def test_simulate(data):
x, t, u, u_fun = data
model = SINDy()
model.fit(x, u=u, t=t)
x1 = model.simulate(x[0], t=t, u=u_fun)
assert len(x1) == len(t)
def test_trapping_inequality_constraints(thresholds, relax_optim,
noise_levels):
t = np.arange(0, 40, 0.05)
x = odeint(lorenz, [-8, 8, 27], t)
x = x + np.random.normal(0.0, noise_levels, x.shape)
# if order is "feature"
constraint_rhs = np.array([-10.0, -2.0])
constraint_matrix = np.zeros((2, 30))
constraint_matrix[0, 6] = 1.0
constraint_matrix[1, 17] = 1.0
feature_names = ["x", "y", "z"]
opt = TrappingSR3(
threshold=thresholds,
constraint_lhs=constraint_matrix,
constraint_rhs=constraint_rhs,
constraint_order="feature",
inequality_constraints=True,
relax_optim=relax_optim,
)
poly_lib = PolynomialLibrary(degree=2)
model = SINDy(
optimizer=opt,
feature_library=poly_lib,
differentiation_method=FiniteDifference(drop_endpoints=True),
feature_names=feature_names,
)
model.fit(x, t=t[1] - t[0])
assert np.all(
np.dot(constraint_matrix, (model.coefficients()).flatten("F")) <=
constraint_rhs) or np.allclose(
np.dot(constraint_matrix,
(model.coefficients()).flatten("F")), constraint_rhs)
def test_simulate(data):
x, t = data
model = SINDy()
model.fit(x, t)
x1 = model.simulate(x[0], t)
assert len(x1) == len(t)
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):
x, t = data_lorenz
model = SINDy(differentiation_method=SmoothedFiniteDifference())
model.fit(x, t=t)
check_is_fitted(model)
def test_predict(data, optimizer):
x, t = data
model = SINDy(optimizer=optimizer)
model.fit(x, t)
x_dot = model.predict(x)
assert x.shape == x_dot.shape
def test_simulate_discrete_time(data_discrete_time_c):
x, u = data_discrete_time_c
model = SINDy(discrete_time=True)
model.fit(x, u=u)
n_steps = x.shape[0]
x1 = model.simulate(x[0], t=n_steps, u=u)
assert len(x1) == n_steps
def test_parallel(data_lorenz_c_1d):
x, t, u, _ = data_lorenz_c_1d
model = SINDy(n_jobs=4)
model.fit(x, u=u, t=t)
x_dot = model.predict(x, u=u)
s = model.score(x, u=u, x_dot=x_dot)
assert s >= 0.95
def test_simulate_with_vector_control_input(data):
x, t, u, _ = data
model = SINDy()
model.fit(x, u=u, t=t)
x1 = model.simulate(x[0], t=t, u=u)
assert len(x1) == len(t) - 1
def test_simulate_discrete_time(data_discrete_time):
x = data_discrete_time
model = SINDy(discrete_time=True)
model.fit(x)
n_steps = x.shape[0]
x1 = model.simulate(x[0], n_steps)
assert len(x1) == n_steps
def test_get_feature_names_len(data_lorenz_c_1d):
x, t, u, _ = data_lorenz_c_1d
model = SINDy()
model.fit(x, u=u, t=t)
# Assumes default library is polynomial features of degree 2
assert len(model.get_feature_names()) == 15
def test_print_discrete_time(data_discrete_time, capsys):
x = data_discrete_time
model = SINDy(discrete_time=True)
model.fit(x)
model.print()
out, _ = capsys.readouterr()
assert len(out) > 0
def test_parallel(data_lorenz):
x, t = data_lorenz
model = SINDy(n_jobs=4)
model.fit(x, t)
x_dot = model.predict(x)
s = model.score(x, x_dot=x_dot)
assert s >= 0.95
def test_simulate_with_interp(data):
x, t, u, _ = data
model = SINDy()
model.fit(x, u=u, t=t)
u_fun = interp1d(t, u, axis=0)
x1 = model.simulate(x[0], t=t[:-1], u=u_fun)
assert len(x1) == len(t) - 1
def test_fit_discrete_time(data_discrete_time_c):
x, u = data_discrete_time_c
model = SINDy(discrete_time=True)
model.fit(x, u=u)
check_is_fitted(model)
model = SINDy(discrete_time=True)
model.fit(x[:-1], u=u[:-1], x_dot=x[1:])
check_is_fitted(model)
def test_print_discrete_time_multiple_trajectories(
data_discrete_time_multiple_trajectories, capsys):
x = data_discrete_time_multiple_trajectories
model = SINDy(discrete_time=True)
model.fit(x, multiple_trajectories=True)
model.print()
out, _ = capsys.readouterr()
assert len(out) > 1
def test_fit_discrete_time(data_discrete_time):
x = data_discrete_time
model = SINDy(discrete_time=True)
model.fit(x)
check_is_fitted(model)
model = SINDy(discrete_time=True)
model.fit(x[:-1], x_dot=x[1:])
check_is_fitted(model)
def test_simulate_errors(data_lorenz_c_1d):
x, t, u, u_fun = data_lorenz_c_1d
model = SINDy()
model.fit(x, u=u, t=t)
with pytest.raises(ValueError):
model.simulate(x[0], t=1, u=u)
model = SINDy(discrete_time=True)
with pytest.raises(ValueError):
model.simulate(x[0], t=[1, 2], u=u)
def test_fit_warn(data_lorenz, params, warning):
x, t = data_lorenz
model = SINDy(optimizer=STLSQ(**params))
with pytest.warns(warning):
model.fit(x, t)
with pytest.warns(None) as warn_record:
model.fit(x, t, quiet=True)
assert len(warn_record) == 0
def test_simulate_errors(data_lorenz):
x, t = data_lorenz
model = SINDy()
model.fit(x, t)
with pytest.raises(ValueError):
model.simulate(x[0], t=1)
model = SINDy(discrete_time=True)
with pytest.raises(ValueError):
model.simulate(x[0], t=[1, 2])
def test_get_feature_names_len(data_lorenz):
x, t = data_lorenz
model = SINDy()
with pytest.raises(NotFittedError):
model.get_feature_names()
model.fit(x, t)
# Assumes default library is polynomial features of degree 2
assert len(model.get_feature_names()) == 10
def test_predict_multiple_trajectories(data_multiple_trajctories):
x, t = data_multiple_trajctories
model = SINDy()
model.fit(x, t=t, multiple_trajectories=True)
# Should fail if multiple_trajectories flag is not set
with pytest.raises(ValueError):
model.predict(x)
p = model.predict(x, multiple_trajectories=True)
assert len(p) == len(x)
def test_equations(data, capsys):
x, t = data
model = SINDy()
model.fit(x, t)
out, _ = capsys.readouterr()
assert len(out) == 0
model.print(precision=2)
out, _ = capsys.readouterr()
assert len(out) > 0
def test_predict_discrete_time_multiple_trajectories(
data_discrete_time_multiple_trajectories, ):
x = data_discrete_time_multiple_trajectories
model = SINDy(discrete_time=True)
model.fit(x, multiple_trajectories=True)
# Should fail if multiple_trajectories flag is not set
with pytest.raises(ValueError):
model.predict(x)
y = model.predict(x, multiple_trajectories=True)
assert len(y) == len(x)
def test_score(data):
x, t, u, _ = data
model = SINDy()
model.fit(x, u=u, t=t)
assert model.score(x, u=u) <= 1
assert model.score(x, u=u, t=t) <= 1
assert model.score(x, u=u, x_dot=x) <= 1
assert model.score(x, u=u, t=t, x_dot=x) <= 1
def test_score(data):
x, t = data
model = SINDy()
model.fit(x, t)
assert model.score(x) <= 1
assert model.score(x, t) <= 1
assert model.score(x, x_dot=x) <= 1
assert model.score(x, t, x_dot=x) <= 1
def test_extra_u_warn_discrete(data_discrete_time_c):
x, u = data_discrete_time_c
model = SINDy(discrete_time=True)
model.fit(x)
with pytest.warns(UserWarning):
model.predict(x, u=u)
with pytest.warns(UserWarning):
model.score(x, u=u)
with pytest.warns(UserWarning):
model.simulate(x[0], u=u, t=10)
def test_predict_multiple_trajectories(data_multiple_trajctories):
x, t = data_multiple_trajctories
u = [np.ones((xi.shape[0], 2)) for xi in x]
model = SINDy()
model.fit(x, u=u, t=t, multiple_trajectories=True)
# Should fail if multiple_trajectories flag is not set
with pytest.raises(ValueError):
model.predict(x, u=u)
p = model.predict(x, u=u, multiple_trajectories=True)
assert len(p) == len(x)
def test_extra_u_warn(data_lorenz_c_1d):
x, t, u, _ = data_lorenz_c_1d
model = SINDy()
model.fit(x, t=t)
with pytest.warns(UserWarning):
model.predict(x, u=u)
with pytest.warns(UserWarning):
model.score(x, u=u)
with pytest.warns(UserWarning):
model.simulate(x[0], t=t, u=u)
def test_u_omitted(data_lorenz_c_1d):
x, t, u, _ = data_lorenz_c_1d
model = SINDy()
model.fit(x, u=u, t=t)
with pytest.raises(TypeError):
model.predict(x)
with pytest.raises(TypeError):
model.score(x)
with pytest.raises(TypeError):
model.simulate(x[0], t=t)
