Docs: reorganize sections

docs/customization/index.html

@@ -138,21 +138,22 @@
         <div class="col-md-3"><div class="bs-sidebar hidden-print affix well" role="complementary">
     <ul class="nav bs-sidenav">
         <li class="first-level active"><a href="#customization">Customization</a></li>
-            <li class="second-level"><a href="#selecting-the-internal-mip-solver">Selecting the internal MIP solver</a></li>
+            <li class="second-level"><a href="#customizing-solver-parameters">Customizing solver parameters</a></li>
                 
-            <li class="second-level"><a href="#selecting-solver-components">Selecting solver components</a></li>
-                
-            <li class="second-level"><a href="#adjusting-component-aggressiveness">Adjusting component aggressiveness</a></li>
-                
-            <li class="second-level"><a href="#evaluating-component-performance">Evaluating component performance</a></li>
+                <li class="third-level"><a href="#selecting-the-internal-mip-solver">Selecting the internal MIP solver</a></li>
+            <li class="second-level"><a href="#customizing-solver-components">Customizing solver components</a></li>
                 
+                <li class="third-level"><a href="#selecting-components">Selecting components</a></li>
+                <li class="third-level"><a href="#adjusting-component-aggressiveness">Adjusting component aggressiveness</a></li>
+                <li class="third-level"><a href="#evaluating-component-performance">Evaluating component performance</a></li>
                 <li class="third-level"><a href="#using-customized-ml-classifiers-and-regressors">Using customized ML classifiers and regressors</a></li>
     </ul>
 </div></div>
         <div class="col-md-9" role="main">
 
 <h1 id="customization">Customization</h1>
-<h2 id="selecting-the-internal-mip-solver">Selecting the internal MIP solver</h2>
+<h2 id="customizing-solver-parameters">Customizing solver parameters</h2>
+<h3 id="selecting-the-internal-mip-solver">Selecting the internal MIP solver</h3>
 <p>By default, <code>LearningSolver</code> uses <a href="https://www.gurobi.com/">Gurobi</a> as its internal MIP solver. Another supported solver is <a href="https://www.ibm.com/products/ilog-cplex-optimization-studio">IBM ILOG CPLEX</a>. To switch between solvers, use the <code>solver</code> constructor argument, as shown below. It is also possible to specify a time limit (in seconds) and a relative MIP gap tolerance.</p>
 <pre><code class="python">from miplearn import LearningSolver
 solver = LearningSolver(solver=&quot;cplex&quot;,
@@ -160,7 +161,7 @@ solver = LearningSolver(solver=&quot;cplex&quot;,
                         gap_tolerance=1e-3)
 </code></pre>
 
-<h2 id="selecting-solver-components">Selecting solver components</h2>
+<h2 id="customizing-solver-components">Customizing solver components</h2>
 <p><code>LearningSolver</code> is composed by a number of individual machine-learning components, each targeting a different part of the solution process. Each component can be individually enabled, disabled or customized. The following components are enabled by default:</p>
 <ul>
 <li><code>LazyConstraintComponent</code>: Predicts which lazy constraint to initially enforce.</li>
@@ -171,6 +172,7 @@ solver = LearningSolver(solver=&quot;cplex&quot;,
 <ul>
 <li><code>BranchPriorityComponent</code>: Predicts good branch priorities for decision variables.</li>
 </ul>
+<h3 id="selecting-components">Selecting components</h3>
 <p>To create a <code>LearningSolver</code> with a specific set of components, the <code>components</code> constructor argument may be used, as the next example shows:</p>
 <pre><code class="python"># Create a solver without any components
 solver1 = LearningSolver(components=[])
@@ -190,7 +192,7 @@ solver = LearningSolver()
 solver.add(LazyConstraintComponent(...))
 </code></pre>
 
-<h2 id="adjusting-component-aggressiveness">Adjusting component aggressiveness</h2>
+<h3 id="adjusting-component-aggressiveness">Adjusting component aggressiveness</h3>
 <p>The aggressiveness of classification components (such as <code>PrimalSolutionComponent</code> and <code>LazyConstraintComponent</code>) can
 be adjusted through the <code>threshold</code> constructor argument. Internally, these components ask the ML models how confident
 they are on each prediction (through the <code>predict_proba</code> method in the sklearn API), and only take into account
@@ -203,7 +205,7 @@ more aggressive, this precision may be lowered.</p>
 <pre><code class="python">PrimalSolutionComponent(threshold=MinPrecisionThreshold(0.95))
 </code></pre>
 
-<h2 id="evaluating-component-performance">Evaluating component performance</h2>
+<h3 id="evaluating-component-performance">Evaluating component performance</h3>
 <p>MIPLearn allows solver components to be modified, trained and evaluated in isolation. In the following example, we build and
 fit <code>PrimalSolutionComponent</code> outside the solver, then evaluate its performance.</p>
 <pre><code class="python">from miplearn import PrimalSolutionComponent

docs/index.html

@@ -273,5 +273,5 @@
 
 <!--
 MkDocs version : 1.1
-Build Date UTC : 2020-05-05 18:54:05
+Build Date UTC : 2020-05-05 18:57:59
 -->

src/docs/customization.md

@@ -1,6 +1,8 @@
 # Customization
 
-## Selecting the internal MIP solver
+## Customizing solver parameters
+
+### Selecting the internal MIP solver
 
 By default, `LearningSolver` uses [Gurobi](https://www.gurobi.com/) as its internal MIP solver. Another supported solver is [IBM ILOG CPLEX](https://www.ibm.com/products/ilog-cplex-optimization-studio). To switch between solvers, use the `solver` constructor argument, as shown below. It is also possible to specify a time limit (in seconds) and a relative MIP gap tolerance.
 
@@ -11,7 +13,7 @@ solver = LearningSolver(solver="cplex",
                         gap_tolerance=1e-3)
 ```
 
-## Selecting solver components
+## Customizing solver components
 
 `LearningSolver` is composed by a number of individual machine-learning components, each targeting a different part of the solution process. Each component can be individually enabled, disabled or customized. The following components are enabled by default:
 
@@ -23,6 +25,8 @@ The following components are also available, but not enabled by default:
 
 * `BranchPriorityComponent`: Predicts good branch priorities for decision variables.
 
+### Selecting components
+
 To create a `LearningSolver` with a specific set of components, the `components` constructor argument may be used, as the next example shows:
 
 ```python
@@ -45,7 +49,7 @@ solver = LearningSolver()
 solver.add(LazyConstraintComponent(...))
 ```
 
-## Adjusting component aggressiveness
+### Adjusting component aggressiveness
 
 The aggressiveness of classification components (such as `PrimalSolutionComponent` and `LazyConstraintComponent`) can
 be adjusted through the `threshold` constructor argument. Internally, these components ask the ML models how confident
@@ -62,7 +66,7 @@ more aggressive, this precision may be lowered.
 PrimalSolutionComponent(threshold=MinPrecisionThreshold(0.95))
 ```
 
-## Evaluating component performance
+### Evaluating component performance
 
 MIPLearn allows solver components to be modified, trained and evaluated in isolation. In the following example, we build and
 fit `PrimalSolutionComponent` outside the solver, then evaluate its performance.