# Source code for pykeen.models.inductive.inductive_nodepiece

```
# -*- coding: utf-8 -*-
"""A wrapper which combines an interaction function with NodePiece entity representations."""
import logging
from typing import Any, Callable, ClassVar, Mapping, Optional
import torch
from class_resolver import Hint, HintOrType, OptionalKwargs
from .base import InductiveERModel
from ...constants import DEFAULT_EMBEDDING_HPO_EMBEDDING_DIM_RANGE
from ...nn import (
DistMultInteraction,
Interaction,
NodePieceRepresentation,
SubsetRepresentation,
representation_resolver,
)
from ...nn.node_piece import RelationTokenizer
from ...triples.triples_factory import CoreTriplesFactory
__all__ = [
"InductiveNodePiece",
]
logger = logging.getLogger(__name__)
[docs]class InductiveNodePiece(InductiveERModel):
"""A wrapper which combines an interaction function with NodePiece entity representations from [galkin2021]_.
This model uses the :class:`pykeen.nn.NodePieceRepresentation` instead of a typical
:class:`pykeen.nn.Embedding` to more efficiently store representations.
INDUCTIVE VERSION
---
citation:
author: Galkin
year: 2021
link: https://arxiv.org/abs/2106.12144
github: https://github.com/migalkin/NodePiece
"""
hpo_default: ClassVar[Mapping[str, Any]] = dict(
embedding_dim=DEFAULT_EMBEDDING_HPO_EMBEDDING_DIM_RANGE,
)
def __init__(
self,
*,
triples_factory: CoreTriplesFactory,
inference_factory: CoreTriplesFactory,
num_tokens: int = 2,
embedding_dim: int = 64,
relation_representations_kwargs: OptionalKwargs = None,
interaction: HintOrType[Interaction] = DistMultInteraction,
aggregation: Hint[Callable[[torch.Tensor, int], torch.Tensor]] = None,
validation_factory: Optional[CoreTriplesFactory] = None,
test_factory: Optional[CoreTriplesFactory] = None,
**kwargs,
) -> None:
"""
Initialize the model.
:param triples_factory:
the triples factory of training triples. Must have create_inverse_triples set to True.
:param inference_factory:
the triples factory of inference triples. Must have create_inverse_triples set to True.
:param validation_factory:
the triples factory of validation triples. Must have create_inverse_triples set to True.
:param test_factory:
the triples factory of testing triples. Must have create_inverse_triples set to True.
:param num_tokens:
the number of relations to use to represent each entity, cf.
:class:`pykeen.nn.NodePieceRepresentation`.
:param embedding_dim:
the embedding dimension. Only used if embedding_specification is not given.
:param relation_representations_kwargs:
the relation representation parameters
:param interaction:
the interaction module, or a hint for it.
:param aggregation:
aggregation of multiple token representations to a single entity representation. By default,
this uses :func:`torch.mean`. If a string is provided, the module assumes that this refers to a top-level
torch function, e.g. "mean" for :func:`torch.mean`, or "sum" for func:`torch.sum`. An aggregation can
also have trainable parameters, .e.g., ``MLP(mean(MLP(tokens)))`` (cf. DeepSets from [zaheer2017]_). In
this case, the module has to be created outside of this component.
Moreover, we support providing "mlp" as a shortcut to use the MLP aggregation version from [galkin2021]_.
We could also have aggregations which result in differently shapes output, e.g. a concatenation of all
token embeddings resulting in shape ``(num_tokens * d,)``. In this case, `shape` must be provided.
The aggregation takes two arguments: the (batched) tensor of token representations, in shape
``(*, num_tokens, *dt)``, and the index along which to aggregate.
:param kwargs:
additional keyword-based arguments passed to :meth:`ERModel.__init__`
:raises ValueError:
if the triples factory does not create inverse triples
"""
if not triples_factory.create_inverse_triples:
raise ValueError(
"The provided triples factory does not create inverse triples. However, for the node piece "
"representations inverse relation representations are required.",
)
# always create representations for normal and inverse relations and padding
relation_representations = representation_resolver.make(
query=None,
pos_kwargs=relation_representations_kwargs,
max_id=2 * triples_factory.real_num_relations + 1,
shape=embedding_dim,
)
if validation_factory is None:
validation_factory = inference_factory
super().__init__(
triples_factory=triples_factory,
interaction=interaction,
entity_representations=NodePieceRepresentation,
entity_representations_kwargs=dict(
triples_factory=triples_factory,
tokenizers=RelationTokenizer,
token_representations=relation_representations,
aggregation=aggregation,
num_tokens=num_tokens,
),
relation_representations=SubsetRepresentation( # hide padding relation
max_id=triples_factory.num_relations,
base=relation_representations,
),
validation_factory=validation_factory,
testing_factory=test_factory,
**kwargs,
)
# note: we need to share the aggregation across representations, since the aggregation may have
# trainable parameters
np: NodePieceRepresentation = self.entity_representations[0]
for representations in self._mode_to_representations.values():
assert len(representations) == 1
np2 = representations[0]
assert isinstance(np2, NodePieceRepresentation)
np2.combination = np.combination
```