Changed target name/series ID divider and added ability to return series ID column with predictions

[christopherbunn]

Resolves #4359

[codecov]

Codecov Report

All modified and coverable lines are covered by tests ✅

Comparison is base (8ffa04f) 99.7% compared to head (36185ae) 99.7%.

Additional details and impacted files

@@           Coverage Diff           @@
##            main   #4357     +/-   ##
=======================================
+ Coverage   99.7%   99.7%   +0.1%     
=======================================
  Files        357     357             
  Lines      39869   39910     +41     
=======================================
+ Hits       39749   39790     +41     
  Misses       120     120             

Files	Coverage Δ
...valml/pipelines/multiseries_regression_pipeline.py	100.0% <100.0%> (ø)
...valml/pipelines/time_series_regression_pipeline.py	100.0% <100.0%> (ø)
evalml/pipelines/utils.py	99.7% <100.0%> (+0.1%)	⬆️
...sts/component_tests/test_time_series_featurizer.py	99.7% <100.0%> (+0.1%)	⬆️
.../tests/component_tests/test_time_series_imputer.py	100.0% <100.0%> (ø)
evalml/tests/conftest.py	98.4% <100.0%> (+0.1%)	⬆️
...line_tests/test_multiseries_regression_pipeline.py	100.0% <100.0%> (ø)
evalml/tests/pipeline_tests/test_pipeline_utils.py	99.6% <ø> (ø)

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[chukarsten]

Do you not just want the X_train/y_train columns here?

[chukarsten]

We're missing testing on this branch?

[eccabay]

Just a few comments, and I think we're missing a test!

[eccabay]

This is a really long chunk of text, where a lot of it's repeated. A few questions:

• Is there a test case that covers this? (i.e. one that fails without this code)
• Are X_unstacked and X_train_unstacked ever going to have different columns? It seems odd that we get those separately from each other, so differently from how y is handled here
• Is the goal here to filter columns, reorder columns, or both? The comment makes me think it's re-ordering, but the code makes me think we're filtering

[christopherbunn]

• Will push an additional test case. Actually our current test case for predict_in_sample() errors out if it isn't in the right order. I could add something explicitly if you think it would be helpful?
• This covers the case when we're forecasting. When we're forecasting, we only pass in the dates + the series IDs. If we're using lagged features (like in the future), we can pull them from X_train even if they're not specified in the current X. We can generally expect the y and y_train values to be consistent since the column names come from the same series ID values.
• The goal is to do both for the reason described above. I can update the comment to clarify.

[eccabay]

When you say the current test case errors out if it isn't in the right order, does that mean you changed it around manually to verify it fails in that case? I'm thinking we'd benefit from an explicit test case that fails if this code isn't in place, no modification required. It'll help stop us from removing or breaking this bit in the future

[christopherbunn]

Added test_multiseries_pipeline_predict_in_sample_series_out_of_order() which evaluates this case.

[jeremyliweishih]

LGTM after recent revisions