rem superflous try-except block in _summarize

flennerhag · May 18, 2017 · 9273cbd · 9273cbd
1 parent 0a04d98
commit 9273cbd
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Showing 2 changed files with 28 additions and 33 deletions.
diff --git a/mlens/model_selection/model_selection.py b/mlens/model_selection/model_selection.py
@@ -541,23 +541,11 @@ def _summarize(self, cv_res):
 
                 if best_data is None:
                     best_data, best_draw = draw_data, draw_num
-
-                    try:
-                        best_data['params'] = \
-                            self.params[case_est][best_draw]
-                    except KeyError:
-                        best_data['params'] = \
-                            self.params[case_est][best_draw]
+                    best_data['params'] = self.params[case_est][best_draw]
 
                 if draw_data['test_score_mean'] > best_data['test_score_mean']:
                     best_data, best_draw = draw_data, draw_num
-
-                    try:
-                        best_data['params'] = \
-                            self.params[case_est][best_draw]
-                    except KeyError:
-                        best_data['params'] = \
-                            self.params[case_est][best_draw]
+                    best_data['params'] = self.params[case_est][best_draw]
 
             # Assign data associated with best test score to summary dict
             # We invert the dictionary nesting here

diff --git a/mlens/model_selection/tests/test_model_selection.py b/mlens/model_selection/tests/test_model_selection.py
@@ -92,13 +92,15 @@ def test_w_prep():
     evl = Evaluator(mape_scorer, cv=5, shuffle=False, random_state=100)
 
     # Preprocessing
-    evl.preprocess(X, y, {'pr': [Scale()], 'no': []})
+    with open(os.devnull, 'w') as f, redirect_stderr(f):
+
+        evl.preprocess(X, y, {'pr': [Scale()], 'no': []})
 
-    # Fitting
-    evl.evaluate(X, y,
-                 estimators=[OLS()],
-                 param_dicts={'ols': {'offset': randint(1, 10)}},
-                 n_iter=3)
+        # Fitting
+        evl.evaluate(X, y,
+                     estimators=[OLS()],
+                     param_dicts={'ols': {'offset': randint(1, 10)}},
+                     n_iter=3)
 
     np.testing.assert_approx_equal(
             evl.summary['test_score_mean'][('no', 'ols')],
@@ -114,13 +116,16 @@ def test_w_prep():
 
 def test_w_prep_fit():
     """[Model Selection] Test run with preprocessing, single step."""
-    evl = Evaluator(mape_scorer, cv=5, shuffle=False, random_state=100)
+    evl = Evaluator(mape_scorer, cv=5, shuffle=False, random_state=100,
+                    verbose=True)
 
-    evl.fit(X, y,
-            estimators=[OLS()],
-            param_dicts={'ols': {'offset': randint(1, 10)}},
-            preprocessing={'pr': [Scale()], 'no': []},
-            n_iter=3)
+    with open(os.devnull, 'w') as f, redirect_stderr(f):
+
+        evl.fit(X, y,
+                estimators=[OLS()],
+                param_dicts={'ols': {'offset': randint(1, 10)}},
+                preprocessing={'pr': [Scale()], 'no': []},
+                n_iter=3)
 
     np.testing.assert_approx_equal(
             evl.summary['test_score_mean'][('no', 'ols')],
@@ -136,18 +141,20 @@ def test_w_prep_fit():
 
 def test_w_prep_set_params():
     """[Model Selection] Test run with preprocessing, sep param dists."""
-    evl = Evaluator(mape_scorer, cv=5, shuffle=False, random_state=100)
+    evl = Evaluator(mape_scorer, cv=5, shuffle=False, random_state=100,
+                    verbose=True)
 
     params = {('no', 'ols'): {'offset': randint(3, 6)},
               ('pr', 'ols'): {'offset': randint(1, 3)},
               }
 
-    # Fitting
-    evl.fit(X, y,
-            estimators={'pr': [OLS()], 'no': [OLS()]},
-            param_dicts=params,
-            preprocessing={'pr': [Scale()], 'no': []},
-            n_iter=3)
+    with open(os.devnull, 'w') as f, redirect_stderr(f):
+
+        evl.fit(X, y,
+                estimators={'pr': [OLS()], 'no': [OLS()]},
+                param_dicts=params,
+                preprocessing={'pr': [Scale()], 'no': []},
+                n_iter=3)
 
     np.testing.assert_approx_equal(
             evl.summary['test_score_mean'][('no', 'ols')],