如何使用此输出应用余弦相似性？

Question

我不确定如何使用此输出计算列车组和测试组之间的相似性。在应用tfidf和余弦相似性后，我得到了这个结果。我如何评估这个结果Python: tf-idf-cosine: to find document similarity我遵循了此代码。有人能解释一下为什么在余弦相似代码之后再次使用tfidf吗

from sklearn.feature_extraction.text import TfidfTransformer
from nltk.corpus import stopwords
import numpy as np
import numpy.linalg as LA

train_set = df4['BOW_COMMENTS_2'] #Documents
test_set = df4["BOW_JOB_LIST"] #Query
stopWords = stopwords.words('english')

vectorizer = CountVectorizer(stop_words = stopWords)
#print vectorizer

#print transformer
transformer=TfidfTransformer()

trainVectorizerArray= vectorizer.fit_transform([' '.join(arr) for arr in train_set]).toarray()
freq_term_matrix = vectorizer.transform([' '.join(arr) for arr in test_set])
print ('Fit Vectorizer to train set', trainVectorizerArray)
print ('Transform Vectorizer to test set',freq_term_matrix.todense())
tfidf = TfidfTransformer()
M=freq_term_matrix.toarray()

cx = lambda a, b : round(np.inner(a, b)/(LA.norm(a)*LA.norm(b)), 3)

for vector in trainVectorizerArray:
    print (vector)
    for testV in M:
        print (testV)
        cosine = cx(vector, testV)
        s=cosine
        print (s)
print(transformer.fit(trainVectorizerArray))

print (transformer.transform(trainVectorizerArray).toarray())

print(transformer.fit(M))

tfidf = transformer.transform(M)
print (tfidf.todense())``` 
`[0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1
 0 0 0 0 0]
0.577
[0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1
 0 0 0 0 0]
0.577
[0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1
 0 0 0 0 0]
0.577
[1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0
 0 0 0 0 0]
0.0
[1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0
 0 0 0 0 0]
0.0
[1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0
 0 0 0 0 0]
0.0
[0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0
 0 0 0 0 0]
0.0
[0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0
 0 0 0 0 0]
0.0
[0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0
 0 0 0 0 0]
0.0
[0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
 0 0 0 1 0]
0.0
[0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
 0 0 0 1 0]
0.0
[0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
 0 0 0 1 0]
0.0
[0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1
 0 0 0 1 0]
0.408
[0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1
 0 0 0 1 0]
0.408
[0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1
 0 0 0 1 0]
0.408
[0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
 0 0 1 1 0]
0.0
[0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
 0 0 1 1 0]
0.0
[0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
 0 0 1 1 0]
0.0
[0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
 0 0 0 0 0]
0.0
[0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
 0 0 0 0 0]
0.0
[0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
 0 0 0 0 0]
0.0
[0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
 0 0 0 0 0]
0.0
[0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
 0 0 0 0 0]
0.0
[0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
 0 0 0 0 0]
0.0
TfidfTransformer(norm='l2', smooth_idf=True, sublinear_tf=False, use_idf=True)
[[0.         0.         0.54634418 ... 0.46500799 0.43218473 0.        ]
 [0.         0.         0.54634418 ... 0.46500799 0.43218473 0.        ]
 [0.         0.         0.54634418 ... 0.46500799 0.43218473 0.        ]
 ...
 [0.         0.         0.         ... 0.         0.         0.        ]
 [0.         0.         0.         ... 0.         0.25836849 0.        ]
 [0.         0.         0.         ... 0.         0.         0.        ]]
TfidfTransformer(norm='l2', smooth_idf=True, sublinear_tf=False, use_idf=True)
[[0.        0.        0.        ... 0.        0.        0.       ]
 [0.7623208 0.        0.        ... 0.        0.        0.       ]
 [0.        0.        0.        ... 0.        0.        0.       ]
 ...
 [0.        0.        0.        ... 0.        0.        0.       ]
 [0.        0.        0.        ... 0.        0.        0.       ]
 [0.        0.        0.        ... 0.        0.        0.       ]]`