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<li><a class="reference internal" href="#">Parallel optimization</a><ul>
<li><a class="reference internal" href="#introduction">Introduction</a></li>
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<section class="sphx-glr-example-title" id="parallel-optimization">
<span id="sphx-glr-auto-examples-parallel-optimization-py"></span><h1>Parallel optimization<a class="headerlink" href="#parallel-optimization" title="Permalink to this headline">¶</a></h1>
<p>Iaroslav Shcherbatyi, May 2017.
Reviewed by Manoj Kumar and Tim Head.
Reformatted by Holger Nahrstaedt 2020</p>
<section id="introduction">
<h2>Introduction<a class="headerlink" href="#introduction" title="Permalink to this headline">¶</a></h2>
<p>For many practical black box optimization problems expensive objective can be
evaluated in parallel at multiple points. This allows to get more objective
evaluations per unit of time, which reduces the time necessary to reach good
objective values when appropriate optimization algorithms are used, see for
example results in <a class="footnote-reference brackets" href="#id3" id="id1">1</a> and the references therein.</p>
<p>One such example task is a selection of number and activation function of a
neural network which results in highest accuracy for some machine learning
problem. For such task, multiple neural networks with different combinations
of number of neurons and activation function type can be evaluated at the same
time in parallel on different cpu cores / computational nodes.</p>
<p>The “ask and tell” API of scikit-optimize exposes functionality that allows to
obtain multiple points for evaluation in parallel. Intended usage of this
interface is as follows:</p>
<ol class="arabic simple">
<li><p>Initialize instance of the <code class="docutils literal notranslate"><span class="pre">Optimizer</span></code> class from skopt</p></li>
<li><p>Obtain n points for evaluation in parallel by calling the <code class="docutils literal notranslate"><span class="pre">ask</span></code> method of an optimizer instance with the <code class="docutils literal notranslate"><span class="pre">n_points</span></code> argument set to n > 0</p></li>
<li><p>Evaluate points</p></li>
<li><p>Provide points and corresponding objectives using the <code class="docutils literal notranslate"><span class="pre">tell</span></code> method of an optimizer instance</p></li>
<li><p>Continue from step 2 until eg maximum number of evaluations reached</p></li>
</ol>
<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>
</pre></div>
</div>
</section>
<section id="example">
<h2>Example<a class="headerlink" href="#example" title="Permalink to this headline">¶</a></h2>
<p>A minimalistic example that uses joblib to parallelize evaluation of the
objective function is given below.</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.Optimizer.html#skopt.Optimizer" title="skopt.Optimizer" class="sphx-glr-backref-module-skopt sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">Optimizer</span></a>
<span class="kn">from</span> <span class="nn">skopt.space</span> <span class="kn">import</span> <span class="n">Real</span>
<span class="kn">from</span> <span class="nn">joblib</span> <span class="kn">import</span> <a href="https://joblib.readthedocs.io/en/latest/generated/joblib.Parallel.html#joblib.Parallel" title="joblib.Parallel" class="sphx-glr-backref-module-joblib sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">Parallel</span></a><span class="p">,</span> <a href="https://joblib.readthedocs.io/en/latest/parallel.html#joblib.delayed" title="joblib.delayed" class="sphx-glr-backref-module-joblib sphx-glr-backref-type-py-function"><span class="n">delayed</span></a>
<span class="c1"># example objective taken from skopt</span>
<span class="kn">from</span> <span class="nn">skopt.benchmarks</span> <span class="kn">import</span> <a href="../modules/generated/skopt.benchmarks.branin.html#skopt.benchmarks.branin" title="skopt.benchmarks.branin" class="sphx-glr-backref-module-skopt-benchmarks sphx-glr-backref-type-py-function"><span class="n">branin</span></a>
<span class="n">optimizer</span> <span class="o">=</span> <a href="../modules/generated/skopt.Optimizer.html#skopt.Optimizer" title="skopt.Optimizer" class="sphx-glr-backref-module-skopt sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">Optimizer</span></a><span class="p">(</span>
<span class="n">dimensions</span><span class="o">=</span><span class="p">[</span><span class="n">Real</span><span class="p">(</span><span class="o">-</span><span class="mf">5.0</span><span class="p">,</span> <span class="mf">10.0</span><span class="p">),</span> <span class="n">Real</span><span class="p">(</span><span class="mf">0.0</span><span class="p">,</span> <span class="mf">15.0</span><span class="p">)],</span>
<span class="n">random_state</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span>
<span class="n">base_estimator</span><span class="o">=</span><span class="s1">'gp'</span>
<span class="p">)</span>
<span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">10</span><span class="p">):</span>
<span class="n">x</span> <span class="o">=</span> <span class="n">optimizer</span><span class="o">.</span><span class="n">ask</span><span class="p">(</span><span class="n">n_points</span><span class="o">=</span><span class="mi">4</span><span class="p">)</span> <span class="c1"># x is a list of n_points points</span>
<span class="n">y</span> <span class="o">=</span> <a href="https://joblib.readthedocs.io/en/latest/generated/joblib.Parallel.html#joblib.Parallel" title="joblib.Parallel" class="sphx-glr-backref-module-joblib sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">Parallel</span></a><span class="p">(</span><span class="n">n_jobs</span><span class="o">=</span><span class="mi">4</span><span class="p">)(</span><a href="https://joblib.readthedocs.io/en/latest/parallel.html#joblib.delayed" title="joblib.delayed" class="sphx-glr-backref-module-joblib sphx-glr-backref-type-py-function"><span class="n">delayed</span></a><span class="p">(</span><a href="../modules/generated/skopt.benchmarks.branin.html#skopt.benchmarks.branin" title="skopt.benchmarks.branin" class="sphx-glr-backref-module-skopt-benchmarks sphx-glr-backref-type-py-function"><span class="n">branin</span></a><span class="p">)(</span><span class="n">v</span><span class="p">)</span> <span class="k">for</span> <span class="n">v</span> <span class="ow">in</span> <span class="n">x</span><span class="p">)</span> <span class="c1"># evaluate points in parallel</span>
<span class="n">optimizer</span><span class="o">.</span><span class="n">tell</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="c1"># takes ~ 20 sec to get here</span>
<span class="nb">print</span><span class="p">(</span><span class="nb">min</span><span class="p">(</span><span class="n">optimizer</span><span class="o">.</span><span class="n">yi</span><span class="p">))</span> <span class="c1"># print the best objective found</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>0.3982974723981023
</pre></div>
</div>
<p>Note that if <code class="docutils literal notranslate"><span class="pre">n_points</span></code> is set to some integer > 0 for the <code class="docutils literal notranslate"><span class="pre">ask</span></code> method, the
result will be a list of points, even for <code class="docutils literal notranslate"><span class="pre">n_points</span></code> = 1. If the argument is
set to <code class="docutils literal notranslate"><span class="pre">None</span></code> (default value) then a single point (but not a list of points)
will be returned.</p>
<p>The default “minimum constant liar” <a class="footnote-reference brackets" href="#id3" id="id2">1</a> parallelization strategy is used in
the example, which allows to obtain multiple points for evaluation with a
single call to the <code class="docutils literal notranslate"><span class="pre">ask</span></code> method with any surrogate or acquisition function.
Parallelization strategy can be set using the “strategy” argument of <code class="docutils literal notranslate"><span class="pre">ask</span></code>.
For supported parallelization strategies see the documentation of
scikit-optimize.</p>
<dl class="footnote brackets">
<dt class="label" id="id3"><span class="brackets">1</span><span class="fn-backref">(<a href="#id1">1</a>,<a href="#id2">2</a>)</span></dt>
<dd><p><a class="reference external" href="https://hal.archives-ouvertes.fr/hal-00732512/document">https://hal.archives-ouvertes.fr/hal-00732512/document</a></p>
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showNavBar = function() {
navBar.style.top = "0";
}
if (hashTargetOnTop()) {
hideNavBar()
}
var prevScrollpos = window.pageYOffset;
hideOnScroll = function(lastScrollTop) {
if (($window.width() < 768) && (navBarToggler.getAttribute("aria-expanded") === 'true')) {
return;
}
if (lastScrollTop > 2 && (prevScrollpos <= lastScrollTop) || hashTargetOnTop()){
hideNavBar()
} else {
showNavBar()
}
prevScrollpos = lastScrollTop;
};
/*** high performance scroll event listener***/
var raf = window.requestAnimationFrame ||
window.webkitRequestAnimationFrame ||
window.mozRequestAnimationFrame ||
window.msRequestAnimationFrame ||
window.oRequestAnimationFrame;
var lastScrollTop = $window.scrollTop();
if (raf) {
loop();
}
function loop() {
var scrollTop = $window.scrollTop();
if (lastScrollTop === scrollTop) {
raf(loop);
return;
} else {
lastScrollTop = scrollTop;
hideOnScroll(lastScrollTop);
raf(loop);
}
}
})();
});
</script>
<script id="MathJax-script" async src="https://cdn.jsdelivr.net/npm/mathjax@3/es5/tex-chtml.js"></script>
<script src="https://scikit-optimize.github.io/versionwarning.js"></script>
</body>
</html>