Metadata-Version: 2.1
Name: reclib
Version: 0.1.0
Summary: A Python Library for Recommender System
Home-page: https://github.com/tingkai-zhang/reclib
Author: Tingkai Zhang
Author-email: tingkai.zhang@gmail.com
License: UNKNOWN
Description: <p align="center"><img width="17%" src="docs/RecLib.png" /></p>
        
        
        [![Build Status](https://dev.azure.com/tingkaizhang/reclib/_apis/build/status/tingkai-zhang.reclib?branchName=master)](https://dev.azure.com/tingkaizhang/reclib/_build/latest?definitionId=1&branchName=master)
        <a style="margin: 0 5px" href="https://opensource.org/licenses/MIT"><img src="https://img.shields.io/github/license/tingkai-zhang/reclib" alt="GitHub license"></a>
        
        
        
        
        A python library that provides many recommendation algorithms and let users train and test on their own datasets.
        
        
        ## Package Overview
        | **reclib** | an Python library for recommender system |
        | --- | --- |
        | **reclib.commands** | functionality for a CLI and web service |
        | **reclib.data** | a data processing module for loading and encoding datasets for representation |
        | **reclib.models** | a collection of state-of-the-art models |
        | **reclib.modules** | a collection of PyTorch modules for use with recommender system |
        | **reclib.nn** | tensor utility functions, such as initializers and activation functions |
        | **reclib.service** | a web server to that can serve demos for your models |
        | **reclib.training** | functionality for training models |
        
        
        ## Installation
        
        reclib requires Python 3.7.1 or later. The preferred way to install reclib is via `pip`.  Just run `pip install reclib` in your Python environment and you're good to go!
        
        If you need pointers on setting up an appropriate Python environment or would like to install reclib using a different method, see below.
        
        Windows is currently not officially supported, although we try to fix issues when they are easily addressed.
        
        ### Installing via pip
        
        #### Setting up a virtual environment
        
        [Conda](https://conda.io/) can be used set up a virtual environment with the
        version of Python required for reclib.  If you already have a Python 3.7 or 3.7
        environment you want to use, you can skip to the 'installing via pip' section.
        
        1.  [Download and install Conda](https://conda.io/projects/conda/en/latest/user-guide/install/index.html).
        
        2.  Create a Conda environment with Python 3.7
        
            ```bash
            conda create -n reclib python=3.7
            ```
        
        3.  Activate the Conda environment. You will need to activate the Conda environment in each terminal in which you want to use reclib.
        
            ```bash
            source activate reclib
            ```
        
        #### Installing the library and dependencies
        
        Installing the library and dependencies is simple using `pip`.
        
           ```bash
           pip install reclib
           ```
        
        That's it! You're now ready to build and train reclib models.
        reclib installs a script when you install the python package, meaning you can run reclib commands just by typing `reclib` into a terminal.
        
        You can now test your installation with `reclib test-install`.
        
        _`pip` currently installs Pytorch for CUDA 9 only (or no GPU). If you require an older version,
        please visit https://pytorch.org/ and install the relevant pytorch binary._
        
        ## Features
        
        - Pythonic
        - Easy to use
        - State-of-the-art
        
        
        ## Models
        
        Please refer to the documents
        
        ### MovieLens 1M
        
        | Model | MAP | nDCG@k | Precision@k | Recall@k | RMSE | MAE | R<sup>2</sup> | Auc | Explained Variance | 
        | --- | --- | --- | --- | --- | --- | --- | --- | --- |--- | 
        | XDeepFM |   |   |   |   |  |    |   | 0.800158 |   | 
        | AutomaticFeatureInteraction |   |   |   |   |  |    |   |  0.7928 |   | 
        
        
        
        ### 
        
        
        ## Issues
        Everyone is welcome to file issues with either feature requests, bug reports, or general questions. As a small team with only one person, we may ask for contributions if a prompt fix doesn't fit into our roadmap. We allow users a two week window to follow up on questions, after which we will close issues. They can be re-opened if there is further discussion.
        
        ## Contributions
        If you would like to contribute a larger feature, we recommend first creating an issue with a proposed design for discussion. This will prevent you from spending significant time on an implementation which has a technical limitation someone could have pointed out early on. Small contributions can be made directly in a pull request.
        
        Pull requests (PRs) must have one approving review and no requested changes before they are merged. 
        
        ## Licence
        Apache 2.0 
        
        
Keywords: recommender-system,machine-learning,deep-learning
Platform: UNKNOWN
Requires-Python: >=3
Description-Content-Type: text/markdown
