def test_polynomial_feature_names(data_small):
x = data_small
model = Polynomial(degree=2).fit(x)
expected_default_names = ["1", "x0", "x1", "x0^2", "x0 x1", "x1^2"]
assert model.get_feature_names() == expected_default_names
custom_names = ["x", "y"]
expected_custom_names = ["1", "x", "y", "x^2", "x y", "y^2"]
assert model.get_feature_names(
input_features=custom_names) == expected_custom_names
def test_bad_polynomial_inputs():
with pytest.raises(ValueError):
Polynomial(degree=0)
assert model.score(x[::2], y=x[1::2]) > 0.8
def test_score_complex_data(data_random_complex):
x = data_random_complex
model = Koopman().fit(x)
with pytest.raises(ValueError):
model.score(x, cast_as_real=False)
@pytest.mark.parametrize(
"observables",
[
Identity(),
Polynomial(),
TimeDelay(),
pytest.lazy_fixture("data_custom_observables"),
],
)
def test_observables_integration(data_random, observables):
x = data_random
model = Koopman(observables=observables).fit(x)
check_is_fitted(model)
y = model.predict(x)
assert y.shape[1] == x.shape[1]
@pytest.mark.parametrize(
"observables",
from numpy import stack
from numpy.testing import assert_allclose
from sklearn.exceptions import NotFittedError
from sklearn.utils.validation import check_is_fitted
from pykoopman.observables import Identity
from pykoopman.observables import Polynomial
@pytest.fixture
def data_small():
t = linspace(0, 5, 10)
return stack((t, t**2), axis=1)
@pytest.mark.parametrize("observables", [Identity(), Polynomial()])
def test_if_fitted(observables, data_random):
x = data_random
with pytest.raises(NotFittedError):
observables.transform(x)
with pytest.raises(NotFittedError):
observables.inverse(x)
with pytest.raises(NotFittedError):
observables.get_feature_names()
observables.fit(x)
check_is_fitted(observables)
from pykoopman.observables import Identity
from pykoopman.observables import Polynomial
from pykoopman.observables import TimeDelay
@pytest.fixture
def data_small():
t = linspace(0, 5, 10)
return stack((t, t**2), axis=1)
@pytest.mark.parametrize(
"observables",
[
Identity(),
Polynomial(),
TimeDelay(),
pytest.lazy_fixture("data_custom_observables"),
],
)
def test_if_fitted(observables, data_random):
x = data_random
with pytest.raises(NotFittedError):
observables.transform(x)
with pytest.raises(NotFittedError):
observables.inverse(x)
with pytest.raises(NotFittedError):
observables.get_feature_names()