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<li><a class="reference internal" href="#">Scikit-learn hyperparameter search wrapper</a><ul>
<li><a class="reference internal" href="#introduction">Introduction</a></li>
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<li><a class="reference internal" href="#advanced-example">Advanced example</a></li>
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<section class="sphx-glr-example-title" id="scikit-learn-hyperparameter-search-wrapper">
<span id="sphx-glr-auto-examples-sklearn-gridsearchcv-replacement-py"></span><h1>Scikit-learn hyperparameter search wrapper<a class="headerlink" href="#scikit-learn-hyperparameter-search-wrapper" title="Permalink to this headline">¶</a></h1>
<p>Iaroslav Shcherbatyi, Tim Head and Gilles Louppe. June 2017.
Reformatted by Holger Nahrstaedt 2020</p>
<section id="introduction">
<h2>Introduction<a class="headerlink" href="#introduction" title="Permalink to this headline">¶</a></h2>
<p>This example assumes basic familiarity with
<a class="reference external" href="http://scikit-learn.org/stable/index.html">scikit-learn</a>.</p>
<p>Search for parameters of machine learning models that result in best
cross-validation performance is necessary in almost all practical
cases to get a model with best generalization estimate. A standard
approach in scikit-learn is using <a class="reference external" href="https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.GridSearchCV.html#sklearn.model_selection.GridSearchCV" title="(in scikit-learn v1.0)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">sklearn.model_selection.GridSearchCV</span></code></a> class, which takes
a set of values for every parameter to try, and simply enumerates all
combinations of parameter values. The complexity of such search grows
exponentially with the addition of new parameters. A more scalable
approach is using <a class="reference external" href="https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.RandomizedSearchCV.html#sklearn.model_selection.RandomizedSearchCV" title="(in scikit-learn v1.0)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">sklearn.model_selection.RandomizedSearchCV</span></code></a>, which however does not take
advantage of the structure of a search space.</p>
<p>Scikit-optimize provides a drop-in replacement for <a class="reference external" href="https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.GridSearchCV.html#sklearn.model_selection.GridSearchCV" title="(in scikit-learn v1.0)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">sklearn.model_selection.GridSearchCV</span></code></a>,
which utilizes Bayesian Optimization where a predictive model referred
to as “surrogate” is used to model the search space and utilized to
arrive at good parameter values combination as soon as possible.</p>
<p>Note: for a manual hyperparameter optimization example, see
“Hyperparameter Optimization” notebook.</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">123</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>
</pre></div>
</div>
</section>
<section id="minimal-example">
<h2>Minimal example<a class="headerlink" href="#minimal-example" title="Permalink to this headline">¶</a></h2>
<p>A minimal example of optimizing hyperparameters of SVC (Support Vector machine Classifier) 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.BayesSearchCV.html#skopt.BayesSearchCV" title="skopt.BayesSearchCV" class="sphx-glr-backref-module-skopt sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">BayesSearchCV</span></a>
<span class="kn">from</span> <span class="nn">sklearn.datasets</span> <span class="kn">import</span> <a href="https://scikit-learn.org/stable/modules/generated/sklearn.datasets.load_digits.html#sklearn.datasets.load_digits" title="sklearn.datasets.load_digits" class="sphx-glr-backref-module-sklearn-datasets sphx-glr-backref-type-py-function"><span class="n">load_digits</span></a>
<span class="kn">from</span> <span class="nn">sklearn.svm</span> <span class="kn">import</span> <a href="https://scikit-learn.org/stable/modules/generated/sklearn.svm.SVC.html#sklearn.svm.SVC" title="sklearn.svm.SVC" class="sphx-glr-backref-module-sklearn-svm sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">SVC</span></a>
<span class="kn">from</span> <span class="nn">sklearn.model_selection</span> <span class="kn">import</span> <a href="https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.train_test_split.html#sklearn.model_selection.train_test_split" title="sklearn.model_selection.train_test_split" class="sphx-glr-backref-module-sklearn-model_selection sphx-glr-backref-type-py-function"><span class="n">train_test_split</span></a>
<span class="n">X</span><span class="p">,</span> <span class="n">y</span> <span class="o">=</span> <a href="https://scikit-learn.org/stable/modules/generated/sklearn.datasets.load_digits.html#sklearn.datasets.load_digits" title="sklearn.datasets.load_digits" class="sphx-glr-backref-module-sklearn-datasets sphx-glr-backref-type-py-function"><span class="n">load_digits</span></a><span class="p">(</span><span class="n">n_class</span><span class="o">=</span><span class="mi">10</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="n">X_train</span><span class="p">,</span> <span class="n">X_test</span><span class="p">,</span> <span class="n">y_train</span><span class="p">,</span> <span class="n">y_test</span> <span class="o">=</span> <a href="https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.train_test_split.html#sklearn.model_selection.train_test_split" title="sklearn.model_selection.train_test_split" class="sphx-glr-backref-module-sklearn-model_selection sphx-glr-backref-type-py-function"><span class="n">train_test_split</span></a><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">train_size</span><span class="o">=</span><span class="mf">0.75</span><span class="p">,</span> <span class="n">test_size</span><span class="o">=</span><span class="mf">.25</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
<span class="c1"># log-uniform: understand as search over p = exp(x) by varying x</span>
<span class="n">opt</span> <span class="o">=</span> <a href="../modules/generated/skopt.BayesSearchCV.html#skopt.BayesSearchCV" title="skopt.BayesSearchCV" class="sphx-glr-backref-module-skopt sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">BayesSearchCV</span></a><span class="p">(</span>
<a href="https://scikit-learn.org/stable/modules/generated/sklearn.svm.SVC.html#sklearn.svm.SVC" title="sklearn.svm.SVC" class="sphx-glr-backref-module-sklearn-svm sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">SVC</span></a><span class="p">(),</span>
<span class="p">{</span>
<span class="s1">'C'</span><span class="p">:</span> <span class="p">(</span><span class="mf">1e-6</span><span class="p">,</span> <span class="mf">1e+6</span><span class="p">,</span> <span class="s1">'log-uniform'</span><span class="p">),</span>
<span class="s1">'gamma'</span><span class="p">:</span> <span class="p">(</span><span class="mf">1e-6</span><span class="p">,</span> <span class="mf">1e+1</span><span class="p">,</span> <span class="s1">'log-uniform'</span><span class="p">),</span>
<span class="s1">'degree'</span><span class="p">:</span> <span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">8</span><span class="p">),</span> <span class="c1"># integer valued parameter</span>
<span class="s1">'kernel'</span><span class="p">:</span> <span class="p">[</span><span class="s1">'linear'</span><span class="p">,</span> <span class="s1">'poly'</span><span class="p">,</span> <span class="s1">'rbf'</span><span class="p">],</span> <span class="c1"># categorical parameter</span>
<span class="p">},</span>
<span class="n">n_iter</span><span class="o">=</span><span class="mi">32</span><span class="p">,</span>
<span class="n">cv</span><span class="o">=</span><span class="mi">3</span>
<span class="p">)</span>
<span class="n">opt</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X_train</span><span class="p">,</span> <span class="n">y_train</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">"val. score: </span><span class="si">%s</span><span class="s2">"</span> <span class="o">%</span> <span class="n">opt</span><span class="o">.</span><span class="n">best_score_</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">"test score: </span><span class="si">%s</span><span class="s2">"</span> <span class="o">%</span> <span class="n">opt</span><span class="o">.</span><span class="n">score</span><span class="p">(</span><span class="n">X_test</span><span class="p">,</span> <span class="n">y_test</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>val. score: 0.985894580549369
test score: 0.9822222222222222
</pre></div>
</div>
</section>
<section id="advanced-example">
<h2>Advanced example<a class="headerlink" href="#advanced-example" title="Permalink to this headline">¶</a></h2>
<p>In practice, one wants to enumerate over multiple predictive model classes,
with different search spaces and number of evaluations per class. An
example of such search over parameters of Linear SVM, Kernel SVM, and
decision trees 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.BayesSearchCV.html#skopt.BayesSearchCV" title="skopt.BayesSearchCV" class="sphx-glr-backref-module-skopt sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">BayesSearchCV</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="p">,</span> <span class="n">Categorical</span><span class="p">,</span> <span class="n">Integer</span>
<span class="kn">from</span> <span class="nn">skopt.plots</span> <span class="kn">import</span> <a href="../modules/generated/skopt.plots.plot_objective.html#skopt.plots.plot_objective" title="skopt.plots.plot_objective" class="sphx-glr-backref-module-skopt-plots sphx-glr-backref-type-py-function"><span class="n">plot_objective</span></a><span class="p">,</span> <a href="../modules/generated/skopt.plots.plot_histogram.html#skopt.plots.plot_histogram" title="skopt.plots.plot_histogram" class="sphx-glr-backref-module-skopt-plots sphx-glr-backref-type-py-function"><span class="n">plot_histogram</span></a>
<span class="kn">from</span> <span class="nn">sklearn.datasets</span> <span class="kn">import</span> <a href="https://scikit-learn.org/stable/modules/generated/sklearn.datasets.load_digits.html#sklearn.datasets.load_digits" title="sklearn.datasets.load_digits" class="sphx-glr-backref-module-sklearn-datasets sphx-glr-backref-type-py-function"><span class="n">load_digits</span></a>
<span class="kn">from</span> <span class="nn">sklearn.svm</span> <span class="kn">import</span> <a href="https://scikit-learn.org/stable/modules/generated/sklearn.svm.LinearSVC.html#sklearn.svm.LinearSVC" title="sklearn.svm.LinearSVC" class="sphx-glr-backref-module-sklearn-svm sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">LinearSVC</span></a><span class="p">,</span> <a href="https://scikit-learn.org/stable/modules/generated/sklearn.svm.SVC.html#sklearn.svm.SVC" title="sklearn.svm.SVC" class="sphx-glr-backref-module-sklearn-svm sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">SVC</span></a>
<span class="kn">from</span> <span class="nn">sklearn.pipeline</span> <span class="kn">import</span> <a href="https://scikit-learn.org/stable/modules/generated/sklearn.pipeline.Pipeline.html#sklearn.pipeline.Pipeline" title="sklearn.pipeline.Pipeline" class="sphx-glr-backref-module-sklearn-pipeline sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">Pipeline</span></a>
<span class="kn">from</span> <span class="nn">sklearn.model_selection</span> <span class="kn">import</span> <a href="https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.train_test_split.html#sklearn.model_selection.train_test_split" title="sklearn.model_selection.train_test_split" class="sphx-glr-backref-module-sklearn-model_selection sphx-glr-backref-type-py-function"><span class="n">train_test_split</span></a>
<span class="n">X</span><span class="p">,</span> <span class="n">y</span> <span class="o">=</span> <a href="https://scikit-learn.org/stable/modules/generated/sklearn.datasets.load_digits.html#sklearn.datasets.load_digits" title="sklearn.datasets.load_digits" class="sphx-glr-backref-module-sklearn-datasets sphx-glr-backref-type-py-function"><span class="n">load_digits</span></a><span class="p">(</span><span class="n">n_class</span><span class="o">=</span><span class="mi">10</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="n">X_train</span><span class="p">,</span> <span class="n">X_test</span><span class="p">,</span> <span class="n">y_train</span><span class="p">,</span> <span class="n">y_test</span> <span class="o">=</span> <a href="https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.train_test_split.html#sklearn.model_selection.train_test_split" title="sklearn.model_selection.train_test_split" class="sphx-glr-backref-module-sklearn-model_selection sphx-glr-backref-type-py-function"><span class="n">train_test_split</span></a><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
<span class="c1"># pipeline class is used as estimator to enable</span>
<span class="c1"># search over different model types</span>
<span class="n">pipe</span> <span class="o">=</span> <a href="https://scikit-learn.org/stable/modules/generated/sklearn.pipeline.Pipeline.html#sklearn.pipeline.Pipeline" title="sklearn.pipeline.Pipeline" class="sphx-glr-backref-module-sklearn-pipeline sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">Pipeline</span></a><span class="p">([</span>
<span class="p">(</span><span class="s1">'model'</span><span class="p">,</span> <a href="https://scikit-learn.org/stable/modules/generated/sklearn.svm.SVC.html#sklearn.svm.SVC" title="sklearn.svm.SVC" class="sphx-glr-backref-module-sklearn-svm sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">SVC</span></a><span class="p">())</span>
<span class="p">])</span>
<span class="c1"># single categorical value of 'model' parameter is</span>
<span class="c1"># sets the model class</span>
<span class="c1"># We will get ConvergenceWarnings because the problem is not well-conditioned.</span>
<span class="c1"># But that's fine, this is just an example.</span>
<span class="n">linsvc_search</span> <span class="o">=</span> <span class="p">{</span>
<span class="s1">'model'</span><span class="p">:</span> <span class="p">[</span><a href="https://scikit-learn.org/stable/modules/generated/sklearn.svm.LinearSVC.html#sklearn.svm.LinearSVC" title="sklearn.svm.LinearSVC" class="sphx-glr-backref-module-sklearn-svm sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">LinearSVC</span></a><span class="p">(</span><span class="n">max_iter</span><span class="o">=</span><span class="mi">1000</span><span class="p">)],</span>
<span class="s1">'model__C'</span><span class="p">:</span> <span class="p">(</span><span class="mf">1e-6</span><span class="p">,</span> <span class="mf">1e+6</span><span class="p">,</span> <span class="s1">'log-uniform'</span><span class="p">),</span>
<span class="p">}</span>
<span class="c1"># explicit dimension classes can be specified like this</span>
<span class="n">svc_search</span> <span class="o">=</span> <span class="p">{</span>
<span class="s1">'model'</span><span class="p">:</span> <span class="n">Categorical</span><span class="p">([</span><a href="https://scikit-learn.org/stable/modules/generated/sklearn.svm.SVC.html#sklearn.svm.SVC" title="sklearn.svm.SVC" class="sphx-glr-backref-module-sklearn-svm sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">SVC</span></a><span class="p">()]),</span>
<span class="s1">'model__C'</span><span class="p">:</span> <span class="n">Real</span><span class="p">(</span><span class="mf">1e-6</span><span class="p">,</span> <span class="mf">1e+6</span><span class="p">,</span> <span class="n">prior</span><span class="o">=</span><span class="s1">'log-uniform'</span><span class="p">),</span>
<span class="s1">'model__gamma'</span><span class="p">:</span> <span class="n">Real</span><span class="p">(</span><span class="mf">1e-6</span><span class="p">,</span> <span class="mf">1e+1</span><span class="p">,</span> <span class="n">prior</span><span class="o">=</span><span class="s1">'log-uniform'</span><span class="p">),</span>
<span class="s1">'model__degree'</span><span class="p">:</span> <span class="n">Integer</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span><span class="mi">8</span><span class="p">),</span>
<span class="s1">'model__kernel'</span><span class="p">:</span> <span class="n">Categorical</span><span class="p">([</span><span class="s1">'linear'</span><span class="p">,</span> <span class="s1">'poly'</span><span class="p">,</span> <span class="s1">'rbf'</span><span class="p">]),</span>
<span class="p">}</span>
<span class="n">opt</span> <span class="o">=</span> <a href="../modules/generated/skopt.BayesSearchCV.html#skopt.BayesSearchCV" title="skopt.BayesSearchCV" class="sphx-glr-backref-module-skopt sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">BayesSearchCV</span></a><span class="p">(</span>
<span class="n">pipe</span><span class="p">,</span>
<span class="c1"># (parameter space, # of evaluations)</span>
<span class="p">[(</span><span class="n">svc_search</span><span class="p">,</span> <span class="mi">40</span><span class="p">),</span> <span class="p">(</span><span class="n">linsvc_search</span><span class="p">,</span> <span class="mi">16</span><span class="p">)],</span>
<span class="n">cv</span><span class="o">=</span><span class="mi">3</span>
<span class="p">)</span>
<span class="n">opt</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X_train</span><span class="p">,</span> <span class="n">y_train</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">"val. score: </span><span class="si">%s</span><span class="s2">"</span> <span class="o">%</span> <span class="n">opt</span><span class="o">.</span><span class="n">best_score_</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">"test score: </span><span class="si">%s</span><span class="s2">"</span> <span class="o">%</span> <span class="n">opt</span><span class="o">.</span><span class="n">score</span><span class="p">(</span><span class="n">X_test</span><span class="p">,</span> <span class="n">y_test</span><span class="p">))</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">"best params: </span><span class="si">%s</span><span class="s2">"</span> <span class="o">%</span> <span class="nb">str</span><span class="p">(</span><span class="n">opt</span><span class="o">.</span><span class="n">best_params_</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/miniconda/envs/testenv/lib/python3.9/site-packages/scikit_learn-1.0-py3.9-linux-x86_64.egg/sklearn/svm/_base.py:1199: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.
warnings.warn(
/home/circleci/miniconda/envs/testenv/lib/python3.9/site-packages/scikit_learn-1.0-py3.9-linux-x86_64.egg/sklearn/svm/_base.py:1199: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.
warnings.warn(
/home/circleci/miniconda/envs/testenv/lib/python3.9/site-packages/scikit_learn-1.0-py3.9-linux-x86_64.egg/sklearn/svm/_base.py:1199: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.
warnings.warn(
/home/circleci/miniconda/envs/testenv/lib/python3.9/site-packages/scikit_learn-1.0-py3.9-linux-x86_64.egg/sklearn/svm/_base.py:1199: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.
warnings.warn(
/home/circleci/miniconda/envs/testenv/lib/python3.9/site-packages/scikit_learn-1.0-py3.9-linux-x86_64.egg/sklearn/svm/_base.py:1199: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.
warnings.warn(
/home/circleci/miniconda/envs/testenv/lib/python3.9/site-packages/scikit_learn-1.0-py3.9-linux-x86_64.egg/sklearn/svm/_base.py:1199: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.
warnings.warn(
/home/circleci/miniconda/envs/testenv/lib/python3.9/site-packages/scikit_learn-1.0-py3.9-linux-x86_64.egg/sklearn/svm/_base.py:1199: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.
warnings.warn(
/home/circleci/miniconda/envs/testenv/lib/python3.9/site-packages/scikit_learn-1.0-py3.9-linux-x86_64.egg/sklearn/svm/_base.py:1199: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.
warnings.warn(
/home/circleci/miniconda/envs/testenv/lib/python3.9/site-packages/scikit_learn-1.0-py3.9-linux-x86_64.egg/sklearn/svm/_base.py:1199: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.
warnings.warn(
/home/circleci/miniconda/envs/testenv/lib/python3.9/site-packages/scikit_learn-1.0-py3.9-linux-x86_64.egg/sklearn/svm/_base.py:1199: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.
warnings.warn(
/home/circleci/miniconda/envs/testenv/lib/python3.9/site-packages/scikit_learn-1.0-py3.9-linux-x86_64.egg/sklearn/svm/_base.py:1199: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.
warnings.warn(
/home/circleci/miniconda/envs/testenv/lib/python3.9/site-packages/scikit_learn-1.0-py3.9-linux-x86_64.egg/sklearn/svm/_base.py:1199: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.
warnings.warn(
/home/circleci/miniconda/envs/testenv/lib/python3.9/site-packages/scikit_learn-1.0-py3.9-linux-x86_64.egg/sklearn/svm/_base.py:1199: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.
warnings.warn(
/home/circleci/miniconda/envs/testenv/lib/python3.9/site-packages/scikit_learn-1.0-py3.9-linux-x86_64.egg/sklearn/svm/_base.py:1199: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.
warnings.warn(
/home/circleci/miniconda/envs/testenv/lib/python3.9/site-packages/scikit_learn-1.0-py3.9-linux-x86_64.egg/sklearn/svm/_base.py:1199: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.
warnings.warn(
/home/circleci/miniconda/envs/testenv/lib/python3.9/site-packages/scikit_learn-1.0-py3.9-linux-x86_64.egg/sklearn/svm/_base.py:1199: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.
warnings.warn(
/home/circleci/miniconda/envs/testenv/lib/python3.9/site-packages/scikit_learn-1.0-py3.9-linux-x86_64.egg/sklearn/svm/_base.py:1199: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.
warnings.warn(
/home/circleci/miniconda/envs/testenv/lib/python3.9/site-packages/scikit_learn-1.0-py3.9-linux-x86_64.egg/sklearn/svm/_base.py:1199: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.
warnings.warn(
/home/circleci/miniconda/envs/testenv/lib/python3.9/site-packages/scikit_learn-1.0-py3.9-linux-x86_64.egg/sklearn/svm/_base.py:1199: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.
warnings.warn(
/home/circleci/miniconda/envs/testenv/lib/python3.9/site-packages/scikit_learn-1.0-py3.9-linux-x86_64.egg/sklearn/svm/_base.py:1199: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.
warnings.warn(
/home/circleci/miniconda/envs/testenv/lib/python3.9/site-packages/scikit_learn-1.0-py3.9-linux-x86_64.egg/sklearn/svm/_base.py:1199: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.
warnings.warn(
/home/circleci/miniconda/envs/testenv/lib/python3.9/site-packages/scikit_learn-1.0-py3.9-linux-x86_64.egg/sklearn/svm/_base.py:1199: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.
warnings.warn(
/home/circleci/miniconda/envs/testenv/lib/python3.9/site-packages/scikit_learn-1.0-py3.9-linux-x86_64.egg/sklearn/svm/_base.py:1199: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.
warnings.warn(
/home/circleci/miniconda/envs/testenv/lib/python3.9/site-packages/scikit_learn-1.0-py3.9-linux-x86_64.egg/sklearn/svm/_base.py:1199: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.
warnings.warn(
/home/circleci/miniconda/envs/testenv/lib/python3.9/site-packages/scikit_learn-1.0-py3.9-linux-x86_64.egg/sklearn/svm/_base.py:1199: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.
warnings.warn(
/home/circleci/miniconda/envs/testenv/lib/python3.9/site-packages/scikit_learn-1.0-py3.9-linux-x86_64.egg/sklearn/svm/_base.py:1199: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.
warnings.warn(
/home/circleci/miniconda/envs/testenv/lib/python3.9/site-packages/scikit_learn-1.0-py3.9-linux-x86_64.egg/sklearn/svm/_base.py:1199: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.
warnings.warn(
val. score: 0.985894580549369
test score: 0.9822222222222222
best params: OrderedDict([('model', SVC(C=0.41571471424085416, gamma=1.0560013164213486, kernel='poly')), ('model__C', 0.41571471424085416), ('model__degree', 3), ('model__gamma', 1.0560013164213486), ('model__kernel', 'poly')])
</pre></div>
</div>
<p>Partial Dependence plot of the objective function for SVC</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">_</span> <span class="o">=</span> <a href="../modules/generated/skopt.plots.plot_objective.html#skopt.plots.plot_objective" title="skopt.plots.plot_objective" class="sphx-glr-backref-module-skopt-plots sphx-glr-backref-type-py-function"><span class="n">plot_objective</span></a><span class="p">(</span><span class="n">opt</span><span class="o">.</span><span class="n">optimizer_results_</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span>
<span class="n">dimensions</span><span class="o">=</span><span class="p">[</span><span class="s2">"C"</span><span class="p">,</span> <span class="s2">"degree"</span><span class="p">,</span> <span class="s2">"gamma"</span><span class="p">,</span> <span class="s2">"kernel"</span><span class="p">],</span>
<span class="n">n_minimum_search</span><span class="o">=</span><span class="nb">int</span><span class="p">(</span><span class="mf">1e8</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>
<img src="../_images/sphx_glr_sklearn-gridsearchcv-replacement_001.png" srcset="../_images/sphx_glr_sklearn-gridsearchcv-replacement_001.png" alt="sklearn gridsearchcv replacement" class = "sphx-glr-single-img"/><p>Plot of the histogram for LinearSVC</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">_</span> <span class="o">=</span> <a href="../modules/generated/skopt.plots.plot_histogram.html#skopt.plots.plot_histogram" title="skopt.plots.plot_histogram" class="sphx-glr-backref-module-skopt-plots sphx-glr-backref-type-py-function"><span class="n">plot_histogram</span></a><span class="p">(</span><span class="n">opt</span><span class="o">.</span><span class="n">optimizer_results_</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="mi">1</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>
<img src="../_images/sphx_glr_sklearn-gridsearchcv-replacement_002.png" srcset="../_images/sphx_glr_sklearn-gridsearchcv-replacement_002.png" alt="sklearn gridsearchcv replacement" class = "sphx-glr-single-img"/></section>
<section id="progress-monitoring-and-control-using-callback-argument-of-fit-method">
<h2>Progress monitoring and control using <code class="docutils literal notranslate"><span class="pre">callback</span></code> argument of <code class="docutils literal notranslate"><span class="pre">fit</span></code> method<a class="headerlink" href="#progress-monitoring-and-control-using-callback-argument-of-fit-method" title="Permalink to this headline">¶</a></h2>
<p>It is possible to monitor the progress of <a class="reference internal" href="../modules/generated/skopt.BayesSearchCV.html#skopt.BayesSearchCV" title="skopt.BayesSearchCV"><code class="xref py py-class docutils literal notranslate"><span class="pre">BayesSearchCV</span></code></a> with an event
handler that is called on every step of subspace exploration. For single job
mode, this is called on every evaluation of model configuration, and for
parallel mode, this is called when n_jobs model configurations are evaluated
in parallel.</p>
<p>Additionally, exploration can be stopped if the callback returns <code class="docutils literal notranslate"><span class="pre">True</span></code>.
This can be used to stop the exploration early, for instance when the
accuracy that you get is sufficiently high.</p>
<p>An example usage is shown 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.BayesSearchCV.html#skopt.BayesSearchCV" title="skopt.BayesSearchCV" class="sphx-glr-backref-module-skopt sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">BayesSearchCV</span></a>
<span class="kn">from</span> <span class="nn">sklearn.datasets</span> <span class="kn">import</span> <a href="https://scikit-learn.org/stable/modules/generated/sklearn.datasets.load_iris.html#sklearn.datasets.load_iris" title="sklearn.datasets.load_iris" class="sphx-glr-backref-module-sklearn-datasets sphx-glr-backref-type-py-function"><span class="n">load_iris</span></a>
<span class="kn">from</span> <span class="nn">sklearn.svm</span> <span class="kn">import</span> <a href="https://scikit-learn.org/stable/modules/generated/sklearn.svm.SVC.html#sklearn.svm.SVC" title="sklearn.svm.SVC" class="sphx-glr-backref-module-sklearn-svm sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">SVC</span></a>
<span class="n">X</span><span class="p">,</span> <span class="n">y</span> <span class="o">=</span> <a href="https://scikit-learn.org/stable/modules/generated/sklearn.datasets.load_iris.html#sklearn.datasets.load_iris" title="sklearn.datasets.load_iris" class="sphx-glr-backref-module-sklearn-datasets sphx-glr-backref-type-py-function"><span class="n">load_iris</span></a><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="n">searchcv</span> <span class="o">=</span> <a href="../modules/generated/skopt.BayesSearchCV.html#skopt.BayesSearchCV" title="skopt.BayesSearchCV" class="sphx-glr-backref-module-skopt sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">BayesSearchCV</span></a><span class="p">(</span>
<a href="https://scikit-learn.org/stable/modules/generated/sklearn.svm.SVC.html#sklearn.svm.SVC" title="sklearn.svm.SVC" class="sphx-glr-backref-module-sklearn-svm sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">SVC</span></a><span class="p">(</span><span class="n">gamma</span><span class="o">=</span><span class="s1">'scale'</span><span class="p">),</span>
<span class="n">search_spaces</span><span class="o">=</span><span class="p">{</span><span class="s1">'C'</span><span class="p">:</span> <span class="p">(</span><span class="mf">0.01</span><span class="p">,</span> <span class="mf">100.0</span><span class="p">,</span> <span class="s1">'log-uniform'</span><span class="p">)},</span>
<span class="n">n_iter</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span>
<span class="n">cv</span><span class="o">=</span><span class="mi">3</span>
<span class="p">)</span>
<span class="c1"># callback handler</span>
<span class="k">def</span> <span class="nf">on_step</span><span class="p">(</span><span class="n">optim_result</span><span class="p">):</span>
<span class="n">score</span> <span class="o">=</span> <span class="o">-</span><span class="n">optim_result</span><span class="p">[</span><span class="s1">'fun'</span><span class="p">]</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">"best score: </span><span class="si">%s</span><span class="s2">"</span> <span class="o">%</span> <span class="n">score</span><span class="p">)</span>
<span class="k">if</span> <span class="n">score</span> <span class="o">>=</span> <span class="mf">0.98</span><span class="p">:</span>
<span class="nb">print</span><span class="p">(</span><span class="s1">'Interrupting!'</span><span class="p">)</span>
<span class="k">return</span> <span class="kc">True</span>
<span class="n">searchcv</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="n">callback</span><span class="o">=</span><span class="n">on_step</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>best score: 0.98
Interrupting!
BayesSearchCV(cv=3, estimator=SVC(), n_iter=10,
search_spaces={'C': (0.01, 100.0, 'log-uniform')})
</pre></div>
</div>
</section>
<section id="counting-total-iterations-that-will-be-used-to-explore-all-subspaces">
<h2>Counting total iterations that will be used to explore all subspaces<a class="headerlink" href="#counting-total-iterations-that-will-be-used-to-explore-all-subspaces" title="Permalink to this headline">¶</a></h2>
<p>Subspaces in previous examples can further increase in complexity if you add
new model subspaces or dimensions for feature extraction pipelines. For
monitoring of progress, you would like to know the total number of
iterations it will take to explore all subspaces. This can be
calculated with <code class="docutils literal notranslate"><span class="pre">total_iterations</span></code> property, as in the code 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.BayesSearchCV.html#skopt.BayesSearchCV" title="skopt.BayesSearchCV" class="sphx-glr-backref-module-skopt sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">BayesSearchCV</span></a>
<span class="kn">from</span> <span class="nn">sklearn.datasets</span> <span class="kn">import</span> <a href="https://scikit-learn.org/stable/modules/generated/sklearn.datasets.load_iris.html#sklearn.datasets.load_iris" title="sklearn.datasets.load_iris" class="sphx-glr-backref-module-sklearn-datasets sphx-glr-backref-type-py-function"><span class="n">load_iris</span></a>
<span class="kn">from</span> <span class="nn">sklearn.svm</span> <span class="kn">import</span> <a href="https://scikit-learn.org/stable/modules/generated/sklearn.svm.SVC.html#sklearn.svm.SVC" title="sklearn.svm.SVC" class="sphx-glr-backref-module-sklearn-svm sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">SVC</span></a>
<span class="n">X</span><span class="p">,</span> <span class="n">y</span> <span class="o">=</span> <a href="https://scikit-learn.org/stable/modules/generated/sklearn.datasets.load_iris.html#sklearn.datasets.load_iris" title="sklearn.datasets.load_iris" class="sphx-glr-backref-module-sklearn-datasets sphx-glr-backref-type-py-function"><span class="n">load_iris</span></a><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="n">searchcv</span> <span class="o">=</span> <a href="../modules/generated/skopt.BayesSearchCV.html#skopt.BayesSearchCV" title="skopt.BayesSearchCV" class="sphx-glr-backref-module-skopt sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">BayesSearchCV</span></a><span class="p">(</span>
<a href="https://scikit-learn.org/stable/modules/generated/sklearn.svm.SVC.html#sklearn.svm.SVC" title="sklearn.svm.SVC" class="sphx-glr-backref-module-sklearn-svm sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">SVC</span></a><span class="p">(),</span>
<span class="n">search_spaces</span><span class="o">=</span><span class="p">[</span>
<span class="p">({</span><span class="s1">'C'</span><span class="p">:</span> <span class="p">(</span><span class="mf">0.1</span><span class="p">,</span> <span class="mf">1.0</span><span class="p">)},</span> <span class="mi">19</span><span class="p">),</span> <span class="c1"># 19 iterations for this subspace</span>
<span class="p">{</span><span class="s1">'gamma'</span><span class="p">:(</span><span class="mf">0.1</span><span class="p">,</span> <span class="mf">1.0</span><span class="p">)}</span>
<span class="p">],</span>
<span class="n">n_iter</span><span class="o">=</span><span class="mi">23</span>
<span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="n">searchcv</span><span class="o">.</span><span class="n">total_iterations</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>42
</pre></div>
</div>
<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 1 minutes 38.127 seconds)</p>
<p><strong>Estimated memory usage:</strong> 10 MB</p>
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