python - 当我尝试为scikit-learn模型增加1个功能时，出现此错误“ValueError:找到的输入变量样本数量不一致”

我的代码工作正常

    df_amazon = pd.read_csv ("datasets/amazon_alexa.tsv", sep="\t")

    X = df_amazon['variation'] # the features we want to analyze
    ylabels = df_amazon['feedback'] # the labels, or answers, we want to test against

    X_train, X_test, y_train, y_test = train_test_split(X, ylabels, test_size=0.3)

    # Create pipeline using Bag of Words
    pipe = Pipeline([('cleaner', predictors()),
                     ('vectorizer', bow_vector),
                     ('classifier', classifier)])

    pipe.fit(X_train,y_train)

但是，如果我尝试向模型添加1个功能，则替换

    X = df_amazon['variation']

通过

    X = df_amazon[['variation','verified_reviews']]

呼叫fit时，我收到来自Sklearn的错误消息：


  ValueError：找到输入样本数量不一致的输入变量：[2，2205]


因此，当fit和X_train具有形状时，y_train可以工作
（2205，）和（2205，）。

但是当形状更改为
（2205，2）和（2205，）。

最好的解决方法是什么？

最佳答案

import pandas as pd
from sklearn.feature_extraction.text import CountVectorizer

df = pd.DataFrame(data = [['Heather Gray Fabric','I received the echo as a gift.',1],['Sandstone Fabric','Without having a cellphone, I cannot use many of her features',0]], columns = ['variation','review','feedback'])


vect = CountVectorizer()
vect.fit_transform(df[['variation','review']])

# now when you look at vocab that has been created
print(vect.vocabulary_)

#o/p, where feature has been generated only for column name and not content of particular column
Out[49]:
{'variation': 1, 'review': 0}

#so you need to make one column which contain which contain variation and review both and that  need to be passed into your model
df['variation_review'] = df['variation'] + df['review']

vect.fit_transform(df['variation_review'])
print(vect.vocabulary_)

{'heather': 8,
'gray': 6,
'fabrici': 3,
'received': 9,
'the': 11,
'echo': 2,
'as': 0,
'gift': 5,
'sandstone': 10,
'fabricwithout': 4,
'having': 7,
'cellphone': 1}

关于python - 当我尝试为scikit-learn模型增加1个功能时，出现此错误“ValueError:找到的输入变量样本数量不一致”，我们在Stack Overflow上找到一个类似的问题：https://stackoverflow.com/questions/56817875/