Skip to main content
Help us improve PyPI by participating in user testing. All experience levels needed!

Matrix completion and feature imputation algorithms

Project description

[![Build Status](https://travis-ci.org/hammerlab/fancyimpute.svg?branch=master)](https://travis-ci.org/hammerlab/fancyimpute) [![Coverage Status](https://coveralls.io/repos/github/hammerlab/fancyimpute/badge.svg?branch=master)](https://coveralls.io/github/hammerlab/fancyimpute?branch=master) [![DOI](https://zenodo.org/badge/doi/10.5281/zenodo.51773.svg)](http://dx.doi.org/10.5281/zenodo.51773)


# fancyimpute

A variety of matrix completion and imputation algorithms implemented in Python.

## Usage

```python
from fancyimpute import BiScaler, KNN, NuclearNormMinimization, SoftImpute

# X is the complete data matrix
# X_incomplete has the same values as X except a subset have been replace with NaN

# Use 3 nearest rows which have a feature to fill in each row's missing features
X_filled_knn = KNN(k=3).complete(X_incomplete)

# matrix completion using convex optimization to find low-rank solution
# that still matches observed values. Slow!
X_filled_nnm = NuclearNormMinimization().complete(X_incomplete)

# Instead of solving the nuclear norm objective directly, instead
# induce sparsity using singular value thresholding
X_filled_softimpute = SoftImpute().complete(X_incomplete_normalized)

# print mean squared error for the three imputation methods above
nnm_mse = ((X_filled_nnm[missing_mask] - X[missing_mask]) ** 2).mean()
print("Nuclear norm minimization MSE: %f" % nnm_mse)

softImpute_mse = ((X_filled_softimpute[missing_mask] - X[missing_mask]) ** 2).mean()
print("SoftImpute MSE: %f" % softImpute_mse)

knn_mse = ((X_filled_knn[missing_mask] - X[missing_mask]) ** 2).mean()
print("knnImpute MSE: %f" % knn_mse)
```

## Algorithms

* `SimpleFill`: Replaces missing entries with the mean or median of each column.

* `KNN`: Nearest neighbor imputations which weights samples using the mean squared difference
on features for which two rows both have observed data.

* `SoftImpute`: Matrix completion by iterative soft thresholding of SVD decompositions. Inspired by the [softImpute](https://web.stanford.edu/~hastie/swData/softImpute/vignette.html) package for R, which is based on [Spectral Regularization Algorithms for Learning Large Incomplete Matrices](http://web.stanford.edu/~hastie/Papers/mazumder10a.pdf) by Mazumder et. al.

* `IterativeSVD`: Matrix completion by iterative low-rank SVD decomposition. Should be similar to SVDimpute from [Missing value estimation methods for DNA microarrays](http://www.ncbi.nlm.nih.gov/pubmed/11395428) by Troyanskaya et. al.

* `MICE`: Reimplementation of [Multiple Imputation by Chained Equations](http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3074241/).

* `MatrixFactorization`: Direct factorization of the incomplete matrix into low-rank `U` and `V`, with an L1 sparsity penalty on the elements of `U` and an L2 penalty on the elements of `V`. Solved by gradient descent.

* `NuclearNormMinimization`: Simple implementation of [Exact Matrix Completion via Convex Optimization](http://statweb.stanford.edu/~candes/papers/MatrixCompletion.pdf
) by Emmanuel Candes and Benjamin Recht using [cvxpy](http://www.cvxpy.org/en/latest/). Too slow for large matrices.

* `BiScaler`: Iterative estimation of row/column means and standard deviations to get doubly normalized
matrix. Not guaranteed to converge but works well in practice. Taken from [Matrix Completion and Low-Rank SVD via Fast Alternating Least Squares](http://arxiv.org/abs/1410.2596).

Project details


Release history Release notifications

This version
History Node

0.3.1

History Node

0.3.0

History Node

0.2.0

History Node

0.1.0

History Node

0.0.19

History Node

0.0.18

History Node

0.0.16

History Node

0.0.15

History Node

0.0.14

History Node

0.0.13

History Node

0.0.12

History Node

0.0.11

History Node

0.0.10

History Node

0.0.9

History Node

0.0.6

History Node

0.0.5

History Node

0.0.4

History Node

0.0.1

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Filename, size & hash SHA256 hash help File type Python version Upload date
fancyimpute-0.3.1.tar.gz (24.1 kB) Copy SHA256 hash SHA256 Source None Feb 12, 2018

Supported by

Elastic Elastic Search Pingdom Pingdom Monitoring Google Google BigQuery Sentry Sentry Error logging CloudAMQP CloudAMQP RabbitMQ AWS AWS Cloud computing Fastly Fastly CDN DigiCert DigiCert EV certificate StatusPage StatusPage Status page