def train_model(start_date, end_date, location, warmup=0, samples=200):
model_type = covid.models.SEIRD_incident.SEIRD
util.run_place(data,
location,
start=start_date,
end=end_date,
model_type=model_type,
rw_scale=1e-1,
num_warmup=warmup,
num_samples=samples)
parser = argparse.ArgumentParser(description='Run Bayesian compartmental models.')
parser.add_argument('place', help='place to use (e.g., US state)')
parser.add_argument('--start', help='start date', default='2020-03-04')
parser.add_argument('--end', help='end date', default=None)
parser.add_argument('--prefix', help='path prefix for saving results', default='results')
parser.add_argument('--config', help='model configuration name', default='SEIRD')
args = parser.parse_args()
if args.config not in dir(configs):
print(f'Invalid config: {args.config}. Options are {dir(configs)}')
exit()
config = getattr(configs, args.config)
data = util.load_data()
util.run_place(data,
args.place,
start=args.start,
end=args.end,
prefix=args.prefix,
model_type=config['model'],
**config['args'])
util.gen_forecasts(data,
args.place,
start=args.start,
prefix=args.prefix,
show=False)
import pandas as pd
import covid
import covid.util as util
import covid.models.SEIRD_variable_detection
import covid.models.SEIRD
if __name__ == "__main__":
place = sys.argv[1]
data = util.load_state_data()
util.run_place(data,
place,
start='2020-03-15',
end='2020-04-27',
T_future=8 * 7,
model_abrv="SEIRD",
model_type=covid.models.SEIRD.SEIRD,
num_warmup=100,
num_samples=100)
util.run_place(data,
place,
start='2020-03-15',
end='2020-04-27',
T_future=8 * 7,
model_abrv="SEIRD_variable_detection",
model_type=covid.models.SEIRD_variable_detection.SEIRD,
num_warmup=100,
num_samples=100)