Fixes bug where SimpleImputer cannot handle dropped columns #846
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@@ Coverage Diff @@
## master #846 +/- ##
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Coverage 99.69% 99.69%
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Files 195 195
Lines 7745 7774 +29
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+ Hits 7721 7750 +29
Misses 24 24
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@angela97lin do we need a similar change for the new per-column imputer @jeremyliweishih added? |
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@dsherry Ah probably! I'll take a look :) |
| super().__init__(parameters=parameters, | ||
| component_obj=imputer, | ||
| random_state=random_state) | ||
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| def fit(self, X, y=None): |
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This will pick up the docstring from the subclass, right? Our pattern has been to docstring everything, even if its inherited. I suggest we continue that until we decide to change it globally
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Yup, picks it up from Transformer! Seems like we haven't really established a proper pattern--some places have docstrings, other places don't--but doesn't hurt to be explicit!
| @@ -35,23 +43,16 @@ def transform(self, X, y=None): | |||
| Returns: | |||
| pd.DataFrame: Transformed X | |||
| """ | |||
| if self._all_null_cols is None: | |||
| raise RuntimeError("Must fit transformer before calling transform!") | |||
| X_t = self._component_obj.transform(X) | |||
| if not isinstance(X_t, pd.DataFrame) and isinstance(X, pd.DataFrame): | |||
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Is there ever a case when this condition isn't true?
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Ya, I guess just when X wasn't a DataFrame to begin with, so this could be simplified to just check isinstance(X, pd.DataFrame), but there would be no need to do that if X_t was a DataFrame already, hence both.
@angela97lin cool thanks. I suggest you merge this, keep #514 open and handle the per-column imputer in a separate PR |
Addresses #514 by storing which columns have all nan and thus will be dropped and manually excluding them; work still needs to be done to support the per-column imputer.