def fill_reference_tables(self):
"""
Fills all of the reference tables including density, node, covariate, age, and time.
:return: self
"""
self.density = reference_tables.construct_density_table()
self.node = reference_tables.construct_node_table(
location_dag=self.inputs.location_dag)
self.covariate = reference_tables.construct_covariate_table(
covariates=self.parent_child_model.covariates)
self.age = reference_tables.construct_age_time_table(
variable_name='age',
variable=self.parent_child_model.get_age_array(),
data_min=self.min_age,
data_max=self.max_age)
self.time = reference_tables.construct_age_time_table(
variable_name='time',
variable=self.parent_child_model.get_time_array(),
data_min=self.min_time,
data_max=self.max_time)
self.integrand = reference_tables.construct_integrand_table(
data_cv_from_settings=data_cv_from_settings(
settings=self.settings))
return self
def test_fill_tables(dm):
unknown_omega_world = 1e-2
known_income_multiplier = -1e-3
adjusted_omega = unknown_omega_world * np.exp(
known_income_multiplier * 1000.0)
dm.integrand = construct_integrand_table()
rate = default_rate_table()
rate.loc[rate.rate_name == 'omega', 'parent_smooth_id'] = 0
dm.rate = rate
dm.density = construct_density_table()
dm.age = pd.DataFrame({'age_id': [0, 1], 'age': [0.0, 100.0]})
dm.time = pd.DataFrame({'time_id': [0, 1], 'time': [1995.0, 2015.0]})
dm.node = pd.DataFrame({
'node_id': [0],
'node_name': ['world'],
'parent': [np.nan]
})
dm.weight = pd.DataFrame({
'weight_id': [0],
'weight_name': ['constant'],
'n_age': [1],
'n_time': [1]
})
dm.weight_grid = pd.DataFrame({
'weight_id': [0],
'weight_grid_id': [0],
'age_id': [0],
'time_id': [0],
'weight': [1]
})
dm.covariate = pd.DataFrame({
'covariate_id': [0],
'covariate_name': ['income'],
'reference': [0],
'max_difference': [0]
})
dm.avgint = pd.DataFrame({
'avgint_id': [0],
'integrand_id': [0],
'node_id': [0],
'weight_id': [0],
'subgroup_id': [0],
'age_lower': [0],
'age_upper': [0],
'time_lower': [1995.],
'time_upper': [1995.],
'x_0': [1.]
})
dm.data = pd.DataFrame({
'data_id': [0],
'data_name': ['world'],
'integrand_id': [0],
'density_id': [1],
'node_id': [0],
'weight_id': [0],
'subgroup_id': [0],
'hold_out': [0],
'meas_value': [np.exp(-1 * adjusted_omega * 50.)],
'meas_std': [np.exp(-1 * adjusted_omega * 50.) / 20.],
'eta': [np.nan],
'nu': [np.nan],
'age_lower': [50.],
'age_upper': [50.],
'time_lower': [2000.],
'time_upper': [2000.],
'x_0': [1.]
})
dm.prior = pd.DataFrame({
'prior_id': [0, 1, 2],
'prior_name':
['prior_not_used', 'prior_omega_parent', 'prior_income_multiplier'],
'density_id': [0, 0, 0],
'lower': [np.nan, 1e-4, known_income_multiplier],
'upper': [np.nan, 1.0, known_income_multiplier],
'mean': [0.0, 1e-1, known_income_multiplier],
'std':
np.nan,
'eta':
np.nan,
'nu':
np.nan
})
dm.smooth = pd.DataFrame({
'smooth_id': [0, 1],
'smooth_name': ['smooth_omega_parent', 'smooth_income_multiplier'],
'n_age': [1, 1],
'n_time': [1, 1],
'mulstd_value_prior_id': [1, 2]
})
dm.smooth_grid = pd.DataFrame({
'smooth_grid_id': [0, 1],
'smooth_id': [0, 1],
'age_id': [0, 0],
'time_id': [0, 0],
'const_value': [1e-3, 1e-3]
})
dm.mulcov = pd.DataFrame({
'mulcov_id': [0],
'mulcov_type': 'rate_value',
'rate_id': [0],
'integrand_id': [0],
'covariate_id': [0],
'group_id': [0],
'subgroup_smooth_id': [1]
})
dm.option = pd.DataFrame({
'option_id': [0, 1, 2, 3],
'option_name':
['parent_node_id', 'ode_step_size', 'age_avg_split', 'rate_case'],
'option_value': ['0', '10.0', '5.0', 'iota_zero_rho_zero']
})
dm.subgroup = pd.DataFrame({
'subgroup_id': [0],
'subgroup_name': ['world'],
'group_id': [0],
'group_name': ['world']
})
dm.nslist = pd.DataFrame({'nslist_id': [0], 'nslist_name': ['parent']})
dm.nslist_pair = pd.DataFrame({
'nslist_id': [0],
'nslist_pair_id': [0],
'node_id': [0],
'smooth_id': [0]
})