embiggen/embedders/karateclub_embedders/laplacian_eigenmaps.py
"""Wrapper for LaplacianEigenmaps model provided from the Karate Club package."""
from typing import Dict, Any
from karateclub.node_embedding import LaplacianEigenmaps
from embiggen.embedders.karateclub_embedders.abstract_karateclub_embedder import AbstractKarateClubEmbedder
class LaplacianEigenmapsKarateClub(AbstractKarateClubEmbedder):
def __init__(
self,
embedding_size: int = 100,
random_state: int = 42,
ring_bell: bool = False,
enable_cache: bool = False
):
"""Return a new LaplacianEigenmaps embedding model.
Parameters
----------------------
embedding_size: int = 100
Size of the embedding to use.
random_state: int = 42
Random state to use for the stocastic
portions of the embedding algorithm.
ring_bell: bool = False,
Whether to play a sound when embedding completes.
enable_cache: bool = False
Whether to enable the cache, that is to
store the computed embedding.
"""
super().__init__(
embedding_size=embedding_size,
enable_cache=enable_cache,
ring_bell=ring_bell,
random_state=random_state
)
def _build_model(self) -> LaplacianEigenmaps:
"""Return new instance of the LaplacianEigenmaps model."""
return LaplacianEigenmaps(
dimensions=self._embedding_size,
seed=self._random_state
)
@classmethod
def model_name(cls) -> str:
"""Returns name of the model"""
return "Laplacian Eigenmaps"
@classmethod
def requires_nodes_sorted_by_decreasing_node_degree(cls) -> bool:
return False
@classmethod
def is_topological(cls) -> bool:
return True
@classmethod
def can_use_edge_weights(cls) -> bool:
"""Returns whether the model can optionally use edge weights."""
return False
@classmethod
def can_use_node_types(cls) -> bool:
"""Returns whether the model can optionally use node types."""
return False
@classmethod
def can_use_edge_types(cls) -> bool:
"""Returns whether the model can optionally use edge types."""
return False