def test_identity_feature_names(data_random):
x = data_random
model = Identity().fit(x)
# Default names
expected_names = [f"x{i}" for i in range(x.shape[1])]
assert model.get_feature_names() == expected_names
# Given names
custome_names = [f"y{i+1}" for i in range(x.shape[1])]
assert model.get_feature_names(
input_features=custome_names) == custome_names
def __init__(self, observables=None, differentiator=None, regressor=None):
if observables is None:
observables = Identity()
if differentiator is None:
differentiator = FiniteDifference()
if regressor is None:
regressor = DMD()
self.observables = observables
self.differentiator = differentiator
self.regressor = regressor
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)
# Test with a target
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(
from pykoopman.observables import CustomObservables
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()