author Adrien Di Mascio <Adrien.DiMascio@logilab.fr>
Tue, 28 Nov 2017 18:15:10 +0100
changeset 518 18c42a345591
parent 400 24351d8749a9
child 524 cdc8b5ada1a2
permissions -rw-r--r--
[aligner] safety belt to avoid crash on empty sets There are several parts in the code that assume that at least refset is not empty (e.g. log_infos() to compute the alignment progress)

# -*- coding:utf-8 -*-
# copyright 2012 LOGILAB S.A. (Paris, FRANCE), all rights reserved.
# contact http://www.logilab.fr -- mailto:contact@logilab.fr
# This program is free software: you can redistribute it and/or modify it under
# the terms of the GNU Lesser General Public License as published by the Free
# Software Foundation, either version 2.1 of the License, or (at your option)
# any later version.
# This program is distributed in the hope that it will be useful, but WITHOUT
# ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS
# FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License for more
# details.
# You should have received a copy of the GNU Lesser General Public License along
# with this program. If not, see <http://www.gnu.org/licenses/>.
import sys
if sys.version_info >= (2, 7):
    import unittest
    import unittest2 as unittest
from functools import partial
from os import path
import random
random.seed(6) ### Make sure tests are repeatable

from nazca.utils.normalize import simplify
from nazca.utils.minhashing import Minlsh, count_vectorizer_func
from nazca.data import FRENCH_LEMMAS

TESTDIR = path.dirname(__file__)

class MinLSHTest(unittest.TestCase):

    def test_iter_wordgrams(self):
        sentence = 'nom de la rose'
        minlsh = Minlsh()
        results = list(minlsh._iter_wordgrams(sentence, 2))
        truth = ['nom de', 'nom', 'de la', 'de', 'la rose', 'la', 'rose']
        self.assertEqual(len(results), len(truth))
        self.assertEqual(set(results), set(truth))

    def test_iter_wordgrams_sklearn(self):
        sentences = ('nom de la rose', 'nom de la')
        tokenizer_func = partial(count_vectorizer_func, min_n=1, max_n=2)
        minlsh = Minlsh(tokenizer_func=tokenizer_func)
        rows, shape = list(minlsh._buildmatrixdocument(sentences, 2))
        self.assertEqual(shape, (2, 7))
        self.assertEqual(rows[0], [0, 1, 2, 3, 4, 5, 6])
        self.assertEqual(rows[1], [0, 1, 2, 4, 5])

    def test_all(self):
        sentences = [u"Un nuage flotta dans le grand ciel bleu.",
                     u"Des grands nuages noirs flottent dans le ciel.",
                     u"Je n'aime pas ce genre de bandes dessinées tristes.",
                     u"J'aime les bandes dessinées de genre comiques.",
                     u"Pour quelle occasion vous êtes-vous apprêtée ?",
                     u"Je les vis ensemble à plusieurs occasions.",
                     u"Je les ai vus ensemble à plusieurs occasions.",
        minlsh = Minlsh()
        # XXX Should works independantly of the seed. Unstability due to the bands number ?
        minlsh.train((simplify(s, FRENCH_LEMMAS, remove_stopwords=True) for s in sentences), 1, 200)
        self.assertEqual(set([(0, 1), (2, 3), (5,6)]), minlsh.predict(0.4))

if __name__ == '__main__':