def run_predictions():
data = Data()
data.fetch_data()
total_df = data.preprocess_cases_data(data.df_us_cases)
daily_df = data.daily_data(total_df)
daily_reg = Regressor(daily_df, 7)
pred_path = "data/"
for state in daily_df.columns.tolist():
tempdf = pd.DataFrame(daily_df[state], index=daily_df.index)
state_reg = Regressor(tempdf, 7)
arima_state_data = state_reg.ARIMA(row=state)
arima_state_data.to_csv(pred_path + "daily_{}_ARIMA.csv".format(state))
xg_state_data = state_reg.XGBoost(row=state)
xg_state_data.to_csv(pred_path + "daily_{}_XGBoost.csv".format(state))
lstm_state_data = state_reg.LSTM(row=state, num_estimators=1)
lstm_state_data.to_csv(pred_path + "daily_{}_LSTM.csv".format(state))
total_df_death = data.preprocess_death_data(data.df_us_deaths)
daily_df_death = data.daily_data(total_df_death)
for state in daily_df_death.columns.tolist():
tempdf = pd.DataFrame(daily_df_death[state], index=daily_df_death.index)
death_reg = Regressor(tempdf, 7)
arima_state_death = death_reg.ARIMA(row=state)
arima_state_death.to_csv(pred_path + "death_{}_ARIMA.csv".format(state))
xg_state_death = death_reg.XGBoost(row=state)
xg_state_death.to_csv(pred_path + "death_{}_XGBoost.csv".format(state))
lstm_state_death = death_reg.LSTM(row=state, num_estimators=1)
lstm_state_death.to_csv(pred_path + "death_{}_LSTM.csv".format(state))
