def split(self):
# get input draws
draws = self._epi_draw_source.content(filters=self.demo_filters.copy())
# get split props
filters = self.ss_filters
filters.update(self.demo_filters)
gprops = self._ss_draw_source.content(filters=filters)
splits = merge_split(draws,
gprops,
group_cols=self.dimensions.index_names,
value_cols=self.dimensions.data_list())
splits = splits.assign(modelable_entity_id=splits['child_meid'])
splits = splits[self.dimensions.index_names + ["modelable_entity_id"] +
self.dimensions.data_list()]
splits = splits.fillna(0)
self.pusher.push(splits, append=False)
def _parallel_merge_split(meid_cause_map, interp_files, output_dir, tmpdir,
location_id):
try:
epi_draw = []
for f in interp_files:
epi_draw.append(
pd.read_hdf(f,
'draws',
where=["location_id=={}".format(location_id)]))
epi_draws = pd.concat(epi_draw)
cd = pd.read_hdf(os.path.join(tmpdir.name, 'source_cause_draws.h5'),
'draws',
where=['location_id=={}'.format(location_id)])
draw_cols = [col for col in cd.columns if 'draw_' in col]
epi_draws = epi_draws[epi_draws['age_group_id'].isin(
cd['age_group_id'].unique())]
# these columns are not needed and cause maths.merge_split to break
drop_cols = ['measure_id', 'model_version_id', 'metric_id']
cd.drop(drop_cols, axis=1, inplace=True, errors='ignore')
epi_draws.drop(drop_cols, axis=1, inplace=True, errors='ignore')
cout = merge_split(
cd, epi_draws,
['year_id', 'age_group_id', 'sex_id', 'location_id'], draw_cols)
cout = cout.merge(cd[[
'year_id', 'age_group_id', 'sex_id', 'location_id', 'envelope'
]],
how='left')
cout['cause_id'] = cout['modelable_entity_id']
cout['cause_id'] = cout['cause_id'].replace(meid_cause_map)
cout['measure_id'] = 1
for cid in cout.cause_id.unique():
cid_dir = '{}/{}'.format(output_dir, int(cid))
cid_dir = cid_dir.replace("\r", "")
if not os.path.exists(cid_dir):
makedirs_safely(cid_dir)
fn = '{}/death_{}.csv'.format(cid_dir, location_id)
cout.query('cause_id=={}'.format(cid)).to_csv(fn, index=False)
return location_id, 0
except Exception:
tb_str = traceback.format_exc()
return location_id, tb_str
def filet(source_meid, target_prop_map, location_id, split_meas_ids,
prop_meas_id, gbd_round_id, mvid_map, source_mvid, decomp_step,
n_draws, downsample):
"""
Splits the draws for source_meid to the target meids given in
target_prop_map by the proportions estimated in the prop_meids. The split
is applied to all GBD years associated with the given gbd_round_id for the
specified location_id. The 'best' version of the meids will be used by
default.
Arguments:
source_meid (int): meid for the draws to be split.
target_prop_map (dict): dictionary whose keys are the target meids and
whose values are the meids for the corresponding proportion models.
location_id (int): location_id to operate on.
split_meas_ids (list of ints): The measure_ids from source_meid to be
split.
prop_meas_id (int): The measure_id that identifies the proportion in
prop_meids to use for the split.
gbd_round_id (int): the gbd_round_id for models being split.
mvid_map (dict): relationship of target MEs to proportion MEs.
source_mvid (int): source model version id.
decomp_step (str): Decomposition step. Allowed values are None,
'iterative', 'step1', 'step2', 'step3', 'step4', and 'step5'
depending on the value of gbd_round_id.
n_draws (Optional[int])
downsample (Optional[bool])
Returns:
A DataFrame containing the draws for the target meids
"""
splits = []
props = []
for key in target_prop_map:
if mvid_map is not None:
version_id = mvid_map[target_prop_map[key]]
else:
version_id = None
this_props = get_draws(gbd_id_type='modelable_entity_id',
gbd_id=target_prop_map[key],
source='epi',
measure_id=prop_meas_id,
location_id=location_id,
version_id=version_id,
gbd_round_id=gbd_round_id,
decomp_step=decomp_step,
n_draws=n_draws,
downsample=downsample)
props.append(this_props)
props = pd.concat(props)
props = props.reset_index(drop=True)
props['target_modelable_entity_id'] = (props.modelable_entity_id.replace(
{v: k
for k, v in target_prop_map.items()}))
props_drawcols = [col for col in props.columns if 'draw_' in col]
if source_mvid is not None:
version_id = source_mvid
else:
version_id = None
for measure_id in split_meas_ids:
source = get_draws(gbd_id_type='modelable_entity_id',
gbd_id=[source_meid],
source='epi',
measure_id=measure_id,
location_id=location_id,
version_id=version_id,
gbd_round_id=gbd_round_id,
decomp_step=decomp_step)
source_drawcols = [col for col in source.columns if 'draw_' in col]
props['measure_id'] = measure_id
props = props[props.age_group_id.isin(source.age_group_id.unique())]
props = props[props.sex_id.isin(source.sex_id.unique())]
# These columns are not needed and break maths.merge_split
drop_cols = ['modelable_entity_id', 'model_version_id', 'metric_id']
source.drop(drop_cols, axis=1, inplace=True, errors='ignore')
if len(source) > 0 and len(props) > 0:
if len(target_prop_map) > 1:
force_scale = True
else:
force_scale = False
if len(props_drawcols) != len(source_drawcols):
raise ValueError(
"props and source drawcols are different lengths")
split = merge_split(source,
props, [
'year_id', 'age_group_id', 'sex_id',
'location_id', 'measure_id'
],
props_drawcols,
force_scale=force_scale)
splits.append(split)
else:
pass
splits = pd.concat(splits)
splits = splits[[
'location_id', 'year_id', 'age_group_id', 'sex_id', 'measure_id',
'target_modelable_entity_id'
] + props_drawcols]
splits.rename(
columns={'target_modelable_entity_id': 'modelable_entity_id'},
inplace=True)
return splits