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<section id="model-persistence">
<span id="id1"></span><h1><span class="section-number">9. </span>Model persistence<a class="headerlink" href="#model-persistence" title="Permalink to this headline">¶</a></h1>
<p>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 sections give
you some hints on how to persist a scikit-learn model.</p>
<section id="python-specific-serialization">
<h2><span class="section-number">9.1. </span>Python specific serialization<a class="headerlink" href="#python-specific-serialization" title="Permalink to this headline">¶</a></h2>
<p>It is possible to save a model in scikit-learn by using Python’s built-in
persistence model, namely <a class="reference external" href="https://docs.python.org/3/library/pickle.html">pickle</a>:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="kn">from</span> <span class="nn">sklearn</span> <span class="kn">import</span> <span class="n">svm</span>
<span class="gp">>>> </span><span class="kn">from</span> <span class="nn">sklearn</span> <span class="kn">import</span> <span class="n">datasets</span>
<span class="gp">>>> </span><span class="n">clf</span> <span class="o">=</span> <span class="n">svm</span><span class="o">.</span><span class="n">SVC</span><span class="p">()</span>
<span class="gp">>>> </span><span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="o">=</span> <span class="n">datasets</span><span class="o">.</span><span class="n">load_iris</span><span class="p">(</span><span class="n">return_X_y</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">clf</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span>
<span class="go">SVC()</span>
<span class="gp">>>> </span><span class="kn">import</span> <span class="nn">pickle</span>
<span class="gp">>>> </span><span class="n">s</span> <span class="o">=</span> <span class="n">pickle</span><span class="o">.</span><span class="n">dumps</span><span class="p">(</span><span class="n">clf</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">clf2</span> <span class="o">=</span> <span class="n">pickle</span><span class="o">.</span><span class="n">loads</span><span class="p">(</span><span class="n">s</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">clf2</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">X</span><span class="p">[</span><span class="mi">0</span><span class="p">:</span><span class="mi">1</span><span class="p">])</span>
<span class="go">array([0])</span>
<span class="gp">>>> </span><span class="n">y</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
<span class="go">0</span>
</pre></div>
</div>
<p>In the specific case of scikit-learn, it may be better to use joblib’s
replacement of pickle (<code class="docutils literal notranslate"><span class="pre">dump</span></code> & <code class="docutils literal notranslate"><span class="pre">load</span></code>), 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:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="kn">from</span> <span class="nn">joblib</span> <span class="kn">import</span> <span class="n">dump</span><span class="p">,</span> <span class="n">load</span>
<span class="gp">>>> </span><span class="n">dump</span><span class="p">(</span><span class="n">clf</span><span class="p">,</span> <span class="s1">'filename.joblib'</span><span class="p">)</span>
</pre></div>
</div>
<p>Later you can load back the pickled model (possibly in another Python process)
with:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">clf</span> <span class="o">=</span> <span class="n">load</span><span class="p">(</span><span class="s1">'filename.joblib'</span><span class="p">)</span>
</pre></div>
</div>
<div class="admonition note">
<p class="admonition-title">Note</p>
<p><code class="docutils literal notranslate"><span class="pre">dump</span></code> and <code class="docutils literal notranslate"><span class="pre">load</span></code> functions also accept file-like object
instead of filenames. More information on data persistence with Joblib is
available <a class="reference external" href="https://joblib.readthedocs.io/en/latest/persistence.html">here</a>.</p>
</div>
<section id="security-maintainability-limitations">
<span id="persistence-limitations"></span><h3><span class="section-number">9.1.1. </span>Security & maintainability limitations<a class="headerlink" href="#security-maintainability-limitations" title="Permalink to this headline">¶</a></h3>
<p>pickle (and joblib by extension), has some issues regarding maintainability
and security. Because of this,</p>
<ul class="simple">
<li><p>Never unpickle untrusted data as it could lead to malicious code being
executed upon loading.</p></li>
<li><p>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.</p></li>
</ul>
<p>In order to rebuild a similar model with future versions of scikit-learn,
additional metadata should be saved along the pickled model:</p>
<ul class="simple">
<li><p>The training data, e.g. a reference to an immutable snapshot</p></li>
<li><p>The python source code used to generate the model</p></li>
<li><p>The versions of scikit-learn and its dependencies</p></li>
<li><p>The cross validation score obtained on the training data</p></li>
</ul>
<p>This should make it possible to check that the cross-validation score is in the
same range as before.</p>
<p>Aside for a few exceptions, pickled models should be portable across
architectures assuming the same versions of dependencies and Python are used.
If you encounter an estimator that is not portable please open an issue on
GitHub. Pickled models are often deployed in production using containers, like
Docker, in order to freeze the environment and dependencies.</p>
<p>If you want to know more about these issues and explore other possible
serialization methods, please refer to this
<a class="reference external" href="https://pyvideo.org/video/2566/pickles-are-for-delis-not-software">talk by Alex Gaynor</a>.</p>
</section>
</section>
<section id="interoperable-formats">
<h2><span class="section-number">9.2. </span>Interoperable formats<a class="headerlink" href="#interoperable-formats" title="Permalink to this headline">¶</a></h2>
<p>For reproducibility and quality control needs, when different architectures
and environments should be taken into account, exporting the model in
<a class="reference external" href="https://onnx.ai/">Open Neural Network
Exchange</a> format or <a class="reference external" href="http://dmg.org/pmml/v4-4-1/GeneralStructure.html">Predictive Model Markup Language
(PMML)</a> format
might be a better approach than using <code class="docutils literal notranslate"><span class="pre">pickle</span></code> alone.
These are helpful where you may want to use your model for prediction in a
different environment from where the model was trained.</p>
<p>ONNX is a binary serialization of the model. It has been developed to improve
the usability of the interoperable representation of data models.
It aims to facilitate the conversion of the data
models between different machine learning frameworks, and to improve their
portability on different computing architectures. More details are available
from the <a class="reference external" href="https://onnx.ai/get-started.html">ONNX tutorial</a>.
To convert scikit-learn model to ONNX a specific tool <a class="reference external" href="http://onnx.ai/sklearn-onnx/">sklearn-onnx</a> has been developed.</p>
<p>PMML is an implementation of the <a class="reference external" href="https://en.wikipedia.org/wiki/XML">XML</a> document standard
defined to represent data models together with the data used to generate them.
Being human and machine readable,
PMML is a good option for model validation on different platforms and
long term archiving. On the other hand, as XML in general, its verbosity does
not help in production when performance is critical.
To convert scikit-learn model to PMML you can use for example <a class="reference external" href="https://github.com/jpmml/sklearn2pmml">sklearn2pmml</a> distributed under the Affero GPLv3
license.</p>
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