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| """ | |
| Tests for IBM Model 3 training methods | |
| """ | |
| import unittest | |
| from collections import defaultdict | |
| from nltk.translate import AlignedSent, IBMModel, IBMModel3 | |
| from nltk.translate.ibm_model import AlignmentInfo | |
| class TestIBMModel3(unittest.TestCase): | |
| def test_set_uniform_distortion_probabilities(self): | |
| # arrange | |
| corpus = [ | |
| AlignedSent(["ham", "eggs"], ["schinken", "schinken", "eier"]), | |
| AlignedSent(["spam", "spam", "spam", "spam"], ["spam", "spam"]), | |
| ] | |
| model3 = IBMModel3(corpus, 0) | |
| # act | |
| model3.set_uniform_probabilities(corpus) | |
| # assert | |
| # expected_prob = 1.0 / length of target sentence | |
| self.assertEqual(model3.distortion_table[1][0][3][2], 1.0 / 2) | |
| self.assertEqual(model3.distortion_table[4][2][2][4], 1.0 / 4) | |
| def test_set_uniform_distortion_probabilities_of_non_domain_values(self): | |
| # arrange | |
| corpus = [ | |
| AlignedSent(["ham", "eggs"], ["schinken", "schinken", "eier"]), | |
| AlignedSent(["spam", "spam", "spam", "spam"], ["spam", "spam"]), | |
| ] | |
| model3 = IBMModel3(corpus, 0) | |
| # act | |
| model3.set_uniform_probabilities(corpus) | |
| # assert | |
| # examine i and j values that are not in the training data domain | |
| self.assertEqual(model3.distortion_table[0][0][3][2], IBMModel.MIN_PROB) | |
| self.assertEqual(model3.distortion_table[9][2][2][4], IBMModel.MIN_PROB) | |
| self.assertEqual(model3.distortion_table[2][9][2][4], 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, | |
| [[3], [1], [4], [], [2], [5, 6]], | |
| ) | |
| distortion_table = defaultdict( | |
| lambda: defaultdict(lambda: defaultdict(lambda: defaultdict(float))) | |
| ) | |
| distortion_table[1][1][5][6] = 0.97 # i -> ich | |
| distortion_table[2][4][5][6] = 0.97 # love -> gern | |
| distortion_table[3][0][5][6] = 0.97 # to -> NULL | |
| distortion_table[4][2][5][6] = 0.97 # eat -> esse | |
| distortion_table[5][5][5][6] = 0.97 # smoked -> räucherschinken | |
| distortion_table[6][5][5][6] = 0.97 # ham -> räucherschinken | |
| 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, | |
| "distortion_table": distortion_table, | |
| "fertility_table": fertility_table, | |
| "alignment_table": None, | |
| } | |
| model3 = IBMModel3(corpus, 0, probabilities) | |
| # act | |
| probability = model3.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 | |
| distortion = 0.97 * 0.97 * 0.97 * 0.97 * 0.97 * 0.97 | |
| expected_probability = ( | |
| null_generation * fertility * lexical_translation * distortion | |
| ) | |
| self.assertEqual(round(probability, 4), round(expected_probability, 4)) | |