Kaggle competition: Santander Customer Transaction Prediction
submit = pd.read_csv('.. /input/sample_submission.csv')
 test_data = CactiImageData(split_data = submit, data_root = test_dir, transform = valid_transf)
 test_loader = DataLoader(dataset = test_data, batch_size=32, shuffle=False, num_workers=0 )<save_to_csv>
df1 = pd.read_csv('.. /input/sctp-blend-data/submission_1.csv')
 df2 = pd.read_csv('.. /input/sctp-blend-data/submission_2.csv')
 df3 = pd.read_csv('.. /input/sctp-blend-data/submission_3.csv')
 df4 = pd.read_csv('.. /input/sctp-blend-data/submission_4.csv' )
model.eval()
predict = []
for batch_i,(data, target)in enumerate(test_loader):
data, target = data.to(device), target.to(device)
 output = model(data)
 pr = output[:,1].detach().cpu().numpy()
for i in pr:
predict.append(i)
 submit['has_cactus'] = predict
submit.to_csv('submission.csv', index=False )<load_from_csv>
blend = df4['target'] *0.25 + df3['target'] * 0.25 + df2['target'] * 0.25 + df1['target'] * 0.25
sample = pd.read_csv('.. /input/santander-customer-transaction-prediction/sample_submission.csv' )
<choose_model_class><EOS> 
sample['target'] = blend
sample.to_csv('blend_ver10.csv',index=False )