126 lines
4.8 KiB
ReStructuredText
126 lines
4.8 KiB
ReStructuredText
.. Places parent toc into the sidebar
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:parenttoc: True
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.. _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 sections give
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you some hints on how to persist a scikit-learn model.
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Python specific serialization
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-----------------------------
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It is possible to save a model in scikit-learn by using Python's built-in
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persistence model, namely `pickle
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<https://docs.python.org/3/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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>>> X, y= datasets.load_iris(return_X_y=True)
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>>> clf.fit(X, y)
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SVC()
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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 scikit-learn, it may be better to use joblib's
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replacement of pickle (``dump`` & ``load``), which is more efficient on
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objects that carry large numpy arrays internally as is often the case for
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fitted scikit-learn estimators, but can only pickle to the disk and not to a
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string::
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>>> from joblib import dump, load
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>>> dump(clf, 'filename.joblib') # 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 = load('filename.joblib') # doctest:+SKIP
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.. note::
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``dump`` and ``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
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<https://joblib.readthedocs.io/en/latest/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 an 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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Aside for a few exceptions, pickled models should be portable across
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architectures assuming the same versions of dependencies and Python are used.
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If you encounter an estimator that is not portable please open an issue on
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GitHub. Pickled models are often deployed in production using containers, like
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Docker, in order to freeze the environment and dependencies.
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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
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<https://pyvideo.org/video/2566/pickles-are-for-delis-not-software>`_.
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Interoperable formats
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---------------------
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For reproducibility and quality control needs, when different architectures
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and environments should be taken into account, exporting the model in
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`Open Neural Network
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Exchange <https://onnx.ai/>`_ format or `Predictive Model Markup Language
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(PMML) <http://dmg.org/pmml/v4-4-1/GeneralStructure.html>`_ format
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might be a better approach than using `pickle` alone.
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These are helpful where you may want to use your model for prediction in a
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different environment from where the model was trained.
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ONNX is a binary serialization of the model. It has been developed to improve
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the usability of the interoperable representation of data models.
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It aims to facilitate the conversion of the data
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models between different machine learning frameworks, and to improve their
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portability on different computing architectures. More details are available
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from the `ONNX tutorial <https://onnx.ai/get-started.html>`_.
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To convert scikit-learn model to ONNX a specific tool `sklearn-onnx
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<http://onnx.ai/sklearn-onnx/>`_ has been developed.
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PMML is an implementation of the `XML
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<https://en.wikipedia.org/wiki/XML>`_ document standard
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defined to represent data models together with the data used to generate them.
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Being human and machine readable,
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PMML is a good option for model validation on different platforms and
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long term archiving. On the other hand, as XML in general, its verbosity does
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not help in production when performance is critical.
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To convert scikit-learn model to PMML you can use for example `sklearn2pmml
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<https://github.com/jpmml/sklearn2pmml>`_ distributed under the Affero GPLv3
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license.
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