if __name__ == "__main__":
class ResultsObject():
def __init__(self, indxs, trace):
self.CMReduction = trace.CMReduction
self.RegionLogR = trace.RegionLogR[:, indxs]
self.InfectedCases = trace.InfectedCases[:, indxs, :]
self.InfectedDeaths = trace.InfectedDeaths[:, indxs, :]
self.ExpectedCases = trace.ExpectedCases[:, indxs, :]
self.ExpectedDeaths = trace.ExpectedDeaths[:, indxs, :]
self.Phi = trace.Phi_1
dp = DataPreprocessor(min_confirmed=100, drop_HS=True)
data = dp.preprocess_data("notebooks/final_data/data_final.csv")
HO_rs = ["DE", "PT", "CZ", "PL", "MX", "NL"]
indxs = [data.Rs.index(rg) for rg in HO_rs]
unmask_all(data)
for region in HO_rs:
mask_region(data, region)
print(f"Growth Noise {args.g}")
with cm_effect.models.CMCombined_Final_V3(data, None) as model:
model.DailyGrowthNoise = args.g
model.build_model()
with model.model:
model.trace = pm.sample(args.nS, chains=args.nC, target_accept=0.95)
elif "Phi_1" in trace.varnames:
self.Phi = trace.Phi_1
if __name__ == "__main__":
folds = [['FR', 'GR', 'NL', 'BA', 'LV'], ['SE', 'DE', 'LT', 'MY', 'BG'],
['FI', 'DK', 'CZ', 'RS', 'BE'], ['NO', 'SK', 'IL', 'CH', 'ES'],
['ZA', 'MX', 'IT', 'IE', 'GE'], ['RO', 'PL', 'MA', 'HU', 'SI'],
['NZ', 'SG', 'PT', 'HR', 'EE']]
eval_fold = ['AL', 'AT', 'GB', 'AD', 'IS', 'MT']
dp = DataPreprocessor()
for fold_i, fold in enumerate(folds):
data = dp.preprocess_data(
"notebooks/double-entry-data/double_entry_final.csv",
last_day="2020-05-30",
schools_unis="whoops")
data.mask_reopenings()
r_is = []
for rg in fold:
c_s, d_s = mask_region(data, rg)
r_is.append(data.Rs.index(rg))
with cm_effect.models.CMCombined_Final_DifEffects(data, None) as model:
model.DailyGrowthNoise = args.growth_noise
model.RegionVariationNoise = args.country_noise
model.build_model()
with model.model:
model.trace = pm.sample(2000, cores=4, chains=4, max_treedepth=12)
from epimodel.pymc3_models import cm_effect
from epimodel.pymc3_models.cm_effect.datapreprocessor import DataPreprocessor
import argparse
import pickle
argparser = argparse.ArgumentParser()
argparser.add_argument("--l", dest="last_date", type=int)
argparser.add_argument("--m", dest="model", type=int)
args = argparser.parse_args()
if __name__ == "__main__":
last_dates = ["2020-04-25", "2020-05-05", "2020-05-15", "2020-05-25", "2020-05-30"]
dp = DataPreprocessor()
data = dp.preprocess_data("notebooks/double-entry-data/double_entry_final.csv",
last_day=last_dates[args.last_date])
data.mask_reopenings()
if args.model == 0:
with cm_effect.models.CMCombined_Final(data, None) as model:
model.build_model(serial_interval_mean=6.7, serial_interval_sigma=2.1)
elif args.model == 1:
with cm_effect.models.CMCombined_Final(data, None) as model:
model.build_model(serial_interval_mean=5.1, serial_interval_sigma=1.8)
elif args.model == 2:
with cm_effect.models.CMCombined_Final(data, None) as model:
model.build_model(serial_interval_mean=6.68, serial_interval_sigma=4.88)
elif args.model == 3:
with cm_effect.models.CMCombined_Final_Reset1(data, None) as model:
model.build_model(serial_interval_mean=6.68, serial_interval_sigma=4.88)
elif args.model == 4: