def test_labeller_with_percentage():
width = 5
n_steps_future = 1
labeller = Labeller(width=width, func=np.max, func_params={},
percentiles=[100], n_steps_future=n_steps_future)
labeller.fit(X)
assert np.max(X) == labeller.thresholds_[0]
def test_labeller_transformed():
width = 5
n_steps_future = 1
labeller = Labeller(width=width, func=np.max, func_params={},
percentiles=None, n_steps_future=n_steps_future)
x, y = labeller.fit_transform_resample(X, X)
assert_almost_equal(x, X[(width-1):-n_steps_future])
def test_labeller_resampled():
width = 5
n_steps_future = 1
labeller = Labeller(width=width, func=np.max, func_params={},
percentiles=None, n_steps_future=n_steps_future)
x, y = labeller.fit_transform_resample(X, X)
assert_almost_equal(y, np.array([5, 6, 7, 8, 9, 9, 9,
9, 9, 9, 5, 6, 7, 8, 9]))
def test_labeller_invalid_percentage():
labeller = Labeller(width=5,
func=np.max,
func_params={},
percentiles=[101],
n_steps_future=2)
with pytest.raises(ValueError):
labeller.fit_transform_resample([], [])
def test_labeller_shape():
width = 3
labeller = Labeller(width=width,
func=np.std,
func_params={},
percentiles=None,
n_steps_future=1)
signal_transformed = labeller.fit_transform(signal)
assert signal_transformed.shape == (20 - (width + 1) + 1, 1)