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 csmr(Demographics, ihme): csmr = CSMR(process_version_id=None, cause_id=587, demographics=Demographics, decomp_step='step3', gbd_round_id=6) csmr.raw = pd.DataFrame( { 'age_group_id': 2, 'cause_id': 587, 'location_id': 70, 'measure_id': 1, 'metric_id': 3, 'sex_id': 2, 'year_id': 1990, 'acause': '', 'age_group_name': '', 'cause_name': '', 'expected': False, 'location_name': 'Canada', 'measure_name': '', 'metric_name': '', 'sex': 'Female', 'val': 5e-06, 'upper': 6e-06, 'lower': 2e-06 }, index=[0]) return csmr
def get_raw_inputs(self): """ Get the raw inputs that need to be used in the modeling. """ LOG.info("Getting all raw inputs.") LOG.warning("FIXME -- gma -- asdr.py and csmr.py were getting different locations -- not sure if they should use location_ids or drill_locations.") LOG.warning("FIXME -- gma -- suspect it should be drill_locations, but it seems Drill leaf node handling is not implemented properly.") 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, ).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()
class MeasurementInputs: def __init__(self, model_version_id: int, gbd_round_id: int, decomp_step_id: int, conn_def: str, country_covariate_id: List[int], csmr_cause_id: int, crosswalk_version_id: int, csmr_process_version_id: Optional[int] = None, location_set_version_id: Optional[int] = None, drill_location_start: Optional[int] = None, drill_location_end: Optional[List[int]] = 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 the model version ID gbd_round_id the GBD round ID decomp_step_id the decomp step ID csmr_process_version_id process version ID for CSMR csmr_cause_id: (int) cause to pull CSMR from crosswalk_version_id crosswalk version to use country_covariate_id list of covariate IDs conn_def connection definition from .odbc file (e.g. 'epi') to connect to the IHME databases location_set_version_id can be None, if it's none, get the best location_set_version_id for estimation hierarchy of this GBD round drill_location_start which location ID to drill from as the parent drill_location_end which immediate children of the drill_location_start parent to include in the drill 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[int, int] 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 Examples -------- >>> from cascade_at.settings.base_case import BASE_CASE >>> from cascade_at.settings.settings import load_settings >>> >>> settings = load_settings(BASE_CASE) >>> covariate_id = [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_id, >>> conn_def='epi', >>> location_set_version_id=settings.location_set_version_id >>> ) >>> i.get_raw_inputs() >>> i.configure_inputs_for_dismod(settings) """ 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.drill_location_start = drill_location_start self.drill_location_end = drill_location_end self.decomp_step = ds.decomp_step_from_decomp_step_id(self.decomp_step_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.demographics = Demographics( gbd_round_id=self.gbd_round_id, location_set_version_id=self.location_set_version_id) self.location_dag = LocationDAG( location_set_version_id=self.location_set_version_id, gbd_round_id=self.gbd_round_id ) # Need to subset the locations to only those needed for # the drill. drill_locations_all is the set of locations # to pull data for, including all descendents. drill_locations # is the set of locations just parent-children in the drill. drill_locations_all, drill_locations = locations_by_drill( drill_location_start=self.drill_location_start, drill_location_end=self.drill_location_end, dag=self.location_dag ) if drill_locations_all: self.demographics.location_id = drill_locations_all self.demographics.drill_locations = drill_locations 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. """ 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: SettingsConfig, midpoint: bool = False, mortality_year_reduction: int = 5): """ Modifies the inputs for DisMod based on model-specific settings. Arguments --------- settings Settings for the model mortality_year_reduction number of years to decimate csmr and asdr """ self.data_eta = data_eta_from_settings(settings) self.density = density_from_settings(settings) self.nu = nu_from_settings(settings) self.measures_to_exclude = 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, midpoint=midpoint, ) 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 = 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 prune_mortality_data(self, parent_location_id: int) -> pd.DataFrame: """ Remove mortality data for descendents that are not children of parent_location_id from the configured dismod data before it gets filled into the dismod database. """ df = self.dismod_data.copy() direct_children = self.location_dag.parent_children(parent_location_id) direct_children = df.location_id.isin(direct_children) mortality_measures = df.measure.isin([ IntegrandEnum.mtall.name, IntegrandEnum.mtspecific.name ]) remove_rows = ~direct_children & mortality_measures df = df.loc[~remove_rows].copy() return df def add_covariates_to_data(self, df: pd.DataFrame) -> pd.DataFrame: """ 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. """ 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: int, sex_id: int) -> pd.DataFrame: """ Converts the demographics of the model to the avgint table. """ LOG.info(f"Getting grid for the avgint table " f"for parent location ID {parent_location_id} " f"and sex_id {sex_id}.") if self.drill_location_start is not None: locations = self.demographics.drill_locations else: locations = self.location_dag.parent_children(parent_location_id) grid = expand_grid({ 'sex_id': [sex_id], 'location_id': locations, '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 interpolate_country_covariate_values(self, df: pd.DataFrame, cov_dict: Dict[Union[float, str], pd.DataFrame]): """ Interpolates the covariate values onto the data so that the non-standard ages and years match up to meaningful covariate values. """ 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 " f"{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: int, sex_id: int) -> CovariateSpecs: """ 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 = self.location_dag.children(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 def reset_index(self, drop, inplace): pass
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