MARS

MARS 接受 Pandas 或 Polars 宽表，提供数据画像、分箱评估、特征筛选、建模、监控、结构化 report、 Excel/HTML 导出和评分卡能力。

安装

MARS 0.0.27 支持 Python 3.8–3.12。Python 3.8 使用冻结兼容依赖栈；可选的建模、调参、Notebook 和文档工具要求 Python 3.10+。

pip install mars-risk==0.0.27

从 GitHub 源码安装当前 main 分支：

pip install "git+https://github.com/leeesq/mars-risk.git"

也可以先克隆仓库，再从本地源码安装：

git clone https://github.com/leeesq/mars-risk.git
cd mars-risk
pip install .

建模与调参需要可选依赖：

pip install "mars-risk[ml,tuning]==0.0.27"

0.0.27 正式发布前，请从源码安装进行文档预览验收。Python 3.8 已停止官方安全维护； MARS 的运行兼容不代表解释器仍有安全支持。

最小风险评估

import polars as pl

from mars.analysis import profile_risk

df = pl.DataFrame(
    {
        "income": [3200, 3600, 5200, 6100, 3400, 4300, 5800, 6800],
        "utilization": [0.72, 0.61, 0.29, 0.18, 0.66, 0.48, 0.24, 0.12],
        "target": [1, 1, 0, 0, 1, 1, 0, 0],
    }
)

risk_profile = profile_risk(
    df,
    target="target",
    features=["income", "utilization"],
    method="quantile",
    n_bins=4,
)

summary = risk_profile.report.summary_table
binner = risk_profile.binner

完整的日期、分组、趋势和报告示例见 10 分钟 Quickstart。

从任务开始

目标	文档
检查缺失、分布和 PSI	数据画像
使用基准规则评估当前数据	分箱与风险评估
统计、线性或重要性筛选	特征筛选
切分、调参、replay 与 Pipeline	Modeling / Pipeline
分布、模型分和表现覆盖率监控	特征与模型监控
Excel、HTML、评分卡与 SQL	报告与评分卡
精确签名、默认值和异常	API Reference

稳定性

Analysis、Feature、Reporting 是当前 Stable 模块。Monitoring、Modeling、Pipeline、Scoring 为 Experimental；受控生产流程应固定精确版本，并为 report 字段、报警结果、评分映射、生成 SQL、 step 契约、replay 和 artifact 路径增加契约回归。

开发检查

python -m ruff check src tests scripts docs/snippets
python -m mypy src/mars
pydoclint src/mars
python scripts/check_private_docstrings.py src/mars
python -m pytest -q
python -m mkdocs build --strict

贡献要求见 CONTRIBUTING.md，许可证见 LICENSE。

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