Metadata-Version: 1.1
Name: regain
Version: 0.0.6
Summary: REGAIN (Regularised Graph Inference)
Home-page: https://github.com/fdtomasi/regain
Author: Federico Tomasi
Author-email: federico.tomasi@dibris.unige.it
License: FreeBSD
Download-URL: https://github.com/fdtomasi/regain/archive/v0.0.6.tar.gz
Description-Content-Type: UNKNOWN
Description: [![develstat](https://travis-ci.org/fdtomasi/regain.svg?branch=master)](https://travis-ci.org/fdtomasi/regain) [![covdevel](http://codecov.io/github/fdtomasi/regain/coverage.svg?branch=master)](http://codecov.io/github/fdtomasi/regain?branch=master) [![licence](https://img.shields.io/badge/licence-BSD-blue.svg)](http://opensource.org/licenses/BSD-3-Clause) [![PyPI](https://img.shields.io/pypi/v/regain.svg)](https://pypi.python.org/pypi/regain) [![Conda](https://img.shields.io/conda/v/fdtomasi/regain.svg)](https://anaconda.org/fdtomasi/regain) [![Python27](https://img.shields.io/badge/python-2.7-blue.svg)](https://badge.fury.io/py/regain) [![Python34](https://img.shields.io/badge/python-3.5-blue.svg)](https://badge.fury.io/py/regain) [![Requirements Status](https://requires.io/github/fdtomasi/regain/requirements.svg?branch=master)](https://requires.io/github/fdtomasi/regain/requirements/?branch=master)
        
        # regain
        Regularised graph inference across multiple time stamps, considering the influence of latent variables.
        It inherits functionalities from the [scikit-learn](https://github.com/scikit-learn/scikit-learn) package.
        
        ## Getting started
        ### Dependencies
        regain requires:
        - Python (>= 2.7 or >= 3.5)
        - NumPy (>= 1.8.2)
        - scikit-learn (>= 0.17)
        
        To use the parameter selection via gaussian process optimisation, [GPyOpt](https://github.com/SheffieldML/GPyOpt) is required.
        You can install dependencies by running:
        ```bash
        pip install -r requirements.txt
        ```
        
        ### Installation
        The simplest way to install regain is using pip
        ```bash
        pip install regain
        ```
        or `conda`
        
        ```bash
        conda install -c fdtomasi regain
        ```
        
        If you'd like to install from source, or want to contribute to the project (e.g. by sending pull requests via github), read on. Clone the repository in GitHub and add it to your $PYTHONPATH.
        ```bash
        git clone https://github.com/fdtomasi/regain.git
        cd regain
        python setup.py develop
        ```
        
        ## Quickstart
        A simple example for how to use LTGL.
        ```python
        import numpy as np
        from regain.admm import LatentTimeGraphLasso
        from regain.datasets import generate_dataset
        from regain.utils import error_norm_time
        
        np.random.seed(42)
        data = generate_dataset(mode='l1l2', n_dim_lat=1, n_dim_obs=10)
        X = data.data
        theta = data.thetas
        
        mdl = LatentTimeGraphLasso(max_iter=50).fit(X)
        print("Error: %.2f" % error_norm_time(theta, mdl.precision_))
        ```
        Note that the input of `LatentTimeGraphLasso` is a three-dimensional matrix with shape `(n_times, n_samples, n_dimensions)`.
        If you have a single time (`n_times = 1`), ensure a `X = X.reshape(1, *X.shape)` before using `LatentTimeGraphLasso`, or, alternatively, use `LatentGraphLasso`.
        
        
        ## Citation
        ```latex
        @ARTICLE{2018arXiv180203987T,
           author = {{Tomasi}, F. and {Tozzo}, V. and {Salzo}, S. and {Verri}, A.},
            title = "{Latent variable time-varying network inference}",
          journal = {ArXiv e-prints},
        archivePrefix = "arXiv",
           eprint = {1802.03987},
         primaryClass = "stat.ML",
         keywords = {Statistics - Machine Learning, Computer Science - Learning},
             year = 2018,
            month = feb,
           adsurl = {http://adsabs.harvard.edu/abs/2018arXiv180203987T},
          adsnote = {Provided by the SAO/NASA Astrophysics Data System}
        }
        ```
        
Keywords: graph inference,latent variables
Platform: UNKNOWN
Classifier: Development Status :: 4 - Beta
Classifier: Environment :: Console
Classifier: Intended Audience :: Science/Research
Classifier: Intended Audience :: Developers
Classifier: Programming Language :: Python
Classifier: License :: OSI Approved :: BSD License
Classifier: Topic :: Software Development
Classifier: Topic :: Scientific/Engineering
Classifier: Natural Language :: English
Classifier: Operating System :: POSIX
Classifier: Operating System :: Unix
Classifier: Operating System :: MacOS
Classifier: Programming Language :: Python
Requires: numpy (>=1.11)
Requires: scipy (>=0.16.1,>=1.0)
Requires: sklearn (>=0.17)
Requires: six
