Python 无法在逻辑回归中使用决策函数（）评估分数

我正在做华盛顿大学的这项作业，我必须使用LogisticRecession中的decision函数（）预测样本测试矩阵（最后几行）的分数。但我得到的错误是

    ValueError: X has 145 features per sample; expecting 113092

代码如下：

   import pandas as pd 
   import numpy as np 
   from sklearn.linear_model import LogisticRegression

   products = pd.read_csv('amazon_baby.csv')

   def remove_punct (text) :
       import string 
       text = str(text)
       for i in string.punctuation:
          text = text.replace(i,"")
       return(text)

   products['review_clean'] = products['review'].apply(remove_punct)
   products = products[products.rating != 3]
   products['sentiment'] = products['rating'].apply(lambda x : +1 if x > 3 else  -1 )

   train_data_index = pd.read_json('module-2-assignment-train-idx.json')
   test_data_index = pd.read_json('module-2-assignment-test-idx.json')

   train_data = products.loc[train_data_index[0], :]
   test_data = products.loc[test_data_index[0], :]
   train_data = train_data.dropna()
   test_data = test_data.dropna()

   from sklearn.feature_extraction.text import CountVectorizer

   train_matrix = vectorizer.fit_transform(train_data['review_clean'])
   test_matrix = vectorizer.fit_transform(test_data['review_clean'])

   sentiment_model = LogisticRegression()
   sentiment_model.fit(train_matrix, train_data['sentiment'])
   print (sentiment_model.coef_)

   sample_data = test_data[10:13]
   print (sample_data)

   sample_test_matrix = vectorizer.transform(sample_data['review_clean'])
   scores = sentiment_model.decision_function(sample_test_matrix)
   print (scores)

以下是产品数据：

          Name                                                         Review                                       Rating  

  0       Planetwise Flannel Wipes                              These flannel wipes are OK, but in my opinion ...       3  


  1       Planetwise Wipe Pouch                                 it came early and was not disappointed. i love...       5  


  2       Annas Dream Full Quilt with 2 Shams                   Very soft and comfortable and warmer than it l...       5  

  3       Stop Pacifier Sucking without tears with Thumb...     This is a product well worth the purchase.  I ...       5

  4       Stop Pacifier Sucking without tears with Thumb...      All of my kids have cried non-stop when I trie...       5 

---

此行导致后续行中出现错误：

test_matrix = vectorizer.fit_transform(test_data['review_clean'])

将上述内容更改为：

test_matrix = vectorizer.transform(test_data['review_clean'])

说明：使用fit_transform（）将在测试数据上重新安装CountVector。因此，有关训练数据的所有信息都将丢失，词汇将仅从测试数据中计算

然后使用

矢量器

对象转换

样本数据['review\u clean']

。因此，其中的功能将仅是从

测试数据中学习的功能

但是
情绪模型
是根据
训练数据
中的词汇进行训练的。因此，特征是不同的

始终在测试数据上使用
transform（）
，永远不要使用
fit\u transform（）
太好了，它起作用了。谢谢你能告诉我为什么会这样吗？@harshi我补充了解释。请仔细检查，如果仍然不理解，请询问。同样，如果这能帮助你考虑投票/接受答案。当然。谢谢