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main.py
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main.py
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from load_data import Data
import numpy as np
import torch
import time
from collections import defaultdict
from model import *
from torch.optim.lr_scheduler import ExponentialLR
import argparse
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}
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):
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(d.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]):
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)
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)
print(time.time()-start_test)
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument("--dataset", type=str, default="FB15k-237", nargs="?",
help="Which dataset to use: FB15k, FB15k-237, WN18 or WN18RR.")
parser.add_argument("--num_iterations", type=int, default=500, nargs="?",
help="Number of iterations.")
parser.add_argument("--batch_size", type=int, default=128, 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=True)
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()