class BestMatchSentimentComparisonTestCase(ChatBotTestCase):
"""
Integration tests for the BestMatch logic adapter
using the similarity of sentiment polarity as a comparison function.
"""
def setUp(self):
super(BestMatchSentimentComparisonTestCase, self).setUp()
from chatterbot.trainers import ListTrainer
from chatterbot.comparisons import sentiment_comparison
self.chatbot.set_trainer(ListTrainer)
self.adapter = BestMatch(
statement_comparison_function=sentiment_comparison
)
self.adapter.set_chatbot(self.chatbot)
def test_exact_input(self):
self.chatbot.train([
'What is your favorite flavor of ice cream?',
'I enjoy raspberry ice cream.',
'I am glad to hear that.',
'Thank you.'
])
happy_statement = Statement('I enjoy raspberry ice cream.')
confidence, response = self.adapter.process(happy_statement)
self.assertEqual(confidence, 1)
self.assertEqual(response.confidence, 1)
self.assertEqual(response.text, 'I am glad to hear that.')
def test_close_input(self):
self.chatbot.train([
'What is your favorite flavor of ice cream?',
'I enjoy raspberry ice cream.',
'I am glad to hear that.',
'Thank you.'
])
happy_statement = Statement('I enjoy raspberry.')
confidence, response = self.adapter.process(happy_statement)
self.assertEqual(response.text, 'I am glad to hear that.')
self.assertAlmostEqual(confidence, 0.75, places=1)
self.assertAlmostEqual(response.confidence, 0.75, places=1)
def test_best_match(self):
from chatterbot.logic import BestMatch
adapter = BestMatch()
adapter.set_chatbot(self.chatbot)
statement1 = Statement(text='Do you like programming?')
statement1.save()
statement2 = Statement(text='Yes')
statement2.save()
response = Response(statement=statement1, response=statement2)
response.save()
response = adapter.process(statement1)
self.assertEqual(response.text, 'Yes')
self.assertEqual(response.confidence, 1)
class BestMatchSentimentComparisonTestCase(ChatBotTestCase):
"""
Integration tests for the BestMatch logic adapter
using the similarity of sentiment polarity as a comparison function.
"""
def setUp(self):
super(BestMatchSentimentComparisonTestCase, self).setUp()
from chatterbot.trainers import ListTrainer
from chatterbot.comparisons import sentiment_comparison
self.chatbot.set_trainer(ListTrainer)
self.adapter = BestMatch(
statement_comparison_function=sentiment_comparison)
self.adapter.set_chatbot(self.chatbot)
def test_exact_input(self):
self.chatbot.train([
'What is your favorite flavor of ice cream?',
'I enjoy raspberry ice cream.', 'I am glad to hear that.',
'Thank you.'
])
happy_statement = Statement('I enjoy raspberry ice cream.')
confidence, response = self.adapter.process(happy_statement)
self.assertEqual(confidence, 1)
self.assertEqual(response.text, 'I am glad to hear that.')
def test_close_input(self):
self.chatbot.train([
'What is your favorite flavor of ice cream?',
'I enjoy raspberry ice cream.', 'I am glad to hear that.',
'Thank you.'
])
happy_statement = Statement('I enjoy raspberry.')
confidence, response = self.adapter.process(happy_statement)
self.assertEqual(response.text, 'I am glad to hear that.')
self.assertAlmostEqual(confidence, 0.75, places=1)
class BestMatchTestCase(ChatBotTestCase):
"""
Unit tests for the BestMatch logic adapter.
"""
def setUp(self):
super(BestMatchTestCase, self).setUp()
self.adapter = BestMatch()
self.adapter.set_chatbot(self.chatbot)
def test_no_choices(self):
"""
An exception should be raised if there is no data in the database.
"""
self.adapter.chatbot.storage.filter = MagicMock(return_value=[])
self.adapter.chatbot.storage.count = MagicMock(return_value=0)
statement = Statement('What is your quest?')
with self.assertRaises(BestMatch.EmptyDatasetException):
self.adapter.get(statement)
def test_best_match(self):
from chatterbot.logic import BestMatch
adapter = BestMatch()
adapter.set_chatbot(self.chatbot)
statement1 = Statement.objects.create(
text='Do you like programming?',
conversation='test'
)
Statement.objects.create(
text='Yes',
in_response_to=statement1.text,
conversation='test'
)
response = adapter.process(statement1)
self.assertEqual(response.text, 'Yes')
self.assertEqual(response.confidence, 1)
class BestMatchTestCase(ChatBotTestCase):
"""
Unit tests for the BestMatch logic adapter.
"""
def setUp(self):
super(BestMatchTestCase, self).setUp()
self.adapter = BestMatch()
self.adapter.set_chatbot(self.chatbot)
def test_no_choices(self):
"""
An exception should be raised if there is no data in the database.
"""
self.adapter.chatbot.storage.filter = MagicMock(return_value=[])
self.adapter.chatbot.storage.count = MagicMock(return_value=0)
statement = Statement('What is your quest?')
with self.assertRaises(BestMatch.EmptyDatasetException):
self.adapter.get(statement)
class BestMatchTestCase(TestCase):
"""
Unit tests for the BestMatch logic adapter.
"""
def setUp(self):
from chatterbot.comparisons import levenshtein_distance
self.adapter = BestMatch()
# Add a mock chatbot to the logic adapter
self.adapter.set_chatbot(MockChatBot())
def test_no_choices(self):
"""
An exception should be raised if there is no data in the database.
"""
self.adapter.chatbot.storage.filter = MagicMock(return_value=[])
statement = Statement('What is your quest?')
with self.assertRaises(BestMatch.EmptyDatasetException):
self.adapter.get(statement)
class BestMatchSynsetDistanceTestCase(ChatBotTestCase):
"""
Integration tests for the BestMatch logic adapter
using the synset_distance comparison function.
"""
def setUp(self):
super(BestMatchSynsetDistanceTestCase, self).setUp()
from chatterbot.comparisons import synset_distance
self.adapter = BestMatch(statement_comparison_function=synset_distance)
self.adapter.initialize()
# Add a mock storage adapter to the logic adapter
self.adapter.set_chatbot(self.chatbot)
def test_get_closest_statement(self):
"""
Note, the content of the in_response_to field for each of the
test statements is only required because the logic adapter will
filter out any statements that are not in response to a known statement.
"""
possible_choices = [
Statement('This is a lovely bog.',
in_response_to=[Response('This is a lovely bog.')]),
Statement('This is a beautiful swamp.',
in_response_to=[Response('This is a beautiful swamp.')]),
Statement('It smells like a swamp.',
in_response_to=[Response('It smells like a swamp.')])
]
self.adapter.chatbot.storage.filter = MagicMock(
return_value=possible_choices)
statement = Statement('This is a lovely swamp.')
match = self.adapter.get(statement)
self.assertEqual('This is a lovely bog.', match)
def test_different_punctuation(self):
possible_choices = [
Statement('Who are you?'),
Statement('Are you good?'),
Statement('You are good')
]
self.adapter.chatbot.storage.get_response_statements = MagicMock(
return_value=possible_choices)
statement = Statement('Are you good')
match = self.adapter.get(statement)
self.assertEqual('Are you good?', match)
def test_no_known_responses(self):
"""
In the case that a match is selected which has no known responses.
In this case a random response will be returned, but the confidence
should be zero because it is a random choice.
"""
self.adapter.chatbot.storage.update = MagicMock()
self.adapter.chatbot.storage.count = MagicMock(return_value=1)
self.adapter.chatbot.storage.get_random = MagicMock(
return_value=Statement('Random'))
match = self.adapter.process(Statement('Blah'))
self.assertEqual(match.confidence, 0)
self.assertEqual(match.text, 'Random')
class BestMatchLevenshteinDistanceTestCase(TestCase):
"""
Integration tests for the BestMatch logic adapter
using Levenshtein distance as a comparison function.
"""
def setUp(self):
from chatterbot.comparisons import levenshtein_distance
self.adapter = BestMatch(
statement_comparison_function=levenshtein_distance)
# Add a mock chatbot to the logic adapter
self.adapter.set_chatbot(MockChatBot())
def test_get_closest_statement(self):
"""
Note, the content of the in_response_to field for each of the
test statements is only required because the logic adapter will
filter out any statements that are not in response to a known statement.
"""
possible_choices = [
Statement(
"Who do you love?",
in_response_to=[Response("I hear you are going on a quest?")]),
Statement("What is the meaning of life?",
in_response_to=[
Response("Yuck, black licorice jelly beans.")
]),
Statement("I am Iron Man.",
in_response_to=[Response("What... is your quest?")]),
Statement("What... is your quest?",
in_response_to=[Response("I am Iron Man.")]),
Statement(
"Yuck, black licorice jelly beans.",
in_response_to=[Response("What is the meaning of life?")]),
Statement("I hear you are going on a quest?",
in_response_to=[Response("Who do you love?")]),
]
self.adapter.chatbot.storage.filter = MagicMock(
return_value=possible_choices)
statement = Statement("What is your quest?")
confidence, match = self.adapter.get(statement)
self.assertEqual("What... is your quest?", match)
def test_confidence_exact_match(self):
possible_choices = [
Statement("What is your quest?",
in_response_to=[Response("What is your quest?")])
]
self.adapter.chatbot.storage.filter = MagicMock(
return_value=possible_choices)
statement = Statement("What is your quest?")
confidence, match = self.adapter.get(statement)
self.assertEqual(confidence, 1)
def test_confidence_half_match(self):
possible_choices = [
Statement("xxyy", in_response_to=[Response("xxyy")])
]
self.adapter.chatbot.storage.filter = MagicMock(
return_value=possible_choices)
statement = Statement("wwxx")
confidence, match = self.adapter.get(statement)
self.assertEqual(confidence, 0.5)
def test_confidence_no_match(self):
possible_choices = [Statement("xxx", in_response_to=[Response("xxx")])]
self.adapter.chatbot.storage.filter = MagicMock(
return_value=possible_choices)
statement = Statement("yyy")
confidence, match = self.adapter.get(statement)
self.assertEqual(confidence, 0)
def test_no_known_responses(self):
"""
In the case that a match is selected which has no known responses.
In this case a random response will be returned, but the confidence
should be zero because it is a random choice.
"""
self.adapter.chatbot.storage.update = MagicMock()
self.adapter.chatbot.storage.filter = MagicMock(return_value=[])
self.adapter.chatbot.storage.get_random = MagicMock(
return_value=Statement("Random"))
confidence, match = self.adapter.process(Statement("Blah"))
self.assertEqual(confidence, 0)
self.assertEqual(match.text, "Random")
class BestMatchSynsetDistanceTestCase(ChatBotTestCase):
"""
Integration tests for the BestMatch logic adapter
using the synset_distance comparison function.
"""
def setUp(self):
super(BestMatchSynsetDistanceTestCase, self).setUp()
from chatterbot.utils import nltk_download_corpus
from chatterbot.comparisons import synset_distance
nltk_download_corpus('stopwords')
nltk_download_corpus('wordnet')
nltk_download_corpus('punkt')
self.adapter = BestMatch(
statement_comparison_function=synset_distance
)
# Add a mock storage adapter to the logic adapter
self.adapter.set_chatbot(self.chatbot)
def test_get_closest_statement(self):
"""
Note, the content of the in_response_to field for each of the
test statements is only required because the logic adapter will
filter out any statements that are not in response to a known statement.
"""
possible_choices = [
Statement('This is a lovely bog.', in_response_to=[Response('This is a lovely bog.')]),
Statement('This is a beautiful swamp.', in_response_to=[Response('This is a beautiful swamp.')]),
Statement('It smells like a swamp.', in_response_to=[Response('It smells like a swamp.')])
]
self.adapter.chatbot.storage.filter = MagicMock(
return_value=possible_choices
)
statement = Statement('This is a lovely swamp.')
match = self.adapter.get(statement)
self.assertEqual('This is a lovely bog.', match)
def test_different_punctuation(self):
possible_choices = [
Statement('Who are you?'),
Statement('Are you good?'),
Statement('You are good')
]
self.adapter.chatbot.storage.get_response_statements = MagicMock(
return_value=possible_choices
)
statement = Statement('Are you good')
match = self.adapter.get(statement)
self.assertEqual('Are you good?', match)
def test_no_known_responses(self):
"""
In the case that a match is selected which has no known responses.
In this case a random response will be returned, but the confidence
should be zero because it is a random choice.
"""
self.adapter.chatbot.storage.update = MagicMock()
self.adapter.chatbot.storage.count = MagicMock(return_value=1)
self.adapter.chatbot.storage.get_random = MagicMock(
return_value=Statement('Random')
)
match = self.adapter.process(Statement('Blah'))
self.assertEqual(match.confidence, 0)
self.assertEqual(match.text, 'Random')
class BestMatchLevenshteinDistanceTestCase(ChatBotTestCase):
"""
Integration tests for the BestMatch logic adapter
using Levenshtein distance as a comparison function.
"""
def setUp(self):
super(BestMatchLevenshteinDistanceTestCase, self).setUp()
from chatterbot.comparisons import levenshtein_distance
self.adapter = BestMatch(
statement_comparison_function=levenshtein_distance
)
self.adapter.set_chatbot(self.chatbot)
def test_get_closest_statement(self):
"""
Note, the content of the in_response_to field for each of the
test statements is only required because the logic adapter will
filter out any statements that are not in response to a known statement.
"""
possible_choices = [
Statement("Who do you love?", in_response_to=[Response("I hear you are going on a quest?")]),
Statement("What is the meaning of life?", in_response_to=[Response("Yuck, black licorice jelly beans.")]),
Statement("I am Iron Man.", in_response_to=[Response("What... is your quest?")]),
Statement("What... is your quest?", in_response_to=[Response("I am Iron Man.")]),
Statement("Yuck, black licorice jelly beans.", in_response_to=[Response("What is the meaning of life?")]),
Statement("I hear you are going on a quest?", in_response_to=[Response("Who do you love?")]),
]
self.adapter.chatbot.storage.filter = MagicMock(return_value=possible_choices)
statement = Statement("What is your quest?")
confidence, match = self.adapter.get(statement)
self.assertEqual("What... is your quest?", match)
def test_confidence_exact_match(self):
possible_choices = [
Statement("What is your quest?", in_response_to=[Response("What is your quest?")])
]
self.adapter.chatbot.storage.filter = MagicMock(return_value=possible_choices)
statement = Statement("What is your quest?")
confidence, match = self.adapter.get(statement)
self.assertEqual(confidence, 1)
def test_confidence_half_match(self):
possible_choices = [
Statement("xxyy", in_response_to=[Response("xxyy")])
]
self.adapter.chatbot.storage.filter = MagicMock(return_value=possible_choices)
statement = Statement("wwxx")
confidence, match = self.adapter.get(statement)
self.assertEqual(confidence, 0.5)
def test_confidence_no_match(self):
possible_choices = [
Statement("xxx", in_response_to=[Response("xxx")])
]
self.adapter.chatbot.storage.filter = MagicMock(return_value=possible_choices)
statement = Statement("yyy")
confidence, match = self.adapter.get(statement)
self.assertEqual(confidence, 0)
def test_no_known_responses(self):
"""
In the case that a match is selected which has no known responses.
In this case a random response will be returned, but the confidence
should be zero because it is a random choice.
"""
self.adapter.chatbot.storage.update = MagicMock()
self.adapter.chatbot.storage.count = MagicMock(return_value=1)
self.adapter.chatbot.storage.get_random = MagicMock(
return_value=Statement("Random")
)
confidence, match = self.adapter.process(Statement("Blah"))
self.assertEqual(confidence, 0)
self.assertEqual(match.text, "Random")
class BestMatchLevenshteinDistanceTestCase(ChatBotTestCase):
"""
Integration tests for the BestMatch logic adapter
using Levenshtein distance as a comparison function.
"""
def setUp(self):
super(BestMatchLevenshteinDistanceTestCase, self).setUp()
from chatterbot.comparisons import levenshtein_distance
self.adapter = BestMatch(
statement_comparison_function=levenshtein_distance)
self.adapter.set_chatbot(self.chatbot)
def test_get_closest_statement(self):
"""
Note, the content of the in_response_to field for each of the
test statements is only required because the logic adapter will
filter out any statements that are not in response to a known statement.
"""
possible_choices = [
Statement('Who do you love?',
in_response_to='I hear you are going on a quest?'),
Statement('What is the meaning of life?',
in_response_to='Yuck, black licorice jelly beans.'),
Statement('I am Iron Man.',
in_response_to='What... is your quest?'),
Statement('What... is your quest?',
in_response_to='I am Iron Man.'),
Statement('Yuck, black licorice jelly beans.',
in_response_to='What is the meaning of life?'),
Statement('I hear you are going on a quest?',
in_response_to='Who do you love?'),
]
self.adapter.chatbot.storage.filter = MagicMock(
return_value=possible_choices)
statement = Statement('What is your quest?')
match = self.adapter.get(statement)
self.assertEqual('What... is your quest?', match)
def test_confidence_exact_match(self):
possible_choices = [
Statement('What is your quest?',
in_response_to='What is your quest?')
]
self.adapter.chatbot.storage.filter = MagicMock(
return_value=possible_choices)
statement = Statement('What is your quest?')
match = self.adapter.get(statement)
self.assertEqual(match.confidence, 1)
def test_confidence_half_match(self):
possible_choices = [Statement('xxyy', in_response_to='xxyy')]
self.adapter.chatbot.storage.filter = MagicMock(
return_value=possible_choices)
statement = Statement('wwxx')
match = self.adapter.get(statement)
self.assertEqual(match.confidence, 0.5)
def test_confidence_no_match(self):
possible_choices = [Statement('xxx', in_response_to='xxx')]
self.adapter.chatbot.storage.filter = MagicMock(
return_value=possible_choices)
statement = Statement('yyy')
match = self.adapter.get(statement)
self.assertEqual(match.confidence, 0)
def test_no_known_responses(self):
"""
In the case that a match is selected which has no known responses.
In this case a random response will be returned, but the confidence
should be zero because it is a random choice.
"""
self.adapter.chatbot.storage.update = MagicMock()
self.adapter.chatbot.storage.count = MagicMock(return_value=1)
self.adapter.chatbot.storage.get_random = MagicMock(
return_value=Statement('Random'))
match = self.adapter.process(Statement('Blah'))
self.assertEqual(match.confidence, 0)
self.assertEqual(match.text, 'Random')
