class SpeechRecognizer:
def __init__(self):
# Services
self.__recognizer = sr.Recognizer()
self.__logger = Logger()
self._exceptions_handler = ExceptionsHandler()
self.__logger.info('SpeechRecognizer was successfully initialized.', __name__)
def recognize_speech(self):
while True:
try:
with sr.Microphone() as source:
speech = self.__recognizer.listen(source)
except BaseException as exception:
error_message = self._exceptions_handler.get_error_message(exception)
self.__logger.error(error_message, __name__)
return error_message
try:
text = self.__recognizer.recognize_google(speech, language="ru-RU").lower().strip()
return text
except BaseException as exception:
error_message = self._exceptions_handler.get_error_message(exception)
if isinstance(exception, sr.WaitTimeoutError):
self.__logger.warning(self._exceptions_handler.get_error_message(exception), __name__)
else:
self.__logger.error(error_message, __name__)
return error_message
class FileReader(QWidget):
def __init__(self):
super().__init__()
self.__logger = Logger()
self.__file_dialog = QFileDialog()
self.__logger.info('FileReader was successfully initialized.',
__name__)
def _detect_encoding(self, filename):
with open(filename, 'rb') as byte_file:
byte_string = byte_file.read()
encoding = chardet.detect(byte_string)['encoding']
self.__logger.info(f"File's encoding: {encoding}", __name__)
return encoding
def get_file_content(self):
try:
filename = self.__file_dialog.getOpenFileName(
self, 'Open file', '/home')[0]
self.__logger.info(f'Filename: {filename}', __name__)
if filename:
with open(filename,
'r',
encoding=self._detect_encoding(filename)) as file:
return file.read()
except BaseException as exception:
self.__logger.error(str(exception), __name__)
class SpellChecker:
def __init__(self):
# Services
self.__logger = Logger()
self._exceptions_handler = ExceptionsHandler()
self.__logger.info('SpellChecker was successfully initialized.',
__name__)
def check_spelling(self, text):
self.__logger.info(f'Start text: {text}', __name__)
try:
response = requests.get(
'https://speller.yandex.net/services/spellservice.json/checkText',
params={
'text': text
}).json()
except BaseException as exception:
self.__logger.error(
self._exceptions_handler.get_error_message(exception),
__name__)
return text
for word in response:
text = text.replace(word['word'], word['s'][0])
self.__logger.info(f'Checked text: {text}', __name__)
return text
class DocumentPreparer:
def __init__(self):
self.__logger = Logger()
self.__logger.info('DocumentPreparer was successfully initialized.',
__name__)
def split_into_unigrams(self, text):
if text:
return re.findall(r'\w+', text)
else:
self.__logger.warning('Got empty text.', __name__)
def split_into_bigrams(self, text):
if not text:
self.__logger.warning('Got empty text.', __name__)
return
unigrams = self.split_into_unigrams(text)
bigrams = list()
if len(unigrams) >= 2:
for unigram_index in range(len(unigrams) - 1):
bigram = ' '.join(
sorted(
[unigrams[unigram_index],
unigrams[unigram_index + 1]])).strip()
bigrams.append(bigram)
return bigrams
else:
self.__logger.info("Text doesn't contain enough words.", __name__)
def split_into_trigrams(self, text):
if not text:
self.__logger.warning('Got empty text.', __name__)
return
unigrams = self.split_into_unigrams(text)
trigrams = list()
if len(unigrams) >= 3:
for unigram_index in range(len(unigrams) - 2):
trigram = ' '.join(
sorted([
unigrams[unigram_index], unigrams[unigram_index + 1],
unigrams[unigram_index + 2]
])).strip()
trigrams.append(trigram)
return trigrams
else:
self.__logger.info("Text doesn't contain enough words.", __name__)
class DatabaseCursor:
def __init__(self):
# Services
self.__logger = Logger()
self._path_service = PathService()
self._configurator = Configurator()
self._exceptions_handler = ExceptionsHandler()
# Data
self._wd = os.getcwd()
self._request_url = None
self.databases_public_keys = None
self.__logger.info('DatabaseCursor was successfully initialized.',
__name__)
def _load_config(self):
path_to_config = os.path.join(self._path_service.path_to_configs,
'database_cursor.json')
if os.path.exists(path_to_config):
with open(path_to_config, 'r', encoding='utf-8') as file:
config = json.load(file)
self._request_url = config['request_url']
self.databases_public_keys = config['database_public_keys']
else:
self.__logger.error(
"Can't load config for DatabaseCursor (doesn't exist).",
__name__)
def __update_connection(self, ngram):
path_to_db = None
if ngram.count(' ') == 0:
path_to_db = self._path_service.get_path_to_database('unigrams.db')
elif ngram.count(' ') == 1:
path_to_db = self._path_service.get_path_to_database('bigrams.db')
elif ngram.count(' ') == 2:
path_to_db = self._path_service.get_path_to_database('trigrams.db')
if path_to_db and os.path.exists(path_to_db):
self.__logger.info(f'Connected to database: {path_to_db}',
__name__)
return sqlite3.connect(path_to_db)
else:
self.__logger.warning(f'Database lost: {path_to_db}', __name__)
self.__logger.info('Trying to download database from cloud...',
__name__)
self._configurator.download_database(path_to_db)
self.__logger.info(f'Connected to database: {path_to_db}',
__name__)
if os.path.exists(path_to_db):
return sqlite3.connect(path_to_db)
else:
self.__logger.fatal("Database doesn't exist.", __name__)
def get_entry(self, ngram):
connection = self.__update_connection(ngram)
cursor = connection.cursor()
request = ("""
SELECT * FROM 'Data' WHERE Ngram='%s'
""") % ngram
self.__logger.info(f'Request to DB: {request.strip()}', __name__)
try:
cursor.execute(request)
self.__logger.info('Request is OK.', __name__)
except BaseException as exception:
connection.close()
self.__logger.error(
self._exceptions_handler.get_error_message(exception),
__name__)
return
result = cursor.fetchone()
self.__logger.info(f'Received data: {str(result)}', __name__)
if result:
connection.close()
return result[1], result[2]
else:
connection.close()
def entry_exists(self, ngram):
connection = self.__update_connection(ngram)
cursor = connection.cursor()
request = ("""
SELECT * FROM 'Data' WHERE Ngram='%s'
""") % ngram
self.__logger.info(f'Request to DB: {request.strip()}', __name__)
try:
cursor.execute(request)
self.__logger.info('Request is OK.', __name__)
except BaseException as exception:
connection.close()
self.__logger.error(
self._exceptions_handler.get_error_message(exception),
__name__)
return
if cursor.fetchone():
connection.close()
self.__logger.info('Entry exists.', __name__)
return True
else:
connection.close()
self.__logger.info("Entry doesn't exist.", __name__)
return False
class MainWindow(QWidget):
def __init__(self):
super().__init__()
self.os = platform.system().lower()
# Services
self._speech_recognizer = SpeechRecognizer()
self._file_reader = FileReader()
self.__logger = Logger()
self._path_service = PathService()
self._text_tonal_analyzer = TextTonalAnalyzer('NBC')
self._config = None
self._load_config()
# GUI Elements
self.line_edit = QLineEdit(self)
self.answer_label = QLabel(self)
self.voice_button = QPushButton(self)
self.answer_button = QPushButton(self)
self.file_dialog_button = QPushButton(self)
self.delete_button = QPushButton(self)
self.message_box = QMessageBox()
def _load_config(self):
path_to_config = os.path.join(self._path_service.path_to_configs, 'demo.json')
if os.path.exists(path_to_config):
with open(path_to_config, 'r', encoding='utf-8') as file:
self._config = json.load(file)
if self.os == 'windows':
self._config = self._config['windows']
else:
self._config = self._config['darwin']
else:
self.__logger.fatal("Config for GUI doesn't exist.", __name__)
exit(-1)
def _configure_main_window(self):
self._set_base_params()
self._configure_line_edit()
self._configure_answer_button()
self.configure_voice_button()
self._configure_delete_button()
self._configure_file_dialog_button()
self._configure_answer_label()
self.__logger.info('Main window was successfully configured.', __name__)
def _set_base_params(self):
self.setFixedSize(*self._config['size'])
self.setStyleSheet('QWidget { background-color: %s }' % self._config['background-color'])
def _configure_line_edit(self):
self.line_edit.setToolTip('Enter the text here')
self.line_edit.returnPressed.connect(self._answer_button_clicked)
self.line_edit.resize(*self._config['line-edit']['size'])
self.line_edit.setStyleSheet('QWidget { background-color: %s }' %
self._config['line-edit']['background-color'])
self.line_edit.move(*self._config['line-edit']['coordinates'])
self.line_edit.setFont(QFont(*self._config['line-edit']['font']))
def _configure_answer_button(self):
self.answer_button.clicked.connect(self._answer_button_clicked)
self.answer_button.setText('Start')
self.answer_button.setToolTip('Push to count tonal')
self.answer_button.setStyleSheet("""
QPushButton:hover { background-color: %s }
QPushButton:!hover { background-color: %s }
QPushButton:pressed { background-color: %s; }
""" % (self._config['answer-button']['background-color']['hover'],
self._config['answer-button']['background-color']['!hover'],
self._config['answer-button']['background-color']['pressed']))
self.answer_button.resize(*self._config['answer-button']['size'])
self.answer_button.move(*self._config['answer-button']['coordinates'])
self.answer_button.setFont(QFont(*self._config['answer-button']['font']))
def configure_voice_button(self):
self.voice_button.setText('🎙')
self.voice_button.setToolTip('Push to enter the text by speech')
self.voice_button.clicked.connect(self._voice_button_clicked)
self.voice_button.resize(*self._config['voice-button']['size'])
self.voice_button.setFont(QFont(*self._config['voice-button']['font']))
self.voice_button.move(*self._config['voice-button']['coordinates'])
self.voice_button.setStyleSheet("""
QPushButton:hover { background-color: %s }
QPushButton:!hover { background-color: %s }
QPushButton:pressed { background-color: %s; }
""" % (self._config['voice-button']['background-color']['hover'],
self._config['voice-button']['background-color']['!hover'],
self._config['voice-button']['background-color']['pressed']))
def _configure_delete_button(self):
self.delete_button.setText('✗')
self.delete_button.setToolTip('Push to clear text box')
self.delete_button.clicked.connect(self._delete_button_clicked)
self.delete_button.resize(*self._config['delete-button']['size'])
self.delete_button.setFont(QFont(*self._config['delete-button']['font']))
self.delete_button.move(*self._config['delete-button']['coordinates'])
self.delete_button.setStyleSheet("""
QPushButton:hover { background-color: %s }
QPushButton:!hover { background-color: %s }
QPushButton:pressed { background-color: %s; }
""" % (self._config['delete-button']['background-color']['hover'],
self._config['delete-button']['background-color']['!hover'],
self._config['delete-button']['background-color']['pressed']))
def _configure_file_dialog_button(self):
self.file_dialog_button.setText('📂')
self.file_dialog_button.setToolTip('Push to open file')
self.file_dialog_button.clicked.connect(self._file_dialog_button_clicked)
self.file_dialog_button.resize(*self._config['file-dialog-button']['size'])
self.file_dialog_button.setFont(QFont(*self._config['file-dialog-button']['font']))
self.file_dialog_button.move(*self._config['file-dialog-button']['coordinates'])
self.file_dialog_button.setStyleSheet("""
QPushButton:hover { background-color: %s }
QPushButton:!hover { background-color: %s }
QPushButton:pressed { background-color: %s; }
""" % (self._config['file-dialog-button']['background-color']['hover'],
self._config['file-dialog-button']['background-color']['!hover'],
self._config['file-dialog-button']['background-color']['pressed']))
def _configure_answer_label(self):
self.answer_label.move(*self._config['answer-label']['coordinates'])
self.answer_label.setFont(QFont(*self._config['answer-label']['font']))
self.answer_label.resize(*self._config['answer-label']['size'])
def launch(self):
self.setWindowIcon(QIcon('icon.ico'))
self.setWindowTitle('Sentiment Analyser')
self._configure_main_window()
self.show()
self.__logger.info('Main window was successfully launched.', __name__)
def _delete_button_clicked(self):
self.line_edit.clear()
self.answer_label.clear()
def _voice_button_clicked(self):
self.message_box.question(self, 'Speak', 'You can start speeking.', QMessageBox.Ok)
speech_text = self._speech_recognizer.recognize_speech()
if speech_text == 'Unknown value':
try_again = QMessageBox.Yes
while try_again == QMessageBox.Yes and speech_text == 'Unknown value':
try_again = self.message_box.question(self, 'Error', 'Unknown value\n Try again?',
QMessageBox.Yes | QMessageBox.No)
if try_again == QMessageBox.No:
break
speech_text = self._speech_recognizer.recognize_speech()
elif speech_text == 'Internet connection lost':
self.message_box.question(self, 'Error', 'Internet connection lost', QMessageBox.Ok)
return
elif speech_text == 'No microphone':
self.message_box.question(self, 'Error', 'Microphone was disconnected', QMessageBox.Ok)
return
if speech_text != 'Unknown value':
self.line_edit.setText(speech_text)
return
def _file_dialog_button_clicked(self):
file_content = self._file_reader.get_file_content()
if file_content:
self.line_edit.setText(file_content)
else:
self.__logger.warning('Empty file.', __name__)
def _answer_button_clicked(self):
self._text_tonal_analyzer.detect_tonal(self.line_edit.text())
if self.os == 'windows':
if self._text_tonal_analyzer.tonal == 'positive':
self.answer_label.setStyleSheet('QLabel {color:rgba(0, 200, 100, 255)}')
self.answer_label.move(193.5, 180)
elif self._text_tonal_analyzer.tonal == 'negative':
self.answer_label.setStyleSheet('QLabel {color:rgba(255, 56, 20, 255)}')
self.answer_label.move(180, 180)
elif self.os == 'darwin':
if self._text_tonal_analyzer.tonal == 'positive':
self.answer_label.setStyleSheet('QLabel {color:rgba(0, 200, 100, 255)}')
self.answer_label.move(230, 210)
elif self._text_tonal_analyzer.tonal == 'negative':
self.answer_label.setStyleSheet('QLabel {color:rgba(255, 56, 20, 255)}')
self.answer_label.move(225, 210)
self.answer_label.setToolTip('Tonal and probability')
if self._text_tonal_analyzer.probability:
self.answer_label.setText(self._text_tonal_analyzer.tonal.capitalize() + '\n' +
str(round(self._text_tonal_analyzer.probability * 100, 3)) + '%')
else:
self.answer_label.setText(self._text_tonal_analyzer.tonal.capitalize())
class PathService(metaclass=Singleton):
def __init__(self):
# Services
self.__logger = Logger()
# Data
self._wd = os.getcwd()
self.path_to_databases = None
self.path_to_configs = None
self._valid_classifiers = None
self._valid_model_types = None
self._valid_databases = None
self._valid_test_results_modes = None
self._valid_datasets = None
self.path_to_stop_words = None
self._path_to_main_directory = None
self.path_to_vector_model = None
self._path_to_classifier_models = None
self._path_to_test_results = None
self.configure()
self.__logger.info('PathService was successfully configured.', __name__)
def _find_main_directory(self):
max_nesting_level = 5
nesting_level = 0
while not os.getcwd().endswith('Python'):
if os.getcwd().endswith('Databases'):
os.chdir(os.path.join('..', 'Python'))
break
else:
os.chdir('..')
nesting_level += 1
if nesting_level > max_nesting_level:
self.__logger.fatal("Can't find main directory (exceeded maximum nesting level).", __name__)
exit(-1)
self._path_to_main_directory = os.getcwd()
self.path_to_configs = os.path.join(self._path_to_main_directory, 'Services', 'Configs')
self.path_to_databases = os.path.abspath(os.path.join('..', 'Databases'))
os.chdir(self._wd)
def _check_paths_existing(self):
if not os.path.exists(self.path_to_configs):
self.__logger.fatal("Directory with config files doesn't exist.", __name__)
exit(-1)
elif not os.path.exists(self.path_to_databases):
self.__logger.fatal("Directory with databases doesn't exist.", __name__)
exit(-1)
elif not os.path.exists(self._path_to_classifier_models):
self.__logger.fatal("Directory with classifier models doesn't exist.", __name__)
exit(-1)
if not os.path.exists(self.path_to_vector_model):
self.path_to_vector_model = None
self.__logger.error("Vector model doesn't exist.", __name__)
if not os.path.exists(self.path_to_stop_words):
self.path_to_stop_words = None
self.__logger.error("File with stop-words doesn't exist.", __name__)
if not os.path.exists(self._path_to_test_results):
self._path_to_test_results = None
self.__logger.warning("Directory with tests reports doesn't exist.", __name__)
def _load_config(self):
path_to_config = os.path.join(self.path_to_configs, 'path_service.json')
if not os.path.exists(path_to_config):
self.__logger.error("Can't find config-file for PathService.", __name__)
with open(path_to_config, 'r', encoding='utf-8') as file:
config = json.load(file)
self._valid_classifiers = config['valid_classifiers']
self._valid_databases = config['valid_databases']
self._valid_datasets = config['valid_datasets']
self._valid_test_results_modes = config['valid_test_results_modes']
self._valid_model_types = config['valid_model_types']
def configure(self):
self._find_main_directory()
self._load_config()
self.path_to_vector_model = os.path.join(self.path_to_databases, 'ruscorpora_upos_skipgram_300_10_2017.bin.gz')
self.path_to_stop_words = os.path.join(self._path_to_main_directory, 'Services', 'Lemmatizer',
'stop_words.json')
self._path_to_classifier_models = os.path.join(self.path_to_databases, 'Models')
self._path_to_test_results = os.path.join(self._path_to_main_directory, 'Tests', 'System', 'Reports')
self._check_paths_existing()
def get_path_to_test_results(self, mode='classifier', classifier_name='NBC'):
if classifier_name not in self._valid_classifiers:
self.__logger.warning('Got incorrect classifier name.', __name__)
classifier_name = 'NBC'
if classifier_name not in self._valid_test_results_modes:
self.__logger.warning('Got incorrect mode.', __name__)
return self._path_to_test_results
if mode.lower().strip() == 'vec_model':
return os.path.join(self._path_to_test_results, 'VectorModel')
elif mode.lower().strip() == 'classifier_main':
return os.path.join(self._path_to_test_results, '..', '..', 'MainReports', 'Classifier', classifier_name)
elif mode.lower().strip() == 'classifier':
return self._path_to_test_results
def get_path_to_model(self, model='unigrams', classifier_name='NBC'):
if classifier_name not in self._valid_classifiers:
self.__logger.warning('Got incorrect classifier name.', __name__)
classifier_name = 'NBC'
if model not in self._valid_model_types:
self.__logger.warning('Got incorrect model type.', __name__)
model = 'unigrams'
path_to_models = os.path.join(self._path_to_classifier_models, classifier_name)
if os.path.exists(path_to_models):
path_to_required_model = os.path.join(path_to_models, f'model_{model}.pkl')
return path_to_required_model
else:
self.__logger.error("Required model wasn't found.", __name__)
def get_path_to_database(self, database_name='unigrams.db'):
if database_name not in self._valid_databases:
self.__logger.warning('Got incorrect database name.', __name__)
database_name = 'unigrams.db'
path_to_database = os.path.join(self.path_to_databases, database_name)
return path_to_database
def get_path_to_dataset(self, dataset):
if dataset not in self._valid_datasets:
self.__logger.warning('Got incorrect dataset name.', __name__)
dataset = 'dataset_with_unigrams.csv'
path_to_dataset = os.path.join(self.path_to_databases, dataset)
return path_to_dataset
def set_path_to_vector_model(self, path_to_vector_model):
self.path_to_vector_model = path_to_vector_model
class ExceptionsHandler:
def __init__(self):
self.__logger = Logger()
self._request_exceptions = [
type(item) for item in [
requests.ConnectionError(),
requests.HTTPError(),
requests.TooManyRedirects(),
requests.Timeout(),
requests.TooManyRedirects(),
requests.RequestException(),
requests.ConnectTimeout(),
requests.ReadTimeout()
]
]
self._system_errors = [
type(item) for item in [
KeyError(),
AttributeError(),
IndexError(),
ZeroDivisionError(),
SystemError(),
ValueError(),
AssertionError()
]
]
self._file_errors = [
type(item) for item in [FileExistsError(),
FileNotFoundError()]
]
self._database_errors = [
type(item) for item in [
sqlite3.Error(),
sqlite3.DataError(),
sqlite3.ProgrammingError(),
sqlite3.DatabaseError(),
sqlite3.NotSupportedError(),
sqlite3.IntegrityError(),
sqlite3.InterfaceError(),
sqlite3.InternalError(),
sqlite3.OperationalError()
]
]
self._speech_recognizer_errors = [
type(item) for item in [
sr.RequestError(),
sr.UnknownValueError(),
sr.WaitTimeoutError(),
sr.RequestError()
]
]
self.__logger.info('ExceptionsHandler was successfully initialized.',
__name__)
@staticmethod
def _handle_system_exception(exception):
if isinstance(exception, KeyError):
return 'Key error occurred.'
elif isinstance(exception, AttributeError):
return 'AttributeError occurred.'
elif isinstance(exception, IndexError):
return 'Index error occurred.'
elif isinstance(exception, ZeroDivisionError):
return 'ZeroDivisionError occurred.'
elif isinstance(exception, SystemError):
return 'SystemError occurred.'
elif isinstance(exception, ValueError):
return 'ValueError occurred.'
elif isinstance(exception, AssertionError):
return 'Assertion error occurred.'
@staticmethod
def _handle_file_exception(exception):
if isinstance(exception, FileNotFoundError):
return f'FileNotFoundError occurred ({str(exception.filename)}).'
elif isinstance(exception, FileExistsError):
return f'FileExistsError occurred ({str(exception.filename)}).'
@staticmethod
def _handle_database_exception(exception):
if isinstance(exception, sqlite3.OperationalError):
return 'sqlite3.Operational occurred.'
elif isinstance(exception, sqlite3.ProgrammingError):
return 'sqlite3.ProgrammingError occurred.'
elif isinstance(exception, sqlite3.InternalError):
return 'sqlite3.InternalError occurred.'
elif isinstance(exception, sqlite3.InterfaceError):
return 'sqlite3.InterfaceError occurred.'
elif isinstance(exception, sqlite3.IntegrityError):
return 'sqlite3.IntegrityError occurred.'
elif isinstance(exception, sqlite3.NotSupportedError):
return 'sqlite3.NotSupportedError occurred.'
elif isinstance(exception, sqlite3.DatabaseError):
return 'sqlite3.DatabaseError occurred.'
elif isinstance(exception, sqlite3.DataError):
return 'sqlite3.DataError occurred.'
elif isinstance(exception, sqlite3.Error):
return 'sqlite3.Error occurred.'
@staticmethod
def _handle_request_exception(exception):
if isinstance(exception, requests.ConnectionError):
return 'Problems with connection (requests.ConnectionError).'
elif isinstance(exception, requests.HTTPError):
return 'HHTP request return unsuccessful status code (requests.HTTPError).'
elif isinstance(exception, requests.Timeout):
return 'Request times out (requests.Timeout).'
elif isinstance(exception, requests.TooManyRedirects):
return 'Request exceeds the configured number of maximum redirections (requests.TooManyRedirects).'
elif isinstance(exception, requests.ConnectTimeout):
return 'ConnectTimeout (requests.ConnectTimeout).'
elif isinstance(exception, requests.ReadTimeout):
return 'ReadTimeout (requests.ReadTimeout).'
else:
return 'Request exception (requests.RequestException).'
@staticmethod
def _handle_speech_recognizer_exception(exception):
if isinstance(exception, sr.WaitTimeoutError):
return 'speech_recognition.WaitTimeoutError occurred.'
elif isinstance(exception, sr.UnknownValueError):
return 'Unknown value (speech_recognoition.UnknownValueError).'
elif isinstance(exception, sr.RequestError):
return 'speech_recognition.RequestError occurred.'
def get_error_message(self, exception):
if type(exception) in self._system_errors:
return self._handle_system_exception(exception)
elif type(exception) in self._file_errors:
return self._handle_file_exception(exception)
elif type(exception) in self._request_exceptions:
return ExceptionsHandler._handle_request_exception(exception)
elif type(exception) in self._speech_recognizer_errors:
return self._handle_speech_recognizer_exception(exception)
return str(exception)
class TextTonalAnalyzer:
def __init__(self, classifier_name='NBC'):
# Services
self._configurator = Configurator()
self._configurator.configure_system()
self._database_cursor = DatabaseCursor()
self._document_preparer = DocumentPreparer()
self._text_weight_counter = TextWeightCounter()
self._classifier = Classifier()
self.__logger = Logger()
self._lemmatizer = Lemmatizer()
self._path_service = PathService()
# Data
self._classifier_name = classifier_name
self._text = None
self.tonal = None
self.probability = 0
self._unigrams = None
self._bigrams = None
self._trigrams = None
self._unigrams_weight = None
self._bigrams_weight = None
self._trigrams_weight = None
self.__logger.info('TextTonalAnalyzer was successfully initialized.',
__name__)
def _reset_data(self):
self._text = None
self.tonal = None
self.probability = 0
self._unigrams = None
self._bigrams = None
self._trigrams = None
self._unigrams_weight = None
self._bigrams_weight = None
self._trigrams_weight = None
self.__logger.info('Data was successfully reset.', __name__)
def _document_prepare(self):
self._unigrams = self._document_preparer.split_into_unigrams(
self._text)
self._bigrams = self._document_preparer.split_into_bigrams(self._text)
self._trigrams = self._document_preparer.split_into_trigrams(
self._text)
def _text_in_dataset(self):
path_to_dataset = self._path_service.get_path_to_dataset(
'dataset_with_unigrams.csv')
with open(path_to_dataset, 'r', encoding='utf-8') as file:
dataset = csv.reader(file)
for doc in dataset:
doc = ''.join(doc).split(';')
if doc[0] == self._text:
self.tonal = doc[1]
self.probability = 1
self.__logger.info('Document is in dataset.', __name__)
return True
return False
def _count_weight_by_unigrams(self):
self._unigrams_weight = self._text_weight_counter.count_weight_by_unigrams(
self._unigrams)
def _count_weight_by_bigrams(self):
self._bigrams_weight = self._text_weight_counter.count_weight_by_bigrams(
self._bigrams)
def _count_weight_by_trigrams(self):
self._trigrams_weight = self._text_weight_counter.count_weight_by_trigrams(
self._trigrams)
def detect_tonal(self, text):
self._reset_data()
self._text = self._lemmatizer.get_text_initial_form(text)
if not self._text:
self.tonal = 'Unknown'
self.__logger.warning('Text is empty.', __name__)
return None
self._document_prepare()
if not self._text_in_dataset():
threads = list()
threads.append(
Thread(target=self._count_weight_by_unigrams, args=()))
threads.append(
Thread(target=self._count_weight_by_bigrams, args=()))
threads.append(
Thread(target=self._count_weight_by_trigrams, args=()))
for thread in threads:
thread.start()
for thread in threads:
while thread.is_alive():
time.sleep(0.1)
thread.join()
self._classifier.customize(self._unigrams_weight,
self._bigrams_weight,
self._trigrams_weight,
self._classifier_name)
self.tonal, self.probability = self._classifier.predict_tonal()
self.__logger.page_break()
class NgramAnalyzer:
def __init__(self):
# Services
self._database_cursor = DatabaseCursor()
self.__logger = Logger()
self._exceptions_hanlder = ExceptionsHandler()
self._lemmatizer = Lemmatizer()
self._path_service = PathService()
self._configurator = Configurator()
self._morph_analyzer = pymorphy2.MorphAnalyzer()
# Data
self._vec_model = None
self._load_vec_model()
self.__logger.info('NgramAnalyzer was successfully initialized.', __name__)
def _load_vec_model(self):
if not self._path_service.path_to_vector_model:
self.__logger.warning("Vector model doesn't exist.", __name__)
self._configurator.download_vector_model()
self._path_service.set_path_to_vector_model(os.path.join(self._path_service.path_to_databases,
'ruscorpora_upos_skipgram_300_10_2017.bin.gz'))
self.__logger.info('Vector model was successfully downloaded.', __name__)
if self._path_service.path_to_vector_model:
self._vec_model = gensim.models.KeyedVectors.load_word2vec_format(self._path_service.path_to_vector_model,
binary=True)
else:
self.__logger.error("Vector model doesn't exist.", __name__)
def _part_of_speech_detect(self, word):
if not word:
return
part_of_speech = self._morph_analyzer.parse(word)[0].tag.POS
if part_of_speech:
if re.match(r'ADJ', part_of_speech):
return 'ADJ'
elif re.match(r'PRT', part_of_speech):
return 'PRT'
elif part_of_speech == 'INFN':
return 'VERB'
elif part_of_speech == 'ADVB' or part_of_speech == 'PRED':
return 'ADV'
elif part_of_speech == 'PRCL':
return 'PART'
return part_of_speech
@staticmethod
def _detect_ngram_type(ngram):
if not ngram:
return
if ngram.count(' ') == 0:
return 'unigram'
elif ngram.count(' ') == 1:
return 'bigram'
elif ngram.count(' ') == 2:
return 'trigram'
def _nearest_synonyms_find(self, word, topn):
if not self._vec_model or not word or topn <= 0:
return
nearest_synonyms = list()
part_of_speech = self._part_of_speech_detect(word)
ngram_type = self._detect_ngram_type(word)
if part_of_speech:
word = word + '_%s' % self._part_of_speech_detect(word)
try:
for synonym in self._vec_model.most_similar(positive=[word], topn=topn * 10):
found_synonym = self._lemmatizer.get_text_initial_form(synonym[0].split('_')[0])
if found_synonym and self._detect_ngram_type(found_synonym) == ngram_type:
nearest_synonyms.append({'word': found_synonym,
'cosine proximity': synonym[1]})
if len(nearest_synonyms) == topn:
break
except BaseException as exception:
self.__logger.warning(self._exceptions_hanlder.get_error_message(exception), __name__)
return
return nearest_synonyms
def relevant_ngram_find(self, ngram):
if not ngram:
return
self.__logger.info(f'Start ngram: {ngram}', __name__)
response = {'synonym_found': False, 'content': dict()}
if self._detect_ngram_type(ngram) == 'unigram':
synonyms_count = 10
nearest_synonyms = self._nearest_synonyms_find(ngram, synonyms_count)
if not nearest_synonyms:
return response
for nearest_synonym in nearest_synonyms:
data = self._database_cursor.get_entry(nearest_synonym['word'])
if data and data[0]:
self.__logger.info(f'Relevant ngram: {nearest_synonym["word"]}', __name__)
response['synonym_found'] = True
response['content']['synonym'] = nearest_synonym['word']
response['content']['pos_docs'] = data[0]
response['content']['neg_docs'] = data[1]
return response
return response
class TextWeightCounter:
def __init__(self):
# Services
self._database_cursor = DatabaseCursor()
self._ngram_analyzer = NgramAnalyzer()
self.__logger = Logger()
self._path_service = PathService()
# Data
self._docs_count = dict()
self._count_all_docs()
self.__logger.info('TextWeightCounter was successfully initialized.',
__name__)
def _count_docs_in_dataset(self, mode):
path_to_dataset = self._path_service.get_path_to_dataset(
f'dataset_with_{mode}.csv')
with open(path_to_dataset, 'r', encoding='utf-8') as file:
negative_docs_shift = 10000
positive_docs = 0
negative_docs = negative_docs_shift
for row in csv.reader(file):
if ''.join(row).split(';')[1] == 'positive':
positive_docs += 1
else:
negative_docs += 1
return positive_docs + negative_docs - negative_docs_shift, positive_docs, negative_docs
def _count_all_docs(self):
modes = ['unigrams', 'bigrams', 'trigrams']
for mode in modes:
self._docs_count[mode] = dict()
self._docs_count[mode]['all_docs'], self._docs_count[mode]['positive_docs'], \
self._docs_count[mode]['negative_docs'] = self._count_docs_in_dataset(mode)
@staticmethod
def _detect_ngram_type(ngram):
if ngram.count(' ') == 0:
return 'unigram'
elif ngram.count(' ') == 1:
return 'bigram'
elif ngram.count(' ') == 2:
return 'trigram'
def _count_ngram_weight(self, ngram):
self.__logger.info(f'Ngram: {ngram}', __name__)
ngram_type = self._detect_ngram_type(ngram)
delta_tf_idf = 0
self.__logger.info(f'Ngram_type: {ngram_type}', __name__)
if self._database_cursor.entry_exists(ngram):
pos_docs_word, neg_docs_word = self._database_cursor.get_entry(
ngram)
delta_tf_idf = math.log10(
(self._docs_count[ngram_type + 's']['negative_docs'] *
pos_docs_word) /
(self._docs_count[ngram_type + 's']['positive_docs'] *
neg_docs_word))
else:
response = self._ngram_analyzer.relevant_ngram_find(ngram)
if response['synonym_found']:
if ngram_type == 'unigram':
pos_docs_word, neg_docs_word = response['content'][
'pos_docs'], response['content']['neg_docs']
if (not (pos_docs_word and neg_docs_word)) or (
pos_docs_word == 1 and neg_docs_word == 1):
return 0
delta_tf_idf = math.log10(
(self._docs_count[ngram_type + 's']['negative_docs'] *
pos_docs_word) /
(self._docs_count[ngram_type + 's']['positive_docs'] *
neg_docs_word))
return delta_tf_idf
def count_weight_by_unigrams(self, unigrams):
checked_unigrams = list()
important_unigrams = list()
unigrams_weight = 0
for unigram in unigrams:
if unigram not in checked_unigrams:
this_doc_unigram = unigrams.count(unigram)
unigram_weight = this_doc_unigram * self._count_ngram_weight(
unigram)
unigrams_weight += unigram_weight
checked_unigrams.append(unigram)
if unigram_weight:
important_unigrams.append(unigram)
if len(important_unigrams) >= round(
len(unigrams) * 0.6) and important_unigrams:
unigrams_weight = unigrams_weight / len(important_unigrams)
self.__logger.info(f'Unigrams weight: {unigrams_weight}', __name__)
return unigrams_weight
def count_weight_by_bigrams(self, bigrams):
if not bigrams:
return None
checked_bigrams = list()
important_bigrams = list()
bigrams_weight = 0
for bigram in bigrams:
if bigram not in checked_bigrams:
this_doc_bigram = bigrams.count(bigram)
bigram_weight = this_doc_bigram * self._count_ngram_weight(
bigram)
bigrams_weight += bigram_weight
checked_bigrams.append(bigram)
if bigram_weight:
important_bigrams.append(bigram)
if len(important_bigrams) >= len(bigrams) // 2 and important_bigrams:
bigrams_weight = bigrams_weight / len(important_bigrams)
self.__logger.info(f'Bigrams weight: {bigrams_weight}', __name__)
return bigrams_weight
def count_weight_by_trigrams(self, trigrams):
if not trigrams:
return None
checked_trigrams = list()
important_trigrams = list()
trigrams_weight = 0
for trigram in trigrams:
if trigram not in checked_trigrams:
this_doc_trigram = trigrams.count(trigram)
trigram_weight = this_doc_trigram * self._count_ngram_weight(
trigram)
trigrams_weight += trigram_weight
checked_trigrams.append(trigram)
if trigram_weight:
important_trigrams.append(trigram)
if len(important_trigrams) >= round(
len(trigrams) * 0.4) and important_trigrams:
trigrams_weight = trigrams_weight / len(important_trigrams)
self.__logger.info(f'Trigrams weight: {trigrams_weight}', __name__)
return trigrams_weight
class Configurator(metaclass=Singleton):
def __init__(self):
# Services
self.__logger = Logger()
self._path_service = PathService()
self._exceptions_handler = ExceptionsHandler()
# Data
self._config = dict()
self._wd = os.getcwd()
self._path_to_databases = None
self._request_url = None
self._vector_model_public_key = None
self._databases_public_keys = None
self._load_public_keys()
self.__logger.info('Configurator was successfully initialized.',
__name__)
def _load_public_keys(self):
path_to_config = os.path.join(self._path_service.path_to_configs,
'configurator.json')
if os.path.exists(path_to_config):
with open(path_to_config, 'r', encoding='utf-8') as file:
config = json.load(file)
self._request_url = config['request_url']
self._vector_model_public_key = config['vector_model_public_key']
self._databases_public_keys = config['databases_public_keys']
else:
self.__logger.error(
"Can't load config for Configrurator (doesn't exist).",
__name__)
def download_database(self, path_to_db):
database_name = os.path.split(path_to_db)[1]
if database_name:
try:
download_url = requests.get(
self._request_url,
params={
'public_key':
self._databases_public_keys[database_name]
}).json()["href"]
with open(path_to_db, 'wb') as database_file:
database_file.write(requests.get(download_url).content)
self._config[path_to_db] = 'downloaded'
except BaseException as exception:
self.__logger.error(
self._exceptions_handler.get_error_message(exception),
__name__)
self._config[path_to_db] = 'error'
def download_vector_model(self):
self._path_service.set_path_to_vector_model(
os.path.join(self._path_service.path_to_databases,
'ruscorpora_upos_skipgram_300_10_2017.bin.gz'))
try:
download_url = requests.get(self._request_url,
params={
'public_key':
self._vector_model_public_key
}).json()["href"]
with open(self._path_service.path_to_vector_model,
'wb') as vec_model:
vec_model.write(requests.get(download_url).content)
self._config[
'ruscorpora_upos_skipgram_300_10_2017.bin.gz'] = 'downloaded'
except BaseException as exception:
self.__logger.error(
self._exceptions_handler.get_error_message(exception),
__name__)
self._config[
'ruscorpora_upos_skipgram_300_10_2017.bin.gz'] = 'error'
def configure_system(self):
self._config['datetime'] = str(datetime.now())
for database in ['unigrams.db', 'bigrams.db', 'trigrams.db']:
path_to_database = self._path_service.get_path_to_database(
database)
if not path_to_database or not os.path.exists(path_to_database):
self.__logger.warning('Database not found: %s' % str(database),
__name__)
self.download_database(
os.path.join(self._path_service.path_to_databases,
database))
else:
self._config[database] = 'exists'
if not self._path_service.path_to_vector_model or not os.path.exists(
self._path_service.path_to_vector_model):
self.__logger.warning('Vector model not found.', __name__)
self.download_vector_model()
else:
self._config[
'ruscorpora_upos_skipgram_300_10_2017.bin.gz'] = 'exists'
self._create_config()
def _create_config(self):
with open(os.path.join('Logs', 'config.json'), 'w',
encoding='utf-8') as config:
json.dump(self._config, config, indent=4)
class Classifier:
def __init__(self):
# Services
self.__logger = Logger()
self._path_service = PathService()
self._exceptions_handler = ExceptionsHandler()
# Data
self._container = ClassificationDataContainer()
self._possible_classifiers = ['NBC', 'LogisticRegression', 'KNN']
self.__logger.info('Classifier was successfully initialized.',
__name__)
def _load_config(self):
path_to_config = os.path.join(self._path_service.path_to_configs,
'classifier.json')
if os.path.exists(path_to_config):
with open(path_to_config, 'r', encoding='utf-8') as file:
config = json.load(file)
self._possible_classifiers = config['possible_classifiers']
else:
self.__logger.error("Can't load Classifier configuration.",
__name__)
def customize(self,
unigrams_weight,
bigrams_weight,
trigrams_weight,
classifier_name='NBC'):
self._container.clear()
if classifier_name in self._possible_classifiers:
self._container.classifiers['name'] = classifier_name
else:
self._container.classifiers['name'] = 'NBC'
self.__logger.error('Got unknown classifier, set default (NBC).',
__name__)
self._container.weights['unigrams'] = unigrams_weight
self._container.weights['bigrams'] = bigrams_weight
self._container.weights['trigrams'] = trigrams_weight
try:
if self._container.weights['unigrams']:
self._container.classifiers['unigrams'] = joblib.load(
self._path_service.get_path_to_model(
'unigrams', self._container.classifiers['name']))
if self._container.weights['bigrams']:
self._container.classifiers['bigrams'] = joblib.load(
self._path_service.get_path_to_model(
'bigrams', self._container.classifiers['name']))
if self._container.weights['trigrams']:
self._container.classifiers['trigrams'] = joblib.load(
self._path_service.get_path_to_model(
'trigrams', self._container.classifiers['name']))
self.__logger.info('Models were successfully loaded.', __name__)
self.__logger.info('Classifier was successfully configured.',
__name__)
except BaseException as exception:
self.__logger.fatal(
self._exceptions_handler.get_error_message(exception),
__name__)
def _predict_tonal_by_unigrams(self):
self._container.tonalities['unigrams'] = self._container.classifiers[
'unigrams'].predict(self._container.weights['unigrams'])[0]
self._container.probabilities['unigrams'] = max(
self._container.classifiers['unigrams'].predict_proba(
self._container.weights['unigrams'])[0])
self.__logger.info(
f'Unigrams tonal: {self._container.tonalities["unigrams"]}',
__name__)
self.__logger.info(
f'Unigrams probability: {self._container.probabilities["unigrams"]}',
__name__)
def _predict_tonal_by_unigrams_bigrams(self):
self._container.tonalities['bigrams'] = self._container.classifiers[
'bigrams'].predict([[
self._container.weights['unigrams'],
self._container.weights['bigrams']
]])[0]
self._container.probabilities['bigrams'] = max(
self._container.classifiers['bigrams'].predict_proba([[
self._container.weights['unigrams'],
self._container.weights['bigrams']
]])[0])
self.__logger.info(
f'Bigrams tonal: {self._container.tonalities["bigrams"]}',
__name__)
self.__logger.info(
f'Bigrams probability: {self._container.probabilities["bigrams"]}',
__name__)
def _predict_tonal_by_unigrams_bigrams_trigrams(self):
self._container.tonalities['trigrams'] = self._container.classifiers[
'trigrams'].predict([[
self._container.weights['unigrams'],
self._container.weights['bigrams'],
self._container.weights['trigrams']
]])[0]
self._container.probabilities['trigrams'] = max(
self._container.classifiers['trigrams'].predict_proba([[
self._container.weights['unigrams'],
self._container.weights['bigrams'],
self._container.weights['trigrams']
]])[0])
self.__logger.info(
f'Trigrams tonal: {self._container.tonalities["trigrams"]}',
__name__)
self.__logger.info(
f'Trigrams probability: {self._container.probabilities["trigrams"]}',
__name__)
def _predict_intermediate_tonalities(self):
threads = list()
if self._container.weights['unigrams']:
threads.append(
Thread(target=self._predict_tonal_by_unigrams, args=()))
if self._container.weights['bigrams']:
threads.append(
Thread(target=self._predict_tonal_by_unigrams_bigrams,
args=()))
if self._container.weights['trigrams']:
threads.append(
Thread(target=self._predict_tonal_by_unigrams_bigrams_trigrams,
args=()))
for thread in threads:
thread.start()
for thread in threads:
while thread.is_alive():
time.sleep(0.1)
thread.join()
def _select_final_tonal(self):
if self._container.tonalities['unigrams'] and self._container.tonalities['bigrams'] and \
self._container.tonalities['trigrams']:
if self._container.tonalities[
'unigrams'] == self._container.tonalities['bigrams']:
self._container.tonalities[
'final'] = self._container.tonalities['unigrams']
self._container.probabilities['final'] = max(
self._container.probabilities['unigrams'],
self._container.probabilities['bigrams'])
elif self._container.tonalities[
'unigrams'] == self._container.tonalities['trigrams']:
self._container.tonalities[
'final'] = self._container.tonalities['unigrams']
self._container.probabilities['final'] = max(
self._container.probabilities['unigrams'],
self._container.probabilities['trigrams'])
elif self._container.tonalities[
'bigrams'] == self._container.tonalities['trigrams']:
self._container.tonalities[
'final'] = self._container.tonalities['bigrams']
self._container.probabilities['final'] = max(
self._container.probabilities['bigrams'],
self._container.probabilities['trigrams'])
elif self._container.tonalities[
'unigrams'] and self._container.tonalities['bigrams']:
if self._container.tonalities[
'unigrams'] != self._container.tonalities['bigrams']:
if self._container.probabilities[
'unigrams'] >= self._container.probabilities['bigrams']:
self._container.tonalities[
'final'] = self._container.tonalities['unigrams']
self._container.probabilities[
'final'] = self._container.probabilities['unigrams']
else:
self._container.tonalities[
'final'] = self._container.tonalities['bigrams']
self._container.probabilities[
'final'] = self._container.probabilities['bigrams']
elif self._container.tonalities[
'unigrams'] == self._container.tonalities['bigrams']:
self._container.tonalities[
'final'] = self._container.tonalities['unigrams']
self._container.probabilities['final'] = max(
self._container.probabilities['bigrams'],
self._container.probabilities['unigrams'])
elif self._container.tonalities['unigrams']:
self._container.tonalities['final'] = self._container.tonalities[
'unigrams']
self._container.probabilities[
'final'] = self._container.probabilities['unigrams']
def predict_tonal(self):
self._predict_intermediate_tonalities()
self._select_final_tonal()
self.__logger.info(
f'Final tonal: {self._container.tonalities["final"]}', __name__)
self.__logger.info(
f'Final probability: {self._container.probabilities["final"]}',
__name__)
return self._container.tonalities[
'final'], self._container.probabilities['final']
class Lemmatizer:
def __init__(self):
# Services
self._spell_checker = SpellChecker()
self.__logger = Logger()
self._path_service = PathService()
self._morph_analyzer = pymorphy2.MorphAnalyzer()
# Data
self._stop_words = self._read_stop_words()
self._parts_of_speech_to_remove = ['NUMR', 'NPRO', 'PREP']
self.__logger.info('Lemmatizer was successfully initialized.',
__name__)
@staticmethod
def _contains_latin_letter(word):
if word:
return all(map(lambda c: c in ascii_letters, word))
def _detect_part_of_speech(self, word):
if word:
return self._morph_analyzer.parse(word)[0].tag.POS
def _is_stop_word(self, word):
if not word:
self.__logger.warning('Got empty word.', __name__)
return
word = f' {word} '
for stop_words in self._stop_words.values():
if word in stop_words:
return True
return False
def _remove_words_without_emotions(self, text):
if not text:
self.__logger.warning('Got empty text.', __name__)
return
cleaned_text = list()
for word in re.findall(r'\w+', text):
if not self._detect_part_of_speech(word) in self._parts_of_speech_to_remove and\
not self._is_stop_word(word):
cleaned_text.append(word)
return ' '.join(cleaned_text).strip()
def _read_stop_words(self):
if os.path.exists(self._path_service.path_to_stop_words):
with open(self._path_service.path_to_stop_words,
'r',
encoding='utf-8') as file:
return json.load(file)
def _delete_words_contains_latin_letters(self, text):
text = ' '.join([
word for word in re.findall(
r'\w+', self._spell_checker.check_spelling(text.lower()))
if not self._contains_latin_letter(word) and word.isalpha()
]).strip()
if text:
return text
else:
self.__logger.warning(
'All words in document contain latin letters or all words are digits.',
__name__)
def _get_text_normal_form(self, text):
return ' '.join([self._morph_analyzer.parse(word)[0].normal_form + ' ' for word in re.findall(r'\w+', text)]) \
.strip()
def get_text_initial_form(self, text):
if not text:
self.__logger.warning('Got empty text.', __name__)
return
self.__logger.info(f'Start text: {text}', __name__)
transformations = [
self._delete_words_contains_latin_letters,
self._get_text_normal_form, self._remove_words_without_emotions
]
for transformation in transformations:
text = transformation(text)
if not text:
return
self.__logger.info(f'Lemmatized text: {text}', __name__)
return text