Kaggle competition: Microsoft Malware Prediction
<SOS> metric: AUC Kaggle data source: microsoft-malware-prediction<predict_on_test>
LABELS = ["HasDetections"]
test = test.values.reshape(-1,28,28,1)
 test = test/255.0
pred = np.argmax(m.predict(test), axis = 1 )<create_dataframe>
predict_list = []
predict_list.append(pd.read_csv(".. /input/aimalware/nffm_submission.csv")[LABELS].values)
 predict_list.append(pd.read_csv(".. /input/last-stages/submission_ashish_v2.csv")[LABELS].values)
 predict_list.append(pd.read_csv(".. /input/laststages/submission_ashish_v3.csv")[LABELS].values)
 predict_list.append(pd.read_csv(".. /input/last-stages/submission_ashish_v4.csv")[LABELS].values)
 predict_list.append(pd.read_csv(".. /input/last-stages/submission_ashish_vb1.csv")[LABELS].values)
 predict_list.append(pd.read_csv(".. /input/finalai/blending.csv")[LABELS].values )
Submission = pd.DataFrame({"ImageId" : range(1, 28001), "Label":pred} )<save_to_csv>
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 )
<train_model><EOS> 
submission = pd.read_csv('.. /input/microsoft-malware-prediction/sample_submission.csv')
 sample_3 = pd.read_csv(".. /input/last-stages/submission_ashish_v2.csv")
 sample_4 = pd.read_csv(".. /input/laststages/submission_ashish_v3.csv")
 sample_5 = pd.read_csv(".. /input/last-stages/submission_ashish_v4.csv")
 sample_6 = pd.read_csv(".. /input/last-stages/submission_ashish_vb1.csv")
 sample_7 = pd.read_csv(".. /input/aimalware/nffm_submission.csv")
 sample_8 = pd.read_csv(".. /input/finalai/blending.csv")
 submission['HasDetections'] = sample_6['HasDetections'] * 0.3 + sample_8['HasDetections'] * 0.3 + sample_3['HasDetections'] * 0.2 + sample_7['HasDetections'] * 0.2
submission.to_csv('super_blend_2.csv', index=False )