Source code for pykeen.nn.modules

# -*- coding: utf-8 -*-

"""Stateful interaction functions."""

from __future__ import annotations

import itertools as itt
import logging
import math
from abc import ABC, abstractmethod
from typing import Any, Callable, Generic, Mapping, MutableMapping, Optional, Sequence, Set, Tuple, Union, cast

import torch
from class_resolver import Resolver
from torch import FloatTensor, nn

from . import functional as pkf
from .combinations import Combination
from ..typing import HeadRepresentation, HintOrType, RelationRepresentation, TailRepresentation
from ..utils import (
    CANONICAL_DIMENSIONS,
    activation_resolver,
    convert_to_canonical_shape,
    ensure_tuple,
    upgrade_to_sequence,
)

__all__ = [
    "interaction_resolver",
    # Base Classes
    "Interaction",
    "FunctionalInteraction",
    "LiteralInteraction",
    "NormBasedInteraction",
    # Adapter classes
    "MonotonicAffineTransformationInteraction",
    # Concrete Classes
    "BoxEInteraction",
    "ComplExInteraction",
    "ConvEInteraction",
    "ConvKBInteraction",
    "CrossEInteraction",
    "DistMultInteraction",
    "DistMAInteraction",
    "ERMLPInteraction",
    "ERMLPEInteraction",
    "HolEInteraction",
    "KG2EInteraction",
    "MuREInteraction",
    "NTNInteraction",
    "PairREInteraction",
    "ProjEInteraction",
    "RESCALInteraction",
    "RotatEInteraction",
    "SimplEInteraction",
    "StructuredEmbeddingInteraction",
    "TorusEInteraction",
    "TransDInteraction",
    "TransEInteraction",
    "TransFInteraction",
    "TransHInteraction",
    "TransRInteraction",
    "TuckerInteraction",
    "UnstructuredModelInteraction",
]

logger = logging.getLogger(__name__)


def _get_batches(z, slice_size):
    for batch in zip(*(hh.split(slice_size, dim=1) for hh in ensure_tuple(z)[0])):
        if len(batch) == 1:
            batch = batch[0]
        yield batch


[docs]class Interaction(nn.Module, Generic[HeadRepresentation, RelationRepresentation, TailRepresentation], ABC): """Base class for interaction functions.""" #: The symbolic shapes for entity representations entity_shape: Sequence[str] = ("d",) #: The symbolic shapes for entity representations for tail entities, if different. This is ony relevant for ConvE. tail_entity_shape: Optional[Sequence[str]] = None #: The symbolic shapes for relation representations relation_shape: Sequence[str] = ("d",)
[docs] @classmethod def get_dimensions(cls) -> Set[str]: """Get all of the relevant dimension keys. This draws from :data:`Interaction.entity_shape`, :data:`Interaction.relation_shape`, and in the case of :class:`ConvEInteraction`, the :data:`Interaction.tail_entity_shape`. :returns: a set of strings representting the dimension keys. """ return set(itt.chain(cls.entity_shape, cls.tail_entity_shape or set(), cls.relation_shape))
[docs] @abstractmethod def forward( self, h: HeadRepresentation, r: RelationRepresentation, t: TailRepresentation, ) -> torch.FloatTensor: """Compute broadcasted triple scores given broadcasted representations for head, relation and tails. :param h: shape: (batch_size, num_heads, 1, 1, ``*``) The head representations. :param r: shape: (batch_size, 1, num_relations, 1, ``*``) The relation representations. :param t: shape: (batch_size, 1, 1, num_tails, ``*``) The tail representations. :return: shape: (batch_size, num_heads, num_relations, num_tails) The scores. """
[docs] def score( self, h: HeadRepresentation, r: RelationRepresentation, t: TailRepresentation, slice_size: Optional[int] = None, slice_dim: Optional[str] = None, ) -> torch.FloatTensor: """Compute broadcasted triple scores with optional slicing. .. note :: At most one of the slice sizes may be not None. :param h: shape: (batch_size, num_heads, `1, 1, `*``) The head representations. :param r: shape: (batch_size, 1, num_relations, 1, ``*``) The relation representations. :param t: shape: (batch_size, 1, 1, num_tails, ``*``) The tail representations. :param slice_size: The slice size. :param slice_dim: The dimension along which to slice. From {"h", "r", "t"} :return: shape: (batch_size, num_heads, num_relations, num_tails) The scores. """ return self._forward_slicing_wrapper(h=h, r=r, t=t, slice_size=slice_size, slice_dim=slice_dim)
def _score( self, h: HeadRepresentation, r: RelationRepresentation, t: TailRepresentation, slice_size: Optional[int] = None, slice_dim: str = None, ) -> torch.FloatTensor: """Compute scores for the score_* methods outside of models. TODO: merge this with the Model utilities? :param h: shape: (b, h, *) :param r: shape: (b, r, *) :param t: shape: (b, t, *) :param slice_size: The slice size. :param slice_dim: The dimension along which to slice. From {"h", "r", "t"} :return: shape: (b, h, r, t) """ args = [] for key, x in zip("hrt", (h, r, t)): value = [] for xx in upgrade_to_sequence(x): # type: torch.FloatTensor # bring to (b, n, *) xx = xx.unsqueeze(dim=1 if key != slice_dim else 0) # bring to (b, h, r, t, *) xx = convert_to_canonical_shape( x=xx, dim=key, num=xx.shape[1], batch_size=xx.shape[0], suffix_shape=xx.shape[2:], ) value.append(xx) # unpack singleton if len(value) == 1: value = value[0] args.append(value) h, r, t = cast(Tuple[HeadRepresentation, RelationRepresentation, TailRepresentation], args) return self._forward_slicing_wrapper(h=h, r=r, t=t, slice_dim=slice_dim, slice_size=slice_size) def _forward_slicing_wrapper( self, h: Union[torch.FloatTensor, Tuple[torch.FloatTensor, ...]], r: Union[torch.FloatTensor, Tuple[torch.FloatTensor, ...]], t: Union[torch.FloatTensor, Tuple[torch.FloatTensor, ...]], slice_size: Optional[int], slice_dim: Optional[str], ) -> torch.FloatTensor: """Compute broadcasted triple scores with optional slicing for representations in canonical shape. .. note :: Depending on the interaction function, there may be more than one representation for h/r/t. In that case, a tuple of at least two tensors is passed. :param h: shape: (batch_size, num_heads, 1, 1, ``*``) The head representations. :param r: shape: (batch_size, 1, num_relations, 1, ``*``) The relation representations. :param t: shape: (batch_size, 1, 1, num_tails, ``*``) The tail representations. :param slice_size: The slice size. :param slice_dim: The dimension along which to slice. From {"h", "r", "t"} :return: shape: (batch_size, num_heads, num_relations, num_tails) The scores. :raises ValueError: If slice_dim is invalid. """ if slice_size is None: scores = self(h=h, r=r, t=t) elif slice_dim == "h": scores = torch.cat( [self(h=h_batch, r=r, t=t) for h_batch in _get_batches(h, slice_size)], dim=CANONICAL_DIMENSIONS[slice_dim], ) elif slice_dim == "r": scores = torch.cat( [self(h=h, r=r_batch, t=t) for r_batch in _get_batches(r, slice_size)], dim=CANONICAL_DIMENSIONS[slice_dim], ) elif slice_dim == "t": scores = torch.cat( [self(h=h, r=r, t=t_batch) for t_batch in _get_batches(t, slice_size)], dim=CANONICAL_DIMENSIONS[slice_dim], ) else: raise ValueError(f"Invalid slice_dim: {slice_dim}") return scores
[docs] def score_hrt( self, h: HeadRepresentation, r: RelationRepresentation, t: TailRepresentation, ) -> torch.FloatTensor: """Score a batch of triples. :param h: shape: (batch_size, d_e) The head representations. :param r: shape: (batch_size, d_r) The relation representations. :param t: shape: (batch_size, d_e) The tail representations. :return: shape: (batch_size, 1) The scores. """ return self._score(h=h, r=r, t=t)[:, 0, 0, 0, None]
[docs] def score_h( self, all_entities: HeadRepresentation, r: RelationRepresentation, t: TailRepresentation, slice_size: Optional[int] = None, ) -> torch.FloatTensor: """Score all head entities. :param all_entities: shape: (num_entities, d_e) The head representations. :param r: shape: (batch_size, d_r) The relation representations. :param t: shape: (batch_size, d_e) The tail representations. :param slice_size: The slice size. :return: shape: (batch_size, num_entities) The scores. """ return self._score(h=all_entities, r=r, t=t, slice_dim="h", slice_size=slice_size)[:, :, 0, 0]
[docs] def score_r( self, h: HeadRepresentation, all_relations: RelationRepresentation, t: TailRepresentation, slice_size: Optional[int] = None, ) -> torch.FloatTensor: """Score all relations. :param h: shape: (batch_size, d_e) The head representations. :param all_relations: shape: (num_relations, d_r) The relation representations. :param t: shape: (batch_size, d_e) The tail representations. :param slice_size: The slice size. :return: shape: (batch_size, num_entities) The scores. """ return self._score(h=h, r=all_relations, t=t, slice_dim="r", slice_size=slice_size)[:, 0, :, 0]
[docs] def score_t( self, h: HeadRepresentation, r: RelationRepresentation, all_entities: TailRepresentation, slice_size: Optional[int] = None, ) -> torch.FloatTensor: """Score all tail entities. :param h: shape: (batch_size, d_e) The head representations. :param r: shape: (batch_size, d_r) The relation representations. :param all_entities: shape: (num_entities, d_e) The tail representations. :param slice_size: The slice size. :return: shape: (batch_size, num_entities) The scores. """ return self._score(h=h, r=r, t=all_entities, slice_dim="t", slice_size=slice_size)[:, 0, 0, :]
[docs] def reset_parameters(self): """Reset parameters the interaction function may have.""" for mod in self.modules(): if mod is self: continue if hasattr(mod, "reset_parameters"): mod.reset_parameters()
[docs]class LiteralInteraction( Interaction, Generic[HeadRepresentation, RelationRepresentation, TailRepresentation], ): """The interaction function shared by literal-containing interactions.""" def __init__( self, base: HintOrType[Interaction[HeadRepresentation, RelationRepresentation, TailRepresentation]], combination: Combination, base_kwargs: Optional[Mapping[str, Any]] = None, ): """Instantiate the module. :param combination: The module used to concatenate the literals to the entity representations :param base: The interaction module :param base_kwargs: Keyword arguments for the interaction module """ super().__init__() self.base = interaction_resolver.make(base, base_kwargs) self.combination = combination # The appended "e" represents the literals that get concatenated # on the entity representations. It does not necessarily have the # same dimension "d" as the entity representations. self.entity_shape = tuple(self.base.entity_shape) + ("e",)
[docs] def forward( self, h: HeadRepresentation, r: RelationRepresentation, t: TailRepresentation, ) -> torch.FloatTensor: """Compute broadcasted triple scores given broadcasted representations for head, relation and tails. :param h: shape: (batch_size, num_heads, 1, 1, ``*``) The head representations. :param r: shape: (batch_size, 1, num_relations, 1, ``*``) The relation representations. :param t: shape: (batch_size, 1, 1, num_tails, ``*``) The tail representations. :return: shape: (batch_size, num_heads, num_relations, num_tails) The scores. """ # alternate way of combining entity embeddings + literals # h = torch.cat(h, dim=-1) # h = self.combination(h.view(-1, h.shape[-1])).view(*h.shape[:-1], -1) # type: ignore # t = torch.cat(t, dim=-1) # t = self.combination(t.view(-1, t.shape[-1])).view(*t.shape[:-1], -1) # type: ignore h_proj = self.combination(*h) t_proj = self.combination(*t) return self.base(h=h_proj, r=r, t=t_proj)
[docs]class FunctionalInteraction(Interaction, Generic[HeadRepresentation, RelationRepresentation, TailRepresentation]): """Base class for interaction functions.""" #: The functional interaction form func: Callable[..., torch.FloatTensor]
[docs] def forward( self, h: HeadRepresentation, r: RelationRepresentation, t: TailRepresentation, ) -> torch.FloatTensor: """Compute broadcasted triple scores given broadcasted representations for head, relation and tails. :param h: shape: (batch_size, num_heads, 1, 1, ``*``) The head representations. :param r: shape: (batch_size, 1, num_relations, 1, ``*``) The relation representations. :param t: shape: (batch_size, 1, 1, num_tails, ``*``) The tail representations. :return: shape: (batch_size, num_heads, num_relations, num_tails) The scores. """ return self.__class__.func(**self._prepare_for_functional(h=h, r=r, t=t))
def _prepare_for_functional( self, h: HeadRepresentation, r: RelationRepresentation, t: TailRepresentation, ) -> Mapping[str, torch.FloatTensor]: """Conversion utility to prepare the arguments for the functional form.""" kwargs = self._prepare_hrt_for_functional(h=h, r=r, t=t) kwargs.update(self._prepare_state_for_functional()) return kwargs @staticmethod def _prepare_hrt_for_functional( h: HeadRepresentation, r: RelationRepresentation, t: TailRepresentation, ) -> MutableMapping[str, torch.FloatTensor]: """Conversion utility to prepare the h/r/t representations for the functional form.""" assert all(torch.is_tensor(x) for x in (h, r, t)) return dict(h=h, r=r, t=t) def _prepare_state_for_functional(self) -> MutableMapping[str, Any]: """Conversion utility to prepare the state to be passed to the functional form.""" return dict()
[docs]class NormBasedInteraction( FunctionalInteraction, Generic[HeadRepresentation, RelationRepresentation, TailRepresentation], ABC, ): """Norm-based interactions use a (powered) $p$-norm in their scoring function.""" def __init__(self, p: int, power_norm: bool = False): """Initialize the norm-based interaction function. :param p: The norm used with :func:`torch.linalg.vector_norm`. Typically is 1 or 2. :param power_norm: Whether to use the p-th power of the $L_p$ norm. It has the advantage of being differentiable around 0, and numerically more stable. """ super().__init__() self.p = p self.power_norm = power_norm def _prepare_state_for_functional(self) -> MutableMapping[str, Any]: # noqa: D102 return dict(p=self.p, power_norm=self.power_norm)
[docs]class TransEInteraction(NormBasedInteraction[FloatTensor, FloatTensor, FloatTensor]): """A stateful module for the TransE interaction function. .. seealso:: :func:`pykeen.nn.functional.transe_interaction` """ func = pkf.transe_interaction
[docs]class TransFInteraction(FunctionalInteraction[FloatTensor, FloatTensor, FloatTensor]): """A stateless module for the TransF interaction function. .. seealso:: :func:`pykeen.nn.functional.transf_interaction` """ func = pkf.transf_interaction
[docs]class ComplExInteraction(FunctionalInteraction[FloatTensor, FloatTensor, FloatTensor]): """A module wrapper for the stateless ComplEx interaction function. .. seealso:: :func:`pykeen.nn.functional.complex_interaction` """ func = pkf.complex_interaction
def _calculate_missing_shape_information( embedding_dim: int, input_channels: Optional[int] = None, width: Optional[int] = None, height: Optional[int] = None, ) -> Tuple[int, int, int]: """Automatically calculates missing dimensions for ConvE. :param embedding_dim: The embedding dimension. :param input_channels: The number of input channels for the convolution. :param width: The width of the embedding "image". :param height: The height of the embedding "image". :return: (input_channels, width, height), such that `embedding_dim = input_channels * width * height` :raises ValueError: If no factorization could be found. """ # Store initial input for error message original = (input_channels, width, height) # All are None -> try and make closest to square if input_channels is None and width is None and height is None: input_channels = 1 result_sqrt = math.floor(math.sqrt(embedding_dim)) height = max(factor for factor in range(1, result_sqrt + 1) if embedding_dim % factor == 0) width = embedding_dim // height # Only input channels is None elif input_channels is None and width is not None and height is not None: input_channels = embedding_dim // (width * height) # Only width is None elif input_channels is not None and width is None and height is not None: width = embedding_dim // (height * input_channels) # Only height is none elif height is None and width is not None and input_channels is not None: height = embedding_dim // (width * input_channels) # Width and input_channels are None -> set input_channels to 1 and calculage height elif input_channels is None and height is None and width is not None: input_channels = 1 height = embedding_dim // width # Width and input channels are None -> set input channels to 1 and calculate width elif input_channels is None and height is not None and width is None: input_channels = 1 width = embedding_dim // height if input_channels * width * height != embedding_dim: # type: ignore raise ValueError(f"Could not resolve {original} to a valid factorization of {embedding_dim}.") return input_channels, width, height # type: ignore
[docs]class ConvEInteraction( FunctionalInteraction[torch.FloatTensor, torch.FloatTensor, Tuple[torch.FloatTensor, torch.FloatTensor]], ): """A stateful module for the ConvE interaction function. .. seealso:: :func:`pykeen.nn.functional.conve_interaction` """ tail_entity_shape = ("d", "k") # with k=1 #: The head-relation encoder operating on 2D "images" hr2d: nn.Module #: The head-relation encoder operating on the 1D flattened version hr1d: nn.Module #: The interaction function func = pkf.conve_interaction def __init__( self, input_channels: Optional[int] = None, output_channels: int = 32, embedding_height: Optional[int] = None, embedding_width: Optional[int] = None, kernel_height: int = 3, kernel_width: int = 3, input_dropout: float = 0.2, output_dropout: float = 0.3, feature_map_dropout: float = 0.2, embedding_dim: int = 200, apply_batch_normalization: bool = True, ): super().__init__() # Automatic calculation of remaining dimensions logger.info(f"Resolving {input_channels} * {embedding_width} * {embedding_height} = {embedding_dim}.") if embedding_dim is None: embedding_dim = input_channels * embedding_width * embedding_height # Parameter need to fulfil: # input_channels * embedding_height * embedding_width = embedding_dim input_channels, embedding_width, embedding_height = _calculate_missing_shape_information( embedding_dim=embedding_dim, input_channels=input_channels, width=embedding_width, height=embedding_height, ) logger.info(f"Resolved to {input_channels} * {embedding_width} * {embedding_height} = {embedding_dim}.") if input_channels * embedding_height * embedding_width != embedding_dim: raise ValueError( f"Product of input channels ({input_channels}), height ({embedding_height}), and width " f"({embedding_width}) does not equal target embedding dimension ({embedding_dim})", ) # encoders # 1: 2D encoder: BN?, DO, Conv, BN?, Act, DO hr2d_layers = [ nn.BatchNorm2d(input_channels) if apply_batch_normalization else None, nn.Dropout(input_dropout), nn.Conv2d( in_channels=input_channels, out_channels=output_channels, kernel_size=(kernel_height, kernel_width), stride=1, padding=0, bias=True, ), nn.BatchNorm2d(output_channels) if apply_batch_normalization else None, nn.ReLU(), nn.Dropout2d(feature_map_dropout), ] self.hr2d = nn.Sequential(*(layer for layer in hr2d_layers if layer is not None)) # 2: 1D encoder: FC, DO, BN?, Act num_in_features = ( output_channels * (2 * embedding_height - kernel_height + 1) * (embedding_width - kernel_width + 1) ) hr1d_layers = [ nn.Linear(num_in_features, embedding_dim), nn.Dropout(output_dropout), nn.BatchNorm1d(embedding_dim) if apply_batch_normalization else None, nn.ReLU(), ] self.hr1d = nn.Sequential(*(layer for layer in hr1d_layers if layer is not None)) # store reshaping dimensions self.embedding_height = embedding_height self.embedding_width = embedding_width self.input_channels = input_channels @staticmethod def _prepare_hrt_for_functional( h: HeadRepresentation, r: RelationRepresentation, t: TailRepresentation, ) -> MutableMapping[str, torch.FloatTensor]: # noqa: D102 return dict(h=h, r=r, t=t[0], t_bias=t[1]) def _prepare_state_for_functional(self) -> MutableMapping[str, Any]: # noqa: D102 return dict( input_channels=self.input_channels, embedding_height=self.embedding_height, embedding_width=self.embedding_width, hr2d=self.hr2d, hr1d=self.hr1d, )
[docs]class ConvKBInteraction(FunctionalInteraction[FloatTensor, FloatTensor, FloatTensor]): """A stateful module for the ConvKB interaction function. .. seealso:: :func:`pykeen.nn.functional.convkb_interaction`` """ func = pkf.convkb_interaction def __init__( self, hidden_dropout_rate: float = 0.0, embedding_dim: int = 200, num_filters: int = 400, ): super().__init__() self.embedding_dim = embedding_dim self.num_filters = num_filters # The interaction model self.conv = nn.Conv2d(in_channels=1, out_channels=num_filters, kernel_size=(1, 3), bias=True) self.activation = nn.ReLU() self.hidden_dropout = nn.Dropout(p=hidden_dropout_rate) self.linear = nn.Linear(embedding_dim * num_filters, 1, bias=True)
[docs] def reset_parameters(self): # noqa: D102 # Use Xavier initialization for weight; bias to zero nn.init.xavier_uniform_(self.linear.weight, gain=nn.init.calculate_gain("relu")) nn.init.zeros_(self.linear.bias) # Initialize all filters to [0.1, 0.1, -0.1], # c.f. https://github.com/daiquocnguyen/ConvKB/blob/master/model.py#L34-L36 nn.init.constant_(self.conv.weight[..., :2], 0.1) nn.init.constant_(self.conv.weight[..., 2], -0.1) nn.init.zeros_(self.conv.bias)
def _prepare_state_for_functional(self) -> MutableMapping[str, Any]: # noqa: D102 return dict( conv=self.conv, activation=self.activation, hidden_dropout=self.hidden_dropout, linear=self.linear, )
[docs]class DistMultInteraction(FunctionalInteraction[FloatTensor, FloatTensor, FloatTensor]): """A module wrapper for the stateless DistMult interaction function. .. seealso:: :func:`pykeen.nn.functional.distmult_interaction` """ func = pkf.distmult_interaction
[docs]class DistMAInteraction(FunctionalInteraction[FloatTensor, FloatTensor, FloatTensor]): """A module wrapper for the stateless DistMA interaction function. .. seealso:: :func:`pykeen.nn.functional.dist_ma_interaction` """ func = pkf.dist_ma_interaction
[docs]class ERMLPInteraction(FunctionalInteraction[FloatTensor, FloatTensor, FloatTensor]): """A stateful module for the ER-MLP interaction. .. seealso:: :func:`pykeen.nn.functional.ermlp_interaction` .. math :: f(h, r, t) = W_2 ReLU(W_1 cat(h, r, t) + b_1) + b_2 """ func = pkf.ermlp_interaction def __init__( self, embedding_dim: int, hidden_dim: int, ): """Initialize the interaction function. :param embedding_dim: The embedding vector dimension. :param hidden_dim: The hidden dimension of the MLP. """ super().__init__() """The multi-layer perceptron consisting of an input layer with 3 * self.embedding_dim neurons, a hidden layer with self.embedding_dim neurons and output layer with one neuron. The input is represented by the concatenation embeddings of the heads, relations and tail embeddings. """ self.hidden = nn.Linear(in_features=3 * embedding_dim, out_features=hidden_dim, bias=True) self.activation = nn.ReLU() self.hidden_to_score = nn.Linear(in_features=hidden_dim, out_features=1, bias=True) def _prepare_state_for_functional(self) -> MutableMapping[str, Any]: # noqa: D102 return dict( hidden=self.hidden, activation=self.activation, final=self.hidden_to_score, )
[docs] def reset_parameters(self): # noqa: D102 # Initialize biases with zero nn.init.zeros_(self.hidden.bias) nn.init.zeros_(self.hidden_to_score.bias) # In the original formulation, nn.init.xavier_uniform_(self.hidden.weight) nn.init.xavier_uniform_( self.hidden_to_score.weight, gain=nn.init.calculate_gain(self.activation.__class__.__name__.lower()), )
[docs]class ERMLPEInteraction(FunctionalInteraction[FloatTensor, FloatTensor, FloatTensor]): """A stateful module for the ER-MLP (E) interaction function. .. seealso:: :func:`pykeen.nn.functional.ermlpe_interaction` """ func = pkf.ermlpe_interaction def __init__( self, hidden_dim: int = 300, input_dropout: float = 0.2, hidden_dropout: float = 0.3, embedding_dim: int = 200, ): super().__init__() self.mlp = nn.Sequential( nn.Dropout(input_dropout), nn.Linear(2 * embedding_dim, hidden_dim), nn.Dropout(hidden_dropout), nn.BatchNorm1d(hidden_dim), nn.ReLU(), nn.Linear(hidden_dim, embedding_dim), nn.Dropout(hidden_dropout), nn.BatchNorm1d(embedding_dim), nn.ReLU(), ) def _prepare_state_for_functional(self) -> MutableMapping[str, Any]: # noqa: D102 return dict(mlp=self.mlp)
[docs]class TransRInteraction( NormBasedInteraction[ torch.FloatTensor, Tuple[torch.FloatTensor, torch.FloatTensor], torch.FloatTensor, ], ): """A stateful module for the TransR interaction function. .. seealso:: :func:`pykeen.nn.functional.transr_interaction` """ relation_shape = ("e", "de") func = pkf.transr_interaction def __init__(self, p: int, power_norm: bool = True): super().__init__(p=p, power_norm=power_norm) @staticmethod def _prepare_hrt_for_functional( h: HeadRepresentation, r: RelationRepresentation, t: TailRepresentation, ) -> MutableMapping[str, torch.FloatTensor]: # noqa: D102 return dict(h=h, r=r[0], t=t, m_r=r[1])
[docs]class RotatEInteraction(FunctionalInteraction[FloatTensor, FloatTensor, FloatTensor]): """A module wrapper for the stateless RotatE interaction function. .. seealso:: :func:`pykeen.nn.functional.rotate_interaction` """ func = pkf.rotate_interaction
[docs]class HolEInteraction(FunctionalInteraction[FloatTensor, FloatTensor, FloatTensor]): """A module wrapper for the stateless HolE interaction function. .. seealso:: :func:`pykeen.nn.functional.hole_interaction` """ func = pkf.hole_interaction
[docs]class ProjEInteraction(FunctionalInteraction[FloatTensor, FloatTensor, FloatTensor]): """A stateful module for the ProjE interaction function. .. seealso:: :func:`pykeen.nn.functional.proje_interaction` """ func = pkf.proje_interaction def __init__( self, embedding_dim: int = 50, inner_non_linearity: Optional[nn.Module] = None, ): super().__init__() # Global entity projection self.d_e = nn.Parameter(torch.empty(embedding_dim), requires_grad=True) # Global relation projection self.d_r = nn.Parameter(torch.empty(embedding_dim), requires_grad=True) # Global combination bias self.b_c = nn.Parameter(torch.empty(embedding_dim), requires_grad=True) # Global combination bias self.b_p = nn.Parameter(torch.empty(1), requires_grad=True) if inner_non_linearity is None: inner_non_linearity = nn.Tanh() self.inner_non_linearity = inner_non_linearity
[docs] def reset_parameters(self): # noqa: D102 embedding_dim = self.d_e.shape[0] bound = math.sqrt(6) / embedding_dim for p in self.parameters(): nn.init.uniform_(p, a=-bound, b=bound)
def _prepare_state_for_functional(self) -> MutableMapping[str, Any]: return dict(d_e=self.d_e, d_r=self.d_r, b_c=self.b_c, b_p=self.b_p, activation=self.inner_non_linearity)
[docs]class RESCALInteraction(FunctionalInteraction[FloatTensor, FloatTensor, FloatTensor]): """A module wrapper for the stateless RESCAL interaction function. .. seealso:: :func:`pykeen.nn.functional.rescal_interaction` """ relation_shape = ("dd",) func = pkf.rescal_interaction
[docs]class StructuredEmbeddingInteraction( NormBasedInteraction[ torch.FloatTensor, Tuple[torch.FloatTensor, torch.FloatTensor], torch.FloatTensor, ], ): """A stateful module for the Structured Embedding (SE) interaction function. .. seealso:: :func:`pykeen.nn.functional.structured_embedding_interaction` """ relation_shape = ("dd", "dd") func = pkf.structured_embedding_interaction @staticmethod def _prepare_hrt_for_functional( h: HeadRepresentation, r: RelationRepresentation, t: TailRepresentation, ) -> MutableMapping[str, torch.FloatTensor]: # noqa: D102 return dict(h=h, t=t, r_h=r[0], r_t=r[1])
[docs]class TuckerInteraction(FunctionalInteraction[FloatTensor, FloatTensor, FloatTensor]): """A stateful module for the stateless Tucker interaction function. .. seealso:: :func:`pykeen.nn.functional.tucker_interaction` """ func = pkf.tucker_interaction def __init__( self, embedding_dim: int = 200, relation_dim: Optional[int] = None, head_dropout: float = 0.3, relation_dropout: float = 0.4, head_relation_dropout: float = 0.5, apply_batch_normalization: bool = True, ): """Initialize the Tucker interaction function. :param embedding_dim: The entity embedding dimension. :param relation_dim: The relation embedding dimension. :param head_dropout: The dropout rate applied to the head representations. :param relation_dropout: The dropout rate applied to the relation representations. :param head_relation_dropout: The dropout rate applied to the combined head and relation representations. :param apply_batch_normalization: Whether to use batch normalization on head representations and the combination of head and relation. """ super().__init__() if relation_dim is None: relation_dim = embedding_dim # Core tensor # Note: we use a different dimension permutation as in the official implementation to match the paper. self.core_tensor = nn.Parameter( torch.empty(embedding_dim, relation_dim, embedding_dim), requires_grad=True, ) # Dropout self.head_dropout = nn.Dropout(head_dropout) self.relation_dropout = nn.Dropout(relation_dropout) self.head_relation_dropout = nn.Dropout(head_relation_dropout) if apply_batch_normalization: self.head_batch_norm = nn.BatchNorm1d(embedding_dim) self.head_relation_batch_norm = nn.BatchNorm1d(embedding_dim) else: self.head_batch_norm = self.head_relation_batch_norm = None
[docs] def reset_parameters(self): # noqa:D102 # Initialize core tensor, cf. https://github.com/ibalazevic/TuckER/blob/master/model.py#L12 nn.init.uniform_(self.core_tensor, -1.0, 1.0)
# batch norm gets reset automatically, since it defines reset_parameters def _prepare_state_for_functional(self) -> MutableMapping[str, Any]: return dict( core_tensor=self.core_tensor, do_h=self.head_dropout, do_r=self.relation_dropout, do_hr=self.head_relation_dropout, bn_h=self.head_batch_norm, bn_hr=self.head_relation_batch_norm, )
[docs]class UnstructuredModelInteraction( NormBasedInteraction[torch.FloatTensor, None, torch.FloatTensor], ): """A stateful module for the UnstructuredModel interaction function. .. seealso:: :func:`pykeen.nn.functional.unstructured_model_interaction` """ # shapes relation_shape: Sequence[str] = tuple() func = pkf.unstructured_model_interaction def __init__(self, p: int, power_norm: bool = True): super().__init__(p=p, power_norm=power_norm) @staticmethod def _prepare_hrt_for_functional( h: HeadRepresentation, r: RelationRepresentation, t: TailRepresentation, ) -> MutableMapping[str, torch.FloatTensor]: # noqa: D102 return dict(h=h, t=t)
[docs]class TorusEInteraction(NormBasedInteraction[torch.FloatTensor, torch.FloatTensor, torch.FloatTensor]): """A stateful module for the TorusE interaction function. .. seealso:: :func:`pykeen.nn.functional.toruse_interaction` """ func = pkf.toruse_interaction def __init__(self, p: int = 2, power_norm: bool = False): super().__init__(p=p, power_norm=power_norm)
[docs]class TransDInteraction( NormBasedInteraction[ Tuple[torch.FloatTensor, torch.FloatTensor], Tuple[torch.FloatTensor, torch.FloatTensor], Tuple[torch.FloatTensor, torch.FloatTensor], ], ): """A stateful module for the TransD interaction function. .. seealso:: :func:`pykeen.nn.functional.transd_interaction` """ entity_shape = ("d", "d") relation_shape = ("e", "e") func = pkf.transd_interaction def __init__(self, p: int = 2, power_norm: bool = True): super().__init__(p=p, power_norm=power_norm) @staticmethod def _prepare_hrt_for_functional( h: Tuple[torch.FloatTensor, torch.FloatTensor], r: Tuple[torch.FloatTensor, torch.FloatTensor], t: Tuple[torch.FloatTensor, torch.FloatTensor], ) -> MutableMapping[str, torch.FloatTensor]: # noqa: D102 h, h_p = h r, r_p = r t, t_p = t return dict(h=h, r=r, t=t, h_p=h_p, r_p=r_p, t_p=t_p)
[docs]class NTNInteraction( FunctionalInteraction[ torch.FloatTensor, Tuple[torch.FloatTensor, torch.FloatTensor, torch.FloatTensor, torch.FloatTensor, torch.FloatTensor], torch.FloatTensor, ], ): """A stateful module for the NTN interaction function. .. seealso:: :func:`pykeen.nn.functional.ntn_interaction` """ relation_shape = ("kdd", "kd", "kd", "k", "k") func = pkf.ntn_interaction def __init__( self, activation: HintOrType[nn.Module] = None, activation_kwargs: Optional[Mapping[str, Any]] = None, ): """Initialize NTN with the given non-linear activation function. :param activation: A non-linear activation function. Defaults to the hyperbolic tangent :class:`torch.nn.Tanh` if none, otherwise uses the :data:`pykeen.utils.activation_resolver` for lookup. :param activation_kwargs: If the ``activation`` is passed as a class, these keyword arguments are used during its instantiation. """ super().__init__() if activation is None: self.non_linearity = nn.Tanh() else: self.non_linearity = activation_resolver.make(activation, activation_kwargs) @staticmethod def _prepare_hrt_for_functional( h: torch.FloatTensor, r: Tuple[torch.FloatTensor, torch.FloatTensor, torch.FloatTensor, torch.FloatTensor, torch.FloatTensor], t: torch.FloatTensor, ) -> MutableMapping[str, torch.FloatTensor]: # noqa: D102 w, vh, vt, b, u = r return dict(h=h, t=t, w=w, b=b, u=u, vh=vh, vt=vt) def _prepare_state_for_functional(self) -> MutableMapping[str, Any]: # noqa: D102 return dict(activation=self.non_linearity)
[docs]class KG2EInteraction( FunctionalInteraction[ Tuple[torch.FloatTensor, torch.FloatTensor], Tuple[torch.FloatTensor, torch.FloatTensor], Tuple[torch.FloatTensor, torch.FloatTensor], ], ): """A stateful module for the KG2E interaction function. .. seealso:: :func:`pykeen.nn.functional.kg2e_interaction` """ entity_shape = ("d", "d") relation_shape = ("d", "d") similarity: str exact: bool func = pkf.kg2e_interaction def __init__(self, similarity: Optional[str] = None, exact: bool = True): super().__init__() if similarity is None: similarity = "KL" self.similarity = similarity self.exact = exact @staticmethod def _prepare_hrt_for_functional( h: Tuple[torch.FloatTensor, torch.FloatTensor], r: Tuple[torch.FloatTensor, torch.FloatTensor], t: Tuple[torch.FloatTensor, torch.FloatTensor], ) -> MutableMapping[str, torch.FloatTensor]: h_mean, h_var = h r_mean, r_var = r t_mean, t_var = t return dict( h_mean=h_mean, h_var=h_var, r_mean=r_mean, r_var=r_var, t_mean=t_mean, t_var=t_var, ) def _prepare_state_for_functional(self) -> MutableMapping[str, Any]: return dict( similarity=self.similarity, exact=self.exact, )
[docs]class TransHInteraction(NormBasedInteraction[FloatTensor, Tuple[FloatTensor, FloatTensor], FloatTensor]): """A stateful module for the TransH interaction function. .. seealso:: :func:`pykeen.nn.functional.transh_interaction` """ relation_shape = ("d", "d") func = pkf.transh_interaction @staticmethod def _prepare_hrt_for_functional( h: HeadRepresentation, r: RelationRepresentation, t: TailRepresentation, ) -> MutableMapping[str, torch.FloatTensor]: # noqa: D102 return dict(h=h, w_r=r[0], d_r=r[1], t=t)
[docs]class MuREInteraction( NormBasedInteraction[ Tuple[FloatTensor, FloatTensor, FloatTensor], Tuple[FloatTensor, FloatTensor], Tuple[FloatTensor, FloatTensor, FloatTensor], ], ): """A stateful module for the MuRE interaction function from [balazevic2019b]_. .. seealso:: :func:`pykeen.nn.functional.mure_interaction` """ # there are separate biases for entities in head and tail position entity_shape = ("d", "", "") relation_shape = ("d", "d") func = pkf.mure_interaction @staticmethod def _prepare_hrt_for_functional( h: Tuple[FloatTensor, FloatTensor, FloatTensor], r: Tuple[FloatTensor, FloatTensor], t: Tuple[FloatTensor, FloatTensor, FloatTensor], ) -> MutableMapping[str, torch.FloatTensor]: # noqa: D102 h, b_h, _ = h t, _, b_t = t r_vec, r_mat = r return dict(h=h, b_h=b_h, r_vec=r_vec, r_mat=r_mat, t=t, b_t=b_t)
[docs]class SimplEInteraction( FunctionalInteraction[ Tuple[torch.FloatTensor, torch.FloatTensor], Tuple[torch.FloatTensor, torch.FloatTensor], Tuple[torch.FloatTensor, torch.FloatTensor], ], ): """A module wrapper for the SimplE interaction function. .. seealso:: :func:`pykeen.nn.functional.simple_interaction` """ func = pkf.simple_interaction entity_shape = ("d", "d") relation_shape = ("d", "d") def __init__(self, clamp_score: Union[None, float, Tuple[float, float]] = None): super().__init__() if isinstance(clamp_score, float): clamp_score = (-clamp_score, clamp_score) self.clamp_score = clamp_score def _prepare_state_for_functional(self) -> MutableMapping[str, Any]: # noqa: D102 return dict(clamp=self.clamp_score) @staticmethod def _prepare_hrt_for_functional( h: HeadRepresentation, r: RelationRepresentation, t: TailRepresentation, ) -> MutableMapping[str, torch.FloatTensor]: # noqa: D102 return dict(h=h[0], h_inv=h[1], r=r[0], r_inv=r[1], t=t[0], t_inv=t[1])
[docs]class PairREInteraction(NormBasedInteraction[FloatTensor, Tuple[FloatTensor, FloatTensor], FloatTensor]): """A stateful module for the PairRE interaction function. .. seealso:: :func:`pykeen.nn.functional.pair_re_interaction` """ relation_shape = ("d", "d") func = pkf.pair_re_interaction @staticmethod def _prepare_hrt_for_functional( h: HeadRepresentation, r: RelationRepresentation, t: TailRepresentation, ) -> MutableMapping[str, torch.FloatTensor]: # noqa: D102 return dict(h=h, r_h=r[0], r_t=r[1], t=t)
class QuatEInteraction( FunctionalInteraction[ torch.FloatTensor, torch.FloatTensor, torch.FloatTensor, ], ): """A module wrapper for the QuatE interaction function. .. seealso:: :func:`pykeen.nn.functional.quat_e_interaction` """ func = pkf.quat_e_interaction
[docs]class MonotonicAffineTransformationInteraction( Interaction[ HeadRepresentation, RelationRepresentation, TailRepresentation, ], ): r""" An adapter of interaction functions which adds a scalar (trainable) monotonic affine transformation of the score. .. math :: score(h, r, t) = \alpha \cdot score'(h, r, t) + \beta This adapter is useful for losses such as BCE, where there is a fixed decision threshold, or margin-based losses, where the margin is not be treated as hyper-parameter, but rather a trainable parameter. This is particularly useful, if the value range of the score function is not known in advance, and thus choosing an appropriate margin becomes difficult. Monotonicity is required to preserve the ordering of the original scoring function, and thus ensures that more plausible triples are still more plausible after the transformation. For example, we can add a bias to a distance-based interaction function to enable positive values: >>> base = TransEInteraction(p=2) >>> interaction = MonotonicAffineTransformationInteraction(base=base, trainable_bias=True, trainable_scale=False) When combined with BCE loss, we can geometrically think about predicting a (soft) sphere at :math:`h + r` with radius equal to the bias of the transformation. When we add a trainable scale, the model can control the "softness" of the decision boundary itself. """ def __init__( self, base: Interaction[HeadRepresentation, RelationRepresentation, TailRepresentation], initial_bias: float = 0.0, trainable_bias: bool = True, initial_scale: float = 1.0, trainable_scale: bool = True, ): """ Initialize the interaction. :param base: The base interaction. :param initial_bias: The initial value for the bias. :param trainable_bias: Whether the bias should be trainable. :param initial_scale: >0 The initial value for the scale. Must be strictly positive. :param trainable_scale: Whether the scale should be trainable. """ super().__init__() # the base interaction self.base = base # forward entity/relation shapes self.entity_shape = base.entity_shape self.relation_shape = base.relation_shape self.tail_entity_shape = base.tail_entity_shape # The parameters of the affine transformation: bias self.bias = nn.Parameter(torch.empty(size=tuple()), requires_grad=trainable_bias) self.initial_bias = torch.as_tensor(data=[initial_bias], dtype=torch.get_default_dtype()) # scale. We model this as log(scale) to ensure scale > 0, and thus monotonicity self.log_scale = nn.Parameter(torch.empty(size=tuple()), requires_grad=trainable_scale) self.initial_log_scale = torch.as_tensor(data=[math.log(initial_scale)], dtype=torch.get_default_dtype())
[docs] def reset_parameters(self): # noqa: D102 self.bias.data = self.initial_bias.to(device=self.bias.device) self.log_scale.data = self.initial_log_scale.to(device=self.bias.device)
[docs] def forward( self, h: HeadRepresentation, r: RelationRepresentation, t: TailRepresentation, ) -> torch.FloatTensor: # noqa: D102 return self.log_scale.exp() * self.base(h=h, r=r, t=t) + self.bias
[docs]class CrossEInteraction(FunctionalInteraction[FloatTensor, Tuple[FloatTensor, FloatTensor], FloatTensor]): """A module wrapper for the CrossE interaction function. .. seealso:: :func:`pykeen.nn.functional.cross_e_interaction` """ func = pkf.cross_e_interaction relation_shape = ("d", "d") def __init__( self, embedding_dim: int = 50, combination_activation: HintOrType[nn.Module] = nn.Tanh, combination_activation_kwargs: Optional[Mapping[str, Any]] = None, combination_dropout: Optional[float] = 0.5, ): """ Instantiate the interaction module. :param embedding_dim: The embedding dimension. :param combination_activation: The combination activation function. :param combination_activation_kwargs: Additional keyword-based arguments passed to the constructor of the combination activation function (if not already instantiated). :param combination_dropout: An optional dropout applied to the combination. """ super().__init__() self.combination_activation = activation_resolver.make( combination_activation, pos_kwargs=combination_activation_kwargs, ) self.combination_bias = nn.Parameter(data=torch.zeros(1, 1, 1, 1, embedding_dim)) self.combination_dropout = nn.Dropout(combination_dropout) if combination_dropout else None def _prepare_state_for_functional(self) -> MutableMapping[str, Any]: # noqa: D102 return dict( bias=self.combination_bias, activation=self.combination_activation, dropout=self.combination_dropout, ) @staticmethod def _prepare_hrt_for_functional( h: FloatTensor, r: Tuple[FloatTensor, FloatTensor], t: FloatTensor, ) -> MutableMapping[str, torch.FloatTensor]: # noqa: D102 r, c_r = r return dict(h=h, r=r, c_r=c_r, t=t)
[docs]class BoxEInteraction( NormBasedInteraction[ Tuple[FloatTensor, FloatTensor], Tuple[FloatTensor, FloatTensor, FloatTensor, FloatTensor, FloatTensor, FloatTensor], Tuple[FloatTensor, FloatTensor], ] ): """An implementation of the BoxE interaction from [abboud2020]_.""" func = pkf.boxe_interaction relation_shape = ("d", "d", "s", "d", "d", "s") # Boxes are 2xd (size) each, x 2 sets of boxes: head and tail entity_shape = ("d", "d") # Base position and bump def __init__(self, tanh_map: bool = True, p: int = 2, power_norm: bool = False): r""" Instantiate the interaction module. :param tanh_map: Should the hyperbolic tangent be applied to all representations prior to model scoring? :param p: the order of the norm :param power_norm: whether to use the p-th power of the norm instead """ super().__init__(p=p, power_norm=power_norm) self.tanh_map = tanh_map @staticmethod def _prepare_hrt_for_functional( h: Tuple[FloatTensor, FloatTensor], r: Tuple[FloatTensor, FloatTensor, FloatTensor, FloatTensor, FloatTensor, FloatTensor], t: Tuple[FloatTensor, FloatTensor], ) -> MutableMapping[str, torch.FloatTensor]: # noqa:D102 rh_base, rh_delta, rh_size, rt_base, rt_delta, rt_size = r h_pos, h_bump = h t_pos, t_bump = t return dict( # head position and bump h_pos=h_pos, h_bump=h_bump, # relation box: head rh_base=rh_base, rh_delta=rh_delta, rh_size=rh_size, # relation box: tail rt_base=rt_base, rt_delta=rt_delta, rt_size=rt_size, # tail position and bump t_pos=t_pos, t_bump=t_bump, ) def _prepare_state_for_functional(self) -> MutableMapping[str, Any]: # noqa: D102 state = super()._prepare_state_for_functional() state["tanh_map"] = self.tanh_map return state
class CPInteraction(FunctionalInteraction[FloatTensor, FloatTensor, FloatTensor]): """ An implementation of the CP interaction as described [lacroix2018]_ (originally from [hitchcock1927]_). .. note :: For $k=1$, this interaction is the same as DistMult (but consider the node below). .. note :: For equivalence to CP, entities should have different representations for head & tail role. This is different to DistMult. """ func = pkf.cp_interaction entity_shape = ("kd",) relation_shape = ("kd",) interaction_resolver = Resolver.from_subclasses( Interaction, # type: ignore skip={NormBasedInteraction, FunctionalInteraction, MonotonicAffineTransformationInteraction}, suffix=Interaction.__name__, )