Source code for nltk.test.unit.translate.test_ibm2

# -*- coding: utf-8 -*-
"""
Tests for IBM Model 2 training methods
"""

import unittest

from collections import defaultdict
from nltk.translate import AlignedSent
from nltk.translate import IBMModel
from nltk.translate import IBMModel2
from nltk.translate.ibm_model import AlignmentInfo


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