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main.py
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main.py
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'''
@Author: your name
@Date: 2020-05-17 13:39:08
LastEditTime: 2022-06-22 10:50:43
LastEditors: xiaomingaaa [email protected]
@Description: In User Settings Edit
@FilePath: /Multi-task-pytorch/main.py
'''
import argparse
from dgl.data.utils import save_graphs, load_graphs
from model import MKDTI, MultiTaskLoss
from layer import Shared_Unit_NL
from data_loader import load_data
import utils
import random
import torch
import numpy as np
import time
import torch.nn.functional as F
import os
import pandas as pd
from sklearn.model_selection import StratifiedKFold, KFold, train_test_split
import warnings
warnings.filterwarnings("ignore")
def cpi_data_iter(batch_size, features, drug2smile=None, target2seq=None):
num_examples = len(features)
indices = list(range(num_examples))
random.shuffle(indices)
features = torch.from_numpy(np.array(features))
for i in range(0, num_examples, batch_size):
drugs = list()
targets = list()
labels = list()
drugids = list()
j = torch.LongTensor(indices[i:min(i+batch_size, num_examples)])
features_select = features.index_select(0, j)
for (drugid, targetid, label) in features_select:
drugs.append(drug2smile[int(drugid)])
targets.append(target2seq[int(targetid)])
labels.append([int(label)])
drugids.append(drugid)
yield np.array(drugs), np.array(targets), np.array(labels), np.array(drugids)
def get_data(features, drug2smiles, target2seq):
drugs = list()
targets = list()
labels = list()
drugids = list()
for (drugid, targetid, label) in features:
drugs.append(drug2smiles[int(drugid)])
targets.append(target2seq[int(targetid)])
labels.append([int(label)])
drugids.append(drugid)
return np.array(drugs), np.array(targets), np.array(labels), np.array(drugids)
def get_dti_data(features):
drugs = list()
targets = list()
labels = list()
drugids = list()
for (drugid, targetid, label) in features:
drugs.append(int(drugid))
targets.append(int(targetid))
labels.append([int(label)])
return np.array(drugs), np.array(targets), np.array(labels)
def dti_data_iter(batch_size, features):
num_examples = len(features)
indices = list(range(num_examples))
random.shuffle(indices)
features = torch.from_numpy(np.array(features))
for i in range(0, num_examples, batch_size):
drugs = list()
targets = list()
labels = list()
#drugids = list()
j = torch.LongTensor(indices[i:min(i+batch_size, num_examples)])
features_select = features.index_select(0, j)
for (drugid, targetid, label) in features_select:
drugs.append(int(drugid))
targets.append(int(targetid))
labels.append([int(label)])
# drugids.append(drugid)
yield np.array(drugs), np.array(targets), np.array(labels)
def graph_data_iter(batch_size, features, protein2seq):
num_examples = len(features)
indices = list(range(num_examples))
random.shuffle(indices)
features = torch.from_numpy(np.array(features))
for i in range(0, num_examples, batch_size):
drugs = list()
targets = list()
labels = list()
drugids = list()
j = torch.LongTensor(indices[i:min(i+batch_size, num_examples)])
features_select = features.index_select(0, j)
for (drugid, targetid, label) in features_select:
drugs.append(int(drugid))
targets.append(protein2seq[int(targetid)])
labels.append([int(label)])
drugids.append(int(drugid))
yield drugs, np.array(targets), np.array(labels), np.array(drugids)
def get_all_graph(features, protein2seq):
drugs = list()
targets = list()
labels = list()
drugids = list()
for (drugid, targetid, label) in features:
drugs.append(int(drugid))
targets.append(protein2seq[int(targetid)])
labels.append([int(label)])
drugids.append(drugid)
return drugs, np.array(targets), np.array(labels), np.array(drugids)
def process_kg(args, train_kg, data, adj_list, degrees, use_cuda, sample_nodes=None):
g, node_id, edge_type, node_norm, grapg_data, labels = utils.generate_sampled_graph_and_labels(
train_kg, args.graph_batch_size, args.graph_split_size, data.num_rels, adj_list, degrees, args.negative_sample, args.edge_sampler, sample_nodes)
#print('Done edge sampling for rgcn')
node_id = torch.from_numpy(node_id).view(-1, 1).long()
edge_type = torch.from_numpy(edge_type)
edge_norm = utils.node_norm_to_edge_norm(
g, torch.from_numpy(node_norm).view(-1, 1))
grapg_data, labels = torch.from_numpy(
grapg_data), torch.from_numpy(labels)
deg = g.in_degrees(range(g.number_of_nodes())).float().view(-1, 1)
if use_cuda:
node_id, deg = node_id.cuda(), deg.cuda()
edge_norm, edge_type = edge_norm.cuda(), edge_type.cuda()
grapg_data, labels = grapg_data.cuda(), labels.cuda()
# test_node_id,test_deg=test_node_id.cuda(),test_deg.cuda()
# test_norm,test_rel=test_norm.cuda(),test_rel.cuda()
return g, node_id, edge_type, node_norm, grapg_data, labels, edge_norm
def main(args):
# get dataset for gnn
data = load_data('dataset/kg',
'dataset/dti_task', 'dataset/cpi_task', cpi_dataset=args.cpi_dataset, dti_dataset=args.dti_dataset, cpi_gnn=True)
train_kg = torch.LongTensor(np.array(data.train_kg))
val_compounds, val_proteins, val_cpi_labels, val_compoundids = get_all_graph(
data.val_set_gnn, data.protein2seq)
test_compounds, test_proteins, test_cpi_labels, test_compoundids = get_all_graph(
data.test_set_gnn, data.protein2seq)
val_cpi_labels = torch.from_numpy(val_cpi_labels)
test_cpi_labels = torch.from_numpy(test_cpi_labels)
val_drugs, val_targets, val_dti_labels = get_dti_data(data.val_dti_set)
val_dti_labels = torch.from_numpy(val_dti_labels).long()
test_drugs, test_targets, test_dti_labels = get_dti_data(data.test_dti_set)
test_dti_labels = torch.from_numpy(test_dti_labels).long()
drug_entities, target_entities, dti_labels = get_dti_data(
data.train_dti_set)
device='cuda:{}'.format(args.gpu) if args.gpu>=0 else 'cpu'
loss_model = MultiTaskLoss(2, args.shared_unit_num, args.
embedd_dim, data.word_length, 3, 2, 0.5, data.num_nodes,
args.embedd_dim, args.embedd_dim, data.num_rels, args.n_bases, variant=args.variant, device=device)
use_cuda = args.gpu >= 0 and torch.cuda.is_available()
if use_cuda:
torch.cuda.set_device(args.gpu)
loss_model.cuda()
dti_labels = torch.from_numpy(dti_labels).float().cuda()
# build adj list and calculate degrees for sampling
print('build adj and degrees....')
if os.path.isfile('data/adj_list.npy'):
adj_list = list(np.load('data/adj_list.npy', allow_pickle=True))
degrees = np.load('data/degrees.npy')
else:
adj_list, degrees = utils.get_adj_and_degrees(data.num_nodes, train_kg)
np.save('data/adj_list.npy', np.array(adj_list))
np.save('data/degrees.npy', degrees)
print('start training....')
lr_globals = [0.001]
### 32, 16 for full negative
batch_sizes = [32]
# loss_lamdas=[0.25,0.5,0.75]
shared_lrs = [0.001]
super_params = [lr_globals, batch_sizes, shared_lrs]
combinations = utils.lists_combination(super_params, ',')
search_performace = dict()
loss_history = []
auc_history = []
for p in combinations:
print('params: {} training...'.format(p))
val_dti_log = []
search_performace[p] = dict()
best_test_cpi_record = [0, 0]
best_test_dti_record = [0, 0]
best_dti_roc = 0.0
best_cpi_roc = 0.0
val_cpi_log = []
epochs_his = []
test_dti_performance = dict()
test_cpi_performance = dict()
l = p.strip().split(',')
lr_g = float(l[0]) # global learning rate for each layer
batch_size = int(l[1]) # batch_size of cpi task
# loss_lamda=float(l[2]) # loss weight for two tasks
shared_lr = float(l[2]) # learning rate for shared unit
early_stop = 0
params_list = []
params = list(
filter(lambda kv: 'shared_unit' in kv[0], loss_model.named_parameters()))
base_params = list(
filter(lambda kv: 'shared_unit' not in kv[0], loss_model.named_parameters()))
for k, v in params:
params_list += [{'params': [v], 'lr': shared_lr}]
for k, v in base_params:
params_list += [{'params': [v], 'lr': lr_g}]
optimizer_global = torch.optim.Adam(params_list, lr=lr_g)
for epoch in range(args.n_epochs):
early_stop += 1
if early_stop >= 10:
print(
'After 10 consecutive epochs, the model stops training because the performance has not improved!')
break
loss_model.train()
if use_cuda:
loss_model.cuda()
g, node_id, edge_type, node_norm, grapg_data, labels, edge_norm = process_kg(
args, train_kg, data, adj_list, degrees, use_cuda, sample_nodes=list(data.sample_nodes))
loss_epoch_cpi = 0
loss_epoch_dti = 0
loss_epoch_total = 0
for (compounds, proteins, cpi_labels, compoundids) in graph_data_iter(batch_size, data.train_set_gnn, data.protein2seq):
cpi_labels = torch.from_numpy(cpi_labels).float().cuda()
loss_total, loss_cpi, loss_dti, cpi_pred, dti_pred, loss_params = loss_model(g, node_id, edge_type, edge_norm,
compounds, torch.LongTensor(proteins).cuda(), compoundids, drug_entities, target_entities, smiles2graph=data.smiles2graph, cpi_labels=cpi_labels, dti_labels=dti_labels,mode=args.loss_mode)
loss_total.backward()
# torch.nn.utils.clip_grad_norm_(model.parameters(), args.grad_norm)
optimizer_global.step()
optimizer_global.zero_grad()
loss_epoch_total += loss_total
loss_epoch_cpi += loss_cpi
loss_epoch_dti += loss_dti
if use_cuda:
loss_model.cpu()
loss_model.eval()
cpi_pred, dti_pred = loss_model(g, node_id.cpu(), edge_type.cpu(), edge_norm.cpu(),
val_compounds, torch.LongTensor(val_proteins), val_compoundids, val_drugs, val_targets, smiles2graph=data.smiles2graph, eval_=True)
val_dti_acc, val_dti_roc, val_dti_pre, val_dti_recall, val_dti_aupr = utils.eval_cpi_2(
dti_pred, val_dti_labels)
val_acc, val_roc, val_pre, val_recall, val_aupr = utils.eval_cpi_2(
cpi_pred, val_cpi_labels)
test_dti_performance[str(epoch)] = [
val_dti_acc, val_dti_roc, val_dti_pre, val_dti_recall, val_dti_aupr]
test_cpi_performance[str(epoch)] = [
val_acc, val_roc, val_pre, val_recall, val_aupr]
print("Epoch {:04d}-CPI-val | acc:{:.4f}, roc:{:.4f}, precision:{:.4f}, recall:{:.4f}, aupr:{:.4f}".
format(epoch, val_acc, val_roc, val_pre, val_recall, val_aupr))
val_cpi_log.append(
[val_acc, val_roc, val_pre, val_recall, val_aupr])
print('Epoch {:04d}-DTI-val | acc:{:.4f}, roc:{:.4f}, precision:{:.4f}, recall:{:.4f}, aupr:{:.4f}'.format(
epoch, val_dti_acc, val_dti_roc, val_dti_pre, val_dti_recall, val_dti_aupr))
val_dti_log.append(
[val_dti_acc, val_dti_roc, val_dti_pre, val_dti_recall, val_dti_aupr])
epochs_his.append(epoch)
if best_dti_roc < val_dti_roc and best_cpi_roc < val_roc:
model_path = 'ckl/lr{}_epoch{}_{}_{}_batch{}_slr{}_{}_{}.pkl'.format(
lr_g, epoch, args.cpi_dataset, args.dti_dataset, batch_size, shared_lr, args.embedd_dim, args.variant)
early_stop = 0
best_cpi_roc = val_roc
best_dti_roc = val_dti_roc
print('Best performance: CPI:{}, DTI:{}'.format(
best_cpi_roc, best_dti_roc))
torch.save(loss_model.state_dict(), model_path)
print('Best model saved!')
if args.save_embed:
np.save('ckl/entity_embed_kg-mtl{}.npy'.format(epoch),loss_model.multi_task.entity_embedding.embedding.weight.detach().cpu().numpy(),)
np.save('ckl/relation_embed-kg-mtl.npy',loss_model.multi_task.w_relation.detach().cpu().numpy())
print('emebdding saved!')
loss_model.load_state_dict(torch.load(model_path))
if args.save_embed:
np.save('ckl/entity_embed_{}.npy'.format(args.variant),loss_model.multi_task.entity_embedding.embedding.weight.detach().cpu().numpy(),)
np.save('ckl/relation_embed_{}.npy'.format(args.variant),loss_model.multi_task.w_relation.detach().cpu().numpy())
if use_cuda:
loss_model.cpu()
loss_model.eval()
test_cpi_pred, test_dti_pred = loss_model(g, node_id.cpu(), edge_type.cpu(), edge_norm.cpu(),
test_compounds, torch.LongTensor(test_proteins), test_compoundids, test_drugs, test_targets, smiles2graph=data.smiles2graph, eval_=True)
test_dti_acc, test_dti_roc, test_dti_pre, test_dti_recall, test_dti_aupr = utils.eval_cpi_2(
test_dti_pred, test_dti_labels)
test_cpi_acc, test_cpi_roc, test_cpi_pre, test_cpi_recall, test_cpi_aupr = utils.eval_cpi_2(
test_cpi_pred, test_cpi_labels)
test_dti_performance['final'] = [
test_dti_acc, test_dti_roc, test_dti_pre, test_dti_recall, test_dti_aupr]
test_cpi_performance['final'] = [
test_cpi_acc, test_cpi_roc, test_cpi_pre, test_cpi_recall, test_cpi_aupr]
print("Test CPI | acc:{:.4f}, roc:{:.4f}, precision:{:.4f}, recall:{:.4f}, aupr:{:.4f}".
format(test_cpi_acc, test_cpi_roc, test_cpi_pre, test_cpi_recall, test_cpi_aupr))
print('Test DTI | acc:{:.4f}, roc:{:.4f}, precision:{:.4f}, recall:{:.4f}, aupr:{:.4f}'.format(
test_dti_acc, test_dti_roc, test_dti_pre, test_dti_recall, test_dti_aupr))
return [test_cpi_acc, test_cpi_roc, test_cpi_aupr], [test_dti_acc, test_dti_roc, test_dti_aupr], best_test_cpi_record, best_test_dti_record
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument('--dropout', type=float,
default=0.2, help='dropout probability')
parser.add_argument('--n-hidden', type=int, default=500,
help='number of hidden units')
parser.add_argument('--gpu', type=int, default=0, help='gpu id')
parser.add_argument('--lr_pre', type=float, default=0.01,
help='learning rate of pretrain')
parser.add_argument('--lr_dti', type=float, default=0.001,
help='learning rate of dti task')
parser.add_argument('--n_bases', type=int, default=4,
help='number of weight blocks for each relation')
parser.add_argument('--sample_size', type=int,
default=4, help='size of sample of ')
parser.add_argument("--n-layers", type=int, default=2,
help="number of propagation rounds")
parser.add_argument("--n-epochs", type=int, default=100,
help="number of minimum training epochs")
parser.add_argument("--regularization", type=float,
default=0.01, help="regularization weight")
parser.add_argument("--grad-norm", type=float,
default=1.0, help="norm to clip gradient to")
parser.add_argument("--graph-split-size", type=float, default=0.5,
help="portion of edges used as positive sample")
parser.add_argument("--negative-sample", type=int, default=10,
help="number of negative samples per positive sample")
parser.add_argument("--edge-sampler", type=str, default="neighbor",
help="type of edge sampler: 'uniform' or 'neighbor'")
parser.add_argument("--graph_batch_size", type=int, default=40000)
parser.add_argument("--rgcn_epochs", type=int,
default=0, help="rgcn pre-training rounds")
parser.add_argument("--loss_lamda", type=float,
default=0.75, help="rgcn pre-training rounds")
parser.add_argument('--cpi_dataset', type=str,
default='human', help='dataset used for cpi task')
parser.add_argument('--dti_dataset', type=str,
default='drugcentral', help='dataset used for dti task')
# 共用同一个shared unit layer
parser.add_argument('--shared_unit_num', type=int,
default=1, help='the number of shared units')
parser.add_argument('--embedd_dim', type=int,
default=256, help='the dim of embedding')
parser.add_argument('--variant', type=str,
default='KG-MTL', help='[KG-MTL, KG-MTL-L, KG-MTL-C]')
parser.add_argument('--loss_mode', type=str,
default='weighted', help='the way of caculating total loss [weighted, single]')
parser.add_argument('--save_embed', type=bool,
default=True, help='save the embedding of entity and relation')
args = parser.parse_args()
print(args)
# ('human_sparse','drugcentral_sparse'),
print(args.variant)
# args.cpi_dataset=cpi
# args.dti_dataset=dti
print(args.variant)
results_cpi = []
results_dti = []
best_results_cpi = []
best_results_dti = []
for i in range(1):
print('{}-th iteration'.format(i+1))
cpi_r, dti_r, best_cpi_r, best_dti_r = main(args)
results_cpi.append(cpi_r)
results_dti.append(dti_r)
best_results_cpi.append(best_cpi_r)
best_results_dti.append(best_dti_r)
avg_cpi = np.mean(np.array(results_cpi), axis=0)
std_cpi = np.std(results_cpi, axis=0)
print('test results: ')
print(avg_cpi)
avg_dti = np.mean(np.array(results_dti), axis=0)
std_dti = np.std(np.array(results_cpi), axis=0)
print(avg_dti)
results_cpi.append(avg_cpi)
results_cpi.append(std_cpi)
results_dti.append(avg_dti)
results_dti.append(std_dti)
np.savetxt('results/cpi_{}_result_{}.txt'.format(args.cpi_dataset, args.variant),
np.array(results_cpi), delimiter=",", fmt='%f')
np.savetxt('results/dti_{}_result_{}.txt'.format(args.dti_dataset, args.variant),
np.array(results_dti), delimiter=",", fmt='%f')
best_avg_cpi=np.mean(np.array(best_results_cpi), axis=0)
best_std_cpi=np.std(np.array(best_results_cpi), axis=0)
print('best results: ')
print(best_avg_cpi)
best_results_cpi.append(best_avg_cpi)
best_results_cpi.append(best_std_cpi)
best_avg_dti=np.mean(np.array(best_results_dti), axis=0)
best_std_dti=np.std(np.array(best_results_dti), axis=0)
print(best_avg_dti)
best_results_dti.append(best_avg_dti)
best_results_dti.append(best_std_dti)
np.savetxt('results/cpi_{}_best_result_{}.txt'.format(args.cpi_dataset, args.variant),
np.array(best_results_cpi), delimiter=",", fmt='%f')
np.savetxt('results/dti_{}_best_result_{}.txt'.format(args.dti_dataset, args.variant),
np.array(best_results_dti), delimiter=",", fmt='%f')
print('result saved!!!')