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Difan Deng: allow rf to impute OrdinalHyperparameter (#1065)
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<!DOCTYPE html>

<html lang="en">
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<head>
<meta charset="utf-8" />
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<div>

<section id="experimental">
<h1>Experimental<a class="headerlink" href="#experimental" title="Permalink to this heading"></a></h1>
<h1>Experimental<a class="headerlink" href="#experimental" title="Link to this heading"></a></h1>
<div class="admonition warning">
<p class="admonition-title">Warning</p>
<p>This part is experimental and might not work in each case. If you would like to suggest any changes, please let us know.</p>
</div>
<section id="installation-in-windows-via-wsl">
<h2>Installation in Windows via WSL<a class="headerlink" href="#installation-in-windows-via-wsl" title="Permalink to this heading"></a></h2>
<h2>Installation in Windows via WSL<a class="headerlink" href="#installation-in-windows-via-wsl" title="Link to this heading"></a></h2>
<p>SMAC can be installed in a WSL (Windows Subsystem for Linux) under Windows.</p>
<p><strong>1) Install WSL under Windows</strong></p>
<p>Install WSL under Windows. This SMAC installation workflow was tested with Ubuntu 18.04. For Ubuntu 20.04,
Expand Down Expand Up @@ -1094,7 +1094,7 @@ <h2>Installation in Windows via WSL<a class="headerlink" href="#installation-in-
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<html lang="en">
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<div>

<section id="installation">
<h1>Installation<a class="headerlink" href="#installation" title="Permalink to this heading"></a></h1>
<h1>Installation<a class="headerlink" href="#installation" title="Link to this heading"></a></h1>
<section id="requirements">
<h2>Requirements<a class="headerlink" href="#requirements" title="Permalink to this heading"></a></h2>
<h2>Requirements<a class="headerlink" href="#requirements" title="Link to this heading"></a></h2>
<p>SMAC is written in python3 and therefore requires an environment with python&gt;=3.8.
Furthermore, the Random Forest used in SMAC requires SWIG as a build dependency. Install it either in your
environment or on your system directly. The command to install swig on linux machines is the following:</p>
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<p>SMAC is tested on Linux and Mac machines with python 3.8, 3.9 and 3.10.</p>
</section>
<section id="anaconda">
<h2>Anaconda<a class="headerlink" href="#anaconda" title="Permalink to this heading"></a></h2>
<h2>Anaconda<a class="headerlink" href="#anaconda" title="Link to this heading"></a></h2>
<p>We recommend using Anaconda to create and activate an environment:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">conda</span> <span class="n">create</span> <span class="o">-</span><span class="n">n</span> <span class="n">SMAC</span> <span class="n">python</span><span class="o">=</span><span class="mf">3.10</span>
<span class="n">conda</span> <span class="n">activate</span> <span class="n">SMAC</span>
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</div>
</section>
<section id="conda-forge">
<h2>Conda-forge<a class="headerlink" href="#conda-forge" title="Permalink to this heading"></a></h2>
<h2>Conda-forge<a class="headerlink" href="#conda-forge" title="Link to this heading"></a></h2>
<p>Installing SMAC from the <cite>conda-forge</cite> channel can be achieved by adding <cite>conda-forge</cite> to your channels with:</p>
<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>conda<span class="w"> </span>config<span class="w"> </span>--add<span class="w"> </span>channels<span class="w"> </span>conda-forge
conda<span class="w"> </span>config<span class="w"> </span>--set<span class="w"> </span>channel_priority<span class="w"> </span>strict
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<p>Read <a class="reference external" href="https://github.com/conda-forge/smac-feedstock">SMAC feedstock</a> for more details.</p>
</section>
<section id="windows-via-wsl-experimental">
<h2>Windows via WSL (Experimental)<a class="headerlink" href="#windows-via-wsl-experimental" title="Permalink to this heading"></a></h2>
<h2>Windows via WSL (Experimental)<a class="headerlink" href="#windows-via-wsl-experimental" title="Link to this heading"></a></h2>
<p>SMAC can be installed under Windows in a WSL (Windows Subsystem for Linux).
You can find an instruction on how to do this here: <a class="reference internal" href="10_experimental.html#experimental"><span class="std std-ref">Experimental</span></a>.
However, this is experimental and might not work in each case.
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<!DOCTYPE html>

<html lang="en">
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<head>
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<div>

<section id="package-overview">
<h1>Package Overview<a class="headerlink" href="#package-overview" title="Permalink to this heading"></a></h1>
<h1>Package Overview<a class="headerlink" href="#package-overview" title="Link to this heading"></a></h1>
<p>SMAC supports you in determining well-performing hyperparameter configurations for your algorithms. By being a robust
and flexible framework for <a class="reference internal" href="7_glossary.html#term-BO"><span class="xref std std-term">Bayesian Optimization</span></a>, SMAC can improve performance within a few function
evaluations. It offers several entry points and pre-sets for typical use cases, such as optimizing
hyperparameters, solving low dimensional continuous (artificial) global optimization problems and configuring algorithms
to perform well across multiple problem <a class="reference internal" href="7_glossary.html#term-Instances"><span class="xref std std-term">instances</span></a>.</p>
<section id="features">
<h2>Features<a class="headerlink" href="#features" title="Permalink to this heading"></a></h2>
<h2>Features<a class="headerlink" href="#features" title="Link to this heading"></a></h2>
<p>SMAC has the following characteristics and capabilities:</p>
<dl>
<dt>Global Optimizer</dt><dd><p><a class="reference internal" href="7_glossary.html#term-Bayesian-Optimization"><span class="xref std std-term">Bayesian Optimization</span></a> is used for sample-efficient optimization.</p>
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</dl>
</section>
<section id="comparison">
<h2>Comparison<a class="headerlink" href="#comparison" title="Permalink to this heading"></a></h2>
<h2>Comparison<a class="headerlink" href="#comparison" title="Link to this heading"></a></h2>
<p>The following table provides an overview of SMAC’s capabilities in comparison with other optimization tools.</p>
<table class="table">
<colgroup>
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<!DOCTYPE html>

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<div>

<section id="getting-started">
<h1>Getting Started<a class="headerlink" href="#getting-started" title="Permalink to this heading">¶</a></h1>
<h1>Getting Started<a class="headerlink" href="#getting-started" title="Link to this heading">¶</a></h1>
<p>SMAC needs four core components (configuration space, target function, scenario and a facade) to run an
optimization process, all of which are explained on this page.</p>
<p>They interact in the following way:</p>
<a class="reference internal image-reference" href="_images/smac_components_interaction.jpg"><img alt="Interaction of SMAC's components" src="_images/smac_components_interaction.jpg" style="width: 400px;" /></a>
<section id="configuration-space">
<h2>Configuration Space<a class="headerlink" href="#configuration-space" title="Permalink to this heading">¶</a></h2>
<h2>Configuration Space<a class="headerlink" href="#configuration-space" title="Link to this heading">¶</a></h2>
<p>The configuration space defines the search space of the hyperparameters and, therefore, the tunable parameters’ legal
ranges and default values.</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">ConfigSpace</span> <span class="kn">import</span> <span class="n">ConfigSpace</span>
Expand All @@ -1066,7 +1066,7 @@ <h2>Configuration Space<a class="headerlink" href="#configuration-space" title="
<p>Please see the documentation of <a class="reference external" href="https://automl.github.io/ConfigSpace/main/">ConfigSpace</a> for more details.</p>
</section>
<section id="target-function">
<h2>Target Function<a class="headerlink" href="#target-function" title="Permalink to this heading">¶</a></h2>
<h2>Target Function<a class="headerlink" href="#target-function" title="Link to this heading">¶</a></h2>
<p>The target function takes a configuration from the configuration space and returns a performance value.
For example, you could use a Neural Network to predict on your data and get some validation performance.
If, for instance, you would tune the learning rate of the Network’s optimizer, every learning rate will
Expand Down Expand Up @@ -1100,7 +1100,7 @@ <h2>Target Function<a class="headerlink" href="#target-function" title="Permalin
</div>
</section>
<section id="scenario">
<h2>Scenario<a class="headerlink" href="#scenario" title="Permalink to this heading">¶</a></h2>
<h2>Scenario<a class="headerlink" href="#scenario" title="Link to this heading">¶</a></h2>
<p>The <a class="reference internal" href="api/smac.scenario.html#module-smac.scenario"><span class="std std-ref">Scenario</span></a> is used to provide environment variables. For example,
if you want to limit the optimization process by a time limit or want to specify where to save the results.</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">smac</span> <span class="kn">import</span> <span class="n">Scenario</span>
Expand All @@ -1117,7 +1117,7 @@ <h2>Scenario<a class="headerlink" href="#scenario" title="Permalink to this head
</div>
</section>
<section id="facade">
<h2>Facade<a class="headerlink" href="#facade" title="Permalink to this heading">¶</a></h2>
<h2>Facade<a class="headerlink" href="#facade" title="Link to this heading">¶</a></h2>
<p>A <a class="reference internal" href="api/smac.facade.html#module-smac.facade"><span class="std std-ref">facade</span></a> is the entry point to SMAC, which constructs a default optimization
pipeline for you. SMAC offers various facades, which satisfy many common use cases and are crucial to
achieving peak performance. The idea behind the facades is to provide a simple interface to all of SMAC’s components,
Expand Down Expand Up @@ -1301,7 +1301,7 @@ <h2>Facade<a class="headerlink" href="#facade" title="Permalink to this heading"
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Created using <a href="http://sphinx-doc.org/">Sphinx</a>
7.1.1. Template is modified version of <a
7.2.6. Template is modified version of <a
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