def main():
# initial setup
dataset_list = ['skin_cancer']
score_list = ['Mahalanobis_0.0', 'Mahalanobis_0.01', 'Mahalanobis_0.005', 'Mahalanobis_0.002', 'Mahalanobis_0.0014', 'Mahalanobis_0.001', 'Mahalanobis_0.0005']
# train and measure the performance of Mahalanobis detector
list_best_results, list_best_results_index = [], []
for dataset in dataset_list:
print('In-distribution: ', dataset)
outf = './output/' + args.net_type + '_' + dataset + '/'
out_list = ['imgnet', 'skin_cli', 'skin_derm', 'corrupted', 'corrupted_70', 'nct']
list_best_results_out, list_best_results_index_out = [[] for i in range(len(out_list))], [[] for i in range(len(out_list))]
for out in out_list:
best_lr = None
best_score = None
best_tnr = 0
for score in score_list:
total_X, total_Y = lib_regression.load_characteristics(score, dataset, out, outf)
X_in = total_X[-2533:,:]
Y_in = total_Y[-2533:]
X_out = total_X[:-2533,:]
Y_out = total_Y[:-2533]
l = int(0.8*X_out.shape[0])
X_train = np.concatenate((X_in[:500], X_out[:l]))
Y_train = np.concatenate((Y_in[:500], Y_out[:l]))
X_val_for_test = np.concatenate((X_in[500:550], X_out[l:2*l]))
Y_val_for_test = np.concatenate((Y_in[500:550], Y_out[l:2*l]))
lr = LogisticRegressionCV(n_jobs=-1,max_iter=1000).fit(X_train, Y_train)
results = lib_regression.detection_performance(lr, X_val_for_test, Y_val_for_test, outf)
if best_tnr < results['TMP']['TNR']:
best_tnr = results['TMP']['TNR']
best_lr = lr
best_score = score
for i,out in enumerate(out_list):
print('Out-of-distribution: ', out)
total_X, total_Y = lib_regression.load_characteristics(best_score, dataset, out, outf)
X_in = total_X[-2533:,:]
Y_in = total_Y[-2533:]
X_out = total_X[:-2533,:]
Y_out = total_Y[:-2533]
np.random.seed(seed=0)
np.random.shuffle(X_out)
k = int(0.1*X_out.shape[0])
l = int(0*X_out.shape[0])
# X_train = np.concatenate((X_in[:k], X_out[:l]))
# Y_train = np.concatenate((Y_in[:k], Y_out[:l]))
# X_val_for_test = np.concatenate((X_in[k:2*k], X_out[l:2*l]))
# Y_val_for_test = np.concatenate((Y_in[k:2*k], Y_out[l:2*l]))
X_test = np.concatenate((X_in[550:], X_out[2*l:]))
Y_test = np.concatenate((Y_in[550:], Y_out[2*l:]))
best_result = lib_regression.detection_performance(best_lr, X_test, Y_test, outf)
list_best_results_out[i].append(best_result)
list_best_results_index_out[i].append(best_score)
list_best_results.append(list_best_results_out)
list_best_results_index.append(list_best_results_index_out)
print(len(list_best_results_out))
print(len(list_best_results))
# print the results
count_in = 0
mtypes = ['TNR', 'AUROC', 'DTACC', 'AUIN', 'AUOUT']
for in_list in list_best_results:
print('in_distribution: ' + dataset_list[count_in] + '==========')
# out_list = ['skin_cli', 'skin_derm', 'corrupted', 'corrupted_70', 'imgnet', 'nct', 'final_test']
print(len(in_list))
count_out = 0
for results in in_list:
print('out_distribution: '+ out_list[count_out])
summary_results = {"TMP":{}}
for r in results:
# print(r)
summary_results["TMP"]["TNR"] = summary_results["TMP"].get("TNR",0)+r["TMP"]["TNR"]/len(out_list)
summary_results["TMP"]["AUROC"] = summary_results["TMP"].get("AUROC",0)+r["TMP"]["AUROC"]/len(out_list)
summary_results["TMP"]["DTACC"] = summary_results["TMP"].get("DTACC",0)+r["TMP"]["DTACC"]/len(out_list)
summary_results["TMP"]["AUIN"] = summary_results["TMP"].get("AUIN",0)+r["TMP"]["AUIN"]/len(out_list)
summary_results["TMP"]["AUOUT"] = summary_results["TMP"].get("AUOUT",0)+r["TMP"]["AUOUT"]/len(out_list)
for mtype in mtypes:
print(' {mtype:6s}'.format(mtype=mtype), end='')
print('\n{val:6.2f}'.format(val=100.*summary_results['TMP']['TNR']), end='')
print(' {val:6.2f}'.format(val=100.*summary_results['TMP']['AUROC']), end='')
print(' {val:6.2f}'.format(val=100.*summary_results['TMP']['DTACC']), end='')
print(' {val:6.2f}'.format(val=100.*summary_results['TMP']['AUIN']), end='')
print(' {val:6.2f}\n'.format(val=100.*summary_results['TMP']['AUOUT']), end='')
print('Input noise: ' + str(list_best_results_index[count_in][count_out]))
print('')
count_out += 1
count_in += 1
def main():
# initial setup
dataset_list = ['cifar10', 'cifar100', 'svhn']
score_list = [
'Mahalanobis_0.0', 'Mahalanobis_0.01', 'Mahalanobis_0.005',
'Mahalanobis_0.002', 'Mahalanobis_0.0014', 'Mahalanobis_0.001',
'Mahalanobis_0.0005'
]
# train and measure the performance of Mahalanobis detector
list_best_results, list_best_results_index = [], []
for dataset in dataset_list:
print('In-distribution: ', dataset)
outf = './output/' + args.net_type + '_' + dataset + '/'
out_list = ['svhn', 'imagenet_resize', 'lsun_resize']
if dataset == 'svhn':
out_list = ['cifar10', 'imagenet_resize', 'lsun_resize']
list_best_results_out, list_best_results_index_out = [], []
for out in out_list:
print('Out-of-distribution: ', out)
best_tnr, best_result, best_index = 0, 0, 0
for score in score_list:
total_X, total_Y = lib_regression.load_characteristics(
score, dataset, out, outf)
X_val, Y_val, X_test, Y_test = lib_regression.block_split(
total_X, total_Y, out)
X_train = np.concatenate((X_val[:500], X_val[1000:1500]))
Y_train = np.concatenate((Y_val[:500], Y_val[1000:1500]))
X_val_for_test = np.concatenate(
(X_val[500:1000], X_val[1500:]))
Y_val_for_test = np.concatenate(
(Y_val[500:1000], Y_val[1500:]))
lr = LogisticRegressionCV(n_jobs=-1).fit(X_train, Y_train)
y_pred = lr.predict_proba(X_train)[:, 1]
#print('training mse: {:.4f}'.format(np.mean(y_pred - Y_train)))
y_pred = lr.predict_proba(X_val_for_test)[:, 1]
#print('test mse: {:.4f}'.format(np.mean(y_pred - Y_val_for_test)))
results = lib_regression.detection_performance(
lr, X_val_for_test, Y_val_for_test, outf)
if best_tnr < results['TMP']['TNR']:
best_tnr = results['TMP']['TNR']
best_index = score
best_result = lib_regression.detection_performance(
lr, X_test, Y_test, outf)
list_best_results_out.append(best_result)
list_best_results_index_out.append(best_index)
list_best_results.append(list_best_results_out)
list_best_results_index.append(list_best_results_index_out)
# print the results
count_in = 0
mtypes = ['TNR', 'AUROC', 'DTACC', 'AUIN', 'AUOUT']
for in_list in list_best_results:
print('in_distribution: ' + dataset_list[count_in] + '==========')
out_list = ['svhn', 'imagenet_resize', 'lsun_resize']
if dataset_list[count_in] == 'svhn':
out_list = ['cifar10', 'imagenet_resize', 'lsun_resize']
count_out = 0
for results in in_list:
print('out_distribution: ' + out_list[count_out])
for mtype in mtypes:
print(' {mtype:6s}'.format(mtype=mtype), end='')
print('\n{val:6.2f}'.format(val=100. * results['TMP']['TNR']),
end='')
print(' {val:6.2f}'.format(val=100. * results['TMP']['AUROC']),
end='')
print(' {val:6.2f}'.format(val=100. * results['TMP']['DTACC']),
end='')
print(' {val:6.2f}'.format(val=100. * results['TMP']['AUIN']),
end='')
print(' {val:6.2f}\n'.format(val=100. * results['TMP']['AUOUT']),
end='')
print('Input noise: ' +
list_best_results_index[count_in][count_out])
print('')
count_out += 1
count_in += 1
def main():
# initial setup
dataset_list = ['cifar10', 'cifar100', 'svhn']
dataset_list = ['cifar10']
if args.net_type == 'densenet121':
dataset_list = ['ham10000']
adv_test_list = ['FGSM', 'BIM', 'DeepFool', 'CWL2']
print('evaluate the LID estimator')
score_list = [
'LID_10', 'LID_20', 'LID_30', 'LID_40', 'LID_50', 'LID_60', 'LID_70',
'LID_80', 'LID_90'
]
list_best_results, list_best_results_index = [], []
for dataset in dataset_list:
print('load train data: ', dataset)
outf = './adv_output/' + args.net_type + '_' + dataset + '/'
list_best_results_out, list_best_results_index_out = [], []
for out in adv_test_list:
best_auroc, best_result, best_index = 0, 0, 0
for score in score_list:
print('load train data: ', out, ' of ', score)
total_X, total_Y = lib_regression.load_characteristics(
score, dataset, out, outf)
#X_val, Y_val, X_test, Y_test = lib_regression.block_split_adv(total_X, total_Y)
X_val, Y_val, X_test, Y_test = lib_regression.stratified_split(
total_X, total_Y)
#pivot = int(X_val.shape[0] / 6)
#X_train = np.concatenate((X_val[:pivot], X_val[2*pivot:3*pivot], X_val[4*pivot:5*pivot]))
#Y_train = np.concatenate((Y_val[:pivot], Y_val[2*pivot:3*pivot], Y_val[4*pivot:5*pivot]))
#X_val_for_test = np.concatenate((X_val[pivot:2*pivot], X_val[3*pivot:4*pivot], X_val[5*pivot:]))
#Y_val_for_test = np.concatenate((Y_val[pivot:2*pivot], Y_val[3*pivot:4*pivot], Y_val[5*pivot:]))
X_train, Y_train, X_val_for_test, Y_val_for_test = lib_regression.stratified_split(
X_val, Y_val, test_size=0.5)
lr = LogisticRegressionCV(n_jobs=-1,
max_iter=1000).fit(X_train, Y_train)
y_pred = lr.predict_proba(X_train)[:, 1]
#print('training mse: {:.4f}'.format(np.mean(y_pred - Y_train)))
y_pred = lr.predict_proba(X_val_for_test)[:, 1]
#print('test mse: {:.4f}'.format(np.mean(y_pred - Y_val_for_test)))
results = lib_regression.detection_performance(
lr, X_val_for_test, Y_val_for_test, outf)
if best_auroc < results['TMP']['AUROC']:
best_auroc = results['TMP']['AUROC']
best_index = score
best_result = lib_regression.detection_performance(
lr, X_test, Y_test, outf)
list_best_results_out.append(best_result)
list_best_results_index_out.append(best_index)
list_best_results.append(list_best_results_out)
list_best_results_index.append(list_best_results_index_out)
print('evaluate the Mahalanobis estimator')
score_list = ['Mahalanobis_0.0', 'Mahalanobis_0.01', 'Mahalanobis_0.005', \
'Mahalanobis_0.002', 'Mahalanobis_0.0014', 'Mahalanobis_0.001', 'Mahalanobis_0.0005']
list_best_results_ours, list_best_results_index_ours = [], []
for dataset in dataset_list:
print('load train data: ', dataset)
outf = './adv_output/' + args.net_type + '_' + dataset + '/'
list_best_results_out, list_best_results_index_out = [], []
for out in adv_test_list:
best_auroc, best_result, best_index = 0, 0, 0
for score in score_list:
print('load train data: ', out, ' of ', score)
total_X, total_Y = lib_regression.load_characteristics(
score, dataset, out, outf)
#X_val, Y_val, X_test, Y_test = lib_regression.block_split_adv(total_X, total_Y)
X_val, Y_val, X_test, Y_test = lib_regression.stratified_split(
total_X, total_Y)
pivot = int(X_val.shape[0] / 6)
#X_train = np.concatenate((X_val[:pivot], X_val[2*pivot:3*pivot], X_val[4*pivot:5*pivot]))
#Y_train = np.concatenate((Y_val[:pivot], Y_val[2*pivot:3*pivot], Y_val[4*pivot:5*pivot]))
#X_val_for_test = np.concatenate((X_val[pivot:2*pivot], X_val[3*pivot:4*pivot], X_val[5*pivot:]))
#Y_val_for_test = np.concatenate((Y_val[pivot:2*pivot], Y_val[3*pivot:4*pivot], Y_val[5*pivot:]))
X_train, Y_train, X_val_for_test, Y_val_for_test = lib_regression.stratified_split(
X_val, Y_val, test_size=0.5)
lr = LogisticRegressionCV(n_jobs=-1,
max_iter=1000).fit(X_train, Y_train)
y_pred = lr.predict_proba(X_train)[:, 1]
#print('training mse: {:.4f}'.format(np.mean(y_pred - Y_train)))
y_pred = lr.predict_proba(X_val_for_test)[:, 1]
#print('test mse: {:.4f}'.format(np.mean(y_pred - Y_val_for_test)))
results = lib_regression.detection_performance(
lr, X_val_for_test, Y_val_for_test, outf)
if best_auroc < results['TMP']['AUROC']:
best_auroc = results['TMP']['AUROC']
best_index = score
best_result = lib_regression.detection_performance(
lr, X_test, Y_test, outf)
list_best_results_out.append(best_result)
list_best_results_index_out.append(best_index)
list_best_results_ours.append(list_best_results_out)
list_best_results_index_ours.append(list_best_results_index_out)
count_in = 0
mtypes = ['TNR', 'AUROC', 'DTACC', 'AUIN', 'AUOUT']
print("results of LID")
for in_list in list_best_results:
print('in_distribution: ' + dataset_list[count_in] + '==========')
count_out = 0
for results in in_list:
print('out_distribution: ' + adv_test_list[count_out])
for mtype in mtypes:
print(' {mtype:6s}'.format(mtype=mtype), end='')
print('\n{val:6.2f}'.format(val=100. * results['TMP']['TNR']),
end='')
print(' {val:6.2f}'.format(val=100. * results['TMP']['AUROC']),
end='')
print(' {val:6.2f}'.format(val=100. * results['TMP']['DTACC']),
end='')
print(' {val:6.2f}'.format(val=100. * results['TMP']['AUIN']),
end='')
print(' {val:6.2f}\n'.format(val=100. * results['TMP']['AUOUT']),
end='')
print('Input noise: ' +
list_best_results_index[count_in][count_out])
print('')
count_out += 1
count_in += 1
count_in = 0
print("results of Mahalanobis")
for in_list in list_best_results_ours:
print('in_distribution: ' + dataset_list[count_in] + '==========')
count_out = 0
for results in in_list:
print('out_distribution: ' + adv_test_list[count_out])
for mtype in mtypes:
print(' {mtype:6s}'.format(mtype=mtype), end='')
print('\n{val:6.2f}'.format(val=100. * results['TMP']['TNR']),
end='')
print(' {val:6.2f}'.format(val=100. * results['TMP']['AUROC']),
end='')
print(' {val:6.2f}'.format(val=100. * results['TMP']['DTACC']),
end='')
print(' {val:6.2f}'.format(val=100. * results['TMP']['AUIN']),
end='')
print(' {val:6.2f}\n'.format(val=100. * results['TMP']['AUOUT']),
end='')
print('Input noise: ' +
list_best_results_index_ours[count_in][count_out])
print('')
count_out += 1
count_in += 1
def train_detector(dataset_list, out_list):
net_type = "model"
outf = "/output/"
score_list = [
'Mahalanobis_0.0', 'Mahalanobis_0.01', 'Mahalanobis_0.005',
'Mahalanobis_0.002', 'Mahalanobis_0.0014', 'Mahalanobis_0.001',
'Mahalanobis_0.0005'
]
for dataset in dataset_list:
list_best_results_out, list_best_results_index_out = [], []
for out in out_list:
print('Out-of-distribution: ', out)
best_tnr, best_result, best_index = 0, 0, 0
for score in score_list:
total_X, total_Y = lib_regression.load_characteristics(
score, dataset, out, outf)
X_val, Y_val, X_test, Y_test = lib_regression.stratified_split(
total_X, total_Y)
X_train, Y_train, X_val_for_test, Y_val_for_test = lib_regression.stratified_split(
X_val, Y_val, test_size=0.5)
lr = LogisticRegressionCV(n_jobs=-1,
max_iter=1000).fit(X_train, Y_train)
y_pred = lr.predict_proba(X_train)[:, 1]
y_pred = lr.predict_proba(X_val_for_test)[:, 1]
results = lib_regression.detection_performance(
lr, X_val_for_test, Y_val_for_test, outf)
if best_tnr < results['TMP']['TNR']:
best_tnr = results['TMP']['TNR']
best_index = score
best_result = lib_regression.detection_performance(
lr, X_test, Y_test, outf)
list_best_results_out.append(best_result)
list_best_results_index_out.append(best_index)
list_best_results.append(list_best_results_out)
list_best_results_index.append(list_best_results_index_out)
# print the results
count_in = 0
mtypes = ['TNR', 'AUROC', 'DTACC', 'AUIN', 'AUOUT']
for in_list in list_best_results:
print('in_distribution: ' + dataset_list[count_in] + '==========')
count_out = 0
for results in in_list:
print('out_distribution: ' + out_list[count_out])
for mtype in mtypes:
print(' {mtype:6s}'.format(mtype=mtype), end='')
print('\n{val:6.2f}'.format(val=100. * results['TMP']['TNR']),
end='')
print(' {val:6.2f}'.format(val=100. * results['TMP']['AUROC']),
end='')
print(' {val:6.2f}'.format(val=100. * results['TMP']['DTACC']),
end='')
print(' {val:6.2f}'.format(val=100. * results['TMP']['AUIN']),
end='')
print(' {val:6.2f}\n'.format(val=100. * results['TMP']['AUOUT']),
end='')
print('Input noise: ' +
list_best_results_index[count_in][count_out])
print('')
count_out += 1
count_in += 1
# if __name__ == '__main__':
# main()
def main():
# initial setup
dataset_list = ["cifar10", "cifar100", "svhn"]
score_list = [
"Mahalanobis_0.0",
"Mahalanobis_0.01",
"Mahalanobis_0.005",
"Mahalanobis_0.002",
"Mahalanobis_0.0014",
"Mahalanobis_0.001",
"Mahalanobis_0.0005",
]
# train and measure the performance of Mahalanobis detector
list_best_results, list_best_results_index = [], []
for dataset in dataset_list:
print("In-distribution: ", dataset)
outf = "./output/" + args.net_type + "_" + dataset + "/"
out_list = ["svhn", "imagenet_resize", "lsun_resize"]
if dataset == "svhn":
out_list = ["cifar10", "imagenet_resize", "lsun_resize"]
list_best_results_out, list_best_results_index_out = [], []
for out in out_list:
print("Out-of-distribution: ", out)
best_tnr, best_result, best_index = 0, 0, 0
for score in score_list:
total_X, total_Y = lib_regression.load_characteristics(
score, dataset, out, outf)
X_val, Y_val, X_test, Y_test = lib_regression.block_split(
total_X, total_Y, out)
X_train = np.concatenate((X_val[:500], X_val[1000:1500]))
Y_train = np.concatenate((Y_val[:500], Y_val[1000:1500]))
X_val_for_test = np.concatenate(
(X_val[500:1000], X_val[1500:]))
Y_val_for_test = np.concatenate(
(Y_val[500:1000], Y_val[1500:]))
lr = LogisticRegressionCV(n_jobs=-1).fit(X_train, Y_train)
y_pred = lr.predict_proba(X_train)[:, 1]
# print('training mse: {:.4f}'.format(np.mean(y_pred - Y_train)))
y_pred = lr.predict_proba(X_val_for_test)[:, 1]
# print('test mse: {:.4f}'.format(np.mean(y_pred - Y_val_for_test)))
results = lib_regression.detection_performance(
lr, X_val_for_test, Y_val_for_test, outf)
if best_tnr < results["TMP"]["TNR"]:
best_tnr = results["TMP"]["TNR"]
best_index = score
best_result = lib_regression.detection_performance(
lr, X_test, Y_test, outf)
list_best_results_out.append(best_result)
list_best_results_index_out.append(best_index)
list_best_results.append(list_best_results_out)
list_best_results_index.append(list_best_results_index_out)
# print the results
count_in = 0
mtypes = ["TNR", "AUROC", "DTACC", "AUIN", "AUOUT"]
for in_list in list_best_results:
print("in_distribution: " + dataset_list[count_in] + "==========")
out_list = ["svhn", "imagenet_resize", "lsun_resize"]
if dataset_list[count_in] == "svhn":
out_list = ["cifar10", "imagenet_resize", "lsun_resize"]
count_out = 0
for results in in_list:
print("out_distribution: " + out_list[count_out])
for mtype in mtypes:
print(" {mtype:6s}".format(mtype=mtype), end="")
print("\n{val:6.2f}".format(val=100.0 * results["TMP"]["TNR"]),
end="")
print(" {val:6.2f}".format(val=100.0 * results["TMP"]["AUROC"]),
end="")
print(" {val:6.2f}".format(val=100.0 * results["TMP"]["DTACC"]),
end="")
print(" {val:6.2f}".format(val=100.0 * results["TMP"]["AUIN"]),
end="")
print(" {val:6.2f}\n".format(val=100.0 * results["TMP"]["AUOUT"]),
end="")
print("Input noise: " +
list_best_results_index[count_in][count_out])
print("")
count_out += 1
count_in += 1
def main():
# initial setup
dataset_list = ['cifar10', 'cifar100', 'svhn']
if args.net_type == 'densenet121':
dataset_list = ['ham10000']
elif args.net_type == "parkinsonsNet-rest":
dataset_list = ['mpower-rest']
elif args.net_type == "parkinsonsNet-return":
dataset_list = ['mpower-return']
elif args.net_type == "parkinsonsNet-outbound":
dataset_list = ['mpower-outbound']
score_list = [
'Mahalanobis_0.0', 'Mahalanobis_0.01', 'Mahalanobis_0.005',
'Mahalanobis_0.002', 'Mahalanobis_0.0014', 'Mahalanobis_0.001',
'Mahalanobis_0.0005'
]
# train and measure the performance of Mahalanobis detector
list_best_results, list_best_results_index = [], []
for dataset in dataset_list:
print('In-distribution: ', dataset)
outf = '/home/anasa2/deep_Mahalanobis_detector/output/' + args.net_type + '_' + dataset + '/'
out_list = ['svhn', 'imagenet_resize', 'lsun_resize']
if dataset == 'svhn':
out_list = ['cifar10', 'imagenet_resize', 'lsun_resize']
elif dataset == 'ham10000':
#out_list = ['cifar10', 'cifar100', 'svhn', 'imagenet_resize', 'lsun_resize', 'face', 'face_age', 'isic-2017', 'isic-2016'] #face #face_age
out_list = [
'ham10000-avg-smoothing', 'ham10000-brightness',
'ham10000-contrast', 'ham10000-dilation', 'ham10000-erosion',
'ham10000-med-smoothing', 'ham10000-rotation', 'ham10000-shift'
] #face #face_age
elif dataset == 'mpower-rest':
out_list = [
'mHealth', 'MotionSense', 'oodParkinsonsData', 'mpower-rst'
]
elif dataset == 'mpower-return':
out_list = [
'mHealth', 'MotionSense', 'oodParkinsonsData', 'mpower-ret'
]
elif dataset == 'mpower-outbound':
out_list = [
'mHealth', 'MotionSense', 'oodParkinsonsData', 'mpower-out'
]
list_best_results_out, list_best_results_index_out = [], []
for out in out_list:
print('Out-of-distribution: ', out)
best_tnr, best_result, best_index = 0, 0, 0
for score in score_list:
total_X, total_Y = lib_regression.load_characteristics(
score, dataset, out, outf)
#X_val, Y_val, X_test, Y_test = lib_regression.block_split(total_X, total_Y, out)
#X_val, Y_val, X_test, Y_test = lib_regression.block_split_adv(total_X, total_Y)
X_val, Y_val, X_test, Y_test = lib_regression.stratified_split(
total_X, total_Y)
#X_train = np.concatenate((X_val[:500], X_val[1000:1500]))
#Y_train = np.concatenate((Y_val[:500], Y_val[1000:1500]))
#X_val_for_test = np.concatenate((X_val[500:1000], X_val[1500:]))
#Y_val_for_test = np.concatenate((Y_val[500:1000], Y_val[1500:]))
#partition = int(len(X_val)/2)
#X_train = np.concatenate((X_val[:partition], X_val[:partition]))
#Y_train = np.concatenate((Y_val[:partition], Y_val[:partition]))
#X_val_for_test = np.concatenate((X_val[partition:], X_val[partition:]))
#Y_val_for_test = np.concatenate((Y_val[partition:], Y_val[partition:]))
X_train, Y_train, X_val_for_test, Y_val_for_test = lib_regression.stratified_split(
X_val, Y_val, test_size=0.5)
lr = LogisticRegressionCV(n_jobs=-1,
max_iter=1000).fit(X_train, Y_train)
y_pred = lr.predict_proba(X_train)[:, 1]
#print('training mse: {:.4f}'.format(np.mean(y_pred - Y_train)))
y_pred = lr.predict_proba(X_val_for_test)[:, 1]
#print('test mse: {:.4f}'.format(np.mean(y_pred - Y_val_for_test)))
results = lib_regression.detection_performance(
lr, X_val_for_test, Y_val_for_test, outf)
if best_tnr < results['TMP']['TNR']:
best_tnr = results['TMP']['TNR']
best_index = score
best_result = lib_regression.detection_performance(
lr, X_test, Y_test, outf)
list_best_results_out.append(best_result)
list_best_results_index_out.append(best_index)
list_best_results.append(list_best_results_out)
list_best_results_index.append(list_best_results_index_out)
# print the results
count_in = 0
mtypes = ['TNR', 'AUROC', 'DTACC', 'AUIN', 'AUOUT']
for in_list in list_best_results:
print('in_distribution: ' + dataset_list[count_in] + '==========')
out_list = ['svhn', 'imagenet_resize', 'lsun_resize']
if dataset_list[count_in] == 'svhn':
out_list = ['cifar10', 'imagenet_resize', 'lsun_resize']
elif dataset == 'ham10000':
#out_list = ['cifar10', 'cifar100', 'svhn', 'imagenet_resize', 'lsun_resize', 'face', 'face_age', 'isic-2017', 'isic-2016']
out_list = [
'ham10000-avg-smoothing', 'ham10000-brightness',
'ham10000-contrast', 'ham10000-dilation', 'ham10000-erosion',
'ham10000-med-smoothing', 'ham10000-rotation', 'ham10000-shift'
]
elif dataset == 'mpower-rest':
out_list = [
'mHealth', 'MotionSense', 'oodParkinsonsData', 'mpower-rst'
]
elif dataset == 'mpower-outbound':
out_list = [
'mHealth', 'MotionSense', 'oodParkinsonsData', 'mpower-out'
]
elif dataset == 'mpower-return':
out_list = [
'mHealth', 'MotionSense', 'oodParkinsonsData', 'mpower-ret'
]
count_out = 0
for results in in_list:
print('out_distribution: ' + out_list[count_out])
for mtype in mtypes:
print(' {mtype:6s}'.format(mtype=mtype), end='')
print('\n{val:6.2f}'.format(val=100. * results['TMP']['TNR']),
end='')
print(' {val:6.2f}'.format(val=100. * results['TMP']['AUROC']),
end='')
print(' {val:6.2f}'.format(val=100. * results['TMP']['DTACC']),
end='')
print(' {val:6.2f}'.format(val=100. * results['TMP']['AUIN']),
end='')
print(' {val:6.2f}\n'.format(val=100. * results['TMP']['AUOUT']),
end='')
print('Input noise: ' +
list_best_results_index[count_in][count_out])
print('')
count_out += 1
count_in += 1