Achieving model persistence

When we train a model, it would be nice if we could save it as a file so that it can be used later by simply loading it again.

How to do it…

Let's see how to achieve model persistence programmatically:

1. Add the following lines to regressor.py:

   import cPickle as pickle
   
   output_model_file = 'saved_model.pkl'
   with open(output_model_file, 'w') as f:
       pickle.dump(linear_regressor, f)

2. The regressor object will be saved in the saved_model.pkl file. Let's look at how to load it and use it, as follows:

   with open(output_model_file, 'r') as f:
       model_linregr = pickle.load(f)
   
   y_test_pred_new = model_linregr.predict(X_test)
   print "\nNew mean absolute error =", round(sm.mean_absolute_error(y_test, y_test_pred_new), 2)

3. Here, we just loaded the regressor from the file into the model_linregr variable. You can compare the preceding result with the earlier result to confirm that it's the same.