lightgbm - LightGBM:继续训练模型

Question

我正在使用交叉验证来训练模型，如下所示:

classifier = lgb.Booster(
    params=params, 
    train_set=lgb_train_set,
)

result = lgb.cv(
    init_model=classifier,
    params=params, 
    train_set=lgb_train_set,
    num_boost_round=1000,
    early_stopping_rounds=20,
    verbose_eval=50,
    shuffle=True
)

我想通过多次运行第二个命令(也许使用新的训练集或使用不同的参数)来继续训练模型，它将继续改进模型。
但是，当我尝试此操作时，很明显，每次都会从头开始创建模型。
是否有其他方法可以做我打算做的事情？

Answer 1

可以使用lightgbm.train的init_model选项解决，该选项接受两个对象之一

LightGBM模型的文件名，或

lightgbm Booster对象

代码说明:

import numpy as np 
import lightgbm as lgb

data = np.random.rand(1000, 10) # 1000 entities, each contains 10 features
label = np.random.randint(2, size=1000) # binary target
train_data = lgb.Dataset(data, label=label, free_raw_data=False)
params = {}

#Initialize with 10 iterations
gbm_init = lgb.train(params, train_data, num_boost_round = 10)
print("Initial iter# %d" %gbm_init.current_iteration())

# Example of option #1 (pass a file):
gbm_init.save_model('model.txt')
gbm = lgb.train(params, train_data, num_boost_round = 10,
                init_model='model.txt')
print("Option 1 current iter# %d" %gbm.current_iteration())


# Example of option #2 (pass a lightgbm Booster object):
gbm_2 = lgb.train(params, train_data, num_boost_round = 10,
                init_model = gbm_init)
print("Option 2 current iter# %d" %gbm_2.current_iteration())

https://lightgbm.readthedocs.io/en/latest/pythonapi/lightgbm.train.html