PCQRSCANER/venv/Lib/site-packages/nltk/test/unit/translate/test_ibm4.py

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2019-12-22 21:51:47 +01:00
# -*- coding: utf-8 -*-
"""
Tests for IBM Model 4 training methods
"""
import unittest
from collections import defaultdict
from nltk.translate import AlignedSent
from nltk.translate import IBMModel
from nltk.translate import IBMModel4
from nltk.translate.ibm_model import AlignmentInfo
class TestIBMModel4(unittest.TestCase):
def test_set_uniform_distortion_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']),
]
model4 = IBMModel4(corpus, 0, src_classes, trg_classes)
# act
model4.set_uniform_probabilities(corpus)
# assert
# number of displacement values =
# 2 *(number of words in longest target sentence - 1)
expected_prob = 1.0 / (2 * (4 - 1))
# examine the boundary values for (displacement, src_class, trg_class)
self.assertEqual(model4.head_distortion_table[3][0][0], expected_prob)
self.assertEqual(model4.head_distortion_table[-3][1][2], expected_prob)
self.assertEqual(model4.non_head_distortion_table[3][0], expected_prob)
self.assertEqual(model4.non_head_distortion_table[-3][2], expected_prob)
def test_set_uniform_distortion_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']),
]
model4 = IBMModel4(corpus, 0, src_classes, trg_classes)
# act
model4.set_uniform_probabilities(corpus)
# assert
# examine displacement values that are not in the training data domain
self.assertEqual(model4.head_distortion_table[4][0][0], IBMModel.MIN_PROB)
self.assertEqual(model4.head_distortion_table[100][1][2], IBMModel.MIN_PROB)
self.assertEqual(model4.non_head_distortion_table[4][0], IBMModel.MIN_PROB)
self.assertEqual(model4.non_head_distortion_table[100][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_distortion_table = defaultdict(
lambda: defaultdict(lambda: defaultdict(float))
)
head_distortion_table[1][None][3] = 0.97 # None, i
head_distortion_table[3][2][4] = 0.97 # ich, eat
head_distortion_table[-2][3][4] = 0.97 # esse, love
head_distortion_table[3][4][1] = 0.97 # gern, smoked
non_head_distortion_table = defaultdict(lambda: defaultdict(float))
non_head_distortion_table[1][0] = 0.96 # 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,
'head_distortion_table': head_distortion_table,
'non_head_distortion_table': non_head_distortion_table,
'fertility_table': fertility_table,
'alignment_table': None,
}
model4 = IBMModel4(corpus, 0, src_classes, trg_classes, probabilities)
# act
probability = model4.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 * 1 * 0.97 * 0.97 * 0.96
expected_probability = (
null_generation * fertility * lexical_translation * distortion
)
self.assertEqual(round(probability, 4), round(expected_probability, 4))