FIX _get_estimators_indices() in BaseBagging (scikit-learn#16437)

doc/whats_new/v0.23.rst

@@ -23,7 +23,8 @@ parameters, may produce different models from the previous version. This often
 occurs due to changes in the modelling logic (bug fixes or enhancements), or in
 random sampling procedures.
 
-- list models here
+- :class:`ensemble.BaggingClassifier`, :class:`ensemble.BaggingRegressor`,
+  and :class:`ensemble.IsolationForest`. |Fix|
 
 Details are listed in the changelog below.
 
@@ -140,6 +141,12 @@ Changelog
   samples in the training set. :pr:`14516` by :user:`Johann Faouzi
   <johannfaouzi>`.
 
+- |Fix| Fixed a bug in :class:`ensemble.BaggingClassifier`,
+  :class:`ensemble.BaggingRegressor` and :class:`ensemble.IsolationForest`
+  where the attribute `estimators_samples_` did not generate the proper indices
+  used during `fit`.
+  :pr:`16437` by :user:`Jin-Hwan CHO <chofchof>`.
+
 :mod:`sklearn.feature_extraction`
 .................................
 

sklearn/ensemble/_bagging.py

@@ -82,7 +82,7 @@ def _parallel_build_estimators(n_estimators, ensemble, X, y, sample_weight,
             print("Building estimator %d of %d for this parallel run "
                   "(total %d)..." % (i + 1, n_estimators, total_n_estimators))
 
-        random_state = np.random.RandomState(seeds[i])
+        random_state = seeds[i]
         estimator = ensemble._make_estimator(append=False,
                                              random_state=random_state)
 
@@ -405,9 +405,8 @@ def _get_estimators_indices(self):
         for seed in self._seeds:
             # Operations accessing random_state must be performed identically
             # to those in `_parallel_build_estimators()`
-            random_state = np.random.RandomState(seed)
             feature_indices, sample_indices = _generate_bagging_indices(
-                random_state, self.bootstrap_features, self.bootstrap,
+                seed, self.bootstrap_features, self.bootstrap,
                 self.n_features_, self._n_samples, self._max_features,
                 self._max_samples)
 

sklearn/ensemble/tests/test_bagging.py

@@ -878,3 +878,25 @@ def test_bagging_small_max_features():
     bagging = BaggingClassifier(LogisticRegression(),
                                 max_features=0.3, random_state=1)
     bagging.fit(X, y)
+
+
+def test_bagging_get_estimators_indices():
+    # Check that Bagging estimator can generate sample indices properly
+    # Non-regression test for:
+    # https://github.com/scikit-learn/scikit-learn/issues/16436
+
+    rng = np.random.RandomState(0)
+    X = rng.randn(13, 4)
+    y = np.arange(13)
+
+    class MyEstimator(DecisionTreeRegressor):
+        """An estimator which stores y indices information at fit."""
+        def fit(self, X, y):
+            self._sample_indices = y
+
+    clf = BaggingRegressor(base_estimator=MyEstimator(),
+                           n_estimators=1, random_state=0)
+    clf.fit(X, y)
+
+    assert_array_equal(clf.estimators_[0]._sample_indices,
+                       clf.estimators_samples_[0])

sklearn/tree/_classes.py

@@ -1668,7 +1668,7 @@ class ExtraTreeRegressor(DecisionTreeRegressor):
     >>> reg = BaggingRegressor(extra_tree, random_state=0).fit(
     ...     X_train, y_train)
     >>> reg.score(X_test, y_test)
-    0.7788...
+    0.7447...
     """
     def __init__(self,
                  criterion="mse",