# -*- coding: utf-8 -*-
"""Implementation of UM."""
from typing import Any, ClassVar, Mapping
from ..nbase import ERModel
from ...constants import DEFAULT_EMBEDDING_HPO_EMBEDDING_DIM_RANGE
from ...nn.init import xavier_normal_
from ...nn.modules import UMInteraction
from ...typing import Hint, Initializer
__all__ = [
"UM",
]
[docs]class UM(ERModel):
r"""An implementation of the Unstructured Model (UM) published by [bordes2014]_.
UM computes the distance between head and tail entities then applies the $l_p$ norm.
.. math::
f(h, r, t) = - \|\textbf{e}_h - \textbf{e}_t\|_p^2
A small distance between the embeddings for the head and tail entity indicates a plausible triple. It is
appropriate for networks with a single relationship type that is undirected.
.. warning::
In UM, neither the relations nor the directionality are considered, so it can't distinguish between them.
However, it may serve as a baseline for comparison against relation-aware models.
---
name: Unstructured Model
citation:
author: Bordes
year: 2014
link: https://link.springer.com/content/pdf/10.1007%2Fs10994-013-5363-6.pdf
"""
#: The default strategy for optimizing the model's hyper-parameters
hpo_default: ClassVar[Mapping[str, Any]] = dict(
embedding_dim=DEFAULT_EMBEDDING_HPO_EMBEDDING_DIM_RANGE,
scoring_fct_norm=dict(type=int, low=1, high=2),
)
def __init__(
self,
*,
embedding_dim: int = 50,
scoring_fct_norm: int = 1,
entity_initializer: Hint[Initializer] = xavier_normal_,
**kwargs,
) -> None:
r"""Initialize UM.
:param embedding_dim: The entity embedding dimension $d$. Is usually $d \in [50, 300]$.
:param scoring_fct_norm: The $l_p$ norm. Usually 1 for UM.
:param entity_initializer: The initializer for the entity embeddings.
Defaults to the xavier normal distribution.
:param kwargs: Remaining keyword arguments passed through to :class:`pykeen.models.ERModel`.
"""
super().__init__(
interaction=UMInteraction,
interaction_kwargs=dict(p=scoring_fct_norm),
entity_representations_kwargs=dict(
shape=embedding_dim,
initializer=entity_initializer,
),
relation_representations=[],
**kwargs,
)