HaHafeng
98d862dbd4
AIA V2.0 Major Updates: - Add StreamingService with OpenAI Compatible format (backend/common/streaming) - Upgrade Chat component V2 with Ant Design X deep integration - Implement 12 intelligent agents (5 phases: topic/design/review/data/writing) - Create AgentHub with 100% prototype V11 restoration - Create ChatWorkspace with fullscreen immersive experience - Add ThinkingBlock for deep thinking display - Add useAIStream Hook for stream handling - Add ConversationList for conversation management Backend (~1300 lines): - common/streaming: OpenAI adapter and streaming service - modules/aia: 12 agents config, conversation service, attachment service - Unified API routes to /api/v1 (RVW, PKB, AIA modules) - Update authentication and permission helpers Frontend (~3500 lines): - modules/aia: AgentHub + ChatWorkspace + AgentCard components - shared/Chat: AIStreamChat, ThinkingBlock, useAIStream, useConversations - Update all modules API endpoints to v1 - Modern design with theme colors (blue/yellow/teal/purple) Documentation (~2500 lines): - AIA module status and development guide - Universal capabilities catalog (11 services) - Quick reference card - System overview updates - All module documentation synchronization Other Updates: - DC Tool C: Python operations and frontend components - IIT Manager: session memory and wechat service - PKB/RVW/ASL: API route updates - Docker configs and deployment scripts - Database migrations and scripts - Test files and documentation Tested: AIA streaming verified, authentication working, core features functional Status: AIA V2.0 completed (85%), all changes synchronized
131 lines
2.3 KiB
Python
131 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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