sparsebm 1.0

An implementation of Stochastic Bloc model and Latent Block model efficient with sparse matrices.

SparseBM: a python module for handling sparse graphs with Block Models

Installing

From pypi:

pip3 install sparsebm

To use GPU acceleration:

pip3 install sparsebm[gpu]

Or

pip3 install sparsebm
pip3 install cupy

Example with Stochastic Block Model

Generate SBM Synthetic graph

• Generate a synthetic graph to analyse with SBM:

from sparsebm import generate_SBM_dataset

dataset = generate_SBM_dataset(symmetric=True)
graph = dataset["data"]
cluster_indicator = dataset["cluster_indicator"]

Infere with sparsebm SBM:

• Use the bernoulli Stochastic Bloc Model:

    from sparsebm import SBM

    number_of_clusters = cluster_indicator.shape[1]

    # A number of classes must be specify. Otherwise see model selection.
    model = SBM(number_of_clusters)
    model.fit(graph, symmetric=True)
    print("Labels:", model.labels)

Compute performances:

    from sparsebm.utils import ARI
    ari = ARI(cluster_indicator.argmax(1), model.labels)
    print("Adjusted Rand index is {:.2f}".format(ari))

To use GPU acceleration, CUPY needs to be installed and replace gpu_number to the desired GPU index.

Example with Latent Block Model

Generate LBM Synthetic graph

• Generate a synthetic graph to analyse with LBM:

from sparsebm import generate_LBM_dataset

dataset = generate_LBM_dataset()
graph = dataset["data"]
row_cluster_indicator = dataset["row_cluster_indicator"]
column_cluster_indicator = dataset["column_cluster_indicator"]

Infere with sparsebm LBM:

• Use the bernoulli Latent Bloc Model:

    from sparsebm import LBM

    number_of_row_clusters = row_cluster_indicator.shape[1]
    number_of_columns_clusters = column_cluster_indicator.shape[1]

    # A number of classes must be specify. Otherwise see model selection.
    model = LBM(
        number_of_row_clusters,
        number_of_columns_clusters,
        n_init_total_run=1,
    )
    model.fit(graph)
    print("Row Labels:", model.row_labels)
    print("Column Labels:", model.column_labels)

Compute performances:

    from sparsebm.utils import CARI
    cari = CARI(
        row_cluster_indicator.argmax(1),
        column_cluster_indicator.argmax(1),
        model.row_labels,
        model.column_labels,
    )
    print("Co-Adjusted Rand index is {:.2f}".format(cari))