return training samples. Combines, filters, and returns data-points that have been either added by add_ground_truth or were the result of a prediction after which their trace got feedback through the RESP API-s /traces/{trace_id}/feedback endpoint (end-to-end feedback). Filtering can be used to only return points matching all given tags (or the single given tag) and by time of creation. Examples: >>> query_ground_truth () [...] Parameters: Name Type Description Default conjunctive_tags Union [ List [ str ], str ] Single tag or a list of tags which the returned traces have to match. The relationship between the tags is conjunctive (AND). [] since Optional [ datetime ] Only return traces created after the given timestamp. None means no filtering. None until Optional [ datetime ] Only return traces created before the given timestamp. None means no filtering. None return_max_count Optional [ int ] Return at-most this many traces. (take, limit) None Source code in great_ai/tracing/query_ground_truth.py 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 def query_ground_truth ( conjunctive_tags : Union [ List [ str ], str ] = [], * , since : Optional [ datetime ] = None , until : Optional [ datetime ] = None , return_max_count : Optional [ int ] = None ) -> List [ Trace ]: \"\"\"Return training samples. Combines, filters, and returns data-points that have been either added by `add_ground_truth` or were the result of a prediction after which their trace got feedback through the RESP API-s `/traces/{trace_id}/feedback` endpoint (end-to-end feedback). Filtering can be used to only return points matching all given tags (or the single given tag) and by time of creation. Examples: >>> query_ground_truth() [...] Args: conjunctive_tags: Single tag or a list of tags which the returned traces have to match. The relationship between the tags is conjunctive (AND). since: Only return traces created after the given timestamp. `None` means no filtering. until: Only return traces created before the given timestamp. `None` means no filtering. return_max_count: Return at-most this many traces. (take, limit) \"\"\" tags = ( conjunctive_tags if isinstance ( conjunctive_tags , list ) else [ conjunctive_tags ] ) db = get_context () . tracing_database items , length = db . query ( conjunctive_tags = tags , since = since , until = until , take = return_max_count , has_feedback = True , ) return items","title":"query_ground_truth()"},{"location":"reference/#great_ai.delete_ground_truth","text":"Delete traces matching the given criteria. Takes the same arguments as query_ground_truth but instead of returning them, it simply deletes them. You can rely on the efficiency of the delete's implementation. Examples: >>> delete_ground_truth ([ 'train' , 'test' , 'validation' ]) Parameters: Name Type Description Default conjunctive_tags Union [ List [ str ], str ] Single tag or a list of tags which the deleted traces have to match. The relationship between the tags is conjunctive (AND). [] since Optional [ datetime ] Only delete traces created after the given timestamp. None means no filtering. None until Optional [ datetime ] Only delete traces created before the given timestamp. None means no filtering. None Source code in great_ai/tracing/delete_ground_truth.py 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 def delete_ground_truth ( conjunctive_tags : Union [ List [ str ], str ] = [], * , since : Optional [ datetime ] = None , until : Optional [ datetime ] = None , ) -> None : \"\"\"Delete traces matching the given criteria. Takes the same arguments as `query_ground_truth` but instead of returning them, it simply deletes them. You can rely on the efficiency of the delete's implementation. Examples: >>> delete_ground_truth(['train', 'test', 'validation']) Args: conjunctive_tags: Single tag or a list of tags which the deleted traces have to match. The relationship between the tags is conjunctive (AND). since: Only delete traces created after the given timestamp. `None` means no filtering. until: Only delete traces created before the given timestamp. `None` means no filtering. \"\"\" tags = ( conjunctive_tags if isinstance ( conjunctive_tags , list ) else [ conjunctive_tags ] ) db = get_context () . tracing_database items , length = db . query ( conjunctive_tags = tags , until = until , since = since , has_feedback = True ) db . delete_batch ([ i . trace_id for i in items ])","title":"delete_ground_truth()"},{"location":"reference/#tracing-databases","text":"","title":"Tracing databases"},{"location":"reference/#great_ai.TracingDatabaseDriver","text":"Bases: ABC Interface expected from a database to be used for storing traces. Source code in great_ai/persistence/tracing_database_driver.py 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 class TracingDatabaseDriver ( ABC ): \"\"\"Interface expected from a database to be used for storing traces.\"\"\" is_production_ready : bool initialized : bool = False @classmethod def configure_credentials_from_file ( cls , secrets : Union [ Path , str , ConfigFile ], ) -> None : if not isinstance ( secrets , ConfigFile ): secrets = ConfigFile ( secrets ) cls . configure_credentials ( ** { k . lower (): v for k , v in secrets . items ()}) @classmethod def configure_credentials ( cls , ) -> None : cls . initialized = True @abstractmethod def save ( self , document : Trace ) -> str : pass @abstractmethod def save_batch ( self , documents : List [ Trace ], ) -> List [ str ]: pass @abstractmethod def get ( self , id : str ) -> Optional [ Trace ]: pass @abstractmethod def query ( self , * , skip : int = 0 , take : Optional [ int ] = None , conjunctive_filters : Sequence [ Filter ] = [], conjunctive_tags : Sequence [ str ] = [], until : Optional [ datetime ] = None , since : Optional [ datetime ] = None , has_feedback : Optional [ bool ] = None , sort_by : Sequence [ SortBy ] = [] ) -> Tuple [ List [ Trace ], int ]: pass @abstractmethod def update ( self , id : str , new_version : Trace ) -> None : pass @abstractmethod def delete ( self , id : str ) -> None : pass @abstractmethod def delete_batch ( self , ids : List [ str ], ) -> None : pass","title":"TracingDatabaseDriver"},{"location":"reference/#great_ai.MongoDbDriver","text":"Bases: TracingDatabaseDriver TracingDatabaseDriver implementation using MongoDB as a backend. A production-ready database driver suitable for efficiently handling semi-structured data. Checkout MongoDB Atlas for a hosted MongoDB solution. Source code in great_ai/persistence/mongodb_driver.py 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 class MongoDbDriver ( TracingDatabaseDriver ): \"\"\"TracingDatabaseDriver implementation using MongoDB as a backend. A production-ready database driver suitable for efficiently handling semi-structured data. Checkout [MongoDB Atlas](https://www.mongodb.com/cloud/atlas/register) for a hosted MongoDB solution. \"\"\" is_production_ready = True mongo_connection_string : str mongo_database : str def __init__ ( self ) -> None : super () . __init__ () if self . mongo_connection_string is None or self . mongo_database is None : raise ValueError ( \"Please configure the MongoDB access options by calling \" \"MongoDbDriver.configure_credentials\" ) with MongoClient [ Any ]( self . mongo_connection_string ) as client : client [ self . mongo_database ] . traces . create_index ( [( \"tags\" , ASCENDING ), ( \"created\" , DESCENDING )], background = True ) @classmethod def configure_credentials ( # type: ignore cls , * , mongo_connection_string : str , mongo_database : str , ** _ : Any , ) -> None : \"\"\"Configure the connection details for MongoDB. Args: mongo_connection_string: For example: 'mongodb://my_user:my_pass@localhost:27017' mongo_database: Name of the database to use. If doesn't exist, it is created and initialised. \"\"\" cls . mongo_connection_string = mongo_connection_string cls . mongo_database = mongo_database super () . configure_credentials () def save ( self , trace : Trace ) -> str : serialized = trace . to_flat_dict () serialized [ \"_id\" ] = trace . trace_id with MongoClient [ Any ]( self . mongo_connection_string ) as client : return client [ self . mongo_database ] . traces . insert_one ( serialized ) . inserted_id def save_batch ( self , documents : List [ Trace ]) -> List [ str ]: serialized = [ d . to_flat_dict () for d in documents ] for s in serialized : s [ \"_id\" ] = s [ \"trace_id\" ] with MongoClient [ Any ]( self . mongo_connection_string ) as client : return ( client [ self . mongo_database ] . traces . insert_many ( serialized , ordered = False ) . inserted_ids ) def get ( self , id : str ) -> Optional [ Trace ]: with MongoClient [ Any ]( self . mongo_connection_string ) as client : value = client [ self . mongo_database ] . traces . find_one ( id ) if value : value = Trace . parse_obj ( value ) return value def _get_operator ( self , filter : Filter ) -> str : if filter . operator == \"contains\" and not isinstance ( filter . value , str ): return operator_mapping [ \"=\" ] return operator_mapping [ filter . operator ] def query ( self , * , skip : int = 0 , take : Optional [ int ] = None , conjunctive_filters : Sequence [ Filter ] = [], conjunctive_tags : Sequence [ str ] = [], since : Optional [ datetime ] = None , until : Optional [ datetime ] = None , has_feedback : Optional [ bool ] = None , sort_by : Sequence [ SortBy ] = [], ) -> Tuple [ List [ Trace ], int ]: query : Dict [ str , Any ] = { \"filter\" : {}, } and_query : List [ Dict [ str , Any ]] = [] and_query . extend ({ \"tags\" : tag } for tag in conjunctive_tags ) and_query . extend ( { f . property : { self . _get_operator ( f ): f . value }} for f in conjunctive_filters ) if not and_query : and_query . append ({}) if since : and_query . append ({ \"created\" : { \"$gte\" : since }}) if until : and_query . append ({ \"created\" : { \"$lte\" : until }}) if has_feedback is not None : and_query . append ( { \"feedback\" : { \"$ne\" : None }} if has_feedback else { \"feedback\" : None } ) query [ \"filter\" ][ \"$and\" ] = and_query with MongoClient [ Any ]( self . mongo_connection_string ) as client : count = client [ self . mongo_database ] . traces . count_documents ( ** query ) if skip : query [ \"skip\" ] = skip if take : query [ \"limit\" ] = take query [ \"sort\" ] = [ ( col . column_id , 1 if col . direction == \"asc\" else - 1 ) for col in sort_by ] with client [ self . mongo_database ] . traces . find ( ** query ) as cursor : documents = [ Trace [ Any ] . parse_obj ( t ) for t in cursor ] return documents , count def update ( self , id : str , new_version : Trace ) -> None : serialized = new_version . to_flat_dict () serialized [ \"_id\" ] = new_version . trace_id with MongoClient [ Any ]( self . mongo_connection_string ) as client : client [ self . mongo_database ] . traces . update_one ({ \"_id\" : id }, serialized ) def delete ( self , id : str ) -> None : with MongoClient [ Any ]( self . mongo_connection_string ) as client : client [ self . mongo_database ] . traces . delete_one ({ \"_id\" : id }) def delete_batch ( self , ids : List [ str ]) -> None : with MongoClient [ Any ]( self . mongo_connection_string ) as client : for c in chunk ( ids , chunk_size = 10000 ): # avoid: 'delete' command document too large delete_filter = { \"_id\" : { \"$in\" : c }} client [ self . mongo_database ] . traces . delete_many ( delete_filter )","title":"MongoDbDriver"},{"location":"reference/#great_ai.persistence.mongodb_driver.MongoDbDriver.configure_credentials","text":"Configure the connection details for MongoDB. Parameters: Name Type Description Default mongo_connection_string str For example: 'mongodb://my_user:my_pass@localhost:27017' required mongo_database str Name of the database to use. If doesn't exist, it is created and initialised. required Source code in great_ai/persistence/mongodb_driver.py 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 @classmethod def configure_credentials ( # type: ignore cls , * , mongo_connection_string : str , mongo_database : str , ** _ : Any , ) -> None : \"\"\"Configure the connection details for MongoDB. Args: mongo_connection_string: For example: 'mongodb://my_user:my_pass@localhost:27017' mongo_database: Name of the database to use. If doesn't exist, it is created and initialised. \"\"\" cls . mongo_connection_string = mongo_connection_string cls . mongo_database = mongo_database super () . configure_credentials ()","title":"configure_credentials()"},{"location":"reference/#great_ai.ParallelTinyDbDriver","text":"Bases: TracingDatabaseDriver TracingDatabaseDriver with TinyDB as a backend. Saves the database as a JSON into a single file. Highly inefficient on inserting, not advised for production use. A multiprocessing lock protects the database file to avoid parallelisation issues. Source code in great_ai/persistence/parallel_tinydb_driver.py 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 class ParallelTinyDbDriver ( TracingDatabaseDriver ): \"\"\"TracingDatabaseDriver with TinyDB as a backend. Saves the database as a JSON into a single file. Highly inefficient on inserting, not advised for production use. A multiprocessing lock protects the database file to avoid parallelisation issues. \"\"\" is_production_ready = False path_to_db = Path ( DEFAULT_TRACING_DB_FILENAME ) def save ( self , trace : Trace ) -> str : return self . _safe_execute ( lambda db : db . insert ( trace . dict ())) def save_batch ( self , documents : List [ Trace ]) -> List [ str ]: traces = [ d . dict () for d in documents ] return self . _safe_execute ( lambda db : db . insert_multiple ( traces )) def get ( self , id : str ) -> Optional [ Trace ]: value = self . _safe_execute ( lambda db : db . get ( lambda d : d [ \"trace_id\" ] == id )) if value : value = Trace . parse_obj ( value ) return value def query ( self , * , skip : int = 0 , take : Optional [ int ] = None , conjunctive_filters : Sequence [ Filter ] = [], conjunctive_tags : Sequence [ str ] = [], since : Optional [ datetime ] = None , until : Optional [ datetime ] = None , has_feedback : Optional [ bool ] = None , sort_by : Sequence [ SortBy ] = [] ) -> Tuple [ List [ Trace ], int ]: def does_match ( d : Dict [ str , Any ]) -> bool : return ( not set ( conjunctive_tags ) - set ( d [ \"tags\" ]) and ( since is None or datetime . fromisoformat ( d [ \"created\" ]) >= since ) and ( until is None or datetime . fromisoformat ( d [ \"created\" ]) <= until ) and ( has_feedback is None or has_feedback == ( d [ \"feedback\" ] is not None ) ) ) documents : List [ Trace ] = [ Trace . parse_obj ( t ) for t in self . _safe_execute ( lambda db : db . search ( does_match )) ] if not documents : return [], 0 df = pd . DataFrame ([ d . to_flat_dict () for d in documents ]) for f in conjunctive_filters : operator = f . operator . lower () if operator in operator_mapping : df = df . loc [ getattr ( df [ f . property ], operator_mapping [ f . operator ])( f . value ) ] elif operator == \"contains\" : df = df . loc [ df [ f . property ] . str . contains ( f . value , case = False )] if sort_by : df . sort_values ( [ col . column_id for col in sort_by ], ascending = [ col . direction == \"asc\" for col in sort_by ], inplace = True , ) count = len ( df ) result = df . iloc [ skip :] if take is None else df . iloc [ skip : skip + take ] return [ next ( d for d in documents if d . trace_id == trace_id ) for trace_id in result [ \"trace_id\" ] ], count def update ( self , id : str , new_version : Trace ) -> None : self . _safe_execute ( lambda db : db . update ( new_version . dict (), lambda d : d [ \"trace_id\" ] == id ) ) def delete ( self , id : str ) -> None : self . _safe_execute ( lambda db : db . remove ( lambda d : d [ \"trace_id\" ] == id )) def delete_batch ( self , ids : List [ str ]) -> None : for i in ids : self . delete ( i ) def _safe_execute ( self , func : Callable [[ TinyDB ], Any ]) -> Any : with lock : with TinyDB ( self . path_to_db ) as db : return func ( db )","title":"ParallelTinyDbDriver"},{"location":"reference/large-file/","text":"LargeFile # from great_ai.large_file import * For more details about using LargeFiles, check out the how to guide . LargeFileLocal # Bases: LargeFileBase LargeFile implementation using local filesystem as a backend. Store large files remotely using the familiar API of open() . With built-in versioning, pruning and local cache. IMPORTANT: If LargeFileLocal.max_cache_size is too small, it won't be enough to store all your files and they can end up deleted. See parent for more details. Examples: >>> LargeFileLocal . cache_path = Path ( \".cache\" ) >>> LargeFileLocal . max_cache_size = \"30GB\" >>> with LargeFileLocal ( \"my_test.txt\" , \"w\" , keep_last_n = 2 ) as f : ... f . write ( 'test' ) 4 >>> with LargeFileLocal ( \"my_test.txt\" ) as f : ... print ( f . read ()) test Source code in great_ai/large_file/large_file/large_file_local.py 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 class LargeFileLocal ( LargeFileBase ): \"\"\"LargeFile implementation using local filesystem as a backend. Store large files remotely using the familiar API of `open()`. With built-in versioning, pruning and local cache. IMPORTANT: If LargeFileLocal.max_cache_size is too small, it won't be enough to store all your files and they can end up deleted. See parent for more details. Examples: >>> LargeFileLocal.cache_path = Path(\".cache\") >>> LargeFileLocal.max_cache_size = \"30GB\" >>> with LargeFileLocal(\"my_test.txt\", \"w\", keep_last_n=2) as f: ... f.write('test') 4 >>> with LargeFileLocal(\"my_test.txt\") as f: ... print(f.read()) test \"\"\" def __init__ ( self , name : str , mode : str = \"r\" , * , buffering : int = - 1 , encoding : Optional [ str ] = None , errors : Optional [ str ] = None , newline : Optional [ str ] = None , version : Optional [ int ] = None , keep_last_n : Optional [ int ] = None , ): super () . __init__ ( name , mode , buffering = buffering , encoding = encoding , errors = errors , newline = newline , version = version , keep_last_n = keep_last_n , cache_only_mode = True , ) super () . configure_credentials () def _find_remote_instances ( self ) -> List [ DataInstance ]: return [] def _download ( self , remote_path : Any , local_path : Path , hide_progress : bool ) -> None : # This will never be called because the file must be in the cache raise NotImplementedError () def _upload ( self , local_path : Path , hide_progress : bool ) -> None : pass # the \"upload\" is already done py the parent's caching mechanism def _delete_old_remote_versions ( self ) -> None : if self . _keep_last_n is not None : for i in ( self . _instances [: - self . _keep_last_n ] if self . _keep_last_n > 0 else self . _instances ): logger . info ( f \"Removing old version (keep_last_n= { self . _keep_last_n } ): { i . remote_path } \" ) i . remote_path . unlink () LargeFileS3 # Bases: LargeFileBase LargeFile implementation using S3-compatible storage as a backend. Store large files remotely using the familiar API of open() . With built-in versioning, pruning and local cache. See parent for more details. Source code in great_ai/large_file/large_file/large_file_s3.py 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 class LargeFileS3 ( LargeFileBase ): \"\"\"LargeFile implementation using S3-compatible storage as a backend. Store large files remotely using the familiar API of `open()`. With built-in versioning, pruning and local cache. See parent for more details. \"\"\" region_name = None access_key_id = None secret_access_key = None bucket_name = None endpoint_url = None @classmethod def configure_credentials ( # type: ignore cls , * , aws_region_name : str , aws_access_key_id : str , aws_secret_access_key : str , large_files_bucket_name : str , aws_endpoint_url : Optional [ str ] = None , ** _ : Any , ) -> None : cls . region_name = aws_region_name cls . access_key_id = aws_access_key_id cls . secret_access_key = aws_secret_access_key cls . bucket_name = large_files_bucket_name cls . endpoint_url = aws_endpoint_url super () . configure_credentials () @cached_property def _client ( self ) -> boto3 . client : if ( self . region_name is None or self . access_key_id is None or self . secret_access_key is None or self . bucket_name is None ): raise ValueError ( \"Please configure the S3 access options by calling LargeFileS3.configure_credentials or set offline_mode=True in the constructor.\" ) return boto3 . client ( \"s3\" , aws_access_key_id = self . access_key_id , aws_secret_access_key = self . secret_access_key , region_name = self . region_name , endpoint_url = self . endpoint_url , ) def _find_remote_instances ( self ) -> List [ DataInstance ]: logger . debug ( f \"Fetching S3 versions of { self . _name } \" ) found_objects = self . _client . list_objects_v2 ( Bucket = self . bucket_name , Prefix = self . _name ) return ( [ DataInstance ( name = o [ \"Key\" ] . split ( S3_NAME_VERSION_SEPARATOR )[ 0 ], version = int ( o [ \"Key\" ] . split ( S3_NAME_VERSION_SEPARATOR )[ - 1 ]), remote_path = o [ \"Key\" ], ) for o in found_objects [ \"Contents\" ] if o [ \"Key\" ] . split ( S3_NAME_VERSION_SEPARATOR )[ 0 ] == self . _name ] if \"Contents\" in found_objects else [] ) def _download ( self , remote_path : Any , local_path : Path , hide_progress : bool ) -> None : logger . info ( f \"Downloading { remote_path } from S3\" ) size = self . _client . head_object ( Bucket = self . bucket_name , Key = remote_path )[ \"ContentLength\" ] self . _client . download_file ( Bucket = self . bucket_name , Key = remote_path , Filename = str ( local_path ), Callback = None if hide_progress else DownloadProgressBar ( name = str ( remote_path ), size = size , logger = logger ), ) def _upload ( self , local_path : Path , hide_progress : bool ) -> None : key = f \" { self . _name } / { self . version } \" logger . info ( f \"Uploading { self . _local_name } to S3 as { key } \" ) self . _client . upload_file ( Filename = str ( local_path ), Bucket = self . bucket_name , Key = key , Callback = None if hide_progress else UploadProgressBar ( path = local_path , logger = logger ), ) def _delete_old_remote_versions ( self ) -> None : if self . _keep_last_n is not None : for i in ( self . _instances [: - self . _keep_last_n ] if self . _keep_last_n > 0 else self . _instances ): logger . info ( f \"Removing old version from S3 (keep_last_n= { self . _keep_last_n } ): { i . remote_path } \" ) self . _client . delete_object ( Bucket = self . bucket_name , Key = i . remote_path ) LargeFileMongo # Bases: LargeFileBase LargeFile implementation using GridFS (MongoDB) as a backend. Store large files remotely using the familiar API of open() . With built-in versioning, pruning and local cache. See parent for more details. Source code in great_ai/large_file/large_file/large_file_mongo.py 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 class LargeFileMongo ( LargeFileBase ): \"\"\"LargeFile implementation using GridFS (MongoDB) as a backend. Store large files remotely using the familiar API of `open()`. With built-in versioning, pruning and local cache. See parent for more details. \"\"\" mongo_connection_string = None mongo_database = None @classmethod def configure_credentials ( # type: ignore cls , * , mongo_connection_string : str , mongo_database : str , ** _ : Any , ) -> None : cls . mongo_connection_string = mongo_connection_string cls . mongo_database = mongo_database super () . configure_credentials () @cached_property def _client ( self ) -> GridFSBucket : if self . mongo_connection_string is None or self . mongo_database is None : raise ValueError ( \"Please configure the MongoDB access options by calling LargeFileMongo.configure_credentials or set offline_mode=True in the constructor.\" ) db : Database = MongoClient ( self . mongo_connection_string )[ self . mongo_database ] return GridFSBucket ( db ) def _find_remote_instances ( self ) -> List [ DataInstance ]: logger . debug ( f \"Fetching Mongo (GridFS) versions of { self . _name } \" ) return [ DataInstance ( name = MONGO_NAME_VERSION_SEPARATOR . join ( f . name . split ( MONGO_NAME_VERSION_SEPARATOR )[: - 1 ] ), version = int ( f . name . split ( MONGO_NAME_VERSION_SEPARATOR )[ - 1 ]), remote_path = ( f . _id , f . length ), origin = \"mongodb\" , ) for f in self . _client . find ( { \"filename\" : re . compile ( re . escape ( self . _name + MONGO_NAME_VERSION_SEPARATOR ) + \".*\" ) } ) ] def _download ( self , remote_path : Any , local_path : Path , hide_progress : bool ) -> None : logger . info ( f \"Downloading { remote_path [ 0 ] } from Mongo (GridFS)\" ) progress = ( DownloadProgressBar ( name = str ( remote_path [ 0 ]), size = remote_path [ 1 ], logger = logger ) if not hide_progress else None ) with self . _client . open_download_stream ( remote_path [ 0 ]) as stream : with open ( local_path , \"wb\" ) as f : while True : content = stream . read ( DEFAULT_CHUNK_SIZE ) f . write ( content ) if progress : progress ( len ( content )) if len ( content ) < DEFAULT_CHUNK_SIZE : break def _upload ( self , local_path : Path , hide_progress : bool ) -> None : logger . info ( f \"Uploading { local_path } to Mongo (GridFS)\" ) progress = ( UploadProgressBar ( path = local_path , logger = logger ) if not hide_progress else None ) with self . _client . open_upload_stream ( f \" { self . _name }{ MONGO_NAME_VERSION_SEPARATOR }{ self . version } \" ) as stream : with open ( local_path , \"rb\" ) as f : while True : content = f . read ( DEFAULT_CHUNK_SIZE ) stream . write ( content ) if progress : progress ( len ( content )) if len ( content ) < DEFAULT_CHUNK_SIZE : break def _delete_old_remote_versions ( self ) -> None : if self . _keep_last_n is not None : for i in ( self . _instances [: - self . _keep_last_n ] if self . _keep_last_n > 0 else self . _instances ): logger . info ( f \"Removing old version from MongoDB (GridFS) (keep_last_n= { self . _keep_last_n } ): { i . name }{ MONGO_NAME_VERSION_SEPARATOR }{ i . version } \" ) self . _client . delete ( i . remote_path [ 0 ]) LargeFileBase # Bases: ABC Base for LargeFile implementations with different backends. Store large files remotely using the familiar API of open() . With built-in versioning, pruning and local cache. By default, files are stored in the \".cache\" folder and the least recently used is deleted after the overall size reaches 30 GBs. Examples: >>> LargeFileBase . cache_path = Path ( \".cache\" ) >>> LargeFileBase . max_cache_size = \"30GB\" Attributes: Name Type Description initialized Tell whether configure_credentials or configure_credentials_from_file has been already called. cache_path Storage location for cached files. max_cache_size Optional [ str ] Delete files until the folder at cache_path is smaller than this value. Examples: \"5 GB\", \"10MB\", \"0.3 TB\". Set to None for no automatic cache-pruning. Source code in great_ai/large_file/large_file/large_file_base.py 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 class LargeFileBase ( ABC ): \"\"\"Base for LargeFile implementations with different backends. Store large files remotely using the familiar API of `open()`. With built-in versioning, pruning and local cache. By default, files are stored in the \".cache\" folder and the least recently used is deleted after the overall size reaches 30 GBs. Examples: >>> LargeFileBase.cache_path = Path(\".cache\") >>> LargeFileBase.max_cache_size = \"30GB\" Attributes: initialized: Tell whether `configure_credentials` or `configure_credentials_from_file` has been already called. cache_path: Storage location for cached files. max_cache_size: Delete files until the folder at `cache_path` is smaller than this value. Examples: \"5 GB\", \"10MB\", \"0.3 TB\". Set to `None` for no automatic cache-pruning. \"\"\" initialized = False cache_path = Path ( \".cache\" ) max_cache_size : Optional [ str ] = \"30GB\" def __init__ ( self , name : str , mode : str = \"r\" , * , buffering : int = - 1 , encoding : Optional [ str ] = None , errors : Optional [ str ] = None , newline : Optional [ str ] = None , version : Optional [ int ] = None , keep_last_n : Optional [ int ] = None , cache_only_mode : bool = False , ): self . _name = name self . _version = version self . _mode = mode self . _keep_last_n = keep_last_n self . _cache_only_mode = cache_only_mode self . _buffering = buffering self . _encoding = encoding if errors is not None and sys . version_info [ 1 ] < 8 : raise RuntimeError ( \"The `errors` kwarg is only supported in 3.8 <= Python versions.\" ) self . _errors = errors self . _newline = newline LargeFileBase . cache_path . mkdir ( parents = True , exist_ok = True ) self . _find_instances () self . _check_mode_and_set_version () @classmethod def configure_credentials_from_file ( cls , secrets : Union [ Path , str , ConfigFile ], ) -> None : \"\"\"Load file and feed its content to `configure_credentials`. Extra keys are ignored. \"\"\" if not isinstance ( secrets , ConfigFile ): secrets = ConfigFile ( secrets ) cls . configure_credentials ( ** { k . lower (): v for k , v in secrets . items ()}) @classmethod def configure_credentials ( cls , ** kwargs : str ) -> None : \"\"\"Configure required credentials for the LargeFile backend.\"\"\" cls . initialized = True def __enter__ ( self ) -> IO : params = dict ( mode = self . _mode , buffering = self . _buffering , encoding = self . _encoding , newline = self . _newline , delete = False , prefix = \"large_file-\" , ) if sys . version_info [ 1 ] >= 8 : params [ \"errors\" ] = self . _errors self . _file : IO [ Any ] = ( tempfile . NamedTemporaryFile ( ** params ) # type: ignore if \"w\" in self . _mode else open ( self . get (), mode = self . _mode , buffering = self . _buffering , encoding = self . _encoding , newline = self . _newline , errors = self . _errors , ) ) return self . _file def __exit__ ( self , type : Optional [ Type [ BaseException ]], exc : Optional [ BaseException ], traceback : Optional [ TracebackType ], ) -> Literal [ False ]: self . _file . close () if type is None : if \"w\" in self . _mode : self . push ( Path ( self . _file . name )) os . unlink ( self . _file . name ) else : logger . exception ( \"Could not finish operation.\" ) return False @property def version ( self ) -> int : \"\"\"Numeric version of the file proxied by this LargeFile instance.\"\"\" return cast ( int , self . _version ) @lru_cache ( 1 ) def get ( self , hide_progress : bool = False ) -> Path : \"\"\"Return path to the proxy of a file (or directory). If not available in the local cache, an attempt is made to download it. Args: hide_progress: Do not show a progress update after each 10% of progress. \"\"\" remote_path = next ( i . remote_path for i in self . _instances if i . version == self . _version ) destination = self . cache_path / self . _local_name if not destination . exists (): logger . info ( f \"File { self . _local_name } does not exist locally\" ) with tempfile . TemporaryDirectory () as tmp : local_root_path = Path ( tmp ) tmp_file_archive = ( local_root_path / f \" { self . _local_name }{ ARCHIVE_EXTENSION } \" ) self . _download ( remote_path , tmp_file_archive , hide_progress = hide_progress ) logger . info ( f \"Decompressing { self . _local_name } \" ) shutil . unpack_archive ( str ( tmp_file_archive ), tmp , COMPRESSION_ALGORITHM ) shutil . move ( str ( local_root_path / self . _local_name ), str ( destination )) else : logger . info ( f \"File { self . _local_name } found in cache\" ) return destination def push ( self , path : Union [ Path , str ], hide_progress : bool = False ) -> None : \"\"\"Upload a file (or directory) as a new version of `key`. The file/directory is compressed before upload. Args: hide_progress: Do not show a progress update after each 10% of progress. \"\"\" if isinstance ( path , str ): path = Path ( path ) with tempfile . TemporaryDirectory () as tmp : if path . is_file (): logger . info ( f \"Copying file for { self . _local_name } \" ) copy : Any = shutil . copy else : logger . info ( f \"Copying directory for { self . _local_name } \" ) copy = shutil . copytree try : # Make local copy in the cache shutil . rmtree ( self . cache_path / self . _local_name , ignore_errors = True ) copy ( str ( path ), str ( self . cache_path / self . _local_name )) except shutil . SameFileError : pass # No worries copy ( str ( path ), str ( Path ( tmp ) / self . _local_name )) with tempfile . TemporaryDirectory () as tmp2 : # A directory has to be zipped and it cannot contain the output of the zipping logger . info ( f \"Compressing { self . _local_name } \" ) shutil . make_archive ( str ( Path ( tmp2 ) / self . _local_name ), COMPRESSION_ALGORITHM , tmp , ) file_to_be_uploaded = ( Path ( tmp2 ) / f \" { self . _local_name }{ ARCHIVE_EXTENSION } \" ) self . _upload ( file_to_be_uploaded , hide_progress = hide_progress ) self . clean_up () def delete ( self ) -> None : \"\"\"Delete all versions of the files under this `key`.\"\"\" self . _keep_last_n = 0 self . _delete_old_remote_versions () @property def versions_pretty ( self ) -> str : \"\"\"Formatted string of all available versions.\"\"\" return \", \" . join (( str ( i . version ) for i in self . _instances )) def clean_up ( self ) -> None : \"\"\"Delete local and remote versions according to currently set cache and retention policy.\"\"\" self . _delete_old_remote_versions () self . _prune_cache () @property def _local_name ( self ) -> str : return f \" { self . _name }{ CACHE_NAME_VERSION_SEPARATOR }{ self . version } \" def _find_instances ( self ) -> None : if self . _cache_only_mode : self . _instances = self . _find_instances_from_cache () else : self . _instances = self . _find_remote_instances () self . _instances = sorted ( self . _instances , key = lambda i : i . version ) def _find_instances_from_cache ( self ) -> List [ DataInstance ]: logger . info ( f \"Fetching cached versions of { self . _name } \" ) candidates = [ DataInstance ( name = CACHE_NAME_VERSION_SEPARATOR . join ( f . name . split ( CACHE_NAME_VERSION_SEPARATOR )[: - 1 ] ), version = int ( f . name . split ( CACHE_NAME_VERSION_SEPARATOR )[ - 1 ]), remote_path = f , ) for f in self . cache_path . glob ( f \" { self . _name }{ CACHE_NAME_VERSION_SEPARATOR } *\" ) ] return [ c for c in candidates if c . name == self . _name ] def _check_mode_and_set_version ( self ) -> None : if \"+\" in self . _mode : raise ValueError ( f \"File mode ` { self . _mode } ` is not allowed3, remove the `+`.\" ) if \"w\" in self . _mode : if self . _version is not None : raise ValueError ( \"Providing a version is not allowed in write mode.\" ) self . _version = self . _instances [ - 1 ] . version + 1 if self . _instances else 0 elif \"r\" in self . _mode : if not self . _instances : raise FileNotFoundError ( f \"File { self . _name } not found. No versions are available.\" ) if self . _version is None : self . _version = self . _instances [ - 1 ] . version logger . info ( f \"Latest version of { self . _name } is { self . _version } \" + f \"(from versions: { self . versions_pretty } )\" ) elif self . _version not in [ i . version for i in self . _instances ]: raise FileNotFoundError ( f \"File { self . _name } not found with version { self . _version } . \" + f \"(from versions: { self . versions_pretty } )\" ) else : raise ValueError ( \"Unsupported file mode.\" ) def _prune_cache ( self ) -> None : self . cache_path . mkdir ( parents = True , exist_ok = True ) if self . max_cache_size is None : return allowed_size = human_readable_to_byte ( self . max_cache_size ) assert allowed_size >= 0 least_recently_read = sorted ( [ f for f in self . cache_path . glob ( \"*\" )], key = lambda f : f . stat () . st_atime ) while sum ( os . path . getsize ( f ) for f in least_recently_read ) > allowed_size : file = least_recently_read . pop ( 0 ) logger . info ( f \"Deleting file from cache to meet quota (max_cache_size= { self . max_cache_size } ): { file } \" ) os . unlink ( file ) @abstractmethod def _find_remote_instances ( self ) -> List [ DataInstance ]: pass @abstractmethod def _download ( self , remote_path : Any , local_path : Path , hide_progress : bool ) -> None : pass @abstractmethod def _upload ( self , local_path : Path , hide_progress : bool ) -> None : pass @abstractmethod def _delete_old_remote_versions ( self ) -> None : pass clean_up () # Delete local and remote versions according to currently set cache and retention policy. Source code in great_ai/large_file/large_file/large_file_base.py 250 251 252 253 254 def clean_up ( self ) -> None : \"\"\"Delete local and remote versions according to currently set cache and retention policy.\"\"\" self . _delete_old_remote_versions () self . _prune_cache () configure_credentials ( ** kwargs ) classmethod # Configure required credentials for the LargeFile backend. Source code in great_ai/large_file/large_file/large_file_base.py 102 103 104 105 106 @classmethod def configure_credentials ( cls , ** kwargs : str ) -> None : \"\"\"Configure required credentials for the LargeFile backend.\"\"\" cls . initialized = True configure_credentials_from_file ( secrets ) classmethod # Load file and feed its content to configure_credentials . Extra keys are ignored. Source code in great_ai/large_file/large_file/large_file_base.py 88 89 90 91 92 93 94 95 96 97 98 99 100 @classmethod def configure_credentials_from_file ( cls , secrets : Union [ Path , str , ConfigFile ], ) -> None : \"\"\"Load file and feed its content to `configure_credentials`. Extra keys are ignored. \"\"\" if not isinstance ( secrets , ConfigFile ): secrets = ConfigFile ( secrets ) cls . configure_credentials ( ** { k . lower (): v for k , v in secrets . items ()}) delete () # Delete all versions of the files under this key . Source code in great_ai/large_file/large_file/large_file_base.py 239 240 241 242 243 def delete ( self ) -> None : \"\"\"Delete all versions of the files under this `key`.\"\"\" self . _keep_last_n = 0 self . _delete_old_remote_versions () get ( hide_progress = False ) cached # Return path to the proxy of a file (or directory). If not available in the local cache, an attempt is made to download it. Parameters: Name Type Description Default hide_progress bool Do not show a progress update after each 10% of progress. False Source code in great_ai/large_file/large_file/large_file_base.py 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 @lru_cache ( 1 ) def get ( self , hide_progress : bool = False ) -> Path : \"\"\"Return path to the proxy of a file (or directory). If not available in the local cache, an attempt is made to download it. Args: hide_progress: Do not show a progress update after each 10% of progress. \"\"\" remote_path = next ( i . remote_path for i in self . _instances if i . version == self . _version ) destination = self . cache_path / self . _local_name if not destination . exists (): logger . info ( f \"File { self . _local_name } does not exist locally\" ) with tempfile . TemporaryDirectory () as tmp : local_root_path = Path ( tmp ) tmp_file_archive = ( local_root_path / f \" { self . _local_name }{ ARCHIVE_EXTENSION } \" ) self . _download ( remote_path , tmp_file_archive , hide_progress = hide_progress ) logger . info ( f \"Decompressing { self . _local_name } \" ) shutil . unpack_archive ( str ( tmp_file_archive ), tmp , COMPRESSION_ALGORITHM ) shutil . move ( str ( local_root_path / self . _local_name ), str ( destination )) else : logger . info ( f \"File { self . _local_name } found in cache\" ) return destination push ( path , hide_progress = False ) # Upload a file (or directory) as a new version of key . The file/directory is compressed before upload. Parameters: Name Type Description Default hide_progress bool Do not show a progress update after each 10% of progress. False Source code in great_ai/large_file/large_file/large_file_base.py 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 def push ( self , path : Union [ Path , str ], hide_progress : bool = False ) -> None : \"\"\"Upload a file (or directory) as a new version of `key`. The file/directory is compressed before upload. Args: hide_progress: Do not show a progress update after each 10% of progress. \"\"\" if isinstance ( path , str ): path = Path ( path ) with tempfile . TemporaryDirectory () as tmp : if path . is_file (): logger . info ( f \"Copying file for { self . _local_name } \" ) copy : Any = shutil . copy else : logger . info ( f \"Copying directory for { self . _local_name } \" ) copy = shutil . copytree try : # Make local copy in the cache shutil . rmtree ( self . cache_path / self . _local_name , ignore_errors = True ) copy ( str ( path ), str ( self . cache_path / self . _local_name )) except shutil . SameFileError : pass # No worries copy ( str ( path ), str ( Path ( tmp ) / self . _local_name )) with tempfile . TemporaryDirectory () as tmp2 : # A directory has to be zipped and it cannot contain the output of the zipping logger . info ( f \"Compressing { self . _local_name } \" ) shutil . make_archive ( str ( Path ( tmp2 ) / self . _local_name ), COMPRESSION_ALGORITHM , tmp , ) file_to_be_uploaded = ( Path ( tmp2 ) / f \" { self . _local_name }{ ARCHIVE_EXTENSION } \" ) self . _upload ( file_to_be_uploaded , hide_progress = hide_progress ) self . clean_up () version () property # Numeric version of the file proxied by this LargeFile instance. Source code in great_ai/large_file/large_file/large_file_base.py 153 154 155 156 157 @property def version ( self ) -> int : \"\"\"Numeric version of the file proxied by this LargeFile instance.\"\"\" return cast ( int , self . _version ) versions_pretty () property # Formatted string of all available versions. Source code in great_ai/large_file/large_file/large_file_base.py 245 246 247 248 @property def versions_pretty ( self ) -> str : \"\"\"Formatted string of all available versions.\"\"\" return \", \" . join (( str ( i . version ) for i in self . _instances ))","title":"LargeFile"},{"location":"reference/large-file/#largefile","text":"from great_ai.large_file import * For more details about using LargeFiles, check out the how to guide .","title":"LargeFile"},{"location":"reference/large-file/#great_ai.large_file.LargeFileLocal","text":"Bases: LargeFileBase LargeFile implementation using local filesystem as a backend. Store large files remotely using the familiar API of open() . With built-in versioning, pruning and local cache. IMPORTANT: If LargeFileLocal.max_cache_size is too small, it won't be enough to store all your files and they can end up deleted. See parent for more details. Examples: >>> LargeFileLocal . cache_path = Path ( \".cache\" ) >>> LargeFileLocal . max_cache_size = \"30GB\" >>> with LargeFileLocal ( \"my_test.txt\" , \"w\" , keep_last_n = 2 ) as f : ... f . write ( 'test' ) 4 >>> with LargeFileLocal ( \"my_test.txt\" ) as f : ... print ( f . read ()) test Source code in great_ai/large_file/large_file/large_file_local.py 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 class LargeFileLocal ( LargeFileBase ): \"\"\"LargeFile implementation using local filesystem as a backend. Store large files remotely using the familiar API of `open()`. With built-in versioning, pruning and local cache. IMPORTANT: If LargeFileLocal.max_cache_size is too small, it won't be enough to store all your files and they can end up deleted. See parent for more details. Examples: >>> LargeFileLocal.cache_path = Path(\".cache\") >>> LargeFileLocal.max_cache_size = \"30GB\" >>> with LargeFileLocal(\"my_test.txt\", \"w\", keep_last_n=2) as f: ... f.write('test') 4 >>> with LargeFileLocal(\"my_test.txt\") as f: ... print(f.read()) test \"\"\" def __init__ ( self , name : str , mode : str = \"r\" , * , buffering : int = - 1 , encoding : Optional [ str ] = None , errors : Optional [ str ] = None , newline : Optional [ str ] = None , version : Optional [ int ] = None , keep_last_n : Optional [ int ] = None , ): super () . __init__ ( name , mode , buffering = buffering , encoding = encoding , errors = errors , newline = newline , version = version , keep_last_n = keep_last_n , cache_only_mode = True , ) super () . configure_credentials () def _find_remote_instances ( self ) -> List [ DataInstance ]: return [] def _download ( self , remote_path : Any , local_path : Path , hide_progress : bool ) -> None : # This will never be called because the file must be in the cache raise NotImplementedError () def _upload ( self , local_path : Path , hide_progress : bool ) -> None : pass # the \"upload\" is already done py the parent's caching mechanism def _delete_old_remote_versions ( self ) -> None : if self . _keep_last_n is not None : for i in ( self . _instances [: - self . _keep_last_n ] if self . _keep_last_n > 0 else self . _instances ): logger . info ( f \"Removing old version (keep_last_n= { self . _keep_last_n } ): { i . remote_path } \" ) i . remote_path . unlink ()","title":"LargeFileLocal"},{"location":"reference/large-file/#great_ai.large_file.LargeFileS3","text":"Bases: LargeFileBase LargeFile implementation using S3-compatible storage as a backend. Store large files remotely using the familiar API of open() . With built-in versioning, pruning and local cache. See parent for more details. Source code in great_ai/large_file/large_file/large_file_s3.py 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 class LargeFileS3 ( LargeFileBase ): \"\"\"LargeFile implementation using S3-compatible storage as a backend. Store large files remotely using the familiar API of `open()`. With built-in versioning, pruning and local cache. See parent for more details. \"\"\" region_name = None access_key_id = None secret_access_key = None bucket_name = None endpoint_url = None @classmethod def configure_credentials ( # type: ignore cls , * , aws_region_name : str , aws_access_key_id : str , aws_secret_access_key : str , large_files_bucket_name : str , aws_endpoint_url : Optional [ str ] = None , ** _ : Any , ) -> None : cls . region_name = aws_region_name cls . access_key_id = aws_access_key_id cls . secret_access_key = aws_secret_access_key cls . bucket_name = large_files_bucket_name cls . endpoint_url = aws_endpoint_url super () . configure_credentials () @cached_property def _client ( self ) -> boto3 . client : if ( self . region_name is None or self . access_key_id is None or self . secret_access_key is None or self . bucket_name is None ): raise ValueError ( \"Please configure the S3 access options by calling LargeFileS3.configure_credentials or set offline_mode=True in the constructor.\" ) return boto3 . client ( \"s3\" , aws_access_key_id = self . access_key_id , aws_secret_access_key = self . secret_access_key , region_name = self . region_name , endpoint_url = self . endpoint_url , ) def _find_remote_instances ( self ) -> List [ DataInstance ]: logger . debug ( f \"Fetching S3 versions of { self . _name } \" ) found_objects = self . _client . list_objects_v2 ( Bucket = self . bucket_name , Prefix = self . _name ) return ( [ DataInstance ( name = o [ \"Key\" ] . split ( S3_NAME_VERSION_SEPARATOR )[ 0 ], version = int ( o [ \"Key\" ] . split ( S3_NAME_VERSION_SEPARATOR )[ - 1 ]), remote_path = o [ \"Key\" ], ) for o in found_objects [ \"Contents\" ] if o [ \"Key\" ] . split ( S3_NAME_VERSION_SEPARATOR )[ 0 ] == self . _name ] if \"Contents\" in found_objects else [] ) def _download ( self , remote_path : Any , local_path : Path , hide_progress : bool ) -> None : logger . info ( f \"Downloading { remote_path } from S3\" ) size = self . _client . head_object ( Bucket = self . bucket_name , Key = remote_path )[ \"ContentLength\" ] self . _client . download_file ( Bucket = self . bucket_name , Key = remote_path , Filename = str ( local_path ), Callback = None if hide_progress else DownloadProgressBar ( name = str ( remote_path ), size = size , logger = logger ), ) def _upload ( self , local_path : Path , hide_progress : bool ) -> None : key = f \" { self . _name } / { self . version } \" logger . info ( f \"Uploading { self . _local_name } to S3 as { key } \" ) self . _client . upload_file ( Filename = str ( local_path ), Bucket = self . bucket_name , Key = key , Callback = None if hide_progress else UploadProgressBar ( path = local_path , logger = logger ), ) def _delete_old_remote_versions ( self ) -> None : if self . _keep_last_n is not None : for i in ( self . _instances [: - self . _keep_last_n ] if self . _keep_last_n > 0 else self . _instances ): logger . info ( f \"Removing old version from S3 (keep_last_n= { self . _keep_last_n } ): { i . remote_path } \" ) self . _client . delete_object ( Bucket = self . bucket_name , Key = i . remote_path )","title":"LargeFileS3"},{"location":"reference/large-file/#great_ai.large_file.LargeFileMongo","text":"Bases: LargeFileBase LargeFile implementation using GridFS (MongoDB) as a backend. Store large files remotely using the familiar API of open() . With built-in versioning, pruning and local cache. See parent for more details. Source code in great_ai/large_file/large_file/large_file_mongo.py 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 class LargeFileMongo ( LargeFileBase ): \"\"\"LargeFile implementation using GridFS (MongoDB) as a backend. Store large files remotely using the familiar API of `open()`. With built-in versioning, pruning and local cache. See parent for more details. \"\"\" mongo_connection_string = None mongo_database = None @classmethod def configure_credentials ( # type: ignore cls , * , mongo_connection_string : str , mongo_database : str , ** _ : Any , ) -> None : cls . mongo_connection_string = mongo_connection_string cls . mongo_database = mongo_database super () . configure_credentials () @cached_property def _client ( self ) -> GridFSBucket : if self . mongo_connection_string is None or self . mongo_database is None : raise ValueError ( \"Please configure the MongoDB access options by calling LargeFileMongo.configure_credentials or set offline_mode=True in the constructor.\" ) db : Database = MongoClient ( self . mongo_connection_string )[ self . mongo_database ] return GridFSBucket ( db ) def _find_remote_instances ( self ) -> List [ DataInstance ]: logger . debug ( f \"Fetching Mongo (GridFS) versions of { self . _name } \" ) return [ DataInstance ( name = MONGO_NAME_VERSION_SEPARATOR . join ( f . name . split ( MONGO_NAME_VERSION_SEPARATOR )[: - 1 ] ), version = int ( f . name . split ( MONGO_NAME_VERSION_SEPARATOR )[ - 1 ]), remote_path = ( f . _id , f . length ), origin = \"mongodb\" , ) for f in self . _client . find ( { \"filename\" : re . compile ( re . escape ( self . _name + MONGO_NAME_VERSION_SEPARATOR ) + \".*\" ) } ) ] def _download ( self , remote_path : Any , local_path : Path , hide_progress : bool ) -> None : logger . info ( f \"Downloading { remote_path [ 0 ] } from Mongo (GridFS)\" ) progress = ( DownloadProgressBar ( name = str ( remote_path [ 0 ]), size = remote_path [ 1 ], logger = logger ) if not hide_progress else None ) with self . _client . open_download_stream ( remote_path [ 0 ]) as stream : with open ( local_path , \"wb\" ) as f : while True : content = stream . read ( DEFAULT_CHUNK_SIZE ) f . write ( content ) if progress : progress ( len ( content )) if len ( content ) < DEFAULT_CHUNK_SIZE : break def _upload ( self , local_path : Path , hide_progress : bool ) -> None : logger . info ( f \"Uploading { local_path } to Mongo (GridFS)\" ) progress = ( UploadProgressBar ( path = local_path , logger = logger ) if not hide_progress else None ) with self . _client . open_upload_stream ( f \" { self . _name }{ MONGO_NAME_VERSION_SEPARATOR }{ self . version } \" ) as stream : with open ( local_path , \"rb\" ) as f : while True : content = f . read ( DEFAULT_CHUNK_SIZE ) stream . write ( content ) if progress : progress ( len ( content )) if len ( content ) < DEFAULT_CHUNK_SIZE : break def _delete_old_remote_versions ( self ) -> None : if self . _keep_last_n is not None : for i in ( self . _instances [: - self . _keep_last_n ] if self . _keep_last_n > 0 else self . _instances ): logger . info ( f \"Removing old version from MongoDB (GridFS) (keep_last_n= { self . _keep_last_n } ): { i . name }{ MONGO_NAME_VERSION_SEPARATOR }{ i . version } \" ) self . _client . delete ( i . remote_path [ 0 ])","title":"LargeFileMongo"},{"location":"reference/large-file/#great_ai.large_file.LargeFileBase","text":"Bases: ABC Base for LargeFile implementations with different backends. Store large files remotely using the familiar API of open() . With built-in versioning, pruning and local cache. By default, files are stored in the \".cache\" folder and the least recently used is deleted after the overall size reaches 30 GBs. Examples: >>> LargeFileBase . cache_path = Path ( \".cache\" ) >>> LargeFileBase . max_cache_size = \"30GB\" Attributes: Name Type Description initialized Tell whether configure_credentials or configure_credentials_from_file has been already called. cache_path Storage location for cached files. max_cache_size Optional [ str ] Delete files until the folder at cache_path is smaller than this value. Examples: \"5 GB\", \"10MB\", \"0.3 TB\". Set to None for no automatic cache-pruning. Source code in great_ai/large_file/large_file/large_file_base.py 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 class LargeFileBase ( ABC ): \"\"\"Base for LargeFile implementations with different backends. Store large files remotely using the familiar API of `open()`. With built-in versioning, pruning and local cache. By default, files are stored in the \".cache\" folder and the least recently used is deleted after the overall size reaches 30 GBs. Examples: >>> LargeFileBase.cache_path = Path(\".cache\") >>> LargeFileBase.max_cache_size = \"30GB\" Attributes: initialized: Tell whether `configure_credentials` or `configure_credentials_from_file` has been already called. cache_path: Storage location for cached files. max_cache_size: Delete files until the folder at `cache_path` is smaller than this value. Examples: \"5 GB\", \"10MB\", \"0.3 TB\". Set to `None` for no automatic cache-pruning. \"\"\" initialized = False cache_path = Path ( \".cache\" ) max_cache_size : Optional [ str ] = \"30GB\" def __init__ ( self , name : str , mode : str = \"r\" , * , buffering : int = - 1 , encoding : Optional [ str ] = None , errors : Optional [ str ] = None , newline : Optional [ str ] = None , version : Optional [ int ] = None , keep_last_n : Optional [ int ] = None , cache_only_mode : bool = False , ): self . _name = name self . _version = version self . _mode = mode self . _keep_last_n = keep_last_n self . _cache_only_mode = cache_only_mode self . _buffering = buffering self . _encoding = encoding if errors is not None and sys . version_info [ 1 ] < 8 : raise RuntimeError ( \"The `errors` kwarg is only supported in 3.8 <= Python versions.\" ) self . _errors = errors self . _newline = newline LargeFileBase . cache_path . mkdir ( parents = True , exist_ok = True ) self . _find_instances () self . _check_mode_and_set_version () @classmethod def configure_credentials_from_file ( cls , secrets : Union [ Path , str , ConfigFile ], ) -> None : \"\"\"Load file and feed its content to `configure_credentials`. Extra keys are ignored. \"\"\" if not isinstance ( secrets , ConfigFile ): secrets = ConfigFile ( secrets ) cls . configure_credentials ( ** { k . lower (): v for k , v in secrets . items ()}) @classmethod def configure_credentials ( cls , ** kwargs : str ) -> None : \"\"\"Configure required credentials for the LargeFile backend.\"\"\" cls . initialized = True def __enter__ ( self ) -> IO : params = dict ( mode = self . _mode , buffering = self . _buffering , encoding = self . _encoding , newline = self . _newline , delete = False , prefix = \"large_file-\" , ) if sys . version_info [ 1 ] >= 8 : params [ \"errors\" ] = self . _errors self . _file : IO [ Any ] = ( tempfile . NamedTemporaryFile ( ** params ) # type: ignore if \"w\" in self . _mode else open ( self . get (), mode = self . _mode , buffering = self . _buffering , encoding = self . _encoding , newline = self . _newline , errors = self . _errors , ) ) return self . _file def __exit__ ( self , type : Optional [ Type [ BaseException ]], exc : Optional [ BaseException ], traceback : Optional [ TracebackType ], ) -> Literal [ False ]: self . _file . close () if type is None : if \"w\" in self . _mode : self . push ( Path ( self . _file . name )) os . unlink ( self . _file . name ) else : logger . exception ( \"Could not finish operation.\" ) return False @property def version ( self ) -> int : \"\"\"Numeric version of the file proxied by this LargeFile instance.\"\"\" return cast ( int , self . _version ) @lru_cache ( 1 ) def get ( self , hide_progress : bool = False ) -> Path : \"\"\"Return path to the proxy of a file (or directory). If not available in the local cache, an attempt is made to download it. Args: hide_progress: Do not show a progress update after each 10% of progress. \"\"\" remote_path = next ( i . remote_path for i in self . _instances if i . version == self . _version ) destination = self . cache_path / self . _local_name if not destination . exists (): logger . info ( f \"File { self . _local_name } does not exist locally\" ) with tempfile . TemporaryDirectory () as tmp : local_root_path = Path ( tmp ) tmp_file_archive = ( local_root_path / f \" { self . _local_name }{ ARCHIVE_EXTENSION } \" ) self . _download ( remote_path , tmp_file_archive , hide_progress = hide_progress ) logger . info ( f \"Decompressing { self . _local_name } \" ) shutil . unpack_archive ( str ( tmp_file_archive ), tmp , COMPRESSION_ALGORITHM ) shutil . move ( str ( local_root_path / self . _local_name ), str ( destination )) else : logger . info ( f \"File { self . _local_name } found in cache\" ) return destination def push ( self , path : Union [ Path , str ], hide_progress : bool = False ) -> None : \"\"\"Upload a file (or directory) as a new version of `key`. The file/directory is compressed before upload. Args: hide_progress: Do not show a progress update after each 10% of progress. \"\"\" if isinstance ( path , str ): path = Path ( path ) with tempfile . TemporaryDirectory () as tmp : if path . is_file (): logger . info ( f \"Copying file for { self . _local_name } \" ) copy : Any = shutil . copy else : logger . info ( f \"Copying directory for { self . _local_name } \" ) copy = shutil . copytree try : # Make local copy in the cache shutil . rmtree ( self . cache_path / self . _local_name , ignore_errors = True ) copy ( str ( path ), str ( self . cache_path / self . _local_name )) except shutil . SameFileError : pass # No worries copy ( str ( path ), str ( Path ( tmp ) / self . _local_name )) with tempfile . TemporaryDirectory () as tmp2 : # A directory has to be zipped and it cannot contain the output of the zipping logger . info ( f \"Compressing { self . _local_name } \" ) shutil . make_archive ( str ( Path ( tmp2 ) / self . _local_name ), COMPRESSION_ALGORITHM , tmp , ) file_to_be_uploaded = ( Path ( tmp2 ) / f \" { self . _local_name }{ ARCHIVE_EXTENSION } \" ) self . _upload ( file_to_be_uploaded , hide_progress = hide_progress ) self . clean_up () def delete ( self ) -> None : \"\"\"Delete all versions of the files under this `key`.\"\"\" self . _keep_last_n = 0 self . _delete_old_remote_versions () @property def versions_pretty ( self ) -> str : \"\"\"Formatted string of all available versions.\"\"\" return \", \" . join (( str ( i . version ) for i in self . _instances )) def clean_up ( self ) -> None : \"\"\"Delete local and remote versions according to currently set cache and retention policy.\"\"\" self . _delete_old_remote_versions () self . _prune_cache () @property def _local_name ( self ) -> str : return f \" { self . _name }{ CACHE_NAME_VERSION_SEPARATOR }{ self . version } \" def _find_instances ( self ) -> None : if self . _cache_only_mode : self . _instances = self . _find_instances_from_cache () else : self . _instances = self . _find_remote_instances () self . _instances = sorted ( self . _instances , key = lambda i : i . version ) def _find_instances_from_cache ( self ) -> List [ DataInstance ]: logger . info ( f \"Fetching cached versions of { self . _name } \" ) candidates = [ DataInstance ( name = CACHE_NAME_VERSION_SEPARATOR . join ( f . name . split ( CACHE_NAME_VERSION_SEPARATOR )[: - 1 ] ), version = int ( f . name . split ( CACHE_NAME_VERSION_SEPARATOR )[ - 1 ]), remote_path = f , ) for f in self . cache_path . glob ( f \" { self . _name }{ CACHE_NAME_VERSION_SEPARATOR } *\" ) ] return [ c for c in candidates if c . name == self . _name ] def _check_mode_and_set_version ( self ) -> None : if \"+\" in self . _mode : raise ValueError ( f \"File mode ` { self . _mode } ` is not allowed3, remove the `+`.\" ) if \"w\" in self . _mode : if self . _version is not None : raise ValueError ( \"Providing a version is not allowed in write mode.\" ) self . _version = self . _instances [ - 1 ] . version + 1 if self . _instances else 0 elif \"r\" in self . _mode : if not self . _instances : raise FileNotFoundError ( f \"File { self . _name } not found. No versions are available.\" ) if self . _version is None : self . _version = self . _instances [ - 1 ] . version logger . info ( f \"Latest version of { self . _name } is { self . _version } \" + f \"(from versions: { self . versions_pretty } )\" ) elif self . _version not in [ i . version for i in self . _instances ]: raise FileNotFoundError ( f \"File { self . _name } not found with version { self . _version } . \" + f \"(from versions: { self . versions_pretty } )\" ) else : raise ValueError ( \"Unsupported file mode.\" ) def _prune_cache ( self ) -> None : self . cache_path . mkdir ( parents = True , exist_ok = True ) if self . max_cache_size is None : return allowed_size = human_readable_to_byte ( self . max_cache_size ) assert allowed_size >= 0 least_recently_read = sorted ( [ f for f in self . cache_path . glob ( \"*\" )], key = lambda f : f . stat () . st_atime ) while sum ( os . path . getsize ( f ) for f in least_recently_read ) > allowed_size : file = least_recently_read . pop ( 0 ) logger . info ( f \"Deleting file from cache to meet quota (max_cache_size= { self . max_cache_size } ): { file } \" ) os . unlink ( file ) @abstractmethod def _find_remote_instances ( self ) -> List [ DataInstance ]: pass @abstractmethod def _download ( self , remote_path : Any , local_path : Path , hide_progress : bool ) -> None : pass @abstractmethod def _upload ( self , local_path : Path , hide_progress : bool ) -> None : pass @abstractmethod def _delete_old_remote_versions ( self ) -> None : pass","title":"LargeFileBase"},{"location":"reference/large-file/#great_ai.large_file.large_file.large_file_base.LargeFileBase.clean_up","text":"Delete local and remote versions according to currently set cache and retention policy. Source code in great_ai/large_file/large_file/large_file_base.py 250 251 252 253 254 def clean_up ( self ) -> None : \"\"\"Delete local and remote versions according to currently set cache and retention policy.\"\"\" self . _delete_old_remote_versions () self . _prune_cache ()","title":"clean_up()"},{"location":"reference/large-file/#great_ai.large_file.large_file.large_file_base.LargeFileBase.configure_credentials","text":"Configure required credentials for the LargeFile backend. Source code in great_ai/large_file/large_file/large_file_base.py 102 103 104 105 106 @classmethod def configure_credentials ( cls , ** kwargs : str ) -> None : \"\"\"Configure required credentials for the LargeFile backend.\"\"\" cls . initialized = True","title":"configure_credentials()"},{"location":"reference/large-file/#great_ai.large_file.large_file.large_file_base.LargeFileBase.configure_credentials_from_file","text":"Load file and feed its content to configure_credentials . Extra keys are ignored. Source code in great_ai/large_file/large_file/large_file_base.py 88 89 90 91 92 93 94 95 96 97 98 99 100 @classmethod def configure_credentials_from_file ( cls , secrets : Union [ Path , str , ConfigFile ], ) -> None : \"\"\"Load file and feed its content to `configure_credentials`. Extra keys are ignored. \"\"\" if not isinstance ( secrets , ConfigFile ): secrets = ConfigFile ( secrets ) cls . configure_credentials ( ** { k . lower (): v for k , v in secrets . items ()})","title":"configure_credentials_from_file()"},{"location":"reference/large-file/#great_ai.large_file.large_file.large_file_base.LargeFileBase.delete","text":"Delete all versions of the files under this key . Source code in great_ai/large_file/large_file/large_file_base.py 239 240 241 242 243 def delete ( self ) -> None : \"\"\"Delete all versions of the files under this `key`.\"\"\" self . _keep_last_n = 0 self . _delete_old_remote_versions ()","title":"delete()"},{"location":"reference/large-file/#great_ai.large_file.large_file.large_file_base.LargeFileBase.get","text":"Return path to the proxy of a file (or directory). If not available in the local cache, an attempt is made to download it. Parameters: Name Type Description Default hide_progress bool Do not show a progress update after each 10% of progress. False Source code in great_ai/large_file/large_file/large_file_base.py 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 @lru_cache ( 1 ) def get ( self , hide_progress : bool = False ) -> Path : \"\"\"Return path to the proxy of a file (or directory). If not available in the local cache, an attempt is made to download it. Args: hide_progress: Do not show a progress update after each 10% of progress. \"\"\" remote_path = next ( i . remote_path for i in self . _instances if i . version == self . _version ) destination = self . cache_path / self . _local_name if not destination . exists (): logger . info ( f \"File { self . _local_name } does not exist locally\" ) with tempfile . TemporaryDirectory () as tmp : local_root_path = Path ( tmp ) tmp_file_archive = ( local_root_path / f \" { self . _local_name }{ ARCHIVE_EXTENSION } \" ) self . _download ( remote_path , tmp_file_archive , hide_progress = hide_progress ) logger . info ( f \"Decompressing { self . _local_name } \" ) shutil . unpack_archive ( str ( tmp_file_archive ), tmp , COMPRESSION_ALGORITHM ) shutil . move ( str ( local_root_path / self . _local_name ), str ( destination )) else : logger . info ( f \"File { self . _local_name } found in cache\" ) return destination","title":"get()"},{"location":"reference/large-file/#great_ai.large_file.large_file.large_file_base.LargeFileBase.push","text":"Upload a file (or directory) as a new version of key . The file/directory is compressed before upload. Parameters: Name Type Description Default hide_progress bool Do not show a progress update after each 10% of progress. False Source code in great_ai/large_file/large_file/large_file_base.py 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 def push ( self , path : Union [ Path , str ], hide_progress : bool = False ) -> None : \"\"\"Upload a file (or directory) as a new version of `key`. The file/directory is compressed before upload. Args: hide_progress: Do not show a progress update after each 10% of progress. \"\"\" if isinstance ( path , str ): path = Path ( path ) with tempfile . TemporaryDirectory () as tmp : if path . is_file (): logger . info ( f \"Copying file for { self . _local_name } \" ) copy : Any = shutil . copy else : logger . info ( f \"Copying directory for { self . _local_name } \" ) copy = shutil . copytree try : # Make local copy in the cache shutil . rmtree ( self . cache_path / self . _local_name , ignore_errors = True ) copy ( str ( path ), str ( self . cache_path / self . _local_name )) except shutil . SameFileError : pass # No worries copy ( str ( path ), str ( Path ( tmp ) / self . _local_name )) with tempfile . TemporaryDirectory () as tmp2 : # A directory has to be zipped and it cannot contain the output of the zipping logger . info ( f \"Compressing { self . _local_name } \" ) shutil . make_archive ( str ( Path ( tmp2 ) / self . _local_name ), COMPRESSION_ALGORITHM , tmp , ) file_to_be_uploaded = ( Path ( tmp2 ) / f \" { self . _local_name }{ ARCHIVE_EXTENSION } \" ) self . _upload ( file_to_be_uploaded , hide_progress = hide_progress ) self . clean_up ()","title":"push()"},{"location":"reference/large-file/#great_ai.large_file.large_file.large_file_base.LargeFileBase.version","text":"Numeric version of the file proxied by this LargeFile instance. Source code in great_ai/large_file/large_file/large_file_base.py 153 154 155 156 157 @property def version ( self ) -> int : \"\"\"Numeric version of the file proxied by this LargeFile instance.\"\"\" return cast ( int , self . _version )","title":"version()"},{"location":"reference/large-file/#great_ai.large_file.large_file.large_file_base.LargeFileBase.versions_pretty","text":"Formatted string of all available versions. Source code in great_ai/large_file/large_file/large_file_base.py 245 246 247 248 @property def versions_pretty ( self ) -> str : \"\"\"Formatted string of all available versions.\"\"\" return \", \" . join (( str ( i . version ) for i in self . _instances ))","title":"versions_pretty()"},{"location":"reference/utilities/","text":"Utilities # from great_ai.utilities import * NLP tools # Well-tested tools that can be used in production with confidence. The toolbox of feature-extraction functions is expected to grow to cover other domains as well. clean ( text , ignore_xml = False , ignore_latex = False , remove_brackets = False , convert_to_ascii = False ) # Clean all XML, LaTeX, PDF-extraction, and Unicode artifacts from the text. The cleaning is quite heavy-weight and can be destructive. However, when working with text, this is usually required to achieve sufficient cleanliness before further processing. Optionally, the text can be turned into ASCII. Carefully consider whether this is absolutely needed for your use-case. Examples: >>> clean ( '

Bj \\\\ \" {o} rn is \\t \\\\ textit {happy} \ud83d\ude42 <3

' ) 'Bj\u00f6rn is happy \ud83d\ude42 <3' >>> clean ( ... '

Bj \\\\ \" {o} rn is \\t \\\\ textit {happy} \ud83d\ude42 <3

' , ... convert_to_ascii = True ... ) 'Bjorn is happy <3' >>> clean ( ... '

Bj \\\\ \" {o} rn is \\t \\\\ textit {happy} \ud83d\ude42 <3

' , ... ignore_xml = True ... ) '

Bj\u00f6rn is happy \ud83d\ude42 lt;3

' Parameters: Name Type Description Default text str Text to be cleaned. required ignore_xml bool Do not process/remove XML-tags. False ignore_latex bool Do not process/remove LaTeX-tags. False remove_brackets bool Do not remove brackets ([]) False convert_to_ascii bool Strip (or convert) non-ascii characters. False Returns: Type Description str The cleaned input text with sensibly collapsed whitespace and optionally no markup. Source code in great_ai/utilities/clean.py 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 def clean ( text : str , ignore_xml : bool = False , ignore_latex : bool = False , remove_brackets : bool = False , convert_to_ascii : bool = False , ) -> str : \"\"\"Clean all XML, LaTeX, PDF-extraction, and Unicode artifacts from the text. The cleaning is quite heavy-weight and can be destructive. However, when working with text, this is usually required to achieve sufficient cleanliness before further processing. Optionally, the text can be turned into ASCII. Carefully consider whether this is absolutely needed for your use-case. Examples: >>> clean('

Bj\\\\\\\\\"{o}rn is \\\\t \\\\\\\\textit{happy} \ud83d\ude42 <3

') 'Bj\u00f6rn is happy \ud83d\ude42 <3' >>> clean( ... '

Bj\\\\\\\\\"{o}rn is \\\\t \\\\\\\\textit{happy} \ud83d\ude42 <3

', ... convert_to_ascii=True ... ) 'Bjorn is happy <3' >>> clean( ... '

Bj\\\\\\\\\"{o}rn is \\\\t \\\\\\\\textit{happy} \ud83d\ude42 <3

', ... ignore_xml=True ... ) '

Bj\u00f6rn is happy \ud83d\ude42 lt;3

' Args: text: Text to be cleaned. ignore_xml: Do not process/remove XML-tags. ignore_latex: Do not process/remove LaTeX-tags. remove_brackets: Do not remove brackets ([]) convert_to_ascii: Strip (or convert) non-ascii characters. Returns: The cleaned input text with sensibly collapsed whitespace and optionally no markup. \"\"\" if not ignore_xml : text = re . sub ( r \"<[^>]*>\" , \" \" , text ) text = html . unescape ( text ) if not ignore_latex : text = text . replace ( \"%\" , \" \\\\ %\" ) # escape LaTeX comments before parsing as LaTeX try : text = latex . latex_to_text ( text , tolerant_parsing = True , strict_braces = False ) text = text . replace ( \" %s \" , \" \" ) except : logger . exception ( \"Latex parsing error\" ) if convert_to_ascii : text = unicodedata . normalize ( \"NFKD\" , text ) try : text . encode ( \"ASCII\" , errors = \"strict\" ) except UnicodeEncodeError : text = \"\" . join ([ c for c in text if not unicodedata . combining ( c )]) text = unidecode . unidecode ( text ) text = re . sub ( r \"\\b[a-zA-Z](?:[\\t ]+[a-zA-Z]\\b)+\" , lambda m : re . sub ( r \"[\\t ]\" , \"\" , m [ 0 ]), text ) # A R T I C L E => ARTICLE if remove_brackets : text = re . sub ( r \"\\[[^\\]]*\\]\" , \" \" , text ) # fix hypens: break- word => break-word text = re . sub ( r \"(\\S)-\\s+\" , r \"\\1-\" , text ) text = re . sub ( r \"\\s+-(\\S)\" , r \"-\\1\" , text ) # collapse whitespace text = re . sub ( r \"\\s+\" , \" \" , text ) # fix punctuation text = re . sub ( rf \" ([ { joined_left_punctuations } ])\" , r \"\\1\" , text ) text = re . sub ( rf \"([ { joined_right_punctuations } ]) \" , r \"\\1\" , text ) text = text . strip () return text get_sentences ( text , ignore_partial = False , true_case = False , remove_punctuation = False ) # Return the list of sentences found in the input text. Use syntok to segment the sentences. Further processing can be enabled with optional arguments. Examples: >>> get_sentences ( 'This is a sentence. This is a half' ) ['This is a sentence.', 'This is a half'] >>> get_sentences ( 'This is a sentence. This is a half' , ignore_partial = True ) ['This is a sentence.'] >>> get_sentences ( 'I like Apple.' , true_case = True , remove_punctuation = True ) ['i like Apple'] Parameters: Name Type Description Default text str Text to be segmented into sentences. required ignore_partial bool Filter out sentences that are not capitalised/don't end with a punctuation. False true_case bool Crude method: lowercase the first word of each sentence. False remove_punctuation bool Remove all kinds of punctuation. False Returns: Type Description List [ str ] The found sentences (with partial sentences optionally filtered out). Source code in great_ai/utilities/get_sentences.py 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 def get_sentences ( text : str , ignore_partial : bool = False , true_case : bool = False , remove_punctuation : bool = False , ) -> List [ str ]: \"\"\"Return the list of sentences found in the input text. Use [syntok](https://github.com/fnl/syntok) to segment the sentences. Further processing can be enabled with optional arguments. Examples: >>> get_sentences('This is a sentence. This is a half') ['This is a sentence.', 'This is a half'] >>> get_sentences('This is a sentence. This is a half', ignore_partial=True) ['This is a sentence.'] >>> get_sentences('I like Apple.', true_case=True, remove_punctuation=True) ['i like Apple'] Args: text: Text to be segmented into sentences. ignore_partial: Filter out sentences that are not capitalised/don't end with a punctuation. true_case: Crude method: lowercase the first word of each sentence. remove_punctuation: Remove all kinds of punctuation. Returns: The found sentences (with partial sentences optionally filtered out). \"\"\" tokenizer = Tokenizer ( emit_hyphen_or_underscore_sep = True , replace_not_contraction = False ) token_stream = tokenizer . tokenize ( text ) def process ( sentence : str ) -> str : if true_case : sentence = sentence [ 0 ] . lower () + sentence [ 1 :] # very crude method if remove_punctuation : sentence = re . sub ( punctuations_pattern , \" \" , sentence ) return sentence . strip () sentences = [ process ( tokenizer . to_text ( sentence )) for sentence in segment ( token_stream ) ] if ignore_partial : sentences = [ sentence for sentence in sentences if sentence [ 0 ] . isupper () and sentence [ - 1 ] in sentence_ending_punctuations ] return sentences predict_language ( text ) # Predict the language code from text. A thin wrapper over langcodes for convenient language tagging. Examples: >>> predict_language ( 'This is a sentence.' ) 'en' Parameters: Name Type Description Default text Optional [ str ] Text used for prediction. required Returns: Type Description str The predicted language code (en, en-US) or und if a prediction could not be made. Source code in great_ai/utilities/language/predict_language.py 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 def predict_language ( text : Optional [ str ]) -> str : \"\"\"Predict the language code from text. A thin wrapper over [langcodes](https://github.com/rspeer/langcodes) for convenient language tagging. Examples: >>> predict_language('This is a sentence.') 'en' Args: text: Text used for prediction. Returns: The predicted language code (en, en-US) or `und` if a prediction could not be made. \"\"\" if not text : return Language . make () . to_tag () try : language_code = detect ( text ) except LangDetectException : return Language . make () . to_tag () return Language . get ( language_code ) . to_tag () english_name_of_language ( language_code ) # Human-friendly English name of language from its language_code . A thin wrapper over langcodes for convenient language tagging. Examples: >>> english_name_of_language ( 'en-US' ) 'English (United States)' >>> english_name_of_language ( 'und' ) 'Unknown language' Parameters: Name Type Description Default language_code Optional [ str ] Language code, for example, returned by great_ai.utilities.language.predict_language.predict_language . required Returns: Type Description str English name of language. Source code in great_ai/utilities/language/english_name_of_language.py 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 def english_name_of_language ( language_code : Optional [ str ]) -> str : \"\"\"Human-friendly English name of language from its `language_code`. A thin wrapper over [langcodes](https://github.com/rspeer/langcodes) for convenient language tagging. Examples: >>> english_name_of_language('en-US') 'English (United States)' >>> english_name_of_language('und') 'Unknown language' Args: language_code: Language code, for example, returned by [great_ai.utilities.language.predict_language.predict_language][]. Returns: English name of language. \"\"\" if not language_code : language_code = \"und\" return Language . get ( language_code ) . display_name () is_english ( language_code ) # Decide whether the language_code is of an English language. A thin wrapper over langcodes for convenient language tagging. Examples: >>> is_english ( 'en-US' ) True >>> is_english ( None ) False >>> is_english ( 'und' ) False Parameters: Name Type Description Default language_code Optional [ str ] Language code, for example, returned by ` great_ai.utilities.language.predict_language.predict_language . required Returns: Type Description bool Boolean indicating whether it's English. Source code in great_ai/utilities/language/is_english.py 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 def is_english ( language_code : Optional [ str ]) -> bool : \"\"\"Decide whether the `language_code` is of an English language. A thin wrapper over [langcodes](https://github.com/rspeer/langcodes) for convenient language tagging. Examples: >>> is_english('en-US') True >>> is_english(None) False >>> is_english('und') False Args: language_code: Language code, for example, returned by `[great_ai.utilities.language.predict_language.predict_language][]. Returns: Boolean indicating whether it's English. \"\"\" if not language_code : language_code = \"und\" language_code = standardize_tag ( language_code ) return tag_distance ( language_code , \"en\" ) < 15 evaluate_ranking ( expected , actual_scores , target_recall , title = '' , disable_interpolation = False , axes = None , output_svg = None , reverse_order = False , plot = True ) # Render the Precision-Recall curve of a ranking. And improved version of scikit-learn's PR-curve Parameters: Name Type Description Default expected List [ T ] Expected ordering of the elements (rank if it's an integer, alphabetical if a string) required actual_scores List [ float ] Actual ranking scores (need not be on the same scale as expected ) required title Optional [ str ] Title of the plot. '' disable_interpolation bool Do not interpolate. False axes Optional [ plt . Axes ] Matplotlib axes for plotting inside a subplot. None output_svg Optional [ Path ] If specified, save the chart as an svg to the given Path. None reverse_order bool Reverse the ranking specified by expected . False plot bool Display a plot on the screen. True Returns: Type Description Dict [ T , float ] Precision values at given recall. Source code in great_ai/utilities/evaluate_ranking/evaluate_ranking.py 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 def evaluate_ranking ( expected : List [ T ], actual_scores : List [ float ], target_recall : float , title : Optional [ str ] = \"\" , disable_interpolation : bool = False , axes : Optional [ plt . Axes ] = None , output_svg : Optional [ Path ] = None , reverse_order : bool = False , plot : bool = True , ) -> Dict [ T , float ]: \"\"\"Render the Precision-Recall curve of a ranking. And improved version of scikit-learn's [PR-curve](https://scikit-learn.org/stable/auto_examples/model_selection/plot_precision_recall.html#sphx-glr-auto-examples-model-selection-plot-precision-recall-py) Args: expected: Expected ordering of the elements (rank if it's an integer, alphabetical if a string) actual_scores: Actual ranking scores (need not be on the same scale as `expected`) title: Title of the plot. disable_interpolation: Do not interpolate. axes: Matplotlib axes for plotting inside a subplot. output_svg: If specified, save the chart as an svg to the given Path. reverse_order: Reverse the ranking specified by `expected`. plot: Display a plot on the screen. Returns: Precision values at given recall. \"\"\" assert 0 <= target_recall <= 1 if plot and axes is None : fig = plt . figure ( figsize = ( 20 , 20 )) fig . patch . set_facecolor ( \"white\" ) ax = plt . axes () else : ax = axes classes = sorted ( unique ( expected ), reverse = reverse_order ) str_classes = [ str ( c ) for c in classes ] with matplotlib . rc_context ({ \"font.size\" : 20 }): if plot : ax . set_xmargin ( 0 ) draw_f1_iso_lines ( axes = ax ) results : Dict [ T , float ] = {} for i in range ( len ( classes ) - 1 ): binarized_expected = [ ( v < classes [ i ]) if reverse_order else ( v > classes [ i ]) for v in expected ] sorted_expected_actual = sorted ( zip ( binarized_expected , actual_scores ), key = lambda v : v [ 1 ], reverse = True ) precision = [] recall = [] correct = 0 for all , ( e , score ) in enumerate ( sorted_expected_actual , start = 1 ): correct += int ( e ) precision . append ( correct / all ) recall . append ( all / len ( sorted_expected_actual )) if not disable_interpolation : for j in range ( len ( precision ) - 2 , - 1 , - 1 ): precision [ j ] = max ( precision [ j ], precision [ j + 1 ]) closest_recall_index = np . argmin ( np . abs ( np . array ( recall ) - target_recall )) precision_at_closest_recall = precision [ closest_recall_index ] average_precision = average_precision_score ( binarized_expected , actual_scores ) results [ classes [ i ]] = precision_at_closest_recall if plot : ax . plot ( recall , precision , label = f \" { '|' . join ( str_classes [: i + 1 ]) } \u2194 { '|' . join ( str_classes [ i + 1 :]) } (P@ { target_recall : .2f } = { precision_at_closest_recall : .2f } , AP= { average_precision : .2f } )\" , ) if plot : ax . legend ( loc = \"upper right\" ) ax . axvline ( x = target_recall , linestyle = \"--\" , color = \"#55c6bb\" , linewidth = 2.0 ) if title is None : title = \"Ranking evaluation\" ax . set_title ( f ' { title } ( { \" < \" . join ( str_classes ) } )' , pad = 20 ) ax . set_xlabel ( \"Recall\" ) ax . set_ylabel ( \"Precision\" ) ax . set_xticks ([ target_recall ] + sorted ( ax . get_xticks ())) if plot and output_svg is None : if axes is None : plt . show () elif output_svg : plt . savefig ( output_svg , format = \"svg\" ) return results Parallel processing # Multiprocessing and multithreading-based parallelism with support for async functions. Its main purpose is to implement great_ai.GreatAI.process_batch , however, the parallel processing functions are also convenient for covering other types of mapping needs with a friendlier API than joblib or multiprocess . simple_parallel_map ( func , input_values , * , chunk_size = None , concurrency = None ) # Execute a map operation on an list mimicking the API of the built-in map() . A thin-wrapper over parallel_map . For more options, consult the documentation of parallel_map . Examples: >>> import math >>> list ( simple_parallel_map ( math . sqrt , [ 9 , 4 , 1 ])) [3.0, 2.0, 1.0] Parameters: Name Type Description Default func Callable [[ T ], Union [ V , Awaitable [ V ]]] The function that should be applied to each element of input_values . It can async , in that case, a new event loop is started for each chunk. required input_values Sequence [ T ] An iterable of items that func is applied to. required chunk_size Optional [ int ] Tune the number of items processed in each step. Larger numbers result in smaller communication overhead but less parallelism at the start and end. If chunk_size has a __len__ property, the chunk_size is calculated automatically if not given. None concurrency Optional [ int ] Number of new processes to start. Shouldn't be too much more than the number of physical cores. None Returns: Type Description List [ V ] An iterable of results obtained from applying func to each input value. Raises: Type Description WorkerException If there was an error in the func function in a background process. Source code in great_ai/utilities/parallel_map/simple_parallel_map.py 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 def simple_parallel_map ( func : Callable [[ T ], Union [ V , Awaitable [ V ]]], input_values : Sequence [ T ], * , chunk_size : Optional [ int ] = None , concurrency : Optional [ int ] = None , ) -> List [ V ]: \"\"\"Execute a map operation on an list mimicking the API of the built-in `map()`. A thin-wrapper over [parallel_map][great_ai.utilities.parallel_map.parallel_map.parallel_map]. For more options, consult the documentation of [parallel_map][great_ai.utilities.parallel_map.parallel_map.parallel_map]. Examples: >>> import math >>> list(simple_parallel_map(math.sqrt, [9, 4, 1])) [3.0, 2.0, 1.0] Args: func: The function that should be applied to each element of `input_values`. It can `async`, in that case, a new event loop is started for each chunk. input_values: An iterable of items that `func` is applied to. chunk_size: Tune the number of items processed in each step. Larger numbers result in smaller communication overhead but less parallelism at the start and end. If `chunk_size` has a `__len__` property, the `chunk_size` is calculated automatically if not given. concurrency: Number of new processes to start. Shouldn't be too much more than the number of physical cores. Returns: An iterable of results obtained from applying `func` to each input value. Raises: WorkerException: If there was an error in the `func` function in a background process. \"\"\" input_values = list ( input_values ) # in case the input is mistakenly not a sequence generator = parallel_map ( func = func , input_values = input_values , chunk_size = chunk_size , concurrency = concurrency , ) return list ( tqdm ( generator , total = len ( input_values ), ) ) parallel_map ( func , input_values , * , chunk_size = None , ignore_exceptions = False , concurrency = None , unordered = False ) # Execute a map operation on an iterable stream. A custom parallel map operation supporting both synchronous and async map functions. The func function is serialised with dill . Exceptions encountered in the map function are sent to the host process where they are either raised (default) or ignored. The new processes are forked if the OS allows it, otherwise, new Python processes are bootstrapped which can incur some start-up cost. Each process processes a single chunk at once. Examples: >>> import math >>> list ( parallel_map ( math . sqrt , [ 9 , 4 , 1 ], concurrency = 2 )) [3.0, 2.0, 1.0] Parameters: Name Type Description Default func Callable [[ T ], Union [ V , Awaitable [ V ]]] The function that should be applied to each element of input_values . It can async , in that case, a new event loop is started for each chunk. required input_values Union [ Iterable [ T ], Sequence [ T ]] An iterable of items that func is applied to. required chunk_size Optional [ int ] Tune the number of items processed in each step. Larger numbers result in smaller communication overhead but less parallelism at the start and end. If chunk_size has a __len__ property, the chunk_size is calculated automatically if not given. None ignore_exceptions bool Ignore chunks if next() raises an exception on input_values . And return None if func raised an exception in a worker process. False concurrency Optional [ int ] Number of new processes to start. Shouldn't be too much more than the number of physical cores. None unordered bool Do not preserve the order of the elements, yield them as soon as they have been processed. This decreases the latency caused by difficult-to-process items. False Yields: Type Description Iterable [ Optional [ V ]] The next result obtained from applying func to each input value. May contain None -s if ignore_exceptions=True . May have different order than the input if unordered=True . Raises: Type Description WorkerException If there was an error in the func function in a background process and ignore_exceptions=False . Source code in great_ai/utilities/parallel_map/parallel_map.py 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 def parallel_map ( func : Callable [[ T ], Union [ V , Awaitable [ V ]]], input_values : Union [ Iterable [ T ], Sequence [ T ]], * , chunk_size : Optional [ int ] = None , ignore_exceptions : bool = False , concurrency : Optional [ int ] = None , unordered : bool = False , ) -> Iterable [ Optional [ V ]]: \"\"\"Execute a map operation on an iterable stream. A custom parallel map operation supporting both synchronous and `async` map functions. The `func` function is serialised with `dill`. Exceptions encountered in the map function are sent to the host process where they are either raised (default) or ignored. The new processes are forked if the OS allows it, otherwise, new Python processes are bootstrapped which can incur some start-up cost. Each process processes a single chunk at once. Examples: >>> import math >>> list(parallel_map(math.sqrt, [9, 4, 1], concurrency=2)) [3.0, 2.0, 1.0] Args: func: The function that should be applied to each element of `input_values`. It can `async`, in that case, a new event loop is started for each chunk. input_values: An iterable of items that `func` is applied to. chunk_size: Tune the number of items processed in each step. Larger numbers result in smaller communication overhead but less parallelism at the start and end. If `chunk_size` has a `__len__` property, the `chunk_size` is calculated automatically if not given. ignore_exceptions: Ignore chunks if `next()` raises an exception on `input_values`. And return `None` if `func` raised an exception in a worker process. concurrency: Number of new processes to start. Shouldn't be too much more than the number of physical cores. unordered: Do not preserve the order of the elements, yield them as soon as they have been processed. This decreases the latency caused by difficult-to-process items. Yields: The next result obtained from applying `func` to each input value. May contain `None`-s if `ignore_exceptions=True`. May have different order than the input if `unordered=True`. Raises: WorkerException: If there was an error in the `func` function in a background process and `ignore_exceptions=False`. \"\"\" config = get_config ( function = func , input_values = input_values , chunk_size = chunk_size , concurrency = concurrency , ) ctx = ( mp . get_context ( \"fork\" ) if \"fork\" in mp . get_all_start_methods () else mp . get_context ( \"spawn\" ) ) ctx . freeze_support () manager = ctx . Manager () input_queue = manager . Queue ( config . concurrency * 2 ) output_queue = manager . Queue ( config . concurrency * 2 ) should_stop = ctx . Event () serialized_map_function = dill . dumps ( func , byref = True , recurse = False ) processes = [ ctx . Process ( name = f \"parallel_map_ { config . function_name } _ { i } \" , target = mapper_function , daemon = True , kwargs = dict ( input_queue = input_queue , output_queue = output_queue , should_stop = should_stop , func = serialized_map_function , ), ) for i in range ( config . concurrency ) ] for p in processes : p . start () try : yield from manage_communication ( input_values = input_values , chunk_size = config . chunk_size , input_queue = input_queue , output_queue = output_queue , unordered = unordered , ignore_exceptions = ignore_exceptions , ) should_stop . set () except WorkerException : should_stop . set () raise except Exception : for p in processes : p . terminate () p . kill () raise finally : for p in processes : p . join () # terminated processes have to be joined else they remain zombies p . close () manager . shutdown () threaded_parallel_map ( func , input_values , * , chunk_size = None , ignore_exceptions = False , concurrency = None , unordered = False ) # Execute a map operation on an iterable stream. Similar to parallel_map but uses threads instead of processes. Hence, it is not helpful in CPU-bound situations. A custom parallel map operation supporting both synchronous and async map functions. Exceptions encountered in the map function are sent to the host thread where they are either raised (default) or ignored. Each process processes a single chunk at once. Examples: >>> list ( threaded_parallel_map ( lambda x : x ** 2 , [ 1 , 2 , 3 ])) [1, 4, 9] Parameters: Name Type Description Default func Callable [[ T ], Union [ V , Awaitable [ V ]]] The function that should be applied to each element of input_values . It can async , in that case, a new event loop is started for each chunk. required input_values Union [ Iterable [ T ], Sequence [ T ]] An iterable of items that func is applied to. required chunk_size Optional [ int ] Tune the number of items processed in each step. Larger numbers result in smaller communication overhead but less parallelism at the start and end. If chunk_size has a __len__ property, the chunk_size is calculated automatically if not given. None ignore_exceptions bool Ignore chunks if next() raises an exception on input_values . And return None if func raised an exception in a worker process. False concurrency Optional [ int ] Number of new threads to start. None unordered bool Do not preserve the order of the elements, yield them as soon as they have been processed. This decreases the latency caused by difficult-to-process items. False Yields: Type Description Iterable [ Optional [ V ]] The next result obtained from applying func to each input value. May contain None -s if ignore_exceptions=True . May have different order than the input if unordered=True . Raises: Type Description WorkerException If there was an error in the func function in a background thread and ignore_exceptions=False . Source code in great_ai/utilities/parallel_map/threaded_parallel_map.py 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 def threaded_parallel_map ( func : Callable [[ T ], Union [ V , Awaitable [ V ]]], input_values : Union [ Iterable [ T ], Sequence [ T ]], * , chunk_size : Optional [ int ] = None , ignore_exceptions : bool = False , concurrency : Optional [ int ] = None , unordered : bool = False , ) -> Iterable [ Optional [ V ]]: \"\"\"Execute a map operation on an iterable stream. Similar to [parallel_map][great_ai.utilities.parallel_map.parallel_map.parallel_map] but uses threads instead of processes. Hence, it is not helpful in CPU-bound situations. A custom parallel map operation supporting both synchronous and `async` map functions. Exceptions encountered in the map function are sent to the host thread where they are either raised (default) or ignored. Each process processes a single chunk at once. Examples: >>> list(threaded_parallel_map(lambda x: x ** 2, [1, 2, 3])) [1, 4, 9] Args: func: The function that should be applied to each element of `input_values`. It can `async`, in that case, a new event loop is started for each chunk. input_values: An iterable of items that `func` is applied to. chunk_size: Tune the number of items processed in each step. Larger numbers result in smaller communication overhead but less parallelism at the start and end. If `chunk_size` has a `__len__` property, the `chunk_size` is calculated automatically if not given. ignore_exceptions: Ignore chunks if `next()` raises an exception on `input_values`. And return `None` if `func` raised an exception in a worker process. concurrency: Number of new threads to start. unordered: Do not preserve the order of the elements, yield them as soon as they have been processed. This decreases the latency caused by difficult-to-process items. Yields: The next result obtained from applying `func` to each input value. May contain `None`-s if `ignore_exceptions=True`. May have different order than the input if `unordered=True`. Raises: WorkerException: If there was an error in the `func` function in a background thread and `ignore_exceptions=False`. \"\"\" config = get_config ( function = func , input_values = input_values , chunk_size = chunk_size , concurrency = concurrency , ) input_queue : queue . Queue = queue . Queue ( config . concurrency * 2 ) output_queue : queue . Queue = queue . Queue ( config . concurrency * 2 ) should_stop = threading . Event () threads = [ threading . Thread ( name = f \"threaded_parallel_map_ { config . function_name } _ { i } \" , target = mapper_function , daemon = True , kwargs = dict ( input_queue = input_queue , output_queue = output_queue , should_stop = should_stop , func = func , ), ) for i in range ( config . concurrency ) ] for t in threads : t . start () yield from manage_communication ( input_values = input_values , chunk_size = config . chunk_size , input_queue = input_queue , output_queue = output_queue , unordered = unordered , ignore_exceptions = ignore_exceptions , ) should_stop . set () for t in threads : t . join ( 1 ) Composable parallel processing # Because both threaded_parallel_map and parallel_map have a streaming interface, it is easy to compose them and end up with, for example, a process for each CPU core with its own thread-pool or event-loop. Longer pipelines are also easy to imagine. The chunking methods help in these compositions. chunk ( values , chunk_size ) # Turn an iterable of items into an iterable of lists (chunks) of items. Each returned chunk is of length chunk_size except the last one the length of which is between 1 and chunk_size . Useful for parallel processing. Examples: >>> list ( chunk ( range ( 10 ), chunk_size = 3 )) [[0, 1, 2], [3, 4, 5], [6, 7, 8], [9]] Parameters: Name Type Description Default values Iterable [ T ] The stream of items to pack into chunks. required chunk_size int Desired length of each (but the last) chunk. required Yields: Type Description Iterable [ List [ T ]] The next chunk. Source code in great_ai/utilities/chunk.py 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 def chunk ( values : Iterable [ T ], chunk_size : int ) -> Iterable [ List [ T ]]: \"\"\"Turn an iterable of items into an iterable of lists (chunks) of items. Each returned chunk is of length `chunk_size` except the last one the length of which is between 1 and `chunk_size`. Useful for parallel processing. Examples: >>> list(chunk(range(10), chunk_size=3)) [[0, 1, 2], [3, 4, 5], [6, 7, 8], [9]] Args: values: The stream of items to pack into chunks. chunk_size: Desired length of each (but the last) chunk. Yields: The next chunk. \"\"\" assert chunk_size >= 1 result : List [ T ] = [] for v in values : result . append ( v ) if len ( result ) == chunk_size : yield result result = [] if len ( result ) > 0 : yield result unchunk ( chunks ) # Turn a stream of chunks of items into a stream of items (flatten operation). The inverse operation of chunk . Useful for parallel processing. Similar to itertools.chain but ignores None chunks. Examples: >>> list ( unchunk ([[ 0 , 1 , 2 ], [ 3 , 4 , 5 ], [ 6 , 7 , 8 ], [ 9 ]])) [0, 1, 2, 3, 4, 5, 6, 7, 8, 9] Parameters: Name Type Description Default chunks Iterable [ Optional [ Iterable [ T ]]] Stream of chunks to unpack. required Yields: Type Description Iterable [ T ] The next item in the flattened iterable. Source code in great_ai/utilities/unchunk.py 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 def unchunk ( chunks : Iterable [ Optional [ Iterable [ T ]]]) -> Iterable [ T ]: \"\"\"Turn a stream of chunks of items into a stream of items (flatten operation). The inverse operation of [chunk][great_ai.utilities.chunk.chunk]. Useful for parallel processing. Similar to itertools.chain but ignores `None` chunks. Examples: >>> list(unchunk([[0, 1, 2], [3, 4, 5], [6, 7, 8], [9]])) [0, 1, 2, 3, 4, 5, 6, 7, 8, 9] Args: chunks: Stream of chunks to unpack. Yields: The next item in the flattened iterable. \"\"\" for chunk in chunks : if chunk is not None : yield from chunk Operations # ConfigFile # Bases: Mapping [ str , str ] A small and safe INI -style configuration loader with dict and ENV support. The values can be accessed using both dot- and index-notation. It is compatible with the dict interface. File format example: # comments are allowed everywhere key = value # you can leave or omit whitespace around the equal-sign my_hashtag = \"#great_ai\" # the r-value can be quoted with \" or ' or `. my_var = my_default_value # Default values can be given to env-vars, # see next line. The default value must come first. my_var = ENV : MY_ENV_VAR # If the value starts with the `ENV:` prefix, # it is looked up from the environment variables. Examples: >>> ConfigFile ( 'tests/utilities/data/simple.conf' ) ConfigFile(path=tests/utilities/data/simple.conf) {'zeroth_key': 'test', 'first_key': 'Andr\u00e1s'} >>> ConfigFile ( 'tests/utilities/data/simple.conf' ) . zeroth_key 'test' >>> ConfigFile ( 'tests/utilities/data/simple.conf' ) . second_key Traceback (most recent call last): ... KeyError : 'Key `second_key` is not found in configuration file ... >>> a = ConfigFile ( 'tests/utilities/data/simple.conf' ) >>> { ** a } {'zeroth_key': 'test', 'first_key': 'Andr\u00e1s'} Source code in great_ai/utilities/config_file/config_file.py 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 class ConfigFile ( Mapping [ str , str ]): \"\"\"A small and safe `INI`-style configuration loader with `dict` and `ENV` support. The values can be accessed using both dot- and index-notation. It is compatible with the `dict` interface. File format example: ```toml # comments are allowed everywhere key = value # you can leave or omit whitespace around the equal-sign my_hashtag = \"#great_ai\" # the r-value can be quoted with \" or ' or `. my_var = my_default_value # Default values can be given to env-vars, # see next line. The default value must come first. my_var = ENV:MY_ENV_VAR # If the value starts with the `ENV:` prefix, # it is looked up from the environment variables. ``` Examples: >>> ConfigFile('tests/utilities/data/simple.conf') ConfigFile(path=tests/utilities/data/simple.conf) {'zeroth_key': 'test', 'first_key': 'Andr\u00e1s'} >>> ConfigFile('tests/utilities/data/simple.conf').zeroth_key 'test' >>> ConfigFile('tests/utilities/data/simple.conf').second_key Traceback (most recent call last): ... KeyError: 'Key `second_key` is not found in configuration file ... >>> a = ConfigFile('tests/utilities/data/simple.conf') >>> {**a} {'zeroth_key': 'test', 'first_key': 'Andr\u00e1s'} \"\"\" ENVIRONMENT_VARIABLE_KEY_PREFIX = \"ENV\" def __init__ ( self , path : Union [ Path , str ], * , ignore_missing : bool = False ) -> None : \"\"\"Load and parse a configuration file. Everything is eager-loaded, thus, exceptions may be thrown here. Args: path: Local path of the configuration file. ignore_missing: Don't raise an exception on missing environment variables. Raises: FileNotFoundError: If there is no file at the specified path. ParseError: If the provided file does not conform to the expected format. KeyError: If there is duplication in the keys. ValueError: If an environment variable is referenced but it is not set in the system and `ignore_missing=False`. \"\"\" if not isinstance ( path , Path ): path = Path ( path ) if not path . exists (): raise FileNotFoundError ( path . absolute ()) self . _ignore_missing = ignore_missing self . _path = path self . _key_values : Dict [ str , str ] = {} self . _parse () @property def path ( self ) -> Path : \"\"\"Original path from where the configuration was loaded.\"\"\" return self . _path def _parse ( self ) -> None : with open ( self . _path , encoding = \"utf-8\" ) as f : lines : str = f . read () matches = pattern . findall ( lines ) for key , * values in matches : try : value = next ( v for v in values if v ) except StopIteration : raise ParseError ( f \"\"\"Cannot parse config file ( { self . _path . absolute () } ), error at key ` { key } `\"\"\" ) already_exists = key in self . _key_values if already_exists and not value . startswith ( f \" { self . ENVIRONMENT_VARIABLE_KEY_PREFIX } :\" ): raise KeyError ( f \"Key ` { key } ` has been already defined and its value is ` { self . _key_values [ key ] } `\" ) if value . startswith ( f \" { self . ENVIRONMENT_VARIABLE_KEY_PREFIX } :\" ): _ , value = value . split ( \":\" ) if value not in os . environ : issue = f \"\"\"The value of ` { key } ` contains the \" { self . ENVIRONMENT_VARIABLE_KEY_PREFIX } ` prefix but ` { value } ` is not defined as an environment variable\"\"\" if already_exists : logger . warning ( f \"\"\" { issue } , using the default value defined above (` { self . _key_values [ key ] } `)\"\"\" ) continue elif self . _ignore_missing : logger . warning ( issue ) else : raise ValueError ( f \" { issue } and no default value has been provided\" ) else : value = os . environ [ value ] self . _key_values [ key ] = value def __getattr__ ( self , key : str ) -> str : if key in self . _key_values : return self . _key_values [ key ] raise KeyError ( f \"Key ` { key } ` is not found in configuration file ( { self . _path . absolute () } )\" ) __getitem__ = __getattr__ def __iter__ ( self ) -> Iterator [ str ]: return iter ( self . _key_values ) def __len__ ( self ) -> int : return len ( self . _key_values ) def keys ( self ) -> KeysView [ str ]: return self . _key_values . keys () def values ( self ) -> ValuesView [ str ]: return self . _key_values . values () def items ( self ) -> ItemsView [ str , str ]: return self . _key_values . items () def __repr__ ( self ) -> str : return f \" { type ( self ) . __name__ } (path= { self . _path . as_posix () } ) { self . _key_values } \" __init__ ( path , * , ignore_missing = False ) # Load and parse a configuration file. Everything is eager-loaded, thus, exceptions may be thrown here. Parameters: Name Type Description Default path Union [ Path , str ] Local path of the configuration file. required ignore_missing bool Don't raise an exception on missing environment variables. False Raises: Type Description FileNotFoundError If there is no file at the specified path. ParseError If the provided file does not conform to the expected format. KeyError If there is duplication in the keys. ValueError If an environment variable is referenced but it is not set in the system and ignore_missing=False . Source code in great_ai/utilities/config_file/config_file.py 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 def __init__ ( self , path : Union [ Path , str ], * , ignore_missing : bool = False ) -> None : \"\"\"Load and parse a configuration file. Everything is eager-loaded, thus, exceptions may be thrown here. Args: path: Local path of the configuration file. ignore_missing: Don't raise an exception on missing environment variables. Raises: FileNotFoundError: If there is no file at the specified path. ParseError: If the provided file does not conform to the expected format. KeyError: If there is duplication in the keys. ValueError: If an environment variable is referenced but it is not set in the system and `ignore_missing=False`. \"\"\" if not isinstance ( path , Path ): path = Path ( path ) if not path . exists (): raise FileNotFoundError ( path . absolute ()) self . _ignore_missing = ignore_missing self . _path = path self . _key_values : Dict [ str , str ] = {} self . _parse () path () property # Original path from where the configuration was loaded. Source code in great_ai/utilities/config_file/config_file.py 83 84 85 86 @property def path ( self ) -> Path : \"\"\"Original path from where the configuration was loaded.\"\"\" return self . _path get_logger ( name , level = logging . INFO , disable_colors = False ) # Return a customised logger used throughout the GreatAI codebase. Uses colors, and only prints timestamps when not running inside notebook. Source code in great_ai/utilities/logger/get_logger.py 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 def get_logger ( name : str , level : int = logging . INFO , disable_colors : bool = False ) -> logging . Logger : \"\"\"Return a customised logger used throughout the GreatAI codebase. Uses colors, and only prints timestamps when not running inside notebook. \"\"\" if name not in loggers : logger = logging . getLogger ( name ) logger . setLevel ( level ) try : get_ipython () # type: ignore log_format = \" %(message)s \" except NameError : # will fail outside of a notebook https://ipython.org/ log_format = \" %(asctime)s | %(levelname)8s | %(message)s \" stdout_handler = logging . StreamHandler () stdout_handler . setLevel ( level ) if not disable_colors : stdout_handler . setFormatter ( CustomFormatter ( log_format )) logger . addHandler ( stdout_handler ) loggers [ name ] = logger return loggers [ name ]","title":"Utilities"},{"location":"reference/utilities/#utilities","text":"from great_ai.utilities import *","title":"Utilities"},{"location":"reference/utilities/#nlp-tools","text":"Well-tested tools that can be used in production with confidence. The toolbox of feature-extraction functions is expected to grow to cover other domains as well.","title":"NLP tools"},{"location":"reference/utilities/#great_ai.utilities.clean.clean","text":"Clean all XML, LaTeX, PDF-extraction, and Unicode artifacts from the text. The cleaning is quite heavy-weight and can be destructive. However, when working with text, this is usually required to achieve sufficient cleanliness before further processing. Optionally, the text can be turned into ASCII. Carefully consider whether this is absolutely needed for your use-case. Examples: >>> clean ( '

Bj \\\\ \" {o} rn is \\t \\\\ textit {happy} \ud83d\ude42 <3

' ) 'Bj\u00f6rn is happy \ud83d\ude42 <3' >>> clean ( ... '

Bj \\\\ \" {o} rn is \\t \\\\ textit {happy} \ud83d\ude42 <3

' , ... convert_to_ascii = True ... ) 'Bjorn is happy <3' >>> clean ( ... '

Bj \\\\ \" {o} rn is \\t \\\\ textit {happy} \ud83d\ude42 <3

' , ... ignore_xml = True ... ) '

Bj\u00f6rn is happy \ud83d\ude42 lt;3

' Parameters: Name Type Description Default text str Text to be cleaned. required ignore_xml bool Do not process/remove XML-tags. False ignore_latex bool Do not process/remove LaTeX-tags. False remove_brackets bool Do not remove brackets ([]) False convert_to_ascii bool Strip (or convert) non-ascii characters. False Returns: Type Description str The cleaned input text with sensibly collapsed whitespace and optionally no markup. Source code in great_ai/utilities/clean.py 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 def clean ( text : str , ignore_xml : bool = False , ignore_latex : bool = False , remove_brackets : bool = False , convert_to_ascii : bool = False , ) -> str : \"\"\"Clean all XML, LaTeX, PDF-extraction, and Unicode artifacts from the text. The cleaning is quite heavy-weight and can be destructive. However, when working with text, this is usually required to achieve sufficient cleanliness before further processing. Optionally, the text can be turned into ASCII. Carefully consider whether this is absolutely needed for your use-case. Examples: >>> clean('

Bj\\\\\\\\\"{o}rn is \\\\t \\\\\\\\textit{happy} \ud83d\ude42 <3

') 'Bj\u00f6rn is happy \ud83d\ude42 <3' >>> clean( ... '

Bj\\\\\\\\\"{o}rn is \\\\t \\\\\\\\textit{happy} \ud83d\ude42 <3

', ... convert_to_ascii=True ... ) 'Bjorn is happy <3' >>> clean( ... '

Bj\\\\\\\\\"{o}rn is \\\\t \\\\\\\\textit{happy} \ud83d\ude42 <3

', ... ignore_xml=True ... ) '

Bj\u00f6rn is happy \ud83d\ude42 lt;3

' Args: text: Text to be cleaned. ignore_xml: Do not process/remove XML-tags. ignore_latex: Do not process/remove LaTeX-tags. remove_brackets: Do not remove brackets ([]) convert_to_ascii: Strip (or convert) non-ascii characters. Returns: The cleaned input text with sensibly collapsed whitespace and optionally no markup. \"\"\" if not ignore_xml : text = re . sub ( r \"<[^>]*>\" , \" \" , text ) text = html . unescape ( text ) if not ignore_latex : text = text . replace ( \"%\" , \" \\\\ %\" ) # escape LaTeX comments before parsing as LaTeX try : text = latex . latex_to_text ( text , tolerant_parsing = True , strict_braces = False ) text = text . replace ( \" %s \" , \" \" ) except : logger . exception ( \"Latex parsing error\" ) if convert_to_ascii : text = unicodedata . normalize ( \"NFKD\" , text ) try : text . encode ( \"ASCII\" , errors = \"strict\" ) except UnicodeEncodeError : text = \"\" . join ([ c for c in text if not unicodedata . combining ( c )]) text = unidecode . unidecode ( text ) text = re . sub ( r \"\\b[a-zA-Z](?:[\\t ]+[a-zA-Z]\\b)+\" , lambda m : re . sub ( r \"[\\t ]\" , \"\" , m [ 0 ]), text ) # A R T I C L E => ARTICLE if remove_brackets : text = re . sub ( r \"\\[[^\\]]*\\]\" , \" \" , text ) # fix hypens: break- word => break-word text = re . sub ( r \"(\\S)-\\s+\" , r \"\\1-\" , text ) text = re . sub ( r \"\\s+-(\\S)\" , r \"-\\1\" , text ) # collapse whitespace text = re . sub ( r \"\\s+\" , \" \" , text ) # fix punctuation text = re . sub ( rf \" ([ { joined_left_punctuations } ])\" , r \"\\1\" , text ) text = re . sub ( rf \"([ { joined_right_punctuations } ]) \" , r \"\\1\" , text ) text = text . strip () return text","title":"clean()"},{"location":"reference/utilities/#great_ai.utilities.get_sentences.get_sentences","text":"Return the list of sentences found in the input text. Use syntok to segment the sentences. Further processing can be enabled with optional arguments. Examples: >>> get_sentences ( 'This is a sentence. This is a half' ) ['This is a sentence.', 'This is a half'] >>> get_sentences ( 'This is a sentence. This is a half' , ignore_partial = True ) ['This is a sentence.'] >>> get_sentences ( 'I like Apple.' , true_case = True , remove_punctuation = True ) ['i like Apple'] Parameters: Name Type Description Default text str Text to be segmented into sentences. required ignore_partial bool Filter out sentences that are not capitalised/don't end with a punctuation. False true_case bool Crude method: lowercase the first word of each sentence. False remove_punctuation bool Remove all kinds of punctuation. False Returns: Type Description List [ str ] The found sentences (with partial sentences optionally filtered out). Source code in great_ai/utilities/get_sentences.py 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 def get_sentences ( text : str , ignore_partial : bool = False , true_case : bool = False , remove_punctuation : bool = False , ) -> List [ str ]: \"\"\"Return the list of sentences found in the input text. Use [syntok](https://github.com/fnl/syntok) to segment the sentences. Further processing can be enabled with optional arguments. Examples: >>> get_sentences('This is a sentence. This is a half') ['This is a sentence.', 'This is a half'] >>> get_sentences('This is a sentence. This is a half', ignore_partial=True) ['This is a sentence.'] >>> get_sentences('I like Apple.', true_case=True, remove_punctuation=True) ['i like Apple'] Args: text: Text to be segmented into sentences. ignore_partial: Filter out sentences that are not capitalised/don't end with a punctuation. true_case: Crude method: lowercase the first word of each sentence. remove_punctuation: Remove all kinds of punctuation. Returns: The found sentences (with partial sentences optionally filtered out). \"\"\" tokenizer = Tokenizer ( emit_hyphen_or_underscore_sep = True , replace_not_contraction = False ) token_stream = tokenizer . tokenize ( text ) def process ( sentence : str ) -> str : if true_case : sentence = sentence [ 0 ] . lower () + sentence [ 1 :] # very crude method if remove_punctuation : sentence = re . sub ( punctuations_pattern , \" \" , sentence ) return sentence . strip () sentences = [ process ( tokenizer . to_text ( sentence )) for sentence in segment ( token_stream ) ] if ignore_partial : sentences = [ sentence for sentence in sentences if sentence [ 0 ] . isupper () and sentence [ - 1 ] in sentence_ending_punctuations ] return sentences","title":"get_sentences()"},{"location":"reference/utilities/#great_ai.utilities.language.predict_language.predict_language","text":"Predict the language code from text. A thin wrapper over langcodes for convenient language tagging. Examples: >>> predict_language ( 'This is a sentence.' ) 'en' Parameters: Name Type Description Default text Optional [ str ] Text used for prediction. required Returns: Type Description str The predicted language code (en, en-US) or und if a prediction could not be made. Source code in great_ai/utilities/language/predict_language.py 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 def predict_language ( text : Optional [ str ]) -> str : \"\"\"Predict the language code from text. A thin wrapper over [langcodes](https://github.com/rspeer/langcodes) for convenient language tagging. Examples: >>> predict_language('This is a sentence.') 'en' Args: text: Text used for prediction. Returns: The predicted language code (en, en-US) or `und` if a prediction could not be made. \"\"\" if not text : return Language . make () . to_tag () try : language_code = detect ( text ) except LangDetectException : return Language . make () . to_tag () return Language . get ( language_code ) . to_tag ()","title":"predict_language()"},{"location":"reference/utilities/#great_ai.utilities.language.english_name_of_language.english_name_of_language","text":"Human-friendly English name of language from its language_code . A thin wrapper over langcodes for convenient language tagging. Examples: >>> english_name_of_language ( 'en-US' ) 'English (United States)' >>> english_name_of_language ( 'und' ) 'Unknown language' Parameters: Name Type Description Default language_code Optional [ str ] Language code, for example, returned by great_ai.utilities.language.predict_language.predict_language . required Returns: Type Description str English name of language. Source code in great_ai/utilities/language/english_name_of_language.py 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 def english_name_of_language ( language_code : Optional [ str ]) -> str : \"\"\"Human-friendly English name of language from its `language_code`. A thin wrapper over [langcodes](https://github.com/rspeer/langcodes) for convenient language tagging. Examples: >>> english_name_of_language('en-US') 'English (United States)' >>> english_name_of_language('und') 'Unknown language' Args: language_code: Language code, for example, returned by [great_ai.utilities.language.predict_language.predict_language][]. Returns: English name of language. \"\"\" if not language_code : language_code = \"und\" return Language . get ( language_code ) . display_name ()","title":"english_name_of_language()"},{"location":"reference/utilities/#great_ai.utilities.language.is_english.is_english","text":"Decide whether the language_code is of an English language. A thin wrapper over langcodes for convenient language tagging. Examples: >>> is_english ( 'en-US' ) True >>> is_english ( None ) False >>> is_english ( 'und' ) False Parameters: Name Type Description Default language_code Optional [ str ] Language code, for example, returned by ` great_ai.utilities.language.predict_language.predict_language . required Returns: Type Description bool Boolean indicating whether it's English. Source code in great_ai/utilities/language/is_english.py 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 def is_english ( language_code : Optional [ str ]) -> bool : \"\"\"Decide whether the `language_code` is of an English language. A thin wrapper over [langcodes](https://github.com/rspeer/langcodes) for convenient language tagging. Examples: >>> is_english('en-US') True >>> is_english(None) False >>> is_english('und') False Args: language_code: Language code, for example, returned by `[great_ai.utilities.language.predict_language.predict_language][]. Returns: Boolean indicating whether it's English. \"\"\" if not language_code : language_code = \"und\" language_code = standardize_tag ( language_code ) return tag_distance ( language_code , \"en\" ) < 15","title":"is_english()"},{"location":"reference/utilities/#great_ai.utilities.evaluate_ranking.evaluate_ranking.evaluate_ranking","text":"Render the Precision-Recall curve of a ranking. And improved version of scikit-learn's PR-curve Parameters: Name Type Description Default expected List [ T ] Expected ordering of the elements (rank if it's an integer, alphabetical if a string) required actual_scores List [ float ] Actual ranking scores (need not be on the same scale as expected ) required title Optional [ str ] Title of the plot. '' disable_interpolation bool Do not interpolate. False axes Optional [ plt . Axes ] Matplotlib axes for plotting inside a subplot. None output_svg Optional [ Path ] If specified, save the chart as an svg to the given Path. None reverse_order bool Reverse the ranking specified by expected . False plot bool Display a plot on the screen. True Returns: Type Description Dict [ T , float ] Precision values at given recall. Source code in great_ai/utilities/evaluate_ranking/evaluate_ranking.py 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 def evaluate_ranking ( expected : List [ T ], actual_scores : List [ float ], target_recall : float , title : Optional [ str ] = \"\" , disable_interpolation : bool = False , axes : Optional [ plt . Axes ] = None , output_svg : Optional [ Path ] = None , reverse_order : bool = False , plot : bool = True , ) -> Dict [ T , float ]: \"\"\"Render the Precision-Recall curve of a ranking. And improved version of scikit-learn's [PR-curve](https://scikit-learn.org/stable/auto_examples/model_selection/plot_precision_recall.html#sphx-glr-auto-examples-model-selection-plot-precision-recall-py) Args: expected: Expected ordering of the elements (rank if it's an integer, alphabetical if a string) actual_scores: Actual ranking scores (need not be on the same scale as `expected`) title: Title of the plot. disable_interpolation: Do not interpolate. axes: Matplotlib axes for plotting inside a subplot. output_svg: If specified, save the chart as an svg to the given Path. reverse_order: Reverse the ranking specified by `expected`. plot: Display a plot on the screen. Returns: Precision values at given recall. \"\"\" assert 0 <= target_recall <= 1 if plot and axes is None : fig = plt . figure ( figsize = ( 20 , 20 )) fig . patch . set_facecolor ( \"white\" ) ax = plt . axes () else : ax = axes classes = sorted ( unique ( expected ), reverse = reverse_order ) str_classes = [ str ( c ) for c in classes ] with matplotlib . rc_context ({ \"font.size\" : 20 }): if plot : ax . set_xmargin ( 0 ) draw_f1_iso_lines ( axes = ax ) results : Dict [ T , float ] = {} for i in range ( len ( classes ) - 1 ): binarized_expected = [ ( v < classes [ i ]) if reverse_order else ( v > classes [ i ]) for v in expected ] sorted_expected_actual = sorted ( zip ( binarized_expected , actual_scores ), key = lambda v : v [ 1 ], reverse = True ) precision = [] recall = [] correct = 0 for all , ( e , score ) in enumerate ( sorted_expected_actual , start = 1 ): correct += int ( e ) precision . append ( correct / all ) recall . append ( all / len ( sorted_expected_actual )) if not disable_interpolation : for j in range ( len ( precision ) - 2 , - 1 , - 1 ): precision [ j ] = max ( precision [ j ], precision [ j + 1 ]) closest_recall_index = np . argmin ( np . abs ( np . array ( recall ) - target_recall )) precision_at_closest_recall = precision [ closest_recall_index ] average_precision = average_precision_score ( binarized_expected , actual_scores ) results [ classes [ i ]] = precision_at_closest_recall if plot : ax . plot ( recall , precision , label = f \" { '|' . join ( str_classes [: i + 1 ]) } \u2194 { '|' . join ( str_classes [ i + 1 :]) } (P@ { target_recall : .2f } = { precision_at_closest_recall : .2f } , AP= { average_precision : .2f } )\" , ) if plot : ax . legend ( loc = \"upper right\" ) ax . axvline ( x = target_recall , linestyle = \"--\" , color = \"#55c6bb\" , linewidth = 2.0 ) if title is None : title = \"Ranking evaluation\" ax . set_title ( f ' { title } ( { \" < \" . join ( str_classes ) } )' , pad = 20 ) ax . set_xlabel ( \"Recall\" ) ax . set_ylabel ( \"Precision\" ) ax . set_xticks ([ target_recall ] + sorted ( ax . get_xticks ())) if plot and output_svg is None : if axes is None : plt . show () elif output_svg : plt . savefig ( output_svg , format = \"svg\" ) return results","title":"evaluate_ranking()"},{"location":"reference/utilities/#parallel-processing","text":"Multiprocessing and multithreading-based parallelism with support for async functions. Its main purpose is to implement great_ai.GreatAI.process_batch , however, the parallel processing functions are also convenient for covering other types of mapping needs with a friendlier API than joblib or multiprocess .","title":"Parallel processing"},{"location":"reference/utilities/#great_ai.utilities.simple_parallel_map","text":"Execute a map operation on an list mimicking the API of the built-in map() . A thin-wrapper over parallel_map . For more options, consult the documentation of parallel_map . Examples: >>> import math >>> list ( simple_parallel_map ( math . sqrt , [ 9 , 4 , 1 ])) [3.0, 2.0, 1.0] Parameters: Name Type Description Default func Callable [[ T ], Union [ V , Awaitable [ V ]]] The function that should be applied to each element of input_values . It can async , in that case, a new event loop is started for each chunk. required input_values Sequence [ T ] An iterable of items that func is applied to. required chunk_size Optional [ int ] Tune the number of items processed in each step. Larger numbers result in smaller communication overhead but less parallelism at the start and end. If chunk_size has a __len__ property, the chunk_size is calculated automatically if not given. None concurrency Optional [ int ] Number of new processes to start. Shouldn't be too much more than the number of physical cores. None Returns: Type Description List [ V ] An iterable of results obtained from applying func to each input value. Raises: Type Description WorkerException If there was an error in the func function in a background process. Source code in great_ai/utilities/parallel_map/simple_parallel_map.py 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 def simple_parallel_map ( func : Callable [[ T ], Union [ V , Awaitable [ V ]]], input_values : Sequence [ T ], * , chunk_size : Optional [ int ] = None , concurrency : Optional [ int ] = None , ) -> List [ V ]: \"\"\"Execute a map operation on an list mimicking the API of the built-in `map()`. A thin-wrapper over [parallel_map][great_ai.utilities.parallel_map.parallel_map.parallel_map]. For more options, consult the documentation of [parallel_map][great_ai.utilities.parallel_map.parallel_map.parallel_map]. Examples: >>> import math >>> list(simple_parallel_map(math.sqrt, [9, 4, 1])) [3.0, 2.0, 1.0] Args: func: The function that should be applied to each element of `input_values`. It can `async`, in that case, a new event loop is started for each chunk. input_values: An iterable of items that `func` is applied to. chunk_size: Tune the number of items processed in each step. Larger numbers result in smaller communication overhead but less parallelism at the start and end. If `chunk_size` has a `__len__` property, the `chunk_size` is calculated automatically if not given. concurrency: Number of new processes to start. Shouldn't be too much more than the number of physical cores. Returns: An iterable of results obtained from applying `func` to each input value. Raises: WorkerException: If there was an error in the `func` function in a background process. \"\"\" input_values = list ( input_values ) # in case the input is mistakenly not a sequence generator = parallel_map ( func = func , input_values = input_values , chunk_size = chunk_size , concurrency = concurrency , ) return list ( tqdm ( generator , total = len ( input_values ), ) )","title":"simple_parallel_map()"},{"location":"reference/utilities/#great_ai.utilities.parallel_map.parallel_map.parallel_map","text":"Execute a map operation on an iterable stream. A custom parallel map operation supporting both synchronous and async map functions. The func function is serialised with dill . Exceptions encountered in the map function are sent to the host process where they are either raised (default) or ignored. The new processes are forked if the OS allows it, otherwise, new Python processes are bootstrapped which can incur some start-up cost. Each process processes a single chunk at once. Examples: >>> import math >>> list ( parallel_map ( math . sqrt , [ 9 , 4 , 1 ], concurrency = 2 )) [3.0, 2.0, 1.0] Parameters: Name Type Description Default func Callable [[ T ], Union [ V , Awaitable [ V ]]] The function that should be applied to each element of input_values . It can async , in that case, a new event loop is started for each chunk. required input_values Union [ Iterable [ T ], Sequence [ T ]] An iterable of items that func is applied to. required chunk_size Optional [ int ] Tune the number of items processed in each step. Larger numbers result in smaller communication overhead but less parallelism at the start and end. If chunk_size has a __len__ property, the chunk_size is calculated automatically if not given. None ignore_exceptions bool Ignore chunks if next() raises an exception on input_values . And return None if func raised an exception in a worker process. False concurrency Optional [ int ] Number of new processes to start. Shouldn't be too much more than the number of physical cores. None unordered bool Do not preserve the order of the elements, yield them as soon as they have been processed. This decreases the latency caused by difficult-to-process items. False Yields: Type Description Iterable [ Optional [ V ]] The next result obtained from applying func to each input value. May contain None -s if ignore_exceptions=True . May have different order than the input if unordered=True . Raises: Type Description WorkerException If there was an error in the func function in a background process and ignore_exceptions=False . Source code in great_ai/utilities/parallel_map/parallel_map.py 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 def parallel_map ( func : Callable [[ T ], Union [ V , Awaitable [ V ]]], input_values : Union [ Iterable [ T ], Sequence [ T ]], * , chunk_size : Optional [ int ] = None , ignore_exceptions : bool = False , concurrency : Optional [ int ] = None , unordered : bool = False , ) -> Iterable [ Optional [ V ]]: \"\"\"Execute a map operation on an iterable stream. A custom parallel map operation supporting both synchronous and `async` map functions. The `func` function is serialised with `dill`. Exceptions encountered in the map function are sent to the host process where they are either raised (default) or ignored. The new processes are forked if the OS allows it, otherwise, new Python processes are bootstrapped which can incur some start-up cost. Each process processes a single chunk at once. Examples: >>> import math >>> list(parallel_map(math.sqrt, [9, 4, 1], concurrency=2)) [3.0, 2.0, 1.0] Args: func: The function that should be applied to each element of `input_values`. It can `async`, in that case, a new event loop is started for each chunk. input_values: An iterable of items that `func` is applied to. chunk_size: Tune the number of items processed in each step. Larger numbers result in smaller communication overhead but less parallelism at the start and end. If `chunk_size` has a `__len__` property, the `chunk_size` is calculated automatically if not given. ignore_exceptions: Ignore chunks if `next()` raises an exception on `input_values`. And return `None` if `func` raised an exception in a worker process. concurrency: Number of new processes to start. Shouldn't be too much more than the number of physical cores. unordered: Do not preserve the order of the elements, yield them as soon as they have been processed. This decreases the latency caused by difficult-to-process items. Yields: The next result obtained from applying `func` to each input value. May contain `None`-s if `ignore_exceptions=True`. May have different order than the input if `unordered=True`. Raises: WorkerException: If there was an error in the `func` function in a background process and `ignore_exceptions=False`. \"\"\" config = get_config ( function = func , input_values = input_values , chunk_size = chunk_size , concurrency = concurrency , ) ctx = ( mp . get_context ( \"fork\" ) if \"fork\" in mp . get_all_start_methods () else mp . get_context ( \"spawn\" ) ) ctx . freeze_support () manager = ctx . Manager () input_queue = manager . Queue ( config . concurrency * 2 ) output_queue = manager . Queue ( config . concurrency * 2 ) should_stop = ctx . Event () serialized_map_function = dill . dumps ( func , byref = True , recurse = False ) processes = [ ctx . Process ( name = f \"parallel_map_ { config . function_name } _ { i } \" , target = mapper_function , daemon = True , kwargs = dict ( input_queue = input_queue , output_queue = output_queue , should_stop = should_stop , func = serialized_map_function , ), ) for i in range ( config . concurrency ) ] for p in processes : p . start () try : yield from manage_communication ( input_values = input_values , chunk_size = config . chunk_size , input_queue = input_queue , output_queue = output_queue , unordered = unordered , ignore_exceptions = ignore_exceptions , ) should_stop . set () except WorkerException : should_stop . set () raise except Exception : for p in processes : p . terminate () p . kill () raise finally : for p in processes : p . join () # terminated processes have to be joined else they remain zombies p . close () manager . shutdown ()","title":"parallel_map()"},{"location":"reference/utilities/#great_ai.utilities.threaded_parallel_map","text":"Execute a map operation on an iterable stream. Similar to parallel_map but uses threads instead of processes. Hence, it is not helpful in CPU-bound situations. A custom parallel map operation supporting both synchronous and async map functions. Exceptions encountered in the map function are sent to the host thread where they are either raised (default) or ignored. Each process processes a single chunk at once. Examples: >>> list ( threaded_parallel_map ( lambda x : x ** 2 , [ 1 , 2 , 3 ])) [1, 4, 9] Parameters: Name Type Description Default func Callable [[ T ], Union [ V , Awaitable [ V ]]] The function that should be applied to each element of input_values . It can async , in that case, a new event loop is started for each chunk. required input_values Union [ Iterable [ T ], Sequence [ T ]] An iterable of items that func is applied to. required chunk_size Optional [ int ] Tune the number of items processed in each step. Larger numbers result in smaller communication overhead but less parallelism at the start and end. If chunk_size has a __len__ property, the chunk_size is calculated automatically if not given. None ignore_exceptions bool Ignore chunks if next() raises an exception on input_values . And return None if func raised an exception in a worker process. False concurrency Optional [ int ] Number of new threads to start. None unordered bool Do not preserve the order of the elements, yield them as soon as they have been processed. This decreases the latency caused by difficult-to-process items. False Yields: Type Description Iterable [ Optional [ V ]] The next result obtained from applying func to each input value. May contain None -s if ignore_exceptions=True . May have different order than the input if unordered=True . Raises: Type Description WorkerException If there was an error in the func function in a background thread and ignore_exceptions=False . Source code in great_ai/utilities/parallel_map/threaded_parallel_map.py 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 def threaded_parallel_map ( func : Callable [[ T ], Union [ V , Awaitable [ V ]]], input_values : Union [ Iterable [ T ], Sequence [ T ]], * , chunk_size : Optional [ int ] = None , ignore_exceptions : bool = False , concurrency : Optional [ int ] = None , unordered : bool = False , ) -> Iterable [ Optional [ V ]]: \"\"\"Execute a map operation on an iterable stream. Similar to [parallel_map][great_ai.utilities.parallel_map.parallel_map.parallel_map] but uses threads instead of processes. Hence, it is not helpful in CPU-bound situations. A custom parallel map operation supporting both synchronous and `async` map functions. Exceptions encountered in the map function are sent to the host thread where they are either raised (default) or ignored. Each process processes a single chunk at once. Examples: >>> list(threaded_parallel_map(lambda x: x ** 2, [1, 2, 3])) [1, 4, 9] Args: func: The function that should be applied to each element of `input_values`. It can `async`, in that case, a new event loop is started for each chunk. input_values: An iterable of items that `func` is applied to. chunk_size: Tune the number of items processed in each step. Larger numbers result in smaller communication overhead but less parallelism at the start and end. If `chunk_size` has a `__len__` property, the `chunk_size` is calculated automatically if not given. ignore_exceptions: Ignore chunks if `next()` raises an exception on `input_values`. And return `None` if `func` raised an exception in a worker process. concurrency: Number of new threads to start. unordered: Do not preserve the order of the elements, yield them as soon as they have been processed. This decreases the latency caused by difficult-to-process items. Yields: The next result obtained from applying `func` to each input value. May contain `None`-s if `ignore_exceptions=True`. May have different order than the input if `unordered=True`. Raises: WorkerException: If there was an error in the `func` function in a background thread and `ignore_exceptions=False`. \"\"\" config = get_config ( function = func , input_values = input_values , chunk_size = chunk_size , concurrency = concurrency , ) input_queue : queue . Queue = queue . Queue ( config . concurrency * 2 ) output_queue : queue . Queue = queue . Queue ( config . concurrency * 2 ) should_stop = threading . Event () threads = [ threading . Thread ( name = f \"threaded_parallel_map_ { config . function_name } _ { i } \" , target = mapper_function , daemon = True , kwargs = dict ( input_queue = input_queue , output_queue = output_queue , should_stop = should_stop , func = func , ), ) for i in range ( config . concurrency ) ] for t in threads : t . start () yield from manage_communication ( input_values = input_values , chunk_size = config . chunk_size , input_queue = input_queue , output_queue = output_queue , unordered = unordered , ignore_exceptions = ignore_exceptions , ) should_stop . set () for t in threads : t . join ( 1 )","title":"threaded_parallel_map()"},{"location":"reference/utilities/#composable-parallel-processing","text":"Because both threaded_parallel_map and parallel_map have a streaming interface, it is easy to compose them and end up with, for example, a process for each CPU core with its own thread-pool or event-loop. Longer pipelines are also easy to imagine. The chunking methods help in these compositions.","title":"Composable parallel processing"},{"location":"reference/utilities/#great_ai.utilities.chunk.chunk","text":"Turn an iterable of items into an iterable of lists (chunks) of items. Each returned chunk is of length chunk_size except the last one the length of which is between 1 and chunk_size . Useful for parallel processing. Examples: >>> list ( chunk ( range ( 10 ), chunk_size = 3 )) [[0, 1, 2], [3, 4, 5], [6, 7, 8], [9]] Parameters: Name Type Description Default values Iterable [ T ] The stream of items to pack into chunks. required chunk_size int Desired length of each (but the last) chunk. required Yields: Type Description Iterable [ List [ T ]] The next chunk. Source code in great_ai/utilities/chunk.py 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 def chunk ( values : Iterable [ T ], chunk_size : int ) -> Iterable [ List [ T ]]: \"\"\"Turn an iterable of items into an iterable of lists (chunks) of items. Each returned chunk is of length `chunk_size` except the last one the length of which is between 1 and `chunk_size`. Useful for parallel processing. Examples: >>> list(chunk(range(10), chunk_size=3)) [[0, 1, 2], [3, 4, 5], [6, 7, 8], [9]] Args: values: The stream of items to pack into chunks. chunk_size: Desired length of each (but the last) chunk. Yields: The next chunk. \"\"\" assert chunk_size >= 1 result : List [ T ] = [] for v in values : result . append ( v ) if len ( result ) == chunk_size : yield result result = [] if len ( result ) > 0 : yield result","title":"chunk()"},{"location":"reference/utilities/#great_ai.utilities.unchunk.unchunk","text":"Turn a stream of chunks of items into a stream of items (flatten operation). The inverse operation of chunk . Useful for parallel processing. Similar to itertools.chain but ignores None chunks. Examples: >>> list ( unchunk ([[ 0 , 1 , 2 ], [ 3 , 4 , 5 ], [ 6 , 7 , 8 ], [ 9 ]])) [0, 1, 2, 3, 4, 5, 6, 7, 8, 9] Parameters: Name Type Description Default chunks Iterable [ Optional [ Iterable [ T ]]] Stream of chunks to unpack. required Yields: Type Description Iterable [ T ] The next item in the flattened iterable. Source code in great_ai/utilities/unchunk.py 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 def unchunk ( chunks : Iterable [ Optional [ Iterable [ T ]]]) -> Iterable [ T ]: \"\"\"Turn a stream of chunks of items into a stream of items (flatten operation). The inverse operation of [chunk][great_ai.utilities.chunk.chunk]. Useful for parallel processing. Similar to itertools.chain but ignores `None` chunks. Examples: >>> list(unchunk([[0, 1, 2], [3, 4, 5], [6, 7, 8], [9]])) [0, 1, 2, 3, 4, 5, 6, 7, 8, 9] Args: chunks: Stream of chunks to unpack. Yields: The next item in the flattened iterable. \"\"\" for chunk in chunks : if chunk is not None : yield from chunk","title":"unchunk()"},{"location":"reference/utilities/#operations","text":"","title":"Operations"},{"location":"reference/utilities/#great_ai.utilities.ConfigFile","text":"Bases: Mapping [ str , str ] A small and safe INI -style configuration loader with dict and ENV support. The values can be accessed using both dot- and index-notation. It is compatible with the dict interface. File format example: # comments are allowed everywhere key = value # you can leave or omit whitespace around the equal-sign my_hashtag = \"#great_ai\" # the r-value can be quoted with \" or ' or `. my_var = my_default_value # Default values can be given to env-vars, # see next line. The default value must come first. my_var = ENV : MY_ENV_VAR # If the value starts with the `ENV:` prefix, # it is looked up from the environment variables. Examples: >>> ConfigFile ( 'tests/utilities/data/simple.conf' ) ConfigFile(path=tests/utilities/data/simple.conf) {'zeroth_key': 'test', 'first_key': 'Andr\u00e1s'} >>> ConfigFile ( 'tests/utilities/data/simple.conf' ) . zeroth_key 'test' >>> ConfigFile ( 'tests/utilities/data/simple.conf' ) . second_key Traceback (most recent call last): ... KeyError : 'Key `second_key` is not found in configuration file ... >>> a = ConfigFile ( 'tests/utilities/data/simple.conf' ) >>> { ** a } {'zeroth_key': 'test', 'first_key': 'Andr\u00e1s'} Source code in great_ai/utilities/config_file/config_file.py 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 class ConfigFile ( Mapping [ str , str ]): \"\"\"A small and safe `INI`-style configuration loader with `dict` and `ENV` support. The values can be accessed using both dot- and index-notation. It is compatible with the `dict` interface. File format example: ```toml # comments are allowed everywhere key = value # you can leave or omit whitespace around the equal-sign my_hashtag = \"#great_ai\" # the r-value can be quoted with \" or ' or `. my_var = my_default_value # Default values can be given to env-vars, # see next line. The default value must come first. my_var = ENV:MY_ENV_VAR # If the value starts with the `ENV:` prefix, # it is looked up from the environment variables. ``` Examples: >>> ConfigFile('tests/utilities/data/simple.conf') ConfigFile(path=tests/utilities/data/simple.conf) {'zeroth_key': 'test', 'first_key': 'Andr\u00e1s'} >>> ConfigFile('tests/utilities/data/simple.conf').zeroth_key 'test' >>> ConfigFile('tests/utilities/data/simple.conf').second_key Traceback (most recent call last): ... KeyError: 'Key `second_key` is not found in configuration file ... >>> a = ConfigFile('tests/utilities/data/simple.conf') >>> {**a} {'zeroth_key': 'test', 'first_key': 'Andr\u00e1s'} \"\"\" ENVIRONMENT_VARIABLE_KEY_PREFIX = \"ENV\" def __init__ ( self , path : Union [ Path , str ], * , ignore_missing : bool = False ) -> None : \"\"\"Load and parse a configuration file. Everything is eager-loaded, thus, exceptions may be thrown here. Args: path: Local path of the configuration file. ignore_missing: Don't raise an exception on missing environment variables. Raises: FileNotFoundError: If there is no file at the specified path. ParseError: If the provided file does not conform to the expected format. KeyError: If there is duplication in the keys. ValueError: If an environment variable is referenced but it is not set in the system and `ignore_missing=False`. \"\"\" if not isinstance ( path , Path ): path = Path ( path ) if not path . exists (): raise FileNotFoundError ( path . absolute ()) self . _ignore_missing = ignore_missing self . _path = path self . _key_values : Dict [ str , str ] = {} self . _parse () @property def path ( self ) -> Path : \"\"\"Original path from where the configuration was loaded.\"\"\" return self . _path def _parse ( self ) -> None : with open ( self . _path , encoding = \"utf-8\" ) as f : lines : str = f . read () matches = pattern . findall ( lines ) for key , * values in matches : try : value = next ( v for v in values if v ) except StopIteration : raise ParseError ( f \"\"\"Cannot parse config file ( { self . _path . absolute () } ), error at key ` { key } `\"\"\" ) already_exists = key in self . _key_values if already_exists and not value . startswith ( f \" { self . ENVIRONMENT_VARIABLE_KEY_PREFIX } :\" ): raise KeyError ( f \"Key ` { key } ` has been already defined and its value is ` { self . _key_values [ key ] } `\" ) if value . startswith ( f \" { self . ENVIRONMENT_VARIABLE_KEY_PREFIX } :\" ): _ , value = value . split ( \":\" ) if value not in os . environ : issue = f \"\"\"The value of ` { key } ` contains the \" { self . ENVIRONMENT_VARIABLE_KEY_PREFIX } ` prefix but ` { value } ` is not defined as an environment variable\"\"\" if already_exists : logger . warning ( f \"\"\" { issue } , using the default value defined above (` { self . _key_values [ key ] } `)\"\"\" ) continue elif self . _ignore_missing : logger . warning ( issue ) else : raise ValueError ( f \" { issue } and no default value has been provided\" ) else : value = os . environ [ value ] self . _key_values [ key ] = value def __getattr__ ( self , key : str ) -> str : if key in self . _key_values : return self . _key_values [ key ] raise KeyError ( f \"Key ` { key } ` is not found in configuration file ( { self . _path . absolute () } )\" ) __getitem__ = __getattr__ def __iter__ ( self ) -> Iterator [ str ]: return iter ( self . _key_values ) def __len__ ( self ) -> int : return len ( self . _key_values ) def keys ( self ) -> KeysView [ str ]: return self . _key_values . keys () def values ( self ) -> ValuesView [ str ]: return self . _key_values . values () def items ( self ) -> ItemsView [ str , str ]: return self . _key_values . items () def __repr__ ( self ) -> str : return f \" { type ( self ) . __name__ } (path= { self . _path . as_posix () } ) { self . _key_values } \"","title":"ConfigFile"},{"location":"reference/utilities/#great_ai.utilities.config_file.config_file.ConfigFile.__init__","text":"Load and parse a configuration file. Everything is eager-loaded, thus, exceptions may be thrown here. Parameters: Name Type Description Default path Union [ Path , str ] Local path of the configuration file. required ignore_missing bool Don't raise an exception on missing environment variables. False Raises: Type Description FileNotFoundError If there is no file at the specified path. ParseError If the provided file does not conform to the expected format. KeyError If there is duplication in the keys. ValueError If an environment variable is referenced but it is not set in the system and ignore_missing=False . Source code in great_ai/utilities/config_file/config_file.py 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 def __init__ ( self , path : Union [ Path , str ], * , ignore_missing : bool = False ) -> None : \"\"\"Load and parse a configuration file. Everything is eager-loaded, thus, exceptions may be thrown here. Args: path: Local path of the configuration file. ignore_missing: Don't raise an exception on missing environment variables. Raises: FileNotFoundError: If there is no file at the specified path. ParseError: If the provided file does not conform to the expected format. KeyError: If there is duplication in the keys. ValueError: If an environment variable is referenced but it is not set in the system and `ignore_missing=False`. \"\"\" if not isinstance ( path , Path ): path = Path ( path ) if not path . exists (): raise FileNotFoundError ( path . absolute ()) self . _ignore_missing = ignore_missing self . _path = path self . _key_values : Dict [ str , str ] = {} self . _parse ()","title":"__init__()"},{"location":"reference/utilities/#great_ai.utilities.config_file.config_file.ConfigFile.path","text":"Original path from where the configuration was loaded. Source code in great_ai/utilities/config_file/config_file.py 83 84 85 86 @property def path ( self ) -> Path : \"\"\"Original path from where the configuration was loaded.\"\"\" return self . _path","title":"path()"},{"location":"reference/utilities/#great_ai.utilities.get_logger","text":"Return a customised logger used throughout the GreatAI codebase. Uses colors, and only prints timestamps when not running inside notebook. Source code in great_ai/utilities/logger/get_logger.py 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 def get_logger ( name : str , level : int = logging . INFO , disable_colors : bool = False ) -> logging . Logger : \"\"\"Return a customised logger used throughout the GreatAI codebase. Uses colors, and only prints timestamps when not running inside notebook. \"\"\" if name not in loggers : logger = logging . getLogger ( name ) logger . setLevel ( level ) try : get_ipython () # type: ignore log_format = \" %(message)s \" except NameError : # will fail outside of a notebook https://ipython.org/ log_format = \" %(asctime)s | %(levelname)8s | %(message)s \" stdout_handler = logging . StreamHandler () stdout_handler . setLevel ( level ) if not disable_colors : stdout_handler . setFormatter ( CustomFormatter ( log_format )) logger . addHandler ( stdout_handler ) loggers [ name ] = logger return loggers [ name ]","title":"get_logger()"},{"location":"reference/views/","text":"View models # Trace # Bases: Generic [ T ] , HashableBaseModel Universal structure for storing prediction traces and training data. Attributes: Name Type Description trace_id str UUID4 identifier for uniquely referring to a trace. created str Timestamp of its (original) construction. original_execution_time_ms float Wall-time elapsed while its generating TracingContext was alive. logged_values Dict [ str , Any ] Values persisted through using @parameter or log_metric() . models List [ Model ] Marks left by each encountered @use_model decorated function. exception Optional [ str ] Exception description if any was encountered. output Optional [ T ] Return value of the function wrapped by GreatAI. feedback Any Feedback obtained using the REST API of add_ground_truth . tags List [ str ] Tags used for filtering traces. Contains the name of the original function, value of ENVIRONMENT , its split if has any, and either ground_truth or online depending on the origin of the Trace. Source code in great_ai/views/trace.py 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 class Trace ( Generic [ T ], HashableBaseModel ): \"\"\"Universal structure for storing prediction traces and training data. Attributes: trace_id: UUID4 identifier for uniquely referring to a trace. created: Timestamp of its (original) construction. original_execution_time_ms: Wall-time elapsed while its generating TracingContext was alive. logged_values: Values persisted through using `@parameter` or `log_metric()`. models: Marks left by each encountered `@use_model` decorated function. exception: Exception description if any was encountered. output: Return value of the function wrapped by GreatAI. feedback: Feedback obtained using the REST API of `add_ground_truth`. tags: Tags used for filtering traces. Contains the name of the original function, value of `ENVIRONMENT`, its split if has any, and either `ground_truth` or `online` depending on the origin of the Trace. \"\"\" trace_id : str created : str original_execution_time_ms : float logged_values : Dict [ str , Any ] models : List [ Model ] exception : Optional [ str ] output : Optional [ T ] feedback : Any = None tags : List [ str ] class Config : extra = Extra . ignore @property def input ( self ) -> Any : return ( self . logged_values [ \"input\" ] if list ( self . logged_values . keys ()) == [ \"input\" ] else self . logged_values ) @property def models_flat ( self ) -> str : return \", \" . join ( f \" { m . key } : { m . version } \" for m in self . models ) @property def output_flat ( self ) -> str : return pformat ( self . output , indent = 2 , compact = True ) @property def exception_flat ( self ) -> str : return ( \"null\" if self . exception is None else pformat ( self . exception , indent = 2 , compact = True ) ) @property def feedback_flat ( self ) -> str : return ( \"null\" if self . feedback is None else pformat ( self . feedback , indent = 2 , compact = True ) ) @property def tags_flat ( self ) -> str : return \", \\n \" . join ( self . tags ) def to_flat_dict ( self , include_original : bool = True ) -> Dict [ str , Any ]: return { ** ( self . dict () if include_original else { \"trace_id\" : self . trace_id , \"created\" : self . created , \"original_execution_time_ms\" : self . original_execution_time_ms , } ), ** { k : pformat ( v , indent = 2 , compact = True ) for k , v in self . logged_values . items () }, \"models_flat\" : self . models_flat , \"exception_flat\" : self . exception_flat , \"output_flat\" : self . output_flat , \"feedback_flat\" : self . feedback_flat , \"tags_flat\" : self . tags_flat , } def __repr__ ( self ) -> str : return f \"\"\"Trace[ { type ( self . output ) . __name__ } ]( { pformat ( self . dict (), indent = 2 , compact = True ) . replace ( '{ ' , '{' , 1 ) } )\"\"\" RouteConfig # Bases: BaseModel Source code in great_ai/views/route_config.py 4 5 6 7 8 9 10 class RouteConfig ( BaseModel ): prediction_endpoint_enabled : bool = True docs_endpoints_enabled : bool = True dashboard_enabled : bool = True feedback_endpoints_enabled : bool = True trace_endpoints_enabled : bool = True meta_endpoints_enabled : bool = True ClassificationOutput # Bases: HashableBaseModel Source code in great_ai/views/outputs/classification_output.py 6 7 8 9 class ClassificationOutput ( HashableBaseModel ): label : Union [ str , int ] confidence : float explanation : Optional [ Any ] MultiLabelClassificationOutput # Bases: HashableBaseModel Source code in great_ai/views/outputs/multi_label_classification_output.py 7 8 class MultiLabelClassificationOutput ( HashableBaseModel ): labels : List [ ClassificationOutput ] = [] RegressionOutput # Bases: HashableBaseModel Source code in great_ai/views/outputs/regression_output.py 6 7 8 class RegressionOutput ( HashableBaseModel ): value : Union [ int , float ] explanation : Optional [ Any ] SequenceLabelingOutput # Bases: HashableBaseModel Source code in great_ai/views/outputs/sequence_labeling_output.py 15 16 17 class SequenceLabelingOutput ( HashableBaseModel ): labeled_tokens : List [ LabeledToken ] explanation : Optional [ Any ]","title":"View models"},{"location":"reference/views/#view-models","text":"","title":"View models"},{"location":"reference/views/#great_ai.Trace","text":"Bases: Generic [ T ] , HashableBaseModel Universal structure for storing prediction traces and training data. Attributes: Name Type Description trace_id str UUID4 identifier for uniquely referring to a trace. created str Timestamp of its (original) construction. original_execution_time_ms float Wall-time elapsed while its generating TracingContext was alive. logged_values Dict [ str , Any ] Values persisted through using @parameter or log_metric() . models List [ Model ] Marks left by each encountered @use_model decorated function. exception Optional [ str ] Exception description if any was encountered. output Optional [ T ] Return value of the function wrapped by GreatAI. feedback Any Feedback obtained using the REST API of add_ground_truth . tags List [ str ] Tags used for filtering traces. Contains the name of the original function, value of ENVIRONMENT , its split if has any, and either ground_truth or online depending on the origin of the Trace. Source code in great_ai/views/trace.py 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 class Trace ( Generic [ T ], HashableBaseModel ): \"\"\"Universal structure for storing prediction traces and training data. Attributes: trace_id: UUID4 identifier for uniquely referring to a trace. created: Timestamp of its (original) construction. original_execution_time_ms: Wall-time elapsed while its generating TracingContext was alive. logged_values: Values persisted through using `@parameter` or `log_metric()`. models: Marks left by each encountered `@use_model` decorated function. exception: Exception description if any was encountered. output: Return value of the function wrapped by GreatAI. feedback: Feedback obtained using the REST API of `add_ground_truth`. tags: Tags used for filtering traces. Contains the name of the original function, value of `ENVIRONMENT`, its split if has any, and either `ground_truth` or `online` depending on the origin of the Trace. \"\"\" trace_id : str created : str original_execution_time_ms : float logged_values : Dict [ str , Any ] models : List [ Model ] exception : Optional [ str ] output : Optional [ T ] feedback : Any = None tags : List [ str ] class Config : extra = Extra . ignore @property def input ( self ) -> Any : return ( self . logged_values [ \"input\" ] if list ( self . logged_values . keys ()) == [ \"input\" ] else self . logged_values ) @property def models_flat ( self ) -> str : return \", \" . join ( f \" { m . key } : { m . version } \" for m in self . models ) @property def output_flat ( self ) -> str : return pformat ( self . output , indent = 2 , compact = True ) @property def exception_flat ( self ) -> str : return ( \"null\" if self . exception is None else pformat ( self . exception , indent = 2 , compact = True ) ) @property def feedback_flat ( self ) -> str : return ( \"null\" if self . feedback is None else pformat ( self . feedback , indent = 2 , compact = True ) ) @property def tags_flat ( self ) -> str : return \", \\n \" . join ( self . tags ) def to_flat_dict ( self , include_original : bool = True ) -> Dict [ str , Any ]: return { ** ( self . dict () if include_original else { \"trace_id\" : self . trace_id , \"created\" : self . created , \"original_execution_time_ms\" : self . original_execution_time_ms , } ), ** { k : pformat ( v , indent = 2 , compact = True ) for k , v in self . logged_values . items () }, \"models_flat\" : self . models_flat , \"exception_flat\" : self . exception_flat , \"output_flat\" : self . output_flat , \"feedback_flat\" : self . feedback_flat , \"tags_flat\" : self . tags_flat , } def __repr__ ( self ) -> str : return f \"\"\"Trace[ { type ( self . output ) . __name__ } ]( { pformat ( self . dict (), indent = 2 , compact = True ) . replace ( '{ ' , '{' , 1 ) } )\"\"\"","title":"Trace"},{"location":"reference/views/#great_ai.RouteConfig","text":"Bases: BaseModel Source code in great_ai/views/route_config.py 4 5 6 7 8 9 10 class RouteConfig ( BaseModel ): prediction_endpoint_enabled : bool = True docs_endpoints_enabled : bool = True dashboard_enabled : bool = True feedback_endpoints_enabled : bool = True trace_endpoints_enabled : bool = True meta_endpoints_enabled : bool = True","title":"RouteConfig"},{"location":"reference/views/#great_ai.ClassificationOutput","text":"Bases: HashableBaseModel Source code in great_ai/views/outputs/classification_output.py 6 7 8 9 class ClassificationOutput ( HashableBaseModel ): label : Union [ str , int ] confidence : float explanation : Optional [ Any ]","title":"ClassificationOutput"},{"location":"reference/views/#great_ai.MultiLabelClassificationOutput","text":"Bases: HashableBaseModel Source code in great_ai/views/outputs/multi_label_classification_output.py 7 8 class MultiLabelClassificationOutput ( HashableBaseModel ): labels : List [ ClassificationOutput ] = []","title":"MultiLabelClassificationOutput"},{"location":"reference/views/#great_ai.RegressionOutput","text":"Bases: HashableBaseModel Source code in great_ai/views/outputs/regression_output.py 6 7 8 class RegressionOutput ( HashableBaseModel ): value : Union [ int , float ] explanation : Optional [ Any ]","title":"RegressionOutput"},{"location":"reference/views/#great_ai.SequenceLabelingOutput","text":"Bases: HashableBaseModel Source code in great_ai/views/outputs/sequence_labeling_output.py 15 16 17 class SequenceLabelingOutput ( HashableBaseModel ): labeled_tokens : List [ LabeledToken ] explanation : Optional [ Any ]","title":"SequenceLabelingOutput"},{"location":"tutorial/","text":"Train and deploy a SOTA model # Let's see great-ai in action by going over the life-cycle of a simple service. Objectives # You will see how the great_ai.utilities can integrate into your Data Science workflow. You will use great_ai.large_file to version and store your trained model. You will use GreatAI to prepare your model for a robust and responsible deployment. Overview # You are going to train a field of study (domain) classifier for scientific sentences. The exact task was proposed by the SciBERT paper in which SciBERT achieved an F1-score of 0.6571 . We are going to outperform it using a trivial text classification model: a Linear SVM . We use the same synthetic dataset derived from the Microsoft Academic Graph . The dataset is available here . Success You are ready to start the tutorial. Feel free to come back to the summary section once you're finished. Train it Deploy it Summary # Training notebook # We load and preprocess the dataset while relying on great_ai.utilities.clean for doing the heavy-lifting. Additionally, the preprocessing is parallelised using great_ai.utilities.simple_parallel_map After training and evaluating a model, it is exported using great_ai.save_model . Remote storage To store your model remotely, you need to set your credentials before calling save_model . For example, to use AWS S3 : from great_ai.large_file import LargeFileS3 LargeFileS3 . configure ( aws_region_name = 'eu-west-2' , aws_access_key_id = 'MY_AWS_ACCESS_KEY' , aws_secret_access_key = 'MY_AWS_SECRET_KEY' , large_files_bucket_name = 'my_bucket_for_models' ) from great_ai import save_model save_model ( model , key = 'my-domain-predictor' ) For more info, checkout the configuration how-to page . Deployment notebook # We create an inference function that can be hardened by wrapping it in a GreatAI instance. from great_ai import GreatAI , use_model from great_ai.utilities import clean @GreatAI . create @use_model ( 'my-domain-predictor' ) #(1) def predict_domain ( sentence , model ): inputs = [ clean ( sentence )] return str ( model . predict ( inputs )[ 0 ]) @use_model loads and injects your model into the predict_domain function's model argument. You can freely reference it knowing that the function is always provided with it. Finally, we test the model's inference function through the GreatAI dashboard. The only thing left is to deploy the hardened service properly. Learn about all the features Look at more examples","title":"Tutorial overview"},{"location":"tutorial/#train-and-deploy-a-sota-model","text":"Let's see great-ai in action by going over the life-cycle of a simple service.","title":"Train and deploy a SOTA model"},{"location":"tutorial/#objectives","text":"You will see how the great_ai.utilities can integrate into your Data Science workflow. You will use great_ai.large_file to version and store your trained model. You will use GreatAI to prepare your model for a robust and responsible deployment.","title":"Objectives"},{"location":"tutorial/#overview","text":"You are going to train a field of study (domain) classifier for scientific sentences. The exact task was proposed by the SciBERT paper in which SciBERT achieved an F1-score of 0.6571 . We are going to outperform it using a trivial text classification model: a Linear SVM . We use the same synthetic dataset derived from the Microsoft Academic Graph . The dataset is available here . Success You are ready to start the tutorial. Feel free to come back to the summary section once you're finished. Train it Deploy it","title":"Overview"},{"location":"tutorial/#summary","text":"","title":"Summary"},{"location":"tutorial/#training-notebook","text":"We load and preprocess the dataset while relying on great_ai.utilities.clean for doing the heavy-lifting. Additionally, the preprocessing is parallelised using great_ai.utilities.simple_parallel_map After training and evaluating a model, it is exported using great_ai.save_model . Remote storage To store your model remotely, you need to set your credentials before calling save_model . For example, to use AWS S3 : from great_ai.large_file import LargeFileS3 LargeFileS3 . configure ( aws_region_name = 'eu-west-2' , aws_access_key_id = 'MY_AWS_ACCESS_KEY' , aws_secret_access_key = 'MY_AWS_SECRET_KEY' , large_files_bucket_name = 'my_bucket_for_models' ) from great_ai import save_model save_model ( model , key = 'my-domain-predictor' ) For more info, checkout the configuration how-to page .","title":"Training notebook"},{"location":"tutorial/#deployment-notebook","text":"We create an inference function that can be hardened by wrapping it in a GreatAI instance. from great_ai import GreatAI , use_model from great_ai.utilities import clean @GreatAI . create @use_model ( 'my-domain-predictor' ) #(1) def predict_domain ( sentence , model ): inputs = [ clean ( sentence )] return str ( model . predict ( inputs )[ 0 ]) @use_model loads and injects your model into the predict_domain function's model argument. You can freely reference it knowing that the function is always provided with it. Finally, we test the model's inference function through the GreatAI dashboard. The only thing left is to deploy the hardened service properly. Learn about all the features Look at more examples","title":"Deployment notebook"},{"location":"tutorial/deploy/","text":"(function (global, factory) { typeof exports === 'object' && typeof module !== 'undefined' ? module.exports = factory() : typeof define === 'function' && define.amd ? define(factory) : (global = global || self, global.ClipboardCopyElement = factory()); }(this, function () { 'use strict'; function createNode(text) { const node = document.createElement('pre'); node.style.width = '1px'; node.style.height = '1px'; node.style.position = 'fixed'; node.style.top = '5px'; node.textContent = text; return node; } function copyNode(node) { if ('clipboard' in navigator) { // eslint-disable-next-line flowtype/no-flow-fix-me-comments // $FlowFixMe Clipboard is not defined in Flow yet. return navigator.clipboard.writeText(node.textContent); } const selection = getSelection(); if (selection == null) { return Promise.reject(new Error()); } selection.removeAllRanges(); const range = document.createRange(); 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const node = root.getElementById(id); if (node) copyTarget(node).then(trigger); } } function copyTarget(content) { if (content instanceof HTMLInputElement || content instanceof HTMLTextAreaElement) { return copyText(content.value); } else if (content instanceof HTMLAnchorElement && content.hasAttribute('href')) { return copyText(content.href); } else { return copyNode(content); } } function clicked(event) { const button = event.currentTarget; if (button instanceof HTMLElement) { copy(button); } } function keydown(event) { if (event.key === ' ' || event.key === 'Enter') { const button = event.currentTarget; if (button instanceof HTMLElement) { event.preventDefault(); copy(button); } } } function focused(event) { event.currentTarget.addEventListener('keydown', keydown); } function blurred(event) { event.currentTarget.removeEventListener('keydown', keydown); } class ClipboardCopyElement extends HTMLElement { constructor() { super(); this.addEventListener('click', clicked); this.addEventListener('focus', focused); this.addEventListener('blur', blurred); } connectedCallback() { if (!this.hasAttribute('tabindex')) { this.setAttribute('tabindex', '0'); } if (!this.hasAttribute('role')) { this.setAttribute('role', 'button'); } } get value() { return this.getAttribute('value') || ''; } set value(text) { this.setAttribute('value', text); } } if (!window.customElements.get('clipboard-copy')) { window.ClipboardCopyElement = ClipboardCopyElement; window.customElements.define('clipboard-copy', ClipboardCopyElement); } return ClipboardCopyElement; })); document.addEventListener('clipboard-copy', function(event) { const notice = event.target.querySelector('.notice') notice.hidden = false setTimeout(function() { notice.hidden = true }, 1000) }) pre { line-height: 125%; } td.linenos .normal { color: inherit; background-color: transparent; padding-left: 5px; padding-right: 5px; } span.linenos { color: inherit; background-color: transparent; padding-left: 5px; padding-right: 5px; } td.linenos .special { color: #000000; background-color: #ffffc0; padding-left: 5px; padding-right: 5px; } span.linenos.special { color: #000000; background-color: #ffffc0; padding-left: 5px; padding-right: 5px; } .highlight-ipynb .hll { background-color: var(--jp-cell-editor-active-background) } .highlight-ipynb { background: var(--jp-cell-editor-background); color: var(--jp-mirror-editor-variable-color) } .highlight-ipynb .c { color: var(--jp-mirror-editor-comment-color); font-style: italic } /* Comment */ .highlight-ipynb .err { color: var(--jp-mirror-editor-error-color) } /* Error */ .highlight-ipynb .k { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword */ .highlight-ipynb .o { color: var(--jp-mirror-editor-operator-color); font-weight: bold } /* Operator */ .highlight-ipynb .p { color: var(--jp-mirror-editor-punctuation-color) } /* Punctuation */ .highlight-ipynb .ch { color: var(--jp-mirror-editor-comment-color); font-style: italic } /* Comment.Hashbang */ .highlight-ipynb .cm { color: var(--jp-mirror-editor-comment-color); font-style: italic } /* Comment.Multiline */ .highlight-ipynb .cp { color: var(--jp-mirror-editor-comment-color); font-style: italic } /* Comment.Preproc */ .highlight-ipynb .cpf { color: var(--jp-mirror-editor-comment-color); font-style: italic } /* Comment.PreprocFile */ .highlight-ipynb .c1 { color: var(--jp-mirror-editor-comment-color); font-style: italic } /* Comment.Single */ .highlight-ipynb .cs { color: var(--jp-mirror-editor-comment-color); font-style: italic } /* Comment.Special */ .highlight-ipynb .kc { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword.Constant */ .highlight-ipynb .kd { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword.Declaration */ .highlight-ipynb .kn { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword.Namespace */ .highlight-ipynb .kp { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword.Pseudo */ .highlight-ipynb .kr { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword.Reserved */ .highlight-ipynb .kt { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword.Type */ .highlight-ipynb .m { color: var(--jp-mirror-editor-number-color) } /* Literal.Number */ .highlight-ipynb .s { color: var(--jp-mirror-editor-string-color) } /* Literal.String */ .highlight-ipynb .ow { color: var(--jp-mirror-editor-operator-color); font-weight: bold } /* Operator.Word */ .highlight-ipynb .w { color: var(--jp-mirror-editor-variable-color) } /* Text.Whitespace */ .highlight-ipynb .mb { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Bin */ .highlight-ipynb .mf { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Float */ .highlight-ipynb .mh { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Hex */ .highlight-ipynb .mi { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Integer */ .highlight-ipynb .mo { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Oct */ .highlight-ipynb .sa { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Affix */ .highlight-ipynb .sb { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Backtick */ .highlight-ipynb .sc { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Char */ .highlight-ipynb .dl { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Delimiter */ .highlight-ipynb .sd { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Doc */ .highlight-ipynb .s2 { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Double */ .highlight-ipynb .se { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Escape */ .highlight-ipynb .sh { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Heredoc */ .highlight-ipynb .si { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Interpol */ .highlight-ipynb .sx { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Other */ .highlight-ipynb .sr { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Regex */ .highlight-ipynb .s1 { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Single */ .highlight-ipynb .ss { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Symbol */ .highlight-ipynb .il { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Integer.Long */ /* This file is taken from the built JupyterLab theme.css Found on share/nbconvert/templates/lab/static Some changes have been made and marked with CHANGE */ .jupyter-wrapper { /* Elevation * * We style box-shadows using Material Design's idea of elevation. These particular numbers are taken from here: * * https://github.com/material-components/material-components-web * https://material-components-web.appspot.com/elevation.html */ --jp-shadow-base-lightness: 0; --jp-shadow-umbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.2 ); --jp-shadow-penumbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.14 ); --jp-shadow-ambient-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.12 ); --jp-elevation-z0: none; --jp-elevation-z1: 0px 2px 1px -1px var(--jp-shadow-umbra-color), 0px 1px 1px 0px var(--jp-shadow-penumbra-color), 0px 1px 3px 0px var(--jp-shadow-ambient-color); --jp-elevation-z2: 0px 3px 1px -2px var(--jp-shadow-umbra-color), 0px 2px 2px 0px var(--jp-shadow-penumbra-color), 0px 1px 5px 0px var(--jp-shadow-ambient-color); --jp-elevation-z4: 0px 2px 4px -1px var(--jp-shadow-umbra-color), 0px 4px 5px 0px var(--jp-shadow-penumbra-color), 0px 1px 10px 0px var(--jp-shadow-ambient-color); --jp-elevation-z6: 0px 3px 5px -1px var(--jp-shadow-umbra-color), 0px 6px 10px 0px var(--jp-shadow-penumbra-color), 0px 1px 18px 0px var(--jp-shadow-ambient-color); --jp-elevation-z8: 0px 5px 5px -3px var(--jp-shadow-umbra-color), 0px 8px 10px 1px var(--jp-shadow-penumbra-color), 0px 3px 14px 2px var(--jp-shadow-ambient-color); --jp-elevation-z12: 0px 7px 8px -4px var(--jp-shadow-umbra-color), 0px 12px 17px 2px var(--jp-shadow-penumbra-color), 0px 5px 22px 4px var(--jp-shadow-ambient-color); --jp-elevation-z16: 0px 8px 10px -5px var(--jp-shadow-umbra-color), 0px 16px 24px 2px var(--jp-shadow-penumbra-color), 0px 6px 30px 5px var(--jp-shadow-ambient-color); --jp-elevation-z20: 0px 10px 13px -6px var(--jp-shadow-umbra-color), 0px 20px 31px 3px var(--jp-shadow-penumbra-color), 0px 8px 38px 7px var(--jp-shadow-ambient-color); --jp-elevation-z24: 0px 11px 15px -7px var(--jp-shadow-umbra-color), 0px 24px 38px 3px var(--jp-shadow-penumbra-color), 0px 9px 46px 8px var(--jp-shadow-ambient-color); /* Borders * * The following variables, specify the visual styling of borders in JupyterLab. */ --jp-border-width: 1px; --jp-border-color0: var(--md-grey-400); --jp-border-color1: var(--md-grey-400); --jp-border-color2: var(--md-grey-300); --jp-border-color3: var(--md-grey-200); --jp-border-radius: 2px; /* UI Fonts * * The UI font CSS variables are used for the typography all of the JupyterLab * user interface elements that are not directly user generated content. * * The font sizing here is done assuming that the body font size of --jp-ui-font-size1 * is applied to a parent element. When children elements, such as headings, are sized * in em all things will be computed relative to that body size. */ --jp-ui-font-scale-factor: 1.2; --jp-ui-font-size0: 0.83333em; --jp-ui-font-size1: 13px; /* Base font size */ --jp-ui-font-size2: 1.2em; --jp-ui-font-size3: 1.44em; --jp-ui-font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Helvetica, Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\"; /* * Use these font colors against the corresponding main layout colors. * In a light theme, these go from dark to light. */ /* Defaults use Material Design specification */ --jp-ui-font-color0: rgba(0, 0, 0, 1); --jp-ui-font-color1: rgba(0, 0, 0, 0.87); --jp-ui-font-color2: rgba(0, 0, 0, 0.54); --jp-ui-font-color3: rgba(0, 0, 0, 0.38); /* * Use these against the brand/accent/warn/error colors. * These will typically go from light to darker, in both a dark and light theme. */ --jp-ui-inverse-font-color0: rgba(255, 255, 255, 1); --jp-ui-inverse-font-color1: rgba(255, 255, 255, 1); --jp-ui-inverse-font-color2: rgba(255, 255, 255, 0.7); --jp-ui-inverse-font-color3: rgba(255, 255, 255, 0.5); /* Content Fonts * * Content font variables are used for typography of user generated content. * * The font sizing here is done assuming that the body font size of --jp-content-font-size1 * is applied to a parent element. When children elements, such as headings, are sized * in em all things will be computed relative to that body size. */ --jp-content-line-height: 1.6; --jp-content-font-scale-factor: 1.2; --jp-content-font-size0: 0.83333em; --jp-content-font-size1: 14px; /* Base font size */ --jp-content-font-size2: 1.2em; --jp-content-font-size3: 1.44em; --jp-content-font-size4: 1.728em; --jp-content-font-size5: 2.0736em; /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-content-presentation-font-size1: 17px; --jp-content-heading-line-height: 1; --jp-content-heading-margin-top: 1.2em; --jp-content-heading-margin-bottom: 0.8em; --jp-content-heading-font-weight: 500; /* Defaults use Material Design specification */ --jp-content-font-color0: rgba(0, 0, 0, 1); --jp-content-font-color1: rgba(0, 0, 0, 0.87); --jp-content-font-color2: rgba(0, 0, 0, 0.54); --jp-content-font-color3: rgba(0, 0, 0, 0.38); --jp-content-link-color: var(--md-blue-700); --jp-content-font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Helvetica, Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\"; /* * Code Fonts * * Code font variables are used for typography of code and other monospaces content. */ --jp-code-font-size: 13px; --jp-code-line-height: 1.3077; /* 17px for 13px base */ --jp-code-padding: 5px; /* 5px for 13px base, codemirror highlighting needs integer px value */ --jp-code-font-family-default: Menlo, Consolas, \"DejaVu Sans Mono\", monospace; --jp-code-font-family: var(--jp-code-font-family-default); /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-code-presentation-font-size: 16px; /* may need to tweak cursor width if you change font size */ --jp-code-cursor-width0: 1.4px; --jp-code-cursor-width1: 2px; --jp-code-cursor-width2: 4px; /* Layout * * The following are the main layout colors use in JupyterLab. In a light * theme these would go from light to dark. */ --jp-layout-color0: white; --jp-layout-color1: white; --jp-layout-color2: var(--md-grey-200); --jp-layout-color3: var(--md-grey-400); --jp-layout-color4: var(--md-grey-600); /* Inverse Layout * * The following are the inverse layout colors use in JupyterLab. In a light * theme these would go from dark to light. */ --jp-inverse-layout-color0: #111111; --jp-inverse-layout-color1: var(--md-grey-900); --jp-inverse-layout-color2: var(--md-grey-800); --jp-inverse-layout-color3: var(--md-grey-700); --jp-inverse-layout-color4: var(--md-grey-600); /* Brand/accent */ --jp-brand-color0: var(--md-blue-900); --jp-brand-color1: var(--md-blue-700); --jp-brand-color2: var(--md-blue-300); --jp-brand-color3: var(--md-blue-100); --jp-brand-color4: var(--md-blue-50); --jp-accent-color0: var(--md-green-900); --jp-accent-color1: var(--md-green-700); --jp-accent-color2: var(--md-green-300); --jp-accent-color3: var(--md-green-100); /* State colors (warn, error, success, info) */ --jp-warn-color0: var(--md-orange-900); --jp-warn-color1: var(--md-orange-700); --jp-warn-color2: var(--md-orange-300); --jp-warn-color3: var(--md-orange-100); --jp-error-color0: var(--md-red-900); --jp-error-color1: var(--md-red-700); --jp-error-color2: var(--md-red-300); --jp-error-color3: var(--md-red-100); --jp-success-color0: var(--md-green-900); --jp-success-color1: var(--md-green-700); --jp-success-color2: var(--md-green-300); --jp-success-color3: var(--md-green-100); --jp-info-color0: var(--md-cyan-900); --jp-info-color1: var(--md-cyan-700); --jp-info-color2: var(--md-cyan-300); --jp-info-color3: var(--md-cyan-100); /* Cell specific styles */ --jp-cell-padding: 5px; --jp-cell-collapser-width: 8px; --jp-cell-collapser-min-height: 20px; --jp-cell-collapser-not-active-hover-opacity: 0.6; --jp-cell-editor-background: var(--md-grey-100); --jp-cell-editor-border-color: var(--md-grey-300); --jp-cell-editor-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-cell-editor-active-background: var(--jp-layout-color0); --jp-cell-editor-active-border-color: var(--jp-brand-color1); --jp-cell-prompt-width: 64px; --jp-cell-prompt-font-family: var(--jp-code-font-family-default); --jp-cell-prompt-letter-spacing: 0px; --jp-cell-prompt-opacity: 1; --jp-cell-prompt-not-active-opacity: 0.5; --jp-cell-prompt-not-active-font-color: var(--md-grey-700); /* A custom blend of MD grey and blue 600 * See https://meyerweb.com/eric/tools/color-blend/#546E7A:1E88E5:5:hex */ --jp-cell-inprompt-font-color: #307fc1; /* A custom blend of MD grey and orange 600 * https://meyerweb.com/eric/tools/color-blend/#546E7A:F4511E:5:hex */ --jp-cell-outprompt-font-color: #bf5b3d; /* Notebook specific styles */ --jp-notebook-padding: 10px; --jp-notebook-select-background: var(--jp-layout-color1); --jp-notebook-multiselected-color: var(--md-blue-50); /* The scroll padding is calculated to fill enough space at the bottom of the notebook to show one single-line cell (with appropriate padding) at the top when the notebook is scrolled all the way to the bottom. We also subtract one pixel so that no scrollbar appears if we have just one single-line cell in the notebook. This padding is to enable a 'scroll past end' feature in a notebook. */ --jp-notebook-scroll-padding: calc( 100% - var(--jp-code-font-size) * var(--jp-code-line-height) - var(--jp-code-padding) - var(--jp-cell-padding) - 1px ); /* Rendermime styles */ --jp-rendermime-error-background: #fdd; --jp-rendermime-table-row-background: var(--md-grey-100); --jp-rendermime-table-row-hover-background: var(--md-light-blue-50); /* Dialog specific styles */ --jp-dialog-background: rgba(0, 0, 0, 0.25); /* Console specific styles */ --jp-console-padding: 10px; /* Toolbar specific styles */ --jp-toolbar-border-color: var(--jp-border-color1); --jp-toolbar-micro-height: 8px; --jp-toolbar-background: var(--jp-layout-color1); --jp-toolbar-box-shadow: 0px 0px 2px 0px rgba(0, 0, 0, 0.24); --jp-toolbar-header-margin: 4px 4px 0px 4px; --jp-toolbar-active-background: var(--md-grey-300); /* Statusbar specific styles */ --jp-statusbar-height: 24px; /* Input field styles */ --jp-input-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-input-active-background: var(--jp-layout-color1); --jp-input-hover-background: var(--jp-layout-color1); --jp-input-background: var(--md-grey-100); --jp-input-border-color: var(--jp-border-color1); --jp-input-active-border-color: var(--jp-brand-color1); --jp-input-active-box-shadow-color: rgba(19, 124, 189, 0.3); /* General editor styles */ --jp-editor-selected-background: #d9d9d9; --jp-editor-selected-focused-background: #d7d4f0; --jp-editor-cursor-color: var(--jp-ui-font-color0); /* Code mirror specific styles */ --jp-mirror-editor-keyword-color: #008000; --jp-mirror-editor-atom-color: #88f; --jp-mirror-editor-number-color: #080; --jp-mirror-editor-def-color: #00f; --jp-mirror-editor-variable-color: var(--md-grey-900); --jp-mirror-editor-variable-2-color: #05a; --jp-mirror-editor-variable-3-color: #085; --jp-mirror-editor-punctuation-color: #05a; --jp-mirror-editor-property-color: #05a; --jp-mirror-editor-operator-color: #aa22ff; --jp-mirror-editor-comment-color: #408080; --jp-mirror-editor-string-color: #ba2121; --jp-mirror-editor-string-2-color: #708; --jp-mirror-editor-meta-color: #aa22ff; --jp-mirror-editor-qualifier-color: #555; --jp-mirror-editor-builtin-color: #008000; --jp-mirror-editor-bracket-color: #997; --jp-mirror-editor-tag-color: #170; --jp-mirror-editor-attribute-color: #00c; --jp-mirror-editor-header-color: blue; --jp-mirror-editor-quote-color: #090; --jp-mirror-editor-link-color: #00c; --jp-mirror-editor-error-color: #f00; --jp-mirror-editor-hr-color: #999; /* Vega extension styles */ --jp-vega-background: white; /* Sidebar-related styles */ --jp-sidebar-min-width: 250px; /* Search-related styles */ --jp-search-toggle-off-opacity: 0.5; --jp-search-toggle-hover-opacity: 0.8; --jp-search-toggle-on-opacity: 1; --jp-search-selected-match-background-color: rgb(245, 200, 0); --jp-search-selected-match-color: black; --jp-search-unselected-match-background-color: var( --jp-inverse-layout-color0 ); --jp-search-unselected-match-color: var(--jp-ui-inverse-font-color0); /* Icon colors that work well with light or dark backgrounds */ --jp-icon-contrast-color0: var(--md-purple-600); --jp-icon-contrast-color1: var(--md-green-600); --jp-icon-contrast-color2: var(--md-pink-600); --jp-icon-contrast-color3: var(--md-blue-600); } [data-md-color-scheme=\"slate\"] .jupyter-wrapper { /* Elevation * * We style box-shadows using Material Design's idea of elevation. These particular numbers are taken from here: * * https://github.com/material-components/material-components-web * https://material-components-web.appspot.com/elevation.html */ /* The dark theme shadows need a bit of work, but this will probably also require work on the core layout * colors used in the theme as well. */ --jp-shadow-base-lightness: 32; --jp-shadow-umbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.2 ); --jp-shadow-penumbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.14 ); --jp-shadow-ambient-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.12 ); --jp-elevation-z0: none; --jp-elevation-z1: 0px 2px 1px -1px var(--jp-shadow-umbra-color), 0px 1px 1px 0px var(--jp-shadow-penumbra-color), 0px 1px 3px 0px var(--jp-shadow-ambient-color); --jp-elevation-z2: 0px 3px 1px -2px var(--jp-shadow-umbra-color), 0px 2px 2px 0px var(--jp-shadow-penumbra-color), 0px 1px 5px 0px var(--jp-shadow-ambient-color); --jp-elevation-z4: 0px 2px 4px -1px var(--jp-shadow-umbra-color), 0px 4px 5px 0px var(--jp-shadow-penumbra-color), 0px 1px 10px 0px var(--jp-shadow-ambient-color); --jp-elevation-z6: 0px 3px 5px -1px var(--jp-shadow-umbra-color), 0px 6px 10px 0px var(--jp-shadow-penumbra-color), 0px 1px 18px 0px var(--jp-shadow-ambient-color); --jp-elevation-z8: 0px 5px 5px -3px var(--jp-shadow-umbra-color), 0px 8px 10px 1px var(--jp-shadow-penumbra-color), 0px 3px 14px 2px var(--jp-shadow-ambient-color); --jp-elevation-z12: 0px 7px 8px -4px var(--jp-shadow-umbra-color), 0px 12px 17px 2px var(--jp-shadow-penumbra-color), 0px 5px 22px 4px var(--jp-shadow-ambient-color); --jp-elevation-z16: 0px 8px 10px -5px var(--jp-shadow-umbra-color), 0px 16px 24px 2px var(--jp-shadow-penumbra-color), 0px 6px 30px 5px var(--jp-shadow-ambient-color); --jp-elevation-z20: 0px 10px 13px -6px var(--jp-shadow-umbra-color), 0px 20px 31px 3px var(--jp-shadow-penumbra-color), 0px 8px 38px 7px var(--jp-shadow-ambient-color); --jp-elevation-z24: 0px 11px 15px -7px var(--jp-shadow-umbra-color), 0px 24px 38px 3px var(--jp-shadow-penumbra-color), 0px 9px 46px 8px var(--jp-shadow-ambient-color); /* Borders * * The following variables, specify the visual styling of borders in JupyterLab. */ --jp-border-width: 1px; --jp-border-color0: var(--md-grey-700); --jp-border-color1: var(--md-grey-700); --jp-border-color2: var(--md-grey-800); --jp-border-color3: var(--md-grey-900); --jp-border-radius: 2px; /* UI Fonts * * The UI font CSS variables are used for the typography all of the JupyterLab * user interface elements that are not directly user generated content. * * The font sizing here is done assuming that the body font size of --jp-ui-font-size1 * is applied to a parent element. When children elements, such as headings, are sized * in em all things will be computed relative to that body size. */ --jp-ui-font-scale-factor: 1.2; --jp-ui-font-size0: 0.83333em; --jp-ui-font-size1: 13px; /* Base font size */ --jp-ui-font-size2: 1.2em; --jp-ui-font-size3: 1.44em; --jp-ui-font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Helvetica, Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\"; /* * Use these font colors against the corresponding main layout colors. * In a light theme, these go from dark to light. */ /* Defaults use Material Design specification */ --jp-ui-font-color0: rgba(255, 255, 255, 1); --jp-ui-font-color1: rgba(255, 255, 255, 0.87); --jp-ui-font-color2: rgba(255, 255, 255, 0.54); --jp-ui-font-color3: rgba(255, 255, 255, 0.38); /* * Use these against the brand/accent/warn/error colors. * These will typically go from light to darker, in both a dark and light theme. */ --jp-ui-inverse-font-color0: rgba(0, 0, 0, 1); --jp-ui-inverse-font-color1: rgba(0, 0, 0, 0.8); --jp-ui-inverse-font-color2: rgba(0, 0, 0, 0.5); --jp-ui-inverse-font-color3: rgba(0, 0, 0, 0.3); /* Content Fonts * * Content font variables are used for typography of user generated content. * * The font sizing here is done assuming that the body font size of --jp-content-font-size1 * is applied to a parent element. When children elements, such as headings, are sized * in em all things will be computed relative to that body size. */ --jp-content-line-height: 1.6; --jp-content-font-scale-factor: 1.2; --jp-content-font-size0: 0.83333em; --jp-content-font-size1: 14px; /* Base font size */ --jp-content-font-size2: 1.2em; --jp-content-font-size3: 1.44em; --jp-content-font-size4: 1.728em; --jp-content-font-size5: 2.0736em; /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-content-presentation-font-size1: 17px; --jp-content-heading-line-height: 1; --jp-content-heading-margin-top: 1.2em; --jp-content-heading-margin-bottom: 0.8em; --jp-content-heading-font-weight: 500; /* Defaults use Material Design specification */ --jp-content-font-color0: rgba(255, 255, 255, 1); --jp-content-font-color1: rgba(255, 255, 255, 1); --jp-content-font-color2: rgba(255, 255, 255, 0.7); --jp-content-font-color3: rgba(255, 255, 255, 0.5); --jp-content-link-color: var(--md-blue-300); --jp-content-font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Helvetica, Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\"; /* * Code Fonts * * Code font variables are used for typography of code and other monospaces content. */ --jp-code-font-size: 13px; --jp-code-line-height: 1.3077; /* 17px for 13px base */ --jp-code-padding: 5px; /* 5px for 13px base, codemirror highlighting needs integer px value */ --jp-code-font-family-default: Menlo, Consolas, \"DejaVu Sans Mono\", monospace; --jp-code-font-family: var(--jp-code-font-family-default); /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-code-presentation-font-size: 16px; /* may need to tweak cursor width if you change font size */ --jp-code-cursor-width0: 1.4px; --jp-code-cursor-width1: 2px; --jp-code-cursor-width2: 4px; /* Layout * * The following are the main layout colors use in JupyterLab. In a light * theme these would go from light to dark. */ --jp-layout-color0: #111111; --jp-layout-color1: var(--md-grey-900); --jp-layout-color2: var(--md-grey-800); --jp-layout-color3: var(--md-grey-700); --jp-layout-color4: var(--md-grey-600); /* Inverse Layout * * The following are the inverse layout colors use in JupyterLab. In a light * theme these would go from dark to light. */ --jp-inverse-layout-color0: white; --jp-inverse-layout-color1: white; --jp-inverse-layout-color2: var(--md-grey-200); --jp-inverse-layout-color3: var(--md-grey-400); --jp-inverse-layout-color4: var(--md-grey-600); /* Brand/accent */ --jp-brand-color0: var(--md-blue-700); --jp-brand-color1: var(--md-blue-500); --jp-brand-color2: var(--md-blue-300); --jp-brand-color3: var(--md-blue-100); --jp-brand-color4: var(--md-blue-50); --jp-accent-color0: var(--md-green-700); --jp-accent-color1: var(--md-green-500); --jp-accent-color2: var(--md-green-300); --jp-accent-color3: var(--md-green-100); /* State colors (warn, error, success, info) */ --jp-warn-color0: var(--md-orange-700); --jp-warn-color1: var(--md-orange-500); --jp-warn-color2: var(--md-orange-300); --jp-warn-color3: var(--md-orange-100); --jp-error-color0: var(--md-red-700); --jp-error-color1: var(--md-red-500); --jp-error-color2: var(--md-red-300); --jp-error-color3: var(--md-red-100); --jp-success-color0: var(--md-green-700); --jp-success-color1: var(--md-green-500); --jp-success-color2: var(--md-green-300); --jp-success-color3: var(--md-green-100); --jp-info-color0: var(--md-cyan-700); --jp-info-color1: var(--md-cyan-500); --jp-info-color2: var(--md-cyan-300); --jp-info-color3: var(--md-cyan-100); /* Cell specific styles */ --jp-cell-padding: 5px; --jp-cell-collapser-width: 8px; --jp-cell-collapser-min-height: 20px; --jp-cell-collapser-not-active-hover-opacity: 0.6; --jp-cell-editor-background: var(--jp-layout-color1); --jp-cell-editor-border-color: var(--md-grey-700); --jp-cell-editor-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-cell-editor-active-background: var(--jp-layout-color0); --jp-cell-editor-active-border-color: var(--jp-brand-color1); --jp-cell-prompt-width: 64px; --jp-cell-prompt-font-family: var(--jp-code-font-family-default); --jp-cell-prompt-letter-spacing: 0px; --jp-cell-prompt-opacity: 1; --jp-cell-prompt-not-active-opacity: 1; --jp-cell-prompt-not-active-font-color: var(--md-grey-300); /* A custom blend of MD grey and blue 600 * See https://meyerweb.com/eric/tools/color-blend/#546E7A:1E88E5:5:hex */ --jp-cell-inprompt-font-color: #307fc1; /* A custom blend of MD grey and orange 600 * https://meyerweb.com/eric/tools/color-blend/#546E7A:F4511E:5:hex */ --jp-cell-outprompt-font-color: #bf5b3d; /* Notebook specific styles */ --jp-notebook-padding: 10px; --jp-notebook-select-background: var(--jp-layout-color1); --jp-notebook-multiselected-color: rgba(33, 150, 243, 0.24); /* The scroll padding is calculated to fill enough space at the bottom of the notebook to show one single-line cell (with appropriate padding) at the top when the notebook is scrolled all the way to the bottom. We also subtract one pixel so that no scrollbar appears if we have just one single-line cell in the notebook. This padding is to enable a 'scroll past end' feature in a notebook. */ --jp-notebook-scroll-padding: calc( 100% - var(--jp-code-font-size) * var(--jp-code-line-height) - var(--jp-code-padding) - var(--jp-cell-padding) - 1px ); /* Rendermime styles */ --jp-rendermime-error-background: rgba(244, 67, 54, 0.28); --jp-rendermime-table-row-background: var(--md-grey-900); --jp-rendermime-table-row-hover-background: rgba(3, 169, 244, 0.2); /* Dialog specific styles */ --jp-dialog-background: rgba(0, 0, 0, 0.6); /* Console specific styles */ --jp-console-padding: 10px; /* Toolbar specific styles */ --jp-toolbar-border-color: var(--jp-border-color2); --jp-toolbar-micro-height: 8px; --jp-toolbar-background: var(--jp-layout-color1); --jp-toolbar-box-shadow: 0px 0px 2px 0px rgba(0, 0, 0, 0.8); --jp-toolbar-header-margin: 4px 4px 0px 4px; --jp-toolbar-active-background: var(--jp-layout-color0); /* Statusbar specific styles */ --jp-statusbar-height: 24px; /* Input field styles */ --jp-input-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-input-active-background: var(--jp-layout-color0); --jp-input-hover-background: var(--jp-layout-color2); --jp-input-background: var(--md-grey-800); --jp-input-border-color: var(--jp-border-color1); --jp-input-active-border-color: var(--jp-brand-color1); --jp-input-active-box-shadow-color: rgba(19, 124, 189, 0.3); /* General editor styles */ --jp-editor-selected-background: var(--jp-layout-color2); --jp-editor-selected-focused-background: rgba(33, 150, 243, 0.24); --jp-editor-cursor-color: var(--jp-ui-font-color0); /* Code mirror specific styles */ --jp-mirror-editor-keyword-color: var(--md-green-500); --jp-mirror-editor-atom-color: var(--md-blue-300); --jp-mirror-editor-number-color: var(--md-green-400); --jp-mirror-editor-def-color: var(--md-blue-600); --jp-mirror-editor-variable-color: var(--md-grey-300); --jp-mirror-editor-variable-2-color: var(--md-blue-400); --jp-mirror-editor-variable-3-color: var(--md-green-600); --jp-mirror-editor-punctuation-color: var(--md-blue-400); --jp-mirror-editor-property-color: var(--md-blue-400); --jp-mirror-editor-operator-color: #aa22ff; --jp-mirror-editor-comment-color: #408080; --jp-mirror-editor-string-color: #ff7070; --jp-mirror-editor-string-2-color: var(--md-purple-300); --jp-mirror-editor-meta-color: #aa22ff; --jp-mirror-editor-qualifier-color: #555; --jp-mirror-editor-builtin-color: var(--md-green-600); --jp-mirror-editor-bracket-color: #997; --jp-mirror-editor-tag-color: var(--md-green-700); --jp-mirror-editor-attribute-color: var(--md-blue-700); --jp-mirror-editor-header-color: var(--md-blue-500); --jp-mirror-editor-quote-color: var(--md-green-300); --jp-mirror-editor-link-color: var(--md-blue-700); --jp-mirror-editor-error-color: #f00; --jp-mirror-editor-hr-color: #999; /* Vega extension styles */ --jp-vega-background: var(--md-grey-400); /* Sidebar-related styles */ --jp-sidebar-min-width: 250px; /* Search-related styles */ --jp-search-toggle-off-opacity: 0.6; --jp-search-toggle-hover-opacity: 0.8; --jp-search-toggle-on-opacity: 1; --jp-search-selected-match-background-color: rgb(255, 225, 0); --jp-search-selected-match-color: black; --jp-search-unselected-match-background-color: var( --jp-inverse-layout-color0 ); --jp-search-unselected-match-color: var(--jp-ui-inverse-font-color0); /* scrollbar related styles. Supports every browser except Edge. */ /* colors based on JetBrain's Darcula theme */ --jp-scrollbar-background-color: #3f4244; --jp-scrollbar-thumb-color: 88, 96, 97; /* need to specify thumb color as an RGB triplet */ --jp-scrollbar-endpad: 3px; /* the minimum gap between the thumb and the ends of a scrollbar */ /* hacks for setting the thumb shape. These do nothing in Firefox */ --jp-scrollbar-thumb-margin: 3.5px; /* the space in between the sides of the thumb and the track */ --jp-scrollbar-thumb-radius: 9px; /* set to a large-ish value for rounded endcaps on the thumb */ /* Icon colors that work well with light or dark backgrounds */ --jp-icon-contrast-color0: var(--md-purple-600); --jp-icon-contrast-color1: var(--md-green-600); --jp-icon-contrast-color2: var(--md-pink-600); --jp-icon-contrast-color3: var(--md-blue-600); } :root{--md-red-50: #ffebee;--md-red-100: #ffcdd2;--md-red-200: #ef9a9a;--md-red-300: #e57373;--md-red-400: #ef5350;--md-red-500: #f44336;--md-red-600: #e53935;--md-red-700: #d32f2f;--md-red-800: #c62828;--md-red-900: #b71c1c;--md-red-A100: #ff8a80;--md-red-A200: #ff5252;--md-red-A400: #ff1744;--md-red-A700: #d50000;--md-pink-50: #fce4ec;--md-pink-100: #f8bbd0;--md-pink-200: #f48fb1;--md-pink-300: #f06292;--md-pink-400: #ec407a;--md-pink-500: #e91e63;--md-pink-600: #d81b60;--md-pink-700: #c2185b;--md-pink-800: #ad1457;--md-pink-900: #880e4f;--md-pink-A100: #ff80ab;--md-pink-A200: #ff4081;--md-pink-A400: #f50057;--md-pink-A700: #c51162;--md-purple-50: #f3e5f5;--md-purple-100: #e1bee7;--md-purple-200: #ce93d8;--md-purple-300: #ba68c8;--md-purple-400: #ab47bc;--md-purple-500: #9c27b0;--md-purple-600: #8e24aa;--md-purple-700: #7b1fa2;--md-purple-800: #6a1b9a;--md-purple-900: #4a148c;--md-purple-A100: #ea80fc;--md-purple-A200: #e040fb;--md-purple-A400: #d500f9;--md-purple-A700: #aa00ff;--md-deep-purple-50: #ede7f6;--md-deep-purple-100: #d1c4e9;--md-deep-purple-200: #b39ddb;--md-deep-purple-300: #9575cd;--md-deep-purple-400: #7e57c2;--md-deep-purple-500: #673ab7;--md-deep-purple-600: #5e35b1;--md-deep-purple-700: #512da8;--md-deep-purple-800: #4527a0;--md-deep-purple-900: #311b92;--md-deep-purple-A100: #b388ff;--md-deep-purple-A200: #7c4dff;--md-deep-purple-A400: #651fff;--md-deep-purple-A700: #6200ea;--md-indigo-50: #e8eaf6;--md-indigo-100: #c5cae9;--md-indigo-200: #9fa8da;--md-indigo-300: #7986cb;--md-indigo-400: #5c6bc0;--md-indigo-500: #3f51b5;--md-indigo-600: #3949ab;--md-indigo-700: #303f9f;--md-indigo-800: #283593;--md-indigo-900: #1a237e;--md-indigo-A100: #8c9eff;--md-indigo-A200: #536dfe;--md-indigo-A400: #3d5afe;--md-indigo-A700: #304ffe;--md-blue-50: #e3f2fd;--md-blue-100: #bbdefb;--md-blue-200: #90caf9;--md-blue-300: #64b5f6;--md-blue-400: #42a5f5;--md-blue-500: #2196f3;--md-blue-600: #1e88e5;--md-blue-700: #1976d2;--md-blue-800: #1565c0;--md-blue-900: #0d47a1;--md-blue-A100: #82b1ff;--md-blue-A200: #448aff;--md-blue-A400: #2979ff;--md-blue-A700: #2962ff;--md-light-blue-50: #e1f5fe;--md-light-blue-100: #b3e5fc;--md-light-blue-200: #81d4fa;--md-light-blue-300: #4fc3f7;--md-light-blue-400: #29b6f6;--md-light-blue-500: #03a9f4;--md-light-blue-600: #039be5;--md-light-blue-700: #0288d1;--md-light-blue-800: #0277bd;--md-light-blue-900: #01579b;--md-light-blue-A100: #80d8ff;--md-light-blue-A200: #40c4ff;--md-light-blue-A400: #00b0ff;--md-light-blue-A700: #0091ea;--md-cyan-50: #e0f7fa;--md-cyan-100: #b2ebf2;--md-cyan-200: #80deea;--md-cyan-300: #4dd0e1;--md-cyan-400: #26c6da;--md-cyan-500: #00bcd4;--md-cyan-600: #00acc1;--md-cyan-700: #0097a7;--md-cyan-800: #00838f;--md-cyan-900: #006064;--md-cyan-A100: #84ffff;--md-cyan-A200: #18ffff;--md-cyan-A400: #00e5ff;--md-cyan-A700: #00b8d4;--md-teal-50: #e0f2f1;--md-teal-100: #b2dfdb;--md-teal-200: #80cbc4;--md-teal-300: #4db6ac;--md-teal-400: #26a69a;--md-teal-500: #009688;--md-teal-600: #00897b;--md-teal-700: #00796b;--md-teal-800: #00695c;--md-teal-900: #004d40;--md-teal-A100: #a7ffeb;--md-teal-A200: #64ffda;--md-teal-A400: #1de9b6;--md-teal-A700: #00bfa5;--md-green-50: #e8f5e9;--md-green-100: #c8e6c9;--md-green-200: #a5d6a7;--md-green-300: #81c784;--md-green-400: #66bb6a;--md-green-500: #4caf50;--md-green-600: #43a047;--md-green-700: #388e3c;--md-green-800: #2e7d32;--md-green-900: #1b5e20;--md-green-A100: #b9f6ca;--md-green-A200: #69f0ae;--md-green-A400: #00e676;--md-green-A700: #00c853;--md-light-green-50: #f1f8e9;--md-light-green-100: #dcedc8;--md-light-green-200: #c5e1a5;--md-light-green-300: #aed581;--md-light-green-400: #9ccc65;--md-light-green-500: #8bc34a;--md-light-green-600: #7cb342;--md-light-green-700: #689f38;--md-light-green-800: #558b2f;--md-light-green-900: #33691e;--md-light-green-A100: #ccff90;--md-light-green-A200: #b2ff59;--md-light-green-A400: #76ff03;--md-light-green-A700: #64dd17;--md-lime-50: #f9fbe7;--md-lime-100: #f0f4c3;--md-lime-200: #e6ee9c;--md-lime-300: #dce775;--md-lime-400: #d4e157;--md-lime-500: #cddc39;--md-lime-600: #c0ca33;--md-lime-700: #afb42b;--md-lime-800: #9e9d24;--md-lime-900: #827717;--md-lime-A100: #f4ff81;--md-lime-A200: #eeff41;--md-lime-A400: #c6ff00;--md-lime-A700: #aeea00;--md-yellow-50: #fffde7;--md-yellow-100: #fff9c4;--md-yellow-200: #fff59d;--md-yellow-300: #fff176;--md-yellow-400: #ffee58;--md-yellow-500: #ffeb3b;--md-yellow-600: #fdd835;--md-yellow-700: #fbc02d;--md-yellow-800: #f9a825;--md-yellow-900: #f57f17;--md-yellow-A100: #ffff8d;--md-yellow-A200: #ffff00;--md-yellow-A400: #ffea00;--md-yellow-A700: #ffd600;--md-amber-50: #fff8e1;--md-amber-100: #ffecb3;--md-amber-200: #ffe082;--md-amber-300: #ffd54f;--md-amber-400: #ffca28;--md-amber-500: #ffc107;--md-amber-600: #ffb300;--md-amber-700: #ffa000;--md-amber-800: #ff8f00;--md-amber-900: #ff6f00;--md-amber-A100: #ffe57f;--md-amber-A200: #ffd740;--md-amber-A400: #ffc400;--md-amber-A700: #ffab00;--md-orange-50: #fff3e0;--md-orange-100: #ffe0b2;--md-orange-200: #ffcc80;--md-orange-300: #ffb74d;--md-orange-400: #ffa726;--md-orange-500: #ff9800;--md-orange-600: #fb8c00;--md-orange-700: #f57c00;--md-orange-800: #ef6c00;--md-orange-900: #e65100;--md-orange-A100: #ffd180;--md-orange-A200: #ffab40;--md-orange-A400: #ff9100;--md-orange-A700: #ff6d00;--md-deep-orange-50: #fbe9e7;--md-deep-orange-100: #ffccbc;--md-deep-orange-200: #ffab91;--md-deep-orange-300: #ff8a65;--md-deep-orange-400: #ff7043;--md-deep-orange-500: 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sourceMappingURL=mkdocs-jupyter.css.map*/ init_mathjax = function() { if (window.MathJax) { // MathJax loaded MathJax.Hub.Config({ TeX: { equationNumbers: { autoNumber: \"AMS\", useLabelIds: true } }, tex2jax: { inlineMath: [ ['$','$'], [\"\\\\(\",\"\\\\)\"] ], displayMath: [ ['$$','$$'], [\"\\\\[\",\"\\\\]\"] ], processEscapes: true, processEnvironments: true }, displayAlign: 'center', CommonHTML: { linebreaks: { automatic: true } } }); MathJax.Hub.Queue([\"Typeset\", MathJax.Hub]); } } init_mathjax(); Harden and deploy your app \u00b6 Finally, it's time to deploy your model. But before that, you have to make sure you follow AI deployment best practices . In the past, this step was too often either the source of unexpected struggle, or worse, simply ignored. With GreatAI , it has become a matter of 4 lines of code. In [1]: Copied! from great_ai import GreatAI , use_model from great_ai.utilities import clean @GreatAI . create @use_model ( \"my-domain-predictor\" ) def predict_domain ( sentence , model ): inputs = [ clean ( sentence )] return str ( model . predict ( inputs )[ 0 ]) from great_ai import GreatAI, use_model from great_ai.utilities import clean @GreatAI.create @use_model(\"my-domain-predictor\") def predict_domain(sentence, model): inputs = [clean(sentence)] return str(model.predict(inputs)[0]) Environment variable ENVIRONMENT is not set, defaulting to development mode \u203c\ufe0f Cannot find credentials files, defaulting to using ParallelTinyDbDriver The selected tracing database (ParallelTinyDbDriver) is not recommended for production Cannot find credentials files, defaulting to using LargeFileLocal GreatAI (v0.1.4): configured \u2705 \ud83d\udd29 tracing_database: ParallelTinyDbDriver \ud83d\udd29 large_file_implementation: LargeFileLocal \ud83d\udd29 is_production: False \ud83d\udd29 should_log_exception_stack: True \ud83d\udd29 prediction_cache_size: 512 \ud83d\udd29 dashboard_table_size: 50 You still need to check whether you follow all best practices before trusting your deployment. > Find out more at https://se-ml.github.io/practices Fetching cached versions of my-domain-predictor Latest version of my-domain-predictor is 9 (from versions: 0, 1, 2, 3, 4, 5, 6, 7, 8, 9) File my-domain-predictor-9 found in cache In [2]: Copied! predict_domain ( \"Mountains are just big rocks.\" ) predict_domain(\"Mountains are just big rocks.\") Out[2]: Trace[str]({'created': '2022-07-12T13:34:26.743292', 'exception': None, 'feedback': None, 'logged_values': { 'arg:sentence:length': 29, 'arg:sentence:value': 'Mountains are just big rocks.'}, 'models': [{'key': 'my-domain-predictor', 'version': 9}], 'original_execution_time_ms': 6.9699, 'output': 'geography', 'tags': ['predict_domain', 'online', 'development'], 'trace_id': 'c80bdee3-602b-49dd-a84d-6eef80127e5a'}) Notice how the original return value is under the .output key. Additionally, a plethora of metadata has been added which will be useful later on. Running your app in development-mode is as easy as executing great-ai deploy.ipynb from your terminal. In [3]: Copied! ! great - ai deploy . ipynb # leave this running and open http://127.0.0.1:6060 !great-ai deploy.ipynb # leave this running and open http://127.0.0.1:6060 2022-07-12 15:34:28 | INFO | Converting notebook to Python script 2022-07-12 15:34:29 | INFO | Found `predict_domain` to be the GreatAI app 2022-07-12 15:34:29 | INFO | Uvicorn running on http://0.0.0.0:6060 (Press CTRL+C to quit) 2022-07-12 15:34:31 | WARNING | Environment variable ENVIRONMENT is not set, defaulting to development mode \u203c\ufe0f 2022-07-12 15:34:31 | WARNING | Cannot find credentials files, defaulting to using ParallelTinyDbDriver 2022-07-12 15:34:31 | WARNING | The selected tracing database (ParallelTinyDbDriver) is not recommended for production 2022-07-12 15:34:31 | WARNING | Cannot find credentials files, defaulting to using LargeFileLocal 2022-07-12 15:34:31 | INFO | GreatAI (v0.1.4): configured \u2705 2022-07-12 15:34:31 | INFO | \ud83d\udd29 tracing_database: ParallelTinyDbDriver 2022-07-12 15:34:31 | INFO | \ud83d\udd29 large_file_implementation: LargeFileLocal 2022-07-12 15:34:31 | INFO | \ud83d\udd29 is_production: False 2022-07-12 15:34:31 | INFO | \ud83d\udd29 should_log_exception_stack: True 2022-07-12 15:34:31 | INFO | \ud83d\udd29 prediction_cache_size: 512 2022-07-12 15:34:31 | INFO | \ud83d\udd29 dashboard_table_size: 50 2022-07-12 15:34:31 | WARNING | You still need to check whether you follow all best practices before trusting your deployment. 2022-07-12 15:34:31 | WARNING | > Find out more at https://se-ml.github.io/practices 2022-07-12 15:34:31 | INFO | Fetching cached versions of my-domain-predictor 2022-07-12 15:34:31 | INFO | Latest version of my-domain-predictor is 9 (from versions: 0, 1, 2, 3, 4, 5, 6, 7, 8, 9) 2022-07-12 15:34:31 | INFO | File my-domain-predictor-9 found in cache 2022-07-12 15:34:31 | INFO | Started server process [199794] 2022-07-12 15:34:31 | INFO | Waiting for application startup. 2022-07-12 15:34:31 | INFO | Application startup complete. ^C 2022-07-12 15:34:33 | INFO | Shutting down 2022-07-12 15:34:33 | INFO | Waiting for application shutdown. 2022-07-12 15:34:33 | INFO | Application shutdown complete. 2022-07-12 15:34:33 | INFO | Finished server process [199794] Congrats, you've just created your first GreatAI service! \ud83c\udf89 Now that you've made sure your application is hardened enough for the intended use case, it is time to deploy it. The responsibilities of GreatAI end when it wraps your inference function and model into a production-ready service. You're given the freedom and responsibility to deploy this service. Fortunately, you (or your organisation) probably already have an established routine for deploying services. There are three main approaches to deploy a GreatAI service: For more info about them, check out the deployment how-to . For more thorough examples, see the examples page . Go back to the summary \u00b6","title":"Harden and deploy your app"},{"location":"tutorial/deploy/#harden-and-deploy-your-app","text":"Finally, it's time to deploy your model. But before that, you have to make sure you follow AI deployment best practices . In the past, this step was too often either the source of unexpected struggle, or worse, simply ignored. With GreatAI , it has become a matter of 4 lines of code. In [1]: Copied! from great_ai import GreatAI , use_model from great_ai.utilities import clean @GreatAI . create @use_model ( \"my-domain-predictor\" ) def predict_domain ( sentence , model ): inputs = [ clean ( sentence )] return str ( model . predict ( inputs )[ 0 ]) from great_ai import GreatAI, use_model from great_ai.utilities import clean @GreatAI.create @use_model(\"my-domain-predictor\") def predict_domain(sentence, model): inputs = [clean(sentence)] return str(model.predict(inputs)[0]) Environment variable ENVIRONMENT is not set, defaulting to development mode \u203c\ufe0f Cannot find credentials files, defaulting to using ParallelTinyDbDriver The selected tracing database (ParallelTinyDbDriver) is not recommended for production Cannot find credentials files, defaulting to using LargeFileLocal GreatAI (v0.1.4): configured \u2705 \ud83d\udd29 tracing_database: ParallelTinyDbDriver \ud83d\udd29 large_file_implementation: LargeFileLocal \ud83d\udd29 is_production: False \ud83d\udd29 should_log_exception_stack: True \ud83d\udd29 prediction_cache_size: 512 \ud83d\udd29 dashboard_table_size: 50 You still need to check whether you follow all best practices before trusting your deployment. > Find out more at https://se-ml.github.io/practices Fetching cached versions of my-domain-predictor Latest version of my-domain-predictor is 9 (from versions: 0, 1, 2, 3, 4, 5, 6, 7, 8, 9) File my-domain-predictor-9 found in cache In [2]: Copied! predict_domain ( \"Mountains are just big rocks.\" ) predict_domain(\"Mountains are just big rocks.\") Out[2]: Trace[str]({'created': '2022-07-12T13:34:26.743292', 'exception': None, 'feedback': None, 'logged_values': { 'arg:sentence:length': 29, 'arg:sentence:value': 'Mountains are just big rocks.'}, 'models': [{'key': 'my-domain-predictor', 'version': 9}], 'original_execution_time_ms': 6.9699, 'output': 'geography', 'tags': ['predict_domain', 'online', 'development'], 'trace_id': 'c80bdee3-602b-49dd-a84d-6eef80127e5a'}) Notice how the original return value is under the .output key. Additionally, a plethora of metadata has been added which will be useful later on. Running your app in development-mode is as easy as executing great-ai deploy.ipynb from your terminal. In [3]: Copied! ! great - ai deploy . ipynb # leave this running and open http://127.0.0.1:6060 !great-ai deploy.ipynb # leave this running and open http://127.0.0.1:6060 2022-07-12 15:34:28 | INFO | Converting notebook to Python script 2022-07-12 15:34:29 | INFO | Found `predict_domain` to be the GreatAI app 2022-07-12 15:34:29 | INFO | Uvicorn running on http://0.0.0.0:6060 (Press CTRL+C to quit) 2022-07-12 15:34:31 | WARNING | Environment variable ENVIRONMENT is not set, defaulting to development mode \u203c\ufe0f 2022-07-12 15:34:31 | WARNING | Cannot find credentials files, defaulting to using ParallelTinyDbDriver 2022-07-12 15:34:31 | WARNING | The selected tracing database (ParallelTinyDbDriver) is not recommended for production 2022-07-12 15:34:31 | WARNING | Cannot find credentials files, defaulting to using LargeFileLocal 2022-07-12 15:34:31 | INFO | GreatAI (v0.1.4): configured \u2705 2022-07-12 15:34:31 | INFO | \ud83d\udd29 tracing_database: ParallelTinyDbDriver 2022-07-12 15:34:31 | INFO | \ud83d\udd29 large_file_implementation: LargeFileLocal 2022-07-12 15:34:31 | INFO | \ud83d\udd29 is_production: False 2022-07-12 15:34:31 | INFO | \ud83d\udd29 should_log_exception_stack: True 2022-07-12 15:34:31 | INFO | \ud83d\udd29 prediction_cache_size: 512 2022-07-12 15:34:31 | INFO | \ud83d\udd29 dashboard_table_size: 50 2022-07-12 15:34:31 | WARNING | You still need to check whether you follow all best practices before trusting your deployment. 2022-07-12 15:34:31 | WARNING | > Find out more at https://se-ml.github.io/practices 2022-07-12 15:34:31 | INFO | Fetching cached versions of my-domain-predictor 2022-07-12 15:34:31 | INFO | Latest version of my-domain-predictor is 9 (from versions: 0, 1, 2, 3, 4, 5, 6, 7, 8, 9) 2022-07-12 15:34:31 | INFO | File my-domain-predictor-9 found in cache 2022-07-12 15:34:31 | INFO | Started server process [199794] 2022-07-12 15:34:31 | INFO | Waiting for application startup. 2022-07-12 15:34:31 | INFO | Application startup complete. ^C 2022-07-12 15:34:33 | INFO | Shutting down 2022-07-12 15:34:33 | INFO | Waiting for application shutdown. 2022-07-12 15:34:33 | INFO | Application shutdown complete. 2022-07-12 15:34:33 | INFO | Finished server process [199794] Congrats, you've just created your first GreatAI service! \ud83c\udf89 Now that you've made sure your application is hardened enough for the intended use case, it is time to deploy it. The responsibilities of GreatAI end when it wraps your inference function and model into a production-ready service. You're given the freedom and responsibility to deploy this service. Fortunately, you (or your organisation) probably already have an established routine for deploying services. There are three main approaches to deploy a GreatAI service: For more info about them, check out the deployment how-to . For more thorough examples, see the examples page .","title":"Harden and deploy your app"},{"location":"tutorial/deploy/#go-back-to-the-summary","text":"","title":"Go back to the summary"},{"location":"tutorial/train/","text":"(function (global, factory) { typeof exports === 'object' && typeof module !== 'undefined' ? module.exports = factory() : typeof define === 'function' && define.amd ? define(factory) : (global = global || self, global.ClipboardCopyElement = factory()); }(this, function () { 'use strict'; function createNode(text) { const node = document.createElement('pre'); node.style.width = '1px'; node.style.height = '1px'; node.style.position = 'fixed'; node.style.top = '5px'; node.textContent = text; return node; } function copyNode(node) { if ('clipboard' in navigator) { // eslint-disable-next-line flowtype/no-flow-fix-me-comments // $FlowFixMe Clipboard is not defined in Flow yet. return navigator.clipboard.writeText(node.textContent); } const selection = getSelection(); if (selection == null) { return Promise.reject(new Error()); 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When children elements, such as headings, are sized * in em all things will be computed relative to that body size. */ --jp-content-line-height: 1.6; --jp-content-font-scale-factor: 1.2; --jp-content-font-size0: 0.83333em; --jp-content-font-size1: 14px; /* Base font size */ --jp-content-font-size2: 1.2em; --jp-content-font-size3: 1.44em; --jp-content-font-size4: 1.728em; --jp-content-font-size5: 2.0736em; /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-content-presentation-font-size1: 17px; --jp-content-heading-line-height: 1; --jp-content-heading-margin-top: 1.2em; --jp-content-heading-margin-bottom: 0.8em; --jp-content-heading-font-weight: 500; /* Defaults use Material Design specification */ --jp-content-font-color0: rgba(0, 0, 0, 1); --jp-content-font-color1: rgba(0, 0, 0, 0.87); --jp-content-font-color2: rgba(0, 0, 0, 0.54); --jp-content-font-color3: rgba(0, 0, 0, 0.38); --jp-content-link-color: var(--md-blue-700); --jp-content-font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Helvetica, Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\"; /* * Code Fonts * * Code font variables are used for typography of code and other monospaces content. */ --jp-code-font-size: 13px; --jp-code-line-height: 1.3077; /* 17px for 13px base */ --jp-code-padding: 5px; /* 5px for 13px base, codemirror highlighting needs integer px value */ --jp-code-font-family-default: Menlo, Consolas, \"DejaVu Sans Mono\", monospace; --jp-code-font-family: var(--jp-code-font-family-default); /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-code-presentation-font-size: 16px; /* may need to tweak cursor width if you change font size */ --jp-code-cursor-width0: 1.4px; --jp-code-cursor-width1: 2px; --jp-code-cursor-width2: 4px; /* Layout * * The following are the main layout colors use in JupyterLab. In a light * theme these would go from light to dark. */ --jp-layout-color0: white; --jp-layout-color1: white; --jp-layout-color2: var(--md-grey-200); --jp-layout-color3: var(--md-grey-400); --jp-layout-color4: var(--md-grey-600); /* Inverse Layout * * The following are the inverse layout colors use in JupyterLab. In a light * theme these would go from dark to light. */ --jp-inverse-layout-color0: #111111; --jp-inverse-layout-color1: var(--md-grey-900); --jp-inverse-layout-color2: var(--md-grey-800); --jp-inverse-layout-color3: var(--md-grey-700); --jp-inverse-layout-color4: var(--md-grey-600); /* Brand/accent */ --jp-brand-color0: var(--md-blue-900); --jp-brand-color1: var(--md-blue-700); --jp-brand-color2: var(--md-blue-300); --jp-brand-color3: var(--md-blue-100); --jp-brand-color4: var(--md-blue-50); --jp-accent-color0: var(--md-green-900); --jp-accent-color1: var(--md-green-700); --jp-accent-color2: var(--md-green-300); --jp-accent-color3: var(--md-green-100); /* State colors (warn, error, success, info) */ --jp-warn-color0: var(--md-orange-900); --jp-warn-color1: var(--md-orange-700); --jp-warn-color2: var(--md-orange-300); --jp-warn-color3: var(--md-orange-100); --jp-error-color0: var(--md-red-900); --jp-error-color1: var(--md-red-700); --jp-error-color2: var(--md-red-300); --jp-error-color3: var(--md-red-100); --jp-success-color0: var(--md-green-900); --jp-success-color1: var(--md-green-700); --jp-success-color2: var(--md-green-300); --jp-success-color3: var(--md-green-100); --jp-info-color0: var(--md-cyan-900); --jp-info-color1: var(--md-cyan-700); --jp-info-color2: var(--md-cyan-300); --jp-info-color3: var(--md-cyan-100); /* Cell specific styles */ --jp-cell-padding: 5px; --jp-cell-collapser-width: 8px; --jp-cell-collapser-min-height: 20px; --jp-cell-collapser-not-active-hover-opacity: 0.6; --jp-cell-editor-background: var(--md-grey-100); --jp-cell-editor-border-color: var(--md-grey-300); --jp-cell-editor-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-cell-editor-active-background: var(--jp-layout-color0); --jp-cell-editor-active-border-color: var(--jp-brand-color1); --jp-cell-prompt-width: 64px; --jp-cell-prompt-font-family: var(--jp-code-font-family-default); --jp-cell-prompt-letter-spacing: 0px; --jp-cell-prompt-opacity: 1; --jp-cell-prompt-not-active-opacity: 0.5; --jp-cell-prompt-not-active-font-color: var(--md-grey-700); /* A custom blend of MD grey and blue 600 * See https://meyerweb.com/eric/tools/color-blend/#546E7A:1E88E5:5:hex */ --jp-cell-inprompt-font-color: #307fc1; /* A custom blend of MD grey and orange 600 * https://meyerweb.com/eric/tools/color-blend/#546E7A:F4511E:5:hex */ --jp-cell-outprompt-font-color: #bf5b3d; /* Notebook specific styles */ --jp-notebook-padding: 10px; --jp-notebook-select-background: var(--jp-layout-color1); --jp-notebook-multiselected-color: var(--md-blue-50); /* The scroll padding is calculated to fill enough space at the bottom of the notebook to show one single-line cell (with appropriate padding) at the top when the notebook is scrolled all the way to the bottom. We also subtract one pixel so that no scrollbar appears if we have just one single-line cell in the notebook. This padding is to enable a 'scroll past end' feature in a notebook. */ --jp-notebook-scroll-padding: calc( 100% - var(--jp-code-font-size) * var(--jp-code-line-height) - var(--jp-code-padding) - var(--jp-cell-padding) - 1px ); /* Rendermime styles */ --jp-rendermime-error-background: #fdd; --jp-rendermime-table-row-background: var(--md-grey-100); --jp-rendermime-table-row-hover-background: var(--md-light-blue-50); /* Dialog specific styles */ --jp-dialog-background: rgba(0, 0, 0, 0.25); /* Console specific styles */ --jp-console-padding: 10px; /* Toolbar specific styles */ --jp-toolbar-border-color: var(--jp-border-color1); --jp-toolbar-micro-height: 8px; --jp-toolbar-background: var(--jp-layout-color1); --jp-toolbar-box-shadow: 0px 0px 2px 0px rgba(0, 0, 0, 0.24); --jp-toolbar-header-margin: 4px 4px 0px 4px; --jp-toolbar-active-background: var(--md-grey-300); /* Statusbar specific styles */ --jp-statusbar-height: 24px; /* Input field styles */ --jp-input-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-input-active-background: var(--jp-layout-color1); --jp-input-hover-background: var(--jp-layout-color1); --jp-input-background: var(--md-grey-100); --jp-input-border-color: var(--jp-border-color1); --jp-input-active-border-color: var(--jp-brand-color1); --jp-input-active-box-shadow-color: rgba(19, 124, 189, 0.3); /* General editor styles */ --jp-editor-selected-background: #d9d9d9; --jp-editor-selected-focused-background: #d7d4f0; --jp-editor-cursor-color: var(--jp-ui-font-color0); /* Code mirror specific styles */ --jp-mirror-editor-keyword-color: #008000; --jp-mirror-editor-atom-color: #88f; --jp-mirror-editor-number-color: #080; --jp-mirror-editor-def-color: #00f; --jp-mirror-editor-variable-color: var(--md-grey-900); --jp-mirror-editor-variable-2-color: #05a; --jp-mirror-editor-variable-3-color: #085; --jp-mirror-editor-punctuation-color: #05a; --jp-mirror-editor-property-color: #05a; --jp-mirror-editor-operator-color: #aa22ff; --jp-mirror-editor-comment-color: #408080; --jp-mirror-editor-string-color: #ba2121; --jp-mirror-editor-string-2-color: #708; --jp-mirror-editor-meta-color: #aa22ff; --jp-mirror-editor-qualifier-color: #555; --jp-mirror-editor-builtin-color: #008000; --jp-mirror-editor-bracket-color: #997; --jp-mirror-editor-tag-color: #170; --jp-mirror-editor-attribute-color: #00c; --jp-mirror-editor-header-color: blue; --jp-mirror-editor-quote-color: #090; --jp-mirror-editor-link-color: #00c; --jp-mirror-editor-error-color: #f00; --jp-mirror-editor-hr-color: #999; /* Vega extension styles */ --jp-vega-background: white; /* Sidebar-related styles */ --jp-sidebar-min-width: 250px; /* Search-related styles */ --jp-search-toggle-off-opacity: 0.5; --jp-search-toggle-hover-opacity: 0.8; --jp-search-toggle-on-opacity: 1; --jp-search-selected-match-background-color: rgb(245, 200, 0); --jp-search-selected-match-color: black; --jp-search-unselected-match-background-color: var( --jp-inverse-layout-color0 ); --jp-search-unselected-match-color: var(--jp-ui-inverse-font-color0); /* Icon colors that work well with light or dark backgrounds */ --jp-icon-contrast-color0: var(--md-purple-600); --jp-icon-contrast-color1: var(--md-green-600); --jp-icon-contrast-color2: var(--md-pink-600); --jp-icon-contrast-color3: var(--md-blue-600); } [data-md-color-scheme=\"slate\"] .jupyter-wrapper { /* Elevation * * We style box-shadows using Material Design's idea of elevation. These particular numbers are taken from here: * * https://github.com/material-components/material-components-web * https://material-components-web.appspot.com/elevation.html */ /* The dark theme shadows need a bit of work, but this will probably also require work on the core layout * colors used in the theme as well. */ --jp-shadow-base-lightness: 32; --jp-shadow-umbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.2 ); --jp-shadow-penumbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.14 ); --jp-shadow-ambient-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.12 ); --jp-elevation-z0: none; --jp-elevation-z1: 0px 2px 1px -1px var(--jp-shadow-umbra-color), 0px 1px 1px 0px var(--jp-shadow-penumbra-color), 0px 1px 3px 0px var(--jp-shadow-ambient-color); --jp-elevation-z2: 0px 3px 1px -2px var(--jp-shadow-umbra-color), 0px 2px 2px 0px var(--jp-shadow-penumbra-color), 0px 1px 5px 0px var(--jp-shadow-ambient-color); --jp-elevation-z4: 0px 2px 4px -1px var(--jp-shadow-umbra-color), 0px 4px 5px 0px var(--jp-shadow-penumbra-color), 0px 1px 10px 0px var(--jp-shadow-ambient-color); --jp-elevation-z6: 0px 3px 5px -1px var(--jp-shadow-umbra-color), 0px 6px 10px 0px var(--jp-shadow-penumbra-color), 0px 1px 18px 0px var(--jp-shadow-ambient-color); --jp-elevation-z8: 0px 5px 5px -3px var(--jp-shadow-umbra-color), 0px 8px 10px 1px var(--jp-shadow-penumbra-color), 0px 3px 14px 2px var(--jp-shadow-ambient-color); --jp-elevation-z12: 0px 7px 8px -4px var(--jp-shadow-umbra-color), 0px 12px 17px 2px var(--jp-shadow-penumbra-color), 0px 5px 22px 4px var(--jp-shadow-ambient-color); --jp-elevation-z16: 0px 8px 10px -5px var(--jp-shadow-umbra-color), 0px 16px 24px 2px var(--jp-shadow-penumbra-color), 0px 6px 30px 5px var(--jp-shadow-ambient-color); --jp-elevation-z20: 0px 10px 13px -6px var(--jp-shadow-umbra-color), 0px 20px 31px 3px var(--jp-shadow-penumbra-color), 0px 8px 38px 7px var(--jp-shadow-ambient-color); --jp-elevation-z24: 0px 11px 15px -7px var(--jp-shadow-umbra-color), 0px 24px 38px 3px var(--jp-shadow-penumbra-color), 0px 9px 46px 8px var(--jp-shadow-ambient-color); /* Borders * * The following variables, specify the visual styling of borders in JupyterLab. */ --jp-border-width: 1px; --jp-border-color0: var(--md-grey-700); --jp-border-color1: var(--md-grey-700); --jp-border-color2: var(--md-grey-800); --jp-border-color3: var(--md-grey-900); --jp-border-radius: 2px; /* UI Fonts * * The UI font CSS variables are used for the typography all of the JupyterLab * user interface elements that are not directly user generated content. * * The font sizing here is done assuming that the body font size of --jp-ui-font-size1 * is applied to a parent element. When children elements, such as headings, are sized * in em all things will be computed relative to that body size. */ --jp-ui-font-scale-factor: 1.2; --jp-ui-font-size0: 0.83333em; --jp-ui-font-size1: 13px; /* Base font size */ --jp-ui-font-size2: 1.2em; --jp-ui-font-size3: 1.44em; --jp-ui-font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Helvetica, Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\"; /* * Use these font colors against the corresponding main layout colors. * In a light theme, these go from dark to light. */ /* Defaults use Material Design specification */ --jp-ui-font-color0: rgba(255, 255, 255, 1); --jp-ui-font-color1: rgba(255, 255, 255, 0.87); --jp-ui-font-color2: rgba(255, 255, 255, 0.54); --jp-ui-font-color3: rgba(255, 255, 255, 0.38); /* * Use these against the brand/accent/warn/error colors. * These will typically go from light to darker, in both a dark and light theme. */ --jp-ui-inverse-font-color0: rgba(0, 0, 0, 1); --jp-ui-inverse-font-color1: rgba(0, 0, 0, 0.8); --jp-ui-inverse-font-color2: rgba(0, 0, 0, 0.5); --jp-ui-inverse-font-color3: rgba(0, 0, 0, 0.3); /* Content Fonts * * Content font variables are used for typography of user generated content. * * The font sizing here is done assuming that the body font size of --jp-content-font-size1 * is applied to a parent element. When children elements, such as headings, are sized * in em all things will be computed relative to that body size. */ --jp-content-line-height: 1.6; --jp-content-font-scale-factor: 1.2; --jp-content-font-size0: 0.83333em; --jp-content-font-size1: 14px; /* Base font size */ --jp-content-font-size2: 1.2em; --jp-content-font-size3: 1.44em; --jp-content-font-size4: 1.728em; --jp-content-font-size5: 2.0736em; /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-content-presentation-font-size1: 17px; --jp-content-heading-line-height: 1; --jp-content-heading-margin-top: 1.2em; --jp-content-heading-margin-bottom: 0.8em; --jp-content-heading-font-weight: 500; /* Defaults use Material Design specification */ --jp-content-font-color0: rgba(255, 255, 255, 1); --jp-content-font-color1: rgba(255, 255, 255, 1); --jp-content-font-color2: rgba(255, 255, 255, 0.7); --jp-content-font-color3: rgba(255, 255, 255, 0.5); --jp-content-link-color: var(--md-blue-300); --jp-content-font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Helvetica, Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\"; /* * Code Fonts * * Code font variables are used for typography of code and other monospaces content. */ --jp-code-font-size: 13px; --jp-code-line-height: 1.3077; /* 17px for 13px base */ --jp-code-padding: 5px; /* 5px for 13px base, codemirror highlighting needs integer px value */ --jp-code-font-family-default: Menlo, Consolas, \"DejaVu Sans Mono\", monospace; --jp-code-font-family: var(--jp-code-font-family-default); /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-code-presentation-font-size: 16px; /* may need to tweak cursor width if you change font size */ --jp-code-cursor-width0: 1.4px; --jp-code-cursor-width1: 2px; --jp-code-cursor-width2: 4px; /* Layout * * The following are the main layout colors use in JupyterLab. In a light * theme these would go from light to dark. */ --jp-layout-color0: #111111; --jp-layout-color1: var(--md-grey-900); --jp-layout-color2: var(--md-grey-800); --jp-layout-color3: var(--md-grey-700); --jp-layout-color4: var(--md-grey-600); /* Inverse Layout * * The following are the inverse layout colors use in JupyterLab. In a light * theme these would go from dark to light. */ --jp-inverse-layout-color0: white; --jp-inverse-layout-color1: white; --jp-inverse-layout-color2: var(--md-grey-200); --jp-inverse-layout-color3: var(--md-grey-400); --jp-inverse-layout-color4: var(--md-grey-600); /* Brand/accent */ --jp-brand-color0: var(--md-blue-700); --jp-brand-color1: var(--md-blue-500); --jp-brand-color2: var(--md-blue-300); --jp-brand-color3: var(--md-blue-100); --jp-brand-color4: var(--md-blue-50); --jp-accent-color0: var(--md-green-700); --jp-accent-color1: var(--md-green-500); --jp-accent-color2: var(--md-green-300); --jp-accent-color3: var(--md-green-100); /* State colors (warn, error, success, info) */ --jp-warn-color0: var(--md-orange-700); --jp-warn-color1: var(--md-orange-500); --jp-warn-color2: var(--md-orange-300); --jp-warn-color3: var(--md-orange-100); --jp-error-color0: var(--md-red-700); --jp-error-color1: var(--md-red-500); --jp-error-color2: var(--md-red-300); --jp-error-color3: var(--md-red-100); --jp-success-color0: var(--md-green-700); --jp-success-color1: var(--md-green-500); --jp-success-color2: var(--md-green-300); --jp-success-color3: var(--md-green-100); --jp-info-color0: var(--md-cyan-700); --jp-info-color1: var(--md-cyan-500); --jp-info-color2: var(--md-cyan-300); --jp-info-color3: var(--md-cyan-100); /* Cell specific styles */ --jp-cell-padding: 5px; --jp-cell-collapser-width: 8px; --jp-cell-collapser-min-height: 20px; --jp-cell-collapser-not-active-hover-opacity: 0.6; --jp-cell-editor-background: var(--jp-layout-color1); --jp-cell-editor-border-color: var(--md-grey-700); --jp-cell-editor-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-cell-editor-active-background: var(--jp-layout-color0); --jp-cell-editor-active-border-color: var(--jp-brand-color1); --jp-cell-prompt-width: 64px; --jp-cell-prompt-font-family: var(--jp-code-font-family-default); --jp-cell-prompt-letter-spacing: 0px; --jp-cell-prompt-opacity: 1; --jp-cell-prompt-not-active-opacity: 1; --jp-cell-prompt-not-active-font-color: var(--md-grey-300); /* A custom blend of MD grey and blue 600 * See https://meyerweb.com/eric/tools/color-blend/#546E7A:1E88E5:5:hex */ --jp-cell-inprompt-font-color: #307fc1; /* A custom blend of MD grey and orange 600 * https://meyerweb.com/eric/tools/color-blend/#546E7A:F4511E:5:hex */ --jp-cell-outprompt-font-color: #bf5b3d; /* Notebook specific styles */ --jp-notebook-padding: 10px; --jp-notebook-select-background: var(--jp-layout-color1); --jp-notebook-multiselected-color: rgba(33, 150, 243, 0.24); /* The scroll padding is calculated to fill enough space at the bottom of the notebook to show one single-line cell (with appropriate padding) at the top when the notebook is scrolled all the way to the bottom. We also subtract one pixel so that no scrollbar appears if we have just one single-line cell in the notebook. This padding is to enable a 'scroll past end' feature in a notebook. */ --jp-notebook-scroll-padding: calc( 100% - var(--jp-code-font-size) * var(--jp-code-line-height) - var(--jp-code-padding) - var(--jp-cell-padding) - 1px ); /* Rendermime styles */ --jp-rendermime-error-background: rgba(244, 67, 54, 0.28); --jp-rendermime-table-row-background: var(--md-grey-900); --jp-rendermime-table-row-hover-background: rgba(3, 169, 244, 0.2); /* Dialog specific styles */ --jp-dialog-background: rgba(0, 0, 0, 0.6); /* Console specific styles */ --jp-console-padding: 10px; /* Toolbar specific styles */ --jp-toolbar-border-color: var(--jp-border-color2); --jp-toolbar-micro-height: 8px; --jp-toolbar-background: var(--jp-layout-color1); --jp-toolbar-box-shadow: 0px 0px 2px 0px rgba(0, 0, 0, 0.8); --jp-toolbar-header-margin: 4px 4px 0px 4px; --jp-toolbar-active-background: var(--jp-layout-color0); /* Statusbar specific styles */ --jp-statusbar-height: 24px; /* Input field styles */ --jp-input-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-input-active-background: var(--jp-layout-color0); --jp-input-hover-background: var(--jp-layout-color2); --jp-input-background: var(--md-grey-800); --jp-input-border-color: var(--jp-border-color1); --jp-input-active-border-color: var(--jp-brand-color1); --jp-input-active-box-shadow-color: rgba(19, 124, 189, 0.3); /* General editor styles */ --jp-editor-selected-background: var(--jp-layout-color2); --jp-editor-selected-focused-background: rgba(33, 150, 243, 0.24); --jp-editor-cursor-color: var(--jp-ui-font-color0); /* Code mirror specific styles */ --jp-mirror-editor-keyword-color: var(--md-green-500); --jp-mirror-editor-atom-color: var(--md-blue-300); --jp-mirror-editor-number-color: var(--md-green-400); --jp-mirror-editor-def-color: var(--md-blue-600); --jp-mirror-editor-variable-color: var(--md-grey-300); --jp-mirror-editor-variable-2-color: var(--md-blue-400); --jp-mirror-editor-variable-3-color: var(--md-green-600); --jp-mirror-editor-punctuation-color: var(--md-blue-400); --jp-mirror-editor-property-color: var(--md-blue-400); --jp-mirror-editor-operator-color: #aa22ff; --jp-mirror-editor-comment-color: #408080; --jp-mirror-editor-string-color: #ff7070; --jp-mirror-editor-string-2-color: var(--md-purple-300); --jp-mirror-editor-meta-color: #aa22ff; --jp-mirror-editor-qualifier-color: #555; --jp-mirror-editor-builtin-color: var(--md-green-600); --jp-mirror-editor-bracket-color: #997; --jp-mirror-editor-tag-color: var(--md-green-700); --jp-mirror-editor-attribute-color: var(--md-blue-700); --jp-mirror-editor-header-color: var(--md-blue-500); --jp-mirror-editor-quote-color: var(--md-green-300); --jp-mirror-editor-link-color: var(--md-blue-700); --jp-mirror-editor-error-color: #f00; --jp-mirror-editor-hr-color: #999; /* Vega extension styles */ --jp-vega-background: var(--md-grey-400); /* Sidebar-related styles */ --jp-sidebar-min-width: 250px; /* Search-related styles */ --jp-search-toggle-off-opacity: 0.6; --jp-search-toggle-hover-opacity: 0.8; --jp-search-toggle-on-opacity: 1; --jp-search-selected-match-background-color: rgb(255, 225, 0); --jp-search-selected-match-color: black; --jp-search-unselected-match-background-color: var( --jp-inverse-layout-color0 ); --jp-search-unselected-match-color: var(--jp-ui-inverse-font-color0); /* scrollbar related styles. Supports every browser except Edge. */ /* colors based on JetBrain's Darcula theme */ --jp-scrollbar-background-color: #3f4244; --jp-scrollbar-thumb-color: 88, 96, 97; /* need to specify thumb color as an RGB triplet */ --jp-scrollbar-endpad: 3px; /* the minimum gap between the thumb and the ends of a scrollbar */ /* hacks for setting the thumb shape. 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sourceMappingURL=mkdocs-jupyter.css.map*/ init_mathjax = function() { if (window.MathJax) { // MathJax loaded MathJax.Hub.Config({ TeX: { equationNumbers: { autoNumber: \"AMS\", useLabelIds: true } }, tex2jax: { inlineMath: [ ['$','$'], [\"\\\\(\",\"\\\\)\"] ], displayMath: [ ['$$','$$'], [\"\\\\[\",\"\\\\]\"] ], processEscapes: true, processEnvironments: true }, displayAlign: 'center', CommonHTML: { linebreaks: { automatic: true } } }); MathJax.Hub.Queue([\"Typeset\", MathJax.Hub]); } } init_mathjax(); Train your model \u00b6 The first step is, of course, to install great-ai in your Python environment. In [1]: Copied! % pip install great - ai > / dev / null %pip install great-ai > /dev/null Note: you may need to restart the kernel to use updated packages. First, we have to get some data. After downloading it from here , we might notice that the dataset is in JSON Lines format (each line is a separate JSON document). Let's write a function which takes a single line and returns the sentence and the corresponding label from it. Before returning, the sentence is also cleaned to remove any LaTeX, XML, unicode, PDF-extraction artifacts. In [2]: Copied! import json from great_ai.utilities import clean def preprocess ( line ): data_point = json . loads ( line ) sentence = data_point [ \"text\" ] label = data_point [ \"label\" ] return clean ( sentence ), label import json from great_ai.utilities import clean def preprocess(line): data_point = json.loads(line) sentence = data_point[\"text\"] label = data_point[\"label\"] return clean(sentence), label Now, we can load the dataset and extract the training samples from it. Since we're impatient, we can do it in parallel using the simple_parallel_map function. Open files in Python are iterable: in text mode, each iteration returns the next line. In [3]: Copied! from great_ai.utilities import simple_parallel_map with open ( \"data/train.txt\" , encoding = \"utf-8\" ) as f : training_data = simple_parallel_map ( preprocess , f ) X_train = [ d [ 0 ] for d in training_data ] y_train = [ d [ 1 ] for d in training_data ] from great_ai.utilities import simple_parallel_map with open(\"data/train.txt\", encoding=\"utf-8\") as f: training_data = simple_parallel_map(preprocess, f) X_train = [d[0] for d in training_data] y_train = [d[1] for d in training_data] 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 84000/84000 [00:09<00:00, 8960.63it/s] Let's do the same for the test data. In [4]: Copied! with open ( \"data/test.txt\" , encoding = \"utf-8\" ) as f : test_data = simple_parallel_map ( preprocess , f ) X_test = [ d [ 0 ] for d in test_data ] y_test = [ d [ 1 ] for d in test_data ] with open(\"data/test.txt\", encoding=\"utf-8\") as f: test_data = simple_parallel_map(preprocess, f) X_test = [d[0] for d in test_data] y_test = [d[1] for d in test_data] 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 22399/22399 [00:03<00:00, 6078.02it/s] After obtaining some clean data, it's time to create and train a model on it. We are going to use scikit-learn for this purpose. In this case, we opted for a linear Support Vector Machine because it's known to be an adequate baseline for simple classification tasks. In [5]: Copied! from sklearn.pipeline import make_pipeline from sklearn.feature_extraction.text import TfidfVectorizer from sklearn.svm import LinearSVC model = make_pipeline ( TfidfVectorizer (), LinearSVC ()) # todo: hyperparameter-optimisation model . fit ( X_train , y_train ) from sklearn.pipeline import make_pipeline from sklearn.feature_extraction.text import TfidfVectorizer from sklearn.svm import LinearSVC model = make_pipeline(TfidfVectorizer(), LinearSVC()) # todo: hyperparameter-optimisation model.fit(X_train, y_train) Out[5]: #sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"\u25b8\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"\u25be\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;} Pipeline(steps=[('tfidfvectorizer', TfidfVectorizer()), ('linearsvc', LinearSVC())]) In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org. Pipeline Pipeline(steps=[('tfidfvectorizer', TfidfVectorizer()), ('linearsvc', LinearSVC())]) TfidfVectorizer TfidfVectorizer() LinearSVC LinearSVC() It's already finished. Let's see how well it performs. But first, we need to configure matplotlib to make the charts more legible. ConfusionMatrixDisplay relies on matplotlib for rendering. In [6]: Copied! import matplotlib.pyplot as plt % matplotlib inline plt . rcParams [ \"figure.figsize\" ] = ( 30 , 15 ) plt . rcParams [ \"figure.facecolor\" ] = \"white\" plt . rcParams [ \"font.size\" ] = 12 import matplotlib.pyplot as plt %matplotlib inline plt.rcParams[\"figure.figsize\"] = (30, 15) plt.rcParams[\"figure.facecolor\"] = \"white\" plt.rcParams[\"font.size\"] = 12 Next, we check the quality of the model on the test split. We can use the classification report to check the common metrics, such as the macro F1-score. We also draw the confusion matrix to get some insights into the types of mistakes being made by the model. In [7]: Copied! from sklearn import metrics y_predicted = model . predict ( X_test ) print ( metrics . classification_report ( y_test , y_predicted )) metrics . ConfusionMatrixDisplay . from_predictions ( y_test , y_predicted ) None from sklearn import metrics y_predicted = model.predict(X_test) print(metrics.classification_report(y_test, y_predicted)) metrics.ConfusionMatrixDisplay.from_predictions(y_test, y_predicted) None precision recall f1-score support business 0.67 0.69 0.68 3198 economics 0.69 0.71 0.70 3189 geography 0.70 0.73 0.72 3207 medicine 0.90 0.91 0.90 3187 politics 0.63 0.57 0.60 3169 psychology 0.73 0.73 0.73 3252 sociology 0.51 0.51 0.51 3197 accuracy 0.69 22399 macro avg 0.69 0.69 0.69 22399 weighted avg 0.69 0.69 0.69 22399 Great work, we can be rightfully satisfied with our model. Seeing the results, we achieved an F1-score of 0.69 which is about 5% better than SciBERT's 0.6571! You might wonder that \"this is great, but besides some utility functions ( clean , simple_parallel_map , ...) what more value does GreatAI add?\" . This would be a valid argument because the scope of GreatAI actually only starts here. Not coincidentally, this is the point where the scope of Data Science ends but it's still a grey zone for software engineering. In order to use this model in production, we have to make it available on some possibly shared infrastructure. In [8]: Copied! from great_ai import save_model save_model ( model , key = \"my-domain-predictor\" ) from great_ai import save_model save_model(model, key=\"my-domain-predictor\") Environment variable ENVIRONMENT is not set, defaulting to development mode \u203c\ufe0f Cannot find credentials files, defaulting to using ParallelTinyDbDriver The selected tracing database (ParallelTinyDbDriver) is not recommended for production Cannot find credentials files, defaulting to using LargeFileLocal GreatAI (v0.1.4): configured \u2705 \ud83d\udd29 tracing_database: ParallelTinyDbDriver \ud83d\udd29 large_file_implementation: LargeFileLocal \ud83d\udd29 is_production: False \ud83d\udd29 should_log_exception_stack: True \ud83d\udd29 prediction_cache_size: 512 \ud83d\udd29 dashboard_table_size: 50 You still need to check whether you follow all best practices before trusting your deployment. > Find out more at https://se-ml.github.io/practices Fetching cached versions of my-domain-predictor Copying file for my-domain-predictor-9 Compressing my-domain-predictor-9 Model my-domain-predictor uploaded with version 9 Out[8]: 'my-domain-predictor:9' Let's continue by finishing the deployment in the next notebook. \u00b6","title":"Train your model"},{"location":"tutorial/train/#train-your-model","text":"The first step is, of course, to install great-ai in your Python environment. In [1]: Copied! % pip install great - ai > / dev / null %pip install great-ai > /dev/null Note: you may need to restart the kernel to use updated packages. First, we have to get some data. After downloading it from here , we might notice that the dataset is in JSON Lines format (each line is a separate JSON document). Let's write a function which takes a single line and returns the sentence and the corresponding label from it. Before returning, the sentence is also cleaned to remove any LaTeX, XML, unicode, PDF-extraction artifacts. In [2]: Copied! import json from great_ai.utilities import clean def preprocess ( line ): data_point = json . loads ( line ) sentence = data_point [ \"text\" ] label = data_point [ \"label\" ] return clean ( sentence ), label import json from great_ai.utilities import clean def preprocess(line): data_point = json.loads(line) sentence = data_point[\"text\"] label = data_point[\"label\"] return clean(sentence), label Now, we can load the dataset and extract the training samples from it. Since we're impatient, we can do it in parallel using the simple_parallel_map function. Open files in Python are iterable: in text mode, each iteration returns the next line. In [3]: Copied! from great_ai.utilities import simple_parallel_map with open ( \"data/train.txt\" , encoding = \"utf-8\" ) as f : training_data = simple_parallel_map ( preprocess , f ) X_train = [ d [ 0 ] for d in training_data ] y_train = [ d [ 1 ] for d in training_data ] from great_ai.utilities import simple_parallel_map with open(\"data/train.txt\", encoding=\"utf-8\") as f: training_data = simple_parallel_map(preprocess, f) X_train = [d[0] for d in training_data] y_train = [d[1] for d in training_data] 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 84000/84000 [00:09<00:00, 8960.63it/s] Let's do the same for the test data. In [4]: Copied! with open ( \"data/test.txt\" , encoding = \"utf-8\" ) as f : test_data = simple_parallel_map ( preprocess , f ) X_test = [ d [ 0 ] for d in test_data ] y_test = [ d [ 1 ] for d in test_data ] with open(\"data/test.txt\", encoding=\"utf-8\") as f: test_data = simple_parallel_map(preprocess, f) X_test = [d[0] for d in test_data] y_test = [d[1] for d in test_data] 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 22399/22399 [00:03<00:00, 6078.02it/s] After obtaining some clean data, it's time to create and train a model on it. We are going to use scikit-learn for this purpose. In this case, we opted for a linear Support Vector Machine because it's known to be an adequate baseline for simple classification tasks. In [5]: Copied! from sklearn.pipeline import make_pipeline from sklearn.feature_extraction.text import TfidfVectorizer from sklearn.svm import LinearSVC model = make_pipeline ( TfidfVectorizer (), LinearSVC ()) # todo: hyperparameter-optimisation model . fit ( X_train , y_train ) from sklearn.pipeline import make_pipeline from sklearn.feature_extraction.text import TfidfVectorizer from sklearn.svm import LinearSVC model = make_pipeline(TfidfVectorizer(), LinearSVC()) # todo: hyperparameter-optimisation model.fit(X_train, y_train) Out[5]: #sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"\u25b8\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"\u25be\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;} Pipeline(steps=[('tfidfvectorizer', TfidfVectorizer()), ('linearsvc', LinearSVC())]) In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org. Pipeline Pipeline(steps=[('tfidfvectorizer', TfidfVectorizer()), ('linearsvc', LinearSVC())]) TfidfVectorizer TfidfVectorizer() LinearSVC LinearSVC() It's already finished. Let's see how well it performs. But first, we need to configure matplotlib to make the charts more legible. ConfusionMatrixDisplay relies on matplotlib for rendering. In [6]: Copied! import matplotlib.pyplot as plt % matplotlib inline plt . rcParams [ \"figure.figsize\" ] = ( 30 , 15 ) plt . rcParams [ \"figure.facecolor\" ] = \"white\" plt . rcParams [ \"font.size\" ] = 12 import matplotlib.pyplot as plt %matplotlib inline plt.rcParams[\"figure.figsize\"] = (30, 15) plt.rcParams[\"figure.facecolor\"] = \"white\" plt.rcParams[\"font.size\"] = 12 Next, we check the quality of the model on the test split. We can use the classification report to check the common metrics, such as the macro F1-score. We also draw the confusion matrix to get some insights into the types of mistakes being made by the model. In [7]: Copied! from sklearn import metrics y_predicted = model . predict ( X_test ) print ( metrics . classification_report ( y_test , y_predicted )) metrics . ConfusionMatrixDisplay . from_predictions ( y_test , y_predicted ) None from sklearn import metrics y_predicted = model.predict(X_test) print(metrics.classification_report(y_test, y_predicted)) metrics.ConfusionMatrixDisplay.from_predictions(y_test, y_predicted) None precision recall f1-score support business 0.67 0.69 0.68 3198 economics 0.69 0.71 0.70 3189 geography 0.70 0.73 0.72 3207 medicine 0.90 0.91 0.90 3187 politics 0.63 0.57 0.60 3169 psychology 0.73 0.73 0.73 3252 sociology 0.51 0.51 0.51 3197 accuracy 0.69 22399 macro avg 0.69 0.69 0.69 22399 weighted avg 0.69 0.69 0.69 22399 Great work, we can be rightfully satisfied with our model. Seeing the results, we achieved an F1-score of 0.69 which is about 5% better than SciBERT's 0.6571! You might wonder that \"this is great, but besides some utility functions ( clean , simple_parallel_map , ...) what more value does GreatAI add?\" . This would be a valid argument because the scope of GreatAI actually only starts here. Not coincidentally, this is the point where the scope of Data Science ends but it's still a grey zone for software engineering. In order to use this model in production, we have to make it available on some possibly shared infrastructure. In [8]: Copied! from great_ai import save_model save_model ( model , key = \"my-domain-predictor\" ) from great_ai import save_model save_model(model, key=\"my-domain-predictor\") Environment variable ENVIRONMENT is not set, defaulting to development mode \u203c\ufe0f Cannot find credentials files, defaulting to using ParallelTinyDbDriver The selected tracing database (ParallelTinyDbDriver) is not recommended for production Cannot find credentials files, defaulting to using LargeFileLocal GreatAI (v0.1.4): configured \u2705 \ud83d\udd29 tracing_database: ParallelTinyDbDriver \ud83d\udd29 large_file_implementation: LargeFileLocal \ud83d\udd29 is_production: False \ud83d\udd29 should_log_exception_stack: True \ud83d\udd29 prediction_cache_size: 512 \ud83d\udd29 dashboard_table_size: 50 You still need to check whether you follow all best practices before trusting your deployment. > Find out more at https://se-ml.github.io/practices Fetching cached versions of my-domain-predictor Copying file for my-domain-predictor-9 Compressing my-domain-predictor-9 Model my-domain-predictor uploaded with version 9 Out[8]: 'my-domain-predictor:9'","title":"Train your model"},{"location":"tutorial/train/#lets-continue-by-finishing-the-deployment-in-the-next-notebook","text":"","title":"Let's continue by finishing the deployment in the next notebook."}]} \ No newline at end of file diff --git a/sitemap.xml b/sitemap.xml index 0ff6e82..7dbee8b 100644 --- a/sitemap.xml +++ b/sitemap.xml @@ -2,132 +2,132 @@ None - 2022-08-04 + 2022-08-06 daily None - 2022-08-04 + 2022-08-06 daily None - 2022-08-04 + 2022-08-06 daily None - 2022-08-04 + 2022-08-06 daily None - 2022-08-04 + 2022-08-06 daily None - 2022-08-04 + 2022-08-06 daily None - 2022-08-04 + 2022-08-06 daily None - 2022-08-04 + 2022-08-06 daily None - 2022-08-04 + 2022-08-06 daily None - 2022-08-04 + 2022-08-06 daily None - 2022-08-04 + 2022-08-06 daily None - 2022-08-04 + 2022-08-06 daily None - 2022-08-04 + 2022-08-06 daily None - 2022-08-04 + 2022-08-06 daily None - 2022-08-04 + 2022-08-06 daily None - 2022-08-04 + 2022-08-06 daily None - 2022-08-04 + 2022-08-06 daily None - 2022-08-04 + 2022-08-06 daily None - 2022-08-04 + 2022-08-06 daily None - 2022-08-04 + 2022-08-06 daily None - 2022-08-04 + 2022-08-06 daily None - 2022-08-04 + 2022-08-06 daily None - 2022-08-04 + 2022-08-06 daily None - 2022-08-04 + 2022-08-06 daily None - 2022-08-04 + 2022-08-06 daily None - 2022-08-04 + 2022-08-06 daily \ No newline at end of file diff --git a/sitemap.xml.gz b/sitemap.xml.gz index 4b95ce4..b6ff1ff 100644 Binary files a/sitemap.xml.gz and b/sitemap.xml.gz differ diff --git a/thesis/chapters/1_introduction.tex b/thesis/chapters/1_introduction.tex index ca9f03b..9d71ae0 100644 --- a/thesis/chapters/1_introduction.tex +++ b/thesis/chapters/1_introduction.tex @@ -1,6 +1,6 @@ \chapter{Introduction} -Artificial intelligence techniques have recently started enjoying widespread industry awareness and adoption; the use of AI is increasingly prevalent in all sectors \cite{wirtz2019artificial,bosch2021engineering}. The reasons behind this are manifold \cite{jordan2015machine}, to name a few: recent breakthroughs in deep learning (DL), increased public awareness, abundance of available data, access to powerful low-cost commodity hardware, education, but most interestingly, the rise of high-level libraries making ready-to-use state-of-the-art (SOTA) models easily available. The latter practically abolishes the barrier of entry for applying AI --- and with that --- can help use cases in various areas. +Artificial intelligence techniques have recently started enjoying widespread industry awareness and adoption; the use of AI is increasingly prevalent in all sectors \cite{wirtz2019artificial,bosch2021engineering}. The reasons behind this are manifold \cite{jordan2015machine}, to name a few: recent breakthroughs in deep learning (DL), increased public awareness, abundance of available data, access to powerful low-cost commodity hardware, education, but most interestingly, the rise of high-level libraries making ready-to-use state-of-the-art (SOTA) models easily available. The latter practically abolishes the barrier of entry for applying AI --- and with that --- can help use-cases in various areas. However, the successful integration of AI components into production-ready applications demands strong engineering methods in order to achieve robust deployments \cite{serban2020adoption}. That is why it is as important as ever to also focus on the quality and robustness of deployed models and software. For instance, the lack of a proper overview of data transformation steps may lead to suboptimal performance and to introducing unintended biases which might contribute to the ever-increasing negative externality of misused AI \cite{o2016weapons}. @@ -23,7 +23,7 @@ I hypothesise that facilitating the adoption of AI deployment best practices is \item How suitable is the design of \textit{GreatAI} for helping to apply best practices in other contexts? \end{rqlist} -In this case, complexity refers to the difficulty faced by professionals (Data Scientists and Software Engineers alike) when integrating third-party libraries with their solutions. This could also be described as the barrier of entry or steepness of the learning curve. If the aforementioned hypothesis is correct, the adoption of best practices can be efficiently increased by decreasing this complexity. AI deployment best practices entail the technical steps ought to be taken in order to achieve robust, end-to-end, automated, and trustworthy deployments. These are detailed in Section \ref{section:requirements}. +In this case, complexity refers to the difficulty faced by professionals (Data Scientists and Software Engineers alike) when integrating third-party libraries with their solutions. This could be also described as the barrier of entry or steepness of the learning curve. If the aforementioned hypothesis is correct, the adoption of best practices can be efficiently increased by decreasing this complexity. AI deployment best practices entail the technical steps ought to be taken in order to achieve robust, end-to-end, automated, and trustworthy deployments. These are detailed in Section \ref{section:requirements}. The existence question regarding the problem itself (\textbf{RQ1}) is answered by reviewing the literature of more than 30 published case studies in Chapter \ref{chapter:background}. \textbf{RQ2} and \textbf{RQ3} are closely connected, the design and evaluation phases utilised to answer them follow an iterative process. They are examined in Chapters \ref{chapter:design} and \ref{chapter:case} respectively. The final evaluation step is to ascertain the capability of the framework's design to generalise beyond a single subdomain and problem context. This question, \textbf{RQ4}, is investigated through interviews with industry professionals in Chapter \ref{section:interviews}. diff --git a/thesis/chapters/2_background.tex b/thesis/chapters/2_background.tex index 6acc92a..5134918 100644 --- a/thesis/chapters/2_background.tex +++ b/thesis/chapters/2_background.tex @@ -6,7 +6,7 @@ In the following, the context of the problem is presented from three perspective \section{Accessible AI} \label{section:accessible-ai} -Most companies prefer not to develop new models but instead reuse prior ones \cite{bosch2021engineering} and they are able to do so increasingly easier. In recent years, there has been a proliferation of highly accessible AI libraries. For example, let us consider the domain of natural language processing (NLP). There are various options for finding AI solutions that work out of the box: FLAIR \cite{akbik2019flair} and Huggingface's transformers \cite{wolf2019huggingface} let developers access the state-of-the-art models and methods in only a couple of lines of code (in many cases 2 or 3). Using transfer-learning, Huggingface enables developers to leverage vast amounts of knowledge learned by pretrained models (such as BERT \cite{devlin2018bert} and its many improved variations) and fine-tune them for their specific use case. The API exposing this is also extremely accessible. +Most companies prefer not to develop new models but instead reuse prior ones \cite{bosch2021engineering} and they are able to do so increasingly easier. In recent years, there has been a proliferation of highly accessible AI libraries. For example, let us consider the domain of natural language processing (NLP). There are various options for finding AI solutions that work out of the box: FLAIR \cite{akbik2019flair} and Huggingface's transformers \cite{wolf2019huggingface} let developers access the state-of-the-art models and methods in only a couple of lines of code (in many cases 2 or 3). Using transfer-learning, Huggingface enables developers to leverage vast amounts of knowledge learned by pretrained models (such as BERT \cite{devlin2018bert} and its many improved variations) and fine-tune them for their specific use-case. The API exposing this is also extremely accessible. It is not just these two packages, the list of readily available tools is vast: SpaCy \cite{srinivasa2018natural}, Gensim \cite{vrehuuvrek2011gensim}, and scikit-learn \cite{pedregosa2011scikit}, XGBoost \cite{Chen_2016} are other great examples. The situation is similar in all subdomains of artificial intelligence: some domain expertise is --- admittedly --- beneficial but not a hard-requirement. This, combined with the exponentially increasing computing power affordably available to consumers and business alike \cite{sun2019summarizing}, results in AI that is accessible by many. @@ -36,7 +36,7 @@ From the previous section, it is noticeable that given enough resources and at t Given the nature of problems faced and amount of available resources, it is not surprising the both of these high-tech, Fortune 500 companies needed to, and did overcome the problems presented by deploying AI. We can learn from their approaches, nonetheless, using them may be infeasible for individuals and SMEs, thus, the issues remain for the majority of practitioners. Luckily, the open-source scene of AI/ML/DS tools, libraries, frameworks\footnote{The terms \textit{framework} and \textit{library} will be used interchangeably in this work stemming from their vague and often holistic differentiation.}, and platforms is thriving. Additionally, there is a considerable number of closed-source --- usually platforms-as-a-service (PaaS) --- solutions next to them. Let us look at some prominent examples. -IBM's AutoAI \cite{wang2020autoai} promises to provide automation for the entire machine learning lifecycle, including deployment. It is a closed-sourced, paid service which --- from their documentation --- seems to focus mostly on non-technical users by providing them with a UI for authoring models. The restrictions caused by the encapsulation of the entire process can be severe. The challenges of integration were emphasised above \cite{sculley2015hidden}. Additionally, an engineer working on Microsoft's comparable solution, the Azure ML Studio, highlighted that once users gain enough understanding of ML, such visual tools can get in their way, and they may need to seek out other solutions \cite{amershi2019software}. Unfortunately, the main value proposition of Azure ML Studio is also to provide a UI for laypeople, and it has also been set to be retired by 2024. Its successor is Azure Machine Learning which shares many similarities with AWS's SageMaker suite \cite{joshi2020amazon}. +IBM's AutoAI \cite{wang2020autoai} promises to provide automation for the entire machine learning lifecycle, including deployment. It is a closed-sourced, paid service which --- from their documentation --- seems to focus mostly on non-technical users by providing them with a UI for authoring models. The restrictions caused by the encapsulation of the entire process can be severe. The challenges of integration were emphasised above \cite{sculley2015hidden}. Additionally, an engineer working on Microsoft's comparable solution, the Azure ML Studio, highlighted that once users gain enough understanding of ML, such visual tools can get in their way, and they may need to seek out other solutions \cite{amershi2019software}. Unfortunately, the main value proposition of Azure ML Studio is also to provide a UI for laypeople, and it has been also set to be retired by 2024. Its successor is Azure Machine Learning which shares many similarities with AWS's SageMaker suite \cite{joshi2020amazon}. SageMaker offers the most comprehensive suite of tools and services; most importantly it has a set of features called \textit{AWS SageMaker MLOps}. This provides easy and/or default implementations for multiple industry best practices described in \cite{serban2020adoption,serban2021practices,john2020ai}. Among others, it promotes the use of CI/CD, model monitoring, tracing, model versioning, storing both data and models on shared infrastructure, numerous collaboration tools, etc. Nonetheless, SageMaker does not enjoy universal adoption as indicated by the survey data. The cause of this may be the lack of self-hosting option and its relatively high prices: many companies prefer on-premise hosting for privacy and financial reasons \cite{bosch2021engineering}. Additionally, vendor lock-in, and possibly --- in the case where it is not already used for the project --- the initial effort required for setting up AWS integration could be likely deterrents. diff --git a/thesis/chapters/3_methods.tex b/thesis/chapters/3_methods.tex index faf170d..e9cab90 100644 --- a/thesis/chapters/3_methods.tex +++ b/thesis/chapters/3_methods.tex @@ -14,9 +14,9 @@ in order to decrease the negative externality of misused AI.} The problem context is the difficulty in responsibly transitioning (while following best practices) from prototype industrial AI applications to production-ready deployments. With the possible treatment being libraries with high-level APIs and a set of default settings. It is important to note that \textit{GreatAI} is merely a proof-of-concept, and its aim is to serve as a proxy for the design decisions behind it. Through this, the design can be indirectly evaluated. Hopefully, a by-product will be a library that can be effectively applied to this problem context. -The practical cases used for the evaluation are further elaborated in Chapter \ref{chapter:case}. In short, they focus on individual components of a growing commercial platform\footnote{\href{https://dashboard.scoutinscience.com/}{dashboard.scoutinscience.com}} with the aim of finding tech-transfer opportunities in academic publications. The main input of the system as a whole are PDFs while the output is a list of metrics describing various aspects of each paper, such as interesting sentences, scientific domains, and the scientific contribution. The output also includes a predicted score used for ranking. This ranking is subsequently processed by the business developers of Technology Transfer Offices (TTOs) of multiple Dutch and German universities who later give feedback on the results. +The practical cases used for the evaluation are further elaborated in Chapter \ref{chapter:case}. In short, they focus on individual components of a growing commercial platform with the aim of finding tech-transfer opportunities in academic publications. The main input of the system as a whole are PDFs while the output is a list of metrics describing various aspects of each paper, such as interesting sentences, scientific domains, and the scientific contribution. The output also includes a predicted score used for ranking. This ranking is subsequently processed by the business developers of Technology Transfer Offices (TTOs) of multiple Dutch and German universities who later give feedback on the results. -Overall, this problem context carries the properties of typical industry use cases: it utilises a wide-range of natural language processing methods, contains complex interactions between the services, benefits from the integration of end-to-end feedback, and has to provide the clients with a platform that they can rely on in their organisation's core processes. Since the final ranking affects real people, explainability and robustness are also central questions. +Overall, this problem context carries the properties of typical industry use-cases: it utilises a wide-range of natural language processing methods, contains complex interactions between the services, benefits from the integration of end-to-end feedback, and has to provide the clients with a platform that they can rely on in their organisation's core processes. Since the final ranking affects real people, explainability and robustness are also central questions. Before generalising, the design of the framework is iteratively refined using the feedback acquired from applying it in practical contexts which in this case is the research and development of a smaller and a more complex AI component using the work-in-progress framework. The treatment is finding a simple, less cognitively straining to use, design which still leads to high-quality deployments as defined in Section \ref{section:requirements}. @@ -24,7 +24,7 @@ Before generalising, the design of the framework is iteratively refined using th In order to conclusively answer \textbf{RQ3} and \textbf{RQ4}, interviews are conducted from a population of software engineers and data scientists with varying levels of professional background. Since me and my colleagues are likely to have a bias for (or against) the proposed design, the first step of checking its applicability in other practical contexts is to ask the opinion of non-affiliated practitioners. -First, before their interview, interviewees are requested to complete a questionnaire (shown in Appendix \ref{appendix:practices}) about their last completed AI project; the questions refer to the best practices implemented by \textit{GreatAI} as described in Tables \ref{table:best-practices-1} and \ref{table:best-practices-2}. They are also advised to take a quick look at the tutorial page\footnote{\href{https://great-ai.scoutinscience.com/tutorial/}{great-ai.scoutinscience.com/tutorial}} of the documentation. The interviews are divided into two halves. In the first part, after a brief introduction, participants are asked to solve a real-world task by finishing a partially completed example application using \textit{GreatAI}, they are also encouraged to think out loud so that their feedback can be noted. Successfully completing the task creates a system implementing a known number of best practices. This way, the added value --- in terms of larger number of implemented best practices --- can be quantitatively analysed by comparing the qualities of the finished implementation with the previously given answers. +First, before their interview, interviewees are requested to complete a questionnaire (shown in Appendix \ref{appendix:practices}) about their last completed AI project; the questions refer to the best practices implemented by \textit{GreatAI} as described in Tables \ref{table:best-practices-1} and \ref{table:best-practices-2}. They are also advised to take a quick look at the tutorial page of the documentation. The interviews are divided into two halves. In the first part, after a brief introduction, participants are asked to solve a real-world task by finishing a partially completed example application using \textit{GreatAI}, they are also encouraged to think out loud so that their feedback can be noted. Successfully completing the task creates a system implementing a known number of best practices. This way, the added value --- in terms of larger number of implemented best practices --- can be quantitatively analysed by comparing the qualities of the finished implementation with the previously given answers. Notes are taken throughout the interviews and subsequently extended using reflective journaling \cite{halcomb2006verbatim} combined with thematic coding. After which, the insights from the interviewed professionals are distilled using the techniques of thematic analysis \cite{fereday2006demonstrating} following the methodologies of \cite{cruz2019catalog} and \cite{haakman2021ai}. These insights can then be combined with the numerical results to explain and elaborate on them. diff --git a/thesis/chapters/4_design.tex b/thesis/chapters/4_design.tex index 43fa4bb..04c5e80 100644 --- a/thesis/chapters/4_design.tex +++ b/thesis/chapters/4_design.tex @@ -16,11 +16,11 @@ There have been attempts that at least partially address this issue, however, as \label{fig:scope} \end{figure} -It is interesting to mention that there is a proliferation\footnote{\href{https://xkcd.com/927/}{xkcd.com/927}} of platform/software as a service (PaaS/SaaS) products for deploying AI\footnote{Such as \href{https://mlem.ai/}{MLEM}, \href{https://streamlit.io/cloud}{Streamlit} or any AutoML SaaS platform, for example, \href{https://www.akkio.com/role/software-engineers}{Akkio} as these often have a one-click deployment feature as well.}. At first, these may look intriguing, however, they tend to only focus on getting code easily deployed in the cloud: AI best practices are not prioritised in this setup. Nevertheless, in many cases, it may be a suitable option to use such a service and these can also complement \textit{GreatAI} as illustrated in Figure \ref{fig:scope}: first, the prototype is transformed into a GREAT service and materialised as a common software artifact implementing the best practices. Then, it is either deployed using a deployment SaaS, or by using the organisation's existing software deployment setup. +It is interesting to mention that \href{https://xkcd.com/927/}{there is a proliferation} of platform/software as a service (PaaS/SaaS) products for deploying AI\footnote{Such as \href{https://mlem.ai/}{MLEM}, \href{https://streamlit.io/cloud}{Streamlit} or any AutoML SaaS platform, for example, \href{https://www.akkio.com/role/software-engineers}{Akkio} as these often have a one-click deployment feature as well.}. At first, these may look intriguing, however, they tend to only focus on getting code easily deployed in the cloud: AI best practices are not prioritised in this setup. Nevertheless, in many cases, it may be a suitable option to use such a service and these can also complement \textit{GreatAI} as illustrated in Figure \ref{fig:scope}: first, the prototype is transformed into a GREAT service and materialised as a common software artifact implementing the best practices. Then, it is either deployed using a deployment SaaS, or by using the organisation's existing software deployment setup. \section{Requirements} \label{section:requirements} -The best practices (which are referenced throughout the thesis) with which the design is concerned are a subset of those compiled by Serban et al. \cite{serban2020adoption,serban2021practices} and John et al. \cite{john2020architecting}. The core requirements --- set of covered best practices --- for a software solution that has the potential of improving our problem context are presented in the following along with some explanation and clarification of each of them. +The best practices (which are referenced throughout the thesis) with which the design is concerned are a subset of those compiled by Serban et al. \cite{serban2020adoption,serban2021practices} and John et al. \cite{john2020architecting}. The core requirements --- set of covered best practices --- for a software solution that has the potential of improving our problem context are presented in the following along with some explanation and clarification for each of them. \paragraph{General} Albeit not explicitly in the list of best practices, compatibility is vital in encouraging adoption. Large projects oftentimes end up depending on numerous packages, each of which may impose some restrictions on the code: since these all have to be satisfied simultaneously, this can result in severe constraints. @@ -40,17 +40,17 @@ These requirements were chosen stemming from their general importance and potent \section{Design principles} -Before diving into the concrete issues solved, let us detail the principles that should be used for implementing them in the scope of this framework. As implied in Section \ref{section:scope}, the Unix philosophy \cite{ritchie1978unix,salus1994quarter} of software design is followed. Most notably, the design goal that encourages to \textit{write programs that do one thing and do it well.}\footnote{Of course, \textit{write programs to work together} is also very much applicable, since allowing interoperability is one of the core requirements for \textit{GreatAI}.}. Apart from providing a clear and simple picture of the intended use cases for the library, this is also in line with the main notion of \textit{A Philosophy of Software Design} \cite{ousterhout2018philosophy}: APIs should be narrow and deep. A narrow width refers to having a small exposed surface area, i.e. having a small number of functions and classes in the public API. While depth implies each of them accomplishing an involved, complex goal. +Before diving into the concrete issues solved, let us detail the principles that should be used for implementing them in the scope of this framework. As implied in Section \ref{section:scope}, the Unix philosophy \cite{ritchie1978unix,salus1994quarter} of software design is followed. Most notably, the design goal that encourages to \textit{write programs that do one thing and do it well.}\footnote{Of course, \textit{write programs to work together} is also very much applicable, since allowing interoperability is one of the core requirements for \textit{GreatAI}.}. Apart from providing a clear and simple picture of the intended use-cases for the library, this is also in line with the main notion of \textit{A Philosophy of Software Design} \cite{ousterhout2018philosophy}: APIs should be narrow and deep. A narrow width refers to having a small exposed surface area, i.e. having a small number of functions and classes in the public API. While depth implies each of them accomplishing an involved, complex goal. -In a way, the width of an API is the price users have to pay (the effort required for learning it) to use it, while the depth is analogous to the return they get from it. Having to learn little and being provided by a lot of functionality maximises return on investment (ROI), hence, developer experience (DX). The theoretical frameworks presented in \textit{The Programmer's Brain} \cite{hermans2021programmer} provides us with explanations and vocabulary from psychology for arguing about the cognitive aspects of API design. In the following, two of them will be used for detailing the design principles: cognitive dimensions of code bases (CDCB) which is an extension of the cognitive dimensions of notation (CDN) framework \cite{blackwell2001cognitive}, and linguistic antipatterns \cite{arnaoudova2016linguistic}. The former comes with a set of dimensions which describe different (often competing) cognitive aspects of code that influence one's ability to perform certain tasks with it. +In a way, the width of an API is the price users have to pay (the effort required for learning it) to use it, while the depth is analogous to the return they get from it. Having to learn little and being provided by a lot of functionality maximises return on investment (ROI), hence, developer experience (DX). The theoretical frameworks presented in \textit{The Programmer's Brain} \cite{hermans2021programmer} provides us with explanations and vocabulary from psychology for arguing about the cognitive aspects of API design. In the following, two of them will be used for detailing the design principles: cognitive dimensions of code bases (CDCB) which is an extension of the cognitive dimensions of notation (CDN) framework \cite{blackwell2001cognitive}, and linguistic anti-patterns \cite{arnaoudova2016linguistic}. The former comes with a set of dimensions which describe different (often competing) cognitive aspects of code that influence one's ability to perform certain tasks on it. -Linguistic antipatterns provide guidelines for improving consistency and decreasing the false sense of consistency when there is none. Also, choosing the right names for identifiers can help activate information stored in the long-term memory which makes it quicker to comprehend and easier to reason about the code \cite{deissenboeck2006concise}. Finding the most accurate and useful names is harder than it first seems. Accuracy and usefulness are already often competing goals. The more precise the name, the longer and therefore less convenient to use \cite{butler2009relating}. In short, good names are key to good APIs; consciously considering the implications of names should be an integral part of the design process. +Linguistic anti-patterns provide guidelines for improving consistency and decreasing the false sense of consistency when there is none. Also, choosing the right names for identifiers can help activate information stored in the long-term memory which makes it quicker to comprehend and easier to reason about the code \cite{deissenboeck2006concise}. Finding the most accurate and useful names is harder than it first seems. Accuracy and usefulness are already often competing goals. The more precise the name, the longer and therefore less convenient to use \cite{butler2009relating}. In short, good names are key to good APIs; consciously considering the implications of names should be an integral part of the design process. -Nonetheless, simple APIs come at a high technical cost. The library has to implement these in a way that still allows high-performance in production \cite{kleppmann2017designing} and avoids being tied to specific libraries or technologies. Inspiration for the latter may be gained from the ML pipelines of Prado et al. \cite{prado2020bonseyes}: they show that more freedom can be achieved with plug-and-play steps and preconfigured defaults. +Nonetheless, simple APIs come at a high technical cost. The library has to implement these in a way that still allows for high performance in production \cite{kleppmann2017designing} and avoids being tied to specific libraries or technologies. Inspiration for the latter may be gained from the ML pipelines of Prado et al. \cite{prado2020bonseyes}: they show that more freedom can be achieved with plug-and-play steps and preconfigured defaults. \subsection{Default configuration} -\href{https://grugbrain.dev/#grug-on-apis}{Existing frameworks oftentimes suffer from the entanglement of numerous levels of abstractions.} Instead of exposing each implementation detail and encouraging users to interact with most of them, many of these could be abstracted away in a more high-level layer. Where configuration may be helpful for advanced users, default values can still be chosen automatically while providing an override option where necessary. +\href{https://grugbrain.dev/#grug-on-apis}{Existing frameworks oftentimes suffer from the entanglement of numerous levels of abstractions.} Instead of exposing each implementation detail and encouraging users to interact with most of them, many of these could be abstracted away in a more high-level layer. Even where configuration may be helpful for advanced users, default values can still be chosen automatically while providing an override option where necessary. For example, tracing the evaluations and the model versions used in a distributed fashion is very much expected of a trustworthy system. Hence, turning this feature on by default but allowing opting-out from it can result in less scaffolding required from the library's users. It also decreases their up-front cognitive load which by definition flattens the learning-curve \cite{hermans2021programmer}. Similar features can be imagined for providing a service API for the algorithms and for giving feedback, marking outliers, etc. @@ -58,17 +58,17 @@ Being \textit{automated} is listed as a requirement but it is imperative to only Learning from this, any kind of guessing must be avoided for creating a pleasant API. However, this conflicts with providing defaults for each configuration value. Even if these would be reasonable defaults derived from educated guesses, they are still merely guesses. Nevertheless, if the users were required to specify each configuration option, that would lead to considerably more boilerplate code. This verbosity is captured by the \textit{Diffuseness} dimension of CDCB and, of course, should be minimised. -To resolve this conflict, \textit{GreatAI} should have recommended values instead of defaults. This can mean a context object (as suggested in \cite{ousterhout2018philosophy}), which contains the result of each design decision that has to be made for a service's deployment. If not configured manually, the recommended values are applied automatically, just like defaults. The values chosen for each parameter must be clearly highlighted. Coming from the library's single responsibility, the number of parameters should not be immense, hence, the user can be expected to comprehend them instead of just being overwhelmed and skimming it. +To resolve this conflict, \textit{GreatAI} should have recommended values instead of defaults. This can mean a context object (as suggested in \cite{ousterhout2018philosophy}), which contains the result of each design consideration that has to be made for a service's deployment. If not configured manually, the recommended values are applied automatically, just like defaults. The values chosen for each parameter must be clearly highlighted. Coming from the library's single responsibility, the number of parameters should not be immense, hence, the user can be expected to comprehend them instead of just being overwhelmed and skimming it. This way, the library attempts to notify its user about the existence of these decisions but does not force them to manually decide. As a result, no initial configuration is needed for starting out with the library (high \textit{Provisionality}, low \textit{Diffuseness}) and the dependencies are not hidden since they are explicitly highlighted. \subsection{Documentation} -Little value can be derived from software without good documentation; without a doubt, good documentation is a prerequisite for adoption. Documentation comes in many shapes: modern integrated development environments (IDEs) tend to show a popup of a function's description when requested (on mouse hover for instance), at the same time a more comprehensive online manual and example projects are also still expected. But descriptive error messages can be also viewed as documentation. +Little value can be derived from software without good documentation; undoubtedly, good documentation is a prerequisite for adoption. Documentation comes in many shapes: modern integrated development environments (IDEs) tend to show a popup of a function's description when requested (on mouse hover for instance), at the same time, a more comprehensive online manual and example projects are also still expected. But descriptive error messages can be also viewed as documentation. The library must have quality documentation for all categories. Accordingly, for structuring it, the \textit{Diátaxis} philosophy is preferred \cite{Procida_Diataxis_documentation_framework} which prescribes dividing documentation into 4 parts along 2 axes: practical-theoretical and passive-active consumption. The four quadrants derived from this are: tutorials, how-to guides, reference, and explanation. -Once again, we might notice two competing interests: the level-of-detail and the length of the documentation. For example, FastAPI\footnote{\href{https://fastapi.tiangolo.com/async/\#concurrent-burgers}{fastapi.tiangolo.com}}, a popular Python web framework, has extensive descriptions and explanations on all topics related to Python's import system, the HTTP protocol, concurrency, deployment, etc. The actual framework's documentation is sprinkled over these very broad topics. This is certainly helpful for beginners to acquire knowledge from a single place. Nevertheless, this high-level of accessibility actually hinders the process of finding the relevant sections (in CDCB, this shows a trade-off between the support of \textit{Searching} and \textit{Comprehension} tasks). My (and Diátaxis') take is that linking to external resources about the library's domain are welcome, but the documentation must have a single responsibility: describing the library itself. +Once again, we might notice two competing interests: the level-of-detail and the length of the documentation. For example, FastAPI\footnote{\href{https://fastapi.tiangolo.com/async/\#concurrent-burgers}{fastapi.tiangolo.com}}, a popular Python web framework, has extensive descriptions and explanations on all topics related to Python's import system, the HTTP protocol, concurrency, deployment, etc. The actual framework's documentation is sprinkled over these very broad topics. This is certainly helpful for beginners to acquire knowledge from a single place. Nevertheless, this high-level of accessibility actually hinders the process of finding the relevant sections (in CDCB, this shows a trade-off between the support of \textit{Searching} and \textit{Comprehension} tasks). Diátaxis' take is that linking to external resources about the library's domain are welcome, but the documentation must have a single responsibility: describing the library itself. A large portion of software documentations is automatically generated from source code. This has the advantage of always keeping it in sync with code changes, however, it might also signal that the API is too large because it is inconvenient for the developers to document it by hand. Striking the right balance between handcrafted and automatically extracted documentation may be a vital component of good documentation. @@ -80,4 +80,4 @@ Subjectively, a key component of good DX is \textit{Progressive evaluation} thro At the same time, Python codebases are rarely strictly object-oriented (OO), they are a mix of the functional, data-driven, and OO paradigms. Consequently, relying on classes for grouping related functions is not always desirable. Therefore, it is even more imperative to name similar functions similarly. This helps discoverability and chunking \cite{hermans2021programmer} which amount to quicker comprehension. -There is one more reason to prefer consistency: humans have a limited short-term memory (STM) \cite{miller1956magical}. Even though flags as function parameters are frowned upon by some \cite{martin2009clean}, they are useful, especially, when configuring libraries. However, if there is no convention for the default value of a flag, clients have to remember the flag's name and initial value at the same time, quickly overloading their STM. Thus, in the codebase, all defaults must be \texttt{False}. Sometimes, it can result in a \textit{disable} prefix which may turn into a double negation, nevertheless, users should not ever encounter this themselves since the doubly-negated version is the default, thus when overriding it, it is only singly-negated. This approach also implies, something may be recommended to be turned on by default. +There is one more reason to prefer consistency: humans have a limited short-term memory (STM) \cite{miller1956magical}. Even though flags as function parameters are frowned upon by some \cite{martin2009clean}, they are useful, especially, when configuring libraries. However, if there is no convention for the default value of a flag, clients have to remember the flag's name and initial value at the same time, quickly overloading their STM. Thus, in the codebase, all defaults must be the same, let us say, \texttt{False}. Sometimes, it can result in a \textit{disable} prefix which may turn into a double negation, nevertheless, users should not ever encounter this themselves since the doubly-negated version is the default, thus when overriding it, it is only singly-negated. This approach also implies, something may be recommended to be turned on by default. diff --git a/thesis/chapters/5_cases/main.tex b/thesis/chapters/5_cases/main.tex index 9344822..9eb31de 100644 --- a/thesis/chapters/5_cases/main.tex +++ b/thesis/chapters/5_cases/main.tex @@ -1,14 +1,14 @@ \chapter{The ScoutinScience platform} \label{chapter:case} -The core product of \href{https://scoutinscience.com/}{ScoutinScience B.V.} is its platform. The clients are technology-transfer offices of Dutch and German universities, government organisations (e.g.: Wetsus), and corporates (e.g.: Heraeus Group, Ruma Rubber B.V.) who wish to extend the scope of their R\&D activities. ScoutinScience connects to multiple data sources of academic publications and integrates them into a single database. Each new publication is evaluated with a suite of AI components that ultimately determine its technology transfer potential. Other features are also extracted that help the users get a quick overview of the authors, topics, and contributions of a given piece of research. +The core product of \href{https://scoutinscience.com/}{ScoutinScience B.V.} is its platform\footnote{\href{https://dashboard.scoutinscience.com/}{dashboard.scoutinscience.com}}. The clients are technology-transfer offices of Dutch and German universities, government organisations (e.g.: Wetsus), and corporates (e.g.: Heraeus Group, Ruma Rubber B.V.) who wish to extend the scope of their R\&D activities. ScoutinScience connects to multiple data sources of academic publications and integrates them into a single database. Each new publication is evaluated with a suite of AI components that ultimately determine its technology transfer potential. Other features are also extracted that help the users get a quick overview of the authors, topics, and contributions of a given piece of research. -Each client organisation gets to see a different filtered view of this database ranked by the predicted probability of technology transfer opportunities being present. The main motivation is to make these business developers' and other professionals work more efficient by showing them which papers have the largest likelihood of being considered interesting by them. +Each client organisation gets to see a different filtered view of this database ranked by the predicted probability of technology transfer opportunities being present. The main motivation is to make these business developers' and other professionals work more efficient by showing them which papers have the highest chance of being considered interesting by them. -To achieve this, we have a service-based architecture \cite{kleppmann2017designing} on the backend, apart from the data integration, communication, and business logic, it is made up of services wrapping simpler (phrase-matching, Naïve Bayes) and more sophisticated (conditional random fields, transformer) models. As we will soon see, these can also depend on each other, for instance, based on the predicted scientific domain, a different model can be chosen for scoring certain aspects of papers. +To achieve this, we have a service-based architecture \cite{kleppmann2017designing} on the backend-side --- apart from the data integration, communication, and business logic --- it is made up of services wrapping simpler (phrase-matching, Naïve Bayes) and more sophisticated (conditional random fields, transformer) models. As we will soon see, these can also depend on each other, for instance, based on the predicted scientific domain, a different model can be chosen for scoring certain aspects of papers. -I was among the first engineers on the team which has grown considerably in the past two years. While architecting, designing, and integrating more and better models into our software solution, I experienced the same difficulties as described in Chapter \ref{chapter:background}. The gap between prototypes and production-ready services is larger than it seems. It is also larger than it should be. This motivated me to investigate the state-of-the-art and had found that it is insufficient in many cases. Since the ScoutinScience platform is a quite typical example of applying AI in the industry, it will serve as the real-life case, problem context, and testbed for attempting to design a solution which can advance the state-of-the-art. +I was among the first engineers on the team which has grown considerably in the past two years. While architecting, designing, and integrating more and better models into our software solution, I experienced the same difficulties as were described in Chapter \ref{chapter:background}. The gap between prototypes and production-ready services is larger than it seems. It is also larger than it should be. This had motivated me to investigate the state-of-the-art and I have found that it is insufficient in many cases. Since the ScoutinScience platform is a quite typical example of applying AI in the industry, it will serve as the real-life case, problem context, and testbed for attempting to design a solution which can hopefully advance the state-of-the-art. -In this chapter, the process of designing \textit{GreatAI} is described along with how it fits into real-life use cases. First, a simple experiment is presented which leads to the implementation of a service, subsequently, as the feature-set of the library grows and matures, a more complex software service is developed. Lastly, the final version of the design is presented and qualitatively evaluated to verify how well it satisfies the requirements described in Section \ref{section:requirements}. +In this chapter, the process of designing \textit{GreatAI} is described along with how it fits into real-life use-cases. First, a simple experiment is presented which leads to the implementation of a software service, subsequently, as the feature-set of the library grows and matures, a more complex component is developed. Lastly, the final version of the design is presented and qualitatively evaluated to verify how well it satisfies the requirements described in Section \ref{section:requirements}. \input{chapters/5_cases/naive-bayes} \input{chapters/5_cases/scibert} diff --git a/thesis/chapters/5_cases/naive-bayes.tex b/thesis/chapters/5_cases/naive-bayes.tex index f71bdd1..9839545 100644 --- a/thesis/chapters/5_cases/naive-bayes.tex +++ b/thesis/chapters/5_cases/naive-bayes.tex @@ -6,7 +6,7 @@ Using different models for slight variations of the same problem is quite common Fortunately, this is one of the oldest text classification tasks. In fact, Maron introduced the Naïve Bayes classifier in 1961 \cite{maron1961automatic} for exactly this purpose: classifying documents' subjects. However, it is still an active problem when it comes to academic texts as indicated by Elsevier funded research carried out by Rivest et al. \cite{rivest2021level}. They created a 176-class classification problem for comparing bibliometric and deep-learning approaches but this comparison is made difficult because 44\% of the labels are \textit{assigned suboptimally} in the ground-truth dataset. -Prior work evaluated SciBERT \cite{beltagy2019scibert} --- a BERT \cite{devlin2018bert} model pretrained on academic publications --- on a simpler version of the task in which the domains of sentences\footnote{Sentences are more appropriate units for processing due to SciBERT's maximum token length of 512 (which comes from its attention mechanism's quadratic complexity \cite{vaswani2017attention}).} have to be decided\footnote{\href{https://paperswithcode.com/sota/sentence-classification-on-paper-field}{paperswithcode.com/sota/sentence-classification-on-paper-field}}. It achieved an F1-score of $0.6571$ after being pretrained on the Semantic Scholar Corpus (SSC) \cite{Lo2020S2ORCTS} and finetuned on the train split of the Microsoft Academic Graph (MAG) dataset \cite{wang2019review}\footnote{SciBERT was applied to a preprocessed version of this dataset available at \href{https://github.com/allenai/scibert/tree/master/data/text_classification/mag}{github.com/allenai/scibert/tree/master/data/text\_classification/mag}}. To the best of my knowledge, no other published work exists on this sentence-classification task. This may be explained by the lack of practical relevance and contrived nature (uniform label distribution) of the task as we will see in the next subsection. +Prior work evaluated SciBERT \cite{beltagy2019scibert} --- a BERT \cite{devlin2018bert} model pretrained on academic publications --- on a simpler version of the task in which the domains of sentences\footnote{Sentences are more appropriate units for processing due to SciBERT's maximum token length of 512 which comes from its attention mechanism's quadratic complexity \cite{vaswani2017attention}.} have to be decided\footnote{\href{https://paperswithcode.com/sota/sentence-classification-on-paper-field}{paperswithcode.com/sota/sentence-classification-on-paper-field}}. It achieved an F1-score of $0.6571$ after being pretrained on the Semantic Scholar Corpus (SSC) \cite{Lo2020S2ORCTS} and finetuned on the train split of the Microsoft Academic Graph (MAG) dataset \cite{wang2019review}\footnote{SciBERT was applied to a preprocessed version (\href{https://github.com/allenai/scibert/tree/master/data/text_classification/mag}{github.com/allenai/scibert/tree/master/data/text\_classification/mag}) of this dataset.}. To the best of my knowledge, no other published work exists on this sentence-classification task. This may be explained by the lack of practical relevance and contrived nature (uniform label distribution) of the task as we will see in the next subsection. \begin{displayquote} \textbf{Design note} After getting familiar with the context, it is time to focus on experimenting and developing our domain prediction service. At the same time, the difficulties encountered should be noted and integrated into \textit{GreatAI}'s design. @@ -14,7 +14,7 @@ Prior work evaluated SciBERT \cite{beltagy2019scibert} --- a BERT \cite{devlin20 \subsection{Data} -Two datasets will be considered for the experiments. SciBERT's MAG and the SSC. The former is used to compare the results with SciBERT's, while the latter is utilised for training a model for production purposes because it has 19 labels compared with MAG's 7 and it also contains abstracts instead of just sentences, thus, it is more fitting for our use-case. +Two datasets will be considered for the experiments. SciBERT's MAG and the SSC. The former is used to compare the results with SciBERT's, while the latter is utilised for training a model for production purposes because it has 19 labels compared with MAG's 7 and it also contains abstracts instead of just sentences, thus, it is more fitting for our practical use-case. SciBERT's version of the MAG dataset has 84 thousand and 22.3 thousand sentences in its train and test splits respectively. These are mostly in English and have all punctuation and casing removed. Each sentence is classified as belonging to one of seven fields. Figure \ref{fig:mag-distribtion} shows that the classes have a uniform distribution. @@ -26,7 +26,7 @@ SciBERT's version of the MAG dataset has 84 thousand and 22.3 thousand sentences \label{fig:mag-distribtion} \end{figure} -SSC is much larger: it contains over 80 million abstracts. Having more data certainly helps in sampling the term distribution more accurately, nonetheless, the law of diminishing returns apply, especially when using simple models. Therefore, the data will be randomly downsampled to leave us with a more manageable couple of hundreds of megabytes of abstracts. We can see the distribution of class labels in Figure \ref{fig:ss-distribution}. The dataset is considerably less balanced: \textit{medicine} is by far the most voluminous field. +SSC is much larger: it contains over 80 million abstracts. Having more data certainly helps in sampling the term distribution more accurately, nonetheless, the law of diminishing returns applies, especially when using simple models. Therefore, the data will be randomly downsampled to leave us with a more manageable couple of hundreds of megabytes of abstracts. We can see the distribution of class labels in Figure \ref{fig:ss-distribution}. The dataset is considerably less balanced: \textit{medicine} is by far the most voluminous field. \begin{figure} \centering @@ -37,27 +37,27 @@ SSC is much larger: it contains over 80 million abstracts. Having more data cert \end{figure} \begin{displayquote} -\textbf{Where should we store this data?} ``On my machine'' seems like an easy answer. However, if we have a team working with the data or it has intrinsic value, it must be stored in an easy-to-access, potentially redundant way. Serban et al. \cite{serban2020adoption} expressed this need in the following best practice: \textit{Make Data Sets Available on Shared Infrastructure (private or public)}. Meanwhile, wherever data is stored, it should also be versioned to satisfy the next best practice: \textit{Use Versioning for Data, Model, Configurations and Training Scripts}. +\textbf{Where should we store this data?} ``On my machine'' seems like an easy answer. However, if we have a team working with the data or it has intrinsic value, it must be stored in an easy-to-access, potentially redundant way. Serban et al. \cite{serban2020adoption} expressed this need in the following best practice: \textit{Make Data Sets Available on Shared Infrastructure (private or public)}. Meanwhile, wherever data is stored, it should be also versioned to satisfy the next best practice: \textit{Use Versioning for Data, Model, Configurations and Training Scripts}. \end{displayquote} -MAG needs no further preprocessing if we aim to match SciBERT's setup \cite{beltagy2019scibert}. But since SSC contains a heap of metadata, the relevant parts have to be extracted and preprocessed. In this case, these are the concatenation of the abstract's text, paper's title and the journal's name along with the paper's domains (there can be multiple domains for a single paper, it is a multi-label classification task). Lastly, the non-English entries are discarded because we only expect to process papers in English. +MAG needs no further preprocessing if we aim to match SciBERT's setup \cite{beltagy2019scibert}. But since SSC contains heaps of metadata, the relevant parts have to be extracted and preprocessed. In this case, these are the concatenation of the abstract's text and the paper's title along with the paper's domains (there can be multiple domains for a single paper: it is a multi-label classification task). Lastly, the non-English entries are discarded because we only expect to process papers in English. \begin{displayquote} -\textbf{How should we preprocess the data?} These simple processing steps (filter, map, project) are almost always present in the data science lifecycle. For example, cleaning the input text from various HTML, OCR, PDF, or \LaTeX \hskip 0.12cm extraction artifacts is almost always necessary for text analysis. This is captured in the AI best practices collection under the following category: \textit{Write Reusable Scripts for Data Cleaning and Merging}. Also, the best practice of \textit{Test all Feature Extraction Code} is somewhat applicable: the applied processing steps must not introduce unwanted artifacts. +\textbf{How should we preprocess the data?} These simple processing steps (filter, map, project) are almost always present in the data science lifecycle. For example, cleaning the input text from various HTML, OCR, PDF, or \LaTeX \hskip 0.12cm extraction artifacts is almost always necessary for text analysis. This is captured in the \href{https://se-ml.github.io/practices}{AI best practices collection} under the following category: \textit{Write Reusable Scripts for Data Cleaning and Merging}. Also, the best practice of \textit{Test all Feature Extraction Code} is somewhat applicable: the applied processing steps must not introduce unwanted artifacts. \end{displayquote} \subsection{Methods} Our aims are twofold: (1) to evaluate a sentence classification model on MAG and compare it with prior art; and (2) to retrain and apply this model for classifying publication metadata (including abstracts). This would allow the ScoutinScience platform to select an appropriate processing pipeline which has been trained on a matching vocabulary (and domain) for each publication. -It seems reasonable that only considering the distribution (frequencies) of individual terms may be sufficient. To test this hypothesis, a unigram language model (Multinomial Naïve Bayes) is constructed and its accuracy is compared with SciBERT's. The former definitely aligns with the advice to \textit{Use The Most Efficient Models}\footnote{\href{https://se-ml.github.io/best_practices/02-efficient-models/}{se-ml.github.io/best\_practices/02-efficient-models}}. +It seems reasonable that only considering the distribution (frequencies) of individual terms may be sufficient. To test this hypothesis, a unigram language model (Multinomial Naïve Bayes) is constructed and its accuracy is compared with SciBERT's. The former definitely aligns with the advice to \textit{Use The Most Efficient Models}. Using the MNB implementation of scikit-learn \cite{pedregosa2011scikit}, it only took a couple of lines to create, hyperparameter-optimise, and test a text classifier. Including data loading and visualisations, it takes 71 lines of code (LOC) to be more precise. \footnote{The code is available at \href{https://great-ai.scoutinscience.com/tutorial/}{great-ai.scoutinscience.com/tutorial}.} This further proves relatively how simple it is to use standard packages. The code can be considered for satisfying the \textit{Automate Hyper-Parameter Optimisation} best practice, since it also implements an automated hyperparameter sweep. -The sentences are tokenised into words and vectorised with TF-IDF (with logarithmic term frequency) \cite{buckley1985implementation}, the hyperparameters found via 3-fold cross-validation on the \textit{train} split lead to filtering out tokens which occur in fewer than 5 documents or more than 5\% of the documents. +The sentences are tokenised into words and vectorised with TF-IDF (with logarithmic term frequency) \cite{buckley1985implementation}, the hyperparameters found via 10-fold cross-validation on the \textit{train} split lead to filtering out tokens which occur in fewer than 5 documents or more than 5\% of the documents. \begin{displayquote} -\textbf{What could be automated here?} As discussed in Section \ref{section:accessible-ai}, libraries exposing algorithms and state-of-the-art models can already be considered mature and accessible. In this case, only scikit-learn was utilised, but subjectively, most popular libraries have a similarly easy to use use API. Therefore, I see no urgent need for further action regarding the \textit{experimentation} step of the lifecycle in connection with the AI best practices. +\textbf{What could be automated here?} As discussed in Section \ref{section:accessible-ai}, libraries exposing algorithms and even SOTA models can already be considered mature and accessible. In this case, only scikit-learn was utilised, but subjectively, most popular libraries have a similarly easy to use API. Therefore, I see no urgent need for further action regarding the \textit{experimentation} step of the lifecycle in connection with the AI best practices. \end{displayquote} \subsection{Results \& Discussion} @@ -80,11 +80,11 @@ The sentences are tokenised into words and vectorised with TF-IDF (with logarith When this model is applied to the \textit{test} split of MAG, we get the confusion matrix of Figure \ref{fig:mag-confusion}. This Naïve Bayes classifier achieves a whopping $0.6795$ F1-score. This is $2.3\%$ more than SciBERT's on the same dataset. Thus, it seems, MNB clearly outperforms SciBERT for this particular use-case: it is not only more accurate, its model is magnitudes smaller, while it is also considerably faster to train (or finetune in the case of SciBERT) and use (its running time is in the order of milliseconds per publication). It also has no upper-limit on the input length. Thus, this experiment validates the choice of picking MNB for the task over SciBERT. -It is, of course, not entirely surprising that the sophisticated transformer architecture of SciBERT is not necessary for a plain task like this. Apart from phrases, the relation between separate words of a sentence do not carry nearly as much discriminative power as the identity of the terms\footnote{On a similar note, the independence assumption of Naïve Bayes is often less wrong than it might seem \cite{hand2001idiot}.}, hence there is little reason for using an attention mechanism. The fact that SciBERT even works in any way on this task is already a testament to its general applicability. Nevertheless, this short experiment has proved that we can safely opt for using MNB for production. +It is, of course, not entirely surprising that the sophisticated transformer architecture of SciBERT is not necessary for a plain task like this. Apart from phrases, the relation between separate words of a sentence do not carry nearly as much discriminative power as the identity of the terms \cite{hand2001idiot}, hence, there is little reason for using an attention mechanism. The fact that SciBERT even works in any way on this task is already a testament to its general applicability. Nevertheless, this short experiment has proved that we can safely opt for using MNB for production. Since Multinomial Naïve Bayes is best at returning a single label and SSC has multiple labels per datapoint: for evaluation purposes, it is checked whether the returned label is contained in the labels of the ground truth. On this dataset, MNB achieves lower macro-average F1-score which is 0.59.\footnote{The code for this is available at \href{https://great-ai.scoutinscience.com/examples/simple/deploy}{great-ai.scoutinscience.com/examples/simple/deploy}.} The weighted-average F1 is 0.70 and the overall accuracy is also 70\%. The substantial difference between the macro and weighted averages come from the unbalanced distribution of the labels. -The lower F1-score is not surprising because there are more than twice as many classes in this dataset, Additionally, the mistakes made are defensible when we look at Figure \ref{fig:ss-confusion}: most of them are between close or related classes. +The lower F1-score is not surprising because there are more than twice as many classes in this dataset. Additionally, the mistakes made are defensible when we look at Figure \ref{fig:ss-confusion}: most of them are between close or related classes. \begin{displayquote} This is the usual point where papers conclude: a proof-of-concept/prototype has been built and its performance demonstrated, measured --- and usually --- explained. Nonetheless, in an industrial setting, our problem is far from being solved: it has yet to be deployed. @@ -95,44 +95,51 @@ This is the usual point where papers conclude: a proof-of-concept/prototype has First, an inference function needs to be written that can take an input on the fly and calculate a corresponding prediction. Since we aim to follow the best practices, namely: \textit{Explain Results and Decisions to Users} and \textit{Employ Interpretable Models When Possible}, giving an explanation of the results is expected. Fortunately, with our simple model it is easy to determine the most influential weights, thus, words; the explanations are derived by taking the top 5 tokens from the input text ranked by their feature weights. The last deployment step may be to provide access to our model for others. \begin{displayquote} -\textbf{How do we provide an interface for the inference function?} We either have an offline or online inference workflow (or both). For the former, we have to provide a way to use it in batch processing; a simple Python function may be adequate for this purpose, though, allowing it to be easily (or automatically) parallelised would make its consumers' DX better. If it is an online-workflow, we must have a service running continuously and accepting input at any time. This can be achieved by a remote procedure call (RPC) interface, or more commonly, a web API. Developers usually refer to these as REST APIs, sometimes, they even follow the conventions of REST. Either way, we must develop a wrapper over the service in order to make it available for other internal/external consumers. +\textbf{How do we provide an interface for the inference function?} We either have an offline or online inference workflow (or both). For the former, we have to provide a way to use it in batch processing; a simple Python function may be adequate for this purpose, though, allowing it to be easily (or automatically) parallelised would make its consumers' DX better. If it is an online workflow, we must have a service running continuously and accepting input at any time. This can be achieved by a remote procedure call (RPC) interface, or more commonly, a web API. Developers usually refer to these as REST APIs, sometimes, they even follow the conventions of REST. Either way, we must develop a wrapper over the service in order to make it available for other internal/external consumers. \end{displayquote} According to the body of research on the adoption of best practices, this is where many real-world projects conclude. This also happens to be \textbf{the gap}. Believing that solely focusing on the research and experiments is good enough is a fallacy: when following this approach, the deployment step ends up being a rushed attempt of wrapping the \textit{AI} and putting it in the production environment. This is inarguably a deployment. However, it likely follows very few of the best practices which can lead to suboptimal real-life performance, lack of accountability, lack of opportunity to improve, and possibly an overall negative societal impact. \begin{displayquote} -\textbf{How could we implement more best practices?} The most notable missing best practices are the lack of automated deployment, automated regression testing, online monitoring, persisting the traces, graceful error-handling, taking feedback on the results (if possible in the use-case), calculating the online accuracy based on the feedback, and retraining the model if necessary using novel data. These all correspond to best practices. +\textbf{How could we implement more best practices?} The most notable missing software/operations features are the lack of automated deployment, automated regression testing, online monitoring, persisting the traces, graceful error-handling, taking feedback on the results (if possible in the use-case), calculating the online accuracy based on the feedback, and retraining the model if necessary using novel data. These all correspond to best practices. \end{displayquote} \section{Bridging the gap with GreatAI} -First, let us revisit the library's scope for clarification. As concluded in Section \ref{section:scope}, \textit{GreatAI} should ease the \textit{transition} step between prototypes and production-ready deployments. However, this leaves open the question of what constitutes to this step? There are cross-cutting concerns, for example, feature extraction is implemented and used in the training phase but it is also deployed alongside the model. The robustness criterion has to be met by this procedure even though its implementation is only in focus in the earlier stages of the project. Since having an untested function deployed into production can have severe repercussions, I conclude, assuring its correctness lies within the scope of \textit{GreatAI}. +First, let us revisit the library's scope for clarification. As concluded in Section \ref{section:scope}, \textit{GreatAI} should ease the \textit{transition} step between prototypes and production-ready deployments. However, this leaves open the question of what constitutes to this step? There are cross-cutting concerns, for example, feature extraction is implemented and used in the training phase, but it is also deployed alongside the model. The robustness criterion has to be met by this procedure even though its implementation is only in focus in the earlier stages of the project. Since having an untested function deployed into production can have severe repercussions, I conclude, assuring its correctness lies within the scope of \textit{GreatAI}. -This section briefly explores how the problems raised can be solved using \textit{GreatAI}, and the API it provides in order to best fit the needs of its users. We first focus on the aspects of data, then, the automated wrapping of services, lastly we discuss the utility of helper functions. +This section briefly explores how the problems raised can be solved using \textit{GreatAI}, and the API it provides in order to best fit the needs of its users. We first focus on the aspects of data, then, we discuss the utility of helper functions, and lastly, the automated wrapping of services. \subsection{Handling data} \label{subsection:large-file} -The obstacles coming from the intertwined nature of different models is widely recognised \cite{haakman2021ai,amershi2019software,sculley2015hidden}. This can lead to non-monotonic error propagation, meaning that improvements in one part of the system might decrease the overall system quality \cite{amershi2019software}. The importance of schema versioning in an environment of rapidly changing models and transformations is highlighted for a specific use-case in \cite{van2017versioning} and more generally by the \textit{Use Versioning for Data, Model, Configurations and Training Scripts} best practice. These emphasise the requirement for versioning models and in general, data. +The obstacles coming from the intertwined nature of different models is widely recognised \cite{haakman2021ai,amershi2019software,sculley2015hidden}. This can lead to non-monotonic error propagation, meaning that improvements in one part of the system might decrease the overall system quality \cite{amershi2019software}. The importance of schema versioning in an environment of rapidly changing models and transformations is highlighted for a specific use-case in \cite{van2017versioning} and more generally by the \textit{Use Versioning for Data, Model, Configurations and Training Scripts} best practice. These emphasise the requirement for versioning models and, in general, data. There are two kinds of data storage needs we have to address: training data and trained models. Because our code is probably already tracked under Git (and \href{https://octoverse.github.com/#lets-look-back-at-the-code-and-communities-built-on-git-hub-this-year}{likely synchronised with GitHub}), using the Git Large File Storage (LFS)\footnote{\href{https://git-lfs.github.com/}{git-lfs.github.com}} might seem intriguing. However, it is a paid (and surprisingly expensive) service of GitHub especially when we factor in the expected sizes of the models and training data with the fact that the only way to remove files counting towards our quota is to \href{https://docs.github.com/en/repositories/working-with-files/managing-large-files/removing-files-from-git-large-file-storage#git-lfs-objects-in-your-repository}{delete the entire repository}. -An open-source tool, the Data Version Control (DVC)\footnote{\href{https://dvc.org/}{dvc.org}} provides a nearly perfect alternative. It comes with a command-line interface (CLI) inspired by Git's, and it can be integrated with several backend storage servers. Its only downside is, of course, that it is one more tool that increases the complexity of the project and the initial setup time. If this is an acceptable price to pay, then I personally recommend opting for DVC. Nevertheless, if this may prohibit a team\footnote{As was the case with MLFlow tracking in an ING team that we saw in Section \ref{section:industry}.} from properly handling data according to the best practices, I present a simpler solution. +An open-source tool, the Data Version Control (DVC)\footnote{\href{https://dvc.org/}{dvc.org}} provides a nearly perfect alternative. It comes with a command-line interface (CLI) inspired by Git's, and it can be integrated with several backend storage servers. Its only downside is, of course, that it is one more tool that increases the complexity of the project and the initial setup time. If this is an acceptable price to pay, then I personally recommend opting for DVC. Nevertheless, if this may prohibit a team\footnote{As was the case with MLFlow tracking in an ING team described in Section \ref{section:industry}.} from properly handling data according to the best practices, I present a simpler solution. -The complexity of an API can be decreased by relying on its users preexisting knowledge and known patterns \cite{hermans2021programmer,ousterhout2018philosophy}. Therefore, we can reuse familiar APIs, such as the \texttt{open()} method from Python. Therefore, a method is proposed which provides the same interface, however, the backing storage can be a mixture of local disk space, S3-compatible storage, MongoDB, or any other storage backend. It provides a superset of \texttt{open()}'s interface\footnote{\href{https://docs.python.org/3/library/functions.html\#open}{docs.python.org/3/library/functions.html\#open}}; the same parameters can be used with it resulting in similar observed behaviour. +The complexity of an API can be decreased by relying on its users preexisting knowledge and known patterns \cite{hermans2021programmer,ousterhout2018philosophy}. Therefore, we can reuse familiar APIs, such as the \texttt{open()} method from Python. Therefore, a method is proposed which provides the same interface, however, the backing storage can be a mixture of local disk space, S3-compatible storage, MongoDB, or any other storage backend. It provides a superset of \texttt{open()}'s interface\footnote{\href{https://docs.python.org/3/library/functions.html\#open}{docs.python.org/3/library/functions.html\#open}}: the same parameters can be used with it resulting in similar observed behaviour. The expected features: versioning, progress bars, caching, garbage collecting the cache, automatically deleting old remote version are all present and come with recommended --- but easy to see and change --- configuration. -The expected features: versioning, progress bars, caching, garbage collecting the cache, automatically deleting old remote version are all present and come with recommended --- but easy to see and change --- configuration. +Easing development is not merely about automating everything but also making the code easy to change (which is the \textit{Viscosity} dimension of CDCB). Going from opening a local file on the disk with the built-in open method, to opening a file from S3 is as easy as changing \texttt{open('file.txt', 'w')} to \texttt{LargeFileS3('file.txt', 'w')}. In the case of the latter, an additional \texttt{version} keyword argument can be also given to lock ourselves in using a certain version which is very much desired in the case of models. -Easing development is not merely about automating everything but also making the code easy to change (which is the \textit{Viscosity} dimension of CDCB). Going from opening a local file on the disk with the built-in open method, to opening a file from S3 is as easy as changing \texttt{open('file.txt', 'w')} to \texttt{LargeFileS3('file.txt', 'w')}. In the case of the latter, an additional \texttt{version} keyword argument can also be given to lock ourselves in using a certain version which is very much desired in the case of models. +\subsection{Utilities} + +It is easy to notice multiple recurring tasks when it comes to processing text. Cleaning it from various extraction artifacts and normalising characters is one of the most common. But splitting sentences, language tagging, robustly lemmatizing are also often recurring tasks. Because having reusable and tested feature extraction code covers two best practices, it seems straightforward that a utility module could be created for this which could be extensively tested by means of unit testing. + +This is exactly the motivation behind \texttt{great\_ai.utilities}. Extra care has to be taken not to overfit these utilities on the cases considered in this chapter; however, I believe these are versatile enough to be helpful in many text-related contexts. A conclusive answer to this assumption will be found during the interviews. + +Implementing the unit tests uncovered multiple edge cases and even runtime errors, hence, the merit of \textit{Test all Feature Extraction Code} best practice is unequivocal. There is one more best practice that could be partially covered here, especially, because its solution also helps both during batch inference, but also at training/feature extraction time: \textit{Enable Parallel Training Experiments}. + +A function called \texttt{parallel\_map()} is implemented which closely mimics the API of the built-in Python function: \texttt{map}. And it exemplifies how even a close to trivial function is able to improve the DX by magnitudes. Rooted in the global interpreter lock (GIL)\footnote{\href{https://wiki.python.org/moin/GlobalInterpreterLock}{wiki.python.org/moin/GlobalInterpreterLock}} of CPython, in almost all cases, multi-threading does not lead to higher performance of CPU-bound tasks. For this purpose, multiprocessing has to be used. Fortunately, the built-in \texttt{multiprocessing} library has a great API, however, it still takes about a dozen lines to do a parallel mapping task with a progress bar. This can deter people (at least me) from taking advantage of more than just a single CPU core during exploratory experimentation. With \texttt{parallel\_map()}, this challenge becomes a single-line, routine task. \subsection{Deployment approach} -Some of the expectations one might have for data-intensive (such as AI) software are similar to that for software in general. These are also captured by the best practices: \textit{Use Continuous Integration}, \textit{Automate Model Deployment}, \textit{Enable Automatic Roll Backs for Production Model} to name a few. It is important to notice that these have been already solved by software engineering, more specifically, by the DevOps paradigm \cite{leite2019survey}. +Some of the expectations one might have for data-intensive (such as AI) software are similar to that for software in general. These are also captured by the best practices: \textit{Use Continuous Integration}, \textit{Automate Model Deployment}, and \textit{Enable Automatic Roll Backs for Production Model} to name a few. It is important to notice that these have been already solved by software engineering, more specifically, by the DevOps paradigm \cite{leite2019survey}. +In line with the findings of John et al. \cite{john2020architecting} on the SOTA of AI deployments, I suggest we wrap the applications in a format which is more compatible with existing DevOps toolkits. Instead of reinventing the wheel, we should rely on more established DevOps best practices for implementing the SE4ML deployment best practices. Besides, organisations are expected to have their deployment processes for classical applications, thus, allowing them to reuse those for AI applications seems to be the most convenient approach. -Inline with the findings of John et al. \cite{john2020architecting} on the SOTA of AI deployments, I suggest we wrap the applications in a format which is more compatible with existing DevOps tool-kits. Instead of reinventing the wheel, we should rely on more established DevOps best practices for implementing the SE4ML deployment best practices. Besides, organisations are expected to have their deployment processes for classical applications, thus, allowing them to reuse those for AI applications seems to be the most convenient approach. +Based on personal empirical evidence, three types of software artifacts are identified (in the context of Python) for which a wide range of established practices exist. WSGI server\footnote{\href{https://peps.python.org/pep-3333/}{peps.python.org/pep-3333}} compatible applications, executable scripts, and Docker Images\footnote{\href{https://docs.docker.com/registry/spec/manifest-v2-2/}{docs.docker.com/registry/spec/manifest-v2-2}}. To achieve this, \textit{GreatAI} provides a compatibility layer between simple Python inference functions and all the above common artifacts. Taking functions as input for the first step also satisfies the requirement to be \textbf{General}. Nevertheless, in order to also allow customisation, additional configuration, metadata, and behavioural specification can be given as well. -Based on personal empirical evidence, three types of software artifacts are identified (in the context of Python) for which a wide range of established practices exist. WSGI server\footnote{\href{https://peps.python.org/pep-3333/}{peps.python.org/pep-3333}} compatible applications, executable scripts, and Docker Images\footnote{\href{https://docs.docker.com/registry/spec/manifest-v2-2/}{docs.docker.com/registry/spec/manifest-v2-2}}. To achieve this, \textit{GreatAI} provides a compatibility layer between simple Python inference functions and all the above common artifacts. Taking functions as input for the first step is inline with the requirement to be \textbf{General}. Nevertheless, in order to also allow customisation, additional configuration, metadata, and behavioural specification can be given as well. - -The main advantage of the wrapping approach is that it does not require any input from the clients (by default). I opted for a decorator which lets users wrap their function by adding a single additional line of code as shown in Listing \ref{listing:hello-world}. After which the created WSGI application can be accessed through the \texttt{greeter.app} property. A CLI script (\texttt{great-ai}), along with a \texttt{Dockerfile} are also provided to cover the other two deployment artifacts. +The main advantage of the wrapping approach is that it does not require any input from the clients (by default). I opted for a decorator \cite{gamma1995design} which lets users wrap their function by adding a single additional line of code as shown in Listing \ref{listing:hello-world}. After which the created WSGI application can be accessed through the \texttt{greeter.app} property where \texttt{greeter} is the identifier of the user-defined function. A CLI script (\texttt{great-ai}), along with a \texttt{Dockerfile} are also provided to cover the other two deployment artifacts. \begin{listing}[!ht] \begin{minted}[ @@ -147,13 +154,12 @@ from great_ai import GreatAI def greeter(name: str) -> str: return f"Hello {name}!" \end{minted} +\captionsetup{width=.9\linewidth} \caption{Simplest example using \textit{GreatAI} for wrapping a function. In practice, \texttt{greeter} probably would be the inference function of an ML model.} \label{listing:hello-world} \end{listing} -Coincidentally, deployment best practices can be easily implemented in this wrapper layer. In the first iteration these are: input validation, persisting traces, online monitoring, and generating documentation. Input validation may be used to \textit{Check that Input Data is Complete, Balanced and Well Distributed}. Traces are important for both \textit{Log Production Predictions with the Model's Version and Input Data} and \textit{Provide Audit Trails}. However, traces can also indirectly help \textbf{Robustness}, because even production systems cannot be expected to be perfect. Saving and letting the users filter on encountered errors while allowing them to correlate it with the input causing it is imperative for facilitating debugging. - -Lastly, monitoring and documentation correspond with helping best practices: \textit{Continuously Monitor the Behaviour of Deployed Models} and \textit{Communicate, Align, and Collaborate With Others} respectively. +Coincidentally, deployment best practices can be easily implemented in this wrapper layer. In the first iteration these are: input validation, persisting traces, online monitoring, and generating documentation. Input validation may be used to \textit{Check that Input Data is Complete, Balanced and Well Distributed}. Traces are important for both \textit{Log Production Predictions with the Model's Version and Input Data} and \textit{Provide Audit Trails}. However, traces can also indirectly help \textbf{Robustness}, because even production systems cannot be expected to be perfect. Saving and letting the users filter on encountered errors while allowing them to correlate it with the input causing it is imperative for facilitating debugging. Lastly, monitoring and documentation correspond with helping best practices: \textit{Continuously Monitor the Behaviour of Deployed Models} and \textit{Communicate, Align, and Collaborate With Others} respectively. To allow customising the service's behaviour to fit different use-cases, the default configurations can be overridden by calling some functions of the library. An example of this can be seen in Listing \ref{listing:complex}, while more details of the semantics can be found in the documentation\footnote{\href{https://great-ai.scoutinscience.com/how-to-guides/create-service/}{great-ai.scoutinscience.com/how-to-guides/create-service}}. @@ -176,23 +182,14 @@ def add_to_secret_number(positive_number: int, model: int) -> int: assert add_number(1).output == 5 \end{minted} +\captionsetup{width=.9\linewidth} \caption{A simple \textit{GreatAI} service with behavioural customisations. In practice, the function would probably be the inference function for an ML model.} \label{listing:complex} \end{listing} -\subsection{Utilities} - -It is easy to notice multiple recurring tasks when it comes to processing text. Cleaning it from various extraction artifacts and normalising characters is one of the most common. But splitting sentences, language tagging, robustly lemmatizing are also often recurring tasks. Because having reusable and tested feature extraction code covers two best practices, it seems straightforward that a utility module could be created for this which can also be extensively tested by means of unit testing. - -This is exactly the motivation behind \texttt{great\_ai.utilities}. Extra care has to be taken not to overfit these utilities on the cases considered in this chapter; however, I believe these are versatile enough to be helpful in many text-related context. A conclusive answer to this assumption will be found during the interviews. - -Implementing the unit tests uncovered multiple edge cases and even runtime errors, hence, the merit of \textit{Test all Feature Extraction Code} best practice is unequivocal. There is one more best practice that could be partially covered here, especially, because its solution also helps both during batch inference, but also at training/feature extraction time: \textit{Enable Parallel Training Experiments}. - -A function called \texttt{parallel\_map()} is implemented which closely mimics the API of the built-in Python function: \texttt{map}. And it exemplifies how even a close to trivial function is able to improve the DX by magnitudes. Rooted in the global interpreter lock (GIL)\footnote{\href{https://wiki.python.org/moin/GlobalInterpreterLock}{wiki.python.org/moin/GlobalInterpreterLock}} of CPython, in almost all cases, multi-threading does not lead to higher performance of CPU-bound tasks. For this purpose, multiprocessing has to be used. Fortunately, the built-in \texttt{multiprocessing} library has a great API, however, it still takes about a dozen lines to do a parallel mapping task with a progress bar. This can deter people (at least me) from taking advantage of more than just a single CPU core during exploratory experimentation. With \texttt{parallel\_map()}, this challenge becomes a single-line, routine task. - \subsection{Summary} -After implementing some features of the library it can be already used for deploying the previously discussed domain prediction model. In this case, online prediction is expected, hence, the REST API-based deployment is chosen which is created by \textit{GreatAI} and packaged in a Docker image. This image can be instantiated by the company's existing DevOps pipeline and cloud infrastructure. At the end, users can see one more tag in the header section of publication evaluations where they can also see the explanation behind the model's decision as seen in Figure \ref{fig:dashboard-domains}. Let us now explore how it fares in a more complex case. +After implementing some features of the library, it can be already used for deploying the previously discussed domain prediction model. In this case, online prediction is expected, hence, the REST API-based deployment is chosen which is created by \texttt{GreatAI.create} and packaged in a Docker image. This image can be instantiated by the company's existing DevOps pipeline and cloud infrastructure. At the end, users can see one more tag in the header section of publication evaluations where they can also see the explanation behind the model's decision as demonstrated in Figure \ref{fig:dashboard-domains}. Let us now explore how it fares in a more complex case. \begin{figure} \centering diff --git a/thesis/chapters/5_cases/scibert.tex b/thesis/chapters/5_cases/scibert.tex index fed1602..e29415f 100644 --- a/thesis/chapters/5_cases/scibert.tex +++ b/thesis/chapters/5_cases/scibert.tex @@ -1,6 +1,6 @@ \section{Text summarisation with SciBERT} \label{section:complex-case} -Let us now turn our attention towards a more complex component. The ScoutinScience Dashboard\footnote{\href{https://dashboard.scoutinscience.com/}{dashboard.scoutinscience.com}} contains a full-page evaluation view for academic publications. On this, the known metadata, historical trends about the paper's topics, social media mentions, a PDF viewer showing the document, and other augmentation tools are displayed. One of these is the \textit{Highlights} section, which aims to summarise the paper from a technology-transfer perspective. +The ScoutinScience Dashboard contains a full-page evaluation view for academic publications. On this, the known metadata, historical trends about the paper's topics, social media mentions, a PDF viewer showing the document, and other augmentation tools are displayed. One of these is the \textit{Highlights} section, which aims to summarise the paper from a technology-transfer perspective. The current approach uses a simple heuristic based on a set of phrases selected by business developers and extended by the help of a word2vec model \cite{mikolov2013efficient}. The user feedback deemed this implementation slightly helpful but not adequate for providing an accurate overview. Thus, this is the baseline that I attempt to improve on in this section. @@ -14,15 +14,15 @@ Automatic text summarisation (ATS) is also one of the earliest established tasks Our problem requires generating a special type of summary: it must only concern a single aspect (tech-transfer) of the document. Aspect-based text summarisation has also seen some progress over the last decades \cite{berkovsky2008aspect,hayashi2021wikiasp} but these methods require concretely defined topics. Unfortunately, \textit{tech-transfer potential} is anything but a clear topic definition. -Numerous discussions and interviews with business developers over the last two years made it clear that there is no universally agreed on definition for it. At least, all of them agree that they know it when they see it. Additionally, most of them agree that they can confidently make a decision at the granularity of sentences. This gives rise to an obvious idea: show the experts something that they can annotate. Because the time of experts is valuable, and relevant sentences are few and far between, extra care needs to be taken to improve the ratio of positive examples in the dataset. The research of Iwatsuki Kenichi on formulaic expressions (FE) \cite{iwatsuki2020evaluation,iwatsuki2021extraction,iwatsuki2021communicative,iwatsuki2022extraction} provides a promising direction to do so. +Numerous discussions and interviews with business developers over the last two years made it clear that there is no universally agreed on definition of it. At least, all of them agree that they know it when they see it. Additionally, most of them agree that they can confidently make a decision at the granularity of sentences. This gives rise to an obvious idea: show the experts something that they can annotate. Because the time of experts is valuable, and relevant sentences are few and far between, extra care needs to be taken to improve the ratio of positive examples in the dataset. The research of Iwatsuki Kenichi on formulaic expressions (FEs) \cite{iwatsuki2020evaluation,iwatsuki2021extraction,iwatsuki2021communicative,iwatsuki2022extraction} provides a promising direction to do so. -A formulaic expression is a phrase with zero or more ``slots'' which when filled appropriately, leads to expressing a certain intent. In the context of scientific texts, an example\footnote{Taken from the ground-truth data available at \href{https://github.com/Alab-NII/FECFevalDataset/blob/master/human_evaluation/background.tsv}{github.com/Alab-NII/FECFevalDataset}.} could be: \texttt{it was not until * that}. The asterisk can be substituted with multiple terms and the intention of this expression is (likely) to describe the \textit{History of the related topics}. Iwatsuki et al. identified a set of 39 intentions, compiled a manually labelled dataset \cite{iwatsuki2020evaluation}, and developed multiple approaches for automatically extracting and classifying formulaic expressions in large corpora \cite{iwatsuki2021communicative,iwatsuki2022extraction}. +A formulaic expression is a phrase with zero or more ``slots'' which when filled appropriately, leads to expressing a certain intent. In the context of scientific text, an example\footnote{Taken from the ground-truth data available at \href{https://github.com/Alab-NII/FECFevalDataset/blob/master/human_evaluation/background.tsv}{github.com/Alab-NII/FECFevalDataset}.} could be: \texttt{it was not until * that}. The asterisk can be substituted with multiple terms and the intention of this expression is (likely) to describe the \textit{History of the related topics}. Iwatsuki et al. identified a set of 39 intentions, compiled a manually labelled dataset \cite{iwatsuki2020evaluation}, and developed multiple approaches for automatically extracting and classifying formulaic expressions in large corpora \cite{iwatsuki2021communicative,iwatsuki2022extraction}. \subsection{Methods} -In order to compile a new dataset, experts are asked to judge sentences that passed an \textit{intention check}. This pooling approach is commonly used in the field of information retrieval \cite{schutze2008introduction}. The filtering is expected to sieve out sentences that are probably not relevant from a technology-transfer perspective using Iwatsuki's formulaic expression intention labels. Subsequently, relevance judgements --- in the form of \textit{interesting} or \textit{not interesting} labels --- are gathered for these sentences. This method turns the extractive summarisation into a binary classification task for which a SciBERT model \cite{beltagy2019scibert} can be finetuned. Ultimately, the summaries are derived from sentences that are selected by the classifier trained on the experts' annotations. +In order to compile a new dataset, experts are asked to judge sentences that passed an \textit{intention check}. This pooling approach is commonly used in the field of information retrieval \cite{schutze2008introduction}. The filtering is expected to sieve out sentences that are probably not relevant from a technology-transfer perspective using Iwatsuki's formulaic expression intention classes. Subsequently, relevance judgements --- in the form of \textit{interesting} or \textit{not interesting} labels --- are gathered for the remaining sentences. This method turns the extractive summarisation into a binary classification task for which a SciBERT model \cite{beltagy2019scibert} can be finetuned. Ultimately, the summaries are derived from sentences that are selected by the classifier trained on the experts' annotations. -We have to note two possible shortcomings of this setup: firstly, we assume the FE intentions are strongly correlated with the sought-after aspect, this may or may not be true. secondly, only the individual relevance of the sentences is considered instead of the overall relevance (utility) of the summary. Nonetheless, it is expected that stemming from the length of the documents and the sparseness of the selected sentences, that any combination of them is likely to have low redundancy. +We have to note two possible shortcomings of this setup: firstly, the FE intentions are assumed to be strongly correlated with the sought-after aspect, this may or may not be true. Secondly, only the individual relevance of the sentences is considered instead of the overall relevance (utility) of the summary. Nonetheless, it is expected that stemming from the length of the documents and the sparseness of the selected sentences, that any combination of them is likely to have low redundancy. \subsection{Results} @@ -40,10 +40,10 @@ For the first iteration, 1500 sentences were selected for 2 experts to annotate \kappa_{agreement} \equiv \frac{p_{observed} - p_{expected}}{1 - p_{expected}} = 1 - \frac{1 - p_{observed}}{1 - p_{expected}} \end{equation} -The next step is finetuning SciBERT with the help of HuggingFace transformers \cite{wolf2019huggingface}. The data are divided into training and test splits with a ratio of 4:1. From the train split, a validation split is also derived which is used for early stopping. The objective function is the positive class' F1-score and the early stopping patience is 5 epochs. The learning rate is $5 \times 10^{-5}$ and AdamW \cite{loshchilov2017decoupled} is used for optimisation with a weight decay of 0.05. The code can be found in the documentation\footnote{\href{https://great-ai.scoutinscience.com/examples/scibert/train/}{great-ai.scoutinscience.com/examples/scibert/train/}}, it is surprisingly slightly shorter than the code of Section \ref{section:simple-case}. +The next step is finetuning SciBERT with the help of HuggingFace transformers \cite{wolf2019huggingface}. The data are divided into training and test splits with a ratio of 4:1. From the train split, a validation split is also derived which is used for early stopping. The objective function is the F1-score of the positive class and the early stopping patience is 5 epochs. The learning rate is $5 \times 10^{-5}$ and AdamW \cite{loshchilov2017decoupled} is used for optimisation with a weight decay of 0.05. The code can be found in the documentation\footnote{\href{https://great-ai.scoutinscience.com/examples/scibert/train/}{great-ai.scoutinscience.com/examples/scibert/train}}, it is surprisingly slightly shorter than the code of Section \ref{section:simple-case}. \begin{displayquote} -\textbf{Reproducability} Reproducible experiments are generally preferred. It is easy to forget to set some seeds values and, for example, end up with different datapoints in the test-train splits during training and validation in a Continuous Integration (CI) pipeline. To facilitate reproducability, it would be useful to reset the seeds of each imported library's random number generators (RNGs) when \textit{GreatAI} is configured. Thus, a feature has been added to detect and reset RNGs of installed and imported libraries. This certainly will not solve the reproducibility crisis \cite{hutson2018artificial} on its own, however, in some cases, it can result in one fewer step to miss. +\textbf{Reproducibility} Reproducible experiments are generally preferred. It is easy to forget to set some seeds values and, for example, end up with different datapoints in the test-train splits during training and validation in a Continuous Integration (CI) pipeline. To facilitate reproducibility, it would be useful to reset the seeds of each imported library's random number generators (RNGs) when \textit{GreatAI} is configured. Thus, a feature has been added to detect and reset RNGs of installed and imported libraries. This certainly will not solve the reproducibility crisis \cite{hutson2018artificial} on its own, however, in some cases, it can result in one fewer step to miss. \end{displayquote} \begin{displayquote} @@ -62,7 +62,7 @@ The best validation results were achieved after 8 epochs which was slightly more Let us check how well the selected sentences correspond with the tech-transfer potential. Users and in-house experts can rate publications (from a tech-transfer perspective) by assigning them to one of four categories: \texttt{A}, \texttt{B}, \texttt{C}, and \texttt{D} with \texttt{A} being the most and \texttt{D} the least promising. This feedback is stored and used for analytic and training purposes. Since both the feedback grade and the ``highlights'' are supposed to reflect the same aspect of papers, therefore, we can reasonably expect some correlation between them. -Figure \ref{fig:histograms} shows the ratio of summary candidate sentences as predicted by the finetuned model in 4 categories (grades) of papers. The two datasets come from non-overlapping sets of papers, hence, the results come solely from the model's ability to generalise. It is interesting to see that the Spearman's rank correlation coefficient \cite{spearman1961proof} between the normalised ``highlights'' counts and the ratings of papers is \textbf{0.4784} and is statistically significant ($P = 5.4 \times 10^{-74}$). This proves the presence of a monotonic association. For context, the correlation between the grades and the number of sentences found by the baseline approach is 0.06597 ($P = 0.03$). We can conclude that the classifier's output is indicative of the publications' tech-transfer potential. +Figure \ref{fig:histograms} shows the ratio of summary candidate sentences as predicted by the finetuned model in 4 categories (grades) of papers. The two datasets come from non-overlapping sets of papers, hence, the results come solely from the model's ability to generalise. It is interesting to see that the Spearman's rank correlation coefficient \cite{spearman1961proof} between the normalised ``highlights'' counts and the ratings of papers is \textbf{0.4784} and is statistically significant ($P = 5.4 \times 10^{-74}$). This proves the presence of a monotonic association. For context, the correlation between the grades and the number of sentences found by the baseline approach is 0.06597 ($P = 0.03$). We can conclude that the classifier's output is indicative of publications' tech-transfer potential. \begin{figure} \centering @@ -77,13 +77,13 @@ Figure \ref{fig:histograms} shows the ratio of summary candidate sentences as pr To implement the summarisation, at most the top 7 selected sentences are chosen as ranked by their log-probabilities. They are subsequently reordered according to their position in the text. As a quasi-explanation, the tokens' attention scores are visualised and overlaid on the highlighted sentences. The \textit{i}-th token's visualised attention comes from summing up the attention weights of each of the last layer's heads between the \texttt{[CLS]} and the \textit{i}-th token. To improve the end-user experience, a high-pass filter and a stop-word list is applied to the scores in order not to highlight the syntax-related tokens (punctuation, determiners). The service --- after being integrated into the dashboard --- can be seen in Figure \ref{fig:dashboard-highlights}. \begin{displayquote} -In order to get insights into their inner workings, HuggingFace models can be given \texttt{output\_attentions=True} in their constructor which results in a new property becoming accessible on the results for querying the attentions. The only issue with it is that it is a 5-dimensional matrix which makes exploring and understanding it non-obvious. In short, it has very low \textit{Discoveribility}. For example, the attentions for the UI are calculated with this expression: +\textbf{Design inspiration} In order to get insights into their inner workings, HuggingFace models can be given \texttt{output\_attentions=True} in their constructor which results in a new property becoming accessible on the results for querying the attentions. The only issue with it is that it is a 5-dimensional matrix which makes exploring and understanding it non-obvious. In short, it has very low \textit{Discoveribility}. For example, the attentions for the UI are calculated with this expression: \begin{minted}[ baselinestretch=1, ]{python} np.sum(result.attentions[-1].numpy()[0], axis=0)[0][1:-1] \end{minted} -Even though the operation is conceptually simple, because of the opaque datastructure, this is anything but obvious to comprehend. Therefore, it is clear that this needs to be avoided in my library design; it has to have a clear and discoverable API which can be achieved by the use of typehints, descriptive property names, and docstrings. +Even though the operation is conceptually simple, because of the opaque data structure, this is anything but obvious to comprehend. Therefore, it is clear that this needs to be avoided in my library design; it has to have an explicit and discoverable API which can be achieved by the use of typehints, descriptive property names, and docstrings. \end{displayquote} \begin{figure} @@ -96,7 +96,7 @@ Even though the operation is conceptually simple, because of the opaque datastru \section{Improving GreatAI} -After having solved two problems by implementing two standalone services and integrating them into an existing ecosystem while relying on \textit{GreatAI} as a primary tool, a wide variety of insights have been gained. In the next couple of subsections, the extra features and design decisions are presented that have been motivated by the \textit{Highlights service}. After which, the final surface of the API is described and evaluated by its relation to the SE4ML \cite{serban2020adoption,serban2021practices} and AI engineering \cite{john2020architecting,john2020ai} best practices. +After having solved two problems by implementing two standalone services and integrating them into an existing ecosystem while relying on \textit{GreatAI} as a primary tool, a wide variety of insights have been gained. In the next couple of subsections, the extra features and design decisions are presented that were motivated by the \textit{Highlights service}. After which, the final surface of the API is described and evaluated by its relation to the SE4ML \cite{serban2020adoption,serban2021practices} and AI engineering \cite{john2020architecting,john2020ai} best practices. \subsection{Caching} @@ -122,9 +122,9 @@ Additionally, a REST API is generated with its accompanying OpenAPI schema\footn \label{fig:greatai-api} \end{figure} -\subsection{\textit{Human} integration} +\subsection{Human integration} -Even though the REST API of \textit{GreatAI} services exposes all necessary features\footnote{Such as providing feedback per prediction, complexly filtering and sorting traces, create-read-update-delete (CRUD) operations for the feedback and traces, accessing live monitoring info (current configuration, versions, cache statistics), etc.} which are great for programmatic access, these are not ideal for direct human comprehension. In order to ease the introduction of \textit{GreatAI} services, a rudimentary dashboard is --- optionally --- generated next to the REST API. The dashboard's main features can be observed in Figures \ref{fig:greatai-header}, \ref{fig:greatai-table}, and \ref{fig:greatai-parallel}. The diagrams and filterable/sortable table are interconnected and are automatically updated, the reactive behaviour is provided by the Dash framework \cite{shammamah_hossain-proc-scipy-2019}. +Even though the REST API of \textit{GreatAI} services exposes all necessary features\footnote{Such as providing feedback per prediction, complexly filtering and sorting traces, create-read-update-delete (CRUD) operations for the feedback and traces, accessing live monitoring info (current configuration, versions, cache statistics), etc.} which are great for programmatic access, these are not ideal for direct human consumption. In order to ease the introduction of \textit{GreatAI} services, a rudimentary dashboard is --- optionally --- generated next to the REST API. The dashboard's main features can be observed in Figures \ref{fig:greatai-header}, \ref{fig:greatai-table}, and \ref{fig:greatai-parallel}. The diagrams and filterable/sortable table are interconnected and are automatically updated, the reactive behaviour is provided by the Dash framework \cite{shammamah_hossain-proc-scipy-2019}. \begin{figure} \centering diff --git a/thesis/chapters/6_results.tex b/thesis/chapters/6_results.tex index f370602..799812d 100644 --- a/thesis/chapters/6_results.tex +++ b/thesis/chapters/6_results.tex @@ -2,9 +2,9 @@ It should not be surprising that neither data scientists nor software engineers can be replaced by software libraries. However, a non-negligible subset of their processes can be partially or fully automated, especially when it comes to packaging and deploying AI/ML services. My goal was to design a library with an API that finds the balance between being simple enough to adopt without friction, yet useful/powerful enough to be adopted. Simplicity is subjective and it will be discussed separately in Section \ref{section:interviews}. For now, let us look at the utility of \textit{GreatAI}. -For answering \textbf{RQ3} --- To what extent can \textit{GreatAI} automatically implement AI deployment best practices? --- a comparison is presented in the following that illustrates which best-practices can be implemented/scaffolded/configured with little user input; hence, through a simple and streamlined API. Tables \ref{table:best-practices-1} and \ref{table:best-practices-2} summarise the implemented best practices in the context of practices found by prior surveys of scientific and grey literature \cite{serban2020adoption,serban2021practices,john2020architecting}. +For answering \textbf{RQ3} --- \textit{To what extent can \textit{GreatAI} automatically implement AI deployment best practices?} --- a comparison is presented in the following that illustrates which best-practices can be implemented/scaffolded/configured with little user input; hence, through a simple and streamlined API. Tables \ref{table:best-practices-1} and \ref{table:best-practices-2} summarise the implemented best practices in the context of practices found by prior surveys of scientific and grey literature \cite{serban2020adoption,serban2021practices,john2020architecting}. -In order to show an accurately nuanced representation, a \textit{Level of support} is determined for each best practice on a scale of \textit{Fully automated}, \textit{Supported}, and \textit{Partially supported}. For instance, \textit{Use static analysis to check code quality} from Table \ref{table:best-practices-1} is \textit{Supported} because the entire public interface of \textit{GreatAI} is correctly typed (including generics and asynchronous coroutines) and compatible with \href{https://mypy.readthedocs.io/en/stable/index.html#}{\texttt{mypy}} and \href{https://marketplace.visualstudio.com/items?itemName=ms-python.vscode-pylance}{\texttt{Pylance}}. This means that when \textit{GreatAI} is used in any Python project, these tools can be applied to statically check the soundness of the projects' integration with \textit{GreatAI}. However, if the library's user does not use typehints in their code and it contains more complex control flow, it can only be partially typechecked. In short, this best practice is supported, and a considerable part of it is already implemented by \textit{GreatAI}, but clients should still keep in mind that they might also need to make effort to fully implement it. +In order to show an accurately nuanced representation, a \textit{Level of support} is determined for each best practice on a scale of \textit{Fully automated}, \textit{Supported}, and \textit{Partially supported}. For instance, \textit{Use static analysis to check code quality} from Table \ref{table:best-practices-1} is \textit{Supported} because the entire public interface of \textit{GreatAI} is correctly typed (including generics and asynchronous coroutines) and compatible with \href{https://mypy.readthedocs.io/en/stable/index.html#}{\texttt{mypy}} and \href{https://marketplace.visualstudio.com/items?itemName=ms-python.vscode-pylance}{\texttt{Pylance}}. This means that when \textit{GreatAI} is used in any Python project, these tools can be applied to statically check the soundness of the project's integration with \textit{GreatAI}. However, if the library's user does not use typehints in their code and it contains more complex control flow, it can only be partially typechecked. In short, this best practice is supported, and a considerable part of it is already implemented by \textit{GreatAI}, but clients should still keep in mind that they might also need to make effort to fully implement it. This is not the case for \textit{Log production predictions with the model's version and input data} because by default, it is automatically implemented when calling \texttt{@GreatAI.create}. Users can still specify the exact expected behaviour, e.g.: where to store traces, additional metrics to log, or disabling the logging of sensitive input. Nevertheless, without input from the library's user, the best practice is already reasonably well implemented. @@ -85,13 +85,13 @@ Common schemas for common prediction tasks\textsuperscript{3} \end{threeparttable} \end{table} -Quantifying the number of implemented best practices would be misleading since their scope and importance cover a wide --- sometimes overlapping --- range. Especially because there is some overlap between the different reviews and even within the reviews. However, it is still clear that a large number of best practices can be given a \textit{Fully automated} implementation by \textit{GreatAI}'s design while and even larger number of them can be augmented by the library. This proves the feasibility of designing simple API's using the techniques of Chapter \ref{chapter:design} for decreasing the complexity of correctly deploying AI services (\textbf{RQ2}). +Quantifying the number of implemented best practices would be misleading since their scope and importance cover a wide --- sometimes overlapping --- range. Especially because there is some overlap between the different reviews and even within the reviews. However, it is still clear that a large number of best practices can be given a \textit{Fully automated} implementation by \textit{GreatAI}'s design while and even larger number of them can be augmented by the library. This proves the feasibility of designing simple APIs using the techniques of Chapter \ref{chapter:design} for decreasing the complexity of correctly deploying AI services (\textbf{RQ2}). \section{Interviews} \label{section:interviews} -Let us finally tackle the question of generalisability using the interview methodology described in Sectino \ref{section:interview-setup}. +Let us finally tackle the question of generalisability using the interview methodology described in Section \ref{section:interview-setup}. -One of the takeaways of Chapter \ref{chapter:background} was that Seldon Core is useful for implementing or helping to implement most best practices. Nonetheless, it also has an initial threshold that has to be surmounted before implementing even a single best practice. According to the adoption rate surveys, this stops a large portion of practitioners from using it or other similar frameworks. \textit{GreatAI} offers a different mix of features, the initial threshold is virtually non-existent: best practices can be immediately applied. But at the same time, the presented solution covers a smaller number of practices. The hypothesis is that the latter approach aligns better with the expectations of professionals. +One of the takeaways of Chapter \ref{chapter:background} was that Seldon Core is useful for implementing or helping to implement most best practices. Nonetheless, it also has an initial threshold that must be surmounted before implementing even a single best practice. According to the adoption rate surveys, this discourages a large portion of practitioners from using it or other similar frameworks. \textit{GreatAI} offers a different mix of features, the initial threshold is virtually non-existent: best practices can be immediately applied. But at the same time, the presented solution covers a smaller number of practices. The hypothesis is that the latter approach aligns better with the expectations of professionals. \subsection{Threats to validity} @@ -101,7 +101,7 @@ The primary purpose of \textit{GreatAI} was to serve as a proxy through which it \subsection{More data science} -The cases presented in Chapter \ref{chapter:case} revolved around NLP. This, unsurprisingly, heavily influenced the design process. The two most notable effects can be found in the REST API's \texttt{/predict} endpoint and some \texttt{utilities} functions. The former is streamlined to accept JSON compatible data while the latter gives robust feature extraction support for only textual inputs. Supporting the easy, direct upload of larger non-JSON files and extending \texttt{utilities} to handle multimedia formats should be sufficient for widely extending the scope of applicability of \textit{GreatAI}. +The cases presented in Chapter \ref{chapter:case} revolved around NLP. This, unsurprisingly, heavily influenced the design process. The two most notable effects can be found in the REST API's \texttt{/predict} endpoint and some \texttt{utilities} functions. The former is streamlined to accept JSON compatible data while the latter gives robust feature extraction support for only textual inputs. Supporting the easy, direct upload of larger non-JSON files --- e.g. by saving them to S3 and showing a preview for them on the Dashboard's trace table --- and extending \texttt{utilities} to handle multimedia formats should be sufficient for widely extending the scope of applicability of \textit{GreatAI}. \subsection{More software engineering} @@ -111,4 +111,4 @@ As described in Designing Data-intensive Applications \cite{kleppmann2017designi Some metrics of \textit{GreatAI}, such as the cache statistics, versions, and derived data from traces can be already conveniently queried from its REST API. Nevertheless, adding support for the de facto standard metric gathering tool \href{https://prometheus.io/}{Prometheus} could save the library's users from one more integration steps. -The common theme among the above-mentioned opportunities is that they could be reasonably well implemented without any user input, making them inline with the library's philosophy. Of course, the open-source nature of \textit{GreatAI} also allows anyone to already provide support for a wide range of integrations. +The common theme among the above-mentioned opportunities is that they could be reasonably well implemented without any user input, making them inline with the library's philosophy. Of course, the open-source nature of \textit{GreatAI} also allows anyone to already provide support for a wide range of integrations. Additionally, the scope could be also reasonably extended, i.e. more practices could be covered by including more criteria next to the GREAT ones. diff --git a/thesis/chapters/7_conclusion.tex b/thesis/chapters/7_conclusion.tex index 033016e..a9b1271 100644 --- a/thesis/chapters/7_conclusion.tex +++ b/thesis/chapters/7_conclusion.tex @@ -4,6 +4,6 @@ todo \textit{GreatAI} may have the potential to bridge the gap between data science and software engineering. Stemming from the bidirectional nature of bridges, we can look at the framework from two perspectives: for professionals closer to the field of data science, it provides an automatic scaffolding of software facilities that are required for deploying, monitoring, and iterating on their models. For software engineers, it highlights the necessary steps required for robust and improvable deployments --- while at the same time --- saves them from the menial work of implementing these constructs manually. While most importantly, it proves that increasing the adoption rate of AI/ML deployment best practices is viable by designing narrower and deeper APIs. -Good deployments benefit all of us. Continued research into the means of good deployments remains crucial. However, next to that --- as the presented results show --- better deployments can also be achieved by facilitating the \textit{transition} step of the AI lifecycle. Having automated implementations, even if for just simpler best practices, leaves professionals more time to tackle other deployment challenges and less opportunities to miss crucial steps. Overall, resulting in more implemented practices, hence, robust and trustworthy production software. +Good deployments benefit all of us. Continued research into the means of good deployments remains crucial. However, next to that --- as the presented results show --- better deployments can be also achieved by facilitating the \textit{transition} step of the AI lifecycle. Having automated implementations, even if for just simpler best practices, leaves professionals more time to tackle other deployment challenges and less opportunities to miss crucial steps. Overall, resulting in more implemented practices, hence, robust and trustworthy production software. \section{Concluding remarks} diff --git a/thesis/main.pdf b/thesis/main.pdf new file mode 100644 index 0000000..eda4194 Binary files /dev/null and b/thesis/main.pdf differ diff --git a/thesis/main.tex b/thesis/main.tex index b4084c8..1a5487f 100644 --- a/thesis/main.tex +++ b/thesis/main.tex @@ -17,11 +17,11 @@ \usepackage{minted} \usepackage{tocloft} \usepackage[a4paper, - bindingoffset=0pt, - left=3cm, - right=3cm, - top=3cm, - bottom=4cm, + bindingoffset=0cm, + left=2.5cm, + right=2.5cm, + top=2.5cm, + bottom=3.5cm, footskip=1.5cm]{geometry} % Header & footer diff --git a/thesis/ref.bib b/thesis/ref.bib index 817a2c3..52eda45 100644 --- a/thesis/ref.bib +++ b/thesis/ref.bib @@ -850,4 +850,11 @@ numpages = {3} pages={87--89}, year={2006}, publisher={IEEE} -} \ No newline at end of file +} + +@book{gamma1995design, + title={Design patterns: elements of reusable object-oriented software}, + author={Gamma, Erich and Helm, Richard and Johnson, Ralph and Johnson, Ralph E and Vlissides, John and others}, + year={1995}, + publisher={Pearson Deutschland GmbH} +}