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Merge pull request #4294 from rvraghav93/model_selection
[MRG+1] Reorganize grid_search, cross_validation and learning_curve into model_selection
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‎doc/whats_new.rst

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@@ -25,6 +25,16 @@ New features
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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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@@ -62,8 +62,8 @@
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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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@@ -22,7 +22,7 @@
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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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@@ -152,7 +152,7 @@ def fit(self, X, y, sample_weight=None):
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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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@@ -3,7 +3,7 @@
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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.split(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
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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
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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 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)],
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voting='hard')
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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')
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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])
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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')
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assert_almost_equal(scores.mean(), 0.93, decimal=2)
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