def get_label():
pad = 24
data_dict = data_loader.get_data_pyro(countries,
smart_start=False,
pad=pad,
rebuttal=True)
data_dict = smooth_daily(data_dict)
test_start = [str(x.date())
for x in list(data_dict['date_list'])].index('2020-04-25')
test_len = 30
actual_14 = data_dict['actual_daily_death'][test_start:test_start +
test_len].numpy()
eval_days = [
str(data_dict['date_list'][test_start].date() + timedelta(days=x))
for x in range(test_len)
]
return actual_14, eval_days
args = parser.parse_args()
days = int(args.days)
register_matplotlib_converters()
countries = [
'United Kingdom', 'Italy', 'Germany', 'Spain', 'US', 'France', 'Belgium',
'Korea, South', 'Brazil', 'Iran', 'Netherlands', 'Canada', 'Turkey',
'Romania', 'Portugal', 'Sweden', 'Switzerland', 'Ireland', 'Hungary',
'Denmark', 'Austria', 'Mexico', 'India', 'Ecuador', 'Russia', 'Peru',
'Indonesia', 'Poland', 'Philippines', 'Japan', 'Pakistan'
]
niter = 2000
n_sample = 500
pad = 24
data_dict = data_loader.get_data_pyro(countries, smart_start=False, pad=pad)
data_dict = pyro_model.helper.smooth_daily(data_dict)
train_len = data_dict['cum_death'].shape[0] - days
n_country = len(countries)
covariates_notime = pyro_model.helper.get_covariates_intervention(data_dict,
train_len,
notime=True)
Y_train = pyro_model.helper.get_Y(data_dict, train_len)
total_len = len(data_dict['date_list'])
covariates_full_notime = pyro_model.helper.get_covariates_intervention(
data_dict, total_len, notime=True)
Y_full = pyro_model.helper.get_Y(data_dict, total_len)
Y_daily = data_dict['daily_death']
'country_feat': data_dict['country_feat']
}
countries = [
'United Kingdom', 'Italy', 'Germany', 'Spain', 'US', 'France', 'Belgium',
'Korea, South', 'Brazil', 'Iran', 'Netherlands', 'Canada', 'Turkey',
'Romania', 'Portugal', 'Sweden', 'Switzerland', 'Ireland', 'Hungary',
'Denmark', 'Austria', 'Mexico', 'India', 'Ecuador', 'Russia', 'Peru',
'Indonesia', 'Poland', 'Philippines', 'Japan', 'Pakistan'
]
pad = 24
data_dict = data_loader.get_data_pyro(countries,
smart_start=False,
pad=pad,
rebuttal=True)
data_dict = smooth_daily(data_dict)
test_start = [str(x.date())
for x in list(data_dict['date_list'])].index('2020-04-25')
test_len = 30
actual_14 = data_dict['actual_daily_death'][test_start:test_start +
test_len].numpy()
eval_days = [
str(data_dict['date_list'][test_start].date() + timedelta(days=x))
for x in range(test_len)
'Portugal',
'Sweden',
'Switzerland',
'Ireland',
'Hungary',
'Denmark',
'Austria',
'Poland',
]
model_id = 'testing'
days = 14
niter = 500
n_sample = 1000
data_dict = data_loader.get_data_pyro(countries)
data_dict = pyro_model.helper.smooth_daily(data_dict)
train_len = data_dict['cum_death'].shape[0] - days
n_country = len(countries)
covariates_notime = pyro_model.helper.get_covariates_intervention(data_dict,
train_len,
notime=True)
Y_train = data_dict['actual_cum_death'][:train_len, :]
total_len = len(data_dict['date_list'])
covariates_full_notime = pyro_model.helper.get_covariates_intervention(
data_dict, total_len, notime=True)
Y_full = data_dict['actual_cum_death'][:total_len, :]
Y_daily = data_dict['actual_daily_death']