# python-adaptive/adaptive

:chart_with_upwards_trend: Adaptive: parallel active learning of mathematical functions

Repository: https://github.com/python-adaptive/adaptive
Canonical: https://ross.abutalabs.com/products/adaptive
Homepage: http://adaptive.readthedocs.io/
Language: Python
License: BSD-3-Clause
License Family: permissive
Topics: adaptive-learning, machine-learning, python, live-plots, parallel-computing, adaptive-sampling, parallel, adaptive, active-learning
Last push: 2026-08-24T22:07:44+00:00

## Health v2 (maintenance only)
Score: 91/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 99, release rhythm 75, longevity 100
- inputs: {"age_days": 3189, "days_push": 9, "days_rel": 84, "gap_med": 55, "n_releases_24m": 8}
- flags: none
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1227, forks 63 (observed 2026-08-28T04:04:03.314772+00:00)

## What it is
Adaptive is an open-source Python library for parallel active learning of mathematical functions. It intelligently selects the most informative points in a parameter space instead of evaluating a dense grid, with support for cluster execution and live plotting.

## Use cases
- adaptively sample expensive mathematical functions in parallel
- plot functions with fewer evaluation points than a dense grid
- run adaptive function evaluations on a computing cluster
- visualize live plots of function convergence in Jupyter
- reduce simulation cost by sampling only interesting regions of parameter space

## When to choose
- each function evaluation takes roughly 50ms or more
- you need efficient sampling of 1D-ND functions without dense grids
- you want parallel evaluation across workers or a cluster with live plots

## When to avoid
- function evaluations are very fast (well under ~50ms) where sampling overhead dominates
- you need general supervised machine learning on datasets rather than function sampling

## Facets
- artifact type: library
- maturity: active
- function: machine-learning, data-science, math, benchmarking
- domain: machine-learning, mathematics, data-science
- platform: python, cross-platform
- tags: active-learning, adaptive-sampling, parallel-computing, live-plots, function-evaluation, algorithms

## Member repositories
- python-adaptive/adaptive (main) score 91

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:04:03.314772+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-30T06:15:16.465821+00:00, confidence not recorded.
  - readme: https://github.com/python-adaptive/adaptive (fetched 2026-08-28T04:04:03.314772+00:00, sha 07394ce7a354)
  - registry_pypi: https://pypi.org/pypi/adaptive/json (fetched 2026-08-29T12:23:46.946035+00:00, sha 198c298ad1a6)
- Data as of 2026-08-30T08:39:29.467469+00:00.