def fit(self, kg: KG) -> None:
"""Fits the embedding network based on provided Knowledge Graph.
Args:
kg: The Knowledge Graph.
"""
super().fit(kg)
self.counts = {}
for vertex in kg._vertices:
if not vertex.predicate:
self.counts[vertex.name] = len(kg.get_inv_neighbors(vertex))
def fit(self, kg: KG) -> None:
"""Fits the embedding network based on provided Knowledge Graph.
Args:
kg: The Knowledge Graph.
"""
if kg.is_remote and not self.remote_supported:
raise ValueError("This sampler is not supported for remote KGs.")
if self.split:
self.degrees = {}
for vertex in kg._vertices:
if not vertex.predicate:
self.degrees[vertex.name] = len(
kg.get_inv_neighbors(vertex))
def _create_label(self, kg: KG, vertex: Vertex, n: int):
"""Creates a label.
kg: The Knowledge Graph.
The graph from which the neighborhoods are extracted for the
provided instances.
vertex: The vertex.
n: The position.
"""
neighbor_names = [
self._label_map[neighbor][n - 1]
for neighbor in kg.get_inv_neighbors(vertex)
]
suffix = "-".join(sorted(set(map(str, neighbor_names))))
return self._label_map[vertex][n - 1] + "-" + suffix