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])