shenjianZ
a39d8b2fd2
搭建完整的前后端分离架构，实现数据概览、预测分析、聚类分析等核心功能模块 详细版： feat: 初始化员工缺勤分析系统项目 - 后端：基于 Flask 搭建 RESTful API，包含数据概览、特征分析、预测模型、聚类分析四大模块 - 前端：基于 Vue.js 构建单页应用，实现 Dashboard、预测、聚类、因子分析等页面 - 模型：集成随机森林、XGBoost、LightGBM、Stacking 等多种机器学习模型 - 文档：完成需求规格说明、系统架构设计、接口设计、数据设计、UI原型设计等文档
149 lines
3.3 KiB
Python
149 lines
3.3 KiB
Python
import os
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BASE_DIR = os.path.dirname(os.path.abspath(__file__))
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DATA_DIR = os.path.join(BASE_DIR, 'data')
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RAW_DATA_DIR = os.path.join(DATA_DIR, 'raw')
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PROCESSED_DATA_DIR = os.path.join(DATA_DIR, 'processed')
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MODELS_DIR = os.path.join(BASE_DIR, 'models')
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RAW_DATA_PATH = os.path.join(RAW_DATA_DIR, 'Absenteeism_at_work.csv')
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CLEAN_DATA_PATH = os.path.join(PROCESSED_DATA_DIR, 'clean_data.csv')
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RF_MODEL_PATH = os.path.join(MODELS_DIR, 'rf_model.pkl')
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XGB_MODEL_PATH = os.path.join(MODELS_DIR, 'xgb_model.pkl')
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KMEANS_MODEL_PATH = os.path.join(MODELS_DIR, 'kmeans_model.pkl')
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SCALER_PATH = os.path.join(MODELS_DIR, 'scaler.pkl')
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ENCODER_PATH = os.path.join(MODELS_DIR, 'encoder.pkl')
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CSV_SEPARATOR = ';'
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RANDOM_STATE = 42
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TEST_SIZE = 0.2
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FEATURE_NAMES = [
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'ID',
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'Reason for absence',
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'Month of absence',
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'Day of the week',
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'Seasons',
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'Transportation expense',
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'Distance from Residence to Work',
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'Service time',
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'Age',
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'Work load Average/day ',
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'Hit target',
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'Disciplinary failure',
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'Education',
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'Son',
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'Social drinker',
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'Social smoker',
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'Pet',
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'Weight',
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'Height',
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'Body mass index',
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'Absenteeism time in hours'
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]
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CATEGORICAL_FEATURES = [
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'Reason for absence',
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'Month of absence',
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'Day of the week',
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'Seasons',
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'Disciplinary failure',
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'Education',
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'Social drinker',
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'Social smoker'
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]
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NUMERICAL_FEATURES = [
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'Transportation expense',
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'Distance from Residence to Work',
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'Service time',
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'Age',
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'Work load Average/day ',
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'Hit target',
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'Son',
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'Pet',
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'Body mass index'
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]
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REASON_NAMES = {
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0: '未知原因',
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1: '传染病',
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2: '肿瘤',
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3: '血液疾病',
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4: '内分泌疾病',
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5: '精神行为障碍',
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6: '神经系统疾病',
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7: '眼部疾病',
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8: '耳部疾病',
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9: '循环系统疾病',
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10: '呼吸系统疾病',
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11: '消化系统疾病',
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12: '皮肤疾病',
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13: '肌肉骨骼疾病',
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14: '泌尿生殖疾病',
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15: '妊娠相关',
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16: '围产期疾病',
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17: '先天性畸形',
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18: '症状体征',
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19: '损伤中毒',
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20: '外部原因',
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21: '健康因素',
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22: '医疗随访',
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23: '医疗咨询',
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24: '献血',
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25: '实验室检查',
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26: '无故缺勤',
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27: '理疗',
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28: '牙科咨询'
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}
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WEEKDAY_NAMES = {
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2: '周一',
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3: '周二',
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4: '周三',
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5: '周四',
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6: '周五'
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}
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SEASON_NAMES = {
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1: '夏季',
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2: '秋季',
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3: '冬季',
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4: '春季'
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}
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EDUCATION_NAMES = {
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1: '高中',
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2: '本科',
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3: '研究生',
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4: '博士'
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}
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FEATURE_NAME_CN = {
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'ID': '员工标识',
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'Reason for absence': '缺勤原因',
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'Month of absence': '缺勤月份',
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'Day of the week': '星期几',
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'Seasons': '季节',
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'Transportation expense': '交通费用',
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'Distance from Residence to Work': '通勤距离',
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'Service time': '工龄',
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'Age': '年龄',
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'Work load Average/day ': '日均工作负荷',
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'Hit target': '达标率',
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'Disciplinary failure': '违纪记录',
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'Education': '学历',
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'Son': '子女数量',
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'Social drinker': '饮酒习惯',
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'Social smoker': '吸烟习惯',
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'Pet': '宠物数量',
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'Weight': '体重',
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'Height': '身高',
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'Body mass index': 'BMI指数',
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'Absenteeism time in hours': '缺勤时长'
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}
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