重采样期间出现TypeError

如何解决重采样期间出现TypeError

我正在尝试对具有不平衡类的数据集应用重采样。 我所做的是：

from sklearn.utils import resample

y = df.Label

vectorizer = CountVectorizer()
X = vectorizer.fit_transform(df['Text'].replace(np.NaN,""))

X_train,X_test,y_train,y_test = train_test_split(X,y,test_size = 0.30,stratify=y)

# concatenate our training data back together
X = pd.concat([X_train,y_train],axis=1)

# separate minority and majority classes
not_df = X[X.Label==0]
df = X[X.Label==1]

# upsample minority
df_upsampled = resample(df,replace=True,n_samples=len(not_df),random_state=27) 

# combine majority and upsampled minority
upsampled = pd.concat([not_df,df_upsampled])

很遗憾，我在此步骤遇到了一些问题：X = pd.concat([X_train,axis=1)，即

/anaconda3/lib/python3.7/site-packages/pandas/core/reshape/concat.py in concat(objs,axis,join,ignore_index,keys,levels,names,verify_integrity,sort,copy)
    279         verify_integrity=verify_integrity,280         copy=copy,--> 281         sort=sort,282     )
    283 

/anaconda3/lib/python3.7/site-packages/pandas/core/reshape/concat.py in __init__(self,objs,copy,sort)
    355                     "only Series and DataFrame objs are valid".format(typ=type(obj))
    356                 )
--> 357                 raise TypeError(msg)
    358 
    359             # consolidate

TypeError: cannot concatenate object of type '<class 'scipy.sparse.csr.csr_matrix'>'; only Series and DataFrame objs are valid

您可以将“文本”列视为

Text
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希望您能帮助我解决这个问题。

解决方法

在使用X_train之前，您必须将concat转换为数据框

X = pd.concat([pd.DataFrame(X_train),y_train],axis=1)