From e8235bf3caf215007124ce534983975863d8667a Mon Sep 17 00:00:00 2001 From: shuo Date: Sat, 4 Apr 2026 07:15:46 +0800 Subject: [PATCH] =?UTF-8?q?=20=20=20feat:=20=E6=B7=BB=E5=8A=A0=20JD-R=20?= =?UTF-8?q?=E7=90=86=E8=AE=BA=E5=88=86=E6=9E=90=E6=A8=A1=E5=9D=97=E4=B8=8E?= =?UTF-8?q?=20SHAP=20=E5=8F=AF=E8=A7=A3=E9=87=8A=E6=80=A7=E5=88=86?= =?UTF-8?q?=E6=9E=90=E5=8A=9F=E8=83=BD?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit - 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 --- backend/0.43.0 | 30 + backend/=0.43.0 | 38 + backend/api/__init__.py | 4 + backend/api/jdr_routes.py | 51 + backend/api/predict_routes.py | 15 + backend/api/shap_routes.py | 50 + backend/app.py | 13 + backend/config.py | 59 + backend/core/generate_dataset.py | 181 +- backend/core/model_features.py | 89 + backend/core/shap_analysis.py | 399 +++ backend/core/train_model.py | 74 + .../outputs/eval_figures/01_模型性能对比.png | Bin 0 -> 105581 bytes .../eval_figures/02_LSTM真实值_vs_预测值.png | Bin 0 -> 304584 bytes .../outputs/eval_figures/03_LSTM残差分析.png | Bin 0 -> 439662 bytes .../eval_figures/04_LSTM风险等级混淆矩阵.png | Bin 0 -> 64649 bytes .../eval_figures/05_特征重要性_Top15.png | Bin 0 -> 98057 bytes .../eval_figures/evaluation_summary.json | 50 + .../outputs/eval_figures/lstm_predictions.csv | 2401 +++++++++++++++++ backend/requirements.txt | 1 + backend/services/jdr_service.py | 212 ++ backend/services/predict_service.py | 76 + backend/services/shap_service.py | 31 + frontend/package-lock.json | 1720 ++++++++++++ frontend/src/App.vue | 10 +- frontend/src/api/jdr.js | 21 + frontend/src/api/shap.js | 17 + frontend/src/router/index.js | 6 + frontend/src/views/JDRAnalysis.vue | 670 +++++ frontend/src/views/Prediction.vue | 94 +- 30 files changed, 6302 insertions(+), 10 deletions(-) create mode 100644 backend/0.43.0 create mode 100644 backend/=0.43.0 create mode 100644 backend/api/jdr_routes.py create mode 100644 backend/api/shap_routes.py create mode 100644 backend/core/shap_analysis.py create mode 100644 backend/outputs/eval_figures/01_模型性能对比.png create mode 100644 backend/outputs/eval_figures/02_LSTM真实值_vs_预测值.png create mode 100644 backend/outputs/eval_figures/03_LSTM残差分析.png create mode 100644 backend/outputs/eval_figures/04_LSTM风险等级混淆矩阵.png create mode 100644 backend/outputs/eval_figures/05_特征重要性_Top15.png create mode 100644 backend/outputs/eval_figures/evaluation_summary.json create mode 100644 backend/outputs/eval_figures/lstm_predictions.csv create mode 100644 backend/services/jdr_service.py create mode 100644 backend/services/shap_service.py create mode 100644 frontend/package-lock.json create mode 100644 frontend/src/api/jdr.js create mode 100644 frontend/src/api/shap.js create mode 100644 frontend/src/views/JDRAnalysis.vue diff --git a/backend/0.43.0 b/backend/0.43.0 new file mode 100644 index 0000000..6b5e534 --- /dev/null +++ b/backend/0.43.0 @@ -0,0 +1,30 @@ +Looking in indexes: https://pypi.tuna.tsinghua.edu.cn/simple +Collecting shap + Downloading https://pypi.tuna.tsinghua.edu.cn/packages/a5/8e/cee1ee136a4e54fe2fbb63a60d72d7c25e21a4ffe6aa05779cab7669cb31/shap-0.51.0-cp311-cp311-win_amd64.whl (554 kB) + ---------------------------------------- 554.9/554.9 kB 6.2 MB/s 0:00:00 +Requirement already satisfied: numpy>=2 in D:\anaconda\envs\ml-nlp\Lib\site-packages (from shap) (2.3.5) +Requirement already satisfied: scipy in D:\anaconda\envs\ml-nlp\Lib\site-packages (from shap) (1.17.1) +Requirement already satisfied: scikit-learn in D:\anaconda\envs\ml-nlp\Lib\site-packages (from shap) (1.8.0) +Requirement already satisfied: pandas in D:\anaconda\envs\ml-nlp\Lib\site-packages (from shap) (3.0.1) +Requirement already satisfied: tqdm>=4.27.0 in 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(from scikit-learn->shap) (3.6.0) +Installing collected packages: slicer, llvmlite, cloudpickle, numba, shap + +Successfully installed cloudpickle-3.1.2 llvmlite-0.47.0 numba-0.65.0 shap-0.51.0 slicer-0.0.8 diff --git a/backend/=0.43.0 b/backend/=0.43.0 new file mode 100644 index 0000000..ff1997a --- /dev/null +++ b/backend/=0.43.0 @@ -0,0 +1,38 @@ +Collecting shap + Downloading shap-0.51.0-cp312-cp312-win_amd64.whl.metadata (26 kB) +Collecting numpy>=2 (from shap) + Downloading numpy-2.4.4-cp312-cp312-win_amd64.whl.metadata (6.6 kB) +Requirement already satisfied: scipy in d:\anaconda\lib\site-packages (from shap) (1.13.1) +Requirement already satisfied: scikit-learn in d:\anaconda\lib\site-packages (from shap) (1.5.1) +Requirement already satisfied: pandas in d:\anaconda\lib\site-packages (from shap) (2.2.2) +Requirement already satisfied: tqdm>=4.27.0 in d:\anaconda\lib\site-packages (from shap) (4.66.5) +Requirement already satisfied: packaging>20.9 in d:\anaconda\lib\site-packages 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into account all the packages that are installed. This behaviour is the source of the following dependency conflicts. +contourpy 1.2.0 requires numpy<2.0,>=1.20, but you have numpy 2.0.2 which is incompatible. +gensim 4.3.3 requires numpy<2.0,>=1.18.5, but you have numpy 2.0.2 which is incompatible. +Successfully installed numpy-2.0.2 shap-0.51.0 slicer-0.0.8 diff --git a/backend/api/__init__.py b/backend/api/__init__.py index 4bd9fd3..341a0a9 100644 --- a/backend/api/__init__.py +++ b/backend/api/__init__.py @@ -2,6 +2,8 @@ from .overview_routes import overview_bp from .analysis_routes import analysis_bp from .predict_routes import predict_bp from .cluster_routes import cluster_bp +from .jdr_routes import jdr_bp +from .shap_routes import shap_bp def register_blueprints(app): @@ -9,3 +11,5 @@ def register_blueprints(app): app.register_blueprint(analysis_bp) app.register_blueprint(predict_bp) app.register_blueprint(cluster_bp) + app.register_blueprint(jdr_bp) + app.register_blueprint(shap_bp) diff --git a/backend/api/jdr_routes.py b/backend/api/jdr_routes.py new file mode 100644 index 0000000..4075813 --- /dev/null +++ b/backend/api/jdr_routes.py @@ -0,0 +1,51 @@ +from flask import Blueprint, jsonify, request + +from services.jdr_service import jdr_service + +jdr_bp = Blueprint('jdr', __name__, url_prefix='/api/jdr') + + +@jdr_bp.route('/dimensions', methods=['GET']) +def get_dimensions(): + try: + result = jdr_service.get_dimension_scores() + return jsonify({'code': 200, 'message': 'success', 'data': result}) + except Exception as e: + return jsonify({'code': 500, 'message': str(e), 'data': None}), 500 + + +@jdr_bp.route('/burnout-engagement', methods=['GET']) +def get_burnout_engagement(): + try: + result = jdr_service.get_burnout_engagement_analysis() + return jsonify({'code': 200, 'message': 'success', 'data': result}) + except Exception as e: + return jsonify({'code': 500, 'message': str(e), 'data': None}), 500 + + +@jdr_bp.route('/path-analysis', methods=['GET']) +def get_path_analysis(): + try: + result = jdr_service.get_jdr_path_analysis() + return jsonify({'code': 200, 'message': 'success', 'data': result}) + except Exception as e: + return jsonify({'code': 500, 'message': str(e), 'data': None}), 500 + + +@jdr_bp.route('/profile', methods=['GET']) +def get_profile(): + try: + dimension = request.args.get('dimension', '所属行业') + result = jdr_service.get_jdr_profile(dimension) + return jsonify({'code': 200, 'message': 'success', 'data': result}) + except Exception as e: + return jsonify({'code': 500, 'message': str(e), 'data': None}), 500 + + +@jdr_bp.route('/risk-distribution', methods=['GET']) +def get_risk_distribution(): + try: + result = jdr_service.get_risk_distribution() + return jsonify({'code': 200, 'message': 'success', 'data': result}) + except Exception as e: + return jsonify({'code': 500, 'message': str(e), 'data': None}), 500 diff --git a/backend/api/predict_routes.py b/backend/api/predict_routes.py index 6d87939..0f9561b 100644 --- a/backend/api/predict_routes.py +++ b/backend/api/predict_routes.py @@ -100,3 +100,18 @@ def get_model_info(): 'message': str(e), 'data': None }), 500 + + +@predict_bp.route('/risk-classify', methods=['POST']) +def risk_classify(): + try: + data = request.get_json() + if not data: + return jsonify({'code': 400, 'message': 'Request body is required', 'data': None}), 400 + model_type = data.get('model_type') + result = predict_service.predict_risk_classification(data, model_type) + if result is None: + return jsonify({'code': 404, 'message': 'No classifier available', 'data': None}), 404 + return jsonify({'code': 200, 'message': 'success', 'data': result}) + except Exception as e: + return jsonify({'code': 500, 'message': str(e), 'data': None}), 500 diff --git a/backend/api/shap_routes.py b/backend/api/shap_routes.py new file mode 100644 index 0000000..67270a6 --- /dev/null +++ b/backend/api/shap_routes.py @@ -0,0 +1,50 @@ +from flask import Blueprint, jsonify, request + +from services.shap_service import shap_service + +shap_bp = Blueprint('shap', __name__, url_prefix='/api/shap') + + +@shap_bp.route('/global', methods=['GET']) +def get_global_importance(): + try: + model_type = request.args.get('model', 'random_forest') + result = shap_service.get_global_importance(model_type) + return jsonify({'code': 200, 'message': 'success', 'data': result}) + except Exception as e: + return jsonify({'code': 500, 'message': str(e), 'data': None}), 500 + + +@shap_bp.route('/local', methods=['POST']) +def get_local_explanation(): + try: + data = request.get_json() + if not data: + return jsonify({'code': 400, 'message': 'Request body is required', 'data': None}), 400 + model_type = data.get('model_type', 'random_forest') + result = shap_service.get_local_explanation(data, model_type) + return jsonify({'code': 200, 'message': 'success', 'data': result}) + except Exception as e: + return jsonify({'code': 500, 'message': str(e), 'data': None}), 500 + + +@shap_bp.route('/interaction', methods=['GET']) +def get_interactions(): + try: + model_type = request.args.get('model', 'random_forest') + top_n = int(request.args.get('top_n', 10)) + result = shap_service.get_interactions(model_type, top_n) + return jsonify({'code': 200, 'message': 'success', 'data': result}) + except Exception as e: + return jsonify({'code': 500, 'message': str(e), 'data': None}), 500 + + +@shap_bp.route('/dependence', methods=['GET']) +def get_dependence(): + try: + feature = request.args.get('feature', '月均加班时长') + model_type = request.args.get('model', 'random_forest') + result = shap_service.get_dependence(feature, model_type) + return jsonify({'code': 200, 'message': 'success', 'data': result}) + except Exception as e: + return jsonify({'code': 500, 'message': str(e), 'data': None}), 500 diff --git a/backend/app.py b/backend/app.py index 65fa0cd..8ddc068 100644 --- a/backend/app.py +++ b/backend/app.py @@ -39,6 +39,19 @@ def create_app(): '/api/cluster/result', '/api/cluster/profile', '/api/cluster/scatter' + ], + 'jdr': [ + '/api/jdr/dimensions', + '/api/jdr/burnout-engagement', + '/api/jdr/path-analysis', + '/api/jdr/profile', + '/api/jdr/risk-distribution' + ], + 'shap': [ + '/api/shap/global', + '/api/shap/local', + '/api/shap/interaction', + '/api/shap/dependence' ] } } diff --git a/backend/config.py b/backend/config.py index 1e37791..020f9a9 100644 --- a/backend/config.py +++ b/backend/config.py @@ -147,4 +147,63 @@ FEATURE_NAME_CN = { '年龄分层': '年龄分层', '通勤分层': '通勤分层', '加班分层': '加班分层', + # JD-R 工作要求维度 + '工作自主性': '工作自主性', + '情绪劳动强度': '情绪劳动强度', + '时间压力感知': '时间压力感知', + '角色模糊度': '角色模糊度', + '工作家庭冲突': '工作家庭冲突', + # JD-R 工作资源维度 + '上级支持': '上级支持', + '同事支持': '同事支持', + '技能多样性': '技能多样性', + '职业发展机会': '职业发展机会', + '参与决策': '参与决策', + '组织公平感': '组织公平感', + # JD-R 个人资源维度 + '自我效能感': '自我效能感', + '心理韧性': '心理韧性', + '乐观程度': '乐观程度', + # JD-R 中介变量 + '工作倦怠': '工作倦怠', + '工作投入': '工作投入', + # JD-R 复合指数 + '工作要求指数': '工作要求指数', + '工作资源指数': '工作资源指数', + '个人资源指数': '个人资源指数', + 'JD-R平衡度': 'JD-R平衡度', + '倦怠风险指数': '倦怠风险指数', + '工作投入指数': '工作投入指数', } + +# JD-R 理论维度映射 +JDR_DIMENSIONS = { + 'job_demands': { + 'name_cn': '工作要求', + 'features': ['月均加班时长', '通勤时长分钟', '是否夜班岗位', '工作自主性', + '情绪劳动强度', '时间压力感知', '角色模糊度', '工作家庭冲突'], + }, + 'job_resources': { + 'name_cn': '工作资源', + 'features': ['工作自主性', '上级支持', '同事支持', '技能多样性', + '职业发展机会', '参与决策', '组织公平感'], + }, + 'personal_resources': { + 'name_cn': '个人资源', + 'features': ['自我效能感', '心理韧性', '乐观程度'], + }, + 'mediators': { + 'name_cn': '中介变量', + 'features': ['工作倦怠', '工作投入'], + }, +} + +# 风险等级配置 +RISK_LEVELS = { + 'low': {'max_hours': 4, 'label': '低风险', 'color': '#22c55e'}, + 'medium': {'min_hours': 4, 'max_hours': 8, 'label': '中风险', 'color': '#f59e0b'}, + 'high': {'min_hours': 8, 'label': '高风险', 'color': '#ef4444'}, +} + +# JD-R 数据版本标记 +JDR_DATA_VERSION = '1.0' diff --git a/backend/core/generate_dataset.py b/backend/core/generate_dataset.py index 3c8e8f5..a5fb67a 100644 --- a/backend/core/generate_dataset.py +++ b/backend/core/generate_dataset.py @@ -387,16 +387,181 @@ def generate_dataset(output_path=None, sample_count=12000, random_state=None): return df -def ensure_dataset(): - if not os.path.exists(config.RAW_DATA_PATH): - generate_dataset(config.RAW_DATA_PATH) - return +def enrich_with_jdr_columns(df): + """为现有数据追加 JD-R（工作要求-资源）理论维度列。 - try: - df = pd.read_csv(config.RAW_DATA_PATH) - validate_dataset(df) - except Exception: + 在已有的员工/事件属性基础上，合成 16 个新列： + - 工作要求：工作自主性、情绪劳动强度、时间压力感知、角色模糊度、工作家庭冲突 + - 工作资源：上级支持、同事支持、技能多样性、职业发展机会、参与决策、组织公平感 + - 个人资源：自我效能感、心理韧性、乐观程度 + - 中介变量：工作倦怠、工作投入 + """ + rng = np.random.default_rng(config.RANDOM_STATE + 100) + df = df.copy() + n = len(df) + + # ── 辅助：条件性 Likert 生成 ── + def likert(mean_offset, std=0.8, low=1.0, high=5.0): + return np.clip(rng.normal(mean_offset, std, size=n), low, high) + + # ── 预提取列 ── + overtime = df['月均加班时长'].values + commute = df['通勤时长分钟'].values + night = df['是否夜班岗位'].values + children = df['子女数量'].values + married_arr = (df['婚姻状态'] == '已婚').astype(int).values + tenure = df['司龄年数'].values + team_size = df['团队人数'].values + manager_span = df['直属上级管理跨度'].values + exercise = df['每周运动频次'].values + sleep = df['近30天睡眠时长均值'].values + chronic = df['是否慢性病史'].values + perf_a = (df['绩效等级'] == 'A').astype(int).values + perf_ab = df['绩效等级'].isin(['A', 'B']).astype(int).values + level_map = {'初级': 0, '中级': 1, '高级': 2, '主管': 3, '经理及以上': 4} + level_vals = df['岗位级别'].map(level_map).fillna(1).values + industry_vals = df['所属行业'].values + employment_type = df['用工类型'].values + job_family = df['岗位序列'].values + company_scale_map = { + '100人以下': 0, '100-499人': 1, '500-999人': 2, '1000-4999人': 3, '5000人及以上': 4 + } + scale_vals = df['企业规模'].map(company_scale_map).fillna(1).values + + formal_employee = (df['用工类型'] == '正式员工').astype(int).values + edu_map = {'中专及以下': 0, '大专': 1, '本科': 2, '硕士': 3, '博士': 4} + edu_vals = df['最高学历'].map(edu_map).fillna(2).values + + # ── 工作要求维度 (5 列) ── + df['工作自主性'] = likert( + 3.2 + level_vals * 0.25 + + np.isin(industry_vals, ['互联网', '金融服务']).astype(int) * 0.3 + - night * 0.4 + ).round(1) + + df['情绪劳动强度'] = likert( + 2.8 + + np.isin(job_family, ['客服坐席', '销售业务']).astype(int) * 0.6 + + np.isin(industry_vals, ['医药健康', '零售连锁']).astype(int) * 0.3 + ).round(1) + + df['时间压力感知'] = likert( + 3.0 + overtime * 0.02 + commute * 0.01 + + np.isin(industry_vals, ['互联网', '金融服务']).astype(int) * 0.2 + ).round(1) + + df['角色模糊度'] = likert( + 2.5 + + np.isin(employment_type, ['劳务派遣', '外包驻场']).astype(int) * 0.5 + - tenure * 0.05 + ).round(1) + + df['工作家庭冲突'] = likert( + 2.6 + overtime * 0.02 + children * 0.3 + married_arr * 0.3 + ).round(1) + + # ── 工作资源维度 (6 列) ── + df['上级支持'] = likert( + 3.4 - manager_span * 0.02 + level_vals * 0.2 + ).round(1) + + df['同事支持'] = likert( + 3.3 + team_size * 0.02 + + np.isin(job_family, ['管理', '专业技术']).astype(int) * 0.2 + ).round(1) + + df['技能多样性'] = likert( + 3.0 + + np.isin(job_family, ['专业技术', '管理']).astype(int) * 0.5 + - np.isin(job_family, ['生产操作']).astype(int) * 0.3 + ).round(1) + + df['职业发展机会'] = likert( + 3.1 + + np.isin(industry_vals, ['互联网', '金融服务']).astype(int) * 0.4 + + scale_vals * 0.1 + ).round(1) + + df['参与决策'] = likert( + 2.8 + level_vals * 0.35 + ).round(1) + + df['组织公平感'] = likert( + 3.3 + formal_employee * 0.4 + perf_ab * 0.3 + ).round(1) + + # ── 个人资源维度 (3 列) ── + df['自我效能感'] = likert( + 3.3 + perf_a * 0.4 + perf_ab * 0.2 + tenure * 0.03 + edu_vals * 0.08 + ).round(1) + + df['心理韧性'] = likert( + 3.2 + exercise * 0.1 + sleep * 0.15 + tenure * 0.02 + ).round(1) + + df['乐观程度'] = likert( + 3.3 + perf_ab * 0.3 - chronic * 0.3 + married_arr * 0.15 + ).round(1) + + # ── 中介变量 (2 列) ── + # 工作倦怠 (1-7)：健康损伤过程 — 高需求→高倦怠 + df['工作倦怠'] = np.clip( + rng.normal(3.0, 0.8, size=n) + + overtime * 0.015 + night * 0.3 + commute * 0.008 + + df['情绪劳动强度'].values * 0.25 + + df['时间压力感知'].values * 0.25 + + df['工作家庭冲突'].values * 0.2 + + df['角色模糊度'].values * 0.15 + - df['工作自主性'].values * 0.2 + - df['上级支持'].values * 0.15 + - df['自我效能感'].values * 0.2 + - df['心理韧性'].values * 0.15, + 1.0, 7.0 + ).round(1) + + # 工作投入 (1-7)：激励过程 — 高资源→高投入 + df['工作投入'] = np.clip( + rng.normal(3.5, 0.8, size=n) + + df['工作自主性'].values * 0.2 + + df['上级支持'].values * 0.2 + + df['同事支持'].values * 0.15 + + df['技能多样性'].values * 0.15 + + df['职业发展机会'].values * 0.15 + + df['参与决策'].values * 0.1 + + df['组织公平感'].values * 0.1 + + df['自我效能感'].values * 0.2 + + df['心理韧性'].values * 0.15 + + df['乐观程度'].values * 0.15 + - df['工作倦怠'].values * 0.2, + 1.0, 7.0 + ).round(1) + + # JD-R 数据版本标记 + df['_jdr_version'] = config.JDR_DATA_VERSION + + return df + + +def ensure_dataset(): + needs_regenerate = not os.path.exists(config.RAW_DATA_PATH) + + if not needs_regenerate: + try: + df = pd.read_csv(config.RAW_DATA_PATH) + validate_dataset(df) + except Exception: + needs_regenerate = True + + if needs_regenerate: generate_dataset(config.RAW_DATA_PATH) + df = pd.read_csv(config.RAW_DATA_PATH) + + # 检查是否需要 JD-R 数据丰富 + jdr_columns = ['工作自主性', '上级支持', '自我效能感', '工作倦怠', '工作投入'] + if not all(col in df.columns for col in jdr_columns): + df = enrich_with_jdr_columns(df) + os.makedirs(os.path.dirname(config.RAW_DATA_PATH), exist_ok=True) + df.to_csv(config.RAW_DATA_PATH, index=False, encoding='utf-8-sig') if __name__ == '__main__': diff --git a/backend/core/model_features.py b/backend/core/model_features.py index d66d9d5..09a2253 100644 --- a/backend/core/model_features.py +++ b/backend/core/model_features.py @@ -35,6 +35,11 @@ NUMERICAL_OUTLIER_COLUMNS = [ 'BMI', '近30天睡眠时长均值', '每周运动频次', + # JD-R 维度列 + '工作自主性', '情绪劳动强度', '时间压力感知', '角色模糊度', '工作家庭冲突', + '上级支持', '同事支持', '技能多样性', '职业发展机会', '参与决策', '组织公平感', + '自我效能感', '心理韧性', '乐观程度', + '工作倦怠', '工作投入', ] DEFAULT_PREDICTION_INPUT = { 'industry': '制造业', @@ -82,6 +87,26 @@ DEFAULT_PREDICTION_INPUT = { 'urgent_leave_flag': 1, 'continuous_absence_flag': 0, 'previous_day_overtime_flag': 1, + # JD-R 工作要求维度 + 'work_autonomy': 3.0, + 'emotional_labor': 3.0, + 'time_pressure': 3.0, + 'role_ambiguity': 3.0, + 'work_family_conflict': 3.0, + # JD-R 工作资源维度 + 'supervisor_support': 3.0, + 'coworker_support': 3.0, + 'skill_variety': 3.0, + 'career_development': 3.0, + 'decision_participation': 3.0, + 'organizational_justice': 3.0, + # JD-R 个人资源维度 + 'self_efficacy': 3.0, + 'resilience': 3.0, + 'optimism': 3.0, + # JD-R 中介变量 + 'burnout': 3.5, + 'work_engagement': 3.5, } @@ -171,6 +196,50 @@ def engineer_features(df): ) df['管理负荷指数'] = df['团队人数'] * 0.4 + df['直属上级管理跨度'] * 0.25 + # ── JD-R 复合指数 ── + autonomy = df.get('工作自主性', pd.Series(3.0, index=df.index)) + df['工作要求指数'] = ( + df['月均加班时长'] * 0.20 + + df['通勤时长分钟'] * 0.08 + + df['是否夜班岗位'] * 1.5 + + (5 - autonomy) * 0.3 + + df.get('情绪劳动强度', pd.Series(3.0, index=df.index)) * 0.25 + + df.get('时间压力感知', pd.Series(3.0, index=df.index)) * 0.25 + + df.get('角色模糊度', pd.Series(3.0, index=df.index)) * 0.20 + + df.get('工作家庭冲突', pd.Series(3.0, index=df.index)) * 0.20 + ) / 2 + + df['工作资源指数'] = ( + autonomy * 0.18 + + df.get('上级支持', pd.Series(3.0, index=df.index)) * 0.18 + + df.get('同事支持', pd.Series(3.0, index=df.index)) * 0.14 + + df.get('技能多样性', pd.Series(3.0, index=df.index)) * 0.14 + + df.get('职业发展机会', pd.Series(3.0, index=df.index)) * 0.14 + + df.get('参与决策', pd.Series(3.0, index=df.index)) * 0.10 + + df.get('组织公平感', pd.Series(3.0, index=df.index)) * 0.12 + ) + + df['个人资源指数'] = ( + df.get('自我效能感', pd.Series(3.0, index=df.index)) * 0.35 + + df.get('心理韧性', pd.Series(3.0, index=df.index)) * 0.35 + + df.get('乐观程度', pd.Series(3.0, index=df.index)) * 0.30 + ) + + df['JD-R平衡度'] = df['工作资源指数'] - df['工作要求指数'] * 0.5 + + df['倦怠风险指数'] = ( + df.get('工作倦怠', pd.Series(3.5, index=df.index)) * 0.40 + + df['工作要求指数'] * 0.30 + - df['工作资源指数'] * 0.20 + - df['个人资源指数'] * 0.10 + ) + + df['工作投入指数'] = ( + df.get('工作投入', pd.Series(3.5, index=df.index)) * 0.40 + + df['工作资源指数'] * 0.30 + + df['个人资源指数'] * 0.30 + ) + df['工龄分层'] = pd.cut(df['司龄年数'], bins=[0, 2, 5, 10, 40], labels=['1', '2', '3', '4']) df['年龄分层'] = pd.cut(df['年龄'], bins=[18, 25, 32, 40, 60], labels=['1', '2', '3', '4']) df['通勤分层'] = pd.cut(df['通勤时长分钟'], bins=[0, 25, 45, 70, 180], labels=['1', '2', '3', '4']) @@ -299,6 +368,26 @@ def build_prediction_dataframe(data): 'previous_day_overtime_flag', DEFAULT_PREDICTION_INPUT['previous_day_overtime_flag'], ), + # JD-R 工作要求维度 + '工作自主性': data.get('work_autonomy', DEFAULT_PREDICTION_INPUT['work_autonomy']), + '情绪劳动强度': data.get('emotional_labor', DEFAULT_PREDICTION_INPUT['emotional_labor']), + '时间压力感知': data.get('time_pressure', DEFAULT_PREDICTION_INPUT['time_pressure']), + '角色模糊度': data.get('role_ambiguity', DEFAULT_PREDICTION_INPUT['role_ambiguity']), + '工作家庭冲突': data.get('work_family_conflict', DEFAULT_PREDICTION_INPUT['work_family_conflict']), + # JD-R 工作资源维度 + '上级支持': data.get('supervisor_support', DEFAULT_PREDICTION_INPUT['supervisor_support']), + '同事支持': data.get('coworker_support', DEFAULT_PREDICTION_INPUT['coworker_support']), + '技能多样性': data.get('skill_variety', DEFAULT_PREDICTION_INPUT['skill_variety']), + '职业发展机会': data.get('career_development', DEFAULT_PREDICTION_INPUT['career_development']), + '参与决策': data.get('decision_participation', DEFAULT_PREDICTION_INPUT['decision_participation']), + '组织公平感': data.get('organizational_justice', DEFAULT_PREDICTION_INPUT['organizational_justice']), + # JD-R 个人资源维度 + '自我效能感': data.get('self_efficacy', DEFAULT_PREDICTION_INPUT['self_efficacy']), + '心理韧性': data.get('resilience', DEFAULT_PREDICTION_INPUT['resilience']), + '乐观程度': data.get('optimism', DEFAULT_PREDICTION_INPUT['optimism']), + # JD-R 中介变量 + '工作倦怠': data.get('burnout', DEFAULT_PREDICTION_INPUT['burnout']), + '工作投入': data.get('work_engagement', DEFAULT_PREDICTION_INPUT['work_engagement']), } return pd.DataFrame([feature_row]) diff --git a/backend/core/shap_analysis.py b/backend/core/shap_analysis.py new file mode 100644 index 0000000..0f22d60 --- /dev/null +++ b/backend/core/shap_analysis.py @@ -0,0 +1,399 @@ +import os + +import joblib +import numpy as np +import pandas as pd + +import config + +try: + import shap + SHAP_AVAILABLE = True +except ImportError: + SHAP_AVAILABLE = False + + +class SHAPAnalyzer: + """基于 SHAP 值的可解释性分析器，按 JD-R 维度聚合解释结果。""" + + def __init__(self): + self.explainers = {} + self.models = {} + self.scaler = None + self.feature_names = None + self.selected_features = None + self.label_encoders = {} + self.background_data = None + self._initialized = False + + def _ensure_initialized(self): + if self._initialized: + return + + # 加载回归模型（SHAP 分析基于回归模型） + models_dir = config.MODELS_DIR + model_files = { + 'random_forest': 'random_forest_model.pkl', + 'xgboost': 'xgboost_model.pkl', + 'lightgbm': 'lightgbm_model.pkl', + 'gradient_boosting': 'gradient_boosting_model.pkl', + 'extra_trees': 'extra_trees_model.pkl', + } + for name, filename in model_files.items(): + path = os.path.join(models_dir, filename) + if os.path.exists(path): + try: + self.models[name] = joblib.load(path) + except Exception: + pass + + # 加载预处理工件 + if os.path.exists(config.SCALER_PATH): + self.scaler = joblib.load(config.SCALER_PATH) + for filename, attr in [ + ('feature_names.pkl', 'feature_names'), + ('selected_features.pkl', 'selected_features'), + ('label_encoders.pkl', 'label_encoders'), + ]: + path = os.path.join(models_dir, filename) + if os.path.exists(path): + try: + setattr(self, attr, joblib.load(path)) + except Exception: + pass + + self._initialized = True + + def _get_tree_explainer(self, model_type='random_forest'): + """获取或创建 TreeExplainer""" + if not SHAP_AVAILABLE: + return None + + if model_type in self.explainers: + return self.explainers[model_type] + + model = self.models.get(model_type) + if model is None: + return None + + try: + explainer = shap.TreeExplainer(model) + self.explainers[model_type] = explainer + return explainer + except Exception: + return None + + def _get_background_sample(self, n_samples=500): + """获取背景数据样本""" + if self.background_data is not None: + return self.background_data + + try: + from core.preprocessing import get_clean_data + from core.model_features import ( + normalize_columns, prepare_modeling_dataframe, + apply_outlier_bounds, fit_outlier_bounds, + engineer_features, extract_xy, fit_label_encoders, + apply_label_encoders, align_feature_frame, to_float_array, + NUMERICAL_OUTLIER_COLUMNS, ORDINAL_COLUMNS, + ) + + raw_df = normalize_columns(get_clean_data()) + df = prepare_modeling_dataframe(raw_df) + + bounds = fit_outlier_bounds(df, NUMERICAL_OUTLIER_COLUMNS) + df = apply_outlier_bounds(df, bounds) + df = engineer_features(df) + X_df, _ = extract_xy(df) + X_df, encoders = fit_label_encoders(X_df, ORDINAL_COLUMNS) + + if self.feature_names: + X_df = align_feature_frame(X_df, self.feature_names) + + if n_samples < len(X_df): + X_df = X_df.sample(n=n_samples, random_state=config.RANDOM_STATE) + + if self.scaler is not None: + X = self.scaler.transform(to_float_array(X_df)) + else: + X = to_float_array(X_df) + + if self.selected_features and self.feature_names: + selected_indices = [self.feature_names.index(n) for n in self.selected_features if n in self.feature_names] + if selected_indices: + X = X[:, selected_indices] + + self.background_data = X + return X + except Exception: + return None + + def _get_feature_display_names(self): + """获取特征显示名称映射""" + feature_names = self.selected_features or self.feature_names or [] + return {name: config.FEATURE_NAME_CN.get(name, name) for name in feature_names} + + def _map_feature_to_dimension(self, feature_name): + """将特征映射到 JD-R 维度""" + for dim_key, dim_info in config.JDR_DIMENSIONS.items(): + if feature_name in dim_info['features']: + return dim_key + # 事件/上下文特征 + context_features = ['缺勤月份', '星期几', '是否节假日前后', '季节', + '请假类型', '请假原因大类', '是否提供医院证明', + '是否临时请假', '是否连续缺勤', '前一工作日是否加班'] + if feature_name in context_features: + return 'event_context' + return 'other' + + def global_shap_values(self, model_type='random_forest'): + """计算全局 SHAP 重要性，按 JD-R 维度分组""" + if not SHAP_AVAILABLE: + return {'error': 'SHAP library not installed'} + + self._ensure_initialized() + explainer = self._get_tree_explainer(model_type) + if explainer is None: + return {'error': f'No tree model available for {model_type}'} + + X = self._get_background_sample() + if X is None: + return {'error': 'Failed to prepare background data'} + + try: + shap_values = explainer.shap_values(X) + if isinstance(shap_values, list): + shap_values = shap_values[0] + + mean_abs_shap = np.abs(shap_values).mean(axis=0) + feature_names = self.selected_features or self.feature_names or [] + name_map = self._get_feature_display_names() + + # 按维度分组 + dimensions = {} + for dim_key, dim_info in config.JDR_DIMENSIONS.items(): + dim_features = [] + for fname in feature_names: + if fname in dim_info['features']: + idx = list(feature_names).index(fname) + dim_features.append({ + 'name': fname, + 'name_cn': name_map.get(fname, fname), + 'importance': round(float(mean_abs_shap[idx]), 4), + }) + if dim_features: + dimensions[dim_key] = { + 'name_cn': dim_info['name_cn'], + 'features': sorted(dim_features, key=lambda x: x['importance'], reverse=True), + } + + # 事件上下文维度 + context_features = [] + for fname in feature_names: + if self._map_feature_to_dimension(fname) == 'event_context': + idx = list(feature_names).index(fname) + context_features.append({ + 'name': fname, + 'name_cn': name_map.get(fname, fname), + 'importance': round(float(mean_abs_shap[idx]), 4), + }) + if context_features: + dimensions['event_context'] = { + 'name_cn': '事件上下文', + 'features': sorted(context_features, key=lambda x: x['importance'], reverse=True), + } + + # Top 特征列表 + top_indices = np.argsort(mean_abs_shap)[::-1][:20] + top_features = [] + for idx in top_indices: + fname = feature_names[idx] if idx < len(feature_names) else f'f{idx}' + top_features.append({ + 'name': fname, + 'name_cn': name_map.get(fname, fname), + 'importance': round(float(mean_abs_shap[idx]), 4), + 'dimension': self._map_feature_to_dimension(fname), + }) + + return { + 'model_type': model_type, + 'dimensions': dimensions, + 'top_features': top_features, + } + except Exception as exc: + return {'error': str(exc)} + + def local_shap_values(self, data, model_type='random_forest'): + """计算单条预测的 SHAP 解释""" + if not SHAP_AVAILABLE: + return {'error': 'SHAP library not installed'} + + self._ensure_initialized() + explainer = self._get_tree_explainer(model_type) + if explainer is None: + return {'error': f'No tree model available for {model_type}'} + + try: + from core.model_features import ( + build_prediction_dataframe, engineer_features, + apply_label_encoders, align_feature_frame, to_float_array, + ) + + X_df = build_prediction_dataframe(data) + X_df = engineer_features(X_df) + X_df = apply_label_encoders(X_df, self.label_encoders) + if self.feature_names: + X_df = align_feature_frame(X_df, self.feature_names) + features = self.scaler.transform(to_float_array(X_df)) + if self.selected_features and self.feature_names: + selected_indices = [self.feature_names.index(n) for n in self.selected_features if n in self.feature_names] + if selected_indices: + features = features[:, selected_indices] + + shap_values = explainer.shap_values(features) + if isinstance(shap_values, list): + shap_values = shap_values[0] + + base_value = float(explainer.expected_value) + if isinstance(base_value, (list, np.ndarray)): + base_value = float(base_value[0]) + + feature_names = self.selected_features or self.feature_names or [] + name_map = self._get_feature_display_names() + + feature_contributions = [] + dimension_contribution = {} + for idx, fname in enumerate(feature_names): + sv = float(shap_values[0][idx]) + fv = float(features[0][idx]) + dim = self._map_feature_to_dimension(fname) + feature_contributions.append({ + 'name': fname, + 'name_cn': name_map.get(fname, fname), + 'shap_value': round(sv, 4), + 'feature_value': round(fv, 4), + 'dimension': dim, + }) + dimension_contribution[dim] = dimension_contribution.get(dim, 0) + sv + + feature_contributions.sort(key=lambda x: abs(x['shap_value']), reverse=True) + + # 维度标签 + dim_labels = {} + for dk, di in config.JDR_DIMENSIONS.items(): + dim_labels[dk] = di['name_cn'] + dim_labels['event_context'] = '事件上下文' + dim_labels['other'] = '其他' + + return { + 'base_value': round(base_value, 4), + 'features': feature_contributions[:20], + 'dimension_contribution': { + dim_labels.get(k, k): round(v, 4) + for k, v in sorted(dimension_contribution.items(), key=lambda x: abs(x[1]), reverse=True) + }, + } + except Exception as exc: + return {'error': str(exc)} + + def shap_interaction(self, model_type='random_forest', top_n=10): + """计算 SHAP 交互值""" + if not SHAP_AVAILABLE: + return {'error': 'SHAP library not installed'} + + self._ensure_initialized() + explainer = self._get_tree_explainer(model_type) + if explainer is None: + return {'error': f'No tree model available for {model_type}'} + + X = self._get_background_sample(n_samples=200) + if X is None: + return {'error': 'Failed to prepare background data'} + + try: + interaction_values = explainer.shap_interaction_values(X) + if isinstance(interaction_values, list): + interaction_values = interaction_values[0] + + mean_interaction = np.abs(interaction_values).mean(axis=0) + feature_names = self.selected_features or self.feature_names or [] + + # 获取 top_n 特征的交互 + mean_abs = np.abs(interaction_values.mean(axis=0)) + np.fill_diagonal(mean_abs, 0) + flat_idx = np.argsort(mean_abs.ravel())[::-1][:top_n * 2] + top_pairs = [] + seen = set() + for idx in flat_idx: + i, j = divmod(idx, mean_abs.shape[1]) + if i >= j: + continue + pair_key = (min(i, j), max(i, j)) + if pair_key in seen: + continue + seen.add(pair_key) + fi = feature_names[i] if i < len(feature_names) else f'f{i}' + fj = feature_names[j] if j < len(feature_names) else f'f{j}' + name_map = self._get_feature_display_names() + top_pairs.append({ + 'feature_1': fi, + 'feature_1_cn': name_map.get(fi, fi), + 'feature_2': fj, + 'feature_2_cn': name_map.get(fj, fj), + 'strength': round(float(mean_interaction[i, j]), 4), + }) + if len(top_pairs) >= top_n: + break + + return { + 'model_type': model_type, + 'top_interactions': top_pairs, + } + except Exception as exc: + return {'error': str(exc)} + + def shap_dependence(self, feature_name, model_type='random_forest'): + """计算单个特征的 SHAP 依赖图数据""" + if not SHAP_AVAILABLE: + return {'error': 'SHAP library not installed'} + + self._ensure_initialized() + explainer = self._get_tree_explainer(model_type) + if explainer is None: + return {'error': f'No tree model available for {model_type}'} + + X = self._get_background_sample() + if X is None: + return {'error': 'Failed to prepare background data'} + + try: + feature_names = self.selected_features or self.feature_names or [] + if feature_name not in feature_names: + return {'error': f'Feature {feature_name} not found'} + + col_idx = list(feature_names).index(feature_name) + shap_values = explainer.shap_values(X) + if isinstance(shap_values, list): + shap_values = shap_values[0] + + feature_vals = X[:, col_idx].tolist() + shap_vals = shap_values[:, col_idx].tolist() + + # 下采样用于可视化 + max_points = 300 + if len(feature_vals) > max_points: + indices = np.random.RandomState(config.RANDOM_STATE).choice( + len(feature_vals), max_points, replace=False + ) + feature_vals = [feature_vals[i] for i in indices] + shap_vals = [shap_vals[i] for i in indices] + + name_map = self._get_feature_display_names() + return { + 'feature': feature_name, + 'feature_cn': name_map.get(feature_name, feature_name), + 'values': [round(v, 4) for v in feature_vals], + 'shap_values': [round(v, 4) for v in shap_vals], + } + except Exception as exc: + return {'error': str(exc)} diff --git a/backend/core/train_model.py b/backend/core/train_model.py index efa6c53..782fa6f 100644 --- a/backend/core/train_model.py +++ b/backend/core/train_model.py @@ -7,8 +7,10 @@ from datetime import datetime import joblib import numpy as np from sklearn.ensemble import ExtraTreesRegressor, GradientBoostingRegressor, RandomForestRegressor +from sklearn.ensemble import GradientBoostingClassifier, RandomForestClassifier from sklearn.feature_selection import SelectKBest, f_regression from sklearn.metrics import mean_absolute_error, mean_squared_error, r2_score +from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score, confusion_matrix from sklearn.model_selection import RandomizedSearchCV, train_test_split from sklearn.preprocessing import RobustScaler @@ -351,9 +353,81 @@ class OptimizedModelTrainer: ) self.save_models() + + # 风险分类模型训练 + print('\nRisk Classification Training') + risk_trainer = RiskClassifierTrainer(self) + risk_trainer.train_all(X_train, y_train, X_test, y_test) + risk_trainer.save() + return self.model_metrics +class RiskClassifierTrainer: + """风险等级分类模型训练器：低(<4h) / 中(4-8h) / 高(>8h)""" + + RISK_MAP = {'low': 0, 'medium': 1, 'high': 2} + RISK_LABELS = ['low', 'medium', 'high'] + + def __init__(self, regression_trainer): + self.regression_trainer = regression_trainer + self.classifiers = {} + self.classification_metrics = {} + + def _make_target(self, y_hours): + y_class = np.full(len(y_hours), 1, dtype=int) + y_class[y_hours < 4] = 0 + y_class[y_hours > 8] = 2 + return y_class + + def train_all(self, X_train, y_train_hours, X_test, y_test_hours): + y_train_cls = self._make_target(y_train_hours) + y_test_cls = self._make_target(y_test_hours) + + classifier_configs = { + 'random_forest': RandomForestClassifier( + n_estimators=300, max_depth=14, random_state=config.RANDOM_STATE, n_jobs=-1, + ), + 'gradient_boosting': GradientBoostingClassifier( + n_estimators=200, max_depth=4, learning_rate=0.05, random_state=config.RANDOM_STATE, + ), + } + + if lgb is not None: + classifier_configs['lightgbm'] = lgb.LGBMClassifier( + n_estimators=260, max_depth=7, learning_rate=0.05, + random_state=config.RANDOM_STATE, n_jobs=-1, verbose=-1, + ) + if xgb is not None: + classifier_configs['xgboost'] = xgb.XGBClassifier( + n_estimators=260, max_depth=6, learning_rate=0.05, + random_state=config.RANDOM_STATE, n_jobs=-1, + ) + + for name, clf in classifier_configs.items(): + try: + clf.fit(X_train, y_train_cls) + y_pred = clf.predict(X_test) + self.classifiers[name] = clf + self.classification_metrics[name] = { + 'accuracy': round(accuracy_score(y_test_cls, y_pred), 4), + 'precision_macro': round(precision_score(y_test_cls, y_pred, average='macro', zero_division=0), 4), + 'recall_macro': round(recall_score(y_test_cls, y_pred, average='macro', zero_division=0), 4), + 'f1_macro': round(f1_score(y_test_cls, y_pred, average='macro', zero_division=0), 4), + 'confusion_matrix': confusion_matrix(y_test_cls, y_pred).tolist(), + } + m = self.classification_metrics[name] + print(f' {name:20s} Acc={m["accuracy"]:.4f} F1={m["f1_macro"]:.4f}') + except Exception as exc: + print(f' {name:20s} Skipped: {exc}') + + def save(self): + for name, clf in self.classifiers.items(): + path = os.path.join(config.MODELS_DIR, f'risk_{name}_classifier.pkl') + 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JDRService: + """JD-R（工作要求-资源）理论分析服务""" + + def __init__(self): + self._df = None + + def _ensure_data(self): + if self._df is None: + self._df = get_clean_data() + self._df = engineer_features(self._df) + + def get_dimension_scores(self): + """JD-R 三维度统计分布""" + self._ensure_data() + df = self._df + + result = {} + for dim_key, col_name in [ + ('demands', '工作要求指数'), + ('resources', '工作资源指数'), + ('personal', '个人资源指数'), + ]: + if col_name not in df.columns: + continue + vals = df[col_name].dropna() + bins = np.linspace(vals.min(), vals.max(), 8) + hist, edges = np.histogram(vals, bins=bins) + result[dim_key] = { + 'mean': round(float(vals.mean()), 2), + 'std': round(float(vals.std()), 2), + 'median': round(float(vals.median()), 2), + 'distribution': [ + {'range': f'{round(edges[i], 1)}-{round(edges[i+1], 1)}', 'count': int(hist[i])} + for i in range(len(hist)) + ], + } + + # JD-R 平衡度 + if 'JD-R平衡度' in df.columns: + balance = df['JD-R平衡度'].dropna() + result['balance'] = { + 'mean': round(float(balance.mean()), 2), + 'positive_ratio': round(float((balance > 0).mean()) * 100, 1), + } + + return result + + def get_burnout_engagement_analysis(self): + """倦怠与投入分析""" + self._ensure_data() + df = self._df + + result = {} + + if '工作倦怠' in df.columns: + burnout = df['工作倦怠'].dropna() + result['burnout'] = { + 'mean': round(float(burnout.mean()), 2), + 'std': round(float(burnout.std()), 2), + 'high_risk_ratio': round(float((burnout >= 5).mean()) * 100, 1), + 'distribution': self._make_distribution(burnout, 1, 7, 7), + } + + if '工作投入' in df.columns: + engagement = df['工作投入'].dropna() + result['engagement'] = { + 'mean': round(float(engagement.mean()), 2), + 'std': round(float(engagement.std()), 2), + 'low_engagement_ratio': round(float((engagement <= 3).mean()) * 100, 1), + 'distribution': self._make_distribution(engagement, 1, 7, 7), + } + + # 相关性分析 + corr_cols = {} + if '工作倦怠' in df.columns: + corr_cols['burnout'] = '工作倦怠' + if '工作投入' in df.columns: + corr_cols['engagement'] = '工作投入' + if '工作要求指数' in df.columns: + corr_cols['demands'] = '工作要求指数' + if '工作资源指数' in df.columns: + corr_cols['resources'] = '工作资源指数' + if config.TARGET_COLUMN in df.columns: + corr_cols['absence_hours'] = config.TARGET_COLUMN + + if len(corr_cols) >= 2: + corr_df = df[[v for v in corr_cols.values()]].dropna() + corr_matrix = corr_df.corr() + correlations = {} + for k1, v1 in corr_cols.items(): + for k2, v2 in corr_cols.items(): + if k1 != k2 and v1 in corr_matrix.index and v2 in corr_matrix.columns: + correlations[f'{k1}_vs_{k2}'] = round(float(corr_matrix.loc[v1, v2]), 3) + result['correlations'] = correlations + + return result + + def get_jdr_path_analysis(self): + """JD-R 双路径中介分析""" + self._ensure_data() + df = self._df + + result = {} + + target = config.TARGET_COLUMN + + # 健康损伤路径: demands -> burnout -> absence + if all(col in df.columns for col in ['工作要求指数', '工作倦怠', target]): + cols = ['工作要求指数', '工作倦怠', target] + sub = df[cols].dropna() + if len(sub) > 30: + r_demands_burnout = sub['工作要求指数'].corr(sub['工作倦怠']) + r_burnout_absence = sub['工作倦怠'].corr(sub[target]) + r_demands_absence = sub['工作要求指数'].corr(sub[target]) + indirect = r_demands_burnout * r_burnout_absence + result['health_impairment'] = { + 'direct_effect_demands': round(float(r_demands_absence), 3), + 'indirect_via_burnout': round(float(indirect), 3), + 'mediation_ratio': round(float(indirect / r_demands_absence) if r_demands_absence != 0 else 0, 3), + 'demands_to_burnout': round(float(r_demands_burnout), 3), + 'burnout_to_absence': round(float(r_burnout_absence), 3), + } + + # 激励路径: resources -> engagement -> lower absence + if all(col in df.columns for col in ['工作资源指数', '工作投入', target]): + cols = ['工作资源指数', '工作投入', target] + sub = df[cols].dropna() + if len(sub) > 30: + r_resources_engagement = sub['工作资源指数'].corr(sub['工作投入']) + r_engagement_absence = sub['工作投入'].corr(sub[target]) + r_resources_absence = sub['工作资源指数'].corr(sub[target]) + indirect = r_resources_engagement * r_engagement_absence + result['motivational'] = { + 'direct_effect_resources': round(float(r_resources_absence), 3), + 'indirect_via_engagement': round(float(indirect), 3), + 'mediation_ratio': round(float(indirect / r_resources_absence) if r_resources_absence != 0 else 0, 3), + 'resources_to_engagement': round(float(r_resources_engagement), 3), + 'engagement_to_absence': round(float(r_engagement_absence), 3), + } + + return result + + def get_jdr_profile(self, dimension='所属行业'): + """按维度分组的 JD-R 轮廓""" + self._ensure_data() + df = self._df + + if dimension not in df.columns: + return {'error': f'Dimension {dimension} not found'} + + 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'] + existing_cols = [c for c in score_cols if c in df.columns] + if not existing_cols: + return {'error': 'JD-R scores not computed'} + + group_cols = [dimension] + existing_cols + if config.TARGET_COLUMN in df.columns: + group_cols.append(config.TARGET_COLUMN) + + grouped = df[group_cols].groupby(dimension).agg(['mean', 'std']).round(2) + + profiles = [] + for group_name in grouped.index: + profile = {'group_name': str(group_name)} + for col in existing_cols: + profile[col] = round(float(grouped.loc[group_name, (col, 'mean')]), 2) + if config.TARGET_COLUMN in df.columns: + profile['avg_absence_hours'] = round(float(grouped.loc[group_name, (config.TARGET_COLUMN, 'mean')]), 2) + profiles.append(profile) + + return {'dimension': dimension, 'profiles': profiles} + + def get_risk_distribution(self): + """风险等级分布""" + self._ensure_data() + df = self._df + + target = config.TARGET_COLUMN + if target not in df.columns: + return {'error': 'Target column not found'} + + hours = df[target] + levels = [ + {'level': 'low', 'label': '低风险', 'color': '#22c55e', 'count': int((hours < 4).sum()), + 'percentage': round(float((hours < 4).mean()) * 100, 1), 'avg_hours': round(float(hours[hours < 4].mean()), 2) if (hours < 4).any() else 0}, + {'level': 'medium', 'label': '中风险', 'color': '#f59e0b', 'count': int(((hours >= 4) & (hours <= 8)).sum()), + 'percentage': round(float(((hours >= 4) & (hours <= 8)).mean()) * 100, 1), + 'avg_hours': round(float(hours[(hours >= 4) & (hours <= 8)].mean()), 2) if ((hours >= 4) & (hours <= 8)).any() else 0}, + {'level': 'high', 'label': '高风险', 'color': '#ef4444', 'count': int((hours > 8).sum()), + 'percentage': round(float((hours > 8).mean()) * 100, 1), 'avg_hours': round(float(hours[hours > 8].mean()), 2) if (hours > 8).any() else 0}, + ] + + return {'levels': levels, 'total': len(hours)} + + def _make_distribution(self, series, low, high, n_bins): + bins = np.linspace(low, high, n_bins + 1) + hist, edges = np.histogram(series, bins=bins) + return [ + {'range': f'{round(edges[i], 1)}-{round(edges[i+1], 1)}', 'count': int(hist[i])} + for i in range(len(hist)) + ] + + +jdr_service = JDRService() diff --git a/backend/services/predict_service.py b/backend/services/predict_service.py index 2e451b6..322e876 100644 --- a/backend/services/predict_service.py +++ b/backend/services/predict_service.py @@ -32,6 +32,8 @@ MODEL_INFO = { class PredictService: def __init__(self): self.models = {} + self.classifiers = {} + self.classification_metrics = {} self.scaler = None self.feature_names = None self.selected_features = None @@ -94,6 +96,21 @@ class PredictService: if valid_metrics: self.default_model = max(valid_metrics.items(), key=lambda item: item[1]['r2'])[0] + # 加载风险分类模型 + for name in ['random_forest', 'gradient_boosting', 'lightgbm', 'xgboost']: + path = os.path.join(config.MODELS_DIR, f'risk_{name}_classifier.pkl') + if os.path.exists(path): + try: + self.classifiers[name] = joblib.load(path) + except Exception: + pass + cls_metrics_path = os.path.join(config.MODELS_DIR, 'classification_metrics.pkl') + if os.path.exists(cls_metrics_path): + try: + self.classification_metrics = joblib.load(cls_metrics_path) + except Exception: + pass + def get_available_models(self): self._ensure_models_loaded() models = [] @@ -131,10 +148,15 @@ class PredictService: risk_level, risk_label = self._get_risk_level(predicted_hours) confidence = max(0.5, self.model_metrics.get(model_type, {}).get('r2', 0.82)) + + # 风险分类概率 + risk_probability = self._get_risk_probability(features, model_type) + return { 'predicted_hours': round(predicted_hours, 2), 'risk_level': risk_level, 'risk_label': risk_label, + 'risk_probability': risk_probability, 'confidence': round(confidence, 2), 'model_used': model_type, 'model_name_cn': MODEL_INFO.get(model_type, {}).get('name_cn', model_type), @@ -198,11 +220,65 @@ class PredictService: 'predicted_hours': round(max(0.5, base_hours), 2), 'risk_level': risk_level, 'risk_label': risk_label, + 'risk_probability': {'low': 0.0, 'medium': 1.0, 'high': 0.0}, 'confidence': 0.72, 'model_used': 'default', 'model_name_cn': '默认规则', } + def _get_risk_probability(self, features, model_type): + """获取分类器预测的风险概率""" + classifier = self.classifiers.get(model_type) + if classifier is None: + classifier = self.classifiers.get('random_forest') + if classifier is None: + return {'low': 0.0, 'medium': 1.0, 'high': 0.0} + try: + proba = classifier.predict_proba([features])[0] + classes = list(classifier.classes_) + result = {'low': 0.0, 'medium': 0.0, 'high': 0.0} + label_map = {0: 'low', 1: 'medium', 2: 'high'} + for idx, cls in enumerate(classes): + if cls in label_map: + result[label_map[cls]] = round(float(proba[idx]), 4) + return result + except Exception: + return {'low': 0.0, 'medium': 1.0, 'high': 0.0} + + def predict_risk_classification(self, data, model_type=None): + """使用分类模型直接预测风险等级""" + self._ensure_models_loaded() + model_type = model_type or self.default_model + classifier = self.classifiers.get(model_type) + if classifier is None: + classifier = self.classifiers.get('random_forest') + if classifier is None or self.scaler is None: + return None + + features = self._prepare_features(data) + try: + pred_class = int(classifier.predict([features])[0]) + proba = classifier.predict_proba([features])[0] + label_map = {0: 'low', 1: 'medium', 2: 'high'} + risk_labels_map = {'low': '低风险', 'medium': '中风险', 'high': '高风险'} + risk_level = label_map.get(pred_class, 'medium') + + classes = list(classifier.classes_) + probabilities = {'low': 0.0, 'medium': 0.0, 'high': 0.0} + for idx, cls in enumerate(classes): + if cls in label_map: + probabilities[label_map[cls]] = round(float(proba[idx]), 4) + + return { + 'risk_level': risk_level, + 'risk_label': risk_labels_map[risk_level], + 'risk_probability': probabilities, + 'model_used': model_type, + 'classification_metrics': self.classification_metrics.get(model_type, {}), + } + except Exception: + return None + def get_model_info(self): self._ensure_models_loaded() return { diff --git a/backend/services/shap_service.py b/backend/services/shap_service.py new file mode 100644 index 0000000..931e646 --- /dev/null +++ b/backend/services/shap_service.py @@ -0,0 +1,31 @@ +from core.shap_analysis import SHAPAnalyzer + + +class SHAPService: + """SHAP 可解释性分析服务""" + + def __init__(self): + self._analyzer = None + + def _ensure_analyzer(self): + if self._analyzer is None: + self._analyzer = SHAPAnalyzer() + + def get_global_importance(self, model_type='random_forest'): + self._ensure_analyzer() + return self._analyzer.global_shap_values(model_type) + + def get_local_explanation(self, data, model_type='random_forest'): + self._ensure_analyzer() + return self._analyzer.local_shap_values(data, model_type) + + def get_interactions(self, model_type='random_forest', top_n=10): + self._ensure_analyzer() + return self._analyzer.shap_interaction(model_type, top_n) + + def get_dependence(self, feature_name, model_type='random_forest'): + self._ensure_analyzer() + return self._analyzer.shap_dependence(feature_name, model_type) + + +shap_service = SHAPService() diff --git a/frontend/package-lock.json b/frontend/package-lock.json new file mode 100644 index 0000000..611c26d --- /dev/null +++ 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} + }, + "node_modules/zrender": { + "version": "5.6.1", + "resolved": "https://registry.npmjs.org/zrender/-/zrender-5.6.1.tgz", + "integrity": "sha512-OFXkDJKcrlx5su2XbzJvj/34Q3m6PvyCZkVPHGYpcCJ52ek4U/ymZyfuV1nKE23AyBJ51E/6Yr0mhZ7xGTO4ag==", + "license": "BSD-3-Clause", + "dependencies": { + "tslib": "2.3.0" + } + } + } +} diff --git a/frontend/src/App.vue b/frontend/src/App.vue index ddef2dd..f8ccbe6 100644 --- a/frontend/src/App.vue +++ b/frontend/src/App.vue @@ -28,6 +28,10 @@ 员工画像 + + + JD-R分析 + @@ -67,7 +71,7 @@ + + diff --git a/frontend/src/views/Prediction.vue b/frontend/src/views/Prediction.vue index 4529b40..a7352a7 100644 --- a/frontend/src/views/Prediction.vue +++ b/frontend/src/views/Prediction.vue @@ -297,15 +297,36 @@ + + +

    + + {{ dim }}: {{ val >= 0 ? '+' : '' }}{{ val }} + +
    +
    + +