print('method', method)
print('dataset_id', dataset_id)
input_dir = 'cascade/{}/'.format(dataset_id)
if method != 'our':
output_dir = 'output/{}/{}/'.format(method, dataset_id)
eval_result_path = 'eval/{}/{}.pkl'.format(method, dataset_id)
else:
output_dir = 'output/{}-{}/{}/'.format(method, root_sampler,
dataset_id)
eval_result_path = 'eval/{}-{}/{}.pkl'.format(
method, root_sampler, dataset_id)
eval_dir = os.path.dirname(eval_result_path)
print('eval_dir', eval_dir)
makedir_if_not_there(eval_dir)
makedir_if_not_there(output_dir)
if parallel:
print('parallel: ON')
if method == 'min-steiner-tree':
n_jobs = 4 # memory reason
else:
n_jobs = max_n_jobs
print('n_jobs', n_jobs)
rows = Parallel(n_jobs=n_jobs)(
delayed(one_run)(g,
edge_weights,
input_path,
output_dir,
method,
edge_weights = g.edge_properties['weights']
dataset_id = "{}-m{}-s{}-o{}-omuniform".format(graph, cascade_model,
cascade_fraction,
obs_fraction)
print('method', method)
print('dataset_id', dataset_id)
input_dir = 'cascade/{}/'.format(dataset_id)
output_dir = 'output/{}-{}/{}/'.format(method, root_sampler_name,
dataset_id)
eval_result_path = 'eval/{}-{}/{}.pkl'.format(method,
root_sampler_name,
dataset_id)
makedir_if_not_there(output_dir)
makedir_if_not_there(os.path.dirname(eval_result_path))
rows = Parallel(n_jobs=n_jobs)(
delayed(one_run)(g,
edge_weights,
input_path,
output_dir,
method,
root_sampler_name=root_sampler_name,
n_sample=n_sample)
for input_path in tqdm(glob(input_dir + '*.pkl'))
if not is_processed(input_path, output_dir))
# assert len(rows) > 0, 'nothing calculated'
mu_mo=br.mu_mo,
k_days=int(br.k_days),
x0_pt=br.x0_pt)
pkl.dump(params, open('output/params_after_lockdown.pkl', 'wb'))
total, delta, increase, trans_data, aux = do_simulation(total_days + 60,
bed_info,
params,
p0_time=start_date)
I_true_all = df[(df['date'] > start_date)]['infected'].values
I_pred_all = increase[1:len(I_true_all) + 1, STATE.I]
I_pred = increase[1:(len(I_true) + 1), STATE.I]
dates = pd.date_range(start_date + timedelta(days=1),
end_date + timedelta(days=2))
data = {
'date': dates,
'true_I': I_true_all,
'pred_I': I_pred_all,
'abs_error': np.abs(I_true_all - I_pred_all),
'squared_error': np.power(I_true_all - I_pred_all, 2),
'used_in_fitting': [(d >= start_date) & (d <= end_date) for d in dates]
}
fit_df = pd.DataFrame.from_dict(data)
makedir_if_not_there('output/tbl/parameter_fitting')
fit_df.to_csv('output/tbl/parameter_fitting/daily-data.csv', index=None)
stages=[days_to_p0]
)
total, delta, increase, trans, stats = do_simulation(total_days, bed_info, params, p0_time=p0_time)
bundle = [total, delta, increase, trans]
return assumed_ld_date, delta_t, bundle, stats
file_path_factor='output/params_after_lockdown/'
file_list_factor=os.listdir(file_path_factor)
file_path_bed='data/bed_info/'
file_list_bed=os.listdir(file_path_bed)
for f_factor in file_list_factor:
params_after=pkl.load(open(file_path_factor+f_factor, 'rb'))
factor_one=f_factor.split(".pkl")[0].split("_")[3]
for f_bed in file_list_bed:
bed_info=pkl.load(open(file_path_bed+f_bed, 'rb'))
bed_one=f_bed.split(".pkl")[0].split("_")[2]
rows = Parallel(n_jobs=1)(delayed(one_run)(params_after,bed_info,delta_t, total_days) for delta_t in range(-7, 8))
makedir_if_not_there('figs/advance-or-delay-lockdown/')
for dt, days, bundle, stats in rows:
dt_str = dt.strftime('%y-%m-%d')
print(days)
print(dt_str)
fig, ax = plot_total(bundle[0], p0_time, total_days)
fig.savefig(f'figs/advance-or-delay-lockdown/factor-{factor_one}-bed-{bed_one}-{dt_str}({days}).pdf')
save_bundle(bundle, p0_time, total_days, f'output/tbl/advance-or-delay-lockdown/factor-{factor_one}-bed-{bed_one}-{dt_str}-({days})/')
save_to_json(stats, f'output/tbl/advance-or-delay-lockdown/factor-{factor_one}-bed-{bed_one}-{dt_str}-({days})/stats.txt')
params
# In[14]:
stats
# In[15]:
p0_time + timedelta(days=total_days)
# In[16]:
from helpers import plot_total
fig, ax = plot_total(total, p0_time, total_days)
fig.savefig('figs/start2end.pdf')
# In[2]:
makedir_if_not_there('output/tbl/start2end')
# In[17]:
save_bundle([total, delta, increase, trans_data], p0_time, total_days,
'output/tbl/start2end')
# In[18]:
path = 'output/tbl/start2end/stats.txt'
save_to_json(stats, path)
# t is the number of days back
rows = Parallel(n_jobs=1)(delayed(one_run)(t) for t in tqdm(range(20, 61)))
df = pd.DataFrame(rows,
columns=('t', 'actual_I', 'pred_I', 'mse_I', 'mse_E',
'mse_M', 'mse_IM', 'mse_IEM', 'r0_info'))
df.sort_values(by='mse_I').head(10)
best_t = int(df.sort_values(by='mse_IM').iloc[0].t)
p0_time = T('2020/3/20') - timedelta(days=best_t)
print(p0_time)
makedir_if_not_there('output/tbl/p0-time/')
df.to_csv('output/tbl/p0-time/error.csv', index=False)
pkl.dump(p0_time, open('output/p0_time.pkl', 'wb'))
params = copy(params_jan27)
params.alpha = infection_factor * params.alpha
params.beta = infection_factor * params.beta
params.initial_num_E = 1
params.initial_num_I = 0
params.initial_num_M = 0
total, delta, increase, trans, stats = do_simulation(best_t,
bed_info,
params,
