numpyro.set_host_device_count(args.num_chains)
if __name__ == "__main__":
print(f"Running Sensitivity Analysis {__file__} with config:")
config = load_model_config(args.model_config)
pprint_mb_dict(config)
print("Loading Data")
data = preprocess_data(get_data_path())
data.featurize(**config["featurize_kwargs"])
data.mask_new_variant(
new_variant_fraction_fname=get_new_variant_path(),
)
data.mask_from_date("2021-01-09")
print("Loading EpiParam")
ep = EpidemiologicalParameters()
model_func = get_model_func_from_str(args.model_type)
ta = get_target_accept_from_model_str(args.model_type)
td = get_tree_depth_from_model_str(args.model_type)
base_outpath = generate_base_output_dir(
args.model_type, args.model_config, args.exp_tag
)
ts_str = datetime.now().strftime("%Y-%m-%d;%H:%M:%S")
summary_output = os.path.join(base_outpath, f"{ts_str}_summary.json")
full_output = os.path.join(base_outpath, f"{ts_str}_full.netcdf")
model_build_dict = config["model_kwargs"]
posterior_samples, _, info_dict, _ = run_model(
numpyro.set_host_device_count(args.num_chains)
if __name__ == "__main__":
print(f"Running Sensitivity Analysis {__file__} with config:")
config = load_model_config(args.model_config)
pprint_mb_dict(config)
print("Loading Data")
data = preprocess_data(get_data_path())
data.featurize(**config["featurize_kwargs"])
data.mask_new_variant(new_variant_fraction_fname=get_new_variant_path(), )
data.mask_from_date("2021-01-09")
print("Loading EpiParam")
ep = EpidemiologicalParameters()
# shift delays
ep.generation_interval["mean"] = (ep.generation_interval["mean"] +
args.gen_int_mean_shift)
ep.onset_to_death_delay["mean"] = (ep.onset_to_death_delay["mean"] +
args.death_delay_mean_shift)
ep.onset_to_case_delay["mean"] = (ep.onset_to_case_delay["mean"] +
args.cases_delay_mean_shift)
ep.generate_delays()
model_func = get_model_func_from_str(args.model_type)
ta = get_target_accept_from_model_str(args.model_type)
r0_scale = np.clip(0.3 + 0.1 * np.random.normal(), a_min=0.1, a_max=0.5)
rw_period = np.random.choice([5, 7, 9, 11, 14])
n_days_seeding = np.random.choice([5, 7, 9, 11, 14])
print("Loading Data")
data = preprocess_data(get_data_path())
data.featurize(**config["featurize_kwargs"])
data.mask_new_variant(
new_variant_fraction_fname=get_new_variant_path(),
maximum_fraction_voc=float(max_frac_voc),
)
data.mask_from_date("2021-01-09")
print("Loading EpiParam")
ep = EpidemiologicalParameters()
# shift delays
ep.generation_interval["mean"] = (ep.generation_interval["mean"] +
gi_shift)
ep.onset_to_death_delay["mean"] = (ep.onset_to_death_delay["mean"] +
dd_shift)
ep.onset_to_case_delay["mean"] = (ep.onset_to_case_delay["mean"] +
cd_shift)
ep.generate_delays()
model_func = get_model_func_from_str(args.model_type)
ta = get_target_accept_from_model_str(args.model_type)