diff --git a/doc/whats_new.rst b/doc/whats_new.rst
index 66e8212947cf015e5b7df4c5f36c683a95807ed5..34f82c30e981e4b4a17ebf3908b2f4e06703a17e 100644
--- a/doc/whats_new.rst
+++ b/doc/whats_new.rst
@@ -110,6 +110,10 @@ Enhancements
attributes, ``n_skips_*``.
:issue:`7914` by :user:`Michael Horrell <mthorrell>`.
+ - :func:`model_selection.cross_val_predict` now returns output of the
+ correct shape for all values of the argument ``method``.
+ :issue:`7863` by :user:`Aman Dalmia <dalmia>`.
+
- Fix a bug where :class:`sklearn.feature_selection.SelectFdr` did not
exactly implement Benjamini-Hochberg procedure. It formerly may have
selected fewer features than it should.
diff --git a/sklearn/model_selection/_validation.py b/sklearn/model_selection/_validation.py
index 91f60366f871703ac0dbcf4256a9aee0e4b3caee..ab18d9035b4d207b6b9259c68e2026c24f7629e0 100644
--- a/sklearn/model_selection/_validation.py
+++ b/sklearn/model_selection/_validation.py
@@ -28,6 +28,7 @@ from ..externals.joblib import Parallel, delayed, logger
from ..metrics.scorer import check_scoring
from ..exceptions import FitFailedWarning
from ._split import check_cv
+from ..preprocessing import LabelEncoder
__all__ = ['cross_val_score', 'cross_val_predict', 'permutation_test_score',
'learning_curve', 'validation_curve']
@@ -364,7 +365,9 @@ def cross_val_predict(estimator, X, y=None, groups=None, cv=None, n_jobs=1,
as in '2*n_jobs'
method : string, optional, default: 'predict'
- Invokes the passed method name of the passed estimator.
+ Invokes the passed method name of the passed estimator. For
+ method='predict_proba', the columns correspond to the classes
+ in sorted order.
Returns
-------
@@ -390,6 +393,10 @@ def cross_val_predict(estimator, X, y=None, groups=None, cv=None, n_jobs=1,
raise AttributeError('{} not implemented in estimator'
.format(method))
+ if method in ['decision_function', 'predict_proba', 'predict_log_proba']:
+ le = LabelEncoder()
+ y = le.fit_transform(y)
+
# 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,
@@ -472,6 +479,14 @@ def _fit_and_predict(estimator, X, y, train, test, verbose, fit_params,
estimator.fit(X_train, y_train, **fit_params)
func = getattr(estimator, method)
predictions = func(X_test)
+ if method in ['decision_function', 'predict_proba', 'predict_log_proba']:
+ n_classes = len(set(y))
+ predictions_ = np.zeros((X_test.shape[0], n_classes))
+ if method == 'decision_function' and len(estimator.classes_) == 2:
+ predictions_[:, estimator.classes_[-1]] = predictions
+ else:
+ predictions_[:, estimator.classes_] = predictions
+ predictions = predictions_
return predictions, test
diff --git a/sklearn/model_selection/tests/test_validation.py b/sklearn/model_selection/tests/test_validation.py
index d1f83b469d6c8dd65a19a7039a63b22788815e29..c6ae5f3fdd18a553e6de886957c95f607f0cfefc 100644
--- a/sklearn/model_selection/tests/test_validation.py
+++ b/sklearn/model_selection/tests/test_validation.py
@@ -51,6 +51,7 @@ from sklearn.svm import SVC
from sklearn.cluster import KMeans
from sklearn.preprocessing import Imputer
+from sklearn.preprocessing import LabelEncoder
from sklearn.pipeline import Pipeline
from sklearn.externals.six.moves import cStringIO as StringIO
@@ -940,6 +941,79 @@ def test_cross_val_predict_with_method():
cv=kfold)
assert_array_almost_equal(expected_predictions, predictions)
+ # Test alternative representations of y
+ predictions_y1 = cross_val_predict(est, X, y + 1, method=method,
+ cv=kfold)
+ assert_array_equal(predictions, predictions_y1)
+
+ predictions_y2 = cross_val_predict(est, X, y - 2, method=method,
+ cv=kfold)
+ assert_array_equal(predictions, predictions_y2)
+
+ predictions_ystr = cross_val_predict(est, X, y.astype('str'),
+ method=method, cv=kfold)
+ assert_array_equal(predictions, predictions_ystr)
+
+
+def get_expected_predictions(X, y, cv, classes, est, method):
+
+ expected_predictions = np.zeros([len(y), classes])
+ func = getattr(est, method)
+
+ for train, test in cv.split(X, y):
+ est.fit(X[train], y[train])
+ expected_predictions_ = func(X[test])
+ # To avoid 2 dimensional indexing
+ exp_pred_test = np.zeros((len(test), classes))
+ if method is 'decision_function' and len(est.classes_) == 2:
+ exp_pred_test[:, est.classes_[-1]] = expected_predictions_
+ else:
+ exp_pred_test[:, est.classes_] = expected_predictions_
+ expected_predictions[test] = exp_pred_test
+
+ return expected_predictions
+
+
+def test_cross_val_predict_class_subset():
+
+ X = np.arange(8).reshape(4, 2)
+ y = np.array([0, 0, 1, 2])
+ classes = 3
+
+ kfold3 = KFold(n_splits=3)
+ kfold4 = KFold(n_splits=4)
+
+ le = LabelEncoder()
+
+ methods = ['decision_function', 'predict_proba', 'predict_log_proba']
+ for method in methods:
+ est = LogisticRegression()
+
+ # Test with n_splits=3
+ predictions = cross_val_predict(est, X, y, method=method,
+ cv=kfold3)
+
+ # Runs a naive loop (should be same as cross_val_predict):
+ expected_predictions = get_expected_predictions(X, y, kfold3, classes,
+ est, method)
+ assert_array_almost_equal(expected_predictions, predictions)
+
+ # Test with n_splits=4
+ predictions = cross_val_predict(est, X, y, method=method,
+ cv=kfold4)
+ expected_predictions = get_expected_predictions(X, y, kfold4, classes,
+ est, method)
+ assert_array_almost_equal(expected_predictions, predictions)
+
+ # Testing unordered labels
+ y = [1, 1, -4, 6]
+ predictions = cross_val_predict(est, X, y, method=method,
+ cv=kfold3)
+ y = le.fit_transform(y)
+ expected_predictions = get_expected_predictions(X, y, kfold3, classes,
+ est, method)
+ assert_array_almost_equal(expected_predictions, predictions)
+
def test_score_memmap():
# Ensure a scalar score of memmap type is accepted