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

# Natural Language Toolkit: Stack decoder
#
# Copyright (C) 2001-2022 NLTK Project
# Author: Tah Wei Hoon <hoon.tw@gmail.com>
# URL: <https://www.nltk.org/>
# For license information, see LICENSE.TXT

"""
Tests for stack decoder
"""

import unittest
from collections import defaultdict
from math import log

from nltk.translate import PhraseTable, StackDecoder
from nltk.translate.stack_decoder import _Hypothesis, _Stack


[docs]class TestStackDecoder(unittest.TestCase):
[docs] def test_find_all_src_phrases(self): # arrange phrase_table = TestStackDecoder.create_fake_phrase_table() stack_decoder = StackDecoder(phrase_table, None) sentence = ("my", "hovercraft", "is", "full", "of", "eels") # act src_phrase_spans = stack_decoder.find_all_src_phrases(sentence) # assert self.assertEqual(src_phrase_spans[0], [2]) # 'my hovercraft' self.assertEqual(src_phrase_spans[1], [2]) # 'hovercraft' self.assertEqual(src_phrase_spans[2], [3]) # 'is' self.assertEqual(src_phrase_spans[3], [5, 6]) # 'full of', 'full of eels' self.assertFalse(src_phrase_spans[4]) # no entry starting with 'of' self.assertEqual(src_phrase_spans[5], [6]) # 'eels'
[docs] def test_distortion_score(self): # arrange stack_decoder = StackDecoder(None, None) stack_decoder.distortion_factor = 0.5 hypothesis = _Hypothesis() hypothesis.src_phrase_span = (3, 5) # act score = stack_decoder.distortion_score(hypothesis, (8, 10)) # assert expected_score = log(stack_decoder.distortion_factor) * (8 - 5) self.assertEqual(score, expected_score)
[docs] def test_distortion_score_of_first_expansion(self): # arrange stack_decoder = StackDecoder(None, None) stack_decoder.distortion_factor = 0.5 hypothesis = _Hypothesis() # act score = stack_decoder.distortion_score(hypothesis, (8, 10)) # assert # expansion from empty hypothesis always has zero distortion cost self.assertEqual(score, 0.0)
[docs] def test_compute_future_costs(self): # arrange phrase_table = TestStackDecoder.create_fake_phrase_table() language_model = TestStackDecoder.create_fake_language_model() stack_decoder = StackDecoder(phrase_table, language_model) sentence = ("my", "hovercraft", "is", "full", "of", "eels") # act future_scores = stack_decoder.compute_future_scores(sentence) # assert self.assertEqual( future_scores[1][2], ( phrase_table.translations_for(("hovercraft",))[0].log_prob + language_model.probability(("hovercraft",)) ), ) self.assertEqual( future_scores[0][2], ( phrase_table.translations_for(("my", "hovercraft"))[0].log_prob + language_model.probability(("my", "hovercraft")) ), )
[docs] def test_compute_future_costs_for_phrases_not_in_phrase_table(self): # arrange phrase_table = TestStackDecoder.create_fake_phrase_table() language_model = TestStackDecoder.create_fake_language_model() stack_decoder = StackDecoder(phrase_table, language_model) sentence = ("my", "hovercraft", "is", "full", "of", "eels") # act future_scores = stack_decoder.compute_future_scores(sentence) # assert self.assertEqual( future_scores[1][3], # 'hovercraft is' is not in phrase table future_scores[1][2] + future_scores[2][3], ) # backoff
[docs] def test_future_score(self): # arrange: sentence with 8 words; words 2, 3, 4 already translated hypothesis = _Hypothesis() hypothesis.untranslated_spans = lambda _: [(0, 2), (5, 8)] # mock future_score_table = defaultdict(lambda: defaultdict(float)) future_score_table[0][2] = 0.4 future_score_table[5][8] = 0.5 stack_decoder = StackDecoder(None, None) # act future_score = stack_decoder.future_score(hypothesis, future_score_table, 8) # assert self.assertEqual(future_score, 0.4 + 0.5)
[docs] def test_valid_phrases(self): # arrange hypothesis = _Hypothesis() # mock untranslated_spans method hypothesis.untranslated_spans = lambda _: [(0, 2), (3, 6)] all_phrases_from = [[1, 4], [2], [], [5], [5, 6, 7], [], [7]] # act phrase_spans = StackDecoder.valid_phrases(all_phrases_from, hypothesis) # assert self.assertEqual(phrase_spans, [(0, 1), (1, 2), (3, 5), (4, 5), (4, 6)])
[docs] @staticmethod def create_fake_phrase_table(): phrase_table = PhraseTable() phrase_table.add(("hovercraft",), ("",), 0.8) phrase_table.add(("my", "hovercraft"), ("", ""), 0.7) phrase_table.add(("my", "cheese"), ("", ""), 0.7) phrase_table.add(("is",), ("",), 0.8) phrase_table.add(("is",), ("",), 0.5) phrase_table.add(("full", "of"), ("", ""), 0.01) phrase_table.add(("full", "of", "eels"), ("", "", ""), 0.5) phrase_table.add(("full", "of", "spam"), ("", ""), 0.5) phrase_table.add(("eels",), ("",), 0.5) phrase_table.add(("spam",), ("",), 0.5) return phrase_table
[docs] @staticmethod def create_fake_language_model(): # nltk.model should be used here once it is implemented language_prob = defaultdict(lambda: -999.0) language_prob[("my",)] = log(0.1) language_prob[("hovercraft",)] = log(0.1) language_prob[("is",)] = log(0.1) language_prob[("full",)] = log(0.1) language_prob[("of",)] = log(0.1) language_prob[("eels",)] = log(0.1) language_prob[("my", "hovercraft")] = log(0.3) language_model = type( "", (object,), {"probability": lambda _, phrase: language_prob[phrase]} )() return language_model
[docs]class TestHypothesis(unittest.TestCase):
[docs] def setUp(self): root = _Hypothesis() child = _Hypothesis( raw_score=0.5, src_phrase_span=(3, 7), trg_phrase=("hello", "world"), previous=root, ) grandchild = _Hypothesis( raw_score=0.4, src_phrase_span=(1, 2), trg_phrase=("and", "goodbye"), previous=child, ) self.hypothesis_chain = grandchild
[docs] def test_translation_so_far(self): # act translation = self.hypothesis_chain.translation_so_far() # assert self.assertEqual(translation, ["hello", "world", "and", "goodbye"])
[docs] def test_translation_so_far_for_empty_hypothesis(self): # arrange hypothesis = _Hypothesis() # act translation = hypothesis.translation_so_far() # assert self.assertEqual(translation, [])
[docs] def test_total_translated_words(self): # act total_translated_words = self.hypothesis_chain.total_translated_words() # assert self.assertEqual(total_translated_words, 5)
[docs] def test_translated_positions(self): # act translated_positions = self.hypothesis_chain.translated_positions() # assert translated_positions.sort() self.assertEqual(translated_positions, [1, 3, 4, 5, 6])
[docs] def test_untranslated_spans(self): # act untranslated_spans = self.hypothesis_chain.untranslated_spans(10) # assert self.assertEqual(untranslated_spans, [(0, 1), (2, 3), (7, 10)])
[docs] def test_untranslated_spans_for_empty_hypothesis(self): # arrange hypothesis = _Hypothesis() # act untranslated_spans = hypothesis.untranslated_spans(10) # assert self.assertEqual(untranslated_spans, [(0, 10)])
[docs]class TestStack(unittest.TestCase):
[docs] def test_push_bumps_off_worst_hypothesis_when_stack_is_full(self): # arrange stack = _Stack(3) poor_hypothesis = _Hypothesis(0.01) # act stack.push(_Hypothesis(0.2)) stack.push(poor_hypothesis) stack.push(_Hypothesis(0.1)) stack.push(_Hypothesis(0.3)) # assert self.assertFalse(poor_hypothesis in stack)
[docs] def test_push_removes_hypotheses_that_fall_below_beam_threshold(self): # arrange stack = _Stack(3, 0.5) poor_hypothesis = _Hypothesis(0.01) worse_hypothesis = _Hypothesis(0.009) # act stack.push(poor_hypothesis) stack.push(worse_hypothesis) stack.push(_Hypothesis(0.9)) # greatly superior hypothesis # assert self.assertFalse(poor_hypothesis in stack) self.assertFalse(worse_hypothesis in stack)
[docs] def test_push_does_not_add_hypothesis_that_falls_below_beam_threshold(self): # arrange stack = _Stack(3, 0.5) poor_hypothesis = _Hypothesis(0.01) # act stack.push(_Hypothesis(0.9)) # greatly superior hypothesis stack.push(poor_hypothesis) # assert self.assertFalse(poor_hypothesis in stack)
[docs] def test_best_returns_the_best_hypothesis(self): # arrange stack = _Stack(3) best_hypothesis = _Hypothesis(0.99) # act stack.push(_Hypothesis(0.0)) stack.push(best_hypothesis) stack.push(_Hypothesis(0.5)) # assert self.assertEqual(stack.best(), best_hypothesis)
[docs] def test_best_returns_none_when_stack_is_empty(self): # arrange stack = _Stack(3) # assert self.assertEqual(stack.best(), None)