def main():
warnings.simplefilter("error")
# 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)
# Setting
eg_tau = 1440
eg_population = 1_000_000
model = cs.SIRFV
eg_population = model.EXAMPLE["population"]
set_param_dict = model.EXAMPLE["param_dict"]
# Simulation
example_data = cs.ExampleData(tau=eg_tau)
example_data.add(model)
# Non-dimensional
nondim_df = example_data.non_dim(model)
nondim_df.to_csv(output_dir.joinpath(
f"{model.NAME}_non_dim.csv"), index=False)
cs.line_plot(
nondim_df.set_index("t"),
title=f"{model.NAME}: Example data (non-dimensional)",
ylabel=str(),
h=1.0,
filename=output_dir.joinpath(f"{model.NAME}_non_dim.png")
)
# Dimensional
dim_df = example_data.cleaned()
dim_df.to_csv(output_dir.joinpath(f"{model.NAME}_dim.csv"), index=False)
cs.line_plot(
dim_df.set_index("Date").drop("Confirmed", axis=1),
title=f"{model.NAME}: Example data (dimensional)",
h=eg_population,
y_integer=True,
filename=output_dir.joinpath(f"{model.NAME}_dim.png")
)
# Hyperparameter estimation of example data
estimator = cs.Estimator(
example_data, model=model, population=eg_population,
country=model.NAME, province=None, tau=eg_tau, omega=0.001
)
estimator.run()
estimated_df = estimator.summary(name=model.NAME)
estimated_df = estimated_df.append(
pd.Series({**set_param_dict, "tau": eg_tau}, name="set")
)
estimated_df["tau"] = estimated_df["tau"].astype(np.int64)
estimated_df.to_csv(
output_dir.joinpath(f"{model.NAME}_estimate_parameter.csv"), index=True
)
# Show the history of optimization
estimator.history(filename=output_dir.joinpath(
f"{model.NAME}_estimate_history.png")
)
# Show the accuracy as a figure
estimator.accuracy(filename=output_dir.joinpath(
f"{model.NAME}_estimate_accuracy.png")
)
def main():
# 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)
# Setting
eg_population = 1_000_000
eg_tau = 1440
start_date = "22Jan2020"
model = cs.SIRF
set_param_dict = {
"theta": 0.002, "kappa": 0.005, "rho": 0.2, "sigma": 0.075
}
set_param_dict_2 = {
"theta": 0.002, "kappa": 0.005, "rho": 0.1, "sigma": 0.075
}
y0_dict = {
"Susceptible": 999_000, "Infected": 1000, "Recovered": 0, "Fatal": 0
}
# Dataset for S-R trend analysis
simulator = cs.ODESimulator(country="Trend", province=model.NAME)
simulator.add(
model=model, step_n=30, population=eg_population,
param_dict=set_param_dict, y0_dict=y0_dict
)
simulator.add(
model=model, step_n=30, population=eg_population,
param_dict=set_param_dict_2
)
simulator.run()
dim_df = simulator.dim(tau=eg_tau, start_date=start_date)
cs.line_plot(
dim_df.set_index("Date"),
title=f"{model.NAME}: Example data for S-R trend analysis",
h=eg_population,
y_integer=True,
filename=output_dir.joinpath("trend_data.png")
)
restored_df = model.restore(dim_df)
# 1st trial of S-R trend analysis
change_finder = cs.ChangeFinder(restored_df, eg_population, "Trend")
change_finder.run(n_points=1, n_jobs=1, seed=0)
change_finder.show(
filename=output_dir.joinpath("trend_1st.png")
)
# 2nd trial of S-R trend analysis
change_finder = cs.ChangeFinder(restored_df, eg_population, "Trend")
change_finder.run(n_points=1, n_jobs=1, seed=0)
change_finder.show(
filename=output_dir.joinpath("trend_2nd.png")
)
# Dataset for parameter estimation
simulator = cs.ODESimulator(country="Param", province=model.NAME)
simulator.add(
model=model, step_n=150, population=eg_population,
param_dict=set_param_dict, y0_dict=y0_dict
)
simulator.run()
dim_df = simulator.dim(tau=eg_tau, start_date=start_date)
cs.line_plot(
dim_df.set_index("Date"),
title=f"{model.NAME}: Example data for parameter estimation",
h=eg_population,
y_integer=True,
filename=output_dir.joinpath("estimate_data.png")
)
# 1st trial of parameter estimation
estimator = cs.Estimator(
clean_df=dim_df, model=model, population=eg_population,
country="Param", province=model.NAME, tau=eg_tau
)
estimator.run(n_jobs=1, seed=0)
estimator.history(filename=output_dir.joinpath("estimate_history_1st.png"))
# 2nd trial of parameter estimation
estimator = cs.Estimator(
clean_df=dim_df, model=model, population=eg_population,
country="Param", province=model.NAME, tau=eg_tau
)
estimator.run(n_jobs=1, seed=0)
estimator.history(filename=output_dir.joinpath("estimate_history_2nd.png"))
def main():
# 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)
# Setting
eg_population = 1_000_000
eg_tau = 1440
start_date = "22Jan2020"
model = cs.SIR
set_param_dict = {"rho": 0.2, "sigma": 0.075}
y0_dict = {
"Susceptible": 999_000,
"Infected": 1000,
"Fatal or Recovered": 0
}
# Simulation
simulator = cs.ODESimulator(country="Example", province=model.NAME)
simulator.add(model=model,
step_n=180,
population=eg_population,
param_dict=set_param_dict,
y0_dict=y0_dict)
simulator.run()
# Non-dimensional
nondim_df = simulator.non_dim()
nondim_df.to_csv(output_dir.joinpath(f"{model.NAME}_non_dim.csv"),
index=False)
cs.line_plot(nondim_df.set_index("t"),
title=f"{model.NAME}: Example data (non-dimensional)",
ylabel=str(),
h=1.0,
filename=output_dir.joinpath(f"{model.NAME}_non_dim.png"))
# Dimensional
dim_df = simulator.dim(tau=eg_tau, start_date=start_date)
dim_df.to_csv(output_dir.joinpath("dim.csv"), index=False)
cs.line_plot(dim_df.set_index("Date"),
title=f"{model.NAME}: Example data (dimensional)",
h=eg_population,
y_integer=True,
filename=output_dir.joinpath(f"{model.NAME}_dim.png"))
# Hyperparameter estimation of example data
estimator = cs.Estimator(clean_df=dim_df,
model=model,
population=eg_population,
country="Example",
province=model.NAME,
tau=eg_tau)
estimator.run()
estimated_df = estimator.summary(name=model.NAME)
estimated_df.loc["Setted"] = pd.Series({**set_param_dict, "tau": eg_tau})
estimated_df["tau"] = estimated_df["tau"].astype(np.int64)
estimated_df.to_csv(
output_dir.joinpath(f"{model.NAME}_estimate_parameter.csv"),
index=True)
# Show the history of optimization
estimator.history(
filename=output_dir.joinpath(f"{model.NAME}_estimate_history.png"))
# Show the accuracy as a figure
estimator.accuracy(
filename=output_dir.joinpath(f"{model.NAME}_estimate_accuracy.png"))
