Merge branch 'master' into issue_1085

.github/workflows/linter.yml

@@ -20,4 +20,4 @@ jobs:
           isort -p mlxtend --check --diff --line-length 88 --multi-line 3 --py 39 --profile black mlxtend/*
           black --check --diff mlxtend/*
           # exit-zero treats all errors as warnings.
-          flake8 . --config=.flake8 --count --exit-zero --statistics 
+          flake8 . --config=.flake8 --count --exit-zero --statistics

docs/sources/CHANGELOG.md

@@ -21,6 +21,20 @@ The CHANGELOG for the current development version is available at
 
 
 
+### Version 0.23.2 (TBD)
+
+##### Downloads
+
+- [Source code (zip)](https://github.com/rasbt/mlxtend/archive/v0.23.2.zip)
+
+- [Source code (tar.gz)](https://github.com/rasbt/mlxtend/archive/v0.23.2.tar.gz)
+
+##### Changes
+
+- Add `n_classes_` attribute to stacking classifiers for compatibility with scikit-learn 1.3 ([#1091](https://github.com/rasbt/mlxtend/issues/1091)
+
+
+
 ### Version 0.23.1 (5 Jan 2024)
 
 ##### Downloads

mlxtend/__init__.py

@@ -4,4 +4,4 @@
 #
 # License: BSD 3 clause
 
-__version__ = "0.23.1"
+__version__ = "0.23.2dev"

mlxtend/classifier/stacking_classification.py

@@ -13,6 +13,7 @@
 import numpy as np
 from scipy import sparse
 from sklearn.base import TransformerMixin, clone
+from sklearn.preprocessing import LabelEncoder
 
 from ..externals.estimator_checks import check_is_fitted
 from ..externals.name_estimators import _name_estimators
@@ -95,6 +96,9 @@ class StackingClassifier(_BaseXComposition, _BaseStackingClassifier, Transformer
         Fitted classifiers (clones of the original classifiers)
     meta_clf_ : estimator
         Fitted meta-classifier (clone of the original meta-estimator)
+    classes_ : ndarray of shape (n_classes,) or list of ndarray if `y` \
+            is of type `"multilabel-indicator"`.
+            Class labels.
     train_meta_features : numpy array, shape = [n_samples, n_classifiers]
         meta-features for training data, where n_samples is the
         number of samples
@@ -175,6 +179,13 @@ def fit(self, X, y, sample_weight=None):
             self.clfs_ = self.classifiers
             self.meta_clf_ = self.meta_classifier
 
+        if y.ndim > 1:
+            self._label_encoder = [LabelEncoder().fit(yk) for yk in y.T]
+            self.classes_ = [le.classes_ for le in self._label_encoder]
+        else:
+            self._label_encoder = LabelEncoder().fit(y)
+            self.classes_ = self._label_encoder.classes_
+
         if self.fit_base_estimators:
             if self.verbose > 0:
                 print("Fitting %d classifiers..." % (len(self.classifiers)))

mlxtend/classifier/stacking_cv_classification.py

@@ -14,6 +14,7 @@
 from sklearn.base import TransformerMixin, clone
 from sklearn.model_selection import cross_val_predict
 from sklearn.model_selection._split import check_cv
+from sklearn.preprocessing import LabelEncoder
 
 from ..externals.estimator_checks import check_is_fitted
 from ..externals.name_estimators import _name_estimators
@@ -129,6 +130,9 @@ class StackingCVClassifier(
         Fitted classifiers (clones of the original classifiers)
     meta_clf_ : estimator
         Fitted meta-classifier (clone of the original meta-estimator)
+    classes_ : ndarray of shape (n_classes,) or list of ndarray if `y` \
+            is of type `"multilabel-indicator"`.
+            Class labels.
     train_meta_features : numpy array, shape = [n_samples, n_classifiers]
         meta-features for training data, where n_samples is the
         number of samples
@@ -220,6 +224,13 @@ def fit(self, X, y, groups=None, sample_weight=None):
         if self.verbose > 0:
             print("Fitting %d classifiers..." % (len(self.classifiers)))
 
+        if y.ndim > 1:
+            self._label_encoder = [LabelEncoder().fit(yk) for yk in y.T]
+            self.classes_ = [le.classes_ for le in self._label_encoder]
+        else:
+            self._label_encoder = LabelEncoder().fit(y)
+            self.classes_ = self._label_encoder.classes_
+
         final_cv = check_cv(self.cv, y, classifier=self.stratify)
         if isinstance(self.cv, int):
             # Override shuffle parameter in case of self generated

mlxtend/classifier/tests/test_stacking_classifier.py

@@ -4,6 +4,7 @@
 #
 # License: BSD 3 clause
 
+import platform
 import random
 
 import numpy as np
@@ -549,8 +550,12 @@ def test_decision_function():
 
     if Version(sklearn_version) < Version("0.21"):
         assert scores_mean == 0.96, scores_mean
-    else:
-        assert scores_mean == 0.93, scores_mean
+
+    min_allowed_score = 0.92
+    max_allowed_score = 0.95
+    assert (
+        min_allowed_score <= scores_mean <= max_allowed_score
+    ), "Score is out of the allowed range."
 
     # another test
     meta = SVC(decision_function_shape="ovo")
@@ -565,7 +570,11 @@ def test_decision_function():
     if Version(sklearn_version) < Version("0.22"):
         assert scores_mean == 0.95, scores_mean
     else:
-        assert scores_mean == 0.94, scores_mean
+        min_allowed_score = 0.92
+        max_allowed_score = 0.95
+        assert (
+            min_allowed_score <= scores_mean <= max_allowed_score
+        ), "Score is out of the allowed range."
 
 
 def test_drop_col_unsupported():

mlxtend/regressor/tests/test_stacking_cv_regression.py

@@ -382,6 +382,7 @@ def test_weight_unsupported_with_no_weight():
     stack.fit(X1, y).predict(X1)
 
 
+@pytest.mark.skip(reason="scikit-learn implemented a StackingRegressor in 0.22.")
 def test_gridsearch_replace_mix():
     svr_lin = SVR(kernel="linear", gamma="auto")
     ridge = Ridge(random_state=1)