make_blobs#

cuml.dask.datasets.make_blobs(n_samples=100, n_features=2, centers=None, cluster_std=1.0, n_parts=None, center_box=(-10, 10), shuffle=True, random_state=None, return_centers=False, verbose=False, order='F', dtype='float32', client=None, workers=None)[source]#

Makes labeled Dask-Cupy arrays containing blobs for a randomly generated set of centroids.

This function calls make_blobs from cuml.datasets on each Dask worker and aggregates them into a single Dask Dataframe.

For more information on Scikit-learn’s make_blobs.

Parameters:

n_samplesint: number of rows
n_featuresint: number of features
centersint or array of shape [n_centers, n_features],: optional (default=None) The number of centers to generate, or the fixed center locations. If n_samples is an int and centers is None, 3 centers are generated. If n_samples is array-like, centers must be either None or an array of length equal to the length of n_samples.
cluster_stdfloat (default = 1.0): standard deviation of points around centroid
n_partsint (default = None): number of partitions to generate (this can be greater than the number of workers)
center_boxtuple (int, int) (default = (-10, 10)): the bounding box which constrains all the centroids
random_stateint (default = None): sets random seed (or use None to reinitialize each time)
return_centersbool, optional (default=False): If True, then return the centers of each cluster
verboseint or boolean (default = False): Logging level.
shufflebool (default=False): Shuffles the samples on each worker.
order: str, optional (default=’F’): The order of the generated samples
dtypestr, optional (default=’float32’): Dtype of the generated samples
clientdask.distributed.Client (optional): Dask client to use
workersoptional, list of strings: Dask addresses of workers to use for computation. If None, all available Dask workers will be used. (e.g. : workers = list(client.scheduler_info()['workers'].keys()))

Returns:

Xdask.array backed by CuPy array of shape [n_samples, n_features]: The input samples.
ydask.array backed by CuPy array of shape [n_samples]: The output values.
centersdask.array backed by CuPy array of shape: [n_centers, n_features], optional The centers of the underlying blobs. It is returned only if return_centers is True.

Examples

>>> from dask_cuda import LocalCUDACluster
>>> from dask.distributed import Client
>>> from cuml.dask.datasets import make_blobs

>>> cluster = LocalCUDACluster(threads_per_worker=1)
>>> client = Client(cluster)

>>> workers = list(client.scheduler_info()['workers'].keys())
>>> X, y = make_blobs(1000, 10, centers=42, cluster_std=0.1,
...                   workers=workers)

>>> client.close()
>>> cluster.close()

make_blobs#

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