classification.py

import logging
import time
import sys
import os
from tqdm import tqdm
import numpy as np
import warnings
import shutil
import json
import torch
import torch.nn as nn

from models.TGAT import TGAT
from models.MemoryModel import MemoryModel, compute_src_dst_node_time_shifts
from models.CAWN import CAWN
from models.TCL import TCL
from models.GraphMixer import GraphMixer
from models.DyGFormer import DyGFormer
from models.modules import BinaryLoss, MLP, LinkPredictor, EdgeClassifier, EdgeClassifierLearnable
from utils.utils import set_random_seed, convert_to_gpu, get_parameter_sizes, create_optimizer
from utils.utils import get_neighbor_sampler
from evaluate_models_utils import evaluate_model_classification
from utils.metrics import get_classification_metrics
from utils.DataLoader import get_idx_data_loader, get_classification_data
from utils.EarlyStopping import EarlyStopping
from utils.load_configs import get_args

if __name__ == "__main__":

    warnings.filterwarnings('ignore')

    # get arguments
    args = get_args()

    # get data for training, validation and testing
    node_raw_features, edge_raw_features, full_data, train_data, val_data, test_data, new_node_val_data, new_node_test_data = \
        get_classification_data(dataset_name=args.dataset_name, full_ratio=args.full_ratio, val_ratio=args.val_ratio, test_ratio=args.test_ratio)

    # initialize validation and test neighbor sampler to retrieve temporal graph
    full_neighbor_sampler = get_neighbor_sampler(data=full_data, sample_neighbor_strategy=args.sample_neighbor_strategy,
                                                 time_scaling_factor=args.time_scaling_factor, seed=1)

    # get data loaders
    train_idx_data_loader = get_idx_data_loader(indices_list=list(range(len(train_data.src_node_ids))), batch_size=args.batch_size, shuffle=False)
    val_idx_data_loader = get_idx_data_loader(indices_list=list(range(len(val_data.src_node_ids))), batch_size=args.batch_size, shuffle=False)
    new_node_val_idx_data_loader = get_idx_data_loader(indices_list=list(range(len(new_node_val_data.src_node_ids))), batch_size=args.batch_size, shuffle=False)
    test_idx_data_loader = get_idx_data_loader(indices_list=list(range(len(test_data.src_node_ids))), batch_size=args.batch_size, shuffle=False)
    new_node_test_idx_data_loader = get_idx_data_loader(indices_list=list(range(len(new_node_test_data.src_node_ids))), batch_size=args.batch_size, shuffle=False)

    val_metric_all_runs, new_node_val_metric_all_runs, test_metric_all_runs, new_node_test_metric_all_runs = [], [], [], []

    for run in range(args.num_runs):

        if args.num_runs > 1: args.seed = run
        set_random_seed(args.seed)
        args.load_model_name = 'link_prediction'
        args.save_model_name = f'classification_{args.classifier}'

        # set up logger
        logging.basicConfig(level=logging.INFO)
        logger = logging.getLogger()
        logger.setLevel(logging.DEBUG)
        os.makedirs(f"./logs/{args.model_name}/{args.dataset_name}/seed_{args.seed}/{args.save_model_name}/", exist_ok=True)
        # create file handler that logs debug and higher level messages
        fh = logging.FileHandler(f"./logs/{args.model_name}/{args.dataset_name}/seed_{args.seed}/{args.save_model_name}/{str(time.time())}.log")
        fh.setLevel(logging.DEBUG)
        # create console handler with a higher log level
        ch = logging.StreamHandler()
        ch.setLevel(logging.WARNING)
        # create formatter and add it to the handlers
        formatter = logging.Formatter('%(asctime)s - %(name)s - %(levelname)s - %(message)s')
        fh.setFormatter(formatter)
        ch.setFormatter(formatter)
        # add the handlers to logger
        logger.addHandler(fh)
        logger.addHandler(ch)

        run_start_time = time.time()
        logger.info(f"********** Run {run + 1} starts. **********")

        logger.info(f'configuration is {args}')

        save_model_folder = f"./saved_models/{args.model_name}/{args.dataset_name}/seed_{args.seed}/"
        os.makedirs(save_model_folder, exist_ok=True)

        save_result_folder = f"./saved_results/{args.model_name}/{args.dataset_name}/seed_{args.seed}/{args.save_model_name}/"
        shutil.rmtree(save_result_folder, ignore_errors=True)
        os.makedirs(save_result_folder, exist_ok=True)

        # create model
        if args.model_name == 'TGAT':
            dynamic_backbone = TGAT(node_raw_features=node_raw_features, edge_raw_features=edge_raw_features, neighbor_sampler=full_neighbor_sampler,
                                    time_feat_dim=args.time_feat_dim, num_layers=args.num_layers, num_heads=args.num_heads, dropout=args.dropout, device=args.device)
        elif args.model_name in ['JODIE', 'DyRep', 'TGN']:
            # four floats that represent the mean and standard deviation of source and destination node time shifts in the training data, which is used for JODIE
            src_node_mean_time_shift, src_node_std_time_shift, dst_node_mean_time_shift_dst, dst_node_std_time_shift = \
                compute_src_dst_node_time_shifts(train_data.src_node_ids, train_data.dst_node_ids, train_data.node_interact_times)
            dynamic_backbone = MemoryModel(node_raw_features=node_raw_features, edge_raw_features=edge_raw_features, neighbor_sampler=full_neighbor_sampler,
                                           time_feat_dim=args.time_feat_dim, model_name=args.model_name, num_layers=args.num_layers, num_heads=args.num_heads,
                                           dropout=args.dropout, src_node_mean_time_shift=src_node_mean_time_shift, src_node_std_time_shift=src_node_std_time_shift,
                                           dst_node_mean_time_shift_dst=dst_node_mean_time_shift_dst, dst_node_std_time_shift=dst_node_std_time_shift, device=args.device)
        elif args.model_name == 'CAWN':
            dynamic_backbone = CAWN(node_raw_features=node_raw_features, edge_raw_features=edge_raw_features, neighbor_sampler=full_neighbor_sampler,
                                    time_feat_dim=args.time_feat_dim, position_feat_dim=args.position_feat_dim, walk_length=args.walk_length,
                                    num_walk_heads=args.num_walk_heads, dropout=args.dropout, device=args.device)
        elif args.model_name == 'TCL':
            dynamic_backbone = TCL(node_raw_features=node_raw_features, edge_raw_features=edge_raw_features, neighbor_sampler=full_neighbor_sampler,
                                   time_feat_dim=args.time_feat_dim, num_layers=args.num_layers, num_heads=args.num_heads,
                                   num_depths=args.num_neighbors + 1, dropout=args.dropout, device=args.device)
        elif args.model_name == 'GraphMixer':
            dynamic_backbone = GraphMixer(node_raw_features=node_raw_features, edge_raw_features=edge_raw_features, neighbor_sampler=full_neighbor_sampler,
                                          time_feat_dim=args.time_feat_dim, num_tokens=args.num_neighbors, num_layers=args.num_layers, dropout=args.dropout, device=args.device)
        elif args.model_name == 'DyGFormer':
            dynamic_backbone = DyGFormer(node_raw_features=node_raw_features, edge_raw_features=edge_raw_features, neighbor_sampler=full_neighbor_sampler,
                                         time_feat_dim=args.time_feat_dim, channel_embedding_dim=args.channel_embedding_dim, patch_size=args.patch_size,
                                         num_layers=args.num_layers, num_heads=args.num_heads, dropout=args.dropout,
                                         max_input_sequence_length=args.max_input_sequence_length, device=args.device)
        else:
            raise ValueError(f"Wrong value for model_name {args.model_name}!")
        link_predictor = LinkPredictor(prompt_dim=2*node_raw_features.shape[1], lamb=args.lamb, dropout=args.dropout)
        model = nn.Sequential(dynamic_backbone, link_predictor)

        # load the saved model in the link prediction task
        early_stopping = EarlyStopping(patience=0, save_model_folder=save_model_folder,
                                       save_model_name=args.load_model_name, logger=logger, model_name=args.model_name)
        early_stopping.load_checkpoint(model, map_location='cpu')

        if args.classifier != 'mlp':
            for param in model[1].mlp.parameters():
                param.requires_grad = False

        # create the model for the node classification task
        num_classes = train_data.labels.max().item() + 1
        match args.classifier:
            case 'mean':
                edge_classifier = EdgeClassifier(args, train_data, train_idx_data_loader, prompt_dim=2*node_raw_features.shape[1], mlp=model[1].mlp, lamb=args.lamb)
            case 'learnable':
                edge_classifier = EdgeClassifierLearnable(num_classes=num_classes, prompt_dim=2*node_raw_features.shape[1], mlp=model[1].mlp, lamb=args.lamb)
            case 'mlp':
                edge_classifier = MLP(input_dim=node_raw_features.shape[1], output_dim=num_classes if num_classes > 2 else 1, dropout=args.dropout)
        model = nn.Sequential(model[0], edge_classifier)
        logger.info(f'model -> {model}')
        logger.info(f'model name: {args.model_name}, #parameters: {get_parameter_sizes(model) * 4} B, '
                    f'{get_parameter_sizes(model) * 4 / 1024} KB, {get_parameter_sizes(model) * 4 / 1024 / 1024} MB.')

        # follow previous work, we freeze the dynamic_backbone and only optimize the edge_classifier
        optimizer = create_optimizer(model=model[1], optimizer_name=args.optimizer, learning_rate=args.learning_rate, weight_decay=args.weight_decay)

        model = convert_to_gpu(model, device=args.device)
        # put the node raw messages of memory-based models on device
        if args.model_name in ['JODIE', 'DyRep', 'TGN']:
            for node_id, node_raw_messages in model[0].memory_bank.node_raw_messages.items():
                new_node_raw_messages = []
                for node_raw_message in node_raw_messages:
                    new_node_raw_messages.append((node_raw_message[0].to(args.device), node_raw_message[1]))
                model[0].memory_bank.node_raw_messages[node_id] = new_node_raw_messages

        early_stopping = EarlyStopping(patience=args.patience, save_model_folder=save_model_folder,
                                       save_model_name=args.save_model_name, logger=logger, model_name=args.model_name)

        loss_func = nn.CrossEntropyLoss() if num_classes > 2 else BinaryLoss()

        # set the dynamic_backbone in evaluation mode
        model[0].eval()

        for epoch in range(args.num_epochs):

            model[1].train()
            if args.model_name in ['DyRep', 'TGAT', 'TGN', 'CAWN', 'TCL', 'GraphMixer', 'DyGFormer']:
                # training process, set the neighbor sampler
                model[0].set_neighbor_sampler(full_neighbor_sampler)
            if args.model_name in ['JODIE', 'DyRep', 'TGN']:
                # reinitialize memory of memory-based models at the start of each epoch
                model[0].memory_bank.__init_memory_bank__()

            # store train losses, trues and predicts
            train_total_loss, train_y_trues, train_y_predicts = 0.0, [], []
            train_idx_data_loader_tqdm = tqdm(train_idx_data_loader, ncols=120)
            for batch_idx, train_data_indices in enumerate(train_idx_data_loader_tqdm):
                model[1].prototypical_encoding(model[0])
                batch_src_node_ids, batch_dst_node_ids, batch_node_interact_times, batch_edge_ids, batch_labels = \
                    train_data.src_node_ids[train_data_indices], train_data.dst_node_ids[train_data_indices], train_data.node_interact_times[train_data_indices], \
                    train_data.edge_ids[train_data_indices], train_data.labels[train_data_indices]

                with torch.no_grad():
                    if args.model_name in ['TGAT', 'CAWN', 'TCL']:
                        # get temporal embedding of source and destination nodes
                        # two Tensors, with shape (batch_size, node_feat_dim)
                        batch_src_node_embeddings, batch_dst_node_embeddings = \
                            model[0].compute_src_dst_node_temporal_embeddings(src_node_ids=batch_src_node_ids,
                                                                              dst_node_ids=batch_dst_node_ids,
                                                                              node_interact_times=batch_node_interact_times,
                                                                              num_neighbors=args.num_neighbors)
                    elif args.model_name in ['JODIE', 'DyRep', 'TGN']:
                        # get temporal embedding of source and destination nodes
                        # two Tensors, with shape (batch_size, node_feat_dim)
                        batch_src_node_embeddings, batch_dst_node_embeddings = \
                            model[0].compute_src_dst_node_temporal_embeddings(src_node_ids=batch_src_node_ids,
                                                                              dst_node_ids=batch_dst_node_ids,
                                                                              node_interact_times=batch_node_interact_times,
                                                                              edge_ids=batch_edge_ids,
                                                                              edges_are_positive=True,
                                                                              num_neighbors=args.num_neighbors)
                    elif args.model_name in ['GraphMixer']:
                        # get temporal embedding of source and destination nodes
                        # two Tensors, with shape (batch_size, node_feat_dim)
                        batch_src_node_embeddings, batch_dst_node_embeddings = \
                            model[0].compute_src_dst_node_temporal_embeddings(src_node_ids=batch_src_node_ids,
                                                                              dst_node_ids=batch_dst_node_ids,
                                                                              node_interact_times=batch_node_interact_times,
                                                                              num_neighbors=args.num_neighbors,
                                                                              time_gap=args.time_gap)
                    elif args.model_name in ['DyGFormer']:
                        # get temporal embedding of source and destination nodes
                        # two Tensors, with shape (batch_size, node_feat_dim)
                        batch_src_node_embeddings, batch_dst_node_embeddings = \
                            model[0].compute_src_dst_node_temporal_embeddings(src_node_ids=batch_src_node_ids,
                                                                              dst_node_ids=batch_dst_node_ids,
                                                                              node_interact_times=batch_node_interact_times)
                    else:
                        raise ValueError(f"Wrong value for model_name {args.model_name}!")
                # get predicted probabilities, shape (batch_size, )
                predicts = model[1](input_1=batch_src_node_embeddings, input_2=batch_dst_node_embeddings, times=batch_node_interact_times)
                labels = torch.from_numpy(batch_labels).to(predicts.device)
                if num_classes <= 2: labels = labels.float()

                loss = loss_func(input=predicts, target=labels)

                train_total_loss += loss.item()

                train_y_trues.append(labels)
                train_y_predicts.append(predicts)

                optimizer.zero_grad()
                loss.backward()
                optimizer.step()

                train_idx_data_loader_tqdm.set_description(f'Epoch: {epoch + 1}, train for the {batch_idx + 1}-th batch, train loss: {loss.item()}')

            train_total_loss /= (batch_idx + 1)
            train_y_trues = torch.cat(train_y_trues, dim=0)
            train_y_predicts = torch.cat(train_y_predicts, dim=0)

            train_metrics = get_classification_metrics(predicts=train_y_predicts, labels=train_y_trues, multiclass=num_classes>2)

            if args.model_name in ['JODIE', 'DyRep', 'TGN']:
                # backup memory bank after training so it can be used for new validation nodes
                train_backup_memory_bank = model[0].memory_bank.backup_memory_bank()

            val_total_loss, val_metrics = evaluate_model_classification(model_name=args.model_name,
                                                                        model=model,
                                                                        neighbor_sampler=full_neighbor_sampler,
                                                                        evaluate_idx_data_loader=val_idx_data_loader,
                                                                        evaluate_data=val_data,
                                                                        loss_func=loss_func,
                                                                        num_neighbors=args.num_neighbors,
                                                                        time_gap=args.time_gap)

            if args.model_name in ['JODIE', 'DyRep', 'TGN']:
                # backup memory bank after validating so it can be used for testing nodes (since test edges are strictly later in time than validation edges)
                val_backup_memory_bank = model[0].memory_bank.backup_memory_bank()

                # reload training memory bank for new validation nodes
                model[0].memory_bank.reload_memory_bank(train_backup_memory_bank)

            new_node_val_total_loss, new_node_val_metrics = evaluate_model_classification(model_name=args.model_name,
                                                                                       model=model,
                                                                                       neighbor_sampler=full_neighbor_sampler,
                                                                                       evaluate_idx_data_loader=new_node_val_idx_data_loader,
                                                                                       evaluate_data=new_node_val_data,
                                                                                       loss_func=loss_func,
                                                                                       num_neighbors=args.num_neighbors,
                                                                                       time_gap=args.time_gap)

            if args.model_name in ['JODIE', 'DyRep', 'TGN']:
                # reload validation memory bank for testing nodes or saving models
                # note that since model treats memory as parameters, we need to reload the memory to val_backup_memory_bank for saving models
                model[0].memory_bank.reload_memory_bank(val_backup_memory_bank)

            logger.info(f'Epoch: {epoch + 1}, learning rate: {optimizer.param_groups[0]["lr"]}, train loss: {train_total_loss:.4f}')
            for metric_name in train_metrics.keys():
                logger.info(f'train {metric_name}, {train_metrics[metric_name]:.4f}')
            logger.info(f'validate loss: {val_total_loss:.4f}')
            for metric_name in val_metrics.keys():
                logger.info(f'validate {metric_name}, {val_metrics[metric_name]:.4f}')
            logger.info(f'new node validate loss: {new_node_val_total_loss:.4f}')
            for metric_name in new_node_val_metrics.keys():
                logger.info(f'new node validate {metric_name}, {new_node_val_metrics[metric_name]:.4f}')

            # perform testing once after test_interval_epochs
            if (epoch + 1) % args.test_interval_epochs == 0:
                test_total_loss, test_metrics = evaluate_model_classification(model_name=args.model_name,
                                                                            model=model,
                                                                            neighbor_sampler=full_neighbor_sampler,
                                                                            evaluate_idx_data_loader=test_idx_data_loader,
                                                                            evaluate_data=test_data,
                                                                            loss_func=loss_func,
                                                                            num_neighbors=args.num_neighbors,
                                                                            time_gap=args.time_gap)

                if args.model_name in ['JODIE', 'DyRep', 'TGN']:
                    # reload validation memory bank for new testing nodes
                    model[0].memory_bank.reload_memory_bank(val_backup_memory_bank)

                new_node_test_total_loss, new_node_test_metrics = evaluate_model_classification(model_name=args.model_name,
                                                                                             model=model,
                                                                                             neighbor_sampler=full_neighbor_sampler,
                                                                                             evaluate_idx_data_loader=new_node_test_idx_data_loader,
                                                                                             evaluate_data=new_node_test_data,
                                                                                             loss_func=loss_func,
                                                                                             num_neighbors=args.num_neighbors,
                                                                                             time_gap=args.time_gap)

                if args.model_name in ['JODIE', 'DyRep', 'TGN']:
                    # reload validation memory bank for testing nodes or saving models
                    # note that since model treats memory as parameters, we need to reload the memory to val_backup_memory_bank for saving models
                    model[0].memory_bank.reload_memory_bank(val_backup_memory_bank)

                logger.info(f'test loss: {test_total_loss:.4f}')
                for metric_name in test_metrics.keys():
                    logger.info(f'test {metric_name}, {test_metrics[metric_name]:.4f}')
                logger.info(f'new node test loss: {new_node_test_total_loss:.4f}')
                for metric_name in new_node_test_metrics.keys():
                    logger.info(f'new node test {metric_name}, {new_node_test_metrics[metric_name]:.4f}')

            # select the best model based on all the validate metrics
            val_metric_indicator = []
            for metric_name in val_metrics.keys():
                val_metric_indicator.append((metric_name, val_metrics[metric_name], True))
            early_stop = early_stopping.step(val_metric_indicator, model)

            if early_stop:
                break

        # load the best model
        early_stopping.load_checkpoint(model)

        # evaluate the best model
        logger.info(f'get final performance on dataset {args.dataset_name}...')

        # the saved best model of memory-based models cannot perform validation since the stored memory has been updated by validation data
        if args.model_name not in ['JODIE', 'DyRep', 'TGN']:
            val_total_loss, val_metrics = evaluate_model_classification(model_name=args.model_name,
                                                                        model=model,
                                                                        neighbor_sampler=full_neighbor_sampler,
                                                                        evaluate_idx_data_loader=val_idx_data_loader,
                                                                        evaluate_data=val_data,
                                                                        loss_func=loss_func,
                                                                        num_neighbors=args.num_neighbors,
                                                                        time_gap=args.time_gap,
                                                                        fp=f'{save_result_folder}/val_best.json')

            new_node_val_total_loss, new_node_val_metrics = evaluate_model_classification(model_name=args.model_name,
                                                                                       model=model,
                                                                                       neighbor_sampler=full_neighbor_sampler,
                                                                                       evaluate_idx_data_loader=new_node_val_idx_data_loader,
                                                                                       evaluate_data=new_node_val_data,
                                                                                       loss_func=loss_func,
                                                                                       num_neighbors=args.num_neighbors,
                                                                                       time_gap=args.time_gap,
                                                                                       fp=f'{save_result_folder}/new_node_val_best.json')

        if args.model_name in ['JODIE', 'DyRep', 'TGN']:
            # the memory in the best model has seen the validation edges, we need to backup the memory for new testing nodes
            val_backup_memory_bank = model[0].memory_bank.backup_memory_bank()

        test_total_loss, test_metrics = evaluate_model_classification(model_name=args.model_name,
                                                                    model=model,
                                                                    neighbor_sampler=full_neighbor_sampler,
                                                                    evaluate_idx_data_loader=test_idx_data_loader,
                                                                    evaluate_data=test_data,
                                                                    loss_func=loss_func,
                                                                    num_neighbors=args.num_neighbors,
                                                                    time_gap=args.time_gap,
                                                                    fp=f'{save_result_folder}/test_best.json')

        if args.model_name in ['JODIE', 'DyRep', 'TGN']:
            # reload validation memory bank for new testing nodes
            model[0].memory_bank.reload_memory_bank(val_backup_memory_bank)

        new_node_test_total_loss, new_node_test_metrics = evaluate_model_classification(model_name=args.model_name,
                                                                                     model=model,
                                                                                     neighbor_sampler=full_neighbor_sampler,
                                                                                     evaluate_idx_data_loader=new_node_test_idx_data_loader,
                                                                                     evaluate_data=new_node_test_data,
                                                                                     loss_func=loss_func,
                                                                                     num_neighbors=args.num_neighbors,
                                                                                     time_gap=args.time_gap,
                                                                                     fp=f'{save_result_folder}/new_node_test_best.json')

        # store the evaluation metrics at the current run
        val_metric_dict, new_node_val_metric_dict, test_metric_dict, new_node_test_metric_dict = {}, {}, {}, {}

        if args.model_name not in ['JODIE', 'DyRep', 'TGN']:
            logger.info(f'validate loss: {val_total_loss:.4f}')
            for metric_name in val_metrics.keys():
                val_metric = val_metrics[metric_name]
                logger.info(f'validate {metric_name}, {val_metric:.4f}')
                val_metric_dict[metric_name] = val_metric

            logger.info(f'new node validate loss: {new_node_val_total_loss:.4f}')
            for metric_name in new_node_val_metrics.keys():
                new_node_val_metric = new_node_val_metrics[metric_name]
                logger.info(f'new node validate {metric_name}, {new_node_val_metric:.4f}')
                new_node_val_metric_dict[metric_name] = new_node_val_metric

        logger.info(f'test loss: {test_total_loss:.4f}')
        for metric_name in test_metrics.keys():
            test_metric = test_metrics[metric_name]
            logger.info(f'test {metric_name}, {test_metric:.4f}')
            test_metric_dict[metric_name] = test_metric

        logger.info(f'new node test loss: {new_node_test_total_loss:.4f}')
        for metric_name in new_node_test_metrics.keys():
            new_node_test_metric = new_node_test_metrics[metric_name]
            logger.info(f'new node test {metric_name}, {new_node_test_metric:.4f}')
            new_node_test_metric_dict[metric_name] = new_node_test_metric

        single_run_time = time.time() - run_start_time
        logger.info(f'Run {run + 1} cost {single_run_time:.2f} seconds.')

        if args.model_name not in ['JODIE', 'DyRep', 'TGN']:
            val_metric_all_runs.append(val_metric_dict)
            new_node_val_metric_all_runs.append(new_node_val_metric_dict)
        test_metric_all_runs.append(test_metric_dict)
        new_node_test_metric_all_runs.append(new_node_test_metric_dict)

        # avoid the overlap of logs
        if run < args.num_runs - 1:
            logger.removeHandler(fh)
            logger.removeHandler(ch)

        # save model result
        if args.model_name not in ['JODIE', 'DyRep', 'TGN']:
            result_json = {
                "validate metrics": {metric_name: f'{val_metric_dict[metric_name]:.4f}' for metric_name in val_metric_dict},
                "new node validate metrics": {metric_name: f'{new_node_val_metric_dict[metric_name]:.4f}' for metric_name in new_node_val_metric_dict},
                "test metrics": {metric_name: f'{test_metric_dict[metric_name]:.4f}' for metric_name in test_metric_dict},
                "new node test metrics": {metric_name: f'{new_node_test_metric_dict[metric_name]:.4f}' for metric_name in new_node_test_metric_dict}
            }
        else:
            result_json = {
                "test metrics": {metric_name: f'{test_metric_dict[metric_name]:.4f}' for metric_name in test_metric_dict},
                "new node test metrics": {metric_name: f'{new_node_test_metric_dict[metric_name]:.4f}' for metric_name in new_node_test_metric_dict}
            }
        result_json = json.dumps(result_json, indent=4)

        with open(f'{save_result_folder}/main.json', 'w') as file:
            file.write(result_json)

    # store the average metrics at the log of the last run
    logger.info(f'metrics over {args.num_runs} runs:')

    if args.model_name not in ['JODIE', 'DyRep', 'TGN']:
        for metric_name in val_metric_all_runs[0].keys():
            logger.info(f'validate {metric_name}, {[val_metric_single_run[metric_name] for val_metric_single_run in val_metric_all_runs]}')
            logger.info(f'average validate {metric_name}, {np.mean([val_metric_single_run[metric_name] for val_metric_single_run in val_metric_all_runs]):.4f} '
                        f'± {np.std([val_metric_single_run[metric_name] for val_metric_single_run in val_metric_all_runs], ddof=1):.4f}')

        for metric_name in new_node_val_metric_all_runs[0].keys():
            logger.info(f'new node validate {metric_name}, {[new_node_val_metric_single_run[metric_name] for new_node_val_metric_single_run in new_node_val_metric_all_runs]}')
            logger.info(f'average new node validate {metric_name}, {np.mean([new_node_val_metric_single_run[metric_name] for new_node_val_metric_single_run in new_node_val_metric_all_runs]):.4f} '
                        f'± {np.std([new_node_val_metric_single_run[metric_name] for new_node_val_metric_single_run in new_node_val_metric_all_runs], ddof=1):.4f}')

    for metric_name in test_metric_all_runs[0].keys():
        logger.info(f'test {metric_name}, {[test_metric_single_run[metric_name] for test_metric_single_run in test_metric_all_runs]}')
        logger.info(f'average test {metric_name}, {np.mean([test_metric_single_run[metric_name] for test_metric_single_run in test_metric_all_runs]):.4f} '
                    f'± {np.std([test_metric_single_run[metric_name] for test_metric_single_run in test_metric_all_runs], ddof=1):.4f}')

    for metric_name in new_node_test_metric_all_runs[0].keys():
        logger.info(f'new node test {metric_name}, {[new_node_test_metric_single_run[metric_name] for new_node_test_metric_single_run in new_node_test_metric_all_runs]}')
        logger.info(f'average new node test {metric_name}, {np.mean([new_node_test_metric_single_run[metric_name] for new_node_test_metric_single_run in new_node_test_metric_all_runs]):.4f} '
                    f'± {np.std([new_node_test_metric_single_run[metric_name] for new_node_test_metric_single_run in new_node_test_metric_all_runs], ddof=1):.4f}')

    sys.exit()