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@@ -5,7 +5,7 @@
<head>
<meta charset="utf-8" />
<meta name="viewport" content="width=device-width, initial-scale=1.0" />
<title>3. Feature Extractors &#8212; MIPLearn 0.3</title>
<title>6. Feature Extractors &#8212; MIPLearn 0.3</title>
<link href="../../_static/css/theme.css" rel="stylesheet" />
<link href="../../_static/css/index.c5995385ac14fb8791e8eb36b4908be2.css" rel="stylesheet" />
@@ -24,6 +24,8 @@
<link rel="stylesheet" href="../../_static/pygments.css" type="text/css" />
<link rel="stylesheet" href="../../_static/sphinx-book-theme.acff12b8f9c144ce68a297486a2fa670.css" type="text/css" />
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<link rel="stylesheet" type="text/css" href="../../_static/custom.css" />
<link rel="preload" as="script" href="../../_static/js/index.1c5a1a01449ed65a7b51.js">
@@ -38,8 +40,8 @@
<script type="text/x-mathjax-config">MathJax.Hub.Config({"tex2jax": {"inlineMath": [["\\(", "\\)"]], "displayMath": [["\\[", "\\]"]], "processRefs": false, "processEnvironments": false}})</script>
<link rel="index" title="Index" href="../../genindex/" />
<link rel="search" title="Search" href="../../search/" />
<link rel="next" title="4. Primal Components" href="../primal/" />
<link rel="prev" title="2. Training Data Collectors" href="../collectors/" />
<link rel="next" title="7. Primal Components" href="../primal/" />
<link rel="prev" title="5. Training Data Collectors" href="../collectors/" />
<meta name="viewport" content="width=device-width, initial-scale=1" />
<meta name="docsearch:language" content="en" />
@@ -68,6 +70,28 @@
</form><nav class="bd-links" id="bd-docs-nav" aria-label="Main navigation">
<div class="bd-toc-item active">
<p class="caption">
<span class="caption-text">
Tutorials
</span>
</p>
<ul class="nav bd-sidenav">
<li class="toctree-l1">
<a class="reference internal" href="../../tutorials/getting-started-pyomo/">
1. Getting started (Pyomo)
</a>
</li>
<li class="toctree-l1">
<a class="reference internal" href="../../tutorials/getting-started-gurobipy/">
2. Getting started (Gurobipy)
</a>
</li>
<li class="toctree-l1">
<a class="reference internal" href="../../tutorials/getting-started-jump/">
3. Getting started (JuMP)
</a>
</li>
</ul>
<p class="caption">
<span class="caption-text">
User Guide
</span>
@@ -75,59 +99,59 @@
<ul class="current nav bd-sidenav">
<li class="toctree-l1">
<a class="reference internal" href="../problems/">
1. Benchmark Problems
4. Benchmark Problems
</a>
</li>
<li class="toctree-l1">
<a class="reference internal" href="../collectors/">
2. Training Data Collectors
5. Training Data Collectors
</a>
</li>
<li class="toctree-l1 current active">
<a class="current reference internal" href="#">
3. Feature Extractors
6. Feature Extractors
</a>
</li>
<li class="toctree-l1">
<a class="reference internal" href="../primal/">
4. Primal Components
7. Primal Components
</a>
</li>
<li class="toctree-l1">
<a class="reference internal" href="../solvers/">
5. Solvers
8. Solvers
</a>
</li>
</ul>
<p class="caption">
<span class="caption-text">
API Reference
Python API Reference
</span>
</p>
<ul class="nav bd-sidenav">
<li class="toctree-l1">
<a class="reference internal" href="../../api/problems/">
6. Benchmark Problems
9. Benchmark Problems
</a>
</li>
<li class="toctree-l1">
<a class="reference internal" href="../../api/collectors/">
7. Collectors &amp; Extractors
10. Collectors &amp; Extractors
</a>
</li>
<li class="toctree-l1">
<a class="reference internal" href="../../api/components/">
8. Components
11. Components
</a>
</li>
<li class="toctree-l1">
<a class="reference internal" href="../../api/solvers/">
9. Solvers
12. Solvers
</a>
</li>
<li class="toctree-l1">
<a class="reference internal" href="../../api/helpers/">
10. Helpers
13. Helpers
</a>
</li>
</ul>
@@ -199,12 +223,12 @@
<ul class="visible nav section-nav flex-column">
<li class="toc-h2 nav-item toc-entry">
<a class="reference internal nav-link" href="#Overview">
3.1. Overview
6.1. Overview
</a>
</li>
<li class="toc-h2 nav-item toc-entry">
<a class="reference internal nav-link" href="#H5FieldsExtractor">
3.2. H5FieldsExtractor
6.2. H5FieldsExtractor
</a>
<ul class="nav section-nav flex-column">
<li class="toc-h3 nav-item toc-entry">
@@ -216,7 +240,7 @@
</li>
<li class="toc-h2 nav-item toc-entry">
<a class="reference internal nav-link" href="#AlvLouWeh2017Extractor">
3.3. AlvLouWeh2017Extractor
6.3. AlvLouWeh2017Extractor
</a>
<ul class="nav section-nav flex-column">
<li class="toc-h3 nav-item toc-entry">
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<div class="section" id="Feature-Extractors">
<h1><span class="section-number">3. </span>Feature Extractors<a class="headerlink" href="#Feature-Extractors" title="Permalink to this headline"></a></h1>
<div class="section" id="Feature-Extractors">
<h1><span class="section-number">6. </span>Feature Extractors<a class="headerlink" href="#Feature-Extractors" title="Permalink to this headline"></a></h1>
<p>In the previous page, we introduced <em>training data collectors</em>, which solve the optimization problem and collect raw training data, such as the optimal solution. In this page, we introduce <strong>feature extractors</strong>, which take the raw training data, stored in HDF5 files, and extract relevant information in order to train a machine learning model. We describe the extractors readily available in MIPLearn.</p>
<div class="section" id="Overview">
<h2><span class="section-number">3.1. </span>Overview<a class="headerlink" href="#Overview" title="Permalink to this headline"></a></h2>
<h2><span class="section-number">6.1. </span>Overview<a class="headerlink" href="#Overview" title="Permalink to this headline"></a></h2>
<p>Feature extraction is an important step of the process of building a machine learning model because it helps to reduce the complexity of the data and convert it into a format that is more easily processed. Previous research has proposed converting absolute variable coefficients, for example, into relative values which are invariant to various transformations, such as problem scaling, making them more amenable to learning. Various other transformations have also been described.</p>
<p>In the framework, we treat data collection and feature extraction as two separate steps to accelerate the model development cycle. Specifically, collectors are typically time-consuming, as they often need to solve the problem to optimality, and therefore focus on collecting and storing all data that may or may not be relevant, in its raw format. Feature extractors, on the other hand, focus entirely on filtering the data and improving its representation, and are therefore much faster to run.
Experimenting with new data representations, therefore, can be done without resolving the instances.</p>
@@ -506,13 +273,13 @@ Experimenting with new data representations, therefore, can be done without reso
subset of these methods, if it is known that it will not be used with a machine learning component that requires the other types of features.</p>
</div>
<div class="section" id="H5FieldsExtractor">
<h2><span class="section-number">3.2. </span>H5FieldsExtractor<a class="headerlink" href="#H5FieldsExtractor" title="Permalink to this headline"></a></h2>
<p><a class="reference external" href="#h5fieldsextractor">H5FieldsExtractor</a>, the most simple extractor in MIPLearn, simple extracts data that is already available in the HDF5 file, assembles it into a matrix and returns it as-is. The fields used to build instance, variable and constraint features are user-specified. The class also performs checks to ensure that the shapes of the returned matrices make sense.</p>
<h2><span class="section-number">6.2. </span>H5FieldsExtractor<a class="headerlink" href="#H5FieldsExtractor" title="Permalink to this headline"></a></h2>
<p><a class="reference internal" href="#H5FieldsExtractor"><span class="std std-ref">H5FieldsExtractor</span></a>, the most simple extractor in MIPLearn, simple extracts data that is already available in the HDF5 file, assembles it into a matrix and returns it as-is. The fields used to build instance, variable and constraint features are user-specified. The class also performs checks to ensure that the shapes of the returned matrices make sense.</p>
<div class="section" id="Example">
<h3>Example<a class="headerlink" href="#Example" title="Permalink to this headline"></a></h3>
<p>The example below demonstrates the usage of H5FieldsExtractor in a randomly generated instance of the multi-dimensional knapsack problem.</p>
<div class="nbinput docutils container">
<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[1]:
<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[5]:
</pre></div>
</div>
<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">glob</span> <span class="kn">import</span> <span class="n">glob</span>
@@ -522,12 +289,12 @@ subset of these methods, if it is known that it will not be used with a machine
<span class="kn">from</span> <span class="nn">scipy.stats</span> <span class="kn">import</span> <span class="n">uniform</span><span class="p">,</span> <span class="n">randint</span>
<span class="kn">from</span> <span class="nn">miplearn.collectors.basic</span> <span class="kn">import</span> <span class="n">BasicCollector</span>
<span class="kn">from</span> <span class="nn">miplearn.features.fields</span> <span class="kn">import</span> <span class="n">H5FieldsExtractor</span>
<span class="kn">from</span> <span class="nn">miplearn.extractors.fields</span> <span class="kn">import</span> <span class="n">H5FieldsExtractor</span>
<span class="kn">from</span> <span class="nn">miplearn.h5</span> <span class="kn">import</span> <span class="n">H5File</span>
<span class="kn">from</span> <span class="nn">miplearn.io</span> <span class="kn">import</span> <span class="n">save</span>
<span class="kn">from</span> <span class="nn">miplearn.io</span> <span class="kn">import</span> <span class="n">write_pkl_gz</span>
<span class="kn">from</span> <span class="nn">miplearn.problems.multiknapsack</span> <span class="kn">import</span> <span class="p">(</span>
<span class="n">MultiKnapsackGenerator</span><span class="p">,</span>
<span class="n">build_multiknapsack_model</span>
<span class="n">build_multiknapsack_model</span><span class="p">,</span>
<span class="p">)</span>
<span class="c1"># Set random seed to make example reproducible</span>
@@ -535,7 +302,7 @@ subset of these methods, if it is known that it will not be used with a machine
<span class="c1"># Generate some random multiknapsack instances</span>
<span class="n">rmtree</span><span class="p">(</span><span class="s2">&quot;data/multiknapsack/&quot;</span><span class="p">,</span> <span class="n">ignore_errors</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
<span class="n">save</span><span class="p">(</span>
<span class="n">write_pkl_gz</span><span class="p">(</span>
<span class="n">MultiKnapsackGenerator</span><span class="p">(</span>
<span class="n">n</span><span class="o">=</span><span class="n">randint</span><span class="p">(</span><span class="n">low</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span> <span class="n">high</span><span class="o">=</span><span class="mi">11</span><span class="p">),</span>
<span class="n">m</span><span class="o">=</span><span class="n">randint</span><span class="p">(</span><span class="n">low</span><span class="o">=</span><span class="mi">5</span><span class="p">,</span> <span class="n">high</span><span class="o">=</span><span class="mi">6</span><span class="p">),</span>
@@ -547,7 +314,7 @@ subset of these methods, if it is known that it will not be used with a machine
<span class="n">p_jitter</span><span class="o">=</span><span class="n">uniform</span><span class="p">(</span><span class="n">loc</span><span class="o">=</span><span class="mf">0.75</span><span class="p">,</span> <span class="n">scale</span><span class="o">=</span><span class="mf">0.5</span><span class="p">),</span>
<span class="n">fix_w</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
<span class="p">)</span><span class="o">.</span><span class="n">generate</span><span class="p">(</span><span class="mi">10</span><span class="p">),</span>
<span class="s2">&quot;data/multiknapsack&quot;</span>
<span class="s2">&quot;data/multiknapsack&quot;</span><span class="p">,</span>
<span class="p">)</span>
<span class="c1"># Run the basic collector</span>
@@ -578,7 +345,7 @@ subset of these methods, if it is known that it will not be used with a machine
<span class="s2">&quot;static_constr_rhs&quot;</span><span class="p">,</span>
<span class="s2">&quot;lp_constr_dual_values&quot;</span><span class="p">,</span>
<span class="s2">&quot;lp_constr_slacks&quot;</span><span class="p">,</span>
<span class="p">]</span>
<span class="p">],</span>
<span class="p">)</span>
<span class="k">with</span> <span class="n">H5File</span><span class="p">(</span><span class="s2">&quot;data/multiknapsack/00000.h5&quot;</span><span class="p">)</span> <span class="k">as</span> <span class="n">h5</span><span class="p">:</span>
@@ -631,16 +398,16 @@ constraint features (5, 3)
</div>
</div>
<div class="section" id="AlvLouWeh2017Extractor">
<h2><span class="section-number">3.3. </span>AlvLouWeh2017Extractor<a class="headerlink" href="#AlvLouWeh2017Extractor" title="Permalink to this headline"></a></h2>
<p>Alvarez, Louveaux and Wehenkel (2017) proposed a set features to describe a particular decision variable in a given node of the branch-and-bound tree, and applied it to the problem of mimicking strong branching decisions. The class <a class="reference external" href="#alvlouweh2017extractor">AlvLouWeh2017Extractor</a> implements a subset of these features (40 out of 64), which are available outside of the branch-and-bound tree. Some features are derived from the static defintion of the problem (i.e. from objective function and
<h2><span class="section-number">6.3. </span>AlvLouWeh2017Extractor<a class="headerlink" href="#AlvLouWeh2017Extractor" title="Permalink to this headline"></a></h2>
<p>Alvarez, Louveaux and Wehenkel (2017) proposed a set features to describe a particular decision variable in a given node of the branch-and-bound tree, and applied it to the problem of mimicking strong branching decisions. The class <a class="reference internal" href="#AlvLouWeh2017Extractor"><span class="std std-ref">AlvLouWeh2017Extractor</span></a> implements a subset of these features (40 out of 64), which are available outside of the branch-and-bound tree. Some features are derived from the static defintion of the problem (i.e. from objective function and
constraint data), while some features are derived from the solution to the LP relaxation. The features have been designed to be: (i) independent of the size of the problem; (ii) invariant with respect to irrelevant problem transformations, such as row and column permutation; and (iii) independent of the scale of the problem. We refer to the paper for a more complete description.</p>
<div class="section" id="id1">
<h3>Example<a class="headerlink" href="#id1" title="Permalink to this headline"></a></h3>
<div class="nbinput docutils container">
<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[5]:
<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[6]:
</pre></div>
</div>
<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">miplearn.features.AlvLouWeh2017</span> <span class="kn">import</span> <span class="n">AlvLouWeh2017Extractor</span>
<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">miplearn.extractors.AlvLouWeh2017</span> <span class="kn">import</span> <span class="n">AlvLouWeh2017Extractor</span>
<span class="kn">from</span> <span class="nn">miplearn.h5</span> <span class="kn">import</span> <span class="n">H5File</span>
<span class="c1"># Build the extractor</span>
@@ -648,7 +415,6 @@ constraint data), while some features are derived from the solution to the LP re
<span class="c1"># Open previously-created multiknapsack training data</span>
<span class="k">with</span> <span class="n">H5File</span><span class="p">(</span><span class="s2">&quot;data/multiknapsack/00000.h5&quot;</span><span class="p">)</span> <span class="k">as</span> <span class="n">h5</span><span class="p">:</span>
<span class="c1"># Extract and print variable features</span>
<span class="n">x1</span> <span class="o">=</span> <span class="n">ext</span><span class="o">.</span><span class="n">get_var_features</span><span class="p">(</span><span class="n">h5</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;x1&quot;</span><span class="p">,</span> <span class="n">x1</span><span class="o">.</span><span class="n">shape</span><span class="p">,</span> <span class="s2">&quot;</span><span class="se">\n</span><span class="s2">&quot;</span><span class="p">,</span> <span class="n">x1</span><span class="o">.</span><span class="n">round</span><span class="p">(</span><span class="mi">1</span><span class="p">))</span>
@@ -740,8 +506,8 @@ x1 (10, 40)
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<a class='left-prev' id="prev-link" href="../collectors/" title="previous page"><span class="section-number">2. </span>Training Data Collectors</a>
<a class='right-next' id="next-link" href="../primal/" title="next page"><span class="section-number">4. </span>Primal Components</a>
<a class='left-prev' id="prev-link" href="../collectors/" title="previous page"><span class="section-number">5. </span>Training Data Collectors</a>
<a class='right-next' id="next-link" href="../primal/" title="next page"><span class="section-number">7. </span>Primal Components</a>
</div>
@@ -751,7 +517,7 @@ x1 (10, 40)
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