Public record
Software health reportschema 0.34.0 · metrics 2.10.0 · 2026-10-19 03:05 UTC

csinva / imodels

Interpretable ML package 🔍 for concise, transparent, and accurate predictive modeling (sklearn-compatible).

Jupyter Notebook · PythonMIT★ 1,629 stars⑂ 148 forkssince Jul 2019View on GitHub ↗
KindLibraryhow this is determined
#interpretability#machine-learning#data-science#artificial-intelligence#ml#ai#statistics#scikit-learn#python#optimal-classification-tree#rulefit#imodels#rule-learning#supervised-learning#explainable-ml#explainable-ai#rules#bayesian-rule-list

csinva/imodels holds a health index of 83 out of 100, placing it in the Excellent band. It scores highest on Vitality (75/100) and lowest on AI Readiness (55/100). It was last updated 2 days ago. A single contributor accounts for most of its recent work.

83
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.

83
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 70 is calibrated to 83 on the published index scale (record calibration 2026-08-02).

Ownership

Chandan SinghPersonal account
541 followers79 public repossince Jul 2013Staff research scientist

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 publish
PyPIimodels3.0.428,6926012 days ago

Metrics by category

Vitality

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

75Good · 21% of overall

Development activity

74Good
How it's scored
36/36Push recency — last push 2 days ago
9.7/36Commit cadence — 14/52 weeks with commits
18/18Commit volume — 377 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_year377
human_commit_share0.96
days_since_last_push2
active_weeks_last_year14

Release discipline

76Good
How it's scored
27/27Ships releases — 42 releases published
36/36Release recency — latest release 12 days ago
12.6/27Release cadence — a release every ~121.9 days
0/10OpenSSF Scorecard: Signed-Releases — Project has not signed or included provenance with any releases.
Inputs used
releases_count42
latest_release_tagv3.0.4
releases_from_tagsno
days_since_latest_release12
mean_days_between_releases121.9

Community & Adoption

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

74Good · 17% of overall

Popularity & adoption

77Good
How it's scored
52.1/60Stars — 1,629 stars
18.1/25Forks — 148 forks
7/15Watchers — 19 watchers
Inputs used
forks148
stars1,629
watchers19
growth_stateunverified
growth_factor_pct100
growth_unverified_reasonno_history

Community health

70Good
How it's scored
22.5/22.5README
22.5/22.5License — recognized license (MIT)
18/18CONTRIBUTING guide
0/13.5Code of conduct
0/7.2Issue template
0/6.3PR template
Inputs used
has_readmeyes
has_licenseyes
readme_badges5
has_contributingyes
has_issue_templateno
has_code_of_conductno
readme_badge_servicesgithub.com, shields.io
has_pull_request_templateno

Ecosystem adoption (downloads)

74Good
How it's scored
59.4/80Monthly downloads — 28,692 downloads/month across pypi
0/20Registry dependents — not reported by this ecosystem
Inputs used
packagesimodels
dependents—
ecosystemspypi
total_downloads—
monthly_downloads28,692
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?

71Good · 23% of overall

Maintainer resilience (bus factor)

41Weak
How it's scored
9/54Bus factor — 1 contributor(s) cover half of all commits
8.4/22.5Commit distribution — top contributor authored 63% of commits
13.5/13.5Contributor breadth — 25 contributors
10/10OpenSSF Scorecard: Contributors — project has 9 contributing companies or organizations
Inputs used
bus_factor1
contributors_sampled25
top_contributor_share0.628

Issue & PR responsiveness

86Excellent
How it's scored
40/42Issue resolution — 95% of issues closed
28.6/30PR acceptance — 198/208 decided PRs merged
13/13Newcomer PR acceptance — 3/3 first-time contributors' PRs merged in 30d
4.5/15OpenSSF Scorecard: Code-Review — Found 3/8 approved changesets -- score normalized to 3
Inputs used
merged_prs198
open_issues5
closed_issues99
prs_merged_7d5
prs_decided_7d5
prs_merged_30d12
prs_decided_30d12
issue_closed_ratio0.952
closed_unmerged_prs10
first_time_authors_30d3
first_time_prs_merged_30d3
first_time_prs_decided_30d3

Ownership & stewardship

68Good
How it's scored
10/30Ownership backing — personal (user) account
0/20Verified domain — not applicable to user accounts
19.7/25Owner reach — 541 followers of csinva
25/25Track record — 79 public repos, account ~13 yr old
Inputs used
followers541
owner_typeUser
is_verified—
owner_logincsinva
public_repos79
account_age_days4,851
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 12 days ago
20/20Version history — 60 published versions
20/20Not deprecated — active, not deprecated or yanked
Inputs used
packagesimodels
ecosystemspypi
any_deprecatedno
min_days_since_publish12

Engineering Quality

Are baseline engineering and documentation practices in place?

75Good · 19% of overall

Engineering practices

58Moderate
How it's scored
24/24CI workflows — 1 workflow(s)
24/24Tests present
0/16Linter config
0/9.6Pre-commit hooks
0/6.4.editorconfig
10/20OpenSSF Scorecard: CI-Tests — 4 out of 7 merged PRs checked by a CI test -- score normalized to 5
Inputs used
has_ciyes
has_testsyes
has_editorconfigno
has_linter_configno
has_precommit_configno

Documentation

100Exceptional
How it's scored
30/30README
25/25Documentation directory
15/15Documentation / homepage site — https://csinva.io/imodels
10/10Repository description
10/10Topics — 18 topics
10/10Wiki
Inputs used
topicsinterpretability, machine-learning, data-science, artificial-intelligence, ml, ai, statistics, scikit-learn, python, optimal-classification-tree, rulefit, imodels, rule-learning, supervised-learning, explainable-ml, explainable-ai, rules, bayesian-rule-list
has_wikiyes
homepagehttps://csinva.io/imodels
docs_sitehttps://csinva.io/imodels
has_readmeyes
has_docs_diryes
has_descriptionyes

Security

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

55Moderate · 16% of overall

Security posture

44Weak
How it's scored
7.5/7.5Binary-Artifacts — no binaries found in the repo
0/7.5Branch-Protection — no data
1.2/2.5CI-Tests — 4 out of 7 merged PRs checked by a CI test -- score normalized to 5
0/2.5CII-Best-Practices — no effort to earn an OpenSSF best practices badge detected
2.2/7.5Code-Review — Found 3/8 approved changesets -- score normalized to 3
2.5/2.5Contributors — project has 9 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
0/5Packaging — no data
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 — Project has not signed or included provenance with any releases.
0/7.5Token-Permissions — detected GitHub workflow tokens with excessive permissions
0/7.5Vulnerabilities — 67 existing vulnerabilities detected
Inputs used
sourceopenssf_scorecard
checks_evaluated16
scorecard_versionv5.5.0
checks_inconclusive2
scorecard_aggregate4.4
Excluded from scoring (no data or not applicable): Branch-Protection, Packaging. 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_packages25
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:imodels@3.0.4 runtime dependency closure — what installing the published package pulls in — 25 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.

55Moderate · 4% of overall

Agent context & guidance

40Weak
How it's scored
0/45Agent instructions — no CLAUDE.md / AGENTS.md / editor rules
0/15Machine-readable docs (llms.txt)
40/40Legible commit history — 83 of 96 human commits state their intent (structured subject or explanatory body)
Inputs used
has_llms_txtno
llms_txt_url—
legible_history_share0.865
agent_instruction_files—
agent_instruction_max_bytes—

Verify loop (build / test / typecheck)

50Moderate
How it's scored
0/18One-command bootstrap
22/22Automated tests
0/11Lint / format config
0/11Static type checking
10/10Reproducible environment — lockfile
10/10Demonstrated agent practice — 72 of the last 100 commits agent-authored or agent-credited
8/8Automated maintenance — 4 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_files—
has_devcontainerno
has_linter_configno
typecheck_configs—
agent_commit_share0.72
toolchain_manifests—
dependency_bot_commit_share0.04

Code legibility for models

54Moderate
How it's scored
0/45Type-checkable code — Jupyter Notebook without a type-check config
54.3/55Manageable file sizes — 3/252 source files over 60KB
Inputs used
primary_languageJupyter Notebook
largest_source_bytes195,094
source_files_sampled252
oversized_source_files3

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 — notebooks
Inputs used
example_dirsnotebooks
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

1,629GitHub stars
25contributors
377commits, last 12 months
2days since last push
42releases
1bus factor
5open 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 ★ / 148 ⇿
0Stars
148Forks
41Releases

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.

025507510012515014582019-082023-032026-10
Major 2Minor 4Patch 34

Each point covers 7 days.

OpenSSF Scorecard 4.4 / 10
4.4aggregate

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 · 2026-10-19 03:05 UTC

10Binary-Artifactsno binaries found in the repo
n/aBranch-Protectioninternal 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
5CI-Tests4 out of 7 merged PRs checked by a CI test -- score normalized to 5
0CII-Best-Practicesno effort to earn an OpenSSF best practices badge detected
3Code-ReviewFound 3/8 approved changesets -- score normalized to 3
10Contributorsproject has 9 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
n/aPackagingpackaging workflow not 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
0Signed-ReleasesProject has not signed or included provenance with any releases.
0Token-Permissionsdetected GitHub workflow tokens with excessive permissions
0Vulnerabilities67 existing vulnerabilities detected
Direct dependencies 8
RegistryPackageVersion constraintManifest
PyPImatplotlib—pyproject.toml
PyPImlxtend—pyproject.toml
PyPInumpy—pyproject.toml
PyPIpandas—pyproject.toml
PyPIrequests—pyproject.toml
PyPIscipy—pyproject.toml
PyPIscikit-learn—pyproject.toml
PyPItqdm—pyproject.toml
All dependencies 235

Full resolved dependency set from the GitHub dependency graph: 13 direct and 222 indirect (transitive) packages. The transitive closure is complete when the repository commits a lockfile.

RegistryPackageVersionRelation
PyPImatplotlib3.10.5direct
PyPImatplotlib3.2.2direct
PyPImlxtend0.23.4direct
PyPInumpy2.0.2direct
PyPInumpy2.2.6direct
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PyPIpandas2.3.2direct
PyPIrequests2.32.5direct
PyPIscikit-learn1.5.2direct
PyPIscipy1.13.1direct
PyPIscipy1.15.3direct
PyPIscipy1.16.1direct
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PyPIjson50.12.1indirect
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PyPIjupyterlab-pygments0.3.0indirect
PyPIjupyterlab-server2.28.0indirect
PyPIjupyterlab-widgets3.0.15indirect
PyPIjupytext1.17.2indirect
PyPIkiwisolver1.4.7indirect
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PyPImarkdown3.8.2indirect
PyPImarkdown-it-py3.0.0indirect
PyPImarkdown-it-py4.0.0indirect
PyPImarkupsafe3.0.2indirect
PyPImatplotlib-inline0.1.7indirect
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PyPImdurl0.1.2indirect
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PyPInvidia-cuda-runtime-cu1212.8.90indirect
PyPInvidia-cudnn-cu129.10.2.21indirect
PyPInvidia-cufft-cu1211.3.3.83indirect
PyPInvidia-cufile-cu121.13.1.3indirect
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PyPIpygments2.19.2indirect
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PyPIpysocks1.7.1indirect
PyPIpytest8.4.1indirect
PyPIpytest-cov6.2.1indirect
PyPIpytest-xdist3.8.0indirect
PyPIpython-dateutil2.9.0.post0indirect
PyPIpython-json-logger3.3.0indirect
PyPIpytz2025.2indirect
PyPIpywin32311indirect
PyPIpywinpty3.0.0indirect
PyPIpyyaml6.0.2indirect
PyPIpyzmq27.0.2indirect
PyPIreferencing0.36.2indirect
PyPIretrying1.4.2indirect
PyPIrfc3339-validator0.1.4indirect
PyPIrfc3986-validator0.1.1indirect
PyPIrfc3987-syntax1.1.0indirect
PyPIrpds-py0.27.0indirect
PyPIsalib1.4.8indirect
PyPIsalib1.5.1indirect
PyPIscs3.2.8indirect
PyPIseaborn0.12.2indirect
PyPIseaborn0.13.2indirect
PyPIsend2trash1.8.3indirect
PyPIsetuptools80.9.0indirect
PyPIshap0.46.0indirect
PyPIsix1.17.0indirect
PyPIslicer0.0.8indirect
PyPIslurmpy0.0.8indirect
PyPIsniffio1.3.1indirect
PyPIsoupsieve2.10indirect
PyPIsoupsieve2.8.4indirect
PyPIstack-data0.6.3indirect
PyPIstatsmodels0.14.5indirect
PyPIsympy1.14.0indirect
PyPIterminado0.18.1indirect
PyPIthreadpoolctl3.6.0indirect
PyPItinycss21.4.0indirect
PyPItomli2.2.1indirect
PyPItorch2.8.0indirect
PyPItornado6.5.10indirect
PyPItraitlets5.14.3indirect
PyPItriton3.4.0indirect
PyPItypes-python-dateutil2.9.0.20250809indirect
PyPItyping-extensions4.14.1indirect
PyPItzdata2025.2indirect
PyPIuri-template1.3.0indirect
PyPIurllib32.5.0indirect
PyPIwcwidth0.2.13indirect
PyPIwebcolors24.11.1indirect
PyPIwebencodings0.5.1indirect
PyPIwebsocket-client1.8.0indirect
PyPIwerkzeug3.0.6indirect
PyPIwidgetsnbextension4.0.14indirect
PyPIzipp3.23.0indirect
PyPIzope-event5.1.1indirect
PyPIzope-interface7.2indirect
Dependency advisories 0

Installing pypi:imodels@3.0.4 pulls in 25 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

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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.