def analyzeClustering_ratio_cut(G, n_clusters, clustering):
"""
Compute the ratio cut score for a partitioning/clustering
Parameters
----------
G : cugraph.Graph
cuGraph graph descriptor. This graph should have edge weights.
n_clusters : integer
Specifies the number of clusters in the given clustering
clustering : cudf.Series
The cluster assignment to analyze.
Returns
-------
score : float
The computed ratio cut score
Examples
--------
>>> M = cudf.read_csv('datasets/karate.csv',
delimiter = ' ',
dtype=['int32', 'int32', 'float32'],
header=None)
>>> G = cugraph.Graph()
>>> G.from_cudf_edgelist(M, source='0', destination='1', edge_attr='2')
>>> df = cugraph.spectralBalancedCutClustering(G, 5)
>>> score = cugraph.analyzeClustering_ratio_cut(G, 5, df['cluster'])
"""
score = spectral_clustering_wrapper.analyzeClustering_ratio_cut(
G, n_clusters, clustering)
return score
def analyzeClustering_ratio_cut(G,
n_clusters,
clustering,
vertex_col_name='vertex',
cluster_col_name='cluster'):
"""
Compute the ratio cut score for a partitioning/clustering
Parameters
----------
G : cugraph.Graph
cuGraph graph descriptor. This graph should have edge weights.
n_clusters : integer
Specifies the number of clusters in the given clustering
clustering : cudf.DataFrame
The cluster assignment to analyze.
vertex_col_name : str
The name of the column in the clustering dataframe identifying
the external vertex id
cluster_col_name : str
The name of the column in the clustering dataframe identifying
the cluster id
Returns
-------
score : float
The computed ratio cut score
Examples
--------
>>> M = cudf.read_csv('datasets/karate.csv',
delimiter = ' ',
dtype=['int32', 'int32', 'float32'],
header=None)
>>> G = cugraph.Graph()
>>> G.from_cudf_edgelist(M, source='0', destination='1', edge_attr='2')
>>> df = cugraph.spectralBalancedCutClustering(G, 5)
>>> score = cugraph.analyzeClustering_ratio_cut(G, 5, df,
>>> 'vertex', 'cluster')
"""
if type(vertex_col_name) is not str:
raise Exception("vertex_col_name must be a string")
if type(cluster_col_name) is not str:
raise Exception("cluster_col_name must be a string")
if G.renumbered:
clustering = G.add_internal_vertex_id(clustering,
vertex_col_name,
vertex_col_name,
drop=True)
clustering = clustering.sort_values(vertex_col_name)
score = spectral_clustering_wrapper.analyzeClustering_ratio_cut(
G, n_clusters, clustering[cluster_col_name])
return score