Metadata-Version: 1.1
Name: impyute
Version: 0.0.5
Summary: Cross-sectional and time-series data imputation algorithms
Home-page: http://impyute.readthedocs.io/en/latest/
Author: Elton Law
Author-email: eltonlaw296@gmail.com
License: GPL-3.0
Download-URL: https://github.com/eltonlaw/impyute
Description-Content-Type: UNKNOWN
Description: .. image:: https://travis-ci.org/eltonlaw/impyute.svg?branch=master
            :target: https://travis-ci.org/eltonlaw/impyute
        
        .. image:: https://img.shields.io/pypi/v/impyute.svg
            :target: https://pypi.python.org/pypi/impyute
        
        Impyute
        ========
        
        Impyute is a library of missing data imputation algorithms written in Python 3. This library was designed to be super lightweight, here's a sneak peak at what impyute can do. 
        
        .. code-block:: python
        
            >>> from impyute.datasets import random_uniform
            >>> raw_data = random_uniform(shape=(5, 5), missingness="mcar", th=0.2)
            >>> print(raw_data)
            [[  1.   0.   4.   0.   1.]
             [  1.  nan   6.   4.  nan]
             [  5.   0.  nan   1.   3.]
             [  2.   1.   5.   4.   6.]
             [  2.   1.   0.   0.   6.]]
            >>> from impyute.imputations.cs import mean_imputation   
            >>> complete_data = random_imputation(raw_data) 
            >>> print(complete_data)
            [[ 1.    0.    4.    0.    1.  ]
             [ 1.    0.5   6.    4.    4.  ]
             [ 5.    0.    3.75  1.    3.  ]
             [ 2.    1.    5.    4.    6.  ]
             [ 2.    1.    0.    0.    6.  ]]
        
        Feature Support
        ---------------
        
        * Imputation of Cross Sectional Data
            * Multivariate Imputation by Chained Equations
            * Expectation Maximization
            * Mean Imputation
            * Mode Imputation
            * Median Imputation
            * Random Imputation
        * Imputation of Time Series Data
            * Last Observation Carried Forward
            * Autoregressive Integrated Moving Average (WIP)
            * Expectation Maximization with the Kalman Filter (WIP)
        * Dataset Generation
            * Datasets
                * MNIST
                * Random uniform distribution
                * Random gaussian distribution
            * Missingness Corruptors
                * MCAR
                * MAR (WIP)
                * MNAR (WIP)
        * Diagnostic Tools
            * Loggers
            * Distribution of Null Values
            * Comparison of imputations
            * Little's MCAR Test (WIP)
        
        Installation
        ------------
        
        To install impyute, run the following:
        
        .. code-block:: bash
        
            $ pip install impyute
        
        Or to get the most current version:
        
        .. code-block:: bash
            
            $ git clone https://github.com/eltonlaw/impyute
            $ cd impyute
            $ python setup.py install
        
        Documentation
        -------------
        
        Documentation is available here: http://impyute.readthedocs.io/
        
        
        How to Contribute
        -----------------
        
        Check out CONTRIBUTING_
        
        .. _CONTRIBUTING: https://github.com/eltonlaw/impyute/blob/master/CONTRIBUTING.md
        
        
Keywords: imputation
Platform: UNKNOWN
Classifier: Development Status :: 3 - Alpha
Classifier: Intended Audience :: Science/Research
Classifier: Intended Audience :: Developers
Classifier: Programming Language :: Python
Classifier: Topic :: Software Development
Classifier: Topic :: Scientific/Engineering
