scikit-learn · ogrisel · Feb 1, 2022 · Jan 20, 2022 · Jan 20, 2022 · Jan 20, 2022
diff --git a/doc/whats_new/v1.1.rst b/doc/whats_new/v1.1.rst
@@ -314,6 +314,12 @@ Changelog
   `max_iter` and `tol`.
   :pr:`21341` by :user:`Arturo Amor <ArturoAmorQ>`.
 
+:mod:`sklearn.isotonic`
+.......................
+
+- |Enhancement| Adds :term:`get_feature_names_out` to `IsotonicRegression`.
+  :pr:`22249` by `Thomas Fan`_.
+
 :mod:`sklearn.linear_model`
 ...........................
 

diff --git a/sklearn/isotonic.py b/sklearn/isotonic.py
@@ -416,6 +416,26 @@ def predict(self, T):
         """
         return self.transform(T)
 
+    # We implement get_feature_names_out here instead of using
+    # `_ClassNamePrefixFeaturesOutMixin`` because `input_features` are ignored.
+    # `input_features` are ignored because `IsotonicRegression` accepts 1d
+    # arrays and the semantics of `feature_names_in_` are not clear for 1d arrays.
+    def get_feature_names_out(self, input_features=None):
+        """Get output feature names for transformation.
+
+        Parameters
+        ----------
+        input_features : array-like of str or None, default=None
+            Ignored.
+
+        Returns
+        -------
+        feature_names_out : ndarray of str objects
+            An ndarray with one string i.e. ["isotonicregression0"].
+        """
+        class_name = self.__class__.__name__.lower()
+        return np.asarray([f"{class_name}0"], dtype=object)
+
     def __getstate__(self):
         """Pickle-protocol - return state of the estimator."""
         state = super().__getstate__()

diff --git a/sklearn/tests/test_common.py b/sklearn/tests/test_common.py
@@ -382,7 +382,6 @@ def test_pandas_column_name_consistency(estimator):
 GET_FEATURES_OUT_MODULES_TO_IGNORE = [
     "cluster",
     "ensemble",
-    "isotonic",
     "kernel_approximation",
     "preprocessing",
     "manifold",

diff --git a/sklearn/tests/test_isotonic.py b/sklearn/tests/test_isotonic.py
@@ -695,3 +695,18 @@ def test_isotonic_regression_sample_weight_not_overwritten():
 
     IsotonicRegression().fit(X, y, sample_weight=sample_weight_fit)
     assert_allclose(sample_weight_fit, sample_weight_original)
+
+
+@pytest.mark.parametrize("shape", ["1d", "2d"])
+def test_get_feature_names_out(shape):
+    """Check `get_feature_names_out` for `IsotonicRegression`."""
+    X = np.arange(10)
+    if shape == "2d":
+        X = X.reshape(-1, 1)
+    y = np.arange(10)
+
+    iso = IsotonicRegression().fit(X, y)
+    names = iso.get_feature_names_out()
+    assert isinstance(names, np.ndarray)
+    assert names.dtype == object
+    assert_array_equal(["isotonicregression0"], names)