tinyconformal 0.1.2

A small toolbox for conformal prediction

TinyConformal

TinyConformal 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.

Recent updates

• Added support for exactness-bound-based calibration through ExactnessBound for ICP and CQR workflows.
• Added unlabeled_fit support for conformal classifiers and regressors, enabling calibration without labeled calibration data when an exactness bound is available.
• Classifiers can now be calibrated from unlabeled data using pseudo-labels derived from model predictions, while regressors can use a pre-estimated exactness bound to build the conformity scores.

Previously, calibrate used Balanced Accuracy Score; it can now also be calibrated with Matthews Correlation Coefficient or Bookmaker Informedness Score for improved reliability. The evaluate method also reports bm and mcc.

Currently, TinyConformal supports Out-of-Bag (OOB) solutions for RandomForestClassifier in binary classification problems, as well as RandomForestRegressor and RandomForestQuantileRegressor for regression tasks. For additional options and advanced features, you may want to explore Crepes.

Installation

Install TinyConformal using pip:

pip install tinyconformal

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

poetry install --E plot

Usage

Importing Classifiers

Import the conformal classifiers from the tinyconformal.classifier module:

from tinyconformal.classifier import BinaryClassConditionalConformalClassifier
from tinyconformal.classifier import BinaryMarginalConformalClassifier

Importing Regressors

Import the conformal regressors from the tinyconformal.regressor module:

from tinyconformal.regressor import ConformalizedRegressor
from tinyconformal.regressor import ConformalizedQuantileRegressor

Example

Example usage of BinaryClassConditionalConformalClassifier:

from sklearn.ensemble import RandomForestClassifier
from tinyconformal.classifier import BinaryClassConditionalConformalClassifier

# 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 = BinaryClassConditionalConformalClassifier(learner)
conformal_classifier.fit(X=X_train, y=y_train, oob=True)

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

Unlabeled calibration example

For settings where labeled calibration data are unavailable, you can fit the conformal model directly on unlabeled data:

from sklearn.ensemble import RandomForestClassifier
from tinyconformal.classifier import BinaryMarginalConformalClassifier

learner = RandomForestClassifier(n_estimators=100, oob_score=True)
learner.fit(X_train, y_train)

conformal_classifier = BinaryMarginalConformalClassifier(learner)
conformal_classifier.unlabeled_fit(X_unlabeled)

predictions = conformal_classifier.predict(X_test)

For regressors, you can combine an exactness bound estimate with unlabeled calibration:

from sklearn.ensemble import RandomForestRegressor
from tinyconformal import ConformalizedRegressor, ExactnessBound

learner = RandomForestRegressor(random_state=42)
tilde_beta = ExactnessBound.estimate_icp_bound(learner, X_train, y_train, p=0.95, cv=5)

regressor = ConformalizedRegressor(learner, alpha=0.05)
regressor.unlabeled_fit(X_unlabeled, tilde_beta=tilde_beta)

intervals = regressor.predict_interval(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

BinaryMarginalConformalClassifier

BinaryMarginalConformalClassifier is a marginal-coverage conformal classifier that uses a classifier as the underlying learner.

• Training via labeled calibration: fit(X, y)
• Training via OOB calibration: fit(X, y, oob=True)
• Training via unlabeled calibration: unlabeled_fit(X)

BinaryClassConditionalConformalClassifier

BinaryClassConditionalConformalClassifier is a class-conditional conformal classifier that uses a classifier as the underlying learner.

• Training via labeled calibration: fit(X, y)
• Training via OOB calibration: fit(X, y, oob=True)
• Training via unlabeled calibration: unlabeled_fit(X) using pseudo-labels derived from the model probabilities

ConformalizedRegressor

ConformalizedRegressor is a conformal regressor built on a regression learner.

• Training via labeled calibration: fit(X, y)
• Training via OOB calibration: fit(X, y, oob=True)
• Training via unlabeled calibration: unlabeled_fit(X, tilde_beta=...) using an exactness bound

ConformalizedQuantileRegressor

ConformalizedQuantileRegressor is a conformal quantile regressor built on a quantile regressor.

• Training via labeled calibration: fit(X, y)
• Training via OOB calibration: fit(X, y, oob=True)
• Training via unlabeled calibration: unlabeled_fit(X, tilde_beta=...) using an exactness bound

ExactnessBound

ExactnessBound provides helper methods to estimate the exactness bound used in unlabeled conformal calibration for ICP and CQR workflows.

License

This project is licensed under the MIT License.