def test_cutoff_warning(): X_data = [1, 2, 0.5, 0.7, 100, -1, -23, 0] cutoff = CutOff() with pytest.warns(UserWarning): cutoff.fit_transform(X_data)
def test_cutoff_warning(): X_data = [1, 2, 0.5, 0.7, 100, -1, -23, 0] cutoff = CutOff() with pytest.raises(ValueError): cutoff.fit_transform(X_data)
def test_cutoff_transformer(): cutoff = CutOff() X_data = np.array([1, 2, 0.5, 0.7, 100, -1, -23, 0]).reshape(-1, 1) assert np.all( cutoff.fit_transform(X_data) == np.array([1, 1, 0.5, 0.7, 1, 0, 0, 0 ]).reshape(-1, 1))
def test_dsapp_lr(data): dsapp_lr = ScaledLogisticRegression() dsapp_lr.fit(data["X_train"], data["y_train"]) minmax_scaler = preprocessing.MinMaxScaler() dsapp_cutoff = CutOff() lr = linear_model.LogisticRegression() pipeline = Pipeline([("minmax_scaler", minmax_scaler), ("dsapp_cutoff", dsapp_cutoff), ("lr", lr)]) pipeline.fit(data["X_train"], data["y_train"]) assert np.all( dsapp_lr.predict(data["X_test"]) == pipeline.predict(data["X_test"]))
def test_cutoff_inside_a_pipeline(data): minmax_scaler = preprocessing.MinMaxScaler() dsapp_cutoff = CutOff() pipeline = Pipeline([("minmax_scaler", minmax_scaler), ("dsapp_cutoff", dsapp_cutoff)]) pipeline.fit(data["X_train"], data["y_train"]) X_fake_new_data = data["X_test"][-1, :].reshape(1, -1) + 0.5 mms = preprocessing.MinMaxScaler().fit(data["X_train"]) assert np.all((mms.transform(X_fake_new_data) > 1) == ( pipeline.transform(X_fake_new_data) == 1))
def test_dsapp_lr(data): dsapp_lr = ScaledLogisticRegression() dsapp_lr.fit(data['X_train'], data['y_train']) minmax_scaler = preprocessing.MinMaxScaler() dsapp_cutoff = CutOff() lr = linear_model.LogisticRegression() pipeline = Pipeline([('minmax_scaler', minmax_scaler), ('dsapp_cutoff', dsapp_cutoff), ('lr', lr)]) pipeline.fit(data['X_train'], data['y_train']) assert np.all( dsapp_lr.predict(data['X_test']) == pipeline.predict(data['X_test']))
def test_cutoff_transformer(): cutoff = CutOff() X_data = [1, 2, 0.5, 0.7, 100, -1, -23, 0] assert np.all(cutoff.fit_transform(X_data) == [1, 1, 0.5, 0.7, 1, 0, 0, 0])