def test_too_few_points_for_polyfit_warning():
"""Test that the correct warning is raised if data is not enough to fit."""
fac = PolyFactory(X_VALS, order=2)
fac._instack = [
{
"scale_factor": 1.0,
"shots": 100
},
{
"scale_factor": 2.0,
"shots": 100
},
]
fac._outstack = [1.0, 2.0]
with warns(
ExtrapolationWarning,
match=r"The extrapolation fit may be ill-conditioned.",
):
fac.reduce()
# test also the static "extrapolate" method.
with warns(
ExtrapolationWarning,
match=r"The extrapolation fit may be ill-conditioned.",
):
PolyFactory.extrapolate([1.0, 2.0], [1.0, 2.0], order=2)
def test_params_cov_and_zne_std():
"""Tests the variance of the parametes and of the zne are produced."""
x_values = [0, 0, 1]
y_values = [-1, 1, 0]
zne_limit = PolyFactory.extrapolate(x_values, y_values, order=1)
assert np.isclose(zne_limit, 0.0, atol=1.0e-4)
(
zne_limit,
zne_std,
opt_params,
params_cov,
zne_curve,
) = PolyFactory.extrapolate(x_values, y_values, order=1, full_output=True)
assert len(opt_params) == 2
assert np.isclose(zne_limit, 0.0)
assert np.isclose(0.0, opt_params[1])
assert np.isclose(0.0, opt_params[0])
assert np.allclose(params_cov, [[3.0, -1.0], [-1.0, 1.0]])
assert np.isclose(zne_std, 1.0)
assert np.isclose(zne_curve(0), 0.0)
assert np.isclose(zne_curve(0.5), 0.0)
def test_full_output_keyword_cov_std():
"""Tests the full_output keyword in extrapolate method."""
zne_limit = PolyFactory.extrapolate([1, 2, 3], [1, 4, 9], order=2)
assert np.isclose(zne_limit, 0.0)
(
zne_limit,
zne_std,
opt_params,
params_cov,
zne_curve,
) = PolyFactory.extrapolate(
[1, 2, 3], [1, 4, 9], order=2, full_output=True
)
assert len(opt_params) == 3
assert np.isclose(zne_limit, 0.0)
assert np.isclose(0.0, opt_params[1])
assert np.isclose(1.0, opt_params[0])
assert params_cov is None
assert zne_std is None
assert np.isclose(zne_curve(0), 0.0)
assert np.isclose(zne_curve(2), 4.0)
assert np.isclose(zne_curve(3), 9.0)
def test_poly_extr():
"""Test of polynomial extrapolator."""
# test (order=1)
fac = PolyFactory(X_VALS, order=1)
fac.run_classical(f_lin)
assert np.isclose(fac.reduce(), f_lin(0, err=0), atol=CLOSE_TOL)
# test that, for some non-linear functions,
# order=1 is bad while order=2 is better.
seeded_f = apply_seed_to_func(f_non_lin, SEED)
fac = PolyFactory(X_VALS, order=1)
fac.run_classical(seeded_f)
assert not np.isclose(fac.reduce(), seeded_f(0, err=0), atol=NOT_CLOSE_TOL)
seeded_f = apply_seed_to_func(f_non_lin, SEED)
fac = PolyFactory(X_VALS, order=2)
fac.run_classical(seeded_f)
zne_value = fac.reduce()
assert np.isclose(fac.reduce(), seeded_f(0, err=0), atol=CLOSE_TOL)
exp_vals = fac.get_expectation_values()
assert np.isclose(fac.extrapolate(X_VALS, exp_vals, order=2), zne_value)
assert np.isclose(
fac.extrapolate(X_VALS, exp_vals, order=2, full_output=True)[0],
zne_value,
)