# -*- coding: utf-8 -*-
"""
Tests for IBM Model 5 training methods
"""
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
from nltk.align.ibm4 import IBMModel4
from nltk.align.ibm5 import IBMModel5
[docs]class TestIBMModel5(unittest.TestCase):
[docs] def test_set_uniform_distortion_probabilities_of_non_domain_values(self):
# arrange
src_classes = {'schinken': 0, 'eier': 0, 'spam': 1}
trg_classes = {'ham': 0, 'eggs': 1, 'spam': 2}
corpus = [
AlignedSent(['ham', 'eggs'], ['schinken', 'schinken', 'eier']),
AlignedSent(['spam', 'spam', 'spam', 'spam'], ['spam', 'spam']),
]
model5 = IBMModel5(corpus, 0, src_classes, trg_classes)
# act
model5.set_uniform_distortion_probabilities(corpus)
# assert
# examine dv and max_v values that are not in the training data domain
self.assertEqual(model5.head_vacancy_table[5][4][0],
IBMModel.MIN_PROB)
self.assertEqual(model5.head_vacancy_table[-4][1][2],
IBMModel.MIN_PROB)
self.assertEqual(model5.head_vacancy_table[4][0][0],
IBMModel.MIN_PROB)
self.assertEqual(model5.non_head_vacancy_table[5][4][0],
IBMModel.MIN_PROB)
self.assertEqual(model5.non_head_vacancy_table[-4][1][2],
IBMModel.MIN_PROB)
[docs] def test_prob_t_a_given_s(self):
# arrange
src_sentence = ["ich", 'esse', 'ja', 'gern', 'räucherschinken']
trg_sentence = ['i', 'love', 'to', 'eat', 'smoked', 'ham']
src_classes = {'räucherschinken': 0, 'ja': 1, 'ich': 2, 'esse': 3,
'gern': 4}
trg_classes = {'ham': 0, 'smoked': 1, 'i': 3, 'love': 4, 'to': 2,
'eat': 4}
corpus = [AlignedSent(trg_sentence, src_sentence)]
alignment_info = AlignmentInfo((0, 1, 4, 0, 2, 5, 5),
[None] + src_sentence,
['UNUSED'] + trg_sentence,
[[3], [1], [4], [], [2], [5, 6]])
head_vacancy_table = defaultdict(
lambda: defaultdict(lambda: defaultdict(float)))
head_vacancy_table[1 - 0][6][3] = 0.97 # ich -> i
head_vacancy_table[3 - 0][5][4] = 0.97 # esse -> eat
head_vacancy_table[1 - 2][4][4] = 0.97 # gern -> love
head_vacancy_table[2 - 0][2][1] = 0.97 # räucherschinken -> smoked
non_head_vacancy_table = defaultdict(
lambda: defaultdict(lambda: defaultdict(float)))
non_head_vacancy_table[1 - 0][1][0] = 0.96 # räucherschinken -> ham
translation_table = defaultdict(lambda: defaultdict(float))
translation_table['i']['ich'] = 0.98
translation_table['love']['gern'] = 0.98
translation_table['to'][None] = 0.98
translation_table['eat']['esse'] = 0.98
translation_table['smoked']['räucherschinken'] = 0.98
translation_table['ham']['räucherschinken'] = 0.98
fertility_table = defaultdict(lambda: defaultdict(float))
fertility_table[1]['ich'] = 0.99
fertility_table[1]['esse'] = 0.99
fertility_table[0]['ja'] = 0.99
fertility_table[1]['gern'] = 0.99
fertility_table[2]['räucherschinken'] = 0.999
fertility_table[1][None] = 0.99
probabilities = {
'p1': 0.167,
'translation_table': translation_table,
'fertility_table': fertility_table,
'head_vacancy_table': head_vacancy_table,
'non_head_vacancy_table': non_head_vacancy_table,
'head_distortion_table': None,
'non_head_distortion_table': None,
'alignment_table': None
}
model5 = IBMModel5(corpus, 0, src_classes, trg_classes,
probabilities)
# act
probability = model5.prob_t_a_given_s(alignment_info)
# assert
null_generation = 5 * pow(0.167, 1) * pow(0.833, 4)
fertility = 1*0.99 * 1*0.99 * 1*0.99 * 1*0.99 * 2*0.999
lexical_translation = 0.98 * 0.98 * 0.98 * 0.98 * 0.98 * 0.98
vacancy = 0.97 * 0.97 * 1 * 0.97 * 0.97 * 0.96
expected_probability = (null_generation * fertility *
lexical_translation * vacancy)
self.assertEqual(round(probability, 4), round(expected_probability, 4))
[docs] def test_prune(self):
# arrange
alignment_infos = [
AlignmentInfo((1, 1), None, None, None),
AlignmentInfo((1, 2), None, None, None),
AlignmentInfo((2, 1), None, None, None),
AlignmentInfo((2, 2), None, None, None),
AlignmentInfo((0, 0), None, None, None)
]
min_factor = IBMModel5.MIN_SCORE_FACTOR
best_score = 0.9
scores = {
(1, 1): min(min_factor * 1.5, 1) * best_score, # above threshold
(1, 2): best_score,
(2, 1): min_factor * best_score, # at threshold
(2, 2): min_factor * best_score * 0.5, # low score
(0, 0): min(min_factor * 1.1, 1) * 1.2 # above threshold
}
corpus = [AlignedSent(['a'], ['b'])]
original_prob_function = IBMModel4.model4_prob_t_a_given_s
# mock static method
IBMModel4.model4_prob_t_a_given_s = staticmethod(
lambda a, model: scores[a.alignment])
model5 = IBMModel5(corpus, 0, None, None)
# act
pruned_alignments = model5.prune(alignment_infos)
# assert
self.assertEqual(len(pruned_alignments), 3)
# restore static method
IBMModel4.model4_prob_t_a_given_s = original_prob_function