From c11880483d5b847dc1c208bebf85e2765eae42c6 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Andr=C3=A1s=20Schmelczer?= Date: Sun, 3 Apr 2022 21:46:35 +0200 Subject: [PATCH] More experimentation MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Signed-off-by: András Schmelczer --- .vscode/settings.json | 18 +- example/.gitignore | 1 + example/README.md | 12 + example/main.py | 16 +- example/predict_domain.py | 2 +- example/train.ipynb | 564 +++++++----------- good_ai/src/__init__.py | 0 good_ai/src/good_ai/good_ai/__init__.py | 1 + good_ai/src/good_ai/good_ai/core/__init__.py | 2 - .../good_ai/good_ai/core/function_registry.py | 23 - good_ai/src/good_ai/good_ai/core/plugin.py | 19 - .../good_ai/good_ai/deploy/process_batch.py | 19 +- .../src/good_ai/good_ai/models/load_model.py | 12 +- .../src/good_ai/good_ai/models/save_model.py | 4 + .../src/good_ai/good_ai/models/use_model.py | 43 +- .../good_ai/models/use_model_plugin.py | 28 - .../src/good_ai/good_ai/set_default_config.py | 55 ++ .../src/good_ai/good_ai/tracing/__init__.py | 4 + good_ai/src/good_ai/good_ai/tracing/model.py | 6 + .../good_ai/tracing/persistence/__init__.py | 3 + .../tracing/persistence/mongodb_driver.py | 5 + .../tracing/persistence/persistence_driver.py | 8 + .../tracing/persistence/tinydb_driver.py | 15 + good_ai/src/good_ai/good_ai/tracing/trace.py | 14 + .../good_ai/tracing/tracing_context.py | 71 +++ good_ai/src/good_ai/open_s3/__main__.py | 4 +- .../open_s3/helper/bytes_to_megabytes.py | 2 + .../good_ai/open_s3/helper/progress_bar.py | 10 +- good_ai/src/good_ai/open_s3/large_file.py | 12 +- .../open_s3/test_human_readable_to_byte.py | 2 +- good_ai/tests/open_s3/test_large_file.py | 12 +- good_ai/tests/sus/data/parsed.py | 2 +- good_ai/tests/sus/test_clean.py | 2 +- good_ai/tests/sus/test_evaluate_ranking.py | 2 +- good_ai/tests/sus/test_get_sentences.py | 2 +- good_ai/tests/sus/test_language.py | 6 +- good_ai/tests/sus/test_lemmatize_text.py | 2 +- good_ai/tests/sus/test_lemmatize_token.py | 4 +- good_ai/tests/sus/test_match_names.py | 2 +- good_ai/tests/sus/test_parallel_map.py | 2 +- good_ai/tests/sus/test_publication_tei.py | 2 +- good_ai/tests/sus/test_unique.py | 2 +- ideas.drawio | 230 +++++++ 43 files changed, 749 insertions(+), 496 deletions(-) create mode 100644 example/README.md create mode 100644 good_ai/src/__init__.py delete mode 100644 good_ai/src/good_ai/good_ai/core/__init__.py delete mode 100644 good_ai/src/good_ai/good_ai/core/function_registry.py delete mode 100644 good_ai/src/good_ai/good_ai/core/plugin.py delete mode 100644 good_ai/src/good_ai/good_ai/models/use_model_plugin.py create mode 100644 good_ai/src/good_ai/good_ai/set_default_config.py create mode 100644 good_ai/src/good_ai/good_ai/tracing/__init__.py create mode 100644 good_ai/src/good_ai/good_ai/tracing/model.py create mode 100644 good_ai/src/good_ai/good_ai/tracing/persistence/__init__.py create mode 100644 good_ai/src/good_ai/good_ai/tracing/persistence/mongodb_driver.py create mode 100644 good_ai/src/good_ai/good_ai/tracing/persistence/persistence_driver.py create mode 100644 good_ai/src/good_ai/good_ai/tracing/persistence/tinydb_driver.py create mode 100644 good_ai/src/good_ai/good_ai/tracing/trace.py create mode 100644 good_ai/src/good_ai/good_ai/tracing/tracing_context.py create mode 100644 good_ai/src/good_ai/open_s3/helper/bytes_to_megabytes.py create mode 100644 ideas.drawio diff --git a/.vscode/settings.json b/.vscode/settings.json index e65cd97..a4135c2 100644 --- a/.vscode/settings.json +++ b/.vscode/settings.json @@ -1,3 +1,19 @@ { - "cSpell.words": ["pydantic", "pyplot", "sklearn", "Tfidf", "Vectorizer"] + "cSpell.words": [ + "botocore", + "pydantic", + "pyplot", + "sklearn", + "Tfidf", + "tinydb", + "Vectorizer", + "xmargin" + ], + "files.exclude": { + ".env": true, + "**/.cache": true, + "**/.ipynb_checkpoints": true, + "**/.mypy_cache": true, + "**/.pytest_cache": true + } } diff --git a/example/.gitignore b/example/.gitignore index 71c9115..2f87778 100644 --- a/example/.gitignore +++ b/example/.gitignore @@ -1,2 +1,3 @@ data .ipynb_checkpoints +tracing_database.json diff --git a/example/README.md b/example/README.md new file mode 100644 index 0000000..7d84112 --- /dev/null +++ b/example/README.md @@ -0,0 +1,12 @@ +# Train Domain classifier from the [semantic scholar dataset](https://api.semanticscholar.org/corpus) + +## Upload the dataset (or a part of it) to shared infrastructure + +```sh +mkdir ss-data && cd ss-data +wget https://s3-us-west-2.amazonaws.com/ai2-s2-research-public/open-corpus/2022-02-01/manifest.txt +wget -B https://s3-us-west-2.amazonaws.com/ai2-s2-research-public/open-corpus/2022-02-01/ -i manifest.txt +cd - +python3 -m good_ai.open_s3 --secrets s3.ini --push ss-data +rm -rf ss-data +``` diff --git a/example/main.py b/example/main.py index 6b9672c..4621f2e 100644 --- a/example/main.py +++ b/example/main.py @@ -1,15 +1,21 @@ import json +from random import shuffle +from devtools import debug from predict_domain import predict_domain -from good_ai import LargeFile, process_batch +from good_ai import process_batch if __name__ == "__main__": - with open("data/s2-corpus-1583.json") as f: + with open(".cache/ss-data-0/s2-corpus-1583.json") as f: raw = json.load(f) - LargeFile.configure_credentials_from_file("s3.ini") - + shuffle(raw) data = {f'{r["title"]} {r["abstract"]}': r["domain"] for r in raw[:5]} - print(process_batch(predict_domain, ["We have found a new type of chemical."])) + results = process_batch(predict_domain, data.keys()) + + for predicted, actual in zip(results, data.values()): + print(", ".join(actual)) + debug(predicted) + print() diff --git a/example/predict_domain.py b/example/predict_domain.py index 1c7d735..e2ace96 100644 --- a/example/predict_domain.py +++ b/example/predict_domain.py @@ -45,7 +45,7 @@ def predict_domain( ) ) - if sum(r.probability for r in results) >= cut_off_probability: + if sum(r.probability for r in results) >= cut_off_probability * 100: break return results diff --git a/example/train.ipynb b/example/train.ipynb index 369093d..47bc9d7 100644 --- a/example/train.ipynb +++ b/example/train.ipynb @@ -4,60 +4,33 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# Train Domain classifier from the [semantic scholar dataset](https://api.semanticscholar.org/corpus)\n", - "\n", - "## Download dataset (or a part of it)\n", - "\n", - "```sh\n", - "wget https://s3-us-west-2.amazonaws.com/ai2-s2-research-public/open-corpus/2022-02-01/manifest.txt\n", - "wget -B https://s3-us-west-2.amazonaws.com/ai2-s2-research-public/open-corpus/2022-02-01/ -i manifest.txt\n", - "```" + "# Train Domain classifier from the [semantic scholar dataset](https://api.semanticscholar.org/corpus)" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, - "outputs": [ - { - "ename": "ModuleNotFoundError", - "evalue": "No module named 'open_s3'", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mModuleNotFoundError\u001b[0m Traceback (most recent call last)", - "\u001b[1;32m/Users/andras/Projects/good-ai/example/train.ipynb Cell 2'\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 16\u001b[0m \u001b[39mimport\u001b[39;00m \u001b[39mre\u001b[39;00m\n\u001b[1;32m 17\u001b[0m \u001b[39mfrom\u001b[39;00m \u001b[39mconfig\u001b[39;00m \u001b[39mimport\u001b[39;00m model_key\n\u001b[0;32m---> 18\u001b[0m \u001b[39mfrom\u001b[39;00m \u001b[39mgood_ai\u001b[39;00m\u001b[39m.\u001b[39;00m\u001b[39mutilities\u001b[39;00m\u001b[39m.\u001b[39;00m\u001b[39mclean\u001b[39;00m \u001b[39mimport\u001b[39;00m clean\n\u001b[1;32m 19\u001b[0m \u001b[39mfrom\u001b[39;00m \u001b[39mgood_ai\u001b[39;00m\u001b[39m.\u001b[39;00m\u001b[39mutilities\u001b[39;00m\u001b[39m.\u001b[39;00m\u001b[39mparallel_map\u001b[39;00m \u001b[39mimport\u001b[39;00m parallel_map\n\u001b[1;32m 20\u001b[0m \u001b[39mfrom\u001b[39;00m \u001b[39mgood_ai\u001b[39;00m\u001b[39m.\u001b[39;00m\u001b[39mutilities\u001b[39;00m\u001b[39m.\u001b[39;00m\u001b[39mlanguage\u001b[39;00m \u001b[39mimport\u001b[39;00m is_english, predict_language\n", - "File \u001b[0;32m~/Projects/good-ai/good_ai/src/good_ai/__init__.py:1\u001b[0m, in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[39mfrom\u001b[39;00m \u001b[39m.\u001b[39;00m\u001b[39mgood_ai\u001b[39;00m \u001b[39mimport\u001b[39;00m \u001b[39m*\u001b[39m\n\u001b[1;32m 2\u001b[0m \u001b[39mfrom\u001b[39;00m \u001b[39m.\u001b[39;00m\u001b[39mutilities\u001b[39;00m \u001b[39mimport\u001b[39;00m \u001b[39m*\u001b[39m\n\u001b[1;32m 3\u001b[0m \u001b[39mfrom\u001b[39;00m \u001b[39m.\u001b[39;00m\u001b[39mopen_s3\u001b[39;00m \u001b[39mimport\u001b[39;00m \u001b[39m*\u001b[39m\n", - "File \u001b[0;32m~/Projects/good-ai/good_ai/src/good_ai/good_ai/__init__.py:2\u001b[0m, in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[39mfrom\u001b[39;00m \u001b[39m.\u001b[39;00m\u001b[39mdeploy\u001b[39;00m \u001b[39mimport\u001b[39;00m process_batch\n\u001b[0;32m----> 2\u001b[0m \u001b[39mfrom\u001b[39;00m \u001b[39m.\u001b[39;00m\u001b[39mmodels\u001b[39;00m \u001b[39mimport\u001b[39;00m save_model, use_model\n", - "File \u001b[0;32m~/Projects/good-ai/good_ai/src/good_ai/good_ai/models/__init__.py:1\u001b[0m, in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[39mfrom\u001b[39;00m \u001b[39m.\u001b[39;00m\u001b[39msave_model\u001b[39;00m \u001b[39mimport\u001b[39;00m save_model\n\u001b[1;32m 2\u001b[0m \u001b[39mfrom\u001b[39;00m \u001b[39m.\u001b[39;00m\u001b[39muse_model\u001b[39;00m \u001b[39mimport\u001b[39;00m use_model\n", - "File \u001b[0;32m~/Projects/good-ai/good_ai/src/good_ai/good_ai/models/save_model.py:6\u001b[0m, in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[39mfrom\u001b[39;00m \u001b[39mtyping\u001b[39;00m \u001b[39mimport\u001b[39;00m Optional, Union\n\u001b[1;32m 5\u001b[0m \u001b[39mfrom\u001b[39;00m \u001b[39mjoblib\u001b[39;00m \u001b[39mimport\u001b[39;00m dump\n\u001b[0;32m----> 6\u001b[0m \u001b[39mfrom\u001b[39;00m \u001b[39mopen_s3\u001b[39;00m \u001b[39mimport\u001b[39;00m LargeFile\n\u001b[1;32m 8\u001b[0m logger \u001b[39m=\u001b[39m logging\u001b[39m.\u001b[39mgetLogger(\u001b[39m\"\u001b[39m\u001b[39mmodels\u001b[39m\u001b[39m\"\u001b[39m)\n\u001b[1;32m 11\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39msave_model\u001b[39m(\n\u001b[1;32m 12\u001b[0m model: Union[Path, \u001b[39mstr\u001b[39m, \u001b[39mobject\u001b[39m], key: \u001b[39mstr\u001b[39m, keep_last_n: Optional[\u001b[39mint\u001b[39m] \u001b[39m=\u001b[39m \u001b[39mNone\u001b[39;00m\n\u001b[1;32m 13\u001b[0m ) \u001b[39m-\u001b[39m\u001b[39m>\u001b[39m \u001b[39mint\u001b[39m:\n", - "\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'open_s3'" - ] - } - ], + "outputs": [], "source": [ "import json\n", "import matplotlib.pyplot as plt\n", - "from sklearn import metrics\n", - "from pathlib import Path\n", - "from sklearn.naive_bayes import ComplementNB\n", - "from sklearn.model_selection import train_test_split\n", "from pathlib import Path\n", "import pandas as pd\n", - "from joblib import dump\n", + "from sklearn import metrics, set_config\n", + "from sklearn.model_selection import train_test_split\n", + "from sklearn.naive_bayes import ComplementNB\n", "from sklearn.pipeline import Pipeline\n", "from sklearn.feature_extraction.text import TfidfVectorizer\n", - "from sklearn import metrics\n", - "from random import shuffle, seed\n", "from sklearn.model_selection import GridSearchCV\n", - "from pprint import pprint\n", - "from config import model_key\n", + "\n", "from good_ai.utilities.clean import clean\n", "from good_ai.utilities.parallel_map import parallel_map\n", "from good_ai.utilities.language import is_english, predict_language\n", - "from good_ai import LargeFile, save_model\n", + "from good_ai import save_model, set_default_config, LargeFile\n", + "\n", "from preprocess import preprocess\n", - "from predict_domain import predict_domain" + "from config import model_key" ] }, { @@ -73,25 +46,9 @@ "metadata": {}, "outputs": [], "source": [ - "SS_CORPUS_PATH = Path(\"./data\")\n", "PREFIX = \"domain-\"\n", - "SEED = 42\n", - "HYPERPARAMETER_OPTIMISATION_SIZE = 1000000\n", - "MAX_FILE_COUNT = 5\n", - "\n", - "LargeFile.configure_credentials_from_file(\"s3.ini\")\n", - "plt.rcParams[\"figure.figsize\"] = (30, 15)\n", - "plt.rcParams[\"figure.facecolor\"] = \"white\"\n", - "plt.rcParams[\"font.size\"] = 12\n", - "plt.rcParams[\"axes.xmargin\"] = 0\n", - "seed(SEED)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Preprocessing" + "DATASET_KEY = \"ss-data\"\n", + "MAX_FILE_COUNT = 5" ] }, { @@ -103,12 +60,47 @@ "name": "stderr", "output_type": "stream", "text": [ - "100%|██████████| 5/5 [00:00<00:00, 50412.31it/s]\n" + "INFO:good_ai:Defaults: configured ✅\n", + "INFO:open_s3:Fetching online versions of ss-data\n", + "INFO:open_s3:Found versions: [0]\n", + "INFO:open_s3:Lastest version of ss-data-0 is 0\n", + "INFO:open_s3:File ss-data-0 found in cache\n" ] } ], "source": [ - "def clean_file(p: Path):\n", + "set_default_config()\n", + "corpus_path = LargeFile(DATASET_KEY).get()\n", + "\n", + "set_config(display=\"diagram\")\n", + "plt.rcParams[\"figure.figsize\"] = (30, 15)\n", + "plt.rcParams[\"figure.facecolor\"] = \"white\"\n", + "plt.rcParams[\"font.size\"] = 12\n", + "plt.rcParams[\"axes.xmargin\"] = 0" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Preprocessing" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:00<00:00, 43873.47it/s]\n" + ] + } + ], + "source": [ + "def clean_file(p: Path) -> None:\n", " try:\n", " processed_path = p.with_name(f\"{PREFIX}{p.stem}{p.suffix}\")\n", "\n", @@ -120,7 +112,7 @@ "\n", " result = {\n", " preprocess(\n", - " clean(f'{c[\"title\"]} {c[\"abstract\"]}'), convert_to_ascii=True\n", + " clean(f'{c[\"title\"]} {c[\"abstract\"]}', convert_to_ascii=True)\n", " ): c[\"domain\"]\n", " for c in content\n", " if (\n", @@ -138,7 +130,7 @@ "\n", "parallel_map(\n", " clean_file,\n", - " list(SS_CORPUS_PATH.glob(\"s2-corpus-*.json\"))[:MAX_FILE_COUNT],\n", + " list(corpus_path.glob(\"s2-corpus-*.json\"))[:MAX_FILE_COUNT],\n", " chunk_size=1,\n", ")\n", "None" @@ -146,7 +138,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 5, "metadata": {}, "outputs": [ { @@ -159,19 +151,18 @@ } ], "source": [ - "corpora = list(SS_CORPUS_PATH.glob(f\"{PREFIX}*.json\"))\n", - "shuffle(corpora)\n", + "corpora = list(corpus_path.glob(f\"{PREFIX}*.json\"))[:MAX_FILE_COUNT]\n", "print(f\"Found {len(corpora)} files\")\n", "\n", "data = []\n", - "for p in corpora[:MAX_FILE_COUNT]:\n", + "for p in corpora:\n", " with open(p) as f:\n", " data.extend(json.load(f).items())\n", "\n", "print(f\"Found {len(data)} documents\")\n", "\n", "X_train, X_test, y_train, y_test = train_test_split(\n", - " [d[0] for d in data], [d[1] for d in data], test_size=0.1, random_state=SEED\n", + " [d[0] for d in data], [d[1] for d in data], test_size=0.1, random_state=1\n", ")\n", "\n", "X_train = [x for x, y in zip(X_train, y_train) for domain in y]\n", @@ -185,35 +176,6 @@ "## Naive Bayes" ] }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], - "source": [ - "# classifier = GridSearchCV(\n", - "# Pipeline(steps=[(\"vectorizer\", TfidfVectorizer()), (\"classifier\", ComplementNB())]),\n", - "# {\n", - "# \"vectorizer__max_df\": [0.05, 0.1, 0.3],\n", - "# \"vectorizer__min_df\": [5, 10, 30, 100],\n", - "# \"vectorizer__sublinear_tf\": [True, False],\n", - "# \"classifier__alpha\": [0.001, 0.1, 0.5, 1],\n", - "# \"classifier__fit_prior\": [True, False],\n", - "# },\n", - "# scoring=\"f1_macro\",\n", - "# cv=3,\n", - "# n_jobs=12,\n", - "# verbose=7,\n", - "# )\n", - "# classifier.fit(\n", - "# X_train[:HYPERPARAMETER_OPTIMISATION_SIZE],\n", - "# y_train[:HYPERPARAMETER_OPTIMISATION_SIZE],\n", - "# )\n", - "\n", - "# results = pd.DataFrame(classifier.cv_results_)\n", - "# results.sort_values(\"rank_test_score\")" - ] - }, { "cell_type": "code", "execution_count": 6, @@ -223,20 +185,71 @@ "name": "stdout", "output_type": "stream", "text": [ - "[Pipeline] ........ (step 1 of 2) Processing vectorizer, total= 7.5s\n", - "[Pipeline] ........ (step 2 of 2) Processing classifier, total= 0.2s\n" + "Fitting 3 folds for each of 144 candidates, totalling 432 fits\n" ] }, + { + "ename": "KeyboardInterrupt", + "evalue": "", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)", + "\u001b[1;32m/Users/andras/Projects/good-ai/example/train.ipynb Cell 10'\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m classifier \u001b[39m=\u001b[39m GridSearchCV(\n\u001b[1;32m 2\u001b[0m Pipeline(steps\u001b[39m=\u001b[39m[(\u001b[39m\"\u001b[39m\u001b[39mvectorizer\u001b[39m\u001b[39m\"\u001b[39m, TfidfVectorizer()), (\u001b[39m\"\u001b[39m\u001b[39mclassifier\u001b[39m\u001b[39m\"\u001b[39m, ComplementNB())]),\n\u001b[1;32m 3\u001b[0m {\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 13\u001b[0m verbose\u001b[39m=\u001b[39m\u001b[39m1\u001b[39m,\n\u001b[1;32m 14\u001b[0m )\n\u001b[0;32m---> 15\u001b[0m classifier\u001b[39m.\u001b[39;49mfit(X_train, y_train)\n\u001b[1;32m 17\u001b[0m results \u001b[39m=\u001b[39m pd\u001b[39m.\u001b[39mDataFrame(classifier\u001b[39m.\u001b[39mcv_results_)\n\u001b[1;32m 18\u001b[0m results\u001b[39m.\u001b[39msort_values(\u001b[39m\"\u001b[39m\u001b[39mrank_test_score\u001b[39m\u001b[39m\"\u001b[39m)\n", + "File \u001b[0;32m~/Projects/good-ai/.env/lib/python3.8/site-packages/sklearn/model_selection/_search.py:891\u001b[0m, in \u001b[0;36mBaseSearchCV.fit\u001b[0;34m(self, X, y, groups, **fit_params)\u001b[0m\n\u001b[1;32m 885\u001b[0m results \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_format_results(\n\u001b[1;32m 886\u001b[0m all_candidate_params, n_splits, all_out, all_more_results\n\u001b[1;32m 887\u001b[0m )\n\u001b[1;32m 889\u001b[0m \u001b[39mreturn\u001b[39;00m results\n\u001b[0;32m--> 891\u001b[0m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_run_search(evaluate_candidates)\n\u001b[1;32m 893\u001b[0m \u001b[39m# multimetric is determined here because in the case of a callable\u001b[39;00m\n\u001b[1;32m 894\u001b[0m \u001b[39m# self.scoring the return type is only known after calling\u001b[39;00m\n\u001b[1;32m 895\u001b[0m first_test_score \u001b[39m=\u001b[39m all_out[\u001b[39m0\u001b[39m][\u001b[39m\"\u001b[39m\u001b[39mtest_scores\u001b[39m\u001b[39m\"\u001b[39m]\n", + "File \u001b[0;32m~/Projects/good-ai/.env/lib/python3.8/site-packages/sklearn/model_selection/_search.py:1392\u001b[0m, in \u001b[0;36mGridSearchCV._run_search\u001b[0;34m(self, evaluate_candidates)\u001b[0m\n\u001b[1;32m 1390\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39m_run_search\u001b[39m(\u001b[39mself\u001b[39m, evaluate_candidates):\n\u001b[1;32m 1391\u001b[0m \u001b[39m\"\"\"Search all candidates in param_grid\"\"\"\u001b[39;00m\n\u001b[0;32m-> 1392\u001b[0m evaluate_candidates(ParameterGrid(\u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mparam_grid))\n", + "File \u001b[0;32m~/Projects/good-ai/.env/lib/python3.8/site-packages/sklearn/model_selection/_search.py:838\u001b[0m, in \u001b[0;36mBaseSearchCV.fit..evaluate_candidates\u001b[0;34m(candidate_params, cv, more_results)\u001b[0m\n\u001b[1;32m 830\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mverbose \u001b[39m>\u001b[39m \u001b[39m0\u001b[39m:\n\u001b[1;32m 831\u001b[0m \u001b[39mprint\u001b[39m(\n\u001b[1;32m 832\u001b[0m \u001b[39m\"\u001b[39m\u001b[39mFitting \u001b[39m\u001b[39m{0}\u001b[39;00m\u001b[39m folds for each of \u001b[39m\u001b[39m{1}\u001b[39;00m\u001b[39m candidates,\u001b[39m\u001b[39m\"\u001b[39m\n\u001b[1;32m 833\u001b[0m \u001b[39m\"\u001b[39m\u001b[39m totalling \u001b[39m\u001b[39m{2}\u001b[39;00m\u001b[39m fits\u001b[39m\u001b[39m\"\u001b[39m\u001b[39m.\u001b[39mformat(\n\u001b[1;32m 834\u001b[0m n_splits, n_candidates, n_candidates \u001b[39m*\u001b[39m n_splits\n\u001b[1;32m 835\u001b[0m )\n\u001b[1;32m 836\u001b[0m )\n\u001b[0;32m--> 838\u001b[0m out \u001b[39m=\u001b[39m parallel(\n\u001b[1;32m 839\u001b[0m delayed(_fit_and_score)(\n\u001b[1;32m 840\u001b[0m clone(base_estimator),\n\u001b[1;32m 841\u001b[0m X,\n\u001b[1;32m 842\u001b[0m y,\n\u001b[1;32m 843\u001b[0m train\u001b[39m=\u001b[39;49mtrain,\n\u001b[1;32m 844\u001b[0m test\u001b[39m=\u001b[39;49mtest,\n\u001b[1;32m 845\u001b[0m parameters\u001b[39m=\u001b[39;49mparameters,\n\u001b[1;32m 846\u001b[0m split_progress\u001b[39m=\u001b[39;49m(split_idx, n_splits),\n\u001b[1;32m 847\u001b[0m candidate_progress\u001b[39m=\u001b[39;49m(cand_idx, n_candidates),\n\u001b[1;32m 848\u001b[0m \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mfit_and_score_kwargs,\n\u001b[1;32m 849\u001b[0m )\n\u001b[1;32m 850\u001b[0m \u001b[39mfor\u001b[39;49;00m (cand_idx, parameters), (split_idx, (train, test)) \u001b[39min\u001b[39;49;00m product(\n\u001b[1;32m 851\u001b[0m \u001b[39menumerate\u001b[39;49m(candidate_params), \u001b[39menumerate\u001b[39;49m(cv\u001b[39m.\u001b[39;49msplit(X, y, groups))\n\u001b[1;32m 852\u001b[0m )\n\u001b[1;32m 853\u001b[0m )\n\u001b[1;32m 855\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mlen\u001b[39m(out) \u001b[39m<\u001b[39m \u001b[39m1\u001b[39m:\n\u001b[1;32m 856\u001b[0m \u001b[39mraise\u001b[39;00m \u001b[39mValueError\u001b[39;00m(\n\u001b[1;32m 857\u001b[0m \u001b[39m\"\u001b[39m\u001b[39mNo fits were performed. \u001b[39m\u001b[39m\"\u001b[39m\n\u001b[1;32m 858\u001b[0m \u001b[39m\"\u001b[39m\u001b[39mWas the CV iterator empty? \u001b[39m\u001b[39m\"\u001b[39m\n\u001b[1;32m 859\u001b[0m \u001b[39m\"\u001b[39m\u001b[39mWere there no candidates?\u001b[39m\u001b[39m\"\u001b[39m\n\u001b[1;32m 860\u001b[0m )\n", + "File \u001b[0;32m~/Projects/good-ai/.env/lib/python3.8/site-packages/joblib/parallel.py:1056\u001b[0m, in \u001b[0;36mParallel.__call__\u001b[0;34m(self, iterable)\u001b[0m\n\u001b[1;32m 1053\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_iterating \u001b[39m=\u001b[39m \u001b[39mFalse\u001b[39;00m\n\u001b[1;32m 1055\u001b[0m \u001b[39mwith\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_backend\u001b[39m.\u001b[39mretrieval_context():\n\u001b[0;32m-> 1056\u001b[0m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mretrieve()\n\u001b[1;32m 1057\u001b[0m \u001b[39m# Make sure that we get a last message telling us we are done\u001b[39;00m\n\u001b[1;32m 1058\u001b[0m elapsed_time \u001b[39m=\u001b[39m time\u001b[39m.\u001b[39mtime() \u001b[39m-\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_start_time\n", + "File \u001b[0;32m~/Projects/good-ai/.env/lib/python3.8/site-packages/joblib/parallel.py:935\u001b[0m, in \u001b[0;36mParallel.retrieve\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 933\u001b[0m \u001b[39mtry\u001b[39;00m:\n\u001b[1;32m 934\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mgetattr\u001b[39m(\u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_backend, \u001b[39m'\u001b[39m\u001b[39msupports_timeout\u001b[39m\u001b[39m'\u001b[39m, \u001b[39mFalse\u001b[39;00m):\n\u001b[0;32m--> 935\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_output\u001b[39m.\u001b[39mextend(job\u001b[39m.\u001b[39;49mget(timeout\u001b[39m=\u001b[39;49m\u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mtimeout))\n\u001b[1;32m 936\u001b[0m \u001b[39melse\u001b[39;00m:\n\u001b[1;32m 937\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_output\u001b[39m.\u001b[39mextend(job\u001b[39m.\u001b[39mget())\n", + "File \u001b[0;32m~/Projects/good-ai/.env/lib/python3.8/site-packages/joblib/_parallel_backends.py:542\u001b[0m, in \u001b[0;36mLokyBackend.wrap_future_result\u001b[0;34m(future, timeout)\u001b[0m\n\u001b[1;32m 539\u001b[0m \u001b[39m\"\"\"Wrapper for Future.result to implement the same behaviour as\u001b[39;00m\n\u001b[1;32m 540\u001b[0m \u001b[39mAsyncResults.get from multiprocessing.\"\"\"\u001b[39;00m\n\u001b[1;32m 541\u001b[0m \u001b[39mtry\u001b[39;00m:\n\u001b[0;32m--> 542\u001b[0m \u001b[39mreturn\u001b[39;00m future\u001b[39m.\u001b[39;49mresult(timeout\u001b[39m=\u001b[39;49mtimeout)\n\u001b[1;32m 543\u001b[0m \u001b[39mexcept\u001b[39;00m CfTimeoutError \u001b[39mas\u001b[39;00m e:\n\u001b[1;32m 544\u001b[0m \u001b[39mraise\u001b[39;00m \u001b[39mTimeoutError\u001b[39;00m \u001b[39mfrom\u001b[39;00m \u001b[39me\u001b[39;00m\n", + "File \u001b[0;32m/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/concurrent/futures/_base.py:434\u001b[0m, in \u001b[0;36mFuture.result\u001b[0;34m(self, timeout)\u001b[0m\n\u001b[1;32m 431\u001b[0m \u001b[39melif\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_state \u001b[39m==\u001b[39m FINISHED:\n\u001b[1;32m 432\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m__get_result()\n\u001b[0;32m--> 434\u001b[0m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_condition\u001b[39m.\u001b[39;49mwait(timeout)\n\u001b[1;32m 436\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_state \u001b[39min\u001b[39;00m [CANCELLED, CANCELLED_AND_NOTIFIED]:\n\u001b[1;32m 437\u001b[0m \u001b[39mraise\u001b[39;00m CancelledError()\n", + "File \u001b[0;32m/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/threading.py:302\u001b[0m, in \u001b[0;36mCondition.wait\u001b[0;34m(self, timeout)\u001b[0m\n\u001b[1;32m 300\u001b[0m \u001b[39mtry\u001b[39;00m: \u001b[39m# restore state no matter what (e.g., KeyboardInterrupt)\u001b[39;00m\n\u001b[1;32m 301\u001b[0m \u001b[39mif\u001b[39;00m timeout \u001b[39mis\u001b[39;00m \u001b[39mNone\u001b[39;00m:\n\u001b[0;32m--> 302\u001b[0m waiter\u001b[39m.\u001b[39;49macquire()\n\u001b[1;32m 303\u001b[0m gotit \u001b[39m=\u001b[39m \u001b[39mTrue\u001b[39;00m\n\u001b[1;32m 304\u001b[0m \u001b[39melse\u001b[39;00m:\n", + "\u001b[0;31mKeyboardInterrupt\u001b[0m: " + ] + } + ], + "source": [ + "classifier = GridSearchCV(\n", + " Pipeline(steps=[(\"vectorizer\", TfidfVectorizer()), (\"classifier\", ComplementNB())]),\n", + " {\n", + " \"vectorizer__max_df\": [0.05, 0.1, 0.3],\n", + " \"vectorizer__min_df\": [5, 10, 30],\n", + " \"vectorizer__sublinear_tf\": [True, False],\n", + " \"classifier__alpha\": [0.001, 0.1, 0.5, 1],\n", + " \"classifier__fit_prior\": [True, False],\n", + " },\n", + " scoring=\"f1_macro\",\n", + " cv=3,\n", + " n_jobs=8,\n", + " verbose=1,\n", + ")\n", + "classifier.fit(X_train, y_train)\n", + "\n", + "results = pd.DataFrame(classifier.cv_results_)\n", + "results.sort_values(\"rank_test_score\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ { "data": { + "text/html": [ + "
Pipeline(steps=[('vectorizer',\n",
+       "                 TfidfVectorizer(max_df=0.1, min_df=10, token_pattern='[^ ]+')),\n",
+       "                ('classifier', ComplementNB(alpha=0.5, fit_prior=False))])
Please rerun this cell to show the HTML repr or trust the notebook.
" + ], "text/plain": [ "Pipeline(steps=[('vectorizer',\n", " TfidfVectorizer(max_df=0.1, min_df=10, token_pattern='[^ ]+')),\n", - " ('classifier', ComplementNB(alpha=0.5, fit_prior=False))],\n", - " verbose=1)" + " ('classifier', ComplementNB(alpha=0.5, fit_prior=False))])" ] }, - "execution_count": 6, + "execution_count": 7, "metadata": {}, "output_type": "execute_result" } @@ -246,8 +259,7 @@ " steps=[\n", " (\"vectorizer\", TfidfVectorizer(min_df=10, max_df=0.1, token_pattern=r\"[^ ]+\")),\n", " (\"classifier\", ComplementNB(alpha=0.5, fit_prior=False)),\n", - " ],\n", - " verbose=1,\n", + " ]\n", ")\n", "\n", "classifier.fit(X_train, y_train)" @@ -255,7 +267,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": null, "metadata": {}, "outputs": [ { @@ -264,35 +276,35 @@ "text": [ " precision recall f1-score support\n", "\n", - " Art 0.40 0.42 0.41 67\n", - " Biology 0.76 0.72 0.74 579\n", - " Business 0.56 0.64 0.60 195\n", - " Chemistry 0.77 0.71 0.74 677\n", - " Computer Science 0.64 0.86 0.73 807\n", - " Economics 0.70 0.53 0.61 173\n", - " Engineering 0.62 0.31 0.41 537\n", - "Environmental Science 0.60 0.34 0.43 156\n", - " Geography 0.64 0.30 0.41 152\n", - " Geology 0.67 0.63 0.65 133\n", - " History 0.32 0.11 0.17 62\n", - " Materials Science 0.63 0.86 0.73 613\n", - " Mathematics 0.86 0.66 0.75 306\n", - " Medicine 0.88 0.92 0.90 2144\n", - " Philosophy 0.56 0.27 0.36 52\n", - " Physics 0.75 0.61 0.67 350\n", - " Political Science 0.48 0.55 0.51 214\n", - " Psychology 0.54 0.67 0.60 317\n", - " Sociology 0.35 0.35 0.35 184\n", + " Art 0.44 0.43 0.43 86\n", + " Biology 0.79 0.75 0.77 567\n", + " Business 0.56 0.58 0.57 223\n", + " Chemistry 0.75 0.73 0.74 651\n", + " Computer Science 0.61 0.85 0.71 773\n", + " Economics 0.68 0.55 0.60 179\n", + " Engineering 0.56 0.32 0.41 499\n", + "Environmental Science 0.48 0.32 0.38 132\n", + " Geography 0.57 0.19 0.29 182\n", + " Geology 0.67 0.66 0.66 144\n", + " History 0.43 0.22 0.30 58\n", + " Materials Science 0.66 0.84 0.74 630\n", + " Mathematics 0.79 0.59 0.67 285\n", + " Medicine 0.89 0.94 0.91 2151\n", + " Philosophy 0.68 0.23 0.35 64\n", + " Physics 0.75 0.62 0.68 364\n", + " Political Science 0.47 0.52 0.49 221\n", + " Psychology 0.55 0.62 0.58 321\n", + " Sociology 0.32 0.35 0.33 188\n", "\n", - " accuracy 0.72 7718\n", - " macro avg 0.62 0.55 0.57 7718\n", - " weighted avg 0.72 0.72 0.70 7718\n", + " accuracy 0.71 7718\n", + " macro avg 0.61 0.54 0.56 7718\n", + " weighted avg 0.71 0.71 0.70 7718\n", "\n" ] }, { "data": { - "image/png": "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", + "image/png": "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", "text/plain": [ "
" ] @@ -319,7 +331,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": null, "metadata": {}, "outputs": [ { @@ -327,76 +339,76 @@ "output_type": "stream", "text": [ "INFO:open_s3:Fetching online versions of domain-prediction-v2\n", - "INFO:open_s3:Found versions: [1, 2]\n", - "INFO:open_s3:Copying file for domain-prediction-v2-3\n", - "INFO:open_s3:Compressing domain-prediction-v2-3\n", - "INFO:open_s3:Uploading domain-prediction-v2-3 to S3 from /var/folders/5g/yd_5_wg548q0yb4lnvg0y3q40000gn/T/large-file-hkyimkq5\n", - "INFO:open_s3:Uploading domain-prediction-v2-3.tar.gz 262144/15169091 bytes (1.7%)\n", - "INFO:open_s3:Uploading domain-prediction-v2-3.tar.gz 524288/15169091 bytes (3.5%)\n", - "INFO:open_s3:Uploading domain-prediction-v2-3.tar.gz 786432/15169091 bytes (5.2%)\n", - "INFO:open_s3:Uploading domain-prediction-v2-3.tar.gz 1048576/15169091 bytes (6.9%)\n", - "INFO:open_s3:Uploading domain-prediction-v2-3.tar.gz 1310720/15169091 bytes (8.6%)\n", - "INFO:open_s3:Uploading domain-prediction-v2-3.tar.gz 1572864/15169091 bytes (10.4%)\n", - "INFO:open_s3:Uploading domain-prediction-v2-3.tar.gz 1835008/15169091 bytes (12.1%)\n", - "INFO:open_s3:Uploading domain-prediction-v2-3.tar.gz 2097152/15169091 bytes (13.8%)\n", - "INFO:open_s3:Uploading domain-prediction-v2-3.tar.gz 2359296/15169091 bytes (15.6%)\n", - "INFO:open_s3:Uploading domain-prediction-v2-3.tar.gz 2621440/15169091 bytes (17.3%)\n", - "INFO:open_s3:Uploading domain-prediction-v2-3.tar.gz 2883584/15169091 bytes (19.0%)\n", - "INFO:open_s3:Uploading domain-prediction-v2-3.tar.gz 3145728/15169091 bytes (20.7%)\n", - "INFO:open_s3:Uploading domain-prediction-v2-3.tar.gz 3407872/15169091 bytes (22.5%)\n", - "INFO:open_s3:Uploading domain-prediction-v2-3.tar.gz 3670016/15169091 bytes (24.2%)\n", - "INFO:open_s3:Uploading domain-prediction-v2-3.tar.gz 3932160/15169091 bytes (25.9%)\n", - "INFO:open_s3:Uploading domain-prediction-v2-3.tar.gz 4194304/15169091 bytes (27.7%)\n", - "INFO:open_s3:Uploading domain-prediction-v2-3.tar.gz 4456448/15169091 bytes (29.4%)\n", - "INFO:open_s3:Uploading domain-prediction-v2-3.tar.gz 4718592/15169091 bytes (31.1%)\n", - "INFO:open_s3:Uploading domain-prediction-v2-3.tar.gz 4980736/15169091 bytes (32.8%)\n", - "INFO:open_s3:Uploading domain-prediction-v2-3.tar.gz 5242880/15169091 bytes (34.6%)\n", - "INFO:open_s3:Uploading domain-prediction-v2-3.tar.gz 5505024/15169091 bytes (36.3%)\n", - "INFO:open_s3:Uploading domain-prediction-v2-3.tar.gz 5767168/15169091 bytes (38.0%)\n", - "INFO:open_s3:Uploading domain-prediction-v2-3.tar.gz 6029312/15169091 bytes (39.7%)\n", - "INFO:open_s3:Uploading domain-prediction-v2-3.tar.gz 6291456/15169091 bytes (41.5%)\n", - "INFO:open_s3:Uploading domain-prediction-v2-3.tar.gz 6553600/15169091 bytes (43.2%)\n", - "INFO:open_s3:Uploading domain-prediction-v2-3.tar.gz 6815744/15169091 bytes (44.9%)\n", - "INFO:open_s3:Uploading domain-prediction-v2-3.tar.gz 7077888/15169091 bytes (46.7%)\n", - "INFO:open_s3:Uploading domain-prediction-v2-3.tar.gz 7340032/15169091 bytes (48.4%)\n", - "INFO:open_s3:Uploading domain-prediction-v2-3.tar.gz 7602176/15169091 bytes (50.1%)\n", - "INFO:open_s3:Uploading domain-prediction-v2-3.tar.gz 7864320/15169091 bytes (51.8%)\n", - "INFO:open_s3:Uploading domain-prediction-v2-3.tar.gz 8126464/15169091 bytes (53.6%)\n", - "INFO:open_s3:Uploading domain-prediction-v2-3.tar.gz 8388608/15169091 bytes (55.3%)\n", - "INFO:open_s3:Uploading domain-prediction-v2-3.tar.gz 8650752/15169091 bytes (57.0%)\n", - "INFO:open_s3:Uploading domain-prediction-v2-3.tar.gz 8912896/15169091 bytes (58.8%)\n", - "INFO:open_s3:Uploading domain-prediction-v2-3.tar.gz 9175040/15169091 bytes (60.5%)\n", - "INFO:open_s3:Uploading domain-prediction-v2-3.tar.gz 9437184/15169091 bytes (62.2%)\n", - "INFO:open_s3:Uploading domain-prediction-v2-3.tar.gz 9699328/15169091 bytes (63.9%)\n", - "INFO:open_s3:Uploading domain-prediction-v2-3.tar.gz 9961472/15169091 bytes (65.7%)\n", - "INFO:open_s3:Uploading domain-prediction-v2-3.tar.gz 10223616/15169091 bytes (67.4%)\n", - "INFO:open_s3:Uploading domain-prediction-v2-3.tar.gz 10485760/15169091 bytes (69.1%)\n", - "INFO:open_s3:Uploading domain-prediction-v2-3.tar.gz 10747904/15169091 bytes (70.9%)\n", - "INFO:open_s3:Uploading domain-prediction-v2-3.tar.gz 11010048/15169091 bytes (72.6%)\n", - "INFO:open_s3:Uploading domain-prediction-v2-3.tar.gz 11272192/15169091 bytes (74.3%)\n", - "INFO:open_s3:Uploading domain-prediction-v2-3.tar.gz 11534336/15169091 bytes (76.0%)\n", - "INFO:open_s3:Uploading domain-prediction-v2-3.tar.gz 11796480/15169091 bytes (77.8%)\n", - "INFO:open_s3:Uploading domain-prediction-v2-3.tar.gz 12023363/15169091 bytes (79.3%)\n", - "INFO:open_s3:Uploading domain-prediction-v2-3.tar.gz 12285507/15169091 bytes (81.0%)\n", - "INFO:open_s3:Uploading domain-prediction-v2-3.tar.gz 12547651/15169091 bytes (82.7%)\n", - "INFO:open_s3:Uploading domain-prediction-v2-3.tar.gz 12809795/15169091 bytes (84.4%)\n", - "INFO:open_s3:Uploading domain-prediction-v2-3.tar.gz 13071939/15169091 bytes (86.2%)\n", - "INFO:open_s3:Uploading domain-prediction-v2-3.tar.gz 13334083/15169091 bytes (87.9%)\n", - "INFO:open_s3:Uploading domain-prediction-v2-3.tar.gz 13596227/15169091 bytes (89.6%)\n", - "INFO:open_s3:Uploading domain-prediction-v2-3.tar.gz 13858371/15169091 bytes (91.4%)\n", - "INFO:open_s3:Uploading domain-prediction-v2-3.tar.gz 14120515/15169091 bytes (93.1%)\n", - "INFO:open_s3:Uploading domain-prediction-v2-3.tar.gz 14382659/15169091 bytes (94.8%)\n", - "INFO:open_s3:Uploading domain-prediction-v2-3.tar.gz 14644803/15169091 bytes (96.5%)\n", - "INFO:open_s3:Uploading domain-prediction-v2-3.tar.gz 14906947/15169091 bytes (98.3%)\n", - "INFO:open_s3:Uploading domain-prediction-v2-3.tar.gz 15169091/15169091 bytes (100.0%)\n", - "INFO:open_s3:Removing old version (keep_last_n=1): domain-prediction-v2/1\n", - "INFO:models:Model domain-prediction-v2 uploaded with version 3\n" + "INFO:open_s3:Found versions: [3, 4]\n", + "INFO:open_s3:Copying file for domain-prediction-v2-5\n", + "INFO:open_s3:Compressing domain-prediction-v2-5\n", + "INFO:open_s3:Uploading domain-prediction-v2-5 to S3 from /var/folders/5g/yd_5_wg548q0yb4lnvg0y3q40000gn/T/large-file-e5pc9s_c\n", + "INFO:open_s3:Uploading domain-prediction-v2-5.tar.gz 262144/15169569 bytes (1.7%)\n", + "INFO:open_s3:Uploading domain-prediction-v2-5.tar.gz 524288/15169569 bytes (3.5%)\n", + "INFO:open_s3:Uploading domain-prediction-v2-5.tar.gz 786432/15169569 bytes (5.2%)\n", + "INFO:open_s3:Uploading domain-prediction-v2-5.tar.gz 1048576/15169569 bytes (6.9%)\n", + "INFO:open_s3:Uploading domain-prediction-v2-5.tar.gz 1310720/15169569 bytes (8.6%)\n", + "INFO:open_s3:Uploading domain-prediction-v2-5.tar.gz 1572864/15169569 bytes (10.4%)\n", + "INFO:open_s3:Uploading domain-prediction-v2-5.tar.gz 1835008/15169569 bytes (12.1%)\n", + "INFO:open_s3:Uploading domain-prediction-v2-5.tar.gz 2097152/15169569 bytes (13.8%)\n", + "INFO:open_s3:Uploading domain-prediction-v2-5.tar.gz 2359296/15169569 bytes (15.6%)\n", + "INFO:open_s3:Uploading domain-prediction-v2-5.tar.gz 2621440/15169569 bytes (17.3%)\n", + "INFO:open_s3:Uploading domain-prediction-v2-5.tar.gz 2883584/15169569 bytes (19.0%)\n", + "INFO:open_s3:Uploading domain-prediction-v2-5.tar.gz 3145728/15169569 bytes (20.7%)\n", + "INFO:open_s3:Uploading domain-prediction-v2-5.tar.gz 3407872/15169569 bytes (22.5%)\n", + "INFO:open_s3:Uploading domain-prediction-v2-5.tar.gz 3670016/15169569 bytes (24.2%)\n", + "INFO:open_s3:Uploading domain-prediction-v2-5.tar.gz 3932160/15169569 bytes (25.9%)\n", + "INFO:open_s3:Uploading domain-prediction-v2-5.tar.gz 4194304/15169569 bytes (27.6%)\n", + "INFO:open_s3:Uploading domain-prediction-v2-5.tar.gz 4456448/15169569 bytes (29.4%)\n", + "INFO:open_s3:Uploading domain-prediction-v2-5.tar.gz 4718592/15169569 bytes (31.1%)\n", + "INFO:open_s3:Uploading domain-prediction-v2-5.tar.gz 4980736/15169569 bytes (32.8%)\n", + "INFO:open_s3:Uploading domain-prediction-v2-5.tar.gz 5242880/15169569 bytes (34.6%)\n", + "INFO:open_s3:Uploading domain-prediction-v2-5.tar.gz 5505024/15169569 bytes (36.3%)\n", + "INFO:open_s3:Uploading domain-prediction-v2-5.tar.gz 5767168/15169569 bytes (38.0%)\n", + "INFO:open_s3:Uploading domain-prediction-v2-5.tar.gz 6029312/15169569 bytes (39.7%)\n", + "INFO:open_s3:Uploading domain-prediction-v2-5.tar.gz 6291456/15169569 bytes (41.5%)\n", + "INFO:open_s3:Uploading domain-prediction-v2-5.tar.gz 6553600/15169569 bytes (43.2%)\n", + "INFO:open_s3:Uploading domain-prediction-v2-5.tar.gz 6815744/15169569 bytes (44.9%)\n", + "INFO:open_s3:Uploading domain-prediction-v2-5.tar.gz 7077888/15169569 bytes (46.7%)\n", + "INFO:open_s3:Uploading domain-prediction-v2-5.tar.gz 7340032/15169569 bytes (48.4%)\n", + "INFO:open_s3:Uploading domain-prediction-v2-5.tar.gz 7602176/15169569 bytes (50.1%)\n", + "INFO:open_s3:Uploading domain-prediction-v2-5.tar.gz 7864320/15169569 bytes (51.8%)\n", + "INFO:open_s3:Uploading domain-prediction-v2-5.tar.gz 8126464/15169569 bytes (53.6%)\n", + "INFO:open_s3:Uploading domain-prediction-v2-5.tar.gz 8388608/15169569 bytes (55.3%)\n", + "INFO:open_s3:Uploading domain-prediction-v2-5.tar.gz 8650752/15169569 bytes (57.0%)\n", + "INFO:open_s3:Uploading domain-prediction-v2-5.tar.gz 8912896/15169569 bytes (58.8%)\n", + "INFO:open_s3:Uploading domain-prediction-v2-5.tar.gz 9175040/15169569 bytes (60.5%)\n", + "INFO:open_s3:Uploading domain-prediction-v2-5.tar.gz 9437184/15169569 bytes (62.2%)\n", + "INFO:open_s3:Uploading domain-prediction-v2-5.tar.gz 9699328/15169569 bytes (63.9%)\n", + "INFO:open_s3:Uploading domain-prediction-v2-5.tar.gz 9961472/15169569 bytes (65.7%)\n", + "INFO:open_s3:Uploading domain-prediction-v2-5.tar.gz 10223616/15169569 bytes (67.4%)\n", + "INFO:open_s3:Uploading domain-prediction-v2-5.tar.gz 10485760/15169569 bytes (69.1%)\n", + "INFO:open_s3:Uploading domain-prediction-v2-5.tar.gz 10747904/15169569 bytes (70.9%)\n", + "INFO:open_s3:Uploading domain-prediction-v2-5.tar.gz 11010048/15169569 bytes (72.6%)\n", + "INFO:open_s3:Uploading domain-prediction-v2-5.tar.gz 11272192/15169569 bytes (74.3%)\n", + "INFO:open_s3:Uploading domain-prediction-v2-5.tar.gz 11534336/15169569 bytes (76.0%)\n", + "INFO:open_s3:Uploading domain-prediction-v2-5.tar.gz 11796480/15169569 bytes (77.8%)\n", + "INFO:open_s3:Uploading domain-prediction-v2-5.tar.gz 12058624/15169569 bytes (79.5%)\n", + "INFO:open_s3:Uploading domain-prediction-v2-5.tar.gz 12320768/15169569 bytes (81.2%)\n", + "INFO:open_s3:Uploading domain-prediction-v2-5.tar.gz 12582912/15169569 bytes (82.9%)\n", + "INFO:open_s3:Uploading domain-prediction-v2-5.tar.gz 12845056/15169569 bytes (84.7%)\n", + "INFO:open_s3:Uploading domain-prediction-v2-5.tar.gz 13107200/15169569 bytes (86.4%)\n", + "INFO:open_s3:Uploading domain-prediction-v2-5.tar.gz 13369344/15169569 bytes (88.1%)\n", + "INFO:open_s3:Uploading domain-prediction-v2-5.tar.gz 13631488/15169569 bytes (89.9%)\n", + "INFO:open_s3:Uploading domain-prediction-v2-5.tar.gz 13893632/15169569 bytes (91.6%)\n", + "INFO:open_s3:Uploading domain-prediction-v2-5.tar.gz 14155776/15169569 bytes (93.3%)\n", + "INFO:open_s3:Uploading domain-prediction-v2-5.tar.gz 14417920/15169569 bytes (95.0%)\n", + "INFO:open_s3:Uploading domain-prediction-v2-5.tar.gz 14680064/15169569 bytes (96.8%)\n", + "INFO:open_s3:Uploading domain-prediction-v2-5.tar.gz 14907425/15169569 bytes (98.3%)\n", + "INFO:open_s3:Uploading domain-prediction-v2-5.tar.gz 15169569/15169569 bytes (100.0%)\n", + "INFO:open_s3:Removing old version (keep_last_n=1): domain-prediction-v2/3\n", + "INFO:models:Model domain-prediction-v2 uploaded with version 5\n" ] }, { "data": { "text/plain": [ - "3" + "5" ] }, "execution_count": 8, @@ -405,182 +417,8 @@ } ], "source": [ - "import logging\n", - "import sys\n", - "\n", - "logging.basicConfig(stream=sys.stdout, level=logging.INFO)\n", - "\n", "save_model(classifier, key=model_key, keep_last_n=1)" ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Chemistry\n", - "[DomainPrediction(domain='Chemistry', probability=16.0, explanation=['octane', 'woNUM', 'gasoline', 'benzene', 'alNUMoNUM'])]\n", - "\n", - "Medicine\n", - "[DomainPrediction(domain='Medicine', probability=85.0, explanation=['jerk', 'cec', 'rectal', 'ankle', 'prospectively'])]\n", - "\n", - "Medicine\n", - "[DomainPrediction(domain='Medicine', probability=23.0, explanation=['valent', 'pneumococcal', 'serotype', 'herd', 'pneumoniae'])]\n", - "\n", - "Computer Science, Mathematics\n", - "[DomainPrediction(domain='Computer Science', probability=17.0, explanation=['cryptographic', 'lin', 'encryption', 'unbounded', 'bilinear'])]\n", - "\n", - "Chemistry\n", - "[DomainPrediction(domain='Chemistry', probability=17.0, explanation=['syrup', 'plum', 'malt', 'aroma', 'cream'])]\n", - "\n", - "Chemistry\n", - "[DomainPrediction(domain='Chemistry', probability=16.0, explanation=['nitroso', 'azo', 'enantioselective', 'blooming', 'com'])]\n", - "\n", - "Engineering\n", - "[DomainPrediction(domain='Materials Science', probability=8.0, explanation=['ply', 'preload', 'factorial', 'fabric', 'transducer'])]\n", - "\n", - "Political Science\n", - "[DomainPrediction(domain='Political Science', probability=12.0, explanation=['diplomacy', 'colonialism', 'terrorism', 'policymaker', 'sexuality'])]\n", - "\n", - "Medicine\n", - "[DomainPrediction(domain='Medicine', probability=83.0, explanation=['radiculopathy', 'acinar', 'personalise', 'prostatic', 'adenocarcinoma'])]\n", - "\n", - "Computer Science\n", - "[DomainPrediction(domain='Computer Science', probability=15.0, explanation=['maintainability', 'attacker', 'emulate', 'injector', 'truncation'])]\n", - "\n", - "Computer Science\n", - "[DomainPrediction(domain='Computer Science', probability=19.0, explanation=['timer', 'remind', 'broadcasting', 'alarm', 'lighting'])]\n", - "\n", - "Geology\n", - "[DomainPrediction(domain='Geology', probability=19.0, explanation=['archean', 'metamorphose', 'thickened', 'collisional', 'crust'])]\n", - "\n", - "Chemistry\n", - "[DomainPrediction(domain='Chemistry', probability=18.0, explanation=['tetrachloride', 'aliphatic', 'thiol', 'benzene', 'acceptor'])]\n", - "\n", - "Medicine\n", - "[DomainPrediction(domain='Medicine', probability=31.0, explanation=['scrotum', 'futile', 'hernia', 'protrusion', 'anxious'])]\n", - "\n", - "Biology\n", - "[DomainPrediction(domain='Biology', probability=11.0, explanation=['workable', 'disinfectant', 'larva', 'decontamination', 'disinfection'])]\n", - "\n", - "Computer Science\n", - "[DomainPrediction(domain='Computer Science', probability=20.0, explanation=['synthesiser', 'audio', 'fitness', 'topology', 'programming'])]\n", - "\n", - "Medicine\n", - "[DomainPrediction(domain='Medicine', probability=68.0, explanation=['concordant', 'acr', 'percentile', 'glomerular', 'sibling'])]\n", - "\n", - "Computer Science\n", - "[DomainPrediction(domain='Computer Science', probability=23.0, explanation=['reachability', 'cryptographic', 'secrecy', 'yao', 'authentication'])]\n", - "\n", - "Mathematics\n", - "[DomainPrediction(domain='Mathematics', probability=11.0, explanation=['averse', 'skewness', 'bilinear', 'kalman', 'backward'])]\n", - "\n", - "Computer Science\n", - "[DomainPrediction(domain='Psychology', probability=17.0, explanation=['kemampuan', 'deskriptif', 'aplikasi', 'smp', 'melalui'])]\n", - "\n", - "Sociology\n", - "[DomainPrediction(domain='Sociology', probability=10.0, explanation=['roland', 'geographer', 'naturalist', 'naturalise', 'julia'])]\n", - "\n", - "Computer Science\n", - "[DomainPrediction(domain='Computer Science', probability=13.0, explanation=['preset', 'yes', 'permission', 'client', 'request'])]\n", - "\n", - "Engineering\n", - "[DomainPrediction(domain='Engineering', probability=9.0, explanation=['steepness', 'mooring', 'bow', 'deck', 'asme'])]\n", - "\n", - "Chemistry\n", - "[DomainPrediction(domain='Chemistry', probability=14.0, explanation=['silanol', 'fluorouracil', 'vienna', 'hydrated', 'fu'])]\n", - "\n", - "Engineering\n", - "[DomainPrediction(domain='Chemistry', probability=6.0, explanation=['simulant', 'bill', 'sludge', 'faecal', 'gates'])]\n", - "\n", - "Physics\n", - "[DomainPrediction(domain='Physics', probability=9.0, explanation=['photon', 'gate', 'detector', 'quantum', 'pulse'])]\n", - "\n", - "Mathematics\n", - "[DomainPrediction(domain='Chemistry', probability=9.0, explanation=['roasted', 'garlic', 'butter', 'sweetening', 'sesame'])]\n", - "\n", - "Medicine, Biology\n", - "[DomainPrediction(domain='Medicine', probability=45.0, explanation=['treg', 'debilitate', 'runner', 'chemoattractant', 'ccrNUM'])]\n", - "\n", - "Materials Science\n", - "[DomainPrediction(domain='Materials Science', probability=18.0, explanation=['pyrolytic', 'densification', 'furnace', 'residence', 'microstructure'])]\n", - "\n", - "Computer Science\n", - "[DomainPrediction(domain='Computer Science', probability=16.0, explanation=['scheduling', 'execute', 'pseudo', 'instruction', 'request'])]\n", - "\n", - "Psychology\n", - "[DomainPrediction(domain='Psychology', probability=6.0, explanation=['methodical', 'breathing', 'russia', 'manual', 'teach'])]\n", - "\n", - "Economics\n", - "[DomainPrediction(domain='Political Science', probability=9.0, explanation=['bloc', 'dquo', 'paradoxically', 'cuba', 'capitalist'])]\n", - "\n", - "Medicine\n", - "[DomainPrediction(domain='Psychology', probability=8.0, explanation=['unconditional', 'apartment', 'mentally', 'hub', 'aforementioned'])]\n", - "\n", - "Computer Science\n", - "[DomainPrediction(domain='Computer Science', probability=15.0, explanation=['recourse', 'dissimilar', 'nn', 'categorization', 'vote'])]\n", - "\n", - "Medicine\n", - "[DomainPrediction(domain='Medicine', probability=73.0, explanation=['thyroiditis', 'subtotal', 'goitre', 'thyroidectomy', 'sequelae'])]\n", - "\n", - "Computer Science, Psychology\n", - "[DomainPrediction(domain='Psychology', probability=10.0, explanation=['dissonance', 'coworker', 'prosocial', 'pls', 'iranian'])]\n", - "\n", - "Chemistry, Medicine\n", - "[DomainPrediction(domain='Chemistry', probability=12.0, explanation=['multimeric', 'nat', 'xie', 'trimer', 'robinson'])]\n", - "\n", - "Medicine\n", - "[DomainPrediction(domain='Medicine', probability=21.0, explanation=['pharmacogenetic', 'analgesic', 'opioid', 'hamper', 'vast'])]\n", - "\n", - "Medicine\n", - "[DomainPrediction(domain='Medicine', probability=76.0, explanation=['docetaxel', 'stomatitis', 'anthracycline', 'fluorouracil', 'febrile'])]\n", - "\n", - "Biology\n", - "[DomainPrediction(domain='Biology', probability=8.0, explanation=['striped', 'vole', 'thermoregulation', 'thermogenesis', 'northeastern'])]\n", - "\n", - "Physics\n", - "[DomainPrediction(domain='Physics', probability=11.0, explanation=['pseudopotential', 'superconductivity', 'rigorously', 'coulomb', 'imaginary'])]\n", - "\n", - "Chemistry, Medicine\n", - "[DomainPrediction(domain='Medicine', probability=14.0, explanation=['presenilin', 'pharynx', 'secretase', 'terminally', 'sulfoxide'])]\n", - "\n", - "Chemistry\n", - "[DomainPrediction(domain='Chemistry', probability=22.0, explanation=['molybdate', 'methanogen', 'reducer', 'sulfonic', 'dichloromethane'])]\n", - "\n", - "Medicine\n", - "[DomainPrediction(domain='Medicine', probability=15.0, explanation=['discrepant', 'ttg', 'transglutaminase', 'colocalization', 'tempting'])]\n", - "\n", - "Geology\n", - "[DomainPrediction(domain='Engineering', probability=9.0, explanation=['hertz', 'sleeper', 'kn', 'wh', 'irregularity'])]\n", - "\n", - "Engineering\n", - "[DomainPrediction(domain='Political Science', probability=8.0, explanation=['beloved', 'riot', 'doubtless', 'symbolise', 'demon'])]\n", - "\n", - "Biology, Medicine\n", - "[DomainPrediction(domain='Biology', probability=16.0, explanation=['counterintuitive', 'parsimony', 'linnaeus', 'conservatism', 'discernible'])]\n", - "\n", - "Medicine\n", - "[DomainPrediction(domain='Medicine', probability=52.0, explanation=['putaman', 'caudate', 'hyperglycemia', 'retrospectively', 'ketone'])]\n", - "\n", - "Physics\n", - "[DomainPrediction(domain='Physics', probability=13.0, explanation=['grb', 'interplanetary', 'reanalyze', 'catalogue', 'burst'])]\n", - "\n", - "Biology\n", - "[DomainPrediction(domain='Biology', probability=31.0, explanation=['tuberosum', 'hirsutum', 'gossypium', 'lycopersicon', 'esculentum'])]\n", - "\n" - ] - } - ], - "source": [ - "for X, y in zip(X_test[:50], y_test):\n", - " print(\", \".join(y))\n", - " pprint(predict_domain(X, classifier))\n", - " print()" - ] } ], "metadata": { diff --git a/good_ai/src/__init__.py b/good_ai/src/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/good_ai/src/good_ai/good_ai/__init__.py b/good_ai/src/good_ai/good_ai/__init__.py index 1a83f45..2bc2318 100644 --- a/good_ai/src/good_ai/good_ai/__init__.py +++ b/good_ai/src/good_ai/good_ai/__init__.py @@ -1,2 +1,3 @@ from .deploy import process_batch from .models import save_model, use_model +from .set_default_config import set_default_config, set_default_config_if_uninitialized diff --git a/good_ai/src/good_ai/good_ai/core/__init__.py b/good_ai/src/good_ai/good_ai/core/__init__.py deleted file mode 100644 index 51c52bf..0000000 --- a/good_ai/src/good_ai/good_ai/core/__init__.py +++ /dev/null @@ -1,2 +0,0 @@ -from .function_registry import function_registry -from .plugin import Plugin diff --git a/good_ai/src/good_ai/good_ai/core/function_registry.py b/good_ai/src/good_ai/good_ai/core/function_registry.py deleted file mode 100644 index b648768..0000000 --- a/good_ai/src/good_ai/good_ai/core/function_registry.py +++ /dev/null @@ -1,23 +0,0 @@ -from collections import defaultdict -from typing import Any, Callable, DefaultDict, List - -from .plugin import Plugin - - -class FunctionRegistry: - def __init__(self) -> None: - self._registered_functions: DefaultDict[int, List[Plugin]] = defaultdict( - lambda: [] - ) - - def add_plugin(self, function: Callable[..., Any], plugin: Plugin): - self._registered_functions[id(function)].append(plugin) - - def get_plugins(self, function: Callable[..., Any]) -> List[Plugin]: - plugins = self._registered_functions[id(function)] - for p in plugins: - p.initialize() - return plugins - - -function_registry = FunctionRegistry() diff --git a/good_ai/src/good_ai/good_ai/core/plugin.py b/good_ai/src/good_ai/good_ai/core/plugin.py deleted file mode 100644 index 30d5e12..0000000 --- a/good_ai/src/good_ai/good_ai/core/plugin.py +++ /dev/null @@ -1,19 +0,0 @@ -from typing import Any, Callable - - -class Plugin: - def __init__(self, function: Callable[[Any], Any]): - self._function = function - self._initialized = False - - def initialize(self): - if not self._initialized: - self.on_initialize() - self._initialized = True - - def on_initialize(self): - pass - - def __call__(self, *args: Any, **kwargs: Any) -> Any: - assert self._initialized - return self._function(*args, **kwargs) diff --git a/good_ai/src/good_ai/good_ai/deploy/process_batch.py b/good_ai/src/good_ai/good_ai/deploy/process_batch.py index a910f5f..2e76485 100644 --- a/good_ai/src/good_ai/good_ai/deploy/process_batch.py +++ b/good_ai/src/good_ai/good_ai/deploy/process_batch.py @@ -1,16 +1,23 @@ -from functools import partial, reduce from typing import Any, Callable, Iterable, Optional, Sequence from good_ai.utilities.parallel_map import parallel_map -from ..core import function_registry +from ..set_default_config import set_default_config_if_uninitialized +from ..tracing import Trace, TracingContext def process_batch( function: Callable[..., Any], batch: Iterable[Any], concurrency: Optional[int] = None, -) -> Sequence[Any]: - plugins = function_registry.get_plugins(function) - composed = partial(reduce, lambda r, f: f(r), plugins) - return parallel_map(composed, batch, concurrency=concurrency) +) -> Sequence[Trace]: + set_default_config_if_uninitialized() + + def inner(input: Any) -> Trace: + with TracingContext() as t: + t.log_input(input) + result = function(input) + output = t.log_output(result) + return output + + return parallel_map(inner, batch, concurrency=concurrency) diff --git a/good_ai/src/good_ai/good_ai/models/load_model.py b/good_ai/src/good_ai/good_ai/models/load_model.py index 916ec61..b30a611 100644 --- a/good_ai/src/good_ai/good_ai/models/load_model.py +++ b/good_ai/src/good_ai/good_ai/models/load_model.py @@ -1,20 +1,24 @@ import logging -from typing import Any, Optional +from typing import Any, Optional, Tuple from joblib import load from good_ai.open_s3 import LargeFile +from ..set_default_config import set_default_config_if_uninitialized + logger = logging.getLogger("models") def load_model( key: str, version: Optional[int] = None, return_path: bool = False -) -> Any: +) -> Tuple[Any, int]: + set_default_config_if_uninitialized() + file = LargeFile(name=key, mode="rb", version=version) if return_path: - return file.get() + return file.get(), file.version with file as f: - return load(f) + return load(f), file.version diff --git a/good_ai/src/good_ai/good_ai/models/save_model.py b/good_ai/src/good_ai/good_ai/models/save_model.py index 4d42b86..1702b42 100644 --- a/good_ai/src/good_ai/good_ai/models/save_model.py +++ b/good_ai/src/good_ai/good_ai/models/save_model.py @@ -6,12 +6,16 @@ from joblib import dump from good_ai.open_s3 import LargeFile +from ..set_default_config import set_default_config_if_uninitialized + logger = logging.getLogger("models") def save_model( model: Union[Path, str, object], key: str, keep_last_n: Optional[int] = None ) -> int: + set_default_config_if_uninitialized() + file = LargeFile(name=key, mode="wb", keep_last_n=keep_last_n) if isinstance(model, Path) or isinstance(model, str): diff --git a/good_ai/src/good_ai/good_ai/models/use_model.py b/good_ai/src/good_ai/good_ai/models/use_model.py index b8101a6..680e443 100644 --- a/good_ai/src/good_ai/good_ai/models/use_model.py +++ b/good_ai/src/good_ai/good_ai/models/use_model.py @@ -1,24 +1,33 @@ -from typing import Literal, Union +from functools import wraps +from typing import Any, Callable, Literal, Union -from ..core import function_registry -from .use_model_plugin import UseModelPlugin +from ..tracing import Model, TracingContext +from .load_model import load_model def use_model( - key: str, version: Union[int, Literal["latest"]] = None, return_path: bool = False -): + key: str, + *, + version: Union[int, Literal["latest"]], + return_path: bool = False, + model_kwarg_name: str = "model" +) -> Callable[..., Any]: assert isinstance(version, int) or version == "latest" - def inner(f): - function_registry.add_plugin( - f, - UseModelPlugin( - f, - key=key, - version=version if isinstance(version, int) else None, - return_path=return_path, - ), - ) - return f + model, actual_version = load_model( + key=key, + version=None if version == "latest" else version, + return_path=return_path, + ) - return inner + def decorator(func: Callable[..., Any]) -> Callable[..., Any]: + @wraps(func) + def wrapper(*args: Any, **kwargs: Any) -> Any: + context = TracingContext.get_current_context() + if context: + context.log_model(Model(key=key, version=actual_version)) + return func(*args, **kwargs, **{model_kwarg_name: model}) + + return wrapper + + return decorator diff --git a/good_ai/src/good_ai/good_ai/models/use_model_plugin.py b/good_ai/src/good_ai/good_ai/models/use_model_plugin.py deleted file mode 100644 index 2bf142a..0000000 --- a/good_ai/src/good_ai/good_ai/models/use_model_plugin.py +++ /dev/null @@ -1,28 +0,0 @@ -from typing import Any, Callable, Optional - -from ..core import Plugin -from .load_model import load_model - - -class UseModelPlugin(Plugin): - def __init__( - self, - function: Callable[[Any], Any], - key: str, - version: Optional[int], - return_path: bool, - ): - def wrapper(*args, **kwargs): - return function(*args, **kwargs, model=self._model) - - super().__init__(wrapper) - - self._key = key - self._version = version - self._return_path = return_path - - def on_initialize(self) -> None: - super().on_initialize() - self._model = load_model( - key=self._key, version=self._version, return_path=self._return_path - ) diff --git a/good_ai/src/good_ai/good_ai/set_default_config.py b/good_ai/src/good_ai/good_ai/set_default_config.py new file mode 100644 index 0000000..e7cd4f0 --- /dev/null +++ b/good_ai/src/good_ai/good_ai/set_default_config.py @@ -0,0 +1,55 @@ +import logging +import random +from pathlib import Path + +from good_ai.good_ai.tracing.tracing_context import TracingContext +from good_ai.open_s3 import LargeFile + +from .tracing import PersistenceDriver, TinyDbDriver + +logger = logging.getLogger("good_ai") + +_initialized = False + + +def set_default_config_if_uninitialized() -> None: + if not _initialized: + set_default_config() + + +def set_default_config( + log_level: int = logging.INFO, + s3_config: Path = Path("s3.ini"), + seed: int = 42, + tracing_db_driver: PersistenceDriver = TinyDbDriver(Path("tracing_database.json")), +) -> None: + global _initialized + logging.basicConfig(level=log_level) + + _initialize_large_file(s3_config) + _set_seed(seed) + + TracingContext.persistence_driver = tracing_db_driver + + _initialized = True + + logger.info(f"Defaults: configured ✅") + + +def _initialize_large_file(s3_config: Path) -> None: + if s3_config.exists(): + LargeFile.configure_credentials_from_file(s3_config) + else: + logger.info( + f"Provided S3 config ({s3_config.resolve()}) not found, skipping LargeFile initialisation" + ) + + +def _set_seed(seed: int) -> None: + random.seed(seed) + try: + import numpy + + numpy.random.seed(seed + 1) + except ImportError: + pass diff --git a/good_ai/src/good_ai/good_ai/tracing/__init__.py b/good_ai/src/good_ai/good_ai/tracing/__init__.py new file mode 100644 index 0000000..510390e --- /dev/null +++ b/good_ai/src/good_ai/good_ai/tracing/__init__.py @@ -0,0 +1,4 @@ +from .model import Model +from .persistence import MongoDbDriver, PersistenceDriver, TinyDbDriver +from .trace import Trace +from .tracing_context import TracingContext diff --git a/good_ai/src/good_ai/good_ai/tracing/model.py b/good_ai/src/good_ai/good_ai/tracing/model.py new file mode 100644 index 0000000..12a1ed6 --- /dev/null +++ b/good_ai/src/good_ai/good_ai/tracing/model.py @@ -0,0 +1,6 @@ +from pydantic import BaseModel + + +class Model(BaseModel): + key: str + version: int diff --git a/good_ai/src/good_ai/good_ai/tracing/persistence/__init__.py b/good_ai/src/good_ai/good_ai/tracing/persistence/__init__.py new file mode 100644 index 0000000..7bddbe7 --- /dev/null +++ b/good_ai/src/good_ai/good_ai/tracing/persistence/__init__.py @@ -0,0 +1,3 @@ +from .mongodb_driver import MongoDbDriver +from .persistence_driver import PersistenceDriver +from .tinydb_driver import TinyDbDriver diff --git a/good_ai/src/good_ai/good_ai/tracing/persistence/mongodb_driver.py b/good_ai/src/good_ai/good_ai/tracing/persistence/mongodb_driver.py new file mode 100644 index 0000000..7b27a82 --- /dev/null +++ b/good_ai/src/good_ai/good_ai/tracing/persistence/mongodb_driver.py @@ -0,0 +1,5 @@ +from .persistence_driver import PersistenceDriver + + +class MongoDbDriver(PersistenceDriver): + pass diff --git a/good_ai/src/good_ai/good_ai/tracing/persistence/persistence_driver.py b/good_ai/src/good_ai/good_ai/tracing/persistence/persistence_driver.py new file mode 100644 index 0000000..dd37537 --- /dev/null +++ b/good_ai/src/good_ai/good_ai/tracing/persistence/persistence_driver.py @@ -0,0 +1,8 @@ +from abc import ABC, abstractmethod +from typing import Any, Dict + + +class PersistenceDriver(ABC): + @abstractmethod + def save_document(self, document: Dict[str, Any]) -> str: + pass diff --git a/good_ai/src/good_ai/good_ai/tracing/persistence/tinydb_driver.py b/good_ai/src/good_ai/good_ai/tracing/persistence/tinydb_driver.py new file mode 100644 index 0000000..f5208d0 --- /dev/null +++ b/good_ai/src/good_ai/good_ai/tracing/persistence/tinydb_driver.py @@ -0,0 +1,15 @@ +from pathlib import Path +from typing import Any, Dict + +from tinydb import TinyDB + +from .persistence_driver import PersistenceDriver + + +class TinyDbDriver(PersistenceDriver): + def __init__(self, path_to_db: Path) -> None: + super().__init__() + self._db = TinyDB(path_to_db) + + def save_document(self, document: Dict[str, Any]) -> str: + return self._db.insert(document) diff --git a/good_ai/src/good_ai/good_ai/tracing/trace.py b/good_ai/src/good_ai/good_ai/tracing/trace.py new file mode 100644 index 0000000..25809ea --- /dev/null +++ b/good_ai/src/good_ai/good_ai/tracing/trace.py @@ -0,0 +1,14 @@ +from typing import Any, List + +from pydantic import BaseModel + +from .model import Model + + +class Trace(BaseModel): + created: str + execution_time_ms: float + input: Any + models: List[Model] + output: Any + evaluation: Any = None diff --git a/good_ai/src/good_ai/good_ai/tracing/tracing_context.py b/good_ai/src/good_ai/good_ai/tracing/tracing_context.py new file mode 100644 index 0000000..bbfda4d --- /dev/null +++ b/good_ai/src/good_ai/good_ai/tracing/tracing_context.py @@ -0,0 +1,71 @@ +import logging +import threading +from collections import defaultdict +from datetime import datetime +from types import TracebackType +from typing import Any, DefaultDict, List, Optional, Type + +from .persistence import PersistenceDriver + +logger = logging.getLogger("good_ai") + +from .model import Model +from .trace import Trace + + +class TracingContext: + persistence_driver: PersistenceDriver + _contexts: DefaultDict[int, List["TracingContext"]] = defaultdict(lambda: []) + + def __init__(self) -> None: + self._models: List[Model] = [] + self._input: Any = None + self._output: Any = None + self._trace: Optional[Trace] = None + self._start_time = datetime.utcnow() + + def log_input(self, input: Any) -> None: + self._input = input + + def log_model(self, model: Model) -> None: + self._models.append(model) + + def log_output(self, output: Any) -> Trace: + self._output = output + + delta_time = (datetime.utcnow() - self._start_time).microseconds / 1000 + self._trace = Trace( + created=self._start_time.isoformat(), + execution_time_ms=delta_time, + input=self._input, + models=self._models, + output=self._output, + ) + return self._trace + + @classmethod + def get_current_context(cls) -> Optional["TracingContext"]: + if cls._contexts[threading.get_ident()]: + return cls._contexts[threading.get_ident()][-1] + return None + + def __enter__(self) -> "TracingContext": + self._contexts[threading.get_ident()].append(self) + return self + + def __exit__( + self, + type: Optional[Type[BaseException]], + exception: Optional[BaseException], + traceback: Optional[TracebackType], + ) -> bool: + assert self._contexts[threading.get_ident()][-1] == self + self._contexts[threading.get_ident()].remove(self) + + if type is None: + assert self._trace is not None + self.persistence_driver.save_document(self._trace.dict()) + else: + logger.exception(f"Could not finish operation: {exception}") + + return True diff --git a/good_ai/src/good_ai/open_s3/__main__.py b/good_ai/src/good_ai/open_s3/__main__.py index 9e5a5b8..3b96e7c 100644 --- a/good_ai/src/good_ai/open_s3/__main__.py +++ b/good_ai/src/good_ai/open_s3/__main__.py @@ -3,8 +3,8 @@ import logging from pathlib import Path -from large_file import LargeFile -from parse_arguments import parse_arguments +from .large_file import LargeFile +from .parse_arguments import parse_arguments if __name__ == "__main__": logging.basicConfig(level=logging.INFO) diff --git a/good_ai/src/good_ai/open_s3/helper/bytes_to_megabytes.py b/good_ai/src/good_ai/open_s3/helper/bytes_to_megabytes.py new file mode 100644 index 0000000..7b1808f --- /dev/null +++ b/good_ai/src/good_ai/open_s3/helper/bytes_to_megabytes.py @@ -0,0 +1,2 @@ +def bytes_to_megabytes(bytes: int) -> str: + return f"{round(bytes / 1000 / 1000, 2):.2f}" diff --git a/good_ai/src/good_ai/open_s3/helper/progress_bar.py b/good_ai/src/good_ai/open_s3/helper/progress_bar.py index 545db48..8e6d1d1 100644 --- a/good_ai/src/good_ai/open_s3/helper/progress_bar.py +++ b/good_ai/src/good_ai/open_s3/helper/progress_bar.py @@ -3,6 +3,8 @@ import threading from logging import Logger from pathlib import Path +from .bytes_to_megabytes import bytes_to_megabytes + class ProgressBar: def __init__(self, file_size: int, logger: Logger, prefix: str): @@ -17,10 +19,12 @@ class ProgressBar: with self._lock: self._seen_so_far += bytes_amount percentage = (self._seen_so_far / float(self._file_size)) * 100 - size_length = len(str(self._file_size)) - progress = str(self._seen_so_far).rjust(size_length) + + file_size_mb = bytes_to_megabytes(self._file_size) + seen_so_far_mb = bytes_to_megabytes(self._seen_so_far) + progress = seen_so_far_mb.rjust(len(file_size_mb)) self._logger.info( - f"{self._prefix} {progress}/{self._file_size} bytes ({percentage:.1f}%)" + f"{self._prefix} {progress}/{file_size_mb} MB ({percentage:.1f}%)" ) diff --git a/good_ai/src/good_ai/open_s3/large_file.py b/good_ai/src/good_ai/open_s3/large_file.py index 00e643b..d0175d7 100644 --- a/good_ai/src/good_ai/open_s3/large_file.py +++ b/good_ai/src/good_ai/open_s3/large_file.py @@ -5,7 +5,7 @@ import shutil import tempfile from pathlib import Path from types import TracebackType -from typing import IO, Any, Dict, List, Optional, Type, Union +from typing import IO, Any, List, Mapping, Optional, Type, Union, cast import boto3 @@ -65,7 +65,7 @@ class LargeFile: offline_mode: bool = False, ): self._name: str = name - self._version = version + self._version: int = cast(int, version) self._mode: str = mode self._keep_last_n = keep_last_n self._offline_mode = offline_mode @@ -89,7 +89,7 @@ class LargeFile: aws_secret_access_key: str, large_files_bucket_name: str, endpoint_url: Optional[str] = None, - **_: Dict[str, Any], + **_: Mapping[str, Any], ) -> None: cls.region_name = aws_region_name cls.access_key_id = aws_access_key_id @@ -188,7 +188,7 @@ class LargeFile: Filename=str(tmp_file_archive), Callback=None if hide_progress - else DownloadProgressBar(size=size, name=key, logger=logger), + else DownloadProgressBar(name=str(key), size=size, logger=logger), ) logger.info(f"Decompressing {self._local_name}") shutil.unpack_archive(str(tmp_file_archive), tmp, "gztar") @@ -199,7 +199,7 @@ class LargeFile: return destination - def push(self, path: Union[Path, str], hide_progress: bool = False) -> int: + def push(self, path: Union[Path, str], hide_progress: bool = False) -> None: if isinstance(path, str): path = Path(path) @@ -235,7 +235,7 @@ class LargeFile: Key=self._s3_name, Callback=None if hide_progress - else UploadProgressBar(file_to_be_uploaded, logger=logger), + else UploadProgressBar(path=file_to_be_uploaded, logger=logger), ) self.clean_up() diff --git a/good_ai/tests/open_s3/test_human_readable_to_byte.py b/good_ai/tests/open_s3/test_human_readable_to_byte.py index f4fe84d..f8752b7 100644 --- a/good_ai/tests/open_s3/test_human_readable_to_byte.py +++ b/good_ai/tests/open_s3/test_human_readable_to_byte.py @@ -1,6 +1,6 @@ import unittest -from src.open_s3.helper import human_readable_to_byte +from src.good_ai.open_s3.helper import human_readable_to_byte class TestHumanReadableToByte(unittest.TestCase): diff --git a/good_ai/tests/open_s3/test_large_file.py b/good_ai/tests/open_s3/test_large_file.py index e37d60b..3537f52 100644 --- a/good_ai/tests/open_s3/test_large_file.py +++ b/good_ai/tests/open_s3/test_large_file.py @@ -8,7 +8,7 @@ import botocore.session PATH = Path(__file__).parent.resolve() -from src.open_s3 import LargeFile +from src.good_ai import LargeFile credentials = { "aws_region_name": "your_region_like_eu-west-2", @@ -30,7 +30,7 @@ class TestLargeFile(unittest.TestCase): self.assertRaises(ValueError, LargeFile, "test-file", "test") @patch("botocore.session") - def test_initialized_with_dict(self, session) -> None: + def test_initialized_with_dict(self, session: Any) -> None: session_mock = Mock() session.get_session = create_autospec( botocore.session.get_session, return_value=session_mock @@ -58,7 +58,13 @@ class TestLargeFile(unittest.TestCase): session_mock.create_client = Mock(return_value=s3) - LargeFile.configure_credentials(**credentials) + LargeFile.configure_credentials( + aws_region_name=credentials["aws_region_name"], + aws_access_key_id=credentials["aws_access_key_id"], + aws_secret_access_key=credentials["aws_secret_access_key"], + large_files_bucket_name=credentials["large_files_bucket_name"], + endpoint_url=credentials["endpoint_url"], + ) lf = LargeFile("test-file") session_mock.set_credentials.assert_called_once_with( access_key=credentials["aws_access_key_id"], diff --git a/good_ai/tests/sus/data/parsed.py b/good_ai/tests/sus/data/parsed.py index 4826438..a0dc626 100644 --- a/good_ai/tests/sus/data/parsed.py +++ b/good_ai/tests/sus/data/parsed.py @@ -1,4 +1,4 @@ -from sus.publication_tei.models import ( +from good_ai.utilities.publication_tei.models import ( Affiliation, Author, Meta, diff --git a/good_ai/tests/sus/test_clean.py b/good_ai/tests/sus/test_clean.py index bafea9c..7c94868 100644 --- a/good_ai/tests/sus/test_clean.py +++ b/good_ai/tests/sus/test_clean.py @@ -1,6 +1,6 @@ import unittest -from src.sus.clean import clean +from src.good_ai.utilities.clean import clean class TestClean(unittest.TestCase): diff --git a/good_ai/tests/sus/test_evaluate_ranking.py b/good_ai/tests/sus/test_evaluate_ranking.py index fd8d20d..aa52736 100644 --- a/good_ai/tests/sus/test_evaluate_ranking.py +++ b/good_ai/tests/sus/test_evaluate_ranking.py @@ -1,7 +1,7 @@ import unittest from pathlib import Path -from src.sus.evaluate_ranking import evaluate_ranking +from src.good_ai.utilities.evaluate_ranking import evaluate_ranking class TestEvaluateRanking(unittest.TestCase): diff --git a/good_ai/tests/sus/test_get_sentences.py b/good_ai/tests/sus/test_get_sentences.py index 4146811..848bcde 100644 --- a/good_ai/tests/sus/test_get_sentences.py +++ b/good_ai/tests/sus/test_get_sentences.py @@ -1,6 +1,6 @@ import unittest -from src.sus.get_sentences import get_sentences +from src.good_ai.utilities.get_sentences import get_sentences class TestGetSentences(unittest.TestCase): diff --git a/good_ai/tests/sus/test_language.py b/good_ai/tests/sus/test_language.py index 84d1093..be0e1aa 100644 --- a/good_ai/tests/sus/test_language.py +++ b/good_ai/tests/sus/test_language.py @@ -1,6 +1,10 @@ import unittest -from src.sus.language import english_name_of_language, is_english, predict_language +from src.good_ai.utilities.language import ( + english_name_of_language, + is_english, + predict_language, +) class TestLanguage(unittest.TestCase): diff --git a/good_ai/tests/sus/test_lemmatize_text.py b/good_ai/tests/sus/test_lemmatize_text.py index 99da45b..418a864 100644 --- a/good_ai/tests/sus/test_lemmatize_text.py +++ b/good_ai/tests/sus/test_lemmatize_text.py @@ -1,6 +1,6 @@ import unittest -from src.sus.lemmatize_text import lemmatize_text +from src.good_ai.utilities.lemmatize_text import lemmatize_text class TestLemmatizeText(unittest.TestCase): diff --git a/good_ai/tests/sus/test_lemmatize_token.py b/good_ai/tests/sus/test_lemmatize_token.py index 18bd1f3..76cfc22 100644 --- a/good_ai/tests/sus/test_lemmatize_token.py +++ b/good_ai/tests/sus/test_lemmatize_token.py @@ -1,7 +1,7 @@ import unittest -from src.sus.lemmatize_text import lemmatize_token -from src.sus.nlp import nlp +from src.good_ai.utilities.lemmatize_text import lemmatize_token +from src.good_ai.utilities.nlp import nlp class TestLemmatizeToken(unittest.TestCase): diff --git a/good_ai/tests/sus/test_match_names.py b/good_ai/tests/sus/test_match_names.py index 893f24c..20451fd 100644 --- a/good_ai/tests/sus/test_match_names.py +++ b/good_ai/tests/sus/test_match_names.py @@ -1,6 +1,6 @@ import unittest -from src.sus.match_names.match_names import match_names +from src.good_ai.utilities.match_names.match_names import match_names class TestMatchNames(unittest.TestCase): diff --git a/good_ai/tests/sus/test_parallel_map.py b/good_ai/tests/sus/test_parallel_map.py index 0664e14..ee9def7 100644 --- a/good_ai/tests/sus/test_parallel_map.py +++ b/good_ai/tests/sus/test_parallel_map.py @@ -1,6 +1,6 @@ import unittest -from src.sus.parallel_map import parallel_map +from src.good_ai.utilities.parallel_map import parallel_map COUNT = int(1e5) + 3 diff --git a/good_ai/tests/sus/test_publication_tei.py b/good_ai/tests/sus/test_publication_tei.py index 82c8814..c7c227d 100644 --- a/good_ai/tests/sus/test_publication_tei.py +++ b/good_ai/tests/sus/test_publication_tei.py @@ -1,7 +1,7 @@ import unittest from pathlib import Path -from src.sus.publication_tei import PublicationTEI +from src.good_ai.utilities.publication_tei import PublicationTEI from .data.parsed import authors, content, metadata, sentences diff --git a/good_ai/tests/sus/test_unique.py b/good_ai/tests/sus/test_unique.py index 8b90e08..ee8cdfd 100644 --- a/good_ai/tests/sus/test_unique.py +++ b/good_ai/tests/sus/test_unique.py @@ -1,6 +1,6 @@ import unittest -from src.sus.unique import unique +from src.good_ai.utilities.unique import unique original = [ ("a", 1), diff --git a/ideas.drawio b/ideas.drawio new file mode 100644 index 0000000..c85f02d --- /dev/null +++ b/ideas.drawio @@ -0,0 +1,230 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + \ No newline at end of file