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
bags=bags,
labels=labels,
model=SMILa,
folds=folds,
parameters=parameters_smil)
cknn_classifier = CKNN()
parameters_cknn = {'references': 3, 'citers': 5}
accuracie, results_accuracie, auc, results_auc = mil_cross_val(
bags=bags,
labels=labels,
model=cknn_classifier,
folds=folds,
parameters=parameters_cknn)
maxDD_classifier = maxDD()
accuracie, results_accuracie, auc, results_auc = mil_cross_val(
bags=bags,
labels=labels,
model=maxDD_classifier,
folds=folds,
parameters={})
emdd_classifier = EMDD()
accuracie, results_accuracie, auc, results_auc = mil_cross_val(
bags=bags,
labels=labels,
model=emdd_classifier,
folds=folds,
parameters={})
#CNN#
#####
cknn_classifier = CKNN()
cknn_classifier.fit(train_bags, train_labels, references=3, citers=5)
predictions = cknn_classifier.predict(test_bags)
accuracie = np.average(test_labels.T == np.sign(predictions))
print '\n Accuracy: %.2f%%' % (100 * accuracie)
fpr, tpr, thresholds = metrics.roc_curve(test_labels,
predictions,
pos_label=1.)
metrics.auc(fpr, tpr)
#######
#MAXDD#
#######
maxdd_classifier = maxDD()
maxdd_classifier.fit(train_bags=train_bags,
train_labels=train_labels) #Train Classifier
predictions, out = maxdd_classifier.predict(test_bags)
accuracie = np.average(test_labels.T == np.sign(predictions))
print '\n Accuracy: %.2f%%' % (100 * accuracie)
fpr, tpr, thresholds = metrics.roc_curve(test_labels, out, pos_label=1.)
metrics.auc(fpr, tpr)
######
#EMDD#
######
emdd_classifier = EMDD()
emdd_classifier.fit(train_bags=train_bags,
train_labels=train_labels) #Train Classifier
predictions, out = emdd_classifier.predict(test_bags)