ml_edm package: a Python toolkit for Machine Learning based Early Decision Making

TL;DR

The paper presents ml_edm, a Python toolkit for Machine Learning based Early Decision Making, focused on early decision making in sequential/time-series tasks. It consolidates state-of-the-art Early Classification of Time Series algorithms into a modular architecture with a scikit-learn like API and emphasizes cost-sensitive learning through a dedicated CostMatrices mechanism. The library enables reproducible ECTS research, supports parallel training for trigger models, and targets univariate time series while outlining paths to broader extensions. Practically, ml_edm provides ready-to-use classifiers and triggers to balance Earliness and Accuracy, with evaluation metrics such as Average cost, Accuracy, and Earliness, facilitating method comparison and new ECTS development.

Abstract

\texttt{ml\_edm} is a Python 3 library, designed for early decision making of any learning tasks involving temporal/sequential data. The package is also modular, providing researchers an easy way to implement their own triggering strategy for classification, regression or any machine learning task. As of now, many Early Classification of Time Series (ECTS) state-of-the-art algorithms, are efficiently implemented in the library leveraging parallel computation. The syntax follows the one introduce in \texttt{scikit-learn}, making estimators and pipelines compatible with \texttt{ml\_edm}. This software is distributed over the BSD-3-Clause license, source code can be found at \url{https://github.com/ML-EDM/ml_edm}.