main.py

from load_data import Data
import numpy as np
import torch
import time
from collections import defaultdict
from model import TuckER
from torch.optim.lr_scheduler import ExponentialLR
import argparse
import os

RES = 'result.txt'


class Experiment:

    def __init__(self, learning_rate=0.0005, ent_vec_dim=200, rel_vec_dim=200,
                 num_iterations=500, batch_size=128, decay_rate=0., cuda=False,
                 input_dropout=0.3, hidden_dropout1=0.4, hidden_dropout2=0.5,
                 label_smoothing=0.):
        self.learning_rate = learning_rate
        self.ent_vec_dim = ent_vec_dim
        self.rel_vec_dim = rel_vec_dim
        self.num_iterations = num_iterations
        self.batch_size = batch_size
        self.decay_rate = decay_rate
        self.label_smoothing = label_smoothing
        self.cuda = cuda
        self.kwargs = {"input_dropout": input_dropout, "hidden_dropout1": hidden_dropout1,
                       "hidden_dropout2": hidden_dropout2}
        if not os.path.exists('./results'):
            os.mkdir('./results/')
        with open(f'./results/{RES}', 'w') as _:
            pass

    def get_data_idxs(self, data):
        data_idxs = [(self.entity_idxs[data[i][0]], self.relation_idxs[data[i][1]], \
                      self.entity_idxs[data[i][2]]) for i in range(len(data))]
        return data_idxs

    def get_er_vocab(self, data):
        er_vocab = defaultdict(list)
        for triple in data:
            er_vocab[(triple[0], triple[1])].append(triple[2])
        return er_vocab

    def get_batch(self, er_vocab, er_vocab_pairs, idx):
        batch = er_vocab_pairs[idx:idx + self.batch_size]
        targets = np.zeros((len(batch), len(d.entities)))
        for idx, pair in enumerate(batch):
            targets[idx, er_vocab[pair]] = 1.
        targets = torch.FloatTensor(targets)
        if self.cuda:
            targets = targets.cuda()
        return np.array(batch), targets

    def evaluate(self, model, data, mode='valid'):
        hits = []
        ranks = []
        for i in range(10):
            hits.append([])

        test_data_idxs = self.get_data_idxs(data)
        er_vocab = self.get_er_vocab(self.get_data_idxs(self.data))

        print("Number of data points: %d" % len(test_data_idxs))

        for i in range(0, len(test_data_idxs), self.batch_size):
            data_batch, _ = self.get_batch(er_vocab, test_data_idxs, i)
            e1_idx = torch.tensor(data_batch[:, 0])
            r_idx = torch.tensor(data_batch[:, 1])
            e2_idx = torch.tensor(data_batch[:, 2])
            if self.cuda:
                e1_idx = e1_idx.cuda()
                r_idx = r_idx.cuda()
                e2_idx = e2_idx.cuda()
            predictions = model.forward(e1_idx, r_idx)

            for j in range(data_batch.shape[0]):
                filt = er_vocab[(data_batch[j][0], data_batch[j][1])]
                target_value = predictions[j, e2_idx[j]].item()
                predictions[j, filt] = 0.0
                predictions[j, e2_idx[j]] = target_value

            sort_values, sort_idxs = torch.sort(predictions, dim=1, descending=True)
            sort_idxs = sort_idxs.cpu().numpy()

            for j in range(data_batch.shape[0]):

                if mode == 'test':
                    with open(f'./results/{RES}', 'a+') as fp:
                        head_name = d.idx_to_entity[e1_idx[j].item()]
                        relation_name = d.idx_to_relation[r_idx[j].item()]
                        top_tail_names = [d.idx_to_entity[idx] for idx in sort_idxs[j][:10]]
                        fp.write(f"{head_name} {relation_name} {' '.join(top_tail_names)}\n")

                rank = np.where(sort_idxs[j] == e2_idx[j].item())[0][0]
                ranks.append(rank + 1)

                for hits_level in range(10):
                    if rank <= hits_level:
                        hits[hits_level].append(1.0)
                    else:
                        hits[hits_level].append(0.0)

        if mode == 'test':
            return
        print('Hits @10: {0}'.format(np.mean(hits[9])))
        print('Hits @3: {0}'.format(np.mean(hits[2])))
        print('Hits @1: {0}'.format(np.mean(hits[0])))
        print('Mean rank: {0}'.format(np.mean(ranks)))
        print('Mean reciprocal rank: {0}'.format(np.mean(1. / np.array(ranks))))

    def train_and_eval(self):
        print("Training the TuckER model...")
        self.entity_idxs = {d.entities[i]: i for i in range(len(d.entities))}
        self.relation_idxs = {d.relations[i]: i for i in range(len(d.relations))}

        train_data_idxs = self.get_data_idxs(d.train_data)
        print("Number of training data points: %d" % len(train_data_idxs))

        model = TuckER(d, self.ent_vec_dim, self.rel_vec_dim, **self.kwargs)
        if self.cuda:
            model.cuda()
        model.init()
        opt = torch.optim.Adam(model.parameters(), lr=self.learning_rate)
        if self.decay_rate:
            scheduler = ExponentialLR(opt, self.decay_rate)

        er_vocab = self.get_er_vocab(train_data_idxs)
        er_vocab_pairs = list(er_vocab.keys())

        print("Starting training...")
        for it in range(1, self.num_iterations + 1):
            start_train = time.time()
            model.train()
            losses = []
            np.random.shuffle(er_vocab_pairs)
            for j in range(0, len(er_vocab_pairs), self.batch_size):
                data_batch, targets = self.get_batch(er_vocab, er_vocab_pairs, j)
                opt.zero_grad()
                e1_idx = torch.tensor(data_batch[:, 0])
                r_idx = torch.tensor(data_batch[:, 1])
                if self.cuda:
                    e1_idx = e1_idx.cuda()
                    r_idx = r_idx.cuda()
                predictions = model.forward(e1_idx, r_idx)
                if self.label_smoothing:
                    targets = ((1.0 - self.label_smoothing) * targets) + (1.0 / targets.size(1))
                loss = model.loss(predictions, targets)
                loss.backward()
                opt.step()
                losses.append(loss.item())
            if self.decay_rate:
                scheduler.step()
            print(it)
            print(time.time() - start_train)
            print(np.mean(losses))
            model.eval()
            with torch.no_grad():
                print("Validation:")
                self.evaluate(model, d.valid_data)
                if not it % 2:
                    print("Test:")
                    start_test = time.time()
                    self.evaluate(model, d.test_data, 'test')
                    print(time.time() - start_test)


if __name__ == '__main__':
    parser = argparse.ArgumentParser()
    parser.add_argument("--dataset", type=str, default="OpenBG500", nargs="?",
                        help="Which dataset to use: OpenBG500.")
    parser.add_argument("--num_iterations", type=int, default=150, nargs="?",
                        help="Number of iterations.")
    parser.add_argument("--batch_size", type=int, default=200, nargs="?",
                        help="Batch size.")
    parser.add_argument("--lr", type=float, default=0.0005, nargs="?",
                        help="Learning rate.")
    parser.add_argument("--dr", type=float, default=1.0, nargs="?",
                        help="Decay rate.")
    parser.add_argument("--edim", type=int, default=200, nargs="?",
                        help="Entity embedding dimensionality.")
    parser.add_argument("--rdim", type=int, default=200, nargs="?",
                        help="Relation embedding dimensionality.")
    parser.add_argument("--cuda", type=bool, default=True, nargs="?",
                        help="Whether to use cuda (GPU) or not (CPU).")
    parser.add_argument("--input_dropout", type=float, default=0.3, nargs="?",
                        help="Input layer dropout.")
    parser.add_argument("--hidden_dropout1", type=float, default=0.4, nargs="?",
                        help="Dropout after the first hidden layer.")
    parser.add_argument("--hidden_dropout2", type=float, default=0.5, nargs="?",
                        help="Dropout after the second hidden layer.")
    parser.add_argument("--label_smoothing", type=float, default=0.1, nargs="?",
                        help="Amount of label smoothing.")

    args = parser.parse_args()
    dataset = args.dataset
    data_dir = "data/%s/" % dataset
    torch.backends.cudnn.deterministic = True
    seed = 20
    np.random.seed(seed)
    torch.manual_seed(seed)
    if torch.cuda.is_available:
        torch.cuda.manual_seed_all(seed)
    d = Data(data_dir=data_dir, reverse=False)
    experiment = Experiment(num_iterations=args.num_iterations, batch_size=args.batch_size, learning_rate=args.lr,
                            decay_rate=args.dr, ent_vec_dim=args.edim, rel_vec_dim=args.rdim, cuda=args.cuda,
                            input_dropout=args.input_dropout, hidden_dropout1=args.hidden_dropout1,
                            hidden_dropout2=args.hidden_dropout2, label_smoothing=args.label_smoothing)
    experiment.train_and_eval()