PCQRSCANER/venv/Lib/site-packages/nltk/test/unit/translate/test_ibm1.py

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2019-12-22 21:51:47 +01:00
# -*- coding: utf-8 -*-
"""
Tests for IBM Model 1 training methods
"""
import unittest
from collections import defaultdict
from nltk.translate import AlignedSent
from nltk.translate import IBMModel
from nltk.translate import IBMModel1
from nltk.translate.ibm_model import AlignmentInfo
class TestIBMModel1(unittest.TestCase):
def test_set_uniform_translation_probabilities(self):
# arrange
corpus = [
AlignedSent(['ham', 'eggs'], ['schinken', 'schinken', 'eier']),
AlignedSent(['spam', 'spam', 'spam', 'spam'], ['spam', 'spam']),
]
model1 = IBMModel1(corpus, 0)
# act
model1.set_uniform_probabilities(corpus)
# assert
# expected_prob = 1.0 / (target vocab size + 1)
self.assertEqual(model1.translation_table['ham']['eier'], 1.0 / 3)
self.assertEqual(model1.translation_table['eggs'][None], 1.0 / 3)
def test_set_uniform_translation_probabilities_of_non_domain_values(self):
# arrange
corpus = [
AlignedSent(['ham', 'eggs'], ['schinken', 'schinken', 'eier']),
AlignedSent(['spam', 'spam', 'spam', 'spam'], ['spam', 'spam']),
]
model1 = IBMModel1(corpus, 0)
# act
model1.set_uniform_probabilities(corpus)
# assert
# examine target words that are not in the training data domain
self.assertEqual(model1.translation_table['parrot']['eier'], 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
model1 = IBMModel1(corpus, 0)
model1.translation_table = translation_table
# act
probability = model1.prob_t_a_given_s(alignment_info)
# assert
lexical_translation = 0.98 * 0.98 * 0.98 * 0.98 * 0.98 * 0.98
expected_probability = lexical_translation
self.assertEqual(round(probability, 4), round(expected_probability, 4))