removed trailing commas - one time activity

Author: akash-suresh

examples/applications/porto_seguro_keras_under_sampling.py

@@ -226,7 +226,7 @@ def fit_predict_balanced_model(X_train, y_train, X_test, y_test):
 )
 df_time = (
     pd.DataFrame(
-        {"Balanced model": cv_time_balanced, "Imbalanced model": cv_time_imbalanced,}
+        {"Balanced model": cv_time_balanced, "Imbalanced model": cv_time_imbalanced}
     )
     .unstack()
     .reset_index()

examples/plot_outlier_rejections.py

@@ -71,7 +71,7 @@ def plot_scatter(X, y, title):
 )
 X_test = np.vstack([moons, blobs])
 y_test = np.hstack(
-    [np.ones(moons.shape[0], dtype=np.int8), np.zeros(blobs.shape[0], dtype=np.int8),]
+    [np.ones(moons.shape[0], dtype=np.int8), np.zeros(blobs.shape[0], dtype=np.int8)]
 )
 
 plot_scatter(X_test, y_test, "Testing dataset")

examples/under-sampling/plot_illustration_nearmiss.py

@@ -40,7 +40,7 @@ def make_plot_despine(ax):
 
 # minority class
 X_minority = np.transpose(
-    [[1.1, 1.3, 1.15, 0.8, 0.8, 0.6, 0.55], [1.0, 1.5, 1.7, 2.5, 2.0, 1.2, 0.55],]
+    [[1.1, 1.3, 1.15, 0.8, 0.8, 0.6, 0.55], [1.0, 1.5, 1.7, 2.5, 2.0, 1.2, 0.55]]
 )
 # majority class
 X_majority = np.transpose(

examples/under-sampling/plot_illustration_tomek_links.py

@@ -88,7 +88,7 @@ def make_plot_despine(ax):
 for ax, title, sampler in zip(
     ax_arr,
     title_arr,
-    [TomekLinks(sampling_strategy="auto"), TomekLinks(sampling_strategy="all"),],
+    [TomekLinks(sampling_strategy="auto"), TomekLinks(sampling_strategy="all")],
 ):
     X_res, y_res = sampler.fit_resample(
         np.vstack((X_minority, X_majority)),

imblearn/metrics/_classification.py

@@ -617,7 +617,7 @@ class is unrecognized by the classifier, G-mean resolves to zero. To
         else:
             n_labels = len(labels)
             labels = np.hstack(
-                [labels, np.setdiff1d(present_labels, labels, assume_unique=True),]
+                [labels, np.setdiff1d(present_labels, labels, assume_unique=True)]
             )
 
         le = LabelEncoder()

imblearn/metrics/tests/test_classification.py

@@ -439,7 +439,7 @@ def test_iba_sklearn_metrics(score, expected_score):
 
 @pytest.mark.parametrize(
     "score_loss",
-    [average_precision_score, brier_score_loss, cohen_kappa_score, roc_auc_score,],
+    [average_precision_score, brier_score_loss, cohen_kappa_score, roc_auc_score],
 )
 def test_iba_error_y_score_prob_error(score_loss):
     y_true, y_pred, _ = make_prediction(binary=True)

imblearn/over_sampling/tests/test_adasyn.py

@@ -79,7 +79,7 @@ def test_ada_fit_resample():
         ]
     )
     y_gt = np.array(
-        [0, 1, 0, 0, 0, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 0, 1, 0, 0, 0, 0, 0,]
+        [0, 1, 0, 0, 0, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 0, 1, 0, 0, 0, 0, 0]
     )
     assert_allclose(X_resampled, X_gt, rtol=R_TOL)
     assert_array_equal(y_resampled, y_gt)
@@ -118,7 +118,7 @@ def test_ada_fit_resample_nn_obj():
         ]
     )
     y_gt = np.array(
-        [0, 1, 0, 0, 0, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 0, 1, 0, 0, 0, 0, 0,]
+        [0, 1, 0, 0, 0, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 0, 1, 0, 0, 0, 0, 0]
     )
     assert_allclose(X_resampled, X_gt, rtol=R_TOL)
     assert_array_equal(y_resampled, y_gt)

imblearn/over_sampling/tests/test_smote.py

@@ -76,7 +76,7 @@ def test_sample_regular():
         ]
     )
     y_gt = np.array(
-        [0, 1, 0, 0, 0, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 0, 1, 0, 0, 0, 0, 0,]
+        [0, 1, 0, 0, 0, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 0, 1, 0, 0, 0, 0, 0]
     )
     assert_allclose(X_resampled, X_gt, rtol=R_TOL)
     assert_array_equal(y_resampled, y_gt)
@@ -149,7 +149,7 @@ def test_sample_regular_with_nn():
         ]
     )
     y_gt = np.array(
-        [0, 1, 0, 0, 0, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 0, 1, 0, 0, 0, 0, 0,]
+        [0, 1, 0, 0, 0, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 0, 1, 0, 0, 0, 0, 0]
     )
     assert_allclose(X_resampled, X_gt, rtol=R_TOL)
     assert_array_equal(y_resampled, y_gt)

imblearn/tests/test_pipeline.py

@@ -1248,7 +1248,7 @@ def test_pipeline_param_error():
                 r"\[Pipeline\].*\(step 2 of 2\) Processing clf.* total=.*\n$",
             ),
             (
-                Pipeline([("transf", Transf()), ("noop", None), ("clf", FitParamT()),]),
+                Pipeline([("transf", Transf()), ("noop", None), ("clf", FitParamT())]),
                 r"\[Pipeline\].*\(step 1 of 3\) Processing transf.* total=.*\n"
                 r"\[Pipeline\].*\(step 2 of 3\) Processing noop.* total=.*\n"
                 r"\[Pipeline\].*\(step 3 of 3\) Processing clf.* total=.*\n$",

imblearn/under_sampling/_prototype_selection/tests/test_repeated_edited_nearest_neighbours.py

@@ -151,7 +151,7 @@ def test_renn_fit_resample():
         ]
     )
     y_gt = np.array(
-        [0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,]
+        [0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2]
     )
     assert_array_equal(X_resampled, X_gt)
     assert_array_equal(y_resampled, y_gt)
@@ -337,7 +337,7 @@ def test_renn_not_good_object():
 
 
 @pytest.mark.parametrize(
-    "max_iter, n_iter", [(2, 2), (5, 3),],
+    "max_iter, n_iter", [(2, 2), (5, 3)],
 )
 def test_renn_iter_attribute(max_iter, n_iter):
     renn = RepeatedEditedNearestNeighbours(max_iter=max_iter)