def to_gbd_avgint(self, parent_location_id, sex_id):
"""
Converts the demographics of the model to the avgint table.
:return:
"""
LOG.info(f"Getting grid for the avgint table "
f"for parent location ID {parent_location_id} "
f"and sex_id {sex_id}.")
grid = expand_grid({
'sex_id': [sex_id],
'location_id':
self.location_dag.parent_children(parent_location_id),
'year_id':
self.demographics.year_id,
'age_group_id':
self.demographics.age_group_id
})
grid['time_lower'] = grid['year_id'].astype(int)
grid['time_upper'] = grid['year_id'] + 1.
grid = BaseInput(
gbd_round_id=self.gbd_round_id).convert_to_age_lower_upper(df=grid)
LOG.info("Adding covariates to avgint grid.")
grid = self.add_covariates_to_data(df=grid)
return grid
def get_prior_avgint_grid(settings,
integrands,
sexes,
locations,
midpoint=False):
"""
Get a data frame to use for setting up posterior predictions on a grid.
Will still need to have covariates added to it, and prep data from
dismod.api.data_tables.prep_data_avgint to convert nodes and covariate names
before it can be input into the avgint table in a database.
Args:
settings: (cascade_at.settings.settings_configuration.SettingsConfiguration)
integrands: (list of str)
sexes: (list of int)
locations: (list of int)
midpoint: (bool)
Returns: (pd.DataFrame) with columns
"avgint_id", "integrand_id", "location_id", "weight_id", "subgroup_id",
"age_lower", "age_upper", "time_lower", "time_upper", "sex_id"
"""
posterior_dfs = pd.DataFrame()
alchemy = Alchemy(settings)
grids = integrand_grids(alchemy=alchemy, integrands=integrands)
for k, v in grids.items():
if midpoint:
time = vec_to_midpoint(v['time'])
age = vec_to_midpoint(v['age'])
else:
time = v['time']
age = v['age']
posterior_df = expand_grid({
'age_lower': age,
'time_lower': time,
'location_id': locations,
'sex_id': sexes
})
posterior_df['time_upper'] = posterior_df['time_lower']
posterior_df['age_upper'] = posterior_df['age_lower']
posterior_df['rate'] = k
posterior_df['integrand'] = posterior_df['rate'].map(RateToIntegrand)
posterior_df['integrand_id'] = posterior_df['integrand'].apply(
lambda x: IntegrandEnum[x].value)
posterior_df['weight_id'] = posterior_df["integrand"].apply(
lambda x: INTEGRAND_TO_WEIGHT[x].value)
posterior_df['subgroup_id'] = 0
posterior_dfs = posterior_dfs.append(posterior_df)
return posterior_dfs[[
"integrand_id", "location_id", "weight_id", "subgroup_id", "age_lower",
"age_upper", "time_lower", "time_upper", "sex_id"
]]
def population(Demographics, ihme):
pop = Population(demographics=Demographics,
decomp_step='step3',
gbd_round_id=6)
raw = expand_grid({
'age_group_id': Demographics.age_group_id,
'location_id': np.array([70., 72.]),
'year_id': np.array([1990.]),
'sex_id': np.array([1., 2.])
})
raw['population'] = 3000.
raw['run_id'] = np.nan
pop.raw = raw
return pop
def get_prior_avgint_grid(grids: Dict[str, Dict[str, np.ndarray]],
sexes: List[int],
locations: List[int],
midpoint: bool = False) -> pd.DataFrame:
"""
Get a data frame to use for setting up posterior predictions on a grid.
The grids are specified in the grids parameter.
Will still need to have covariates added to it, and prep data from
dismod.api.data_tables.prep_data_avgint to convert nodes and covariate names
before it can be input into the avgint table in a database.
Parameters
---------
grids
A dictionary of grids with keys for each integrand,
which are dictionaries for "age" and "time".
sexes
A list of sexes
locations
A list of locations
midpoint
Whether to midpoint the grid lower and upper values (recommended for rates).
Returns
-------
Dataframe with columns
"avgint_id", "integrand_id", "location_id", "weight_id", "subgroup_id",
"age_lower", "age_upper", "time_lower", "time_upper", "sex_id"
"""
posterior_dfs = pd.DataFrame()
for k, v in grids.items():
if midpoint:
time = vec_to_midpoint(v['time'])
age = vec_to_midpoint(v['age'])
else:
time = v['time']
age = v['age']
posterior_df = expand_grid({
'age_lower': age,
'time_lower': time,
'location_id': locations,
'sex_id': sexes
})
posterior_df['time_upper'] = posterior_df['time_lower']
posterior_df['age_upper'] = posterior_df['age_lower']
posterior_df['rate'] = k
posterior_df['integrand'] = posterior_df['rate'].map(RateToIntegrand)
posterior_df['integrand_id'] = posterior_df['integrand'].apply(
lambda x: IntegrandEnum[x].value
)
posterior_df['weight_id'] = posterior_df["integrand"].apply(
lambda x: INTEGRAND_TO_WEIGHT[x].value
)
posterior_df['subgroup_id'] = 0
posterior_dfs = posterior_dfs.append(posterior_df)
return posterior_dfs[[
"integrand_id", "location_id", "weight_id", "subgroup_id",
"age_lower", "age_upper", "time_lower", "time_upper", "sex_id"
]]