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/services/analysis_service.py

@@ -1,6 +1,6 @@
 import os
+
 import joblib
-import numpy as np
 
 import config
 from core.feature_mining import get_correlation_for_heatmap, group_comparison
@@ -10,109 +10,95 @@ class AnalysisService:
     def __init__(self):
         self.models = {}
         self.feature_names = None
-    
+        self.selected_features = None
+        self.training_metadata = {}
+
     def _ensure_models_loaded(self):
-        if not self.models:
-            model_files = {
-                'random_forest': 'random_forest_model.pkl',
-                'xgboost': 'xgboost_model.pkl',
-                'lightgbm': 'lightgbm_model.pkl',
-            }
-            
-            for name, filename in model_files.items():
-                model_path = os.path.join(config.MODELS_DIR, filename)
-                if os.path.exists(model_path):
-                    try:
-                        self.models[name] = joblib.load(model_path)
-                    except Exception as e:
-                        print(f"Failed to load {name}: {e}")
-            
-            feature_names_path = os.path.join(config.MODELS_DIR, 'feature_names.pkl')
-            if os.path.exists(feature_names_path):
-                self.feature_names = joblib.load(feature_names_path)
-    
+        if self.models:
+            return
+        metadata_path = os.path.join(config.MODELS_DIR, 'training_metadata.pkl')
+        if os.path.exists(metadata_path):
+            self.training_metadata = joblib.load(metadata_path)
+        model_files = {
+            'random_forest': 'random_forest_model.pkl',
+            'xgboost': 'xgboost_model.pkl',
+            'lightgbm': 'lightgbm_model.pkl',
+            'gradient_boosting': 'gradient_boosting_model.pkl',
+        }
+        allowed_models = self.training_metadata.get('available_models')
+        if allowed_models:
+            model_files = {k: v for k, v in model_files.items() if k in allowed_models}
+        for name, filename in model_files.items():
+            path = os.path.join(config.MODELS_DIR, filename)
+            if os.path.exists(path):
+                try:
+                    self.models[name] = joblib.load(path)
+                except Exception as exc:
+                    print(f'Failed to load model {name}: {exc}')
+        for filename, attr in [('feature_names.pkl', 'feature_names'), ('selected_features.pkl', 'selected_features')]:
+            path = os.path.join(config.MODELS_DIR, filename)
+            if os.path.exists(path):
+                try:
+                    setattr(self, attr, joblib.load(path))
+                except Exception as exc:
+                    print(f'Failed to load artifact {filename}: {exc}')
+
     def get_feature_importance(self, model_type='random_forest'):
         self._ensure_models_loaded()
-        
         if model_type not in self.models:
-            if self.models:
-                model_type = list(self.models.keys())[0]
-            else:
-                return self._get_default_importance()
-        
+            model_type = next(iter(self.models), 'default')
+        if model_type == 'default':
+            return self._get_default_importance()
         model = self.models[model_type]
-        
-        try:
-            if hasattr(model, 'feature_importances_'):
-                importances = model.feature_importances_
-            else:
-                return self._get_default_importance()
-            
-            feature_names = self.feature_names or [f'feature_{i}' for i in range(len(importances))]
-            
-            if len(feature_names) != len(importances):
-                feature_names = [f'feature_{i}' for i in range(len(importances))]
-            
-            feature_importance = list(zip(feature_names, importances))
-            feature_importance.sort(key=lambda x: x[1], reverse=True)
-            
-            features = []
-            for i, (name, imp) in enumerate(feature_importance[:15]):
-                features.append({
+        if not hasattr(model, 'feature_importances_'):
+            return self._get_default_importance()
+
+        importances = model.feature_importances_
+        feature_names = self.selected_features or self.feature_names or []
+        if len(feature_names) != len(importances):
+            feature_names = [f'feature_{idx}' for idx in range(len(importances))]
+        ranked = sorted(zip(feature_names, importances), key=lambda item: item[1], reverse=True)[:15]
+        return {
+            'model_type': model_type,
+            'features': [
+                {
                     'name': name,
                     'name_cn': config.FEATURE_NAME_CN.get(name, name),
-                    'importance': round(float(imp), 4),
-                    'rank': i + 1
-                })
-            
-            return {
-                'model_type': model_type,
-                'features': features
-            }
-        except Exception as e:
-            print(f"Error getting feature importance: {e}")
-            return self._get_default_importance()
-    
+                    'importance': round(float(importance), 4),
+                    'rank': idx + 1,
+                }
+                for idx, (name, importance) in enumerate(ranked)
+            ],
+        }
+
     def _get_default_importance(self):
-        default_features = [
-            ('Reason for absence', 0.25),
-            ('Transportation expense', 0.12),
-            ('Distance from Residence to Work', 0.10),
-            ('Service time', 0.08),
-            ('Age', 0.07),
-            ('Work load Average/day', 0.06),
-            ('Body mass index', 0.05),
-            ('Social drinker', 0.04),
-            ('Hit target', 0.03),
-            ('Son', 0.03),
-            ('Pet', 0.02),
-            ('Education', 0.02),
-            ('Social smoker', 0.01)
+        defaults = [
+            ('加班通勤压力指数', 0.24),
+            ('健康风险指数', 0.18),
+            ('请假类型', 0.12),
+            ('通勤时长分钟', 0.1),
+            ('月均加班时长', 0.08),
+            ('近90天缺勤次数', 0.07),
+            ('心理压力等级', 0.06),
+            ('家庭负担指数', 0.05),
         ]
-        
-        features = []
-        for i, (name, imp) in enumerate(default_features):
-            features.append({
-                'name': name,
-                'name_cn': config.FEATURE_NAME_CN.get(name, name),
-                'importance': imp,
-                'rank': i + 1
-            })
-        
         return {
             'model_type': 'default',
-            'features': features
+            'features': [
+                {
+                    'name': name,
+                    'name_cn': config.FEATURE_NAME_CN.get(name, name),
+                    'importance': importance,
+                    'rank': idx + 1,
+                }
+                for idx, (name, importance) in enumerate(defaults)
+            ],
         }
-    
+
     def get_correlation(self):
         return get_correlation_for_heatmap()
-    
+
     def get_group_comparison(self, dimension):
-        valid_dimensions = ['drinker', 'smoker', 'education', 'children', 'pet']
-        
-        if dimension not in valid_dimensions:
-            raise ValueError(f"Invalid dimension: {dimension}. Must be one of {valid_dimensions}")
-        
         return group_comparison(dimension)