Kaggle competition: Microsoft Malware Prediction
<SOS> metric: AUC Kaggle data source: microsoft-malware-prediction<set_options>
LABELS = ["HasDetections"]
torch.cuda.empty_cache()<train_model>
predict_list = []
predict_list.append(pd.read_csv(".. /input/ensmbl2/submission.csv")[LABELS].values)
 predict_list.append(pd.read_csv(".. /input/lightgbm-baseline-model-using-sparse-matrix/lgb_submission.csv")[LABELS].values)
 predict_list.append(pd.read_csv(".. /input/nffm-baseline-0-690-on-lb/nffm_submission.csv")[LABELS].values)
<load_pretrained><EOS> 
print("Rank averaging on ", len(predict_list), " files")
 predictions = np.zeros_like(predict_list[0])
 for predict in predict_list:
for i in range(1):
predictions[:, i] = np.add(predictions[:, i], rankdata(predict[:, i])/predictions.shape[0])
 predictions /= len(predict_list)
 submission = pd.read_csv('.. /input/microsoft-malware-prediction/sample_submission.csv')
 submission[LABELS] = predictions
submission.to_csv('super_blend.csv', index=False )