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<li><a class="reference internal" href="#">Store and load <code class="docutils literal notranslate"><span class="pre">skopt</span></code> optimization results</a><ul>
<li><a class="reference internal" href="#problem-statement">Problem statement</a></li>
<li><a class="reference internal" href="#simple-example">Simple example</a></li>
<li><a class="reference internal" href="#using-skopt-dump-and-skopt-load">Using <code class="docutils literal notranslate"><span class="pre">skopt.dump()</span></code> and <code class="docutils literal notranslate"><span class="pre">skopt.load()</span></code></a><ul>
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<p class="admonition-title">Note</p>
<p>Click <a class="reference internal" href="#sphx-glr-download-auto-examples-store-and-load-results-py"><span class="std std-ref">here</span></a>
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<section class="sphx-glr-example-title" id="store-and-load-skopt-optimization-results">
<span id="sphx-glr-auto-examples-store-and-load-results-py"></span><h1>Store and load <code class="docutils literal notranslate"><span class="pre">skopt</span></code> optimization results<a class="headerlink" href="#store-and-load-skopt-optimization-results" title="Permalink to this headline">¶</a></h1>
<p>Mikhail Pak, October 2016.
Reformatted by Holger Nahrstaedt 2020</p>
<section id="problem-statement">
<h2>Problem statement<a class="headerlink" href="#problem-statement" title="Permalink to this headline">¶</a></h2>
<p>We often want to store optimization results in a file. This can be useful,
for example,</p>
<ul class="simple">
<li><p>if you want to share your results with colleagues;</p></li>
<li><p>if you want to archive and/or document your work;</p></li>
<li><p>or if you want to postprocess your results in a different Python instance or on an another computer.</p></li>
</ul>
<p>The process of converting an object into a byte stream that can be stored in
a file is called _serialization_.
Conversely, _deserialization_ means loading an object from a byte stream.</p>
<p><strong>Warning:</strong> Deserialization is not secure against malicious or erroneous
code. Never load serialized data from untrusted or unauthenticated sources!</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="nb">print</span><span class="p">(</span><span class="vm">__doc__</span><span class="p">)</span>
<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
<span class="kn">import</span> <span class="nn">os</span>
<span class="kn">import</span> <span class="nn">sys</span>
</pre></div>
</div>
</section>
<section id="simple-example">
<h2>Simple example<a class="headerlink" href="#simple-example" title="Permalink to this headline">¶</a></h2>
<p>We will use the same optimization problem as in the
<a class="reference internal" href="bayesian-optimization.html#sphx-glr-auto-examples-bayesian-optimization-py"><span class="std std-ref">Bayesian optimization with skopt</span></a> notebook:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">skopt</span> <span class="kn">import</span> <a href="../modules/generated/skopt.gp_minimize.html#skopt.gp_minimize" title="skopt.gp_minimize" class="sphx-glr-backref-module-skopt sphx-glr-backref-type-py-function"><span class="n">gp_minimize</span></a>
<span class="n">noise_level</span> <span class="o">=</span> <span class="mf">0.1</span>
<span class="k">def</span> <span class="nf">obj_fun</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">noise_level</span><span class="o">=</span><span class="n">noise_level</span><span class="p">):</span>
<span class="k">return</span> <a href="https://numpy.org/doc/stable/reference/generated/numpy.sin.html#numpy.sin" title="numpy.sin" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-data"><span class="n">np</span><span class="o">.</span><span class="n">sin</span></a><span class="p">(</span><span class="mi">5</span> <span class="o">*</span> <span class="n">x</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span> <span class="o">*</span> <span class="p">(</span><span class="mi">1</span> <span class="o">-</span> <a href="https://numpy.org/doc/stable/reference/generated/numpy.tanh.html#numpy.tanh" title="numpy.tanh" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-data"><span class="n">np</span><span class="o">.</span><span class="n">tanh</span></a><span class="p">(</span><span class="n">x</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">**</span> <span class="mi">2</span><span class="p">))</span> <span class="o">+</span> <a href="https://numpy.org/doc/stable/reference/random/generated/numpy.random.randn.html#numpy.random.randn" title="numpy.random.randn" class="sphx-glr-backref-module-numpy-random sphx-glr-backref-type-py-function"><span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">randn</span></a><span class="p">()</span> \
<span class="o">*</span> <span class="n">noise_level</span>
<span class="n">res</span> <span class="o">=</span> <a href="../modules/generated/skopt.gp_minimize.html#skopt.gp_minimize" title="skopt.gp_minimize" class="sphx-glr-backref-module-skopt sphx-glr-backref-type-py-function"><span class="n">gp_minimize</span></a><span class="p">(</span><span class="n">obj_fun</span><span class="p">,</span> <span class="c1"># the function to minimize</span>
<span class="p">[(</span><span class="o">-</span><span class="mf">2.0</span><span class="p">,</span> <span class="mf">2.0</span><span class="p">)],</span> <span class="c1"># the bounds on each dimension of x</span>
<span class="n">x0</span><span class="o">=</span><span class="p">[</span><span class="mf">0.</span><span class="p">],</span> <span class="c1"># the starting point</span>
<span class="n">acq_func</span><span class="o">=</span><span class="s2">"LCB"</span><span class="p">,</span> <span class="c1"># the acquisition function (optional)</span>
<span class="n">n_calls</span><span class="o">=</span><span class="mi">15</span><span class="p">,</span> <span class="c1"># the number of evaluations of f including at x0</span>
<span class="n">n_random_starts</span><span class="o">=</span><span class="mi">3</span><span class="p">,</span> <span class="c1"># the number of random initial points</span>
<span class="n">random_state</span><span class="o">=</span><span class="mi">777</span><span class="p">)</span>
</pre></div>
</div>
<p class="sphx-glr-script-out">Out:</p>
<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>/home/circleci/project/skopt/optimizer/optimizer.py:449: UserWarning: The objective has been evaluated at this point before.
warnings.warn("The objective has been evaluated "
</pre></div>
</div>
<p>As long as your Python session is active, you can access all the
optimization results via the <code class="docutils literal notranslate"><span class="pre">res</span></code> object.</p>
<p>So how can you store this data in a file? <code class="docutils literal notranslate"><span class="pre">skopt</span></code> conveniently provides
functions <a class="reference internal" href="../modules/generated/skopt.dump.html#skopt.dump" title="skopt.dump"><code class="xref py py-class docutils literal notranslate"><span class="pre">skopt.dump</span></code></a> and <a class="reference internal" href="../modules/generated/skopt.load.html#skopt.load" title="skopt.load"><code class="xref py py-class docutils literal notranslate"><span class="pre">skopt.load</span></code></a> that handle this for you.
These functions are essentially thin wrappers around the
<a class="reference external" href="https://joblib.readthedocs.io/en/latest/">joblib</a> module’s <a class="reference external" href="https://joblib.readthedocs.io/en/latest/generated/joblib.dump.html#joblib.dump" title="(in joblib v1.1.0)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">joblib.dump</span></code></a> and <a class="reference external" href="https://joblib.readthedocs.io/en/latest/generated/joblib.load.html#joblib.load" title="(in joblib v1.1.0)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">joblib.load</span></code></a>.</p>
<p>We will now show how to use <a class="reference internal" href="../modules/generated/skopt.dump.html#skopt.dump" title="skopt.dump"><code class="xref py py-class docutils literal notranslate"><span class="pre">skopt.dump</span></code></a> and <a class="reference internal" href="../modules/generated/skopt.load.html#skopt.load" title="skopt.load"><code class="xref py py-class docutils literal notranslate"><span class="pre">skopt.load</span></code></a> for storing
and loading results.</p>
</section>
<section id="using-skopt-dump-and-skopt-load">
<h2>Using <code class="docutils literal notranslate"><span class="pre">skopt.dump()</span></code> and <code class="docutils literal notranslate"><span class="pre">skopt.load()</span></code><a class="headerlink" href="#using-skopt-dump-and-skopt-load" title="Permalink to this headline">¶</a></h2>
<p>For storing optimization results into a file, call the <a class="reference internal" href="../modules/generated/skopt.dump.html#skopt.dump" title="skopt.dump"><code class="xref py py-class docutils literal notranslate"><span class="pre">skopt.dump</span></code></a>
function:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">skopt</span> <span class="kn">import</span> <a href="../modules/generated/skopt.dump.html#skopt.dump" title="skopt.dump" class="sphx-glr-backref-module-skopt sphx-glr-backref-type-py-function"><span class="n">dump</span></a><span class="p">,</span> <a href="../modules/generated/skopt.load.html#skopt.load" title="skopt.load" class="sphx-glr-backref-module-skopt sphx-glr-backref-type-py-function"><span class="n">load</span></a>
<a href="../modules/generated/skopt.dump.html#skopt.dump" title="skopt.dump" class="sphx-glr-backref-module-skopt sphx-glr-backref-type-py-function"><span class="n">dump</span></a><span class="p">(</span><span class="n">res</span><span class="p">,</span> <span class="s1">'result.pkl'</span><span class="p">)</span>
</pre></div>
</div>
<p>And load from file using <a class="reference internal" href="../modules/generated/skopt.load.html#skopt.load" title="skopt.load"><code class="xref py py-class docutils literal notranslate"><span class="pre">skopt.load</span></code></a>:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">res_loaded</span> <span class="o">=</span> <a href="../modules/generated/skopt.load.html#skopt.load" title="skopt.load" class="sphx-glr-backref-module-skopt sphx-glr-backref-type-py-function"><span class="n">load</span></a><span class="p">(</span><span class="s1">'result.pkl'</span><span class="p">)</span>
<span class="n">res_loaded</span><span class="o">.</span><span class="n">fun</span>
</pre></div>
</div>
<p class="sphx-glr-script-out">Out:</p>
<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>-0.08361149010622955
</pre></div>
</div>
<p>You can fine-tune the serialization and deserialization process by calling
<a class="reference internal" href="../modules/generated/skopt.dump.html#skopt.dump" title="skopt.dump"><code class="xref py py-class docutils literal notranslate"><span class="pre">skopt.dump</span></code></a> and <a class="reference internal" href="../modules/generated/skopt.load.html#skopt.load" title="skopt.load"><code class="xref py py-class docutils literal notranslate"><span class="pre">skopt.load</span></code></a> with additional keyword arguments. See the
<a class="reference external" href="https://joblib.readthedocs.io/en/latest/">joblib</a> documentation
<a class="reference external" href="https://joblib.readthedocs.io/en/latest/generated/joblib.dump.html#joblib.dump" title="(in joblib v1.1.0)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">joblib.dump</span></code></a> and
<a class="reference external" href="https://joblib.readthedocs.io/en/latest/generated/joblib.load.html#joblib.load" title="(in joblib v1.1.0)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">joblib.load</span></code></a> for the additional parameters.</p>
<p>For instance, you can specify the compression algorithm and compression
level (highest in this case):</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><a href="../modules/generated/skopt.dump.html#skopt.dump" title="skopt.dump" class="sphx-glr-backref-module-skopt sphx-glr-backref-type-py-function"><span class="n">dump</span></a><span class="p">(</span><span class="n">res</span><span class="p">,</span> <span class="s1">'result.gz'</span><span class="p">,</span> <span class="n">compress</span><span class="o">=</span><span class="mi">9</span><span class="p">)</span>
<span class="kn">from</span> <span class="nn">os.path</span> <span class="kn">import</span> <a href="https://docs.python.org/3/library/os.path.html#os.path.getsize" title="os.path.getsize" class="sphx-glr-backref-module-os-path sphx-glr-backref-type-py-function"><span class="n">getsize</span></a>
<span class="nb">print</span><span class="p">(</span><span class="s1">'Without compression: </span><span class="si">{}</span><span class="s1"> bytes'</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><a href="https://docs.python.org/3/library/os.path.html#os.path.getsize" title="os.path.getsize" class="sphx-glr-backref-module-os-path sphx-glr-backref-type-py-function"><span class="n">getsize</span></a><span class="p">(</span><span class="s1">'result.pkl'</span><span class="p">)))</span>
<span class="nb">print</span><span class="p">(</span><span class="s1">'Compressed with gz: </span><span class="si">{}</span><span class="s1"> bytes'</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><a href="https://docs.python.org/3/library/os.path.html#os.path.getsize" title="os.path.getsize" class="sphx-glr-backref-module-os-path sphx-glr-backref-type-py-function"><span class="n">getsize</span></a><span class="p">(</span><span class="s1">'result.gz'</span><span class="p">)))</span>
</pre></div>
</div>
<p class="sphx-glr-script-out">Out:</p>
<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Without compression: 74076 bytes
Compressed with gz: 26466 bytes
</pre></div>
</div>
<section id="unserializable-objective-functions">
<h3>Unserializable objective functions<a class="headerlink" href="#unserializable-objective-functions" title="Permalink to this headline">¶</a></h3>
<p>Notice that if your objective function is non-trivial (e.g. it calls MATLAB
engine from Python), it might be not serializable and <a class="reference internal" href="../modules/generated/skopt.dump.html#skopt.dump" title="skopt.dump"><code class="xref py py-class docutils literal notranslate"><span class="pre">skopt.dump</span></code></a> will
raise an exception when you try to store the optimization results.
In this case you should disable storing the objective function by calling
<a class="reference internal" href="../modules/generated/skopt.dump.html#skopt.dump" title="skopt.dump"><code class="xref py py-class docutils literal notranslate"><span class="pre">skopt.dump</span></code></a> with the keyword argument <code class="docutils literal notranslate"><span class="pre">store_objective=False</span></code>:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><a href="../modules/generated/skopt.dump.html#skopt.dump" title="skopt.dump" class="sphx-glr-backref-module-skopt sphx-glr-backref-type-py-function"><span class="n">dump</span></a><span class="p">(</span><span class="n">res</span><span class="p">,</span> <span class="s1">'result_without_objective.pkl'</span><span class="p">,</span> <span class="n">store_objective</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
</pre></div>
</div>
<p>Notice that the entry <code class="docutils literal notranslate"><span class="pre">'func'</span></code> is absent in the loaded object but is still
present in the local variable:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">res_loaded_without_objective</span> <span class="o">=</span> <a href="../modules/generated/skopt.load.html#skopt.load" title="skopt.load" class="sphx-glr-backref-module-skopt sphx-glr-backref-type-py-function"><span class="n">load</span></a><span class="p">(</span><span class="s1">'result_without_objective.pkl'</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="s1">'Loaded object: '</span><span class="p">,</span> <span class="n">res_loaded_without_objective</span><span class="o">.</span><span class="n">specs</span><span class="p">[</span><span class="s1">'args'</span><span class="p">]</span><span class="o">.</span><span class="n">keys</span><span class="p">())</span>
<span class="nb">print</span><span class="p">(</span><span class="s1">'Local variable:'</span><span class="p">,</span> <span class="n">res</span><span class="o">.</span><span class="n">specs</span><span class="p">[</span><span class="s1">'args'</span><span class="p">]</span><span class="o">.</span><span class="n">keys</span><span class="p">())</span>
</pre></div>
</div>
<p class="sphx-glr-script-out">Out:</p>
<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Loaded object: dict_keys(['dimensions', 'base_estimator', 'n_calls', 'n_random_starts', 'n_initial_points', 'initial_point_generator', 'acq_func', 'acq_optimizer', 'x0', 'y0', 'random_state', 'verbose', 'callback', 'n_points', 'n_restarts_optimizer', 'xi', 'kappa', 'n_jobs', 'model_queue_size'])
Local variable: dict_keys(['func', 'dimensions', 'base_estimator', 'n_calls', 'n_random_starts', 'n_initial_points', 'initial_point_generator', 'acq_func', 'acq_optimizer', 'x0', 'y0', 'random_state', 'verbose', 'callback', 'n_points', 'n_restarts_optimizer', 'xi', 'kappa', 'n_jobs', 'model_queue_size'])
</pre></div>
</div>
</section>
</section>
<section id="possible-problems">
<h2>Possible problems<a class="headerlink" href="#possible-problems" title="Permalink to this headline">¶</a></h2>
<ul class="simple">
<li><p><strong>Python versions incompatibility:</strong> In general, objects serialized in
Python 2 cannot be deserialized in Python 3 and vice versa.</p></li>
<li><p><strong>Security issues:</strong> Once again, do not load any files from untrusted
sources.</p></li>
<li><p><strong>Extremely large results objects:</strong> If your optimization results object</p></li>
</ul>
<p>is extremely large, calling <a class="reference internal" href="../modules/generated/skopt.dump.html#skopt.dump" title="skopt.dump"><code class="xref py py-class docutils literal notranslate"><span class="pre">skopt.dump</span></code></a> with <code class="docutils literal notranslate"><span class="pre">store_objective=False</span></code> might
cause performance issues. This is due to creation of a deep copy without the
objective function. If the objective function it is not critical to you, you
can simply delete it before calling <a class="reference internal" href="../modules/generated/skopt.dump.html#skopt.dump" title="skopt.dump"><code class="xref py py-class docutils literal notranslate"><span class="pre">skopt.dump</span></code></a>. In this case, no deep
copy is created:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="k">del</span> <span class="n">res</span><span class="o">.</span><span class="n">specs</span><span class="p">[</span><span class="s1">'args'</span><span class="p">][</span><span class="s1">'func'</span><span class="p">]</span>
<a href="../modules/generated/skopt.dump.html#skopt.dump" title="skopt.dump" class="sphx-glr-backref-module-skopt sphx-glr-backref-type-py-function"><span class="n">dump</span></a><span class="p">(</span><span class="n">res</span><span class="p">,</span> <span class="s1">'result_without_objective_2.pkl'</span><span class="p">)</span>
</pre></div>
</div>
<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 2.013 seconds)</p>
<p><strong>Estimated memory usage:</strong> 14 MB</p>
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