lightgbm || ValueError：Series.dtypes必须为int，float或bool

如何解决lightgbm || ValueError：Series.dtypes必须为int，float或bool

数据框已填充na值。

数据集的架构没有文档中指定的 object dtype。

df.info() 

输出：

<class 'pandas.core.frame.DataFrame'>
Int64Index: 429 entries,351 to 559
Data columns (total 11 columns):
 #   Column             Non-Null Count  Dtype   
---  ------             --------------  -----   
 0   Gender             429 non-null    category
 1   Married            429 non-null    category
 2   Dependents         429 non-null    category
 3   Education          429 non-null    category
 4   Self_Employed      429 non-null    category
 5   ApplicantIncome    429 non-null    int64   
 6   CoapplicantIncome  429 non-null    float64 
 7   LoanAmount         429 non-null    float64 
 8   Loan_Amount_Term   429 non-null    float64 
 9   Credit_History     429 non-null    float64 
 10  Property_Area      429 non-null    category
dtypes: category(6),float64(4),int64(1)
memory usage: 23.3 KB

我有以下代码....................................... ................................................... ................................................... ................................................... ................................................... ................................................... ...................

• 我正在尝试使用lightgbm对数据集进行分类

import lightgbm as lgb

train_data=lgb.Dataset(x_train,label=y_train,categorical_feature=cat_cols)


#define parameters
params = {'learning_rate':0.001}


model= lgb.train(params,train_data,100,categorical_feature=cat_cols) 

出现以下错误：

---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
<ipython-input-178-aaa91a2d8719> in <module>
      6 
      7 
----> 8 model= lgb.train(params,categorical_feature=cat_cols)

~\Anaconda3\lib\site-packages\lightgbm\engine.py in train(params,train_set,num_boost_round,valid_sets,valid_names,fobj,feval,init_model,feature_name,categorical_feature,early_stopping_rounds,evals_result,verbose_eval,learning_rates,keep_training_booster,callbacks)
    229     # construct booster
    230     try:
--> 231         booster = Booster(params=params,train_set=train_set)
    232         if is_valid_contain_train:
    233             booster.set_train_data_name(train_data_name)

~\Anaconda3\lib\site-packages\lightgbm\basic.py in __init__(self,params,model_file,model_str,silent)
   1981                     break
   1982             # construct booster object
-> 1983             train_set.construct()
   1984             # copy the parameters from train_set
   1985             params.update(train_set.get_params())

~\Anaconda3\lib\site-packages\lightgbm\basic.py in construct(self)
   1319             else:
   1320                 # create train
-> 1321                 self._lazy_init(self.data,label=self.label,1322                                 weight=self.weight,group=self.group,1323                                 init_score=self.init_score,predictor=self._predictor,~\Anaconda3\lib\site-packages\lightgbm\basic.py in _lazy_init(self,data,label,reference,weight,group,init_score,predictor,silent,params)
   1133                 raise TypeError('Cannot initialize Dataset from {}'.format(type(data).__name__))
   1134         if label is not None:
-> 1135             self.set_label(label)
   1136         if self.get_label() is None:
   1137             raise ValueError("Label should not be None")

~\Anaconda3\lib\site-packages\lightgbm\basic.py in set_label(self,label)
   1648         self.label = label
   1649         if self.handle is not None:
-> 1650             label = list_to_1d_numpy(_label_from_pandas(label),name='label')
   1651             self.set_field('label',label)
   1652             self.label = self.get_field('label')  # original values can be modified at cpp side

~\Anaconda3\lib\site-packages\lightgbm\basic.py in list_to_1d_numpy(data,dtype,name)
     88     elif isinstance(data,Series):
     89         if _get_bad_pandas_dtypes([data.dtypes]):
---> 90             raise ValueError('Series.dtypes must be int,float or bool')
     91         return np.array(data,dtype=dtype,copy=False)  # SparseArray should be supported as well
     92     else:

ValueError: Series.dtypes must be int,float or bool

解决方法

有人帮助过你吗？如果不是：答案在于转换您的变量。

转到此链接：GitHub Discussion lightGBM

LightGBM 的创建者曾经遇到过同样的问题。 在上面的链接中，他们 (STRIKER) 告诉您，您应该：使用 astype("category") (pandas/scikit) 转换变量，并且您应该对它们进行 labelEncode，因为您需要一个 INT ！特征列中的值，尤其是 INT32。

然而，labelEncoding 和 astype('category') 通常应该做同样的事情： Encoding

另一个有用的链接是关于分类特征的高级文档：Categorical feature light gbm homepage，其中他们告诉您他们无法像在您的数据中那样处理对象（字符串）dtype。

如果你仍然对这个解释感到不舒服，这里是我来自 kaggle space_race_set 的代码片段。如果您仍然遇到问题。就问吧。

cat_feats = ['Company Name','Night_and_Day','Rocket Type','Rocket Mission Type','State','Country']
labelencoder = LabelEncoder()

for col in cat_feats:
    train_df[col] = labelencoder.fit_transform(train_df[col])

for col in cat_feats:
    train_df[col] = train_df[col].astype('int')
    

y = train_df[["Status Mission"]]
X = train_df.drop(["Status Mission"],axis=1)

X_train,X_test,y_train,y_test = train_test_split(X,y,random_state = 42)    

train_data = lgb.Dataset(X_train,label=y_train,categorical_feature=['Company Name','Country'],free_raw_data=False)
test_data = lgb.Dataset(X_test,label=y_test,free_raw_data=False)