ANL-CEEESA/MIPLearn
1
0
Fork 0
mirror of https://github.com/ANL-CEEESA/MIPLearn.git synced 2025-12-07 18:08:51 -06:00
Code Issues Packages Projects Releases Wiki Activity

Docs: reorganize sections

Browse Source
This commit is contained in:
Alinson S. Xavier
2020-05-05 13:58:01 -05:00
parent 92cc924fee
commit 857e6af1a0
5 changed files with 22 additions and 16 deletions
Download Patch File Download Diff File Expand all files Collapse all files

12
src/docs/customization.md
View File

@@ -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.