evaluate_link_classification.py

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

from models.TGAT import TGAT
from models.MemoryModel import MemoryModel, compute_src_dst_node_time_shifts
from models.DyGFormer import DyGFormer
from models.simpleKT import SimpleKT
from models.DyGFormer import DyGFormer
from models.CTNCM import CTNCM
from models.DKT import DKT
from models.DIMKT import DIMKT
from models.QIKT import QIKT
from models.IPKT import IPKT
from models.IEKT import IEKT
from models.AKT import AKT
from models.DyGKT import DyGKT
from models.modules import MergeLayer
from utils.utils import set_random_seed, convert_to_gpu, get_parameter_sizes
from utils.utils import get_neighbor_sampler, NegativeEdgeSampler
from evaluate_models_utils import evaluate_model_link_classification
from utils.DataLoader import get_idx_data_loader, get_link_classification_data
from utils.EarlyStopping import EarlyStopping
from utils.load_configs import get_link_classification_args

if __name__ == "__main__":

    warnings.filterwarnings('ignore')

    # get arguments
    args = get_link_classification_args(is_evaluation=True)

    # 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_link_classification_data(dataset_name=args.dataset_name, 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)

    # initialize negative samplers, set seeds for validation and testing so negatives are the same across different runs
    # in the inductive setting, negatives are sampled only amongst other new nodes
    if args.negative_sample_strategy != 'random':
        val_neg_edge_sampler = NegativeEdgeSampler(src_node_ids=full_data.src_node_ids, dst_node_ids=full_data.dst_node_ids,
                                                   interact_times=full_data.node_interact_times, last_observed_time=train_data.node_interact_times[-1],
                                                   negative_sample_strategy=args.negative_sample_strategy, seed=0)
        new_node_val_neg_edge_sampler = NegativeEdgeSampler(src_node_ids=new_node_val_data.src_node_ids, dst_node_ids=new_node_val_data.dst_node_ids,
                                                            interact_times=new_node_val_data.node_interact_times, last_observed_time=train_data.node_interact_times[-1],
                                                            negative_sample_strategy=args.negative_sample_strategy, seed=1)
        test_neg_edge_sampler = NegativeEdgeSampler(src_node_ids=full_data.src_node_ids, dst_node_ids=full_data.dst_node_ids,
                                                    interact_times=full_data.node_interact_times, last_observed_time=val_data.node_interact_times[-1],
                                                    negative_sample_strategy=args.negative_sample_strategy, seed=2)
        new_node_test_neg_edge_sampler = NegativeEdgeSampler(src_node_ids=new_node_test_data.src_node_ids, dst_node_ids=new_node_test_data.dst_node_ids,
                                                             interact_times=new_node_test_data.node_interact_times, last_observed_time=val_data.node_interact_times[-1],
                                                             negative_sample_strategy=args.negative_sample_strategy, seed=3)
    else:
        val_neg_edge_sampler = NegativeEdgeSampler(src_node_ids=full_data.src_node_ids, dst_node_ids=full_data.dst_node_ids, seed=0)
        new_node_val_neg_edge_sampler = NegativeEdgeSampler(src_node_ids=new_node_val_data.src_node_ids, dst_node_ids=new_node_val_data.dst_node_ids, seed=1)
        test_neg_edge_sampler = NegativeEdgeSampler(src_node_ids=full_data.src_node_ids, dst_node_ids=full_data.dst_node_ids, seed=2)
        new_node_test_neg_edge_sampler = NegativeEdgeSampler(src_node_ids=new_node_test_data.src_node_ids, dst_node_ids=new_node_test_data.dst_node_ids, seed=3)

    # get data loaders
    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):

        set_random_seed(seed=run)

        args.seed = run
        args.load_model_name = f'{args.model_name}_seed{args.seed}'
        args.save_result_name = f'{args.negative_sample_strategy}_negative_sampling_{args.model_name}_seed{args.seed}'

        # 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}/{args.save_result_name}/", exist_ok=True)
        inf = 'ablation paper'
        fh = logging.FileHandler(f"./logs/{args.model_name}/{args.dataset_name}/{args.save_result_name}/{inf + 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}')

        # create model
        if args.model_name == 'DKT':
            dynamic_backbone = DKT(node_raw_features=node_raw_features, 
                                        edge_raw_features=edge_raw_features, 
                                        dropout=args.dropout,
                                        num_neighbors=args.num_neighbors, 
                                        device=args.device)
        elif args.model_name == 'DyGKT':
            dynamic_backbone = DyGKT(node_raw_features=node_raw_features,
                                        edge_raw_features=edge_raw_features,
                                        dropout=args.dropout, 
                                        num_neighbors=args.num_neighbors,
                                        device=args.device,
                                        ablation=args.ablation)

        elif args.model_name == 'CTNCM':
            dynamic_backbone = CTNCM(node_raw_features=node_raw_features,
                                        edge_raw_features=edge_raw_features,
                                        dropout=args.dropout, 
                                        num_neighbors=args.num_neighbors,
                                        device=args.device)

        elif args.model_name == 'AKT':
            dynamic_backbone = AKT(node_raw_features=node_raw_features,
                                        edge_raw_features=edge_raw_features,
                                        dropout=args.dropout, 
                                        num_neighbors=args.num_neighbors,
                                        device=args.device)
        elif args.model_name == 'DIMKT':
            dynamic_backbone = DIMKT(node_raw_features=node_raw_features,
                                    edge_raw_features=edge_raw_features,
                                    dropout=args.dropout,
                                    dataset_name=args.dataset_name,
                                    device=args.device)
        elif args.model_name == 'IPKT':
            dynamic_backbone = IPKT(node_raw_features=node_raw_features,
                                    edge_raw_features=edge_raw_features,
                                    dropout=args.dropout,
                                    device=args.device)
            
        elif args.model_name == 'IEKT':
            dynamic_backbone = IEKT(node_raw_features=node_raw_features,
                                    edge_raw_features=edge_raw_features,
                                    dropout=args.dropout,
                                    device=args.device) 
        elif args.model_name == 'QIKT':
            dynamic_backbone = QIKT(node_raw_features=node_raw_features,
                                    edge_raw_features=edge_raw_features,
                                    dropout=args.dropout,
                                    device=args.device)              
            
        elif args.model_name == 'simpleKT':
            dynamic_backbone = SimpleKT(node_raw_features=node_raw_features,
                                        edge_raw_features=edge_raw_features,
                                        dropout=args.dropout, 
                                        num_neighbors=args.num_neighbors,
                                        device=args.device)
        elif 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 == '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 = MergeLayer(input_dim1=node_raw_features.shape[1], input_dim2=node_raw_features.shape[1],
                                    hidden_dim=node_raw_features.shape[1], output_dim=1)
        model = nn.Sequential(dynamic_backbone, link_predictor)
        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.')

        # load the saved model
        load_model_folder = f"./saved_models/{args.model_name}/{args.dataset_name}/{args.load_model_name}"
        early_stopping = EarlyStopping(patience=0, save_model_folder=load_model_folder,
                                        save_model_name=args.load_model_name, logger=logger, model_name=args.model_name)
        early_stopping.load_checkpoint(model, map_location='cpu')

        model = convert_to_gpu(model, device=args.device)
        # put the node raw messages of memory-based models on device
        if args.model_name in ['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

        loss_func = nn.BCELoss()

        # 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 ['TGN']:
            val_losses, val_metrics = evaluate_model_link_classification(model_name=args.model_name,
                                                                        model=model,
                                                                        neighbor_sampler=full_neighbor_sampler,
                                                                        evaluate_idx_data_loader=val_idx_data_loader,
                                                                        evaluate_neg_edge_sampler=val_neg_edge_sampler,
                                                                        evaluate_data=val_data,
                                                                        loss_func=loss_func,
                                                                        num_neighbors=args.num_neighbors,
                                                                        time_gap=args.time_gap)

            new_node_val_losses, new_node_val_metrics = evaluate_model_link_classification(model_name=args.model_name,
                                                                                        model=model,
                                                                                        neighbor_sampler=full_neighbor_sampler,
                                                                                        evaluate_idx_data_loader=new_node_val_idx_data_loader,
                                                                                        evaluate_neg_edge_sampler=new_node_val_neg_edge_sampler,
                                                                                        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 [ '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_losses, test_metrics = evaluate_model_link_classification(model_name=args.model_name,
                                                                    model=model,
                                                                    neighbor_sampler=full_neighbor_sampler,
                                                                    evaluate_idx_data_loader=test_idx_data_loader,
                                                                    evaluate_neg_edge_sampler=test_neg_edge_sampler,
                                                                    evaluate_data=test_data,
                                                                    loss_func=loss_func,
                                                                    num_neighbors=args.num_neighbors,
                                                                    time_gap=args.time_gap)

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

        new_node_test_losses, new_node_test_metrics = evaluate_model_link_classification(model_name=args.model_name,
                                                                                        model=model,
                                                                                        neighbor_sampler=full_neighbor_sampler,
                                                                                        evaluate_idx_data_loader=new_node_test_idx_data_loader,
                                                                                        evaluate_neg_edge_sampler=new_node_test_neg_edge_sampler,
                                                                                        evaluate_data=new_node_test_data,
                                                                                        loss_func=loss_func,
                                                                                        num_neighbors=args.num_neighbors,
                                                                                        time_gap=args.time_gap)
        # 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 ['TGN']:
            logger.info(f'validate loss: {np.mean(val_losses):.4f}')
            for metric_name in val_metrics[0].keys():
                average_val_metric = np.mean([val_metric[metric_name] for val_metric in val_metrics])
                logger.info(f'validate {metric_name}, {average_val_metric:.4f}')
                val_metric_dict[metric_name] = average_val_metric

            logger.info(f'new node validate loss: {np.mean(new_node_val_losses):.4f}')
            for metric_name in new_node_val_metrics[0].keys():
                average_new_node_val_metric = np.mean([new_node_val_metric[metric_name] for new_node_val_metric in new_node_val_metrics])
                logger.info(f'new node validate {metric_name}, {average_new_node_val_metric:.4f}')
                new_node_val_metric_dict[metric_name] = average_new_node_val_metric

        logger.info(f'test loss: {np.mean(test_losses):.4f}')
        for metric_name in test_metrics[0].keys():
            average_test_metric = np.mean([test_metric[metric_name] for test_metric in test_metrics])
            logger.info(f'test {metric_name}, {average_test_metric:.4f}')
            test_metric_dict[metric_name] = average_test_metric

        logger.info(f'new node test loss: {np.mean(new_node_test_losses):.4f}')
        for metric_name in new_node_test_metrics[0].keys():
            average_new_node_test_metric = np.mean([new_node_test_metric[metric_name] for new_node_test_metric in new_node_test_metrics])
            logger.info(f'new node test {metric_name}, {average_new_node_test_metric:.4f}')
            new_node_test_metric_dict[metric_name] = average_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 ['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 ['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)

        save_result_folder = f"./saved_results/{args.model_name}/{args.dataset_name}"
        os.makedirs(save_result_folder, exist_ok=True)
        save_result_path = os.path.join(save_result_folder, f"{args.save_result_name}.json")
        with open(save_result_path, 'w') as file:
            file.write(result_json)
        logger.info(f'save negative sampling results at {save_result_path}')

    # 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 ['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()