90 lines
3.4 KiB
Python
90 lines
3.4 KiB
Python
# -*- coding: utf-8 -*-
|
|
"""
|
|
Tests for IBM Model 2 training methods
|
|
"""
|
|
|
|
import unittest
|
|
|
|
from collections import defaultdict
|
|
from nltk.translate import AlignedSent
|
|
from nltk.translate import IBMModel
|
|
from nltk.translate import IBMModel2
|
|
from nltk.translate.ibm_model import AlignmentInfo
|
|
|
|
|
|
class TestIBMModel2(unittest.TestCase):
|
|
def test_set_uniform_alignment_probabilities(self):
|
|
# arrange
|
|
corpus = [
|
|
AlignedSent(['ham', 'eggs'], ['schinken', 'schinken', 'eier']),
|
|
AlignedSent(['spam', 'spam', 'spam', 'spam'], ['spam', 'spam']),
|
|
]
|
|
model2 = IBMModel2(corpus, 0)
|
|
|
|
# act
|
|
model2.set_uniform_probabilities(corpus)
|
|
|
|
# assert
|
|
# expected_prob = 1.0 / (length of source sentence + 1)
|
|
self.assertEqual(model2.alignment_table[0][1][3][2], 1.0 / 4)
|
|
self.assertEqual(model2.alignment_table[2][4][2][4], 1.0 / 3)
|
|
|
|
def test_set_uniform_alignment_probabilities_of_non_domain_values(self):
|
|
# arrange
|
|
corpus = [
|
|
AlignedSent(['ham', 'eggs'], ['schinken', 'schinken', 'eier']),
|
|
AlignedSent(['spam', 'spam', 'spam', 'spam'], ['spam', 'spam']),
|
|
]
|
|
model2 = IBMModel2(corpus, 0)
|
|
|
|
# act
|
|
model2.set_uniform_probabilities(corpus)
|
|
|
|
# assert
|
|
# examine i and j values that are not in the training data domain
|
|
self.assertEqual(model2.alignment_table[99][1][3][2], IBMModel.MIN_PROB)
|
|
self.assertEqual(model2.alignment_table[2][99][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,
|
|
None,
|
|
)
|
|
|
|
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
|
|
|
|
alignment_table = defaultdict(
|
|
lambda: defaultdict(lambda: defaultdict(lambda: defaultdict(float)))
|
|
)
|
|
alignment_table[0][3][5][6] = 0.97 # None -> to
|
|
alignment_table[1][1][5][6] = 0.97 # ich -> i
|
|
alignment_table[2][4][5][6] = 0.97 # esse -> eat
|
|
alignment_table[4][2][5][6] = 0.97 # gern -> love
|
|
alignment_table[5][5][5][6] = 0.96 # räucherschinken -> smoked
|
|
alignment_table[5][6][5][6] = 0.96 # räucherschinken -> ham
|
|
|
|
model2 = IBMModel2(corpus, 0)
|
|
model2.translation_table = translation_table
|
|
model2.alignment_table = alignment_table
|
|
|
|
# act
|
|
probability = model2.prob_t_a_given_s(alignment_info)
|
|
|
|
# assert
|
|
lexical_translation = 0.98 * 0.98 * 0.98 * 0.98 * 0.98 * 0.98
|
|
alignment = 0.97 * 0.97 * 0.97 * 0.97 * 0.96 * 0.96
|
|
expected_probability = lexical_translation * alignment
|
|
self.assertEqual(round(probability, 4), round(expected_probability, 4))
|