Source code for nltk.test.unit.translate.test_ibm1

# -*- 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


[docs]class TestIBMModel1(unittest.TestCase):
[docs] 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)
[docs] 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)
[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 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))