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Docs: minor fixes

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Alinson S. Xavier
2020-05-05 13:38:04 -05:00
parent 9355ab9158
commit c15ec9e2c8
5 changed files with 12 additions and 10 deletions
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9
docs/customization/index.html
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@@ -204,13 +204,13 @@ more aggressive, this precision may be lowered.</p>
<h2 id="evaluating-component-performance">Evaluating component performance</h2> <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 <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 the solver, then evaluate its performance.</p>
<pre><code class="python">from miplearn import PrimalSolutionComponent <pre><code class="python">from miplearn import PrimalSolutionComponent
# User-provided set os solved training instances # User-provided set of previously-solved instances
train_instances = [...] train_instances = [...]
# Construct and fit component on a subset of the training set # Construct and fit component on a subset of training instances
comp = PrimalSolutionComponent() comp = PrimalSolutionComponent()
comp.fit(train_instances[:100]) comp.fit(train_instances[:100])
@@ -247,7 +247,8 @@ False negative (%) 29.720000
dtype: float64 dtype: float64
</code></pre> </code></pre>
<p>Regression components (such as <code>ObjectiveValueComponent</code>) can also be used similarly, as shown in the next example:</p> <p>Regression components (such as <code>ObjectiveValueComponent</code>) can also be trained and evaluated similarly,
as the next example shows:</p>
<pre><code class="python">from miplearn import ObjectiveValueComponent <pre><code class="python">from miplearn import ObjectiveValueComponent
comp = ObjectiveValueComponent() comp = ObjectiveValueComponent()
comp.fit(train_instances[:100]) comp.fit(train_instances[:100])

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@@ -273,5 +273,5 @@
<!-- <!--
MkDocs version : 1.1 MkDocs version : 1.1
Build Date UTC : 2020-05-05 18:32:57 Build Date UTC : 2020-05-05 18:38:02
--> -->

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src/docs/customization.md
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@@ -65,15 +65,15 @@ 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 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 the solver, then evaluate its performance.
```python ```python
from miplearn import PrimalSolutionComponent from miplearn import PrimalSolutionComponent
# User-provided set os solved training instances # User-provided set of previously-solved instances
train_instances = [...] train_instances = [...]
# Construct and fit component on a subset of the training set # Construct and fit component on a subset of training instances
comp = PrimalSolutionComponent() comp = PrimalSolutionComponent()
comp.fit(train_instances[:100]) comp.fit(train_instances[:100])
@@ -112,7 +112,8 @@ False negative (%) 29.720000
dtype: float64 dtype: float64
``` ```
Regression components (such as `ObjectiveValueComponent`) can also be used similarly, as shown in the next example: Regression components (such as `ObjectiveValueComponent`) can also be trained and evaluated similarly,
as the next example shows:
```python ```python
from miplearn import ObjectiveValueComponent from miplearn import ObjectiveValueComponent