def test_set_params(): meta = Stacking( LogisticRegression(), [('tree', DecisionTreeClassifier(max_depth=1, random_state=0)), ('svm', SVC(probability=True, random_state=0))], probabilities=True) assert 2 == len(meta) meta.set_params(tree__min_samples_split=7, svm__C=0.05) assert 7 == meta.get_params()["tree__min_samples_split"] assert 0.05 == meta.get_params()["svm__C"] assert isinstance(meta.get_params()["meta_estimator"], LogisticRegression) assert meta.get_params()["probabilities"] meta.set_params(meta_estimator=DecisionTreeClassifier(), probabilities=False) assert isinstance(meta.get_params()["meta_estimator"], DecisionTreeClassifier) assert not meta.get_params()["probabilities"] p = meta.get_params(deep=False) assert set(p.keys()) == { "meta_estimator", "base_estimators", "probabilities" }
def test_set_params(self): meta = Stacking(_PredictDummy(), [('coxph', CoxPHSurvivalAnalysis()), ('svm', FastSurvivalSVM(random_state=0))], probabilities=False) meta.set_params(coxph__alpha=1.0, svm__alpha=0.4132) self.assertEqual(1.0, meta.get_params()["coxph__alpha"]) self.assertEqual(0.4132, meta.get_params()["svm__alpha"])
def test_set_params(self): meta = Stacking(LogisticRegression(), [('tree', DecisionTreeClassifier(max_depth=1, random_state=0)), ('svm', SVC(probability=True, random_state=0))], probabilities=True) self.assertEqual(2, len(meta)) meta.set_params(tree__min_samples_split=7, svm__C=0.05) self.assertEqual(7, meta.get_params()["tree__min_samples_split"]) self.assertEqual(0.05, meta.get_params()["svm__C"]) self.assertIsInstance(meta.get_params()["meta_estimator"], LogisticRegression) self.assertTrue(meta.get_params()["probabilities"]) meta.set_params(meta_estimator=DecisionTreeClassifier(), probabilities=False) self.assertIsInstance(meta.get_params()["meta_estimator"], DecisionTreeClassifier) self.assertFalse(meta.get_params()["probabilities"])