PCA with missing values in Python

python numpy pca
Emily picture Emily · Apr 2, 2015 · Viewed 12.8k times · Source

I'm trying to do a PCA analysis on a masked array. From what I can tell, matplotlib.mlab.PCA doesn't work if the original 2D matrix has missing values. Does anyone have recommendations for doing a PCA with missing values in Python?

Thanks.

Answer

LudvigH picture LudvigH · Jun 13, 2019

Imputing data will skew the result in ways that might bias the PCA estimates. A better approach is to use a PPCA algorithm, which gives the same result as PCA, but in some implementations can deal with missing data more robustly.

I have found two libraries. You have

  1. Package PPCA on PyPI, which is called PCA-magic on github
  2. Package PyPPCA, having the same name on PyPI and github

Since the packages are in low maintenance, you might want to implement it yourself instead. The code above build on theory presented in the well quoted (and well written!) paper by Tipping and Bishop 1999. It is available on Tippings home page if you want guidance on how to implement PPCA properly.

As an aside, the sklearn implementation of PCA is actually a PPCA implementation based on TippingBishop1999, but they have not chosen to implement it in such a way that it handles missing values.

EDIT: both the libraries above had issues so I could not use them directly myself. I forked PyPPCA and bug fixed it. Available on github.

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