TST set random state in scoring tests

scikit-learn-contrib · Aug 23, 2017 · a2de53a · a2de53a
1 parent 108105d
commit a2de53a
Showing 1 changed file with 32 additions and 16 deletions.
diff --git a/imblearn/metrics/tests/test_score_objects.py b/imblearn/metrics/tests/test_score_objects.py
@@ -32,86 +32,102 @@ def test_imblearn_classification_scorers():
 
     # sensitivity scorer
     scorer = make_scorer(sensitivity_score, pos_label=None, average='macro')
-    grid = GridSearchCV(LinearSVC(), param_grid={'C': [1, 10]}, scoring=scorer)
+    grid = GridSearchCV(LinearSVC(random_state=0), param_grid={'C': [1, 10]},
+                        scoring=scorer)
     grid.fit(X_train, y_train).predict(X_test)
     assert_allclose(grid.best_score_, 0.92, rtol=R_TOL)
 
     scorer = make_scorer(sensitivity_score, pos_label=None, average='weighted')
-    grid = GridSearchCV(LinearSVC(), param_grid={'C': [1, 10]}, scoring=scorer)
+    grid = GridSearchCV(LinearSVC(random_state=0), param_grid={'C': [1, 10]},
+                        scoring=scorer)
     grid.fit(X_train, y_train).predict(X_test)
     assert_allclose(grid.best_score_, 0.92, rtol=R_TOL)
 
     scorer = make_scorer(sensitivity_score, pos_label=None, average='micro')
-    grid = GridSearchCV(LinearSVC(), param_grid={'C': [1, 10]}, scoring=scorer)
+    grid = GridSearchCV(LinearSVC(random_state=0), param_grid={'C': [1, 10]},
+                        scoring=scorer)
     grid.fit(X_train, y_train).predict(X_test)
     assert_allclose(grid.best_score_, 0.92, rtol=R_TOL)
 
     scorer = make_scorer(sensitivity_score, pos_label=1)
-    grid = GridSearchCV(LinearSVC(), param_grid={'C': [1, 10]}, scoring=scorer)
+    grid = GridSearchCV(LinearSVC(random_state=0), param_grid={'C': [1, 10]},
+                        scoring=scorer)
     grid.fit(X_train, y_train).predict(X_test)
     assert_allclose(grid.best_score_, 0.92, rtol=R_TOL)
 
     # specificity scorer
     scorer = make_scorer(specificity_score, pos_label=None, average='macro')
-    grid = GridSearchCV(LinearSVC(), param_grid={'C': [1, 10]}, scoring=scorer)
+    grid = GridSearchCV(LinearSVC(random_state=0), param_grid={'C': [1, 10]},
+                        scoring=scorer)
     grid.fit(X_train, y_train).predict(X_test)
     assert_allclose(grid.best_score_, 0.92, rtol=R_TOL)
 
     scorer = make_scorer(specificity_score, pos_label=None, average='weighted')
-    grid = GridSearchCV(LinearSVC(), param_grid={'C': [1, 10]}, scoring=scorer)
+    grid = GridSearchCV(LinearSVC(random_state=0), param_grid={'C': [1, 10]},
+                        scoring=scorer)
     grid.fit(X_train, y_train).predict(X_test)
     assert_allclose(grid.best_score_, 0.92, rtol=R_TOL)
 
     scorer = make_scorer(specificity_score, pos_label=None, average='micro')
-    grid = GridSearchCV(LinearSVC(), param_grid={'C': [1, 10]}, scoring=scorer)
+    grid = GridSearchCV(LinearSVC(random_state=0), param_grid={'C': [1, 10]},
+                        scoring=scorer)
     grid.fit(X_train, y_train).predict(X_test)
     assert_allclose(grid.best_score_, 0.92, rtol=R_TOL)
 
     scorer = make_scorer(specificity_score, pos_label=1)
-    grid = GridSearchCV(LinearSVC(), param_grid={'C': [1, 10]}, scoring=scorer)
+    grid = GridSearchCV(LinearSVC(random_state=0), param_grid={'C': [1, 10]},
+                        scoring=scorer)
     grid.fit(X_train, y_train).predict(X_test)
     assert_allclose(grid.best_score_, 0.95, rtol=R_TOL)
 
     # geometric_mean scorer
     scorer = make_scorer(geometric_mean_score, pos_label=None, average='macro')
-    grid = GridSearchCV(LinearSVC(), param_grid={'C': [1, 10]}, scoring=scorer)
+    grid = GridSearchCV(LinearSVC(random_state=0), param_grid={'C': [1, 10]},
+                        scoring=scorer)
     grid.fit(X_train, y_train).predict(X_test)
     assert_allclose(grid.best_score_, 0.92, rtol=R_TOL)
 
     scorer = make_scorer(
         geometric_mean_score, pos_label=None, average='weighted')
-    grid = GridSearchCV(LinearSVC(), param_grid={'C': [1, 10]}, scoring=scorer)
+    grid = GridSearchCV(LinearSVC(random_state=0), param_grid={'C': [1, 10]},
+                        scoring=scorer)
     grid.fit(X_train, y_train).predict(X_test)
     assert_allclose(grid.best_score_, 0.92, rtol=R_TOL)
 
     scorer = make_scorer(geometric_mean_score, pos_label=None, average='micro')
-    grid = GridSearchCV(LinearSVC(), param_grid={'C': [1, 10]}, scoring=scorer)
+    grid = GridSearchCV(LinearSVC(random_state=0), param_grid={'C': [1, 10]},
+                        scoring=scorer)
     grid.fit(X_train, y_train).predict(X_test)
     assert_allclose(grid.best_score_, 0.92, rtol=R_TOL)
 
     scorer = make_scorer(geometric_mean_score, pos_label=1)
-    grid = GridSearchCV(LinearSVC(), param_grid={'C': [1, 10]}, scoring=scorer)
+    grid = GridSearchCV(LinearSVC(random_state=0), param_grid={'C': [1, 10]},
+                        scoring=scorer)
     grid.fit(X_train, y_train).predict(X_test)
     assert_allclose(grid.best_score_, 0.92, rtol=R_TOL)
 
     # make a iba metric before a scorer
     geo_mean_iba = make_index_balanced_accuracy()(geometric_mean_score)
     scorer = make_scorer(geo_mean_iba, pos_label=None, average='macro')
-    grid = GridSearchCV(LinearSVC(), param_grid={'C': [1, 10]}, scoring=scorer)
+    grid = GridSearchCV(LinearSVC(random_state=0), param_grid={'C': [1, 10]},
+                        scoring=scorer)
     grid.fit(X_train, y_train).predict(X_test)
     assert_allclose(grid.best_score_, 0.85, rtol=R_TOL)
 
     scorer = make_scorer(geo_mean_iba, pos_label=None, average='weighted')
-    grid = GridSearchCV(LinearSVC(), param_grid={'C': [1, 10]}, scoring=scorer)
+    grid = GridSearchCV(LinearSVC(random_state=0), param_grid={'C': [1, 10]},
+                        scoring=scorer)
     grid.fit(X_train, y_train).predict(X_test)
     assert_allclose(grid.best_score_, 0.85, rtol=R_TOL)
 
     scorer = make_scorer(geo_mean_iba, pos_label=None, average='micro')
-    grid = GridSearchCV(LinearSVC(), param_grid={'C': [1, 10]}, scoring=scorer)
+    grid = GridSearchCV(LinearSVC(random_state=0), param_grid={'C': [1, 10]},
+                        scoring=scorer)
     grid.fit(X_train, y_train).predict(X_test)
     assert_allclose(grid.best_score_, 0.85, rtol=R_TOL)
 
     scorer = make_scorer(geo_mean_iba, pos_label=1)
-    grid = GridSearchCV(LinearSVC(), param_grid={'C': [1, 10]}, scoring=scorer)
+    grid = GridSearchCV(LinearSVC(random_state=0), param_grid={'C': [1, 10]},
+                        scoring=scorer)
     grid.fit(X_train, y_train).predict(X_test)
     assert_allclose(grid.best_score_, 0.84, rtol=R_TOL)