161 lines
6.4 KiB
Python
161 lines
6.4 KiB
Python
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"""
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Tests for IBM Model 5 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, IBMModel4, IBMModel5
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from nltk.translate.ibm_model import AlignmentInfo
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class TestIBMModel5(unittest.TestCase):
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def test_set_uniform_vacancy_probabilities_of_max_displacements(self):
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# arrange
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src_classes = {"schinken": 0, "eier": 0, "spam": 1}
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trg_classes = {"ham": 0, "eggs": 1, "spam": 2}
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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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model5 = IBMModel5(corpus, 0, src_classes, trg_classes)
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# act
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model5.set_uniform_probabilities(corpus)
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# assert
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# number of vacancy difference values =
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# 2 * number of words in longest target sentence
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expected_prob = 1.0 / (2 * 4)
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# examine the boundary values for (dv, max_v, trg_class)
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self.assertEqual(model5.head_vacancy_table[4][4][0], expected_prob)
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self.assertEqual(model5.head_vacancy_table[-3][1][2], expected_prob)
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self.assertEqual(model5.non_head_vacancy_table[4][4][0], expected_prob)
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self.assertEqual(model5.non_head_vacancy_table[-3][1][2], expected_prob)
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def test_set_uniform_vacancy_probabilities_of_non_domain_values(self):
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# arrange
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src_classes = {"schinken": 0, "eier": 0, "spam": 1}
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trg_classes = {"ham": 0, "eggs": 1, "spam": 2}
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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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model5 = IBMModel5(corpus, 0, src_classes, trg_classes)
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# act
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model5.set_uniform_probabilities(corpus)
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# assert
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# examine dv and max_v values that are not in the training data domain
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self.assertEqual(model5.head_vacancy_table[5][4][0], IBMModel.MIN_PROB)
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self.assertEqual(model5.head_vacancy_table[-4][1][2], IBMModel.MIN_PROB)
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self.assertEqual(model5.head_vacancy_table[4][0][0], IBMModel.MIN_PROB)
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self.assertEqual(model5.non_head_vacancy_table[5][4][0], IBMModel.MIN_PROB)
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self.assertEqual(model5.non_head_vacancy_table[-4][1][2], 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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src_classes = {"räucherschinken": 0, "ja": 1, "ich": 2, "esse": 3, "gern": 4}
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trg_classes = {"ham": 0, "smoked": 1, "i": 3, "love": 4, "to": 2, "eat": 4}
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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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head_vacancy_table = defaultdict(
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lambda: defaultdict(lambda: defaultdict(float))
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)
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head_vacancy_table[1 - 0][6][3] = 0.97 # ich -> i
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head_vacancy_table[3 - 0][5][4] = 0.97 # esse -> eat
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head_vacancy_table[1 - 2][4][4] = 0.97 # gern -> love
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head_vacancy_table[2 - 0][2][1] = 0.97 # räucherschinken -> smoked
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non_head_vacancy_table = defaultdict(
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lambda: defaultdict(lambda: defaultdict(float))
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)
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non_head_vacancy_table[1 - 0][1][0] = 0.96 # räucherschinken -> ham
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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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"fertility_table": fertility_table,
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"head_vacancy_table": head_vacancy_table,
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"non_head_vacancy_table": non_head_vacancy_table,
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"head_distortion_table": None,
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"non_head_distortion_table": None,
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"alignment_table": None,
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}
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model5 = IBMModel5(corpus, 0, src_classes, trg_classes, probabilities)
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# act
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probability = model5.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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vacancy = 0.97 * 0.97 * 1 * 0.97 * 0.97 * 0.96
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expected_probability = (
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null_generation * fertility * lexical_translation * vacancy
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)
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self.assertEqual(round(probability, 4), round(expected_probability, 4))
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def test_prune(self):
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# arrange
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alignment_infos = [
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AlignmentInfo((1, 1), None, None, None),
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AlignmentInfo((1, 2), None, None, None),
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AlignmentInfo((2, 1), None, None, None),
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AlignmentInfo((2, 2), None, None, None),
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AlignmentInfo((0, 0), None, None, None),
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]
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min_factor = IBMModel5.MIN_SCORE_FACTOR
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best_score = 0.9
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scores = {
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(1, 1): min(min_factor * 1.5, 1) * best_score, # above threshold
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(1, 2): best_score,
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(2, 1): min_factor * best_score, # at threshold
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(2, 2): min_factor * best_score * 0.5, # low score
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(0, 0): min(min_factor * 1.1, 1) * 1.2, # above threshold
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}
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corpus = [AlignedSent(["a"], ["b"])]
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original_prob_function = IBMModel4.model4_prob_t_a_given_s
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# mock static method
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IBMModel4.model4_prob_t_a_given_s = staticmethod(
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lambda a, model: scores[a.alignment]
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)
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model5 = IBMModel5(corpus, 0, None, None)
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# act
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pruned_alignments = model5.prune(alignment_infos)
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# assert
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self.assertEqual(len(pruned_alignments), 3)
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# restore static method
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IBMModel4.model4_prob_t_a_given_s = original_prob_function
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