我正在尝试在python上使用xgboost。这是我的代码。 xgb.train可以工作，但是xgb.cv出现错误，尽管似乎我使用了正确的方式。
以下为我工作：
###### XGBOOST ######
import datetime
startTime = datetime.datetime.now()
import xgboost as xgb
data_train = np.array(traindata.drop('Category',axis=1))
labels_train = np.array(traindata['Category'].cat.codes)
data_valid = np.array(validdata.drop('Category',axis=1))
labels_valid = np.array(validdata['Category'].astype('category').cat.codes)
weights_train = np.ones(len(labels_train))
weights_valid = np.ones(len(labels_valid ))
dtrain = xgb.DMatrix( data_train, label=labels_train,weight = weights_train)
dvalid = xgb.DMatrix( data_valid , label=labels_valid ,weight = weights_valid )
param = {'bst:max_depth':5, 'bst:eta':0.05, # eta [default=0.3]
#'min_child_weight':1,'gamma':0,'subsample':1,'colsample_bytree':1,'scale_pos_weight':0, # default
# max_delta_step:0 # default
'min_child_weight':5,'scale_pos_weight':0, 'max_delta_step':2,
'subsample':0.8,'colsample_bytree':0.8,
'silent':1, 'objective':'multi:softprob' }
param['nthread'] = 4
param['eval_metric'] = 'mlogloss'
param['lambda'] = 2
param['num_class']=39
evallist = [(dtrain,'train'),(dvalid,'eval')] # if there is a validation set
# evallist = [(dtrain,'train')] # if there is no validation set
plst = param.items()
plst += [('ams@0','eval_metric')]
num_round = 100
bst = xgb.train( plst, dtrain, num_round, evallist,early_stopping_rounds=5 ) # early_stopping_rounds=10 # when there is a validation set
# bst.res=xgb.cv(plst,dtrain,num_round,nfold = 5,evallist,early_stopping_rounds=5)
bst.save_model('0001.model')
# dump model
bst.dump_model('dump.raw.txt')
# dump model with feature map
# bst.dump_model('dump.raw.txt','featmap.txt')
x = datetime.datetime.now() - startTime
print(x)
但是如果我换行...
bst = xgb.train( plst, dtrain, num_round, evallist,early_stopping_rounds=5 )
...到这一个...
bst.res = xgb.cv(plst,dtrain,num_round,nfold = 5,evallist,early_stopping_rounds=5)
...我收到以下意外错误：
文件“”，第45行bst.res = xgb.cv（plst，dtrain，num_round，nfold = 5，evallist，early_stopping_rounds = 5）语法错误：之后非关键字arg关键字arg
EDIT1：我也尝试更改关键字的顺序：
bst.res = xgb.cv(plst,dtrain,num_round,evallist,nfold = 5,early_stopping_rounds=5)
...我收到以下错误：
---------------------------------------------------------------------------
TypeError
Traceback (most recent call last) <ipython-input-49-36177ef64bab> in <module>()
43 # bst = xgb.train( plst, dtrain, num_round, evallist,early_stopping_rounds=5 ) # early_stopping_rounds=10 # when there is a validation set
44
---> 45 bst.res=xgb.cv(plst,dtrain,num_round,evallist,nfold =5 ,early_stopping_rounds=5)
46
47 bst.save_model('0001.model')
TypeError: cv() got multiple values for keyword argument 'nfold'
EDIT2毕竟，CV中不需要验证集。xgb.cv的签名中没有参数evals（尽管xgb.train中存在）所以我将其删除并将行更改为：
bst.res=xgb.cv(params=plst,dtrain=dtrain,num_boost_round=num_round,nfold = 5,early_stopping_rounds=5)
然后我收到此错误
/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/xgboost/training.pyc
in cv(params, dtrain, num_boost_round, nfold, metrics, obj, feval,
maximize, early_stopping_rounds, fpreproc, as_pandas, show_progress,
show_stdv, seed)
413 best_score_i = 0
414 results = []
--> 415 cvfolds = mknfold(dtrain, nfold, params, seed, metrics, fpreproc)
416 for i in range(num_boost_round):
417 for fold in cvfolds:
/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/xgboost/training.pyc
in mknfold(dall, nfold, param, seed, evals, fpreproc)
280 else:
281 tparam = param
--> 282 plst = list(tparam.items()) + [('eval_metric', itm) for itm in evals]
283 ret.append(CVPack(dtrain, dtest, plst))
284 return ret
AttributeError: 'list' object has no attribute 'items'
xgboost.cv的签名，从文档复制而来>>xgboost.cv(params, dtrain, num_boost_round=10, nfold=3, stratified=False,
folds=None, metrics=(), obj=None, feval=None, maximize=False,
early_stopping_rounds=None, fpreproc=None, as_pandas=True,
verbose_eval=None, show_stdv=True, seed=0, callbacks=None)
请注意，确切地有个严格的位置参数（two
params, dtrain，并且第四个位置的参数是nfold。您的电话是：xgb.cv(plst, dtrain, num_round, evallist, nfold=5, early_stopping_rounds=5)
当python解析函数调用时，它首先匹配您在位置上[传递的所有参数。因此，在您的情况下，python像这样进行匹配
Formal Parameter <-- What You Passed In
params <-- plst
dtrain <-- dtrain
num_boost_round <-- num_round
nfold <-- evallist
by name传递的所有参数。因此，在您的情况下，python像这样进行匹配
Formal Parameter <-- What You Passed In
nfold <-- 5
early_stopping_rounds <-- 5
nfold被分配了两次，这是生成此参数的原因TypeError: cv() got multiple values for keyword argument 'nfold'可能最简单，最清晰的解决方法是将
all您的参数作为关键字传递。通常，最佳做法是将位置参数限制为很小的数目，大多数程序员似乎最多只针对两个位置参数。
但是我遇到了另一个错误，,不明白
好像您要传递一个列表，该列表应包含字典。再次使用文档，第一个参数：
params（dict）– Booster params。
应该是字典。