Kaggle competition: OpenVaccine: COVID-19 mRNA Vaccine Degradation Prediction
<SOS> metric: MWCRMSE Kaggle data source: stanford-covid-vaccine<prepare_output>
import numpy as np
import pandas as pd
import os
ypc.insert(0,"ForecastId",forecast,False)
 ypc.insert(1,"ConfirmedCases",ypred,False)
 ypc.insert(2, "Fatalities",ypred2,False )<save_to_csv>
sub1 = pd.read_csv('.. /input/ae-pretrained-local/submission_ae_nonpretrained_local.csv')
 sub2 = pd.read_csv('.. /input/ae-pretrained-local/submission_ae_pretrained_local.csv')
 sub3 = pd.read_csv('.. /input/ae-pretrained-local/submission_ae_pretrained_local2.csv')
 sub4 = pd.read_csv('.. /input/openvaccine-pytorch-ae-pretrain/submission.csv' )
ypc.to_csv('submission.csv', index=False )<import_modules>
sub5 = pd.read_csv('.. /input/24551-ae-gcn/submission(3 ).csv')
 sub6 = pd.read_csv('.. /input/hawkey-ae-pretrained-gcn-3ensemble/submission(4 ).csv' )
import numpy as np
import pandas as pd
import datetime as dt
import matplotlib.pyplot as plt
from sklearn.linear_model import LinearRegression
import pickle<set_options>
sub7 = pd.read_csv('.. /input/lstm-gru-5fold-2seeds-knncv/submission.csv')
 sub8 = pd.read_csv('.. /input/hrunic-lstm-25177/submission(6 ).csv')
 sub9 = pd.read_csv('.. /input/hrunic-gru-25365/submission(7 ).csv')
 sub10 = pd.read_csv('.. /input/gru-lstm-with-feature-engineering-and-augmentation/submission.csv')
warnings.filterwarnings('ignore' )<load_from_csv>
sub11 = pd.read_csv('.. /input/hawkey-gcn-only-25301/submission(5 ).csv' )
train = pd.read_csv(".. /input/covid19-global-forecasting-week-4/train.csv")
 test = pd.read_csv(".. /input/covid19-global-forecasting-week-4/test.csv" )<load_from_csv>
final = sub1.set_index('id_seqpos')
 sub2 = sub2[sub1.columns].set_index('id_seqpos' ).loc[final.index]
sub3 = sub3[sub1.columns].set_index('id_seqpos' ).loc[final.index]
sub4 = sub4[sub1.columns].set_index('id_seqpos' ).loc[final.index]
sub5 = sub5[sub1.columns].set_index('id_seqpos' ).loc[final.index]
sub6 = sub6[sub1.columns].set_index('id_seqpos' ).loc[final.index]
sub7 = sub7[sub1.columns].set_index('id_seqpos' ).loc[final.index]
sub8 = sub8[sub1.columns].set_index('id_seqpos' ).loc[final.index]
sub9 = sub9[sub1.columns].set_index('id_seqpos' ).loc[final.index]
sub10 = sub10[sub1.columns].set_index('id_seqpos' ).loc[final.index]
sub11 = sub11[sub1.columns].set_index('id_seqpos' ).loc[final.index]
sub1 = sub1.set_index('id_seqpos' )
submission = pd.read_csv(".. /input/covid19-global-forecasting-week-4/submission.csv" )<rename_columns>
ac_pretrained =(sub1 + sub2 + sub3 + sub4)/ 4 * 0.5 +(sub5 + sub6)/ 2 * 0.5
non_ac_pretrained =(sub7 + sub8 + sub9 + sub10)/4 *0.6 + sub11 * 0.4
train.columns = ['ForecastId', 'Province_State', 'Country_Region', 'Date', 'ConfirmedCases', 'Fatalities']<filter>
final = ac_pretrained *.5 + non_ac_pretrained *.5
test = test[test['Date'] > train_date_end]<concatenate>
final.to_csv('final_v1.csv' )