feat: 初始化员工缺勤分析系统项目
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
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backend/core/clustering.py
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229
backend/core/clustering.py
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import pandas as pd
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import numpy as np
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from sklearn.cluster import KMeans
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from sklearn.preprocessing import MinMaxScaler
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import joblib
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import os
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import config
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from core.preprocessing import get_clean_data
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class KMeansAnalyzer:
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def __init__(self, n_clusters=3):
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self.n_clusters = n_clusters
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self.model = None
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self.scaler = MinMaxScaler()
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self.data = None
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self.data_scaled = None
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self.labels = None
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def _get_feature_columns(self, df):
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df.columns = [col.strip() for col in df.columns]
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feature_map = {
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'Age': None,
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'Service time': None,
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'Work load Average/day': None,
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'Body mass index': None,
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'Absenteeism time in hours': None
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}
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for key in feature_map:
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if key in df.columns:
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feature_map[key] = key
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else:
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for col in df.columns:
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if key.replace(' ', '').lower() == col.replace(' ', '').lower():
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feature_map[key] = col
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break
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actual_features = [v for v in feature_map.values() if v is not None]
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return actual_features
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def fit(self, n_clusters=None):
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if n_clusters:
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self.n_clusters = n_clusters
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df = get_clean_data()
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df = df.reset_index(drop=True)
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feature_cols = self._get_feature_columns(df)
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if not feature_cols:
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feature_cols = ['Age', 'Service time', 'Body mass index', 'Absenteeism time in hours']
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feature_cols = [c for c in feature_cols if c in df.columns]
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self.data = df[feature_cols].values
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self.scaler = MinMaxScaler()
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self.data_scaled = self.scaler.fit_transform(self.data)
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self.model = KMeans(
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n_clusters=self.n_clusters,
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random_state=config.RANDOM_STATE,
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n_init=10
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)
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self.labels = self.model.fit_predict(self.data_scaled)
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return self.model
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def get_cluster_results(self, n_clusters=3):
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if self.model is None or self.n_clusters != n_clusters:
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self.fit(n_clusters)
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centers = self.scaler.inverse_transform(self.model.cluster_centers_)
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unique, counts = np.unique(self.labels, return_counts=True)
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total = len(self.labels)
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cluster_names = self._generate_cluster_names(centers)
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feature_cols = self._get_feature_columns(get_clean_data())
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clusters = []
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for i, (cluster_id, count) in enumerate(zip(unique, counts)):
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center_dict = {}
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for j, fname in enumerate(feature_cols):
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if j < len(centers[i]):
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center_dict[fname] = round(centers[i][j], 2)
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clusters.append({
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'id': int(cluster_id),
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'name': cluster_names.get(cluster_id, f'群体{cluster_id+1}'),
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'member_count': int(count),
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'percentage': round(count / total * 100, 1),
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'center': center_dict,
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'description': self._generate_description(cluster_names.get(cluster_id, ''))
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})
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return {
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'n_clusters': self.n_clusters,
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'clusters': clusters
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}
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def get_cluster_profile(self, n_clusters=3):
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if self.model is None or self.n_clusters != n_clusters:
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self.fit(n_clusters)
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centers_scaled = self.model.cluster_centers_
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df = get_clean_data()
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df.columns = [col.strip() for col in df.columns]
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feature_cols = self._get_feature_columns(df)
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dimensions = ['年龄', '工龄', '工作负荷', 'BMI', '缺勤倾向'][:len(feature_cols)]
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cluster_names = self._generate_cluster_names(
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self.scaler.inverse_transform(centers_scaled)
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)
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clusters = []
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for i in range(self.n_clusters):
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clusters.append({
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'id': i,
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'name': cluster_names.get(i, f'群体{i+1}'),
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'values': [round(v, 2) for v in centers_scaled[i]]
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})
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return {
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'dimensions': dimensions,
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'dimension_keys': feature_cols,
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'clusters': clusters
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}
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def get_scatter_data(self, n_clusters=3, x_axis='Age', y_axis='Absenteeism time in hours'):
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if self.model is None or self.n_clusters != n_clusters:
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self.fit(n_clusters)
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df = get_clean_data()
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df = df.reset_index(drop=True)
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df.columns = [col.strip() for col in df.columns]
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x_col = None
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y_col = None
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for col in df.columns:
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if x_axis.replace(' ', '').lower() in col.replace(' ', '').lower():
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x_col = col
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if y_axis.replace(' ', '').lower() in col.replace(' ', '').lower():
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y_col = col
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if x_col is None:
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x_col = df.columns[0]
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if y_col is None:
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y_col = df.columns[-1]
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points = []
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for idx in range(min(len(df), len(self.labels))):
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row = df.iloc[idx]
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points.append({
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'employee_id': int(row['ID']),
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'x': float(row[x_col]),
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'y': float(row[y_col]),
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'cluster_id': int(self.labels[idx])
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})
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cluster_colors = {
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'0': '#67C23A',
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'1': '#E6A23C',
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'2': '#F56C6C',
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'3': '#909399',
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'4': '#409EFF'
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}
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return {
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'x_axis': x_col,
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'x_axis_name': config.FEATURE_NAME_CN.get(x_col, x_col),
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'y_axis': y_col,
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'y_axis_name': config.FEATURE_NAME_CN.get(y_col, y_col),
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'points': points[:500],
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'cluster_colors': cluster_colors
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}
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def _generate_cluster_names(self, centers):
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names = {}
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for i, center in enumerate(centers):
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if len(center) >= 5:
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service_time = center[1]
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work_load = center[2]
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bmi = center[3]
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absent = center[4]
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else:
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service_time = center[1] if len(center) > 1 else 0
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work_load = 0
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bmi = center[2] if len(center) > 2 else 0
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absent = center[3] if len(center) > 3 else 0
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if service_time > 15 and absent < 3:
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names[i] = '模范型员工'
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elif work_load > 260 and absent > 5:
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names[i] = '压力型员工'
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elif bmi > 28:
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names[i] = '生活习惯型员工'
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else:
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names[i] = f'群体{i+1}'
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return names
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def _generate_description(self, name):
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descriptions = {
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'模范型员工': '工龄长、工作稳定、缺勤率低',
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'压力型员工': '工作负荷大、缺勤较多',
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'生活习惯型员工': 'BMI偏高、需关注健康'
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}
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return descriptions.get(name, '常规员工群体')
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def save_model(self):
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os.makedirs(config.MODELS_DIR, exist_ok=True)
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joblib.dump(self.model, config.KMEANS_MODEL_PATH)
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def load_model(self):
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if os.path.exists(config.KMEANS_MODEL_PATH):
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self.model = joblib.load(config.KMEANS_MODEL_PATH)
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self.n_clusters = self.model.n_clusters
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kmeans_analyzer = KMeansAnalyzer()
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