[python-package] Correctly recognize LGBMClassifier(num_class=2, objective="multiclass") as multiclass classification

python-package/lightgbm/sklearn.py

@@ -467,7 +467,7 @@ def _extract_evaluation_meta_data(
         # It's possible, for example, to pass 3 eval sets through `eval_set`,
         # but only 1 init_score through `eval_init_score`.
         #
-        # This if-else accounts for that possiblity.
+        # This if-else accounts for that possibility.
         if len(collection) > i:
             return collection[i]
         else:
@@ -1011,7 +1011,7 @@ def predict(
                 f"match the input. Model n_features_ is {self._n_features} and "
                 f"input n_features is {n_features}"
             )
-        # retrive original params that possibly can be used in both training and prediction
+        # retrieve original params that possibly can be used in both training and prediction
         # and then overwrite them (considering aliases) with params that were passed directly in prediction
         predict_params = self._process_params(stage="predict")
         for alias in _ConfigAliases.get_by_alias(
@@ -1361,7 +1361,7 @@ def predict_proba(
                 "Returning raw scores instead."
             )
             return result
-        elif self._n_classes > 2 or raw_score or pred_leaf or pred_contrib:  # type: ignore [operator]
+        elif self._n_classes > 2 or len(result.shape) > 1 or raw_score or pred_leaf or pred_contrib:  # type: ignore [operator]
             return result
         else:
             return np.vstack((1.0 - result, result)).transpose()

tests/python_package_test/test_sklearn.py

@@ -719,6 +719,21 @@ def test_predict():
     with pytest.raises(AssertionError):
         np.testing.assert_allclose(res_engine, res_sklearn_params)
 
+    # Test multiclass binary classification
+    num_samples, num_classes = 20, 2
+    rng = np.random.Generator(np.random.PCG64())
+    X_train = rng.uniform(low=0, high=1, size=[num_samples, 3])
+    y_train = rng.choice([0, 1], size=num_samples)
+
+    gbm = lgb.train({"objective": "multiclass", "num_class": num_classes, "verbose": -1}, lgb.Dataset(X_train, y_train))
+    clf = lgb.LGBMClassifier(objective="multiclass", num_classes=num_classes).fit(X_train, y_train)
+
+    res_engine = gbm.predict(X_train)
+    res_sklearn = clf.predict_proba(X_train)
+    assert res_engine.shape == (num_samples, num_classes)
+    assert res_sklearn.shape == (num_samples, num_classes)
+    np.testing.assert_allclose(res_engine, res_sklearn)
+
 
 def test_predict_with_params_from_init():
     X, y = load_iris(return_X_y=True)