class BestMatchSentimentComparisonTestCase(ChatBotTestCase):
"""
Integration tests for the BestMatch logic adapter
using the similarity of sentiment polarity as a comparison function.
"""
def setUp(self):
super().setUp()
from chatterbot.trainers import ListTrainer
from chatterbot.comparisons import sentiment_comparison
self.trainer = ListTrainer(
self.chatbot,
show_training_progress=False
)
self.adapter = BestMatch(
self.chatbot,
statement_comparison_function=sentiment_comparison
)
self.adapter.initialize()
def test_exact_input(self):
self.trainer.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(text='I enjoy raspberry ice cream.')
response = self.adapter.process(happy_statement)
self.assertEqual(response.confidence, 1)
self.assertEqual(response.text, 'I am glad to hear that.')
def test_close_input(self):
self.trainer.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(text='I enjoy raspberry.')
response = self.adapter.process(happy_statement)
self.assertEqual(response.text, 'I am glad to hear that.')
self.assertAlmostEqual(response.confidence, 0.75, places=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.trainer = ListTrainer(
self.chatbot,
show_training_progress=False
)
self.adapter = BestMatch(
statement_comparison_function=sentiment_comparison
)
self.adapter.set_chatbot(self.chatbot)
self.adapter.initialize()
def test_exact_input(self):
self.trainer.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.')
response = self.adapter.process(happy_statement)
self.assertEqual(response.confidence, 1)
self.assertEqual(response.text, 'I am glad to hear that.')
def test_close_input(self):
self.trainer.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.')
response = self.adapter.process(happy_statement)
self.assertEqual(response.text, 'I am glad to hear that.')
self.assertAlmostEqual(response.confidence, 0.75, places=1)
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 BestMatchSynsetDistanceTestCase(ChatBotTestCase):
"""
Integration tests for the BestMatch logic adapter
using the synset_distance comparison function.
"""
def setUp(self):
super().setUp()
from chatterbot.comparisons import synset_distance
self.adapter = BestMatch(
self.chatbot,
statement_comparison_function=synset_distance
)
self.adapter.initialize()
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(text='This is a lovely bog.', in_response_to='This is a lovely bog.'),
Statement(text='This is a beautiful swamp.', in_response_to='This is a beautiful swamp.'),
Statement(text='It smells like a swamp.', in_response_to='It smells like a swamp.')
]
self.adapter.chatbot.storage.filter = MagicMock(
return_value=possible_choices
)
statement = Statement(text='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(text='Who are you?'),
Statement(text='Are you good?'),
Statement(text='You are good')
]
self.adapter.chatbot.storage.get_response_statements = MagicMock(
return_value=possible_choices
)
statement = Statement(text='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(text='Random')
)
match = self.adapter.process(Statement(text='Blah'))
self.assertEqual(match.confidence, 0)
self.assertEqual(match.text, 'Random')
