Kaggle competition: OpenVaccine: COVID-19 mRNA Vaccine Degradation Prediction
<create_dataframe><EOS> 
import pandas as pd
import numpy as np
<SOS> metric: MWCRMSE Kaggle data source: stanford-covid-vaccine<concatenate>
import pandas as pd
import numpy as np
name_cols = bottom_lvl_pred_df.id.str.split(pat='_',expand=True)
 name_cols['dept_id']=name_cols[0]+'_'+name_cols[1]
name_cols['store_id']=name_cols[3]+'_'+name_cols[4]
name_cols = name_cols.rename(columns={0: "cat_id", 3: "state_id"})
 name_cols = name_cols.drop([1,2,4,5],axis=1)
 bottom_lvl_pred_df = pd.concat([name_cols,bottom_lvl_pred_df],axis=1 )<count_unique_values>
df1 = pd.read_csv('/kaggle/input/gru-lstm-mix-with-custom-loss-tunning/ensemble_final.csv')
 df2 = pd.read_csv('/kaggle/input/mvan-covid-mrna-vaccine-analysis-notebook-268/submission.csv')
 df3 = pd.read_csv('/kaggle/input/gru-lstm-mix-with-custom-loss/ensemble_final.csv' )
cat_cols = ['id', 'dept_id', 'cat_id', 'store_id', 'state_id']
ts_cols = [col for col in bottom_lvl_pred_df.columns if col not in cat_cols]
ts_dict = {t: int(t[1:])for t in ts_cols}
print(' unique forecasts: %i' % bottom_lvl_pred_df.shape[0])
 for col in cat_cols:
print(' N_unique %s: %i' %(col, bottom_lvl_pred_df[col].nunique())) 
<create_dataframe>
sub = df1
sub['reactivity'] =(df1.reactivity.values + df2.reactivity.values + df3.reactivity.values)/3
sub['deg_Mg_pH10'] =(df1.deg_Mg_pH10.values + df2.deg_Mg_pH10.values + df3.deg_Mg_pH10.values)/3
sub['deg_pH10'] =(df1.deg_pH10.values + df2.deg_pH10.values + df3.deg_pH10.values)/3
sub['deg_Mg_50C'] =(df1.deg_Mg_50C.values + df2.deg_Mg_50C.values +df3.deg_Mg_50C.values)/3
sub['deg_50C'] =(df1.deg_50C.values + df2.deg_50C.values + df3.deg_50C.values)/3
<groupby><EOS> 
sub.to_csv('submission.csv', index = False )