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Unnecessary call to check_X_y in RFE #10821

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jimmywan opened this issue Mar 16, 2018 · 1 comment · Fixed by #11635
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Unnecessary call to check_X_y in RFE #10821

jimmywan opened this issue Mar 16, 2018 · 1 comment · Fixed by #11635
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Description

Can't use RFE/RFECV on X that is sparse(has NaN values) or y that is 2d.
Both of these decisions should be up to the underlying estimator.
The rejection of sparse X values does not make sense in a world with pipelines.
The reject of 2d output data should be up to the underlying estimator.

Steps/Code to Reproduce

Example partial stack trace from 2d y:

  File "/mypythoninstalldir/lib/python3.6/site-packages/sklearn/pipeline.py", line 250, in fit
    self._final_estimator.fit(Xt, y, **fit_params)
  File "/mypythoninstalldir/lib/python3.6/site-packages/sklearn/feature_selection/rfe.py", line 134, in fit
    return self._fit(X, y)
  File "/mypythoninstalldir/lib/python3.6/site-packages/sklearn/feature_selection/rfe.py", line 142, in _fit
    X, y = check_X_y(X, y, "csc")
  File "/mypythoninstalldir/lib/python3.6/site-packages/sklearn/utils/validation.py", line 578, in check_X_y
    y = column_or_1d(y, warn=True)
  File "/mypythoninstalldir/lib/python3.6/site-packages/sklearn/utils/validation.py", line 614, in column_or_1d
    raise ValueError("bad input shape {0}".format(shape))
ValueError: bad input shape (256, 2)

Expected Results

No error is thrown.
@jimmywan jimmywan mentioned this issue Mar 16, 2018
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@jimmywan
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Attached PR.

This was referenced Jul 19, 2018
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@glemaitre @jimmywan