class SearchComparisonFunctionSpacySimilarityTests(ChatBotTestCase):
"""
Test that the search algorithm works correctly with the
spacy similarity comparison function.
"""
def setUp(self):
super().setUp()
self.search_algorithm = IndexedTextSearch(
self.chatbot,
statement_comparison_function=comparisons.spacy_similarity)
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.
"""
self.chatbot.storage.create_many([
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.')
])
statement = Statement(text='This is a lovely swamp.')
results = list(self.search_algorithm.search(statement))
self.assertIsLength(results, 2)
results_text = [result.text for result in results]
self.assertIn('This is a lovely bog.', results_text)
self.assertIn('This is a beautiful swamp.', results_text)
self.assertGreater(results[0].confidence, 0)
self.assertGreater(results[1].confidence, 0)
def test_different_punctuation(self):
self.chatbot.storage.create_many([
Statement(text='Who are you?'),
Statement(text='Are you good?'),
Statement(text='You are good')
])
statement = Statement(text='Are you good')
results = list(self.search_algorithm.search(statement))
self.assertIsLength(results, 1)
self.assertEqual(results[0].text, 'Are you good?')
class SearchComparisonFunctionSentimentComparisonTests(ChatBotTestCase):
"""
Test that the search algorithm works correctly with the
sentiment comparison function by using the similarity
of sentiment polarity.
"""
def setUp(self):
super().setUp()
self.search_algorithm = IndexedTextSearch(
self.chatbot,
statement_comparison_function=comparisons.sentiment_comparison
)
# Make sure the required NLTK data files are downloaded
for function in utils.get_initialization_functions(
self.search_algorithm,
'compare_statements'
):
function()
def test_exact_input(self):
self.chatbot.storage.create(text='What is your favorite flavor of ice cream?')
self.chatbot.storage.create(text='I enjoy raspberry ice cream.')
self.chatbot.storage.create(text='I am glad to hear that.')
self.chatbot.storage.create(text='Thank you.')
happy_statement = Statement(text='I enjoy raspberry ice cream.')
results = list(self.search_algorithm.search(happy_statement))
self.assertIsLength(results, 1)
self.assertEqual(results[0].text, 'I enjoy raspberry ice cream.')
self.assertEqual(results[0].confidence, 1)
def test_close_input(self):
self.chatbot.storage.create(text='What is your favorite flavor of ice cream?')
self.chatbot.storage.create(text='I enjoy raspberry ice cream.')
self.chatbot.storage.create(text='I am glad to hear that.')
self.chatbot.storage.create(text='Thank you, what is yours?')
self.chatbot.storage.create(text='Mine is chocolate.')
happy_statement = Statement(text='I enjoy raspberry.')
results = list(self.search_algorithm.search(happy_statement))
self.assertIsLength(results, 1)
self.assertEqual(results[0].text, 'I enjoy raspberry ice cream.')
self.assertAlmostEqual(results[0].confidence, 0.75, places=1)
class SearchComparisonFunctionSpacySimilarityTests(ChatBotTestCase):
"""
Test that the search algorithm works correctly with the
spacy similarity comparison function.
"""
def setUp(self):
super().setUp()
self.search_algorithm = IndexedTextSearch(
self.chatbot,
statement_comparison_function=comparisons.SpacySimilarity
)
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.
"""
self.chatbot.storage.create_many([
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.')
])
statement = Statement(text='This is a lovely swamp.')
results = list(self.search_algorithm.search(statement))
self.assertIsLength(results, 1)
self.assertEqual(results[0].text, 'This is a beautiful swamp.')
self.assertGreater(results[0].confidence, 0)
def test_different_punctuation(self):
self.chatbot.storage.create_many([
Statement(text='Who are you?'),
Statement(text='Are you good?'),
Statement(text='You are good')
])
statement = Statement(text='Are you good')
results = list(self.search_algorithm.search(statement))
self.assertEqual(len(results), 2, msg=[r.search_text for r in results])
# Note: the last statement in the list always has the highest confidence
self.assertEqual(results[-1].text, 'Are you good?')
class SearchTestCase(ChatBotTestCase):
def setUp(self):
super().setUp()
self.search_algorithm = IndexedTextSearch(self.chatbot)
def test_search_no_results(self):
"""
An exception should be raised if there is no data to return.
"""
statement = Statement(text='What is your quest?')
with self.assertRaises(StopIteration):
next(self.search_algorithm.search(statement))
def test_search_cast_to_list_no_results(self):
"""
An empty list should be returned when the generator is
cast to a list and there are no results to return.
"""
statement = Statement(text='What is your quest?')
results = list(self.search_algorithm.search(statement))
self.assertEqual(results, [])
def test_search_additional_parameters(self):
"""
It should be possible to pass additional parameters in to use for searching.
"""
self.chatbot.storage.create_many([
Statement(text='A', conversation='test_1'),
Statement(text='A', conversation='test_2')
])
statement = Statement(text='A')
results = list(self.search_algorithm.search(
statement, conversation='test_1'
))
self.assertIsLength(results, 1)
self.assertEqual(results[0].text, 'A')
self.assertEqual(results[0].conversation, 'test_1')
class SearchTestCase(ChatBotTestCase):
def setUp(self):
super().setUp()
self.search_algorithm = IndexedTextSearch(self.chatbot)
def test_search_no_results(self):
"""
An exception should be raised if there is no data to return.
"""
statement = Statement(text='What is your quest?')
with self.assertRaises(StopIteration):
next(self.search_algorithm.search(statement))
def test_search_cast_to_list_no_results(self):
"""
An empty list should be returned when the generator is
cast to a list and there are no results to return.
"""
statement = Statement(text='What is your quest?')
results = list(self.search_algorithm.search(statement))
self.assertEqual(results, [])
def test_search_additional_parameters(self):
"""
It should be possible to pass additional parameters in to use for searching.
"""
self.chatbot.storage.create_many([
Statement(text='A', conversation='test_1'),
Statement(text='A', conversation='test_2')
])
statement = Statement(text='A')
results = list(self.search_algorithm.search(
statement, conversation='test_1'
))
self.assertIsLength(results, 1)
self.assertEqual(results[0].text, 'A')
self.assertEqual(results[0].conversation, 'test_1')
class SearchTestCase(ChatBotTestCase):
def setUp(self):
super().setUp()
self.search_algorithm = IndexedTextSearch(self.chatbot)
def test_search_no_results(self):
"""
An exception should be raised if there is no data to return.
"""
statement = Statement(text='What is your quest?')
with self.assertRaises(StopIteration):
next(self.search_algorithm.search(statement))
def test_search_cast_to_list_no_results(self):
"""
An empty list should be returned when the generator is
cast to a list and there are no results to return.
"""
statement = Statement(text='What is your quest?')
results = list(self.search_algorithm.search(statement))
self.assertEqual(results, [])
class SearchComparisonFunctionLevenshteinDistanceComparisonTests(
ChatBotTestCase):
"""
Test that the search algorithm works correctly with the
Levenshtein distance comparison function.
"""
def setUp(self):
super().setUp()
self.search_algorithm = IndexedTextSearch(
self.chatbot,
statement_comparison_function=comparisons.levenshtein_distance)
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.
"""
self.chatbot.storage.create_many([
Statement(text='Who do you love?',
in_response_to='I hear you are going on a quest?'),
Statement(text='What is the meaning of life?',
in_response_to='Yuck, black licorice jelly beans.'),
Statement(text='I am Iron Man.',
in_response_to='What... is your quest?'),
Statement(text='What... is your quest?',
in_response_to='I am Iron Man.'),
Statement(text='Yuck, black licorice jelly beans.',
in_response_to='What is the meaning of life?'),
Statement(text='I hear you are going on a quest?',
in_response_to='Who do you love?'),
])
statement = Statement(text='What is your quest?')
results = list(self.search_algorithm.search(statement))
self.assertIsLength(results, 1)
self.assertEqual(results[0].text, 'What... is your quest?')
def test_confidence_exact_match(self):
self.chatbot.storage.create(text='What is your quest?',
in_response_to='What is your quest?')
statement = Statement(text='What is your quest?')
results = list(self.search_algorithm.search(statement))
self.assertIsLength(results, 1)
self.assertEqual(results[0].confidence, 1)
def test_confidence_half_match(self):
from unittest.mock import MagicMock
# Assume that the storage adapter returns a partial match
self.chatbot.storage.filter = MagicMock(
return_value=[Statement(text='xxyy')])
statement = Statement(text='wwxx')
results = list(self.search_algorithm.search(statement))
self.assertIsLength(results, 1)
self.assertEqual(results[0].confidence, 0.5)
def test_confidence_no_match(self):
from unittest.mock import MagicMock
# Assume that the storage adapter returns a partial match
self.search_algorithm.chatbot.storage.filter = MagicMock(
return_value=[Statement(text='xxx', in_response_to='xxx')])
statement = Statement(text='yyy')
results = list(self.search_algorithm.search(statement))
self.assertIsLength(results, 0)
class SearchComparisonFunctionLevenshteinDistanceComparisonTests(ChatBotTestCase):
"""
Test that the search algorithm works correctly with the
Levenshtein distance comparison function.
"""
def setUp(self):
super().setUp()
self.search_algorithm = IndexedTextSearch(
self.chatbot,
statement_comparison_function=comparisons.LevenshteinDistance
)
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 search process will
filter out any statements that are not in response to something.
"""
self.chatbot.storage.create_many([
Statement(text='What is the meaning of life?', in_response_to='...'),
Statement(text='I am Iron Man.', in_response_to='...'),
Statement(text='What... is your quest?', in_response_to='...'),
Statement(text='Yuck, black licorice jelly beans.', in_response_to='...'),
Statement(text='I hear you are going on a quest?', in_response_to='...'),
])
statement = Statement(text='What is your quest?')
results = list(self.search_algorithm.search(statement))
self.assertEqual(len(results), 1, msg=[r.text for r in results])
self.assertEqual(results[0].text, 'What... is your quest?')
def test_confidence_exact_match(self):
self.chatbot.storage.create(text='What is your quest?', in_response_to='What is your quest?')
statement = Statement(text='What is your quest?')
results = list(self.search_algorithm.search(statement))
self.assertIsLength(results, 1)
self.assertEqual(results[0].confidence, 1)
def test_confidence_half_match(self):
from unittest.mock import MagicMock
# Assume that the storage adapter returns a partial match
self.chatbot.storage.filter = MagicMock(return_value=[
Statement(text='xxyy')
])
statement = Statement(text='wwxx')
results = list(self.search_algorithm.search(statement))
self.assertIsLength(results, 1)
self.assertEqual(results[0].confidence, 0.5)
def test_confidence_no_match(self):
from unittest.mock import MagicMock
# Assume that the storage adapter returns a partial match
self.search_algorithm.chatbot.storage.filter = MagicMock(return_value=[
Statement(text='xxx', in_response_to='xxx')
])
statement = Statement(text='yyy')
results = list(self.search_algorithm.search(statement))
self.assertIsLength(results, 0)
