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Enhance PCA Decomposition

See original GitHub issue

We should enhance the PCADecomposition visualizer to provide many of the features the Manifold visualizer provides, including things like:

Color points by class with a legend (See #458)
Color points by heatmap for continuous y and add a colorbar
Add alpha parameter (see #475)
Add random state to pass to PCA
Allow user to pass in a PCA transformer/pipeline
Update tests with better random data sets (more points; see manifold tests)
Include explained variance/noise variance (or explained variance ratio) in chart
Enhance biplots documentation

See also #455 as another enhancement that might not be related to this enhancement.

Issue Analytics

State:
Created 5 years ago
Comments:12 (10 by maintainers)

Top GitHub Comments

1reaction

BrandonKMLeecommented, Mar 12, 2022

@bbengfort n_components_in_ for FastICA, but at the same time explained variance could be a problem, as each components are expected to have well-distributed significance instead of being ordered, and also such a function currently does not exist for FastICA.

0reactions

bbengfortcommented, May 21, 2022

@BrandonKMLee ok, that makes sense so potentially FastICA make not work unless we create a specialized manifold for them.

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