def test_score(data_vandermonde):
data = data_vandermonde
weak_model = SSPOR(n_sensors=3)
weak_model.fit(data)
# You must pass in data with as many features as the training set
with pytest.raises(ValueError):
weak_model.score(data[:, :5])
strong_model = SSPOR(n_sensors=8)
strong_model.fit(data)
assert weak_model.score(data) < strong_model.score(data)
def test_not_fitted(data_vandermonde):
x = data_vandermonde
model = SSPOR()
# Should not be able to call any of these methods before fitting
with pytest.raises(NotFittedError):
model.predict(x)
with pytest.raises(NotFittedError):
model.get_selected_sensors()
with pytest.raises(NotFittedError):
model.get_all_sensors()
with pytest.raises(NotFittedError):
model.set_number_of_sensors(20)
with pytest.raises(NotFittedError):
model.score(x)
with pytest.raises(NotFittedError):
model.reconstruction_error(x)