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[MRG+2] MAINT merge _check_cv into check_cv as indices argument is removed in 0.17 #4829

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Merged
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Jun 8, 2015
Merged

[MRG+2] MAINT merge _check_cv into check_cv as indices argument is removed in 0.17 #4829

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4 changes: 2 additions & 2 deletions sklearn/calibration.py
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Expand Up @@ -22,7 +22,7 @@
from .utils.validation import check_is_fitted
from .isotonic import IsotonicRegression
from .svm import LinearSVC
from .cross_validation import _check_cv
from .cross_validation import check_cv
from .metrics.classification import _check_binary_probabilistic_predictions


Expand Down Expand Up @@ -141,7 +141,7 @@ def fit(self, X, y, sample_weight=None):
calibrated_classifier.fit(X, y)
self.calibrated_classifiers_.append(calibrated_classifier)
else:
cv = _check_cv(self.cv, X, y, classifier=True)
cv = check_cv(self.cv, X, y, classifier=True)
arg_names = inspect.getargspec(base_estimator.fit)[0]
estimator_name = type(base_estimator).__name__
if (sample_weight is not None
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2 changes: 1 addition & 1 deletion sklearn/covariance/graph_lasso_.py
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Expand Up @@ -21,7 +21,7 @@
from ..utils.validation import check_random_state, check_array
from ..linear_model import lars_path
from ..linear_model import cd_fast
from ..cross_validation import _check_cv as check_cv, cross_val_score
from ..cross_validation import check_cv, cross_val_score
from ..externals.joblib import Parallel, delayed
import collections

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11 changes: 3 additions & 8 deletions sklearn/cross_validation.py
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Expand Up @@ -1033,7 +1033,7 @@ def cross_val_predict(estimator, X, y=None, cv=None, n_jobs=1,
"""
X, y = indexable(X, y)

cv = _check_cv(cv, X, y, classifier=is_classifier(estimator))
cv = check_cv(cv, X, y, classifier=is_classifier(estimator))
# We clone the estimator to make sure that all the folds are
# independent, and that it is pickle-able.
parallel = Parallel(n_jobs=n_jobs, verbose=verbose,
Expand Down Expand Up @@ -1191,7 +1191,7 @@ def cross_val_score(estimator, X, y=None, scoring=None, cv=None, n_jobs=1,
"""
X, y = indexable(X, y)

cv = _check_cv(cv, X, y, classifier=is_classifier(estimator))
cv = check_cv(cv, X, y, classifier=is_classifier(estimator))
scorer = check_scoring(estimator, scoring=scoring)
# We clone the estimator to make sure that all the folds are
# independent, and that it is pickle-able.
Expand Down Expand Up @@ -1428,11 +1428,6 @@ def check_cv(cv, X=None, y=None, classifier=False):
The return value is guaranteed to be a cv generator instance, whatever
the input type.
"""
return _check_cv(cv, X=X, y=y, classifier=classifier)


def _check_cv(cv, X=None, y=None, classifier=False):
# This exists for internal use while indices is being deprecated.
is_sparse = sp.issparse(X)
if cv is None:
cv = 3
Expand Down Expand Up @@ -1523,7 +1518,7 @@ def permutation_test_score(estimator, X, y, cv=None,

"""
X, y = indexable(X, y)
cv = _check_cv(cv, X, y, classifier=is_classifier(estimator))
cv = check_cv(cv, X, y, classifier=is_classifier(estimator))
scorer = check_scoring(estimator, scoring=scoring)
random_state = check_random_state(random_state)

Expand Down
2 changes: 1 addition & 1 deletion sklearn/feature_selection/rfe.py
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Expand Up @@ -14,7 +14,7 @@
from ..base import MetaEstimatorMixin
from ..base import clone
from ..base import is_classifier
from ..cross_validation import _check_cv as check_cv
from ..cross_validation import check_cv
from ..cross_validation import _safe_split, _score
from ..metrics.scorer import check_scoring
from .base import SelectorMixin
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2 changes: 1 addition & 1 deletion sklearn/grid_search.py
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Expand Up @@ -21,7 +21,7 @@

from .base import BaseEstimator, is_classifier, clone
from .base import MetaEstimatorMixin, ChangedBehaviorWarning
from .cross_validation import _check_cv as check_cv
from .cross_validation import check_cv
from .cross_validation import _fit_and_score
from .externals.joblib import Parallel, delayed
from .externals import six
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6 changes: 3 additions & 3 deletions sklearn/learning_curve.py
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Expand Up @@ -9,7 +9,7 @@
import numpy as np

from .base import is_classifier, clone
from .cross_validation import _check_cv
from .cross_validation import check_cv
from .externals.joblib import Parallel, delayed
from .cross_validation import _safe_split, _score, _fit_and_score
from .metrics.scorer import check_scoring
Expand Down Expand Up @@ -108,7 +108,7 @@ def learning_curve(estimator, X, y, train_sizes=np.linspace(0.1, 1.0, 5),

X, y = indexable(X, y)
# Make a list since we will be iterating multiple times over the folds
cv = list(_check_cv(cv, X, y, classifier=is_classifier(estimator)))
cv = list(check_cv(cv, X, y, classifier=is_classifier(estimator)))
scorer = check_scoring(estimator, scoring=scoring)

# HACK as long as boolean indices are allowed in cv generators
Expand Down Expand Up @@ -297,7 +297,7 @@ def validation_curve(estimator, X, y, param_name, param_range, cv=None,
<example_model_selection_plot_validation_curve.py>`
"""
X, y = indexable(X, y)
cv = _check_cv(cv, X, y, classifier=is_classifier(estimator))
cv = check_cv(cv, X, y, classifier=is_classifier(estimator))
scorer = check_scoring(estimator, scoring=scoring)

parallel = Parallel(n_jobs=n_jobs, pre_dispatch=pre_dispatch,
Expand Down
2 changes: 1 addition & 1 deletion sklearn/linear_model/coordinate_descent.py
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Expand Up @@ -17,7 +17,7 @@
from .base import center_data, sparse_center_data
from ..utils import check_array, check_X_y, deprecated
from ..utils.validation import check_random_state
from ..cross_validation import _check_cv as check_cv
from ..cross_validation import check_cv
from ..externals.joblib import Parallel, delayed
from ..externals import six
from ..externals.six.moves import xrange
Expand Down
2 changes: 1 addition & 1 deletion sklearn/linear_model/least_angle.py
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Expand Up @@ -22,7 +22,7 @@
from .base import LinearModel
from ..base import RegressorMixin
from ..utils import arrayfuncs, as_float_array, check_X_y
from ..cross_validation import _check_cv as check_cv
from ..cross_validation import check_cv
from ..utils import ConvergenceWarning
from ..externals.joblib import Parallel, delayed
from ..externals.six.moves import xrange
Expand Down
4 changes: 2 additions & 2 deletions sklearn/linear_model/logistic.py
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Expand Up @@ -28,7 +28,7 @@
check_X_y)
from ..utils.fixes import expit
from ..externals.joblib import Parallel, delayed
from ..cross_validation import _check_cv
from ..cross_validation import check_cv
from ..externals import six
from ..metrics import SCORERS

Expand Down Expand Up @@ -1345,7 +1345,7 @@ def fit(self, X, y):
check_consistent_length(X, y)

# init cross-validation generator
cv = _check_cv(self.cv, X, y, classifier=True)
cv = check_cv(self.cv, X, y, classifier=True)
folds = list(cv)

self._enc = LabelEncoder()
Expand Down
2 changes: 1 addition & 1 deletion sklearn/linear_model/omp.py
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Expand Up @@ -15,7 +15,7 @@
from .base import LinearModel, _pre_fit
from ..base import RegressorMixin
from ..utils import as_float_array, check_array, check_X_y
from ..cross_validation import _check_cv as check_cv
from ..cross_validation import check_cv
from ..externals.joblib import Parallel, delayed

import scipy
Expand Down
12 changes: 6 additions & 6 deletions sklearn/tests/test_cross_validation.py
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Expand Up @@ -950,31 +950,31 @@ def test_permutation_test_score_allow_nans():

def test_check_cv_return_types():
X = np.ones((9, 2))
cv = cval._check_cv(3, X, classifier=False)
cv = cval.check_cv(3, X, classifier=False)
assert_true(isinstance(cv, cval.KFold))

y_binary = np.array([0, 1, 0, 1, 0, 0, 1, 1, 1])
cv = cval._check_cv(3, X, y_binary, classifier=True)
cv = cval.check_cv(3, X, y_binary, classifier=True)
assert_true(isinstance(cv, cval.StratifiedKFold))

y_multiclass = np.array([0, 1, 0, 1, 2, 1, 2, 0, 2])
cv = cval._check_cv(3, X, y_multiclass, classifier=True)
cv = cval.check_cv(3, X, y_multiclass, classifier=True)
assert_true(isinstance(cv, cval.StratifiedKFold))

X = np.ones((5, 2))
y_seq_of_seqs = [[], [1, 2], [3], [0, 1, 3], [2]]

with warnings.catch_warnings(record=True):
# deprecated sequence of sequence format
cv = cval._check_cv(3, X, y_seq_of_seqs, classifier=True)
cv = cval.check_cv(3, X, y_seq_of_seqs, classifier=True)
assert_true(isinstance(cv, cval.KFold))

y_indicator_matrix = LabelBinarizer().fit_transform(y_seq_of_seqs)
cv = cval._check_cv(3, X, y_indicator_matrix, classifier=True)
cv = cval.check_cv(3, X, y_indicator_matrix, classifier=True)
assert_true(isinstance(cv, cval.KFold))

y_multioutput = np.array([[1, 2], [0, 3], [0, 0], [3, 1], [2, 0]])
cv = cval._check_cv(3, X, y_multioutput, classifier=True)
cv = cval.check_cv(3, X, y_multioutput, classifier=True)
assert_true(isinstance(cv, cval.KFold))


Expand Down