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
#Load Data
#bags,labels,_ = load_data('musk1_scaled')
#bags,labels,_ = load_data('musk2_scaled')
#bags,labels,_ = load_data('fox_scaled')
#bags,labels,_ = load_data('tiger_scaled')
#bags,labels,_ = load_data('elephant_scaled')
bags, labels, _ = load_data('data_gauss')
#Shuffle Data
bags, labels = shuffle(bags, labels, random_state=rand.randint(0, 100))
#Number of Folds
folds = 5
bow_classifier = BOW()
#parameters_bow = {'k':100,'covar_type':'diag','n_iter':20}
parameters_bow = {'k': 10, 'covar_type': 'diag', 'n_iter': 20}
accuracie, results_accuracie, auc, results_auc = mil_cross_val(
bags=bags,
labels=labels,
model=bow_classifier,
folds=folds,
parameters=parameters_bow)
SMILa = simpleMIL()
parameters_smil = {'type': 'max'}
#En este me funciono maxDD porque no tiene problem con parametros
accuracie, results_accuracie, auc, results_auc, elapsed = mil_cross_val(
bags=bags,
labels=labels,
seed = 66
#seed = 70
#Split Data
#seed= 90
#Shuffle Data
bags, labels = shuffle(bags, labels, random_state=rand.randint(0, 100))
train_bags, test_bags, train_labels, test_labels = cross_validation.train_test_split(
bags, labels, test_size=0.1, random_state=seed)
################
#Bags Of Words #
################
bow_classifier = BOW()
#bow_classifier.fit(train_bags, train_labels,k=100,covar_type = 'diag',n_iter = 20)
bow_classifier.fit(train_bags,
train_labels,
k=10,
covar_type='diag',
n_iter=20)
predictions = bow_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)
#####################