106 lines
4.0 KiB
Python
106 lines
4.0 KiB
Python
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"""
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Tests for IBM Model 3 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, IBMModel3
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from nltk.translate.ibm_model import AlignmentInfo
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class TestIBMModel3(unittest.TestCase):
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def test_set_uniform_distortion_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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model3 = IBMModel3(corpus, 0)
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# act
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model3.set_uniform_probabilities(corpus)
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# assert
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# expected_prob = 1.0 / length of target sentence
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self.assertEqual(model3.distortion_table[1][0][3][2], 1.0 / 2)
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self.assertEqual(model3.distortion_table[4][2][2][4], 1.0 / 4)
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def test_set_uniform_distortion_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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model3 = IBMModel3(corpus, 0)
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# act
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model3.set_uniform_probabilities(corpus)
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# assert
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# examine i and j values that are not in the training data domain
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self.assertEqual(model3.distortion_table[0][0][3][2], IBMModel.MIN_PROB)
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self.assertEqual(model3.distortion_table[9][2][2][4], IBMModel.MIN_PROB)
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self.assertEqual(model3.distortion_table[2][9][2][4], 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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[[3], [1], [4], [], [2], [5, 6]],
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)
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distortion_table = defaultdict(
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lambda: defaultdict(lambda: defaultdict(lambda: defaultdict(float)))
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)
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distortion_table[1][1][5][6] = 0.97 # i -> ich
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distortion_table[2][4][5][6] = 0.97 # love -> gern
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distortion_table[3][0][5][6] = 0.97 # to -> NULL
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distortion_table[4][2][5][6] = 0.97 # eat -> esse
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distortion_table[5][5][5][6] = 0.97 # smoked -> räucherschinken
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distortion_table[6][5][5][6] = 0.97 # ham -> räucherschinken
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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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fertility_table = defaultdict(lambda: defaultdict(float))
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fertility_table[1]["ich"] = 0.99
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fertility_table[1]["esse"] = 0.99
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fertility_table[0]["ja"] = 0.99
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fertility_table[1]["gern"] = 0.99
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fertility_table[2]["räucherschinken"] = 0.999
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fertility_table[1][None] = 0.99
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probabilities = {
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"p1": 0.167,
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"translation_table": translation_table,
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"distortion_table": distortion_table,
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"fertility_table": fertility_table,
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"alignment_table": None,
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}
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model3 = IBMModel3(corpus, 0, probabilities)
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# act
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probability = model3.prob_t_a_given_s(alignment_info)
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# assert
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null_generation = 5 * pow(0.167, 1) * pow(0.833, 4)
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fertility = 1 * 0.99 * 1 * 0.99 * 1 * 0.99 * 1 * 0.99 * 2 * 0.999
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lexical_translation = 0.98 * 0.98 * 0.98 * 0.98 * 0.98 * 0.98
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distortion = 0.97 * 0.97 * 0.97 * 0.97 * 0.97 * 0.97
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expected_probability = (
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null_generation * fertility * lexical_translation * distortion
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)
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self.assertEqual(round(probability, 4), round(expected_probability, 4))
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