Pipeline¶
Pipeline dedicated to automate training model.
Parameters¶
-
epochs
-
eval_every – defaults to
1 -
early_stopping_rounds – defaults to
3 -
device – defaults to
cuda
Examples¶
>>> from ckb import compose
>>> from ckb import datasets
>>> from ckb import evaluation
>>> from ckb import losses
>>> from ckb import models
>>> from ckb import sampling
>>> from ckb import scoring
>>> import torch
>>> _ = torch.manual_seed(42)
>>> device = 'cpu'
>>> train = [('mkb', 'is_a', 'library')]
>>> valid = [('ckb', 'is_a', 'library'), ('github', 'is_a', 'tool')]
>>> test = [('mkb', 'is_a', 'tool'), ('ckb', 'is_a', 'tool')]
>>> dataset = datasets.Dataset(
... batch_size = 1,
... train = train,
... valid = valid,
... test = test,
... seed = 42,
... )
>>> model = models.DistillBert(
... entities = dataset.entities,
... relations = dataset.relations,
... gamma = 9,
... scoring = scoring.TransE(),
... device = device,
... )
>>> model = model.to(device)
>>> optimizer = torch.optim.Adam(
... filter(lambda p: p.requires_grad, model.parameters()),
... lr = 0.00005,
... )
>>> evaluation = evaluation.Evaluation(
... entities = dataset.entities,
... relations = dataset.relations,
... true_triples = dataset.train + dataset.valid + dataset.test,
... batch_size = 1,
... device = device,
... )
>>> sampling = sampling.NegativeSampling(
... size = 1,
... entities = dataset.entities,
... relations = dataset.relations,
... train_triples = dataset.train,
... )
>>> pipeline = compose.Pipeline(
... epochs = 1,
... eval_every = 1,
... early_stopping_rounds = 1,
... device = device,
... )
>>> pipeline = pipeline.learn(
... model = model,
... dataset = dataset,
... evaluation = evaluation,
... sampling = sampling,
... optimizer = optimizer,
... loss = losses.Adversarial(alpha=0.5),
... )
<BLANKLINE>
Epoch: 0.
Validation:
MRR: 0.375
MR: 2.75
HITS@1: 0.0
HITS@3: 1.0
HITS@10: 1.0
MRR_relations: 1.0
MR_relations: 1.0
HITS@1_relations: 1.0
HITS@3_relations: 1.0
HITS@10_relations: 1.0
Test:
MRR: 0.375
MR: 2.75
HITS@1: 0.0
HITS@3: 1.0
HITS@10: 1.0
MRR_relations: 1.0
MR_relations: 1.0
HITS@1_relations: 1.0
HITS@3_relations: 1.0
HITS@10_relations: 1.0
<BLANKLINE>
Epoch: 0.
<BLANKLINE>
Validation:
MRR: 0.375
MR: 2.75
HITS@1: 0.0
HITS@3: 1.0
HITS@10: 1.0
MRR_relations: 1.0
MR_relations: 1.0
HITS@1_relations: 1.0
HITS@3_relations: 1.0
HITS@10_relations: 1.0
Test:
MRR: 0.375
MR: 2.75
HITS@1: 0.0
HITS@3: 1.0
HITS@10: 1.0
MRR_relations: 1.0
MR_relations: 1.0
HITS@1_relations: 1.0
HITS@3_relations: 1.0
HITS@10_relations: 1.0