Public record
Software health reportschema 0.34.0 · metrics 2.10.0 · 2026-09-14 00:14 UTC

mmschlk / shapiq

Shapley Interactions and Shapley Values for Machine Learning

Python · C++MIT★ 782 stars⑂ 74 forkssince Oct 2023View on GitHub ↗
KindLibraryhow this is determined
#python#feature-interactions#explainability#interpretability#machine-learning#shap#shapley#shapiq#shapley-interactions#banzhaf-index#explainable-ai#feature-importance#game-theory#interpretable-machine-learning#feature-attribution

mmschlk/shapiq holds a health index of 91 out of 100, placing it in the Excellent band. It scores highest on Engineering Quality (94/100) and lowest on Security (63/100). It was last updated 12 days ago. A single contributor accounts for most of its recent work.

91
overall / 100
Excellent

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.

91
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)
VitalityCommunity &AdoptionSustainability &GovernanceEngineeringQualitySecurityAI Readiness

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 77 is calibrated to 91 on the published index scale (record calibration 2026-08-02).

Ownership

MaximilianPersonal account
60 followers11 public repossince Nov 2018AIML@LMU

This repository is owned by a personal account. A single-owner project carries more continuity risk than an organization-backed one.

Package ecosystems

RegistryPackageVersionDownloads / moVersionsLast publishTags
PyPIshapiq1.7.0-2417 days agopythonmachine-learninginterpretable-machine-learningshapxaiexplainable-aiinteractionshapley-interactionsshapley-valuesfeature-interaction

Metrics by category

Vitality

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

84Excellent · 21% of overall

Development activity

78Good
How it's scored
28.8/36Push recency — last push 12 days ago
20.8/36Commit cadence — 30/52 weeks with commits
18/18Commit volume — 136 commits in the last year
10/10OpenSSF Scorecard: Maintained — 30 commit(s) and 9 issue activity found in the last 90 days -- score normalized to 10
Inputs used
commits_last_year136
human_commit_share0.87
days_since_last_push12
active_weeks_last_year30

Release discipline

92Excellent
How it's scored
27/27Ships releases — 23 releases published
36/36Release recency — latest release 17 days ago
19.8/27Release cadence — a release every ~48.5 days
0/10OpenSSF Scorecard: Signed-Releases — no data
Inputs used
releases_count23
latest_release_tagv1.7.0
releases_from_tagsno
days_since_latest_release17
mean_days_between_releases48.5
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?

79Good · 17% of overall

Popularity & adoption

68Good
How it's scored
46.9/60Stars — 782 stars
15.5/25Forks — 74 forks
5.3/15Watchers — 10 watchers
Inputs used
forks74
stars782
watchers10
growth_stateunverified
growth_factor_pct100
growth_unverified_reasonno_history

Community health

92Excellent
How it's scored
22.5/22.5README
22.5/22.5License — recognized license (MIT)
18/18CONTRIBUTING guide
13.5/13.5Code of conduct
0/7.2Issue template
6.3/6.3PR template
Inputs used
has_readmeyes
has_licenseyes
readme_badges9
has_contributingyes
has_issue_templateno
has_code_of_conductyes
readme_badge_servicesbadge.fury.io, codecov.io, github.com, readthedocs.org, shields.io
has_pull_request_templateyes

Sustainability & Governance

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

64Moderate · 23% of overall

Maintainer resilience (bus factor)

39Weak
How it's scored
9/54Bus factor — 1 contributor(s) cover half of all commits
6.7/22.5Commit distribution — top contributor authored 70% of commits
13.5/13.5Contributor breadth — 28 contributors
10/10OpenSSF Scorecard: Contributors — project has 10 contributing companies or organizations
Inputs used
bus_factor1
contributors_sampled28
top_contributor_share0.702

Issue & PR responsiveness

77Good
How it's scored
38.9/42Issue resolution — 92% of issues closed
25.1/30PR acceptance — 270/323 decided PRs merged
4.3/13Newcomer PR acceptance — 2/6 first-time contributors' PRs merged in 30d
9/15OpenSSF Scorecard: Code-Review — Found 18/27 approved changesets -- score normalized to 6
Inputs used
merged_prs270
open_issues19
closed_issues233
prs_merged_7d0
prs_decided_7d0
prs_merged_30d14
prs_decided_30d18
issue_closed_ratio0.925
closed_unmerged_prs53
first_time_authors_30d4
first_time_prs_merged_30d2
first_time_prs_decided_30d6

Ownership & stewardship

53Moderate
How it's scored
10/30Ownership backing — personal (user) account
0/20Verified domain — not applicable to user accounts
12.8/25Owner reach — 60 followers of mmschlk
19.9/25Track record — 11 public repos, account ~7 yr old
Inputs used
followers60
owner_typeUser
is_verified—
owner_loginmmschlk
public_repos11
account_age_days2,858
Excluded from scoring (no data or not applicable): Verified domain. Remaining weights renormalized.

Package maintenance

100Exceptional
How it's scored
25/25Published & resolvable — 1 package(s) on pypi
35/35Publish recency — latest publish 17 days ago
20/20Version history — 24 published versions
20/20Not deprecated — active, not deprecated or yanked
Inputs used
packagesshapiq
ecosystemspypi
any_deprecatedno
min_days_since_publish17

Engineering Quality

Are baseline engineering and documentation practices in place?

94Exceptional · 19% of overall

Engineering practices

90Excellent
How it's scored
24/24CI workflows — 3 workflow(s)
24/24Tests present
16/16Linter config — pyproject.toml ([tool.ruff])
9.6/9.6Pre-commit hooks
0/6.4.editorconfig
16/20OpenSSF Scorecard: CI-Tests — 22 out of 25 merged PRs checked by a CI test -- score normalized to 8
Inputs used
has_ciyes
has_testsyes
has_editorconfigno
has_linter_configyes
has_precommit_configyes

Documentation

100Exceptional
How it's scored
30/30README
25/25Documentation directory
15/15Documentation / homepage site — https://shapiq.readthedocs.io
10/10Repository description
10/10Topics — 15 topics
10/10Wiki
Inputs used
topicspython, feature-interactions, explainability, interpretability, machine-learning, shap, shapley, shapiq, shapley-interactions, banzhaf-index, explainable-ai, feature-importance, game-theory, interpretable-machine-learning, feature-attribution
has_wikiyes
homepagehttps://shapiq.readthedocs.io
docs_sitehttps://shapiq.readthedocs.io
has_readmeyes
has_docs_diryes
has_descriptionyes

Security

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

63Moderate · 16% of overall

Security posture

54Moderate
How it's scored
7.5/7.5Binary-Artifacts — no binaries found in the repo
3.8/7.5Branch-Protection — branch protection is not maximal on development and all release branches
2/2.5CI-Tests — 22 out of 25 merged PRs checked by a CI test -- score normalized to 8
0/2.5CII-Best-Practices — no effort to earn an OpenSSF best practices badge detected
4.5/7.5Code-Review — Found 18/27 approved changesets -- score normalized to 6
2.5/2.5Contributors — project has 10 contributing companies or organizations
10/10Dangerous-Workflow — no dangerous workflow patterns detected
7.5/7.5Dependency-Update-Tool — update tool detected
0/5Fuzzing — project is not fuzzed
2.5/2.5License — license file detected
7.5/7.5Maintained — 30 commit(s) and 9 issue activity found in the last 90 days -- score normalized to 10
5/5Packaging — packaging workflow detected
0/5Pinned-Dependencies — dependency not pinned by hash detected -- score normalized to 0
0/5SAST — SAST tool is not run on all commits -- score normalized to 0
0/5Security-Policy — security policy file not detected
0/7.5Signed-Releases — no data
0/7.5Token-Permissions — detected GitHub workflow tokens with excessive permissions
0/7.5Vulnerabilities — 88 existing vulnerabilities detected
Inputs used
sourceopenssf_scorecard
checks_evaluated17
scorecard_versionv5.5.0
checks_inconclusive1
scorecard_aggregate5.4
Excluded from scoring (no data or not applicable): Signed-Releases. Remaining weights renormalized.

Dependency advisories

100Exceptional
How it's scored
35/35Direct dependencies free of known advisories — no direct dependency carries a known advisory
25/25Indirect dependencies free of known advisories — no indirect dependency carries a known advisory
0/40No advisories left outstanding — no advisory carries a publication date
Inputs used
sourceosv
advisories0
affected_packages0
assessed_packages26
unassessed_packages0
affected_by_severitynone
direct_affected_packages0
Excluded from scoring (no data or not applicable): No advisories left outstanding. Remaining weights renormalized. Matched the pypi:shapiq@1.7.0 runtime dependency closure — what installing the published package pulls in — 26 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.

88Excellent · 4% of overall

Agent context & guidance

84Excellent
How it's scored
45/45Agent instructions — AGENTS.md, CLAUDE.md
0/15Machine-readable docs (llms.txt)
39.2/40Legible commit history — 64 of 87 human commits state their intent (structured subject or explanatory body)
Inputs used
has_llms_txtno
llms_txt_url—
legible_history_share0.736
agent_instruction_filesAGENTS.md, CLAUDE.md
agent_instruction_max_bytes2,746

Verify loop (build / test / typecheck)

90Excellent
How it's scored
18/18One-command bootstrap — docs/Makefile
22/22Automated tests
11/11Lint / format config — pyproject.toml ([tool.ruff])
11/11Static type checking — src/shapiq/py.typed
10/10Reproducible environment — lockfile
10/10Demonstrated agent practice — 18 of the last 100 commits agent-authored or agent-credited
8/8Automated maintenance — 13 of the last 100 commits are automated dependency updates
0/10OpenSSF Scorecard: Pinned-Dependencies — dependency not pinned by hash detected -- score normalized to 0
Inputs used
has_nixno
has_testsyes
lockfilesuv.lock
has_dockerfileno
typed_languageno
bootstrap_filesdocs/Makefile
has_devcontainerno
has_linter_configyes
typecheck_configssrc/shapiq/py.typed
agent_commit_share0.18
toolchain_manifests—
dependency_bot_commit_share0.13

Code legibility for models

82Excellent
How it's scored
27/45Type-checkable code — Python with type-check config (src/shapiq/py.typed)
54.7/55Manageable file sizes — 2/377 source files over 60KB
Inputs used
primary_languagePython
largest_source_bytes105,954
source_files_sampled377
oversized_source_files2

Machine-readable interfaces

100Exceptional
How it's scored
0/40API schema (OpenAPI/GraphQL/proto) — not applicable to this kind of software
0/20MCP server — not applicable to this kind of software
40/40Runnable examples — examples
Inputs used
example_dirsexamples
has_mcp_signalno
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

782GitHub stars
28contributors
136commits, last 12 months
12days since last push
23releases
1bus factor
19open issues
PyPIpackage ecosystems

Data collection warnings

  • Star history unavailable: GitHub GraphQL error: Resource not accessible by personal access token
  • pypi download statistics for 'shapiq' unavailable this scan (stats endpoint failed after retries); adoption may be under-evidenced

More detail

Star and fork history 0 ★ / 74 ⇿
0Stars
74Forks
20Releases

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.

01325385063757162024-022025-062026-09
Major 1Minor 7Patch 9

Each point covers 3 days.

OpenSSF Scorecard 5.4 / 10
5.4aggregate

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 · 2026-09-14 00:14 UTC

10Binary-Artifactsno binaries found in the repo
5Branch-Protectionbranch protection is not maximal on development and all release branches
8CI-Tests22 out of 25 merged PRs checked by a CI test -- score normalized to 8
0CII-Best-Practicesno effort to earn an OpenSSF best practices badge detected
6Code-ReviewFound 18/27 approved changesets -- score normalized to 6
10Contributorsproject has 10 contributing companies or organizations
10Dangerous-Workflowno dangerous workflow patterns detected
10Dependency-Update-Toolupdate tool detected
0Fuzzingproject is not fuzzed
10Licenselicense file detected
10Maintained30 commit(s) and 9 issue activity found in the last 90 days -- score normalized to 10
10Packagingpackaging workflow detected
0Pinned-Dependenciesdependency not pinned by hash detected -- score normalized to 0
0SASTSAST tool is not run on all commits -- score normalized to 0
0Security-Policysecurity policy file not detected
n/aSigned-Releasesno releases found
0Token-Permissionsdetected GitHub workflow tokens with excessive permissions
0Vulnerabilities88 existing vulnerabilities detected
Direct dependencies 11
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PyPImatplotlib—pyproject.toml
PyPInetworkx—pyproject.toml
PyPIcolour—pyproject.toml
PyPIpillow—pyproject.toml
All dependencies 272

Full resolved dependency set from the GitHub dependency graph: 11 direct and 261 indirect (transitive) packages. The transitive closure is complete when the repository commits a lockfile.

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PyPItifffile2026.3.3indirect
PyPItinycss21.4.0indirect
PyPItokenizers0.22.2indirect
PyPItoolz1.1.0indirect
PyPItorch2.9.1indirect
PyPItorchvision0.24.1indirect
PyPItornado6.5.5indirect
PyPItraitlets5.14.3indirect
PyPItransformers5.3.0indirect
PyPItreelite4.7.1indirect
PyPItriton3.5.1indirect
PyPIty0.0.21indirect
PyPItyper0.24.1indirect
PyPItyping-extensions4.15.0indirect
PyPItyping-inspect0.9.0indirect
PyPItyping-inspection0.4.2indirect
PyPItzdata2025.3indirect
PyPIuri-template1.3.0indirect
PyPIurllib32.6.3indirect
PyPIvirtualenv21.2.0indirect
PyPIwadler-lindig0.1.7indirect
PyPIwcwidth0.6.0indirect
PyPIwebcolors25.10.0indirect
PyPIwebencodings0.5.1indirect
PyPIwebsocket-client1.9.0indirect
PyPIwheel0.46.3indirect
PyPIwidgetsnbextension4.0.15indirect
PyPIwoodelf-explainer0.4.6indirect
PyPIwrapt2.1.2indirect
PyPIxgboost3.2.0indirect
PyPIzict3.0.0indirect
Dependency advisories 0

Installing pypi:shapiq@1.7.0 pulls in 26 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.

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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.