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test_ppi_ESM2.py
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import numpy as np
import torch
import pandas as pd
from torch.utils.data import DataLoader
import argparse
from sklearn.metrics import roc_auc_score,accuracy_score,f1_score,precision_score,recall_score,precision_recall_curve,auc
import logging
# from torch.utils.tensorboard import SummaryWriter
import random
import os
from torch.utils.data import Dataset
from torch.nn import functional as F
import warnings
warnings.filterwarnings("ignore")
import torch.nn as nn
from transformers import AutoTokenizer, AutoModel
from Bio import SeqIO
from tqdm import tqdm
from models.models import baseline, DeepNano_seq,DeepNano
from utils.dataloader import seqData_Dscript
from utils.evaluate import evaluate
# ESM2_MODEL = 'esm2_t6_8M_UR50D'
# ESM2_MODEL = 'esm2_t12_35M_UR50D'
# ESM2_MODEL = 'esm2_t30_150M_UR50D'
ESM2_MODEL = 'esm2_t33_650M_UR50D'
def predicting(model, device, loader, Model_type):
model.eval()
total_preds_ave = torch.Tensor()
total_preds_min = torch.Tensor()
total_preds_max = torch.Tensor()
total_labels = torch.Tensor()
logging.info('Make prediction for {} samples...'.format(len(loader.dataset)))
with torch.no_grad():
for data in tqdm(loader):
# for data in loader:
#Get input
seqs_nanobody = data[0]
seqs_antigen = data[1]
#Calculate output
g = data[2]
if Model_type == 0:
predictions = model(seqs_nanobody,seqs_antigen,device)
total_preds_ave = torch.cat((total_preds_ave, predictions.cpu()), 0)
elif Model_type == 1:
p_ave, p_min, p_max = model(seqs_nanobody,seqs_antigen,device)
total_preds_ave = torch.cat((total_preds_ave, p_ave.cpu()), 0)
total_preds_min = torch.cat((total_preds_min, p_min.cpu()), 0)
total_preds_max = torch.cat((total_preds_max, p_max.cpu()), 0)
total_labels = torch.cat((total_labels, g), 0)
if Model_type == 1:
return total_labels.numpy().flatten(),total_preds_ave.numpy().flatten(),total_preds_min.numpy().flatten(),total_preds_max.numpy().flatten()
else:
return total_labels.numpy().flatten(),total_preds_ave.numpy().flatten()
###装载训练好的模型
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
from torch.utils.data import Dataset
import pandas as pd
import numpy as np
from Bio import SeqIO
import random
import torch
from torch import nn
print('##########################在DeepNano-seq(DScriptData),{}模型：'.format(ESM2_MODEL))
###装载训练好的模型baseline#################
# model = baseline().to(device)
if ESM2_MODEL == 'esm2_t6_8M_UR50D':
model = DeepNano_seq(pretrained_model=r'./models/esm2_t6_8M_UR50D',hidden_size=320, finetune=0).to(device)
if ESM2_MODEL == 'esm2_t12_35M_UR50D':
model = DeepNano_seq(pretrained_model=r'./models/esm2_t12_35M_UR50D',hidden_size=480,finetune=0).to(device)
if ESM2_MODEL == 'esm2_t30_150M_UR50D':
model = DeepNano_seq(pretrained_model=r'./models/esm2_t30_150M_UR50D',hidden_size=640,finetune=0).to(device)
if ESM2_MODEL == 'esm2_t33_650M_UR50D':
model = DeepNano_seq(pretrained_model=r'./models/esm2_t33_650M_UR50D',hidden_size=1280,finetune=0).to(device)
if ESM2_MODEL == 'esm2_t36_3B_UR50D':
model = DeepNano_seq(pretrained_model=r'./models/esm2_t36_3B_UR50D',hidden_size=2560,finetune=0).to(device)
if ESM2_MODEL == 'esm2_t48_15B_UR50D':
model = DeepNano_seq(pretrained_model=r'./models/esm2_t48_15B_UR50D',hidden_size=5120,finetune=0).to(device)
model_dir = './output/checkpoint/'
model_name = 'DeepNano_seq({})_DScriptData_finetune1_best.model'.format(ESM2_MODEL)
model_path = model_dir + model_name
weights = torch.load(model_path) # map_location=torch.device('cpu')
model.load_state_dict(weights)
# model.load_state_dict({k.replace('module.',''):v for k,v in weights.items()})
###装载测试数据
pair_list = ['mouse_test.tsv','fly_test.tsv','worm_test.tsv','yeast_test.tsv','ecoli_test.tsv']
seqs_list = ['mouse_dedup.fasta','fly_dedup.fasta','worm_dedup.fasta','yeast_dedup.fasta','ecoli_dedup.fasta']
for n in range(5):
pair_file = pair_list[n]
seqs_file = seqs_list[n]
testDataset = seqData_Dscript(pair_path='./data/D_script/pairs/{}'.format(pair_file),seqs_path='./data/D_script/seqs/{}'.format(seqs_file),addNeg=True)
test_loader = DataLoader(testDataset, batch_size=128, shuffle=False)
#Test
g,p_ave,p_min,p_max = predicting(model, device, test_loader,Model_type=1)
p = (p_ave+p_min+p_max)/3
np.save('./output/results_DeepNano-seq(DScriptData){}_{}.npy'.format(ESM2_MODEL,pair_file),[g,p,p_ave,p_min,p_max])
##Ensemble
precision,recall,accuracy,F1_score,Top10,Top20,Top50,AUC_ROC,AUC_PR = evaluate(g,p)
print("{}: Top10 = {:.4f},Top20 = {:.4f},Top50 = {:.4f},accuracy={:.4f},Recall = {:.4f},Precision={:.4f},F1 score={:.4f},AUC_ROC={:.4f},AUC_PR={:.4f}".format(
pair_file,Top10,Top20,Top50,accuracy,recall,precision,F1_score,AUC_ROC,AUC_PR))
##Ave
precision,recall,accuracy,F1_score,Top10,Top20,Top50,AUC_ROC,AUC_PR = evaluate(g,p_ave)
print("{}: Top10 = {:.4f},Top20 = {:.4f},Top50 = {:.4f},accuracy={:.4f},Recall = {:.4f},Precision={:.4f},F1 score={:.4f},AUC_ROC={:.4f},AUC_PR={:.4f}".format(
'Ave',Top10,Top20,Top50,accuracy,recall,precision,F1_score,AUC_ROC,AUC_PR))
##Min
precision,recall,accuracy,F1_score,Top10,Top20,Top50,AUC_ROC,AUC_PR = evaluate(g,p_min)
print("{}: Top10 = {:.4f},Top20 = {:.4f},Top50 = {:.4f},accuracy={:.4f},Recall = {:.4f},Precision={:.4f},F1 score={:.4f},AUC_ROC={:.4f},AUC_PR={:.4f}".format(
'Min',Top10,Top20,Top50,accuracy,recall,precision,F1_score,AUC_ROC,AUC_PR))
##Max
precision,recall,accuracy,F1_score,Top10,Top20,Top50,AUC_ROC,AUC_PR = evaluate(g,p_max)
print("{}: Top10 = {:.4f},Top20 = {:.4f},Top50 = {:.4f},accuracy={:.4f},Recall = {:.4f},Precision={:.4f},F1 score={:.4f},AUC_ROC={:.4f},AUC_PR={:.4f}".format(
'Max',Top10,Top20,Top50,accuracy,recall,precision,F1_score,AUC_ROC,AUC_PR))