280 lines
9.7 KiB
Python
280 lines
9.7 KiB
Python
# -*- coding: utf-8 -*-
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"""
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Tests for common methods of IBM translation models
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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
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from nltk.translate import IBMModel
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from nltk.translate.ibm_model import AlignmentInfo
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class TestIBMModel(unittest.TestCase):
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__TEST_SRC_SENTENCE = ["j'", 'aime', 'bien', 'jambon']
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__TEST_TRG_SENTENCE = ['i', 'love', 'ham']
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def test_vocabularies_are_initialized(self):
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parallel_corpora = [
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AlignedSent(['one', 'two', 'three', 'four'], ['un', 'deux', 'trois']),
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AlignedSent(['five', 'one', 'six'], ['quatre', 'cinq', 'six']),
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AlignedSent([], ['sept']),
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]
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ibm_model = IBMModel(parallel_corpora)
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self.assertEqual(len(ibm_model.src_vocab), 8)
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self.assertEqual(len(ibm_model.trg_vocab), 6)
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def test_vocabularies_are_initialized_even_with_empty_corpora(self):
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parallel_corpora = []
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ibm_model = IBMModel(parallel_corpora)
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self.assertEqual(len(ibm_model.src_vocab), 1) # addition of NULL token
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self.assertEqual(len(ibm_model.trg_vocab), 0)
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def test_best_model2_alignment(self):
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# arrange
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sentence_pair = AlignedSent(
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TestIBMModel.__TEST_TRG_SENTENCE, TestIBMModel.__TEST_SRC_SENTENCE
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)
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# None and 'bien' have zero fertility
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translation_table = {
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'i': {"j'": 0.9, 'aime': 0.05, 'bien': 0.02, 'jambon': 0.03, None: 0},
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'love': {"j'": 0.05, 'aime': 0.9, 'bien': 0.01, 'jambon': 0.01, None: 0.03},
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'ham': {"j'": 0, 'aime': 0.01, 'bien': 0, 'jambon': 0.99, None: 0},
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}
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alignment_table = defaultdict(
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lambda: defaultdict(lambda: defaultdict(lambda: defaultdict(lambda: 0.2)))
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)
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ibm_model = IBMModel([])
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ibm_model.translation_table = translation_table
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ibm_model.alignment_table = alignment_table
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# act
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a_info = ibm_model.best_model2_alignment(sentence_pair)
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# assert
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self.assertEqual(a_info.alignment[1:], (1, 2, 4)) # 0th element unused
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self.assertEqual(a_info.cepts, [[], [1], [2], [], [3]])
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def test_best_model2_alignment_does_not_change_pegged_alignment(self):
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# arrange
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sentence_pair = AlignedSent(
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TestIBMModel.__TEST_TRG_SENTENCE, TestIBMModel.__TEST_SRC_SENTENCE
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)
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translation_table = {
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'i': {"j'": 0.9, 'aime': 0.05, 'bien': 0.02, 'jambon': 0.03, None: 0},
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'love': {"j'": 0.05, 'aime': 0.9, 'bien': 0.01, 'jambon': 0.01, None: 0.03},
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'ham': {"j'": 0, 'aime': 0.01, 'bien': 0, 'jambon': 0.99, None: 0},
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}
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alignment_table = defaultdict(
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lambda: defaultdict(lambda: defaultdict(lambda: defaultdict(lambda: 0.2)))
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)
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ibm_model = IBMModel([])
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ibm_model.translation_table = translation_table
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ibm_model.alignment_table = alignment_table
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# act: force 'love' to be pegged to 'jambon'
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a_info = ibm_model.best_model2_alignment(sentence_pair, 2, 4)
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# assert
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self.assertEqual(a_info.alignment[1:], (1, 4, 4))
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self.assertEqual(a_info.cepts, [[], [1], [], [], [2, 3]])
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def test_best_model2_alignment_handles_fertile_words(self):
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# arrange
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sentence_pair = AlignedSent(
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['i', 'really', ',', 'really', 'love', 'ham'],
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TestIBMModel.__TEST_SRC_SENTENCE,
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)
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# 'bien' produces 2 target words: 'really' and another 'really'
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translation_table = {
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'i': {"j'": 0.9, 'aime': 0.05, 'bien': 0.02, 'jambon': 0.03, None: 0},
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'really': {"j'": 0, 'aime': 0, 'bien': 0.9, 'jambon': 0.01, None: 0.09},
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',': {"j'": 0, 'aime': 0, 'bien': 0.3, 'jambon': 0, None: 0.7},
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'love': {"j'": 0.05, 'aime': 0.9, 'bien': 0.01, 'jambon': 0.01, None: 0.03},
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'ham': {"j'": 0, 'aime': 0.01, 'bien': 0, 'jambon': 0.99, None: 0},
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}
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alignment_table = defaultdict(
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lambda: defaultdict(lambda: defaultdict(lambda: defaultdict(lambda: 0.2)))
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)
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ibm_model = IBMModel([])
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ibm_model.translation_table = translation_table
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ibm_model.alignment_table = alignment_table
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# act
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a_info = ibm_model.best_model2_alignment(sentence_pair)
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# assert
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self.assertEqual(a_info.alignment[1:], (1, 3, 0, 3, 2, 4))
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self.assertEqual(a_info.cepts, [[3], [1], [5], [2, 4], [6]])
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def test_best_model2_alignment_handles_empty_src_sentence(self):
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# arrange
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sentence_pair = AlignedSent(TestIBMModel.__TEST_TRG_SENTENCE, [])
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ibm_model = IBMModel([])
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# act
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a_info = ibm_model.best_model2_alignment(sentence_pair)
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# assert
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self.assertEqual(a_info.alignment[1:], (0, 0, 0))
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self.assertEqual(a_info.cepts, [[1, 2, 3]])
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def test_best_model2_alignment_handles_empty_trg_sentence(self):
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# arrange
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sentence_pair = AlignedSent([], TestIBMModel.__TEST_SRC_SENTENCE)
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ibm_model = IBMModel([])
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# act
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a_info = ibm_model.best_model2_alignment(sentence_pair)
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# assert
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self.assertEqual(a_info.alignment[1:], ())
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self.assertEqual(a_info.cepts, [[], [], [], [], []])
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def test_neighboring_finds_neighbor_alignments(self):
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# arrange
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a_info = AlignmentInfo(
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(0, 3, 2),
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(None, 'des', 'œufs', 'verts'),
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('UNUSED', 'green', 'eggs'),
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[[], [], [2], [1]],
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)
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ibm_model = IBMModel([])
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# act
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neighbors = ibm_model.neighboring(a_info)
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# assert
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neighbor_alignments = set()
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for neighbor in neighbors:
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neighbor_alignments.add(neighbor.alignment)
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expected_alignments = set(
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[
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# moves
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(0, 0, 2),
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(0, 1, 2),
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(0, 2, 2),
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(0, 3, 0),
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(0, 3, 1),
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(0, 3, 3),
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# swaps
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(0, 2, 3),
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# original alignment
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(0, 3, 2),
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]
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)
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self.assertEqual(neighbor_alignments, expected_alignments)
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def test_neighboring_sets_neighbor_alignment_info(self):
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# arrange
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a_info = AlignmentInfo(
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(0, 3, 2),
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(None, 'des', 'œufs', 'verts'),
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('UNUSED', 'green', 'eggs'),
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[[], [], [2], [1]],
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)
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ibm_model = IBMModel([])
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# act
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neighbors = ibm_model.neighboring(a_info)
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# assert: select a few particular alignments
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for neighbor in neighbors:
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if neighbor.alignment == (0, 2, 2):
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moved_alignment = neighbor
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elif neighbor.alignment == (0, 3, 2):
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swapped_alignment = neighbor
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self.assertEqual(moved_alignment.cepts, [[], [], [1, 2], []])
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self.assertEqual(swapped_alignment.cepts, [[], [], [2], [1]])
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def test_neighboring_returns_neighbors_with_pegged_alignment(self):
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# arrange
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a_info = AlignmentInfo(
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(0, 3, 2),
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(None, 'des', 'œufs', 'verts'),
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('UNUSED', 'green', 'eggs'),
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[[], [], [2], [1]],
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)
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ibm_model = IBMModel([])
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# act: peg 'eggs' to align with 'œufs'
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neighbors = ibm_model.neighboring(a_info, 2)
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# assert
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neighbor_alignments = set()
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for neighbor in neighbors:
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neighbor_alignments.add(neighbor.alignment)
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expected_alignments = set(
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[
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# moves
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(0, 0, 2),
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(0, 1, 2),
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(0, 2, 2),
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# no swaps
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# original alignment
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(0, 3, 2),
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]
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)
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self.assertEqual(neighbor_alignments, expected_alignments)
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def test_hillclimb(self):
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# arrange
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initial_alignment = AlignmentInfo((0, 3, 2), None, None, None)
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def neighboring_mock(a, j):
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if a.alignment == (0, 3, 2):
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return set(
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[
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AlignmentInfo((0, 2, 2), None, None, None),
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AlignmentInfo((0, 1, 1), None, None, None),
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]
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)
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elif a.alignment == (0, 2, 2):
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return set(
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[
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AlignmentInfo((0, 3, 3), None, None, None),
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AlignmentInfo((0, 4, 4), None, None, None),
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]
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)
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return set()
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def prob_t_a_given_s_mock(a):
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prob_values = {
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(0, 3, 2): 0.5,
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(0, 2, 2): 0.6,
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(0, 1, 1): 0.4,
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(0, 3, 3): 0.6,
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(0, 4, 4): 0.7,
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}
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return prob_values.get(a.alignment, 0.01)
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ibm_model = IBMModel([])
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ibm_model.neighboring = neighboring_mock
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ibm_model.prob_t_a_given_s = prob_t_a_given_s_mock
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# act
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best_alignment = ibm_model.hillclimb(initial_alignment)
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# assert: hill climbing goes from (0, 3, 2) -> (0, 2, 2) -> (0, 4, 4)
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self.assertEqual(best_alignment.alignment, (0, 4, 4))
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def test_sample(self):
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# arrange
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sentence_pair = AlignedSent(
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TestIBMModel.__TEST_TRG_SENTENCE, TestIBMModel.__TEST_SRC_SENTENCE
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)
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ibm_model = IBMModel([])
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ibm_model.prob_t_a_given_s = lambda x: 0.001
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# act
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samples, best_alignment = ibm_model.sample(sentence_pair)
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# assert
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self.assertEqual(len(samples), 61)
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