[MRG] FIX raise error for max_samples if no bootstrap & optimize forest tests

doc/whats_new/v1.1.rst

@@ -53,6 +53,13 @@ Changelog
   :class:`ensemble.HistGradientBoostingRegressor`.
   :pr:`21130` :user:`Christian Lorentzen <lorentzenchr>`.
 
+- |Fix| :class:`ensemble.RandomForestClassifier`,
+  :class:`ensemble.RandomForestRegressor`,
+  :class:`ensemble.ExtraTreesClassifier`, :class:`ensemble.ExtraTreesRegressor`,
+  and :class:`ensemble.RandomTreesEmbedding` now raise a ``ValueError`` when
+  ``bootstrap=False`` and ``max_samples`` is not ``None``.
+  :pr:`21295` :user:`Haoyin Xu <PSSF23>`.
+
 :mod:`sklearn.linear_model`
 ...........................
 

sklearn/ensemble/_forest.py

@@ -374,6 +374,9 @@ def fit(self, X, y, sample_weight=None):
             else:
                 sample_weight = expanded_class_weight
 
+        if not self.bootstrap and self.max_samples is not None:
+            raise ValueError("Sub-sample size only available if bootstrap=True")
+
         # Get bootstrap sample size
         n_samples_bootstrap = _get_n_samples_bootstrap(
             n_samples=X.shape[0], max_samples=self.max_samples

sklearn/ensemble/tests/test_forest.py

@@ -1613,6 +1613,16 @@ def test_forest_degenerate_feature_importances():
     assert_array_equal(gbr.feature_importances_, np.zeros(10, dtype=np.float64))
 
 
+@pytest.mark.parametrize("name", FOREST_CLASSIFIERS_REGRESSORS)
+def test_max_samples_bootstrap(name):
+    # Check invalid `max_samples` values
+    est = FOREST_CLASSIFIERS_REGRESSORS[name](bootstrap=False, max_samples=0.5)
+    with pytest.raises(
+        ValueError, match=r"Sub-sample size only available if bootstrap=True"
+    ):
+        est.fit(X, y)
+
+
 @pytest.mark.parametrize("name", FOREST_CLASSIFIERS_REGRESSORS)
 @pytest.mark.parametrize(
     "max_samples, exc_type, exc_msg",
@@ -1657,7 +1667,7 @@ def test_forest_degenerate_feature_importances():
 )
 def test_max_samples_exceptions(name, max_samples, exc_type, exc_msg):
     # Check invalid `max_samples` values
-    est = FOREST_CLASSIFIERS_REGRESSORS[name](max_samples=max_samples)
+    est = FOREST_CLASSIFIERS_REGRESSORS[name](bootstrap=True, max_samples=max_samples)
     with pytest.raises(exc_type, match=exc_msg):
         est.fit(X, y)
 
@@ -1668,10 +1678,14 @@ def test_max_samples_boundary_regressors(name):
         X_reg, y_reg, train_size=0.7, test_size=0.3, random_state=0
     )
 
-    ms_1_model = FOREST_REGRESSORS[name](max_samples=1.0, random_state=0)
+    ms_1_model = FOREST_REGRESSORS[name](
+        bootstrap=True, max_samples=1.0, random_state=0
+    )
     ms_1_predict = ms_1_model.fit(X_train, y_train).predict(X_test)
 
-    ms_None_model = FOREST_REGRESSORS[name](max_samples=None, random_state=0)
+    ms_None_model = FOREST_REGRESSORS[name](
+        bootstrap=True, max_samples=None, random_state=0
+    )
     ms_None_predict = ms_None_model.fit(X_train, y_train).predict(X_test)
 
     ms_1_ms = mean_squared_error(ms_1_predict, y_test)
@@ -1686,10 +1700,14 @@ def test_max_samples_boundary_classifiers(name):
         X_large, y_large, random_state=0, stratify=y_large
     )
 
-    ms_1_model = FOREST_CLASSIFIERS[name](max_samples=1.0, random_state=0)
+    ms_1_model = FOREST_CLASSIFIERS[name](
+        bootstrap=True, max_samples=1.0, random_state=0
+    )
     ms_1_proba = ms_1_model.fit(X_train, y_train).predict_proba(X_test)
 
-    ms_None_model = FOREST_CLASSIFIERS[name](max_samples=None, random_state=0)
+    ms_None_model = FOREST_CLASSIFIERS[name](
+        bootstrap=True, max_samples=None, random_state=0
+    )
     ms_None_proba = ms_None_model.fit(X_train, y_train).predict_proba(X_test)
 
     np.testing.assert_allclose(ms_1_proba, ms_None_proba)