pyrdf2vec.walkers.random module¶
- class pyrdf2vec.walkers.random.RandomWalker(max_depth, max_walks=None, sampler=NOTHING, n_jobs=None, *, with_reverse=False, random_state=None, md5_bytes=8)¶
Bases:
pyrdf2vec.walkers.walker.WalkerRandom walking strategy which extracts walks from a root node using the Depth First Search (DFS) algorithm if a maximum number of walks is specified, otherwise the Breadth First Search (BFS) algorithm is used.
- _is_support_remote¶
True if the walking strategy can be used with a remote Knowledge Graph, False Otherwise Defaults to True.
- kg¶
The global KG used later on for the worker process. Defaults to None.
- max_depth¶
The maximum depth of one walk.
- max_walks¶
The maximum number of walks per entity. Defaults to None.
- md5_bytes¶
The number of bytes to keep after hashing objects in MD5. Hasher allows to reduce the memory occupied by a long text. If md5_bytes is None, no hash is applied. Defaults to 8.
- random_state¶
The random state to use to keep random determinism with the walking strategy. Defaults to None.
- sampler¶
The sampling strategy. Defaults to UniformSampler.
- with_reverse¶
True to extracts parents and children hops from an entity, creating (max_walks * max_walks) walks of 2 * depth, allowing also to centralize this entity in the walks. False otherwise. Defaults to False.
- extract_walks(kg, entity)¶
Extracts random walks for an entity based on Knowledge Graph using the Depth First Search (DFS) algorithm if a maximum number of walks is specified, otherwise the Breadth First Search (BFS) algorithm is used.