FIX Improves error message in partial_fit when early_stopping=True (#25694)

thomasjpfan · jeremiedbb · web-flow · commit 1a33e5655217 · 2023-02-28T18:20:04.000+01:00
Co-authored-by: Jérémie du Boisberranger &lt;34657725+jeremiedbb@users.noreply.github.com&gt;
diff --git a/doc/whats_new/v1.2.rst b/doc/whats_new/v1.2.rst
@@ -204,6 +204,10 @@ Changelog
   no longer raise warnings when fitting data with feature names.
   :pr:`24873` by :user:`Tim Head <betatim>`.
 
+- |Fix| Improves error message in :class:`neural_network.MLPClassifier` and
+  :class:`neural_network.MLPRegressor`, when `early_stopping=True` and
+  :meth:`partial_fit` is called. :pr:`25694` by `Thomas Fan`_.
+
 :mod:`sklearn.preprocessing`
 ............................
 
diff --git a/sklearn/neural_network/_multilayer_perceptron.py b/sklearn/neural_network/_multilayer_perceptron.py
@@ -575,7 +575,9 @@ def _fit_stochastic(
                 )
 
         # early_stopping in partial_fit doesn't make sense
-        early_stopping = self.early_stopping and not incremental
+        if self.early_stopping and incremental:
+            raise ValueError("partial_fit does not support early_stopping=True")
+        early_stopping = self.early_stopping
         if early_stopping:
             # don't stratify in multilabel classification
             should_stratify = is_classifier(self) and self.n_outputs_ == 1
diff --git a/sklearn/neural_network/tests/test_mlp.py b/sklearn/neural_network/tests/test_mlp.py
@@ -953,3 +953,16 @@ def test_mlp_warm_start_no_convergence(MLPEstimator, solver):
     with pytest.warns(ConvergenceWarning):
         model.fit(X_iris, y_iris)
     assert model.n_iter_ == 20
+
+
+@pytest.mark.parametrize("MLPEstimator", [MLPClassifier, MLPRegressor])
+def test_mlp_partial_fit_after_fit(MLPEstimator):
+    """Check partial fit does not fail after fit when early_stopping=True.
+
+    Non-regression test for gh-25693.
+    """
+    mlp = MLPEstimator(early_stopping=True, random_state=0).fit(X_iris, y_iris)
+
+    msg = "partial_fit does not support early_stopping=True"
+    with pytest.raises(ValueError, match=msg):
+        mlp.partial_fit(X_iris, y_iris)
