Metadata-Version: 2.1
Name: ml_recsys_tools
Version: 0.5.3
Summary: Tools for recommendation systems development
Home-page: https://github.com/DomainGroupOSS/ml-recsys-tools
Author: Domain group (Arthur Deygin)
Author-email: arthurdgn@gmail.com
License: UNKNOWN
Description: # ml-recsys-tools
        
        #### Open source repo for various tools for recommender systems development work. Work in progress.
        
        #### 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: includes only basic dependecies + lightfm/implicit for now: `pip install ml_recsys_tools`
        
        Docker (build and run interactively): 
        ```
        docker build -t ml_recsys_tools:local .
        docker run -it --rm ml_recsys_tools:local python
        ```     
        
        #### Basic example:
            # 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.
        * #### [Spotlight](https://github.com/maciejkula/spotlight) package based implicit 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)
                * early stopping fit (for iterative models: LightFM, ALS, Spotlight)
                * hyperparameter search
                * 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
            * mappers for geo features, observations, recommendations, similarities etc.
                
        * #### 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:
            * hyperparameter tuning utils (by skopt)
            * similarity calculation helpers (similarities, dot, top N, top N on sparse)
            * parallelism utils
            * sklearn transformer extenstions (for feature engineering)
            * google maps util for displaying geographical data
            * logging, debug printouts decorators and other isntrumentation and inspection tools
            * pandas utils
            * data helpers: redis, s3    
            * basic rankings server that updates models from S3 
         
        
        * #### Examples:
            * a basic example on movielens 1M demonstrating:
                * basic data ingestion without any item/user features
                * LightFM recommender:
                    fit, evaluation, early stopping,
                    hyper-param search, recommendations, similarities
                * Cooccurrence recommender
                * Two combination ensembles (Ranks and Simils)
        
        * #### Still to add:
            * add and reorganize examples 
            * much more comments and docstrings
            * more tests
        
        
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
