test: 增加测试
This commit is contained in:
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.gitignore
vendored
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build/
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dist/
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old/
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AGENT.md
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AGENTS.md
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*.log.*
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# Byte-compiled / optimized / DLL files
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485
ARCHITECTURE.md
485
ARCHITECTURE.md
@@ -1,62 +1,467 @@
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# 项目架构
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## 架构图(待更新 0.5.0)
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以下 Mermaid 图展示了 HeurAMS 的主要组件及其关系:
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## 整体架构概览
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```mermaid
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graph TB
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subgraph "用户界面层 (TUI)"
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TUI[Textual TUI]
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Widgets[界面组件]
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TUI[Textual App]
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Screens[应用屏幕]
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Widgets[谜题组件]
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end
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subgraph "内核层 Kernel"
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Reactor[调度反应器]
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Algorithms[算法模块]
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Particles[数据模型]
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Puzzles[谜题引擎]
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RepoLib[仓库系统]
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Auxiliary[辅助工具]
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end
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subgraph "服务层"
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Config[配置管理]
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Config[配置管理 ConfigDict]
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Logger[日志系统]
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Timer[时间服务]
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AudioService[音频服务]
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TTSService[TTS服务]
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SyncService[同步服务]
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OtherServices[其他服务]
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end
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subgraph "内核层"
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Algorithms[算法模块]
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Particles[数据模型]
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Puzzles[谜题模块]
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Reactor[调度反应器]
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Audio[音频服务]
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TTS[TTS 服务]
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Favorites[收藏管理]
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Attic[持久化]
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Hasher[哈希服务]
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end
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subgraph "提供者层"
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AudioProvider[音频提供者]
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TTSProvider[TTS提供者]
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OtherProviders[其他提供者]
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AudioProv[音频提供者]
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TTSProv[TTS 提供者]
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LLMProv[LLM 提供者]
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end
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subgraph "数据层"
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Files[本地文件数据]
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RepoDir[TOML/JSON 仓库目录]
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ConfigDir[TOML 配置目录]
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Logs[日志文件]
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end
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subgraph "上下文管理"
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Context[ConfigContext]
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CtxVar[config_var]
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end
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TUI --> Config
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TUI --> Logger
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TUI --> AudioService
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TUI --> TTSService
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TUI --> OtherServices
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Config --> Files
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Config --> Context
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AudioService --> AudioProvider
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TTSService --> TTSProvider
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OtherServices --> OtherProviders
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TUI --> Screens
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Screens --> Reactor
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Screens --> RepoLib
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Screens --> Widgets
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Widgets --> Puzzles
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Widgets --> Reactor
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Reactor --> Algorithms
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Reactor --> Particles
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Reactor --> Puzzles
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Particles --> Files
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Algorithms --> Files
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Particles --> RepoLib
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RepoLib --> Config
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RepoLib --> Auxiliary
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Auxiliary --> Lict
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Auxiliary --> Evalizer
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TUI --> Config
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TUI --> Logger
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TUI --> Audio
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TUI --> TTS
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Config --> ConfigDir
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Audio --> AudioProv
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TTS --> TTSProv
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Attic --> RepoDir
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```
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## 数据模型
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项目以物理粒子隐喻为核心, 将记忆单元拆解为三个模型:
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### Nucleon (核子) — 内容层
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```
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Nucleon(ident, payload, common)
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```
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- **只读**内容容器. 通过 `Evalizer` (基于 `eval()` 的模板系统)对 payload 和 common 进行编译展开.
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- 包含 `puzzles` 字段, 定义该记忆单元支持哪些谜题类型.
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- 从 `repo.payload` 和 `repo.typedef["common"]` 配对创建.
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- 一旦创建, 内容不可修改 (`__setitem__` 抛出 `AttributeError`).
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### Electron (电子) — 状态层
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```
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Electron(ident, algodata, algo_name)
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```
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- 算法状态数据的包装器. 每个 Electron 绑定一个算法 (`algorithms[algo_name]`).
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- `algodata` 是到仓库 `algodata.lict` 中对应字典的**引用**, 修改即持久化.
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- 核心方法：`activate()` (标记激活)、`revisor()` (评分迭代)、`is_due()` (到期判断).
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### Orbital (轨道) — 策略层
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```
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orbital = {
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"schedule": ["quick_review", "recognition"],
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"routes": {
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"quick_review": [["MCQ", "1.0"], ["Cloze", "0.5"]],
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"recognition": [["Recognition", "1.0"]],
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}
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}
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```
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- 定义复习阶段流程和各阶段内谜题选择策略的纯字典.
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- 每个阶段对应一组 `(谜题类型, 概率系数)` 元组列表, 概率系数 >1 的部分表示强制重复次数.
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### Atom (原子) — 运行时组装
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```
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Atom(nucleon, electron, orbital)
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```
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- 三者的运行时组合, 附带 `runtime` 运行时标志 (`locked`, `min_rate`, `new_activation`).
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- 是 UI 和调度层操作的基本单位.
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- `revise()` 方法在 `locked` 为真时调用 `electron.revisor(min_rate)`, 执行最终评分迭代.
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**关系图**：
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```mermaid
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graph LR
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subgraph "持久化存储"
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Payload[(payload.toml)]
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Common[(typedef.toml)]
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Algodata[(algodata.json)]
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Schedule[(schedule.toml)]
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end
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subgraph "运行时组装"
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Nucleon -->|内容| Atom
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Electron -->|状态| Atom
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Orbital -->|策略| Atom
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end
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Payload --> Nucleon
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Common --> Nucleon
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Algodata --> Electron
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Schedule --> Orbital
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```
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---
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## 调度反应器 (Reactor)
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调度反应器是核心业务流程引擎, 采用三层嵌套的有限状态机设计 (基于 `transitions` 库).
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### 状态枚举定义
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| 状态机 | 状态 | 说明 |
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|--------|------|------|
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| **RouterState** | `unsure` | 初始状态, 自动推进 |
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| | `quick_review` | 快速复习阶段 |
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| | `recognition` | 新记忆识别阶段 |
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| | `final_review` | 最终总复习阶段 |
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| | `finished` | 完成, 执行评分 |
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| **ProcessionState** | `active` | 进行中 |
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| | `finished` | 已完成 |
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| **ExpanderState** | `exammode` | 考试模式 (正面答题) |
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| | `retronly` | 回溯模式 (仅识别) |
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### 三层嵌套结构
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```mermaid
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graph TB
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subgraph "Router (全局路由器)"
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R[Router<br/>状态: unsure→quick_review→recognition→final_review→finished]
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P1[Procession 队列1: 快速复习]
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P2[Procession 队列2: 新记忆]
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P3[Procession 队列3: 总复习]
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R --> P1
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R --> P2
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R --> P3
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end
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subgraph "Procession (单阶段队列)"
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P1 --> E1[Expander 原子A]
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P1 --> E2[Expander 原子B]
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P1 --> E3[Expander 原子C]
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M{forward 推进} --> |完成| Finish((FINISHED))
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end
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subgraph "Expander (单原子展开器)"
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E1 --> S[(轨道策略)]
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S -->|概率展开| PZ1[谜题1: MCQ]
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S -->|概率展开| PZ2[谜题2: Cloze]
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PZ1 -->|评分| RPT[report]
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PZ2 -->|评分| RPT
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RPT -->|finish| RETRO[retronly 回溯模式]
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end
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```
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### 数据流详解
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```
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Router.__init__(atoms)
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│
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├─ 新旧原子分流
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│ ├─ old_atoms → Procession(quick_review) "初始复习"
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│ └─ new_atoms → Procession(recognition) "新记忆"
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│
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└─ 所有原子 → Procession(final_review) "总体复习"
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│
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└─ Procession.forward()
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│
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├─ cursor >= len(atoms) → finish()
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└─ cursor < len(atoms) → next_atom
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│
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└─ Procession.get_expander()
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│
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└─ Expander(atom, route)
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│
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├─ 读取 orbital.routes[route_value]
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├─ 概率展开为谜题列表 self.puzzles_inf
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├─ exammode → 依次展示谜题
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├─ report(rating) → 记录最低评分
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├─ forward() → 下一个谜题或 finish → retronly
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└─ retronly → 展示 Recognition
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│
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└─ Atom.revise()
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│
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└─ Electron.revisor(min_rate)
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│
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└─ Algorithm.revisor(algodata, feedback)
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```
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---
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## 算法系统
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所有算法继承自 `BaseAlgorithm`, 以类方法的风格实现, 通过 `algorithms` 字典注册.
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| 算法 | 文件 | 状态 | 说明 |
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|------|------|------|------|
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| **SM-2** | `sm2.py` | ✅ 完成 | 经典 SuperMemo 1987 算法 |
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| **NSP-0** | `nsp0.py` | ✅ 完成 | 非间隔过滤调度器 |
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| **SM-15M** | `sm15m.py` + `sm15m_calc.py` | ✅ 完成 | 从 CoffeeScript 移植的 SM-15 |
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| **FSRS** | `fsrs.py` | ❌ 未实现 | 进度: `logger.info("尚未实现")` |
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| **Base** | `base.py` | ✅ 基类 | 定义 `AlgodataDict` 结构和默认值 |
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每个算法提供以下类方法：
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| 方法 | 功能 |
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|------|------|
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| `revisor(algodata, feedback, is_new_activation)` | 根据评分迭代记忆数据 |
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| `is_due(algodata)` | 判断是否到期复习 |
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| `get_rating(algodata)` | 获取评分信息 |
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| `nextdate(algodata)` | 获取下一次复习时间戳 |
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| `check_integrity(algodata)` | 校验 algodata 数据结构完整性 |
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### 算法数据结构 (AlgodataDict)
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```python
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{
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"real_rept": int, # 实际复习次数
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"rept": int, # 当前重复计数
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"interval": int, # 间隔天数
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"last_date": int, # 上次复习日期
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"next_date": int, # 下次到期日期
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"is_activated": int, # 是否已激活 (0/1)
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"last_modify": float, # 最后修改时间戳
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}
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```
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---
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## 仓库系统 (Repo)
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仓库是 TOML/JSON 文件目录, 无数据库依赖.
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### 目录结构
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```
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data/repo/<package_name>/
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├── manifest.toml # 元信息: title, author, package, desc
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├── typedef.toml # 通用元数据、谜题定义、注解
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├── payload.toml # 记忆条目 (key=ident)
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├── algodata.json # 算法状态 (key=ident)
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└── schedule.toml # 轨道/复习策略
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```
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### Repo 类设计
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```mermaid
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classDiagram
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class Repo {
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+dict schedule
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+Lict payload
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+dict manifest
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+dict typedef
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+Lict algodata
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+Path source
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+Lict nucleonic_data_lict
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+dict orbitic_data
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+Lict electronic_data_lict
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+from_repodir(source) ~Repo
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+from_dict(dictdata) ~Repo
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+create_new_repo() ~Repo
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+persist_to_repodir(save_list, source)
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+export_to_dict() dict
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}
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```
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- `payload` 和 `algodata` 使用 `Lict` (列表+字典混合容器), 支持双模式访问.
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- `_generate_particles_data()` 在初始化时自动将 payload 数据转换为 `Nucleon` 所需的格式.
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- 默认保存列表 `default_save_list = ["algodata"]`, 仅持久化算法状态.
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---
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## Lict 集合
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`Lict` 继承 `MutableSequence`, 同时维护列表和字典访问：
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```python
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lict = Lict()
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lict.append(("key1", value1)) # 列表追加
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lict["key1"] # 字典访问
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lict[0] # 索引访问
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lict.keys() # 所有键
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lict.dicted_data # 纯字典导出
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```
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脏同步机制：修改列表时自动同步字典, 修改字典时自动同步列表. 用于 `payload` 和 `algodata` 的双模式访问需求.
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---
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## 配置系统 (ConfigDict)
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`ConfigDict` 继承 `UserDict`, 是**单例模式**的 TOML 懒加载配置管理器.
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### 配置目录约定
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```
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data/config/
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├── _.toml # 顶层默认值 (递归合并)
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├── interface/
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│ ├── _.toml # interface 层默认值
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│ ├── global.toml
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│ └── puzzles.toml
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├── services/
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│ ├── _.toml # services 层默认值
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│ ├── audio.toml
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│ └── tts.toml
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└── repo/
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└── _.toml
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```
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- `_.toml` 文件 = 该目录层级的默认值, 合并到父级
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- 带后缀文件 = 按需懒加载
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- 子目录 = 递归子配置
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### 上下文管理
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```python
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from heurams.context import config_var, ConfigContext
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# 全局访问
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config = config_var.get()
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algo = config["interface"]["global"]["algorithm"]
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# 作用域覆盖
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with ConfigContext(test_config):
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... # 临时使用测试配置
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```
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||||
---
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||||
## 提供者系统 (Providers)
|
||||
|
||||
可插拔的后端实现, 通过 `providers/__init__.py` 中的字典注册.
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||||
|
||||
| 类别 | 提供者 | 说明 |
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||||
|------|--------|------|
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||||
| **TTS** | `edge_tts` | Microsoft Edge TTS (在线) |
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||||
| | `basetts` | 桩基类 (未实现) |
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||||
| **Audio** | `playsound` | 跨平台音频播放 |
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||||
| | `termux` | Android Termux 环境 |
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||||
| **LLM** | `openai` | OpenAI 兼容 API (未完整实现) |
|
||||
|
||||
选择方式：`services/*.toml` 中的 `provider` 字段.
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||||
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||||
---
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||||
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||||
## 谜题系统 (Puzzles)
|
||||
|
||||
谜题引擎用于在复习阶段生成评估视图：
|
||||
|
||||
| 谜题 | 文件 | 说明 |
|
||||
|------|------|------|
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||||
| **MCQ** | `mcq.py` | 选择题 (Multiple Choice) |
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||||
| **Cloze** | `cloze.py` | 完形填空 (Cloze Deletion) |
|
||||
| **Recognition** | `recognition.py` | 认读识别 |
|
||||
| **Guess** | `guess.py` | 猜测词义 |
|
||||
| **Base** | `base.py` | 抽象基类 |
|
||||
|
||||
谜题通过轨道策略 (Orbital)在 `Expander` 中按概率展开, 每个原子可产生多个谜题, 每个谜题独立评分.
|
||||
|
||||
---
|
||||
|
||||
## 服务层
|
||||
|
||||
| 服务 | 文件 | 说明 |
|
||||
|------|------|------|
|
||||
| **Config** | `config.py` | `ConfigDict(UserDict)` TOML 懒加载单例 |
|
||||
| **Logger** | `logger.py` | `get_logger(name)` → 层级日志 (`heurams.*`) |
|
||||
| **Timer** | `timer.py` | `get_daystamp()` / `get_timestamp()`, 支持可配置覆盖 |
|
||||
| **Audio** | `audio_service.py` | 音频播放, 路由到配置的音频提供者 |
|
||||
| **TTS** | `tts_service.py` | 文本转语音, 路由到配置的 TTS 提供者 |
|
||||
| **Favorites** | `favorite_service.py` | JSON5 持久化的收藏管理器 (单例) |
|
||||
| **Attic** | `attic.py` | 结构化 pickle 持久化, 支持 `<DAYSTAMP>`/`<TIMESTAMP>` 占位符 |
|
||||
| **Hasher** | `hasher.py` | MD5 哈希 |
|
||||
| **Epath** | `epath.py` | 点符号嵌套字典访问 (`epath(dct, "a.b.c")`) |
|
||||
| **TextProc** | `textproc.py` | `truncate()`, `domize()`, `undomize()` |
|
||||
|
||||
日志系统：每个模块通过 `get_logger(__name__)` 创建自己的日志器, 日志文件 10MB 轮转, 最多 5 个备份, 追加到 `heurams.log`.
|
||||
|
||||
---
|
||||
|
||||
## 复习全流程
|
||||
|
||||
```mermaid
|
||||
sequenceDiagram
|
||||
participant User as 用户
|
||||
participant UI as TUI
|
||||
participant Router as Router
|
||||
participant Procession as Procession
|
||||
participant Expander as Expander
|
||||
participant Atom as Atom
|
||||
participant Electron as Electron
|
||||
participant Algo as Algorithm
|
||||
|
||||
User->>UI: 开始复习
|
||||
UI->>Router: Router(atoms)
|
||||
Router->>Procession: 创建复习队列
|
||||
Router->>Procession: 创建新记忆队列
|
||||
Router->>Procession: 创建总复习队列
|
||||
Procession->>Expander: 展开当前原子
|
||||
Expander->>Expander: 解析轨道策略
|
||||
Expander-->>UI: 展示谜题
|
||||
User->>UI: 评分 (1-5)
|
||||
UI->>Expander: report(rating)
|
||||
Expander->>Expander: forward() 下一个谜题
|
||||
Expander-->>UI: 下一个谜题或回溯
|
||||
Expander->>Expander: finish() → retronly
|
||||
Expander-->>UI: Recognition 回溯
|
||||
User->>UI: 最终评分
|
||||
UI->>Atom: revise()
|
||||
Atom->>Electron: revisor(min_rate)
|
||||
Electron->>Algo: revisor(algodata, feedback)
|
||||
Algo-->>Electron: 更新 algodata
|
||||
Algo-->>Atom: 更新 interval, next_date
|
||||
Procession->>Procession: forward() 下一原子
|
||||
Procession-->>Router: 队列完成
|
||||
Router->>Router: 切换阶段
|
||||
Router-->>UI: 完成 (finished)
|
||||
UI->>User: 显示总结
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## 关键设计决策
|
||||
|
||||
1. **无数据库** — 所有持久化基于 TOML/JSON 文件目录, 方便版本管理和手动编辑.
|
||||
2. **Lict 双模式访问** — payload 和 algodata 同时支持列表迭代和字典查找, 兼顾批处理和随机访问.
|
||||
3. **物理隐喻分离** — 内容 (Nucleon)、状态 (Electron)、策略 (Orbital) 三者正交, 可独立替换, 便于组合不同算法和内容类型.
|
||||
4. **transitions 状态机** — 使用 `transitions` 库实现 Router → Procession → Expander 三层嵌套状态机, 每个层次职责明确.
|
||||
5. **Evalizer eval 模板** — 使用 `eval()` 实现动态模板替换, 功能灵活但存在安全风险 (标记为待替换).
|
||||
6. **配置单例** — `ConfigDict` 以规范化路径为键实现单例, 避免多实例导致的配置不一致问题.
|
||||
7. **评分累积** — 原子在多谜题阶段的最终评分取所有谜题的最低评分 (`min_rate`), 确保严格评估.
|
||||
|
||||
@@ -13,6 +13,7 @@ license = "AGPL-3.0-or-later"
|
||||
license-files = ["LICENSE"]
|
||||
|
||||
dependencies = [
|
||||
"fsrs>=6.3.1",
|
||||
#"edge-tts>=7.2.8",
|
||||
#"jieba>=0.42.1",
|
||||
#"openai>=2.32.0",
|
||||
@@ -34,6 +35,21 @@ Issues = "https://github.com/heurams/heurams/issues"
|
||||
heurams = "heurams.__main__:main"
|
||||
tui = "heurams.interface.__main__:main"
|
||||
|
||||
[dependency-groups]
|
||||
dev = [
|
||||
"pytest>=8.0.0",
|
||||
"pytest-cov>=6.0.0",
|
||||
]
|
||||
|
||||
[tool.pytest.ini_options]
|
||||
testpaths = ["tests"]
|
||||
python_files = ["test_*.py", "*_test.py"]
|
||||
pythonpath = ["src"]
|
||||
markers = [
|
||||
"slow: marks tests as slow (deselect with '-m \"not slow\"')",
|
||||
"integration: marks tests as integration tests",
|
||||
]
|
||||
|
||||
[build-system]
|
||||
requires = ["uv_build>=0.7.19"]
|
||||
build-backend = "uv_build"
|
||||
|
||||
21
src/heurams/vendor/pyfsrs/LICENSE
vendored
21
src/heurams/vendor/pyfsrs/LICENSE
vendored
@@ -1,21 +0,0 @@
|
||||
MIT License
|
||||
|
||||
Copyright (c) 2022 Open Spaced Repetition
|
||||
|
||||
Permission is hereby granted, free of charge, to any person obtaining a copy
|
||||
of this software and associated documentation files (the "Software"), to deal
|
||||
in the Software without restriction, including without limitation the rights
|
||||
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
|
||||
copies of the Software, and to permit persons to whom the Software is
|
||||
furnished to do so, subject to the following conditions:
|
||||
|
||||
The above copyright notice and this permission notice shall be included in all
|
||||
copies or substantial portions of the Software.
|
||||
|
||||
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
|
||||
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
|
||||
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
|
||||
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
|
||||
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
|
||||
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
|
||||
SOFTWARE.
|
||||
29
src/heurams/vendor/pyfsrs/__init__.py
vendored
29
src/heurams/vendor/pyfsrs/__init__.py
vendored
@@ -1,29 +0,0 @@
|
||||
"""
|
||||
py-fsrs
|
||||
-------
|
||||
|
||||
Py-FSRS is the official Python implementation of the FSRS scheduler algorithm, which can be used to develop spaced repetition systems.
|
||||
"""
|
||||
|
||||
from fsrs.scheduler import Scheduler
|
||||
from fsrs.state import State
|
||||
from fsrs.card import Card
|
||||
from fsrs.rating import Rating
|
||||
from fsrs.review_log import ReviewLog
|
||||
from typing import TYPE_CHECKING
|
||||
|
||||
if TYPE_CHECKING:
|
||||
from fsrs.optimizer import Optimizer
|
||||
|
||||
|
||||
# lazy load the Optimizer module due to heavy dependencies
|
||||
def __getattr__(name: str) -> type:
|
||||
if name == "Optimizer":
|
||||
global Optimizer
|
||||
from fsrs.optimizer import Optimizer
|
||||
|
||||
return Optimizer
|
||||
raise AttributeError
|
||||
|
||||
|
||||
__all__ = ["Scheduler", "Card", "Rating", "ReviewLog", "State", "Optimizer"]
|
||||
167
src/heurams/vendor/pyfsrs/card.py
vendored
167
src/heurams/vendor/pyfsrs/card.py
vendored
@@ -1,167 +0,0 @@
|
||||
"""
|
||||
fsrs.card
|
||||
---------
|
||||
|
||||
This module defines the Card and State classes.
|
||||
|
||||
Classes:
|
||||
Card: Represents a flashcard in the FSRS system.
|
||||
State: Enum representing the learning state of a Card object.
|
||||
"""
|
||||
|
||||
from __future__ import annotations
|
||||
from dataclasses import dataclass
|
||||
from datetime import datetime, timezone
|
||||
import time
|
||||
import json
|
||||
from typing import TypedDict
|
||||
from typing_extensions import Self
|
||||
from fsrs.state import State
|
||||
|
||||
|
||||
class CardDict(TypedDict):
|
||||
"""
|
||||
JSON-serializable dictionary representation of a Card object.
|
||||
"""
|
||||
|
||||
card_id: int
|
||||
state: int
|
||||
step: int | None
|
||||
stability: float | None
|
||||
difficulty: float | None
|
||||
due: str
|
||||
last_review: str | None
|
||||
|
||||
|
||||
@dataclass(init=False)
|
||||
class Card:
|
||||
"""
|
||||
Represents a flashcard in the FSRS system.
|
||||
|
||||
Attributes:
|
||||
card_id: The id of the card. Defaults to the epoch milliseconds of when the card was created.
|
||||
state: The card's current learning state.
|
||||
step: The card's current learning or relearning step or None if the card is in the Review state.
|
||||
stability: Core mathematical parameter used for future scheduling.
|
||||
difficulty: Core mathematical parameter used for future scheduling.
|
||||
due: The date and time when the card is due next.
|
||||
last_review: The date and time of the card's last review.
|
||||
"""
|
||||
|
||||
card_id: int
|
||||
state: State
|
||||
step: int | None
|
||||
stability: float | None
|
||||
difficulty: float | None
|
||||
due: datetime
|
||||
last_review: datetime | None
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
card_id: int | None = None,
|
||||
state: State = State.Learning,
|
||||
step: int | None = None,
|
||||
stability: float | None = None,
|
||||
difficulty: float | None = None,
|
||||
due: datetime | None = None,
|
||||
last_review: datetime | None = None,
|
||||
) -> None:
|
||||
if card_id is None:
|
||||
# epoch milliseconds of when the card was created
|
||||
card_id = int(datetime.now(timezone.utc).timestamp() * 1000)
|
||||
# wait 1ms to prevent potential card_id collision on next Card creation
|
||||
time.sleep(0.001)
|
||||
self.card_id = card_id
|
||||
|
||||
self.state = state
|
||||
|
||||
if self.state == State.Learning and step is None:
|
||||
step = 0
|
||||
self.step = step
|
||||
|
||||
self.stability = stability
|
||||
self.difficulty = difficulty
|
||||
|
||||
if due is None:
|
||||
due = datetime.now(timezone.utc)
|
||||
self.due = due
|
||||
|
||||
self.last_review = last_review
|
||||
|
||||
def to_dict(self) -> CardDict:
|
||||
"""
|
||||
Returns a dictionary representation of the Card object.
|
||||
|
||||
Returns:
|
||||
CardDict: A dictionary representation of the Card object.
|
||||
"""
|
||||
|
||||
return {
|
||||
"card_id": self.card_id,
|
||||
"state": self.state.value,
|
||||
"step": self.step,
|
||||
"stability": self.stability,
|
||||
"difficulty": self.difficulty,
|
||||
"due": self.due.isoformat(),
|
||||
"last_review": self.last_review.isoformat() if self.last_review else None,
|
||||
}
|
||||
|
||||
@classmethod
|
||||
def from_dict(cls, source_dict: CardDict) -> Self:
|
||||
"""
|
||||
Creates a Card object from an existing dictionary.
|
||||
|
||||
Args:
|
||||
source_dict: A dictionary representing an existing Card object.
|
||||
|
||||
Returns:
|
||||
Self: A Card object created from the provided dictionary.
|
||||
"""
|
||||
|
||||
return cls(
|
||||
card_id=int(source_dict["card_id"]),
|
||||
state=State(int(source_dict["state"])),
|
||||
step=source_dict["step"],
|
||||
stability=(
|
||||
float(source_dict["stability"]) if source_dict["stability"] else None
|
||||
),
|
||||
difficulty=(
|
||||
float(source_dict["difficulty"]) if source_dict["difficulty"] else None
|
||||
),
|
||||
due=datetime.fromisoformat(source_dict["due"]),
|
||||
last_review=(
|
||||
datetime.fromisoformat(source_dict["last_review"])
|
||||
if source_dict["last_review"]
|
||||
else None
|
||||
),
|
||||
)
|
||||
|
||||
def to_json(self, indent: int | str | None = None) -> str:
|
||||
"""
|
||||
Returns a JSON-serialized string of the Card object.
|
||||
|
||||
Args:
|
||||
indent: Equivalent argument to the indent in json.dumps()
|
||||
|
||||
Returns:
|
||||
str: A JSON-serialized string of the Card object.
|
||||
"""
|
||||
return json.dumps(self.to_dict(), indent=indent)
|
||||
|
||||
@classmethod
|
||||
def from_json(cls, source_json: str) -> Self:
|
||||
"""
|
||||
Creates a Card object from a JSON-serialized string.
|
||||
|
||||
Args:
|
||||
source_json: A JSON-serialized string of an existing Card object.
|
||||
|
||||
Returns:
|
||||
Self: A Card object created from the JSON string.
|
||||
"""
|
||||
|
||||
source_dict: CardDict = json.loads(source_json)
|
||||
return cls.from_dict(source_dict=source_dict)
|
||||
|
||||
|
||||
__all__ = ["Card"]
|
||||
674
src/heurams/vendor/pyfsrs/optimizer.py
vendored
674
src/heurams/vendor/pyfsrs/optimizer.py
vendored
@@ -1,674 +0,0 @@
|
||||
"""
|
||||
fsrs.optimizer
|
||||
---------
|
||||
|
||||
This module defines the optional Optimizer class.
|
||||
"""
|
||||
|
||||
from fsrs.card import Card
|
||||
from fsrs.review_log import ReviewLog, Rating
|
||||
from fsrs.scheduler import (
|
||||
Scheduler,
|
||||
DEFAULT_PARAMETERS,
|
||||
LOWER_BOUNDS_PARAMETERS,
|
||||
UPPER_BOUNDS_PARAMETERS,
|
||||
)
|
||||
|
||||
import math
|
||||
from datetime import datetime, timezone
|
||||
from copy import deepcopy
|
||||
from random import Random
|
||||
from statistics import mean
|
||||
|
||||
try:
|
||||
import torch
|
||||
from torch.nn import BCELoss
|
||||
from torch import optim
|
||||
import pandas as pd
|
||||
from tqdm import tqdm
|
||||
|
||||
# weight clipping
|
||||
LOWER_BOUNDS_PARAMETERS_TENSORS = torch.tensor(
|
||||
LOWER_BOUNDS_PARAMETERS,
|
||||
dtype=torch.float64,
|
||||
)
|
||||
|
||||
UPPER_BOUNDS_PARAMETERS_TENSORS = torch.tensor(
|
||||
UPPER_BOUNDS_PARAMETERS,
|
||||
dtype=torch.float64,
|
||||
)
|
||||
|
||||
# hyper parameters
|
||||
num_epochs = 5
|
||||
mini_batch_size = 512
|
||||
learning_rate = 4e-2
|
||||
max_seq_len = (
|
||||
64 # up to the first 64 reviews of each card are used for optimization
|
||||
)
|
||||
|
||||
class Optimizer:
|
||||
"""
|
||||
The FSRS optimizer.
|
||||
|
||||
Enables the optimization of FSRS scheduler parameters from existing review logs for more accurate interval calculations.
|
||||
|
||||
Attributes:
|
||||
review_logs: A collection of previous ReviewLog objects from a user.
|
||||
_revlogs_train: The collection of review logs, sorted and formatted for optimization.
|
||||
"""
|
||||
|
||||
review_logs: tuple[ReviewLog, ...]
|
||||
_revlogs_train: dict
|
||||
|
||||
def __init__(
|
||||
self, review_logs: tuple[ReviewLog, ...] | list[ReviewLog]
|
||||
) -> None:
|
||||
"""
|
||||
Initializes the Optimizer with a set of ReviewLogs. Also formats a copy of the review logs for optimization.
|
||||
|
||||
Note that the ReviewLogs provided by the user don't need to be in order.
|
||||
"""
|
||||
|
||||
def _format_revlogs() -> dict:
|
||||
"""
|
||||
Sorts and converts the tuple of ReviewLog objects to a dictionary format for optimizing
|
||||
"""
|
||||
|
||||
revlogs_train = {}
|
||||
for review_log in self.review_logs:
|
||||
# pull data out of current ReviewLog object
|
||||
card_id = review_log.card_id
|
||||
rating = review_log.rating
|
||||
review_datetime = review_log.review_datetime
|
||||
review_duration = review_log.review_duration
|
||||
|
||||
# if the card was rated Again, it was not recalled
|
||||
recall = 0 if rating == Rating.Again else 1
|
||||
|
||||
# as a ML problem, [x, y] = [ [review_datetime, rating, review_duration], recall ]
|
||||
datum = [[review_datetime, rating, review_duration], recall]
|
||||
|
||||
if card_id not in revlogs_train:
|
||||
revlogs_train[card_id] = []
|
||||
|
||||
revlogs_train[card_id].append((datum))
|
||||
revlogs_train[card_id] = sorted(
|
||||
revlogs_train[card_id], key=lambda x: x[0][0]
|
||||
) # keep reviews sorted
|
||||
|
||||
# sort the dictionary in order of when each card history starts
|
||||
revlogs_train = dict(sorted(revlogs_train.items()))
|
||||
|
||||
return revlogs_train
|
||||
|
||||
self.review_logs = deepcopy(tuple(review_logs))
|
||||
|
||||
# format the ReviewLog data for optimization
|
||||
self._revlogs_train = _format_revlogs()
|
||||
|
||||
def _compute_batch_loss(self, *, parameters: list[float]) -> float:
|
||||
"""
|
||||
Computes the current total loss for the entire batch of review logs.
|
||||
"""
|
||||
|
||||
card_ids = list(self._revlogs_train.keys())
|
||||
params = torch.tensor(parameters, dtype=torch.float64)
|
||||
loss_fn = BCELoss()
|
||||
scheduler = Scheduler(parameters=params)
|
||||
step_losses = []
|
||||
|
||||
for card_id in card_ids:
|
||||
card_review_history = self._revlogs_train[card_id][:max_seq_len]
|
||||
|
||||
for i in range(len(card_review_history)):
|
||||
review = card_review_history[i]
|
||||
|
||||
x_date = review[0][0]
|
||||
y_retrievability = review[1]
|
||||
u_rating = review[0][1]
|
||||
|
||||
if i == 0:
|
||||
card = Card(card_id=card_id, due=x_date)
|
||||
|
||||
y_pred_retrievability = scheduler.get_card_retrievability(
|
||||
card=card, current_datetime=x_date
|
||||
)
|
||||
y_retrievability = torch.tensor(
|
||||
y_retrievability, dtype=torch.float64
|
||||
)
|
||||
|
||||
if card.last_review and (x_date - card.last_review).days > 0:
|
||||
step_loss = loss_fn(y_pred_retrievability, y_retrievability)
|
||||
step_losses.append(step_loss)
|
||||
|
||||
card, _ = scheduler.review_card(
|
||||
card=card,
|
||||
rating=u_rating,
|
||||
review_datetime=x_date,
|
||||
review_duration=None,
|
||||
)
|
||||
|
||||
batch_loss = torch.sum(torch.stack(step_losses))
|
||||
batch_loss = batch_loss.item() / len(step_losses)
|
||||
|
||||
return batch_loss
|
||||
|
||||
def compute_optimal_parameters(self, verbose: bool = False) -> list[float]:
|
||||
"""
|
||||
Computes a set of optimized parameters for the FSRS scheduler and returns it as a list of floats.
|
||||
|
||||
High level explanation of optimization:
|
||||
---------------------------------------
|
||||
FSRS is a many-to-many sequence model where the "State" at each step is a Card object at a given point in time,
|
||||
the input is the time of the review and the output is the predicted retrievability of the card at the time of review.
|
||||
|
||||
Each card's review history can be thought of as a sequence, each review as a step and each collection of card review histories
|
||||
as a batch.
|
||||
|
||||
The loss is computed by comparing the predicted retrievability of the Card at each step with whether the Card was actually
|
||||
sucessfully recalled or not (0/1).
|
||||
|
||||
Finally, the card objects at each step in their sequences are updated using the current parameters of the Scheduler
|
||||
as well as the rating given to that card by the user. The parameters of the Scheduler is what is being optimized.
|
||||
"""
|
||||
|
||||
def _num_reviews() -> int:
|
||||
"""
|
||||
Computes how many Review-state reviews there are in the dataset.
|
||||
Only the loss from Review-state reviews count for optimization and their number must
|
||||
be computed in advance to properly initialize the Cosine Annealing learning rate scheduler.
|
||||
"""
|
||||
|
||||
scheduler = Scheduler()
|
||||
num_reviews = 0
|
||||
# iterate through the card review histories
|
||||
card_ids = list(self._revlogs_train.keys())
|
||||
for card_id in card_ids:
|
||||
card_review_history = self._revlogs_train[card_id][:max_seq_len]
|
||||
|
||||
# iterate through the current Card's review history
|
||||
for i in range(len(card_review_history)):
|
||||
review = card_review_history[i]
|
||||
|
||||
review_datetime = review[0][0]
|
||||
rating = review[0][1]
|
||||
|
||||
# if this is the first review, create the Card object
|
||||
if i == 0:
|
||||
card = Card(card_id=card_id, due=review_datetime)
|
||||
|
||||
# only non-same-day reviews count
|
||||
if (
|
||||
card.last_review
|
||||
and (review_datetime - card.last_review).days > 0
|
||||
):
|
||||
num_reviews += 1
|
||||
|
||||
card, _ = scheduler.review_card(
|
||||
card=card,
|
||||
rating=rating,
|
||||
review_datetime=review_datetime,
|
||||
review_duration=None,
|
||||
)
|
||||
|
||||
return num_reviews
|
||||
|
||||
def _update_parameters(
|
||||
*,
|
||||
step_losses: list,
|
||||
adam_optimizer: torch.optim.Adam,
|
||||
params: torch.Tensor,
|
||||
lr_scheduler: torch.optim.lr_scheduler.CosineAnnealingLR,
|
||||
) -> None:
|
||||
"""
|
||||
Computes and updates the current FSRS parameters based on the step losses. Also updates the learning rate scheduler.
|
||||
"""
|
||||
|
||||
# Backpropagate through the loss
|
||||
mini_batch_loss = torch.sum(torch.stack(step_losses))
|
||||
adam_optimizer.zero_grad() # clear previous gradients
|
||||
mini_batch_loss.backward() # compute gradients
|
||||
adam_optimizer.step() # Update parameters
|
||||
|
||||
# clamp the weights in place without modifying the computational graph
|
||||
with torch.no_grad():
|
||||
params.clamp_(
|
||||
min=LOWER_BOUNDS_PARAMETERS_TENSORS,
|
||||
max=UPPER_BOUNDS_PARAMETERS_TENSORS,
|
||||
)
|
||||
|
||||
# update the learning rate
|
||||
lr_scheduler.step()
|
||||
|
||||
# set local random seed for reproducibility
|
||||
rng = Random(42)
|
||||
|
||||
card_ids = list(self._revlogs_train.keys())
|
||||
|
||||
num_reviews = _num_reviews()
|
||||
|
||||
if num_reviews < mini_batch_size:
|
||||
return list(DEFAULT_PARAMETERS)
|
||||
|
||||
# Define FSRS Scheduler parameters as torch tensors with gradients
|
||||
params = torch.tensor(
|
||||
DEFAULT_PARAMETERS, requires_grad=True, dtype=torch.float64
|
||||
)
|
||||
|
||||
loss_fn = BCELoss()
|
||||
adam_optimizer = optim.Adam([params], lr=learning_rate)
|
||||
lr_scheduler = optim.lr_scheduler.CosineAnnealingLR(
|
||||
optimizer=adam_optimizer,
|
||||
T_max=math.ceil(num_reviews / mini_batch_size) * num_epochs,
|
||||
)
|
||||
|
||||
best_params = None
|
||||
best_loss = math.inf
|
||||
# iterate through the epochs
|
||||
for _ in tqdm(
|
||||
range(num_epochs),
|
||||
desc="Optimizing",
|
||||
unit="epoch",
|
||||
disable=(not verbose),
|
||||
):
|
||||
# randomly shuffle the order of which Card's review histories get computed first
|
||||
# at the beginning of each new epoch
|
||||
rng.shuffle(card_ids)
|
||||
|
||||
# initialize new scheduler with updated parameters each epoch
|
||||
scheduler = Scheduler(parameters=params)
|
||||
|
||||
# stores the computed loss of each individual review
|
||||
step_losses = []
|
||||
|
||||
# iterate through the card review histories (sequences)
|
||||
for card_id in card_ids:
|
||||
card_review_history = self._revlogs_train[card_id][:max_seq_len]
|
||||
|
||||
# iterate through the current Card's review history (steps)
|
||||
for i in range(len(card_review_history)):
|
||||
review = card_review_history[i]
|
||||
|
||||
# input
|
||||
x_date = review[0][0]
|
||||
# target
|
||||
y_retrievability = review[1]
|
||||
# update
|
||||
u_rating = review[0][1]
|
||||
|
||||
# if this is the first review, create the Card object
|
||||
if i == 0:
|
||||
card = Card(card_id=card_id, due=x_date)
|
||||
|
||||
# predicted target
|
||||
y_pred_retrievability = scheduler.get_card_retrievability(
|
||||
card=card, current_datetime=x_date
|
||||
)
|
||||
y_retrievability = torch.tensor(
|
||||
y_retrievability, dtype=torch.float64
|
||||
)
|
||||
|
||||
# only compute step-loss on non-same-day reviews
|
||||
if card.last_review and (x_date - card.last_review).days > 0:
|
||||
step_loss = loss_fn(y_pred_retrievability, y_retrievability)
|
||||
step_losses.append(step_loss)
|
||||
|
||||
# update the card's state
|
||||
card, _ = scheduler.review_card(
|
||||
card=card,
|
||||
rating=u_rating,
|
||||
review_datetime=x_date,
|
||||
review_duration=None,
|
||||
)
|
||||
|
||||
# take a gradient step after each mini-batch
|
||||
if len(step_losses) == mini_batch_size:
|
||||
_update_parameters(
|
||||
step_losses=step_losses,
|
||||
adam_optimizer=adam_optimizer,
|
||||
params=params,
|
||||
lr_scheduler=lr_scheduler,
|
||||
)
|
||||
|
||||
# update the scheduler's with the new parameters
|
||||
scheduler = Scheduler(parameters=params)
|
||||
# clear the step losses for next batch
|
||||
step_losses = []
|
||||
|
||||
# remove gradient history from tensor card parameters for next batch
|
||||
card.stability = card.stability.detach()
|
||||
card.difficulty = card.difficulty.detach()
|
||||
|
||||
# update params on remaining review logs
|
||||
if len(step_losses) > 0:
|
||||
_update_parameters(
|
||||
step_losses=step_losses,
|
||||
adam_optimizer=adam_optimizer,
|
||||
params=params,
|
||||
lr_scheduler=lr_scheduler,
|
||||
)
|
||||
|
||||
# compute the current batch loss after each epoch
|
||||
detached_params = [
|
||||
x.detach().item() for x in list(params.detach())
|
||||
] # convert to floats
|
||||
with torch.no_grad():
|
||||
epoch_batch_loss = self._compute_batch_loss(
|
||||
parameters=detached_params
|
||||
)
|
||||
|
||||
# if the batch loss is better with the current parameters, update the current best parameters
|
||||
if epoch_batch_loss < best_loss:
|
||||
best_loss = epoch_batch_loss
|
||||
best_params = detached_params
|
||||
|
||||
return best_params
|
||||
|
||||
def _compute_probs_and_costs(self) -> dict[str, float]:
|
||||
review_log_df = pd.DataFrame(
|
||||
vars(review_log) for review_log in self.review_logs
|
||||
)
|
||||
|
||||
review_log_df = review_log_df.sort_values(
|
||||
by=["card_id", "review_datetime"], ascending=[True, True]
|
||||
).reset_index(drop=True)
|
||||
|
||||
# dictionary to return
|
||||
probs_and_costs_dict = {}
|
||||
|
||||
# compute the probabilities and costs of the first rating
|
||||
first_reviews_df = review_log_df.loc[
|
||||
~review_log_df["card_id"].duplicated(keep="first")
|
||||
].reset_index(drop=True)
|
||||
|
||||
first_again_reviews_df = first_reviews_df.loc[
|
||||
first_reviews_df["rating"] == Rating.Again
|
||||
]
|
||||
first_hard_reviews_df = first_reviews_df.loc[
|
||||
first_reviews_df["rating"] == Rating.Hard
|
||||
]
|
||||
first_good_reviews_df = first_reviews_df.loc[
|
||||
first_reviews_df["rating"] == Rating.Good
|
||||
]
|
||||
first_easy_reviews_df = first_reviews_df.loc[
|
||||
first_reviews_df["rating"] == Rating.Easy
|
||||
]
|
||||
|
||||
# compute the probability of the user clicking again/hard/good/easy given it's their first review
|
||||
num_first_again = len(first_again_reviews_df)
|
||||
num_first_hard = len(first_hard_reviews_df)
|
||||
num_first_good = len(first_good_reviews_df)
|
||||
num_first_easy = len(first_easy_reviews_df)
|
||||
|
||||
num_first_review = (
|
||||
num_first_again + num_first_hard + num_first_good + num_first_easy
|
||||
)
|
||||
|
||||
prob_first_again = num_first_again / num_first_review
|
||||
prob_first_hard = num_first_hard / num_first_review
|
||||
prob_first_good = num_first_good / num_first_review
|
||||
prob_first_easy = num_first_easy / num_first_review
|
||||
|
||||
probs_and_costs_dict["prob_first_again"] = prob_first_again
|
||||
probs_and_costs_dict["prob_first_hard"] = prob_first_hard
|
||||
probs_and_costs_dict["prob_first_good"] = prob_first_good
|
||||
probs_and_costs_dict["prob_first_easy"] = prob_first_easy
|
||||
|
||||
# compute the cost of the user clicking again/hard/good/easy on their first review
|
||||
first_again_review_durations = list(
|
||||
first_again_reviews_df["review_duration"]
|
||||
)
|
||||
first_hard_review_durations = list(first_hard_reviews_df["review_duration"])
|
||||
first_good_review_durations = list(first_good_reviews_df["review_duration"])
|
||||
first_easy_review_durations = list(first_easy_reviews_df["review_duration"])
|
||||
|
||||
avg_first_again_review_duration = (
|
||||
mean(first_again_review_durations)
|
||||
if first_again_review_durations
|
||||
else 0
|
||||
)
|
||||
avg_first_hard_review_duration = (
|
||||
mean(first_hard_review_durations) if first_hard_review_durations else 0
|
||||
)
|
||||
avg_first_good_review_duration = (
|
||||
mean(first_good_review_durations) if first_good_review_durations else 0
|
||||
)
|
||||
avg_first_easy_review_duration = (
|
||||
mean(first_easy_review_durations) if first_easy_review_durations else 0
|
||||
)
|
||||
|
||||
probs_and_costs_dict["avg_first_again_review_duration"] = (
|
||||
avg_first_again_review_duration
|
||||
)
|
||||
probs_and_costs_dict["avg_first_hard_review_duration"] = (
|
||||
avg_first_hard_review_duration
|
||||
)
|
||||
probs_and_costs_dict["avg_first_good_review_duration"] = (
|
||||
avg_first_good_review_duration
|
||||
)
|
||||
probs_and_costs_dict["avg_first_easy_review_duration"] = (
|
||||
avg_first_easy_review_duration
|
||||
)
|
||||
|
||||
# compute the probabilities and costs of non-first ratings
|
||||
non_first_reviews_df = review_log_df.loc[
|
||||
review_log_df["card_id"].duplicated(keep="first")
|
||||
].reset_index(drop=True)
|
||||
|
||||
again_reviews_df = non_first_reviews_df.loc[
|
||||
non_first_reviews_df["rating"] == Rating.Again
|
||||
]
|
||||
hard_reviews_df = non_first_reviews_df.loc[
|
||||
non_first_reviews_df["rating"] == Rating.Hard
|
||||
]
|
||||
good_reviews_df = non_first_reviews_df.loc[
|
||||
non_first_reviews_df["rating"] == Rating.Good
|
||||
]
|
||||
easy_reviews_df = non_first_reviews_df.loc[
|
||||
non_first_reviews_df["rating"] == Rating.Easy
|
||||
]
|
||||
|
||||
# compute the probability of the user clicking hard/good/easy given they correctly recalled the card
|
||||
num_hard = len(hard_reviews_df)
|
||||
num_good = len(good_reviews_df)
|
||||
num_easy = len(easy_reviews_df)
|
||||
|
||||
num_recall = num_hard + num_good + num_easy
|
||||
|
||||
prob_hard = num_hard / num_recall
|
||||
prob_good = num_good / num_recall
|
||||
prob_easy = num_easy / num_recall
|
||||
|
||||
probs_and_costs_dict["prob_hard"] = prob_hard
|
||||
probs_and_costs_dict["prob_good"] = prob_good
|
||||
probs_and_costs_dict["prob_easy"] = prob_easy
|
||||
|
||||
again_review_durations = list(again_reviews_df["review_duration"])
|
||||
hard_review_durations = list(hard_reviews_df["review_duration"])
|
||||
good_review_durations = list(good_reviews_df["review_duration"])
|
||||
easy_review_durations = list(easy_reviews_df["review_duration"])
|
||||
|
||||
avg_again_review_duration = (
|
||||
mean(again_review_durations) if again_review_durations else 0
|
||||
)
|
||||
avg_hard_review_duration = (
|
||||
mean(hard_review_durations) if hard_review_durations else 0
|
||||
)
|
||||
avg_good_review_duration = (
|
||||
mean(good_review_durations) if good_review_durations else 0
|
||||
)
|
||||
avg_easy_review_duration = (
|
||||
mean(easy_review_durations) if easy_review_durations else 0
|
||||
)
|
||||
|
||||
probs_and_costs_dict["avg_again_review_duration"] = (
|
||||
avg_again_review_duration
|
||||
)
|
||||
probs_and_costs_dict["avg_hard_review_duration"] = avg_hard_review_duration
|
||||
probs_and_costs_dict["avg_good_review_duration"] = avg_good_review_duration
|
||||
probs_and_costs_dict["avg_easy_review_duration"] = avg_easy_review_duration
|
||||
|
||||
return probs_and_costs_dict
|
||||
|
||||
def _simulate_cost(
|
||||
self,
|
||||
*,
|
||||
desired_retention: float,
|
||||
parameters: tuple[float, ...] | list[float],
|
||||
num_cards_simulate: int,
|
||||
probs_and_costs_dict: dict[str, float],
|
||||
) -> float:
|
||||
rng = Random(42)
|
||||
|
||||
# simulate from the beginning of 2025 till before the beginning of 2026
|
||||
start_date = datetime(2025, 1, 1, 0, 0, 0, 0, timezone.utc)
|
||||
end_date = datetime(2026, 1, 1, 0, 0, 0, 0, timezone.utc)
|
||||
|
||||
scheduler = Scheduler(
|
||||
parameters=parameters,
|
||||
desired_retention=desired_retention,
|
||||
enable_fuzzing=False,
|
||||
)
|
||||
|
||||
# unpack probs_and_costs_dict
|
||||
prob_first_again = probs_and_costs_dict["prob_first_again"]
|
||||
prob_first_hard = probs_and_costs_dict["prob_first_hard"]
|
||||
prob_first_good = probs_and_costs_dict["prob_first_good"]
|
||||
prob_first_easy = probs_and_costs_dict["prob_first_easy"]
|
||||
|
||||
avg_first_again_review_duration = probs_and_costs_dict[
|
||||
"avg_first_again_review_duration"
|
||||
]
|
||||
avg_first_hard_review_duration = probs_and_costs_dict[
|
||||
"avg_first_hard_review_duration"
|
||||
]
|
||||
avg_first_good_review_duration = probs_and_costs_dict[
|
||||
"avg_first_good_review_duration"
|
||||
]
|
||||
avg_first_easy_review_duration = probs_and_costs_dict[
|
||||
"avg_first_easy_review_duration"
|
||||
]
|
||||
|
||||
prob_hard = probs_and_costs_dict["prob_hard"]
|
||||
prob_good = probs_and_costs_dict["prob_good"]
|
||||
prob_easy = probs_and_costs_dict["prob_easy"]
|
||||
|
||||
avg_again_review_duration = probs_and_costs_dict[
|
||||
"avg_again_review_duration"
|
||||
]
|
||||
avg_hard_review_duration = probs_and_costs_dict["avg_hard_review_duration"]
|
||||
avg_good_review_duration = probs_and_costs_dict["avg_good_review_duration"]
|
||||
avg_easy_review_duration = probs_and_costs_dict["avg_easy_review_duration"]
|
||||
|
||||
simulation_cost = 0
|
||||
for i in range(num_cards_simulate):
|
||||
card = Card()
|
||||
curr_date = start_date
|
||||
while curr_date < end_date:
|
||||
# the card is new
|
||||
if curr_date == start_date:
|
||||
rating = rng.choices(
|
||||
[Rating.Again, Rating.Hard, Rating.Good, Rating.Easy],
|
||||
weights=[
|
||||
prob_first_again,
|
||||
prob_first_hard,
|
||||
prob_first_good,
|
||||
prob_first_easy,
|
||||
],
|
||||
)[0]
|
||||
|
||||
if rating == Rating.Again:
|
||||
simulation_cost += avg_first_again_review_duration
|
||||
|
||||
elif rating == Rating.Hard:
|
||||
simulation_cost += avg_first_hard_review_duration
|
||||
|
||||
elif rating == Rating.Good:
|
||||
simulation_cost += avg_first_good_review_duration
|
||||
|
||||
elif rating == Rating.Easy:
|
||||
simulation_cost += avg_first_easy_review_duration
|
||||
|
||||
# the card is not new
|
||||
else:
|
||||
rating = rng.choices(
|
||||
["recall", Rating.Again],
|
||||
weights=[desired_retention, 1.0 - desired_retention],
|
||||
)[0]
|
||||
|
||||
if rating == "recall":
|
||||
# compute probability that the user chose hard/good/easy, GIVEN that they correctly recalled the card
|
||||
rating = rng.choices(
|
||||
[Rating.Hard, Rating.Good, Rating.Easy],
|
||||
weights=[prob_hard, prob_good, prob_easy],
|
||||
)[0]
|
||||
|
||||
if rating == Rating.Again:
|
||||
simulation_cost += avg_again_review_duration
|
||||
|
||||
elif rating == Rating.Hard:
|
||||
simulation_cost += avg_hard_review_duration
|
||||
|
||||
elif rating == Rating.Good:
|
||||
simulation_cost += avg_good_review_duration
|
||||
|
||||
elif rating == Rating.Easy:
|
||||
simulation_cost += avg_easy_review_duration
|
||||
|
||||
card, _ = scheduler.review_card(
|
||||
card=card, rating=rating, review_datetime=curr_date
|
||||
)
|
||||
curr_date = card.due
|
||||
|
||||
total_knowledge = desired_retention * num_cards_simulate
|
||||
simulation_cost = simulation_cost / total_knowledge
|
||||
|
||||
return simulation_cost
|
||||
|
||||
def compute_optimal_retention(
|
||||
self, parameters: tuple[float, ...] | list[float]
|
||||
) -> list[float]:
|
||||
def _validate_review_logs() -> None:
|
||||
if len(self.review_logs) < 512:
|
||||
raise ValueError(
|
||||
"Not enough ReviewLog's: at least 512 ReviewLog objects are required to compute optimal retention"
|
||||
)
|
||||
|
||||
for review_log in self.review_logs:
|
||||
if review_log.review_duration is None:
|
||||
raise ValueError(
|
||||
"ReviewLog.review_duration cannot be None when computing optimal retention"
|
||||
)
|
||||
|
||||
_validate_review_logs()
|
||||
|
||||
NUM_CARDS_SIMULATE = 1000
|
||||
DESIRED_RETENTIONS = [0.7, 0.75, 0.8, 0.85, 0.9, 0.95]
|
||||
|
||||
probs_and_costs_dict = self._compute_probs_and_costs()
|
||||
|
||||
simulation_costs = []
|
||||
for desired_retention in DESIRED_RETENTIONS:
|
||||
simulation_cost = self._simulate_cost(
|
||||
desired_retention=desired_retention,
|
||||
parameters=parameters,
|
||||
num_cards_simulate=NUM_CARDS_SIMULATE,
|
||||
probs_and_costs_dict=probs_and_costs_dict,
|
||||
)
|
||||
simulation_costs.append(simulation_cost)
|
||||
|
||||
min_index = simulation_costs.index(min(simulation_costs))
|
||||
optimal_retention = DESIRED_RETENTIONS[min_index]
|
||||
|
||||
return optimal_retention
|
||||
|
||||
except ImportError:
|
||||
|
||||
class Optimizer:
|
||||
def __init__(self, *args, **kwargs) -> None:
|
||||
raise ImportError(
|
||||
'Optimizer is not installed.\nInstall it with: pip install "fsrs[optimizer]"'
|
||||
)
|
||||
|
||||
|
||||
__all__ = ["Optimizer"]
|
||||
0
src/heurams/vendor/pyfsrs/py.typed
vendored
0
src/heurams/vendor/pyfsrs/py.typed
vendored
15
src/heurams/vendor/pyfsrs/rating.py
vendored
15
src/heurams/vendor/pyfsrs/rating.py
vendored
@@ -1,15 +0,0 @@
|
||||
from enum import IntEnum
|
||||
|
||||
|
||||
class Rating(IntEnum):
|
||||
"""
|
||||
Enum representing the four possible ratings when reviewing a card.
|
||||
"""
|
||||
|
||||
Again = 1
|
||||
Hard = 2
|
||||
Good = 3
|
||||
Easy = 4
|
||||
|
||||
|
||||
__all__ = ["Rating"]
|
||||
117
src/heurams/vendor/pyfsrs/review_log.py
vendored
117
src/heurams/vendor/pyfsrs/review_log.py
vendored
@@ -1,117 +0,0 @@
|
||||
"""
|
||||
fsrs.review_log
|
||||
---------
|
||||
|
||||
This module defines the ReviewLog and Rating classes.
|
||||
|
||||
Classes:
|
||||
ReviewLog: Represents the log entry of a Card that has been reviewed.
|
||||
Rating: Enum representing the four possible ratings when reviewing a card.
|
||||
"""
|
||||
|
||||
from __future__ import annotations
|
||||
from dataclasses import dataclass
|
||||
from datetime import datetime
|
||||
from typing import TypedDict
|
||||
import json
|
||||
from typing_extensions import Self
|
||||
from fsrs.rating import Rating
|
||||
|
||||
|
||||
class ReviewLogDict(TypedDict):
|
||||
"""
|
||||
JSON-serializable dictionary representation of a ReviewLog object.
|
||||
"""
|
||||
|
||||
card_id: int
|
||||
rating: int
|
||||
review_datetime: str
|
||||
review_duration: int | None
|
||||
|
||||
|
||||
@dataclass
|
||||
class ReviewLog:
|
||||
"""
|
||||
Represents the log entry of a Card object that has been reviewed.
|
||||
|
||||
Attributes:
|
||||
card_id: The id of the card being reviewed.
|
||||
rating: The rating given to the card during the review.
|
||||
review_datetime: The date and time of the review.
|
||||
review_duration: The number of milliseconds it took to review the card or None if unspecified.
|
||||
"""
|
||||
|
||||
card_id: int
|
||||
rating: Rating
|
||||
review_datetime: datetime
|
||||
review_duration: int | None
|
||||
|
||||
def to_dict(
|
||||
self,
|
||||
) -> ReviewLogDict:
|
||||
"""
|
||||
Returns a dictionary representation of the ReviewLog object.
|
||||
|
||||
Returns:
|
||||
ReviewLogDict: A dictionary representation of the ReviewLog object.
|
||||
"""
|
||||
|
||||
return {
|
||||
"card_id": self.card_id,
|
||||
"rating": int(self.rating),
|
||||
"review_datetime": self.review_datetime.isoformat(),
|
||||
"review_duration": self.review_duration,
|
||||
}
|
||||
|
||||
@classmethod
|
||||
def from_dict(
|
||||
cls,
|
||||
source_dict: ReviewLogDict,
|
||||
) -> Self:
|
||||
"""
|
||||
Creates a ReviewLog object from an existing dictionary.
|
||||
|
||||
Args:
|
||||
source_dict: A dictionary representing an existing ReviewLog object.
|
||||
|
||||
Returns:
|
||||
Self: A ReviewLog object created from the provided dictionary.
|
||||
"""
|
||||
|
||||
return cls(
|
||||
card_id=source_dict["card_id"],
|
||||
rating=Rating(int(source_dict["rating"])),
|
||||
review_datetime=datetime.fromisoformat(source_dict["review_datetime"]),
|
||||
review_duration=source_dict["review_duration"],
|
||||
)
|
||||
|
||||
def to_json(self, indent: int | str | None = None) -> str:
|
||||
"""
|
||||
Returns a JSON-serialized string of the ReviewLog object.
|
||||
|
||||
Args:
|
||||
indent: Equivalent argument to the indent in json.dumps()
|
||||
|
||||
Returns:
|
||||
str: A JSON-serialized string of the ReviewLog object.
|
||||
"""
|
||||
|
||||
return json.dumps(self.to_dict(), indent=indent)
|
||||
|
||||
@classmethod
|
||||
def from_json(cls, source_json: str) -> Self:
|
||||
"""
|
||||
Creates a ReviewLog object from a JSON-serialized string.
|
||||
|
||||
Args:
|
||||
source_json: A JSON-serialized string of an existing ReviewLog object.
|
||||
|
||||
Returns:
|
||||
Self: A ReviewLog object created from the JSON string.
|
||||
"""
|
||||
|
||||
source_dict: ReviewLogDict = json.loads(source_json)
|
||||
return cls.from_dict(source_dict=source_dict)
|
||||
|
||||
|
||||
__all__ = ["ReviewLog"]
|
||||
856
src/heurams/vendor/pyfsrs/scheduler.py
vendored
856
src/heurams/vendor/pyfsrs/scheduler.py
vendored
@@ -1,856 +0,0 @@
|
||||
"""
|
||||
fsrs.scheduler
|
||||
---------
|
||||
|
||||
This module defines the Scheduler class as well as the various constants used in its calculations.
|
||||
|
||||
Classes:
|
||||
Scheduler: The FSRS spaced-repetition scheduler.
|
||||
"""
|
||||
|
||||
from __future__ import annotations
|
||||
from collections.abc import Sequence
|
||||
import math
|
||||
from datetime import datetime, timezone, timedelta
|
||||
from copy import copy
|
||||
import json
|
||||
from random import random
|
||||
from dataclasses import dataclass
|
||||
from fsrs.state import State
|
||||
from fsrs.card import Card
|
||||
from fsrs.rating import Rating
|
||||
from fsrs.review_log import ReviewLog
|
||||
from typing import TYPE_CHECKING, TypedDict, overload
|
||||
|
||||
if TYPE_CHECKING:
|
||||
from torch import Tensor # torch is optional; import only for type checking
|
||||
from typing_extensions import Self
|
||||
|
||||
FSRS_DEFAULT_DECAY = 0.1542
|
||||
DEFAULT_PARAMETERS = (
|
||||
0.212,
|
||||
1.2931,
|
||||
2.3065,
|
||||
8.2956,
|
||||
6.4133,
|
||||
0.8334,
|
||||
3.0194,
|
||||
0.001,
|
||||
1.8722,
|
||||
0.1666,
|
||||
0.796,
|
||||
1.4835,
|
||||
0.0614,
|
||||
0.2629,
|
||||
1.6483,
|
||||
0.6014,
|
||||
1.8729,
|
||||
0.5425,
|
||||
0.0912,
|
||||
0.0658,
|
||||
FSRS_DEFAULT_DECAY,
|
||||
)
|
||||
|
||||
STABILITY_MIN = 0.001
|
||||
LOWER_BOUNDS_PARAMETERS = (
|
||||
STABILITY_MIN,
|
||||
STABILITY_MIN,
|
||||
STABILITY_MIN,
|
||||
STABILITY_MIN,
|
||||
1.0,
|
||||
0.001,
|
||||
0.001,
|
||||
0.001,
|
||||
0.0,
|
||||
0.0,
|
||||
0.001,
|
||||
0.001,
|
||||
0.001,
|
||||
0.001,
|
||||
0.0,
|
||||
0.0,
|
||||
1.0,
|
||||
0.0,
|
||||
0.0,
|
||||
0.0,
|
||||
0.1,
|
||||
)
|
||||
|
||||
INITIAL_STABILITY_MAX = 100.0
|
||||
UPPER_BOUNDS_PARAMETERS = (
|
||||
INITIAL_STABILITY_MAX,
|
||||
INITIAL_STABILITY_MAX,
|
||||
INITIAL_STABILITY_MAX,
|
||||
INITIAL_STABILITY_MAX,
|
||||
10.0,
|
||||
4.0,
|
||||
4.0,
|
||||
0.75,
|
||||
4.5,
|
||||
0.8,
|
||||
3.5,
|
||||
5.0,
|
||||
0.25,
|
||||
0.9,
|
||||
4.0,
|
||||
1.0,
|
||||
6.0,
|
||||
2.0,
|
||||
2.0,
|
||||
0.8,
|
||||
0.8,
|
||||
)
|
||||
|
||||
MIN_DIFFICULTY = 1.0
|
||||
MAX_DIFFICULTY = 10.0
|
||||
|
||||
FUZZ_RANGES = [
|
||||
{
|
||||
"start": 2.5,
|
||||
"end": 7.0,
|
||||
"factor": 0.15,
|
||||
},
|
||||
{
|
||||
"start": 7.0,
|
||||
"end": 20.0,
|
||||
"factor": 0.1,
|
||||
},
|
||||
{
|
||||
"start": 20.0,
|
||||
"end": math.inf,
|
||||
"factor": 0.05,
|
||||
},
|
||||
]
|
||||
|
||||
|
||||
class SchedulerDict(TypedDict):
|
||||
"""
|
||||
JSON-serializable dictionary representation of a Scheduler object.
|
||||
"""
|
||||
|
||||
parameters: list[float]
|
||||
desired_retention: float
|
||||
learning_steps: list[int]
|
||||
relearning_steps: list[int]
|
||||
maximum_interval: int
|
||||
enable_fuzzing: bool
|
||||
|
||||
|
||||
@dataclass(init=False)
|
||||
class Scheduler:
|
||||
"""
|
||||
The FSRS scheduler.
|
||||
|
||||
Enables the reviewing and future scheduling of cards according to the FSRS algorithm.
|
||||
|
||||
Attributes:
|
||||
parameters: The model weights of the FSRS scheduler.
|
||||
desired_retention: The desired retention rate of cards scheduled with the scheduler.
|
||||
learning_steps: Small time intervals that schedule cards in the Learning state.
|
||||
relearning_steps: Small time intervals that schedule cards in the Relearning state.
|
||||
maximum_interval: The maximum number of days a Review-state card can be scheduled into the future.
|
||||
enable_fuzzing: Whether to apply a small amount of random 'fuzz' to calculated intervals.
|
||||
"""
|
||||
|
||||
parameters: tuple[float, ...]
|
||||
desired_retention: float
|
||||
learning_steps: tuple[timedelta, ...]
|
||||
relearning_steps: tuple[timedelta, ...]
|
||||
maximum_interval: int
|
||||
enable_fuzzing: bool
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
parameters: Sequence[float] = DEFAULT_PARAMETERS,
|
||||
desired_retention: float = 0.9,
|
||||
learning_steps: tuple[timedelta, ...] | list[timedelta] = (
|
||||
timedelta(minutes=1),
|
||||
timedelta(minutes=10),
|
||||
),
|
||||
relearning_steps: tuple[timedelta, ...] | list[timedelta] = (
|
||||
timedelta(minutes=10),
|
||||
),
|
||||
maximum_interval: int = 36500,
|
||||
enable_fuzzing: bool = True,
|
||||
) -> None:
|
||||
self._validate_parameters(parameters=parameters)
|
||||
|
||||
self.parameters = tuple(parameters)
|
||||
self.desired_retention = desired_retention
|
||||
self.learning_steps = tuple(learning_steps)
|
||||
self.relearning_steps = tuple(relearning_steps)
|
||||
self.maximum_interval = maximum_interval
|
||||
self.enable_fuzzing = enable_fuzzing
|
||||
|
||||
self._DECAY = -self.parameters[20]
|
||||
self._FACTOR = 0.9 ** (1 / self._DECAY) - 1
|
||||
|
||||
def _validate_parameters(self, *, parameters: Sequence[float]) -> None:
|
||||
if len(parameters) != len(LOWER_BOUNDS_PARAMETERS):
|
||||
raise ValueError(
|
||||
f"Expected {len(LOWER_BOUNDS_PARAMETERS)} parameters, got {len(parameters)}."
|
||||
)
|
||||
|
||||
error_messages = []
|
||||
for index, (parameter, lower_bound, upper_bound) in enumerate(
|
||||
zip(parameters, LOWER_BOUNDS_PARAMETERS, UPPER_BOUNDS_PARAMETERS)
|
||||
):
|
||||
if not lower_bound <= parameter <= upper_bound:
|
||||
error_message = f"parameters[{index}] = {parameter} is out of bounds: ({lower_bound}, {upper_bound})"
|
||||
error_messages.append(error_message)
|
||||
|
||||
if len(error_messages) > 0:
|
||||
raise ValueError(
|
||||
"One or more parameters are out of bounds:\n"
|
||||
+ "\n".join(error_messages)
|
||||
)
|
||||
|
||||
def get_card_retrievability(
|
||||
self, card: Card, current_datetime: datetime | None = None
|
||||
) -> float:
|
||||
"""
|
||||
Calculates a Card object's current retrievability for a given date and time.
|
||||
|
||||
The retrievability of a card is the predicted probability that the card is correctly recalled at the provided datetime.
|
||||
|
||||
Args:
|
||||
card: The card whose retrievability is to be calculated
|
||||
current_datetime: The current date and time
|
||||
|
||||
Returns:
|
||||
float: The retrievability of the Card object.
|
||||
"""
|
||||
|
||||
if card.last_review is None or card.stability is None:
|
||||
return 0
|
||||
|
||||
if current_datetime is None:
|
||||
current_datetime = datetime.now(timezone.utc)
|
||||
|
||||
elapsed_days = max(0, (current_datetime - card.last_review).days)
|
||||
|
||||
return (1 + self._FACTOR * elapsed_days / card.stability) ** self._DECAY
|
||||
|
||||
def review_card(
|
||||
self,
|
||||
card: Card,
|
||||
rating: Rating,
|
||||
review_datetime: datetime | None = None,
|
||||
review_duration: int | None = None,
|
||||
) -> tuple[Card, ReviewLog]:
|
||||
"""
|
||||
Reviews a card with a given rating at a given time for a specified duration.
|
||||
|
||||
Args:
|
||||
card: The card being reviewed.
|
||||
rating: The chosen rating for the card being reviewed.
|
||||
review_datetime: The date and time of the review.
|
||||
review_duration: The number of miliseconds it took to review the card or None if unspecified.
|
||||
|
||||
Returns:
|
||||
tuple[Card,ReviewLog]: A tuple containing the updated, reviewed card and its corresponding review log.
|
||||
|
||||
Raises:
|
||||
ValueError: If the `review_datetime` argument is not timezone-aware and set to UTC.
|
||||
"""
|
||||
|
||||
if review_datetime is not None and (
|
||||
(review_datetime.tzinfo is None) or (review_datetime.tzinfo != timezone.utc)
|
||||
):
|
||||
raise ValueError("datetime must be timezone-aware and set to UTC")
|
||||
|
||||
card = copy(card)
|
||||
|
||||
if review_datetime is None:
|
||||
review_datetime = datetime.now(timezone.utc)
|
||||
|
||||
days_since_last_review = (
|
||||
(review_datetime - card.last_review).days if card.last_review else None
|
||||
)
|
||||
|
||||
match card.state:
|
||||
case State.Learning:
|
||||
assert card.step is not None
|
||||
|
||||
# update the card's stability and difficulty
|
||||
if card.stability is None or card.difficulty is None:
|
||||
card.stability = self._initial_stability(rating=rating)
|
||||
card.difficulty = self._initial_difficulty(
|
||||
rating=rating, clamp=True
|
||||
)
|
||||
|
||||
elif days_since_last_review is not None and days_since_last_review < 1:
|
||||
card.stability = self._short_term_stability(
|
||||
stability=card.stability, rating=rating
|
||||
)
|
||||
card.difficulty = self._next_difficulty(
|
||||
difficulty=card.difficulty, rating=rating
|
||||
)
|
||||
|
||||
else:
|
||||
card.stability = self._next_stability(
|
||||
difficulty=card.difficulty,
|
||||
stability=card.stability,
|
||||
retrievability=self.get_card_retrievability(
|
||||
card,
|
||||
current_datetime=review_datetime,
|
||||
),
|
||||
rating=rating,
|
||||
)
|
||||
card.difficulty = self._next_difficulty(
|
||||
difficulty=card.difficulty, rating=rating
|
||||
)
|
||||
|
||||
# calculate the card's next interval
|
||||
## first if-clause handles edge case where the Card in the Learning state was previously
|
||||
## scheduled with a Scheduler with more learning_steps than the current Scheduler
|
||||
if len(self.learning_steps) == 0 or (
|
||||
card.step >= len(self.learning_steps)
|
||||
and rating in (Rating.Hard, Rating.Good, Rating.Easy)
|
||||
):
|
||||
card.state = State.Review
|
||||
card.step = None
|
||||
|
||||
next_interval_days = self._next_interval(stability=card.stability)
|
||||
next_interval = timedelta(days=next_interval_days)
|
||||
|
||||
else:
|
||||
match rating:
|
||||
case Rating.Again:
|
||||
card.step = 0
|
||||
next_interval = self.learning_steps[card.step]
|
||||
|
||||
case Rating.Hard:
|
||||
# card step stays the same
|
||||
|
||||
if card.step == 0 and len(self.learning_steps) == 1:
|
||||
next_interval = self.learning_steps[0] * 1.5
|
||||
elif card.step == 0 and len(self.learning_steps) >= 2:
|
||||
next_interval = (
|
||||
self.learning_steps[0] + self.learning_steps[1]
|
||||
) / 2.0
|
||||
else:
|
||||
next_interval = self.learning_steps[card.step]
|
||||
|
||||
case Rating.Good:
|
||||
if card.step + 1 == len(
|
||||
self.learning_steps
|
||||
): # the last step
|
||||
card.state = State.Review
|
||||
card.step = None
|
||||
|
||||
next_interval_days = self._next_interval(
|
||||
stability=card.stability
|
||||
)
|
||||
next_interval = timedelta(days=next_interval_days)
|
||||
|
||||
else:
|
||||
card.step += 1
|
||||
next_interval = self.learning_steps[card.step]
|
||||
|
||||
case Rating.Easy:
|
||||
card.state = State.Review
|
||||
card.step = None
|
||||
|
||||
next_interval_days = self._next_interval(
|
||||
stability=card.stability
|
||||
)
|
||||
next_interval = timedelta(days=next_interval_days)
|
||||
|
||||
case _:
|
||||
raise ValueError(f"Unknown rating: {rating}")
|
||||
|
||||
case State.Review:
|
||||
assert card.stability is not None
|
||||
assert card.difficulty is not None
|
||||
|
||||
# update the card's stability and difficulty
|
||||
if days_since_last_review is not None and days_since_last_review < 1:
|
||||
card.stability = self._short_term_stability(
|
||||
stability=card.stability, rating=rating
|
||||
)
|
||||
else:
|
||||
card.stability = self._next_stability(
|
||||
difficulty=card.difficulty,
|
||||
stability=card.stability,
|
||||
retrievability=self.get_card_retrievability(
|
||||
card,
|
||||
current_datetime=review_datetime,
|
||||
),
|
||||
rating=rating,
|
||||
)
|
||||
|
||||
card.difficulty = self._next_difficulty(
|
||||
difficulty=card.difficulty, rating=rating
|
||||
)
|
||||
|
||||
# calculate the card's next interval
|
||||
match rating:
|
||||
case Rating.Again:
|
||||
# if there are no relearning steps (they were left blank)
|
||||
if len(self.relearning_steps) == 0:
|
||||
next_interval_days = self._next_interval(
|
||||
stability=card.stability
|
||||
)
|
||||
next_interval = timedelta(days=next_interval_days)
|
||||
|
||||
else:
|
||||
card.state = State.Relearning
|
||||
card.step = 0
|
||||
|
||||
next_interval = self.relearning_steps[card.step]
|
||||
|
||||
case Rating.Hard | Rating.Good | Rating.Easy:
|
||||
next_interval_days = self._next_interval(
|
||||
stability=card.stability
|
||||
)
|
||||
next_interval = timedelta(days=next_interval_days)
|
||||
|
||||
case _:
|
||||
raise ValueError(f"Unknown rating: {rating}")
|
||||
|
||||
case State.Relearning:
|
||||
assert card.stability is not None
|
||||
assert card.difficulty is not None
|
||||
assert card.step is not None
|
||||
|
||||
# update the card's stability and difficulty
|
||||
if days_since_last_review is not None and days_since_last_review < 1:
|
||||
card.stability = self._short_term_stability(
|
||||
stability=card.stability, rating=rating
|
||||
)
|
||||
card.difficulty = self._next_difficulty(
|
||||
difficulty=card.difficulty, rating=rating
|
||||
)
|
||||
|
||||
else:
|
||||
card.stability = self._next_stability(
|
||||
difficulty=card.difficulty,
|
||||
stability=card.stability,
|
||||
retrievability=self.get_card_retrievability(
|
||||
card,
|
||||
current_datetime=review_datetime,
|
||||
),
|
||||
rating=rating,
|
||||
)
|
||||
card.difficulty = self._next_difficulty(
|
||||
difficulty=card.difficulty, rating=rating
|
||||
)
|
||||
|
||||
# calculate the card's next interval
|
||||
## first if-clause handles edge case where the Card in the Relearning state was previously
|
||||
## scheduled with a Scheduler with more relearning_steps than the current Scheduler
|
||||
if len(self.relearning_steps) == 0 or (
|
||||
card.step >= len(self.relearning_steps)
|
||||
and rating in (Rating.Hard, Rating.Good, Rating.Easy)
|
||||
):
|
||||
card.state = State.Review
|
||||
card.step = None
|
||||
|
||||
next_interval_days = self._next_interval(stability=card.stability)
|
||||
next_interval = timedelta(days=next_interval_days)
|
||||
|
||||
else:
|
||||
match rating:
|
||||
case Rating.Again:
|
||||
card.step = 0
|
||||
next_interval = self.relearning_steps[card.step]
|
||||
|
||||
case Rating.Hard:
|
||||
# card step stays the same
|
||||
|
||||
if card.step == 0 and len(self.relearning_steps) == 1:
|
||||
next_interval = self.relearning_steps[0] * 1.5
|
||||
elif card.step == 0 and len(self.relearning_steps) >= 2:
|
||||
next_interval = (
|
||||
self.relearning_steps[0] + self.relearning_steps[1]
|
||||
) / 2.0
|
||||
else:
|
||||
next_interval = self.relearning_steps[card.step]
|
||||
|
||||
case Rating.Good:
|
||||
if card.step + 1 == len(
|
||||
self.relearning_steps
|
||||
): # the last step
|
||||
card.state = State.Review
|
||||
card.step = None
|
||||
|
||||
next_interval_days = self._next_interval(
|
||||
stability=card.stability
|
||||
)
|
||||
next_interval = timedelta(days=next_interval_days)
|
||||
|
||||
else:
|
||||
card.step += 1
|
||||
next_interval = self.relearning_steps[card.step]
|
||||
|
||||
case Rating.Easy:
|
||||
card.state = State.Review
|
||||
card.step = None
|
||||
|
||||
next_interval_days = self._next_interval(
|
||||
stability=card.stability
|
||||
)
|
||||
next_interval = timedelta(days=next_interval_days)
|
||||
|
||||
case _:
|
||||
raise ValueError(f"Unknown rating: {rating}")
|
||||
|
||||
case _:
|
||||
raise ValueError(f"Unknown card state: {card.state}")
|
||||
|
||||
if self.enable_fuzzing and card.state == State.Review:
|
||||
next_interval = self._get_fuzzed_interval(interval=next_interval)
|
||||
|
||||
card.due = review_datetime + next_interval
|
||||
card.last_review = review_datetime
|
||||
|
||||
review_log = ReviewLog(
|
||||
card_id=card.card_id,
|
||||
rating=rating,
|
||||
review_datetime=review_datetime,
|
||||
review_duration=review_duration,
|
||||
)
|
||||
|
||||
return card, review_log
|
||||
|
||||
def reschedule_card(self, card: Card, review_logs: list[ReviewLog]) -> Card:
|
||||
"""
|
||||
Reschedules/updates the given card with the current scheduler provided that card's review logs.
|
||||
|
||||
If the current card was previously scheduled with a different scheduler, you may want to reschedule/update
|
||||
it as if it had always been scheduled with this current scheduler. For example, you may want to reschedule
|
||||
each of your cards with a new scheduler after computing the optimal parameters with the Optimizer.
|
||||
|
||||
Args:
|
||||
card: The card to be rescheduled/updated.
|
||||
review_logs: A list of that card's review logs (order doesn't matter).
|
||||
|
||||
Returns:
|
||||
Card: A new card that has been rescheduled/updated with this current scheduler.
|
||||
|
||||
Raises:
|
||||
ValueError: If any of the review logs are for a card other than the one specified, this will raise an error.
|
||||
|
||||
"""
|
||||
|
||||
for review_log in review_logs:
|
||||
if review_log.card_id != card.card_id:
|
||||
raise ValueError(
|
||||
f"ReviewLog card_id {review_log.card_id} does not match Card card_id {card.card_id}"
|
||||
)
|
||||
|
||||
review_logs = sorted(review_logs, key=lambda log: log.review_datetime)
|
||||
|
||||
rescheduled_card = Card(card_id=card.card_id, due=card.due)
|
||||
|
||||
for review_log in review_logs:
|
||||
rescheduled_card, _ = self.review_card(
|
||||
card=rescheduled_card,
|
||||
rating=review_log.rating,
|
||||
review_datetime=review_log.review_datetime,
|
||||
)
|
||||
|
||||
return rescheduled_card
|
||||
|
||||
def to_dict(
|
||||
self,
|
||||
) -> SchedulerDict:
|
||||
"""
|
||||
Returns a dictionary representation of the Scheduler object.
|
||||
|
||||
Returns:
|
||||
SchedulerDict: A dictionary representation of the Scheduler object.
|
||||
"""
|
||||
|
||||
return {
|
||||
"parameters": list(self.parameters),
|
||||
"desired_retention": self.desired_retention,
|
||||
"learning_steps": [
|
||||
int(learning_step.total_seconds())
|
||||
for learning_step in self.learning_steps
|
||||
],
|
||||
"relearning_steps": [
|
||||
int(relearning_step.total_seconds())
|
||||
for relearning_step in self.relearning_steps
|
||||
],
|
||||
"maximum_interval": self.maximum_interval,
|
||||
"enable_fuzzing": self.enable_fuzzing,
|
||||
}
|
||||
|
||||
@classmethod
|
||||
def from_dict(cls, source_dict: SchedulerDict) -> Self:
|
||||
"""
|
||||
Creates a Scheduler object from an existing dictionary.
|
||||
|
||||
Args:
|
||||
source_dict: A dictionary representing an existing Scheduler object.
|
||||
|
||||
Returns:
|
||||
Self: A Scheduler object created from the provided dictionary.
|
||||
"""
|
||||
|
||||
return cls(
|
||||
parameters=source_dict["parameters"],
|
||||
desired_retention=source_dict["desired_retention"],
|
||||
learning_steps=[
|
||||
timedelta(seconds=learning_step)
|
||||
for learning_step in source_dict["learning_steps"]
|
||||
],
|
||||
relearning_steps=[
|
||||
timedelta(seconds=relearning_step)
|
||||
for relearning_step in source_dict["relearning_steps"]
|
||||
],
|
||||
maximum_interval=source_dict["maximum_interval"],
|
||||
enable_fuzzing=source_dict["enable_fuzzing"],
|
||||
)
|
||||
|
||||
def to_json(self, indent: int | str | None = None) -> str:
|
||||
"""
|
||||
Returns a JSON-serialized string of the Scheduler object.
|
||||
|
||||
Args:
|
||||
indent: Equivalent argument to the indent in json.dumps()
|
||||
|
||||
Returns:
|
||||
str: A JSON-serialized string of the Scheduler object.
|
||||
"""
|
||||
|
||||
return json.dumps(self.to_dict(), indent=indent)
|
||||
|
||||
@classmethod
|
||||
def from_json(cls, source_json: str) -> Self:
|
||||
"""
|
||||
Creates a Scheduler object from a JSON-serialized string.
|
||||
|
||||
Args:
|
||||
source_json: A JSON-serialized string of an existing Scheduler object.
|
||||
|
||||
Returns:
|
||||
Self: A Scheduler object created from the JSON string.
|
||||
"""
|
||||
|
||||
source_dict: SchedulerDict = json.loads(source_json)
|
||||
return cls.from_dict(source_dict=source_dict)
|
||||
|
||||
@overload
|
||||
def _clamp_difficulty(self, *, difficulty: float) -> float: ...
|
||||
@overload
|
||||
def _clamp_difficulty(self, *, difficulty: Tensor) -> Tensor: ...
|
||||
def _clamp_difficulty(self, *, difficulty: float | Tensor) -> float | Tensor:
|
||||
if isinstance(difficulty, (int, float)):
|
||||
difficulty = min(max(difficulty, MIN_DIFFICULTY), MAX_DIFFICULTY)
|
||||
else:
|
||||
difficulty = difficulty.clamp(min=MIN_DIFFICULTY, max=MAX_DIFFICULTY)
|
||||
|
||||
return difficulty
|
||||
|
||||
@overload
|
||||
def _clamp_stability(self, *, stability: float) -> float: ...
|
||||
@overload
|
||||
def _clamp_stability(self, *, stability: Tensor) -> Tensor: ...
|
||||
def _clamp_stability(self, *, stability: float | Tensor) -> float | Tensor:
|
||||
if isinstance(stability, (int, float)):
|
||||
stability = max(stability, STABILITY_MIN)
|
||||
else:
|
||||
stability = stability.clamp(min=STABILITY_MIN)
|
||||
|
||||
return stability
|
||||
|
||||
def _initial_stability(self, *, rating: Rating) -> float:
|
||||
initial_stability = self.parameters[rating - 1]
|
||||
|
||||
initial_stability = self._clamp_stability(stability=initial_stability)
|
||||
|
||||
return initial_stability
|
||||
|
||||
def _initial_difficulty(self, *, rating: Rating, clamp: bool) -> float:
|
||||
initial_difficulty = (
|
||||
self.parameters[4] - (math.e ** (self.parameters[5] * (rating - 1))) + 1
|
||||
)
|
||||
|
||||
if clamp:
|
||||
initial_difficulty = self._clamp_difficulty(difficulty=initial_difficulty)
|
||||
|
||||
return initial_difficulty
|
||||
|
||||
def _next_interval(self, *, stability: float) -> int:
|
||||
next_interval = (stability / self._FACTOR) * (
|
||||
(self.desired_retention ** (1 / self._DECAY)) - 1
|
||||
)
|
||||
|
||||
if not isinstance(next_interval, (int, float)):
|
||||
next_interval = next_interval.detach().item()
|
||||
|
||||
next_interval = round(next_interval) # intervals are full days
|
||||
|
||||
# must be at least 1 day long
|
||||
next_interval = max(next_interval, 1)
|
||||
|
||||
# can not be longer than the maximum interval
|
||||
next_interval = min(next_interval, self.maximum_interval)
|
||||
|
||||
return next_interval
|
||||
|
||||
def _short_term_stability(self, *, stability: float, rating: Rating) -> float:
|
||||
short_term_stability_increase = (
|
||||
math.e ** (self.parameters[17] * (rating - 3 + self.parameters[18]))
|
||||
) * (stability ** -self.parameters[19])
|
||||
|
||||
if rating in (Rating.Good, Rating.Easy):
|
||||
if isinstance(short_term_stability_increase, (int, float)):
|
||||
short_term_stability_increase = max(short_term_stability_increase, 1.0)
|
||||
else:
|
||||
short_term_stability_increase = short_term_stability_increase.clamp(
|
||||
min=1.0
|
||||
)
|
||||
|
||||
short_term_stability = stability * short_term_stability_increase
|
||||
|
||||
short_term_stability = self._clamp_stability(stability=short_term_stability)
|
||||
|
||||
return short_term_stability
|
||||
|
||||
def _next_difficulty(self, *, difficulty: float, rating: Rating) -> float:
|
||||
def _linear_damping(*, delta_difficulty: float, difficulty: float) -> float:
|
||||
return (10.0 - difficulty) * delta_difficulty / 9.0
|
||||
|
||||
def _mean_reversion(*, arg_1: float, arg_2: float) -> float:
|
||||
return self.parameters[7] * arg_1 + (1 - self.parameters[7]) * arg_2
|
||||
|
||||
arg_1 = self._initial_difficulty(rating=Rating.Easy, clamp=False)
|
||||
|
||||
delta_difficulty = -(self.parameters[6] * (rating - 3))
|
||||
arg_2 = difficulty + _linear_damping(
|
||||
delta_difficulty=delta_difficulty, difficulty=difficulty
|
||||
)
|
||||
|
||||
next_difficulty = _mean_reversion(arg_1=arg_1, arg_2=arg_2)
|
||||
|
||||
next_difficulty = self._clamp_difficulty(difficulty=next_difficulty)
|
||||
|
||||
return next_difficulty
|
||||
|
||||
def _next_stability(
|
||||
self,
|
||||
*,
|
||||
difficulty: float,
|
||||
stability: float,
|
||||
retrievability: float,
|
||||
rating: Rating,
|
||||
) -> float:
|
||||
if rating == Rating.Again:
|
||||
next_stability = self._next_forget_stability(
|
||||
difficulty=difficulty,
|
||||
stability=stability,
|
||||
retrievability=retrievability,
|
||||
)
|
||||
|
||||
elif rating in (Rating.Hard, Rating.Good, Rating.Easy):
|
||||
next_stability = self._next_recall_stability(
|
||||
difficulty=difficulty,
|
||||
stability=stability,
|
||||
retrievability=retrievability,
|
||||
rating=rating,
|
||||
)
|
||||
|
||||
else:
|
||||
raise ValueError(f"Unknown rating: {rating}")
|
||||
|
||||
next_stability = self._clamp_stability(stability=next_stability)
|
||||
|
||||
return next_stability
|
||||
|
||||
def _next_forget_stability(
|
||||
self, *, difficulty: float, stability: float, retrievability: float
|
||||
) -> float:
|
||||
next_forget_stability_long_term_params = (
|
||||
self.parameters[11]
|
||||
* (difficulty ** -self.parameters[12])
|
||||
* (((stability + 1) ** (self.parameters[13])) - 1)
|
||||
* (math.e ** ((1 - retrievability) * self.parameters[14]))
|
||||
)
|
||||
|
||||
next_forget_stability_short_term_params = stability / (
|
||||
math.e ** (self.parameters[17] * self.parameters[18])
|
||||
)
|
||||
|
||||
return min(
|
||||
next_forget_stability_long_term_params,
|
||||
next_forget_stability_short_term_params,
|
||||
)
|
||||
|
||||
def _next_recall_stability(
|
||||
self,
|
||||
*,
|
||||
difficulty: float,
|
||||
stability: float,
|
||||
retrievability: float,
|
||||
rating: Rating,
|
||||
) -> float:
|
||||
hard_penalty = self.parameters[15] if rating == Rating.Hard else 1
|
||||
easy_bonus = self.parameters[16] if rating == Rating.Easy else 1
|
||||
|
||||
return stability * (
|
||||
1
|
||||
+ (math.e ** (self.parameters[8]))
|
||||
* (11 - difficulty)
|
||||
* (stability ** -self.parameters[9])
|
||||
* ((math.e ** ((1 - retrievability) * self.parameters[10])) - 1)
|
||||
* hard_penalty
|
||||
* easy_bonus
|
||||
)
|
||||
|
||||
def _get_fuzzed_interval(self, *, interval: timedelta) -> timedelta:
|
||||
"""
|
||||
Takes the current calculated interval and adds a small amount of random fuzz to it.
|
||||
For example, a card that would've been due in 50 days, after fuzzing, might be due in 49, or 51 days.
|
||||
|
||||
Args:
|
||||
interval: The calculated next interval, before fuzzing.
|
||||
|
||||
Returns:
|
||||
timedelta: The new interval, after fuzzing.
|
||||
"""
|
||||
|
||||
interval_days = interval.days
|
||||
|
||||
if interval_days < 2.5: # fuzz is not applied to intervals less than 2.5
|
||||
return interval
|
||||
|
||||
def _get_fuzz_range(*, interval_days: int) -> tuple[int, int]:
|
||||
"""
|
||||
Helper function that computes the possible upper and lower bounds of the interval after fuzzing.
|
||||
"""
|
||||
|
||||
delta = 1.0
|
||||
for fuzz_range in FUZZ_RANGES:
|
||||
delta += fuzz_range["factor"] * max(
|
||||
min(float(interval_days), fuzz_range["end"]) - fuzz_range["start"],
|
||||
0.0,
|
||||
)
|
||||
|
||||
min_ivl = int(round(interval_days - delta))
|
||||
max_ivl = int(round(interval_days + delta))
|
||||
|
||||
# make sure the min_ivl and max_ivl fall into a valid range
|
||||
min_ivl = max(2, min_ivl)
|
||||
max_ivl = min(max_ivl, self.maximum_interval)
|
||||
min_ivl = min(min_ivl, max_ivl)
|
||||
|
||||
return min_ivl, max_ivl
|
||||
|
||||
min_ivl, max_ivl = _get_fuzz_range(interval_days=interval_days)
|
||||
|
||||
fuzzed_interval_days = (
|
||||
random() * (max_ivl - min_ivl + 1)
|
||||
) + min_ivl # the next interval is a random value between min_ivl and max_ivl
|
||||
|
||||
fuzzed_interval_days = min(round(fuzzed_interval_days), self.maximum_interval)
|
||||
|
||||
fuzzed_interval = timedelta(days=fuzzed_interval_days)
|
||||
|
||||
return fuzzed_interval
|
||||
|
||||
|
||||
__all__ = ["Scheduler"]
|
||||
14
src/heurams/vendor/pyfsrs/state.py
vendored
14
src/heurams/vendor/pyfsrs/state.py
vendored
@@ -1,14 +0,0 @@
|
||||
from enum import IntEnum
|
||||
|
||||
|
||||
class State(IntEnum):
|
||||
"""
|
||||
Enum representing the learning state of a Card object.
|
||||
"""
|
||||
|
||||
Learning = 1
|
||||
Review = 2
|
||||
Relearning = 3
|
||||
|
||||
|
||||
__all__ = ["State"]
|
||||
54
tests/conftest.py
Normal file
54
tests/conftest.py
Normal file
@@ -0,0 +1,54 @@
|
||||
"""
|
||||
Pytest shared fixtures for HeurAMS test suite.
|
||||
|
||||
Provides:
|
||||
- timer_config: A ConfigDict with deterministic timer overrides for reproducible tests.
|
||||
- timer_context: A ConfigContext that applies timer_config for the duration of a test.
|
||||
- sample_algodata_sm2: A fresh SM-2 algodata dict.
|
||||
- sample_algodata_nsp0: A fresh NSP-0 algodata dict.
|
||||
"""
|
||||
|
||||
import pathlib
|
||||
from copy import deepcopy
|
||||
|
||||
import pytest
|
||||
|
||||
from heurams.context import ConfigContext, config_var, workdir
|
||||
from heurams.kernel.algorithms import algorithms, nsp0
|
||||
from heurams.services.config import ConfigDict
|
||||
|
||||
|
||||
@pytest.fixture
|
||||
def timer_config():
|
||||
"""A ConfigDict with deterministic timer overrides (daystamp=20000, timestamp=1e9).
|
||||
|
||||
This allows reproducible algorithm tests without relying on wall-clock time.
|
||||
"""
|
||||
# Use the real config path as base, then overlay timer overrides
|
||||
config = ConfigDict(workdir / "data" / "config")
|
||||
# Override timer values in-place via the nested ConfigDict
|
||||
timer_cfg = config["services"]["timer"]
|
||||
timer_cfg["daystamp_override"] = 20000
|
||||
timer_cfg["timestamp_override"] = 1000000000.0
|
||||
return config
|
||||
|
||||
|
||||
@pytest.fixture
|
||||
def timer_context(timer_config):
|
||||
"""Context manager fixture that applies the timer overrides."""
|
||||
with ConfigContext(timer_config):
|
||||
yield
|
||||
|
||||
|
||||
@pytest.fixture
|
||||
def sample_algodata_sm2():
|
||||
"""A fresh SM-2 algodata dict (pre-activation)."""
|
||||
algo = algorithms["SM-2"]
|
||||
return {algo.algo_name: deepcopy(algo.defaults)}
|
||||
|
||||
|
||||
@pytest.fixture
|
||||
def sample_algodata_nsp0():
|
||||
"""A fresh NSP-0 algodata dict (pre-activation)."""
|
||||
algo = algorithms["NSP-0"]
|
||||
return {algo.algo_name: deepcopy(algo.defaults)}
|
||||
58
tests/test_base_algorithm.py
Normal file
58
tests/test_base_algorithm.py
Normal file
@@ -0,0 +1,58 @@
|
||||
"""Tests for heurams.kernel.algorithms.base.BaseAlgorithm"""
|
||||
|
||||
from copy import deepcopy
|
||||
|
||||
import pytest
|
||||
|
||||
from heurams.kernel.algorithms import BaseAlgorithm
|
||||
from heurams.services import timer
|
||||
|
||||
|
||||
class TestBaseAlgorithmDefaults:
|
||||
def test_defaults_have_required_keys(self):
|
||||
required = {
|
||||
"real_rept",
|
||||
"rept",
|
||||
"interval",
|
||||
"last_date",
|
||||
"next_date",
|
||||
"is_activated",
|
||||
"last_modify",
|
||||
}
|
||||
assert required.issubset(BaseAlgorithm.defaults.keys())
|
||||
|
||||
def test_defaults_last_modify_is_reasonable(self):
|
||||
# defaults is evaluated at module import, not test time
|
||||
ts = BaseAlgorithm.defaults["last_modify"]
|
||||
assert isinstance(ts, float)
|
||||
assert ts > 1e9 # reasonable UNIX timestamp
|
||||
|
||||
|
||||
class TestBaseAlgorithmMethods:
|
||||
def test_revisor_does_nothing(self):
|
||||
d = {"SM-2": {"rept": 0}}
|
||||
BaseAlgorithm.revisor(d, feedback=5)
|
||||
# Base.revisor is a no-op — dict unchanged
|
||||
assert d["SM-2"]["rept"] == 0
|
||||
|
||||
def test_is_due_returns_one(self):
|
||||
assert BaseAlgorithm.is_due({}) == 1
|
||||
|
||||
def test_get_rating_returns_empty(self):
|
||||
assert BaseAlgorithm.get_rating({}) == ""
|
||||
|
||||
def test_nextdate_returns_negative_one(self):
|
||||
assert BaseAlgorithm.nextdate({}) == -1
|
||||
|
||||
|
||||
class TestBaseAlgorithmIntegrity:
|
||||
def test_check_integrity_valid(self, sample_algodata_sm2):
|
||||
# BaseAlgorithm.algo_name is "BaseAlgorithm", not "SM-2"
|
||||
data = {"BaseAlgorithm": sample_algodata_sm2["SM-2"]}
|
||||
assert BaseAlgorithm.check_integrity(data) == 1
|
||||
|
||||
def test_check_integrity_invalid(self):
|
||||
assert BaseAlgorithm.check_integrity({"SM-2": {}}) == 0
|
||||
|
||||
def test_check_integrity_missing_key(self):
|
||||
assert BaseAlgorithm.check_integrity({}) == 0
|
||||
150
tests/test_electron.py
Normal file
150
tests/test_electron.py
Normal file
@@ -0,0 +1,150 @@
|
||||
"""Tests for heurams.kernel.particles.electron.Electron"""
|
||||
|
||||
from copy import deepcopy
|
||||
|
||||
import pytest
|
||||
|
||||
from heurams.kernel.algorithms import algorithms
|
||||
from heurams.kernel.particles.electron import Electron
|
||||
from heurams.services import timer
|
||||
|
||||
|
||||
class TestElectronInit:
|
||||
def test_default_algo_is_sm2(self, timer_context):
|
||||
e = Electron("test-id", {})
|
||||
assert e.algoname == "SM-2"
|
||||
assert e.ident == "test-id"
|
||||
|
||||
def test_specific_algo(self, timer_context):
|
||||
e = Electron("test-id", {}, algo_name="NSP-0")
|
||||
assert e.algoname == "NSP-0"
|
||||
|
||||
def test_integrity_check_fills_defaults(self, timer_context):
|
||||
e = Electron("test-id", {})
|
||||
assert "SM-2" in e.algodata
|
||||
assert e.algodata["SM-2"]["efactor"] == 2.5
|
||||
|
||||
def test_existing_data_preserved(self, timer_context):
|
||||
data = {"SM-2": {"efactor": 1.5, "rept": 3, "real_rept": 5, "interval": 10,
|
||||
"last_date": 100, "next_date": 200, "is_activated": 1,
|
||||
"last_modify": 1e9}}
|
||||
e = Electron("test-id", data)
|
||||
assert e.algodata["SM-2"]["efactor"] == 1.5
|
||||
assert e.algodata["SM-2"]["rept"] == 3
|
||||
|
||||
|
||||
class TestElectronActivation:
|
||||
def test_activate_sets_flag(self, timer_context):
|
||||
e = Electron("test-id", {})
|
||||
assert e.is_activated() == 0
|
||||
e.activate()
|
||||
assert e.is_activated() == 1
|
||||
|
||||
def test_is_due_requires_activation(self, timer_context):
|
||||
e = Electron("test-id", {})
|
||||
e.algodata["SM-2"]["next_date"] = 0 # past
|
||||
assert e.is_due() == 0 # not activated
|
||||
|
||||
e.activate()
|
||||
assert e.is_due() == 1
|
||||
|
||||
def test_is_due_returns_false_when_not_due(self, timer_context):
|
||||
e = Electron("test-id", {})
|
||||
e.activate()
|
||||
e.algodata["SM-2"]["next_date"] = 999999
|
||||
assert e.is_due() is False
|
||||
|
||||
|
||||
class TestElectronModify:
|
||||
def test_modify_valid_key(self, timer_context):
|
||||
e = Electron("test-id", {})
|
||||
e.modify("efactor", 3.0)
|
||||
assert e.algodata["SM-2"]["efactor"] == 3.0
|
||||
|
||||
def test_modify_invalid_key_raises(self, timer_context):
|
||||
e = Electron("test-id", {})
|
||||
with pytest.raises(AttributeError):
|
||||
e.modify("nonexistent", 42)
|
||||
|
||||
|
||||
class TestElectronRevisor:
|
||||
def test_revisor_delegates_to_algo(self, timer_context):
|
||||
e = Electron("test-id", {})
|
||||
e.activate()
|
||||
e.algodata["SM-2"]["next_date"] = 0
|
||||
assert e.is_due() == 1
|
||||
|
||||
e.revisor(quality=5)
|
||||
# After good review, interval > 0
|
||||
assert e.algodata["SM-2"]["interval"] >= 1
|
||||
|
||||
def test_revisor_nsp0(self, timer_context):
|
||||
e = Electron("test-id", {}, algo_name="NSP-0")
|
||||
e.activate()
|
||||
e.algodata["NSP-0"]["next_date"] = 0
|
||||
assert e.is_due() == 1
|
||||
|
||||
e.revisor(quality=3) # bad feedback
|
||||
assert e.algodata["NSP-0"]["interval"] == 1
|
||||
|
||||
|
||||
class TestElectronProperties:
|
||||
def test_rept(self, timer_context):
|
||||
e = Electron("test-id", {})
|
||||
assert e.rept() == 0
|
||||
|
||||
def test_rept_real(self, timer_context):
|
||||
e = Electron("test-id", {})
|
||||
assert e.rept(real_rept=True) == 0
|
||||
|
||||
def test_get_rating(self, timer_context):
|
||||
e = Electron("test-id", {})
|
||||
rating = e.get_rating()
|
||||
assert isinstance(rating, str)
|
||||
|
||||
def test_nextdate_returns_int(self, timer_context):
|
||||
e = Electron("test-id", {})
|
||||
nd = e.nextdate()
|
||||
assert isinstance(nd, int)
|
||||
|
||||
def test_hash(self, timer_context):
|
||||
e = Electron("test-id", {})
|
||||
assert hash(e) == hash("test-id")
|
||||
|
||||
def test_len(self, timer_context):
|
||||
e = Electron("test-id", {})
|
||||
assert len(e) == len(algorithms["SM-2"].defaults)
|
||||
|
||||
|
||||
class TestElectronGetSetItem:
|
||||
def test_getitem_ident(self, timer_context):
|
||||
e = Electron("test-id", {})
|
||||
assert e["ident"] == "test-id"
|
||||
|
||||
def test_getitem_algo_key(self, timer_context):
|
||||
e = Electron("test-id", {})
|
||||
assert e["efactor"] == 2.5
|
||||
|
||||
def test_getitem_missing_key_raises(self, timer_context):
|
||||
e = Electron("test-id", {})
|
||||
with pytest.raises(KeyError):
|
||||
_ = e["nonexistent"]
|
||||
|
||||
def test_setitem_valid_key(self, timer_context):
|
||||
e = Electron("test-id", {})
|
||||
e["efactor"] = 3.5
|
||||
assert e["efactor"] == 3.5
|
||||
|
||||
def test_setitem_ident_raises(self, timer_context):
|
||||
e = Electron("test-id", {})
|
||||
with pytest.raises(AttributeError):
|
||||
e["ident"] = "new-id"
|
||||
|
||||
|
||||
class TestElectronFromData:
|
||||
def test_from_data_creates_electron(self, timer_context):
|
||||
data = {"SM-2": {}}
|
||||
e = Electron.from_data(("my-ident", data), algo_name="SM-2")
|
||||
assert e.ident == "my-ident"
|
||||
assert e.algoname == "SM-2"
|
||||
assert "SM-2" in e.algodata
|
||||
72
tests/test_epath.py
Normal file
72
tests/test_epath.py
Normal file
@@ -0,0 +1,72 @@
|
||||
"""Tests for heurams.services.epath"""
|
||||
|
||||
from heurams.services.epath import epath
|
||||
|
||||
|
||||
class TestEpathRead:
|
||||
def test_empty_path_returns_self(self):
|
||||
d = {"a": 1}
|
||||
assert epath(d, "") is d
|
||||
|
||||
def test_simple_key(self):
|
||||
d = {"a": 1}
|
||||
assert epath(d, "a") == 1
|
||||
|
||||
def test_nested_key(self):
|
||||
d = {"a": {"b": {"c": 42}}}
|
||||
assert epath(d, "a.b.c") == 42
|
||||
|
||||
def test_missing_key_returns_default(self):
|
||||
d = {"a": 1}
|
||||
assert epath(d, "b", default=None) is None
|
||||
|
||||
def test_missing_key_no_default(self):
|
||||
d = {"a": 1}
|
||||
assert epath(d, "b") is None
|
||||
|
||||
def test_list_index_access(self):
|
||||
d = {"items": [10, 20, 30]}
|
||||
assert epath(d, "items.[1]") == 20
|
||||
|
||||
def test_leading_dot_stripped(self):
|
||||
d = {"a": 1}
|
||||
assert epath(d, ".a") == 1
|
||||
|
||||
def test_trailing_dot_stripped(self):
|
||||
d = {"a": 1}
|
||||
assert epath(d, "a.") == 1
|
||||
|
||||
|
||||
class TestEpathParents:
|
||||
def test_parents_creates_missing_dict_keys(self):
|
||||
d = {}
|
||||
result = epath(d, "a.b.c", parents=True, default=None)
|
||||
# parents=True creates all intermediate keys including the leaf
|
||||
assert result == {}
|
||||
assert d == {"a": {"b": {"c": {}}}}
|
||||
|
||||
|
||||
class TestEpathModify:
|
||||
def test_modify_dict_key(self):
|
||||
d = {"a": 1}
|
||||
result = epath(d, "a", enable_modify=True, new_value=99)
|
||||
assert result == 99
|
||||
assert d["a"] == 99
|
||||
|
||||
def test_modify_nested_key(self):
|
||||
d = {"a": {"b": 2}}
|
||||
epath(d, "a.b", enable_modify=True, new_value=42)
|
||||
assert d["a"]["b"] == 42
|
||||
|
||||
def test_modify_list_index(self):
|
||||
d = {"items": [10, 20]}
|
||||
epath(d, "items.[0]", enable_modify=True, new_value=99)
|
||||
assert d["items"][0] == 99
|
||||
|
||||
def test_modify_list_index_with_parents(self):
|
||||
d = {"items": []}
|
||||
result = epath(
|
||||
d, "items.[3]", enable_modify=True, new_value=42, parents=True
|
||||
)
|
||||
assert result == 42
|
||||
assert d["items"] == [None, None, None, 42]
|
||||
47
tests/test_evalizor.py
Normal file
47
tests/test_evalizor.py
Normal file
@@ -0,0 +1,47 @@
|
||||
"""Tests for heurams.kernel.auxiliary.evalizor.Evalizer"""
|
||||
|
||||
from heurams.kernel.auxiliary.evalizor import Evalizer
|
||||
|
||||
|
||||
class TestEvalizer:
|
||||
def test_noop_on_plain_string(self):
|
||||
e = Evalizer({"x": 42})
|
||||
assert e("hello") == "hello"
|
||||
|
||||
def test_eval_expression(self):
|
||||
e = Evalizer({"x": 42})
|
||||
assert e("eval: x") == 42
|
||||
|
||||
def test_eval_arithmetic(self):
|
||||
e = Evalizer({"a": 10, "b": 20})
|
||||
assert e("eval: a + b") == 30
|
||||
|
||||
def test_traverses_dict(self):
|
||||
e = Evalizer({"val": 99})
|
||||
data = {"key_a": "plain", "key_b": "eval: val + 1"}
|
||||
result = e(data)
|
||||
assert result == {"key_a": "plain", "key_b": 100}
|
||||
|
||||
def test_traverses_list(self):
|
||||
e = Evalizer({"val": 5})
|
||||
data = ["eval: val", "plain", "eval: val * 2"]
|
||||
result = e(data)
|
||||
assert result == [5, "plain", 10]
|
||||
|
||||
def test_traverses_nested(self):
|
||||
e = Evalizer({"val": 3})
|
||||
data = {"outer": {"inner": "eval: val ** 2"}}
|
||||
result = e(data)
|
||||
assert result == {"outer": {"inner": 9}}
|
||||
|
||||
def test_traverses_tuple(self):
|
||||
e = Evalizer({"val": 7})
|
||||
data = ("eval: val", "other")
|
||||
result = e(data)
|
||||
assert result == (7, "other")
|
||||
|
||||
def test_non_string_passthrough(self):
|
||||
e = Evalizer({})
|
||||
assert e(42) == 42
|
||||
assert e(None) is None
|
||||
assert e([1, 2, 3]) == [1, 2, 3]
|
||||
25
tests/test_hasher.py
Normal file
25
tests/test_hasher.py
Normal file
@@ -0,0 +1,25 @@
|
||||
"""Tests for heurams.services.hasher"""
|
||||
|
||||
from heurams.services.hasher import get_md5, hash
|
||||
|
||||
|
||||
class TestGetMD5:
|
||||
def test_known_value(self):
|
||||
# MD5 of "hello" is known
|
||||
assert get_md5("hello") == "5d41402abc4b2a76b9719d911017c592"
|
||||
|
||||
def test_empty_string(self):
|
||||
assert get_md5("") == "d41d8cd98f00b204e9800998ecf8427e"
|
||||
|
||||
def test_unicode(self):
|
||||
result = get_md5("中文测试")
|
||||
assert isinstance(result, str)
|
||||
assert len(result) == 32
|
||||
|
||||
def test_different_inputs_differ(self):
|
||||
assert get_md5("abc") != get_md5("abcd")
|
||||
|
||||
|
||||
class TestHash:
|
||||
def test_hash_delegates_to_md5(self):
|
||||
assert hash("hello") == get_md5("hello")
|
||||
198
tests/test_lict.py
Normal file
198
tests/test_lict.py
Normal file
@@ -0,0 +1,198 @@
|
||||
"""Tests for heurams.kernel.auxiliary.lict.Lict"""
|
||||
|
||||
import pytest
|
||||
|
||||
from heurams.kernel.auxiliary.lict import Lict
|
||||
|
||||
|
||||
class TestLictInit:
|
||||
def test_empty(self):
|
||||
l = Lict()
|
||||
assert len(l) == 0
|
||||
assert list(l) == []
|
||||
|
||||
def test_from_list(self):
|
||||
l = Lict(initlist=[("a", 1), ("b", 2)])
|
||||
assert l["a"] == 1
|
||||
assert l["b"] == 2
|
||||
assert len(l) == 2
|
||||
|
||||
def test_from_dict(self):
|
||||
l = Lict(initdict={"x": 10, "y": 20})
|
||||
assert l["x"] == 10
|
||||
assert l["y"] == 20
|
||||
assert len(l) == 2
|
||||
|
||||
|
||||
class TestLictListInterface:
|
||||
def test_list_getitem(self):
|
||||
l = Lict(initlist=[("a", 1), ("b", 2)])
|
||||
assert l[0] == ("a", 1)
|
||||
assert l[1] == ("b", 2)
|
||||
|
||||
def test_list_setitem(self):
|
||||
l = Lict(initlist=[("a", 1), ("b", 2)])
|
||||
l[0] = ("c", 3)
|
||||
assert l["c"] == 3
|
||||
assert l[0] == ("c", 3)
|
||||
|
||||
def test_list_delitem(self):
|
||||
l = Lict(initlist=[("a", 1), ("b", 2)])
|
||||
del l[0]
|
||||
assert "a" not in l
|
||||
assert len(l) == 1
|
||||
|
||||
def test_append(self):
|
||||
l = Lict()
|
||||
l.append(("k", "v"))
|
||||
assert l["k"] == "v"
|
||||
assert l[0] == ("k", "v")
|
||||
|
||||
def test_insert(self):
|
||||
l = Lict(initlist=[("a", 1), ("c", 3)])
|
||||
l.insert(1, ("b", 2))
|
||||
assert l[1] == ("b", 2)
|
||||
assert l["b"] == 2
|
||||
assert len(l) == 3
|
||||
|
||||
def test_pop(self):
|
||||
l = Lict(initlist=[("a", 1), ("b", 2)])
|
||||
item = l.pop()
|
||||
assert item == ("b", 2)
|
||||
assert "b" not in l
|
||||
|
||||
def test_remove_by_key(self):
|
||||
l = Lict(initlist=[("a", 1), ("b", 2)])
|
||||
l.remove("a")
|
||||
assert "a" not in l
|
||||
assert len(l) == 1
|
||||
|
||||
def test_remove_by_tuple(self):
|
||||
l = Lict(initlist=[("a", 1), ("b", 2)])
|
||||
l.remove(("a", 1))
|
||||
assert "a" not in l
|
||||
|
||||
def test_clear(self):
|
||||
l = Lict(initlist=[("a", 1)])
|
||||
l.clear()
|
||||
assert len(l) == 0
|
||||
assert list(l) == []
|
||||
|
||||
|
||||
class TestLictDictInterface:
|
||||
def test_dict_getitem(self):
|
||||
l = Lict(initlist=[("a", 1)])
|
||||
assert l["a"] == 1
|
||||
|
||||
def test_dict_setitem(self):
|
||||
l = Lict()
|
||||
l["k"] = "v"
|
||||
assert l["k"] == "v"
|
||||
# dict set marks list dirty — sync on access
|
||||
assert l[0] == ("k", "v")
|
||||
|
||||
def test_dict_delitem(self):
|
||||
l = Lict(initlist=[("a", 1), ("b", 2)])
|
||||
del l["a"]
|
||||
assert "a" not in l
|
||||
assert len(l) == 1
|
||||
|
||||
def test_keys(self):
|
||||
l = Lict(initlist=[("a", 1), ("b", 2)])
|
||||
assert set(l.keys()) == {"a", "b"}
|
||||
|
||||
def test_values(self):
|
||||
l = Lict(initlist=[("a", 1), ("b", 2)])
|
||||
assert set(l.values()) == {1, 2}
|
||||
|
||||
def test_items(self):
|
||||
l = Lict(initlist=[("a", 1), ("b", 2)])
|
||||
assert set(l.items()) == {("a", 1), ("b", 2)}
|
||||
|
||||
def test_get_itemic_unit(self):
|
||||
l = Lict(initlist=[("a", 1)])
|
||||
assert l.get_itemic_unit("a") == ("a", 1)
|
||||
|
||||
|
||||
class TestLictSync:
|
||||
def test_dict_to_list_sync(self):
|
||||
"""After dict modification, list access triggers sync."""
|
||||
l = Lict(initdict={"a": 1})
|
||||
assert l[0] == ("a", 1)
|
||||
|
||||
def test_list_to_dict_sync(self):
|
||||
"""After list modification, dict access triggers sync."""
|
||||
l = Lict(initlist=[("a", 1)])
|
||||
assert l["a"] == 1
|
||||
|
||||
def test_append_list_maintained(self):
|
||||
l = Lict()
|
||||
l.append(("x", 100))
|
||||
l.append(("y", 200))
|
||||
# List order preserved
|
||||
assert list(l) == [("x", 100), ("y", 200)]
|
||||
|
||||
|
||||
class TestLictEdgeCases:
|
||||
def test_append_non_tuple_raises(self):
|
||||
l = Lict()
|
||||
with pytest.raises(NotImplementedError):
|
||||
l.append("not_a_tuple") # type: ignore
|
||||
|
||||
def test_append_bad_tuple_raises(self):
|
||||
l = Lict()
|
||||
with pytest.raises(NotImplementedError):
|
||||
l.append((1, 2, 3)) # type: ignore
|
||||
|
||||
def test_contains_by_key(self):
|
||||
l = Lict(initlist=[("a", 1)])
|
||||
assert "a" in l
|
||||
|
||||
def test_contains_by_value(self):
|
||||
l = Lict(initlist=[("a", 1)])
|
||||
assert 1 in l
|
||||
|
||||
def test_contains_by_tuple(self):
|
||||
l = Lict(initlist=[("a", 1)])
|
||||
assert ("a", 1) in l
|
||||
|
||||
def test_not_contains(self):
|
||||
l = Lict(initlist=[("a", 1)])
|
||||
assert "z" not in l
|
||||
|
||||
def test_forced_order(self):
|
||||
l = Lict(initlist=[("b", 2), ("a", 1)], forced_order=True)
|
||||
assert l[0] == ("a", 1)
|
||||
assert l[1] == ("b", 2)
|
||||
|
||||
def test_append_if_not_exists(self):
|
||||
l = Lict()
|
||||
l.append_if_it_doesnt_exist_before(("k", "v"))
|
||||
assert l["k"] == "v"
|
||||
l.append_if_it_doesnt_exist_before(("k", "v"))
|
||||
assert len(l) == 1
|
||||
|
||||
def test_keys_equal_with(self):
|
||||
a = Lict(initlist=[("x", 1), ("y", 2)])
|
||||
b = Lict(initlist=[("y", 3), ("x", 4)])
|
||||
assert a.keys_equal_with(b)
|
||||
|
||||
def test_index_raises(self):
|
||||
l = Lict()
|
||||
with pytest.raises(NotImplementedError):
|
||||
l.index()
|
||||
|
||||
def test_extend_raises(self):
|
||||
l = Lict()
|
||||
with pytest.raises(NotImplementedError):
|
||||
l.extend()
|
||||
|
||||
def test_sort_raises(self):
|
||||
l = Lict()
|
||||
with pytest.raises(NotImplementedError):
|
||||
l.sort()
|
||||
|
||||
def test_reverse_raises(self):
|
||||
l = Lict()
|
||||
with pytest.raises(NotImplementedError):
|
||||
l.reverse()
|
||||
70
tests/test_nsp0.py
Normal file
70
tests/test_nsp0.py
Normal file
@@ -0,0 +1,70 @@
|
||||
"""Tests for heurams.kernel.algorithms.nsp0.NSP0Algorithm"""
|
||||
|
||||
from copy import deepcopy
|
||||
|
||||
import pytest
|
||||
|
||||
from heurams.kernel.algorithms import algorithms
|
||||
from heurams.services import timer
|
||||
|
||||
|
||||
@pytest.fixture
|
||||
def algo():
|
||||
return algorithms["NSP-0"]
|
||||
|
||||
|
||||
@pytest.fixture
|
||||
def algodata(sample_algodata_nsp0):
|
||||
return sample_algodata_nsp0
|
||||
|
||||
|
||||
class TestNSP0Defaults:
|
||||
def test_defaults_have_important(self, algo):
|
||||
assert algo.defaults["important"] == 0
|
||||
|
||||
def test_algo_name(self, algo):
|
||||
assert algo.algo_name == "NSP-0"
|
||||
|
||||
|
||||
class TestNSP0Revisor:
|
||||
def test_negative_one_skip(self, algo, algodata):
|
||||
d = deepcopy(algodata)
|
||||
algo.revisor(d, feedback=-1)
|
||||
assert d == algodata
|
||||
|
||||
def test_feedback_three_or_less_sets_interval_one(self, algo, algodata):
|
||||
for fb in (0, 1, 2, 3):
|
||||
d = deepcopy(algodata)
|
||||
algo.revisor(d, feedback=fb)
|
||||
assert d["NSP-0"]["interval"] == 1
|
||||
assert d["NSP-0"]["important"] == 1
|
||||
|
||||
def test_feedback_greater_than_three_sets_infinite_interval(self, algo, algodata):
|
||||
for fb in (4, 5):
|
||||
d = deepcopy(algodata)
|
||||
algo.revisor(d, feedback=fb)
|
||||
assert d["NSP-0"]["interval"] == float("inf")
|
||||
assert d["NSP-0"]["important"] == 0
|
||||
|
||||
def test_revisor_updates_dates(self, algo, algodata, timer_context):
|
||||
d = deepcopy(algodata)
|
||||
algo.revisor(d, feedback=3)
|
||||
assert d["NSP-0"]["last_date"] == timer.get_daystamp()
|
||||
assert d["NSP-0"]["next_date"] == timer.get_daystamp() + 1
|
||||
|
||||
|
||||
class TestNSP0IsDue:
|
||||
def test_due_when_past(self, algo, algodata, timer_context):
|
||||
d = deepcopy(algodata)
|
||||
d["NSP-0"]["next_date"] = 100
|
||||
assert algo.is_due(d) is True
|
||||
|
||||
def test_not_due_when_future(self, algo, algodata, timer_context):
|
||||
d = deepcopy(algodata)
|
||||
d["NSP-0"]["next_date"] = 999999
|
||||
assert algo.is_due(d) is False
|
||||
|
||||
def test_nextdate_returns_stored(self, algo, algodata):
|
||||
d = deepcopy(algodata)
|
||||
d["NSP-0"]["next_date"] = 42
|
||||
assert algo.nextdate(d) == 42
|
||||
123
tests/test_sm2.py
Normal file
123
tests/test_sm2.py
Normal file
@@ -0,0 +1,123 @@
|
||||
"""Tests for heurams.kernel.algorithms.sm2.SM2Algorithm"""
|
||||
|
||||
from copy import deepcopy
|
||||
|
||||
import pytest
|
||||
|
||||
from heurams.kernel.algorithms import algorithms
|
||||
from heurams.services import timer
|
||||
|
||||
|
||||
@pytest.fixture
|
||||
def algo():
|
||||
return algorithms["SM-2"]
|
||||
|
||||
|
||||
@pytest.fixture
|
||||
def algodata(sample_algodata_sm2):
|
||||
return sample_algodata_sm2
|
||||
|
||||
|
||||
class TestSM2Defaults:
|
||||
def test_defaults_have_efactor(self, algo):
|
||||
assert algo.defaults["efactor"] == 2.5
|
||||
|
||||
def test_algo_name(self, algo):
|
||||
assert algo.algo_name == "SM-2"
|
||||
|
||||
|
||||
class TestSM2Revisor:
|
||||
def test_feedback_negative_one_skips(self, algo, algodata):
|
||||
"""feedback == -1 should be a no-op."""
|
||||
d = deepcopy(algodata)
|
||||
algo.revisor(d, feedback=-1)
|
||||
assert d == algodata # unchanged
|
||||
|
||||
def test_good_feedback_increases_efactor(self, algo, algodata):
|
||||
d = deepcopy(algodata)
|
||||
ef_before = d["SM-2"]["efactor"]
|
||||
algo.revisor(d, feedback=5)
|
||||
assert d["SM-2"]["efactor"] > ef_before
|
||||
|
||||
def test_bad_feedback_resets_rept(self, algo, algodata):
|
||||
d = deepcopy(algodata)
|
||||
d["SM-2"]["rept"] = 5
|
||||
algo.revisor(d, feedback=2)
|
||||
assert d["SM-2"]["rept"] == 0
|
||||
assert d["SM-2"]["interval"] == 1
|
||||
|
||||
def test_efactor_minimum_floor(self, algo, algodata):
|
||||
d = deepcopy(algodata)
|
||||
d["SM-2"]["efactor"] = 0.5
|
||||
algo.revisor(d, feedback=2)
|
||||
assert d["SM-2"]["efactor"] >= 1.3
|
||||
|
||||
def test_rept_increments_on_good_feedback(self, algo, algodata):
|
||||
d = deepcopy(algodata)
|
||||
algo.revisor(d, feedback=4)
|
||||
assert d["SM-2"]["rept"] == 1
|
||||
|
||||
def test_new_activation_resets_state(self, algo, algodata):
|
||||
d = deepcopy(algodata)
|
||||
d["SM-2"]["rept"] = 10
|
||||
d["SM-2"]["efactor"] = 3.0
|
||||
algo.revisor(d, feedback=5, is_new_activation=True)
|
||||
assert d["SM-2"]["rept"] == 0
|
||||
assert d["SM-2"]["efactor"] == 2.5
|
||||
|
||||
def test_interval_at_rept_zero(self, algo, algodata):
|
||||
d = deepcopy(algodata)
|
||||
algo.revisor(d, feedback=2)
|
||||
assert d["SM-2"]["interval"] == 1
|
||||
|
||||
def test_interval_at_rept_one(self, algo, algodata):
|
||||
d = deepcopy(algodata)
|
||||
# rept=0 + feedback>=3 -> rept becomes 1 -> interval=6
|
||||
algo.revisor(d, feedback=5)
|
||||
assert d["SM-2"]["interval"] == 6
|
||||
|
||||
def test_interval_for_rept_gt_one(self, algo, algodata):
|
||||
d = deepcopy(algodata)
|
||||
d["SM-2"]["rept"] = 2
|
||||
d["SM-2"]["interval"] = 6
|
||||
d["SM-2"]["efactor"] = 2.0
|
||||
algo.revisor(d, feedback=5)
|
||||
# efactor 2.0 + 0.1(feedback=5) = 2.1; interval = round(6 * 2.1) = 13
|
||||
assert d["SM-2"]["interval"] == 13
|
||||
|
||||
def test_real_rept_always_increments(self, algo, algodata):
|
||||
d = deepcopy(algodata)
|
||||
algo.revisor(d, feedback=5)
|
||||
assert d["SM-2"]["real_rept"] == 1
|
||||
algo.revisor(d, feedback=0)
|
||||
assert d["SM-2"]["real_rept"] == 2
|
||||
|
||||
|
||||
class TestSM2DueDate:
|
||||
def test_is_due_when_past(self, algo, algodata, timer_context):
|
||||
d = deepcopy(algodata)
|
||||
d["SM-2"]["next_date"] = 100 # far in the past
|
||||
assert algo.is_due(d) is True
|
||||
|
||||
def test_not_due_when_future(self, algo, algodata, timer_context):
|
||||
d = deepcopy(algodata)
|
||||
d["SM-2"]["next_date"] = 999999 # far in the future
|
||||
assert algo.is_due(d) is False
|
||||
|
||||
def test_nextdate_returns_stored(self, algo, algodata):
|
||||
d = deepcopy(algodata)
|
||||
d["SM-2"]["next_date"] = 12345
|
||||
assert algo.nextdate(d) == 12345
|
||||
|
||||
def test_revisor_updates_dates(self, algo, algodata, timer_context):
|
||||
d = deepcopy(algodata)
|
||||
algo.revisor(d, feedback=5)
|
||||
assert d["SM-2"]["last_date"] == timer.get_daystamp()
|
||||
assert d["SM-2"]["next_date"] > timer.get_daystamp()
|
||||
|
||||
|
||||
class TestSM2Rating:
|
||||
def test_get_rating_returns_efactor(self, algo, algodata):
|
||||
d = deepcopy(algodata)
|
||||
d["SM-2"]["efactor"] = 2.5
|
||||
assert algo.get_rating(d) == "2.5"
|
||||
35
tests/test_textproc.py
Normal file
35
tests/test_textproc.py
Normal file
@@ -0,0 +1,35 @@
|
||||
"""Tests for heurams.services.textproc"""
|
||||
|
||||
from heurams.services.textproc import domize, truncate, undomize
|
||||
|
||||
|
||||
class TestTruncate:
|
||||
def test_short_string_unchanged(self):
|
||||
assert truncate("ab") == "ab"
|
||||
|
||||
def test_three_char_unchanged(self):
|
||||
assert truncate("abc") == "abc"
|
||||
|
||||
def test_longer_string_truncated(self):
|
||||
assert truncate("abcd") == "abc>"
|
||||
|
||||
def test_empty_string(self):
|
||||
assert truncate("") == ""
|
||||
|
||||
|
||||
class TestDomizeUndomize:
|
||||
def test_domize_replaces_dot(self):
|
||||
assert domize("a.b.c") == "a--DOT--b--DOT--c"
|
||||
|
||||
def test_domize_no_dot(self):
|
||||
assert domize("abc") == "abc"
|
||||
|
||||
def test_undomize_restores_dot(self):
|
||||
assert undomize("a--DOT--b") == "a.b"
|
||||
|
||||
def test_undomize_no_marker(self):
|
||||
assert undomize("abc") == "abc"
|
||||
|
||||
def test_roundtrip(self):
|
||||
original = "config.key.subkey"
|
||||
assert undomize(domize(original)) == original
|
||||
278
uv.lock
generated
278
uv.lock
generated
@@ -84,11 +84,163 @@ wheels = [
|
||||
{ url = "https://files.pythonhosted.org/packages/ae/3a/dbeec9d1ee0844c679f6bb5d6ad4e9f198b1224f4e7a32825f47f6192b0c/cffi-2.0.0-cp314-cp314t-win_arm64.whl", hash = "sha256:0a1527a803f0a659de1af2e1fd700213caba79377e27e4693648c2923da066f9", size = 184195, upload-time = "2025-09-08T23:23:43.004Z" },
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "colorama"
|
||||
version = "0.4.6"
|
||||
source = { registry = "https://pypi.org/simple" }
|
||||
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[[package]]
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Reference in New Issue
Block a user