def run_world(year, cascade, drop_emr=False, reimport=False):
cl = Cascade_loc(1, 0, year, cascade, timespan=50, reimport=reimport)
if drop_emr:
cl.gen_data(1, 0, drop_emr=True)
cl.run_dismod()
cl.summarize_posterior()
cl.draw()
cl.predict()
return cascade
def run_loc(args):
'''Meant to be called in parallel using multiprocessing. Run
dismod.
Args:
args(Tuple[int, int, int, Bool, Bool]): tuple of
(location_id, sex_id, year_id, full_timespan, debug)
Returns:
Tuple of location_id and either a string error message or integer 0,
representing no error
'''
gc.collect()
loc_id, sex_id, year, full_timespan, debug = args
if debug:
if full_timespan:
cl = Cascade_loc(loc_id,
sex_id,
year,
cascade,
timespan=50,
parent_loc=cl_parent)
else:
cl = Cascade_loc(loc_id,
sex_id,
year,
cascade,
parent_loc=cl_parent)
cl.run_dismod()
cl.summarize_posterior()
cl.draw()
cl.predict()
return loc_id, 0
else:
try:
if full_timespan:
cl = Cascade_loc(loc_id,
sex_id,
year,
cascade,
timespan=50,
parent_loc=cl_parent)
else:
cl = Cascade_loc(loc_id,
sex_id,
year,
cascade,
parent_loc=cl_parent)
cl.run_dismod()
cl.summarize_posterior()
cl.draw()
cl.predict()
return loc_id, 0
except Exception as e:
logging.exception("Failure running location {}".format(loc_id))
return loc_id, str(e)
def run_world(year, cascade, drop_emr=False, reimport=False):
''' Instantiates a global cascade_loc object and runs dismod.
Since we're running dismod, that means this function reads from and writes
to file system.
Args:
year (int): Year to run dismod for
cascade (drill.cascade): Cascade object of the model_version_id we're
running dismod for
drop_emr (bool, False): If True, exclude excess mortality data before
running dismod
reimport (bool, False): if True, read input data from database via
importer.Importer object. If False, rely on cached csv files
written during a previous cascade instantiation
(if a file is missing, will automatically read from db)
Returns:
cascade object that was passed in
'''
log = logging.getLogger(__name__)
cl = Cascade_loc(1, 0, year, cascade, timespan=50, reimport=reimport)
if drop_emr:
cl.gen_data(1, 0, drop_emr=True)
log.info("Starting dismod for year {} loc {}".format(year, cl.loc))
cl.run_dismod()
log.info("dismod finished")
log.info("summarizing posterior")
cl.summarize_posterior()
log.info("summarizing posterior finished")
log.info("begin draw")
cl.draw()
log.info("draw finished")
log.info("beginning predict")
cl.predict()
log.info("predict finished")
return cascade
def run_loc(args):
gc.collect()
loc_id, sex_id, year, full_timespan, debug = args
if debug:
if full_timespan:
cl = Cascade_loc(loc_id,
sex_id,
year,
c,
timespan=50,
parent_loc=cl_parent)
else:
cl = Cascade_loc(loc_id, sex_id, year, c, parent_loc=cl_parent)
cl.run_dismod()
cl.summarize_posterior()
cl.draw()
cl.predict()
return loc_id, 0
else:
try:
if full_timespan:
cl = Cascade_loc(loc_id,
sex_id,
year,
c,
timespan=50,
parent_loc=cl_parent)
else:
cl = Cascade_loc(loc_id, sex_id, year, c, parent_loc=cl_parent)
cl.run_dismod()
cl.summarize_posterior()
cl.draw()
cl.predict()
return loc_id, 0
except Exception as e:
logging.exception("Failure running location {}".format(loc_id))
return loc_id, str(e)