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
d_s = np.nonzero(np.cumsum(d.NewDeaths.data[i, :] > 0) == days + 1)[0] if len(d_s) > 0: d_s = d_s[0] else: d_s = len(d.Ds) d.Active.mask[i, c_s:] = True d.Confirmed.mask[i, c_s:] = True d.Deaths.mask[i, d_s:] = True d.NewDeaths.mask[i, d_s:] = True d.NewCases.mask[i, c_s:] = True if __name__ == "__main__": dp = DataPreprocessor() exp_num = args.exp print(f"running exp {exp_num}") # structural sensitivity if exp_num == 1: data = dp.preprocess_data( "notebooks/double-entry-data/double_entry_final.csv", last_day="2020-05-30", schools_unis="whoops") data.mask_reopenings() with cm_effect.models.CMCombined_Additive(data, None) as model: model.build_model() elif exp_num == 2:
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 = [['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)
with cm_effect.models.CMCombined_ICL_NoNoise_3(data, None) as model: model.build_model() elif args.model == 2: with cm_effect.models.CMCombined_ICL_NoNoise_4(data, None) as model: model.build_model() elif args.model == 3: with cm_effect.models.CMCombined_ICL_NoNoise_5(data, None) as model: model.build_model() elif args.model == 4: with cm_effect.models.CMCombined_ICL_NoNoise_6(data, None) as model: model.build_model() elif args.model == 5: with cm_effect.models.CMCombined_ICL_NoNoise_7(data, None) as model: model.build_model() elif args.model == 6: dp = DataPreprocessor(drop_HS=True) dp.N_smooth = 1 data = dp.preprocess_data("notebooks/final_data/data_final.csv") with cm_effect.models.CMCombined_ICL_NoNoise_7(data, None) as model: model.build_model() with model.model: model.trace = pm.sample(1500, chains=6, target_accept=0.9) np.savetxt(f"icl_bugs/model_{args.model+2}.csv", model.trace.CMReduction)
warnings.simplefilter(action="ignore", category=FutureWarning) 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: