def initialize():
anotacoes = parser.parseAnnotation()
dominios = parser.parseDominio()
participantes = {}
atributos = ["type", "size", "colour", "hpos", "vpos", "near", "left", "right", "below", "above", "in-front-of"]
targets = {"01f-t1n":"h", "01f-t1r":"h", "01f-t2n":"h", "01f-t2r":"h", "01o-t1n":"h", "01o-t1r":"h", "01o-t2n":"h", "01o-t2r":"h", "02f-t1n":"o", "02f-t1r":"o", "02f-t2n":"o", "02f-t2r":"o", "02o-t1n":"o", "02o-t1r":"o", "02o-t2n":"o", "02o-t2r":"o", "03f-t1n":"m", "03f-t1r":"m", "03f-t2n":"m", "03f-t2r":"m", "03o-t1n":"m", "03o-t1r":"m", "03o-t2n":"m", "03o-t2r":"m", "04f-t1n":"a", "04f-t1r":"a", "04f-t2n":"a", "04f-t2r":"a", "04o-t1n":"a", "04o-t1r":"a", "04o-t2n":"a", "04o-t2r":"a", "05f-t1n":"m", "05f-t2n":"m", "05f-t1r":"m", "05f-t2r":"m", "05o-t1n":"m", "05o-t1r":"m", "05o-t2n":"m", "05o-t2r":"m", "06f-t1n":"h", "06f-t1r":"h", "06f-t2n":"h", "06f-t2r":"h", "06o-t1n":"h", "06o-t1r":"h", "06o-t2n":"h", "06o-t2r":"h", "07f-t1n":"i", "07f-t1r":"i", "07f-t2n":"i", "07f-t2r":"i", "07o-t1n":"i", "07o-t1r":"i", "07o-t2n":"i", "07o-t2r":"i", "08f-t1n":"a", "08f-t1r":"a", "08f-t2n":"a", "08f-t2r":"a", "08o-t1n":"a", "08o-t1r":"a", "08o-t2n":"a", "08o-t2r":"a" }
return dominios, targets, anotacoes, atributos, participantes
def initialize():
anotacoes = parser.parseAnnotation()
dominios = parser.parseDominio()
participantes = {}
atributos = ["type", "colour", "hpos", "vpos", "near", "left", "right", "below", "above"]
targets = {"abs1":"e2", "abs3":"e2", "abs5":"e3", "proj1":"e2", "proj3":"e4", "proj5":"e2" }
return dominios, targets, anotacoes, atributos, participantes
def run_validation(dominios, folds, featureVector, participantesInput, incluirParticipante):
[inputs, frequencias, participantes] = [{}, {}, {}]
# Inicializa Vetor de Caracteristicas
for fold in folds:
frequencias[fold] = utils.countAttributeFrequencyIndividual(folds, fold)
participantes[fold] = parser.descriptionsMeans(folds, fold, dominios)
inputs[fold] = parser.parseSVMInput(folds[fold], featureVector, participantes[fold], frequencias[fold], incluirParticipante)
[acertos, total] = [{}, {}]
keys = folds.keys()
keys.sort()
for fold in folds:
# print "Fold: ", fold
# print 50 * "-"
resultados = svm.run(inputs, fold)
for combination in resultados.keys():
if combination not in acertos.keys():
acertos[combination] = {}
total[combination] = {}
for svm1 in resultados[combination][0].keys():
if svm1 not in acertos[combination].keys():
acertos[combination][svm1] = 0.0
total[combination][svm1] = 0.0
acertos[combination][svm1] = acertos[combination][svm1] + resultados[combination][0][svm1]
total[combination][svm1] = total[combination][svm1] + resultados[combination][1][svm1]
combinacoes = {}
acuracias = {}
for combination in acertos.keys():
aux = combination.split(",")
C = float(aux[0])
gamma = float(aux[1])
# print 20 * "-"
# print str(C) + " - " + str(gamma)
# print 20 * "-"
for svm1 in acertos[combination].keys():
if total[combination][svm1] == 0:
acuracia = 0
else:
acuracia = acertos[combination][svm1] / total[combination][svm1]
# print str(svm1) + ": " + str(acuracia)
if svm1 not in combinacoes.keys():
acuracias[svm1] = acuracia
combinacoes[svm1] = {}
combinacoes[svm1]["C"] = C
combinacoes[svm1]["gamma"] = gamma
elif acuracia > acuracias[svm1]:
acuracias[svm1] = acuracia
combinacoes[svm1]["C"] = C
combinacoes[svm1]["gamma"] = gamma
return combinacoes
def crossValidation(anotacoes = {}):
if len(anotacoes) == 0:
anotacoes = parser.parseAnnotation()
folds = {}
for i in range(1,7):
folds[i] = {}
for anotacao in anotacoes:
contexto = anotacao["caracteristicas"]["context"]
participante = anotacao["caracteristicas"]["trial"]
for i in range(1,7):
if participante not in folds[i]:
folds[i][participante] = anotacao
break
return folds
def crossValidation(fold = int, anotacoes = {}):
if len(anotacoes) == 0:
anotacoes = parser.parseAnnotation()
folds = {}
nroParticipantes = contabilizaParticipantes(anotacoes)
nroAnotacoes = len(anotacoes)
expressoesParticipantePorFold = (nroAnotacoes / nroParticipantes) / fold
nroExpressoes = {}
for i in range(1,fold+1):
folds[str(i)] = {}
nroExpressoes[str(i)] = {}
for anotacao in anotacoes:
contexto = anotacao["caracteristicas"]["context"]
participante = anotacao["caracteristicas"]["trial"]
achou = False
for i in range(1,fold+1):
if participante not in folds[str(i)]:
folds[str(i)][participante] = []
nroExpressoes[str(i)][participante] = expressoesParticipantePorFold
if nroExpressoes[str(i)][participante] > 0:
folds[str(i)][participante].append(anotacao)
nroExpressoes[str(i)][participante] = nroExpressoes[str(i)][participante] -1
achou = True
break
if achou == False:
for i in range(1,fold+1):
if nroExpressoes[str(i)][participante] == 0:
folds[str(i)][participante].append(anotacao)
nroExpressoes[str(i)][participante] = nroExpressoes[str(i)][participante] -1
break
return folds
def run(dominios, targets, folds, atributos, participantes, incluirParticipante):
featureVector = parser.parseFeatureVector(dominios, targets)
inputs = {}
for fold in folds:
frequencias = countAttributeFrequencyIndividual(folds, fold)
inputs[fold] = parser.parseSVMInput(folds[fold], featureVector, participantes, frequencias, incluirParticipante)
resultadoTotal = []
dice = []
masi = []
acuracia = 0.0
# print 10 * "*"
# if incluirParticipante == True:
# print "SVM com o id do participante"
# else:
# print "SVM sem o id do participante"
# print 10 * "*"
for fold in ["1","2","3","4","5","6"]:
print "FOLD: " + fold
resultados = svm.run(inputs, fold)
resultadoTotal.append(resultados)
acertos = resultados[0]
total = resultados[1]
expressoes = resultados[2]
tp = resultados[3]
tn = resultados[4]
fp = resultados[5]
fn = resultados[6]
for expressao in expressoes:
contexto = expressao["anotacao"]["caracteristicas"]["context"]
descricao = defineExpressao(expressao["previsoes"], dominios[contexto], targets[contexto])
descricaoR = expressao["anotacao"]["descricao"]
A = ass.parse(descricao)
B = ass.parse(descricaoR)
DICE = ass.dice(A, B)
MASI = ass.masi(A, B)
if DICE == 1.0:
acuracia = acuracia + 1.0
dice.append(DICE)
masi.append(MASI)
# if expressao["previsoes"]["relation"] > 0:
# print "Descricao Prevista : " + str(expressao["previsoes"])
# print "Descricao Prevista : " + str(descricao)
# print "Descricao Real : " + str(expressao["classes"])
# print "Descricao Real : " + str(descricaoR)
# print "Dice: " + str(DICE)
# print 30 * "-"
for atributo in acertos.keys():
print "Atributo: " + str(atributo)
print "Acuracia: " + str(acertos[atributo] / total[atributo])
# print "Verdadeiros Positivos: " + str(tp[atributo])
# print "Falsos Positivos: " + str(fp[atributo])
# print "Verdadeiros Negativos: " + str(tn[atributo])
# print "Falsos Negativos: " + str(fn[atributo])
print 10 * "-"
print 50 * "*"
print 50 * "*"
print "Expressoes: "
print "Dice Total: " + str(np.mean(dice))
print "Masi Total: " + str(np.mean(masi))
print "Acuracia: " + str(acuracia / len(dice))
print 50 * "*"
acertosT = {}
totalT = {}
for resultados in resultadoTotal:
acertos = resultados[0]
total = resultados[1]
for atributo in acertos.keys():
if atributo not in acertosT:
acertosT[atributo] = 0.0
totalT[atributo] = 0.0
acertosT[atributo] = acertosT[atributo] + acertos[atributo]
totalT[atributo] = totalT[atributo] + total[atributo]
for atributo in acertosT.keys():
print "Atributo: " + str(atributo)
print "Acuracia: " + str(acertosT[atributo] / totalT[atributo])
print 10 * "-"
return resultadoTotal, dice, masi, acuracia
def run(dominios, targets, folds, atributos, participantesInput, incluirParticipante):
featureVector = parser.parseFeatureVector(dominios, targets)
inputs = {}
frequencias = {}
participantes = {}
# Inicializa Vetor de Caracteristicas
for fold in folds:
frequencias[fold] = utils.countAttributeFrequencyIndividual(folds, fold)
participantes[fold] = parser.descriptionsMeans(folds, fold, dominios)
inputs[fold] = parser.parseSVMInput(folds[fold], featureVector, participantes[fold], frequencias[fold], incluirParticipante)
resultadoTotal = []
diceTotal = []
masiTotal = []
acuraciaTotal = 0.0
for testeFold in folds:
teste = folds[testeFold]
inputTeste = inputs[testeFold]
treinamentoFolds = {}
inputsTreinamento = {}
for foldAux in folds:
if foldAux != testeFold:
treinamentoFolds[foldAux] = folds[foldAux]
inputsTreinamento[foldAux] = inputs[foldAux]
combinacoes = run_validation(dominios, treinamentoFolds, featureVector, participantesInput, incluirParticipante)
resultados = svm.run2(inputsTreinamento, inputTeste, combinacoes)
resultadoTotal.append(resultados)
acertos = resultados[0]
total = resultados[1]
expressoes = resultados[2]
tp = resultados[3]
tn = resultados[4]
fp = resultados[5]
fn = resultados[6]
dice = []
masi = []
acuracia = 0
for expressao in expressoes:
contexto = expressao["anotacao"]["caracteristicas"]["context"]
descricao = utils.defineExpressao(expressao["previsoes"], dominios[contexto], targets[contexto])
descricaoR = expressao["anotacao"]["descricao"]
A = ass.parse(descricao)
B = ass.parse(descricaoR)
DICE = ass.dice(A, B)
MASI = ass.masi(A, B)
# print 20 * "-"
# print descricaoR
# print descricao
# print DICE
# print 20 * "-"
if DICE == 1.0:
acuracia = acuracia + 1.0
acuraciaTotal = acuraciaTotal + 1.0
dice.append(DICE)
masi.append(MASI)
diceTotal.append(DICE)
masiTotal.append(MASI)
# for atributo in acertos.keys():
# print "Attribute: " + str(atributo)
# print "Accuracy: " + str(acertos[atributo] / total[atributo])
# print 10 * "-"
#
# print 50 * "*"
#
# print "\n"
# print "General:"
# print 50 * "*"
# print "Expressions: "
# print "Dice: " + str(np.mean(dice))
# print "Masi: " + str(np.mean(masi))
# print "Accuracy: " + str(acuracia / len(dice))
# print "\n"
# print "\n"
# print "General:"
# print 50 * "*"
# print "Expressions: "
# print "Dice: " + str(np.mean(diceTotal))
# print "Masi: " + str(np.mean(masiTotal))
# print "Accuracy: " + str(acuraciaTotal / len(diceTotal))
# print "\n"
acertosT = {}
totalT = {}
for resultados in resultadoTotal:
acertos = resultados[0]
total = resultados[1]
for atributo in acertos.keys():
if atributo not in acertosT:
acertosT[atributo] = 0.0
totalT[atributo] = 0.0
acertosT[atributo] = acertosT[atributo] + acertos[atributo]
totalT[atributo] = totalT[atributo] + total[atributo]
# print "Attributes:"
# print 15 * "-"
# for atributo in acertosT.keys():
# print "Attribute: " + str(atributo)
# print "Accuracy: " + str(acertosT[atributo] / totalT[atributo])
# print 10 * "-"
return resultadoTotal, diceTotal, masiTotal, acuraciaTotal
def run(dominios, targets, folds, atributos, participantes, incluirParticipante):
featureVector = parser.parseFeatureVector(dominios, targets)
inputs = {}
# Cross Validation
#folds = cross.crossValidation(6, annotations)
# Inicializa Vetor de Caracteristicas
for fold in folds:
frequencias = countAttributeFrequencyIndividual(folds, fold)
inputs[fold] = parser.parseSVMInputSecondVersion(folds[fold], featureVector, participantes, frequencias, incluirParticipante)
resultadoTotal = []
diceTotal = []
masiTotal = []
acuraciaTotal = 0.0
for testeFold in folds:
teste = folds[testeFold]
inputTeste = inputs[testeFold]
treinamentoFolds = {}
inputsTreinamento = {}
for foldAux in folds:
if foldAux != testeFold:
treinamentoFolds[foldAux] = folds[foldAux]
inputsTreinamento[foldAux] = inputs[foldAux]
acertos = {}
total = {}
tp = {}
tn = {}
fp = {}
fn = {}
keys = folds.keys()
keys.sort()
for fold in treinamentoFolds:
# print "Fold: ", fold
# print 50 * "-"
resultados = svm.run(inputsTreinamento, fold)
for combination in resultados.keys():
if combination not in acertos.keys():
acertos[combination] = {}
total[combination] = {}
tp[combination] = {}
tn[combination] = {}
fp[combination] = {}
fn[combination] = {}
for svm1 in resultados[combination][0].keys():
if svm1 not in acertos[combination].keys():
acertos[combination][svm1] = 0.0
total[combination][svm1] = 0.0
tp[combination][svm1] = 0.0
tn[combination][svm1] = 0.0
fp[combination][svm1] = 0.0
fn[combination][svm1] = 0.0
acertos[combination][svm1] = acertos[combination][svm1] + resultados[combination][0][svm1]
total[combination][svm1] = total[combination][svm1] + resultados[combination][1][svm1]
tp[combination][svm1] = tp[combination][svm1] + resultados[combination][3][svm1]
tn[combination][svm1] = tn[combination][svm1] + resultados[combination][4][svm1]
fp[combination][svm1] = fp[combination][svm1] + resultados[combination][5][svm1]
fn[combination][svm1] = fn[combination][svm1] + resultados[combination][6][svm1]
combinacoes = {}
acuracias = {}
for combination in acertos.keys():
aux = combination.split(",")
C = float(aux[0])
gamma = float(aux[1])
# print 20 * "-"
# print str(C) + " - " + str(gamma)
# print 20 * "-"
for svm1 in acertos[combination].keys():
acuracia = acertos[combination][svm1] / total[combination][svm1]
# print str(svm1) + ": " + str(acuracia)
if svm1 not in combinacoes.keys():
acuracias[svm1] = acuracia
combinacoes[svm1] = {}
combinacoes[svm1]["C"] = C
combinacoes[svm1]["gamma"] = gamma
elif acuracia > acuracias[svm1]:
acuracias[svm1] = acuracia
combinacoes[svm1]["C"] = C
combinacoes[svm1]["gamma"] = gamma
# print "\n"
# print "Combinacao: " + str(combinacoes)
resultados = svm.run2(inputsTreinamento, inputTeste, combinacoes)
resultadoTotal.append(resultados)
acertos = resultados[0]
total = resultados[1]
expressoes = resultados[2]
tp = resultados[3]
tn = resultados[4]
fp = resultados[5]
fn = resultados[6]
dice = []
masi = []
for expressao in expressoes:
contexto = expressao["anotacao"]["caracteristicas"]["context"]
descricao = defineExpressao(expressao["previsoes"], dominios[contexto], targets[contexto])
descricaoR = expressao["anotacao"]["descricao"]
A = ass.parse(descricao)
B = ass.parse(descricaoR)
DICE = ass.dice(A, B)
MASI = ass.masi(A, B)
# print 20 * "-"
# print descricaoR
# print descricao
# print DICE
# print 20 * "-"
if DICE == 1.0:
acuracia = acuracia + 1.0
acuraciaTotal = acuraciaTotal + 1.0
dice.append(DICE)
masi.append(MASI)
diceTotal.append(DICE)
masiTotal.append(MASI)
# for atributo in acertos.keys():
# print "Attribute: " + str(atributo)
# print "Accuracy: " + str(acertos[atributo] / total[atributo])
# print 10 * "-"
#
# print 50 * "*"
#
# print "\n"
# print "General:"
# print 50 * "*"
# print "Expressions: "
# print "Dice: " + str(np.mean(dice))
# print "Masi: " + str(np.mean(masi))
# print "Accuracy: " + str(acuracia / len(dice))
# print "\n"
print "\n"
print "General:"
print 50 * "*"
print "Expressions: "
print "Dice: " + str(np.mean(diceTotal))
print "Masi: " + str(np.mean(masiTotal))
print "Accuracy: " + str(acuraciaTotal / len(diceTotal))
print "\n"
acertosT = {}
totalT = {}
for resultados in resultadoTotal:
acertos = resultados[0]
total = resultados[1]
for atributo in acertos.keys():
if atributo not in acertosT:
acertosT[atributo] = 0.0
totalT[atributo] = 0.0
acertosT[atributo] = acertosT[atributo] + acertos[atributo]
totalT[atributo] = totalT[atributo] + total[atributo]
print "Attributes:"
print 15 * "-"
for atributo in acertosT.keys():
print "Attribute: " + str(atributo)
print "Accuracy: " + str(acertosT[atributo] / totalT[atributo])
print 10 * "-"
