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- # -*- coding: utf-8 -*-
- """
- @author: yq
- @time: 2024/11/27
- @desc:
- """
- import time
- from entitys import DataSplitEntity
- from online_learning import OnlineLearningTrainer
- if __name__ == "__main__":
- time_now = time.time()
- import scorecardpy as sc
- # 加载数据
- dat = sc.germancredit()
- dat_columns = dat.columns.tolist()
- dat_columns = [c.replace(".","_") for c in dat_columns]
- dat.columns = dat_columns
- dat["creditability"] = dat["creditability"].apply(lambda x: 1 if x == "bad" else 0)
- data = DataSplitEntity(train_data=dat[:709], test_data=dat[709:])
- # 特征处理
- cfg = {
- # 模型系数,分箱信息等,请参考ol_resources_demo目录下文件
- # 模型系数文件 coef.dict(如果有常数项(截距)请用const作为key)
- # 分箱信息文件 feature.csv(数值型的分箱信息请按升序排列)
- "path_resources": "/root/notebook/ol_resources_demo",
- # 项目名称,影响数据存储位置
- "project_name": "OnlineLearningDemo",
- "y_column": "creditability",
- # 学习率
- "lr": 0.01,
- # 单次更新批大小
- "batch_size": 64,
- # 训练轮数
- "epochs": 20,
- "jupyter_print": True,
- # 压力测试
- "stress_test": True,
- # 压力测试抽样次数
- "stress_sample_times": 10,
- }
- # 训练并生成报告
- trainer = OnlineLearningTrainer(data=data, **cfg)
- trainer.train()
- trainer.report()
- print(time.time() - time_now)
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