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MIT License
GNU AFFERO GENERAL PUBLIC LICENSE
Version 3, 19 November 2007
Copyright (c) 2026 Yuanzhen Yang (yangyuan-zhen)
Copyright (C) 2007 Free Software Foundation, Inc. <https://fsf.org/>
Everyone is permitted to copy and distribute verbatim copies
of this license document, but changing it is not allowed.
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solutions will be better for different programs; see section 13 for the
specific requirements.
You should also get your employer (if you work as a programmer) or school,
if any, to sign a "copyright disclaimer" for the program, if necessary.
For more information on this, and how to apply and follow the GNU AGPL, see
<https://www.gnu.org/licenses/>.
README.md
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Production weather-intelligence stack for temperature settlement markets.
Official dashboard: [polyweather-pro.vercel.app](https://polyweather-pro.vercel.app/)
中文说明: [README_ZH.md](README_ZH.md)
Public docs center: `/docs/intro` on the main site (bilingual product documentation, including intraday signals, TAF, settlement sources, history, and extension).
@@ -32,14 +33,15 @@ Public docs center: `/docs/intro` on the main site (bilingual product documentat
- Trade cue now combines upper-air structure, `TAF`, market crowding, and `edge_percent`.
- Browser extension now uses `DEB` for multi-day forecast and stays positioned as a lightweight lead-in to the main site.
## Open-Core Boundary (Important)
## License & Commercial Boundary
This repository follows an **Open-Core** strategy:
This repository is licensed under **GNU AGPL-3.0 only** from `2026-03-30` onward.
- Public in repo: weather aggregation, core analysis, dashboard, bot baseline, standard payment flow.
- Private in production: commercial risk rules, operational thresholds, pricing strategy details, internal reconciliation policies, and growth operations tooling.
- Public in repo: weather aggregation, core analysis, dashboard, bot baseline, and standard payment flow.
- Not included in this repository: private production data, internal operating thresholds, commercial risk rules, pricing strategy details, and growth tooling.
- Trademark, brand, domain, production databases, and hosted-service operations are **not** granted by the code license.
See: [Open-Core & Commercial Boundary](docs/OPEN_CORE_POLICY.md)
See: [AGPL-3.0 & Commercial Boundary](docs/OPEN_CORE_POLICY.md)
## Core Capabilities
@@ -168,7 +170,7 @@ docker compose logs -f polyweather | egrep "polymarket wallet activity watcher s
- Chinese API guide: [docs/API_ZH.md](docs/API_ZH.md)
- TAF signal guide (ZH): [docs/TAF_SIGNAL_ZH.md](docs/TAF_SIGNAL_ZH.md)
- Commercialization: [docs/COMMERCIALIZATION.md](docs/COMMERCIALIZATION.md)
- Open-Core policy: [docs/OPEN_CORE_POLICY.md](docs/OPEN_CORE_POLICY.md)
- AGPL-3.0 policy: [docs/OPEN_CORE_POLICY.md](docs/OPEN_CORE_POLICY.md)
- Supabase setup (ZH): [docs/SUPABASE_SETUP_ZH.md](docs/SUPABASE_SETUP_ZH.md)
- Configuration & secrets (ZH): [docs/CONFIGURATION_ZH.md](docs/CONFIGURATION_ZH.md)
- LightGBM daily-high model (ZH): [docs/LGBM_DAILY_HIGH_ZH.md](docs/LGBM_DAILY_HIGH_ZH.md)
README_ZH.md
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@@ -26,14 +26,15 @@
- 运行态状态与缓存已支持 SQLite 渐进迁移：`file / dual / sqlite`。
- 已接入 EMOS/CRPS 校准链路，但当前仍保持 `emos_shadow`。
## 开源边界（重要）
## 许可证与商用边界（重要）
本项目采用 **Open-Core** 策略：
本仓库自 `2026-03-30` 起采用 **GNU AGPL-3.0-only**。
- 仓库公开部分：天气聚合、基础分析、前端看板、Bot 基础能力、支付标准流程示例。
- 生产私有部分：商业风控规则、运营阈值、收费策略细节、付费用户运营脚本、内部对账与审计策略。
- 仓库公开部分：天气聚合、基础分析、前端看板、Bot 基础能力、标准支付流程。
- 不包含在仓库中的部分：生产私有数据、商业风控规则、运营阈值、收费策略细节、内部对账与增长工具。
- 商标、品牌、域名、生产数据库与托管服务运营能力，不因代码许可证一并授权。
详细见：[Open-Core 与商用边界](docs/OPEN_CORE_POLICY.md)
详细见：[AGPL-3.0 与商用边界](docs/OPEN_CORE_POLICY.md)
## 核心能力
@@ -158,7 +159,7 @@ docker compose logs -f polyweather | egrep "polymarket wallet activity watcher s
- 英文总览：[README.md](README.md)
- API 文档（中文）：[docs/API_ZH.md](docs/API_ZH.md)
- 商业化说明：[docs/COMMERCIALIZATION.md](docs/COMMERCIALIZATION.md)
- Open-Core 边界：[docs/OPEN_CORE_POLICY.md](docs/OPEN_CORE_POLICY.md)
- AGPL-3.0 边界：[docs/OPEN_CORE_POLICY.md](docs/OPEN_CORE_POLICY.md)
- Supabase 接入：[docs/SUPABASE_SETUP_ZH.md](docs/SUPABASE_SETUP_ZH.md)
- 配置与密钥管理：[docs/CONFIGURATION_ZH.md](docs/CONFIGURATION_ZH.md)
- 前端部署（Vercel）：[docs/FRONTEND_DEPLOYMENT_ZH.md](docs/FRONTEND_DEPLOYMENT_ZH.md)
contracts/PolyWeatherCheckout.sol
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@@ -1,4 +1,4 @@
// SPDX-License-Identifier: MIT
// SPDX-License-Identifier: AGPL-3.0-only
pragma solidity ^0.8.24;
interface IERC20 {
contracts/PolyWeatherCheckoutV2.sol
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@@ -1,4 +1,4 @@
// SPDX-License-Identifier: MIT
// SPDX-License-Identifier: AGPL-3.0-only
pragma solidity ^0.8.24;
interface IERC20 {
docs/API_ZH.md
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@@ -235,8 +235,8 @@ curl -s http://127.0.0.1:8000/api/system/status | python3 -m json.tool
docker compose logs -f polyweather | egrep "payment event loop started|payment confirm loop started|payment auto-confirmed"
```
## 10. 开源口径说明
## 10. AGPL 与公开口径说明
对外公开文档仅覆盖通用 API 契约。生产商业策略参数不在公开文档披露。
本仓库代码自 `2026-03-30` 起采用 `AGPL-3.0-only`。对外公开文档仅覆盖通用 API 契约；生产商业策略参数、私有运营阈值与托管服务能力不在公开文档披露。
详见：[Open-Core 与商用边界](OPEN_CORE_POLICY.md)
详见：[AGPL-3.0 与商用边界](OPEN_CORE_POLICY.md)
docs/COMMERCIALIZATION.md
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@@ -41,14 +41,14 @@ PolyWeather 是面向温度结算场景的气象决策层，不是通用天气
> 说明：具体运营策略可按阶段调整，生产参数建议放私有仓库。
## 5. 建议的开源边界
## 5. 许可证与商用边界
请按 Open-Core 执行：
当前仓库代码采用 `AGPL-3.0-only`：
- 开源：基础能力与通用支付流程。
- 私有：商业风控、营销策略、关键运营参数、内部审计策略。
- 公开：基础能力与通用支付流程源码。
- 不随代码许可证授权：商业风控、营销策略、关键运营参数、内部审计策略、品牌与托管服务资产。
详见：[Open-Core 与商用边界](OPEN_CORE_POLICY.md)
详见：[AGPL-3.0 与商用边界](OPEN_CORE_POLICY.md)
## 6. 上线检查清单（收费前）
docs/OPEN_CORE_POLICY.md
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# Open-Core 与商用边界
# AGPL-3.0 与商用边界
最后更新：`2026-03-14`
最后更新：`2026-03-30`
## 1. 目标
## 1. 当前许可证
在保持社区可用性的前提下，保护商业化阶段的核心经营资产。
- 本仓库代码自 `2026-03-30` 起采用 **GNU Affero General Public License v3.0 only**（`AGPL-3.0-only`）。
- 该许可证适用于仓库中未另行声明许可证的源代码与文档。
- 如果你修改本项目并通过网络向用户提供服务，需按 AGPL 第 13 条向用户提供对应源码。
## 2. 仓库公开范围（可开源）
## 2. 旧版本说明
- 天气数据采集与标准化（METAR / Open-Meteo / MGM 接口层）。
- DEB 与基础趋势分析、概率桶计算。
- Dashboard 基础体验与 API/BFF 结构。
- Telegram Bot 基础命令与基础积分机制。
- 合约支付标准流程（钱包绑定、intent、提交、确认、补单）。
- 在本次切换前已经发布的 MIT 版本，仍按其原始许可证生效。
- 本次变更不会追溯撤销既往已发布版本的 MIT 授权。
## 3. 生产私有范围（建议不公开）
## 3. 仓库公开范围
- 商业风控参数与规则库：
- 错价信号阈值组合、推送阈值、异常检测策略。
- 运营策略资产：
- 用户分层规则、促销规则、留存策略、活动模板。
- 付费系统敏感细节：
- 实时对账容错阈值、退款审计策略、内部财务映射规则。
- 私有运维资产：
- 生产告警路由、内部频道映射、应急脚本与排障手册。
- 天气数据采集与标准化（METAR / Open-Meteo / MGM / 官方结算源接口层）。
- DEB、基础趋势分析、概率桶、历史对账、前端看板与 Bot 基础能力。
- 标准支付流程、链上收款合约与公开 API/BFF 结构。
## 4. 配置与数据安全红线
## 4. 不在仓库许可证授权范围内的资产
- 不提交：`.env`、私钥、API key、机器人 token。
- 不提交：生产数据库、运行时状态文件、支付流水快照。
- 不提交：用户身份信息、钱包映射、订阅原始审计日志。
- 商标、品牌名、域名、Logo、商店素材与市场宣传文案。
- 生产数据库、用户资料、钱包映射、订阅审计日志、内部报表。
- 私有运营脚本、增长工具、内部风控参数、收费策略细节与内部阈值。
- 托管服务本身、SLA、客服、运维值守与内部告警路由。
## 5. 推荐发布模式
## 5. 配置与数据安全红线
### 5.1 Community Edition（开源）
- 不提交：`.env`、私钥、API key、机器人 token、第三方 service role key。
- 不提交：生产数据库、运行态快照、支付流水快照、用户身份信息。
- 不提交：仅用于线上商业判断的私有规则库与内部操作手册。
- 提供基础分析与基础看板。
- 可选保留只读市场扫描。
- 默认关闭商业化运营规则。
## 6. 对部署者的要求
### 5.2 Production Edition（私有）
- 若你提供公开网络服务，应在产品界面中提供清晰可访问的源码入口。
- 若你修改了本项目再对外提供网络服务，应公开与你实际运行版本对应的源码。
- 若你使用了仓库外的私有数据、商标或运营资产，这些额外资产不因 AGPL 自动获得授权。
- 启用收费、订阅、积分抵扣、风控、私有监控。
- 仅在私有仓库维护运营策略与敏感参数。
## 7. 法务与运营建议
## 6. 文档口径规范
对外文档仅描述：
- 能力边界与使用方式。
- 可公开的技术架构。
- 不包含可被直接复刻的商业参数。
不对外文档描述：
- 具体策略阈值、用户分层细则、收益归因规则。
## 7. 许可证与法务建议（简版）
- 建议保持仓库代码许可证与商标/品牌授权分离。
- 若提供商业服务，建议在官网补充服务条款与隐私政策。
- 对“订阅权益”与“可用性”做明确 SLA 与免责边界。
- 代码许可证与商标/品牌授权应继续分离管理。
- 官网应补充服务条款、隐私政策与付费权益说明。
- 若后续接受外部贡献，再次调整许可证前应先确认贡献者版权归属与再许可条件。
docs/deep-research-report.md
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## 执行摘要
PolyWeather（仓库：`yangyuan-zhen/PolyWeather`）定位为**面向温度类结算预测市场（如 Polymarket 的温度结算合约）**的“生产级气象情报系统”，核心在于把多源天气观测/预报转化为**结算导向的概率桶（μ + bucket distribution）**，并进一步映射到市场报价完成**错价扫描**；同时提供 Web 仪表盘与 Telegram Bot 两套交互入口，并包含 Polygon 链上 USDC/USDC.e 支付、自动补单与订阅/积分体系。项目 README 明确其“Open-Core”边界：仓库公开天气聚合、基础分析、看板、Bot、标准支付流程；生产私有部分包含商业风控、阈值与运营工具等。
PolyWeather（仓库：`yangyuan-zhen/PolyWeather`）定位为**面向温度类结算预测市场（如 Polymarket 的温度结算合约）**的“生产级气象情报系统”，核心在于把多源天气观测/预报转化为**结算导向的概率桶（μ + bucket distribution）**，并进一步映射到市场报价完成**错价扫描**；同时提供 Web 仪表盘与 Telegram Bot 两套交互入口，并包含 Polygon 链上 USDC/USDC.e 支付、自动补单与订阅/积分体系。项目 README 现明确仓库代码采用 `AGPL-3.0-only`，同时将品牌、商标、生产私有数据与运营阈值保留在代码许可证之外。
从工程实现看，截至 `2026-03-21`，项目已经完成一轮明确的工程化收口：多源天气采集仍保持现有业务能力，同时已完成采集层与 Web API 大文件拆分、CI 质量门禁、配置分级（`.env.example` / `.env.secrets.example` / 中文部署文档）、EMOS/CRPS 校准链路、运行态状态与缓存向 SQLite 的渐进迁移，以及基础可观测性接口（`/healthz`、`/api/system/status`、`/metrics`）。
这意味着报告里最初最突出的“工程地基缺失”问题，已经有一部分被关闭：`src/data_collection/weather_sources.py` 与 `web/app.py` 不再是原来的超大单文件；GitHub Actions 已覆盖 Python、前端和 Docker build；配置与密钥治理已成体系；运行态状态不再只能依赖 JSON/JSONL 文件；EMOS 也不再只是概念，而是进入了可训练、可评估、可 shadow、可门禁判断的阶段。
但项目仍处在“从可用走向稳态”的中段，而不是终局。当前真正的高优先级问题已收敛为三类：第一，**SQLite 迁移仍处于推荐的 dual 过渡模式**，线上真正切主读路径前仍需跑一段时间验证；第二，**可观测性只完成了轻量级指标层**，还没有形成完整的外部监控、阈值告警与趋势面板；第三，**EMOS 仍未达到生产切换标准**，当前门禁结论明确为 `hold`，阻塞原因是 shadow bucket brier 明显退化。支付链路方面，链下审计与容灾已明显增强：事件重放、SQLite 审计事件、RPC 多节点容灾、合约静态检查、`/ops` 支付异常单、按邮箱恢复脚本都已补齐；当前剩余风险主要集中在**链上合约本身仍是最小实现**，尚未升级到 SafeERC20、Pausable、链上套餐绑定等更强防护版本。
@@ -18,7 +18,7 @@ DEB（Dynamic Error Balancing）基于过去 N 天模型误差（MAE）倒数加
**市场层（Polymarket 行情对照）**：只读模式从 Gamma API 发现市场、从 CLOB（`py-clob-client` 或 REST 回退）读取价格/盘口并计算 edge（模型概率 − 市场概率）生成信号标签。
**商业化与支付**：订阅（`Pro Monthly 5 USDC`）、积分抵扣、Polygon 链上收款合约（USDC/USDC.e），并提供“事件监听 + 周期确认”的自动补单机制。
**支持的数据集/数据源**：项目不是传统“训练数据集+模型训练”的机器学习仓库；其“数据集”本质是外部实时/预报 API 与站点观测数据。对外部数据的使用需要遵守来源方的访问与速率限制，例如 AviationWeather Data API 明确限制请求频率（含每分钟请求上限/建议降低频率与使用缓存文件）。
**许可证**：仓库根目录 `LICENSE` 为 MIT。 同时 README 强调 Open-Core 策略与生产私有组件边界，意味着“可复现/可审计”的范围以公开部分为准。
**许可证**：仓库根目录 `LICENSE` 当前为 `AGPL-3.0-only`。同时 README 与策略文档明确：品牌、商标、生产私有数据与运营策略不随代码许可证一并授权。
（插图：项目 README 中包含产品截图，可用于快速理解信息架构与 UI 形态）
![PolyWeather demo map](https://raw.githubusercontent.com/yangyuan-zhen/PolyWeather/main/docs/images/demo_map.png)
@@ -147,7 +147,7 @@ Web/Telegram 请求 → FastAPI 调用采集器抓取/复用缓存 → 分析引
项目强依赖外部 API（Open-Meteo、AviationWeather、NWS、HKO、CWA、Polymarket、Supabase）。其中 AviationWeather Data API 有明确速率限制；Polymarket 官方说明 Gamma/Data/CLOB 三套 API 分属不同域，CLOB 交易端点需鉴权且策略可能变化；Supabase 明确强调 `service_role`/secret keys 绝不可暴露。若缺乏集中治理（重试/退避/熔断/降级/配额监控/密钥轮换），稳定性与合规不可控。
**可观测性已起步，但仍不构成完整监控体系**：项目现在已有 `/healthz`、`/api/system/status`、`/metrics`，并为 HTTP 与关键第三方源增加了轻量指标；但仍缺少 Prometheus/Grafana 级别的外部抓取、告警阈值、趋势面板和运行日报。这部分现在属于“已开始，不算完成”。
**EMOS 已完成工程接入，但未完成生产发布**：EMOS/CRPS 校准、shadow 观测、rollout report、上线门禁都已实现；当前真实门禁结果为 `hold`，阻塞原因是 shadow bucket brier 明显退化。因此概率引擎标准化并非未做，而是“工程完成、发布未通过”。
**许可证/商业使用的潜在冲突点**：仓库自身是 MIT，但如果未来尝试引入外部 AI 预报模型，需要非常谨慎：GraphCast 仓库代码 Apache-2.0，但权重使用 CC BY-NC-SA 4.0（非商业），Pangu-Weather 权重同样 BY-NC-SA 且明确禁止商业用途；不加区分地把这些模型用于付费产品会留下法律风险。
**许可证/商业使用的潜在冲突点**：仓库自身现为 `AGPL-3.0-only`，但如果未来尝试引入外部 AI 预报模型，仍需单独核验第三方代码与权重的商用条件：GraphCast 仓库代码 Apache-2.0，但权重使用 CC BY-NC-SA 4.0（非商业），Pangu-Weather 权重同样 BY-NC-SA 且明确禁止商业用途；不加区分地把这些模型用于付费产品会留下法律风险。
## 对标分析
为满足“至少 3 个相似开源项目或近期论文”对标，本报告选择三类代表：
@@ -158,7 +158,7 @@ Web/Telegram 请求 → FastAPI 调用采集器抓取/复用缓存 → 分析引
| 项目/论文 | 解决的问题 | 输出形态 | 性能/效果（公开描述） | 易用性与依赖 | 许可证要点 |
| --------------------------------------------------------- | ---------------------------------------------------- | ------------------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------- | ---------------------------------------------------------------------------------------- | -------------------------------------------------------------------------------------------------- |
| **PolyWeather**（本仓库） | 温度结算市场气象情报：多源→概率桶→错价扫描→订阅/支付 | 生产级应用（Web+Bot+API+支付） | 以工程能力为主；内置 DEB、概率桶、死盘判定、市场扫描；覆盖 20 城市。 | 主要依赖外部 API；Docker Compose 一键启动。 | 仓库 MIT；Open-Core（部分生产规则私有）。 |
| **PolyWeather**（本仓库） | 温度结算市场气象情报：多源→概率桶→错价扫描→订阅/支付 | 生产级应用（Web+Bot+API+支付） | 以工程能力为主；内置 DEB、概率桶、死盘判定、市场扫描；覆盖 20 城市。 | 主要依赖外部 API；Docker Compose 一键启动。 | 仓库 `AGPL-3.0-only`；品牌、生产私有数据与运营规则不随代码许可证授权。 |
| **GraphCast**（google-deepmind/graphcast） | 10 天全球中期预报（ML 替代/增强 NWP） | 模型代码+权重+notebooks | 论文与介绍提到在大量指标上优于主流确定性系统；仓库提供预训练权重与示例数据入口，并提示 ERA5/HRES 数据条款需另行遵守。 | 完整训练需 ERA5 等；更适合科研/平台级推理，不是产品级 BFF。 | 代码 Apache-2.0；权重 CC BY-NC-SA 4.0（商业限制）。 |
| **FourCastNet**（NVlabs/FourCastNet） | 高分辨率 data-driven 全球预报（AFNO/ViT） | 模型训练/推理代码+数据/权重链接 | README 描述：0.25° 分辨率、周尺度推理非常快，并可做大规模集合；适合平台型预报。 | 训练/数据依赖大（ERA5 子集 TB 级）；工程集成成本高。 | BSD 3-Clause（代码）。 |
| **Pangu-Weather**（198808xc/Pangu-Weather + Nature 论文） | 3D Transformer 架构的中期全球预报 | ONNX 推理代码+预训练模型 | Nature 论文称在 reanalysis 上对比 IFS 有更强确定性预报表现，并强调速度优势；仓库提供 ONNX 推理与 lite 版训练说明。 | 模型文件大（多份 ~GB 级），训练资源需求高；更适合科研推理或内部平台。 | 权重 BY-NC-SA 4.0、明确禁止商业用途。 |
@@ -244,7 +244,7 @@ PolyWeather 的评测应围绕“结算场景”而非传统数值天气预报
**密钥泄露与权限滥用**：Supabase 明确强调 `service_role` 属高权限密钥，绝不可出现在前端或公开环境。缓解：密钥分级、CI secret scan、运行时最小权限、日志脱敏。
**支付链路最终一致性与链上不确定性**：链上事件索引延迟、RPC 不稳定、交易确认数不足都会导致误判。当前项目已经补齐“事件监听 + 确认补单”双路径、事件重放脚本、SQLite 审计事件与多 RPC fallback；现阶段的主要剩余风险不再是“没有防护”，而是链上合约仍为最小实现，owner 为单地址管理，且没有 pause 开关与 SafeERC20。
**引入外部 AI 预报模型的商业合规风险**：GraphCast/Pangu-Weather 的权重许可均带非商业限制（CC BY-NC-SA/BY-NC-SA）；若 PolyWeather 是付费产品，必须先做法务与授权评审。缓解：只在研究环境评估；商用优先选择可商用权重/购买授权/自研。
**Open-Core 边界导致的“公开仓库与生产行为不一致”**：README 明确生产存在私有风控与阈值。缓解：把“公开核心”的可复现与评测做扎实（接口/数据 schema/测试/评测），私有策略只作为可插拔 policy layer 接入。
**代码公开与生产私有资产边界导致的“公开仓库与生产行为不一致”**：README 明确品牌、商标、生产私有数据与运营阈值不在代码许可证授权范围内。缓解：把“公开核心”的可复现与评测做扎实（接口/数据 schema/测试/评测），私有策略只作为可插拔 policy layer 接入。
## 参考链接
- PolyWeather 仓库（本次评估对象）：https://github.com/yangyuan-zhen/PolyWeather
frontend/README.md
+3 -3
View File
@@ -170,10 +170,10 @@ Ops：
- `summary?force_refresh=true`：`no-store`
- 支付相关路由：`no-store`
## 开源边界说明
## AGPL 与商用边界说明
此前端仓库包含通用产品界面和标准支付体验。
商业策略调优、私有运营流程和敏感生产参数不在公开文档范围内。
此前端代码随仓库一起采用 `AGPL-3.0-only`。
生产私有运营流程、商业策略调优、敏感生产参数、品牌与托管服务能力不在代码许可证授权范围内。
详见根目录策略文档：`docs/OPEN_CORE_POLICY.md`
scripts/train_lgbm_daily_high.py
+31 -21
View File
@@ -4,21 +4,14 @@ import json
import os
import sys
from datetime import datetime
from typing import Any, Dict, List
from typing import TYPE_CHECKING, Any, Dict, List
ROOT_DIR = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
if ROOT_DIR not in sys.path:
sys.path.insert(0, ROOT_DIR)
if TYPE_CHECKING:
import lightgbm as lgb
import numpy as np
from src.models.lgbm_features import (
FEATURE_NAMES,
build_training_samples,
schema_payload,
)
MODEL_PATH = os.path.join(ROOT_DIR, "artifacts", "models", "lgbm_daily_high.txt")
SCHEMA_PATH = os.path.join(ROOT_DIR, "artifacts", "models", "lgbm_daily_high_schema.json")
@@ -58,15 +51,21 @@ def _chronological_split(samples: List[Dict[str, Any]]) -> tuple[List[Dict[str,
return ordered[:-validation_count], ordered[-validation_count:]
def _dataset_from_samples(samples: List[Dict[str, Any]]) -> tuple[np.ndarray, np.ndarray]:
def _dataset_from_samples(samples: List[Dict[str, Any]], np: Any) -> tuple[Any, Any]:
features = np.asarray([row["vector"] for row in samples], dtype=np.float32)
targets = np.asarray([row["target"] for row in samples], dtype=np.float32)
return features, targets
def _train_model(train_samples: List[Dict[str, Any]], valid_samples: List[Dict[str, Any]]) -> lgb.Booster:
train_x, train_y = _dataset_from_samples(train_samples)
train_data = lgb.Dataset(train_x, label=train_y, feature_name=FEATURE_NAMES, free_raw_data=True)
def _train_model(
train_samples: List[Dict[str, Any]],
valid_samples: List[Dict[str, Any]],
lgb: Any,
np: Any,
feature_names: List[str],
) -> Any:
train_x, train_y = _dataset_from_samples(train_samples, np)
train_data = lgb.Dataset(train_x, label=train_y, feature_name=feature_names, free_raw_data=True)
valid_sets = [train_data]
valid_names = ["train"]
@@ -85,8 +84,8 @@ def _train_model(train_samples: List[Dict[str, Any]], valid_samples: List[Dict[s
callbacks = []
if valid_samples:
valid_x, valid_y = _dataset_from_samples(valid_samples)
valid_data = lgb.Dataset(valid_x, label=valid_y, feature_name=FEATURE_NAMES, reference=train_data)
valid_x, valid_y = _dataset_from_samples(valid_samples, np)
valid_data = lgb.Dataset(valid_x, label=valid_y, feature_name=feature_names, reference=train_data)
valid_sets.append(valid_data)
valid_names.append("valid")
callbacks.append(lgb.early_stopping(stopping_rounds=15, verbose=False))
@@ -101,7 +100,7 @@ def _train_model(train_samples: List[Dict[str, Any]], valid_samples: List[Dict[s
)
def _evaluate(booster: lgb.Booster, samples: List[Dict[str, Any]]) -> Dict[str, Any]:
def _evaluate(booster: Any, samples: List[Dict[str, Any]], np: Any) -> Dict[str, Any]:
if not samples:
return {
"sample_count": 0,
@@ -111,7 +110,7 @@ def _evaluate(booster: lgb.Booster, samples: List[Dict[str, Any]]) -> Dict[str,
"median_mae": None,
}
features, _ = _dataset_from_samples(samples)
features, _ = _dataset_from_samples(samples, np)
preds = booster.predict(features, num_iteration=booster.best_iteration)
lgbm_pairs: List[tuple[float, float]] = []
deb_pairs: List[tuple[float, float]] = []
@@ -144,14 +143,25 @@ def _evaluate(booster: lgb.Booster, samples: List[Dict[str, Any]]) -> Dict[str,
def main() -> int:
if ROOT_DIR not in sys.path:
sys.path.insert(0, ROOT_DIR)
import lightgbm as lgb
import numpy as np
from src.models.lgbm_features import (
FEATURE_NAMES,
build_training_samples,
schema_payload,
)
samples = build_training_samples()
if len(samples) < 16:
raise SystemExit(f"Not enough supervised samples for LightGBM training: {len(samples)}")
train_samples, valid_samples = _chronological_split(samples)
booster = _train_model(train_samples, valid_samples)
booster = _train_model(train_samples, valid_samples, lgb, np, FEATURE_NAMES)
all_features, all_targets = _dataset_from_samples(samples)
all_features, all_targets = _dataset_from_samples(samples, np)
final_train = lgb.Dataset(all_features, label=all_targets, feature_name=FEATURE_NAMES, free_raw_data=True)
final_booster = lgb.train(
params={
@@ -174,8 +184,8 @@ def main() -> int:
final_booster.save_model(MODEL_PATH)
metrics = {
"validation": _evaluate(booster, valid_samples),
"full_sample": _evaluate(final_booster, samples),
"validation": _evaluate(booster, valid_samples, np),
"full_sample": _evaluate(final_booster, samples, np),
}
schema = schema_payload(
model_path=os.path.relpath(MODEL_PATH, ROOT_DIR),