This release is a pre-release and may not be stable for production use.
wedata3-feature-engineering
WeData 3.0 特征工程与 Feature Store 客户端 SDK，封装 Feast 0.49 并对接 WeData 平台（Tclake、腾讯云 SDK、MLflow）。
PyPI 包名：
wedata3-feature-engineeringPython 入口：import wedata
功能特性
- Feature Store 客户端：基于 Feast 的特征注册、查询、在线 / 离线特征获取（Redis / Postgres 后端）
- 特征工程客户端：训练集构造、特征关联（feature lookup）、特征表管理
- MLflow 集成：以
wedata.feature_store.mlflow_model/wedata.feature_engineering.mlflow_model形式将特征查询逻辑随模型一起持久化，预测时自动取在线特征 - Spark 支持：内置 Spark Client，离线特征通过 PySpark 处理
- 腾讯云生态：通过
tencentcloud-sdk-python与 Tclake、CAM 等平台能力集成
目录结构
feature-engineering/
├── README.md # 本文件
├── setup.py # 打包配置
├── build.sh # 本地构建 + 上传 PyPI 脚本
├── requirements.txt # 运行依赖
├── test_valid.py # 冒烟测试入口
├── tests/ # 单元测试
└── wedata/ # 主包（顶层 import 名）
├── __init__.py # 暴露版本号
├── feature_store/ # Feature Store 客户端（与 MLflow 集成）
│ ├── client.py
│ ├── mlflow_model.py
│ ├── feature_table_client/
│ └── training_set_client/
├── feature_engineering/ # 特征工程客户端（封装 Feast）
│ ├── client.py
│ ├── mlflow_model.py
│ ├── feast/
│ ├── ml_training_client/
│ └── table_client/
└── common/ # 公共能力
├── base_table_client/ # 表客户端基类
├── cloud_sdk_client/ # 腾讯云 SDK 封装
├── feast_client/ # Feast 适配
├── spark_client/ # Spark / PySpark 封装
├── entities/ # 领域实体
├── constants/ # 常量
├── protos/ # protobuf 定义
├── log/ # 日志
└── utils/ # 工具函数
环境要求
- Python：>= 3.10
- PySpark：3.5.0（与
py4j==0.10.9.7配合） - Feast：0.49.0（
extras=[redis,postgres]） - MLflow：根据使用场景二选一
mlflow2extra：mlflow==2.17.2（WeData 2.0）mlflow3extra：mlflow>=3.10.0,<3.11.0（WeData 3.0）
安装
# 仅运行时，使用 MLflow 3.x（WeData 3.0 默认）
pip install "wedata3-feature-engineering[mlflow3]"
# WeData 2.0 场景
pip install "wedata3-feature-engineering[mlflow2]"
# 开发依赖（flake8 / pytest / python-dotenv）
pip install "wedata3-feature-engineering[dev]"
快速使用
# 特征工程：构造训练集
from wedata.feature_engineering.client import FeatureEngineeringClient
fe_client = FeatureEngineeringClient()
training_set = fe_client.create_training_set(
df=labels_df,
feature_lookups=[...],
label="label",
)
# Feature Store：在线特征查询
from wedata.feature_store.client import FeatureStoreClient
fs_client = FeatureStoreClient()
features = fs_client.get_online_features(
feature_refs=["user_features:age", "user_features:city"],
entity_rows=[{"user_id": "u_001"}],
)
构建与发布
# 本地打包并上传 PyPI（需要在 ~/.pypirc 配置好凭据）
bash build.sh
build.sh 会：
- 清理旧的
dist//build//*.egg-info/ - 读取
wedata/__init__.py中的__version__ - 安装
build/twine python3 -m build生成 wheel + sdisttwine upload推送到 PyPI 镜像
与本仓库其他子项目的关系
| 项目 | 关系 |
|---|---|
../feast-server/ |
本 SDK 默认通过 Feast 的 RemoteRegistry 访问 feast-server 的 gRPC 接口，作为特征注册表 |
../mlflow-server/ |
mlflow_model.py 序列化的特征逻辑可被 MLflow Server 加载与下发 |
../wedata-ml-runtime/ |
Notebook Kernel 启动时会注入 Feast / MLflow 的网关代理与租户隔离逻辑，本 SDK 在其上层使用 |
许可证
Apache License 2.0
14 maintainers
Metadata
14 maintainers
Release files for tencent-wedata-feature-engineering 1.1.1.dev1
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