link_prediction.py

import logging
import time
import sys
import os
from tqdm import tqdm
import warnings
import shutil
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, LinkPredictor, MLP
from utils.utils import set_random_seed, convert_to_gpu, get_parameter_sizes, create_optimizer
from utils.utils import get_neighbor_sampler, NegativeEdgeSampler
from utils.metrics import get_link_prediction_metrics
from utils.DataLoader import get_idx_data_loader, get_link_prediction_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, train_data = get_link_prediction_data(dataset_name=args.dataset_name, full_ratio=args.full_ratio)

    # initialize training neighbor sampler to retrieve temporal graph
    train_neighbor_sampler = get_neighbor_sampler(data=train_data, sample_neighbor_strategy=args.sample_neighbor_strategy,
                                                  time_scaling_factor=args.time_scaling_factor, seed=0)

    train_neg_edge_sampler = NegativeEdgeSampler(dst_node_ids=train_data.dst_node_ids)

    # 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)

    for run in range(args.num_runs):

        if args.num_runs > 1: args.seed = run
        set_random_seed(args.seed)
        args.save_model_name = 'link_prediction'

        # 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)

        # create model
        if args.model_name == 'TGAT':
            dynamic_backbone = TGAT(node_raw_features=node_raw_features, edge_raw_features=edge_raw_features, neighbor_sampler=train_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=train_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=train_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=train_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=train_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=train_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)
        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.')

        optimizer = create_optimizer(model=model, optimizer_name=args.optimizer, learning_rate=args.learning_rate, weight_decay=args.weight_decay)

        model = convert_to_gpu(model, device=args.device)

        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 = BinaryLoss()

        for epoch in range(args.num_epochs):

            model.train()
            if args.model_name in ['DyRep', 'TGAT', 'TGN', 'CAWN', 'TCL', 'GraphMixer', 'DyGFormer']:
                # training, only use training graph
                model[0].set_neighbor_sampler(train_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):
                batch_src_node_ids, batch_dst_node_ids, batch_node_interact_times, batch_edge_ids = \
                    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]

                batch_neg_dst_node_ids = train_neg_edge_sampler.sample(batch_src_node_ids)
                batch_neg_src_node_ids = batch_src_node_ids

                # we need to compute for positive and negative edges respectively, because the new sampling strategy (for evaluation) allows the negative source nodes to be
                # different from the source nodes, this is different from previous works that just replace destination nodes with negative destination nodes
                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)

                    # get temporal embedding of negative source and negative destination nodes
                    # two Tensors, with shape (batch_size, node_feat_dim)
                    batch_neg_src_node_embeddings, batch_neg_dst_node_embeddings = \
                        model[0].compute_src_dst_node_temporal_embeddings(src_node_ids=batch_neg_src_node_ids,
                                                                          dst_node_ids=batch_neg_dst_node_ids,
                                                                          node_interact_times=batch_node_interact_times,
                                                                          num_neighbors=args.num_neighbors)
                elif args.model_name in ['JODIE', 'DyRep', 'TGN']:
                    # note that negative nodes do not change the memories while the positive nodes change the memories,
                    # we need to first compute the embeddings of negative nodes for memory-based models
                    # get temporal embedding of negative source and negative destination nodes
                    # two Tensors, with shape (batch_size, node_feat_dim)
                    batch_neg_src_node_embeddings, batch_neg_dst_node_embeddings = \
                        model[0].compute_src_dst_node_temporal_embeddings(src_node_ids=batch_neg_src_node_ids,
                                                                          dst_node_ids=batch_neg_dst_node_ids,
                                                                          node_interact_times=batch_node_interact_times,
                                                                          edge_ids=None,
                                                                          edges_are_positive=False,
                                                                          num_neighbors=args.num_neighbors)

                    # 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)

                    # get temporal embedding of negative source and negative destination nodes
                    # two Tensors, with shape (batch_size, node_feat_dim)
                    batch_neg_src_node_embeddings, batch_neg_dst_node_embeddings = \
                        model[0].compute_src_dst_node_temporal_embeddings(src_node_ids=batch_neg_src_node_ids,
                                                                          dst_node_ids=batch_neg_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)

                    # get temporal embedding of negative source and negative destination nodes
                    # two Tensors, with shape (batch_size, node_feat_dim)
                    batch_neg_src_node_embeddings, batch_neg_dst_node_embeddings = \
                        model[0].compute_src_dst_node_temporal_embeddings(src_node_ids=batch_neg_src_node_ids,
                                                                          dst_node_ids=batch_neg_dst_node_ids,
                                                                          node_interact_times=batch_node_interact_times)
                else:
                    raise ValueError(f"Wrong value for model_name {args.model_name}!")
                # get positive and negative probabilities, shape (batch_size, )
                positive_probabilities = model[1](input_1=batch_src_node_embeddings, input_2=batch_dst_node_embeddings, times=batch_node_interact_times)
                negative_probabilities = model[1](input_1=batch_neg_src_node_embeddings, input_2=batch_neg_dst_node_embeddings, times=batch_node_interact_times)
                predicts = torch.cat([positive_probabilities, negative_probabilities], dim=0)
                labels = torch.cat([torch.ones_like(positive_probabilities), torch.zeros_like(negative_probabilities)], dim=0).squeeze(1)
                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()}')

                if args.model_name in ['JODIE', 'DyRep', 'TGN']:
                    # detach the memories and raw messages of nodes in the memory bank after each batch, so we don't back propagate to the start of time
                    model[0].memory_bank.detach_memory_bank()

            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_link_prediction_metrics(predicts=train_y_predicts, labels=train_y_trues)
            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}')

            # select the best model based on all the validate metrics
            train_metric_indicator = []
            for metric_name in train_metrics.keys():
                train_metric_indicator.append((metric_name, train_metrics[metric_name], True))

            early_stop = early_stopping.step(train_metric_indicator, model)
            # early_stop = early_stopping.step([("loss", train_total_loss, False)], model)

            if early_stop:
                break

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

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

    sys.exit()