dasf.ml.cluster.dbscan

DBSCAN algorithm module.

Classes

DBSCAN

Perform DBSCAN clustering from vector array or distance matrix.

Module Contents

class dasf.ml.cluster.dbscan.DBSCAN(eps=0.5, leaf_size=40, metric='euclidean', min_samples=5, p=None, output_type=None, calc_core_sample_indices=True, verbose=False, **kwargs)[source]

Bases: dasf.ml.cluster.classifier.ClusterClassifier

Perform DBSCAN clustering from vector array or distance matrix.

DBSCAN - Density-Based Spatial Clustering of Applications with Noise. Finds core samples of high density and expands clusters from them. Good for data which contains clusters of similar density.

Parameters

epsfloat, default=0.5: The maximum distance between two samples for one to be considered as in the neighborhood of the other. This is not a maximum bound on the distances of points within a cluster. This is the most important DBSCAN parameter to choose appropriately for your data set and distance function.
min_samplesint, default=5: The number of samples (or total weight) in a neighborhood for a point to be considered as a core point. This includes the point itself.
metricstring, or callable, default=’euclidean’: The metric to use when calculating distance between instances in a feature array. If metric is a string or callable, it must be one of the options allowed by sklearn.metrics.pairwise_distances() for its metric parameter. If metric is “precomputed”, X is assumed to be a distance matrix and must be square. X may be a Glossary, in which case only “nonzero” elements may be considered neighbors for DBSCAN.

Added in version 0.17: metric precomputed to accept precomputed sparse matrix.
metric_paramsdict, default=None: Additional keyword arguments for the metric function.

Added in version 0.19.
algorithm{‘auto’, ‘ball_tree’, ‘kd_tree’, ‘brute’}, default=’auto’: The algorithm to be used by the NearestNeighbors module to compute pointwise distances and find nearest neighbors. See NearestNeighbors module documentation for details.
leaf_sizeint, default=30: Leaf size passed to BallTree or cKDTree. This can affect the speed of the construction and query, as well as the memory required to store the tree. The optimal value depends on the nature of the problem.
pfloat, default=None: The power of the Minkowski metric to be used to calculate distance between points. If None, then p=2 (equivalent to the Euclidean distance).
n_jobsint, default=None: The number of parallel jobs to run. None means 1 unless in a joblib.parallel_backend context. -1 means using all processors. See Glossary for more details.
output_type{‘input’, ‘cudf’, ‘cupy’, ‘numpy’, ‘numba’}, default=None: Variable to control output type of the results and attributes of the estimator. If None, it’ll inherit the output type set at the module level, cuml.global_settings.output_type. See Output Data Type Configuration for more info.
calc_core_sample_indices(optional)boolean, default = True: Indicates whether the indices of the core samples should be calculated. The the attribute core_sample_indices_ will not be used, setting this to False will avoid unnecessary kernel launches.

Examples

>>> from dasf.ml.cluster import DBSCAN
>>> import numpy as np
>>> X = np.array([[1, 2], [2, 2], [2, 3],
...               [8, 7], [8, 8], [25, 80]])
>>> clustering = DBSCAN(eps=3, min_samples=2).fit(X)
>>> clustering
DBSCAN(eps=3, min_samples=2)

For further informations see: - https://scikit-learn.org/stable/modules/generated/sklearn.cluster.DBSCAN.html#sklearn.cluster.DBSCAN - https://docs.rapids.ai/api/cuml/stable/api.html#dbscan - https://docs.rapids.ai/api/cuml/stable/api.html#dbscan-clustering

References

Ester, M., H. P. Kriegel, J. Sander, and X. Xu, “A Density-Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise”. In: Proceedings of the 2nd International Conference on Knowledge Discovery and Data Mining, Portland, OR, AAAI Press, pp. 226-231. 1996

Schubert, E., Sander, J., Ester, M., Kriegel, H. P., & Xu, X. (2017). DBSCAN revisited, revisited: why and how you should (still) use DBSCAN. ACM Transactions on Database Systems (TODS), 42(3), 19.

Constructor of the class DBSCAN.

eps

leaf_size

metric

min_samples

p

output_type

calc_core_sample_indices

verbose

__dbscan_cpu

_lazy_fit_gpu(X, y=None, out_dtype='int32')[source]

Perform DBSCAN clustering from features, or distance matrix using Dask with GPUs only (from CuML).

Parameters

X{array-like, sparse matrix} of shape (n_samples, n_features), or (n_samples, n_samples): Training instances to cluster, or distances between instances if metric='precomputed'. If a sparse matrix is provided, it will be converted into a sparse csr_matrix.
yIgnored: Not used, present here for API consistency by convention.
sample_weightarray-like of shape (n_samples,), default=None: Weight of each sample, such that a sample with a weight of at least min_samples is by itself a core sample; a sample with a negative weight may inhibit its eps-neighbor from being core. Note that weights are absolute, and default to 1.

Returns

selfobject: Returns a fitted instance of self.

_fit_cpu(X, y=None, sample_weight=None)[source]

Perform DBSCAN clustering from features, or distance matrix using CPU only.

Parameters

X{array-like, sparse matrix} of shape (n_samples, n_features), or (n_samples, n_samples): Training instances to cluster, or distances between instances if metric='precomputed'. If a sparse matrix is provided, it will be converted into a sparse csr_matrix.
yIgnored: Not used, present here for API consistency by convention.
sample_weightarray-like of shape (n_samples,), default=None: Weight of each sample, such that a sample with a weight of at least min_samples is by itself a core sample; a sample with a negative weight may inhibit its eps-neighbor from being core. Note that weights are absolute, and default to 1.

Returns

selfobject: Returns a fitted instance of self.

_fit_gpu(X, y=None, out_dtype='int32')[source]

Perform DBSCAN clustering from features, or distance matrix using GPU only (from CuML).

Parameters

X{array-like, sparse matrix} of shape (n_samples, n_features), or (n_samples, n_samples): Training instances to cluster, or distances between instances if metric='precomputed'. If a sparse matrix is provided, it will be converted into a sparse csr_matrix.
yIgnored: Not used, present here for API consistency by convention.
sample_weightarray-like of shape (n_samples,), default=None: Weight of each sample, such that a sample with a weight of at least min_samples is by itself a core sample; a sample with a negative weight may inhibit its eps-neighbor from being core. Note that weights are absolute, and default to 1.

Returns

selfobject: Returns a fitted instance of self.

_lazy_fit_predict_gpu(X, y=None, out_dtype='int32')[source]

Compute clusters from a data or distance matrix and predict labels using Dask and GPUs (from CuML).

Parameters

X{array-like, sparse matrix} of shape (n_samples, n_features), or (n_samples, n_samples): Training instances to cluster, or distances between instances if metric='precomputed'. If a sparse matrix is provided, it will be converted into a sparse csr_matrix.
yIgnored: Not used, present here for API consistency by convention.
sample_weightarray-like of shape (n_samples,), default=None: Weight of each sample, such that a sample with a weight of at least min_samples is by itself a core sample; a sample with a negative weight may inhibit its eps-neighbor from being core. Note that weights are absolute, and default to 1.

Returns

labelsndarray of shape (n_samples,): Cluster labels. Noisy samples are given the label -1.

_fit_predict_cpu(X, y=None, sample_weight=None)[source]

Compute clusters from a data or distance matrix and predict labels using CPU only.

Parameters

X{array-like, sparse matrix} of shape (n_samples, n_features), or (n_samples, n_samples): Training instances to cluster, or distances between instances if metric='precomputed'. If a sparse matrix is provided, it will be converted into a sparse csr_matrix.
yIgnored: Not used, present here for API consistency by convention.
sample_weightarray-like of shape (n_samples,), default=None: Weight of each sample, such that a sample with a weight of at least min_samples is by itself a core sample; a sample with a negative weight may inhibit its eps-neighbor from being core. Note that weights are absolute, and default to 1.

Returns

labelsndarray of shape (n_samples,): Cluster labels. Noisy samples are given the label -1.

_fit_predict_gpu(X, y=None, out_dtype='int32')[source]

Compute clusters from a data or distance matrix and predict labels using GPU only (from CuML).

Parameters

X{array-like, sparse matrix} of shape (n_samples, n_features), or (n_samples, n_samples): Training instances to cluster, or distances between instances if metric='precomputed'. If a sparse matrix is provided, it will be converted into a sparse csr_matrix.
yIgnored: Not used, present here for API consistency by convention.
sample_weightarray-like of shape (n_samples,), default=None: Weight of each sample, such that a sample with a weight of at least min_samples is by itself a core sample; a sample with a negative weight may inhibit its eps-neighbor from being core. Note that weights are absolute, and default to 1.

Returns

labelsndarray of shape (n_samples,): Cluster labels. Noisy samples are given the label -1.

dasf.ml.cluster.dbscan

Classes

Module Contents

Parameters

Examples

See Also

References

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