python - 无法在逻辑回归中使用 Decision_function() 评估分数

Question

我正在读这所大学。在华盛顿作业中，我必须使用 LogisticRegression 中的 Decision_function() 来预测sample_test_matrix(最后几行)的分数。但我收到的错误是

    ValueError: X has 145 features per sample; expecting 113092

这是代码:

   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)

这是产品数据:

          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 

Answer 1

该行导致后续行出现错误:

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

将上面的内容更改为:

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

说明:使用 fit_transform() 将根据测试数据重新拟合 CountVectorizer。因此，有关训练数据的所有信息都将丢失，词汇量将仅根据测试数据计算。

然后您将使用该vectorizer对象来转换sample_data['review_clean']。因此其中的功能将只是从 test_data 中学习的功能。

但是 sentiment_model 是根据 train_data 中的词汇进行训练的。因此功能不同。

始终对测试数据使用 transform()，切勿使用 fit_transform()。