Adds Imputer to allow different imputation strategies for numerical and categorical dtypes

[angela97lin]

Closes #881

Notes:

• Leaving fill_value as is right now to be used by both the numeric and categorical imputers. Default of None works for both right now, but could be an issue if user sets it. Could file separate issue or fix here, open to thoughts! (Filed Imputer: same fill value used for numeric/categorical for "constant" strategy #1000)

[codecov]

Codecov Report

Merging #991 into main will increase coverage by 0.00%.
The diff coverage is 100.00%.

@@           Coverage Diff            @@
##             main     #991    +/-   ##
========================================
  Coverage   99.86%   99.86%            
========================================
  Files         179      181     +2     
  Lines        9436     9584   +148     
========================================
+ Hits         9423     9571   +148     
  Misses         13       13            

Impacted Files	Coverage Δ
evalml/pipelines/components/__init__.py	100.00% <ø> (ø)
evalml/data_checks/invalid_targets_data_check.py	100.00% <100.00%> (ø)
evalml/data_checks/label_leakage_data_check.py	100.00% <100.00%> (ø)
evalml/data_checks/outliers_data_check.py	100.00% <100.00%> (ø)
...alml/pipelines/components/transformers/__init__.py	100.00% <100.00%> (ø)
...lines/components/transformers/imputers/__init__.py	100.00% <100.00%> (ø)
...elines/components/transformers/imputers/imputer.py	100.00% <100.00%> (ø)
...components/transformers/imputers/simple_imputer.py	100.00% <100.00%> (ø)
evalml/pipelines/regression_pipeline.py	100.00% <100.00%> (ø)
evalml/pipelines/utils.py	100.00% <100.00%> (ø)
... and 9 more

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[angela97lin]

@kmax12 @dsherry @freddyaboulton Might refactor tests to try all imputation strategies in this PR or in a latter one but implementation is ready for review again and I wanted to try kicking off the perf testing so please re-review if you get a chance! :')

[dsherry]

@angela97lin thanks for hustling to get this updated! It looks great to me.

Approved, pending perf test results comparison to ensure that a) timing and accuracy aren't degraded by this change, and b) we don't see any new errors on the perf test datasets introduced by this change.

I also left some comments I think are blocking, mostly minor:

• Let's resolve the discussion in fit about defining a list of allowed categorical types. I think we should do that rather than just filtering out the numerics, to avoid bugs.
• Remove "constant" strategy from automl search hyperparameters
• Small docstring update
• Add another release notes entry to highlight the fact that this is a potential performance improvement on mixed-type data.

Your test coverage looks great. A couple things I thought of:

• What happens if the provided data isn't empty, but also doesn't contain any numeric or categorical cols? I.e. all datetimes or something. Related to discussion in fit about datatype selection.
• Checking that the default parameters match what we expect.

[dsherry]

@angela97lin well done on this!!

I did some poking around at the first batch perf test results earlier. Looked good to me at first glance. I'd say since this is a low-risk PR and we're more interested in defense than we are in performance gain here, feel free to merge this now so we can start the release moving, and post the perf test plots after that. That ok?

[angela97lin]

@dsherry I just generated the plot!