# -*- coding: utf-8 -*- """ Tests for IBM Model 5 training methods """ import unittest from collections import defaultdict from nltk.translate import AlignedSent from nltk.translate import IBMModel from nltk.translate import IBMModel4 from nltk.translate import IBMModel5 from nltk.translate.ibm_model import AlignmentInfo class TestIBMModel5(unittest.TestCase): def test_set_uniform_vacancy_probabilities_of_max_displacements(self): # arrange src_classes = {'schinken': 0, 'eier': 0, 'spam': 1} trg_classes = {'ham': 0, 'eggs': 1, 'spam': 2} corpus = [ AlignedSent(['ham', 'eggs'], ['schinken', 'schinken', 'eier']), AlignedSent(['spam', 'spam', 'spam', 'spam'], ['spam', 'spam']), ] model5 = IBMModel5(corpus, 0, src_classes, trg_classes) # act model5.set_uniform_probabilities(corpus) # assert # number of vacancy difference values = # 2 * number of words in longest target sentence expected_prob = 1.0 / (2 * 4) # examine the boundary values for (dv, max_v, trg_class) self.assertEqual(model5.head_vacancy_table[4][4][0], expected_prob) self.assertEqual(model5.head_vacancy_table[-3][1][2], expected_prob) self.assertEqual(model5.non_head_vacancy_table[4][4][0], expected_prob) self.assertEqual(model5.non_head_vacancy_table[-3][1][2], expected_prob) def test_set_uniform_vacancy_probabilities_of_non_domain_values(self): # arrange src_classes = {'schinken': 0, 'eier': 0, 'spam': 1} trg_classes = {'ham': 0, 'eggs': 1, 'spam': 2} corpus = [ AlignedSent(['ham', 'eggs'], ['schinken', 'schinken', 'eier']), AlignedSent(['spam', 'spam', 'spam', 'spam'], ['spam', 'spam']), ] model5 = IBMModel5(corpus, 0, src_classes, trg_classes) # act model5.set_uniform_probabilities(corpus) # assert # examine dv and max_v values that are not in the training data domain self.assertEqual(model5.head_vacancy_table[5][4][0], IBMModel.MIN_PROB) self.assertEqual(model5.head_vacancy_table[-4][1][2], IBMModel.MIN_PROB) self.assertEqual(model5.head_vacancy_table[4][0][0], IBMModel.MIN_PROB) self.assertEqual(model5.non_head_vacancy_table[5][4][0], IBMModel.MIN_PROB) self.assertEqual(model5.non_head_vacancy_table[-4][1][2], IBMModel.MIN_PROB) def test_prob_t_a_given_s(self): # arrange src_sentence = ["ich", 'esse', 'ja', 'gern', 'räucherschinken'] trg_sentence = ['i', 'love', 'to', 'eat', 'smoked', 'ham'] src_classes = {'räucherschinken': 0, 'ja': 1, 'ich': 2, 'esse': 3, 'gern': 4} trg_classes = {'ham': 0, 'smoked': 1, 'i': 3, 'love': 4, 'to': 2, 'eat': 4} corpus = [AlignedSent(trg_sentence, src_sentence)] alignment_info = AlignmentInfo( (0, 1, 4, 0, 2, 5, 5), [None] + src_sentence, ['UNUSED'] + trg_sentence, [[3], [1], [4], [], [2], [5, 6]], ) head_vacancy_table = defaultdict( lambda: defaultdict(lambda: defaultdict(float)) ) head_vacancy_table[1 - 0][6][3] = 0.97 # ich -> i head_vacancy_table[3 - 0][5][4] = 0.97 # esse -> eat head_vacancy_table[1 - 2][4][4] = 0.97 # gern -> love head_vacancy_table[2 - 0][2][1] = 0.97 # räucherschinken -> smoked non_head_vacancy_table = defaultdict( lambda: defaultdict(lambda: defaultdict(float)) ) non_head_vacancy_table[1 - 0][1][0] = 0.96 # räucherschinken -> ham 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 fertility_table = defaultdict(lambda: defaultdict(float)) fertility_table[1]['ich'] = 0.99 fertility_table[1]['esse'] = 0.99 fertility_table[0]['ja'] = 0.99 fertility_table[1]['gern'] = 0.99 fertility_table[2]['räucherschinken'] = 0.999 fertility_table[1][None] = 0.99 probabilities = { 'p1': 0.167, 'translation_table': translation_table, 'fertility_table': fertility_table, 'head_vacancy_table': head_vacancy_table, 'non_head_vacancy_table': non_head_vacancy_table, 'head_distortion_table': None, 'non_head_distortion_table': None, 'alignment_table': None, } model5 = IBMModel5(corpus, 0, src_classes, trg_classes, probabilities) # act probability = model5.prob_t_a_given_s(alignment_info) # assert null_generation = 5 * pow(0.167, 1) * pow(0.833, 4) fertility = 1 * 0.99 * 1 * 0.99 * 1 * 0.99 * 1 * 0.99 * 2 * 0.999 lexical_translation = 0.98 * 0.98 * 0.98 * 0.98 * 0.98 * 0.98 vacancy = 0.97 * 0.97 * 1 * 0.97 * 0.97 * 0.96 expected_probability = ( null_generation * fertility * lexical_translation * vacancy ) self.assertEqual(round(probability, 4), round(expected_probability, 4)) def test_prune(self): # arrange alignment_infos = [ AlignmentInfo((1, 1), None, None, None), AlignmentInfo((1, 2), None, None, None), AlignmentInfo((2, 1), None, None, None), AlignmentInfo((2, 2), None, None, None), AlignmentInfo((0, 0), None, None, None), ] min_factor = IBMModel5.MIN_SCORE_FACTOR best_score = 0.9 scores = { (1, 1): min(min_factor * 1.5, 1) * best_score, # above threshold (1, 2): best_score, (2, 1): min_factor * best_score, # at threshold (2, 2): min_factor * best_score * 0.5, # low score (0, 0): min(min_factor * 1.1, 1) * 1.2, # above threshold } corpus = [AlignedSent(['a'], ['b'])] original_prob_function = IBMModel4.model4_prob_t_a_given_s # mock static method IBMModel4.model4_prob_t_a_given_s = staticmethod( lambda a, model: scores[a.alignment] ) model5 = IBMModel5(corpus, 0, None, None) # act pruned_alignments = model5.prune(alignment_infos) # assert self.assertEqual(len(pruned_alignments), 3) # restore static method IBMModel4.model4_prob_t_a_given_s = original_prob_function