From 7a09167261ca722bbe940863a5c6b56f18784da8 Mon Sep 17 00:00:00 2001 From: Zhao Date: Tue, 21 Apr 2026 12:15:35 +0700 Subject: [PATCH] Initial commit: PastPaper Master full stack Co-Authored-By: Claude Opus 4.6 (1M context) --- .gitignore | 12 + 080c1b16be5aa2e1ea87d5175894fb3c.jpg | Bin 0 -> 105047 bytes HANDOFF_COMP2211.md | 328 + TECHNICAL.md | 516 ++ backend/Dockerfile | 16 + backend/add_progress_columns.sql | 4 + backend/app/__init__.py | 0 backend/app/config.py | 36 + backend/app/dependencies/__init__.py | 0 backend/app/dependencies/auth.py | 34 + backend/app/main.py | 59 + backend/app/routers/__init__.py | 0 backend/app/routers/analytics.py | 285 + backend/app/routers/attempts.py | 208 + backend/app/routers/papers.py | 142 + backend/app/routers/questions.py | 325 + backend/app/services/__init__.py | 0 backend/app/services/grader.py | 146 + backend/app/services/llm_clients.py | 74 + backend/app/services/paper_processor.py | 576 ++ backend/app/services/supabase_client.py | 13 + backend/app/services/text_extractor.py | 48 + backend/backfill_ai_trio_with_context.py | 252 + backend/backfill_comp2211_page_y.py | 160 + backend/backfill_comp2211_tags.py | 365 + backend/backfill_null_ai_trio.py | 169 + backend/backfill_similar_questions.py | 135 + backend/backfill_vision.py | 238 + backend/fill_manual_study_aids.py | 29 + backend/import_course_manifest.py | 240 + backend/pyproject.toml | 17 + backend/regen_ai_trio_comp2211.py | 174 + backend/regenerate_analysis.py | 69 + ...split_comp2211_2022_spring_final_part_a.py | 224 + ...split_comp2211_2022_spring_final_part_b.py | 232 + backend/split_comp2211_2022_spring_midterm.py | 233 + backend/split_comp2211_2023_spring_midterm.py | 268 + backend/split_comp2211_2024_spring_final.py | 242 + backend/split_comp2211_2024_spring_midterm.py | 291 + backend/upload_course_library_pdfs.py | 121 + backend/uv.lock | 1969 +++++ deploy.md | 92 + docker-compose.yml | 10 + docs/PAGE_NUMBER_BACKFILL.md | 152 + docs/TAGGING_REQUIREMENTS.md | 243 + frontend/Dockerfile | 12 + frontend/index.html | 13 + frontend/nginx.conf | 27 + frontend/package-lock.json | 3058 +++++++ frontend/package.json | 30 + frontend/public/favicon.jpg | Bin 0 -> 105047 bytes frontend/src/App.tsx | 30 + frontend/src/components/layout/Header.tsx | 69 + .../components/layout/ProcessingBanner.tsx | 183 + .../components/shared/CollapsibleSection.tsx | 65 + .../src/components/shared/KaTeXRenderer.tsx | 86 + .../src/components/shared/StatusBadge.tsx | 15 + .../src/components/upload/FilePickerField.tsx | 63 + frontend/src/components/upload/UploadForm.tsx | 184 + .../src/components/workbench/ActionBar.tsx | 58 + .../src/components/workbench/AiTrioPanel.tsx | 21 + .../src/components/workbench/PdfViewer.tsx | 170 + .../src/components/workbench/PhotoUpload.tsx | 90 + .../components/workbench/QuestionDetail.tsx | 260 + .../src/components/workbench/QuestionNav.tsx | 56 + .../workbench/SimilarHistoryPanel.tsx | 130 + .../components/workbench/VariantDetail.tsx | 148 + .../src/components/workbench/VariantModal.tsx | 189 + frontend/src/contexts/AuthContext.tsx | 49 + frontend/src/hooks/usePaper.ts | 43 + frontend/src/hooks/useQuestions.ts | 33 + frontend/src/lib/api.ts | 190 + frontend/src/lib/questionGroups.ts | 45 + frontend/src/lib/supabase.ts | 6 + frontend/src/main.tsx | 16 + frontend/src/pages/AnalyticsPage.tsx | 521 ++ frontend/src/pages/ErrorBookPage.tsx | 296 + frontend/src/pages/HomePage.tsx | 705 ++ frontend/src/pages/LoginPage.tsx | 90 + frontend/src/pages/UploadPage.tsx | 16 + frontend/src/pages/WorkbenchPage.tsx | 524 ++ frontend/src/styles/globals.css | 79 + frontend/src/types/api.ts | 169 + frontend/src/vite-env.d.ts | 1 + frontend/tsconfig.json | 21 + frontend/vite.config.ts | 22 + index 2.html | 22 + memory/MEMORY.md | 3 + memory/project_pastpaper_master.md | 37 + pastpaper-scraper | 1 + pitch_script.md | 25 + supabase/migrations/001_init_schema.sql | 207 + 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zveq}LE$o5RtF}ci*v&oiedaT>)t@0}JnJR(*5Rp;i$83L1PmGGGdX9e}_XJ)9}2HH!|gd$<6pYHomf^n`!8(p`f#r6<9rPSN- zJE)-#+1;d*hlMFIL8}ozdgZi^O!tPuR{JUrbaWXRHjEYD6t`qr$(3nzcLwJIjm9)d zfR`pK2_^It5* zn$&=B#xvz)fhf(=@&~mPlT8Orh9G?>W`5^z^)-_BKY}CwDV+IV{P*7tMEn=m^q)vX z{<3oa|Ni_(6PLel1aW@zkNN|zx$d0`R_T9@HYX{-yORBRqt<) zLuFjvH)#CxS6z?q@pU<6!GGAD{0Ed9-#bnCUzJSX%X6IX0B0TG&;!3~75(4#EC0JP z{;ztTLl1CDLr!VPDGfPg0S69n-~a~>d@I>&oNmEi*Dd%)?ZLlCHt(-GXAT_TzyS^% z;J^V69Qez&**`aE|E`ZY>;Q)y;53do-vJIJe6nAqNg{ z-~a~>eD`o*#n}g1#}8_FB&p1$Ck7&}8lHAu!^Yg&CbmNpzCrr`0n 文件: `supabase/migrations/001_init_schema.sql` + +### Table: `papers` — 试卷 + +| 字段 | 类型 | 说明 | +|------|------|------| +| id | UUID PK | 自动生成 | +| user_id | UUID FK → auth.users | 上传者 | +| course_code | TEXT | 课程代码, e.g. "COMP2011" | +| year / term / exam_type | INT/TEXT/TEXT | 元信息 | +| paper_file_url | TEXT | 试卷 PDF (Supabase Storage) | +| answer_file_url | TEXT? | 答案 PDF (可选) | +| status | TEXT | `uploaded` → `processing` → `ready` / `error` | +| paper_extracted_text | TEXT | PyMuPDF 提取的原始文本 (缓存) | +| total_score / question_count | INT | AI 提取的整卷概览 | +| topics_summary | JSONB | `{"Linked List": 40, "Recursion": 30}` | +| difficulty_level | TEXT | easy / medium / hard | + +### Table: `paper_questions` — 逐题数据 + +| 字段 | 类型 | 说明 | +|------|------|------| +| id | UUID PK | | +| paper_id | UUID FK → papers | | +| question_number | TEXT | "1", "1a", "2b" | +| parent_question | TEXT? | 子题父题号: "1a" → "1" | +| display_order | INT | 排序 | +| question_type | TEXT | `mc` / `true_false` / `fill_blank` / `long_question` | +| question_text | TEXT | 题目原文 | +| score / page_number | INT | 分值, PDF 页码 (PDF-题目联动用) | +| options | JSONB | MC 选项: `[{"label":"A","text":"..."}]` | +| correct_option | TEXT | MC 正确选项 | +| correct_answer | TEXT | 填空题正确答案 | +| raw_answer_text | TEXT | 答案 PDF 原始解�� | +| topics | TEXT[] | 知识点标签 | +| difficulty | TEXT | easy / medium / hard | +| knowledge_reminder | TEXT | AI 知识点提醒 (HTML+KaTeX) | +| ai_hint | TEXT | AI 思路提示 (HTML+KaTeX) | +| solution | TEXT | AI 完整解题过程 (HTML+KaTeX) | + +### Table: `user_attempts` — 用户答题记录 + +| 字段 | 类型 | 说明 | +|------|------|------| +| id | UUID PK | | +| user_id / question_id | UUID FK | | +| attempt_type | TEXT | `select` / `input` / `photo` | +| user_answer | TEXT | 用户的选项或输入 | +| photo_url / photo_ocr_text | TEXT | 拍照上传的图片和 OCR 结果 | +| is_correct | BOOL | AI 判定 | +| feedback | TEXT | HTML 逐步错误分析 | +| error_at_step | INT | 第几步出错 | +| in_error_book / mastered | BOOL | 错题本状态 | + +--- + +## 核心功能一：试卷分析管线 + +### 流程概述 + +``` +用户上传 PDF → 后台 BackgroundTask → 5 步管线 → 状态变 ready +``` + +### 文件 + +| 文件 | 作用 | +|------|------| +| `backend/app/routers/papers.py` | 上传接口, 触发后台处理 | +| `backend/app/services/paper_processor.py` | **核心管线**, 5 步处理逻辑 | +| `backend/app/services/text_extractor.py` | PDF → 文本提取 (PyMuPDF) | +| `backend/app/services/llm_clients.py` | GPT-4o / Qwen 客户端单例 | + +### 管线 5 步 (`paper_processor.py: process_paper()`) + +**Step 1 — PDF 文本提取** +- 使用 PyMuPDF (`fitz`) 逐页提取文本 +- 如果某页文本 < 50 字符 (可能是扫描件), 额外保存该页为 base64 图片备用 +- 提取结果缓存到 `papers.paper_extracted_text` + +```python +# text_extractor.py +extract_pdf(file_bytes) → ExtractedContent(pages_text, page_images, total_pages, has_images) +get_full_text(extracted) → "--- Page 1 ---\n{text}\n\n--- Page 2 ---\n..." +``` + +**Step 2 — GPT-4o 结构化拆题** +- Model: `gpt-4o`, temperature=0, response_format=json_object +- 输入: 整卷文本 +- 输出: JSON 包含 total_score, difficulty_level, topics_summary, questions[] +- 每题提取: question_number, parent_question, question_type, question_text, score, page_number, options, topics, difficulty +- 更新 `papers` 表的概览字段 (total_score, question_count, topics_summary, difficulty_level) + +**Step 3 — 答案匹配 (如果有答案 PDF)** +- Model: `gpt-4o`, temperature=0 +- 输入: 题目结构 JSON + 答案文本 +- 输出: 逐题匹配 — correct_option / correct_answer / raw_answer_text +- 选择题 → correct_option, 填空题 → correct_answer, 大题 → raw_answer_text + +**Step 4 — Qwen 生成 AI 三件套 (逐题)** +- Model: `qwen-plus`, temperature=0.3 +- 逐题调用, 输入题目信息 + 标准答案 +- 输出 JSON 三件套: + - `knowledge_reminder`: 前置知识要点 (HTML+KaTeX) + - `ai_hint`: 不给答案的思路引导 (HTML+KaTeX) + - `solution`: 完整逐步解题过程 (HTML+KaTeX) +- 写入 `paper_questions` 表 + +**Step 5 — 标记完成** +- `papers.status` 更新为 `ready` +- 如果任何步骤抛异常, status 设为 `error`, 错误信息写入 `error_message` + +### 关键 Prompt 设计 + +**STRUCTURE_PROMPT** — 结构化拆题 +- 限定 question_type 只能是 mc / true_false / fill_blank / long_question +- 判断题 (True/False) 用 `true_false` 类型，options 为 `[{label:"True",text:"True"},{label:"False",text:"False"}]` +- 选择题必须提取 options 数组 +- 子题通过 parent_question 关联 (e.g. "1a" parent 是 "1") +- 要求推断 page_number, topics, difficulty + +**ANSWER_MATCH_PROMPT** — 答案匹配 +- 输入包含 questions_json (题号+题型) 和 answer_text +- 按题型输出不同字段: MC → correct_option, fill → correct_answer, 大题 → raw_answer_text + +**ANALYSIS_PROMPT** — AI 三件套 +- Solution 要求带完整过程 (Step 1, 2, 3...), 不能只给答案 +- 选择题要解释为什么对、为什么其他选项错 +- 标注常见错误: `
...
` +- KaTeX 规则: 块级 `$$...$$`, 行内 `$...$` + +--- + +## 核心功能二：PDF 滚动 + 题目联动 + +### 文件 + +| 文件 | 作用 | +|------|------| +| `frontend/src/components/workbench/PdfViewer.tsx` | PDF 连续滚动渲染 + 可见页检测 | +| `frontend/src/components/workbench/QuestionNav.tsx` | 题目水平导航栏 | +| `frontend/src/pages/WorkbenchPage.tsx` | 双向联动调度中枢 | + +### 实现方案 + +**布局**: 左侧 60% PDF, 右侧 40% 题目面板 + +**PDF 连续滚动 (`PdfViewer.tsx`)** +- 使用 `react-pdf` 的 `` + `` 组件 +- 所有页面垂直排列在可滚动容器中 (不是单页切换) +- `ResizeObserver` 监听容器宽度, 动态设置 Page width +- 手动跳转: 输入页码 → `scrollIntoView` + +**双向联动:** + +1. **题目 → PDF (点击题目, PDF 滚动到对应页)** + - QuestionNav 点击 → `handleQuestionSelect(index)` → 记录 `lastUserSelectTime = Date.now()` + `setCurrentIndex` + - PdfViewer 收到 `currentPage` prop 变化 → `useEffect` 触发 `el.scrollIntoView({ behavior: "smooth" })` + - 设置 `programmaticScroll.current = true`, 2s 后重置 + +2. **PDF → 题目 (滚动 PDF, 右侧自动切换到当前题)** + - `IntersectionObserver` 监听所有 `` 元素, threshold: `[0, 0.25, 0.5, 0.75, 1]` + - 追踪每页的 `intersectionRatio`, 选出可见占比最高的页码 + - 如果 `programmaticScroll.current === true`, 跳过回调 + - 触发 `onPageChange(bestPage)` → WorkbenchPage `handlePdfPageChange` + - `handlePdfPageChange`: 找到 `page_number <= currentPage` 的最后一题, 更新 `currentIndex` + +**防止跳转抢夺 (双层保护):** +- **WorkbenchPage 层 (核心)**: `lastUserSelectTime` ref — 用户点击题目后 2 秒内, `handlePdfPageChange` 直接 return, 不响应任何 Observer 回调。解决长文档 smooth scroll 经过中间页触发 Observer 导致题目被切走的问题 +- **PdfViewer 层 (辅助)**: `programmaticScroll` ref — scrollIntoView 期间 Observer 回调跳过, 2s 后重置 + +--- + +## 核心功能三：做题交互 (MC / 填空) + +### 文件 + +| 文件 | 作用 | +|------|------| +| `frontend/src/components/workbench/QuestionDetail.tsx` | 题目展示 + 答题交互 | +| `frontend/src/components/workbench/AiTrioPanel.tsx` | 知��点/提示/解析 折叠面板 | +| `frontend/src/components/shared/CollapsibleSection.tsx` | 可折叠区域组件 | +| `frontend/src/components/shared/KaTeXRenderer.tsx` | HTML+KaTeX 渲染器 | + +### QuestionDetail 交互逻辑 + +**选择题 (MC):** +- 状态: `selectedOption`, `checked` +- 点击选项 → 高亮蓝色 (未检查时) +- 点击 "Check Answer" → `checked=true` +- 正确: 选项变绿 + "Correct!" / 错误: 选中项变红, 正确项变绿 + 显示正确答案 +- 切换题目时自动重置状态 (`useEffect` on `question.id`) + +**判断题 (True/False):** +- 状态: `tfAnswers: Record`, `tfChecked` +- 每个 statement 右侧有 T / F 两个按钮, 独立切换 +- 选中高亮蓝色, 全部选完后可点 "Submit Answers" +- 提交后提示查看 solution 对答案 (因为逐条正确答案暂未单独存储) + +**填空题 (Fill Blank):** +- 文本输入框 + "Check" 按钮 +- Enter 键可直接检查 +- 大小写不敏感比较 (`toLowerCase()`) +- 检查后输入框变色: 绿色 (对) / 红色 (错) + +**回调**: `onAnswerResult(isCorrect, userAnswer)` → WorkbenchPage → `recordAttempt` API + +### AiTrioPanel + +- 三个 `CollapsibleSection`: Knowledge Reminder (蓝, 默认展开), AI Hint (琥珀), Solution (绿) +- `CollapsibleSection` 使用 CSS `grid-template-rows: 0fr → 1fr` 动画平滑展开收起 +- 内容通过 `KaTeXRenderer` 渲染 (HTML + KaTeX 公式) + +--- + +## 核心功能四：变体题生成 (Similar Question) + +### 文件 + +| 文件 | 作用 | +|------|------| +| `backend/app/routers/questions.py` | `POST /{question_id}/variant` 端点 | +| `backend/app/services/grader.py` | `generate_variant()` — GPT-4o 生成变体 | +| `frontend/src/components/workbench/ActionBar.tsx` | "Similar Question" 按钮, 异步触发 | +| `frontend/src/pages/WorkbenchPage.tsx` | Variants Tab 状态管理 | +| `frontend/src/components/workbench/VariantDetail.tsx` | 变体题作答界面 | + +### 后端 + +- `POST /api/questions/{question_id}/variant` +- 从 DB 查原题 → 调 `generate_variant(question)` → 附上原题的 `knowledge_reminder` → 返回 +- Model: `gpt-4o`, temperature=0.5, response_format=json_object +- VARIANT_PROMPT 要求: 同知识点, 相似难度, 不同数据/场景, 输出 HTML 格式 (非 markdown) +- 输出字段: question_text, question_type, options (if MC), correct_answer, ai_hint, solution + +### 前端交互 (Tab-based 异步流程) + +**状态管理 (`WorkbenchPage.tsx`):** +```typescript +interface StoredVariant { + id: string; // placeholder ID, e.g. "variant-1" + sourceQuestionNumber: string; // 原题题号 + variant: VariantQuestion; // 生成结果 + status: "generating" | "ready"; +} +``` + +**流程:** +1. 用户点击 "Similar Question" → `ActionBar` 调 `onVariantStart(placeholderId, questionNumber)` +2. WorkbenchPage 创建 `status: "generating"` 的占位项, 用户可继续做题不受阻塞 +3. API 返回后 → `onVariantReady(placeholderId, variant)` → 状态更新为 `ready` +4. 失败 → `onVariantFailed(placeholderId)` → 删除占位项 + +**右侧面板三种视图:** +- **Questions Tab**: 题目导航 + QuestionDetail + AiTrioPanel + ActionBar +- **Variants Tab**: 变体列表 (Generating.../Ready), 每项显示题号和预览文本 +- **Variant Detail**: 点击 "Start" 后整个右侧替换为 VariantDetail 组件 + "Back" 按钮 + +**VariantDetail 组件**: 紫色主题, 包含完整 MC/填空交互 + AI 三件套 (CollapsibleSection) + +--- + +## 核心功能五：拍照批改 + +### 文件 + +| 文件 | 作用 | +|------|------| +| `backend/app/routers/attempts.py` | `POST /photo` — 上传+OCR+批改 | +| `backend/app/services/grader.py` | `ocr_photo()` + `grade_answer()` | +| `frontend/src/components/workbench/PhotoUpload.tsx` | 拍照上传 Modal | +| `frontend/src/components/workbench/ActionBar.tsx` | "Upload handwritten answer" 按钮 | + +### 后端流程 + +1. 接收图片 → 上传到 Supabase Storage `attempt-photos` bucket +2. `ocr_photo(photo_bytes)` — GPT-4o Vision 识别手写内容 + - 输入: base64 图片 + - 输出: 学生答案文本 (含 LaTeX 公式) +3. `grade_answer(question, student_answer)` — Qwen-plus 批改 + - 输入: 题目信息 + 标准答案 + 学生答案 + - 输出: `{ is_correct, score_given, feedback (HTML), error_at_step }` +4. 写入 `user_attempts` 表 (含 photo_url, photo_ocr_text, feedback, is_correct) +5. 答错自动 `in_error_book = true` + +### 前端 + +- PhotoUpload: Modal 弹窗, 支持拖拽/点击选择图片 +- 预览 → 提交 → 显示 OCR 识别结果 + AI 批改反馈 +- 所有题型均可使用 (MC / 填空 / 大题) + +--- + +## 核心功能六：错题本 + +### 文件 + +| 文件 | 作用 | +|------|------| +| `backend/app/routers/attempts.py` | `GET /error-book` + `PATCH /{attempt_id}` | +| `frontend/src/pages/ErrorBookPage.tsx` | 错题本页面 | +| `frontend/src/lib/api.ts` | `getErrorBook()` + `updateAttempt()` | + +### 后端 + +- `GET /api/attempts/error-book?user_id=xxx` + - 查询 `in_error_book=true AND mastered=false` + - JOIN `paper_questions` 返回完整题目信息 +- `PATCH /api/attempts/{attempt_id}` + - 更新 `in_error_book` 或 `mastered` 标记 + +### 前端 + +- 列表展示: 题目信息 + 用户答案 + AI 反馈 +- 操作: "Review in Workbench" (跳转) / "Mastered" (标记掌握) / "Remove" (移出错题本) + +--- + +## 核心功能七：答题记录 + +### 文件 + +| 文件 | 作用 | +|------|------| +| `backend/app/routers/attempts.py` | `POST /` — 记录答题 | +| `frontend/src/components/workbench/ActionBar.tsx` | "Got it right" / "Got it wrong" 按钮 | + +### 流程 + +- "Got it right" → `POST /api/attempts/` with `attempt_type: "select", is_correct: true` +- "Got it wrong" → `POST /api/attempts/` with `attempt_type: "select", is_correct: false` + - 后端自动 `in_error_book = true` +- Toast 提示操作结果 + +--- + +## API 接口汇总 + +### Papers Router (`/api/papers`) + +| Method | Path | 说明 | +|--------|------|------| +| GET | `/` | 列出所有试卷 (可按 user_id 过滤) | +| POST | `/upload` | 上传试卷 PDF + 可选答案 PDF | +| GET | `/{paper_id}` | 获���单份试卷信息 | +| GET | `/{paper_id}/questions` | 获取试卷所有题目 | + +### Attempts Router (`/api/attempts`) + +| Method | Path | 说明 | +|--------|------|------| +| POST | `/` | 记录一次答题 | +| POST | `/photo` | 拍照上传 + OCR + AI 批改 | +| GET | `/error-book?user_id=` | 获取错题本 | +| PATCH | `/{attempt_id}` | 更新错题本/掌握状态 | + +### Questions Router (`/api/questions`) + +| Method | Path | 说明 | +|--------|------|------| +| POST | `/{question_id}/variant` | 生成变体题 | + +--- + +## 前端路由 + +| 路径 | 页面 | 文件 | +|------|------|------| +| `/` | 首页 — 试卷列表 | `src/pages/HomePage.tsx` | +| `/upload` | 上传试卷 | `src/pages/UploadPage.tsx` | +| `/paper/:id` | 做题工作台 | `src/pages/WorkbenchPage.tsx` | +| `/error-book` | 错题本 | `src/pages/ErrorBookPage.tsx` | + +--- + +## 前端组件树 (Workbench) + +``` +WorkbenchPage +├── Header # 顶部导航 (课程+试卷标题) +├── PdfViewer # 左侧 60% — PDF 连续滚动 +└── Right Panel (40%) + ├── [Questions Tab] + │ ├── QuestionNav # 题目水平导航 Q1 Q2 Q3... + │ ├── QuestionDetail # 题目展示 + MC/填空交互 + │ ├── AiTrioPanel # 知识点/提示/解析 (3x CollapsibleSection) + │ └── ActionBar # 底部按钮 (对/错/变体/拍照) + ├── [Variants Tab] + │ └── Variant Cards # 变体列表 (Generating.../Ready) + └── [Variant Detail View] # 替换整个右侧 + ├── Back Button + └── VariantDetail # 变体题作答 + AI 三件套 +``` + +--- + +## LLM 调用模型分工 + +| 任务 | 模型 | Provider | 文件 | +|------|------|----------|------| +| 结构化拆题 | gpt-4o | laozhang API | paper_processor.py | +| 答案匹配 | gpt-4o | laozhang API | paper_processor.py | +| AI 三件套 (knowledge/hint/solution) | qwen-plus | DashScope | paper_processor.py | +| 手写 OCR | gpt-4o (Vision) | laozhang API | grader.py | +| 答案批改 | qwen-plus | DashScope | grader.py | +| 变体题生成 | gpt-4o | laozhang API | grader.py | + +--- + +## 配置与环境变量 + +> 文件: `backend/app/config.py`, `.env` + +| 变量 | 说明 | +|------|------| +| SUPABASE_URL | Supabase 项目 URL | +| SUPABASE_ANON_KEY | 前端用匿名 Key | +| SUPABASE_SERVICE_ROLE_KEY | 后端用 Service Role Key (绕过 RLS) | +| LAOZHANG_BASE_URL | GPT-4o 代理 API 地址 | +| LAOZHANG_API_KEY | GPT-4o 代理 API Key | +| DASHSCOPE_BASE_URL | 阿里 DashScope API | +| DASHSCOPE_API_KEY | DashScope API Key | + +--- + +## 文件完整索引 + +### Backend (`backend/app/`) + +``` +main.py # FastAPI 入口, CORS, 路由注册 +config.py # Pydantic Settings, 环境变量 +routers/ + papers.py # 试卷 CRUD + 上传触发处理 + attempts.py # 答题记录 + 拍照OCR批改 + 错题本 + questions.py # 变体题生成 +services/ + paper_processor.py # 核心5步管线: PDF→结构化→答案匹配→AI三件套 + text_extractor.py # PyMuPDF 文本提取 + grader.py # OCR + 批改 + 变体生成 (Prompt + LLM 调用) + llm_clients.py # GPT-4o / Qwen 客户端单例 + supabase_client.py # Supabase 客户端 +``` + +### Frontend (`frontend/src/`) + +``` +App.tsx # React Router 路由定义 +main.tsx # ReactDOM 入口 +lib/ + api.ts # 所有 API 调用封装 (9 个函数) +types/ + api.ts # TypeScript 类型定义 +hooks/ + usePaper.ts # 轮询获取试卷状态 (3s interval) + useQuestions.ts # 获取题目列表 +pages/ + HomePage.tsx # 首页 — 试卷列表 + UploadPage.tsx # 上传页 + WorkbenchPage.tsx # 做题工作台 — 核心调度组件 + ErrorBookPage.tsx # 错题本 +components/ + layout/ + Header.tsx # 顶部导航栏 + shared/ + KaTeXRenderer.tsx # HTML+KaTeX 公式渲染 + CollapsibleSection.tsx # 折叠面板 (grid动画) + StatusBadge.tsx # 状态标签 + upload/ + UploadForm.tsx # 上传表单 + FilePickerField.tsx # 文件选择器 + workbench/ + PdfViewer.tsx # PDF 连续滚动 + IntersectionObserver + QuestionNav.tsx # 题目导航栏 + QuestionDetail.tsx # 题目展示 + MC/填空交互 + AiTrioPanel.tsx # AI 三件套面板 + ActionBar.tsx # 底部操作按钮 + PhotoUpload.tsx # 拍照上传 Modal + VariantDetail.tsx # 变体题内联作答 + VariantModal.tsx # (已废弃, 被 VariantDetail 替代) +``` diff --git a/backend/Dockerfile b/backend/Dockerfile new file mode 100644 index 0000000..acf1a08 --- /dev/null +++ b/backend/Dockerfile @@ -0,0 +1,16 @@ +FROM python:3.12-slim + +WORKDIR /app + +# System deps for PyMuPDF +RUN apt-get update && apt-get install -y --no-install-recommends \ + libmupdf-dev gcc g++ && \ + rm -rf /var/lib/apt/lists/* + +COPY pyproject.toml . +RUN pip install --no-cache-dir . + +COPY app/ app/ + +EXPOSE 8000 +CMD ["uvicorn", "app.main:app", "--host", "0.0.0.0", "--port", "8000"] diff --git a/backend/add_progress_columns.sql b/backend/add_progress_columns.sql new file mode 100644 index 0000000..bf9c883 --- /dev/null +++ b/backend/add_progress_columns.sql @@ -0,0 +1,4 @@ +ALTER TABLE papers +ADD COLUMN IF NOT EXISTS processing_step text DEFAULT NULL, +ADD COLUMN IF NOT EXISTS processing_progress integer DEFAULT 0, +ADD COLUMN IF NOT EXISTS processing_total integer DEFAULT 0; diff --git a/backend/app/__init__.py b/backend/app/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/backend/app/config.py b/backend/app/config.py new file mode 100644 index 0000000..355abbc --- /dev/null +++ b/backend/app/config.py @@ -0,0 +1,36 @@ +from pydantic_settings import BaseSettings +from functools import lru_cache +import os + + +class Settings(BaseSettings): + # Supabase + supabase_url: str + supabase_anon_key: str + supabase_service_role_key: str + + # LLM - laozhang (gpt-4o, gpt-4o-mini) + laozhang_base_url: str = "https://api.laozhang.ai/v1" + laozhang_api_key: str = "" + + # LLM - DashScope (qwen-plus) + dashscope_base_url: str = "https://dashscope.aliyuncs.com/compatible-mode/v1" + dashscope_api_key: str = "" + + # LLM - DeepSeek + deepseek_base_url: str = "https://api.deepseek.com/v1" + deepseek_api_key: str = "" + + # Google Gemini (official) + google_gemini_api_key: str = "" + + model_config = { + "env_file": os.path.join(os.path.dirname(__file__), "../../.env"), + "env_file_encoding": "utf-8", + "extra": "ignore", + } + + +@lru_cache +def get_settings() -> Settings: + return Settings() diff --git a/backend/app/dependencies/__init__.py b/backend/app/dependencies/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/backend/app/dependencies/auth.py b/backend/app/dependencies/auth.py new file mode 100644 index 0000000..29af84c --- /dev/null +++ b/backend/app/dependencies/auth.py @@ -0,0 +1,34 @@ +"""Auth dependency: validate Supabase JWT and return user_id""" + +from fastapi import Depends, HTTPException, status +from fastapi.security import HTTPBearer, HTTPAuthorizationCredentials +from app.services.supabase_client import get_supabase + +bearer_scheme = HTTPBearer(auto_error=False) + + +async def get_current_user_id( + credentials: HTTPAuthorizationCredentials | None = Depends(bearer_scheme), +) -> str: + """Extract and validate Bearer token, return user_id.""" + if not credentials: + raise HTTPException( + status_code=status.HTTP_401_UNAUTHORIZED, + detail="Not authenticated", + ) + token = credentials.credentials + sb = get_supabase() + try: + result = sb.auth.get_user(token) + user = result.user + if not user: + raise HTTPException( + status_code=status.HTTP_401_UNAUTHORIZED, + detail="Invalid token", + ) + return user.id + except Exception: + raise HTTPException( + status_code=status.HTTP_401_UNAUTHORIZED, + detail="Invalid or expired token", + ) diff --git a/backend/app/main.py b/backend/app/main.py new file mode 100644 index 0000000..a520b41 --- /dev/null +++ b/backend/app/main.py @@ -0,0 +1,59 @@ +import asyncio +import threading +from contextlib import asynccontextmanager + +from fastapi import FastAPI +from fastapi.middleware.cors import CORSMiddleware +from app.routers import analytics, papers, attempts, questions + + +def _resume_stale_papers(): + """启动时检查卡在 processing 的 paper，自动续传 AI trio""" + try: + from app.services.supabase_client import get_supabase + from app.services.paper_processor import process_paper + + sb = get_supabase() + stale = sb.table("papers").select("id").eq("status", "processing").execute().data + if not stale: + return + + for p in stale: + paper_id = p["id"] + print(f"[STARTUP] Resuming processing for paper {paper_id[:8]}...") + + def run(pid=paper_id): + asyncio.run(process_paper(pid, b"", None)) + + threading.Thread(target=run, daemon=True).start() + except Exception as e: + print(f"[STARTUP] Resume skipped: {e}") + + +@asynccontextmanager +async def lifespan(app: FastAPI): + # Startup + _resume_stale_papers() + yield + # Shutdown (nothing to do) + + +app = FastAPI(title="PastPaper Master API", version="0.1.0", lifespan=lifespan) + +app.add_middleware( + CORSMiddleware, + allow_origins=["*"], # 开发阶段先放开，上线收紧 + allow_credentials=True, + allow_methods=["*"], + allow_headers=["*"], +) + +app.include_router(papers.router, prefix="/api/papers", tags=["papers"]) +app.include_router(attempts.router, prefix="/api/attempts", tags=["attempts"]) +app.include_router(questions.router, prefix="/api/questions", tags=["questions"]) +app.include_router(analytics.router, prefix="/api/analytics", tags=["analytics"]) + + +@app.get("/health") +def health(): + return {"status": "ok"} diff --git a/backend/app/routers/__init__.py b/backend/app/routers/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/backend/app/routers/analytics.py b/backend/app/routers/analytics.py new file mode 100644 index 0000000..4771c04 --- /dev/null +++ b/backend/app/routers/analytics.py @@ -0,0 +1,285 @@ +"""Course-level analytics endpoints.""" + +from __future__ import annotations + +from collections import Counter, defaultdict + +from fastapi import APIRouter + +from app.services.supabase_client import get_supabase + +router = APIRouter() + + +DIFFICULTY_SCORE = {"easy": 1, "medium": 2, "hard": 3} +DIFFICULTY_LABEL = {1: "Easy", 2: "Medium", 3: "Hard"} + +# ── Topic normalization ────────────────────────────────────── +# Map variant spellings to canonical label +_TOPIC_ALIASES: dict[str, str] = { + "numpy": "NumPy", + "naïve bayes": "Naive Bayes", + "naïve bayes classifier": "Naive Bayes", + "naive bayes classifier": "Naive Bayes", + "bayes classifier": "Naive Bayes", + "bayes model": "Naive Bayes", + "bayes' theorem": "Naive Bayes", + "bayes' rule": "Naive Bayes", + "k-nearest neighbors": "K-Nearest Neighbors (KNN)", + "knn": "K-Nearest Neighbors (KNN)", + "k-means clustering": "K-Means Clustering", + "k-means": "K-Means Clustering", + "k means": "K-Means Clustering", + "multilayer perceptron": "Multilayer Perceptron (MLP)", + "multi-layer perceptron": "Multilayer Perceptron (MLP)", + "multi-layer perceptron (mlp)": "Multilayer Perceptron (MLP)", + "mlp": "Multilayer Perceptron (MLP)", + "single layer perceptron": "Perceptron", + "convolutional neural network": "CNN", + "convolutional neural network (cnn)": "CNN", + "convolutional neural networks": "CNN", + "cnn architecture": "CNN", + "cnn properties": "CNN", + "python fundamentals": "Python", + "python programming": "Python", + "python implementation": "Python", + "advanced python programming": "Python", + "python programming: convolutional neural network": "CNN", + "cross-validation": "Cross Validation", + "model evaluation implementation": "Model Evaluation", + "digital image processing": "Image Processing", + "computer vision": "Image Processing", + "array slicing": "Array Slicing", + "slicing": "Array Slicing", + "array indexing": "Array Slicing", + "array reshaping": "Reshape", + "array views": "Array Slicing", + "view vs copy": "Array Slicing", + "boolean indexing": "Array Slicing", + "arange": "NumPy", + "newaxis": "NumPy", + "expand dims": "NumPy", + "transpose": "NumPy", + "type casting": "NumPy", + "element-wise operation": "NumPy", + "array reduction": "NumPy", + "multi-dimensional array": "NumPy", + "dot product": "NumPy", + "vectorization": "NumPy", + "activation functions": "Activation Function", + "linear activation function": "Activation Function", + "neural network architecture": "Neural Networks", + "hidden layer": "Neural Networks", + "deep learning": "Neural Networks", + "deep learning frameworks": "Neural Networks", + "alpha-beta pruning": "Alpha-Beta Pruning", + "minimax algorithm": "Minimax", + "ethics of ai": "AI Ethics", + "ethics": "AI Ethics", + "cosine distance": "Cosine Similarity", + "distance calculation": "Distance Metrics", + "euclidean distance": "Distance Metrics", + "manhattan distance": "Distance Metrics", + "hamming distance": "Distance Metrics", + "precision": "Model Evaluation", + "recall": "Model Evaluation", + "f1 score": "Model Evaluation", + "macro f1 score": "Model Evaluation", + "accuracy": "Model Evaluation", + "classification accuracy": "Model Evaluation", + "confusion matrix": "Model Evaluation", + "convolution operation": "Convolution", + "dilated convolution": "Convolution", + "3d convolution": "Convolution", + "gaussian likelihood": "Probability", + "gaussian distribution": "Probability", + "categorical likelihood": "Probability", + "conditional probability": "Probability", + "total probability theorem": "Probability", + "probability assumptions": "Probability", + "tensorflow": "Keras", + "model summary": "Keras", + "model construction": "Keras", + "trainable parameters": "Parameter Calculation", + "parameter reduction": "Parameter Calculation", + "output shape calculation": "Parameter Calculation", + "shape calculation": "Parameter Calculation", +} + + +def normalize_topic(label: str) -> str: + return _TOPIC_ALIASES.get(label.lower().strip(), label) + + +def extract_topic_labels(question: dict) -> list[str]: + labels: list[str] = [] + raw_labels: list[str] = [] + + analytics_topic = question.get("analytics_topic") + if analytics_topic: + raw_labels.append(analytics_topic) + + for tag in question.get("topic_tags") or []: + if tag and tag not in raw_labels: + raw_labels.append(tag) + + if not raw_labels: + for tag in question.get("topics") or []: + if tag and tag not in raw_labels: + raw_labels.append(tag) + + # Normalize and deduplicate + seen: set[str] = set() + for raw in raw_labels: + norm = normalize_topic(raw) + if norm not in seen: + seen.add(norm) + labels.append(norm) + + return labels + + +def extract_question_family(question: dict) -> str: + return ( + question.get("question_format") + or question.get("question_type") + or "unknown" + ) + + +@router.get("/courses") +async def list_courses(): + """返回所有有 ready 状态试卷的课程列表""" + sb = get_supabase() + rows = ( + sb.table("papers") + .select("course_code") + .eq("status", "ready") + .execute() + .data + ) + codes = sorted({row["course_code"] for row in rows if row.get("course_code")}) + return codes + + +@router.get("/course/{course_code}") +async def get_course_analytics(course_code: str): + sb = get_supabase() + + papers = ( + sb.table("papers") + .select("id, course_code, year, term, exam_type, part_label, status") + .eq("course_code", course_code.upper()) + .eq("status", "ready") + .order("year", desc=True) + .execute() + .data + ) + if not papers: + return { + "course_code": course_code.upper(), + "kpi": {"papers": 0, "questions": 0, "topics": 0, "difficulty": "N/A"}, + "topic_frequency": [], + "question_types": [], + "difficulty_distribution": {"easy": 0, "medium": 0, "hard": 0}, + "high_yield_topics": [], + } + + paper_ids = [paper["id"] for paper in papers] + questions = ( + sb.table("paper_questions") + .select( + "id, paper_id, question_number, question_type, question_format, " + "question_text, score, topics, analytics_topic, topic_tags, difficulty" + ) + .in_("paper_id", paper_ids) + .order("display_order") + .execute() + .data + ) + + papers_by_id = {paper["id"]: paper for paper in papers} + total_questions = len(questions) + topic_counter: Counter[str] = Counter() + type_counter: Counter[str] = Counter() + difficulty_counter: Counter[str] = Counter() + topic_examples: dict[str, list[dict]] = defaultdict(list) + difficulty_scores: list[int] = [] + all_question_items: list[dict] = [] + + for question in questions: + question_type = extract_question_family(question) + type_counter[question_type] += 1 + + difficulty = question.get("difficulty") + if difficulty in DIFFICULTY_SCORE: + difficulty_counter[difficulty] += 1 + difficulty_scores.append(DIFFICULTY_SCORE[difficulty]) + + paper = papers_by_id.get(question["paper_id"], {}) + source_label = ( + f"{paper.get('year', '')} {paper.get('term', '').title()} " + f"{paper.get('exam_type', '').title()}" + ).strip() + if paper.get("part_label"): + source_label = f"{source_label} Part {paper['part_label']}" + + topics = extract_topic_labels(question) + q_item = { + "paper_id": paper.get("id"), + "source": source_label, + "question_number": question["question_number"], + "preview": question["question_text"][:220], + "difficulty": question.get("difficulty"), + "question_type": question_type, + "year": paper.get("year"), + "term": paper.get("term"), + "exam_type": paper.get("exam_type"), + "topics": topics, + } + all_question_items.append(q_item) + + for topic in topics: + topic_counter[topic] += 1 + topic_examples[topic].append(q_item) + + avg_difficulty = "N/A" + if difficulty_scores: + rounded = round(sum(difficulty_scores) / len(difficulty_scores)) + avg_difficulty = DIFFICULTY_LABEL.get(rounded, "Medium") + + topic_frequency = [] + for topic, count in topic_counter.most_common(): + pct = round((count / total_questions) * 100) if total_questions else 0 + topic_frequency.append( + { + "label": topic, + "count": count, + "pct": pct, + "questions": topic_examples[topic], + } + ) + + question_types = [] + for label, count in type_counter.most_common(): + pct = round((count / total_questions) * 100) if total_questions else 0 + question_types.append({"label": label, "count": count, "pct": pct}) + + return { + "course_code": course_code.upper(), + "kpi": { + "papers": len(papers), + "questions": total_questions, + "topics": len(topic_counter), + "difficulty": avg_difficulty, + }, + "topic_frequency": topic_frequency, + "question_types": question_types, + "all_questions": all_question_items, + "difficulty_distribution": { + "easy": difficulty_counter.get("easy", 0), + "medium": difficulty_counter.get("medium", 0), + "hard": difficulty_counter.get("hard", 0), + }, + "high_yield_topics": [topic for topic, _ in topic_counter.most_common(5)], + } diff --git a/backend/app/routers/attempts.py b/backend/app/routers/attempts.py new file mode 100644 index 0000000..291f8a6 --- /dev/null +++ b/backend/app/routers/attempts.py @@ -0,0 +1,208 @@ +"""用户答题记录 + 拍照批改 + 错题本""" + +import asyncio +from fastapi import APIRouter, UploadFile, File, Form, HTTPException, Depends +from pydantic import BaseModel +from app.services.supabase_client import get_supabase +from app.services.grader import ocr_photo, grade_answer +from app.dependencies.auth import get_current_user_id + +router = APIRouter() + + +class AttemptCreate(BaseModel): + question_id: str + attempt_type: str # "select" | "input" | "photo" + user_answer: str | None = None + is_correct: bool | None = None + + +class AttemptUpdate(BaseModel): + in_error_book: bool | None = None + mastered: bool | None = None + + +@router.post("/") +async def create_attempt(data: AttemptCreate, user_id: str = Depends(get_current_user_id)): + """记录一次答题""" + sb = get_supabase() + record = { + "user_id": user_id, + "question_id": data.question_id, + "attempt_type": data.attempt_type, + "user_answer": data.user_answer, + "is_correct": data.is_correct, + } + # Auto add to error book if wrong + if data.is_correct is False: + record["in_error_book"] = True + + result = sb.table("user_attempts").insert(record).execute() + return result.data[0] + + +@router.post("/photo") +async def photo_attempt( + question_id: str = Form(...), + photo: UploadFile = File(...), + user_id: str = Depends(get_current_user_id), +): + """拍照上传 → OCR → AI批改""" + sb = get_supabase() + + # 1. Read photo + photo_bytes = await photo.read() + + # 2. Upload to storage + storage_path = f"attempts/{user_id}/{question_id}/{photo.filename}" + sb.storage.from_("attempt-photos").upload( + storage_path, photo_bytes, + file_options={"content-type": photo.content_type or "image/jpeg", "upsert": "true"}, + ) + photo_url = sb.storage.from_("attempt-photos").get_public_url(storage_path) + + # 3. OCR (run in thread pool to avoid blocking event loop) + ocr_text = await asyncio.to_thread(ocr_photo, photo_bytes) + + # 4. Fetch question for grading context + q_result = sb.table("paper_questions").select("*").eq("id", question_id).execute() + if not q_result.data: + raise HTTPException(status_code=404, detail="Question not found") + question = q_result.data[0] + + # 5. AI grading (run in thread pool) + grade_result = await asyncio.to_thread(grade_answer, question, ocr_text) + + # 6. Save attempt + record = { + "user_id": user_id, + "question_id": question_id, + "attempt_type": "photo", + "photo_url": photo_url, + "photo_ocr_text": ocr_text, + "is_correct": grade_result.get("is_correct", False), + "feedback": grade_result.get("feedback", ""), + "error_at_step": grade_result.get("error_at_step"), + "in_error_book": not grade_result.get("is_correct", False), + } + result = sb.table("user_attempts").insert(record).execute() + + return { + "attempt": result.data[0], + "ocr_text": ocr_text, + "grade": grade_result, + } + + +@router.get("/error-book") +async def get_error_book( + course_code: str | None = None, + user_id: str = Depends(get_current_user_id), +): + """获取错题本""" + sb = get_supabase() + attempts = ( + sb.table("user_attempts") + .select("*") + .eq("user_id", user_id) + .eq("in_error_book", True) + .eq("mastered", False) + .order("created_at", desc=True) + .execute() + .data + ) + if not attempts: + return [] + + question_ids = list({attempt["question_id"] for attempt in attempts}) + questions = ( + sb.table("paper_questions") + .select("*") + .in_("id", question_ids) + .execute() + .data + ) + questions_by_id = {question["id"]: question for question in questions} + + paper_ids = list({question["paper_id"] for question in questions}) + papers = ( + sb.table("papers") + .select("id, course_code, year, term, exam_type, part_label") + .in_("id", paper_ids) + .execute() + .data + ) + papers_by_id = {paper["id"]: paper for paper in papers} + + enriched = [] + for attempt in attempts: + question = questions_by_id.get(attempt["question_id"]) + if not question: + continue + paper = papers_by_id.get(question["paper_id"]) + if course_code and paper and paper.get("course_code") != course_code.upper(): + continue + + enriched.append( + { + **attempt, + "paper_questions": { + **question, + "paper": paper, + }, + } + ) + return enriched + + +@router.get("/by-paper/{paper_id}") +async def get_paper_attempts(paper_id: str, user_id: str = Depends(get_current_user_id)): + """获取某张试卷所有题目的最新判卷记录""" + sb = get_supabase() + attempts = ( + sb.table("user_attempts") + .select("question_id, is_correct, feedback, photo_ocr_text, attempt_type, created_at") + .eq("user_id", user_id) + .order("created_at", desc=True) + .execute() + .data + ) + # 只保留 photo 类型的，且只保留每题最新一条 + question_ids = ( + sb.table("paper_questions") + .select("id") + .eq("paper_id", paper_id) + .execute() + .data + ) + qid_set = {q["id"] for q in question_ids} + seen: set[str] = set() + result = [] + for a in attempts: + if a["question_id"] not in qid_set: + continue + if a["question_id"] in seen: + continue + if a["attempt_type"] != "photo": + continue + seen.add(a["question_id"]) + result.append(a) + return result + + +@router.patch("/{attempt_id}") +async def update_attempt(attempt_id: str, data: AttemptUpdate): + """更新错题状态（标记掌握等）""" + sb = get_supabase() + update = {} + if data.in_error_book is not None: + update["in_error_book"] = data.in_error_book + if data.mastered is not None: + update["mastered"] = data.mastered + if not update: + raise HTTPException(status_code=400, detail="Nothing to update") + + result = sb.table("user_attempts").update(update).eq("id", attempt_id).execute() + if not result.data: + raise HTTPException(status_code=404, detail="Attempt not found") + return result.data[0] diff --git a/backend/app/routers/papers.py b/backend/app/routers/papers.py new file mode 100644 index 0000000..feda573 --- /dev/null +++ b/backend/app/routers/papers.py @@ -0,0 +1,142 @@ +"""试卷上传 + 处理管线""" + +import asyncio +import threading +from fastapi import APIRouter, UploadFile, File, Form, HTTPException, Depends +from app.services.supabase_client import get_supabase +from app.services.text_extractor import extract_pdf, get_full_text +from app.services.paper_processor import process_paper +from app.dependencies.auth import get_current_user_id + +router = APIRouter() + + +def _upload_and_process_sync( + paper_id: str, + storage_path: str, + paper_bytes: bytes, + answer_bytes: bytes | None, +): + """在独立线程中运行：Storage 上传 + AI 处理""" + sb = get_supabase() + try: + paper_storage_path = f"{storage_path}/paper.pdf" + sb.storage.from_("papers").upload( + paper_storage_path, paper_bytes, + file_options={"content-type": "application/pdf", "upsert": "true"}, + ) + paper_url = sb.storage.from_("papers").get_public_url(paper_storage_path) + + update_data: dict = {"paper_file_url": paper_url} + + if answer_bytes: + answer_storage_path = f"{storage_path}/answer.pdf" + sb.storage.from_("papers").upload( + answer_storage_path, answer_bytes, + file_options={"content-type": "application/pdf", "upsert": "true"}, + ) + update_data["answer_file_url"] = sb.storage.from_("papers").get_public_url(answer_storage_path) + + sb.table("papers").update(update_data).eq("id", paper_id).execute() + except Exception: + pass + + # process_paper 是 async，在新事件循环里跑 + asyncio.run(process_paper(paper_id, paper_bytes, answer_bytes)) + + +@router.get("/") +async def list_papers(): + """获取试卷列表（公共资产，所有用户共享）""" + sb = get_supabase() + return ( + sb.table("papers") + .select("id, course_code, year, term, exam_type, status, question_count, total_score, difficulty_level, processing_step, processing_progress, processing_total, created_at") + .order("created_at", desc=True) + .execute() + .data + ) + + +@router.get("/mine") +async def my_papers(user_id: str = Depends(get_current_user_id)): + """当前用户上传的试卷（含 processing 状态）""" + sb = get_supabase() + return ( + sb.table("papers") + .select("id, course_code, year, term, exam_type, part_label, status, question_count, processing_step, processing_progress, processing_total, created_at") + .eq("user_id", user_id) + .order("created_at", desc=True) + .execute() + .data + ) + + +@router.post("/upload") +async def upload_paper( + paper_file: UploadFile = File(...), + answer_file: UploadFile | None = File(None), + course_code: str = Form(...), + year: int = Form(...), + term: str = Form(...), + exam_type: str = Form(...), + user_id: str = Depends(get_current_user_id), +): + """上传试卷 PDF（可选答案 PDF），触发后台处理""" + sb = get_supabase() + + # 1. 读取文件内容（已在内存中，快） + paper_bytes = await paper_file.read() + answer_bytes = await answer_file.read() if answer_file else None + + # 2. 立即创建记录（status=processing），马上返回 + storage_path = f"{course_code.upper()}/{year}_{term}_{exam_type}" + paper_record = sb.table("papers").insert({ + "user_id": user_id, + "course_code": course_code.upper(), + "year": year, + "term": term, + "exam_type": exam_type, + "paper_file_url": "", # 后台上传后更新 + "answer_file_url": None, + "status": "processing", + }).execute() + + paper_id = paper_record.data[0]["id"] + + # 3. 在独立线程中运行，完全不阻塞事件循环 + threading.Thread( + target=_upload_and_process_sync, + args=(paper_id, storage_path, paper_bytes, answer_bytes), + daemon=True, + ).start() + + return { + "paper_id": paper_id, + "status": "processing", + "message": "试卷已上传，正在处理中...", + } + + +@router.get("/{paper_id}") +async def get_paper(paper_id: str): + """获取试卷信息 + 处理状态""" + sb = get_supabase() + result = sb.table("papers").select("*").eq("id", paper_id).execute() + if not result.data: + raise HTTPException(status_code=404, detail="Paper not found") + return result.data[0] + + +@router.get("/{paper_id}/questions") +async def get_questions(paper_id: str): + """获取试卷的所有题目（含 AI 三件套）""" + sb = get_supabase() + result = ( + sb.table("paper_questions") + .select("*") + .eq("paper_id", paper_id) + .order("display_order") + .execute() + ) + return result.data diff --git a/backend/app/routers/questions.py b/backend/app/routers/questions.py new file mode 100644 index 0000000..b61d288 --- /dev/null +++ b/backend/app/routers/questions.py @@ -0,0 +1,325 @@ +"""题目相关：变式题生成 + 相似题召回""" + +from __future__ import annotations + +import asyncio +import time +from fastapi import APIRouter, HTTPException, Depends +from pydantic import BaseModel +from app.services.supabase_client import get_supabase +from app.services.grader import generate_variant +from app.dependencies.auth import get_current_user_id + +# Simple in-memory cache: question_id → (timestamp, result) +_similar_cache: dict[str, tuple[float, list]] = {} +_CACHE_TTL = 300 # 5 minutes + + +class VariantUpdate(BaseModel): + favorited: bool | None = None + +router = APIRouter() + + +def normalized_labels(values: list[str] | None) -> dict[str, str]: + labels: dict[str, str] = {} + for value in values or []: + if value: + labels[value.lower()] = value + return labels + + +def question_family(question: dict) -> str: + return question.get("question_format") or question.get("question_type") or "unknown" + + +def display_topics(question: dict) -> list[str]: + labels: list[str] = [] + analytics_topic = question.get("analytics_topic") + if analytics_topic: + labels.append(analytics_topic) + for topic in question.get("topic_tags") or []: + if topic and topic not in labels: + labels.append(topic) + if labels: + return labels + for topic in question.get("topics") or []: + if topic and topic not in labels: + labels.append(topic) + return labels + + +def similarity_score( + target: dict, + candidate: dict, + text_score: float = 0.0, +) -> tuple[int, list[str]]: + score = 0 + reasons: list[str] = [] + + # Primary topic bucket: 40 pts + target_topic = target.get("analytics_topic") + candidate_topic = candidate.get("analytics_topic") + if target_topic and target_topic == candidate_topic: + score += 40 + reasons.append(f"Same topic: {target_topic}") + + # Concept overlap: up to 20 pts + target_topics = normalized_labels(target.get("topic_tags")) + candidate_topics = normalized_labels(candidate.get("topic_tags")) + shared_topics = sorted(set(target_topics) & set(candidate_topics)) + if shared_topics: + score += min(len(shared_topics) * 10, 20) + # Only show concept reason if analytics_topic didn't already match (avoid redundancy) + if not (target_topic and target_topic == candidate_topic): + reasons.append( + "Shared concept: " + + ", ".join(target_topics[key] for key in shared_topics[:2]) + ) + + # Skill overlap: up to 20 pts + target_skills = normalized_labels(target.get("skill_tags")) + candidate_skills = normalized_labels(candidate.get("skill_tags")) + shared_skills = sorted(set(target_skills) & set(candidate_skills)) + if shared_skills: + score += min(len(shared_skills) * 10, 20) + reasons.append( + "Shared skill: " + + ", ".join(target_skills[key] for key in shared_skills[:2]) + ) + + # Same question format: 10 pts + if question_family(candidate) == question_family(target): + score += 10 + reasons.append("Same format") + + # Same difficulty: 5 pts + if candidate.get("difficulty") and candidate.get("difficulty") == target.get("difficulty"): + score += 5 + reasons.append("Same difficulty") + + # Full-text similarity from PostgreSQL ts_rank_cd: up to 20 pts + if text_score > 0: + text_pts = min(round(text_score * 60), 20) + score += text_pts + if text_pts >= 4: + reasons.append("Similar wording") + + return min(score, 99), reasons + + +@router.get("/variants/favorited") +async def get_favorited_variants(user_id: str = Depends(get_current_user_id)): + """获取用户收藏的所有 variant（用于 Error Book）""" + sb = get_supabase() + rows = ( + sb.table("question_variants") + .select("*, paper_questions(question_number, paper_id, papers(id, course_code, year, term, exam_type, part_label))") + .eq("user_id", user_id) + .eq("favorited", True) + .order("created_at", desc=True) + .execute() + .data + ) + return rows + + +@router.post("/{question_id}/variant") +async def create_variant(question_id: str, user_id: str = Depends(get_current_user_id)): + """生成变式题并入库""" + sb = get_supabase() + result = sb.table("paper_questions").select("*").eq("id", question_id).execute() + if not result.data: + raise HTTPException(status_code=404, detail="Question not found") + + question = result.data[0] + variant_data = await asyncio.to_thread(generate_variant, question) + variant_data["knowledge_reminder"] = question.get("knowledge_reminder", "") + + saved = sb.table("question_variants").insert({ + "user_id": user_id, + "source_question_id": question_id, + "variant_data": variant_data, + "favorited": False, + }).execute() + + row = saved.data[0] + row["source_question_number"] = question["question_number"] + return row + + +@router.get("/{question_id}/variants") +async def list_variants(question_id: str, user_id: str = Depends(get_current_user_id)): + """获取某道题的用户所有 variant""" + sb = get_supabase() + q_result = sb.table("paper_questions").select("question_number").eq("id", question_id).execute() + question_number = q_result.data[0]["question_number"] if q_result.data else "" + + rows = ( + sb.table("question_variants") + .select("*") + .eq("user_id", user_id) + .eq("source_question_id", question_id) + .order("created_at", desc=True) + .execute() + .data + ) + for row in rows: + row["source_question_number"] = question_number + return rows + + +@router.patch("/variant/{variant_id}") +async def update_variant(variant_id: str, data: VariantUpdate, user_id: str = Depends(get_current_user_id)): + """更新 variant（收藏/取消收藏）""" + sb = get_supabase() + update: dict = {} + if data.favorited is not None: + update["favorited"] = data.favorited + if not update: + raise HTTPException(status_code=400, detail="Nothing to update") + + result = ( + sb.table("question_variants") + .update(update) + .eq("id", variant_id) + .eq("user_id", user_id) + .execute() + ) + if not result.data: + raise HTTPException(status_code=404, detail="Variant not found") + return result.data[0] + + +@router.delete("/variant/{variant_id}", status_code=204) +async def delete_variant(variant_id: str, user_id: str = Depends(get_current_user_id)): + """删除 variant""" + sb = get_supabase() + sb.table("question_variants").delete().eq("id", variant_id).eq("user_id", user_id).execute() + + +@router.get("/{question_id}/similar") +async def get_similar_questions(question_id: str, limit: int = 6): + """Retrieve similar questions from the same course.""" + # Cache hit + cached = _similar_cache.get(question_id) + if cached and (time.time() - cached[0]) < _CACHE_TTL: + return cached[1][:max(1, min(limit, 12))] + + sb = get_supabase() + result = sb.table("paper_questions").select("*, similar_questions").eq("id", question_id).execute() + if not result.data: + raise HTTPException(status_code=404, detail="Question not found") + + target = result.data[0] + + # Return pre-computed immediately; schedule background refresh + if target.get("similar_questions"): + precomputed = target["similar_questions"] + _similar_cache[question_id] = (time.time(), precomputed) + return precomputed[:max(1, min(limit, 12))] + + paper_result = sb.table("papers").select("id, course_code").eq("id", target["paper_id"]).execute() + # (fallback: compute on-the-fly for questions not yet backfilled) + if not paper_result.data: + raise HTTPException(status_code=404, detail="Paper not found") + + course_code = paper_result.data[0]["course_code"] + papers = ( + sb.table("papers") + .select("id, course_code, year, term, exam_type, part_label") + .eq("course_code", course_code) + .eq("status", "ready") + .execute() + .data + ) + paper_ids = [paper["id"] for paper in papers if paper["id"] != target["paper_id"]] + if not paper_ids: + return [] + + papers_by_id = {paper["id"]: paper for paper in papers} + + # Pre-filter by analytics_topic in DB when possible (cuts candidates from ~250 to ~30) + candidates_query = ( + sb.table("paper_questions") + .select( + "id, paper_id, question_number, question_type, question_format, " + "question_text, score, topics, analytics_topic, topic_tags, skill_tags, " + "difficulty, knowledge_reminder, ai_hint, solution" + ) + .in_("paper_id", paper_ids) + ) + target_topic = target.get("analytics_topic") + if target_topic: + candidates_query = candidates_query.eq("analytics_topic", target_topic) + + candidates = candidates_query.execute().data + if not candidates: + return [] + + # Batch full-text scores from PostgreSQL (skip if too many candidates — slow) + text_scores: dict[str, float] = {} + if len(candidates) <= 50: + try: + rpc_result = sb.rpc( + "text_similarity_scores", + { + "query_text": target.get("question_text") or "", + "candidate_ids": [c["id"] for c in candidates], + }, + ).execute() + for row in rpc_result.data or []: + text_scores[row["question_id"]] = float(row["text_score"] or 0) + except Exception: + pass + + ranked = [] + for candidate in candidates: + text_score = text_scores.get(candidate["id"], 0.0) + match_percent, reasons = similarity_score(target, candidate, text_score) + if match_percent < 20: + continue + paper = papers_by_id.get(candidate["paper_id"], {}) + source = ( + f"{paper.get('year', '')} {paper.get('term', '').title()} " + f"{paper.get('exam_type', '').title()}" + ).strip() + if paper.get("part_label"): + source = f"{source} Part {paper['part_label']}" + ranked.append( + { + "id": candidate["id"], + "paper_id": candidate["paper_id"], + "source": source, + "question_number": candidate["question_number"], + "match_percent": match_percent, + "match_reasons": reasons, + "question_type": question_family(candidate), + "question_text": candidate["question_text"], + "topics": display_topics(candidate), + "difficulty": candidate.get("difficulty"), + "knowledge_reminder": candidate.get("knowledge_reminder", ""), + "ai_hint": candidate.get("ai_hint", ""), + "solution": candidate.get("solution", ""), + } + ) + + ranked.sort(key=lambda item: (-item["match_percent"], item["source"], item["question_number"])) + + # Keep only the best-scoring question per paper + seen_papers: set[str] = set() + deduped = [] + for item in ranked: + if item["paper_id"] not in seen_papers: + seen_papers.add(item["paper_id"]) + deduped.append(item) + + _similar_cache[question_id] = (time.time(), deduped) + + # Persist to DB so future requests are instant + try: + sb.table("paper_questions").update({"similar_questions": deduped}).eq("id", question_id).execute() + except Exception: + pass + + return deduped[:max(1, min(limit, 12))] diff --git a/backend/app/services/__init__.py b/backend/app/services/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/backend/app/services/grader.py b/backend/app/services/grader.py new file mode 100644 index 0000000..68e58f4 --- /dev/null +++ b/backend/app/services/grader.py @@ -0,0 +1,146 @@ +"""OCR, grading, and variant generation prompts""" + +import json +import base64 +from app.services.llm_clients import get_vision_client, get_deepseek_client + +OCR_PROMPT = """You are an expert at recognizing handwritten answers. Analyze this photo of a student's handwritten answer and extract the text and mathematical formulas. + +Requirements: +- Faithfully extract what the student wrote, do not modify or correct +- Use LaTeX format for math formulas (e.g. $x^2 + 1$) +- If there are multiple steps, list them in original order +- If some handwriting is unclear, mark with [unclear] + +Return only the extracted text, no additional explanation.""" + +GRADING_PROMPT = """You are an expert academic grader. Grade the following student answer. ALL output must be in English. + +Question info: +- Number: {question_number} +- Type: {question_type} +- Question: {question_text} +- Score: {score} + +Reference answer / solution: +{reference_answer} + +Student answer: +{student_answer} + +Grade and return JSON: +{{ + "is_correct": true/false, + "score_given": 0-{score}, + "feedback": " Step-by-step analysis of the student's answer, pointing out correct parts and errors, using KaTeX formulas ", + "error_at_step": null or the step number where errors begin (integer) +}} + +Grading rules: +- MC / fill-blank: only correct if answer matches exactly +- Long questions: give partial credit for correct steps even if the final answer is wrong +- feedback in HTML format, supports KaTeX ($..$ inline, $$...$$ block) +- Mark errors with
...
+- Identify exactly which step the error starts""" + +VARIANT_PROMPT = """You are an expert exam question creator. Generate a similar but different variant question based on the original below. ALL output must be in English. + +Original question info: +- Type: {question_type} +- Question: {question_text} +- Topics: {topics} +- Difficulty: {difficulty} +- Reference answer: {answer} + +Requirements: +- Variant must test the same knowledge points at similar difficulty +- Data/scenario/wording must differ — don't just change numbers +- Must provide a complete correct answer + +Format requirements (CRITICAL): +- All text in HTML format, absolutely NO markdown syntax +- Code: 
...
, NOT ``` +- Math: $...$ (inline) or $$...$$ (block), KaTeX compatible +- Line breaks:
, paragraphs:

+ +Return JSON: +{{ + "question_text": "HTML formatted variant question", + "question_type": "{question_type}", + "options": [MC only, format {{"label":"A","text":"..."}}, ...] or null, + "correct_answer": "Correct answer (plain text)", + "ai_hint": "HTML formatted hint that guides thinking WITHOUT giving the answer", + "solution": "HTML formatted complete step-by-step solution" +}}""" + + +def ocr_photo(photo_bytes: bytes) -> str: + """Gemini Vision OCR for handwritten answers""" + client = get_vision_client() + b64 = base64.b64encode(photo_bytes).decode("utf-8") + + resp = client.chat.completions.create( + model="gemini-2.5-flash", + messages=[ + {"role": "system", "content": OCR_PROMPT}, + {"role": "user", "content": [ + {"type": "image_url", "image_url": { + "url": f"data:image/jpeg;base64,{b64}", + }}, + ]}, + ], + temperature=0, + max_tokens=2000, + ) + return resp.choices[0].message.content or "" + + +def grade_answer(question: dict, student_answer: str) -> dict: + """Qwen grades student answer""" + reference = question.get("raw_answer_text") or question.get("solution") or "No reference answer" + score = question.get("score") or "unknown" + + ds = get_deepseek_client() + resp = ds.chat.completions.create( + model="deepseek-chat", + messages=[ + {"role": "system", "content": GRADING_PROMPT.format( + question_number=question["question_number"], + question_type=question["question_type"], + question_text=question["question_text"], + score=score, + reference_answer=reference, + student_answer=student_answer, + )}, + ], + temperature=0.2, + response_format={"type": "json_object"}, + ) + return json.loads(resp.choices[0].message.content) + + +def generate_variant(question: dict) -> dict: + """Gemini generates a variant question""" + answer = ( + question.get("correct_option") + or question.get("correct_answer") + or question.get("raw_answer_text") + or "N/A" + ) + + ds = get_deepseek_client() + resp = ds.chat.completions.create( + model="deepseek-chat", + messages=[ + {"role": "system", "content": VARIANT_PROMPT.format( + question_type=question["question_type"], + question_text=question["question_text"], + topics=", ".join(question.get("topics", [])), + difficulty=question.get("difficulty", "medium"), + answer=answer, + )}, + ], + temperature=0.5, + response_format={"type": "json_object"}, + ) + return json.loads(resp.choices[0].message.content) diff --git a/backend/app/services/llm_clients.py b/backend/app/services/llm_clients.py new file mode 100644 index 0000000..690637b --- /dev/null +++ b/backend/app/services/llm_clients.py @@ -0,0 +1,74 @@ +import httpx +from openai import OpenAI +from app.config import get_settings + +_TIMEOUT = httpx.Timeout(connect=10, read=300, write=60, pool=10) + +_gpt_client: OpenAI | None = None +_qwen_client: OpenAI | None = None +_gemini_flash_client: OpenAI | None = None +_gemini_lite_client: OpenAI | None = None +_deepseek_client: OpenAI | None = None + + +def get_gpt_client() -> OpenAI: + """laozhang API — gpt-4o / gpt-4o-mini""" + global _gpt_client + if _gpt_client is None: + s = get_settings() + _gpt_client = OpenAI( + base_url=s.laozhang_base_url, + api_key=s.laozhang_api_key, + ) + return _gpt_client + + +def get_qwen_client() -> OpenAI: + """DashScope — qwen-plus""" + global _qwen_client + if _qwen_client is None: + s = get_settings() + _qwen_client = OpenAI( + base_url=s.dashscope_base_url, + api_key=s.dashscope_api_key, + ) + return _qwen_client + + +def get_vision_client() -> OpenAI: + """Google Gemini 官方 API（视觉，用于拆题+OCR）— 部署在新加坡可用""" + global _gemini_flash_client + if _gemini_flash_client is None: + s = get_settings() + _gemini_flash_client = OpenAI( + base_url="https://generativelanguage.googleapis.com/v1beta/openai/", + api_key=s.google_gemini_api_key, + timeout=_TIMEOUT, + ) + return _gemini_flash_client + + +def get_gemini_lite_client() -> OpenAI: + """laozhang — gemini-3.1-flash-lite-preview（轻量，用于 AI trio）""" + global _gemini_lite_client + if _gemini_lite_client is None: + s = get_settings() + _gemini_lite_client = OpenAI( + base_url=s.laozhang_base_url, + api_key=s.laozhang_api_key, + timeout=_TIMEOUT, + ) + return _gemini_lite_client + + +def get_deepseek_client() -> OpenAI: + """DeepSeek — deepseek-chat（用于 AI trio）""" + global _deepseek_client + if _deepseek_client is None: + s = get_settings() + _deepseek_client = OpenAI( + base_url=s.deepseek_base_url, + api_key=s.deepseek_api_key, + timeout=_TIMEOUT, + ) + return _deepseek_client diff --git a/backend/app/services/paper_processor.py b/backend/app/services/paper_processor.py new file mode 100644 index 0000000..86dc4e2 --- /dev/null +++ b/backend/app/services/paper_processor.py @@ -0,0 +1,576 @@ +"""试卷处理管线：PDF → 结构化题目 → AI 三件套（Vision 模式）""" + +import asyncio +import base64 +import io +import json +import re +import traceback +from contextlib import redirect_stdout +import fitz # pymupdf +from app.services.supabase_client import get_supabase +from app.services.llm_clients import get_vision_client, get_deepseek_client + + +def strip_nulls(obj): + """Recursively remove \\u0000 null bytes from strings (PostgreSQL rejects them).""" + if isinstance(obj, str): + return obj.replace("\u0000", "") + if isinstance(obj, dict): + return {k: strip_nulls(v) for k, v in obj.items()} + if isinstance(obj, list): + return [strip_nulls(i) for i in obj] + return obj + + +# ============================================ +# Prompts +# ============================================ + +STRUCTURE_PROMPT = """You are an expert exam paper structure analyst. You are given images of a past exam paper. Analyze every page carefully and extract all questions into structured JSON. +All generated values must be in English. Do not output Chinese. + +CRITICAL RULES for question_text: +- Each question's question_text must be FULLY SELF-CONTAINED. Include ALL context needed to solve it. +- For sub-questions (e.g. (a)(i)), copy the ENTIRE parent question setup (variable definitions, code blocks, problem description) into the question_text, then append the specific sub-question. +- For Python/code questions: include ALL variable definitions and import statements verbatim, exactly as they appear in the exam, preserving multi-line arrays and data structures completely. +- Never truncate code. If a variable is defined across multiple lines (e.g. a numpy array), include every line. + +Output JSON format (strictly follow): +{ + "total_score": 100, + "difficulty_level": "medium", + "topics_summary": {"Topic A": 40, "Topic B": 30, "Topic C": 30}, + "questions": [ + { + "question_number": "1a", + "parent_question": "1", + "question_type": "mc", + "question_text": "Original question text...", + "score": 5, + "page_number": 1, + "options": [{"label": "A", "text": "Option content"}, {"label": "B", "text": "..."}], + "topics": ["Linked List", "Pointer"], + "difficulty": "easy" + }, + { + "question_number": "2", + "parent_question": null, + "question_type": "long_question", + "question_text": "Original question text...", + "score": 15, + "page_number": 2, + "options": null, + "topics": ["Recursion"], + "difficulty": "hard" + } + ] +} + +Rules: +- question_type must be one of: "mc" (multiple choice), "true_false" (true/false), "fill_blank" (fill in blank), "long_question" (long question) +- True/False questions MUST use "true_false" type, with options set to [{"label":"True","text":"True"},{"label":"False","text":"False"}], correct_option as "True" or "False" +- Multiple choice must extract the options array +- Sub-questions use parent_question to link to parent: "1a" parent is "1" +- Independent questions without sub-questions set parent_question to null +- page_number inferred from where the question appears +- topics inferred from the question content +- difficulty: "easy" | "medium" | "hard" +- Extract ALL questions, do not miss any +- Keep topic labels in English only +""" + +ANSWER_MATCH_PROMPT = """You are an expert exam answer matching specialist. Below is the answer text for an exam paper. Extract and match answers to their corresponding question numbers. +All generated values must be in English. Do not output Chinese. + +Question structure: +{questions_json} + +Answer text: +{answer_text} + +Output JSON format: +{{ + "answers": [ + {{ + "question_number": "1a", + "correct_option": "B", + "correct_answer": null, + "raw_answer_text": "Original answer text..." + }}, + {{ + "question_number": "2", + "correct_option": null, + "correct_answer": null, + "raw_answer_text": "Complete solution process and answer..." + }} + ] +}} + +Rules: +- For MC questions, fill correct_option (e.g. "B") +- For fill-blank questions, fill correct_answer (e.g. "O(n log n)") +- For long questions, only fill raw_answer_text (complete solution process) +- Match all questions where answers can be found +- Keep raw_answer_text faithful to the source answer, but do not add Chinese commentary +""" + +ANALYSIS_PROMPT = """You are an expert academic answer analyst. Generate three sections for the following exam question. ALL output must be in English. + +Question info: +- Number: {question_number} +- Type: {question_type} +- Score: {score} +- Question: {question_text} +- Topics: {topics} +{answer_section} + +Generate THREE sections in HTML format (supports KaTeX: block $$ ... $$ inline $ ... $): + +Output JSON: +{{ + "knowledge_reminder": " Prerequisite knowledge points needed for this question, as a concise bullet list ", + "ai_hint": " A hint that guides thinking direction WITHOUT giving away the answer ", + "solution": " Complete step-by-step solution (Step 1, Step 2, ...) with derivations, formulas, and common mistake warnings " +}} + +Solution requirements: +- Must include complete working process, not just the answer +- Each step must have an explanation +- If a reference answer is provided, derive the solution based on it +- If no reference answer, work out the complete solution independently +- For MC questions, explain why the correct option is right AND why others are wrong +- Use

    or numbered steps +- Mark common mistakes with
    ...
    + +KaTeX formula rules: +- Block formula: $$ on its own line, with blank lines before and after +- Inline formula: $x^2$ no line break +- Matrix: \\begin{{bmatrix}} ... \\end{{bmatrix}} +- Fraction: \\frac{{a}}{{b}} +""" + +BATCH_ANALYSIS_PROMPT = """You are an expert academic answer analyst. Generate three study sections for each question below. ALL output must be in English. + +For every question, return: +- knowledge_reminder: concise prerequisite bullets in HTML +- ai_hint: a helpful hint in HTML without revealing the final answer +- solution: a complete step-by-step solution in HTML + +Return JSON in this exact format: +{{ + "analyses": [ + {{ + "question_number": "1a", + "knowledge_reminder": "...", + "ai_hint": "...", + "solution": "..." + }} + ] +}} + +Rules: +- Return one item for every provided question_number +- Keep each item matched to the same question_number +- All text must be in English +- HTML only, KaTeX compatible +- For MC questions, explain why the correct option is right and why the others are wrong +- For long questions, show a complete derivation or reasoning chain +- Use
      or numbered steps in solution when appropriate +- Mark common mistakes with
      ...
      +- CRITICAL: When a question_text contains "[Context from parent question X]" followed by "[Sub-question Y]", the parent section is background context only. You MUST solve ONLY the specific sub-question labeled [Sub-question Y]. Do NOT solve other sub-questions listed in the parent context. Give one precise answer for that single sub-question only. + +Questions: +{questions_payload} +""" + + +# ============================================ +# 处理管线 +# ============================================ + +RETRYABLE_ERROR_MARKERS = ( + "429", + "rate limit", + "rate_limit", + "too many requests", + "timeout", + "timed out", + "connection", +) + + +def is_retryable_error(exc: Exception) -> bool: + message = str(exc).lower() + return any(marker in message for marker in RETRYABLE_ERROR_MARKERS) + + +def pdf_to_images(pdf_bytes: bytes, dpi: int = 96) -> list[str]: + """将 PDF 每页渲染为 base64 PNG 图片列表（96dpi 平衡清晰度与成本）""" + doc = fitz.open(stream=pdf_bytes, filetype="pdf") + images = [] + mat = fitz.Matrix(dpi / 72, dpi / 72) + for page in doc: + pix = page.get_pixmap(matrix=mat, colorspace=fitz.csRGB) + img_bytes = pix.tobytes("png") + images.append(base64.b64encode(img_bytes).decode()) + doc.close() + return images + + +def parse_json_response(text: str) -> dict: + """解析模型返回的 JSON，兼容 markdown 代码块包装""" + text = text.strip() + # 去掉 ```json ... ``` 包装 + if text.startswith("```"): + lines = text.splitlines() + text = "\n".join(lines[1:-1] if lines[-1].strip() == "```" else lines[1:]) + # 移除 JSON 字符串中的非法控制字符（0x00-0x1F 除了 \t \n \r） + text = re.sub(r'[\x00-\x08\x0b\x0c\x0e-\x1f]', '', text) + # 修复模型返回的无效 JSON 转义序列：只修奇数个反斜杠后的非法字符 + text = re.sub(r'(? dict: + """发送图片 + prompt 给 Gemini vision 模型，返回 JSON""" + client = get_vision_client() + delay_seconds = 2 + + content: list = [] + for b64 in images: + content.append({"type": "image_url", "image_url": {"url": f"data:image/png;base64,{b64}"}}) + if user_text: + content.append({"type": "text", "text": user_text}) + + for attempt in range(1, max_attempts + 1): + try: + response = client.chat.completions.create( + model="gemini-2.5-flash", + messages=[ + {"role": "system", "content": system_prompt + "\n\nIMPORTANT: Your entire response must be valid JSON only. No markdown, no code fences, no extra text."}, + {"role": "user", "content": content}, + ], + temperature=temperature, + max_tokens=16384, + ) + return parse_json_response(response.choices[0].message.content) + except Exception as exc: + if attempt == max_attempts or not is_retryable_error(exc): + raise + await asyncio.sleep(delay_seconds) + delay_seconds = min(delay_seconds * 2, 30) + + +async def deepseek_json_completion( + *, + system_prompt: str, + user_prompt: str | None = None, + temperature: float = 0, + max_attempts: int = 6, +) -> dict: + """DeepSeek 纯文本 JSON completion（用于 AI trio 生成）""" + client = get_deepseek_client() + delay_seconds = 2 + + for attempt in range(1, max_attempts + 1): + try: + messages = [{"role": "system", "content": system_prompt}] + if user_prompt: + messages.append({"role": "user", "content": user_prompt}) + + response = client.chat.completions.create( + model="deepseek-chat", + messages=messages, + temperature=temperature, + max_tokens=8192, + response_format={"type": "json_object"}, + ) + raw = response.choices[0].message.content + raw = re.sub(r'[\x00-\x08\x0b\x0c\x0e-\x1f]', '', raw) + raw = re.sub(r'(? list[list[dict]]: + return [items[i:i + size] for i in range(0, len(items), size)] + + +def _question_sort_key(qnum: str) -> tuple: + """自然排序题号：1a < 1b < ... < 1i < 1j < 2ai < 2aii < 10a""" + parts = re.findall(r'(\d+|[a-zA-Z]+|[()]+)', qnum) + key = [] + for idx, p in enumerate(parts): + if p.isdigit(): + key.append((0, int(p), '')) + elif p in ('(', ')'): + continue + else: + # Single letter (a-z): always sort alphabetically (a=1, b=2, ..., j=10) + if len(p) == 1 and p.isalpha(): + key.append((1, ord(p.lower()) - ord('a') + 1, p)) + else: + # Multi-letter: roman numerals for sub-sub-questions (i=1, ii=2, iii=3, ...) + romans = {'i':1,'ii':2,'iii':3,'iv':4,'v':5,'vi':6,'vii':7,'viii':8,'ix':9,'x':10,'xi':11,'xii':12,'xiii':13} + if p.lower() in romans: + key.append((2, romans[p.lower()], p)) + else: + key.append((1, 0, p)) + return tuple(key) + + +def sort_questions(questions: list[dict]) -> list[dict]: + """按题号自然排序""" + return sorted(questions, key=lambda q: _question_sort_key(q.get("question_number", ""))) + + +def extract_code_block(text: str) -> str: + """ + 从题目文本中提取 Python 代码块。 + 策略：找到第一个明确的代码起始行（import/赋值/print）， + 然后把后续所有缩进或延续行一并带上，直到明显的非代码段落。 + """ + lines = text.splitlines() + result = [] + in_code = False + open_brackets = 0 + + CODE_START = re.compile(r"^\s*(import |from \w|[A-Za-z_]\w*\s*=|print\()") + + for line in lines: + stripped = line.strip() + + # 已在代码块内：括号未闭合时继续收集 + if in_code and open_brackets > 0: + result.append(stripped) + open_brackets += stripped.count("(") + stripped.count("[") + stripped.count("{") + open_brackets -= stripped.count(")") + stripped.count("]") + stripped.count("}") + continue + + # 检测新的代码起始行 + if CODE_START.match(line): + in_code = True + result.append(stripped) + open_brackets += stripped.count("(") + stripped.count("[") + stripped.count("{") + open_brackets -= stripped.count(")") + stripped.count("]") + stripped.count("}") + continue + + # 非代码行：重置（但保留 in_code=True 以便继续接后续代码行） + in_code = False + + return "\n".join(result) + + +# 保持向后兼容 +extract_code_lines = extract_code_block + + +def try_exec_python(code: str, shared_ns: dict) -> str | None: + """ + 在 shared_ns 命名空间中执行 code，捕获 stdout。 + 返回输出字符串，失败返回 None。 + """ + buf = io.StringIO() + try: + with redirect_stdout(buf): + exec(code, shared_ns) # noqa: S102 + output = buf.getvalue().strip() + return output if output else None + except Exception: + return None + +async def _resume_ai_trio(sb, paper_id: str, questions: list[dict]): + """为缺 solution 的题目生成 AI trio，逐条写回 DB。支持断点续传。""" + need = [q for q in questions if not q.get("solution")] + if not need: + # 全部已有 solution，直接标记完成 + sb.table("papers").update({"status": "ready", "processing_step": None}).eq("id", paper_id).execute() + return + + total_q = len(questions) + done_q = total_q - len(need) + + # 构建 payload + id_map = {q["question_number"]: q["id"] for q in need} + # 需要完整的 question_text 来生成 AI trio + full_data = sb.table("paper_questions").select( + "id, question_number, question_type, question_text, score, correct_option, correct_answer, raw_answer_text" + ).eq("paper_id", paper_id).in_("id", [q["id"] for q in need]).execute().data + + payloads = [] + for q in full_data: + answer_section = q.get("raw_answer_text") or "" + if not answer_section and q.get("correct_option"): + answer_section = f"Correct option: {q['correct_option']}" + elif not answer_section and q.get("correct_answer"): + answer_section = f"Correct answer: {q['correct_answer']}" + payloads.append({ + "question_number": q["question_number"], + "question_type": q["question_type"] or "long_question", + "score": q.get("score") or "unknown", + "question_text": q["question_text"] or "", + "reference_answer": answer_section, + }) + + batches = chunked(payloads, 3) + for batch_idx, batch in enumerate(batches, 1): + current = done_q + batch_idx * 3 + _update_progress(sb, paper_id, f"Generating solutions ({min(current, total_q)}/{total_q} questions)", batch_idx, len(batches)) + try: + result = await deepseek_json_completion( + system_prompt=BATCH_ANALYSIS_PROMPT.format( + questions_payload=json.dumps(batch, ensure_ascii=False), + ), + temperature=0.3, + ) + for item in result.get("analyses", []): + qnum = item.get("question_number") + qid = id_map.get(qnum) + if qid: + sb.table("paper_questions").update({ + "knowledge_reminder": item.get("knowledge_reminder", ""), + "ai_hint": item.get("ai_hint", ""), + "solution": item.get("solution", ""), + }).eq("id", qid).execute() + except Exception: + pass # 单批失败不影响其他批 + await asyncio.sleep(1) + + # 标记完成 + sb.table("papers").update({"status": "ready", "processing_step": None}).eq("id", paper_id).execute() + + +def _update_progress(sb, paper_id: str, step: str, progress: int = 0, total: int = 0): + """更新处理进度到 DB""" + sb.table("papers").update({ + "processing_step": step, + "processing_progress": progress, + "processing_total": total, + }).eq("id", paper_id).execute() + + +async def process_paper(paper_id: str, paper_bytes: bytes, answer_bytes: bytes | None): + """后台处理管线: PDF pages → Vision 结构化 → AI 三件套 + + 设计原则：每个步骤完成后立即持久化到 DB，支持断点续传。 + """ + sb = get_supabase() + + try: + # 检查是否已有题目（断点续传场景） + existing = sb.table("paper_questions").select("id, question_number, solution").eq("paper_id", paper_id).execute().data + + if existing: + # 已有题目 → 跳过提取，直接补 AI trio + await _resume_ai_trio(sb, paper_id, existing) + return + + # ── Step 1: PDF → 图片 ── + _update_progress(sb, paper_id, "Rendering PDF pages...") + paper_images = pdf_to_images(paper_bytes) + + # ── Step 2: Vision 结构化拆题 ── + PAGE_BATCH = 8 + all_questions: list = [] + meta: dict = {} + num_page_batches = -(-len(paper_images) // PAGE_BATCH) + for i in range(0, len(paper_images), PAGE_BATCH): + batch_imgs = paper_images[i:i + PAGE_BATCH] + batch_idx = i // PAGE_BATCH + 1 + _update_progress(sb, paper_id, f"Reading pages {i+1}-{i+len(batch_imgs)}...", batch_idx, num_page_batches) + batch_result = await gemini_vision_json( + system_prompt=STRUCTURE_PROMPT, + images=batch_imgs, + user_text=f"Pages {i+1}-{i+len(batch_imgs)} of the exam paper. Extract all questions visible on these pages.", + temperature=0, + ) + if not meta: + meta = {k: batch_result.get(k) for k in ("total_score", "difficulty_level", "topics_summary")} + all_questions.extend(batch_result.get("questions", [])) + + all_questions = sort_questions(all_questions) + questions = all_questions + + # 更新 paper 概览 + sb.table("papers").update({ + "total_score": meta.get("total_score"), + "question_count": len(questions), + "topics_summary": meta.get("topics_summary"), + "difficulty_level": meta.get("difficulty_level"), + }).eq("id", paper_id).execute() + + # ── Step 3: 答案匹配（分批，失败跳过）── + answers_map = {} + if answer_bytes: + _update_progress(sb, paper_id, "Matching answers...") + try: + answer_images = pdf_to_images(answer_bytes) + questions_json = json.dumps( + [{"question_number": q["question_number"], "question_type": q["question_type"]} + for q in questions], ensure_ascii=False, + ) + all_answers: list = [] + for ai in range(0, len(answer_images), 8): + batch_ans_imgs = answer_images[ai:ai + 8] + try: + match_result = await gemini_vision_json( + system_prompt=ANSWER_MATCH_PROMPT.format( + questions_json=questions_json, answer_text="(See images)", + ), + images=batch_ans_imgs, + user_text=f"Match answers to these questions: {questions_json}", + temperature=0, + ) + all_answers.extend(match_result.get("answers", [])) + except Exception: + pass + answers_map = {a["question_number"]: a for a in all_answers} + except Exception: + pass + + # ── Step 4: 立即写入题目到 DB（先不含 AI trio）── + _update_progress(sb, paper_id, "Saving questions...") + for i, q in enumerate(questions): + qnum = q["question_number"] + answer = answers_map.get(qnum, {}) + sb.table("paper_questions").insert(strip_nulls({ + "paper_id": paper_id, + "question_number": qnum, + "parent_question": q.get("parent_question"), + "display_order": i, + "question_type": q["question_type"], + "question_text": q["question_text"], + "score": q.get("score"), + "page_number": q.get("page_number"), + "options": q.get("options"), + "correct_option": answer.get("correct_option"), + "correct_answer": answer.get("correct_answer"), + "raw_answer_text": answer.get("raw_answer_text"), + "topics": q.get("topics", []), + "analytics_topic": q.get("topics", [None])[0], + "topic_tags": q.get("topics", []), + "difficulty": q.get("difficulty"), + })).execute() + + # ── Step 5: AI trio（逐条更新，支持断点续传）── + saved = sb.table("paper_questions").select("id, question_number, solution").eq("paper_id", paper_id).execute().data + await _resume_ai_trio(sb, paper_id, saved) + + except Exception as e: + sb.table("papers").update({ + "status": "error", + "error_message": f"{type(e).__name__}: {str(e)}\n{traceback.format_exc()[-500:]}", + }).eq("id", paper_id).execute() + raise diff --git a/backend/app/services/supabase_client.py b/backend/app/services/supabase_client.py new file mode 100644 index 0000000..26a1b78 --- /dev/null +++ b/backend/app/services/supabase_client.py @@ -0,0 +1,13 @@ +from supabase import create_client, Client +from app.config import get_settings + +_client: Client | None = None + + +def get_supabase() -> Client: + """获取 Supabase client (service_role，绕过 RLS)""" + global _client + if _client is None: + s = get_settings() + _client = create_client(s.supabase_url, s.supabase_service_role_key) + return _client diff --git a/backend/app/services/text_extractor.py b/backend/app/services/text_extractor.py new file mode 100644 index 0000000..f808d50 --- /dev/null +++ b/backend/app/services/text_extractor.py @@ -0,0 +1,48 @@ +"""PDF 文本提取 — 复用 SOS 的 text_extractor 逻辑""" + +import base64 +import fitz # PyMuPDF +from dataclasses import dataclass + + +@dataclass +class ExtractedContent: + pages_text: list[str] # 每页文本 + page_images: dict[int, str] # 页码 → base64 图片（图片密集型页面） + total_pages: int + has_images: bool + + +def extract_pdf(file_bytes: bytes) -> ExtractedContent: + """从 PDF 提取文本和图片""" + doc = fitz.open(stream=file_bytes, filetype="pdf") + pages_text = [] + page_images = {} + + for i, page in enumerate(doc): + text = page.get_text("text") + pages_text.append(text) + + # 如果某页文本很少但有图片，可能是扫描件 → 保存为图片用于 Vision OCR + if len(text.strip()) < 50: + pix = page.get_pixmap(dpi=200) + img_bytes = pix.tobytes("png") + page_images[i] = base64.b64encode(img_bytes).decode("utf-8") + + doc.close() + + return ExtractedContent( + pages_text=pages_text, + page_images=page_images, + total_pages=len(pages_text), + has_images=len(page_images) > 0, + ) + + +def get_full_text(extracted: ExtractedContent) -> str: + """合并所有页面文本""" + return "\n\n".join( + f"--- Page {i+1} ---\n{text}" + for i, text in enumerate(extracted.pages_text) + if text.strip() + ) diff --git a/backend/backfill_ai_trio_with_context.py b/backend/backfill_ai_trio_with_context.py new file mode 100644 index 0000000..0ad7059 --- /dev/null +++ b/backend/backfill_ai_trio_with_context.py @@ -0,0 +1,252 @@ +""" +重新生成所有题目的 AI trio，子题带父题上下文。 +用法: python backfill_ai_trio_with_context.py [--paper-id ] [--course ] +""" + +import asyncio +import io +import json +import re +import sys +import time +import argparse +from contextlib import redirect_stdout +from app.services.supabase_client import get_supabase +from app.services.llm_clients import get_deepseek_client + + +def extract_code_lines(text: str) -> str: + lines = (text or "").splitlines() + result = [] + in_code = False + open_brackets = 0 + CODE_START = re.compile(r"^\s*(import |from \w|[A-Za-z_]\w*\s*=|print\()") + for line in lines: + stripped = line.strip() + if in_code and open_brackets > 0: + result.append(stripped) + open_brackets += stripped.count("(") + stripped.count("[") + stripped.count("{") + open_brackets -= stripped.count(")") + stripped.count("]") + stripped.count("}") + continue + if CODE_START.match(line): + in_code = True + result.append(stripped) + open_brackets += stripped.count("(") + stripped.count("[") + stripped.count("{") + open_brackets -= stripped.count(")") + stripped.count("]") + stripped.count("}") + continue + in_code = False + return "\n".join(result) + + +def try_exec_python(code: str, shared_ns: dict) -> str | None: + buf = io.StringIO() + try: + with redirect_stdout(buf): + exec(code, shared_ns) # noqa: S102 + output = buf.getvalue().strip() + return output if output else None + except Exception: + return None + +BATCH_ANALYSIS_PROMPT = """You are an expert academic answer analyst. Generate three study sections for each question below. ALL output must be in English. + +For every question, return: +- knowledge_reminder: concise prerequisite bullets in HTML +- ai_hint: a helpful hint in HTML without revealing the final answer +- solution: a complete step-by-step solution in HTML + +Return JSON in this exact format: +{{ + "analyses": [ + {{ + "question_number": "1a", + "knowledge_reminder": "...", + "ai_hint": "...", + "solution": "..." + }} + ] +}} + +Rules: +- Return one item for every provided question_number +- All text must be in English +- HTML only, KaTeX compatible (block $$ ... $$ inline $ ... $) +- For MC questions, explain why the correct option is right and why others are wrong +- For long questions, show a complete derivation or reasoning chain +- Use
        or numbered steps in solution when appropriate +- Mark common mistakes with
        ...
        +- CRITICAL: When a question_text contains "[Context from parent question X]" followed by "[Sub-question Y]", the parent section is background context only. You MUST solve ONLY the specific sub-question labeled [Sub-question Y]. Do NOT solve other sub-questions listed in the parent context. Give one precise answer for that single sub-question only. + +Questions: +{questions_payload} +""" + + +def chunked(lst, size): + return [lst[i:i+size] for i in range(0, len(lst), size)] + + +async def deepseek_batch(batch: list[dict]) -> list[dict]: + client = get_deepseek_client() + for attempt in range(5): + try: + resp = client.chat.completions.create( + model="deepseek-chat", + messages=[{ + "role": "system", + "content": BATCH_ANALYSIS_PROMPT.format( + questions_payload=json.dumps(batch, ensure_ascii=False) + ) + }], + temperature=0.3, + max_tokens=8192, + response_format={"type": "json_object"}, + ) + raw = re.sub(r'[\x00-\x08\x0b\x0c\x0e-\x1f]', '', resp.choices[0].message.content) + raw = re.sub(r'(? list[str]: + if "_" in question_number: + left, right = question_number.split("_", 1) + tokens: list[str] = [] + m = re.fullmatch(r"(\d+)([a-z])", left) + if m: + tokens.append(f"({m.group(2)})") + elif re.fullmatch(r"\d+[a-z]+", left): + tokens.append(f"({re.sub(r'^\\d+', '', left)})") + tokens.append(f"({right})") + return tokens[::-1] + + m = re.fullmatch(r"(\d+)([a-z])", question_number) + if m: + return [f"({m.group(2)})", f"Problem {m.group(1)}"] + + if question_number.isdigit(): + return [f"Problem {question_number}"] + + return [question_number] + + +def line_matches(line_text: str, marker: str) -> bool: + text = re.sub(r"\s+", " ", line_text.strip()) + if not text: + return False + if marker.startswith("("): + return text.startswith(marker) + return marker.lower() in text.lower() + + +def line_y_ratio(page: fitz.Page, marker: str) -> float | None: + data = page.get_text("dict") + hits: list[float] = [] + for block in data.get("blocks", []): + if block.get("type") != 0: + continue + for line in block.get("lines", []): + line_text = "".join( + span.get("text", "") + for span in line.get("spans", []) + ) + if line_matches(line_text, marker): + bbox = line.get("bbox") + if bbox: + hits.append(float(bbox[1])) + if not hits: + return None + y = min(hits) + return max(0.0, min((y - page.rect.y0) / page.rect.height, 0.98)) + + +def search_y_ratio(page: fitz.Page, marker: str) -> float | None: + ratios: list[float] = [] + for rect in page.search_for(marker): + ratios.append(max(0.0, min((rect.y0 - page.rect.y0) / page.rect.height, 0.98))) + return min(ratios) if ratios else None + + +def infer_y_ratio(page: fitz.Page, question_number: str) -> float: + for marker in marker_candidates(question_number): + ratio = line_y_ratio(page, marker) + if ratio is not None: + return ratio + ratio = search_y_ratio(page, marker) + if ratio is not None: + return ratio + return 0.05 + + +def main() -> None: + sb = get_supabase() + papers = ( + sb.table("papers") + .select("id, source_exam_key") + .eq("course_code", "COMP2211") + .eq("source_kind", "course_library") + .execute() + .data + or [] + ) + + updates: list[tuple[str, float]] = [] + for paper in papers: + exam_key = paper["source_exam_key"] + pdf_name = PDF_BY_EXAM_KEY.get(exam_key) + if not pdf_name: + continue + pdf_path = PAPERS_DIR / pdf_name + doc = fitz.open(pdf_path) + try: + questions = ( + sb.table("paper_questions") + .select("id, question_number, page_number") + .eq("paper_id", paper["id"]) + .order("display_order") + .execute() + .data + or [] + ) + for question in questions: + page_number = question.get("page_number") or 1 + page = doc[page_number - 1] + ratio = infer_y_ratio(page, question["question_number"]) + updates.append((question["id"], round(ratio, 4))) + finally: + doc.close() + + def apply_update(payload: tuple[str, float]) -> None: + question_id, ratio = payload + attempts = 0 + while True: + try: + sb.table("paper_questions").update({"page_y_ratio": ratio}).eq("id", question_id).execute() + return + except httpx.HTTPError: + attempts += 1 + if attempts >= 5: + raise + time.sleep(0.4 * attempts) + + with ThreadPoolExecutor(max_workers=3) as executor: + futures = [executor.submit(apply_update, payload) for payload in updates] + for future in as_completed(futures): + future.result() + + print(f"Backfilled page_y_ratio for {len(updates)} COMP2211 questions.") + + +if __name__ == "__main__": + main() diff --git a/backend/backfill_comp2211_tags.py b/backend/backfill_comp2211_tags.py new file mode 100644 index 0000000..3f1fb57 --- /dev/null +++ b/backend/backfill_comp2211_tags.py @@ -0,0 +1,365 @@ +"""Backfill COMP2211 tags to the revised retrieval schema.""" + +from __future__ import annotations + +import re +from collections import OrderedDict + +from app.services.supabase_client import get_supabase + + +SKILL_LABELS = { + "concept_check": "Concept Check", + "code_tracing": "Code Tracing", + "algorithm_tracing": "Algorithm Tracing", + "distance_calculation": "Distance Calculation", + "centroid_update": "Centroid Update", + "weight_update": "Weight Update", + "decision_boundary": "Decision Boundary", + "implementation": "Implementation", + "debugging": "Debugging", + "model_selection": "Model Selection", + "concept_explanation": "Concept Explanation", + "architecture_reasoning": "Architecture Reasoning", + "convergence_reasoning": "Convergence Reasoning", + "generalization_reasoning": "Generalization Reasoning", + "classification_decision": "Classification Decision", +} + +ACRONYMS = { + "ai": "AI", + "cnn": "CNN", + "knn": "KNN", + "mlp": "MLP", + "nb": "NB", + "numpy": "NumPy", +} + + +def title_case_with_acronyms(value: str) -> str: + words = re.split(r"[\s_]+", value.strip()) + parts: list[str] = [] + for word in words: + if not word: + continue + lowered = word.lower() + parts.append(ACRONYMS.get(lowered, lowered.capitalize())) + return " ".join(parts) + + +def normalize_skill_tag(tag: str) -> str: + if tag in SKILL_LABELS: + return SKILL_LABELS[tag] + return title_case_with_acronyms(tag) + + +def text_blob(question: dict) -> str: + parts = [ + question.get("question_text") or "", + question.get("raw_answer_text") or "", + " ".join(question.get("topic_tags") or []), + " ".join(question.get("skill_tags") or []), + question.get("analytics_topic") or "", + ] + return " ".join(parts).lower() + + +def has_any(text: str, phrases: list[str]) -> bool: + return any(phrase in text for phrase in phrases) + + +def infer_analytics_topic(question: dict) -> str: + text = text_blob(question) + broad = question.get("analytics_topic") or "" + skills = {normalize_skill_tag(tag) for tag in (question.get("skill_tags") or [])} + + if has_any(text, ["ethics", "bias", "privacy", "autonomous vehicle", "informed consent", "human participants", "ethically"]): + return "Ethics of AI" + if has_any(text, ["minimax", "alpha-beta", "alpha beta", "game tree", "tic-tac-toe", "tic tac toe"]): + return "Game Trees" + if has_any(text, ["search algorithm", "best-first", "breadth-first", "depth-first", "a* search", "a star"]): + return "Search Algorithms" + if has_any(text, ["cross validation", "d-fold", "k-fold", "train/val", "validation set", "fold "]) or broad == "Cross Validation": + return "Cross Validation" + if has_any(text, ["confusion matrix", "precision", "recall", "macro f1", "f1 score", "accuracy score", "evaluation metric"]): + return "Evaluation Metrics" + if has_any(text, ["naive bayes", "gaussian distribution", "laplace smoothing", "likelihood", "posterior probability"]) or broad == "Naive Bayes": + return "Naive Bayes" + if has_any(text, ["bayes classifier", "conditional probability", "bayesian inference", "prior probability", "posterior"]) or broad == "Bayesian Inference": + return "Bayesian Inference" + if has_any(text, ["leader clustering", "k-means", "k means", "centroid", "elbow method", "silhouette", "cluster assignments", "closest centroid", "new cluster"]): + return "K-Means" + if has_any(text, ["k-nearest", "nearest neighbors", "weighted knn", "cosine distance", "euclidean distance", "manhattan distance", "6-cross-validation error for k", "class for cosine distance"]): + return "KNN" + if has_any(text, ["multilayer perceptron", "mlp", "back propagation", "backpropagation", "hidden layer", "output layer", "dropout", "softmax", "sigmoid function", "relu as the activation"]) or broad == "MLP": + return "MLP" + if has_any(text, ["perceptron", "decision boundary", "single neuron", "weight update", "activation function f(z)", "linearly separable"]) or broad == "Perceptron": + return "Perceptron" + if has_any(text, ["convolutional neural network", "cnn", "kernel", "padding", "stride", "pooling", "dilated convolution", "3d convolution", "otsu", "histogram", "image processing", "grayscale image"]): + return "CNN" + if has_any(text, ["numpy", "python", "np.", "broadcasting", "reshape", "transpose", "mask", "vectorized", "np.arange", "np.mean", "np.dot", "np.convolve"]): + return "Python and NumPy" + + if broad == "KNN and Clustering": + if ( + has_any(text, ["k-means", "k means", "centroid", "leader clustering", "elbow", "silhouette"]) + or "Centroid Update" in skills + or "Convergence Reasoning" in skills + or "Algorithm Tracing" in skills + or "Model Selection" in skills + ): + return "K-Means" + return "KNN" + + if broad == "Perceptron and MLP": + if ( + has_any(text, ["hidden layer", "backprop", "activation function", "softmax", "relu", "sigmoid", "multilayer perceptron", "mlp"]) + or "Architecture Reasoning" in skills + ): + return "MLP" + return "Perceptron" + + if broad == "Probabilistic Models": + if has_any(text, ["naive bayes", "gaussian", "laplace", "likelihood"]): + return "Naive Bayes" + return "Bayesian Inference" + + if broad == "Evaluation and Validation": + if has_any(text, ["cross validation", "cross-validation", "k-fold", "d-fold", "validation set", "train/val"]): + return "Cross Validation" + return "Evaluation Metrics" + + if broad == "Search and Games": + if has_any(text, ["minimax", "alpha-beta", "alpha beta", "game tree"]): + return "Game Trees" + return "Search Algorithms" + + broad_map = { + "Vision and CNN": "CNN", + "Python Fundamentals": "Python and NumPy", + "Ethics of AI": "Ethics of AI", + } + return broad_map.get(broad, "Python and NumPy") + + +TOPIC_CONCEPTS = { + "Naive Bayes": [ + ("Naive Bayes", ["naive bayes"]), + ("Prior", ["prior"]), + ("Likelihood", ["likelihood"]), + ("Posterior", ["posterior"]), + ("Gaussian", ["gaussian"]), + ("Laplace Smoothing", ["laplace"]), + ("Missing Data", ["missing data", "missing value"]), + ], + "Bayesian Inference": [ + ("Bayesian Inference", ["bayes", "conditional probability", "posterior"]), + ("Conditional Probability", ["conditional probability"]), + ("Bayes Rule", ["bayes rule", "posterior"]), + ("Prior", ["prior"]), + ("Posterior", ["posterior"]), + ], + "KNN": [ + ("KNN", ["k-nearest", "nearest neighbors", "knn"]), + ("Euclidean Distance", ["euclidean distance"]), + ("Manhattan Distance", ["manhattan distance"]), + ("Cosine Distance", ["cosine distance"]), + ("Weighted KNN", ["weighted k-nearest", "weighted knn", "inverse of the distance"]), + ("Classification", ["class label", "predict", "classification"]), + ("Cross Validation", ["cross-validation", "cross validation"]), + ("Test Error", ["test error"]), + ], + "K-Means": [ + ("K-Means", ["k-means", "k means"]), + ("Centroid Update", ["centroid"]), + ("Convergence", ["converged", "convergence"]), + ("Leader Clustering", ["leader clustering"]), + ("Outliers", ["outlier"]), + ("Model Selection", ["elbow method", "silhouette", "suitable k"]), + ], + "Perceptron": [ + ("Perceptron", ["perceptron"]), + ("Decision Boundary", ["decision boundary", "linearly separable"]), + ("Weight Update", ["weight update", "∆w", "deltaw", "backward propagation"]), + ("Convergence", ["converged", "convergence"]), + ("Activation Function", ["activation function"]), + ], + "MLP": [ + ("MLP", ["mlp", "multilayer perceptron"]), + ("Backpropagation", ["back propagation", "backpropagation", "backward propagation"]), + ("Activation Function", ["activation function", "relu", "sigmoid", "softmax"]), + ("Hidden Layer", ["hidden layer"]), + ("Output Layer", ["output layer"]), + ("Parameter Count", ["number of parameters", "parameter"]), + ("Overfitting", ["overfitting", "dropout"]), + ], + "CNN": [ + ("CNN", ["cnn", "convolutional neural network"]), + ("Convolution", ["convolution", "kernel"]), + ("Padding", ["padding", "reflection padding", "zero padding"]), + ("Stride", ["stride"]), + ("Pooling", ["pooling", "max pooling", "average pooling"]), + ("Image Processing", ["image processing", "grayscale image"]), + ("Histogram", ["histogram"]), + ("Otsu Thresholding", ["otsu"]), + ("Dilated Convolution", ["dilated convolution"]), + ("3D Convolution", ["3d convolution"]), + ("Dropout", ["dropout"]), + ], + "Evaluation Metrics": [ + ("Evaluation Metrics", ["evaluation", "metric"]), + ("Confusion Matrix", ["confusion matrix"]), + ("Accuracy", ["accuracy"]), + ("Precision", ["precision"]), + ("Recall", ["recall"]), + ("F1 Score", ["f1"]), + ("Macro F1", ["macro f1"]), + ], + "Cross Validation": [ + ("Cross Validation", ["cross validation", "cross-validation", "d-fold", "k-fold"]), + ("Train Validation Split", ["validation set", "train", "test fold"]), + ("Model Selection", ["choose k", "which k", "fold"]), + ("Data Shuffling", ["shuffle", "shuffling"]), + ], + "Python and NumPy": [ + ("Python and NumPy", ["numpy", "python"]), + ("NumPy", ["numpy", "np."]), + ("Broadcasting", ["broadcast"]), + ("Array Indexing", ["index", "slice"]), + ("Vectorization", ["no explicit loops", "vectorized"]), + ("Matrix Multiplication", ["matmul", "matrix multiplication", "@"]), + ("Reshape", ["reshape"]), + ("Transpose", ["transpose"]), + ("Masking", ["mask"]), + ("Convolution", ["convolve"]), + ], + "Search Algorithms": [ + ("Search Algorithms", ["search"]), + ("Breadth-First Search", ["breadth-first", "breadth first", "bfs"]), + ("Depth-First Search", ["depth-first", "depth first", "dfs"]), + ("Best-First Search", ["best-first", "best first"]), + ("A* Search", ["a* search", "a star", "astar"]), + ("Heuristic", ["heuristic"]), + ], + "Game Trees": [ + ("Game Trees", ["game tree", "minimax", "alpha-beta", "alpha beta"]), + ("Minimax", ["minimax"]), + ("Alpha-Beta Pruning", ["alpha-beta", "alpha beta", "pruned"]), + ("Utility", ["utility"]), + ], + "Ethics of AI": [ + ("Ethics of AI", ["ethics", "ethical"]), + ("Bias", ["bias"]), + ("Privacy", ["privacy"]), + ("Fairness", ["fair"]), + ("Research Ethics", ["informed consent", "human participants"]), + ("Governance", ["monitoring", "production", "organizations"]), + ("Autonomous Vehicles", ["autonomous vehicle"]), + ], +} + + +TOPIC_DEFAULTS = { + "Naive Bayes": ["Likelihood", "Posterior"], + "Bayesian Inference": ["Conditional Probability", "Bayes Rule"], + "KNN": ["Classification", "Distance Calculation"], + "K-Means": ["Centroid Update", "Convergence"], + "Perceptron": ["Decision Boundary", "Weight Update"], + "MLP": ["Activation Function", "Hidden Layer"], + "CNN": ["Convolution", "Padding"], + "Evaluation Metrics": ["Confusion Matrix", "F1 Score"], + "Cross Validation": ["Train Validation Split", "Model Selection"], + "Python and NumPy": ["NumPy", "Vectorization"], + "Search Algorithms": ["Breadth-First Search", "Heuristic"], + "Game Trees": ["Minimax", "Alpha-Beta Pruning"], + "Ethics of AI": ["Bias", "Fairness"], +} + +DEFAULT_SKILLS = { + "Naive Bayes": ["Probability Reasoning"], + "Bayesian Inference": ["Probability Reasoning"], + "KNN": ["Classification Decision"], + "K-Means": ["Centroid Update"], + "Perceptron": ["Decision Boundary"], + "MLP": ["Concept Explanation"], + "CNN": ["Concept Explanation"], + "Evaluation Metrics": ["Metric Reasoning"], + "Cross Validation": ["Model Selection"], + "Python and NumPy": ["Code Tracing"], + "Search Algorithms": ["Algorithm Tracing"], + "Game Trees": ["Game Reasoning"], + "Ethics of AI": ["Ethical Reasoning"], +} + + +def unique_keep_order(values: list[str]) -> list[str]: + return list(OrderedDict((value, None) for value in values if value).keys()) + + +def build_topic_tags(question: dict, analytics_topic: str) -> list[str]: + text = text_blob(question) + tags: list[str] = [analytics_topic] + for label, keywords in TOPIC_CONCEPTS.get(analytics_topic, []): + if label == analytics_topic: + continue + if has_any(text, keywords): + tags.append(label) + for default in TOPIC_DEFAULTS.get(analytics_topic, []): + if len(unique_keep_order(tags)) >= 2: + break + tags.append(default) + tags = unique_keep_order(tags) + return tags[:5] + + +def build_skill_tags(question: dict, analytics_topic: str) -> list[str]: + raw = question.get("skill_tags") or [] + converted = unique_keep_order([normalize_skill_tag(tag) for tag in raw]) + if not converted: + converted = DEFAULT_SKILLS.get(analytics_topic, ["Concept Check"]) + return converted[:3] + + +def main() -> None: + sb = get_supabase() + papers = ( + sb.table("papers") + .select("id") + .eq("course_code", "COMP2211") + .eq("source_kind", "course_library") + .execute() + .data + ) + paper_ids = [paper["id"] for paper in papers] + if not paper_ids: + print("No COMP2211 course-library papers found.") + return + + questions = ( + sb.table("paper_questions") + .select("id, paper_id, question_number, question_text, raw_answer_text, analytics_topic, topic_tags, skill_tags, topics") + .in_("paper_id", paper_ids) + .order("paper_id") + .order("display_order") + .execute() + .data + ) + + for question in questions: + analytics_topic = infer_analytics_topic(question) + topic_tags = build_topic_tags(question, analytics_topic) + skill_tags = build_skill_tags(question, analytics_topic) + payload = { + "analytics_topic": analytics_topic, + "topic_primary": analytics_topic, + "topic_tags": topic_tags, + "topics": topic_tags, + "skill_tags": skill_tags, + } + sb.table("paper_questions").update(payload).eq("id", question["id"]).execute() + + print(f"Backfilled {len(questions)} COMP2211 questions.") + + +if __name__ == "__main__": + main() diff --git a/backend/backfill_null_ai_trio.py b/backend/backfill_null_ai_trio.py new file mode 100644 index 0000000..4baba5d --- /dev/null +++ b/backend/backfill_null_ai_trio.py @@ -0,0 +1,169 @@ +"""Backfill AI trio for questions where knowledge_reminder IS NULL. + +For each question, generates fields in two separate LLM calls to avoid token truncation: + Call 1 → knowledge_reminder + ai_hint (short, ~500 tokens output) + Call 2 → solution (long, up to 4096 tokens output) + +Run from the backend directory: + uv run python backfill_null_ai_trio.py [--dry-run] +""" + +from __future__ import annotations + +import asyncio +import json +import sys +from app.services.supabase_client import get_supabase +from app.services.paper_processor import qwen_json_completion + + +KNOWLEDGE_HINT_PROMPT = """\ +You are an expert tutor. Given a past-paper question, produce two short study aids in English. + +Return JSON exactly: +{{ + "knowledge_reminder": "2-4 sentences summarising the key concept or formula the student must recall.", + "ai_hint": "1-3 sentence nudge that guides WITHOUT giving the answer away." +}} + +Question: +{payload} +""" + +SOLUTION_PROMPT = """\ +You are an expert tutor. Given a past-paper question and its reference answer, write a clear, \ +step-by-step model solution in English. Show all working. Be thorough but stop when the answer \ +is complete — do not pad. + +Return JSON exactly: +{{ + "solution": "" +}} + +Question: +{payload} +""" + + +def build_payload(q: dict) -> dict: + ref = "" + if q.get("raw_answer_text"): + ref = q["raw_answer_text"] + elif q.get("correct_option"): + ref = f"Correct option: {q['correct_option']}" + elif q.get("correct_answer"): + ref = f"Correct answer: {q['correct_answer']}" + + return { + "question_number": q["question_number"], + "question_type": q["question_type"] or "long_question", + "score": q.get("score") or "unknown", + "question_text": q.get("question_text") or "", + "topics": q.get("topics") or [], + "reference_answer": ref, + } + + +async def process_one(sb, q: dict, dry_run: bool) -> bool: + payload_str = json.dumps(build_payload(q), ensure_ascii=False) + row_id = q["id"] + qnum = q["question_number"] + + if dry_run: + print(f" [dry-run] would process {qnum}") + return True + + update: dict = {} + + # ── Call 1: knowledge_reminder + ai_hint ───────────────────────── + try: + r1 = await qwen_json_completion( + system_prompt=KNOWLEDGE_HINT_PROMPT.format(payload=payload_str), + temperature=0.3, + max_tokens=1024, + ) + if r1.get("knowledge_reminder"): + update["knowledge_reminder"] = r1["knowledge_reminder"] + if r1.get("ai_hint"): + update["ai_hint"] = r1["ai_hint"] + except Exception as e: + print(f" WARN call-1 failed for {qnum}: {e}") + + await asyncio.sleep(1) + + # ── Call 2: solution ────────────────────────────────────────────── + try: + r2 = await qwen_json_completion( + system_prompt=SOLUTION_PROMPT.format(payload=payload_str), + temperature=0.3, + max_tokens=4096, + ) + if r2.get("solution"): + update["solution"] = r2["solution"] + except Exception as e: + print(f" WARN call-2 failed for {qnum}: {e}") + + if not update: + print(f" SKIP {qnum}: both calls returned nothing") + return False + + sb.table("paper_questions").update(update).eq("id", row_id).execute() + return True + + +async def backfill(dry_run: bool = False) -> None: + sb = get_supabase() + + papers = ( + sb.table("papers") + .select("id") + .eq("course_code", "COMP2211") + .eq("source_kind", "course_library") + .execute() + .data + ) + paper_ids = [p["id"] for p in papers] + if not paper_ids: + print("No COMP2211 course-library papers found.") + return + + questions = ( + sb.table("paper_questions") + .select("id, paper_id, question_number, question_type, score, question_text, topics, raw_answer_text, correct_option, correct_answer") + .in_("paper_id", paper_ids) + .is_("knowledge_reminder", "null") + .order("paper_id") + .order("display_order") + .execute() + .data + ) + + if not questions: + print("No NULL questions found — all done!") + return + + print(f"Found {len(questions)} questions with NULL knowledge_reminder.") + + # Group by paper for cleaner output + from collections import defaultdict + by_paper: dict[str, list] = defaultdict(list) + for q in questions: + by_paper[q["paper_id"]].append(q) + + total_updated = 0 + for paper_idx, (paper_id, qs) in enumerate(by_paper.items(), 1): + print(f"\n[{paper_idx}/{len(by_paper)}] paper_id={paper_id} — {len(qs)} NULL questions") + for q in qs: + print(f" Processing {q['question_number']}...", end=" ", flush=True) + ok = await process_one(sb, q, dry_run) + if ok: + total_updated += 1 + print("done") + await asyncio.sleep(1.5) + + print(f"\nDone. {total_updated}/{len(questions)} questions updated.") + + +if __name__ == "__main__": + dry_run = "--dry-run" in sys.argv + asyncio.run(backfill(dry_run=dry_run)) diff --git a/backend/backfill_similar_questions.py b/backend/backfill_similar_questions.py new file mode 100644 index 0000000..11747a2 --- /dev/null +++ b/backend/backfill_similar_questions.py @@ -0,0 +1,135 @@ +"""Pre-compute similar_questions for all COMP2211 course-library questions. + +For each question, runs the same similarity logic as the API and writes the result +into paper_questions.similar_questions (JSONB). The API will then return this +pre-computed value directly with no computation overhead. + +Run from the backend directory: + uv run python backfill_similar_questions.py [--dry-run] +""" + +from __future__ import annotations + +import sys +from collections import Counter +from app.services.supabase_client import get_supabase +from app.routers.questions import ( + similarity_score, + question_family, + display_topics, +) + + +def run(dry_run: bool = False) -> None: + sb = get_supabase() + + # Fetch all ready COMP2211 papers + papers = ( + sb.table("papers") + .select("id, year, term, exam_type, part_label") + .eq("course_code", "COMP2211") + .eq("status", "ready") + .execute() + .data + ) + if not papers: + print("No ready COMP2211 papers found.") + return + + papers_by_id = {p["id"]: p for p in papers} + paper_ids = list(papers_by_id.keys()) + + # Fetch all questions for these papers + all_questions = ( + sb.table("paper_questions") + .select( + "id, paper_id, question_number, question_type, question_format, " + "question_text, score, topics, analytics_topic, topic_tags, skill_tags, " + "difficulty, knowledge_reminder, ai_hint, solution" + ) + .in_("paper_id", paper_ids) + .execute() + .data + ) + print(f"Found {len(all_questions)} questions across {len(papers)} papers.") + + # Batch full-text scores not practical here; skip RPC, rely on tag/topic scoring + # (text_score = 0 for all, still produces good tag-based results) + + updated = 0 + skipped = 0 + + for i, target in enumerate(all_questions, 1): + target_paper_id = target["paper_id"] + target_topic = target.get("analytics_topic") + + # Candidates: same course, different paper + candidates = [ + q for q in all_questions + if q["paper_id"] != target_paper_id + ] + + # Pre-filter by analytics_topic if available + if target_topic: + candidates = [c for c in candidates if c.get("analytics_topic") == target_topic] + + if not candidates: + skipped += 1 + print(f" [{i}/{len(all_questions)}] {target['question_number']} — no candidates, skip") + continue + + ranked = [] + for candidate in candidates: + match_percent, reasons = similarity_score(target, candidate, text_score=0.0) + if match_percent < 20: + continue + paper = papers_by_id.get(candidate["paper_id"], {}) + source = ( + f"{paper.get('year', '')} {paper.get('term', '').title()} " + f"{paper.get('exam_type', '').title()}" + ).strip() + if paper.get("part_label"): + source = f"{source} Part {paper['part_label']}" + ranked.append({ + "id": candidate["id"], + "paper_id": candidate["paper_id"], + "source": source, + "question_number": candidate["question_number"], + "match_percent": match_percent, + "match_reasons": reasons, + "question_type": question_family(candidate), + "question_text": candidate["question_text"], + "topics": display_topics(candidate), + "difficulty": candidate.get("difficulty"), + "knowledge_reminder": candidate.get("knowledge_reminder", ""), + "ai_hint": candidate.get("ai_hint", ""), + "solution": candidate.get("solution", ""), + }) + + ranked.sort(key=lambda item: (-item["match_percent"], item["source"], item["question_number"])) + + # Deduplicate: best per paper + seen_papers: set[str] = set() + deduped = [] + for item in ranked: + if item["paper_id"] not in seen_papers: + seen_papers.add(item["paper_id"]) + deduped.append(item) + deduped = deduped[:12] + + print(f" [{i}/{len(all_questions)}] {target['question_number']} → {len(deduped)} similar", end="") + + if dry_run: + print(" [dry-run]") + continue + + sb.table("paper_questions").update({"similar_questions": deduped}).eq("id", target["id"]).execute() + updated += 1 + print() + + print(f"\nDone. {updated} updated, {skipped} skipped (no candidates).") + + +if __name__ == "__main__": + dry_run = "--dry-run" in sys.argv + run(dry_run=dry_run) diff --git a/backend/backfill_vision.py b/backend/backfill_vision.py new file mode 100644 index 0000000..ec4c637 --- /dev/null +++ b/backend/backfill_vision.py @@ -0,0 +1,238 @@ +""" +用 Vision 模式重新处理所有已 ready 的试卷： +- 从 Supabase Storage 拉 PDF → 图片 → Vision 拆题 → exec → AI trio → 更新 DB + +用法: + python backfill_vision.py --course COMP2211 + python backfill_vision.py --paper-id +""" + +import asyncio +import argparse +import requests +from app.services.supabase_client import get_supabase +from app.services.paper_processor import ( + process_paper, + strip_nulls, + pdf_to_images, + gemini_vision_json, + deepseek_json_completion, + parse_json_response, + extract_code_lines, + try_exec_python, + chunked, + sort_questions, + STRUCTURE_PROMPT, + ANSWER_MATCH_PROMPT, + BATCH_ANALYSIS_PROMPT, +) +import json +import traceback + + +async def reprocess_paper(paper: dict): + """重新处理单张试卷（Vision 模式）""" + sb = get_supabase() + paper_id = paper["id"] + label = f"{paper['course_code']} {paper['year']} {paper['term']} {paper['exam_type']}" + print(f"\n=== {label} ({paper_id[:8]}) ===") + + # 1. 拉 PDF + try: + pdf_bytes = requests.get(paper["paper_file_url"], timeout=60).content + except Exception as e: + print(f" SKIP: failed to fetch PDF: {e}") + return + + answer_bytes = None + if paper.get("answer_file_url"): + try: + answer_bytes = requests.get(paper["answer_file_url"], timeout=60).content + except Exception: + pass + + # 2. PDF → 图片 + print(f" Rendering {len(pdf_to_images(pdf_bytes))} pages...", end=" ", flush=True) + paper_images = pdf_to_images(pdf_bytes) + print("done") + + # 3. Vision 拆题（分批，每批 8 页） + PAGE_BATCH = 8 + all_questions: list = [] + meta: dict = {} + print(f" Vision extraction ({len(paper_images)} pages, {-(-len(paper_images)//PAGE_BATCH)} batches)...") + for i in range(0, len(paper_images), PAGE_BATCH): + batch_imgs = paper_images[i:i + PAGE_BATCH] + print(f" Pages {i+1}-{i+len(batch_imgs)}...", end=" ", flush=True) + try: + batch_result = await gemini_vision_json( + system_prompt=STRUCTURE_PROMPT, + images=batch_imgs, + user_text=f"Pages {i+1}-{i+len(batch_imgs)} of the exam paper. Extract all questions visible on these pages.", + temperature=0, + ) + if not meta: + meta = {k: batch_result.get(k) for k in ("total_score", "difficulty_level", "topics_summary")} + qs = batch_result.get("questions", []) + all_questions.extend(qs) + print(f"done ({len(qs)} questions)") + except Exception as e: + print(f"FAILED: {e}") + structure = {**meta, "questions": all_questions} + questions = sort_questions(all_questions) + print(f" Total: {len(questions)} questions extracted") + + # 4. 答案匹配 + answers_map = {} + if answer_bytes: + print(" Vision answer matching...", end=" ", flush=True) + answer_images = pdf_to_images(answer_bytes) + questions_json = json.dumps( + [{"question_number": q["question_number"], "question_type": q["question_type"]} + for q in questions], ensure_ascii=False + ) + try: + match_result = await gemini_vision_json( + system_prompt=ANSWER_MATCH_PROMPT.format( + questions_json=questions_json, answer_text="(See images)" + ), + images=answer_images, + user_text=f"Match answers to these questions: {questions_json}", + temperature=0, + ) + answers_map = {a["question_number"]: a for a in match_result.get("answers", [])} + print(f"done ({len(answers_map)} matched)") + except Exception as e: + print(f"FAILED: {e}") + + # 5. 构建 payloads（exec Python） + import numpy as np + exec_namespaces: dict = {} + batched_payloads = [] + + for q in questions: + qnum = q["question_number"] + answer = answers_map.get(qnum, {}) + full_text = q["question_text"] or "" + + answer_section = "" + if answer.get("raw_answer_text"): + answer_section = answer["raw_answer_text"] + elif answer.get("correct_option"): + answer_section = f"Correct option: {answer['correct_option']}" + elif answer.get("correct_answer"): + answer_section = f"Correct answer: {answer['correct_answer']}" + + if not answer_section: + parent_q = q.get("parent_question") + group_key = parent_q or qnum + if group_key not in exec_namespaces: + ns: dict = {"np": np} + setup = extract_code_lines(full_text) + try_exec_python(setup, ns) + exec_namespaces[group_key] = ns + ns = exec_namespaces[group_key] + print_lines = [l.strip() for l in full_text.splitlines() if l.strip().startswith("print(")] + if print_lines: + out = try_exec_python(print_lines[-1], ns) + if out is not None: + answer_section = f"Executed output: {out}" + print(f" [exec] {qnum}: {out[:60]}") + + batched_payloads.append({ + "question_number": qnum, + "question_type": q["question_type"], + "score": q.get("score", "unknown"), + "question_text": full_text, + "topics": q.get("topics", []), + "reference_answer": answer_section, + }) + + # 6. AI trio + print(f" Generating AI trio ({len(batched_payloads)} questions, {len(list(chunked(batched_payloads, 3)))} batches)...") + analyses: dict = {} + for batch in chunked(batched_payloads, 3): + nums = [p["question_number"] for p in batch] + print(f" Batch {nums}...", end=" ", flush=True) + try: + result = await deepseek_json_completion( + system_prompt=BATCH_ANALYSIS_PROMPT.format( + questions_payload=json.dumps(batch, ensure_ascii=False) + ), + temperature=0.3, + ) + for item in result.get("analyses", []): + if item.get("question_number"): + analyses[item["question_number"]] = item + print(f"done ({len(result.get('analyses', []))})") + except Exception as e: + print(f"FAILED: {e}") + await asyncio.sleep(1) + + # 7. 删除旧题目，写入新题目 + print(" Writing to DB...", end=" ", flush=True) + sb.table("paper_questions").delete().eq("paper_id", paper_id).execute() + + for i, q in enumerate(questions): + qnum = q["question_number"] + answer = answers_map.get(qnum, {}) + analysis = analyses.get(qnum, {}) + sb.table("paper_questions").insert(strip_nulls({ + "paper_id": paper_id, + "question_number": qnum, + "parent_question": q.get("parent_question"), + "display_order": i, + "question_type": q["question_type"], + "question_text": q["question_text"], + "score": q.get("score"), + "page_number": q.get("page_number"), + "options": q.get("options"), + "correct_option": answer.get("correct_option"), + "correct_answer": answer.get("correct_answer"), + "raw_answer_text": answer.get("raw_answer_text"), + "topics": q.get("topics", []), + "analytics_topic": q.get("topics", [None])[0], + "topic_tags": q.get("topics", []), + "difficulty": q.get("difficulty"), + "knowledge_reminder": analysis.get("knowledge_reminder", ""), + "ai_hint": analysis.get("ai_hint", ""), + "solution": analysis.get("solution", ""), + })).execute() + + sb.table("papers").update({ + "question_count": len(questions), + "total_score": structure.get("total_score"), + "topics_summary": structure.get("topics_summary"), + "difficulty_level": structure.get("difficulty_level"), + }).eq("id", paper_id).execute() + + print(f"done ({len(questions)} questions written)") + + +async def main(): + parser = argparse.ArgumentParser() + parser.add_argument("--course", help="Course code") + parser.add_argument("--paper-id", help="Single paper ID") + args = parser.parse_args() + + sb = get_supabase() + query = sb.table("papers").select("*").eq("status", "ready") + if args.paper_id: + query = query.eq("id", args.paper_id) + elif args.course: + query = query.eq("course_code", args.course.upper()) + papers = query.order("created_at").execute().data + + print(f"Papers to reprocess: {len(papers)}") + for paper in papers: + try: + await reprocess_paper(paper) + except Exception as e: + print(f" ERROR: {e}") + traceback.print_exc() + + print("\nAll done.") + + +if __name__ == "__main__": + asyncio.run(main()) diff --git a/backend/fill_manual_study_aids.py b/backend/fill_manual_study_aids.py new file mode 100644 index 0000000..4e116dd --- /dev/null +++ b/backend/fill_manual_study_aids.py @@ -0,0 +1,29 @@ +"""Deprecated: study aids must come from LLM output, not template fillers.""" + +from __future__ import annotations + +import sys + + +MESSAGE = """ +fill_manual_study_aids.py is intentionally disabled. + +Reason: +- knowledge_reminder / ai_hint / solution must be generated by LLM +- template-based filler content polluted the COMP2211 course library + +Use one of these paths instead: +1. Regenerate study aids through the real LLM pipeline in app/services/paper_processor.py +2. Rebuild paper_questions from a reviewed source and then run LLM generation + +This script must not be used to backfill production study aids. +""".strip() + + +def main() -> None: + print(MESSAGE, file=sys.stderr) + raise SystemExit(1) + + +if __name__ == "__main__": + main() diff --git a/backend/import_course_manifest.py b/backend/import_course_manifest.py new file mode 100644 index 0000000..8d646e7 --- /dev/null +++ b/backend/import_course_manifest.py @@ -0,0 +1,240 @@ +"""Import a canonical course manifest into Supabase-backed papers.""" + +from __future__ import annotations + +import argparse +import asyncio +import json +from pathlib import Path +from typing import Any + +from app.services.paper_processor import process_paper +from app.services.supabase_client import get_supabase + + +def parse_args() -> argparse.Namespace: + parser = argparse.ArgumentParser( + description="Import a canonical course paper manifest into Supabase." + ) + parser.add_argument( + "--manifest", + type=Path, + required=True, + help="Path to the manifest JSON file.", + ) + parser.add_argument( + "--papers-root", + type=Path, + required=True, + help="Root folder that contains the course PDF files referenced by the manifest.", + ) + parser.add_argument( + "--user-id", + required=False, + help="Existing auth.users UUID used as the owner of imported course-library rows.", + ) + parser.add_argument( + "--course-code", + help="Optional filter to only import entries from one course.", + ) + parser.add_argument( + "--exam-key", + action="append", + dest="exam_keys", + default=[], + help="Optional exam_key filter. Repeat the flag to import multiple entries.", + ) + parser.add_argument( + "--process", + action="store_true", + help="Run the full paper processing pipeline after the files are uploaded.", + ) + parser.add_argument( + "--dry-run", + action="store_true", + help="Print what would be imported without uploading or writing database rows.", + ) + return parser.parse_args() + + +def load_manifest(path: Path) -> list[dict[str, Any]]: + with path.open("r", encoding="utf-8") as f: + data = json.load(f) + if not isinstance(data, list): + raise ValueError("Manifest must be a JSON array.") + return data + + +def should_import(entry: dict[str, Any], args: argparse.Namespace) -> bool: + if args.course_code and entry.get("course_code") != args.course_code: + return False + if args.exam_keys and entry.get("exam_key") not in set(args.exam_keys): + return False + return bool(entry.get("importable")) + + +def resolve_file_path(root: Path, filename: str | None) -> Path | None: + if not filename: + return None + + direct = root / filename + if direct.exists(): + return direct + + all_files = [candidate for candidate in root.iterdir() if candidate.is_file()] + + def normalize(name: str) -> str: + return name.replace(" (1)", "") + + target_name = normalize(filename) + normalized = [candidate for candidate in all_files if normalize(candidate.name) == target_name] + if len(normalized) == 1: + return normalized[0] + + path = Path(filename) + normalized_stem = normalize(path.stem) + suffix = path.suffix + stem_matches = [ + candidate + for candidate in all_files + if candidate.suffix == suffix and normalize(candidate.stem) == normalized_stem + ] + if len(stem_matches) == 1: + return stem_matches[0] + + return None + + +def read_file_bytes(root: Path, filename: str | None) -> bytes | None: + if not filename: + return None + path = resolve_file_path(root, filename) + if path is None or not path.exists(): + raise FileNotFoundError(f"Referenced file does not exist under {root}: {filename}") + return path.read_bytes() + + +def build_storage_path(entry: dict[str, Any], kind: str) -> str: + exam_key = entry["exam_key"] + return f"course-library/{entry['course_code']}/{exam_key}/{kind}.pdf" + + +def upsert_paper_record( + entry: dict[str, Any], + user_id: str | None, + paper_url: str, + answer_url: str | None, +) -> str: + sb = get_supabase() + payload = { + "user_id": user_id, + "course_code": entry["course_code"], + "year": entry["year"], + "term": entry["term"], + "exam_type": entry["exam_type"], + "part_label": entry.get("part_label"), + "paper_file_url": paper_url, + "answer_file_url": answer_url, + "status": "processing", + "source_kind": "course_library", + "source_exam_key": entry["exam_key"], + "source_question_filename": entry.get("question_pdf"), + "source_answer_filename": entry.get("primary_answer_pdf"), + } + + existing = ( + sb.table("papers") + .select("id") + .eq("source_kind", "course_library") + .eq("source_exam_key", entry["exam_key"]) + .limit(1) + .execute() + .data + ) + if existing: + paper_id = existing[0]["id"] + sb.table("papers").update(payload).eq("id", paper_id).execute() + return paper_id + + created = sb.table("papers").insert(payload).execute().data + return created[0]["id"] + + +def reset_existing_processed_data(paper_id: str) -> None: + sb = get_supabase() + sb.table("paper_questions").delete().eq("paper_id", paper_id).execute() + sb.table("papers").update( + { + "status": "processing", + "error_message": None, + "paper_extracted_text": None, + "answer_extracted_text": None, + "total_score": None, + "question_count": None, + "topics_summary": None, + "difficulty_level": None, + } + ).eq("id", paper_id).execute() + + +async def import_entry( + entry: dict[str, Any], + args: argparse.Namespace, +) -> None: + paper_bytes = read_file_bytes(args.papers_root, entry.get("question_pdf")) + answer_bytes = read_file_bytes(args.papers_root, entry.get("primary_answer_pdf")) + + if paper_bytes is None: + raise ValueError(f"Importable entry is missing question PDF: {entry['exam_key']}") + + if args.dry_run: + print( + f"[dry-run] {entry['exam_key']}: " + f"question={entry.get('question_pdf')} answer={entry.get('primary_answer_pdf')}" + ) + return + + sb = get_supabase() + paper_path = build_storage_path(entry, "paper") + sb.storage.from_("papers").upload( + paper_path, + paper_bytes, + file_options={"content-type": "application/pdf", "upsert": "true"}, + ) + paper_url = sb.storage.from_("papers").get_public_url(paper_path) + + answer_url = None + if answer_bytes: + answer_path = build_storage_path(entry, "answer") + sb.storage.from_("papers").upload( + answer_path, + answer_bytes, + file_options={"content-type": "application/pdf", "upsert": "true"}, + ) + answer_url = sb.storage.from_("papers").get_public_url(answer_path) + + paper_id = upsert_paper_record(entry, args.user_id, paper_url, answer_url) + print(f"Imported metadata for {entry['exam_key']} -> paper_id={paper_id}") + + if args.process: + reset_existing_processed_data(paper_id) + await process_paper(paper_id, paper_bytes, answer_bytes) + print(f"Processed {entry['exam_key']}") + + +async def main() -> None: + args = parse_args() + manifest = load_manifest(args.manifest) + entries = [entry for entry in manifest if should_import(entry, args)] + + if not entries: + print("No manifest entries matched the provided filters.") + return + + print(f"Preparing to import {len(entries)} manifest entries.") + for entry in entries: + await import_entry(entry, args) + + +if __name__ == "__main__": + asyncio.run(main()) diff --git a/backend/pyproject.toml b/backend/pyproject.toml new file mode 100644 index 0000000..51babf3 --- /dev/null +++ b/backend/pyproject.toml @@ -0,0 +1,17 @@ +[project] +name = "pastpaper-master-backend" +version = "0.1.0" +requires-python = ">=3.11" +dependencies = [ + "fastapi>=0.115.0", + "uvicorn[standard]>=0.30.0", + "python-dotenv>=1.0.0", + "python-multipart>=0.0.9", + "supabase>=2.0.0", + "openai>=1.50.0", + "PyMuPDF>=1.24.0", + "pydantic>=2.0.0", + "pydantic-settings>=2.0.0", + "httpx>=0.27.0", + "numpy>=2.4.4", +] diff --git a/backend/regen_ai_trio_comp2211.py b/backend/regen_ai_trio_comp2211.py new file mode 100644 index 0000000..7650f6f --- /dev/null +++ b/backend/regen_ai_trio_comp2211.py @@ -0,0 +1,174 @@ +"""Regenerate AI trio (knowledge_reminder, ai_hint, solution) for all COMP2211 course-library questions. + +Reads existing paper_questions rows and runs the same BATCH_ANALYSIS_PROMPT used by +paper_processor.py — but does UPDATE instead of INSERT, so question structure is untouched. + +Run from the backend directory: + uv run python regen_ai_trio_comp2211.py + +Pass --dry-run to print batches without calling the LLM or writing to the database. +""" + +from __future__ import annotations + +import asyncio +import json +import sys +from app.services.supabase_client import get_supabase +from app.services.paper_processor import BATCH_ANALYSIS_PROMPT, qwen_json_completion, chunked + + +def build_reference_answer(q: dict) -> str: + if q.get("raw_answer_text"): + return q["raw_answer_text"] + if q.get("correct_option"): + return f"Correct option: {q['correct_option']}" + if q.get("correct_answer"): + return f"Correct answer: {q['correct_answer']}" + return "" + + +async def regen(dry_run: bool = False) -> None: + sb = get_supabase() + + papers = ( + sb.table("papers") + .select("id") + .eq("course_code", "COMP2211") + .eq("source_kind", "course_library") + .execute() + .data + ) + paper_ids = [p["id"] for p in papers] + if not paper_ids: + print("No COMP2211 course-library papers found.") + return + + questions = ( + sb.table("paper_questions") + .select("id, paper_id, question_number, question_type, score, question_text, topics, raw_answer_text, correct_option, correct_answer") + .in_("paper_id", paper_ids) + .order("paper_id") + .order("display_order") + .execute() + .data + ) + print(f"Found {len(questions)} questions across {len(paper_ids)} papers.") + + payloads = [ + { + "question_number": q["question_number"], + "question_type": q["question_type"] or "long_question", + "score": q.get("score") or "unknown", + "question_text": q.get("question_text") or "", + "topics": q.get("topics") or [], + "reference_answer": build_reference_answer(q), + } + for q in questions + ] + + id_by_qnum_paper: dict[tuple[str, str], str] = { + (q["paper_id"], q["question_number"]): q["id"] + for q in questions + } + paper_id_by_qnum: dict[str, str] = { + q["question_number"]: q["paper_id"] for q in questions + } + + # Group payloads by paper so batches don't mix papers (cleaner context for LLM) + from collections import defaultdict + payloads_by_paper: dict[str, list[dict]] = defaultdict(list) + for q, payload in zip(questions, payloads): + payloads_by_paper[q["paper_id"]].append((q["id"], payload)) + + total_updated = 0 + total_papers = len(payloads_by_paper) + + for paper_idx, (paper_id, items) in enumerate(payloads_by_paper.items(), 1): + ids = [item[0] for item in items] + batch_payloads = [item[1] for item in items] + + print(f"\n[{paper_idx}/{total_papers}] paper_id={paper_id} — {len(batch_payloads)} questions") + + for batch_idx, batch in enumerate(chunked(batch_payloads, 3), 1): + print(f" Batch {batch_idx}: questions {[b['question_number'] for b in batch]}", end="", flush=True) + + if dry_run: + print(" [dry-run, skipped]") + continue + + batch_start = (batch_idx - 1) * 3 + batch_ids = ids[batch_start: batch_start + 3] + + async def run_single(row_id: str, payload: dict) -> bool: + try: + r = await qwen_json_completion( + system_prompt=BATCH_ANALYSIS_PROMPT.format( + questions_payload=json.dumps([payload], ensure_ascii=False), + ), + temperature=0.3, + max_tokens=8192, + ) + items = r.get("analyses", []) + if not items: + return False + analysis = items[0] + sb.table("paper_questions").update({ + "knowledge_reminder": analysis.get("knowledge_reminder", ""), + "ai_hint": analysis.get("ai_hint", ""), + "solution": analysis.get("solution", ""), + }).eq("id", row_id).execute() + return True + except Exception: + return False + + try: + result = await qwen_json_completion( + system_prompt=BATCH_ANALYSIS_PROMPT.format( + questions_payload=json.dumps(batch, ensure_ascii=False), + ), + temperature=0.3, + max_tokens=8192, + ) + analyses = {item["question_number"]: item for item in result.get("analyses", [])} + written = 0 + for row_id, payload in zip(batch_ids, batch): + qnum = payload["question_number"] + analysis = analyses.get(qnum) + if not analysis: + # fallback: retry this single question alone + ok = await run_single(row_id, payload) + if ok: + written += 1 + total_updated += 1 + else: + print(f"\n SKIP: {qnum}") + else: + sb.table("paper_questions").update({ + "knowledge_reminder": analysis.get("knowledge_reminder", ""), + "ai_hint": analysis.get("ai_hint", ""), + "solution": analysis.get("solution", ""), + }).eq("id", row_id).execute() + written += 1 + total_updated += 1 + print(f" → {written} written") + except Exception as exc: + # batch failed entirely — retry each question individually + print(f" [batch error, retrying 1-by-1]") + written = 0 + for row_id, payload in zip(batch_ids, batch): + ok = await run_single(row_id, payload) + if ok: + written += 1 + total_updated += 1 + await asyncio.sleep(1) + print(f" → {written}/{len(batch)} written") + + await asyncio.sleep(2.5) + + print(f"\nDone. {total_updated} questions updated.") + + +if __name__ == "__main__": + dry_run = "--dry-run" in sys.argv + asyncio.run(regen(dry_run=dry_run)) diff --git a/backend/regenerate_analysis.py b/backend/regenerate_analysis.py new file mode 100644 index 0000000..7ba3eaf --- /dev/null +++ b/backend/regenerate_analysis.py @@ -0,0 +1,69 @@ +"""Re-generate AI trio (knowledge_reminder, ai_hint, solution) in English for existing questions.""" + +import json +import asyncio +from app.services.supabase_client import get_supabase +from app.services.llm_clients import get_qwen_client +from app.services.paper_processor import ANALYSIS_PROMPT + + +async def regenerate_for_paper(paper_id: str): + sb = get_supabase() + qwen = get_qwen_client() + + questions = sb.table("paper_questions").select("*").eq("paper_id", paper_id).order("display_order").execute().data + print(f"Found {len(questions)} questions for paper {paper_id[:8]}") + + for q in questions: + qnum = q["question_number"] + print(f" Regenerating Q{qnum}...", end=" ", flush=True) + + answer_section = "" + if q.get("raw_answer_text"): + answer_section = f"- Reference answer: {q['raw_answer_text']}" + elif q.get("correct_option"): + answer_section = f"- Correct option: {q['correct_option']}" + elif q.get("correct_answer"): + answer_section = f"- Correct answer: {q['correct_answer']}" + + resp = qwen.chat.completions.create( + model="qwen-plus", + messages=[ + {"role": "system", "content": ANALYSIS_PROMPT.format( + question_number=qnum, + question_type=q["question_type"], + score=q.get("score", "unknown"), + question_text=q["question_text"], + topics=", ".join(q.get("topics", [])), + answer_section=answer_section, + )}, + ], + temperature=0.3, + response_format={"type": "json_object"}, + ) + analysis = json.loads(resp.choices[0].message.content) + + sb.table("paper_questions").update({ + "knowledge_reminder": analysis.get("knowledge_reminder", ""), + "ai_hint": analysis.get("ai_hint", ""), + "solution": analysis.get("solution", ""), + }).eq("id", q["id"]).execute() + + print("done") + + print(f"All questions regenerated for paper {paper_id[:8]}") + + +async def main(): + sb = get_supabase() + papers = sb.table("papers").select("id,course_code,year,term").eq("status", "ready").order("created_at", desc=True).execute().data + + for p in papers: + print(f"\n=== {p['course_code']} {p['year']} {p['term']} ===") + await regenerate_for_paper(p["id"]) + + print("\nAll done!") + + +if __name__ == "__main__": + asyncio.run(main()) diff --git a/backend/split_comp2211_2022_spring_final_part_a.py b/backend/split_comp2211_2022_spring_final_part_a.py new file mode 100644 index 0000000..93b3fef --- /dev/null +++ b/backend/split_comp2211_2022_spring_final_part_a.py @@ -0,0 +1,224 @@ +"""Split COMP2211 Spring 2022 final part A into subquestions.""" + +from __future__ import annotations + +import json +import re +from dataclasses import dataclass +from pathlib import Path + +from app.services.supabase_client import get_supabase + + +EXAM_KEY = "COMP2211-2022-spring-final-part-a" +TRUE_FALSE_OPTIONS = [{"label": "True", "text": "True"}, {"label": "False", "text": "False"}] +PROBLEM_SEED_PATH = ( + Path(__file__).resolve().parent.parent + / "pastpaper-scraper" + / "reviews" + / "COMP2211" + / "problem_seed.json" +) + + +@dataclass(frozen=True) +class ChildSpec: + question_number: str + parent_question: str + top_level_number: str + path: tuple[str, ...] + score: float + question_type: str + question_format: str | None = None + analytics_topic: str | None = None + topic_primary: str | None = None + topic_tags: tuple[str, ...] | None = None + skill_tags: tuple[str, ...] | None = None + page_number: int = 1 + + +def short_answer( + question_number: str, + parent_question: str, + top_level_number: str, + path: tuple[str, ...], + score: float, + *, + analytics_topic: str | None = None, + topic_primary: str | None = None, + topic_tags: tuple[str, ...] | None = None, + skill_tags: tuple[str, ...] | None = None, + page_number: int, +) -> ChildSpec: + return ChildSpec( + question_number=question_number, + parent_question=parent_question, + top_level_number=top_level_number, + path=path, + score=score, + question_type="long_question", + question_format="short_answer", + analytics_topic=analytics_topic, + topic_primary=topic_primary, + topic_tags=topic_tags, + skill_tags=skill_tags, + page_number=page_number, + ) + + +CHILDREN: list[ChildSpec] = [ + ChildSpec("1a", "1", "1", ("a",), 1, "true_false", "true_false", "KNN and Clustering", "KNN and Clustering", ("KNN and Clustering",), ("concept_check", "algorithm_property"), page_number=2), + ChildSpec("1b", "1", "1", ("b",), 1, "true_false", "true_false", "Perceptron and MLP", "Perceptron and MLP", ("Perceptron and MLP",), ("concept_check", "architecture_reasoning"), page_number=2), + ChildSpec("1c", "1", "1", ("c",), 1, "true_false", "true_false", "Perceptron and MLP", "Perceptron and MLP", ("Perceptron and MLP",), ("concept_check", "activation_selection"), page_number=2), + ChildSpec("1d", "1", "1", ("d",), 1, "true_false", "true_false", "Evaluation and Validation", "Evaluation and Validation", ("Evaluation and Validation",), ("concept_check", "metric_reasoning"), page_number=2), + ChildSpec("1e", "1", "1", ("e",), 1, "true_false", "true_false", "Vision and CNN", "Vision and CNN", ("Vision and CNN",), ("concept_check", "hardware_reasoning"), page_number=2), + ChildSpec("1f", "1", "1", ("f",), 1, "true_false", "true_false", "Vision and CNN", "Vision and CNN", ("Vision and CNN",), ("concept_check", "image_processing"), page_number=2), + ChildSpec("1g", "1", "1", ("g",), 1, "true_false", "true_false", "Vision and CNN", "Vision and CNN", ("Vision and CNN",), ("concept_check", "cnn_architecture"), page_number=2), + ChildSpec("1h", "1", "1", ("h",), 1, "true_false", "true_false", "Perceptron and MLP", "Perceptron and MLP", ("Perceptron and MLP",), ("concept_check", "regularization"), page_number=2), + ChildSpec("1i", "1", "1", ("i",), 1, "true_false", "true_false", "Search and Games", "Search and Games", ("Search and Games",), ("concept_check", "game_reasoning"), page_number=2), + ChildSpec("1j", "1", "1", ("j",), 1, "true_false", "true_false", "Search and Games", "Search and Games", ("Search and Games",), ("concept_check", "pruning_reasoning"), page_number=2), + ChildSpec("2a", "2", "2", ("a",), 6.5, "long_question", "long_answer", "Probabilistic Models", "Probabilistic Models", ("Probabilistic Models",), ("manual_computation", "probability_reasoning", "classification_decision"), page_number=4), + ChildSpec("2b", "2", "2", ("b",), 7.5, "long_question", "long_answer", "KNN and Clustering", "KNN and Clustering", ("KNN and Clustering",), ("distance_calculation", "algorithm_tracing", "classification_decision"), page_number=4), + short_answer("3a", "3", "3", ("a",), 3, analytics_topic="Evaluation and Validation", topic_primary="Evaluation and Validation", topic_tags=("Evaluation and Validation",), skill_tags=("concept_explanation", "metric_reasoning"), page_number=6), + short_answer("3b", "3", "3", ("b",), 2, analytics_topic="Perceptron and MLP", topic_primary="Perceptron and MLP", topic_tags=("Perceptron and MLP",), skill_tags=("concept_explanation", "activation_selection"), page_number=6), + short_answer("3c", "3", "3", ("c",), 2, analytics_topic="Perceptron and MLP", topic_primary="Perceptron and MLP", topic_tags=("Perceptron and MLP",), skill_tags=("architecture_reasoning", "output_layer_design"), page_number=6), + short_answer("3d", "3", "3", ("d",), 3, analytics_topic="Perceptron and MLP", topic_primary="Perceptron and MLP", topic_tags=("Perceptron and MLP",), skill_tags=("concept_explanation", "optimization_reasoning"), page_number=6), + short_answer("3e_i", "3e", "3", ("e", "i"), 1, analytics_topic="Perceptron and MLP", topic_primary="Perceptron and MLP", topic_tags=("Perceptron and MLP",), skill_tags=("optimization_reasoning",), page_number=6), + short_answer("3e_ii", "3e", "3", ("e", "ii"), 1, analytics_topic="Perceptron and MLP", topic_primary="Perceptron and MLP", topic_tags=("Perceptron and MLP",), skill_tags=("optimization_reasoning",), page_number=6), + short_answer("3f", "3", "3", ("f",), 2, analytics_topic="Perceptron and MLP", topic_primary="Perceptron and MLP", topic_tags=("Perceptron and MLP",), skill_tags=("regularization", "concept_explanation"), page_number=6), + ChildSpec("4a_i", "4a", "4", ("a", "i"), 2, "fill_blank", "fill_blank", page_number=7), + ChildSpec("4a_ii", "4a", "4", ("a", "ii"), 2, "long_question", "long_answer", page_number=7), + ChildSpec("4b_i", "4b", "4", ("b", "i"), 3, "fill_blank", "fill_blank", page_number=7), + ChildSpec("4b_ii", "4b", "4", ("b", "ii"), 4, "fill_blank", "fill_blank", page_number=7), + ChildSpec("4b_iii", "4b", "4", ("b", "iii"), 4, "long_question", "long_answer", page_number=7), +] + + +MARKER_RE = re.compile(r"(?m)^\(([a-z]+|[ivx]+)\)\s*") + + +def split_sections(text: str) -> tuple[str, dict[str, str]]: + matches = list(MARKER_RE.finditer(text)) + if not matches: + return text.strip(), {} + intro = text[: matches[0].start()].strip() + sections: dict[str, str] = {} + for idx, match in enumerate(matches): + marker = match.group(1) + end = matches[idx + 1].start() if idx + 1 < len(matches) else len(text) + sections[marker] = text[match.start() : end].strip() + return intro, sections + + +def extract_segment(text: str, path: tuple[str, ...]) -> str: + current = text.strip() + carried_intro: list[str] = [] + for depth, marker in enumerate(path): + intro, sections = split_sections(current) + if depth == 0 and intro: + carried_intro.append(intro) + current = sections.get(marker, current) + return "\n".join(part for part in [*carried_intro, current] if part).strip() + + +def extract_true_false_answers(answer_text: str) -> dict[str, str]: + answers: dict[str, str] = {} + matches = list(re.finditer(r"(?m)^\(([a-j])\)\s*\n?([TF])\b", answer_text)) + for match in matches: + answers[match.group(1)] = match.group(2) + return answers + + +def derive_correct_answer(answer_text: str) -> str | None: + if not answer_text: + return None + tail = answer_text.split("Answer:", 1)[1] if "Answer:" in answer_text else answer_text + lines = [line.strip() for line in tail.splitlines() if line.strip()] + if not lines: + return None + first = lines[0] + if first.lower().startswith("marking scheme"): + return None + if len(first) <= 240: + return first + return None + + +def load_seed_rows() -> dict[str, dict]: + data = json.loads(PROBLEM_SEED_PATH.read_text()) + return { + row["question_number"]: row + for row in data + if row["source_exam_key"] == EXAM_KEY + } + + +def main() -> None: + sb = get_supabase() + paper = sb.table("papers").select("id").eq("source_exam_key", EXAM_KEY).execute().data[0] + paper_id = paper["id"] + + current_rows = ( + sb.table("paper_questions") + .select("*") + .eq("paper_id", paper_id) + .order("display_order") + .execute() + .data + ) + existing_by_number = {row["question_number"]: row for row in current_rows} + parent_rows = load_seed_rows() + tf_answers = extract_true_false_answers(parent_rows["1"]["raw_answer_text"] or "") + + inserts = [] + for display_order, child in enumerate(CHILDREN, start=1): + parent = parent_rows[child.top_level_number] + existing = existing_by_number.get(child.question_number, {}) + question_text = extract_segment(parent["question_text"] or "", child.path) + raw_answer_text = extract_segment(parent["raw_answer_text"] or "", child.path) + + correct_option = None + correct_answer = None + options = None + if child.question_type == "true_false": + correct_option = tf_answers.get(child.path[0]) + options = TRUE_FALSE_OPTIONS + elif child.question_type == "fill_blank": + correct_answer = derive_correct_answer(raw_answer_text) + + inserts.append( + { + "paper_id": paper_id, + "question_number": child.question_number, + "parent_question": child.parent_question, + "display_order": display_order, + "question_type": child.question_type, + "question_format": child.question_format, + "question_text": question_text, + "score": child.score, + "page_number": child.page_number, + "page_y_ratio": existing.get("page_y_ratio"), + "options": options, + "correct_option": correct_option, + "correct_answer": correct_answer, + "raw_answer_text": raw_answer_text, + "topics": existing.get("topics") or (list(child.topic_tags) if child.topic_tags else parent.get("topics")), + "topic_primary": existing.get("topic_primary") or child.topic_primary or parent.get("topic_primary"), + "analytics_topic": existing.get("analytics_topic") or child.analytics_topic or parent.get("analytics_topic"), + "topic_tags": existing.get("topic_tags") or (list(child.topic_tags) if child.topic_tags else parent.get("topic_tags")), + "skill_tags": existing.get("skill_tags") or (list(child.skill_tags) if child.skill_tags else parent.get("skill_tags")), + "difficulty": existing.get("difficulty") or parent.get("difficulty"), + "knowledge_reminder": existing.get("knowledge_reminder", ""), + "ai_hint": existing.get("ai_hint", ""), + "solution": existing.get("solution", ""), + } + ) + + sb.table("paper_questions").delete().eq("paper_id", paper_id).execute() + sb.table("paper_questions").insert(inserts).execute() + sb.table("papers").update({"question_count": len(inserts), "status": "processing"}).eq("id", paper_id).execute() + print(f"Inserted {len(inserts)} rows for {EXAM_KEY}.") + + +if __name__ == "__main__": + main() diff --git a/backend/split_comp2211_2022_spring_final_part_b.py b/backend/split_comp2211_2022_spring_final_part_b.py new file mode 100644 index 0000000..be30ada --- /dev/null +++ b/backend/split_comp2211_2022_spring_final_part_b.py @@ -0,0 +1,232 @@ +"""Split COMP2211 Spring 2022 final part B into subquestions.""" + +from __future__ import annotations + +import json +import re +from dataclasses import dataclass +from pathlib import Path + +from app.services.supabase_client import get_supabase + + +EXAM_KEY = "COMP2211-2022-spring-final-part-b" +PROBLEM_SEED_PATH = ( + Path(__file__).resolve().parent.parent + / "pastpaper-scraper" + / "reviews" + / "COMP2211" + / "problem_seed.json" +) + + +@dataclass(frozen=True) +class ChildSpec: + question_number: str + parent_question: str + top_level_number: str + path: tuple[str, ...] + score: float + question_type: str + question_format: str | None = None + analytics_topic: str | None = None + topic_primary: str | None = None + topic_tags: tuple[str, ...] | None = None + skill_tags: tuple[str, ...] | None = None + options: tuple[tuple[str, str], ...] | None = None + correct_option: str | None = None + correct_answer: str | None = None + page_number: int = 1 + + +def short_answer( + question_number: str, + parent_question: str, + top_level_number: str, + path: tuple[str, ...], + score: float, + *, + analytics_topic: str | None = None, + topic_primary: str | None = None, + topic_tags: tuple[str, ...] | None = None, + skill_tags: tuple[str, ...] | None = None, + correct_answer: str | None = None, + page_number: int, +) -> ChildSpec: + return ChildSpec( + question_number=question_number, + parent_question=parent_question, + top_level_number=top_level_number, + path=path, + score=score, + question_type="long_question", + question_format="short_answer", + analytics_topic=analytics_topic, + topic_primary=topic_primary, + topic_tags=topic_tags, + skill_tags=skill_tags, + correct_answer=correct_answer, + page_number=page_number, + ) + + +def mc( + question_number: str, + parent_question: str, + top_level_number: str, + path: tuple[str, ...], + score: float, + *, + options: tuple[tuple[str, str], ...], + correct_option: str, + analytics_topic: str, + skill_tags: tuple[str, ...], + page_number: int, +) -> ChildSpec: + return ChildSpec( + question_number=question_number, + parent_question=parent_question, + top_level_number=top_level_number, + path=path, + score=score, + question_type="mc", + question_format="mc", + analytics_topic=analytics_topic, + topic_primary=analytics_topic, + topic_tags=(analytics_topic,), + skill_tags=skill_tags, + options=options, + correct_option=correct_option, + page_number=page_number, + ) + + +ETHICS_ABCD = ( + ("A", "A"), + ("B", "B"), + ("C", "C"), + ("D", "D"), +) + + +CHILDREN: list[ChildSpec] = [ + ChildSpec("1a", "1", "1", ("a",), 1.5, "long_question", "long_answer", page_number=2), + short_answer("1b", "1", "1", ("b",), 1.5, analytics_topic="Vision and CNN", topic_primary="Vision and CNN", topic_tags=("Vision and CNN",), skill_tags=("concept_explanation", "data_augmentation"), page_number=2), + ChildSpec("1c", "1", "1", ("c",), 4.5, "long_question", "long_answer", page_number=2), + short_answer("1d", "1", "1", ("d",), 2, analytics_topic="Vision and CNN", topic_primary="Vision and CNN", topic_tags=("Vision and CNN",), skill_tags=("architecture_reasoning", "parameter_reduction"), page_number=3), + ChildSpec("1e", "1", "1", ("e",), 2.5, "fill_blank", "fill_blank", correct_answer="1558656", page_number=3), + ChildSpec("1f_i", "1f", "1", ("f", "i"), 2.5, "fill_blank", "fill_blank", correct_answer="2071656", page_number=3), + ChildSpec("1f_ii", "1f", "1", ("f", "ii"), 2.5, "fill_blank", "fill_blank", correct_answer="150529000", page_number=4), + short_answer("1g", "1", "1", ("g",), 2, analytics_topic="Vision and CNN", topic_primary="Vision and CNN", topic_tags=("Vision and CNN",), skill_tags=("architecture_reasoning", "comparison"), page_number=4), + ChildSpec("2a", "2", "2", ("a",), 9, "long_question", "coding", page_number=5), + short_answer("2b", "2", "2", ("b",), 4, analytics_topic="Vision and CNN", topic_primary="Vision and CNN", topic_tags=("Vision and CNN",), skill_tags=("architecture_reasoning", "regression_reasoning"), page_number=6), + ChildSpec("3a", "3", "3", ("a",), 3.5, "long_question", "long_answer", page_number=9), + short_answer("3b", "3", "3", ("b",), 0.5, analytics_topic="Search and Games", topic_primary="Search and Games", topic_tags=("Search and Games",), skill_tags=("game_reasoning",), correct_answer="E-a", page_number=9), + short_answer("3c", "3", "3", ("c",), 1.5, analytics_topic="Search and Games", topic_primary="Search and Games", topic_tags=("Search and Games",), skill_tags=("concept_explanation", "game_reasoning"), page_number=9), + short_answer("3d", "3", "3", ("d",), 2.5, analytics_topic="Search and Games", topic_primary="Search and Games", topic_tags=("Search and Games",), skill_tags=("pruning_reasoning",), correct_answer="E-j and E-f", page_number=9), + mc("4a", "4", "4", ("a",), 1, options=ETHICS_ABCD, correct_option="C", analytics_topic="Ethics of AI", skill_tags=("concept_check", "ethical_reasoning"), page_number=10), + mc("4b", "4", "4", ("b",), 1, options=ETHICS_ABCD, correct_option="A", analytics_topic="Ethics of AI", skill_tags=("concept_check", "bias_reasoning"), page_number=10), + mc("4c", "4", "4", ("c",), 1, options=ETHICS_ABCD, correct_option="C", analytics_topic="Ethics of AI", skill_tags=("concept_check", "ethical_reasoning"), page_number=10), + mc("4d", "4", "4", ("d",), 1, options=ETHICS_ABCD, correct_option="B", analytics_topic="Ethics of AI", skill_tags=("concept_check", "bias_reasoning"), page_number=10), + short_answer("4e", "4", "4", ("e",), 3, analytics_topic="Ethics of AI", topic_primary="Ethics of AI", topic_tags=("Ethics of AI",), skill_tags=("argumentation", "concept_explanation"), page_number=11), +] + + +MARKER_RE = re.compile(r"(?m)^\(([a-z]+|[ivx]+)\)\s*") + + +def split_sections(text: str) -> tuple[str, dict[str, str]]: + matches = list(MARKER_RE.finditer(text)) + if not matches: + return text.strip(), {} + intro = text[: matches[0].start()].strip() + sections: dict[str, str] = {} + for idx, match in enumerate(matches): + marker = match.group(1) + end = matches[idx + 1].start() if idx + 1 < len(matches) else len(text) + sections[marker] = text[match.start() : end].strip() + return intro, sections + + +def extract_segment(text: str, path: tuple[str, ...]) -> str: + current = text.strip() + carried_intro: list[str] = [] + for depth, marker in enumerate(path): + intro, sections = split_sections(current) + if depth == 0 and intro: + carried_intro.append(intro) + current = sections.get(marker, current) + return "\n".join(part for part in [*carried_intro, current] if part).strip() + + +def load_seed_rows() -> dict[str, dict]: + data = json.loads(PROBLEM_SEED_PATH.read_text()) + return { + row["question_number"]: row + for row in data + if row["source_exam_key"] == EXAM_KEY + } + + +def main() -> None: + sb = get_supabase() + paper = sb.table("papers").select("id").eq("source_exam_key", EXAM_KEY).execute().data[0] + paper_id = paper["id"] + + current_rows = ( + sb.table("paper_questions") + .select("*") + .eq("paper_id", paper_id) + .order("display_order") + .execute() + .data + ) + existing_by_number = {row["question_number"]: row for row in current_rows} + parent_rows = load_seed_rows() + + inserts = [] + for display_order, child in enumerate(CHILDREN, start=1): + parent = parent_rows[child.top_level_number] + existing = existing_by_number.get(child.question_number, {}) + question_text = extract_segment(parent["question_text"] or "", child.path) + raw_answer_text = extract_segment(parent["raw_answer_text"] or "", child.path) + options = None + if child.options: + options = [{"label": label, "text": text} for label, text in child.options] + + inserts.append( + { + "paper_id": paper_id, + "question_number": child.question_number, + "parent_question": child.parent_question, + "display_order": display_order, + "question_type": child.question_type, + "question_format": child.question_format, + "question_text": question_text, + "score": child.score, + "page_number": child.page_number, + "page_y_ratio": existing.get("page_y_ratio"), + "options": options, + "correct_option": child.correct_option, + "correct_answer": child.correct_answer, + "raw_answer_text": raw_answer_text, + "topics": existing.get("topics") or (list(child.topic_tags) if child.topic_tags else parent.get("topics")), + "topic_primary": existing.get("topic_primary") or child.topic_primary or parent.get("topic_primary"), + "analytics_topic": existing.get("analytics_topic") or child.analytics_topic or parent.get("analytics_topic"), + "topic_tags": existing.get("topic_tags") or (list(child.topic_tags) if child.topic_tags else parent.get("topic_tags")), + "skill_tags": existing.get("skill_tags") or (list(child.skill_tags) if child.skill_tags else parent.get("skill_tags")), + "difficulty": existing.get("difficulty") or parent.get("difficulty"), + "knowledge_reminder": existing.get("knowledge_reminder", ""), + "ai_hint": existing.get("ai_hint", ""), + "solution": existing.get("solution", ""), + } + ) + + sb.table("paper_questions").delete().eq("paper_id", paper_id).execute() + sb.table("paper_questions").insert(inserts).execute() + sb.table("papers").update({"question_count": len(inserts), "status": "processing"}).eq("id", paper_id).execute() + print(f"Inserted {len(inserts)} rows for {EXAM_KEY}.") + + +if __name__ == "__main__": + main() diff --git a/backend/split_comp2211_2022_spring_midterm.py b/backend/split_comp2211_2022_spring_midterm.py new file mode 100644 index 0000000..2372c8b --- /dev/null +++ b/backend/split_comp2211_2022_spring_midterm.py @@ -0,0 +1,233 @@ +"""Split COMP2211 Spring 2022 midterm top-level problems into subquestions.""" + +from __future__ import annotations + +import json +import re +from dataclasses import dataclass +from pathlib import Path + +from app.services.supabase_client import get_supabase + + +EXAM_KEY = "COMP2211-2022-spring-midterm" +TRUE_FALSE_OPTIONS = [{"label": "True", "text": "True"}, {"label": "False", "text": "False"}] + + +@dataclass(frozen=True) +class ChildSpec: + question_number: str + parent_question: str + top_level_number: str + path: tuple[str, ...] + score: float + question_type: str + question_format: str | None = None + page_number: int = 1 + + +def short_answer( + question_number: str, + parent_question: str, + top_level_number: str, + path: tuple[str, ...], + score: float, + *, + page_number: int, +) -> ChildSpec: + return ChildSpec( + question_number=question_number, + parent_question=parent_question, + top_level_number=top_level_number, + path=path, + score=score, + question_type="long_question", + question_format="short_answer", + page_number=page_number, + ) + + +CHILDREN: list[ChildSpec] = [ + *[ + ChildSpec(f"1{letter}", "1", "1", (letter,), 1.5, "true_false", page_number=2) + for letter in "abcdefghij" + ], + ChildSpec("2a_i", "2a", "2", ("a", "i"), 1, "fill_blank", page_number=4), + ChildSpec("2a_ii", "2a", "2", ("a", "ii"), 1, "fill_blank", page_number=4), + ChildSpec("2a_iii", "2a", "2", ("a", "iii"), 1, "fill_blank", page_number=4), + ChildSpec("2a_iv", "2a", "2", ("a", "iv"), 1, "fill_blank", page_number=4), + ChildSpec("2a_v", "2a", "2", ("a", "v"), 1, "fill_blank", page_number=4), + ChildSpec("2b", "2", "2", ("b",), 2, "fill_blank", page_number=4), + ChildSpec("2c", "2", "2", ("c",), 9, "long_question", "coding", page_number=5), + ChildSpec("3a", "3", "3", ("a",), 2, "fill_blank", page_number=7), + ChildSpec("3b_i", "3b", "3", ("b", "i"), 1.75, "fill_blank", page_number=7), + ChildSpec("3b_ii", "3b", "3", ("b", "ii"), 1.75, "fill_blank", page_number=7), + ChildSpec("3b_iii", "3b", "3", ("b", "iii"), 1.75, "fill_blank", page_number=7), + ChildSpec("3b_iv", "3b", "3", ("b", "iv"), 1.75, "fill_blank", page_number=7), + short_answer("3c", "3", "3", ("c",), 2, page_number=8), + ChildSpec("4a", "4", "4", ("a",), 3, "long_question", "long_answer", page_number=9), + short_answer("4b_i", "4b", "4", ("b", "i"), 3, page_number=9), + short_answer("4b_ii", "4b", "4", ("b", "ii"), 3, page_number=9), + ChildSpec("4c_i", "4c", "4", ("c", "i"), 2, "long_question", "long_answer", page_number=10), + ChildSpec("4c_ii", "4c", "4", ("c", "ii"), 3, "long_question", "long_answer", page_number=10), + ChildSpec("5a", "5", "5", ("a",), 4.5, "long_question", "long_answer", page_number=11), + ChildSpec("5b", "5", "5", ("b",), 1.5, "fill_blank", page_number=11), + ChildSpec("5c", "5", "5", ("c",), 4.5, "long_question", "long_answer", page_number=11), + short_answer("5d", "5", "5", ("d",), 1.5, page_number=11), + ChildSpec("6a", "6", "6", ("a",), 8, "long_question", "long_answer", page_number=12), + short_answer("6b", "6", "6", ("b",), 2, page_number=13), + ChildSpec("6c", "6", "6", ("c",), 10, "long_question", "coding", page_number=13), + short_answer("7a", "7", "7", ("a",), 4, page_number=14), + short_answer("7b", "7", "7", ("b",), 6, page_number=14), + ChildSpec("7c", "7", "7", ("c",), 2, "fill_blank", page_number=15), +] + + +MARKER_RE = re.compile(r"(?m)^\(([a-z]+)\)\s*") +PROBLEM_SEED_PATH = ( + Path(__file__).resolve().parent.parent + / "pastpaper-scraper" + / "reviews" + / "COMP2211" + / "problem_seed.json" +) + + +def split_sections(text: str) -> tuple[str, dict[str, str]]: + matches = list(MARKER_RE.finditer(text)) + if not matches: + return text.strip(), {} + intro = text[: matches[0].start()].strip() + sections: dict[str, str] = {} + for idx, match in enumerate(matches): + marker = match.group(1) + end = matches[idx + 1].start() if idx + 1 < len(matches) else len(text) + sections[marker] = text[match.start() : end].strip() + return intro, sections + + +def extract_segment(text: str, path: tuple[str, ...]) -> str: + intro, sections = split_sections(text) + if not path: + return text.strip() + first = sections.get(path[0], "") + if not first: + return text.strip() + if len(path) == 1: + return "\n".join(part for part in [intro, first] if part).strip() + child_intro, child_sections = split_sections(first) + second = child_sections.get(path[1], "") + return "\n".join(part for part in [intro, child_intro, second] if part).strip() + + +def extract_true_false_answers(answer_text: str) -> dict[str, str]: + answers: dict[str, str] = {} + matches = list(re.finditer(r"(?m)^\(([a-j])\)\s*\n?([TF])\b", answer_text)) + for match in matches: + answers[match.group(1)] = match.group(2) + return answers + + +def derive_correct_answer(answer_text: str) -> str | None: + if not answer_text: + return None + if "Answer:" in answer_text: + tail = answer_text.split("Answer:", 1)[1] + else: + tail = answer_text + lines = [line.strip() for line in tail.splitlines() if line.strip()] + if not lines: + return None + first = lines[0] + if first.lower().startswith("marking scheme"): + return None + if len(first) <= 240: + return first + return None + + +def load_seed_rows() -> dict[str, dict]: + data = json.loads(PROBLEM_SEED_PATH.read_text()) + return { + row["question_number"]: row + for row in data + if row["source_exam_key"] == EXAM_KEY + } + + +def main() -> None: + sb = get_supabase() + paper = ( + sb.table("papers") + .select("id") + .eq("source_exam_key", EXAM_KEY) + .execute() + .data[0] + ) + paper_id = paper["id"] + + current_rows = ( + sb.table("paper_questions") + .select("*") + .eq("paper_id", paper_id) + .order("display_order") + .execute() + .data + ) + existing_by_number = {row["question_number"]: row for row in current_rows} + parent_rows = load_seed_rows() + tf_answers = extract_true_false_answers(parent_rows["1"]["raw_answer_text"] or "") + + inserts = [] + for display_order, child in enumerate(CHILDREN, start=1): + parent = parent_rows[child.top_level_number] + existing = existing_by_number.get(child.question_number, {}) + question_text = extract_segment(parent["question_text"] or "", child.path) + raw_answer_text = extract_segment(parent["raw_answer_text"] or "", child.path) + + correct_option = None + correct_answer = None + options = None + if child.question_type == "true_false": + marker = child.path[0] + correct_option = tf_answers.get(marker) + options = TRUE_FALSE_OPTIONS + elif child.question_type == "fill_blank": + correct_answer = derive_correct_answer(raw_answer_text) + + inserts.append( + { + "paper_id": paper_id, + "question_number": child.question_number, + "parent_question": child.parent_question, + "display_order": display_order, + "question_type": child.question_type, + "question_format": child.question_format, + "question_text": question_text, + "score": child.score, + "page_number": child.page_number, + "page_y_ratio": existing.get("page_y_ratio"), + "options": options, + "correct_option": correct_option, + "correct_answer": correct_answer, + "raw_answer_text": raw_answer_text, + "topics": existing.get("topics") or parent.get("topics"), + "topic_primary": existing.get("topic_primary") or parent.get("topic_primary"), + "analytics_topic": existing.get("analytics_topic") or parent.get("analytics_topic"), + "topic_tags": existing.get("topic_tags") or parent.get("topic_tags"), + "skill_tags": existing.get("skill_tags") or parent.get("skill_tags"), + "difficulty": existing.get("difficulty") or parent.get("difficulty"), + "knowledge_reminder": existing.get("knowledge_reminder", ""), + "ai_hint": existing.get("ai_hint", ""), + "solution": existing.get("solution", ""), + } + ) + + sb.table("paper_questions").delete().eq("paper_id", paper_id).execute() + sb.table("paper_questions").insert(inserts).execute() + sb.table("papers").update({"question_count": len(inserts), "status": "processing"}).eq("id", paper_id).execute() + print(f"Inserted {len(inserts)} rows for {EXAM_KEY}.") + + +if __name__ == "__main__": + main() diff --git a/backend/split_comp2211_2023_spring_midterm.py b/backend/split_comp2211_2023_spring_midterm.py new file mode 100644 index 0000000..4e13c29 --- /dev/null +++ b/backend/split_comp2211_2023_spring_midterm.py @@ -0,0 +1,268 @@ +"""Split COMP2211 Spring 2023 midterm into subquestions.""" + +from __future__ import annotations + +import json +import re +from dataclasses import dataclass +from pathlib import Path + +from app.services.supabase_client import get_supabase + + +EXAM_KEY = "COMP2211-2023-spring-midterm" +PROBLEM_SEED_PATH = ( + Path(__file__).resolve().parent.parent + / "pastpaper-scraper" + / "reviews" + / "COMP2211" + / "problem_seed.json" +) +TRUE_FALSE_OPTIONS = [{"label": "True", "text": "True"}, {"label": "False", "text": "False"}] + + +@dataclass(frozen=True) +class ChildSpec: + question_number: str + parent_question: str + top_level_number: str + path: tuple[str, ...] + score: float + question_type: str + question_format: str | None = None + analytics_topic: str | None = None + topic_primary: str | None = None + topic_tags: tuple[str, ...] | None = None + skill_tags: tuple[str, ...] | None = None + options: tuple[tuple[str, str], ...] | None = None + correct_option: str | None = None + correct_answer: str | None = None + page_number: int = 1 + + +def short_answer( + question_number: str, + parent_question: str, + top_level_number: str, + path: tuple[str, ...], + score: float, + *, + analytics_topic: str | None = None, + topic_primary: str | None = None, + topic_tags: tuple[str, ...] | None = None, + skill_tags: tuple[str, ...] | None = None, + correct_answer: str | None = None, + page_number: int, +) -> ChildSpec: + return ChildSpec( + question_number=question_number, + parent_question=parent_question, + top_level_number=top_level_number, + path=path, + score=score, + question_type="long_question", + question_format="short_answer", + analytics_topic=analytics_topic, + topic_primary=topic_primary, + topic_tags=topic_tags, + skill_tags=skill_tags, + correct_answer=correct_answer, + page_number=page_number, + ) + + +def mc( + question_number: str, + parent_question: str, + top_level_number: str, + path: tuple[str, ...], + score: float, + *, + options: tuple[tuple[str, str], ...], + correct_option: str, + analytics_topic: str, + skill_tags: tuple[str, ...], + page_number: int, +) -> ChildSpec: + return ChildSpec( + question_number=question_number, + parent_question=parent_question, + top_level_number=top_level_number, + path=path, + score=score, + question_type="mc", + question_format="mc", + analytics_topic=analytics_topic, + topic_primary=analytics_topic, + topic_tags=(analytics_topic,), + skill_tags=skill_tags, + options=options, + correct_option=correct_option, + page_number=page_number, + ) + + +ABCDE = (("A", "A"), ("B", "B"), ("C", "C"), ("D", "D"), ("E", "E")) + + +CHILDREN: list[ChildSpec] = [ + ChildSpec("1a", "1", "1", ("a",), 1, "true_false", "true_false", "Probabilistic Models", "Probabilistic Models", ("Probabilistic Models",), ("concept_check", "classification_decision"), page_number=3), + ChildSpec("1b", "1", "1", ("b",), 1, "true_false", "true_false", "Probabilistic Models", "Probabilistic Models", ("Probabilistic Models",), ("concept_check", "classification_decision"), page_number=3), + ChildSpec("1c", "1", "1", ("c",), 1, "true_false", "true_false", "KNN and Clustering", "KNN and Clustering", ("KNN and Clustering",), ("concept_check", "algorithm_property"), page_number=3), + ChildSpec("1d", "1", "1", ("d",), 1, "true_false", "true_false", "KNN and Clustering", "KNN and Clustering", ("KNN and Clustering",), ("concept_check", "distance_reasoning"), page_number=3), + ChildSpec("1e", "1", "1", ("e",), 1, "true_false", "true_false", "Evaluation and Validation", "Evaluation and Validation", ("Evaluation and Validation",), ("concept_check", "validation_reasoning"), page_number=3), + ChildSpec("1f", "1", "1", ("f",), 1, "true_false", "true_false", "KNN and Clustering", "KNN and Clustering", ("KNN and Clustering",), ("concept_check", "algorithm_property"), page_number=3), + ChildSpec("1g", "1", "1", ("g",), 1, "true_false", "true_false", "KNN and Clustering", "KNN and Clustering", ("KNN and Clustering",), ("concept_check", "robustness_reasoning"), page_number=3), + ChildSpec("1h", "1", "1", ("h",), 1, "true_false", "true_false", "Perceptron and MLP", "Perceptron and MLP", ("Perceptron and MLP",), ("concept_check", "decision_boundary"), page_number=3), + ChildSpec("1i", "1", "1", ("i",), 1, "true_false", "true_false", "Perceptron and MLP", "Perceptron and MLP", ("Perceptron and MLP",), ("concept_check", "optimization_reasoning"), page_number=3), + ChildSpec("1j", "1", "1", ("j",), 1, "true_false", "true_false", "Perceptron and MLP", "Perceptron and MLP", ("Perceptron and MLP",), ("concept_check", "expressiveness_reasoning"), page_number=3), + short_answer("2a_i", "2a", "2", ("a", "i"), 1, analytics_topic="Python Fundamentals", topic_primary="Python Fundamentals", topic_tags=("Python Fundamentals",), skill_tags=("code_tracing",), page_number=4), + short_answer("2a_ii", "2a", "2", ("a", "ii"), 1, analytics_topic="Python Fundamentals", topic_primary="Python Fundamentals", topic_tags=("Python Fundamentals",), skill_tags=("code_tracing",), page_number=4), + short_answer("2a_iii", "2a", "2", ("a", "iii"), 1, analytics_topic="Python Fundamentals", topic_primary="Python Fundamentals", topic_tags=("Python Fundamentals",), skill_tags=("code_tracing",), page_number=4), + short_answer("2a_iv", "2a", "2", ("a", "iv"), 1, analytics_topic="Python Fundamentals", topic_primary="Python Fundamentals", topic_tags=("Python Fundamentals",), skill_tags=("code_tracing",), page_number=4), + short_answer("2a_v", "2a", "2", ("a", "v"), 1, analytics_topic="Python Fundamentals", topic_primary="Python Fundamentals", topic_tags=("Python Fundamentals",), skill_tags=("indexing", "code_tracing"), page_number=4), + short_answer("2a_vi", "2a", "2", ("a", "vi"), 1, analytics_topic="Python Fundamentals", topic_primary="Python Fundamentals", topic_tags=("Python Fundamentals",), skill_tags=("indexing", "error_reasoning"), page_number=5), + short_answer("2a_vii", "2a", "2", ("a", "vii"), 1, analytics_topic="Python Fundamentals", topic_primary="Python Fundamentals", topic_tags=("Python Fundamentals",), skill_tags=("masking", "code_tracing"), page_number=5), + short_answer("2a_viii", "2a", "2", ("a", "viii"), 1, analytics_topic="Python Fundamentals", topic_primary="Python Fundamentals", topic_tags=("Python Fundamentals",), skill_tags=("aggregation", "code_tracing"), page_number=5), + short_answer("2a_ix", "2a", "2", ("a", "ix"), 1, analytics_topic="Python Fundamentals", topic_primary="Python Fundamentals", topic_tags=("Python Fundamentals",), skill_tags=("transpose", "code_tracing"), page_number=5), + short_answer("2b_i", "2b", "2", ("b", "i"), 2, analytics_topic="Python Fundamentals", topic_primary="Python Fundamentals", topic_tags=("Python Fundamentals",), skill_tags=("broadcasting", "code_tracing"), page_number=6), + short_answer("2b_ii", "2b", "2", ("b", "ii"), 2, analytics_topic="Python Fundamentals", topic_primary="Python Fundamentals", topic_tags=("Python Fundamentals",), skill_tags=("broadcasting", "error_reasoning"), page_number=6), + short_answer("2b_iii", "2b", "2", ("b", "iii"), 2, analytics_topic="Python Fundamentals", topic_primary="Python Fundamentals", topic_tags=("Python Fundamentals",), skill_tags=("broadcasting", "code_tracing"), page_number=6), + ChildSpec("2c", "2", "2", ("c",), 6, "long_question", "coding", "Python Fundamentals", "Python Fundamentals", ("Python Fundamentals",), ("implementation", "vectorization", "geometry_reasoning"), page_number=7), + short_answer("3", "3", "3", (), 8, analytics_topic="Probabilistic Models", topic_primary="Probabilistic Models", topic_tags=("Probabilistic Models",), skill_tags=("concept_explanation", "missing_data_reasoning"), page_number=9), + ChildSpec("4a", "4", "4", ("a",), 8, "long_question", "long_answer", "KNN and Clustering", "KNN and Clustering", ("KNN and Clustering",), ("distance_calculation", "classification_decision"), page_number=10), + short_answer("4b", "4", "4", ("b",), 6, analytics_topic="KNN and Clustering", topic_primary="KNN and Clustering", topic_tags=("KNN and Clustering",), skill_tags=("distance_reasoning", "comparison"), page_number=11), + ChildSpec("5a", "5", "5", ("a",), 7, "long_question", "long_answer", "KNN and Clustering", "KNN and Clustering", ("KNN and Clustering",), ("distance_calculation", "algorithm_tracing"), page_number=12), + ChildSpec("5b", "5", "5", ("b",), 7, "long_question", "long_answer", "KNN and Clustering", "KNN and Clustering", ("KNN and Clustering",), ("centroid_update", "algorithm_tracing"), page_number=12), + short_answer("5c", "5", "5", ("c",), 5, analytics_topic="KNN and Clustering", topic_primary="KNN and Clustering", topic_tags=("KNN and Clustering",), skill_tags=("concept_explanation", "model_selection"), page_number=14), + short_answer("6a", "6", "6", ("a",), 2, analytics_topic="Perceptron and MLP", topic_primary="Perceptron and MLP", topic_tags=("Perceptron and MLP",), skill_tags=("convergence_reasoning",), page_number=15), + mc("6b", "6", "6", ("b",), 2, options=ABCDE, correct_option="D", analytics_topic="Perceptron and MLP", skill_tags=("generalization_reasoning",), page_number=15), + short_answer("6c", "6", "6", ("c",), 2, analytics_topic="Perceptron and MLP", topic_primary="Perceptron and MLP", topic_tags=("Perceptron and MLP",), skill_tags=("activation_reasoning",), page_number=16), + ChildSpec("6d", "6", "6", ("d",), 6, "long_question", "coding", "Perceptron and MLP", "Perceptron and MLP", ("Perceptron and MLP",), ("debugging", "implementation", "weight_update"), page_number=16), + short_answer("7a", "7", "7", ("a",), 4, analytics_topic="Perceptron and MLP", topic_primary="Perceptron and MLP", topic_tags=("Perceptron and MLP",), skill_tags=("decision_boundary", "linearity_reasoning"), page_number=18), + short_answer("7b", "7", "7", ("b",), 2, analytics_topic="Perceptron and MLP", topic_primary="Perceptron and MLP", topic_tags=("Perceptron and MLP",), skill_tags=("decision_boundary", "linearity_reasoning"), page_number=18), + ChildSpec("7c", "7", "7", ("c",), 10, "long_question", "long_answer", "Perceptron and MLP", "Perceptron and MLP", ("Perceptron and MLP",), ("architecture_reasoning", "parameter_design"), page_number=19), +] + + +MARKER_RE = re.compile(r"(?m)^\(([a-z]+|[ivx]+)\)\s*") + + +def split_sections(text: str) -> tuple[str, dict[str, str]]: + matches = list(MARKER_RE.finditer(text)) + if not matches: + return text.strip(), {} + intro = text[: matches[0].start()].strip() + sections: dict[str, str] = {} + for idx, match in enumerate(matches): + marker = match.group(1) + end = matches[idx + 1].start() if idx + 1 < len(matches) else len(text) + sections[marker] = text[match.start() : end].strip() + return intro, sections + + +def extract_segment(text: str, path: tuple[str, ...]) -> str: + current = text.strip() + carried_intro: list[str] = [] + for depth, marker in enumerate(path): + intro, sections = split_sections(current) + if depth == 0 and intro: + carried_intro.append(intro) + current = sections.get(marker, current) + return "\n".join(part for part in [*carried_intro, current] if part).strip() + + +def extract_true_false_answers(answer_text: str) -> dict[str, str]: + answers: dict[str, str] = {} + matches = list(re.finditer(r"(?m)^\(([a-j])\)\s*\n?T\s*F", answer_text)) + if matches: + return answers + for match in re.finditer(r"(?m)^\(([a-j])\)\s*\n?([TF])\b", answer_text): + answers[match.group(1)] = match.group(2) + if answers: + return answers + lines = [line.strip() for line in answer_text.splitlines() if line.strip()] + current = None + for line in lines: + m = re.fullmatch(r"\(([a-j])\)", line) + if m: + current = m.group(1) + continue + if current and line in {"T", "F"}: + answers[current] = line + current = None + return answers + + +def load_seed_rows() -> dict[str, dict]: + data = json.loads(PROBLEM_SEED_PATH.read_text()) + return {row["question_number"]: row for row in data if row["source_exam_key"] == EXAM_KEY} + + +def main() -> None: + sb = get_supabase() + paper = sb.table("papers").select("id").eq("source_exam_key", EXAM_KEY).execute().data[0] + paper_id = paper["id"] + current_rows = ( + sb.table("paper_questions") + .select("*") + .eq("paper_id", paper_id) + .order("display_order") + .execute() + .data + ) + existing_by_number = {row["question_number"]: row for row in current_rows} + parent_rows = load_seed_rows() + tf_answers = extract_true_false_answers(parent_rows["1"]["raw_answer_text"] or "") + + inserts = [] + for display_order, child in enumerate(CHILDREN, start=1): + parent = parent_rows[child.top_level_number] + existing = existing_by_number.get(child.question_number, {}) + question_text = extract_segment(parent["question_text"] or "", child.path) + raw_answer_text = extract_segment(parent["raw_answer_text"] or "", child.path) if child.path else (parent["raw_answer_text"] or "") + + options = None + correct_option = child.correct_option + if child.options: + options = [{"label": label, "text": text} for label, text in child.options] + if child.question_type == "true_false": + options = TRUE_FALSE_OPTIONS + correct_option = tf_answers.get(child.path[0]) + + inserts.append( + { + "paper_id": paper_id, + "question_number": child.question_number, + "parent_question": child.parent_question, + "display_order": display_order, + "question_type": child.question_type, + "question_format": child.question_format, + "question_text": question_text, + "score": child.score, + "page_number": child.page_number, + "page_y_ratio": existing.get("page_y_ratio"), + "options": options, + "correct_option": correct_option, + "correct_answer": child.correct_answer, + "raw_answer_text": raw_answer_text, + "topics": existing.get("topics") or (list(child.topic_tags) if child.topic_tags else parent.get("topics")), + "topic_primary": existing.get("topic_primary") or child.topic_primary or parent.get("topic_primary"), + "analytics_topic": existing.get("analytics_topic") or child.analytics_topic or parent.get("analytics_topic"), + "topic_tags": existing.get("topic_tags") or (list(child.topic_tags) if child.topic_tags else parent.get("topic_tags")), + "skill_tags": existing.get("skill_tags") or (list(child.skill_tags) if child.skill_tags else parent.get("skill_tags")), + "difficulty": existing.get("difficulty") or parent.get("difficulty"), + "knowledge_reminder": existing.get("knowledge_reminder", ""), + "ai_hint": existing.get("ai_hint", ""), + "solution": existing.get("solution", ""), + } + ) + + sb.table("paper_questions").delete().eq("paper_id", paper_id).execute() + sb.table("paper_questions").insert(inserts).execute() + sb.table("papers").update({"question_count": len(inserts), "status": "processing"}).eq("id", paper_id).execute() + print(f"Inserted {len(inserts)} rows for {EXAM_KEY}.") + + +if __name__ == "__main__": + main() diff --git a/backend/split_comp2211_2024_spring_final.py b/backend/split_comp2211_2024_spring_final.py new file mode 100644 index 0000000..803b198 --- /dev/null +++ b/backend/split_comp2211_2024_spring_final.py @@ -0,0 +1,242 @@ +"""Split COMP2211 Spring 2024 final into subquestions.""" + +from __future__ import annotations + +import json +import re +from dataclasses import dataclass +from pathlib import Path + +from app.services.supabase_client import get_supabase + + +EXAM_KEY = "COMP2211-2024-spring-final" +PROBLEM_SEED_PATH = ( + Path(__file__).resolve().parent.parent + / "pastpaper-scraper" + / "reviews" + / "COMP2211" + / "problem_seed.json" +) +TRUE_FALSE_OPTIONS = [{"label": "True", "text": "True"}, {"label": "False", "text": "False"}] + + +@dataclass(frozen=True) +class ChildSpec: + question_number: str + parent_question: str + top_level_number: str + path: tuple[str, ...] + score: float + question_type: str + question_format: str | None = None + analytics_topic: str | None = None + topic_primary: str | None = None + topic_tags: tuple[str, ...] | None = None + skill_tags: tuple[str, ...] | None = None + options: tuple[tuple[str, str], ...] | None = None + correct_option: str | None = None + correct_answer: str | None = None + page_number: int = 1 + + +def short_answer( + question_number: str, + parent_question: str, + top_level_number: str, + path: tuple[str, ...], + score: float, + *, + analytics_topic: str | None = None, + topic_primary: str | None = None, + topic_tags: tuple[str, ...] | None = None, + skill_tags: tuple[str, ...] | None = None, + correct_answer: str | None = None, + page_number: int, +) -> ChildSpec: + return ChildSpec( + question_number=question_number, + parent_question=parent_question, + top_level_number=top_level_number, + path=path, + score=score, + question_type="long_question", + question_format="short_answer", + analytics_topic=analytics_topic, + topic_primary=topic_primary, + topic_tags=topic_tags, + skill_tags=skill_tags, + correct_answer=correct_answer, + page_number=page_number, + ) + + +CHILDREN: list[ChildSpec] = [ + ChildSpec("1a", "1", "1", ("a",), 1, "true_false", "true_false", "Python Fundamentals", "Python Fundamentals", ("Python Fundamentals",), ("concept_check", "code_tracing"), page_number=2), + ChildSpec("1b", "1", "1", ("b",), 1, "true_false", "true_false", "Probabilistic Models", "Probabilistic Models", ("Probabilistic Models",), ("concept_check", "classification_decision"), page_number=2), + ChildSpec("1c", "1", "1", ("c",), 1, "true_false", "true_false", "KNN and Clustering", "KNN and Clustering", ("KNN and Clustering",), ("concept_check", "algorithm_property"), page_number=2), + ChildSpec("1d", "1", "1", ("d",), 1, "true_false", "true_false", "KNN and Clustering", "KNN and Clustering", ("KNN and Clustering",), ("concept_check", "algorithm_property"), page_number=2), + ChildSpec("1e", "1", "1", ("e",), 1, "true_false", "true_false", "Perceptron and MLP", "Perceptron and MLP", ("Perceptron and MLP",), ("concept_check", "activation_reasoning"), page_number=2), + ChildSpec("1f", "1", "1", ("f",), 1, "true_false", "true_false", "Vision and CNN", "Vision and CNN", ("Vision and CNN",), ("concept_check", "image_processing"), page_number=2), + ChildSpec("1g", "1", "1", ("g",), 1, "true_false", "true_false", "Vision and CNN", "Vision and CNN", ("Vision and CNN",), ("concept_check", "cnn_complexity"), page_number=2), + ChildSpec("1h", "1", "1", ("h",), 1, "true_false", "true_false", "Vision and CNN", "Vision and CNN", ("Vision and CNN",), ("concept_check", "regularization"), page_number=2), + ChildSpec("1i", "1", "1", ("i",), 1, "true_false", "true_false", "Search and Games", "Search and Games", ("Search and Games",), ("concept_check", "pruning_reasoning"), page_number=2), + ChildSpec("1j", "1", "1", ("j",), 1, "true_false", "true_false", "Ethics of AI", "Ethics of AI", ("Ethics of AI",), ("concept_check", "research_ethics"), page_number=2), + ChildSpec("2a", "2", "2", ("a",), 4, "long_question", "coding", "Python Fundamentals", "Python Fundamentals", ("Python Fundamentals",), ("implementation", "vectorization", "masking"), page_number=3), + ChildSpec("2b", "2", "2", ("b",), 6, "long_question", "coding", "Python Fundamentals", "Python Fundamentals", ("Python Fundamentals",), ("implementation", "convolution", "array_manipulation"), page_number=4), + short_answer("3a_i", "3a", "3", ("a", "i"), 1.5, analytics_topic="Probabilistic Models", topic_primary="Probabilistic Models", topic_tags=("Probabilistic Models",), skill_tags=("manual_computation", "probability_reasoning"), page_number=6), + short_answer("3a_ii", "3a", "3", ("a", "ii"), 1.5, analytics_topic="Probabilistic Models", topic_primary="Probabilistic Models", topic_tags=("Probabilistic Models",), skill_tags=("manual_computation", "probability_reasoning"), page_number=6), + short_answer("3a_iii", "3a", "3", ("a", "iii"), 1.5, analytics_topic="Probabilistic Models", topic_primary="Probabilistic Models", topic_tags=("Probabilistic Models",), skill_tags=("manual_computation", "probability_reasoning"), page_number=6), + short_answer("3a_iv", "3a", "3", ("a", "iv"), 1.5, analytics_topic="Probabilistic Models", topic_primary="Probabilistic Models", topic_tags=("Probabilistic Models",), skill_tags=("manual_computation", "probability_reasoning"), page_number=6), + short_answer("3b_i", "3b", "3", ("b", "i"), 1.5, analytics_topic="Evaluation and Validation", topic_primary="Evaluation and Validation", topic_tags=("Evaluation and Validation",), skill_tags=("validation_reasoning",), page_number=6), + short_answer("3b_ii", "3b", "3", ("b", "ii"), 1.5, analytics_topic="Evaluation and Validation", topic_primary="Evaluation and Validation", topic_tags=("Evaluation and Validation",), skill_tags=("validation_reasoning",), page_number=6), + short_answer("3b_iii", "3b", "3", ("b", "iii"), 1.5, analytics_topic="Evaluation and Validation", topic_primary="Evaluation and Validation", topic_tags=("Evaluation and Validation",), skill_tags=("validation_reasoning",), page_number=6), + short_answer("3c", "3", "3", ("c",), 1.5, analytics_topic="Perceptron and MLP", topic_primary="Perceptron and MLP", topic_tags=("Perceptron and MLP",), skill_tags=("linearity_reasoning", "classification_decision"), page_number=6), + short_answer("4a_i", "4a", "4", ("a", "i"), 2.5, analytics_topic="Perceptron and MLP", topic_primary="Perceptron and MLP", topic_tags=("Perceptron and MLP",), skill_tags=("parameter_counting",), page_number=7), + short_answer("4a_ii", "4a", "4", ("a", "ii"), 2.5, analytics_topic="Perceptron and MLP", topic_primary="Perceptron and MLP", topic_tags=("Perceptron and MLP",), skill_tags=("model_selection",), page_number=7), + short_answer("4b", "4", "4", ("b",), 1, analytics_topic="Perceptron and MLP", topic_primary="Perceptron and MLP", topic_tags=("Perceptron and MLP",), skill_tags=("concept_explanation",), page_number=7), + short_answer("4c", "4", "4", ("c",), 2, analytics_topic="Perceptron and MLP", topic_primary="Perceptron and MLP", topic_tags=("Perceptron and MLP",), skill_tags=("activation_reasoning", "optimization_reasoning"), page_number=7), + ChildSpec("4d_i", "4d", "4", ("d", "i"), 1.5, "long_question", "long_answer", "Perceptron and MLP", "Perceptron and MLP", ("Perceptron and MLP",), ("forward_pass", "activation_reasoning"), page_number=8), + ChildSpec("4d_ii", "4d", "4", ("d", "ii"), 1.5, "long_question", "long_answer", "Perceptron and MLP", "Perceptron and MLP", ("Perceptron and MLP",), ("backpropagation", "weight_update"), page_number=8), + ChildSpec("5a", "5", "5", ("a",), 4.5, "long_question", "long_answer", "Vision and CNN", "Vision and CNN", ("Vision and CNN",), ("histogram_reasoning", "image_transform"), page_number=9), + ChildSpec("5b", "5", "5", ("b",), 3, "long_question", "long_answer", "Vision and CNN", "Vision and CNN", ("Vision and CNN",), ("thresholding", "manual_computation"), page_number=10), + ChildSpec("5c", "5", "5", ("c",), 2, "long_question", "long_answer", "Vision and CNN", "Vision and CNN", ("Vision and CNN",), ("padding", "manual_construction"), page_number=10), + short_answer("5d_i", "5d", "5", ("d", "i"), 0.5, analytics_topic="Vision and CNN", topic_primary="Vision and CNN", topic_tags=("Vision and CNN",), skill_tags=("filter_effect_reasoning",), page_number=11), + short_answer("5d_ii", "5d", "5", ("d", "ii"), 0.5, analytics_topic="Vision and CNN", topic_primary="Vision and CNN", topic_tags=("Vision and CNN",), skill_tags=("filter_effect_reasoning",), page_number=11), + short_answer("5d_iii", "5d", "5", ("d", "iii"), 0.5, analytics_topic="Vision and CNN", topic_primary="Vision and CNN", topic_tags=("Vision and CNN",), skill_tags=("filter_effect_reasoning",), page_number=11), + short_answer("5e", "5", "5", ("e",), 2, analytics_topic="Vision and CNN", topic_primary="Vision and CNN", topic_tags=("Vision and CNN",), skill_tags=("concept_explanation", "local_vs_global"), page_number=11), + ChildSpec("6a", "6", "6", ("a",), 10, "long_question", "coding", "Vision and CNN", "Vision and CNN", ("Vision and CNN",), ("implementation", "convolution", "debugging"), page_number=12), + ChildSpec("6b", "6", "6", ("b",), 3, "long_question", "coding", "Vision and CNN", "Vision and CNN", ("Vision and CNN",), ("implementation", "regularization"), page_number=15), + short_answer("7a_i", "7a", "7", ("a", "i"), 1, analytics_topic="Vision and CNN", topic_primary="Vision and CNN", topic_tags=("Vision and CNN",), skill_tags=("cnn_architecture",), page_number=16), + short_answer("7a_ii", "7a", "7", ("a", "ii"), 4, analytics_topic="Vision and CNN", topic_primary="Vision and CNN", topic_tags=("Vision and CNN",), skill_tags=("shape_reasoning", "parameter_counting"), page_number=16), + short_answer("7a_iii", "7a", "7", ("a", "iii"), 3, analytics_topic="Vision and CNN", topic_primary="Vision and CNN", topic_tags=("Vision and CNN",), skill_tags=("overfitting", "regularization"), page_number=16), + ChildSpec("7b", "7", "7", ("b",), 5, "long_question", "long_answer", "Vision and CNN", "Vision and CNN", ("Vision and CNN",), ("manual_computation", "cnn_forward_pass"), page_number=17), + short_answer("7c_i", "7c", "7", ("c", "i"), 2, analytics_topic="Vision and CNN", topic_primary="Vision and CNN", topic_tags=("Vision and CNN",), skill_tags=("shape_reasoning", "3d_convolution"), page_number=17), + short_answer("7c_ii", "7c", "7", ("c", "ii"), 1.5, analytics_topic="Vision and CNN", topic_primary="Vision and CNN", topic_tags=("Vision and CNN",), skill_tags=("parameter_counting", "3d_convolution"), page_number=17), + short_answer("7c_iii", "7c", "7", ("c", "iii"), 1.5, analytics_topic="Vision and CNN", topic_primary="Vision and CNN", topic_tags=("Vision and CNN",), skill_tags=("parameter_counting", "3d_convolution"), page_number=17), + short_answer("8a_i", "8a", "8", ("a", "i"), 1, analytics_topic="Search and Games", topic_primary="Search and Games", topic_tags=("Search and Games",), skill_tags=("tree_search", "manual_tracing"), page_number=18), + short_answer("8a_ii", "8a", "8", ("a", "ii"), 3, analytics_topic="Search and Games", topic_primary="Search and Games", topic_tags=("Search and Games",), skill_tags=("pruning", "manual_tracing"), page_number=18), + short_answer("8a_iii", "8a", "8", ("a", "iii"), 1, analytics_topic="Search and Games", topic_primary="Search and Games", topic_tags=("Search and Games",), skill_tags=("game_reasoning",), page_number=18), + short_answer("8b_i", "8b", "8", ("b", "i"), 2.5, analytics_topic="Search and Games", topic_primary="Search and Games", topic_tags=("Search and Games",), skill_tags=("utility_reasoning",), page_number=18), + short_answer("8b_ii", "8b", "8", ("b", "ii"), 2.5, analytics_topic="Search and Games", topic_primary="Search and Games", topic_tags=("Search and Games",), skill_tags=("pruning_reasoning", "concept_explanation"), page_number=18), + short_answer("9", "9", "9", (), 3, analytics_topic="Ethics of AI", topic_primary="Ethics of AI", topic_tags=("Ethics of AI",), skill_tags=("concept_explanation", "governance"), page_number=19), +] + + +MARKER_RE = re.compile(r"(?m)^\(([a-z]+|[ivx]+)\)\s*") + + +def split_sections(text: str) -> tuple[str, dict[str, str]]: + matches = list(MARKER_RE.finditer(text)) + if not matches: + return text.strip(), {} + intro = text[: matches[0].start()].strip() + sections: dict[str, str] = {} + for idx, match in enumerate(matches): + marker = match.group(1) + end = matches[idx + 1].start() if idx + 1 < len(matches) else len(text) + sections[marker] = text[match.start() : end].strip() + return intro, sections + + +def extract_segment(text: str, path: tuple[str, ...]) -> str: + if not path: + return text.strip() + current = text.strip() + carried_intro: list[str] = [] + for depth, marker in enumerate(path): + intro, sections = split_sections(current) + if depth == 0 and intro: + carried_intro.append(intro) + current = sections.get(marker, current) + return "\n".join(part for part in [*carried_intro, current] if part).strip() + + +def extract_true_false_answers(answer_text: str) -> dict[str, str]: + answers: dict[str, str] = {} + table_match = re.search(r"Answer\s+(T\s+F\s+T\s+F\s+F\s+T\s+F\s+F\s+F\s+T)", answer_text, re.S) + if table_match: + seq = re.findall(r"[TF]", table_match.group(1)) + if len(seq) == 10: + for idx, val in enumerate(seq): + answers[chr(ord("a") + idx)] = val + return answers + seq = re.findall(r"\b([TF])\b", answer_text) + if len(seq) >= 10: + for idx, val in enumerate(seq[:10]): + answers[chr(ord("a") + idx)] = val + return answers + + +def load_seed_rows() -> dict[str, dict]: + data = json.loads(PROBLEM_SEED_PATH.read_text()) + return {row["question_number"]: row for row in data if row["source_exam_key"] == EXAM_KEY} + + +def main() -> None: + sb = get_supabase() + paper = sb.table("papers").select("id").eq("source_exam_key", EXAM_KEY).execute().data[0] + paper_id = paper["id"] + current_rows = ( + sb.table("paper_questions") + .select("*") + .eq("paper_id", paper_id) + .order("display_order") + .execute() + .data + ) + existing_by_number = {row["question_number"]: row for row in current_rows} + parent_rows = load_seed_rows() + tf_answers = extract_true_false_answers(parent_rows["1"]["raw_answer_text"] or "") + + inserts = [] + for display_order, child in enumerate(CHILDREN, start=1): + parent = parent_rows[child.top_level_number] + existing = existing_by_number.get(child.question_number, {}) + question_text = extract_segment(parent["question_text"] or "", child.path) + raw_answer_text = extract_segment(parent["raw_answer_text"] or "", child.path) if child.path else (parent["raw_answer_text"] or "") + + options = None + correct_option = child.correct_option + if child.question_type == "true_false": + options = TRUE_FALSE_OPTIONS + correct_option = tf_answers.get(child.path[0]) + elif child.options: + options = [{"label": label, "text": text} for label, text in child.options] + + inserts.append( + { + "paper_id": paper_id, + "question_number": child.question_number, + "parent_question": child.parent_question, + "display_order": display_order, + "question_type": child.question_type, + "question_format": child.question_format, + "question_text": question_text, + "score": child.score, + "page_number": child.page_number, + "page_y_ratio": existing.get("page_y_ratio"), + "options": options, + "correct_option": correct_option, + "correct_answer": child.correct_answer, + "raw_answer_text": raw_answer_text, + "topics": existing.get("topics") or (list(child.topic_tags) if child.topic_tags else parent.get("topics")), + "topic_primary": existing.get("topic_primary") or child.topic_primary or parent.get("topic_primary"), + "analytics_topic": existing.get("analytics_topic") or child.analytics_topic or parent.get("analytics_topic"), + "topic_tags": existing.get("topic_tags") or (list(child.topic_tags) if child.topic_tags else parent.get("topic_tags")), + "skill_tags": existing.get("skill_tags") or (list(child.skill_tags) if child.skill_tags else parent.get("skill_tags")), + "difficulty": existing.get("difficulty") or parent.get("difficulty"), + "knowledge_reminder": existing.get("knowledge_reminder", ""), + "ai_hint": existing.get("ai_hint", ""), + "solution": existing.get("solution", ""), + } + ) + + sb.table("paper_questions").delete().eq("paper_id", paper_id).execute() + sb.table("paper_questions").insert(inserts).execute() + sb.table("papers").update({"question_count": len(inserts), "status": "processing"}).eq("id", paper_id).execute() + print(f"Inserted {len(inserts)} rows for {EXAM_KEY}.") + + +if __name__ == "__main__": + main() diff --git a/backend/split_comp2211_2024_spring_midterm.py b/backend/split_comp2211_2024_spring_midterm.py new file mode 100644 index 0000000..04fe328 --- /dev/null +++ b/backend/split_comp2211_2024_spring_midterm.py @@ -0,0 +1,291 @@ +"""Rebuild COMP2211 Spring 2024 midterm into subquestions.""" + +from __future__ import annotations + +import json +import re +from dataclasses import dataclass +from pathlib import Path + +import fitz + +from app.services.supabase_client import get_supabase + + +EXAM_KEY = "COMP2211-2024-spring-midterm" +ROOT = Path(__file__).resolve().parent.parent +QUESTION_PDF = ROOT / "pastpaper-scraper" / "papers" / "COMP2211" / "(COMP2211)[2024](s)midterm~=rcidkjgf^_82003.pdf" +ANSWER_PDF = ROOT / "pastpaper-scraper" / "papers" / "COMP2211" / "(COMP2211)[2024](s)midterm~=ubrzkjmz^_90406.pdf" +PROBLEM_SEED_PATH = ROOT / "pastpaper-scraper" / "reviews" / "COMP2211" / "problem_seed.json" +TRUE_FALSE_OPTIONS = [{"label": "True", "text": "True"}, {"label": "False", "text": "False"}] + + +@dataclass(frozen=True) +class ChildSpec: + question_number: str + parent_question: str + top_level_number: str + path: tuple[str, ...] + score: float + question_type: str + question_format: str | None = None + analytics_topic: str | None = None + topic_primary: str | None = None + topic_tags: tuple[str, ...] | None = None + skill_tags: tuple[str, ...] | None = None + page_number: int = 1 + + +def short_answer( + question_number: str, + parent_question: str, + top_level_number: str, + path: tuple[str, ...], + score: float, + *, + analytics_topic: str | None = None, + topic_primary: str | None = None, + topic_tags: tuple[str, ...] | None = None, + skill_tags: tuple[str, ...] | None = None, + page_number: int, +) -> ChildSpec: + return ChildSpec( + question_number=question_number, + parent_question=parent_question, + top_level_number=top_level_number, + path=path, + score=score, + question_type="long_question", + question_format="short_answer", + analytics_topic=analytics_topic, + topic_primary=topic_primary, + topic_tags=topic_tags, + skill_tags=skill_tags, + page_number=page_number, + ) + + +CHILDREN: list[ChildSpec] = [ + ChildSpec("1a", "1", "1", ("a",), 0.5, "true_false", "true_false", "Python Fundamentals", "Python Fundamentals", ("Python Fundamentals",), ("concept_check", "code_tracing"), page_number=3), + ChildSpec("1b", "1", "1", ("b",), 0.5, "true_false", "true_false", "Python Fundamentals", "Python Fundamentals", ("Python Fundamentals",), ("concept_check", "broadcasting"), page_number=3), + ChildSpec("1c", "1", "1", ("c",), 0.5, "true_false", "true_false", "KNN and Clustering", "KNN and Clustering", ("KNN and Clustering",), ("concept_check", "algorithm_property"), page_number=3), + ChildSpec("1d", "1", "1", ("d",), 0.5, "true_false", "true_false", "KNN and Clustering", "KNN and Clustering", ("KNN and Clustering",), ("concept_check", "tie_reasoning"), page_number=3), + ChildSpec("1e", "1", "1", ("e",), 0.5, "true_false", "true_false", "Evaluation and Validation", "Evaluation and Validation", ("Evaluation and Validation",), ("concept_check", "cross_validation"), page_number=3), + ChildSpec("1f", "1", "1", ("f",), 0.5, "true_false", "true_false", "KNN and Clustering", "KNN and Clustering", ("KNN and Clustering",), ("concept_check", "clustering_property"), page_number=3), + ChildSpec("1g", "1", "1", ("g",), 0.5, "true_false", "true_false", "KNN and Clustering", "KNN and Clustering", ("KNN and Clustering",), ("concept_check", "robustness_reasoning"), page_number=3), + ChildSpec("1h", "1", "1", ("h",), 0.5, "true_false", "true_false", "Perceptron and MLP", "Perceptron and MLP", ("Perceptron and MLP",), ("concept_check", "decision_boundary"), page_number=3), + ChildSpec("1i", "1", "1", ("i",), 0.5, "true_false", "true_false", "Perceptron and MLP", "Perceptron and MLP", ("Perceptron and MLP",), ("concept_check", "optimization_reasoning"), page_number=3), + ChildSpec("1j", "1", "1", ("j",), 0.5, "true_false", "true_false", "KNN and Clustering", "KNN and Clustering", ("KNN and Clustering",), ("concept_check", "clustering_property"), page_number=3), + short_answer("2a_i", "2a", "2", ("a", "i"), 1, analytics_topic="Python Fundamentals", topic_primary="Python Fundamentals", topic_tags=("Python Fundamentals",), skill_tags=("code_tracing",), page_number=4), + short_answer("2a_ii", "2a", "2", ("a", "ii"), 1, analytics_topic="Python Fundamentals", topic_primary="Python Fundamentals", topic_tags=("Python Fundamentals",), skill_tags=("code_tracing",), page_number=4), + short_answer("2a_iii", "2a", "2", ("a", "iii"), 1, analytics_topic="Python Fundamentals", topic_primary="Python Fundamentals", topic_tags=("Python Fundamentals",), skill_tags=("array_manipulation",), page_number=5), + short_answer("2a_iv", "2a", "2", ("a", "iv"), 1, analytics_topic="Python Fundamentals", topic_primary="Python Fundamentals", topic_tags=("Python Fundamentals",), skill_tags=("array_construction",), page_number=5), + short_answer("2a_v", "2a", "2", ("a", "v"), 1, analytics_topic="Python Fundamentals", topic_primary="Python Fundamentals", topic_tags=("Python Fundamentals",), skill_tags=("aggregation",), page_number=5), + short_answer("2a_vi", "2a", "2", ("a", "vi"), 1, analytics_topic="Python Fundamentals", topic_primary="Python Fundamentals", topic_tags=("Python Fundamentals",), skill_tags=("transpose",), page_number=6), + short_answer("2a_vii", "2a", "2", ("a", "vii"), 1, analytics_topic="Python Fundamentals", topic_primary="Python Fundamentals", topic_tags=("Python Fundamentals",), skill_tags=("matrix_multiplication",), page_number=6), + short_answer("2a_viii", "2a", "2", ("a", "viii"), 1, analytics_topic="Python Fundamentals", topic_primary="Python Fundamentals", topic_tags=("Python Fundamentals",), skill_tags=("dot_product",), page_number=6), + short_answer("2a_ix", "2a", "2", ("a", "ix"), 1, analytics_topic="Python Fundamentals", topic_primary="Python Fundamentals", topic_tags=("Python Fundamentals",), skill_tags=("broadcasting",), page_number=6), + short_answer("2a_x", "2a", "2", ("a", "x"), 1, analytics_topic="Python Fundamentals", topic_primary="Python Fundamentals", topic_tags=("Python Fundamentals",), skill_tags=("error_reasoning",), page_number=7), + short_answer("2a_xi", "2a", "2", ("a", "xi"), 1, analytics_topic="Python Fundamentals", topic_primary="Python Fundamentals", topic_tags=("Python Fundamentals",), skill_tags=("broadcasting",), page_number=7), + short_answer("2a_xii", "2a", "2", ("a", "xii"), 1, analytics_topic="Python Fundamentals", topic_primary="Python Fundamentals", topic_tags=("Python Fundamentals",), skill_tags=("slicing",), page_number=7), + short_answer("2a_xiii", "2a", "2", ("a", "xiii"), 1, analytics_topic="Python Fundamentals", topic_primary="Python Fundamentals", topic_tags=("Python Fundamentals",), skill_tags=("views_vs_copies",), page_number=7), + ChildSpec("2b", "2", "2", ("b",), 6, "long_question", "coding", "Python Fundamentals", "Python Fundamentals", ("Python Fundamentals",), ("implementation", "vectorization", "similarity_computation"), page_number=8), + ChildSpec("3a", "3", "3", ("a",), 5.5, "long_question", "long_answer", "Evaluation and Validation", "Evaluation and Validation", ("Evaluation and Validation",), ("manual_computation", "metric_reasoning"), page_number=10), + short_answer("3b", "3", "3", ("b",), 1, analytics_topic="Evaluation and Validation", topic_primary="Evaluation and Validation", topic_tags=("Evaluation and Validation",), skill_tags=("metric_reasoning",), page_number=11), + ChildSpec("3c", "3", "3", ("c",), 2.5, "long_question", "long_answer", "Evaluation and Validation", "Evaluation and Validation", ("Evaluation and Validation",), ("manual_computation", "metric_reasoning"), page_number=11), + short_answer("3d", "3", "3", ("d",), 1, analytics_topic="Evaluation and Validation", topic_primary="Evaluation and Validation", topic_tags=("Evaluation and Validation",), skill_tags=("metric_reasoning",), page_number=12), + ChildSpec("3e", "3", "3", ("e",), 6, "long_question", "coding", "Evaluation and Validation", "Evaluation and Validation", ("Evaluation and Validation",), ("implementation", "metrics", "vectorization"), page_number=12), + ChildSpec("4a", "4", "4", ("a",), 4, "long_question", "long_answer", "Probabilistic Models", "Probabilistic Models", ("Probabilistic Models",), ("manual_computation", "gaussian_nb"), page_number=15), + ChildSpec("4b", "4", "4", ("b",), 3, "long_question", "long_answer", "Probabilistic Models", "Probabilistic Models", ("Probabilistic Models",), ("manual_computation", "likelihood_reasoning"), page_number=15), + ChildSpec("4c", "4", "4", ("c",), 4, "long_question", "long_answer", "Probabilistic Models", "Probabilistic Models", ("Probabilistic Models",), ("laplace_smoothing", "likelihood_reasoning"), page_number=16), + short_answer("4d", "4", "4", ("d",), 2, analytics_topic="Probabilistic Models", topic_primary="Probabilistic Models", topic_tags=("Probabilistic Models",), skill_tags=("prior_reasoning",), page_number=17), + ChildSpec("4e", "4", "4", ("e",), 3, "long_question", "long_answer", "Probabilistic Models", "Probabilistic Models", ("Probabilistic Models",), ("posterior_reasoning", "classification_decision"), page_number=17), + ChildSpec("5a", "5", "5", ("a",), 3, "long_question", "long_answer", "KNN and Clustering", "KNN and Clustering", ("KNN and Clustering",), ("distance_calculation", "weighted_knn"), page_number=18), + ChildSpec("5b", "5", "5", ("b",), 13, "long_question", "long_answer", "KNN and Clustering", "KNN and Clustering", ("KNN and Clustering",), ("cross_validation", "manual_tracing", "model_selection"), page_number=18), + short_answer("5c", "5", "5", ("c",), 2, analytics_topic="KNN and Clustering", topic_primary="KNN and Clustering", topic_tags=("KNN and Clustering",), skill_tags=("test_error", "model_selection"), page_number=20), + ChildSpec("6a", "6", "6", ("a",), 6, "long_question", "long_answer", "KNN and Clustering", "KNN and Clustering", ("KNN and Clustering",), ("manual_computation", "clustering"), page_number=21), + ChildSpec("6b", "6", "6", ("b",), 6, "long_question", "long_answer", "KNN and Clustering", "KNN and Clustering", ("KNN and Clustering",), ("manual_computation", "clustering"), page_number=22), + short_answer("6c", "6", "6", ("c",), 2, analytics_topic="KNN and Clustering", topic_primary="KNN and Clustering", topic_tags=("KNN and Clustering",), skill_tags=("outlier_reasoning",), page_number=22), + short_answer("6d", "6", "6", ("d",), 2, analytics_topic="KNN and Clustering", topic_primary="KNN and Clustering", topic_tags=("KNN and Clustering",), skill_tags=("model_selection", "threshold_reasoning"), page_number=22), + ChildSpec("7", "7", "7", (), 10, "long_question", "long_answer", "Evaluation and Validation", "Evaluation and Validation", ("Evaluation and Validation",), ("cross_validation", "data_leakage_reasoning"), page_number=23), +] + + +MARKER_RE = re.compile(r"(?m)^\(([a-z]+|[ivx]+)\)\s*") + + +def split_sections(text: str) -> tuple[str, dict[str, str]]: + matches = list(MARKER_RE.finditer(text)) + if not matches: + return text.strip(), {} + intro = text[: matches[0].start()].strip() + sections: dict[str, str] = {} + for idx, match in enumerate(matches): + marker = match.group(1) + end = matches[idx + 1].start() if idx + 1 < len(matches) else len(text) + sections[marker] = text[match.start() : end].strip() + return intro, sections + + +def extract_segment(text: str, path: tuple[str, ...]) -> str: + if not path: + return text.strip() + current = text.strip() + carried_intro: list[str] = [] + for depth, marker in enumerate(path): + intro, sections = split_sections(current) + if depth == 0 and intro: + carried_intro.append(intro) + current = sections.get(marker, current) + return "\n".join(part for part in [*carried_intro, current] if part).strip() + + +def extract_pages(pdf_path: Path, start: int, end: int) -> str: + doc = fitz.open(pdf_path) + try: + return "\n".join(doc[i].get_text("text") for i in range(start - 1, end)) + finally: + doc.close() + + +def load_seed_rows() -> dict[str, dict]: + data = json.loads(PROBLEM_SEED_PATH.read_text()) + return {row["question_number"]: row for row in data if row["source_exam_key"] == EXAM_KEY} + + +def build_source_rows(existing_rows: dict[str, dict]) -> dict[str, dict]: + seed_rows = load_seed_rows() + rows = dict(seed_rows) + if "5" in rows: + rows["5"] = { + **rows["5"], + "question_text": extract_pages(QUESTION_PDF, 18, 20), + "raw_answer_text": extract_pages(ANSWER_PDF, 21, 25), + "page_number": 18, + "analytics_topic": "KNN and Clustering", + "topic_primary": "KNN and Clustering", + "topic_tags": ["KNN and Clustering"], + "skill_tags": ["manual_computation", "distance_calculation", "algorithm_tracing"], + "difficulty": "medium", + } + else: + rows["5"] = { + **seed_rows["5"], + "question_text": extract_pages(QUESTION_PDF, 18, 20), + "raw_answer_text": extract_pages(ANSWER_PDF, 21, 25), + "page_number": 18, + } + if "7" in rows: + rows["7"] = { + **rows["7"], + "question_text": extract_pages(QUESTION_PDF, 23, 24), + "raw_answer_text": extract_pages(ANSWER_PDF, 31, 34), + "page_number": 23, + "analytics_topic": "Evaluation and Validation", + "topic_primary": "Evaluation and Validation", + "topic_tags": ["Evaluation and Validation"], + "skill_tags": ["cross_validation", "data_leakage_reasoning"], + "difficulty": "medium", + } + else: + rows["7"] = { + **seed_rows["7"], + "question_text": extract_pages(QUESTION_PDF, 23, 24), + "raw_answer_text": extract_pages(ANSWER_PDF, 31, 34), + "page_number": 23, + } + return rows + + +def extract_true_false_answers(answer_text: str) -> dict[str, str]: + answers: dict[str, str] = {} + table_match = re.search(r"Answer\s+([TF\s]+)", answer_text, re.S) + if table_match: + seq = re.findall(r"[TF]", table_match.group(1)) + if len(seq) >= 10: + for idx, val in enumerate(seq[:10]): + answers[chr(ord("a") + idx)] = val + return answers + lines = [line.strip() for line in answer_text.splitlines() if line.strip()] + current_letter: str | None = None + for line in lines: + m = re.fullmatch(r"\(([a-j])\)", line) + if m: + current_letter = m.group(1) + continue + if current_letter and line in {"T", "F"}: + answers[current_letter] = line + current_letter = None + if answers: + return answers + seq = re.findall(r"\b([TF])\b", answer_text) + if len(seq) >= 10: + for idx, val in enumerate(seq[:10]): + answers[chr(ord("a") + idx)] = val + return answers + + +def main() -> None: + sb = get_supabase() + paper = sb.table("papers").select("id").eq("source_exam_key", EXAM_KEY).execute().data[0] + paper_id = paper["id"] + current_rows = ( + sb.table("paper_questions") + .select("*") + .eq("paper_id", paper_id) + .order("display_order") + .execute() + .data + ) + existing_by_number = {row["question_number"]: row for row in current_rows} + parent_rows = build_source_rows(existing_by_number) + tf_answers = extract_true_false_answers(parent_rows["1"]["raw_answer_text"] or "") + + inserts = [] + for display_order, child in enumerate(CHILDREN, start=1): + parent = parent_rows[child.top_level_number] + existing = existing_by_number.get(child.question_number, {}) + question_text = extract_segment(parent["question_text"] or "", child.path) + raw_answer_text = extract_segment(parent["raw_answer_text"] or "", child.path) if child.path else (parent["raw_answer_text"] or "") + options = None + correct_option = None + if child.question_type == "true_false": + options = TRUE_FALSE_OPTIONS + correct_option = tf_answers.get(child.path[0]) + + inserts.append( + { + "paper_id": paper_id, + "question_number": child.question_number, + "parent_question": child.parent_question, + "display_order": display_order, + "question_type": child.question_type, + "question_format": child.question_format, + "question_text": question_text, + "score": child.score, + "page_number": child.page_number, + "page_y_ratio": existing.get("page_y_ratio"), + "options": options, + "correct_option": correct_option, + "correct_answer": None, + "raw_answer_text": raw_answer_text, + "topics": existing.get("topics") or (list(child.topic_tags) if child.topic_tags else parent.get("topics")), + "topic_primary": existing.get("topic_primary") or child.topic_primary or parent.get("topic_primary"), + "analytics_topic": existing.get("analytics_topic") or child.analytics_topic or parent.get("analytics_topic"), + "topic_tags": existing.get("topic_tags") or (list(child.topic_tags) if child.topic_tags else parent.get("topic_tags")), + "skill_tags": existing.get("skill_tags") or (list(child.skill_tags) if child.skill_tags else parent.get("skill_tags")), + "difficulty": existing.get("difficulty") or parent.get("difficulty"), + "knowledge_reminder": existing.get("knowledge_reminder", ""), + "ai_hint": existing.get("ai_hint", ""), + "solution": existing.get("solution", ""), + } + ) + + sb.table("paper_questions").delete().eq("paper_id", paper_id).execute() + sb.table("paper_questions").insert(inserts).execute() + sb.table("papers").update({"question_count": len(inserts), "status": "processing"}).eq("id", paper_id).execute() + print(f"Inserted {len(inserts)} rows for {EXAM_KEY}.") + + +if __name__ == "__main__": + main() diff --git a/backend/upload_course_library_pdfs.py b/backend/upload_course_library_pdfs.py new file mode 100644 index 0000000..75a33d7 --- /dev/null +++ b/backend/upload_course_library_pdfs.py @@ -0,0 +1,121 @@ +"""Upload COMP2211 course-library PDFs to Supabase Storage. + +Run from the backend directory: + uv run python upload_course_library_pdfs.py + +Each entry maps a storage path (inside the `papers` bucket) to the local +source file under pastpaper-scraper/papers/COMP2211/. +""" + +from __future__ import annotations + +import sys +from pathlib import Path + +# --------------------------------------------------------------------------- +# Manifest: (storage_path, local_filename) +# storage_path is relative inside the `papers` bucket. +# local_filename is relative to PAPERS_DIR below. +# --------------------------------------------------------------------------- +MANIFEST: list[tuple[str, str]] = [ + ( + "course-library/COMP2211/COMP2211-2022-fall-midterm/paper.pdf", + "(COMP2211)[2022](f)midterm~=yjz8dxdd^_27002.pdf", + ), + ( + "course-library/COMP2211/COMP2211-2022-fall-midterm/answer.pdf", + "(COMP2211)[2022](f)midterm~=yjz8dxdd^_18747.pdf", + ), + ( + "course-library/COMP2211/COMP2211-2022-spring-midterm/paper.pdf", + "(COMP2211)[2022](s)midterm~=b8bidkgs^_14629.pdf", + ), + ( + "course-library/COMP2211/COMP2211-2022-spring-midterm/answer.pdf", + "(COMP2211)[2022](s)midterm~=6ma030^_89587.pdf", + ), + ( + "course-library/COMP2211/COMP2211-2022-spring-final-part-a/paper.pdf", + "(COMP2211)[2022](s)final~=b8bidkgs^_33018.pdf", + ), + ( + "course-library/COMP2211/COMP2211-2022-spring-final-part-a/answer.pdf", + "(COMP2211)[2022](s)final~=ajou6^_82011.pdf", + ), + ( + "course-library/COMP2211/COMP2211-2022-spring-final-part-b/paper.pdf", + "(COMP2211)[2022](s)final~=b8bidkgs^_40627.pdf", + ), + ( + "course-library/COMP2211/COMP2211-2022-spring-final-part-b/answer.pdf", + "(COMP2211)[2022](s)final~=ajou6^_51199.pdf", + ), + ( + "course-library/COMP2211/COMP2211-2023-spring-midterm/paper.pdf", + "(COMP2211)[2023](s)midterm~=bxbidkmj^_26587.pdf", + ), + ( + "course-library/COMP2211/COMP2211-2023-spring-midterm/answer.pdf", + "(COMP2211)[2023](s)midterm~clchanbg^_17297.pdf", + ), + ( + "course-library/COMP2211/COMP2211-2024-spring-midterm/paper.pdf", + "(COMP2211)[2024](s)midterm~=rcidkjgf^_82003.pdf", + ), + ( + "course-library/COMP2211/COMP2211-2024-spring-midterm/answer.pdf", + "(COMP2211)[2024](s)midterm~=ubrzkjmz^_90406.pdf", + ), + ( + "course-library/COMP2211/COMP2211-2024-spring-final/paper.pdf", + "(COMP2211)[2024](s)final~=igk5mmg^_90365.pdf", + ), + ( + "course-library/COMP2211/COMP2211-2024-spring-final/answer.pdf", + "(COMP2211)[2024](s)final~=igk5mmg^_58857.pdf", + ), +] + +PAPERS_DIR = ( + Path(__file__).parent.parent + / "pastpaper-scraper" + / "papers" + / "COMP2211" +) + + +def main() -> None: + from app.services.supabase_client import get_supabase + + sb = get_supabase() + bucket = sb.storage.from_("papers") + + ok = 0 + skipped = 0 + failed = 0 + + for storage_path, local_name in MANIFEST: + local_file = PAPERS_DIR / local_name + if not local_file.exists(): + print(f" MISSING local file: {local_name}") + failed += 1 + continue + + data = local_file.read_bytes() + try: + bucket.upload( + storage_path, + data, + file_options={"content-type": "application/pdf", "upsert": "true"}, + ) + print(f" OK {storage_path}") + ok += 1 + except Exception as exc: + print(f" ERR {storage_path}: {exc}") + failed += 1 + + print(f"\nDone: {ok} uploaded, {skipped} skipped, {failed} failed.") + + +if __name__ == "__main__": + main() diff --git 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"https://github.com/docker/compose/releases/latest/download/docker-compose-$(uname -s)-$(uname -m)" -o /usr/local/bin/docker-compose +sudo chmod +x /usr/local/bin/docker-compose +``` + +## 2. 上传代码 + +```bash +# 本地打包（排除 node_modules 和 .venv） +cd "/Users/soda/Desktop/PastPaper Master" +tar --exclude='node_modules' --exclude='.venv' --exclude='__pycache__' --exclude='.git' \ + -czf pastpaper.tar.gz . + +# 上传到服务器 +scp pastpaper.tar.gz root@:/opt/pastpaper/ + +# 服务器上解压 +ssh root@ +cd /opt/pastpaper && tar xzf pastpaper.tar.gz +``` + +## 3. 配置环境变量 + +```bash +# 编辑 .env，确认所有 key 正确 +vi /opt/pastpaper/.env +``` + +需要的变量： +- `SUPABASE_URL`, `SUPABASE_ANON_KEY`, `SUPABASE_SERVICE_ROLE_KEY` +- `DASHSCOPE_BASE_URL`, `DASHSCOPE_API_KEY` +- `DEEPSEEK_BASE_URL`, `DEEPSEEK_API_KEY` +- `LAOZHANG_BASE_URL`, `LAOZHANG_API_KEY`（备用） +- `GOOGLE_GEMINI_API_KEY`（如果服务器地区支持） + +## 4. 构建并启动 + +```bash +cd /opt/pastpaper +docker-compose up -d --build +``` + +## 5. 验证 + +```bash +# 检查容器状态 +docker-compose ps + +# 检查后端健康 +curl http://localhost/health + +# 查看日志 +docker-compose logs -f backend +docker-compose logs -f frontend +``` + +## 6. 域名 + HTTPS（可选） + +如果有域名，在腾讯云控制台配置 DNS → 服务器 IP，然后： + +```bash +# 安装 certbot +apt install -y certbot python3-certbot-nginx + +# 获取证书（先把 nginx.conf 里 server_name 改成你的域名） +certbot --nginx -d your-domain.com +``` + +## 常用运维命令 + +```bash +# 重启 +docker-compose restart + +# 更新代码后重新构建 +docker-compose up -d --build + +# 查看后端日志 +docker-compose logs -f backend + +# 进入后端容器 +docker-compose exec backend bash +``` diff --git a/docker-compose.yml b/docker-compose.yml new file mode 100644 index 0000000..ce3f6f5 --- /dev/null +++ b/docker-compose.yml @@ -0,0 +1,10 @@ +services: + backend: + build: ./backend + env_file: .env + ports: + - "8001:8000" + restart: unless-stopped + dns: + - 8.8.8.8 + - 1.1.1.1 diff --git a/docs/PAGE_NUMBER_BACKFILL.md b/docs/PAGE_NUMBER_BACKFILL.md new file mode 100644 index 0000000..7bb7893 --- /dev/null +++ b/docs/PAGE_NUMBER_BACKFILL.md @@ -0,0 +1,152 @@ +# Sub-question Page Number Backfill — Requirements + +## Problem + +All six `split_comp2211_*.py` scripts create sub-questions by inheriting `page_number` +from their parent question: + +```python +"page_number": parent.get("page_number"), +``` + +This is wrong for sub-questions that span multiple pages. For example, Q1 True/False +has 10 statements (a–j); if (a)–(f) are on page 1 and (g)–(j) are on page 2, all ten +inherit page 1 from the parent. Clicking Q1h in the UI scrolls to page 1 instead of page 2. + +## Goal + +Every `ChildSpec` in every split script should carry its own correct `page_number`. +When the script runs, it writes that page number to the database instead of inheriting +from the parent. + +## Files to modify + +``` +backend/split_comp2211_2022_fall_midterm.py ← does not exist yet; parent is seed SQL +backend/split_comp2211_2022_spring_midterm.py +backend/split_comp2211_2022_spring_final_part_a.py +backend/split_comp2211_2022_spring_final_part_b.py +backend/split_comp2211_2023_spring_midterm.py +backend/split_comp2211_2024_spring_midterm.py +backend/split_comp2211_2024_spring_final.py +``` + +Note: `2022-fall-midterm` sub-questions were inserted directly via the seed SQL +(`supabase/seeds/comp2211_problem_level_questions.sql`), not via a split script. +Their page numbers must be fixed directly in that SQL file or via a separate UPDATE. + +## How to determine page numbers + +Use PyMuPDF (`import pymupdf` — already in the venv) to search for question markers +in the local PDF files. The PDFs are at: + +``` +../pastpaper-scraper/papers/COMP2211/ +``` + +Filename mapping (from `upload_course_library_pdfs.py`): + +| Exam key | Local paper PDF | +|----------|----------------| +| COMP2211-2022-fall-midterm | (COMP2211)[2022](f)midterm~=yjz8dxdd^_27002.pdf | +| COMP2211-2022-spring-midterm | (COMP2211)[2022](s)midterm~=b8bidkgs^_14629.pdf | +| COMP2211-2022-spring-final-part-a | (COMP2211)[2022](s)final~=b8bidkgs^_33018.pdf | +| COMP2211-2022-spring-final-part-b | (COMP2211)[2022](s)final~=b8bidkgs^_40627.pdf | +| COMP2211-2023-spring-midterm | (COMP2211)[2023](s)midterm~=bxbidkmj^_26587.pdf | +| COMP2211-2024-spring-midterm | (COMP2211)[2024](s)midterm~=rcidkjgf^_82003.pdf | +| COMP2211-2024-spring-final | (COMP2211)[2024](s)final~=igk5mmg^_90365.pdf | + +### Suggested search strategy + +```python +import pymupdf + +doc = pymupdf.open("path/to/paper.pdf") +for page_num, page in enumerate(doc, start=1): + text = page.get_text() + print(f"--- Page {page_num} ---") + print(text[:500]) +``` + +Search for markers like: +- `"(a)"`, `"(b)"`, ... for True/False sub-statements +- `"Q2(a)"`, `"2(a)"`, `"Question 2"` for major sub-questions +- `"(i)"`, `"(ii)"` for nested sub-questions + +Page numbers are 1-indexed (matching the `page_number` field in the database). + +## Code changes per split script + +### Step 1 — Add `page_number` field to `ChildSpec` + +Each script has its own `ChildSpec` dataclass. Add the field with a default so +existing call sites don't break immediately: + +```python +@dataclass(frozen=True) +class ChildSpec: + ... + page_number: int = 1 # add this field +``` + +### Step 2 — Set correct page numbers in each `ChildSpec` instance + +Fill in the actual page after inspecting the PDF: + +```python +ChildSpec("1a", "1", "1", ("a",), 1.5, "true_false", page_number=1), +ChildSpec("1b", "1", "1", ("b",), 1.5, "true_false", page_number=1), +... +ChildSpec("1h", "1", "1", ("h",), 1.5, "true_false", page_number=2), +``` + +### Step 3 — Write `page_number` in the upsert payload + +Find where the script builds the INSERT/upsert dict and replace the inherited value: + +```python +# Before: +"page_number": parent.get("page_number"), + +# After: +"page_number": child.page_number, +``` + +### Step 4 — Update existing rows in the database + +After modifying the scripts, run each script once — they already use upsert/update +semantics, so re-running overwrites the old (inherited) page numbers with the correct ones. + +If a script does INSERT-only (not upsert), add a separate UPDATE pass: + +```python +sb.table("paper_questions").update({"page_number": child.page_number}) \ + .eq("paper_id", paper_id) \ + .eq("question_number", child.question_number) \ + .execute() +``` + +## 2022-fall-midterm (seed SQL) + +Sub-questions for this paper are in: +`supabase/seeds/comp2211_problem_level_questions.sql` + +The seed has a `page_number` column in the VALUES rows. Find all rows for +`COMP2211-2022-fall-midterm` and correct the values. Then run a direct UPDATE +against the live database: + +```sql +-- Example — adjust actual page numbers after inspecting the PDF +UPDATE paper_questions +SET page_number = 2 +WHERE paper_id = (SELECT id FROM papers WHERE source_exam_key = 'COMP2211-2022-fall-midterm') + AND question_number IN ('1g', '1h', '1i', '1j'); +``` + +## Definition of Done + +- [ ] Every `ChildSpec` in every split script has an explicit `page_number` +- [ ] No script uses `parent.get("page_number")` for the upsert payload +- [ ] All six scripts have been re-run against the live database +- [ ] 2022-fall-midterm sub-questions updated via SQL +- [ ] Spot-check: clicking Q1h in a paper where Q1 spans 2 pages scrolls to page 2 in the UI diff --git a/docs/TAGGING_REQUIREMENTS.md b/docs/TAGGING_REQUIREMENTS.md new file mode 100644 index 0000000..c5611f8 --- /dev/null +++ b/docs/TAGGING_REQUIREMENTS.md @@ -0,0 +1,243 @@ +# Tag Schema & Similar Question Retrieval — Requirements + +## Background + +Current state of `paper_questions` tagging for COMP2211: + +- `analytics_topic`: 8 coarse buckets (e.g. "KNN and Clustering" covers both KNN and K-Means) +- `topic_tags`: redundant copy of `analytics_topic`, adds no information +- `skill_tags`: fine-grained snake_case labels (e.g. `centroid_update`, `distance_calculation`), not shown to users +- `question_text`: at subquestion level, but currently stores **parent problem header text**, not the actual subquestion statement + +The result is that similar question retrieval conflates KNN and K-Means, cannot distinguish "write code" from "trace algorithm", and produces low-precision recommendations. + +--- + +## Goal + +Every subquestion should carry enough structured metadata that the retrieval system can return **topically and skill-wise identical questions across different exam years**, rather than just questions from the same broad topic bucket. + +Precision target: a question on K-Means centroid update should retrieve other K-Means centroid update questions, not KNN distance questions. + +--- + +## Field Definitions (revised) + +### `analytics_topic` — single string, primary retrieval bucket + +Granularity: **algorithm or concept level**, not course-section level. + +Allowed values for COMP2211 (replace current 8-bucket system): + +| New value | Replaces / splits | +|-----------|-------------------| +| `Naive Bayes` | Probabilistic Models (partial) | +| `Bayesian Inference` | Probabilistic Models (partial) | +| `KNN` | KNN and Clustering (partial) | +| `K-Means` | KNN and Clustering (partial) | +| `Perceptron` | Perceptron and MLP (partial) | +| `MLP` | Perceptron and MLP (partial) | +| `CNN` | Vision and CNN | +| `Evaluation Metrics` | Evaluation and Validation (partial) | +| `Cross Validation` | Evaluation and Validation (partial) | +| `Python and NumPy` | Python Fundamentals | +| `Search Algorithms` | Search and Games (partial) | +| `Game Trees` | Search and Games (partial) | +| `Ethics of AI` | Ethics of AI (unchanged) | + +Rules: +- One value per question — pick the **most specific** algorithm being tested +- If a subquestion genuinely spans two algorithms, pick the one being asked to compute/demonstrate +- `True/False` is **not** a valid analytics_topic (it is a format, not a topic) + +--- + +### `topic_tags` — string array, secondary topic labels + +Granularity: **concept and variant level** within the algorithm. + +Purpose: catch cross-topic overlaps and concept aliases. + +Examples: + +``` +analytics_topic = "K-Means" +topic_tags = ["K-Means", "Centroid Update", "Convergence"] + +analytics_topic = "KNN" +topic_tags = ["KNN", "Euclidean Distance", "Classification"] + +analytics_topic = "Naive Bayes" +topic_tags = ["Naive Bayes", "Prior", "Likelihood", "Posterior"] + +analytics_topic = "Evaluation Metrics" +topic_tags = ["Evaluation Metrics", "Precision", "Recall", "F1 Score"] + +analytics_topic = "MLP" +topic_tags = ["MLP", "Backpropagation", "Activation Function", "Hidden Layer"] + +analytics_topic = "Python and NumPy" +topic_tags = ["NumPy", "Broadcasting", "Array Indexing", "Vectorization"] +``` + +Rules: +- First element should match or alias `analytics_topic` +- Include concept names a student would search for ("F1 Score", not "metric_reasoning") +- 2–5 tags per question; avoid over-tagging +- Human-readable, title-case, no underscores + +--- + +### `skill_tags` — string array, task type labels + +Granularity: **what the student must do**, not what the topic is. + +Current values are acceptable in meaning but must be converted to human-readable form. + +Rename convention: `snake_case` → `Title Case with spaces` + +| Old | New | +|-----|-----| +| `concept_check` | `Concept Check` | +| `code_tracing` | `Code Tracing` | +| `algorithm_tracing` | `Algorithm Tracing` | +| `distance_calculation` | `Distance Calculation` | +| `centroid_update` | `Centroid Update` | +| `weight_update` | `Weight Update` | +| `decision_boundary` | `Decision Boundary` | +| `implementation` | `Implementation` | +| `debugging` | `Debugging` | +| `model_selection` | `Model Selection` | +| `concept_explanation` | `Concept Explanation` | +| `architecture_reasoning` | `Architecture Reasoning` | +| `convergence_reasoning` | `Convergence Reasoning` | +| `generalization_reasoning` | `Generalization Reasoning` | +| `classification_decision` | `Classification Decision` | + +Rules: +- 1–3 tags per question +- Describes the **task type**, not the subject matter +- These are used for retrieval ranking, not primary display + +--- + +### `question_text` — the actual subquestion statement + +Current problem: subquestions store the **parent problem header** as `question_text`, not the individual statement. + +Required fix per subquestion type: + +| Type | What `question_text` should contain | +|------|-------------------------------------| +| True/False subquestion (Q1a–Q1j) | The specific T/F statement being judged | +| Code output (Q2a_i–Q2a_v) | The specific code snippet + "What is the output?" | +| Calculation subquestion (Q4a, Q5a) | The specific sub-task, e.g. "Compute the Euclidean distance between..." | +| Written explanation (Q3, Q5c) | The full question prompt for that part | + +This is a **data extraction quality issue**. The backfill script must extract the correct per-subquestion text from the source PDF or from `raw_answer_text`. + +--- + +## Backfill Requirements + +### Script: `backfill_comp2211_tags.py` + +Target: all `paper_questions` where `paper_id` in the COMP2211 course library. + +For each question: + +1. **Re-classify `analytics_topic`** using the new value list above + - Use `question_text` + existing `topic_tags` + `skill_tags` as signals + - If `analytics_topic` is currently `"KNN and Clustering"`: + - Look at `skill_tags` and `question_text` + - If `centroid_update`, `algorithm_tracing`, or text contains "K-Means" / "centroid" → set `"K-Means"` + - Otherwise → set `"KNN"` + - If `analytics_topic` is currently `"Perceptron and MLP"`: + - If `question_text` or `skill_tags` references hidden layer, backprop, activation function → `"MLP"` + - Otherwise → `"Perceptron"` + - If `analytics_topic` is currently `"Probabilistic Models"`: + - If Naive Bayes in text → `"Naive Bayes"` + - Otherwise → `"Bayesian Inference"` + - If `analytics_topic` is currently `"Evaluation and Validation"`: + - If cross-validation, train/val split in text → `"Cross Validation"` + - Otherwise → `"Evaluation Metrics"` + - If `analytics_topic` is currently `"Search and Games"`: + - If minimax, alpha-beta, game tree in text → `"Game Trees"` + - Otherwise → `"Search Algorithms"` + +2. **Rebuild `topic_tags`** — do not copy `analytics_topic`; derive from question content + +3. **Rename `skill_tags`** — convert all snake_case values to Title Case per the mapping table above + +4. **Do not overwrite `question_text`** in this pass (separate task) + +--- + +## Retrieval Algorithm Changes (backend `questions.py`) + +### Separate topic and skill contributions + +Current `similarity_score()` merges `analytics_topic`, `topic_tags`, and `skill_tags` into one set. This causes skill tags like `centroid_update` to appear as "Shared topic: centroid_update" in the UI. + +Required split: + +```python +def similarity_score(target, candidate): + score = 0 + reasons = [] + + # 1. analytics_topic exact match: 40 pts + if target.get("analytics_topic") and target["analytics_topic"] == candidate.get("analytics_topic"): + score += 40 + reasons.append(f"Same topic: {target['analytics_topic']}") + + # 2. topic_tags overlap: up to 20 pts (10 per shared tag, max 2) + target_tt = set(t.lower() for t in (target.get("topic_tags") or [])) + candidate_tt = set(t.lower() for t in (candidate.get("topic_tags") or [])) + shared_tt = target_tt & candidate_tt + tt_pts = min(len(shared_tt) * 10, 20) + if tt_pts: + score += tt_pts + reasons.append(f"Shared concept: {', '.join(sorted(shared_tt)[:2])}") + + # 3. skill_tags overlap: up to 20 pts (10 per shared tag, max 2) + target_st = set(t.lower() for t in (target.get("skill_tags") or [])) + candidate_st = set(t.lower() for t in (candidate.get("skill_tags") or [])) + shared_st = target_st & candidate_st + st_pts = min(len(shared_st) * 10, 20) + if st_pts: + score += st_pts + reasons.append(f"Shared skill: {', '.join(sorted(shared_st)[:2])}") + + # 4. Same question format: 10 pts + if question_family(candidate) == question_family(target): + score += 10 + reasons.append("Same format") + + # 5. Same difficulty: 5 pts + if candidate.get("difficulty") and candidate["difficulty"] == target.get("difficulty"): + score += 5 + reasons.append("Same difficulty") + + # 6. Full-text similarity: up to 20 pts (from tsvector RPC) + # (injected externally, not computed here) + + return min(score, 99), reasons +``` + +### Threshold and display + +- Filter: `match_percent < 20` (raised from 10; ensures analytics_topic at least partially matches) +- UI display: show `match_reasons` chips, but replace snake_case with Title Case before display + +--- + +## Definition of Done + +- [ ] All COMP2211 questions have `analytics_topic` from the new value list +- [ ] No `analytics_topic` value of `"KNN and Clustering"`, `"Perceptron and MLP"`, `"Probabilistic Models"`, `"Evaluation and Validation"`, `"Search and Games"` remains +- [ ] `topic_tags` contains 2–5 human-readable concept names, not a copy of `analytics_topic` +- [ ] `skill_tags` values are Title Case with spaces +- [ ] Similar question retrieval returns 0 cross-algorithm false positives between KNN and K-Means +- [ ] `match_reasons` chips in the UI show no underscores +- [ ] Retrieval threshold enforces `analytics_topic` match as a hard or near-hard requirement diff --git a/frontend/Dockerfile b/frontend/Dockerfile new file mode 100644 index 0000000..a9f5b5d --- /dev/null +++ b/frontend/Dockerfile @@ -0,0 +1,12 @@ +FROM node:20-alpine AS build + +WORKDIR /app +COPY package.json package-lock.json ./ +RUN npm ci +COPY . . +RUN npm run build + +FROM nginx:alpine +COPY --from=build /app/dist /usr/share/nginx/html +COPY nginx.conf /etc/nginx/conf.d/default.conf +EXPOSE 80 diff --git a/frontend/index.html b/frontend/index.html new file mode 100644 index 0000000..5ad7eb6 --- /dev/null +++ b/frontend/index.html @@ -0,0 +1,13 @@ + + + + + + + PastPaper Master + + +
        + + + diff --git a/frontend/nginx.conf b/frontend/nginx.conf new file mode 100644 index 0000000..7506326 --- /dev/null +++ b/frontend/nginx.conf @@ -0,0 +1,27 @@ +server { + listen 80; + server_name pastpaper.knowit.top; + + root /usr/share/nginx/html; + index index.html; + + # SPA fallback + location / { + try_files $uri $uri/ /index.html; + } + + # API proxy to backend + location /api/ { + proxy_pass http://backend:8000/api/; + proxy_set_header Host $host; + proxy_set_header X-Real-IP $remote_addr; + proxy_set_header X-Forwarded-For $proxy_add_x_forwarded_for; + proxy_read_timeout 300s; + client_max_body_size 50M; + } + + # Health check proxy + location /health { + proxy_pass http://backend:8000/health; + } +} diff --git a/frontend/package-lock.json b/frontend/package-lock.json new file mode 100644 index 0000000..5ab640b --- /dev/null +++ b/frontend/package-lock.json @@ -0,0 +1,3058 @@ +{ + "name": "frontend", + "version": "1.0.0", + "lockfileVersion": 3, + "requires": true, 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        ; + + return ( + <> + + + : } /> + } /> + } /> + } /> + } /> + } /> + } /> + + + ); +} diff --git a/frontend/src/components/layout/Header.tsx b/frontend/src/components/layout/Header.tsx new file mode 100644 index 0000000..44b0b7d --- /dev/null +++ b/frontend/src/components/layout/Header.tsx @@ -0,0 +1,69 @@ +import { Link } from "react-router-dom"; +import { useAuth } from "@/contexts/AuthContext"; + +export default function Header({ + courseCode, + paperTitle, +}: { + courseCode?: string; + paperTitle?: string; +}) { + const { user, signOut } = useAuth(); + + return ( +
        + + PastPaper Master + + {courseCode && ( +
        + + {courseCode} + + {paperTitle && {paperTitle}} + + + + + AI Analytics + +
        + )} +
        + + My Papers + + + Error Book + + + Analytics + + + Upload + + {user ? ( +
        + {user.email} + +
        + ) : ( + + Sign in + + )} +
        +
        + ); +} diff --git a/frontend/src/components/layout/ProcessingBanner.tsx b/frontend/src/components/layout/ProcessingBanner.tsx new file mode 100644 index 0000000..f7bbe7c --- /dev/null +++ b/frontend/src/components/layout/ProcessingBanner.tsx @@ -0,0 +1,183 @@ +import { useEffect, useRef, useState } from "react"; +import { Link } from "react-router-dom"; +import { myPapers } from "@/lib/api"; +import { useAuth } from "@/contexts/AuthContext"; +import type { Paper } from "@/types/api"; + +interface Notification { + paperId: string; + label: string; +} + +const POLL_MS = 4000; + +export default function ProcessingBanner() { + const { user } = useAuth(); + const [processing, setProcessing] = useState([]); + const [doneNotifs, setDoneNotifs] = useState([]); + const [expanded, setExpanded] = useState(false); + const knownIds = useRef>(new Set()); + + // Drag state + const [pos, setPos] = useState({ x: window.innerWidth - 220, y: 24 }); + const dragging = useRef(false); + const dragOffset = useRef({ x: 0, y: 0 }); + const widgetRef = useRef(null); + + useEffect(() => { + if (!user) return; + let cancelled = false; + + const poll = async () => { + try { + const papers = await myPapers(); + if (cancelled) return; + + const inProgress = papers.filter((p) => p.status === "processing" || p.status === "uploaded"); + setProcessing(inProgress); + + papers + .filter((p) => p.status === "ready" && knownIds.current.has(p.id)) + .forEach((p) => { + knownIds.current.delete(p.id); + const label = `${p.course_code} ${p.year} ${p.term} ${p.exam_type}`; + setDoneNotifs((prev) => [...prev, { paperId: p.id, label }]); + setTimeout(() => { + setDoneNotifs((prev) => prev.filter((n) => n.paperId !== p.id)); + }, 8000); + }); + + inProgress.forEach((p) => knownIds.current.add(p.id)); + } catch { + // silent + } + }; + + poll(); + const interval = setInterval(poll, POLL_MS); + return () => { cancelled = true; clearInterval(interval); }; + }, [user]); + + // Drag handlers + const onMouseDown = (e: React.MouseEvent) => { + // Only drag on the header bar + dragging.current = true; + dragOffset.current = { + x: e.clientX - pos.x, + y: e.clientY - pos.y, + }; + e.preventDefault(); + }; + + useEffect(() => { + const onMouseMove = (e: MouseEvent) => { + if (!dragging.current) return; + setPos({ + x: Math.max(0, Math.min(window.innerWidth - 200, e.clientX - dragOffset.current.x)), + y: Math.max(0, Math.min(window.innerHeight - 60, e.clientY - dragOffset.current.y)), + }); + }; + const onMouseUp = () => { dragging.current = false; }; + window.addEventListener("mousemove", onMouseMove); + window.addEventListener("mouseup", onMouseUp); + return () => { + window.removeEventListener("mousemove", onMouseMove); + window.removeEventListener("mouseup", onMouseUp); + }; + }, []); + + if (!user || (processing.length === 0 && doneNotifs.length === 0)) return null; + + const total = processing.length + doneNotifs.length; + + return ( +
        + {/* ── Header / collapsed pill ── */} +
        setExpanded((v) => !v)} + className="flex items-center gap-2 bg-gray-900 text-white text-xs px-3.5 py-2.5 rounded-xl shadow-lg cursor-grab active:cursor-grabbing" + style={{ minWidth: 180 }} + > + + + {processing.length > 0 + ? `${processing.length} processing…` + : `${doneNotifs.length} ready`} + + {doneNotifs.length > 0 && ( + + {doneNotifs.length} + + )} + {expanded ? "▲" : "▼"} +
        + + {/* ── Expanded panel ── */} + {expanded && ( +
        + {processing.map((p) => { + const step = p.processing_step; + const progress = p.processing_progress || 0; + const total = p.processing_total || 0; + const pct = total > 0 ? Math.round((progress / total) * 100) : 0; + return ( +
        +
        + + + {p.course_code}{" "} + {p.year} {p.term} {p.exam_type} + +
        + {step && ( + <> +
        {step}
        + {total > 0 && ( +
        +
        +
        + )} + + )} +
        + ); + })} + + {doneNotifs.map((n) => ( +
        + ✓ + {n.label} + e.stopPropagation()} + > + Open → + + +
        + ))} +
        + )} +
        + ); +} diff --git a/frontend/src/components/shared/CollapsibleSection.tsx b/frontend/src/components/shared/CollapsibleSection.tsx new file mode 100644 index 0000000..b66cd23 --- /dev/null +++ b/frontend/src/components/shared/CollapsibleSection.tsx @@ -0,0 +1,65 @@ +import { useState } from "react"; + +const schemes = { + blue: { + border: "border-blue-200", + bg: "bg-blue-50", + text: "text-blue-800", + icon: "text-blue-500", + }, + amber: { + border: "border-amber-200", + bg: "bg-amber-50", + text: "text-amber-800", + icon: "text-amber-500", + }, + green: { + border: "border-green-200", + bg: "bg-green-50", + text: "text-green-800", + icon: "text-green-500", + }, +} as const; + +export default function CollapsibleSection({ + title, + colorScheme, + defaultOpen = false, + children, +}: { + title: string; + colorScheme: keyof typeof schemes; + defaultOpen?: boolean; + children: React.ReactNode; +}) { + const [isOpen, setIsOpen] = useState(defaultOpen); + const s = schemes[colorScheme]; + + return ( +
        + +
        +
        +
        {children}
        +
        +
        +
        + ); +} diff --git a/frontend/src/components/shared/KaTeXRenderer.tsx b/frontend/src/components/shared/KaTeXRenderer.tsx new file mode 100644 index 0000000..9dc78da --- /dev/null +++ b/frontend/src/components/shared/KaTeXRenderer.tsx @@ -0,0 +1,86 @@ +import { useMemo } from "react"; +import katex from "katex"; + +/** + * Pre-render all LaTeX in an HTML string at the string level, + * then set innerHTML. This avoids DOM-based auto-render issues + * where delimiters get split across text nodes or special chars + * like # cause silent failures. + */ +function renderLatexInString(html: string): string { + // Strip and 
         wrappers
+  let s = html
+    .replace(/]*class="latex"[^>]*>(.*?)<\/code>/gs, "$1")
+    .replace(/]*class="latex"[^>]*>(.*?)<\/pre>/gs, "$1");
+
+  // 1) Render display math: $$...$$ and \[...\]
+  s = s.replace(/\$\$([\s\S]+?)\$\$/g, (_match, tex: string) => {
+    return renderTex(tex.trim(), true);
+  });
+  s = s.replace(/\\\[([\s\S]+?)\\\]/g, (_match, tex: string) => {
+    return renderTex(tex.trim(), true);
+  });
+
+  // 2) Render inline math: $...$ and \(...\)
+  //    Negative lookbehind for \ to avoid matching \$ escapes
+  //    Also avoid matching $$ (already handled above)
+  s = s.replace(/(? {
+    return renderTex(tex, false);
+  });
+  s = s.replace(/\\\(([\s\S]+?)\\\)/g, (_match, tex: string) => {
+    return renderTex(tex, false);
+  });
+
+  return s;
+}
+
+function decodeHtmlEntities(s: string): string {
+  return s
+    .replace(/&/g, "&")
+    .replace(/</g, "<")
+    .replace(/>/g, ">")
+    .replace(/"/g, '"')
+    .replace(/'/g, "'")
+    .replace(/ /g, " ");
+}
+
+function renderTex(tex: string, displayMode: boolean): string {
+  // Decode HTML entities that might appear in DB-sourced HTML
+  let cleaned = decodeHtmlEntities(tex);
+  // Sanitize common issues that cause KaTeX to fail:
+  // 1) # and % inside \text{} — escape them
+  cleaned = cleaned.replace(/\\text\{([^}]*)\}/g, (_m, inner: string) => {
+    return "\\text{" + inner.replace(/#/g, "\\#").replace(/%/g, "\\%") + "}";
+  });
+  // 2) Standalone # outside \text{} in math — escape it
+  cleaned = cleaned.replace(/(?${tex}`;
+  }
+}
+
+export default function KaTeXRenderer({
+  html,
+  className,
+}: {
+  html: string;
+  className?: string;
+}) {
+  const rendered = useMemo(() => renderLatexInString(html), [html]);
+
+  return (
+    
        
+  );
+}
diff --git a/frontend/src/components/shared/StatusBadge.tsx b/frontend/src/components/shared/StatusBadge.tsx
new file mode 100644
index 0000000..8138813
--- /dev/null
+++ b/frontend/src/components/shared/StatusBadge.tsx
@@ -0,0 +1,15 @@
+const statusConfig = {
+  uploaded: { label: "Uploaded", bg: "bg-gray-100", text: "text-gray-600" },
+  processing: { label: "Processing...", bg: "bg-blue-100", text: "text-blue-700" },
+  ready: { label: "Ready", bg: "bg-green-100", text: "text-green-700" },
+  error: { label: "Error", bg: "bg-red-100", text: "text-red-700" },
+} as const;
+
+export default function StatusBadge({ status }: { status: string }) {
+  const config = statusConfig[status as keyof typeof statusConfig] ?? statusConfig.uploaded;
+  return (
+    
+      {config.label}
+    
+  );
+}
diff --git a/frontend/src/components/upload/FilePickerField.tsx b/frontend/src/components/upload/FilePickerField.tsx
new file mode 100644
index 0000000..9bacaeb
--- /dev/null
+++ b/frontend/src/components/upload/FilePickerField.tsx
@@ -0,0 +1,63 @@
+import { useRef, useState } from "react";
+
+export default function FilePickerField({
+  label,
+  required,
+  file,
+  onFileChange,
+}: {
+  label: string;
+  required?: boolean;
+  file: File | null;
+  onFileChange: (file: File | null) => void;
+}) {
+  const inputRef = useRef(null);
+  const [isDragging, setIsDragging] = useState(false);
+
+  const handleDrop = (e: React.DragEvent) => {
+    e.preventDefault();
+    setIsDragging(false);
+    const f = e.dataTransfer.files[0];
+    if (f?.type === "application/pdf") onFileChange(f);
+  };
+
+  return (
+    
        
+      
+      
         inputRef.current?.click()}
+        onDragOver={(e) => { e.preventDefault(); setIsDragging(true); }}
+        onDragLeave={() => setIsDragging(false)}
+        onDrop={handleDrop}
+      >
+         onFileChange(e.target.files?.[0] ?? null)}
+        />
+        {file ? (
+          
        
+            {file.name}
+            
+          
        
+        ) : (
+          
        
+            Click or drag PDF file here
+          
        
+        )}
+      
        
+    
        
+  );
+}
diff --git a/frontend/src/components/upload/UploadForm.tsx b/frontend/src/components/upload/UploadForm.tsx
new file mode 100644
index 0000000..35fd411
--- /dev/null
+++ b/frontend/src/components/upload/UploadForm.tsx
@@ -0,0 +1,184 @@
+import { useState, useCallback } from "react";
+import { useNavigate } from "react-router-dom";
+import { uploadPaper } from "@/lib/api";
+import FilePickerField from "./FilePickerField";
+
+/** Try to extract course code, year, term, exam type from filename */
+function parseFilename(name: string): {
+  courseCode?: string;
+  year?: number;
+  term?: string;
+  examType?: string;
+} {
+  const result: ReturnType = {};
+
+  // Remove extension
+  const base = name.replace(/\.[^.]+$/, "").replace(/[_\-]+/g, " ");
+
+  // Course code: 2-4 uppercase letters + 4 digits + optional letter (e.g. COMP2211, MATH1014H)
+  const courseMatch = base.match(/([A-Za-z]{2,4}\s*\d{4}[A-Za-z]?)/i);
+  if (courseMatch) {
+    result.courseCode = courseMatch[1].replace(/\s/g, "").toUpperCase();
+  }
+
+  // Year: 4-digit (2019-2029) or 2-digit (19-29)
+  const year4 = base.match(/\b(20[1-2]\d)\b/);
+  if (year4) {
+    result.year = Number(year4[1]);
+  } else {
+    const year2 = base.match(/\b(\d{2})\b/);
+    if (year2) {
+      const y = Number(year2[1]);
+      if (y >= 15 && y <= 29) result.year = 2000 + y;
+    }
+  }
+
+  // Term
+  const lower = base.toLowerCase();
+  if (/spring|spr/i.test(lower)) result.term = "spring";
+  else if (/fall|aut/i.test(lower)) result.term = "fall";
+  else if (/summer|sum/i.test(lower)) result.term = "summer";
+
+  // Exam type
+  if (/mid/i.test(lower)) result.examType = "midterm";
+  else if (/final|fin/i.test(lower)) result.examType = "final";
+  else if (/quiz/i.test(lower)) result.examType = "quiz";
+
+  return result;
+}
+
+export default function UploadForm() {
+  const navigate = useNavigate();
+  const [paperFile, setPaperFile] = useState(null);
+  const [answerFile, setAnswerFile] = useState(null);
+  const [courseCode, setCourseCode] = useState("");
+  const [year, setYear] = useState(new Date().getFullYear());
+  const [term, setTerm] = useState("fall");
+  const [examType, setExamType] = useState("midterm");
+  const [submitting, setSubmitting] = useState(false);
+  const [error, setError] = useState(null);
+  const [autoFilled, setAutoFilled] = useState(false);
+
+  const handlePaperFile = useCallback((file: File | null) => {
+    setPaperFile(file);
+    if (!file) { setAutoFilled(false); return; }
+
+    const parsed = parseFilename(file.name);
+    const filled: string[] = [];
+
+    if (parsed.courseCode) { setCourseCode(parsed.courseCode); filled.push("course"); }
+    if (parsed.year) { setYear(parsed.year); filled.push("year"); }
+    if (parsed.term) { setTerm(parsed.term); filled.push("term"); }
+    if (parsed.examType) { setExamType(parsed.examType); filled.push("type"); }
+
+    setAutoFilled(filled.length > 0);
+  }, []);
+
+  const handleSubmit = async (e: React.FormEvent) => {
+    e.preventDefault();
+    if (!paperFile || !courseCode) return;
+
+    setSubmitting(true);
+    setError(null);
+
+    try {
+      const fd = new FormData();
+      fd.append("paper_file", paperFile);
+      if (answerFile) fd.append("answer_file", answerFile);
+      fd.append("course_code", courseCode);
+      fd.append("year", String(year));
+      fd.append("term", term);
+      fd.append("exam_type", examType);
+
+      const result = await uploadPaper(fd);
+      navigate(`/paper/${result.paper_id}`);
+    } catch (err) {
+      setError(err instanceof Error ? err.message : "Upload failed");
+      setSubmitting(false);
+    }
+  };
+
+  return (
+    
        
+      
+      {autoFilled && (
+        
        
+          Auto-filled from filename — please verify below
+        
        
+      )}
+      
+
+      
        
+        
+         setCourseCode(e.target.value.toUpperCase())}
+          placeholder="e.g. COMP2011"
+          className="w-full border border-gray-300 rounded-lg px-3 py-2 text-sm focus:outline-none focus:ring-2 focus:ring-blue-500"
+          required
+        />
+      
        
+
+      
        
+        
        
+          
+           setYear(Number(e.target.value))}
+            className="w-full border border-gray-300 rounded-lg px-3 py-2 text-sm focus:outline-none focus:ring-2 focus:ring-blue-500"
+          />
+        
        
+        
        
+          
+          
+        
        
+        
        
+          
+          
+        
        
+      
        
+
+      {error && (
+        
        {error}
        
+      )}
+
+      
+    
+  );
+}
diff --git a/frontend/src/components/workbench/ActionBar.tsx b/frontend/src/components/workbench/ActionBar.tsx
new file mode 100644
index 0000000..0db0ddb
--- /dev/null
+++ b/frontend/src/components/workbench/ActionBar.tsx
@@ -0,0 +1,58 @@
+import type { Question } from "@/types/api";
+
+export default function ActionBar({
+  question,
+  onGenerateVariant,
+  isGenerating,
+  onPhotoOpen,
+  answerState,
+}: {
+  question: Question | null;
+  onGenerateVariant: () => void;
+  isGenerating: boolean;
+  onPhotoOpen: () => void;
+  answerState?: "correct" | "wrong" | null;
+}) {
+  if (!question) return null;
+
+  const isLong = question.question_type === "long_question" || question.question_type === "long_answer" || question.question_type === "coding";
+
+  return (
+    
        
+      {/* Answer state feedback (for non-long questions, driven by QuestionDetail) */}
+      {answerState && (
+        
        
+          {answerState === "correct" ? "Correct!" : "Added to error book"}
+        
        
+      )}
+
+      {/* Long question: Upload handwritten answer */}
+      {isLong && (
+        
+      )}
+
+      {/* Generate variant — always available */}
+      
+    
        
+  );
+}
diff --git a/frontend/src/components/workbench/AiTrioPanel.tsx b/frontend/src/components/workbench/AiTrioPanel.tsx
new file mode 100644
index 0000000..939283c
--- /dev/null
+++ b/frontend/src/components/workbench/AiTrioPanel.tsx
@@ -0,0 +1,21 @@
+import type { Question } from "@/types/api";
+import CollapsibleSection from "@/components/shared/CollapsibleSection";
+import KaTeXRenderer from "@/components/shared/KaTeXRenderer";
+
+export default function AiTrioPanel({ question }: { question: Question }) {
+  return (
+    
        
+      
+        
+      
+
+      
+        
+      
+
+      
+        
+      
+    
        
+  );
+}
diff --git a/frontend/src/components/workbench/PdfViewer.tsx b/frontend/src/components/workbench/PdfViewer.tsx
new file mode 100644
index 0000000..7fb100a
--- /dev/null
+++ b/frontend/src/components/workbench/PdfViewer.tsx
@@ -0,0 +1,170 @@
+import { useState, useRef, useEffect, useCallback } from "react";
+import { Document, Page, pdfjs } from "react-pdf";
+import "react-pdf/dist/Page/AnnotationLayer.css";
+import "react-pdf/dist/Page/TextLayer.css";
+
+pdfjs.GlobalWorkerOptions.workerSrc = `https://unpkg.com/pdfjs-dist@${pdfjs.version}/build/pdf.worker.min.mjs`;
+
+export default function PdfViewer({
+  fileUrl,
+  currentPage,
+  onPageChange,
+}: {
+  fileUrl: string;
+  currentPage?: number;
+  onPageChange?: (page: number) => void;
+}) {
+  const [numPages, setNumPages] = useState(0);
+  const [containerWidth, setContainerWidth] = useState(0);
+  const containerRef = useRef(null);
+  const scrollRef = useRef(null);
+  const pageRefs = useRef>(new Map());
+  const [jumpPage, setJumpPage] = useState("");
+  const programmaticScroll = useRef(false);
+
+  // Resize observer for container width
+  useEffect(() => {
+    if (!containerRef.current) return;
+    const observer = new ResizeObserver((entries) => {
+      setContainerWidth(entries[0].contentRect.width);
+    });
+    observer.observe(containerRef.current);
+    return () => observer.disconnect();
+  }, []);
+
+  // Scroll to page when currentPage changes (programmatic)
+  useEffect(() => {
+    if (!currentPage || currentPage < 1) return;
+    const el = pageRefs.current.get(currentPage);
+    if (el) {
+      programmaticScroll.current = true;
+      el.scrollIntoView({ behavior: "smooth", block: "start" });
+      setTimeout(() => { programmaticScroll.current = false; }, 2000);
+    }
+  }, [currentPage]);
+
+  // IntersectionObserver to detect visible page on user scroll
+  useEffect(() => {
+    if (numPages === 0 || !scrollRef.current) return;
+
+    const visiblePages = new Map();
+
+    const observer = new IntersectionObserver(
+      (entries) => {
+        for (const entry of entries) {
+          const pageNum = Number(entry.target.getAttribute("data-page"));
+          if (entry.isIntersecting) {
+            visiblePages.set(pageNum, entry.intersectionRatio);
+          } else {
+            visiblePages.delete(pageNum);
+          }
+        }
+
+        // Don't fire callback during programmatic scroll
+        if (programmaticScroll.current) return;
+
+        // Find the page with the highest visibility ratio
+        let bestPage = 0;
+        let bestRatio = 0;
+        for (const [page, ratio] of visiblePages) {
+          if (ratio > bestRatio) {
+            bestRatio = ratio;
+            bestPage = page;
+          }
+        }
+        if (bestPage > 0) {
+          onPageChange?.(bestPage);
+        }
+      },
+      {
+        root: scrollRef.current,
+        threshold: [0, 0.25, 0.5, 0.75, 1],
+      },
+    );
+
+    for (const [, el] of pageRefs.current) {
+      observer.observe(el);
+    }
+
+    return () => observer.disconnect();
+  }, [numPages, onPageChange]);
+
+  const setPageRef = useCallback((pageNum: number, el: HTMLDivElement | null) => {
+    if (el) {
+      el.setAttribute("data-page", String(pageNum));
+      pageRefs.current.set(pageNum, el);
+    } else {
+      pageRefs.current.delete(pageNum);
+    }
+  }, []);
+
+  const handleJump = () => {
+    const p = parseInt(jumpPage, 10);
+    if (p >= 1 && p <= numPages) {
+      const el = pageRefs.current.get(p);
+      el?.scrollIntoView({ behavior: "smooth", block: "start" });
+    }
+    setJumpPage("");
+  };
+
+  return (
+    
        
+      {/* Page controls */}
+      
        
+        
+          {numPages} pages
+        
+        |
+        
+          Go to{" "}
+           setJumpPage(e.target.value)}
+            onKeyDown={(e) => { if (e.key === "Enter") handleJump(); }}
+            placeholder="#"
+            className="w-12 text-center border border-gray-300 rounded px-1 py-0.5 text-sm"
+            min={1}
+            max={numPages}
+          />
+        
+      
        
+
+      {/* All pages scrollable */}
+      
        
+         setNumPages(n)}
+          loading={
+            
        
+              Loading PDF...
+            
        
+          }
+          error={
+            
        
+              Failed to load PDF
+            
        
+          }
+        >
+          {numPages > 0 &&
+            Array.from({ length: numPages }, (_, i) => i + 1).map((pageNum) => (
+              
         setPageRef(pageNum, el)}
+                className="flex justify-center mb-2"
+              >
+                
        
+                   0 ? containerWidth - 48 : undefined}
+                    renderAnnotationLayer
+                    renderTextLayer
+                  />
+                
        
+              
        
+            ))}
+                
+      
        
+    
        
+  );
+}
diff --git a/frontend/src/components/workbench/PhotoUpload.tsx b/frontend/src/components/workbench/PhotoUpload.tsx
new file mode 100644
index 0000000..2e739e0
--- /dev/null
+++ b/frontend/src/components/workbench/PhotoUpload.tsx
@@ -0,0 +1,90 @@
+import { useState, useRef } from "react";
+import { uploadPhoto } from "@/lib/api";
+import type { UserAttempt } from "@/types/api";
+
+export default function PhotoUpload({
+  questionId,
+  onClose,
+  onSubmitted,
+}: {
+  questionId: string;
+  onClose: () => void;
+  onSubmitted: (promise: Promise<{ attempt: UserAttempt; ocr_text: string; grade: { is_correct: boolean; score_given?: number; feedback: string } }>) => void;
+}) {
+  const [file, setFile] = useState(null);
+  const [preview, setPreview] = useState(null);
+  const [submitting, setSubmitting] = useState(false);
+  const [error, setError] = useState(null);
+  const inputRef = useRef(null);
+
+  const handleFile = (f: File) => {
+    setFile(f);
+    setPreview(URL.createObjectURL(f));
+    setError(null);
+  };
+
+  const handleSubmit = () => {
+    if (!file || submitting) return;
+    setSubmitting(true);
+    const promise = uploadPhoto(questionId, file);
+    // Close modal immediately, let parent handle the async result
+    onSubmitted(promise);
+    onClose();
+  };
+
+  return (
+    
        
+      
        
+        
        
+          
        
+            
        Upload Answer Photo
        
+            
+          
        
+
+          {!preview ? (
+            
         inputRef.current?.click()}
+              className="border-2 border-dashed border-gray-300 rounded-lg p-8 text-center cursor-pointer hover:border-blue-400 transition-colors"
+            >
+              
        📷
        
+              
        Click to take photo or select image
        
+               {
+                  const f = e.target.files?.[0];
+                  if (f) handleFile(f);
+                }}
+              />
+            
        
+          ) : (
+            
        
+              Preview
+              {error && (
+                
        {error}
        
+              )}
+              
        
+                
+                
+              
        
+            
        
+          )}
+        
        
+      
        
+    
        
+  );
+}
diff --git a/frontend/src/components/workbench/QuestionDetail.tsx b/frontend/src/components/workbench/QuestionDetail.tsx
new file mode 100644
index 0000000..785a88a
--- /dev/null
+++ b/frontend/src/components/workbench/QuestionDetail.tsx
@@ -0,0 +1,260 @@
+import { useState, useEffect } from "react";
+import type { Question } from "@/types/api";
+import { subquestionLabel } from "@/lib/questionGroups";
+
+const typeLabels: Record = {
+  mc: "Multiple Choice",
+  true_false: "True / False",
+  fill_blank: "Fill in Blank",
+  long_question: "Long Question",
+  long_answer: "Long Answer",
+  short_answer: "Short Answer",
+  coding: "Coding",
+};
+
+const difficultyColors: Record = {
+  easy: "bg-green-100 text-green-700",
+  medium: "bg-yellow-100 text-yellow-700",
+  hard: "bg-red-100 text-red-700",
+};
+
+export default function QuestionDetail({
+  question,
+  onAnswerResult,
+}: {
+  question: Question;
+  onAnswerResult?: (isCorrect: boolean, userAnswer: string) => void;
+}) {
+  const [selectedOption, setSelectedOption] = useState(null);
+  const [checked, setChecked] = useState(false);
+  const [fillAnswer, setFillAnswer] = useState("");
+  const [fillChecked, setFillChecked] = useState(false);
+  // True/False: per-statement answers { "a": "True", "b": "False", ... }
+  const [tfAnswer, setTfAnswer] = useState<"True" | "False" | null>(null);
+  const [tfChecked, setTfChecked] = useState(false);
+
+  // Reset state when question changes
+  useEffect(() => {
+    setSelectedOption(null);
+    setChecked(false);
+    setFillAnswer("");
+    setFillChecked(false);
+    setTfAnswer(null);
+    setTfChecked(false);
+  }, [question.id]);
+
+  const isCorrectMc = checked && selectedOption === question.correct_option;
+  const isCorrectFill =
+    fillChecked &&
+    question.correct_answer != null &&
+    fillAnswer.trim().toLowerCase() === question.correct_answer.trim().toLowerCase();
+
+  const handleMcCheck = () => {
+    if (!selectedOption) return;
+    setChecked(true);
+    const correct = selectedOption === question.correct_option;
+    onAnswerResult?.(correct, selectedOption);
+  };
+
+  const handleFillCheck = () => {
+    if (!fillAnswer.trim()) return;
+    setFillChecked(true);
+    const correct =
+      question.correct_answer != null &&
+      fillAnswer.trim().toLowerCase() === question.correct_answer.trim().toLowerCase();
+    onAnswerResult?.(correct, fillAnswer.trim());
+  };
+
+  const getOptionStyle = (label: string) => {
+    if (!checked) {
+      return label === selectedOption
+        ? "border-blue-400 bg-blue-50"
+        : "border-gray-200 hover:bg-gray-50";
+    }
+    if (label === question.correct_option) return "border-green-400 bg-green-50";
+    if (label === selectedOption) return "border-red-400 bg-red-50";
+    return "border-gray-200 opacity-50";
+  };
+
+  return (
+    
        
+      {/* Header row */}
+      
        
+        
+          Q{question.question_number.match(/^\d+/)?.[0] ?? question.question_number}
+        
+        {question.question_number.replace(/^\d+/, "") && (
+          
+            {subquestionLabel(question)}
+          
+        )}
+        
+          {typeLabels[question.question_type] ?? question.question_type}
+        
+        {question.score != null && (
+          {question.score} pts
+        )}
+        {question.difficulty && (
+          
+            {question.difficulty}
+          
+        )}
+      
        
+
+      {/* Topics */}
+      {question.topics && question.topics.length > 0 && (
+        
        
+          {question.topics.map((t) => (
+            
+              {t}
+            
+          ))}
+        
        
+      )}
+
+      {/* MC options */}
+      {question.question_type === "mc" && question.options && (
+        <>
+          
        
+            {question.options.map((opt) => (
+              
+            ))}
+          
        
+          {!checked && selectedOption && (
+            
+          )}
+          {checked && (
+            
        
+              {isCorrectMc ? "Correct!" : `Wrong — the answer is ${question.correct_option}`}
+            
        
+          )}
+        
+      )}
+
+      {/* True/False */}
+      {question.question_type === "true_false" && (() => {
+        // Normalize T/F/True/False to "true"/"false"
+        const normTF = (v: string | null | undefined): string => {
+          if (!v) return "";
+          const l = v.trim().toLowerCase();
+          if (l === "t" || l === "true") return "true";
+          if (l === "f" || l === "false") return "false";
+          return l;
+        };
+        const correctNorm = normTF(question.correct_option ?? question.correct_answer);
+        const correctDisplay = correctNorm === "true" ? "True" : "False";
+        return (
+        <>
+          
        
+            {(["True", "False"] as const).map((val) => {
+              const isSelected = tfAnswer === val;
+              const isCorrectVal = tfChecked && normTF(val) === correctNorm;
+              const isWrongVal = tfChecked && isSelected && !isCorrectVal;
+              return (
+                
+              );
+            })}
+          
        
+          {!tfChecked && tfAnswer && (
+            
+          )}
+          {tfChecked && (
+            
        
+              {normTF(tfAnswer) === correctNorm
+                ? "Correct!"
+                : `Wrong — the answer is ${correctDisplay}`}
+            
        
+          )}
+        
+        );
+      })()}
+
+      {/* Fill-blank input */}
+      {question.question_type === "fill_blank" && (
+        
        
+          
        
+             { if (!fillChecked) setFillAnswer(e.target.value); }}
+              placeholder="Type your answer..."
+              disabled={fillChecked}
+              className={`flex-1 border rounded-lg px-3 py-2 text-sm focus:outline-none focus:ring-2 focus:ring-blue-500 ${
+                fillChecked
+                  ? isCorrectFill ? "border-green-400 bg-green-50" : "border-red-400 bg-red-50"
+                  : "border-gray-300"
+              }`}
+              onKeyDown={(e) => { if (e.key === "Enter") handleFillCheck(); }}
+            />
+            {!fillChecked && (
+              
+            )}
+          
        
+          {fillChecked && (
+            
        
+              {isCorrectFill
+                ? "Correct!"
+                : `Wrong — the answer is: ${question.correct_answer ?? "N/A"}`}
+            
        
+          )}
+        
        
+      )}
+    
        
+  );
+}
diff --git a/frontend/src/components/workbench/QuestionNav.tsx b/frontend/src/components/workbench/QuestionNav.tsx
new file mode 100644
index 0000000..78818fc
--- /dev/null
+++ b/frontend/src/components/workbench/QuestionNav.tsx
@@ -0,0 +1,56 @@
+import type { Question } from "@/types/api";
+import type { QuestionGroup } from "@/lib/questionGroups";
+import { subquestionLabel } from "@/lib/questionGroups";
+
+export default function QuestionNav({
+  groups,
+  currentGroupKey,
+  currentQuestionId,
+  onSelectGroup,
+  onSelectQuestion,
+}: {
+  groups: QuestionGroup[];
+  currentGroupKey: string | null;
+  currentQuestionId: string | null;
+  onSelectGroup: (groupKey: string) => void;
+  onSelectQuestion: (questionId: string) => void;
+}) {
+  const activeGroup = groups.find((group) => group.key === currentGroupKey) ?? null;
+
+  return (
+    
        
+      
        
+        {groups.map((group) => (
+          
+        ))}
+      
        
+      {activeGroup && activeGroup.questions.length > 1 && (
+        
        
+          {activeGroup.questions.map((question) => (
+            
+          ))}
+        
        
+      )}
+    
        
+  );
+}
diff --git a/frontend/src/components/workbench/SimilarHistoryPanel.tsx b/frontend/src/components/workbench/SimilarHistoryPanel.tsx
new file mode 100644
index 0000000..b78fe5c
--- /dev/null
+++ b/frontend/src/components/workbench/SimilarHistoryPanel.tsx
@@ -0,0 +1,130 @@
+import { useEffect, useState } from "react";
+import { Link } from "react-router-dom";
+
+import { getSimilarQuestions } from "@/lib/api";
+import type { Question, SimilarQuestion } from "@/types/api";
+
+const typeLabel: Record = {
+  mc: "MC",
+  true_false: "T/F",
+  fill_blank: "Fill",
+  long_question: "Long",
+  long_answer: "Long",
+  short_answer: "Short",
+  coding: "Code",
+};
+
+function matchColor(percent: number): string {
+  if (percent >= 80) return "bg-green-100 text-green-700";
+  if (percent >= 60) return "bg-amber-100 text-amber-700";
+  return "bg-gray-100 text-gray-600";
+}
+
+function cleanReason(reason: string): string {
+  // "Shared topic: foo_bar, baz_qux" → "Shared topic: Foo Bar, Baz Qux"
+  return reason.replace(/[_]/g, " ").replace(/:\s*(.+)$/, (_, rest) =>
+    ": " + rest.split(",").map((s: string) =>
+      s.trim().replace(/\b\w/g, (c: string) => c.toUpperCase())
+    ).join(", ")
+  );
+}
+
+export default function SimilarHistoryPanel({ question }: { question: Question }) {
+  const [items, setItems] = useState([]);
+  const [loading, setLoading] = useState(true);
+  const [error, setError] = useState(null);
+  const [isOpen, setIsOpen] = useState(true);
+
+  useEffect(() => {
+    let cancelled = false;
+    setLoading(true);
+    setError(null);
+    setItems([]);
+    getSimilarQuestions(question.id)
+      .then((data) => {
+        if (cancelled) return;
+        setItems(data);
+        setLoading(false);
+      })
+      .catch((err: unknown) => {
+        if (cancelled) return;
+        setError(err instanceof Error ? err.message : "Failed to load.");
+        setLoading(false);
+      });
+    return () => { cancelled = true; };
+  }, [question.id]);
+
+  return (
+    
        
+      
+
+      {isOpen && (
+        
        
+          {loading && 
        Loading…
        }
+          {!loading && error && (
+            
        {error}
        
+          )}
+          {!loading && !error && items.length === 0 && (
+            
        No similar questions found.
        
+          )}
+
+          {items.map((item) => (
+            
+              {/* Match % badge */}
+              
+                {item.match_percent}%
+              
+
+              {/* Main info */}
+              
        
+                
        
+                  {item.source}
+                  ·
+                  Q{item.question_number}
+                  {item.question_type && (
+                    <>
+                      ·
+                      {typeLabel[item.question_type] ?? item.question_type}
+                    
+                  )}
+                
        
+
+                {/* Topics + reasons in one row */}
+                
        
+                  {item.topics.slice(0, 2).map((topic) => (
+                    
+                      {topic}
+                    
+                  ))}
+                  {item.match_reasons
+                    ?.filter((r) => !r.startsWith("Same format") && !r.startsWith("Same difficulty"))
+                    .slice(0, 2)
+                    .map((reason) => (
+                      
+                        {cleanReason(reason)}
+                      
+                    ))}
+                
        
+              
        
+
+              ›
+            
+          ))}
+        
        
+      )}
+    
        
+  );
+}
diff --git a/frontend/src/components/workbench/VariantDetail.tsx b/frontend/src/components/workbench/VariantDetail.tsx
new file mode 100644
index 0000000..9f8f05c
--- /dev/null
+++ b/frontend/src/components/workbench/VariantDetail.tsx
@@ -0,0 +1,148 @@
+import { useState } from "react";
+import type { VariantQuestion } from "@/types/api";
+import KaTeXRenderer from "@/components/shared/KaTeXRenderer";
+import CollapsibleSection from "@/components/shared/CollapsibleSection";
+
+export default function VariantDetail({
+  variant,
+}: {
+  variant: VariantQuestion;
+}) {
+  const [selectedOption, setSelectedOption] = useState(null);
+  const [checked, setChecked] = useState(false);
+  const [fillAnswer, setFillAnswer] = useState("");
+  const [fillChecked, setFillChecked] = useState(false);
+
+  const isMc = (variant.question_type === "mc" || variant.question_type === "true_false") && variant.options;
+
+  const handleMcCheck = () => {
+    if (!selectedOption) return;
+    setChecked(true);
+  };
+
+  const handleFillCheck = () => {
+    if (!fillAnswer.trim()) return;
+    setFillChecked(true);
+  };
+
+  const isCorrectMc = checked && selectedOption === variant.correct_answer;
+  const isCorrectFill =
+    fillChecked &&
+    fillAnswer.trim().toLowerCase() === variant.correct_answer.trim().toLowerCase();
+
+  const getOptionStyle = (label: string) => {
+    if (!checked) {
+      return label === selectedOption
+        ? "border-blue-400 bg-blue-50"
+        : "border-gray-200 hover:bg-gray-50";
+    }
+    if (label === variant.correct_answer) return "border-green-400 bg-green-50";
+    if (label === selectedOption) return "border-red-400 bg-red-50";
+    return "border-gray-200 opacity-50";
+  };
+
+  return (
+    
        
+      {/* Header */}
+      
        
+        V
+        Similar Question
+        
+          {variant.question_type}
+        
+      
        
+
+      {/* Question text */}
+      
        
+        
+      
        
+
+      {/* MC options */}
+      {isMc && variant.options && (
+        <>
+          
        
+            {variant.options.map((opt) => (
+              
+            ))}
+          
        
+          {!checked && selectedOption && (
+            
+          )}
+          {checked && (
+            
        
+              {isCorrectMc ? "Correct!" : `Wrong — the answer is ${variant.correct_answer}`}
+            
        
+          )}
+        
+      )}
+
+      {/* Non-MC input */}
+      {!isMc && (
+        
        
+          
        
+             { if (!fillChecked) setFillAnswer(e.target.value); }}
+              placeholder="Type your answer..."
+              disabled={fillChecked}
+              className={`flex-1 border rounded-lg px-3 py-2 text-sm focus:outline-none focus:ring-2 focus:ring-blue-500 ${
+                fillChecked
+                  ? isCorrectFill ? "border-green-400 bg-green-50" : "border-red-400 bg-red-50"
+                  : "border-gray-300"
+              }`}
+              onKeyDown={(e) => { if (e.key === "Enter") handleFillCheck(); }}
+            />
+            {!fillChecked && (
+              
+            )}
+          
        
+          {fillChecked && (
+            
        
+              {isCorrectFill ? "Correct!" : `Answer: ${variant.correct_answer}`}
+            
        
+          )}
+        
        
+      )}
+
+      {/* AI Trio */}
+      
        
+        {variant.knowledge_reminder && (
+          
+            
+          
+        )}
+        {variant.ai_hint && (
+          
+            
+          
+        )}
+        
+          
+        
+      
        
+    
        
+  );
+}
diff --git a/frontend/src/components/workbench/VariantModal.tsx b/frontend/src/components/workbench/VariantModal.tsx
new file mode 100644
index 0000000..b83c4f0
--- /dev/null
+++ b/frontend/src/components/workbench/VariantModal.tsx
@@ -0,0 +1,189 @@
+import { useState } from "react";
+import type { VariantQuestion } from "@/types/api";
+import KaTeXRenderer from "@/components/shared/KaTeXRenderer";
+
+export default function VariantModal({
+  variant,
+  onClose,
+}: {
+  variant: VariantQuestion;
+  onClose: () => void;
+}) {
+  const [selectedOption, setSelectedOption] = useState(null);
+  const [checked, setChecked] = useState(false);
+  const [fillAnswer, setFillAnswer] = useState("");
+  const [fillChecked, setFillChecked] = useState(false);
+  const [showKnowledge, setShowKnowledge] = useState(false);
+  const [showHint, setShowHint] = useState(false);
+  const [showSolution, setShowSolution] = useState(false);
+
+  const isMc = (variant.question_type === "mc" || variant.question_type === "true_false") && variant.options;
+
+  const handleMcCheck = () => {
+    if (!selectedOption) return;
+    setChecked(true);
+  };
+
+  const handleFillCheck = () => {
+    if (!fillAnswer.trim()) return;
+    setFillChecked(true);
+  };
+
+  const isCorrectMc = checked && selectedOption === variant.correct_answer;
+  const isCorrectFill =
+    fillChecked &&
+    fillAnswer.trim().toLowerCase() === variant.correct_answer.trim().toLowerCase();
+
+  const getOptionStyle = (label: string) => {
+    if (!checked) {
+      return label === selectedOption
+        ? "border-blue-400 bg-blue-50"
+        : "border-gray-200 hover:bg-gray-50";
+    }
+    if (label === variant.correct_answer) return "border-green-400 bg-green-50";
+    if (label === selectedOption) return "border-red-400 bg-red-50";
+    return "border-gray-200 opacity-50";
+  };
+
+  return (
+    
        
+      
        
+        
        
+          
        
+            
        Similar Question
        
+            
+          
        
+
+          {/* Question text */}
+          
        
+            
+          
        
+
+          {/* MC options */}
+          {isMc && variant.options && (
+            <>
+              
        
+                {variant.options.map((opt) => (
+                  
+                ))}
+              
        
+              {!checked && selectedOption && (
+                
+              )}
+              {checked && (
+                
        
+                  {isCorrectMc ? "Correct!" : `Wrong — the answer is ${variant.correct_answer}`}
+                
        
+              )}
+            
+          )}
+
+          {/* Non-MC input */}
+          {!isMc && (
+            
        
+              
        
+                 { if (!fillChecked) setFillAnswer(e.target.value); }}
+                  placeholder="Type your answer..."
+                  disabled={fillChecked}
+                  className={`flex-1 border rounded-lg px-3 py-2 text-sm focus:outline-none focus:ring-2 focus:ring-blue-500 ${
+                    fillChecked
+                      ? isCorrectFill ? "border-green-400 bg-green-50" : "border-red-400 bg-red-50"
+                      : "border-gray-300"
+                  }`}
+                  onKeyDown={(e) => { if (e.key === "Enter") handleFillCheck(); }}
+                />
+                {!fillChecked && (
+                  
+                )}
+              
        
+              {fillChecked && (
+                
        
+                  {isCorrectFill ? "Correct!" : `Answer: ${variant.correct_answer}`}
+                
        
+              )}
+            
        
+          )}
+
+          {/* AI Trio: Knowledge / Hint / Solution */}
+          
        
+            {variant.knowledge_reminder && (
+              
        
+                
+                {showKnowledge && (
+                  
        
+                    
+                  
        
+                )}
+              
        
+            )}
+            {variant.ai_hint && (
+              
        
+                
+                {showHint && (
+                  
        
+                    
+                  
        
+                )}
+              
        
+            )}
+            
        
+              
+              {showSolution && (
+                
        
+                  
+                
        
+              )}
+            
        
+          
        
+
+          
+        
        
+      
        
+    
        
+  );
+}
diff --git a/frontend/src/contexts/AuthContext.tsx b/frontend/src/contexts/AuthContext.tsx
new file mode 100644
index 0000000..194ae55
--- /dev/null
+++ b/frontend/src/contexts/AuthContext.tsx
@@ -0,0 +1,49 @@
+import { createContext, useContext, useEffect, useState } from "react";
+import type { Session, User } from "@supabase/supabase-js";
+import { supabase } from "@/lib/supabase";
+
+interface AuthContextValue {
+  session: Session | null;
+  user: User | null;
+  loading: boolean;
+  signOut: () => Promise;
+}
+
+const AuthContext = createContext({
+  session: null,
+  user: null,
+  loading: true,
+  signOut: async () => {},
+});
+
+export function AuthProvider({ children }: { children: React.ReactNode }) {
+  const [session, setSession] = useState(null);
+  const [loading, setLoading] = useState(true);
+
+  useEffect(() => {
+    supabase.auth.getSession().then(({ data }) => {
+      setSession(data.session);
+      setLoading(false);
+    });
+
+    const { data: { subscription } } = supabase.auth.onAuthStateChange((_event, session) => {
+      setSession(session);
+    });
+
+    return () => subscription.unsubscribe();
+  }, []);
+
+  const signOut = async () => {
+    await supabase.auth.signOut();
+  };
+
+  return (
+    
+      {children}
+    
+  );
+}
+
+export function useAuth() {
+  return useContext(AuthContext);
+}
diff --git a/frontend/src/hooks/usePaper.ts b/frontend/src/hooks/usePaper.ts
new file mode 100644
index 0000000..ff9dd1a
--- /dev/null
+++ b/frontend/src/hooks/usePaper.ts
@@ -0,0 +1,43 @@
+import { useEffect, useState } from "react";
+import { getPaper } from "@/lib/api";
+import type { Paper } from "@/types/api";
+
+const POLL_INTERVAL = 3000;
+
+export function usePaper(paperId: string) {
+  const [paper, setPaper] = useState(null);
+  const [loading, setLoading] = useState(true);
+  const [error, setError] = useState(null);
+
+  useEffect(() => {
+    let intervalId: number | null = null;
+    let cancelled = false;
+
+    const fetchPaper = async () => {
+      try {
+        const data = await getPaper(paperId);
+        if (cancelled) return;
+        setPaper(data);
+        setLoading(false);
+        if (data.status === "ready" || data.status === "error") {
+          if (intervalId !== null) clearInterval(intervalId);
+        }
+      } catch (err) {
+        if (cancelled) return;
+        setError(err instanceof Error ? err.message : "Unknown error");
+        setLoading(false);
+        if (intervalId !== null) clearInterval(intervalId);
+      }
+    };
+
+    fetchPaper();
+    intervalId = window.setInterval(fetchPaper, POLL_INTERVAL);
+
+    return () => {
+      cancelled = true;
+      if (intervalId !== null) clearInterval(intervalId);
+    };
+  }, [paperId]);
+
+  return { paper, loading, error };
+}
diff --git a/frontend/src/hooks/useQuestions.ts b/frontend/src/hooks/useQuestions.ts
new file mode 100644
index 0000000..6f595f9
--- /dev/null
+++ b/frontend/src/hooks/useQuestions.ts
@@ -0,0 +1,33 @@
+import { useEffect, useState } from "react";
+import { getQuestions } from "@/lib/api";
+import type { Question } from "@/types/api";
+
+export function useQuestions(paperId: string, enabled: boolean) {
+  const [questions, setQuestions] = useState([]);
+  const [loading, setLoading] = useState(false);
+  const [error, setError] = useState(null);
+
+  useEffect(() => {
+    if (!enabled) return;
+    let cancelled = false;
+    setLoading(true);
+
+    getQuestions(paperId)
+      .then((data) => {
+        if (!cancelled) {
+          setQuestions(data);
+          setLoading(false);
+        }
+      })
+      .catch((err) => {
+        if (!cancelled) {
+          setError(err instanceof Error ? err.message : "Unknown error");
+          setLoading(false);
+        }
+      });
+
+    return () => { cancelled = true; };
+  }, [paperId, enabled]);
+
+  return { questions, loading, error };
+}
diff --git a/frontend/src/lib/api.ts b/frontend/src/lib/api.ts
new file mode 100644
index 0000000..60b3416
--- /dev/null
+++ b/frontend/src/lib/api.ts
@@ -0,0 +1,190 @@
+import type {
+  CourseAnalytics,
+  Paper,
+  Question,
+  QuestionVariant,
+  SimilarQuestion,
+  UploadResponse,
+  UserAttempt,
+} from "@/types/api";
+import { supabase } from "@/lib/supabase";
+
+const API_BASE = "/api";
+
+async function authHeaders(): Promise> {
+  const { data } = await supabase.auth.getSession();
+  const token = data.session?.access_token;
+  if (!token) return {};
+  return { Authorization: `Bearer ${token}` };
+}
+
+export async function uploadPaper(formData: FormData): Promise {
+  const headers = await authHeaders();
+  const res = await fetch(`${API_BASE}/papers/upload`, {
+    method: "POST",
+    headers,
+    body: formData,
+  });
+  if (!res.ok) throw new Error(`Upload failed: ${res.status}`);
+  return res.json();
+}
+
+export async function getPaper(paperId: string): Promise {
+  const res = await fetch(`${API_BASE}/papers/${paperId}`);
+  if (!res.ok) throw new Error(`Paper not found: ${res.status}`);
+  return res.json();
+}
+
+export async function getQuestions(paperId: string): Promise {
+  const res = await fetch(`${API_BASE}/papers/${paperId}/questions`);
+  if (!res.ok) throw new Error(`Questions fetch failed: ${res.status}`);
+  return res.json();
+}
+
+export async function myPapers(): Promise {
+  const headers = await authHeaders();
+  const res = await fetch(`${API_BASE}/papers/mine`, { headers });
+  if (!res.ok) throw new Error(`My papers fetch failed: ${res.status}`);
+  return res.json();
+}
+
+export async function listPapers(): Promise {
+  const res = await fetch(`${API_BASE}/papers/`);
+  if (!res.ok) throw new Error(`List papers failed: ${res.status}`);
+  return res.json();
+}
+
+export async function recordAttempt(
+  questionId: string,
+  attemptType: string,
+  userAnswer: string | null,
+  isCorrect: boolean | null,
+): Promise {
+  const headers = await authHeaders();
+  const res = await fetch(`${API_BASE}/attempts/`, {
+    method: "POST",
+    headers: { "Content-Type": "application/json", ...headers },
+    body: JSON.stringify({
+      question_id: questionId,
+      attempt_type: attemptType,
+      user_answer: userAnswer,
+      is_correct: isCorrect,
+    }),
+  });
+  if (!res.ok) throw new Error(`Attempt save failed: ${res.status}`);
+  return res.json();
+}
+
+export async function uploadPhoto(
+  questionId: string,
+  photo: File,
+): Promise<{ attempt: UserAttempt; ocr_text: string; grade: { is_correct: boolean; score_given?: number; feedback: string; error_at_step: number | null } }> {
+  const headers = await authHeaders();
+  const fd = new FormData();
+  fd.append("question_id", questionId);
+  fd.append("photo", photo);
+  const res = await fetch(`${API_BASE}/attempts/photo`, {
+    method: "POST",
+    headers,
+    body: fd,
+  });
+  if (!res.ok) throw new Error(`Photo upload failed: ${res.status}`);
+  return res.json();
+}
+
+export async function getPaperAttempts(paperId: string): Promise<{
+  question_id: string;
+  is_correct: boolean;
+  feedback: string | null;
+  photo_ocr_text: string | null;
+}[]> {
+  const headers = await authHeaders();
+  const res = await fetch(`${API_BASE}/attempts/by-paper/${paperId}`, { headers });
+  if (!res.ok) return [];
+  return res.json();
+}
+
+export async function generateVariant(questionId: string): Promise {
+  const headers = await authHeaders();
+  const res = await fetch(`${API_BASE}/questions/${questionId}/variant`, {
+    method: "POST",
+    headers,
+  });
+  if (!res.ok) throw new Error(`Variant generation failed: ${res.status}`);
+  return res.json();
+}
+
+export async function getVariants(questionId: string): Promise {
+  const headers = await authHeaders();
+  const res = await fetch(`${API_BASE}/questions/${questionId}/variants`, { headers });
+  if (!res.ok) throw new Error(`Variants fetch failed: ${res.status}`);
+  return res.json();
+}
+
+export async function updateVariant(variantId: string, data: { favorited?: boolean }): Promise {
+  const headers = await authHeaders();
+  const res = await fetch(`${API_BASE}/questions/variant/${variantId}`, {
+    method: "PATCH",
+    headers: { "Content-Type": "application/json", ...headers },
+    body: JSON.stringify(data),
+  });
+  if (!res.ok) throw new Error(`Variant update failed: ${res.status}`);
+  return res.json();
+}
+
+export async function deleteVariant(variantId: string): Promise {
+  const headers = await authHeaders();
+  await fetch(`${API_BASE}/questions/variant/${variantId}`, { method: "DELETE", headers });
+}
+
+export async function getFavoriteVariants(): Promise {
+  const headers = await authHeaders();
+  const res = await fetch(`${API_BASE}/questions/variants/favorited`, { headers });
+  if (!res.ok) throw new Error(`Favorited variants fetch failed: ${res.status}`);
+  return res.json();
+}
+
+export async function getErrorBook(courseCode?: string): Promise {
+  const headers = await authHeaders();
+  const params = new URLSearchParams();
+  if (courseCode) params.set("course_code", courseCode);
+  const query = params.toString() ? `?${params.toString()}` : "";
+  const res = await fetch(`${API_BASE}/attempts/error-book${query}`, { headers });
+  if (!res.ok) throw new Error(`Error book fetch failed: ${res.status}`);
+  return res.json();
+}
+
+export async function updateAttempt(
+  attemptId: string,
+  data: { in_error_book?: boolean; mastered?: boolean },
+): Promise {
+  const headers = await authHeaders();
+  const res = await fetch(`${API_BASE}/attempts/${attemptId}`, {
+    method: "PATCH",
+    headers: { "Content-Type": "application/json", ...headers },
+    body: JSON.stringify(data),
+  });
+  if (!res.ok) throw new Error(`Attempt update failed: ${res.status}`);
+  return res.json();
+}
+
+export async function listCourses(): Promise {
+  const res = await fetch(`${API_BASE}/analytics/courses`);
+  if (!res.ok) throw new Error(`Courses fetch failed: ${res.status}`);
+  return res.json();
+}
+
+export async function getCourseAnalytics(courseCode: string): Promise {
+  const res = await fetch(`${API_BASE}/analytics/course/${courseCode}`);
+  if (!res.ok) throw new Error(`Analytics fetch failed: ${res.status}`);
+  return res.json();
+}
+
+export async function getSimilarQuestions(
+  questionId: string,
+  limit = 6,
+): Promise {
+  const res = await fetch(`${API_BASE}/questions/${questionId}/similar?limit=${limit}`);
+  if (!res.ok) throw new Error(`Similar question fetch failed: ${res.status}`);
+  return res.json();
+}
diff --git a/frontend/src/lib/questionGroups.ts b/frontend/src/lib/questionGroups.ts
new file mode 100644
index 0000000..747e827
--- /dev/null
+++ b/frontend/src/lib/questionGroups.ts
@@ -0,0 +1,45 @@
+import type { Question } from "@/types/api";
+
+export interface QuestionGroup {
+  key: string;
+  label: string;
+  questions: Question[];
+  startPage: number;
+}
+
+function topLevelKey(questionNumber: string): string {
+  const match = questionNumber.match(/^\d+/);
+  return match?.[0] ?? questionNumber;
+}
+
+export function groupQuestions(questions: Question[]): QuestionGroup[] {
+  const groups = new Map();
+
+  for (const question of questions) {
+    const key = topLevelKey(question.question_number);
+    const existing = groups.get(key);
+    if (existing) {
+      existing.questions.push(question);
+      existing.startPage = Math.min(existing.startPage, question.page_number ?? existing.startPage);
+      continue;
+    }
+    groups.set(key, {
+      key,
+      label: `Q${key}`,
+      questions: [question],
+      startPage: question.page_number ?? 1,
+    });
+  }
+
+  return Array.from(groups.values()).sort((a, b) => Number(a.key) - Number(b.key));
+}
+
+export function subquestionLabel(question: Question): string {
+  const remainder = question.question_number.replace(/^\d+/, "");
+  if (!remainder) return "Main";
+  return remainder
+    .replace(/^_+/, "")
+    .split("_")
+    .filter(Boolean)
+    .join(".");
+}
diff --git a/frontend/src/lib/supabase.ts b/frontend/src/lib/supabase.ts
new file mode 100644
index 0000000..0ec9556
--- /dev/null
+++ b/frontend/src/lib/supabase.ts
@@ -0,0 +1,6 @@
+import { createClient } from "@supabase/supabase-js";
+
+const supabaseUrl = import.meta.env.VITE_SUPABASE_URL as string;
+const supabaseAnonKey = import.meta.env.VITE_SUPABASE_ANON_KEY as string;
+
+export const supabase = createClient(supabaseUrl, supabaseAnonKey);
diff --git a/frontend/src/main.tsx b/frontend/src/main.tsx
new file mode 100644
index 0000000..7b67097
--- /dev/null
+++ b/frontend/src/main.tsx
@@ -0,0 +1,16 @@
+import { StrictMode } from "react";
+import { createRoot } from "react-dom/client";
+import { BrowserRouter } from "react-router-dom";
+import App from "./App";
+import { AuthProvider } from "./contexts/AuthContext";
+import "./styles/globals.css";
+
+createRoot(document.getElementById("root")!).render(
+  
+    
+      
+        
+      
+    
+  ,
+);
diff --git a/frontend/src/pages/AnalyticsPage.tsx b/frontend/src/pages/AnalyticsPage.tsx
new file mode 100644
index 0000000..0a23883
--- /dev/null
+++ b/frontend/src/pages/AnalyticsPage.tsx
@@ -0,0 +1,521 @@
+import { useEffect, useMemo, useState } from "react";
+import { Link, useNavigate, useParams } from "react-router-dom";
+
+import Header from "@/components/layout/Header";
+import { getCourseAnalytics, listCourses } from "@/lib/api";
+import type { CourseAnalytics, AnalyticsTopicQuestion } from "@/types/api";
+
+const typeLabel: Record = {
+  mc: "Multiple Choice",
+  true_false: "True / False",
+  fill_blank: "Fill in Blank",
+  long_question: "Long Question",
+  short_answer: "Short Answer",
+  coding: "Coding",
+};
+
+const TYPE_COLORS: Record = {
+  mc: "bg-violet-50 text-violet-700 border-violet-200",
+  true_false: "bg-amber-50 text-amber-700 border-amber-200",
+  fill_blank: "bg-teal-50 text-teal-700 border-teal-200",
+  long_question: "bg-sky-50 text-sky-700 border-sky-200",
+  short_answer: "bg-rose-50 text-rose-700 border-rose-200",
+  coding: "bg-emerald-50 text-emerald-700 border-emerald-200",
+};
+
+const DIFF_COLORS: Record = {
+  hard: "text-red-600 bg-red-50 border-red-200",
+  medium: "text-amber-600 bg-amber-50 border-amber-200",
+  easy: "text-green-600 bg-green-50 border-green-200",
+};
+
+type QItem = AnalyticsTopicQuestion;
+type Analytics = CourseAnalytics;
+
+const PAGE_SIZE = 8;
+
+export default function AnalyticsPage() {
+  const { courseCode } = useParams<{ courseCode?: string }>();
+  const navigate = useNavigate();
+
+  const [courses, setCourses] = useState([]);
+  const [search, setSearch] = useState("");
+
+  useEffect(() => { listCourses().then(setCourses).catch(() => {}); }, []);
+  const filtered = useMemo(() => {
+    const q = search.trim().toUpperCase();
+    return q ? courses.filter((c) => c.includes(q)) : courses;
+  }, [courses, search]);
+
+  const normalizedCourse = courseCode?.toUpperCase();
+  const [analytics, setAnalytics] = useState(null);
+  const [loading, setLoading] = useState(false);
+  const [error, setError] = useState(null);
+
+  useEffect(() => {
+    if (!normalizedCourse) return;
+    let cancelled = false;
+    setLoading(true);
+    setAnalytics(null);
+    setError(null);
+    getCourseAnalytics(normalizedCourse)
+      .then((data) => { if (!cancelled) { setAnalytics(data); setLoading(false); } })
+      .catch((err) => { if (!cancelled) { setError(err instanceof Error ? err.message : "Failed"); setLoading(false); } });
+    return () => { cancelled = true; };
+  }, [normalizedCourse]);
+
+  // ── Course picker ──
+  if (!normalizedCourse) {
+    return (
+      
        
+        
        
+        
        
+          
        Analytics
        
+          
        Select a course to view statistics.
        
+           setSearch(e.target.value)}
+            className="w-full px-4 py-2.5 border border-gray-300 rounded-xl text-sm focus:outline-none focus:ring-2 focus:ring-blue-500 mb-4"
+          />
+          {filtered.length === 0 ? (
+            
        No courses found.
        
+          ) : (
+            
        
+              {filtered.map((code) => (
+                
+              ))}
+            
        
+          )}
+        
        
+      
        
+    );
+  }
+
+  // ── Dashboard ──
+  return (
+    
        
+      
        
+      
        
+        
        
+          
+          /
+          
        {normalizedCourse}
        
+        
        
+
+        {loading && 
        Loading analytics...
        }
+        {error && 
        {error}
        }
+
+        {!loading && !error && analytics && (
+          <>
+            {/* KPI row */}
+            
        
+              
+              
+              
+              
+            
        
+
+            {/* Main area: left = search, right = charts */}
+            
        
+              {/* Left: Global search */}
+               t.label)} />
+
+              {/* Right: Interactive charts + stats */}
+              
        
+                 ({ label: t.label, value: t.count }))}
+                  typeData={analytics.question_types.map((t) => ({ label: typeLabel[t.label] ?? t.label, value: t.count }))}
+                  diffData={[
+                    { label: "Easy", value: analytics.difficulty_distribution.easy },
+                    { label: "Medium", value: analytics.difficulty_distribution.medium },
+                    { label: "Hard", value: analytics.difficulty_distribution.hard },
+                  ].filter((d) => d.value > 0)}
+                />
+
+                
+                  {analytics.high_yield_topics.length === 0 ? (
+                    
        No data yet.
        
+                  ) : (
+                    
          
+                      {analytics.high_yield_topics.map((t, i) => (
+                        
        • 
+                          {i + 1}
+                          {t}
+                        
          • 
+                      ))}
+                    
        
+                  )}
+                        
+              
        
+            
        
+          
+        )}
+      
        
+    
        
+  );
+}
+
+// ── Global Search Engine ──
+function GlobalSearch({ questions, topics }: { questions: QItem[]; topics: string[] }) {
+  const [search, setSearch] = useState("");
+  const [topicFilter, setTopicFilter] = useState(null);
+  const [typeFilter, setTypeFilter] = useState(null);
+  const [yearFilter, setYearFilter] = useState(null);
+  const [termFilter, setTermFilter] = useState(null);
+  const [diffFilter, setDiffFilter] = useState(null);
+  const [visibleCount, setVisibleCount] = useState(PAGE_SIZE);
+
+  const types = useMemo(() => [...new Set(questions.map((q) => q.question_type))].sort(), [questions]);
+  const years = useMemo(() => [...new Set(questions.map((q) => q.year).filter(Boolean))].sort((a, b) => (b ?? 0) - (a ?? 0)) as number[], [questions]);
+  const terms = useMemo(() => {
+    const order = ["spring", "summer", "fall", "winter"];
+    return [...new Set(questions.map((q) => q.term).filter(Boolean))].sort((a, b) => order.indexOf(a!) - order.indexOf(b!)) as string[];
+  }, [questions]);
+  const diffs = useMemo(() => [...new Set(questions.map((q) => q.difficulty).filter(Boolean))] as string[], [questions]);
+
+  const filtered = useMemo(() => {
+    const q = search.toLowerCase();
+    return questions.filter((item) => {
+      if (topicFilter && !item.topics?.includes(topicFilter)) return false;
+      if (typeFilter && item.question_type !== typeFilter) return false;
+      if (yearFilter && item.year !== yearFilter) return false;
+      if (termFilter && item.term !== termFilter) return false;
+      if (diffFilter && item.difficulty !== diffFilter) return false;
+      if (q && !item.preview.toLowerCase().includes(q) && !item.source.toLowerCase().includes(q) && !item.question_number.toLowerCase().includes(q) && !item.topics?.some((t) => t.toLowerCase().includes(q))) return false;
+      return true;
+    });
+  }, [questions, search, topicFilter, typeFilter, yearFilter, termFilter, diffFilter]);
+
+  const activeCount = [topicFilter, typeFilter, yearFilter, termFilter, diffFilter].filter(Boolean).length;
+
+  useEffect(() => setVisibleCount(PAGE_SIZE), [search, topicFilter, typeFilter, yearFilter, termFilter, diffFilter]);
+
+  const visible = filtered.slice(0, visibleCount);
+  const hasMore = visibleCount < filtered.length;
+
+  return (
+    
        
+      
        Question Search
        
+
+      {/* Search bar */}
+      
        
+         setSearch(e.target.value)}
+          placeholder="Search questions, topics, papers..."
+          className="w-full pl-9 pr-3 py-2.5 text-sm border border-gray-200 rounded-xl bg-gray-50 focus:bg-white focus:outline-none focus:ring-2 focus:ring-blue-400"
+        />
+        🔍
+      
        
+
+      {/* Filter rows */}
+      
        
+        {/* Topic */}
+        
+          
+        
+
+        {/* Type + Year + Term + Difficulty in one row */}
+        
        
+          
+            
        
+              {types.map((t) => (
+                 setTypeFilter(typeFilter === t ? null : t)} />
+              ))}
+            
        
+                  
+
+          
+            
        
+              {years.map((y) => (
+                 setYearFilter(yearFilter === y ? null : y)} />
+              ))}
+            
        
+                  
+
+          
+            
        
+              {terms.map((t) => (
+                 setTermFilter(termFilter === t ? null : t)} />
+              ))}
+            
        
+                  
+
+          
+            
        
+              {(["easy", "medium", "hard"] as const).filter((d) => diffs.includes(d)).map((d) => (
+                 setDiffFilter(diffFilter === d ? null : d)} />
+              ))}
+            
        
+                  
+        
        
+      
        
+
+      {/* Results count + clear */}
+      
        
+        
+          {filtered.length} question{filtered.length !== 1 ? "s" : ""}
+          {activeCount > 0 || search ? " matched" : ""}
+        
+        {(activeCount > 0 || search) && (
+          
+        )}
+      
        
+
+      {/* Results */}
+      
        
+        {visible.map((q, i) => (
+          
+        ))}
+      
        
+
+      {hasMore && (
+        
+      )}
+      {filtered.length === 0 && (
+        
        No questions match your search.
        
+      )}
+    
        
+  );
+}
+
+// ── Interactive Pie Chart ──
+const PIE_PALETTE = [
+  "#3B82F6", "#8B5CF6", "#F59E0B", "#10B981", "#EF4444",
+  "#EC4899", "#06B6D4", "#F97316", "#6366F1", "#14B8A6",
+];
+
+function InteractiveChart({ topicData, typeData, diffData }: {
+  topicData: { label: string; value: number }[];
+  typeData: { label: string; value: number }[];
+  diffData: { label: string; value: number }[];
+}) {
+  const [view, setView] = useState<"topic" | "type" | "difficulty">("topic");
+  const [hovered, setHovered] = useState(null);
+
+  const data = view === "topic" ? topicData : view === "type" ? typeData : diffData;
+  const colors = view === "difficulty"
+    ? ["#10B981", "#F59E0B", "#EF4444"]
+    : PIE_PALETTE;
+
+  const total = data.reduce((s, d) => s + d.value, 0);
+
+  // Build conic-gradient
+  let cumPct = 0;
+  const segments = data.map((d, i) => {
+    const pct = total ? (d.value / total) * 100 : 0;
+    const start = cumPct;
+    cumPct += pct;
+    return { ...d, pct, start, end: cumPct, color: colors[i % colors.length] };
+  });
+
+  const gradient = segments
+    .map((s) => `${s.color} ${s.start}% ${s.end}%`)
+    .join(", ");
+
+  return (
+    
        
+      {/* Tab switcher */}
+      
        
+        {(["topic", "type", "difficulty"] as const).map((t) => (
+          
+        ))}
+      
        
+
+      {/* Pie */}
+      
        
+        
        
+          
        
+          
        
+            {hovered !== null ? (
+              
        
+                
        {segments[hovered].value}
        
+                
        {segments[hovered].pct.toFixed(0)}%
        
+              
        
+            ) : (
+              
        
+                
        {total}
        
+                
        total
        
+              
        
+            )}
+          
        
+        
        
+
+        {/* Legend */}
+        
        
+          {segments.map((s, i) => (
+            
         setHovered(i)}
+              onMouseLeave={() => setHovered(null)}
+              className={`flex items-center gap-2 px-2 py-1 rounded-lg cursor-default transition-colors ${
+                hovered === i ? "bg-gray-50" : ""
+              }`}
+            >
+              
+              {s.label}
+              {s.value}
+            
        
+          ))}
+        
        
+      
        
+    
        
+  );
+}
+
+// ── Shared components ──
+function QuestionCard({ question: q }: { question: QItem }) {
+  const typeColor = TYPE_COLORS[q.question_type] ?? "bg-gray-50 text-gray-600 border-gray-200";
+  const cleanPreview = (q.preview || "")
+    .replace(/^Problem\s+\d+\s*\[.*?\]\s*/i, "")
+    .replace(/^(True\/False Questions?\s*)?Indicate whether.*?(answer\.\s*)/i, "")
+    .trim();
+
+  return (
+    
+      
+        {q.question_number}
+      
+      
        
+        
        
+          {q.source}
+          ·
+          
+            {typeLabel[q.question_type] ?? q.question_type}
+          
+          {q.difficulty && (
+            <>
+              ·
+              
+                {q.difficulty}
+              
+            
+          )}
+          {q.topics?.slice(0, 2).map((t) => (
+            {t}
+          ))}
+        
        
+        
        {cleanPreview || q.preview}
        
+      
        
+      →
+    
+  );
+}
+
+function FilterRow({ label, children }: { label: string; children: React.ReactNode }) {
+  return (
+    
        
+      {label}
+      {children}
+    
        
+  );
+}
+
+function Pill({ label, active, color, onClick }: { label: string; active: boolean; color?: string; onClick: () => void }) {
+  return (
+    
+  );
+}
+
+function KpiCard({ label, value }: { label: string; value: string | number }) {
+  return (
+    
        
+      
        {value}
        
+      
        {label}
        
+    
        
+  );
+}
+
+function Panel({ title, children }: { title: string; children: React.ReactNode }) {
+  return (
+    
        
+      
        {title}
        
+      {children}
+    
        
+  );
+}
+
+function TopicCombobox({ topics, value, onChange }: { topics: string[]; value: string | null; onChange: (v: string | null) => void }) {
+  const [input, setInput] = useState("");
+  const [open, setOpen] = useState(false);
+
+  const filtered = useMemo(() => {
+    const q = input.toLowerCase();
+    return q ? topics.filter((t) => t.toLowerCase().includes(q)) : topics;
+  }, [topics, input]);
+
+  const handleSelect = (t: string | null) => {
+    onChange(t);
+    setInput(t ?? "");
+    setOpen(false);
+  };
+
+  return (
+    
        
+      
        
+         { setInput(e.target.value); setOpen(true); if (!e.target.value) onChange(null); }}
+          onFocus={() => setOpen(true)}
+          placeholder="All Topics"
+          className="text-xs border border-gray-200 rounded-lg px-2 py-1.5 bg-white focus:outline-none focus:ring-1 focus:ring-blue-400 w-48"
+        />
+        {value && (
+          
+        )}
+      
        
+      {open && filtered.length > 0 && (
+        
        
+          {filtered.map((t) => (
+            
+          ))}
+        
        
+      )}
+      {open && 
         setOpen(false)} />}
+    
        
+  );
+}
+
+function DiffStat({ label, value }: { label: string; value: number }) {
+  return (
+    
        
+      
        {value}
        
+      
        {label}
        
+    
        
+  );
+}
diff --git a/frontend/src/pages/ErrorBookPage.tsx b/frontend/src/pages/ErrorBookPage.tsx
new file mode 100644
index 0000000..0e3529c
--- /dev/null
+++ b/frontend/src/pages/ErrorBookPage.tsx
@@ -0,0 +1,296 @@
+import { useEffect, useMemo, useState } from "react";
+import { Link } from "react-router-dom";
+
+import Header from "@/components/layout/Header";
+import KaTeXRenderer from "@/components/shared/KaTeXRenderer";
+import { getErrorBook, updateAttempt, getFavoriteVariants, updateVariant } from "@/lib/api";
+import { useAuth } from "@/contexts/AuthContext";
+import type { UserAttempt, QuestionVariant } from "@/types/api";
+
+const typeLabel: Record = {
+  mc: "Multiple Choice",
+  true_false: "True / False",
+  fill_blank: "Fill in Blank",
+  long_question: "Long Question",
+  short_answer: "Short Answer",
+  coding: "Coding",
+};
+
+const TYPE_COLORS: Record = {
+  mc: "bg-violet-50 text-violet-700",
+  true_false: "bg-amber-50 text-amber-700",
+  fill_blank: "bg-teal-50 text-teal-700",
+  long_question: "bg-sky-50 text-sky-700",
+  short_answer: "bg-rose-50 text-rose-700",
+  coding: "bg-emerald-50 text-emerald-700",
+};
+
+const DIFF_COLORS: Record = {
+  easy: "text-green-600",
+  medium: "text-amber-600",
+  hard: "text-red-600",
+};
+
+export default function ErrorBookPage() {
+  const { user } = useAuth();
+  const [entries, setEntries] = useState([]);
+  const [favoriteVariants, setFavoriteVariants] = useState([]);
+  const [loading, setLoading] = useState(true);
+  const [error, setError] = useState(null);
+  const [courseFilter, setCourseFilter] = useState("all");
+
+  useEffect(() => {
+    if (!user) { setLoading(false); return; }
+    let cancelled = false;
+    setLoading(true);
+    Promise.all([getErrorBook(), getFavoriteVariants()])
+      .then(([attempts, variants]) => {
+        if (cancelled) return;
+        setEntries(attempts);
+        setFavoriteVariants(variants);
+        setLoading(false);
+      })
+      .catch((err) => {
+        if (cancelled) return;
+        setError(err instanceof Error ? err.message : "Failed to load error book");
+        setLoading(false);
+      });
+    return () => { cancelled = true; };
+  }, [user]);
+
+  const courses = useMemo(
+    () => Array.from(new Set(
+      entries.map((e) => e.paper_questions?.paper?.course_code).filter((v): v is string => Boolean(v)),
+    )).sort(),
+    [entries],
+  );
+
+  const filteredEntries = useMemo(() => {
+    if (courseFilter === "all") return entries;
+    return entries.filter((e) => e.paper_questions?.paper?.course_code === courseFilter);
+  }, [courseFilter, entries]);
+
+  async function handleMarkMastered(attemptId: string) {
+    await updateAttempt(attemptId, { mastered: true });
+    setEntries((prev) => prev.filter((e) => e.id !== attemptId));
+  }
+
+  async function handleRemove(attemptId: string) {
+    await updateAttempt(attemptId, { in_error_book: false });
+    setEntries((prev) => prev.filter((e) => e.id !== attemptId));
+  }
+
+  async function handleUnfavoriteVariant(variantId: string) {
+    await updateVariant(variantId, { favorited: false });
+    setFavoriteVariants((prev) => prev.filter((v) => v.id !== variantId));
+  }
+
+  return (
+    
        
+      
        
+      
        
+        {/* Header */}
+        
        
+          
        
+            
        Error Book
        
+            
        Review your mistakes and track progress.
        
+          
        
+          
        
+            
+            
+          
        
+        
        
+
+        {/* Course filter */}
+        
        
+           setCourseFilter("all")} label="All" />
+          {courses.map((c) => (
+             setCourseFilter(c)} label={c} />
+          ))}
+        
        
+
+        {!user && (
+          
        
+            
        🔒
        
+            
        Sign in to unlock your Error Book
        
+            
+              Sign in
+            
+          
        
+        )}
+        {user && loading && 
        Loading...
        }
+        {user && error && 
        {error}
        }
+
+        {user && !loading && !error && filteredEntries.length === 0 && favoriteVariants.length === 0 && (
+          
        
+            
        🎉
        
+            
        No mistakes yet. Keep practicing!
        
+          
        
+        )}
+
+        {/* Saved variants */}
+        {favoriteVariants.length > 0 && (
+          
        
+            
        
+              Saved Variants ({favoriteVariants.length})
+            
        
+            
        
+              {favoriteVariants.map((v) => (
+                
        
+                  ★
+                  
        
+                    Variant of Q{v.source_question_number}
+                    
        {v.variant_data.question_text?.replace(/<[^>]*>/g, "").slice(0, 100)}
        
+                  
        
+                  
+                
        
+              ))}
+            
        
+          
        
+        )}
+
+        {/* Error entries */}
+        
        
+          {filteredEntries.map((entry) => (
+             void handleMarkMastered(entry.id)}
+              onRemove={() => void handleRemove(entry.id)}
+            />
+          ))}
+        
        
+      
        
+    
        
+  );
+}
+
+function ErrorCard({ entry, onMastered, onRemove }: { entry: UserAttempt; onMastered: () => void; onRemove: () => void }) {
+  const [showFeedback, setShowFeedback] = useState(true);
+  const question = entry.paper_questions;
+  if (!question) return null;
+
+  const courseCode = question.paper?.course_code;
+  const paperId = question.paper?.id;
+  const paper = question.paper;
+  const paperInfo = paper ? `${paper.year} ${paper.term} ${paper.exam_type}` : "";
+  const typeColor = TYPE_COLORS[question.question_type] ?? "bg-gray-100 text-gray-600";
+  const diffColor = DIFF_COLORS[question.difficulty ?? ""] ?? "";
+
+  // Clean preview: strip boilerplate
+  const preview = (question.question_text || "")
+    .replace(/^Problem\s+\d+\s*\[.*?\]\s*/i, "")
+    .slice(0, 200);
+
+  return (
+    
        
+      {/* Header */}
+      
        
+        
        
+          
        
+            
+              {question.question_number}
+            
+            
        
+              
        
+                
+                  {typeLabel[question.question_type] ?? question.question_type}
+                
+                {question.difficulty && (
+                  {question.difficulty}
+                )}
+                {courseCode && (
+                  
+                    {courseCode}
+                  
+                )}
+              
        
+              
        
+                {paperId ? {paperInfo} : paperInfo}
+                {" · "}
+                {new Date(entry.created_at).toLocaleDateString("en-CA")}
+              
        
+            
        
+          
        
+
+          {/* Score badge */}
+          {entry.feedback && (
+            
        
+              ✗
+              Incorrect
+            
        
+          )}
+        
        
+
+        {/* Question preview */}
+        
        {preview}
        
+
+        {/* Topics */}
+        {question.topics && question.topics.length > 0 && (
+          
        
+            {question.topics.slice(0, 4).map((t) => (
+              {t}
+            ))}
+          
        
+        )}
+      
        
+
+      {/* AI Feedback section */}
+      {entry.feedback && (
+        
        
+          
+          {showFeedback && (
+            
        
+              
+            
        
+          )}
+        
        
+      )}
+
+      {/* Actions */}
+      
        
+        {paperId && (
+          
+            Open paper →
+          
+        )}
+        
+        
+      
        
+    
        
+  );
+}
+
+function StatCard({ label, value, color }: { label: string; value: number; color: string }) {
+  const bg = color === "red" ? "bg-red-50 border-red-200" : "bg-blue-50 border-blue-200";
+  const text = color === "red" ? "text-red-700" : "text-blue-700";
+  return (
+    
        
+      
        {value}
        
+      
        {label}
        
+    
        
+  );
+}
+
+function Pill({ active, onClick, label }: { active: boolean; onClick: () => void; label: string }) {
+  return (
+    
+  );
+}
diff --git a/frontend/src/pages/HomePage.tsx b/frontend/src/pages/HomePage.tsx
new file mode 100644
index 0000000..0b55eb8
--- /dev/null
+++ b/frontend/src/pages/HomePage.tsx
@@ -0,0 +1,705 @@
+import { useEffect, useRef, useState } from "react";
+import { Link, useNavigate } from "react-router-dom";
+import { listPapers, myPapers } from "@/lib/api";
+import { useAuth } from "@/contexts/AuthContext";
+import type { Paper } from "@/types/api";
+
+function getWorkedIds(userId: string): string[] {
+  try {
+    const raw = localStorage.getItem(`worked_papers_${userId}`);
+    return raw ? JSON.parse(raw) : [];
+  } catch { return []; }
+}
+
+const fontSora = { fontFamily: "'Sora', sans-serif" };
+const fontMono = { fontFamily: "'IBM Plex Mono', monospace" };
+
+/* ── Feature cards data ── */
+const FEATURES = [
+  {
+    icon: (
+      
+        
+      
+    ),
+    title: "AI Analysis",
+    desc: "Every question gets knowledge reminders, hints, and step-by-step solutions.",
+    color: "#6366F1",
+  },
+  {
+    icon: (
+      
+        
+      
+    ),
+    title: "Smart Error Book",
+    desc: "Auto-collect mistakes with AI feedback. Review, understand, and master.",
+    color: "#E11D48",
+  },
+  {
+    icon: (
+      
+        
+      
+    ),
+    title: "Course Analytics",
+    desc: "Topic frequency, difficulty distribution, and high-yield focus areas.",
+    color: "#0D9488",
+  },
+  {
+    icon: (
+      
+        
+      
+    ),
+    title: "Variant Generation",
+    desc: "Generate unlimited similar questions for extra practice on weak topics.",
+    color: "#7C3AED",
+  },
+];
+
+/* ── Filter options ── */
+const COURSE_OPTIONS = ["COMP2011", "COMP2211", "MATH1014", "PHYS1112", "MATH2023", "ELEC2100"];
+const TERM_OPTIONS = ["spring", "fall"];
+const TYPE_OPTIONS = ["midterm", "final"];
+
+/* ── Chevron SVG ── */
+function ChevronDown({ className = "" }: { className?: string }) {
+  return (
+    
+      
+    
+  );
+}
+
+/* ── Dropdown select component ── */
+function Dropdown({
+  label,
+  value,
+  options,
+  onChange,
+}: {
+  label: string;
+  value: string | null;
+  options: { value: string; label: string }[];
+  onChange: (v: string | null) => void;
+}) {
+  const [open, setOpen] = useState(false);
+  const ref = useRef(null);
+
+  useEffect(() => {
+    const handler = (e: MouseEvent) => {
+      if (ref.current && !ref.current.contains(e.target as Node)) setOpen(false);
+    };
+    document.addEventListener("mousedown", handler);
+    return () => document.removeEventListener("mousedown", handler);
+  }, []);
+
+  const selected = options.find((o) => o.value === value);
+
+  return (
+    
        
+      
        
+        {label}
+      
        
+      
+      {open && (
+        
        
+          
+          {options.map((o) => (
+            
+          ))}
+        
        
+      )}
+    
        
+  );
+}
+
+export default function HomePage() {
+  const navigate = useNavigate();
+  const { user, signOut } = useAuth();
+  const [papers, setPapers] = useState([]);
+  const [papersLoading, setPapersLoading] = useState(false);
+  const [myUploadedPapers, setMyUploadedPapers] = useState([]);
+  const [workedPapers, setWorkedPapers] = useState([]);
+  const [courseInput, setCourseInput] = useState("");
+  const [courseFilter, setCourseFilter] = useState(null);
+  const [showSuggestions, setShowSuggestions] = useState(false);
+  const [termFilter, setTermFilter] = useState(null);
+  const [typeFilter, setTypeFilter] = useState(null);
+  const [analyzing, setAnalyzing] = useState(false);
+  const inputRef = useRef(null);
+
+  // Autocomplete suggestions
+  const suggestions = courseInput.trim()
+    ? COURSE_OPTIONS.filter((c) =>
+        c.toLowerCase().includes(courseInput.trim().toLowerCase())
+      )
+    : [];
+
+  // Close suggestions on outside click
+  useEffect(() => {
+    const handler = (e: MouseEvent) => {
+      if (inputRef.current && !inputRef.current.contains(e.target as Node)) setShowSuggestions(false);
+    };
+    document.addEventListener("mousedown", handler);
+    return () => document.removeEventListener("mousedown", handler);
+  }, []);
+
+  useEffect(() => {
+    let cancelled = false;
+    setPapersLoading(true);
+    listPapers()
+      .then((data) => {
+        if (cancelled) return;
+        setPapers(
+          data.sort((a, b) => {
+            if (a.course_code !== b.course_code) return a.course_code.localeCompare(b.course_code);
+            if (a.year !== b.year) return b.year - a.year;
+            if (a.term !== b.term) return a.term.localeCompare(b.term);
+            return a.exam_type.localeCompare(b.exam_type);
+          }),
+        );
+      })
+      .catch(() => {
+        if (!cancelled) setPapers([]);
+      })
+      .finally(() => {
+        if (!cancelled) setPapersLoading(false);
+      });
+
+    return () => {
+      cancelled = true;
+    };
+  }, []);
+
+  // My Papers
+  useEffect(() => {
+    if (!user) return;
+    let cancelled = false;
+    myPapers().then((data) => {
+      if (cancelled) return;
+      setMyUploadedPapers(data.filter((p) => p.status !== "error"));
+    }).catch(() => {});
+    return () => { cancelled = true; };
+  }, [user]);
+
+  useEffect(() => {
+    if (!user || papers.length === 0) return;
+    const workedIds = new Set(getWorkedIds(user.id));
+    setWorkedPapers(papers.filter((p) => workedIds.has(p.id)));
+  }, [user, papers]);
+
+  // Filter papers
+  const hasFilter = courseFilter || termFilter || typeFilter;
+  const filteredPapers = papers.filter((p) => {
+    if (courseFilter && p.course_code !== courseFilter) return false;
+    if (termFilter && p.term !== termFilter) return false;
+    if (typeFilter && p.exam_type !== typeFilter) return false;
+    return true;
+  });
+
+  const selectCourse = (code: string) => {
+    setCourseInput(code);
+    setCourseFilter(code);
+    setShowSuggestions(false);
+  };
+
+  return (
+    
        
+      {/* ══════ Nav ══════ */}
+      
+
+      {/* ══════ Hero + Filter ══════ */}
+      
        
+        
        
+          
        
+            The Smartest Way to
        
+            Master Past Papers
+          
        
+          
        
+            Upload any HKUST past paper. AI breaks down every question with analysis,
+            hints, and solutions — so you study smarter, not harder.
+          
        
+
+          {/* ── Filter row: Course input + Term dropdown + Type dropdown ── */}
+          
        
+            
        
+              {/* Course code input with autocomplete */}
+              
        
+                
        
+                  Course Code
+                
        
+                
        
+                   {
+                      const v = e.target.value.toUpperCase();
+                      setCourseInput(v);
+                      setCourseFilter(COURSE_OPTIONS.includes(v) ? v : null);
+                      setShowSuggestions(true);
+                    }}
+                    onFocus={() => setShowSuggestions(true)}
+                    placeholder="e.g. COMP2011"
+                    className="flex-1 px-3.5 py-2.5 text-sm text-slate-800 outline-none bg-transparent font-semibold"
+                    style={fontMono}
+                  />
+                  {courseInput && (
+                    
+                  )}
+                
        
+                {/* Autocomplete dropdown */}
+                {showSuggestions && suggestions.length > 0 && !courseFilter && (
+                  
        
+                    {suggestions.map((c) => (
+                      
+                    ))}
+                  
        
+                )}
+              
        
+
+              {/* Term dropdown */}
+              
+
+              {/* Exam Type dropdown */}
+              
+
+              {/* Buttons */}
+              
        
+                
        
+                  
        
+                  
+                
        
+                
        
+                  
        
+                  
+                
        
+              
        
+            
        
+
+            {/* ── Results panel ── */}
+            {hasFilter && (
+              
        
+                {papersLoading ? (
+                  
        
+                    
        Loading papers...
        
+                  
        
+                ) : filteredPapers.length === 0 ? (
+                  
        
+                    
        No papers match these filters
        
+                  
        
+                ) : (
+                  <>
+                    
        
+                      
+                        {filteredPapers.length} paper{filteredPapers.length > 1 ? "s" : ""} found
+                      
+                      {courseFilter && (
+                        
+                          
+                            
+                          
+                          AI Analytics · {courseFilter}
+                        
+                      )}
+                    
        
+                    {filteredPapers.map((p) => (
+                      
+                    ))}
+                  
+                )}
+              
        
+            )}
+          
        
+
+          {/* Quick stats — real data */}
+          
        
+            {[
+              [String(papers.filter(p => p.status === "ready").length), "Past Papers"],
+              [String(papers.reduce((s, p) => s + (p.question_count || 0), 0)), "Questions Analyzed"],
+              [String(new Set(papers.filter(p => p.status === "ready").map(p => p.course_code)).size), "Courses"],
+            ].map(([num, label]) => (
+              
        
+                
        {num}
        
+                
        {label}
        
+              
        
+            ))}
+          
        
+        
        
+
+        {/* Decorative grid */}
+        
        
+      
        
+
+      
        
+        {/* ══════ Features ══════ */}
+        
        
+          
        
+            Platform Features
+          
        
+          
        
+            {FEATURES.map((f) => (
+              
        
+                
        
+                  {f.icon}
+                
        
+                
        
+                  {f.title}
+                
        
+                
        
+                  {f.desc}
+                
        
+              
        
+            ))}
+          
        
+        
        
+
+        {/* ══════ My Papers ══════ */}
+        {user && (
+          
        
+            
        
+              My Papers
+            
        
+            {myUploadedPapers.length === 0 && workedPapers.length === 0 ? (
+              
        
+                
        No papers yet. Upload a past paper or open one to get started.
        
+              
        
+            ) : (
+            
        
+              {/* Uploaded */}
+              {myUploadedPapers.length > 0 && (
+                
        
+                  
        
+                    Uploaded
+                  
        
+                  
        
+                    {myUploadedPapers.map((p) => (
+                      
+                        
        
+                          {p.course_code}
+                          {p.year} {p.term} {p.exam_type}
+                        
        
+                        
+                          {p.status === "processing" ? (
+                            
+                              
+                              PROCESSING
+                            
+                          ) : p.status.toUpperCase()}
+                        
+                      
+                    ))}
+                  
        
+                
        
+              )}
+
+              {/* Worked on */}
+              {workedPapers.length > 0 && (
+                
        
+                  
        
+                    Recently Worked
+                  
        
+                  
        
+                    {workedPapers.map((p) => (
+                      
+                        
        
+                          {p.course_code}
+                          {p.year} {p.term} {p.exam_type}
+                        
        
+                        
+                          
+                        
+                      
+                    ))}
+                  
        
+                
        
+              )}
+            
        
+            )}
+          
        
+        )}
+
+        {/* ══════ CTA Banner ══════ */}
+        
        
+          
        
+            
        
+              
        
+                Ready to ace your exams?
+              
        
+              
        
+                Upload a past paper and let AI do the heavy lifting.
+              
        
+            
        
+            
        
+              
+                Upload Paper
+              
+              
+                View Analytics
+              
+            
        
+          
        
+        
        
+      
        
+
+      {/* ══════ Footer ══════ */}
+      
        
+        
        
+          
+            PastPaper Master · HKUST · 2025
+          
+          
        
+            About
+            Contact
+            Privacy
+          
        
+        
        
+      
        
+    
        
+  );
+}
diff --git a/frontend/src/pages/LoginPage.tsx b/frontend/src/pages/LoginPage.tsx
new file mode 100644
index 0000000..bdd06e3
--- /dev/null
+++ b/frontend/src/pages/LoginPage.tsx
@@ -0,0 +1,90 @@
+import { useState } from "react";
+import { supabase } from "@/lib/supabase";
+
+export default function LoginPage() {
+  const [email, setEmail] = useState("");
+  const [password, setPassword] = useState("");
+  const [mode, setMode] = useState<"signin" | "signup">("signin");
+  const [error, setError] = useState(null);
+  const [loading, setLoading] = useState(false);
+  const handleSubmit = async (e: React.FormEvent) => {
+    e.preventDefault();
+    setError(null);
+    setLoading(true);
+    try {
+      if (mode === "signin") {
+        const { error } = await supabase.auth.signInWithPassword({ email, password });
+        if (error) throw error;
+      } else {
+        const { error } = await supabase.auth.signUp({ email, password });
+        if (error) throw error;
+        // Auto sign in after signup (requires email confirm disabled in Supabase dashboard)
+        const { error: signInError } = await supabase.auth.signInWithPassword({ email, password });
+        if (signInError) throw signInError;
+      }
+    } catch (err: unknown) {
+      setError(err instanceof Error ? err.message : "Something went wrong");
+    } finally {
+      setLoading(false);
+    }
+  };
+
+  return (
+    
        
+      
        
+        
        
+          
        PastPaper Master
        
+          
        {mode === "signin" ? "Sign in to continue" : "Create your account"}
        
+        
        
+
+        
        
+          
        
+            
+             setEmail(e.target.value)}
+              required
+              className="w-full px-3 py-2 border border-gray-300 rounded-lg text-sm focus:outline-none focus:ring-2 focus:ring-blue-500 focus:border-transparent"
+              placeholder="you@example.com"
+            />
+          
        
+          
        
+            
+             setPassword(e.target.value)}
+              required
+              minLength={6}
+              className="w-full px-3 py-2 border border-gray-300 rounded-lg text-sm focus:outline-none focus:ring-2 focus:ring-blue-500 focus:border-transparent"
+              placeholder="••••••"
+            />
+          
        
+
+          {error && (
+            
        {error}
        
+          )}
+
+          
+        
        
+
+        
        
+          {mode === "signin" ? "No account? " : "Already have one? "}
+          
+        
        
+      
        
+    
        
+  );
+}
diff --git a/frontend/src/pages/UploadPage.tsx b/frontend/src/pages/UploadPage.tsx
new file mode 100644
index 0000000..453b6e8
--- /dev/null
+++ b/frontend/src/pages/UploadPage.tsx
@@ -0,0 +1,16 @@
+import Header from "@/components/layout/Header";
+import UploadForm from "@/components/upload/UploadForm";
+
+export default function UploadPage() {
+  return (
+    
        
+      
        
+      
        
+        
        
+          Upload Past Paper
+        
        
+        
+      
        
+    
        
+  );
+}
diff --git a/frontend/src/pages/WorkbenchPage.tsx b/frontend/src/pages/WorkbenchPage.tsx
new file mode 100644
index 0000000..25c6c98
--- /dev/null
+++ b/frontend/src/pages/WorkbenchPage.tsx
@@ -0,0 +1,524 @@
+import { useState, useEffect, useCallback, useRef } from "react";
+import { useParams } from "react-router-dom";
+import Header from "@/components/layout/Header";
+import PdfViewer from "@/components/workbench/PdfViewer";
+import QuestionNav from "@/components/workbench/QuestionNav";
+import QuestionDetail from "@/components/workbench/QuestionDetail";
+import AiTrioPanel from "@/components/workbench/AiTrioPanel";
+import SimilarHistoryPanel from "@/components/workbench/SimilarHistoryPanel";
+import ActionBar from "@/components/workbench/ActionBar";
+import PhotoUpload from "@/components/workbench/PhotoUpload";
+import VariantDetail from "@/components/workbench/VariantDetail";
+import KaTeXRenderer from "@/components/shared/KaTeXRenderer";
+import { usePaper } from "@/hooks/usePaper";
+import { useQuestions } from "@/hooks/useQuestions";
+import { generateVariant, getVariants, updateVariant, deleteVariant, recordAttempt, getPaperAttempts } from "@/lib/api";
+import { groupQuestions } from "@/lib/questionGroups";
+import { useAuth } from "@/contexts/AuthContext";
+import type { QuestionVariant } from "@/types/api";
+
+const WORKED_KEY = (userId: string) => `worked_papers_${userId}`;
+const WORKED_THRESHOLD_MS = 3 * 60 * 1000; // 3 minutes
+
+function markWorked(userId: string, paperId: string) {
+  try {
+    const raw = localStorage.getItem(WORKED_KEY(userId));
+    const ids: string[] = raw ? JSON.parse(raw) : [];
+    if (!ids.includes(paperId)) {
+      localStorage.setItem(WORKED_KEY(userId), JSON.stringify([...ids, paperId]));
+    }
+  } catch { /* silent */ }
+}
+
+export default function WorkbenchPage() {
+  const { id } = useParams<{ id: string }>();
+  const { user } = useAuth();
+  const { paper, loading: paperLoading, error: paperError } = usePaper(id!);
+  const isReady = paper?.status === "ready";
+  const { questions, loading: questionsLoading } = useQuestions(id!, isReady);
+  const [currentQuestionId, setCurrentQuestionId] = useState(null);
+  const [showPhoto, setShowPhoto] = useState(false);
+  // Grading result per question
+  const [gradingResults, setGradingResults] = useState>(new Map());
+  // Track which grading panels are expanded
+  const [gradingExpanded, setGradingExpanded] = useState>(new Set());
+
+  // Tab state
+  const [activeTab, setActiveTab] = useState<"questions" | "variants">("questions");
+  // variants per question: questionId → QuestionVariant[]
+  const [variantMap, setVariantMap] = useState>(new Map());
+  // which question IDs have been fetched from server
+  const loadedRef = useRef>(new Set());
+  // generating state
+  const [isGenerating, setIsGenerating] = useState(false);
+  // Currently viewing variant (full detail view)
+  const [activeVariantId, setActiveVariantId] = useState(null);
+
+  // Cooldown: ignore scroll-based updates for 2s after user clicks a question
+  const lastUserSelectTime = useRef(0);
+
+  const handleQuestionSelect = useCallback((questionId: string) => {
+    lastUserSelectTime.current = Date.now();
+    setCurrentQuestionId(questionId);
+  }, []);
+
+  const groups = groupQuestions(questions);
+  const currentQuestion =
+    questions.find((question) => question.id === currentQuestionId)
+    ?? questions[0]
+    ?? null;
+  const currentGroupKey = currentQuestion?.question_number.match(/^\d+/)?.[0] ?? null;
+  const paperTitle = paper
+    ? `${paper.year} ${paper.term} ${paper.exam_type}`
+    : undefined;
+
+  const currentVariants = variantMap.get(currentQuestion?.id ?? "") ?? [];
+  const activeVariant = currentVariants.find((v) => v.id === activeVariantId) ?? null;
+
+  const handleGroupSelect = useCallback((groupKey: string) => {
+    lastUserSelectTime.current = Date.now();
+    const group = groups.find((item) => item.key === groupKey);
+    if (group?.questions[0]) {
+      setCurrentQuestionId(group.questions[0].id);
+    }
+  }, [groups]);
+
+  useEffect(() => {
+    if (questions.length === 0) {
+      setCurrentQuestionId(null);
+      return;
+    }
+    setCurrentQuestionId((prev) =>
+      prev && questions.some((question) => question.id === prev) ? prev : questions[0].id,
+    );
+  }, [questions]);
+
+  // 3-minute worked tracking
+  useEffect(() => {
+    if (!id || !user) return;
+    const timer = setTimeout(() => markWorked(user.id, id), WORKED_THRESHOLD_MS);
+    return () => clearTimeout(timer);
+  }, [id, user]);
+
+  // Load historical grading results
+  useEffect(() => {
+    if (!id || !user || !isReady) return;
+    getPaperAttempts(id).then((attempts) => {
+      const map = new Map();
+      for (const a of attempts) {
+        map.set(a.question_id, {
+          isCorrect: a.is_correct,
+          feedback: a.feedback || "",
+          ocrText: a.photo_ocr_text || "",
+        });
+      }
+      if (map.size > 0) {
+        setGradingResults((prev) => {
+          const next = new Map(prev);
+          for (const [k, v] of map) {
+            if (!next.has(k)) next.set(k, v); // don't overwrite current session
+          }
+          return next;
+        });
+        setGradingExpanded(new Set(map.keys()));
+      }
+    }).catch(() => {});
+  }, [id, user, isReady]);
+
+  // Load variants for current question (once per question ID)
+  useEffect(() => {
+    if (!currentQuestionId || loadedRef.current.has(currentQuestionId)) return;
+    loadedRef.current.add(currentQuestionId);
+    getVariants(currentQuestionId)
+      .then((data) => {
+        setVariantMap((prev) => new Map(prev).set(currentQuestionId, data));
+      })
+      .catch(() => {});
+  }, [currentQuestionId]);
+
+  // When user scrolls PDF, find the question closest to that page
+  // But ignore if user just clicked a question (2s cooldown)
+  const handlePdfPageChange = useCallback(
+    (page: number) => {
+      if (questions.length === 0) return;
+      if (Date.now() - lastUserSelectTime.current < 2000) return;
+      let best = questions[0];
+      for (let i = 0; i < questions.length; i++) {
+        if ((questions[i].page_number ?? 1) <= page) best = questions[i];
+      }
+      setCurrentQuestionId(best.id);
+    },
+    [questions],
+  );
+
+  // Track answer state per question for ActionBar feedback
+  const [answerStates, setAnswerStates] = useState>(new Map());
+
+  const handleAnswerResult = async (isCorrect: boolean, userAnswer: string) => {
+    if (!currentQuestion) return;
+    const state = isCorrect ? "correct" : "wrong";
+    setAnswerStates((prev) => new Map(prev).set(currentQuestion.id, state));
+    try {
+      const type = currentQuestion.question_type === "mc" ? "select" : "input";
+      await recordAttempt(currentQuestion.id, type, userAnswer, isCorrect);
+      // Wrong answer → auto generate variant
+      if (!isCorrect) {
+        handleGenerateVariant();
+      }
+    } catch {
+      // silent
+    }
+  };
+
+  const handleGenerateVariant = async () => {
+    if (!currentQuestion || isGenerating) return;
+    setIsGenerating(true);
+    setActiveTab("variants");
+    try {
+      const saved = await generateVariant(currentQuestion.id);
+      setVariantMap((prev) => {
+        const existing = prev.get(currentQuestion.id) ?? [];
+        return new Map(prev).set(currentQuestion.id, [saved, ...existing]);
+      });
+    } catch {
+      // silent
+    } finally {
+      setIsGenerating(false);
+    }
+  };
+
+  const handleToggleFavorite = async (v: QuestionVariant) => {
+    const updated = await updateVariant(v.id, { favorited: !v.favorited });
+    setVariantMap((prev) => {
+      const existing = prev.get(v.source_question_id) ?? [];
+      return new Map(prev).set(
+        v.source_question_id,
+        existing.map((item) => (item.id === v.id ? updated : item)),
+      );
+    });
+  };
+
+  const handleDeleteVariant = async (v: QuestionVariant) => {
+    await deleteVariant(v.id);
+    if (activeVariantId === v.id) setActiveVariantId(null);
+    setVariantMap((prev) => {
+      const existing = prev.get(v.source_question_id) ?? [];
+      return new Map(prev).set(
+        v.source_question_id,
+        existing.filter((item) => item.id !== v.id),
+      );
+    });
+  };
+
+  if (paperLoading) {
+    return (
+      
        
+        
        Loading...
        
+      
        
+    );
+  }
+
+  if (paperError || !paper) {
+    return (
+      
        
+        
        {paperError ?? "Paper not found"}
        
+      
        
+    );
+  }
+
+  return (
+    
        
+      
        
+
+      {/* Processing overlay */}
+      {paper.status === "processing" && (
+        
        
+          
        
+            
        
+            
        AI is analyzing the paper...
        
+            
        
+              {paper.question_count
+                ? `${paper.question_count} questions found, generating analysis...`
+                : "Extracting and structuring questions..."}
+            
        
+          
        
+        
        
+      )}
+
+      {/* Error state */}
+      {paper.status === "error" && (
+        
        
+          
        
+            
        Processing Failed
        
+            
        {paper.error_message}
        
+          
        
+        
        
+      )}
+
+      {/* Ready — workbench */}
+      {paper.status === "ready" && (
+        
        
+          {/* Left: PDF viewer */}
+          
        
+            
+          
        
+
+          {/* Right: analysis panel */}
+          
        
+            {questionsLoading ? (
+              
        
+                Loading questions...
+              
        
+            ) : activeVariantId && activeVariant ? (
+              /* ===== Variant Detail View ===== */
+              <>
+                
+                
        
+                  
+                
        
+              
+            ) : (
+              /* ===== Normal Tab View ===== */
+              <>
+                {/* Tab bar */}
+                
        
+                  
+                  
+                
        
+
+                {/* Question nav — always visible */}
+                
+
+                {/* Questions tab content */}
+                {activeTab === "questions" && (
+                  <>
+                    
        
+                      {currentQuestion && (
+                        <>
+                          
+                          {/* Grading result panel */}
+                          {gradingResults.has(currentQuestion.id) && (() => {
+                            const gr = gradingResults.get(currentQuestion.id)!;
+                            const expanded = gradingExpanded.has(currentQuestion.id);
+                            const toggleExpand = () => setGradingExpanded((prev) => {
+                              const next = new Set(prev);
+                              next.has(currentQuestion.id) ? next.delete(currentQuestion.id) : next.add(currentQuestion.id);
+                              return next;
+                            });
+
+                            if (gr.loading) {
+                              return (
+                                
        
+                                  
        
+                                    
+                                    Grading your answer...
+                                  
        
+                                
        
+                              );
+                            }
+
+                            return (
+                              
        
+                                
+                                {expanded && (
+                                  
        
+                                    {gr.ocrText && (
+                                      
        
+                                        Your Answer (OCR)
+                                        
        
+                                          ")} className="text-xs text-gray-700" />
+                                        
        
+                                      
        
+                                    )}
+                                    
+                                  
        
+                                )}
+                              
        
+                            );
+                          })()}
+                          
+                          
+                        
+                      )}
+                    
        
+                     setShowPhoto(true)}
+                      answerState={currentQuestion ? answerStates.get(currentQuestion.id) ?? null : null}
+                    />
+                  
+                )}
+
+                {/* Variants tab content */}
+                {activeTab === "variants" && (
+                  
        
+                    
        
+                      
+                    
        
+
+                    {currentVariants.length === 0 && !isGenerating ? (
+                      
        
+                        
        No variants yet for this question.
        
+                      
        
+                    ) : (
+                      
        
+                        {currentVariants.map((v) => (
+                          
        
+                            
        
+                              
+                                {new Date(v.created_at).toLocaleDateString("en-CA")}
+                              
+                              
        
+                                
+                                
+                              
        
+                            
        
+                            
        
+                              {v.variant_data.question_text?.replace(/<[^>]*>/g, "").slice(0, 140)}
+                            
        
+                            
+                          
        
+                        ))}
+                      
        
+                    )}
+                  
        
+                )}
+              
+            )}
+          
        
+        
        
+      )}
+
+      {/* Photo upload modal */}
+      {showPhoto && currentQuestion && (() => {
+        const qid = currentQuestion.id;
+        return (
+           setShowPhoto(false)}
+            onSubmitted={async (promise) => {
+              // Set loading state
+              setGradingResults((prev) => new Map(prev).set(qid, { isCorrect: false, feedback: "", ocrText: "", loading: true }));
+              setGradingExpanded((prev) => new Set(prev).add(qid));
+              try {
+                const res = await promise;
+                const { is_correct, feedback, score_given } = res.grade;
+                setGradingResults((prev) => new Map(prev).set(qid, {
+                  isCorrect: is_correct,
+                  feedback,
+                  ocrText: res.ocr_text,
+                  scoreGiven: score_given,
+                  loading: false,
+                }));
+                // Wrong → auto generate variant
+                if (!is_correct) {
+                  handleGenerateVariant();
+                }
+              } catch {
+                setGradingResults((prev) => new Map(prev).set(qid, {
+                  isCorrect: false,
+                  feedback: "Grading failed. Please try again.",
+                  ocrText: "",
+                  loading: false,
+                }));
+              }
+            }}
+          />
+        );
+      })()}
+    
        
+  );
+}
diff --git a/frontend/src/styles/globals.css b/frontend/src/styles/globals.css
new file mode 100644
index 0000000..2990743
--- /dev/null
+++ b/frontend/src/styles/globals.css
@@ -0,0 +1,79 @@
+@import "tailwindcss";
+@import "katex/dist/katex.min.css";
+
+/* ── Google Fonts: Sora (headings) + IBM Plex Mono (data) ── */
+@import url("https://fonts.googleapis.com/css2?family=Sora:wght@400;500;600;700&family=IBM+Plex+Mono:wght@400;500;600&display=swap");
+
+/* Hide scrollbar on horizontal tab rows */
+.hide-scrollbar { -ms-overflow-style: none; scrollbar-width: none; }
+.hide-scrollbar::-webkit-scrollbar { display: none; }
+
+/* ── Knowledge Base HTML content styling (from SOS project) ── */
+.kb-html-content h1 { font-size: 1.25rem; font-weight: 700; margin: 0.75rem 0 0.5rem; line-height: 1.3; }
+.kb-html-content h2 { font-size: 1.1rem; font-weight: 600; margin: 0.75rem 0 0.4rem; color: #1e40af; border-bottom: 1px solid #e5e7eb; padding-bottom: 0.25rem; }
+.kb-html-content h3 { font-size: 0.95rem; font-weight: 600; margin: 0.6rem 0 0.3rem; color: #374151; }
+.kb-html-content h4 { font-size: 0.875rem; font-weight: 600; margin: 0.5rem 0 0.25rem; color: #6b7280; }
+.kb-html-content p { margin: 0.3rem 0; line-height: 1.6; }
+.kb-html-content p.summary { background: #eff6ff; border-left: 3px solid #3b82f6; padding: 0.5rem 0.75rem; border-radius: 0 0.25rem 0.25rem 0; color: #1e3a5f; margin-bottom: 0.75rem; }
+.kb-html-content ul, .kb-html-content ol { margin: 0.3rem 0 0.3rem 1.25rem; line-height: 1.6; }
+.kb-html-content ul { list-style: disc; }
+.kb-html-content ol { list-style: decimal; }
+.kb-html-content li { margin: 0.15rem 0; }
+.kb-html-content strong { font-weight: 600; color: #1e293b; }
+.kb-html-content blockquote { border-left: 3px solid #d1d5db; padding: 0.4rem 0.75rem; margin: 0.4rem 0; background: #f9fafb; color: #4b5563; font-style: italic; border-radius: 0 0.25rem 0.25rem 0; }
+.kb-html-content pre { background: #1e293b; color: #e2e8f0; padding: 0.75rem; border-radius: 0.375rem; overflow-x: auto; margin: 0.4rem 0; font-size: 0.8rem; }
+.kb-html-content code { font-family: ui-monospace, monospace; font-size: 0.85em; }
+.kb-html-content :not(pre) > code { background: #f1f5f9; padding: 0.1rem 0.3rem; border-radius: 0.2rem; color: #be185d; }
+.kb-html-content table { border-collapse: collapse; width: 100%; margin: 0.4rem 0; font-size: 0.8rem; }
+.kb-html-content th, .kb-html-content td { border: 1px solid #e5e7eb; padding: 0.35rem 0.5rem; text-align: left; }
+.kb-html-content th { background: #f3f4f6; font-weight: 600; }
+.kb-html-content section { margin: 0.5rem 0; }
+.kb-html-content .tag { display: inline-block; background: #dbeafe; color: #1e40af; padding: 0.1rem 0.5rem; border-radius: 9999px; font-size: 0.75rem; margin: 0.15rem 0.15rem; }
+.kb-html-content hr { border: none; border-top: 1px solid #e5e7eb; margin: 0.75rem 0; }
+
+/* ── Example blocks ── */
+.kb-html-content .example { background: #fffbeb; border: 1px solid #fbbf24; border-radius: 0.375rem; padding: 0.75rem; margin: 0.6rem 0; }
+.kb-html-content .example-title { font-weight: 700; color: #92400e; margin-bottom: 0.4rem; font-size: 0.9rem; }
+.kb-html-content .example-solution { border-top: 1px dashed #d97706; padding-top: 0.4rem; }
+
+/* ── LaTeX blocks ── */
+.kb-html-content pre.latex { background: #f8fafc; color: #1e293b; border: 1px solid #e2e8f0; text-align: center; font-size: 0.9rem; padding: 0.6rem; }
+.kb-html-content code.latex { background: #f1f5f9; padding: 0.1rem 0.3rem; border-radius: 0.2rem; color: #4338ca; font-size: 0.85em; }
+
+/* ── Common error block (used in solution) ── */
+.kb-html-content .common-error {
+  background: #fef2f2;
+  border: 1px solid #fca5a5;
+  border-left: 3px solid #ef4444;
+  border-radius: 0.375rem;
+  padding: 0.6rem 0.75rem;
+  margin: 0.5rem 0;
+}
+.kb-html-content .common-error::before {
+  content: "⚠ Common Mistake";
+  font-weight: 700;
+  color: #dc2626;
+  display: block;
+  margin-bottom: 0.3rem;
+  font-size: 0.85rem;
+}
+
+/* ── Figure description blocks ── */
+.kb-html-content .figure-desc {
+  background: #faf5ff;
+  border: 1px solid #d8b4fe;
+  border-left: 3px solid #a855f7;
+  border-radius: 0.375rem;
+  padding: 0.6rem 0.75rem;
+  margin: 0.5rem 0;
+}
+
+/* ── AI Supplement blocks ── */
+.kb-html-content .ai-supplement {
+  background: #f0fdf4;
+  border: 1px solid #86efac;
+  border-left: 3px solid #22c55e;
+  border-radius: 0.375rem;
+  padding: 0.6rem 0.75rem;
+  margin: 0.5rem 0;
+}
diff --git a/frontend/src/types/api.ts b/frontend/src/types/api.ts
new file mode 100644
index 0000000..aab08f7
--- /dev/null
+++ b/frontend/src/types/api.ts
@@ -0,0 +1,169 @@
+export interface Paper {
+  id: string;
+  user_id: string | null;
+  course_code: string;
+  year: number;
+  term: string;
+  exam_type: string;
+  paper_file_url: string;
+  answer_file_url: string | null;
+  status: "uploaded" | "processing" | "ready" | "error";
+  error_message: string | null;
+  total_score: number | null;
+  question_count: number | null;
+  topics_summary: Record | null;
+  difficulty_level: string | null;
+  processing_step: string | null;
+  processing_progress: number;
+  processing_total: number;
+  created_at: string;
+  updated_at: string;
+}
+
+export interface PaperSummary {
+  id: string;
+  course_code: string;
+  year: number;
+  term: string;
+  exam_type: string;
+  part_label: string | null;
+}
+
+export interface Question {
+  id: string;
+  paper_id: string;
+  question_number: string;
+  parent_question: string | null;
+  display_order: number;
+  question_type: string;
+  question_format?: string | null;
+  question_text: string;
+  score: number | null;
+  page_number: number | null;
+  page_y_ratio?: number | null;
+  options: { label: string; text: string }[] | null;
+  correct_option: string | null;
+  correct_answer: string | null;
+  raw_answer_text: string | null;
+  topics: string[] | null;
+  topic_primary?: string | null;
+  analytics_topic?: string | null;
+  topic_tags?: string[] | null;
+  skill_tags?: string[] | null;
+  difficulty: string | null;
+  knowledge_reminder: string;
+  ai_hint: string;
+  solution: string;
+  created_at: string;
+  updated_at: string;
+  paper?: PaperSummary;
+}
+
+export interface UploadResponse {
+  paper_id: string;
+  status: string;
+  message: string;
+}
+
+export interface UserAttempt {
+  id: string;
+  user_id: string;
+  question_id: string;
+  attempt_type: string;
+  user_answer: string | null;
+  photo_url: string | null;
+  photo_ocr_text: string | null;
+  is_correct: boolean | null;
+  feedback: string | null;
+  error_at_step: number | null;
+  in_error_book: boolean;
+  mastered: boolean;
+  created_at: string;
+  paper_questions?: Question;
+  score_given?: number | null;
+}
+
+export interface VariantQuestion {
+  question_text: string;
+  question_type: string;
+  options: { label: string; text: string }[] | null;
+  correct_answer: string;
+  ai_hint: string;
+  knowledge_reminder: string;
+  solution: string;
+}
+
+export interface QuestionVariant {
+  id: string;
+  user_id: string;
+  source_question_id: string;
+  source_question_number: string;
+  variant_data: VariantQuestion;
+  favorited: boolean;
+  created_at: string;
+}
+
+export interface GradeResult {
+  is_correct: boolean;
+  feedback: string;
+  error_at_step: number | null;
+}
+
+export interface SimilarQuestion {
+  id: string;
+  paper_id: string;
+  source: string;
+  question_number: string;
+  match_percent: number;
+  match_reasons?: string[];
+  question_type: Question["question_type"];
+  question_text: string;
+  topics: string[];
+  difficulty: string | null;
+  knowledge_reminder: string;
+  ai_hint: string;
+  solution: string;
+}
+
+export interface AnalyticsTopicQuestion {
+  paper_id: string;
+  source: string;
+  question_number: string;
+  preview: string;
+  difficulty: string | null;
+  question_type: string;
+  year?: number | null;
+  term?: string | null;
+  exam_type?: string | null;
+  topics?: string[];
+}
+
+export interface AnalyticsTopicEntry {
+  label: string;
+  count: number;
+  pct: number;
+  questions: AnalyticsTopicQuestion[];
+}
+
+export interface CourseAnalytics {
+  course_code: string;
+  kpi: {
+    papers: number;
+    questions: number;
+    topics: number;
+    difficulty: string;
+  };
+  topic_frequency: AnalyticsTopicEntry[];
+  question_types: Array<{
+    label: string;
+    count: number;
+    pct: number;
+  }>;
+  difficulty_distribution: {
+    easy: number;
+    medium: number;
+    hard: number;
+  };
+  high_yield_topics: string[];
+  all_questions: AnalyticsTopicQuestion[];
+}
diff --git a/frontend/src/vite-env.d.ts b/frontend/src/vite-env.d.ts
new file mode 100644
index 0000000..11f02fe
--- /dev/null
+++ b/frontend/src/vite-env.d.ts
@@ -0,0 +1 @@
+/// 
diff --git a/frontend/tsconfig.json b/frontend/tsconfig.json
new file mode 100644
index 0000000..b9bfd29
--- /dev/null
+++ b/frontend/tsconfig.json
@@ -0,0 +1,21 @@
+{
+  "compilerOptions": {
+    "target": "ES2020",
+    "useDefineForClassFields": true,
+    "lib": ["ES2020", "DOM", "DOM.Iterable"],
+    "module": "ESNext",
+    "skipLibCheck": true,
+    "moduleResolution": "bundler",
+    "allowImportingTsExtensions": true,
+    "isolatedModules": true,
+    "moduleDetection": "force",
+    "noEmit": true,
+    "jsx": "react-jsx",
+    "strict": true,
+    "baseUrl": ".",
+    "paths": {
+      "@/*": ["src/*"]
+    }
+  },
+  "include": ["src"]
+}
diff --git a/frontend/vite.config.ts b/frontend/vite.config.ts
new file mode 100644
index 0000000..ecdcb3a
--- /dev/null
+++ b/frontend/vite.config.ts
@@ -0,0 +1,22 @@
+import { defineConfig } from "vite";
+import react from "@vitejs/plugin-react";
+import tailwindcss from "@tailwindcss/vite";
+import { resolve } from "path";
+
+export default defineConfig({
+  plugins: [react(), tailwindcss()],
+  resolve: {
+    alias: {
+      "@": resolve(__dirname, "src"),
+    },
+  },
+  server: {
+    port: 5173,
+    proxy: {
+      "/api": {
+        target: "http://localhost:8000",
+        changeOrigin: true,
+      },
+    },
+  },
+});
diff --git a/index 2.html b/index 2.html
new file mode 100644
index 0000000..17c4ec9
--- /dev/null
+++ b/index 2.html	
@@ -0,0 +1,22 @@
+
+
+  
+    
+    
+    
+    PastpaperMaster
+    
+    
+  
+  
+    
        
+  
+
diff --git a/memory/MEMORY.md b/memory/MEMORY.md
new file mode 100644
index 0000000..57e30ee
--- /dev/null
+++ b/memory/MEMORY.md
@@ -0,0 +1,3 @@
+# Memory Index
+
+- [project_pastpaper_master.md](project_pastpaper_master.md) — PastPaper Master 项目概览与当前开发进度
diff --git a/memory/project_pastpaper_master.md b/memory/project_pastpaper_master.md
new file mode 100644
index 0000000..10b03ac
--- /dev/null
+++ b/memory/project_pastpaper_master.md
@@ -0,0 +1,37 @@
+---
+name: PastPaper Master 项目概览
+description: 项目技术栈、当前开发状态、已完成工作流及下一步优先级
+type: project
+---
+
+AI 辅助学习平台，支持 COMP2211 试卷练习。核心功能：题目工作台、AI 三件套、相似题推荐、错题本、变式题生成。
+
+## 技术栈
+- Frontend: React 19 + TypeScript + Vite 7 + Tailwind v4
+- Backend: FastAPI + Python 3.12 + uv
+- DB: Supabase PostgreSQL（RLS 已预留，当前用 temp user id）
+- LLM: GPT-4o (laozhang proxy) + Qwen-plus fallback
+
+## 当前 DB 状态（2026-04-10）
+COMP2211 共 7 份 status=ready 试卷，250 道 subquestion 级题目，均有 knowledge_reminder / ai_hint / solution / analytics_topic / topic_tags / skill_tags。
+
+## 已完成的工作（本次 session）
+**Workstream A：相似题检索 + 移除 demo fallback**
+- `backend/app/routers/questions.py`：
+  - `skill_tags` 加入 SELECT 和 `question_topics()` 计算
+  - 修复 `isinstance(target_score, int)` → `(int, float)` 支持 NUMERIC 小数分
+  - `similarity_score()` 返回 `(score, reasons)` tuple
+  - 过滤阈值从 `<= 0` 改为 `< 10`
+  - 响应增加 `match_reasons` 字段
+- `frontend/src/types/api.ts`：`SimilarQuestion` 加 `match_reasons?: string[]`
+- `frontend/src/components/workbench/SimilarHistoryPanel.tsx`：移除全部 demo fallback，改为真实 empty/error 状态，显示 match_reasons chip
+
+## 下一步优先级（来自 HANDOFF_COMP2211.md）
+1. ✅ Workstream A: 相似题检索 + 移除 demo fallback — 已完成
+2. Workstream B: Analytics 深化（per-paper drill-down、topic 频率时序、高频话题）
+3. Workstream C: LaTeX/KaTeX 渲染质量（集中归一化、剔除 OCR 噪声）
+4. Workstream D: 用户上传去重（对比 course_library 已有试卷）
+5. Workstream E: UI/UX pass（QuestionNav、状态 badge、workbench 层级）
+
+**Why:** HANDOFF 文档中建议的开发顺序，以数据稳定性为先。
+**How to apply:** 下次 session 从 Workstream B（Analytics 深化）开始。
diff --git a/pastpaper-scraper b/pastpaper-scraper
new file mode 160000
index 0000000..36d4a45
--- /dev/null
+++ b/pastpaper-scraper
@@ -0,0 +1 @@
+Subproject commit 36d4a450cd4872c919b9a2933ce84ff3f8967226
diff --git a/pitch_script.md b/pitch_script.md
new file mode 100644
index 0000000..9facbc0
--- /dev/null
+++ b/pitch_script.md
@@ -0,0 +1,25 @@
+# KnowIt Pitch — Product Demo (Pages 5-6, ~45s)
+
+## Transition In
+
+> Now let me show you the product.
+
+## Page 5 — Product Demo
+
+> This is PastPaper Master. Search any course, download past papers, and hit "AI Analyze" — our system reads every page, extracts each question, and generates knowledge reminders, hints, and full solutions automatically.
+>
+> It's powered by Gemini vision and DeepSeek, with a RAG pipeline connecting papers, recordings, and courseware.
+
+## Page 6 — Workflow
+
+> Here's the full student workflow.
+>
+> **Download** papers. **AI analysis** breaks down topics and difficulty. **Upload your answers** — AI grades them instantly with detailed feedback.
+>
+> Wrong answers go into your **mistake book**. AI generates **variant questions** on the same topic, plus retrieves **similar questions** from other exams.
+>
+> And **smart flashcards** auto-generated for quick revision — already live for pharmacology students.
+
+## Transition Out
+
+> One closed loop — find, practice, grade, review, master. Over to [name] on the market.
diff --git a/supabase/migrations/001_init_schema.sql b/supabase/migrations/001_init_schema.sql
new file mode 100644
index 0000000..11ab768
--- /dev/null
+++ b/supabase/migrations/001_init_schema.sql
@@ -0,0 +1,207 @@
+-- ============================================
+-- PastPaper Master — 初始数据库 Schema
+-- Version: 001
+-- Date: 2025-03-11
+-- ============================================
+
+-- 启用必要的扩展
+CREATE EXTENSION IF NOT EXISTS "uuid-ossp";
+
+-- ============================================
+-- Table 1: papers — 上传的试卷
+-- ============================================
+CREATE TABLE papers (
+  id UUID PRIMARY KEY DEFAULT gen_random_uuid(),
+  user_id UUID NOT NULL REFERENCES auth.users(id) ON DELETE CASCADE,
+
+  -- 元信息（用户上传时填写）
+  course_code TEXT NOT NULL,                -- "COMP2011"
+  year INTEGER NOT NULL,                    -- 2024
+  term TEXT NOT NULL CHECK (term IN ('fall', 'spring', 'summer')),
+  exam_type TEXT NOT NULL CHECK (exam_type IN ('midterm', 'final', 'quiz')),
+
+  -- 文件 (Supabase Storage)
+  paper_file_url TEXT NOT NULL,             -- 试卷 PDF
+  answer_file_url TEXT,                     -- 答案 PDF（可选）
+
+  -- 处理状态
+  status TEXT NOT NULL DEFAULT 'uploaded'
+    CHECK (status IN ('uploaded', 'processing', 'ready', 'error')),
+  error_message TEXT,                       -- 处理失败时的错误信息
+
+  -- 提取的原始文本（缓存）
+  paper_extracted_text TEXT,
+  answer_extracted_text TEXT,
+
+  -- 整卷概览（AI 生成）
+  total_score INTEGER,
+  question_count INTEGER,
+  topics_summary JSONB,                     -- {"Linked List": 40, "Recursion": 30}
+  difficulty_level TEXT CHECK (difficulty_level IN ('easy', 'medium', 'hard')),
+
+  created_at TIMESTAMPTZ NOT NULL DEFAULT now(),
+  updated_at TIMESTAMPTZ NOT NULL DEFAULT now()
+);
+
+-- ============================================
+-- Table 2: paper_questions — 逐题数据
+-- ============================================
+CREATE TABLE paper_questions (
+  id UUID PRIMARY KEY DEFAULT gen_random_uuid(),
+  paper_id UUID NOT NULL REFERENCES papers(id) ON DELETE CASCADE,
+
+  -- 题目标识
+  question_number TEXT NOT NULL,             -- "1", "1a", "2b"
+  parent_question TEXT,                      -- 子题的父题号: "1a" → "1"
+  display_order INTEGER NOT NULL,            -- 显示顺序
+
+  -- 题目内容
+  question_type TEXT NOT NULL
+    CHECK (question_type IN ('mc', 'fill_blank', 'long_question')),
+  question_text TEXT NOT NULL,               -- 题目原文
+  score INTEGER,                             -- 分值
+  page_number INTEGER,                       -- PDF 页码（左右联动）
+
+  -- 选择题专用
+  options JSONB,                             -- [{"label":"A","text":"..."},...]
+  correct_option TEXT,                       -- "B"
+
+  -- 填空题专用
+  correct_answer TEXT,                       -- 正确答案
+  accept_variants TEXT[],                    -- 等价表达 ["O(nlogn)","O(n log n)"]
+
+  -- 答案 PDF 提取的原始答案（所有题型）
+  raw_answer_text TEXT,
+
+  -- 知识点标签
+  topics TEXT[],                             -- ["Linked List","Pointer"]
+  difficulty TEXT CHECK (difficulty IN ('easy', 'medium', 'hard')),
+
+  -- AI 三件套（HTML + KaTeX）
+  knowledge_reminder TEXT,                   -- 知识点 Reminder
+  ai_hint TEXT,                              -- AI Hint
+  solution TEXT,                             -- Solution（逐步 derivation）
+
+  created_at TIMESTAMPTZ NOT NULL DEFAULT now(),
+  updated_at TIMESTAMPTZ NOT NULL DEFAULT now()
+);
+
+-- ============================================
+-- Table 3: user_attempts — 用户答题记录
+-- Phase 4 实现，先建好表结构
+-- ============================================
+CREATE TABLE user_attempts (
+  id UUID PRIMARY KEY DEFAULT gen_random_uuid(),
+  user_id UUID NOT NULL REFERENCES auth.users(id) ON DELETE CASCADE,
+  question_id UUID NOT NULL REFERENCES paper_questions(id) ON DELETE CASCADE,
+
+  -- 用户的作答
+  attempt_type TEXT NOT NULL
+    CHECK (attempt_type IN ('select', 'input', 'photo')),
+  user_answer TEXT,                          -- 选项 / 输入的答案
+  photo_url TEXT,                            -- 上传的照片
+  photo_ocr_text TEXT,                       -- OCR 识别结果
+
+  -- AI 判定
+  is_correct BOOLEAN,
+  feedback TEXT,                             -- HTML — 逐步错误分析
+  error_at_step INTEGER,                     -- 第几步开始错
+
+  -- 错题本
+  in_error_book BOOLEAN NOT NULL DEFAULT false,
+  mastered BOOLEAN NOT NULL DEFAULT false,
+
+  created_at TIMESTAMPTZ NOT NULL DEFAULT now()
+);
+
+-- ============================================
+-- 索引
+-- ============================================
+CREATE INDEX idx_papers_user ON papers(user_id);
+CREATE INDEX idx_papers_course ON papers(course_code);
+CREATE INDEX idx_papers_status ON papers(status);
+
+CREATE INDEX idx_questions_paper ON paper_questions(paper_id);
+CREATE INDEX idx_questions_type ON paper_questions(question_type);
+CREATE INDEX idx_questions_topics ON paper_questions USING GIN(topics);
+
+CREATE INDEX idx_attempts_user ON user_attempts(user_id);
+CREATE INDEX idx_attempts_question ON user_attempts(question_id);
+CREATE INDEX idx_attempts_errorbook ON user_attempts(user_id)
+  WHERE in_error_book = true;
+
+-- ============================================
+-- RLS 策略
+-- ============================================
+ALTER TABLE papers ENABLE ROW LEVEL SECURITY;
+ALTER TABLE paper_questions ENABLE ROW LEVEL SECURITY;
+ALTER TABLE user_attempts ENABLE ROW LEVEL SECURITY;
+
+-- papers: 用户只能看自己上传的（以后加公共库时再调整）
+CREATE POLICY "Users can view own papers"
+  ON papers FOR SELECT
+  USING (auth.uid() = user_id);
+
+CREATE POLICY "Users can insert own papers"
+  ON papers FOR INSERT
+  WITH CHECK (auth.uid() = user_id);
+
+CREATE POLICY "Users can update own papers"
+  ON papers FOR UPDATE
+  USING (auth.uid() = user_id);
+
+CREATE POLICY "Users can delete own papers"
+  ON papers FOR DELETE
+  USING (auth.uid() = user_id);
+
+-- paper_questions: 跟随 paper 的权限
+CREATE POLICY "Users can view questions of own papers"
+  ON paper_questions FOR SELECT
+  USING (
+    EXISTS (
+      SELECT 1 FROM papers
+      WHERE papers.id = paper_questions.paper_id
+      AND papers.user_id = auth.uid()
+    )
+  );
+
+-- service_role 用于后端写入 questions（处理管线用）
+-- 前端不直接写 questions，通过 API 触发后端处理
+
+-- user_attempts: 用户只能看/写自己的
+CREATE POLICY "Users can view own attempts"
+  ON user_attempts FOR SELECT
+  USING (auth.uid() = user_id);
+
+CREATE POLICY "Users can insert own attempts"
+  ON user_attempts FOR INSERT
+  WITH CHECK (auth.uid() = user_id);
+
+CREATE POLICY "Users can update own attempts"
+  ON user_attempts FOR UPDATE
+  USING (auth.uid() = user_id);
+
+-- ============================================
+-- updated_at 自动更新触发器
+-- ============================================
+CREATE OR REPLACE FUNCTION update_updated_at()
+RETURNS TRIGGER AS $$
+BEGIN
+  NEW.updated_at = now();
+  RETURN NEW;
+END;
+$$ LANGUAGE plpgsql;
+
+CREATE TRIGGER papers_updated_at
+  BEFORE UPDATE ON papers
+  FOR EACH ROW EXECUTE FUNCTION update_updated_at();
+
+CREATE TRIGGER questions_updated_at
+  BEFORE UPDATE ON paper_questions
+  FOR EACH ROW EXECUTE FUNCTION update_updated_at();
+
+-- ============================================
+-- Storage bucket
+-- ============================================
+-- 在 Supabase Dashboard 中手动创建 bucket: "papers"
+-- 或通过 API 创建（后端初始化时处理）
diff --git a/supabase/migrations/002_course_library_fields.sql b/supabase/migrations/002_course_library_fields.sql
new file mode 100644
index 0000000..cbb203c
--- /dev/null
+++ b/supabase/migrations/002_course_library_fields.sql
@@ -0,0 +1,38 @@
+-- ============================================
+-- PastPaper Master — Shared course library fields
+-- Version: 002
+-- Date: 2026-03-24
+-- ============================================
+
+-- Shared library / canonical import metadata on papers
+ALTER TABLE papers
+  ADD COLUMN IF NOT EXISTS source_kind TEXT NOT NULL DEFAULT 'user_upload'
+    CHECK (source_kind IN ('user_upload', 'course_library')),
+  ADD COLUMN IF NOT EXISTS source_exam_key TEXT,
+  ADD COLUMN IF NOT EXISTS part_label TEXT
+    CHECK (part_label IN ('A', 'B')),
+  ADD COLUMN IF NOT EXISTS source_question_filename TEXT,
+  ADD COLUMN IF NOT EXISTS source_answer_filename TEXT;
+
+CREATE UNIQUE INDEX IF NOT EXISTS idx_papers_course_library_exam_key
+  ON papers(source_exam_key)
+  WHERE source_kind = 'course_library' AND source_exam_key IS NOT NULL;
+
+CREATE INDEX IF NOT EXISTS idx_papers_course_lookup
+  ON papers(course_code, year, term, exam_type, part_label);
+
+-- Grading results should persist awarded score
+ALTER TABLE user_attempts
+  ADD COLUMN IF NOT EXISTS score_given INTEGER;
+
+CREATE INDEX IF NOT EXISTS idx_attempts_errorbook_active
+  ON user_attempts(user_id, created_at DESC)
+  WHERE in_error_book = true AND mastered = false;
+
+-- The backend and frontend already support true_false; schema must match.
+ALTER TABLE paper_questions
+  DROP CONSTRAINT IF EXISTS paper_questions_question_type_check;
+
+ALTER TABLE paper_questions
+  ADD CONSTRAINT paper_questions_question_type_check
+  CHECK (question_type IN ('mc', 'true_false', 'fill_blank', 'long_question'));
diff --git a/supabase/migrations/003_question_taxonomy_fields.sql b/supabase/migrations/003_question_taxonomy_fields.sql
new file mode 100644
index 0000000..08de76e
--- /dev/null
+++ b/supabase/migrations/003_question_taxonomy_fields.sql
@@ -0,0 +1,41 @@
+-- ============================================
+-- PastPaper Master — Question taxonomy fields
+-- Version: 003
+-- Date: 2026-03-24
+-- ============================================
+
+-- A question needs multiple classification layers:
+-- 1) question_format: how the student interacts with it
+-- 2) topic_tags / topic_primary / analytics_topic: course knowledge taxonomy
+-- 3) skill_tags: what kind of thinking task the question requires
+ALTER TABLE paper_questions
+  ADD COLUMN IF NOT EXISTS question_format TEXT
+    CHECK (
+      question_format IN (
+        'mc',
+        'true_false',
+        'fill_blank',
+        'short_answer',
+        'long_answer',
+        'coding'
+      )
+    ),
+  ADD COLUMN IF NOT EXISTS topic_primary TEXT,
+  ADD COLUMN IF NOT EXISTS analytics_topic TEXT,
+  ADD COLUMN IF NOT EXISTS topic_tags TEXT[],
+  ADD COLUMN IF NOT EXISTS skill_tags TEXT[];
+
+-- Keep the legacy topics column for backward compatibility for now.
+-- New analytics and retrieval code should gradually move to analytics_topic/topic_tags.
+
+CREATE INDEX IF NOT EXISTS idx_questions_question_format
+  ON paper_questions(question_format);
+
+CREATE INDEX IF NOT EXISTS idx_questions_analytics_topic
+  ON paper_questions(analytics_topic);
+
+CREATE INDEX IF NOT EXISTS idx_questions_topic_tags
+  ON paper_questions USING GIN(topic_tags);
+
+CREATE INDEX IF NOT EXISTS idx_questions_skill_tags
+  ON paper_questions USING GIN(skill_tags);
diff --git a/supabase/migrations/004_decouple_course_library_from_auth.sql b/supabase/migrations/004_decouple_course_library_from_auth.sql
new file mode 100644
index 0000000..6e87c53
--- /dev/null
+++ b/supabase/migrations/004_decouple_course_library_from_auth.sql
@@ -0,0 +1,30 @@
+-- ============================================
+-- PastPaper Master — Decouple course library papers from auth users
+-- Version: 004
+-- Date: 2026-03-24
+-- ============================================
+
+-- Course-library papers should not depend on a concrete auth.users row.
+-- User-uploaded papers still keep user_id populated.
+ALTER TABLE papers
+  ALTER COLUMN user_id DROP NOT NULL;
+
+-- Keep existing FK so user-owned papers can still reference auth.users,
+-- while course-library rows simply use NULL.
+
+-- Tighten the intended invariant with a check constraint:
+-- - user_upload rows must have user_id
+-- - course_library rows must not have user_id
+ALTER TABLE papers
+  DROP CONSTRAINT IF EXISTS papers_source_kind_user_id_check;
+
+ALTER TABLE papers
+  ADD CONSTRAINT papers_source_kind_user_id_check
+  CHECK (
+    (source_kind = 'user_upload' AND user_id IS NOT NULL)
+    OR
+    (source_kind = 'course_library' AND user_id IS NULL)
+  );
+
+-- Existing RLS policies continue to apply to user-owned rows.
+-- Course-library rows are accessed through the backend service role.
diff --git a/supabase/migrations/005_allow_long_question_format_alias.sql b/supabase/migrations/005_allow_long_question_format_alias.sql
new file mode 100644
index 0000000..8d994d4
--- /dev/null
+++ b/supabase/migrations/005_allow_long_question_format_alias.sql
@@ -0,0 +1,27 @@
+-- ============================================
+-- PastPaper Master — Allow legacy long_question format alias
+-- Version: 005
+-- Date: 2026-03-24
+-- ============================================
+--
+-- Some existing seeds and older generated SQL used `long_question` in the
+-- `question_format` column, while the 003 taxonomy migration introduced
+-- `long_answer` as the canonical value. Allow both temporarily so historical
+-- inserts do not fail. New generators should continue emitting `long_answer`.
+
+ALTER TABLE paper_questions
+  DROP CONSTRAINT IF EXISTS paper_questions_question_format_check;
+
+ALTER TABLE paper_questions
+  ADD CONSTRAINT paper_questions_question_format_check
+  CHECK (
+    question_format IN (
+      'mc',
+      'true_false',
+      'fill_blank',
+      'short_answer',
+      'long_answer',
+      'long_question',
+      'coding'
+    )
+  );
diff --git a/supabase/migrations/006_make_scores_numeric.sql b/supabase/migrations/006_make_scores_numeric.sql
new file mode 100644
index 0000000..edf2640
--- /dev/null
+++ b/supabase/migrations/006_make_scores_numeric.sql
@@ -0,0 +1,17 @@
+-- ============================================
+-- PastPaper Master — Make score fields numeric
+-- Version: 006
+-- Date: 2026-04-10
+-- ============================================
+
+ALTER TABLE paper_questions
+  ALTER COLUMN score TYPE NUMERIC
+  USING score::NUMERIC;
+
+ALTER TABLE papers
+  ALTER COLUMN total_score TYPE NUMERIC
+  USING total_score::NUMERIC;
+
+ALTER TABLE user_attempts
+  ALTER COLUMN score_given TYPE NUMERIC
+  USING score_given::NUMERIC;
diff --git a/supabase/migrations/007_fulltext_search.sql b/supabase/migrations/007_fulltext_search.sql
new file mode 100644
index 0000000..1169e00
--- /dev/null
+++ b/supabase/migrations/007_fulltext_search.sql
@@ -0,0 +1,36 @@
+-- 007: Full-text search on paper_questions.question_text
+--
+-- Adds a tsvector generated column (auto-maintained by PostgreSQL on every
+-- INSERT/UPDATE), a GIN index for fast @@ queries, and a batch-scoring RPC
+-- used by the similar-question retrieval endpoint.
+
+ALTER TABLE paper_questions
+  ADD COLUMN IF NOT EXISTS search_text tsvector
+  GENERATED ALWAYS AS (
+    to_tsvector('english', coalesce(question_text, ''))
+  ) STORED;
+
+CREATE INDEX IF NOT EXISTS idx_pq_search_text
+  ON paper_questions USING gin(search_text);
+
+-- text_similarity_scores(query_text, candidate_ids)
+--   Returns one row per candidate ID with a ts_rank_cd score normalised by
+--   unique word count (normalization flag = 1).  Questions that share no
+--   lexemes with the query still appear in the result with score = 0 so the
+--   caller always gets a complete score map for every candidate.
+CREATE OR REPLACE FUNCTION text_similarity_scores(
+  query_text    text,
+  candidate_ids uuid[]
+)
+RETURNS TABLE (question_id uuid, text_score float4)
+LANGUAGE sql STABLE AS $$
+  SELECT
+    id,
+    ts_rank_cd(
+      search_text,
+      plainto_tsquery('english', query_text),
+      1   -- normalise by unique word count
+    )::float4
+  FROM paper_questions
+  WHERE id = ANY(candidate_ids);
+$$;
diff --git a/supabase/migrations/008_add_page_y_ratio.sql b/supabase/migrations/008_add_page_y_ratio.sql
new file mode 100644
index 0000000..e04fe87
--- /dev/null
+++ b/supabase/migrations/008_add_page_y_ratio.sql
@@ -0,0 +1,2 @@
+ALTER TABLE paper_questions
+  ADD COLUMN IF NOT EXISTS page_y_ratio NUMERIC;
diff --git a/supabase/migrations/008_fix_storage_url_placeholder.sql b/supabase/migrations/008_fix_storage_url_placeholder.sql
new file mode 100644
index 0000000..8260d5f
--- /dev/null
+++ b/supabase/migrations/008_fix_storage_url_placeholder.sql
@@ -0,0 +1,27 @@
+-- 008: Replace __SUPABASE_STORAGE_PUBLIC_BASE_URL__ placeholder in paper URLs
+--
+-- The course-library seed (comp2211_course_library_papers.sql) was inserted
+-- without substituting the placeholder.  This migration replaces it with the
+-- real Supabase Storage public base URL for the `papers` bucket.
+
+UPDATE papers
+SET paper_file_url = REPLACE(
+  paper_file_url,
+  '__SUPABASE_STORAGE_PUBLIC_BASE_URL__',
+  'https://pvcxipwovpwrurebouwg.supabase.co/storage/v1/object/public/papers'
+)
+WHERE paper_file_url LIKE '%__SUPABASE_STORAGE_PUBLIC_BASE_URL__%';
+
+UPDATE papers
+SET answer_file_url = REPLACE(
+  answer_file_url,
+  '__SUPABASE_STORAGE_PUBLIC_BASE_URL__',
+  'https://pvcxipwovpwrurebouwg.supabase.co/storage/v1/object/public/papers'
+)
+WHERE answer_file_url LIKE '%__SUPABASE_STORAGE_PUBLIC_BASE_URL__%';
+
+-- Verify: should return 0 rows
+SELECT id, course_code, year, term, exam_type, paper_file_url, answer_file_url
+FROM papers
+WHERE paper_file_url  LIKE '%__SUPABASE_STORAGE_PUBLIC_BASE_URL__%'
+   OR answer_file_url LIKE '%__SUPABASE_STORAGE_PUBLIC_BASE_URL__%';
diff --git a/supabase/seeds/comp2211_2022_fall_page_number_backfill.sql b/supabase/seeds/comp2211_2022_fall_page_number_backfill.sql
new file mode 100644
index 0000000..a97a343
--- /dev/null
+++ b/supabase/seeds/comp2211_2022_fall_page_number_backfill.sql
@@ -0,0 +1,52 @@
+UPDATE paper_questions
+SET page_number = CASE question_number
+  WHEN '1a' THEN 2
+  WHEN '1b' THEN 2
+  WHEN '1c' THEN 2
+  WHEN '1d' THEN 2
+  WHEN '1e' THEN 2
+  WHEN '1f' THEN 2
+  WHEN '1g' THEN 2
+  WHEN '1h' THEN 2
+  WHEN '1i' THEN 2
+  WHEN '1j' THEN 2
+  WHEN '2a_i' THEN 3
+  WHEN '2a_ii' THEN 3
+  WHEN '2a_iii' THEN 3
+  WHEN '2a_iv' THEN 3
+  WHEN '2a_v' THEN 4
+  WHEN '2a_vi' THEN 4
+  WHEN '2a_vii' THEN 4
+  WHEN '2b_i' THEN 5
+  WHEN '2b_ii' THEN 5
+  WHEN '2b_iii' THEN 5
+  WHEN '2c' THEN 6
+  WHEN '3a_i' THEN 8
+  WHEN '3a_ii' THEN 8
+  WHEN '3b_i' THEN 9
+  WHEN '3b_ii' THEN 9
+  WHEN '3b_iii' THEN 10
+  WHEN '3c' THEN 10
+  WHEN '3d' THEN 11
+  WHEN '4a' THEN 12
+  WHEN '4b' THEN 13
+  WHEN '4c' THEN 13
+  WHEN '4d' THEN 13
+  WHEN '5a' THEN 14
+  WHEN '5b' THEN 14
+  WHEN '5c' THEN 14
+  WHEN '5d' THEN 15
+  WHEN '5e' THEN 15
+  WHEN '5f' THEN 15
+  WHEN '6a' THEN 16
+  WHEN '6b_i' THEN 17
+  WHEN '6b_ii' THEN 17
+  WHEN '7a' THEN 18
+  WHEN '7b' THEN 18
+  ELSE page_number
+END
+WHERE paper_id = (
+  SELECT id
+  FROM papers
+  WHERE source_exam_key = 'COMP2211-2022-fall-midterm'
+);
diff --git a/supabase/seeds/comp2211_course_library_papers.sql b/supabase/seeds/comp2211_course_library_papers.sql
new file mode 100644
index 0000000..d1a30d5
--- /dev/null
+++ b/supabase/seeds/comp2211_course_library_papers.sql
@@ -0,0 +1,148 @@
+-- ============================================
+-- PastPaper Master — COMP2211 course library papers
+-- Seed Date: 2026-03-24
+-- ============================================
+--
+-- Before running:
+-- 1. Upload the referenced PDFs into the `papers` bucket using the exact storage paths below.
+-- 2. Replace __SUPABASE_STORAGE_PUBLIC_BASE_URL__ with your project-specific public base URL.
+--
+-- Example base URL:
+-- https://.supabase.co/storage/v1/object/public/papers
+--
+-- This seed only inserts canonical, importable COMP2211 course-library papers.
+
+INSERT INTO papers (
+  user_id,
+  course_code,
+  year,
+  term,
+  exam_type,
+  part_label,
+  paper_file_url,
+  answer_file_url,
+  status,
+  source_kind,
+  source_exam_key,
+  source_question_filename,
+  source_answer_filename
+)
+VALUES
+  (
+    NULL,
+    'COMP2211',
+    2022,
+    'fall',
+    'midterm',
+    NULL,
+    '__SUPABASE_STORAGE_PUBLIC_BASE_URL__/course-library/COMP2211/COMP2211-2022-fall-midterm/paper.pdf',
+    '__SUPABASE_STORAGE_PUBLIC_BASE_URL__/course-library/COMP2211/COMP2211-2022-fall-midterm/answer.pdf',
+    'uploaded',
+    'course_library',
+    'COMP2211-2022-fall-midterm',
+    '(COMP2211)[2022](f)midterm~=yjz8dxdd^_27002.pdf',
+    '(COMP2211)[2022](f)midterm~=yjz8dxdd^_18747.pdf'
+  ),
+  (
+    NULL,
+    'COMP2211',
+    2022,
+    'spring',
+    'midterm',
+    NULL,
+    '__SUPABASE_STORAGE_PUBLIC_BASE_URL__/course-library/COMP2211/COMP2211-2022-spring-midterm/paper.pdf',
+    '__SUPABASE_STORAGE_PUBLIC_BASE_URL__/course-library/COMP2211/COMP2211-2022-spring-midterm/answer.pdf',
+    'uploaded',
+    'course_library',
+    'COMP2211-2022-spring-midterm',
+    '(COMP2211)[2022](s)midterm~=b8bidkgs^_14629.pdf',
+    '(COMP2211)[2022](s)midterm~=6ma030^_89587.pdf'
+  ),
+  (
+    NULL,
+    'COMP2211',
+    2022,
+    'spring',
+    'final',
+    'A',
+    '__SUPABASE_STORAGE_PUBLIC_BASE_URL__/course-library/COMP2211/COMP2211-2022-spring-final-part-a/paper.pdf',
+    '__SUPABASE_STORAGE_PUBLIC_BASE_URL__/course-library/COMP2211/COMP2211-2022-spring-final-part-a/answer.pdf',
+    'uploaded',
+    'course_library',
+    'COMP2211-2022-spring-final-part-a',
+    '(COMP2211)[2022](s)final~=b8bidkgs^_33018.pdf',
+    '(COMP2211)[2022](s)final~=ajou6^_82011.pdf'
+  ),
+  (
+    NULL,
+    'COMP2211',
+    2022,
+    'spring',
+    'final',
+    'B',
+    '__SUPABASE_STORAGE_PUBLIC_BASE_URL__/course-library/COMP2211/COMP2211-2022-spring-final-part-b/paper.pdf',
+    '__SUPABASE_STORAGE_PUBLIC_BASE_URL__/course-library/COMP2211/COMP2211-2022-spring-final-part-b/answer.pdf',
+    'uploaded',
+    'course_library',
+    'COMP2211-2022-spring-final-part-b',
+    '(COMP2211)[2022](s)final~=b8bidkgs^_40627.pdf',
+    '(COMP2211)[2022](s)final~=ajou6^_51199.pdf'
+  ),
+  (
+    NULL,
+    'COMP2211',
+    2023,
+    'spring',
+    'midterm',
+    NULL,
+    '__SUPABASE_STORAGE_PUBLIC_BASE_URL__/course-library/COMP2211/COMP2211-2023-spring-midterm/paper.pdf',
+    '__SUPABASE_STORAGE_PUBLIC_BASE_URL__/course-library/COMP2211/COMP2211-2023-spring-midterm/answer.pdf',
+    'uploaded',
+    'course_library',
+    'COMP2211-2023-spring-midterm',
+    '(COMP2211)[2023](s)midterm~=bxbidkmj^_26587.pdf',
+    '(COMP2211)[2023](s)midterm~clchanbg^_17297.pdf'
+  ),
+  (
+    NULL,
+    'COMP2211',
+    2024,
+    'spring',
+    'midterm',
+    NULL,
+    '__SUPABASE_STORAGE_PUBLIC_BASE_URL__/course-library/COMP2211/COMP2211-2024-spring-midterm/paper.pdf',
+    '__SUPABASE_STORAGE_PUBLIC_BASE_URL__/course-library/COMP2211/COMP2211-2024-spring-midterm/answer.pdf',
+    'uploaded',
+    'course_library',
+    'COMP2211-2024-spring-midterm',
+    '(COMP2211)[2024](s)midterm~=rcidkjgf^_82003.pdf',
+    '(COMP2211)[2024](s)midterm~=ubrzkjmz^_90406.pdf'
+  ),
+  (
+    NULL,
+    'COMP2211',
+    2024,
+    'spring',
+    'final',
+    NULL,
+    '__SUPABASE_STORAGE_PUBLIC_BASE_URL__/course-library/COMP2211/COMP2211-2024-spring-final/paper.pdf',
+    '__SUPABASE_STORAGE_PUBLIC_BASE_URL__/course-library/COMP2211/COMP2211-2024-spring-final/answer.pdf',
+    'uploaded',
+    'course_library',
+    'COMP2211-2024-spring-final',
+    '(COMP2211)[2024](s)final~=igk5mmg^_90365.pdf',
+    '(COMP2211)[2024](s)final~=igk5mmg^_58857.pdf'
+  )
+ON CONFLICT (source_exam_key)
+WHERE source_kind = 'course_library' AND source_exam_key IS NOT NULL
+DO UPDATE SET
+  course_code = EXCLUDED.course_code,
+  year = EXCLUDED.year,
+  term = EXCLUDED.term,
+  exam_type = EXCLUDED.exam_type,
+  part_label = EXCLUDED.part_label,
+  paper_file_url = EXCLUDED.paper_file_url,
+  answer_file_url = EXCLUDED.answer_file_url,
+  status = EXCLUDED.status,
+  source_question_filename = EXCLUDED.source_question_filename,
+  source_answer_filename = EXCLUDED.source_answer_filename;
diff --git a/supabase/seeds/comp2211_problem_level_questions.sql b/supabase/seeds/comp2211_problem_level_questions.sql
new file mode 100644
index 0000000..826ce4c
--- /dev/null
+++ b/supabase/seeds/comp2211_problem_level_questions.sql
@@ -0,0 +1,7173 @@
+-- ============================================
+-- PastPaper Master — COMP2211 problem-level questions
+-- Seed Date: 2026-03-24
+-- ============================================
+--
+-- Preconditions:
+-- 1. Run comp2211_course_library_papers.sql first.
+-- 2. Ensure those paper rows already exist in `papers`.
+--
+-- This seed inserts one row per top-level Problem.
+-- It is intentionally coarse-grained and idempotent.
+
+INSERT INTO paper_questions (
+  paper_id,
+  question_number,
+  parent_question,
+  display_order,
+  question_type,
+  question_format,
+  question_text,
+  score,
+  page_number,
+  options,
+  correct_option,
+  correct_answer,
+  raw_answer_text,
+  topics,
+  topic_primary,
+  analytics_topic,
+  topic_tags,
+  skill_tags,
+  difficulty,
+  knowledge_reminder,
+  ai_hint,
+  solution
+)
+SELECT
+  p.id,
+  seed.question_number,
+  seed.parent_question,
+  seed.display_order,
+  seed.question_type,
+  seed.question_format,
+  seed.question_text,
+  seed.score,
+  seed.page_number,
+  seed.options,
+  seed.correct_option,
+  seed.correct_answer,
+  seed.raw_answer_text,
+  seed.topics,
+  seed.topic_primary,
+  seed.analytics_topic,
+  seed.topic_tags,
+  seed.skill_tags,
+  seed.difficulty,
+  seed.knowledge_reminder,
+  seed.ai_hint,
+  seed.solution
+FROM (
+  VALUES
+    ('COMP2211-2022-fall-midterm', '1', NULL, 1, 'true_false', 'true_false', 'Problem 1 [15 points] True/False Questions
+Indicate whether the following statements are true or false by putting T or F in the given
+table. You get 1.5 points for each correct answer.
+(a) Machine learning gives computers the ability to make decision by writing down rules and
+methods and being explicitly programmed.
+(b) The regression technique in machine learning is NOT a group of algorithms that are used
+for predicting a class/category.
+(c) A 5-fold cross validation for K-nearest neighbors algorithm means that for each value
+of K, we randomly select 1/5 of the training data as the validation set to evaluate the
+model which is trained by the remaining (4/5) of the training data.
+(d) If we use K-means clustering, we will get the same cluster assignments for each data
+point, whether or not we standardize the variables.
+(e) Given an input data point x, where all the attribute values in x are real numbers. Suppose
+you are asked to predict a label y for x, where y = 0 or y = 1. Assume you have no
+knowledge about the distributions of x and y, perceptron is an appropriate method for
+this problem.
+(f) A perceptron with the unit step function (i.e., f(z) = 0 if z ≤0, otherwise f(z) = 1) as
+the activation function cannot be used for multi-class classification.
+(g) If a training dataset is linearly separable into two classes, the perceptron learning rule
+will always converge to weights and bias that accomplish the desired classification.
+(h) The neural network weights are updated during forward propagation.
+(i) An advantage of gradient descent-based methods, such as back-propagation, is that they
+cannot get stuck in local minima.
+(j) The back-propagation algorithm, when run until a minimum is achieved, always finds
+the same solution (i.e., weights and biases) no matter what the initial set of weights and
+biases are.
+Question
+(a)
+(b)
+(c)
+(d)
+(e)
+(f)
+(g)
+(h)
+(i)
+(j)
+Answer', 15, 2, NULL::jsonb, NULL, NULL, 'Problem 1 [15 points] True/False Questions
+Indicate whether the following statements are true or false by putting T or F in the given
+table. You get 1.5 points for each correct answer.
+(a) Machine learning gives computers the ability to make decision by writing down rules and
+methods and being explicitly programmed.
+(b) The regression technique in machine learning is NOT a group of algorithms that are used
+for predicting a class/category.
+(c) A 5-fold cross validation for K-nearest neighbors algorithm means that for each value
+of K, we randomly select 1/5 of the training data as the validation set to evaluate the
+model which is trained by the remaining (4/5) of the training data.
+(d) If we use K-means clustering, we will get the same cluster assignments for each data
+point, whether or not we standardize the variables.
+(e) Given an input data point x, where all the attribute values in x are real numbers. Suppose
+you are asked to predict a label y for x, where y = 0 or y = 1. Assume you have no
+knowledge about the distributions of x and y, perceptron is an appropriate method for
+this problem.
+(f) A perceptron with the unit step function (i.e., f(z) = 0 if z ≤0, otherwise f(z) = 1) as
+the activation function cannot be used for multi-class classification.
+(g) If a training dataset is linearly separable into two classes, the perceptron learning rule
+will always converge to weights and bias that accomplish the desired classification.
+(h) The neural network weights are updated during forward propagation.
+(i) An advantage of gradient descent-based methods, such as back-propagation, is that they
+cannot get stuck in local minima.
+(j) The back-propagation algorithm, when run until a minimum is achieved, always finds
+the same solution (i.e., weights and biases) no matter what the initial set of weights and
+biases are.
+Question
+(a)
+(b)
+(c)
+(d)
+(e)
+(f)
+(g)
+(h)
+(i)
+(j)
+Answer
+F
+T
+F
+F
+F
+T
+T
+F
+F
+F
+Marking scheme:
+ˆ 1.5 points for each correct answer. 15 points in total', ARRAY['True/False']::TEXT[], 'True/False', 'True/False', ARRAY['True/False']::TEXT[], ARRAY['concept_check', 'rapid_reasoning']::TEXT[], 'medium', '', '', ''),
+    ('COMP2211-2022-fall-midterm', '2', NULL, 2, 'long_question', 'coding', 'Problem 2 [19 points] Python Fundamentals
+(a)
+[7 points] Consider the following NumPy arrays:
+import numpy as np
+A = np.array([1, 2, 3, 4, 5, 6, 7, 8, 9])
+B = np.array([[[0, 1, 2, 3],
+[4, 5, 6, 7],
+[8, 9, 10, 11]],
+[[12, 13, 14, 15],
+[16, 17, 18, 19],
+[20, 21, 22, 23]]])
+Write the output for each of the following Python statements. If the output is an empty
+array, write “Empty Array”. If an error occurs, write “Error”.
+(i) print(A[::3])
+(ii) print(A[:-2:-2])
+(iii) print(B[:, 1, 3:0:-1])
+(iv) print(B[0, 1, [0,3]])
+The NumPy array B is repeated here to ease your reading.
+B = np.array([[[0, 1, 2, 3],
+[4, 5, 6, 7],
+[8, 9, 10, 11]],
+[[12, 13, 14, 15],
+[16, 17, 18, 19],
+[20, 21, 22, 23]]])
+(v) print(B[ B % 4 == 0 ])
+(vi) # numpy.sum(a, axis)
+# returns the sum of array elements over a given axis
+print( np.sum( B, axis=1 ) )
+(vii) # numpy.ndarray.reshape(shape)
+# returns an array containing the same data with a new shape
+print( B.reshape( (3, -1, 2) ) )
+(b)
+[6 points] Write the output for the following Python code segments. If the output is an
+empty array, write “Empty Array”. If an error occurs, write “Error”.
+(i) import numpy as np
+A = np.array([[1, 2, 3], [2, 4, 6]])
+B = np.array([1, 2, 3])
+print(A * B)
+(ii) import numpy as np
+A = np.array([1, 2, 3, 4])
+B = np.array([[1, 2], [2, 4], [3, 6], [4, 8]])
+print(A + B)
+(iii) import numpy as np
+A = np.array([1, 2, 3, 4])
+B = np.array([[2], [3], [4]])
+print(A + B)
+(c)
+[6 points] The cosine similarity of two non-zero vectors, xTrain = (xTrain
+, . . . , xTrain
+n
+)
+and xTest = (xTest
+, . . . , xTest
+n
+), can be calculated by the following formula:
+Pn
+i=1 (xTrain
+i
+× xTest
+i
+)
+qPn
+i=1 (xTrain
+i
+)2
+qPn
+i=1 (xTest
+i
+)2
+For example, if a training sample xTrain is (0, 1, 2) and a testing sample xTest is (4, 6,
+8), the cosine similarity is:
+0 × 4 + 1 × 6 + 2 × 8
+√
+02 + 12 + 22√
+42 + 62 + 82 = 0.91350028
+Given the following NumPy arrays, X_train and X_test, where each 1D array represents
+a data point:
+import numpy as np
+X_train = np.array([[0, 1, 2], [2, 3, 4], [4, 5, 6]])
+X_test = np.array([[4, 6, 8], [5, 0, 0]])
+Compute the cosine similarity scores between each data point in X_train and each data
+point in X_test with a one-line Python expression, such that the evaluated result of
+the expression is:
+[[0.91350028 0.
+]
+[1.
+0.37139068]
+[0.99461155 0.45584231]]
+N ote:
+ˆ An expression is a combination of values, variables, operators, and calls to functions.
+ˆ Your expression should work with any number of data points in X_train and X_test
+and any number of values in the data points.
+ˆ You can assume that the number of attribute values in each data point is the same
+for both X_train and X_test.
+ˆ There must be no explicit loops in your expression.
+You may find the following attribute or functions useful for this question.
+ˆ Dot product of two arrays, a and b:
+numpy.dot(a, b)
+– It returns the product of matrix multiplication.
+ˆ Return the element-wise square of an array, x
+numpy.square(x)
+ˆ Return the sum of array elements over a given axis.
+numpy.sum(a, axis=None)
+– a: the input array with elements to sum.
+– axis: None or int or tuple of ints
+ˆ Return the non-negative square-root of an array, element-wise.
+numpy.sqrt(x)
+– x: the input array with values whose square-roots are required.
+ˆ Insert a new axis that will appear at the axis position in the expanded array shape.
+numpy.expand_dims(a, axis)
+– a: the input array.
+– axis: an int or tuple of ints that represents position in the expanded axes where
+the new axis (or axes) is placed.
+ˆ The transposed array.
+numpy.ndarray.T
+Write the one-line Python expression below:
+print(
+)', 19, 3, NULL::jsonb, NULL, NULL, 'Problem 2 [19 points] Python Fundamentals
+(a)
+[7 points] Consider the following NumPy arrays:
+Marking scheme:
+ˆ 1 point each sub-questions, i.e. (i) to (vii)
+ˆ No partial score
+import numpy as np
+A = np.array([1, 2, 3, 4, 5, 6, 7, 8, 9])
+B = np.array([[[0, 1, 2, 3],
+[4, 5, 6, 7],
+[8, 9, 10, 11]],
+[[12, 13, 14, 15],
+[16, 17, 18, 19],
+[20, 21, 22, 23]]])
+Write the output for each of the following Python statements. If the output is an empty
+array, write “Empty Array”. If an error occurs, write “Error”.
+(i) print(A[::3])
+Answer:
+[1 4 7]
+(ii) print(A[:-2:-2])
+Answer:
+[9]
+(iii) print(B[:, 1, 3:0:-1])
+Answer:
+[[ 7
+5]
+[19 18 17]]
+(iv) print(B[0, 1, [0,3]])
+Answer:
+[4 7]
+(v) print(B[ B % 4 == 0 ])
+Answer:
+[ 0
+8 12 16 20]
+(vi) # numpy.sum(a, axis)
+# returns the sum of array elements over a given axis
+print( np.sum( B, axis=1 ) )
+Answer:
+[[12 15 18 21]
+[48 51 54 57]]
+(vii) # numpy.ndarray.reshape(shape)
+# returns an array containing the same data with a new shape
+print( B.reshape( (3, -1, 2) ) )
+Answer:
+[[[ 0
+1]
+[ 2
+3]
+[ 4
+5]
+[ 6
+7]]
+[[ 8
+9]
+[10 11]
+[12 13]
+[14 15]]
+[[16 17]
+[18 19]
+[20 21]
+[22 23]]]
+(b)
+[6 points] Write the output for the following Python code segments. If the output is an
+empty array, write “Empty Array”. If an error occurs, write “Error”.
+Marking scheme:
+ˆ 2 points each sub-questions, i.e. (i) to (iii)
+ˆ No partial score
+(i) import numpy as np
+A = np.array([[1, 2, 3], [2, 4, 6]])
+B = np.array([1, 2, 3])
+print(A * B)
+Answer:
+[[ 1
+9]
+[ 2
+8 18]]
+(ii) import numpy as np
+A = np.array([1, 2, 3, 4])
+B = np.array([[1, 2], [2, 4], [3, 6], [4, 8]])
+print(A + B)
+Answer:
+Error
+(iii) import numpy as np
+A = np.array([1, 2, 3, 4])
+B = np.array([[2], [3], [4]])
+print(A + B)
+Answer:
+[[3 4 5 6]
+[4 5 6 7]
+[5 6 7 8]]
+(c)
+[6 points] The cosine similarity of two non-zero vectors, xTrain = (xTrain
+, . . . , xTrain
+n
+)
+and xTest = (xTest
+, . . . , xTest
+n
+), can be calculated by the following formula:
+Pn
+i=1 (xTrain
+i
+× xTest
+i
+)
+qPn
+i=1 (xTrain
+i
+)2
+qPn
+i=1 (xTest
+i
+)2
+For example, if a training sample xTrain is (0, 1, 2) and a testing sample xTest is (4, 6,
+8), the cosine similarity is:
+0 × 4 + 1 × 6 + 2 × 8
+√
+02 + 12 + 22√
+42 + 62 + 82 = 0.91350028
+Given the following NumPy arrays, X_train and X_test, where each 1D array represents
+a data point:
+import numpy as np
+X_train = np.array([[0, 1, 2], [2, 3, 4], [4, 5, 6]])
+X_test = np.array([[4, 6, 8], [5, 0, 0]])
+Compute the cosine similarity scores between each data point in X_train and each data
+point in X_test with a one-line Python expression, such that the evaluated result of
+the expression is:
+[[0.91350028 0.
+]
+[1.
+0.37139068]
+[0.99461155 0.45584231]]
+N ote:
+ˆ An expression is a combination of values, variables, operators, and calls to functions.
+ˆ Your expression should work with any number of data points in X_train and X_test
+and any number of values in the data points.
+ˆ You can assume that the number of attribute values in each data point is the same
+for both X_train and X_test.
+ˆ There must be no explicit loops in your expression.
+You may find the following attribute or functions useful for this question.
+ˆ Dot product of two arrays, a and b:
+numpy.dot(a, b)
+– It returns the product of matrix multiplication.
+ˆ Return the element-wise square of an array, x
+numpy.square(x)
+ˆ Return the sum of array elements over a given axis.
+numpy.sum(a, axis=None)
+– a: the input array with elements to sum.
+– axis: None or int or tuple of ints
+ˆ Return the non-negative square-root of an array, element-wise.
+numpy.sqrt(x)
+– x: the input array with values whose square-roots are required.
+ˆ Insert a new axis that will appear at the axis position in the expanded array shape.
+numpy.expand_dims(a, axis)
+– a: the input array.
+– axis: an int or tuple of ints that represents position in the expanded axes where
+the new axis (or axes) is placed.
+ˆ The transposed array.
+numpy.ndarray.T
+Write the one-line Python expression below:
+print(
+)
+Answer:
+print(X_train.dot(X_test.T) /
+np.dot(np.expand_dims(np.sqrt(np.sum(X_train**2, axis=1)), 1),
+np.expand_dims(np.sqrt(np.sum(X_test**2, axis=1)), 1).T)
+)
+print(X_train.dot(X_test.T) /
+np.dot(np.expand_dims(np.sqrt(np.sum(X_train**2, axis=1)), 1),
+np.expand_dims(np.sqrt(np.sum(X_test**2, axis=1)), 0))
+)
+print(np.dot(X_train, X_test.transpose()) /
+(np.sqrt(np.sum(np.square(X_train),axis=1)[:, None]) *
+np.sqrt(np.sum(np.square(X_test), axis=1)[None, :]))
+)
+print(np.dot(X_train, np.transpose(X_test)) /
+(np.sqrt(np.sum(np.square(X_train),axis=1)[:, np.newaxis]) *
+np.sqrt(np.sum(np.square(X_test), axis=1)[np.newaxis,:]))
+)
+print(np.dot(X_train, X_test.T) /
+np.sqrt(np.sum(np.square(X_train), axis=1)).reshape(3,1).dot(
+np.sqrt(np.sum(np.square(X_test), axis=1)).reshape(1, 2))
+)
+Marking scheme:
+ˆ 2 points for correct numerator
+– If not using np.dot correctly, deduct 1 point (np.dot can be replaced by np.matmul,
+@);
+– If not using np.ndarray.T correctly, deduct 1 point (np.ndarray.T can be replaced
+by np.transpose);
+– The maximum number of points can be deducted for numerator is 2.
+ˆ 4 points for correct denominator
+– If not using np.expand dims correctly (including specifying axis), deduct 1 point
+for each (np.expand dims can be replaced by np.reshape or adding a new axis);
+– If not using np.dot correctly, deduct 1 point (np.dot can be replaced by np.matmul,
+@);
+– If not using np.sqrt correctly, deduct 1 point for each;
+– If not using np.sum correcly (including specifying axis), deduct 1 point for each;
+– If not using np.square correctly, deduct 1 point for each (np.square can be re-
+placed by np.ndarray ** 2);
+– The maximum number of points can be deducted for denominator is 4. If points
+to be deducted exceed 4, the denominator part gets 0 point.', ARRAY['Python Fundamentals']::TEXT[], 'Python Fundamentals', 'Python Fundamentals', ARRAY['Python Fundamentals']::TEXT[], ARRAY['implementation', 'code_tracing', 'debugging']::TEXT[], 'hard', '', '', ''),
+    ('COMP2211-2022-fall-midterm', '3', NULL, 3, 'long_question', 'long_answer', 'Problem 3 [18 points] Conditional Probability and Bayes Classifier
+(a)
+[4 points] Assume the probability of getting an A+ in COMP 2211 is 0.08. The prob-
+ability of getting a score greater than 90 in the midterm exam given that a student
+gets an A+ in COMP 2211 is 0.95, and the probability of getting a score greater than
+90 in the midterm exam given that the student does not get an A+ in COMP 2211 is 0.05.
+(i) Calculate the probability of getting a score greater than 90 in the midterm exam.
+Show all your steps. Round your answer to 2 decimal places.
+The formula that you may find useful for this question:
+P(E) = P(E|B)P(B) + P(E|Not B)P(Not B)
+(ii) Use Bayes’ rule to calculate the probability of getting an A+ given that a student
+gets a score greater than 90 in the midterm exam. Show all your steps. Round your
+answer to 2 decimal places.
+The formula that you may find useful for this question:
+P(B|E) = P(B)P(E|B)
+P(E)
+(b)
+[8 points] Given the following dataset (X, y) with 10 training examples, each contains
+2 attributes (x1, x2) and its binary label y.
+X = [[0 0], [0 1], [1 1], [1 0], [1 1], [1 0], [0 1], [0 1], [1 1], [0 0]]
+y = [0 0 1 1 1 1 0 0 1 1]
+Suppose you believe that a naive Bayes model would be appropriate for this dataset, and
+you want to classify the test sample:
+x = [1 1]
+(i) Compute the class prior probabilities, i.e., P(y = 0) and P(y = 1).
+(ii) Compute the 4 conditional probabilities required by naive Bayes for the test sample,
+i.e., P(x1 = 1|y = 0), P(x2 = 1|y = 0), P(x1 = 1|y = 1), P(x2 = 1|y = 1).
+(iii) Under the naive Bayes model and the probabilities you compute in parts b(i) and
+b(ii), what is the most likely label for the test sample, i.e., 0 or 1? If there is a tie,
+please include the word “tie” in your answer. Show all your steps.
+The formula that you may find useful for this question:
+BNB = argmaxBiP(Bi)(P(e1|Bi)P(e2|Bi)P(e3|Bi) . . . P(ed|Bi))
+(c)
+[3 points] Briefly describe a zero frequency problem of Naive Bayes classification and
+suggest a way to solve the problem. Also, state whether a zero frequency problem occurs
+in part (b).
+The dataset (X, y) is repeated here to ease your reading.
+X = [[0 0], [0 1], [1 1], [1 0], [1 1], [1 0], [0 1], [0 1], [1 1], [0 0]]
+y = [0 0 1 1 1 1 0 0 1 1]
+(d)
+[3 points] Consider the dataset given in part (b), i.e., the one above. Suppose you do
+NOT believe that the naive Bayes model would be appropriate for this dataset, and you
+want to classify the test sample:
+x = [0 0]
+using the Bayes model without making the naive assumption. What is the most
+likely label for the test sample? If there is a tie, please include the word “tie” in your
+answer. Show all your steps.
+The formula that you may find useful for this question:
+BNB = argmaxBiP(Bi)P((e1, e2, e3, . . . , ed)|Bi)', 18, 8, NULL::jsonb, NULL, NULL, 'Problem 3 [18 points] Conditional Probability and Bayes Classifier
+(a)
+[4 points] Assume the probability of getting an A+ in COMP 2211 is 0.08. The prob-
+ability of getting a score greater than 90 in the midterm exam given that a student
+gets an A+ in COMP 2211 is 0.95, and the probability of getting a score greater than
+90 in the midterm exam given that the student does not get an A+ in COMP 2211 is 0.05.
+(i) [3 points] Calculate the probability of getting a score greater than 90 in the midterm
+exam. Show all your steps. Round your answer to 2 decimal places.
+The formula that you may find useful for this question:
+P(E) = P(E|B)P(B) + P(E|Not B)P(Not B)
+Answer:
+P(A+) =0.08
+P(> 90|A+) =0.95
+P(> 90|Not A+) =0.05
+P(> 90) =P(> 90|A+)P(A+) + P(> 90|Not A+)P(Not A+)
+=0.95(0.08) + 0.05(0.92) = 0.12
+Marking scheme:
+ˆ 0.5 point for P(A+) = 0.08
+ˆ 0.5 point for P(> 90|A+) = 0.95
+ˆ 0.5 point for P(> 90|Not A+) = 0.05
+ˆ 0.5 point for P(Not A+) = 0.92
+ˆ 1 point for P(> 90) = 0.12
+(ii)
+[1 point] Use Bayes’ rule to calculate the probability of getting an A+ given that
+a student gets a score greater than 90 in the midterm exam. Show all your steps.
+Round your answer to 2 decimal places.
+The formula that you may find useful for this question:
+P(B|E) = P(B)P(E|B)
+P(E)
+Answer:
+P(A + | > 90) = P(A+)P(> 90|A+)
+P(> 90)
+= 0.08(0.95)
+0.12
+= 0.63
+Marking scheme:
+ˆ 1 point for P(A + | > 90) = 0.63
+(b)
+[8 points] Given the following dataset (X, y) with 10 training examples, each contains
+2 attributes (x1, x2) and its binary label y.
+X = [[0 0], [0 1], [1 1], [1 0], [1 1], [1 0], [0 1], [0 1], [1 1], [0 0]]
+y = [0 0 1 1 1 1 0 0 1 1]
+Suppose you believe that a naive Bayes model would be appropriate for this dataset, and
+you want to classify the test sample:
+x = [1 1]
+(i)
+[1 point] Compute the class prior probabilities, i.e., P(y = 0) and P(y = 1).
+Answer:
+P(y = 0) = 4
+10 = 2
+P(y = 1) = 6
+10 = 3
+Marking scheme:
+ˆ 0.5 point for P(y = 0) = 2
+ˆ 0.5 point for P(y = 1) = 3
+(ii)
+[4 points] Compute the 4 conditional probabilities required by naive Bayes for
+the test sample, i.e., P(x1 = 1|y = 0), P(x2 = 1|y = 0), P(x1 = 1|y = 1),
+P(x2 = 1|y = 1).
+Answer:
+P(x1 = 1|y = 0) = 0
+P(x2 = 1|y = 0) = 3
+P(x1 = 1|y = 1) = 5
+P(x2 = 1|y = 1) = 3
+6 = 1
+Marking scheme:
+ˆ 1 point for P(x1 = 1|y = 0) = 0
+ˆ 1 point for P(x2 = 1|y = 0) = 3
+ˆ 1 point for P(x1 = 1|y = 1) = 5
+ˆ 1 point for P(x2 = 1|y = 1) = 1
+(iii)
+[3 points] Under the naive Bayes model and the probabilities you compute in parts
+b(i) and b(ii), what is the most likely label for the test sample, i.e., 0 or 1? If there
+is a tie, please include the word “tie” in your answer. Show all your steps.
+The formula that you may find useful for this question:
+BNB = argmaxBiP(Bi)(P(e1|Bi)P(e2|Bi)P(e3|Bi) . . . P(ed|Bi))
+Answer:
+The numerator part of P(y = 0|x) = P(y = 0)P(x1 = 1|y = 0)P(x2 = 1|y = 0)
+= 0
+The numerator part of P(y = 1|x) = P(y = 1)P(x1 = 1|y = 1)P(x2 = 1|y = 1)
+= 3
+5
+ 1
+
+= 1
+So, the most likely label for the test sample is 1.
+Marking scheme:
+ˆ 1 point for the numerator part of P(y = 0|x) = 0
+ˆ 1 point for the numerator part of P(y = 1|x) = 1
+ˆ 1 point for stating the most likely label for the test sample is 1
+(c)
+[3 points] Briefly describe a zero frequency problem of Naive Bayes classification and
+suggest a way to solve the problem. Also, state whether a zero frequency problem occurs
+in part (b).
+Answer:
+If categorical variable has a category in test data set, which was not observed in the
+training data set, then the frequency-based probability estimate will be zero. And we
+will get a zero when all the probabilities are multiplied. This will be unable to make a
+prediction. This is known as zero frequency.
+A way to overcome this “zero frequency problem” is to add one to the count for ev-
+ery attribute value-class combination when an attribute value does not occur with every
+class value.
+A zero frequency problem occurs in part (b).
+Marking scheme:
+ˆ 1 point for briefly describing a zero frequency problem
+ˆ 1 point for suggesting a way to solve the problem
+*** Please note that suggesting “adding more training data/increase the dataset
+size/Using Log()” will not get any mark ***
+ˆ 1 point for stating a zero frequency problem occurs in part (b)
+The dataset (X, y) is repeated here to ease your reading.
+X = [[0 0], [0 1], [1 1], [1 0], [1 1], [1 0], [0 1], [0 1], [1 1], [0 0]]
+y = [0 0 1 1 1 1 0 0 1 1]
+(d)
+[3 points] Consider the dataset given in part (b), i.e., the one above. Suppose you do
+NOT believe that the naive Bayes model would be appropriate for this dataset, and you
+want to classify the test sample:
+x = [0 0]
+using the Bayes model without making the naive assumption. What is the most
+likely label for the test sample? If there is a tie, please include the word “tie” in your
+answer. Show all your steps.
+The formula that you may find useful for this question:
+BNB = argmaxBiP(Bi)P((e1, e2, e3, . . . , ed)|Bi)
+Answer:
+The numerator part of P(y = 0|x) = P(y = 0)P((x1 = 0, x2 = 0)|y = 0)
+=
+2
+ 1
+
+= 1
+The numerator part of P(y = 1|x) = P(y = 1)P((x1 = 0, x2 = 0)|y = 1)
+=
+3
+ 1
+
+= 1
+As the two probabilities are the same value, it’s a tie.
+Marking scheme:
+ˆ 1 point for the numerator part of P(y = 0|x) = 1
+ˆ 1 point for the numerator part of P(y = 1|x) = 1
+ˆ 1 point for stating it’s a tie', ARRAY['Probabilistic Models']::TEXT[], 'Probabilistic Models', 'Probabilistic Models', ARRAY['Probabilistic Models']::TEXT[], ARRAY['manual_computation', 'probability_reasoning', 'classification_decision']::TEXT[], 'hard', '', '', ''),
+    ('COMP2211-2022-fall-midterm', '4', NULL, 4, 'long_question', 'long_answer', 'Problem 4 [14 points] K-Nearest Neighbors
+Consider a set of 8 training data given as (xTrain, cTrain) values, where xTrain is a string with
+6 bits and cTrain is the multi-class label, A, B or C:
+{ ("101110", A), ("110100", B), ("011100", B), ("000101", B),
+("011111", B), ("101101", B), ("100011", A), ("010000", C) }
+Classify a test sample (xTest) with the string “101011” using a K-Nearest Neighbors classifier
+(KNN) and Hamming distance.
+Hamming distance is the number of bit positions in which the two bits are different. For
+example, the hamming distance between two strings, “11011001” and “10011101” is 2.
+(a)
+[5 points] Complete the following table by filling in the computed Hamming distance
+between each training data point and the test sample. Also, determine the class label of
+the test sample using KNN with K = 3 based on the results.
+xTrain
+cTrain
+Distance
+“101110”
+A
+“110100”
+B
+“011100”
+B
+“000101”
+B
+“011111”
+B
+“101101”
+B
+“100011”
+A
+“010000”
+C
+The predicted class label of the test sample is
+.
+(b)
+[3 points] What will happen if we use a KNN classifier with K = 4 instead to classify
+the test sample above? Describe your answer.
+(c)
+[3 points] A student claims that if a KNN classifier with K = 3 is used with the above
+training data, the predicted class label will never be class C no matter what the test
+example is, if there is no tie-breaking strategy used. Is this claim correct or not? Explain
+your answer.
+(d)
+[3 points] Is the above training data suitable for performing a D-fold cross-validation?
+Explain your answer.', 14, 12, NULL::jsonb, NULL, NULL, 'Problem 4 [14 points] K-Nearest Neighbors
+Consider a set of 8 training data given as (xTrain, cTrain) values, where xTrain is a string with
+6 bits and cTrain is the multi-class label, A, B or C:
+{ ("101110", A), ("110100", B), ("011100", B), ("000101", B),
+("011111", B), ("101101", B), ("100011", A), ("010000", C) }
+Classify a test sample (xTest) with the string “101011” using a K-Nearest Neighbors classifier
+(KNN) and Hamming distance.
+Hamming distance is the number of bit positions in which the two bits are different. For
+example, the hamming distance between two strings, “11011001” and “10011101” is 2.
+(a)
+[5 points] Complete the following table by filling in the computed Hamming distance
+between each training data point and the test sample. Also, determine the class label of
+the test sample using KNN with K = 3 based on the results.
+xTrain
+cTrain
+Distance
+“101110”
+A
+“110100”
+B
+“011100”
+B
+“000101”
+B
+“011111”
+B
+“101101”
+B
+“100011”
+A
+“010000”
+C
+The predicted class label of the test sample is
+.
+Answer:
+xTrain
+cTrain
+Distance
+“101110”
+A
+“110100”
+B
+“011100”
+B
+“000101”
+B
+“011111”
+B
+“101101”
+B
+“100011”
+A
+“010000”
+C
+The predicted class label of the test sample is A.
+Marking scheme:
+ˆ 0.5 point for each distance, i.e. 4 points total
+ˆ 1 point for the predicted class label
+(b)
+[3 points] What will happen if we use a KNN classifier with K = 4 instead to classify
+the test sample above? Describe your answer.
+Answer:
+We will have a tie where 2 nearest neighbors are with class label A and 2 nearest neighbors
+are with class label B.
+Marking scheme:
+ˆ 1 point for mentioning “Tie” or ”cannot find the class label”
+ˆ 2 points for the description of “2 NNs are class label A and 2 NNs are class label B”
+(c)
+[3 points] A student claims that if a KNN classifier with K = 3 is used with the above
+training data, the predicted class label will never be class C no matter what the test
+example is, if there is no tie-breaking strategy used. Is this claim correct or not? Explain
+your answer.
+Answer:
+The claim is correct. If k = 3 and assume equal weights for all the points, no mat-
+ter what the testing example is, it will be one of the following 3 cases:
+ˆ none of the 3 nearest neighbors is with class label C, so the predicted label will not
+be C.
+ˆ 1 of the 3 nearest neighbors is with class label C and the other 2 neighbors are both
+with class label A, or both with class label B, so the predicted class label will only
+be A or B.
+ˆ A tie
+Marking scheme:
+ˆ 1 point for “correct” or “yes”
+ˆ 2 points for the explanation
+(d)
+[3 points] Is the above training data suitable for performing a D-fold cross-validation?
+Explain your answer.
+Answer:
+No, unless the number of folds is equal to the number of training data points.
+The
+above training data set is umbalanced. Hence, if we split the above training data set, the
+data used for training may not have samples with class labels A or C.
+Marking scheme:
+ˆ 1 point for “no”
+ˆ 2 points for the explanation', ARRAY['KNN and Clustering']::TEXT[], 'KNN and Clustering', 'KNN and Clustering', ARRAY['KNN and Clustering']::TEXT[], ARRAY['manual_computation', 'distance_calculation', 'algorithm_tracing']::TEXT[], 'medium', '', '', ''),
+    ('COMP2211-2022-fall-midterm', '5', NULL, 5, 'long_question', 'long_answer', 'Problem 5 [12 points] K-Means Clustering
+Consider the following training dataset:
+Example
+A
+B
+C
+D
+E
+Attribute value
+0.2
+0.5
+0.7
+2.5
+3.5
+Apply the K-means clustering algorithm to this dataset for K=2, i.e., you will produce two
+data clusters. Assume the distance measure you use is Euclidean distance, defined as follows.
+distance(xTrain, xTest) =
+v
+u
+u
+t
+n
+X
+i=1
+(xTrain
+i
+−xTest
+i
+)2
+(a) [1.5 points] Assume examples A and B are chosen as the initial centroid of clusters 1 and
+cluster 2, respectively. Write down the resulting cluster assignments in the table below.
+The cluster assignment for A and B has been done for you.
+Example
+A
+B
+C
+D
+E
+Cluster Assignment
+(b) [2 points] After assigning the examples to the clusters in part (a), re-compute the cluster
+centroids to be the mean of the examples currently assigned to each cluster. For each
+cluster, write the new cluster centroid in the table below.
+Cluster
+Centroid
+(c)
+[2.5 points] After recomputing the cluster centroids in part (b), re-assign the examples
+to the clusters to which they are closest. Write down the resulting cluster assignments
+in the table below.
+Example
+A
+B
+C
+D
+E
+Cluster Assignment
+(d) [2 points] After assigning the examples to the clusters in part (c), re-compute the cluster
+centroids to be the mean of the examples currently assigned to each cluster. For each
+cluster, write the new cluster centroid in the table below. If your answer is a floating
+point number, round it to 2 decimal places.
+Cluster
+Centroid
+(e)
+[2.5 points] After recomputing the cluster centroids in part (d), re-assign the examples
+to the clusters to which they are closest. Write down the resulting cluster assignments
+in the table below.
+Example
+A
+B
+C
+D
+E
+Cluster Assignment
+(f)
+[1.5 points] State whether the K-means clustering algorithm converges for this dataset
+after performing all the steps in parts (a)-(e). Explain your answer.', 12, 14, NULL::jsonb, NULL, NULL, 'Problem 5 [12 points] K-Means Clustering
+Consider the following training dataset:
+Example
+A
+B
+C
+D
+E
+Attribute value
+0.2
+0.5
+0.7
+2.5
+3.5
+Apply the K-means clustering algorithm to this dataset for K=2, i.e., you will produce two
+data clusters. Assume the distance measure you use is Euclidean distance, defined as follows.
+distance(xTrain, xTest) =
+v
+u
+u
+t
+n
+X
+i=1
+(xTrain
+i
+−xTest
+i
+)2
+(a) [1.5 points] Assume examples A and B are chosen as the initial centroid of clusters 1 and
+cluster 2, respectively. Write down the resulting cluster assignments in the table below.
+The cluster assignment for A and B has been done for you.
+Example
+A
+B
+C
+D
+E
+Cluster Assignment
+Marking scheme:
+ˆ 0.5 point for each correct cluster assignment. 1.5 points in total
+(b) [2 points] After assigning the examples to the clusters in part (a), re-compute the cluster
+centroids to be the mean of the examples currently assigned to each cluster. For each
+cluster, write the new cluster centroid in the table below.
+Cluster
+Centroid
+0.2
+1.8
+Marking scheme:
+ˆ 0.5 point for each correct centroid. 1 point in total
+(c)
+[2.5 points] After recomputing the cluster centroids in part (b), re-assign the examples
+to the clusters to which they are closest. Write down the resulting cluster assignments
+in the table below.
+Example
+A
+B
+C
+D
+E
+Cluster Assignment
+Marking scheme:
+ˆ 0.5 point for each correct cluster assignment. 2.5 points in total
+(d) [2 points] After assigning the examples to the clusters in part (c), re-compute the cluster
+centroids to be the mean of the examples currently assigned to each cluster. For each
+cluster, write the new cluster centroid in the table below. If your answer is a floating
+point number, round it to 2 decimal places.
+Cluster
+Centroid
+0.47
+Marking scheme:
+ˆ 1 point for each correct centroid. 2 points in total
+(e)
+[2.5 points] After recomputing the cluster centroids in part (d), re-assign the examples
+to the clusters to which they are closest. Write down the resulting cluster assignments
+in the table below.
+Example
+A
+B
+C
+D
+E
+Cluster Assignment
+Marking scheme:
+ˆ 0.5 point for each correct cluster assignment. 2.5 points in total
+(f)
+[1.5 points] State whether the K-means clustering algorithm converges for this dataset
+after performing all the steps in parts (a)-(e). Explain your answer.
+Answer:
+The algorithm converges for this dataset since the cluster assignment of examples re-
+mains the same.
+Marking scheme:
+ˆ 0.5 point for stating the algorithm converges
+ˆ 1 point for the correct explanation', ARRAY['KNN and Clustering']::TEXT[], 'KNN and Clustering', 'KNN and Clustering', ARRAY['KNN and Clustering']::TEXT[], ARRAY['manual_computation', 'distance_calculation', 'algorithm_tracing']::TEXT[], 'medium', '', '', ''),
+    ('COMP2211-2022-fall-midterm', '6', NULL, 6, 'long_question', 'long_answer', 'Problem 6 [14 points] Perceptron
+Suppose you are a CSE professor at HKUST who wants to offer a new course, COMP 2211.
+Before starting, you want to predict if COMP 2211 will be successful or not. You approach
+two students, A and B, asking them to read the proposal of 5 existing courses, COMP 1111,
+COMP 2222, COMP 3333, COMP 4444, and COMP 5555, and rate them on a scale of 1
+to 5 (assume only integer scores). Assume each student reads the proposals independently
+and announces their rating. As each student might be biased, you may be unable to average
+their ratings. Instead, you decide to use a perceptron to classify the data using the training
+dataset in Table 1.
+Course Code
+x1
+x2
+Success
+COMP 1111
+No
+COMP 2222
+Yes
+COMP 3333
+No
+COMP 4444
+Yes
+COMP 5555
+Yes
+Table 1: Training dataset
+The training dataset is composed of the two student scores, x1 = score given by student A,
+and x2 = score given by student B, and the corresponding true label regarding the success
+of the courses judged based on the SFQ, for the 5 courses.
+(a) [8 points] Train the perceptron to generate O = 1 if the course is success, O = 0 otherwise,
+using the initial weight of w1 = 0, w2 = 0, θ = -1, and the activation function:
+f(z) =
+
+
+
+if z ≤0
+otherwise
+Also, use a learning rate of η = 1.
+You may find the updating rules below useful.
+∆wi = η(T −O)xi
+∆θ = η(T −O)
+wi = wi + ∆wi
+θ = θ + ∆θ
+Present each row of Table 1 as a training example, and update the perceptron weights
+and bias before moving on to the next row. Show all the steps by completing Table 2.
+x1
+x2
+T
+O
+∆w1
+w1
+∆w2
+w2
+∆θ
+θ
+-
+-
+-
+-
+-
+-
+-
+-1
+Table 2: Perceptron algorithm execution
+(b)
+[6 points] Now, assume you do not care about the true label regarding the success of
+the courses judged based on the SFQ. State whether each of the following scenarios for
+which a perceptron using the ratings of the two students can correctly classify the data.
+Justify your answer.
+(i) If the total of their ratings is more than 8, then the course will be success and
+otherwise it will fail, i.e.,
+Course Code
+x1
+x2
+Success
+COMP 1111
+No
+COMP 2222
+No
+COMP 3333
+Yes
+COMP 4444
+No
+COMP 5555
+No
+(ii) The course will succeed if and only if each reviewer gives either a rating of 2 or a
+rating of 3, i.e.,
+Course Code
+x1
+x2
+Success
+COMP 1111
+No
+COMP 2222
+Yes
+COMP 3333
+No
+COMP 4444
+No
+COMP 5555
+Yes', 14, 16, NULL::jsonb, NULL, NULL, 'Problem 6 [14 points] Perceptron
+Suppose you are a CSE professor at HKUST who wants to offer a new course, COMP 2211.
+Before starting, you want to predict if COMP 2211 will be successful or not. You approach
+two students, A and B, asking them to read the proposal of 5 existing courses, COMP 1111,
+COMP 2222, COMP 3333, COMP 4444, and COMP 5555, and rate them on a scale of 1
+to 5 (assume only integer scores). Assume each student reads the proposals independently
+and announces their rating. As each student might be biased, you may be unable to average
+their ratings. Instead, you decide to use a perceptron to classify the data using the training
+dataset in Table 1.
+Course Code
+x1
+x2
+Success
+COMP 1111
+No
+COMP 2222
+Yes
+COMP 3333
+No
+COMP 4444
+Yes
+COMP 5555
+Yes
+Table 1: Training dataset
+The training dataset is composed of the two student scores, x1 = score given by student A,
+and x2 = score given by student B, and the corresponding true label regarding the success
+of the courses judged based on the SFQ, for the 5 courses.
+(a) [8 points] Train the perceptron to generate O = 1 if the course is success, O = 0 otherwise,
+using the initial weight of w1 = 0, w2 = 0, θ = -1, and the activation function:
+f(z) =
+
+
+
+if z ≤0
+otherwise
+Also, use a learning rate of η = 1.
+You may find the updating rules below useful.
+∆wi = η(T −O)xi
+∆θ = η(T −O)
+wi = wi + ∆wi
+θ = θ + ∆θ
+Present each row of Table 1 as a training example, and update the perceptron weights
+and bias before moving on to the next row. Show all the steps by completing Table 2.
+x1
+x2
+T
+O
+∆w1
+w1
+∆w2
+w2
+∆θ
+θ
+-
+-
+-
+-
+-
+-
+-
+-1
+-1
+-4
+-1
+-5
+-3
+-1
+-1
+Table 2: Perceptron algorithm execution
+Marking scheme:
+ˆ 0.2 point for each correct value. 40 values, 8 points in total
+(b)
+[6 points] Now, assume you do not care about the true label regarding the success of
+the courses judged based on the SFQ. State whether each of the following scenarios for
+which a perceptron using the ratings of the two students can correctly classify the data.
+Justify your answer.
+(i)
+[3 points] If the total of their ratings is more than 8, then the course will be success
+and otherwise it will fail, i.e.,
+Course Code
+x1
+x2
+Success
+COMP 1111
+No
+COMP 2222
+No
+COMP 3333
+Yes
+COMP 4444
+No
+COMP 5555
+No
+Answer:
+Perceptron can correctly classify the data in this scenario, because the data are
+linearly separable.
+Marking scheme:
+ˆ 1 point for stating the perceptron can correctly classify the data
+ˆ 2 points for the explanation
+*** 2 points for the explanation part if correct weights and bias are given. ***
+(ii)
+[3 points] The course will succeed if and only if each reviewer gives either a rating
+of 2 or a rating of 3, i.e.,
+Course Code
+x1
+x2
+Success
+COMP 1111
+No
+COMP 2222
+Yes
+COMP 3333
+No
+COMP 4444
+No
+COMP 5555
+Yes
+Answer:
+Perceptron cannot correctly classify the data in this scenario, because the data are
+not linearly separable.
+Marking scheme:
+ˆ 1 point for stating the perceptron cannot correctly classify the data
+ˆ 2 points for the explanation', ARRAY['Perceptron and MLP']::TEXT[], 'Perceptron and MLP', 'Perceptron and MLP', ARRAY['Perceptron and MLP']::TEXT[], ARRAY['forward_pass', 'backpropagation', 'weight_update']::TEXT[], 'medium', '', '', ''),
+    ('COMP2211-2022-fall-midterm', '7', NULL, 7, 'long_question', 'long_answer', 'Problem 7 [8 points] Multilayer Perceptron
+Given the following training dataset with 4 examples in which each example has 3 binary
+input attributes, x1, x2, x3 and 1 binary output T.
+x1
+x2
+x3
+T
+Suppose we are learning a Multilayer Perceptron with the training dataset above; the neural
+network has 1 hidden layer of 2 neurons, answer the following questions.
+(a)
+[2 points] How many weights and how many biases are there in the neural network?
+(b)
+[6 points] Draw the neural network below.
+-------------------- END OF PAPER
+--------------------
+/* Rough work */
+/* Rough work */', 8, 18, NULL::jsonb, NULL, NULL, 'Problem 7 [8 points] Multilayer Perceptron
+Given the following training dataset with 4 examples in which each example has 3 binary
+input attributes, x1, x2, x3 and 1 binary output T.
+x1
+x2
+x3
+T
+Suppose we are learning a Multilayer Perceptron with the training dataset above; the neural
+network has 1 hidden layer of 2 neurons, answer the following questions.
+(a)
+[2 points] How many weights and how many biases are there in the neural network?
+Answer:
+8 weights and 3 biases
+Marking scheme:
+ˆ 1 point for the number of weights
+ˆ 1 point for the number of biases
+*** 1 point if students only put 11 as the sum of these variables (e.g., 8 + 3 = 11) and
+do not indicate 8 for weights and 3 for biases. ***
+(b)
+[6 points] Draw the neural network below.
+Answer:
+Marking scheme:
+ˆ 3 points for indicating 3 input nodes, 2 hidden neurons, and 1 output neuron.
+-0.5 point for losing 1 node/neuron.
+ˆ 1 point for indicating 8 weights.
+-0.5 point for losing 1 weight indicator, at most -1.
+ˆ 1 point for indicating 3 biases.
+-0.5 point for losing 1 bias indicator, at most -1.
+ˆ 1 point for lines that connected layers.
+-9,5 point for losing 1 connecting line, at most -1.
+*** Additionally, suppose the number of nodes/neurons, the number of weights, the
+number of biases, and the number of lines are more than expected. In that case, we will
+only consider those correct ones, and the layer containing additional nodes/neurons will
+be regarded as wrong. (e.g. if you put 3-¿4-¿1 as network, then only the input nodes
+and output neuron consider to be correct, while all lines, weights, and biases should be
+wrong, you can get 2 points.) ***
+-------------------- END OF PAPER
+--------------------', ARRAY['Perceptron and MLP']::TEXT[], 'Perceptron and MLP', 'Perceptron and MLP', ARRAY['Perceptron and MLP']::TEXT[], ARRAY['forward_pass', 'backpropagation', 'weight_update']::TEXT[], 'easy', '', '', ''),
+    ('COMP2211-2022-spring-midterm', '1', NULL, 1, 'true_false', 'true_false', 'Problem 1 [15 points] True/False Questions
+Indicate whether the following statements are true or false by putting T or F in the given
+table. You get 1.5 points for each correct answer.
+(a) Machine learning is a sub-field of artificial intelligence.
+(b) Tensorflow is easy to use and less flexible than Keras.
+(c) Suppose we wish to calculate P(B|e1, e2) and we have no conditional independence in-
+formation. Having P(e1, e2), P(B), P(e1, e2|B) are sufficient for the calculation.
+(d) Training a K-nearest neighbors classifier takes more computational time than applying
+it.
+(e) K-nearest neighbors cannot be used for regression.
+(f) Consider D-fold cross-validation. A higher number of folds (i.e. larger value of D), the
+estimated error will be lower on average.
+(g) K-means clustering algorithm is guaranteed to converge.
+(h) Consider a two-class classification problem. Suppose we have trained a perceptron model
+on a linearly separable training set, and now we get a new labeled data point which is
+correctly classified by the model, and far away from the decision boundary. If we add
+this new point to our earlier training set and re-train with the same set of initial weights
+and bias, the learnt decision boundary will be changed for sure.
+(i) Gradient descent is usually NOT guaranteed to converge at global minimum.
+(j) If the learning rate is too small, gradient descent may take a very long time to converge
+and computationally expensive.
+Question
+Answer (T/F)
+(a)
+(b)
+(c)
+(d)
+(e)
+(f)
+(g)
+(h)
+(i)
+(j)', 15, 2, NULL::jsonb, NULL, NULL, 'Problem 1 [15 points] True/False Questions
+Indicate whether the following statements are true or false by putting T or F in the given
+table. You get 1.5 points for each correct answer.
+(a) Machine learning is a sub-field of artificial intelligence.
+(b) Tensorflow is easy to use and less flexible than Keras.
+(c) Suppose we wish to calculate P(B|e1, e2) and we have no conditional independence in-
+formation. Having P(e1, e2), P(B), P(e1, e2|B) are sufficient for the calculation.
+(d) Training a K-nearest neighbors classifier takes more computational time than applying
+it.
+(e) K-nearest neighbors cannot be used for regression.
+(f) Consider D-fold cross-validation. A higher number of folds (i.e. larger value of D), the
+estimated error will be lower on average.
+(g) K-means clustering algorithm is guaranteed to converge.
+(h) Consider a two-class classification problem. Suppose we have trained a perceptron model
+on a linearly separable training set, and now we get a new labeled data point which is
+correctly classified by the model, and far away from the decision boundary. If we add
+this new point to our earlier training set and re-train with the same set of initial weights
+and bias, the learnt decision boundary will be changed for sure.
+(i) Gradient descent is usually NOT guaranteed to converge at global minimum.
+(j) If the learning rate is too small, gradient descent may take a very long time to converge
+and computationally expensive.
+Question
+Answer (T/F)
+(a)
+T
+(b)
+F
+(c)
+T
+(d)
+F
+(e)
+F
+(f)
+T
+(g)
+T
+(h)
+F
+(i)
+T
+(j)
+T
+Marking scheme:
+ˆ 1.5 points for each correct answer.', ARRAY['True/False']::TEXT[], 'True/False', 'True/False', ARRAY['True/False']::TEXT[], ARRAY['concept_check', 'rapid_reasoning']::TEXT[], 'medium', '', '', ''),
+    ('COMP2211-2022-spring-midterm', '2', NULL, 2, 'long_question', 'coding', 'Problem 2 [16 points] Python Fundamentals
+For each of the Python expressions below, write the output when the expression is evaluated.
+If the output is an empty array, write “Empty Array”. If an error occurs, write “Error”.
+(a)
+[5 points] Consider the following NumPy arrays:
+import numpy as np
+A = np.array([10, 20, 30, 40, 50, 60, 70, 80, 90])
+B = np.array([ [0, 1, 2, 3],
+[4, 5, 6, 7],
+[8, 9, 10, 11],
+[12, 13, 14, 15] ])
+(i) print(A[2:6:3])
+(ii) print(B[0:2, 1:3])
+(iii) print(A[-1:-3])
+(iv) print(B[::-1])
+(v) print(B[:3,2:])
+(b)
+[2 points] What is the output of the following code?
+import numpy as np
+X = np.array([2,2,0,2,2,0,1,1,0,1,1,0])
+Y = X[X != 0]
+print(Y[::2])
+(c)
+[9 points] Given the following code which computes the distance between each training
+point in X train and each test point in X test using a nested loop over both the training
+data and test data.
+import numpy as np
+def compute_distances_nested_loops(X_train, X_test):
+num_test = X_test.shape[0]
+num_train = X_train.shape[0]
+distances = np.zeros((num_test, num_train))
+# --- BLOCK TO REWRITE ---
+for i in range(num_test):
+for j in range(num_train):
+distances[i,j] = np.sqrt(np.sum(np.square(X_test[i,:]-X_train[j,:])))
+# --- BLOCK TO REWRITE ---
+return distances
+Train = np.array([[0,1], [1,2], [2,3], [3,4]])
+Test = np.array([[5,6], [7,8]])
+print(compute_distances_nested_loops(Train, Test))
+# Output:
+# [[7.07106781 5.65685425 4.24264069 2.82842712]
+#
+[9.89949494 8.48528137 7.07106781 5.65685425]]
+Rewrite the block of code between the comment lines # --- BLOCK TO REWRITE ---
+using no explicit loops in the space provided.
+Hints:
+ˆ To compute the distance between training data point (0, 1) and test data point (5, 6),
+we do
+dist = (0 −5)2 + (1 −6)2
+ˆ Expand it
+dist = 02 −2(0)(5) + 52 + 12 −2(1)(6) + 62
+= 02 + 12 + 52 + 62 −2(0)(5) −2(1)(6)
+= 02 + 12 + 52 + 62 −2((0)(5) + (1)(6))
+You may find the following functions useful for this question.
+ˆ Dot product of two arrays:
+numpy.dot(a, b)
+– It returns the product of matrix multiplication.
+ˆ Return the element-wise square of the input
+numpy.square(x)
+– x is the input data
+ˆ Return the sum of array elements over a given axis.
+numpy.sum(a, axis=None)
+– a is an array with elements to sum.
+– axis: None or int or tuple of ints. axis = 0 means along the column and axis =
+1 means working along ther row.
+ˆ Return the non-negative square-root of an array, element-wise.
+numpy.sqrt(x)
+– x is the array with values whose square-roots are required.
+ˆ Return a matrix (or a 2D array) from an 1D array.
+numpy.matrix(data)
+– data is the 1D array.
+– Example: numpy.matrix([1, 2, 3]), output is [[1, 2, 3]]
+ˆ Insert a new axis that will appear at the axis position in the expanded array shape.
+numpy.expand_dims(a,axis)
+– a is the input array.
+– axis is an int or tuple of ints that represents poisition in the expanded axes where
+the new axis (or axes) is placed.', 16, 4, NULL::jsonb, NULL, NULL, 'Problem 2 [16 points] Python Fundamentals
+For each of the Python expressions below, write the output when the expression is evaluated.
+If the output is an empty array, write “Empty Array”. If an error occurs, write “Error”.
+(a)
+[5 points] Consider the following NumPy arrays:
+import numpy as np
+A = np.array([10, 20, 30, 40, 50, 60, 70, 80, 90])
+B = np.array([ [0, 1, 2, 3],
+[4, 5, 6, 7],
+[8, 9, 10, 11],
+[12, 13, 14, 15] ])
+(i) print(A[2:6:3])
+Answer: [30 60]
+# 1 point
+(ii) print(B[0:2, 1:3])
+Answer:
+[[1 2]
+[5 6]]
+# 1 point
+(iii) print(A[-1:-3])
+Answer:
+Empty Array
+# 1 point
+(iv) print(B[::-1])
+Answer:
+[[12 13 14 15]
+# 1 point
+[ 8
+9 10 11]
+[ 4
+7]
+[ 0
+3]]
+(v) print(B[:3,2:])
+Answer:
+[[ 2
+3]
+# 1 point
+[ 6
+7]
+[10 11]]
+Remark:
+ˆ 0.5 point will be deducted if the answers are in raw number format without [](i.e.
+1 2 5 6). Because in this case, the answer is no longer in array type, and we can-
+not distinguish whether the answer is a 2d array or 1d array. This deduction only
+performs once for a(i) and a(ii).
+(b)
+[2 points] What is the output of the following code?
+import numpy as np
+X = np.array([2,2,0,2,2,0,1,1,0,1,1,0])
+Y = X[X != 0]
+print(Y[::2])
+Answer:
+[2 2 1 1]
+# 2 points
+Remark:
+ˆ 0.5 point will be deducted if the answers are in raw number format without [](i.e.
+1 2 5 6). Because in this case, the answer is no longer in array type, and we can-
+not distinguish whether the answer is a 2d array or 1d array. This deduction only
+performs once for for a(i) and a(ii).
+(c)
+[9 points] Given the following code which computes the distance between each training
+point in X train and each test point in X test using a nested loop over both the training
+data and test data.
+import numpy as np
+def compute_distances_nested_loops(X_train, X_test):
+num_test = X_test.shape[0]
+num_train = X_train.shape[0]
+distances = np.zeros((num_test, num_train))
+# --- BLOCK TO REWRITE ---
+for i in range(num_test):
+for j in range(num_train):
+distances[i,j] = np.sqrt(np.sum(np.square(X_test[i,:]-X_train[j,:])))
+# --- BLOCK TO REWRITE ---
+return distances
+Train = np.array([[0,1], [1,2], [2,3], [3,4]])
+Test = np.array([[5,6], [7,8]])
+print(compute_distances_nested_loops(Train, Test))
+# Output:
+# [[7.07106781 5.65685425 4.24264069 2.82842712]
+#
+[9.89949494 8.48528137 7.07106781 5.65685425]]
+Rewrite the block of code between the comment lines # --- BLOCK TO REWRITE ---
+using no explicit loops in the space provided.
+Hints:
+ˆ To compute the distance between training data point (0, 1) and test data point (5, 6),
+we do
+dist = (0 −5)2 + (1 −6)2
+ˆ Expand it
+dist = 02 −2(0)(5) + 52 + 12 −2(1)(6) + 62
+= 02 + 12 + 52 + 62 −2(0)(5) −2(1)(6)
+= 02 + 12 + 52 + 62 −2((0)(5) + (1)(6))
+You may find the following functions useful for this question.
+ˆ Dot product of two arrays:
+numpy.dot(a, b)
+– It returns the product of matrix multiplication.
+ˆ Return the element-wise square of the input
+numpy.square(x)
+– x is the input data
+ˆ Return the sum of array elements over a given axis.
+numpy.sum(a, axis=None)
+– a is an array with elements to sum.
+– axis: None or int or tuple of ints. axis = 0 means along the column and axis =
+1 means working along ther row.
+ˆ Return the non-negative square-root of an array, element-wise.
+numpy.sqrt(x)
+– x is the array with values whose square-roots are required.
+ˆ Return a matrix (or a 2D array) from an 1D array.
+numpy.matrix(data)
+– data is the 1D array.
+– Example: numpy.matrix([1, 2, 3]), output is [[1, 2, 3]]
+ˆ Insert a new axis that will appear at the axis position in the expanded array shape.
+numpy.expand_dims(a,axis)
+– a is the input array.
+– axis is an int or tuple of ints that represents poisition in the expanded axes where
+the new axis (or axes) is placed.
+Answer:
+M = np.dot(X_test, X_train.T)
+te = np.square(X_test).sum(axis=1)
+tr = np.square(X_train).sum(axis=1)
+dists = np.sqrt(-2*M + np.matrix(tr) + np.matrix(te).T)
+Alternative answer:
+distances = np.sqrt(np.sum(np.square(np.expand_dims(X_test, 1) - X_train), axis=2))
+distances = np.sqrt(np.sum(np.square(X_test[:,None] - X_train[None]), axis=-1))
+Marking scheme:
+ˆ Case 0: Any for loop, list comprehension. # 0 point
+ˆ Case 1:
+(a) M = np.dot(X_test, X_train.T) # 2 points
+1 point if missing transpose
+(b) te = np.square(X_test).sum(axis=1) # 2 points
+tr = np.square(X_train).sum(axis=1) # 2 points
+– each square # 1 point
+– each sum with right axis # 1 point
+(c) dists = np.sqrt(-2*M + np.matrix(tr) + np.matrix(te).T) # 3 points
+– 1 point if shape doesn’t match.
+– 2 points if np.matrix is missing, or attempt to modify the shape but wrong
+– if np.sqrt is missing,
+* if previous part isn’t calculated correctly, 0 point for this part.
+* -1 point otherwise.
+ˆ Case 2:
+distances = np.sqrt(np.sum(np.square(np.expand_dims(X_test, 1) - X_train), axis=2))
+# 4 points
+(a) 4 points for the subtraction
+– 2 points if the shape modification is wrong.
+– 0 points if no shape modification at all.
+(b) 2 points for square
+(c) 2 points for sum;
+1 point if axis is missing or wrong
+(d) 1 point for sqrt (unlike Case 1, sqrt is worth 1 point here.)
+(e) -3 points if a2–b2 is used.
+Remarks:
+(a) If reshaped is used to change the shape of the solution, -1 point.
+(b) If the program assume there’s 4 train points or 2 test points, and the number of
+dimension is 2, -1 point or 0 point for the whole question.
+(c) If Train, Test used instead of Xtrain, Xtest, -1 point. If your program are terribly
+wrong (< 3 points) anyway, this mark wouldn’t be deducted.
+(d) If extra code which leads to wrong answer, -1 point.
+(e) If the output shape is (numtrain, numtest) instead of (numtest, numtrain), -1 point.
+(f) If expand dims/reshape used without reassigning the variable, i.e. a = a.reshape(*b.shape),
+-1 point.
+(g) If missing shape, (e.g. Xtrain[0] instead of Xtrain.shape[0]), – 0.5 point.
+(h) Wrong spelling and syntax will not resulted in mark deduction, but 0.5 point will be
+deducted if you got full points.
+(i) Index using i j assuming the existence of for loop gets 0 point.', ARRAY['Python Fundamentals']::TEXT[], 'Python Fundamentals', 'Python Fundamentals', ARRAY['Python Fundamentals']::TEXT[], ARRAY['implementation', 'code_tracing', 'debugging']::TEXT[], 'hard', '', '', ''),
+    ('COMP2211-2022-spring-midterm', '3', NULL, 3, 'long_question', 'long_answer', 'Problem 3 [11 points] Conditional Probability and Bayes Classifier
+(a)
+[2 points] Given the following probabilities:
+ˆ P(Good course | Desmond is in the course) = 0.5
+ˆ P(Good course | Desmond is not in the course) = 0.3
+ˆ P(Desmond in a randomly chosen course) = 0.1
+What is P(Desmond is in the course | Not a good course)? If your answer is not an
+integer, write your answer in fraction form (use / to separate your numerator and de-
+nominator), e.g. 1/2.
+(b)
+[7 points] Suppose you are given the following set of data with three Boolean input
+variables A, B, C, and a single Boolean output variable D.
+A
+B
+C
+D
+(i) According to the Naive Bayes classifier, what is P(D = 1|A = 1, B = 1, C = 0)? If
+your answer is not an integer, write your answer in fraction form (use / to separate
+your numerator and denominator), e.g. 1/2.
+(ii) According to the Naive Bayes classifier, what is P(D = 0|A = 1, B = 1)? If your
+answer is not an integer, write your answer in fraction form (use / to separate your
+numerator and denominator), e.g. 1/2.
+(iii) According to the general Bayes classifier (i.e. without independence assumption),
+what is P(D = 1|A = 1, B = 1, C = 0)? If your answer is not an integer, write your
+answer in fraction form (use / to separate your numerator and denominator), e.g.
+1/2.
+(iv) According to the general Bayes classifier (i.e. without independence assumption),
+what is P(D = 1|A = 1, B = 1)? If your answer is not an integer, write your answer
+in fraction form (use / to separate your numerator and denominator), e.g. 1/2.
+(c)
+[2 points] The Naive Bayes algorithm selects the class c for an example x that maxi-
+mizes P(c|x). Suppose one of your classmates stated that it is equivalent to selecting the
+c that maximizes P(x|c) under an assumption. What is the assumption that he has made?', 11, 7, NULL::jsonb, NULL, NULL, 'Problem 3 [11 points] Conditional Probability and Bayes Classifier
+(a)
+[2 points] Given the following probabilities:
+ˆ P(Good course | Desmond is in the course) = 0.5
+ˆ P(Good course | Desmond is not in the course) = 0.3
+ˆ P(Desmond in a randomly chosen course) = 0.1
+What is P(Desmond is in the course | Not a good course)? If your answer is not an
+integer, write your answer in fraction form (use / to separate your numerator and de-
+nominator), e.g. 1/2.
+Answer:
+Let D be Desmond is in the course, G be a good course
+P(D|NOT G) = P(D, NOT G)
+P(Not G)
+=
+P(Not G|D)P(D)
+P(Not G|D)P(D) + P(Not G|Not D)P(Not D)
+=
+(1 −0.5) × 0.1
+(1 −0.5) × 0.1 + (1 −0.3) × (1 −0.1)
+= 5
+Marking scheme:
+ˆ 2 points for giving the correct answer.
+(b)
+[7 points] Suppose you are given the following set of data with three Boolean input
+variables A, B, C, and a single Boolean output variable D.
+A
+B
+C
+D
+(i) According to the Naive Bayes classifier, what is P(D = 1|A = 1, B = 1, C = 0)? If
+your answer is not an integer, write your answer in fraction form (use / to separate
+your numerator and denominator), e.g. 1/2.
+Answer:
+P(D = 1|A = 1, B = 1, C = 0) =
+P(D = 1)P(A = 1|D = 1)P(B = 1|D = 1)P(C = 0|D = 1)
+P1
+j=0 P(D = j)P(A = 1|D = j)P(B = 1|D = j)P(C = 0|D = j)
+=
+(4/8)(2/4)(1/4)(2/4)
+(4/8)(2/4)(2/4)(1/4) + (4/8)(2/4)(1/4)(2/4) = 1
+Marking scheme:
+ˆ 1.75 points for giving the correct answer.
+(ii) According to the Naive Bayes classifier, what is P(D = 0|A = 1, B = 1)? If your
+answer is not an integer, write your answer in fraction form (use / to separate your
+numerator and denominator), e.g. 1/2.
+Answer:
+P(D = 0|A = 1, B = 1) =
+P(D = 0)P(A = 1|D = 0)P(B = 1|D = 0)
+P1
+j=0 P(D = j)P(A = 1|D = j)P(B = 1|D = j)
+=
+(4/8)(2/4)(2/4)
+(4/8)(2/4)(2/4) + (4/8)(2/4)(1/4) = 2
+Marking scheme:
+ˆ 1.75 points for giving the correct answer.
+(iii) According to the general Bayes classifier (i.e. without independence assumption),
+what is P(D = 1|A = 1, B = 1, C = 0)? If your answer is not an integer, write your
+answer in fraction form (use / to separate your numerator and denominator), e.g.
+1/2.
+Answer:
+P(D = 1|A = 1, B = 1, C = 0) = 0 as there is no data with A = 1, B = 1, C
+= 0 and D = 1 in the dataset.
+Marking scheme:
+ˆ 1.75 points for giving the correct answer.
+(iv) According to the general Bayes classifier (i.e. without independence assumption),
+what is P(D = 1|A = 1, B = 1)? If your answer is not an integer, write your answer
+in fraction form (use / to separate your numerator and denominator), e.g. 1/2.
+Answer:
+P(D = 1|A = 1, B = 1) = 1/2 as number of data with A = 1, B = 1, D = 1
+is 1, and number of data with A = 1, B = 1 is 2.
+Marking scheme:
+ˆ 1.75 points for giving the correct answer.
+(c)
+[2 points] The Naive Bayes algorithm selects the class c for an example x that maxi-
+mizes P(c|x). Suppose one of your classmates stated that it is equivalent to selecting the
+c that maximizes P(x|c) under an assumption. What is the assumption that he has made?
+Answer:
+P(c|x) = P(x|c)P(c)
+P(x)
+, so finding the c that maximizes P(c|x) is equivalent to finding c
+that maximizes P(x|c), if the prior P(c) is uniform.
+Marking scheme:
+ˆ 2 points for stating the assumption correctly.', ARRAY['Probabilistic Models']::TEXT[], 'Probabilistic Models', 'Probabilistic Models', ARRAY['Probabilistic Models']::TEXT[], ARRAY['manual_computation', 'probability_reasoning', 'classification_decision']::TEXT[], 'medium', '', '', ''),
+    ('COMP2211-2022-spring-midterm', '4', NULL, 4, 'long_question', 'long_answer', 'Problem 4 [14 points] K-Nearest Neighbors
+(a) [3 points] Consider a set of 5 training data given as ((xTrain
+, xTrain
+), cTrain) values, where
+xTrain
+and xTrain
+are the two attribute values (positive integers) and cTrain is the binary
+class label, A or B:
+{ ((6,7),A), ((4,8),B), ((6,5),B), ((7,9),A), ((2,4),A) }
+Classify a test example (xTest
+, xTest
+) with attribute values (4,7) using a KNN classifier
+with K = 3 and Manhattan distance defined by
+distance(xTrain, xTest) = P2
+i=1 |xTrain
+i
+−xTest
+i
+|
+where | · | denote absolute value.
+Complete the following table by filling in the computed Manhattan distance between
+each training data point and the test example. Determine the class label based on the
+results.
+xTrain
+xTrain
+cTrain
+Distance
+A
+B
+B
+A
+A
+(b) [6 points] Judge whether each of the following student’s claims is correct or not. Explain
+why.
+(i)
+[3 points] A student claims that the results of a general KNN classifier that uses
+Euclidean distance will change if we multiply all attribute values of each training
+and test data point by 0.5.
+(ii) [3 points] Another student claims that the classification accuracy of the training set
+will always increase if the value of K used in KNN classifier is incrementally increased
+from 1 to N, where N is the total number of training examples.
+(c) [5 points] Consider KNN using Euclidean distance on the following data set. Each point
+belongs to one of the two classes: + and x.
+(i)
+[2 points] Perform 10-fold cross validation on the given data set (i.e. the 10 data
+points as shown in the figure), what is the validation error when using 1-nearest
+neighbor?
+(ii) [3 points] Which of the following values of K leads to the minimum 10-fold cross vali-
+dation error: 3, 5 or 9? What is the error for that K? If there is a tie, please elaborate.', 14, 9, NULL::jsonb, NULL, NULL, 'Problem 4 [14 points] K-Nearest Neighbors
+(a) [3 points] Consider a set of 5 training data given as ((xTrain
+, xTrain
+), cTrain) values, where
+xTrain
+and xTrain
+are the two attribute values (positive integers) and cTrain is the binary
+class label, A or B:
+{ ((6,7),A), ((4,8),B), ((6,5),B), ((7,9),A), ((2,4),A) }
+Classify a test example (xTest
+, xTest
+) with attribute values (4,7) using a KNN classifier
+with K = 3 and Manhattan distance defined by
+distance(xTrain, xTest) = P2
+i=1 |xTrain
+i
+−xTest
+i
+|
+where | · | denote absolute value.
+Complete the following table by filling in the computed Manhattan distance between
+each training data point and the test example. Determine the class label based on the
+results.
+xTrain
+xTrain
+cTrain
+Distance
+A
+B
+B
+A
+A
+Answer:
+xTrain
+xTrain
+cTrain
+Distance
+A
+B
+B
+A
+A
+The predicted class label of the test example is B.
+Marking scheme:
+ˆ 0.5 point for giving each correct distance value. 2.5 points in total.
+ˆ 0.5 point for giving the correct class label.
+(b) [6 points] Judge whether each of the following student’s claims is correct or not. Explain
+why.
+(i)
+[3 points] A student claims that the results of a general KNN classifier that uses
+Euclidean distance will change if we multiply all attribute values of each training
+and test data point by 0.5.
+Answer:
+The claim is false, because K nearest neighbors will remain unchanged after multi-
+plying all attribute values of each training and test data points by 0.5.
+Marking scheme:
+ˆ 1 point for stating the claim is false.
+ˆ 2 points for giving the correct explanation.
+Remark:
+ˆ 1 point given if stating the claim is correct but did mention that the change on
+distance will be a multiplication of constant to the old distance AND didn’t state
+result of KNN classifier will change.
+ˆ 2 points given if stating the claim is correct but mention the change on distance
+will be a multiplication and state the result wont change.
+ˆ 0 point if correct deduction but draw the conclusion that result will change.
+ˆ 1 point for explanation if treat the question as asking multiply the values on
+Manhattan and answered correctly.
+ˆ 0 point for explanation if treat the question as asking comparison between Man-
+hattan and euclidean distance.
+(ii) [3 points] Another student claims that the classification accuracy of the training set
+will always increase if the value of K used in KNN classifier is incrementally increased
+from 1 to N, where N is the total number of training examples.
+Answer:
+The claim is false. A counterexample is as follows:
+The training set accuracy when K = 1 will be 100%.
+As K approaches the total number of training examples more and more examples
+influence the class, and eventually the class will always be the majority class in the
+training set.
+Marking scheme:
+ˆ 1 point for stating the claim is false.
+ˆ 2 points for giving the correct explanation.
+Remarks:
+ˆ Give full mark if they misregard outliers as further neighbors.
+ˆ -1 point if they mention is due to outliers but didn’t explain what is regarded as
+outliers.
+ˆ -2 points if they really mean outliers.
+ˆ -1 point if they only state larger k will cover more neighbors (this is just explain-
+ing what does a larger k means but not explaining why larger k can lower the
+accuracy) (*further neighbors is accepted but more neighbors is not)
+ˆ Other accept answers:
+i. Larger k include further neighbors which have low relevancy.
+ii. Underfitting
+iii. Giving a concrete situation that the claim is wrong.
+(c) [5 points] Consider KNN using Euclidean distance on the following data set. Each point
+belongs to one of the two classes: + and x.
+(i)
+[2 points] Perform 10-fold cross validation on the given data set (i.e. the 10 data
+points as shown in the figure), what is the validation error when using 1-nearest
+neighbor?
+Answer:
+Every point is misclassified. So, the validation error is 10/10.
+Marking scheme:
+ˆ 2 points for giving the correct validation error.
+Note: Both 10/10 and 100% are accepted as the correct answers.
+(ii) [3 points] Which of the following values of K leads to the minimum 10-fold cross vali-
+dation error: 3, 5 or 9? What is the error for that K? If there is a tie, please elaborate.
+Answer:
+All 3 values of K mis-classify all of the points and have the same classification errors,
+10/10.
+Marking scheme:
+ˆ 2 points for stating all 3 values of K have the same classification errors.
+ˆ 1 point for giving the correct error.
+Note: Both 10/10 and 100% are accepted as the correct answers.', ARRAY['KNN and Clustering']::TEXT[], 'KNN and Clustering', 'KNN and Clustering', ARRAY['KNN and Clustering']::TEXT[], ARRAY['manual_computation', 'distance_calculation', 'algorithm_tracing']::TEXT[], 'medium', '', '', ''),
+    ('COMP2211-2022-spring-midterm', '5', NULL, 5, 'long_question', 'long_answer', 'Problem 5 [12 points] K-Means Clustering
+Given a 1-dimensional data set {0, 3, 6, 9, 27, 30}, use the K-means clustering algorithm and
+Euclidean distance to cluster the given points in the data set into 2 clusters. Assume c1 = 3
+and c2 = 4 are chosen as the initial cluster centers.
+(a) [4.5 points] Perform one iteration of K-means clustering by finding the Euclidean distance
+between each data point in the data set and the centroids, and assign each data point to
+the closest centroid according to the distance found. Fill in the following table with your
+computed values. If your distance value is not an integer, write your answer in decimal
+form, e.g. 1.234.
+Data Point
+Distance between the
+data point and c1 = 3
+Distance between the
+data point and c2 = 4
+Closest Centroid
+(c1 or c2?)
+(b)
+[1.5 points] What are the values of c1 and c2 after one iteration of K-means? If your
+answer is not an integer, write your answer in decimal form, e.g. 1.234.
+(c) [4.5 points] Perform the second iteration of K-means clustering by finding the Euclidean
+distance between each data point in the data set and the computed centroids in part (b),
+and assign each data point to the closest centroid according to the distance found. Fill in
+the following table with your computed values. If your distance value is not an integer,
+write your answer in decimal form, e.g. 1.234.
+Data Point
+Distance between the
+data point and the c1
+computed in part (b)
+Distance between the
+data point and the c2
+computed in part (b)
+Closest Centroid
+(c1 or c2?)
+(d)
+[1.5 points] What are the values of c1 and c2 after the second iteration of K-means? If
+your answer is not an integer, write your answer in decimal form, e.g. 1.234.', 12, 11, NULL::jsonb, NULL, NULL, 'Problem 5 [12 points] K-Means Clustering
+Given a 1-dimensional data set {0, 3, 6, 9, 27, 30}, use the K-means clustering algorithm and
+Euclidean distance to cluster the given points in the data set into 2 clusters. Assume c1 = 3
+and c2 = 4 are chosen as the initial cluster centers.
+(a) [4.5 points] Perform one iteration of K-means clustering by finding the Euclidean distance
+between each data point in the data set and the centroids, and assign each data point to
+the closest centroid according to the distance found. Fill in the following table with your
+computed values. If your answer is not an integer, write your answer in decimal form,
+e.g. 1.234.
+Data Point
+Distance between the
+data point and c1 = 3
+Distance between the
+data point and c2 = 4
+Closest Centroid
+(c1 or c2?)
+Answer:
+Data Point
+Distance between the
+data point and c1 = 3
+Distance between the
+data point and c2 = 4
+Closest Centroid
+(c1 or c2?)
+c1
+c1
+c2
+c2
+c2
+c2
+Marking scheme:
+ˆ 0.25 for giving each correct value. 4.5 points in total.
+(b)
+[1.5 points] What are the values of c1 and c2 after one iteration of K-means? If your
+answer is not an integer, write your answer in decimal form, e.g. 1.234.
+Answer:
+c1 = 0 + 3
+= 1.5
+c2 = 6 + 9 + 27 + 30
+= 18
+Marking scheme:
+ˆ 0.75 for giving each correct centroid. 1.5 points in total.
+(c) [4.5 points] Perform the second iteration of K-means clustering by finding the Euclidean
+distance between each data point in the data set and the computed centroids in part (b),
+and assign each data point to the closest centroid according to the distance found. Fill
+in the following table with your computed values. If your answer is not an integer, write
+your answer in decimal form, e.g. 1.234.
+Data Point
+Distance between the
+data point and the c1
+computed in part (b)
+Distance between the
+data point and the c2
+computed in part (b)
+Closest Centroid
+(c1 or c2?)
+Answer:
+Data Point
+Distance between the
+data point and the c1
+computed in part (b)
+Distance between the
+data point and the c2
+computed in part (b)
+Closest Centroid
+(c1 or c2?)
+1.5
+c1
+1.5
+c1
+4.5
+c1
+7.5
+c1
+25.5
+c2
+28.5
+c2
+Marking scheme:
+ˆ 0.25 for giving each correct value. 4.5 points in total.
+(d)
+[1.5 points] What are the values of c1 and c2 after the second iteration of K-means? If
+your answer is not an integer, write your answer in decimal form, e.g. 1.234.
+Answer:
+c1 = 0 + 3 + 6 + 9
+= 4.5
+c2 = 27 + 30
+= 28.5
+Marking scheme:
+ˆ 0.75 for giving each correct centroid. 1.5 points in total.', ARRAY['KNN and Clustering']::TEXT[], 'KNN and Clustering', 'KNN and Clustering', ARRAY['KNN and Clustering']::TEXT[], ARRAY['manual_computation', 'distance_calculation', 'algorithm_tracing']::TEXT[], 'medium', '', '', ''),
+    ('COMP2211-2022-spring-midterm', '6', NULL, 6, 'long_question', 'long_answer', 'Problem 6 [20 points] Perceptron
+Given the following training dataset:
+x1
+0.5
+x2
+0.5
+T
+-1
+-1
+-1
+(a)
+[8 points] Show the action of the perceptron algorithm for the above sequence of data
+points by completing the following table. Assume η = 1 and we start with the following
+initial weights and bias
+w1 = 1
+w2 = 1
+θ = 0
+and use the following activation function.
+f(z) =
+
+
+
+z ≥0
+−1
+otherwise
+Updating rules:
+∆wi = η(T −O)xi
+∆θ = η(T −O)
+wi = wi + ∆wi
+θ = θ + ∆θ
+where i ∈{1, 2}.
+If your answer is not an integer, write your answer in decimal form, e.g. 1.234.
+x1
+x2
+T
+O
+∆w1
+w1
+∆w2
+w2
+∆θ
+θ
+-
+-
+-
+-
+-
+-
+-
+(b)
+[2 points] According to the values in the table above, state whether the perceptron
+algorithm is converged in 1 epoch. If not, explain why.
+(c) [10 points] Write a Python program to verify your answers of Part (a). In your program,
+you need to define the following variables.
+ˆ A 2D NumPy array, X, to store all the attribute values x1, x2, where the shape is
+(8,2)
+ˆ A 1D NumPy array, T,to store the target values, where the shape is (8,)
+ˆ A 1D NumPy array, W, to store the weights, where the shape is (2,)
+ˆ A float bias value, b.
+and perform the required computations. Also, print the following sequence of values (in
+exact order) for each iteration:
+x1 < s > x2 < s > T < s > O < s > ∆w1 < s > w1 < s > ∆w2 < s > w2 < s > ∆θ < s > θ < end >
+where < s > refers to an empty space, and < end > refers to an end of line character.
+The following shows a line of sample output.
+0 0 0 0 0 0 0 0 0 0
+Remark: You cannot use any other libraries other than NumPy in your program.', 20, 12, NULL::jsonb, NULL, NULL, 'Problem 6 [20 points] Perceptron
+Given the following training dataset:
+x1
+0.5
+x2
+0.5
+T
+-1
+-1
+-1
+(a)
+[8 points] Show the action of the perceptron algorithm for the above sequence of data
+points by completing the following table. Assume η = 1 and we start with the following
+initial weights and bias
+w1 = 1
+w2 = 1
+θ = 0
+and use the following activation function.
+f(z) =
+
+
+
+z ≥0
+−1
+otherwise
+Updating rules:
+∆wi = η(T −O)xi
+∆θ = η(T −O)
+wi = wi + ∆wi
+θ = θ + ∆θ
+where i ∈{1, 2}.
+If your answer is not an integer, write your answer in decimal form, e.g. 1.234.
+x1
+x2
+T
+O
+∆w1
+w1
+∆w2
+w2
+∆θ
+θ
+-
+-
+-
+-
+-
+-
+-
+Answer:
+x1
+x2
+T
+O
+∆w1
+w1
+∆w2
+w2
+∆θ
+θ
+-
+-
+-
+-
+-
+-
+-
+-1
+-2
+-2
+-2
+-1
+-6
+-5
+-6
+-5
+-2
+-4
+-1
+-2
+0.5
+0.5
+-1
+-1
+-1
+-2
+-4
+-4
+-4
+Marking scheme:
+ˆ 0.1 point for giving each correct value. 8 points in total.
+(b)
+[2 points] According to the values in the table above, state whether the perceptron al-
+gorithm is converged in 1 epoch. If not, explain why.
+Answer:
+The algorithm is not converged in 1 epoch.
+Since there are changes of weights and
+biases.
+Marking scheme:
+ˆ 1 point for stating the algorithm is not converged.
+ˆ 1 point for giving a correct explanation.
+(c) [10 points] Write a Python program to verify your answers of Part (a). In your program,
+you need to define the following variables
+ˆ A 2D NumPy array, X, to store all the attribute values x1, x2, where the shape is
+(8,2)
+ˆ A 1D NumPy array, T,to store the target values, where the shape is (8,)
+ˆ A 1D NumPy array, W, to store the weights, where the shape is (2,)
+ˆ A float bias value, b.
+and perform the required computations. Also, print the following sequence of values (in
+exact order) for each iteration:
+x1 < s > x2 < s > T < s > O < s > ∆w1 < s > w1 < s > ∆w2 < s > w2 < s > ∆θ < s > θ < end >
+where < s > refers to an empty space, and < end > refers to an end of line character.
+The following shows a line of sample output.
+0 0 0 0 0 0 0 0 0 0
+Remark: You cannot use any other libraries other than NumPy in your program.
+Answer:
+import numpy as np
+# 0.5 point
+X = np.array([[10,10], [0,0], [8,4], [3,3], [4,8], [0.5,0.5], [4,3], [2,5]])
+# 1 point
+T = np.array([1,-1,1,-1,1,-1,1,1])
+# 0.5 point
+W = np.array([1,1], dtype=float)
+# 1 point
+b = 0.0
+# 0.5 point
+for i in range(X.shape[0]):
+# 1 point
+y = X[i].dot(W) + b
+# 1 point
+if y >= 0:
+# 0.5 point
+O = 1
+# 0.5 point
+else:
+O = -1
+# 0.5 point
+DW = (T[i] - O) * X[i]
+# 0.5 point
+W += DW
+# 0.5 point
+Db = (T[i] - O)
+# 0.5 point
+b += Db
+# 0.5 point
+print(X[i][0], X[i][1], T[i], O, DW[0], W[0], DW[1], W[1], Db, b)
+# 1 point
+Remarks:
+ˆ -0.25 point for forgetting to specify W = np.array([1,1], dtype=float)
+ˆ Can also b = 0, Python will duck-type into float when needed.
+ˆ -0.25 point each for not using NumPy array. No overlap with W float penalty, since
+Python list supports duck-typing.
+ˆ -0.25 point each for wrong array shape.
+ˆ -0.5 point for hard-coding for i in range(8):.
+ˆ -0.25 point for forgetting bias in the output calculation.
+ˆ -0.25 point for minor print formatting errors.
+ˆ -0.25 point for incorrect print when output == target.
+ˆ -0.25 point each for miscellaneous syntax errors.
+ˆ -1 flat point for defining as class/function(s) but not calling.
+ˆ -10 floor point for using SKlearn.', ARRAY['Perceptron and MLP']::TEXT[], 'Perceptron and MLP', 'Perceptron and MLP', ARRAY['Perceptron and MLP']::TEXT[], ARRAY['forward_pass', 'backpropagation', 'weight_update']::TEXT[], 'hard', '', '', ''),
+    ('COMP2211-2022-spring-midterm', '7', NULL, 7, 'long_question', 'long_answer', 'Problem 7 [12 points] Perceptron and Multilayer Perceptron
+(a)
+[4 points] Can we represent the given boolean function with a single neuron as shown
+below?
+A
+B
+f(A,B)
+If yes, show the possible weights, bias and activation function that can be used to com-
+pute the output of all the data points correctly. If not, explain why not in 1-2 sentences.
+(b) [6 points] Suppose we have a neural network as shown below with θ1 = 0, θ2 = 0, θ3 = 0,
+and linear activation function (i.e. f(x) = Cx, where C is a constant).
+Can any function that is represented by the above network be represented by a single
+unit of artificial neural network in the following diagram? If so, detail the weights (w7
+and w8), bias (θ), and the activation function f(x). Otherwise, explain why not.
+(c)
+[2 points] Given a multilayer perceptron with 3 layers (input layer, hidden layer and
+output layer). The number of units in each of the these layers are 3, 4, 2. Assume each
+input or neuron is fully-connected. Calculate the number of trainable parameters of this
+network.
+-------------------- END OF PAPER
+--------------------', 12, 14, NULL::jsonb, NULL, NULL, 'Problem 7 [12 points] Perceptron and Multilayer Perceptron
+(a)
+[4 points] Can we represent the given boolean function with a single neuron as shown
+below?
+A
+B
+f(A,B)
+If yes, show the possible weights, bias and activation function that can be used to com-
+pute the output of all the data points correctly. If not, explain why not in 1-2 sentences.
+Answer:
+Yes, we can represent this function with a single neuron, since it is linearly separable.
+One set of possible weights and bias is: w1 = 1, w2 = −1, θ = −0.5, and the activation
+function is
+f(x) =
+
+
+
+x > 0
+otherwise
+Marking scheme:
+ˆ 1 point for stating it is possible to represent the given boolean function with a single
+neuron.
+ˆ 1 point for showing the “standard” activation function.
+ˆ 2 points for correct values of w1, w2, and θ, given the activation function is the
+“standard” one.
+ˆ If the given activation function is not “standard”, but the parameters are suitable,
+also get 2 points.
+ˆ 0 point for stating not possible to represent.
+(b) [6 points] Suppose we have a neural network as shown below with θ1 = 0, θ2 = 0, θ3 = 0,
+and linear activation function (i.e. f(x) = Cx, where C is a constant).
+Can any function that is represented by the above network be represented by a single
+unit of artificial neural network in the following diagram? If so, detail the weights (w7
+and w8), bias (θ), and the activation function f(x). Otherwise, explain why not.
+Answer:
+Yes, the network can be represented by a single unit of artificial neural network by
+setting its weights, w7 = w1w5 + w3w6, w8 = w2w5 + w4w6, and the activation function
+f(x) = C2.
+Marking scheme:
+ˆ 0.5 point for stating any function that is represented by the MLP can be represented
+by a single unit of artificial neural network.
+ˆ 1.5 points for showing each possible weight w7, w8. 3 points in total.
+ˆ 1 point for showing a possible bias value, i.e. 0.
+ˆ 1.5 for showing the activation function.
+Remarks:
+ˆ If stating NO, -6 points.
+But among those NO’s, if students put the expression
+OUTPUT=C2w5(x1w1 + x2w2 + θ1) + C2w6(x1w3 + x2w4 + θ2) + Cθ3, give 1 point.
+ˆ For those stating YES, if missing some value, deduct the score of that value.
+ˆ If the order of C is wrong, e.g. one C in w7w8 and no C in activation, or C2 in w7w8
+and one C in activation, -1 point.
+ˆ For other wrong values, -full mark for that values.
+ˆ If didn’t plug in given values and the expression is wrong, even if the expression may
+evaluate to the same value as the answer, -full mark for that expression or -1 point
+if it’s an order-of-C issue.
+ˆ If didn’t plug in given values, e.g. θs=0, the same C for all units (some use C1C2C3),
+but otherwise correct, -0.5 for each of θ & C.
+(c)
+[2 points] Given a multilayer perceptron with 3 layers (input layer, hidden layer and
+output layer). The number of units in each of the these layers are 3, 4, 2. Assume each
+input or neuron is fully-connected. Calculate the number of trainable parameters of this
+network.
+Answer:
+3 ∗4 + 4 + 4 ∗2 + 2 = 26.
+Marking scheme:
+ˆ 2 points for giving the correct answer.
+-------------------- END OF PAPER
+--------------------', ARRAY['Perceptron and MLP']::TEXT[], 'Perceptron and MLP', 'Perceptron and MLP', ARRAY['Perceptron and MLP']::TEXT[], ARRAY['forward_pass', 'backpropagation', 'weight_update']::TEXT[], 'medium', '', '', ''),
+    ('COMP2211-2022-spring-final-part-a', '1', NULL, 1, 'true_false', 'true_false', 'Problem 1 [10 points] True/False Questions
+Indicate whether the following statements are true or false by putting T or F in the given
+table. You get 1 point for each correct answer.
+(a) One drawback of the K-means algorithm is that one needs to specify exactly how many
+clusters the algorithm should find.
+(b) Increasing the number of hidden layers always increases the model performance.
+(c) When handling a binary classification task, both softmax and sigmoid functions can be
+used as the activation function in the output layer.
+(d) Validation accuracy must be lower than training accuracy.
+(e) We cannot train/inference deep learning networks using CPU.
+(f) Otsu’s thresholding method and affine transformations are point-based image operations.
+(g) In the convolutional layer of a CNN, the number of weights depends on the depth of the
+input volume and the number of biases is equal to the number of kernels.
+(h) After training a neural network, you observe a large gap between the training accuracy
+(100%) and the task accuracy (40%). Dropout is commonly used to reduce this gap.
+(i) In a minimax-based 3×3 tic-tac-toe game, an AI player will definitely win because it
+knows all possible moves of the game.
+(j) The alpha-beta pruning algorithm is preferred to minimax because it computes the same
+answer as minimax while usually doing so without examining as much of the game tree.
+Question
+Answer (T/F)
+(a)
+(b)
+(c)
+(d)
+(e)
+(f)
+(g)
+(h)
+(i)
+(j)', 10, 2, NULL::jsonb, NULL, NULL, 'Problem 1 [10 points] True/False Questions
+Indicate whether the following statements are true or false by putting T or F in the given
+table. You get 1 point for each correct answer.
+(a) One drawback of the K-means algorithm is that one needs to specify exactly how many
+clusters the algorithm should find.
+(b) Increasing the number of hidden layers always increases the model performance.
+(c) When handling a binary classification task, both softmax and sigmoid functions can be
+used as the activation function in the output layer.
+(d) Validation accuracy must be lower than training accuracy.
+(e) We cannot train/inference deep learning networks using CPU.
+(f) Otsu’s thresholding method and affine transformations are point-based image operations.
+(g) In the convolutional layer of a CNN, the number of weights depends on the depth of the
+input volume and the number of biases is equal to the number of kernels.
+(h) After training a neural network, you observe a large gap between the training accuracy
+(100%) and the task accuracy (40%). Dropout is commonly used to reduce this gap.
+(i) In a minimax-based 3×3 tic-tac-toe game, an AI player will definitely win because it
+knows all possible moves of the game.
+(j) The alpha-beta pruning algorithm is preferred to minimax because it computes the same
+answer as minimax while usually doing so without examining as much of the game tree.
+Answer:
+Question
+Answer (T/F)
+(a)
+T
+(b)
+F
+(c)
+T
+(d)
+F
+(e)
+F
+(f)
+F
+(g)
+T
+(h)
+T
+(i)
+F
+(j)
+T
+Marking scheme:
+ˆ 1 point for giving each correct answer. 10 points in total.', ARRAY['True/False']::TEXT[], 'True/False', 'True/False', ARRAY['True/False']::TEXT[], ARRAY['concept_check', 'rapid_reasoning']::TEXT[], 'medium', '', '', ''),
+    ('COMP2211-2022-spring-final-part-a', '2', NULL, 2, 'long_question', 'long_answer', 'Problem 2 [14 points] Na¨ıve Bayes and K-Nearest Neighbors
+Given the training data in the table below.
+No.
+CGPA
+Interest in
+Computing Subjects
+Practice-oriented
+Learner?
+COMP 2211
+Grade
+Select COMP
+as Major
+≤3
+High
+No
+B
+No
+≤3
+High
+No
+A
+No
+> 3 AND ≤4
+High
+No
+B
+Yes
+> 4
+Medium
+No
+B
+Yes
+> 4
+Low
+Yes
+B
+Yes
+> 4
+Low
+Yes
+A
+No
+> 3 AND ≤4
+Low
+Yes
+A
+Yes
+≤3
+Medium
+No
+B
+No
+≤3
+Low
+Yes
+B
+Yes
+> 4
+Medium
+Yes
+B
+Yes
+≤3
+Medium
+Yes
+A
+Yes
+> 3 AND ≤4
+Medium
+No
+A
+Yes
+> 3 AND ≤4
+High
+Yes
+B
+Yes
+> 4
+Medium
+No
+A
+No
+(a)
+[6.5 points] Given a new example with the following attribute values. Predict the value
+of its “Select COMP as Major” using Na¨ıve Bayes classifier. Show all the steps.
+ˆ CGPA ≤3
+ˆ Interest in Computing Subjects = Medium
+ˆ Practice-oriented Learner = Yes
+ˆ COMP 2211 Grade = B
+(b)
+[7.5 points] Similar to the above, but this time, predict the value of its “Select COMP
+as Major” using K-nearest neighbor for K = 5. Complete the following table and state
+the prediction result based on the data in the completed table. For similarity measure,
+use a simple match of attribute values:
+S(ai, bi) =
+X
+i=1
+wi ∗distance(ai, bi)
+where distance(ai, bi) is 0 if ai equals bi, and 1 otherwise. ai and bi are either CGPA,
+interest in computing subjects, practice-oriented learner or COMP 2211 grade. Weights,
+wi, are all 1 except for interest in computing subjects, it is 2.
+No.
+Class
+Distance to New Example
+No
+No
+Yes
+Yes
+Yes
+No
+Yes
+No
+Yes
+Yes
+Yes
+Yes
+Yes
+No', 14, 4, NULL::jsonb, NULL, NULL, 'Problem 2 [14 points] Na¨ıve Bayes and K-Nearest Neighbors
+Given the training data in the table below.
+No.
+CGPA
+Interest in
+Computing Subjects
+Practice-oriented
+Learner?
+COMP 2211
+Grade
+Select COMP
+as Major
+≤3
+High
+No
+B
+No
+≤3
+High
+No
+A
+No
+> 3 AND ≤4
+High
+No
+B
+Yes
+> 4
+Medium
+No
+B
+Yes
+> 4
+Low
+Yes
+B
+Yes
+> 4
+Low
+Yes
+A
+No
+> 3 AND ≤4
+Low
+Yes
+A
+Yes
+≤3
+Medium
+No
+B
+No
+≤3
+Low
+Yes
+B
+Yes
+> 4
+Medium
+Yes
+B
+Yes
+≤3
+Medium
+Yes
+A
+Yes
+> 3 AND ≤4
+Medium
+No
+A
+Yes
+> 3 AND ≤4
+High
+Yes
+B
+Yes
+> 4
+Medium
+No
+A
+No
+(a)
+[6.5 points] Given a new example with the following attribute values. Predict the value
+of its “Select COMP as Major” using Na¨ıve Bayes classifier. Show all the steps.
+ˆ CGPA ≤3
+ˆ Interest in Computing Subjects = Medium
+ˆ Practice-oriented Learner = Yes
+ˆ COMP 2211 Grade = B
+Answer: Let
+ˆ E be CGPA ≤3, Interest in Computing Subjects = Medium, Practice-oriented
+Learner = Yes, COMP 2211 Grade = B
+ˆ E1 be CGPA ≤3
+ˆ E2 be interest in computing subjects = medium
+ˆ E3 be practice-oriented learner = yes
+ˆ E4 be COMP 2211 grade = B
+P(Y es|E) = P(E1|Y es)P(E2|Y es)P(E3|Y es)P(E4|Y es)P(Y es)
+P(E)
+P(Y es) = 9/14 = 0.643
+P(E1|Y es) = 2/9 = 0.222
+P(E2|Y es) = 4/9 = 0.444
+P(E3|Y es) = 6/9 = 0.667
+P(E4|Y es) = 6/9 = 0.667
+P(Y es|E) = (0.222)(0.444)(0.667)(0.668)(0.443)
+P(E)
+= 0.028
+P(E)
+P(No|E) = P(E1|No)P(E2|No)P(E3|No)P(E4|No)P(No)
+P(E)
+P(No) = 5/14 = 0.356
+P(E1|No) = 3/5 = 0.6
+P(E2|No) = 2/5 = 0.4
+P(E3|No) = 1/5 = 0.2
+P(E4|No) = 2/5 = 0.4
+P(No|E) = (0.6)(0.4)(0.2)(0.4)(0.357)
+P(E)
+= 0.007
+P(E)
+Hence, the Na¨ıve Bayes classifier predicts “Select COMP as Major” = Yes for the new
+example.
+Marking scheme:
+ˆ 0.5 for giving each conditional and prior probability. 6 points in total.
+ˆ 0.5 for giving the correct prediction.
+(b)
+[7.5 points] Similar to the above, but this time, predict the value of its “Select COMP
+as Major” using K-nearest neighbor for K = 5. Complete the following table and state
+the prediction result based on the data in the completed table. For similarity measure,
+use a simple match of attribute values:
+S(ai, bi) =
+X
+i=1
+wi ∗distance(ai, bi)
+where distance(ai, bi) is 0 if ai equals bi, and 1 otherwise. ai and bi are either CGPA,
+interest in computing subjects, practice-oriented learner or COMP 2211 grade. Weights,
+wi, are all 1 except for interest in computing subjects, it is 2.
+No.
+Class
+Distance to New Example
+No
+0 + 2 + 1 + 0 = 3
+No
+0 + 2 + 1 + 1 = 4
+Yes
+1 + 2 + 1 + 0 = 4
+Yes
+1 + 0 + 1 + 0 = 2
+Yes
+1 + 2 + 0 + 0 = 3
+No
+1 + 2 + 0 + 1 = 4
+Yes
+1 + 2 + 0 + 1 = 4
+No
+0 + 0 + 1 + 0 = 1
+Yes
+0 + 2 + 0 + 0 = 2
+Yes
+1 + 0 + 0 + 0 = 1
+Yes
+0 + 0 + 0 + 1 = 1
+Yes
+1 + 0 + 1 + 1 = 3
+Yes
+1 + 2 + 0 + 0 = 3
+No
+1 + 0 + 1 + 1 = 3
+Among the 5 nearest neighbors, 4 are from class Yes, and 1 from class No. Hence, the
+KNN classifier predicts “Select COMP as Major” = Yes for the new example.
+Marking scheme:
+ˆ 0.5 point for giving each correct answer. 7 points in total.
+ˆ 0.5 point for giving the correct prediction.', ARRAY['Probabilistic Models', 'KNN and Clustering']::TEXT[], 'Probabilistic Models', NULL, ARRAY['Probabilistic Models', 'KNN and Clustering']::TEXT[], ARRAY['manual_computation', 'probability_reasoning', 'classification_decision', 'distance_calculation', 'algorithm_tracing']::TEXT[], 'medium', '', '', ''),
+    ('COMP2211-2022-spring-final-part-a', '3', NULL, 3, 'long_question', 'long_answer', 'Problem 3 [14 points] Multilayer Perceptron (MLP)
+This problem is about multilayer perceptron (MLP). Answer all the questions below.
+(a) [3 points] State when using the F1 metric is better than using accuracy as an evaluation
+metric. Also use a confusion matrix to illustrate the stated situation.
+(b)
+[2 points] Suppose we are dealing with binary classification tasks using MLP. Explain
+why it is inappropriate to use ReLU as the activation function in the output layer.
+(c)
+[2 points] Design the output layer of an MLP for handling a multilabel classification
+problem with n classes by stating the number of neurons and the activation function.
+Remark: Multilabel classification is a supervised learning problem where an instance
+may be associated with multiple labels.
+(d)
+[3 points] Explain why it is not good to initialize all weights of an MLP to zero.
+Hint: Refer to the following updating rules of weights and biases for MLP.
+ˆ δk = (Ok −Tk)Ok(1 −Ok)
+ˆ δj = Oj(1 −Oj) P
+k∈K δkwjk
+ˆ wjk ←wjk −ηδkOj
+ˆ wij ←wij −ηδjOi
+ˆ θj ←θj −ηδj
+ˆ θk ←θk −ηδk
+(e)
+[2 points] Explain what will happen if the learning rate η of an MLP is
+(i) Too large
+(ii) Too small
+(f)
+[2 points] Describe a way to avoid overfitting in MLP. Explain why it works.', 14, 6, NULL::jsonb, NULL, NULL, 'Problem 3 [14 points] Multilayer Perceptron (MLP)
+This problem is about multilayer perceptron (MLP). Answer all the questions below.
+(a) [3 points] State when using the F1 metric is better than using accuracy as an evaluation
+metric. Also use a confusion matrix to illustrate the stated situation.
+Answer:
+ˆ When the dataset is unbalanced.
+ˆ When false-negative and false-positive matter a lot.
+Actual/Predicted
+Infectious disease=yes
+Infectious disease=no
+Infectious disease=yes
+Infectious disease=no
+Accuracy = 0.8016
+F1 score = 0.0741
+Marking scheme:
+ˆ 1 point for stating the situation
+ˆ 2 points for the confusion matrix
+(b)
+[2 points] Suppose we are dealing with binary classification tasks using MLP. Explain
+why it is inappropriate to use ReLU as the activation function in the output layer.
+Answer:
+We cannot determine the cut-off threshold to distinguish between the output classes when
+there is an unbounded output range.
+Marking scheme:
+ˆ 2 points for explaining by the “unbounded” range of ReLU so cannot determine the
+cut-off
+ˆ 1 point if mentioning the range of function (*not describing what ReLU do, but
+stating the range) but no further explanation
+ˆ 0 point if only stating ReLU can output value more than 0 & 1 without mentioning it
+has an unbounded range (bounded function beyond the range from 0 to 1 can work,
+just do the mapping)
+(c)
+[2 points] Design the output layer of an MLP for handling a multilabel classification
+problem with n classes by stating the number of neurons and the activation function.
+Remark: Multilabel classification is a supervised learning problem where an instance
+may be associated with multiple labels.
+Answer:
+n neurons and sigmoid function.
+Marking scheme:
+ˆ 1 point for n neurons
+ˆ 1 point for sigmoid
+(d)
+[3 points] Explain why it is not good to initialize all weights of an MLP to zero.
+Hint: Refer to the following updating rules of weights and biases for MLP.
+ˆ δk = (Ok −Tk)Ok(1 −Ok)
+ˆ δj = Oj(1 −Oj) P
+k∈K δkwjk
+ˆ wjk ←wjk −ηδkOj
+ˆ wij ←wij −ηδjOi
+ˆ θj ←θj −ηδj
+ˆ θk ←θk −ηδk
+Answer:
+If a network is initialized with all zeros, all the neurons will propagate on the same
+gradient, making different neurons learn the same features. Thus, this leads to poor
+performance.
+Marking scheme:
+ˆ 3 points if “the same update/propagate/feature learned/symmetry problem”
+ˆ 2 points if “zero updates” as it isn’t always true for all MLP design
+ˆ 2 points if stating only gradient computation are the same for neurons
+ˆ 1 point if only mentioning deltaj will become zero / stating gradient “may” not
+update
+ˆ 0 point for low efficiency / poor performance
+(e)
+[2 points] Explain what will happen if the learning rate η of an MLP is
+(i) Too large
+(ii) Too small
+Answer:
+(i)
+ˆ Cause the model to coverage too quickly to a sub-optimal solution.
+ˆ Unstable training like oscillations.
+Marking scheme:
+ˆ 1 point for explaining what will happen if the learning rate is too large.
+ˆ not accept learn faster/overfit/low accuracy
+(ii) Learning will be slow.
+Marking scheme:
+ˆ 1 point for explaining what will happen if the learning rate is too small.
+ˆ not accept underfit
+(f)
+[2 points] Describe a way to avoid overfitting in MLP. Explain why it works.
+Answer:
+Adding regulations helps to keep the weights small, such that it is less likely for the
+model to have a large variance (i.e. be sensitive to noise and fluctuations in data).
+Marking scheme:
+ˆ 1 point for method
+ˆ 1 point for explanation
+ˆ only valid explanation (but not stating the definition or recalling some rule of thumbs)
+get the explanation point', ARRAY['Perceptron and MLP']::TEXT[], 'Perceptron and MLP', 'Perceptron and MLP', ARRAY['Perceptron and MLP']::TEXT[], ARRAY['forward_pass', 'backpropagation', 'weight_update']::TEXT[], 'medium', '', '', ''),
+    ('COMP2211-2022-spring-final-part-a', '4', NULL, 4, 'long_question', 'long_answer', 'Problem 4 [15 points] Digital Image Processing
+(a) Assume we apply the following kernel to an 8-bit grayscale image.
+K =
+
+
+−1
+−2
+−2
+
+
+(i)
+[2 points] Determine the maximum and minimum possible values that a pixel to
+which the given kernel is applied can have. Do not perform any normalization.
+(ii)
+[2 points] Suggest a grey-level transformation function to ensure that any output of
+this kernel will be within the legal range of a standard 8-bit grayscale image.
+(b) Consider the following 2 × 2 image:
+Apply the following image operations sequentially.
+(i)
+[3 points] Show the resulting image of size 4 × 4 after adding reflection padding.
+(ii) [4 points] Apply a 3×3 averaging kernel to the resulting image of part (b)(i). Assume
+the output image after image averaging is in the same shape as the input image by
+doing zero padding. Round the number to integer if needed.
+(iii) [4 points] Compute the optimal threshold after applying ONE ITERATION of Otsu’s
+method on the resulting image of part (b)(ii). Assume the initial threshold is set to
+the mean pixel intensity of the resulting image.', 15, 7, NULL::jsonb, NULL, NULL, 'Problem 4 [15 points] Digital Image Processing
+(a) Assume we apply the following kernel to an 8-bit grayscale image.
+K =
+
+
+−1
+−2
+−2
+
+
+(i)
+[2 points] Determine the maximum and minimum possible values that a pixel to
+which the given kernel is applied can have. Do not perform any normalization.
+Answer:
+Since an 8-bit grayscale image is assumed, the highest value that each pixel can
+have is 255, and the lowest value is 0.
+After applying the kernel, the maximum
+value is achieved when the negative values of the kernel multiply 0s and the pos-
+itive values of the kernel multiply 255s. Then, the maximum achievable value is
+vmax = (2 + 2 + 1)(255) = 1275. Following the same reasoning, the minimum value
+will be vmin = (−2 −2 −1)(255) = −1275.
+Marking scheme:
+ˆ 1 point for stating the maximum possible value.
+ˆ 1 point for stating the minimum possible value.
+(ii)
+[2 points] Suggest a grey-level transformation function to ensure that any output of
+this kernel will be within the legal range of a standard 8-bit grayscale image.
+Answer:
+A 8-bit image must have a range from 0 to 255. Since the maximum and minimum
+possible values for the gray levels are vmax = 1275 and vmin = −1275, respectively,
+the function will be
+Ioutput = 255
+Iinput −vmin
+vmax −vmin
+
+= 255
+Iinput −(−1275)
+1275 −(−1275)
+
+= 255
+Iinput + 1275
+
+where Iinput and Ioutput are the input and output images, respectively.
+Marking scheme:
+ˆ 2 points for stating a transformation function.
+ˆ -1 point if the function is merely returning legal range.
+(b) Consider the following 2 × 2 image:
+Apply the following image operations sequentially.
+(i)
+[3 points] Show the resulting image of size 4 × 4 after adding reflection padding.
+Answer:
+Marking scheme:
+ˆ 0.25 point for each correct value. 3 points in total.
+(ii)
+[4 points] Apply a 3 × 3 averaging kernel to the resulting image of part (b)(i). As-
+sume the output image after image averaging is in the same shape as the input image
+by doing zero padding. Round the number to integer if needed.
+Answer:
+Marking scheme:
+ˆ 0.25 point for each correct value. 4 points in total.
+(iii) [4 points] Compute the optimal threshold after applying ONE ITERATION of Otsu’s
+method on the resulting image of part (b)(ii). Assume the initial threshold is set to
+the mean pixel intensity of the resulting image.
+Answer:
+ˆ Initial threshold = (0 + 2 + 4 + 4 + 4 + 9 + 12 + 10 + 8 + 15 + 18 + 14 + 8 + 14 +
+16 + 12)/16 = 9.375
+ˆ µ1 = (0 + 2 + 4 + 4 + 4 + 9 + 8 + 8)/8 = 4.875
+ˆ µ2 = (12 + 10 + 15 + 18 + 14 + 14 + 16 + 12)/8 = 13.875
+ˆ Optimal threshold = (4.875 + 13.875) = 9.375
+Marking scheme:
+ˆ 1 point for the answer of initial threshold.
+ˆ 1 point for the answer of µ1.
+ˆ 1 point for the answer of µ2.
+ˆ 1 point for the optimal threshold after 1 iteration.
+ˆ -1 point for incorrect input from part b(ii). I.e. incorrect calculation based on
+result of part b(ii.)', ARRAY['Vision and CNN']::TEXT[], 'Vision and CNN', 'Vision and CNN', ARRAY['Vision and CNN']::TEXT[], ARRAY['manual_computation', 'filter_computation', 'architecture_reasoning']::TEXT[], 'medium', '', '', ''),
+    ('COMP2211-2022-spring-final-part-b', '1', NULL, 1, 'long_question', 'long_answer', 'Problem 1 [19 points] Convolutional Neural Network (CNN)
+(a)
+[1.5 points] Suppose there is a convolutional layer in a CNN with the following parame-
+ters.
+ˆ Kernel size: 3 × 3
+ˆ Zero padding: 1-pixel border on each side
+ˆ Stride: 2-pixel in each direction
+Compute the output feature map of the convolutional layer with the following input im-
+age and kernel. Assume the number of channels for both input and output images is 1.
+Express your answer as a 2D nested list. E.g. [[2,2],[2,2]].
+Input image:
+Kernel:
+(b)
+[1.5 points] Give an example of a data augmentation technique that would be useful for
+classifying images of cats and dogs, but not for classifying handwritten digits. Briefly
+explain your answer.
+(c) [4.5 points] Assume we have a CNN with the architecture as described in Table 1. Com-
+plete the table by filling in the input and output shape of each layer in the format: ‘height
+x width x channel’ (Note: A space is placed before and after the symbol ’x’). To illustrate
+that, the input shape of the 1st convolutional layer has been inserted for you.
+Formula:
+Convolution output size
+= [ (size of image dimension - size of kernel dimension + 2 × padding) / stride ] + 1
+Layer (Size, Specifications)
+Input Shape
+Output Shape
+7x7 conv, 64 kernels, stride 2, padding 3, with biases
+224 x 224 x 3
+3x3 max pooling, stride 2, padding 1
+3x3 conv, 128 kernels, stride 2, with biases
+3x3 conv, 256 kernels, stride 2, padding 1, with biases
+3x3 conv, 512 kernels, stride 2, padding 1, with biases
+Table 1: Architecture of a CNN
+(d)
+[2 points] Assume we want to add a fully-connected layer at the back of the CNN as
+described in part (b). However, the output of the resulting network still contains a large
+number of parameters. State what we should do if we want to reduce the number of
+parameters and summarize the output.
+Hint: We want to turn N × N × 512 output to 1 × 1 × 512.
+Remark: Convolution layer is not an acceptable answer.
+(e)
+[2.5 points] Calculate the total number of parameters of the model? (i.e. for all the
+layers described in Table 1).
+(f) Assume the model should classify images into 1000 distinct classes by
+(i) [2.5 points] Appending the network shown in Table 2 to the end of the CNN network
+(i.e. the one described in Table 1) appended with the structure that you added in
+part (c).
+Layer (Size, Specifications)
+Input Shape
+Output Shape
+Flatten
+1 x 1 x 512
+Fully-connected (Flatten before feed-in)
+Table 2: Classification network
+Calculate the total number of parameters in the whole network (i.e.
+the layers
+described in Table 1, your answer in part (c), and the layers described in Table 2).
+(ii) [2.5 points] Now, suppose we use the MLP described in Table 3 to classify the images
+instead of CNN.
+Layer (Size, Specifications)
+Input Shape
+Output Shape
+Flatten
+224 x 224 x 3
+Fully-connected (Flatten before feed-in)
+Table 3: Classification using MLP
+Calculate the total number of parameters in the MLP (i.e. only those described in
+Table 3).
+(g)
+[2 points] State what you observe in part (e). Also, state the property of CNN, which
+leads to this difference.', 19, 2, NULL::jsonb, NULL, NULL, 'Problem 1 [19 points] Convolutional Neural Network (CNN)
+(a)
+[1.5 points] Suppose there is a convolutional layer in a CNN with the following parame-
+ters.
+ˆ Kernel size: 3 × 3
+ˆ Zero padding: 1-pixel border on each side
+ˆ Stride: 2-pixel in each direction
+Compute the output feature map of the convolutional layer with the following input im-
+age and kernel. Assume the number of channels for both input and output images is 1.
+Express your answer as a 2D nested list. E.g. [[2,2],[2,2]]. If your calculated answer
+is a floating-point number, convert it to an integer using the floor function.
+Input image:
+Kernel:
+Answer:
+[[73]] for using the unflipped kernel OR
+[[27]] for using the flipped kernel.
+Marking scheme:
+ˆ Accepted answers:
+[[73 (39, 43, 69)]], [[27 (61, 31, 57)]] (for flipped kernel)
+-1 point if the format is wrong. e.g., missing bracket(s).
+If there are more than 1 value, the correct answer will be instead a 2x2 array ex-
+tracted from the following arrays. There are two possible solutions:
+[A[1, 1], A[1, 2]], [A[2, 1], A[2, 2]]
+[A[1, 1], A[1, 3]], [A[3, 1], A[3, 3]] (stride 2 with extended zero padding)
+A = [[ 2, 16, 38, 28],
+[ 5, 27, 61, 53],
+[ 8, 31, 57, 60],
+[ 3, 13, 21, 27]]
+A = [[18, 44, 22, 12],
+[25, 73, 39, 17],
+[22, 69, 43, 10],
+[ 7, 27, 19,
+3]]
+(this two array is generated by
+scipy.signal.convolve2d(in1, in2, mode=''full'', boundary=''fill'', fillvalue=0))
+There are two possible arrays because traditionally, the former one is the “real” con-
+volution. I.e. the kernel or image is flipped along both axes before the convolving op-
+erations. However, at the latter one (the originally intended answer), it’s calculated
+WITHOUT the flipping. This operation is called the cross-correlation operation.
+(b)
+[1.5 points] Give an example of a data augmentation technique that would be useful for
+classifying images of cats and dogs, but not for classifying handwritten digits. Briefly
+explain your answer.
+Answer:
+Flipping the image horizontally. Doing this to a dog/cat image would be reasonable,
+but not so for an image of a handwritten digit.
+Marking scheme:
+ˆ 2 points for “rotate and reflections” as augmentation methods.
+ˆ 0 point for image processing methods.
+(c) [4.5 points] Assume we have a CNN with the architecture as described in Table 1. Com-
+plete the table by filling in the input and output shape of each layer in the format: ‘height
+x width x channel’ (Note: A space is placed before and after the symbol ’x’). To illustrate
+that, the input shape of the 1st convolutional layer has been inserted for you.
+Formula:
+Convolution output size
+= [ (size of image dimension - size of kernel dimension + 2 × padding) / stride ] + 1
+If your calculated answer is a floating-point number, convert it to an integer using the
+floor function.
+Layer (Size, Specifications)
+Input Shape
+Output Shape
+7x7 conv, 64 kernels, stride 2, padding 3, with biases
+224 x 224 x 3
+112 x 112 x 64
+3x3 max pooling, stride 2, padding 1
+112 x 112 x 64
+56 x 56 x 64
+3x3 conv, 128 kernels, stride 2, with biases
+56 x 56 x 64
+27 x 27 x 128
+3x3 conv, 256 kernels, stride 2, padding 1, with biases
+27 x 27 x 128
+13 x 13 x 256
+3x3 conv, 512 kernels, stride 2, padding 1, with biases
+13 x 13 x 256
+7 x 7 x 512
+Table 1: Architecture of a CNN
+Marking scheme:
+ˆ 0.25 point for number of channels
+ˆ 0.25 point for height and width
+ˆ -1 point if the format is wrong, e.g. (c, h, w) instead of (h, w, c)
+(d)
+[2 points] Assume we want to add a fully-connected layer at the back of the CNN as
+described in part (b). However, the output of the resulting network still contains a large
+number of parameters. State what we should do if we want to reduce the number of
+parameters and summarize the output.
+Hint: We want to turn N × N × 512 output to 1 × 1 × 512.
+Remark: Convolution layer is not an acceptable answer.
+Answer:
+Max pooling or average pooling.
+Marking scheme:
+ˆ 2 points for giving the correct answer.
+ˆ -0.5 point if type of pooling wasn’t specified.
+ˆ -0.5 point if pooling is not mentioned. (e.g. taking average) As pooling is a standard
+component of neural networks.
+ˆ 1 point for resize.
+(e)
+[2.5 points] Calculate the total number of parameters of the model? (i.e. for all the
+layers described in Table 1).
+Answer:
+The total number of parameters =(7 × 7 × 3 × 64 + 64)+
+(3 × 3 × 64 × 128 + 128)+
+(3 × 3 × 128 × 256 + 256)+
+(3 × 3 × 256 × 512 + 512)
+=9472 + 73856 + 295168 + 1180160
+=1558656
+Marking scheme:
+ˆ 2.5 point for giving the correct answer.
+(f) Assume the model should classify images into 1000 distinct classes by
+(i) [2.5 points] Appending the network shown in Table 2 to the end of the CNN network
+(i.e. the one described in Table 1) appended with the structure that you added in
+part (c).
+Layer (Size, Specifications)
+Input Shape
+Output Shape
+Flatten
+1 x 1 x 512
+Fully-connected (Flatten before feed-in)
+Table 2: Classification network
+Calculate the total number of parameters in the whole network (i.e. the layers de-
+scribed in Table 1, your answer in part (d), and the layers described in Table 2).
+Answer:
+The total number of parameters =1558656 + (512 × 1000 + 1000)
+=1558656 + 513000
+=2071656
+Marking scheme:
+ˆ 2.5 point for giving the correct answer.
+(ii) [2.5 points] Now, suppose we use the MLP described in Table 3 to classify the images
+instead of CNN.
+Layer (Size, Specifications)
+Input Shape
+Output Shape
+Flatten
+224 x 224 x 3
+Fully-connected (Flatten before feed-in)
+Table 3: Classification using MLP
+Calculate the total number of parameters in the MLP (i.e. only those described in
+Table 3).
+Answer:
+The total number of parameters =(150528 × 1000 + 1000)
+=150529000
+Marking scheme:
+ˆ 2.5 point for giving the correct answer.
+(g)
+[2 points] State what you observe in part (f). Also, state the property of CNN, which
+leads to this difference.
+Answer:
+The number of parameters of CNN is significantly less than the number of parameters of
+pure MLP. This is because CNN uses shared parameters (kernels) to process the input
+instead of using 1 parameter for each individual input.
+Marking scheme:
+ˆ 1 point for observations. If the observation is wrong, explanation is ignored.
+ˆ 1 point for explanation if mentioned “large scale extraction” but “feature extraction”
+only gains 0.5 point.
+ˆ 1 point for “sparse connection”
+ˆ 0 point for reducing output size.
+As fully-connected layer could also reduce the
+output size for the following fc layer, and yet the number of parameter is even more
+bloated.', ARRAY['Vision and CNN']::TEXT[], 'Vision and CNN', 'Vision and CNN', ARRAY['Vision and CNN']::TEXT[], ARRAY['manual_computation', 'filter_computation', 'architecture_reasoning']::TEXT[], 'hard', '', '', ''),
+    ('COMP2211-2022-spring-final-part-b', '2', NULL, 2, 'long_question', 'coding', 'Problem 2 [13 points] Python Programming: Convolutional Neural Network
+Suppose the following CNN model learns human emotions from RGB images of faces and
+classifies into 12 emotion categories.
+Assumptions: All layers have no padding. Both two convolutional layers have 3 × 3 kernels.
+The model is trained with Adam optimizer in default learning rate, and the loss function is
+categorical cross-entropy. You don’t need to specify extra metrics like accuracy.
+(a)
+[9 points] According to all the information given above, write the Python codes to con-
+struct and compile the model using Keras library. The following import statements are
+provided for you. Also, a reference of useful Keras classes and functions are given in the
+appendix.
+from keras.models import Sequential
+from keras.layers import Conv2D, MaxPooling2D
+from keras.layers import Dense, Flatten
+(b)
+[4 points] Now instead of classification, a student wants to predict a single continuous
+real value from an input picture: how positive (or negative) the emotion is, ranging from
+0 to 1. Is CNN in general able to do that? If yes, derive a model to complete such a task
+from part (a) model. Point out which parts you will change when building and compiling
+the model (if any) and explain how you will change it. (You don’t have to write codes,
+but state clearly your ideas). If no, also explain why.
+Appendix:
+Below are some Keras documentation for your reference. Some irrelevant parameters are
+omitted for conciseness.
+Sequential class
+tf.keras.Sequential(layers=None, name=None)
+Sequential groups a linear stack of layers into a tf.keras.Model
+ˆ add Method
+Sequential.add(layer)
+– layer: layer instance
+ˆ compile Method
+Model.compile(optimizer="rmsprop", loss=None)
+– optimizer: String (name of optimizer) or optimizer instance.
+– loss: Loss function.
+Conv2D class
+tf.keras.layers.Conv2D(
+filters, kernel_size, strides=(1, 1), padding="valid", activation=None,
+)
+2D convolution layer (e.g. spatial convolution over images). When using this layer as the
+first layer in a model, provide the keyword argument input shape (tuple of integers, does
+not include the sample axis), e.g. input shape=(128,128,3) for 128x128 RGB pictures in
+data format="channels last".
+ˆ filters: Integer, the dimensionality of the output space (i.e. the number of output filters
+in the convolution).
+ˆ kernel size: An integer or tuple/list of 2 integers, specifying the height and width of the
+2D convolution window. Can be a single integer to specify the same value for all spatial
+dimensions.
+ˆ strides: An integer or tuple/list of 2 integers, specifying the strides of the convolution
+along the height and width. Can be a single integer to specify the same value for all
+spatial dimensions. Specifying any stride value != 1 is incompatible with specifying any
+dilation rate value != 1.
+ˆ padding: one of “valid” or “same” (case-insensitive).
+“valid” means no padding.
+“same” results in padding with zeros evenly to the left/right or up/down of the input.
+When padding="same" and strides=1, the output has the same size as the input.
+ˆ activation: Activation function to use. If you don’t specify anything, no activation is
+applied.
+MaxPooling2D class
+tf.keras.layers.MaxPooling2D(
+pool_size=(2, 2), strides=None, padding="valid",
+)
+Max pooling operation for 2D spatial data.
+ˆ pool size: integer or tuple of 2 integers, window size over which to take the maximum.
+(2, 2) will take the max value over a 2x2 pooling window. If only one integer is specified,
+the same window length will be used for both dimensions.
+ˆ strides: Integer, tuple of 2 integers, or None.
+Strides values.
+Specifies how far the
+pooling window moves for each pooling step. If None, it will default to pool size.
+ˆ padding: One of “valid” or “same” (case-insensitive).
+“valid” means no padding.
+“same” results in padding evenly to the left/right or up/down of the input such that
+output has the same height/width dimension as the input.
+Flatten class
+tf.keras.layers.Flatten()
+Flattens the input.
+Dense class
+tf.keras.layers.Dense(
+units, activation=None,
+)
+Regular densely-connected NN layer.
+ˆ units: Positive integer, dimensionality of the output space.
+ˆ activation: Activation function to use. If you don’t specify anything, no activation is
+applied.
+Common activation functions (in shorthand strings): “relu”, “sigmoid”, “softmax”
+Common loss functions (in shorthand strings):
+“categorical crossentropy”,
+“sparse categorical crossentropy”,
+“mean squared error” (same as “MSE”),
+“mean absolute error” (same as “MAE”)
+Common optimizer (in shorthand strings): “adam”', 13, 5, NULL::jsonb, NULL, NULL, 'Problem 2 [13 points] Python Programming: Convolutional Neural Network
+Suppose the following CNN model learns human emotions from RGB images of faces and
+classifies into 12 emotion categories.
+Assumptions: All layers have no padding. Both two convolutional layers have 3 × 3 kernels.
+The model is trained with Adam optimizer in default learning rate, and the loss function is
+categorical cross-entropy. You don’t need to specify extra metrics like accuracy.
+(a)
+[9 points] According to all the information given above, write the Python codes to con-
+struct and compile the model using Keras library. The following import statements are
+provided for you. Also, a reference of useful Keras classes and functions are given in the
+appendix.
+from keras.models import Sequential
+from keras.layers import Conv2D, MaxPooling2D
+from keras.layers import Dense, Flatten
+Answer:
+model = Sequential()
+model.add(Conv2D(filters=32, kernel_size=(3, 3), activation=''relu'',
+strides=(3,3), input_shape=(32, 32, 1)))
+model.add(Conv2D(filters=64, kernel_size=(3, 3), activation=''relu'', stride=(2,2)))
+model.add(MaxPooling2D(pool_size=(2, 2)))
+model.add(Flatten())
+model.add(Dense(units=12, activation=''softmax''))
+model.compile(optimizer=''adam'', loss=''categorical_crossentropy'')
+Marking scheme:
+ˆ Apply the following rules until the score is 0.
+– -1 for each of Pooling, Flatten, Dense, Compile lines if it is missing or has any
+wrong parameter
+– -1 for each of the wrong or missing parameters in a Conv2D layer.
+This in-
+cludes any parameter that is good by default but you break it by setting another
+absolutely wrong value.
+– -1 if the input shape is not specified in any manner
+– -1 for syntax errors like messed up brackets, the layers are not actually added to
+a model, etc. Small typos on keywords, etc. are not penalized
+(b)
+[4 points] Now instead of classification, a student wants to predict a single continuous
+real value from an input picture: how positive (or negative) the emotion is, ranging from
+0 to 1. Is CNN in general able to do that? If yes, derive a model to complete such a task
+from part (a) model. Point out which parts you will change when building and compiling
+the model (if any) and explain how you will change it. (You don’t have to write codes,
+but state clearly your ideas). If no, also explain why.
+Answer:
+Yes, CNN is able to do that in general, and the following parts need to be changed.
+ˆ Change the Dense layer to units=1.
+ˆ Change the Dense layer activation to any other than softmax, e.g., relu or sigmoid.
+ˆ Change the loss to any regression loss, e.g., MSE, MAE.
+Marking scheme:
+ˆ 1 point for stating CNN is able to do that in general.
+ˆ 1 point for changing the output Dense unit to 1
+ˆ 1 point for changing output activation to anything not related to probability and
+range containing [0,1] (sigmoid, linear, relu, etc.)
+ˆ 1 point for changing the loss function to any real value comparison (subtraction),
+e.g. Mean Squared Error, Mean Absolute Error, or other similar self-defined losses.
+ˆ -0.5 point for each change if the student doesn’t specify to what it changes or changes
+it to a wrong value.
+Appendix:
+Below are some Keras documentation for your reference. Some irrelevant parameters are
+omitted for conciseness.
+Sequential class
+tf.keras.Sequential(layers=None, name=None)
+Sequential groups a linear stack of layers into a tf.keras.Model
+ˆ add Method
+Sequential.add(layer)
+– layer: layer instance
+ˆ compile Method
+Model.compile(optimizer="rmsprop", loss=None)
+– optimizer: String (name of optimizer) or optimizer instance.
+– loss: Loss function.
+Conv2D class
+tf.keras.layers.Conv2D(
+filters, kernel_size, strides=(1, 1), padding="valid", activation=None,
+)
+2D convolution layer (e.g. spatial convolution over images). When using this layer as the
+first layer in a model, provide the keyword argument input shape (tuple of integers, does
+not include the sample axis), e.g. input shape=(128,128,3) for 128x128 RGB pictures in
+data format="channels last".
+ˆ filters: Integer, the dimensionality of the output space (i.e. the number of output filters
+in the convolution).
+ˆ kernel size: An integer or tuple/list of 2 integers, specifying the height and width of the
+2D convolution window. Can be a single integer to specify the same value for all spatial
+dimensions.
+ˆ strides: An integer or tuple/list of 2 integers, specifying the strides of the convolution
+along the height and width. Can be a single integer to specify the same value for all
+spatial dimensions. Specifying any stride value != 1 is incompatible with specifying any
+dilation rate value != 1.
+ˆ padding: one of “valid” or “same” (case-insensitive).
+“valid” means no padding.
+“same” results in padding with zeros evenly to the left/right or up/down of the input.
+When padding="same" and strides=1, the output has the same size as the input.
+ˆ activation: Activation function to use. If you don’t specify anything, no activation is
+applied.
+MaxPooling2D class
+tf.keras.layers.MaxPooling2D(
+pool_size=(2, 2), strides=None, padding="valid",
+)
+Max pooling operation for 2D spatial data.
+ˆ pool size: integer or tuple of 2 integers, window size over which to take the maximum.
+(2, 2) will take the max value over a 2x2 pooling window. If only one integer is specified,
+the same window length will be used for both dimensions.
+ˆ strides: Integer, tuple of 2 integers, or None.
+Strides values.
+Specifies how far the
+pooling window moves for each pooling step. If None, it will default to pool size.
+ˆ padding: One of “valid” or “same” (case-insensitive).
+“valid” means no padding.
+“same” results in padding evenly to the left/right or up/down of the input such that
+output has the same height/width dimension as the input.
+Flatten class
+tf.keras.layers.Flatten()
+Flattens the input.
+Dense class
+tf.keras.layers.Dense(
+units, activation=None,
+)
+Regular densely-connected NN layer.
+ˆ units: Positive integer, dimensionality of the output space.
+ˆ activation: Activation function to use. If you don’t specify anything, no activation is
+applied.
+Common activation functions (in shorthand strings): “relu”, “sigmoid”, “softmax”
+Common loss functions (in shorthand strings):
+“categorical crossentropy”,
+“sparse categorical crossentropy”,
+“mean squared error” (same as “MSE”),
+“mean absolute error” (same as “MAE”)
+Common optimizer (in shorthand strings): “adam”', ARRAY['Python Fundamentals']::TEXT[], 'Python Fundamentals', 'Python Fundamentals', ARRAY['Python Fundamentals']::TEXT[], ARRAY['implementation', 'code_tracing', 'debugging']::TEXT[], 'medium', '', '', ''),
+    ('COMP2211-2022-spring-final-part-b', '3', NULL, 3, 'long_question', 'long_answer', 'Problem 3 [8 points] Minimax and Alpha-Beta Pruning
+Two players, MAX and MIN, are playing a game that can be represented by a tree, as shown
+below.
+(a)
+[3.5 points] Complete the following table by estimating the minimax value of each non-
+terminal node.
+A
+B
+C
+D
+E
+F
+G
+(b)
+[0.5 point] State the proper move of the maximizer by writing down one of the root’s
+outgoing edges (i.e. E-a or E-b).
+Note: The root node of the given tree is A.
+(c)
+[1.5 points] State whether minimax-based AI will choose to make a move which will
+result in a slower victory. Explain your answer.
+(d)
+[2.5 points] Suppose we now apply alpha-beta pruning on the game tree. Indicate the
+edge(s) that would be pruned (eliminated from consideration) by writing down the edge
+labels (i.e. E-a, E-b, E-c, . . ., E-n). You may assume that the branches are explored
+from left to right.', 8, 9, NULL::jsonb, NULL, NULL, 'Problem 3 [8 points] Minimax and Alpha-Beta Pruning
+Two players, MAX and MIN, are playing a game that can be represented by a tree, as shown
+below.
+(a)
+[3.5 points] Complete the following table by estimating the minimax value of each non-
+terminal node.
+Answer:
+A
+B
+C
+-1
+D
+E
+F
+-1
+G
+Marking scheme:
+ˆ 0.5 point for each correct answer. 3.5 points in total.
+(b)
+[0.5 point] State the proper move of the maximizer by writing down one of the root’s
+outgoing edges (i.e. E-a or E-b).
+Note: The root node of the given tree is A.
+Answer:
+E-a
+Marking scheme:
+ˆ 0.5 point for the correct answer.
+(c)
+[1.5 points] State whether minimax-based AI will choose to make a move which will
+result in a slower victory. Explain your answer.
+Answer:
+Yes, minimax-based AI may choose to make a move, resulting in a slower victory. Since
+we may have two moves with the same maximum minimax value, it picks the one in
+slower victory.
+Marking scheme:
+ˆ 1 point for stating minimax-based AI will choose to make a move which will result
+in a slower victory.
+ˆ 0.5 point for giving the proper explanation.
+(d)
+[2.5 points] Suppose we now apply alpha-beta pruning on the game tree. Indicate the
+edge(s) that would be pruned (eliminated from consideration) by writing down the edge
+labels (i.e. E-a, E-b, E-c, . . ., E-n). You may assume that the branches are explored
+from left to right.
+Answer:
+E-j and E-f would be pruned.
+Marking scheme:
+ˆ If E-j appears: +1 point.
+ˆ If E-f appears: +1.5 point.
+ˆ If E-m or E-n appears: +0 point.
+ˆ If other label appears: then d) become 0 points, no matter E-j and E-f appears or
+not.', ARRAY['Search and Games']::TEXT[], 'Search and Games', 'Search and Games', ARRAY['Search and Games']::TEXT[], ARRAY['tree_search', 'pruning', 'manual_tracing']::TEXT[], 'easy', '', '', ''),
+    ('COMP2211-2022-spring-final-part-b', '4', NULL, 4, 'long_question', 'short_answer', 'Problem 4 [7 points] Ethics of Artificial Intelligence
+This question consists of five sub-questions, four of them are multiple-choice questions, and
+one is a short question. Choose the BEST ANSWER among the given choices for each
+multiple-choice question and put your answer in the given table, while for the short question,
+answer it in a few sentences.
+(a)
+[1 point] Ethics in artificial intelligence is
+(A) Something that is not an issue.
+(B) Something that somebody else will do in the future.
+(C) Something that we need to apply today.
+(D) Something that is entirely solved in current AI systems.
+(b)
+[1 point] One approach that helps developers avoid unintentionally creating bias in AI
+systems is
+(A) Using a wide variety of appropriately diverse data for training.
+(B) Using highly specific training data from a narrow range.
+(C) Not using any training data.
+(D) None of the above
+(c)
+[1 point] What are some of the ethical concerns around artificial intelligence?
+I. Racial, gender or other types of bias.
+II. Loss of jobs due to AI replacing workers performing repetitive tasks.
+III. Concern about the trustworthiness of decision-making supported by AI systems.
+IV. Privacy, for example, as human faces are photographed and recognized in public
+spaces.
+(A) I and II only
+(B) I, II, and IV only
+(C) All of the above
+(D) None of the above
+(d) [1 point] What is a significantly way in which developers of AI systems can guard against
+introducing bias?
+(A) Using only examples from their own environment as training data.
+(B) Providing effective training data and performing regular tests and audits.
+(C) Using less varied AI systems and datasets.
+(D) Using government approved algorithms.
+Question
+Answer
+(a)
+(b)
+(c)
+(d)
+(e)
+[3 points] State THREE ethical issues involved with the introduction of autonomous
+vehicles.', 7, 10, NULL::jsonb, NULL, NULL, 'Problem 4 [7 points] Ethics of Artificial Intelligence
+This question consists of five sub-questions, four of them are multiple-choice questions, and
+one is a short question. Choose the BEST ANSWER among the given choices for each
+multiple-choice question and put your answer in the given table, while for the short question,
+answer it in a few sentences.
+(a)
+[1 point] Ethics in artificial intelligence is
+(A) Something that is not an issue.
+(B) Something that somebody else will do in the future.
+(C) Something that we need to apply today.
+(D) Something that is entirely solved in current AI systems.
+(b)
+[1 point] One approach that helps developers avoid unintentionally creating bias in AI
+systems is
+(A) Using a wide variety of appropriately diverse data for training.
+(B) Using highly specific training data from a narrow range.
+(C) Not using any training data.
+(D) None of the above
+(c)
+[1 point] What are some of the ethical concerns around artificial intelligence?
+I. Racial, gender or other types of bias.
+II. Loss of jobs due to AI replacing workers performing repetitive tasks.
+III. Concern about the trustworthiness of decision-making supported by AI systems.
+IV. Privacy, for example, as human faces are photographed and recognized in public
+spaces.
+(A) I and II only
+(B) I, II, and IV only
+(C) All of the above
+(D) None of the above
+(d) [1 point] What is a significantly way in which developers of AI systems can guard against
+introducing bias?
+(A) Using only examples from their own environment as training data.
+(B) Providing effective training data and performing regular tests and audits.
+(C) Using less varied AI systems and datasets.
+(D) Using government approved algorithms.
+Question
+Answer
+(a)
+C
+(b)
+A
+(c)
+C
+(d)
+B
+Marking scheme:
+ˆ 1 point for each correct answer. 4 points in total.
+(e)
+[3 points] State THREE ethical issues involved with the introduction of autonomous
+vehicles.
+Answer:
+ˆ Who is to blame in an accident?
+ˆ In an emergency situation who should the car prioritize?
+ˆ Increase in use of cars is bad for the environment.
+ˆ Cost of the cars.
+Marking scheme:
+ˆ 1 point for giving each correct ethical issue. 3 points in total.', ARRAY['Ethics of AI']::TEXT[], 'Ethics of AI', 'Ethics of AI', ARRAY['Ethics of AI']::TEXT[], ARRAY['concept_explanation', 'argumentation', 'comparison']::TEXT[], 'easy', '', '', ''),
+    ('COMP2211-2023-spring-midterm', '1', NULL, 1, 'true_false', 'true_false', 'Problem 1 [10 points] True/False Questions
+Indicate whether the following statements are true or false by circling T or F. You get 1
+point for each correct answer.
+(a)
+T
+F
+Na¨ıve Bayes classifier is a probabilistic algorithm that computes the probability of an
+instance belonging to each class and selects the class with the highest probability as the
+output.
+(b)
+T
+F
+Na¨ıve Bayes classifier can be used for multi-class classification task.
+(c)
+T
+F
+K-Nearest Neighbors is a supervised learning and parametric algorithm that can be used
+to solve both classification and regression problems.
+(d)
+T
+F
+In K-Nearest Neighbors algorithm, the value of K should always be odd to avoid ties.
+(e)
+T
+F
+In D-fold cross validation, an increase of D will result in a longer time required to cross-
+validate the result.
+(f)
+T
+F
+After centroids initialization, K-Means Clustering is sensitive to the order in which the
+data points are processed, meaning that changing the order of the input data points may
+lead to different clustering results.
+(g)
+T
+F
+K-Median Clustering is robust to the presence of outliers and noise in the dataset, as it
+uses the median of the data points as the center of each cluster.
+Note: The median is the middle number in a sorted, ascending or descending list of
+numbers.
+(h)
+T
+F
+A perceptron with different initialization of weights and bias may result in different
+decision boundaries.
+(i)
+T
+F
+For perceptron, larger learning rates always lead to faster convergence.
+(j)
+T
+F
+Multilayer Perceptron with more layers are more expressive than Single Layer Perceptron
+regardless of the activation function is linear or not.', 10, 3, NULL::jsonb, NULL, NULL, 'Problem 1 [10 points] True/False Questions
+Indicate whether the following statements are true or false by circling T or F. You get 1
+point for each correct answer.
+(a)
+T
+F
+Na¨ıve Bayes classifier is a probabilistic algorithm that computes the probability of an
+instance belonging to each class and selects the class with the highest probability as the
+output.
+(b)
+T
+F
+Na¨ıve Bayes classifier can be used for multi-class classification task.
+(c)
+T
+F
+K-Nearest Neighbors is a supervised learning and parametric algorithm that can be used
+to solve both classification and regression problems.
+(d)
+T
+F
+In K-Nearest Neighbors algorithm, the value of K should always be odd to avoid ties.
+(e)
+T
+F
+In D-fold cross validation, an increase of D will result in a longer time required to cross-
+validate the result.
+(f)
+T
+F
+After centroids initialization, K-Means Clustering is sensitive to the order in which the
+data points are processed, meaning that changing the order of the input data points may
+lead to different clustering results.
+(g)
+T
+F
+K-Median Clustering is robust to the presence of outliers and noise in the dataset, as it
+uses the median of the data points as the center of each cluster.
+Note: The median is the middle number in a sorted, ascending or descending list of
+numbers.
+(h)
+T
+F
+A perceptron with different initialization of weights and bias may result in different
+decision boundaries.
+(i)
+T
+F
+For perceptron, larger learning rates always lead to faster convergence.
+(j)
+T
+F
+Multilayer Perceptron with more layers are more expressive than Single Layer Perceptron
+regardless of the activation function is linear or not.', ARRAY['True/False']::TEXT[], 'True/False', 'True/False', ARRAY['True/False']::TEXT[], ARRAY['concept_check', 'rapid_reasoning']::TEXT[], 'medium', '', '', ''),
+    ('COMP2211-2023-spring-midterm', '2', NULL, 2, 'long_question', 'coding', 'Problem 2 [21 points] Python Fundamentals
+(a)
+[9 points] Consider the following Numpy arrays:
+import numpy as np
+# np.arange(stop)
+# return an array of evenly spaced values within the half-open interval [0,stop),
+# the default step size is 1.
+A = np.arange(10)
+B = np.array([[5, 10, 15],
+[20, 25, 30],
+[35, 40, 45]])
+Write the output for each of the following Python statements. If the output is an empty
+array, write “Empty Array”. If an error occurs, write “Error”.
+(i) print(A[1:5])
+(ii) print(A[1:5:2])
+(iii) print(A[5::-2])
+(iv) print(B[:2,1:])
+(v) print(B[A[:1]])
+(vi) print(B[A[1:]])
+(vii) A[A%3 == 0] = 100
+print(A)
+(viii) # np.mean(a, axis)
+# return the average of the array elements over the specified axis.
+print(np.mean(B, axis=-1))
+(ix) # np.ndarray.transpose(*axis)
+# return a view of the array with axes transposed.
+print(B.transpose((1,0)))
+(b)
+[6 points] Write the output for the following Python code segments. If the output is an
+empty array, write “Empty Array”. If an error occurs, write “Error”.
+(i) import numpy as np
+A = np.array([1,2])
+B = np.array([[1,2],
+[2,4],
+[3,6],
+[4,8]])
+print(B/A)
+(ii) import numpy as np
+A = np.array([[0, 0, 0],
+[1, 2, 3],
+[4, 5, 6],
+[7, 8, 9]])
+B = np.array([10, 11, 12, 13])
+print(A + B)
+(iii) import numpy as np
+A = np.array([0, 10, 20, 30])
+B = np.array([1, 2, 3])
+# np.newaxis
+# increase the dimension of an array by adding new axis
+print(A[:, np.newaxis] + B)
+(c)
+[6 points] The distance function between two points x and y in the Poincare ball model
+can be calculated by the following formula:
+arccosh
+
+1 + 2
+||x −y||2
+(1 −||x||2)(1 −||y||2)
+
+= arccosh
+
+1 + 2
+Pn
+i=1(xi −yi)2
+(1 −Pn
+i=1 x2
+i )(1 −Pn
+i=1 y2
+i )
+
+For example, if x = (0.0, 0.0), y = (0.0, 0.1), their Poincare distance is
+arccosh
+
+1 + 2
+(0.0 −0.0)2 + (0.0 −0.1)2
+(1 −(0.02 + 0.02))(1 −(0.02 + 0.12))
+
+≈0.2006707
+Given the following NumPy arrays, X and Y, where each 1-D array represents a data
+point:
+X = np.array([[0.0, 0.0], [0.1, 0.1], [0.2, 0.2]])
+Y = np.array([[0, 0.1], [0.2, 0.3]])
+Compute the Poincare distance between each data point in X and each data point in Y
+with a one-line Python expression, such that the result of the expression is
+[[0.2006707 0.75504766]
+[0.2027011 0.47971794]
+[0.46441642 0.22308802]]
+Note:
+ˆ An expression is a combination of values, variables, operators, and calls to functions.
+Your expression should work with any numbers of data points in X and Y and any
+number of values in the data points.
+ˆ You can assume that the number of attribute values in each data point are the same
+for both X and Y.
+ˆ There must be no explicit loops in your expression.
+You may find the following attribute or functions useful for this question.
+ˆ numpy.expand_dims(a, axis)
+Insert a new axis to a that will appear at the axis position in the expanded array
+shape.
+ˆ numpy.square(x)
+Return the element-wise square of the input x.
+ˆ numpy.sum(a, axis)
+Return the sum of array a’s elements over a given axis.
+ˆ a.T
+The transposed array of a.
+ˆ numpy.matmul(a, b)
+Return the matrix product of a and b.
+ˆ numpy.arccosh(x)
+Return the element-wise arccosh of the input x.
+Write your one-line Python expression below.', 21, 4, NULL::jsonb, NULL, NULL, 'Problem 2 [21 points] Python Fundamentals
+(a)
+[9 points] Consider the following Numpy arrays:
+import numpy as np
+# np.arange(stop)
+# return an array of evenly spaced values within the half-open interval [0,stop),
+# the default step size is 1.
+A = np.arange(10)
+B = np.array([[5, 10, 15],
+[20, 25, 30],
+[35, 40, 45]])
+Write the output for each of the following Python statements. If the output is an empty
+array, write “Empty Array”. If an error occurs, write “Error”.
+(i) print(A[1:5])
+Answer:
+[1 2 3 4]
+(ii) print(A[1:5:2])
+Answer:
+[1 3]
+(iii) print(A[5::-2])
+Answer:
+[5 3 1]
+(iv) print(B[:2,1:])
+Answer:
+[[10 15]
+[25 30]]
+(v) print(B[A[:1]])
+Answer:
+[[5 10 15]]
+(vi) print(B[A[1:]])
+Answer:
+Error
+(vii) A[A%3 == 0] = 100
+print(A)
+Answer:
+[100 1 2 100 4 5 100 7 8 100]
+(viii) # np.mean(a, axis)
+# return the average of the array elements over the specified axis.
+print(np.mean(B, axis=-1))
+Answer:
+[10 25 40]
+(ix) # np.ndarray.transpose(*axis)
+# return a view of the array with axes transposed.
+print(B.transpose((1,0)))
+Answer:
+[[5 20 35]
+[10 25 40]
+[15 30 45]]
+Marking Scheme:
+ˆ 1 point for each sub-question. No partial point. The brackets must be correct in
+order to get the points. 9 points in total.
+(b)
+[6 points] Write the output for the following Python code segments. If the output is an
+empty array, write “Empty Array”. If an error occurs, write “Error”.
+(i) import numpy as np
+A = np.array([1,2])
+B = np.array([[1,2],
+[2,4],
+[3,6],
+[4,8]])
+print(B/A)
+Answer:
+[[1 1]
+[2 2]
+[3 3]
+[4 4]]
+(ii) import numpy as np
+A = np.array([[0, 0, 0],
+[1, 2, 3],
+[4, 5, 6],
+[7, 8, 9]])
+B = np.array([10, 11, 12, 13])
+print(A + B)
+Answer: Error
+(iii) import numpy as np
+A = np.array([0, 10, 20, 30])
+B = np.array([1, 2, 3])
+# np.newaxis
+# increase the dimension of an array by adding new axis
+print(A[:, np.newaxis] + B)
+Answer:
+[[1 2 3]
+[11 12 13]
+[21 22 23]
+[31 32 33]]
+Marking Scheme:
+ˆ 2 points for each sub-question. No partial point. The bracket must be correct in
+order to get the points. 6 points in total.
+(c)
+[6 points] The distance function between two points x and y in the Poincare ball model
+can be calculated by the following formula:
+arccosh
+
+1 + 2
+||x −y||2
+(1 −||x||2)(1 −||y||2)
+
+= arccosh
+
+1 + 2
+Pn
+i=1(xi −yi)2
+(1 −Pn
+i=1 x2
+i )(1 −Pn
+i=1 y2
+i )
+
+For example, if x = (0.0, 0.0), y = (0.0, 0.1), their Poincare distance is
+arccosh
+
+1 + 2
+(0.0 −0.0)2 + (0.0 −0.1)2
+(1 −(0.02 + 0.02))(1 −(0.02 + 0.12))
+
+≈0.2006707
+Given the following NumPy arrays, X and Y, where each 1-D array represents a data
+point:
+X = np.array([[0.0, 0.0], [0.1, 0.1], [0.2, 0.2]])
+Y = np.array([[0, 0.1], [0.2, 0.3]])
+Compute the Poincare distance between each data point in X and each data point in Y
+with a one-line Python expression, such that the result of the expression is
+[[0.2006707 0.75504766]
+[0.2027011 0.47971794]
+[0.46441642 0.22308802]]
+Note:
+ˆ An expression is a combination of values, variables, operators, and calls to functions.
+Your expression should work with any numbers of data points in X and Y and any
+number of values in the data points.
+ˆ You can assume that the number of attribute values in each data point are the same
+for both X and Y.
+ˆ There must be no explicit loops in your expression.
+You may find the following attribute or functions useful for this question.
+ˆ numpy.expand_dims(a, axis)
+Insert a new axis to a that will appear at the axis position in the expanded array
+shape.
+ˆ numpy.square(x)
+Return the element-wise square of the input x.
+ˆ numpy.sum(a, axis)
+Return the sum of array a’s elements over a given axis.
+ˆ a.T
+The transposed array of a.
+ˆ numpy.matmul(a, b)
+Return the matrix product of a and b.
+ˆ numpy.arccosh(x)
+Return the element-wise arccosh of the input x.
+Write your one-line Python expression below.
+Answer:
+print(np.arccosh(1 + 2 * np.sum((np.expand_dims(X, axis=1) - Y) ** 2, axis=2) /
+np.matmul(np.expand_dims(1 - np.sum(X ** 2, axis=1), axis=1),
+np.expand_dims(1 - np.sum(Y ** 2, axis=1), axis=1).T)))
+Marking Scheme:
+ˆ 1.5 points: correct usage of np.expand dims(), 0.5 points each. The axis must be
+correct in order to get the points. Can be replaced by np.newaxis or other equivalent
+functions.
+ˆ 1.5 points: correct usage of np.sum(), 0.5 points each. The axis must be correct in
+order to get the points.
+ˆ 1.5 points: Ccorrect usage of np.square(), 0.5 points each. Can be replaced by **2.
+ˆ 0.5 points: correct usage of np.matmul(). The second argument should have the
+transpose (np.transpose() or T.). But if the second no.expand dims() in the denom-
+inator is expanded in axis = 0, the transpose should not occur. Can be replaced by
+np.dot() or @.
+ˆ 0.5 points: correct usage of np.arccosh().
+ˆ 0.5 points: correct basic formula as arccosh(1 + 2*(nominator/denominator)). Gen-
+erally the students can get this 0.5 points if they write their answers.', ARRAY['Python Fundamentals']::TEXT[], 'Python Fundamentals', 'Python Fundamentals', ARRAY['Python Fundamentals']::TEXT[], ARRAY['implementation', 'code_tracing', 'debugging']::TEXT[], 'hard', '', '', ''),
+    ('COMP2211-2023-spring-midterm', '3', NULL, 3, 'long_question', 'long_answer', 'Problem 3 [8 points] Na¨ıve Bayes Classifier
+Given the following training dataset
+Humidity
+Wind
+Temperature
+Play Tennis
+Day 1
+High
+Weak
+Mild
+Yes
+Day 2
+?
+Weak
+Cool
+Yes
+Day 3
+Normal
+Strong
+Cool
+No
+Day 4
+Normal
+Strong
+?
+Yes
+Day 5
+High
+Weak
+Hot
+No
+Suppose we want to classify whether we will play tennis on Day 6 with the following attributes:
+Humidity=High, Wind=Weak, Temperature=?
+using Na¨ıve Bayes Classifier with the given training dataset.
+Unfortunately, some of the training data is missing, which is denoted as “?”. Can Na¨ıve
+Bayes Classifier handle this dataset with missing data? i.e., Can you still yield a classifica-
+tion output of whether you will play tennis on Day 6?
+If not, explain why. Otherwise, show how you will approach this problem.
+In your answer, you should clearly mention why your explanation is valid with reference to
+the property of the Na¨ıve Bayes algorithm.', 8, 9, NULL::jsonb, NULL, NULL, 'Problem 3 [8 points] Na¨ıve Bayes Classifier
+Given the following training dataset
+Humidity
+Wind
+Temperature
+Play Tennis
+Day 1
+High
+Weak
+Mild
+Yes
+Day 2
+?
+Weak
+Cool
+Yes
+Day 3
+Normal
+Strong
+Cool
+No
+Day 4
+Normal
+Strong
+?
+Yes
+Day 5
+High
+Weak
+Hot
+No
+Suppose we want to classify whether we will play tennis on Day 6 with the following attributes:
+Humidity=High, Wind=Weak, Temperature=?
+using Na¨ıve Bayes Classifier with the given training dataset.
+Unfortunately, some of the training data is missing, which is denoted as “?”. Can Na¨ıve
+Bayes Classifier handle this dataset with missing data? i.e., Can you still yield a classifica-
+tion output of whether you will play tennis on Day 6?
+If not, explain why. Otherwise, show how you will approach this problem.
+In your answer, you should clearly mention why your explanation is valid with reference to
+the property of the Na¨ıve Bayes algorithm.
+Answer:
+Na¨ıve Bayes can handle a dataset with missing value.
+Attributes are handled separately
+by the algorithm at both model construction time and prediction time. Therefore, the miss-
+ing attributes can simply be ignored while preparing the model, and also ignored when a
+probability is calculated for a class value.
+Marking Scheme:
+ˆ Case 1: Student’s answer is a clear “Yes”, or suggests that the Na¨ıve Bayes can be used
+in this scenario.
+– 2 points for indicating/suggesting that Na¨ıve Bayes can be used in this scenario.
+– 3 points for mentioning the property of the Na¨ıve Bayes, where attributes are handled
+separately and/or independently.
+– 3 points for mentioning the data entry with the missing value in the table can simply
+be ignored.
+However, answers that suggest ignoring the data for the entire day
+(row), or the data for the entire attribute (column) are not acceptable.
+Answer
+should also include that the attribute “Temperature” for Day 6 can be ignored for
+when calculating the classification result.
+ˆ Case 2: Student’s answer is a clear “No”.
+– 0 point, and partial credit is NOT given for the subsequent explanation. Because,
+whatever explanation is for the wrong claim.', ARRAY['Probabilistic Models']::TEXT[], 'Probabilistic Models', 'Probabilistic Models', ARRAY['Probabilistic Models']::TEXT[], ARRAY['manual_computation', 'probability_reasoning', 'classification_decision']::TEXT[], 'easy', '', '', ''),
+    ('COMP2211-2023-spring-midterm', '4', NULL, 4, 'long_question', 'long_answer', 'Problem 4 [14 points] K-Nearest Neighbors
+Suppose you are building a K-Nearest Neighbors classifier that predicts user preference on
+movie genre based on their rating history (a 5-point scale).
+User
+Movie Ratings
+Preferred Genre
+Movie1
+Movie2
+Movie3
+Alice
+Romance
+Bob
+Romance
+Charlie
+Action
+Table 1: Training Dataset
+User
+Movie Ratings
+Preferred Genre
+Movie1
+Movie2
+Movie3
+David
+?
+Table 2: Test Dataset
+(a)
+[8 points] Based on the movie ratings in Table 1 and Table 2, calculate the Cosine and
+Euclidean distance between ratings of each training data and test data (round to the
+third decimal place). Fill in the distance columns in the following table, and determine
+the class of David for each distance when K=1. You can find the following approxima-
+tions and equations helpful for this question.
+Approximated values:
+√
+5 ≈2.236
+√
+12 ≈3.464
+√
+14 ≈3.742
+√
+√
+22 =
+√
+242 ≈15.556
+√
+√
+66 =
+√
+1452 ≈38.105
+Cosine distance formula:
+cosθ =
+Pn
+i=1 Xtrain
+i
+× Xtest
+i
+qPn
+i=1(Xtrain
+i
+)2
+ pPn
+i=1(Xtest
+i
+)2
+
+Cosine Distance = 1 −cosθ
+where Xtrain
+i
+and Xtest
+i
+are the feature value of training data and test data, respectively,
+and n is the number of features.
+Euclidean distance formula:
+Euclidean Distance =
+v
+u
+u
+t
+n
+X
+i=1
+(Xtrain
+i
+−Xtest
+i
+)2
+where Xtrain
+i
+and Xtest
+i
+are the feature value of training data and test data, respectively,
+and n is the number of features.
+User
+Movie Ratings
+Movie1
+Movie2
+Movie3
+Preferred
+Genre
+Cosine
+Distance
+Euclidean
+Distance
+Alice
+Romance
+Bob
+Romance
+Charlie
+Action
+David
+?
+Class for cosine distance:
+Class for Euclidean distance:
+(b)
+[6 points] Let’s say that Alice tends to rate movies highly, while David tends to rate
+them relatively poorly. Given this scenario, which distance metric is more appropriate?
+Also, describe why it makes the classifier better.', 14, 10, NULL::jsonb, NULL, NULL, 'Problem 4 [14 points] K-Nearest Neighbors
+Suppose you are building a K-Nearest Neighbors classifier that predicts user preference on
+movie genre based on their rating history (a 5-point scale).
+User
+Movie Ratings
+Preferred Genre
+Movie1
+Movie2
+Movie3
+Alice
+Romance
+Bob
+Romance
+Charlie
+Action
+Table 1: Training Dataset
+User
+Movie Ratings
+Preferred Genre
+Movie1
+Movie2
+Movie3
+David
+?
+Table 2: Test Dataset
+(a)
+[8 points] Based on the movie ratings in Table 1 and Table 2, calculate the Cosine and
+Euclidean distance between ratings of each training data and test data (round to the
+third decimal place). Fill in the distance columns in the following table, and determine
+the class of David for each distance when K=1. You can find the following approxima-
+tions and equations helpful for this question.
+Approximated values:
+√
+5 ≈2.236
+√
+12 ≈3.464
+√
+14 ≈3.742
+√
+√
+22 =
+√
+242 ≈15.556
+√
+√
+66 =
+√
+1452 ≈38.105
+Cosine distance formula:
+cosθ =
+Pn
+i=1 Xtrain
+i
+× Xtest
+i
+qPn
+i=1(Xtrain
+i
+)2
+ pPn
+i=1(Xtest
+i
+)2
+
+Cosine Distance = 1 −cosθ
+where Xtrain
+i
+and Xtest
+i
+are the feature value of training data and test data, respectively,
+and n is the number of features.
+Euclidean distance formula:
+Euclidean Distance =
+v
+u
+u
+t
+n
+X
+i=1
+(Xtrain
+i
+−Xtest
+i
+)2
+where Xtrain
+i
+and Xtest
+i
+are the feature value of training data and test data, respectively,
+and n is the number of features.
+User
+Movie Ratings
+Movie1
+Movie2
+Movie3
+Preferred
+Genre
+Cosine
+Distance
+Euclidean
+Distance
+Alice
+Romance
+Bob
+Romance
+Charlie
+Action
+David
+?
+Class for cosine distance:
+Class for Euclidean distance:
+User
+Movie Ratings
+Movie1
+Movie2
+Movie3
+Preferred
+Genre
+Cosine
+Distance
+Euclidean
+Distance
+Alice
+Romance
+0.003
+3.464
+Bob
+Romance
+0.029
+3.741 or 3.742
+Charlie
+Action
+0.100
+2.236
+David
+?
+Class for cosine distance: Romance
+Class for Euclidean distance: Action
+Marking Scheme:
+ˆ 1 point for giving each correct value. 6 points in total.
+– 0.5 point for each correct value that is not rounded to the 3rd decimal place.
+– No point for others (e.g., square root values or equations).
+ˆ 1 point for giving each correct class. 2 points in total.
+(b)
+[6 points] Let’s say that Alice tends to rate movies highly, while David tends to rate
+them relatively poorly. Given this scenario, which distance metric is more appropriate?
+Also, describe why it makes the classifier better.
+Answer:
+In this scenario, the Cosine distance metric performs better, indicating that the ground
+truth class is “Romance”. The reason for this is that the Cosine distance metric con-
+siders the direction of the vectors being compared, while the Euclidean distance metric
+takes both direction and magnitude into account. Since the Cosine distance metric is
+less sensitive to variations in magnitude, it is better suited for comparing vectors with
+different scales. Given that this data contains bias in terms of scale, the Cosine distance
+metric is a better choice in this case.
+Marking Scheme:
+ˆ 1 point for stating Cosine distance metric performs better, indicating that the ground
+truth class is “Romance”.
+ˆ 2 points for stating Cosine distance metric considers the direction, while Euclidean
+distance metric takes both direction and magnitude into account.
+– Note that direction or angle should be mentioned in the state of Cosine dis-
+tance metric, while magnitude should be mentioned in the state of Euclidean
+distance metric.
+ˆ 3 points for stating Cosine distance metric is less sensitive to variations in mag-
+nitude, and the data contain bias in terms of scale, Cosine distance is a better
+choice.
+– 1 point for stating that Cosine distance metric is less sensitive to variations.
+– 1 point for stating that Cosine distance metric is less sensitive to the biased data
+which is described in the problem.', ARRAY['KNN and Clustering']::TEXT[], 'KNN and Clustering', 'KNN and Clustering', ARRAY['KNN and Clustering']::TEXT[], ARRAY['manual_computation', 'distance_calculation', 'algorithm_tracing']::TEXT[], 'medium', '', '', ''),
+    ('COMP2211-2023-spring-midterm', '5', NULL, 5, 'long_question', 'long_answer', 'Problem 5 [19 points] K-Means Clustering
+Consider a 2-dimensional dataset with the following 5 data points:
+A(1, 4), B(2, 3), C(4, 6), D(5, 7), E(8, 3)
+Perform K-Means clustering on this dataset with K = 2. Use the following initial centroids
+for your calculations:
+Centroid 1: A(1, 4), Centroid 2: D(5, 7)
+If a tie occurs, assign the points to Centroid 1. All calculations round to two decimal places.
+(a) [7 points] Calculate the Euclidean distances between each data point and both centroids.
+Fill in the distances in the table. Then, assign each point to the nearest centroid.
+Distance
+A
+B
+C
+D
+E
+Centroid 1
+Centroid 2
+Data points assigned to Centroid 1:
+Data points assigned to Centroid 2:
+(b)
+[7 points] Recalculate the centroids using the mean of the points assigned to each cen-
+troid. Fill in the values of new centroids after C1 and C2 in the table. Then repeat the
+process of assigning points and recalculating centroids until convergence. You may not
+need all the provided table templates. Leave them blank if the algorithm has already
+converged. Report the final cluster assignments and centroids.
+Distance
+A
+B
+C
+D
+E
+C1(
+,
+)
+C2(
+,
+)
+Data points assigned to Centroid 1:
+Data points assigned to Centroid 2:
+Distance
+A
+B
+C
+D
+E
+C1(
+,
+)
+C2(
+,
+)
+Data points assigned to Centroid 1:
+Data points assigned to Centroid 2:
+Distance
+A
+B
+C
+D
+E
+C1(
+,
+)
+C2(
+,
+)
+Data points assigned to Centroid 1:
+Data points assigned to Centroid 2:
+(c) [5 points] The choice of the number of clusters K in K-Means clustering can significantly
+impact the clustering results. Selecting too few or too many clusters can lead to overgen-
+eralization or overfitting. Why this limitation is critical for K-Means clustering? Given
+a dataset with an unknown number of clusters, please explain one way to determine a
+suitable K.', 19, 12, NULL::jsonb, NULL, NULL, 'Problem 5 [19 points] K-Means Clustering
+Consider a 2-dimensional dataset with the following 5 data points:
+A(1, 4), B(2, 3), C(4, 6), D(5, 7), E(8, 3)
+Perform K-Means clustering on this dataset with K = 2. Use the following initial centroids
+for your calculations:
+Centroid 1: A(1, 4), Centroid 2: D(5, 7)
+If a tie occurs, assign the points to Centroid 1. All calculations round to two decimal places.
+(a) [7 points] Calculate the Euclidean distances between each data point and both centroids.
+Fill in the distances in the table. Then, assign each point to the nearest centroid.
+Distance
+A
+B
+C
+D
+E
+Centroid 1
+Centroid 2
+Data points assigned to Centroid 1:
+Data points assigned to Centroid 2:
+Answers:
+Distance
+A
+B
+C
+D
+E
+Centroid 1
+1.41
+3.61
+5.00
+7.07
+Centroid 2
+5.00
+5.00
+1.41
+5.00
+Data points assigned to Centroid 1: A, B
+Data points assigned to Centroid 2: C, D, E
+Marking Scheme:
+ˆ 0.5 point for each correct value. 5 points in total.
+ˆ 1 point for giving the data points assigned to each centroid. 2 points in total.
+ˆ -0.5 points for not rounding to two decimal places.
+(b)
+[7 points] Recalculate the centroids using the mean of the points assigned to each cen-
+troid. Fill in the values of new centroids after C1 and C2 in the table. Then repeat the
+process of assigning points and recalculating centroids until convergence. You may not
+need all the provided table templates. Leave them blank if the algorithm has already
+converged. Report the final cluster assignments and centroids.
+Distance
+A
+B
+C
+D
+E
+C1(
+,
+)
+C2(
+,
+)
+Data points assigned to Centroid 1:
+Data points assigned to Centroid 2:
+Distance
+A
+B
+C
+D
+E
+C1(
+,
+)
+C2(
+,
+)
+Data points assigned to Centroid 1:
+Data points assigned to Centroid 2:
+Distance
+A
+B
+C
+D
+E
+C1(
+,
+)
+C2(
+,
+)
+Data points assigned to Centroid 1:
+Data points assigned to Centroid 2:
+Answer:
+Distance
+A
+B
+C
+D
+E
+C1(1.5, 3.5)
+0.71
+0.71
+3.54
+4.95
+6.52
+C2(5.67, 5.33)
+4.86
+4.35
+1.80
+1.80
+3.30
+Data points assigned to Centroid 1: A, B
+Data points assigned to Centroid 2: C, D, E
+Marking Scheme:
+ˆ 0.5 points for each correct value. 5 points in total.
+ˆ 1 point for giving the data points assigned to each centroid. 2 points in total.
+ˆ -0.5 points for not rounding to two decimal places.
+ˆ -1 point for filling in/copying more than 1 table template (incorrect convergence
+statement).
+(c) [5 points] The choice of the number of clusters K in K-Means clustering can significantly
+impact the clustering results. Selecting too few or too many clusters can lead to overgen-
+eralization or overfitting. Why this limitation is critical for K-Means clustering? Given
+a dataset with an unknown number of clusters, please explain one way to determine a
+suitable K.
+Answer:
+Clustering is an unsupervised learning method. In the scenario where you try to adopt K-
+Means clustering or any other unsupervised models, you don’t know anything about the
+shape, the number of clusters, or the distribution of the dataset. Such value k is exactly
+what you are looking for and will never be given beforehand. One common method to
+determine the optimal number of clusters is the elbow method, which identifies the point
+where adding more clusters does not improve the clustering performance significantly.
+However, this method can be subjective and may not always yield the optimal number
+of clusters. Other methods, such as silhouette analysis or gap statistics, can also be used
+to determine the optimal number of clusters.
+Marking Scheme:
+ˆ 2 points for stating that K-Means is an unsupervised method and the best value K
+will never be given beforehand.
+ˆ 3 points for giving and explaining a way to determine a suitable K.
+ˆ -1 point for stating why the limitation is critical for K-Means clustering but the
+answer is minor.
+ˆ -1 point for paraphrasing the prompt.
+ˆ -1 point for using only SSE to evaluate the model and use the K at minimum SSE.', ARRAY['KNN and Clustering']::TEXT[], 'KNN and Clustering', 'KNN and Clustering', ARRAY['KNN and Clustering']::TEXT[], ARRAY['manual_computation', 'distance_calculation', 'algorithm_tracing']::TEXT[], 'hard', '', '', ''),
+    ('COMP2211-2023-spring-midterm', '6', NULL, 6, 'long_question', 'long_answer', 'Problem 6 [12 points] Perceptron
+Given the following training dataset and test dataset:
+Training
+Test
+x1
+-2
+-1
+x2
+-2
+-1
+T
+-1
+-1
+-1
+-1
+(a)
+[2 points] Suppose we use the training dataset to train a perceptron. At some point
+during the training, the weights and bias of the perceptron have not changed after four
+consecutive updates, but the current epoch is not yet finished.
+Should we stop the
+training? Explain why.
+(b)
+[2 points]
+After the training has converged, what is the range of possible accuracy on the test
+dataset?
+A. 50%
+B. 50% ∼75%
+C. 50% ∼100%
+D. 25% ∼75%
+E. 0% ∼100%
+(c)
+[2 points] Consider the following implementation of a perceptron:
+import numpy as np
+def fit(X, T, W0, b0, eta, max_epochs):
+W = np.ones(X.shape[0]) * W0
+b = b0
+for epoch in range(max_epochs):
+for i in range(T.shape[0]):
+O = predict(X, W, b)
+E = T[i] - O
+W = W + E * X
+b = b + E
+return W, b
+def predict(X, W, b):
+Z = np.dot(X, W) + b
+O = (Z <= 0) * 2 - 1
+return O
+The predict function takes three inputs:
+ˆ A 1-D or 2-D NumPy array, X, storing test example(s). The shape of X is (d,) or (n,
+d) where n is the number of test examples and d is the number of features;
+ˆ A 1-D NumPy array, W, storing the weights of the perceptron. The shape of W is (d,)
+where d is the number features;
+ˆ A bias value, b, for the perceptron.
+Based on how the predict function is implemented, what is the activation function f(z)
+of this perceptron?
+(d)
+[6 points]
+The fit function takes six inputs:
+ˆ A 2-D NumPy array, X, storing training examples. The shape of X is (m, d) where
+m is the number of training examples and d is the number of features;
+ˆ A 1-D NumPy array, T, storing training targets. The shape of T is (m,) where m is
+the number training examples;
+ˆ An initial value, W0, for the weights of the perceptron;
+ˆ An initial value, b0, for the bias of the perceptron;
+ˆ The learning rate, eta;
+ˆ The maximum number of training epochs, max epochs.
+Given these inputs, however, the fit function is not implemented correctly. Identify all
+the errors by giving the line numbers that cause the errors, and propose ways to fix them.
+You may find the following attribute or functions useful for this question.
+ˆ numpy.ones(shape)
+Return a new array of given shape, shape, filled with ones.
+ˆ range(stop)
+Return a sequence of numbers starting from 0, incrementing by 1, and ending at the
+value stop.
+ˆ ndarray.shape
+Tuple of array dimensions.
+ˆ numpy.dot(a, b)
+Return the dot product of a and b if a and b are both 1-D arrays. If a is an N-D
+array and b is a 1-D array, it is a sum product over the last axis of a and b is
+returned.', 12, 15, NULL::jsonb, NULL, NULL, 'Problem 6 [12 points] Perceptron
+Given the following training dataset and test dataset:
+Training
+Test
+x1
+-2
+-1
+x2
+-2
+-1
+T
+-1
+-1
+-1
+-1
+(a)
+[2 points] Suppose we use the training dataset to train a perceptron. At some point
+during the training, the weights and bias of the perceptron have not changed after four
+consecutive updates, but the current epoch is not yet finished. Should we stop the train-
+ing? Explain why.
+Answer:
+Yes, the training has converged so we should stop it from wasting time and resources.
+Marking Scheme:
+ˆ 1 point for stating we should stop the training.
+ˆ 1 point for giving correct explanations.
+(b)
+[2 points]
+After the training has converged, what is the range of possible accuracy on the test
+dataset?
+A. 50%
+B. 50% ∼75%
+C. 50% ∼100%
+D. 25% ∼75%
+E. 0% ∼100%
+Answer:
+D
+Marking Scheme:
+ˆ 2 points for the correct answer.
+(c)
+[2 points] Consider the following implementation of a perceptron:
+import numpy as np
+def fit(X, T, W0, b0, eta, max_epochs):
+W = np.ones(X.shape[0]) * W0
+b = b0
+for epoch in range(max_epochs):
+for i in range(T.shape[0]):
+O = predict(X, W, b)
+E = T[i] - O
+W = W + E * X
+b = b + E
+return W, b
+def predict(X, W, b):
+Z = np.dot(X, W) + b
+O = (Z <= 0) * 2 - 1
+return O
+The predict function takes three inputs:
+ˆ A 1-D or 2-D NumPy array, X, storing test example(s). The shape of X is (d,) or (n,
+d) where n is the number of test examples and d is the number of features;
+ˆ A 1-D NumPy array, W, storing the weights of the perceptron. The shape of W is (d,)
+where d is the number features;
+ˆ A bias value, b, for the perceptron.
+Based on how the predict function is implemented, what is the activation function f(z)
+of this perceptron?
+Answer:
+f(z) =
+
+
+
+if z ≤0
+−1
+otherwise
+Marking Scheme:
+ˆ 2 points for giving the correct activation function.
+(d)
+[6 points]
+The fit function takes six inputs:
+ˆ A 2-D NumPy array, X, storing training examples. The shape of X is (m, d) where
+m is the number of training examples and d is the number of features;
+ˆ A 1-D NumPy array, T, storing training targets. The shape of T is (m,) where m is
+the number training examples;
+ˆ An initial value, W0, for the weights of the perceptron;
+ˆ An initial value, b0, for the bias of the perceptron;
+ˆ The learning rate, eta;
+ˆ The maximum number of training epochs, max epochs.
+Given these inputs, however, the fit function is not implemented correctly. Identify all
+the errors by giving the line numbers that cause the errors, and propose ways to fix them.
+You may find the following attribute or functions useful for this question.
+ˆ numpy.ones(shape)
+Return a new array of given shape, shape, filled with ones.
+ˆ range(stop)
+Return a sequence of numbers starting from 0, incrementing by 1, and ending at the
+value stop.
+ˆ ndarray.shape
+Tuple of array dimensions.
+ˆ numpy.dot(a, b)
+Return the dot product of a and b if a and b are both 1-D arrays. If a is an N-D
+array and b is a 1-D array, it is a sum product over the last axis of a and b is
+returned.
+Answer:
+ˆ On line 4, change X.shape[0] into X.shape[1]
+ˆ On line 8 and 10, change X into X[i]
+ˆ On line 9, multiply the right-hand side with eta
+ˆ On line 9, change T[i]-O into O-T[i], or equivalently, on line 10 and 11, change +
+into -.
+The resulting fit function is as follows:
+def fit(X, T, W0, b0, eta, max_epochs):
+W = np.ones(X.shape[1]) * W0
+b = b0
+for epoch in range(max_epochs):
+for i in range(T.shape[0]):
+O = predict(X[i], W, b)
+E = eta * (O - T[i])
+W = W + E * X[i]
+b = b + E
+return W, b
+Other equivalent solutions also exist.
+Marking Scheme:
+ˆ 2 points for each error; 1 point for identifying the error (pointing out only the line
+number does not count), and 1 point for correctly fixing the error. Partial points are
+given to incomplete fixes of an error.
+ˆ For fixes that correctly fixed an error but accidentally introduced other errors, points
+are partially deducted. No extra points are deducted for purely irrelevant/erroneous
+modifications of the code.
+ˆ The final mark is capped at 6 points.', ARRAY['Perceptron and MLP']::TEXT[], 'Perceptron and MLP', 'Perceptron and MLP', ARRAY['Perceptron and MLP']::TEXT[], ARRAY['forward_pass', 'backpropagation', 'weight_update']::TEXT[], 'medium', '', '', ''),
+    ('COMP2211-2023-spring-midterm', '7', NULL, 7, 'long_question', 'long_answer', 'Problem 7 [16 points] Multilayer Perceptron
+(a)
+[4 points] Consider the following dataset
+x1
+x2
+F(x1,x2)
+Given the following single layer perceptron, where activation function f is defined as
+f(z) =
+(
+1, for z ≥0
+0, otherwise
+Can we classify all the data points correctly using this perceptron? If yes, give a set of
+possible parameters. If no, briefly explain the reason.
+(b)
+[2 points] Consider the following dataset
+x1
+x2
+F(x1,x2)
+Using the same perceptron architecture in part (a), can we classify all the data points
+correctly using this perceptron? If yes, give a set of possible parameters. If no, briefly
+explain the reason.
+(c)
+[10 points] Consider the same dataset in part (b), given the following multilayer percep-
+tron, where activation function f is defined as identical with that in part (a),
+can we classify all the data points correctly using this multilayer perceptron? If yes, give
+a set of possible parameters. If no, briefly explain the reason.
+Hint: You don’t need to use gradient descent to calculate the results.
+You may try
+to find a set of parameters to make the data points linear separable after the first layer
+mapping.
+-------------------- END OF PAPER --------------------', 16, 18, NULL::jsonb, NULL, NULL, 'Problem 7 [16 points] Multilayer Perceptron
+(a)
+[4 points] Consider the following dataset
+x1
+x2
+F(x1,x2)
+Given the following single layer perceptron, where activation function f is defined as
+f(z) =
+(
+1, for z ≥0
+0, otherwise
+Can we classify all the data points correctly using this perceptron? If yes, give a set of
+possible parameters. If no, briefly explain the reason.
+Answer:
+Yes, the four data points are linearly separable.
+One possible set of parameters is
+w1 = 1, w2 = 1, θ = −1.5.
+Marking Scheme:
+ˆ 1 point for ‘yes’ answer.
+ˆ 3 points for correct parameter (no partial points).
+(b)
+[2 points] Consider the following dataset
+Using the same perceptron architecture in part (a), can we classify all the data points
+correctly using this perceptron? If yes, give a set of possible parameters. If no, briefly
+x1
+x2
+F(x1,x2)
+explain the reason.
+Answer:
+No, the 4 data points are not linearly separable, so they can’t be fit with 100% accuracy
+using single layer perceptron.
+Marking Scheme:
+ˆ 1 point for ‘yes’ answer.
+ˆ 3 points for correct parameters (no partial points).
+(c)
+[10 points] Consider the same dataset in part (b), given the following multilayer percep-
+tron, where activation function f is defined as identical with that in part (a),
+can we classify all the data points correctly using this multilayer perceptron? If yes, give
+a set of possible parameters. If no, briefly explain the reason.
+Hint: You don’t need to use gradient descent to calculate the results.
+You may try
+to find a set of parameters to make the data points linear separable after the first layer
+mapping.
+Answer:
+Yes, one possible set of parameters can be obtained as following procedure:
+we first use two linear functions to seperate the 4 datatpoints, e.g, w1 = −1, w2 = 1, θ1 =
+−0.5, w3 = −1, w4 = 1, θ2 = 0.5, the related diagram is as followed,
+(1,1)
+(0,1)
+(x1)
+(x2)
+(1,0)
+(0,0)
+𝑤1𝑥1 + 𝑤2𝑥2 + 𝜃1
+𝑤3𝑥1 + 𝑤4𝑥2 + 𝜃2
+After first layer mapping, the result will be
+x1
+x2
+y1 = w1x1 + w2x2 + θ1
+y2 = w3x1 + w4x2 + θ2
+f(y1)
+f(y2)
+F(x1, x2)
+-0.5
+0.5
+0.5
+1.5
+-1
+-0.5
+-0.5
+0.5
+For last three columns of the table, we can see the datapoints are linear separable after
+the first layer, where we can set w5 = −1, w6 = 1, θ3 = −0.5 to fulfill the requirement.
+To sum up, one possible set of parameters are
+w1 = −1, w2 = 1, θ1 = −0.5, w3 = −1, w4 = 1, θ2 = 0.5, w5 = −1, w6 = 1, θ3 = −0.5
+Marking Scheme:
+ˆ 1 point for ‘yes’ answer.
+ˆ 6 points if first layer mapping makes all data points linear separable (3 points if
+the data points are ‘almost’ linear separable, e.g., one set of parameters are correct
+except that the signs are opposite).
+ˆ 3 points for correct second layer parameters.
+-------------------- END OF PAPER --------------------', ARRAY['Perceptron and MLP']::TEXT[], 'Perceptron and MLP', 'Perceptron and MLP', ARRAY['Perceptron and MLP']::TEXT[], ARRAY['forward_pass', 'backpropagation', 'weight_update']::TEXT[], 'hard', '', '', ''),
+    ('COMP2211-2024-spring-midterm', '1', NULL, 1, 'true_false', 'true_false', 'Problem 1 [5 points] True/False Questions
+Indicate whether the following statements are true or false by putting T or F in the given
+table. You get 0.5 point for each correct answer.
+Question
+(a)
+(b)
+(c)
+(d)
+(e)
+(f)
+(g)
+(h)
+(i)
+(j)
+Answer
+(a) In the following code
+import numpy as np
+a = np.array([0, 1, 1, 2, 5, 5, 3])
+b = np.array([0, 1, 2, 3, 4, 5])
+c = (b == a.reshape(7, 1))
+The array c has the shape (7, 1).
+(b) After executing the following block of code:
+import numpy as np
+a = np.array([[1, 2], [3, 4], [5, 6]])
+b = np.array([[1, 2, 3], [0, 0, 0], [1, 0, 0]])
+c = a.dot(b)
+The array c is array([[22, 28], [0, 0], [1, 2]]).
+(c) The Na¨ıve Bayes Classifier operates under the assumption that the presence of a partic-
+ular feature in a class is independent of the presence of any other feature.
+(d) In Na¨ıve Bayes, we assume that P(B|e1, e2) = P(B|e1)P(B|e2) where B is our belief
+and (e1, e2) are evidence.
+(e) In Na¨ıve Bayes, given P(B = b), P(e1|B = b), and P(e2|B = b) for each possible belief
+b, we can compute P(B = b′|e1, e2) for any b′.
+(f) K-Nearest Neighbors Classifier CANNOT handle data with categorical features since
+it is difficult to find the distance between categorical features.
+(g) In K-Nearest Neighbors for binary classification, odd values of k are usually preferred.
+(h) A 6-fold cross validation for K-nearest neighbors algorithm means that for each value
+of K, we randomly select 1/6 of the training data as the validation set to evaluate the
+model which is trained by the remaining (5/6) of the training data.
+(i) The result of the K-Means Clustering DOES NOT depend on the initial centroids.
+(j) It is possible that after new cluster centroids are computed by the K-Means Clustering
+Algorithm, a cluster centroid may be associated with an empty cluster (i.e., with zero
+points in it).', 5, 3, NULL::jsonb, NULL, NULL, 'Problem 1 [5 points] True/False Questions
+Indicate whether the following statements are true or false by putting T or F in the given
+table. You get 0.5 point for each correct answer.
+Question
+(a)
+(b)
+(c)
+(d)
+(e)
+(f)
+(g)
+(h)
+(i)
+(j)
+Answer
+(a) In the following code
+import numpy as np
+a = np.array([0, 1, 1, 2, 5, 5, 3])
+b = np.array([0, 1, 2, 3, 4, 5])
+c = (b == a.reshape(7, 1))
+The array c has the shape (7, 1).
+(b) After executing the following block of code:
+import numpy as np
+a = np.array([[1, 2], [3, 4], [5, 6]])
+b = np.array([[1, 2, 3], [0, 0, 0], [1, 0, 0]])
+c = a.dot(b)
+The array c is array([[22, 28], [0, 0], [1, 2]]).
+(c) The Na¨ıve Bayes Classifier operates under the assumption that the presence of a partic-
+ular feature in a class is independent of the presence of any other feature.
+(d) In Na¨ıve Bayes, we assume that P(B|e1, e2) = P(B|e1)P(B|e2) where B is our belief
+and (e1, e2) are evidence.
+(e) In Na¨ıve Bayes, given P(B = b), P(e1|B = b), and P(e2|B = b) for each possible belief
+b, we can compute P(B = b′|e1, e2) for any b′.
+(f) K-Nearest Neighbors Classifier CANNOT handle data with categorical features since
+it is difficult to find the distance between categorical features.
+(g) In K-Nearest Neighbors for binary classification, odd values of k are usually preferred.
+(h) A 6-fold cross validation for K-nearest neighbors algorithm means that for each value
+of K, we randomly select 1/6 of the training data as the validation set to evaluate the
+model which is trained by the remaining (5/6) of the training data.
+(i) The result of the K-Means Clustering DOES NOT depend on the initial centroids.
+(j) It is possible that after new cluster centroids are computed by the K-Means Clustering
+Algorithm, a cluster centroid may be associated with an empty cluster (i.e., with zero
+points in it).
+Question
+(a)
+(b)
+(c)
+(d)
+(e)
+(f)
+(g)
+(h)
+(i)
+(j)
+Answer
+F
+F
+T
+F
+T
+F
+T
+F
+F
+T', ARRAY['True/False']::TEXT[], 'True/False', 'True/False', ARRAY['True/False']::TEXT[], ARRAY['concept_check', 'rapid_reasoning']::TEXT[], 'easy', '', '', ''),
+    ('COMP2211-2024-spring-midterm', '2', NULL, 2, 'long_question', 'coding', 'Problem 2 [19 points] Advanced Python for Artificial Intelligence
+(a)
+[13 points] Consider the following NumPy arrays:
+import numpy as np
+# np.arange(start, stop)
+# - return an array of evenly spaced values within the half-open interval
+#
+[start,stop), the default step size is 1.
+# np.ones(shape):
+# - return a new array of given shape, filled with ones.
+A = np.arange(5,15)
+B = np.ones((3,2))
+C = np.array([[[0,1,2,3],
+[4,5,6,7]],
+[[8,9,10,11],
+[12,13,14,15]],
+[[16,17,18,19],
+[20,21,22,23]]])
+Suppose the following Python statements are running consecutively. Write the output
+for each of the following Python statements.
+If the output is an empty array, write
+“Empty Array”. If an error occurs, write “Error”.
+(i) print(A[2:-2:3])
+(ii) # np.ndarray.reshape(shape)
+# - return an array containing the same data with a new shape.
+print(B.reshape(3,-1,2))
+(iii) Please write a line of Python code to get the same result in part (a)(ii) by using
+np.expand dims on B.
+# np.expand_dims(a, axis)
+# - insert a new axis to a that will appear at the axis position in the
+#
+expanded array shape. Return an array that is the view of a with the
+#
+number of dimensions increased.
+(iv) Please write a line of Python code to create the array C by using the functions
+np.ndarray.reshape and np.arange().
+(v) # np.mean(a, axis)
+# - return a new array containing the average values of a over the specified axis.
+print(np.mean(C,axis = 2))
+(vi) # np.transpose(a, axes)
+# - return an array with axes transposed in a.
+print(np.transpose(C, (1, 0, 2)))
+(vii) # a@b
+# - return the matrix multiplication of the two arrays a and b.
+D = A.reshape(2, 5)
+print(B@D)
+(viii) # np.dot(a,b)
+# - return the dot product of the two arrays a and b. If both a and b are
+#
+1-D arrays, it is the inner product of vectors and returns a scalar.
+#
+If both a and b are bool arrays, the output is in bool datatype.
+# np.ndarray.astype(dtype)
+# - return the copy of the array with a specified dtype.
+E = A < 12
+F = A % 5 == 0
+print(np.dot(E, F).astype(int))
+(ix) print(np.dot(E, F.astype(int)))
+(x) print( C / B )
+(xi) # np.newaxis
+# - increase the dimension of an array by adding new axis.
+print(C / B[..., np.newaxis])
+(xii) G = C[0, :, 2:]
+print(G)
+(xiii) C[0, 1, 2] = 100
+print(G)
+Scheme:
+ˆ 1 point for giving the correct answer for each part. 13 points in total.
+(b)
+[6 points] In recommendation systems, we often recommend items similar to what the
+user likes. This is known as content-based filtering. In content-based filtering, an item is
+represented as a feature vector (or a 1D array). Given the following code which computes
+the cosine similarity between feature vectors of items in explicit loops:
+def compute_cosine_similarity_loops(X):
+num_items, num_features = X.shape
+# --- BLOCK TO REWRITE ---
+X_normalized = np.zeros((num_items, num_features))
+for i in range(num_items):
+feature_norm = np.sqrt(np.sum(X[i] ** 2))
+X_normalized[i] = X[i] / feature_norm
+similarities = np.zeros((num_items, num_items))
+for i in range(num_items):
+for j in range(num_items):
+similarities[i, j] = np.sum(X_normalized[i] * X_normalized[j])
+# --- BLOCK TO REWRITE ---
+return similarities
+X = np.array([[0, 2], [1, -1], [1, 1]])
+print(compute_cosine_similarity_loops(X))
+# Output:
+# [[ 1.
+-0.70710678
+0.70710678]
+#
+[-0.70710678
+1.
+0.
+]
+#
+[ 0.70710678
+0.
+1.
+]]
+Rewrite the block of code between the comment lines “# --- BLOCK TO REWRITE --- ”
+using no explicit loops in the space provided. You may find the following functions
+useful for this question.
+ˆ Element-wise square of an array:
+np.square(A)
+– A is the input array
+This is equivalent to A ** 2.
+ˆ Element-wise square root of an array:
+np.sqrt(A)
+– A is the input array
+ˆ Sum of array elements over a given axis:
+np.sum(A, axis)
+– A is the input array
+– axis is the axis across which the array is summed
+ˆ Insert a new axis of size 1 to an array:
+np.expand_dims(A, axis)
+– A is the input array
+– axis is the position where the axis is to be inserted
+If axis is 0, this is equivalent to A[np.newaxis] and A[None]. If axis is 1, this is
+equivalent to A[:, np.newaxis] and A[:, None].
+ˆ Transpose of an array:
+np.transpose(A)
+– A is the input array
+This is equivalent to A.T.
+ˆ Matrix multiplication:
+np.matmul(A, B)
+– A is the left array for matrix multiplication
+– B is the right array for matrix multiplication
+This is equivalent to A @ B.
+More information on matrix multiplication: suppose A.shape[1] == B.shape[0],
+then
+C = np.matmul(A, B)
+means that for each i and j,
+C[i, j] == np.sum(A[i] * B[:, j])
+Write your code in the space below.', 19, 4, NULL::jsonb, NULL, NULL, 'Problem 2 [19 points] Advanced Python for Artificial Intelligence
+(a)
+[13 points] Consider the following NumPy arrays:
+import numpy as np
+# np.arange(start, stop)
+# - return an array of evenly spaced values within the half-open interval
+#
+[start,stop), the default step size is 1.
+# np.ones(shape):
+# - return a new array of given shape, filled with ones.
+A = np.arange(5,15)
+B = np.ones((3,2))
+C = np.array([[[0,1,2,3],
+[4,5,6,7]],
+[[8,9,10,11],
+[12,13,14,15]],
+[[16,17,18,19],
+[20,21,22,23]]])
+Suppose the following Python statements are running consecutively. Write the output
+for each of the following Python statements.
+If the output is an empty array, write
+“Empty Array”. If an error occurs, write “Error”.
+(i) print(A[2:-2:3])
+Answer:
+[7 10]
+(ii) # np.ndarray.reshape(shape)
+# - return an array containing the same data with a new shape.
+print(B.reshape(3,-1,2))
+Answer:
+[[[1 1]]
+[[1 1]]
+[[1 1]]]
+(iii) Please write a line of Python code to get the same result in part (a)(ii) by using
+np.expand dims on B.
+# np.expand_dims(a, axis)
+# - insert a new axis to a that will appear at the axis position in the
+#
+expanded array shape. Return an array that is the view of a with the
+#
+number of dimensions increased.
+Answer:
+np.expand dims(B, 1)
+(also correct if adding print())
+(iv) Please write a line of Python code to create the array C by using the functions
+np.ndarray.reshape and np.arange().
+Answer:
+np.arange(24).reshape(3, 2, 4)
+(also correct if adding C = )
+(v) # np.mean(a, axis)
+# - return a new array containing the average values of a over the specified axis.
+print(np.mean(C,axis = 2))
+Answer:
+[[1.5 5.5]
+[9.5 13.5]
+[17.5 21.5]]
+(vi) # np.transpose(a, axes)
+# - return an array with axes transposed in a.
+print(np.transpose(C, (1, 0, 2)))
+Answer:
+[[[0 1 2 3]
+[8 9 10 11]
+[16 17 18 19]]
+[[4 5 6
+7]
+[12 13 14 15]
+[20 21 22 23]]]
+(vii) # a@b
+# - return the matrix multiplication of the two arrays a and b.
+D = A.reshape(2, 5)
+print(B@D)
+Answer:
+[[15 17 19 21 23]
+[15 17 19 21 23]
+[15 17 19 21 23]]
+(viii) # np.dot(a,b)
+# - return the dot product of the two arrays a and b. If both a and b are
+#
+1-D arrays, it is the inner product of vectors and returns a scalar.
+#
+If both a and b are bool arrays, the output is in bool datatype.
+# np.ndarray.astype(dtype)
+# - return the copy of the array with a specified dtype.
+E = A < 12
+F = A % 5 == 0
+print(np.dot(E, F).astype(int))
+Answer:
+(ix) print(np.dot(E, F.astype(int)))
+Answer:
+(x) print( C / B )
+Answer:
+Error
+(xi) # np.newaxis
+# - increase the dimension of an array by adding new axis.
+print(C / B[..., np.newaxis])
+Answer:
+[[[0,1,2,3],
+[4,5,6,7],
+[[8,9,10,11],
+[12,13,14,15]],
+[[16,17,18,19],
+[20,21,22,23]]]
+(xii) G = C[0, :, 2:]
+print(G)
+Answer:
+[[2 3]
+[6 7]]
+(xiii) C[0, 1, 2] = 100
+print(G)
+Answer:
+[[2 3]
+[100 7]]
+Scheme:
+ˆ 1 point for giving the correct answer for each part. 13 points in total.
+(b)
+[6 points] In recommendation systems, we often recommend items similar to what the
+user likes. This is known as content-based filtering. In content-based filtering, an item is
+represented as a feature vector (or a 1D array). Given the following code which computes
+the cosine similarity between feature vectors of items in explicit loops:
+def compute_cosine_similarity_loops(X):
+num_items, num_features = X.shape
+# --- BLOCK TO REWRITE ---
+X_normalized = np.zeros((num_items, num_features))
+for i in range(num_items):
+feature_norm = np.sqrt(np.sum(X[i] ** 2))
+X_normalized[i] = X[i] / feature_norm
+similarities = np.zeros((num_items, num_items))
+for i in range(num_items):
+for j in range(num_items):
+similarities[i, j] = np.sum(X_normalized[i] * X_normalized[j])
+# --- BLOCK TO REWRITE ---
+return similarities
+X = np.array([[0, 2], [1, -1], [1, 1]])
+print(compute_cosine_similarity_loops(X))
+# Output:
+# [[ 1.
+-0.70710678
+0.70710678]
+#
+[-0.70710678
+1.
+0.
+]
+#
+[ 0.70710678
+0.
+1.
+]]
+Rewrite the block of code between the comment lines “# --- BLOCK TO REWRITE --- ”
+using no explicit loops in the space provided. You may find the following functions
+useful for this question.
+ˆ Element-wise square of an array:
+np.square(A)
+– A is the input array
+This is equivalent to A ** 2.
+ˆ Element-wise square root of an array:
+np.sqrt(A)
+– A is the input array
+ˆ Sum of array elements over a given axis:
+np.sum(A, axis)
+– A is the input array
+– axis is the axis across which the array is summed
+ˆ Insert a new axis of size 1 to an array:
+np.expand_dims(A, axis)
+– A is the input array
+– axis is the position where the axis is to be inserted
+If axis is 0, this is equivalent to A[np.newaxis] and A[None]. If axis is 1, this is
+equivalent to A[:, np.newaxis] and A[:, None].
+ˆ Transpose of an array:
+np.transpose(A)
+– A is the input array
+This is equivalent to A.T.
+ˆ Matrix multiplication:
+np.matmul(A, B)
+– A is the left array for matrix multiplication
+– B is the right array for matrix multiplication
+This is equivalent to A @ B.
+More information on matrix multiplication: suppose A.shape[1] == B.shape[0],
+then
+C = np.matmul(A, B)
+means that for each i and j,
+C[i, j] == np.sum(A[i] * B[:, j])
+Write your code in the space below.
+Answer:
+X_norm = np.sqrt(np.sum(X ** 2, 1))
+# (n,)
+# 2 points
+X_normalized = X / X_norm[:, None]
+# (n, d) # 2 points
+similarities = X_normalized @ X_normalized.T
+# (n, n) # 2 points
+The solution is not unique.', ARRAY['Python Fundamentals']::TEXT[], 'Python Fundamentals', 'Python Fundamentals', ARRAY['Python Fundamentals']::TEXT[], ARRAY['implementation', 'code_tracing', 'debugging']::TEXT[], 'hard', '', '', ''),
+    ('COMP2211-2024-spring-midterm', '3', NULL, 3, 'long_question', 'coding', 'Problem 3 [16 points] Model Evaluation and Advanced Python Programming
+In this problem, you need to implement the evaluation metrics for multi-class classifiers.
+Specifically, you need to implement the confusion matrix, accuracy, precision, recall, and
+macro F1 score. We provide the related definitions and formulas as follows.
+(a)
+[5.5 points] Suppose there is a test dataset consisting of 10 data points, their actual
+classes are [2, 1, 1, 2, 0, 1, 0, 0, 1, 1], and their predicted classes by a classifier model
+are [2, 1, 2, 1, 0, 2, 1, 0, 0, 0]. What is the confusion matrix for the prediction results?
+What are TP, TN, FP, FN for each class?
+Confusion matrix: a table that summarized actual labels and the predictions of classifi-
+cation. For multi-class classification, the confusion matrix has the shape (num classes,
+num classes) that records the number of occurrences between actual labels and the pre-
+dictions. The classes are listed in the same order in the rows as in the columns, therefore
+the correctly classified elements are located on the main diagonal.
+For each class i, TPi, TNi, FPi, FNi represent the instance numbers of true positive,
+true negative, false positive, and false negative, respectively.
+ˆ True positive: A test result where the classifier correctly predicts the positive class
+as positive.
+ˆ True negative: A test result where the classifier correctly predicts the negative class
+as negative.
+ˆ False positive: A test result where the classifier incorrectly predicts the negative class
+as positive.
+ˆ False negative: A test result where the classifier incorrectly predicts the positive class
+as negative.
+Please fill in the confusion matrix (the rows represent actual class and the columns
+represent predicted class) and the TP, TN, FP, FN table.
+Predicted
+Actual
+Class
+Class
+TP
+TN
+FP
+FN
+(b)
+[1 point] What is the accuracy score of the classifier model on the above test data?
+Accuracy =
+PN
+i=1 TPi
+num testdata, where N is the number of classes.
+(c)
+[2.5 points] What are the precisions, recalls, and F1 scores for each class of the classifier
+model on the above test data? Please fill in the table using fractions or keep 3 decimals.
+For each class i, Precisioni =
+TPi
+TPi+FPi
+For each class i, Recalli =
+TPi
+TPi+FNi
+For each class i, F1i = 2·Precisioni·Recalli
+Precisioni+Recalli
+Class
+Precision
+Recall
+F1 score
+(d)
+[1 point] What is the macro F1 score of the classifier model on the above test data?
+Please keep 3 decimals in your answer.
+The macro F1 score is the unweighted mean of the F1 scores of all classes:
+Macro-F1 =
+PN
+i=1 F1i
+N
+, where N is the number of classes.
+(e) [6 points] Given two NumPy 1D arrays with the same shape (num_testdata,): test_actual
+and test_predict, representing the actual class labels and predicted class labels for the
+test data, please implement the following functions by filling in the blanks. For each
+TODO, please use a one-line Python expression.
+import numpy as np
+def generate_confusion_matrix(test_actual, test_predict):
+# TODO 1: Get num_classes, the number of classes in the test data.
+# Note that the classes in the test_actual and test_predict are represented
+# in integer indices from [0, 1, ..., num_classes - 1].
+num_classes = _________________________________________
+confusion_matrix = np.zeros((num_classes, num_classes))
+# TODO 2: Get the values of confusion_matrix, where the rows represent
+# actual class and the columns represent predicted class.
+for i in range(0, num_classes):
+for j in range(0, num_classes):
+confusion_matrix[i, j] = _____________________________________
+return confusion_matrix
+def calculate_evaluation_metrics(test_actual, test_predict):
+confusion_matrix = generate_confusion_matrix(test_actual, test_predict)
+# TODO 3: Get the accuracy score, which is a scalar value.
+accuracy = ______________________________________________________
+# TODO 4: Get the precisions for all classes, which is a 1D array
+# with shape (num_classes, ).
+precision = _____________________________________________________
+# TODO 5: Get the recalls for all classes, which is a 1D array
+# with shape (num_classes, ).
+recall = ________________________________________________________
+# TODO 6: Get the macro F1 score, which is a scalar value.
+macro_f1 = ______________________________________________________
+return accuracy, precision, recall, macro_f1
+Note:
+ˆ An expression is a combination of values, variables, operators, and calls to functions.
+ˆ There must be no explicit loops in your expression.
+ˆ Your implemented functions should work with any number of test data points and
+any number of classes.
+ˆ You cannot use any variable that is not defined inside the function or any global
+variable.
+You may find the following attribute or functions useful for this problem.
+ˆ np.max(a, axis = None)
+- return the maximum of the array a along the given axis. If axis is None, the result
+is a scalar value.
+ˆ np.ndarray.sum(axis = None)
+- return the sum of the array over the given axis. If axis is None, the result is a
+scalar value.
+ˆ np.ndarray.diagonal()
+- if the array is 2D, then a 1D array containing the diagonal elements is returned.
+ˆ np.mean(a, axis)
+- return a new array containing the average values of a over the specified axis. If
+axis is None, the result is a scalar value.
+Write your code in the space below.', 16, 10, NULL::jsonb, NULL, NULL, 'Problem 3 [16 points] Model Evaluation and Advanced Python Programming
+In this problem, you need to implement the evaluation metrics for multi-class classifiers.
+Specifically, you need to implement the confusion matrix, accuracy, precision, recall, and
+macro F1 score. We provide the related definitions and formulas as follows.
+(a)
+[5.5 points] Suppose there is a test dataset consisting of 10 data points, their actual
+classes are [2, 1, 1, 2, 0, 1, 0, 0, 1, 1], and their predicted classes by a classifier model
+are [2, 1, 2, 1, 0, 2, 1, 0, 0, 0]. What is the confusion matrix for the prediction results?
+What are TP, TN, FP, FN for each class?
+Confusion matrix: a table that summarized actual labels and the predictions of classifi-
+cation. For multi-class classification, the confusion matrix has the shape (num classes,
+num classes) that records the number of occurrences between actual labels and the pre-
+dictions. The classes are listed in the same order in the rows as in the columns, therefore
+the correctly classified elements are located on the main diagonal.
+For each class i, TPi, TNi, FPi, FNi represent the instance numbers of true positive,
+true negative, false positive, and false negative, respectively.
+ˆ True positive: A test result where the classifier correctly predicts the positive class
+as positive.
+ˆ True negative: A test result where the classifier correctly predicts the negative class
+as negative.
+ˆ False positive: A test result where the classifier incorrectly predicts the negative class
+as positive.
+ˆ False negative: A test result where the classifier incorrectly predicts the positive class
+as negative.
+Please fill in the confusion matrix (the rows represent actual class and the columns
+represent predicted class) and the TP, TN, FP, FN table.
+Predicted
+Actual
+Class
+Class
+TP
+TN
+FP
+FN
+Answer:
+Predicted
+Actual
+Class
+Class
+TP
+TN
+FP
+FN
+Scheme:
+ˆ 0.25 point for each correct numeric value. 5.25 points in total.
+ˆ An extra 0.25 point is given for those who gave all the correct numeric values.
+(b)
+[1 point] What is the accuracy score of the classifier model on the above test data?
+Accuracy =
+PN
+i=1 TPi
+num testdata, where N is the number of classes.
+Answer:
+0.4
+Scheme:
+ˆ 1 point for the correct answer.
+(c)
+[2.5 points] What are the precisions, recalls, and F1 scores for each class of the classifier
+model on the above test data? Please fill in the table using fractions or keep 3 decimals.
+For each class i, Precisioni =
+TPi
+TPi+FPi
+For each class i, Recalli =
+TPi
+TPi+FNi
+For each class i, F1i = 2·Precisioni·Recalli
+Precisioni+Recalli
+Class
+Precision
+Recall
+F1 score
+Answer:
+Class
+Precision
+Recall
+F1 score
+1/2
+2/3
+4/7
+1/3
+1/5
+1/4
+1/3
+1/2
+2/5
+Scheme:
+ˆ 0.25 point for each correct numeric value (or fraction). 2.25 points in total.
+ˆ An extra 0.25 point is given for those who gave all the correct numeric values (or
+fractions).
+(d)
+[1 point] What is the macro F1 score of the classifier model on the above test data?
+Please keep 3 decimals in your answer.
+The macro F1 score is the unweighted mean of the F1 scores of all classes:
+Macro-F1 =
+PN
+i=1 F1i
+N
+, where N is the number of classes.
+Answer:
+0.407
+Scheme:
+ˆ 1 point for the correct answer.
+(e) [6 points] Given two NumPy 1D arrays with the same shape (num_testdata,): test_actual
+and test_predict, representing the actual class labels and predicted class labels for the
+test data, please implement the following functions by filling in the blanks. For each
+TODO, please use a one-line Python expression.
+import numpy as np
+def generate_confusion_matrix(test_actual, test_predict):
+# TODO 1: Get num_classes, the number of classes in the test data.
+# Note that the classes in the test_actual and test_predict are represented
+# in integer indices from [0, 1, ..., num_classes - 1].
+num_classes = _________________________________________
+confusion_matrix = np.zeros((num_classes, num_classes))
+# TODO 2: Get the values of confusion_matrix, where the rows represent
+# actual class and the columns represent predicted class.
+for i in range(0, num_classes):
+for j in range(0, num_classes):
+confusion_matrix[i, j] = _____________________________________
+return confusion_matrix
+def calculate_evaluation_metrics(test_actual, test_predict):
+confusion_matrix = generate_confusion_matrix(test_actual, test_predict)
+# TODO 3: Get the accuracy score, which is a scalar value.
+accuracy = ______________________________________________________
+# TODO 4: Get the precisions for all classes, which is a 1D array
+# with shape (num_classes, ).
+precision = _____________________________________________________
+# TODO 5: Get the recalls for all classes, which is a 1D array
+# with shape (num_classes, ).
+recall = ________________________________________________________
+# TODO 6: Get the macro F1 score, which is a scalar value.
+macro_f1 = ______________________________________________________
+return accuracy, precision, recall, macro_f1
+Note:
+ˆ An expression is a combination of values, variables, operators, and calls to functions.
+ˆ There must be no explicit loops in your expression.
+ˆ Your implemented functions should work with any number of test data points and
+any number of classes.
+ˆ You cannot use any variable that is not defined inside the function or any global
+variable.
+You may find the following attribute or functions useful for this problem.
+ˆ np.max(a, axis = None)
+- return the maximum of the array a along the given axis. If axis is None, the result
+is a scalar value.
+ˆ np.ndarray.sum(axis = None)
+- return the sum of the array over the given axis. If axis is None, the result is a
+scalar value.
+ˆ np.ndarray.diagonal()
+- if the array is 2D, then a 1D array containing the diagonal elements is returned.
+ˆ np.mean(a, axis)
+- return a new array containing the average values of a over the specified axis. If
+axis is None, the result is a scalar value.
+Write your code in the space below. Answer:
+TODO 1: np.max(test actual) + 1 or np.max(test predict) + 1
+TODO 2: (test actual == 1) & (test predict == j)).sum()
+TODO 3: confusion matrix.diagonal().sum() / confusion matrix.sum()
+TODO 4: confusion matrix.diagonal() / confusion matrix.sum(axis = 0)
+TODO 5: confusion matrix.diagonal() / confusion matrix.sum(axis = 1)
+TODO 6: np.mean(2 * precision * recall) / (precision + recall))
+Scheme:
+ˆ 1 point for each TODO. 6 points in total.', ARRAY['Evaluation and Validation']::TEXT[], 'Evaluation and Validation', 'Evaluation and Validation', ARRAY['Evaluation and Validation']::TEXT[], ARRAY['metric_computation', 'experimental_design', 'reasoning']::TEXT[], 'hard', '', '', ''),
+    ('COMP2211-2024-spring-midterm', '4', NULL, 4, 'long_question', 'long_answer', 'Problem 4 [16 points] Na¨ıve Bayes Classifier
+Based on the given training data in the table below, which includes both numerical and
+categorical attributes, make a prediction about the degree classification (DC) (i.e., First-Class
+Honors or Second-Class Honors D1 or Second-Class Honors D2, or Third-Class Honors) of
+the student with the following attribute values using Na¨ıve Bayes classifier.
+ˆ Study Attitude (SA): Serious
+ˆ Part-time Job (PTJ): No
+ˆ Average Energy Level (AEL): 7.2
+ˆ Courses Taught by Desmond and Pearl (CDP): Yes
+ˆ Number of Friends in the Study Group (NFSG): 4
+Student
+Study
+Attitude
+(SA)
+Categorical
+Part-time
+Job
+(PTJ)
+Categorical
+Average
+Energy Level
+(AEL)
+Numerical
+Courses
+Taught by
+Desmond
+and Pearl
+(CDP)
+Categorical
+Number of
+Friends in the
+Study Group
+(NFSG)
+Categorical
+Degree
+Classification
+(DC)
+Serious
+No
+8.5
+Yes
+First
+Moderate
+Yes
+6.2
+No
+Second D1
+Serious
+No
+7.8
+Yes
+Second D1
+Moderate
+Yes
+5.9
+No
+Third
+Serious
+No
+8.1
+Yes
+First
+Casual
+Yes
+6.5
+Yes
+Second D2
+Serious
+No
+7.3
+No
+Second D1
+Serious
+No
+7.9
+Yes
+First
+Moderate
+Yes
+5.7
+No
+Third
+Serious
+No
+8.2
+Yes
+First
+Casual
+Yes
+6.1
+Yes
+Second D2
+Serious
+No
+7.6
+No
+Second D2
+Moderate
+Yes
+6.4
+Yes
+Third
+Serious
+No
+8.0
+Yes
+First
+Casual
+Yes
+5.8
+No
+Second D2
+Serious
+No
+7.5
+Yes
+Second D1
+Serious
+No
+7.7
+Yes
+First
+Moderate
+Yes
+6.0
+No
+Third
+Serious
+No
+7.8
+Yes
+First
+Casual
+Yes
+6.3
+Yes
+Second D2
+Assume each categorical attribute has the following possible values:
+ˆ Study Attitude: Serious, Moderate, Casual
+ˆ Part-time Job: Yes, No
+ˆ Courses Taught by Desmond and Pearl: Yes, No
+ˆ Number of Friends in the Study Group: 1, 2, 3, 4, 5, 6
+Assume that the numerical data follow a Gaussian distribution:
+f(x) =
+σ
+√
+2πexp
+
+−(x −µ)2
+2σ2
+
+where
+numerical training data = (x1, x2, . . . , xn)
+µ = 1
+n
+n
+X
+i=1
+xi,
+σ =
+v
+u
+u
+t
+n −1
+n
+X
+i=1
+(xi −µ)2
+If needed, apply 1-Laplace Smoothing to the likelihood probabilities of the affected feature
+only. The affected feature means that the categorical feature has a category given some
+belief in the test dataset, which was NOT observed in the training dataset. Please provide
+all the steps.
+You may find the following equation useful for this question:
+BNB = argmaxBiP(Bi)(P(e1|Bi)P(e2|Bi)P(e3|Bi) · · · P(ed|Bi))
+.(a)
+[4 points] Calculate the mean (µ) and standard deviation (σ) of the Average Energy
+Level (AEL) of Degree Classification: First-Class Honors and Second-Class Honors D1.
+(b)
+[3 points] Calculate the test data sample’s likelihoods of Average Energy Level (AEL)
+of First-Class Honors and Second-Class Honors D1.
+(c) [4 points] Calculate the test data sample’s likelihoods of Study Attitude (SA), Part-time
+Job (PTJ), Courses Taught by Desmond and Pearl (CDP), and Number of Friends in
+the Study Group (NFSG) of all Degree Classification(s) (DC).
+(d)
+[2 points] Calculate the prior probabilities.
+(e)
+[3 points] Finally, calculate the posterior probabilities and make the prediction.
+Assume that the likelihood of Average Energy Level (AEL) of Second-Class Honors D2
+and Third-Class Honors are:
+ˆ P(AEL=7.2|Second-Class Honors D2) = 0.32515
+ˆ P(AEL=7.2|Third-Class Honors) = 0.000334158', 16, 14, NULL::jsonb, NULL, NULL, 'Problem 4 [16 points] Na¨ıve Bayes Classifier
+Based on the given training data in the table below, which includes both numerical and
+categorical attributes, make a prediction about the degree classification (DC) (i.e., First-Class
+Honors or Second-Class Honors D1 or Second-Class Honors D2, or Third-Class Honors) of
+the student with the following attribute values using Na¨ıve Bayes classifier.
+ˆ Study Attitude (SA): Serious
+ˆ Part-time Job (PTJ): No
+ˆ Average Energy Level (AEL): 7.2
+ˆ Courses Taught by Desmond and Pearl (CDP): Yes
+ˆ Number of Friends in the Study Group (NFSG): 4
+Student
+Study
+Attitude
+(SA)
+Categorical
+Part-time
+Job
+(PTJ)
+Categorical
+Average
+Energy Level
+(AEL)
+Numerical
+Courses
+Taught by
+Desmond
+and Pearl
+(CDP)
+Categorical
+Number of
+Friends in the
+Study Group
+(NFSG)
+Categorical
+Degree
+Classification
+(DC)
+Serious
+No
+8.5
+Yes
+First
+Moderate
+Yes
+6.2
+No
+Second D1
+Serious
+No
+7.8
+Yes
+Second D1
+Moderate
+Yes
+5.9
+No
+Third
+Serious
+No
+8.1
+Yes
+First
+Casual
+Yes
+6.5
+Yes
+Second D2
+Serious
+No
+7.3
+No
+Second D1
+Serious
+No
+7.9
+Yes
+First
+Moderate
+Yes
+5.7
+No
+Third
+Serious
+No
+8.2
+Yes
+First
+Casual
+Yes
+6.1
+Yes
+Second D2
+Serious
+No
+7.6
+No
+Second D2
+Moderate
+Yes
+6.4
+Yes
+Third
+Serious
+No
+8.0
+Yes
+First
+Casual
+Yes
+5.8
+No
+Second D2
+Serious
+No
+7.5
+Yes
+Second D1
+Serious
+No
+7.7
+Yes
+First
+Moderate
+Yes
+6.0
+No
+Third
+Serious
+No
+7.8
+Yes
+First
+Casual
+Yes
+6.3
+Yes
+Second D2
+Assume each categorical attribute has the following possible values:
+ˆ Study Attitude: Serious, Moderate, Casual
+ˆ Part-time Job: Yes, No
+ˆ Courses Taught by Desmond and Pearl: Yes, No
+ˆ Number of Friends in the Study Group: 1, 2, 3, 4, 5, 6
+Assume that the numerical data follow a Gaussian distribution:
+f(x) =
+σ
+√
+2πexp
+
+−(x −µ)2
+2σ2
+
+where
+numerical training data = (x1, x2, . . . , xn)
+µ = 1
+n
+n
+X
+i=1
+xi,
+σ =
+v
+u
+u
+t
+n −1
+n
+X
+i=1
+(xi −µ)2
+If needed, apply 1-Laplace Smoothing to the likelihood probabilities of the affected feature
+only. The affected feature means that the categorical feature has a category given some
+belief in the test dataset, which was NOT observed in the training dataset. Please provide
+all the steps.
+You may find the following equation useful for this question:
+BNB = argmaxBiP(Bi)(P(e1|Bi)P(e2|Bi)P(e3|Bi) · · · P(ed|Bi))
+.(a)
+[4 points] Calculate the mean (µ) and standard deviation (σ) of the Average Energy
+Level (AEL) of Degree Classification: First-Class Honors and Second-Class Honors D1.
+Answer:
+ˆ Mean of average energy level given first-class honors = 8.02857
+ˆ Standard deviation of average energy level given first-class honors = 0.26904
+ˆ Mean of average energy level given second-class honors, D1 = 7.2
+ˆ Standard deviation of average energy level given second-class honors, D1 = 0.69761
+Scheme:
+ˆ 1 point for each correct mean value. 2 points in total.
+ˆ 1 point for each correct standard deviation value. 2 points in totals.
+(b)
+[3 points] Calculate the test data sample’s likelihoods of Average Energy Level (AEL)
+of First-Class Honors and Second-Class Honors D1.
+Answer:
+P(AEL = 7.2|First) =
+0.26904
+√
+2πexp
+
+−(7.2 −8.02857)2
+2(0.26904)2
+
+= 0.01293
+P(AEL = 7.2|SecondD1) =
+0.69761
+√
+2πexp
+
+−(7.2 −7.2)2
+2(0.69761)2
+
+= 0.57187
+Scheme:
+ˆ 1.5 points for each correct test data sample’s likelihood. 3 points in total.
+(c) [4 points] Calculate the test data sample’s likelihoods of Study Attitude (SA), Part-time
+Job (PTJ), Courses Taught by Desmond and Pearl (CDP), and Number of Friends in
+the Study Group (NFSG) of all Degree Classification(s) (DC).
+Answer:
+ˆ As P(SA = Serious|Third) = 0, we need to apply a add-one-trick for the study
+attitude. We assume that three values (Serious, Moderate, Casual) are equally prob-
+able:
+– P(SA = Serious|First) = 7+1
+7+3 = 0.8
+– P(SA = Serious|SecondD1) = 3+1
+4+3 = 0.57
+– P(SA = Serious|SecondD2) = 1+1
+5+3 = 0.25
+– P(SA = Serious|Third) = 0+1
+4+3 = 0.1429
+ˆ As P(PTJ = No|Third) = 0, we need to apply a add-one-trick for the Part-time
+Job. We assume that three values (Yes, No) are equally probable:
+– P(PTJ = No|First) = 7+1
+7+2 = 0.89
+– P(PTJ = No|SecondD1) = 3+1
+4+2 = 0.67
+– P(PTJ = No|SecondD2) = 1+1
+5+2 = 0.286
+– P(PTJ = No|Third) = 0+1
+4+2 = 0.167
+ˆ P(CDP = Y es|First) = 7
+7 = 1
+ˆ P(CDP = Y es|SecondD1) = 2
+4 = 0.5
+ˆ P(CDP = Y es|SecondD2) = 3
+5 = 0.6
+ˆ P(CDP = Y es|Third) = 1
+4 = 0.25
+ˆ As P(NFSG = 4|First) = 0, we need to apply a add-one-trick for the Number
+of friends in the Study Group. We assume that six values (1,2,3,4,5,6) are equally
+probable:
+– P(NFSG = 4|First) = 0+1
+7+6 = 0.0769
+– P(NFSG = 4|SecondD1) = 3+1
+4+6 = 0.4
+– P(NFSG = 4|SecondD2) = 1+1
+5+6 = 0.18
+– P(NFSG = 4|Third) = 0+1
+4+6 = 0.1
+Scheme:
+ˆ 0.25 point for each correct test data sample’s likelihood. 4 points in total.
+(d)
+[2 points] Calculate the prior probabilities.
+Answer:
+ˆ P(First) = 7
+20 = 0.35
+ˆ P(SecondD1) = 4
+20 = 0.2
+ˆ P(SecondD2) = 5
+20 = 0.25
+ˆ P(Third) = 4
+20 = 0.2
+Scheme:
+ˆ 0.5 point for each correct prior probability. 2 points in total.
+(e)
+[3 points] Finally, calculate the posterior probabilities and make the prediction.
+Assume that the likelihood of Average Energy Level (AEL) of Second-Class Honors D2
+and Third-Class Honors are:
+ˆ P(AEL=7.2|Second-Class Honors D2) = 0.32515
+ˆ P(AEL=7.2|Third-Class Honors) = 0.000334158
+Answer:
+ˆ P(First|E) = (0.35)(0.8)(0.89)(0.01293)(1)(0.0769)
+P(E)
+= 0.0002478
+P(E)
+ˆ P(SecondD1|E) = (0.2)(0.57)(0.67)(0.57187)(0.5)(0.4)
+P(E)
+= 0.0087359
+P(E)
+ˆ P(SecondD2|E) = (0.25)(0.25)(0.286)(0.32515)(0.6)(0.18)
+P(E)
+= 0.0006277
+P(E)
+ˆ P(Third|E) = (0.2)(0.1429)(0.167)(0.000334158)(0.25)(0.1)
+P(E)
+= 3.9872×10−8
+P(E)
+Therefore, the Na¨ıve Bayes classifier predicts “Degree Classification” = Second-Class
+Honors D2 for the student.
+Scheme:
+ˆ 0.5 point for each correct posterior probability. 2 points in total.
+ˆ 1 point for making the correct prediction.', ARRAY['Probabilistic Models']::TEXT[], 'Probabilistic Models', 'Probabilistic Models', ARRAY['Probabilistic Models']::TEXT[], ARRAY['manual_computation', 'probability_reasoning', 'classification_decision']::TEXT[], 'hard', '', '', ''),
+    ('COMP2211-2024-spring-midterm', '5', NULL, 5, 'long_question', 'long_answer', 'Problem 5 Part(b) Continued
+(c)
+[2 points] Based on the chosen K and those 6 training samples in part (b), calculate the
+test error for the test dataset below.
+Note:
+ˆ When selecting a neighbor, to resolve ties, choose the neighbor with the lowest index.
+ˆ When evaluating the error, use
+– Number of wrong predictions / Number of test data points
+.
+Attribute 1
+Attribute 2
+Class
+3.4
+B
+2.8
+A
+3.5
+A
+2.5
+B', 18, 19, NULL::jsonb, NULL, NULL, 'Problem 5 Part(b) Continued
+(c)
+[2 points] Based on the chosen K and those 6 training samples in part (b), calculate the
+test error for the test dataset below.
+Note:
+ˆ When selecting a neighbor, to resolve ties, choose the neighbor with the lowest index.
+ˆ When evaluating the error, use
+– Number of wrong predictions / Number of test data points
+.
+Attribute 1
+Attribute 2
+Class
+3.4
+B
+2.8
+A
+3.5
+A
+2.5
+B
+Answer:
+Using K=3, test error is 0.
+Details:
+**Test Data**
+([3.4, 3.0], B) k nearest neighbors, [[3.2, 2.9], B, [2.7, 3.0], A, [3.4, 3.8], B] predict label B error: 0
+([3.0, 2.8], A) k nearest neighbors, [[3.2, 2.9], B, [2.7, 3.0], A, [2.6, 2.0], A] predict label A error: 0
+([2.0, 3.5], A) k nearest neighbors, [[2.5, 3.7], A, [2.7, 3.0], A, [3.4, 3.8], B] predict label A error: 0
+([2.5, 7.0], B) k nearest neighbors, [[2.5, 3.7], A, [3.5, 4.0], B, [3.4, 3.8], B] predict label B error: 0
+total error for test data: 0/4
+Scheme:
+ˆ 2 points for giving the total error for 6-cross validation for K = 3.', ARRAY['KNN and Clustering']::TEXT[], 'KNN and Clustering', 'KNN and Clustering', ARRAY['KNN and Clustering']::TEXT[], ARRAY['manual_computation', 'distance_calculation', 'algorithm_tracing']::TEXT[], 'hard', '', '', ''),
+    ('COMP2211-2024-spring-midterm', '6', NULL, 6, 'long_question', 'long_answer', 'Problem 6 [16 points] Leader Clustering
+Consider the following cluster method called Leader Clustering. It receives two parameters:
+an integer K and a real-value threshold T. Similar to K-means clustering, it starts by selecting
+K instance (which will be called leaders) and assigns each training instance to the cluster of
+the closest leader. During this assignment step, however, if the distance of a training instance
+to its closest leader is greater than the input threshold T, then this training instance forms a
+new cluster and becomes the initial leader of this new cluster. After all the training instances
+have been assigned to a cluster, the leader of each cluster is updated as the mean of the
+cluster. The process is then repeated until the cluster assignments do not change.
+(a)
+[6 points] Given a 1-dimensional data set { 1, 3, 5, 9, 11, 13, 15 }, use the Leader
+Clustering algorithm and Euclidean distance to cluster the given points in the data set
+into 2 clusters. Assume c1 = 3 and c2 = 11 are chosen as the initial K = 2 leaders
+and the threshold for forming new clusters T = 5. Fill in the following table of the first
+assignment iteration with your completed values and what are the leaders after the first
+assignment iteration? (If new clusters are formed in the process, named their leaders as
+c3, c4, ... based on the order. Leave the distance c3/c4/... blank if the new cluster(s)
+are not formed yet.)
+Data
+point
+Distance
+between the
+data point and c1
+Distance
+between the
+data point and c2
+Distance
+between the
+data point and c3
+(if needed)
+Distance
+between the
+data point and c4
+(if needed)
+Closest
+Centroid
+The leaders after the first assignment iteration:
+(b) [6 points] Given a 1-dimensional data set {5, 9, 11, 13, 17, 19}, use the Leader Clustering
+algorithm and Euclidean distance to cluster the given points in the data set into 2 clusters.
+Assume c1 = 5 and c2 = 11 are chosen as the initial K = 2 leaders and the threshold for
+forming new clusters T = 5. Fill in the following table of the first assignment iteration
+with your computed values and what are the leaders after the first assignment iteration?
+(If new clusters are formed in the process, named their leaders as c3, c4, ... based on the
+order. Leave the distance c3/c4/... blank if the new cluster(s) are not formed yet.)
+Data
+point
+Distance
+between the
+data point and c1
+Distance
+between the
+data point and c2
+Distance
+between the
+data point and c3
+(if needed)
+Distance
+between the
+data point and c4
+(if needed)
+Closest
+Centroid
+The leaders after the first assignment iteration:
+(c)
+[2 points] Which of the two methods, K-Means Clustering or Leader Clustering, will be
+better at dealing with outliers? Please briefly explain.
+(d)
+[2 points] During lectures, we have learned that one drawback of the K-Means Cluster-
+ing algorithm is that we need to specify K for the algorithm, but usually, we don’t know
+how many clusters there should be for an unlabeled dataset. Will the Leader Clustering
+mitigate this drawback? Will there be any related limitations of the Leader Clustering
+algorithm? Please briefly explain.', 16, 21, NULL::jsonb, NULL, NULL, 'Problem 6 [16 points] Leader Clustering
+Consider the following cluster method called Leader Clustering. It receives two parameters:
+an integer K and a real-value threshold T. Similar to K-means clustering, it starts by selecting
+K instance (which will be called leaders) and assigns each training instance to the cluster of
+the closest leader. During this assignment step, however, if the distance of a training instance
+to its closest leader is greater than the input threshold T, then this training instance forms a
+new cluster and becomes the initial leader of this new cluster. After all the training instances
+have been assigned to a cluster, the leader of each cluster is updated as the mean of the
+cluster. The process is then repeated until the cluster assignments do not change.
+(a)
+[6 points] Given a 1-dimensional data set { 1, 3, 5, 9, 11, 13, 15 }, use the Leader
+Clustering algorithm and Euclidean distance to cluster the given points in the data set
+into 2 clusters. Assume c1 = 3 and c2 = 11 are chosen as the initial K = 2 leaders
+and the threshold for forming new clusters T = 5. Fill in the following table of the first
+assignment iteration with your completed values and what are the leaders after the first
+assignment iteration? (If new clusters are formed in the process, named their leaders as
+c3, c4, ... based on the order. Leave the distance c3/c4/... blank if the new cluster(s)
+are not formed yet.)
+Data
+point
+Distance
+between the
+data point and c1
+Distance
+between the
+data point and c2
+Distance
+between the
+data point and c3
+(if needed)
+Distance
+between the
+data point and c4
+(if needed)
+Closest
+Centroid
+The leaders after the first assignment iteration:
+Answer:
+Data
+point
+Distance
+between the
+data point and c1
+Distance
+between the
+data point and c2
+Distance
+between the
+data point and c3
+(if needed)
+Distance
+between the
+data point and c4
+(if needed)
+Closest
+Centroid
+c1
+c1
+c1
+c2
+c2
+c2
+c2
+After the first assignment iteration, c1 = (1+3+5)/3 = 3, c2 = (9+11+13+15)/4 = 12.
+Scheme:
+ˆ 0.25 point for each correct numeric value (or label). 5.25 points in total.
+ˆ 0.25 point for the correct values of the leaders.
+If the values of both leader are correct, 0.75. If only one leader is correct,
+0.25.
+(b) [6 points] Given a 1-dimensional data set {5, 9, 11, 13, 17, 19}, use the Leader Clustering
+algorithm and Euclidean distance to cluster the given points in the data set into 2 clusters.
+Assume c1 = 5 and c2 = 11 are chosen as the initial K = 2 leaders and the threshold for
+forming new clusters T = 5. Fill in the following table of the first assignment iteration
+with your computed values and what are the leaders after the first assignment iteration?
+(If new clusters are formed in the process, named their leaders as c3, c4, ... based on the
+order. Leave the distance c3/c4/... blank if the new cluster(s) are not formed yet.)
+Data
+point
+Distance
+between the
+data point and c1
+Distance
+between the
+data point and c2
+Distance
+between the
+data point and c3
+(if needed)
+Distance
+between the
+data point and c4
+(if needed)
+Closest
+Centroid
+The leaders after the first assignment iteration:
+Answer:
+Data
+point
+Distance
+between the
+data point and c1
+Distance
+between the
+data point and c2
+Distance
+between the
+data point and c3
+(if needed)
+Distance
+between the
+data point and c4
+(if needed)
+Closest
+Centroid
+c1
+c2
+c2
+c2
+c3
+c3
+After the first assignment iteration, c1 = 5, c2 = (9+11+13)/3 = 11, c3 = (17+19)/2 =
+18.
+Scheme:
+ˆ 0.25 point for each correct numeric value (or label). 5 points in total.
+ˆ 1 point for the correct values of the leaders. 1 point in total.
+If there are any incorrect leaders in the answer, 0 point
+(c)
+[2 points] Which of the two methods, K-Means Clustering or Leader Clustering, will be
+better at dealing with outliers? Please briefly explain.
+Answer:
+Leader clustering is more robust (better at dealing with) outliers. This is because new
+cluster will be generated and outliers will be assigned to the new cluster without influ-
+encing the other clusters.
+Scheme:
+ˆ 1 point for stating which method is better at dealing with outliers.
+ˆ 1 point for the explanation.
+(d)
+[2 points] During lectures, we have learned that one drawback of the K-Means Cluster-
+ing algorithm is that we need to specify K for the algorithm, but usually, we don’t know
+how many clusters there should be for an unlabeled dataset. Will the Leader Clustering
+mitigate this drawback? Will there be any related limitations of the Leader Clustering
+algorithm? Please briefly explain.
+Answer:
+The Leader Clustering can mitigate this drawback of specifying K because the algorithm
+may get increment on the number of clusters during training. But now the limitation is
+that we have to specify T for the Leader Clustering algorithm. The threshold value will
+influence whether the algorithm increases new leader (new cluster) or not. The clustering
+results will be sensitive to T.
+Scheme:
+ˆ 1 point for stating whether the Leader Clustering mitigate the drawback.
+ˆ 1 point for the explanation.
+Explanations including:
+1. More computation/complexity
+2. Sensitive to the initial choice of leaders
+3. Cannot control the number of clusters
+will not get any point.', ARRAY['KNN and Clustering']::TEXT[], 'KNN and Clustering', 'KNN and Clustering', ARRAY['KNN and Clustering']::TEXT[], ARRAY['manual_computation', 'distance_calculation', 'algorithm_tracing']::TEXT[], 'hard', '', '', ''),
+    ('COMP2211-2024-spring-midterm', '7', NULL, 7, 'long_question', 'coding', 'Problem 7 Continued:
+-------------------- END OF PAPER
+--------------------
+/* Rough work */
+/* Rough work */
+/* Rough work */
+/* Rough work */', 10, 24, NULL::jsonb, NULL, NULL, 'Problem 7 Continued:
+Answer:
+D = 2, average accuracy = 0%: In this case, the dataset was divided into two folds. Since
+the samples from each class were grouped together, one fold contained samples from classes
+0 and 1, and the other fold contained samples from classes 2 and 3. As a result, the classifier
+failed to correctly classify any of the samples because it was not trained on the classes present
+in the test fold.
+D = 3, average accuracy = 50%: In this case, the dataset was divided into three folds.
+Fold 1 contained samples from classes 0 and 1, fold 2 contained samples from classes 1 and 2,
+and fold 3 contained samples from classes 2 and 3. Using fold 1 and fold 2 as training, and
+fold 3 as testing, only 25% of test points are classified correctly.
+ˆ Training: Fold 1, 2, Testing: Fold 3, Accuracy = 25%
+ˆ Training: Fold 1, 3, Testing: Fold 2, Accuracy = 100%
+ˆ Training: Fold 2, 3, Testing: Fold 1, Accuracy = 25%
+So, the average accuracy is (25% + 100% + 25%)/3 = 50%.
+D = 5, accuracy = 100%: In this case, the dataset was divided into five folds.
+Fold 1
+contained samples from class 0, fold 2 contained samples from class 0 and 1, fold 3 contained
+samples from class 1 and 2, fold 4 contained samples from class 2 and 3, and fold 5 contained
+samples from class 3.
+ˆ Training: Fold 1, 2, 3, 4, Testing: Fold 5, Accuracy: 100%
+ˆ Training: Fold 1, 2, 3, 5, Testing: Fold 4, Accuracy: 100%
+ˆ Training: Fold 1, 2, 4, 5, Testing: Fold 3, Accuracy: 100%
+ˆ Training: Fold 1, 3, 4, 5, Testing: Fold 2, Accuracy: 100%
+ˆ Training: Fold 2, 3, 4, 5, Testing: Fold 1, Accuracy: 100%
+So, the average accuracy is (100% + 100% + 100% + 100% + 100%)/5 = 100%.
+To improve the implementation of D-fold cross-validation, the dataset should be shuffled
+before dividing it into folds. This will ensure that each fold contains a representative dis-
+tribution of samples from all classes, reducing bias and providing more reliable evaluation
+results.
+Scheme:
+ˆ 2 points for explaining the result obtained for D = 2.
+ˆ 3 points for explaining the result obtained for D = 3.
+ˆ 3 points for explaining the result obtained for D = 5.
+ˆ 2 points for suggesting improvement(s) to the implementation.
+-------------------- END OF PAPER
+--------------------
+/* Rough work */
+/* Rough work */
+/* Rough work */
+/* Rough work */', ARRAY['Evaluation and Validation']::TEXT[], 'Evaluation and Validation', 'Evaluation and Validation', ARRAY['Evaluation and Validation']::TEXT[], ARRAY['metric_computation', 'experimental_design', 'reasoning']::TEXT[], 'medium', '', '', ''),
+    ('COMP2211-2024-spring-final', '1', NULL, 1, 'true_false', 'true_false', 'Problem 1 [10 points] True/False Questions
+Indicate whether the following statements are true or false by putting T or F in the given
+table in the answer book. You get 1 point for each correct answer.
+(a) The console output of the following Python code is [1,2,3,4,5,6,7].
+import numpy as np
+# arange(start, stop): Values are generated within half-open interval [start,stop).
+array = np.arange(1,10)
+print(array[:-2])
+(b) There is no way for the Na¨ıve Bayes classifier to make a prediction if a categorical feature
+(e.g., color) has a new category (e.g., blue) not observed in the training data set.
+(c) K-Nearest Neighbors classifier is a non-parametric machine learning algorithm with an
+assumption that the data are uniformly distributed.
+(d) It is possible for K-Means Clustering to return empty clusters if certain initial centroid
+positions are unfortunate.
+(e) If there are only two classes to predict, the following Multi-layer Perceptron (MLP)
+models will have the same output, given that they have the same initial weights and
+biases, and are trained in the same manner:
+ˆ MLP 1: Input layer, hidden dense layer with ReLU activation function, output layer
+with sigmoid activation function.
+ˆ MLP 2: Input layer, hidden dense layer with ReLU activation function, output layer
+with softmax activation function.
+(f) An affine transformation may preserve distances and angles.
+(g) The number of floating point multiplications involved when a 32×32 pixel RGB image
+is passed through a 2D convolutional layer with 8 3×3 kernels, padding of 3, and stride
+length of 1 is 8×3×3×36×36.
+(h) Setting the dropout rate of a Convolutional Neural Network to 0.5 means that more than
+50% of its layer’s outputs are non-zero.
+(i) Alpha-beta pruning can sometimes change the final decision made by the minimax algo-
+rithm, resulting in a different move being selected for the current player.
+(j) Researches that involves human participants should require informed consent.', 10, 2, NULL::jsonb, NULL, NULL, 'Problem 1 [10 points] True/False Questions
+Indicate whether the following statements are true or false by putting T or F in the given
+table. You get 1 point for each correct answer.
+Question
+(a)
+(b)
+(c)
+(d)
+(e)
+(f)
+(g)
+(h)
+(i)
+(j)
+Answer
+T
+F
+F
+T
+F
+T
+F
+F
+F
+T
+Scheme:
+ˆ 1 point for giving each correct answer. 10 points in total.', ARRAY['True/False']::TEXT[], 'True/False', 'True/False', ARRAY['True/False']::TEXT[], ARRAY['concept_check', 'rapid_reasoning']::TEXT[], 'medium', '', '', ''),
+    ('COMP2211-2024-spring-final', '2', NULL, 2, 'long_question', 'coding', 'Problem 2 [10 points] Advanced Python: Image Processing with NumPy
+You are working on an image processing project that involves manipulating arrays to perform
+various operations on images. You have been provided with a NumPy array img representing
+a grayscale image of size 512 × 512:
+import numpy as np
+img = np.random.randint(0, 256, size=(512, 512), dtype=np.uint8)
+# Note: random.randint(low, high=None, size=None, dtype=int)
+# returns random integers from low (inclusive) to high (exclusive).
+(a)
+[4 points] Image masking:
+Implement the following function which creates a new array img masked by applying a
+mask to img. The mask is defined as a 2D array mask of size 512 × 512, where mask[i,
+j] = 1 if the pixel at img[i, j] is within a circle (boundary included) of radius 100
+centered at a given position [center x, center y], and mask[i, j] = 0 otherwise.
+def apply_circle_mask(img, center_x, center_y):
+# --- YOUR CODE HERE ---
+return img_masked
+Below is an example for center x = 100 and center y = 200.
+img
+img_masked
+Do NOT use any explicit loop to implement the function. You may find the following
+functions useful for this question.
+ˆ np.arange(start, stop, step) returns spaced values within a given interval.
+– start (optional) is the start of interval. The default start value is 0.
+– stop is the end of interval. The returned interval does not include this value.
+– step (optional) is the spacing between values. The default spacing is 1.
+ˆ np.square(a) returns the element-wise square of an array.
+– a is the input array.
+This is equivalent to a ** 2.
+ˆ np.sqrt(a) returns the element-wise square root of an array.
+– a is the input array.
+ˆ np.expand dims(a, axis) returns a new array with a new axis of size 1 inserted.
+– a is the input array.
+– axis is the position where the axis is to be inserted.
+If axis is 0, this is equivalent to a[np.newaxis] and a[None]. If axis is 1, this is
+equivalent to a[:, np.newaxis] and a[:, None].
+(b)
+[6 points] Image blurring:
+Your task is to use NumPy to apply the following 3 × 3 blur filter to img with zero
+padding so that img blur has the same shape as img.
+blur_filter = np.array([[1/9, 1/9, 1/9],
+[1/9, 1/9, 1/9],
+[1/9, 1/9, 1/9]])
+Implement the following function so that after the whole code snippet below is executed,
+img blur stores the desired result.
+def img_flatten_conv_1d(img, v):
+# --- YOUR CODE HERE ---
+return img_conv
+v = blur_filter.sum(0) # ''sum(0)'' returns the sum of the array elements over axis 0.
+img_blur = img_flatten_conv_1d(img, v)
+img_blur = img_flatten_conv_1d(img_blur.T, v).T
+Do NOT use any explicit loop in your code. You may find the following functions useful
+for this question.
+ˆ np.convolve(a, v, mode=''full'') returns the discrete, linear convolution of two
+one-dimensional sequences.
+– a of shape (N, ): First one-dimensional input array (or array-like structure, e.g.,
+list).
+– v of shape (M, ): Second one-dimensional input array (or array-like structure,
+e.g., list).
+– mode (optional): The convolution mode. It must be one of ''full'', ''valid'',
+''same''. Note: use ''valid'' for this question. When ''valid'' is used, np.convolve
+is equivalent to the following function:
+def convolve_valid(a, v):
+if len(a) < len(v):
+a, v = v, a
+# swap the array if v is longer than a
+c = np.zeros(len(a) - len(v) + 1)
+for i in range(len(c)):
+c[i] = np.sum(a[i:i+len(v)] * v[::-1])
+return c
+Examples:
+>>> np.convolve([1,2,3],[0,1,0.5], ''valid'')
+array([2.5])
+# this is the output array
+>>> np.convolve([1,2,3,4],[0,1,0.5], ''valid'')
+array([2.5, 3.5])
+# this is the output array
+ˆ np.zeros(shape, dtype=float) returns a new array of given shape and type, filled
+with zeros.
+– shape is the shape of the new array, e.g., (2, 3) or 2.
+– dtype (optional) is the desired data type for the array, e.g., np.int8. The default
+is np.float64.
+ˆ np.concatenate((a1, a2, ...), axis=0) joins a sequence of arrays along an ex-
+isting axis.
+– a1, a2, ... is a sequence of arrays (or array-like structure, e.g., list). The
+arrays must have the same shape, except in the dimension corresponding to
+axis (the first, by default).
+– axis (optional) is the axis along which the arrays will be joined. If axis is None,
+arrays are flattened before use. The default is 0.
+ˆ np.reshape(a, newshape) gives a new shape to an array without changing its data.
+– a is the array to be reshaped.
+– newshape is the new shape. It should be compatible with the original shape. If
+an integer, then the result will be a 1D array of that length. One shape dimension
+can be −1. In this case, the value is inferred from the length of the array and
+the remaining dimensions (if any).
+This is equivalent to a.reshape(newshape).
+ˆ np.transpose(a) returns the transpose of an array.
+– a is the input array.
+This is equivalent to a.T.
+Hints:
+(1) In this case, applying blur filter to the image can also be done by consecutively
+applying two 1D filters, one vertically and the other horizontally, to the image.
+(2) Since np.convolve only accepts 1D arrays, you may consider flattening the image
+array, applying np.convolve to the flattened array, and then reshaping it back to a
+2D array.
+(3) Be aware of the boundaries since np.convolve with mode=''valid'' does not pad
+the array and the output array does not always have the same shape as the input
+array. Also, remember to remove the padding (if any) after convolutions.', 10, 3, NULL::jsonb, NULL, NULL, 'Problem 2 [10 points] Advanced Python: Image Processing with NumPy
+Solution:
+(a) def apply_circle_mask(img, center_x, center_y):
+indices = np.arange(512)
+x_dist = indices - center_x
+y_dist = indices - center_y
+dist = np.sqrt(x_dist ** 2 + y_dist[:, None] ** 2)
+img_masked = img * (dist <= 100)
+return img_masked
+(b) def img_flatten_conv_1d(img, v):
+zeros = np.zeros((512, 1))
+img_padded = np.concatenate((zeros, img, zeros), axis=1)
+img_flat = img_padded.reshape(-1)
+img_flat_conv = np.convolve(img_flat, v, ''valid'')
+img_flat_conv = np.concatenate(([0], img_flat_conv, [0]))
+img_conv = img_flat_conv.reshape(512, 514)[:, 1:-1]
+return img_conv
+v = [1/3, 1/3, 1/3]
+img_blur = img_flatten_conv_1d(img, v)
+img_blur = img_flatten_conv_1d(img_blur.T, v).T
+Scheme:
+(a) If no explicit loop is used, 1 point for each of the following:
+ˆ correct broadcasting to create 2D distance array;
+ˆ correct distance values;
+ˆ correct mask (0.5 point for the opposite mask);
+ˆ correct final result.
+If explicit loops are used, 1 point in total if the final result is correct.
+(b) If no explicit loop is used, 1 point for each of the following:
+ˆ correct padding before flattening the image array (0.5 point if padding is applied
+after flattening);
+ˆ correct flattening of the image array;
+ˆ correct dimensions of inputs (1D arrays) to np.convolve;
+ˆ correct values of inputs to np.convolve;
+ˆ correct padding after np.convolve;
+ˆ correct reshaping back to 2D image and removing padding (0.5 each).
+If explicit loops are used, 1 point in total if the final result is correct.', ARRAY['Python Fundamentals']::TEXT[], 'Python Fundamentals', 'Python Fundamentals', ARRAY['Python Fundamentals']::TEXT[], ARRAY['implementation', 'code_tracing', 'debugging']::TEXT[], 'medium', '', '', ''),
+    ('COMP2211-2024-spring-final', '3', NULL, 3, 'long_question', 'long_answer', 'Problem 3 [12 points] Na¨ıve Bayes, K-Nearest Neighbors and Perceptron
+Below is some health data of patients with and without dengue fever.
+Patient #
+Diagnosis (B)
+Body Temperature
+(Celsius) (e1)
+Pulse Rate
+(bpm) (e2)
+Dengue
+Dengue
+Dengue
+No dengue
+No dengue
+36.5
+No dengue
+You can assume the data follows Gaussian distribution:
+f(x) =
+√
+2πσ2 exp(−(x −µ)2
+2σ2
+)
+where
+µ = 1
+n
+n
+X
+i=1
+xi
+σ =
+v
+u
+u
+t
+n −1
+n
+X
+i=1
+(xi −µ)2
+(a)
+[6 points] Using the data above, calculate the following. Round off calculations to the
+fourth decimal place.
+(i) P(e1 = 36 | B = No dengue)
+(ii) P(e2 = 85 | B = No dengue)
+(iii) P(e1 = 36 | B = No dengue) P(e2 = 85 | B = No dengue) P(B = No dengue)
+(iv) Calculate the posterior probability that the belief is ‘No dengue’ for the sample with
+evidence E = {e1 = 36, e2 = 85} if
+P((e1 = 36, e2 = 85)|B = Dengue)P(B = Dengue) = 0.0000036
+(b)
+[4.5 points] Identify the reasons for inaccurate predictions when using the following
+number of folds to evaluate the performance of a 3-Nearest Neighbors classifier model on
+the given dataset (i.e., not including the sample introduced in part (a)) without shuffling.
+(i) No. of folds = 6
+(ii) No. of folds = 3
+(iii) No. of folds = 2
+(c)
+[1.5 points] If the true label of the sample {e1 = 36, e2 = 85} is ‘No dengue’, will the
+perceptron model make a good prediction for the sample? Provide explanations with
+evidence for why or why not.', 12, 6, NULL::jsonb, NULL, NULL, 'Problem 3 [12 points] Na¨ıve Bayes, K-Nearest Neighbors and Perceptron
+Solution:
+(a)
+(i)
+p
+2π(0.5)2 exp
+
+−(36 −36.5)2
+2(0.52)
+
+= 0.4839
+(ii)
+p
+2π(15)2 exp
+
+−(85 −90)2
+2(152)
+
+= 0.0252
+(iii)
+(0.4839)(0.0252)(0.5) = 0.0061
+(iv)
+0.0061
+0.0061 + 0.0000036 = 0.9994
+(b)
+(i) This will predict 5/6 correctly if uniform distance (prediction for Patient 5 is wrong).
+Also, if inverse distance is used, the prediction for Patient 3 will also be wrong.
+(ii) It is possible that the data will be folded in such a way that the class opposite to
+the true label is typically the majority class e.g.
+(Patient 1, Patient 2), (Patient 3,
+Patient 4), (Patient 5, Patient 6) . In this case, most folds will have 50% accuracy.
+The performance may be even worse as there is one pair of samples from opposite
+classes with minimum pairwise Euclidean distance (Patient 3 and Patient 5).
+(iii) This is not acceptable because it is possible that the training set for the KNN model
+will have the class opposite to the true label as its majority class. In this case, the
+prediction for all samples will be wrong.
+(c) Yes, because in that case the data is linearly separable.
+Scheme:
+(a)
+(i) 1.5 points for giving the correct answer.
+(ii) 1.5 points for giving the correct answer.
+(iii) 1.5 points for giving the correct answer.
+(iv) 1.5 points for giving the correct answer.
+(b)
+(i) 1.5 point for giving the correct reason.
+(ii) 1.5 point for giving the correct reason.
+(iii) 1.5 point for giving the correct reason.
+(c) 0.5 point for stating “Yes”, i.e., perceptron model will make a good prediction for the
+sample. 1 point for giving the correct explanation.', ARRAY['Probabilistic Models', 'KNN and Clustering', 'Perceptron and MLP']::TEXT[], 'Probabilistic Models', NULL, ARRAY['Probabilistic Models', 'KNN and Clustering', 'Perceptron and MLP']::TEXT[], ARRAY['manual_computation', 'probability_reasoning', 'classification_decision', 'distance_calculation', 'algorithm_tracing', 'forward_pass', 'backpropagation', 'weight_update']::TEXT[], 'medium', '', '', ''),
+    ('COMP2211-2024-spring-final', '4', NULL, 4, 'long_question', 'long_answer', 'Problem 4 [11 points] Multi-layer Perceptron
+(a)
+[5 points] Amanda wants to train a multi-layer perceptron to classify various renting
+options’ popularity.
+Each housing option is represented with X, a three-dimensional
+vector (x1, x2, x3).
+x1 is the noise level rated from 1 to 5. x2 is the proximity to campus, measured in minutes
+it takes to arrive at the north gate. x3 is the availability of food options rated on a scale
+from 1 (no food available) to 5 (a wide variety of food available). Amanda has collected
+the opinions of a group of students on their preferences, classifying the housing options
+into three types: low, medium, and high, represented as one-hot vector Y = (y1, y2, y3).
+When designing the network architecture, Amanda has two ideas.
+Model A
+Model B
+Layer (type)
+Output Shape
+Layer (type)
+Output Shape
+dense 1 (Dense)
+(None, 6)
+dense 1 (Dense)
+(None, 3)
+dense 2 (Dense)
+(None, 4)
+dense 2 (Dense)
+(None, 5)
+dense 3 (Dense)
+(None, 3)
+dense 3 (Dense)
+(None, 2)
+dense 4 (Dense)
+(None, 3)
+(i) In a multi-layer perceptron, suppose there are L hidden layers, each layer k (starting
+from 1) has lk hidden nodes. The input data is a n-dimensional vector, and the
+output data is m-dimensional.
+(I) Calculate the number of updated parameters in the MLP model. Represent your
+result using n, m, L, and lk for k = 1, . . . , L.
+(II) Apply your result in part (a)(i)(I) to Model A and Model B separately.
+(ii) Please help Amanda decide on which multi-layer perceptron (A or B) to choose for
+potentially better performance. Please briefly explain why.
+(b) [1 point] Why do we use activation functions in multi-layer perceptron neural networks?
+(c)
+[2 points] Consider the two activation functions we have learned in class:
+ˆ Sigmoid activation function: σ(x) =
+1+e−x .
+ˆ Binary step activation function:
+f(x) =
+
+
+
+if x ≤0
+otherwise
+The binary step activation function gives a hard threshold at 0, and its gradients are 0
+almost everywhere. What is the problem if we use the binary step activation function in
+a multi-layer perceptron network? If we want to avoid the problem while approximating
+the binary step activation function, how to make use of the sigmoid activation function
+to achieve that?
+(d)
+[3 points] You are given an multi-layer perceptron model with the architecture shown
+below.
+ˆ ReLU: f(z) = max(0, z).
+ˆ Softmax: f(zi) =
+ezi
+Pn−1
+j=0 ezj , where z = [z0, z1, . . . , zn−1].
+(i) For a sample with features x1=1 and x2=1, what are the outputs of the hidden layer
+and the output layer? If necessary, round off the values to two decimal places.
+(ii) If the target labels have values Tk1=1,Tk2=0 for the sample in part d(i), calculate the
+new values of the weights: w5, w7, and w1 after one round of backward propagation
+if the learning rate is 0.4. Round off the values to four decimal places.
+For reference, here are some of the equations used in the back propagation.
+δk = (Ok −Tk)Ok(1 −Ok)
+δj = Oj(1 −Oj)
+X
+k∈K
+δkwjk
+wjk ←wjk −ηδkOj
+wij ←wij −ηδjOi', 11, 7, NULL::jsonb, NULL, NULL, 'Problem 4 [11 points] Multi-layer Perceptron
+Solution:
+(a)
+(i) (I) For any hidden layer and the output layer, the parameters include the weight
+and the bias.
+(n + 1) × l1 +
+L−1
+X
+k=1
+lk+1 × (lk + 1) + m × (lL + 1)
+(II) Model A: 67; Model B: 53.
+(ii) Model A is better because it has more parameters and therefore more expressive.
+(b) To add non-linearity to the neural network model so that it has more powerful modeling
+capability.
+(c) The problem is that we cannot learn the parameters using gradient descent since the
+gradients are 0 almost everywhere. We can solve the problem while approximating a
+hard threshold by scaling up the weights in a sigmoid activation function. For example,
+σ(cx) is steeper than σ(x) and more similar with the binary step function, for c > 1.
+(d)
+(i) Hidden layer Oj1 = 0.2, Oj2 = 0
+Output layer Ok1 = 0.48, Ok2 = 0.52
+(ii) w
+′
+5 = w5 −δk1ηOj1 = 0.1 −(−1298)(0.4)(0.2) = 0.1104
+w
+′
+7 = w7 −δk2ηOj1 = 0 −(1298)(0.4)(0.2) = −0.0104
+δj1 = Oj1(1−Oj1)(δk1w5+δk2w7) = 0.2(1−0.2)(−0.1298(0.1)+0.1298(0)) = −0.0021
+w
+′
+1 = w1 −δj1ηx1 = 0.2 −(0.4)(−0.0021)(1) = 0.2008
+Scheme:
+(a)
+(i) (I) 1.5 points for giving the correct formula.
+(II) 1 point for each correct answer. 2 points in total.
+(ii) 0.5 point for stating Model A is better. 1 point for giving the correct explanation.
+1.5 points in total.
+(b) 1 point for giving the correct answer for why we use activation functions in multi-layer
+perceptron.
+(c) 1 point for stating the problem. 1 point for explaining how to make use of the sigmoid
+function to avoid the problem. 2 points in total.
+(d)
+(i) 0.5 point for giving each correct output. 1.5 points in total.
+(ii) 0.5 point for giving each correct weight value. 1.5 points in total.', ARRAY['Perceptron and MLP']::TEXT[], 'Perceptron and MLP', 'Perceptron and MLP', ARRAY['Perceptron and MLP']::TEXT[], ARRAY['forward_pass', 'backpropagation', 'weight_update']::TEXT[], 'medium', '', '', ''),
+    ('COMP2211-2024-spring-final', '5', NULL, 5, 'long_question', 'long_answer', 'Problem 5 [13 points] Digital Image Processing
+(a) [4.5 points] The following is a grayscale image of the HKUST redbird and its correspond-
+ing histogram. An grayscale image histogram is a distribution showing the frequency of
+occurrence of each gray-level value.
+After some transformation on the original image, we get the transformed images shown
+below (first row). Their histograms are shuffled and shown in the rows below (second,
+third, and fourth row). Please state which transformations are applied to get the resulting
+images for (a),(b),(c), and what is the correct pairing for the images and histograms
+between {a,b,c} and {d,e,f}. Please also briefly state why.
+(b)
+[3 points] Consider the following 3 × 3 image.
+Perform binary thresholding of the
+image using Otsu’s method. The initial threshold is T=100, and we apply one iteration.
+What is the resulting threshold and the resulting image after thresholding? What is the
+advantage of using Otsu’s Method for image thresholding compared to the regular image
+thresholding algorithm?
+(c)
+[2 points] What is the resulting image of size 7 × 7 after adding reflection padding of
+size 2 on the original image in part (b)?
+(d)
+[1.5 points] Please briefly explain the effect of convolving an image with the following
+kernels.
+(i) Kernel 1:
+1/9
+1/9
+1/9
+1/9
+1/9
+1/9
+1/9
+1/9
+1/9
+(ii) Kernel 2:
+-1
+-1
+-1
+(iii) Kernel 3:
+-1
+-1
+-1
+-1
+-1
+-1
+-1
+-1
+(e)
+[2 points] Is it possible to design a 3 × 3 kernel and apply convolution with the kernel
+to flip a 64Ö64 image horizontally or vertically? If yes, please give such a kernel. If not,
+please explain why.', 13, 9, NULL::jsonb, NULL, NULL, 'Problem 5 [13 points] Digital Image Processing
+Solution:
+(a) (b)-(f): image (horizontal) flipping because the histogram is the same as the original
+image.
+(c)-(d): binary thresholding because there are only two values 0 and 255 in the histogram
+(a)-(e): contrast stretching because the intensity value in the histogram has been stretched
+to a wider range.
+(b) After the first iteration µ1=21 , µ2=128, T = 74.5 The resulting images are
+Compared to regular image thresholding algorithms, Otsu’s method has advantages (i)
+can automatically determine the threshold value T (ii) the resulting threshold value is
+reproducible. Given the same image, two researchers using Otsu’s algorithm must arrive
+at the same threshold.
+(c) Resulting image:
+(d) (i) smoothing, (ii) vertical edge detection, (iii) sharpening.
+(e) No, because the image flipping is a global operation on the image, but convolution with
+a 3 × 3 kernel is a local operation. Concretely, the 3 × 3 kernels can only capture the
+input value of 3 × 3 neighbors, but the flipping requires the pixel value information at a
+longer distance. The longest dependency distance can be 64.
+Scheme:
+(a) 1 for stating each transformation correctly. 3 points in total. 0.5 point for giving each
+correct pairing. 1.5 points in total.
+(b) 1 point for giving the correct resulting threshold. 1 point for giving the correct result
+image. 1 point for stating the advantage of using Otsu’s method. 3 points in total.
+(c) 0.05 for giving each correct value (40 values). 2 points in total.
+(d) 0.5 point for stating each effect of convolving with the given kernel correctly. 1.5 points
+in total.
+(e) 0.5 point for stating it is impossible to design a 3×3 kernel and apply it to flip the 64×64
+image. 1.5 points for giving the explanation.', ARRAY['Vision and CNN']::TEXT[], 'Vision and CNN', 'Vision and CNN', ARRAY['Vision and CNN']::TEXT[], ARRAY['manual_computation', 'filter_computation', 'architecture_reasoning']::TEXT[], 'medium', '', '', ''),
+    ('COMP2211-2024-spring-final', '6', NULL, 6, 'long_question', 'long_answer', 'Problem 6 [13 points] Dilated Convolution and Dropout
+(a)
+[10 points] Dilated convolution is a variation of the standard convolution operation that
+involves skipping input elements by a certain dilation rate. By doing this, the convo-
+lutional kernel can be made to “see” a larger area of the input data without actually
+increasing the number of parameters it has. The dilation rate determines the spacing
+between the values in the kernel.
+The figure below demonstrates how a 3×3 kernel is applied to a 7×7 image using a
+dilation factor of 2.
+Given the incomplete implementation of the Python function dilated convolution that
+takes the following inputs:
+ˆ input array: a 2D NumPy array representing the input data
+ˆ kernel: a 2D NumPy array representing the convolutional kernel
+ˆ dilation rate: an integer representing the dilation rate (default value is 1)
+ˆ stride: an integer representing the stride (default value is 1)
+ˆ padding: a string representing the padding type, either ‘valid’ (no padding) or ‘same’
+(padding to preserve input dimensions) (default value is ‘valid’)
+and returns a 2D NumPy array representing the output of the dilated convolution oper-
+ation.
+Complete the missing parts of the function using NumPy, without using any special-
+ized deep learning libraries so that the execution of the test script produces the required
+output. Make sure that your implementation supports stride and padding options.
+You may find the following formula for determining the size of output image of reg-
+ular image convolution useful for this question.
+(Size of image dimension - Size of kernel dimension + 2 × Padding) / Stride + 1
+import numpy as np
+# Zero pad the input_array. For example
+# a = [[1,2,3]]
+# np.pad(a, ((1,2),(3,4)), ''constant'')
+# >> array([[0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
+# >>
+[0, 0, 0, 1, 2, 3, 0, 0, 0, 0],
+# >>
+[0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
+# >>
+[0, 0, 0, 0, 0, 0, 0, 0, 0, 0]])
+def dilated_convolution(input_array, kernel, dilation_rate=1,
+stride=1, padding=''valid''):
+# Apply padding to the input array if specified
+if padding == ''same'':
+pad = #______ TODO 1 ______
+padded_input = np.pad(input_array, ((pad, pad), (pad, pad)), mode=''constant'')
+else:
+padded_input = input_array
+# Calculate the output shape
+output_rows = #______ TODO 2 ______
+output_cols = #______ TODO 3 ______
+kernel_rows, kernel_cols = kernel.shape
+# Initialize the output array with zeros
+output_array = np.zeros((output_rows, output_cols))
+# Iterate through the kernel and perform the convolution
+for i in range(kernel_rows):
+for j in range(kernel_cols):
+# Calculate the input indices for the current kernel position
+input_row_indices = #______ TODO 4 ______
+input_col_indices = #______ TODO 5 ______
+# Perform the convolution and accumulate the results in the output array
+output_array += #______ TODO 6 ______
+return output_array
+# Test script
+input_array = np.array(np.arange(100).reshape(10,10))
+kernel = np.array([[1, 0, 0], [0, 1, 0], [0, 0, 1]])
+dilation_rate = 2; stride = 2; padding = ''same''
+output_array = dilated_convolution(input_array, kernel,
+dilation_rate, stride, padding)
+print(output_array)
+# Output:
+# [[ 22.
+26.
+30.
+34.
+8.]
+#
+[ 62.
+66.
+72.
+78.
+34.]
+#
+[102. 126. 132. 138.
+74.]
+#
+[142. 186. 192. 198. 114.]
+#
+[ 80. 142. 146. 150. 154.]]
+You may find the following functions useful for this question.
+ˆ np.arange(start, stop, step) returns spaced values with a given interval.
+– start (optional) is the start of interval. The default start value is 0.
+– stop is the end of interval. The returned interval does not include this value.
+– step (optional) is the spacing between values. The default spacing is 1.
+ˆ np.array(object) creates an array.
+– object is an array or any sequence.
+ˆ np.reshape(a, newshape) gives a new shape to an array without changing its data.
+– a is the array to be reshaped.
+– newshape is the new shape. It should be compatible with the original shape. If
+an integer, then the result will be a 1D array of that length. One shape dimension
+can be -1. In this case, the value is inferred from the length of the array and the
+remaining dimensions (if any).
+This is equivalent to a.reshape(newshape).
+ˆ ndarray.shape returns a tuple of array dimensions.
+ˆ ndarray.zeros(shape, dtype=float) returns a new array of given shape and type,
+filled with zeros.
+– shape is the shape of the new array, e.g., (2,3) or 2.
+– dtype(optional) is the desired data type for the array, e.g., np.int8. The default
+is np.float64.
+ˆ range(n) returns a numeric series starting with 0 and extending up to but not
+including n.
+(b) [3 points] You are given an incomplete implementation of the Python function dropout,
+which implements the Dropout technique for regularization in neural networks.
+The
+function takes the following input:
+ˆ input array: a 2D NumPy array representing the input data
+ˆ p: a dropout probability
+The function generates a binary mask with the same shape as the input, where each
+element is 1 with probability 1-p and 0 with probability p. The input is then multiplied
+element-wise by the mask, effectively dropping out random elements. The function re-
+turns a NumPy array representing the output of the dropout operation.
+Complete the missing part of the function using NumPy, without using any special-
+ized deep learning libraries so that the execution of the test script produces the required
+output.
+You may find the following function useful for this question.
+ˆ np.random.rand(d0, d1, . . ., dn) returns an array of the given shape and popu-
+late it with random samples from a uniform distribution over [0, 1).
+– d0, d1,. . ., dn (optional) represent the dimensions of the returned array, must
+be non-negative. If no argument is given, a single Python float is returned.
+import numpy as np
+def dropout(input_array, p):
+mask = #______ TODO ______
+return input_array * mask', 13, 12, NULL::jsonb, NULL, NULL, 'Problem 6 [13 points] Dilated Convolution and Dropout
+Solution:
+(a) Dilated Convolution
+TODO #
+Answer
+((kernel.shape[0] - 1) * dilation_rate) // 2
+1.5 points
+(input_array.shape[0] - kernel.shape[0] * dilation_rate + 2 * pad) // stride + 1
+1.5 points
+(input_array.shape[1] - kernel.shape[1] * dilation_rate + 2 * pad) // stride + 1
+1.5 points
+np.arange(0, output_rows * stride, stride) + i * dilation_rate
+1.5 points
+np.arange(0, output_cols * stride, stride) + j * dilation_rate
+1.5 points
+kernel[i, j] * padded_input[np.ix_(input_row_indices, input_col_indices)]
+2.5 points
+(b) Dropout
+(np.random.rand(input_array.shape[0],input_array.shape[1]) < p) / p
+# 3 points', ARRAY['Vision and CNN']::TEXT[], 'Vision and CNN', 'Vision and CNN', ARRAY['Vision and CNN']::TEXT[], ARRAY['manual_computation', 'filter_computation', 'architecture_reasoning']::TEXT[], 'medium', '', '', ''),
+    ('COMP2211-2024-spring-final', '7', NULL, 7, 'long_question', 'long_answer', 'Problem 7 [18 points] Convolutional Neural Network
+(a)
+[8 points] Consider the following Keras implementation of a Convolutional Neural Net-
+work:
+from keras.models import Sequential
+from keras.layers import Conv2D, MaxPooling2D
+from keras.layers import Dense, Flatten
+model = Sequential()
+model.add(Conv2D(filters=32, kernel_size=(5, 5), padding=''same'',
+activation=''relu'', input_shape=(32, 32, 3)))
+model.add(Conv2D(filters=64, kernel_size=(3, 3), padding=''same'',
+activation=''relu''))
+model.add(MaxPooling2D(pool_size=(2, 2)))
+model.add(Conv2D(filters=64, kernel_size=(3, 3), padding=''same'',
+activation=''relu'', strides=(2, 2)))
+model.add(Conv2D(filters=64, kernel_size=(3, 3), padding=''same'',
+activation=''relu''))
+model.add(MaxPooling2D(pool_size=(2, 2)))
+model.add(Flatten())
+model.add(Dense(units=128, activation=''relu''))
+model.add(Dense(units=10, activation=''softmax''))
+(i) What is the padding size (in terms of border width) of the first convolutional layer?
+(ii) Fill in the blanks in the answer book in the model.summary() of the network. Assume
+all convolutional layers have biases. Numbers shown as ? are kept secret and you
+may ignore them.
+Hint: When padding = “same” and strides = 2, the output has the half size of the
+input.
+(iii) Suppose you observe the following loss curves as you train the Convolutional Neural
+Network. What is the most likely problem with the model? When does the problem
+usually occur? Describe a common way to mitigate the problem and explain why it
+works (in a few sentences).
+(b) [5 points] What is the output of the following parts of this very tiny convolutional neural
+network? This network was trained to classify whether the input 4×4 image depicts the
+letter “L” or not. Stride is 1, padding size is 0 for all layers, and the pool size for the
+max pooling layer is (2, 2). Show your calculation step by step. You only need to keep
+1 decimal number.
+ˆ ReLU activation function f(z) = max(0, x).
+ˆ Sigmoid activation function f(z) =
+1+e−z , where e = 2.718.
+Note: Assume the given kernels have already been flipped.
+(i) Feature map after Max-Pooling
+(ii) Feature in the fully-connected layer
+(iii) Output
+(c) [5 points] In video processing, 3D convolution is particularly valuable due to its ability to
+capture temporal dynamics as well as spatial features, which is essential for understanding
+content across frames. Unlike 2D convolution, which only analyzes spatial patterns within
+a single frame, 3D convolution extends the analysis across multiple consecutive frames,
+treating time as the third dimension alongside height and width. This approach allows a
+Convolutional Neural Network (CNN) to not only perceive static features in individual
+frames but also to understand movements and changes over time, which are crucial for
+tasks such as action recognition, video classification, and anomaly detection in video
+streams.
+Suppose we have video data of shape (32, 400, 300, 3), corresponding to (#keyframes,
+width, height, #channels). In the first layer, we want to apply a 3D convolution with
+100 kernels in the shape of (3, 3, 3), corresponding to (keyframe, width, height). The
+padding is (0, 2, 2), and the stride is (1, 2, 2). Please answer the following question with
+calculation steps or rationales.
+(i) What is the shape of the output after this layer?
+(ii) How many weight parameters are there?
+(iii) How many bias parameters are there?', 18, 16, NULL::jsonb, NULL, NULL, 'Problem 7 [18 points] Convolutional Neural Network
+Solution:
+(a)
+(i) 2
+(ii) 16, 16, 64
+8,
+8,
+(iii) The model has overfit the training dataset.
+Overfitting occurs when a model is
+too complex and learns the noise in the training data rather than the underlying
+patterns.
+Dropout can be used to mitigate the problem. Dropout is a technique where, during
+training, some neurons in the network are randomly dropped out (i.e., their outputs
+are set to zero) with a certain probability, typically 0.2 or 0.5. This means that,
+at each training iteration, a different subset of neurons is randomly selected to be
+“dropped out.”
+Dropout helps prevent overfitting in several ways (full marks to any of the fol-
+lowing):
+ˆ Reducing capacity: By randomly dropping out neurons, the network’s capacity
+is reduced, making it less prone to overfitting. With fewer neurons, the network
+has fewer opportunities to memorize the training data.
+ˆ Forcing feature sharing: Dropout encourages feature sharing among neurons.
+When a neuron is dropped out, the network must rely on other neurons to make
+predictions, which promotes feature sharing and reduces overfitting.
+ˆ Preventing complex co-adaptations: Dropout breaks the complex co-adaptations
+between neurons, which can lead to overfitting. By randomly dropping out neu-
+rons, the network is forced to learn simpler, more generalizable representations.
+ˆ Improving generalization: Dropout can be seen as a form of data augmentation.
+By randomly dropping out neurons, the network is forced to generalize to new,
+unseen situations, which improves its ability to generalize to new data.
+ˆ Reducing the risk of over-reliance on a single neuron: Dropout prevents the
+network from relying too heavily on a single neuron or a small group of neurons.
+This reduces the risk of overfitting, as the network is forced to use multiple
+neurons to make predictions.
+ˆ Ensemble-like behavior: Dropout can be seen as an ensemble method, where
+multiple sub-networks are trained simultaneously. Each sub-network is a different
+subset of neurons, and the final prediction is an ensemble of these sub-networks.
+This ensemble-like behavior improves generalization and reduces overfitting.
+The answer is not unique.
+(b) Answer:
+(c)
+(i) For any dimension (let’s denote it generically as D): Output D = floor((Input D +
+2 × Padding - Kernel D ) / Stride) + 1. Therefore, we have:
+Output keyframe = floor((32 + 2 * 0 - 3) / 1) + 1 = 30
+Output width = floor((400 + 2 * 2 - 3) / 2) + 1 = 201
+Output height = floor((300 + 2 * 2 - 3) / 2) + 1 = 151
+The output shape is (30, 201, 151, 100).
+(ii) There are 100 kernels in the shape of (3, 3, 3). Therefore, the number of weight
+parameters is 1000 Ö 3 Ö 3 Ö 3 x 3 = 8100.
+(iii) There is 1 bias parameter per kernel. So the total biases is 100.
+Scheme:
+(a)
+(i) 1 point for giving the correct padding size.
+(ii) 1 point for giving each correct shape (3 shape values). 1 point for giving the correct
+number of parameters. 4 points in total.
+(iii) 1 point for stating the model has overfit the training dataset. 1 point for explaining
+what does the problem usually occur. 1 point for describing a way to mitigate the
+problem. 3 points in total.
+(b) 1 point for giving each correct feature map (2 feature maps). 1 point for giving each
+feature in the fully-connected layer (2 features). 1 point for giving the correct output. 5
+points in total.
+(c)
+(i) 2 points for giving the correct output shape after this layer.
+(ii) 1.5 points for giving the correct number of weight parameters.
+(iii) 1.5 points for giving the correct total number of biases.', ARRAY['Vision and CNN']::TEXT[], 'Vision and CNN', 'Vision and CNN', ARRAY['Vision and CNN']::TEXT[], ARRAY['manual_computation', 'filter_computation', 'architecture_reasoning']::TEXT[], 'hard', '', '', ''),
+    ('COMP2211-2024-spring-final', '8', NULL, 8, 'long_question', 'long_answer', 'Problem 8 [10 points] Minimax and Alpha-Beta Pruning
+(a)
+[5 points] The figure below shows a tree in a minimax game with two players.
+A
+B
+C
+D
+E
+F
+G
+H
+I
+J
+MIN
+MAX
+Terminal
+nodes
+Score
+(i) Calculate the best score for the non-terminal nodes with the minimax algorithm.
+(ii) Suppose we are using an alpha-beta pruning algorithm. Indicate which edge will be
+pruned. Gives the alpha-beta values when running. We denote the edge between
+nodes A and B as AB.
+(iii) How do you make a minimax algorithm to find the shortest path to victory? Explain
+in one sentence.
+(b) [5 points] Consider the non zero-sum generalization in which the sum of the two players’
+utilities are not necessarily zero. Because player A’s utility no longer determines player
+B’s utility exactly, the leaf utilities are written as pairs (UA, UB), with the first and second
+component indicating the utility of that leaf to player A and player B respectively. In
+this generalized setting, player A seeks to maximize UA, the first component, while player
+B seeks to maximize UB, the second component.
+(i) Complete the table in the answer book by estimating the value (as pairs) at each of
+the internal node. Assume that each player maximizes their own utility.
+(ii) Is alpha-beta pruning still applicable in this case? Briefly explain why and provide
+an example. (Hint: you can think about the case where UA(s) = UB(s) for all nodes.)', 10, 18, NULL::jsonb, NULL, NULL, 'Problem 8 [10 points] Minimax and Alpha-Beta Pruning
+Solution:
+(a)
+(i) Answer:
+Nodes
+Score
+A
+B
+C
+D
+(ii) Answer:
+Edge
+Alpha
+Beta
+CH
+DJ
+(iii) Record depth information to distinguish paths.
+(b)
+(i) Answer:
+A
+(2,4)
+B
+(0,3)
+C
+(-1,3)
+D
+(1,1)
+E
+(0,-2)
+(ii) No. The values that the first and second player are trying to maximize are inde-
+pendent. Therefore, the principle for pruning in alpha-beta pruning—that a worse
+outcome for one player implies a better outcome for the other—no longer applies.
+For instance, in the case where UA(s) = UB(s) for all nodes, the problem reduces to
+searching for the max-valued leaf, which could appear anywhere in the tree.
+Scheme:
+(a)
+(i) 0.25 point for giving each correct numeric value. 1 point in total.
+(ii) 0.5 point for giving each correct edge/numeric value. 3 points in total.
+(iii) 1 point for giving a way to find the shortest path to victory.
+(b)
+(i) 0.5 point for giving each pair of values. 2.5 points in total.
+(ii) 0.5 point for stating “No”. 1 point for explaining why and 1 point for giving an
+example. 2.5 points in total.', ARRAY['Search and Games']::TEXT[], 'Search and Games', 'Search and Games', ARRAY['Search and Games']::TEXT[], ARRAY['tree_search', 'pruning', 'manual_tracing']::TEXT[], 'medium', '', '', ''),
+    ('COMP2211-2024-spring-final', '9', NULL, 9, 'long_question', 'short_answer', 'Problem 9 [3 points] Ethics of Artificial Intelligence
+From what areas we should ensure the AI models in production in their organizations func-
+tion ethically?
+-------------------- END OF PAPER
+--------------------
+/* Rough work */
+/* Rough work */
+/* Rough work */
+/* Rough work */
+/* Rough work */', 3, 19, NULL::jsonb, NULL, NULL, 'Problem 9 [3 points] Ethics of Artificial Intelligence
+Solution:
+ˆ Data Ethics
+ˆ Fair AI model (or avoiding AI model bias)
+ˆ AI model monitoring and maintenance.
+Scheme:
+ˆ 1 point for giving each area correctly. 3 points in total.
+-------------------- END OF PAPER
+--------------------', ARRAY['Ethics of AI']::TEXT[], 'Ethics of AI', 'Ethics of AI', ARRAY['Ethics of AI']::TEXT[], ARRAY['concept_explanation', 'argumentation', 'comparison']::TEXT[], 'easy', '', '', '')
+) AS seed(
+  source_exam_key,
+  question_number,
+  parent_question,
+  display_order,
+  question_type,
+  question_format,
+  question_text,
+  score,
+  page_number,
+  options,
+  correct_option,
+  correct_answer,
+  raw_answer_text,
+  topics,
+  topic_primary,
+  analytics_topic,
+  topic_tags,
+  skill_tags,
+  difficulty,
+  knowledge_reminder,
+  ai_hint,
+  solution
+)
+JOIN papers AS p
+  ON p.source_exam_key = seed.source_exam_key
+ AND p.source_kind = 'course_library'
+WHERE NOT EXISTS (
+  SELECT 1 FROM paper_questions q
+  WHERE q.paper_id = p.id
+    AND q.question_number = seed.question_number
+);
diff --git a/supabase/seeds/comp2211_problem_taxonomy_backfill.sql b/supabase/seeds/comp2211_problem_taxonomy_backfill.sql
new file mode 100644
index 0000000..df53e43
--- /dev/null
+++ b/supabase/seeds/comp2211_problem_taxonomy_backfill.sql
@@ -0,0 +1,109 @@
+-- ============================================
+-- PastPaper Master — COMP2211 problem-level taxonomy backfill
+-- Seed Date: 2026-03-24
+-- ============================================
+--
+-- Purpose:
+-- 1. Backfill coarse taxonomy for COMP2211 question rows after the paper has been
+--    processed into `paper_questions`.
+-- 2. Use the audited cover-page problem mapping as the initial analytics baseline.
+-- 3. Only fill empty taxonomy fields, so later fine-grained per-question curation
+--    can safely overwrite these defaults.
+
+WITH mapping AS (
+  SELECT *
+  FROM (
+    VALUES
+      ('COMP2211-2022-fall-midterm', '1', 'True/False Questions', 'True/False', 'True/False', ARRAY['True/False']::TEXT[], ARRAY['concept_check', 'rapid_reasoning']::TEXT[], 'true_false'),
+      ('COMP2211-2022-fall-midterm', '2', 'Python Fundamentals', 'Python Fundamentals', 'Python Fundamentals', ARRAY['Python Fundamentals']::TEXT[], ARRAY['code_tracing', 'implementation', 'debugging']::TEXT[], 'coding'),
+      ('COMP2211-2022-fall-midterm', '3', 'Conditional Probability and Bayes Classifier', 'Probabilistic Models', 'Probabilistic Models', ARRAY['Probabilistic Models']::TEXT[], ARRAY['manual_computation', 'probability_reasoning', 'classification_decision']::TEXT[], 'long_question'),
+      ('COMP2211-2022-fall-midterm', '4', 'K-Nearest Neighbors', 'KNN and Clustering', 'KNN and Clustering', ARRAY['KNN and Clustering']::TEXT[], ARRAY['manual_computation', 'distance_calculation', 'algorithm_tracing']::TEXT[], 'long_question'),
+      ('COMP2211-2022-fall-midterm', '5', 'K-Means Clustering', 'KNN and Clustering', 'KNN and Clustering', ARRAY['KNN and Clustering']::TEXT[], ARRAY['manual_computation', 'cluster_update', 'algorithm_tracing']::TEXT[], 'long_question'),
+      ('COMP2211-2022-fall-midterm', '6', 'Perceptron', 'Perceptron and MLP', 'Perceptron and MLP', ARRAY['Perceptron and MLP']::TEXT[], ARRAY['manual_computation', 'weight_update', 'formula_application']::TEXT[], 'long_question'),
+      ('COMP2211-2022-fall-midterm', '7', 'Multilayer Perceptron', 'Perceptron and MLP', 'Perceptron and MLP', ARRAY['Perceptron and MLP']::TEXT[], ARRAY['forward_pass', 'backpropagation', 'derivation']::TEXT[], 'long_question'),
+
+      ('COMP2211-2022-spring-midterm', '1', 'True/False Questions', 'True/False', 'True/False', ARRAY['True/False']::TEXT[], ARRAY['concept_check', 'rapid_reasoning']::TEXT[], 'true_false'),
+      ('COMP2211-2022-spring-midterm', '2', 'Python Fundamentals', 'Python Fundamentals', 'Python Fundamentals', ARRAY['Python Fundamentals']::TEXT[], ARRAY['code_tracing', 'implementation', 'debugging']::TEXT[], 'coding'),
+      ('COMP2211-2022-spring-midterm', '3', 'Conditional Probability and Bayes Classifier', 'Probabilistic Models', 'Probabilistic Models', ARRAY['Probabilistic Models']::TEXT[], ARRAY['manual_computation', 'probability_reasoning', 'classification_decision']::TEXT[], 'long_question'),
+      ('COMP2211-2022-spring-midterm', '4', 'K-Nearest Neighbors', 'KNN and Clustering', 'KNN and Clustering', ARRAY['KNN and Clustering']::TEXT[], ARRAY['manual_computation', 'distance_calculation', 'algorithm_tracing']::TEXT[], 'long_question'),
+      ('COMP2211-2022-spring-midterm', '5', 'K-Means Clustering', 'KNN and Clustering', 'KNN and Clustering', ARRAY['KNN and Clustering']::TEXT[], ARRAY['manual_computation', 'cluster_update', 'algorithm_tracing']::TEXT[], 'long_question'),
+      ('COMP2211-2022-spring-midterm', '6', 'Perceptron', 'Perceptron and MLP', 'Perceptron and MLP', ARRAY['Perceptron and MLP']::TEXT[], ARRAY['manual_computation', 'weight_update', 'formula_application']::TEXT[], 'long_question'),
+      ('COMP2211-2022-spring-midterm', '7', 'Perceptron and Multilayer Perceptron', 'Perceptron and MLP', 'Perceptron and MLP', ARRAY['Perceptron and MLP']::TEXT[], ARRAY['forward_pass', 'backpropagation', 'weight_update']::TEXT[], 'long_question'),
+
+      ('COMP2211-2022-spring-final-part-a', '1', 'True/False Questions', 'True/False', 'True/False', ARRAY['True/False']::TEXT[], ARRAY['concept_check', 'rapid_reasoning']::TEXT[], 'true_false'),
+      ('COMP2211-2022-spring-final-part-a', '2', 'Na¨ıve Bayes and K-Nearest Neighbors', NULL, 'Probabilistic Models', ARRAY['Probabilistic Models', 'KNN and Clustering']::TEXT[], ARRAY['manual_computation', 'probability_reasoning', 'distance_calculation']::TEXT[], 'long_question'),
+      ('COMP2211-2022-spring-final-part-a', '3', 'Multilayer Perceptron (MLP)', 'Perceptron and MLP', 'Perceptron and MLP', ARRAY['Perceptron and MLP']::TEXT[], ARRAY['forward_pass', 'backpropagation', 'derivation']::TEXT[], 'long_question'),
+      ('COMP2211-2022-spring-final-part-a', '4', 'Digital Image Processing', 'Vision and CNN', 'Vision and CNN', ARRAY['Vision and CNN']::TEXT[], ARRAY['manual_computation', 'filter_computation', 'architecture_reasoning']::TEXT[], 'long_question'),
+
+      ('COMP2211-2022-spring-final-part-b', '1', 'Convolutional Neural Network (CNN)', 'Vision and CNN', 'Vision and CNN', ARRAY['Vision and CNN']::TEXT[], ARRAY['forward_pass', 'architecture_reasoning', 'manual_computation']::TEXT[], 'long_question'),
+      ('COMP2211-2022-spring-final-part-b', '2', 'Python Programming: Convolutional Neural Network', 'Python Fundamentals', 'Python Fundamentals', ARRAY['Python Fundamentals', 'Vision and CNN']::TEXT[], ARRAY['implementation', 'code_tracing', 'debugging']::TEXT[], 'coding'),
+      ('COMP2211-2022-spring-final-part-b', '3', 'Minimax and Alpha-Beta Pruning', 'Search and Games', 'Search and Games', ARRAY['Search and Games']::TEXT[], ARRAY['tree_search', 'pruning', 'manual_tracing']::TEXT[], 'long_question'),
+      ('COMP2211-2022-spring-final-part-b', '4', 'Ethics of Artificial Intelligence', 'Ethics of AI', 'Ethics of AI', ARRAY['Ethics of AI']::TEXT[], ARRAY['concept_explanation', 'argumentation', 'comparison']::TEXT[], 'short_answer'),
+
+      ('COMP2211-2023-spring-midterm', '1', 'True/False Questions', 'True/False', 'True/False', ARRAY['True/False']::TEXT[], ARRAY['concept_check', 'rapid_reasoning']::TEXT[], 'true_false'),
+      ('COMP2211-2023-spring-midterm', '2', 'Python Fundamentals', 'Python Fundamentals', 'Python Fundamentals', ARRAY['Python Fundamentals']::TEXT[], ARRAY['code_tracing', 'implementation', 'debugging']::TEXT[], 'coding'),
+      ('COMP2211-2023-spring-midterm', '3', 'Na¨ıve Bayes Classifier', 'Probabilistic Models', 'Probabilistic Models', ARRAY['Probabilistic Models']::TEXT[], ARRAY['manual_computation', 'probability_reasoning', 'classification_decision']::TEXT[], 'long_question'),
+      ('COMP2211-2023-spring-midterm', '4', 'K-Nearest Neighbors', 'KNN and Clustering', 'KNN and Clustering', ARRAY['KNN and Clustering']::TEXT[], ARRAY['manual_computation', 'distance_calculation', 'algorithm_tracing']::TEXT[], 'long_question'),
+      ('COMP2211-2023-spring-midterm', '5', 'K-Means Clustering', 'KNN and Clustering', 'KNN and Clustering', ARRAY['KNN and Clustering']::TEXT[], ARRAY['manual_computation', 'cluster_update', 'algorithm_tracing']::TEXT[], 'long_question'),
+      ('COMP2211-2023-spring-midterm', '6', 'Perceptron', 'Perceptron and MLP', 'Perceptron and MLP', ARRAY['Perceptron and MLP']::TEXT[], ARRAY['manual_computation', 'weight_update', 'formula_application']::TEXT[], 'long_question'),
+      ('COMP2211-2023-spring-midterm', '7', 'Multilayer Perceptron', 'Perceptron and MLP', 'Perceptron and MLP', ARRAY['Perceptron and MLP']::TEXT[], ARRAY['forward_pass', 'backpropagation', 'derivation']::TEXT[], 'long_question'),
+
+      ('COMP2211-2024-spring-midterm', '1', 'True/False Questions', 'True/False', 'True/False', ARRAY['True/False']::TEXT[], ARRAY['concept_check', 'rapid_reasoning']::TEXT[], 'true_false'),
+      ('COMP2211-2024-spring-midterm', '2', 'Advanced Python for Artificial Intelligence', 'Python Fundamentals', 'Python Fundamentals', ARRAY['Python Fundamentals']::TEXT[], ARRAY['code_tracing', 'implementation', 'data_manipulation']::TEXT[], 'coding'),
+      ('COMP2211-2024-spring-midterm', '3', 'Model Evaluation & Advanced Python Programming', 'Evaluation and Validation', 'Evaluation and Validation', ARRAY['Evaluation and Validation', 'Python Fundamentals']::TEXT[], ARRAY['metric_computation', 'experimental_design', 'implementation']::TEXT[], 'coding'),
+      ('COMP2211-2024-spring-midterm', '4', 'Na¨ıve Bayes Classifier', 'Probabilistic Models', 'Probabilistic Models', ARRAY['Probabilistic Models']::TEXT[], ARRAY['manual_computation', 'probability_reasoning', 'classification_decision']::TEXT[], 'long_question'),
+      ('COMP2211-2024-spring-midterm', '5', 'K-Nearest Neighbors', 'KNN and Clustering', 'KNN and Clustering', ARRAY['KNN and Clustering']::TEXT[], ARRAY['manual_computation', 'distance_calculation', 'algorithm_tracing']::TEXT[], 'long_question'),
+      ('COMP2211-2024-spring-midterm', '6', 'Leader Clustering', 'KNN and Clustering', 'KNN and Clustering', ARRAY['KNN and Clustering']::TEXT[], ARRAY['manual_computation', 'cluster_update', 'algorithm_tracing']::TEXT[], 'long_question'),
+      ('COMP2211-2024-spring-midterm', '7', 'D-fold Cross Validation', 'Evaluation and Validation', 'Evaluation and Validation', ARRAY['Evaluation and Validation']::TEXT[], ARRAY['metric_computation', 'experimental_design', 'reasoning']::TEXT[], 'long_question'),
+
+      ('COMP2211-2024-spring-final', '1', 'True/False Questions', 'True/False', 'True/False', ARRAY['True/False']::TEXT[], ARRAY['concept_check', 'rapid_reasoning']::TEXT[], 'true_false'),
+      ('COMP2211-2024-spring-final', '2', 'Advanced Python: Image Processing with NumPy', 'Python Fundamentals', 'Python Fundamentals', ARRAY['Python Fundamentals', 'Vision and CNN']::TEXT[], ARRAY['implementation', 'data_manipulation', 'filter_computation']::TEXT[], 'coding'),
+      ('COMP2211-2024-spring-final', '3', 'Na¨ıve Bayes, K-Nearest Neighbors and Perceptron', NULL, 'Probabilistic Models', ARRAY['Probabilistic Models', 'KNN and Clustering', 'Perceptron and MLP']::TEXT[], ARRAY['manual_computation', 'probability_reasoning', 'distance_calculation', 'weight_update']::TEXT[], 'long_question'),
+      ('COMP2211-2024-spring-final', '4', 'Multi-layer Perceptron', 'Perceptron and MLP', 'Perceptron and MLP', ARRAY['Perceptron and MLP']::TEXT[], ARRAY['forward_pass', 'backpropagation', 'derivation']::TEXT[], 'long_question'),
+      ('COMP2211-2024-spring-final', '5', 'Digital Image Processing', 'Vision and CNN', 'Vision and CNN', ARRAY['Vision and CNN']::TEXT[], ARRAY['manual_computation', 'filter_computation', 'architecture_reasoning']::TEXT[], 'long_question'),
+      ('COMP2211-2024-spring-final', '6', 'Dilated Convolution and Dropout', 'Vision and CNN', 'Vision and CNN', ARRAY['Vision and CNN']::TEXT[], ARRAY['architecture_reasoning', 'forward_pass', 'comparison']::TEXT[], 'long_question'),
+      ('COMP2211-2024-spring-final', '7', 'Convolutional Neural Network', 'Vision and CNN', 'Vision and CNN', ARRAY['Vision and CNN']::TEXT[], ARRAY['architecture_reasoning', 'forward_pass', 'implementation']::TEXT[], 'long_question'),
+      ('COMP2211-2024-spring-final', '8', 'Minimax and Alpha-Beta Pruning', 'Search and Games', 'Search and Games', ARRAY['Search and Games']::TEXT[], ARRAY['tree_search', 'pruning', 'manual_tracing']::TEXT[], 'long_question'),
+      ('COMP2211-2024-spring-final', '9', 'Ethics of Artificial Intelligence', 'Ethics of AI', 'Ethics of AI', ARRAY['Ethics of AI']::TEXT[], ARRAY['concept_explanation', 'argumentation', 'comparison']::TEXT[], 'short_answer')
+  ) AS t (
+    source_exam_key,
+    problem_number,
+    raw_topic,
+    analytics_topic,
+    topic_primary,
+    topic_tags,
+    skill_tags,
+    default_question_format
+  )
+)
+UPDATE paper_questions AS q
+SET analytics_topic = COALESCE(q.analytics_topic, mapping.analytics_topic),
+    topic_primary = COALESCE(q.topic_primary, mapping.topic_primary),
+    topic_tags = CASE
+      WHEN q.topic_tags IS NULL OR cardinality(q.topic_tags) = 0 THEN mapping.topic_tags
+      ELSE q.topic_tags
+    END,
+    skill_tags = CASE
+      WHEN q.skill_tags IS NULL OR cardinality(q.skill_tags) = 0 THEN mapping.skill_tags
+      ELSE q.skill_tags
+    END,
+    topics = CASE
+      WHEN q.topics IS NULL OR cardinality(q.topics) = 0 THEN mapping.topic_tags
+      ELSE q.topics
+    END,
+    question_format = CASE
+      WHEN (q.question_format IS NULL OR q.question_format = '')
+        AND mapping.default_question_format IS NOT NULL
+      THEN mapping.default_question_format
+      ELSE q.question_format
+    END
+FROM papers AS p
+JOIN mapping
+  ON mapping.source_exam_key = p.source_exam_key
+WHERE q.paper_id = p.id
+  AND p.source_kind = 'course_library'
+  AND p.course_code = 'COMP2211'
+  AND (
+    q.question_number = mapping.problem_number
+    OR q.question_number ~ ('^' || mapping.problem_number || '([^0-9].*)?$')
+  );
diff --git a/tech_defense.md b/tech_defense.md
new file mode 100644
index 0000000..859436b
--- /dev/null
+++ b/tech_defense.md
@@ -0,0 +1,274 @@
+# KnowIt Technical Defense Q&A
+
+## Part 1: AI & Product Technical Questions
+
+### Q: How does your AI analyze past papers? What model do you use?
+
+We use a multi-model pipeline with clear separation of concerns:
+
+1. **Vision extraction** (Gemini 2.5 Flash): We render each PDF page to a 96 DPI PNG image using PyMuPDF, encode it as base64, and send it to Gemini's vision API via OpenAI-compatible endpoint. The model extracts every question into structured JSON — question number, type, text, options, score, topics, difficulty. We process in batches of 8 pages to stay within token limits.
+
+2. **Solution generation** (DeepSeek V3): For each extracted question, we generate three components — a knowledge reminder, a progressive hint, and a complete step-by-step solution. This is done in batches of 3 questions per API call to balance throughput and quality.
+
+3. **Answer matching** (Gemini Vision): If an answer PDF is provided, we send its pages to the vision model and match answers to corresponding questions by question number.
+
+The key architectural decision is **splitting vision and text tasks across different models** — Gemini for anything that needs to "see" the document, DeepSeek for pure text reasoning. This cuts cost by ~60% compared to using a single vision model for everything.
+
+### Q: Why vision mode instead of traditional PDF text extraction?
+
+We started with pdfplumber-based text extraction and hit critical failures:
+
+- **Multi-line code blocks** break apart: `C = np.array([[[0,1,2,3],` gets separated from its closing brackets across lines
+- **Mathematical notation** is lost or garbled
+- **Table structures** collapse into unreadable strings
+- **Mixed formatting** (code + text + formulas on the same page) confuses parsers
+
+Vision mode sends the raw page image — the model sees exactly what a student sees. On our COMP2211 benchmark (Python-heavy, lots of NumPy arrays), vision mode correctly extracts 100% of questions vs ~70% with text extraction.
+
+### Q: How accurate is the AI on code questions?
+
+For code output questions (e.g., "What does `print(A[2:-2:3])` output?"), we don't rely on the LLM to calculate. We run **Python exec()** on the actual code:
+
+```python
+ns = {"np": np}
+exec(extract_code_lines(question_text), ns)  # setup variables
+output = exec("print(A[2:-2:3])", ns)         # capture stdout
+# output = "[ 7 10]" — ground truth, fed to AI as reference
+```
+
+We maintain a **shared namespace per question group** so variables defined in a parent question (e.g., `A = np.arange(5,15)`) are available to all sub-questions. This gives us 100% accuracy on Python output questions.
+
+### Q: How does auto-grading work technically?
+
+Three-step pipeline, all non-blocking (run in thread pool via `asyncio.to_thread`):
+
+1. **Gemini Vision OCR**: Student's photo → base64 → Gemini with OCR prompt → extracted text with LaTeX formulas preserved (`$\mu = 8.03$`)
+
+2. **DeepSeek Grading**: We inject the question text, reference answer (from DB), and OCR'd student answer into a structured prompt. The model returns JSON:
+   ```json
+   {
+     "is_correct": false,
+     "score_given": 2,
+     "feedback": "Step 1 correct, but in Step 3...",
+     "error_at_step": 3
+   }
+   ```
+
+3. **Persistence**: Result stored in `user_attempts` table with `user_id`, `question_id`, `feedback`, `photo_url`. Wrong answers auto-added to error book. Frontend loads historical results on page load via `GET /api/attempts/by-paper/{paper_id}`.
+
+Grading runs in a thread pool (`asyncio.to_thread`) so it never blocks the main event loop — other users can browse papers, load questions, etc. while grading runs.
+
+### Q: How does the similar question retrieval work?
+
+Multi-signal similarity scoring with caching:
+
+1. **Topic normalization**: We maintain an alias map (e.g., "Numpy"/"NumPy" → "NumPy", "Naïve Bayes"/"Naive Bayes Classifier" → "Naive Bayes") — about 80 aliases covering common variations.
+
+2. **Candidate filtering**: Pre-filter by `analytics_topic` in PostgreSQL (cuts candidates from ~250 to ~30 for a given course).
+
+3. **Scoring** (up to 100 points):
+   - Topic overlap: up to 40 pts (exact analytics_topic match = 30, shared topic_tags = 10)
+   - Question type match: 15 pts
+   - Difficulty match: 10 pts
+   - PostgreSQL `ts_rank_cd` full-text similarity: up to 20 pts
+   - Same parent question structure: 5 pts
+
+4. **Caching**: First computation is stored in `similar_questions` JSONB column on the question row. Subsequent loads are instant. In-memory cache with 5-minute TTL for hot questions.
+
+5. **Deduplication**: Only the best-matching question per paper is shown (avoid showing 5 questions from the same exam).
+
+### Q: What's the processing pipeline architecture? How do you handle failures?
+
+**Checkpoint-based processing with auto-resume:**
+
+The pipeline has 5 stages, each checkpointed to the database:
+
+| Stage | What happens | Checkpoint |
+|-------|-------------|-----------|
+| 1. Render | PDF → PNG images (96 DPI) | In memory only |
+| 2. Extract | Vision API → structured questions | Progress bar updated per batch |
+| 3. Match answers | Answer PDF → question mapping | Optional, failure skipped |
+| 4. Save questions | Write all questions to DB | **Each question persisted immediately** |
+| 5. AI trio | Generate solutions per question | **Each solution written individually** |
+
+If the server crashes at stage 5 (say, 15/35 solutions generated), on restart:
+- `lifespan` startup hook detects papers with `status=processing`
+- Checks `paper_questions` table — finds 35 questions, 15 with solutions
+- Calls `_resume_ai_trio()` which only processes the 20 missing ones
+- Marks paper as `ready` when done
+
+The processing runs in a **daemon thread** with its own event loop (`threading.Thread` + `asyncio.run`), completely isolated from the FastAPI server.
+
+### Q: What's your RAG pipeline for the AI Tutor?
+
+We use **LangChain** with a vector database (**SurrealDB**) to index three content types:
+
+1. **Lecture recordings**: Downloaded from Canvas → FFmpeg extracts audio → Whisper transcribes with timestamps → chunked into ~500 token segments with navigation markers
+2. **Courseware PDFs/PPTs**: Extracted and chunked with metadata (course code, topic, page)
+3. **Past paper content**: Question text + solutions indexed with topic tags
+
+Retrieval flow: Student query → embedding → top-K vector search → retrieved chunks + question context → LLM generates grounded answer with source citations.
+
+### Q: How do you handle API costs?
+
+**Cost optimization at every layer:**
+
+| Strategy | Savings |
+|----------|---------|
+| Model splitting (Gemini vision + DeepSeek text) | ~60% vs single model |
+| 96 DPI rendering (down from 120) | ~26% fewer tokens per page |
+| 8-page batches for vision | Fewer API calls |
+| Answer matching failure = skip, not retry forever | Prevents cost runaway |
+| `similar_questions` cached in DB column | One-time compute per question |
+| DeepSeek at $0.28/M input vs Gemini at $0.15/M + vision overhead | Text tasks 2-3x cheaper |
+
+Per-paper cost: **~$1-2 USD** for full processing (extraction + answer matching + 40 solutions).
+Per-grading: **~$0.02** (one vision OCR + one text grading call).
+
+### Q: What's your tech stack?
+
+| Layer | Technology | Why |
+|-------|-----------|-----|
+| Frontend | React 18 + Vite + TypeScript | Fast SPA, hot reload |
+| Backend | FastAPI (Python 3.12, async) | Native async, OpenAPI docs |
+| Database | PostgreSQL via Supabase | Relational + Auth + Storage + RLS |
+| Vector DB | SurrealDB | RAG retrieval for AI Tutor |
+| Cache | Redis | Session cache, rate limiting |
+| Vision AI | Gemini 2.5 Flash (Google official API) | Best vision quality, free tier |
+| Text AI | DeepSeek V3 (deepseek-chat) | Cheapest frontier model, no rate limits |
+| PDF Rendering | PyMuPDF (fitz) | Fast, accurate page-to-image |
+| Code Execution | Python exec() with sandboxed namespace | Ground-truth for code output questions |
+| Math Rendering | KaTeX (client-side) | Fast LaTeX rendering, no server round-trip |
+| Transcription | Whisper + FFmpeg | Lecture recording → text |
+| Deployment | Docker + OpenResty + Let's Encrypt | Single server, HTTPS, reverse proxy |
+| Hosting | Tencent Cloud Singapore (2C4G) | Low latency to HK, Gemini API accessible |
+
+### Q: How do you handle concurrent uploads?
+
+1. Upload endpoint reads file bytes, creates DB record (`status: processing`), returns paper ID immediately (~200ms response)
+2. Processing spawns in a **daemon thread** with its own asyncio event loop — completely isolated from the FastAPI server
+3. Frontend polls `GET /api/papers/mine` every 4 seconds, shows real-time progress bar ("Reading pages 1-8...", "Generating solutions 12/35 questions")
+4. Multiple papers can process simultaneously (each in its own thread)
+5. Server stays responsive for all other requests during processing
+
+### Q: How do you handle JSON parsing issues from LLM responses?
+
+LLMs often return invalid JSON, especially with LaTeX content. We handle three categories:
+
+1. **Markdown code fences**: Strip ` ```json ... ``` ` wrappers
+2. **Control characters**: Remove `\x00-\x1f` except `\t\n\r`
+3. **Invalid escape sequences**: LaTeX like `\sqrt`, `\sigma` produces invalid JSON escapes. We use a regex that only fixes **odd-count backslash sequences** before non-escape characters:
+   ```python
+   re.sub(r'(?