FIX do not ignore target_names when output_dict=True in classification_report_imbalanced

doc/whats_new/v0.11.rst

@@ -9,6 +9,10 @@ Changelog
 Bug fixes
 .........
 
+- Fix a bug in :func:`~imblearn.metrics.classification_report_imbalanced` where the
+  parameter `target_names` was not taken into account when `output_dict=True`.
+  :pr:`989` by :user:`AYY7 <AYY7>`.
+
 - :class:`~imblearn.over_sampling.SMOTENC` now handles mix types of data type such as
   `bool` and `pd.category` by delegating the conversion to scikit-learn encoder.
   :pr:`1002` by :user:`Guillaume Lemaitre <glemaitre>`.

imblearn/metrics/_classification.py

@@ -1038,7 +1038,7 @@ class 2       1.00      0.67      1.00      0.80      0.82      0.64\
         report_dict_label[headers[-1]] = support[i]
         report += fmt % tuple(values)
 
-        report_dict[label] = report_dict_label
+        report_dict[target_names[i]] = report_dict_label
 
     report += "\n"
 

imblearn/metrics/tests/test_classification.py

@@ -459,7 +459,7 @@ def test_iba_error_y_score_prob_error(score_loss):
         aps(y_true, y_pred)
 
 
-def test_classification_report_imbalanced_dict():
+def test_classification_report_imbalanced_dict_with_target_names():
     iris = datasets.load_iris()
     y_true, y_pred, _ = make_prediction(dataset=iris, binary=False)
 
@@ -471,12 +471,44 @@ def test_classification_report_imbalanced_dict():
         output_dict=True,
     )
     outer_keys = set(report.keys())
-    inner_keys = set(report[0].keys())
+    inner_keys = set(report["setosa"].keys())
 
     expected_outer_keys = {
-        0,
-        1,
-        2,
+        "setosa",
+        "versicolor",
+        "virginica",
+        "avg_pre",
+        "avg_rec",
+        "avg_spe",
+        "avg_f1",
+        "avg_geo",
+        "avg_iba",
+        "total_support",
+    }
+    expected_inner_keys = {"spe", "f1", "sup", "rec", "geo", "iba", "pre"}
+
+    assert outer_keys == expected_outer_keys
+    assert inner_keys == expected_inner_keys
+
+
+def test_classification_report_imbalanced_dict_without_target_names():
+    iris = datasets.load_iris()
+    y_true, y_pred, _ = make_prediction(dataset=iris, binary=False)
+    print(iris.target_names)
+    report = classification_report_imbalanced(
+        y_true,
+        y_pred,
+        labels=np.arange(len(iris.target_names)),
+        output_dict=True,
+    )
+    print(report.keys())
+    outer_keys = set(report.keys())
+    inner_keys = set(report["0"].keys())
+
+    expected_outer_keys = {
+        "0",
+        "1",
+        "2",
         "avg_pre",
         "avg_rec",
         "avg_spe",