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"""
Tests for IBM Model 3 training methods
"""
import unittest
from collections import defaultdict
from nltk.translate import AlignedSent, IBMModel, 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))
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