def __new__(self, annotationFile="../annotate/emo20q.txt"):
# read in tournament, do some testing, get some stats
tournament = HumanHumanTournament(annotationFile)
#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.MultiDiGraph()
G.add_node("Emotion")
#add emo20q data to graph
for m in tournament.matches():
H = nx.MultiDiGraph()
parent = "Emotion"
prevAns = "yes"
H.add_node(parent)
for t in m.turns():
if (qcounts[t.qgloss] < 2): continue
#deal with guesses:
guess = re.search(r'^e==(\w+)$', t.qgloss)
if (guess):
H.add_edge(parent, guess.group(1), ans=prevAns)
#G.add_nodes_from(H)
continue
#deal with questions
if (t.qgloss.find("non-yes-no") == 0): continue
if (t.qgloss.find("giveup") == 0): continue
ans = "other"
if t.agloss.find("yes") == 0: ans = "yes"
if t.agloss.find("no") == 0: ans = "no"
#if ans == "other": continue
H.add_edge(parent, t.qgloss, ans=prevAns)
parent = t.qgloss
prevAns = ans
else: #python has for... else!
# if(parent == "Emotion"): # add intermediate node
# H.add_edge(parent,"LowFrequencyGuesses")
# parent = "LowFrequencyGuesses"
emotionSynonyms = re.search(r'(\w+)(?:/(\w+))*$', m.emotion())
for e in emotionSynonyms.groups():
if e is not None: H.add_edge(parent, e, ans=prevAns)
# plt.figure(figsize=(18,18))
# pos=nx.graphviz_layout(H,prog='twopi',root='Emotion',args='',)
# nx.draw(H,pos,node_size=10,alpha=0.5,node_color="blue", with_labels=True)
# edge_labels=dict([((u,v,),d['ans'])
# for u,v,d in H.edges(data=True)])
# nx.draw_networkx_edge_labels(G,pos,edge_labels=edge_labels)
# plt.show()
G.add_edges_from(H.edges(data=True))
return G
def __new__(self, annotationFile="../annotate/emo20q.txt"):
# read in tournament, do some testing, get some stats
tournament = HumanHumanTournament(annotationFile)
#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.MultiDiGraph()
G.add_node("Emotion")
#add emo20q data to graph
for m in tournament.matches():
H = nx.MultiDiGraph()
parent = "Emotion"
prevAns = "yes"
H.add_node(parent)
for t in m.turns():
if(qcounts[t.qgloss]<2): continue
#deal with guesses:
guess = re.search(r'^e==(\w+)$',t.qgloss )
if(guess):
H.add_edge(parent,guess.group(1),ans=prevAns)
#G.add_nodes_from(H)
continue
#deal with questions
if (t.qgloss.find("non-yes-no")==0): continue
if (t.qgloss.find("giveup")==0): continue
ans = "other"
if t.agloss.find("yes") == 0 : ans = "yes"
if t.agloss.find("no") == 0 : ans = "no"
#if ans == "other": continue
H.add_edge(parent,t.qgloss,ans=prevAns)
parent = t.qgloss
prevAns = ans
else: #python has for... else!
# if(parent == "Emotion"): # add intermediate node
# H.add_edge(parent,"LowFrequencyGuesses")
# parent = "LowFrequencyGuesses"
emotionSynonyms = re.search(r'(\w+)(?:/(\w+))*$',m.emotion() )
for e in emotionSynonyms.groups():
if e is not None: H.add_edge(parent,e,ans=prevAns)
# plt.figure(figsize=(18,18))
# pos=nx.graphviz_layout(H,prog='twopi',root='Emotion',args='',)
# nx.draw(H,pos,node_size=10,alpha=0.5,node_color="blue", with_labels=True)
# edge_labels=dict([((u,v,),d['ans'])
# for u,v,d in H.edges(data=True)])
# nx.draw_networkx_edge_labels(G,pos,edge_labels=edge_labels)
# plt.show()
G.add_edges_from(H.edges(data=True))
return G
def __new__(self, annotationFile="../annotate/emo20q.txt"):
# read in tournament, do some testing, get some stats
tournament = HumanHumanTournament(annotationFile)
#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] > 1):
#deal with guesses:
guess = re.search(r'^e==(\w+)$', t.qgloss)
if (guess):
H.add_edge(parent, guess.group(1))
#G.add_nodes_from(H)
continue
#deal with questions
if (t.qgloss.find("non-yes-no") == 0): continue
if (t.qgloss.find("giveup") == 0): continue
ans = "other"
if t.agloss.find("yes") == 0: ans = "yes"
if t.agloss.find("no") == 0: ans = "no"
#if ans == "other": continue
newNode = (t.qgloss, ans)
H.add_edge(parent, newNode)
parent = newNode
else: #python has for... else!
# if(parent == "Emotion"): # add intermediate node
# H.add_edge(parent,"LowFrequencyGuesses")
# parent = "LowFrequencyGuesses"
emotionSynonyms = re.search(r'(\w+)(?:/(\w+))*$', m.emotion())
for e in emotionSynonyms.groups():
if e is not None: H.add_edge(parent, e)
G.add_edges_from(H.edges())
return G
def __init__(self):
# read in tournament, do some testing, get some stats
tournament = HumanHumanTournament()
self._dictionary = defaultdict(list)
for m in tournament.matches():
for t in m.turns():
self._dictionary[t.questionId()].append(t.q)
def __init__(self):
# read in tournament, do some testing, get some stats
tournament = HumanHumanTournament()
self._dictionary = defaultdict(list)
for m in tournament.matches():
for t in m.turns():
self._dictionary[t.questionId()].append(t.q)
out['param'] = {'type':x.type}
if x.provenance:
out['param'] = {'provenance':x.provenance}
out['container'] = [{'type':'Match',
'container':x.turns()}]
#out['turns'] = []
return out
if isinstance(x,Turn):
out['type'] = 'Turn'
out['container'] = [{'type':'Question',
'param':{'text':x.q,
'gloss':x.qgloss}},
{'type':'Answer',
'param':{'text':x.a,
'gloss':x.agloss}}]
return out
#return {}
for m in hh.matches():
m.type='dialog:human-human'
m.provenance= ['xmpp','text','students','generation0']
#create empty dialog in couch
#print dir(m)
#print json.dumps(m,sys.stdout,default=match2JsonEncoder,sort_keys=True, indent=2)
#time.sleep(0.1)
doc_id, doc_rev = db.save(json.loads(json.dumps(m,sys.stdout,default=match2JsonEncoder,sort_keys=False, indent=2)))
print
# print t.qgloss
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
#!/usr/bin/python
from emo20q.data.base import HumanHumanTournament
import networkx as nx
from networkx import graphviz_layout
import matplotlib.pyplot as plt
from collections import defaultdict
import re
# read in tournament, do some testing, get some stats
tournament = HumanHumanTournament("../annotate/emo20q.txt")
#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",
#parent = ()
H.add_node(parent)
for t in m.turns():
if (qcounts[t.qgloss] > 10):
#deal with guesses:
from emo20q.data.base import HumanHumanTournament
import networkx as nx
from networkx import graphviz_layout
import matplotlib.pyplot as plt
from collections import defaultdict
import re
# read in tournament, do some testing, get some stats
tournament = HumanHumanTournament("../annotate/emo20q.txt")
#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",
#parent = ()
H.add_node(parent)
for t in m.turns():
if(qcounts[t.qgloss]>10):
'param': {
'text': x.q,
'gloss': x.qgloss
}
}, {
'type': 'Answer',
'param': {
'text': x.a,
'gloss': x.agloss
}
}]
return out
#return {}
for m in hh.matches():
m.type = 'dialog:human-human'
m.provenance = ['xmpp', 'text', 'students', 'generation0']
#create empty dialog in couch
#print dir(m)
#print json.dumps(m,sys.stdout,default=match2JsonEncoder,sort_keys=True, indent=2)
#time.sleep(0.1)
doc_id, doc_rev = db.save(
json.loads(
json.dumps(m,
sys.stdout,
default=match2JsonEncoder,
sort_keys=False,
indent=2)))
