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 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 setUp(self):
super().setUp()
from chatterbot.comparisons import synset_distance
self.adapter = BestMatch(self.chatbot,
statement_comparison_function=synset_distance)
self.adapter.initialize()
def setUp(self):
super().setUp()
from chatterbot.comparisons import levenshtein_distance
self.adapter = BestMatch(
self.chatbot,
statement_comparison_function=levenshtein_distance
)
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)
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 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_levenshtein_distance_comparisons(self):
"""
Test the levenshtein distance comparison algorithm.
"""
self.chatbot.logic_adapters[0] = BestMatch(
self.chatbot,
statement_comparison_function=comparisons.LevenshteinDistance,
response_selection_method=response_selection.get_first_response)
trainer = get_list_trainer(self.chatbot)
trainer.train(STATEMENT_LIST)
self.assert_response_duration_is_less_than(1)
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_spacy_similarity_comparisons(self):
"""
Test the spacy similarity comparison algorithm.
"""
self.chatbot.logic_adapters[0] = BestMatch(
self.chatbot,
statement_comparison_function=comparisons.SpacySimilarity,
response_selection_method=response_selection.get_first_response)
trainer = get_list_trainer(self.chatbot)
trainer.train(STATEMENT_LIST)
self.assert_response_duration_is_less_than(3)
def test_synset_distance_comparisons(self):
"""
Test the synset distance comparison algorithm.
"""
self.chatbot.logic_adapters[0] = BestMatch(
self.chatbot,
statement_comparison_function=
'chatterbot.comparisons.synset_distance',
response_selection_method=
'chatterbot.response_selection.get_first_response')
trainer = get_list_trainer(self.chatbot)
trainer.train(STATEMENT_LIST)
self.assert_response_duration_is_less_than(3)
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_best_match(self):
from chatterbot.logic import BestMatch
adapter = BestMatch(self.chatbot)
statement1 = self.chatbot.storage.create(
text='Do you like programming?', conversation='test')
self.chatbot.storage.create(text='Yes',
in_response_to=statement1.text,
conversation='test')
response = adapter.process(statement1)
self.assertEqual(response.text, 'Yes')
self.assertEqual(response.confidence, 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)
def test_text_search_algorithm(self):
"""
Test that a close match is found when the text_search algorithm is used.
"""
self.adapter = BestMatch(self.chatbot,
search_algorithm_name='text_search')
self.chatbot.storage.create(text='I am hungry.')
self.chatbot.storage.create(text='Okay, what would you like to eat?',
in_response_to='I am hungry.')
self.chatbot.storage.create(text='Can you help me?')
self.chatbot.storage.create(text='Sure, what seems to be the problem?',
in_response_to='Can you help me?')
statement = Statement(text='Could you help me?')
match = self.adapter.process(statement)
self.assertEqual(match.confidence, 0.82)
self.assertEqual(match.text, 'Sure, what seems to be the problem?')
def setUp(self):
super(BestMatchTestCase, self).setUp()
self.adapter = BestMatch()
self.adapter.set_chatbot(self.chatbot)
def setUp(self):
super().setUp()
self.adapter = BestMatch(self.chatbot)
