xgboost.cv gives TypeError: 'StratifiedKFold' object is not iterable

Question

我一直在尝试在 python 2.7 中实现此代码。它给了我这个错误。我将不胜感激帮助。我有最新版本的 sklearn(0.18.1) 和 xgboost(0.6)

import xgboost as xgb
from sklearn.model_selection import StratifiedKFold
from sklearn.metrics import f1_score, roc_auc_score, confusion_matrix

nfold = 3
kf = StratifiedKFold(nfold, shuffle=True)

dtrain = xgb.DMatrix(x_train, label=y_train)
dtest = xgb.DMatrix(x_test)

params = {
    'objective' : 'binary:logistic',
    'eval_metric': 'auc',
    'min_child_weight':10,
    'scale_pos_weight':scale,
}
hist = xgb.cv(params, dtrain, num_boost_round=10000, folds=kf, early_stopping_rounds=50, as_pandas=True, verbose_eval=100, show_stdv=True, seed=0)

我收到这个错误：

TypeErrorTraceback (most recent call last)
<ipython-input-52-41c415e116d7> in <module>()
      5     'scale_pos_weight':scale,
      6 }
----> 7 hist = xgb.cv(params, dtrain, num_boost_round=10000, folds=kf, early_stopping_rounds=50, as_pandas=True, verbose_eval=100, show_stdv=True, seed=0)
      8 
      9 

/opt/conda/lib/python2.7/site-packages/xgboost/training.pyc in cv(params, dtrain, num_boost_round, nfold, stratified, folds, metrics, obj, feval, maximize, early_stopping_rounds, fpreproc, as_pandas, verbose_eval, show_stdv, seed, callbacks)
    369 
    370     results = {}
--> 371     cvfolds = mknfold(dtrain, nfold, params, seed, metrics, fpreproc, stratified, folds)
    372 
    373     # setup callbacks

/opt/conda/lib/python2.7/site-packages/xgboost/training.pyc in mknfold(dall, nfold, param, seed, evals, fpreproc, stratified, folds)
    236         idset = [randidx[(i * kstep): min(len(randidx), (i + 1) * kstep)] for i in range(nfold)]
    237     elif folds is not None:
--> 238         idset = [x[1] for x in folds]
    239         nfold = len(idset)
    240     else:

TypeError: 'StratifiedKFold' object is not iterable

Answer 1

在xgb.cv函数内，尝试替换

folds=kf

和

folds=list(kf.split(x_train,y_train))

应用 split method 以拆分为训练和验证。然后我们将其转换为 list 以便它成为一个可迭代对象。

如果这不起作用，请尝试不使用 list。即：

folds=kf.split(x_train,y_train)

Answer 2

如错误所示，kf 是一个 'StratifiedKFold' 对象。

这个对象有一个 .split() 方法，它会给你一个包含不同 train/valid 元素索引的生成器。

folds_generator = kf.split(x_train, y_train)

然而，阅读 xgb.cv doc,

folds : list, provides a possibility to use a list of pre-defined CV folds (each element must be a vector of test fold's indices). When folds are supplied, the nfold and stratified parameters are ignored.

folds 需要一个类型为 'list' 的参数。您可以使用以下代码将生成器转换为列表

folds_list = list(folds_generator)

xgboost.cv gives TypeError: 'StratifiedKFold' object is not iterable

xgboost.cv gives TypeError: 'StratifiedKFold' object is not iterable

python

machine-learning

scikit-learn

xgboost

grid-search