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
134 lines
5.2 KiB
Python
134 lines
5.2 KiB
Python
import numpy as np
|
|
from sklearn.cluster import KMeans
|
|
from sklearn.preprocessing import MinMaxScaler
|
|
|
|
import config
|
|
from core.preprocessing import get_clean_data
|
|
|
|
|
|
class KMeansAnalyzer:
|
|
def __init__(self, n_clusters=3):
|
|
self.n_clusters = n_clusters
|
|
self.model = None
|
|
self.scaler = MinMaxScaler()
|
|
self.labels = None
|
|
self.feature_cols = [
|
|
'年龄',
|
|
'司龄年数',
|
|
'月均加班时长',
|
|
'通勤时长分钟',
|
|
'BMI',
|
|
'缺勤时长（小时）',
|
|
]
|
|
|
|
def fit(self, n_clusters=None):
|
|
if n_clusters:
|
|
self.n_clusters = n_clusters
|
|
df = get_clean_data().reset_index(drop=True)
|
|
data = df[self.feature_cols].values
|
|
data_scaled = self.scaler.fit_transform(data)
|
|
self.model = KMeans(n_clusters=self.n_clusters, random_state=config.RANDOM_STATE, n_init=10)
|
|
self.labels = self.model.fit_predict(data_scaled)
|
|
return self.model
|
|
|
|
def get_cluster_results(self, n_clusters=3):
|
|
if self.model is None or self.n_clusters != n_clusters:
|
|
self.fit(n_clusters)
|
|
centers = self.scaler.inverse_transform(self.model.cluster_centers_)
|
|
unique, counts = np.unique(self.labels, return_counts=True)
|
|
total = len(self.labels)
|
|
names = self._generate_cluster_names(centers)
|
|
clusters = []
|
|
for cluster_id, count in zip(unique, counts):
|
|
center = centers[int(cluster_id)]
|
|
clusters.append({
|
|
'id': int(cluster_id),
|
|
'name': names.get(int(cluster_id), f'群体{int(cluster_id) + 1}'),
|
|
'member_count': int(count),
|
|
'percentage': round(count / total * 100, 1),
|
|
'center': {
|
|
feature: round(float(value), 2)
|
|
for feature, value in zip(self.feature_cols, center)
|
|
},
|
|
'description': self._generate_description(names.get(int(cluster_id), '')),
|
|
})
|
|
return {'n_clusters': self.n_clusters, 'clusters': clusters}
|
|
|
|
def get_cluster_profile(self, n_clusters=3):
|
|
if self.model is None or self.n_clusters != n_clusters:
|
|
self.fit(n_clusters)
|
|
centers_scaled = self.model.cluster_centers_
|
|
names = self._generate_cluster_names(self.scaler.inverse_transform(centers_scaled))
|
|
return {
|
|
'dimensions': ['年龄', '司龄', '加班', '通勤', 'BMI', '缺勤'],
|
|
'dimension_keys': self.feature_cols,
|
|
'clusters': [
|
|
{
|
|
'id': idx,
|
|
'name': names.get(idx, f'群体{idx + 1}'),
|
|
'values': [round(float(v), 2) for v in centers_scaled[idx]],
|
|
}
|
|
for idx in range(self.n_clusters)
|
|
],
|
|
}
|
|
|
|
def get_scatter_data(self, n_clusters=3, x_axis='月均加班时长', y_axis='缺勤时长（小时）'):
|
|
if self.model is None or self.n_clusters != n_clusters:
|
|
self.fit(n_clusters)
|
|
df = get_clean_data().reset_index(drop=True)
|
|
if x_axis not in df.columns:
|
|
x_axis = '月均加班时长'
|
|
if y_axis not in df.columns:
|
|
y_axis = config.TARGET_COLUMN
|
|
points = []
|
|
for idx in range(min(len(df), len(self.labels))):
|
|
row = df.iloc[idx]
|
|
points.append({
|
|
'employee_id': str(row[config.EMPLOYEE_ID_COLUMN]),
|
|
'x': float(row[x_axis]),
|
|
'y': float(row[y_axis]),
|
|
'cluster_id': int(self.labels[idx]),
|
|
})
|
|
return {
|
|
'x_axis': x_axis,
|
|
'x_axis_name': config.FEATURE_NAME_CN.get(x_axis, x_axis),
|
|
'y_axis': y_axis,
|
|
'y_axis_name': config.FEATURE_NAME_CN.get(y_axis, y_axis),
|
|
'points': points[:500],
|
|
'cluster_colors': {
|
|
'0': '#5B8FF9',
|
|
'1': '#61DDAA',
|
|
'2': '#F6BD16',
|
|
'3': '#E8684A',
|
|
'4': '#6DC8EC',
|
|
},
|
|
}
|
|
|
|
def _generate_cluster_names(self, centers):
|
|
names = {}
|
|
for idx, center in enumerate(centers):
|
|
_, tenure, overtime, commute, bmi, absence = center
|
|
if overtime > 38 and commute > 55 and absence > 8:
|
|
names[idx] = '高压通勤型'
|
|
elif bmi > 27 and absence > 8:
|
|
names[idx] = '健康波动型'
|
|
elif tenure > 8 and absence < 6:
|
|
names[idx] = '稳定低风险型'
|
|
elif overtime > 28 and absence > 7:
|
|
names[idx] = '轮班负荷型'
|
|
else:
|
|
names[idx] = f'群体{idx + 1}'
|
|
return names
|
|
|
|
def _generate_description(self, name):
|
|
descriptions = {
|
|
'高压通勤型': '加班和通勤压力都高，缺勤时长偏长。',
|
|
'健康波动型': '健康相关风险更高，需要重点关注。',
|
|
'稳定低风险型': '司龄较长，缺勤水平稳定且偏低。',
|
|
'轮班负荷型': '排班和工作负荷较重，缺勤风险较高。',
|
|
}
|
|
return descriptions.get(name, '常规员工群体。')
|
|
|
|
|
|
kmeans_analyzer = KMeansAnalyzer()
|