Update pipelines and make_pipelines to accept Woodwork DataTables

[angela97lin]

Addresses of #1288 and #1367 to update pipeline classes and make_pipelines to handle Woodwork data types. Components will be handled in a separate PR.

[codecov]

Codecov Report

Merging #1393 (af6bc93) into main (554102c) will increase coverage by 0.1%.
The diff coverage is 100.0%.

@@            Coverage Diff            @@
##             main    #1393     +/-   ##
=========================================
+ Coverage   100.0%   100.0%   +0.1%     
=========================================
  Files         214      214             
  Lines       14040    14107     +67     
=========================================
+ Hits        14033    14100     +67     
  Misses          7        7             

Impacted Files	Coverage Δ
evalml/pipelines/binary_classification_pipeline.py	100.0% <ø> (ø)
evalml/automl/automl_search.py	99.7% <100.0%> (-<0.1%)	⬇️
evalml/pipelines/classification_pipeline.py	100.0% <100.0%> (ø)
evalml/pipelines/pipeline_base.py	100.0% <100.0%> (ø)
evalml/pipelines/regression_pipeline.py	100.0% <100.0%> (ø)
evalml/pipelines/utils.py	100.0% <100.0%> (ø)
evalml/tests/automl_tests/test_automl.py	100.0% <100.0%> (ø)
...assification_pipeline_tests/test_classification.py	100.0% <100.0%> (ø)
...tests/regression_pipeline_tests/test_regression.py	100.0% <100.0%> (ø)
evalml/tests/pipeline_tests/test_pipelines.py	100.0% <100.0%> (ø)
... and 1 more

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[angela97lin]

Note: we want warnings for users who pass in non-Woodwork data structures. Will either update this PR or put up one shortly after (depending on where reviews are) to address this.

EDIT: Per discussion with @dsherry For now, it is okay to just warn when users use AutoML, and not for individual pipelines/components. This aligns with our methodology that AutoML is smart, pipelines/components are not. Later, when we tackle #1289 (passing Woodwork data structures directly to pipelines), we could think about adding in warnings for pipelines/components as we will trigger the warning only once in AutoMLSearch, but there needs to be some work in Woodwork to make that a viable thing for EvalML.

[freddyaboulton]

@angela97lin I think this looks good! I am interested in your thoughts on whether we should convert back to pandas in make_pipeline!

[freddyaboulton]

How come we need this change?

[dsherry]

@freddyaboulton "category" is the woodwork "semantic tag" applied to indicate a feature is categorical. So, this change is necessary because we're using woodwork's feature types here instead of pandas data types.

[dsherry]

Its confusing because "category" is also the physical type used by pandas, hence the usage on line 11 below.

[dsherry]

@angela97lin amazing!

Blocking:

• delete pdb import from pipeline base
• Resolve comment in make_pipelines about not needing _convert_woodwork_types_wrapper
• Resolve @freddyaboulton comment about test_woodwork_classification_pipeline

[dsherry]

Its confusing because "category" is also the physical type used by pandas, hence the usage on line 11 below.