def upload_forecast(forecast_name):
path = get_forecast_info(forecast_name)
db = read_validation_db()
metadata = metadata_dict_for_file(
list(Path(path).parent.glob('metadata-*.txt'))[0])
if f"{metadata['team_abbr']}-{metadata['model_abbr']}" not in [
m.abbreviation for m in models
]:
create_model(path, metadata)
time_zero_date = '-'.join(forecast_name.split('-')[:3])
if time_zero_date not in [
timezero.timezero_date for timezero in project_obj.timezeros
]:
create_timezero(time_zero_date)
# print(forecast_name, metadata, time_zero_date)
if path is not None:
errors_from_validation = validate_quantile_csv_file(path)
if errors_from_validation != "no errors":
print(errors_from_validation)
return errors_from_validation, True
with open(path) as fp:
print('uploading %s' % path)
checksum = hashlib.md5(str(fp.read()).encode('utf-8')).hexdigest()
fp.seek(0)
quantile_json, error_from_transformation = json_io_dict_from_quantile_csv_file(
fp, COVID_TARGETS, covid19_row_validator, COVID_ADDL_REQ_COLS)
if len(error_from_transformation) > 0:
return error_from_transformation, True
try:
fr = util.upload_forecast(
conn, quantile_json, forecast, project_name,
f"{metadata['team_abbr']}-{metadata['model_abbr']}",
time_zero_date)
db[forecast_name] = checksum
write_db(db)
return None, fr
except Exception as e:
raise e
return e, True
pass
def upload_covid_all_forecasts(path_to_processed_model_forecasts, dir_name):
global models
global model_names
# Get all forecasts in the directory of this model
forecasts = os.listdir(path_to_processed_model_forecasts)
conn.re_authenticate_if_necessary()
# Get model name or create a new model if it's not in the current Zoltar project
try:
metadata = metadata_dict_for_file(path_to_processed_model_forecasts +
'metadata-' + dir_name + '.txt')
except Exception as ex:
return ex
model_name = metadata['model_name']
if model_name not in model_names:
model_config = {}
model_config['name'], model_config['abbreviation'], model_config['team_name'], \
model_config['description'], model_config['home_url'], model_config['aux_data_url'] \
= metadata['model_name'], metadata['team_abbr'] + '-' + metadata['model_abbr'], \
metadata['team_name'], metadata['methods'], metadata['website_url'] if metadata.get(
'website_url') != None else url + dir_name, 'NA'
try:
print('Create model %s' % model_name)
project_obj.create_model(model_config)
models = project_obj.models
model_names = [model.name for model in models]
except Exception as ex:
return ex
print('Time: %s \t Model: %s' % (datetime.now(), model_name))
model = [model for model in models if model.name == model_name][0]
# Get names of existing forecasts to avoid re-upload
existing_time_zeros = [
forecast.timezero.timezero_date for forecast in model.forecasts
]
# Batch upload
json_io_dict_batch = []
forecast_filename_batch = []
timezero_date_batch = []
for forecast in forecasts:
# Default config
over_write = False
checksum = 0
time_zero_date = forecast.split(dir_name)[0][:-1]
# Check if forecast is already on zoltar
with open(path_to_processed_model_forecasts + forecast, "rb") as f:
# Get the current hash of a processed file
checksum = hashlib.md5(f.read()).hexdigest()
f.close()
# Check this hash against the previous version of hash
# if db.get(forecast, None) != checksum:
# print(forecast, db.get(forecast, None))
# if time_zero_date in existing_time_zeros:
# over_write = True
# else:
# continue
# if timezero existing, then don't write again
if time_zero_date in existing_time_zeros:
#update checksum
# db[forecast] = checksum
continue
# Skip metadata text file
if '.txt' in forecast:
continue
with open(path_to_processed_model_forecasts + forecast) as fp:
# Create timezero on zoltar if not existed
if time_zero_date not in project_timezeros:
try:
project_obj.create_timezero(time_zero_date)
project_timezeros.append(time_zero_date)
except Exception as ex:
return ex
# Validate covid19 file
errors_from_validation = validate_quantile_csv_file(
path_to_processed_model_forecasts + forecast)
# Upload forecast
if "no errors" == errors_from_validation:
quantile_json, error_from_transformation = json_io_dict_from_quantile_csv_file(
fp, COVID_TARGETS, covid19_row_validator,
COVID_ADDL_REQ_COLS)
if len(error_from_transformation) > 0:
return error_from_transformation
else:
try:
print('Upload forecast for model: %s \t|\t File: %s' %
(model_name, forecast))
print()
util.upload_forecast(conn,
quantile_json,
forecast,
project_name,
model_name,
time_zero_date,
overwrite=over_write)
db[forecast] = checksum
except Exception as ex:
print(ex)
return ex
json_io_dict_batch.append(quantile_json)
timezero_date_batch.append(time_zero_date)
forecast_filename_batch.append(forecast)
else:
return errors_from_validation
fp.close()
# # Batch upload for better performance
# if len(json_io_dict_batch) > 0:
# try:
# util.upload_forecast_batch(conn, json_io_dict_batch, forecast_filename_batch, project_name, model_name, timezero_date_batch, overwrite = over_write)
# except Exception as ex:
# return ex
return "Pass"
def upload_covid_all_forecasts(path_to_processed_model_forecasts, dir_name):
global models
global model_names
# Get all forecasts in the directory of this model
forecasts = os.listdir(path_to_processed_model_forecasts)
# Get model name or create a new model if it's not in the current Zoltar project
try:
metadata = metadata_dict_for_file(path_to_processed_model_forecasts+'metadata-'+dir_name+'.txt')
except Exception as ex:
return ex
model_name = metadata['model_name']
if model_name not in model_names:
model_config = {}
model_config['name'], model_config['abbreviation'], model_config['team_name'], model_config['description'], model_config['home_url'], model_config['aux_data_url'] \
= metadata['model_name'], metadata['team_abbr']+'-'+metadata['model_abbr'], metadata['team_name'], metadata['methods'], url + dir_name, 'NA'
try:
project_obj.create_model(model_config)
models = project_obj.models
model_names = [model.name for model in models]
except Exception as ex:
return ex
model = [model for model in models if model.name == model_name][0]
# Get names of existing forecasts to avoid re-upload
existing_forecasts = [forecast.source for forecast in model.forecasts]
# Batch upload
json_io_dict_batch = []
forecast_filename_batch = []
timezero_date_batch = []
for forecast in forecasts:
over_write = False
checksum = 0
# Check if forecast is already on zoltar
with open(path_to_processed_model_forecasts+forecast, "rb") as f:
# Get the current hash of a processed file
checksum = hashlib.md5(f.read()).hexdigest()
f.close()
# Check this hash against the previous version of hash
if db.get(forecast, None) != checksum:
print(forecast)
if forecast in existing_forecasts:
over_write = True
else:
continue
# Skip metadata text file
if '.txt' in forecast:
continue
with open(path_to_processed_model_forecasts+forecast) as fp:
# Get timezero and create timezero on zoltar if not existed
time_zero_date = forecast.split(dir_name)[0][:-1]
# if time_zero_date != "2020-05-25":
# continue
if time_zero_date not in project_timezeros:
try:
project_obj.create_timezero(time_zero_date)
project_timezeros.append(time_zero_date)
except Exception as ex:
return ex
# Validate covid19 file
errors_from_validation = validate_quantile_csv_file(path_to_processed_model_forecasts+forecast)
# Upload forecast
if "no errors" == errors_from_validation:
quantile_json, error_from_transformation = json_io_dict_from_quantile_csv_file(fp, VALID_TARGET_NAMES, covid19_row_validator)
if len(error_from_transformation) >0 :
return error_from_transformation
else:
try:
util.upload_forecast(conn, quantile_json, forecast,
project_name, model_name , time_zero_date, overwrite=over_write)
db[forecast] = checksum
except Exception as ex:
print(ex)
return ex
json_io_dict_batch.append(quantile_json)
timezero_date_batch.append(time_zero_date)
forecast_filename_batch.append(forecast)
else:
return errors_from_validation
fp.close()
# # Batch upload for better performance
# if len(json_io_dict_batch) > 0:
# try:
# util.upload_forecast_batch(conn, json_io_dict_batch, forecast_filename_batch, project_name, model_name, timezero_date_batch, overwrite = over_write)
# except Exception as ex:
# return ex
return "Pass"
def upload_covid_all_forecasts(path_to_processed_model_forecasts):
# meta info
project_name = 'COVID-19 Forecasts'
project_obj = None
project_timezeros = []
forecasts = os.listdir(path_to_processed_model_forecasts)
conn = util.authenticate()
# Get all existing timezeros in the project
for project in conn.projects:
if project.name == project_name:
project_obj = project
for timezero in project.timezeros:
project_timezeros.append(timezero.timezero_date)
break
# Get model name
separator = '-'
dir_name = separator.join(
forecasts[0].split(separator)[3:]).split('.csv')[0]
metadata = metadata_dict_for_file(path_to_processed_model_forecasts +
'metadata-' + dir_name + '.txt')
model_name = metadata['model_name']
model = [
model for model in project_obj.models if model.name == model_name
][0]
# Get names of existing forecasts to avoid re-upload
existing_forecasts = [forecast.source for forecast in model.forecasts]
for forecast in forecasts:
# Skip if forecast is already on zoltar
if forecast in existing_forecasts:
continue
# Skip metadata text file
if '.txt' in forecast:
continue
with open(path_to_processed_model_forecasts + forecast) as fp:
# Get timezero and create timezero on zoltar if not existed
time_zero_date = forecast.split(dir_name)[0][:-1]
if time_zero_date not in project_timezeros:
try:
project_obj.create_timezero(time_zero_date)
except Exception as ex:
print(ex)
# Validate covid19 file
errors_from_validation = validate_quantile_csv_file(
path_to_processed_model_forecasts + forecast)
# Upload forecast
if "no errors" == errors_from_validation:
quantile_json, error_from_transformation = json_io_dict_from_quantile_csv_file(
fp, COVID19_TARGET_NAMES, covid19_row_validator)
if len(error_from_transformation) > 0:
print(error_from_transformation)
else:
try:
util.upload_forecast(conn,
quantile_json,
forecast,
project_name,
model_name,
time_zero_date,
overwrite=False)
except Exception as ex:
print(ex)
else:
print(errors_from_validation)
fp.close()
def upload_covid_all_forecasts(path_to_processed_model_forecasts, dir_name):
global models
global model_abbrs
# Get all forecasts in the directory of this model
forecasts = os.listdir(path_to_processed_model_forecasts)
# Get model name or create a new model if it's not in the current Zoltar project
try:
metadata = metadata_dict_for_file(path_to_processed_model_forecasts +
'metadata-' + dir_name + '.txt')
except Exception as ex:
return ex
model_abbreviation = metadata['model_abbr']
# get the corresponding model_config for the metadata file
model_config = zoltar_config_from_metadata(metadata)
if model_abbreviation not in model_abbrs:
pprint.pprint('%s not in models' % model_abbreviation)
if 'home_url' not in model_config:
model_config['home_url'] = url + dir_name
try:
logger.info(f"Creating model {model_config}")
models.append(project_obj.create_model(model_config))
model_abbrs = [model.abbreviation for model in models]
except Exception as ex:
return ex
# fetch model based on model_abbr
model = [
model for model in models if model.abbreviation == model_abbreviation
][0]
if has_changed(metadata, model):
# model metadata has changed, call the edit function in zoltpy to update metadata
print(
f"{metadata['model_abbr']!r} model has changed metadata contents. Updating on Zoltar..."
)
model.edit(model_config)
# Get names of existing forecasts to avoid re-upload
existing_time_zeros = [
forecast.timezero.timezero_date for forecast in model.forecasts
]
# Batch upload
json_io_dict_batch = []
forecast_filename_batch = []
timezero_date_batch = []
for forecast in forecasts:
# Skip metadata text file
if not forecast.endswith('.csv'):
continue
# Default config
over_write = False
checksum = 0
time_zero_date = forecast.split(dir_name)[0][:-1]
# Check if forecast is already on zoltar
with open(path_to_processed_model_forecasts + forecast, "rb") as f:
# Get the current hash of a processed file
checksum = hashlib.md5(f.read()).hexdigest()
f.close()
# Check this hash against the previous version of hash
if db.get(forecast, None) != checksum:
print(forecast, db.get(forecast, None))
if time_zero_date in existing_time_zeros:
over_write = True
else:
continue
with open(path_to_processed_model_forecasts + forecast) as fp:
# Create timezero on zoltar if not existed
if time_zero_date not in project_timezeros:
try:
project_obj.create_timezero(time_zero_date)
project_timezeros.append(time_zero_date)
except Exception as ex:
print(ex)
return ex
# Validate covid19 file
print(f"Validating {forecast}")
errors_from_validation = validate_quantile_csv_file(
path_to_processed_model_forecasts + forecast)
# Upload forecast
if "no errors" == errors_from_validation:
quantile_json, error_from_transformation = json_io_dict_from_quantile_csv_file(
fp, COVID_TARGETS, covid19_row_validator,
COVID_ADDL_REQ_COLS)
if len(error_from_transformation) > 0:
return error_from_transformation
else:
try:
logger.debug(
'Upload forecast for model: %s \t|\t File: %s\n' %
(metadata['model_abbr'], forecast))
util.upload_forecast(conn,
quantile_json,
forecast,
project_name,
metadata['model_abbr'],
time_zero_date,
overwrite=over_write)
db[forecast] = checksum
except Exception as ex:
logger.error(ex)
return ex
json_io_dict_batch.append(quantile_json)
timezero_date_batch.append(time_zero_date)
forecast_filename_batch.append(forecast)
else:
return errors_from_validation
fp.close()
# # Batch upload for better performance
# if len(json_io_dict_batch) > 0:
# try:
# util.upload_forecast_batch(conn, json_io_dict_batch, forecast_filename_batch, project_name, model_name, timezero_date_batch, overwrite = over_write)
# except Exception as ex:
# return ex
return "Pass"
def upload_to_zoltar_fte(conn, proj_config):
project, existing_timezeros = get_project_and_timezeros(conn, proj_config)
pres_nat = pd.read_csv(DOWNLOAD_PATHS[FTE]['pres-nat'])
pres_state = pd.read_csv(DOWNLOAD_PATHS[FTE]['pres-state'])
# calculate Dem share in two-party popular vote
# Dem share in two-party popular vote = (avg Dem share / (avg Dem share + avg Rep share)) * 100
# (multiplied by 100 for percentage)
# (subject to revision?)
pres_nat['voteshare_dem_twoparty'] = \
(pres_nat['national_voteshare_chal'] / (pres_nat['national_voteshare_inc'] +
pres_nat['national_voteshare_chal'])) * 100
pres_state['voteshare_dem_twoparty'] = \
(pres_state['voteshare_chal'] / (pres_state['voteshare_inc'] +
pres_state['voteshare_chal'])) * 100
forecasts_by_timezero = {}
jobs = []
# Presidential, national
for i in range(len(pres_nat)):
row = pres_nat.iloc[i]
month, day, year = row.at['modeldate'].split('/')
forecast = check_and_make_timezero_and_get_forecast_object(year, month, day, project,
existing_timezeros, forecasts_by_timezero)
# each row in raw 538 csv has multiple targets for us
for target in proj_config['targets']:
target_name = target['name']
if target_name == 'popvote_win_dem':
forecast['predictions'].append({
'unit': 'US-pres',
'target': target_name,
'class': 'bin',
'prediction': {
'cat': [True, False],
'prob': list(row.loc[['popwin_chal', 'popwin_inc']])
}
})
elif target_name == 'ec_win_dem':
forecast['predictions'].append({
'unit': 'US-pres',
'target': target_name,
'class': 'bin',
'prediction': {
'cat': ['Dem win', 'Rep win', 'tie'],
'prob': list(row.loc[['ecwin_chal', 'ecwin_inc', 'ec_nomajority']])
}
})
elif target_name == 'ev_won_dem':
forecast['predictions'].extend([
{
'unit': 'US-pres',
'target': target_name,
'class': 'point',
'prediction': {
'value': row.at['ev_chal']
}
},
{
'unit': 'US-pres',
'target': target_name,
'class': 'quantile',
'prediction': {
'quantile': [0.1, 0.9],
'value': list(row.loc[['ev_chal_lo', 'ev_chal_hi']].astype('float64'))
}
}
])
elif target_name == 'voteshare_dem_twoparty':
forecast['predictions'].extend([
{
'unit': 'US-pres',
'target': target_name,
'class': 'point',
'prediction': {
'value': row.at['voteshare_dem_twoparty']
}
}
])
# Presidential, state-level
for i in range(len(pres_state)):
row = pres_state.iloc[i]
month, day, year = row.at['modeldate'].split('/')
forecast = check_and_make_timezero_and_get_forecast_object(year, month, day, project,
existing_timezeros, forecasts_by_timezero)
for target in proj_config['targets']:
target_name = target['name']
if target_name == 'popvote_win_dem':
forecast['predictions'].append({
'unit': f'{LOCATION_CODES_REVERSE[row.at["state"]]}-pres',
'target': target_name,
'class': 'bin',
'prediction': {
'cat': [True, False],
'prob': list(row.loc[['winstate_chal', 'winstate_inc']])
}
})
elif target_name == 'voteshare_dem_twoparty':
forecast['predictions'].append({
'unit': f'{LOCATION_CODES_REVERSE[row.at["state"]]}-pres',
'target': target_name,
'class': 'point',
'prediction': {
'value': row.at['voteshare_dem_twoparty']
}
})
# 538_pp model ends here, senatorial forecasts are done by 3 different models
for timezero in forecasts_by_timezero:
util.upload_forecast(
conn=conn,
json_io_dict=forecasts_by_timezero[timezero],
forecast_filename='538-agg.csv',
project_name=proj_config['name'],
model_abbr='538_pp',
timezero_date=timezero.isoformat(),
overwrite=True
)
# senatorial elections
senate_nat = pd.read_csv(DOWNLOAD_PATHS[FTE]['senate-nat'])
senate_state = pd.read_csv(DOWNLOAD_PATHS[FTE]['senate-state'])
total_dem_voteshare = senate_state.loc[:, ['voteshare_mean_D1', 'voteshare_mean_D2',
'voteshare_mean_D3', 'voteshare_mean_D4']].sum(axis=1)
total_rep_voteshare = senate_state.loc[:, ['voteshare_mean_R1', 'voteshare_mean_R2',
'voteshare_mean_R3', 'voteshare_mean_R4']].sum(axis=1)
senate_state['voteshare_dem_twoparty'] = total_dem_voteshare / (total_dem_voteshare + total_rep_voteshare)
forecasts_by_timezero = {'_lite': {}, '_classic': {}, '_deluxe': {}}
# Senate, national
for i in range(len(senate_nat)):
row = senate_nat.iloc[i]
model = row.at['expression']
month, day, year = row.at['forecastdate'].split('/')
forecast = check_and_make_timezero_and_get_forecast_object('20' + year, month, day, project, existing_timezeros,
forecasts_by_timezero, model=model)
forecast['predictions'].extend([
{
'unit': 'US-sen',
'target': 'senate_win_dem',
'class': 'bin',
'prediction': {
'cat': [True, False],
'prob': list(row.loc[['chamber_Dparty', 'chamber_Rparty']])
}
},
{
'unit': 'US-sen',
'target': 'senate_seats_won_dem',
'class': 'quantile',
'prediction': {
'quantile': [0.1, 0.5, 0.9],
'value': list(row.loc[['p10_seats_Dparty',
'median_seats_Dparty',
'p90_seats_Dparty'
]].astype('int32'))
}
}
])
# Senate, state-level
for i in range(len(senate_state)):
row = senate_state.iloc[i]
model = row.at['expression']
seat = row.at['district'].split('-')
if seat[0] == 'GA':
if seat[1] == 'S3':
unit = 'GA-sen-sp'
else:
unit = 'GA-sen'
else:
unit = f'{seat[0]}-sen'
month, day, year = row.at['forecastdate'].split('/')
forecast = check_and_make_timezero_and_get_forecast_object('20' + year, month, day, project, existing_timezeros,
forecasts_by_timezero, model=model)
forecast['predictions'].extend([
{
'unit': unit,
'target': 'popvote_win_dem',
'class': 'bin',
'prediction': {
'cat': [True, False],
'prob': list(row.loc[['winner_Dparty', 'winner_Rparty']])
}
},
{
'unit': unit,
'target': 'voteshare_dem_twoparty',
'class': 'point',
'prediction': {
'value': row.at['voteshare_dem_twoparty']
}
}
])
for model in forecasts_by_timezero:
for timezero in forecasts_by_timezero[model]:
util.upload_forecast(
conn=conn,
json_io_dict=forecasts_by_timezero[model][timezero],
forecast_filename='538-agg.csv',
project_name=proj_config['name'],
model_abbr=f'538{model}',
timezero_date=timezero.isoformat(),
overwrite=True
)
def upload_to_zoltar_economist(conn, proj_config):
project, existing_timezeros = get_project_and_timezeros(conn, proj_config)
ec_sims = pd.read_csv(f'{DOWNLOAD_PATHS[ECONOMIST]["root"]}electoral_college_simulations.csv')
ec_prob = pd.read_csv(f'{DOWNLOAD_PATHS[ECONOMIST]["root"]}electoral_college_probability_over_time.csv')
ec_votes = pd.read_csv(f'{DOWNLOAD_PATHS[ECONOMIST]["root"]}electoral_college_votes_over_time.csv')
pres_nat = pd.read_csv(f'{DOWNLOAD_PATHS[ECONOMIST]["root"]}projected_eday_vote_over_time.csv')
pres_nat_topline = pd.read_csv(f'{DOWNLOAD_PATHS[ECONOMIST]["root"]}national_ec_popvote_topline.csv')
pres_state_topline = pd.read_csv(f'{DOWNLOAD_PATHS[ECONOMIST]["root"]}state_averages_and_predictions_topline.csv')
for year, month, day in map(
lambda date_str: date_str.split('-'),
pres_nat['model_run_date']
):
check_and_make_timezero(year, month, day, project, existing_timezeros)
with open(f'{DOWNLOAD_PATHS[ECONOMIST]["root"]}timestamp.json', 'rb') as timestamp_file:
model_most_recent_date = date.fromtimestamp(json.load(timestamp_file)['timestamp'])
timestamp_file.close()
forecasts = {}
# process electoral college simulations
# 1. sample 1000 out of 40000 simulations provided
# 2. convert to Zoltar format
# 3. save for upload
ec_sims_sampled_1000 = ec_sims.sample(n=1000, random_state=SEED)
forecast = get_forecast_object_of_timezero(model_most_recent_date, forecasts)
ec_win_dem_samples, ev_won_dem_samples, popvote_win_dem_samples, voteshare_dem_twoparty_samples = [], [], [], []
state_list = list(LOCATION_CODES.keys())[1:52]
state_won_dem_samples, state_voteshare_samples = {k: [] for k in state_list}, {k: [] for k in state_list}
for sample in ec_sims_sampled_1000.itertuples(name='Sample'):
dem_ev = int(sample.dem_ev)
if dem_ev > 270:
ec_result = 'Dem win'
elif dem_ev < 270:
ec_result = 'Rep win'
else: # dev_ev == 270
ec_result = 'tie'
# extremely unlikely to be exactly 0.5 due to floating point value (and reality)
voteshare_dem_twoparty = sample.natl_pop_vote
popvote_win_dem = False if voteshare_dem_twoparty < 0.5 else True
ec_win_dem_samples.append(ec_result)
ev_won_dem_samples.append(dem_ev)
popvote_win_dem_samples.append(popvote_win_dem)
voteshare_dem_twoparty_samples.append(voteshare_dem_twoparty)
# get the state popular voteshares
state_list = list(LOCATION_CODES.keys())[1:52]
for state, state_voteshare in zip(state_list, sample[4:]):
state_won_dem = False if state_voteshare < 0.5 else True
state_won_dem_samples[state].append(state_won_dem)
state_voteshare_samples[state].append(state_voteshare)
# converting to Zoltar format
forecast['predictions'].extend([
{
'unit': 'US-pres',
'target': 'ec_win_dem',
'class': 'sample',
'prediction': {
'sample': ec_win_dem_samples
}
},
{
'unit': 'US-pres',
'target': 'ev_won_dem',
'class': 'sample',
'prediction': {
'sample': ev_won_dem_samples
}
},
{
'unit': 'US-pres',
'target': 'popvote_win_dem',
'class': 'sample',
'prediction': {
'sample': popvote_win_dem_samples
}
},
{
'unit': 'US-pres',
'target': 'voteshare_dem_twoparty',
'class': 'sample',
'prediction': {
'sample': voteshare_dem_twoparty_samples
}
},
])
for state in state_list:
forecast['predictions'].extend([
{
'unit': f'{state}-pres',
'target': 'popvote_win_dem',
'class': 'sample',
'prediction': {
'sample': state_won_dem_samples[state]
}
},
{
'unit': f'{state}-pres',
'target': 'voteshare_dem_twoparty',
'class': 'sample',
'prediction': {
'sample': state_voteshare_samples[state]
}
}
])
# process electoral college win probabilities over time
ec_prob_dem = ec_prob[ec_prob['party'] == 'Democratic']
ec_prob_rep = ec_prob[ec_prob['party'] == 'Republican']
for row_dem, row_rep in zip(ec_prob_dem.itertuples(name='Row'), ec_prob_rep.itertuples(name='Row')):
year, month, day = row_dem.date.split('-')
timezero = date(int(year), int(month), int(day))
forecast = get_forecast_object_of_timezero(timezero, forecasts)
tie_prob = 1 - row_dem.win_prob - row_rep.win_prob
forecast['predictions'].append({
'unit': 'US-pres',
'target': 'ec_win_dem',
'class': 'bin',
'prediction': {
'cat': ['Dem win', 'Rep win', 'tie'],
'prob': [row_dem.win_prob, row_rep.win_prob, tie_prob]
}
})
# process electoral college votes over time
ec_votes_dem = ec_votes[ec_votes['party'] == 'Democratic']
for row in ec_votes_dem.itertuples(name='Row'):
year, month, day = row.date.split('-')
timezero = date(int(year), int(month), int(day))
forecast = get_forecast_object_of_timezero(timezero, forecasts)
forecast['predictions'].append({
'unit': 'US-pres',
'target': 'ev_won_dem',
'class': 'quantile',
'prediction': {
'quantile': [0.05, 0.4, 0.5, 0.6, 0.95],
'value': [float(ev) for ev in row[2:]]
}
})
# process national popular voteshare over time
for row in pres_nat.itertuples(name='Row'):
year, month, day = row.model_run_date.split('-')
timezero = date(int(year), int(month), int(day))
forecast = get_forecast_object_of_timezero(timezero, forecasts)
forecast['predictions'].extend([
{
'unit': 'US-pres',
'target': 'voteshare_dem_twoparty',
'class': 'quantile',
'prediction': {
'quantile': [0.05, 0.5, 0.95],
'value': [row.lower_95_dem_vote, row.mean_dem_vote, row.upper_95_dem_vote]
}
},
])
# process national popular vote topline (for popular vote win probability)
for row in pres_nat_topline.itertuples(name='Row'):
year, month, day = row.date.split('-')
timezero = date(int(year), int(month), int(day))
forecast = get_forecast_object_of_timezero(timezero, forecasts)
forecast['predictions'].append({
'unit': 'US-pres',
'target': 'popvote_win_dem',
'class': 'bin',
'prediction': {
'cat': [True, False],
'prob': [row.dem_vote_win_prob, row.rep_vote_win_prob]
}
})
# process state popular vote topline:
for row in pres_state_topline.itertuples(name='Row'):
year, month, day = row.date.split('-')
timezero = date(int(year), int(month), int(day))
forecast = get_forecast_object_of_timezero(timezero, forecasts)
forecast['predictions'].extend([
{
'unit': f'{row.state}-pres',
'target': 'popvote_win_dem',
'class': 'bin',
'prediction': {
'cat': [True, False],
'prob': [row.projected_win_prob, 1 - row.projected_win_prob]
}
},
{
'unit': f'{row.state}-pres',
'target': 'voteshare_dem_twoparty',
'class': 'quantile',
'prediction': {
'quantile': [0.05, 0.5, 0.95],
'value': [row.projected_vote_low, row.projected_vote_mean, row.projected_vote_high]
}
}
])
# upload to Zoltar
for timezero in forecasts:
util.upload_forecast(
conn=conn,
json_io_dict=forecasts[timezero],
forecast_filename='economist-agg.csv',
project_name=proj_config['name'],
model_abbr='economist',
timezero_date=timezero.isoformat(),
overwrite=True
)
EW2submitDates = sortDateOfSubmissions()
EW2submissionFile = relateEW2SubmissionFiles()
EW2submitDates = {
'EW42': '20191014',
'EW43': '20191021',
'EW44': '20191028',
'EW45': '20191104',
'EW46': '20191111',
'EW47': '20191118'
}
for EW in EW2submitDates:
try:
timezero_date = EW2submitDates[EW]
forecast_file_path = EW2submissionFile[EW]
predx_json, forecast_filename = util.convert_cdc_csv_to_json_io_dict(
forecast_file_path)
conn = util.authenticate()
util.upload_forecast(conn,
predx_json,
forecast_filename,
project_name,
model_name,
timezero_date,
overwrite=True)
print('uploaded {:s}'.format(forecast_file_path))
except Exception as e:
print('error uploading {:s}'.format(forecast_file_path))
print('{:s}'.format(str(e)))
