scikit-learn-contrib · glemaitre · Jul 8, 2023 · Jul 8, 2023 · Jul 8, 2023
diff --git a/doc/ensemble.rst b/doc/ensemble.rst
@@ -77,7 +77,9 @@ each tree of the forest will be provided a balanced bootstrap sample
 :class:`~sklearn.ensemble.RandomForestClassifier`::
 
   >>> from imblearn.ensemble import BalancedRandomForestClassifier
-  >>> brf = BalancedRandomForestClassifier(n_estimators=100, random_state=0)
+  >>> brf = BalancedRandomForestClassifier(
+  ...     n_estimators=100, random_state=0, sampling_strategy="all", replacement=True
+  ... )
   >>> brf.fit(X_train, y_train)
   BalancedRandomForestClassifier(...)
   >>> y_pred = brf.predict(X_test)

diff --git a/doc/whats_new/v0.11.rst b/doc/whats_new/v0.11.rst
@@ -30,6 +30,11 @@ Deprecation
   and will be removed in version 0.13. Use `categorical_encoder_` instead.
   :pr:`1000` by :user:`Guillaume Lemaitre <glemaitre>`.
 
+- The default of the parameters `sampling_strategy` and `replacement` will change in
+  :class:`~imblearn.ensemble.BalancedRandomForestClassifier` to follow the
+  implementation of the original paper. This changes will take effect in version 0.13.
+  :pr:`1006` by :user:`Guillaume Lemaitre <glemaitre>`.
+
 Enhancements
 ............
 

diff --git a/examples/applications/plot_impact_imbalanced_classes.py b/examples/applications/plot_impact_imbalanced_classes.py
@@ -319,7 +319,9 @@
 
 rf_clf = make_pipeline(
     preprocessor_tree,
-    BalancedRandomForestClassifier(random_state=42, n_jobs=2),
+    BalancedRandomForestClassifier(
+        sampling_strategy="all", replacement=True, random_state=42, n_jobs=2
+    ),
 )
 
 # %%

diff --git a/examples/ensemble/plot_comparison_ensemble_classifier.py b/examples/ensemble/plot_comparison_ensemble_classifier.py
@@ -143,7 +143,9 @@
 from imblearn.ensemble import BalancedRandomForestClassifier
 
 rf = RandomForestClassifier(n_estimators=50, random_state=0)
-brf = BalancedRandomForestClassifier(n_estimators=50, random_state=0)
+brf = BalancedRandomForestClassifier(
+    n_estimators=50, sampling_strategy="all", replacement=True, random_state=0
+)
 
 rf.fit(X_train, y_train)
 brf.fit(X_train, y_train)

diff --git a/imblearn/ensemble/_forest.py b/imblearn/ensemble/_forest.py
@@ -36,10 +36,9 @@
 from ..base import _ParamsValidationMixin
 from ..pipeline import make_pipeline
 from ..under_sampling import RandomUnderSampler
-from ..under_sampling.base import BaseUnderSampler
 from ..utils import Substitution
 from ..utils._docstring import _n_jobs_docstring, _random_state_docstring
-from ..utils._param_validation import Interval, StrOptions
+from ..utils._param_validation import Hidden, Interval, StrOptions
 from ..utils._validation import check_sampling_strategy
 from ..utils.fixes import _fit_context
 from ._common import _random_forest_classifier_parameter_constraints
@@ -100,7 +99,6 @@ def _local_parallel_build_trees(
 
 
 @Substitution(
-    sampling_strategy=BaseUnderSampler._sampling_strategy_docstring,
     n_jobs=_n_jobs_docstring,
     random_state=_random_state_docstring,
 )
@@ -193,11 +191,56 @@ class BalancedRandomForestClassifier(_ParamsValidationMixin, RandomForestClassif
         Whether to use out-of-bag samples to estimate
         the generalization accuracy.
 
-    {sampling_strategy}
+    sampling_strategy : float, str, dict, callable, default="auto"
+        Sampling information to sample the data set.
+
+        - When ``float``, it corresponds to the desired ratio of the number of
+          samples in the minority class over the number of samples in the
+          majority class after resampling. Therefore, the ratio is expressed as
+          :math:`\\alpha_{{us}} = N_{{m}} / N_{{rM}}` where :math:`N_{{m}}` is the
+          number of samples in the minority class and
+          :math:`N_{{rM}}` is the number of samples in the majority class
+          after resampling.
+
+          .. warning::
+             ``float`` is only available for **binary** classification. An
+             error is raised for multi-class classification.
+
+        - When ``str``, specify the class targeted by the resampling. The
+          number of samples in the different classes will be equalized.
+          Possible choices are:
+
+            ``'majority'``: resample only the majority class;
+
+            ``'not minority'``: resample all classes but the minority class;
+
+            ``'not majority'``: resample all classes but the majority class;
+
+            ``'all'``: resample all classes;
+
+            ``'auto'``: equivalent to ``'not minority'``.
+
+        - When ``dict``, the keys correspond to the targeted classes. The
+          values correspond to the desired number of samples for each targeted
+          class.
+
+        - When callable, function taking ``y`` and returns a ``dict``. The keys
+          correspond to the targeted classes. The values correspond to the
+          desired number of samples for each class.
+
+        .. versionchanged:: 0.11
+           The default of `sampling_strategy` will change from `"auto"` to
+           `"all"` in version 0.13. This forces to use a bootstrap of the
+           minority class as proposed in [1]_.
 
     replacement : bool, default=False
         Whether or not to sample randomly with replacement or not.
 
+        .. versionchanged:: 0.11
+           The default of `replacement` will change from `False` to `True` in
+           version 0.13. This forces to use a bootstrap of the
+           minority class and draw with replacement as proposed in [1]_.
+
     {n_jobs}
 
     {random_state}
@@ -351,7 +394,8 @@ class labels (multi-output problem).
     >>> X, y = make_classification(n_samples=1000, n_classes=3,
     ...                            n_informative=4, weights=[0.2, 0.3, 0.5],
     ...                            random_state=0)
-    >>> clf = BalancedRandomForestClassifier(max_depth=2, random_state=0)
+    >>> clf = BalancedRandomForestClassifier(
+    ...     sampling_strategy="all", replacement=True, max_depth=2, random_state=0)
     >>> clf.fit(X, y)
     BalancedRandomForestClassifier(...)
     >>> print(clf.feature_importances_)
@@ -376,8 +420,9 @@ class labels (multi-output problem).
                 StrOptions({"auto", "majority", "not minority", "not majority", "all"}),
                 dict,
                 callable,
+                Hidden(StrOptions({"warn"})),
             ],
-            "replacement": ["boolean"],
+            "replacement": ["boolean", Hidden(StrOptions({"warn"}))],
         }
     )
 
@@ -395,8 +440,8 @@ def __init__(
         min_impurity_decrease=0.0,
         bootstrap=True,
         oob_score=False,
-        sampling_strategy="auto",
-        replacement=False,
+        sampling_strategy="warn",
+        replacement="warn",
         n_jobs=None,
         random_state=None,
         verbose=0,
@@ -450,7 +495,7 @@ def _validate_estimator(self, default=DecisionTreeClassifier()):
 
         self.base_sampler_ = RandomUnderSampler(
             sampling_strategy=self._sampling_strategy,
-            replacement=self.replacement,
+            replacement=self._replacement,
         )
 
     def _make_sampler_estimator(self, random_state=None):
@@ -496,6 +541,31 @@ def fit(self, X, y, sample_weight=None):
             The fitted instance.
         """
         self._validate_params()
+        # TODO: remove in 0.13
+        if self.sampling_strategy == "warn":
+            warn(
+                "The default of `sampling_strategy` will change from `'auto'` to "
+                "`'all'` in version 0.13. This change will follow the implementation "
+                "proposed in the original paper. Set to `'all'` to silence this "
+                "warning and adopt the future behaviour.",
+                FutureWarning,
+            )
+            self._sampling_strategy = "auto"
+        else:
+            self._sampling_strategy = self.sampling_strategy
+
+        if self.replacement == "warn":
+            warn(
+                "The default of `replacement` will change from `False` to "
+                "`True` in version 0.13. This change will follow the implementation "
+                "proposed in the original paper. Set to `True` to silence this "
+                "warning and adopt the future behaviour.",
+                FutureWarning,
+            )
+            self._replacement = False
+        else:
+            self._replacement = self.replacement
+
         # Validate or convert input data
         if issparse(y):
             raise ValueError("sparse multilabel-indicator for y is not supported.")
@@ -533,7 +603,7 @@ def fit(self, X, y, sample_weight=None):
         if getattr(y, "dtype", None) != DOUBLE or not y.flags.contiguous:
             y_encoded = np.ascontiguousarray(y_encoded, dtype=DOUBLE)
 
-        if isinstance(self.sampling_strategy, dict):
+        if isinstance(self._sampling_strategy, dict):
             self._sampling_strategy = {
                 np.where(self.classes_[0] == key)[0][0]: value
                 for key, value in check_sampling_strategy(
@@ -543,7 +613,7 @@ def fit(self, X, y, sample_weight=None):
                 ).items()
             }
         else:
-            self._sampling_strategy = self.sampling_strategy
+            self._sampling_strategy = self._sampling_strategy
 
         if expanded_class_weight is not None:
             if sample_weight is not None:

diff --git a/imblearn/ensemble/tests/test_forest.py b/imblearn/ensemble/tests/test_forest.py
@@ -28,7 +28,9 @@ def imbalanced_dataset():
 
 
 def test_balanced_random_forest_error_warning_warm_start(imbalanced_dataset):
-    brf = BalancedRandomForestClassifier(n_estimators=5)
+    brf = BalancedRandomForestClassifier(
+        n_estimators=5, sampling_strategy="all", replacement=True
+    )
     brf.fit(*imbalanced_dataset)
 
     with pytest.raises(ValueError, match="must be larger or equal to"):
@@ -44,7 +46,12 @@ def test_balanced_random_forest_error_warning_warm_start(imbalanced_dataset):
 
 def test_balanced_random_forest(imbalanced_dataset):
     n_estimators = 10
-    brf = BalancedRandomForestClassifier(n_estimators=n_estimators, random_state=0)
+    brf = BalancedRandomForestClassifier(
+        n_estimators=n_estimators,
+        random_state=0,
+        sampling_strategy="all",
+        replacement=True,
+    )
     brf.fit(*imbalanced_dataset)
 
     assert len(brf.samplers_) == n_estimators
@@ -56,7 +63,12 @@ def test_balanced_random_forest(imbalanced_dataset):
 def test_balanced_random_forest_attributes(imbalanced_dataset):
     X, y = imbalanced_dataset
     n_estimators = 10
-    brf = BalancedRandomForestClassifier(n_estimators=n_estimators, random_state=0)
+    brf = BalancedRandomForestClassifier(
+        n_estimators=n_estimators,
+        random_state=0,
+        sampling_strategy="all",
+        replacement=True,
+    )
     brf.fit(X, y)
 
     for idx in range(n_estimators):
@@ -80,7 +92,9 @@ def test_balanced_random_forest_sample_weight(imbalanced_dataset):
     rng = np.random.RandomState(42)
     X, y = imbalanced_dataset
     sample_weight = rng.rand(y.shape[0])
-    brf = BalancedRandomForestClassifier(n_estimators=5, random_state=0)
+    brf = BalancedRandomForestClassifier(
+        n_estimators=5, random_state=0, sampling_strategy="all", replacement=True
+    )
     brf.fit(X, y, sample_weight)
 
 
@@ -95,6 +109,8 @@ def test_balanced_random_forest_oob(imbalanced_dataset):
         random_state=0,
         n_estimators=1000,
         min_samples_leaf=2,
+        sampling_strategy="all",
+        replacement=True,
     )
 
     est.fit(X_train, y_train)
@@ -104,14 +120,19 @@ def test_balanced_random_forest_oob(imbalanced_dataset):
 
     # Check warning if not enough estimators
     est = BalancedRandomForestClassifier(
-        oob_score=True, random_state=0, n_estimators=1, bootstrap=True
+        oob_score=True,
+        random_state=0,
+        n_estimators=1,
+        bootstrap=True,
+        sampling_strategy="all",
+        replacement=True,
     )
     with pytest.warns(UserWarning) and np.errstate(divide="ignore", invalid="ignore"):
         est.fit(X, y)
 
 
 def test_balanced_random_forest_grid_search(imbalanced_dataset):
-    brf = BalancedRandomForestClassifier()
+    brf = BalancedRandomForestClassifier(sampling_strategy="all", replacement=True)
     grid = GridSearchCV(brf, {"n_estimators": (1, 2), "max_depth": (1, 2)}, cv=3)
     grid.fit(*imbalanced_dataset)
 
@@ -127,13 +148,17 @@ def test_little_tree_with_small_max_samples():
         n_estimators=1,
         random_state=rng,
         max_samples=None,
+        sampling_strategy="all",
+        replacement=True,
     )
 
     # Second fit with max samples restricted to just 2
     est2 = BalancedRandomForestClassifier(
         n_estimators=1,
         random_state=rng,
         max_samples=2,
+        sampling_strategy="all",
+        replacement=True,
     )
 
     est1.fit(X, y)
@@ -147,11 +172,13 @@ def test_little_tree_with_small_max_samples():
 
 
 def test_balanced_random_forest_pruning(imbalanced_dataset):
-    brf = BalancedRandomForestClassifier()
+    brf = BalancedRandomForestClassifier(sampling_strategy="all", replacement=True)
     brf.fit(*imbalanced_dataset)
     n_nodes_no_pruning = brf.estimators_[0].tree_.node_count
 
-    brf_pruned = BalancedRandomForestClassifier(ccp_alpha=0.015)
+    brf_pruned = BalancedRandomForestClassifier(
+        ccp_alpha=0.015, sampling_strategy="all", replacement=True
+    )
     brf_pruned.fit(*imbalanced_dataset)
     n_nodes_pruning = brf_pruned.estimators_[0].tree_.node_count
 
@@ -168,25 +195,47 @@ def test_balanced_random_forest_oob_binomial(ratio):
     X = np.arange(n_samples).reshape(-1, 1)
     y = rng.binomial(1, ratio, size=n_samples)
 
-    erf = BalancedRandomForestClassifier(oob_score=True, random_state=42)
+    erf = BalancedRandomForestClassifier(
+        oob_score=True,
+        random_state=42,
+        sampling_strategy="not minority",
+        replacement=False,
+    )
     erf.fit(X, y)
     assert np.abs(erf.oob_score_ - 0.5) < 0.1
 
 
 def test_balanced_bagging_classifier_n_features():
     """Check that we raise a FutureWarning when accessing `n_features_`."""
     X, y = load_iris(return_X_y=True)
-    estimator = BalancedRandomForestClassifier().fit(X, y)
+    estimator = BalancedRandomForestClassifier(
+        sampling_strategy="all", replacement=True
+    ).fit(X, y)
     with pytest.warns(FutureWarning, match="`n_features_` was deprecated"):
         estimator.n_features_
 
 
 @pytest.mark.skipif(
     sklearn_version < parse_version("1.2"), reason="requires scikit-learn>=1.2"
 )
-def test_balanced_bagging_classifier_base_estimator():
+def test_balanced_random_forest_classifier_base_estimator():
     """Check that we raise a FutureWarning when accessing `base_estimator_`."""
     X, y = load_iris(return_X_y=True)
-    estimator = BalancedRandomForestClassifier().fit(X, y)
+    estimator = BalancedRandomForestClassifier(
+        sampling_strategy="all", replacement=True
+    ).fit(X, y)
     with pytest.warns(FutureWarning, match="`base_estimator_` was deprecated"):
         estimator.base_estimator_
+
+
+# TODO: remove in 0.13
+def test_balanced_random_forest_change_behaviour(imbalanced_dataset):
+    """Check that we raise a change of behaviour for the parameters `sampling_strategy`
+    and `replacement`.
+    """
+    estimator = BalancedRandomForestClassifier(sampling_strategy="all")
+    with pytest.warns(FutureWarning, match="The default of `replacement`"):
+        estimator.fit(*imbalanced_dataset)
+    estimator = BalancedRandomForestClassifier(replacement=True)
+    with pytest.warns(FutureWarning, match="The default of `sampling_strategy`"):
+        estimator.fit(*imbalanced_dataset)
diff --git a/imblearn/utils/estimator_checks.py b/imblearn/utils/estimator_checks.py
@@ -75,6 +75,10 @@ def _set_checking_parameters(estimator):
         )
     if name == "KMeansSMOTE":
         estimator.set_params(kmeans_estimator=12)
+    if name == "BalancedRandomForestClassifier":
+        # TODO: remove in 0.13
+        # future default in 0.13
+        estimator.set_params(replacement=True, sampling_strategy="all")
 
 
 def _yield_sampler_checks(sampler):