Source code for nltk.test.unit.translate.test_ibm4

# -*- 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


[docs]class TestIBMModel4(unittest.TestCase):
[docs] 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)
[docs] 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)
[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'] 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))