ANL-CEEESA/MIPLearn
1
0
Fork 0
mirror of https://github.com/ANL-CEEESA/MIPLearn.git synced 2025-12-06 09:28:51 -06:00
Code Issues Packages Projects Releases Wiki Activity

Docs: minor fixes

Browse Source
This commit is contained in:
Alinson S. Xavier
2020-05-05 13:33:00 -05:00
parent 2d88a41767
commit 9355ab9158
5 changed files with 14 additions and 12 deletions
Download Patch File Download Diff File Expand all files Collapse all files

11
docs/customization/index.html
View File

@@ -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

2
docs/index.html
View File

@@ -273,5 +273,5 @@
<!--
MkDocs version : 1.1
Build Date UTC : 2020-05-05 18:30:25
Build Date UTC : 2020-05-05 18:32:57
-->

2
docs/search/search_index.json
View File

File diff suppressed because one or more lines are too long

BIN
docs/sitemap.xml.gz
View File

Binary file not shown.

11
src/docs/customization.md
View File

@@ -62,9 +62,9 @@ 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 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.
```python
@@ -88,7 +88,7 @@ and for each type of prediction the component makes. To obtain a summary across
import pandas as pd
pd.DataFrame(ev["Fix one"]).mean(axis=1)
```
```
```text
Predicted positive 3.120000
Predicted negative 196.880000
Condition positive 62.500000
@@ -123,7 +123,7 @@ ev = comp.evaluate(train_instances[100:150])
import pandas as pd
pd.DataFrame(ev).mean(axis=1)
```
```
```text
Mean squared error 7001.977827
Explained variance 0.519790
Max error 242.375804
@@ -131,4 +131,5 @@ Mean absolute error 65.843924
R2 0.517612
Median absolute error 65.843924
dtype: float64
```
```