Simple implementation of N-Gram, tf-idf and Cosine similarity in Python
Check out NLTK package: http://www.nltk.org it has everything what you need
For the cosine_similarity:
def cosine_distance(u, v): """ Returns the cosine of the angle between vectors v and u. This is equal to u.v / |u||v|. """ return numpy.dot(u, v) / (math.sqrt(numpy.dot(u, u)) * math.sqrt(numpy.dot(v, v))) For ngrams:
def ngrams(sequence, n, pad_left=False, pad_right=False, pad_symbol=None): """ A utility that produces a sequence of ngrams from a sequence of items. For example: >>> ngrams([1,2,3,4,5], 3) [(1, 2, 3), (2, 3, 4), (3, 4, 5)] Use ingram for an iterator version of this function. Set pad_left or pad_right to true in order to get additional ngrams: >>> ngrams([1,2,3,4,5], 2, pad_right=True) [(1, 2), (2, 3), (3, 4), (4, 5), (5, None)] @param sequence: the source data to be converted into ngrams @type sequence: C{sequence} or C{iterator} @param n: the degree of the ngrams @type n: C{int} @param pad_left: whether the ngrams should be left-padded @type pad_left: C{boolean} @param pad_right: whether the ngrams should be right-padded @type pad_right: C{boolean} @param pad_symbol: the symbol to use for padding (default is None) @type pad_symbol: C{any} @return: The ngrams @rtype: C{list} of C{tuple}s """ if pad_left: sequence = chain((pad_symbol,) * (n-1), sequence) if pad_right: sequence = chain(sequence, (pad_symbol,) * (n-1)) sequence = list(sequence) count = max(0, len(sequence) - n + 1) return [tuple(sequence[i:i+n]) for i in range(count)] for tf-idf you will have to compute distribution first, I am using Lucene to do that, but you may very well do something similar with NLTK, use FreqDist:
http://nltk.googlecode.com/svn/trunk/doc/book/ch01.html#frequency_distribution_index_term
if you like pylucene, this will tell you how to comute tf.idf
# reader = lucene.IndexReader(FSDirectory.open(index_loc)) docs = reader.numDocs() for i in xrange(docs): tfv = reader.getTermFreqVector(i, fieldname) if tfv: rec = {} terms = tfv.getTerms() frequencies = tfv.getTermFrequencies() for (t,f,x) in zip(terms,frequencies,xrange(maxtokensperdoc)): df= searcher.docFreq(Term(fieldname, t)) # number of docs with the given term tmap.setdefault(t, len(tmap)) rec[t] = sim.tf(f) * sim.idf(df, max_doc) #compute TF.IDF # and normalize the values using cosine normalization if cosine_normalization: denom = sum([x**2 for x in rec.values()])**0.5 for k,v in rec.items(): rec[k] = v / denom
Here's an answer with just python + numpy, in short:
Cosine:
def cosine_sim(u,v): return np.dot(u,v) / (sqrt(np.dot(u,u)) * sqrt(np.dot(v,v)))Ngrams:
def ngrams(sentence, n): return zip(*[sentence.split()[i:] for i in range(n)])TF-IDF (it's a little weird but it works):
def tfidf(corpus, vocab): """ INPUT: corpus = [('this is a foo bar', [1, 1, 0, 1, 1, 0, 0, 1]), ('foo bar bar black sheep', [0, 2, 1, 1, 0, 0, 1, 0]), ('this is a sentence', [1, 0, 0, 0, 1, 1, 0, 1])] vocab = ['a', 'bar', 'black', 'foo', 'is', 'sentence', 'sheep', 'this'] OUTPUT: [[0.300, 0.300, 0.0, 0.300, 0.300, 0.0, 0.0, 0.300], [0.0, 0.600, 0.600, 0.300, 0.0, 0.0, 0.600, 0.0], [0.375, 0.0, 0.0, 0.0, 0.375, 0.75, 0.0, 0.375]] """ def termfreq(matrix, doc, term): try: return matrix[doc][term] / float(sum(matrix[doc].values())) except ZeroDivisionError: return 0 def inversedocfreq(matrix, term): try: return float(len(matrix)) /sum([1 for i,_ in enumerate(matrix) if matrix[i][term] > 0]) except ZeroDivisionError: return 0 matrix = [{k:v for k,v in zip(vocab, i[1])} for i in corpus] tfidf = defaultdict(dict) for doc,_ in enumerate(matrix): for term in matrix[doc]: tf = termfreq(matrix,doc,term) idf = inversedocfreq(matrix, term) tfidf[doc][term] = tf*idf return [[tfidf[doc][term] for term in vocab] for doc,_ in enumerate(tfidf)]Here's the long answer with the tests:
import numpy as npfrom math import sqrt, logfrom itertools import chain, productfrom collections import defaultdictdef cosine_sim(u,v): return np.dot(u,v) / (sqrt(np.dot(u,u)) * sqrt(np.dot(v,v)))def ngrams(sentence, n): return zip(*[sentence.split()[i:] for i in range(n)])def tfidf(corpus, vocab): """ INPUT: corpus = [('this is a foo bar', [1, 1, 0, 1, 1, 0, 0, 1]), ('foo bar bar black sheep', [0, 2, 1, 1, 0, 0, 1, 0]), ('this is a sentence', [1, 0, 0, 0, 1, 1, 0, 1])] vocab = ['a', 'bar', 'black', 'foo', 'is', 'sentence', 'sheep', 'this'] OUTPUT: [[0.300, 0.300, 0.0, 0.300, 0.300, 0.0, 0.0, 0.300], [0.0, 0.600, 0.600, 0.300, 0.0, 0.0, 0.600, 0.0], [0.375, 0.0, 0.0, 0.0, 0.375, 0.75, 0.0, 0.375]] """ def termfreq(matrix, doc, term): try: return matrix[doc][term] / float(sum(matrix[doc].values())) except ZeroDivisionError: return 0 def inversedocfreq(matrix, term): try: return float(len(matrix)) /sum([1 for i,_ in enumerate(matrix) if matrix[i][term] > 0]) except ZeroDivisionError: return 0 matrix = [{k:v for k,v in zip(vocab, i[1])} for i in corpus] tfidf = defaultdict(dict) for doc,_ in enumerate(matrix): for term in matrix[doc]: tf = termfreq(matrix,doc,term) idf = inversedocfreq(matrix, term) tfidf[doc][term] = tf*idf return [[tfidf[doc][term] for term in vocab] for doc,_ in enumerate(tfidf)]def corpus2vectors(corpus): def vectorize(sentence, vocab): return [sentence.split().count(i) for i in vocab] vectorized_corpus = [] vocab = sorted(set(chain(*[i.lower().split() for i in corpus]))) for i in corpus: vectorized_corpus.append((i, vectorize(i, vocab))) return vectorized_corpus, vocabdef create_test_corpus(): sent1 = "this is a foo bar" sent2 = "foo bar bar black sheep" sent3 = "this is a sentence" all_sents = [sent1,sent2,sent3] corpus, vocab = corpus2vectors(all_sents) return corpus, vocabdef test_cosine(): corpus, vocab = create_test_corpus() for sentx, senty in product(corpus, corpus): print sentx[0] print senty[0] print "cosine =", cosine_sim(sentx[1], senty[1]) printdef test_ngrams(): corpus, vocab = create_test_corpus() for sentx in corpus: print sentx[0] print ngrams(sentx[0],2) print ngrams(sentx[0],3) printdef test_tfidf(): corpus, vocab = create_test_corpus() print corpus print vocab print tfidf(corpus, vocab)print "Testing cosine..."test_cosine()printprint "Testing ngrams..."test_ngrams()printprint "Testing tfidf..."test_tfidf()print[out]:
Testing cosine...this is a foo barthis is a foo barcosine = 1.0this is a foo barfoo bar bar black sheepcosine = 0.507092552837this is a foo barthis is a sentencecosine = 0.67082039325foo bar bar black sheepthis is a foo barcosine = 0.507092552837foo bar bar black sheepfoo bar bar black sheepcosine = 1.0foo bar bar black sheepthis is a sentencecosine = 0.0this is a sentencethis is a foo barcosine = 0.67082039325this is a sentencefoo bar bar black sheepcosine = 0.0this is a sentencethis is a sentencecosine = 1.0Testing ngrams...this is a foo bar[('this', 'is'), ('is', 'a'), ('a', 'foo'), ('foo', 'bar')][('this', 'is', 'a'), ('is', 'a', 'foo'), ('a', 'foo', 'bar')]foo bar bar black sheep[('foo', 'bar'), ('bar', 'bar'), ('bar', 'black'), ('black', 'sheep')][('foo', 'bar', 'bar'), ('bar', 'bar', 'black'), ('bar', 'black', 'sheep')]this is a sentence[('this', 'is'), ('is', 'a'), ('a', 'sentence')][('this', 'is', 'a'), ('is', 'a', 'sentence')]Testing tfidf...[('this is a foo bar', [1, 1, 0, 1, 1, 0, 0, 1]), ('foo bar bar black sheep', [0, 2, 1, 1, 0, 0, 1, 0]), ('this is a sentence', [1, 0, 0, 0, 1, 1, 0, 1])]['a', 'bar', 'black', 'foo', 'is', 'sentence', 'sheep', 'this'][[0.30000000000000004, 0.30000000000000004, 0.0, 0.30000000000000004, 0.30000000000000004, 0.0, 0.0, 0.30000000000000004], [0.0, 0.6000000000000001, 0.6000000000000001, 0.30000000000000004, 0.0, 0.0, 0.6000000000000001, 0.0], [0.375, 0.0, 0.0, 0.0, 0.375, 0.75, 0.0, 0.375]]