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evaluate.py
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evaluate.py
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"""Evaluate SigGraInferNet.
"""
import numpy as np
import random
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.utils.data as data
import util
from args import get_test_args
from model import SigGraInferNet_GCN,SigGraInferNet_GAT,FocalLoss
from tqdm import tqdm
import matplotlib.pyplot as plt
from matplotlib.backends.backend_pdf import PdfPages
import os
from json import dumps
def get_model(log,args):
if args.model_name == "GAT":
model = SigGraInferNet_GAT(feature_input_size=args.feature_input_size,
feature_output_size=args.feature_output_size,
PPI_input_size=args.PPI_input_size,
PPI_output_size=args.PPI_output_size,
num_GAT=args.num_GNN,
num_head=args.num_head,
drop_prob=args.drop_prob)
elif args.model_name == "GCN":
model = SigGraInferNet_GCN(feature_input_size=args.feature_input_size,
feature_output_size=args.feature_output_size,
PPI_input_size=args.PPI_input_size,
PPI_output_size=args.PPI_output_size,
num_GCN=args.num_GNN,
drop_prob=args.drop_prob)
else:
raise ValueError("Model name doesn't exist.")
model = nn.DataParallel(model, args.gpu_ids)
for p in model.parameters():
if p.dim() > 1:
nn.init.xavier_uniform_(p)
if args.load_path:
log.info(f'Loading checkpoint from {args.load_path}...')
model, step = util.load_model(model, args.load_path, args.gpu_ids)
else:
step = 0
return model,step
def main(args):
# Set up logging and devices
args.save_dir = util.get_save_dir(args.save_dir, args.name, type="evaluation")
log = util.get_logger(args.save_dir, args.name)
device, args.gpu_ids = util.get_available_devices()
log.info(f'Args: {dumps(vars(args), indent=4, sort_keys=True)}')
args.batch_size *= max(1, len(args.gpu_ids))
# Get your model
log.info('Building model...')
model, step=get_model(log,args)
model = model.to(device)
model.eval()
# Get data loader
log.info('Building dataset...')
dev_dataset = util.load_dataset(args.test_file,args.PPI_dir,args.PPI_gene_feature_dir,
args.PPI_gene_query_dict_dir,args.max_nodes,train=False)
dev_loader = data.DataLoader(dev_dataset,
batch_size=args.batch_size,
shuffle=False,
num_workers=args.num_workers,
collate_fn=util.collate_fn)
# Train
log.info('Evaluating...')
#get loss computer
cri=FocalLoss(alpha=torch.tensor([args.alpha,1]).to(device),gamma=args.gamma)
loss_meter = util.AverageMeter()
ground_true = dev_loader.dataset.y_list
ground_true = ground_true.to(device)
predict_list=torch.zeros([dev_loader.dataset.__len__(),2],dtype=torch.float)
predict_list = predict_list.to(device)
sample_index=0
with torch.no_grad(), \
tqdm(total=len(dev_loader.dataset)) as progress_bar:
for batch_a, batch_bio_a, batch_A, batch_b, batch_bio_b, batch_B, batch_y in dev_loader:
# Setup for forward
batch_a = batch_a.to(device)
batch_bio_a = batch_bio_a.to(device)
batch_A = batch_A.to(device)
batch_bio_b = batch_bio_b.to(device)
batch_b = batch_b.to(device)
batch_B = batch_B.to(device)
batch_y = batch_y.to(device)
batch_y = batch_y.long()
batch_size = batch_bio_a.size(0)
# Forward
output= model(batch_a, batch_bio_a, batch_A, batch_b, batch_bio_b, batch_B)
loss = cri(output, batch_y)
loss_val = loss.item()
loss_meter.update(loss_val, batch_size)
predict_list[sample_index:sample_index+batch_size]=output
sample_index=sample_index+batch_size
# Log info
progress_bar.update(batch_size)
progress_bar.set_postfix(NLL=loss_meter.avg)
results = util.metrics_compute(predict_list, ground_true)
log.info("Evaluation result of model:")
log.info(f"Loss in test dataset is {loss_meter.avg}")
log.info(f"Accuracy:{results['Accuracy']}, AUC:{results['AUC']}, Recall:{results['Recall']},Precision:{results['Precision']},Specificity:{results['Specificity']}")
log.info(f"TP:{results['TP']},FN:{results['FN']}")
log.info(f"FP:{results['FP']},TN:{results['TN']}")
log.info("plot prediction curve...")
ROC_AUC(results["fpr"],results["tpr"],results["AUC"],os.path.join(args.save_dir,"ROC_curve.pdf"))
log.info("Save evaluation result...")
np.savez(os.path.join(args.save_dir,"results.npz"),predict=np.array(predict_list.cpu().tolist()),result=results)
def ROC_AUC(fpr,tpr,auc_score,path,figsize=(16,9)):
pdf = PdfPages(path)
fig = plt.figure(figsize=figsize)
plt.plot(fpr,tpr,color='darkorange',
lw=2, label='ROC curve (area = %0.3f)' % auc_score)
plt.plot([0, 1], [0, 1], color='navy', lw=2, linestyle='--')
plt.xlim([0.0, 1.0])
plt.ylim([0.0, 1.05])
plt.xlabel('False Positive Rate')
plt.ylabel('True Positive Rate')
plt.title('ROC and AUC Result of Model')
plt.legend(loc="lower right")
pdf.savefig(orientation="landscape")
plt.close()
pdf.close()
return auc_score
if __name__ == '__main__':
main(get_test_args())