def test_location_drill_start_end(ihme):
these_settings = deepcopy(BASE_CASE)
model_settings = these_settings["model"]
tree = LocationDAG(these_settings['location_set_version_id'],
these_settings['gbd_round_id'])
region_ids = tree.parent_children(1)
parent_test_loc = choice(region_ids)
test_children = list(tree.parent_children(parent_test_loc))
num_test_children = randint(2, len(test_children))
children_test_locs = sample(test_children, num_test_children)
num_descendants = 0
for child in children_test_locs:
num_descendants += len(tree.descendants(child))
model_settings['drill_location_end'] = children_test_locs
model_settings['drill_location_start'] = parent_test_loc
these_settings['model'] = model_settings
s = load_settings(these_settings)
mi = MeasurementInputsFromSettings(settings=s)
# demographics.location_id shoul be set to all descendants of each
# location in drill_location_end, plus drill_location_end locations
# themselves, plus the drill_location_start location
assert len(mi.demographics.location_id) == (num_descendants +
len(children_test_locs) + 1)
assert len(mi.demographics.drill_locations) == (len(children_test_locs) +
1)
def test_no_drill(ihme):
these_settings = deepcopy(BASE_CASE)
model_settings = these_settings["model"]
tree = LocationDAG(these_settings['location_set_version_id'],
these_settings['gbd_round_id'])
num_descendants = len(tree.descendants(1))
model_settings.pop('drill_location_end')
model_settings.pop('drill_location_start')
these_settings['model'] = model_settings
s = load_settings(these_settings)
mi = MeasurementInputsFromSettings(settings=s)
# since we haven't set either drill_location_start or
# drill_location_end, demographics.location_id should be set
# to the entire hierarchy
assert len(mi.demographics.location_id) == num_descendants + 1
assert len(mi.demographics.drill_locations) == num_descendants + 1
def test_location_drill_start_only(ihme):
these_settings = deepcopy(BASE_CASE)
model_settings = these_settings["model"]
tree = LocationDAG(these_settings['location_set_version_id'],
these_settings['gbd_round_id'])
region_ids = tree.parent_children(1)
test_loc = choice(region_ids)
num_descendants = len(tree.descendants(test_loc))
num_mr_locs = len(tree.parent_children(test_loc))
model_settings.pop("drill_location_end")
model_settings['drill_location_start'] = test_loc
these_settings["model"] = model_settings
s = load_settings(these_settings)
mi = MeasurementInputsFromSettings(settings=s)
# with drill_location_end unset, demographics.location_id should
# be set to all descendants of the test loc, plus the test loc itself
assert len(mi.demographics.location_id) == num_descendants + 1
assert len(mi.demographics.drill_locations) == num_mr_locs
class MeasurementInputs:
def __init__(self,
model_version_id,
gbd_round_id,
decomp_step_id,
csmr_process_version_id,
csmr_cause_id,
crosswalk_version_id,
country_covariate_id,
conn_def,
location_set_version_id=None,
drill=None):
"""
The class that constructs all of the measurement inputs. Pulls ASDR, CSMR, crosswalk versions,
and country covariates, and puts them into one data frame that then formats itself
for the dismod database. Performs covariate value interpolation if age and year ranges
don't match up with GBD age and year ranges.
Parameters:
model_version_id: (int) the model version ID
gbd_round_id: (int) the GBD round ID
decomp_step_id: (int) the decomp step ID
csmr_process_version_id: (int) process version ID for CSMR
csmr_cause_id: (int) cause to pull CSMR from
crosswalk_version_id: (int) crosswalk version to use
country_covariate_id: (list of int) list of covariate IDs
conn_def: (str) connection definition from .odbc file (e.g. 'epi')
location_set_version_id: (int) can be None, if it's none, get the
best location_set_version_id for estimation hierarchy of this GBD round.
drill: (int) optional, which location ID to drill from as the parent
Attributes:
self.decomp_step: (str) the decomp step in string form
self.demographics: (cascade_at.inputs.demographics.Demographics) a demographics object
that specifies the age group, sex, location, and year IDs to grab
self.integrand_map: (dict) dictionary mapping from GBD measure IDs to DisMod IDs
self.asdr: (cascade_at.inputs.asdr.ASDR) all-cause mortality input object
self.csmr: (cascade_at.inputs.csmr.CSMR) cause-specific mortality input object from cause
csmr_cause_id
self.data: (cascade_at.inputs.data.CrosswalkVersion) crosswalk version data from IHME database
self.covariate_data: (List[cascade_at.inputs.covariate_data.CovariateData]) list of covariate
data objects that contains the raw covariate data mapped to IDs
self.location_dag: (cascade_at.inputs.locations.LocationDAG) DAG of locations to be used
self.population: (cascade_at.inputs.population.Population) population object that is used
for covariate weighting
self.data_eta: (Dict[str, float]): dictionary of eta value to be applied to each measure
self.density: (Dict[str, str]): dictionary of density to be applied to each measure
self.nu: (Dict[str, float]): dictionary of nu value to be applied to each measure
self.dismod_data: (pd.DataFrame) resulting dismod data formatted to be used in the dismod database
Usage:
>>> from cascade_at.settings.base_case import BASE_CASE
>>> from cascade_at.settings.settings import load_settings
>>> settings = load_settings(BASE_CASE)
>>> covariate_ids = [i.country_covariate_id for i in settings.country_covariate]
>>> i = MeasurementInputs(model_version_id=settings.model.model_version_id,
>>> gbd_round_id=settings.gbd_round_id,
>>> decomp_step_id=settings.model.decomp_step_id,
>>> csmr_process_version_id=None,
>>> csmr_cause_id = settings.model.add_csmr_cause,
>>> crosswalk_version_id=settings.model.crosswalk_version_id,
>>> country_covariate_id=covariate_ids,
>>> conn_def='epi',
>>> location_set_version_id=settings.location_set_version_id)
>>> i.get_raw_inputs()
>>> i.configure_inputs_for_dismod()
"""
LOG.info(
f"Initializing input object for model version ID {model_version_id}."
)
LOG.info(f"GBD Round ID {gbd_round_id}.")
LOG.info(f"Pulling from connection {conn_def}.")
self.model_version_id = model_version_id
self.gbd_round_id = gbd_round_id
self.decomp_step_id = decomp_step_id
self.csmr_process_version_id = csmr_process_version_id
self.csmr_cause_id = csmr_cause_id
self.crosswalk_version_id = crosswalk_version_id
self.country_covariate_id = country_covariate_id
self.conn_def = conn_def
self.decomp_step = ds.decomp_step_from_decomp_step_id(
self.decomp_step_id)
self.demographics = Demographics(gbd_round_id=self.gbd_round_id)
if location_set_version_id is None:
self.location_set_version_id = get_location_set_version_id(
gbd_round_id=self.gbd_round_id)
else:
self.location_set_version_id = location_set_version_id
self.location_dag = LocationDAG(
location_set_version_id=self.location_set_version_id,
gbd_round_id=self.gbd_round_id)
if drill:
LOG.info(
f"This is a DRILL model, so only going to pull data associated with "
f"drill location start {drill} and its descendants.")
drill_descendants = list(
self.location_dag.descendants(location_id=drill))
self.demographics.location_id = [drill] + drill_descendants
self.exclude_outliers = True
self.asdr = None
self.csmr = None
self.population = None
self.data = None
self.covariates = None
self.age_groups = None
self.data_eta = None
self.density = None
self.nu = None
self.measures_to_exclude = None
self.dismod_data = None
self.covariate_data = None
self.country_covariate_data = None
self.covariate_specs = None
self.omega = None
def get_raw_inputs(self):
"""
Get the raw inputs that need to be used
in the modeling.
:return:
"""
LOG.info("Getting all raw inputs.")
self.asdr = ASDR(demographics=self.demographics,
decomp_step=self.decomp_step,
gbd_round_id=self.gbd_round_id).get_raw()
self.csmr = CSMR(
cause_id=self.csmr_cause_id,
demographics=self.demographics,
decomp_step=self.decomp_step,
gbd_round_id=self.gbd_round_id,
process_version_id=self.csmr_process_version_id).get_raw()
self.data = CrosswalkVersion(
crosswalk_version_id=self.crosswalk_version_id,
exclude_outliers=self.exclude_outliers,
demographics=self.demographics,
conn_def=self.conn_def,
gbd_round_id=self.gbd_round_id).get_raw()
self.covariate_data = [
CovariateData(covariate_id=c,
demographics=self.demographics,
decomp_step=self.decomp_step,
gbd_round_id=self.gbd_round_id).get_raw()
for c in self.country_covariate_id
]
self.population = Population(
demographics=self.demographics,
decomp_step=self.decomp_step,
gbd_round_id=self.gbd_round_id).get_population()
def configure_inputs_for_dismod(self,
settings,
mortality_year_reduction=5):
"""
Modifies the inputs for DisMod based on model-specific settings.
:param settings: (cascade.settings.configuration.Configuration)
:param mortality_year_reduction: (int) number of years to decimate csmr and asdr
:return: self
"""
self.data_eta = self.data_eta_from_settings(settings)
self.density = self.density_from_settings(settings)
self.nu = self.nu_from_settings(settings)
self.measures_to_exclude = self.measures_to_exclude_from_settings(
settings)
# If we are constraining omega, then we want to hold out the data
# from the DisMod fit for ASDR (but never CSMR -- always want to fit CSMR).
data = self.data.configure_for_dismod(
measures_to_exclude=self.measures_to_exclude,
relabel_incidence=settings.model.relabel_incidence)
asdr = self.asdr.configure_for_dismod(
hold_out=settings.model.constrain_omega)
csmr = self.csmr.configure_for_dismod(hold_out=0)
if settings.model.constrain_omega:
self.omega = self.calculate_omega(asdr=asdr, csmr=csmr)
else:
self.omega = None
if not csmr.empty:
csmr = decimate_years(data=csmr,
num_years=mortality_year_reduction)
if not asdr.empty:
asdr = decimate_years(data=asdr,
num_years=mortality_year_reduction)
self.dismod_data = pd.concat([data, asdr, csmr], axis=0, sort=True)
self.dismod_data.reset_index(drop=True, inplace=True)
self.dismod_data["density"] = self.dismod_data.measure.apply(
self.density.__getitem__)
self.dismod_data["eta"] = self.dismod_data.measure.apply(
self.data_eta.__getitem__)
self.dismod_data["nu"] = self.dismod_data.measure.apply(
self.nu.__getitem__)
# This makes the specs not just for the country covariate but adds on the
# sex and one covariates.
self.covariate_specs = CovariateSpecs(
country_covariates=settings.country_covariate,
study_covariates=settings.study_covariate)
self.country_covariate_data = {
c.covariate_id: c.configure_for_dismod(
pop_df=self.population.configure_for_dismod(),
loc_df=self.location_dag.df)
for c in self.covariate_data
}
self.dismod_data = self.add_covariates_to_data(df=self.dismod_data)
self.dismod_data.loc[self.dismod_data.hold_out.isnull(),
'hold_out'] = 0.
self.dismod_data.drop(['age_group_id'], inplace=True, axis=1)
return self
def add_covariates_to_data(self, df):
"""
Add on covariates to a data frame that has age_group_id, year_id
or time-age upper / lower, and location_id and sex_id. Adds both
country-level and study-level covariates.
:return:
"""
cov_dict_for_interpolation = {
c.name: self.country_covariate_data[c.covariate_id]
for c in self.covariate_specs.covariate_specs
if c.study_country == 'country'
}
df = self.interpolate_country_covariate_values(
df=df, cov_dict=cov_dict_for_interpolation)
df = self.transform_country_covariates(df=df)
df['s_sex'] = df.sex_id.map(SEX_ID_TO_NAME).map(
StudyCovConstants.SEX_COV_VALUE_MAP)
df['s_one'] = StudyCovConstants.ONE_COV_VALUE
return df
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
@staticmethod
def calculate_omega(asdr, csmr):
"""
Calculates other cause mortality (omega) from ASDR (mtall -- all-cause mortality)
and CSMR (mtspecific -- cause-specific mortality). For most diseases, mtall is a
good approximation to omega, but we calculate omega = mtall - mtspecific in case it isn't.
For diseases without CSMR (self.csmr_cause_id = None), then omega = mtall.
"""
join_columns = [
'location_id', 'time_lower', 'time_upper', 'age_lower',
'age_upper', 'sex_id'
]
mtall = asdr[join_columns + ['meas_value']].copy()
mtall.rename(columns={'meas_value': 'mtall'}, inplace=True)
if csmr.empty:
omega = mtall.copy()
omega.rename(columns={'mtall': 'mean'}, inplace=True)
else:
mtspecific = csmr[join_columns + ['meas_value']].copy()
mtspecific.rename(columns={'meas_value': 'mtspecific'},
inplace=True)
omega = mtall.merge(mtspecific, on=join_columns)
omega['mean'] = omega['mtall'] - omega['mtspecific']
omega.drop(columns=['mtall', 'mtspecific'], inplace=True)
negative_omega = omega['mean'] < 0
if any(negative_omega):
raise ValueError("There are negative values for omega. Must fix.")
return omega
def interpolate_country_covariate_values(self, df, cov_dict):
"""
Interpolates the covariate values onto the data
so that the non-standard ages and years match up to meaningful
covariate values.
:param df: (pd.DataFrame)
:param cov_dict: (Dict)
"""
LOG.info(f"Interpolating and merging the country covariates.")
interp_df = get_interpolated_covariate_values(
data_df=df,
covariate_dict=cov_dict,
population_df=self.population.configure_for_dismod())
return interp_df
def transform_country_covariates(self, df):
"""
Transforms the covariate data with the transformation ID.
:param df: (pd.DataFrame)
:return: self
"""
for c in self.covariate_specs.covariate_specs:
if c.study_country == 'country':
LOG.info(
f"Transforming the data for country covariate {c.covariate_id}."
)
df[c.name] = df[c.name].apply(
lambda x: COVARIATE_TRANSFORMS[c.transformation_id](x))
return df
def calculate_country_covariate_reference_values(self, parent_location_id,
sex_id):
"""
Gets the country covariate reference value for a covariate ID and a parent location ID.
Also gets the maximum difference between the reference value and covariate values observed.
Run this when you're going to make a DisMod AT database for a specific parent location
and sex ID.
:param: (int)
:param parent_location_id: (int)
:param sex_id: (int)
:return: List[CovariateSpec] list of the covariate specs with the correct reference values and max diff.
"""
covariate_specs = copy(self.covariate_specs)
age_min = self.dismod_data.age_lower.min()
age_max = self.dismod_data.age_upper.max()
time_min = self.dismod_data.time_lower.min()
time_max = self.dismod_data.time_upper.max()
children = list(self.location_dag.dag.successors(parent_location_id))
for c in covariate_specs.covariate_specs:
if c.study_country == 'study':
if c.name == 's_sex':
c.reference = StudyCovConstants.SEX_COV_VALUE_MAP[
SEX_ID_TO_NAME[sex_id]]
c.max_difference = StudyCovConstants.MAX_DIFFERENCE_SEX_COV
elif c.name == 's_one':
c.reference = StudyCovConstants.ONE_COV_VALUE
c.max_difference = StudyCovConstants.MAX_DIFFERENCE_ONE_COV
else:
raise ValueError(
f"The only two study covariates allowed are sex and one, you tried {c.name}."
)
elif c.study_country == 'country':
LOG.info(
f"Calculating the reference and max difference for country covariate {c.covariate_id}."
)
cov_df = self.country_covariate_data[c.covariate_id]
parent_df = cov_df.loc[cov_df.location_id ==
parent_location_id].copy()
child_df = cov_df.loc[cov_df.location_id.isin(children)].copy()
all_loc_df = pd.concat([child_df, parent_df], axis=0)
# if there is no data for the parent location at all (which there should be provided by Central Comp)
# then we are going to set the reference value to 0.
if cov_df.empty:
reference_value = 0
max_difference = np.nan
else:
pop_df = self.population.configure_for_dismod()
pop_df = pop_df.loc[pop_df.location_id ==
parent_location_id].copy()
df_to_interp = pd.DataFrame({
'location_id': parent_location_id,
'sex_id': [sex_id],
'age_lower': [age_min],
'age_upper': [age_max],
'time_lower': [time_min],
'time_upper': [time_max]
})
reference_value = get_interpolated_covariate_values(
data_df=df_to_interp,
covariate_dict={c.name: parent_df},
population_df=pop_df)[c.name].iloc[0]
max_difference = np.max(
np.abs(all_loc_df.mean_value - reference_value)
) + CascadeConstants.PRECISION_FOR_REFERENCE_VALUES
c.reference = reference_value
c.max_difference = max_difference
covariate_specs.create_covariate_list()
return covariate_specs
@staticmethod
def measures_to_exclude_from_settings(settings):
"""
Gets the measures to exclude from the data from the model
settings configuration.
:param settings: (cascade.settings.configuration.Configuration)
:return:
"""
if not settings.model.is_field_unset("exclude_data_for_param"):
measures_to_exclude = [
INTEGRAND_MAP[m].name
for m in settings.model.exclude_data_for_param
if m in INTEGRAND_MAP
]
else:
measures_to_exclude = list()
if settings.policies.exclude_relative_risk:
measures_to_exclude.append("relrisk")
return measures_to_exclude
@staticmethod
def data_eta_from_settings(settings):
"""
Gets the data eta from the settings Configuration.
The default data eta is np.nan.
settings.eta.data: (Dict[str, float]): Default value for eta parameter on distributions
as a dictionary from measure name to float
:param settings: (cascade.settings.configuration.Configuration)
:return:
"""
data_eta = defaultdict(lambda: np.nan)
if not settings.eta.is_field_unset("data") and settings.eta.data:
data_eta = defaultdict(lambda: float(settings.eta.data))
for set_eta in settings.data_eta_by_integrand:
data_eta[INTEGRAND_MAP[set_eta.integrand_measure_id].name] = float(
set_eta.value)
return data_eta
@staticmethod
def density_from_settings(settings):
"""
Gets the density from the settings Configuration.
The default density is "gaussian".
settings.model.data_density: (Dict[str, float]): Default values for density parameter on distributions
as a dictionary from measure name to string
:param settings: (cascade.settings.configuration.Configuration)
:return:
"""
density = defaultdict(lambda: "gaussian")
if not settings.model.is_field_unset(
"data_density") and settings.model.data_density:
density = defaultdict(lambda: settings.model.data_density)
for set_density in settings.data_density_by_integrand:
density[INTEGRAND_MAP[
set_density.integrand_measure_id].name] = set_density.value
return density
@staticmethod
def data_cv_from_settings(settings, default=0.0):
"""
Gets the data min coefficient of variation from the settings Configuration
Args:
settings: (cascade.settings.configuration.Configuration)
default: (float) default data cv
Returns:
dictionary of data cv's from settings
"""
data_cv = defaultdict(lambda: default)
if not settings.model.is_field_unset(
"minimum_meas_cv") and settings.model.minimum_meas_cv:
data_cv = defaultdict(
lambda: float(settings.model.minimum_meas_cv))
for set_data_cv in settings.data_cv_by_integrand:
data_cv[INTEGRAND_MAP[
set_data_cv.integrand_measure_id].name] = float(
set_data_cv.value)
return data_cv
@staticmethod
def nu_from_settings(settings):
"""
Gets nu from the settings Configuration.
The default nu is np.nan.
settings.students_dof.data: (Dict[str, float]): The parameter for students-t distributions
settings.log_students_dof.data: (Dict[str, float]): The parameter for students-t distributions in log-space
:param settings: (cascade.settings.configuration.Configuration)
:return:
"""
nu = defaultdict(lambda: np.nan)
nu["students"] = settings.students_dof.data
nu["log_students"] = settings.log_students_dof.data
return nu
def reset_index(self, drop, inplace):
pass
def test_dag_from_df(df):
dag = LocationDAG(df=df, root=1)
assert set(dag.dag.successors(1)) == {2, 3}
assert set(dag.dag.successors(2)) == {4, 5}
assert set(dag.descendants(1)) == {2, 3, 4, 5}