Add unit tests for standard metric objectives

[angela97lin]

Closes #619

Adds the following tests:

• input contains NaN
• input contains inf
• inputs are different lengths
• inputs are zero lengths
• probabilities are not in [0, 1] range
• binary classification objective that doesn't need probability estimates gets input with more than two unique values

[codecov]

Codecov Report

Merging #741 into master will increase coverage by 0.01%.
The diff coverage is 100.00%.

@@            Coverage Diff             @@
##           master     #741      +/-   ##
==========================================
+ Coverage   99.36%   99.38%   +0.01%     
==========================================
  Files         151      151              
  Lines        5378     5529     +151     
==========================================
+ Hits         5344     5495     +151     
  Misses         34       34              

Impacted Files	Coverage Δ
evalml/exceptions/exceptions.py	100.00% <ø> (ø)
evalml/objectives/standard_metrics.py	100.00% <ø> (ø)
evalml/exceptions/__init__.py	100.00% <100.00%> (ø)
...alml/objectives/binary_classification_objective.py	100.00% <100.00%> (ø)
evalml/objectives/objective_base.py	100.00% <100.00%> (ø)
...alml/tests/objective_tests/test_fraud_detection.py	100.00% <100.00%> (ø)
evalml/tests/objective_tests/test_lead_scoring.py	100.00% <100.00%> (ø)
...lml/tests/objective_tests/test_standard_metrics.py	100.00% <100.00%> (ø)

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[dsherry]

FYI I think you can say np.isnan(y_true).any()

[angela97lin]

@dsherry Oh, I used np.any because of our previous discussion about numpy methods being faster?

[dsherry]

Both call np.any! np.isnan will return a np.array. I believenp.any(value) and value.any() call the same method. Also lol this was a total nit-pick on my part, feel free to ignore 😂

[dsherry]

@angela97lin this is great! It's nice to have this sort of thing in the codebase :) this will help people quickly diagnose problems with their data and problem setup. Nice going!

I left comments but I have two main suggestions. First, I left a discussion about what we should do for nans/infs in the input. Second, can you please also add this input validation to our custom objectives (fraud cost and lead scoring) too?

I could imagine us wanting something similar in the plot metrics, but let's handle that another time, in a separate PR.