Update pipelines and make_pipelines to accept Woodwork DataTables
#1393
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angela97lin
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## main #1393 +/- ##
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+ Coverage 100.0% 100.0% +0.1%
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Files 214 214
Lines 14040 14107 +67
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+ Hits 14033 14100 +67
Misses 7 7
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angela97lin
changed the title
make_pipelines to accept Woodwork DataTables
…lml into 1288_ww_pipelines_components
angela97lin
requested review from
dsherry,
freddyaboulton,
eccabay and
bchen1116
and removed request for
dsherry
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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. |
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@angela97lin I think this looks good! I am interested in your thoughts on whether we should convert back to pandas in make_pipeline!
evalml/tests/pipeline_tests/classification_pipeline_tests/test_classification.py
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| def test_invalid_targets_regression_pipeline(target_type, dummy_regression_pipeline_class): | ||
| X, y = load_wine() | ||
| if target_type == "categorical": | ||
| y = pd.Categorical(y) | ||
| if target_type == "category": |
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How come we need this change?
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@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.
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Its confusing because "category" is also the physical type used by pandas, hence the usage on line 11 below.
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@angela97lin amazing!
Blocking:
- delete pdb import from pipeline base
- Resolve comment in
make_pipelinesabout not needing_convert_woodwork_types_wrapper - Resolve @freddyaboulton comment about
test_woodwork_classification_pipeline
| def test_invalid_targets_regression_pipeline(target_type, dummy_regression_pipeline_class): | ||
| X, y = load_wine() | ||
| if target_type == "categorical": | ||
| y = pd.Categorical(y) | ||
| if target_type == "category": |
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Its confusing because "category" is also the physical type used by pandas, hence the usage on line 11 below.
Addresses of #1288 and #1367 to update pipeline classes and
make_pipelinesto handle Woodwork data types. Components will be handled in a separate PR.