def run_dataset(paras):
dataset, sparsity, trade_off, learning_rate, max_iter, epsilon, write_to_dir, tao, num_nodes, deg, normalize = paras
if not write_to_dir:
logger = logging.getLogger('fei')
else:
log_fn = '{}_nodes_{}_deg_{}_sparsity_{:d}_trade_{}_lr_{}_{}_parallel.txt'.format(
DATASET, num_nodes, deg, sparsity, trade_off, learning_rate,
normalize)
if os.path.isfile(os.path.join(write_to_dir, log_fn)):
print('file exist !!!')
return
logger = logging.getLogger(log_fn)
formatter = logging.Formatter('')
file_handler = logging.FileHandler(filename=os.path.join(
write_to_dir, log_fn),
mode='w')
file_handler.setFormatter(formatter)
logger.addHandler(file_handler)
all_performance = []
logger.debug('-' * 5 + ' setting ' + '-' * 5)
logger.debug('sparsity: {:d}'.format(sparsity))
logger.debug('learning rate: {:.5f}'.format(learning_rate))
logger.debug('trade off: {:.5f}'.format(trade_off))
logger.debug('tao: {:d}'.format(tao))
for i, instance in enumerate(dataset):
logger.debug('instance: {:d}'.format(i))
# normalize = True
if normalize:
logger.debug('feature normalized')
instance['features'] = instance['features'] / np.max(
instance['features'])
opt_x, run_time = optimize(instance,
sparsity,
trade_off,
learning_rate,
max_iter,
epsilon,
logger=None,
tao=tao)
logger.debug('run time: {:.5f}'.format(run_time))
raw_pred_subgraph = np.nonzero(opt_x)[0]
prec, rec, fm, iou = evaluate_block(instance['true_subgraph'],
raw_pred_subgraph)
logger.debug('-' * 5 + ' performance of raw prediction ' + '-' * 5)
logger.debug('precision: {:.5f}'.format(prec))
logger.debug('recall : {:.5f}'.format(rec))
logger.debug('f-measure: {:.5f}'.format(fm))
logger.debug('iou : {:.5f}'.format(iou))
refined_pred_subgraph = post_process_block(instance['graph'],
raw_pred_subgraph,
dataset=DATASET)
refined_prec, refined_rec, refined_fm, refined_iou = evaluate_block(
instance['true_subgraph'], refined_pred_subgraph)
logger.debug('-' * 5 + ' performance of refined prediction ' + '-' * 5)
logger.debug('refined precision: {:.5f}'.format(refined_prec))
logger.debug('refined recall : {:.5f}'.format(refined_rec))
logger.debug('refined f-measure: {:.5f}'.format(refined_fm))
logger.debug('refined iou : {:.5f}'.format(refined_iou))
all_performance.append((prec, rec, fm, iou, refined_prec, refined_rec,
refined_fm, refined_iou, run_time))
all_performance = np.array(all_performance)
avg_performance = np.mean(all_performance, axis=0)
logger.debug('-' * 5 + ' average performance ' + '-' * 5)
logger.debug('average presision: {:.5f}'.format(avg_performance[0]))
logger.debug('average recall : {:.5f}'.format(avg_performance[1]))
logger.debug('average f-measure: {:.5f}'.format(avg_performance[2]))
logger.debug('average iou : {:.5f}'.format(avg_performance[3]))
logger.debug('avg refined prec : {:.5f}'.format(avg_performance[4]))
logger.debug('avg refined rec : {:.5f}'.format(avg_performance[5]))
logger.debug('avg refined fm : {:.5f}'.format(avg_performance[6]))
logger.debug('avg refined iou : {:.5f}'.format(avg_performance[7]))
logger.debug('average run time : {:.5f}'.format(avg_performance[8]))
def run_dataset(paras):
dataset, sparsity, threshold, trade_off, learning_rate, max_iter, epsilon, write_to_dir = paras
# print log setting
if not write_to_dir:
logger = logging.getLogger('fei')
else:
log_fn = '{}_sparsity_{:d}_threshold_{}_trade_off_{}_lr_{}.txt'.format(
DATASET, sparsity, threshold, trade_off, learning_rate)
if os.path.isfile(os.path.join(write_to_dir, log_fn)):
print('file exists !!!')
return
logger = logging.getLogger(log_fn)
formatter = logging.Formatter('')
file_handler = logging.FileHandler(filename=os.path.join(
write_to_dir, log_fn),
mode='w')
file_handler.setFormatter(formatter)
logger.addHandler(file_handler)
all_performance = []
logger.debug('-' * 5 + ' setting ' + '-' * 5)
logger.debug('sparsity: {:d}'.format(sparsity))
logger.debug('learning rate: {:.5f}'.format(learning_rate))
logger.debug('trade off: {:.5f}'.format(trade_off))
for i, instance in enumerate(dataset):
logger.debug('instance: {:d}'.format(i))
opt_x, opt_y, run_time = optimize(instance,
sparsity,
threshold,
trade_off,
learning_rate,
max_iter,
epsilon,
logger=logger)
logger.debug('run time: {:.5f}'.format(run_time))
second_graph = instance['second_graph']
raw_pred_subgraph_x = np.nonzero(opt_x)[0]
x_prec, x_rec, x_fm, x_iou = evaluate_block(instance['true_subgraph'],
raw_pred_subgraph_x)
logger.debug('-' * 5 + ' performance of x prediction ' + '-' * 5)
logger.debug('precision: {:.5f}'.format(x_prec))
logger.debug('recall : {:.5f}'.format(x_rec))
logger.debug('f-measure: {:.5f}'.format(x_fm))
logger.debug('iou : {:.5f}'.format(x_iou))
logger.debug('raw x density second graph: {:.5f}'.format(
nx.density(nx.subgraph(second_graph, list(raw_pred_subgraph_x)))
)) # note, calculating density is conducted on the second graph
raw_pred_subgraph_y = np.nonzero(opt_y)[0]
print('length y', len(raw_pred_subgraph_y))
y_prec, y_rec, y_fm, y_iou = evaluate_block(instance['true_subgraph'],
raw_pred_subgraph_y)
logger.debug('-' * 5 + ' performance of y prediction ' + '-' * 5)
logger.debug('precision: {:.5f}'.format(y_prec))
logger.debug('recall : {:.5f}'.format(y_rec))
logger.debug('f-measure: {:.5f}'.format(y_fm))
logger.debug('iou : {:.5f}'.format(y_iou))
logger.debug('raw y density second graph: {:.5f}'.format(
nx.density(nx.subgraph(second_graph, list(raw_pred_subgraph_y)))))
# connected in first graph
connected_pred_subgraph_x = post_process_block(
instance['first_graph'], raw_pred_subgraph_x, dataset=DATASET
) # note, connected operation is conducted on the first graph, raw_x is necessarily connected since some entries may be zeros
connected_x_prec, connected_x_rec, connected_x_fm, connected_x_iou = evaluate_block(
instance['true_subgraph'], connected_pred_subgraph_x)
logger.debug('-' * 5 + ' performance of connected x prediction ' +
'-' * 5)
logger.debug('refined precision: {:.5f}'.format(connected_x_prec))
logger.debug('refined recall : {:.5f}'.format(connected_x_rec))
logger.debug('refined f-measure: {:.5f}'.format(connected_x_fm))
logger.debug('refined iou : {:.5f}'.format(connected_x_iou))
logger.debug('connected x density second graph: {:.5f}'.format(
nx.density(nx.subgraph(second_graph, connected_pred_subgraph_x))))
connected_pred_subgraph_y = post_process_block(instance['first_graph'],
raw_pred_subgraph_y,
dataset=DATASET)
connected_y_prec, connected_y_rec, connected_y_fm, connected_y_iou = evaluate_block(
instance['true_subgraph'], connected_pred_subgraph_y)
logger.debug('-' * 5 + ' performance of connected y prediction ' +
'-' * 5)
logger.debug('refined precision: {:.5f}'.format(connected_y_prec))
logger.debug('refined recall : {:.5f}'.format(connected_y_rec))
logger.debug('refined f-measure: {:.5f}'.format(connected_y_fm))
logger.debug('refined iou : {:.5f}'.format(connected_y_iou))
logger.debug('refined y density second graph: {:.5f}'.format(
nx.density(nx.subgraph(second_graph, connected_pred_subgraph_y))))
# print(type(raw_pred_subgraph_x))
# print(type(raw_pred_subgraph_y))
intersect_pred_subgraph = set(list(raw_pred_subgraph_x)) & set(
list(raw_pred_subgraph_y))
intersect_prec, intersect_rec, intersect_fm, intersect_iou = evaluate_block(
instance['true_subgraph'], intersect_pred_subgraph)
logger.debug('-' * 5 + ' intersect performance of prediction ' +
'-' * 5)
logger.debug('precision: {:.5f}'.format(intersect_prec))
logger.debug('recall : {:.5f}'.format(intersect_rec))
logger.debug('f-measure: {:.5f}'.format(intersect_fm))
logger.debug('iou : {:.5f}'.format(intersect_iou))
logger.debug('intersect density second graph: {:.5f}'.format(
nx.density(nx.subgraph(second_graph, intersect_pred_subgraph))))
connected_intersect_pred_subgraph = post_process_block(
instance['first_graph'], intersect_pred_subgraph, dataset=DATASET)
connected_intersect_prec, connected_intersect_rec, connected_intersect_fm, connected_intersect_iou = evaluate_block(
instance['true_subgraph'], connected_intersect_pred_subgraph)
logger.debug('-' * 5 +
' connected intersect performance of prediction ' +
'-' * 5)
logger.debug('precision: {:.5f}'.format(connected_intersect_prec))
logger.debug('recall : {:.5f}'.format(connected_intersect_rec))
logger.debug('f-measure: {:.5f}'.format(connected_intersect_fm))
logger.debug('iou : {:.5f}'.format(connected_intersect_iou))
logger.debug('connected intersect density second graph: {:.5f}'.format(
nx.density(
nx.subgraph(second_graph, connected_intersect_pred_subgraph))))
union_pred_subgraph = set(list(raw_pred_subgraph_x)) | set(
list(raw_pred_subgraph_y))
union_prec, union_rec, union_fm, union_iou = evaluate_block(
instance['true_subgraph'], union_pred_subgraph)
logger.debug('-' * 5 + ' union performance of prediction ' + '-' * 5)
logger.debug('precision: {:.5f}'.format(union_prec))
logger.debug('recall : {:.5f}'.format(union_rec))
logger.debug('f-measure: {:.5f}'.format(union_fm))
logger.debug('iou : {:.5f}'.format(union_iou))
logger.debug('union density second graph: {:.5f}'.format(
nx.density(nx.subgraph(second_graph, union_pred_subgraph))))
connected_union_pred_subgraph = post_process_block(
instance['first_graph'], union_pred_subgraph, dataset=DATASET)
connected_union_prec, connected_union_rec, connected_union_fm, connected_union_iou = evaluate_block(
instance['true_subgraph'], connected_union_pred_subgraph)
logger.debug('-' * 5 + ' connected union performance of prediction ' +
'-' * 5)
logger.debug('precision: {:.5f}'.format(connected_union_prec))
logger.debug('recall : {:.5f}'.format(connected_union_rec))
logger.debug('f-measure: {:.5f}'.format(connected_union_fm))
logger.debug('iou : {:.5f}'.format(connected_union_iou))
logger.debug('connected union density second graph: {:.5f}'.format(
nx.density(nx.subgraph(second_graph,
connected_union_pred_subgraph))))
def run_dataset(paras):
dataset, sparsity, threshold, trade_off, learning_rate, max_iter, epsilon, write_to_dir = paras
if not write_to_dir:
logger = logging.getLogger('fei')
else:
log_fn = '{}_sparsity_{:d}_threshold_{}_trade_{}_lr_{}.txt'.format(DATASET, sparsity, threshold, trade_off, learning_rate)
if os.path.isfile(os.path.join(write_to_dir, log_fn)):
print('file exists !!!')
return
logger = logging.getLogger(log_fn)
formatter = logging.Formatter('')
file_handler = logging.FileHandler(filename=os.path.join(write_to_dir, log_fn), mode='w')
file_handler.setFormatter(formatter)
logger.addHandler(file_handler)
all_performance = []
logger.debug('-' * 5 + ' setting ' + '-' * 5)
logger.debug('sparsity: {:d}'.format(sparsity))
logger.debug('learning rate: {:.5f}'.format(learning_rate))
logger.debug('trade off: {:.5f}'.format(trade_off))
for i, instance in enumerate(dataset):
logger.debug('instance: {:d}'.format(i))
true_subgraph = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14]
# if normalize:
# logger.debug('feature normalized')
# instance['features'] = instance['features'] / np.max(instance['features'])
opt_x, opt_y, run_time = optimize(instance, sparsity, threshold, trade_off, learning_rate, max_iter, epsilon, logger=logger)
logger.debug('run time: {:.5f}'.format(run_time))
raw_pred_subgraph_x = np.nonzero(opt_x)[0]
print('rawx', raw_pred_subgraph_x)
# prec, rec, fm, iou = evaluate_block(instance['true_subgraph'], raw_pred_subgraph_x)
prec, rec, fm, iou = evaluate_block(true_subgraph, raw_pred_subgraph_x)
logger.debug('-' * 5 + ' performance of x prediction ' + '-' * 5)
logger.debug('precision: {:.5f}'.format(prec))
logger.debug('recall : {:.5f}'.format(rec))
logger.debug('f-measure: {:.5f}'.format(fm))
logger.debug('iou : {:.5f}'.format(iou))
raw_pred_subgraph_y = np.nonzero(opt_y)[0]
print('rawy', raw_pred_subgraph_y)
# prec, rec, fm, iou = evaluate_block(instance['true_subgraph'], raw_pred_subgraph_y)
prec, rec, fm, iou = evaluate_block(true_subgraph, raw_pred_subgraph_y)
logger.debug('-' * 5 + ' performance of y prediction ' + '-' * 5)
logger.debug('precision: {:.5f}'.format(prec))
logger.debug('recall : {:.5f}'.format(rec))
logger.debug('f-measure: {:.5f}'.format(fm))
logger.debug('iou : {:.5f}'.format(iou))
# connected in first graph
refined_pred_subgraph_x = post_process_block(instance['first_graph'], raw_pred_subgraph_x, dataset=DATASET)
# refined_prec, refined_rec, refined_fm, refined_iou = evaluate_block(instance['true_subgraph'], refined_pred_subgraph_x)
refined_prec, refined_rec, refined_fm, refined_iou = evaluate_block(true_subgraph, refined_pred_subgraph_x)
print('refined x', refined_pred_subgraph_x)
logger.debug('-' * 5 + ' performance of refined x prediction ' + '-' * 5)
logger.debug('refined precision: {:.5f}'.format(refined_prec))
logger.debug('refined recall : {:.5f}'.format(refined_rec))
logger.debug('refined f-measure: {:.5f}'.format(refined_fm))
logger.debug('refined iou : {:.5f}'.format(refined_iou))
refined_pred_subgraph_y = post_process_block(instance['first_graph'], raw_pred_subgraph_y, dataset=DATASET)
# refined_prec, refined_rec, refined_fm, refined_iou = evaluate_block(instance['true_subgraph'], refined_pred_subgraph_y)
refined_prec, refined_rec, refined_fm, refined_iou = evaluate_block(true_subgraph, refined_pred_subgraph_y)
print('refined y', refined_pred_subgraph_y)
logger.debug('-' * 5 + ' performance of refined y prediction ' + '-' * 5)
logger.debug('refined precision: {:.5f}'.format(refined_prec))
logger.debug('refined recall : {:.5f}'.format(refined_rec))
logger.debug('refined f-measure: {:.5f}'.format(refined_fm))
logger.debug('refined iou : {:.5f}'.format(refined_iou))
combine_pred_subgraph = raw_pred_subgraph_x & raw_pred_subgraph_y
print('combine', combine_pred_subgraph)
# combine_prec, combine_rec, combine_fm, combine_iou = evaluate_block(instance['true_subgraph'], raw_pred_subgraph_y)
combine_prec, combine_rec, combine_fm, combine_iou = evaluate_block(true_subgraph, raw_pred_subgraph_y)
logger.debug('-' * 5 + ' performance of combine prediction ' + '-' * 5)
logger.debug('precision: {:.5f}'.format(combine_prec))
logger.debug('recall : {:.5f}'.format(combine_rec))
logger.debug('f-measure: {:.5f}'.format(combine_fm))
logger.debug('iou : {:.5f}'.format(combine_iou))
refined_combine_pred_subgraph = post_process_block(instance['first_graph'], combine_pred_subgraph, dataset=DATASET)
print('refined combine', refined_combine_pred_subgraph)
# refined_combine_prec, refined_combine_rec, refined_combine_fm, refined_combine_iou = evaluate_block(instance['true_subgraph'], raw_pred_subgraph_y)
refined_combine_prec, refined_combine_rec, refined_combine_fm, refined_combine_iou = evaluate_block(true_subgraph, refined_combine_pred_subgraph)
logger.debug('-' * 5 + ' performance of y prediction ' + '-' * 5)
logger.debug('precision: {:.5f}'.format(refined_combine_prec))
logger.debug('recall : {:.5f}'.format(refined_combine_rec))
logger.debug('f-measure: {:.5f}'.format(refined_combine_fm))
logger.debug('iou : {:.5f}'.format(refined_combine_iou))