d.NewCases.mask[i, c_s:] = True
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)
if "Phi" in trace.varnames:
self.Phi = trace.Phi
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
if "Phi" in trace.varnames:
self.Phi = trace.Phi
elif "Phi_1" in trace.varnames:
self.Phi = trace.Phi_1
if __name__ == "__main__":
folds = [['DE', 'HU', 'FI', 'IE', 'RS', 'BE'],
['DK', 'GR', 'NO', 'FR', 'RO', 'MA'],
['ES', 'CZ', 'NL', 'CH', 'PT', 'AT'],
['IL', 'SE', 'IT', 'MX', 'GB', 'PL']]
fold_rs = folds[args.fold]
dp = DataPreprocessor(drop_HS=True)
data = dp.preprocess_data("notebooks/final_data/data_final.csv")
major_interventions = ["School Closure", "Stay Home Order", "Some Businesses Suspended",
"Most Businesses Suspended", "Gatherings <10", "Gatherings <1000", "Gatherings <100"]
minor_interventions = ["Mask Wearing", "Symptomatic Testing"]
ActiveCMs = copy.deepcopy(data.ActiveCMs)
maj_indxs = np.array([data.CMs.index(x) for x in major_interventions])
min_indxs = np.array([data.CMs.index(x) for x in minor_interventions])
nRs, nCMs, nDs = ActiveCMs.shape
for r in range(nRs):
maj_active = np.sum(data.ActiveCMs[r, maj_indxs, :], axis=0)