[MRG+1] copy not passed from linear_model/base.py:_pre_fit to _preprocess_data #9347
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…a in the sparse case
agramfort
reviewed
Jul 13, 2017
sklearn/linear_model/base.py
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| @@ -523,7 +523,7 @@ def _pre_fit(X, y, Xy, precompute, normalize, fit_intercept, copy): | |||
| precompute = False | |||
| X, y, X_offset, y_offset, X_scale = _preprocess_data( | |||
| X, y, fit_intercept=fit_intercept, normalize=normalize, | |||
| return_mean=True) | |||
| copy=copy, return_mean=True) | |||
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I suspect you should never copy for a sparse matrix as X will not be densified hence modified anyway. So copy should be False all the time. Can you check?
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Ok, got it. Let me change to copy=False.
Read through this now
copy : boolean (default=False)
Whether a forced copy will be triggered. If copy=False, a copy might
be triggered by a conversion.
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LGTM +1 for MRG |
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why |
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because if you pass copy=True it makes a copy of X although X is not modified inplace when X is sparse. clear? |
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@agramfort not really clear unless you go read the code of the |
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+1 to add a comment of why this is False here.
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Added a comment. |
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thx @bmanohar16 |
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…cess_data (scikit-learn#9347) * copy not passed from linear_model/base.py:_pre_fit to _preprocess_data in the sparse case * Pass copy as False for sparse matrix to _preprocess_data * Add comment with reason for copy=False
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…cess_data (scikit-learn#9347) * copy not passed from linear_model/base.py:_pre_fit to _preprocess_data in the sparse case * Pass copy as False for sparse matrix to _preprocess_data * Add comment with reason for copy=False
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…cess_data (scikit-learn#9347) * copy not passed from linear_model/base.py:_pre_fit to _preprocess_data in the sparse case * Pass copy as False for sparse matrix to _preprocess_data * Add comment with reason for copy=False
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…cess_data (scikit-learn#9347) * copy not passed from linear_model/base.py:_pre_fit to _preprocess_data in the sparse case * Pass copy as False for sparse matrix to _preprocess_data * Add comment with reason for copy=False
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…cess_data (scikit-learn#9347) * copy not passed from linear_model/base.py:_pre_fit to _preprocess_data in the sparse case * Pass copy as False for sparse matrix to _preprocess_data * Add comment with reason for copy=False
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…cess_data (scikit-learn#9347) * copy not passed from linear_model/base.py:_pre_fit to _preprocess_data in the sparse case * Pass copy as False for sparse matrix to _preprocess_data * Add comment with reason for copy=False
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…cess_data (scikit-learn#9347) * copy not passed from linear_model/base.py:_pre_fit to _preprocess_data in the sparse case * Pass copy as False for sparse matrix to _preprocess_data * Add comment with reason for copy=False
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…cess_data (scikit-learn#9347) * copy not passed from linear_model/base.py:_pre_fit to _preprocess_data in the sparse case * Pass copy as False for sparse matrix to _preprocess_data * Add comment with reason for copy=False
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Reference Issue
Fixes #9345
What does this implement/fix? Explain your changes.
Added copy parameter to _preprocess_data method
Fixes issue discussed in lgtmhq/lgtm-queries#14