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Docs: minor fixes

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Alinson S. Xavier
2020-05-05 13:33:00 -05:00
parent 2d88a41767
commit 9355ab9158
5 changed files with 14 additions and 12 deletions
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docs/customization/index.html
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@@ -144,7 +144,8 @@
<li class="second-level"><a href="#adjusting-component-aggresiveness">Adjusting component aggresiveness</a></li>
<li class="third-level"><a href="#evaluating-component-performance">Evaluating component performance</a></li>
<li class="second-level"><a href="#evaluating-component-performance">Evaluating component performance</a></li>
</ul>
</div></div>
<div class="col-md-9" role="main">
@@ -201,8 +202,8 @@ more aggressive, this precision may be lowered.</p>
<pre><code class="python">PrimalSolutionComponent(threshold=MinPrecisionThreshold(0.95))
</code></pre>
<h3 id="evaluating-component-performance">Evaluating component performance</h3>
<p>MIPLearn allows solver components to be modified and evaluated in isolation. In the following example, we build and
<h2 id="evaluating-component-performance">Evaluating component performance</h2>
<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 a solver, then evaluate its performance.</p>
<pre><code class="python">from miplearn import PrimalSolutionComponent
@@ -223,7 +224,7 @@ and for each type of prediction the component makes. To obtain a summary across
pd.DataFrame(ev[&quot;Fix one&quot;]).mean(axis=1)
</code></pre>
<pre><code>Predicted positive 3.120000
<pre><code class="text">Predicted positive 3.120000
Predicted negative 196.880000
Condition positive 62.500000
Condition negative 137.500000
@@ -256,7 +257,7 @@ import pandas as pd
pd.DataFrame(ev).mean(axis=1)
</code></pre>
<pre><code>Mean squared error 7001.977827
<pre><code class="text">Mean squared error 7001.977827
Explained variance 0.519790
Max error 242.375804
Mean absolute error 65.843924