shenjianZ
e63267cef6
- 新增中国企业员工缺勤模拟数据集生成脚本(generate_dataset.py)，覆盖7个行业、180家企业、2600名员工 - 重构 config.py，更新特征字段为中文名称，调整目标列、员工ID、行业类型等配置 - 重构 clustering.py，简化聚类逻辑，更新聚类特征和群体命名（高压通勤型、健康波动型等） - 重构 feature_mining.py，更新相关性分析和群体比较维度（按行业、班次、婚姻状态等） - 新增 model_features.py 定义模型训练特征 - 更新 preprocessing.py 和 train_model.py 适配新数据结构 - 更新各 API 路由默认参数（model: random_forest, dimension: industry） - 前端更新主题样式和各视图组件适配中文字段 - 更新系统名称为 China Enterprise Absence Analysis System
327 lines
12 KiB
Python
327 lines
12 KiB
Python
import numpy as np
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import pandas as pd
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from sklearn.preprocessing import LabelEncoder
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import config
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TARGET_COLUMN = config.TARGET_COLUMN
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ID_COLUMN = config.EMPLOYEE_ID_COLUMN
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COMPANY_COLUMN = config.COMPANY_ID_COLUMN
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LEAKY_COLUMNS = [ID_COLUMN, COMPANY_COLUMN]
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ORDINAL_COLUMNS = [
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'企业规模',
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'所在城市等级',
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'岗位级别',
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'最高学历',
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'绩效等级',
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'心理压力等级',
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'工龄分层',
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'年龄分层',
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'通勤分层',
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'加班分层',
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]
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NUMERICAL_OUTLIER_COLUMNS = [
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'年龄',
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'司龄年数',
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'月均加班时长',
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'近30天出勤天数',
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'近90天缺勤次数',
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'近180天请假总时长',
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'通勤时长分钟',
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'通勤距离公里',
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'团队人数',
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'直属上级管理跨度',
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'BMI',
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'近30天睡眠时长均值',
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'每周运动频次',
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]
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DEFAULT_PREDICTION_INPUT = {
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'industry': '制造业',
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'company_size': '1000-4999人',
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'city_tier': '新一线',
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'age': 31,
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'tenure_years': 4.5,
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'education_level': '本科',
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'marital_status': '已婚',
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'job_family': '专业技术',
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'job_level': '中级',
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'employment_type': '正式员工',
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'shift_type': '标准白班',
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'is_night_shift': 0,
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'monthly_overtime_hours': 26,
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'attendance_days_30d': 22,
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'absence_count_90d': 1,
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'leave_hours_180d': 18,
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'commute_minutes': 42,
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'commute_km': 18,
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'cross_city_commute': 0,
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'performance_level': 'B',
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'disciplinary_count_12m': 0,
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'team_size': 10,
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'manager_span': 14,
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'bmi': 24.5,
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'chronic_disease_flag': 0,
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'annual_check_abnormal_flag': 0,
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'sleep_hours': 7.1,
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'exercise_frequency': 2,
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'smoking_flag': 0,
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'drinking_flag': 0,
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'stress_level': '中',
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'sedentary_job_flag': 1,
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'local_hukou_flag': 1,
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'children_count': 1,
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'single_child_burden_flag': 0,
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'absence_month': 5,
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'weekday': 2,
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'near_holiday_flag': 0,
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'leave_channel': '系统申请',
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'leave_type': '病假',
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'leave_reason_category': '身体不适',
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'medical_certificate_flag': 1,
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'urgent_leave_flag': 1,
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'continuous_absence_flag': 0,
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'previous_day_overtime_flag': 1,
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}
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def make_target_bins(y):
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y_series = pd.Series(y)
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bins = pd.cut(
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y_series,
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bins=[0, 4, 8, 12, np.inf],
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labels=['low', 'medium', 'high', 'extreme'],
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include_lowest=True,
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)
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return bins.astype(str)
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def normalize_columns(df):
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df = df.copy()
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df.columns = [col.strip() for col in df.columns]
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return df
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def prepare_modeling_dataframe(df):
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df = normalize_columns(df)
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drop_cols = [col for col in LEAKY_COLUMNS if col in df.columns]
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if drop_cols:
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df = df.drop(columns=drop_cols)
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return df
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def fit_outlier_bounds(df, columns, lower_pct=1, upper_pct=99):
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bounds = {}
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for col in columns:
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if col in df.columns and pd.api.types.is_numeric_dtype(df[col]):
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bounds[col] = (
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float(df[col].quantile(lower_pct / 100)),
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float(df[col].quantile(upper_pct / 100)),
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)
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return bounds
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def apply_outlier_bounds(df, bounds):
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df = df.copy()
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for col, (lower, upper) in bounds.items():
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if col in df.columns:
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df[col] = df[col].clip(lower, upper)
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return df
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def engineer_features(df):
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df = df.copy()
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df['加班通勤压力指数'] = (
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df['月均加班时长'] * 0.45
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+ df['通勤时长分钟'] * 0.35
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+ df['是否夜班岗位'] * 12
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+ df['前一工作日是否加班'] * 6
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) / 10
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df['家庭负担指数'] = (
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df['子女数量'] * 1.2
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+ df['是否独生子女家庭负担'] * 1.5
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+ (df['婚姻状态'] == '已婚').astype(int) * 0.6
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)
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df['健康风险指数'] = (
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df['是否慢性病史'] * 2
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+ df['年度体检异常标记'] * 1.2
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+ (df['BMI'] >= 28).astype(int) * 1.1
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+ df['是否吸烟'] * 0.8
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+ df['是否饮酒'] * 0.4
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+ (df['近30天睡眠时长均值'] < 6.5).astype(int) * 1.2
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)
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df['岗位稳定性指数'] = (
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df['司龄年数'] * 0.3
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+ (df['绩效等级'] == 'A').astype(int) * 1.2
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+ (df['绩效等级'] == 'B').astype(int) * 0.8
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- df['近12月违纪次数'] * 0.7
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)
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df['节假日风险标记'] = (
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(df['是否节假日前后'] == 1) | (df['请假类型'].isin(['事假', '年假', '调休']))
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).astype(int)
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df['排班压力标记'] = (
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(df['班次类型'].isin(['两班倒', '三班倒'])) | (df['是否夜班岗位'] == 1)
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).astype(int)
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df['缺勤历史强度'] = df['近90天缺勤次数'] * 1.5 + df['近180天请假总时长'] / 12
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df['生活规律指数'] = (
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df['近30天睡眠时长均值'] * 0.6
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+ df['每周运动频次'] * 0.7
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- df['是否吸烟'] * 1.1
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- df['是否饮酒'] * 0.5
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)
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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
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df['工龄分层'] = pd.cut(df['司龄年数'], bins=[0, 2, 5, 10, 40], labels=['1', '2', '3', '4'])
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df['年龄分层'] = pd.cut(df['年龄'], bins=[18, 25, 32, 40, 60], labels=['1', '2', '3', '4'])
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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'])
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df['加班分层'] = pd.cut(df['月均加班时长'], bins=[-1, 10, 25, 45, 120], labels=['1', '2', '3', '4'])
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return df
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def fit_label_encoders(df, ordinal_columns=None):
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ordinal_columns = ordinal_columns or ORDINAL_COLUMNS
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df = df.copy()
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encoders = {}
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object_columns = df.select_dtypes(include=['object', 'category']).columns.tolist()
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encode_columns = sorted(set(object_columns + [col for col in ordinal_columns if col in df.columns]))
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for col in encode_columns:
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encoder = LabelEncoder()
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df[col] = encoder.fit_transform(df[col].astype(str))
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encoders[col] = encoder
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return df, encoders
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def apply_label_encoders(df, encoders):
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df = df.copy()
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for col, encoder in encoders.items():
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if col not in df.columns:
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continue
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value_map = {cls: idx for idx, cls in enumerate(encoder.classes_)}
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df[col] = df[col].astype(str).map(lambda value: value_map.get(value, 0))
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return df
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def extract_xy(df):
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y = df[TARGET_COLUMN].values if TARGET_COLUMN in df.columns else None
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X_df = df.drop(columns=[TARGET_COLUMN]) if TARGET_COLUMN in df.columns else df.copy()
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return X_df, y
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def build_prediction_dataframe(data):
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feature_row = {
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'企业编号': 'PREDICT_COMPANY',
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'所属行业': data.get('industry', DEFAULT_PREDICTION_INPUT['industry']),
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'企业规模': data.get('company_size', DEFAULT_PREDICTION_INPUT['company_size']),
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'所在城市等级': data.get('city_tier', DEFAULT_PREDICTION_INPUT['city_tier']),
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'用工类型': data.get('employment_type', DEFAULT_PREDICTION_INPUT['employment_type']),
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'部门条线': data.get('department_line', '研发'),
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'岗位序列': data.get('job_family', DEFAULT_PREDICTION_INPUT['job_family']),
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'岗位级别': data.get('job_level', DEFAULT_PREDICTION_INPUT['job_level']),
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'员工编号': 'PREDICT_EMPLOYEE',
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'性别': data.get('gender', '男'),
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'年龄': data.get('age', DEFAULT_PREDICTION_INPUT['age']),
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'司龄年数': data.get('tenure_years', DEFAULT_PREDICTION_INPUT['tenure_years']),
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'最高学历': data.get('education_level', DEFAULT_PREDICTION_INPUT['education_level']),
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'婚姻状态': data.get('marital_status', DEFAULT_PREDICTION_INPUT['marital_status']),
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'是否本地户籍': data.get('local_hukou_flag', DEFAULT_PREDICTION_INPUT['local_hukou_flag']),
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'子女数量': data.get('children_count', DEFAULT_PREDICTION_INPUT['children_count']),
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'是否独生子女家庭负担': data.get(
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'single_child_burden_flag',
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DEFAULT_PREDICTION_INPUT['single_child_burden_flag'],
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),
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'居住类型': data.get('housing_type', '租房'),
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'班次类型': data.get('shift_type', DEFAULT_PREDICTION_INPUT['shift_type']),
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'是否夜班岗位': data.get('is_night_shift', DEFAULT_PREDICTION_INPUT['is_night_shift']),
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'月均加班时长': data.get(
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'monthly_overtime_hours',
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DEFAULT_PREDICTION_INPUT['monthly_overtime_hours'],
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),
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'近30天出勤天数': data.get(
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'attendance_days_30d',
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DEFAULT_PREDICTION_INPUT['attendance_days_30d'],
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),
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'近90天缺勤次数': data.get('absence_count_90d', DEFAULT_PREDICTION_INPUT['absence_count_90d']),
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'近180天请假总时长': data.get('leave_hours_180d', DEFAULT_PREDICTION_INPUT['leave_hours_180d']),
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'通勤时长分钟': data.get('commute_minutes', DEFAULT_PREDICTION_INPUT['commute_minutes']),
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'通勤距离公里': data.get('commute_km', DEFAULT_PREDICTION_INPUT['commute_km']),
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'是否跨城通勤': data.get(
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'cross_city_commute',
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DEFAULT_PREDICTION_INPUT['cross_city_commute'],
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),
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'绩效等级': data.get('performance_level', DEFAULT_PREDICTION_INPUT['performance_level']),
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'近12月违纪次数': data.get(
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'disciplinary_count_12m',
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DEFAULT_PREDICTION_INPUT['disciplinary_count_12m'],
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),
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'团队人数': data.get('team_size', DEFAULT_PREDICTION_INPUT['team_size']),
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'直属上级管理跨度': data.get('manager_span', DEFAULT_PREDICTION_INPUT['manager_span']),
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'BMI': data.get('bmi', DEFAULT_PREDICTION_INPUT['bmi']),
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'是否慢性病史': data.get(
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'chronic_disease_flag',
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DEFAULT_PREDICTION_INPUT['chronic_disease_flag'],
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),
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'年度体检异常标记': data.get(
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'annual_check_abnormal_flag',
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DEFAULT_PREDICTION_INPUT['annual_check_abnormal_flag'],
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),
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'近30天睡眠时长均值': data.get('sleep_hours', DEFAULT_PREDICTION_INPUT['sleep_hours']),
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'每周运动频次': data.get(
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'exercise_frequency',
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DEFAULT_PREDICTION_INPUT['exercise_frequency'],
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),
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'是否吸烟': data.get('smoking_flag', DEFAULT_PREDICTION_INPUT['smoking_flag']),
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'是否饮酒': data.get('drinking_flag', DEFAULT_PREDICTION_INPUT['drinking_flag']),
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'心理压力等级': data.get('stress_level', DEFAULT_PREDICTION_INPUT['stress_level']),
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'是否长期久坐岗位': data.get(
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'sedentary_job_flag',
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DEFAULT_PREDICTION_INPUT['sedentary_job_flag'],
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),
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'缺勤月份': data.get('absence_month', DEFAULT_PREDICTION_INPUT['absence_month']),
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'星期几': data.get('weekday', DEFAULT_PREDICTION_INPUT['weekday']),
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'是否节假日前后': data.get('near_holiday_flag', DEFAULT_PREDICTION_INPUT['near_holiday_flag']),
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'季节': _season_from_month(data.get('absence_month', DEFAULT_PREDICTION_INPUT['absence_month'])),
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'请假申请渠道': data.get('leave_channel', DEFAULT_PREDICTION_INPUT['leave_channel']),
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'请假类型': data.get('leave_type', DEFAULT_PREDICTION_INPUT['leave_type']),
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'请假原因大类': data.get(
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'leave_reason_category',
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DEFAULT_PREDICTION_INPUT['leave_reason_category'],
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),
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'是否提供医院证明': data.get(
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'medical_certificate_flag',
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DEFAULT_PREDICTION_INPUT['medical_certificate_flag'],
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),
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'是否临时请假': data.get('urgent_leave_flag', DEFAULT_PREDICTION_INPUT['urgent_leave_flag']),
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'是否连续缺勤': data.get(
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'continuous_absence_flag',
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DEFAULT_PREDICTION_INPUT['continuous_absence_flag'],
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),
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'前一工作日是否加班': data.get(
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'previous_day_overtime_flag',
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DEFAULT_PREDICTION_INPUT['previous_day_overtime_flag'],
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),
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}
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return pd.DataFrame([feature_row])
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def _season_from_month(month):
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month = int(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 align_feature_frame(df, feature_names):
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aligned = df.copy()
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for feature in feature_names:
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if feature not in aligned.columns:
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aligned[feature] = 0
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return aligned[feature_names]
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def to_float_array(df):
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return df.values.astype(float)
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