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.. _model_persistence:
=================
Model persistence
=================
After training a scikit-learn model, it is desirable to have a way to persist
the model for future use without having to retrain. The following section gives
you an example of how to persist a model with pickle. We'll also review a few
security and maintainability issues when working with pickle serialization.
Persistence example
-------------------
It is possible to save a model in the scikit by using Python's built-in
persistence model, namely `pickle <https://docs.python.org/2/library/pickle.html>`_::
>>> from sklearn import svm
>>> from sklearn import datasets
>>> clf = svm.SVC()
>>> iris = datasets.load_iris()
>>> X, y = iris.data, iris.target
>>> clf.fit(X, y) # doctest: +NORMALIZE_WHITESPACE
SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0,
decision_function_shape=None, degree=3, gamma='auto', kernel='rbf',
max_iter=-1, probability=False, random_state=None, shrinking=True,
tol=0.001, verbose=False)
>>> import pickle
>>> s = pickle.dumps(clf)
>>> clf2 = pickle.loads(s)
>>> clf2.predict(X[0:1])
array([0])
>>> y[0]
0
In the specific case of the scikit, it may be more interesting to use
joblib's replacement of pickle (``joblib.dump`` & ``joblib.load``),
which is more efficient on objects that carry large numpy arrays internally as
is often the case for fitted scikit-learn estimators, but can only pickle to the
disk and not to a string::
>>> from sklearn.externals import joblib
>>> joblib.dump(clf, 'filename.pkl') # doctest: +SKIP
Later you can load back the pickled model (possibly in another Python process)
with::
>>> clf = joblib.load('filename.pkl') # doctest:+SKIP
.. note::
``joblib.dump`` and ``joblib.load`` functions also accept file-like object
instead of filenames. More information on data persistence with Joblib is
available `here <https://pythonhosted.org/joblib/persistence.html>`_.
Security & maintainability limitations
--------------------------------------
pickle (and joblib by extension), has some issues regarding maintainability
and security. Because of this,
* Never unpickle untrusted data as it could lead to malicious code being
executed upon loading.
* While models saved using one version of scikit-learn might load in
other versions, this is entirely unsupported and inadvisable. It should
also be kept in mind that operations performed on such data could give
different and unexpected results.
In order to rebuild a similar model with future versions of scikit-learn,
additional metadata should be saved along the pickled model:
* The training data, e.g. a reference to a immutable snapshot
* The python source code used to generate the model
* The versions of scikit-learn and its dependencies
* The cross validation score obtained on the training data
This should make it possible to check that the cross-validation score is in the
same range as before.
If you want to know more about these issues and explore other possible
serialization methods, please refer to this
`talk by Alex Gaynor <http://pyvideo.org/video/2566/pickles-are-for-delis-not-software>`_.
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