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)