Merge pull request #5161 from beepee14/sparse_prediction_check

ogrisel · ogrisel · commit 9486a7a93d13 · 2015-08-27T15:22:23.000+02:00
[MRG+1] Add check for sparse prediction in cross_val_predict (fixes #5132)
diff --git a/sklearn/cross_validation.py b/sklearn/cross_validation.py
@@ -1042,14 +1042,20 @@ def cross_val_predict(estimator, X, y=None, cv=None, n_jobs=1,
                                                       train, test, verbose,
                                                       fit_params)
                             for train, test in cv)
-    p = np.concatenate([p for p, _ in preds_blocks])
+
+    preds = [p for p, _ in preds_blocks]
     locs = np.concatenate([loc for _, loc in preds_blocks])
     if not _check_is_partition(locs, _num_samples(X)):
         raise ValueError('cross_val_predict only works for partitions')
-    preds = p.copy()
-    preds[locs] = p
-    return preds
-
+    inv_locs = np.empty(len(locs), dtype=int)
+    inv_locs[locs] = np.arange(len(locs))
+
+    # Check for sparse predictions
+    if sp.issparse(preds[0]):
+        preds = sp.vstack(preds, format=preds[0].format)
+    else :
+        preds = np.concatenate(preds)
+    return preds[inv_locs]
 
 def _fit_and_predict(estimator, X, y, train, test, verbose, fit_params):
     """Fit estimator and predict values for a given dataset split.
diff --git a/sklearn/tests/test_cross_validation.py b/sklearn/tests/test_cross_validation.py
@@ -4,6 +4,7 @@
 
 import numpy as np
 from scipy.sparse import coo_matrix
+from scipy.sparse import csr_matrix
 from scipy import stats
 
 from sklearn.utils.testing import assert_true
@@ -25,14 +26,15 @@
 from sklearn.datasets import load_boston
 from sklearn.datasets import load_digits
 from sklearn.datasets import load_iris
+from sklearn.datasets import make_multilabel_classification
 from sklearn.metrics import explained_variance_score
 from sklearn.metrics import make_scorer
 from sklearn.metrics import precision_score
-
 from sklearn.externals import six
 from sklearn.externals.six.moves import zip
 
 from sklearn.linear_model import Ridge
+from sklearn.multiclass import OneVsRestClassifier
 from sklearn.neighbors import KNeighborsClassifier
 from sklearn.svm import SVC
 from sklearn.cluster import KMeans
@@ -1094,3 +1096,18 @@ def test_check_is_partition():
 
     p[0] = 23
     assert_false(cval._check_is_partition(p, 100))
+
+def test_cross_val_predict_sparse_prediction():
+    # check that cross_val_predict gives same result for sparse and dense input
+    X, y = make_multilabel_classification(n_classes=2, n_labels=1,
+                                          allow_unlabeled=False,
+                                          return_indicator=True,
+                                          random_state=1)
+    X_sparse = csr_matrix(X)
+    y_sparse = csr_matrix(y)
+    classif = OneVsRestClassifier(SVC(kernel='linear'))
+    preds = cval.cross_val_predict(classif, X, y, cv=10)
+    preds_sparse = cval.cross_val_predict(classif, X_sparse,y_sparse, cv=10)
+    preds_sparse = preds_sparse.toarray()
+    assert_array_almost_equal(preds_sparse, preds)
+
