CODEWITHSUNDEEP
pythonscikit-learntf-idfcosine-similarity
brucezepplin
brucezepplin

Reputation: 9752

Python: compare items within two different tfidf matrices of different dimensions

I want to use TfidfVectorizer() on a file that contains many lines, each a phrase. I then want to take a test file with a small subset of phrases, do TfidfVectorizer() and then take the cosine similarity between the original and the test file so that for a given phrase in the test file, I retrieve the top N matches within the original file. Here is my attempt:

corpus = tuple(open("original.txt").read().split('\n'))
test = tuple(open("test.txt").read().split('\n'))


from sklearn.feature_extraction.text import TfidfVectorizer

tf = TfidfVectorizer(analyzer='word', ngram_range=(1,3), min_df = 0, stop_words = 'english')
tfidf_matrix =  tf.fit_transform(corpus)
tfidf_matrix2 =  tf.fit_transform(test)

from sklearn.metrics.pairwise import linear_kernel 


def new_find_similar(tfidf_matrix2, index, tfidf_matrix, top_n = 5):
    cosine_similarities = linear_kernel(tfidf_matrix2[index:index+1], tfidf_matrix).flatten()
    related_docs_indices = [i for i in cosine_similarities.argsort()[::-1] if i != index]
    return [(index, cosine_similarities[index]) for index in related_docs_indices][0:top_n]


for index, score in find_similar(tfidf_matrix, 1234567):
       print score, corpus[index]

However I get:

for index, score in new_find_similar(tfidf_matrix2, 1000, tfidf_matrix):
       print score, test[index]
Traceback (most recent call last):

  File "<ipython-input-53-2bf1cd465991>", line 1, in <module>
    for index, score in new_find_similar(tfidf_matrix2, 1000, tfidf_matrix):

  File "<ipython-input-51-da874b8d3076>", line 2, in new_find_similar
    cosine_similarities = linear_kernel(tfidf_matrix2[index:index+1], tfidf_matrix).flatten()

  File "C:\Users\arron\AppData\Local\Continuum\Anaconda2\lib\site-packages\sklearn\metrics\pairwise.py", line 734, in linear_kernel
    X, Y = check_pairwise_arrays(X, Y)

  File "C:\Users\arron\AppData\Local\Continuum\Anaconda2\lib\site-packages\sklearn\metrics\pairwise.py", line 122, in check_pairwise_arrays
    X.shape[1], Y.shape[1]))

ValueError: Incompatible dimension for X and Y matrices: X.shape[1] == 66662 while Y.shape[1] == 3332088

I wouldn't mind combining both files and then transforming, but I want to b sure I do not compare any of the phrases from the test file with in of the other phrases within the test file.

Any pointers?

Upvotes: 1

Views: 1354

Answers (1)

σηγ
σηγ

Reputation: 1314

Fit the TfidfVectorizer with data from corpus, then transform the test data with the already fitted vectorizer (i.e., do not call fit_transform twice):

tfidf_matrix =  tf.fit_transform(corpus)
tfidf_matrix2 =  tf.transform(test)

Upvotes: 4

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