pyrdf2vec.samplers.wide module¶
- class pyrdf2vec.samplers.wide.WideSampler(inverse=False, split=False)¶
Bases:
pyrdf2vec.samplers.sampler.Sampler
Wide sampling node-centric sampling strategy which gives priority to walks containing edges with the highest degree of predicates and objects. The degree of a predicate and an object being defined by the number of predicates and objects present in its neighborhood, but also by their number of occurrence in a Knowledge Graph.
- _is_support_remote¶
True if the sampling strategy can be used with a remote Knowledge Graph, False Otherwise Defaults to False.
- _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)¶
Fits the sampling strategy by couting the number of available neighbors for each vertex, but also by counting the number of occurrence that a predicate and an object appears in the Knowledge Graph.
- 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
- Returns
The weight of a given hop.
- Raises
ValueError – If there is an attempt to access the weight of a hop without the sampling strategy having been trained.