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https://github.com/mcapuccini/scala-cp
Conformal Prediction in Scala
https://github.com/mcapuccini/scala-cp
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Conformal Prediction in Scala
- Host: GitHub
- URL: https://github.com/mcapuccini/scala-cp
- Owner: mcapuccini
- License: apache-2.0
- Created: 2017-05-11T10:20:38.000Z (over 7 years ago)
- Default Branch: master
- Last Pushed: 2020-10-13T02:10:13.000Z (about 4 years ago)
- Last Synced: 2024-02-13T08:05:38.665Z (10 months ago)
- Language: Scala
- Size: 428 KB
- Stars: 15
- Watchers: 3
- Forks: 6
- Open Issues: 2
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
- awesome-conformal-prediction - Scala-CP
README
# Scala-CP
[](https://travis-ci.org/mcapuccini/scala-cp)
[](https://www.codacy.com/app/m-capuccini/scala-cp?utm_source=github.com&utm_medium=referral&utm_content=mcapuccini/scala-cp&utm_campaign=Badge_Grade)
Scala-CP is a Scala implementation of the Conformal Prediction (CP) framework, introduced by Vovk *et. al.* in the book Algorithmic Learning in a Random World. When assigning confidence to machine learning models, CP is a nice alternative to cross-validation. Instead of predicting a value for a certain feature vector, a conformal predictor outputs a prediction set/region that contains the correct prediction with probability *1-𝜺*, where *𝜺* is a user-defined significance level. The choose of the significance level will of course influence the size of the prediction set/region. In alternative, using CP one can predict object-specific p-values for unseen examples.
## Table of Contents
- [Getting started](#getting-started)
- [Documentation](#documentation)
- [Examples](#examples)
- [Scala-CP with Spark MLlib](https://github.com/mcapuccini/scala-cp/blob/master/cp/src/test/scala/se/uu/it/cp/SparkTest.scala)
- [Scala-CP with LIBLINEAR](https://github.com/mcapuccini/scala-cp/blob/master/cp/src/test/scala/se/uu/it/cp/LibLinTest.scala)
- [ZeppelinHub: Scala-CP with Spark MLlib](https://www.zepl.com/viewer/notebooks/bm90ZTovL21hcmNvY2FwL3plcHBlbGluLWNwLzUyNjVlOGQyYjkxOTRmNGU4MWM4OGJjMzQyMDMzZDk5L25vdGUuanNvbg)
- [List of publications](#list-of-publications)
- [Roadmap](#roadmap)
## Getting started
Scala-CP can be used along with any Scala/Java machine learning library and algorithm. All you have to do is to add the Scala-CP dependency to your *pom.xml* file:
```xml
...
se.uu.it
cp
0.1.0
...
```
## Documentation
The API documentation is available at: https://mcapuccini.github.io/scala-cp/scaladocs/.
## Examples
For some usage examples please refer to the unit tests:
- [Scala-CP with Spark MLlib](https://github.com/mcapuccini/scala-cp/blob/master/cp/src/test/scala/se/uu/it/cp/SparkTest.scala)
- [Scala-CP with LIBLINEAR](https://github.com/mcapuccini/scala-cp/blob/master/cp/src/test/scala/se/uu/it/cp/LibLinTest.scala)
You can also refer to this Apache Zeppelin notebooks for more examples:
- [ZeppelinHub: Scala-CP with Spark MLlib](https://www.zepl.com/viewer/notebooks/bm90ZTovL21hcmNvY2FwL3plcHBlbGluLWNwLzUyNjVlOGQyYjkxOTRmNGU4MWM4OGJjMzQyMDMzZDk5L25vdGUuanNvbg)
## List of publications
- [M. Capuccini, L. Carlsson, U. Norinder and O. Spjuth, "Conformal Prediction in Spark: Large-Scale Machine Learning with Confidence," 2015 IEEE/ACM 2nd International Symposium on Big Data Computing (BDC), Limassol, 2015, pp. 61-67.](http://ieeexplore.ieee.org/document/7406330/)
- [Ahmed, L., Georgiev, V., Capuccini, M., Toor, S., Schaal, W., Laure, E., & Spjuth, O. (2018). Efficient iterative virtual screening with Apache Spark and conformal prediction. Journal of cheminformatics, 10(1), 1-8.](https://jcheminf.biomedcentral.com/articles/10.1186/s13321-018-0265-z)
## Roadmap
### Inductive Conformal Prediction
- [x] Classification
- [ ] Regression
### Transductive Conformal Prediction
- [ ] Classification
- [ ] Regression