Achieving model persistence_Python Machine Learning Cookbook-QQ阅读中文都市网

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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:

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)

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)

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.