How to use sclearn CountVectorizer with analyzer "word" and "char"? - python

Question

How to use sklearn CountVectorizer with word and char analyzer? http://scikit-learn.org/stable/modules/generated/sklearn.feature_extraction.text.CountVectorizer.html

I could extract text functions by word or char separately, but how to create charword_vectorizer ? Is there a way to combine vectorizers? or use more than one analyzer?

 >>> from sklearn.feature_extraction.text import CountVectorizer >>> word_vectorizer = CountVectorizer(analyzer='word', ngram_range=(1, 2), min_df=1) >>> char_vectorizer = CountVectorizer(analyzer='char', ngram_range=(1, 2), min_df=1) >>> x = ['this is a foo bar', 'you are a foo bar black sheep'] >>> word_vectorizer.fit_transform(x) <2x15 sparse matrix of type '<type 'numpy.int64'>' with 18 stored elements in Compressed Sparse Column format> >>> char_vectorizer.fit_transform(x) <2x47 sparse matrix of type '<type 'numpy.int64'>' with 64 stored elements in Compressed Sparse Column format> >>> char_vectorizer.get_feature_names() [u' ', u' a', u' b', u' f', u' i', u' s', u'a', u'a ', u'ac', u'ar', u'b', u'ba', u'bl', u'c', u'ck', u'e', u'e ', u'ee', u'ep', u'f', u'fo', u'h', u'he', u'hi', u'i', u'is', u'k', u'k ', u'l', u'la', u'o', u'o ', u'oo', u'ou', u'p', u'r', u'r ', u're', u's', u ', u'sh', u't', u'th', u'u', u'u ', u'y', u'yo'] >>> word_vectorizer.get_feature_names() [u'are', u'are foo', u'bar', u'bar black', u'black', u'black sheep', u'foo', u'foo bar', u'is', u'is foo', u'sheep', u'this', u'this is', u'you', u'you are'] 

Answer 1

You can pass analyzer called as an argument to get complete control over the tokenization, for example

 >>> from pprint import pprint >>> import re >>> x = ['this is a foo bar', 'you are a foo bar black sheep'] >>> def words_and_char_bigrams(text): ... words = re.findall(r'\w{3,}', text) ... for w in words: ... yield w ... for i in range(len(w) - 2): ... yield w[i:i+2] ... >>> v = CountVectorizer(analyzer=words_and_char_bigrams) >>> pprint(v.fit(x).vocabulary_) {'ac': 0, 'ar': 1, 'are': 2, 'ba': 3, 'bar': 4, 'bl': 5, 'black': 6, 'ee': 7, 'fo': 8, 'foo': 9, 'he': 10, 'hi': 11, 'la': 12, 'sh': 13, 'sheep': 14, 'th': 15, 'this': 16, 'yo': 17, 'you': 18} 

Answer 2

You can combine arbitrary function extraction steps using the FeatureUnion evaluation unit: http://scikit-learn.org/dev/modules/pipeline.html#featureunion-combining-feature-extractors

In this case, it is probably less efficient than the larsmans solution, but may be easier to use.