getting_started.rst

Getting Started

Installation

This tool can be installed from PyPI:

$ pip install nclustgen

NOTICE: Nclustgen installs by default the dgl build with no cuda support, in case you want to use gpu you can override this by installing the correct dgl build, more information at: https://www.dgl.ai/pages/start.html.

Basic Usage

Biclustering Dataset

.. seealso:: Detailed API at :doc:`/api-reference/bicluster`.

## Generate biclustering dataset

from nclustgen import BiclusterGenerator

# Initialize generator
generator = BiclusterGenerator(
     dstype='NUMERIC',
     patterns=[['CONSTANT', 'CONSTANT'], ['CONSTANT', 'NONE']],
     bktype='UNIFORM',
     in_memory=True,
     silence=True
)

# Get parameters
generator.get_params()

# Generate dataset
x, y = generator.generate(nrows=50, ncols=100, nclusters=3)

# Build graph
graph = generator.to_graph(x, framework='dgl', device='cpu')

# Save data files
generator.save(file_name='example', single_file=True)

Triclustering Dataset

.. seealso:: Detailed API at :doc:`/api-reference/tricluster`.

## Generate triclustering dataset

from nclustgen import TriclusterGenerator

# Initialize generator
generator = TriclusterGenerator(
     dstype='NUMERIC',
     patterns=[['CONSTANT', 'CONSTANT', 'CONSTANT'], ['CONSTANT', 'NONE', 'NONE']],
     bktype='UNIFORM',
     in_memory=True,
     silence=True
)

# Get parameters
generator.get_params()

# Generate dataset
x, y = generator.generate(nrows=50, ncols=100, ncontexts=10, nclusters=25)

# Build graph
graph = generator.to_graph(x, framework='dgl', device='cpu')

# Save data files
generator.save(file_name='example', single_file=True)

.. seealso:: This is a basic example, more detail at :doc:`/getting-started/generating_data`.