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

# -*- coding: utf-8 -*-
"""
Tests for IBM Model 4 training methods
"""

import unittest

from collections import defaultdict
from nltk.align import AlignedSent
from nltk.align.ibm_model import AlignmentInfo
from nltk.align.ibm_model import IBMModel
from nltk.align.ibm4 import IBMModel4


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_distortion_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_distortion_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))