447 lines
14 KiB
Python
447 lines
14 KiB
Python
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# -*- coding: utf-8 -*-
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# Natural Language Toolkit: Language Model Unit Tests
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#
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# Copyright (C) 2001-2019 NLTK Project
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# Author: Ilia Kurenkov <ilia.kurenkov@gmail.com>
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# URL: <http://nltk.org/>
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# For license information, see LICENSE.TXT
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from __future__ import division
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import math
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import sys
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import unittest
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from six import add_metaclass
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from nltk.lm import (
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Vocabulary,
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MLE,
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Lidstone,
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Laplace,
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WittenBellInterpolated,
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KneserNeyInterpolated,
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)
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from nltk.lm.preprocessing import padded_everygrams
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def _prepare_test_data(ngram_order):
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return (
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Vocabulary(["a", "b", "c", "d", "z", "<s>", "</s>"], unk_cutoff=1),
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[
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list(padded_everygrams(ngram_order, sent))
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for sent in (list("abcd"), list("egadbe"))
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],
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)
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class ParametrizeTestsMeta(type):
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"""Metaclass for generating parametrized tests."""
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def __new__(cls, name, bases, dct):
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contexts = (
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("a",),
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("c",),
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(u"<s>",),
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("b",),
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(u"<UNK>",),
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("d",),
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("e",),
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("r",),
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("w",),
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)
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for i, c in enumerate(contexts):
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dct["test_sumto1_{0}".format(i)] = cls.add_sum_to_1_test(c)
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scores = dct.get("score_tests", [])
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for i, (word, context, expected_score) in enumerate(scores):
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dct["test_score_{0}".format(i)] = cls.add_score_test(
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word, context, expected_score
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)
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return super(ParametrizeTestsMeta, cls).__new__(cls, name, bases, dct)
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@classmethod
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def add_score_test(cls, word, context, expected_score):
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if sys.version_info > (3, 5):
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message = "word='{word}', context={context}"
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else:
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# Python 2 doesn't report the mismatched values if we pass a custom
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# message, so we have to report them manually.
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message = (
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"{score} != {expected_score} within 4 places, "
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"word='{word}', context={context}"
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)
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def test_method(self):
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score = self.model.score(word, context)
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self.assertAlmostEqual(
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score, expected_score, msg=message.format(**locals()), places=4
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)
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return test_method
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@classmethod
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def add_sum_to_1_test(cls, context):
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def test(self):
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s = sum(self.model.score(w, context) for w in self.model.vocab)
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self.assertAlmostEqual(s, 1.0, msg="The context is {}".format(context))
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return test
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@add_metaclass(ParametrizeTestsMeta)
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class MleBigramTests(unittest.TestCase):
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"""unit tests for MLENgramModel class"""
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score_tests = [
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("d", ["c"], 1),
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# Unseen ngrams should yield 0
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("d", ["e"], 0),
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# Unigrams should also be 0
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("z", None, 0),
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# N unigrams = 14
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# count('a') = 2
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("a", None, 2.0 / 14),
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# count('y') = 3
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("y", None, 3.0 / 14),
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]
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def setUp(self):
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vocab, training_text = _prepare_test_data(2)
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self.model = MLE(2, vocabulary=vocab)
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self.model.fit(training_text)
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def test_logscore_zero_score(self):
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# logscore of unseen ngrams should be -inf
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logscore = self.model.logscore("d", ["e"])
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self.assertTrue(math.isinf(logscore))
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def test_entropy_perplexity_seen(self):
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# ngrams seen during training
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trained = [
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("<s>", "a"),
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("a", "b"),
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("b", "<UNK>"),
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("<UNK>", "a"),
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("a", "d"),
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("d", "</s>"),
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]
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# Ngram = Log score
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# <s>, a = -1
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# a, b = -1
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# b, UNK = -1
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# UNK, a = -1.585
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# a, d = -1
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# d, </s> = -1
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# TOTAL logscores = -6.585
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# - AVG logscores = 1.0975
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H = 1.0975
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perplexity = 2.1398
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self.assertAlmostEqual(H, self.model.entropy(trained), places=4)
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self.assertAlmostEqual(perplexity, self.model.perplexity(trained), places=4)
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def test_entropy_perplexity_unseen(self):
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# In MLE, even one unseen ngram should make entropy and perplexity infinite
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untrained = [("<s>", "a"), ("a", "c"), ("c", "d"), ("d", "</s>")]
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self.assertTrue(math.isinf(self.model.entropy(untrained)))
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self.assertTrue(math.isinf(self.model.perplexity(untrained)))
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def test_entropy_perplexity_unigrams(self):
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# word = score, log score
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# <s> = 0.1429, -2.8074
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# a = 0.1429, -2.8074
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# c = 0.0714, -3.8073
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# UNK = 0.2143, -2.2224
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# d = 0.1429, -2.8074
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# c = 0.0714, -3.8073
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# </s> = 0.1429, -2.8074
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# TOTAL logscores = -21.6243
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# - AVG logscores = 3.0095
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H = 3.0095
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perplexity = 8.0529
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text = [("<s>",), ("a",), ("c",), ("-",), ("d",), ("c",), ("</s>",)]
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self.assertAlmostEqual(H, self.model.entropy(text), places=4)
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self.assertAlmostEqual(perplexity, self.model.perplexity(text), places=4)
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@add_metaclass(ParametrizeTestsMeta)
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class MleTrigramTests(unittest.TestCase):
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"""MLE trigram model tests"""
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score_tests = [
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# count(d | b, c) = 1
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# count(b, c) = 1
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("d", ("b", "c"), 1),
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# count(d | c) = 1
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# count(c) = 1
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("d", ["c"], 1),
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# total number of tokens is 18, of which "a" occured 2 times
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("a", None, 2.0 / 18),
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# in vocabulary but unseen
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("z", None, 0),
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# out of vocabulary should use "UNK" score
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("y", None, 3.0 / 18),
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]
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def setUp(self):
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vocab, training_text = _prepare_test_data(3)
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self.model = MLE(3, vocabulary=vocab)
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self.model.fit(training_text)
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@add_metaclass(ParametrizeTestsMeta)
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class LidstoneBigramTests(unittest.TestCase):
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"""unit tests for Lidstone class"""
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score_tests = [
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# count(d | c) = 1
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# *count(d | c) = 1.1
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# Count(w | c for w in vocab) = 1
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# *Count(w | c for w in vocab) = 1.8
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("d", ["c"], 1.1 / 1.8),
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# Total unigrams: 14
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# Vocab size: 8
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# Denominator: 14 + 0.8 = 14.8
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# count("a") = 2
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# *count("a") = 2.1
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("a", None, 2.1 / 14.8),
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# in vocabulary but unseen
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# count("z") = 0
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# *count("z") = 0.1
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("z", None, 0.1 / 14.8),
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# out of vocabulary should use "UNK" score
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# count("<UNK>") = 3
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# *count("<UNK>") = 3.1
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("y", None, 3.1 / 14.8),
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]
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def setUp(self):
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vocab, training_text = _prepare_test_data(2)
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self.model = Lidstone(0.1, 2, vocabulary=vocab)
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self.model.fit(training_text)
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def test_gamma(self):
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self.assertEqual(0.1, self.model.gamma)
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def test_entropy_perplexity(self):
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text = [
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("<s>", "a"),
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("a", "c"),
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("c", "<UNK>"),
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("<UNK>", "d"),
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("d", "c"),
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("c", "</s>"),
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]
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# Unlike MLE this should be able to handle completely novel ngrams
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# Ngram = score, log score
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# <s>, a = 0.3929, -1.3479
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# a, c = 0.0357, -4.8074
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# c, UNK = 0.0(5), -4.1699
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# UNK, d = 0.0263, -5.2479
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# d, c = 0.0357, -4.8074
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# c, </s> = 0.0(5), -4.1699
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# TOTAL logscore: −24.5504
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# - AVG logscore: 4.0917
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H = 4.0917
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perplexity = 17.0504
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self.assertAlmostEqual(H, self.model.entropy(text), places=4)
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self.assertAlmostEqual(perplexity, self.model.perplexity(text), places=4)
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@add_metaclass(ParametrizeTestsMeta)
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class LidstoneTrigramTests(unittest.TestCase):
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score_tests = [
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# Logic behind this is the same as for bigram model
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("d", ["c"], 1.1 / 1.8),
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# if we choose a word that hasn't appeared after (b, c)
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("e", ["c"], 0.1 / 1.8),
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# Trigram score now
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("d", ["b", "c"], 1.1 / 1.8),
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("e", ["b", "c"], 0.1 / 1.8),
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]
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def setUp(self):
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vocab, training_text = _prepare_test_data(3)
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self.model = Lidstone(0.1, 3, vocabulary=vocab)
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self.model.fit(training_text)
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@add_metaclass(ParametrizeTestsMeta)
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class LaplaceBigramTests(unittest.TestCase):
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"""unit tests for Laplace class"""
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score_tests = [
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# basic sanity-check:
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# count(d | c) = 1
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# *count(d | c) = 2
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# Count(w | c for w in vocab) = 1
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# *Count(w | c for w in vocab) = 9
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("d", ["c"], 2.0 / 9),
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# Total unigrams: 14
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# Vocab size: 8
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# Denominator: 14 + 8 = 22
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# count("a") = 2
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# *count("a") = 3
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("a", None, 3.0 / 22),
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# in vocabulary but unseen
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# count("z") = 0
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# *count("z") = 1
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("z", None, 1.0 / 22),
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# out of vocabulary should use "UNK" score
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# count("<UNK>") = 3
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# *count("<UNK>") = 4
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("y", None, 4.0 / 22),
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]
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def setUp(self):
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vocab, training_text = _prepare_test_data(2)
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self.model = Laplace(2, vocabulary=vocab)
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self.model.fit(training_text)
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def test_gamma(self):
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# Make sure the gamma is set to 1
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self.assertEqual(1, self.model.gamma)
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def test_entropy_perplexity(self):
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text = [
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("<s>", "a"),
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("a", "c"),
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("c", "<UNK>"),
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("<UNK>", "d"),
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("d", "c"),
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("c", "</s>"),
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]
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# Unlike MLE this should be able to handle completely novel ngrams
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# Ngram = score, log score
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# <s>, a = 0.2, -2.3219
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# a, c = 0.1, -3.3219
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# c, UNK = 0.(1), -3.1699
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# UNK, d = 0.(09), 3.4594
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# d, c = 0.1 -3.3219
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# c, </s> = 0.(1), -3.1699
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# Total logscores: −18.7651
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# - AVG logscores: 3.1275
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H = 3.1275
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perplexity = 8.7393
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self.assertAlmostEqual(H, self.model.entropy(text), places=4)
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self.assertAlmostEqual(perplexity, self.model.perplexity(text), places=4)
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@add_metaclass(ParametrizeTestsMeta)
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class WittenBellInterpolatedTrigramTests(unittest.TestCase):
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def setUp(self):
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vocab, training_text = _prepare_test_data(3)
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self.model = WittenBellInterpolated(3, vocabulary=vocab)
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self.model.fit(training_text)
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score_tests = [
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# For unigram scores by default revert to MLE
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# Total unigrams: 18
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# count('c'): 1
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("c", None, 1.0 / 18),
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# in vocabulary but unseen
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# count("z") = 0
|
|||
|
|
("z", None, 0.0 / 18),
|
|||
|
|
# out of vocabulary should use "UNK" score
|
|||
|
|
# count("<UNK>") = 3
|
|||
|
|
("y", None, 3.0 / 18),
|
|||
|
|
# gamma(['b']) = 0.1111
|
|||
|
|
# mle.score('c', ['b']) = 0.5
|
|||
|
|
# (1 - gamma) * mle + gamma * mle('c') ~= 0.45 + .3 / 18
|
|||
|
|
("c", ["b"], (1 - 0.1111) * 0.5 + 0.1111 * 1 / 18),
|
|||
|
|
# building on that, let's try 'a b c' as the trigram
|
|||
|
|
# gamma(['a', 'b']) = 0.0667
|
|||
|
|
# mle("c", ["a", "b"]) = 1
|
|||
|
|
("c", ["a", "b"], (1 - 0.0667) + 0.0667 * ((1 - 0.1111) * 0.5 + 0.1111 / 18)),
|
|||
|
|
]
|
|||
|
|
|
|||
|
|
|
|||
|
|
@add_metaclass(ParametrizeTestsMeta)
|
|||
|
|
class KneserNeyInterpolatedTrigramTests(unittest.TestCase):
|
|||
|
|
def setUp(self):
|
|||
|
|
vocab, training_text = _prepare_test_data(3)
|
|||
|
|
self.model = KneserNeyInterpolated(3, vocabulary=vocab)
|
|||
|
|
self.model.fit(training_text)
|
|||
|
|
|
|||
|
|
score_tests = [
|
|||
|
|
# For unigram scores revert to uniform
|
|||
|
|
# Vocab size: 8
|
|||
|
|
# count('c'): 1
|
|||
|
|
("c", None, 1.0 / 8),
|
|||
|
|
# in vocabulary but unseen, still uses uniform
|
|||
|
|
("z", None, 1 / 8),
|
|||
|
|
# out of vocabulary should use "UNK" score, i.e. again uniform
|
|||
|
|
("y", None, 1.0 / 8),
|
|||
|
|
# alpha = count('bc') - discount = 1 - 0.1 = 0.9
|
|||
|
|
# gamma(['b']) = discount * number of unique words that follow ['b'] = 0.1 * 2
|
|||
|
|
# normalizer = total number of bigrams with this context = 2
|
|||
|
|
# the final should be: (alpha + gamma * unigram_score("c"))
|
|||
|
|
("c", ["b"], (0.9 + 0.2 * (1 / 8)) / 2),
|
|||
|
|
# building on that, let's try 'a b c' as the trigram
|
|||
|
|
# alpha = count('abc') - discount = 1 - 0.1 = 0.9
|
|||
|
|
# gamma(['a', 'b']) = 0.1 * 1
|
|||
|
|
# normalizer = total number of trigrams with prefix "ab" = 1 => we can ignore it!
|
|||
|
|
("c", ["a", "b"], 0.9 + 0.1 * ((0.9 + 0.2 * (1 / 8)) / 2)),
|
|||
|
|
]
|
|||
|
|
|
|||
|
|
|
|||
|
|
class NgramModelTextGenerationTests(unittest.TestCase):
|
|||
|
|
"""Using MLE estimator, generate some text."""
|
|||
|
|
|
|||
|
|
def setUp(self):
|
|||
|
|
vocab, training_text = _prepare_test_data(3)
|
|||
|
|
self.model = MLE(3, vocabulary=vocab)
|
|||
|
|
self.model.fit(training_text)
|
|||
|
|
|
|||
|
|
def test_generate_one_no_context(self):
|
|||
|
|
self.assertEqual(self.model.generate(random_seed=3), "<UNK>")
|
|||
|
|
|
|||
|
|
def test_generate_one_limiting_context(self):
|
|||
|
|
# We don't need random_seed for contexts with only one continuation
|
|||
|
|
self.assertEqual(self.model.generate(text_seed=["c"]), "d")
|
|||
|
|
self.assertEqual(self.model.generate(text_seed=["b", "c"]), "d")
|
|||
|
|
self.assertEqual(self.model.generate(text_seed=["a", "c"]), "d")
|
|||
|
|
|
|||
|
|
def test_generate_one_varied_context(self):
|
|||
|
|
# When context doesn't limit our options enough, seed the random choice
|
|||
|
|
self.assertEqual(
|
|||
|
|
self.model.generate(text_seed=("a", "<s>"), random_seed=2), "a"
|
|||
|
|
)
|
|||
|
|
|
|||
|
|
def test_generate_cycle(self):
|
|||
|
|
# Add a cycle to the model: bd -> b, db -> d
|
|||
|
|
more_training_text = [list(padded_everygrams(self.model.order, list("bdbdbd")))]
|
|||
|
|
self.model.fit(more_training_text)
|
|||
|
|
# Test that we can escape the cycle
|
|||
|
|
self.assertEqual(
|
|||
|
|
self.model.generate(7, text_seed=("b", "d"), random_seed=5),
|
|||
|
|
["b", "d", "b", "d", "b", "d", "</s>"],
|
|||
|
|
)
|
|||
|
|
|
|||
|
|
def test_generate_with_text_seed(self):
|
|||
|
|
self.assertEqual(
|
|||
|
|
self.model.generate(5, text_seed=("<s>", "e"), random_seed=3),
|
|||
|
|
["<UNK>", "a", "d", "b", "<UNK>"],
|
|||
|
|
)
|
|||
|
|
|
|||
|
|
def test_generate_oov_text_seed(self):
|
|||
|
|
self.assertEqual(
|
|||
|
|
self.model.generate(text_seed=("aliens",), random_seed=3),
|
|||
|
|
self.model.generate(text_seed=("<UNK>",), random_seed=3),
|
|||
|
|
)
|
|||
|
|
|
|||
|
|
def test_generate_None_text_seed(self):
|
|||
|
|
# should crash with type error when we try to look it up in vocabulary
|
|||
|
|
with self.assertRaises(TypeError):
|
|||
|
|
self.model.generate(text_seed=(None,))
|
|||
|
|
|
|||
|
|
# This will work
|
|||
|
|
self.assertEqual(
|
|||
|
|
self.model.generate(text_seed=None, random_seed=3),
|
|||
|
|
self.model.generate(random_seed=3),
|
|||
|
|
)
|