def cla_filter():
aux = []
resul1 = [[], [], [], [], [], [], []]
resul2 = [[], [], [], [], [], [], []]
resul3 = [[], [], [], [], [], [], []]
resul4 = [[], [], [], [], [], [], []]
resul5 = [[], [], [], [], [], [], []]
resul6 = [[], [], [], [], [], [], []]
resul7 = [[], [], [], [], [], [], []]
resul8 = [[], [], [], [], [], [], []]
resul9 = [[], [], [], [], [], [], []]
roc_m_1 = [[], [], [], [], [], [], []]
roc_m_2 = [[], [], [], [], [], [], []]
roc_m_3 = [[], [], [], [], [], [], []]
roc_m_4 = [[], [], [], [], [], [], []]
roc_m_5 = [[], [], [], [], [], [], []]
roc_m_6 = [[], [], [], [], [], [], []]
roc_m_7 = [[], [], [], [], [], [], []]
roc_m_8 = [[], [], [], [], [], [], []]
roc_m_9 = [[], [], [], [], [], [], []]
SMILaMax = [simpleMIL(), {'type': 'max'}, 'MIL max', resul1, roc_m_1]
SMILaMin = [simpleMIL(), {'type': 'min'}, 'MIL min', resul2, roc_m_2]
SMILaExt = [
simpleMIL(), {
'type': 'extreme'
}, 'MIL Extreme', resul3, roc_m_3
]
BOW_clas = [
BOW(), {
'k': 90,
'covar_type': 'diag',
'n_iter': 20
}, 'BOW', resul4, roc_m_4
]
CKNN_cla = [
CKNN(), {
'references': 3,
'citers': 5
}, 'CKNN', resul5, roc_m_5
]
maxDD_cl = [maxDD(), {}, 'DIVERSE DENSITY', resul6, roc_m_6]
EMDD_cla = [EMDD(), {}, 'EM-DD', resul7, roc_m_7]
MILB_cla = [MILBoost(), {}, 'MILBOOST', resul8, roc_m_8]
MILES_cl = [MILES(), {}, 'MILES', resul9, roc_m_9]
aux.append(SMILaMax)
# aux.append(SMILaMin)
# aux.append(SMILaExt)
aux.append(BOW_clas)
# aux.append(CKNN_cla)
aux.append(maxDD_cl)
# aux.append(EMDD_cla)
# aux.append(MILB_cla)
# aux.append(MILES_cl)
return aux
def cla_filter_ipf():
aux = []
resul1 = [[],[],[],[],[],[],[]]
roc_m_1 = [[],[],[],[],[],[],[]]
SMILaMax = [simpleMIL(),{'type': 'max'},'MIL max',copy.deepcopy(resul1),copy.deepcopy(roc_m_1)]
SMILaMin = [simpleMIL(),{'type': 'min'},'MIL min',copy.deepcopy(resul1),copy.deepcopy(roc_m_1)]
SMILaExt = [simpleMIL(),{'type': 'extreme'},'MIL Extreme',copy.deepcopy(resul1),copy.deepcopy(roc_m_1)]
BOW_clas = [BOW(),{'k':90,'covar_type':'diag','n_iter':20},'BOW',copy.deepcopy(resul1),copy.deepcopy(roc_m_1)]
CKNN_cla = [CKNN(),{'references': 3, 'citers': 5},'CKNN',copy.deepcopy(resul1),copy.deepcopy(roc_m_1)]
maxDD_cl = [maxDD(),{},'DIVERSE DENSITY',copy.deepcopy(resul1),copy.deepcopy(roc_m_1)]
EMDD_cla = [EMDD(),{},'EM-DD',copy.deepcopy(resul1),copy.deepcopy(roc_m_1)]
MILB_cla = [MILBoost(),{},'MILBOOST',copy.deepcopy(resul1),copy.deepcopy(roc_m_1)]
# MILES_cl = [MILES(),{},'MILES',copy.deepcopy(resul1),copy.deepcopy(roc_m_1)]
aux.append(SMILaMax)
aux.append(SMILaMin)
aux.append(SMILaExt)
aux.append(BOW_clas)
aux.append(CKNN_cla)
aux.append(maxDD_cl)
aux.append(EMDD_cla)
aux.append(MILB_cla)
# aux.append(MILES_cl)
return aux
for i in range(runs):
# print '\n run #'+ str(i)
bags, labels = shuffle(bags, labels, random_state=rand.randint(0, 100))
accuracie, results_accuracie, auc, results_auc, elapsed = mil_cross_val(
bags=bags,
labels=labels.ravel(),
model=emdd_classifier,
folds=folds,
parameters={},
timer=True)
# print '\n'+'AUC: ' + str(auc)+'\n'+'Accuracie: '+ str(accuracie)+'\n'+'Elapsed: '+ str(round(elapsed,2))
AUC.append(auc)
ACCURACIE.append(accuracie)
print '\n MEAN AUC: ' + str(np.mean(AUC)) + '\n MEAN ACCURACIE: ' + str(
np.mean(ACCURACIE))
milboost_classifier = MILBoost()
print '\n========= MILBOOST RESULT ========='
AUC = []
ACCURACIE = []
for i in range(runs):
# print '\n run #'+ str(i)
bags, labels = shuffle(bags, labels, random_state=rand.randint(0, 100))
accuracie, results_accuracie, auc, results_auc, elapsed = mil_cross_val(
bags=bags,
labels=labels,
model=milboost_classifier,
folds=folds,
parameters={},
timer=True)
# print '\n'+'AUC: ' + str(auc)+'\n'+'Accuracie: '+ str(accuracie)+'\n'+'Elapsed: '+ str(round(elapsed,2))
AUC.append(auc)
accuracie = np.average(test_labels.T == np.sign(predictions))
print '\n Accuracy: %.2f%%' % (100 * accuracie)
fpr, tpr, thresholds = metrics.roc_curve(test_labels, 1 - out, pos_label=1.)
metrics.auc(fpr, tpr)
##########
#MILBoost#
##########
#Nota Importante: Solo Funciona Con musk1 original.
#Load Data
bags, labels, _ = load_data('musk1_original') #Musk1 Original
seed = 90
train_bags, test_bags, train_labels, test_labels = cross_validation.train_test_split(
bags, labels, test_size=0.1, random_state=seed)
milboost_classifier = MILBoost()
milboost_classifier.fit(train_bags, train_labels)
out = milboost_classifier.predict(test_bags)
fpr, tpr, thresholds = metrics.roc_curve(test_labels, out, pos_label=1.)
metrics.auc(fpr, tpr)
#######
#MILES#
#######
bags, labels, _ = load_data('data_gauss') #Gaussian data
seed = 66
train_bags, test_bags, train_labels, test_labels = cross_validation.train_test_split(
bags, labels, test_size=0.1, random_state=seed)
#Miles, sobre el entrenaod y probado en el training bueno,
#Me hace pensar que depende mucho de datos de entrenamiento