"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "y_predicted = model.predict(X_test)\n",
+ "print(\n",
+ " sklearn.metrics.classification_report(\n",
+ " [y > 0 for y in y_test], [y > 0 for y in y_predicted]\n",
+ " )\n",
+ ")\n",
+ "sklearn.metrics.ConfusionMatrixDisplay.from_predictions(\n",
+ " [y > 0 for y in y_test],\n",
+ " [y > 0 for y in y_predicted],\n",
+ " xticks_rotation=\"vertical\",\n",
+ " values_format=\".2f\",\n",
+ ")\n",
+ "None"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3.10.4 ('.env': venv)",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.10.4"
+ },
+ "orig_nbformat": 4,
+ "vscode": {
+ "interpreter": {
+ "hash": "590d04b986175369d2a3cfc6a3cb86687cb152d8a3ff3e5e61bf670e70f6d913"
+ }
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/examples/scibert/config.ini b/examples/scibert/config.ini
new file mode 100644
index 0000000..86a3fd9
--- /dev/null
+++ b/examples/scibert/config.ini
@@ -0,0 +1,10 @@
+ENVIRONMENT = DEVELOPMENT
+ENVIRONMENT = ENV:ENVIRONMENT
+
+MONGO_CONNECTION_STRING=ENV:MONGO_CONNECTION_STRING
+MONGO_DATABASE=highlights
+
+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
diff --git a/examples/scibert/data/evaluation-experiment-2-stage #1-2m6dmb.json b/examples/scibert/data/evaluation-experiment-2-stage #1-2m6dmb.json
new file mode 100644
index 0000000..1efc308
--- /dev/null
+++ b/examples/scibert/data/evaluation-experiment-2-stage #1-2m6dmb.json
@@ -0,0 +1,501 @@
+{
+ "While promising advancements in improved photosynthesis may be achieved by genetic modification CITATION, we argue that using natural genetic variation for photosynthesis holds an equally promising potential for improvement.": true,
+ "We find that market parameters have a more significant impact on pricing decision and the profit of the platform than logistics parameters, while logistics parameters affect the time decision of the platform more significantly than market parameters.": false,
+ "We find that local informal competition has a robust negative effect on product innovation intensity of formal firms, while within industry informal competition enhances innovative sales.": false,
+ "Then we substantiate our claim of a need for a new chaplain-led intervention, building on various promising and effective interventions of spiritual care that have been developed in recent years.": false,
+ "The framework allows us to: i better qualify the categories of sustaining and disruptive innovation; ii understand the evolution of hybrid patterns of market innovation, since the elements of emerging disruptive innovations sometimes sustain the established technology, and; iii assess and map emerging market patterns.": false,
+ "More importantly, we develop a novel hypothesis about the complementarity between founder social capital and a startup's technological capabilities to suggest that the bargaining impact of founders' social capital becomes more pronounced when a startup is also endowed with superior technological capabilities.": false,
+ "In this setting we will develop novel analysis tools for generic identifiability of subnetworks that are formulated in terms of disconnecting sets in the graph of the network models, and we will show that this leads to a new and effective synthesis procedure for allocating external excitation signals for achieving identifiability of a subnetwork.": false,
+ "However, we show that increased portfolio disclosure creates additional skill re-assessment risk from mutual fund investors for the fund managers, which can lead to an increase in management fees, a decrease in risk taking and thus a decline in net fund performance.": false,
+ "Here, we critically review the most important pre-clinical and clinical findings, recent advances in CAR-T against myeloma, as well as discoveries in the biology of a still incurable disease, that, all together, will further improve safety and efficacy in relapsedrefractory patients, urgently in need of novel treatment options.": false,
+ "For the funds that are forced by the regulation to increase their disclosure frequency hereafter, \"semi-annual funds\", we find that mandating more frequent portfolio disclosure increases the skill re-assessment risk from mutual fund investors for managers of high-volatility funds relative to those of low-volatility funds.": false,
+ "While some of these described competitions overlap the structures and goals of existing alternatives, we believe that the benefits of our system, combined with the novel and varied ideas we present, will make Ludii one of the most attractive and popular competition platforms in future years.": false,
+ "We described KIs' potential to improve combination therapies for solid cancers, and we anticipate that ongoing clinical trials will ultimately guide the implementation of optimal combinatorial approaches to maximize the immune system's anti-tumor activity.": false,
+ "We conclude that the integrative model-based approach of evaluating the potential of new cropping systems in complex systems and fine-tuning alternative combinations will support the enhanced adaptability of innovative cropping practices to provide a sustainable livelihood to marginal households.": false,
+ "We argue therefore that the time has come to focus on advancing the DBS system in terms of technological properties to better meet individual patient needs, leading to more effective symptom control, improved patient quality of life and reduced healthcare costs.": true,
+ "Our findings can be used to improve existing school programs, for example, by integrating effective components in programs, or by developing new promising school-based programs that comprise the most effective components.": true,
+ "In this article, we likewise argue that articulating the drivers and infrastructures that enable the introduction of novel technologies in our case: in PPH will aid the moral assessment of these developments, while it may also allow us to develop tools for critical self-analysis by stakeholders involved in such developments.": false,
+ "Convinced of the potential of the VKB to revolutionize clinical trial data sharing among biopharmaceutical companies, our group in the EAPM is currently working, in cooperation with representatives of the biopharmaceutical industry, to resolve the issues mentioned above and to develop the web-based platform.": false,
+ "Confirming previous work, we showed the potential to reduce GHG emissions from agricultural soils by the application of organic amendments CITATION; however, also the addition of MF in our mesocosm led to a promising development of the cumulative GHG.": true,
+ "Compared to previous studies for instrument segmentation in the NUMBER D US, our proposed method achieves a higher accuracy with a considerably higher prediction efficiency, which offers a promising value for clinical implementation.": true,
+ "Although innovation is the cornerstone of our conceptual model, which hypothetically affects firm performance, our results theoretically contribute to BMI research by highlighting the significance of the organisational capabilities as a mediator when planning to introduce innovations to enhance firm performance.": false,
+ "With its clinical-driven design and easy-to-use setup, our robotic device for hand sensorimotor rehabilitation has the potential for high clinical acceptance, applicability and effectiveness.": true,
+ "We expect that such freedom of choice, in turn, facilitates competition on quality or price, thereby making the private-client segment of the audit market a well-suited setting to study the price and quality effects of market structure and competition.": false,
+ "We also propose improvements to the IKK attack by showing that the accuracy of the attack can be improved significantly by combining multiple attack runs, as we show that median recovery rates can be increased up to NUMBER percentage points, whereas the variance of recovery rates of simulation can be decreased up to NUMBER percentage points.": false,
+ "There is great potential for the digital tools to increase access to eye care and we expect the accuracy of the current tools to improve with every iteration in technology development.": false,
+ "Second, we propose a novel modeling approach, which allows to disentangle between product and process innovation, to empirically investigate innovation activity over the industry life-cycle on a comprehensive database of NUMBER European manufacturing industries.": false,
+ "Our main finding is that to understand investor behaviour, different dimensions of investor characteristics and behaviour should be analysed concurrently; investors' scale of operation, ownership composition, type of capital, and locational and strategic behaviour help configure investment decisions in relation to property market shifts.": false,
+ "From additional policy experiments, we find that labor market reforms aimed at weakening labor unions, by boosting profit margins and innovation, can foster a profit-led growth.": false,
+ "First, since there is already some evidence on the effect of innovation vouchers on future collaborations CITATION, the main contribution of our paper lies in understanding if this policy tool and the use of external knowledge providers by SMEs also leads to improved innovation outcomes.": false,
+ "By adopting a data set from a leading O NUMBER O freight platform in China, we find that the proposed model can effectively increase the revenue of the platform by more than NUMBER if the platform increases the price appropriately.": false,
+ "Based on our findings we demonstrate that the framework of IMSI and SDT can effectively be applied as a frame of analysis to identify essential features of sustainability in educational innovations and we discuss how concepts of SDT deepen the knowledge of sustainable educational innovation.": false,
+ "While these tools represent a significant progress in automated rapid generation and modification of NUMBER D molecular geometries, we sought to develop an easy-to-use tool which can quickly create reasonably accurate molecular geometries in the local chemical space of a given molecular scaffold.": true,
+ "While AI-systems have the potential to increase market efficiency and economic welfare through improved predictions, these positive effects can only unfold in so far as the individual market actors i recognize that potential and ii subsequently engage with them.": false,
+ "We propose a novel approach to address the path explosion problem: A smart triaging system that leverages supervised machine learning techniques to replicate human expertise, leading to vulnerable path discovery.": false,
+ "We conclude that the most promising packages are combining innovation support and information provision with either a carbon tax and adoption subsidy, or with a carbon market.": false,
+ "Therefore we employ a more objective indicator-market share of the niche-as the second axis, which helps to sketch socio-technical trajectories of niche innovations in the market overtime both regime sustaining and disruptive innovations.": false,
+ "The increased complexity of modern sociotechnical systems STS necessitates the need for a manageable representation of their attributes, to augment our understanding and enable the development of ways through which we can increase their effectiveness, efficiency, and safety.": false,
+ "Motivated by the potential for improving the capacities of socially intelligent systems, we investigate the potential of recognizing an individual's interdependence perceptions through an analysis of audiovisual recordings of dyadic conversational interaction.": false,
+ "More precisely, our results suggest that the structure of knowledge networks are positively correlated with the innovative capacity, with particular reference to the cohesion of inventor networks, while social proximity offers a different, and precisely negative, contribution for breakthrough innovation.": false,
+ "Methods: Using the case of the nationwide implementation of WGS into clinical practice in lung cancer in the Dutch healthcare system, we developed a simulation model to show that including service delivery features across the diagnostic pathway can provide essential insight into the affordability and accessibility of care at the systems level.": false,
+ "In section four, we develop some illustrative examples to demonstrate the functionalities of the database: we look at water to illustrate the potential for studying access to amenities and we look at assets to illustrate the potential for studying inequalities.": false,
+ "In general, our results indicate that an enabling performance measurement system design and an enabling system development process both independently increase procedural fairness and decrease red tape.": false,
+ "FOREC demonstrates robust effectiveness, consistently improving the performance compared to competitive baselines on NUMBER target markets we selected for our study.": false,
+ "Consistent with our conjecture that managers require more compensation for bearing the elevated skill re-assessment risk from fund investors, we find that following the regulatory change, high volatility semi-annual funds significantly increase management fees, relative to low volatility semi-annual funds.": false,
+ "We encourage researchers to continue contribut-ing insights to enable the development and assessment of retail service innovations that create positive outcomes for shoppers, investors and the implementing retail firm.": false,
+ "We are still in the very early stages of mobile-health implementation in psychiatry; however, if successfully developed, a mobile platform for early detection has the potential to help achieve major translational goals, such as early recognition of mental problems, accelerating access to care, and personalized monitoring of relapse.": false,
+ "Recognizing the promising clinical potential of RSPOs as novel therapeutic targets, a clinical trial has been set-up that tested the safety and efficacy of the neutralizing monoclonal anti-RSPO NUMBER antibody OMP NUMBER R NUMBER Rosmantuzumab in cancer patients with advanced solid tumors and metastatic CRC CITATION.": false,
+ "Immigrant inventors played a crucial role in making the United States an innovation powerhouse, by bringing new knowledge from their countries of origin CITATION and contributing to the longterm technological development of the US innovation system CITATION.": false,
+ "Finally, we illustrated the applicability of the proposed implementations and design algorithms in a network consensus application, where we have shown that CEV-GF filters achieve machine-precision accuracy at a significantly lower communication cost than existing methods.": false,
+ "We suggest that these differences may lead to a fractured market and an AI crisis in which different members of the EU will adopt nation-centric strategies to exploit AI, thus preventing the development of a frictionless market as envisaged by the EU.": false,
+ "We show that, unlike in lemons markets, adverse selection in gems markets affects low-quality goods, leaving only high-quality goods on the market, pushing up prices.": false,
+ "We provide theoretical foundations and empirical evidence for the impact of oil discoveries on the level of tariffs, illustrating that such discoveries can lead to economically significant higher levels of protectionism.": false,
+ "We propose that IT governance process capability can improve IT performance, which in turn can improve business performance.": false,
+ "We have therefore demonstrated the innovative performance of our pilot-scale melt-electrospinning device, which bridges the gap between laboratory-scale and pilot-scale manufacturing and achieves fiber diameters comparable to those produced by conventional solution electrospinning.": true,
+ "We have therefore demonstrated the innovative performance of our pilot-scale melt electrospinning device, which bridges the gap between laboratory-scale and pilot-scale manufacturing and achieves fiber diameters comparable to those produced by conventional solution electrospinning.": true,
+ "We demonstrate that markets with a high share of supply from VRES yield a significantly lower forward premium than markets with a low market share of wind or solar supply.": false,
+ "We build a novel dataset of immigrant inventors to examine their impact on the US inventing activity between NUMBER and NUMBER Did native inventors benefit from immigrants' inventive activity?": false,
+ "We believe the framework may add value to the development of robust HTA methods and effective implementation, which helps meet the needs for novel HTA methods due to emerging health technologies.": false,
+ "To develop a conceptual framework for the translocal diffusion of sustainability innovations, we will use both the transition literature as well as the regional innovation systems literature, because of their complementary character on the topic of translocal diffusion of sustainability innovations.": false,
+ "Therefore, we posit a need for novel approaches that enable studying host-microbe dynamics in a community ecology perspective, which can be utilised for devising effective diagnostic and therapeutic strategies.": false,
+ "The use of multiple cameras and RGB-D devices may indeed improve the acquisition performance and precision, but renders the system more complex and costly, going against our goal to keep the system as simple and low-cost as possible.": false,
+ "The development of influenza vaccines produced by transient expression is therefore one of the key niches of molecular farming that show unambiguous advantages over all other current manufacturing platforms, and this is reflected by the keen interest and investment in the platform by the US and Canadian governments.": false,
+ "The collaboration and outsourcing of economic activities, we argued, enable registered firms to take strategic advantage of the'local' market acceptance of informal enterprises to expand market size and perform better with product innovations.": false,
+ "Our results demonstrate the feasibility of such co-flocculation strategy for designing new high-rate anode material, which outperforms the original bulk HTiNbO NUMBER compound, making it a promising candidate for application in ultrafast lithium-ion batteries.": true,
+ "Our findings demonstrate the potential effectiveness of an online evidence-based patient information portal in improving knowledge in patients with congenital heart disease, but also underline the crucial importance of effective implementation and active use of the portal.": false,
+ "Moreover, our data show the potential of the PEA as a biomarker discovery tool in vitreous samples by highlighting CD NUMBER as a novel marker with potential diagnostic value for PVRL.": true,
+ "Lastly, we provide a benchmarking tool that uses datasets following this standard to measure the performance of equivalent mutant problem tools, showing the performance in greater detail than state of the art EMP evaluation tools.": false,
+ "Here, we propose a different method to achieve better scalability and higher accuracy using quantum computing, outperforming classical Bayesian neural networks for large datasets significantly.": true,
+ "Here, we also synthetize the design of the architecture of the wireless monitoring network, the sensor technology adopted to develop an effective real time environmental monitoring system and management platform, to construct a Wireless Sensor Network WSN-early warning and reporting system, which can be applied as a prevention measure.": false,
+ "By developing a new capital endogenization method that addresses the above temporality issue, we present a novel analysis on how China's capital development and the associated resource use and emissions over the past NUMBER decades CITATION are linked to meeting the final consumption of China and other countries throughout this time period.": false,
+ "Although several ABS frameworks exist that achieve significant speedups using GPUs in the field of ABS, there is still significant room for improvement, which we wish to address in this article.": false,
+ "Additionally, we developed a novel decentralized market clearing method called DACO that provides a near-optimal market solution in terms of maximum social welfare within limited number of stages.": false,
+ "We broadly define market innovation as purposive actions by market stakeholders that result in a distinctively new or altered form of market.": false,
+ "Using the variability of profitability among segments as our measure of the disclosure of crosssegment differences in performance, we find that existing competition is positively associated with cross-segment variability in profitability, whereas potential competition is negatively associated with cross-segment variability in profitability.": false,
+ "To demonstrate the potential of PPO in industrial use, we show that for larger problem instances where the optimal solution is unknown, the algorithm finds solutions that outperform the benchmark an aggregate modified base-stock heuristic by NUMBER.": false,
+ "To accomplish such goals as mentioned above, we need novel, sophisticated imaging tools CITATION CITATION CITATION as well as innovations to cushion the joint so that we are able to diagnose and treat the cartilage damage at the early subclinical phase.": false,
+ "Overall, the application of our sectoral approach reveals that the need and potential for global governance to contribute to effective climate protection varies significantly across sectoral systems.": false,
+ "Our work enables us to open the black box of the digitally enabled innovation activity by shedding light on collaborative activities between a focal firm and its customers and suppliers to advance innovation while coping with information overload.": false,
+ "Our work demonstrates the possibility of using the BIL functionalized polymer as a tailorable and robust electrolyte material platform for scalable in situ NUMBER D printing of implantable energy storage devices that can potentially be utilized as biomedical devices and implants for healthcare applications.": true,
+ "Our results demonstrate the efficacy of the proposed method for the optimization of light-matter coupling and its potential application for the enhanced performance of optoelectronic devices.": true,
+ "However, substantial challenges, such as production cost, scalability, and compatibility with current manufacturing pipelines in industry, remain and need to be overcome before we can see the full potential of the materials benefit the public.": false,
+ "For the area of automated machine learning, our work clearly demonstrates the potential of using multi-objective algorithm configurators within an integrated system leveraging many state-of-the-art techniques-an idea that can be extended to many other problems in machine learning in which multiple conflicting performance objectives arise.": false,
+ "Finally, we devised a novel strategy for fusing medium resolution Sentinel-NUMBER and high resolution TerraSAR-X data to increase mapping accuracy by using the potential of both sensing systems.": false,
+ "Compared to the earlier version, we have CITATION redefined our proposed system model; CITATION performed five additional novel experiments that demonstrate the efficacy of our solution; CITATION added a section about future work.": false,
+ "While many MLD systems suffer from low deposition rates that limit their potential for use in new applications, we believe this work can help to give directions in optimizing the already created deposition recipes and in developing new MLD precursors, processes and equipment for lowcost and high-throughput applications.": false,
+ "We also show that improvements in the classification performance can subsequently lead to a significantly better performance in the segmentation task.": false,
+ "Our study shows that the inclusion of SNM, unlocks a certain market focus into DT that makes considerations on desirability, viability and feasibility less about the product alone, and more about the product and its deployment to the market.": false,
+ "Our study delineates the impact of knowledge inflows from specific external sources, such as market-and science-based sources, on innovation performance in FFs and non-FFs and identifies which knowledge inflow sources FFs can convert into innovation outcomes more or less effectively than non-FFs.": false,
+ "Our novel methodological approach builds on the theory of a problem-solution space for mission-oriented innovation systems proposed by CITATION NUMBER, allowing identification of interdependencies and hierarchies in and between missions that may lead to trade-offs.": false,
+ "Our hybrid approach has great potential for the development of novel potential protein therapies, as it in part sidesteps the extrapolation challenges from NUMBER D or animal models to complex human physiology by efficiently combining in silico approaches with biologically relevant in vitro test systems.": false,
+ "On this basis, we can assess the potential of international cooperation to address the challenges specific sectoral systems face in the climate transition as well as the extent to which existing sectoral institutional complexes deliver on this potential.": false,
+ "In addition, we will further assess the proposed market using the data from other European countries and we will consider more complex trading strategies to quantify the maximum potential of the proposed market.": false,
+ "In Chapter NUMBER we develop the theory of p-automata, which is essential to us for solving some algebraic equations in chapter NUMBER We start by defining in Section NUMBER concepts such as p-automaton and p-automatic sequences, and we finish with the theorem of Christol relating algebraic power series to automatic sequences.": false,
+ "However, in second-generation processes, significant residual amounts of stillage are produced, and the development of effective valorization routes towards added-value platform chemicals for this material is key in boosting the profitability of the NUMBER generation bioethanol industry CITATION.": false,
+ "Furthermore, we proposed an improved projection random sample consensus RANSAC method, which can effectively divide the detection plane of catenary cantilever devices to solve the multicantilever device occlusion problem.": false,
+ "First, our study indicated that the collaborative process of collaborative innovation contests, facilitated by DT, can potentially bridge the mismatch between open innovation outcomes and company capabilities to adopt such innovation.": false,
+ "By doing so, we are better prepared to adopt the framework for the analysis of electric propulsion systems in cars, a potentially disruptive innovation that has slowly been entering mainstream markets.": false,
+ "As we enter an age of ubiquitous electronic health records EHRs, clinical decision support systems CDSS, and large, linked data sets with the potential to accommodate artificial intelligence AI algorithms, the need to better understand individual and collective decisionmaking has reached a critical point.": false,
+ "Among others, we show that worse labor market prospects of the worker relative to the leader make it more costly to employ an altruistic leader and less costly to employ a spiteful leader, rendering the use of unfriendly leadership styles more attractive for the firm.": false,
+ "All in all, using data across multiple asset classes and markets in an extended sample period, we conclude that r ROD positively and significantly predicts r LH and this robust pattern better describes market intraday momentum everywhere.": false,
+ "While these tools represent a significant progress in automated and rapid generation of molecular geometries, we sought to develop an easy-to-use tool which can quickly create reasonably accurate molecular geometries in the local chemical space of a given molecular scaffold.": false,
+ "We, therefore, have to wonder whether public organizations' use of social media can live up to the expectations that were raised more than a decade ago and whether a more effective tapping into the engaging potential of social media can indeed increase the visibility of public organizations on social media platforms.": false,
+ "We show that the production of heterologous protein and MCFA contents are significantly enhanced when using the novel expression system, and we show that transformant strains are not impaired in growth.": true,
+ "We found that employees with greater perceived future opportunities at work attached greater importance to mastery, meaning and affiliation needs i.e., approach needs, which in turn lead to higher engagement in need-congruent job and off-job crafting efforts.": false,
+ "We argue in this paper that SNM aspects could serve as a necessary addition to DT to increase the chance of successful market implementation of sustainable innovations.": false,
+ "To gain a better grasp of the evolving nature of markets in the NUMBER century, I return to the early theorizing by neoliberal thinkers of the market as an epistemic device that effectively and efficiently mediates information and knowledge between individuals and collective society.": false,
+ "To address the unsatisfied needs in robotic sensorimotor rehabilitation, we aimed at developing a novel clinical-driven robotic hand rehabilitation device that is capable of high quality haptic rendering and that supports physiological full flexionextension of the fingers while offering an effortless setup.": false,
+ "Therefore, we see the need of analyzing the potential benefits and trade-offs of the long-term implementation of these circular business models, especially in a market where they are gaining acceptance to further develop strategies to secure their possible environmental and material benefits.": false,
+ "Our study therefore contributes to emergent research about the micro-foundations of firm strategy and performance e.g., CITATION, NUMBER;Foss and Pedersen, NUMBER; by emphasizing the role of founders in enabling a startup to capture superior value in their RD partnerships.": false,
+ "More precisely, we show that for the tested problems such a single configuration switch can result in performance gains of up to NUMBER.With such a significant indication for improvement potential, we hope that our results trigger an intensified discussion of online algorithm configuration for CMA-ES variants.": false,
+ "In doing so, we hope that B NUMBER B innovation managers will improve their conceptual perspective on innovation by including service design as an effective tool to foster stakeholder engagement throughout the project life cycle in the innovation network.": false,
+ "In addition, we discuss what is needed to improve the situation further, in order to use this technology to its fullest advantage and avoid \"leaks in the pipeline\", when a promising device fails to take the next step of the valorization pathway and is abandoned.": false,
+ "Experimental evaluations demonstrate that while state-of-the-art methods have limited capability of reconstructing USCT image from frequency domain data, our proposed method can effectively reconstruct details for USCT images, which could lead to faster diagnoses and treatment decisions for breast cancer patients.": true,
+ "Drawing on self-determination and play theories, we develop a process model that proposes that daily playful work design PWD; designing fun, designing competition positively relates to employees' daily work engagement through basic psychological need satisfaction.": false,
+ "In particular, we introduce a novel EarlyTSC framework addressing the MO-CASH problem for EarlyTSC that achieves the following: In Fig. NUMBER c, we illustrate the improvements that can be achieved by an automated search over an expanded space of multiple algorithms and all their hyperparameters.": false,
+ "We suggest that significantly underexploited opportunities exist to enhance integration of such short-and long-term technology and policy interventions, which together could provide a more effective pathway to enable the appropriate intensification of groundwater use for irrigation in Nepal and the EIGP.": false,
+ "We show that it can outperform state-of-the-art multiway and two-way Linear Discriminant Analysis classifiers in asynchronous detection of individual finger movements from intracranial recordings, an essential feature to achieve a sense of dexterity with hand prosthetics and exoskeletons.": false,
+ "We provide recommendations to help achieve ASP systems designed to use climate forecasts, arguing that tailored seasonal forecast products have potential in some countries to improve the lead time of interventions to address climate-induced disasters.": false,
+ "We have presented a novel and improved minimization algorithm for t-SNE, providing significant performance improvements above the state-of-the-art, especially for large datasets and higher dimensional embeddings.": true,
+ "We find corroborating evidence for a temperature signal in brGMGT assemblages, further demonstrating the potential to develop novel proxies with more extensive studies of modern distributions.": false,
+ "We detail how our algorithm can be implemented on the target platform or on NISQ computers, bringing forth a promising application of these devices.": false,
+ "To overcome these limitations, we developed a highthroughput TCR repertoire and discovery platform that enables linking T cell phenotype with TCR gene and function data at single-cell level.": false,
+ "To demonstrate the societal benefits of adherence improvement, we recommend that the most promising interventions are subjected to rigorous evaluation of clinical effectiveness in pragmaticallydesigned, randomized, controlled trials.": false,
+ "The high-minus-low return spread is - NUMBER and - NUMBER for UK investors and Japanese investors, respectively, in the universe of all currencies, and - NUMBER and - NUMBER in the universe of developed markets' currencies, confirming that the US equity tail risk factor has a global component.": false,
+ "Our study shows that there are performance trade-offs for each BPM orientation with regard to potential, selectivity, and stability: the forward bias lowers the overall cell potential by reducing the chemical potential, while the reverse bias gives a stable product formation of CO NUMBER conversion products.": false,
+ "Our results when using this customized Six Sigma methodology demonstrate the suitability of this method for improving the SCA process in its different stages; from the basis of the process which is improving the quality of the acquired side-channel measurement to the performance of any kind of side-channel attack or leakage assessment technique.": false,
+ "Our results show that the novel MRcollar design enables achieving comparable heating capabilities as compared to the current clinical HYPERcollar NUMBER D.": false,
+ "Our main aim is to create a European research agenda, data sharing, and development of best practice for clinical and translational science to achieve breakthroughs with clinically feasible HIV cure strategies.": false,
+ "Moreover, building also on estimates of NETs' achievable potential, which are well below biophysical limits CITATION, we calculate the actual deployment level of the most promising NETs that would be necessary to meet the NUMBER C target under three different scenarios CITATION, NUMBER b.": false,
+ "In this review, we demonstrate that a web-based scoring platform can mitigate potential risk factors when including sTILs in clinical trials, and we argue that this framework can be applied for any future biomarker-driven clinical trial setting.": false,
+ "Here, we examined the feasibility of, and initial responses to, large walking perturbations in COPD, as well as the adaptation potential of people with COPD to repeated walking perturbations that might indicate potential for perturbation-based balance training in COPD.": false,
+ "Here we investigated interneuron abnormalities in two experimental models of EoP and explored the potential of two promising treatment strategies, namely intranasal mesenchymal stem cells MSCs or insulin-like growth factor I CITATION, to restore interneuron development.": false,
+ "From the dynamic capabilities perspective, we assume that the development of CE in the firm implies the use of firm capabilities and the alignment of these in the development of a dynamic process of innovation towards the development of CE in firms.": false,
+ "Due to the unique single-particle and single-molecule properties and high-throughput capabilities of SMLM techniques, we strongly believe these results highlight their potential to be used in the routine design, quality control and optimization of nanomaterials with improved biological efficacy.": false,
+ "Comparison with several state-of-the-art baselines shows that our FairMatch algorithm is able to significantly improve the performance of recommendation results in terms of visibility of different items and suppliers with a negligible loss in the recommendation accuracy in some cases.": false,
+ "By unpacking two key mechanisms for basic research investments to improve firms' innovation performance, our results provide a more detailed understanding of the changing rationale for basic research in firms in the context of declining corporate basic research investments.": false,
+ "As such, there is increasing awareness of the need to address significant TR in a manner that comes with i an acceptable level of risk and ii a meaningful clinical outcome.": false,
+ "With those in mind, we believe that improved knowledge on marine ES flows can also support the implementation of policy responses and actions as part of the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services IPBES agenda, to achieve sustainable governance and management of seascapes, oceans, and marine systems.": false,
+ "While numerous studies have demonstrated that augmentation can improve classification and regression accuracies, to date none have explored augmenting multivariate time series i.e. Our research aims to fill this gap by identifying methods capable of successfully augmenting multivariate time series for regression analysis.": false,
+ "We show that the proposed loss function significantly reduces the quantile crossing problem to near NUMBER in all markets considered, while in some cases simultaneously increasing forecasting performance based on classical point forecast metrics applied to the expected value of the probabilistic forecast.": false,
+ "We show that optimal performance is achieved when all atomic levels experience the same potential outside the dimple and we quantify the cooling for various NA by evaluating the dependence of the final entropy densities and temperatures as functions of the initial entropy.": false,
+ "We see a similar potential in the imaging context, which would allow for an improvement in the convergence of the inference and further enhance the sen-sitivity beyond the capabilities that we have demonstrated with this publication.": false,
+ "We have found that labor market integration happens gradually in three stages of entering the labor market, integrating into the labor market and sub-merging with the labor market.": false,
+ "We have developed the novel ChemoTopoChip platform to screen the potential of both chemistry and topography in producing immunomodulatory materials suitable for bone regenerative applications.": true,
+ "We further propose several research perspectives that could support the LC-NE system as a promising target for the identification of at-risk individuals in the preclinical stages of AD, and for the development of novel preventive interventions.": false,
+ "We find that mandatory disclosure of portfolio holdings exposes managers of highvolatility funds to considerable skill re-assessment risk from mutual fund investors, which steer their behavior towards actions that ultimately hurt investors.": false,
+ "We find that a higher number of walk-in customer orders leads to optimal online order fulfillment policies with higher delivery frequencies, i.e., shorter staging times, which are required to mitigate the effect of increasing competition among the two sales channels for the same inventory positions on online service level.": false,
+ "We explored whether the need for achievement and need for affiliation enhance the effects of course characteristics on students' entrepreneurial self-efficacy and study engagement, which advances our knowledge on how to teach entrepreneurship while taking individual differences into account.": false,
+ "We evaluate the proposed method in simulation, showing that our framework can improve prediction performance over baseline methods and avoid catastrophic forgetting, and in experiments with a mobile robot, showing that our framework can continuously improve a prediction model without the need for external supervision.": false,
+ "We discuss the collective, cumulative and uncertain characteristics of innovation, highlighting the lack of transparency in the biomedical RD system, the need for public investment in the innovation process, and the \"time-lag\" between risk-taking and reward.": false,
+ "We conclude that our solution is a promising tool for privacy-preserving machine learning tasks on distributed patient data, potentially leading to an improvement of the quality of healthcare, while respecting the privacy of involved patients.": false,
+ "We assume that actors can contribute to building up resources for innovation success like new knowledge, early market structures, financial capital or legitimacy, for example through RD, networking, investments, or institutional work, Musiolik and Markard, NUMBER CITATION, NUMBER b.": false,
+ "We argue that industries using general and generally available knowledge can easily live with a deregulated hire fire labor market regime like in the US, while the functioning of the routine innovation model in mature but knowledge-intensive industries takes advantage of low rates of job turnover due to well-protected insider positions.": false,
+ "Together, we developed cheap and easily synthesized compounds that dramatically outperform known crosslinking tools, providing the community with a novel strategy for understanding RNA NUMBER D structures and alternative conformations in cells.": true,
+ "To overcome previous limitations, we devised novel methodology, enabling simultaneous invasive assessment of both LV and coronary hemodynamics for the first time.": false,
+ "The reference distances of EP inventors referring to US inventors are much larger than those of US inventors referring to US inventors.": false,
+ "The Seminar has also highlighted the need to pay due attention to technological developments that may help to overcome at least some of the problems that nowadays we face in the application of a healthy diet: new technologies may help modern husbandmen and farmers to produce healthy foods sticking to local traditions but also at a reasonable cost.": false,
+ "Second, we find a substitutive interaction effect between market orientation and new product development stage, indicating that for exits through acquisitions, a high level of market orientation can compensate for an early stage of product development.": false,
+ "Reconstruction results based on simulated data are very promising, and we think that, by addressing the practical issues discussed above, we will indeed make significant progress towards a reliable EPT reconstruction method that provides us with accurate dielectric tissue maps in practice.": false,
+ "Our study establishes CXCL NUMBER as a key component in fibrosis development and the potential of blocking CXCL NUMBER as a promising therapeutic strategy.": false,
+ "Our future work mainly lies in extending the proposed approach with more advanced network clustering algorithms, and our long term goal is to develop a highly efficient and intelligent system to automate the management of complex WMNs.": false,
+ "On the basis of a feasibility analysis, we conclude that the identified challenges can most likely be successfully overcome by platform co-ops that organise taxi rides and professional jobs, while it may prove much more difficult in food delivery, homecare and microtasking.": false,
+ "NUMBER We develop a tailor-made Frank-Wolfe algorithm that can solve the dual estimation problem orders of magnitude faster than state-of-the-art general purpose solvers.": false,
+ "It is clear that we need to continue co-inventing the future of POC technology with multiple stakeholder groups towards the development of a global paradigm to address critical needs, and provide quality healthcare at affordable cost.": false,
+ "Furthermore, our results have proved that the proposed modified U-Net is capable of considering the neighborhood effects effectively, whose advantage is eliminating the need of using a CA to combine with our method.": false,
+ "For the second task, we obtain a question representation that contains all possible answers in equal quantum superposition, and we implement Grover's quantum search algorithm to find the correct answer, agnostic to the specific question, an implementation with the potential of delivering a result with quadratic speedup.": false,
+ "Following on from this we show the results of applying the approaches to the robot platform demonstrating the effectiveness and accuracy that can be achieved.": false,
+ "Finally, we expect that the disorder of metallic electrodes at the atomic scale leads to uncontrolled local electrostatic potential surrounding the nanoscale object, a problem that NUMBER D covalent crystals have the potential to overcome.": false,
+ "Experimental results show that an upstream signaling rate of NUMBER kbps can be achieved with our simple and low-cost prototype, enabling NUMBER Gbits link per user.": false,
+ "Drawing on the literature on sustainability-oriented innovation and innovation resistance theory, we explore the potential of blockchain technology to contribute to sustainable transformations within food supply chains.": false,
+ "Despite our high-quality data and correction for competing risks in our prediction model for the development of SPTs, it should be further developed to allow clinical use.": false,
+ "Based on the measurements and visual observations of the wake flow, we develop a hypothesis to explain how the presence of developed cavitation leads to increasing compressibility Mach number in the wake flow that, in turn, results in a change in the underlying flow pattern of the near-wake, leading to an increase in the vortex formation rate.": false,
+ "As a proof of principle, to demonstrate the feasibility of releasing drugs, we also showed the effect of HASF hydrogel composition on the release of small hydrophobic anti-inflammatory and anabolic drugs, that had previously been identified as promising agents.": false,
+ "Accordingly, we perform many experiments to demonstrate that the proposed method can be implemented on academic papers in any period after publication with a significantly higher degree of accuracy and robustness than the existing algorithms applied to new papers.": false,
+ "And as the challenges faced by cities seem to become greater with time, the need for novel, critical, and creating approaches on how we could use the potential of play to improve urban life, foster sustainability, consolidate resilient and inclusive communities becomes even more urgent.": false,
+ "While still at its infancy, we hope that the PCA initiative has the potential to serve as a nucleator, bringing together scientists and engineers from a wide range of fields to solve fundamental problems in plant biology with innovative solutions from emerging technologies.": true,
+ "While markets can be understood in many different ways, we argue that a network perspective to markets, instead of a channel perspective, offers a promising entry point to reflect on a fruitful role that stakeholder involvement could play for the transition to a CBE.": false,
+ "We will elaborate on the engineering potential of both liposomes and EVs to enhance favorable pharmacokinetic characteristics in order for these vesicles to function as effective drug delivery systems DDS.": false,
+ "We will demonstrate how, through an ongoing process of reflexive innovation, our responses to the above questions have shifted and evolved, leading to a more sophisticated and diversified understanding of the well-being challenge.": false,
+ "We show that within the context of our application, the FPGA accelerated implementation can achieve close to NUMBER GBs of parsing and conversion throughput when the accelerator host-to-device interface provides enough bandwidth and when enough FPGA resources are available.": false,
+ "We show that this exact approach can solve instances with up to NUMBER customers to proven optimality, improving upon existing exact methods that can solve similar problems with up to ten customers only.": false,
+ "We discuss necessary preconditions for successful development and implementation of eGovernment services in a multi-level polity and propose the following recommendations: firstly the EU needs to assess the potential impact of proposed eGovernment systems during the phase of policy development.": false,
+ "We describe the implementation of spectral polarization modulation in a prototype satellite instrument, the SPEX prototype CITATION and in groundSPEX CITATION, which is specifically developed for ground based measurement.": true,
+ "We conclude by identifying the most promising directions where advances in these material systems will enable progress in qubit technology.": true,
+ "We achieve a better performance, in terms of robustness to catastrophic forgetting, than the state-of-the-art regularization and architectural methods using a fixed model capacity, outperforming the regularization methods by a big margin.": true,
+ "To reach these goals, we need to continue the search for biomarkers addressing real clinical needs, to increase the number of prospective studies to show clinical benefits of the putative markers already known and to analyse the costs of using biomarkers in the clinic from a societal perspective.": false,
+ "To ensure computational tractability, we develop a reduced-space formulation for trained one-class support vector machines and show that our formulation outperforms common full-space formulations by a factor of over NUMBER making it a viable tool for engineering applications.": false,
+ "To address this research challenge, we pose the following research objective: To develop a method for the definition of business model key performance indicators KPIs catered to the characteristics of the business model innovation process to support business model decision making.": false,
+ "Through our results, we have demonstrated that oxygen saturation imaging using a dual-wavelength LED-based photoacoustic system has potential in pre-clinical and clinical applications.": true,
+ "The predicted melting points for these interatomic potentials are provided in Section NUMBER Note that our fully automatized approach allows to predict the melting point of any given interatomic potential with a very high numerical precision but not necessarily with high accuracy.": false,
+ "The high variation in adenoidectomies might indicate a lack of agreement on indications for surgery, and our findings underscore the need for high-quality effectiveness research to improve evidence-based guidelines on this topic.": false,
+ "The average share of manufacturing increased in all developing countries between NUMBER and In comparative perspective we observe a long-run increase in the shares of manufacturing in developing countries, and a long-run contraction in the shares of manufacturing in the advanced economies.": false,
+ "Subsequently, in a comparison of our automated failure prediction models, we discovered that all four AI algorithms, RF, GB, LSTM, and GRU, that utilise combined system metrics or multimodal inputs outperformed the state-of-the-art studies.": false,
+ "Recognizing that the total demand and demand flexibility may increase significantly in the future, we included a high share of electric vehicles to test the robustness of the market designs.": false,
+ "Recently, we described the use of a high-throughput organ-on-a-chip platform, the OrganoPlate, for therapy response testing of breast cancer, showing the potential of the platform for three-dimensional tumor models and its application in assessing the resistance of cells to chemotherapeutic agents for personalized medicine CITATION.": true,
+ "Our work demonstrates important progress in the implementation and performance of quantum optimization algorithms on a real device, and underscores the challenges in applying these algorithms beyond those natively realized by hardware interaction graphs.": true,
+ "Our study demonstrates that asynchronous advantage actor-critic A NUMBER C, a deep reinforcement learning DRL method, can be successfully utilised to develop an autonomous trading agent capable of exploiting arbitrage opportunities.": true,
+ "Our results show that the innovation voucher program has an immediate, short-term impact on the execution of these innovation projects with positive effects on product and service development, internal processes, and intellectual property protection.": false,
+ "Our diagnosis approach not only provides a practical tool for managers to develop context-specific solutions to variability, its development also enables us to contribute important insights that strengthen the OM literature on flow improvement.": false,
+ "Moreover, we are the first to explore the potential of two promising treatment strategies in the field of neonatal brain injury, i.e., intranasal MSCs and IGF NUMBER to restore interneuron deficits and improve sociability in our EoP mouse model.": true,
+ "Importantly, Amsterdam and Paris already had highly-developed housing markets, and unique microlevel data survived in the archives of both cities, allowing us to track mortality and the developments in the housing market following an epidemic.": false,
+ "For the future, we propose a model in which the DCRT is reimbursed by national payers for each successfully developed ASO, so these funds can then be invested in the development of new individualized ASOs for additional patients.": false,
+ "Extensive experiments based on real-world datasets demonstrate that our approach achieves a high efficacy of detection performance against the state-of-the-art.": true,
+ "Experimental results show that ArtSAGENet outperforms several strong baselines and obtains state-of-the-art performance in fine art analysis, while qualitative analysis of the representations learned by our approach indicates that it is capable of capturing interesting properties of fine art.": false,
+ "As genome-scale computational modelling of underground metabolism successfully predicts the potential to utilize new nutrient sources in E. coli CITATION, we reasoned that a similar approach could be employed to characterize the theoretical potential of underground reactions to produce industrially relevant chemical compounds.": false,
+ "As concerns the role of mobile phones for the displaced in particular, the United Nations High Commissioner for Refugees has stated that, a connected refugee population can play a critical role in enabling organizations such as UNHCR to innovate effectively and to improve the quality of services that we provide.": false,
+ "Addressing this gap is beneficial at least in two respects: it allows us to look, for the first time, into technology commercialization and the context specific factors that affect it in Iran; it helps us to see how commercialization boosting policies work in a developing country under economic sanctions.": false,
+ "Additionally, we examined whether paternal characteristics could improve model performance of the developed prediction models within a subgroup of our population and we examined the predictive performance of the developed prediction models on secondary maternal, delivery and neonatal complications.": false,
+ "We presented a particular method to generate NUMBER D engineered cultures for the first time with human-derived neurons coupled to MEAs, overcoming some of the limitations related to NUMBER D and NUMBER D neuronal networks and thus increasing the therapeutic target potential of these models for biomedical applications.": false,
+ "To build on these promising results, the development and use of CM optimization techniques, for example similar to the recently developed framework by CITATION NUMBER are important for identifying the most promising combinations of scaffold properties.": false,
+ "Subsequently, in we discuss redirecting enforcement towards the demand side of the market and inquire whether certain practices by employers may be condemned as antitrust infringements when they harm competition in labour markets and whether this approach can contribute to addressing the concerns addressed here.": false,
+ "While the preponderance of evidence from previous studies identifies supply chain complexity as detrimental to firm performance, our results illustrate that although supply chain complexity has a negative effect on operational performance, it has a positive effect on innovation performance and financial performance.": false,
+ "Whereas the Prototypical Part Network ProtoPNet CITATION presents a user a large number of prototypes, our novel architecture with end-to-end training procedure improves interpretability by arranging the prototypes in a hierarchical tree structure.": false,
+ "Well known businesses like Lufax, Flipkart, Snapdeal and Lianjia represent examples of Unicorn companies born in China and India with a market value of over NUMBER billion US dollars, thereby attesting to the potential of emerging markets on giving birth to fast growing start-ups.": false,
+ "We argue that besides improving the quality of the provided diagnostics, allowing some tolerance in deviations assessment also enhances the flexibility of conformance checking techniques and, indirectly, paves the way for improving the resilience of the overall process management system.": false,
+ "Using a case study on agricultural diversification in the former homeland of Venda, South Africa, we explore the usefulness of the nested markets concept to make sense of smallholders' patterning of markets by combining tree crops for export with seasonal vegetables for local markets.": false,
+ "To this end, we propose an integrated dynamic market mechanism which combines the real-time market and frequency regulation, allowing competitive market players, including renewable generation, to negotiate electricity prices while using the most recent information on the grid frequency.": false,
+ "To demonstrate the clinical potential of a robust functional parcellation of the PAG, we propose an exploratory application that can be used to study connectivity changes between different subregions of the PAG related to changes in bladder fullness and bladder sensations.": false,
+ "The NUMBER D organoid systems that we describe in this review CITATION represent a promising platform to further understand the neuropathology of PKU by providing tools to specifically address PKU-related targets and modulate key aspects of oxidative stress, brain L-Phe clearance, neurotransmitter deficiencies and LNAAs brain uptake.": false,
+ "Our studies highlight, for the first time, the therapeutic potential of Epac NUMBER inhibition in hippocampal neuronal cells, and the importance of developing new pharmaceuticals to treat neurodegenerative diseases.": true,
+ "Our results show that both approaches improve performance in the basal and apical regions, although only the classification and segmentation approach produced significantly better results across all labels and datasets.": true,
+ "Our research shows that the concepts serve distinct purposes at different stages of the business model innovation process, and we discuss these findings and their broader implications for the literature on business model innovation and for innovation management practices in B NUMBER B companies.": false,
+ "Our research is situated within these debates, drawing attention to the development-led aspects of planning processes tied to density bonusing, emphasizing its ascent over the past NUMBER years from a development control to development enabling policy.": false,
+ "Our findings suggest that the market reacts positively to a switch to exploration, which signals that the firm is ready to engage in high-quality innovation, once it has completed a series of exploitative acquisitions to build the slack necessary to fund it.": false,
+ "Motivated by recent market developments and the potential of elastic CDNs, we proposed the new problem of dynamic cache rental, file caching and user association for wireless edge caching networks.": false,
+ "Moreover,the possibility to identify the inhibitory selectivity among endogenous LOX isoenzymes will facilitate our understanding of LOXinhibition and will thus facilitate drug discovery.T herefore,w ea im to develop novel tools to investigate the activity of endogenous LOXi soenzymes to advance LOX-oriented research and drug discovery.": false,
+ "Moreover, we present the potential of these applications in cardiovascular and cardiothoracic medicine, and additionally, we will provide key facilitators, challenges, and recommendations to adopting these technologies in clinical practice.": true,
+ "Moreover, we demonstrated the positive impact of generated answers on the performance of the retrieval model of the conversational search system, as the performance significantly increased when the answers to clarifying questions were taken into account.": false,
+ "In this review, we will address the progress that has been made using non-ribosomally produced peptides and ribosomally synthesized and post-translationally modified peptides as scaffolds for designed biosynthetic pathways or combinatorial synthesis for the creation of novel peptide antimicrobials.": false,
+ "In particular, our numerical studies for the Le Mans race track indicated that the lap time of a CVT-equipped electric race car can significantly outperform the one achievable with an FGT, as a CVT can counteract its higher mass with a more effective motor operation, although strongly dependent on the powertrain design choices.": true,
+ "In doing so, we are able to illustrate how new actors-in this case the European Commission and the Commissioner for Competition-were able to terminate long-existing policies of state aid to shipbuilders under the auspices of improving competition and the free market at the start of the NUMBER s.": false,
+ "In addition, we find that our proposed unsupervised method significantly outperforms the state-of-the-art baselines in cross-lingual knowledge selection.": true,
+ "Here, we show that with the proposed implementation, and by selectively making the proposed simplifications, as well as selectively choosing the grid parameters, a model can be obtained that has a minor impact on model accuracy, achieving a simulation time of over NUMBER times faster than real-time.": false,
+ "Due to the promising application of highly rigid anionic liposomes in the field of antigen-specific tolerance and the need for methods to efficiently load tolerogenic adjuvants, we developed a microfluidicsbased approach.": true,
+ "Consistently with previous work, we found that the use of InSAR coherence leads to a significant improvement of the classification performances, for example, with improvements in the user accuracy for most classes considered in the order of NUMBER p.p.": true,
+ "As intensive operations are to be performed on sensors, we demonstrated the feasibility of such a construction by implementing a proof of concept while using a resource-constrained device as a sensor.": false,
+ "Although we expect our findings to be generalizable to other creative industries, it would be interesting to extend this research to investments in start-ups in non-creative industries, focusing on key serial entrepreneurs CITATION with the CEO and the CTO role e.g. the software industry or the CEO and the CSO role e.g. the biotech industry.": false,
+ "We suggest that future studies further investigate the potential of these techniques for the improvement of freshwater invertebrate detection and abundance estimation, which might eventually lead to a robust application of DNA techniques for a total freshwater census.": false,
+ "We report the potentialities and pitfalls of one of the first commercially available devices capable of recording brain local field potentials LFPs from the implanted DBS leads, chronically and during stimulation.": false,
+ "We propose a novel network data envelopment analysis NDEA model, capable of setting store-level performance standards more accurately than state-of-the-art models.": true,
+ "We hypothesize that an enabling design and an enabling development process, as compared to a coercive design and a coercive development process, lead to perceptions of greater procedural fairness and less red tape.": false,
+ "We hope to discover which virtual environment design aids the performance of both the sexes, thus establishing a framework for designing virtual training environments and possibly GPS-based navigation systems that successfully comply with the both sexes, without impairing the performance of either one.": false,
+ "We expect future developments in machine learning and optimization together with novel fabrication techniques to lead to unprecedented nanotechnology within the next decade.": true,
+ "We demonstrated that the proposed idea can improve the performance of the baseline DeepLabv NUMBER by about NUMBER in mIoU sense on two of the most modern and commonly-used datasets PASCAL VOC NUMBER Cityscapes while boosting the performance in certain classes up to NUMBER without introducing any extra delay at inference.": false,
+ "We demonstrate the successful development of a realtime data infrastructure that enabled both data-driven care and decision making and rapid answering of critical clinical questions during pressing times, such as the COVID-NUMBER pandemic.": false,
+ "We believe that, with the relentless advances in scalable manufacturing techniques such as cutting, casting, and coating, findings from this study can facilitate the development of multitech combined devices, that is, photovoltaic-thermoelectric PV-TE coupling system which to date is still at its infancy stage.": false,
+ "To understand this innovation gap, we reviewed the literature to describe the potential of AMPs as alternative to antibiotics and the challenges toward clinical application of AMPs.": false,
+ "To that end, we developed a novel asynchronous arm-crank ergometry platform for use in a clinical magnetic resonance MR scanner with NUMBER P spectroscopy capability to study arm muscle energetics.": true,
+ "Therefore, addressing the psychological patient factors found in our study preoperatively might lead to low-cost interventions to improve return to work after The graphs show the HRs for a simulated patient in which the baseline characteristics are fixed CITATION.": false,
+ "Said differently, even if the focal firm may shy away from acquiring a larger target due to the fear of retaliation, some degree of synergies and gains in market power can also be achieved by acquiring a smaller firm, especially if we consider acquisitions within the same industry.": false,
+ "Our results indicated that the combination of an open innovation contest and design thinking could, through the creation of constant feedback loops, lead to increased collaboration between the contests participants, the companies proposing a challenge, and other relevant stakeholders.": false,
+ "Our novel method provides a simple way to produce customized NUMBER D ultrathin fibrous scaffolds, with great potential for TE applications, in particular those for which anisotropy is of importance.": true,
+ "Our findings are promising as they point to the potential of LKM and self-compassion to serve as a practically attainable way for entrepreneurs to develop resilience and more effectively cope with entrepreneurial fear of failure.": false,
+ "Novel prospective research should include trials investigating the efficacy of CBT-I for patients with previously under-investigated comorbidities, the efficacy of adaptations of the CBT-I protocol compared to the standard protocol, follow-up periods longer than NUMBER mo, and the potential of CBT-I for the prevention of mental disorders.": false,
+ "Motivated by our experimental findings that the quality of the information passed to the central system through final orders is significantly higher when the uniform allocation principle is applied, we propose a new application of the uniform principle that has several attractive properties in this setting.": false,
+ "In this communication we investigate a novel facile and fast nebulization method with a continuous jet atomization device to nebulize formulations at a throughput well over NUMBER mL min with a minimum amount of shear stress enabling a large window to formulate lipid based nanoparticle drug carriers CITATION.": true,
+ "In summary, we have developed novel divergent syntheses of NUMBER yaequinolone-related natural products, which are synthesized for the first time, by late-stage C-H olefination of NUMBER dioxygenated NUMBER aryl-NUMBER hydroxyquinolin-NUMBER NUMBER H-ones, core structures of this family of natural products.": true,
+ "In our study, we find further heterogeneity across workers with a different job-skill CITATION complementary of human capital investments and NUMBER the multiplier potential of these investments for their skill development.": false,
+ "In conclusion, we have demonstrated an integrated MWP signal processor using a network of three RRs in a single photonic chip that spectrally shape the PM spectrum, for the first time, to the best of our knowledge, to create a MWP notch filter with a high stopband rejection and enhanced filter performance.": true,
+ "In Section NUMBER we demonstrate the potential of extending the proposed design to develop latticebased shells.": false,
+ "Furthermore, while scholars have accumulated knowledge about innovative behavior in business contexts where innovation is nurtured, to fully understand'innovation by employees' we need to also address business contexts where innovation by employees is not overtly expected.": false,
+ "Finally, we will propose potential developments that may help to improve the performance of current devices.": true,
+ "Finally, we describe three potential randomized registrybased clinical trials in an adjuvant setting and for advanced disease with a high potential to be executed within the framework of an advanced ENSAT registry.": false,
+ "By connecting several junctions, we can study effective NUMBER spin systems demonstrating the potential of our approach for analog spin-glass simulation.": false,
+ "Both European and US experts NUMBER , NUMBER have emphasized the need for preclinical studies in the animal, for testing various methodologies to produce RDN lesions, evaluating safety and efficacy of novel RDN systems, developing various biomarkers and assessing potential non-blood pressure related benefits.": false,
+ "As a proof of concept, we further discuss a prototype implementation for supporting future-based data streams on top of ABS, and discuss the impact of the use of these data streams in ABS on the performance in the implementation of a distributed application for the generation of social networks.": true,
+ "Although models learned on sparse annotations achieve relatively lower accuracies than those using dense annotations, we show that using a semisupervised deep learning approach can help to close this performance gap while leveraging sparse annotations that can significantly reduce the costs of label generation.": false,
+ "Addressing how to achieve community-based solutions in a malaria elimination trial in The Gambia, we developed the Community Lab of Ideas for Health CLIH: a participatory approach that enabled communities to shape trial implementation.": false,
+ "Using a large panel of innovative firms in the Netherlands, this study shows that partner type diversity in a firm's alliance portfolio has an inverted U-shaped relationship with productivity and radical innovative performance and a positive relationship with incremental innovative performance.": false,
+ "For the deployment of our prototype, we have chosen Amazon Web Service AWS IoT and its extension for edge computing i.e., Greengrass as the baseline IoT platform as it is one of the more mature IoT platforms available on the market and provide the machinery necessary for the realization of the defined deployment models.": false,
+ "We summarized pre-clinical and clinical studies investigating whether physical exercise would improve muscle performance and whether this improvement would translate in a clinically meaningful benefit for patients with cancer, in terms of survival and quality of life.": false,
+ "We then chose the most effective EO to continue with further tests using exposure to the treated oilseed rape plant surface as the method of exposure, expecting to find an effective concentration threshold for significant insecticidal efficacy.": false,
+ "We show that LMX mediates the association between SDT and team performance and innovation as rated by team members, while collective narcissism mediates the association between SDT and supervisory ratings of team innovation and team performance.": false,
+ "We propose a sample average approximation with importance sampling and pruning of dominated activities to solve the problem, and demonstrate that this method solves large instances quickly.": false,
+ "We present a reliable, yet stable time-domain implementation of MDD that includes physical priors as a mechanism to improve kernel conditioning by transforming the model vector into a domain where the ill-posed nature of the inverse problem is significantly mitigated.": false,
+ "We hope that the following chapters will provide the reader with many novel insights into the complex interaction between nutrition and exercise, allowing them to define more effective dietary strategies to improve health and performance.": false,
+ "We find that while the decoupling method leads to an acceptable and convergent effective potential, the method does not solve the fine-tuning problem that is inherent to the hierarchy problem of multiple-scale theories.": false,
+ "We find that contrary to popular belief, contracting has positive and significant impact on malt barley production, intensification, commercialization, quality improvement, and farm gate prices, ultimately resulting in increased net income and spillover into the productivity of other food crops.": false,
+ "We experimentally show that, for the unknown disturbance situation at hand, the variable-gain controller can be automatically tuned using both ESC approaches to achieve the optimal system performance.": false,
+ "We evaluated the assessment tool in two online studies-with participants in their own homes completing the task on their own digital devices-to show that the assessment tool can significantly predict scores from the Patient Health Questionnaire CITATION-a standard self-report tool for assessing depression in clinical contexts.": false,
+ "We discover significant differences between middle slices and both basal and apical slices after training a state-of-the-art segmentation model on a large clinical dataset CITATION and testing it on three independent test datasets.": false,
+ "We conducted interviews with NUMBER solo diners to investigate their experiences while eating alone, their motivations for using an ICT device, as well as the potential of such devices to contribute to healthy eating practices.": false,
+ "We agree that there is a need for further research into the mechanisms of co-existence of cancer and comorbid diseases and the need for development and application of comorbidity assessment and management tools that are validated in the younger cohort.": false,
+ "To this end, we propose a novel online market mechanism, EdgeDR, to achieve cost efficiency in edge demand response programs.": true,
+ "To provide platform leaders with advice on how to create a sustainable digital platform, we examine how retail store activity on the digital platform affects consumer activity, and vice versa also known as cross-side network effects.": false,
+ "To overcome this problem, we developed perfusion fixation methods for needle and wedge biopsies, NUMBER which significantly improved the preservation of the sinusoids, sinusoidal cells and endothelial fenestrae, enabling high resolution transmission electron microscopy TEM of all hepatic cell types in human liver biopsies.": true,
+ "To demonstrate the model potential, we tested our platform at an immature NUMBER days in vitro and mature state NUMBER days in vitro of development.": false,
+ "To address these clinical challenges, we have developed a bilayered, modular hydrogel system that enables the click functionalization of cartilage-and bone-specific biochemical cues to each layer.": true,
+ "Third, on the basis of our first two hypotheses, we hypothesize that individual stakeholders observing a stakeholder-oriented firm will show more moral consideration for other stakeholders than individual stakeholders observing a profit-oriented firm, mediated by individual stakeholders' humanization of other stakeholders.": false,
+ "Taken together, we not only provide a proof of concept by development of inhibitors to target disordered proteins, but we also provide novel technologies and simple methods to develop inhibitors for IDPsIDPRs.": false,
+ "Recently, we have demonstrated on trapezia that the image quality of the CBCT device NewTom NUMBER G Cefla, Italy CITATION can be enhanced to reach an accuracy comparable to HR-pQCT in quantifying bone trabecular parameters CITATION.": true,
+ "Our results show that LMX mediates the association between SDT and team performance and innovation as rated by team members, while collective narcissism mediates the association between SDT and supervisory ratings of team innovation and team performance.": false,
+ "Our results demonstrate the potential of combining MB and DD methods in autonomous systems, while providing a proof of concept for KalmanNet in a challenging real-life scenario.": true,
+ "Our research highlights the importance of developing a holistic picture of the mechanisms by which different signals of product quality affect consumption and decision-making in entertainment markets, considering simultaneous exposure to a range of market signals instead of considering different factors independently.": false,
+ "Our findings reinforce the elevated potential of the Brazilian large families in revealing novel candidate genes or mechanisms of mutation leading to hearing loss.": false,
+ "Our current findings have the potential to inform assessment of clinical reasoning performance in authentic e.g. Such work could also advance our understanding of context specificity, which leads to unwanted variation in physician performance.": false,
+ "Now data are produced at a rapid pace that enables firms to analyze the effectiveness of potential CE relationships between firms, we are motivated to study how information systems can also facilitate circular decision making.": false,
+ "Moreover, we analysed the combinatorial activity in a newly designed ex vivo bacteraemia model as well as the potential of the best combination peptide D-NUMBER with rifampicin to treat K. pneumonia e infections in vivo in a mouse abscess infection model, demonstrating effective clearing of the infection.": false,
+ "More broadly, we point to significant opportunities from applied linguistics and big data analytics for the development of novel indicators to track tangible and intangible dimensions in the geography of innovation.": false,
+ "Many supramolecular systems are complex to synthesize or to selfassemble into functional architectures, yet with progress we increase scalability as well as the simplicity of the architectures and the robustness of the self-assembly process.": false,
+ "Lastly, we believe there is a dire need of additional clinical benchmark data-sets to improve upon the state-of-the-art in this area.": false,
+ "In summary, we propose that Gibbs energy released during catalysis can be harnessed by enzymes in the form of work, which will ultimately lead to an enhancement of their effective diffusion Fig NUMBER A.": false,
+ "In conclusion, we have developed a novel genetic adjuvant that for the first time employs the pyroptosis pathway to improve DNA vaccination against cancer.": true,
+ "In chapter NUMBER we sought to develop a novel flow-cytometry-based high-throughput method that allows quantitation of the agglutinating potential of anti-pneumococcal antibodies.": true,
+ "In addition, we discuss the clinical potential of TEVs as markers of cell state transitions including the acquisition of a treatmentresistant phenotype, and their potential as therapeutic targets for interventions such as the use of extracellular vesicle EV inhibitors to block their pro-tumoral activities.": false,
+ "In addition, we discuss the clinical applicability of therapeutic ultrasound with respect to the main challenges that must be addressed to enable the further progression of therapeutic ultrasound towards an effective, safe and easy-to-use treatment tailored for drug delivery in patients.": false,
+ "If we want to facilitate standardization in OoC by the development of an open platform, it will be essential that the platform finds widespread support.": false,
+ "If we know how to quickly select the optimal or close to optimal trigger injecting position in backdoor attacks on GNNs, we can achieve high attack performance and good evasion of the defender's detection mechanisms.": false,
+ "HTP in clinical decision making: Seven problems that occur in daily clinical practice are described and we show how HTP can enhance insight to formulate an adequate treatment strategy.": false,
+ "Given advancements in mobile technology, an effective-CBT-I app could offer an opportunity to enhance the advantages and coverage provided by other formats of CBT-I, enabling real-time and personalized monitoring, assessment, and interventions CITATION.": true,
+ "Finally, we use all the knowledge we acquired to propose recommendations for practitioners i.e., how they can use the findings of this paper to improve their testing skills, tool makers i.e., a list of tools that would improve the way developers do testing, and educators i.e., testing topics that should be taught at university-level.": false,
+ "Finally, we suggest that B NUMBER B innovation practitioners adopt the stakeholder engagement journey as a process tool that links service design to innovation stages.": false,
+ "Finally, we develop a Frank-Wolfe algorithm that can solve this convex program orders of magnitude faster than state-of-the-art general purpose solvers.": true,
+ "Comparing with state of the art in localization, we demonstrate the superiority and robustness of the performance of FEEL.": true,
+ "CONCLUSION: Our work NUMBER demonstrates the potential of our previously developed automated quality assurance methods to NUMBER generalize to external datasets.": false,
+ "By elucidating the nature of the PD as a function of the potential, we can explain the universally observed bell-shaped curve for the FECO as a function of potential in relation to the effect of PD, cation concentration, and mass transport on the rate of the competing HER.": true,
+ "As we have already discussed, convergence of leader and follower views with regard to their LMX quality will lead to increased development opportunities and career resources that can contribute to the follower's learning and growth, employability, and marketability.": false,
+ "As a proof-of-concept, we develop a data-driven AI-based service scaling prototype to automate the service scaling operation to meet the service requirements while minimizing the consumption of resources.": true,
+ "Applied to gold medal-winning parameterized SAT solvers, we show that our approach can produce significantly better-performing SAT solvers than state-ofthe-art parallel solvers constructed by human experts, reducing time-outs by NUMBER and running time PAR NUMBER score by NUMBER under competition conditions.": false,
+ "Accordingly, we expect institutional investors to affect the implementation of strategy uniqueness, since they relate to both the type of investor putting the most pressure on CEOs CITATION, and also to the type of investor being best equipped to provide good monitoring and governance CITATION.": false,
+ "A remarkable result in our model is that competition in the healthcare market does not necessarily improve care quality, which differs from previous literature.": true,
+ "These encouraging data suggest that the novel enzyme and strain engineering approach represent a promising platform for the clinical development of CDEPT.": true,
+ "As the paintings conservation field has rapidly discovered the potential of Evolon CR for varnish removal from oil paintings, the need to advance our understanding of this material and the steps necessary for safe and optimal usage increases.": false,
+ "According to these latest concepts of clinical translation, we firmly believed that the a-TOS married NUMBER D MoS NUMBER as a promising theranostic platform can be employed to achieve the compelling efficacy and safety benefits for cancer theranostics.": false,
+ "With great interest we read the article by CITATION CITATION, in which they demonstrate the potential of home monitoring to reduce hospital admissions by safely surveying clinical symptoms and vital parameters.": false,
+ "While we showed how this may be effectively conducted for hard-sphere models, further research is needed to develop effective implementations for perfect sampling from a broad class of Gibbs point processes.": false,
+ "While our work suggests potential for developing a SIGS approach for implementation in B. aeneus management, further experiments are needed to more fully explore the potential for incorporating this approach.": false,
+ "We show that the proposed approaches compare favorably to several standard and state-of-the-art imputation methods in terms of predictive performance and runtime.": true,
+ "We proceed by rigorously establishing limitations and unprecedented merits of this controller, further proposing appropriate modifications that guarantee improvements in terms of stability, robustness, and performance.": false,
+ "We introduce an attention-based sequence-to-sequence OIE model that outperforms state-of-the-art network architectures, hereby demonstrating its effectiveness in information extraction tasks.": true,
+ "We have developed a translation of CSX to SMT constraints which enables us to use constraint solving to find optimal configurations for finishers.": false,
+ "We have demonstrated the potential of MCR chemistry by utilizing this privileged scaffold of proven interest in materials science and drug discovery.": true,
+ "We first show that disjoint bilinear optimization problems can be cast as two-stage robust linear optimization problems with fixed-recourse and right-hand-side uncertainty, which enables us to apply robust optimization techniques to solve the resulting problems.": false,
+ "We find that inventory allocations between the high and the low value channel are significantly different from optimal independent of whether profit differences between channels are high or low what we call profit conditions.": false,
+ "We encourage researchers to investigate the usability, acceptance, feasibility, reliability, and clinical validity of wearable sensors in clinical populations to facilitate the application of wearable movement sensors in motor rehabilitation.": false,
+ "We do believe that our general findings-that organizations' capacities and perspectives, embeddedness in a wide network, and other development work create multiple forms of representation, which may contribute to inclusive development-have the potential to form a starting point for this further research.": false,
+ "We discuss implications for further adaptation of annotation tools, and the potential for deriving reference data from such rich annotation datasets for the evaluation of automatic pattern discovery algorithms in the future.": false,
+ "We demonstrate experimentally for the first time that the proposed soft handover mechanism enables the seamless transmission to the mobile UD in the BS-ILC system.": true,
+ "We conduct three sets of studies to demonstrate the potential of the proposed pseudonymization approach: NUMBER First, we validate the proposed approach through ABX pilot tests.": true,
+ "We conclude that this form of data regime competition between China and the EU can give China an advantage in terms of promoting innovation in the electric car industry.": false,
+ "We conclude that existing genetic risk scores can already improve life insurance underwriting, which stresses the urgency of policymakers to balance competing interests between stakeholders as this technology develops.": false,
+ "We conclude it is important that all centres use an adequate, preferably uniform, registration system on innovation indicators and propose to select the system we used because it has been developed and approved by the sector itself.": false,
+ "We argue that to advance service design opportunities for stakeholder engagement, we need to address the unique complexities and challenges of stakeholder engagement during innovation from a systemic and dynamic process perspective.": false,
+ "We address this challenge by proposing alternative tariff designs, in terms of variable and fixed charges together with PV installation cost subsidies, that a regulator can implement to achieve a minimum solar energy target while guaranteeing network financing.": false,
+ "Upon application of an RG step, the new effective local Hamiltonian that we find inside the causal cone will be renormalized to new effective couplings Ja', Jb', which correspond to effective lattice points A', B', respectively.": false,
+ "To utilize the full potential of-omics driven biomarkers discovery studies, it will be essential to reach agreement on the best outcome reference standard in future studies, and we propose our diagnostic phenotyping algorithm as the best possible way to do so at present.": false,
+ "To help address this need, our interdisciplinary team of clinicians, social scientists, bioethicists, designers, and computer scientists developed an innovative ICT framework for knowledge generation in chronic-pain research.": false,
+ "To address this key gap, we broaden the theory of problemistic search for innovation by proposing a digitally enabled collaborative problemistic search CPS capability.": true,
+ "To achieve a high level of UX, we address these user needs by introducing an interactive car door that enables passengers to explore the landscape in a more detailed and informative way using AR on the side window and a touch-sensitive door panel.": false,
+ "This demonstrates the practicality of our study: we believe that researchers can build upon our work's findings by creating improved compiler validation methods and tools.": true,
+ "These findings echo themes in how to improve humanitarian operations more generally, suggesting that finding effective ways to address these enduring challenges may significantly increase effective multi-hazard planning and response.": false,
+ "The discovered effect is further confirmed by comparing the measurements to an advanced numerical model that we have developed earlier CITATION, with further implementation of the free-boundary conditions, showing excellent agreement to the measured data.": false,
+ "Specifically, we extend the literature on social exchanges during opportunity development CITATION, NUMBER; by describing how entrepreneurs make sense of critical feedback from their stakeholders and by compiling the cognitive changes from novel sensemaking.": false,
+ "Since our founding in NUMBER we have developed visualisation tools in our lab, deploying a number of immersion devices across a diversity of data sets and platforms CITATION, as well as across disciplines CITATION.": false,
+ "Second, to improve the performance of MetaVC on large MinVC instances, we introduce a neural-network-based approach to enhance automatic configuration.": false,
+ "Our study provided initial and promising evidence for the psychometric properties of the Italian version of a newly developed tool to measure burnout, namely the Burnout Assessment Tool, which intends to overcome some of the conceptual, methodological and practical limitations of the MBI.": true,
+ "Our study demonstrated that the framework we conceived of IMSI and SDT can effectively be applied as a frame of analysis to identify essential features of sustainability in educational innovations.": false,
+ "Our speed-accuracy trade-offs and effective spatial-contextual feature fusion allow us to outperform the previous state-of-the-art approaches for real-time semantic segmentation on two public datasets, namely Cityscapes dataset CITATION and UAVid dataset CITATION.": true,
+ "Our results have demonstrated the feasibility and the great potential of on-chip SD-OCT systems.": true,
+ "Our findings suggest that as nutrient availability increases under future climatic conditions, subarctic shrubs will develop increased resistance to biotic and abiotic stresses under rising temperature as opposed to under increased cloudiness, which could lead to decreased resistance.": true,
+ "Our findings showed that these biomimetic micro-patterned substrates enabled cell disposal along architectural directions, thus appearing as promising substrates for developing functional TM replacements via TE.": true,
+ "Only if the user knows how to integrate decision support tools into the complex tasks clinical judgement and decision-making we are able to test the full potential of these tools.": false,
+ "Moreover, from our analysis, we conclude that the network assistance provides significant performance improvement, especially when the clients with identical interests compete for a bottleneck link's capacity.": false,
+ "More interestingly, we find that using Bayesian shrinkage priors leads to superior out-of-sample performance for long-term investors.": true,
+ "Merging findings with strands of social movement theory CITATION, I argued that state-funded sites of adult education in francophone Belgium demonstrate the potential to create empowering linkages to social movements.": false,
+ "Introduction Our aim is to develop a novel approach to hyperkinetic movement disorder classification, that combines clinical information, electromyography, accelerometry and video in a computer-aided classification tool.": true,
+ "In this work, we present numerical tools to explore such nonequilibrium effects in spatially confined three-dimensional systems with a variable disorder potential, giving exact solutions to leading order in the disorder potential and the applied electric field.": true,
+ "In this review, we describe the application of several imaging techniques in the clinical practice of BM management, and the promising new developments that lie ahead.": false,
+ "In this paper, the development and application of a public-client-led method is investigated that enabled the development and implementation of a radical green innovation in a civil engineering project.": false,
+ "In this article, we showed that exploiting such information in a GBO setting, specifically one that allows for partial evaluations, can lead to substantial improvements in performance and scalability.": false,
+ "In the past NUMBER years as a result of advances in hardware and software, DSS research has advanced dramatically, which has revealed the potential of this approach to substantially improve clinical care.": true,
+ "In the next step of research, we can use deep learning to automatically extract the features of sound and images, and further improve the detection accuracy and generalization of the AVF method and we can also use better experimental platforms and equipment to improve the real-time performance of our algorithms.": false,
+ "In supplementary fig. NUMBER we demonstrate initial feasibility Deep-ULM in clinical practice, by performing inference on clinical CEUS measurements of a patient's prostate using a standard ultrasound system Philips i CITATION in combination with a standard transrectal probe.": false,
+ "In section NUMBER we propose our model for extracting needs from customer reviews; preliminary evaluation results appear in section NUMBER We revisit the need for needs in our discussion and conclusion.": false,
+ "In line with Clarke and Crane's NUMBER definition of systemic change, therefore, we define transformative change as the result of actions that lead to a significant alteration a transformation within a system, potentially leading to substantial impacts involving economic, social, and ecological issues.": false,
+ "In conclusion, we showed that bimiralisib gel NUMBER for topical use leads to I meaningful cutaneous drug levels, II well-tolerated systemic drug exposure in patients with MF and III a lack of clinical efficacy.": false,
+ "If effective at improving lifestyle behaviors, this approach could be an important measure for all women with overweight or obesity with the potential to improve clinical outcomes for these women who are in the preconception period and are at high risk for pregnancy complications.": false,
+ "However, we do suggest companies mix other adaptive innovation models such as agile development CITATION or the hybrid Agile-Stage-Gateapproach CITATION with their NPD processes to mitigate the negative effects of innovation process formality.": false,
+ "However, the advantages of high avidity and the possibility of modulating their circulation time mean that MMIAs hold great potential, as revealed by clinical trials such as those for NUMBER I-cRGDY-PEG-C dots CITATION.": true,
+ "Here we review the pharmacology of fevipiprant, the oral DP NUMBER receptor antagonist that reached the most advanced state of development to date in a worldwide Phase III clinical trial programme; however, the demonstrated efficacy did not support submission.": false,
+ "Hence, by adding the temporal analysis to our DML framework, we help the system to discover higher semantic movements that enhance the discovery of discriminative personality patterns, and therefore, improve the personality recognition task.": false,
+ "Furthermore, we extend existing findings on innovation vouchers that are limited to a narrow scope in terms of industry, type of collaboration partner and region CITATION by making use of a large-scale field experiment to test the effectiveness of a nationwide, all-industry program with a broad scope of potential partners.": false,
+ "From the current literature, we incorporate two theoretical perspectives within the dynamic capability framework to explain how corporations achieve sustainable competitive advantages: organizational ambidexterity and open innovation CITATION.": false,
+ "First, we develop a model that captures how family social capital is enhanced by shifting the level of analysis from the family business to the enterprise family CITATION.": false,
+ "Finally, we will present some perspectives on future multidisciplinary clinical and experimental research to develop new, more effective sleep therapies to improve both sleep and PTSD.": true,
+ "Finally, we propose a series of recommendations that should be applied to polyester clothing at all stages along the value chain, offering the potential for meaningful and effective change to improve the environmental sustainability of polyester textiles on a global scale.": false,
+ "Finally, our work shows a systematic road toward the development of accurate effective patchy particle potentials.": false,
+ "Finally, by prototyping real-life applications on both FPGA-based MPSoCs and desktop multi-core platforms, we demonstrate that mapping the alternative application specification results in a large performance gain compared to those approaches, in which alternative application specifications are not taken into account.": false,
+ "Endovascular innovations such as steerable sheath technology, our continuously evolving technical skills, and development of dedicated mating stents for fenestrations have the potential to improve technical success and durability of this OTS device in the near future.": true,
+ "Earlier reports developed by our group demonstrated that the spontaneous AsIII oxidation by granular activated carbon GAC combined with the biological FeII oxidation led to the precipitation of biogenic scorodite in batch experiments CITATION,providing a promising green strategy for the removal of arsenic from acidic wastewaters.": true,
+ "Development of in situ and operando XPS techniques that can completely overcome the pressure gap is underway, and we believe it will lead to cornerstone advancements in understanding metal-support interactions in catalysis CITATION.": false,
+ "CITATION Arguing for the potential of an indisciplinary space of action to facilitate \"democracy of experience\", CITATION we seek to develop a non-hierarchical design research that leads into the'de-compartmentalization of each discipline'.": false,
+ "By performing detailed assessments of the predictive performance for the question pattern and content tasks, we ind that CNQG enables us to produce accurate patterns and semantically relevant topics, which provides an explanation for its strong performance.": false,
+ "By carefully tweaking the imprinting parameters, we were able to increase the number of imprints on our chips, validated by SEM and fluorescence microscopy, leading to an improvement of the LoD by an entire order of magnitude for the thermal sensing platform.": true,
+ "Because we have shown that a substantial number of essential developmental genes are significantly downregulated upon SON haploinsufficiency, SON thus represents a master regulator of genes essential for human neurodevelopmental processes.": false,
+ "As an outlook, here, we formulated a proposal for a design concept that allows us to connect cultured nervous system tissues in a platform that will enable the detailed study of PD by NoCs as it is envisaged in the CONNECT NUMBER project as well as other devastating nervous system diseases.": false,
+ "As a consequence, we were able to successfully improve the efficiency of speaking based learning using an adaptive system: Learners who studied using the response time-based SlimStampen algorithm produced faster responses with NUMBER percentage points higher accuracy compared to learners who used the accuracy-based Leitner learning algorithm.": false,
+ "Conflicts of interest Prof. Geusens reports grants, speaker fees and advisory board from Amgen, grants from Pfizer, grants from MSD, grants from UCB, grants from Abbott, grants and speaker fees from Lilly, grants from BMS, grants from Novartis, grants from Roche, and grants from Will Pharma, outside the submitted work.": false,
+ "As the material combination used in this work is superior for the optomechanical quantum transduction task than any other approach to date, additional adjustments to our platform can be used to significantly improve the performance of this new class of devices by several orders of magnitude.": true,
+ "While our evaluation shows that we can link appropriate segments of the clinical study to risk factors, it is the development of the query interface that will demonstrate the significant reduction in manual effort that this work requires, and will greatly widen its impact.": false,
+ "We suggest that acquirers can resolve this coordination-autonomy dilemma by recognizing that the effect of structural form on innovation outcomes depends on the developmental stage of acquired firms' innovation trajectories.": false,
+ "We show that usual MHD boundary conditions can lead to the violation of this physical limit and we implement this current density limitation through a boundary condition for the electrostatic potential.": false,
+ "We show that current approaches for solving multiple-follower problems are unsuitable for our new class of problems and instead we propose a novel analytics-based heuristic decomposition approach.": true,
+ "We recognise that these initial reflections on the different regulatory tools may sound sceptical of the potential for regulators to successfully address the possible negative effects of AR.": false,
+ "We propose a novel approach capable of efficiently solving the multi-year task allocation problem for a fleet of aircraft in a few minutes.": true,
+ "We improve each of the individual defenses' performance over the state of the art, achieving overheads well below NUMBER for each of them.": false,
+ "We implemented the proposed controller on an embedded platform, developed by Cohda Wireless and NXP, with the aim to validate that the theoretical performance of our approach also translates to a good performance in a realistic scenario.": true,
+ "We hypothesized that by CITATION using a simple convolutional architecture, and CITATION-distribution, it is possible to achieve state-of-the-art probabilistic forecasting performance compared to existing Transformer-based methods whilst requiring significantly fewer parameters.": true,
+ "We have successfully applied computational methods to develop HIPe which exhibits high potential to inhibit histone H NUMBER induced cell lysis and consequently shows therapeutic benefit effects in an atherosclerotic mouse model.": false,
+ "We have shown that by leveraging the potential of many existing EarlyTSC algorithms, our approach can outperform any single algorithm, even when their hyperparameters are optimised.": true,
+ "We have presented a scenario-and platform-aware design flow SPADe for IBC system implementation that considers both pipelining and parallelism in an integral fashion to improve QoC of multiprocessor IBC implementations.": false,
+ "We find that dynamic, rule-based investment strategies can outperform traditional static strategies, by which we mean that the investor may achieve the target retirement income with a higher probability or limit the shortfall when the target is not met.": true,
+ "We extend the current state-of-the-art by proposing a novel adaptive approach, called aDynaMOSA Adaptive Dy-naMOSA, to address the two challenges described above.": true,
+ "We demonstrate that the TempEasy system has the ability to screen phenotypic and functional effects of cell-cell interactions in a quantitative way and we believe that it may serve as a promising platform for future cell-cell interaction studies.": true,
+ "We compare results from these past experiments in Appendix B. We are able to experimentally resolve, for the first time, increased performance with greater QAOA depth and apply QAOA to cost functions on graphs that deviate significantly from our hardware connectivity.": true,
+ "We believe that this hybrid ML-PB approach offers the potential in the long term-with the rise of exascale, quantum and analogue processing-to deliver novel pandemic drugs at pandemic speed.": true,
+ "We argue that the concept mapping intervention can accelerate the development of adaptive capabilities as it facilitates the development of the knowledge structures that are needed for team adaptation, namely the team members' development of a shared understanding about their task, roles, and each other's expertise.": false,
+ "We adopt an explanatory case study approach to contribute to the development of theory of disruptive innovation.": false,
+ "We address this outstanding problem by developing a general framework for computing physical properties of quantum manybody states efficiently on NISQ devices.": false,
+ "Using the novel classification system, we identified different types of papillae significantly associated with a lower efficacy of NKF and a prolonged time to obtain successful biliary cannulation using NKF.": false,
+ "Using data from simulated power markets, we analyse the forward premium in three identical power markets with a varying market share of VRES supplied to the system.": false,
+ "Ultimately, we strongly advise the implementation of funding schemes for sustainably supporting the development and maintenance of research software based on clear and transparent criteria, for creating incentives to produce high quality community software, and for enabling career paths as research software engineer RSE.": false,
+ "To this end, we develop a systematic framework for assessment of sustainable hydropower potential that makes three folds improvements to existing methods.": false,
+ "To this end we propose our AQOSA toolkit which has been developed to enable component-based development and automatically improve the non-functional properties of an architectural design, thus allow architects to focus on the higher-level design decisions.": false,
+ "These empirical inconsistencies provide the impetus for our research as they demonstrate that there are gaps in our understanding of how and when team social capital affects team innovation, which may obscure the true nature of the team social network-team innovation relation.": false,
+ "There are various potential models for development i development in children first by academia then to industry; ii joint development by academia and industry; iii standalone academic development within a business model.": false,
+ "The unique advantage of our methodology is that it secures meaningful process waste analysis in two ways: CITATION via the RPG score, which enables scientists to compare the sustainability performance of their process with industry averages.": false,
+ "Since most of the systems around us exhibit some form of nonlinear behavior, nonlinear system identification techniques are the tools that will help us gain a better understanding of our surroundings and potentially let us improve their performance.": false,
+ "Overall, we hope to show in this Review and in the years to come that oxalic acid has great potential the future because of its large potential applications in the polymer market.": false,
+ "Overall, as a proof-of-concept, here we investigated the therapeutic potential of TA-coated AcDXSp NPs, which allowed successfully deliver two drug compounds for cardiac proliferation in CMs.": false,
+ "Our work, which takes a different route, not only sheds new light onto the unveiling of a gradient flow structure for CITATION, but also provides the well-posedness CITATION for a general class of interaction potentials, including the Newtonian potential, which we believe to be novel.": true,
+ "Our study explains the size differentiation in creative industries on the basis of a founder's attention focus and values, which may lead to firm development and, possibly, growth.": false,
+ "Our study demonstrates that entering foreign markets does not necessarily nor directly leads to growth in terms of employees or revenues, but more to an accretion of a creative firm's symbolic capital or impact.": false,
+ "Our results lead us to the major conclusions: Although porosity observations converge to an effective value with increasing observation scale andor number of samples; spatial correlation of samples lead to higher REV levels as typically assumed.": false,
+ "Our research shows the added value of iterative development and user feedback for improving and further development of the tool's usability and functionality.": false,
+ "Our paper only introduces the REF as a way of structuring market evolution, and we suggest future studies are necessary to elaborate on how markets evolve in the cases we addressed, as well as in others.": false,
+ "Our novel modular approach to operating an automated microfluidic system for parallelized cell culture will enable greater experimental flexibility and facilitate the cooperation of different chips from different labs.": false,
+ "Our novel coproduction framework, based on a set of five principles and thirteen criteria, proved useful as an assessment tool, stimulating critical reflection from which we were able to evaluate the actual performance of the thirteen criteria.": false,
+ "Our model outperforms two state-of-the-art matrix factorization-based approaches, demonstrating the effectiveness of integrating neighborhood features in a deep neural network for POI recommendation.": false,
+ "Our experiments also indicate that our proposed semi-supervised reasoning method achieves a comparable performance as state-of-the-art fully supervised learning baselines for physician policy learning.": false,
+ "Our experimental results indicate great potential for improving the accuracy of energy consumption prediction by using automated machine learning approaches.": true,
+ "Our aim was to identify potential opportunities for improving weather and market advisory dissemination to rural communities and remote and underserved farmers in developing countries.": false,
+ "Our aim was to define a minimum Overall Adult Health Standard Set that will enable outcome measurement in routine clinical practice to improve decision making between providers and patients aged NUMBER years or older, to facilitate quality improvement, and to allow for benchmarking across organizations.": false,
+ "Likewise, the networks that grow out of prestigious US business schools and Wall Street institutional investors in the world's largest capital market have extended into CIC corporate headquarters and its asset allocation strategy.": false,
+ "In future work, we plan to develop more sophisticated methods for identifying and terminating unpromising target algorithm runs, to further enhance automatic configuration.": false,
+ "In all these application, the convolution surface based modelling method proposed in our work can show great advantages in its effectiveness and scalability.": true,
+ "In agreement with our hypothesis, results show that interaural segregation cues led to improved behavioral word-recognition performance and stronger cortical segregation of the distractor speakers.": true,
+ "In addition, we overview a proof-of-concept prototype of our tool that facilitates existing software engineering mechanisms to achieve the above-mentioned features of our framework.": true,
+ "In addition, our models will enable more rapid, cost-effective assessment of the efficacy and cellular toxicity of future novel therapeutic compounds to treat the main pathologies related to endothelial dysfunction.": false,
+ "In a previous search for existing drugs with the potential of targeting Cantu Syndrome, also resulting from increased I KATP, we found a set of candidate drugs that may also possess the potential to target DEND syndrome.": false,
+ "In NUMBER we therefore started to study the feasibility of integrating the hyperthermia HN device into an MR scanner for noninvasive MR thermometry by developing a novel applicator: the MRcollar.": true,
+ "In LoTSS-DR NUMBER we also performed significant post processing of the radio catalogues to enhance their scientific potential.": false,
+ "Here, we follow an \"actualization\" perspective within the entrepreneurial opportunity discourse wherein entrepreneurial opportunities are defined as \"the propensity of market demand to be actualized into profits through the introduction of novel products or services\" CITATION.": false,
+ "Hence, we have demonstrated the need for researchers to distinguish different groups of solo self-employed when identifying factors that may enhance their well-being and performance.": false,
+ "Furthermore, we elaborate on ongoing developments such as the rise of comparable registries, increasing support for preregistration in the Netherlands-which led to the funding of PCT by the Dutch government-and pilots of mandatory preregistration by several funding bodies.": false,
+ "Furthermore, we demonstrate that a further developed version of our system may be applied in a wide range of settings by showing the successful application of the system in an elderly population.": true,
+ "Finally, we explored sustainable development pathways of the capital region and the potential of this framework to inform and guide policy.": false,
+ "Extensive experiments and comprehensive analysis performed on two widely used datasets-Microsoft COCO and Flickr NUMBER K-show the effectiveness of the hybrid feature fusion framework in CMHF, in which the stateof-the-art matching performance is achieved by our proposed CMHF method.": false,
+ "By connecting the composition of the food basket to the supply chain that is necessary to deliver it, we are able to increase the efficiency and effectiveness of WFP operations, ensuring that, with the funds available, WFP can continue to supply life-saving assistance to as many people in need as possible.": false,
+ "Both the first steps that the US state appears to be taking towards a stronger market-direction role, and the ongoing market-direction role of the Chinese party state parallel to its extensive internal and external market creation, must be seen in the context of an increasing Sino-US geopolitical rivalry.": false,
+ "Before a risk assessment tool can be implemented we need to know its discriminative accuracy, predictive value, cost-effectiveness, transportability e.g., to particular populations, ages and gender etc., and the general availability of its variables e.g., to enable cross-study comparison and result verification.": false,
+ "As AB in early life have a major impact on microbiota development which may lead to aberrant immune maturation, NUMBER our findings accentuate the need for finding strategies to modify microbiome development after AB exposure to decrease the risk of allergies.": false,
+ "An analysis of the tiles by a domain expert shows that our approach can lead to the discovery of novel insights.": false,
+ "Altogether, our study demonstrates the potential of trehalosepullulan as the main material to produce antigen-containing dMNAs able to induce an immune response after application.": true,
+ "Also, as a result of analyzing market information, the venture would develop an understanding of which potential corporate partners operate in those end markets that demonstrate best sales and profit potential for the end products that could be developed.": false,
+ "Scope and approach: In this commentary we argue that addressing these governance challenges requires the development and adoption of novel research and innovation RI approaches that will provide evidence to inform food system transformation and will serve as catalysts for change.": false,
+ "To improve the costtime performance of these algorithms, we developed gpuZoo which implements GPU-accelerated calculations, dramatically improving the performance of these algorithms.": true,
+ "To facilitate and support effective implementation of the framework, we propose CITATION implementing strategies to facilitate the necessary behavioral changes in the PHW; CITATION implementing strategies to facilitate system changes and NUMBER identification of potential barriers and obstacles for the implementation of these strategies.": false,
+ "This restricts their development from on-station prototypes a prototype developed at an experimental farm to farm-scale adoption adoption of these experimental prototypes on a large scale by farmers.": false,
+ "These novel results show that development in manufacturing have potential for significant improvements in thermal-hydraulic efficiency.": false,
+ "Section NUMBER demonstrates the efficacy of the proposed algorithm; discusses the properties of the discovered SIMPs; compares our approach to existing methods; and presents a case study in aviation, highlighting how our approach could help improve an analyst's understanding of the problem.": false,
+ "Here, we demonstrate the potential for achieving worldwide standardization, ensured by PT, although international implementation the three-leveled sRM is a prerequisite for the success of such a program.": false,
+ "Although our paper addresses a number of significant issues related to dynamic capabilities, open innovation and competitive firm performance, future studies are recommended to contribute to a further elaboration of the third component of open innovation: the inside-in innovation processes introduced in this paper.": false,
+ "As we see that the tourism industry worldwide is facing several developments and challenges, including overcrowding, sustainability, and climate change, applying this technique to measure tourists' preferences and choices for new solutions and strategies to handle these problems seems like a promising approach.": false,
+ "While the landscape of WIMP searches in the traditional GeV-TeV range appears to offer faltering returns for dark matter discovery, our study illustrates how future MeV gammaray detectors provide very promising prospects over several orders of magnitude in the dark matter mass.": false,
+ "What we see is that the transfer price rule improves market performance as long as...rm A remains in the downstream market.": false,
+ "We will also show that such a reduction in the number of reactive components leads to improved performance robustness to variations in the inductive link coupling factor.": false,
+ "We were able to optimize our pilot-scale device to produce fibers with a diameter below NUMBER mm for the first time, thus offering an industrial solution for the preparation of melt-electrospun nanofibers.": true,
+ "We want to stress the importance of tracing back how the methods we propose are implemented in the field to shape a more realistic picture and state of the art, providing us with the possibility to improve and iterate on our methods.": false,
+ "We successfully developed a method for de novo FAIRification via an EDC system that automatically transforms eCRF data entered into the system into machine-readable, FAIR data by the use of a semantic data model and a data transformation application.": false,
+ "We show that semibetas stemming from negative market and negative asset return covariation predict significantly higher future returns, while semibetas attributable to negative market and positive asset return covariation predict significantly lower future returns.": false,
+ "We show that following the regulatory change, the sensitivity of fund flows and managerial turnover to portfolio holdings information significantly increases for high, relative to low, volatility funds.": false,
+ "We show that a magnetization gradient of the vortex can act as an effective SOC, which leads to spin accumulation at the rims of the device.": false,
+ "We propose a novel surrogate-assisted Evolutionary Algorithm for solving expensive combinatorial optimization problems.": true,
+ "We propose a new attacker model, based on dynamic optimization, where we demonstrate that large, initial, fixed costs of exploit development induce attackers to delay implementation and deployment of exploits of vulnerabilities.": false,
+ "We performed a small-scale proof-of-concept study to demonstrate that the platform can be used for drug development.": true,
+ "We note several contextual factors that appear to affect actors' need fulfillment, including health-care structures and institutions e.g. New technologies encourage the development of innovative services that can create beneficial outcomes for multiple actors CITATION.": false,
+ "We hope the workshop can serve as a platform for fruitful discussions of known problems with modeling user features to personalize conversational systems, and for connecting people working on and interested in personalized intelligent conversational systems that lead to new collaborations.": false,
+ "We hope that further development of the model and advancement in experimental techniques will lead to better accuracy in the determination of the parameters of real metal surfaces.": false,
+ "We hope that by sharing our practical experiences, we may enable future researchers to more effectively conduct clinical trials in these populations who could still gain much from improved clinical care.": false,
+ "We have presented the detailed design of our approach, and our experimental results have shown that our solution can achieve significant performance improvement compared to a benchmark solution.": false,
+ "We have developed a novel snapshot hyperspectral imaging camera with a large field of view as a prototype system for non-invasively interrogating superficial anatomic structures and for monitoring changes in microvascular perfusion and oxygenation.": true,
+ "We explore the innovation performance benefits of alliances for spin-off firms, in particular spin-offs either from other firms or from public research organizations.": false,
+ "We envisage that this work will serve as a platform for the accelerated development of isoxazoles and other novel chemotypes for the effective allosteric targeting of RORgt.": false,
+ "We demonstrated the latter benefit for the problem of hierarchical time series forecasting, where we observed up to NUMBER improvement in point performance and up to NUMBER improvement in probabilistic forecasting performance.": false,
+ "We demonstrate that in oxide growth at low temperatures two key points should be highlighted: i the strong dependence of surface potential on reactive oxygen coverage; ii the interrelation between exposure conditions and crystalline oxide formation.": false,
+ "We demonstrate a novel automated solution for creating high-quality knowledge-based plans KBPs using proton and photon beams to identify patients for proton treatment based on their normal tissue complication probabilities NTCP.": true,
+ "We conclude that increased funding availability, research development and data generation, and prioritization within a coordinated binational agenda are needed to advance in terms of water security for groundwater systems in the border region.": false,
+ "We conclude that despite provincial government intervention in regional planning, the impact of market pressures, growth coalitions and institutional coordination problems prevent growth management policies from delivering the significant changes promised by the Ontario government.": false,
+ "We coin this merged approach'Design to Market Thinking' D NUMBER MT, i.e. an integrative thinking approach that could help sustainable innovations to successfully enter the market.": false
+}
\ No newline at end of file
diff --git a/examples/scibert/data/evaluation-experiment-2-stage #1-sa6a0y.json b/examples/scibert/data/evaluation-experiment-2-stage #1-sa6a0y.json
new file mode 100644
index 0000000..c2e25e5
--- /dev/null
+++ b/examples/scibert/data/evaluation-experiment-2-stage #1-sa6a0y.json
@@ -0,0 +1,502 @@
+{
+ "While promising advancements in improved photosynthesis may be achieved by genetic modification CITATION, we argue that using natural genetic variation for photosynthesis holds an equally promising potential for improvement.": true,
+ "We find that market parameters have a more significant impact on pricing decision and the profit of the platform than logistics parameters, while logistics parameters affect the time decision of the platform more significantly than market parameters.": false,
+ "We find that local informal competition has a robust negative effect on product innovation intensity of formal firms, while within industry informal competition enhances innovative sales.": false,
+ "Then we substantiate our claim of a need for a new chaplain-led intervention, building on various promising and effective interventions of spiritual care that have been developed in recent years.": true,
+ "The framework allows us to: i better qualify the categories of sustaining and disruptive innovation; ii understand the evolution of hybrid patterns of market innovation, since the elements of emerging disruptive innovations sometimes sustain the established technology, and; iii assess and map emerging market patterns.": true,
+ "More importantly, we develop a novel hypothesis about the complementarity between founder social capital and a startup's technological capabilities to suggest that the bargaining impact of founders' social capital becomes more pronounced when a startup is also endowed with superior technological capabilities.": false,
+ "In this setting we will develop novel analysis tools for generic identifiability of subnetworks that are formulated in terms of disconnecting sets in the graph of the network models, and we will show that this leads to a new and effective synthesis procedure for allocating external excitation signals for achieving identifiability of a subnetwork.": true,
+ "However, we show that increased portfolio disclosure creates additional skill re-assessment risk from mutual fund investors for the fund managers, which can lead to an increase in management fees, a decrease in risk taking and thus a decline in net fund performance.": false,
+ "Here, we critically review the most important pre-clinical and clinical findings, recent advances in CAR-T against myeloma, as well as discoveries in the biology of a still incurable disease, that, all together, will further improve safety and efficacy in relapsedrefractory patients, urgently in need of novel treatment options.": false,
+ "For the funds that are forced by the regulation to increase their disclosure frequency hereafter, \"semi-annual funds\", we find that mandating more frequent portfolio disclosure increases the skill re-assessment risk from mutual fund investors for managers of high-volatility funds relative to those of low-volatility funds.": false,
+ "While some of these described competitions overlap the structures and goals of existing alternatives, we believe that the benefits of our system, combined with the novel and varied ideas we present, will make Ludii one of the most attractive and popular competition platforms in future years.": true,
+ "We described KIs' potential to improve combination therapies for solid cancers, and we anticipate that ongoing clinical trials will ultimately guide the implementation of optimal combinatorial approaches to maximize the immune system's anti-tumor activity.": true,
+ "We conclude that the integrative model-based approach of evaluating the potential of new cropping systems in complex systems and fine-tuning alternative combinations will support the enhanced adaptability of innovative cropping practices to provide a sustainable livelihood to marginal households.": true,
+ "We argue therefore that the time has come to focus on advancing the DBS system in terms of technological properties to better meet individual patient needs, leading to more effective symptom control, improved patient quality of life and reduced healthcare costs.": false,
+ "Our findings can be used to improve existing school programs, for example, by integrating effective components in programs, or by developing new promising school-based programs that comprise the most effective components.": true,
+ "In this article, we likewise argue that articulating the drivers and infrastructures that enable the introduction of novel technologies in our case: in PPH will aid the moral assessment of these developments, while it may also allow us to develop tools for critical self-analysis by stakeholders involved in such developments.": false,
+ "Convinced of the potential of the VKB to revolutionize clinical trial data sharing among biopharmaceutical companies, our group in the EAPM is currently working, in cooperation with representatives of the biopharmaceutical industry, to resolve the issues mentioned above and to develop the web-based platform.": true,
+ "Confirming previous work, we showed the potential to reduce GHG emissions from agricultural soils by the application of organic amendments CITATION; however, also the addition of MF in our mesocosm led to a promising development of the cumulative GHG.": true,
+ "Compared to previous studies for instrument segmentation in the NUMBER D US, our proposed method achieves a higher accuracy with a considerably higher prediction efficiency, which offers a promising value for clinical implementation.": true,
+ "Although innovation is the cornerstone of our conceptual model, which hypothetically affects firm performance, our results theoretically contribute to BMI research by highlighting the significance of the organisational capabilities as a mediator when planning to introduce innovations to enhance firm performance.": false,
+ "With its clinical-driven design and easy-to-use setup, our robotic device for hand sensorimotor rehabilitation has the potential for high clinical acceptance, applicability and effectiveness.": true,
+ "We expect that such freedom of choice, in turn, facilitates competition on quality or price, thereby making the private-client segment of the audit market a well-suited setting to study the price and quality effects of market structure and competition.": false,
+ "We also propose improvements to the IKK attack by showing that the accuracy of the attack can be improved significantly by combining multiple attack runs, as we show that median recovery rates can be increased up to NUMBER percentage points, whereas the variance of recovery rates of simulation can be decreased up to NUMBER percentage points.": false,
+ "There is great potential for the digital tools to increase access to eye care and we expect the accuracy of the current tools to improve with every iteration in technology development.": true,
+ "Second, we propose a novel modeling approach, which allows to disentangle between product and process innovation, to empirically investigate innovation activity over the industry life-cycle on a comprehensive database of NUMBER European manufacturing industries.": false,
+ "Our main finding is that to understand investor behaviour, different dimensions of investor characteristics and behaviour should be analysed concurrently; investors' scale of operation, ownership composition, type of capital, and locational and strategic behaviour help configure investment decisions in relation to property market shifts.": false,
+ "From additional policy experiments, we find that labor market reforms aimed at weakening labor unions, by boosting profit margins and innovation, can foster a profit-led growth.": false,
+ "First, since there is already some evidence on the effect of innovation vouchers on future collaborations CITATION, the main contribution of our paper lies in understanding if this policy tool and the use of external knowledge providers by SMEs also leads to improved innovation outcomes.": false,
+ "By adopting a data set from a leading O NUMBER O freight platform in China, we find that the proposed model can effectively increase the revenue of the platform by more than NUMBER if the platform increases the price appropriately.": false,
+ "Based on our findings we demonstrate that the framework of IMSI and SDT can effectively be applied as a frame of analysis to identify essential features of sustainability in educational innovations and we discuss how concepts of SDT deepen the knowledge of sustainable educational innovation.": true,
+ "While these tools represent a significant progress in automated rapid generation and modification of NUMBER D molecular geometries, we sought to develop an easy-to-use tool which can quickly create reasonably accurate molecular geometries in the local chemical space of a given molecular scaffold.": true,
+ "While AI-systems have the potential to increase market efficiency and economic welfare through improved predictions, these positive effects can only unfold in so far as the individual market actors i recognize that potential and ii subsequently engage with them.": false,
+ "We propose a novel approach to address the path explosion problem: A smart triaging system that leverages supervised machine learning techniques to replicate human expertise, leading to vulnerable path discovery.": true,
+ "We conclude that the most promising packages are combining innovation support and information provision with either a carbon tax and adoption subsidy, or with a carbon market.": false,
+ "Therefore we employ a more objective indicator-market share of the niche-as the second axis, which helps to sketch socio-technical trajectories of niche innovations in the market overtime both regime sustaining and disruptive innovations.": false,
+ "The increased complexity of modern sociotechnical systems STS necessitates the need for a manageable representation of their attributes, to augment our understanding and enable the development of ways through which we can increase their effectiveness, efficiency, and safety.": false,
+ "Motivated by the potential for improving the capacities of socially intelligent systems, we investigate the potential of recognizing an individual's interdependence perceptions through an analysis of audiovisual recordings of dyadic conversational interaction.": false,
+ "More precisely, our results suggest that the structure of knowledge networks are positively correlated with the innovative capacity, with particular reference to the cohesion of inventor networks, while social proximity offers a different, and precisely negative, contribution for breakthrough innovation.": false,
+ "Methods: Using the case of the nationwide implementation of WGS into clinical practice in lung cancer in the Dutch healthcare system, we developed a simulation model to show that including service delivery features across the diagnostic pathway can provide essential insight into the affordability and accessibility of care at the systems level.": false,
+ "In section four, we develop some illustrative examples to demonstrate the functionalities of the database: we look at water to illustrate the potential for studying access to amenities and we look at assets to illustrate the potential for studying inequalities.": false,
+ "In general, our results indicate that an enabling performance measurement system design and an enabling system development process both independently increase procedural fairness and decrease red tape.": false,
+ "FOREC demonstrates robust effectiveness, consistently improving the performance compared to competitive baselines on NUMBER target markets we selected for our study.": true,
+ "Consistent with our conjecture that managers require more compensation for bearing the elevated skill re-assessment risk from fund investors, we find that following the regulatory change, high volatility semi-annual funds significantly increase management fees, relative to low volatility semi-annual funds.": false,
+ "We encourage researchers to continue contribut-ing insights to enable the development and assessment of retail service innovations that create positive outcomes for shoppers, investors and the implementing retail firm.": false,
+ "We are still in the very early stages of mobile-health implementation in psychiatry; however, if successfully developed, a mobile platform for early detection has the potential to help achieve major translational goals, such as early recognition of mental problems, accelerating access to care, and personalized monitoring of relapse.": true,
+ "Recognizing the promising clinical potential of RSPOs as novel therapeutic targets, a clinical trial has been set-up that tested the safety and efficacy of the neutralizing monoclonal anti-RSPO NUMBER antibody OMP NUMBER R NUMBER Rosmantuzumab in cancer patients with advanced solid tumors and metastatic CRC CITATION.": true,
+ "Immigrant inventors played a crucial role in making the United States an innovation powerhouse, by bringing new knowledge from their countries of origin CITATION and contributing to the longterm technological development of the US innovation system CITATION.": false,
+ "Finally, we illustrated the applicability of the proposed implementations and design algorithms in a network consensus application, where we have shown that CEV-GF filters achieve machine-precision accuracy at a significantly lower communication cost than existing methods.": true,
+ "We suggest that these differences may lead to a fractured market and an AI crisis in which different members of the EU will adopt nation-centric strategies to exploit AI, thus preventing the development of a frictionless market as envisaged by the EU.": false,
+ "We show that, unlike in lemons markets, adverse selection in gems markets affects low-quality goods, leaving only high-quality goods on the market, pushing up prices.": false,
+ "We provide theoretical foundations and empirical evidence for the impact of oil discoveries on the level of tariffs, illustrating that such discoveries can lead to economically significant higher levels of protectionism.": false,
+ "We propose that IT governance process capability can improve IT performance, which in turn can improve business performance.": false,
+ "We have therefore demonstrated the innovative performance of our pilot-scale melt-electrospinning device, which bridges the gap between laboratory-scale and pilot-scale manufacturing and achieves fiber diameters comparable to those produced by conventional solution electrospinning.": true,
+ "We have therefore demonstrated the innovative performance of our pilot-scale melt electrospinning device, which bridges the gap between laboratory-scale and pilot-scale manufacturing and achieves fiber diameters comparable to those produced by conventional solution electrospinning.": true,
+ "We demonstrate that markets with a high share of supply from VRES yield a significantly lower forward premium than markets with a low market share of wind or solar supply.": false,
+ "We build a novel dataset of immigrant inventors to examine their impact on the US inventing activity between NUMBER and NUMBER Did native inventors benefit from immigrants' inventive activity?": false,
+ "We believe the framework may add value to the development of robust HTA methods and effective implementation, which helps meet the needs for novel HTA methods due to emerging health technologies.": true,
+ "To develop a conceptual framework for the translocal diffusion of sustainability innovations, we will use both the transition literature as well as the regional innovation systems literature, because of their complementary character on the topic of translocal diffusion of sustainability innovations.": false,
+ "Therefore, we posit a need for novel approaches that enable studying host-microbe dynamics in a community ecology perspective, which can be utilised for devising effective diagnostic and therapeutic strategies.": false,
+ "The use of multiple cameras and RGB-D devices may indeed improve the acquisition performance and precision, but renders the system more complex and costly, going against our goal to keep the system as simple and low-cost as possible.": false,
+ "The development of influenza vaccines produced by transient expression is therefore one of the key niches of molecular farming that show unambiguous advantages over all other current manufacturing platforms, and this is reflected by the keen interest and investment in the platform by the US and Canadian governments.": true,
+ "The collaboration and outsourcing of economic activities, we argued, enable registered firms to take strategic advantage of the'local' market acceptance of informal enterprises to expand market size and perform better with product innovations.": false,
+ "Our results demonstrate the feasibility of such co-flocculation strategy for designing new high-rate anode material, which outperforms the original bulk HTiNbO NUMBER compound, making it a promising candidate for application in ultrafast lithium-ion batteries.": true,
+ "Our findings demonstrate the potential effectiveness of an online evidence-based patient information portal in improving knowledge in patients with congenital heart disease, but also underline the crucial importance of effective implementation and active use of the portal.": true,
+ "Moreover, our data show the potential of the PEA as a biomarker discovery tool in vitreous samples by highlighting CD NUMBER as a novel marker with potential diagnostic value for PVRL.": true,
+ "Lastly, we provide a benchmarking tool that uses datasets following this standard to measure the performance of equivalent mutant problem tools, showing the performance in greater detail than state of the art EMP evaluation tools.": false,
+ "Here, we propose a different method to achieve better scalability and higher accuracy using quantum computing, outperforming classical Bayesian neural networks for large datasets significantly.": true,
+ "Here, we also synthetize the design of the architecture of the wireless monitoring network, the sensor technology adopted to develop an effective real time environmental monitoring system and management platform, to construct a Wireless Sensor Network WSN-early warning and reporting system, which can be applied as a prevention measure.": false,
+ "By developing a new capital endogenization method that addresses the above temporality issue, we present a novel analysis on how China's capital development and the associated resource use and emissions over the past NUMBER decades CITATION are linked to meeting the final consumption of China and other countries throughout this time period.": false,
+ "Although several ABS frameworks exist that achieve significant speedups using GPUs in the field of ABS, there is still significant room for improvement, which we wish to address in this article.": true,
+ "Additionally, we developed a novel decentralized market clearing method called DACO that provides a near-optimal market solution in terms of maximum social welfare within limited number of stages.": true,
+ "We broadly define market innovation as purposive actions by market stakeholders that result in a distinctively new or altered form of market.": false,
+ "Using the variability of profitability among segments as our measure of the disclosure of crosssegment differences in performance, we find that existing competition is positively associated with cross-segment variability in profitability, whereas potential competition is negatively associated with cross-segment variability in profitability.": false,
+ "To demonstrate the potential of PPO in industrial use, we show that for larger problem instances where the optimal solution is unknown, the algorithm finds solutions that outperform the benchmark an aggregate modified base-stock heuristic by NUMBER.": true,
+ "To accomplish such goals as mentioned above, we need novel, sophisticated imaging tools CITATION CITATION CITATION as well as innovations to cushion the joint so that we are able to diagnose and treat the cartilage damage at the early subclinical phase.": false,
+ "Overall, the application of our sectoral approach reveals that the need and potential for global governance to contribute to effective climate protection varies significantly across sectoral systems.": false,
+ "Our work enables us to open the black box of the digitally enabled innovation activity by shedding light on collaborative activities between a focal firm and its customers and suppliers to advance innovation while coping with information overload.": false,
+ "Our work demonstrates the possibility of using the BIL functionalized polymer as a tailorable and robust electrolyte material platform for scalable in situ NUMBER D printing of implantable energy storage devices that can potentially be utilized as biomedical devices and implants for healthcare applications.": true,
+ "Our results demonstrate the efficacy of the proposed method for the optimization of light-matter coupling and its potential application for the enhanced performance of optoelectronic devices.": true,
+ "However, substantial challenges, such as production cost, scalability, and compatibility with current manufacturing pipelines in industry, remain and need to be overcome before we can see the full potential of the materials benefit the public.": false,
+ "For the area of automated machine learning, our work clearly demonstrates the potential of using multi-objective algorithm configurators within an integrated system leveraging many state-of-the-art techniques-an idea that can be extended to many other problems in machine learning in which multiple conflicting performance objectives arise.": true,
+ "Finally, we devised a novel strategy for fusing medium resolution Sentinel-NUMBER and high resolution TerraSAR-X data to increase mapping accuracy by using the potential of both sensing systems.": true,
+ "Compared to the earlier version, we have CITATION redefined our proposed system model; CITATION performed five additional novel experiments that demonstrate the efficacy of our solution; CITATION added a section about future work.": true,
+ "While many MLD systems suffer from low deposition rates that limit their potential for use in new applications, we believe this work can help to give directions in optimizing the already created deposition recipes and in developing new MLD precursors, processes and equipment for lowcost and high-throughput applications.": true,
+ "We also show that improvements in the classification performance can subsequently lead to a significantly better performance in the segmentation task.": true,
+ "Our study shows that the inclusion of SNM, unlocks a certain market focus into DT that makes considerations on desirability, viability and feasibility less about the product alone, and more about the product and its deployment to the market.": false,
+ "Our study delineates the impact of knowledge inflows from specific external sources, such as market-and science-based sources, on innovation performance in FFs and non-FFs and identifies which knowledge inflow sources FFs can convert into innovation outcomes more or less effectively than non-FFs.": false,
+ "Our novel methodological approach builds on the theory of a problem-solution space for mission-oriented innovation systems proposed by CITATION NUMBER, allowing identification of interdependencies and hierarchies in and between missions that may lead to trade-offs.": false,
+ "Our hybrid approach has great potential for the development of novel potential protein therapies, as it in part sidesteps the extrapolation challenges from NUMBER D or animal models to complex human physiology by efficiently combining in silico approaches with biologically relevant in vitro test systems.": true,
+ "On this basis, we can assess the potential of international cooperation to address the challenges specific sectoral systems face in the climate transition as well as the extent to which existing sectoral institutional complexes deliver on this potential.": false,
+ "In addition, we will further assess the proposed market using the data from other European countries and we will consider more complex trading strategies to quantify the maximum potential of the proposed market.": false,
+ "In Chapter NUMBER we develop the theory of p-automata, which is essential to us for solving some algebraic equations in chapter NUMBER We start by defining in Section NUMBER concepts such as p-automaton and p-automatic sequences, and we finish with the theorem of Christol relating algebraic power series to automatic sequences.": false,
+ "However, in second-generation processes, significant residual amounts of stillage are produced, and the development of effective valorization routes towards added-value platform chemicals for this material is key in boosting the profitability of the NUMBER generation bioethanol industry CITATION.": false,
+ "Furthermore, we proposed an improved projection random sample consensus RANSAC method, which can effectively divide the detection plane of catenary cantilever devices to solve the multicantilever device occlusion problem.": true,
+ "First, our study indicated that the collaborative process of collaborative innovation contests, facilitated by DT, can potentially bridge the mismatch between open innovation outcomes and company capabilities to adopt such innovation.": false,
+ "By doing so, we are better prepared to adopt the framework for the analysis of electric propulsion systems in cars, a potentially disruptive innovation that has slowly been entering mainstream markets.": true,
+ "As we enter an age of ubiquitous electronic health records EHRs, clinical decision support systems CDSS, and large, linked data sets with the potential to accommodate artificial intelligence AI algorithms, the need to better understand individual and collective decisionmaking has reached a critical point.": false,
+ "Among others, we show that worse labor market prospects of the worker relative to the leader make it more costly to employ an altruistic leader and less costly to employ a spiteful leader, rendering the use of unfriendly leadership styles more attractive for the firm.": false,
+ "All in all, using data across multiple asset classes and markets in an extended sample period, we conclude that r ROD positively and significantly predicts r LH and this robust pattern better describes market intraday momentum everywhere.": true,
+ "While these tools represent a significant progress in automated and rapid generation of molecular geometries, we sought to develop an easy-to-use tool which can quickly create reasonably accurate molecular geometries in the local chemical space of a given molecular scaffold.": true,
+ "We, therefore, have to wonder whether public organizations' use of social media can live up to the expectations that were raised more than a decade ago and whether a more effective tapping into the engaging potential of social media can indeed increase the visibility of public organizations on social media platforms.": false,
+ "We show that the production of heterologous protein and MCFA contents are significantly enhanced when using the novel expression system, and we show that transformant strains are not impaired in growth.": true,
+ "We found that employees with greater perceived future opportunities at work attached greater importance to mastery, meaning and affiliation needs i.e., approach needs, which in turn lead to higher engagement in need-congruent job and off-job crafting efforts.": false,
+ "We argue in this paper that SNM aspects could serve as a necessary addition to DT to increase the chance of successful market implementation of sustainable innovations.": false,
+ "To gain a better grasp of the evolving nature of markets in the NUMBER century, I return to the early theorizing by neoliberal thinkers of the market as an epistemic device that effectively and efficiently mediates information and knowledge between individuals and collective society.": false,
+ "To address the unsatisfied needs in robotic sensorimotor rehabilitation, we aimed at developing a novel clinical-driven robotic hand rehabilitation device that is capable of high quality haptic rendering and that supports physiological full flexionextension of the fingers while offering an effortless setup.": true,
+ "Therefore, we see the need of analyzing the potential benefits and trade-offs of the long-term implementation of these circular business models, especially in a market where they are gaining acceptance to further develop strategies to secure their possible environmental and material benefits.": false,
+ "Our study therefore contributes to emergent research about the micro-foundations of firm strategy and performance e.g., CITATION, NUMBER;Foss and Pedersen, NUMBER; by emphasizing the role of founders in enabling a startup to capture superior value in their RD partnerships.": false,
+ "More precisely, we show that for the tested problems such a single configuration switch can result in performance gains of up to NUMBER.With such a significant indication for improvement potential, we hope that our results trigger an intensified discussion of online algorithm configuration for CMA-ES variants.": true,
+ "In doing so, we hope that B NUMBER B innovation managers will improve their conceptual perspective on innovation by including service design as an effective tool to foster stakeholder engagement throughout the project life cycle in the innovation network.": false,
+ "In addition, we discuss what is needed to improve the situation further, in order to use this technology to its fullest advantage and avoid \"leaks in the pipeline\", when a promising device fails to take the next step of the valorization pathway and is abandoned.": false,
+ "Experimental evaluations demonstrate that while state-of-the-art methods have limited capability of reconstructing USCT image from frequency domain data, our proposed method can effectively reconstruct details for USCT images, which could lead to faster diagnoses and treatment decisions for breast cancer patients.": true,
+ "Drawing on self-determination and play theories, we develop a process model that proposes that daily playful work design PWD; designing fun, designing competition positively relates to employees' daily work engagement through basic psychological need satisfaction.": false,
+ "In particular, we introduce a novel EarlyTSC framework addressing the MO-CASH problem for EarlyTSC that achieves the following: In Fig. NUMBER c, we illustrate the improvements that can be achieved by an automated search over an expanded space of multiple algorithms and all their hyperparameters.": true,
+ "We suggest that significantly underexploited opportunities exist to enhance integration of such short-and long-term technology and policy interventions, which together could provide a more effective pathway to enable the appropriate intensification of groundwater use for irrigation in Nepal and the EIGP.": false,
+ "We show that it can outperform state-of-the-art multiway and two-way Linear Discriminant Analysis classifiers in asynchronous detection of individual finger movements from intracranial recordings, an essential feature to achieve a sense of dexterity with hand prosthetics and exoskeletons.": true,
+ "We provide recommendations to help achieve ASP systems designed to use climate forecasts, arguing that tailored seasonal forecast products have potential in some countries to improve the lead time of interventions to address climate-induced disasters.": false,
+ "We have presented a novel and improved minimization algorithm for t-SNE, providing significant performance improvements above the state-of-the-art, especially for large datasets and higher dimensional embeddings.": true,
+ "We find corroborating evidence for a temperature signal in brGMGT assemblages, further demonstrating the potential to develop novel proxies with more extensive studies of modern distributions.": true,
+ "We detail how our algorithm can be implemented on the target platform or on NISQ computers, bringing forth a promising application of these devices.": true,
+ "To overcome these limitations, we developed a highthroughput TCR repertoire and discovery platform that enables linking T cell phenotype with TCR gene and function data at single-cell level.": true,
+ "To demonstrate the societal benefits of adherence improvement, we recommend that the most promising interventions are subjected to rigorous evaluation of clinical effectiveness in pragmaticallydesigned, randomized, controlled trials.": false,
+ "The high-minus-low return spread is - NUMBER and - NUMBER for UK investors and Japanese investors, respectively, in the universe of all currencies, and - NUMBER and - NUMBER in the universe of developed markets' currencies, confirming that the US equity tail risk factor has a global component.": false,
+ "Our study shows that there are performance trade-offs for each BPM orientation with regard to potential, selectivity, and stability: the forward bias lowers the overall cell potential by reducing the chemical potential, while the reverse bias gives a stable product formation of CO NUMBER conversion products.": false,
+ "Our results when using this customized Six Sigma methodology demonstrate the suitability of this method for improving the SCA process in its different stages; from the basis of the process which is improving the quality of the acquired side-channel measurement to the performance of any kind of side-channel attack or leakage assessment technique.": false,
+ "Our results show that the novel MRcollar design enables achieving comparable heating capabilities as compared to the current clinical HYPERcollar NUMBER D.": true,
+ "Our main aim is to create a European research agenda, data sharing, and development of best practice for clinical and translational science to achieve breakthroughs with clinically feasible HIV cure strategies.": false,
+ "Moreover, building also on estimates of NETs' achievable potential, which are well below biophysical limits CITATION, we calculate the actual deployment level of the most promising NETs that would be necessary to meet the NUMBER C target under three different scenarios CITATION, NUMBER b.": false,
+ "In this review, we demonstrate that a web-based scoring platform can mitigate potential risk factors when including sTILs in clinical trials, and we argue that this framework can be applied for any future biomarker-driven clinical trial setting.": false,
+ "Here, we examined the feasibility of, and initial responses to, large walking perturbations in COPD, as well as the adaptation potential of people with COPD to repeated walking perturbations that might indicate potential for perturbation-based balance training in COPD.": false,
+ "Here we investigated interneuron abnormalities in two experimental models of EoP and explored the potential of two promising treatment strategies, namely intranasal mesenchymal stem cells MSCs or insulin-like growth factor I CITATION, to restore interneuron development.": false,
+ "From the dynamic capabilities perspective, we assume that the development of CE in the firm implies the use of firm capabilities and the alignment of these in the development of a dynamic process of innovation towards the development of CE in firms.": false,
+ "Due to the unique single-particle and single-molecule properties and high-throughput capabilities of SMLM techniques, we strongly believe these results highlight their potential to be used in the routine design, quality control and optimization of nanomaterials with improved biological efficacy.": true,
+ "Comparison with several state-of-the-art baselines shows that our FairMatch algorithm is able to significantly improve the performance of recommendation results in terms of visibility of different items and suppliers with a negligible loss in the recommendation accuracy in some cases.": true,
+ "By unpacking two key mechanisms for basic research investments to improve firms' innovation performance, our results provide a more detailed understanding of the changing rationale for basic research in firms in the context of declining corporate basic research investments.": false,
+ "As such, there is increasing awareness of the need to address significant TR in a manner that comes with i an acceptable level of risk and ii a meaningful clinical outcome.": false,
+ "With those in mind, we believe that improved knowledge on marine ES flows can also support the implementation of policy responses and actions as part of the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services IPBES agenda, to achieve sustainable governance and management of seascapes, oceans, and marine systems.": false,
+ "While numerous studies have demonstrated that augmentation can improve classification and regression accuracies, to date none have explored augmenting multivariate time series i.e. Our research aims to fill this gap by identifying methods capable of successfully augmenting multivariate time series for regression analysis.": true,
+ "We show that the proposed loss function significantly reduces the quantile crossing problem to near NUMBER in all markets considered, while in some cases simultaneously increasing forecasting performance based on classical point forecast metrics applied to the expected value of the probabilistic forecast.": true,
+ "We show that optimal performance is achieved when all atomic levels experience the same potential outside the dimple and we quantify the cooling for various NA by evaluating the dependence of the final entropy densities and temperatures as functions of the initial entropy.": true,
+ "We see a similar potential in the imaging context, which would allow for an improvement in the convergence of the inference and further enhance the sen-sitivity beyond the capabilities that we have demonstrated with this publication.": true,
+ "We have found that labor market integration happens gradually in three stages of entering the labor market, integrating into the labor market and sub-merging with the labor market.": false,
+ "We have developed the novel ChemoTopoChip platform to screen the potential of both chemistry and topography in producing immunomodulatory materials suitable for bone regenerative applications.": true,
+ "We further propose several research perspectives that could support the LC-NE system as a promising target for the identification of at-risk individuals in the preclinical stages of AD, and for the development of novel preventive interventions.": true,
+ "We find that mandatory disclosure of portfolio holdings exposes managers of highvolatility funds to considerable skill re-assessment risk from mutual fund investors, which steer their behavior towards actions that ultimately hurt investors.": false,
+ "We find that a higher number of walk-in customer orders leads to optimal online order fulfillment policies with higher delivery frequencies, i.e., shorter staging times, which are required to mitigate the effect of increasing competition among the two sales channels for the same inventory positions on online service level.": false,
+ "We explored whether the need for achievement and need for affiliation enhance the effects of course characteristics on students' entrepreneurial self-efficacy and study engagement, which advances our knowledge on how to teach entrepreneurship while taking individual differences into account.": false,
+ "We evaluate the proposed method in simulation, showing that our framework can improve prediction performance over baseline methods and avoid catastrophic forgetting, and in experiments with a mobile robot, showing that our framework can continuously improve a prediction model without the need for external supervision.": true,
+ "We discuss the collective, cumulative and uncertain characteristics of innovation, highlighting the lack of transparency in the biomedical RD system, the need for public investment in the innovation process, and the \"time-lag\" between risk-taking and reward.": false,
+ "We conclude that our solution is a promising tool for privacy-preserving machine learning tasks on distributed patient data, potentially leading to an improvement of the quality of healthcare, while respecting the privacy of involved patients.": true,
+ "We assume that actors can contribute to building up resources for innovation success like new knowledge, early market structures, financial capital or legitimacy, for example through RD, networking, investments, or institutional work, Musiolik and Markard, NUMBER CITATION, NUMBER b.": false,
+ "We argue that industries using general and generally available knowledge can easily live with a deregulated hire fire labor market regime like in the US, while the functioning of the routine innovation model in mature but knowledge-intensive industries takes advantage of low rates of job turnover due to well-protected insider positions.": false,
+ "Together, we developed cheap and easily synthesized compounds that dramatically outperform known crosslinking tools, providing the community with a novel strategy for understanding RNA NUMBER D structures and alternative conformations in cells.": true,
+ "To overcome previous limitations, we devised novel methodology, enabling simultaneous invasive assessment of both LV and coronary hemodynamics for the first time.": true,
+ "The reference distances of EP inventors referring to US inventors are much larger than those of US inventors referring to US inventors.": false,
+ "The Seminar has also highlighted the need to pay due attention to technological developments that may help to overcome at least some of the problems that nowadays we face in the application of a healthy diet: new technologies may help modern husbandmen and farmers to produce healthy foods sticking to local traditions but also at a reasonable cost.": false,
+ "Second, we find a substitutive interaction effect between market orientation and new product development stage, indicating that for exits through acquisitions, a high level of market orientation can compensate for an early stage of product development.": false,
+ "Reconstruction results based on simulated data are very promising, and we think that, by addressing the practical issues discussed above, we will indeed make significant progress towards a reliable EPT reconstruction method that provides us with accurate dielectric tissue maps in practice.": true,
+ "Our study establishes CXCL NUMBER as a key component in fibrosis development and the potential of blocking CXCL NUMBER as a promising therapeutic strategy.": true,
+ "Our future work mainly lies in extending the proposed approach with more advanced network clustering algorithms, and our long term goal is to develop a highly efficient and intelligent system to automate the management of complex WMNs.": true,
+ "On the basis of a feasibility analysis, we conclude that the identified challenges can most likely be successfully overcome by platform co-ops that organise taxi rides and professional jobs, while it may prove much more difficult in food delivery, homecare and microtasking.": true,
+ "NUMBER We develop a tailor-made Frank-Wolfe algorithm that can solve the dual estimation problem orders of magnitude faster than state-of-the-art general purpose solvers.": true,
+ "It is clear that we need to continue co-inventing the future of POC technology with multiple stakeholder groups towards the development of a global paradigm to address critical needs, and provide quality healthcare at affordable cost.": false,
+ "Furthermore, our results have proved that the proposed modified U-Net is capable of considering the neighborhood effects effectively, whose advantage is eliminating the need of using a CA to combine with our method.": true,
+ "For the second task, we obtain a question representation that contains all possible answers in equal quantum superposition, and we implement Grover's quantum search algorithm to find the correct answer, agnostic to the specific question, an implementation with the potential of delivering a result with quadratic speedup.": false,
+ "Following on from this we show the results of applying the approaches to the robot platform demonstrating the effectiveness and accuracy that can be achieved.": true,
+ "Finally, we expect that the disorder of metallic electrodes at the atomic scale leads to uncontrolled local electrostatic potential surrounding the nanoscale object, a problem that NUMBER D covalent crystals have the potential to overcome.": true,
+ "Experimental results show that an upstream signaling rate of NUMBER kbps can be achieved with our simple and low-cost prototype, enabling NUMBER Gbits link per user.": true,
+ "Drawing on the literature on sustainability-oriented innovation and innovation resistance theory, we explore the potential of blockchain technology to contribute to sustainable transformations within food supply chains.": false,
+ "Despite our high-quality data and correction for competing risks in our prediction model for the development of SPTs, it should be further developed to allow clinical use.": true,
+ "Based on the measurements and visual observations of the wake flow, we develop a hypothesis to explain how the presence of developed cavitation leads to increasing compressibility Mach number in the wake flow that, in turn, results in a change in the underlying flow pattern of the near-wake, leading to an increase in the vortex formation rate.": false,
+ "As a proof of principle, to demonstrate the feasibility of releasing drugs, we also showed the effect of HASF hydrogel composition on the release of small hydrophobic anti-inflammatory and anabolic drugs, that had previously been identified as promising agents.": true,
+ "Accordingly, we perform many experiments to demonstrate that the proposed method can be implemented on academic papers in any period after publication with a significantly higher degree of accuracy and robustness than the existing algorithms applied to new papers.": true,
+ "And as the challenges faced by cities seem to become greater with time, the need for novel, critical, and creating approaches on how we could use the potential of play to improve urban life, foster sustainability, consolidate resilient and inclusive communities becomes even more urgent.": false,
+ "While still at its infancy, we hope that the PCA initiative has the potential to serve as a nucleator, bringing together scientists and engineers from a wide range of fields to solve fundamental problems in plant biology with innovative solutions from emerging technologies.": true,
+ "While markets can be understood in many different ways, we argue that a network perspective to markets, instead of a channel perspective, offers a promising entry point to reflect on a fruitful role that stakeholder involvement could play for the transition to a CBE.": false,
+ "We will elaborate on the engineering potential of both liposomes and EVs to enhance favorable pharmacokinetic characteristics in order for these vesicles to function as effective drug delivery systems DDS.": true,
+ "We will demonstrate how, through an ongoing process of reflexive innovation, our responses to the above questions have shifted and evolved, leading to a more sophisticated and diversified understanding of the well-being challenge.": false,
+ "We show that within the context of our application, the FPGA accelerated implementation can achieve close to NUMBER GBs of parsing and conversion throughput when the accelerator host-to-device interface provides enough bandwidth and when enough FPGA resources are available.": false,
+ "We show that this exact approach can solve instances with up to NUMBER customers to proven optimality, improving upon existing exact methods that can solve similar problems with up to ten customers only.": true,
+ "We discuss necessary preconditions for successful development and implementation of eGovernment services in a multi-level polity and propose the following recommendations: firstly the EU needs to assess the potential impact of proposed eGovernment systems during the phase of policy development.": false,
+ "We describe the implementation of spectral polarization modulation in a prototype satellite instrument, the SPEX prototype CITATION and in groundSPEX CITATION, which is specifically developed for ground based measurement.": false,
+ "We conclude by identifying the most promising directions where advances in these material systems will enable progress in qubit technology.": false,
+ "We achieve a better performance, in terms of robustness to catastrophic forgetting, than the state-of-the-art regularization and architectural methods using a fixed model capacity, outperforming the regularization methods by a big margin.": true,
+ "To reach these goals, we need to continue the search for biomarkers addressing real clinical needs, to increase the number of prospective studies to show clinical benefits of the putative markers already known and to analyse the costs of using biomarkers in the clinic from a societal perspective.": false,
+ "To ensure computational tractability, we develop a reduced-space formulation for trained one-class support vector machines and show that our formulation outperforms common full-space formulations by a factor of over NUMBER making it a viable tool for engineering applications.": true,
+ "To address this research challenge, we pose the following research objective: To develop a method for the definition of business model key performance indicators KPIs catered to the characteristics of the business model innovation process to support business model decision making.": false,
+ "Through our results, we have demonstrated that oxygen saturation imaging using a dual-wavelength LED-based photoacoustic system has potential in pre-clinical and clinical applications.": true,
+ "The predicted melting points for these interatomic potentials are provided in Section NUMBER Note that our fully automatized approach allows to predict the melting point of any given interatomic potential with a very high numerical precision but not necessarily with high accuracy.": false,
+ "The high variation in adenoidectomies might indicate a lack of agreement on indications for surgery, and our findings underscore the need for high-quality effectiveness research to improve evidence-based guidelines on this topic.": false,
+ "The average share of manufacturing increased in all developing countries between NUMBER and In comparative perspective we observe a long-run increase in the shares of manufacturing in developing countries, and a long-run contraction in the shares of manufacturing in the advanced economies.": false,
+ "Subsequently, in a comparison of our automated failure prediction models, we discovered that all four AI algorithms, RF, GB, LSTM, and GRU, that utilise combined system metrics or multimodal inputs outperformed the state-of-the-art studies.": true,
+ "Recognizing that the total demand and demand flexibility may increase significantly in the future, we included a high share of electric vehicles to test the robustness of the market designs.": false,
+ "Recently, we described the use of a high-throughput organ-on-a-chip platform, the OrganoPlate, for therapy response testing of breast cancer, showing the potential of the platform for three-dimensional tumor models and its application in assessing the resistance of cells to chemotherapeutic agents for personalized medicine CITATION.": true,
+ "Our work demonstrates important progress in the implementation and performance of quantum optimization algorithms on a real device, and underscores the challenges in applying these algorithms beyond those natively realized by hardware interaction graphs.": true,
+ "Our study demonstrates that asynchronous advantage actor-critic A NUMBER C, a deep reinforcement learning DRL method, can be successfully utilised to develop an autonomous trading agent capable of exploiting arbitrage opportunities.": true,
+ "Our results show that the innovation voucher program has an immediate, short-term impact on the execution of these innovation projects with positive effects on product and service development, internal processes, and intellectual property protection.": true,
+ "Our diagnosis approach not only provides a practical tool for managers to develop context-specific solutions to variability, its development also enables us to contribute important insights that strengthen the OM literature on flow improvement.": false,
+ "Moreover, we are the first to explore the potential of two promising treatment strategies in the field of neonatal brain injury, i.e., intranasal MSCs and IGF NUMBER to restore interneuron deficits and improve sociability in our EoP mouse model.": true,
+ "Importantly, Amsterdam and Paris already had highly-developed housing markets, and unique microlevel data survived in the archives of both cities, allowing us to track mortality and the developments in the housing market following an epidemic.": false,
+ "For the future, we propose a model in which the DCRT is reimbursed by national payers for each successfully developed ASO, so these funds can then be invested in the development of new individualized ASOs for additional patients.": false,
+ "Extensive experiments based on real-world datasets demonstrate that our approach achieves a high efficacy of detection performance against the state-of-the-art.": true,
+ "Experimental results show that ArtSAGENet outperforms several strong baselines and obtains state-of-the-art performance in fine art analysis, while qualitative analysis of the representations learned by our approach indicates that it is capable of capturing interesting properties of fine art.": true,
+ "As genome-scale computational modelling of underground metabolism successfully predicts the potential to utilize new nutrient sources in E. coli CITATION, we reasoned that a similar approach could be employed to characterize the theoretical potential of underground reactions to produce industrially relevant chemical compounds.": false,
+ "As concerns the role of mobile phones for the displaced in particular, the United Nations High Commissioner for Refugees has stated that, a connected refugee population can play a critical role in enabling organizations such as UNHCR to innovate effectively and to improve the quality of services that we provide.": false,
+ "Addressing this gap is beneficial at least in two respects: it allows us to look, for the first time, into technology commercialization and the context specific factors that affect it in Iran; it helps us to see how commercialization boosting policies work in a developing country under economic sanctions.": false,
+ "Additionally, we examined whether paternal characteristics could improve model performance of the developed prediction models within a subgroup of our population and we examined the predictive performance of the developed prediction models on secondary maternal, delivery and neonatal complications.": false,
+ "We presented a particular method to generate NUMBER D engineered cultures for the first time with human-derived neurons coupled to MEAs, overcoming some of the limitations related to NUMBER D and NUMBER D neuronal networks and thus increasing the therapeutic target potential of these models for biomedical applications.": true,
+ "To build on these promising results, the development and use of CM optimization techniques, for example similar to the recently developed framework by CITATION NUMBER are important for identifying the most promising combinations of scaffold properties.": false,
+ "Subsequently, in we discuss redirecting enforcement towards the demand side of the market and inquire whether certain practices by employers may be condemned as antitrust infringements when they harm competition in labour markets and whether this approach can contribute to addressing the concerns addressed here.": false,
+ "While the preponderance of evidence from previous studies identifies supply chain complexity as detrimental to firm performance, our results illustrate that although supply chain complexity has a negative effect on operational performance, it has a positive effect on innovation performance and financial performance.": false,
+ "Whereas the Prototypical Part Network ProtoPNet CITATION presents a user a large number of prototypes, our novel architecture with end-to-end training procedure improves interpretability by arranging the prototypes in a hierarchical tree structure.": true,
+ "Well known businesses like Lufax, Flipkart, Snapdeal and Lianjia represent examples of Unicorn companies born in China and India with a market value of over NUMBER billion US dollars, thereby attesting to the potential of emerging markets on giving birth to fast growing start-ups.": false,
+ "We argue that besides improving the quality of the provided diagnostics, allowing some tolerance in deviations assessment also enhances the flexibility of conformance checking techniques and, indirectly, paves the way for improving the resilience of the overall process management system.": false,
+ "Using a case study on agricultural diversification in the former homeland of Venda, South Africa, we explore the usefulness of the nested markets concept to make sense of smallholders' patterning of markets by combining tree crops for export with seasonal vegetables for local markets.": false,
+ "To this end, we propose an integrated dynamic market mechanism which combines the real-time market and frequency regulation, allowing competitive market players, including renewable generation, to negotiate electricity prices while using the most recent information on the grid frequency.": false,
+ "To demonstrate the clinical potential of a robust functional parcellation of the PAG, we propose an exploratory application that can be used to study connectivity changes between different subregions of the PAG related to changes in bladder fullness and bladder sensations.": true,
+ "The NUMBER D organoid systems that we describe in this review CITATION represent a promising platform to further understand the neuropathology of PKU by providing tools to specifically address PKU-related targets and modulate key aspects of oxidative stress, brain L-Phe clearance, neurotransmitter deficiencies and LNAAs brain uptake.": true,
+ "Our studies highlight, for the first time, the therapeutic potential of Epac NUMBER inhibition in hippocampal neuronal cells, and the importance of developing new pharmaceuticals to treat neurodegenerative diseases.": true,
+ "Our results show that both approaches improve performance in the basal and apical regions, although only the classification and segmentation approach produced significantly better results across all labels and datasets.": true,
+ "Our research shows that the concepts serve distinct purposes at different stages of the business model innovation process, and we discuss these findings and their broader implications for the literature on business model innovation and for innovation management practices in B NUMBER B companies.": false,
+ "Our research is situated within these debates, drawing attention to the development-led aspects of planning processes tied to density bonusing, emphasizing its ascent over the past NUMBER years from a development control to development enabling policy.": false,
+ "Our findings suggest that the market reacts positively to a switch to exploration, which signals that the firm is ready to engage in high-quality innovation, once it has completed a series of exploitative acquisitions to build the slack necessary to fund it.": false,
+ "Motivated by recent market developments and the potential of elastic CDNs, we proposed the new problem of dynamic cache rental, file caching and user association for wireless edge caching networks.": false,
+ "Moreover,the possibility to identify the inhibitory selectivity among endogenous LOX isoenzymes will facilitate our understanding of LOXinhibition and will thus facilitate drug discovery.T herefore,w ea im to develop novel tools to investigate the activity of endogenous LOXi soenzymes to advance LOX-oriented research and drug discovery.": true,
+ "Moreover, we present the potential of these applications in cardiovascular and cardiothoracic medicine, and additionally, we will provide key facilitators, challenges, and recommendations to adopting these technologies in clinical practice.": false,
+ "Moreover, we demonstrated the positive impact of generated answers on the performance of the retrieval model of the conversational search system, as the performance significantly increased when the answers to clarifying questions were taken into account.": false,
+ "In this review, we will address the progress that has been made using non-ribosomally produced peptides and ribosomally synthesized and post-translationally modified peptides as scaffolds for designed biosynthetic pathways or combinatorial synthesis for the creation of novel peptide antimicrobials.": false,
+ "In particular, our numerical studies for the Le Mans race track indicated that the lap time of a CVT-equipped electric race car can significantly outperform the one achievable with an FGT, as a CVT can counteract its higher mass with a more effective motor operation, although strongly dependent on the powertrain design choices.": false,
+ "In doing so, we are able to illustrate how new actors-in this case the European Commission and the Commissioner for Competition-were able to terminate long-existing policies of state aid to shipbuilders under the auspices of improving competition and the free market at the start of the NUMBER s.": false,
+ "In addition, we find that our proposed unsupervised method significantly outperforms the state-of-the-art baselines in cross-lingual knowledge selection.": true,
+ "Here, we show that with the proposed implementation, and by selectively making the proposed simplifications, as well as selectively choosing the grid parameters, a model can be obtained that has a minor impact on model accuracy, achieving a simulation time of over NUMBER times faster than real-time.": true,
+ "Due to the promising application of highly rigid anionic liposomes in the field of antigen-specific tolerance and the need for methods to efficiently load tolerogenic adjuvants, we developed a microfluidicsbased approach.": true,
+ "Consistently with previous work, we found that the use of InSAR coherence leads to a significant improvement of the classification performances, for example, with improvements in the user accuracy for most classes considered in the order of NUMBER p.p.": true,
+ "As intensive operations are to be performed on sensors, we demonstrated the feasibility of such a construction by implementing a proof of concept while using a resource-constrained device as a sensor.": true,
+ "Although we expect our findings to be generalizable to other creative industries, it would be interesting to extend this research to investments in start-ups in non-creative industries, focusing on key serial entrepreneurs CITATION with the CEO and the CTO role e.g. the software industry or the CEO and the CSO role e.g. the biotech industry.": false,
+ "We suggest that future studies further investigate the potential of these techniques for the improvement of freshwater invertebrate detection and abundance estimation, which might eventually lead to a robust application of DNA techniques for a total freshwater census.": false,
+ "We report the potentialities and pitfalls of one of the first commercially available devices capable of recording brain local field potentials LFPs from the implanted DBS leads, chronically and during stimulation.": false,
+ "We propose a novel network data envelopment analysis NDEA model, capable of setting store-level performance standards more accurately than state-of-the-art models.": true,
+ "We hypothesize that an enabling design and an enabling development process, as compared to a coercive design and a coercive development process, lead to perceptions of greater procedural fairness and less red tape.": false,
+ "We hope to discover which virtual environment design aids the performance of both the sexes, thus establishing a framework for designing virtual training environments and possibly GPS-based navigation systems that successfully comply with the both sexes, without impairing the performance of either one.": false,
+ "We expect future developments in machine learning and optimization together with novel fabrication techniques to lead to unprecedented nanotechnology within the next decade.": false,
+ "We demonstrated that the proposed idea can improve the performance of the baseline DeepLabv NUMBER by about NUMBER in mIoU sense on two of the most modern and commonly-used datasets PASCAL VOC NUMBER Cityscapes while boosting the performance in certain classes up to NUMBER without introducing any extra delay at inference.": true,
+ "We demonstrate the successful development of a realtime data infrastructure that enabled both data-driven care and decision making and rapid answering of critical clinical questions during pressing times, such as the COVID-NUMBER pandemic.": false,
+ "We believe that, with the relentless advances in scalable manufacturing techniques such as cutting, casting, and coating, findings from this study can facilitate the development of multitech combined devices, that is, photovoltaic-thermoelectric PV-TE coupling system which to date is still at its infancy stage.": false,
+ "To understand this innovation gap, we reviewed the literature to describe the potential of AMPs as alternative to antibiotics and the challenges toward clinical application of AMPs.": false,
+ "To that end, we developed a novel asynchronous arm-crank ergometry platform for use in a clinical magnetic resonance MR scanner with NUMBER P spectroscopy capability to study arm muscle energetics.": false,
+ "Therefore, addressing the psychological patient factors found in our study preoperatively might lead to low-cost interventions to improve return to work after The graphs show the HRs for a simulated patient in which the baseline characteristics are fixed CITATION.": false,
+ "Said differently, even if the focal firm may shy away from acquiring a larger target due to the fear of retaliation, some degree of synergies and gains in market power can also be achieved by acquiring a smaller firm, especially if we consider acquisitions within the same industry.": false,
+ "Our results indicated that the combination of an open innovation contest and design thinking could, through the creation of constant feedback loops, lead to increased collaboration between the contests participants, the companies proposing a challenge, and other relevant stakeholders.": false,
+ "Our novel method provides a simple way to produce customized NUMBER D ultrathin fibrous scaffolds, with great potential for TE applications, in particular those for which anisotropy is of importance.": true,
+ "Our findings are promising as they point to the potential of LKM and self-compassion to serve as a practically attainable way for entrepreneurs to develop resilience and more effectively cope with entrepreneurial fear of failure.": true,
+ "Novel prospective research should include trials investigating the efficacy of CBT-I for patients with previously under-investigated comorbidities, the efficacy of adaptations of the CBT-I protocol compared to the standard protocol, follow-up periods longer than NUMBER mo, and the potential of CBT-I for the prevention of mental disorders.": false,
+ "Motivated by our experimental findings that the quality of the information passed to the central system through final orders is significantly higher when the uniform allocation principle is applied, we propose a new application of the uniform principle that has several attractive properties in this setting.": true,
+ "In this communication we investigate a novel facile and fast nebulization method with a continuous jet atomization device to nebulize formulations at a throughput well over NUMBER mL min with a minimum amount of shear stress enabling a large window to formulate lipid based nanoparticle drug carriers CITATION.": true,
+ "In summary, we have developed novel divergent syntheses of NUMBER yaequinolone-related natural products, which are synthesized for the first time, by late-stage C-H olefination of NUMBER dioxygenated NUMBER aryl-NUMBER hydroxyquinolin-NUMBER NUMBER H-ones, core structures of this family of natural products.": false,
+ "In our study, we find further heterogeneity across workers with a different job-skill CITATION complementary of human capital investments and NUMBER the multiplier potential of these investments for their skill development.": false,
+ "In conclusion, we have demonstrated an integrated MWP signal processor using a network of three RRs in a single photonic chip that spectrally shape the PM spectrum, for the first time, to the best of our knowledge, to create a MWP notch filter with a high stopband rejection and enhanced filter performance.": true,
+ "In Section NUMBER we demonstrate the potential of extending the proposed design to develop latticebased shells.": true,
+ "Furthermore, while scholars have accumulated knowledge about innovative behavior in business contexts where innovation is nurtured, to fully understand'innovation by employees' we need to also address business contexts where innovation by employees is not overtly expected.": false,
+ "Finally, we will propose potential developments that may help to improve the performance of current devices.": true,
+ "Finally, we describe three potential randomized registrybased clinical trials in an adjuvant setting and for advanced disease with a high potential to be executed within the framework of an advanced ENSAT registry.": false,
+ "By connecting several junctions, we can study effective NUMBER spin systems demonstrating the potential of our approach for analog spin-glass simulation.": true,
+ "Both European and US experts NUMBER , NUMBER have emphasized the need for preclinical studies in the animal, for testing various methodologies to produce RDN lesions, evaluating safety and efficacy of novel RDN systems, developing various biomarkers and assessing potential non-blood pressure related benefits.": false,
+ "As a proof of concept, we further discuss a prototype implementation for supporting future-based data streams on top of ABS, and discuss the impact of the use of these data streams in ABS on the performance in the implementation of a distributed application for the generation of social networks.": true,
+ "Although models learned on sparse annotations achieve relatively lower accuracies than those using dense annotations, we show that using a semisupervised deep learning approach can help to close this performance gap while leveraging sparse annotations that can significantly reduce the costs of label generation.": true,
+ "Addressing how to achieve community-based solutions in a malaria elimination trial in The Gambia, we developed the Community Lab of Ideas for Health CLIH: a participatory approach that enabled communities to shape trial implementation.": false,
+ "Using a large panel of innovative firms in the Netherlands, this study shows that partner type diversity in a firm's alliance portfolio has an inverted U-shaped relationship with productivity and radical innovative performance and a positive relationship with incremental innovative performance.": false,
+ "For the deployment of our prototype, we have chosen Amazon Web Service AWS IoT and its extension for edge computing i.e., Greengrass as the baseline IoT platform as it is one of the more mature IoT platforms available on the market and provide the machinery necessary for the realization of the defined deployment models.": false,
+ "We summarized pre-clinical and clinical studies investigating whether physical exercise would improve muscle performance and whether this improvement would translate in a clinically meaningful benefit for patients with cancer, in terms of survival and quality of life.": false,
+ "We then chose the most effective EO to continue with further tests using exposure to the treated oilseed rape plant surface as the method of exposure, expecting to find an effective concentration threshold for significant insecticidal efficacy.": false,
+ "We show that LMX mediates the association between SDT and team performance and innovation as rated by team members, while collective narcissism mediates the association between SDT and supervisory ratings of team innovation and team performance.": false,
+ "We propose a sample average approximation with importance sampling and pruning of dominated activities to solve the problem, and demonstrate that this method solves large instances quickly.": true,
+ "We present a reliable, yet stable time-domain implementation of MDD that includes physical priors as a mechanism to improve kernel conditioning by transforming the model vector into a domain where the ill-posed nature of the inverse problem is significantly mitigated.": true,
+ "We hope that the following chapters will provide the reader with many novel insights into the complex interaction between nutrition and exercise, allowing them to define more effective dietary strategies to improve health and performance.": false,
+ "We find that while the decoupling method leads to an acceptable and convergent effective potential, the method does not solve the fine-tuning problem that is inherent to the hierarchy problem of multiple-scale theories.": false,
+ "We find that contrary to popular belief, contracting has positive and significant impact on malt barley production, intensification, commercialization, quality improvement, and farm gate prices, ultimately resulting in increased net income and spillover into the productivity of other food crops.": true,
+ "We experimentally show that, for the unknown disturbance situation at hand, the variable-gain controller can be automatically tuned using both ESC approaches to achieve the optimal system performance.": true,
+ "We evaluated the assessment tool in two online studies-with participants in their own homes completing the task on their own digital devices-to show that the assessment tool can significantly predict scores from the Patient Health Questionnaire CITATION-a standard self-report tool for assessing depression in clinical contexts.": false,
+ "We discover significant differences between middle slices and both basal and apical slices after training a state-of-the-art segmentation model on a large clinical dataset CITATION and testing it on three independent test datasets.": false,
+ "We conducted interviews with NUMBER solo diners to investigate their experiences while eating alone, their motivations for using an ICT device, as well as the potential of such devices to contribute to healthy eating practices.": false,
+ "We agree that there is a need for further research into the mechanisms of co-existence of cancer and comorbid diseases and the need for development and application of comorbidity assessment and management tools that are validated in the younger cohort.": false,
+ "To this end, we propose a novel online market mechanism, EdgeDR, to achieve cost efficiency in edge demand response programs.": true,
+ "To provide platform leaders with advice on how to create a sustainable digital platform, we examine how retail store activity on the digital platform affects consumer activity, and vice versa also known as cross-side network effects.": false,
+ "To overcome this problem, we developed perfusion fixation methods for needle and wedge biopsies, NUMBER which significantly improved the preservation of the sinusoids, sinusoidal cells and endothelial fenestrae, enabling high resolution transmission electron microscopy TEM of all hepatic cell types in human liver biopsies.": true,
+ "To demonstrate the model potential, we tested our platform at an immature NUMBER days in vitro and mature state NUMBER days in vitro of development.": false,
+ "To address these clinical challenges, we have developed a bilayered, modular hydrogel system that enables the click functionalization of cartilage-and bone-specific biochemical cues to each layer.": true,
+ "Third, on the basis of our first two hypotheses, we hypothesize that individual stakeholders observing a stakeholder-oriented firm will show more moral consideration for other stakeholders than individual stakeholders observing a profit-oriented firm, mediated by individual stakeholders' humanization of other stakeholders.": false,
+ "Taken together, we not only provide a proof of concept by development of inhibitors to target disordered proteins, but we also provide novel technologies and simple methods to develop inhibitors for IDPsIDPRs.": true,
+ "Recently, we have demonstrated on trapezia that the image quality of the CBCT device NewTom NUMBER G Cefla, Italy CITATION can be enhanced to reach an accuracy comparable to HR-pQCT in quantifying bone trabecular parameters CITATION.": true,
+ "Our results show that LMX mediates the association between SDT and team performance and innovation as rated by team members, while collective narcissism mediates the association between SDT and supervisory ratings of team innovation and team performance.": false,
+ "Our results demonstrate the potential of combining MB and DD methods in autonomous systems, while providing a proof of concept for KalmanNet in a challenging real-life scenario.": true,
+ "Our research highlights the importance of developing a holistic picture of the mechanisms by which different signals of product quality affect consumption and decision-making in entertainment markets, considering simultaneous exposure to a range of market signals instead of considering different factors independently.": false,
+ "Our findings reinforce the elevated potential of the Brazilian large families in revealing novel candidate genes or mechanisms of mutation leading to hearing loss.": false,
+ "Our current findings have the potential to inform assessment of clinical reasoning performance in authentic e.g. Such work could also advance our understanding of context specificity, which leads to unwanted variation in physician performance.": false,
+ "Now data are produced at a rapid pace that enables firms to analyze the effectiveness of potential CE relationships between firms, we are motivated to study how information systems can also facilitate circular decision making.": false,
+ "Moreover, we analysed the combinatorial activity in a newly designed ex vivo bacteraemia model as well as the potential of the best combination peptide D-NUMBER with rifampicin to treat K. pneumonia e infections in vivo in a mouse abscess infection model, demonstrating effective clearing of the infection.": false,
+ "More broadly, we point to significant opportunities from applied linguistics and big data analytics for the development of novel indicators to track tangible and intangible dimensions in the geography of innovation.": true,
+ "Many supramolecular systems are complex to synthesize or to selfassemble into functional architectures, yet with progress we increase scalability as well as the simplicity of the architectures and the robustness of the self-assembly process.": true,
+ "Lastly, we believe there is a dire need of additional clinical benchmark data-sets to improve upon the state-of-the-art in this area.": false,
+ "In summary, we propose that Gibbs energy released during catalysis can be harnessed by enzymes in the form of work, which will ultimately lead to an enhancement of their effective diffusion Fig NUMBER A.": false,
+ "In conclusion, we have developed a novel genetic adjuvant that for the first time employs the pyroptosis pathway to improve DNA vaccination against cancer.": true,
+ "In chapter NUMBER we sought to develop a novel flow-cytometry-based high-throughput method that allows quantitation of the agglutinating potential of anti-pneumococcal antibodies.": true,
+ "In addition, we discuss the clinical potential of TEVs as markers of cell state transitions including the acquisition of a treatmentresistant phenotype, and their potential as therapeutic targets for interventions such as the use of extracellular vesicle EV inhibitors to block their pro-tumoral activities.": true,
+ "In addition, we discuss the clinical applicability of therapeutic ultrasound with respect to the main challenges that must be addressed to enable the further progression of therapeutic ultrasound towards an effective, safe and easy-to-use treatment tailored for drug delivery in patients.": true,
+ "If we want to facilitate standardization in OoC by the development of an open platform, it will be essential that the platform finds widespread support.": false,
+ "If we know how to quickly select the optimal or close to optimal trigger injecting position in backdoor attacks on GNNs, we can achieve high attack performance and good evasion of the defender's detection mechanisms.": false,
+ "HTP in clinical decision making: Seven problems that occur in daily clinical practice are described and we show how HTP can enhance insight to formulate an adequate treatment strategy.": false,
+ "Given advancements in mobile technology, an effective-CBT-I app could offer an opportunity to enhance the advantages and coverage provided by other formats of CBT-I, enabling real-time and personalized monitoring, assessment, and interventions CITATION.": false,
+ "Finally, we use all the knowledge we acquired to propose recommendations for practitioners i.e., how they can use the findings of this paper to improve their testing skills, tool makers i.e., a list of tools that would improve the way developers do testing, and educators i.e., testing topics that should be taught at university-level.": false,
+ "Finally, we suggest that B NUMBER B innovation practitioners adopt the stakeholder engagement journey as a process tool that links service design to innovation stages.": false,
+ "Finally, we develop a Frank-Wolfe algorithm that can solve this convex program orders of magnitude faster than state-of-the-art general purpose solvers.": true,
+ "Comparing with state of the art in localization, we demonstrate the superiority and robustness of the performance of FEEL.": true,
+ "CONCLUSION: Our work NUMBER demonstrates the potential of our previously developed automated quality assurance methods to NUMBER generalize to external datasets.": true,
+ "By elucidating the nature of the PD as a function of the potential, we can explain the universally observed bell-shaped curve for the FECO as a function of potential in relation to the effect of PD, cation concentration, and mass transport on the rate of the competing HER.": false,
+ "As we have already discussed, convergence of leader and follower views with regard to their LMX quality will lead to increased development opportunities and career resources that can contribute to the follower's learning and growth, employability, and marketability.": false,
+ "As a proof-of-concept, we develop a data-driven AI-based service scaling prototype to automate the service scaling operation to meet the service requirements while minimizing the consumption of resources.": true,
+ "Applied to gold medal-winning parameterized SAT solvers, we show that our approach can produce significantly better-performing SAT solvers than state-ofthe-art parallel solvers constructed by human experts, reducing time-outs by NUMBER and running time PAR NUMBER score by NUMBER under competition conditions.": true,
+ "Accordingly, we expect institutional investors to affect the implementation of strategy uniqueness, since they relate to both the type of investor putting the most pressure on CEOs CITATION, and also to the type of investor being best equipped to provide good monitoring and governance CITATION.": false,
+ "A remarkable result in our model is that competition in the healthcare market does not necessarily improve care quality, which differs from previous literature.": false,
+ "These encouraging data suggest that the novel enzyme and strain engineering approach represent a promising platform for the clinical development of CDEPT.": true,
+ "As the paintings conservation field has rapidly discovered the potential of Evolon CR for varnish removal from oil paintings, the need to advance our understanding of this material and the steps necessary for safe and optimal usage increases.": false,
+ "According to these latest concepts of clinical translation, we firmly believed that the a-TOS married NUMBER D MoS NUMBER as a promising theranostic platform can be employed to achieve the compelling efficacy and safety benefits for cancer theranostics.": true,
+ "With great interest we read the article by CITATION CITATION, in which they demonstrate the potential of home monitoring to reduce hospital admissions by safely surveying clinical symptoms and vital parameters.": false,
+ "While we showed how this may be effectively conducted for hard-sphere models, further research is needed to develop effective implementations for perfect sampling from a broad class of Gibbs point processes.": false,
+ "While our work suggests potential for developing a SIGS approach for implementation in B. aeneus management, further experiments are needed to more fully explore the potential for incorporating this approach.": true,
+ "We show that the proposed approaches compare favorably to several standard and state-of-the-art imputation methods in terms of predictive performance and runtime.": true,
+ "We proceed by rigorously establishing limitations and unprecedented merits of this controller, further proposing appropriate modifications that guarantee improvements in terms of stability, robustness, and performance.": false,
+ "We introduce an attention-based sequence-to-sequence OIE model that outperforms state-of-the-art network architectures, hereby demonstrating its effectiveness in information extraction tasks.": true,
+ "We have developed a translation of CSX to SMT constraints which enables us to use constraint solving to find optimal configurations for finishers.": true,
+ "We have demonstrated the potential of MCR chemistry by utilizing this privileged scaffold of proven interest in materials science and drug discovery.": true,
+ "We first show that disjoint bilinear optimization problems can be cast as two-stage robust linear optimization problems with fixed-recourse and right-hand-side uncertainty, which enables us to apply robust optimization techniques to solve the resulting problems.": true,
+ "We find that inventory allocations between the high and the low value channel are significantly different from optimal independent of whether profit differences between channels are high or low what we call profit conditions.": false,
+ "We encourage researchers to investigate the usability, acceptance, feasibility, reliability, and clinical validity of wearable sensors in clinical populations to facilitate the application of wearable movement sensors in motor rehabilitation.": false,
+ "We do believe that our general findings-that organizations' capacities and perspectives, embeddedness in a wide network, and other development work create multiple forms of representation, which may contribute to inclusive development-have the potential to form a starting point for this further research.": false,
+ "We discuss implications for further adaptation of annotation tools, and the potential for deriving reference data from such rich annotation datasets for the evaluation of automatic pattern discovery algorithms in the future.": false,
+ "We demonstrate experimentally for the first time that the proposed soft handover mechanism enables the seamless transmission to the mobile UD in the BS-ILC system.": true,
+ "We conduct three sets of studies to demonstrate the potential of the proposed pseudonymization approach: NUMBER First, we validate the proposed approach through ABX pilot tests.": false,
+ "We conclude that this form of data regime competition between China and the EU can give China an advantage in terms of promoting innovation in the electric car industry.": false,
+ "We conclude that existing genetic risk scores can already improve life insurance underwriting, which stresses the urgency of policymakers to balance competing interests between stakeholders as this technology develops.": false,
+ "We conclude it is important that all centres use an adequate, preferably uniform, registration system on innovation indicators and propose to select the system we used because it has been developed and approved by the sector itself.": false,
+ "We argue that to advance service design opportunities for stakeholder engagement, we need to address the unique complexities and challenges of stakeholder engagement during innovation from a systemic and dynamic process perspective.": false,
+ "We address this challenge by proposing alternative tariff designs, in terms of variable and fixed charges together with PV installation cost subsidies, that a regulator can implement to achieve a minimum solar energy target while guaranteeing network financing.": false,
+ "Upon application of an RG step, the new effective local Hamiltonian that we find inside the causal cone will be renormalized to new effective couplings Ja', Jb', which correspond to effective lattice points A', B', respectively.": false,
+ "To utilize the full potential of-omics driven biomarkers discovery studies, it will be essential to reach agreement on the best outcome reference standard in future studies, and we propose our diagnostic phenotyping algorithm as the best possible way to do so at present.": true,
+ "To help address this need, our interdisciplinary team of clinicians, social scientists, bioethicists, designers, and computer scientists developed an innovative ICT framework for knowledge generation in chronic-pain research.": true,
+ "To address this key gap, we broaden the theory of problemistic search for innovation by proposing a digitally enabled collaborative problemistic search CPS capability.": true,
+ "To achieve a high level of UX, we address these user needs by introducing an interactive car door that enables passengers to explore the landscape in a more detailed and informative way using AR on the side window and a touch-sensitive door panel.": false,
+ "This demonstrates the practicality of our study: we believe that researchers can build upon our work's findings by creating improved compiler validation methods and tools.": false,
+ "These findings echo themes in how to improve humanitarian operations more generally, suggesting that finding effective ways to address these enduring challenges may significantly increase effective multi-hazard planning and response.": true,
+ "The discovered effect is further confirmed by comparing the measurements to an advanced numerical model that we have developed earlier CITATION, with further implementation of the free-boundary conditions, showing excellent agreement to the measured data.": false,
+ "Specifically, we extend the literature on social exchanges during opportunity development CITATION, NUMBER; by describing how entrepreneurs make sense of critical feedback from their stakeholders and by compiling the cognitive changes from novel sensemaking.": false,
+ "Since our founding in NUMBER we have developed visualisation tools in our lab, deploying a number of immersion devices across a diversity of data sets and platforms CITATION, as well as across disciplines CITATION.": false,
+ "Second, to improve the performance of MetaVC on large MinVC instances, we introduce a neural-network-based approach to enhance automatic configuration.": true,
+ "Our study provided initial and promising evidence for the psychometric properties of the Italian version of a newly developed tool to measure burnout, namely the Burnout Assessment Tool, which intends to overcome some of the conceptual, methodological and practical limitations of the MBI.": false,
+ "Our study demonstrated that the framework we conceived of IMSI and SDT can effectively be applied as a frame of analysis to identify essential features of sustainability in educational innovations.": true,
+ "Our speed-accuracy trade-offs and effective spatial-contextual feature fusion allow us to outperform the previous state-of-the-art approaches for real-time semantic segmentation on two public datasets, namely Cityscapes dataset CITATION and UAVid dataset CITATION.": true,
+ "Our results have demonstrated the feasibility and the great potential of on-chip SD-OCT systems.": true,
+ "Our findings suggest that as nutrient availability increases under future climatic conditions, subarctic shrubs will develop increased resistance to biotic and abiotic stresses under rising temperature as opposed to under increased cloudiness, which could lead to decreased resistance.": false,
+ "Our findings showed that these biomimetic micro-patterned substrates enabled cell disposal along architectural directions, thus appearing as promising substrates for developing functional TM replacements via TE.": true,
+ "Only if the user knows how to integrate decision support tools into the complex tasks clinical judgement and decision-making we are able to test the full potential of these tools.": false,
+ "Moreover, from our analysis, we conclude that the network assistance provides significant performance improvement, especially when the clients with identical interests compete for a bottleneck link's capacity.": false,
+ "More interestingly, we find that using Bayesian shrinkage priors leads to superior out-of-sample performance for long-term investors.": true,
+ "Merging findings with strands of social movement theory CITATION, I argued that state-funded sites of adult education in francophone Belgium demonstrate the potential to create empowering linkages to social movements.": false,
+ "Introduction Our aim is to develop a novel approach to hyperkinetic movement disorder classification, that combines clinical information, electromyography, accelerometry and video in a computer-aided classification tool.": true,
+ "In this work, we present numerical tools to explore such nonequilibrium effects in spatially confined three-dimensional systems with a variable disorder potential, giving exact solutions to leading order in the disorder potential and the applied electric field.": false,
+ "In this review, we describe the application of several imaging techniques in the clinical practice of BM management, and the promising new developments that lie ahead.": false,
+ "In this paper, the development and application of a public-client-led method is investigated that enabled the development and implementation of a radical green innovation in a civil engineering project.": false,
+ "In this article, we showed that exploiting such information in a GBO setting, specifically one that allows for partial evaluations, can lead to substantial improvements in performance and scalability.": true,
+ "In the past NUMBER years as a result of advances in hardware and software, DSS research has advanced dramatically, which has revealed the potential of this approach to substantially improve clinical care.": true,
+ "In the next step of research, we can use deep learning to automatically extract the features of sound and images, and further improve the detection accuracy and generalization of the AVF method and we can also use better experimental platforms and equipment to improve the real-time performance of our algorithms.": true,
+ "In supplementary fig. NUMBER we demonstrate initial feasibility Deep-ULM in clinical practice, by performing inference on clinical CEUS measurements of a patient's prostate using a standard ultrasound system Philips i CITATION in combination with a standard transrectal probe.": true,
+ "In section NUMBER we propose our model for extracting needs from customer reviews; preliminary evaluation results appear in section NUMBER We revisit the need for needs in our discussion and conclusion.": false,
+ "In line with Clarke and Crane's NUMBER definition of systemic change, therefore, we define transformative change as the result of actions that lead to a significant alteration a transformation within a system, potentially leading to substantial impacts involving economic, social, and ecological issues.": false,
+ "In conclusion, we showed that bimiralisib gel NUMBER for topical use leads to I meaningful cutaneous drug levels, II well-tolerated systemic drug exposure in patients with MF and III a lack of clinical efficacy.": true,
+ "If effective at improving lifestyle behaviors, this approach could be an important measure for all women with overweight or obesity with the potential to improve clinical outcomes for these women who are in the preconception period and are at high risk for pregnancy complications.": false,
+ "However, we do suggest companies mix other adaptive innovation models such as agile development CITATION or the hybrid Agile-Stage-Gateapproach CITATION with their NPD processes to mitigate the negative effects of innovation process formality.": false,
+ "However, the advantages of high avidity and the possibility of modulating their circulation time mean that MMIAs hold great potential, as revealed by clinical trials such as those for NUMBER I-cRGDY-PEG-C dots CITATION.": true,
+ "Here we review the pharmacology of fevipiprant, the oral DP NUMBER receptor antagonist that reached the most advanced state of development to date in a worldwide Phase III clinical trial programme; however, the demonstrated efficacy did not support submission.": false,
+ "Hence, by adding the temporal analysis to our DML framework, we help the system to discover higher semantic movements that enhance the discovery of discriminative personality patterns, and therefore, improve the personality recognition task.": true,
+ "Furthermore, we extend existing findings on innovation vouchers that are limited to a narrow scope in terms of industry, type of collaboration partner and region CITATION by making use of a large-scale field experiment to test the effectiveness of a nationwide, all-industry program with a broad scope of potential partners.": false,
+ "From the current literature, we incorporate two theoretical perspectives within the dynamic capability framework to explain how corporations achieve sustainable competitive advantages: organizational ambidexterity and open innovation CITATION.": false,
+ "First, we develop a model that captures how family social capital is enhanced by shifting the level of analysis from the family business to the enterprise family CITATION.": false,
+ "Finally, we will present some perspectives on future multidisciplinary clinical and experimental research to develop new, more effective sleep therapies to improve both sleep and PTSD.": false,
+ "Finally, we propose a series of recommendations that should be applied to polyester clothing at all stages along the value chain, offering the potential for meaningful and effective change to improve the environmental sustainability of polyester textiles on a global scale.": false,
+ "Finally, our work shows a systematic road toward the development of accurate effective patchy particle potentials.": true,
+ "Finally, by prototyping real-life applications on both FPGA-based MPSoCs and desktop multi-core platforms, we demonstrate that mapping the alternative application specification results in a large performance gain compared to those approaches, in which alternative application specifications are not taken into account.": true,
+ "Endovascular innovations such as steerable sheath technology, our continuously evolving technical skills, and development of dedicated mating stents for fenestrations have the potential to improve technical success and durability of this OTS device in the near future.": false,
+ "Earlier reports developed by our group demonstrated that the spontaneous AsIII oxidation by granular activated carbon GAC combined with the biological FeII oxidation led to the precipitation of biogenic scorodite in batch experiments CITATION,providing a promising green strategy for the removal of arsenic from acidic wastewaters.": true,
+ "Development of in situ and operando XPS techniques that can completely overcome the pressure gap is underway, and we believe it will lead to cornerstone advancements in understanding metal-support interactions in catalysis CITATION.": false,
+ "CITATION Arguing for the potential of an indisciplinary space of action to facilitate \"democracy of experience\", CITATION we seek to develop a non-hierarchical design research that leads into the'de-compartmentalization of each discipline'.": false,
+ "By performing detailed assessments of the predictive performance for the question pattern and content tasks, we ind that CNQG enables us to produce accurate patterns and semantically relevant topics, which provides an explanation for its strong performance.": false,
+ "By carefully tweaking the imprinting parameters, we were able to increase the number of imprints on our chips, validated by SEM and fluorescence microscopy, leading to an improvement of the LoD by an entire order of magnitude for the thermal sensing platform.": true,
+ "Because we have shown that a substantial number of essential developmental genes are significantly downregulated upon SON haploinsufficiency, SON thus represents a master regulator of genes essential for human neurodevelopmental processes.": false,
+ "As an outlook, here, we formulated a proposal for a design concept that allows us to connect cultured nervous system tissues in a platform that will enable the detailed study of PD by NoCs as it is envisaged in the CONNECT NUMBER project as well as other devastating nervous system diseases.": false,
+ "As a consequence, we were able to successfully improve the efficiency of speaking based learning using an adaptive system: Learners who studied using the response time-based SlimStampen algorithm produced faster responses with NUMBER percentage points higher accuracy compared to learners who used the accuracy-based Leitner learning algorithm.": true,
+ "Conflicts of interest Prof. Geusens reports grants, speaker fees and advisory board from Amgen, grants from Pfizer, grants from MSD, grants from UCB, grants from Abbott, grants and speaker fees from Lilly, grants from BMS, grants from Novartis, grants from Roche, and grants from Will Pharma, outside the submitted work.": false,
+ "As the material combination used in this work is superior for the optomechanical quantum transduction task than any other approach to date, additional adjustments to our platform can be used to significantly improve the performance of this new class of devices by several orders of magnitude.": true,
+ "While our evaluation shows that we can link appropriate segments of the clinical study to risk factors, it is the development of the query interface that will demonstrate the significant reduction in manual effort that this work requires, and will greatly widen its impact.": false,
+ "We suggest that acquirers can resolve this coordination-autonomy dilemma by recognizing that the effect of structural form on innovation outcomes depends on the developmental stage of acquired firms' innovation trajectories.": false,
+ "We show that usual MHD boundary conditions can lead to the violation of this physical limit and we implement this current density limitation through a boundary condition for the electrostatic potential.": false,
+ "We show that current approaches for solving multiple-follower problems are unsuitable for our new class of problems and instead we propose a novel analytics-based heuristic decomposition approach.": true,
+ "We recognise that these initial reflections on the different regulatory tools may sound sceptical of the potential for regulators to successfully address the possible negative effects of AR.": false,
+ "We propose a novel approach capable of efficiently solving the multi-year task allocation problem for a fleet of aircraft in a few minutes.": true,
+ "We improve each of the individual defenses' performance over the state of the art, achieving overheads well below NUMBER for each of them.": true,
+ "We implemented the proposed controller on an embedded platform, developed by Cohda Wireless and NXP, with the aim to validate that the theoretical performance of our approach also translates to a good performance in a realistic scenario.": true,
+ "We hypothesized that by CITATION using a simple convolutional architecture, and CITATION-distribution, it is possible to achieve state-of-the-art probabilistic forecasting performance compared to existing Transformer-based methods whilst requiring significantly fewer parameters.": false,
+ "We have successfully applied computational methods to develop HIPe which exhibits high potential to inhibit histone H NUMBER induced cell lysis and consequently shows therapeutic benefit effects in an atherosclerotic mouse model.": true,
+ "We have shown that by leveraging the potential of many existing EarlyTSC algorithms, our approach can outperform any single algorithm, even when their hyperparameters are optimised.": true,
+ "We have presented a scenario-and platform-aware design flow SPADe for IBC system implementation that considers both pipelining and parallelism in an integral fashion to improve QoC of multiprocessor IBC implementations.": false,
+ "We find that dynamic, rule-based investment strategies can outperform traditional static strategies, by which we mean that the investor may achieve the target retirement income with a higher probability or limit the shortfall when the target is not met.": false,
+ "We extend the current state-of-the-art by proposing a novel adaptive approach, called aDynaMOSA Adaptive Dy-naMOSA, to address the two challenges described above.": true,
+ "We demonstrate that the TempEasy system has the ability to screen phenotypic and functional effects of cell-cell interactions in a quantitative way and we believe that it may serve as a promising platform for future cell-cell interaction studies.": true,
+ "We compare results from these past experiments in Appendix B. We are able to experimentally resolve, for the first time, increased performance with greater QAOA depth and apply QAOA to cost functions on graphs that deviate significantly from our hardware connectivity.": true,
+ "We believe that this hybrid ML-PB approach offers the potential in the long term-with the rise of exascale, quantum and analogue processing-to deliver novel pandemic drugs at pandemic speed.": true,
+ "We argue that the concept mapping intervention can accelerate the development of adaptive capabilities as it facilitates the development of the knowledge structures that are needed for team adaptation, namely the team members' development of a shared understanding about their task, roles, and each other's expertise.": false,
+ "We adopt an explanatory case study approach to contribute to the development of theory of disruptive innovation.": false,
+ "We address this outstanding problem by developing a general framework for computing physical properties of quantum manybody states efficiently on NISQ devices.": false,
+ "Using the novel classification system, we identified different types of papillae significantly associated with a lower efficacy of NKF and a prolonged time to obtain successful biliary cannulation using NKF.": false,
+ "Using data from simulated power markets, we analyse the forward premium in three identical power markets with a varying market share of VRES supplied to the system.": false,
+ "Ultimately, we strongly advise the implementation of funding schemes for sustainably supporting the development and maintenance of research software based on clear and transparent criteria, for creating incentives to produce high quality community software, and for enabling career paths as research software engineer RSE.": false,
+ "To this end, we develop a systematic framework for assessment of sustainable hydropower potential that makes three folds improvements to existing methods.": true,
+ "To this end we propose our AQOSA toolkit which has been developed to enable component-based development and automatically improve the non-functional properties of an architectural design, thus allow architects to focus on the higher-level design decisions.": true,
+ "These empirical inconsistencies provide the impetus for our research as they demonstrate that there are gaps in our understanding of how and when team social capital affects team innovation, which may obscure the true nature of the team social network-team innovation relation.": false,
+ "There are various potential models for development i development in children first by academia then to industry; ii joint development by academia and industry; iii standalone academic development within a business model.": false,
+ "The unique advantage of our methodology is that it secures meaningful process waste analysis in two ways: CITATION via the RPG score, which enables scientists to compare the sustainability performance of their process with industry averages.": true,
+ "Since most of the systems around us exhibit some form of nonlinear behavior, nonlinear system identification techniques are the tools that will help us gain a better understanding of our surroundings and potentially let us improve their performance.": false,
+ "Overall, we hope to show in this Review and in the years to come that oxalic acid has great potential the future because of its large potential applications in the polymer market.": false,
+ "Overall, as a proof-of-concept, here we investigated the therapeutic potential of TA-coated AcDXSp NPs, which allowed successfully deliver two drug compounds for cardiac proliferation in CMs.": true,
+ "Our work, which takes a different route, not only sheds new light onto the unveiling of a gradient flow structure for CITATION, but also provides the well-posedness CITATION for a general class of interaction potentials, including the Newtonian potential, which we believe to be novel.": true,
+ "Our study explains the size differentiation in creative industries on the basis of a founder's attention focus and values, which may lead to firm development and, possibly, growth.": false,
+ "Our study demonstrates that entering foreign markets does not necessarily nor directly leads to growth in terms of employees or revenues, but more to an accretion of a creative firm's symbolic capital or impact.": false,
+ "Our results lead us to the major conclusions: Although porosity observations converge to an effective value with increasing observation scale andor number of samples; spatial correlation of samples lead to higher REV levels as typically assumed.": false,
+ "Our research shows the added value of iterative development and user feedback for improving and further development of the tool's usability and functionality.": false,
+ "Our paper only introduces the REF as a way of structuring market evolution, and we suggest future studies are necessary to elaborate on how markets evolve in the cases we addressed, as well as in others.": false,
+ "Our novel modular approach to operating an automated microfluidic system for parallelized cell culture will enable greater experimental flexibility and facilitate the cooperation of different chips from different labs.": true,
+ "Our novel coproduction framework, based on a set of five principles and thirteen criteria, proved useful as an assessment tool, stimulating critical reflection from which we were able to evaluate the actual performance of the thirteen criteria.": false,
+ "Our model outperforms two state-of-the-art matrix factorization-based approaches, demonstrating the effectiveness of integrating neighborhood features in a deep neural network for POI recommendation.": true,
+ "Our experiments also indicate that our proposed semi-supervised reasoning method achieves a comparable performance as state-of-the-art fully supervised learning baselines for physician policy learning.": true,
+ "Our experimental results indicate great potential for improving the accuracy of energy consumption prediction by using automated machine learning approaches.": true,
+ "Our aim was to identify potential opportunities for improving weather and market advisory dissemination to rural communities and remote and underserved farmers in developing countries.": false,
+ "Our aim was to define a minimum Overall Adult Health Standard Set that will enable outcome measurement in routine clinical practice to improve decision making between providers and patients aged NUMBER years or older, to facilitate quality improvement, and to allow for benchmarking across organizations.": false,
+ "Likewise, the networks that grow out of prestigious US business schools and Wall Street institutional investors in the world's largest capital market have extended into CIC corporate headquarters and its asset allocation strategy.": false,
+ "In future work, we plan to develop more sophisticated methods for identifying and terminating unpromising target algorithm runs, to further enhance automatic configuration.": true,
+ "In all these application, the convolution surface based modelling method proposed in our work can show great advantages in its effectiveness and scalability.": true,
+ "In agreement with our hypothesis, results show that interaural segregation cues led to improved behavioral word-recognition performance and stronger cortical segregation of the distractor speakers.": true,
+ "In addition, we overview a proof-of-concept prototype of our tool that facilitates existing software engineering mechanisms to achieve the above-mentioned features of our framework.": true,
+ "In addition, our models will enable more rapid, cost-effective assessment of the efficacy and cellular toxicity of future novel therapeutic compounds to treat the main pathologies related to endothelial dysfunction.": true,
+ "In a previous search for existing drugs with the potential of targeting Cantu Syndrome, also resulting from increased I KATP, we found a set of candidate drugs that may also possess the potential to target DEND syndrome.": false,
+ "In NUMBER we therefore started to study the feasibility of integrating the hyperthermia HN device into an MR scanner for noninvasive MR thermometry by developing a novel applicator: the MRcollar.": true,
+ "In LoTSS-DR NUMBER we also performed significant post processing of the radio catalogues to enhance their scientific potential.": false,
+ "Here, we follow an \"actualization\" perspective within the entrepreneurial opportunity discourse wherein entrepreneurial opportunities are defined as \"the propensity of market demand to be actualized into profits through the introduction of novel products or services\" CITATION.": false,
+ "Hence, we have demonstrated the need for researchers to distinguish different groups of solo self-employed when identifying factors that may enhance their well-being and performance.": false,
+ "Furthermore, we elaborate on ongoing developments such as the rise of comparable registries, increasing support for preregistration in the Netherlands-which led to the funding of PCT by the Dutch government-and pilots of mandatory preregistration by several funding bodies.": false,
+ "Furthermore, we demonstrate that a further developed version of our system may be applied in a wide range of settings by showing the successful application of the system in an elderly population.": true,
+ "Finally, we explored sustainable development pathways of the capital region and the potential of this framework to inform and guide policy.": false,
+ "Extensive experiments and comprehensive analysis performed on two widely used datasets-Microsoft COCO and Flickr NUMBER K-show the effectiveness of the hybrid feature fusion framework in CMHF, in which the stateof-the-art matching performance is achieved by our proposed CMHF method.": true,
+ "By connecting the composition of the food basket to the supply chain that is necessary to deliver it, we are able to increase the efficiency and effectiveness of WFP operations, ensuring that, with the funds available, WFP can continue to supply life-saving assistance to as many people in need as possible.": false,
+ "Both the first steps that the US state appears to be taking towards a stronger market-direction role, and the ongoing market-direction role of the Chinese party state parallel to its extensive internal and external market creation, must be seen in the context of an increasing Sino-US geopolitical rivalry.": false,
+ "Before a risk assessment tool can be implemented we need to know its discriminative accuracy, predictive value, cost-effectiveness, transportability e.g., to particular populations, ages and gender etc., and the general availability of its variables e.g., to enable cross-study comparison and result verification.": false,
+ "As AB in early life have a major impact on microbiota development which may lead to aberrant immune maturation, NUMBER our findings accentuate the need for finding strategies to modify microbiome development after AB exposure to decrease the risk of allergies.": false,
+ "An analysis of the tiles by a domain expert shows that our approach can lead to the discovery of novel insights.": false,
+ "Altogether, our study demonstrates the potential of trehalosepullulan as the main material to produce antigen-containing dMNAs able to induce an immune response after application.": true,
+ "Also, as a result of analyzing market information, the venture would develop an understanding of which potential corporate partners operate in those end markets that demonstrate best sales and profit potential for the end products that could be developed.": false,
+ "Scope and approach: In this commentary we argue that addressing these governance challenges requires the development and adoption of novel research and innovation RI approaches that will provide evidence to inform food system transformation and will serve as catalysts for change.": false,
+ "To improve the costtime performance of these algorithms, we developed gpuZoo which implements GPU-accelerated calculations, dramatically improving the performance of these algorithms.": true,
+ "To facilitate and support effective implementation of the framework, we propose CITATION implementing strategies to facilitate the necessary behavioral changes in the PHW; CITATION implementing strategies to facilitate system changes and NUMBER identification of potential barriers and obstacles for the implementation of these strategies.": false,
+ "This restricts their development from on-station prototypes a prototype developed at an experimental farm to farm-scale adoption adoption of these experimental prototypes on a large scale by farmers.": false,
+ "These novel results show that development in manufacturing have potential for significant improvements in thermal-hydraulic efficiency.": false,
+ "Section NUMBER demonstrates the efficacy of the proposed algorithm; discusses the properties of the discovered SIMPs; compares our approach to existing methods; and presents a case study in aviation, highlighting how our approach could help improve an analyst's understanding of the problem.": false,
+ "Here, we demonstrate the potential for achieving worldwide standardization, ensured by PT, although international implementation the three-leveled sRM is a prerequisite for the success of such a program.": true,
+ "Although our paper addresses a number of significant issues related to dynamic capabilities, open innovation and competitive firm performance, future studies are recommended to contribute to a further elaboration of the third component of open innovation: the inside-in innovation processes introduced in this paper.": false,
+ "As we see that the tourism industry worldwide is facing several developments and challenges, including overcrowding, sustainability, and climate change, applying this technique to measure tourists' preferences and choices for new solutions and strategies to handle these problems seems like a promising approach.": false,
+ "While the landscape of WIMP searches in the traditional GeV-TeV range appears to offer faltering returns for dark matter discovery, our study illustrates how future MeV gammaray detectors provide very promising prospects over several orders of magnitude in the dark matter mass.": true,
+ "What we see is that the transfer price rule improves market performance as long as...rm A remains in the downstream market.": false,
+ "We will also show that such a reduction in the number of reactive components leads to improved performance robustness to variations in the inductive link coupling factor.": false,
+ "We were able to optimize our pilot-scale device to produce fibers with a diameter below NUMBER mm for the first time, thus offering an industrial solution for the preparation of melt-electrospun nanofibers.": true,
+ "We want to stress the importance of tracing back how the methods we propose are implemented in the field to shape a more realistic picture and state of the art, providing us with the possibility to improve and iterate on our methods.": true,
+ "We successfully developed a method for de novo FAIRification via an EDC system that automatically transforms eCRF data entered into the system into machine-readable, FAIR data by the use of a semantic data model and a data transformation application.": true,
+ "We show that semibetas stemming from negative market and negative asset return covariation predict significantly higher future returns, while semibetas attributable to negative market and positive asset return covariation predict significantly lower future returns.": false,
+ "We show that following the regulatory change, the sensitivity of fund flows and managerial turnover to portfolio holdings information significantly increases for high, relative to low, volatility funds.": false,
+ "We show that a magnetization gradient of the vortex can act as an effective SOC, which leads to spin accumulation at the rims of the device.": false,
+ "We propose a novel surrogate-assisted Evolutionary Algorithm for solving expensive combinatorial optimization problems.": true,
+ "We propose a new attacker model, based on dynamic optimization, where we demonstrate that large, initial, fixed costs of exploit development induce attackers to delay implementation and deployment of exploits of vulnerabilities.": true,
+ "We performed a small-scale proof-of-concept study to demonstrate that the platform can be used for drug development.": true,
+ "We note several contextual factors that appear to affect actors' need fulfillment, including health-care structures and institutions e.g. New technologies encourage the development of innovative services that can create beneficial outcomes for multiple actors CITATION.": false,
+ "We hope the workshop can serve as a platform for fruitful discussions of known problems with modeling user features to personalize conversational systems, and for connecting people working on and interested in personalized intelligent conversational systems that lead to new collaborations.": false,
+ "We hope that further development of the model and advancement in experimental techniques will lead to better accuracy in the determination of the parameters of real metal surfaces.": false,
+ "We hope that by sharing our practical experiences, we may enable future researchers to more effectively conduct clinical trials in these populations who could still gain much from improved clinical care.": false,
+ "We have presented the detailed design of our approach, and our experimental results have shown that our solution can achieve significant performance improvement compared to a benchmark solution.": true,
+ "We have developed a novel snapshot hyperspectral imaging camera with a large field of view as a prototype system for non-invasively interrogating superficial anatomic structures and for monitoring changes in microvascular perfusion and oxygenation.": true,
+ "We explore the innovation performance benefits of alliances for spin-off firms, in particular spin-offs either from other firms or from public research organizations.": false,
+ "We envisage that this work will serve as a platform for the accelerated development of isoxazoles and other novel chemotypes for the effective allosteric targeting of RORgt.": true,
+ "We demonstrated the latter benefit for the problem of hierarchical time series forecasting, where we observed up to NUMBER improvement in point performance and up to NUMBER improvement in probabilistic forecasting performance.": false,
+ "We demonstrate that in oxide growth at low temperatures two key points should be highlighted: i the strong dependence of surface potential on reactive oxygen coverage; ii the interrelation between exposure conditions and crystalline oxide formation.": false,
+ "We demonstrate a novel automated solution for creating high-quality knowledge-based plans KBPs using proton and photon beams to identify patients for proton treatment based on their normal tissue complication probabilities NTCP.": true,
+ "We conclude that increased funding availability, research development and data generation, and prioritization within a coordinated binational agenda are needed to advance in terms of water security for groundwater systems in the border region.": false,
+ "We conclude that despite provincial government intervention in regional planning, the impact of market pressures, growth coalitions and institutional coordination problems prevent growth management policies from delivering the significant changes promised by the Ontario government.": false,
+ "We coin this merged approach'Design to Market Thinking' D NUMBER MT, i.e. an integrative thinking approach that could help sustainable innovations to successfully enter the market.": true,
+ "We also evaluate the hydraulic performance of the most promising network generation approach against the expected performance of the real system.": false
+}
\ No newline at end of file
diff --git a/examples/scibert/deploy.ipynb b/examples/scibert/deploy.ipynb
new file mode 100644
index 0000000..8531de7
--- /dev/null
+++ b/examples/scibert/deploy.ipynb
@@ -0,0 +1,149 @@
+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "\u001b[38;5;226m2022-07-01 14:28:43,377 | WARNING | Environment variable ENVIRONMENT is not set, defaulting to development mode ‼️\u001b[0m\n",
+ "\u001b[38;5;39m2022-07-01 14:28:43,378 | INFO | Found credentials file (/data/projects/scoutinscience/platform/projects/highlights-service2/mongo.ini), initialising MongoDbDriver\u001b[0m\n",
+ "\u001b[38;5;39m2022-07-01 14:28:43,379 | INFO | Found credentials file (/data/projects/scoutinscience/platform/projects/highlights-service2/s3.ini), initialising LargeFileS3\u001b[0m\n",
+ "\u001b[38;5;39m2022-07-01 14:28:43,380 | INFO | Settings: configured ✅\u001b[0m\n",
+ "\u001b[38;5;39m2022-07-01 14:28:43,380 | INFO | 🔩 tracing_database: MongoDbDriver\u001b[0m\n",
+ "\u001b[38;5;39m2022-07-01 14:28:43,381 | INFO | 🔩 large_file_implementation: LargeFileS3\u001b[0m\n",
+ "\u001b[38;5;39m2022-07-01 14:28:43,381 | INFO | 🔩 is_production: False\u001b[0m\n",
+ "\u001b[38;5;39m2022-07-01 14:28:43,382 | INFO | 🔩 should_log_exception_stack: True\u001b[0m\n",
+ "\u001b[38;5;39m2022-07-01 14:28:43,382 | INFO | 🔩 prediction_cache_size: 512\u001b[0m\n",
+ "\u001b[38;5;39m2022-07-01 14:28:43,383 | INFO | 🔩 dashboard_table_size: 50\u001b[0m\n",
+ "\u001b[38;5;226m2022-07-01 14:28:43,384 | WARNING | You still need to check whether you follow all best practices before trusting your deployment.\u001b[0m\n",
+ "\u001b[38;5;226m2022-07-01 14:28:43,385 | WARNING | > Find out more at https://se-ml.github.io/practices/\u001b[0m\n",
+ "\u001b[38;5;39m2022-07-01 14:28:44,082 | INFO | Latest version of scibert-highlights is 0 (from versions: 0)\u001b[0m\n",
+ "\u001b[38;5;39m2022-07-01 14:28:44,083 | INFO | File scibert-highlights-0 found in cache\u001b[0m\n",
+ "\u001b[38;5;39m2022-07-01 14:28:44,084 | INFO | File scibert-highlights-0 found in cache\u001b[0m\n"
+ ]
+ }
+ ],
+ "source": [
+ "from great_ai import GreatAI, use_model, MongoDbDriver, configure\n",
+ "from great_ai.utilities import clean\n",
+ "from pathlib import Path\n",
+ "from transformers import (\n",
+ " AutoConfig,\n",
+ " AutoModelForSequenceClassification,\n",
+ " AutoTokenizer,\n",
+ ")\n",
+ "import re\n",
+ "import numpy as np\n",
+ "import torch\n",
+ "from transformers.modeling_outputs import SequenceClassifierOutput\n",
+ "from transformers import (\n",
+ " PreTrainedModel,\n",
+ " PreTrainedTokenizer,\n",
+ ")\n",
+ "from views import EvaluatedSentence, Match\n",
+ "from great_ai.large_file import LargeFileS3\n",
+ "\n",
+ "LargeFileS3.configure_credentials_from_file(\"config.ini\")\n",
+ "MongoDbDriver.configure_credentials_from_file(\"config.ini\")\n",
+ "configure(dashboard_table_size=100)\n",
+ "\n",
+ "ORIGINAL_MODEL = \"allenai/scibert_scivocab_uncased\"\n",
+ "\n",
+ "loaded_model: PreTrainedModel = None\n",
+ "tokenizer: PreTrainedTokenizer = None\n",
+ "\n",
+ "\n",
+ "@GreatAI.create\n",
+ "@use_model(\"scibert-highlights\", version=\"latest\")\n",
+ "def find_highlights(sentence: str, model: Path) -> EvaluatedSentence:\n",
+ " global loaded_model, tokenizer\n",
+ "\n",
+ " if loaded_model is None:\n",
+ " config = AutoConfig.from_pretrained(\n",
+ " model, output_hidden_states=True, output_attentions=True\n",
+ " )\n",
+ " loaded_model = AutoModelForSequenceClassification.from_pretrained(\n",
+ " model, config=config\n",
+ " )\n",
+ " if tokenizer is None:\n",
+ " tokenizer = AutoTokenizer.from_pretrained(ORIGINAL_MODEL)\n",
+ "\n",
+ " sentence = clean(sentence, convert_to_ascii=True, remove_brackets=True)\n",
+ "\n",
+ " return evaluate_sentence(sentence=sentence)\n",
+ "\n",
+ "\n",
+ "def evaluate_sentence(sentence: str) -> EvaluatedSentence:\n",
+ " tensors = tokenizer(sentence, return_tensors=\"pt\", truncation=True, max_length=512)\n",
+ "\n",
+ " with torch.no_grad():\n",
+ " result: SequenceClassifierOutput = loaded_model(**tensors)\n",
+ " positive_likelihood = torch.nn.Softmax(dim=1)(result.logits)[0][1]\n",
+ " tokens = tensors[\"input_ids\"][0]\n",
+ "\n",
+ " attentions = np.sum(result.attentions[-1].numpy()[0], axis=0)[0][\n",
+ " 1:-1\n",
+ " ] # Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length, sequence_length)`.\n",
+ " matches = []\n",
+ "\n",
+ " token_attentions = list(zip(attentions, tokens[1:-1]))\n",
+ " for token in re.split(r\"([ .,])\", sentence):\n",
+ " token = token.strip()\n",
+ " if not token:\n",
+ " continue\n",
+ " bert_tokens = tokenizer(\n",
+ " token, return_tensors=\"pt\", truncation=True, max_length=512\n",
+ " )[\"input_ids\"][0][\n",
+ " 1:-1\n",
+ " ] # truncation=True needed to fix RuntimeError: Already borrowed\n",
+ " score = 0\n",
+ " for t1 in bert_tokens:\n",
+ " if not token_attentions:\n",
+ " break\n",
+ " a, t2 = token_attentions.pop(0)\n",
+ " assert t1 == t2, sentence\n",
+ " score += a\n",
+ " matches.append(\n",
+ " Match(phrase=token if token in \".,\" else \" \" + token, score=round(score, 4))\n",
+ " )\n",
+ " if not token_attentions:\n",
+ " break\n",
+ "\n",
+ " return EvaluatedSentence(\n",
+ " score=positive_likelihood, text=sentence, explanation=matches\n",
+ " )"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3.10.4 ('.env': venv)",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.10.4"
+ },
+ "orig_nbformat": 4,
+ "vscode": {
+ "interpreter": {
+ "hash": "02dd6d3afbfa9fbbe1037d64ad9014965528a1ccad21929d6e72f466389a68ad"
+ }
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/examples/scibert/index.html b/examples/scibert/index.html
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index 0000000..cb664b8
--- /dev/null
+++ b/examples/scibert/index.html
@@ -0,0 +1,878 @@
+
+
+
+
+
+ Explainable SciBERT - GreatAI documentation
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+Summarising scientific publications from a tech-transfer perspective
+This example shows how great-ai is used in practice at ScoutinScience.
+
+
+
+
+
+ Last update:
+ July 13, 2022
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+[:material-test-tube: Check out the code](/docs/examples/scibert){ .md-button .md-button--primary }
+
diff --git a/examples/scibert/train.ipynb b/examples/scibert/train.ipynb
new file mode 100644
index 0000000..cfc0645
--- /dev/null
+++ b/examples/scibert/train.ipynb
@@ -0,0 +1,902 @@
+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": 48,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "executionInfo": {
+ "elapsed": 2529,
+ "status": "ok",
+ "timestamp": 1656596749103,
+ "user": {
+ "displayName": "Schmelczer András",
+ "userId": "08401926777942666437"
+ },
+ "user_tz": -120
+ },
+ "id": "j7l0nD9hDQbB",
+ "outputId": "88a9931b-396a-4cf1-c659-8a7b098b3cdd"
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "evaluation-experiment-2-stage #1-2m6dmb.json\n",
+ "evaluation-experiment-2-stage #1-sa6a0y.json\n",
+ "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
+ "Requirement already satisfied: transformers in /usr/local/lib/python3.7/dist-packages (4.20.1)\n",
+ "Requirement already satisfied: datasets in /usr/local/lib/python3.7/dist-packages (2.3.2)\n",
+ "\u001b[31mERROR: Could not find a version that satisfies the requirement great-ai==0.0.6 (from versions: none)\u001b[0m\n",
+ "\u001b[31mERROR: No matching distribution found for great-ai==0.0.6\u001b[0m\n"
+ ]
+ }
+ ],
+ "source": [
+ "from pathlib import Path\n",
+ "import json\n",
+ "\n",
+ "annotations = []\n",
+ "for p in Path(\".\").glob(\"*.json\"):\n",
+ " with open(p, encoding=\"utf-8\") as f:\n",
+ " print(p)\n",
+ " annotations.append(json.load(f))\n",
+ "\n",
+ "evaluations = {\n",
+ " sentence: [\n",
+ " annotation[sentence] for annotation in annotations if sentence in annotation\n",
+ " ]\n",
+ " for sentence in {\n",
+ " sentence for annotation in annotations for sentence in annotation.keys()\n",
+ " }\n",
+ "}\n",
+ "\n",
+ "X = [s for s in evaluations.keys()]\n",
+ "y = [int(sum(e) > 0) for e in evaluations.values()]\n",
+ "\n",
+ "# !pip install transformers datasets great-ai==0.0.6"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 22,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 1000,
+ "referenced_widgets": [
+ "9c57de70e68a41ecbde5093bd671715a",
+ "9185277b1e5945a9a6a3ad75811bc86b",
+ "4be5b3c59dc04aa2b92f51654e815589",
+ "8e0ede9d1dd84c149a7e282211c7071b",
+ "9bd5d2fb87bd428796155cc67d06b333",
+ "bf773a86ec0a4899bb3636035f7ab35e",
+ "22119b79eb514ad684cc3f00f519fb4a",
+ "cb7ec6240337466d8833c70083e1c3cb",
+ "34631de39509438aad98cbd3fc64c999",
+ "32adc54185894f0598c2d9ad438c76e2",
+ "981e11fb9d4f4a2ba28c011741a1eaba"
+ ]
+ },
+ "executionInfo": {
+ "elapsed": 118131,
+ "status": "ok",
+ "timestamp": 1656593941974,
+ "user": {
+ "displayName": "Schmelczer András",
+ "userId": "08401926777942666437"
+ },
+ "user_tz": -120
+ },
+ "id": "AL3etUQ3LtKN",
+ "outputId": "fe00589f-64dd-4b70-e612-3873b504c00a"
+ },
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "Could not locate the tokenizer configuration file, will try to use the model config instead.\n",
+ "loading configuration file https://huggingface.co/allenai/scibert_scivocab_uncased/resolve/main/config.json from cache at /root/.cache/huggingface/transformers/858852fd2471ce39075378592ddc87f5a6551e64c6825d1b92c8dab9318e0fc3.03ff9e9f998b9a9d40647a2148a202e3fb3d568dc0f170dda9dda194bab4d5dd\n",
+ "Model config BertConfig {\n",
+ " \"_name_or_path\": \"allenai/scibert_scivocab_uncased\",\n",
+ " \"attention_probs_dropout_prob\": 0.1,\n",
+ " \"classifier_dropout\": null,\n",
+ " \"hidden_act\": \"gelu\",\n",
+ " \"hidden_dropout_prob\": 0.1,\n",
+ " \"hidden_size\": 768,\n",
+ " \"initializer_range\": 0.02,\n",
+ " \"intermediate_size\": 3072,\n",
+ " \"layer_norm_eps\": 1e-12,\n",
+ " \"max_position_embeddings\": 512,\n",
+ " \"model_type\": \"bert\",\n",
+ " \"num_attention_heads\": 12,\n",
+ " \"num_hidden_layers\": 12,\n",
+ " \"pad_token_id\": 0,\n",
+ " \"position_embedding_type\": \"absolute\",\n",
+ " \"transformers_version\": \"4.20.1\",\n",
+ " \"type_vocab_size\": 2,\n",
+ " \"use_cache\": true,\n",
+ " \"vocab_size\": 31090\n",
+ "}\n",
+ "\n",
+ "loading file https://huggingface.co/allenai/scibert_scivocab_uncased/resolve/main/vocab.txt from cache at /root/.cache/huggingface/transformers/33593020f507d72099bd84ea6cd2296feb424fecd62d4a8edcc2a02899af6e29.38339d84e6e392addd730fd85fae32652c4cc7c5423633d6fa73e5f7937bbc38\n",
+ "loading file https://huggingface.co/allenai/scibert_scivocab_uncased/resolve/main/tokenizer.json from cache at None\n",
+ "loading file https://huggingface.co/allenai/scibert_scivocab_uncased/resolve/main/added_tokens.json from cache at None\n",
+ "loading file https://huggingface.co/allenai/scibert_scivocab_uncased/resolve/main/special_tokens_map.json from cache at None\n",
+ "loading file https://huggingface.co/allenai/scibert_scivocab_uncased/resolve/main/tokenizer_config.json from cache at None\n",
+ "loading configuration file https://huggingface.co/allenai/scibert_scivocab_uncased/resolve/main/config.json from cache at /root/.cache/huggingface/transformers/858852fd2471ce39075378592ddc87f5a6551e64c6825d1b92c8dab9318e0fc3.03ff9e9f998b9a9d40647a2148a202e3fb3d568dc0f170dda9dda194bab4d5dd\n",
+ "Model config BertConfig {\n",
+ " \"_name_or_path\": \"allenai/scibert_scivocab_uncased\",\n",
+ " \"attention_probs_dropout_prob\": 0.1,\n",
+ " \"classifier_dropout\": null,\n",
+ " \"hidden_act\": \"gelu\",\n",
+ " \"hidden_dropout_prob\": 0.1,\n",
+ " \"hidden_size\": 768,\n",
+ " \"initializer_range\": 0.02,\n",
+ " \"intermediate_size\": 3072,\n",
+ " \"layer_norm_eps\": 1e-12,\n",
+ " \"max_position_embeddings\": 512,\n",
+ " \"model_type\": \"bert\",\n",
+ " \"num_attention_heads\": 12,\n",
+ " \"num_hidden_layers\": 12,\n",
+ " \"pad_token_id\": 0,\n",
+ " \"position_embedding_type\": \"absolute\",\n",
+ " \"transformers_version\": \"4.20.1\",\n",
+ " \"type_vocab_size\": 2,\n",
+ " \"use_cache\": true,\n",
+ " \"vocab_size\": 31090\n",
+ "}\n",
+ "\n",
+ "loading configuration file https://huggingface.co/allenai/scibert_scivocab_uncased/resolve/main/config.json from cache at /root/.cache/huggingface/transformers/858852fd2471ce39075378592ddc87f5a6551e64c6825d1b92c8dab9318e0fc3.03ff9e9f998b9a9d40647a2148a202e3fb3d568dc0f170dda9dda194bab4d5dd\n",
+ "Model config BertConfig {\n",
+ " \"_name_or_path\": \"allenai/scibert_scivocab_uncased\",\n",
+ " \"attention_probs_dropout_prob\": 0.1,\n",
+ " \"classifier_dropout\": null,\n",
+ " \"hidden_act\": \"gelu\",\n",
+ " \"hidden_dropout_prob\": 0.1,\n",
+ " \"hidden_size\": 768,\n",
+ " \"initializer_range\": 0.02,\n",
+ " \"intermediate_size\": 3072,\n",
+ " \"layer_norm_eps\": 1e-12,\n",
+ " \"max_position_embeddings\": 512,\n",
+ " \"model_type\": \"bert\",\n",
+ " \"num_attention_heads\": 12,\n",
+ " \"num_hidden_layers\": 12,\n",
+ " \"pad_token_id\": 0,\n",
+ " \"position_embedding_type\": \"absolute\",\n",
+ " \"transformers_version\": \"4.20.1\",\n",
+ " \"type_vocab_size\": 2,\n",
+ " \"use_cache\": true,\n",
+ " \"vocab_size\": 31090\n",
+ "}\n",
+ "\n",
+ "loading configuration file https://huggingface.co/allenai/scibert_scivocab_uncased/resolve/main/config.json from cache at /root/.cache/huggingface/transformers/858852fd2471ce39075378592ddc87f5a6551e64c6825d1b92c8dab9318e0fc3.03ff9e9f998b9a9d40647a2148a202e3fb3d568dc0f170dda9dda194bab4d5dd\n",
+ "Model config BertConfig {\n",
+ " \"_name_or_path\": \"allenai/scibert_scivocab_uncased\",\n",
+ " \"attention_probs_dropout_prob\": 0.1,\n",
+ " \"classifier_dropout\": null,\n",
+ " \"hidden_act\": \"gelu\",\n",
+ " \"hidden_dropout_prob\": 0.1,\n",
+ " \"hidden_size\": 768,\n",
+ " \"initializer_range\": 0.02,\n",
+ " \"intermediate_size\": 3072,\n",
+ " \"layer_norm_eps\": 1e-12,\n",
+ " \"max_position_embeddings\": 512,\n",
+ " \"model_type\": \"bert\",\n",
+ " \"num_attention_heads\": 12,\n",
+ " \"num_hidden_layers\": 12,\n",
+ " \"pad_token_id\": 0,\n",
+ " \"position_embedding_type\": \"absolute\",\n",
+ " \"transformers_version\": \"4.20.1\",\n",
+ " \"type_vocab_size\": 2,\n",
+ " \"use_cache\": true,\n",
+ " \"vocab_size\": 31090\n",
+ "}\n",
+ "\n",
+ "loading weights file https://huggingface.co/allenai/scibert_scivocab_uncased/resolve/main/pytorch_model.bin from cache at /root/.cache/huggingface/transformers/de14937a851e8180a2bc5660c0041d385f8a0c62b1b2ccafa46df31043a2390c.74830bb01a0ffcdeaed8be9916312726d0c4cd364ac6fc15b375f789eaff4cbb\n",
+ "Some weights of the model checkpoint at allenai/scibert_scivocab_uncased were not used when initializing BertForSequenceClassification: ['cls.seq_relationship.bias', 'cls.predictions.transform.dense.bias', 'cls.predictions.transform.dense.weight', 'cls.predictions.bias', 'cls.seq_relationship.weight', 'cls.predictions.decoder.bias', 'cls.predictions.decoder.weight', 'cls.predictions.transform.LayerNorm.bias', 'cls.predictions.transform.LayerNorm.weight']\n",
+ "- This IS expected if you are initializing BertForSequenceClassification from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n",
+ "- This IS NOT expected if you are initializing BertForSequenceClassification from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n",
+ "Some weights of BertForSequenceClassification were not initialized from the model checkpoint at allenai/scibert_scivocab_uncased and are newly initialized: ['classifier.bias', 'classifier.weight']\n",
+ "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
+ ]
+ },
+ {
+ "data": {
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+ " 0%| | 0/1 [00:00\n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " Epoch \n",
+ " Training Loss \n",
+ " Validation Loss \n",
+ " F1 \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " 1 \n",
+ " 0.586800 \n",
+ " 0.512138 \n",
+ " 0.719101 \n",
+ " \n",
+ " \n",
+ " 2 \n",
+ " 0.411600 \n",
+ " 0.416675 \n",
+ " 0.849057 \n",
+ " \n",
+ " \n",
+ " 3 \n",
+ " 0.245600 \n",
+ " 0.417070 \n",
+ " 0.864000 \n",
+ " \n",
+ " \n",
+ " 4 \n",
+ " 0.147800 \n",
+ " 0.575878 \n",
+ " 0.852459 \n",
+ " \n",
+ " \n",
+ " 5 \n",
+ " 0.056800 \n",
+ " 0.474259 \n",
+ " 0.896552 \n",
+ " \n",
+ " \n",
+ " 6 \n",
+ " 0.022500 \n",
+ " 0.754236 \n",
+ " 0.843137 \n",
+ " \n",
+ " \n",
+ " 7 \n",
+ " 0.001000 \n",
+ " 0.857636 \n",
+ " 0.834783 \n",
+ " \n",
+ " \n",
+ " 8 \n",
+ " 0.000500 \n",
+ " 0.920232 \n",
+ " 0.869565 \n",
+ " \n",
+ " \n",
+ " 9 \n",
+ " 0.000300 \n",
+ " 0.970790 \n",
+ " 0.877193 \n",
+ " \n",
+ " \n",
+ " 10 \n",
+ " 0.000300 \n",
+ " 0.948689 \n",
+ " 0.862385 \n",
+ " \n",
+ " \n",
+ "
"
+ ],
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "***** Running Evaluation *****\n",
+ " Num examples = 100\n",
+ " Batch size = 32\n",
+ "Saving model checkpoint to models/checkpoint-13\n",
+ "Configuration saved in models/checkpoint-13/config.json\n",
+ "Model weights saved in models/checkpoint-13/pytorch_model.bin\n",
+ "Deleting older checkpoint [models/checkpoint-91] due to args.save_total_limit\n",
+ "***** Running Evaluation *****\n",
+ " Num examples = 100\n",
+ " Batch size = 32\n",
+ "Saving model checkpoint to models/checkpoint-26\n",
+ "Configuration saved in models/checkpoint-26/config.json\n",
+ "Model weights saved in models/checkpoint-26/pytorch_model.bin\n",
+ "Deleting older checkpoint [models/checkpoint-117] due to args.save_total_limit\n",
+ "***** Running Evaluation *****\n",
+ " Num examples = 100\n",
+ " Batch size = 32\n",
+ "Saving model checkpoint to models/checkpoint-39\n",
+ "Configuration saved in models/checkpoint-39/config.json\n",
+ "Model weights saved in models/checkpoint-39/pytorch_model.bin\n",
+ "Deleting older checkpoint [models/checkpoint-130] due to args.save_total_limit\n",
+ "***** Running Evaluation *****\n",
+ " Num examples = 100\n",
+ " Batch size = 32\n",
+ "Saving model checkpoint to models/checkpoint-52\n",
+ "Configuration saved in models/checkpoint-52/config.json\n",
+ "Model weights saved in models/checkpoint-52/pytorch_model.bin\n",
+ "Deleting older checkpoint [models/checkpoint-143] due to args.save_total_limit\n",
+ "***** Running Evaluation *****\n",
+ " Num examples = 100\n",
+ " Batch size = 32\n",
+ "Saving model checkpoint to models/checkpoint-65\n",
+ "Configuration saved in models/checkpoint-65/config.json\n",
+ "Model weights saved in models/checkpoint-65/pytorch_model.bin\n",
+ "Deleting older checkpoint [models/checkpoint-156] due to args.save_total_limit\n",
+ "***** Running Evaluation *****\n",
+ " Num examples = 100\n",
+ " Batch size = 32\n",
+ "Saving model checkpoint to models/checkpoint-78\n",
+ "Configuration saved in models/checkpoint-78/config.json\n",
+ "Model weights saved in models/checkpoint-78/pytorch_model.bin\n",
+ "Deleting older checkpoint [models/checkpoint-13] due to args.save_total_limit\n",
+ "***** Running Evaluation *****\n",
+ " Num examples = 100\n",
+ " Batch size = 32\n",
+ "Saving model checkpoint to models/checkpoint-91\n",
+ "Configuration saved in models/checkpoint-91/config.json\n",
+ "Model weights saved in models/checkpoint-91/pytorch_model.bin\n",
+ "Deleting older checkpoint [models/checkpoint-26] due to args.save_total_limit\n",
+ "***** Running Evaluation *****\n",
+ " Num examples = 100\n",
+ " Batch size = 32\n",
+ "Saving model checkpoint to models/checkpoint-104\n",
+ "Configuration saved in models/checkpoint-104/config.json\n",
+ "Model weights saved in models/checkpoint-104/pytorch_model.bin\n",
+ "Deleting older checkpoint [models/checkpoint-39] due to args.save_total_limit\n",
+ "***** Running Evaluation *****\n",
+ " Num examples = 100\n",
+ " Batch size = 32\n",
+ "Saving model checkpoint to models/checkpoint-117\n",
+ "Configuration saved in models/checkpoint-117/config.json\n",
+ "Model weights saved in models/checkpoint-117/pytorch_model.bin\n",
+ "Deleting older checkpoint [models/checkpoint-52] due to args.save_total_limit\n",
+ "***** Running Evaluation *****\n",
+ " Num examples = 100\n",
+ " Batch size = 32\n",
+ "Saving model checkpoint to models/checkpoint-130\n",
+ "Configuration saved in models/checkpoint-130/config.json\n",
+ "Model weights saved in models/checkpoint-130/pytorch_model.bin\n",
+ "Deleting older checkpoint [models/checkpoint-78] due to args.save_total_limit\n",
+ "\n",
+ "\n",
+ "Training completed. Do not forget to share your model on huggingface.co/models =)\n",
+ "\n",
+ "\n",
+ "Loading best model from models/checkpoint-65 (score: 0.896551724137931).\n"
+ ]
+ }
+ ],
+ "source": [
+ "from transformers import (\n",
+ " AutoModelForSequenceClassification,\n",
+ " AutoTokenizer,\n",
+ " DataCollatorWithPadding,\n",
+ " Trainer,\n",
+ " TrainingArguments,\n",
+ " EarlyStoppingCallback,\n",
+ ")\n",
+ "import numpy as np\n",
+ "from datasets import Dataset, load_metric\n",
+ "\n",
+ "MODEL = \"allenai/scibert_scivocab_uncased\"\n",
+ "BATCH_SIZE = 32\n",
+ "\n",
+ "tokenizer = AutoTokenizer.from_pretrained(MODEL)\n",
+ "model = AutoModelForSequenceClassification.from_pretrained(MODEL, num_labels=2)\n",
+ "data_collator = DataCollatorWithPadding(tokenizer=tokenizer)\n",
+ "\n",
+ "\n",
+ "def tokenize_function(v):\n",
+ " return tokenizer(v[\"text\"])\n",
+ "\n",
+ "\n",
+ "dataset = (\n",
+ " Dataset.from_dict({\"text\": X, \"label\": y})\n",
+ " .map(lambda v: tokenizer(v[\"text\"], truncation=True), batched=True)\n",
+ " .remove_columns(\"text\")\n",
+ " .train_test_split(test_size=0.2, shuffle=True)\n",
+ ")\n",
+ "\n",
+ "f1_score = load_metric(\"f1\")\n",
+ "\n",
+ "\n",
+ "def compute_metrics(p):\n",
+ " pred, labels = p\n",
+ " pred = np.argmax(pred, axis=1)\n",
+ " return f1_score.compute(predictions=pred, references=labels)\n",
+ "\n",
+ "\n",
+ "training_args = TrainingArguments(\n",
+ " output_dir=Path(\"models\"),\n",
+ " per_device_train_batch_size=BATCH_SIZE,\n",
+ " per_device_eval_batch_size=BATCH_SIZE,\n",
+ " save_total_limit=5,\n",
+ " num_train_epochs=50,\n",
+ " save_strategy=\"epoch\",\n",
+ " evaluation_strategy=\"epoch\",\n",
+ " logging_strategy=\"epoch\",\n",
+ " weight_decay=0.01,\n",
+ " metric_for_best_model=\"f1\",\n",
+ " load_best_model_at_end=True,\n",
+ ")\n",
+ "\n",
+ "result = Trainer(\n",
+ " model=model,\n",
+ " args=training_args,\n",
+ " train_dataset=dataset[\"train\"],\n",
+ " eval_dataset=dataset[\"test\"],\n",
+ " data_collator=data_collator,\n",
+ " compute_metrics=compute_metrics,\n",
+ " callbacks=[EarlyStoppingCallback(early_stopping_patience=5)],\n",
+ ").train()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 44,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "executionInfo": {
+ "elapsed": 25368,
+ "status": "ok",
+ "timestamp": 1656594537509,
+ "user": {
+ "displayName": "Schmelczer András",
+ "userId": "08401926777942666437"
+ },
+ "user_tz": -120
+ },
+ "id": "fyNKltdquZSP",
+ "outputId": "e8c2cbb1-78e1-41a3-b7cf-b0cd573bc45d"
+ },
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "Configuration saved in pretrained/config.json\n",
+ "Model weights saved in pretrained/pytorch_model.bin\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ " adding: pretrained/ (stored 0%)\n",
+ " adding: pretrained/config.json (deflated 49%)\n",
+ " adding: pretrained/pytorch_model.bin (deflated 7%)\n"
+ ]
+ }
+ ],
+ "source": [
+ "from great_ai.large_file import LargeFileS3\n",
+ "\n",
+ "LargeFileS3.configure_credentials_from_file(\"config.ini\")\n",
+ "\n",
+ "model.save_pretrained(\"pretrained\")\n",
+ "LargeFileS3(\"scibert-highlights\").push(\"pretrained\")"
+ ]
+ }
+ ],
+ "metadata": {
+ "accelerator": "GPU",
+ "colab": {
+ "collapsed_sections": [],
+ "name": "Copy of Forms snippets",
+ "provenance": [
+ {
+ "file_id": "/v2/external/notebooks/snippets/forms.ipynb",
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+ }
+ ]
+ },
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new file mode 100644
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--- /dev/null
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Evaluated sentence - GreatAI documentation
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index a334fe7..8e93c10 100644
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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 quasi-shadow deployment Version and store models and data on shared infrastructure (MongoDB GridFS, S3-compatible storage, shared local-volume) Automatically scaffolded custom REST API (and OpenAPI schema) for easy integration Input validation Sensible cache-policy Seamless support for both synchronous and async 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 high-level overview and analysing traces 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 hello-world.py from great_ai import GreatAI @GreatAI . create #(1) def hello_world ( name : str ) -> str : #(2) return f \"Hello { name } !\" @GreatAI.create wraps your hello_world 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, hello_world could be an inference function of some AI/ML application. But it could also just wrap a black-box solution of some SaaS. Either ways, it is imperative to have continuos oversight of the services you provide and data you process especially in the context of AI/ML applications. terminal great-ai hello-world.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. 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} } 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(); 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 ]]]: # Extract response = urllib . request . urlopen ( f \"https://s3-us-west-2.amazonaws.com/ai2-s2-research-public/\" \"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]]]: # Extract response = urllib.request.urlopen( f\"https://s3-us-west-2.amazonaws.com/ai2-s2-research-public/\" \"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 [04:42<00:00, 70.62s/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 [8]: 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) 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 ]]]: # Extract response = urllib . request . urlopen ( f \"https://s3-us-west-2.amazonaws.com/ai2-s2-research-public/\" \"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]]]: # Extract response = urllib.request.urlopen( f\"https://s3-us-west-2.amazonaws.com/ai2-s2-research-public/\" \"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 [04:42<00:00, 70.62s/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 [8]: 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)","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()); }(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); 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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: 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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\" , validator = 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\", validator=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 (v0.1.4): 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 small-domain-prediction Latest version of small-domain-prediction is 0 (from versions: 0) File small-domain-prediction-0 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\" ] = ( 30 , 15 ) plt . rcParams [ \"figure.facecolor\" ] = \"white\" plt . rcParams [ \"font.size\" ] = 12 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\" , ) 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\"] = (30, 15) plt.rcParams[\"figure.facecolor\"] = \"white\" plt.rcParams[\"font.size\"] = 12 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\", ) 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 12332/12332 [01:27<00:00, 141.08it/s] precision recall f1-score support Art 0.43 0.35 0.39 125 Biology 0.77 0.83 0.80 1209 Business 0.50 0.71 0.59 312 Chemistry 0.80 0.67 0.73 1194 Computer Science 0.77 0.76 0.77 1293 Economics 0.64 0.58 0.61 251 Engineering 0.55 0.53 0.54 815 Environmental Science 0.54 0.56 0.55 230 Geography 0.54 0.44 0.48 277 Geology 0.76 0.67 0.71 228 History 0.29 0.21 0.24 102 Materials Science 0.73 0.81 0.77 1053 Mathematics 0.80 0.70 0.75 551 Medicine 0.95 0.77 0.85 2794 Philosophy 0.61 0.12 0.21 88 Physics 0.67 0.76 0.72 605 Political Science 0.43 0.52 0.47 297 Psychology 0.53 0.80 0.63 600 Sociology 0.32 0.60 0.42 308 accuracy 0.71 12332 macro avg 0.61 0.60 0.59 12332 weighted avg 0.73 0.71 0.71 12332","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\" , validator = 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\", validator=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 (v0.1.4): 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 small-domain-prediction Latest version of small-domain-prediction is 0 (from versions: 0) File small-domain-prediction-0 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\" ] = ( 30 , 15 ) plt . rcParams [ \"figure.facecolor\" ] = \"white\" plt . rcParams [ \"font.size\" ] = 12 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\" , ) 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\"] = (30, 15) plt.rcParams[\"figure.facecolor\"] = \"white\" plt.rcParams[\"font.size\"] = 12 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\", ) 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 12332/12332 [01:27<00:00, 141.08it/s] precision recall f1-score support Art 0.43 0.35 0.39 125 Biology 0.77 0.83 0.80 1209 Business 0.50 0.71 0.59 312 Chemistry 0.80 0.67 0.73 1194 Computer Science 0.77 0.76 0.77 1293 Economics 0.64 0.58 0.61 251 Engineering 0.55 0.53 0.54 815 Environmental Science 0.54 0.56 0.55 230 Geography 0.54 0.44 0.48 277 Geology 0.76 0.67 0.71 228 History 0.29 0.21 0.24 102 Materials Science 0.73 0.81 0.77 1053 Mathematics 0.80 0.70 0.75 551 Medicine 0.95 0.77 0.85 2794 Philosophy 0.61 0.12 0.21 88 Physics 0.67 0.76 0.72 605 Political Science 0.43 0.52 0.47 297 Psychology 0.53 0.80 0.63 600 Sociology 0.32 0.60 0.42 308 accuracy 0.71 12332 macro avg 0.61 0.60 0.59 12332 weighted avg 0.73 0.71 0.71 12332","title":"Check accuracy on the test split"},{"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()); 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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-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(); 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 (v0.1.4): 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 In [23]: 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 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 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 9.447799 0.785535 4.260490 0.180851 0.5 False 0.05 20 {'classifier__alpha': 0.5, 'classifier__fit_pr... 0.517618 0.518688 0.513803 0.516703 0.002096 1 10 9.750011 0.671769 4.791153 0.544862 0.5 False 0.1 20 {'classifier__alpha': 0.5, 'classifier__fit_pr... 0.513254 0.517940 0.509116 0.513437 0.003605 2 11 9.398448 0.656403 5.194527 0.303382 0.5 False 0.1 100 {'classifier__alpha': 0.5, 'classifier__fit_pr... 0.508306 0.502244 0.501839 0.504129 0.002958 3 8 9.285461 0.869226 5.030563 0.305472 0.5 False 0.05 100 {'classifier__alpha': 0.5, 'classifier__fit_pr... 0.504070 0.502116 0.500603 0.502263 0.001419 4 19 9.662259 0.234167 4.854762 0.126622 1 False 0.05 20 {'classifier__alpha': 1, 'classifier__fit_prio... 0.492205 0.504466 0.495489 0.497387 0.005182 5 23 9.024540 0.469833 3.235823 0.173482 1 False 0.1 100 {'classifier__alpha': 1, 'classifier__fit_prio... 0.501203 0.493465 0.492361 0.495676 0.003934 6 20 9.304917 0.192977 4.370798 0.255480 1 False 0.05 100 {'classifier__alpha': 1, 'classifier__fit_prio... 0.498869 0.494357 0.492431 0.495219 0.002698 7 22 9.642570 0.438559 3.332855 0.435930 1 False 0.1 20 {'classifier__alpha': 1, 'classifier__fit_prio... 0.479585 0.496964 0.485209 0.487253 0.007241 8 6 10.047471 0.473215 4.358744 0.051892 0.5 False 0.05 5 {'classifier__alpha': 0.5, 'classifier__fit_pr... 0.472578 0.486362 0.481865 0.480268 0.005739 9 5 10.287111 0.349778 4.908435 0.099558 0.5 True 0.1 100 {'classifier__alpha': 0.5, 'classifier__fit_pr... 0.474821 0.474690 0.478096 0.475869 0.001576 10 2 10.997282 0.851917 6.131619 0.274400 0.5 True 0.05 100 {'classifier__alpha': 0.5, 'classifier__fit_pr... 0.476218 0.471377 0.478548 0.475381 0.002987 11 9 9.147621 0.290876 5.392678 0.390403 0.5 False 0.1 5 {'classifier__alpha': 0.5, 'classifier__fit_pr... 0.458994 0.475008 0.469998 0.468000 0.006689 12 1 10.299674 0.486001 5.776907 0.462777 0.5 True 0.05 20 {'classifier__alpha': 0.5, 'classifier__fit_pr... 0.461036 0.466296 0.467280 0.464871 0.002741 13 4 10.771393 0.330218 4.961415 0.454726 0.5 True 0.1 20 {'classifier__alpha': 0.5, 'classifier__fit_pr... 0.454689 0.463340 0.457743 0.458591 0.003582 14 14 9.969123 0.715883 4.382175 0.216170 1 True 0.05 100 {'classifier__alpha': 1, 'classifier__fit_prio... 0.444039 0.449822 0.450524 0.448128 0.002906 15 17 9.439775 1.333323 4.914944 0.292653 1 True 0.1 100 {'classifier__alpha': 1, 'classifier__fit_prio... 0.440663 0.448419 0.443441 0.444174 0.003209 16 18 10.025776 0.198347 5.375965 0.220059 1 False 0.05 5 {'classifier__alpha': 1, 'classifier__fit_prio... 0.418284 0.428063 0.428696 0.425014 0.004766 17 13 9.251913 0.441015 5.085037 0.224299 1 True 0.05 20 {'classifier__alpha': 1, 'classifier__fit_prio... 0.400471 0.406275 0.406521 0.404422 0.002796 18 21 9.173751 0.112242 4.271786 0.293579 1 False 0.1 5 {'classifier__alpha': 1, 'classifier__fit_prio... 0.393559 0.402869 0.406431 0.400953 0.005427 19 16 9.683013 0.600089 4.924559 0.321389 1 True 0.1 20 {'classifier__alpha': 1, 'classifier__fit_prio... 0.387535 0.392661 0.393992 0.391396 0.002784 20 0 9.835260 0.220524 6.034349 0.586497 0.5 True 0.05 5 {'classifier__alpha': 0.5, 'classifier__fit_pr... 0.379471 0.381348 0.386078 0.382299 0.002780 21 3 11.905621 0.329256 5.571229 0.541937 0.5 True 0.1 5 {'classifier__alpha': 0.5, 'classifier__fit_pr... 0.368234 0.367599 0.372849 0.369561 0.002339 22 12 10.378068 0.227200 5.247946 0.126687 1 True 0.05 5 {'classifier__alpha': 1, 'classifier__fit_prio... 0.278087 0.278157 0.278143 0.278129 0.000030 23 15 10.375661 0.139826 5.239350 0.298795 1 True 0.1 5 {'classifier__alpha': 1, 'classifier__fit_prio... 0.259347 0.262720 0.258716 0.260261 0.001758 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-1 Compressing small-domain-prediction-1 Model small-domain-prediction uploaded with version 1 Out[6]: 'small-domain-prediction:1' 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 (v0.1.4): 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 In [23]: 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 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 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 9.447799 0.785535 4.260490 0.180851 0.5 False 0.05 20 {'classifier__alpha': 0.5, 'classifier__fit_pr... 0.517618 0.518688 0.513803 0.516703 0.002096 1 10 9.750011 0.671769 4.791153 0.544862 0.5 False 0.1 20 {'classifier__alpha': 0.5, 'classifier__fit_pr... 0.513254 0.517940 0.509116 0.513437 0.003605 2 11 9.398448 0.656403 5.194527 0.303382 0.5 False 0.1 100 {'classifier__alpha': 0.5, 'classifier__fit_pr... 0.508306 0.502244 0.501839 0.504129 0.002958 3 8 9.285461 0.869226 5.030563 0.305472 0.5 False 0.05 100 {'classifier__alpha': 0.5, 'classifier__fit_pr... 0.504070 0.502116 0.500603 0.502263 0.001419 4 19 9.662259 0.234167 4.854762 0.126622 1 False 0.05 20 {'classifier__alpha': 1, 'classifier__fit_prio... 0.492205 0.504466 0.495489 0.497387 0.005182 5 23 9.024540 0.469833 3.235823 0.173482 1 False 0.1 100 {'classifier__alpha': 1, 'classifier__fit_prio... 0.501203 0.493465 0.492361 0.495676 0.003934 6 20 9.304917 0.192977 4.370798 0.255480 1 False 0.05 100 {'classifier__alpha': 1, 'classifier__fit_prio... 0.498869 0.494357 0.492431 0.495219 0.002698 7 22 9.642570 0.438559 3.332855 0.435930 1 False 0.1 20 {'classifier__alpha': 1, 'classifier__fit_prio... 0.479585 0.496964 0.485209 0.487253 0.007241 8 6 10.047471 0.473215 4.358744 0.051892 0.5 False 0.05 5 {'classifier__alpha': 0.5, 'classifier__fit_pr... 0.472578 0.486362 0.481865 0.480268 0.005739 9 5 10.287111 0.349778 4.908435 0.099558 0.5 True 0.1 100 {'classifier__alpha': 0.5, 'classifier__fit_pr... 0.474821 0.474690 0.478096 0.475869 0.001576 10 2 10.997282 0.851917 6.131619 0.274400 0.5 True 0.05 100 {'classifier__alpha': 0.5, 'classifier__fit_pr... 0.476218 0.471377 0.478548 0.475381 0.002987 11 9 9.147621 0.290876 5.392678 0.390403 0.5 False 0.1 5 {'classifier__alpha': 0.5, 'classifier__fit_pr... 0.458994 0.475008 0.469998 0.468000 0.006689 12 1 10.299674 0.486001 5.776907 0.462777 0.5 True 0.05 20 {'classifier__alpha': 0.5, 'classifier__fit_pr... 0.461036 0.466296 0.467280 0.464871 0.002741 13 4 10.771393 0.330218 4.961415 0.454726 0.5 True 0.1 20 {'classifier__alpha': 0.5, 'classifier__fit_pr... 0.454689 0.463340 0.457743 0.458591 0.003582 14 14 9.969123 0.715883 4.382175 0.216170 1 True 0.05 100 {'classifier__alpha': 1, 'classifier__fit_prio... 0.444039 0.449822 0.450524 0.448128 0.002906 15 17 9.439775 1.333323 4.914944 0.292653 1 True 0.1 100 {'classifier__alpha': 1, 'classifier__fit_prio... 0.440663 0.448419 0.443441 0.444174 0.003209 16 18 10.025776 0.198347 5.375965 0.220059 1 False 0.05 5 {'classifier__alpha': 1, 'classifier__fit_prio... 0.418284 0.428063 0.428696 0.425014 0.004766 17 13 9.251913 0.441015 5.085037 0.224299 1 True 0.05 20 {'classifier__alpha': 1, 'classifier__fit_prio... 0.400471 0.406275 0.406521 0.404422 0.002796 18 21 9.173751 0.112242 4.271786 0.293579 1 False 0.1 5 {'classifier__alpha': 1, 'classifier__fit_prio... 0.393559 0.402869 0.406431 0.400953 0.005427 19 16 9.683013 0.600089 4.924559 0.321389 1 True 0.1 20 {'classifier__alpha': 1, 'classifier__fit_prio... 0.387535 0.392661 0.393992 0.391396 0.002784 20 0 9.835260 0.220524 6.034349 0.586497 0.5 True 0.05 5 {'classifier__alpha': 0.5, 'classifier__fit_pr... 0.379471 0.381348 0.386078 0.382299 0.002780 21 3 11.905621 0.329256 5.571229 0.541937 0.5 True 0.1 5 {'classifier__alpha': 0.5, 'classifier__fit_pr... 0.368234 0.367599 0.372849 0.369561 0.002339 22 12 10.378068 0.227200 5.247946 0.126687 1 True 0.05 5 {'classifier__alpha': 1, 'classifier__fit_prio... 0.278087 0.278157 0.278143 0.278129 0.000030 23 15 10.375661 0.139826 5.239350 0.298795 1 True 0.1 5 {'classifier__alpha': 1, 'classifier__fit_prio... 0.259347 0.262720 0.258716 0.260261 0.001758 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-1 Compressing small-domain-prediction-1 Model small-domain-prediction uploaded with version 1 Out[6]: 'small-domain-prediction:1'","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 , 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 ( \"mag/train.txt\" , encoding = \"utf-8\" ) as f : training_data = list ( tqdm ( 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 ( \"mag/test.txt\" , encoding = \"utf-8\" ) as f : test_data = list ( tqdm ( 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, 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(\"mag/train.txt\", encoding=\"utf-8\") as f: training_data = list(tqdm(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(\"mag/test.txt\", encoding=\"utf-8\") as f: test_data = list(tqdm(parallel_map(preprocess, f.readlines()))) X_test = [d[0] for d in test_data] y_test = [d[1] for d in test_data] 84000it [00:09, 8647.60it/s] 22399it [00:03, 6368.63it/s] In [2]: Copied! import pandas as pd from collections import Counter import plotly.express as px df = pd . DataFrame ( Counter ( y_train ) . most_common (), columns = [ \"domain\" , \"count\" ]) px . bar ( df , \"domain\" , \"count\" , width = 1200 , height = 400 ) . show () import pandas as pd from collections import Counter import plotly.express as px df = pd.DataFrame(Counter(y_train).most_common(), columns=[\"domain\", \"count\"]) px.bar(df, \"domain\", \"count\", width=1200, height=400).show() 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 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 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 1.227354 0.011710 0.652844 0.040511 1 True 0.05 5 {'classifier__alpha': 1, 'classifier__fit_prio... 0.677283 0.670398 0.665671 0.671118 0.004768 1 15 1.279783 0.037969 0.634761 0.068606 1 True 0.1 5 {'classifier__alpha': 1, 'classifier__fit_prio... 0.676970 0.670244 0.666123 0.671112 0.004470 2 18 1.332854 0.171405 0.743611 0.109792 1 False 0.05 5 {'classifier__alpha': 1, 'classifier__fit_prio... 0.675678 0.669321 0.665305 0.670101 0.004271 3 21 1.253222 0.072134 0.612344 0.016771 1 False 0.1 5 {'classifier__alpha': 1, 'classifier__fit_prio... 0.675569 0.669482 0.665225 0.670092 0.004245 4 3 1.472035 0.080849 0.654935 0.044302 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 1.380641 0.054306 0.739966 0.053385 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 1.284987 0.113903 0.696876 0.003757 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 1.291148 0.101837 0.686561 0.083989 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 1.268873 0.042412 0.645649 0.022738 1 True 0.05 20 {'classifier__alpha': 1, 'classifier__fit_prio... 0.656459 0.647433 0.644864 0.649585 0.004972 9 16 1.147120 0.009335 0.599006 0.045637 1 True 0.1 20 {'classifier__alpha': 1, 'classifier__fit_prio... 0.656196 0.646960 0.645436 0.649530 0.004754 10 4 1.186988 0.057330 0.642233 0.078248 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 1.413231 0.153288 0.765960 0.099535 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 1.193013 0.079000 0.691768 0.027448 1 False 0.05 20 {'classifier__alpha': 1, 'classifier__fit_prio... 0.655125 0.645586 0.643166 0.647959 0.005163 13 22 1.043618 0.098733 0.450375 0.061660 1 False 0.1 20 {'classifier__alpha': 1, 'classifier__fit_prio... 0.654254 0.645409 0.643514 0.647726 0.004680 14 7 1.301459 0.062143 0.660748 0.056054 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 1.433934 0.155240 0.636608 0.024064 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 1.325535 0.073539 0.672542 0.085835 1 True 0.05 100 {'classifier__alpha': 1, 'classifier__fit_prio... 0.587097 0.577411 0.580170 0.581560 0.004075 17 17 1.237677 0.070063 0.651091 0.102538 1 True 0.1 100 {'classifier__alpha': 1, 'classifier__fit_prio... 0.585713 0.577197 0.580763 0.581224 0.003492 18 2 1.394873 0.105286 0.637073 0.041708 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 1.270732 0.013414 0.620984 0.025393 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 1.177219 0.039633 0.586308 0.044247 1 False 0.05 100 {'classifier__alpha': 1, 'classifier__fit_prio... 0.581096 0.572833 0.576280 0.576736 0.003389 21 8 1.165583 0.046999 0.603675 0.031774 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 0.907388 0.121543 0.354655 0.023775 1 False 0.1 100 {'classifier__alpha': 1, 'classifier__fit_prio... 0.579769 0.572373 0.576699 0.576280 0.003034 23 11 1.210845 0.041339 0.653606 0.028117 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 [6]: Copied! from sklearn import metrics import matplotlib.pyplot as plt y_predicted = classifier . predict ( X_test ) % matplotlib inline plt . rcParams [ \"figure.figsize\" ] = ( 30 , 15 ) plt . rcParams [ \"figure.facecolor\" ] = \"white\" plt . rcParams [ \"font.size\" ] = 12 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\" , ) None from sklearn import metrics import matplotlib.pyplot as plt y_predicted = classifier.predict(X_test) %matplotlib inline plt.rcParams[\"figure.figsize\"] = (30, 15) plt.rcParams[\"figure.facecolor\"] = \"white\" plt.rcParams[\"font.size\"] = 12 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\", ) None 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 , 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 ( \"mag/train.txt\" , encoding = \"utf-8\" ) as f : training_data = list ( tqdm ( 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 ( \"mag/test.txt\" , encoding = \"utf-8\" ) as f : test_data = list ( tqdm ( 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, 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(\"mag/train.txt\", encoding=\"utf-8\") as f: training_data = list(tqdm(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(\"mag/test.txt\", encoding=\"utf-8\") as f: test_data = list(tqdm(parallel_map(preprocess, f.readlines()))) X_test = [d[0] for d in test_data] y_test = [d[1] for d in test_data] 84000it [00:09, 8647.60it/s] 22399it [00:03, 6368.63it/s] In [2]: Copied! import pandas as pd from collections import Counter import plotly.express as px df = pd . DataFrame ( Counter ( y_train ) . most_common (), columns = [ \"domain\" , \"count\" ]) px . bar ( df , \"domain\" , \"count\" , width = 1200 , height = 400 ) . show () import pandas as pd from collections import Counter import plotly.express as px df = pd.DataFrame(Counter(y_train).most_common(), columns=[\"domain\", \"count\"]) px.bar(df, \"domain\", \"count\", width=1200, height=400).show() 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 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 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 1.227354 0.011710 0.652844 0.040511 1 True 0.05 5 {'classifier__alpha': 1, 'classifier__fit_prio... 0.677283 0.670398 0.665671 0.671118 0.004768 1 15 1.279783 0.037969 0.634761 0.068606 1 True 0.1 5 {'classifier__alpha': 1, 'classifier__fit_prio... 0.676970 0.670244 0.666123 0.671112 0.004470 2 18 1.332854 0.171405 0.743611 0.109792 1 False 0.05 5 {'classifier__alpha': 1, 'classifier__fit_prio... 0.675678 0.669321 0.665305 0.670101 0.004271 3 21 1.253222 0.072134 0.612344 0.016771 1 False 0.1 5 {'classifier__alpha': 1, 'classifier__fit_prio... 0.675569 0.669482 0.665225 0.670092 0.004245 4 3 1.472035 0.080849 0.654935 0.044302 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 1.380641 0.054306 0.739966 0.053385 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 1.284987 0.113903 0.696876 0.003757 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 1.291148 0.101837 0.686561 0.083989 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 1.268873 0.042412 0.645649 0.022738 1 True 0.05 20 {'classifier__alpha': 1, 'classifier__fit_prio... 0.656459 0.647433 0.644864 0.649585 0.004972 9 16 1.147120 0.009335 0.599006 0.045637 1 True 0.1 20 {'classifier__alpha': 1, 'classifier__fit_prio... 0.656196 0.646960 0.645436 0.649530 0.004754 10 4 1.186988 0.057330 0.642233 0.078248 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 1.413231 0.153288 0.765960 0.099535 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 1.193013 0.079000 0.691768 0.027448 1 False 0.05 20 {'classifier__alpha': 1, 'classifier__fit_prio... 0.655125 0.645586 0.643166 0.647959 0.005163 13 22 1.043618 0.098733 0.450375 0.061660 1 False 0.1 20 {'classifier__alpha': 1, 'classifier__fit_prio... 0.654254 0.645409 0.643514 0.647726 0.004680 14 7 1.301459 0.062143 0.660748 0.056054 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 1.433934 0.155240 0.636608 0.024064 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 1.325535 0.073539 0.672542 0.085835 1 True 0.05 100 {'classifier__alpha': 1, 'classifier__fit_prio... 0.587097 0.577411 0.580170 0.581560 0.004075 17 17 1.237677 0.070063 0.651091 0.102538 1 True 0.1 100 {'classifier__alpha': 1, 'classifier__fit_prio... 0.585713 0.577197 0.580763 0.581224 0.003492 18 2 1.394873 0.105286 0.637073 0.041708 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 1.270732 0.013414 0.620984 0.025393 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 1.177219 0.039633 0.586308 0.044247 1 False 0.05 100 {'classifier__alpha': 1, 'classifier__fit_prio... 0.581096 0.572833 0.576280 0.576736 0.003389 21 8 1.165583 0.046999 0.603675 0.031774 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 0.907388 0.121543 0.354655 0.023775 1 False 0.1 100 {'classifier__alpha': 1, 'classifier__fit_prio... 0.579769 0.572373 0.576699 0.576280 0.003034 23 11 1.210845 0.041339 0.653606 0.028117 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 [6]: Copied! from sklearn import metrics import matplotlib.pyplot as plt y_predicted = classifier . predict ( X_test ) % matplotlib inline plt . rcParams [ \"figure.figsize\" ] = ( 30 , 15 ) plt . rcParams [ \"figure.facecolor\" ] = \"white\" plt . rcParams [ \"font.size\" ] = 12 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\" , ) None from sklearn import metrics import matplotlib.pyplot as plt y_predicted = classifier.predict(X_test) %matplotlib inline plt.rcParams[\"figure.figsize\"] = (30, 15) plt.rcParams[\"figure.facecolor\"] = \"white\" plt.rcParams[\"font.size\"] = 12 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\", ) None 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 ) 1. 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 throughpout (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 ) 1. 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 throughpout (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 startup 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. 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 necceseary 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 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 startup 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. 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 necceseary 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 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 the @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-heavy operation return f 'Hi { your_name } !' With decorators # GreatAI can decorate already decorated functions. The only restriction is that @GreatAI.create always has to 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 . 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 of 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 @use_model ( 'name_of_my_model' , version = 'latest' ) 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 . 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' , validator = 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 storing models 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 the @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-heavy 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 always has to 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 . 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 of 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 @use_model ( 'name_of_my_model' , version = 'latest' ) 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 . 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' , validator = 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 storing models 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 a 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 on, 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 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 a 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 on, 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 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 3.8 or later is required. This will work on all major operating systems. 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. 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":"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 3.8 or later is required. This will work on all major operating systems.","title":"Installation guide"},{"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. 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/large_file/","text":"How to use LargeFile-s # The functions save_model and @use_model wrap LargeFile instances. Hence, besides configuring LargeFile, users have few reasons to use LargeFile-s 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 ' ) # By default the latest version is returned # 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 \"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 LargeFile-s"},{"location":"how-to-guides/large_file/#how-to-use-largefile-s","text":"The functions save_model and @use_model wrap LargeFile instances. Hence, besides configuring LargeFile, users have few reasons to use LargeFile-s directly.","title":"How to use LargeFile-s"},{"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 ' ) # By default the latest version is returned # 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 \"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/scraping/","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; 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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: 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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 [ ]: Copied! import httpx transport = httpx . AsyncHTTPTransport ( retries = 2 ) limits = httpx . Limits ( max_connections = None ) timeout = httpx . Timeout ( connect = 60.0 , read = 300 , write = 60 , pool = None ) async with httpx . AsyncClient ( transport = transport , limits = limits , timeout = timeout ) as client : pass import httpx transport = httpx.AsyncHTTPTransport(retries=2) limits = httpx.Limits(max_connections=None) timeout = httpx.Timeout(connect=60.0, read=300, write=60, pool=None) async with httpx.AsyncClient( transport=transport, limits=limits, timeout=timeout ) as client: pass In [1]: Copied! from great_ai import configure , RouteConfig from great_ai import GreatAI @GreatAI . create def greeter ( your_name : str ) -> str : return f \"Hi { your_name } \" greeter ( \"Bob\" ) from great_ai import configure, RouteConfig from great_ai import GreatAI @GreatAI.create def greeter(your_name: str) -> str: return f\"Hi {your_name}\" greeter(\"Bob\") 2022-07-11 19:00:27 | WARNING | Environment variable ENVIRONMENT is not set, defaulting to development mode \u203c\ufe0f 2022-07-11 19:00:27 | WARNING | Cannot find credentials files, defaulting to using ParallelTinyDbDriver 2022-07-11 19:00:27 | WARNING | The selected tracing database (ParallelTinyDbDriver) is not recommended for production 2022-07-11 19:00:27 | WARNING | Cannot find credentials files, defaulting to using LargeFileLocal 2022-07-11 19:00:27 | INFO | GreatAI (v0.1.3): configured \u2705 2022-07-11 19:00:27 | INFO | \ud83d\udd29 tracing_database: ParallelTinyDbDriver 2022-07-11 19:00:27 | INFO | \ud83d\udd29 large_file_implementation: LargeFileLocal 2022-07-11 19:00:27 | INFO | \ud83d\udd29 is_production: False 2022-07-11 19:00:27 | INFO | \ud83d\udd29 should_log_exception_stack: True 2022-07-11 19:00:27 | INFO | \ud83d\udd29 prediction_cache_size: 512 2022-07-11 19:00:27 | INFO | \ud83d\udd29 dashboard_table_size: 50 2022-07-11 19:00:27 | WARNING | You still need to check whether you follow all best practices before trusting your deployment. 2022-07-11 19:00:27 | WARNING | > Find out more at https://se-ml.github.io/practices Out[1]: Trace[str]({'created': '2022-07-11T17:00:27.064568', 'exception': None, 'feedback': None, 'logged_values': {'arg:your_name:length': 3, 'arg:your_name:value': 'Bob'}, 'models': [], 'original_execution_time_ms': 0.0896, 'output': 'Hi Bob', 'tags': ['greeter', 'online', 'development'], 'trace_id': '8ff5b268-2613-4e85-96ae-f248666a051f'})","title":"Scraping"},{"location":"how-to-guides/use-service/","text":"How to deploy a GreatAI service # 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 this end, uvicorn is wrapped by the great-ai command-line utility which, among others, 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 # Similarly 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 ) 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 it's 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 deploy a GreatAI service"},{"location":"how-to-guides/use-service/#how-to-deploy-a-greatai-service","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 deploy a GreatAI service"},{"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 this end, uvicorn is wrapped by the great-ai command-line utility which, among others, 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":"Similarly 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 ) 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 it's 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. 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 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. 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] 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 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 @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 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 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] 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 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. Source code in great_ai/models/use_model.py 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 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 , * , validator = lambda _ : True , disable_logging = False ) # Control the validation and logging of function parameters. 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' , validator = 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 validator Callable [[ Any ], bool ] Optional validator 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 def parameter ( parameter_name : str , * , validator : Callable [[ Any ], bool ] = lambda _ : True , disable_logging : bool = False , ) -> Callable [[ F ], F ]: \"\"\"Control the validation and logging of function parameters. 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', validator=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 validator: Optional validator 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 validator ( 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 ) # Source code in great_ai/parameters/log_metric.py 8 9 10 11 12 13 14 15 def log_metric ( argument_name : str , value : Any ) -> None : 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 permissable 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 permissable 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 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 a 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 a 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. 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 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. 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] 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 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 @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 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 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] 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 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. Source code in great_ai/models/use_model.py 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 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. 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' , validator = 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 validator Callable [[ Any ], bool ] Optional validator 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 def parameter ( parameter_name : str , * , validator : Callable [[ Any ], bool ] = lambda _ : True , disable_logging : bool = False , ) -> Callable [[ F ], F ]: \"\"\"Control the validation and logging of function parameters. 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', validator=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 validator: Optional validator 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 validator ( 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":"Source code in great_ai/parameters/log_metric.py 8 9 10 11 12 13 14 15 def log_metric ( argument_name : str , value : Any ) -> None : 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 permissable 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 permissable 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 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 a 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 a 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 * LargeFileLocal # Bases: LargeFileBase 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 class LargeFileLocal ( LargeFileBase ): 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 : 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 Store large files in S3. Use local cache for speed up. Examples: ``` with LargeFile(\"test.txt\", \"w\", keep_last_n=3) as f: for i in range(1000000): f.write('test ') with LargeFile(\"test.txt\", \"r\") as f: print(f.readlines()[0]) path_to_cached_text_file = LargeFile(\"test.txt\", version=0).get() ``` By default, files are stored in the \".cache\" folder and the least recently use is deleted after the overall size reaches 30 GBs. Change it with the following properties. ``` LargeFile.cache_path = Path(\".cache\") LargeFile.max_cache_size = \"30GB\" ``` Source code in great_ai/large_file/large_file/large_file_s3.py 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 class LargeFileS3 ( LargeFileBase ): \"\"\" Store large files in S3. Use local cache for speed up. Examples: ``` with LargeFile(\"test.txt\", \"w\", keep_last_n=3) as f: for i in range(1000000): f.write('test\\n') with LargeFile(\"test.txt\", \"r\") as f: print(f.readlines()[0]) path_to_cached_text_file = LargeFile(\"test.txt\", version=0).get() ``` By default, files are stored in the \".cache\" folder and the least recently use is deleted after the overall size reaches 30 GBs. Change it with the following properties. ``` LargeFile.cache_path = Path(\".cache\") LargeFile.max_cache_size = \"30GB\" ``` \"\"\" 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 Source code in great_ai/large_file/large_file/large_file_mongo.py 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 class LargeFileMongo ( LargeFileBase ): 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 Store large files remotely. Use local cache for speed up. Examples: ``` with LargeFile(\"test.txt\", \"w\", keep_last_n=3) as f: for i in range(1000000): f.write('test ') with LargeFile(\"test.txt\", \"r\") as f: print(f.readlines()[0]) path_to_cached_text_file = LargeFile(\"test.txt\", version=0).get() ``` By default, files are stored in the \".cache\" folder and the least recently use is deleted after the overall size reaches 30 GBs. Change it with the following properties. ``` LargeFile.cache_path = Path(\".cache\") LargeFile.max_cache_size = \"30GB\" ``` Source code in great_ai/large_file/large_file/large_file_base.py 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 class LargeFileBase ( ABC ): \"\"\" Store large files remotely. Use local cache for speed up. Examples: ``` with LargeFile(\"test.txt\", \"w\", keep_last_n=3) as f: for i in range(1000000): f.write('test\\n') with LargeFile(\"test.txt\", \"r\") as f: print(f.readlines()[0]) path_to_cached_text_file = LargeFile(\"test.txt\", version=0).get() ``` By default, files are stored in the \".cache\" folder and the least recently use is deleted after the overall size reaches 30 GBs. Change it with the following properties. ``` LargeFile.cache_path = Path(\".cache\") LargeFile.max_cache_size = \"30GB\" ``` \"\"\" 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 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 : 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 : cls . initialized = True def __enter__ ( self ) -> IO : self . _file : IO [ Any ] = ( tempfile . NamedTemporaryFile ( mode = self . _mode , buffering = self . _buffering , encoding = self . _encoding , newline = self . _newline , errors = self . _errors , delete = False , prefix = \"large_file-\" , ) 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 _local_name ( self ) -> str : return f \" { self . _name }{ CACHE_NAME_VERSION_SEPARATOR }{ self . version } \" @property def version ( self ) -> int : return cast ( int , self . _version ) @lru_cache ( 1 ) def get ( self , hide_progress : bool = False ) -> Path : 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 : 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 : self . _keep_last_n = 0 self . _delete_old_remote_versions () 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.\" ) @property def versions_pretty ( self ) -> str : return \", \" . join (( str ( i . version ) for i in self . _instances )) def clean_up ( self ) -> None : self . _delete_old_remote_versions () self . _prune_cache () 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":"LargeFile"},{"location":"reference/large-file/#largefile","text":"from great_ai.large_file import *","title":"LargeFile"},{"location":"reference/large-file/#great_ai.large_file.LargeFileLocal","text":"Bases: LargeFileBase 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 class LargeFileLocal ( LargeFileBase ): 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 : 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 Store large files in S3. Use local cache for speed up. Examples: ``` with LargeFile(\"test.txt\", \"w\", keep_last_n=3) as f: for i in range(1000000): f.write('test ') with LargeFile(\"test.txt\", \"r\") as f: print(f.readlines()[0]) path_to_cached_text_file = LargeFile(\"test.txt\", version=0).get() ``` By default, files are stored in the \".cache\" folder and the least recently use is deleted after the overall size reaches 30 GBs. Change it with the following properties. ``` LargeFile.cache_path = Path(\".cache\") LargeFile.max_cache_size = \"30GB\" ``` Source code in great_ai/large_file/large_file/large_file_s3.py 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 class LargeFileS3 ( LargeFileBase ): \"\"\" Store large files in S3. Use local cache for speed up. Examples: ``` with LargeFile(\"test.txt\", \"w\", keep_last_n=3) as f: for i in range(1000000): f.write('test\\n') with LargeFile(\"test.txt\", \"r\") as f: print(f.readlines()[0]) path_to_cached_text_file = LargeFile(\"test.txt\", version=0).get() ``` By default, files are stored in the \".cache\" folder and the least recently use is deleted after the overall size reaches 30 GBs. Change it with the following properties. ``` LargeFile.cache_path = Path(\".cache\") LargeFile.max_cache_size = \"30GB\" ``` \"\"\" 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 Source code in great_ai/large_file/large_file/large_file_mongo.py 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 class LargeFileMongo ( LargeFileBase ): 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 Store large files remotely. Use local cache for speed up. Examples: ``` with LargeFile(\"test.txt\", \"w\", keep_last_n=3) as f: for i in range(1000000): f.write('test ') with LargeFile(\"test.txt\", \"r\") as f: print(f.readlines()[0]) path_to_cached_text_file = LargeFile(\"test.txt\", version=0).get() ``` By default, files are stored in the \".cache\" folder and the least recently use is deleted after the overall size reaches 30 GBs. Change it with the following properties. ``` LargeFile.cache_path = Path(\".cache\") LargeFile.max_cache_size = \"30GB\" ``` Source code in great_ai/large_file/large_file/large_file_base.py 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 class LargeFileBase ( ABC ): \"\"\" Store large files remotely. Use local cache for speed up. Examples: ``` with LargeFile(\"test.txt\", \"w\", keep_last_n=3) as f: for i in range(1000000): f.write('test\\n') with LargeFile(\"test.txt\", \"r\") as f: print(f.readlines()[0]) path_to_cached_text_file = LargeFile(\"test.txt\", version=0).get() ``` By default, files are stored in the \".cache\" folder and the least recently use is deleted after the overall size reaches 30 GBs. Change it with the following properties. ``` LargeFile.cache_path = Path(\".cache\") LargeFile.max_cache_size = \"30GB\" ``` \"\"\" 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 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 : 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 : cls . initialized = True def __enter__ ( self ) -> IO : self . _file : IO [ Any ] = ( tempfile . NamedTemporaryFile ( mode = self . _mode , buffering = self . _buffering , encoding = self . _encoding , newline = self . _newline , errors = self . _errors , delete = False , prefix = \"large_file-\" , ) 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 _local_name ( self ) -> str : return f \" { self . _name }{ CACHE_NAME_VERSION_SEPARATOR }{ self . version } \" @property def version ( self ) -> int : return cast ( int , self . _version ) @lru_cache ( 1 ) def get ( self , hide_progress : bool = False ) -> Path : 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 : 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 : self . _keep_last_n = 0 self . _delete_old_remote_versions () 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.\" ) @property def versions_pretty ( self ) -> str : return \", \" . join (( str ( i . version ) for i in self . _instances )) def clean_up ( self ) -> None : self . _delete_old_remote_versions () self . _prune_cache () 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/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 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 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 () 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.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.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 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.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.predict_language][]. Returns: English name of language. \"\"\" if not language_code : language_code = \"und\" return Language . get ( language_code ) . display_name () 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 startup 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 startup 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 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 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. For more info, check-out the scraping guide . 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 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 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 () 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.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.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 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.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.predict_language][]. Returns: English name of language. \"\"\" if not language_code : language_code = \"und\" return Language . get ( language_code ) . display_name ()","title":"get_sentences()"},{"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 startup 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 startup 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 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 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. For more info, check-out the scraping guide .","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 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 class Trace ( Generic [ T ], HashableBaseModel ): 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 13 14 15 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 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 class Trace ( Generic [ T ], HashableBaseModel ): 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 13 14 15 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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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(); 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 struggles, 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 has 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 struggles, 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 has 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 seperate 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, with a line being return (in text mode) with each iteration. 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 seperate 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, with a line being return (in text mode) with each iteration. 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 quasi-shadow deployment Version and store models and data on shared infrastructure (MongoDB GridFS, S3-compatible storage, shared local-volume) Automatically scaffolded custom REST API (and OpenAPI schema) for easy integration Input validation Sensible cache-policy Seamless support for both synchronous and async 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 high-level overview and analysing traces 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 hello-world.py from great_ai import GreatAI @GreatAI . create #(1) def hello_world ( name : str ) -> str : #(2) return f \"Hello { name } !\" @GreatAI.create wraps your hello_world 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, hello_world could be an inference function of some AI/ML application. But it could also just wrap a black-box solution of some SaaS. Either ways, it is imperative to have continuos oversight of the services you provide and data you process especially in the context of AI/ML applications. terminal great-ai hello-world.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, a number of 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 use those, 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 quasi-shadow deployment Version and store models and data on shared infrastructure (MongoDB GridFS, S3-compatible storage, shared local-volume) Automatically scaffolded custom REST API (and OpenAPI schema) for easy integration Input validation Sensible cache-policy Seamless support for both synchronous and async 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 high-level overview and analysing traces","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 hello-world.py from great_ai import GreatAI @GreatAI . create #(1) def hello_world ( name : str ) -> str : #(2) return f \"Hello { name } !\" @GreatAI.create wraps your hello_world 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, hello_world could be an inference function of some AI/ML application. But it could also just wrap a black-box solution of some SaaS. Either ways, it is imperative to have continuos oversight of the services you provide and data you process especially in the context of AI/ML applications. terminal great-ai hello-world.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, a number of 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 use those, 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 (available soon)","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 (available soon)","title":"Explanation"},{"location":"examples/scibert/","text":"Summarising scientific publications from a tech-transfer perspective # This example shows how great-ai is used in practice at ScoutinScience. Check out the code","title":"Explainable SciBERT"},{"location":"examples/scibert/#summarising-scientific-publications-from-a-tech-transfer-perspective","text":"This example shows how great-ai is used in practice at ScoutinScience. Check out the code","title":"Summarising scientific publications from a tech-transfer perspective"},{"location":"examples/scibert/views/__init__/","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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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: 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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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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); 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} /* 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: 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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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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 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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(); 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 ]]]: # Extract response = urllib . request . urlopen ( f \"https://s3-us-west-2.amazonaws.com/ai2-s2-research-public/\" \"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]]]: # Extract response = urllib.request.urlopen( f\"https://s3-us-west-2.amazonaws.com/ai2-s2-research-public/\" \"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 [04:42<00:00, 70.62s/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 [8]: 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) 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 ]]]: # Extract response = urllib . request . urlopen ( f \"https://s3-us-west-2.amazonaws.com/ai2-s2-research-public/\" \"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]]]: # Extract response = urllib.request.urlopen( f\"https://s3-us-west-2.amazonaws.com/ai2-s2-research-public/\" \"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 [04:42<00:00, 70.62s/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 [8]: 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)","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()); }(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); 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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: 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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\" , validator = 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\", validator=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 (v0.1.4): 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 small-domain-prediction Latest version of small-domain-prediction is 0 (from versions: 0) File small-domain-prediction-0 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\" ] = ( 30 , 15 ) plt . rcParams [ \"figure.facecolor\" ] = \"white\" plt . rcParams [ \"font.size\" ] = 12 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\" , ) 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\"] = (30, 15) plt.rcParams[\"figure.facecolor\"] = \"white\" plt.rcParams[\"font.size\"] = 12 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\", ) 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 12332/12332 [01:27<00:00, 141.08it/s] precision recall f1-score support Art 0.43 0.35 0.39 125 Biology 0.77 0.83 0.80 1209 Business 0.50 0.71 0.59 312 Chemistry 0.80 0.67 0.73 1194 Computer Science 0.77 0.76 0.77 1293 Economics 0.64 0.58 0.61 251 Engineering 0.55 0.53 0.54 815 Environmental Science 0.54 0.56 0.55 230 Geography 0.54 0.44 0.48 277 Geology 0.76 0.67 0.71 228 History 0.29 0.21 0.24 102 Materials Science 0.73 0.81 0.77 1053 Mathematics 0.80 0.70 0.75 551 Medicine 0.95 0.77 0.85 2794 Philosophy 0.61 0.12 0.21 88 Physics 0.67 0.76 0.72 605 Political Science 0.43 0.52 0.47 297 Psychology 0.53 0.80 0.63 600 Sociology 0.32 0.60 0.42 308 accuracy 0.71 12332 macro avg 0.61 0.60 0.59 12332 weighted avg 0.73 0.71 0.71 12332","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\" , validator = 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\", validator=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 (v0.1.4): 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 small-domain-prediction Latest version of small-domain-prediction is 0 (from versions: 0) File small-domain-prediction-0 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\" ] = ( 30 , 15 ) plt . rcParams [ \"figure.facecolor\" ] = \"white\" plt . rcParams [ \"font.size\" ] = 12 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\" , ) 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\"] = (30, 15) plt.rcParams[\"figure.facecolor\"] = \"white\" plt.rcParams[\"font.size\"] = 12 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\", ) 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 12332/12332 [01:27<00:00, 141.08it/s] precision recall f1-score support Art 0.43 0.35 0.39 125 Biology 0.77 0.83 0.80 1209 Business 0.50 0.71 0.59 312 Chemistry 0.80 0.67 0.73 1194 Computer Science 0.77 0.76 0.77 1293 Economics 0.64 0.58 0.61 251 Engineering 0.55 0.53 0.54 815 Environmental Science 0.54 0.56 0.55 230 Geography 0.54 0.44 0.48 277 Geology 0.76 0.67 0.71 228 History 0.29 0.21 0.24 102 Materials Science 0.73 0.81 0.77 1053 Mathematics 0.80 0.70 0.75 551 Medicine 0.95 0.77 0.85 2794 Philosophy 0.61 0.12 0.21 88 Physics 0.67 0.76 0.72 605 Political Science 0.43 0.52 0.47 297 Psychology 0.53 0.80 0.63 600 Sociology 0.32 0.60 0.42 308 accuracy 0.71 12332 macro avg 0.61 0.60 0.59 12332 weighted avg 0.73 0.71 0.71 12332","title":"Check accuracy on the test split"},{"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()); 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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-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(); 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 (v0.1.4): 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 In [23]: 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 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 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 9.447799 0.785535 4.260490 0.180851 0.5 False 0.05 20 {'classifier__alpha': 0.5, 'classifier__fit_pr... 0.517618 0.518688 0.513803 0.516703 0.002096 1 10 9.750011 0.671769 4.791153 0.544862 0.5 False 0.1 20 {'classifier__alpha': 0.5, 'classifier__fit_pr... 0.513254 0.517940 0.509116 0.513437 0.003605 2 11 9.398448 0.656403 5.194527 0.303382 0.5 False 0.1 100 {'classifier__alpha': 0.5, 'classifier__fit_pr... 0.508306 0.502244 0.501839 0.504129 0.002958 3 8 9.285461 0.869226 5.030563 0.305472 0.5 False 0.05 100 {'classifier__alpha': 0.5, 'classifier__fit_pr... 0.504070 0.502116 0.500603 0.502263 0.001419 4 19 9.662259 0.234167 4.854762 0.126622 1 False 0.05 20 {'classifier__alpha': 1, 'classifier__fit_prio... 0.492205 0.504466 0.495489 0.497387 0.005182 5 23 9.024540 0.469833 3.235823 0.173482 1 False 0.1 100 {'classifier__alpha': 1, 'classifier__fit_prio... 0.501203 0.493465 0.492361 0.495676 0.003934 6 20 9.304917 0.192977 4.370798 0.255480 1 False 0.05 100 {'classifier__alpha': 1, 'classifier__fit_prio... 0.498869 0.494357 0.492431 0.495219 0.002698 7 22 9.642570 0.438559 3.332855 0.435930 1 False 0.1 20 {'classifier__alpha': 1, 'classifier__fit_prio... 0.479585 0.496964 0.485209 0.487253 0.007241 8 6 10.047471 0.473215 4.358744 0.051892 0.5 False 0.05 5 {'classifier__alpha': 0.5, 'classifier__fit_pr... 0.472578 0.486362 0.481865 0.480268 0.005739 9 5 10.287111 0.349778 4.908435 0.099558 0.5 True 0.1 100 {'classifier__alpha': 0.5, 'classifier__fit_pr... 0.474821 0.474690 0.478096 0.475869 0.001576 10 2 10.997282 0.851917 6.131619 0.274400 0.5 True 0.05 100 {'classifier__alpha': 0.5, 'classifier__fit_pr... 0.476218 0.471377 0.478548 0.475381 0.002987 11 9 9.147621 0.290876 5.392678 0.390403 0.5 False 0.1 5 {'classifier__alpha': 0.5, 'classifier__fit_pr... 0.458994 0.475008 0.469998 0.468000 0.006689 12 1 10.299674 0.486001 5.776907 0.462777 0.5 True 0.05 20 {'classifier__alpha': 0.5, 'classifier__fit_pr... 0.461036 0.466296 0.467280 0.464871 0.002741 13 4 10.771393 0.330218 4.961415 0.454726 0.5 True 0.1 20 {'classifier__alpha': 0.5, 'classifier__fit_pr... 0.454689 0.463340 0.457743 0.458591 0.003582 14 14 9.969123 0.715883 4.382175 0.216170 1 True 0.05 100 {'classifier__alpha': 1, 'classifier__fit_prio... 0.444039 0.449822 0.450524 0.448128 0.002906 15 17 9.439775 1.333323 4.914944 0.292653 1 True 0.1 100 {'classifier__alpha': 1, 'classifier__fit_prio... 0.440663 0.448419 0.443441 0.444174 0.003209 16 18 10.025776 0.198347 5.375965 0.220059 1 False 0.05 5 {'classifier__alpha': 1, 'classifier__fit_prio... 0.418284 0.428063 0.428696 0.425014 0.004766 17 13 9.251913 0.441015 5.085037 0.224299 1 True 0.05 20 {'classifier__alpha': 1, 'classifier__fit_prio... 0.400471 0.406275 0.406521 0.404422 0.002796 18 21 9.173751 0.112242 4.271786 0.293579 1 False 0.1 5 {'classifier__alpha': 1, 'classifier__fit_prio... 0.393559 0.402869 0.406431 0.400953 0.005427 19 16 9.683013 0.600089 4.924559 0.321389 1 True 0.1 20 {'classifier__alpha': 1, 'classifier__fit_prio... 0.387535 0.392661 0.393992 0.391396 0.002784 20 0 9.835260 0.220524 6.034349 0.586497 0.5 True 0.05 5 {'classifier__alpha': 0.5, 'classifier__fit_pr... 0.379471 0.381348 0.386078 0.382299 0.002780 21 3 11.905621 0.329256 5.571229 0.541937 0.5 True 0.1 5 {'classifier__alpha': 0.5, 'classifier__fit_pr... 0.368234 0.367599 0.372849 0.369561 0.002339 22 12 10.378068 0.227200 5.247946 0.126687 1 True 0.05 5 {'classifier__alpha': 1, 'classifier__fit_prio... 0.278087 0.278157 0.278143 0.278129 0.000030 23 15 10.375661 0.139826 5.239350 0.298795 1 True 0.1 5 {'classifier__alpha': 1, 'classifier__fit_prio... 0.259347 0.262720 0.258716 0.260261 0.001758 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-1 Compressing small-domain-prediction-1 Model small-domain-prediction uploaded with version 1 Out[6]: 'small-domain-prediction:1' 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 (v0.1.4): 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 In [23]: 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 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 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 9.447799 0.785535 4.260490 0.180851 0.5 False 0.05 20 {'classifier__alpha': 0.5, 'classifier__fit_pr... 0.517618 0.518688 0.513803 0.516703 0.002096 1 10 9.750011 0.671769 4.791153 0.544862 0.5 False 0.1 20 {'classifier__alpha': 0.5, 'classifier__fit_pr... 0.513254 0.517940 0.509116 0.513437 0.003605 2 11 9.398448 0.656403 5.194527 0.303382 0.5 False 0.1 100 {'classifier__alpha': 0.5, 'classifier__fit_pr... 0.508306 0.502244 0.501839 0.504129 0.002958 3 8 9.285461 0.869226 5.030563 0.305472 0.5 False 0.05 100 {'classifier__alpha': 0.5, 'classifier__fit_pr... 0.504070 0.502116 0.500603 0.502263 0.001419 4 19 9.662259 0.234167 4.854762 0.126622 1 False 0.05 20 {'classifier__alpha': 1, 'classifier__fit_prio... 0.492205 0.504466 0.495489 0.497387 0.005182 5 23 9.024540 0.469833 3.235823 0.173482 1 False 0.1 100 {'classifier__alpha': 1, 'classifier__fit_prio... 0.501203 0.493465 0.492361 0.495676 0.003934 6 20 9.304917 0.192977 4.370798 0.255480 1 False 0.05 100 {'classifier__alpha': 1, 'classifier__fit_prio... 0.498869 0.494357 0.492431 0.495219 0.002698 7 22 9.642570 0.438559 3.332855 0.435930 1 False 0.1 20 {'classifier__alpha': 1, 'classifier__fit_prio... 0.479585 0.496964 0.485209 0.487253 0.007241 8 6 10.047471 0.473215 4.358744 0.051892 0.5 False 0.05 5 {'classifier__alpha': 0.5, 'classifier__fit_pr... 0.472578 0.486362 0.481865 0.480268 0.005739 9 5 10.287111 0.349778 4.908435 0.099558 0.5 True 0.1 100 {'classifier__alpha': 0.5, 'classifier__fit_pr... 0.474821 0.474690 0.478096 0.475869 0.001576 10 2 10.997282 0.851917 6.131619 0.274400 0.5 True 0.05 100 {'classifier__alpha': 0.5, 'classifier__fit_pr... 0.476218 0.471377 0.478548 0.475381 0.002987 11 9 9.147621 0.290876 5.392678 0.390403 0.5 False 0.1 5 {'classifier__alpha': 0.5, 'classifier__fit_pr... 0.458994 0.475008 0.469998 0.468000 0.006689 12 1 10.299674 0.486001 5.776907 0.462777 0.5 True 0.05 20 {'classifier__alpha': 0.5, 'classifier__fit_pr... 0.461036 0.466296 0.467280 0.464871 0.002741 13 4 10.771393 0.330218 4.961415 0.454726 0.5 True 0.1 20 {'classifier__alpha': 0.5, 'classifier__fit_pr... 0.454689 0.463340 0.457743 0.458591 0.003582 14 14 9.969123 0.715883 4.382175 0.216170 1 True 0.05 100 {'classifier__alpha': 1, 'classifier__fit_prio... 0.444039 0.449822 0.450524 0.448128 0.002906 15 17 9.439775 1.333323 4.914944 0.292653 1 True 0.1 100 {'classifier__alpha': 1, 'classifier__fit_prio... 0.440663 0.448419 0.443441 0.444174 0.003209 16 18 10.025776 0.198347 5.375965 0.220059 1 False 0.05 5 {'classifier__alpha': 1, 'classifier__fit_prio... 0.418284 0.428063 0.428696 0.425014 0.004766 17 13 9.251913 0.441015 5.085037 0.224299 1 True 0.05 20 {'classifier__alpha': 1, 'classifier__fit_prio... 0.400471 0.406275 0.406521 0.404422 0.002796 18 21 9.173751 0.112242 4.271786 0.293579 1 False 0.1 5 {'classifier__alpha': 1, 'classifier__fit_prio... 0.393559 0.402869 0.406431 0.400953 0.005427 19 16 9.683013 0.600089 4.924559 0.321389 1 True 0.1 20 {'classifier__alpha': 1, 'classifier__fit_prio... 0.387535 0.392661 0.393992 0.391396 0.002784 20 0 9.835260 0.220524 6.034349 0.586497 0.5 True 0.05 5 {'classifier__alpha': 0.5, 'classifier__fit_pr... 0.379471 0.381348 0.386078 0.382299 0.002780 21 3 11.905621 0.329256 5.571229 0.541937 0.5 True 0.1 5 {'classifier__alpha': 0.5, 'classifier__fit_pr... 0.368234 0.367599 0.372849 0.369561 0.002339 22 12 10.378068 0.227200 5.247946 0.126687 1 True 0.05 5 {'classifier__alpha': 1, 'classifier__fit_prio... 0.278087 0.278157 0.278143 0.278129 0.000030 23 15 10.375661 0.139826 5.239350 0.298795 1 True 0.1 5 {'classifier__alpha': 1, 'classifier__fit_prio... 0.259347 0.262720 0.258716 0.260261 0.001758 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-1 Compressing small-domain-prediction-1 Model small-domain-prediction uploaded with version 1 Out[6]: 'small-domain-prediction:1'","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 , 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 ( \"mag/train.txt\" , encoding = \"utf-8\" ) as f : training_data = list ( tqdm ( 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 ( \"mag/test.txt\" , encoding = \"utf-8\" ) as f : test_data = list ( tqdm ( 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, 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(\"mag/train.txt\", encoding=\"utf-8\") as f: training_data = list(tqdm(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(\"mag/test.txt\", encoding=\"utf-8\") as f: test_data = list(tqdm(parallel_map(preprocess, f.readlines()))) X_test = [d[0] for d in test_data] y_test = [d[1] for d in test_data] 84000it [00:09, 8647.60it/s] 22399it [00:03, 6368.63it/s] In [2]: Copied! import pandas as pd from collections import Counter import plotly.express as px df = pd . DataFrame ( Counter ( y_train ) . most_common (), columns = [ \"domain\" , \"count\" ]) px . bar ( df , \"domain\" , \"count\" , width = 1200 , height = 400 ) . show () import pandas as pd from collections import Counter import plotly.express as px df = pd.DataFrame(Counter(y_train).most_common(), columns=[\"domain\", \"count\"]) px.bar(df, \"domain\", \"count\", width=1200, height=400).show() 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 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 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 1.227354 0.011710 0.652844 0.040511 1 True 0.05 5 {'classifier__alpha': 1, 'classifier__fit_prio... 0.677283 0.670398 0.665671 0.671118 0.004768 1 15 1.279783 0.037969 0.634761 0.068606 1 True 0.1 5 {'classifier__alpha': 1, 'classifier__fit_prio... 0.676970 0.670244 0.666123 0.671112 0.004470 2 18 1.332854 0.171405 0.743611 0.109792 1 False 0.05 5 {'classifier__alpha': 1, 'classifier__fit_prio... 0.675678 0.669321 0.665305 0.670101 0.004271 3 21 1.253222 0.072134 0.612344 0.016771 1 False 0.1 5 {'classifier__alpha': 1, 'classifier__fit_prio... 0.675569 0.669482 0.665225 0.670092 0.004245 4 3 1.472035 0.080849 0.654935 0.044302 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 1.380641 0.054306 0.739966 0.053385 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 1.284987 0.113903 0.696876 0.003757 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 1.291148 0.101837 0.686561 0.083989 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 1.268873 0.042412 0.645649 0.022738 1 True 0.05 20 {'classifier__alpha': 1, 'classifier__fit_prio... 0.656459 0.647433 0.644864 0.649585 0.004972 9 16 1.147120 0.009335 0.599006 0.045637 1 True 0.1 20 {'classifier__alpha': 1, 'classifier__fit_prio... 0.656196 0.646960 0.645436 0.649530 0.004754 10 4 1.186988 0.057330 0.642233 0.078248 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 1.413231 0.153288 0.765960 0.099535 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 1.193013 0.079000 0.691768 0.027448 1 False 0.05 20 {'classifier__alpha': 1, 'classifier__fit_prio... 0.655125 0.645586 0.643166 0.647959 0.005163 13 22 1.043618 0.098733 0.450375 0.061660 1 False 0.1 20 {'classifier__alpha': 1, 'classifier__fit_prio... 0.654254 0.645409 0.643514 0.647726 0.004680 14 7 1.301459 0.062143 0.660748 0.056054 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 1.433934 0.155240 0.636608 0.024064 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 1.325535 0.073539 0.672542 0.085835 1 True 0.05 100 {'classifier__alpha': 1, 'classifier__fit_prio... 0.587097 0.577411 0.580170 0.581560 0.004075 17 17 1.237677 0.070063 0.651091 0.102538 1 True 0.1 100 {'classifier__alpha': 1, 'classifier__fit_prio... 0.585713 0.577197 0.580763 0.581224 0.003492 18 2 1.394873 0.105286 0.637073 0.041708 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 1.270732 0.013414 0.620984 0.025393 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 1.177219 0.039633 0.586308 0.044247 1 False 0.05 100 {'classifier__alpha': 1, 'classifier__fit_prio... 0.581096 0.572833 0.576280 0.576736 0.003389 21 8 1.165583 0.046999 0.603675 0.031774 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 0.907388 0.121543 0.354655 0.023775 1 False 0.1 100 {'classifier__alpha': 1, 'classifier__fit_prio... 0.579769 0.572373 0.576699 0.576280 0.003034 23 11 1.210845 0.041339 0.653606 0.028117 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 [6]: Copied! from sklearn import metrics import matplotlib.pyplot as plt y_predicted = classifier . predict ( X_test ) % matplotlib inline plt . rcParams [ \"figure.figsize\" ] = ( 30 , 15 ) plt . rcParams [ \"figure.facecolor\" ] = \"white\" plt . rcParams [ \"font.size\" ] = 12 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\" , ) None from sklearn import metrics import matplotlib.pyplot as plt y_predicted = classifier.predict(X_test) %matplotlib inline plt.rcParams[\"figure.figsize\"] = (30, 15) plt.rcParams[\"figure.facecolor\"] = \"white\" plt.rcParams[\"font.size\"] = 12 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\", ) None 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 , 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 ( \"mag/train.txt\" , encoding = \"utf-8\" ) as f : training_data = list ( tqdm ( 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 ( \"mag/test.txt\" , encoding = \"utf-8\" ) as f : test_data = list ( tqdm ( 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, 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(\"mag/train.txt\", encoding=\"utf-8\") as f: training_data = list(tqdm(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(\"mag/test.txt\", encoding=\"utf-8\") as f: test_data = list(tqdm(parallel_map(preprocess, f.readlines()))) X_test = [d[0] for d in test_data] y_test = [d[1] for d in test_data] 84000it [00:09, 8647.60it/s] 22399it [00:03, 6368.63it/s] In [2]: Copied! import pandas as pd from collections import Counter import plotly.express as px df = pd . DataFrame ( Counter ( y_train ) . most_common (), columns = [ \"domain\" , \"count\" ]) px . bar ( df , \"domain\" , \"count\" , width = 1200 , height = 400 ) . show () import pandas as pd from collections import Counter import plotly.express as px df = pd.DataFrame(Counter(y_train).most_common(), columns=[\"domain\", \"count\"]) px.bar(df, \"domain\", \"count\", width=1200, height=400).show() 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 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 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 1.227354 0.011710 0.652844 0.040511 1 True 0.05 5 {'classifier__alpha': 1, 'classifier__fit_prio... 0.677283 0.670398 0.665671 0.671118 0.004768 1 15 1.279783 0.037969 0.634761 0.068606 1 True 0.1 5 {'classifier__alpha': 1, 'classifier__fit_prio... 0.676970 0.670244 0.666123 0.671112 0.004470 2 18 1.332854 0.171405 0.743611 0.109792 1 False 0.05 5 {'classifier__alpha': 1, 'classifier__fit_prio... 0.675678 0.669321 0.665305 0.670101 0.004271 3 21 1.253222 0.072134 0.612344 0.016771 1 False 0.1 5 {'classifier__alpha': 1, 'classifier__fit_prio... 0.675569 0.669482 0.665225 0.670092 0.004245 4 3 1.472035 0.080849 0.654935 0.044302 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 1.380641 0.054306 0.739966 0.053385 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 1.284987 0.113903 0.696876 0.003757 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 1.291148 0.101837 0.686561 0.083989 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 1.268873 0.042412 0.645649 0.022738 1 True 0.05 20 {'classifier__alpha': 1, 'classifier__fit_prio... 0.656459 0.647433 0.644864 0.649585 0.004972 9 16 1.147120 0.009335 0.599006 0.045637 1 True 0.1 20 {'classifier__alpha': 1, 'classifier__fit_prio... 0.656196 0.646960 0.645436 0.649530 0.004754 10 4 1.186988 0.057330 0.642233 0.078248 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 1.413231 0.153288 0.765960 0.099535 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 1.193013 0.079000 0.691768 0.027448 1 False 0.05 20 {'classifier__alpha': 1, 'classifier__fit_prio... 0.655125 0.645586 0.643166 0.647959 0.005163 13 22 1.043618 0.098733 0.450375 0.061660 1 False 0.1 20 {'classifier__alpha': 1, 'classifier__fit_prio... 0.654254 0.645409 0.643514 0.647726 0.004680 14 7 1.301459 0.062143 0.660748 0.056054 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 1.433934 0.155240 0.636608 0.024064 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 1.325535 0.073539 0.672542 0.085835 1 True 0.05 100 {'classifier__alpha': 1, 'classifier__fit_prio... 0.587097 0.577411 0.580170 0.581560 0.004075 17 17 1.237677 0.070063 0.651091 0.102538 1 True 0.1 100 {'classifier__alpha': 1, 'classifier__fit_prio... 0.585713 0.577197 0.580763 0.581224 0.003492 18 2 1.394873 0.105286 0.637073 0.041708 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 1.270732 0.013414 0.620984 0.025393 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 1.177219 0.039633 0.586308 0.044247 1 False 0.05 100 {'classifier__alpha': 1, 'classifier__fit_prio... 0.581096 0.572833 0.576280 0.576736 0.003389 21 8 1.165583 0.046999 0.603675 0.031774 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 0.907388 0.121543 0.354655 0.023775 1 False 0.1 100 {'classifier__alpha': 1, 'classifier__fit_prio... 0.579769 0.572373 0.576699 0.576280 0.003034 23 11 1.210845 0.041339 0.653606 0.028117 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 [6]: Copied! from sklearn import metrics import matplotlib.pyplot as plt y_predicted = classifier . predict ( X_test ) % matplotlib inline plt . rcParams [ \"figure.figsize\" ] = ( 30 , 15 ) plt . rcParams [ \"figure.facecolor\" ] = \"white\" plt . rcParams [ \"font.size\" ] = 12 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\" , ) None from sklearn import metrics import matplotlib.pyplot as plt y_predicted = classifier.predict(X_test) %matplotlib inline plt.rcParams[\"figure.figsize\"] = (30, 15) plt.rcParams[\"figure.facecolor\"] = \"white\" plt.rcParams[\"font.size\"] = 12 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\", ) None 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 ) 1. 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 throughpout (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 ) 1. 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 throughpout (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 startup 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. 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 necceseary 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 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 startup 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. 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 necceseary 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 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 the @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-heavy operation return f 'Hi { your_name } !' With decorators # GreatAI can decorate already decorated functions. The only restriction is that @GreatAI.create always has to 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 . 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 of 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 @use_model ( 'name_of_my_model' , version = 'latest' ) 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 . 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' , validator = 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 storing models 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 the @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-heavy 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 always has to 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 . 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 of 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 @use_model ( 'name_of_my_model' , version = 'latest' ) 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 . 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' , validator = 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 storing models 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 a 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 on, 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 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 a 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 on, 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 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 3.8 or later is required. This will work on all major operating systems. 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. 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":"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 3.8 or later is required. This will work on all major operating systems.","title":"Installation guide"},{"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. 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/large_file/","text":"How to use LargeFile-s # The functions save_model and @use_model wrap LargeFile instances. Hence, besides configuring LargeFile, users have few reasons to use LargeFile-s 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 ' ) # By default the latest version is returned # 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 \"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 LargeFile-s"},{"location":"how-to-guides/large_file/#how-to-use-largefile-s","text":"The functions save_model and @use_model wrap LargeFile instances. Hence, besides configuring LargeFile, users have few reasons to use LargeFile-s directly.","title":"How to use LargeFile-s"},{"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 ' ) # By default the latest version is returned # 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 \"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/scraping/","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; 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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: 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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 [ ]: Copied! import httpx transport = httpx . AsyncHTTPTransport ( retries = 2 ) limits = httpx . Limits ( max_connections = None ) timeout = httpx . Timeout ( connect = 60.0 , read = 300 , write = 60 , pool = None ) async with httpx . AsyncClient ( transport = transport , limits = limits , timeout = timeout ) as client : pass import httpx transport = httpx.AsyncHTTPTransport(retries=2) limits = httpx.Limits(max_connections=None) timeout = httpx.Timeout(connect=60.0, read=300, write=60, pool=None) async with httpx.AsyncClient( transport=transport, limits=limits, timeout=timeout ) as client: pass In [1]: Copied! from great_ai import configure , RouteConfig from great_ai import GreatAI @GreatAI . create def greeter ( your_name : str ) -> str : return f \"Hi { your_name } \" greeter ( \"Bob\" ) from great_ai import configure, RouteConfig from great_ai import GreatAI @GreatAI.create def greeter(your_name: str) -> str: return f\"Hi {your_name}\" greeter(\"Bob\") 2022-07-11 19:00:27 | WARNING | Environment variable ENVIRONMENT is not set, defaulting to development mode \u203c\ufe0f 2022-07-11 19:00:27 | WARNING | Cannot find credentials files, defaulting to using ParallelTinyDbDriver 2022-07-11 19:00:27 | WARNING | The selected tracing database (ParallelTinyDbDriver) is not recommended for production 2022-07-11 19:00:27 | WARNING | Cannot find credentials files, defaulting to using LargeFileLocal 2022-07-11 19:00:27 | INFO | GreatAI (v0.1.3): configured \u2705 2022-07-11 19:00:27 | INFO | \ud83d\udd29 tracing_database: ParallelTinyDbDriver 2022-07-11 19:00:27 | INFO | \ud83d\udd29 large_file_implementation: LargeFileLocal 2022-07-11 19:00:27 | INFO | \ud83d\udd29 is_production: False 2022-07-11 19:00:27 | INFO | \ud83d\udd29 should_log_exception_stack: True 2022-07-11 19:00:27 | INFO | \ud83d\udd29 prediction_cache_size: 512 2022-07-11 19:00:27 | INFO | \ud83d\udd29 dashboard_table_size: 50 2022-07-11 19:00:27 | WARNING | You still need to check whether you follow all best practices before trusting your deployment. 2022-07-11 19:00:27 | WARNING | > Find out more at https://se-ml.github.io/practices Out[1]: Trace[str]({'created': '2022-07-11T17:00:27.064568', 'exception': None, 'feedback': None, 'logged_values': {'arg:your_name:length': 3, 'arg:your_name:value': 'Bob'}, 'models': [], 'original_execution_time_ms': 0.0896, 'output': 'Hi Bob', 'tags': ['greeter', 'online', 'development'], 'trace_id': '8ff5b268-2613-4e85-96ae-f248666a051f'})","title":"Scraping"},{"location":"how-to-guides/use-service/","text":"How to deploy a GreatAI service # 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 this end, uvicorn is wrapped by the great-ai command-line utility which, among others, 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 # Similarly 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 ) 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 it's 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 deploy a GreatAI service"},{"location":"how-to-guides/use-service/#how-to-deploy-a-greatai-service","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 deploy a GreatAI service"},{"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 this end, uvicorn is wrapped by the great-ai command-line utility which, among others, 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":"Similarly 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 ) 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 it's 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. 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 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. 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] 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 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 @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 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 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] 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 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. Source code in great_ai/models/use_model.py 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 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 , * , validator = lambda _ : True , disable_logging = False ) # Control the validation and logging of function parameters. 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' , validator = 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 validator Callable [[ Any ], bool ] Optional validator 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 def parameter ( parameter_name : str , * , validator : Callable [[ Any ], bool ] = lambda _ : True , disable_logging : bool = False , ) -> Callable [[ F ], F ]: \"\"\"Control the validation and logging of function parameters. 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', validator=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 validator: Optional validator 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 validator ( 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 ) # Source code in great_ai/parameters/log_metric.py 8 9 10 11 12 13 14 15 def log_metric ( argument_name : str , value : Any ) -> None : 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 permissable 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 permissable 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 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 a 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 a 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. 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 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. 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] 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 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 @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 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 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] 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 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. Source code in great_ai/models/use_model.py 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 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. 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' , validator = 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 validator Callable [[ Any ], bool ] Optional validator 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 def parameter ( parameter_name : str , * , validator : Callable [[ Any ], bool ] = lambda _ : True , disable_logging : bool = False , ) -> Callable [[ F ], F ]: \"\"\"Control the validation and logging of function parameters. 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', validator=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 validator: Optional validator 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 validator ( 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":"Source code in great_ai/parameters/log_metric.py 8 9 10 11 12 13 14 15 def log_metric ( argument_name : str , value : Any ) -> None : 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 permissable 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 permissable 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 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 a 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 a 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 * LargeFileLocal # Bases: LargeFileBase 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 class LargeFileLocal ( LargeFileBase ): 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 : 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 Store large files in S3. Use local cache for speed up. Examples: ``` with LargeFile(\"test.txt\", \"w\", keep_last_n=3) as f: for i in range(1000000): f.write('test ') with LargeFile(\"test.txt\", \"r\") as f: print(f.readlines()[0]) path_to_cached_text_file = LargeFile(\"test.txt\", version=0).get() ``` By default, files are stored in the \".cache\" folder and the least recently use is deleted after the overall size reaches 30 GBs. Change it with the following properties. ``` LargeFile.cache_path = Path(\".cache\") LargeFile.max_cache_size = \"30GB\" ``` Source code in great_ai/large_file/large_file/large_file_s3.py 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 class LargeFileS3 ( LargeFileBase ): \"\"\" Store large files in S3. Use local cache for speed up. Examples: ``` with LargeFile(\"test.txt\", \"w\", keep_last_n=3) as f: for i in range(1000000): f.write('test\\n') with LargeFile(\"test.txt\", \"r\") as f: print(f.readlines()[0]) path_to_cached_text_file = LargeFile(\"test.txt\", version=0).get() ``` By default, files are stored in the \".cache\" folder and the least recently use is deleted after the overall size reaches 30 GBs. Change it with the following properties. ``` LargeFile.cache_path = Path(\".cache\") LargeFile.max_cache_size = \"30GB\" ``` \"\"\" 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 Source code in great_ai/large_file/large_file/large_file_mongo.py 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 class LargeFileMongo ( LargeFileBase ): 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 Store large files remotely. Use local cache for speed up. Examples: ``` with LargeFile(\"test.txt\", \"w\", keep_last_n=3) as f: for i in range(1000000): f.write('test ') with LargeFile(\"test.txt\", \"r\") as f: print(f.readlines()[0]) path_to_cached_text_file = LargeFile(\"test.txt\", version=0).get() ``` By default, files are stored in the \".cache\" folder and the least recently use is deleted after the overall size reaches 30 GBs. Change it with the following properties. ``` LargeFile.cache_path = Path(\".cache\") LargeFile.max_cache_size = \"30GB\" ``` Source code in great_ai/large_file/large_file/large_file_base.py 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 class LargeFileBase ( ABC ): \"\"\" Store large files remotely. Use local cache for speed up. Examples: ``` with LargeFile(\"test.txt\", \"w\", keep_last_n=3) as f: for i in range(1000000): f.write('test\\n') with LargeFile(\"test.txt\", \"r\") as f: print(f.readlines()[0]) path_to_cached_text_file = LargeFile(\"test.txt\", version=0).get() ``` By default, files are stored in the \".cache\" folder and the least recently use is deleted after the overall size reaches 30 GBs. Change it with the following properties. ``` LargeFile.cache_path = Path(\".cache\") LargeFile.max_cache_size = \"30GB\" ``` \"\"\" 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 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 : 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 : cls . initialized = True def __enter__ ( self ) -> IO : self . _file : IO [ Any ] = ( tempfile . NamedTemporaryFile ( mode = self . _mode , buffering = self . _buffering , encoding = self . _encoding , newline = self . _newline , errors = self . _errors , delete = False , prefix = \"large_file-\" , ) 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 _local_name ( self ) -> str : return f \" { self . _name }{ CACHE_NAME_VERSION_SEPARATOR }{ self . version } \" @property def version ( self ) -> int : return cast ( int , self . _version ) @lru_cache ( 1 ) def get ( self , hide_progress : bool = False ) -> Path : 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 : 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 : self . _keep_last_n = 0 self . _delete_old_remote_versions () 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.\" ) @property def versions_pretty ( self ) -> str : return \", \" . join (( str ( i . version ) for i in self . _instances )) def clean_up ( self ) -> None : self . _delete_old_remote_versions () self . _prune_cache () 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":"LargeFile"},{"location":"reference/large-file/#largefile","text":"from great_ai.large_file import *","title":"LargeFile"},{"location":"reference/large-file/#great_ai.large_file.LargeFileLocal","text":"Bases: LargeFileBase 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 class LargeFileLocal ( LargeFileBase ): 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 : 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 Store large files in S3. Use local cache for speed up. Examples: ``` with LargeFile(\"test.txt\", \"w\", keep_last_n=3) as f: for i in range(1000000): f.write('test ') with LargeFile(\"test.txt\", \"r\") as f: print(f.readlines()[0]) path_to_cached_text_file = LargeFile(\"test.txt\", version=0).get() ``` By default, files are stored in the \".cache\" folder and the least recently use is deleted after the overall size reaches 30 GBs. Change it with the following properties. ``` LargeFile.cache_path = Path(\".cache\") LargeFile.max_cache_size = \"30GB\" ``` Source code in great_ai/large_file/large_file/large_file_s3.py 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 class LargeFileS3 ( LargeFileBase ): \"\"\" Store large files in S3. Use local cache for speed up. Examples: ``` with LargeFile(\"test.txt\", \"w\", keep_last_n=3) as f: for i in range(1000000): f.write('test\\n') with LargeFile(\"test.txt\", \"r\") as f: print(f.readlines()[0]) path_to_cached_text_file = LargeFile(\"test.txt\", version=0).get() ``` By default, files are stored in the \".cache\" folder and the least recently use is deleted after the overall size reaches 30 GBs. Change it with the following properties. ``` LargeFile.cache_path = Path(\".cache\") LargeFile.max_cache_size = \"30GB\" ``` \"\"\" 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 Source code in great_ai/large_file/large_file/large_file_mongo.py 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 class LargeFileMongo ( LargeFileBase ): 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 Store large files remotely. Use local cache for speed up. Examples: ``` with LargeFile(\"test.txt\", \"w\", keep_last_n=3) as f: for i in range(1000000): f.write('test ') with LargeFile(\"test.txt\", \"r\") as f: print(f.readlines()[0]) path_to_cached_text_file = LargeFile(\"test.txt\", version=0).get() ``` By default, files are stored in the \".cache\" folder and the least recently use is deleted after the overall size reaches 30 GBs. Change it with the following properties. ``` LargeFile.cache_path = Path(\".cache\") LargeFile.max_cache_size = \"30GB\" ``` Source code in great_ai/large_file/large_file/large_file_base.py 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 class LargeFileBase ( ABC ): \"\"\" Store large files remotely. Use local cache for speed up. Examples: ``` with LargeFile(\"test.txt\", \"w\", keep_last_n=3) as f: for i in range(1000000): f.write('test\\n') with LargeFile(\"test.txt\", \"r\") as f: print(f.readlines()[0]) path_to_cached_text_file = LargeFile(\"test.txt\", version=0).get() ``` By default, files are stored in the \".cache\" folder and the least recently use is deleted after the overall size reaches 30 GBs. Change it with the following properties. ``` LargeFile.cache_path = Path(\".cache\") LargeFile.max_cache_size = \"30GB\" ``` \"\"\" 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 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 : 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 : cls . initialized = True def __enter__ ( self ) -> IO : self . _file : IO [ Any ] = ( tempfile . NamedTemporaryFile ( mode = self . _mode , buffering = self . _buffering , encoding = self . _encoding , newline = self . _newline , errors = self . _errors , delete = False , prefix = \"large_file-\" , ) 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 _local_name ( self ) -> str : return f \" { self . _name }{ CACHE_NAME_VERSION_SEPARATOR }{ self . version } \" @property def version ( self ) -> int : return cast ( int , self . _version ) @lru_cache ( 1 ) def get ( self , hide_progress : bool = False ) -> Path : 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 : 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 : self . _keep_last_n = 0 self . _delete_old_remote_versions () 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.\" ) @property def versions_pretty ( self ) -> str : return \", \" . join (( str ( i . version ) for i in self . _instances )) def clean_up ( self ) -> None : self . _delete_old_remote_versions () self . _prune_cache () 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/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 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 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 () 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.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.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 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.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.predict_language][]. Returns: English name of language. \"\"\" if not language_code : language_code = \"und\" return Language . get ( language_code ) . display_name () 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 startup 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 startup 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 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 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. For more info, check-out the scraping guide . 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 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 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 () 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.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.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 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.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.predict_language][]. Returns: English name of language. \"\"\" if not language_code : language_code = \"und\" return Language . get ( language_code ) . display_name ()","title":"get_sentences()"},{"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 startup 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 startup 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 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 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. For more info, check-out the scraping guide .","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 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 class Trace ( Generic [ T ], HashableBaseModel ): 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 13 14 15 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 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 class Trace ( Generic [ T ], HashableBaseModel ): 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 13 14 15 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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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(); 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 struggles, 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 has 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 struggles, 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 has 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 seperate 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, with a line being return (in text mode) with each iteration. 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 seperate 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, with a line being return (in text mode) with each iteration. 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."}]}
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