def _calculate_free_cov_msm(
internal_estimates,
internal_jacobian,
internal_moments_cov,
internal_weights,
converter,
method,
n_samples,
bounds_handling,
seed,
):
if method == "optimal":
internal_cov = cov_optimal(internal_jacobian, internal_weights)
else:
internal_cov = cov_robust(internal_jacobian, internal_weights,
internal_moments_cov)
rng = get_rng(seed)
free_cov = transform_covariance(
internal_params=internal_estimates,
internal_cov=internal_cov,
converter=converter,
n_samples=n_samples,
rng=rng,
bounds_handling=bounds_handling,
)
return free_cov
def test_transform_covariance_invalid_bounds():
rng = get_rng(seed=1234)
internal_cov = np.eye(2)
converter = FakeConverter()
internal_params = FakeInternalParams(lower_bounds=np.ones(2),
upper_bounds=np.ones(2))
with pytest.raises(ValueError):
transform_covariance(
internal_params=internal_params,
internal_cov=internal_cov,
converter=converter,
rng=rng,
n_samples=10,
bounds_handling="raise",
)
def test_transform_covariance_no_bounds():
internal_cov = np.eye(2)
converter = FakeConverter()
internal_params = FakeInternalParams()
got = transform_covariance(
internal_params=internal_params,
internal_cov=internal_cov,
converter=converter,
rng=get_rng(seed=5687),
n_samples=100,
bounds_handling="ignore",
)
expected_sample = get_rng(seed=5687).multivariate_normal(
np.arange(2), np.eye(2), 100)
expected = np.cov(expected_sample, rowvar=False)
aaae(got, expected)
def test_transform_covariance_with_clipping():
rng = get_rng(seed=1234)
internal_cov = np.eye(2)
converter = FakeConverter()
internal_params = FakeInternalParams(lower_bounds=np.ones(2),
upper_bounds=np.ones(2))
got = transform_covariance(
internal_params=internal_params,
internal_cov=internal_cov,
converter=converter,
rng=rng,
n_samples=100,
bounds_handling="clip",
)
expected = np.zeros((2, 2))
aaae(got, expected)
def _calculate_free_cov_ml(
method,
internal_estimates,
converter,
internal_jacobian,
internal_hessian,
n_samples,
design_info,
bounds_handling,
seed,
):
if method == "jacobian":
int_cov = cov_jacobian(internal_jacobian)
elif method == "hessian":
int_cov = cov_hessian(internal_hessian)
elif method == "robust":
int_cov = cov_robust(jac=internal_jacobian, hess=internal_hessian)
elif method == "cluster_robust":
int_cov = cov_cluster_robust(jac=internal_jacobian,
hess=internal_hessian,
design_info=design_info)
elif method == "strata_robust":
int_cov = cov_strata_robust(jac=internal_jacobian,
hess=internal_hessian,
design_info=design_info)
rng = get_rng(seed)
free_cov = transform_covariance(
internal_params=internal_estimates,
internal_cov=int_cov,
converter=converter,
rng=rng,
n_samples=n_samples,
bounds_handling=bounds_handling,
)
return free_cov
jac=int_jac, hess=int_hess, design_info=design_info)
if "strata_robust" in cov_cases:
int_covs["cov_strata_robust"] = cov_strata_robust(
jac=int_jac, hess=int_hess, design_info=design_info)
# ==================================================================================
# Calculate all available external covs and summaries
# ==================================================================================
covs = {}
summaries = {}
for case in cov_cases:
cov = transform_covariance(
params=estimates,
internal_cov=int_covs[f"cov_{case}"],
constraints=constraints,
n_samples=n_samples,
bounds_handling=bounds_handling,
)
summary = calculate_inference_quantities(
params=estimates,
free_cov=cov,
ci_level=ci_level,
)
covs[f"cov_{case}"] = cov
summaries[f"summary_{case}"] = summary
# ==================================================================================
# Calculate external jac and hess (if no transforming constraints)
# ==================================================================================
)
if cov_type == "jacobian":
numdiff_options = numdiff_options.copy()
numdiff_options["key"] = "contributions"
internal_jac = first_derivative(
internal_loglike,
internal_params,
**numdiff_options,
)
internal_cov = cov_jacobian(internal_jac)
else:
raise NotImplementedError(
"Hessian calculation is not yet implemented.")
# transform internal covariance matrix
free_cov = transform_covariance(
params=params,
internal_cov=internal_cov,
constraints=constraints,
n_samples=n_samples,
bounds_handling=bounds_handling,
)
# calculate quantities
inference_quantities = calculate_inference_quantities(params=params,
free_cov=free_cov)
return inference_quantities, free_cov