model.py

import pickle
import argparse
import numpy as np
import torch
import torch.nn as nn
from torch.utils.data import DataLoader
import torchtext
import dgl
import os
import tqdm

import layers
import sampler as sampler_module
import evaluation

class PinSAGEModel(nn.Module):
    def __init__(self, full_graph, ntype, textsets, hidden_dims, n_layers):
        super().__init__()

        self.proj = layers.LinearProjector(full_graph, ntype, textsets, hidden_dims)
        self.sage = layers.SAGENet(hidden_dims, n_layers)
        self.scorer = layers.ItemToItemScorer(full_graph, ntype)

    def forward(self, pos_graph, neg_graph, blocks):
        h_item = self.get_repr(blocks)
        pos_score = self.scorer(pos_graph, h_item)
        neg_score = self.scorer(neg_graph, h_item)
        return (neg_score - pos_score + 1).clamp(min=0)

    def get_repr(self, blocks):
        h_item = self.proj(blocks[0].srcdata)
        h_item_dst = self.proj(blocks[-1].dstdata)
        return h_item_dst + self.sage(blocks, h_item)

def train(dataset, args):
    g = dataset['train-graph']
    val_matrix = dataset['val-matrix'].tocsr()
    test_matrix = dataset['test-matrix'].tocsr()
    item_texts = dataset['item-texts']
    user_ntype = dataset['user-type']
    item_ntype = dataset['item-type']
    user_to_item_etype = dataset['user-to-item-type']
    timestamp = dataset['timestamp-edge-column']

    device = torch.device(args.device)

    # Assign user and movie IDs and use them as features (to learn an individual trainable
    # embedding for each entity)
    g.nodes[user_ntype].data['id'] = torch.arange(g.number_of_nodes(user_ntype))
    g.nodes[item_ntype].data['id'] = torch.arange(g.number_of_nodes(item_ntype))

    # Prepare torchtext dataset and vocabulary
    fields = {}
    examples = []
    for key, texts in item_texts.items():
        fields[key] = torchtext.legacy.data.Field(include_lengths=True, lower=True, batch_first=True)
    for i in range(g.number_of_nodes(item_ntype)):
        example = torchtext.legacy.data.Example.fromlist(
            [item_texts[key][i] for key in item_texts.keys()],
            [(key, fields[key]) for key in item_texts.keys()])
        examples.append(example)
    textset = torchtext.legacy.data.Dataset(examples, fields)
    for key, field in fields.items():
        field.build_vocab(getattr(textset, key))
        #field.build_vocab(getattr(textset, key), vectors='fasttext.simple.300d')

    # Sampler
    batch_sampler = sampler_module.ItemToItemBatchSampler(
        g, user_ntype, item_ntype, args.batch_size)
    neighbor_sampler = sampler_module.NeighborSampler(
        g, user_ntype, item_ntype, args.random_walk_length,
        args.random_walk_restart_prob, args.num_random_walks, args.num_neighbors,
        args.num_layers)
    collator = sampler_module.PinSAGECollator(neighbor_sampler, g, item_ntype, textset)
    dataloader = DataLoader(
        batch_sampler,
        collate_fn=collator.collate_train,
        num_workers=args.num_workers)
    dataloader_test = DataLoader(
        torch.arange(g.number_of_nodes(item_ntype)),
        batch_size=args.batch_size,
        collate_fn=collator.collate_test,
        num_workers=args.num_workers)
    dataloader_it = iter(dataloader)

    # Model
    model = PinSAGEModel(g, item_ntype, textset, args.hidden_dims, args.num_layers).to(device)
    # Optimizer
    opt = torch.optim.Adam(model.parameters(), lr=args.lr)

    # For each batch of head-tail-negative triplets...
    for epoch_id in range(args.num_epochs):
        model.train()
        for batch_id in tqdm.trange(args.batches_per_epoch):
            pos_graph, neg_graph, blocks = next(dataloader_it)
            # Copy to GPU
            for i in range(len(blocks)):
                blocks[i] = blocks[i].to(device)
            pos_graph = pos_graph.to(device)
            neg_graph = neg_graph.to(device)

            loss = model(pos_graph, neg_graph, blocks).mean()
            opt.zero_grad()
            loss.backward()
            opt.step()

        # Evaluate
        model.eval()
        with torch.no_grad():
            item_batches = torch.arange(g.number_of_nodes(item_ntype)).split(args.batch_size)
            h_item_batches = []
            for blocks in dataloader_test:
                for i in range(len(blocks)):
                    blocks[i] = blocks[i].to(device)

                h_item_batches.append(model.get_repr(blocks))
            h_item = torch.cat(h_item_batches, 0)

            print(evaluation.evaluate_nn(dataset, h_item, args.k, args.batch_size))

if __name__ == '__main__':
    # Arguments
    parser = argparse.ArgumentParser()
    parser.add_argument('dataset_path', type=str)
    parser.add_argument('--random-walk-length', type=int, default=2)
    parser.add_argument('--random-walk-restart-prob', type=float, default=0.5)
    parser.add_argument('--num-random-walks', type=int, default=10)
    parser.add_argument('--num-neighbors', type=int, default=3)
    parser.add_argument('--num-layers', type=int, default=2)
    parser.add_argument('--hidden-dims', type=int, default=16)
    parser.add_argument('--batch-size', type=int, default=32)
    parser.add_argument('--device', type=str, default='cpu')        # can also be "cuda:0"
    parser.add_argument('--num-epochs', type=int, default=1)
    parser.add_argument('--batches-per-epoch', type=int, default=20000)
    parser.add_argument('--num-workers', type=int, default=0)
    parser.add_argument('--lr', type=float, default=3e-5)
    parser.add_argument('-k', type=int, default=10)
    args = parser.parse_args()

    # Load dataset
    data_info_path = os.path.join(args.dataset_path, 'data.pkl')
    with open(data_info_path, 'rb') as f:
        dataset = pickle.load(f)
    train_g_path = os.path.join(args.dataset_path, 'train_g.bin')
    g_list, _ = dgl.load_graphs(train_g_path)
    dataset['train-graph'] = g_list[0]
    train(dataset, args)