e8235bf3ca
- 后端新增 JD-R（工作要求-资源）理论维度数据生成，包含工作要求、工作资源、
个人资源、中介变量共 16 个新特征列
- 新增 JD-R 分析服务与 API（维度统计、倦怠投入分析、双路径中介分析、
分组轮廓、风险分布）
- 新增 SHAP 可解释性分析模块（全局重要性、局部解释、特征交互、依赖图）
- 预测服务增加风险分类模型加载与概率预测能力
- 前端新增 JD-R 分析页面（JDRAnalysis.vue），含雷达图、散点图、路径分析等可视化
- 预测页面增加风险概率展示与 SHAP 特征解释
- 路由与导航菜单同步更新
213 lines
8.9 KiB
Python
213 lines
8.9 KiB
Python
import numpy as np
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import pandas as pd
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import config
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from core.model_features import engineer_features
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from core.preprocessing import get_clean_data
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class JDRService:
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"""JD-R（工作要求-资源）理论分析服务"""
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def __init__(self):
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self._df = None
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def _ensure_data(self):
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if self._df is None:
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self._df = get_clean_data()
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self._df = engineer_features(self._df)
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def get_dimension_scores(self):
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"""JD-R 三维度统计分布"""
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self._ensure_data()
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df = self._df
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result = {}
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for dim_key, col_name in [
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('demands', '工作要求指数'),
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('resources', '工作资源指数'),
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('personal', '个人资源指数'),
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]:
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if col_name not in df.columns:
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continue
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vals = df[col_name].dropna()
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bins = np.linspace(vals.min(), vals.max(), 8)
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hist, edges = np.histogram(vals, bins=bins)
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result[dim_key] = {
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'mean': round(float(vals.mean()), 2),
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'std': round(float(vals.std()), 2),
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'median': round(float(vals.median()), 2),
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'distribution': [
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{'range': f'{round(edges[i], 1)}-{round(edges[i+1], 1)}', 'count': int(hist[i])}
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for i in range(len(hist))
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],
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}
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# JD-R 平衡度
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if 'JD-R平衡度' in df.columns:
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balance = df['JD-R平衡度'].dropna()
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result['balance'] = {
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'mean': round(float(balance.mean()), 2),
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'positive_ratio': round(float((balance > 0).mean()) * 100, 1),
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}
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return result
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def get_burnout_engagement_analysis(self):
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"""倦怠与投入分析"""
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self._ensure_data()
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df = self._df
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result = {}
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if '工作倦怠' in df.columns:
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burnout = df['工作倦怠'].dropna()
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result['burnout'] = {
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'mean': round(float(burnout.mean()), 2),
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'std': round(float(burnout.std()), 2),
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'high_risk_ratio': round(float((burnout >= 5).mean()) * 100, 1),
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'distribution': self._make_distribution(burnout, 1, 7, 7),
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}
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if '工作投入' in df.columns:
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engagement = df['工作投入'].dropna()
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result['engagement'] = {
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'mean': round(float(engagement.mean()), 2),
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'std': round(float(engagement.std()), 2),
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'low_engagement_ratio': round(float((engagement <= 3).mean()) * 100, 1),
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'distribution': self._make_distribution(engagement, 1, 7, 7),
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}
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# 相关性分析
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corr_cols = {}
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if '工作倦怠' in df.columns:
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corr_cols['burnout'] = '工作倦怠'
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if '工作投入' in df.columns:
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corr_cols['engagement'] = '工作投入'
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if '工作要求指数' in df.columns:
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corr_cols['demands'] = '工作要求指数'
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if '工作资源指数' in df.columns:
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corr_cols['resources'] = '工作资源指数'
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if config.TARGET_COLUMN in df.columns:
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corr_cols['absence_hours'] = config.TARGET_COLUMN
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if len(corr_cols) >= 2:
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corr_df = df[[v for v in corr_cols.values()]].dropna()
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corr_matrix = corr_df.corr()
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correlations = {}
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for k1, v1 in corr_cols.items():
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for k2, v2 in corr_cols.items():
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if k1 != k2 and v1 in corr_matrix.index and v2 in corr_matrix.columns:
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correlations[f'{k1}_vs_{k2}'] = round(float(corr_matrix.loc[v1, v2]), 3)
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result['correlations'] = correlations
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return result
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def get_jdr_path_analysis(self):
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"""JD-R 双路径中介分析"""
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self._ensure_data()
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df = self._df
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result = {}
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target = config.TARGET_COLUMN
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# 健康损伤路径: demands -> burnout -> absence
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if all(col in df.columns for col in ['工作要求指数', '工作倦怠', target]):
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cols = ['工作要求指数', '工作倦怠', target]
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sub = df[cols].dropna()
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if len(sub) > 30:
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r_demands_burnout = sub['工作要求指数'].corr(sub['工作倦怠'])
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r_burnout_absence = sub['工作倦怠'].corr(sub[target])
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r_demands_absence = sub['工作要求指数'].corr(sub[target])
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indirect = r_demands_burnout * r_burnout_absence
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result['health_impairment'] = {
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'direct_effect_demands': round(float(r_demands_absence), 3),
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'indirect_via_burnout': round(float(indirect), 3),
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'mediation_ratio': round(float(indirect / r_demands_absence) if r_demands_absence != 0 else 0, 3),
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'demands_to_burnout': round(float(r_demands_burnout), 3),
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'burnout_to_absence': round(float(r_burnout_absence), 3),
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}
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# 激励路径: resources -> engagement -> lower absence
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if all(col in df.columns for col in ['工作资源指数', '工作投入', target]):
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cols = ['工作资源指数', '工作投入', target]
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sub = df[cols].dropna()
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if len(sub) > 30:
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r_resources_engagement = sub['工作资源指数'].corr(sub['工作投入'])
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r_engagement_absence = sub['工作投入'].corr(sub[target])
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r_resources_absence = sub['工作资源指数'].corr(sub[target])
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indirect = r_resources_engagement * r_engagement_absence
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result['motivational'] = {
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'direct_effect_resources': round(float(r_resources_absence), 3),
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'indirect_via_engagement': round(float(indirect), 3),
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'mediation_ratio': round(float(indirect / r_resources_absence) if r_resources_absence != 0 else 0, 3),
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'resources_to_engagement': round(float(r_resources_engagement), 3),
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'engagement_to_absence': round(float(r_engagement_absence), 3),
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}
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return result
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def get_jdr_profile(self, dimension='所属行业'):
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"""按维度分组的 JD-R 轮廓"""
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self._ensure_data()
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df = self._df
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if dimension not in df.columns:
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return {'error': f'Dimension {dimension} not found'}
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score_cols = ['工作要求指数', '工作资源指数', '个人资源指数', '工作倦怠', '工作投入']
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existing_cols = [c for c in score_cols if c in df.columns]
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if not existing_cols:
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return {'error': 'JD-R scores not computed'}
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group_cols = [dimension] + existing_cols
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if config.TARGET_COLUMN in df.columns:
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group_cols.append(config.TARGET_COLUMN)
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grouped = df[group_cols].groupby(dimension).agg(['mean', 'std']).round(2)
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profiles = []
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for group_name in grouped.index:
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profile = {'group_name': str(group_name)}
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for col in existing_cols:
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profile[col] = round(float(grouped.loc[group_name, (col, 'mean')]), 2)
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if config.TARGET_COLUMN in df.columns:
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profile['avg_absence_hours'] = round(float(grouped.loc[group_name, (config.TARGET_COLUMN, 'mean')]), 2)
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profiles.append(profile)
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return {'dimension': dimension, 'profiles': profiles}
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def get_risk_distribution(self):
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"""风险等级分布"""
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self._ensure_data()
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df = self._df
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target = config.TARGET_COLUMN
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if target not in df.columns:
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return {'error': 'Target column not found'}
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hours = df[target]
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levels = [
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{'level': 'low', 'label': '低风险', 'color': '#22c55e', 'count': int((hours < 4).sum()),
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'percentage': round(float((hours < 4).mean()) * 100, 1), 'avg_hours': round(float(hours[hours < 4].mean()), 2) if (hours < 4).any() else 0},
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{'level': 'medium', 'label': '中风险', 'color': '#f59e0b', 'count': int(((hours >= 4) & (hours <= 8)).sum()),
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'percentage': round(float(((hours >= 4) & (hours <= 8)).mean()) * 100, 1),
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'avg_hours': round(float(hours[(hours >= 4) & (hours <= 8)].mean()), 2) if ((hours >= 4) & (hours <= 8)).any() else 0},
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{'level': 'high', 'label': '高风险', 'color': '#ef4444', 'count': int((hours > 8).sum()),
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'percentage': round(float((hours > 8).mean()) * 100, 1), 'avg_hours': round(float(hours[hours > 8].mean()), 2) if (hours > 8).any() else 0},
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]
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return {'levels': levels, 'total': len(hours)}
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def _make_distribution(self, series, low, high, n_bins):
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bins = np.linspace(low, high, n_bins + 1)
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hist, edges = np.histogram(series, bins=bins)
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return [
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{'range': f'{round(edges[i], 1)}-{round(edges[i+1], 1)}', 'count': int(hist[i])}
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for i in range(len(hist))
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]
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jdr_service = JDRService()
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