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<li><a class="reference internal" href="#">Tuning a scikit-learn estimator with <code class="docutils literal notranslate"><span class="pre">skopt</span></code></a><ul>
<li><a class="reference internal" href="#problem-statement">Problem statement</a></li>
<li><a class="reference internal" href="#objective">Objective</a></li>
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<p class="admonition-title">Note</p>
<p>Click <a class="reference internal" href="#sphx-glr-download-auto-examples-hyperparameter-optimization-py"><span class="std std-ref">here</span></a>
to download the full example code or to run this example in your browser via Binder</p>
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<section class="sphx-glr-example-title" id="tuning-a-scikit-learn-estimator-with-skopt">
<span id="sphx-glr-auto-examples-hyperparameter-optimization-py"></span><h1>Tuning a scikit-learn estimator with <code class="docutils literal notranslate"><span class="pre">skopt</span></code><a class="headerlink" href="#tuning-a-scikit-learn-estimator-with-skopt" title="Permalink to this headline">¶</a></h1>
<p>Gilles Louppe, July 2016
Katie Malone, August 2016
Reformatted by Holger Nahrstaedt 2020</p>
<p>If you are looking for a <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> replacement checkout
<a class="reference internal" href="sklearn-gridsearchcv-replacement.html#sphx-glr-auto-examples-sklearn-gridsearchcv-replacement-py"><span class="std std-ref">Scikit-learn hyperparameter search wrapper</span></a> instead.</p>
<section id="problem-statement">
<h2>Problem statement<a class="headerlink" href="#problem-statement" title="Permalink to this headline">¶</a></h2>
<p>Tuning the hyper-parameters of a machine learning model is often carried out
using an exhaustive exploration of (a subset of) the space all hyper-parameter
configurations (e.g., 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>), which
often results in a very time consuming operation.</p>
<p>In this notebook, we illustrate how to couple <a class="reference internal" href="../modules/generated/skopt.gp_minimize.html#skopt.gp_minimize" title="skopt.gp_minimize"><code class="xref py py-class docutils literal notranslate"><span class="pre">gp_minimize</span></code></a> with sklearn’s
estimators to tune hyper-parameters using sequential model-based optimisation,
hopefully resulting in equivalent or better solutions, but within fewer
evaluations.</p>
<p>Note: scikit-optimize provides a dedicated interface for estimator tuning via
<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> class which has a similar interface to those of
<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>. This class uses functions of skopt to perform hyperparameter
search efficiently. For example usage of this class, see
<a class="reference internal" href="sklearn-gridsearchcv-replacement.html#sphx-glr-auto-examples-sklearn-gridsearchcv-replacement-py"><span class="std std-ref">Scikit-learn hyperparameter search wrapper</span></a>
example 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>
</pre></div>
</div>
</section>
<section id="objective">
<h2>Objective<a class="headerlink" href="#objective" title="Permalink to this headline">¶</a></h2>
<p>To tune the hyper-parameters of our model we need to define a model,
decide which parameters to optimize, and define the objective function
we want to minimize.</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><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_boston.html#sklearn.datasets.load_boston" title="sklearn.datasets.load_boston" class="sphx-glr-backref-module-sklearn-datasets sphx-glr-backref-type-py-function"><span class="n">load_boston</span></a>
<span class="kn">from</span> <span class="nn">sklearn.ensemble</span> <span class="kn">import</span> <a href="https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.GradientBoostingRegressor.html#sklearn.ensemble.GradientBoostingRegressor" title="sklearn.ensemble.GradientBoostingRegressor" class="sphx-glr-backref-module-sklearn-ensemble sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">GradientBoostingRegressor</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.cross_val_score.html#sklearn.model_selection.cross_val_score" title="sklearn.model_selection.cross_val_score" class="sphx-glr-backref-module-sklearn-model_selection sphx-glr-backref-type-py-function"><span class="n">cross_val_score</span></a>
<span class="n">boston</span> <span class="o">=</span> <a href="https://scikit-learn.org/stable/modules/generated/sklearn.datasets.load_boston.html#sklearn.datasets.load_boston" title="sklearn.datasets.load_boston" class="sphx-glr-backref-module-sklearn-datasets sphx-glr-backref-type-py-function"><span class="n">load_boston</span></a><span class="p">()</span>
<span class="n">X</span><span class="p">,</span> <span class="n">y</span> <span class="o">=</span> <span class="n">boston</span><span class="o">.</span><span class="n">data</span><span class="p">,</span> <span class="n">boston</span><span class="o">.</span><span class="n">target</span>
<span class="n">n_features</span> <span class="o">=</span> <span class="n">X</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span>
<span class="c1"># gradient boosted trees tend to do well on problems like this</span>
<span class="n">reg</span> <span class="o">=</span> <a href="https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.GradientBoostingRegressor.html#sklearn.ensemble.GradientBoostingRegressor" title="sklearn.ensemble.GradientBoostingRegressor" class="sphx-glr-backref-module-sklearn-ensemble sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">GradientBoostingRegressor</span></a><span class="p">(</span><span class="n">n_estimators</span><span class="o">=</span><span class="mi">50</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">0</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/utils/deprecation.py:87: FutureWarning: Function load_boston is deprecated; `load_boston` is deprecated in 1.0 and will be removed in 1.2.
The Boston housing prices dataset has an ethical problem. You can refer to
the documentation of this function for further details.
The scikit-learn maintainers therefore strongly discourage the use of this
dataset unless the purpose of the code is to study and educate about
ethical issues in data science and machine learning.
In this case special case, you can fetch the dataset from the original
source::
import pandas as pd
import numpy as np
data_url = "http://lib.stat.cmu.edu/datasets/boston"
raw_df = pd.read_csv(data_url, sep="\s+", skiprows=22, header=None)
data = np.hstack([raw_df.values[::2, :], raw_df.values[1::2, :2]])
target = raw_df.values[1::2, 2]
Alternative datasets include the California housing dataset (i.e.
func:`~sklearn.datasets.fetch_california_housing`) and the Ames housing
dataset. You can load the datasets as follows:
from sklearn.datasets import fetch_california_housing
housing = fetch_california_housing()
for the California housing dataset and:
from sklearn.datasets import fetch_openml
housing = fetch_openml(name="house_prices", as_frame=True)
for the Ames housing dataset.
warnings.warn(msg, category=FutureWarning)
</pre></div>
</div>
<p>Next, we need to define the bounds of the dimensions of the search space
we want to explore and pick the objective. In this case the cross-validation
mean absolute error of a gradient boosting regressor over the Boston
dataset, as a function of its hyper-parameters.</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><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">Integer</span>
<span class="kn">from</span> <span class="nn">skopt.utils</span> <span class="kn">import</span> <a href="../modules/generated/skopt.utils.use_named_args.html#skopt.utils.use_named_args" title="skopt.utils.use_named_args" class="sphx-glr-backref-module-skopt-utils sphx-glr-backref-type-py-function"><span class="n">use_named_args</span></a>
<span class="c1"># The list of hyper-parameters we want to optimize. For each one we define the</span>
<span class="c1"># bounds, the corresponding scikit-learn parameter name, as well as how to</span>
<span class="c1"># sample values from that dimension (`'log-uniform'` for the learning rate)</span>
<span class="n">space</span> <span class="o">=</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">5</span><span class="p">,</span> <span class="n">name</span><span class="o">=</span><span class="s1">'max_depth'</span><span class="p">),</span>
<span class="n">Real</span><span class="p">(</span><span class="mi">10</span><span class="o">**-</span><span class="mi">5</span><span class="p">,</span> <span class="mi">10</span><span class="o">**</span><span class="mi">0</span><span class="p">,</span> <span class="s2">"log-uniform"</span><span class="p">,</span> <span class="n">name</span><span class="o">=</span><span class="s1">'learning_rate'</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="n">n_features</span><span class="p">,</span> <span class="n">name</span><span class="o">=</span><span class="s1">'max_features'</span><span class="p">),</span>
<span class="n">Integer</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">100</span><span class="p">,</span> <span class="n">name</span><span class="o">=</span><span class="s1">'min_samples_split'</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">100</span><span class="p">,</span> <span class="n">name</span><span class="o">=</span><span class="s1">'min_samples_leaf'</span><span class="p">)]</span>
<span class="c1"># this decorator allows your objective function to receive a the parameters as</span>
<span class="c1"># keyword arguments. This is particularly convenient when you want to set</span>
<span class="c1"># scikit-learn estimator parameters</span>
<span class="nd">@use_named_args</span><span class="p">(</span><span class="n">space</span><span class="p">)</span>
<span class="k">def</span> <span class="nf">objective</span><span class="p">(</span><span class="o">**</span><span class="n">params</span><span class="p">):</span>
<span class="n">reg</span><span class="o">.</span><span class="n">set_params</span><span class="p">(</span><span class="o">**</span><span class="n">params</span><span class="p">)</span>
<span class="k">return</span> <span class="o">-</span><a href="https://numpy.org/doc/stable/reference/generated/numpy.mean.html#numpy.mean" title="numpy.mean" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-function"><span class="n">np</span><span class="o">.</span><span class="n">mean</span></a><span class="p">(</span><a href="https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.cross_val_score.html#sklearn.model_selection.cross_val_score" title="sklearn.model_selection.cross_val_score" class="sphx-glr-backref-module-sklearn-model_selection sphx-glr-backref-type-py-function"><span class="n">cross_val_score</span></a><span class="p">(</span><span class="n">reg</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">cv</span><span class="o">=</span><span class="mi">5</span><span class="p">,</span> <span class="n">n_jobs</span><span class="o">=-</span><span class="mi">1</span><span class="p">,</span>
<span class="n">scoring</span><span class="o">=</span><span class="s2">"neg_mean_absolute_error"</span><span class="p">))</span>
</pre></div>
</div>
</section>
<section id="optimize-all-the-things">
<h2>Optimize all the things!<a class="headerlink" href="#optimize-all-the-things" title="Permalink to this headline">¶</a></h2>
<p>With these two pieces, we are now ready for sequential model-based
optimisation. Here we use gaussian process-based optimisation.</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">res_gp</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">objective</span><span class="p">,</span> <span class="n">space</span><span class="p">,</span> <span class="n">n_calls</span><span class="o">=</span><span class="mi">50</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="s2">"Best score=</span><span class="si">%.4f</span><span class="s2">"</span> <span class="o">%</span> <span class="n">res_gp</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>'Best score=2.9062'
</pre></div>
</div>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="nb">print</span><span class="p">(</span><span class="s2">"""Best parameters:</span>
<span class="s2">- max_depth=</span><span class="si">%d</span><span class="s2"></span>
<span class="s2">- learning_rate=</span><span class="si">%.6f</span><span class="s2"></span>
<span class="s2">- max_features=</span><span class="si">%d</span><span class="s2"></span>
<span class="s2">- min_samples_split=</span><span class="si">%d</span><span class="s2"></span>
<span class="s2">- min_samples_leaf=</span><span class="si">%d</span><span class="s2">"""</span> <span class="o">%</span> <span class="p">(</span><span class="n">res_gp</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="n">res_gp</span><span class="o">.</span><span class="n">x</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span>
<span class="n">res_gp</span><span class="o">.</span><span class="n">x</span><span class="p">[</span><span class="mi">2</span><span class="p">],</span> <span class="n">res_gp</span><span class="o">.</span><span class="n">x</span><span class="p">[</span><span class="mi">3</span><span class="p">],</span>
<span class="n">res_gp</span><span class="o">.</span><span class="n">x</span><span class="p">[</span><span class="mi">4</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 parameters:
- max_depth=5
- learning_rate=0.143650
- max_features=9
- min_samples_split=100
- min_samples_leaf=1
</pre></div>
</div>
</section>
<section id="convergence-plot">
<h2>Convergence plot<a class="headerlink" href="#convergence-plot" 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.plots</span> <span class="kn">import</span> <a href="../modules/generated/skopt.plots.plot_convergence.html#skopt.plots.plot_convergence" title="skopt.plots.plot_convergence" class="sphx-glr-backref-module-skopt-plots sphx-glr-backref-type-py-function"><span class="n">plot_convergence</span></a>
<a href="../modules/generated/skopt.plots.plot_convergence.html#skopt.plots.plot_convergence" title="skopt.plots.plot_convergence" class="sphx-glr-backref-module-skopt-plots sphx-glr-backref-type-py-function"><span class="n">plot_convergence</span></a><span class="p">(</span><span class="n">res_gp</span><span class="p">)</span>
</pre></div>
</div>
<img src="../_images/sphx_glr_hyperparameter-optimization_001.png" srcset="../_images/sphx_glr_hyperparameter-optimization_001.png" alt="Convergence plot" 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><AxesSubplot:title={'center':'Convergence plot'}, xlabel='Number of calls $n$', ylabel='$\\min f(x)$ after $n$ calls'>
</pre></div>
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