shenjianZ
e63267cef6
- 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
106 lines
3.9 KiB
Python
106 lines
3.9 KiB
Python
import os
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import joblib
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import config
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from core.feature_mining import get_correlation_for_heatmap, group_comparison
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class AnalysisService:
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def __init__(self):
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self.models = {}
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self.feature_names = None
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self.selected_features = None
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self.training_metadata = {}
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def _ensure_models_loaded(self):
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if self.models:
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return
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metadata_path = os.path.join(config.MODELS_DIR, 'training_metadata.pkl')
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if os.path.exists(metadata_path):
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self.training_metadata = joblib.load(metadata_path)
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model_files = {
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'random_forest': 'random_forest_model.pkl',
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'xgboost': 'xgboost_model.pkl',
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'lightgbm': 'lightgbm_model.pkl',
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'gradient_boosting': 'gradient_boosting_model.pkl',
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}
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allowed_models = self.training_metadata.get('available_models')
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if allowed_models:
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model_files = {k: v for k, v in model_files.items() if k in allowed_models}
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for name, filename in model_files.items():
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path = os.path.join(config.MODELS_DIR, filename)
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if os.path.exists(path):
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try:
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self.models[name] = joblib.load(path)
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except Exception as exc:
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print(f'Failed to load model {name}: {exc}')
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for filename, attr in [('feature_names.pkl', 'feature_names'), ('selected_features.pkl', 'selected_features')]:
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path = os.path.join(config.MODELS_DIR, filename)
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if os.path.exists(path):
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try:
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setattr(self, attr, joblib.load(path))
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except Exception as exc:
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print(f'Failed to load artifact {filename}: {exc}')
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def get_feature_importance(self, model_type='random_forest'):
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self._ensure_models_loaded()
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if model_type not in self.models:
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model_type = next(iter(self.models), 'default')
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if model_type == 'default':
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return self._get_default_importance()
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model = self.models[model_type]
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if not hasattr(model, 'feature_importances_'):
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return self._get_default_importance()
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importances = model.feature_importances_
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feature_names = self.selected_features or self.feature_names or []
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if len(feature_names) != len(importances):
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feature_names = [f'feature_{idx}' for idx in range(len(importances))]
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ranked = sorted(zip(feature_names, importances), key=lambda item: item[1], reverse=True)[:15]
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return {
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'model_type': model_type,
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'features': [
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{
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'name': name,
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'name_cn': config.FEATURE_NAME_CN.get(name, name),
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'importance': round(float(importance), 4),
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'rank': idx + 1,
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}
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for idx, (name, importance) in enumerate(ranked)
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],
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}
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def _get_default_importance(self):
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defaults = [
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('加班通勤压力指数', 0.24),
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('健康风险指数', 0.18),
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('请假类型', 0.12),
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('通勤时长分钟', 0.1),
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('月均加班时长', 0.08),
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('近90天缺勤次数', 0.07),
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('心理压力等级', 0.06),
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('家庭负担指数', 0.05),
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]
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return {
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'model_type': 'default',
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'features': [
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{
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'name': name,
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'name_cn': config.FEATURE_NAME_CN.get(name, name),
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'importance': importance,
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'rank': idx + 1,
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}
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for idx, (name, importance) in enumerate(defaults)
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],
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}
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def get_correlation(self):
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return get_correlation_for_heatmap()
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def get_group_comparison(self, dimension):
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return group_comparison(dimension)
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analysis_service = AnalysisService()
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