Using TF-IDF with other features in SKLearn
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What is the best/correct way to combine text analysis with other features? For example, I have a dataset with some text but also other features/categories. SKlearn's TF-IDF vectoriser transforms text data into sparse matrices. I can use these sparse matrices directly with a Naive Bayes classifier for example. But what's the way to also take into account the other features? Should I de-sparsify the tf-idf representation of the text and combine the features and the text into one DataFrame? Or can I keep the sparse matrix as a separate column for example? What's the correct way to do this?
python scikit-learn pandas tfidf
asked Sep 4 '17 at 11:30
lte__lte__
3101720
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add a comment |
$begingroup$
What is the best/correct way to combine text analysis with other features? For example, I have a dataset with some text but also other features/categories. SKlearn's TF-IDF vectoriser transforms text data into sparse matrices. I can use these sparse matrices directly with a Naive Bayes classifier for example. But what's the way to also take into account the other features? Should I de-sparsify the tf-idf representation of the text and combine the features and the text into one DataFrame? Or can I keep the sparse matrix as a separate column for example? What's the correct way to do this?
python scikit-learn pandas tfidf
asked Sep 4 '17 at 11:30
lte__lte__
3101720
$endgroup$
add a comment |
$begingroup$
What is the best/correct way to combine text analysis with other features? For example, I have a dataset with some text but also other features/categories. SKlearn's TF-IDF vectoriser transforms text data into sparse matrices. I can use these sparse matrices directly with a Naive Bayes classifier for example. But what's the way to also take into account the other features? Should I de-sparsify the tf-idf representation of the text and combine the features and the text into one DataFrame? Or can I keep the sparse matrix as a separate column for example? What's the correct way to do this?
python scikit-learn pandas tfidf
asked Sep 4 '17 at 11:30
lte__lte__
3101720
$endgroup$
What is the best/correct way to combine text analysis with other features? For example, I have a dataset with some text but also other features/categories. SKlearn's TF-IDF vectoriser transforms text data into sparse matrices. I can use these sparse matrices directly with a Naive Bayes classifier for example. But what's the way to also take into account the other features? Should I de-sparsify the tf-idf representation of the text and combine the features and the text into one DataFrame? Or can I keep the sparse matrix as a separate column for example? What's the correct way to do this?
python scikit-learn pandas tfidf
python scikit-learn pandas tfidf
asked Sep 4 '17 at 11:30
lte__lte__
3101720
asked Sep 4 '17 at 11:30
lte__lte__
3101720
asked Sep 4 '17 at 11:30
lte__lte__
3101720
asked Sep 4 '17 at 11:30
lte__lte__
3101720
asked Sep 4 '17 at 11:30
lte__lte__
3101720
3101720
add a comment |
add a comment |
2 Answers
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scikit-learn's FeatureUnion concatenates features from different vectorizers. An example of combining heterogeneous data, including text, can be found here.
answered Sep 4 '17 at 14:58
Brian SpieringBrian Spiering
3,5281028
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The link has expired !
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– Abhishek Raj
9 hours ago
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Thanks! Link has been updated.
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– Brian Spiering
25 mins ago
add a comment |
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Usually, if possible, you'd want to keep your matrice sparse as long as possible as it saves a lot of memory. That's why there are sparse matrices after all, otherwise, why bother? So, even if your classifier requires you to use dense input, you might want to keep the TFIDF features as sparse, and add the other features to them in a sparse format. And then only, make the matrix dense.
To do that, you could use scipy.sparse.hstack. It combines two sparse matrices together by column. scipy.sparse.vstack also exists. And of course, scipy also has the non-sparse version scipy.hstack and scipy.vstack
answered Sep 5 '17 at 21:46
Valentin CalommeValentin Calomme
1,210423
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2 Answers
2
active
oldest
votes
2 Answers
2
active
oldest
votes
active
oldest
votes
active
oldest
votes
$begingroup$
scikit-learn's FeatureUnion concatenates features from different vectorizers. An example of combining heterogeneous data, including text, can be found here.
answered Sep 4 '17 at 14:58
Brian SpieringBrian Spiering
3,5281028
$endgroup$
$begingroup$
The link has expired !
$endgroup$
– Abhishek Raj
9 hours ago
$begingroup$
Thanks! Link has been updated.
$endgroup$
– Brian Spiering
25 mins ago
add a comment |
$begingroup$
scikit-learn's FeatureUnion concatenates features from different vectorizers. An example of combining heterogeneous data, including text, can be found here.
answered Sep 4 '17 at 14:58
Brian SpieringBrian Spiering
3,5281028
$endgroup$
$begingroup$
The link has expired !
$endgroup$
– Abhishek Raj
9 hours ago
$begingroup$
Thanks! Link has been updated.
$endgroup$
– Brian Spiering
25 mins ago
add a comment |
$begingroup$
scikit-learn's FeatureUnion concatenates features from different vectorizers. An example of combining heterogeneous data, including text, can be found here.
answered Sep 4 '17 at 14:58
Brian SpieringBrian Spiering
3,5281028
$endgroup$
scikit-learn's FeatureUnion concatenates features from different vectorizers. An example of combining heterogeneous data, including text, can be found here.
answered Sep 4 '17 at 14:58
Brian SpieringBrian Spiering
3,5281028
edited 25 mins ago
answered Sep 4 '17 at 14:58
Brian SpieringBrian Spiering
3,5281028
answered Sep 4 '17 at 14:58
Brian SpieringBrian Spiering
3,5281028
answered Sep 4 '17 at 14:58
Brian SpieringBrian Spiering
3,5281028
3,5281028
$begingroup$
The link has expired !
$endgroup$
– Abhishek Raj
9 hours ago
$begingroup$
Thanks! Link has been updated.
$endgroup$
– Brian Spiering
25 mins ago
add a comment |
$begingroup$
The link has expired !
$endgroup$
– Abhishek Raj
9 hours ago
$begingroup$
Thanks! Link has been updated.
$endgroup$
– Brian Spiering
25 mins ago
$begingroup$
The link has expired !
$endgroup$
– Abhishek Raj
9 hours ago
$begingroup$
The link has expired !
$endgroup$
– Abhishek Raj
9 hours ago
$begingroup$
Thanks! Link has been updated.
$endgroup$
– Brian Spiering
25 mins ago
$begingroup$
Thanks! Link has been updated.
$endgroup$
– Brian Spiering
25 mins ago
add a comment |
$begingroup$
Usually, if possible, you'd want to keep your matrice sparse as long as possible as it saves a lot of memory. That's why there are sparse matrices after all, otherwise, why bother? So, even if your classifier requires you to use dense input, you might want to keep the TFIDF features as sparse, and add the other features to them in a sparse format. And then only, make the matrix dense.
To do that, you could use scipy.sparse.hstack. It combines two sparse matrices together by column. scipy.sparse.vstack also exists. And of course, scipy also has the non-sparse version scipy.hstack and scipy.vstack
answered Sep 5 '17 at 21:46
Valentin CalommeValentin Calomme
1,210423
$endgroup$
add a comment |
$begingroup$
Usually, if possible, you'd want to keep your matrice sparse as long as possible as it saves a lot of memory. That's why there are sparse matrices after all, otherwise, why bother? So, even if your classifier requires you to use dense input, you might want to keep the TFIDF features as sparse, and add the other features to them in a sparse format. And then only, make the matrix dense.
To do that, you could use scipy.sparse.hstack. It combines two sparse matrices together by column. scipy.sparse.vstack also exists. And of course, scipy also has the non-sparse version scipy.hstack and scipy.vstack
answered Sep 5 '17 at 21:46
Valentin CalommeValentin Calomme
1,210423
$endgroup$
add a comment |
$begingroup$
Usually, if possible, you'd want to keep your matrice sparse as long as possible as it saves a lot of memory. That's why there are sparse matrices after all, otherwise, why bother? So, even if your classifier requires you to use dense input, you might want to keep the TFIDF features as sparse, and add the other features to them in a sparse format. And then only, make the matrix dense.
To do that, you could use scipy.sparse.hstack. It combines two sparse matrices together by column. scipy.sparse.vstack also exists. And of course, scipy also has the non-sparse version scipy.hstack and scipy.vstack
answered Sep 5 '17 at 21:46
Valentin CalommeValentin Calomme
1,210423
$endgroup$
Usually, if possible, you'd want to keep your matrice sparse as long as possible as it saves a lot of memory. That's why there are sparse matrices after all, otherwise, why bother? So, even if your classifier requires you to use dense input, you might want to keep the TFIDF features as sparse, and add the other features to them in a sparse format. And then only, make the matrix dense.
To do that, you could use scipy.sparse.hstack. It combines two sparse matrices together by column. scipy.sparse.vstack also exists. And of course, scipy also has the non-sparse version scipy.hstack and scipy.vstack
answered Sep 5 '17 at 21:46
Valentin CalommeValentin Calomme
1,210423
answered Sep 5 '17 at 21:46
Valentin CalommeValentin Calomme
1,210423
answered Sep 5 '17 at 21:46
Valentin CalommeValentin Calomme
1,210423
answered Sep 5 '17 at 21:46
Valentin CalommeValentin Calomme
1,210423
1,210423
add a comment |
add a comment |
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