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| """ | |
| Tests for IBM Model 1 training methods | |
| """ | |
| import unittest | |
| from collections import defaultdict | |
| from nltk.translate import AlignedSent, IBMModel, IBMModel1 | |
| from nltk.translate.ibm_model import AlignmentInfo | |
| class TestIBMModel1(unittest.TestCase): | |
| def test_set_uniform_translation_probabilities(self): | |
| # arrange | |
| corpus = [ | |
| AlignedSent(["ham", "eggs"], ["schinken", "schinken", "eier"]), | |
| AlignedSent(["spam", "spam", "spam", "spam"], ["spam", "spam"]), | |
| ] | |
| model1 = IBMModel1(corpus, 0) | |
| # act | |
| model1.set_uniform_probabilities(corpus) | |
| # assert | |
| # expected_prob = 1.0 / (target vocab size + 1) | |
| self.assertEqual(model1.translation_table["ham"]["eier"], 1.0 / 3) | |
| self.assertEqual(model1.translation_table["eggs"][None], 1.0 / 3) | |
| def test_set_uniform_translation_probabilities_of_non_domain_values(self): | |
| # arrange | |
| corpus = [ | |
| AlignedSent(["ham", "eggs"], ["schinken", "schinken", "eier"]), | |
| AlignedSent(["spam", "spam", "spam", "spam"], ["spam", "spam"]), | |
| ] | |
| model1 = IBMModel1(corpus, 0) | |
| # act | |
| model1.set_uniform_probabilities(corpus) | |
| # assert | |
| # examine target words that are not in the training data domain | |
| self.assertEqual(model1.translation_table["parrot"]["eier"], 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"] | |
| 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 | |
| model1 = IBMModel1(corpus, 0) | |
| model1.translation_table = translation_table | |
| # act | |
| probability = model1.prob_t_a_given_s(alignment_info) | |
| # assert | |
| lexical_translation = 0.98 * 0.98 * 0.98 * 0.98 * 0.98 * 0.98 | |
| expected_probability = lexical_translation | |
| self.assertEqual(round(probability, 4), round(expected_probability, 4)) | |