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why does transform from tfidf vectorizer (sklearn) not work

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0

$begingroup$

I'm transforming a text in tf-idf from sklearn. I made the model:

from sklearn.feature_extraction.text import TfidfVectorizer
corpus = words
vectorizer = TfidfVectorizer(min_df = 15)
tf_idf_model = vectorizer.fit_transform(corpus)

And now I'm making vectors for different sets of words (documents), like:

word_set = ['dog', 'cat', 'foo']
v = vectorizer.transform(word_set)

But I want just one vector of these words, to compare to other documents. But when I use transform, the shape of v becomes:

<3x56492 sparse matrix of type '<class 'numpy.float64'>'
 with 3 stored elements in Compressed Sparse Row format>

I want a vector with shape 1x56492, and not 3x56492.. I'm certainly missing something here. Maybe you guys have some tips?

Thank you very much in advance.

scikit-learn tfidf

share

edited 1 min ago

Simon Larsson

1,195217

asked 7 mins ago

why_notwhy_not

1

New contributor

why_not is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.

$endgroup$

add a comment | 

0

$begingroup$

I'm transforming a text in tf-idf from sklearn. I made the model:

from sklearn.feature_extraction.text import TfidfVectorizer
corpus = words
vectorizer = TfidfVectorizer(min_df = 15)
tf_idf_model = vectorizer.fit_transform(corpus)

And now I'm making vectors for different sets of words (documents), like:

word_set = ['dog', 'cat', 'foo']
v = vectorizer.transform(word_set)

But I want just one vector of these words, to compare to other documents. But when I use transform, the shape of v becomes:

<3x56492 sparse matrix of type '<class 'numpy.float64'>'
 with 3 stored elements in Compressed Sparse Row format>

I want a vector with shape 1x56492, and not 3x56492.. I'm certainly missing something here. Maybe you guys have some tips?

Thank you very much in advance.

scikit-learn tfidf

share

edited 1 min ago

Simon Larsson

1,195217

asked 7 mins ago

why_notwhy_not

1

New contributor

why_not is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.

$endgroup$

add a comment | 

$begingroup$

I'm transforming a text in tf-idf from sklearn. I made the model:

from sklearn.feature_extraction.text import TfidfVectorizer
corpus = words
vectorizer = TfidfVectorizer(min_df = 15)
tf_idf_model = vectorizer.fit_transform(corpus)

And now I'm making vectors for different sets of words (documents), like:

word_set = ['dog', 'cat', 'foo']
v = vectorizer.transform(word_set)

But I want just one vector of these words, to compare to other documents. But when I use transform, the shape of v becomes:

<3x56492 sparse matrix of type '<class 'numpy.float64'>'
 with 3 stored elements in Compressed Sparse Row format>

I want a vector with shape 1x56492, and not 3x56492.. I'm certainly missing something here. Maybe you guys have some tips?

Thank you very much in advance.

scikit-learn tfidf

share

edited 1 min ago

Simon Larsson

1,195217

asked 7 mins ago

why_notwhy_not

1

New contributor

why_not is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.

$endgroup$

from sklearn.feature_extraction.text import TfidfVectorizer
corpus = words
vectorizer = TfidfVectorizer(min_df = 15)
tf_idf_model = vectorizer.fit_transform(corpus)

word_set = ['dog', 'cat', 'foo']
v = vectorizer.transform(word_set)

<3x56492 sparse matrix of type '<class 'numpy.float64'>'
 with 3 stored elements in Compressed Sparse Row format>

share

edited 1 min ago

Simon Larsson

1,195217

asked 7 mins ago

why_notwhy_not

1

New contributor

why_not is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.

share

edited 1 min ago

Simon Larsson

1,195217

asked 7 mins ago

why_notwhy_not

1

New contributor

why_not is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.

edited 1 min ago

Simon Larsson

1,195217

edited 1 min ago

Simon Larsson

1,195217

asked 7 mins ago

why_notwhy_not

1

New contributor

why_not is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.

asked 7 mins ago

why_notwhy_not

1