def response_matrix(last_utterance, chatbot, conversation):
with open("../data/da_matrix.csv") as f_matrix:
mat = np.loadtxt(f_matrix,
delimiter=",",
skiprows=1,
usecols=range(1, 27))
mat = mat / np.sum(mat, axis=0)
da_names = [
da.name for da in DA
if da.name not in ["statement", "question", "response_action"]
]
response_col = da_names.index(last_utterance.dialogue_act.name)
sentiment = np.random.normal(chatbot.personality.mood, 2)
while sentiment < 1 or sentiment > 10:
sentiment = np.random.normal(chatbot.personality.mood, 2)
assertiveness = np.random.normal(chatbot.personality.mood, 2)
while assertiveness < 1 or assertiveness > 10:
assertiveness = np.random.normal(chatbot.personality.mood, 2)
utterance = Utterance(
chatbot.name, DA[np.random.choice(da_names, p=mat[:,
response_col])],
last_utterance.topic, int(sentiment), int(assertiveness))
text = nlg.generate_response_text(utterance, chatbot, conversation)
utterance.set_text(text)
return utterance
def new_conversation(conversation,
chatbot,
user,
personas=None,
last_utterance=None):
# New conversation started
# prev = greeting, then greeting, query, etc.
if last_utterance.dialogue_act == DA.greeting:
#TODO overcome limitation of not making text here. (use meta text)
# Ideal, would be able to be assertive and make
# greeting + question_ or statement_
# call nlg module here, from IA.
assertiveness = response_assertiveness(chatbot)
if respond_passively(assertiveness):
# if passive response, then only greeting.
utterance = Utterance(chatbot.name, DA.greeting, "self_user",
chatbot.personality.mood,
chatbot.personality.assertiveness)
text = nlg.generate_response_text(utterance, chatbot, conversation)
utterance.set_text(text)
return utterance
else: # respond with greeting AND something else.
# else: query user general
greeting_text = generic_response.greeting(chatbot, conversation,
chatbot.personality.mood)
greeting_text = greeting_text[0].upper() + greeting_text[1:]
greeting_text = (greeting_text +
"." if chatbot.personality.mood < 8
and chatbot.personality.assertiveness < 8 else
greeting_text + "!")
utterance = tactic.query_user_general(conversation, chatbot, user,
personas)
text = greeting_text + " " + utterance.text
utterance.set_text(text)
return utterance
elif (not topic_is_self(last_utterance.topic)
and not topic_is_user(last_utterance.topic)
and is_question(last_utterance.dialogue_act)):
return Utterance(chatbot.name,
question_to_statement(last_utterance.dialogue_act),
last_utterance.topic, chatbot.personality.mood,
chatbot.personality.assertiveness)
elif (not topic_is_self(last_utterance.topic)
and not topic_is_user(last_utterance.topic)
and is_statement(last_utterance.dialogue_act)):
return Utterance(chatbot.name,
statement_to_question(last_utterance.dialogue_act),
last_utterance.topic, chatbot.personality.mood,
chatbot.personality.assertiveness)
else:
return tactic.psychiatrist(last_utterance, chatbot.name)
def static_matrix(conversation, chatbot, user, personas=None):
"""
A manually constructed decision tree to implement a rule based system as a
baseline.
:param conversation: current Conversation
:param chatbot: Persona of chatbot
:param user: Persona of user
:param personas: Dictionary of str "persona_id" to Persona
"""
last_utterance = conversation.last_utterance
# Assess mood and magnitude of change to mood necessary
mood_magnitude = chatbot.personality.mood - user.personality.mood
# Assess topic sentiment in relation to desired mood
topic_magnitude = user.topic_magnitude(last_utterance.topic,
chatbot.personality.mood)
# static reactions:
if last_utterance.dialogue_act == DA.farewell:
utterance = Utterance(chatbot.name, DA.farewell, "self_user",
chatbot.personality.mood,
chatbot.personality.assertiveness)
text = nlg.generate_response_text(utterance, chatbot, conversation)
utterance.set_text(text)
return utterance
#if len(conversation.topic_to_utterances.keys()) <= 0:
if conversation.new_convo:
conversation.new_convo = False
# New conversation started
if last_utterance.dialogue_act == DA.greeting:
utterance = Utterance(chatbot.name, DA.greeting, "self_user",
chatbot.personality.mood,
chatbot.personality.assertiveness)
text = nlg.generate_response_text(utterance, chatbot, conversation)
utterance.set_text(text)
return utterance
elif (not topic_is_self(last_utterance.topic)
and not topic_is_user(last_utterance.topic)
and is_question(last_utterance.dialogue_act)):
utterance = Utterance(
chatbot.name,
question_to_statement(last_utterance.dialogue_act),
last_utterance.topic, chatbot.personality.mood,
chatbot.personality.assertiveness)
text = nlg.generate_response_text(utterance, chatbot, conversation)
utterance.set_text(text)
return utterance
elif (not topic_is_self(last_utterance.topic)
and not topic_is_user(last_utterance.topic)
and is_statement(last_utterance.dialogue_act)):
utterance = Utterance(
chatbot.name,
statement_to_question(last_utterance.dialogue_act),
last_utterance.topic, chatbot.personality.mood,
chatbot.personality.assertiveness)
text = nlg.generate_response_text(utterance, chatbot, conversation)
utterance.set_text(text)
return utterance
else:
return tactic.psychiatrist(last_utterance, chatbot.name)
else: # Ongoing Conversation
# topics have been discussed, and conversation ongoing
if topic_is_self(last_utterance.topic) \
or topic_is_user(last_utterance.topic):
return tactic.psychiatrist(last_utterance, chatbot.name)
else:
return response_matrix(last_utterance, chatbot, conversation)