cdlib.ensemble.pool

pool(graph: <Mock id='140410181329728'>, methods: Callable[[<Mock id='140410181329776'>, dict], object], configurations: list) → tuple

Execute on a pool of community discovery internal on the input graph.

Parameters:

• methods – list community discovery methods (from nclib.community)
• graph – networkx/igraph object
• configurations – list of lists (one for each method) of Parameter and BoolParameter objects

Returns:

at each call the generator yields a tuple composed by: the actual method, its current configuration and the obtained communities

Raises:

ValueError – if the number of methods is different from the number of configurations specified

Example:

>>> import networkx as nx
>>> from cdlib import algorithms, ensemble
>>> g = nx.karate_club_graph()
>>> # Louvain
>>> resolution = ensemble.Parameter(name="resolution", start=0.1, end=1, step=0.1)
>>> randomize = ensemble.BoolParameter(name="randomize")
>>> louvain_conf = [resolution, randomize]
>>>
>>> # Angel
>>> threshold = ensemble.Parameter(name="threshold", start=0.1, end=1, step=0.1)
>>> angel_conf = [threshold]
>>>
>>> methods = [algorithms.louvain, algorithms.angel]
>>>
>>> for communities in ensemble.pool(g, methods, [louvain_conf, angel_conf]):
>>>     print(communities)