# HIPS/Spearmint

Spearmint Bayesian optimization codebase

Repository: https://github.com/HIPS/Spearmint
Canonical: https://ross.abutalabs.com/products/spearmint
Language: Python
License: NOASSERTION
License Family: other
Last push: 2019-12-27T21:30:17+00:00

## Health v2 (maintenance only)
Score: 32/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 0, release rhythm 35, longevity 100
- inputs: {"age_days": 4411, "days_push": 2441, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases, no_license
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1572, forks 326 (observed 2026-08-28T04:05:05.535512+00:00)

## What it is
Spearmint is a Python package for Bayesian optimization that automatically tunes experiment parameters to minimize an objective in as few runs as possible. It implements algorithms from NeurIPS/ICML publications including multi-task and constrained Bayesian optimization, using MongoDB to track experiment state.

## Use cases
- optimize hyperparameters of a machine learning model
- minimize an expensive black-box objective function
- run experiments that iteratively adjust parameters automatically
- perform multi-task Bayesian optimization
- optimize functions with unknown constraints
- tune noisy objective functions with Gaussian processes

## When to choose
- you need Bayesian optimization for academic or non-commercial research
- you want a well-cited, publication-backed optimizer with multi-task and constrained variants
- you are comfortable running MongoDB as the experiment backend

## When to avoid
- your use is commercial - the license restricts to academic and non-commercial research
- you need actively maintained software or Python 3 support
- you want a lightweight optimizer without a MongoDB dependency

## Facets
- artifact type: library
- maturity: maintenance
- function: machine-learning, benchmarking, data-science
- domain: machine-learning, artificial-intelligence
- platform: python
- tags: bayesian-optimization, hyperparameter-optimization, gaussian-processes, experiment-runner, mongodb, non-commercial-license, algorithms, linux, macos

## Member repositories
- HIPS/Spearmint (main) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:05:05.535512+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 2026-08-30T03:58:05.044998+00:00, confidence not recorded.
  - readme: https://github.com/HIPS/Spearmint (fetched 2026-08-28T04:05:05.535512+00:00, sha 82664273a73b)
- Data as of 2026-08-30T08:39:29.467469+00:00.