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| """ | |
| Tests for IBM Model 5 training methods | |
| """ | |
| import unittest | |
| from collections import defaultdict | |
| from nltk.translate import AlignedSent, IBMModel, IBMModel4, 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 | |