The ALPBench Python package

The Benchmark for Active Learning Pipelines on Tabular Data ALPBench is a Python package for the specification, execution, and performance monitoring of active learning pipelines (ALP) consisting of a learning algorithm and a query strategy for real-world tabular classification tasks. It has built-in measures to ensure evaluations are done reproducibly, saving exact dataset splits and hyperparameter settings of used algorithms. In total, ALPBench consists of 86 real-world tabular classification datasets and 5 active learning settings, yielding 430 active learning problems. However, the benchmark allows for easy extension such as implementing your own learning algorithm and/or query strategy and benchmark it against existing approaches.

Contents

OVERVIEW

• Installation Guide
• Quickstart Guide

TUTORIALS

• Specify and fit an active learning pipeline
• Evaluating an active learning pipeline using the evaluator
• Run experiments with py_experimenter

API REFERENCE

• 📖 API References
  • alpbench.benchmark
  • alpbench.evaluation
  • alpbench.pipeline
  • alpbench.util

BIBLIOGRAPHY

• 📚 Bibliography