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Update 0.3 docs

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Alinson S. Xavier
2023-06-08 12:41:47 -05:00
parent 739eb2d56a
commit 13930b491e
22 changed files with 120 additions and 266 deletions
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0.3/tutorials/getting-started-pyomo/index.html
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@@ -125,7 +125,7 @@
</li>
<li class="toctree-l1">
<a class="reference internal" href="../../guide/solvers/">
8. Solvers
8. Learning Solver
</a>
</li>
</ul>
@@ -296,31 +296,13 @@
<li><p>Julia version, compatible with the JuMP modeling language.</p></li>
</ul>
<p>In this tutorial, we will demonstrate how to use and install the Python/Pyomo version of the package. The first step is to install Python 3.8+ in your computer. See the <a class="reference external" href="https://www.python.org/downloads/">official Python website for more instructions</a>. After Python is installed, we proceed to install MIPLearn using <code class="docutils literal notranslate"><span class="pre">pip</span></code>:</p>
<div class="nbinput nblast docutils container">
<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[1]:
<div class="highlight-none notranslate"><div class="highlight"><pre><span></span>$ pip install MIPLearn==0.3
</pre></div>
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<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># !pip install MIPLearn==0.3.0</span>
</pre></div>
</div>
</div>
<p>In addition to MIPLearn itself, we will also install Gurobi 10.0, a state-of-the-art commercial MILP solver. This step also install a demo license for Gurobi, which should able to solve the small optimization problems in this tutorial. A license is required for solving larger-scale problems.</p>
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<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[2]:
<div class="highlight-none notranslate"><div class="highlight"><pre><span></span>$ pip install &#39;gurobipy&gt;=10,&lt;10.1&#39;
</pre></div>
</div>
<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="o">!</span>pip install <span class="s1">&#39;gurobipy&gt;=10,&lt;10.1&#39;</span>
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Requirement already satisfied: gurobipy&lt;10.1,&gt;=10 in /home/axavier/Software/anaconda3/envs/miplearn/lib/python3.8/site-packages (10.0.1)
</pre></div></div>
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<div class="admonition note">
<p class="admonition-title">Note</p>
<p>In the code above, we install specific version of all packages to ensure that this tutorial keeps running in the future, even when newer (and possibly incompatible) versions of the packages are released. This is usually a recommended practice for all Python projects.</p>
@@ -586,7 +568,7 @@ machines. The code below generates the files <code class="docutils literal notra
<span class="n">solver_ml</span> <span class="o">=</span> <span class="n">LearningSolver</span><span class="p">(</span><span class="n">components</span><span class="o">=</span><span class="p">[</span><span class="n">comp</span><span class="p">])</span>
<span class="n">solver_ml</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">train_data</span><span class="p">)</span>
<span class="n">solver_ml</span><span class="o">.</span><span class="n">optimize</span><span class="p">(</span><span class="n">test_data</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">build_uc_model</span><span class="p">);</span>
<span class="n">solver_ml</span><span class="o">.</span><span class="n">optimize</span><span class="p">(</span><span class="n">test_data</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">build_uc_model</span><span class="p">)</span>
</pre></div>
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@@ -672,7 +654,7 @@ WARNING: Cannot get duals for MIP.
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<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">solver_baseline</span> <span class="o">=</span> <span class="n">LearningSolver</span><span class="p">(</span><span class="n">components</span><span class="o">=</span><span class="p">[])</span>
<span class="n">solver_baseline</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">train_data</span><span class="p">)</span>
<span class="n">solver_baseline</span><span class="o">.</span><span class="n">optimize</span><span class="p">(</span><span class="n">test_data</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">build_uc_model</span><span class="p">);</span>
<span class="n">solver_baseline</span><span class="o">.</span><span class="n">optimize</span><span class="p">(</span><span class="n">test_data</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">build_uc_model</span><span class="p">)</span>
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</div>