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def load_logs(file_path):

logs = []

with open(file_path, 'r') as file:

for line in file:

timestamp, service_name, response_time = line.strip().split(',')

logs.append((timestamp, service_name, float(response_time)))

return logs

logs = load_logs('service_logs.txt')

from sklearn.model_selection import train_test_split

from sklearn.ensemble import RandomForestRegressor

# 提取特征和标签

features = [[log[0], log[2]] for log in logs] # 使用时间戳和响应时间作为特征

labels = [log[1] for log in logs] # 服务名称作为目标变量

X_train, X_test, y_train, y_test = train_test_split(features, labels, test_size=0.2)

model = RandomForestRegressor()

model.fit(X_train, y_train)

数字迎新管理系统

def rank_services(model, logs):

service_ranks = {}

for log in logs:

prediction = model.predict([[log[0], log[2]]])

if prediction not in service_ranks:

service_ranks[prediction] = 1

else:

service_ranks[prediction] += 1

return sorted(service_ranks.items(), key=lambda x: x[1], reverse=True)

ranked_services = rank_services(model, logs)