Software health reportschema 0.34.0 · metrics 2.10.0 · 2026-10-19 06:56 UTC

scikit-learn-contrib / MAPIE

A scikit-learn-compatible library for estimating prediction intervals and controlling risks, based on conformal predictions.

Jupyter Notebook · PythonBSD-3-Clause★ 1,604 stars⑂ 165 forkssince Mar 2021View on GitHub ↗

KindLibraryhow this is determined

#regression #confidence-intervals #data-science #python #sklearn #classification #conformal-prediction #risk-control

scikit-learn-contrib/MAPIE holds a health index of 97 out of 100, placing it in the Exceptional band. It scores highest on Vitality (92/100) and lowest on Security (74/100). It was last updated 2 days ago. 4 contributors account for most of its recent work.

overall / 100

Exceptional

Software health index

Metrics are grouped into weighted categories on one standardized 1–100 scale. Overall starts as their weighted mean, calibrated against the distribution of the public record so bands carry percentile meaning; when public evidence triggers the High-Risk Jurisdiction Policy, the rating is adjusted and receives an At Risk ceiling of 34.

Exceptional93-100The record's top tier (≈ top 5%); essentially all checked criteria met

Excellent80-92Strong across the board; minor gaps

Good65-79Healthy; gaps are limited and manageable

Moderate50-64Acceptable with notable gaps; review recommended

Weak35-49Material weaknesses across several areas

At Risk20-34Significant weaknesses; adoption warrants caution

Critical1-19Severe problems (abandoned, single-maintainer, no hygiene)

Score profile

Each axis is a category. The shape matters more than the average — a healthy subject fills the whole shape, while a spike-and-crater profile means strength in one dimension is masking risk in another.

The weighted overall 85 is calibrated to 97 on the published index scale (record calibration 2026-08-02).

Ownership

scikit-learn-contribOrganization

646 followers32 public repossince Feb 2016

This repository is backed by an organization — shared, accountable stewardship that can outlive any single maintainer.

Package ecosystems

Registry	Package	Version	Downloads / mo	Versions	Last publish
PyPI	MAPIE	1.5.0	150,037	39	74 days ago

Metrics by category

Vitality

Is the project alive — is code being written and are releases shipping?

92Excellent · 21% of overall

Development activity

92Excellent

How it's scored

36/36	Push recency — last push 2 days ago
28.4/36	Commit cadence — 41/52 weeks with commits
18/18	Commit volume — 244 commits in the last year
10/10	OpenSSF Scorecard: Maintained — 30 commit(s) and 7 issue activity found in the last 90 days -- score normalized to 10

Inputs used

commits_last_year	244
human_commit_share	1
days_since_last_push	2
active_weeks_last_year	41

Release discipline

92Excellent

How it's scored

27/27	Ships releases — 39 releases published
36/36	Release recency — latest release 74 days ago
19.8/27	Release cadence — a release every ~49.7 days
0/10	OpenSSF Scorecard: Signed-Releases — no data

Inputs used

releases_count	39
latest_release_tag	v1.5.0
releases_from_tags	no
days_since_latest_release	74
mean_days_between_releases	49.7

Excluded from scoring (no data or not applicable): OpenSSF Scorecard: Signed-Releases. Remaining weights renormalized.

Community & Adoption

Does the project have users, downloads, attention, and a welcoming setup for contributors?

84Excellent · 17% of overall

Popularity & adoption

76Good

How it's scored

52/60	Stars — 1,604 stars
18.5/25	Forks — 165 forks
5.6/15	Watchers — 11 watchers

Inputs used

forks	165
stars	1,604
watchers	11
growth_state	unverified
growth_factor_pct	100
growth_unverified_reason	no_history

Community health

92Excellent

How it's scored

22.5/22.5	README
22.5/22.5	License — recognized license (BSD-3-Clause)
18/18	CONTRIBUTING guide
13.5/13.5	Code of conduct
0/7.2	Issue template
6.3/6.3	PR template

Inputs used

has_readme	yes
has_license	yes
readme_badges	10
has_contributing	yes
has_issue_template	no
has_code_of_conduct	yes
readme_badge_services	codecov.io, github.com, readthedocs.org, shields.io
has_pull_request_template	yes

Ecosystem adoption (downloads)

86Excellent

How it's scored

69/80	Monthly downloads — 150,037 downloads/month across pypi
0/20	Registry dependents — not reported by this ecosystem

Inputs used

packages	MAPIE
dependents	—
ecosystems	pypi
total_downloads	—
monthly_downloads	150,037
unverified_packages_excluded	—

Excluded from scoring (no data or not applicable): Registry dependents. Remaining weights renormalized.

Sustainability & Governance

Will the project survive its people — bus factor, responsiveness, who backs it, and package upkeep?

84Excellent · 23% of overall

Maintainer resilience (bus factor)

84Excellent

How it's scored

43.2/54	Bus factor — 4 contributor(s) cover half of all commits
17.8/22.5	Commit distribution — top contributor authored 21% of commits
13.5/13.5	Contributor breadth — 58 contributors
10/10	OpenSSF Scorecard: Contributors — project has 6 contributing companies or organizations

Inputs used

bus_factor	4
contributors_sampled	58
top_contributor_share	0.211

Issue & PR responsiveness

84Excellent

How it's scored

39.5/42	Issue resolution — 94% of issues closed
26.1/30	PR acceptance — 594/682 decided PRs merged
10.4/13	Newcomer PR acceptance — 8/10 first-time contributors' PRs merged in 30d
7.5/15	OpenSSF Scorecard: Code-Review — Found 16/30 approved changesets -- score normalized to 5

Inputs used

merged_prs	594
open_issues	18
closed_issues	289
prs_merged_7d	8
prs_decided_7d	8
prs_merged_30d	10
prs_decided_30d	12
issue_closed_ratio	0.941
closed_unmerged_prs	88
first_time_authors_30d	9
first_time_prs_merged_30d	8
first_time_prs_decided_30d	10

Ownership & stewardship

73Good

How it's scored

30/30	Ownership backing — organization-owned
0/20	Verified domain
20.2/25	Owner reach — 646 followers of scikit-learn-contrib
23.1/25	Track record — 32 public repos, account ~10 yr old

Inputs used

followers	646
owner_type	Organization
is_verified	no
owner_login	scikit-learn-contrib
public_repos	32
account_age_days	3,894

Package maintenance

100Exceptional

How it's scored

25/25	Published & resolvable — 1 package(s) on pypi
35/35	Publish recency — latest publish 74 days ago
20/20	Version history — 39 published versions
20/20	Not deprecated — active, not deprecated or yanked

Inputs used

packages	MAPIE
ecosystems	pypi
any_deprecated	no
min_days_since_publish	74

Engineering Quality

Are baseline engineering and documentation practices in place?

92Excellent · 19% of overall

Engineering practices

94Exceptional

How it's scored

24/24	CI workflows — 5 workflow(s)
24/24	Tests present
16/16	Linter config — pyproject.toml ([tool.ruff])
9.6/9.6	Pre-commit hooks
0/6.4	.editorconfig
20/20	OpenSSF Scorecard: CI-Tests — 25 out of 25 merged PRs checked by a CI test -- score normalized to 10

Inputs used

has_ci	yes
has_tests	yes
has_editorconfig	no
has_linter_config	yes
has_precommit_config	yes

Documentation

90Excellent

How it's scored

30/30	README
25/25	Documentation directory
15/15	Documentation / homepage site — https://mapie.readthedocs.io/en/stable/
10/10	Repository description
10/10	Topics — 8 topics
0/10	Wiki

Inputs used

topics	regression, confidence-intervals, data-science, python, sklearn, classification, conformal-prediction, risk-control
has_wiki	no
homepage	https://mapie.readthedocs.io/en/stable/
docs_site	https://mapie.readthedocs.io/en/stable/
has_readme	yes
has_docs_dir	yes
has_description	yes

Security

Are visible security and supply-chain practices strong, without unresolved high-risk jurisdiction exposure?

74Good · 16% of overall

Security posture

68Good

How it's scored

7.5/7.5	Binary-Artifacts — no binaries found in the repo
0/7.5	Branch-Protection — no data
2.5/2.5	CI-Tests — 25 out of 25 merged PRs checked by a CI test -- score normalized to 10
0/2.5	CII-Best-Practices — no effort to earn an OpenSSF best practices badge detected
3.8/7.5	Code-Review — Found 16/30 approved changesets -- score normalized to 5
2.5/2.5	Contributors — project has 6 contributing companies or organizations
10/10	Dangerous-Workflow — no dangerous workflow patterns detected
7.5/7.5	Dependency-Update-Tool — update tool detected
0/5	Fuzzing — project is not fuzzed
2.5/2.5	License — license file detected
7.5/7.5	Maintained — 30 commit(s) and 7 issue activity found in the last 90 days -- score normalized to 10
5/5	Packaging — packaging workflow detected
0/5	Pinned-Dependencies — dependency not pinned by hash detected -- score normalized to 0
5/5	SAST — SAST tool is run on all commits
0/5	Security-Policy — security policy file not detected
0/7.5	Signed-Releases — no data
0/7.5	Token-Permissions — detected GitHub workflow tokens with excessive permissions
7.5/7.5	Vulnerabilities — 0 existing vulnerabilities detected

Inputs used

source	openssf_scorecard
checks_evaluated	16
scorecard_version	v5.5.0
checks_inconclusive	2
scorecard_aggregate	6.8

Excluded from scoring (no data or not applicable): Branch-Protection, Signed-Releases. Remaining weights renormalized.

Dependency advisories

100Exceptional

How it's scored

35/35	Direct dependencies free of known advisories — no direct dependency carries a known advisory
25/25	Indirect dependencies free of known advisories — no indirect dependency carries a known advisory
0/40	No advisories left outstanding — no advisory carries a publication date

Inputs used

source	osv
advisories	0
affected_packages	0
assessed_packages	7
unassessed_packages	0
affected_by_severity	none
direct_affected_packages	0

Excluded from scoring (no data or not applicable): No advisories left outstanding. Remaining weights renormalized. Matched the pypi:MAPIE@1.5.0 runtime dependency closure — what installing the published package pulls in — 7 packages. Reachability is not analyzed.

AI Readiness

How well is the repo equipped to be developed and maintained with AI coding agents? Carries a deliberately small weight (4%): agent tooling is a real maintenance signal, but a repository with none can still reach 100/100.

81Excellent · 4% of overall

Agent context & guidance

85Excellent

How it's scored

45/45	Agent instructions — AGENTS.md, CLAUDE.md
0/15	Machine-readable docs (llms.txt)
40/40	Legible commit history — 93 of 100 human commits state their intent (structured subject or explanatory body)

Inputs used

has_llms_txt	no
llms_txt_url	—
legible_history_share	0.93
agent_instruction_files	AGENTS.md, CLAUDE.md
agent_instruction_max_bytes	7,214

Verify loop (build / test / typecheck)

72Good

How it's scored

18/18	One-command bootstrap — Makefile
22/22	Automated tests
11/11	Lint / format config — pyproject.toml ([tool.ruff])
11/11	Static type checking — mypy.ini
0/10	Reproducible environment
10/10	Demonstrated agent practice — 18 of the last 100 commits agent-authored or agent-credited
0/8	Automated maintenance — no automated dependency updates observed
0/10	OpenSSF Scorecard: Pinned-Dependencies — dependency not pinned by hash detected -- score normalized to 0

Inputs used

has_nix	no
has_tests	yes
lockfiles	—
has_dockerfile	no
typed_language	no
bootstrap_files	Makefile
has_devcontainer	no
has_linter_config	yes
typecheck_configs	mypy.ini
agent_commit_share	0.18
toolchain_manifests	—
dependency_bot_commit_share	0

Code legibility for models

81Excellent

How it's scored

27/45	Type-checkable code — Jupyter Notebook with type-check config (mypy.ini)
54/55	Manageable file sizes — 3/163 source files over 60KB

Inputs used

primary_language	Jupyter Notebook
largest_source_bytes	100,719
source_files_sampled	163
oversized_source_files	3

Machine-readable interfaces

100Exceptional

How it's scored

0/40	API schema (OpenAPI/GraphQL/proto) — not applicable to this kind of software
0/20	MCP server — not applicable to this kind of software
40/40	Runnable examples — examples, notebooks

Inputs used

example_dirs	examples, notebooks
has_mcp_signal	no
api_schema_files	—
interfaces_expected_of	—

Excluded from scoring (no data or not applicable): API schema (OpenAPI/GraphQL/proto), MCP server. Remaining weights renormalized.

Key facts

1,604GitHub stars

58contributors

244commits, last 12 months

2days since last push

39releases

4bus factor

18open issues

PyPIpackage ecosystems

Data collection warnings

Star history unavailable: GitHub GraphQL error: Resource not accessible by personal access token

More detail

Star and fork history 0 ★ / 165 ⇿

0Stars

165Forks

39Releases

When each star and fork was added, collected from GitHub and bucketed by day. Cumulative growth sits directly above the daily additions it is made of, so the two read against each other: steady organic accretion looks nothing like an abrupt, short-lived burst. Where that difference is measurable, it is reported as growth authenticity.

Major 1Minor 13Patch 24

Each point covers 5 days.

OpenSSF Scorecard 6.8 / 10

6.8aggregate

Independent, tool-agnostic security assessment from the open-source OpenSSF Scorecard. Each check rewards a security practice, not a specific vendor's tool. Checks Scorecard could not determine are marked n/a and excluded from the security score (never counted as zero).Scorecard v5.5.0 · 2026-10-19 06:55 UTC

10	Binary-Artifacts	no binaries found in the repo
n/a	Branch-Protection	internal error: error during branchesHandler.setup: internal error: some github tokens can't read classic branch protection rules: https://github.com/ossf/scorecard-action/blob/main/docs/authentication/fine-grained-auth-token.md
10	CI-Tests	25 out of 25 merged PRs checked by a CI test -- score normalized to 10
0	CII-Best-Practices	no effort to earn an OpenSSF best practices badge detected
5	Code-Review	Found 16/30 approved changesets -- score normalized to 5
10	Contributors	project has 6 contributing companies or organizations
10	Dangerous-Workflow	no dangerous workflow patterns detected
10	Dependency-Update-Tool	update tool detected
0	Fuzzing	project is not fuzzed
10	License	license file detected
10	Maintained	30 commit(s) and 7 issue activity found in the last 90 days -- score normalized to 10
10	Packaging	packaging workflow detected
0	Pinned-Dependencies	dependency not pinned by hash detected -- score normalized to 0
10	SAST	SAST tool is run on all commits
0	Security-Policy	security policy file not detected
n/a	Signed-Releases	no releases found
0	Token-Permissions	detected GitHub workflow tokens with excessive permissions
10	Vulnerabilities	0 existing vulnerabilities detected

Direct dependencies 3

Registry	Package	Version constraint	Manifest
PyPI	numpy	`>=1.24.1`	pyproject.toml
PyPI	scikit-learn	`>=1.4`	pyproject.toml
PyPI	scipy	`>=1.10`	pyproject.toml

All dependencies 43

Full resolved dependency set from the GitHub dependency graph: 3 direct and 40 indirect (transitive) packages. The transitive closure is complete when the repository commits a lockfile.

Registry	Package	Version	Relation
PyPI	numpy	`—`	direct
PyPI	scikit-learn	`—`	direct
PyPI	scipy	`—`	direct
PyPI	albumentations	`2.0.8`	indirect
PyPI	build	`1.3.0`	indirect
PyPI	codecov	`2.1.13`	indirect
PyPI	covmetrics	`0.1.2`	indirect
PyPI	cvxpy	`1.9.1`	indirect
PyPI	huggingface-hub	`1.18.0`	indirect
PyPI	ipykernel	`6.31.0`	indirect
PyPI	jupyter	`1.1.1`	indirect
PyPI	jupyterlab	`4.5.10`	indirect
PyPI	jupytext	`1.18.1`	indirect
PyPI	lightgbm	`4.6.0`	indirect
PyPI	matplotlib	`3.9.4`	indirect
PyPI	mkdocs-gallery	`0.10.4`	indirect
PyPI	mkdocs-material	`9.7.6`	indirect
PyPI	mkdocstrings	`1.0.4`	indirect
PyPI	mkdocstrings-python	`2.0.4`	indirect
PyPI	mypy	`1.19.1`	indirect
PyPI	nbconvert	`7.17.1`	indirect
PyPI	opencv-python	`4.13.0.92`	indirect
PyPI	pandas	`2.3.3`	indirect
PyPI	plotly	`6.5.0`	indirect
PyPI	pre-commit	`4.0.1`	indirect
PyPI	pymdown-extensions	`11.0.1`	indirect
PyPI	pytest	`9.0.3`	indirect
PyPI	pytest-cov	`7.0.0`	indirect
PyPI	pytest-xdist	`3.8.0`	indirect
PyPI	pytorch-lightning	`2.6.5`	indirect
PyPI	ruff	`0.14.3`	indirect
PyPI	scikeras	`0.13.0`	indirect
PyPI	seaborn	`0.13.2`	indirect
PyPI	setuptools	`—`	indirect
PyPI	setuptools-scm	`—`	indirect
PyPI	tensorflow	`2.20.0`	indirect
PyPI	tensorflow-datasets	`4.8.3`	indirect
PyPI	torch	`2.13.0`	indirect
PyPI	tqdm	`4.68.1`	indirect
PyPI	twine	`6.2.0`	indirect
PyPI	typing-extensions	`4.15.0`	indirect
PyPI	wheel	`0.46.2`	indirect
PyPI	xgboost	`2.1.4`	indirect

Dependency advisories 0

Installing pypi:MAPIE@1.5.0 pulls in 7 packages, direct and transitive: 0 carry known advisories, of which 0 are direct dependencies.

No known advisories affect the assessed dependencies.

An advisory means the version recorded in the dependency graph falls inside an advisory’s affected range. Reachability is not analysed, and the graph includes development and test pins — a finding may concern tooling rather than shipped software.

Raw JSON report machine-readable

Feedback

Spotted something off in this report, or have thoughts to share? Wrong measurements, missed tooling, ideas, questions — anything is welcome. Every message is read and gets a response.

Scores are signals, not warranties. They reflect publicly visible practices on GitHub — not a code audit, and not a security guarantee.

Missing data is excluded and weights renormalized, never scored as zero. Methodology is versioned and open: metrics v2.10.0, schema v0.34.0 — full methodology · metrics wiki.

How one result sits in the wider record: aggregate statistics — PyPI.

Software health index

Score profile

Ownership

Package ecosystems

Metrics by category

Key facts

Data collection warnings

More detail

Feedback

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