DelayedFeatureTransformer encodes categorical features and targets #1691
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freddyaboulton
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| @@ -173,3 +226,40 @@ def test_target_delay_when_gap_is_0(gap, delayed_features_data): | |||
| answer = answer.drop(columns=["target_delay_0"]) | |||
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| pd.testing.assert_frame_equal(transformer.fit_transform(None, y), answer) | |||
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| @pytest.mark.parametrize('use_woodwork', [True, False]) | |||
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## main #1691 +/- ##
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+ Coverage 100.0% 100.0% +0.1%
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Files 240 240
Lines 18767 18853 +86
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+ Hits 18759 18845 +86
Misses 8 8
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| if y is not None: | ||
| y = _convert_to_woodwork_structure(y) | ||
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| if y.logical_type == logical_types.Categorical: |
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Wouldn't this check fail if y is None?
| if col_name in categorical_columns: | ||
| col = LabelEncoder().fit_transform(col) | ||
| col = pd.Series(col, index=X.index) | ||
| X = X.assign(**{f"{col_name}_delay_{t}": col.shift(t) for t in range(1, self.max_delay + 1)}) |
| for col_name in X: | ||
| col = X[col_name] | ||
| if col_name in categorical_columns: | ||
| col = LabelEncoder().fit_transform(col) | ||
| col = pd.Series(col, index=X.index) |
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Have you considered doing all the encoding over a subset of the data frame:
X[categorical_columns] = X[categorical_columns].apply(LabelEncoder().fit_transform)
Or something like that...I didn't run it ;)
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I'll give this a shot! Great suggestion :)
| else: | ||
| y = _convert_woodwork_types_wrapper(y.to_series()) | ||
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| categorical_columns = {name for name, column in X.columns.items() if |
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So if we were using straight up pandas, we'd do something like df.select_dtypes("object"). Does woodwork not have anything like that?
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There is a select method we can use! This would pair nicely with your suggestion to apply all of the encoding at once.
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I decided to not go with select because ultimately we just need the column names for categorical columns. Using select returns a datatable so we'd need an extra step to return the column names. I figure this is more direct.
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…TS pipeline code.
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Pull Request Description
Fixes #1581
Fixes #1685
After creating the pull request: in order to pass the release_notes_updated check you will need to update the "Future Release" section of
docs/source/release_notes.rstto include this pull request by adding :pr:123.