#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,
model=SMILa,
folds=folds,
parameters=parameters_smil,
timer=True)
parameters_smil = {'type': 'min'}
# accuracie, results_accuracie, auc,results_auc, elapsed = mil_cross_val(bags=bags,labels=labels.ravel(), model=maxDD_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))
emdd_classifier = EMDD()
print '\n========= EM-DD 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.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))
from MILpy.Algorithms.EMDD import EMDD
from MILpy.Algorithms.MILES import MILES
from MILpy.Algorithms.BOW import BOW
bags, labels, X = load_data('musk2_scaled')
folds = 5
runs = 1
print(labels)
cknn_classifier = CKNN()
parameters_cknn = {'references': 3, 'citers': 5}
print '\n========= CKNN 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.ravel(),
model=cknn_classifier,
folds=folds,
parameters=parameters_cknn,
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))