"""
Tests for IBM Model 2 training methods
"""
import unittest
from collections import defaultdict
from nltk.translate import AlignedSent, IBMModel, IBMModel2
from nltk.translate.ibm_model import AlignmentInfo
[docs]class TestIBMModel2(unittest.TestCase):
[docs] 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)
[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"]
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))