Kaggle competition: Microsoft Malware Prediction
<SOS> metric: AUC Kaggle data source: microsoft-malware-prediction<predict_on_test>
LABELS = ["HasDetections"]
y_pred = model.predict(X_test)
 X_test__ = X_test.reshape(X_test.shape[0], 28, 28)
 fig, axis = plt.subplots(4, 4, figsize=(12, 14))
for i, ax in enumerate(axis.flat):
ax.imshow(X_test__[i], cmap='binary')
 ax.set(title = f"Real Number is {y_test[i].argmax() }
Predict Number is {y_pred[i].argmax() }");<predict_on_test>
predict_list = []
predict_list.append(pd.read_csv(".. /input/ensmbl2/submission.csv")[LABELS].values)
 predict_list.append(pd.read_csv(".. /input/blending-0-697/ens_sub_v12.csv")[LABELS].values)
<save_to_csv><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 )