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Docs: minor fixes

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Alinson S. Xavier
2020-05-05 13:41:12 -05:00
parent c15ec9e2c8
commit 929ff0d9d8
10 changed files with 17 additions and 17 deletions
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src/docs/about.md
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@@ -5,7 +5,7 @@
* **Alinson S. Xavier,** Argonne National Laboratory <<axavier@anl.gov>>
* **Feng Qiu,** Argonne National Laboratory <<fqiu@anl.gov>>
### Acknowledgements
### Acknowledgments
* Based upon work supported by Laboratory Directed Research and Development (LDRD) funding from Argonne National Laboratory, provided by the Director, Office of Science, of the U.S. Department of Energy under Contract No. DE-AC02-06CH11357.

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src/docs/customization.md
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@@ -45,12 +45,12 @@ solver = LearningSolver()
solver.add(LazyConstraintComponent(...))
```
## Adjusting component aggresiveness
## 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
they are on each prediction (through the `predict_proba` method in the sklearn API), and only take into account
predictions which have probabilities above the threshold. Lowering a component's threshold increases its aggresiveness,
predictions which have probabilities above the threshold. Lowering a component's threshold increases its aggressiveness,
while raising a component's threshold makes it more conservative.
MIPLearn also includes `MinPrecisionThreshold`, a dynamic threshold which adjusts itself automatically during training

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src/docs/index.md
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@@ -20,9 +20,9 @@ The package uses Machine Learning (ML) to automatically identify patterns in pre
* [Benchmark utilities](benchmark.md)
* [Benchmark problems, challenges and results](problems.md)
* [Customizing the solver](customization.md)
* [License, authors, references and acknowledgements](about.md)
* [License, authors, references and acknowledgments](about.md)
### Souce Code
### Source Code
* [https://github.com/ANL-CEEESA/MIPLearn](https://github.com/ANL-CEEESA/MIPLearn)

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src/docs/usage.md
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@@ -25,7 +25,7 @@ import miplearn
```
!!! note
To install MIPLearn in another Python environment, switch to that enviroment before running `make install`. To install the package in development mode, run `make develop` instead.
To install MIPLearn in another Python environment, switch to that environment before running `make install`. To install the package in development mode, run `make develop` instead.
### Using `LearningSolver`