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Update Models_tests
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@@ -193,7 +193,6 @@ class Datasets:
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}
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def load(self, name, dataframe=False):
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try:
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class_name = self.class_names[self.data_sets.index(name)]
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X, y = self.dataset.load(name, class_name)
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@@ -47,20 +47,20 @@ class Models:
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"Wodt": Wodt(random_state=random_state),
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"SVC": SVC(random_state=random_state),
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"ODTE": Odte(
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base_estimator=Stree(random_state=random_state),
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estimator=Stree(random_state=random_state),
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random_state=random_state,
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),
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"BaggingStree": BaggingClassifier(
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base_estimator=Stree(random_state=random_state),
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estimator=Stree(random_state=random_state),
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random_state=random_state,
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),
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"BaggingWodt": BaggingClassifier(
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base_estimator=Wodt(random_state=random_state),
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estimator=Wodt(random_state=random_state),
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random_state=random_state,
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),
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"XGBoost": XGBClassifier(random_state=random_state),
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"AdaBoostStree": AdaBoostClassifier(
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base_estimator=Stree(
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estimator=Stree(
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random_state=random_state,
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),
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algorithm="SAMME",
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@@ -70,19 +70,19 @@ class ModelTest(TestBase):
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def test_BaggingStree(self):
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clf = Models.get_model("BaggingStree")
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self.assertIsInstance(clf, BaggingClassifier)
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clf_base = clf.base_estimator
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clf_base = clf.estimator
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self.assertIsInstance(clf_base, Stree)
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def test_BaggingWodt(self):
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clf = Models.get_model("BaggingWodt")
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self.assertIsInstance(clf, BaggingClassifier)
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clf_base = clf.base_estimator
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clf_base = clf.estimator
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self.assertIsInstance(clf_base, Wodt)
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def test_AdaBoostStree(self):
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clf = Models.get_model("AdaBoostStree")
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self.assertIsInstance(clf, AdaBoostClassifier)
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clf_base = clf.base_estimator
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clf_base = clf.estimator
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self.assertIsInstance(clf_base, Stree)
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def test_unknown_classifier(self):
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