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@@ Coverage Diff @@
## main #3959 +/- ##
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+ Coverage 99.7% 99.7% +0.1%
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Files 347 347
Lines 36768 36776 +8
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+ Hits 36647 36656 +9
+ Misses 121 120 -1
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| self._boolean_cols = X.ww.schema._filter_cols( | ||
| include=["Boolean", "BooleanNullable"], | ||
| ) |
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I might be being pedantic, but is the preferred way to call a private function on the schema? I thought there was a select function on the ww accessor?
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Certainly. I stole this line directly from set_boolean_columns_to_integer, but I can switch both places over to use select instead.
eccabay
requested review from
Cmancuso,
bchen1116,
christopherbunn,
chukarsten,
jeremyliweishih and
tamargrey
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| new_schema = original_schema.get_subset_schema(X_t.columns) | ||
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| # TODO: Fix this after WW adds inference of object type booleans to BooleanNullable |
| if logical_type in [NaturalLanguage, Categorical]: | ||
| impute_strategy_to_use = "most_frequent" | ||
| if logical_type in [NaturalLanguage, Categorical, Boolean, BooleanNullable]: | ||
| impute_strategy = "most_frequent" |
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Not a huge fan of how this was originally done - with impute_strategy iterating over a subset of the total impute_strategy available and changing it in the test. But that's not your problem...we might want to think about rewriting this.
| X_train = pd.DataFrame({"a": [pd.NA] * 20 + [1.0] + [pd.NA] * 20}) | ||
| y = pd.Series(range(len(X_train))) | ||
| X_test = pd.DataFrame({"a": [pd.NA] * 10}) |
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Times like these, I think it's helpful to docstring the test to get at what exactly you're testing here. The test name doesn't seem to match what's going on. The test case here is that you're train is sparse and your test set happens to not be fully representative of all the classes available in X, right?
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Basically, yeah. It's really just testing having an all-null test set when the training had non-null values. I'll update the test name and add a docstring
chukarsten
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chukarsten
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Pending select change, looks great!
jeremyliweishih
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jeremyliweishih
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Agree with @chukarsten's comments but otherwise LGTM
Co-authored-by: Jeremy Shih <jeremyliweishih@gmail.com>
…booleannullable-fix
3 participants
Fixes the bug where all-null BooleanNullable columns will break the simple imputer during transform, when fit on nullable data that has a non-null value.