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<li><a class="reference internal" href="#">Use different base estimators for optimization</a><ul>
<li><a class="reference internal" href="#toy-example">Toy example</a></li>
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<p>Click <a class="reference internal" href="#sphx-glr-download-auto-examples-optimizer-with-different-base-estimator-py"><span class="std std-ref">here</span></a>
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<section class="sphx-glr-example-title" id="use-different-base-estimators-for-optimization">
<span id="sphx-glr-auto-examples-optimizer-with-different-base-estimator-py"></span><h1>Use different base estimators for optimization<a class="headerlink" href="#use-different-base-estimators-for-optimization" title="Permalink to this headline">¶</a></h1>
<p>Sigurd Carlen, September 2019.
Reformatted by Holger Nahrstaedt 2020</p>
<p>To use different base_estimator or create a regressor with different parameters,
we can create a regressor object and set it as kernel.</p>
<p>This example uses <a class="reference internal" href="../modules/generated/skopt.plots.plot_gaussian_process.html#skopt.plots.plot_gaussian_process" title="skopt.plots.plot_gaussian_process"><code class="xref py py-class docutils literal notranslate"><span class="pre">plots.plot_gaussian_process</span></code></a> which is available
since version 0.8.</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>
<a href="https://numpy.org/doc/stable/reference/random/generated/numpy.random.seed.html#numpy.random.seed" title="numpy.random.seed" 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">seed</span></a><span class="p">(</span><span class="mi">1234</span><span class="p">)</span>
<span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
<span class="kn">from</span> <span class="nn">skopt.plots</span> <span class="kn">import</span> <a href="../modules/generated/skopt.plots.plot_gaussian_process.html#skopt.plots.plot_gaussian_process" title="skopt.plots.plot_gaussian_process" class="sphx-glr-backref-module-skopt-plots sphx-glr-backref-type-py-function"><span class="n">plot_gaussian_process</span></a>
<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>
</pre></div>
</div>
<section id="toy-example">
<h2>Toy example<a class="headerlink" href="#toy-example" title="Permalink to this headline">¶</a></h2>
<p>Let assume the following noisy function <span class="math notranslate nohighlight">\(f\)</span>:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">noise_level</span> <span class="o">=</span> <span class="mf">0.1</span>
<span class="c1"># Our 1D toy problem, this is the function we are trying to</span>
<span class="c1"># minimize</span>
<span class="k">def</span> <span class="nf">objective</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="k">def</span> <span class="nf">objective_wo_noise</span><span class="p">(</span><span class="n">x</span><span class="p">):</span>
<span class="k">return</span> <span class="n">objective</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="mi">0</span><span class="p">)</span>
</pre></div>
</div>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">opt_gp</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="o">-</span><span class="mf">2.0</span><span class="p">,</span> <span class="mf">2.0</span><span class="p">)],</span> <span class="n">base_estimator</span><span class="o">=</span><span class="s2">"GP"</span><span class="p">,</span> <span class="n">n_initial_points</span><span class="o">=</span><span class="mi">5</span><span class="p">,</span>
<span class="n">acq_optimizer</span><span class="o">=</span><span class="s2">"sampling"</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">42</span><span class="p">)</span>
</pre></div>
</div>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">plot_optimizer</span><span class="p">(</span><span class="n">res</span><span class="p">,</span> <span class="n">n_iter</span><span class="p">,</span> <span class="n">max_iters</span><span class="o">=</span><span class="mi">5</span><span class="p">):</span>
<span class="k">if</span> <span class="n">n_iter</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
<span class="n">show_legend</span> <span class="o">=</span> <span class="kc">True</span>
<span class="k">else</span><span class="p">:</span>
<span class="n">show_legend</span> <span class="o">=</span> <span class="kc">False</span>
<span class="n">ax</span> <span class="o">=</span> <a href="https://matplotlib.org/api/_as_gen/matplotlib.pyplot.subplot.html#matplotlib.pyplot.subplot" title="matplotlib.pyplot.subplot" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">subplot</span></a><span class="p">(</span><span class="n">max_iters</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">2</span> <span class="o">*</span> <span class="n">n_iter</span> <span class="o">+</span> <span class="mi">1</span><span class="p">)</span>
<span class="c1"># Plot GP(x) + contours</span>
<span class="n">ax</span> <span class="o">=</span> <a href="../modules/generated/skopt.plots.plot_gaussian_process.html#skopt.plots.plot_gaussian_process" title="skopt.plots.plot_gaussian_process" class="sphx-glr-backref-module-skopt-plots sphx-glr-backref-type-py-function"><span class="n">plot_gaussian_process</span></a><span class="p">(</span><span class="n">res</span><span class="p">,</span> <span class="n">ax</span><span class="o">=</span><span class="n">ax</span><span class="p">,</span>
<span class="n">objective</span><span class="o">=</span><span class="n">objective_wo_noise</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="n">show_legend</span><span class="o">=</span><span class="n">show_legend</span><span class="p">,</span> <span class="n">show_title</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
<span class="n">show_next_point</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">show_acq_func</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
<span class="n">ax</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="s2">""</span><span class="p">)</span>
<span class="n">ax</span><span class="o">.</span><span class="n">set_xlabel</span><span class="p">(</span><span class="s2">""</span><span class="p">)</span>
<span class="k">if</span> <span class="n">n_iter</span> <span class="o"><</span> <span class="n">max_iters</span> <span class="o">-</span> <span class="mi">1</span><span class="p">:</span>
<span class="n">ax</span><span class="o">.</span><span class="n">get_xaxis</span><span class="p">()</span><span class="o">.</span><span class="n">set_ticklabels</span><span class="p">([])</span>
<span class="c1"># Plot EI(x)</span>
<span class="n">ax</span> <span class="o">=</span> <a href="https://matplotlib.org/api/_as_gen/matplotlib.pyplot.subplot.html#matplotlib.pyplot.subplot" title="matplotlib.pyplot.subplot" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">subplot</span></a><span class="p">(</span><span class="n">max_iters</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">2</span> <span class="o">*</span> <span class="n">n_iter</span> <span class="o">+</span> <span class="mi">2</span><span class="p">)</span>
<span class="n">ax</span> <span class="o">=</span> <a href="../modules/generated/skopt.plots.plot_gaussian_process.html#skopt.plots.plot_gaussian_process" title="skopt.plots.plot_gaussian_process" class="sphx-glr-backref-module-skopt-plots sphx-glr-backref-type-py-function"><span class="n">plot_gaussian_process</span></a><span class="p">(</span><span class="n">res</span><span class="p">,</span> <span class="n">ax</span><span class="o">=</span><span class="n">ax</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="n">show_legend</span><span class="o">=</span><span class="n">show_legend</span><span class="p">,</span> <span class="n">show_title</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
<span class="n">show_next_point</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">show_acq_func</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
<span class="n">show_observations</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
<span class="n">show_mu</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
<span class="n">ax</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="s2">""</span><span class="p">)</span>
<span class="n">ax</span><span class="o">.</span><span class="n">set_xlabel</span><span class="p">(</span><span class="s2">""</span><span class="p">)</span>
<span class="k">if</span> <span class="n">n_iter</span> <span class="o"><</span> <span class="n">max_iters</span> <span class="o">-</span> <span class="mi">1</span><span class="p">:</span>
<span class="n">ax</span><span class="o">.</span><span class="n">get_xaxis</span><span class="p">()</span><span class="o">.</span><span class="n">set_ticklabels</span><span class="p">([])</span>
</pre></div>
</div>
</section>
<section id="gp-kernel">
<h2>GP kernel<a class="headerlink" href="#gp-kernel" title="Permalink to this headline">¶</a></h2>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">fig</span> <span class="o">=</span> <a href="https://matplotlib.org/api/_as_gen/matplotlib.pyplot.figure.html#matplotlib.pyplot.figure" title="matplotlib.pyplot.figure" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">figure</span></a><span class="p">()</span>
<span class="n">fig</span><span class="o">.</span><span class="n">suptitle</span><span class="p">(</span><span class="s2">"Standard GP kernel"</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">next_x</span> <span class="o">=</span> <span class="n">opt_gp</span><span class="o">.</span><span class="n">ask</span><span class="p">()</span>
<span class="n">f_val</span> <span class="o">=</span> <span class="n">objective</span><span class="p">(</span><span class="n">next_x</span><span class="p">)</span>
<span class="n">res</span> <span class="o">=</span> <span class="n">opt_gp</span><span class="o">.</span><span class="n">tell</span><span class="p">(</span><span class="n">next_x</span><span class="p">,</span> <span class="n">f_val</span><span class="p">)</span>
<span class="k">if</span> <span class="n">i</span> <span class="o">>=</span> <span class="mi">5</span><span class="p">:</span>
<span class="n">plot_optimizer</span><span class="p">(</span><span class="n">res</span><span class="p">,</span> <span class="n">n_iter</span><span class="o">=</span><span class="n">i</span><span class="o">-</span><span class="mi">5</span><span class="p">,</span> <span class="n">max_iters</span><span class="o">=</span><span class="mi">5</span><span class="p">)</span>
<a href="https://matplotlib.org/api/_as_gen/matplotlib.pyplot.tight_layout.html#matplotlib.pyplot.tight_layout" title="matplotlib.pyplot.tight_layout" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">tight_layout</span></a><span class="p">(</span><span class="n">rect</span><span class="o">=</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mf">0.03</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mf">0.95</span><span class="p">])</span>
<a href="https://matplotlib.org/api/_as_gen/matplotlib.pyplot.plot.html#matplotlib.pyplot.plot" title="matplotlib.pyplot.plot" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">plot</span></a><span class="p">()</span>
</pre></div>
</div>
<img src="../_images/sphx_glr_optimizer-with-different-base-estimator_001.png" srcset="../_images/sphx_glr_optimizer-with-different-base-estimator_001.png" alt="Standard GP kernel, x* = -0.2167, f(x*) = -0.9141, x* = -0.2167, f(x*) = -0.9141, x* = -0.2167, f(x*) = -0.9141, x* = -0.2167, f(x*) = -0.9141, x* = -0.2167, f(x*) = -0.9141" class = "sphx-glr-single-img"/><p class="sphx-glr-script-out">Out:</p>
<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>[]
</pre></div>
</div>
</section>
<section id="test-different-kernels">
<h2>Test different kernels<a class="headerlink" href="#test-different-kernels" title="Permalink to this headline">¶</a></h2>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">skopt.learning</span> <span class="kn">import</span> <a href="../modules/generated/skopt.learning.GaussianProcessRegressor.html#skopt.learning.GaussianProcessRegressor" title="skopt.learning.GaussianProcessRegressor" class="sphx-glr-backref-module-skopt-learning sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">GaussianProcessRegressor</span></a>
<span class="kn">from</span> <span class="nn">skopt.learning.gaussian_process.kernels</span> <span class="kn">import</span> <a href="https://scikit-learn.org/stable/modules/generated/sklearn.gaussian_process.kernels.ConstantKernel.html#sklearn.gaussian_process.kernels.ConstantKernel" title="sklearn.gaussian_process.kernels.ConstantKernel" class="sphx-glr-backref-module-sklearn-gaussian_process-kernels sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">ConstantKernel</span></a><span class="p">,</span> <a href="https://scikit-learn.org/stable/modules/generated/sklearn.gaussian_process.kernels.Matern.html#sklearn.gaussian_process.kernels.Matern" title="sklearn.gaussian_process.kernels.Matern" class="sphx-glr-backref-module-sklearn-gaussian_process-kernels sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">Matern</span></a>
<span class="c1"># Gaussian process with Matérn kernel as surrogate model</span>
<span class="kn">from</span> <span class="nn">sklearn.gaussian_process.kernels</span> <span class="kn">import</span> <span class="p">(</span><a href="https://scikit-learn.org/stable/modules/generated/sklearn.gaussian_process.kernels.RBF.html#sklearn.gaussian_process.kernels.RBF" title="sklearn.gaussian_process.kernels.RBF" class="sphx-glr-backref-module-sklearn-gaussian_process-kernels sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">RBF</span></a><span class="p">,</span> <a href="https://scikit-learn.org/stable/modules/generated/sklearn.gaussian_process.kernels.Matern.html#sklearn.gaussian_process.kernels.Matern" title="sklearn.gaussian_process.kernels.Matern" class="sphx-glr-backref-module-sklearn-gaussian_process-kernels sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">Matern</span></a><span class="p">,</span> <a href="https://scikit-learn.org/stable/modules/generated/sklearn.gaussian_process.kernels.RationalQuadratic.html#sklearn.gaussian_process.kernels.RationalQuadratic" title="sklearn.gaussian_process.kernels.RationalQuadratic" class="sphx-glr-backref-module-sklearn-gaussian_process-kernels sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">RationalQuadratic</span></a><span class="p">,</span>
<a href="https://scikit-learn.org/stable/modules/generated/sklearn.gaussian_process.kernels.ExpSineSquared.html#sklearn.gaussian_process.kernels.ExpSineSquared" title="sklearn.gaussian_process.kernels.ExpSineSquared" class="sphx-glr-backref-module-sklearn-gaussian_process-kernels sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">ExpSineSquared</span></a><span class="p">,</span> <a href="https://scikit-learn.org/stable/modules/generated/sklearn.gaussian_process.kernels.DotProduct.html#sklearn.gaussian_process.kernels.DotProduct" title="sklearn.gaussian_process.kernels.DotProduct" class="sphx-glr-backref-module-sklearn-gaussian_process-kernels sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">DotProduct</span></a><span class="p">,</span>
<a href="https://scikit-learn.org/stable/modules/generated/sklearn.gaussian_process.kernels.ConstantKernel.html#sklearn.gaussian_process.kernels.ConstantKernel" title="sklearn.gaussian_process.kernels.ConstantKernel" class="sphx-glr-backref-module-sklearn-gaussian_process-kernels sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">ConstantKernel</span></a><span class="p">)</span>
<span class="n">kernels</span> <span class="o">=</span> <span class="p">[</span><span class="mf">1.0</span> <span class="o">*</span> <a href="https://scikit-learn.org/stable/modules/generated/sklearn.gaussian_process.kernels.RBF.html#sklearn.gaussian_process.kernels.RBF" title="sklearn.gaussian_process.kernels.RBF" class="sphx-glr-backref-module-sklearn-gaussian_process-kernels sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">RBF</span></a><span class="p">(</span><span class="n">length_scale</span><span class="o">=</span><span class="mf">1.0</span><span class="p">,</span> <span class="n">length_scale_bounds</span><span class="o">=</span><span class="p">(</span><span class="mf">1e-1</span><span class="p">,</span> <span class="mf">10.0</span><span class="p">)),</span>
<span class="mf">1.0</span> <span class="o">*</span> <a href="https://scikit-learn.org/stable/modules/generated/sklearn.gaussian_process.kernels.RationalQuadratic.html#sklearn.gaussian_process.kernels.RationalQuadratic" title="sklearn.gaussian_process.kernels.RationalQuadratic" class="sphx-glr-backref-module-sklearn-gaussian_process-kernels sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">RationalQuadratic</span></a><span class="p">(</span><span class="n">length_scale</span><span class="o">=</span><span class="mf">1.0</span><span class="p">,</span> <span class="n">alpha</span><span class="o">=</span><span class="mf">0.1</span><span class="p">),</span>
<span class="mf">1.0</span> <span class="o">*</span> <a href="https://scikit-learn.org/stable/modules/generated/sklearn.gaussian_process.kernels.ExpSineSquared.html#sklearn.gaussian_process.kernels.ExpSineSquared" title="sklearn.gaussian_process.kernels.ExpSineSquared" class="sphx-glr-backref-module-sklearn-gaussian_process-kernels sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">ExpSineSquared</span></a><span class="p">(</span><span class="n">length_scale</span><span class="o">=</span><span class="mf">1.0</span><span class="p">,</span> <span class="n">periodicity</span><span class="o">=</span><span class="mf">3.0</span><span class="p">,</span>
<span class="n">length_scale_bounds</span><span class="o">=</span><span class="p">(</span><span class="mf">0.1</span><span class="p">,</span> <span class="mf">10.0</span><span class="p">),</span>
<span class="n">periodicity_bounds</span><span class="o">=</span><span class="p">(</span><span class="mf">1.0</span><span class="p">,</span> <span class="mf">10.0</span><span class="p">)),</span>
<a href="https://scikit-learn.org/stable/modules/generated/sklearn.gaussian_process.kernels.ConstantKernel.html#sklearn.gaussian_process.kernels.ConstantKernel" title="sklearn.gaussian_process.kernels.ConstantKernel" class="sphx-glr-backref-module-sklearn-gaussian_process-kernels sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">ConstantKernel</span></a><span class="p">(</span><span class="mf">0.1</span><span class="p">,</span> <span class="p">(</span><span class="mf">0.01</span><span class="p">,</span> <span class="mf">10.0</span><span class="p">))</span>
<span class="o">*</span> <span class="p">(</span><a href="https://scikit-learn.org/stable/modules/generated/sklearn.gaussian_process.kernels.DotProduct.html#sklearn.gaussian_process.kernels.DotProduct" title="sklearn.gaussian_process.kernels.DotProduct" class="sphx-glr-backref-module-sklearn-gaussian_process-kernels sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">DotProduct</span></a><span class="p">(</span><span class="n">sigma_0</span><span class="o">=</span><span class="mf">1.0</span><span class="p">,</span> <span class="n">sigma_0_bounds</span><span class="o">=</span><span class="p">(</span><span class="mf">0.1</span><span class="p">,</span> <span class="mf">10.0</span><span class="p">))</span> <span class="o">**</span> <span class="mi">2</span><span class="p">),</span>
<span class="mf">1.0</span> <span class="o">*</span> <a href="https://scikit-learn.org/stable/modules/generated/sklearn.gaussian_process.kernels.Matern.html#sklearn.gaussian_process.kernels.Matern" title="sklearn.gaussian_process.kernels.Matern" class="sphx-glr-backref-module-sklearn-gaussian_process-kernels sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">Matern</span></a><span class="p">(</span><span class="n">length_scale</span><span class="o">=</span><span class="mf">1.0</span><span class="p">,</span> <span class="n">length_scale_bounds</span><span class="o">=</span><span class="p">(</span><span class="mf">1e-1</span><span class="p">,</span> <span class="mf">10.0</span><span class="p">),</span>
<span class="n">nu</span><span class="o">=</span><span class="mf">2.5</span><span class="p">)]</span>
</pre></div>
</div>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="k">for</span> <span class="n">kernel</span> <span class="ow">in</span> <span class="n">kernels</span><span class="p">:</span>
<span class="n">gpr</span> <span class="o">=</span> <a href="../modules/generated/skopt.learning.GaussianProcessRegressor.html#skopt.learning.GaussianProcessRegressor" title="skopt.learning.GaussianProcessRegressor" class="sphx-glr-backref-module-skopt-learning sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">GaussianProcessRegressor</span></a><span class="p">(</span><span class="n">kernel</span><span class="o">=</span><span class="n">kernel</span><span class="p">,</span> <span class="n">alpha</span><span class="o">=</span><span class="n">noise_level</span> <span class="o">**</span> <span class="mi">2</span><span class="p">,</span>
<span class="n">normalize_y</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">noise</span><span class="o">=</span><span class="s2">"gaussian"</span><span class="p">,</span>
<span class="n">n_restarts_optimizer</span><span class="o">=</span><span class="mi">2</span>
<span class="p">)</span>
<span class="n">opt</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="o">-</span><span class="mf">2.0</span><span class="p">,</span> <span class="mf">2.0</span><span class="p">)],</span> <span class="n">base_estimator</span><span class="o">=</span><span class="n">gpr</span><span class="p">,</span> <span class="n">n_initial_points</span><span class="o">=</span><span class="mi">5</span><span class="p">,</span>
<span class="n">acq_optimizer</span><span class="o">=</span><span class="s2">"sampling"</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">42</span><span class="p">)</span>
<span class="n">fig</span> <span class="o">=</span> <a href="https://matplotlib.org/api/_as_gen/matplotlib.pyplot.figure.html#matplotlib.pyplot.figure" title="matplotlib.pyplot.figure" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">figure</span></a><span class="p">()</span>
<span class="n">fig</span><span class="o">.</span><span class="n">suptitle</span><span class="p">(</span><span class="nb">repr</span><span class="p">(</span><span class="n">kernel</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">next_x</span> <span class="o">=</span> <span class="n">opt</span><span class="o">.</span><span class="n">ask</span><span class="p">()</span>
<span class="n">f_val</span> <span class="o">=</span> <span class="n">objective</span><span class="p">(</span><span class="n">next_x</span><span class="p">)</span>
<span class="n">res</span> <span class="o">=</span> <span class="n">opt</span><span class="o">.</span><span class="n">tell</span><span class="p">(</span><span class="n">next_x</span><span class="p">,</span> <span class="n">f_val</span><span class="p">)</span>
<span class="k">if</span> <span class="n">i</span> <span class="o">>=</span> <span class="mi">5</span><span class="p">:</span>
<span class="n">plot_optimizer</span><span class="p">(</span><span class="n">res</span><span class="p">,</span> <span class="n">n_iter</span><span class="o">=</span><span class="n">i</span> <span class="o">-</span> <span class="mi">5</span><span class="p">,</span> <span class="n">max_iters</span><span class="o">=</span><span class="mi">5</span><span class="p">)</span>
<a href="https://matplotlib.org/api/_as_gen/matplotlib.pyplot.tight_layout.html#matplotlib.pyplot.tight_layout" title="matplotlib.pyplot.tight_layout" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">tight_layout</span></a><span class="p">(</span><span class="n">rect</span><span class="o">=</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mf">0.03</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mf">0.95</span><span class="p">])</span>
<a href="https://matplotlib.org/api/_as_gen/matplotlib.pyplot.show.html#matplotlib.pyplot.show" title="matplotlib.pyplot.show" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">show</span></a><span class="p">()</span>
</pre></div>
</div>
<ul class="sphx-glr-horizontal">
<li><img src="../_images/sphx_glr_optimizer-with-different-base-estimator_002.png" srcset="../_images/sphx_glr_optimizer-with-different-base-estimator_002.png" alt="1**2 * RBF(length_scale=1), x* = -0.5018, f(x*) = -0.4236, x* = -0.5018, f(x*) = -0.4236, x* = -0.5018, f(x*) = -0.4236, x* = -0.5018, f(x*) = -0.4236, x* = -0.5018, f(x*) = -0.4236" class = "sphx-glr-multi-img"/></li>
<li><img src="../_images/sphx_glr_optimizer-with-different-base-estimator_003.png" srcset="../_images/sphx_glr_optimizer-with-different-base-estimator_003.png" alt="1**2 * RationalQuadratic(alpha=0.1, length_scale=1), x* = -0.5018, f(x*) = -0.4792, x* = -0.5018, f(x*) = -0.4792, x* = -0.5018, f(x*) = -0.4792, x* = -0.5018, f(x*) = -0.4792, x* = -0.3767, f(x*) = -0.8734" class = "sphx-glr-multi-img"/></li>
<li><img src="../_images/sphx_glr_optimizer-with-different-base-estimator_004.png" srcset="../_images/sphx_glr_optimizer-with-different-base-estimator_004.png" alt="1**2 * ExpSineSquared(length_scale=1, periodicity=3), x* = -0.5018, f(x*) = -0.4078, x* = -0.5018, f(x*) = -0.4078, x* = -0.5018, f(x*) = -0.4078, x* = -0.2591, f(x*) = -1.0230, x* = -0.2591, f(x*) = -1.0230" class = "sphx-glr-multi-img"/></li>
<li><img src="../_images/sphx_glr_optimizer-with-different-base-estimator_005.png" srcset="../_images/sphx_glr_optimizer-with-different-base-estimator_005.png" alt="0.316**2 * DotProduct(sigma_0=1) ** 2, x* = -0.5018, f(x*) = -0.5936, x* = -0.5018, f(x*) = -0.5936, x* = -0.5018, f(x*) = -0.5936, x* = -0.5018, f(x*) = -0.5936, x* = -0.5018, f(x*) = -0.5936" class = "sphx-glr-multi-img"/></li>
<li><img src="../_images/sphx_glr_optimizer-with-different-base-estimator_006.png" srcset="../_images/sphx_glr_optimizer-with-different-base-estimator_006.png" alt="1**2 * Matern(length_scale=1, nu=2.5), x* = -0.5018, f(x*) = -0.4247, x* = -0.5018, f(x*) = -0.4247, x* = -0.5018, f(x*) = -0.4247, x* = -0.5018, f(x*) = -0.4247, x* = -0.5009, f(x*) = -0.4400" class = "sphx-glr-multi-img"/></li>
</ul>
<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 9.317 seconds)</p>
<p><strong>Estimated memory usage:</strong> 16 MB</p>
<div class="sphx-glr-footer class sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-optimizer-with-different-base-estimator-py">
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<p><a class="reference download internal" download="" href="../_downloads/5514e237431a52400fbf5075b0d92256/optimizer-with-different-base-estimator.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">optimizer-with-different-base-estimator.py</span></code></a></p>
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