feat: 将数据集从国外员工缺勤数据替换为中国企业缺勤模拟数据

- 新增中国企业员工缺勤模拟数据集生成脚本(generate_dataset.py)，覆盖7个行业、180家企业、2600名员工
  - 重构 config.py，更新特征字段为中文名称，调整目标列、员工ID、行业类型等配置
  - 重构 clustering.py，简化聚类逻辑，更新聚类特征和群体命名（高压通勤型、健康波动型等）
  - 重构 feature_mining.py，更新相关性分析和群体比较维度（按行业、班次、婚姻状态等）
  - 新增 model_features.py 定义模型训练特征
  - 更新 preprocessing.py 和 train_model.py 适配新数据结构
  - 更新各 API 路由默认参数（model: random_forest, dimension: industry）
  - 前端更新主题样式和各视图组件适配中文字段
  - 更新系统名称为 China Enterprise Absence Analysis System

backend/core/preprocessing.py

@@ -1,10 +1,11 @@
-import pandas as pd
-import numpy as np
-from sklearn.preprocessing import StandardScaler
-import joblib
 import os
 
+import joblib
+import pandas as pd
+from sklearn.preprocessing import StandardScaler
+
 import config
+from core.generate_dataset import ensure_dataset
 
 
 class DataPreprocessor:
@@ -12,67 +13,57 @@ class DataPreprocessor:
         self.scaler = StandardScaler()
         self.is_fitted = False
         self.feature_names = None
-        
+
     def load_raw_data(self):
+        ensure_dataset()
         df = pd.read_csv(config.RAW_DATA_PATH, sep=config.CSV_SEPARATOR)
         df.columns = df.columns.str.strip()
         return df
-    
+
     def clean_data(self, df):
         df = df.copy()
-        
         df = df.drop_duplicates()
-        
+
         for col in df.columns:
-            if df[col].isnull().sum() > 0:
-                if df[col].dtype in ['int64', 'float64']:
-                    df[col].fillna(df[col].median(), inplace=True)
-                else:
-                    df[col].fillna(df[col].mode()[0], inplace=True)
-        
+            if df[col].isnull().sum() == 0:
+                continue
+            if pd.api.types.is_numeric_dtype(df[col]):
+                df[col] = df[col].fillna(df[col].median())
+            else:
+                df[col] = df[col].fillna(df[col].mode()[0])
+
         return df
-    
+
     def fit_transform(self, df):
         df = self.clean_data(df)
-        
-        if 'Absenteeism time in hours' in df.columns:
-            y = df['Absenteeism time in hours'].values
-            feature_df = df.drop(columns=['Absenteeism time in hours'])
+        if config.TARGET_COLUMN in df.columns:
+            y = df[config.TARGET_COLUMN].values
+            feature_df = df.drop(columns=[config.TARGET_COLUMN])
         else:
             y = None
             feature_df = df
-        
+
         self.feature_names = list(feature_df.columns)
-        
-        X = feature_df.values
-        
-        X = self.scaler.fit_transform(X)
-        
+        X = self.scaler.fit_transform(feature_df.values)
         self.is_fitted = True
-        
         return X, y
-    
+
     def transform(self, df):
         if not self.is_fitted:
             raise ValueError("Preprocessor has not been fitted yet.")
-        
+
         df = self.clean_data(df)
-        
-        if 'Absenteeism time in hours' in df.columns:
-            feature_df = df.drop(columns=['Absenteeism time in hours'])
+        if config.TARGET_COLUMN in df.columns:
+            feature_df = df.drop(columns=[config.TARGET_COLUMN])
         else:
             feature_df = df
-        
-        X = feature_df.values
-        X = self.scaler.transform(X)
-        
-        return X
-    
+        return self.scaler.transform(feature_df.values)
+
     def save_preprocessor(self):
         os.makedirs(config.MODELS_DIR, exist_ok=True)
         joblib.dump(self.scaler, config.SCALER_PATH)
         joblib.dump(self.feature_names, os.path.join(config.MODELS_DIR, 'feature_names.pkl'))
-    
+
     def load_preprocessor(self):
         self.scaler = joblib.load(config.SCALER_PATH)
         feature_names_path = os.path.join(config.MODELS_DIR, 'feature_names.pkl')
@@ -84,22 +75,18 @@ class DataPreprocessor:
 def get_clean_data():
     preprocessor = DataPreprocessor()
     df = preprocessor.load_raw_data()
-    df = preprocessor.clean_data(df)
-    return df
+    return preprocessor.clean_data(df)
 
 
 def save_clean_data():
     preprocessor = DataPreprocessor()
     df = preprocessor.load_raw_data()
     df = preprocessor.clean_data(df)
-    
     os.makedirs(config.PROCESSED_DATA_DIR, exist_ok=True)
     df.to_csv(config.CLEAN_DATA_PATH, index=False, sep=',')
-    
     return df
 
 
 if __name__ == '__main__':
-    df = save_clean_data()
-    print(f"Clean data saved. Shape: {df.shape}")
-    print(df.head())
+    data = save_clean_data()
+    print(f"Clean data saved. Shape: {data.shape}")