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    "info": {
        "author": "Domain group (Arthur Deygin)",
        "author_email": "arthurdgn@gmail.com",
        "bugtrack_url": null,
        "classifiers": [
            "Development Status :: 3 - Alpha",
            "Intended Audience :: Developers",
            "License :: OSI Approved :: MIT License",
            "Programming Language :: Python :: 3",
            "Topic :: Scientific/Engineering :: Artificial Intelligence"
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        "description": "# ml-recsys-tools\n\n#### Open source repo for various tools for recommender systems development work. Work in progress.\n\n#### Main purpose is to provide a single wrapper for various recommender packages to train, tune, evaluate and get data in and recommendations / similarities out.\n\n#### Installation:\nPip: includes only basic dependecies + lightfm/implicit for now: `pip install ml_recsys_tools`\n\nDocker (build and run interactively): \n```\ndocker build -t ml_recsys_tools:local .\ndocker run -it --rm ml_recsys_tools:local python\n```     \n\n#### Basic example:\n    # dataset: download and prepare dataframes\n    from ml_recsys_tools.datasets.prep_movielense_data import get_and_prep_data\n    rating_csv_path, users_csv_path, movies_csv_path = get_and_prep_data()\n\n    # read the interactions dataframe and create a data handler object and  split to train and test\n    import pandas as pd\n\n    ratings_df = pd.read_csv(rating_csv_path)\n    from ml_recsys_tools.data_handlers.interaction_handlers_base import ObservationsDF    \n    obs = ObservationsDF(ratings_df, uid_col='userid', iid_col='itemid')\n    train_obs, test_obs = obs.split_train_test(ratio=0.2)\n\n    # train and test LightFM recommender\n    from ml_recsys_tools.recommenders.lightfm_recommender import LightFMRecommender    \n    lfm_rec = LightFMRecommender()\n    lfm_rec.fit(train_obs, epochs=10)\n\n    # print summary evaluation report:\n    print(lfm_rec.eval_on_test_by_ranking(test_obs.df_obs, prefix='lfm ', n_rec=100))\n\n    # get all recommendations and print a sample (training interactions are filtered out by default)\n    recs = lfm_rec.get_recommendations(lfm_rec.all_users, n_rec=5)\n    print(recs.sample(5))\n\n    # get all similarities and print a sample\n    simils = lfm_rec.get_similar_items(lfm_rec.all_items, n_simil=5)\n    print(simils.sample(10))\n\n\n## Recommender models and tools:\n\n* #### [LightFM](https://github.com/lyst/lightfm) package based recommender.\n* #### [Spotlight](https://github.com/maciejkula/spotlight) package based implicit recommender.\n* #### [Implicit](https://github.com/benfred/implicit) package based ALS recommender.\n* #### Serving / Tuning / Evaluation features added for most recommenders:\n    * Dataframes for all inputs and outputs\n        * adding external features (for LightFM hybrid mode)\n        * early stopping fit (for iterative models: LightFM, ALS, Spotlight)\n        * hyperparameter search\n        * fast batched methods for:\n            * user recommendation sampling\n            * similar items samplilng with different similarity measures\n            * similar users sampling\n            * evaluation by sampling and ranking      \n\n* #### Additional recommender models:\n    * ##### Similarity based:\n        * cooccurence (items, users)\n        * generic similarity based (can be used with external features)  \n\n* #### Ensembles:\n    * subdivision based (multiple recommenders each on subset of data - e.g. geographical region):\n        * geo based: simple grid, equidense grid, geo clustering\n        * LightFM and cooccurrence based\n    * combination based - combining recommendations from multiple recommenders\n    * similarity combination based - similarity based recommender on similarities from multiple recommenders\n    * cascade ensemble \n\n* #### Interaction dataframe and sparse matrix handlers / builders:\n    * sampling, data splitting,\n    * external features matrix creation (additional item features),\n        with feature engineering: binning / one*hot encoding (via pandas_sklearn)\n    * evaluation and ranking helpers\n    * handlers for observations coupled with external features and features with geo coordinates\n    * mappers for geo features, observations, recommendations, similarities etc.\n\n* #### Evaluation utils:\n    * score reports on lightfm metrics (AUC, precision, recall, reciprocal)\n    * n-DCG, and n-MRR metrics, n-precision / recall\n    * references: best possible ranking and chance ranking\n\n* #### Utilities:\n    * hyperparameter tuning utils (by skopt)\n    * similarity calculation helpers (similarities, dot, top N, top N on sparse)\n    * parallelism utils\n    * sklearn transformer extenstions (for feature engineering)\n    * google maps util for displaying geographical data\n    * logging, debug printouts decorators and other isntrumentation and inspection tools\n    * pandas utils\n    * data helpers: redis, s3    \n    * basic rankings server that updates models from S3 \n\n\n* #### Examples:\n    * a basic example on movielens 1M demonstrating:\n        * basic data ingestion without any item/user features\n        * LightFM recommender:\n            fit, evaluation, early stopping,\n            hyper-param search, recommendations, similarities\n        * Cooccurrence recommender\n        * Two combination ensembles (Ranks and Simils)\n\n* #### Still to add:\n    * add and reorganize examples \n    * much more comments and docstrings\n    * more tests\n\n\n\n",
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        "keywords": "recommendations machine learning",
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