Add support for PostalCode, SubRegionCode, CountryCode logical types

[eccabay]

Closes #2856

[codecov]

Codecov Report

Merging #2946 (1ed6e42) into main (c11809c) will increase coverage by 0.1%.
The diff coverage is 100.0%.

@@           Coverage Diff           @@
##            main   #2946     +/-   ##
=======================================
+ Coverage   99.7%   99.7%   +0.1%     
=======================================
  Files        307     307             
  Lines      29049   29197    +148     
=======================================
+ Hits       28958   29106    +148     
  Misses        91      91             

Impacted Files	Coverage Δ
evalml/model_understanding/graphs.py	100.0% <100.0%> (ø)
...es/components/transformers/samplers/oversampler.py	100.0% <100.0%> (ø)
evalml/tests/component_tests/test_oversampler.py	100.0% <100.0%> (ø)
...s/prediction_explanations_tests/test_explainers.py	100.0% <100.0%> (ø)
...del_understanding_tests/test_partial_dependence.py	99.4% <100.0%> (+0.1%)	⬆️
...understanding_tests/test_permutation_importance.py	100.0% <100.0%> (ø)

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[freddyaboulton]

@eccabay Thank you for this! I think this is ready to merge except we should make sure the one-way partial dependence plot is a bar plot for postal code etc instead of a line plot.

[freddyaboulton]

Don't we also need to check for boolean here? Why not do X.ww.select(['category', 'boolean']) ?

[freddyaboulton]

Wait @bchen1116 I think there might be a bug here on main? If there are either categorical or boolean features in the input along with numerics, the sampler should be SMOTENC not SMOTE right? I thought that's why we had changed the one hot encoder to encode the created features as boolean?

Repro on main

from evalml.automl import AutoMLSearch
from evalml.demos import load_fraud
import imblearn.over_sampling as imb

X, y = load_fraud(100)
X = X.ww[["provider", "country", "amount", "region"]]

automl = AutoMLSearch(X, y, "binary", verbose=True)
automl.search()

pipeline_3 = automl.get_pipeline(3)
pipeline_3.fit(X, y)
assert pipeline_3.get_component("Oversampler").sampler == imb.SMOTE

Not blocking this pr since it preserves this behavior but if this is a bug we should file another issue.

[bchen1116]

@freddyaboulton Yeah, you're right! That's a good catch, this should be SMOTENC in the case thata there are both numeric and categorical. It's likely through this line. I think we should be grabbing both categorical and booleans, not just categoricals.

I can file an issue here!

[freddyaboulton]

Thank you @bchen1116 !

[bchen1116]

LGTM! Just left one nit pick just for clarity bcause I was confused for a while.