Metadata-Version: 2.1
Name: ml-recsys-tools
Version: 0.9.0
Summary: Tools for recommendation systems development
Home-page: https://github.com/artdgn/ml-recsys-tools
Author: Arthur Deygin
Author-email: arthurdgn@gmail.com
License: UNKNOWN
Description: ![CI](https://github.com/artdgn/ml-recsys-tools/workflows/CI/badge.svg)
        
        # ml-recsys-tools
        
        ----
        
        ### This is an updated version of the stale ml-recsys-tools source repo
        
        -----
        
        
        #### Open source repo for various tools for recommender systems development work.
        
        #### Main purpose is to provide a single wrapper for various recommender packages to train, tune, evaluate and get data in and recommendations / similarities out.
        
        #### Installation:
        
        Pip: 
        * `pip install git+https://github.com/artdgn/ml-recsys-tools@master#egg=ml_recsys_tools` 
        * As dependency (add to `requirements.txt`): add line `git+https://github.com/artdgn/ml-recsys-tools@master#egg=ml_recsys_tools`
        
        
        #### Basic example:
        
        ```python
        # dataset: download and prepare dataframes
        from ml_recsys_tools.datasets.prep_movielense_data import get_and_prep_data
        rating_csv_path, users_csv_path, movies_csv_path = get_and_prep_data()
        
        # read the interactions dataframe and create a data handler object and  split to train and test
        import pandas as pd
        
        ratings_df = pd.read_csv(rating_csv_path)
        from ml_recsys_tools.data_handlers.interaction_handlers_base import ObservationsDF    
        obs = ObservationsDF(ratings_df, uid_col='userid', iid_col='itemid')
        train_obs, test_obs = obs.split_train_test(ratio=0.2)
        
        # train and test LightFM recommender
        from ml_recsys_tools.recommenders.lightfm_recommender import LightFMRecommender    
        lfm_rec = LightFMRecommender()
        lfm_rec.fit(train_obs, epochs=10)
        
        # print summary evaluation report:
        print(lfm_rec.eval_on_test_by_ranking(test_obs.df_obs, prefix='lfm ', n_rec=100))
        
        # get all recommendations and print a sample (training interactions are filtered out by default)
        recs = lfm_rec.get_recommendations(lfm_rec.all_users, n_rec=5)
        print(recs.sample(5))
        
        # get all similarities and print a sample
        simils = lfm_rec.get_similar_items(lfm_rec.all_items, n_simil=5)
        print(simils.sample(10))
        ```
            
        
        
        ## Recommender models and tools:
        
        * #### [LightFM](https://github.com/lyst/lightfm) package based recommender.
        * #### [Implicit](https://github.com/benfred/implicit) package based ALS recommender.
        * #### Serving / Tuning / Evaluation features added for most recommenders:
            * Dataframes for all inputs and outputs
                * adding external features (for LightFM hybrid mode)
                * fast batched methods for:
                    * user recommendation sampling
                    * similar items samplilng with different similarity measures
                    * similar users sampling
                    * evaluation by sampling and ranking      
                          
        * #### Additional recommender models:
            * ##### Similarity based:
                * cooccurence (items, users)
                * generic similarity based (can be used with external features)  
                      
        * #### Ensembles:
            * subdivision based (multiple recommenders each on subset of data - e.g. geographical region):
                * geo based: simple grid, equidense grid, geo clustering
                * LightFM and cooccurrence based
            * combination based - combining recommendations from multiple recommenders
            * similarity combination based - similarity based recommender on similarities from multiple recommenders
            * cascade ensemble 
                   
        * #### Interaction dataframe and sparse matrix handlers / builders:
            * sampling, data splitting,
            * external features matrix creation (additional item features),
                with feature engineering: binning / one*hot encoding (via pandas_sklearn)
            * evaluation and ranking helpers
            * handlers for observations coupled with external features and features with geo coordinates
                
        * #### Evaluation utils:
            * score reports on lightfm metrics (AUC, precision, recall, reciprocal)
            * n-DCG, and n-MRR metrics, n-precision / recall
            * references: best possible ranking and chance ranking
        
        * #### Utilities:
            * similarity calculation helpers (similarities, dot, top N, top N on sparse)
            * parallelism utils
            * sklearn transformer extenstions (for feature engineering)
            * logging, debug printouts decorators and other instrumentation and inspection tools
            * pandas utils
        
Keywords: recommendations machine learning
Platform: UNKNOWN
Classifier: Development Status :: 3 - Alpha
Classifier: Intended Audience :: Developers
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 3
Description-Content-Type: text/markdown
