HaHafeng
5523ef36ea
Summary: - Implement Prompt management infrastructure and core services - Build admin portal frontend with light theme - Integrate CodeMirror 6 editor for non-technical users Phase 3.5.1: Infrastructure Setup - Create capability_schema for Prompt storage - Add prompt_templates and prompt_versions tables - Add prompt:view/edit/debug/publish permissions - Migrate RVW prompts to database (RVW_EDITORIAL, RVW_METHODOLOGY) Phase 3.5.2: PromptService Core - Implement gray preview logic (DRAFT for debuggers, ACTIVE for users) - Module-level debug control (setDebugMode) - Handlebars template rendering - Variable extraction and validation (extractVariables, validateVariables) - Three-level disaster recovery (database -> cache -> hardcoded fallback) Phase 3.5.3: Management API - 8 RESTful endpoints (/api/admin/prompts/*) - Permission control (PROMPT_ENGINEER can edit, SUPER_ADMIN can publish) Phase 3.5.4: Frontend Management UI - Build admin portal architecture (AdminLayout, OrgLayout) - Add route system (/admin/*, /org/*) - Implement PromptListPage (filter, search, debug switch) - Implement PromptEditor (CodeMirror 6 simplified for clinical users) - Implement PromptEditorPage (edit, save, publish, test, version history) Technical Details: - Backend: 6 files, ~2044 lines (prompt.service.ts 596 lines) - Frontend: 9 files, ~1735 lines (PromptEditorPage.tsx 399 lines) - CodeMirror 6: Line numbers, auto-wrap, variable highlight, search, undo/redo - Chinese-friendly: 15px font, 1.8 line-height, system fonts Next Step: Phase 3.5.5 - Integrate RVW module with PromptService Tested: Backend API tests passed (8/8), Frontend pending user testing Status: Ready for Phase 3.5.5 RVW integration
125 lines
2.3 KiB
Python
125 lines
2.3 KiB
Python
"""
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数值映射（重编码）操作
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将分类变量的原始值映射为新值（如：男→1，女→2）。
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"""
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import pandas as pd
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from typing import Dict, Any, Optional
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def apply_recode(
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df: pd.DataFrame,
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column: str,
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mapping: Dict[Any, Any],
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create_new_column: bool = True,
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new_column_name: Optional[str] = None
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) -> pd.DataFrame:
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"""
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应用数值映射
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Args:
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df: 输入数据框
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column: 要重编码的列名
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mapping: 映射字典，如 {'男': 1, '女': 2}
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create_new_column: 是否创建新列（True）或覆盖原列（False）
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new_column_name: 新列名（create_new_column=True时使用）
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Returns:
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重编码后的数据框
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Examples:
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>>> df = pd.DataFrame({'性别': ['男', '女', '男', '女']})
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>>> mapping = {'男': 1, '女': 2}
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>>> result = apply_recode(df, '性别', mapping, True, '性别_编码')
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>>> result['性别_编码'].tolist()
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[1, 2, 1, 2]
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"""
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if df.empty:
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return df
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# 验证列是否存在
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if column not in df.columns:
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raise KeyError(f"列 '{column}' 不存在")
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if not mapping:
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raise ValueError('映射字典不能为空')
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# 确定目标列名
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if create_new_column:
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target_column = new_column_name or f'{column}_编码'
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else:
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target_column = column
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# 创建结果数据框（避免修改原数据）
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result = df.copy()
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# 应用映射
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result[target_column] = result[column].map(mapping)
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# 统计结果
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mapped_count = result[target_column].notna().sum()
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unmapped_count = result[target_column].isna().sum()
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total_count = len(result)
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print(f'映射完成: {mapped_count} 个值成功映射')
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if unmapped_count > 0:
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print(f'警告: {unmapped_count} 个值未找到对应映射')
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# 找出未映射的唯一值
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unmapped_mask = result[target_column].isna()
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unmapped_values = result.loc[unmapped_mask, column].unique()
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print(f'未映射的值: {list(unmapped_values)[:10]}') # 最多显示10个
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# 映射成功率
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success_rate = (mapped_count / total_count * 100) if total_count > 0 else 0
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print(f'映射成功率: {success_rate:.1f}%')
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return result
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