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evaluate_retrieval.py
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evaluate_retrieval.py
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import os
import argparse
import numpy as np
from tqdm import tqdm
import random
import torch
import torch.nn as nn
import torch.nn.functional as F
from datasets import DrugBank_Datasets_Graph_retrieval, DrugBank_Datasets_Graph_ATC
from torch_geometric.loader import DataLoader as pyg_DataLoader
# For Language Models
from transformers import AutoModel, AutoTokenizer
from utils.bert import prepare_text_tokens
# For Graph Neural Networks
from layers import GNN, GNN_graphpred
def seed_everything(seed=0):
# To fix the random seed
random.seed(seed)
os.environ['PYTHONHASHSEED'] = str(seed)
np.random.seed(seed)
torch.manual_seed(seed)
torch.cuda.manual_seed(seed)
torch.cuda.manual_seed_all(seed)
# backends
torch.backends.cudnn.deterministic = True
torch.backends.cudnn.benchmark = False
def do_CL_eval(X, Y, neg_Y, args):
X = F.normalize(X, dim=-1)
X = X.unsqueeze(1) # B, 1, d
Y = Y.unsqueeze(0)
Y = torch.cat([Y, neg_Y], dim=0) # T, B, d
Y = Y.transpose(0, 1) # B, T, d
Y = F.normalize(Y, dim=-1)
logits = torch.bmm(X, Y.transpose(1, 2)).squeeze() # B*T
B = X.size()[0]
labels = torch.zeros(B).long().to(logits.device) # B*1
criterion = nn.CrossEntropyLoss()
CL_loss = criterion(logits, labels)
pred = logits.argmax(dim=1, keepdim=False)
confidence = logits
CL_conf = confidence.max(dim=1)[0]
CL_conf = CL_conf.cpu().numpy()
CL_acc = pred.eq(labels).sum().detach().cpu().item() * 1. / B
return CL_loss, CL_conf, CL_acc
def get_text_repr(text):
text_tokens_ids, text_masks = prepare_text_tokens(
device=device, description=text, tokenizer=text_tokenizer, max_seq_len=args.max_seq_len)
text_output = text_model(input_ids=text_tokens_ids, attention_mask=text_masks)
text_repr = text_output["pooler_output"]
text_repr = text2latent(text_repr)
return text_repr
def get_molecule_repr(molecule):
molecule_output, _ = molecule_model(molecule.to(device))
molecule_repr = mol2latent(molecule_output)
return molecule_repr
@torch.no_grad()
def eval_epoch(dataloader):
text_model.eval()
molecule_model.eval()
text2latent.eval()
mol2latent.eval()
accum_acc_list = [0 for _ in args.T_list]
for batch in tqdm(dataloader):
text = batch[0]
molecule_data = batch[1]
neg_text = batch[2]
neg_molecule_data = batch[3]
text_repr = get_text_repr(text)
molecule_repr = get_molecule_repr(molecule_data.to(device))
if test_mode == "given_text":
neg_molecule_repr = [get_molecule_repr(neg_molecule_data[idx].to(device)) for idx in range(T_max)]
neg_molecule_repr = torch.stack(neg_molecule_repr)
for T_idx, T in enumerate(args.T_list):
_, _, acc = do_CL_eval(text_repr, molecule_repr, neg_molecule_repr[:T-1], args)
accum_acc_list[T_idx] += acc
elif test_mode == "given_molecule":
neg_text_repr = [get_text_repr(neg_text[idx]) for idx in range(T_max)]
neg_text_repr = torch.stack(neg_text_repr)
for T_idx, T in enumerate(args.T_list):
_, _, acc = do_CL_eval(molecule_repr, text_repr, neg_text_repr[:T-1], args)
accum_acc_list[T_idx] += acc
else:
raise Exception
accum_acc_list = np.array(accum_acc_list)
accum_acc_list /= len(dataloader)
return accum_acc_list
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument("--seed", type=int, default=42)
parser.add_argument("--device", type=int, default=0)
parser.add_argument("--SSL_emb_dim", type=int, default=256)
parser.add_argument("--text_type", type=str, default="SciBERT", choices=["SciBERT", "BioBERT", "SentenceBERT"])
parser.add_argument("--dataspace_path", type=str, default="./data")
parser.add_argument("--dataset", type=str, default="description", choices=["ATC", "description", "pharmacodynamics"])
parser.add_argument("--input_model_dir", type=str, default="./model_checkpoints")
parser.add_argument("--input_model_config", type=str,
default="AMOLE.pth")
parser.add_argument("--test_mode", type=str, default="given_molecule", choices=["given_text", "given_molecule"])
########## for optimization ##########
parser.add_argument("--T_list", type=int, nargs="+", default=[4, 10, 20])
parser.add_argument("--batch_size", type=int, default=128)
########## for BERT model ##########
parser.add_argument("--max_seq_len", type=int, default=512)
########## for 2D GNN ##########
parser.add_argument("--gnn_emb_dim", type=int, default=300)
parser.add_argument("--num_layer", type=int, default=5)
parser.add_argument('--JK', type=str, default='last')
parser.add_argument("--dropout_ratio", type=float, default=0.5)
parser.add_argument("--gnn_type", type=str, default="gin")
parser.add_argument('--graph_pooling', type=str, default='mean')
args = parser.parse_args()
print("arguments\t", args)
if torch.cuda.is_available():
torch.cuda.manual_seed_all(args.seed)
device = torch.device("cuda:" + str(args.device)) \
if torch.cuda.is_available() else torch.device("cpu")
##### prepare text model #####
if args.text_type == "SciBERT":
pretrained_SciBERT_folder = os.path.join(args.dataspace_path, 'PubChemSTM' ,'pretrained_SciBERT')
text_tokenizer = AutoTokenizer.from_pretrained('allenai/scibert_scivocab_uncased', cache_dir=pretrained_SciBERT_folder)
# TODO: check https:/huggingface/transformers/blob/main/src/transformers/models/bert/modeling_bert.py#L1501
text_model = AutoModel.from_pretrained('allenai/scibert_scivocab_uncased', cache_dir=pretrained_SciBERT_folder).to(device)
text_dim = 768
elif args.text_type == "SentenceBERT":
pretrained_SentenceBERT_folder = os.path.join(args.dataspace_path, 'PubChemSTM' ,'pretrained_SentenceBERT')
text_tokenizer = AutoTokenizer.from_pretrained('sentence-transformers/multi-qa-mpnet-base-dot-v1', cache_dir=pretrained_SentenceBERT_folder)
# TODO: check https:/huggingface/transformers/blob/main/src/transformers/models/bert/modeling_bert.py#L1501
text_model = AutoModel.from_pretrained('sentence-transformers/multi-qa-mpnet-base-dot-v1', cache_dir=pretrained_SentenceBERT_folder).to(device)
text_dim = 768
else:
raise Exception
##### prepare molecule model #####
molecule_node_model = GNN(
num_layer=args.num_layer, emb_dim=args.gnn_emb_dim,
JK=args.JK, drop_ratio=args.dropout_ratio,
gnn_type=args.gnn_type)
molecule_model = GNN_graphpred(
num_layer=args.num_layer, emb_dim=args.gnn_emb_dim, JK=args.JK, graph_pooling=args.graph_pooling,
num_tasks=1, molecule_node_model=molecule_node_model)
molecule_dim = args.gnn_emb_dim
text2latent = nn.Linear(text_dim, args.SSL_emb_dim)
mol2latent = nn.Linear(molecule_dim, args.SSL_emb_dim)
# Load pretrained checkpoints
input_model_path_file = args.input_model_config
input_model_path = os.path.join(args.input_model_dir, "text", input_model_path_file)
state_dict = torch.load(input_model_path, map_location='cpu')
text_model.load_state_dict(state_dict)
input_model_path = os.path.join(args.input_model_dir, "molecule", input_model_path_file)
state_dict = torch.load(input_model_path, map_location='cpu')
molecule_model.load_state_dict(state_dict)
input_model_path = os.path.join(args.input_model_dir, "text2latent", input_model_path_file)
state_dict = torch.load(input_model_path, map_location='cpu')
text2latent.load_state_dict(state_dict)
input_model_path = os.path.join(args.input_model_dir, "mol2latent", input_model_path_file)
state_dict = torch.load(input_model_path, map_location='cpu')
mol2latent.load_state_dict(state_dict)
text_model = text_model.to(device)
molecule_model = molecule_model.to(device)
text2latent = text2latent.to(device)
mol2latent = mol2latent.to(device)
test_mode = args.test_mode
T_max = max(args.T_list) - 1
dataset_folder = os.path.join(args.dataspace_path, "Drugbank")
dataloader_class = pyg_DataLoader
test_acc_lists = list()
for i in range(5):
seed_everything(i)
if args.dataset == "ATC":
dataset_class = DrugBank_Datasets_Graph_ATC
prompt_template = "This molecule is for {}."
full_file_name = "SMILES_ATC_5_full.txt"
processed_dir_prefix = "ATC_full_5"
dataset = dataset_class(dataset_folder, full_file_name, processed_dir_prefix, neg_sample_size=T_max, prompt_template=prompt_template)
dataloader = dataloader_class(dataset, batch_size=args.batch_size, shuffle=True, num_workers=0)
else :
dataset_class = DrugBank_Datasets_Graph_retrieval
if args.dataset == "description":
processed_dir_prefix = "molecule_description_removed_PubChem"
template = "SMILES_description_removed_from_PubChem_{}.txt"
elif args.dataset == "pharmacodynamics":
processed_dir_prefix = "molecule_pharmacodynamics_removed_PubChem"
template = "SMILES_pharmacodynamics_removed_from_PubChem_{}.txt"
full_dataset = dataset_class(dataset_folder, 'full', neg_sample_size=T_max, processed_dir_prefix=processed_dir_prefix, template=template)
dataloader = dataloader_class(full_dataset, batch_size=args.batch_size, shuffle=True, num_workers=0)
test_acc_list = eval_epoch(dataloader)
print("arguments\t", args)
print(test_acc_list)
test_acc_lists.append(test_acc_list)
test_acc_lists = np.vstack(test_acc_lists)
print("5 run acc:", test_acc_lists.mean(axis = 0))
print("5 run std:", test_acc_lists.std(axis = 0))
# Write experimental results
WRITE_PATH = "results_ret/"
os.makedirs(WRITE_PATH, exist_ok=True) # Create directory if it does not exist
f = open("results_ret/{}.txt".format(args.dataset), "a")
f.write("--------------------------------------------------------------------------------- \n")
f.write("{}".format(args))
f.write("\n")
f.write("5 run acc: {}".format(test_acc_lists.mean(axis = 0)))
f.write("\n")
f.write("5 run std: {}".format(test_acc_lists.std(axis = 0)))
f.write("\n")
f.write("--------------------------------------------------------------------------------- \n")
f.close()