Metadata-Version: 2.1
Name: cornac
Version: 0.1.0.post2
Summary: A collection of recommendation algorithms and comparisons
Home-page: https://cornac.preferred.ai/
Author: Aghiles Salah
Author-email: asalah@smu.edu.sg
License: UNKNOWN
Download-URL: https://github.com/PreferredAI/cornac/archive/v0.1.0.tar.gz
Description: # Cornac
        
        **Cornac** is python recommender system library for **easy**, **effective** and **efficient** experiments. Cornac is **simple** and **handy**. It is designed from the ground-up to faithfully reflect the standard steps taken by researchers to implement and evaluate personalized recommendation models.
        
        ### Quick Links
        
        [Website](https://cornac.preferred.ai/) |
        [Documentation](https://cornac.readthedocs.io/en/latest/index.html) |
        [Preferred.AI](https://preferred.ai/)
        
        [![Build Status](https://www.travis-ci.org/PreferredAI/cornac.svg?branch=master)](https://www.travis-ci.org/PreferredAI/cornac)
        [![Documentation Status](https://readthedocs.org/projects/cornac/badge/?version=latest)](https://cornac.readthedocs.io/en/latest/?badge=latest)
        [![Codecov](https://codecov.io/gh/PreferredAI/cornac/branch/master/graph/badge.svg)](https://codecov.io/gh/PreferredAI/cornac)
        [![Python Versions](https://img.shields.io/badge/python-3.6-blue.svg)](https://cornac.preferred.ai/)
        [![License](https://img.shields.io/badge/License-Apache%202.0-yellowgreen.svg)](https://opensource.org/licenses/Apache-2.0)
        
        
        ## Installation
        
        Currently, we are supporting Python 3 (version 3.6 is recommended).
        There are two ways to install Cornac:
        
        - **Install Cornac from PyPI (recommended):**
        
        ```sh
        pip3 install cornac
        ```
        
        - **Install Cornac from the GitHub source:**
        
        First, clone Cornac using `git`:
        
        ```sh
        git clone https://github.com/PreferredAI/cornac.git
        ```
        
         Then, `cd` to the Cornac folder and run the install command:
        ```sh
        cd cornac
        python3 setup.py install
        ```
        
        **Note** 
        
        Some installed dependencies are CPU versions. If you want to utilize your GPU, you might consider:
        
        - [TensorFlow installation instructions](https://www.tensorflow.org/install/).
        - [PyTorch installation instructions](https://pytorch.org/get-started/locally/).
        
        
        ## Getting started: your first Cornac experiment
        
        This example will show you how to run your very first experiment using Cornac. 
        
        Load the [MovieLens 100K](https://grouplens.org/datasets/movielens/100k/) dataset (will be automatically downloaded if not cached).
        ```python
        from cornac.datasets import MovieLens100K
        
        ml_100k = MovieLens100K.load_data()
        ```
        
        Instantiate an evaluation strategy.
        ```python
        from cornac.eval_strategies import RatioSplit
        
        ratio_split = RatioSplit(data=ml_100k, test_size=0.2, rating_threshold=4.0, exclude_unknowns=False)
        ```
        
        Instantiate models that we want to evaluate. Here we use `Probabilistic Matrix Factorization (PMF)` as an example.
        ```python
        pmf = cornac.models.PMF(k=10, max_iter=100, learning_rate=0.001, lamda=0.001)
        ```
        
        Instantiate evaluation metrics.
        ```python
        mae = cornac.metrics.MAE()
        rmse = cornac.metrics.RMSE()
        rec_20 = cornac.metrics.Recall(k=20)
        pre_20 = cornac.metrics.Precision(k=20)
        ```
        
        Instantiate and then run an experiment.
        ```python
        exp = cornac.Experiment(eval_strategy=ratio_split,
                                models=[pmf],
                                metrics=[mae, rmse, rec_20, pre_20],
                                user_based=True)
        exp.run()
        ```
        
        **Output**
        
        ```
                  MAE      RMSE  Recall@20  Precision@20
        PMF  0.760277  0.919413   0.081803        0.0462
        ```
        
        For more details, please take a look at our [examples](examples).
        
        ## License
        
        [Apache License 2.0](LICENSE)
        
Keywords: recommender,recommendation,factorization,multimodal
Platform: UNKNOWN
Classifier: Development Status :: 3 - Alpha
Classifier: Intended Audience :: Science/Research
Classifier: Intended Audience :: Education
Classifier: Intended Audience :: Developers
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.6
Classifier: License :: OSI Approved :: Apache Software License
Classifier: Topic :: Software Development
Classifier: Topic :: Scientific/Engineering
Description-Content-Type: text/markdown
