haphazard: A unified library and benchmark for online learning under varying feature availability

Haphazard inputs arise in online learning when feature availability varies across instances and the complete feature space is unknown in advance. However, the landscape of haphazard inputs research has been fragmented due to inconsistent data preprocessing, non-uniform evaluation metrics, limited coverage of streaming regimes and normalization, and model specific experimental code, making direct comparison across existing methods unreliable. We present haphazard, an open source Python library for reproducible benchmarking of haphazard input methods. The library provides an order preserving data pipeline with streaming normalization, unified evaluation, four streaming regimes, and shared interfaces for datasets, models, normalizers, and metrics. The current release supports 22 datasets, 11 model implementations, 5 normalization strategies, and 7 evaluation metrics, enabling systematic benchmarking across datasets and stream settings. By consolidating the experimental pipeline and exposing extensible interfaces for datasets, models, normalizers, metrics, and streaming regimes, haphazard provides a common reference point that can grow with future research on haphazard inputs. The anonymized source code is available at https://anonymous.4open.science/r/haphazard, and the PyPI package and docker container will be released publicly upon acceptance.