https://github.com/donlapark/coverforest
A simple and fast sklearn-compatible conformal predictions with random forests for both classification and regression tasks.
https://github.com/donlapark/coverforest
classification conformal-prediction python random-forest regression sklearn
Last synced: 6 months ago
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A simple and fast sklearn-compatible conformal predictions with random forests for both classification and regression tasks.
- Host: GitHub
- URL: https://github.com/donlapark/coverforest
- Owner: donlapark
- License: bsd-3-clause
- Created: 2025-01-12T10:57:33.000Z (9 months ago)
- Default Branch: main
- Last Pushed: 2025-03-31T12:16:28.000Z (6 months ago)
- Last Synced: 2025-04-02T09:11:15.351Z (6 months ago)
- Topics: classification, conformal-prediction, python, random-forest, regression, sklearn
- Language: Jupyter Notebook
- Homepage:
- Size: 5.7 MB
- Stars: 42
- Watchers: 1
- Forks: 4
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
## 🌳 coverforest - Conformal Predictions with Random Forest
A simple and fast implementation of conformal random forests for both classification and regression tasks. **coverforest** extends [scikit-learn](https://scikit-learn.org)'s random forest implementation to provide prediction sets/intervals with guaranteed coverage using conformal prediction methods.
**coverforest** provides three conformal prediction methods for random forests:
- CV+ (Cross-Validation+) [[1](#1), [2](#2)].
- Jackknife+-after-Bootstrap [[3]](#3).
- Split Conformal [[4]](#4).
The library provides two main classes: `CoverForestRegressor` for interval prediction and `CoverForestClassifier`. for set prediction.
Here are quick runs of the two classes:
```python
from coverforest import CoverForestRegressor
reg = CoverForestRegressor(n_estimators=100, method='bootstrap') # using J+-a-Bootstrap
reg.fit(X_train, y_train)
y_pred, y_intervals = reg.predict(X_test, alpha=0.05) # 95% coverage intervals
```
```python
from coverforest import CoverForestClassifier
clf = CoverForestClassifier(n_estimators=100, method='cv') # using CV+
clf.fit(X_train, y_train)
y_pred, y_sets = clf.predict(X_test, alpha=0.05) # 95% coverage sets
```
You can try these models in Colab: [[Classification](https://colab.research.google.com/github/donlapark/coverforest/blob/main/notebooks/classification_pipeline.ipynb)] [[Regression](https://colab.research.google.com/github/donlapark/coverforest/blob/main/notebooks/regression_pipeline.ipynb)]
For additional examples and package API, see [Documentation](https://donlapark.github.io/coverforest).
## 🔧 Requirements
- Python >=3.9
- Scikit-learn >=1.6.0
## ⚡ Installation
You can install **coverforest** using pip:
```bash
pip install coverforest
```
Or install from source:
```bash
git clone https://github.com/donlapark/coverforest.git
cd coverforest
pip install .
```
### Regularization in conformal set predictions
The classifier includes two regularization parameters $k$ and $\lambda$ that encourage smaller prediction sets [[5]](#5).
```python
clf = CoverForestClassifier(n_estimators=100, method='cv', k_init=2, lambda_init=0.1)
```
Automatic searching for suitable $k$ and $\lambda$ is also possible by specifying `k_init="auto"` and `lambda_init="auto"`, which are the default values of `CoverForestClassifier`.
### Performance Tips
Random forest leverages parallel computation by processing trees concurrently. Use the `n_jobs` parameter in `fit()` and `predict()` to control CPU usage (`n_jobs=-1` uses all cores).
For prediction, conformity score calculations require a memory array of size `(n_train × n_test × n_classes)`. To optimize performance with high `n_jobs` values, split large test sets into smaller batches.
See the documentation for more details and examples.
## 🔗 See Also
- [MAPIE](https://github.com/scikit-learn-contrib/MAPIE): A Python package that provides scikit-learn-compatible wrappers for conformal classification and regression
- [conforest](https://github.com/knrumsey/conforest) An R implementation of random forest with inductive conformal prediction.
- [clover](https://github.com/Monoxido45/clover) A Python implementation of a regression forest method for conditional coverage ($`P(Y \vert X =x)`$) guarantee.
- [Conformal Prediction](https://github.com/aangelopoulos/conformal-prediction): Jupyter Notebook demonstrations of conformal prediction on various tasks, such as image classification, image segmentation, times series forecasting, and outlier detection
- [TorchCP](https://github.com/ml-stat-Sustech/TorchCP) A Python toolbox for Conformal Prediction in Deep Learning built on top of PyTorch
- [crepes](https://github.com/henrikbostrom/crepes) A Python package that implements standard and Mondrian conformal classifiers as well as standard, normalized and Mondrian conformal regressors and predictive systems.
- [nonconformist](https://github.com/donlnz/nonconformist): One of the first Python implementations of conformal prediction
## 📖 References
[1] Yaniv Romano, Matteo Sesia & Emmanuel J. Candès, "Classification with Valid and Adaptive Coverage", NeurIPS 2020.
[2] Rina Foygel Barber, Emmanuel J. Candès, Aaditya Ramdas & Ryan J. Tibshirani, "Predictive inference with the jackknife+", Ann. Statist. 49 (1) 486-507, 2021.
[3] Byol Kim, Chen Xu, Rina Foygel Barber, "Predictive inference is free with the jackknife+-after-bootstrap", NeurIPS 2020.
[4] Vladimir Vovk, Ilia Nouretdinov, Valery Manokhin & Alexander Gammerman, "Cross-conformal predictive distributions", 37-51, COPA 2018.
[5] Anastasios Nikolas Angelopoulos, Stephen Bates, Michael I. Jordan & Jitendra Malik, "Uncertainty Sets for Image Classifiers using Conformal Prediction", ICLR 2021.
[6] Leo Breiman, "Random Forests", Machine Learning, 45(1), 5-32, 2001.
## 📜 License
[BSD-3-Clause license](https://github.com/donlapark/coverforest/blob/main/LICENSE)
## 📝 Citation
If you use **coverforest** in your research, please cite:
```bibtex
@misc{coverforest2025,
Author = {Panisara Meehinkong and Donlapark Ponnoprat},
Title = {coverforest: Conformal Predictions with Random Forest in Python},
Year = {2025},
Eprint = {arXiv:2501.14570},
}
```