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Fix for passing pandas series in sample_weights in RidgeCV leading to an error(#5606) #6307

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Fix for passing pandas series in sample_weights in RidgeCV leading to an error(#5606) #6307

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14f031e
Added new validation test for sample_weight.
kaichogami Feb 13, 2016
777ea36
Merge branch 'master' of https://github.com/scikit-learn/scikit-learn…
kaichogami Feb 18, 2016
89b7e9a
Used object() in check_X_y to check for sample_weight
kaichogami Feb 19, 2016
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5 changes: 3 additions & 2 deletions sklearn/linear_model/ridge.py
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Original file line number Diff line number Diff line change
Expand Up @@ -930,8 +930,9 @@ def fit(self, X, y, sample_weight=None):
-------
self : Returns self.
"""
X, y = check_X_y(X, y, ['csr', 'csc', 'coo'], dtype=np.float64,
multi_output=True, y_numeric=True)
X, y, sample_weight = check_X_y(X, y, ['csr', 'csc', 'coo'],
dtype=np.float64, multi_output=True, y_numeric=True,
sample_weight=sample_weight)

n_samples, n_features = X.shape

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19 changes: 17 additions & 2 deletions sklearn/utils/validation.py
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Original file line number Diff line number Diff line change
Expand Up @@ -21,6 +21,8 @@
from ..exceptions import NonBLASDotWarning as _NonBLASDotWarning
from ..exceptions import NotFittedError as _NotFittedError

NOT_SPECIFIED = object()


@deprecated("DataConversionWarning has been moved into the sklearn.exceptions"
" module. It will not be available here from version 0.19")
Expand Down Expand Up @@ -429,7 +431,7 @@ def check_X_y(X, y, accept_sparse=None, dtype="numeric", order=None,
copy=False, force_all_finite=True, ensure_2d=True,
allow_nd=False, multi_output=False, ensure_min_samples=1,
ensure_min_features=1, y_numeric=False,
warn_on_dtype=False, estimator=None):
warn_on_dtype=False, estimator=None, sample_weight=NOT_SPECIFIED):
"""Input validation for standard estimators.

Checks X and y for consistent length, enforces X 2d and y 1d.
Expand Down Expand Up @@ -503,6 +505,10 @@ def check_X_y(X, y, accept_sparse=None, dtype="numeric", order=None,
estimator : str or estimator instance (default=None)
If passed, include the name of the estimator in warning messages.

sample_weight : nd-array, list
Ensures sample_weight is 1-d.


Returns
-------
X_converted : object
Expand All @@ -525,7 +531,16 @@ def check_X_y(X, y, accept_sparse=None, dtype="numeric", order=None,

check_consistent_length(X, y)

return X, y
if sample_weight is NOT_SPECIFIED:
return X, y

elif sample_weight is None:
return X, y, sample_weight

else:
sample_weight = check_array(sample_weight, ensure_2d=False)
check_consistent_length(y, sample_weight)
return X, y, sample_weight


def column_or_1d(y, warn=False):
Expand Down