Standardize feature importance for estimators

[angela97lin]

Closes #3183

[codecov]

Codecov Report

Merging #3305 (194e60c) into main (e67430c) will increase coverage by 0.1%.
The diff coverage is 100.0%.

@@           Coverage Diff           @@
##            main   #3305     +/-   ##
=======================================
+ Coverage   99.7%   99.7%   +0.1%     
=======================================
  Files        327     327             
  Lines      31814   31840     +26     
=======================================
+ Hits       31689   31715     +26     
  Misses       125     125             

Impacted Files	Coverage Δ
evalml/model_understanding/graphs.py	100.0% <100.0%> (ø)
...ents/estimators/classifiers/baseline_classifier.py	100.0% <100.0%> (ø)
...ents/estimators/classifiers/catboost_classifier.py	100.0% <100.0%> (ø)
...ts/estimators/classifiers/elasticnet_classifier.py	100.0% <100.0%> (ø)
...tors/classifiers/logistic_regression_classifier.py	100.0% <100.0%> (ø)
...nents/estimators/classifiers/xgboost_classifier.py	100.0% <100.0%> (ø)
...valml/pipelines/components/estimators/estimator.py	100.0% <100.0%> (ø)
...onents/estimators/regressors/catboost_regressor.py	100.0% <100.0%> (ø)
...ents/estimators/regressors/elasticnet_regressor.py	100.0% <100.0%> (ø)
...tors/regressors/exponential_smoothing_regressor.py	100.0% <100.0%> (ø)
... and 5 more

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[jeremyliweishih]

Some food for thought but not blocking!

[jeremyliweishih]

Do we need all the pd.Series calls with this call here?

[angela97lin]

Nope since coef_ should be a pd.Series object now. Though we still want the name, so I can update this line to set the name instead.

[jeremyliweishih]

Should we delete any of the previous tests? I think we could remove all the tests don't check equality (i.e the estimators that don't support feature importance) since there's some redundancy between these tests.

[chukarsten]

Indeed, I can see this going both ways. I can see tests here in test_estimators existing for the purpose of determining that no feature_importance() sends back NaN. If we wanted to do any assertion on the shapes of feature importances relative to the input datasets, now and here would be the place to do it. I think if we wanted, for some reason, to test the specific feature_importance() of an individual estimator and make sure that it calculates the right values, we'd want to do it in that specific module.

[angela97lin]

I like what @chukarsten is saying here, where this is more general and just tests that we don't return nan values / the shapes are expected. Specific modules will test for expected values (since many of the feature importances are calculated from an external library) or assert that we can't calculate it.

[chukarsten]

Thanks for standardizing and reducing my OCD :)

[chukarsten]

Indeed, I can see this going both ways. I can see tests here in test_estimators existing for the purpose of determining that no feature_importance() sends back NaN. If we wanted to do any assertion on the shapes of feature importances relative to the input datasets, now and here would be the place to do it. I think if we wanted, for some reason, to test the specific feature_importance() of an individual estimator and make sure that it calculates the right values, we'd want to do it in that specific module.

[ParthivNaresh]

Looks great, just left a comment about whether we want a Series name to be part of the standardization in case people call it from the estimator directly and not from model understanding.

[ParthivNaresh]

Just out of curiosity, why did we separate these?

[angela97lin]

I couldn't get it to work with them together, because of pandas behavior so it reindexes after and returns all NaNs if put on the same line (or that's my rough understanding)?https://pandas.pydata.org/docs/reference/api/pandas.Series.html

[ParthivNaresh]

Do we want to add a name to the feature importance series?

[angela97lin]

Filed #3330 that we can use to track this! :)