def main():
args = sgpr_args()
args.load('./config/config.yml')
tab_printer(args)
trainer = SGTrainer(args, False)
trainer.model.eval()
pred, gt = trainer.eval_batch_pair([
args.pair_file,
])
print("Score:", pred[0])
def main():
"""
Parsing command line parameters, reading data, fitting and scoring a SimGNN model.
"""
args = sgpr_args()
if len(sys.argv) > 1:
args.load(sys.argv[1])
else:
args.load('./config/config.yml')
tab_printer(args)
trainer = SGTrainer(args)
trainer.fit()
trainer.score()
def main():
args = sgpr_args()
if len(sys.argv) > 1:
args.load(sys.argv[1])
else:
args.load('./config/config.yml')
args.load(os.path.abspath('./config/config.yml'))
tab_printer(args)
trainer = SGTrainer(args, args.model)
trainer.model.eval()
if not os.path.exists(args.output_path):
os.makedirs(args.output_path)
for sequence in tqdm(args.sequences):
print("sequence: ", sequence)
gt_db = []
pred_db = []
graph_pairs = load_paires(
os.path.join(args.graph_pairs_dir, sequence + ".txt"),
args.graph_pairs_dir)
batches = [
graph_pairs[graph:graph + args.batch_size]
for graph in range(0, len(graph_pairs), args.batch_size)
]
for batch in tqdm(batches):
pred, gt = trainer.eval_batch_pair(batch)
pred_db.extend(pred)
gt_db.extend(gt)
assert len(pred_db) == len(gt_db)
assert np.sum(gt_db) > 0 # gt_db should have positive samples
# calc metrics
pred_db = np.array(pred_db)
gt_db = np.array(gt_db)
# save results
gt_db_path = os.path.join(args.output_path, sequence + "_gt_db.npy")
pred_db_path = os.path.join(args.output_path, sequence + "_DL_db.npy")
np.save(gt_db_path, gt_db)
np.save(pred_db_path, pred_db)
#####ROC
fpr, tpr, roc_thresholds = metrics.roc_curve(gt_db, pred_db)
roc_auc = metrics.auc(fpr, tpr)
print("fpr: ", fpr)
print("tpr: ", tpr)
print("thresholds: ", roc_thresholds)
print("roc_auc: ", roc_auc)
# plot ROC Curve
plt.figure(0)
lw = 2
plt.plot(fpr,
tpr,
color='darkorange',
lw=lw,
label='ROC curve (area = %0.2f)' % roc_auc)
plt.plot([0, 1], [0, 1], color='navy', lw=lw, linestyle='--')
plt.xlabel('False Positive Rate')
plt.ylabel('True Positive Rate')
plt.title('DL ROC Curve')
plt.legend(loc="lower right")
roc_out = os.path.join(args.output_path,
sequence + "_DL_roc_curve.png")
plt.savefig(roc_out)
#### P-R
precision, recall, pr_thresholds = metrics.precision_recall_curve(
gt_db, pred_db)
# plot p-r curve
plt.figure(1)
lw = 2
plt.plot(recall,
precision,
color='darkorange',
lw=lw,
label='P-R curve')
plt.axis([0, 1, 0, 1])
plt.xlabel('Recall')
plt.ylabel('Precision')
plt.title('DL Precision-Recall Curve')
plt.legend(loc="lower right")
pr_out = os.path.join(args.output_path, sequence + "_DL_pr_curve.png")
plt.savefig(pr_out)
if args.show:
plt.show()
# calc F1-score
F1_score = 2 * precision * recall / (precision + recall)
F1_score = np.nan_to_num(F1_score)
F1_max_score = np.max(F1_score)
f1_out = os.path.join(args.output_path, sequence + "_DL_F1_max.txt")
with open(f1_out, "w") as out:
out.write(str(F1_max_score))