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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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11
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="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> </ul>
</div></div> </div></div>
<div class="col-md-9" role="main"> <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)) <pre><code class="python">PrimalSolutionComponent(threshold=MinPrecisionThreshold(0.95))
</code></pre> </code></pre>
<h3 id="evaluating-component-performance">Evaluating component performance</h3> <h2 id="evaluating-component-performance">Evaluating component performance</h2>
<p>MIPLearn allows solver components to be modified and evaluated in isolation. In the following example, we build and <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> fit <code>PrimalSolutionComponent</code> outside a solver, then evaluate its performance.</p>
<pre><code class="python">from miplearn import PrimalSolutionComponent <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) pd.DataFrame(ev[&quot;Fix one&quot;]).mean(axis=1)
</code></pre> </code></pre>
<pre><code>Predicted positive 3.120000 <pre><code class="text">Predicted positive 3.120000
Predicted negative 196.880000 Predicted negative 196.880000
Condition positive 62.500000 Condition positive 62.500000
Condition negative 137.500000 Condition negative 137.500000
@@ -256,7 +257,7 @@ import pandas as pd
pd.DataFrame(ev).mean(axis=1) pd.DataFrame(ev).mean(axis=1)
</code></pre> </code></pre>
<pre><code>Mean squared error 7001.977827 <pre><code class="text">Mean squared error 7001.977827
Explained variance 0.519790 Explained variance 0.519790
Max error 242.375804 Max error 242.375804
Mean absolute error 65.843924 Mean absolute error 65.843924

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<!-- <!--
MkDocs version : 1.1 MkDocs version : 1.1
Build Date UTC : 2020-05-05 18:30:25 Build Date UTC : 2020-05-05 18:32:57
--> -->

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src/docs/customization.md
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@@ -62,9 +62,9 @@ more aggressive, this precision may be lowered.
PrimalSolutionComponent(threshold=MinPrecisionThreshold(0.95)) PrimalSolutionComponent(threshold=MinPrecisionThreshold(0.95))
``` ```
### Evaluating component performance ## Evaluating component performance
MIPLearn allows solver components to be modified and evaluated in isolation. In the following example, we build and MIPLearn allows solver components to be modified, trained and evaluated in isolation. In the following example, we build and
fit `PrimalSolutionComponent` outside a solver, then evaluate its performance. fit `PrimalSolutionComponent` outside a solver, then evaluate its performance.
```python ```python
@@ -88,7 +88,7 @@ and for each type of prediction the component makes. To obtain a summary across
import pandas as pd import pandas as pd
pd.DataFrame(ev["Fix one"]).mean(axis=1) pd.DataFrame(ev["Fix one"]).mean(axis=1)
``` ```
``` ```text
Predicted positive 3.120000 Predicted positive 3.120000
Predicted negative 196.880000 Predicted negative 196.880000
Condition positive 62.500000 Condition positive 62.500000
@@ -123,7 +123,7 @@ ev = comp.evaluate(train_instances[100:150])
import pandas as pd import pandas as pd
pd.DataFrame(ev).mean(axis=1) pd.DataFrame(ev).mean(axis=1)
``` ```
``` ```text
Mean squared error 7001.977827 Mean squared error 7001.977827
Explained variance 0.519790 Explained variance 0.519790
Max error 242.375804 Max error 242.375804
@@ -131,4 +131,5 @@ Mean absolute error 65.843924
R2 0.517612 R2 0.517612
Median absolute error 65.843924 Median absolute error 65.843924
dtype: float64 dtype: float64
``` ```