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graphcare.py
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graphcare.py
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import pickle
import json
import random
import networkx as nx
from pyhealth.datasets import SampleDataset
from graphcare_ import split_by_patient
from torch_geometric.utils import to_networkx, from_networkx
from tqdm import tqdm
import numpy as np
import torch
from torch_geometric.loader import DataListLoader, DataLoader
from torch_geometric.utils import k_hop_subgraph
from graphcare_ import GAT, GIN, GraphCare
from tqdm import tqdm
from pyhealth.metrics import multilabel_metrics_fn
import torch.nn.functional as F
from sklearn.metrics import average_precision_score, roc_auc_score, accuracy_score, f1_score, precision_score, recall_score, jaccard_score, cohen_kappa_score
import argparse
import logging
import neptune
from copy import deepcopy
def load_everything(dataset, task, kg="", kg_ratio=1.0, th="th015"):
if kg == "GPT-KG":
kg = ""
if task == "drugrec" or task == "lenofstay":
path_1 = "/data/pj20/exp_data/ccscm_ccsproc"
path_2 = "/data/pj20/g/graphs/cond_proc/CCSCM_CCSPROC"
elif task == "mortality" or task == "readmission":
path_1 = "/data/pj20/exp_data/ccscm_ccsproc_atc3"
path_2 = "/data/pj20/g/graphs/cond_proc_drug/CCSCM_CCSPROC_ATC3"
if kg_ratio != 1.0:
sample_dataset_file = f"{path_1}/sample_dataset_{dataset}_{task}_{kg}{th}_kg{kg_ratio}.pkl"
graph_file = f"{path_1}/graph_{dataset}_{task}_{kg}{th}.pkl"
else:
sample_dataset_file = f"{path_1}/sample_dataset_{dataset}_{task}_{kg}{th}.pkl"
graph_file = f"{path_1}/graph_{dataset}_{task}_{kg}{th}.pkl"
map_cluster_file = f"{path_1}/clusters_{th}.json"
map_cluster_inv = f"{path_1}/clusters_inv_{th}.json"
map_cluster_rel = f"{path_1}/clusters_rel_{th}.json"
map_cluster_rel_inv = f"{path_1}/clusters_inv_rel_{th}.json"
ccscm_id2clus = f"{path_1}/ccscm_id2clus.json"
ccsproc_id2clus = f"{path_1}/ccsproc_id2clus.json"
if task == "mortality" or task == "readmission":
atc3_id2clus = f"{path_1}/atc3_id2clus.json"
ent2id_file = f"{path_2}/ent2id.json"
rel2id_file = f"{path_2}/rel2id.json"
ent_emb_file = f"{path_2}/entity_embedding.pkl"
rel_emb_file = f"{path_2}/relation_embedding.pkl"
with open(sample_dataset_file, "rb") as f:
sample_dataset = pickle.load(f)
with open(graph_file, "rb") as f:
graph = pickle.load(f)
with open(ent2id_file, "r") as f:
ent2id = json.load(f)
with open(rel2id_file, "r") as f:
rel2id = json.load(f)
with open(ent_emb_file, "rb") as f:
ent_emb = pickle.load(f)
with open(rel_emb_file, "rb") as f:
rel_emb = pickle.load(f)
with open(map_cluster_file, "r") as f:
map_cluster = json.load(f)
with open(map_cluster_inv, "r") as f:
map_cluster_inv = json.load(f)
with open(map_cluster_rel, "r") as f:
map_cluster_rel = json.load(f)
with open(map_cluster_rel_inv, "r") as f:
map_cluster_rel_inv = json.load(f)
with open(ccscm_id2clus, "r") as f:
ccscm_id2clus = json.load(f)
with open(ccsproc_id2clus, "r") as f:
ccsproc_id2clus = json.load(f)
if task == "mortality" or task == "readmission":
with open(atc3_id2clus, "r") as f:
atc3_id2clus = json.load(f)
else:
atc3_id2clus = None
return sample_dataset, graph, ent2id, rel2id, ent_emb, rel_emb, \
map_cluster, map_cluster_inv, map_cluster_rel, map_cluster_rel_inv, \
ccscm_id2clus, ccsproc_id2clus, atc3_id2clus
def get_mode_and_out_channels_and_loss_func(task, sample_dataset):
mode = ""
if task == "mortality" or task == "readmission":
mode = "binary"
out_channels = 1
loss_function = F.binary_cross_entropy_with_logits
elif task == "drugrec":
mode = "multilabel"
out_channels = len(sample_dataset[0]["drugs_ind"])
loss_function = F.binary_cross_entropy_with_logits
elif task == "lenofstay":
mode = "multiclass"
out_channels = 10
loss_function = F.cross_entropy
return mode, out_channels, loss_function
def flatten(lst):
result = []
for item in lst:
if isinstance(item, list):
result.extend(flatten(item))
else:
result.append(item)
return result
def label_ehr_nodes(task, sample_dataset, max_nodes, ccscm_id2clus, ccsproc_id2clus, atc3_id2clus):
for patient in tqdm(sample_dataset):
nodes = []
for condition in flatten(patient['conditions']):
ehr_node = ccscm_id2clus[condition]
nodes.append(int(ehr_node))
patient['node_set'].append(int(ehr_node))
for procedure in flatten(patient['procedures']):
ehr_node = ccsproc_id2clus[procedure]
nodes.append(int(ehr_node))
patient['node_set'].append(int(ehr_node))
if task == "mortality" or task == "readmission":
for drug in flatten(patient['drugs']):
ehr_node = atc3_id2clus[drug]
nodes.append(int(ehr_node))
patient['node_set'].append(int(ehr_node))
# make one-hot encoding
node_vec = np.zeros(max_nodes)
node_vec[nodes] = 1
patient['ehr_node_set'] = torch.tensor(node_vec)
return sample_dataset
def get_rel_emb(map_cluster_rel):
rel_emb = []
for i in range(len(map_cluster_rel.keys())):
rel_emb.append(map_cluster_rel[str(i)]['embedding'][0])
rel_emb = np.array(rel_emb)
return torch.tensor(rel_emb)
def label_k_hop_nodes(G, dataset, k=1):
for patient in tqdm(dataset):
nodes, _, _, _ = k_hop_subgraph(torch.tensor(patient['node_set']), k, G.edge_index)
patient['node_set'] = nodes.tolist()
return dataset
def get_subgraph(G, dataset, task, idx, strategy="1"):
patient = dataset[idx]
while len(patient['node_set']) == 0:
idx -= 1
patient = dataset[idx]
# less focused
# L = G.subgraph(torch.tensor(patient['node_set']))
# P = L.edge_subgraph(torch.tensor(patient['node_set']))
# more focused
# another way to get subgraph
# if strategy == "1":
# L = G.edge_subgraph(torch.tensor(patient['node_set']))
# P = L.subgraph(torch.tensor(patient['node_set']))
# else:
nodes, _, _, edge_mask = k_hop_subgraph(torch.tensor(patient['node_set']), 2, G.edge_index)
mask_idx = torch.where(edge_mask)[0]
L = G.edge_subgraph(mask_idx)
P = L.subgraph(torch.tensor(patient['node_set']))
if task == "drugrec":
P.label = patient['drugs_ind']
elif task == "lenofstay":
label = np.zeros(10)
label[patient['label']] = 1
P.label = torch.tensor(label)
else:
P.label = patient['label']
P.visit_padded_node = patient['visit_padded_node']
P.ehr_nodes = patient['ehr_node_set']
P.patient_id = patient['patient_id']
return P
class Dataset(torch.utils.data.Dataset):
def __init__(self, G, dataset, task, strategy="1"):
self.G = G
self.dataset=dataset
self.task = task
self.strategy = strategy
def __len__(self):
return len(self.dataset)
def __getitem__(self, idx):
return get_subgraph(G=self.G, dataset=self.dataset, task=self.task, idx=idx, strategy=self.strategy)
def get_dataloader(G_tg, train_dataset, val_dataset, test_dataset, task, batch_size, strategy="1"):
train_set = Dataset(G=G_tg, dataset=train_dataset, task=task, strategy=strategy)
val_set = Dataset(G=G_tg, dataset=val_dataset, task=task, strategy=strategy)
test_set = Dataset(G=G_tg, dataset=test_dataset, task=task, strategy=strategy)
train_loader = DataLoader(train_set, batch_size=batch_size, shuffle=True, drop_last=True)
val_loader = DataLoader(val_set, batch_size=batch_size, shuffle=False, drop_last=True)
test_loader = DataLoader(test_set, batch_size=batch_size, shuffle=False, drop_last=True)
return train_loader, val_loader, test_loader
def train(mode, patient_mode, gnn, model, device, train_loader, optimizer, loss_func):
model.train()
training_loss = 0
tot_loss = 0
pbar= tqdm(enumerate(train_loader))
for i, data in pbar:
pbar.set_description(f'loss: {training_loss}')
data = data.to(device)
optimizer.zero_grad()
node_ids = data.y
rel_ids = data.relation
ehr_nodes = data.ehr_nodes.reshape(int(train_loader.batch_size), int(len(data.ehr_nodes)/train_loader.batch_size)).float() if patient_mode != "graph" else None
visit_node = data.visit_padded_node.reshape(int(train_loader.batch_size), int(len(data.visit_padded_node)/train_loader.batch_size), data.visit_padded_node.shape[1]).float()
out = model(
node_ids = node_ids,
rel_ids = rel_ids,
edge_index = data.edge_index,
batch = data.batch,
visit_node = visit_node,
ehr_nodes = ehr_nodes,
in_drop=True,
)
label = data.label.reshape(int(train_loader.batch_size), int(len(data.label)/train_loader.batch_size))
loss = loss_func(out, label.float())
loss.backward()
training_loss = loss
tot_loss += loss
optimizer.step()
return tot_loss
def evaluate(mode, patient_mode, gnn, model, device, loader):
model.eval()
y_prob_all = []
y_true_all = []
for data in tqdm(loader):
data = data.to(device)
with torch.no_grad():
node_ids = data.y
rel_ids = data.relation
ehr_nodes = data.ehr_nodes.reshape(int(loader.batch_size), int(len(data.ehr_nodes)/loader.batch_size)).float() if patient_mode != "graph" else None
visit_node = data.visit_padded_node.reshape(int(loader.batch_size), int(len(data.visit_padded_node)/loader.batch_size), data.visit_padded_node.shape[1]).float()
model
logits = model(
node_ids = node_ids,
rel_ids = rel_ids,
edge_index = data.edge_index,
batch = data.batch,
visit_node = visit_node,
ehr_nodes = ehr_nodes,
)
if mode == "multiclass":
y_prob = F.softmax(logits, dim=-1)
else:
y_prob = torch.sigmoid(logits)
y_true = data.label.reshape(int(loader.batch_size), int(len(data.label)/loader.batch_size))
y_prob_all.append(y_prob.cpu())
y_true_all.append(y_true.cpu())
y_true_all = np.concatenate(y_true_all, axis=0)
y_prob_all = np.concatenate(y_prob_all, axis=0)
return y_true_all, y_prob_all
def train_loop(dataset, task, mode, patient_mode, gnn, train_loader, val_loader, model, optimizer, loss_func, device, epochs, logger=None, run=None, early_stop=5):
best_roc_auc = 0
best_f1 = 0
early_stop_indicator = 0
for epoch in range(1, epochs+1):
loss = train(mode, patient_mode, gnn, model, device, train_loader, optimizer, loss_func)
y_true_all, y_prob_all = evaluate(mode, patient_mode, gnn, model, device, val_loader)
if mode == "binary":
y_pred_all = (y_prob_all >= 0.5).astype(int)
val_pr_auc = average_precision_score(y_true_all, y_prob_all)
val_roc_auc = roc_auc_score(y_true_all, y_prob_all)
val_jaccard = jaccard_score(y_true_all, y_pred_all, average="macro", zero_division=1)
val_acc = accuracy_score(y_true_all, y_pred_all)
val_f1 = f1_score(y_true_all, y_pred_all, average="macro", zero_division=1)
val_precision = precision_score(y_true_all, y_pred_all, average="macro", zero_division=1)
val_recall = recall_score(y_true_all, y_pred_all, average="macro", zero_division=1)
elif mode == "multilabel":
y_pred_all = (y_prob_all >= 0.5).astype(int)
val_pr_auc = average_precision_score(y_true_all, y_prob_all, average="samples")
val_roc_auc = roc_auc_score(y_true_all, y_prob_all, average="samples")
val_jaccard = jaccard_score(y_true_all, y_pred_all, average="samples", zero_division=1)
val_acc = accuracy_score(y_true_all, y_pred_all)
val_f1 = f1_score(y_true_all, y_pred_all, average="samples", zero_division=1)
val_precision = precision_score(y_true_all, y_pred_all, average="samples", zero_division=1)
val_recall = recall_score(y_true_all, y_pred_all, average="samples", zero_division=1)
elif mode == "multiclass":
y_pred_all = np.argmax(y_prob_all, axis=-1)
y_true_all = np.argmax(y_true_all, axis=-1)
val_pr_auc = 0
val_roc_auc = roc_auc_score(y_true_all, y_prob_all, multi_class="ovr", average="weighted")
val_jaccard = cohen_kappa_score(y_true_all, y_pred_all)
val_acc = accuracy_score(y_true_all, y_pred_all)
val_f1 = f1_score(y_true_all, y_pred_all, average="weighted")
val_precision = 0
val_recall = 0
if val_roc_auc >= best_roc_auc:
torch.save(model.state_dict(), f'../../../data/pj20/exp_data/saved_weights_{dataset}_{task}_{model.gnn}.pkl')
print("best model saved")
best_roc_auc = val_roc_auc
early_stop_indicator = 0
# best_f1 = val_f1
else:
early_stop_indicator += 1
if early_stop_indicator >= early_stop:
break
if run is not None:
run["train/loss"].append(loss)
run["val/pr_auc"].append(val_pr_auc)
run["val/roc_auc"].append(val_roc_auc)
run["val/acc"].append(val_acc)
run["val/f1"].append(val_f1)
run["val/precision"].append(val_precision)
run["val/recall"].append(val_recall)
run["val/jaccard"].append(val_jaccard)
print(f'Epoch: {epoch}, Training loss: {loss}, Val PRAUC: {val_pr_auc:.4f}, Val ROC_AUC: {val_roc_auc:.4f}, Val acc: {val_acc:.4f}, Val F1: {val_f1:.4f}, Val precision: {val_precision:.4f}, Val recall: {val_recall:.4f}, Val jaccard: {val_jaccard:.4f}')
if logger is not None:
logger.info(f'Epoch: {epoch}, Training loss: {loss}, Val PRAUC: {val_pr_auc:.4f}, Val ROC_AUC: {val_roc_auc:.4f}, Val acc: {val_acc:.4f}, Val F1: {val_f1:.4f}, Val precision: {val_precision:.4f}, Val recall: {val_recall:.4f}, Val jaccard: {val_jaccard:.4f}')
def construct_args():
parser = argparse.ArgumentParser()
parser.add_argument('--dataset', type=str, default='mimic3')
parser.add_argument('--task', type=str, default='mortality')
parser.add_argument('--kg', type=str, default='GPT-KG')
parser.add_argument('--batch_size', type=int, default=64)
parser.add_argument('--hidden_dim', type=int, default=128)
parser.add_argument('--epochs', type=int, default=100)
parser.add_argument('--lr', type=float, default=1e-3)
parser.add_argument('--weight_decay', type=float, default=1e-5)
parser.add_argument('--dropout', type=float, default=0.5)
parser.add_argument('--num_layers', type=int, default=3)
parser.add_argument('--decay_rate', type=float, default=0.01)
parser.add_argument('--freeze_emb', type=str, default="False")
parser.add_argument('--device', type=int, default=1)
parser.add_argument('--patient_mode', type=str, default='joint', choices=['joint', 'graph', 'node'])
parser.add_argument('--alpha', type=str, default="True", choices=["True", "False"])
parser.add_argument('--beta', type=str, default="True", choices=["True", "False"])
parser.add_argument('--edge_attn', type=str, default="True", choices=["True", "False"])
parser.add_argument('--self_attn', type=float, default=0.)
parser.add_argument("--gnn", type=str, default="BAT", choices=["GAT", "BAT", "GIN"])
parser.add_argument('--hyperparameter_search', type=bool, default=False)
parser.add_argument('--attn_init', type=str, default="False", choices=["True", "False"])
parser.add_argument('--in_drop_rate', type=float, default=0.)
parser.add_argument('--out_drop_rate', type=float, default=0.)
parser.add_argument('--kg_ratio', type=float, default=1.0)
parser.add_argument('--ehr_feat_ratio', type=float, default=1.0)
args = parser.parse_args()
return args
def get_logger(dataset, task, kg, hidden_dim, epochs, lr, decay_rate, dropout, num_layers):
logger = logging.getLogger(__name__)
logger.setLevel(logging.INFO)
formatter = logging.Formatter('%(asctime)s - %(message)s')
file_handler = logging.FileHandler(f'./training_logs/{dataset}_{task}_{kg}_{hidden_dim}_{epochs}_{lr}_{decay_rate}_{dropout}_{num_layers}.log')
file_handler.setLevel(logging.INFO)
file_handler.setFormatter(formatter)
logger.addHandler(file_handler)
stream_handler = logging.StreamHandler()
stream_handler.setLevel(logging.INFO)
stream_handler.setFormatter(formatter)
logger.addHandler(stream_handler)
return logger
def single_run(args, params):
dataset, task, kg, batch_size, hidden_dim, epochs, lr, weight_decay, dropout, num_layers, decay_rate, gnn, patient_mode, alpha, beta, edge_attn, freeze, attn_init, in_drop_rate, kg_ratio, train_ratio, feat_ratio = \
params['dataset'], params['task'], params['kg'], params['batch_size'], params['hidden_dim'], params['epochs'], params['lr'], params['weight_decay'], params['dropout'], params['num_layers'], params['decay_rate'], params['gnn'], params['patient_mode'], params['alpha'], params['beta'], params['edge_attn'], params['freeze'], params['attn_init'], params['in_drop_rate'], params['kg_ratio'], params['train_ratio'], params['feat_ratio']
run = neptune.init_run(
project="patrick.jiang.cs/GraphCare",
api_token="eyJhcGlfYWRkcmVzcyI6Imh0dHBzOi8vYXBwLm5lcHR1bmUuYWkiLCJhcGlfdXJsIjoiaHR0cHM6Ly9hcHAubmVwdHVuZS5haSIsImFwaV9rZXkiOiJlNDFjZWU1ZC1mZGM5LTQ2MTItYTk3ZC02ODIzOTA4MTY0YmIifQ==",
)
run["parameters"] = params
device = torch.device(f"cuda:{args.device}" if torch.cuda.is_available() else 'cpu')
logger = get_logger(dataset, task, kg, hidden_dim, epochs, lr, decay_rate, dropout, num_layers)
print("device:", device)
# load dataset
sample_dataset, G, ent2id, rel2id, ent_emb, rel_emb, \
map_cluster, map_cluster_inv, map_cluster_rel, map_cluster_rel_inv, \
ccscm_id2clus, ccsproc_id2clus, atc3_id2clus = load_everything(dataset, task, kg, kg_ratio)
mode, out_channels, loss_function = get_mode_and_out_channels_and_loss_func(task=task, sample_dataset=sample_dataset)
# label direct ehr node
print("Labeling direct ehr nodes...")
if kg_ratio == 1.0:
sample_dataset = label_ehr_nodes(task, sample_dataset, len(map_cluster), ccscm_id2clus, ccsproc_id2clus, atc3_id2clus)
print(G)
G_tg = from_networkx(G)
print("Splitting dataset...")
train_dataset, val_dataset, test_dataset = split_by_patient(sample_dataset, [0.8, 0.1, 0.1], train_ratio=train_ratio, seed=528)
if feat_ratio != 1.0:
# with open(f'/data/pj20/exp_data/ccscm_ccsproc/val_dataset_mimic3_{task}_th015_{feat_ratio}.pkl', 'rb') as f:
# val_dataset = pickle.load(f)
# val_dataset = label_ehr_nodes(task, val_dataset, len(map_cluster), ccscm_id2clus, ccsproc_id2clus, atc3_id2clus)
with open(f'/data/pj20/exp_data/ccscm_ccsproc/train_dataset_mimic3_{task}_th015_{feat_ratio}.pkl', 'rb') as f:
train_dataset = pickle.load(f)
train_dataset = label_ehr_nodes(task, train_dataset, len(map_cluster), ccscm_id2clus, ccsproc_id2clus, atc3_id2clus)
# get initial node attention
print("Getting initial node attention...")
if task == "mortality" or task == "readmission":
attn_file = f"/data/pj20/exp_data/ccscm_ccsproc_atc3/attention_weights_{task}.pkl"
elif task == "lenofstay" or task == "drugrec":
attn_file = f"/data/pj20/exp_data/ccscm_ccsproc/attention_weights_{task}.pkl"
else:
raise NotImplementedError
with open(attn_file, "rb") as f:
attn_weights = torch.tensor(pickle.load(f))
# get embedding
print("Getting embedding...")
rel_emb = get_rel_emb(map_cluster_rel)
node_emb = G_tg.x
num_nodes=node_emb.shape[0]
num_rels=rel_emb.shape[0]
max_visit=sample_dataset[0]['visit_padded_node'].shape[0]
# get dataloader
print("Getting dataloader...")
train_loader, val_loader, test_loader = get_dataloader(G_tg, train_dataset, val_dataset, test_dataset, task, batch_size, strategy="1")
# get model
print("Getting model...")
model = GraphCare(
num_nodes=num_nodes,
num_rels=num_rels,
max_visit=max_visit,
embedding_dim=node_emb.shape[1],
hidden_dim=hidden_dim,
out_channels=out_channels,
layers=num_layers,
dropout=dropout,
decay_rate=decay_rate,
node_emb=node_emb,
rel_emb=rel_emb,
patient_mode=patient_mode,
use_alpha=False if alpha == "True" else False,
use_beta=False if beta == "True" else False,
use_edge_attn=True if edge_attn == "True" else False,
gnn=gnn,
# gnn="GIN",
freeze=True if freeze == "True" else False,
attn_init=attn_weights if attn_init == "True" else None,
drop_rate=in_drop_rate,
)
model.to(device)
total_params = sum(
param.numel() for param in model.parameters()
)
print("total params:", total_params)
# print(model)
# train
logger.info("Start training...")
optimizer = torch.optim.Adam(model.parameters(), lr=lr, weight_decay=weight_decay)
train_loop(
dataset=dataset,
task=task,
mode=mode,
patient_mode=patient_mode,
gnn=gnn,
train_loader=train_loader,
val_loader=val_loader,
model=model,
optimizer=optimizer,
loss_func=loss_function,
device=device,
epochs=epochs,
logger=logger,
run=run
)
run.stop()
def hyper_search_(args, params):
hyperparameter_options = {
# 'batch_size': [16, 32, 64],
# 'hidden_dim': [128, 256, 512],
# 'lr': [0.001, 0.0001, 0.00001],
# 'weight_decay': [0.001, 0.0001, 0.00001],
'dropout': [0.1, 0.2, 0.3, 0.4, 0.5],
'num_layers': [1, 2, 3, 4],
'decay_rate': [0.01, 0.02, 0.03],
'patient_mode': [
"joint",
"graph",
"node"
],
# 'gnn' : [
# "GAT",
# "GIN",
# "BAT"
# ],
# 'edge_attn': [True, False]
# "in_drop_rate":[
# 0.1,
# 0.2,
# 0.3,
# 0.5,
# 0.7,
# 0.9
# ]
# "kg_ratio":[
# # 0.0,
# # 0.1,
# # 0.3,
# # 0.5,
# # 0.7,
# # 0.9
# ]
# "train_ratio": [
# 0.001,
# 0.002,
# 0.003,
# 0.004,
# 0.005,
# 0.006,
# 0.007,
# 0.008,
# 0.009,
# 0.01,
# 0.02,
# 0.03,
# 0.04,
# 0.05,
# 0.06,
# 0.07,
# 0.08,
# 0.09,
# 0.1,
# 0.3,
# 0.5,
# 0.7,
# 0.9,
# ],
# "feat_ratio": [
# 0.05,
# 0.1,
# 0.2,
# 0.3,
# 0.4,
# 0.5,
# 0.6,
# 0.7,
# 0.8,
# 0.9,
# ]
}
for task in [
# "mortality",
# "readmission",
# "lenofstay",
"drugrec"
]:
hyperparameter_options["task"] = [task]
for hp_name, hp_options in hyperparameter_options.items():
print(f"now searching for {hp_name}...")
for hp_value in hp_options:
print(f"now searching for {hp_name}={hp_value}...")
params_copy = params.copy()
params_copy[hp_name] = hp_value
for i in range(10):
single_run(args, params_copy)
def main():
args = construct_args()
dataset, task, kg, batch_size, hidden_dim, epochs, lr, weight_decay, dropout, num_layers, \
decay_rate, patient_mode, alpha, beta, edge_attn, gnn, hyper_search, freeze, attn_init, in_drop_rate, kg_ratio = \
args.dataset, args.task, args.kg, args.batch_size, args.hidden_dim, args.epochs, args.lr, args.weight_decay, \
args.dropout, args.num_layers, args.decay_rate, args.patient_mode, args.alpha, args.beta, args.edge_attn, args.gnn, args.hyperparameter_search, args.freeze_emb, args.attn_init, args.in_drop_rate, args.kg_ratio
parameters = {
"dataset": dataset,
"task": task,
"kg": kg,
"batch_size": batch_size,
"hidden_dim": hidden_dim,
"epochs": epochs,
"lr": lr,
"weight_decay": weight_decay,
"dropout": dropout,
"num_layers": num_layers,
"decay_rate": decay_rate,
"patient_mode": patient_mode,
"alpha": alpha,
"beta": beta,
"edge_attn": edge_attn,
"gnn": gnn,
"freeze": freeze,
"attn_init": attn_init,
"in_drop_rate": in_drop_rate,
"kg_ratio": kg_ratio,
"train_ratio": 1.0,
"feat_ratio": 1.0
}
print(parameters)
if hyper_search:
# hyperparameter search
print("Hyperparameter search...")
hyper_search_(args, parameters)
else:
single_run(args, parameters)
if __name__ == '__main__':
main()