def main():
prs = []
rocs = []
times = []
with Parallel(n_jobs=4, verbose=11, backend='multiprocessing') as parallel_pool:
for index in range(ITERATIONS):
print('ITERATION #%s' % str(index+1))
start_time = time.time()
pr, roc = hplsfacev(args, parallel_pool)
end_time = time.time()
abs_time = (end_time - start_time) / FOLD
prs.append(pr)
rocs.append(roc)
times.append(abs_time)
prs_f, rocs_f = iteration_to_fold(prs, rocs)
with open('./files/' + OUTPUT_NAME + '.file', 'w') as outfile:
pickle.dump([prs_f, rocs_f], outfile)
for index in range(len(prs_f)):
plot_precision_recall(prs_f[index], OUTPUT_NAME + '_fold_' + str(index + 1))
for index in range(len(rocs_f)):
plot_roc_curve(rocs_f[index], OUTPUT_NAME + '_fold_' + str(index + 1))
with open('./times/' + OUTPUT_NAME + '.time', 'a') as outtime:
outtime.write(str(abs_time) + '\n')
with open('./times/' + OUTPUT_NAME + '.time', 'a') as outtime:
outtime.write('------\n')
outtime.write(str(np.mean(times)) + '\n')
outtime.write(str(np.std(times)) + '\n')
def main():
os_cmcs = []
oaa_cmcs = []
dets = []
prs = []
rocs = []
fscores = []
with Parallel(n_jobs=-2, verbose=11,
backend='multiprocessing') as parallel_pool:
for index in range(ITERATIONS):
print('ITERATION #%s' % str(index + 1))
os_cmc, oaa_cmc, det, pr, roc, fscore = plshface(
args, parallel_pool)
os_cmcs.append(os_cmc)
oaa_cmcs.append(oaa_cmc)
dets.append(det)
prs.append(pr)
rocs.append(roc)
fscores.append(fscore)
with open('./files/' + OUTPUT_NAME + '.file', 'w') as outfile:
pickle.dump([prs, rocs], outfile)
plot_cmc_curve(os_cmcs, oaa_cmcs, OUTPUT_NAME)
plot_det_curve(dets, OUTPUT_NAME)
plot_precision_recall(prs, OUTPUT_NAME)
plot_roc_curve(rocs, OUTPUT_NAME)
means = mean_results(fscores)
with open('./values/' + OUTPUT_NAME + '.txt', 'a') as outvalue:
for item in fscores:
outvalue.write(str(item) + '\n')
for item in means:
outvalue.write(str(item) + '\n')
print(fscores)
def main():
PATH = str(args.path)
DATASET = str(args.file)
ITERATIONS = int(args.rept)
KNOWN_SET_SIZE = float(args.known_set_size)
TRAIN_SET_SIZE = float(args.train_set_size)
NUM_HASH = int(args.hash)
DATASET = DATASET.replace('-FEATURE-VECTORS.bin', '')
OUTPUT_NAME = 'HSVM_' + DATASET + '_' + str(NUM_HASH) + '_' + str(
KNOWN_SET_SIZE) + '_' + str(TRAIN_SET_SIZE) + '_' + str(ITERATIONS)
prs = []
rocs = []
with Parallel(n_jobs=-2, verbose=11,
backend='multiprocessing') as parallel_pool:
for index in range(ITERATIONS):
print('ITERATION #%s' % str(index + 1))
pr, roc = svmhface(args, parallel_pool)
prs.append(pr)
rocs.append(roc)
with open('./files/' + OUTPUT_NAME + '.file', 'w') as outfile:
pickle.dump([prs, rocs], outfile)
plot_precision_recall(prs, OUTPUT_NAME)
plot_roc_curve(rocs, OUTPUT_NAME)
def main():
fscores = []
prs = []
rocs = []
with Parallel(n_jobs=1, verbose=15,
backend='multiprocessing') as parallel_pool:
for index in range(ITERATIONS):
keras_backend.clear_session()
keras_session = tensorflow.Session()
keras_backend.set_session(keras_session)
print('ITERATION #%s' % str(index + 1))
pr, roc, fscore = fcnhface(args, parallel_pool)
fscores.append(fscore)
prs.append(pr)
rocs.append(roc)
with open('./files/plot_' + OUTPUT_NAME + '.file', 'w') as outfile:
pickle.dump([prs, rocs], outfile)
plot_precision_recall(prs, OUTPUT_NAME)
plot_roc_curve(rocs, OUTPUT_NAME)
means = mean_results(fscores)
with open('./values/' + OUTPUT_NAME + '.txt', 'a') as outvalue:
for item in fscores:
outvalue.write(str(item) + '\n')
for item in means:
outvalue.write(str(item) + '\n')
print(fscores)
def main():
PATH = str(args.path)
DATASET = str(args.file)
DESCRIPTOR = str(args.desc)
ITERATIONS = int(args.rept)
NUM_HASH = int(args.hash)
OUTPUT_NAME = 'OC-SVM_' + DATASET.replace(
'.txt',
'') + '_' + str(NUM_HASH) + '_' + DESCRIPTOR + '_' + str(ITERATIONS)
prs = []
rocs = []
for index in range(ITERATIONS):
print('ITERATION #%s' % str(index + 1))
pr, roc = svm_oneclass(args)
prs.append(pr)
rocs.append(roc)
with open('./files/' + OUTPUT_NAME + '.file', 'w') as outfile:
pickle.dump([prs, rocs], outfile)
plot_precision_recall(prs, OUTPUT_NAME)
plot_roc_curve(rocs, OUTPUT_NAME)
import os
import pickle
from auxiliar import generate_precision_recall, plot_precision_recall
from auxiliar import generate_roc_curve, plot_roc_curve
path_files = os.listdir('./')
print path_files
for file in path_files:
if file.endswith('.file'):
file_path = './' + file
with open(file_path) as infile:
file_prs, file_rocs = pickle.load(infile)
plot_precision_recall(file_prs, file_path.replace('.file', '.jpg'))
plot_roc_curve(file_rocs, file_path.replace('.file', '.jpg'))