公开记录中等
软件健康报告模式 0.23.0 · 指标 1.13.0 · 2026-07-21 18:25 UTC
google-research / tabfm
TabFM (Tabular Foundation Model) is a pretrained tabular foundation model developed by Google Research for tabular data regression and classification.
google-research/tabfm 的健康指数为 100 分中的 68 分，处于「中等」区间。 其得分最高的类别是Community & Adoption（74/100），最低的是AI Readiness（41/100）。 最近一次更新在今天。 近期的大部分工作由 1 位贡献者完成。
68
总分 / 100软件健康指数
指标归入加权类别，统一采用 1–100 量表。总体分先取类别加权平均；当公开证据触发高风险司法辖区政策时，评级会按政策调整，并设置 49（有风险）的上限。AI 就绪度不计入总体分。
68
优秀85-100堪称典范；基本满足所有检验标准
良好70-84健康；仅有轻微不足
中等50-69可接受，但存在明显不足；建议进行审查
存在风险30-49存在重大薄弱环节；采用时应保持审慎
危急1-29问题严重（项目被弃置、仅有单一维护者、缺乏基本工程规范）
评分画像
每条轴代表一个类别。形状比平均值更重要——健康的对象会填满整个图形，而“一峰一谷”式画像意味着某一维度的优势正掩盖另一维度的风险。
所有权
软件包生态系统
| 注册表 | 软件包 | 版本 | 月下载量 | 版本数 | 最近发布 |
|---|---|---|---|---|---|
| PyPI | tabfm | 1.0.1 | - | 2 | 0 天前 |
按类别列示的指标
活力
项目是否仍有生命——是否仍在编写代码，是否仍在发布版本？
69
开发活跃度
59中等
评分方式
| 36/36 | 推送新近度 — 最近一次推送于 0 天前 |
| 4.8/36 | 提交节奏 — 52 周中有 7 周有提交 |
| 18/18 | 提交量 — 最近一年 110 次提交 |
| 0/10 | OpenSSF Scorecard：Maintained — project was created within the last 90 days. Please review its contents carefully |
所用输入
| commits_last_year | 110 |
| human_commit_share | 0.98 |
| days_since_last_push | 0 |
| active_weeks_last_year | 7 |
发布纪律
84良好
评分方式
| 27/27 | 有发布版本 — 已发布 1 个发布版本 |
| 36/36 | 发布时效 — 最近一次发布版本于 0 天前 |
| 12.6/27 | 发布节奏 — 节奏未知（仅一次发布） |
| 0/10 | OpenSSF Scorecard：Signed-Releases — 无数据 |
所用输入
| releases_count | 1 |
| latest_release_tag | v1.0.1 |
| releases_from_tags | 否 |
| days_since_latest_release | 0 |
| mean_days_between_releases | — |
已排除计分（无数据或不适用）：OpenSSF Scorecard：Signed-Releases。 其余权重已重新归一化。
社区与采用
项目是否拥有用户、下载量与关注度，并具备欢迎贡献者参与的配置？
74
流行度与采用
77良好
评分方式
| 53.5/60 | 星标 — 1,988 个星标 |
| 19.2/25 | 复刻 — 200 个复刻 |
| 3.9/15 | 关注者 — 6 位关注者 |
所用输入
| forks | 200 |
| stars | 1,988 |
| watchers | 6 |
| growth_state | unverified |
| growth_factor_pct | 100 |
| growth_unverified_reason | window_too_short |
社区健康
70良好
评分方式
| 22.5/22.5 | README |
| 22.5/22.5 | 许可证 — 可识别的许可证（Apache-2.0） |
| 18/18 | CONTRIBUTING 指南 |
| 0/13.5 | 行为准则 |
| 0/7.2 | 议题模板 |
| 0/6.3 | PR 模板 |
所用输入
| has_readme | 是 |
| has_license | 是 |
| has_contributing | 是 |
| has_issue_template | 否 |
| has_code_of_conduct | 否 |
| has_pull_request_template | 否 |
可持续性与治理
项目能否在其成员之外延续——巴士系数、响应能力、由谁支持，以及软件包的维护状况？
63
维护者韧性（巴士系数）
33存在风险
评分方式
| 9/54 | 巴士系数 — 1 位贡献者贡献了半数提交 |
| 7.1/22.5 | 提交分布 — 头号贡献者编写了 68% 的提交 |
| 13.5/13.5 | 贡献者广度 — 12 位贡献者 |
| 3/10 | OpenSSF Scorecard：Contributors — project has 1 contributing companies or organizations -- score normalized to 3 |
所用输入
| bus_factor | 1 |
| contributors_sampled | 12 |
| top_contributor_share | 0.683 |
议题与 PR 响应能力
59中等
评分方式
| 9.9/46.8 | 议题解决 — 21% 的议题已关闭 |
| 33.8/38.3 | PR 接受 — 已裁定的 PR 中 38/43 已合并 |
| 15/15 | OpenSSF Scorecard：Code-Review — all changesets reviewed |
所用输入
| merged_prs | 38 |
| open_issues | 15 |
| closed_issues | 4 |
| issue_closed_ratio | 0.211 |
| closed_unmerged_prs | 5 |
所有权与托管责任
80良好
评分方式
| 30/30 | 所有权背书 — 组织持有 |
| 0/20 | 已验证域名 |
| 25/25 | 所有者影响力 — google-research 有 16,705 位关注者 |
| 25/25 | 既往记录 — 350 个公开仓库，账户约 7 年 |
所用输入
| followers | 16,705 |
| owner_type | Organization |
| is_verified | — |
| owner_login | google-research |
| public_repos | 350 |
| account_age_days | 2,847 |
软件包维护
92优秀
评分方式
| 25/25 | 已发布且可解析 — pypi 上有 1 个软件包 |
| 35/35 | 发布时效 — 最近一次发布于 0 天前 |
| 12/20 | 版本历史 — 2 个已发布版本 |
| 20/20 | 未被弃用 — 活跃，未被弃用或撤回 |
所用输入
| packages | tabfm |
| ecosystems | pypi |
| any_deprecated | 否 |
| min_days_since_publish | 0 |
工程质量
基础的工程与文档实践是否到位？
72
工程实践
84良好
评分方式
| 24/24 | CI 工作流 — 1 个工作流 |
| 24/24 | 存在测试 |
| 16/16 | Linter 配置 — .pylintrc |
| 0/9.6 | Pre-commit 钩子 |
| 0/6.4 | .editorconfig |
| 20/20 | OpenSSF Scorecard：CI-Tests — 14 out of 14 merged PRs checked by a CI test -- score normalized to 10 |
所用输入
| has_ci | 是 |
| has_tests | 是 |
| has_editorconfig | 否 |
| has_linter_config | 是 |
| has_precommit_config | 否 |
文档
55中等
评分方式
| 30/30 | README |
| 0/25 | 文档目录 |
| 15/15 | 文档 / 主页站点 — https://research.google/blog/introducing-tabfm-a-zero-shot-foundation-model-for-tabular-data/ |
| 10/10 | 仓库描述 |
| 0/10 | 主题标签 |
| 0/10 | Wiki |
所用输入
| topics | — |
| has_wiki | 否 |
| homepage | https://research.google/blog/introducing-tabfm-a-zero-shot-foundation-model-for-tabular-data/ |
| has_readme | 是 |
| has_docs_dir | 否 |
| has_description | 是 |
安全
可见的安全与供应链实践是否稳固，且不存在未解决的高风险司法辖区暴露？
62
安全态势
52中等
评分方式
| 7.5/7.5 | Binary-Artifacts — no binaries found in the repo |
| 3.8/7.5 | Branch-Protection — branch protection is not maximal on development and all release branches |
| 2.5/2.5 | CI-Tests — 14 out of 14 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 |
| 7.5/7.5 | Code-Review — all changesets reviewed |
| 0.8/2.5 | Contributors — project has 1 contributing companies or organizations -- score normalized to 3 |
| 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 | 许可证 — license file detected |
| 0/7.5 | Maintained — project was created within the last 90 days. Please review its contents carefully |
| 0/5 | Packaging — 无数据 |
| 0/5 | Pinned-Dependencies — dependency not pinned by hash detected -- score normalized to 0 |
| 0/5 | SAST — SAST tool is not run on all commits -- score normalized to 0 |
| 0/5 | Security-Policy — security policy file not detected |
| 0/7.5 | Signed-Releases — 无数据 |
| 0/7.5 | Token-Permissions — detected GitHub workflow tokens with excessive permissions |
| 6/7.5 | Vulnerabilities — 2 existing vulnerabilities detected |
所用输入
| source | openssf_scorecard |
| checks_evaluated | 16 |
| scorecard_version | v5.5.0 |
| checks_inconclusive | 2 |
| scorecard_aggregate | 5.2 |
已排除计分（无数据或不适用）：packaging, signed_releases。 其余权重已重新归一化。
依赖安全公告
100优秀
评分方式
| 35/35 | 直接依赖不含已知公告 — 没有直接依赖携带已知公告 |
| 0/25 | 间接依赖不含已知公告 — 在此范围内，传递依赖集合无法与开发和测试依赖区分 |
| 0/40 | 没有长期未处理的公告 — 没有公告带有发布日期 |
所用输入
| source | osv |
| advisories | 2 |
| affected_packages | 2 |
| assessed_packages | 65 |
| unassessed_packages | 10 |
| affected_by_severity | moderate 2 |
| direct_affected_packages | 0 |
已排除计分（无数据或不适用）：间接依赖不含已知公告, 没有长期未处理的公告。 其余权重已重新归一化。 已将 65 个已解析依赖与 OSV 比对。 有 10 项无法评估——没有已解析的版本、生态系统不受支持，或超出所报告的软件包清单。 该仓库未发布任何索引可解析的软件包，因此改为评估仓库依赖图。该图将开发与测试版本固定同交付的依赖混在一起，因此仅对声明的运行时依赖计分；传递性发现仅作为背景信息列出，不计入评分。 未对可达性进行分析。
AI 就绪度
该仓库在多大程度上具备与 AI 编码代理协同开发与维护的条件？这是一枚独立的实验性徽章——权重为 0.0，因此单独呈现，不影响总体健康评分。
41
AI 代理上下文与指引
35存在风险
评分方式
| 0/45 | 代理指令 — 没有 CLAUDE.md / AGENTS.md / 编辑器规则 |
| 0/15 | 机器可读文档（llms.txt） |
| 35.4/40 | 可读的提交历史 — 98 次人类提交中有 65 次说明了意图（结构化标题或解释性正文） |
所用输入
| has_llms_txt | 否 |
| legible_history_share | 0.663 |
| agent_instruction_files | — |
| agent_instruction_max_bytes | — |
验证循环（构建 / 测试 / 类型检查）
41存在风险
评分方式
| 0/18 | 一条命令的引导启动 |
| 22/22 | 自动化测试 |
| 11/11 | Lint / 格式化配置 — .pylintrc |
| 0/11 | 静态类型检查 |
| 0/10 | 可复现环境 |
| 0/10 | 已体现的代理实践 — 最近 100 次提交中没有代理编写的提交 |
| 8/8 | 自动化维护 — 最近 100 次提交中有 2 次为自动依赖更新 |
| 0/10 | OpenSSF Scorecard：Pinned-Dependencies — dependency not pinned by hash detected -- score normalized to 0 |
所用输入
| has_nix | 否 |
| has_tests | 是 |
| lockfiles | — |
| has_dockerfile | 否 |
| typed_language | 否 |
| bootstrap_files | — |
| has_devcontainer | 否 |
| has_linter_config | 是 |
| typecheck_configs | — |
| agent_commit_share | 0 |
| toolchain_manifests | — |
| dependency_bot_commit_share | 0.02 |
面向模型的代码可读性
51中等
评分方式
| 0/45 | 可类型检查的代码 — Python，未配置类型检查 |
| 50.6/55 | 可控的文件大小 — 采样的 25 个源文件中有 2 个超过 60KB |
所用输入
| primary_language | Python |
| largest_source_bytes | 142,134 |
| source_files_sampled | 25 |
| oversized_source_files | 2 |
机器可读接口
40存在风险
评分方式
| 0/40 | API 模式（OpenAPI/GraphQL/proto） |
| 0/20 | MCP 服务器 |
| 40/40 | 可运行示例 — examples |
所用输入
| example_dirs | examples |
| has_mcp_signal | 否 |
| api_schema_files | — |
关键数据
1,988GitHub 星标
12贡献者
110最近 12 个月提交数
0距最近推送天数
1发布版本数
1巴士系数（bus factor）
15开放议题
PyPI软件包生态系统数
数据采集警告
- deps.dev does not index pypi:tabfm@1.0.1; advisories assessed against the repository dependency graph instead
更多细节
Star 与 Fork 历史 1,988 ★ / 200 ⇿
1,988Star
200Fork
每颗 star 和每个 fork 的添加时间，来自 GitHub 并按天汇总。累计增长位于其构成来源——每日新增——的正上方，二者可相互对照：稳定的自然增长与短暂的突增形态截然不同。当这一差别可被衡量时，它会作为增长真实性予以报告。
仅显示最近的历史——该仓库超出采集窗口，因此未采集最早的历史记录。
OpenSSF Scorecard 5.2 / 10
5.2综合
来自开源项目 OpenSSF Scorecard 的独立、工具无关的安全评估。每项检查奖励的是安全实践本身，而非特定供应商的工具。Scorecard 无法判定的检查项标记为 不适用，并从安全评分中剔除（绝不按零分计）。
| 10 | Binary-Artifacts | no binaries found in the repo |
| 5 | Branch-Protection | branch protection is not maximal on development and all release branches |
| 10 | CI-Tests | 14 out of 14 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 |
| 10 | Code-Review | all changesets reviewed |
| 3 | Contributors | project has 1 contributing companies or organizations -- score normalized to 3 |
| 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 |
| 0 | Maintained | project was created within the last 90 days. Please review its contents carefully |
| 不适用 | Packaging | packaging workflow not detected |
| 0 | Pinned-Dependencies | dependency not pinned by hash detected -- score normalized to 0 |
| 0 | SAST | SAST tool is not run on all commits -- score normalized to 0 |
| 0 | Security-Policy | security policy file not detected |
| 不适用 | Signed-Releases | no releases found |
| 0 | Token-Permissions | detected GitHub workflow tokens with excessive permissions |
| 8 | Vulnerabilities | 2 existing vulnerabilities detected |
直接依赖 8
| 注册表 | 软件包 | 版本约束 | 清单文件 |
|---|---|---|---|
| PyPI | absl-py | — | pyproject.toml |
| PyPI | jaxtyping | <0.3 | pyproject.toml |
| PyPI | numpy | — | pyproject.toml |
| PyPI | pandas | — | pyproject.toml |
| PyPI | scikit-learn | — | pyproject.toml |
| PyPI | scipy | — | pyproject.toml |
| PyPI | typeguard | <3 | pyproject.toml |
| PyPI | huggingface-hub | — | pyproject.toml |
全部依赖 75
来自 GitHub 依赖图的完整解析依赖集合：16 个直接依赖与 59 个间接（传递）软件包。仓库提交锁文件时，传递闭包才是完整的。
| 注册表 | 软件包 | 版本 | 关系 |
|---|---|---|---|
| PyPI | absl-py | — | 直接 |
| PyPI | absl-py | 2.4.0 | 直接 |
| PyPI | huggingface-hub | — | 直接 |
| PyPI | huggingface-hub | 1.21.0 | 直接 |
| PyPI | jaxtyping | — | 直接 |
| PyPI | jaxtyping | 0.2.25 | 直接 |
| PyPI | numpy | — | 直接 |
| PyPI | numpy | 2.2.0 | 直接 |
| PyPI | pandas | — | 直接 |
| PyPI | pandas | 2.2.3 | 直接 |
| PyPI | scikit-learn | — | 直接 |
| PyPI | scikit-learn | 1.6.0 | 直接 |
| PyPI | scipy | — | 直接 |
| PyPI | scipy | 1.17.1 | 直接 |
| PyPI | typeguard | — | 直接 |
| PyPI | typeguard | 2.13.3 | 直接 |
| PyPI | aiofiles | 23.2.1 | 间接 |
| PyPI | annotated-doc | 0.0.4 | 间接 |
| PyPI | anyio | 4.14.1 | 间接 |
| PyPI | certifi | 2026.6.17 | 间接 |
| PyPI | chex | 0.1.92 | 间接 |
| PyPI | click | 8.4.2 | 间接 |
| PyPI | einops | 0.8.2 | 间接 |
| PyPI | etils | 1.7.0 | 间接 |
| PyPI | filelock | 3.29.4 | 间接 |
| PyPI | flax | 0.12.7 | 间接 |
| PyPI | flit-core | — | 间接 |
| PyPI | fsspec | 2024.6.0 | 间接 |
| PyPI | h11 | 0.16.0 | 间接 |
| PyPI | hf-xet | 1.5.1 | 间接 |
| PyPI | httpcore | 1.0.9 | 间接 |
| PyPI | httpx | 0.28.1 | 间接 |
| PyPI | humanize | 4.9.0 | 间接 |
| PyPI | idna | 3.18 | 间接 |
| PyPI | importlib-resources | 6.4.0 | 间接 |
| PyPI | jax | 0.10.1 | 间接 |
| PyPI | jaxlib | 0.10.1 | 间接 |
| PyPI | jinja2 | 3.1.6 | 间接 |
| PyPI | joblib | 1.4.2 | 间接 |
| PyPI | markdown-it-py | 3.0.0 | 间接 |
| PyPI | markupsafe | 3.0.3 | 间接 |
| PyPI | mdurl | 0.1.2 | 间接 |
| PyPI | ml-dtypes | 0.5.0 | 间接 |
| PyPI | mpmath | 1.3.0 | 间接 |
| PyPI | msgpack | 1.2.1 | 间接 |
| PyPI | networkx | 3.6.1 | 间接 |
| PyPI | opt-einsum | 3.3.0 | 间接 |
| PyPI | optax | 0.2.8 | 间接 |
| PyPI | orbax-checkpoint | 0.12.0 | 间接 |
| PyPI | packaging | 26.2 | 间接 |
| PyPI | prometheus-client | 0.20.0 | 间接 |
| PyPI | protobuf | 5.29.6 | 间接 |
| PyPI | psutil | 5.9.8 | 间接 |
| PyPI | pygments | 2.20.0 | 间接 |
| PyPI | pylint | — | 间接 |
| PyPI | python-dateutil | 2.9.0.post0 | 间接 |
| PyPI | pytz | 2024.1 | 间接 |
| PyPI | pyyaml | 6.0.1 | 间接 |
| PyPI | rich | 13.7.1 | 间接 |
| PyPI | setuptools | 81.0.0 | 间接 |
| PyPI | shellingham | 1.5.4 | 间接 |
| PyPI | simplejson | 3.19.2 | 间接 |
| PyPI | six | 1.16.0 | 间接 |
| PyPI | sympy | 1.14.0 | 间接 |
| PyPI | tensorstore | 0.1.84 | 间接 |
| PyPI | threadpoolctl | 3.5.0 | 间接 |
| PyPI | toolz | 1.1.0 | 间接 |
| PyPI | torch | 2.12.1+cpu | 间接 |
| PyPI | tqdm | 4.68.3 | 间接 |
| PyPI | treescope | 0.1.10 | 间接 |
| PyPI | typer | 0.24.2 | 间接 |
| PyPI | typing-extensions | 4.15.0 | 间接 |
| PyPI | tzdata | 2024.1 | 间接 |
| PyPI | uvloop | 0.19.0 | 间接 |
| PyPI | zipp | 4.1.0 | 间接 |
依赖安全公告 2
该仓库未发布可被索引解析的包，因此评估的是其自身的依赖图——共 65 个包，其中也包含从不交付的开发与测试版本固定：2 个存在已知公告，0 个为直接依赖。 有 10 个无法评估——没有已解析的版本、生态系统不受支持，或不在所列包清单之内。
| 软件包 | 版本 | 关系 | 严重程度 | 公告数 | 修复版本 |
|---|---|---|---|---|---|
| setuptools | 81.0.0 | 间接 | 中 | 1 | 83.0.0 |
| torch | 2.12.1+cpu | 间接 | 中 | 1 | 2.13.0 |
公告表示依赖图中记录的版本落入某条公告的受影响范围。可达性未经分析，且依赖图包含开发与测试的版本固定——某项发现可能只涉及工具链而非交付的软件。
原始 JSON 报告 机器可读
{
"data": {
"repo": {
"topics": [],
"is_fork": false,
"size_kb": 230,
"has_wiki": false,
"homepage": "https://research.google/blog/introducing-tabfm-a-zero-shot-foundation-model-for-tabular-data/",
"languages": {
"Python": 422498,
"Starlark": 5389
},
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"headline": "Add ICL context caching to TabFM PyTorch backend with int8 KV-cache q…",
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"headline": "Merge pull request #48 from fus3r/fix-estimator-pickle-after-predict",
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"headline": "Merge pull request #45 from fus3r/fix-column-name-handling",
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"headline": "Merge pull request #47 from google-research/fix-pytorch-pickle-activa…",
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"body": "The first predict memoizes nnx.jit-compiled step functions on the\nestimator instance (_predict_step_compiled_with_cat /\n_predict_step_compiled_no_cat in _batch_forward). Those closures cannot\nbe pickled, so saving a fitted TabFMClassifier/TabFMRegressor with\nstdlib pickle crashes with \"Can't pickle \n[…]\n-side fix in #47.\n\nDrop the memoized functions from __getstate__ on both estimators: they\nare pure caches and are rebuilt lazily on the next predict. Restored\nestimators produce identical predictions.",
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"headline": "Skip memory_efficient_attention_test.py collection when jax is not in…",
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"body": "TabFMRegressor.predict on an unfitted estimator raised AttributeError\n('X_encoder_') instead of sklearn's NotFittedError. Add the\ncheck_is_fitted call in _predict_internal that TabFMClassifier already\nhas in _predict_proba_internal.",
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"headline": "Raise NotFittedError from TabFMRegressor.predict before fit",
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"headline": "Fix crashes on duplicate and non-string column names",
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"body": "A classification checkpoint passed to TabFMRegressor crashes deep in\n_predict_internal with numpy's cryptic 'cannot select an axis to squeeze\nout which has size not equal to one' (issue #43), while a regression\ncheckpoint passed to TabFMClassifier silently returns all-1.0\nprobabilities of shape (T, \n[…]\nutputs, and raise a ValueError naming the\nlikely cause and the exact fix. _batch_forward itself stays\nloss-agnostic (its pass-through behavior is pinned by\ntest_regressor_batch_forward_cross_entropy).",
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"headline": "Fail fast when the checkpoint type does not match the estimator",
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"body": "Fix predict crashing on multi-device hosts (IndivisibleError / device mismatch)",
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"headline": "Merge pull request #42 from devYRPauli/fix-multi-device-predict-sharding",
"author_name": "Weihao Kong",
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"body": "README: load the regression checkpoint in the Regression Example",
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"headline": "Merge pull request #39 from damienrj/fix-eager-transform-in-ensemble-…",
"author_name": "Weihao Kong",
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"body": "… mismatch)\n\nThe JAX forward path in TabFMClassifier and TabFMRegressor rebuilt\ndata_sharding over every visible device on first compile, discarding the\nsharding derived from the active mesh just above. With the default batch_size\nof 1 and no user mesh this forced a batch of size 1 into an N-way sha\n[…]\nrough a user-configured mesh.\n\nAdd a regression test that runs the classifier and regressor default-batch\npredict path on simulated CPU devices, so the multi-device path is covered in\nCI without GPUs.",
"is_bot": false,
"headline": "Fix predict crashing on multi-device hosts (IndivisibleError / device…",
"author_name": "Yash Raj Pandey",
"author_login": "devYRPauli",
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"body": "The example called bare load() for both backends, which default to\nmodel_type=\"classification\", so it downloaded the wrong multi-GB\ncheckpoint and crashed on the first predict() with a squeeze\nValueError (issue #32). Add model_type=\"regression\" to both calls,\nmatching examples/regression_example.py.",
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"headline": "README: load the regression checkpoint in the Regression Example",
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"body": "bias_fn hardcoded slice_q_len = 1, collapsing any query-varying mask/bias to\neach chunk's first row (the correct min() slicing was commented out just above);\na kv-broadcastable bias also crashed lax.dynamic_slice. Restore the general\nslicing; the [B, 1, 1, S] masks the model builds today stay bit-identical.\nAdds regression tests against jax.nn.dot_product_attention.",
"is_bot": false,
"headline": "Fix silent query-axis mask/bias collapse in memory-efficient attention",
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"body": null,
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"headline": "Retrigger CLA check",
"author_name": "damienrj",
"author_login": "damienrj",
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"body": "add PyTorchModelHubMixin to TabFM",
"is_bot": false,
"headline": "Merge pull request #33 from kashif/add-pytorch-hub-mixin",
"author_name": "Erez Louidor",
"author_login": "erzel",
"committed_at": "2026-07-02T22:41:24Z",
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"body": "…eatures\n\ngetattr(obj, name, default) evaluates the default eagerly, so\npreprocessor.transform(self.X_) ran on every call per ensemble member\neven though PreprocessingPipeline.fit() always sets X_transformed_.\nReference the cached attribute directly; behavior is unchanged.",
"is_bot": false,
"headline": "Avoid eager full-train re-transform in EnsembleGenerator._transform_f…",
"author_name": "damienrj",
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{
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"body": "# Conflicts:\n#\ttabfm/src/pytorch/tabfm_v1_0_0.py",
"is_bot": false,
"headline": "Merge remote-tracking branch 'origin/main' into add-pytorch-hub-mixin",
"author_name": "Kashif Rasul",
"author_login": "kashif",
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"body": "Run the PyTorch model in bfloat16 to match the JAX compute dtype",
"is_bot": false,
"headline": "Merge pull request #23 from google-research/run-pytorch-in-bf16",
"author_name": "Weihao Kong",
"author_login": "weihaokong",
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"body": null,
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"headline": "move HF hub code from TabFM into TabFM_HF",
"author_name": "Kashif Rasul",
"author_login": "kashif",
"committed_at": "2026-07-02T07:28:09Z",
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"body": "add ModelHubMixin to JAX model and narrow hub download",
"is_bot": false,
"headline": "Merge pull request #34 from kashif/improve-jax-hub-download",
"author_name": "Erez Louidor",
"author_login": "erzel",
"committed_at": "2026-07-02T02:54:46Z",
"body_truncated": false,
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"body": null,
"is_bot": false,
"headline": "Enable activation chunking by default with fixed memory-safe sizes",
"author_name": "Weihao Kong",
"author_login": "weihaokong",
"committed_at": "2026-07-02T00:23:29Z",
"body_truncated": false,
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"body": null,
"is_bot": false,
"headline": "Note that the dtype option may be removed in a future release",
"author_name": "Weihao Kong",
"author_login": "weihaokong",
"committed_at": "2026-07-01T23:44:38Z",
"body_truncated": false,
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"oid": "cc56f135586d707c192d9bede65838b8cc058010",
"body": "The JAX release runs in bf16 (load(dtype=jnp.bfloat16)) and casts its\ninput to bf16 at the very start of the model's __call__\n(jnp.nan_to_num(X, nan=-100.0).astype(self.dtype)). The native PyTorch\nestimator path ran everything in float32: load() never cast the float32\ncheckpoint, and the model never\n[…]\ns no bfloat16).\n\nHalves activation memory (35.6 -> 17.8 GB on the kddcup09 forward; large\ndatasets that previously OOM'd now fit, ~24 GB) and brings PyTorch\ninference in line with the JAX/TPU results.",
"is_bot": false,
"headline": "Run the PyTorch model in bfloat16 to match the JAX compute dtype",
"author_name": "Weihao Kong",
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"body": null,
"is_bot": false,
"headline": "wrap long lines in _from_pretrained to fit 80 cols",
"author_name": "Kashif Rasul",
"author_login": "kashif",
"committed_at": "2026-07-01T22:08:05Z",
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"body": null,
"is_bot": false,
"headline": "make TabFM_HF subclass TabFM instead of wrapping it",
"author_name": "Kashif Rasul",
"author_login": "kashif",
"committed_at": "2026-07-01T21:58:27Z",
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"body": null,
"is_bot": false,
"headline": "fix subfolder support in _from_pretrained",
"author_name": "Kashif Rasul",
"author_login": "kashif",
"committed_at": "2026-07-01T11:12:34Z",
"body_truncated": false,
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"body": null,
"is_bot": false,
"headline": "fix license to other for non-commercial weights",
"author_name": "Kashif Rasul",
"author_login": "kashif",
"committed_at": "2026-07-01T10:42:41Z",
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"oid": "56779a13c26aaeb6570e4376e9a382e8590ad7ad",
"body": "TabFMJax wraps the NNX module with from_pretrained, save_pretrained,\nand push_to_hub. snapshot_download now uses allow_patterns to fetch\nonly the needed model_type subfolder instead of both classification\nand regression weights.",
"is_bot": false,
"headline": "add ModelHubMixin to JAX TabFM and narrow snapshot download",
"author_name": "Kashif Rasul",
"author_login": "kashif",
"committed_at": "2026-07-01T10:37:53Z",
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"oid": "f9c193d66fd529170d88e05ee6cdcf36a4dfc46e",
"body": "TabFM now extends PyTorchModelHubMixin giving it from_pretrained,\nsave_pretrained, and push_to_hub. The load() helper uses\nTabFM.from_pretrained() instead of manual snapshot_download + torch.load.\nsave_pretrained writes model.safetensors which is the preferred format.\nRemove redundant config dataclasses and manual json/bin saving.",
"is_bot": false,
"headline": "add PyTorchModelHubMixin to TabFM pytorch model",
"author_name": "Kashif Rasul",
"author_login": "kashif",
"committed_at": "2026-07-01T10:35:44Z",
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"body": "Fix TabFMClassifier.predict() returning object-dtype labels",
"is_bot": false,
"headline": "Merge pull request #28 from tmacleod/fix-predict-dtype",
"author_name": "Weihao Kong",
"author_login": "weihaokong",
"committed_at": "2026-07-01T05:08:50Z",
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"body": "CategoricalOrdinalEncoder.inverse_transform() returns an object-dtype\r\nndarray (np.empty(..., dtype=object)). predict() flattens this and\r\nreturns it directly, so predicted labels come back as plain Python\r\nints/strs wrapped in an object array instead of a proper numeric/\r\nstring dtype.\r\n\r\nsklearn's\n[…]\nross_val_score(TabFMClassifier(...), X, y, cv=...,\r\nscoring='accuracy') raises the above even with a dummy model\r\nreturning random logits — confirms this is dtype handling, not a\r\ndata or model issue.",
"is_bot": false,
"headline": "Fix TabFMClassifier.predict() returning object-dtype labels",
"author_name": "tmacleod",
"author_login": "tmacleod",
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"body": "Skip backend test modules when their optional extra isn't installed",
"is_bot": false,
"headline": "Merge pull request #26 from google-research/fix-ci-skip-backend-tests",
"author_name": "Weihao Kong",
"author_login": "weihaokong",
"committed_at": "2026-06-30T22:26:20Z",
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"body": "With only .[dev] installed, the backend test modules were skipped (see the\nconftest change). Install both backend extras so the pytorch and jax tests\n(incl. the torch<->jax parity test) actually run in CI. Validated locally:\nthe full suite is 65 passed, 0 failed with both backends present.",
"is_bot": false,
"headline": "ci: install jax and pytorch extras so backend tests run",
"author_name": "Weihao Kong",
"author_login": "weihaokong",
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"body": "The pytorch/jax test modules import torch/chex/flax at import time, but the CI\n\"core tests\" job runs `pip install -e .[dev]`, which pulls neither the\n`pytorch` nor `jax` extra. pytest then fails to *collect* those modules\n(ModuleNotFoundError) and the whole job errors. Add `collect_ignore` to\nconftest.py so a backend's test modules are skipped when that backend isn't\nimportable. pytorch/model_test.py is a torch<->jax parity test (imports both),\nso it's skipped unless both backends are present.",
"is_bot": false,
"headline": "Skip backend test modules when their optional extra isn't installed",
"author_name": "Weihao Kong",
"author_login": "weihaokong",
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{
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"body": "Add Jax TPU results tables",
"is_bot": false,
"headline": "Merge pull request #25 from google-research/weihaokong-patch-1",
"author_name": "Weihao Kong",
"author_login": "weihaokong",
"committed_at": "2026-06-30T20:48:06Z",
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{
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"body": null,
"is_bot": false,
"headline": "Rename classification result tables to the jax-tpu-tabarena convention",
"author_name": "Weihao Kong",
"author_login": "weihaokong",
"committed_at": "2026-06-30T20:46:01Z",
"body_truncated": false,
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{
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"body": null,
"is_bot": false,
"headline": "Rename regression result tables to the jax-tpu-tabarena convention",
"author_name": "Weihao Kong",
"author_login": "weihaokong",
"committed_at": "2026-06-30T20:45:26Z",
"body_truncated": false,
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{
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"body": null,
"is_bot": false,
"headline": "Document evaluation results in README (results/)",
"author_name": "Weihao Kong",
"author_login": "weihaokong",
"committed_at": "2026-06-30T19:05:57Z",
"body_truncated": false,
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},
{
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"body": null,
"is_bot": false,
"headline": "Add files via upload",
"author_name": "Weihao Kong",
"author_login": "weihaokong",
"committed_at": "2026-06-30T18:50:29Z",
"body_truncated": false,
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},
{
"oid": "53f3fcfb8a3355f55c9fb49f04fbb62b8ba29109",
"body": "Add results/ folder",
"is_bot": false,
"headline": "Merge pull request #24 from google-research/add-results-folder",
"author_name": "Weihao Kong",
"author_login": "weihaokong",
"committed_at": "2026-06-30T18:49:38Z",
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"body": null,
"is_bot": false,
"headline": "Add results/ folder",
"author_name": "Weihao Kong",
"author_login": "weihaokong",
"committed_at": "2026-06-30T18:43:14Z",
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"body": "Update documentation and packaging for JAX/PyTorch separation",
"is_bot": false,
"headline": "Merge pull request #22 from erzel/update-docs",
"author_name": "Erez Louidor",
"author_login": "erzel",
"committed_at": "2026-06-30T05:50:44Z",
"body_truncated": false,
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"body": "- Update README.md to show installation options for JAX and PyTorch backends\n- Make examples in README.md and examples/ directory neutral with comments showing PyTorch usage\n- Expose tabfm_v1_0_0_jax and tabfm_v1_0_0_pytorch symmetric loaders in __init__.py\n- Split pyproject.toml dependencies into optional extras (jax and pytorch)\n- Update CHANGELOG.md with recent changes\n- Fix checkpointing_test.py flag parsing when running via unittest discovery",
"is_bot": false,
"headline": "Update documentation and packaging for JAX/PyTorch separation",
"author_name": "Erez Louidor Ilan",
"author_login": "erzel",
"committed_at": "2026-06-30T05:46:03Z",
"body_truncated": false,
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{
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"body": "Fix datetime-as-text detection for pandas>=3 string dtype",
"is_bot": false,
"headline": "Merge pull request #18 from google-research/fix-pandas3-datetime",
"author_name": "Weihao Kong",
"author_login": "weihaokong",
"committed_at": "2026-06-30T05:13:53Z",
"body_truncated": false,
"is_coding_agent": false
},
{
"oid": "b7c7d0bd2dab3bf52c65b6fa9e4965cae5fa9a5c",
"body": "The ensemble calibration path in TabFMClassifier.fit called\nchex.assert_shape, but chex is only imported inside the JAX try/except\nblock. In a JAX-free (PyTorch-only) install this raised\n'NameError: name chex is not defined' during fit(). Replace it with an\nequivalent numpy-shape assert and drop the now-unused chex import.",
"is_bot": false,
"headline": "Fix JAX-free crash: replace chex.assert_shape with plain assert",
"author_name": "Weihao Kong",
"author_login": "weihaokong",
"committed_at": "2026-06-30T05:09:00Z",
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{
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"body": "…tial-date leniency",
"is_bot": false,
"headline": "Make datetime detector private (_looks_like_datetime) + tests for par…",
"author_name": "Weihao Kong",
"author_login": "weihaokong",
"committed_at": "2026-06-30T04:46:54Z",
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{
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"body": "…ing dtypes",
"is_bot": false,
"headline": "Add regression tests for datetime-as-text detection across object/str…",
"author_name": "Weihao Kong",
"author_login": "weihaokong",
"committed_at": "2026-06-30T04:46:25Z",
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"body": null,
"is_bot": false,
"headline": "Fix datetime-as-text detection for pandas>=3 string dtype",
"author_name": "Weihao Kong",
"author_login": "weihaokong",
"committed_at": "2026-06-30T04:45:46Z",
"body_truncated": false,
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{
"oid": "35a8560e892b883781d0c71ae4fd315f3c5163ad",
"body": "Reorganize jax torch",
"is_bot": false,
"headline": "Merge pull request #21 from erzel/reorganize-jax-torch",
"author_name": "Weihao Kong",
"author_login": "weihaokong",
"committed_at": "2026-06-30T02:46:54Z",
"body_truncated": false,
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{
"oid": "76aad7ab80d9dc08cef6af3768e17d07d56f2171",
"body": "- Decoupled JAX and PyTorch in classifier_and_regressor.py\n- Protected JAX tests and added PyTorch integration tests\n- Fixed PyTorch model out-of-bounds index crashes\n- Made JAX and PyTorch model loading thread-safe\n- Reorganized JAX targets in Bazel BUILD files",
"is_bot": false,
"headline": "Implement JAX-free support and PyTorch integration for estimators",
"author_name": "Erez Louidor Ilan",
"author_login": "erzel",
"committed_at": "2026-06-30T02:32:11Z",
"body_truncated": false,
"is_coding_agent": false
},
{
"oid": "b673130d3768d5a88478b7de6235dd7577ca6f93",
"body": "- Moved JAX files to tabfm/src/jax/\n- Moved PyTorch model implementation to tabfm/src/pytorch/\n- Fixed a precision bug inside InducedSelfAttentionBlock in the JAX code\n- Updated import paths across JAX files, estimator tests, and __init__.py\n- Created pytorch/model_test.py unit tests with numerical parity checks\n- Created hugging_face/convert_and_upload.py to convert Orbax weights to PyTorch weights and upload to hugging face\n- Updated HF JAX model repo to google/tabfm-1.0.0-jax",
"is_bot": false,
"headline": "Reorganize repo into jax/ and pytorch/ subdirectories",
"author_name": "Erez Louidor Ilan",
"author_login": "erzel",
"committed_at": "2026-06-30T02:32:11Z",
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{
"oid": "0842bd42dc7c92b9d7ca8159006512fcf2c213e6",
"body": "…ll CV for large datasets. (#19)",
"is_bot": false,
"headline": "Add parameter to use a single val fold for optimization instead of fu…",
"author_name": "tamannarayan",
"author_login": "tamannarayan",
"committed_at": "2026-06-30T01:35:08Z",
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{
"oid": "da5130ac4ef94ca73cc25754d42e5d1659aad309",
"body": "Support restructured HF JAX checkpoints layout",
"is_bot": false,
"headline": "Merge pull request #20 from google-research/fix-jax-load-path",
"author_name": "Erez Louidor",
"author_login": "erzel",
"committed_at": "2026-06-30T00:40:38Z",
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"body": null,
"is_bot": false,
"headline": "Update JAX repository ID to tabfm-1.0.0-jax",
"author_name": "Erez Louidor Ilan",
"author_login": "erzel",
"committed_at": "2026-06-29T23:25:22Z",
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{
"oid": "15422d70898e86bfe8af3cec90d9c0580a6c2b42",
"body": "Use airfoil_self_noise and maternal_health_risk for the examples",
"is_bot": false,
"headline": "Merge pull request #15 from google-research/swap-example-datasets",
"author_name": "Weihao Kong",
"author_login": "weihaokong",
"committed_at": "2026-06-29T16:46:42Z",
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"headline": "Use clf.classes_ directly in the classification example",
"author_name": "Weihao Kong",
"author_login": "weihaokong",
"committed_at": "2026-06-27T00:09:43Z",
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"headline": "Use maternal_health_risk for the classification example",
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"author_login": "weihaokong",
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"body_truncated": false,
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"headline": "Use airfoil_self_noise for the regression example",
"author_name": "Weihao Kong",
"author_login": "weihaokong",
"committed_at": "2026-06-27T00:04:58Z",
"body_truncated": false,
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"body": "…ples\n\nAdd ensemble-capable TabFM estimators and TabArena examples",
"is_bot": false,
"headline": "Merge pull request #14 from google-research/ensemble-presets-and-exam…",
"author_name": "Weihao Kong",
"author_login": "weihaokong",
"committed_at": "2026-06-26T23:33:21Z",
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{
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"body": "…egy, cleanup",
"is_bot": false,
"headline": "Address PR review: restore alphabetical y-encoder, trim crosses/strat…",
"author_name": "Weihao Kong",
"author_login": "weihaokong",
"committed_at": "2026-06-26T23:05:46Z",
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"body": "…20.0\n\nBump pygments from 2.18.0 to 2.20.0",
"is_bot": false,
"headline": "Merge pull request #1 from google-research/dependabot/pip/pygments-2.…",
"author_name": "Weihao Kong",
"author_login": "weihaokong",
"committed_at": "2026-06-26T21:21:03Z",
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"body": "…29.6\n\nBump protobuf from 5.26.1 to 5.29.6",
"is_bot": false,
"headline": "Merge pull request #2 from google-research/dependabot/pip/protobuf-5.…",
"author_name": "Erez Louidor",
"author_login": "erzel",
"committed_at": "2026-06-26T17:57:05Z",
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{
"oid": "b487230883e272e1315f00d6ad768fd5f4b188a2",
"body": "Add tamannarayan to CODEOWNERS",
"is_bot": false,
"headline": "Merge pull request #11 from weihaokong/add-tamannarayan-codeowner",
"author_name": "Erez Louidor",
"author_login": "erzel",
"committed_at": "2026-06-26T17:46:46Z",
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{
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"body": null,
"is_bot": false,
"headline": "Add TabArena default-vs-ensemble examples",
"author_name": "Weihao Kong",
"author_login": "weihaokong",
"committed_at": "2026-06-26T15:11:30Z",
"body_truncated": false,
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{
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"body": null,
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"headline": "Add ensemble-capable TabFM classifier/regressor",
"author_name": "Weihao Kong",
"author_login": "weihaokong",
"committed_at": "2026-06-26T15:11:30Z",
"body_truncated": false,
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{
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"body": "Remove dead code (ssmax, hierarchical classification, unused helper)",
"is_bot": false,
"headline": "Merge pull request #9 from weihaokong/remove-ssmax",
"author_name": "Weihao Kong",
"author_login": "weihaokong",
"committed_at": "2026-06-26T15:00:49Z",
"body_truncated": false,
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},
{
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"body": "Inference perf: checkpoint cache, selectable ICL attention, 128-padding",
"is_bot": false,
"headline": "Merge pull request #8 from weihaokong/perf-inference",
"author_name": "Weihao Kong",
"author_login": "weihaokong",
"committed_at": "2026-06-26T14:54:48Z",
"body_truncated": false,
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},
{
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"body": null,
"is_bot": false,
"headline": "Add tamannarayan to CODEOWNERS",
"author_name": "Weihao Kong",
"author_login": "weihaokong",
"committed_at": "2026-06-25T21:59:54Z",
"body_truncated": false,
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},
{
"oid": "c04506047c46e4b13214ef6507b189c21cbefd60",
"body": "Bumps [protobuf](https://github.com/protocolbuffers/protobuf) from 5.26.1 to 5.29.6.\n- [Release notes](https://github.com/protocolbuffers/protobuf/releases)\n- [Commits](https://github.com/protocolbuffers/protobuf/commits)\n\n---\nupdated-dependencies:\n- dependency-name: protobuf\n dependency-version: 5.29.6\n dependency-type: direct:production\n...\n\nSigned-off-by: dependabot[bot] <support@github.com>",
"is_bot": true,
"headline": "Bump protobuf from 5.26.1 to 5.29.6",
"author_name": "dependabot[bot]",
"author_login": "dependabot[bot]",
"committed_at": "2026-06-24T17:12:25Z",
"body_truncated": false,
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{
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"body": "Bump msgpack from 1.0.8 to 1.2.1",
"is_bot": false,
"headline": "Merge pull request #10 from google-research/dependabot/pip/msgpack-1.2.1",
"author_name": "Weihao Kong",
"author_login": "weihaokong",
"committed_at": "2026-06-24T17:10:11Z",
"body_truncated": false,
"is_coding_agent": false
},
{
"oid": "6007ae780b5770c74b44ea76b2bf2d63db80eeb0",
"body": "Encode class labels alphabetically (sklearn convention)",
"is_bot": false,
"headline": "Merge pull request #7 from weihaokong/y-encoder-alphabetical",
"author_name": "Weihao Kong",
"author_login": "weihaokong",
"committed_at": "2026-06-24T16:53:50Z",
"body_truncated": false,
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{
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"body": "Bumps [msgpack](https://github.com/msgpack/msgpack-python) from 1.0.8 to 1.2.1.\n- [Release notes](https://github.com/msgpack/msgpack-python/releases)\n- [Changelog](https://github.com/msgpack/msgpack-python/blob/main/CHANGELOG.md)\n- [Commits](https://github.com/msgpack/msgpack-python/compare/v1.0.8...v1.2.1)\n\n---\nupdated-dependencies:\n- dependency-name: msgpack\n dependency-version: 1.2.1\n dependency-type: direct:production\n...\n\nSigned-off-by: dependabot[bot] <support@github.com>",
"is_bot": true,
"headline": "Bump msgpack from 1.0.8 to 1.2.1",
"author_name": "dependabot[bot]",
"author_login": "dependabot[bot]",
"committed_at": "2026-06-24T16:50:37Z",
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"body": "Fix pytest CI on a clean environment",
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"headline": "Merge pull request #6 from weihaokong/fix-ci-deps",
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"body": "The classifier already pads the ICL sequence length to a multiple of 128\nfor the flash (memory_efficient_attention) path, but the regressor did\nnot. With flash attention enabled, any regression dataset whose\nin-context sequence length is not a multiple of 128 fails with a reshape\nerror. Mirror the classifier's fix: pad the sequence with -100 and slice\nthe output back to orig_seq_len so padded rows are dropped.",
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"headline": "Apply sequence 128-padding in TabFMRegressor._batch_forward",
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"body": "The config constructor param was stored but never read: the classifier\nnever used it, and the regressor only read self.config as a fallback for\nself.model.loss, which TabFM always sets -- so the fallback was\nunreachable. Drop config from both estimators (and the now-unused\nargparse/flags/logging imp\n[…]\nror was\nunreachable; replace the block with an unconditional output.squeeze(-1).\n\nAlso use self.batch_size directly in the regressor instead of\ngetattr(self, 'batch_size', 1); __init__ always sets it.",
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"headline": "Remove dead config param and regressor loss branch",
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"headline": "Drop specific restore-time figure from cache comment",
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"body": "Address review: explain that _LOAD_CACHE is a dict keyed by load settings\nso the classification/regression variants can coexist in one process and\nso a cached model is never returned for mismatched settings.",
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"headline": "Document why the load cache is keyed by settings",
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"body": "Address review: use explicit branches for 'appearance', 'alphabetical',\nand 'frequency', and raise ValueError for any unrecognized mode instead\nof silently treating it as appearance order.",
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"headline": "Make encoder mode handling explicit; raise on unknown mode",
"author_name": "Weihao Kong",
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"body": "Removes the never-instantiated ClassNode class and the vestigial\nICLearning.root attribute (hierarchical classification was never\nimplemented; the forward path always raised), the unused\n_round_to_multiple_of helper, and scrubs related docstrings. The\nclassifier now raises a clear ValueError up front when the number of\nclasses exceeds max_classes instead of printing a misleading message\nand failing later in the model.",
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"headline": "Remove dead hierarchical-classification code and unused helper",
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"body": "In TabFMClassifier._batch_forward, pad the sequence length to a multiple\nof 128 with -100 before the (jitted) predict step, and slice the padding\noff the output. Padding outside the jit boundary buckets varying dataset\nsizes to the same compiled shape, avoiding a recompile per distinct\nlength; padded rows fall past train_size and are masked out, so results\nare unchanged.",
"is_bot": false,
"headline": "Pad inference batches to a multiple of 128 to cut JIT recompiles",
"author_name": "Weihao Kong",
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"body": "load() gains:\n - a process-wide in-memory cache (_LOAD_CACHE/use_cache) so repeated\n loads (e.g. AutoGluon/TabArena bagging) skip the ~19s Orbax restore;\n - an attention_impl param (default 'flash') injected as\n icl_attention_impl, letting callers pick the ICL attention kernel.\n\nmodel.py keeps its JAX constructor default (single source of truth: the\nload() param overrides it), so this is a one-file change.",
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"headline": "Cache restored checkpoints and add attention_impl param to load()",
"author_name": "Weihao Kong",
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"body": "The v1.0.0 checkpoint is trained with ssmax disabled (ssmax=False\neverywhere; create_ssmax_layer returns None, so no ssmax params exist\nin the checkpoint). Remove the SSMax/SSMaxMLP/QASSMaxMLP classes, the\ncreate_ssmax_layer factory, the ssmax constructor params/pass-throughs,\nthe forward hook, and the related test case. Verified: model loads and\nblood-transfusion ROC AUC is unchanged (~0.748).",
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"headline": "Remove unused scalable-softmax (ssmax) code",
"author_name": "Weihao Kong",
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"body": "Add an 'alphabetical' mode to CategoricalOrdinalEncoder (sort categories\nascending) and use it for the label encoder, so TabFMClassifier.classes_\nand predict_proba column order follow scikit-learn's LabelEncoder\nconvention (classes sorted ascending) instead of order-of-appearance.",
"is_bot": false,
"headline": "Encode class labels alphabetically to match sklearn convention",
"author_name": "Weihao Kong",
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"body": "The pytest CI (pip install -e .[dev] + pytest -n auto) failed at import\ncollection. Root causes and fixes:\n\n1. Declare runtime deps in pyproject.toml (it was empty) and add the\n missing 'typeguard'. Only the deps with known incompatibilities are\n constrained; the rest float:\n - flax>=0.12.7 \n[…]\nt.py parses absl flags so absltest helpers (create_tempdir ->\n --test_tmpdir) work under the pytest runner.\n\nVerified in a fresh Python 3.11 venv (latest jax/numpy/pandas floated):\n32/32 tests pass.",
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"headline": "Merge pull request #5 from weihaokong/fix-deps",
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"name": "Package maintenance",
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"points": 25,
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"ecosystems": "pypi"
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}
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{
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"points": 35,
"status": "met",
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{
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{
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"detail": "2 published versions",
"points": 12,
"status": "partial",
"details": [
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}
],
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{
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"points": 20,
"status": "met",
"details": [
{
"code": "package_not_deprecated",
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],
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]
}
],
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},
{
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"weight": 0.2,
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"note": null,
"notes": [],
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"points": 24,
"status": "met",
"details": [
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"code": "ci_workflows",
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{
"key": "tests_present",
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{
"key": "linter_config",
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{
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{
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{
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"name": "Documentation",
"note": null,
"notes": [],
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"inputs": {
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{
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{
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{
"key": "wiki",
"name": "Wiki",
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}
]
}
],
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{
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"weight": 0.16,
"metrics": [
{
"key": "security_posture",
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"notes": [
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{
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"components": [
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"points": 7.5,
"status": "met",
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{
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"points": 3.8,
"status": "partial",
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{
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"points": 2.5,
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{
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"points": 0,
"status": "missed",
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},
{
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"points": 7.5,
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},
{
"key": "dangerous_workflow",
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"points": 10,
"status": "met",
"details": [],
"max_points": 10
},
{
"key": "dependency_update_tool",
"name": "Dependency-Update-Tool",
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"points": 7.5,
"status": "met",
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{
"key": "fuzzing",
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"points": 0,
"status": "missed",
"details": [],
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{
"key": "license",
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"points": 2.5,
"status": "met",
"details": [],
"max_points": 2.5
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{
"key": "maintained",
"name": "Maintained",
"detail": "project was created within the last 90 days. Please review its contents carefully",
"points": 0,
"status": "missed",
"details": [],
"max_points": 7.5
},
{
"key": "packaging",
"name": "Packaging",
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"points": 0,
"status": "excluded",
"details": [
{
"code": "no_data",
"params": {}
}
],
"max_points": 5
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{
"key": "pinned_dependencies",
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"points": 0,
"status": "missed",
"details": [],
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},
{
"key": "sast",
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"points": 0,
"status": "missed",
"details": [],
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},
{
"key": "security_policy",
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"detail": "security policy file not detected",
"points": 0,
"status": "missed",
"details": [],
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{
"key": "signed_releases",
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"points": 0,
"status": "excluded",
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"code": "no_data",
"params": {}
}
],
"max_points": 7.5
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{
"key": "token_permissions",
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"points": 0,
"status": "missed",
"details": [],
"max_points": 7.5
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{
"key": "vulnerabilities",
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"points": 6,
"status": "partial",
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"max_points": 7.5
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]
},
{
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{
"code": "advisories_scope_repository",
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},
{
"code": "advisories_unassessed",
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},
{
"code": "advisories_repo_graph_caveat",
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{
"code": "advisories_reachability",
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}
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}
],
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{
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"points": 0,
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{
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"note": null,
"notes": [],
"value": 100,
"inputs": {
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{
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{
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],
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"inputs": {
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"components": [
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"detail": "no confirmed policy-scope location match",
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"status": "met",
"details": [
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"code": "jurisdiction_no_match",
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}
],
"max_points": 100
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]
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],
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{
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"metrics": [
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{
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{
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{
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"source_files_sampled": 25,
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"components": [
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"points": 0,
"status": "missed",
"details": [
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"code": "no_typecheck_config_language",
"params": {
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{
"key": "manageable_file_sizes",
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"detail": "2/25 source files over 60KB",
"points": 50.6,
"status": "partial",
"details": [
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"code": "oversized_source_files",
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]
},
{
"key": "ai_interfaces",
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"note": null,
"notes": [],
"value": 40,
"inputs": {
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"components": [
{
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"detail": null,
"points": 0,
"status": "missed",
"details": [],
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{
"key": "mcp_server",
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"detail": null,
"points": 0,
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{
"key": "runnable_examples",
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"code": "file_list",
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