为什么xgboost.cv和sklearn.cross_val_score会给出不同的结果?
问题描述:
我想在数据集上做一个分类器。我第一次用XGBoost:为什么xgboost.cv和sklearn.cross_val_score会给出不同的结果?
import xgboost as xgb
import pandas as pd
import numpy as np
train = pd.read_csv("train_users_processed_onehot.csv")
labels = train["Buy"].map({"Y":1, "N":0})
features = train.drop("Buy", axis=1)
data_dmat = xgb.DMatrix(data=features, label=labels)
params={"max_depth":5, "min_child_weight":2, "eta": 0.1, "subsamples":0.9, "colsample_bytree":0.8, "objective" : "binary:logistic", "eval_metric": "logloss"}
rounds = 180
result = xgb.cv(params=params, dtrain=data_dmat, num_boost_round=rounds, early_stopping_rounds=50, as_pandas=True, seed=23333)
print result
,其结果是:
test-logloss-mean test-logloss-std train-logloss-mean
0 0.683539 0.000141 0.683407
179 0.622302 0.001504 0.606452
我们可以看到它大约是0.622;
但是当我切换到sklearn
使用完全相同的参数(我认为),结果是完全不同的。下面是我的代码:
from sklearn.model_selection import cross_val_score
from xgboost.sklearn import XGBClassifier
import pandas as pd
train_dataframe = pd.read_csv("train_users_processed_onehot.csv")
train_labels = train_dataframe["Buy"].map({"Y":1, "N":0})
train_features = train_dataframe.drop("Buy", axis=1)
estimator = XGBClassifier(learning_rate=0.1, n_estimators=190, max_depth=5, min_child_weight=2, objective="binary:logistic", subsample=0.9, colsample_bytree=0.8, seed=23333)
print cross_val_score(estimator, X=train_features, y=train_labels, scoring="neg_log_loss")
,其结果是:[-4.11429976 -2.08675843 -3.27346662]
,扭转这一局面还远远0.622之后。
我把一个断点转换为cross_val_score
,并且看到分类器正在通过尝试预测测试集中的每个元组为负值,并以0.99的概率进行疯狂的预测。
我想知道我哪里出错了。有人能帮助我吗?
答
这个问题有点老,但我今天遇到了问题,并找出为什么xgboost.cv
和sklearn.model_selection.cross_val_score
给出的结果有很大不同。
默认情况下cross_val_score使用KFold
或StratifiedKFold
其shuffle参数为False,因此折叠不会从数据中随机抽取。
所以,如果你这样做,那么你应该得到相同的结果,
cross_val_score(estimator, X=train_features, y=train_labels, scoring="neg_log_loss", cv = StratifiedKFold(shuffle=True, random_state=23333))
保持random state
在StratifiedKfold
和seed
在xgboost.cv
同样得到准确可重复的结果。