[MRG+2] ENH Loop over candidates as outer loop in search (#8322)

jnothman · lesteve · commit 7ce7134a57ca · 2017-06-01T16:29:34.000+02:00
This encourages concurrent fits to be over *different datasets* so that
fits over the same data subset are more likely to run in serial and
hence generate cache hits where memoisation is used.
diff --git a/sklearn/model_selection/_search.py b/sklearn/model_selection/_search.py
@@ -600,8 +600,8 @@ def fit(self, X, y=None, groups=None, **fit_params):
                                   return_n_test_samples=True,
                                   return_times=True, return_parameters=False,
                                   error_score=self.error_score)
-          for train, test in cv.split(X, y, groups)
-          for parameters in candidate_params)
+          for parameters, (train, test) in product(candidate_params,
+                                                   cv.split(X, y, groups)))
 
         # if one choose to see train score, "out" will contain train score info
         if self.return_train_score:
@@ -615,8 +615,8 @@ def fit(self, X, y=None, groups=None, **fit_params):
         def _store(key_name, array, weights=None, splits=False, rank=False):
             """A small helper to store the scores/times to the cv_results_"""
             # When iterated first by splits, then by parameters
-            array = np.array(array, dtype=np.float64).reshape(n_splits,
-                                                              n_candidates).T
+            array = np.array(array, dtype=np.float64).reshape(n_candidates,
+                                                              n_splits)
             if splits:
                 for split_i in range(n_splits):
                     results["split%d_%s"
@@ -636,7 +636,7 @@ def _store(key_name, array, weights=None, splits=False, rank=False):
 
         # Computed the (weighted) mean and std for test scores alone
         # NOTE test_sample counts (weights) remain the same for all candidates
-        test_sample_counts = np.array(test_sample_counts[::n_candidates],
+        test_sample_counts = np.array(test_sample_counts[:n_splits],
                                       dtype=np.int)
 
         _store('test_score', test_scores, splits=True, rank=True,
