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Main Commits - Major
-------------------- * ENH Reogranize classes/fn from grid_search into search.py * ENH Reogranize classes/fn from cross_validation into split.py * ENH Reogranize cls/fn from cross_validation/learning_curve into validate.py * MAINT Merge _check_cv into check_cv inside the model_selection module * MAINT Update all the imports to point to the model_selection module * FIX use iter_cv to iterate throught the new style/old style cv objs * TST Add tests for the new model_selection members * ENH Wrap the old-style cv obj/iterables instead of using iter_cv * ENH Use scipy's binomial coefficient function comb for calucation of nCk * ENH Few enhancements to the split module * ENH Improve check_cv input validation and docstring * MAINT _get_test_folds(X, y, labels) --> _get_test_folds(labels) * TST if 1d arrays for X introduce any errors * ENH use 1d X arrays for all tests; * ENH X_10 --> X (global var) Minor ----- * ENH _PartitionIterator --> _BaseCrossValidator; * ENH CVIterator --> CVIterableWrapper * TST Import the old SKF locally * FIX/TST Clean up the split module's tests. * DOC Improve documentation of the cv parameter * COSMIT consistently hyphenate cross-validation/cross-validator * TST Calculate n_samples from X * COSMIT Use separate lines for each import. * COSMIT cross_validation_generator --> cross_validator Commits merged manually ----------------------- * FIX Document the random_state attribute in RandomSearchCV * MAINT Use check_cv instead of _check_cv * ENH refactor OVO decision function, use it in SVC for sklearn-like decision_function shape * FIX avoid memory cost when sampling from large parameter grids ENH Major to Minor incremental enhancements to the model_selection Squashed commit messages - (For reference) Major ----- * ENH p --> n_labels * FIX *ShuffleSplit: all float/invalid type errors at init and int error at split * FIX make PredefinedSplit accept test_folds in constructor; Cleanup docstrings * ENH+TST KFold: make rng to be generated at every split call for reproducibility * FIX/MAINT KFold: make shuffle a public attr * FIX Make CVIterableWrapper private. * FIX reuse len_cv instead of recalculating it * FIX Prevent adding *SearchCV estimators from the old grid_search module * re-FIX In all_estimators: the sorting to use only the 1st item (name) To avoid collision between the old and the new GridSearch classes. * FIX test_validate.py: Use 2D X (1D X is being detected as a single sample) * MAINT validate.py --> validation.py * MAINT make the submodules private * MAINT Support old cv/gs/lc until 0.19 * FIX/MAINT n_splits --> get_n_splits * FIX/TST test_logistic.py/test_ovr_multinomial_iris: pass predefined folds as an iterable * MAINT expose BaseCrossValidator * Update the model_selection module with changes from master - From #5161 - - MAINT remove redundant p variable - - Add check for sparse prediction in cross_val_predict - From #5201 - DOC improve random_state param doc - From #5190 - LabelKFold and test - From #4583 - LabelShuffleSplit and tests - From #5300 - shuffle the `labels` not the `indxs` in LabelKFold + tests - From #5378 - Make the GridSearchCV docs more accurate. - From #5458 - Remove shuffle from LabelKFold - From #5466(#4270) - Gaussian Process by Jan Metzen - From #4826 - Move custom error / warnings into sklearn.exception Minor ----- * ENH Make the KFold shuffling test stronger * FIX/DOC Use the higher level model_selection module as ref * DOC in check_cv "y : array-like, optional" * DOC a supervised learning problem --> supervised learning problems * DOC cross-validators --> cross-validation strategies * DOC Correct Olivier Grisel's name ;) * MINOR/FIX cv_indices --> kfold * FIX/DOC Align the 'See also' section of the new KFold, LeaveOneOut * TST/FIX imports on separate lines * FIX use __class__ instead of classmethod * TST/FIX import directly from model_selection * COSMIT Relocate the random_state documentation * COSMIT remove pass * MAINT Remove deprecation warnings from old tests * FIX correct import at test_split * FIX/MAINT Move P_sparse, X, y defns to top; rm unused W_sparse, X_sparse * FIX random state to avoid doctes 8000 t failure * TST n_splits and split wrapping of _CVIterableWrapper * FIX/MAINT Use multilabel indicator matrix directly * TST/DOC clarify why we conflate classes 0 and 1 * DOC add comment that this was taken from BaseEstimator * FIX use of labels is not needed in stratified k fold * Fix cross_validation reference * Fix the labels param doc FIX/DOC/MAINT Addressing the review comments by Arnaud and Andy COSMIT Sort the members alphabetically COSMIT len_cv --> n_splits COSMIT Merge 2 if; FIX Use kwargs DOC Add my name to the authors :D DOC make labels parameter consistent FIX Remove hack for boolean indices; + COSMIT idx --> indices; DOC Add Returns COSMIT preds --> predictions DOC Add Returns and neatly arrange X, y, labels FIX idx(s)/ind(s)--> indice(s) COSMIT Merge if and else to elif COSMIT n --> n_samples COSMIT Use bincount only once COSMIT cls --> class_i / class_i (ith class indices) --> perm_indices_class_i FIX/ENH/TST Addressing the final reviews COSMIT c --> count FIX/TST make check_cv raise ValueError for string cv value TST nested cv (gs inside cross_val_score) works for diff cvs FIX/ENH Raise ValueError when labels is None for label based cvs; TST if labels is being passed correctly to the cv and that the ValueError is being propagated to the cross_val_score/predict and grid search FIX pass labels to cross_val_score FIX use make_classification DOC Add Returns; COSMIT Remove scaffolding TST add a test to check the _build_repr helper REVERT the old GS/RS should also be tested by the common tests. ENH Add a tuple of all/label based CVS FIX raise VE even at get_n_splits if labels is None FIX Fabian's comments PEP8
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‎doc/whats_new.rst

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Enhancements
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............
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- The cross-validation iterators are now modified as cross-validation splitters
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which expose a ``split`` method that takes in the data and yields a generator
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for the different splits. This change makes it possible to do nested cross-validation
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with ease. (`#4294 https://github.com/scikit-learn/scikit-learn/pull/4294>`_) by `Raghav R V`_.
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- The :mod:`cross_validation`, :mod:`grid_search` and :mod:`learning_curve`
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have been deprecated and the classes and functions have been reorganized into
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the :mod:`model_selection` module. (`#4294 https://github.com/scikit-learn/scikit-learn/pull/4294>`_) by `Raghav R V`_.
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Bug fixes
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.........
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‎sklearn/__init__.py

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'ensemble', 'exceptions', 'externals', 'feature_extraction',
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'feature_selection', 'gaussian_process', 'grid_search',
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'isotonic', 'kernel_approximation', 'kernel_ridge',
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'lda', 'learning_curve',
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'linear_model', 'manifold', 'metrics', 'mixture', 'multiclass',
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'lda', 'learning_curve', 'linear_model', 'manifold', 'metrics',
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'mixture', 'model_selection', 'multiclass',
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'naive_bayes', 'neighbors', 'neural_network', 'pipeline',
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'preprocessing', 'qda', 'random_projection', 'semi_supervised',
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'svm', 'tree', 'discriminant_analysis',

‎sklearn/calibration.py

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from .utils.fixes import signature
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from .isotonic import IsotonicRegression
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from .svm import LinearSVC
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from .cross_validation import check_cv
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from .model_selection import check_cv
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from .metrics.classification import _check_binary_probabilistic_predictions
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calibrated_classifier.fit(X, y)
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self.calibrated_classifiers_.append(calibrated_classifier)
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else:
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cv = check_cv(self.cv, X, y, classifier=True)
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cv = check_cv(self.cv, y, classifier=True)
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fit_parameters = signature(base_estimator.fit).parameters
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estimator_name = type(base_estimator).__name__
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if (sample_weight is not None
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base_estimator_sample_weight = None
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else:
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base_estimator_sample_weight = sample_weight
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for train, test in cv:
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for train, test in cv.split(X, y):
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this_estimator = clone(base_estimator)
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if base_estimator_sample_weight is not None:
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this_estimator.fit(

‎sklearn/cluster/tests/test_bicluster.py

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import numpy as np
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from scipy.sparse import csr_matrix, issparse
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from sklearn.grid_search import ParameterGrid
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from sklearn.model_selection import ParameterGrid
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from sklearn.utils.testing import assert_equal
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from sklearn.utils.testing import assert_almost_equal

‎sklearn/covariance/graph_lasso_.py

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from ..utils.validation import check_random_state, check_array
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from ..linear_model import lars_path
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from ..linear_model import cd_fast
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from ..cross_validation import check_cv, cross_val_score
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from ..model_selection import check_cv, cross_val_score
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from ..externals.joblib import Parallel, delayed
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import collections
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emp_cov = empirical_covariance(
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X, assume_centered=self.assume_centered)
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cv = check_cv(self.cv, X, y, classifier=False)
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cv = check_cv(self.cv, y, classifier=False)
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# List of (alpha, scores, covs)
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path = list()
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this_path = Parallel(
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n_jobs=self.n_jobs,
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verbose=self.verbose
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)(
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delayed(graph_lasso_path)(
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X[train], alphas=alphas,
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X_test=X[test], mode=self.mode,
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tol=self.tol, enet_tol=self.enet_tol,
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max_iter=int(.1 * self.max_iter),
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verbose=inner_verbose)
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for train, test in cv)
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)(delayed(graph_lasso_path)(X[train], alphas=alphas,
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X_test=X[test], mode=self.mode,
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tol=self.tol,
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enet_tol=self.enet_tol,
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max_iter=int(.1 * self.max_iter),
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verbose=inner_verbose)
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for train, test in cv.sp 10000 lit(X, y))
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# Little danse to transform the list in what we need
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covs, _, scores = zip(*this_path)

‎sklearn/cross_validation.py

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from .gaussian_process.kernels import Kernel as GPKernel
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from .exceptions import FitFailedWarning
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warnings.warn("This module has been deprecated in favor of the "
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"model_selection module into which all the refactored classes "
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"and functions are moved. Also note that the interface of the "
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"new CV iterators are different from that of this module. "
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"This module will be removed in 0.19.", DeprecationWarning)
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__all__ = ['KFold',
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'LabelKFold',
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'LeaveOneLabelOut',
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See also
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--------
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StratifiedKFold: take label information into account to avoid building
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StratifiedKFold take label information into account to avoid building
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folds with imbalanced class distributions (for binary or multiclass
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classification tasks).
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‎sklearn/decomposition/tests/test_kernel_pca.py

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from sklearn.datasets import make_circles
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from sklearn.linear_model import Perceptron
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from sklearn.pipeline import Pipeline
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from sklearn.grid_search import GridSearchCV
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from sklearn.model_selection import GridSearchCV
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from sklearn.metrics.pairwise import rbf_kernel
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‎sklearn/ensemble/tests/test_bagging.py

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from sklearn.utils.testing import assert_warns_message
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from sklearn.dummy import DummyClassifier, DummyRegressor
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from sklearn.grid_search import GridSearchCV, ParameterGrid
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from sklearn.model_selection import GridSearchCV, ParameterGrid
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from sklearn.ensemble import BaggingClassifier, BaggingRegressor
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from sklearn.linear_model import Perceptron, LogisticRegression
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from sklearn.neighbors import KNeighborsClassifier, KNeighborsRegressor
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from sklearn.tree import DecisionTreeClassifier, DecisionTreeRegressor
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from sklearn.svm import SVC, SVR
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from sklearn.pipeline import make_pipeline
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from sklearn.feature_selection import SelectKBest
32-
from sklearn.cross_validation import train_test_split
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from sklearn.model_selection import train_test_split
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from sklearn.datasets import load_boston, load_iris, make_hastie_10_2
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from sklearn.utils import check_random_state
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‎sklearn/ensemble/tests/test_forest.py

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from sklearn.ensemble import RandomForestClassifier
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from sklearn.ensemble import RandomForestRegressor
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from sklearn.ensemble import RandomTreesEmbedding
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from sklearn.grid_search import GridSearchCV
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from sklearn.model_selection import GridSearchCV
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from sklearn.svm import LinearSVC
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from sklearn.utils.fixes import bincount
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from sklearn.utils.validation import check_random_state

‎sklearn/ensemble/tests/test_voting_classifier.py

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from sklearn.naive_bayes import GaussianNB
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from sklearn.ensemble import RandomForestClassifier
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from sklearn.ensemble import VotingClassifier
10-
from sklearn.grid_search import GridSearchCV
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from sklearn.model_selection import GridSearchCV
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from sklearn import datasets
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from sklearn import cross_validation
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from sklearn.model_selection import cross_val_score
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from sklearn.datasets import make_multilabel_classification
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from sklearn.svm import SVC
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from sklearn.multiclass import OneVsRestClassifier
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eclf = VotingClassifier(estimators=[
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('lr', clf1), ('rf', clf2), ('gnb', clf3)],
2929
voting='hard')
30-
scores = cross_validation.cross_val_score(eclf,
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X,
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y,
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cv=5,
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scoring='accuracy')
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scores = cross_val_score(eclf, X, y, cv=5, scoring='accuracy')
3531
assert_almost_equal(scores.mean(), 0.95, decimal=2)
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('lr', clf1), ('rf', clf2), ('gnb', clf3)],
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voting='soft',
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weights=[1, 2, 10])
58-
scores = cross_validation.cross_val_score(eclf,
59-
X,
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y,
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cv=5,
62-
scoring='accuracy')
54+
scores = cross_val_score(eclf, X, y, cv=5, scoring='accuracy')
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assert_almost_equal(scores.mean(), 0.93, decimal=2)
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‎sklearn/ensemble/tests/test_weight_boosting.py

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from sklearn.utils.testing import assert_raises, assert_raises_regexp
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from sklearn.base import BaseEstimator
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from sklearn.cross_validation import train_test_split
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from sklearn.grid_search import GridSearchCV
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from sklearn.model_selection import train_test_split
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from sklearn.model_selection import GridSearchCV
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from sklearn.ensemble import AdaBoostClassifier
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from sklearn.ensemble import AdaBoostRegressor
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from sklearn.ensemble import weight_boosting

‎sklearn/exceptions.py

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Examples
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--------
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>>> from sklearn.grid_search import GridSearchCV
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>>> from sklearn.model_selection import GridSearchCV
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>>> from sklearn.svm import LinearSVC
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>>> from sklearn.exceptions import FitFailedWarning
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>>> import warnings

‎sklearn/feature_extraction/tests/test_text.py

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from sklearn.feature_extraction.text import ENGLISH_STOP_WORDS
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from sklearn.cross_validation import train_test_split
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from sklearn.cross_validation import cross_val_score
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from sklearn.grid_search import GridSearchCV
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from sklearn.model_selection import train_test_split
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from sklearn.model_selection import cross_val_score
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from sklearn.model_selection import GridSearchCV
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from sklearn.pipeline import Pipeline
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from sklearn.svm import LinearSVC
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‎sklearn/feature_selection/rfe.py

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from ..base import MetaEstimatorMixin
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from ..base import is_classifier
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from ..cross_validation import check_cv
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from ..cross_validation import _safe_split, _score
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from ..model_selection import check_cv
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from ..model_selection._validation import _safe_split, _score
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from ..metrics.scorer import check_scoring
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from .base import SelectorMixin
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X, y = check_X_y(X, y, "csr")
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# Initialization
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cv = check_cv(self.cv, X, y, is_classifier(self.estimator))
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cv = check_cv(self.cv, y, is_classifier(self.estimator))
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scorer = check_scoring(self.estimator, scoring=self.scoring)
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n_features = X.shape[1]
379379
n_features_to_select = 1
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scores = []
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# Cross-validation
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for n, (train, test) in enumerate(cv):
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for n, (train, test) in enumerate(cv.split(X, y)):
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X_train, y_train = _safe_split(self.estimator, X, y, train)
387387
X_test, y_test = _safe_split(self.estimator, X, y, test, train)
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self.estimator_ = clone(self.estimator)
415415
self.estimator_.fit(self.transform(X), y)
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417-
# Fixing a normalization error, n is equal to len(cv) - 1
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# here, the scores are normalized by len(cv)
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self.grid_scores_ = scores / len(cv)
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# Fixing a normalization error, n is equal to get_n_splits(X, y) - 1
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# here, the scores are normalized by get_n_splits(X, y)
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self.grid_scores_ = scores / cv.get_n_splits(X, y)
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‎sklearn/feature_selection/tests/test_rfe.py

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from sklearn.metrics import zero_one_loss
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from sklearn.svm import SVC, SVR
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from sklearn.ensemble import RandomForestClassifier
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from sklearn.cross_validation import cross_val_score
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from sklearn.model_selection import cross_val_score
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from sklearn.utils.testing import ignore_warnings

‎sklearn/grid_search.py

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'ParameterSampler', 'RandomizedSearchCV']
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warnings.warn("This module has been deprecated in favor of the "
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"model_selection module into which all the refactored classes "
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"and functions are moved. This module will be removed in 0.19.",
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DeprecationWarning)
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class ParameterGrid(object):
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‎sklearn/learning_curve.py

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from .utils.fixes import astype
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warnings.warn("This module has been deprecated in favor of the "
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"model_selection module into which all the functions are moved."
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" This module will be removed in 0.19",
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DeprecationWarning)
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__all__ = ['learning_curve', 'validation_curve']
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‎sklearn/linear_model/coordinate_descent.py

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from .base import center_data, sparse_center_data
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from ..utils.validation import check_random_state
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from ..cross_validation import check_cv
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from ..model_selection import check_cv
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from ..externals.joblib import Parallel, delayed
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from ..externals import six
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path_params['copy_X'] = False
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# init cross-validation generator
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cv = check_cv(self.cv, X)
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cv = check_cv(self.cv)
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# Compute path for all folds and compute MSE to get the best alpha
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folds = list(cv)
1126+
folds = list(cv.split(X))
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best_mse = np.inf
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# We do a double for loop folded in one, in order to be able to

‎sklearn/linear_model/least_angle.py

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from .base import LinearModel
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from ..base import RegressorMixin
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from ..model_selection import check_cv
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from ..exceptions import ConvergenceWarning
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from ..externals.joblib import Parallel, delayed
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y = as_float_array(y, copy=self.copy_X)
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# init cross-validation generator
1082-
cv = check_cv(self.cv, X, y, classifier=False)
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cv = check_cv(self.cv, classifier=False)
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10841084
Gram = 'auto' if self.precompute else None
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10891089
method=self.method, verbose=max(0, self.verbose - 1),
10901090
normalize=self.normalize, fit_intercept=self.fit_intercept,
10911091
max_iter=self.max_iter, eps=self.eps, positive=self.positive)
1092-
for train, test in cv)
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for train, test in cv.split(X, y))
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all_alphas = np.concatenate(list(zip(*cv_paths))[0])
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10951095
all_alphas = np.unique(all_alphas)

‎sklearn/linear_model/logistic.py

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"""
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Logistic Regression
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"""
@@ -32,7 +33,7 @@
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from ..utils.fixes import expit
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from ..utils.multiclass import check_classification_targets
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from ..externals.joblib import Parallel, delayed
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from ..cross_validation import check_cv
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from ..model_selection import check_cv
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from ..externals import six
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from ..metrics import SCORERS
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@@ -1309,7 +1310,7 @@ class LogisticRegressionCV(LogisticRegression, BaseEstimator,
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cv : integer or cross-validation generator
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The default cross-validation generator used is Stratified K-Folds.
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If an integer is provided, then it is the number of folds used.
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See the module :mod:`sklearn.cross_validation` module for the
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See the module :mod:`sklearn.model_selection` module for the
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list of possible cross-validation objects.
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penalty : str, 'l1' or 'l2'
@@ -1506,8 +1507,8 @@ def fit(self, X, y, sample_weight=None):
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check_consistent_length(X, y)
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# init cross-validation generator
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cv = check_cv(self.cv, X, y, classifier=True)
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folds = list(cv)
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cv = check_cv(self.cv, y, classifier=True)
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folds = list(cv.split(X, y))
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self._enc = LabelEncoder()
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self._enc.fit(y)

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