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DualNetGO_output.py
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##############################################
## Revised by Zhuoyang CHEN
## Date: 2023-07-17
#############################################
from __future__ import division
from __future__ import print_function
import time
import random
import argparse
import numpy as np
from scipy import sparse
import scipy.io as sio
from sklearn.preprocessing import minmax_scale
import torch
import torch.nn.functional as F
import torch.nn as nn
import torch.optim as optim
from utils import *
from model import *
import uuid
import pickle
import copy
import itertools
from collections import defaultdict
import warnings
from tqdm import tqdm
import pandas as pd
import sys
import os
import ast
import csv
import aslloss
from validation import evaluate_performance
from comet_ml import Experiment #comment this if not needed
warnings.filterwarnings("ignore") #temporary ignoring warning from torch_sparse
# Training settings
example_usage = 'CUDA_VISIBLE_DEVICES=7 python DualNetGO_output.py --org human --aspect P --embedding AE --step1_iter 500 --step2_iter 50 --max_feat_select 4 --num_adj 7'
parser = argparse.ArgumentParser(description='DualNetGO test given model', epilog=example_usage)
parser.add_argument('--seed', type=int, default=42, help='Random seed.')
parser.add_argument('--org', type=str, default='human', help='which species to test')
parser.add_argument('--aspect', type=str, default='P', help='function category')
parser.add_argument('--checkpoint',type=str, default='', help='explicitly provide model checkpoint file')
parser.add_argument('--embedding', type=str, default='AE', help='what kind of embedding to use')
parser.add_argument('--epochs', type=int, default=100, help='Number of epochs to train.')
parser.add_argument('--hidden', type=int, default=512, help='hidden dimensions.')
parser.add_argument('--dropout1', type=float, default=0.5, help='Dropout rate (1 - keep probability).')
parser.add_argument('--dropout2', type=float, default=0.5, help='Dropout rate (1 - keep probability).')
parser.add_argument('--dropout3', type=float, default=0.1, help='Dropout rate (1 - keep probability).')
parser.add_argument('--dev', type=int, default=0, help='device id')
parser.add_argument('--layer_norm',type=int, default=1, help='layer norm')
parser.add_argument('--w_fc3',type=float, default=0.000, help='Weight decay layer-2')
parser.add_argument('--w_fc2',type=float, default=0.000, help='Weight decay layer-2')
parser.add_argument('--w_fc1',type=float, default=0.000, help='Weight decay layer-1')
parser.add_argument('--lr_fc1',type=float, default=0.01, help='Learning rate 2 fully connected layers')
parser.add_argument('--lr_fc2',type=float, default=0.01, help='Learning rate 2 fully connected layers')
parser.add_argument('--lr_sel',type=float, default=0.01, help='Learning rate for selector')
parser.add_argument('--wd_sel',type=float,default=1e-05,help='weight decay selector layer')
parser.add_argument('--step1_iter',type=int, default=100, help='Step-1 iterations')
parser.add_argument('--step2_iter',type=int, default=30, help='Step-2 iterations')
parser.add_argument('--max_feat_select',type=int, default=5, help='Maximum feature matrices that can be selected.')
parser.add_argument('--num_adj',type=int, default=7, help='Number of sparse adjacency matrices(including powers) as input')
parser.add_argument('--modeldir',type=str, default='best', help='folder to save the trained model')
parser.add_argument('--resultdir',type=str, default='.', help='folder to save the csv result')
parser.add_argument('--out',type=str, default='results_AUPR.csv', help='csv result')
args = parser.parse_args()
random.seed(args.seed)
np.random.seed(args.seed)
torch.manual_seed(args.seed)
torch.cuda.manual_seed(args.seed)
os.makedirs(args.modeldir, exist_ok=True)
os.makedirs(args.resultdir, exist_ok=True)
#maximum length of subset to find
feat_select = int(args.max_feat_select)
sec_iter = args.step2_iter
layer_norm = bool(int(args.layer_norm))
print("==========================")
criterion = aslloss.AsymmetricLossOptimized(
gamma_neg=2, gamma_pos=0,
clip=0.0,
disable_torch_grad_focal_loss=False,
eps=1e-5,
)
cudaid = "cuda:"+str(args.dev)
device = torch.device(cudaid)
if args.checkpoint == '':
checkpt_file = f'{args.modeldir}/iter1_{args.step1_iter}_iter2_{args.step2_iter}_feat_{args.max_feat_select}_epoch{args.epochs}_{args.aspect}_{args.embedding}_seed{args.seed}.pt'
elif os.path.isfile(args.checkpoint):
checkpt_file = args.checkpoint
temp_args = os.path.split(checkpt_file)[1].split('_')
args.step1_iter = int(temp_args[1])
args.step2_iter = int(temp_args[3])
args.max_feat_select = int(temp_args[5])
args.aspect = temp_args[7]
args.embedding = temp_args[8]
#set number of adjacency matrices in the input data
num_adj = int(args.num_adj)
def validate_step(model,labels,list_mat,list_ind):
model.eval()
with torch.no_grad():
output = model(list_mat, layer_norm,list_ind)
outs = nn.functional.sigmoid(output.detach()).cpu().numpy()
loss_val = criterion(torch.zeros(2,2,2), output, torch.tensor(labels).to(device))
perf_val = evaluate_performance(labels, outs, (outs > 0.5).astype(int))
return loss_val.item(),perf_val['Fmax'],perf_val['tmax']
def test_step(model,labels,list_mat,list_ind,threshold):
model.load_state_dict(torch.load(checkpt_file))
model.eval()
with torch.no_grad():
output = model(list_mat, layer_norm, list_ind)
outs = nn.functional.sigmoid(output.detach()).cpu().numpy()
loss_test = criterion(torch.zeros(2,2,2), output, torch.tensor(labels).to(device))
perf_test = evaluate_performance(labels, outs, (outs > threshold).astype(int))
return loss_test.item(),perf_test
def test(list_val_mat,list_test_mat,list_label,num_nodes,num_feat,num_labels):
list_val_mat = [mat.to(device) for mat in list_val_mat]
#Set number of linear layers of input adj/feat to create
num_adj_mat = num_adj
num_feat_mat = len(list_train_mat) - num_adj
num_layer = len(list_train_mat)
model = Classifier(nfeat=num_feat,
num_adj_mat=num_adj_mat,
num_feat_mat=num_feat_mat,
nhidden=args.hidden,
nclass=num_labels,
dropout1=args.dropout1,
dropout2=args.dropout2,
dropout3=args.dropout3,
num_nodes=num_nodes, device=int(args.dev)).to(device)
print('Testing: =======================')
input_val_mat = [list_val_mat[ww] for ww in best_mask]
input_test_mat = [list_test_mat[ww] for ww in best_mask]
loss_val,acc_val,tmax = validate_step(model,list_label[1],input_val_mat,best_mask)
test_out = test_step(model,list_label[2],input_test_mat,best_mask,tmax)
perf = test_out[1]
return perf, best_mask
## main process =====================================================
Annot = sio.loadmat(f'data/{args.org}/{args.org}_annot.mat', squeeze_me=True)
train_idx = Annot['indx'][args.aspect].tolist()['train'].tolist().tolist()
valid_idx = Annot['indx'][args.aspect].tolist()['valid'].tolist().tolist()
test_idx = Annot['indx'][args.aspect].tolist()['test'].tolist().tolist()
labels_train = np.array(Annot['GO'][args.aspect].tolist()['train'].tolist())
labels_valid = np.array(Annot['GO'][args.aspect].tolist()['valid'].tolist())
labels_test = np.array(Annot['GO'][args.aspect].tolist()['test'].tolist())
# load adj_mat or embedding:
# embeddings
list_mat = []
if args.embedding != 'None':
for e in ['neighborhood', 'fusion', 'cooccurence', 'coexpression', 'experimental', 'database', 'textmining']:
fn = f'data/{args.org}/{args.org}_net_{e}_{args.embedding}.npy'
y = np.load(fn)
list_mat.append(y)
del y
else:
# adj_mat
for e in ['neighborhood', 'fusion', 'cooccurence', 'coexpression', 'experimental', 'database', 'textmining']:
fn = f'data/{args.org}/{args.org}_net_{e}.mat'
y = sio.loadmat(fn, squeeze_me=True)
y = y['Net'].todense()
y = minmax_scale(y)
list_mat.append(y)
del y
# load features
with open(f'data/{args.org}/features.npy', 'rb') as f:
Z = pickle.load(f)
list_mat.append(Z)
del Z
# load results for best mask
res = pd.read_csv(f'./results_{args.org}_best.csv', header=None)
df = res[(res[12]==args.step1_iter) & (res[13]==args.step2_iter) & (res[15]==args.max_feat_select) & (res[17]==args.aspect) & ((res[18]==args.embedding))]
if len(df) == 0 or (not os.path.isfile(checkpt_file)):
print('Model result not found!')
sys.exit(0)
best_mask = ast.literal_eval(df[25].values[0])
num_nodes = list_mat[-2].shape[1] # mat
num_labels = labels_train.shape[1] # y
num_feat = list_mat[-1].shape[1] # features
for i in range(1):
#Create training and testing split
list_train_mat = []
list_val_mat = []
list_test_mat = []
for mat in list_mat:
mat = torch.from_numpy(mat).float()
list_train_mat.append(mat[train_idx,:])
list_val_mat.append(mat[valid_idx,:])
list_test_mat.append(mat[test_idx,:])
list_label = [labels_train, labels_valid, labels_test]
del mat
accuracy_data, best_mask = test(list_val_mat,list_test_mat,list_label,num_nodes,num_feat,num_labels)
fn = f'{args.resultdir}/{args.out}'
with open(fn, 'a') as f:
csv.writer(f).writerow([args.org, args.w_fc1, args.w_fc2, args.w_fc3, args.dropout1, args.dropout2, args.dropout3, args.lr_fc1, args.lr_fc2, args.lr_sel, args.wd_sel, args.hidden, args.layer_norm, args.step1_iter, args.step2_iter, args.epochs, args.max_feat_select, args.num_adj, args.aspect, args.embedding, accuracy_data['Fmax'], accuracy_data['F1'], accuracy_data['M-F1'], accuracy_data['acc'], accuracy_data['m-aupr'], accuracy_data['M-aupr'], accuracy_data['M-aupr-labels'], best_mask])