def test_load_corpus_file(self):
"""
Test that a file path can be specified for a corpus.
"""
# Create a file for testing
file_path = './test_corpus.yml'
with io.open(file_path, 'w') as test_corpus:
yml_data = u'\n'.join(
['conversations:', '- - Hello', ' - Hi', '- - Hi', ' - Hello']
)
test_corpus.write(yml_data)
data_files = corpus.list_corpus_files(file_path)
corpus_data = list(corpus.load_corpus(*data_files))
# Remove the test file
if os.path.exists(file_path):
os.remove(file_path)
self.assertEqual(len(corpus_data), 1)
# Load the content from the corpus
conversations, _categories, _file_path = corpus_data[0]
self.assertEqual(len(conversations[0]), 2)
def test_load_corpus(self):
"""
Test loading the entire corpus of languages.
"""
corpus_files = corpus.list_corpus_files('chatterbot.corpus')
corpus_data = corpus.load_corpus(*corpus_files)
self.assertTrue(len(list(corpus_data)))
def test_load_corpus_file_non_existent(self):
"""
Test that a file path can be specified for a corpus.
"""
file_path = './test_corpus.yml'
self.assertFalse(os.path.exists(file_path))
with self.assertRaises(IOError):
list(corpus.load_corpus(file_path))
def test_load_corpus_file_non_existent(self):
"""
Test that a file path can be specified for a corpus.
"""
file_path = './test_corpus.yml'
self.assertFalse(os.path.exists(file_path))
with self.assertRaises(IOError):
list(corpus.load_corpus(file_path))
def test_load_corpus_english_categories(self):
data_files = corpus.list_corpus_files('chatterbot.corpus.english.greetings')
corpus_data = list(corpus.load_corpus(*data_files))
self.assertEqual(len(corpus_data), 1)
# Test that each conversation gets labeled with the correct category
for _conversation, categories, _file_path in corpus_data:
self.assertIn('greetings', categories)
def test_load_corpus_english_categories(self):
data_files = corpus.list_corpus_files('chatterbot.corpus.english.greetings')
corpus_data = list(corpus.load_corpus(*data_files))
self.assertEqual(len(corpus_data), 1)
# Test that each conversation gets labeled with the correct category
for _conversation, categories, _file_path in corpus_data:
self.assertIn('greetings', categories)
def test_load_english_corpus_categories(self):
files = corpus.list_corpus_files(
'chatterbot_corpus/data/english/greetings.yml')
corpus_data = list(corpus.load_corpus(*files))
self.assertEqual(len(corpus_data), 1)
# Test that each conversation gets labeled with the correct category
for conversation in corpus_data:
self.assertIn('greetings', conversation[1])
def test_conversation_format(self):
files = corpus.list_corpus_files('chatterbot_corpus')
for dialog_corpus, _categories, _file_path in corpus.load_corpus(
*files):
for conversation in dialog_corpus:
for text in conversation:
if not isinstance(text, str):
self.fail('"{}" must be a string, not {}.'.format(
str(text), type(text)))
def test_load_corpus_english_greetings(self):
data_files = corpus.list_corpus_files('chatterbot.corpus.english.greetings')
corpus_data = list(corpus.load_corpus(*data_files))
self.assertEqual(len(corpus_data), 1)
conversations, categories, file_path = corpus_data[0]
self.assertIn(['Hi', 'Hello'], conversations)
self.assertEqual(['greetings'], categories)
self.assertIn('chatterbot_corpus/data/english/greetings.yml', file_path)
def test_character_count(self):
"""
Test that no line in the corpus exceeds the maximum number of characters.
"""
files = corpus.list_corpus_files('chatterbot_corpus')
for dialog_corpus, _categories, _file_path in corpus.load_corpus(
*files):
for conversation in dialog_corpus:
for text in conversation:
if len(text) > STATEMENT_TEXT_MAX_LENGTH:
self.fail(
'"{}" cannot be longer than {} characters'.format(
text, STATEMENT_TEXT_MAX_LENGTH))
def train(self, *corpus_paths):
from chatterbot.corpus import load_corpus, list_corpus_files
data_file_paths = []
# Get the paths to each file the bot will be trained with
for corpus_path in corpus_paths:
data_file_paths.extend(list_corpus_files(corpus_path))
for corpus, categories, file_path in load_corpus(*data_file_paths):
statements_to_create = []
# Train the chat bot with each statement and response pair
for conversation_count, conversation in enumerate(corpus):
if self.show_training_progress:
utils.print_progress_bar(
'Training ' + str(os.path.basename(file_path)),
conversation_count + 1, len(corpus))
previous_statement_text = None
previous_statement_search_text = ''
for text in conversation:
statement_search_text = self.stemmer.stem(text)
_statement = Statement(
text=text,
search_text=statement_search_text,
in_response_to=previous_statement_text,
search_in_response_to=previous_statement_search_text,
conversation='training')
_statement.add_tags(*categories)
statement = self.get_preprocessed_statement(_statement)
previous_statement_text = statement.text
previous_statement_search_text = statement_search_text
statements_to_create.append({
'text': statement.text,
'in_response_to': statement.in_response_to,
'conversation': statement.conversation,
'tags': statement.tags
})
self.chatbot.storage.create_many(statements_to_create)
def get_sentence_tokenizer(language):
"""
Return the sentence tokenizer callable.
"""
pickle_path = 'sentence_tokenizer.pickle'
try:
input_file = open(pickle_path, 'rb')
sentence_tokenizer = load(input_file)
input_file.close()
except FileNotFoundError:
data_file_paths = []
sentences = []
try:
# Get the paths to each file the bot will be trained with
corpus_files = list_corpus_files('chatterbot.corpus.{language}'.format(
language=language.ENGLISH_NAME.lower()
))
except LookupError:
# Fall back to English sentence splitting rules if a language is not supported
corpus_files = list_corpus_files('chatterbot.corpus.{language}'.format(
language=languages.ENG.ENGLISH_NAME.lower()
))
data_file_paths.extend(corpus_files)
for corpus, _categories, _file_path in load_corpus(*data_file_paths):
for conversation in corpus:
for text in conversation:
sentences.append(text.upper())
sentences.append(text.lower())
trainer = PunktTrainer()
trainer.INCLUDE_ALL_COLLOCS = True
trainer.train('\n'.join(sentences))
sentence_tokenizer = PunktSentenceTokenizer(trainer.get_params())
# Pickle the sentence tokenizer for future use
output_file = open(pickle_path, 'wb')
dump(sentence_tokenizer, output_file, -1)
output_file.close()
return sentence_tokenizer
def train(self, *corpus_paths):
from chatterbot.corpus import load_corpus, list_corpus_files
data_file_paths = []
# Get the paths to each file the bot will be trained with
for corpus_path in corpus_paths:
data_file_paths.extend(list_corpus_files(corpus_path))
for corpus, categories, file_path in load_corpus(*data_file_paths):
statements_to_create = []
# Train the chat bot with each statement and response pair
for conversation_count, conversation in enumerate(corpus):
if self.show_training_progress:
utils.print_progress_bar(
'Training ' + str(os.path.basename(file_path)),
conversation_count + 1,
len(corpus)
)
previous_statement_text = None
previous_statement_search_text = ''
for text in conversation:
statement_search_text = self.stemmer.get_bigram_pair_string(text)
statement = Statement(
text=text,
search_text=statement_search_text,
in_response_to=previous_statement_text,
search_in_response_to=previous_statement_search_text,
conversation='training'
)
statement.add_tags(*categories)
statement = self.get_preprocessed_statement(statement)
previous_statement_text = statement.text
previous_statement_search_text = statement_search_text
statements_to_create.append(statement)
self.chatbot.storage.create_many(statements_to_create)
def test_load_corpus_spanish(self):
data_files = corpus.list_corpus_files('chatterbot.corpus.spanish')
corpus_data = corpus.load_corpus(*data_files)
self.assertTrue(len(list(corpus_data)))
def test_load_corpus_telugu(self):
data_files = corpus.list_corpus_files('chatterbot.corpus.telugu')
corpus_data = corpus.load_corpus(*data_files)
self.assertTrue(len(list(corpus_data)))
def test_load_corpus_italian(self):
files = corpus.list_corpus_files('chatterbot_corpus/data/italian')
corpus_data = list(corpus.load_corpus(*files))
self.assertTrue(len(corpus_data))
def test_load_corpus_chinese(self):
data_files = corpus.list_corpus_files('chatterbot.corpus.chinese')
corpus_data = corpus.load_corpus(*data_files)
self.assertTrue(len(list(corpus_data)))
def test_load_corpus_portuguese(self):
files = corpus.list_corpus_files('chatterbot_corpus/data/portuguese')
corpus_data = list(corpus.load_corpus(*files))
self.assertTrue(len(corpus_data))
def test_load_corpus_traditional_chinese(self):
files = corpus.list_corpus_files('chatterbot_corpus/data/tchinese')
corpus_data = list(corpus.load_corpus(*files))
self.assertTrue(len(corpus_data))
def test_load_english_corpus(self):
files = corpus.list_corpus_files('chatterbot_corpus/data/english/greetings.yml')
corpus_data = list(corpus.load_corpus(*files))
self.assertEqual(len(corpus_data), 1)
self.assertIn(['Hi', 'Hello'], corpus_data[0][0])
def test_load_corpus_english_trailing_slash(self):
file_path = os.path.join(corpus.DATA_DIRECTORY, 'english') + '/'
data_files = corpus.list_corpus_files(file_path)
corpus_data = list(corpus.load_corpus(*data_files))
self.assertGreater(len(list(corpus_data)), 1)
def test_load_corpus_english_trailing_slash(self):
file_path = os.path.join(corpus.DATA_DIRECTORY, 'english') + '/'
data_files = corpus.list_corpus_files(file_path)
corpus_data = list(corpus.load_corpus(*data_files))
self.assertGreater(len(list(corpus_data)), 1)
def test_load_corpus_english_greetings(self):
file_path = os.path.join(corpus.DATA_DIRECTORY, 'english', 'greetings.yml')
data_files = corpus.list_corpus_files(file_path)
corpus_data = corpus.load_corpus(*data_files)
self.assertEqual(len(list(corpus_data)), 1)
def test_load_corpus_telugu(self):
data_files = corpus.list_corpus_files('chatterbot.corpus.telugu')
corpus_data = corpus.load_corpus(*data_files)
self.assertTrue(len(list(corpus_data)))
def test_load_corpus_spanish(self):
data_files = corpus.list_corpus_files('chatterbot.corpus.spanish')
corpus_data = corpus.load_corpus(*data_files)
self.assertTrue(len(list(corpus_data)))
def test_load_corpus_russian(self):
data_files = corpus.list_corpus_files('chatterbot.corpus.russian')
corpus_data = corpus.load_corpus(*data_files)
self.assertTrue(len(list(corpus_data)))
def test_load_corpus_indonesia(self):
data_files = corpus.list_corpus_files('chatterbot.corpus.indonesia')
corpus_data = corpus.load_corpus(*data_files)
self.assertTrue(len(list(corpus_data)))
def test_load_corpus_indonesia(self):
data_files = corpus.list_corpus_files('chatterbot.corpus.indonesia')
corpus_data = corpus.load_corpus(*data_files)
self.assertTrue(len(list(corpus_data)))
def train(self, *corpus_paths):
from chatterbot.corpus import load_corpus, list_corpus_files
data_file_paths = []
# Get the paths to each file the bot will be trained with
for corpus_path in corpus_paths:
data_file_paths.extend(list_corpus_files(corpus_path))
for corpus, categories, file_path in load_corpus(*data_file_paths):
statements_to_create = []
# Train the chat bot with each statement and response pair
for conversation_count, conversations in enumerate(corpus):
if self.show_training_progress:
utils.print_progress_bar(
'Training ' + str(os.path.basename(file_path)),
conversation_count + 1, len(corpus))
previous_statements_texts = [None]
previous_statements_search_texts = ['']
for conversation in conversations:
if isinstance(conversation, str):
conversation = [conversation]
statements_texts = []
statements_search_texts = []
for previous_statement_text, previous_statement_search_text in zip(
previous_statements_texts,
previous_statements_search_texts):
for text in conversation:
statement_search_text = self.chatbot.storage.tagger.get_bigram_pair_string(
text)
statement = Statement(
text=text,
search_text=statement_search_text,
in_response_to=previous_statement_text,
search_in_response_to=
previous_statement_search_text,
conversation='training')
statement.add_tags(*categories)
statement = self.get_preprocessed_statement(
statement)
statements_texts.append(statement.text)
statements_search_texts.append(
statement_search_text)
statements_to_create.append(statement)
previous_statements_texts = statements_texts
previous_statements_search_texts = statements_search_texts
if statements_to_create:
self.chatbot.storage.create_many(statements_to_create)
def test_load_corpus_marathi(self):
data_files = corpus.list_corpus_files('chatterbot.corpus.marathi')
corpus_data = corpus.load_corpus(*data_files)
self.assertTrue(len(list(corpus_data)))
def train(self, *corpus_paths):
from chatterbot.corpus import load_corpus, list_corpus_files
data_file_paths = []
# Get the paths to each file the bot will be trained with
for corpus_path in corpus_paths:
data_file_paths.extend(list_corpus_files(corpus_path))
for corpus, categories, file_path in load_corpus(*data_file_paths):
statements_to_create = []
# Train the chat bot with each statement and response pair
for conversation_count, conversation in enumerate(corpus):
if self.show_training_progress:
utils.print_progress_bar(
'Training ' + str(os.path.basename(file_path)),
conversation_count + 1, len(corpus))
previous_statement_text = None
previous_statement_search_text = ''
for text in conversation:
suggestion_tags = []
if text.strip('.?!/;:\'\"') in constants.AFFIRMATIVES:
text = 'AFF'
elif text.strip('.?!/;:\'\"') in constants.NEGATIVES:
text = 'NEG'
elif text[0] is '^':
(suggestion, text) = text.split(maxsplit=1)
suggestion = suggestion[1:]
if not suggestion.find('/'):
suggestion_tags.append(suggestion)
else:
for suggestion in suggestion.split('/'):
suggestion_tags.append(suggestion)
statement_search_text = self.chatbot.storage.tagger.get_bigram_pair_string(
text)
statement = Statement(
text=text,
search_text=statement_search_text,
in_response_to=previous_statement_text,
search_in_response_to=previous_statement_search_text,
conversation='training')
# YesNoLogicAdapter deals with responses to AFF/NEG via statement tags.
# No need for statements in_response_to = AFF/NEG In fact, it was causing
# erroneous responses
if statement.in_response_to in ['AFF', 'NEG']:
statement.in_response_to = None
statement.search_in_response_to = None
statement.add_tags(*categories)
if suggestion_tags:
for suggestion in suggestion_tags:
statement.add_tags('SUGGESTION:' + suggestion)
if previous_statement_text:
if previous_statement_text == 'AFF':
statements_to_create[-2].add_tags('AFF:' +
statement.text)
elif previous_statement_text == 'NEG':
statements_to_create[-2].add_tags('NEG:' +
statement.text)
statement = self.get_preprocessed_statement(statement)
previous_statement_text = statement.text
previous_statement_search_text = statement_search_text
statements_to_create.append(statement)
# Using update() because create_many() makes duplicate statements. AFF/NEG tag data was lost on some.
for stmnts in statements_to_create:
self.chatbot.storage.update(stmnts)
def test_load_corpus_portuguese(self):
data_files = corpus.list_corpus_files('chatterbot.corpus.portuguese')
corpus_data = corpus.load_corpus(*data_files)
self.assertTrue(len(list(corpus_data)))
def test_load_corpus_english(self):
files = corpus.list_corpus_files('chatterbot_corpus/data/english')
corpus_data = list(corpus.load_corpus(*files))
self.assertTrue(len(corpus_data))
def test_load_corpus_russian(self):
data_files = corpus.list_corpus_files('chatterbot.corpus.russian')
corpus_data = corpus.load_corpus(*data_files)
self.assertTrue(len(list(corpus_data)))
def test_load_corpus_marathi(self):
files = corpus.list_corpus_files('chatterbot_corpus/data/marathi')
corpus_data = list(corpus.load_corpus(*files))
self.assertTrue(len(corpus_data))
def test_load_corpus_marathi(self):
data_files = corpus.list_corpus_files('chatterbot.corpus.marathi')
corpus_data = corpus.load_corpus(*data_files)
self.assertTrue(len(list(corpus_data)))