cdlib.viz.plot_scoring

plot_scoring(graphs: list, ref_partitions: object, graph_names: list, methods: list, scoring: Callable[[object, object], object] = <function adjusted_mutual_information>, nbRuns: int = 5) → object

Plot the scores obtained by a list of methods on a list of graphs.

Parameters:

• graphs – list of graphs on which to make computations
• ref_partitions – list of reference clusterings corresponding to graphs
• graph_names – list of the names of the graphs to display
• methods – list of functions that take a graph as input and return a Clustering as output
• scoring – the scoring function to use, default anmi
• nbRuns – number of runs to do for each method on each graph

Returns:

a seaborn lineplot

Example:

>>> from cdlib import algorithms, viz, evaluation
>>> import networkx as nx
>>> g1 = nx.algorithms.community.LFR_benchmark_graph(1000, 3, 1.5, 0.5, min_community=20, average_degree=5)
>>> g2 = nx.algorithms.community.LFR_benchmark_graph(1000, 3, 1.5, 0.7, min_community=20, average_degree=5)
>>> names = ["g1", "g2"]
>>> graphs = [g1, g2]
>>> for g in graphs:
>>>     references.append(NodeClustering(communities={frozenset(g.nodes[v]['community']) for v in g}, graph=g, method_name="reference"))
>>> algos = [algorithms.crisp_partition.louvain, algorithms.crisp_partition.label_propagation]
>>> viz.plot_scoring(graphs, references, names, algos, nbRuns=2)