# -*- coding: utf-8 -*-
"""Base module for all KGE models."""
from __future__ import annotations
import functools
import logging
import pickle
import warnings
from abc import ABC, abstractmethod
from typing import Any, ClassVar, Iterable, Mapping, Optional, Type, Union
import pandas as pd
import torch
from docdata import parse_docdata
from torch import nn
from ..losses import Loss, MarginRankingLoss
from ..nn import Embedding, EmbeddingSpecification
from ..regularizers import NoRegularizer, Regularizer
from ..triples import TriplesFactory
from ..typing import DeviceHint, ScorePack
from ..utils import NoRandomSeedNecessary, _can_slice, extend_batch, resolve_device, set_random_seed
__all__ = [
'Model',
'_OldAbstractModel',
'EntityEmbeddingModel',
'EntityRelationEmbeddingModel',
'MultimodalModel',
]
logger = logging.getLogger(__name__)
[docs]class Model(nn.Module, ABC):
"""A base module for KGE models.
Subclasses of :class:`Model` can decide however they want on how to store entities' and
relations' representations, how they want to be looked up, and how they should
be scored. The :class:`OModel` provides a commonly used interface for models storing entity
and relation representations in the form of :class:`pykeen.nn.Embedding`.
"""
#: Keep track of if this is a base model
_is_base_model: ClassVar[bool]
#: The default strategy for optimizing the model's hyper-parameters
hpo_default: ClassVar[Mapping[str, Any]]
#: A triples factory with the training triples
triples_factory: TriplesFactory
#: The device on which this model and its submodules are stored
device: torch.device
_random_seed: Optional[int]
#: The default loss function class
loss_default: ClassVar[Type[Loss]] = MarginRankingLoss
#: The default parameters for the default loss function class
loss_default_kwargs: ClassVar[Optional[Mapping[str, Any]]] = dict(margin=1.0, reduction='mean')
#: The instance of the loss
loss: Loss
def __init__(
self,
triples_factory: TriplesFactory,
loss: Optional[Loss] = None,
predict_with_sigmoid: bool = False,
preferred_device: DeviceHint = None,
random_seed: Optional[int] = None,
) -> None:
"""Initialize the module.
:param triples_factory:
The triples factory facilitates access to the dataset.
:param loss:
The loss to use. If None is given, use the loss default specific to the model subclass.
:param predict_with_sigmoid:
Whether to apply sigmoid onto the scores when predicting scores. Applying sigmoid at prediction time may
lead to exactly equal scores for certain triples with very high, or very low score. When not trained with
applying sigmoid (or using BCEWithLogitsLoss), the scores are not calibrated to perform well with sigmoid.
:param preferred_device:
The preferred device for model training and inference.
:param random_seed:
A random seed to use for initialising the model's weights. **Should** be set when aiming at reproducibility.
:param regularizer:
A regularizer to use for training.
"""
super().__init__()
# Initialize the device
self.device = resolve_device(device=preferred_device)
# Random seeds have to set before the embeddings are initialized
if random_seed is None:
logger.warning('No random seed is specified. This may lead to non-reproducible results.')
self._random_seed = None
elif random_seed is not NoRandomSeedNecessary:
set_random_seed(random_seed)
self._random_seed = random_seed
# Loss
if loss is None:
self.loss = self.loss_default(**(self.loss_default_kwargs or {}))
else:
self.loss = loss
# The triples factory facilitates access to the dataset.
self.triples_factory = triples_factory
'''
When predict_with_sigmoid is set to True, the sigmoid function is applied to the logits during evaluation and
also for predictions after training, but has no effect on the training.
'''
self.predict_with_sigmoid = predict_with_sigmoid
def __init_subclass__(cls, autoreset: bool = True, **kwargs): # noqa:D105
cls._is_base_model = not autoreset
if not cls._is_base_model:
_add_post_reset_parameters(cls)
parse_docdata(cls)
"""Properties"""
@property
def can_slice_h(self) -> bool:
"""Whether score_h supports slicing."""
return _can_slice(self.score_h)
@property
def can_slice_r(self) -> bool:
"""Whether score_r supports slicing."""
return _can_slice(self.score_r)
@property
def can_slice_t(self) -> bool:
"""Whether score_t supports slicing."""
return _can_slice(self.score_t)
[docs] def reset_parameters_(self): # noqa: D401
"""Reset all parameters of the model and enforce model constraints."""
self._reset_parameters_()
self.to_device_()
self.post_parameter_update()
return self
@property
def num_entities(self) -> int: # noqa: D401
"""The number of entities in the knowledge graph."""
return self.triples_factory.num_entities
@property
def num_relations(self) -> int: # noqa: D401
"""The number of unique relation types in the knowledge graph."""
return self.triples_factory.num_relations
"""Base methods"""
[docs] def post_forward_pass(self):
"""Run after calculating the forward loss."""
def _free_graph_and_cache(self):
"""Run to free the graph and cache."""
"""Abstract methods"""
@abstractmethod
def _reset_parameters_(self): # noqa: D401
"""Reset all parameters of the model in-place."""
raise NotImplementedError
[docs] def post_parameter_update(self) -> None:
"""Has to be called after each parameter update."""
"""Abstract methods - Scoring"""
[docs] @abstractmethod
def score_hrt(self, hrt_batch: torch.LongTensor) -> torch.FloatTensor:
"""Forward pass.
This method takes head, relation and tail of each triple and calculates the corresponding score.
:param hrt_batch: shape: (batch_size, 3), dtype: long
The indices of (head, relation, tail) triples.
:raises NotImplementedError:
If the method was not implemented for this class.
:return: shape: (batch_size, 1), dtype: float
The score for each triple.
"""
raise NotImplementedError
[docs] @abstractmethod
def score_t(self, hr_batch: torch.LongTensor) -> torch.FloatTensor:
"""Forward pass using right side (tail) prediction.
This method calculates the score for all possible tails for each (head, relation) pair.
:param hr_batch: shape: (batch_size, 2), dtype: long
The indices of (head, relation) pairs.
:return: shape: (batch_size, num_entities), dtype: float
For each h-r pair, the scores for all possible tails.
"""
[docs] @abstractmethod
def score_r(self, ht_batch: torch.LongTensor) -> torch.FloatTensor:
"""Forward pass using middle (relation) prediction.
This method calculates the score for all possible relations for each (head, tail) pair.
:param ht_batch: shape: (batch_size, 2), dtype: long
The indices of (head, tail) pairs.
:return: shape: (batch_size, num_relations), dtype: float
For each h-t pair, the scores for all possible relations.
"""
[docs] @abstractmethod
def score_h(self, rt_batch: torch.LongTensor) -> torch.FloatTensor:
"""Forward pass using left side (head) prediction.
This method calculates the score for all possible heads for each (relation, tail) pair.
:param rt_batch: shape: (batch_size, 2), dtype: long
The indices of (relation, tail) pairs.
:return: shape: (batch_size, num_entities), dtype: float
For each r-t pair, the scores for all possible heads.
"""
[docs] @abstractmethod
def compute_loss(
self,
tensor_1: torch.FloatTensor,
tensor_2: torch.FloatTensor,
) -> torch.FloatTensor:
"""Compute the loss for functions requiring two separate tensors as input.
:param tensor_1: shape: s
The tensor containing predictions or positive scores.
:param tensor_2: shape: s
The tensor containing target values or the negative scores.
:return: dtype: float, scalar
The label loss value.
.. note:: generally the two tensors do not need to have the same shape, but only one which is broadcastable.
"""
"""Concrete methods"""
[docs] def to_device_(self):
"""Transfer model to device."""
self.to(self.device)
torch.cuda.empty_cache()
return self
[docs] def get_grad_params(self) -> Iterable[nn.Parameter]:
"""Get the parameters that require gradients."""
# TODO: Why do we need that? The optimizer takes care of filtering the parameters.
return filter(lambda p: p.requires_grad, self.parameters())
@property
def num_parameter_bytes(self) -> int:
"""Calculate the number of bytes used for all parameters of the model."""
return sum(
param.numel() * param.element_size()
for param in self.parameters(recurse=True)
)
[docs] def save_state(self, path: str) -> None:
"""Save the state of the model.
:param path:
Path of the file where to store the state in.
"""
torch.save(self.state_dict(), path, pickle_protocol=pickle.HIGHEST_PROTOCOL)
[docs] def load_state(self, path: str) -> None:
"""Load the state of the model.
:param path:
Path of the file where to load the state from.
"""
self.load_state_dict(torch.load(path, map_location=self.device))
"""Prediction methods"""
[docs] def predict_hrt(self, hrt_batch: torch.LongTensor) -> torch.FloatTensor:
"""Calculate the scores for triples.
This method takes head, relation and tail of each triple and calculates the corresponding score.
Additionally, the model is set to evaluation mode.
:param hrt_batch: shape: (number of triples, 3), dtype: long
The indices of (head, relation, tail) triples.
:return: shape: (number of triples, 1), dtype: float
The score for each triple.
"""
self.eval() # Enforce evaluation mode
scores = self.score_hrt(hrt_batch)
if self.predict_with_sigmoid:
scores = torch.sigmoid(scores)
return scores
[docs] def predict_h(
self,
rt_batch: torch.LongTensor,
slice_size: Optional[int] = None,
) -> torch.FloatTensor:
"""Forward pass using left side (head) prediction for obtaining scores of all possible heads.
This method calculates the score for all possible heads for each (relation, tail) pair.
.. note::
If the model has been trained with inverse relations, the task of predicting
the head entities becomes the task of predicting the tail entities of the
inverse triples, i.e., $f(*,r,t)$ is predicted by means of $f(t,r_{inv},*)$.
Additionally, the model is set to evaluation mode.
:param rt_batch: shape: (batch_size, 2), dtype: long
The indices of (relation, tail) pairs.
:param slice_size: >0
The divisor for the scoring function when using slicing.
:return: shape: (batch_size, num_entities), dtype: float
For each r-t pair, the scores for all possible heads.
"""
self.eval() # Enforce evaluation mode
if self.triples_factory.create_inverse_triples:
scores = self.score_h_inverse(rt_batch=rt_batch, slice_size=slice_size)
elif slice_size is None:
scores = self.score_h(rt_batch)
else:
scores = self.score_h(rt_batch, slice_size=slice_size) # type: ignore
if self.predict_with_sigmoid:
scores = torch.sigmoid(scores)
return scores
[docs] def predict_t(
self,
hr_batch: torch.LongTensor,
slice_size: Optional[int] = None,
) -> torch.FloatTensor:
"""Forward pass using right side (tail) prediction for obtaining scores of all possible tails.
This method calculates the score for all possible tails for each (head, relation) pair.
Additionally, the model is set to evaluation mode.
:param hr_batch: shape: (batch_size, 2), dtype: long
The indices of (head, relation) pairs.
:param slice_size: >0
The divisor for the scoring function when using slicing.
:return: shape: (batch_size, num_entities), dtype: float
For each h-r pair, the scores for all possible tails.
.. note::
We only expect the right side-side predictions, i.e., $(h,r,*)$ to change its
default behavior when the model has been trained with inverse relations
(mainly because of the behavior of the LCWA training approach). This is why
the :func:`predict_scores_all_heads()` has different behavior depending on
if inverse triples were used in training, and why this function has the same
behavior regardless of the use of inverse triples.
"""
self.eval() # Enforce evaluation mode
if slice_size is None:
scores = self.score_t(hr_batch)
else:
scores = self.score_t(hr_batch, slice_size=slice_size) # type: ignore
if self.predict_with_sigmoid:
scores = torch.sigmoid(scores)
return scores
[docs] def predict_r(
self,
ht_batch: torch.LongTensor,
slice_size: Optional[int] = None,
) -> torch.FloatTensor:
"""Forward pass using middle (relation) prediction for obtaining scores of all possible relations.
This method calculates the score for all possible relations for each (head, tail) pair.
Additionally, the model is set to evaluation mode.
:param ht_batch: shape: (batch_size, 2), dtype: long
The indices of (head, tail) pairs.
:param slice_size: >0
The divisor for the scoring function when using slicing.
:return: shape: (batch_size, num_relations), dtype: float
For each h-t pair, the scores for all possible relations.
"""
self.eval() # Enforce evaluation mode
if slice_size is None:
scores = self.score_r(ht_batch)
else:
scores = self.score_r(ht_batch, slice_size=slice_size) # type: ignore
if self.predict_with_sigmoid:
scores = torch.sigmoid(scores)
return scores
[docs] def get_all_prediction_df(
self,
*,
k: Optional[int] = None,
batch_size: int = 1,
**kwargs,
) -> Union[ScorePack, pd.DataFrame]:
"""Compute scores for all triples, optionally returning only the k highest scoring.
.. note:: This operation is computationally very expensive for reasonably-sized knowledge graphs.
.. warning:: Setting k=None may lead to huge memory requirements.
:param k:
The number of triples to return. Set to None, to keep all.
:param batch_size:
The batch size to use for calculating scores.
:param kwargs: Additional kwargs to pass to :func:`pykeen.models.predict.get_all_prediction_df`.
:return: shape: (k, 3)
A tensor containing the k highest scoring triples, or all possible triples if k=None.
"""
from .predict import get_all_prediction_df
warnings.warn('Use pykeen.models.predict.get_all_prediction_df', DeprecationWarning)
return get_all_prediction_df(model=self, k=k, batch_size=batch_size, **kwargs)
[docs] def get_head_prediction_df(
self,
relation_label: str,
tail_label: str,
**kwargs,
) -> pd.DataFrame:
"""Predict heads for the given relation and tail (given by label).
:param relation_label: The string label for the relation
:param tail_label: The string label for the tail entity
:param kwargs: Keyword arguments passed to :func:`pykeen.models.predict.get_head_prediction_df`
The following example shows that after you train a model on the Nations dataset,
you can score all entities w.r.t a given relation and tail entity.
>>> from pykeen.pipeline import pipeline
>>> result = pipeline(
... dataset='Nations',
... model='RotatE',
... )
>>> df = result.model.get_head_prediction_df('accusation', 'brazil')
"""
from .predict import get_head_prediction_df
warnings.warn('Use pykeen.models.predict.get_head_prediction_df', DeprecationWarning)
return get_head_prediction_df(self, relation_label=relation_label, tail_label=tail_label, **kwargs)
[docs] def get_relation_prediction_df(
self,
head_label: str,
tail_label: str,
**kwargs,
) -> pd.DataFrame:
"""Predict relations for the given head and tail (given by label).
:param head_label: The string label for the head entity
:param tail_label: The string label for the tail entity
:param kwargs: Keyword arguments passed to :func:`pykeen.models.predict.get_relation_prediction_df`
"""
from .predict import get_relation_prediction_df
warnings.warn('Use pykeen.models.predict.get_relation_prediction_df', DeprecationWarning)
return get_relation_prediction_df(self, head_label=head_label, tail_label=tail_label, **kwargs)
[docs] def get_tail_prediction_df(
self,
head_label: str,
relation_label: str,
**kwargs,
) -> pd.DataFrame:
"""Predict tails for the given head and relation (given by label).
:param head_label: The string label for the head entity
:param relation_label: The string label for the relation
:param kwargs: Keyword arguments passed to :func:`pykeen.models.predict.get_tail_prediction_df`
The following example shows that after you train a model on the Nations dataset,
you can score all entities w.r.t a given head entity and relation.
>>> from pykeen.pipeline import pipeline
>>> result = pipeline(
... dataset='Nations',
... model='RotatE',
... )
>>> df = result.model.get_tail_prediction_df('brazil', 'accusation')
"""
from .predict import get_tail_prediction_df
warnings.warn('Use pykeen.models.predict.get_tail_prediction_df', DeprecationWarning)
return get_tail_prediction_df(self, head_label=head_label, relation_label=relation_label, **kwargs)
"""Inverse scoring"""
def _prepare_inverse_batch(self, batch: torch.LongTensor, index_relation: int) -> torch.LongTensor:
if not self.triples_factory.create_inverse_triples:
raise ValueError(
"Your model is not configured to predict with inverse relations."
" Set ``create_inverse_triples=True`` when creating the dataset/triples factory"
" or using the pipeline().",
)
batch_cloned = batch.clone()
# The number of relations stored in the triples factory includes the number of inverse relations
# Id of inverse relation: relation + 1
batch_cloned[:, index_relation] = batch_cloned[:, index_relation] + 1
return batch_cloned.flip(1)
[docs] def score_hrt_inverse(
self,
hrt_batch: torch.LongTensor,
) -> torch.FloatTensor:
r"""Score triples based on inverse triples, i.e., compute $f(h,r,t)$ based on $f(t,r_{inv},h)$.
When training with inverse relations, the model produces two (different) scores for a triple $(h,r,t) \in K$.
The forward score is calculated from $f(h,r,t)$ and the inverse score is calculated from $f(t,r_{inv},h)$.
This function enables users to inspect the scores obtained by using the corresponding inverse triples.
"""
t_r_inv_h = self._prepare_inverse_batch(batch=hrt_batch, index_relation=1)
return self.score_hrt(hrt_batch=t_r_inv_h)
[docs] def score_t_inverse(self, hr_batch: torch.LongTensor, slice_size: Optional[int] = None):
"""Score all tails for a batch of (h,r)-pairs using the head predictions for the inverses $(*,r_{inv},h)$."""
r_inv_h = self._prepare_inverse_batch(batch=hr_batch, index_relation=1)
if slice_size is None:
return self.score_h(rt_batch=r_inv_h)
else:
return self.score_h(rt_batch=r_inv_h, slice_size=slice_size) # type: ignore
[docs] def score_h_inverse(self, rt_batch: torch.LongTensor, slice_size: Optional[int] = None):
"""Score all heads for a batch of (r,t)-pairs using the tail predictions for the inverses $(t,r_{inv},*)$."""
t_r_inv = self._prepare_inverse_batch(batch=rt_batch, index_relation=0)
if slice_size is None:
return self.score_t(hr_batch=t_r_inv)
else:
return self.score_t(hr_batch=t_r_inv, slice_size=slice_size) # type: ignore
[docs]class _OldAbstractModel(Model, ABC, autoreset=False):
"""A base module for PyKEEN 1.0-style KGE models."""
#: The default regularizer class
regularizer_default: ClassVar[Type[Regularizer]] = NoRegularizer # type: ignore
#: The default parameters for the default regularizer class
regularizer_default_kwargs: ClassVar[Optional[Mapping[str, Any]]] = None
#: The instance of the regularizer
regularizer: Regularizer # type: ignore
def __init__(
self,
triples_factory: TriplesFactory,
loss: Optional[Loss] = None,
predict_with_sigmoid: bool = False,
preferred_device: DeviceHint = None,
random_seed: Optional[int] = None,
regularizer: Optional[Regularizer] = None,
) -> None:
"""Initialize the module.
:param triples_factory:
The triples factory facilitates access to the dataset.
:param loss:
The loss to use. If None is given, use the loss default specific to the model subclass.
:param predict_with_sigmoid:
Whether to apply sigmoid onto the scores when predicting scores. Applying sigmoid at prediction time may
lead to exactly equal scores for certain triples with very high, or very low score. When not trained with
applying sigmoid (or using BCEWithLogitsLoss), the scores are not calibrated to perform well with sigmoid.
:param preferred_device:
The preferred device for model training and inference.
:param random_seed:
A random seed to use for initialising the model's weights. **Should** be set when aiming at reproducibility.
:param regularizer:
A regularizer to use for training.
"""
super().__init__(
triples_factory=triples_factory,
loss=loss,
predict_with_sigmoid=predict_with_sigmoid,
preferred_device=preferred_device,
random_seed=random_seed,
)
# Regularizer
if regularizer is None:
regularizer = self.regularizer_default(
**(self.regularizer_default_kwargs or {}),
)
self.regularizer = regularizer
[docs] def post_parameter_update(self) -> None:
"""Has to be called after each parameter update."""
self.regularizer.reset()
[docs] def regularize_if_necessary(self, *tensors: torch.FloatTensor) -> None:
"""Update the regularizer's term given some tensors, if regularization is requested.
:param tensors: The tensors that should be passed to the regularizer to update its term.
"""
if self.training:
self.regularizer.update(*tensors)
[docs] def score_t(self, hr_batch: torch.LongTensor) -> torch.FloatTensor:
"""Forward pass using right side (tail) prediction.
This method calculates the score for all possible tails for each (head, relation) pair.
:param hr_batch: shape: (batch_size, 2), dtype: long
The indices of (head, relation) pairs.
:return: shape: (batch_size, num_entities), dtype: float
For each h-r pair, the scores for all possible tails.
"""
logger.warning(
'Calculations will fall back to using the score_hrt method, since this model does not have a specific '
'score_t function. This might cause the calculations to take longer than necessary.',
)
# Extend the hr_batch such that each (h, r) pair is combined with all possible tails
hrt_batch = extend_batch(batch=hr_batch, all_ids=list(self.triples_factory.get_entity_ids()), dim=2)
# Calculate the scores for each (h, r, t) triple using the generic interaction function
expanded_scores = self.score_hrt(hrt_batch=hrt_batch)
# Reshape the scores to match the pre-defined output shape of the score_t function.
scores = expanded_scores.view(hr_batch.shape[0], -1)
return scores
[docs] def score_h(self, rt_batch: torch.LongTensor) -> torch.FloatTensor:
"""Forward pass using left side (head) prediction.
This method calculates the score for all possible heads for each (relation, tail) pair.
:param rt_batch: shape: (batch_size, 2), dtype: long
The indices of (relation, tail) pairs.
:return: shape: (batch_size, num_entities), dtype: float
For each r-t pair, the scores for all possible heads.
"""
logger.warning(
'Calculations will fall back to using the score_hrt method, since this model does not have a specific '
'score_h function. This might cause the calculations to take longer than necessary.',
)
# Extend the rt_batch such that each (r, t) pair is combined with all possible heads
hrt_batch = extend_batch(batch=rt_batch, all_ids=list(self.triples_factory.get_entity_ids()), dim=0)
# Calculate the scores for each (h, r, t) triple using the generic interaction function
expanded_scores = self.score_hrt(hrt_batch=hrt_batch)
# Reshape the scores to match the pre-defined output shape of the score_h function.
scores = expanded_scores.view(rt_batch.shape[0], -1)
return scores
[docs] def score_r(self, ht_batch: torch.LongTensor) -> torch.FloatTensor:
"""Forward pass using middle (relation) prediction.
This method calculates the score for all possible relations for each (head, tail) pair.
:param ht_batch: shape: (batch_size, 2), dtype: long
The indices of (head, tail) pairs.
:return: shape: (batch_size, num_relations), dtype: float
For each h-t pair, the scores for all possible relations.
"""
logger.warning(
'Calculations will fall back to using the score_hrt method, since this model does not have a specific '
'score_r function. This might cause the calculations to take longer than necessary.',
)
# Extend the ht_batch such that each (h, t) pair is combined with all possible relations
hrt_batch = extend_batch(batch=ht_batch, all_ids=list(self.triples_factory.get_relation_ids()), dim=1)
# Calculate the scores for each (h, r, t) triple using the generic interaction function
expanded_scores = self.score_hrt(hrt_batch=hrt_batch)
# Reshape the scores to match the pre-defined output shape of the score_r function.
scores = expanded_scores.view(ht_batch.shape[0], -1)
return scores
[docs] def compute_loss(
self,
tensor_1: torch.FloatTensor,
tensor_2: torch.FloatTensor,
) -> torch.FloatTensor:
"""Compute the loss for functions requiring two separate tensors as input.
:param tensor_1: shape: s
The tensor containing predictions or positive scores.
:param tensor_2: shape: s
The tensor containing target values or the negative scores.
:return: dtype: float, scalar
The label loss value.
.. note:: generally the two tensors do not need to have the same shape, but only one which is broadcastable.
"""
return self.loss(tensor_1, tensor_2) + self.regularizer.term
[docs] def post_forward_pass(self):
"""Run after calculating the forward loss."""
self.regularizer.reset()
def _free_graph_and_cache(self):
self.regularizer.reset()
[docs]class EntityEmbeddingModel(_OldAbstractModel, ABC, autoreset=False):
"""A base module for most KGE models that have one embedding for entities."""
entity_embedding: Embedding
def __init__(
self,
triples_factory: TriplesFactory,
entity_representations: EmbeddingSpecification,
loss: Optional[Loss] = None,
predict_with_sigmoid: bool = False,
preferred_device: DeviceHint = None,
random_seed: Optional[int] = None,
regularizer: Optional[Regularizer] = None,
) -> None:
"""Initialize the entity embedding model.
.. seealso:: Constructor of the base class :class:`pykeen.models.Model`
"""
super().__init__(
triples_factory=triples_factory,
loss=loss,
preferred_device=preferred_device,
random_seed=random_seed,
regularizer=regularizer,
predict_with_sigmoid=predict_with_sigmoid,
)
self.entity_embeddings = entity_representations.make(
num_embeddings=triples_factory.num_entities,
device=self.device,
)
@property
def embedding_dim(self) -> int: # noqa:D401
"""The entity embedding dimension."""
return self.entity_embeddings.embedding_dim
def _reset_parameters_(self): # noqa: D102
self.entity_embeddings.reset_parameters()
[docs] def post_parameter_update(self) -> None: # noqa: D102
# make sure to call this first, to reset regularizer state!
super().post_parameter_update()
self.entity_embeddings.post_parameter_update()
[docs]class EntityRelationEmbeddingModel(_OldAbstractModel, ABC, autoreset=False):
"""A base module for KGE models that have different embeddings for entities and relations."""
entity_embedding: Embedding
relation_embedding: Embedding
def __init__(
self,
triples_factory: TriplesFactory,
entity_representations: EmbeddingSpecification,
relation_representations: EmbeddingSpecification,
loss: Optional[Loss] = None,
predict_with_sigmoid: bool = False,
preferred_device: DeviceHint = None,
random_seed: Optional[int] = None,
regularizer: Optional[Regularizer] = None,
) -> None:
"""Initialize the entity embedding model.
.. seealso:: Constructor of the base class :class:`pykeen.models.Model`
"""
super().__init__(
triples_factory=triples_factory,
loss=loss,
preferred_device=preferred_device,
random_seed=random_seed,
regularizer=regularizer,
predict_with_sigmoid=predict_with_sigmoid,
)
self.entity_embeddings = entity_representations.make(
num_embeddings=triples_factory.num_entities,
device=self.device,
)
self.relation_embeddings = relation_representations.make(
num_embeddings=triples_factory.num_relations,
device=self.device,
)
@property
def embedding_dim(self) -> int: # noqa:D401
"""The entity embedding dimension."""
return self.entity_embeddings.embedding_dim
@property
def relation_dim(self): # noqa:D401
"""The relation embedding dimension."""
return self.relation_embeddings.embedding_dim
def _reset_parameters_(self): # noqa: D102
self.entity_embeddings.reset_parameters()
self.relation_embeddings.reset_parameters()
[docs] def post_parameter_update(self) -> None: # noqa: D102
# make sure to call this first, to reset regularizer state!
super().post_parameter_update()
self.entity_embeddings.post_parameter_update()
self.relation_embeddings.post_parameter_update()
[docs]class MultimodalModel(_OldAbstractModel, ABC, autoreset=False):
"""A base module for multimodal KGE models."""
[docs] def score_hrt(self, hrt_batch: torch.LongTensor) -> torch.FloatTensor: # noqa: D102
return self(h_indices=hrt_batch[:, 0], r_indices=hrt_batch[:, 1], t_indices=hrt_batch[:, 2]).view(-1, 1)
[docs] def score_t(self, hr_batch: torch.LongTensor) -> torch.FloatTensor: # noqa: D102
return self(h_indices=hr_batch[:, 0], r_indices=hr_batch[:, 1], t_indices=None)
[docs] def score_r(self, ht_batch: torch.LongTensor) -> torch.FloatTensor: # noqa: D102
return self(h_indices=ht_batch[:, 0], r_indices=None, t_indices=ht_batch[:, 1])
[docs] def score_h(self, rt_batch: torch.LongTensor) -> torch.FloatTensor: # noqa: D102
return self(h_indices=None, r_indices=rt_batch[:, 0], t_indices=rt_batch[:, 1])
def _add_post_reset_parameters(cls: Type[Model]) -> None:
# The following lines add in a post-init hook to all subclasses
# such that the reset_parameters_() function is run
_original_init = cls.__init__
@functools.wraps(_original_init)
def _new_init(self, *args, **kwargs):
_original_init(self, *args, **kwargs)
self.reset_parameters_()
# sorry mypy, but this kind of evil must be permitted.
cls.__init__ = _new_init # type: ignore