def continueOrQuit(self,i):
"""checks if user is ready to start match"""
if nlp.isReady(i):
#reset prior
flat = nltk.probability.UniformProbDist(self.semanticKnowledge.entities())
self.semanticKnowledge.setPriors(flat)
self.episodicBuffer.newMatch()
return self.toAskingState()
elif nlp.classifyYN(i) == -1:
self.current_thread().exit()
#sys.exit()
else:
return "let me know when you are ready"
def continueOrQuit(self, i):
"""checks if user is ready to start match"""
if nlp.isReady(i):
#reset prior
flat = nltk.probability.UniformProbDist(
self.semanticKnowledge.entities())
self.semanticKnowledge.setPriors(flat)
self.episodicBuffer.newMatch()
return self.toAskingState()
elif nlp.classifyYN(i) == -1:
self.current_thread().exit()
#sys.exit()
else:
return "let me know when you are ready"
def semanticYnReceivePatiently(self):
"""
receives a YN message while waiting for additional messages
Also, it addes information to the episodic buffer
"""
msg = self.dialogManager.toAgentBuffer.get()
self.dialogManager.toAgentBuffer.task_done()
while True:
time.sleep(2)
if self.dialogManager.toAgentBuffer.empty():
break
msg = msg + "\n" + self.dialogManager.toAgentBuffer.get()
self.dialogManager.toAgentBuffer.task_done()
sem = nlp.classifyYN(msg)
self.episodicBuffer.addTurn("you",msg,self.state,semantics=sem)
return msg
def semanticYnReceivePatiently(self):
"""
receives a YN message while waiting for additional messages
Also, it addes information to the episodic buffer
"""
msg = self.dialogManager.toAgentBuffer.get()
self.dialogManager.toAgentBuffer.task_done()
while True:
time.sleep(2)
if self.dialogManager.toAgentBuffer.empty():
break
msg = msg + "\n" + self.dialogManager.toAgentBuffer.get()
self.dialogManager.toAgentBuffer.task_done()
sem = nlp.classifyYN(msg)
self.episodicBuffer.addTurn("you", msg, self.state, semantics=sem)
return msg
def __new__(self, annotationFile="../annotate/emo20q.txt"):
# read in tournament, do some testing, get some stats
tournament = HumanHumanTournament(
annotationFile) + HumanComputerTournament()
#count turns in a dict, for pruning
qcounts = defaultdict(int)
for m in tournament.matches():
for t in m.turns():
qcounts[t.qgloss] += 1
#create graph
G = nx.DiGraph()
G.add_node(("Emotion", ))
#add emo20q data to graph
for m_idx, m in enumerate(tournament.matches()):
H = nx.DiGraph()
parent = ("Emotion", )
edge = "yes"
H.add_node(parent)
for t in m.turns():
if (qcounts[t.qgloss] > 0):
#deal with guesses:
guess = re.search(r'^e==(\w+)(\|\|.*)*$', t.qgloss)
if (guess):
#if re.search(r'close', t.a ): #connect a close guess
# H.add_edge(parent,guess.group(1), weight=20)
continue
#deal with questions
if (t.qgloss.find("non-yes-no") == 0): continue
if (t.qgloss.find("clarification") == 0): continue
if (t.qgloss.find("giveup") == 0): continue
ans = "other"
# #use nlp module here!
# if t.a.find("yes") == 0 :
# ans = "yes"
# weight = -5
# if t.a.find("no") == 0 :
# ans = "no"
# weight = 2
# if ans == "other":
# weight = 5
if nlp.classifyYN(t.a) == 1:
ans = "yes"
weight = -1
if nlp.classifyYN(t.a) == -1:
ans = "no"
weight = 0
if ans == "other":
weight = 1
newNode = parent, (t.qgloss, ans)
H.add_edge(parent, newNode, weight=weight)
parent = newNode
if t.qgloss == "e.valence==positive" and m.emotion(
) == "happiness" and ans == "no":
print "wtf!"
print t.qgloss, t.a, m.emotion()
for t in m.turns():
print t.qgloss, t.a
else: #python has for... else!
#if(parent == "Emotion"): # add intermediate node
# H.add_edge(parent,"LowFrequencyGuesses")
# parent = "LowFrequencyGuesses"
emotionSynonyms = re.search(r'(\w+)(?:/(\w+))*$', m.emotion())
for e in emotionSynonyms.groups():
if e is not None: H.add_edge(parent, e, weight=20)
G.add_edges_from(H.edges(data=True))
return G
