benchpots

What it is and what it does

BenchPOTS is a benchmarking toolkit for evaluating machine learning algorithms on partially-observed time series (POTS)—datasets with missing or irregularly sampled values. It provides unified preprocessing pipelines for standard POTS datasets and a suite of evaluation tasks to measure algorithm performance on problems like imputation, classification, clustering, and forecasting.

The package wraps datasets like PhysioNet2012 and applies configurable missing-data patterns (e.g., MCAR with specified rates) to create reproducible benchmarks. It depends on torch, scikit-learn, pandas, numpy, and h5py for core functionality, plus specialized time-series libraries (tsdb, pygrinder, nonlinear_benchmarks). It targets researchers and practitioners developing or comparing algorithms for incomplete time series.

Use it for:

• Evaluate imputation algorithms on standard POTS datasets with controlled missing-data rates.
• Benchmark time-series forecasting models on partially-observed data from medical or sensor domains.
• Compare classification and clustering performance across multiple incomplete time-series datasets.
• Preprocess and standardize POTS datasets for reproducible machine learning research.
• Validate new time-series algorithms against established benchmarks before publication.

Worth the install?

AI-flagged interpretation of the facts on this page — verify before relying

BenchPOTS provides standardized preprocessing pipelines and evaluation tasks for benchmarking machine learning algorithms on partially-observed time series datasets.

Yes, if you are developing or comparing machine learning algorithms for partially-observed time series. The package offers low install friction, active maintenance, permissive licensing, and no known vulnerabilities. Install with caution if you have limited disk or memory—the 8 runtime dependencies (especially torch) are substantial. Not necessary for one-off time-series tasks; best suited for research and systematic benchmarking.

Install

benchpots on PyPI

pip install benchpots

uv add benchpots

poetry add benchpots

Installing benchpots

Before you install

Low install friction with a pure Python wheel. Active maintenance with a recent commit on 2026-08-03. Depends on established scientific libraries (numpy, pandas, scikit-learn, torch, h5py) plus specialized time-series packages (tsdb, pygrinder, nonlinear_benchmarks).

License in practice

BSD License (permissive) allows commercial and private use with minimal restrictions—only requires retaining copyright notice and disclaimer in distributions.

Quickstart

pip install benchpots

import benchpots
benchpots.datasets.preprocess_physionet2012(subset="all", rate=0.1)

Requires Python >=3.8; torch and h5py may need system-level dependencies (CUDA, HDF5 libraries) depending on your environment.

Verify before relying

• Whether all 8 runtime dependencies (h5py, numpy, pandas, scikit-learn, torch, tsdb, pygrinder, nonlinear_benchmarks) are required for basic usage or only for specific datasets/tasks.
• Whether the package includes pre-downloaded datasets or requires separate data acquisition.
• Performance characteristics and memory footprint when working with large-scale time series.

Package facts

Evidence: benchpots-1-py3-none-any.whl

Keywords: data mining, benchmark, neural networks, machine learning, deep learning, artificial intelligence, time-series analysis, time series, imputation, classification, clustering, forecasting, partially observed, irregular sampled, partially-observed time series, incomplete time series, missing data, missing values

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Further reading