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Do TF-IDF with scikit-learn and print top features
#!/usr/bin/env python
# encoding: utf-8
import codecs
import os
import sys
import numpy as np
from sklearn.feature_extraction.text import TfidfVectorizer
def get_document_filenames(document_path='/home/tool/document_text'):
return [os.path.join(document_path, each)
for each in os.listdir(document_path)]
def create_vectorizer():
# Arguments here are tweaked for working with a particular data set.
# All that's really needed is the input argument.
return TfidfVectorizer(input='filename', max_features=200,
token_pattern='(?u)\\b[a-zA-Z]\\w{2,}\\b',
max_df=0.05,
stop_words='english',
ngram_range=(1, 3))
def display_scores(vectorizer, tfidf_result):
# http://stackoverflow.com/questions/16078015/
scores = zip(vectorizer.get_feature_names(),
np.asarray(tfidf_result.sum(axis=0)).ravel())
sorted_scores = sorted(scores, key=lambda x: x[1], reverse=True)
for item in sorted_scores:
print "{0:50} Score: {1}".format(item[0], item[1])
def main():
vectorizer = create_vectorizer()
tfidf_result = vectorizer.fit_transform(get_document_filenames())
display_scores(vectorizer, tfidf_result)
if __name__ == '__main__':
sys.stdout = codecs.getwriter('utf-8')(sys.stdout)
main()
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