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