cugraph.analyzeClustering_ratio_cut#

cugraph.analyzeClustering_ratio_cut(G, n_clusters, clustering, vertex_col_name='vertex', cluster_col_name='cluster')[source]#

Compute the ratio cut score for a partitioning/clustering

Parameters:

Gcugraph.Graph: cuGraph graph descriptor. This graph should have edge weights.
n_clustersinteger: Specifies the number of clusters in the given clustering
clusteringcudf.DataFrame: The cluster assignment to analyze.
vertex_col_namestr, optional (default=’vertex’): The name of the column in the clustering dataframe identifying the external vertex id
cluster_col_namestr, optional (default=’cluster’): The name of the column in the clustering dataframe identifying the cluster id

Returns:

scorefloat: The computed ratio cut score

Examples

>>> from cugraph.datasets import karate
>>> G = karate.get_graph(download=True)
>>> df = cugraph.spectralBalancedCutClustering(G, 5)
>>> score = cugraph.analyzeClustering_ratio_cut(G, 5, df, 'vertex',
...                                             'cluster')