def run_online_training(self, ensemble, domain, interpreter=None,
input_channel=None):
from rasa_core.agent import Agent
if interpreter is None:
interpreter = RegexInterpreter()
bot = Agent(domain, ensemble,
featurizer=self.featurizer,
interpreter=interpreter)
bot.toggle_memoization(False)
try:
bot.handle_channel(
input_channel if input_channel else ConsoleInputChannel())
except TrainingFinishedException:
pass # training has finished
def run_online_training(
self,
domain, # type: Domain
interpreter, # type: NaturalLanguageInterpreter
input_channel=None # type: Optional[InputChannel]
):
# type: (...) -> None
from rasa_core.agent import Agent
if interpreter is None:
interpreter = RegexInterpreter()
bot = Agent(domain, self, interpreter=interpreter)
bot.toggle_memoization(False)
try:
bot.handle_channel(
input_channel if input_channel else ConsoleInputChannel())
except TrainingFinishedException:
pass # training has finished
def run_online_training(self,
domain, # type: Domain
interpreter, # type: NaturalLanguageInterpreter
input_channel=None # type: Optional[InputChannel]
):
# type: (...) -> None
from rasa_core.agent import Agent
if interpreter is None:
interpreter = RegexInterpreter()
bot = Agent(domain, self,
interpreter=interpreter)
bot.toggle_memoization(False)
try:
bot.handle_channel(
input_channel if input_channel else ConsoleInputChannel())
except TrainingFinishedException:
pass # training has finished
class Query_answer_unit(object):
""" This object contains tools to answer in natural language any message Fib related
Attributes:
nlu(:class:`Fibot.NLP.nlu.NLU_unit`): Object that interprets queries
training_data_file(:obj:`str`): String indicating the path to the stories markdown file
model_path(:obj:`str`): String indicating where the dialog model is
agent_ca(:class:`rasa_core.agent.Agent`): Agent capable of handling any incoming messages in catalan
agent_es(:class:`rasa_core.agent.Agent`): Agent capable of handling any incoming messages in spanish
agent_en(:class:`rasa_core.agent.Agent`): Agent capable of handling any incoming messages in english
"""
def __init__(self):
self.nlu = NLU_unit()
self.training_data_file = './Fibot/NLP/core/stories.md'
self.domain_path = './Fibot/NLP/core/domain.yml'
self.model_path = './models/dialogue'
self.agent_ca = Agent(self.domain_path,
policies=[MemoizationPolicy(),
KerasPolicy()])
self.agent_es = Agent(self.domain_path,
policies=[MemoizationPolicy(),
KerasPolicy()])
self.agent_en = Agent(self.domain_path,
policies=[MemoizationPolicy(),
KerasPolicy()])
self.agent_ca.toggle_memoization(activate=True)
self.agent_es.toggle_memoization(activate=True)
self.agent_en.toggle_memoization(activate=True)
def log(self, text):
print(colored("LOG: {}".format(text), 'cyan'))
"""
Parameters:
train (:obj:`bool`): Specifies if the agents have to be trained
This function loads the model into the agents, and trains them if necessary
"""
def load(self, trainNLG=False, trainNLU=False, train_list=None):
self.log("Cargando word vectors")
self.nlu.load(trainNLU, train_list=train_list)
self.log("Modelos NLU cargados")
if trainNLG: self.train()
self.agent_ca = Agent.load(self.model_path,
interpreter=self.nlu.interpreter_ca)
self.agent_es = Agent.load(self.model_path,
interpreter=self.nlu.interpreter_es)
self.agent_en = Agent.load(self.model_path,
interpreter=self.nlu.interpreter_en)
self.log("Agentes de diálogo cargados")
"""
Parameters:
augmentation_factor (:obj:`int`): augmentation factor for the training
max_history (:obj:`int`): max_history factor for the training
epochs (:obj:`int`): epochs (steps) for the training
batch_size (:obj:`int`): batch_size for the training
validation_split (:obj:`int`): validation_split factor for the error calculation
This function trains the agents and saves the models in the dialog's model path
"""
def train(self,
augmentation_factor=200,
max_history=7,
epochs=300,
batch_size=256,
validation_split=0.3):
self.agent_es.train(
self.training_data_file,
#augmentation_factor=augmentation_factor,
#max_history=max_history,
epochs=epochs,
batch_size=batch_size,
validation_split=validation_split)
self.agent_es.persist(self.model_path)
"""
Parameters:
augmentation_factor (:obj:`int`): augmentation factor for the training
max_history (:obj:`int`): max_history factor for the training
epochs (:obj:`int`): epochs (steps) for the training
batch_size (:obj:`int`): batch_size for the training
validation_split (:obj:`int`): validation_split factor for the error calculation
This function makes it possible to generate new stories manually.
"""
def train_manual(self,
augmentation_factor=50,
max_history=2,
epochs=500,
batch_size=50,
validation_split=0.2):
self.agent_es.train_online(self.training_data_file,
input_channel=ConsoleInputChannel(),
augmentation_factor=augmentation_factor,
max_history=max_history,
epochs=epochs,
batch_size=batch_size,
validation_split=validation_split)
"""
Parameters:
message (:obj:`str`): the incoming message from some user
sender_id(:obj:`str`): The id (chat_id) of the sender of the messages
language(:obj:`str`): The language of the sender ('ca', 'es' or 'en')
debug(:obj:`bool`): Boolean value indicating wether it has to output model's response
This function returns the response from the agent using the actions
defined in Fibot/NLP/core/actions.py
"""
def get_response(self,
message,
sender_id=UserMessage.DEFAULT_SENDER_ID,
language='es',
debug=True):
confidence = self.nlu.get_intent(message, language)['confidence']
if debug:
print("\n\n\n####### UN USUARIO HA DICHO: {} #######".format(
colored(message, 'magenta')))
print("\n\nINFORMACIÓN DE MENSAJE:")
print("__________________________________________")
print("El intérprete ha predecido la siguiente intención:")
intent = self.nlu.get_intent(message, language)
entities = self.nlu.get_entities(message, language)
print('Intención: ' +
colored(intent['name'], 'cyan', attrs=['bold']))
print('Confianza: ' +
colored(str(intent['confidence'])[:8], 'cyan'))
if entities: print("\nY las siguientes entidades:")
else: print("\nNo se han encontrado entidades en el mensaje")
i = 0
for entity in entities:
print(colored('[' + str(i) + ']', 'red'))
print('Tipo: ' +
colored(entity['entity'], 'cyan', attrs=['bold']))
print('Valor: ' +
colored(entity['value'], 'cyan', attrs=['bold']))
print('Confianza: ' +
colored(str(entity['confidence'])[:8], 'cyan'))
i += 1
if confidence < 0.5:
with open('./Data/error_responses.json', 'rb') as fp:
messages = json.load(fp)['not_understand']
return [{
'recipient_id':
sender_id,
'text':
messages[language][randint(0,
len(messages[language]) - 1)]
}]
if language == 'ca':
return self.agent_ca.handle_message(message, sender_id=sender_id)
elif language == 'es':
return self.agent_es.handle_message(message, sender_id=sender_id)
else:
return self.agent_en.handle_message(message, sender_id=sender_id)