def test_roc_curve_fails_correctly_without_predict_proba(self): dataset = load_demo_dataset("iris") svc = Model(SVC(gamma="scale")) result = svc.score_estimator(dataset) with pytest.raises(VizError): result.plot.roc_curve() plt.close()
def test_dataset_y_from_fetchopenml_with_two_target_columns_works(self): dataset = load_demo_dataset( "openml", name="blood-transfusion-service-center", target_column=["V1", "V2"], ) features_y = dataset.y assert features_y.shape == (748, 2)
def test_load_prediction_data_works_as_expected(self): dataset = load_demo_dataset("iris") dataset.create_train_test(stratify=True) feature_pipeline = Pipeline([("scale", DFStandardScaler())]) model = Model(LogisticRegression(), feature_pipeline=feature_pipeline) model.train_estimator(dataset) result = model.make_prediction(dataset, 5) expected = pd.DataFrame({"Prediction": [0]}) pd.testing.assert_frame_equal(result, expected, check_dtype=False)
def test_pr_curve_fails_correctly_without_predict_proba(self): """ Expect that the plot will raise an exception if the estimator does not have a predict_proba method """ dataset = load_demo_dataset("iris") svc = Model(SVC(gamma="scale")) result = svc.score_estimator(dataset) with pytest.raises(VizError): result.plot.precision_recall_curve() plt.close()
def classifier_result(self) -> Result: """Setup a classiifer Result""" dataset = load_demo_dataset("iris") model = Model(LogisticRegression()) return model.score_estimator(dataset)
def result_cv(self, model: Model) -> Result: """Setup a Result from a cross-validated scoring""" dataset = load_demo_dataset("boston") return model.score_estimator(dataset, cv=2)
def result(self, model: Model) -> Result: """Setup a Result from a score_estimator without cv""" dataset = load_demo_dataset("boston") return model.score_estimator(dataset)
def regression_result(self) -> Result: """Setup a regression Result""" dataset = load_demo_dataset("boston") model = Model(LinearRegression()) return model.score_estimator(dataset)
def dataset(self): return load_demo_dataset("iris")
def classification_result(self) -> Result: """Setup a classification Result""" dataset = load_demo_dataset("breast_cancer") model = Model(LogisticRegression()) return model.score_estimator(dataset)
def iris_result(self) -> Result: dataset = load_demo_dataset("iris") model = Model(LogisticRegression()) return model.score_estimator(dataset)
def test_dataset_x_from_fetchopenml_with_parameters_works(self): dataset = load_demo_dataset("openml", name="blood-transfusion-service-center", target_column="V1") features_x = dataset.x assert features_x.shape == (748, 4)
def test_dataset_from_fetchopenml_works(self): dataset = load_demo_dataset("openml", name="miceprotein") assert len(dataset.x) == 1080
def load_dataset_iris(self) -> Dataset: return load_demo_dataset("iris")
def dataset(self): """Setup a Dataset""" return load_demo_dataset("iris")