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author | Cédric Bonhomme <cedric@cedricbonhomme.org> | 2014-11-28 19:37:16 +0100 |
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committer | Cédric Bonhomme <cedric@cedricbonhomme.org> | 2014-11-28 19:37:16 +0100 |
commit | 7af0dba1c342f099ce66fad2b4a2c61c95bbf437 (patch) | |
tree | 0a7794d7b8aaf5d2616c60c7bb5e181239e1c80e /pyaggr3g470r/duplicate.py | |
parent | Misc fixes for the installation/uage of scipy/numpy/nltk. (diff) | |
download | newspipe-7af0dba1c342f099ce66fad2b4a2c61c95bbf437.tar.gz newspipe-7af0dba1c342f099ce66fad2b4a2c61c95bbf437.tar.bz2 newspipe-7af0dba1c342f099ce66fad2b4a2c61c95bbf437.zip |
Finall the method with nltk is really slow...
Diffstat (limited to 'pyaggr3g470r/duplicate.py')
-rw-r--r-- | pyaggr3g470r/duplicate.py | 44 |
1 files changed, 2 insertions, 42 deletions
diff --git a/pyaggr3g470r/duplicate.py b/pyaggr3g470r/duplicate.py index 6220a3cb..23f4adc0 100644 --- a/pyaggr3g470r/duplicate.py +++ b/pyaggr3g470r/duplicate.py @@ -2,55 +2,15 @@ #-*- coding: utf-8 -*- import itertools -import nltk, string -from sklearn.feature_extraction.text import TfidfVectorizer - import utils -# tokenizers/punkt/english.pickle - - -stemmer = nltk.stem.porter.PorterStemmer() -remove_punctuation_map = dict((ord(char), None) for char in string.punctuation) - -def stem_tokens(tokens): - return [stemmer.stem(item) for item in tokens] - -def normalize(text): - """ - Remove punctuation, lowercase, stem - """ - return stem_tokens(nltk.word_tokenize(text.lower().translate(remove_punctuation_map))) - -vectorizer = TfidfVectorizer(tokenizer=normalize, stop_words='english') - -def cosine_sim(article1, article2): - try: - tfidf = vectorizer.fit_transform([utils.clear_string(article1.content), - utils.clear_string(article2.content)]) - except ValueError as e: - raise e - return ((tfidf * tfidf.T).A)[0,1] - def compare_documents(feed): """ Compare a list of documents by pair. """ - downloaded = nltk.download("punkt") - if not downloaded: - # Ubuntu packaged version still uses old URL - dl = nltk.downloader.Downloader("https://nltk.github.com/nltk_data/") - dl.download("punkt") duplicates = [] for pair in itertools.combinations(feed.articles, 2): - try: - result = cosine_sim(*pair) - if abs(result.item() - 1.0) < 1e-10: - duplicates.append(pair) - #print pair[0].id, pair[0].title, pair[0].link - #print pair[1].id, pair[1].title, pair[1].link - #print - except ValueError: - continue + if pair[0].content != "" and pair[0].content == pair[1].content: + duplicates.append(pair) return duplicates
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