def test_gaussian_v1():
nb_entities = 10
embedding_size = 20
slope = 1.0
seed = 0
np.random.seed(seed)
torch.manual_seed(seed)
with torch.no_grad():
x_emb = nn.Embedding(nb_entities, embedding_size, sparse=True)
y_emb = nn.Embedding(nb_entities, embedding_size, sparse=True)
x_emb.weight.data *= 1e-3
y_emb.weight.data *= 1e-3
kernel = GaussianKernel(slope=slope)
a = kernel(x_emb.weight, y_emb.weight)
b = kernel(x_emb.weight, x_emb.weight)
c = kernel.pairwise(x_emb.weight, y_emb.weight)
d = kernel.pairwise(x_emb.weight, x_emb.weight)
a_np = a.numpy()
b_np = b.numpy()
c_np = c.numpy()
d_np = d.numpy()
np.testing.assert_allclose(a_np, np.diag(c_np), rtol=1e-7, atol=1e-7)
np.testing.assert_allclose(b_np, np.diag(d_np), rtol=1e-7, atol=1e-7)
def test_clutrr_v2():
embedding_size = 20
triples, hops = [], []
xxx = []
for i in range(16):
triples += [(f'a{i}', 'p', f'b{i}'), (f'b{i}', 'q', f'c{i}')]
hops += [(f'a{i}', 'r', f'c{i}')]
xxx += [(f'a{i}', 'p', f'c{i}'), (f'a{i}', 'q', f'c{i}'), (f'a{i}', 'r', f'c{i}')]
entity_lst = sorted({s for (s, _, _) in triples + hops} | {o for (_, _, o) in triples + hops})
predicate_lst = sorted({p for (_, p, _) in triples + hops})
nb_entities, nb_predicates = len(entity_lst), len(predicate_lst)
entity_to_index = {e: i for i, e in enumerate(entity_lst)}
predicate_to_index = {p: i for i, p in enumerate(predicate_lst)}
kernel = GaussianKernel()
entity_embeddings = nn.Embedding(nb_entities, embedding_size, sparse=True)
predicate_embeddings = nn.Embedding(nb_predicates, embedding_size, sparse=True)
for scoring_type in ['concat']: # ['min', 'concat']:
model = NeuralKB(kernel=kernel, scoring_type=scoring_type)
indices = torch.LongTensor(np.array([predicate_to_index['p'], predicate_to_index['q']]))
_hops = SymbolicReformulator(predicate_embeddings, indices)
hoppy = Hoppy(model, hops_lst=[(_hops, False)], depth=1)
for s in entity_lst:
for p in predicate_lst:
for o in entity_lst:
xs_np = np.array([entity_to_index[s]])
xp_np = np.array([predicate_to_index[p]])
xo_np = np.array([entity_to_index[o]])
with torch.no_grad():
xs = torch.LongTensor(xs_np)
xp = torch.LongTensor(xp_np)
xo = torch.LongTensor(xo_np)
xs_emb = entity_embeddings(xs)
xp_emb = predicate_embeddings(xp)
xo_emb = entity_embeddings(xo)
rel_emb = encode_relation(facts=triples, relation_embeddings=predicate_embeddings,
relation_to_idx=predicate_to_index)
arg1_emb, arg2_emb = encode_arguments(facts=triples, entity_embeddings=entity_embeddings,
entity_to_idx=entity_to_index)
facts = [rel_emb, arg1_emb, arg2_emb]
inf = hoppy.score(xp_emb, xs_emb, xo_emb, facts=facts,
entity_embeddings=entity_embeddings.weight)
inf_np = inf.cpu().numpy()
print(s, p, o, inf_np)
assert inf_np[0] > 0.9 if (s, p, o) in (triples + xxx) else inf_np[0] < 0.1
def test_smart_clutrr_v1():
embedding_size = 50
triples, hops = [], []
for i in range(16):
triples += [(f'a{i}', 'p', f'b{i}'), (f'b{i}', 'q', f'c{i}')]
hops += [(f'a{i}', 'r', f'c{i}')]
entity_lst = sorted({e for (e, _, _) in triples + hops} | {e for (e, _, e) in triples + hops})
predicate_lst = sorted({p for (_, p, _) in triples + hops})
nb_entities, nb_predicates = len(entity_lst), len(predicate_lst)
entity_to_index = {e: i for i, e in enumerate(entity_lst)}
predicate_to_index = {p: i for i, p in enumerate(predicate_lst)}
kernel = GaussianKernel()
entity_embeddings = nn.Embedding(nb_entities, embedding_size, sparse=True)
predicate_embeddings = nn.Embedding(nb_predicates, embedding_size, sparse=True)
for scoring_type in ['concat']:
model = NeuralKB(kernel=kernel)
for s in entity_lst:
for p in predicate_lst:
for o in entity_lst:
xs_np = np.array([entity_to_index[s]])
xp_np = np.array([predicate_to_index[p]])
xo_np = np.array([entity_to_index[o]])
with torch.no_grad():
xs = torch.from_numpy(xs_np)
xp = torch.from_numpy(xp_np)
xo = torch.from_numpy(xo_np)
xs_emb = entity_embeddings(xs)
xp_emb = predicate_embeddings(xp)
xo_emb = entity_embeddings(xo)
rel_emb = encode_relation(facts=triples, relation_embeddings=predicate_embeddings,
relation_to_idx=predicate_to_index)
arg1_emb, arg2_emb = encode_arguments(facts=triples, entity_embeddings=entity_embeddings,
entity_to_idx=entity_to_index)
facts = [rel_emb, arg1_emb, arg2_emb]
inf = model.score(xp_emb, xs_emb, xo_emb, facts=facts)
inf_np = inf.cpu().numpy()
assert inf_np[0] > 0.95 if (s, p, o) in triples else inf_np[0] < 0.01
def test_clutrr_v3():
embedding_size = 20
batch_size = 8
torch.manual_seed(0)
triples, hops = [], []
for i in range(32):
triples += [(f'a{i}', 'p', f'b{i}'), (f'b{i}', 'q', f'c{i}')]
hops += [(f'a{i}', 'r', f'c{i}')]
entity_lst = sorted({s
for (s, _, _) in triples + hops}
| {o
for (_, _, o) in triples + hops})
predicate_lst = sorted({p for (_, p, _) in triples + hops})
nb_entities, nb_predicates = len(entity_lst), len(predicate_lst)
entity_to_index = {e: i for i, e in enumerate(entity_lst)}
predicate_to_index = {p: i for i, p in enumerate(predicate_lst)}
kernel = GaussianKernel(slope=None)
entity_embeddings = nn.Embedding(nb_entities, embedding_size, sparse=True)
predicate_embeddings = nn.Embedding(nb_predicates,
embedding_size,
sparse=True)
# _hops = LinearReformulator(2, embedding_size)
_hops = AttentiveReformulator(2, predicate_embeddings)
model = NeuralKB(kernel=kernel, scoring_type='concat')
hoppy = Hoppy(model, hops_lst=[(_hops, False)], depth=1)
params = [
p for p in hoppy.parameters()
if not torch.equal(p, entity_embeddings.weight)
and not torch.equal(p, predicate_embeddings.weight)
]
for tensor in params:
print(f'\t{tensor.size()}\t{tensor.device}')
loss_function = nn.BCELoss()
optimizer = optim.Adagrad(params, lr=0.1)
hops_data = []
for i in range(64):
hops_data += hops
batches = make_batches(len(hops_data), batch_size)
rs = np.random.RandomState()
c, d = 0.0, 0.0
p_emb = predicate_embeddings(
torch.from_numpy(np.array([predicate_to_index['p']])))
q_emb = predicate_embeddings(
torch.from_numpy(np.array([predicate_to_index['q']])))
for batch_start, batch_end in batches:
hops_batch = hops_data[batch_start:batch_end]
s_lst = [s for (s, _, _) in hops_batch]
p_lst = [p for (_, p, _) in hops_batch]
o_lst = [o for (_, _, o) in hops_batch]
nb_positives = len(s_lst)
nb_negatives = nb_positives * 3
s_n_lst = rs.permutation(nb_entities)[:nb_negatives].tolist()
nb_negatives = len(s_n_lst)
o_n_lst = rs.permutation(nb_entities)[:nb_negatives].tolist()
p_n_lst = list(islice(cycle(p_lst), nb_negatives))
xs_np = np.array([entity_to_index[s] for s in s_lst] + s_n_lst)
xp_np = np.array([predicate_to_index[p] for p in p_lst + p_n_lst])
xo_np = np.array([entity_to_index[o] for o in o_lst] + o_n_lst)
xs_emb = entity_embeddings(torch.from_numpy(xs_np))
xp_emb = predicate_embeddings(torch.from_numpy(xp_np))
xo_emb = entity_embeddings(torch.from_numpy(xo_np))
rel_emb = encode_relation(facts=triples,
relation_embeddings=predicate_embeddings,
relation_to_idx=predicate_to_index)
arg1_emb, arg2_emb = encode_arguments(
facts=triples,
entity_embeddings=entity_embeddings,
entity_to_idx=entity_to_index)
facts = [rel_emb, arg1_emb, arg2_emb]
scores = hoppy.score(xp_emb,
xs_emb,
xo_emb,
facts=facts,
entity_embeddings=entity_embeddings.weight)
labels_np = np.zeros(xs_np.shape[0])
labels_np[:nb_positives] = 1
labels = torch.from_numpy(labels_np).float()
# for s, p, o, l in zip(xs_np, xp_np, xo_np, labels):
# print(s, p, o, l)
loss = loss_function(scores, labels)
hop_1_emb = hoppy.hops_lst[0][0].hops_lst[0](xp_emb)
hop_2_emb = hoppy.hops_lst[0][0].hops_lst[1](xp_emb)
c = kernel.pairwise(p_emb, hop_1_emb).mean().cpu().detach().numpy()
d = kernel.pairwise(q_emb, hop_2_emb).mean().cpu().detach().numpy()
print(c, d)
loss.backward()
optimizer.step()
optimizer.zero_grad()
assert c > 0.95 and d > 0.95
def main(argv):
parser = argparse.ArgumentParser('KBC Research', formatter_class=argparse.ArgumentDefaultsHelpFormatter)
parser.add_argument('--train', action='store', required=True, type=str)
parser.add_argument('--dev', action='store', type=str, default=None)
parser.add_argument('--test', action='store', type=str, default=None)
parser.add_argument('--test-i', action='store', type=str, default=None)
parser.add_argument('--test-ii', action='store', type=str, default=None)
parser.add_argument('--embedding-size', '-k', action='store', type=int, default=20)
parser.add_argument('--k-max', '-K', action='store', type=int, default=1)
parser.add_argument('--max-depth', '-d', action='store', type=int, default=1)
parser.add_argument('--hops', nargs='+', type=str, default=['1', '2'])
# training params
parser.add_argument('--epochs', '-e', action='store', type=int, default=100)
parser.add_argument('--learning-rate', '-l', action='store', type=float, default=0.1)
parser.add_argument('--batch-size', '-b', action='store', type=int, default=8)
parser.add_argument('--N2', action='store', type=float, default=None)
parser.add_argument('--N3', action='store', type=float, default=None)
parser.add_argument('--reformulator', '-r', action='store', type=str, default='linear',
choices=['static', 'linear', 'attentive', 'memory'])
parser.add_argument('--nb-rules', '-R', action='store', type=int, default=4)
parser.add_argument('--seed', action='store', type=int, default=0)
parser.add_argument('--validate-every', '-V', action='store', type=int, default=None)
parser.add_argument('--input-type', '-I', action='store', type=str, default='standard',
choices=['standard', 'reciprocal'])
parser.add_argument('--init-size', '-i', action='store', type=float, default=1.0)
parser.add_argument('--load', action='store', type=str, default=None)
parser.add_argument('--save', action='store', type=str, default=None)
parser.add_argument('--quiet', '-q', action='store_true', default=False)
args = parser.parse_args(argv)
import pprint
pprint.pprint(vars(args))
train_path = args.train
dev_path = args.dev
test_path = args.test
test_i_path = args.test_i
test_ii_path = args.test_ii
embedding_size = args.embedding_size
hops_str = args.hops
nb_epochs = args.epochs
learning_rate = args.learning_rate
batch_size = args.batch_size
N2_weight = args.N2
N3_weight = args.N3
reformulator_type = args.reformulator
nb_rules = args.nb_rules
eval_batch_size = batch_size
seed = args.seed
validate_every = args.validate_every
input_type = args.input_type
init_size = args.init_size
load_path = args.load
save_path = args.save
is_quiet = args.quiet
# set the seeds
np.random.seed(seed)
random_state = np.random.RandomState(seed)
torch.manual_seed(seed)
if torch.cuda.is_available():
torch.cuda.manual_seed(seed)
# device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
device = 'cpu'
logger.info(f'Device: {device}')
if torch.cuda.is_available():
torch.set_default_tensor_type(torch.cuda.FloatTensor)
data = Data(train_path=train_path, dev_path=dev_path, test_path=test_path,
test_i_path=test_i_path, test_ii_path=test_ii_path, input_type=input_type)
triples_name_pairs = [
(data.dev_triples, 'dev'),
(data.test_triples, 'test'),
(data.test_i_triples, 'test-I'),
(data.test_ii_triples, 'test-II'),
]
rank = embedding_size
init_size = init_size
entity_embeddings = nn.Embedding(data.nb_entities, rank, sparse=True)
predicate_embeddings = nn.Embedding(data.nb_predicates, rank, sparse=True)
entity_embeddings.weight.data *= init_size
predicate_embeddings.weight.data *= init_size
kernel = GaussianKernel(slope=1.0)
fact_rel = torch.from_numpy(np.array([data.predicate_to_idx[p] for (_, p, _) in data.train_triples])).to(device)
fact_arg1 = torch.from_numpy(np.array([data.entity_to_idx[s] for (s, _, _) in data.train_triples])).to(device)
fact_arg2 = torch.from_numpy(np.array([data.entity_to_idx[o] for (_, _, o) in data.train_triples])).to(device)
facts = [fact_rel, fact_arg1, fact_arg2]
base_model = NeuralKB(entity_embeddings=entity_embeddings,
predicate_embeddings=predicate_embeddings,
facts=facts,
kernel=kernel).to(device)
memory = None
def make_hop(s: str) -> Tuple[BaseReformulator, bool]:
nonlocal memory
if s.isdigit():
nb_hops, is_reversed = int(s), False
else:
nb_hops, is_reversed = int(s[:-1]), True
res = None
if reformulator_type in {'static'}:
res = StaticReformulator(nb_hops, rank)
elif reformulator_type in {'linear'}:
res = LinearReformulator(nb_hops, rank)
elif reformulator_type in {'attentive'}:
res = AttentiveReformulator(nb_hops, predicate_embeddings)
elif reformulator_type in {'memory'}:
memory = MemoryReformulator.Memory(nb_hops, nb_rules, rank) if memory is None else memory
res = MemoryReformulator(memory)
elif reformulator_type in {'ntp'}:
res = NTPReformulator(nb_hops=nb_hops, embedding_size=embedding_size, kernel=kernel)
assert res is not None
return res, is_reversed
hops_lst = [make_hop(s) for s in hops_str]
model = MultiHoppy(model=base_model, entity_embeddings=entity_embeddings, hops_lst=hops_lst).to(device)
params_lst = [p for p in model.parameters()] + [predicate_embeddings.weight]
params = nn.ParameterList(params_lst).to(device)
if load_path is not None:
model.load_state_dict(torch.load(load_path))
for tensor in params_lst:
logger.info(f'\t{tensor.size()}\t{tensor.device}')
optimizer = optim.Adagrad(params, lr=learning_rate)
loss_function = nn.BCELoss()
N2_reg = N2() if N2_weight is not None else None
N3_reg = N3() if N3_weight is not None else None
for epoch_no in range(1, nb_epochs + 1):
batcher = Batcher(data, batch_size, 1, random_state)
nb_batches = len(batcher.batches)
epoch_loss_values = []
for batch_no, (batch_start, batch_end) in enumerate(batcher.batches, 1):
xp_batch_np, xs_batch_np, xo_batch_np, xi_batch_np = batcher.get_batch(batch_start, batch_end)
xp_batch = torch.from_numpy(xp_batch_np.astype('int64')).to(device)
xs_batch = torch.from_numpy(xs_batch_np.astype('int64')).to(device)
xo_batch = torch.from_numpy(xo_batch_np.astype('int64')).to(device)
xi_batch = torch.from_numpy(xi_batch_np.astype('int64')).to(device)
xp_batch_emb = predicate_embeddings(xp_batch)
xs_batch_emb = entity_embeddings(xs_batch)
xo_batch_emb = entity_embeddings(xo_batch)
sp_scores, po_scores = model.forward(xp_batch_emb, xs_batch_emb, xo_batch_emb, mask_indices=xi_batch)
factors = [model.factor(e) for e in [xp_batch_emb, xs_batch_emb, xo_batch_emb]]
sp_objects = [data.sp_to_o_lst.get((xs, xp), None) for xs, xp in zip(xs_batch_np, xp_batch_np)]
po_subjects = [data.po_to_s_lst.get((xp, xo), None) for xp, xo in zip(xp_batch_np, xo_batch_np)]
sp_targets = compute_bce_targets(xp_batch.shape[0], data.nb_entities, sp_objects, device=device)
po_targets = compute_bce_targets(xp_batch.shape[0], data.nb_entities, po_subjects, device=device)
s_loss = loss_function(sp_scores, sp_targets)
o_loss = loss_function(po_scores, po_targets)
loss = s_loss + o_loss
loss += N2_weight * N2_reg(factors) if N2_weight is not None else 0.0
loss += N3_weight * N3_reg(factors) if N3_weight is not None else 0.0
loss.backward()
optimizer.step()
optimizer.zero_grad()
loss_value = loss.item()
epoch_loss_values += [loss_value]
if not is_quiet:
logger.info(f'Epoch {epoch_no}/{nb_epochs}\tBatch {batch_no}/{nb_batches}\tLoss {loss_value:.6f}')
loss_mean, loss_std = np.mean(epoch_loss_values), np.std(epoch_loss_values)
logger.info(f'Epoch {epoch_no}/{nb_epochs}\tLoss {loss_mean:.4f} ± {loss_std:.4f}')
if validate_every is not None and epoch_no % validate_every == 0:
for triples, name in [(t, n) for t, n in triples_name_pairs if len(t) > 0]:
metrics = evaluate(entity_embeddings=entity_embeddings, predicate_embeddings=predicate_embeddings,
test_triples=triples, all_triples=data.all_triples,
entity_to_index=data.entity_to_idx, predicate_to_index=data.predicate_to_idx,
model=model, batch_size=eval_batch_size, device=device)
logger.info(f'Epoch {epoch_no}/{nb_epochs}\t{name} results\t{metrics_to_str(metrics)}')
for triples, name in [(t, n) for t, n in triples_name_pairs if len(t) > 0]:
metrics = evaluate(entity_embeddings=entity_embeddings, predicate_embeddings=predicate_embeddings,
test_triples=triples, all_triples=data.all_triples,
entity_to_index=data.entity_to_idx, predicate_to_index=data.predicate_to_idx,
model=model, batch_size=eval_batch_size, device=device)
logger.info(f'Final \t{name} results\t{metrics_to_str(metrics)}')
if save_path is not None:
torch.save(model.state_dict(), save_path)
logger.info("Training finished")
def main(argv):
parser = argparse.ArgumentParser('KBC Research', formatter_class=argparse.ArgumentDefaultsHelpFormatter)
parser.add_argument('--train', action='store', required=True, type=str)
parser.add_argument('--dev', action='store', type=str, default=None)
parser.add_argument('--test', action='store', type=str, default=None)
parser.add_argument('--test-i', action='store', type=str, default=None)
parser.add_argument('--test-ii', action='store', type=str, default=None)
# model params
parser.add_argument('--model', '-m', action='store', type=str, default='distmult',
choices=['distmult', 'complex'])
parser.add_argument('--embedding-size', '-k', action='store', type=int, default=100)
parser.add_argument('--batch-size', '-b', action='store', type=int, default=100)
parser.add_argument('--eval-batch-size', '-B', action='store', type=int, default=None)
# training params
parser.add_argument('--epochs', '-e', action='store', type=int, default=100)
parser.add_argument('--learning-rate', '-l', action='store', type=float, default=0.1)
parser.add_argument('--optimizer', '-o', action='store', type=str, default='adagrad',
choices=['adagrad', 'adam', 'sgd'])
parser.add_argument('--N2', action='store', type=float, default=None)
parser.add_argument('--N3', action='store', type=float, default=None)
parser.add_argument('--seed', action='store', type=int, default=0)
parser.add_argument('--validate-every', '-V', action='store', type=int, default=None)
parser.add_argument('--input-type', '-I', action='store', type=str, default='standard',
choices=['standard', 'reciprocal'])
parser.add_argument('--gradient-accumulation-steps', '--gas', action='store', type=int, default=1)
parser.add_argument('--load', action='store', type=str, default=None)
parser.add_argument('--save', action='store', type=str, default=None)
parser.add_argument('--quiet', '-q', action='store_true', default=False)
args = parser.parse_args(argv)
import pprint
pprint.pprint(vars(args))
train_path = args.train
dev_path = args.dev
test_path = args.test
test_i_path = args.test_i
test_ii_path = args.test_ii
model_name = args.model
optimizer_name = args.optimizer
embedding_size = args.embedding_size
batch_size = args.batch_size
eval_batch_size = batch_size if args.eval_batch_size is None else args.eval_batch_size
nb_epochs = args.epochs
seed = args.seed
learning_rate = args.learning_rate
N2_weight = args.N2
N3_weight = args.N3
validate_every = args.validate_every
input_type = args.input_type
gradient_accumulation_steps = args.gradient_accumulation_steps
load_path = args.load
save_path = args.save
is_quiet = args.quiet
# set the seeds
np.random.seed(seed)
random_state = np.random.RandomState(seed)
torch.manual_seed(seed)
if torch.cuda.is_available():
torch.cuda.manual_seed(seed)
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
logger.info(f'Device: {device}')
data = Data(train_path=train_path, dev_path=dev_path, test_path=test_path,
test_i_path=test_i_path, test_ii_path=test_ii_path, input_type=input_type)
triples_name_pairs = [
(data.dev_triples, 'dev'),
(data.test_triples, 'test'),
(data.test_i_triples, 'test-I'),
(data.test_ii_triples, 'test-II'),
]
rank = embedding_size * 2 if model_name in {'complex'} else embedding_size
init_size = 1e-3
entity_embeddings = nn.Embedding(data.nb_entities, rank, sparse=True)
predicate_embeddings = nn.Embedding(data.nb_predicates, rank, sparse=True)
entity_embeddings.weight.data *= init_size
predicate_embeddings.weight.data *= init_size
parameters_lst = nn.ModuleDict({
'entities': entity_embeddings,
'predicates': predicate_embeddings
})
parameters_lst.to(device)
if load_path is not None:
parameters_lst.load_state_dict(torch.load(load_path))
kernel = facts = None
if model_name in {'ntpzero'}:
kernel = GaussianKernel(slope=None)
fact_rel = torch.from_numpy(np.array([data.predicate_to_idx[p] for (_, p, _) in data.train_triples])).to(device)
fact_arg1 = torch.from_numpy(np.array([data.entity_to_idx[s] for (s, _, _) in data.train_triples])).to(device)
fact_arg2 = torch.from_numpy(np.array([data.entity_to_idx[o] for (_, _, o) in data.train_triples])).to(device)
facts = [fact_rel, fact_arg1, fact_arg2]
model_factory = {
'distmult': lambda: DistMult(entity_embeddings=entity_embeddings),
'complex': lambda: ComplEx(entity_embeddings=entity_embeddings),
'ntpzero': lambda: NeuralKB(entity_embeddings=entity_embeddings, predicate_embeddings=predicate_embeddings,
kernel=kernel, facts=facts)
}
assert model_name in model_factory
model = model_factory[model_name]()
model.to(device)
logger.info('Model state:')
for param_tensor in parameters_lst.state_dict():
logger.info(f'\t{param_tensor}\t{parameters_lst.state_dict()[param_tensor].size()}')
optimizer_factory = {
'adagrad': lambda: optim.Adagrad(parameters_lst.parameters(), lr=learning_rate),
'adam': lambda: optim.Adam(parameters_lst.parameters(), lr=learning_rate),
'sgd': lambda: optim.SGD(parameters_lst.parameters(), lr=learning_rate)
}
assert optimizer_name in optimizer_factory
optimizer = optimizer_factory[optimizer_name]()
loss_function = nn.CrossEntropyLoss(reduction='mean')
N2_reg = N2() if N2_weight is not None else None
N3_reg = N3() if N3_weight is not None else None
for epoch_no in range(1, nb_epochs + 1):
batcher = Batcher(data, batch_size, 1, random_state)
nb_batches = len(batcher.batches)
epoch_loss_values = []
for batch_no, (batch_start, batch_end) in enumerate(batcher.batches, 1):
xp_batch, xs_batch, xo_batch, xi_batch = batcher.get_batch(batch_start, batch_end)
xp_batch = torch.from_numpy(xp_batch.astype('int64')).to(device)
xs_batch = torch.from_numpy(xs_batch.astype('int64')).to(device)
xo_batch = torch.from_numpy(xo_batch.astype('int64')).to(device)
xi_batch = torch.from_numpy(xi_batch.astype('int64')).to(device)
xp_batch_emb = predicate_embeddings(xp_batch)
xs_batch_emb = entity_embeddings(xs_batch)
xo_batch_emb = entity_embeddings(xo_batch)
sp_scores, po_scores = model.forward(xp_batch_emb, xs_batch_emb, xo_batch_emb)
factors = [model.factor(e) for e in [xp_batch_emb, xs_batch_emb, xo_batch_emb]]
s_loss = loss_function(sp_scores, xo_batch)
o_loss = loss_function(po_scores, xs_batch)
loss = s_loss + o_loss
loss += N2_weight * N2_reg(factors) if N2_weight is not None else 0.0
loss += N3_weight * N3_reg(factors) if N3_weight is not None else 0.0
if gradient_accumulation_steps > 1:
loss = loss / gradient_accumulation_steps
loss.backward()
if batch_no % gradient_accumulation_steps == 0 or batch_no == nb_batches:
optimizer.step()
optimizer.zero_grad()
loss_value = loss.item()
epoch_loss_values += [loss_value]
if not is_quiet:
logger.info(f'Epoch {epoch_no}/{nb_epochs}\tBatch {batch_no}/{nb_batches}\tLoss {loss_value:.6f}')
loss_mean, loss_std = np.mean(epoch_loss_values), np.std(epoch_loss_values)
logger.info(f'Epoch {epoch_no}/{nb_epochs}\tLoss {loss_mean:.4f} ± {loss_std:.4f}')
if validate_every is not None and epoch_no % validate_every == 0:
for triples, name in [(t, n) for t, n in triples_name_pairs if len(t) > 0]:
metrics = evaluate(entity_embeddings=entity_embeddings, predicate_embeddings=predicate_embeddings,
test_triples=triples, all_triples=data.all_triples,
entity_to_index=data.entity_to_idx, predicate_to_index=data.predicate_to_idx,
model=model, batch_size=eval_batch_size, device=device)
logger.info(f'Epoch {epoch_no}/{nb_epochs}\t{name} results\t{metrics_to_str(metrics)}')
for triples, name in [(t, n) for t, n in triples_name_pairs if len(t) > 0]:
metrics = evaluate(entity_embeddings=entity_embeddings, predicate_embeddings=predicate_embeddings,
test_triples=triples, all_triples=data.all_triples,
entity_to_index=data.entity_to_idx, predicate_to_index=data.predicate_to_idx,
model=model, batch_size=eval_batch_size, device=device)
logger.info(f'Final \t{name} results\t{metrics_to_str(metrics)}')
if save_path is not None:
torch.save(parameters_lst.state_dict(), save_path)
logger.info("Training finished")
def test_learning_v2():
embedding_size = 100
torch.manual_seed(0)
triples, hops = [], []
for i in range(16):
triples += [(f'a{i}', 'p', f'b{i}'), (f'b{i}', 'q', f'c{i}')]
hops += [(f'a{i}', 'r', f'c{i}')]
entity_lst = sorted({e
for (e, _, _) in triples + hops}
| {e
for (e, _, e) in triples + hops})
predicate_lst = sorted({p for (_, p, _) in triples + hops})
nb_entities, nb_predicates = len(entity_lst), len(predicate_lst)
entity_to_index = {e: i for i, e in enumerate(entity_lst)}
predicate_to_index = {p: i for i, p in enumerate(predicate_lst)}
kernel = GaussianKernel()
entity_embeddings = nn.Embedding(nb_entities,
embedding_size * 2,
sparse=True)
predicate_embeddings = nn.Embedding(nb_predicates,
embedding_size * 2,
sparse=True)
fact_rel = torch.LongTensor(
np.array([predicate_to_index[p] for (_, p, _) in triples]))
fact_arg1 = torch.LongTensor(
np.array([entity_to_index[s] for (s, _, _) in triples]))
fact_arg2 = torch.LongTensor(
np.array([entity_to_index[o] for (_, _, o) in triples]))
facts = [fact_rel, fact_arg1, fact_arg2]
model = NeuralKB(entity_embeddings=entity_embeddings,
predicate_embeddings=predicate_embeddings,
kernel=kernel,
facts=facts)
reformulator = AttentiveReformulator(2, predicate_embeddings)
hoppy = SimpleHoppy(model, entity_embeddings, hops=reformulator)
for s, p, o in hops:
xs_np = np.array([entity_to_index[s]])
xp_np = np.array([predicate_to_index[p]])
xo_np = np.array([entity_to_index[o]])
with torch.no_grad():
xs = torch.LongTensor(xs_np)
xp = torch.LongTensor(xp_np)
xo = torch.LongTensor(xo_np)
xs_emb = entity_embeddings(xs)
xp_emb = predicate_embeddings(xp)
xo_emb = entity_embeddings(xo)
inf = hoppy.score(xp_emb, xs_emb, xo_emb)
inf_np = inf.cpu().numpy()
assert inf_np < 0.5
def test_masking_v1():
nb_entities = 10
nb_predicates = 5
embedding_size = 10
init_size = 1.0
rs = np.random.RandomState(0)
for _ in range(1):
for position in [0, 1]:
for st in ['min', 'concat']:
with torch.no_grad():
triples = [
('a', 'p', 'b'),
('c', 'q', 'd')
]
entity_to_index = {'a': 0, 'b': 1, 'c': 2, 'd': 3}
predicate_to_index = {'p': 0, 'q': 1}
kernel = GaussianKernel()
entity_embeddings = nn.Embedding(nb_entities, embedding_size * 2, sparse=True)
predicate_embeddings = nn.Embedding(nb_predicates, embedding_size * 2, sparse=True)
entity_embeddings.weight.data *= init_size
predicate_embeddings.weight.data *= init_size
fact_rel = torch.LongTensor(np.array([predicate_to_index[p] for (_, p, _) in triples]))
fact_arg1 = torch.LongTensor(np.array([entity_to_index[s] for (s, _, _) in triples]))
fact_arg2 = torch.LongTensor(np.array([entity_to_index[o] for (_, _, o) in triples]))
facts = [fact_rel, fact_arg1, fact_arg2]
model = NeuralKB(entity_embeddings=entity_embeddings, predicate_embeddings=predicate_embeddings,
kernel=kernel, facts=facts, scoring_type=st)
xs_np = rs.randint(nb_entities, size=32)
xp_np = rs.randint(nb_predicates, size=32)
xo_np = rs.randint(nb_entities, size=32)
xi_np = np.array([position] * xs_np.shape[0])
xs_np[0] = 0
xp_np[0] = 0
xo_np[0] = 1
xs_np[1] = 2
xp_np[1] = 1
xo_np[1] = 3
xs = torch.LongTensor(xs_np)
xp = torch.LongTensor(xp_np)
xo = torch.LongTensor(xo_np)
xi = torch.LongTensor(xi_np)
xs_emb = entity_embeddings(xs)
xp_emb = predicate_embeddings(xp)
xo_emb = entity_embeddings(xo)
model.mask_indices = xi
scores = model.forward(xp_emb, xs_emb, xo_emb)
inf = model.score(xp_emb, xs_emb, xo_emb)
if position == 0:
assert inf[0] < 0.5
assert inf[1] > 0.9
elif position == 1:
assert inf[0] > 0.9
assert inf[1] < 0.5
scores_sp, scores_po = scores
inf = inf.cpu().numpy()
scores_sp = scores_sp.cpu().numpy()
scores_po = scores_po.cpu().numpy()
for i in range(xs.shape[0]):
np.testing.assert_allclose(inf[i], scores_sp[i, xo[i]], rtol=1e-5, atol=1e-5)
np.testing.assert_allclose(inf[i], scores_po[i, xs[i]], rtol=1e-5, atol=1e-5)
kernel_values = kernel(query_repeat, facts).view(batch_size, fact_size)
mask = torch.arange(fact_size).expand(batch_size, fact_size) < nb_facts.unsqueeze(1)
return kernel_values * mask
def uniform(a: Tensor,
b: Tensor,
c: Optional[Tensor] = None) -> Tuple[Tensor, Optional[Tensor]]:
if a.shape[0] > b.shape[0]:
m = a.shape[0] // b.shape[0]
b = b.view(b.shape[0], 1, b.shape[1], b.shape[2]).repeat(1, m, 1, 1).view(-1, b.shape[1], b.shape[2])
if c is not None:
c = c.view(-1, 1).repeat(1, m).view(-1)
return b, c
if __name__ == '__main__':
kernel = GaussianKernel()
batch_size = 8
fact_size = 32
embedding_size = 10
query = torch.rand(batch_size, embedding_size)
facts = torch.rand(batch_size, fact_size, embedding_size)
nb_facts = torch.tensor([1, 2, 3, 4, 5, 6, 7, 8], dtype=torch.int32)
tmp = lookup(query, facts, nb_facts, kernel)
print(tmp)
def main(argv):
argparser = argparse.ArgumentParser('CLUTRR', formatter_class=argparse.ArgumentDefaultsHelpFormatter)
train_path = test_path = "data/clutrr-emnlp/data_test/64.csv"
argparser.add_argument('--train', action='store', type=str, default=train_path)
argparser.add_argument('--test', nargs='+', type=str, default=[test_path])
# model params
argparser.add_argument('--embedding-size', '-k', action='store', type=int, default=20)
argparser.add_argument('--k-max', '-m', action='store', type=int, default=10)
argparser.add_argument('--max-depth', '-d', action='store', type=int, default=2)
argparser.add_argument('--test-max-depth', action='store', type=int, default=None)
argparser.add_argument('--hops', nargs='+', type=str, default=['2', '2', '1R'])
# training params
argparser.add_argument('--epochs', '-e', action='store', type=int, default=100)
argparser.add_argument('--learning-rate', '-l', action='store', type=float, default=0.1)
argparser.add_argument('--batch-size', '-b', action='store', type=int, default=8)
argparser.add_argument('--optimizer', '-o', action='store', type=str, default='adagrad',
choices=['adagrad', 'adam', 'sgd'])
argparser.add_argument('--seed', action='store', type=int, default=0)
argparser.add_argument('--evaluate-every', '-V', action='store', type=int, default=32)
argparser.add_argument('--N2', action='store', type=float, default=None)
argparser.add_argument('--N3', action='store', type=float, default=None)
argparser.add_argument('--entropy', '-E', action='store', type=float, default=None)
argparser.add_argument('--scoring-type', '-s', action='store', type=str, default='concat', choices=['concat', 'min'])
argparser.add_argument('--tnorm', '-t', action='store', type=str, default='min', choices=['min', 'prod'])
argparser.add_argument('--reformulator', '-r', action='store', type=str, default='linear',
choices=['static', 'linear', 'attentive', 'memory', 'ntp'])
argparser.add_argument('--nb-rules', '-R', action='store', type=int, default=4)
argparser.add_argument('--GNTP-R', action='store', type=int, default=None)
argparser.add_argument('--slope', '-S', action='store', type=float, default=None)
argparser.add_argument('--init-size', '-i', action='store', type=float, default=1.0)
argparser.add_argument('--init', action='store', type=str, default='uniform')
argparser.add_argument('--ref-init', action='store', type=str, default='random')
argparser.add_argument('--debug', '-D', action='store_true', default=False)
argparser.add_argument('--load', action='store', type=str, default=None)
argparser.add_argument('--save', action='store', type=str, default=None)
args = argparser.parse_args(argv)
train_path = args.train
test_paths = args.test
embedding_size = args.embedding_size
k_max = args.k_max
max_depth = args.max_depth
test_max_depth = args.test_max_depth
hops_str = args.hops
nb_epochs = args.epochs
learning_rate = args.learning_rate
batch_size = args.batch_size
optimizer_name = args.optimizer
seed = args.seed
evaluate_every = args.evaluate_every
N2_weight = args.N2
N3_weight = args.N3
entropy_weight = args.entropy
scoring_type = args.scoring_type
tnorm_name = args.tnorm
reformulator_name = args.reformulator
nb_rules = args.nb_rules
gntp_R = args.GNTP_R
slope = args.slope
init_size = args.init_size
init_type = args.init
ref_init_type = args.ref_init
is_debug = args.debug
load_path = args.load
save_path = args.save
np.random.seed(seed)
random_state = np.random.RandomState(seed)
torch.manual_seed(seed)
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
logger.info(f'Device: {device}')
if torch.cuda.is_available():
torch.set_default_tensor_type(torch.cuda.FloatTensor)
data = Data(train_path=train_path, test_paths=test_paths)
rel_to_predicate = data.relation_to_predicate
predicate_to_rel = data.predicate_to_relations
entity_lst, predicate_lst, relation_lst = data.entity_lst, data.predicate_lst, data.relation_lst
nb_examples = len(data.train)
nb_entities = len(entity_lst)
nb_relations = len(relation_lst)
entity_to_idx = {e: i for i, e in enumerate(entity_lst)}
relation_to_idx = {r: i for i, r in enumerate(relation_lst)}
kernel = GaussianKernel(slope=slope)
entity_embeddings = nn.Embedding(nb_entities, embedding_size, sparse=False).to(device)
nn.init.uniform_(entity_embeddings.weight, -1.0, 1.0)
entity_embeddings.requires_grad = False
relation_embeddings = nn.Embedding(nb_relations, embedding_size, sparse=False).to(device)
if init_type in {'uniform'}:
nn.init.uniform_(relation_embeddings.weight, -1.0, 1.0)
relation_embeddings.weight.data *= init_size
model = BatchNeuralKB(kernel=kernel, scoring_type=scoring_type).to(device)
memory = None
def make_hop(s: str) -> Tuple[BaseReformulator, bool]:
nonlocal memory
if s.isdigit():
nb_hops, is_reversed = int(s), False
else:
nb_hops, is_reversed = int(s[:-1]), True
res = None
if reformulator_name in {'static'}:
res = StaticReformulator(nb_hops, embedding_size, init_name=ref_init_type)
elif reformulator_name in {'linear'}:
res = LinearReformulator(nb_hops, embedding_size, init_name=ref_init_type)
elif reformulator_name in {'attentive'}:
res = AttentiveReformulator(nb_hops, relation_embeddings, init_name=ref_init_type)
elif reformulator_name in {'memory'}:
if memory is None:
memory = MemoryReformulator.Memory(nb_hops, nb_rules, embedding_size, init_name=ref_init_type)
res = MemoryReformulator(memory)
elif reformulator_name in {'ntp'}:
res = NTPReformulator(nb_hops=nb_hops, embedding_size=embedding_size,
kernel=kernel, init_name=ref_init_type)
assert res is not None
return res, is_reversed
hops_lst = [make_hop(s) for s in hops_str]
hoppy = BatchHoppy(model=model, k=k_max, depth=max_depth, tnorm_name=tnorm_name, hops_lst=hops_lst, R=gntp_R).to(device)
def scoring_function(instances_batch: List[Instance],
relation_lst: List[str],
is_train: bool = False) -> Tuple[Tensor, List[Tensor]]:
rel_emb_lst: List[Tensor] = []
arg1_emb_lst: List[Tensor] = []
arg2_emb_lst: List[Tensor] = []
story_rel_lst: List[Tensor] = []
story_arg1_lst: List[Tensor] = []
story_arg2_lst: List[Tensor] = []
embeddings_lst: List[Tensor] = []
label_lst: List[int] = []
for i, instance in enumerate(instances_batch):
story, target = instance.story, instance.target
s, r, o = target
story_rel = encode_relation(story, relation_embeddings.weight, relation_to_idx, device)
story_arg1, story_arg2 = encode_arguments(story, entity_embeddings.weight, entity_to_idx, device)
embeddings = encode_entities(story, entity_embeddings.weight, entity_to_idx, device)
true_predicate = rel_to_predicate[r]
target_lst: List[Tuple[str, str, str]] = [(s, x, o) for x in relation_lst]
label_lst += [int(true_predicate == rel_to_predicate[r]) for r in relation_lst]
rel_emb = encode_relation(target_lst, relation_embeddings.weight, relation_to_idx, device)
arg1_emb, arg2_emb = encode_arguments(target_lst, entity_embeddings.weight, entity_to_idx, device)
batch_size = rel_emb.shape[0]
fact_size = story_rel.shape[0]
entity_size = embeddings.shape[0]
# [B, E]
rel_emb_lst += [rel_emb]
arg1_emb_lst += [arg1_emb]
arg2_emb_lst += [arg2_emb]
# [B, F, E]
story_rel_lst += [story_rel.view(1, fact_size, -1).repeat(batch_size, 1, 1)]
story_arg1_lst += [story_arg1.view(1, fact_size, -1).repeat(batch_size, 1, 1)]
story_arg2_lst += [story_arg2.view(1, fact_size, -1).repeat(batch_size, 1, 1)]
# [B, N, E]
embeddings_lst += [embeddings.view(1, entity_size, -1).repeat(batch_size, 1, 1)]
def cat_pad(t_lst: List[Tensor]) -> Tuple[Tensor, Tensor]:
lengths: List[int] = [t.shape[1] for t in t_lst]
max_len: int = max(lengths)
res_t: Tensor = torch.cat([F.pad(t, pad=[0, max_len - lengths[i]]) for i, t in enumerate(t_lst)], dim=0)
res_l: Tensor = torch.tensor([t.shape[1] for t in t_lst for _ in range(t.shape[0])], dtype=torch.long)
return res_t, res_l
rel_emb = torch.cat(rel_emb_lst, dim=0)
arg1_emb = torch.cat(arg1_emb_lst, dim=0)
arg2_emb = torch.cat(arg2_emb_lst, dim=0)
story_rel, nb_facts = cat_pad(story_rel_lst)
story_arg1, _ = cat_pad(story_arg1_lst)
story_arg2, _ = cat_pad(story_arg2_lst)
facts = [story_rel, story_arg1, story_arg2]
_embeddings, nb_embeddings = cat_pad(embeddings_lst)
max_depth_ = hoppy.depth
if not is_train and test_max_depth is not None:
hoppy.depth = test_max_depth
scores = hoppy.score(rel_emb, arg1_emb, arg2_emb, facts, nb_facts, _embeddings, nb_embeddings)
if not is_train and test_max_depth is not None:
hoppy.depth = max_depth_
return scores, [rel_emb, arg1_emb, arg2_emb]
def evaluate(instances: List[Instance], path: str, sample_size: Optional[int] = None) -> float:
res = 0.0
if len(instances) > 0:
res = accuracy_b(scoring_function=scoring_function, instances=instances, sample_size=sample_size,
relation_to_predicate=rel_to_predicate, predicate_to_relations=predicate_to_rel,
batch_size=batch_size)
logger.info(f'Test Accuracy on {path}: {res:.6f}')
return res
loss_function = nn.BCELoss()
N2_reg = N2() if N2_weight is not None else None
N3_reg = N3() if N3_weight is not None else None
entropy_reg = Entropy(use_logits=False) if entropy_weight is not None else None
params_lst = [p for p in hoppy.parameters() if not torch.equal(p, entity_embeddings.weight)]
params_lst += relation_embeddings.parameters()
params = nn.ParameterList(params_lst).to(device)
if load_path is not None:
model.load_state_dict(torch.load(load_path))
for tensor in params_lst:
logger.info(f'\t{tensor.size()}\t{tensor.device}')
optimizer_factory = {
'adagrad': lambda arg: optim.Adagrad(arg, lr=learning_rate),
'adam': lambda arg: optim.Adam(arg, lr=learning_rate),
'sgd': lambda arg: optim.SGD(arg, lr=learning_rate)
}
assert optimizer_name in optimizer_factory
optimizer = optimizer_factory[optimizer_name](params)
global_step = 0
for epoch_no in range(1, nb_epochs + 1):
batcher = Batcher(batch_size=batch_size, nb_examples=nb_examples, nb_epochs=1, random_state=random_state)
nb_batches = len(batcher.batches)
epoch_loss_values = []
for batch_no, (batch_start, batch_end) in enumerate(batcher.batches, start=1):
global_step += 1
indices_batch = batcher.get_batch(batch_start, batch_end)
instances_batch = [data.train[i] for i in indices_batch]
label_lst: List[int] = []
for i, instance in enumerate(instances_batch):
story, target = instance.story, instance.target
s, r, o = target
true_predicate = rel_to_predicate[r]
label_lst += [int(true_predicate == rel_to_predicate[r]) for r in relation_lst]
scores, query_emb_lst = scoring_function(instances_batch, relation_lst, is_train=True)
labels = torch.tensor(label_lst, dtype=torch.float32)
loss = loss_function(scores, labels)
factors = [hoppy.factor(e) for e in query_emb_lst]
loss += N2_weight * N2_reg(factors) if N2_weight is not None else 0.0
loss += N3_weight * N3_reg(factors) if N3_weight is not None else 0.0
if entropy_weight is not None:
for hop, _ in hops_lst:
attn_logits = hop.projection(query_emb_lst[0])
attention = torch.softmax(attn_logits, dim=1)
loss += entropy_weight * entropy_reg([attention])
loss_value = loss.item()
epoch_loss_values += [loss_value]
loss.backward()
optimizer.step()
optimizer.zero_grad()
logger.info(f'Epoch {epoch_no}/{nb_epochs}\tBatch {batch_no}/{nb_batches}\tLoss {loss_value:.4f}')
if global_step % evaluate_every == 0:
for test_path in test_paths:
instances = data.test[test_path]
evaluate(instances=instances, path=test_path)
if is_debug is True:
with torch.no_grad():
show_rules(model=hoppy, kernel=kernel, relation_embeddings=relation_embeddings,
data=data, relation_to_idx=relation_to_idx, device=device)
loss_mean, loss_std = np.mean(epoch_loss_values), np.std(epoch_loss_values)
slope = kernel.slope.item() if isinstance(kernel.slope, Tensor) else kernel.slope
logger.info(f'Epoch {epoch_no}/{nb_epochs}\tLoss {loss_mean:.4f} ± {loss_std:.4f}\tSlope {slope:.4f}')
import time
start = time.time()
for test_path in test_paths:
evaluate(instances=data.test[test_path], path=test_path)
end = time.time()
logger.info(f'Evaluation took {end - start} seconds.')
if save_path is not None:
torch.save(model.state_dict(), save_path)
logger.info("Training finished")
def main(argv):
parser = argparse.ArgumentParser('KBC Research', formatter_class=argparse.ArgumentDefaultsHelpFormatter)
parser.add_argument('--train', action='store', required=True, type=str)
parser.add_argument('--dev', action='store', type=str, default=None)
parser.add_argument('--test', action='store', type=str, default=None)
parser.add_argument('--test-i', action='store', type=str, default=None)
parser.add_argument('--test-ii', action='store', type=str, default=None)
parser.add_argument('--embedding-size', '-k', action='store', type=int, default=20)
parser.add_argument('--k-max', '-K', action='store', type=int, default=3)
parser.add_argument('--hops', nargs='+', type=str, default=['1', '2'])
# training params
parser.add_argument('--epochs', '-e', action='store', type=int, default=100)
parser.add_argument('--learning-rate', '-l', action='store', type=float, default=0.001)
parser.add_argument('--batch-size', '-b', action='store', type=int, default=8)
parser.add_argument('--eval-batch-size', '-E', action='store', type=int, default=None)
parser.add_argument('--optimizer', '-o', action='store', type=str, default='adam',
choices=['adagrad', 'adam', 'sgd'])
parser.add_argument('--N2', action='store', type=float, default=None)
parser.add_argument('--N3', action='store', type=float, default=None)
parser.add_argument('--reformulator', '-r', action='store', type=str, default='linear',
choices=['static', 'linear', 'attentive', 'memory', 'ntp'])
parser.add_argument('--nb-rules', '-R', action='store', type=int, default=4)
parser.add_argument('--GNTP-R', action='store', type=int, default=None)
parser.add_argument('--seed', action='store', type=int, default=0)
parser.add_argument('--validate-every', '-V', action='store', type=int, default=None)
parser.add_argument('--input-type', '-I', action='store', type=str, default='standard',
choices=['standard', 'reciprocal'])
parser.add_argument('--init-size', '-i', action='store', type=float, default=1.0)
parser.add_argument('--init', action='store', type=str, default='uniform')
parser.add_argument('--ref-init', action='store', type=str, default='uniform')
parser.add_argument('--load', action='store', type=str, default=None)
parser.add_argument('--save', action='store', type=str, default=None)
parser.add_argument('--nb-negatives', action='store', type=int, default=1)
parser.add_argument('--quiet', '-q', action='store_true', default=False)
parser.add_argument('--freeze-entities', '-f', action='store', type=int, default=None)
parser.add_argument('--refresh-interval', '--refresh', action='store', type=int, default=None)
parser.add_argument('--index-type', '--index', action='store', type=str, default='faiss',
choices=['np', 'faiss', 'nms'])
parser.add_argument('--lower-bound', '--lb', action='store', type=float, default=-1.0)
parser.add_argument('--upper-bound', '--ub', action='store', type=float, default=1.0)
parser.add_argument('--slow-eval', action='store_true', default=False)
args = parser.parse_args(argv)
import pprint
pprint.pprint(vars(args))
train_path = args.train
dev_path = args.dev
test_path = args.test
test_i_path = args.test_i
test_ii_path = args.test_ii
embedding_size = args.embedding_size
k_max = args.k_max
hops_str = args.hops
nb_epochs = args.epochs
learning_rate = args.learning_rate
batch_size = args.batch_size
optimizer_name = args.optimizer
N2_weight = args.N2
N3_weight = args.N3
reformulator_type = args.reformulator
nb_rules = args.nb_rules
gntp_R = args.GNTP_R
eval_batch_size = batch_size if args.eval_batch_size is None else args.eval_batch_size
seed = args.seed
validate_every = args.validate_every
input_type = args.input_type
init_size = args.init_size
init_type = args.init
ref_init_type = args.ref_init
load_path = args.load
save_path = args.save
nb_neg = args.nb_negatives
is_quiet = args.quiet
freeze_entities = args.freeze_entities
refresh_interval = args.refresh_interval
index_type = args.index_type
lower_bound = args.lower_bound
upper_bound = args.upper_bound
slow_eval = args.slow_eval
evaluate_ = evaluate_naive if slow_eval else evaluate
# set the seeds
np.random.seed(seed)
random_state = np.random.RandomState(seed)
torch.manual_seed(seed)
if torch.cuda.is_available():
torch.cuda.manual_seed(seed)
rs = np.random.RandomState(seed)
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
logger.info(f'Device: {device}')
if torch.cuda.is_available():
torch.set_default_tensor_type(torch.cuda.FloatTensor)
data = Data(train_path=train_path, dev_path=dev_path, test_path=test_path,
test_i_path=test_i_path, test_ii_path=test_ii_path, input_type=input_type)
triples_name_pairs = [
(data.dev_triples, 'dev'),
(data.test_triples, 'test'),
(data.test_i_triples, 'test-I'),
(data.test_ii_triples, 'test-II'),
]
entity_embeddings = nn.Embedding(data.nb_entities, embedding_size, sparse=False)
predicate_embeddings = nn.Embedding(data.nb_predicates, embedding_size, sparse=False)
if init_type in {'uniform'}:
nn.init.uniform_(entity_embeddings.weight, lower_bound, upper_bound)
nn.init.uniform_(predicate_embeddings.weight, lower_bound, upper_bound)
nn.init.uniform_(entity_embeddings.weight, lower_bound, upper_bound)
nn.init.uniform_(predicate_embeddings.weight, lower_bound, upper_bound)
entity_embeddings.weight.data *= init_size
predicate_embeddings.weight.data *= init_size
if freeze_entities is not None:
entity_embeddings.weight.requires_grad = False
kernel = GaussianKernel(slope=1.0)
fact_rel = torch.from_numpy(np.array([data.predicate_to_idx[p] for (_, p, _) in data.train_triples])).to(device)
fact_arg1 = torch.from_numpy(np.array([data.entity_to_idx[s] for (s, _, _) in data.train_triples])).to(device)
fact_arg2 = torch.from_numpy(np.array([data.entity_to_idx[o] for (_, _, o) in data.train_triples])).to(device)
facts = [fact_rel, fact_arg1, fact_arg2]
base_model = NeuralKB(entity_embeddings=entity_embeddings, predicate_embeddings=predicate_embeddings,
k=k_max, facts=facts, kernel=kernel, device=device,
index_type=index_type, refresh_interval=refresh_interval).to(device)
memory = None
def make_hop(s: str) -> Tuple[BaseReformulator, bool]:
nonlocal memory
if s.isdigit():
nb_hops, is_reversed = int(s), False
else:
nb_hops, is_reversed = int(s[:-1]), True
res = None
if reformulator_type in {'static'}:
res = StaticReformulator(nb_hops, embedding_size, init_name=ref_init_type,
lower_bound=lower_bound, upper_bound=upper_bound)
elif reformulator_type in {'linear'}:
res = LinearReformulator(nb_hops, embedding_size, init_name=ref_init_type,
lower_bound=lower_bound, upper_bound=upper_bound)
elif reformulator_type in {'attentive'}:
res = AttentiveReformulator(nb_hops, predicate_embeddings, init_name=ref_init_type,
lower_bound=lower_bound, upper_bound=upper_bound)
elif reformulator_type in {'memory'}:
if memory is None:
memory = MemoryReformulator.Memory(nb_hops, nb_rules, embedding_size, init_name=ref_init_type,
lower_bound=lower_bound, upper_bound=upper_bound)
res = MemoryReformulator(memory)
elif reformulator_type in {'ntp'}:
res = NTPReformulator(nb_hops=nb_hops, embedding_size=embedding_size,
kernel=kernel, init_name=ref_init_type,
lower_bound=lower_bound, upper_bound=upper_bound)
assert res is not None
return res, is_reversed
hops_lst = [make_hop(s) for s in hops_str]
# model = MultiHoppy(model=base_model, entity_embeddings=entity_embeddings, hops_lst=hops_lst).to(device)
model = SimpleHoppy(model=base_model, entity_embeddings=entity_embeddings, hops_lst=hops_lst).to(device)
def scoring_function(batch_xs: np.ndarray,
batch_xp: np.ndarray,
batch_xo: np.ndarray) -> np.ndarray:
with torch.no_grad():
tensor_xs = torch.from_numpy(batch_xs).to(device)
tensor_xp = torch.from_numpy(batch_xp).to(device)
tensor_xo = torch.from_numpy(batch_xo).to(device)
tensor_xs_emb = entity_embeddings(tensor_xs)
tensor_xp_emb = predicate_embeddings(tensor_xp)
tensor_xo_emb = entity_embeddings(tensor_xo)
scores_ = model.score(tensor_xp_emb, tensor_xs_emb, tensor_xo_emb)
return scores_.cpu().numpy()
params_lst = {p for p in model.parameters()} | {entity_embeddings.weight, predicate_embeddings.weight}
params = nn.ParameterList(params_lst).to(device)
if load_path is not None:
model.load_state_dict(torch.load(load_path))
for tensor in params_lst:
logger.info(f'\t{tensor.size()}\t{tensor.device}')
optimizer_factory = {
'adagrad': lambda arg: optim.Adagrad(arg, lr=learning_rate),
'adam': lambda arg: optim.Adam(arg, lr=learning_rate),
'sgd': lambda arg: optim.SGD(arg, lr=learning_rate)
}
assert optimizer_name in optimizer_factory
optimizer = optimizer_factory[optimizer_name](params)
# loss_function = nn.BCELoss(reduction="sum")
loss_function = nn.BCELoss()
N2_reg = N2() if N2_weight is not None else None
N3_reg = N3() if N3_weight is not None else None
for epoch_no in range(1, nb_epochs + 1):
batcher = Batcher(data, batch_size, 1, random_state)
nb_batches = len(batcher.batches)
if freeze_entities is not None and epoch_no > freeze_entities:
entity_embeddings.weight.requires_grad = True
epoch_loss_values = []
for batch_no, (batch_start, batch_end) in enumerate(batcher.batches, 1):
xp_batch_np, xs_batch_np, xo_batch_np, xi_batch_np = batcher.get_batch(batch_start, batch_end)
t = xp_batch_np.shape[0]
assert nb_neg > 0
xp_exp_np = np.repeat(xp_batch_np, nb_neg * 3 + 1)
xs_exp_np = np.repeat(xs_batch_np, nb_neg * 3 + 1)
xo_exp_np = np.repeat(xo_batch_np, nb_neg * 3 + 1)
xi_exp_np = np.repeat(xi_batch_np, nb_neg * 3 + 1)
xt_exp_np = np.zeros_like(xp_exp_np)
xt_exp_np[0::nb_neg * 3 + 1] = 1
for i in range(t):
a_ = rs.permutation(data.nb_entities)
b_ = rs.permutation(data.nb_entities)
c_ = rs.permutation(data.nb_entities)
d_ = rs.permutation(data.nb_entities)
while a_.shape[0] < nb_neg:
a_ = np.concatenate([a_, rs.permutation(data.nb_entities)])
b_ = np.concatenate([b_, rs.permutation(data.nb_entities)])
c_ = np.concatenate([c_, rs.permutation(data.nb_entities)])
d_ = np.concatenate([d_, rs.permutation(data.nb_entities)])
a = a_[:nb_neg]
b = b_[:nb_neg]
c = c_[:nb_neg]
d = d_[:nb_neg]
xs_exp_np[(i * nb_neg * 3) + i + 1:(i * nb_neg * 3) + nb_neg + i + 1] = a
xo_exp_np[(i * nb_neg * 3) + nb_neg + i + 1:(i * nb_neg * 3) + nb_neg * 2 + i + 1] = b
xs_exp_np[(i * nb_neg * 3) + nb_neg * 2 + i + 1:(i * nb_neg * 3) + nb_neg * 3 + i + 1] = c
xo_exp_np[(i * nb_neg * 3) + nb_neg * 2 + i + 1:(i * nb_neg * 3) + nb_neg * 3 + i + 1] = d
xp_batch = torch.from_numpy(xp_exp_np.astype('int64')).to(device)
xs_batch = torch.from_numpy(xs_exp_np.astype('int64')).to(device)
xo_batch = torch.from_numpy(xo_exp_np.astype('int64')).to(device)
xi_batch = torch.from_numpy(xi_exp_np.astype('int64')).to(device)
xt_batch = torch.from_numpy(xt_exp_np.astype('int64')).float().to(device)
# Disable masking
# xi_batch = None
xp_batch_emb = predicate_embeddings(xp_batch)
xs_batch_emb = entity_embeddings(xs_batch)
xo_batch_emb = entity_embeddings(xo_batch)
factors = [model.factor(e) for e in [xp_batch_emb, xs_batch_emb, xo_batch_emb]]
scores = model.score(xp_batch_emb, xs_batch_emb, xo_batch_emb, mask_indices=xi_batch)
# scores = base_model.score(xp_batch_emb, xs_batch_emb, xo_batch_emb, mask_indices=xi_batch)
# print(scores)
loss = loss_function(scores, xt_batch)
loss += N2_weight * N2_reg(factors) if N2_weight is not None else 0.0
loss += N3_weight * N3_reg(factors) if N3_weight is not None else 0.0
loss.backward()
optimizer.step()
optimizer.zero_grad()
loss_value = loss.item()
epoch_loss_values += [loss_value]
if not is_quiet:
logger.info(f'Epoch {epoch_no}/{nb_epochs}\tBatch {batch_no}/{nb_batches}\tLoss {loss_value:.6f}')
loss_mean, loss_std = np.mean(epoch_loss_values), np.std(epoch_loss_values)
logger.info(f'Epoch {epoch_no}/{nb_epochs}\tLoss {loss_mean:.4f} ± {loss_std:.4f}')
if validate_every is not None and epoch_no % validate_every == 0:
if 'countries' in train_path:
dev_auc = evaluate_on_countries('dev', data.entity_to_idx, data.predicate_to_idx, scoring_function)
print('Last AUC-PR (dev) {:.4f}'.format(dev_auc))
test_auc = evaluate_on_countries('test', data.entity_to_idx, data.predicate_to_idx, scoring_function)
print('Last AUC-PR (test) {:.4f}'.format(test_auc))
else:
for triples, name in [(t, n) for t, n in triples_name_pairs if len(t) > 0]:
metrics = evaluate_(entity_embeddings=entity_embeddings, predicate_embeddings=predicate_embeddings,
test_triples=triples, all_triples=data.all_triples,
entity_to_index=data.entity_to_idx, predicate_to_index=data.predicate_to_idx,
model=model, batch_size=eval_batch_size, device=device)
logger.info(f'Epoch {epoch_no}/{nb_epochs}\t{name} results\t{metrics_to_str(metrics)}')
if 'countries' in train_path:
dev_auc = evaluate_on_countries('dev', data.entity_to_idx, data.predicate_to_idx, scoring_function)
print('Last AUC-PR (dev) {:.4f}'.format(dev_auc))
test_auc = evaluate_on_countries('test', data.entity_to_idx, data.predicate_to_idx, scoring_function)
print('Last AUC-PR (test) {:.4f}'.format(test_auc))
else:
for triples, name in [(t, n) for t, n in triples_name_pairs if len(t) > 0]:
metrics = evaluate_(entity_embeddings=entity_embeddings, predicate_embeddings=predicate_embeddings,
test_triples=triples, all_triples=data.all_triples,
entity_to_index=data.entity_to_idx, predicate_to_index=data.predicate_to_idx,
model=model, batch_size=eval_batch_size, device=device)
logger.info(f'Final \t{name} results\t{metrics_to_str(metrics)}')
if save_path is not None:
torch.save(model.state_dict(), save_path)
logger.info("Training finished")
def test_smart_v1():
embedding_size = 50
rs = np.random.RandomState(0)
for _ in range(32):
with torch.no_grad():
triples = [('a', 'p', 'b'), ('c', 'q', 'd'), ('e', 'q', 'f'),
('g', 'q', 'h'), ('i', 'q', 'l'), ('m', 'q', 'n'),
('o', 'q', 'p'), ('q', 'q', 'r'), ('s', 'q', 't'),
('u', 'q', 'v')]
entity_lst = sorted({s
for (s, _, _) in triples}
| {o
for (_, _, o) in triples})
predicate_lst = sorted({p for (_, p, _) in triples})
nb_entities, nb_predicates = len(entity_lst), len(predicate_lst)
entity_to_index = {e: i for i, e in enumerate(entity_lst)}
predicate_to_index = {p: i for i, p in enumerate(predicate_lst)}
kernel = GaussianKernel()
entity_embeddings = nn.Embedding(nb_entities,
embedding_size * 2,
sparse=True)
predicate_embeddings = nn.Embedding(nb_predicates,
embedding_size * 2,
sparse=True)
fact_rel = torch.LongTensor(
np.array([predicate_to_index[p] for (_, p, _) in triples]))
fact_arg1 = torch.LongTensor(
np.array([entity_to_index[s] for (s, _, _) in triples]))
fact_arg2 = torch.LongTensor(
np.array([entity_to_index[o] for (_, _, o) in triples]))
facts = [fact_rel, fact_arg1, fact_arg2]
model = NeuralKB(entity_embeddings=entity_embeddings,
predicate_embeddings=predicate_embeddings,
kernel=kernel,
facts=facts)
xs_np = rs.randint(nb_entities, size=32)
xp_np = rs.randint(nb_predicates, size=32)
xo_np = rs.randint(nb_entities, size=32)
xs_np[0] = 0
xp_np[0] = 0
xo_np[0] = 1
xs_np[1] = 2
xp_np[1] = 1
xo_np[1] = 3
xs = torch.LongTensor(xs_np)
xp = torch.LongTensor(xp_np)
xo = torch.LongTensor(xo_np)
xs_emb = entity_embeddings(xs)
xp_emb = predicate_embeddings(xp)
xo_emb = entity_embeddings(xo)
print('xp_emb', xp_emb.shape)
res_sp, res_po = model.forward(xp_emb, xs_emb, xo_emb)
inf = model.score(xp_emb, xs_emb, xo_emb)
assert inf[0] > 0.9
assert inf[1] > 0.9
scores_sp, emb_sp = res_sp
scores_po, emb_po = res_po
print(scores_sp.shape, emb_sp.shape)
print(scores_po.shape, emb_po.shape)
inf = inf.cpu().numpy()
scores_sp = scores_sp.cpu().numpy()
scores_po = scores_po.cpu().numpy()
print('AAA', inf)
print('BBB', scores_sp)
def test_reasoning_v6():
torch.set_num_threads(multiprocessing.cpu_count())
embedding_size = 50
torch.manual_seed(0)
rs = np.random.RandomState(0)
triples = [('a', 'p', 'b'), ('b', 'q', 'c'), ('c', 'p', 'd'),
('d', 'q', 'e'), ('e', 'p', 'f'), ('f', 'q', 'g'),
('g', 'p', 'h'), ('h', 'q', 'i'), ('i', 'p', 'l'),
('l', 'q', 'm'), ('m', 'p', 'n'), ('n', 'q', 'o'),
('o', 'p', 'p'), ('p', 'q', 'q'), ('q', 'p', 'r'),
('r', 'q', 's'), ('s', 'p', 't'), ('t', 'q', 'u'),
('u', 'p', 'v'), ('v', 'q', 'w'), ('x', 'r', 'y'),
('x', 's', 'y')]
entity_lst = sorted({e
for (e, _, _) in triples}
| {e
for (_, _, e) in triples})
predicate_lst = sorted({p for (_, p, _) in triples})
nb_entities = len(entity_lst)
nb_predicates = len(predicate_lst)
entity_to_index = {e: i for i, e in enumerate(entity_lst)}
predicate_to_index = {p: i for i, p in enumerate(predicate_lst)}
for st in ['min', 'concat']:
with torch.no_grad():
kernel = GaussianKernel()
entity_embeddings = nn.Embedding(nb_entities,
embedding_size * 2,
sparse=True)
predicate_embeddings = nn.Embedding(nb_predicates,
embedding_size * 2,
sparse=True)
fact_rel = torch.from_numpy(
np.array([predicate_to_index[p] for (_, p, _) in triples]))
fact_arg1 = torch.from_numpy(
np.array([entity_to_index[s] for (s, _, _) in triples]))
fact_arg2 = torch.from_numpy(
np.array([entity_to_index[o] for (_, _, o) in triples]))
facts = [fact_rel, fact_arg1, fact_arg2]
model = NeuralKB(entity_embeddings=entity_embeddings,
predicate_embeddings=predicate_embeddings,
kernel=kernel,
facts=facts,
scoring_type=st)
indices = torch.from_numpy(
np.array([predicate_to_index['p'], predicate_to_index['q']]))
reformulator = SymbolicReformulator(predicate_embeddings, indices)
k = 5
rhoppy0 = RecursiveHoppy(model,
entity_embeddings,
hops=reformulator,
depth=0,
k=k)
rhoppy1 = RecursiveHoppy(model,
entity_embeddings,
hops=reformulator,
depth=1,
k=k)
rhoppy2 = RecursiveHoppy(model,
entity_embeddings,
hops=reformulator,
depth=2,
k=k)
rhoppy3 = RecursiveHoppy(model,
entity_embeddings,
hops=reformulator,
depth=3,
k=k)
rhoppy4 = RecursiveHoppy(model,
entity_embeddings,
hops=reformulator,
depth=4,
k=k)
xs_np = rs.randint(nb_entities, size=12)
xp_np = rs.randint(nb_predicates, size=12)
xo_np = rs.randint(nb_entities, size=12)
xs_np[0] = entity_to_index['a']
xp_np[0] = predicate_to_index['r']
xo_np[0] = entity_to_index['c']
xs_np[1] = entity_to_index['a']
xp_np[1] = predicate_to_index['r']
xo_np[1] = entity_to_index['e']
xs_np[2] = entity_to_index['a']
xp_np[2] = predicate_to_index['r']
xo_np[2] = entity_to_index['g']
xs_np[3] = entity_to_index['a']
xp_np[3] = predicate_to_index['r']
xo_np[3] = entity_to_index['i']
xs_np[4] = entity_to_index['a']
xp_np[4] = predicate_to_index['r']
xo_np[4] = entity_to_index['m']
xs_np[5] = entity_to_index['a']
xp_np[5] = predicate_to_index['r']
xo_np[5] = entity_to_index['o']
xs_np[6] = entity_to_index['a']
xp_np[6] = predicate_to_index['r']
xo_np[6] = entity_to_index['q']
xs_np[7] = entity_to_index['a']
xp_np[7] = predicate_to_index['r']
xo_np[7] = entity_to_index['s']
xs_np[8] = entity_to_index['a']
xp_np[8] = predicate_to_index['r']
xo_np[8] = entity_to_index['u']
# xs_np[9] = entity_to_index['a']
# xp_np[9] = predicate_to_index['r']
# xo_np[9] = entity_to_index['w']
xs = torch.from_numpy(xs_np)
xp = torch.from_numpy(xp_np)
xo = torch.from_numpy(xo_np)
xs_emb = entity_embeddings(xs)
xp_emb = predicate_embeddings(xp)
xo_emb = entity_embeddings(xo)
scores0 = rhoppy0.forward(xp_emb, xs_emb, xo_emb)
inf0 = rhoppy0.score(xp_emb, xs_emb, xo_emb)
for i in range(xs.shape[0]):
scores_sp, scores_po = scores0
inf_np = inf0.cpu().numpy()
scores_sp_np = scores_sp.cpu().numpy()
scores_po_np = scores_po.cpu().numpy()
np.testing.assert_allclose(inf_np[i],
scores_sp_np[i, xo[i]],
rtol=1e-5,
atol=1e-5)
np.testing.assert_allclose(inf_np[i],
scores_po_np[i, xs[i]],
rtol=1e-5,
atol=1e-5)
scores1 = rhoppy1.forward(xp_emb, xs_emb, xo_emb)
inf1 = rhoppy1.score(xp_emb, xs_emb, xo_emb)
for i in range(xs.shape[0]):
scores_sp, scores_po = scores1
inf_np = inf1.cpu().numpy()
scores_sp_np = scores_sp.cpu().numpy()
scores_po_np = scores_po.cpu().numpy()
np.testing.assert_allclose(inf_np[i],
scores_sp_np[i, xo[i]],
rtol=1e-5,
atol=1e-5)
np.testing.assert_allclose(inf_np[i],
scores_po_np[i, xs[i]],
rtol=1e-5,
atol=1e-5)
scores2 = rhoppy2.forward(xp_emb, xs_emb, xo_emb)
inf2 = rhoppy2.score(xp_emb, xs_emb, xo_emb)
for i in range(xs.shape[0]):
scores_sp, scores_po = scores2
inf_np = inf2.cpu().numpy()
scores_sp_np = scores_sp.cpu().numpy()
scores_po_np = scores_po.cpu().numpy()
np.testing.assert_allclose(inf_np[i],
scores_sp_np[i, xo[i]],
rtol=1e-1,
atol=1e-1)
np.testing.assert_allclose(inf_np[i],
scores_po_np[i, xs[i]],
rtol=1e-1,
atol=1e-1)
scores3 = rhoppy3.forward(xp_emb, xs_emb, xo_emb)
inf3 = rhoppy3.score(xp_emb, xs_emb, xo_emb)
for i in range(xs.shape[0]):
scores_sp, scores_po = scores3
inf_np = inf3.cpu().numpy()
scores_sp_np = scores_sp.cpu().numpy()
scores_po_np = scores_po.cpu().numpy()
np.testing.assert_allclose(inf_np[i],
scores_sp_np[i, xo[i]],
rtol=1e-1,
atol=1e-1)
np.testing.assert_allclose(inf_np[i],
scores_po_np[i, xs[i]],
rtol=1e-1,
atol=1e-1)
scores4 = rhoppy4.forward(xp_emb, xs_emb, xo_emb)
inf4 = rhoppy4.score(xp_emb, xs_emb, xo_emb)
for i in range(xs.shape[0]):
scores_sp, scores_po = scores4
inf_np = inf4.cpu().numpy()
scores_sp_np = scores_sp.cpu().numpy()
scores_po_np = scores_po.cpu().numpy()
np.testing.assert_allclose(inf_np[i],
scores_sp_np[i, xo[i]],
rtol=1e-1,
atol=1e-1)
np.testing.assert_allclose(inf_np[i],
scores_po_np[i, xs[i]],
rtol=1e-1,
atol=1e-1)
print(inf0)
print(inf1)
print(inf2)
print(inf3)
print(inf4)
inf0_np = inf0.cpu().numpy()
inf1_np = inf1.cpu().numpy()
inf2_np = inf2.cpu().numpy()
inf3_np = inf3.cpu().numpy()
inf4_np = inf4.cpu().numpy()
np.testing.assert_allclose(inf0_np,
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
rtol=1e-1,
atol=1e-1)
np.testing.assert_allclose(inf1_np,
[1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
rtol=1e-1,
atol=1e-1)
np.testing.assert_allclose(inf2_np,
[1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
rtol=1e-1,
atol=1e-1)
np.testing.assert_allclose(inf3_np,
[1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0],
rtol=1e-1,
atol=1e-1)
np.testing.assert_allclose(inf4_np,
[1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0],
rtol=1e-1,
atol=1e-1)
def test_reasoning_v5():
torch.set_num_threads(multiprocessing.cpu_count())
nb_entities = 10
nb_predicates = 5
embedding_size = 10
rs = np.random.RandomState(0)
triples = [('a', 'p', 'b'), ('b', 'q', 'c'), ('c', 'r', 'd'),
('d', 's', 'e')]
entity_to_index = {'a': 0, 'b': 1, 'c': 2, 'd': 3, 'e': 4}
predicate_to_index = {'p': 0, 'q': 1, 'r': 2, 's': 3}
for st in ['min', 'concat']:
with torch.no_grad():
kernel = GaussianKernel()
entity_embeddings = nn.Embedding(nb_entities,
embedding_size * 2,
sparse=True)
predicate_embeddings = nn.Embedding(nb_predicates,
embedding_size * 2,
sparse=True)
fact_rel = torch.from_numpy(
np.array([predicate_to_index[p] for (_, p, _) in triples]))
fact_arg1 = torch.from_numpy(
np.array([entity_to_index[s] for (s, _, _) in triples]))
fact_arg2 = torch.from_numpy(
np.array([entity_to_index[o] for (_, _, o) in triples]))
facts = [fact_rel, fact_arg1, fact_arg2]
model = NeuralKB(entity_embeddings=entity_embeddings,
predicate_embeddings=predicate_embeddings,
kernel=kernel,
facts=facts,
scoring_type=st)
indices = torch.from_numpy(
np.array([
predicate_to_index['p'], predicate_to_index['q'],
predicate_to_index['r'], predicate_to_index['s']
]))
reformulator = SymbolicReformulator(predicate_embeddings, indices)
hoppy = SimpleHoppy(model, entity_embeddings, hops=reformulator)
rhoppy = RecursiveHoppy(model,
entity_embeddings,
hops=reformulator,
depth=1)
xs_np = rs.randint(nb_entities, size=32)
xp_np = rs.randint(nb_predicates, size=32)
xo_np = rs.randint(nb_entities, size=32)
xs_np[0] = 0
xp_np[0] = 0
xo_np[0] = 1
xs_np[1] = 1
xp_np[1] = 1
xo_np[1] = 2
xs_np[2] = 0
xp_np[2] = 3
xo_np[2] = 4
xs = torch.from_numpy(xs_np)
xp = torch.from_numpy(xp_np)
xo = torch.from_numpy(xo_np)
xs_emb = entity_embeddings(xs)
xp_emb = predicate_embeddings(xp)
xo_emb = entity_embeddings(xo)
scores = hoppy.forward(xp_emb, xs_emb, xo_emb)
inf = hoppy.score(xp_emb, xs_emb, xo_emb)
scores_h = rhoppy.depth_r_forward(xp_emb, xs_emb, xo_emb, depth=1)
inf_h = rhoppy.depth_r_score(xp_emb, xs_emb, xo_emb, depth=1)
print(inf)
print(inf_h)
assert inf[2] > 0.95
scores_sp, scores_po = scores
scores_h_sp, scores_h_po = scores_h
inf = inf.cpu().numpy()
scores_sp = scores_sp.cpu().numpy()
scores_po = scores_po.cpu().numpy()
inf_h = inf_h.cpu().numpy()
scores_h_sp = scores_h_sp.cpu().numpy()
scores_h_po = scores_h_po.cpu().numpy()
np.testing.assert_allclose(inf, inf_h)
np.testing.assert_allclose(scores_sp, scores_h_sp)
np.testing.assert_allclose(scores_po, scores_h_po)
for i in range(xs.shape[0]):
np.testing.assert_allclose(inf[i],
scores_sp[i, xo[i]],
rtol=1e-5,
atol=1e-5)
np.testing.assert_allclose(inf[i],
scores_po[i, xs[i]],
rtol=1e-5,
atol=1e-5)
np.testing.assert_allclose(inf_h[i],
scores_h_sp[i, xo[i]],
rtol=1e-5,
atol=1e-5)
np.testing.assert_allclose(inf_h[i],
scores_h_po[i, xs[i]],
rtol=1e-5,
atol=1e-5)
def test_clutrr_v4():
embedding_size = 50
rs = np.random.RandomState(0)
for _ in range(32):
with torch.no_grad():
triples = [('a', 'p', 'b'), ('c', 'q', 'd'), ('e', 'q', 'f'),
('g', 'q', 'h'), ('i', 'q', 'l'), ('m', 'q', 'n'),
('o', 'q', 'p'), ('q', 'q', 'r'), ('s', 'q', 't'),
('u', 'q', 'v')]
entity_lst = sorted({s
for (s, _, _) in triples}
| {o
for (_, _, o) in triples})
predicate_lst = sorted({p for (_, p, _) in triples})
nb_entities, nb_predicates = len(entity_lst), len(predicate_lst)
entity_to_index = {e: i for i, e in enumerate(entity_lst)}
predicate_to_index = {p: i for i, p in enumerate(predicate_lst)}
kernel = GaussianKernel()
entity_embeddings = nn.Embedding(nb_entities,
embedding_size * 2,
sparse=True)
predicate_embeddings = nn.Embedding(nb_predicates,
embedding_size * 2,
sparse=True)
rel_emb = encode_relation(facts=triples,
relation_embeddings=predicate_embeddings,
relation_to_idx=predicate_to_index)
arg1_emb, arg2_emb = encode_arguments(
facts=triples,
entity_embeddings=entity_embeddings,
entity_to_idx=entity_to_index)
facts = [rel_emb, arg1_emb, arg2_emb]
model = NeuralKB(kernel=kernel)
xs_np = rs.randint(nb_entities, size=32)
xp_np = rs.randint(nb_predicates, size=32)
xo_np = rs.randint(nb_entities, size=32)
xs_np[0] = 0
xp_np[0] = 0
xo_np[0] = 1
xs_np[1] = 2
xp_np[1] = 1
xo_np[1] = 3
xs = torch.from_numpy(xs_np)
xp = torch.from_numpy(xp_np)
xo = torch.from_numpy(xo_np)
xs_emb = entity_embeddings(xs)
xp_emb = predicate_embeddings(xp)
xo_emb = entity_embeddings(xo)
print('xp_emb', xp_emb.shape)
scores_sp, scores_po = model.forward(
xp_emb,
xs_emb,
xo_emb,
facts=facts,
entity_embeddings=entity_embeddings.weight)
inf = model.score(xp_emb, xs_emb, xo_emb, facts=facts)
assert inf[0] > 0.9
assert inf[1] > 0.9
inf = inf.cpu().numpy()
scores_sp = scores_sp.cpu().numpy()
scores_po = scores_po.cpu().numpy()
print('AAA', inf)
print('BBB', scores_sp)
def test_learning_v3():
embedding_size = 10
batch_size = 16
triples, hops = [], []
for i in range(16):
triples += [(f'a{i}', 'p', f'b{i}'), (f'b{i}', 'q', f'c{i}')]
hops += [(f'a{i}', 'r', f'c{i}')]
entity_lst = sorted({e
for (e, _, _) in triples + hops}
| {e
for (e, _, e) in triples + hops})
predicate_lst = sorted({p for (_, p, _) in triples + hops})
nb_entities, nb_predicates = len(entity_lst), len(predicate_lst)
entity_to_index = {e: i for i, e in enumerate(entity_lst)}
predicate_to_index = {p: i for i, p in enumerate(predicate_lst)}
torch.manual_seed(0)
kernel = GaussianKernel()
entity_embeddings = nn.Embedding(nb_entities,
embedding_size * 2,
sparse=True)
predicate_embeddings = nn.Embedding(nb_predicates,
embedding_size * 2,
sparse=True)
fact_rel = torch.LongTensor(
np.array([predicate_to_index[p] for (_, p, _) in triples]))
fact_arg1 = torch.LongTensor(
np.array([entity_to_index[s] for (s, _, _) in triples]))
fact_arg2 = torch.LongTensor(
np.array([entity_to_index[o] for (_, _, o) in triples]))
facts = [fact_rel, fact_arg1, fact_arg2]
model = NeuralKB(entity_embeddings=entity_embeddings,
predicate_embeddings=predicate_embeddings,
kernel=kernel,
facts=facts)
reformulator = AttentiveReformulator(2, predicate_embeddings)
hoppy = SimpleHoppy(model, entity_embeddings, hops=reformulator)
N3_reg = N3()
params = [
p for p in hoppy.parameters()
if not torch.equal(p, entity_embeddings.weight)
and not torch.equal(p, predicate_embeddings.weight)
]
loss_function = nn.CrossEntropyLoss(reduction='mean')
p_emb = predicate_embeddings(
torch.LongTensor(np.array([predicate_to_index['p']])))
q_emb = predicate_embeddings(
torch.LongTensor(np.array([predicate_to_index['q']])))
# r_emb = predicate_embeddings(torch.LongTensor(np.array([predicate_to_index['r']])))
optimizer = optim.Adagrad(params, lr=0.1)
hops_data = []
for i in range(128):
hops_data += hops
batches = make_batches(len(hops_data), batch_size)
c, d = 0.0, 0.0
for batch_start, batch_end in batches:
hops_batch = hops_data[batch_start:batch_end]
s_lst = [s for (s, _, _) in hops_batch]
p_lst = [p for (_, p, _) in hops_batch]
o_lst = [o for (_, _, o) in hops_batch]
xs_np = np.array([entity_to_index[s] for s in s_lst])
xp_np = np.array([predicate_to_index[p] for p in p_lst])
xo_np = np.array([entity_to_index[o] for o in o_lst])
xs = torch.LongTensor(xs_np)
xp = torch.LongTensor(xp_np)
xo = torch.LongTensor(xo_np)
xs_emb = entity_embeddings(xs)
xp_emb = predicate_embeddings(xp)
xo_emb = entity_embeddings(xo)
sp_scores, po_scores = hoppy.forward(xp_emb, xs_emb, xo_emb)
loss = loss_function(sp_scores, xo) + loss_function(po_scores, xs)
factors = [hoppy.factor(e) for e in [xp_emb, xs_emb, xo_emb]]
loss += 0.1 * N3_reg(factors)
tmp = hoppy.hops(xp_emb)
hop_1_emb = tmp[0]
hop_2_emb = tmp[1]
c = kernel.pairwise(p_emb, hop_1_emb).mean().cpu().detach().numpy()
d = kernel.pairwise(q_emb, hop_2_emb).mean().cpu().detach().numpy()
loss.backward()
optimizer.step()
optimizer.zero_grad()
assert c > 0.95
assert d > 0.95
def test_clutrr_v7():
torch.set_num_threads(multiprocessing.cpu_count())
embedding_size = 50
torch.manual_seed(0)
rs = np.random.RandomState(0)
triples = [('a', 'p', 'b'), ('b', 'q', 'c'), ('c', 'p', 'd'),
('d', 'q', 'e'), ('e', 'p', 'f'), ('f', 'q', 'g'),
('g', 'p', 'h'), ('h', 'q', 'i'), ('i', 'p', 'l'),
('l', 'q', 'm'), ('m', 'p', 'n'), ('n', 'q', 'o'),
('o', 'p', 'p'), ('p', 'q', 'q'), ('q', 'p', 'r'),
('r', 'q', 's'), ('s', 'p', 't'), ('t', 'q', 'u'),
('u', 'p', 'v'), ('v', 'q', 'w'), ('x', 'r', 'y'),
('x', 's', 'y')]
entity_lst = sorted({e
for (e, _, _) in triples}
| {e
for (_, _, e) in triples})
predicate_lst = sorted({p for (_, p, _) in triples})
nb_entities = len(entity_lst)
nb_predicates = len(predicate_lst)
entity_to_index = {e: i for i, e in enumerate(entity_lst)}
predicate_to_index = {p: i for i, p in enumerate(predicate_lst)}
with torch.no_grad():
kernel = GaussianKernel()
entity_embeddings = nn.Embedding(nb_entities,
embedding_size * 2,
sparse=True)
predicate_embeddings = nn.Embedding(nb_predicates,
embedding_size * 2,
sparse=True)
rel_emb = encode_relation(facts=triples,
relation_embeddings=predicate_embeddings,
relation_to_idx=predicate_to_index)
arg1_emb, arg2_emb = encode_arguments(
facts=triples,
entity_embeddings=entity_embeddings,
entity_to_idx=entity_to_index)
facts = [rel_emb, arg1_emb, arg2_emb]
k = 5
model = NeuralKB(kernel=kernel)
indices = torch.from_numpy(
np.array([predicate_to_index['p'], predicate_to_index['q']]))
reformulator = SymbolicReformulator(predicate_embeddings, indices)
hoppy0 = Hoppy(model, hops_lst=[(reformulator, False)], depth=0)
hoppy1 = Hoppy(model, hops_lst=[(reformulator, False)], depth=1)
hoppy2 = Hoppy(model, hops_lst=[(reformulator, False)], depth=2)
hoppy3 = Hoppy(model, hops_lst=[(reformulator, False)], depth=3)
hoppy4 = Hoppy(model, hops_lst=[(reformulator, False)], depth=4)
xs_np = rs.randint(nb_entities, size=12)
xp_np = rs.randint(nb_predicates, size=12)
xo_np = rs.randint(nb_entities, size=12)
xs_np[0] = entity_to_index['a']
xp_np[0] = predicate_to_index['r']
xo_np[0] = entity_to_index['c']
xs_np[1] = entity_to_index['a']
xp_np[1] = predicate_to_index['r']
xo_np[1] = entity_to_index['e']
xs_np[2] = entity_to_index['a']
xp_np[2] = predicate_to_index['r']
xo_np[2] = entity_to_index['g']
xs_np[3] = entity_to_index['a']
xp_np[3] = predicate_to_index['r']
xo_np[3] = entity_to_index['i']
xs_np[4] = entity_to_index['a']
xp_np[4] = predicate_to_index['r']
xo_np[4] = entity_to_index['m']
xs_np[5] = entity_to_index['a']
xp_np[5] = predicate_to_index['r']
xo_np[5] = entity_to_index['o']
xs_np[6] = entity_to_index['a']
xp_np[6] = predicate_to_index['r']
xo_np[6] = entity_to_index['q']
xs_np[7] = entity_to_index['a']
xp_np[7] = predicate_to_index['r']
xo_np[7] = entity_to_index['s']
xs_np[8] = entity_to_index['a']
xp_np[8] = predicate_to_index['r']
xo_np[8] = entity_to_index['u']
xs = torch.from_numpy(xs_np)
xp = torch.from_numpy(xp_np)
xo = torch.from_numpy(xo_np)
xs_emb = entity_embeddings(xs)
xp_emb = predicate_embeddings(xp)
xo_emb = entity_embeddings(xo)
# res0 = hoppy0.forward(xp_emb, xs_emb, xo_emb, facts=facts, entity_embeddings=entity_embeddings)
inf0 = hoppy0.score(xp_emb,
xs_emb,
xo_emb,
facts=facts,
entity_embeddings=entity_embeddings.weight)
# (scores0_sp, subs0_sp), (scores0_po, subs0_po) = res0
# res1 = hoppy1.forward(xp_emb, xs_emb, xo_emb, facts=facts, entity_embeddings=entity_embeddings)
inf1 = hoppy1.score(xp_emb,
xs_emb,
xo_emb,
facts=facts,
entity_embeddings=entity_embeddings.weight)
# (scores1_sp, subs1_sp), (scores1_po, subs1_po) = res1
# res2 = hoppy2.forward(xp_emb, xs_emb, xo_emb, facts=facts, entity_embeddings=entity_embeddings)
inf2 = hoppy2.score(xp_emb,
xs_emb,
xo_emb,
facts=facts,
entity_embeddings=entity_embeddings.weight)
# (scores2_sp, subs2_sp), (scores2_po, subs2_po) = res2
# res3 = hoppy3.forward(xp_emb, xs_emb, xo_emb, facts=facts, entity_embeddings=entity_embeddings)
inf3 = hoppy3.score(xp_emb,
xs_emb,
xo_emb,
facts=facts,
entity_embeddings=entity_embeddings.weight)
# (scores3_sp, subs3_sp), (scores3_po, subs3_po) = res3
# res4 = hoppy4.forward(xp_emb, xs_emb, xo_emb, facts=facts, entity_embeddings=entity_embeddings)
inf4 = hoppy4.score(xp_emb,
xs_emb,
xo_emb,
facts=facts,
entity_embeddings=entity_embeddings.weight)
# (scores4_sp, subs4_sp), (scores4_po, subs4_po) = res4
inf0_np = inf0.cpu().numpy()
inf1_np = inf1.cpu().numpy()
inf2_np = inf2.cpu().numpy()
inf3_np = inf3.cpu().numpy()
inf4_np = inf4.cpu().numpy()
np.testing.assert_allclose(inf0_np,
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
rtol=1e-1,
atol=1e-1)
np.testing.assert_allclose(inf1_np,
[1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
rtol=1e-1,
atol=1e-1)
np.testing.assert_allclose(inf2_np,
[1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
rtol=1e-1,
atol=1e-1)
np.testing.assert_allclose(inf3_np,
[1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0],
rtol=1e-1,
atol=1e-1)
np.testing.assert_allclose(inf4_np,
[1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0],
rtol=1e-1,
atol=1e-1)
print(inf3_np)
print(entity_embeddings.weight[entity_to_index['c'], 0].item())
print(entity_embeddings.weight[entity_to_index['e'], 0].item())
print(entity_embeddings.weight[entity_to_index['g'], 0].item())
print(entity_embeddings.weight[entity_to_index['i'], 0].item())
def test_learning_v1():
embedding_size = 50
triples, hops = [], []
for i in range(16):
triples += [(f'a{i}', 'p', f'b{i}'), (f'b{i}', 'q', f'c{i}')]
hops += [(f'a{i}', 'r', f'c{i}')]
entity_lst = sorted({e
for (e, _, _) in triples + hops}
| {e
for (e, _, e) in triples + hops})
predicate_lst = sorted({p for (_, p, _) in triples + hops})
nb_entities, nb_predicates = len(entity_lst), len(predicate_lst)
entity_to_index = {e: i for i, e in enumerate(entity_lst)}
predicate_to_index = {p: i for i, p in enumerate(predicate_lst)}
kernel = GaussianKernel()
entity_embeddings = nn.Embedding(nb_entities,
embedding_size * 2,
sparse=True)
predicate_embeddings = nn.Embedding(nb_predicates,
embedding_size * 2,
sparse=True)
fact_rel = torch.LongTensor(
np.array([predicate_to_index[p] for (_, p, _) in triples]))
fact_arg1 = torch.LongTensor(
np.array([entity_to_index[s] for (s, _, _) in triples]))
fact_arg2 = torch.LongTensor(
np.array([entity_to_index[o] for (_, _, o) in triples]))
facts = [fact_rel, fact_arg1, fact_arg2]
for st in ['min', 'concat']:
model = NeuralKB(entity_embeddings=entity_embeddings,
predicate_embeddings=predicate_embeddings,
kernel=kernel,
facts=facts,
scoring_type=st)
for s in entity_lst:
for p in predicate_lst:
for o in entity_lst:
xs_np = np.array([entity_to_index[s]])
xp_np = np.array([predicate_to_index[p]])
xo_np = np.array([entity_to_index[o]])
with torch.no_grad():
xs = torch.LongTensor(xs_np)
xp = torch.LongTensor(xp_np)
xo = torch.LongTensor(xo_np)
xs_emb = entity_embeddings(xs)
xp_emb = predicate_embeddings(xp)
xo_emb = entity_embeddings(xo)
inf = model.score(xp_emb, xs_emb, xo_emb)
inf_np = inf.cpu().numpy()
if (s, p, o) in triples:
assert inf_np[0] > 0.95
else:
assert inf_np[0] < 0.01
def test_masking_v2():
nb_entities = 10
nb_predicates = 5
embedding_size = 10
rs = np.random.RandomState(0)
for _ in range(1):
for position in [0, 1, 2]:
for st in ['min', 'concat']:
with torch.no_grad():
triples = [
('a', 'p', 'b'),
('b', 'q', 'c'),
('a', 'p', 'c')
]
entity_to_index = {'a': 0, 'b': 1, 'c': 2, 'd': 3}
predicate_to_index = {'p': 0, 'q': 1}
kernel = GaussianKernel()
entity_emb = nn.Embedding(nb_entities, embedding_size * 2, sparse=True)
predicate_emb = nn.Embedding(nb_predicates, embedding_size * 2, sparse=True)
fact_rel = torch.LongTensor(np.array([predicate_to_index[p] for (_, p, _) in triples]))
fact_arg1 = torch.LongTensor(np.array([entity_to_index[s] for (s, _, _) in triples]))
fact_arg2 = torch.LongTensor(np.array([entity_to_index[o] for (_, _, o) in triples]))
facts = [fact_rel, fact_arg1, fact_arg2]
base = NeuralKB(entity_embeddings=entity_emb, predicate_embeddings=predicate_emb,
kernel=kernel, facts=facts, scoring_type=st)
indices = torch.LongTensor(np.array([predicate_to_index['p'], predicate_to_index['q']]))
reformulator = SymbolicReformulator(predicate_emb, indices)
model = SimpleHoppy(base, entity_emb, hops=reformulator)
xs_np = rs.randint(nb_entities, size=32)
xp_np = rs.randint(nb_predicates, size=32)
xo_np = rs.randint(nb_entities, size=32)
xi_np = np.array([position] * xs_np.shape[0])
xs_np[0] = 0
xp_np[0] = 0
xo_np[0] = 1
xs_np[1] = 1
xp_np[1] = 1
xo_np[1] = 2
xs_np[2] = 0
xp_np[2] = 0
xo_np[2] = 2
xs = torch.LongTensor(xs_np)
xp = torch.LongTensor(xp_np)
xo = torch.LongTensor(xo_np)
xi = torch.LongTensor(xi_np)
xs_emb = entity_emb(xs)
xp_emb = predicate_emb(xp)
xo_emb = entity_emb(xo)
# xi = None
base.mask_indices = xi
scores = model.forward(xp_emb, xs_emb, xo_emb)
inf = model.score(xp_emb, xs_emb, xo_emb)
if position in {0, 1}:
assert inf[2] < 0.5
else:
assert inf[2] > 0.9
scores_sp, scores_po = scores
inf = inf.cpu().numpy()
scores_sp = scores_sp.cpu().numpy()
scores_po = scores_po.cpu().numpy()
for i in range(xs.shape[0]):
np.testing.assert_allclose(inf[i], scores_sp[i, xo[i]], rtol=1e-5, atol=1e-5)
np.testing.assert_allclose(inf[i], scores_po[i, xs[i]], rtol=1e-5, atol=1e-5)
def main(argv):
argparser = argparse.ArgumentParser(
'CLUTRR', formatter_class=argparse.ArgumentDefaultsHelpFormatter)
train_path = test_path = "data/clutrr-emnlp/data_test/64.csv"
argparser.add_argument('--train',
action='store',
type=str,
default=train_path)
argparser.add_argument('--test', nargs='+', type=str, default=[test_path])
# model params
argparser.add_argument('--embedding-size',
'-k',
action='store',
type=int,
default=20)
argparser.add_argument('--k-max',
'-m',
action='store',
type=int,
default=10)
argparser.add_argument('--max-depth',
'-d',
action='store',
type=int,
default=2)
argparser.add_argument('--hops',
nargs='+',
type=str,
default=['2', '2', '1R'])
# training params
argparser.add_argument('--epochs',
'-e',
action='store',
type=int,
default=100)
argparser.add_argument('--learning-rate',
'-l',
action='store',
type=float,
default=0.1)
argparser.add_argument('--batch-size',
'-b',
action='store',
type=int,
default=8)
argparser.add_argument('--optimizer',
'-o',
action='store',
type=str,
default='adagrad',
choices=['adagrad', 'adam', 'sgd'])
argparser.add_argument('--seed', action='store', type=int, default=0)
argparser.add_argument('--evaluate-every',
'-V',
action='store',
type=int,
default=32)
argparser.add_argument('--N2', action='store', type=float, default=None)
argparser.add_argument('--N3', action='store', type=float, default=None)
argparser.add_argument('--entropy',
'-E',
action='store',
type=float,
default=None)
argparser.add_argument('--reformulator',
'-r',
action='store',
type=str,
default='linear',
choices=['static', 'linear', 'attentive', 'memory'])
argparser.add_argument('--nb-rules',
'-R',
action='store',
type=int,
default=4)
argparser.add_argument('--slope',
'-S',
action='store',
type=float,
default=None)
argparser.add_argument('--init-size',
'-i',
action='store',
type=float,
default=1.0)
argparser.add_argument('--debug', '-D', action='store_true', default=False)
args = argparser.parse_args(argv)
train_path = args.train
test_paths = args.test
embedding_size = args.embedding_size
k_max = args.k_max
max_depth = args.max_depth
hops_str = args.hops
nb_epochs = args.epochs
learning_rate = args.learning_rate
batch_size = args.batch_size
optimizer_name = args.optimizer
seed = args.seed
evaluate_every = args.evaluate_every
N2_weight = args.N2
N3_weight = args.N3
entropy_weight = args.entropy
reformulator_name = args.reformulator
nb_rules = args.nb_rules
slope = args.slope
init_size = args.init_size
is_debug = args.debug
np.random.seed(seed)
random_state = np.random.RandomState(seed)
torch.manual_seed(seed)
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
logger.info(f'Device: {device}')
if torch.cuda.is_available():
torch.set_default_tensor_type(torch.cuda.FloatTensor)
data = Data(train_path=train_path, test_paths=test_paths)
relation_to_predicate = data.relation_to_predicate
predicate_to_relations = data.predicate_to_relations
entity_lst, predicate_lst, relation_lst = data.entity_lst, data.predicate_lst, data.relation_lst
nb_examples = len(data.train)
nb_entities = len(entity_lst)
nb_predicates = len(predicate_lst)
nb_relations = len(relation_lst)
entity_to_idx = {e: i for i, e in enumerate(entity_lst)}
relation_to_idx = {r: i for i, r in enumerate(relation_lst)}
kernel = GaussianKernel(slope=slope)
entity_embeddings = nn.Embedding(nb_entities, embedding_size,
sparse=False).to(device)
if entropy_weight is None:
relation_embeddings = nn.Embedding(nb_relations,
embedding_size,
sparse=False).to(device)
relation_embeddings.weight.data *= init_size
else:
relation_embeddings = AttentiveEmbedding(nb_predicates=nb_predicates,
nb_relations=nb_relations,
embedding_size=embedding_size,
device=device).to(device)
make_easy(predicate_lst, predicate_to_relations, relation_to_idx,
relation_embeddings)
model = NeuralKB(kernel=kernel, k=k_max).to(device)
memory = None
def make_hop(s: str) -> Tuple[BaseReformulator, bool]:
nonlocal memory
if s.isdigit():
nb_hops, is_reversed = int(s), False
else:
nb_hops, is_reversed = int(s[:-1]), True
res = None
if reformulator_name in {'static'}:
res = StaticReformulator(nb_hops, embedding_size)
elif reformulator_name in {'linear'}:
res = LinearReformulator(nb_hops, embedding_size)
elif reformulator_name in {'attentive'}:
res = AttentiveReformulator(nb_hops, relation_embeddings)
elif reformulator_name in {'memory'}:
memory = MemoryReformulator.Memory(
nb_hops, nb_rules,
embedding_size) if memory is None else memory
res = MemoryReformulator(memory)
assert res is not None
return res, is_reversed
hops_lst = [make_hop(s) for s in hops_str]
hoppy = Hoppy(model=model, depth=max_depth, hops_lst=hops_lst).to(device)
def scoring_function(story: List[Fact], targets: List[Fact]) -> Tensor:
story_rel = encode_relation(story, relation_embeddings,
relation_to_idx, device)
story_arg1, story_arg2 = encode_arguments(story, entity_embeddings,
entity_to_idx, device)
targets_rel = encode_relation(targets, relation_embeddings,
relation_to_idx, device)
targets_arg1, targets_arg2 = encode_arguments(targets,
entity_embeddings,
entity_to_idx, device)
facts = [story_rel, story_arg1, story_arg2]
scores = hoppy.score(targets_rel, targets_arg1, targets_arg2, facts)
return scores
def evaluate(instances: List[Instance],
path: str,
sample_size: Optional[int] = None) -> float:
res = 0.0
if len(instances) > 0:
res = accuracy(scoring_function=scoring_function,
instances=instances,
sample_size=sample_size,
relation_to_predicate=relation_to_predicate,
predicate_to_relations=predicate_to_relations)
logger.info(f'Test Accuracy on {path}: {res:.6f}')
return res
loss_function = nn.BCELoss()
N2_reg = N2() if N2_weight is not None else None
N3_reg = N3() if N3_weight is not None else None
entropy_reg = Entropy(
use_logits=False) if entropy_weight is not None else None
params_lst = [
p for p in hoppy.parameters()
if not torch.equal(p, entity_embeddings.weight)
]
params_lst += relation_embeddings.parameters()
params = nn.ParameterList(params_lst).to(device)
for tensor in params_lst:
logger.info(f'\t{tensor.size()}\t{tensor.device}')
optimizer_factory = {
'adagrad': lambda arg: optim.Adagrad(arg, lr=learning_rate),
'adam': lambda arg: optim.Adam(arg, lr=learning_rate),
'sgd': lambda arg: optim.SGD(arg, lr=learning_rate)
}
assert optimizer_name in optimizer_factory
optimizer = optimizer_factory[optimizer_name](params)
global_step = 0
hinton = HintonDiagram(max_arr=[0.0, 1.0])
for epoch_no in range(1, nb_epochs + 1):
batcher = Batcher(batch_size=batch_size,
nb_examples=nb_examples,
nb_epochs=1,
random_state=random_state)
nb_batches = len(batcher.batches)
epoch_loss_values = []
for batch_no, (batch_start, batch_end) in enumerate(batcher.batches,
start=1):
global_step += 1
indices_batch = batcher.get_batch(batch_start, batch_end)
instances_batch = [data.train[i] for i in indices_batch]
batch_loss_values = []
for i, instance in enumerate(instances_batch):
story, target = instance.story, instance.target
s, r, o = target
if is_debug is True and i == 0:
# print('STORY', story)
# print('TARGET', target)
r_lst = [
r for p in predicate_lst
for r in predicate_to_relations[p]
]
r_idx_lst = [relation_to_idx[r] for r in r_lst]
with torch.no_grad():
# show_rules(model=hoppy, kernel=kernel, relation_embeddings=relation_embeddings,
# data=data, relation_to_idx=relation_to_idx, device=device)
r_idx_tensor = torch.from_numpy(
np.array(r_idx_lst, dtype=np.int64)).to(device)
r_tensor = relation_embeddings(r_idx_tensor)
k = kernel.pairwise(r_tensor, r_tensor)
# print(r_lst)
print(hinton(k.cpu().numpy()))
story_rel = encode_relation(story, relation_embeddings,
relation_to_idx, device)
story_arg1, story_arg2 = encode_arguments(
story, entity_embeddings, entity_to_idx, device)
facts = [story_rel, story_arg1, story_arg2]
pos_predicate = relation_to_predicate[r]
p_relation_lst = sorted(relation_to_predicate.keys())
target_lst = [(s, x, o) for x in p_relation_lst]
label_lst = [
int(pos_predicate == relation_to_predicate[r])
for r in p_relation_lst
]
rel_emb = encode_relation(target_lst, relation_embeddings,
relation_to_idx, device)
arg1_emb, arg2_emb = encode_arguments(target_lst,
entity_embeddings,
entity_to_idx, device)
scores = hoppy.score(rel_emb, arg1_emb, arg2_emb, facts)
labels = torch.Tensor(label_lst).float()
# if i == 0:
# print(scores)
# print(labels)
loss = loss_function(scores, labels)
factors = [
hoppy.factor(e) for e in [rel_emb, arg1_emb, arg2_emb]
]
loss += N2_weight * N2_reg(
factors) if N2_weight is not None else 0.0
loss += N3_weight * N3_reg(
factors) if N3_weight is not None else 0.0
if entropy_weight is not None:
attention = relation_embeddings.attention
if i == 0:
pass
# print(scores.cpu().detach().numpy())
# print(labels.cpu().detach().numpy())
# print(hinton(attention.cpu().detach().numpy()))
# print(attention.cpu().detach().numpy())
loss += entropy_weight * entropy_reg([attention])
loss_value = loss.item()
batch_loss_values += [loss_value]
epoch_loss_values += [loss_value]
loss.backward()
optimizer.step()
optimizer.zero_grad()
loss_mean, loss_std = np.mean(batch_loss_values), np.std(
batch_loss_values)
logger.info(
f'Epoch {epoch_no}/{nb_epochs}\tBatch {batch_no}/{nb_batches}\tLoss {loss_mean:.4f} ± {loss_std:.4f}'
)
if global_step % evaluate_every == 0:
for test_path in test_paths:
instances = data.test[test_path]
evaluate(instances=instances, path=test_path)
if is_debug is True:
for i in range(3):
story, target = instances[i].story, instances[
i].target
# print('INSTANCE', target, story)
if is_debug is True:
r_lst = [
r for p in predicate_lst
for r in predicate_to_relations[p]
]
r_idx_lst = [relation_to_idx[r] for r in r_lst]
with torch.no_grad():
show_rules(model=hoppy,
kernel=kernel,
relation_embeddings=relation_embeddings,
data=data,
relation_to_idx=relation_to_idx,
device=device)
r_idx_tensor = torch.from_numpy(
np.array(r_idx_lst, dtype=np.int64)).to(device)
r_tensor = relation_embeddings(r_idx_tensor)
k = kernel.pairwise(r_tensor, r_tensor)
# print(r_lst)
print(hinton(k.cpu().numpy()))
loss_mean, loss_std = np.mean(epoch_loss_values), np.std(
epoch_loss_values)
slope = kernel.slope.item() if isinstance(kernel.slope,
Tensor) else kernel.slope
logger.info(
f'Epoch {epoch_no}/{nb_epochs}\tLoss {loss_mean:.4f} ± {loss_std:.4f}\tSlope {slope:.4f}'
)
for test_path in test_paths:
evaluate(instances=data.test[test_path], path=test_path)
logger.info("Training finished")