我尝试在python上使用xgboost
这是我的密码。xgb.列车但我在xgb.cv 虽然看起来我用的方法是正确的
以下是我的工作
###### 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)
但如果我换线
^{pr2}$通过这个
bst.res=xgb.cv(plst,dtrain,num_round,nfold = 5,evallist,early_stopping_rounds=5)
我得到以下意外错误:
File "", line 45 bst.res=xgb.cv(plst,dtrain,num_round,nfold = 5,evallist,early_stopping_rounds=5) SyntaxError: non-keyword arg after keyword arg
EDIT1:我也尝试更改关键字的顺序
bst.res=xgb.cv(plst,dtrain,num_round,evallist,nfold = 5,early_stopping_rounds=5)
我得到以下错误
---------------------------------------------------------------------------
TypeError
Traceback (most recent call last) <ipython-input-49-36177ef64bab> in <module>()
43 # bst = xgb.train( plst, dtrain, num_round, evallist,early_stopping_rounds=5 ) # early_stopping_rounds=10 # when there is a validation set
44
---> 45 bst.res=xgb.cv(plst,dtrain,num_round,evallist,nfold =5 ,early_stopping_rounds=5)
46
47 bst.save_model('0001.model')
TypeError: cv() got multiple values for keyword argument 'nfold'
编辑2
毕竟,cv中不需要验证集。
的签名中没有参数evals
xgb.cv(当它出现在xgb.列车)
所以我把它取下来,改成了
bst.res=xgb.cv(params=plst,dtrain=dtrain,num_boost_round=num_round,nfold = 5,early_stopping_rounds=5)
然后我得到这个错误
/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/xgboost/training.pyc
in cv(params, dtrain, num_boost_round, nfold, metrics, obj, feval,
maximize, early_stopping_rounds, fpreproc, as_pandas, show_progress,
show_stdv, seed)
413 best_score_i = 0
414 results = []
--> 415 cvfolds = mknfold(dtrain, nfold, params, seed, metrics, fpreproc)
416 for i in range(num_boost_round):
417 for fold in cvfolds:
/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/xgboost/training.pyc
in mknfold(dall, nfold, param, seed, evals, fpreproc)
280 else:
281 tparam = param
--> 282 plst = list(tparam.items()) + [('eval_metric', itm) for itm in evals]
283 ret.append(CVPack(dtrain, dtest, plst))
284 return ret
AttributeError: 'list' object has no attribute 'items'
这是
xgboost.cv
的签名,复制自文档注意,有两个严格的位置参数,第四个位置的参数是
nfold
。在你的电话是
当python解析一个函数调用时,它首先匹配按位置传递的所有参数。在您的例子中,python匹配如下
^{pr2}$然后python按照name匹配作为关键字传入的所有参数。在您的例子中,python匹配如下
因此,您可以看到形式参数
nfold
被分配了两次,这就是生成这个参数的原因也许最简单、最清晰的解决方法是将所有参数作为关键字传递给所有。一般来说,将位置参数限制在一个非常小的数字是一个最佳实践,大多数程序员似乎最多只针对两个位置参数。在
看起来你正在传递一个需要字典的列表。再次使用文档,第一个参数
应该是字典。在
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