90 lines
3.3 KiB
Python
90 lines
3.3 KiB
Python
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# -*- coding: utf-8 -*-
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"""
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Tests for IBM Model 2 training methods
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"""
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import unittest
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from collections import defaultdict
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from nltk.translate import AlignedSent
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from nltk.translate import IBMModel
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from nltk.translate import IBMModel2
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from nltk.translate.ibm_model import AlignmentInfo
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class TestIBMModel2(unittest.TestCase):
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def test_set_uniform_alignment_probabilities(self):
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# arrange
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corpus = [
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AlignedSent(['ham', 'eggs'], ['schinken', 'schinken', 'eier']),
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AlignedSent(['spam', 'spam', 'spam', 'spam'], ['spam', 'spam']),
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]
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model2 = IBMModel2(corpus, 0)
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# act
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model2.set_uniform_probabilities(corpus)
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# assert
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# expected_prob = 1.0 / (length of source sentence + 1)
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self.assertEqual(model2.alignment_table[0][1][3][2], 1.0 / 4)
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self.assertEqual(model2.alignment_table[2][4][2][4], 1.0 / 3)
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def test_set_uniform_alignment_probabilities_of_non_domain_values(self):
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# arrange
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corpus = [
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AlignedSent(['ham', 'eggs'], ['schinken', 'schinken', 'eier']),
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AlignedSent(['spam', 'spam', 'spam', 'spam'], ['spam', 'spam']),
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]
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model2 = IBMModel2(corpus, 0)
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# act
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model2.set_uniform_probabilities(corpus)
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# assert
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# examine i and j values that are not in the training data domain
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self.assertEqual(model2.alignment_table[99][1][3][2], IBMModel.MIN_PROB)
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self.assertEqual(model2.alignment_table[2][99][2][4], IBMModel.MIN_PROB)
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def test_prob_t_a_given_s(self):
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# arrange
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src_sentence = ["ich", 'esse', 'ja', 'gern', 'räucherschinken']
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trg_sentence = ['i', 'love', 'to', 'eat', 'smoked', 'ham']
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corpus = [AlignedSent(trg_sentence, src_sentence)]
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alignment_info = AlignmentInfo(
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(0, 1, 4, 0, 2, 5, 5),
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[None] + src_sentence,
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['UNUSED'] + trg_sentence,
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None,
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)
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translation_table = defaultdict(lambda: defaultdict(float))
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translation_table['i']['ich'] = 0.98
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translation_table['love']['gern'] = 0.98
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translation_table['to'][None] = 0.98
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translation_table['eat']['esse'] = 0.98
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translation_table['smoked']['räucherschinken'] = 0.98
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translation_table['ham']['räucherschinken'] = 0.98
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alignment_table = defaultdict(
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lambda: defaultdict(lambda: defaultdict(lambda: defaultdict(float)))
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)
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alignment_table[0][3][5][6] = 0.97 # None -> to
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alignment_table[1][1][5][6] = 0.97 # ich -> i
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alignment_table[2][4][5][6] = 0.97 # esse -> eat
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alignment_table[4][2][5][6] = 0.97 # gern -> love
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alignment_table[5][5][5][6] = 0.96 # räucherschinken -> smoked
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alignment_table[5][6][5][6] = 0.96 # räucherschinken -> ham
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model2 = IBMModel2(corpus, 0)
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model2.translation_table = translation_table
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model2.alignment_table = alignment_table
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# act
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probability = model2.prob_t_a_given_s(alignment_info)
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# assert
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lexical_translation = 0.98 * 0.98 * 0.98 * 0.98 * 0.98 * 0.98
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alignment = 0.97 * 0.97 * 0.97 * 0.97 * 0.96 * 0.96
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expected_probability = lexical_translation * alignment
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self.assertEqual(round(probability, 4), round(expected_probability, 4))
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