161 lines
6.4 KiB
Python
161 lines
6.4 KiB
Python
"""
|
|
Tests for IBM Model 5 training methods
|
|
"""
|
|
|
|
import unittest
|
|
from collections import defaultdict
|
|
|
|
from nltk.translate import AlignedSent, IBMModel, IBMModel4, IBMModel5
|
|
from nltk.translate.ibm_model import AlignmentInfo
|
|
|
|
|
|
class TestIBMModel5(unittest.TestCase):
|
|
def test_set_uniform_vacancy_probabilities_of_max_displacements(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_probabilities(corpus)
|
|
|
|
# assert
|
|
# number of vacancy difference values =
|
|
# 2 * number of words in longest target sentence
|
|
expected_prob = 1.0 / (2 * 4)
|
|
|
|
# examine the boundary values for (dv, max_v, trg_class)
|
|
self.assertEqual(model5.head_vacancy_table[4][4][0], expected_prob)
|
|
self.assertEqual(model5.head_vacancy_table[-3][1][2], expected_prob)
|
|
self.assertEqual(model5.non_head_vacancy_table[4][4][0], expected_prob)
|
|
self.assertEqual(model5.non_head_vacancy_table[-3][1][2], expected_prob)
|
|
|
|
def test_set_uniform_vacancy_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_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)
|
|
|
|
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))
|
|
|
|
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
|