109 lines
4.2 KiB
Python
109 lines
4.2 KiB
Python
# -*- coding: utf-8 -*-
|
|
"""
|
|
Tests for IBM Model 3 training methods
|
|
"""
|
|
|
|
import unittest
|
|
|
|
from collections import defaultdict
|
|
from nltk.translate import AlignedSent
|
|
from nltk.translate import IBMModel
|
|
from nltk.translate import IBMModel3
|
|
from nltk.translate.ibm_model import AlignmentInfo
|
|
|
|
|
|
class TestIBMModel3(unittest.TestCase):
|
|
def test_set_uniform_distortion_probabilities(self):
|
|
# arrange
|
|
corpus = [
|
|
AlignedSent(['ham', 'eggs'], ['schinken', 'schinken', 'eier']),
|
|
AlignedSent(['spam', 'spam', 'spam', 'spam'], ['spam', 'spam']),
|
|
]
|
|
model3 = IBMModel3(corpus, 0)
|
|
|
|
# act
|
|
model3.set_uniform_probabilities(corpus)
|
|
|
|
# assert
|
|
# expected_prob = 1.0 / length of target sentence
|
|
self.assertEqual(model3.distortion_table[1][0][3][2], 1.0 / 2)
|
|
self.assertEqual(model3.distortion_table[4][2][2][4], 1.0 / 4)
|
|
|
|
def test_set_uniform_distortion_probabilities_of_non_domain_values(self):
|
|
# arrange
|
|
corpus = [
|
|
AlignedSent(['ham', 'eggs'], ['schinken', 'schinken', 'eier']),
|
|
AlignedSent(['spam', 'spam', 'spam', 'spam'], ['spam', 'spam']),
|
|
]
|
|
model3 = IBMModel3(corpus, 0)
|
|
|
|
# act
|
|
model3.set_uniform_probabilities(corpus)
|
|
|
|
# assert
|
|
# examine i and j values that are not in the training data domain
|
|
self.assertEqual(model3.distortion_table[0][0][3][2], IBMModel.MIN_PROB)
|
|
self.assertEqual(model3.distortion_table[9][2][2][4], IBMModel.MIN_PROB)
|
|
self.assertEqual(model3.distortion_table[2][9][2][4], 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']
|
|
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]],
|
|
)
|
|
|
|
distortion_table = defaultdict(
|
|
lambda: defaultdict(lambda: defaultdict(lambda: defaultdict(float)))
|
|
)
|
|
distortion_table[1][1][5][6] = 0.97 # i -> ich
|
|
distortion_table[2][4][5][6] = 0.97 # love -> gern
|
|
distortion_table[3][0][5][6] = 0.97 # to -> NULL
|
|
distortion_table[4][2][5][6] = 0.97 # eat -> esse
|
|
distortion_table[5][5][5][6] = 0.97 # smoked -> räucherschinken
|
|
distortion_table[6][5][5][6] = 0.97 # ham -> räucherschinken
|
|
|
|
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,
|
|
'distortion_table': distortion_table,
|
|
'fertility_table': fertility_table,
|
|
'alignment_table': None,
|
|
}
|
|
|
|
model3 = IBMModel3(corpus, 0, probabilities)
|
|
|
|
# act
|
|
probability = model3.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
|
|
distortion = 0.97 * 0.97 * 0.97 * 0.97 * 0.97 * 0.97
|
|
expected_probability = (
|
|
null_generation * fertility * lexical_translation * distortion
|
|
)
|
|
self.assertEqual(round(probability, 4), round(expected_probability, 4))
|