def build_all_wi_scenario_dicts_from_outputs(output_filename):
opti_outputs_df = read_opti_outputs(output_filename)
all_wi_sc_params = {}
for country in OPTI_REGIONS:
all_wi_sc_params[country] = {}
for sc_idx, settings in WI_SCENARIO_MAPPING.items():
decision_vars = read_decision_vars(
opti_outputs_df,
country,
"by_age",
settings["duration"],
settings["objective"],
)
if decision_vars is not None:
all_wi_sc_params[country][
sc_idx] = build_optimised_scenario_dictionary(
country,
sc_idx,
decision_vars,
WI_SCENARIO_MAPPING,
for_waning_immunity=True,
final_mixing=settings["final_mixing"],
)
return all_wi_sc_params
def main():
opti_output_filename = "opti_outputs.csv"
opti_outputs_df = read_opti_outputs(opti_output_filename)
target_objective = {
"deaths": 20,
"yoll": 1000,
}
for direction in ["up", "down"]:
for country in OPTI_REGIONS:
for mode in MODES:
for duration in DURATIONS:
for objective in OBJECTIVES:
print()
print()
print(country + " " + objective + " " + mode + " " +
str(duration) + " " + direction)
run_sensitivity_perturbations(
opti_outputs_df,
country,
duration,
mode,
objective,
target_objective_per_million=target_objective[
objective],
tol=0.02,
direction=direction,
)
def main():
# Reset pyplot style
mpl.rcParams.update(mpl.rcParamsDefault)
mpl.pyplot.style.use("ggplot")
opti_output_filename = "opti_outputs.csv"
opti_outputs_df = read_opti_outputs(opti_output_filename)
for mode in MODES:
print(mode)
optimised_matrices = {}
original_matrices = {}
for country in OPTI_REGIONS:
print(country)
original_matrices[country] = get_optimised_mixing_matrix(
country, [1] * 16, MODES[0], DURATIONS[0])
optimised_matrices[country] = {}
for duration in DURATIONS:
print(duration)
optimised_matrices[country][duration] = {}
for objective in OBJECTIVES:
print(objective)
decision_vars = read_decision_vars(opti_outputs_df,
country, mode, duration,
objective)
optimised_matrices[country][duration][
objective] = get_optimised_mixing_matrix(
country, decision_vars, mode, duration)
plot_multicountry_matrices(original_matrices, optimised_matrices, mode)
def run_sensitivity_minimum_mixing(
opti_output_filename="dummy_vars_for_test.csv"):
opti_outputs_df = read_opti_outputs(opti_output_filename)
results = {}
mode = MODES[0]
objective = OBJECTIVES[1]
for country in OPTI_REGIONS:
results[country] = {}
root_model = run_root_model(country)
for duration in DURATIONS:
results[country][duration] = {}
decision_vars = read_decision_vars(opti_outputs_df, country, mode,
duration, objective)
if decision_vars is None:
continue
for min_mixing in [0.10, 0.20, 0.30, 0.40, 0.50]:
modified_vars = copy.deepcopy(decision_vars)
modified_vars = [max([v, min_mixing]) for v in modified_vars]
print(
f"evaluate objective for {country} | {duration} | {objective}: min_mixing={min_mixing}"
)
h, d, yoll = objective_function(modified_vars, root_model,
mode, country, duration)
res_dict = {
"h": bool(h),
"d": float(d),
"yoll": float(yoll),
}
results[country][duration][min_mixing] = res_dict
file_path = os.path.join(
BASE_PATH,
"apps",
"covid_19",
"mixing_optimisation",
"sensitivity_analyses",
"min_mixing_results.yml",
)
with open(file_path, "w") as f:
yaml.dump(results, f)
return results
def main():
# Reset pyplot style
mpl.rcParams.update(mpl.rcParamsDefault)
mpl.pyplot.style.use("ggplot")
opti_output_filename = "opti_outputs.csv"
opti_outputs_df = read_opti_outputs(opti_output_filename)
all_results = {}
for country in OPTI_REGIONS:
all_results[country] = {}
for mode in MODES:
all_results[country][mode] = {}
for duration in DURATIONS:
all_results[country][mode][duration] = {}
for objective in OBJECTIVES:
all_results[country][mode][duration][objective] = read_decision_vars(
opti_outputs_df, country, mode, duration, objective
)
for mode in MODES:
plot_multicountry_optimal_plan(all_results, mode)