Library of the different imputation algorithms; methods for dealing with ambiguity and handling missing data.
Project description
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.
>>> 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 uniforml distribution
Random gaussian distribution
- Missingness Corruptors
MCAR
MAR (WIP)
MNAR (WIP)
- Diagnostic Tools
Loggers
Distribution of Null Values
Comparison of imputations (WIP)
Little’s MCAR Test (WIP)
Installation
To install impyute, run the following:
$ pip install impyuteDocumentation
Documentation is available here: http://impyute.readthedocs.io/
How to Contribute
Check out CONTRIBUTING
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Source Distributions
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