def main(country="Italy", province=None, file_prefix="ita"):
"""
Run scenario analysis template.
Args:
country (str): country name
pronvince (str or None): province name or None (country level)
file_prefix (str): prefix of the filenames
"""
# This script works with version >= 2.18.0-beta
print(cs.get_version())
# Create output directory in example directory
code_path = Path(__file__)
input_dir = code_path.parent.with_name("input")
output_dir = code_path.with_name("output").joinpath(f"{code_path.stem}_{file_prefix}")
output_dir.mkdir(exist_ok=True, parents=True)
filer = cs.Filer(output_dir, prefix=file_prefix, numbering="01")
# Load datasets
data_loader = cs.DataLoader(input_dir)
jhu_data = data_loader.jhu()
population_data = data_loader.population()
# Extra datasets
oxcgrt_data = data_loader.oxcgrt()
# Start scenario analysis
snl = cs.Scenario(country=country, province=province)
snl.register(jhu_data, population_data, extras=[oxcgrt_data])
# Show records
record_df = snl.records(**filer.png("records"))
record_df.to_csv(**filer.csv("records", index=False))
# Show S-R trend
snl.trend(**filer.png("trend"))
print(snl.summary())
# Parameter estimation
snl.estimate(cs.SIRF)
snl.estimate_accuracy("10th", name="Main", **filer.png("estimate_accuracy"))
# Score of parameter estimation
metrics = ["MAE", "MSE", "MSLE", "RMSE", "RMSLE"]
for metric in metrics:
metric_name = metric.rjust(len(max(metrics, key=len)))
print(f"{metric_name}: {snl.score(metric=metric)}")
# Reproduction number in past phases
snl.history("Rt", **filer.png("history_rt_past"))
# Main scenario: parameters not changed
snl.add(name="Main", days=60)
snl.simulate(name="Main", **filer.png("simulate_main"))
snl.history_rate(name="Main", **filer.png("history-rate_main"))
# Forecast scenario: Short-term prediction with regression and OxCGRT data
fit_dict = snl.fit(delay=(7, 31), name="Forecast")
fit_dict.pop("coef").to_csv(**filer.csv("forecast_coef", index=True))
del fit_dict["dataset"], fit_dict["intercept"]
print(fit_dict)
snl.predict(name="Forecast")
snl.adjust_end()
snl.simulate(name="Forecast", **filer.png("simulate_forecast"))
snl.history_rate(name="Main", **filer.png("history-rate_forecast"))
# Parameter history
for item in ["Rt", "rho", "sigma", "Confirmed", "Infected", "Recovered", "Fatal"]:
snl.history(item, **filer.png(f"history_{item}"))
# Summary
snl.summary().to_csv(**filer.csv("summary", index=True))
def main():
# This script works with version >= 2.18.0-alpha
print(cs.get_version())
# Create output directory in example directory
code_path = Path(__file__)
output_dir = code_path.with_name("output").joinpath(code_path.stem)
output_dir.mkdir(exist_ok=True, parents=True)
filer = cs.Filer(output_dir, numbering="01")
# Setting
models = [cs.SIR, cs.SIRD, cs.SIRF]
step_numbers = list(range(3, 10))
# Execute validation
for step_n in step_numbers:
validator = cs.ModelValidator(tau=1440, n_trials=8, step_n=step_n, seed=2)
for model in models:
validator.run(model)
validator.summary().to_csv(**filer.csv(f"summary_{step_n}-points", index=False))