def test_bad_optimizers(data_derivative_1d):
x, x_dot = data_derivative_1d
x = x.reshape(-1, 1)
with pytest.raises(AttributeError):
opt = SINDyOptimizer(DummyEmptyModel())
with pytest.raises(AttributeError):
opt = SINDyOptimizer(DummyModelNoCoef())
opt.fit(x, x_dot)
def test_fit(data, optimizer):
x, x_dot = data
if len(x.shape) == 1:
x = x.reshape(-1, 1)
opt = SINDyOptimizer(optimizer, unbias=False)
opt.fit(x, x_dot)
check_is_fitted(opt)
assert opt.complexity >= 0
if len(x_dot.shape) > 1:
assert opt.coef_.shape == (x.shape[1], x_dot.shape[1])
else:
assert opt.coef_.shape == (1, x.shape[1])
def test_unbias_external(data_derivative_1d):
x, x_dot = data_derivative_1d
x = x.reshape(-1, 1)
optimizer_biased = SINDyOptimizer(Lasso(alpha=0.1,
fit_intercept=False,
max_iter=1),
unbias=False)
optimizer_biased.fit(x, x_dot)
optimizer_unbiased = SINDyOptimizer(Lasso(alpha=0.1,
fit_intercept=False,
max_iter=1),
unbias=True)
optimizer_unbiased.fit(x, x_dot)
assert (norm(optimizer_biased.coef_ - optimizer_unbiased.coef_) /
(norm(optimizer_unbiased.coef_) + 1e-5) > 1e-9)
def test_unbias(data_derivative_1d):
x, x_dot = data_derivative_1d
x = x.reshape(-1, 1)
optimizer_biased = SINDyOptimizer(STLSQ(threshold=0.01,
alpha=0.1,
max_iter=1),
unbias=False)
optimizer_biased.fit(x, x_dot)
optimizer_unbiased = SINDyOptimizer(STLSQ(threshold=0.01,
alpha=0.1,
max_iter=1),
unbias=True)
optimizer_unbiased.fit(x, x_dot)
assert (norm(optimizer_biased.coef_ - optimizer_unbiased.coef_) /
norm(optimizer_unbiased.coef_) > 1e-9)
def test_sr3_trimming(optimizer, data_linear_oscillator_corrupted):
X, X_dot, trimming_array = data_linear_oscillator_corrupted
optimizer_without_trimming = SINDyOptimizer(optimizer(), unbias=False)
optimizer_without_trimming.fit(X, X_dot)
optimizer_trimming = SINDyOptimizer(optimizer(trimming_fraction=0.15),
unbias=False)
optimizer_trimming.fit(X, X_dot)
# Check that trimming found the right samples to remove
np.testing.assert_array_equal(optimizer_trimming.optimizer.trimming_array,
trimming_array)
# Check that the coefficients found by the optimizer with trimming
# are closer to the true coefficients than the coefficients found by the
# optimizer without trimming
true_coef = np.array([[-2.0, 0.0], [0.0, 1.0]])
assert norm(true_coef - optimizer_trimming.coef_) < norm(
true_coef - optimizer_without_trimming.coef_)
def test_sr3_trimming(data_linear_oscillator_corrupted):
X, X_dot, trimming_array = data_linear_oscillator_corrupted
optimizer_without_trimming = SINDyOptimizer(SR3(), unbias=False)
optimizer_without_trimming.fit(X, X_dot)
optimizer_trimming = SINDyOptimizer(SR3(trimming_fraction=0.15),
unbias=False)
optimizer_trimming.fit(X, X_dot)
# Check that trimming found the right samples to remove
assert (np.sum(
np.abs(optimizer_trimming.optimizer.trimming_array -
trimming_array)) == 0.0)
# Check that the coefficients found by the optimizer with trimming are closer to
# the true coefficients than the coefficients found by the optimizer without
# trimming
true_coef = np.array([[-2.0, 0.0], [0.0, 1.0]])
assert norm(true_coef - optimizer_trimming.coef_) < norm(
true_coef - optimizer_without_trimming.coef_)