pyrdf2vec.samplers.uniform module¶

class pyrdf2vec.samplers.uniform.UniformSampler¶

Bases: pyrdf2vec.samplers.sampler.Sampler

Uniform sampling strategy that assigns a uniform weight to each edge in a Knowledge Graph, in order to prioritizes walks with strongly connected entities.

_is_support_remote¶

True if the sampling strategy can be used with a remote Knowledge Graph, False Otherwise Defaults to True.

Type: bool

_random_state¶: The random state to use to keep random determinism with the sampling strategy. Defaults to None.

_vertices_deg¶: The degree of the vertices. Defaults to {}.

_visited¶: Tags vertices that appear at the max depth or of which all their children are tagged. Defaults to set.

inverse¶: True if the inverse algorithm must be used, False otherwise. Defaults to False.

split¶: True if the split algorithm must be used, False otherwise. Defaults to False.

fit(kg)¶

Since the weights are uniform, this function does nothing.

Parameters: kg (KG) – The Knowledge Graph.
Return type: None

get_weight(hop)¶

Gets the weight of a hop in the Knowledge Graph.

Parameters: hop (Tuple[Any, Any]) – The hop of a vertex in a (predicate, object) form to get the weight.
Return type: int
Returns: The weight of a given hop.