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, participantesInput, 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 run(dominios, targets, folds, atributos, participantesInput, incluirParticipante):
featureVector = parser.parseFeatureVector(dominios, targets)
inputs = {}
frequencias = {}
participantes = {}
for fold in folds:
frequencias[fold] = countAttributeFrequency(folds, fold)
participantes[fold] = parser.descriptionsMeans(folds, fold, participantesInput, dominios)
inputs[fold] = parser.parseSVMInput(folds[fold], featureVector, participantes[fold], frequencias[fold], 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","7","8","9","10"]:
# print "FOLD: " + fold
resultados = tree.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, targets[contexto])
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 = {}
# Cross Validation
# folds = cross.crossValidation(10, annotations)
# Inicializa Vetor de Caracteristicas
for fold in folds:
frequencias[fold] = utils.countAttributeFrequencyIndividual(folds, fold)
participantes[fold] = parser.descriptionsMeans(folds, fold, participantesInput, 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]
dice = []
masi = []
acuracia = 0
for expressao in expressoes:
contexto = expressao["anotacao"]["caracteristicas"]["context"]
participante = expressao["anotacao"]["caracteristicas"]["trial"]
descricao = utils.defineExpressao(
expressao,
frequencias[testeFold][participante],
participantes[testeFold][participante]["numeroExpressoes"],
dominios[contexto],
targets[contexto],
)
descricaoR = expressao["anotacao"]["descricao"]
A = ass.parse(descricao, targets[contexto])
B = ass.parse(descricaoR)
DICE = ass.dice(A, B)
MASI = ass.masi(A, B)
# print descricao
# print descricaoR
# print DICE
# print "\n"
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
