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
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336
backend/core/generate_dataset.py
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336
backend/core/generate_dataset.py
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import os
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import sys
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import numpy as np
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import pandas as pd
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sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
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import config
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INDUSTRIES = {
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'制造业': {'shift_bias': 0.9, 'overtime_bias': 0.8, 'night_bias': 0.8},
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'互联网': {'shift_bias': 0.2, 'overtime_bias': 1.0, 'night_bias': 0.2},
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'零售连锁': {'shift_bias': 0.7, 'overtime_bias': 0.5, 'night_bias': 0.3},
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'物流运输': {'shift_bias': 0.9, 'overtime_bias': 0.7, 'night_bias': 0.9},
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'金融服务': {'shift_bias': 0.1, 'overtime_bias': 0.7, 'night_bias': 0.1},
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'医药健康': {'shift_bias': 0.6, 'overtime_bias': 0.6, 'night_bias': 0.5},
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'建筑工程': {'shift_bias': 0.5, 'overtime_bias': 0.8, 'night_bias': 0.3},
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}
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def season_from_month(month):
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if month in [12, 1, 2]:
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return 1
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if month in [3, 4, 5]:
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return 2
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if month in [6, 7, 8]:
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return 3
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return 4
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def weighted_choice(rng, items, probs):
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probs = np.array(probs, dtype=float)
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probs = probs / probs.sum()
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return rng.choice(items, p=probs)
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def build_company_pool(rng, company_count=180):
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industries = list(INDUSTRIES.keys())
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scales = ['100人以下', '100-499人', '500-999人', '1000-4999人', '5000人及以上']
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city_tiers = ['一线', '新一线', '二线', '三线及以下']
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companies = []
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for idx in range(company_count):
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industry = weighted_choice(rng, industries, [0.22, 0.14, 0.14, 0.14, 0.1, 0.12, 0.14])
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companies.append({
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'企业编号': f'C{idx + 1:03d}',
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'所属行业': industry,
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'企业规模': weighted_choice(rng, scales, [0.15, 0.28, 0.2, 0.24, 0.13]),
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'所在城市等级': weighted_choice(rng, city_tiers, [0.18, 0.34, 0.3, 0.18]),
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})
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return companies
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def build_employee_pool(rng, companies, employee_count=2600):
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genders = ['男', '女']
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employment_types = ['正式员工', '劳务派遣', '外包驻场', '实习生']
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departments = ['生产', '研发', '销售', '客服', '职能', '仓储物流', '门店运营']
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job_families = ['管理', '专业技术', '销售业务', '生产操作', '行政支持', '客服坐席']
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job_levels = ['初级', '中级', '高级', '主管', '经理及以上']
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educations = ['中专及以下', '大专', '本科', '硕士', '博士']
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marital = ['未婚', '已婚', '离异/其他']
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housing = ['自有住房', '租房', '宿舍']
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shifts = ['标准白班', '两班倒', '三班倒', '弹性班']
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performance = ['A', 'B', 'C', 'D']
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stress = ['低', '中', '高']
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employees = []
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for idx in range(employee_count):
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company = companies[rng.integers(0, len(companies))]
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industry = company['所属行业']
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age = int(np.clip(rng.normal(33, 7), 20, 55))
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tenure = round(float(np.clip(age - 21 + rng.normal(0, 2), 0.2, 32)), 1)
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family_bias = 0.6 if age >= 30 else 0.25
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married = weighted_choice(rng, marital, [0.45, 0.48, 0.07] if age < 30 else [0.18, 0.72, 0.1])
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children = int(np.clip(rng.poisson(0.4 if married == '未婚' else family_bias), 0, 3))
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industry_profile = INDUSTRIES[industry]
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shift = weighted_choice(
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rng,
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shifts,
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[
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max(0.1, 1 - industry_profile['shift_bias']),
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0.35 * industry_profile['shift_bias'],
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0.25 * industry_profile['shift_bias'],
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0.2,
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],
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)
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night_flag = int(shift == '三班倒' or (shift == '两班倒' and rng.random() < industry_profile['night_bias']))
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overtime = float(np.clip(rng.normal(22 + 18 * industry_profile['overtime_bias'], 10), 0, 90))
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commute_minutes = float(np.clip(rng.normal(42, 18), 8, 130))
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commute_km = float(np.clip(commute_minutes * rng.uniform(0.35, 0.75), 2, 65))
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performance_level = weighted_choice(rng, performance, [0.18, 0.46, 0.26, 0.1])
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chronic_flag = int(rng.random() < max(0.05, (age - 26) * 0.01))
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check_abnormal = int(chronic_flag == 1 or rng.random() < 0.14)
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sleep_hours = round(float(np.clip(rng.normal(6.9 - 0.35 * night_flag, 0.8), 4.5, 9.0)), 1)
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exercise = int(np.clip(rng.poisson(2.2), 0, 7))
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smoking = int(rng.random() < (0.22 if rng.random() < 0.55 else 0.08))
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drinking = int(rng.random() < 0.27)
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stress_level = weighted_choice(
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rng,
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stress,
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[0.22, 0.52, 0.26 + min(0.15, overtime / 120)],
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)
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bmi = round(float(np.clip(rng.normal(24.2, 3.2), 17.5, 36.5)), 1)
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history_count = int(np.clip(rng.poisson(1.2 + chronic_flag * 0.6 + children * 0.15), 0, 8))
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history_hours = float(np.clip(rng.normal(18 + chronic_flag * 10 + history_count * 3, 10), 0, 120))
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discipline = int(np.clip(rng.poisson(0.2), 0, 4))
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team_size = int(np.clip(rng.normal(11, 5), 3, 40))
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manager_span = int(np.clip(team_size + rng.normal(3, 2), 4, 60))
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local_hukou = int(rng.random() < 0.58)
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cross_city = int(commute_minutes > 65 or (local_hukou == 0 and rng.random() < 0.35))
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sedentary = int(weighted_choice(rng, [0, 1], [0.45, 0.55]) if company['所属行业'] in ['互联网', '金融服务'] else rng.random() < 0.3)
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employees.append({
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'企业编号': company['企业编号'],
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'所属行业': industry,
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'企业规模': company['企业规模'],
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'所在城市等级': company['所在城市等级'],
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'用工类型': weighted_choice(rng, employment_types, [0.74, 0.12, 0.1, 0.04]),
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'部门条线': weighted_choice(rng, departments, [0.18, 0.16, 0.14, 0.11, 0.12, 0.14, 0.15]),
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'岗位序列': weighted_choice(rng, job_families, [0.08, 0.24, 0.16, 0.2, 0.12, 0.2]),
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'岗位级别': weighted_choice(rng, job_levels, [0.34, 0.32, 0.18, 0.11, 0.05]),
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'员工编号': f'E{idx + 1:05d}',
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'性别': weighted_choice(rng, genders, [0.56, 0.44]),
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'年龄': age,
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'司龄年数': tenure,
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'最高学历': weighted_choice(rng, educations, [0.14, 0.28, 0.4, 0.15, 0.03]),
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'婚姻状态': married,
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'是否本地户籍': local_hukou,
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'子女数量': children,
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'是否独生子女家庭负担': int(children >= 2 or (married == '已婚' and rng.random() < 0.18)),
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'居住类型': weighted_choice(rng, housing, [0.38, 0.48, 0.14]),
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'班次类型': shift,
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'是否夜班岗位': night_flag,
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'月均加班时长': round(overtime, 1),
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'近30天出勤天数': int(np.clip(rng.normal(21.5, 2.2), 14, 27)),
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'近90天缺勤次数': history_count,
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'近180天请假总时长': round(history_hours, 1),
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'通勤时长分钟': round(commute_minutes, 1),
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'通勤距离公里': round(commute_km, 1),
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'是否跨城通勤': cross_city,
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'绩效等级': performance_level,
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'近12月违纪次数': discipline,
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'团队人数': team_size,
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'直属上级管理跨度': manager_span,
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'BMI': bmi,
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'是否慢性病史': chronic_flag,
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'年度体检异常标记': check_abnormal,
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'近30天睡眠时长均值': sleep_hours,
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'每周运动频次': exercise,
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'是否吸烟': smoking,
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'是否饮酒': drinking,
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'心理压力等级': stress_level,
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'是否长期久坐岗位': sedentary,
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})
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return employees
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def sample_event(rng, employee):
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month = int(rng.integers(1, 13))
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weekday = int(rng.integers(1, 8))
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near_holiday = int(rng.random() < (0.3 if month in [1, 2, 4, 5, 9, 10] else 0.16))
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leave_type_items = ['病假', '事假', '年假', '调休', '婚假', '丧假', '产检育儿假', '工伤假', '其他']
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leave_type = weighted_choice(rng, leave_type_items, [0.3, 0.22, 0.12, 0.14, 0.03, 0.02, 0.06, 0.02, 0.09])
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if employee['子女数量'] > 0 and rng.random() < 0.14:
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reason_category = '子女照护'
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else:
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reason_category = weighted_choice(
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rng,
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['身体不适', '家庭事务', '交通受阻', '突发事件', '职业疲劳', '就医复查'],
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[0.28, 0.19, 0.09, 0.11, 0.2, 0.13],
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)
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medical_certificate = int(leave_type in ['病假', '工伤假'] or reason_category in ['身体不适', '就医复查'])
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urgent_leave = int(rng.random() < (0.45 if leave_type in ['病假', '事假', '工伤假'] else 0.18))
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continuous_absence = int(rng.random() < (0.2 if leave_type in ['病假', '产检育儿假', '工伤假'] else 0.08))
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previous_overtime = int(rng.random() < min(0.85, employee['月均加班时长'] / 65))
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season = season_from_month(month)
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channel = weighted_choice(rng, ['系统申请', '主管代提', '临时电话报备'], [0.68, 0.18, 0.14])
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base = 0.95
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base += min(employee['月均加班时长'] / 28, 1.8)
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base += min(employee['通勤时长分钟'] / 65, 1.2)
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base += employee['是否夜班岗位'] * 0.9
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base += employee['是否慢性病史'] * 1.25
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base += employee['年度体检异常标记'] * 0.6
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base += 0.35 * employee['子女数量']
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base += 0.5 if employee['心理压力等级'] == '高' else (0.2 if employee['心理压力等级'] == '中' else -0.1)
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base += 0.4 if employee['是否跨城通勤'] else 0
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base += 0.35 if previous_overtime else 0
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base += 0.35 if near_holiday else 0
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base += 0.3 if continuous_absence else 0
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base += 0.3 if employee['近90天缺勤次数'] >= 3 else 0
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base -= 0.35 if employee['绩效等级'] == 'A' else (0.15 if employee['绩效等级'] == 'B' else 0)
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base -= min(employee['司龄年数'] / 40, 0.5)
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base -= min(employee['每周运动频次'] * 0.08, 0.3)
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base -= 0.2 if employee['近30天睡眠时长均值'] >= 7.5 else 0
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leave_bonus = {
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'病假': 2.0,
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'事假': 0.8,
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'年假': 0.1,
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'调休': 0.1,
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'婚假': 3.0,
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'丧假': 2.8,
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'产检育儿假': 2.4,
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'工伤假': 3.8,
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'其他': 0.5,
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}
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reason_bonus = {
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'身体不适': 1.0,
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'家庭事务': 0.5,
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'子女照护': 0.8,
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'交通受阻': 0.2,
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'突发事件': 0.6,
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'职业疲劳': 0.7,
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'就医复查': 1.2,
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}
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industry_bonus = {
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'制造业': 0.35,
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'互联网': 0.2,
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'零售连锁': 0.25,
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'物流运输': 0.4,
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'金融服务': 0.1,
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'医药健康': 0.2,
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'建筑工程': 0.35,
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}
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season_bonus = {1: 0.35, 2: 0.0, 3: 0.15, 4: 0.05}
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weekday_bonus = {1: 0.05, 2: 0.0, 3: 0.0, 4: 0.05, 5: 0.15, 6: 0.25, 7: 0.3}
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duration = base
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duration += leave_bonus[leave_type]
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duration += reason_bonus[reason_category]
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duration += industry_bonus[employee['所属行业']]
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duration += season_bonus[season]
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duration += weekday_bonus[weekday]
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duration += 0.55 if medical_certificate else 0
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duration += 0.4 if urgent_leave else -0.05
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duration += rng.normal(0, 0.9)
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if leave_type in ['婚假', '丧假', '工伤假'] and rng.random() < 0.5:
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duration += rng.uniform(1.5, 5)
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if leave_type == '病假' and employee['是否慢性病史'] == 1 and rng.random() < 0.35:
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duration += rng.uniform(1, 4)
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if leave_type in ['年假', '调休']:
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duration *= rng.uniform(0.7, 0.95)
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duration = round(float(np.clip(duration, 0.5, 24.0)), 1)
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event = employee.copy()
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event.update({
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'缺勤月份': month,
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'星期几': weekday,
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'是否节假日前后': near_holiday,
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'季节': season,
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'请假申请渠道': channel,
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'请假类型': leave_type,
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'请假原因大类': reason_category,
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'是否提供医院证明': medical_certificate,
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'是否临时请假': urgent_leave,
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'是否连续缺勤': continuous_absence,
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'前一工作日是否加班': previous_overtime,
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'缺勤时长（小时）': duration,
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})
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return event
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def validate_dataset(df):
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required_columns = [
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'员工编号',
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'所属行业',
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'岗位序列',
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'月均加班时长',
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'通勤时长分钟',
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'是否慢性病史',
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'请假类型',
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'缺勤时长（小时）',
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]
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for column in required_columns:
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if column not in df.columns:
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raise ValueError(f'Missing required column: {column}')
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if len(df) < 10000:
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raise ValueError('Synthetic dataset is smaller than expected')
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if df['员工编号'].nunique() < 2000:
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raise ValueError('Employee coverage is too small')
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high_risk_ratio = (df['缺勤时长（小时）'] > 8).mean()
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if not 0.15 <= high_risk_ratio <= 0.4:
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raise ValueError(f'High risk ratio out of range: {high_risk_ratio:.3f}')
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medical_mean = df[df['是否提供医院证明'] == 1]['缺勤时长（小时）'].mean()
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no_medical_mean = df[df['是否提供医院证明'] == 0]['缺勤时长（小时）'].mean()
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if medical_mean <= no_medical_mean:
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raise ValueError('Medical certificate signal is not effective')
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night_mean = df[df['是否夜班岗位'] == 1]['缺勤时长（小时）'].mean()
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day_mean = df[df['是否夜班岗位'] == 0]['缺勤时长（小时）'].mean()
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if night_mean <= day_mean:
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raise ValueError('Night shift signal is not effective')
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def generate_dataset(output_path=None, sample_count=12000, random_state=None):
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rng = np.random.default_rng(config.RANDOM_STATE if random_state is None else random_state)
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companies = build_company_pool(rng)
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employees = build_employee_pool(rng, companies)
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events = []
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employee_idx = rng.integers(0, len(employees), size=sample_count)
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for idx in employee_idx:
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events.append(sample_event(rng, employees[int(idx)]))
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df = pd.DataFrame(events)
|
||||
validate_dataset(df)
|
||||
|
||||
if output_path:
|
||||
os.makedirs(os.path.dirname(output_path), exist_ok=True)
|
||||
df.to_csv(output_path, index=False, encoding='utf-8-sig')
|
||||
return df
|
||||
|
||||
|
||||
def ensure_dataset():
|
||||
if not os.path.exists(config.RAW_DATA_PATH):
|
||||
generate_dataset(config.RAW_DATA_PATH)
|
||||
return
|
||||
|
||||
try:
|
||||
df = pd.read_csv(config.RAW_DATA_PATH)
|
||||
validate_dataset(df)
|
||||
except Exception:
|
||||
generate_dataset(config.RAW_DATA_PATH)
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
dataset = generate_dataset(config.RAW_DATA_PATH)
|
||||
print(f'Generated dataset: {config.RAW_DATA_PATH}')
|
||||
print(dataset.head())
|
||||
Reference in New Issue
Block a user