iter

Author: glemaitre

examples/ensemble/plot_comparison_ensemble_classifier.py

@@ -197,7 +197,7 @@
 
 from imblearn.ensemble import EasyEnsembleClassifier, RUSBoostClassifier
 
-estimator = AdaBoostClassifier(n_estimators=10, algorithm="SAMME")
+estimator = AdaBoostClassifier(n_estimators=10)
 eec = EasyEnsembleClassifier(n_estimators=10, estimator=estimator)
 eec.fit(X_train, y_train)
 y_pred_eec = eec.predict(X_test)

imblearn/ensemble/tests/test_easy_ensemble.py

@@ -37,13 +37,10 @@
 Y = np.array([1, 2, 2, 2, 1, 0, 1, 1, 1, 0])
 
 
-@pytest.mark.parametrize("n_estimators", [10, 20])
+@pytest.mark.parametrize("n_estimators", [5, 10])
 @pytest.mark.parametrize(
     "estimator",
-    [
-        AdaBoostClassifier(algorithm="SAMME", n_estimators=5),
-        AdaBoostClassifier(algorithm="SAMME", n_estimators=10),
-    ],
+    [AdaBoostClassifier(n_estimators=5), AdaBoostClassifier(n_estimators=10)],
 )
 def test_easy_ensemble_classifier(n_estimators, estimator):
     # Check classification for various parameter settings.
@@ -89,7 +86,7 @@ def test_estimator():
     assert isinstance(ensemble.estimator_.steps[-1][1], AdaBoostClassifier)
 
     ensemble = EasyEnsembleClassifier(
-        2, AdaBoostClassifier(algorithm="SAMME"), n_jobs=-1, random_state=0
+        2, AdaBoostClassifier(), n_jobs=-1, random_state=0
     ).fit(X_train, y_train)
 
     assert isinstance(ensemble.estimator_.steps[-1][1], AdaBoostClassifier)
@@ -104,9 +101,7 @@ def test_bagging_with_pipeline():
     )
     estimator = EasyEnsembleClassifier(
         n_estimators=2,
-        estimator=make_pipeline(
-            SelectKBest(k=1), AdaBoostClassifier(algorithm="SAMME")
-        ),
+        estimator=make_pipeline(SelectKBest(k=1), AdaBoostClassifier()),
     )
     estimator.fit(X, y).predict(X)
 
@@ -198,7 +193,7 @@ def test_easy_ensemble_classifier_single_estimator():
     clf1 = EasyEnsembleClassifier(n_estimators=1, random_state=0).fit(X_train, y_train)
     clf2 = make_pipeline(
         RandomUnderSampler(random_state=0),
-        AdaBoostClassifier(algorithm="SAMME", random_state=0),
+        AdaBoostClassifier(random_state=0),
     ).fit(X_train, y_train)
 
     assert_array_equal(clf1.predict(X_test), clf2.predict(X_test))
@@ -217,7 +212,7 @@ def test_easy_ensemble_classifier_grid_search():
         "estimator__n_estimators": [3, 4],
     }
     grid_search = GridSearchCV(
-        EasyEnsembleClassifier(estimator=AdaBoostClassifier(algorithm="SAMME")),
+        EasyEnsembleClassifier(estimator=AdaBoostClassifier()),
         parameters,
         cv=5,
     )

imblearn/utils/_validation.py

@@ -649,5 +649,9 @@ def _check_X(estimator, X):
         return X
 
     return validate_data(
-        estimator, X, dtype=None, accept_sparse=["csr", "csc"], ensure_all_finite=False
+        estimator,
+        X=X,
+        dtype=None,
+        accept_sparse=["csr", "csc"],
+        ensure_all_finite=False,
     )