我在同一份数据上使用Scikit-learn和XGBoost训练了2个梯度提升模型。
Scikit-learn模型
GradientBoostingClassifier(
n_estimators=5,
learning_rate=0.17,
max_depth=5,
verbose=2
)
XGBoost模型
XGBClassifier(
n_estimators=5,
learning_rate=0.17,
max_depth=5,
verbosity=2,
eval_metric="logloss"
)
接下来我检查了推断性能：
为什么XGBoost这么慢？
XGBClassifier() 是XGBoost在scikit-learn中的API（详见https://xgboost.readthedocs.io/en/latest/python/python_api.html#xgboost.XGBClassifier）。如果您直接调用函数（而不是通过API），它将更快。为了比较这两个函数的性能，最好分别直接调用每个函数，而不是一个直接调用，另一个通过API调用。以下是示例：# benchmark_xgboost_vs_sklearn.py
# Adapted from `xgboost_test.py` by Jacob Schreiber
# (https://gist.github.com/jmschrei/6b447aada61d631544cd)
"""
Benchmarking scripts for XGBoost versus sklearn (time and accuracy)
"""
import time
import random
import numpy as np
import xgboost as xgb
from sklearn.ensemble import GradientBoostingClassifier
random.seed(0)
np.random.seed(0)
def make_dataset(n=500, d=10, c=2, z=2):
"""
Make a dataset of size n, with d dimensions and m classes,
with a distance of z in each dimension, making each feature equally
informative.
"""
# Generate our data and our labels
X = np.concatenate([np.random.randn(n, d) + z*i for i in range(c)])
y = np.concatenate([np.ones(n) * i for i in range(c)])
# Generate a random indexing
idx = np.arange(n*c)
np.random.shuffle(idx)
# Randomize the dataset, preserving data-label pairing
X = X[idx]
y = y[idx]
# Return x_train, x_test, y_train, y_test
return X[::2], X[1::2], y[::2], y[1::2]
def main():
"""
Run SKLearn, and then run xgboost,
then xgboost via SKLearn XGBClassifier API wrapper
"""
# Generate the dataset
X_train, X_test, y_train, y_test = make_dataset(10, z=100)
n_estimators=5
max_depth=5
learning_rate=0.17
# sklearn first
tic = time.time()
clf = GradientBoostingClassifier(n_estimators=n_estimators,
max_depth=max_depth, learning_rate=learning_rate)
clf.fit(X_train, y_train)
print("SKLearn GBClassifier: {}s".format(time.time() - tic))
print("Acc: {}".format(clf.score(X_test, y_test)))
print(y_test.sum())
print(clf.predict(X_test))
# Convert the data to DMatrix for xgboost
dtrain = xgb.DMatrix(X_train, label=y_train)
dtest = xgb.DMatrix(X_test, label=y_test)
# Loop through multiple thread numbers for xgboost
for threads in 1, 2, 4:
# xgboost's sklearn interface
tic = time.time()
clf = xgb.XGBModel(n_estimators=n_estimators, max_depth=max_depth,
learning_rate=learning_rate, nthread=threads)
clf.fit(X_train, y_train)
print("SKLearn XGBoost API Time: {}s".format(time.time() - tic))
preds = np.round( clf.predict(X_test) )
acc = 1. - (np.abs(preds - y_test).sum() / y_test.shape[0])
print("Acc: {}".format( acc ))
print("{} threads: ".format( threads ))
tic = time.time()
param = {
'max_depth' : max_depth,
'eta' : 0.1,
'silent': 1,
'objective':'binary:logistic',
'nthread': threads
}
bst = xgb.train( param, dtrain, n_estimators,
[(dtest, 'eval'), (dtrain, 'train')] )
print("XGBoost (no wrapper) Time: {}s".format(time.time() - tic))
preds = np.round(bst.predict(dtest) )
acc = 1. - (np.abs(preds - y_test).sum() / y_test.shape[0])
print("Acc: {}".format(acc))
if __name__ == '__main__':
main()
总结结果：
sklearn.ensemble.GradientBoostingClassifier（）
sklearn xgboost API包装器XGBClassifier（）
XGBoost（无包装器）xgb.train（）
您可以尝试使用带有英特尔优化的XGBoost来提高在英特尔硬件上的性能表现。 可通过以下方式安装英特尔优化的XGBoost：
作为Intel® AI Analytics Toolkit（https://www.intel.com/content/www/us/en/developer/tools/oneapi/ai-analytics-toolkit-download.html）的一部分
从PyPI仓库中，使用pip包管理器：pip install xgboost
从Anaconda包管理器中： 使用Intel渠道：conda install xgboost –c intel
使用conda-forge渠道：conda install xgboost –c conda-forge
作为Docker容器（前提是您拥有DockerHub帐户）（https://hub.docker.com/r/intel/intel-optimized-ml/tags?page=1&name=xgboost）
XGBClassifier()不是xgboost的包装器吗？尝试使用xgb.train()，例如：https://xgboost.readthedocs.io/en/latest/get_started.html - jared_mamrot