Metadata-Version: 2.1
Name: mab2rec
Version: 1.0.1
Summary: Mab2Rec: Multi-Armed Bandits Recommender
Home-page: UNKNOWN
Author: FMR LLC
License: UNKNOWN
Project-URL: Source, https://github.com/fidelity/mab2rec
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        # Mab2Rec: Multi-Armed Bandits Recommender 
        
        Mab2Rec is a Python library for building bandit-based recommendation algorithms. It supports **context-free**, **parametric** and **non-parametric** **contextual** bandit models powered by [MABWiser](https://github.com/fidelity/mabwiser) and fairness and recommenders evaluations powered by [Jurity](https://github.com/fidelity/jurity).
        It supports [all bandit policies available in MABWiser](https://github.com/fidelity/mabwiser#available-bandit-policies). The library is designed with rapid experimentation in mind, follows the [PEP-8 standards](https://www.python.org/dev/peps/pep-0008/) and is tested heavily.
        
        Mab2Rec and several other open-source software it is built on is developed by the AI Center at Fidelity:
        
        * [MABWiser](https://github.com/fidelity/mabwiser) to create multi-armed bandit recommendation algorithms ([IJAIT'21](https://www.worldscientific.com/doi/abs/10.1142/S0218213021500214), [ICTAI'19](https://ieeexplore.ieee.org/document/8995418)).
        * [TextWiser](https://github.com/fidelity/textwiser) to create item representations via text featurization ([AAAI'21](https://ojs.aaai.org/index.php/AAAI/article/view/17814)).
        * [Selective](https://github.com/fidelity/selective) to create user representations via feature selection.
        * [Seq2Pat](https://github.com/fidelity/seq2pat) to enhance users representations via sequential pattern mining ([AAAI'22](https://aaai.org/Conferences/AAAI-22/)).
        * [Jurity](https://github.com/fidelity/jurity) to evaluate recommendations including fairness metrics ([ICMLA'21](https://ieeexplore.ieee.org/abstract/document/9680169)).
        * [Spock](https://github.com/fidelity/spock) to define, manage, and use parameter configurations.
        
        An introduction to **content- and context-aware** recommender systems and an overview of the building blocks of the library is [presented at All Things Open 2021](https://www.youtube.com/watch?v=54d_YUalvOA). 
        
        Documentation is available at [fidelity.github.io/mab2rec](https://fidelity.github.io/mab2rec).
        
        ## Usage Patterns
        
        Mab2Rec supports prototyping of **individual** bandit algorithms and benchmarking of **multiple** bandit algorithms. 
        The intended usage for a new user is to experiment with multiple bandits using the tutorial in the [notebooks](notebooks) folder.
        
        ## Quick Start
        
        ### Individual Recommender
        
        ```python
        # Example of how to train an individual recommender to generate top-4 recommendations
        
        # Import 
        from mab2rec import BanditRecommender, LearningPolicy
        from mab2rec.pipeline import train, score
        
        # LinGreedy recommender to select top-4 items with 10% random exploration 
        rec = BanditRecommender(LearningPolicy.LinGreedy(epsilon=0.1), top_k=4)
        
        # Train on (user, item, response) interactions in train data using user features 
        train(rec, data='data/data_train.csv', 
              user_features='data/features_user.csv')
        
        # Score recommendations for users in test data. The output df holds 
        # user_id, item_id, score columns for every test user for top-k items 
        df = score(rec, data='data/data_test.csv', 
                   user_features='data/features_user.csv')
        ```
        
        ### Multiple Recommenders
        
        ```python
        # Example of how to benchmark multiple bandit algorithms to generate top-4 recommendations
        
        from mab2rec import BanditRecommender, LearningPolicy
        from mab2rec.pipeline import benchmark
        from jurity.recommenders import BinaryRecoMetrics, RankingRecoMetrics
        
        # Recommenders (many more available)
        recommenders = {"Random": BanditRecommender(LearningPolicy.Random()),
                        "Popularity": BanditRecommender(LearningPolicy.Popularity()),
                        "LinGreedy": BanditRecommender(LearningPolicy.LinGreedy(epsilon=0.1))}
        
        # Column names for the response, user, and item id columns
        metric_params = {'click_column': 'score', 'user_id_column': 'user_id', 'item_id_column':'item_id'}
        
        # Performance metrics for benchmarking (many more available)
        metrics = []
        for top_k in [3, 5, 10]:
            metrics.append(BinaryRecoMetrics.CTR(**metric_params, k=top_k))
            metrics.append(RankingRecoMetrics.NDCG(**metric_params, k=top_k))
        
        # Benchmarking with a collection of recommenders and metrics 
        # This returns two dictionaries; 
        # reco_to_results: recommendations for each algorithm on cross-validation data
        # reco_to_metrics: evaluation metrics for each algorithm
        reco_to_results, reco_to_metrics = benchmark(recommenders,
                                                     metrics=metrics,
                                                     train_data="data/data_train.csv",
                                                     cv=5,
                                                     user_features="data/features_user.csv")
        ```
        
        ## Usage Examples
        
        We provide an extensive tutorial in the [notebooks](notebooks) folder with guidelines on building recommenders, performing model selection, and evaluating performance.
        
        1. [Data Overview:](https://github.com/fidelity/mab2rec/tree/master/notebooks/1_data_overview.ipynb) Overview of data required to train recommender.
        2. [Feature Engineering:](https://github.com/fidelity/mab2rec/tree/master/notebooks/2_feature_engineering.ipynb) Creating user and item features from structured, unstructured, and sequential data.
        3. [Model Selection:](https://github.com/fidelity/mab2rec/tree/master/notebooks/3_model_selection.ipynb) Model selection by benchmarking recommenders using cross-validation.
        4. [Evaluation:](https://github.com/fidelity/mab2rec/tree/master/notebooks/4_evaluation.ipynb) Benchmarking of selected recommenders and baselines on test data with detailed evaluation.
        5. [Advanced:](https://github.com/fidelity/mab2rec/tree/master/notebooks/5_advanced.ipynb) Demonstration of advanced functionality.
        
        ## Installation
        
        Mab2Rec can be installed from PyPI using ``pip install mab2rec`` or by building from source by following the instructions
        in our [documentation](https://fidelity.github.io/mab2rec/installation.html).
        
        ## Support
        
        Please submit bug reports and feature requests as [Issues](https://github.com/fidelity/mab2rec/issues).
        
        ## License
        
        Mab2Rec is licensed under the [Apache License 2.0](LICENSE).
        
        <br>
        
Platform: UNKNOWN
Classifier: License :: OSI Approved :: Apache Software License
Classifier: Programming Language :: Python :: 3.7
Classifier: Operating System :: OS Independent
Requires-Python: >=3.7
Description-Content-Type: text/markdown
