Remove redundant p variable

Author: dubstack

sklearn/cross_validation.py

@@ -1043,20 +1043,18 @@ def cross_val_predict(estimator, X, y=None, cv=None, n_jobs=1,
                                                       fit_params)
                             for train, test in cv)
 
-    p = [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')
     inv_locs = np.empty(len(locs), dtype=int)
     inv_locs[locs] = np.arange(len(locs))
 
     # Check for sparse predictions
-    if sp.issparse(p[0]) :
-        p = sp.vstack(p, format = 'csr')
+    if sp.issparse(preds[0]):
+        preds = sp.vstack(preds, format=preds[0].format)
     else :
-        p = np.concatenate(p)
-    preds = p.copy()
+        preds = np.concatenate(preds)
     return preds[inv_locs]
 
 def _fit_and_predict(estimator, X, y, train, test, verbose, fit_params):