Question

我正在尝试在python上使用xgboost

这是我的代码。 xgb.train工作，但我得到xgb.cv错误虽然看起来我用它正确的方式

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)

但如果我改变了行

bst = xgb.train( plst, dtrain, num_round, evallist,early_stopping_rounds=5 )

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）SyntaxError：非关键字arg after 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（当xgb.train存在时）所以我删除它并通过

更改了行

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'

Answer 1

以下是从文档中复制的xgboost.cv签名

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 =无，as_pandas = True，verbose_eval =无，show_stdv = True，种子= 0，回调=无）

请注意，确实有两个严格的位置参数，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，它首先匹配您按位置按位置传递的所有参数。所以在你的情况下，python匹配像这样

Formal Parameter <-- What You Passed In
          params <-- plst
          dtrain <-- dtrain
 num_boost_round <-- num_round
           nfold <-- evallist

然后python将您传递的所有参数作为关键字按名称进行匹配。所以在你的情况下，python匹配像这样

Formal Parameter <-- What You Passed In
          nfold <-- 5
          early_stopping_rounds <-- 5

所以你可以看到形式参数nfold被分配了两次，这是产生这个的

TypeError: cv() got multiple values for keyword argument 'nfold'

最简单和最清晰的解决方法可能是将所有您的参数作为关键字传递。通常，最好将位置参数限制为非常小的数字，大多数程序员似乎最多只针对两个位置参数。

但我得到另一个错误，我无法弄明白唉

看起来你正在传递一个预期字典的列表。再次使用文档，第一个参数

params（dict） - Booster params。

应该是字典。

python上的XGBoost：xgb.cv有什么问题？

1 个答案: