Add PCA component

[eccabay]

Closes #1262 by introducing a PCA component for dimensionality reduction.

As an initial sanity test, I ran AutoML on the MNIST dataset with and without the PCA component. The original dataset had 784 sparse features, after running PCA this was reduced to 154 features.

Training with current pipelines:

Training with the same pipelines but including the PCA component with default parameters:

[codecov]

Codecov Report

Merging #1270 into main will increase coverage by 0.00%.
The diff coverage is 100.00%.

@@           Coverage Diff            @@
##             main    #1270    +/-   ##
========================================
  Coverage   99.93%   99.94%            
========================================
  Files         210      213     +3     
  Lines       13247    13357   +110     
========================================
+ Hits        13239    13349   +110     
  Misses          8        8            

Impacted Files	Coverage Δ
evalml/pipelines/components/__init__.py	100.00% <ø> (ø)
...alml/pipelines/components/transformers/__init__.py	100.00% <100.00%> (ø)
.../transformers/dimensionality_reduction/__init__.py	100.00% <100.00%> (ø)
...nents/transformers/dimensionality_reduction/pca.py	100.00% <100.00%> (ø)
evalml/tests/component_tests/test_components.py	100.00% <100.00%> (ø)
evalml/tests/component_tests/test_pca.py	100.00% <100.00%> (ø)
evalml/tests/component_tests/test_utils.py	100.00% <100.00%> (ø)
evalml/utils/gen_utils.py	100.00% <100.00%> (ø)
... and 1 more

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[freddyaboulton]

@eccabay I think this is really cool!

Since this component changes the input data fed to the estimator, it has an impact on our model understanding methods.

As it stands now, graph_partial_dependence will error if the pipeline has a PCA component and the explain_predictions* functions don't show original column names (which is not a bug but maybe we can look into making them display the original columns).

Once this gets merged, I'll file an issue to track that!

[angela97lin]

This is fantastic! Just left minor comments / things I'm curious about but LGTM!

[angela97lin]

Just curious, how did you get these expected values? Wondering if there's a way to write / store these values in a way that will make more sense than just a 3d list of floats :O

[eccabay]

Hm, I'm not sure if there's a better way to write these numbers! PCA centers features and projects them down to a smaller vector space, so there's no simple or easy to anticipate relationship between the input and output features. Very open to any suggestions though!

[eccabay]

Wrote up a slightly more in-depth summary of my testing with MNIST here