cdlib.ensemble.grid_search

cdlib.ensemble.grid_search(graph: ~networkx.classes.graph.Graph, method: ~typing.Callable[[~networkx.classes.graph.Graph, dict], object], parameters: list, quality_score: ~typing.Callable[[~networkx.classes.graph.Graph, object], object], aggregate: ~typing.Callable[[list], object] = <built-in function max>) → tuple

Returns the optimal partition of the specified graph w.r.t. the selected algorithm and quality score.

Parameters:

• method – community discovery method (from nclib.community)

• graph – networkx/igraph object

• parameters – list of Parameter and BoolParameter objects

• quality_score – a fitness function to evaluate the obtained partition (from nclib.evaluation)

• aggregate – function to select the best fitness value. Possible values: min/max

Returns:

at each call the generator yields a tuple composed by: the optimal configuration for the given algorithm, input paramters and fitness function; the obtained communities; the fitness score

Example:

>>> import networkx as nx
>>> from cdlib import algorithms, ensemble
>>> g = nx.karate_club_graph()
>>> resolution = ensemble.Parameter(name="resolution", start=0.1, end=1, step=0.1)
>>> randomize = ensemble.BoolParameter(name="randomize")
>>> communities, scoring = ensemble.grid_search(graph=g, method=algorithms.louvain,
>>>                                                     parameters=[resolution, randomize],
>>>                                                     quality_score=evaluation.erdos_renyi_modularity,
>>>                                                     aggregate=max)
>>> print(communities, scoring)