def __init__(
self,
optimizer=None,
feature_library=None,
differentiation_method=None,
feature_names=None,
t_default=1,
discrete_time=False,
n_jobs=1,
):
if optimizer is None:
optimizer = STLSQ()
self.optimizer = optimizer
if feature_library is None:
feature_library = PolynomialLibrary()
self.feature_library = feature_library
if differentiation_method is None:
differentiation_method = FiniteDifference()
self.differentiation_method = differentiation_method
if not isinstance(t_default, float) and not isinstance(t_default, int):
raise ValueError("t_default must be a positive number")
elif t_default <= 0:
raise ValueError("t_default must be a positive number")
else:
self.t_default = t_default
self.feature_names = feature_names
self.discrete_time = discrete_time
self.n_jobs = n_jobs
def test_bad_parameters():
with pytest.raises(ValueError):
PolynomialLibrary(degree=-1)
with pytest.raises(ValueError):
PolynomialLibrary(degree=1.5)
with pytest.raises(ValueError):
PolynomialLibrary(include_interaction=False, interaction_only=True)
with pytest.raises(ValueError):
FourierLibrary(n_frequencies=-1)
with pytest.raises(ValueError):
FourierLibrary(n_frequencies=-1)
with pytest.raises(ValueError):
FourierLibrary(n_frequencies=2.2)
with pytest.raises(ValueError):
FourierLibrary(include_sin=False, include_cos=False)
with pytest.raises(ValueError):
library_functions = [lambda x: x, lambda x: x**2, lambda x: 0 * x]
function_names = [lambda s: str(s), lambda s: "{}^2".format(s)]
CustomLibrary(library_functions=library_functions,
function_names=function_names)
def test_linear_constraints(data_lorenz):
x, t = data_lorenz
library = PolynomialLibrary().fit(x)
constraint_rhs = np.ones(2)
constraint_lhs = np.zeros((2, x.shape[1] * library.n_output_features_))
target_1, target_2 = 1, 3
constraint_lhs[0, 3] = target_1
constraint_lhs[1, library.n_output_features_] = target_2
optimizer = ConstrainedSR3(constraint_lhs=constraint_lhs,
constraint_rhs=constraint_rhs)
model = SINDy(feature_library=library, optimizer=optimizer).fit(x, t)
coeffs = model.coefficients()
np.testing.assert_allclose(np.array([coeffs[0, 3], coeffs[1, 0]]),
np.array([1 / target_1, 1 / target_2]))
pytest.lazy_fixture("data_1d_bad_shape"),
],
)
def test_simulate(data):
x, t = data
model = SINDy()
model.fit(x, t)
x1 = model.simulate(x[0], t)
assert len(x1) == len(t)
@pytest.mark.parametrize(
"library",
[
PolynomialLibrary(degree=3),
FourierLibrary(n_frequencies=3),
pytest.lazy_fixture("data_custom_library"),
],
)
def test_libraries(data_lorenz, library):
x, t = data_lorenz
model = SINDy(feature_library=library)
model.fit(x, t)
s = model.score(x, t)
assert s <= 1
@pytest.mark.parametrize(
"data",
pytest.lazy_fixture("data_1d_bad_shape"),
],
)
def test_simulate(data):
x, t = data
model = SINDy()
model.fit(x, t)
x1 = model.simulate(x[0], t)
assert len(x1) == len(t)
@pytest.mark.parametrize(
"library",
[
PolynomialLibrary(degree=3),
FourierLibrary(n_frequencies=3),
pytest.lazy_fixture("data_custom_library"),
PolynomialLibrary() + FourierLibrary(),
],
)
def test_libraries(data_lorenz, library):
x, t = data_lorenz
model = SINDy(feature_library=library)
model.fit(x, t)
s = model.score(x, t)
assert s <= 1
def test_integration_smoothed_finite_difference(data_lorenz):
def test_concat():
ident_lib = IdentityLibrary()
poly_lib = PolynomialLibrary()
concat_lib = ident_lib + poly_lib
assert isinstance(concat_lib, ConcatLibrary)
def test_polynomial_options(data_lorenz, kwargs, sparse_format):
x, t = data_lorenz
library = PolynomialLibrary(**kwargs)
library.fit_transform(sparse_format(x))
check_is_fitted(library)
def test_polynomial_sparse_inputs(data_lorenz, sparse_format):
x, t = data_lorenz
library = PolynomialLibrary()
library.fit_transform(sparse_format(x))
check_is_fitted(library)
with pytest.raises(ValueError):
FourierLibrary(n_frequencies=2.2)
with pytest.raises(ValueError):
FourierLibrary(include_sin=False, include_cos=False)
with pytest.raises(ValueError):
library_functions = [lambda x: x, lambda x: x**2, lambda x: 0 * x]
function_names = [lambda s: str(s), lambda s: "{}^2".format(s)]
CustomLibrary(library_functions=library_functions,
function_names=function_names)
@pytest.mark.parametrize(
"library",
[
IdentityLibrary(),
PolynomialLibrary(),
FourierLibrary(),
IdentityLibrary() + PolynomialLibrary(),
pytest.lazy_fixture("data_custom_library"),
],
)
def test_fit_transform(data_lorenz, library):
x, t = data_lorenz
library.fit_transform(x)
check_is_fitted(library)
@pytest.mark.parametrize(
"library",
[
IdentityLibrary(),
with pytest.raises(ValueError):
FourierLibrary(n_frequencies=2.2)
with pytest.raises(ValueError):
FourierLibrary(include_sin=False, include_cos=False)
with pytest.raises(ValueError):
library_functions = [lambda x: x, lambda x: x**2, lambda x: 0 * x]
function_names = [lambda s: str(s), lambda s: "{}^2".format(s)]
CustomLibrary(library_functions=library_functions,
function_names=function_names)
@pytest.mark.parametrize(
"library",
[
IdentityLibrary(),
PolynomialLibrary(),
FourierLibrary(),
pytest.lazy_fixture("data_custom_library"),
],
)
def test_fit_transform(data_lorenz, library):
x, t = data_lorenz
library.fit_transform(x)
check_is_fitted(library)
@pytest.mark.parametrize(
"library",
[
IdentityLibrary(),
PolynomialLibrary(),