def test_identify_relevant_covariates():
params = pd.read_csv(
io.StringIO(
dedent("""
category,name,value
wage_a,constant,1
nonpec_b,upper_upper,1
wage_c,upper_upper_with_spacing_problem,1
""")),
index_col=["category", "name"],
)
options = {
"covariates": {
"constant": "1",
"nested_covariate": "2",
"upper": "nested_covariate > 2",
"upper_upper": "upper == 5",
"unrelated_covariate": "2",
"unrelated_covariate_upper": "unrelated_covariate",
"upper_upper_with_spacing_problem": "upper>2",
}
}
expected = options.copy()
expected["covariates"].pop("unrelated_covariate")
expected["covariates"].pop("unrelated_covariate_upper")
relevant_covariates = remove_irrelevant_covariates(options, params)
assert expected == relevant_covariates
def process_params_and_options(params, options):
"""Process ``params`` and ``options``.
This function is interface for parsing the model specification given by the user.
"""
options = _read_options(options)
params = _read_params(params)
options = {**DEFAULT_OPTIONS, **options}
options = _create_internal_seeds_from_user_seeds(options)
options = remove_irrelevant_covariates(options, params)
validate_options(options)
optim_paras = _parse_parameters(params, options)
optim_paras, options = _sync_optim_paras_and_options(optim_paras, options)
validate_params(params, optim_paras)
return optim_paras, options