本文档提供了XGBoost Python包中使用的回调API的基本概述。在XGBoost 1.3中，为Python包设计了一个新的回调接口，它为设计各种扩展提供了灵活性，用于训练。此外，XGBoost还预定义了许多回调函数，用于支持提前停止early stopping、检查点checkpoints等。
使用内置回调函数
默认情况下，XGBoost 中的训练方法具有参数，如 early_stopping_rounds 和 verbose/verbose_eval，当指定这些参数时，训练过程将在内部定义相应的回调函数。例如，当指定了 early_stopping_rounds 时，EarlyStopping 回调将在迭代循环内调用。也可以直接将此回调函数传递给 XGBoost：
python
from sklearn.datasets import load_breast_cancer
from sklearn.model_selection import train_test_split
import xgboost as xgb
import numpy as np
X, y = load_breast_cancer(return_X_y=True)
X_train, X_valid, y_train, y_valid = train_test_split(X, y, stratify=y, random_state=94)
D_train = xgb.DMatrix(X_train, y_train)
D_valid = xgb.DMatrix(X_valid, y_valid)
# Define a custom evaluation metric used for early stopping.
def eval_error_metric(predt, dtrain: xgb.DMatrix):
label = dtrain.get_label()
r = np.zeros(predt.shape)
gt = predt > 0.5
r[gt] = 1 - label[gt]
le = predt <= 0.5
r[le] = label[le]
return 'CustomErr', np.sum(r)
# Specify which dataset and which metric should be used for early stopping.
early_stop = xgb.callback.EarlyStopping(rounds=early_stopping_rounds,
metric_name='CustomErr',
data_name='Train')
booster = xgb.train(
{'objective': 'binary:logistic',
'eval_metric': ['error', 'rmse'],
'tree_method': 'hist'}, D_train,
evals=[(D_train, 'Train'), (D_valid, 'Valid')],
feval=eval_error_metric,
num_boost_round=1000,
callbacks=[early_stop],
verbose_eval=False)
dump = booster.get_dump(dump_format='json')
assert len(early_stop.stopping_history['Train']['CustomErr']) == len(dump)定义自己的回调函数
XGBoost提供了一个回调接口类：TrainingCallback，用户定义的回调应该继承这个类并覆盖相应的方法。在示例中有使用和定义回调函数的工作示例。
python
import argparse
import os
import tempfile
from typing import Dict
import numpy as np
from matplotlib import pyplot as plt
from sklearn.datasets import load_breast_cancer
from sklearn.model_selection import train_test_split
import xgboost as xgb
class Plotting(xgb.callback.TrainingCallback):
"""Plot evaluation result during training. Only for demonstration purpose as it's
quite slow to draw using matplotlib.
"""
def __init__(self, rounds: int) -> None:
self.fig = plt.figure()
self.ax = self.fig.add_subplot(111)
self.rounds = rounds
self.lines: Dict[str, plt.Line2D] = {}
self.fig.show()
self.x = np.linspace(0, self.rounds, self.rounds)
plt.ion()
def _get_key(self, data: str, metric: str) -> str:
return f"{data}-{metric}"
def after_iteration(
self, model: xgb.Booster, epoch: int, evals_log: Dict[str, dict]
) -> bool:
"""Update the plot."""
if not self.lines:
for data, metric in evals_log.items():
for metric_name, log in metric.items():
key = self._get_key(data, metric_name)
expanded = log + [0] * (self.rounds - len(log))
(self.lines[key],) = self.ax.plot(self.x, expanded, label=key)
self.ax.legend()
else:
# https://pythonspot.com/matplotlib-update-plot/
for data, metric in evals_log.items():
for metric_name, log in metric.items():
key = self._get_key(data, metric_name)
expanded = log + [0] * (self.rounds - len(log))
self.lines[key].set_ydata(expanded)
self.fig.canvas.draw()
# False to indicate training should not stop.
return False
def custom_callback() -> None:
"""Demo for defining a custom callback function that plots evaluation result during
training."""
X, y = load_breast_cancer(return_X_y=True)
X_train, X_valid, y_train, y_valid = train_test_split(X, y, random_state=0)
D_train = xgb.DMatrix(X_train, y_train)
D_valid = xgb.DMatrix(X_valid, y_valid)
num_boost_round = 100
plotting = Plotting(num_boost_round)
# Pass it to the `callbacks` parameter as a list.
xgb.train(
{
"objective": "binary:logistic",
"eval_metric": ["error", "rmse"],
"tree_method": "hist",
"device": "cuda",
},
D_train,
evals=[(D_train, "Train"), (D_valid, "Valid")],
num_boost_round=num_boost_round,
callbacks=[plotting],
)
def check_point_callback() -> None:
"""Demo for using the checkpoint callback. Custom logic for handling output is
usually required and users are encouraged to define their own callback for
checkpointing operations. The builtin one can be used as a starting point.
"""
# Only for demo, set a larger value (like 100) in practice as checkpointing is quite
# slow.
rounds = 2
def check(as_pickle: bool) -> None:
for i in range(0, 10, rounds):
if i == 0:
continue
if as_pickle:
path = os.path.join(tmpdir, "model_" + str(i) + ".pkl")
else:
path = os.path.join(
tmpdir,
f"model_{i}.{xgb.callback.TrainingCheckPoint.default_format}",
)
assert os.path.exists(path)
X, y = load_breast_cancer(return_X_y=True)
m = xgb.DMatrix(X, y)
# Check point to a temporary directory for demo
with tempfile.TemporaryDirectory() as tmpdir:
# Use callback class from xgboost.callback
# Feel free to subclass/customize it to suit your need.
check_point = xgb.callback.TrainingCheckPoint(
directory=tmpdir, interval=rounds, name="model"
)
xgb.train(
{"objective": "binary:logistic"},
m,
num_boost_round=10,
verbose_eval=False,
callbacks=[check_point],
)
check(False)
# This version of checkpoint saves everything including parameters and
# model. See: doc/tutorials/saving_model.rst
check_point = xgb.callback.TrainingCheckPoint(
directory=tmpdir, interval=rounds, as_pickle=True, name="model"
)
xgb.train(
{"objective": "binary:logistic"},
m,
num_boost_round=10,
verbose_eval=False,
callbacks=[check_point],
)
check(True)
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument("--plot", default=1, type=int)
args = parser.parse_args()
check_point_callback()
if args.plot:
custom_callback()