Full Code of fidelity/mab2rec for AI

Repository: fidelity/mab2rec
Branch: main
Commit: 1cd7ebfd5fa7
Files: 74
Total size: 1.2 MB

Directory structure:
gitextract_avjheom_/

├── .github/
│   └── workflows/
│       └── ci.yml
├── .gitignore
├── CHANGELOG.txt
├── CODEOWNERS
├── CONTRIBUTING.md
├── LICENSE
├── README.md
├── docs/
│   ├── .buildinfo
│   ├── .nojekyll
│   ├── _sources/
│   │   ├── api.rst.txt
│   │   ├── contributing.rst.txt
│   │   ├── examples.rst.txt
│   │   ├── index.rst.txt
│   │   ├── installation.rst.txt
│   │   └── quick.rst.txt
│   ├── _static/
│   │   ├── basic.css
│   │   ├── css/
│   │   │   ├── badge_only.css
│   │   │   └── theme.css
│   │   ├── doctools.js
│   │   ├── documentation_options.js
│   │   ├── jquery-3.5.1.js
│   │   ├── jquery.js
│   │   ├── js/
│   │   │   ├── badge_only.js
│   │   │   └── theme.js
│   │   ├── language_data.js
│   │   ├── pygments.css
│   │   ├── searchtools.js
│   │   ├── underscore-1.13.1.js
│   │   └── underscore.js
│   ├── api.html
│   ├── contributing.html
│   ├── examples.html
│   ├── genindex.html
│   ├── index.html
│   ├── installation.html
│   ├── objects.inv
│   ├── py-modindex.html
│   ├── quick.html
│   ├── search.html
│   └── searchindex.js
├── docsrc/
│   ├── Makefile
│   ├── api.rst
│   ├── conf.py
│   ├── contributing.rst
│   ├── examples.rst
│   ├── index.rst
│   ├── installation.rst
│   ├── make.bat
│   ├── quick.rst
│   └── requirements.txt
├── mab2rec/
│   ├── __init__.py
│   ├── _version.py
│   ├── pipeline.py
│   ├── rec.py
│   ├── utils.py
│   └── visualization.py
├── requirements.txt
├── scripts/
│   └── data_prep/
│       ├── concat_files.sh
│       ├── concat_first_two_columns.sh
│       ├── insert_header.sh
│       ├── remove_columns.sh
│       ├── remove_duplicate_lines.sh
│       ├── remove_empty_lines.sh
│       ├── remove_header.sh
│       ├── rename_header.sh
│       └── sort_except_header.sh
├── setup.py
└── tests/
    ├── __init__.py
    ├── run_all.py
    ├── test_base.py
    ├── test_invalid.py
    ├── test_pipeline.py
    ├── test_rec.py
    └── test_visualization.py

================================================
FILE CONTENTS
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================================================
FILE: .github/workflows/ci.yml
================================================
name: ci

on:
  push:
    branches:
      - main
  pull_request:
    branches:
      - main
  schedule:
    - cron: '00 12 * * 1' # Runs every Monday at 8:00 AM EST

jobs:
  Test:
    runs-on: ${{ matrix.os }}
    strategy:
      matrix:
        python-version: ['3.8', '3.9', '3.10']
        os: [ubuntu-latest, macos-latest, windows-latest]
      fail-fast: false
    steps:
      - uses: actions/checkout@v2
      - name: Set up Python ${{ matrix.python-version }}
        uses: actions/setup-python@v2
        with:
          python-version: ${{ matrix.python-version }}

      - name: Check
        shell: bash
        run: |
          python3 -m pip install --upgrade pip
          pip install -e .
          python3 -m unittest discover -v tests
          python3 setup.py install


================================================
FILE: .gitignore
================================================
# Pycharm cache files
.cache/*
.idea/*
.pytest_cache/*
tests/__pycache__/*
mab2rec/__pycache__/*
dist/*

# Environments
.env
.venv
env/
venv/
ENV/
env.bak/
venv.bak/
*.swp

# Jupyter Notebook
.ipynb_checkpoints
#*.ipynb

# IPython
profile_default/
ipython_config.py

# Compiled python modules.
*.pyc

# Python egg metadata, regenerated from source files by setuptools.
/*.egg-info

# Dev and data folders on working branch
dev/*
data/*
notebooks/*

# Metadata
.DS_Store

# Build folder
build/*
eggs/
.eggs/

# Unit test / coverage reports
htmlcov/
.tox/
.nox/
.coverage
.coverage.*
.cache
nosetests.xml
coverage.xml
*.cover
.hypothesis/
.pytest_cache/

# Spyder project settings
.spyderproject
.spyproject

# Documentation
docsrc/_build/


================================================
FILE: CHANGELOG.txt
================================================
=========
CHANGELOG
=========

-------------------------------------------------------------------------------
September, 03, 2024 1.3.1
-------------------------------------------------------------------------------
Minor:
- Updated requirements to use mabwiser>=2.7.4 to reflect change from np.Inf to np.inf in mabwiser.
- Fixed default KMeans n_init parameters in tests instead of using 'auto' used in scikit-learn>=1.4

-------------------------------------------------------------------------------
February, 22, 2024 1.3.0
-------------------------------------------------------------------------------

Major:
- Added optional `apply_sigmoid` argument to recommend() method, to
  control whether sigmoid transformation is applied to scores or not.
Minor:
- Fixed bug when recommending single context.


-------------------------------------------------------------------------------
February, 05, 2024 1.2.1
-------------------------------------------------------------------------------

Minor:
- Replaced NoReturn type hinting with None - thank you @SaraEkmanSVT

-------------------------------------------------------------------------------
August, 16, 2023 1.2.0
-------------------------------------------------------------------------------

Major:
- Removed spock-config dependency and train/test scripts using Spock
- Updated tests and documentation to reflect Python 3.8+ support

-------------------------------------------------------------------------------
February, 23, 2023 1.1.0
-------------------------------------------------------------------------------

Major:
- Updated requirements to use mabwiser>=2.7 to benefit from enhancements,
  including vectorized predict for Linear policies and tracking of arm status.
- Fixed tests due to changes in random seeding for Linear policies.
Minor:
- Added Diversity metrics to available MAB evaluation metrics.

-------------------------------------------------------------------------------
August, 16, 2022 1.0.3
-------------------------------------------------------------------------------

Minor:
- Fixed bug with inconsistency between scored and eligible items.

-------------------------------------------------------------------------------
July, 5, 2022 1.0.2
-------------------------------------------------------------------------------

Minor:
- Fixed bug with arguments not being passed correctly to load_response_data.

-------------------------------------------------------------------------------
May, 12, 2022 1.0.1
-------------------------------------------------------------------------------

Minor:
- Add encodings generation to Seq2Pat section in feature_engineering notebook.
- Explicitly claim Mab2Rec requires Python 3.7+ for Installation in README.
- Library source scripts are not updated, thus the pypi version is not changed.

-------------------------------------------------------------------------------
April, 15, 2022 1.0.1
-------------------------------------------------------------------------------

Minor:
- Fix missing top-k recommendations when top messages are excluded - thanks @nateewall!

-------------------------------------------------------------------------------
March, 18, 2022 1.0.0
-------------------------------------------------------------------------------

- Initial public release.


================================================
FILE: CODEOWNERS
================================================
# These owners will be the default owners for everything in the repo.
*       @bkleyn @skadio


================================================
FILE: CONTRIBUTING.md
================================================
# Contributing to Mab2Rec

Thank you for contributing to Mab2Rec! This guide will help you get started and know what to expect. All contributions and project spaces are subject to our [Code of Conduct](https://github.com/fidelity/.github/blob/main/CODE_OF_CONDUCT.md).

We welcome all types of contributions, including:

* Code contributions
* Bug reports
* Responsibly disclosed security concerns
* Documentation fixes
* Feature requests and user stories (although we can't guarantee we'll get to all requests, it's helpful to know where we can improve)

If you end up using our library in a project, give us a star on GitHub!

Please note that we periodically fork upstream repos to stage contributions from Fidelity. We do not accept contributions against these forked repos, and request you make contributions against upstream projects directly.

If you have any questions, please contact [opensource@fmr.com](mailto:opensource@fmr.com).

## How to report a bug

Please [open an issue](https://github.com/fidelity/mab2rec/issues) **unless** you are making a significant security disclosure.

When reporting a bug, please start from a fresh pull of the default branch and document how you encountered the issue. Reports with insufficient detail and which we can't reproduce may be closed without action.

While bugs can be frustrating, we ask participants to contribute positively and professionally to the discourse. While we commit to take the contents of the report seriously, abusive behavior be will not be tolerated.

## How to disclose security concerns responsibly

Please follow the instructions in our [security policy](https://github.com/fidelity/.github/blob/main/SECURITY.md) (also visible in the Security tab on the project's repo).

## How to contribute documentation fixes

Minor documentation fixes can be submitted directly as a pull request without filing an issue in advance. More significant changes (e.g., refactoring to support a new documentation format, major reorganizations of content, etc.) should first be discussed in an issue to ensure everyone's time is used effectively.

When opening a PR or issue with a documentation change, please add a `documentation` label.

## How to request features or submit a user story

To request a feature please open an issue and tag it as `feature enhancement`. If you already have an implementation, please [link the pull request to the issue](https://docs.github.com/en/issues/tracking-your-work-with-issues/linking-a-pull-request-to-an-issue#linking-a-pull-request-to-an-issue-using-a-keyword).

Please include as much information and context as you can. Understanding how the feature solves a specific problem will help us prioritize the request. Please understand that we will not be able to provide an implementation timeline on all requests, although requests that include an implementation are more likely to land sooner.

If you won't do the work yourself, please also add a `good first issue` or `help wanted` label. These are special issue tags which are intended to help new and existing contributors get involved in a meaningful and accessible way.

* `good first issue` - Small changes that are suitable for a beginner
* `help wanted` - More involved changes This will help match your request with others who are looking for a way to get involved.

## Code contributions

Code contributions are welcome in all of our projects as long as you follow a few rules:
* With any piece of code, please adhere to PEP-8 standards.
* If you're fixing an issue with an existing piece of code, please make sure all the tests pass, and there is no change in functionality.
* If you want to add a new feature, please open up an issue first.
* When adding a new feature, make sure you have relevant test coverage.
* Any changes to the public API should conform to the current standards, be properly documented, typed, and be intuitive.
* Your contribution must be received under the project's open source license.
* You must have permission to make the contribution. We strongly recommend including a Signed-off-by line to indicate your adherence to the [Developer Certificate of Origin](https://developercertificate.org/).
* All code contributions must be made via PR, and all checks must pass before merging.

While not strictly necessary, we encourage you to open an issue prior to your pull request to let the project know to expect your code. This helps the team plan for the next release and may result in your feature being a higher priority, and also decreases the likelihood of two independent contributions that do the same thing.

## Documentation contributions

* Make sure you follow the standards set by the rest of the repo.
* Be concise, but do not omit details. Verbose documentation is preferred to incomplete documentation.

## Getting started (and helping others find their footing)

Anyone may open an issue and apply a `good first issue` or `help wanted` label for others to work on. We only ask that when someone else picks up your issue and decides to work on it that you be responsive to their questions.

## Getting help

If you have other questions about this project, please [open an issue](https://github.com/fidelity/mab2rec/issues). To reach the Fidelity OSPO directly, please email [opensource@fmr.com](mailto:opensource@fmr.com).


================================================
FILE: LICENSE
================================================
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================================================
FILE: README.md
================================================
[![ci](https://github.com/fidelity/mab2rec/actions/workflows/ci.yml/badge.svg?branch=main)](https://github.com/fidelity/mab2rec/actions/workflows/ci.yml) [![PyPI version fury.io](https://badge.fury.io/py/mab2rec.svg)](https://pypi.python.org/pypi/mab2rec/) [![PyPI license](https://img.shields.io/pypi/l/mab2rec.svg)](https://pypi.python.org/pypi/mab2rec/) [![PRs Welcome](https://img.shields.io/badge/PRs-welcome-brightgreen.svg?style=flat-square)](http://makeapullrequest.com) [![Downloads](https://static.pepy.tech/personalized-badge/mab2rec?period=total&units=international_system&left_color=grey&right_color=orange&left_text=Downloads)](https://pepy.tech/project/mab2rec)

# Mab2Rec: Multi-Armed Bandits Recommender 

Mab2Rec ([AAAI'24](https://ojs.aaai.org/index.php/AAAI/article/view/30341)) 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).
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 is built on top of several other open-source software developed at the AI Center at Fidelity:

* [MABWiser](https://github.com/fidelity/mabwiser) to create multi-armed bandit recommendation algorithms ([Bridge@AAAI'24](http://osullivan.ucc.ie/CPML2024/papers/06.pdf), [TMLR'22](https://openreview.net/pdf?id=sX9d3gfwtE), [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 ([AMAI'24](https://link.springer.com/article/10.1007/s10472-024-09941-x), [CPAIOR'21](https://link.springer.com/chapter/10.1007/978-3-030-78230-6_27), [DSO@IJCAI'21](https://arxiv.org/abs/2112.03105)).
* [Seq2Pat](https://github.com/fidelity/seq2pat) to create user representations via sequential pattern mining ([AI Magazine'23](https://onlinelibrary.wiley.com/doi/epdf/10.1002/aaai.12081), [AAAI'22](https://ojs.aaai.org/index.php/AAAI/article/view/21542), [Bridge@AAAI'23](http://osullivan.ucc.ie/CPML2023/submissions/09.pdf), [Frontiers'22](https://www.frontiersin.org/articles/10.3389/frai.2022.868085/full), [KDF@AAAI'22](https://arxiv.org/abs/2201.09178), [CMU Blog Post](https://www.cmu.edu/tepper/news/stories/2023/may/fidelity-ai.html))
* [Jurity](https://github.com/fidelity/jurity) to evaluate recommendations including fairness metrics ([ACM'24](https://dl.acm.org/doi/10.1145/3700145), [LION'23](https://link.springer.com/chapter/10.1007/978-3-031-44505-7_29), [CIKM'22](https://ceur-ws.org/Vol-3318/short6.pdf), [ICMLA'21](https://ieeexplore.ieee.org/abstract/document/9680169)).

An introduction to **content- and context-aware** recommender systems and an overview of the building blocks of the library is presented at [AAAI 2024](https://underline.io/lecture/91479-building-higher-order-abstractions-from-the-components-of-recommender-systems) and [All Things Open 2021](https://www.youtube.com/watch?v=54d_YUalvOA). There is a corresponding [blogpost](https://2022.allthingsopen.org/introducing-mab2rec-a-multi-armed-bandit-recommender-library/) to serve as a starting point for practioners to build and deploy bandit-based recommenders using Mab2Rec.

Documentation is available at [fidelity.github.io/mab2rec](https://fidelity.github.io/mab2rec).

## Usage Patterns

Mab2Rec supports prototyping with a **single** bandit algorithm or benchmarking with **multiple** bandit algorithms. 
If you are new user, the best place to start is to experiment with multiple bandits using the [tutorial notebooks](notebooks).

## Quick Start

### Single Recommender

```python
# Example of how to train an singler 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 recommenders 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 extensive tutorials 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 such as persistency, eligibility, item availability, and memory efficiency.

## Installation

Mab2Rec requires **Python 3.8+** and can be installed from PyPI using ``pip install mab2rec`` or by building from source as shown in [installation instructions](https://fidelity.github.io/mab2rec/installation.html).

## Citation

If you use Mab2Rec in a publication, please cite it as:

```bibtex
    @inproceedings{DBLP:conf/aaai/KadiogluK24,
      author       = {Serdar Kadioglu and Bernard Kleynhans},
      title        = {Building Higher-Order Abstractions from the Components of Recommender Systems},
      booktitle    = {Thirty-Eighth {AAAI} Conference on Artificial Intelligence, {AAAI} 2024, Thirty-Sixth Conference on Innovative Applications of Artificial Intelligence, {IAAI} 2024, Fourteenth Symposium on Educational Advances in Artificial Intelligence, {EAAI} 2014, February 20-27, 2024, Vancouver, Canada},
      pages        = {22998--23004},
      publisher    = {{AAAI} Press},
      year         = {2024},
      url          = {https://doi.org/10.1609/aaai.v38i21.30341},
      doi          = {10.1609/AAAI.V38I21.30341}
    }
```

## 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>


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================================================
FILE: docs/_sources/api.rst.txt
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.. _Mab2Rec API:

Mab2Rec Public API
==================

.. automodule:: mab2rec
    :members:
    :undoc-members:
    :show-inheritance:

BanditRecommender
^^^^^^^^^^^^^^^^^

.. autoclass:: mab2rec.BanditRecommender
    :members:
    :undoc-members:
    :show-inheritance:

LearningPolicy
^^^^^^^^^^^^^^

.. autoclass:: mab2rec.LearningPolicy
    :members:
    :undoc-members:

NeighborhoodPolicy
^^^^^^^^^^^^^^^^^^

.. autoclass:: mab2rec.NeighborhoodPolicy
    :members:
    :undoc-members:

Pipeline
^^^^^^^^

.. automodule:: mab2rec.pipeline
    :members:
    :undoc-members:

Visualization
^^^^^^^^^^^^^

.. automodule:: mab2rec.visualization
    :members:
    :undoc-members:


================================================
FILE: docs/_sources/contributing.rst.txt
================================================
.. _contributing:

Contributing
============

We welcome contributions of all from everybody, and we will make an effort
to respond to any questions and requests. Code is not the only way to make
a contribution!

If you end up using our library in a project, give us a star on GitHub!

Code Contributions
^^^^^^^^^^^^^^^^^^

- With any piece of code, please adhere to PEP-8 standards.
- If you're fixing an issue with an existing piece of code, please make sure all the tests pass, and there is no change in functionality.
- If you want to add a new feature, please open up an issue first.
- When adding a new feature, make sure you have relevant test coverage.
- Any changes to the public API should conform to the current standards, be properly documented, typed, and be intuitive.

Documentation Contributions
^^^^^^^^^^^^^^^^^^^^^^^^^^^

- Make sure you follow the standards set by the rest of the repo.
- Be concise, but do not omit details. Verbose documentation is preferred to incomplete documentation.


================================================
FILE: docs/_sources/examples.rst.txt
================================================
.. _examples:

Usage Examples
==============

We provide extensive tutorials in Jupyter notebooks under the :repo:`notebooks </tree/main/notebooks>` folder for guidelines on building recommenders, performing model selection, and evaluating performance:

- :repo:`Data Overview </tree/main/notebooks/1_data_overview.ipynb>` provides an overview of data required to train recommender.
- :repo:`Feature Engineering </tree/main/notebooks/2_feature_engineering.ipynb>` gives an overview of methods to create user and item features from structured, unstructured, and sequential data.
- :repo:`Model Selection </tree/main/notebooks/3_model_selection.ipynb>` shows to do model selection by benchmarking recommenders using cross-validation.
- :repo:`Evaluation </tree/main/notebooks/4_evaluation.ipynb>` benchmarks selected recommenders and baselines on test data with detailed evaluations.
- :repo:`Advanced </tree/main/notebooks/5_advanced.ipynb>` demonstrates some advanced functionality.


================================================
FILE: docs/_sources/index.rst.txt
================================================
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 of the open-source software it is built on is developed by the Artificial Intelligence Center at Fidelity Investments, including:

* `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>`_).

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>`_.

.. include:: quick.rst

Source Code
===========
The source code is hosted on :repo:`GitHub <>`.

.. sidebar:: Contents

   .. toctree::
    :maxdepth: 2

    installation
    quick
    examples
    contributing
    api



Indices and tables
==================

* :ref:`genindex`
* :ref:`modindex`


================================================
FILE: docs/_sources/installation.rst.txt
================================================
.. _installation:

Installation
============

.. admonition:: Installation Options

	There are two options to install the library:

	1. Install from PyPI using the prebuilt wheel package (``pip install mab2rec``)
	2. Build from the source code

Requirements
------------

The library requires Python **3.7+**. The ``requirements.txt`` lists the necessary
packages.

Source Code
-----------

You can build a wheel package on your platform from scratch using the source code:

.. code-block:: python

	git clone https://github.com/fidelity/mab2rec.git
	cd mab2rec
	pip install setuptools wheel # if wheel is not installed
	python setup.py sdist bdist_wheel
	pip install dist/mab2rec-X.X.X-py3-none-any.whl

Test Your Setup
---------------

To confirm that cloning was successful, run the tests included in the project.

All tests should pass.

.. code-block:: python

	git clone https://github.com/fidelity/mab2rec.git
	cd mab2rec
	python -m unittest discover tests

Upgrade the Library
-------------------

To upgrade to the latest version of the library, run ``pip install --upgrade mab2rec``.

If you installed from the source code:

.. code-block:: python

	git pull origin master
	python setup.py sdist bdist_wheel
	pip install --upgrade --no-cache-dir dist/mab2rec-X.X.X-py3-none-any.whl


================================================
FILE: docs/_sources/quick.rst.txt
================================================
.. _quick:

Quick Start
===========

Individual Recommender
----------------------

.. code-block:: 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
---------------------

.. code-block:: 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")



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================================================
FILE: docs/_static/doctools.js
================================================
/*
 * doctools.js
 * ~~~~~~~~~~~
 *
 * Sphinx JavaScript utilities for all documentation.
 *
 * :copyright: Copyright 2007-2022 by the Sphinx team, see AUTHORS.
 * :license: BSD, see LICENSE for details.
 *
 */

/**
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 */
$u = _.noConflict();

/**
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}
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/**
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jQuery.urldecode = function(x) {
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/**
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 */
jQuery.getQueryParameters = function(s) {
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};

/**
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    if (node.nodeType === 3) {
      var val = node.nodeValue;
      var pos = val.toLowerCase().indexOf(text);
      if (pos >= 0 &&
          !jQuery(node.parentNode).hasClass(className) &&
          !jQuery(node.parentNode).hasClass("nohighlight")) {
        var span;
        var isInSVG = jQuery(node).closest("body, svg, foreignObject").is("svg");
        if (isInSVG) {
          span = document.createElementNS("http://www.w3.org/2000/svg", "tspan");
        } else {
          span = document.createElement("span");
          span.className = className;
        }
        span.appendChild(document.createTextNode(val.substr(pos, text.length)));
        node.parentNode.insertBefore(span, node.parentNode.insertBefore(
          document.createTextNode(val.substr(pos + text.length)),
          node.nextSibling));
        node.nodeValue = val.substr(0, pos);
        if (isInSVG) {
          var rect = document.createElementNS("http://www.w3.org/2000/svg", "rect");
          var bbox = node.parentElement.getBBox();
          rect.x.baseVal.value = bbox.x;
          rect.y.baseVal.value = bbox.y;
          rect.width.baseVal.value = bbox.width;
          rect.height.baseVal.value = bbox.height;
          rect.setAttribute('class', className);
          addItems.push({
              "parent": node.parentNode,
              "target": rect});
        }
      }
    }
    else if (!jQuery(node).is("button, select, textarea")) {
      jQuery.each(node.childNodes, function() {
        highlight(this, addItems);
      });
    }
  }
  var addItems = [];
  var result = this.each(function() {
    highlight(this, addItems);
  });
  for (var i = 0; i < addItems.length; ++i) {
    jQuery(addItems[i].parent).before(addItems[i].target);
  }
  return result;
};

/*
 * backward compatibility for jQuery.browser
 * This will be supported until firefox bug is fixed.
 */
if (!jQuery.browser) {
  jQuery.uaMatch = function(ua) {
    ua = ua.toLowerCase();

    var match = /(chrome)[ \/]([\w.]+)/.exec(ua) ||
      /(webkit)[ \/]([\w.]+)/.exec(ua) ||
      /(opera)(?:.*version|)[ \/]([\w.]+)/.exec(ua) ||
      /(msie) ([\w.]+)/.exec(ua) ||
      ua.indexOf("compatible") < 0 && /(mozilla)(?:.*? rv:([\w.]+)|)/.exec(ua) ||
      [];

    return {
      browser: match[ 1 ] || "",
      version: match[ 2 ] || "0"
    };
  };
  jQuery.browser = {};
  jQuery.browser[jQuery.uaMatch(navigator.userAgent).browser] = true;
}

/**
 * Small JavaScript module for the documentation.
 */
var Documentation = {

  init : function() {
    this.fixFirefoxAnchorBug();
    this.highlightSearchWords();
    this.initIndexTable();
    if (DOCUMENTATION_OPTIONS.NAVIGATION_WITH_KEYS) {
      this.initOnKeyListeners();
    }
  },

  /**
   * i18n support
   */
  TRANSLATIONS : {},
  PLURAL_EXPR : function(n) { return n === 1 ? 0 : 1; },
  LOCALE : 'unknown',

  // gettext and ngettext don't access this so that the functions
  // can safely bound to a different name (_ = Documentation.gettext)
  gettext : function(string) {
    var translated = Documentation.TRANSLATIONS[string];
    if (typeof translated === 'undefined')
      return string;
    return (typeof translated === 'string') ? translated : translated[0];
  },

  ngettext : function(singular, plural, n) {
    var translated = Documentation.TRANSLATIONS[singular];
    if (typeof translated === 'undefined')
      return (n == 1) ? singular : plural;
    return translated[Documentation.PLURALEXPR(n)];
  },

  addTranslations : function(catalog) {
    for (var key in catalog.messages)
      this.TRANSLATIONS[key] = catalog.messages[key];
    this.PLURAL_EXPR = new Function('n', 'return +(' + catalog.plural_expr + ')');
    this.LOCALE = catalog.locale;
  },

  /**
   * add context elements like header anchor links
   */
  addContextElements : function() {
    $('div[id] > :header:first').each(function() {
      $('<a class="headerlink">\u00B6</a>').
      attr('href', '#' + this.id).
      attr('title', _('Permalink to this headline')).
      appendTo(this);
    });
    $('dt[id]').each(function() {
      $('<a class="headerlink">\u00B6</a>').
      attr('href', '#' + this.id).
      attr('title', _('Permalink to this definition')).
      appendTo(this);
    });
  },

  /**
   * workaround a firefox stupidity
   * see: https://bugzilla.mozilla.org/show_bug.cgi?id=645075
   */
  fixFirefoxAnchorBug : function() {
    if (document.location.hash && $.browser.mozilla)
      window.setTimeout(function() {
        document.location.href += '';
      }, 10);
  },

  /**
   * highlight the search words provided in the url in the text
   */
  highlightSearchWords : function() {
    var params = $.getQueryParameters();
    var terms = (params.highlight) ? params.highlight[0].split(/\s+/) : [];
    if (terms.length) {
      var body = $('div.body');
      if (!body.length) {
        body = $('body');
      }
      window.setTimeout(function() {
        $.each(terms, function() {
          body.highlightText(this.toLowerCase(), 'highlighted');
        });
      }, 10);
      $('<p class="highlight-link"><a href="javascript:Documentation.' +
        'hideSearchWords()">' + _('Hide Search Matches') + '</a></p>')
          .appendTo($('#searchbox'));
    }
  },

  /**
   * init the domain index toggle buttons
   */
  initIndexTable : function() {
    var togglers = $('img.toggler').click(function() {
      var src = $(this).attr('src');
      var idnum = $(this).attr('id').substr(7);
      $('tr.cg-' + idnum).toggle();
      if (src.substr(-9) === 'minus.png')
        $(this).attr('src', src.substr(0, src.length-9) + 'plus.png');
      else
        $(this).attr('src', src.substr(0, src.length-8) + 'minus.png');
    }).css('display', '');
    if (DOCUMENTATION_OPTIONS.COLLAPSE_INDEX) {
        togglers.click();
    }
  },

  /**
   * helper function to hide the search marks again
   */
  hideSearchWords : function() {
    $('#searchbox .highlight-link').fadeOut(300);
    $('span.highlighted').removeClass('highlighted');
    var url = new URL(window.location);
    url.searchParams.delete('highlight');
    window.history.replaceState({}, '', url);
  },

  /**
   * make the url absolute
   */
  makeURL : function(relativeURL) {
    return DOCUMENTATION_OPTIONS.URL_ROOT + '/' + relativeURL;
  },

  /**
   * get the current relative url
   */
  getCurrentURL : function() {
    var path = document.location.pathname;
    var parts = path.split(/\//);
    $.each(DOCUMENTATION_OPTIONS.URL_ROOT.split(/\//), function() {
      if (this === '..')
        parts.pop();
    });
    var url = parts.join('/');
    return path.substring(url.lastIndexOf('/') + 1, path.length - 1);
  },

  initOnKeyListeners: function() {
    $(document).keydown(function(event) {
      var activeElementType = document.activeElement.tagName;
      // don't navigate when in search box, textarea, dropdown or button
      if (activeElementType !== 'TEXTAREA' && activeElementType !== 'INPUT' && activeElementType !== 'SELECT'
          && activeElementType !== 'BUTTON' && !event.altKey && !event.ctrlKey && !event.metaKey
          && !event.shiftKey) {
        switch (event.keyCode) {
          case 37: // left
            var prevHref = $('link[rel="prev"]').prop('href');
            if (prevHref) {
              window.location.href = prevHref;
              return false;
            }
            break;
          case 39: // right
            var nextHref = $('link[rel="next"]').prop('href');
            if (nextHref) {
              window.location.href = nextHref;
              return false;
            }
            break;
        }
      }
    });
  }
};

// quick alias for translations
_ = Documentation.gettext;

$(document).ready(function() {
  Documentation.init();
});


================================================
FILE: docs/_static/documentation_options.js
================================================
var DOCUMENTATION_OPTIONS = {
    URL_ROOT: document.getElementById("documentation_options").getAttribute('data-url_root'),
    VERSION: '1.2.0',
    LANGUAGE: 'None',
    COLLAPSE_INDEX: false,
    BUILDER: 'html',
    FILE_SUFFIX: '.html',
    LINK_SUFFIX: '.html',
    HAS_SOURCE: true,
    SOURCELINK_SUFFIX: '.txt',
    NAVIGATION_WITH_KEYS: false
};

================================================
FILE: docs/_static/jquery-3.5.1.js
================================================
/*!
 * jQuery JavaScript Library v3.5.1
 * https://jquery.com/
 *
 * Includes Sizzle.js
 * https://sizzlejs.com/
 *
 * Copyright JS Foundation and other contributors
 * Released under the MIT license
 * https://jquery.org/license
 *
 * Date: 2020-05-04T22:49Z
 */
( function( global, factory ) {

	"use strict";

	if ( typeof module === "object" && typeof module.exports === "object" ) {

		// For CommonJS and CommonJS-like environments where a proper `window`
		// is present, execute the factory and get jQuery.
		// For environments that do not have a `window` with a `document`
		// (such as Node.js), expose a factory as module.exports.
		// This accentuates the need for the creation of a real `window`.
		// e.g. var jQuery = require("jquery")(window);
		// See ticket #14549 for more info.
		module.exports = global.document ?
			factory( global, true ) :
			function( w ) {
				if ( !w.document ) {
					throw new Error( "jQuery requires a window with a document" );
				}
				return factory( w );
			};
	} else {
		factory( global );
	}

// Pass this if window is not defined yet
} )( typeof window !== "undefined" ? window : this, function( window, noGlobal ) {

// Edge <= 12 - 13+, Firefox <=18 - 45+, IE 10 - 11, Safari 5.1 - 9+, iOS 6 - 9.1
// throw exceptions when non-strict code (e.g., ASP.NET 4.5) accesses strict mode
// arguments.callee.caller (trac-13335). But as of jQuery 3.0 (2016), strict mode should be common
// enough that all such attempts are guarded in a try bl