Fixes #1248 Adds Sklearn Pipeline tests to ModelVisualizers - CPE, ClsRpt, ConfustionMatrix, PRC, ROCAUC,DroppingCurve

[lwgray]

Adding test for utilization of Sklearn pipelines within these ModelVisualizers

• - ClassPredictionError
• - ClassificationReport
• - ConfusionMatrix
• - PrecisionRecallCurve
• - ROCAUC
• - DroppingCurve

CHECKLIST

• Is the commit message formatted correctly?
• Have you noted the new functionality/bugfix in the release notes of the next release?

• Included a sample plot to visually illustrate your changes?
• Do all of your functions and methods have docstrings?
• Have you added/updated unit tests where appropriate?
• Have you updated the baseline images if necessary?
• Have you run the unit tests using pytest?
• Is your code style correct (are you using PEP8, pyflakes)?
• Have you documented your new feature/functionality in the docs?

• Have you built the docs using make html?

[codecov]

Codecov Report

Merging #1249 (a0bcb55) into develop (ad0d133) will increase coverage by 0.03%.
The diff coverage is n/a.

@@             Coverage Diff             @@
##           develop    #1249      +/-   ##
===========================================
+ Coverage    90.58%   90.61%   +0.03%     
===========================================
  Files           92       92              
  Lines         5213     5213              
===========================================
+ Hits          4722     4724       +2     
+ Misses         491      489       -2     

Impacted Files	Coverage Δ
yellowbrick/classifier/rocauc.py	96.94% <0.00%> (+0.76%)	⬆️
yellowbrick/classifier/confusion_matrix.py	93.68% <0.00%> (+1.05%)	⬆️

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[bbengfort]

@lwgray thank you for taking on this work!

[bbengfort]

Looks great, thank you for doing all the hard work adding these tests for pipelines! I'm really glad to see that the new sklearn Pipeline supports visualizers; that's going to make things a lot easier! I also think that the 4 tests you're adding a perfect, testing visualizers in a pipeline and testing visualizers with pipelined models; excellent coverage!

For this test I:

• spot checked old vs new baseline images for obvious big changes
• spot checked new baseline images to make sure they had data
• spot checked the right model was used in the tests.

Nice one!