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