Fix broken warning and reduce complexity of feature validation #35 #36
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This overhauls the feature validation to dramatically simplify changes that were introduced by #22 and #34. I think the easiest way to explain the changes of this commit are to explain how we ended up with that complexity. In #22, we needed to ensure that our estimators got feature names when fitted with dataframe inputs. This was complicated by the fact that they received transformed inputs, and our transformers cast dataframes to arrays, dropping the names. We dealt with this using a `NamedFeatureArray` class that would retain names after being cast. In #34, we implemented the `set_output` API and used this instead to ensure that our transformers returned dataframe outputs. At the same time, we decided that estimator feature names should match the names that original names, not the transformed names, and implemented the `feature_names_in_` property. What we failed to realize is that the `feature_names_in_` property removed any need to extract feature names from the transformed inputs because that extraction is handled entirely by the transformers! This removed the need to use the `set_output` API. The other major change is that the `_check_feature_names` method that we overrode in #34 does not need to be implemented directly because we can instead use that method from the transformer (via `_validate_data`) without having to worry about mismatched feature names during fitting.
aazuspan
added
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grovduck
approved these changes
Jun 8, 2023
| @@ -69,23 +69,26 @@ def test_estimators_support_continuous_multioutput(estimator, moscow_euclidean): | |||
| estimator.predict(moscow_euclidean.X) | |||
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| @pytest.mark.parametrize("with_names", [True, False]) | |||
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Nice parameterization. That's pretty cool!
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I hadn't used parametrize much before this, but I'm finding it's pretty powerful!
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This closes #35 and simplifies feature validation. This comment and this commit go into more depth on the changes here, but the bullet points are:
set_temp_outputbecause we no longer need transformers to pass dataframes into transformed estimators since we can get feature names from the transformer.TransformedKNeighborsMixin._check_feature_names. That is now handled by callingtransform_._validate_data. This method now just serves to override the internal check that would incorrectly fail when our estimator is fit with transformed features that mismatch thefeature_names_in_from the transformer.