tinycp 0.0.2

A small toolbox for conformal prediction

TinyCP

TinyCP is an experimental Python library for conformal predictions, providing tools to generate valid prediction sets with a specified significance level (alpha). This project aims to facilitate the implementation of personal and future projects on the topic.

For more information on a previous project related to Out-of-Bag (OOB) solutions, visit this link.

Changes about previous work

• calibrate: instead of Balanced Accuracy Score, it can be calibrated either Matthews Correlation Coefficient or Bookmaker Informedness Score, for better reliability
• evaluate: scores bm and mcc for more reliability

Currently, TinyCP supports Out-of-Bag (OOB) solutions for RandomForestClassifier in binary classification problems. For more options and advanced features, consider exploring Crepes.

Installation

Install TinyCP using pip:

pip install tinycp

Note: If you want to enable plotting capabilities, you need to install the extras using Poetry:

poetry install --all-extras

Usage

Importing Classes

Import the conformal classifiers from the tinycp.classifier module:

from tinycp.classifier.class_conditional import OOBBinaryClassConditionalConformalClassifier
from tinycp.classifier.marginal import OOBBinaryMarginalConformalClassifier

Example

Example usage of OOBBinaryClassConditionalConformalClassifier:

from sklearn.ensemble import RandomForestClassifier
from tinycp.classifier.class_conditional import OOBBinaryClassConditionalConformalClassifier

# Create and fit a RandomForestClassifier
learner = RandomForestClassifier(n_estimators=100, oob_score=True)
X_train, y_train = ...  # your training data
learner.fit(X_train, y_train)

# Create and fit the conformal classifier
conformal_classifier = OOBBinaryClassConditionalConformalClassifier(learner)
conformal_classifier.fit(y_train)

# Make predictions
X_test = ...  # your test data
predictions = conformal_classifier.predict(X_test)

Evaluating the Classifier

Evaluate the performance of the conformal classifier using the evaluate method:

results = conformal_classifier.evaluate(X_test, y_test)
print(results)

Classes

BaseConformalClassifier

BaseConformalClassifier is a base class for conformal prediction using a RandomForestClassifier and Venn-Abers calibration for confidence estimation.

OOBBinaryClassConditionalConformalClassifier

OOBBinaryClassConditionalConformalClassifier is a class conditional conformal classifier based on OOB methodology, using a random forest classifier as the learner.

OOBBinaryMarginalConformalClassifier

OOBBinaryMarginalConformalClassifier is a conformal classifier based on OOB predictions, using RandomForestClassifier and Venn-Abers calibration.

License

This project is licensed under the MIT License.