74 lines
2.5 KiB
Python
74 lines
2.5 KiB
Python
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"""
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Tests for IBM Model 1 training methods
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"""
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import unittest
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from collections import defaultdict
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from nltk.translate import AlignedSent, IBMModel, IBMModel1
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from nltk.translate.ibm_model import AlignmentInfo
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class TestIBMModel1(unittest.TestCase):
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def test_set_uniform_translation_probabilities(self):
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# arrange
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corpus = [
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AlignedSent(["ham", "eggs"], ["schinken", "schinken", "eier"]),
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AlignedSent(["spam", "spam", "spam", "spam"], ["spam", "spam"]),
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]
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model1 = IBMModel1(corpus, 0)
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# act
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model1.set_uniform_probabilities(corpus)
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# assert
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# expected_prob = 1.0 / (target vocab size + 1)
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self.assertEqual(model1.translation_table["ham"]["eier"], 1.0 / 3)
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self.assertEqual(model1.translation_table["eggs"][None], 1.0 / 3)
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def test_set_uniform_translation_probabilities_of_non_domain_values(self):
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# arrange
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corpus = [
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AlignedSent(["ham", "eggs"], ["schinken", "schinken", "eier"]),
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AlignedSent(["spam", "spam", "spam", "spam"], ["spam", "spam"]),
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]
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model1 = IBMModel1(corpus, 0)
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# act
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model1.set_uniform_probabilities(corpus)
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# assert
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# examine target words that are not in the training data domain
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self.assertEqual(model1.translation_table["parrot"]["eier"], IBMModel.MIN_PROB)
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def test_prob_t_a_given_s(self):
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# arrange
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src_sentence = ["ich", "esse", "ja", "gern", "räucherschinken"]
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trg_sentence = ["i", "love", "to", "eat", "smoked", "ham"]
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corpus = [AlignedSent(trg_sentence, src_sentence)]
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alignment_info = AlignmentInfo(
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(0, 1, 4, 0, 2, 5, 5),
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[None] + src_sentence,
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["UNUSED"] + trg_sentence,
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None,
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)
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translation_table = defaultdict(lambda: defaultdict(float))
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translation_table["i"]["ich"] = 0.98
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translation_table["love"]["gern"] = 0.98
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translation_table["to"][None] = 0.98
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translation_table["eat"]["esse"] = 0.98
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translation_table["smoked"]["räucherschinken"] = 0.98
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translation_table["ham"]["räucherschinken"] = 0.98
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model1 = IBMModel1(corpus, 0)
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model1.translation_table = translation_table
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# act
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probability = model1.prob_t_a_given_s(alignment_info)
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# assert
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lexical_translation = 0.98 * 0.98 * 0.98 * 0.98 * 0.98 * 0.98
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expected_probability = lexical_translation
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self.assertEqual(round(probability, 4), round(expected_probability, 4))
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