Uncertainty-Aware Machine Learning
uaml is a Python module for easy and effective uncertainty-aware machine learning based on probabilistic ensembles and the Jensen–Shannon divergence. Currently, it is built on top of scikit-learn and supports all probabilistic base classifiers.
Dependencies
Following modules are required:
- NumPy
- Scikit-learn
Basic usage
import numpy as np
from sklearn.svm import SVC
from uaml.classifier import UAClassifier
# Some example data
X_train, X_test, y_train = np.random.randn(1000,100), np.random.randn(100,100), np.random.randint(0,5,1000)
# Use SVC as base (probabilistic) estimator
estm = SVC(gamma=2, C=1, probability=True)
# Constuct and fit an uncertainty-aware classifier with 500 estimators and parallelize over 5 cores
clf = UAClassifier(estm, mc_sample_size=0.5, n_mc_samples=500, n_jobs=5)
clf.fit(X_train, y_train)
# Obtain predictions by means of majority voting and calculate aleatoric and epistemic uncertainty
yhat = clf.predict(X_test, avg=True)
ua, ue = clf.get_uncertainty(X_test)
References
[1] Aleatoric and Epistemic Uncertainty in Machine Learning: An Introduction to Concepts and Methods (https://arxiv.org/pdf/1910.09457.pdf)
