Source code for nltk.test.unit.translate.test_ibm3

# -*- coding: utf-8 -*-
"""
Tests for IBM Model 3 training methods
"""

import unittest

from collections import defaultdict
from nltk.translate import AlignedSent
from nltk.translate import IBMModel
from nltk.translate import IBMModel3
from nltk.translate.ibm_model import AlignmentInfo


[docs]class TestIBMModel3(unittest.TestCase):
[docs] 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)
[docs] 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)
[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, [[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))