661 lines
24 KiB
Python
661 lines
24 KiB
Python
import warnings
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from sklearn.feature_extraction.text import strip_tags
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from sklearn.feature_extraction.text import strip_accents_unicode
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from sklearn.feature_extraction.text import strip_accents_ascii
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from sklearn.feature_extraction.text import CountVectorizer
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from sklearn.feature_extraction.text import TfidfTransformer
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from sklearn.feature_extraction.text import TfidfVectorizer
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from sklearn.grid_search import GridSearchCV
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from sklearn.pipeline import Pipeline
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from sklearn.svm import LinearSVC
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import numpy as np
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from nose import SkipTest
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from nose.tools import assert_equal, assert_equals, \
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assert_false, assert_not_equal, assert_true
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from numpy.testing import assert_array_almost_equal
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from numpy.testing import assert_array_equal
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from numpy.testing import assert_raises
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from sklearn.utils.testing import assert_in, assert_less, assert_greater
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from collections import defaultdict, Mapping
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from functools import partial
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import pickle
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from StringIO import StringIO
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JUNK_FOOD_DOCS = (
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"the pizza pizza beer copyright",
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"the pizza burger beer copyright",
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"the the pizza beer beer copyright",
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"the burger beer beer copyright",
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"the coke burger coke copyright",
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"the coke burger burger",
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)
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NOTJUNK_FOOD_DOCS = (
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"the salad celeri copyright",
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"the salad salad sparkling water copyright",
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"the the celeri celeri copyright",
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"the tomato tomato salad water",
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"the tomato salad water copyright",
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)
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ALL_FOOD_DOCS = JUNK_FOOD_DOCS + NOTJUNK_FOOD_DOCS
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def uppercase(s):
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return strip_accents_unicode(s).upper()
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def strip_eacute(s):
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return s.replace(u'\xe9', u'e')
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def split_tokenize(s):
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return s.split()
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def lazy_analyze(s):
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return ['the_ultimate_feature']
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def test_strip_accents():
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# check some classical latin accentuated symbols
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a = u'\xe0\xe1\xe2\xe3\xe4\xe5\xe7\xe8\xe9\xea\xeb'
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expected = u'aaaaaaceeee'
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assert_equal(strip_accents_unicode(a), expected)
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a = u'\xec\xed\xee\xef\xf1\xf2\xf3\xf4\xf5\xf6\xf9\xfa\xfb\xfc\xfd'
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expected = u'iiiinooooouuuuy'
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assert_equal(strip_accents_unicode(a), expected)
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# check some arabic
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a = u'\u0625' # halef with a hamza below
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expected = u'\u0627' # simple halef
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assert_equal(strip_accents_unicode(a), expected)
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# mix letters accentuated and not
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a = u"this is \xe0 test"
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expected = u'this is a test'
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assert_equal(strip_accents_unicode(a), expected)
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def test_to_ascii():
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# check some classical latin accentuated symbols
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a = u'\xe0\xe1\xe2\xe3\xe4\xe5\xe7\xe8\xe9\xea\xeb'
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expected = u'aaaaaaceeee'
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assert_equal(strip_accents_ascii(a), expected)
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a = u'\xec\xed\xee\xef\xf1\xf2\xf3\xf4\xf5\xf6\xf9\xfa\xfb\xfc\xfd'
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expected = u'iiiinooooouuuuy'
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assert_equal(strip_accents_ascii(a), expected)
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# check some arabic
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a = u'\u0625' # halef with a hamza below
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expected = u'' # halef has no direct ascii match
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assert_equal(strip_accents_ascii(a), expected)
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# mix letters accentuated and not
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a = u"this is \xe0 test"
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expected = u'this is a test'
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assert_equal(strip_accents_ascii(a), expected)
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def test_word_analyzer_unigrams():
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wa = CountVectorizer(strip_accents='ascii').build_analyzer()
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text = u"J'ai mang\xe9 du kangourou ce midi, c'\xe9tait pas tr\xeas bon."
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expected = [u'ai', u'mange', u'du', u'kangourou', u'ce', u'midi',
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u'etait', u'pas', u'tres', u'bon']
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assert_equal(wa(text), expected)
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text = "This is a test, really.\n\n I met Harry yesterday."
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expected = [u'this', u'is', u'test', u'really', u'met', u'harry',
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u'yesterday']
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assert_equal(wa(text), expected)
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wa = CountVectorizer(input='file').build_analyzer()
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text = StringIO("This is a test with a file-like object!")
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expected = [u'this', u'is', u'test', u'with', u'file', u'like',
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u'object']
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assert_equal(wa(text), expected)
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# with custom preprocessor
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wa = CountVectorizer(preprocessor=uppercase).build_analyzer()
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text = u"J'ai mang\xe9 du kangourou ce midi, c'\xe9tait pas tr\xeas bon."
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expected = [u'AI', u'MANGE', u'DU', u'KANGOUROU', u'CE', u'MIDI',
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u'ETAIT', u'PAS', u'TRES', u'BON']
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assert_equal(wa(text), expected)
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# with custom tokenizer
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wa = CountVectorizer(tokenizer=split_tokenize,
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strip_accents='ascii').build_analyzer()
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text = u"J'ai mang\xe9 du kangourou ce midi, c'\xe9tait pas tr\xeas bon."
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expected = [u"j'ai", u'mange', u'du', u'kangourou', u'ce', u'midi,',
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u"c'etait", u'pas', u'tres', u'bon.']
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assert_equal(wa(text), expected)
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def test_word_analyzer_unigrams_and_bigrams():
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wa = CountVectorizer(analyzer="word", strip_accents='unicode',
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ngram_range=(1, 2)).build_analyzer()
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text = u"J'ai mang\xe9 du kangourou ce midi, c'\xe9tait pas tr\xeas bon."
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expected = [u'ai', u'mange', u'du', u'kangourou', u'ce', u'midi', u'etait',
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u'pas', u'tres', u'bon', u'ai mange', u'mange du',
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u'du kangourou', u'kangourou ce', u'ce midi', u'midi etait',
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u'etait pas', u'pas tres', u'tres bon']
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assert_equal(wa(text), expected)
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def test_unicode_decode_error():
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# decode_error default to strict, so this should fail
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# First, encode (as bytes) a unicode string.
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text = u"J'ai mang\xe9 du kangourou ce midi, c'\xe9tait pas tr\xeas bon."
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text_bytes = text.encode('utf-8')
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# Then let the Analyzer try to decode it as ascii. It should fail,
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# because we have given it an incorrect charset.
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wa = CountVectorizer(ngram_range=(1, 2), charset='ascii').build_analyzer()
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assert_raises(UnicodeDecodeError, wa, text_bytes)
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ca = CountVectorizer(analyzer='char', ngram_range=(3, 6),
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charset='ascii').build_analyzer()
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assert_raises(UnicodeDecodeError, ca, text_bytes)
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def test_char_ngram_analyzer():
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cnga = CountVectorizer(analyzer='char', strip_accents='unicode',
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ngram_range=(3, 6)).build_analyzer()
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text = u"J'ai mang\xe9 du kangourou ce midi, c'\xe9tait pas tr\xeas bon"
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expected = [u"j'a", u"'ai", u'ai ', u'i m', u' ma']
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assert_equal(cnga(text)[:5], expected)
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expected = [u's tres', u' tres ', u'tres b', u'res bo', u'es bon']
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assert_equal(cnga(text)[-5:], expected)
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text = "This \n\tis a test, really.\n\n I met Harry yesterday"
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expected = [u'thi', u'his', u'is ', u's i', u' is']
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assert_equal(cnga(text)[:5], expected)
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expected = [u' yeste', u'yester', u'esterd', u'sterda', u'terday']
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assert_equal(cnga(text)[-5:], expected)
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cnga = CountVectorizer(input='file', analyzer='char',
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ngram_range=(3, 6)).build_analyzer()
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text = StringIO("This is a test with a file-like object!")
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expected = [u'thi', u'his', u'is ', u's i', u' is']
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assert_equal(cnga(text)[:5], expected)
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def test_char_wb_ngram_analyzer():
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cnga = CountVectorizer(analyzer='char_wb', strip_accents='unicode',
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ngram_range=(3, 6)).build_analyzer()
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text = "This \n\tis a test, really.\n\n I met Harry yesterday"
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expected = [u' th', u'thi', u'his', u'is ', u' thi']
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assert_equal(cnga(text)[:5], expected)
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expected = [u'yester', u'esterd', u'sterda', u'terday', u'erday ']
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assert_equal(cnga(text)[-5:], expected)
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cnga = CountVectorizer(input='file', analyzer='char_wb',
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ngram_range=(3, 6)).build_analyzer()
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text = StringIO("A test with a file-like object!")
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expected = [u' a ', u' te', u'tes', u'est', u'st ', u' tes']
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assert_equal(cnga(text)[:6], expected)
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def test_countvectorizer_custom_vocabulary():
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vocab = {"pizza": 0, "beer": 1}
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terms = set(vocab.keys())
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# Try a few of the supported types.
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for typ in [dict, list, iter, partial(defaultdict, int)]:
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v = typ(vocab)
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vect = CountVectorizer(vocabulary=v)
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vect.fit(JUNK_FOOD_DOCS)
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if isinstance(v, Mapping):
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assert_equal(vect.vocabulary_, vocab)
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else:
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assert_equal(set(vect.vocabulary_), terms)
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X = vect.transform(JUNK_FOOD_DOCS)
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assert_equal(X.shape[1], len(terms))
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def test_countvectorizer_custom_vocabulary_pipeline():
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what_we_like = ["pizza", "beer"]
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pipe = Pipeline([
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('count', CountVectorizer(vocabulary=what_we_like)),
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('tfidf', TfidfTransformer())])
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X = pipe.fit_transform(ALL_FOOD_DOCS)
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assert_equal(set(pipe.named_steps['count'].vocabulary_),
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set(what_we_like))
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assert_equal(X.shape[1], len(what_we_like))
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def test_countvectorizer_empty_vocabulary():
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try:
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CountVectorizer(vocabulary=[])
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assert False, "we shouldn't get here"
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except ValueError as e:
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assert_in("empty vocabulary", str(e))
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try:
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v = CountVectorizer(min_df=1, max_df=1.0, stop_words="english")
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# fit on stopwords only
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v.fit(["to be or not to be", "and me too", "and so do you"])
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assert False, "we shouldn't get here"
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except ValueError as e:
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assert_in("empty vocabulary", str(e))
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def test_fit_countvectorizer_twice():
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cv = CountVectorizer()
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X1 = cv.fit_transform(ALL_FOOD_DOCS[:5])
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X2 = cv.fit_transform(ALL_FOOD_DOCS[5:])
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assert_not_equal(X1.shape[1], X2.shape[1])
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def test_tf_idf_smoothing():
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X = [[1, 1, 1],
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[1, 1, 0],
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[1, 0, 0]]
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tr = TfidfTransformer(smooth_idf=True, norm='l2')
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tfidf = tr.fit_transform(X).toarray()
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assert_true((tfidf >= 0).all())
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# check normalization
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assert_array_almost_equal((tfidf ** 2).sum(axis=1), [1., 1., 1.])
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# this is robust to features with only zeros
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X = [[1, 1, 0],
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[1, 1, 0],
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[1, 0, 0]]
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tr = TfidfTransformer(smooth_idf=True, norm='l2')
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tfidf = tr.fit_transform(X).toarray()
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assert_true((tfidf >= 0).all())
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def test_tfidf_no_smoothing():
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X = [[1, 1, 1],
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[1, 1, 0],
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[1, 0, 0]]
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tr = TfidfTransformer(smooth_idf=False, norm='l2')
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tfidf = tr.fit_transform(X).toarray()
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assert_true((tfidf >= 0).all())
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# check normalization
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assert_array_almost_equal((tfidf ** 2).sum(axis=1), [1., 1., 1.])
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# the lack of smoothing make IDF fragile in the presence of feature with
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# only zeros
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X = [[1, 1, 0],
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[1, 1, 0],
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[1, 0, 0]]
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tr = TfidfTransformer(smooth_idf=False, norm='l2')
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# First we need to verify that numpy here provides div 0 warnings
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with warnings.catch_warnings(record=True) as w:
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1. / np.array([0.])
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numpy_provides_div0_warning = len(w) == 1
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with warnings.catch_warnings(record=True) as w:
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tfidf = tr.fit_transform(X).toarray()
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if not numpy_provides_div0_warning:
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raise SkipTest("Numpy does not provide div 0 warnings.")
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assert_equal(len(w), 1)
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# For Python 3 compatibility
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if hasattr(w[0].message, 'args'):
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assert_true("divide by zero" in w[0].message.args[0])
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else:
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assert_true("divide by zero" in w[0].message)
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def test_sublinear_tf():
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X = [[1], [2], [3]]
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tr = TfidfTransformer(sublinear_tf=True, use_idf=False, norm=None)
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tfidf = tr.fit_transform(X).toarray()
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assert_equal(tfidf[0], 1)
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assert_greater(tfidf[1], tfidf[0])
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assert_greater(tfidf[2], tfidf[1])
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assert_less(tfidf[1], 2)
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assert_less(tfidf[2], 3)
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def test_vectorizer():
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# raw documents as an iterator
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train_data = iter(ALL_FOOD_DOCS[:-1])
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test_data = [ALL_FOOD_DOCS[-1]]
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n_train = len(ALL_FOOD_DOCS) - 1
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# test without vocabulary
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v1 = CountVectorizer(max_df=0.5, min_df=1)
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counts_train = v1.fit_transform(train_data)
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if hasattr(counts_train, 'tocsr'):
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counts_train = counts_train.tocsr()
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assert_equal(counts_train[0, v1.vocabulary_[u"pizza"]], 2)
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# build a vectorizer v1 with the same vocabulary as the one fitted by v1
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v2 = CountVectorizer(vocabulary=v1.vocabulary_)
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# compare that the two vectorizer give the same output on the test sample
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for v in (v1, v2):
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counts_test = v.transform(test_data)
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if hasattr(counts_test, 'tocsr'):
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counts_test = counts_test.tocsr()
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vocabulary = v.vocabulary_
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assert_equal(counts_test[0, vocabulary[u"salad"]], 1)
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assert_equal(counts_test[0, vocabulary[u"tomato"]], 1)
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assert_equal(counts_test[0, vocabulary[u"water"]], 1)
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# stop word from the fixed list
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assert_false(u"the" in vocabulary)
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# stop word found automatically by the vectorizer DF thresholding
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# words that are high frequent across the complete corpus are likely
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# to be not informative (either real stop words of extraction
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# artifacts)
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assert_false(u"copyright" in vocabulary)
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# not present in the sample
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assert_equal(counts_test[0, vocabulary[u"coke"]], 0)
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assert_equal(counts_test[0, vocabulary[u"burger"]], 0)
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assert_equal(counts_test[0, vocabulary[u"beer"]], 0)
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assert_equal(counts_test[0, vocabulary[u"pizza"]], 0)
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# test tf-idf
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t1 = TfidfTransformer(norm='l1')
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tfidf = t1.fit(counts_train).transform(counts_train).toarray()
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assert_equal(len(t1.idf_), len(v1.vocabulary_))
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assert_equal(tfidf.shape, (n_train, len(v1.vocabulary_)))
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# test tf-idf with new data
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tfidf_test = t1.transform(counts_test).toarray()
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assert_equal(tfidf_test.shape, (len(test_data), len(v1.vocabulary_)))
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# test tf alone
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t2 = TfidfTransformer(norm='l1', use_idf=False)
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tf = t2.fit(counts_train).transform(counts_train).toarray()
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assert_equal(t2.idf_, None)
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# L1-normalized term frequencies sum to one
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assert_array_almost_equal(np.sum(tf, axis=1), [1.0] * n_train)
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# test the direct tfidf vectorizer
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# (equivalent to term count vectorizer + tfidf transformer)
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train_data = iter(ALL_FOOD_DOCS[:-1])
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tv = TfidfVectorizer(norm='l1', min_df=1)
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assert_false(tv.fixed_vocabulary)
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tv.max_df = v1.max_df
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tfidf2 = tv.fit_transform(train_data).toarray()
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assert_array_almost_equal(tfidf, tfidf2)
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# test the direct tfidf vectorizer with new data
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tfidf_test2 = tv.transform(test_data).toarray()
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assert_array_almost_equal(tfidf_test, tfidf_test2)
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# test transform on unfitted vectorizer with empty vocabulary
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v3 = CountVectorizer(vocabulary=None)
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assert_raises(ValueError, v3.transform, train_data)
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def test_feature_names():
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cv = CountVectorizer(max_df=0.5, min_df=1)
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X = cv.fit_transform(ALL_FOOD_DOCS)
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n_samples, n_features = X.shape
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assert_equal(len(cv.vocabulary_), n_features)
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feature_names = cv.get_feature_names()
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assert_equal(len(feature_names), n_features)
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assert_array_equal(['beer', 'burger', 'celeri', 'coke', 'pizza',
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'salad', 'sparkling', 'tomato', 'water'],
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feature_names)
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for idx, name in enumerate(feature_names):
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assert_equal(idx, cv.vocabulary_.get(name))
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def test_vectorizer_max_features():
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vec_factories = (
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CountVectorizer,
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TfidfVectorizer,
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)
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expected_vocabulary = set(['burger', 'beer', 'salad', 'pizza'])
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for vec_factory in vec_factories:
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# test bounded number of extracted features
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vectorizer = vec_factory(max_df=0.6, max_features=4)
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vectorizer.fit(ALL_FOOD_DOCS)
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assert_equals(set(vectorizer.vocabulary_), expected_vocabulary)
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def test_vectorizer_max_df():
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test_data = [u'abc', u'dea'] # the letter a occurs in both strings
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vect = CountVectorizer(analyzer='char', max_df=1.0, min_df=1)
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vect.fit(test_data)
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assert_true(u'a' in vect.vocabulary_.keys())
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assert_equals(len(vect.vocabulary_.keys()), 5)
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|
|
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vect.max_df = 0.5
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vect.fit(test_data)
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assert_true(u'a' not in vect.vocabulary_.keys()) # 'a' is ignored
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assert_equals(len(vect.vocabulary_.keys()), 4) # the others remain
|
|
|
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# absolute count: if in more than one
|
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vect.max_df = 1
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vect.fit(test_data)
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assert_true(u'a' not in vect.vocabulary_.keys()) # 'a' is ignored
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assert_equals(len(vect.vocabulary_.keys()), 4) # the others remain
|
|
|
|
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def test_vectorizer_min_df():
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test_data = [u'abc', u'dea', u'eat'] # the letter a occurs in both strings
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vect = CountVectorizer(analyzer='char', max_df=1.0, min_df=1)
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vect.fit(test_data)
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|
assert_true(u'a' in vect.vocabulary_.keys())
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assert_equals(len(vect.vocabulary_.keys()), 6)
|
|
|
|
vect.min_df = 2
|
|
vect.fit(test_data)
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|
assert_true(u'c' not in vect.vocabulary_.keys()) # 'c' is ignored
|
|
assert_equals(len(vect.vocabulary_.keys()), 2) # only e, a remain
|
|
|
|
vect.min_df = .5
|
|
vect.fit(test_data)
|
|
assert_true(u'c' not in vect.vocabulary_.keys()) # 'c' is ignored
|
|
assert_equals(len(vect.vocabulary_.keys()), 2) # only e, a remain
|
|
|
|
|
|
def test_binary_occurrences():
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# by default multiple occurrences are counted as longs
|
|
test_data = [u'aaabc', u'abbde']
|
|
vect = CountVectorizer(analyzer='char', max_df=1.0, min_df=1)
|
|
X = vect.fit_transform(test_data).toarray()
|
|
assert_array_equal(['a', 'b', 'c', 'd', 'e'], vect.get_feature_names())
|
|
assert_array_equal([[3, 1, 1, 0, 0],
|
|
[1, 2, 0, 1, 1]], X)
|
|
|
|
# using boolean features, we can fetch the binary occurrence info
|
|
# instead.
|
|
vect = CountVectorizer(analyzer='char', max_df=1.0,
|
|
binary=True, min_df=1)
|
|
X = vect.fit_transform(test_data).toarray()
|
|
assert_array_equal([[1, 1, 1, 0, 0],
|
|
[1, 1, 0, 1, 1]], X)
|
|
|
|
# check the ability to change the dtype
|
|
vect = CountVectorizer(analyzer='char', max_df=1.0,
|
|
binary=True, dtype=np.float32)
|
|
X_sparse = vect.fit_transform(test_data)
|
|
assert_equal(X_sparse.dtype, np.float32)
|
|
|
|
|
|
def test_vectorizer_inverse_transform():
|
|
# raw documents
|
|
data = ALL_FOOD_DOCS
|
|
for vectorizer in (TfidfVectorizer(min_df=1), CountVectorizer(min_df=1)):
|
|
transformed_data = vectorizer.fit_transform(data)
|
|
inversed_data = vectorizer.inverse_transform(transformed_data)
|
|
analyze = vectorizer.build_analyzer()
|
|
for doc, inversed_terms in zip(data, inversed_data):
|
|
terms = np.sort(np.unique(analyze(doc)))
|
|
inversed_terms = np.sort(np.unique(inversed_terms))
|
|
assert_array_equal(terms, inversed_terms)
|
|
|
|
# Test that inverse_transform also works with numpy arrays
|
|
transformed_data = transformed_data.toarray()
|
|
inversed_data2 = vectorizer.inverse_transform(transformed_data)
|
|
for terms, terms2 in zip(inversed_data, inversed_data2):
|
|
assert_array_equal(np.sort(terms), np.sort(terms2))
|
|
|
|
|
|
def test_count_vectorizer_pipeline_grid_selection():
|
|
# raw documents
|
|
data = JUNK_FOOD_DOCS + NOTJUNK_FOOD_DOCS
|
|
# simulate iterables
|
|
train_data = iter(data[1:-1])
|
|
test_data = iter([data[0], data[-1]])
|
|
|
|
# label junk food as -1, the others as +1
|
|
y = np.ones(len(data))
|
|
y[:6] = -1
|
|
y_train = y[1:-1]
|
|
y_test = np.array([y[0], y[-1]])
|
|
|
|
pipeline = Pipeline([('vect', CountVectorizer(min_df=1)),
|
|
('svc', LinearSVC())])
|
|
|
|
parameters = {
|
|
'vect__ngram_range': [(1, 1), (1, 2)],
|
|
'svc__loss': ('l1', 'l2')
|
|
}
|
|
|
|
# find the best parameters for both the feature extraction and the
|
|
# classifier
|
|
grid_search = GridSearchCV(pipeline, parameters, n_jobs=1)
|
|
|
|
# cross-validation doesn't work if the length of the data is not known,
|
|
# hence use lists instead of iterators
|
|
pred = grid_search.fit(list(train_data), y_train).predict(list(test_data))
|
|
assert_array_equal(pred, y_test)
|
|
|
|
# on this toy dataset bigram representation which is used in the last of
|
|
# the grid_search is considered the best estimator since they all converge
|
|
# to 100% accuracy models
|
|
assert_equal(grid_search.best_score_, 1.0)
|
|
best_vectorizer = grid_search.best_estimator_.named_steps['vect']
|
|
assert_equal(best_vectorizer.ngram_range, (1, 1))
|
|
|
|
|
|
def test_vectorizer_pipeline_grid_selection():
|
|
# raw documents
|
|
data = JUNK_FOOD_DOCS + NOTJUNK_FOOD_DOCS
|
|
# simulate iterables
|
|
train_data = iter(data[1:-1])
|
|
test_data = iter([data[0], data[-1]])
|
|
|
|
# label junk food as -1, the others as +1
|
|
y = np.ones(len(data))
|
|
y[:6] = -1
|
|
y_train = y[1:-1]
|
|
y_test = np.array([y[0], y[-1]])
|
|
|
|
pipeline = Pipeline([('vect', TfidfVectorizer(min_df=1)),
|
|
('svc', LinearSVC())])
|
|
|
|
parameters = {
|
|
'vect__ngram_range': [(1, 1), (1, 2)],
|
|
'vect__norm': ('l1', 'l2'),
|
|
'svc__loss': ('l1', 'l2'),
|
|
}
|
|
|
|
# find the best parameters for both the feature extraction and the
|
|
# classifier
|
|
grid_search = GridSearchCV(pipeline, parameters, n_jobs=1)
|
|
|
|
# cross-validation doesn't work if the length of the data is not known,
|
|
# hence use lists instead of iterators
|
|
pred = grid_search.fit(list(train_data), y_train).predict(list(test_data))
|
|
assert_array_equal(pred, y_test)
|
|
|
|
# on this toy dataset bigram representation which is used in the last of
|
|
# the grid_search is considered the best estimator since they all converge
|
|
# to 100% accuracy models
|
|
assert_equal(grid_search.best_score_, 1.0)
|
|
best_vectorizer = grid_search.best_estimator_.named_steps['vect']
|
|
assert_equal(best_vectorizer.ngram_range, (1, 1))
|
|
assert_equal(best_vectorizer.norm, 'l2')
|
|
assert_false(best_vectorizer.fixed_vocabulary)
|
|
|
|
|
|
def test_count_vectorizer_unicode():
|
|
# tests that the count vectorizer works with cyrillic.
|
|
document = (u"\xd0\x9c\xd0\xb0\xd1\x88\xd0\xb8\xd0\xbd\xd0\xbd\xd0\xbe\xd0"
|
|
u"\xb5 \xd0\xbe\xd0\xb1\xd1\x83\xd1\x87\xd0\xb5\xd0\xbd\xd0\xb8\xd0"
|
|
u"\xb5 \xe2\x80\x94 \xd0\xbe\xd0\xb1\xd1\x88\xd0\xb8\xd1\x80\xd0\xbd"
|
|
u"\xd1\x8b\xd0\xb9 \xd0\xbf\xd0\xbe\xd0\xb4\xd1\x80\xd0\xb0\xd0\xb7"
|
|
u"\xd0\xb4\xd0\xb5\xd0\xbb \xd0\xb8\xd1\x81\xd0\xba\xd1\x83\xd1\x81"
|
|
u"\xd1\x81\xd1\x82\xd0\xb2\xd0\xb5\xd0\xbd\xd0\xbd\xd0\xbe\xd0\xb3"
|
|
u"\xd0\xbe \xd0\xb8\xd0\xbd\xd1\x82\xd0\xb5\xd0\xbb\xd0\xbb\xd0"
|
|
u"\xb5\xd0\xba\xd1\x82\xd0\xb0, \xd0\xb8\xd0\xb7\xd1\x83\xd1\x87"
|
|
u"\xd0\xb0\xd1\x8e\xd1\x89\xd0\xb8\xd0\xb9 \xd0\xbc\xd0\xb5\xd1\x82"
|
|
u"\xd0\xbe\xd0\xb4\xd1\x8b \xd0\xbf\xd0\xbe\xd1\x81\xd1\x82\xd1\x80"
|
|
u"\xd0\xbe\xd0\xb5\xd0\xbd\xd0\xb8\xd1\x8f \xd0\xb0\xd0\xbb\xd0\xb3"
|
|
u"\xd0\xbe\xd1\x80\xd0\xb8\xd1\x82\xd0\xbc\xd0\xbe\xd0\xb2, \xd1\x81"
|
|
u"\xd0\xbf\xd0\xbe\xd1\x81\xd0\xbe\xd0\xb1\xd0\xbd\xd1\x8b\xd1\x85 "
|
|
u"\xd0\xbe\xd0\xb1\xd1\x83\xd1\x87\xd0\xb0\xd1\x82\xd1\x8c\xd1\x81\xd1"
|
|
u"\x8f.")
|
|
vect = CountVectorizer(min_df=1)
|
|
X = vect.fit_transform([document])
|
|
assert_equal(X.shape, (1, 15))
|
|
|
|
|
|
def test_tfidf_vectorizer_with_fixed_vocabulary():
|
|
# non regression smoke test for inheritance issues
|
|
vocabulary = ['pizza', 'celeri']
|
|
vect = TfidfVectorizer(vocabulary=vocabulary)
|
|
assert_true(vect.fixed_vocabulary)
|
|
X_1 = vect.fit_transform(ALL_FOOD_DOCS)
|
|
X_2 = vect.transform(ALL_FOOD_DOCS)
|
|
assert_array_almost_equal(X_1.toarray(), X_2.toarray())
|
|
assert_true(vect.fixed_vocabulary)
|
|
|
|
|
|
def test_pickling_vectorizer():
|
|
instances = [
|
|
CountVectorizer(),
|
|
CountVectorizer(preprocessor=strip_tags),
|
|
CountVectorizer(analyzer=lazy_analyze),
|
|
CountVectorizer(preprocessor=strip_tags).fit(JUNK_FOOD_DOCS),
|
|
CountVectorizer(strip_accents=strip_eacute).fit(JUNK_FOOD_DOCS),
|
|
TfidfVectorizer(),
|
|
TfidfVectorizer(analyzer=lazy_analyze),
|
|
TfidfVectorizer().fit(JUNK_FOOD_DOCS),
|
|
]
|
|
|
|
for orig in instances:
|
|
s = pickle.dumps(orig)
|
|
copy = pickle.loads(s)
|
|
assert_equal(type(copy), orig.__class__)
|
|
assert_array_equal(
|
|
copy.fit_transform(JUNK_FOOD_DOCS).toarray(),
|
|
orig.fit_transform(JUNK_FOOD_DOCS).toarray())
|
|
|
|
|
|
def test_pickling_transformer():
|
|
X = CountVectorizer().fit_transform(JUNK_FOOD_DOCS)
|
|
orig = TfidfTransformer().fit(X)
|
|
s = pickle.dumps(orig)
|
|
copy = pickle.loads(s)
|
|
assert_equal(type(copy), orig.__class__)
|
|
assert_array_equal(
|
|
copy.fit_transform(X).toarray(),
|
|
orig.fit_transform(X).toarray())
|