dasf.ml.cluster.agglomerative

Agglomerative Clustering algorithm module.

Classes

AgglomerativeClustering

Agglomerative Clustering

Module Contents

class dasf.ml.cluster.agglomerative.AgglomerativeClustering(n_clusters=2, affinity='euclidean', connectivity=None, linkage='single', memory=None, compute_full_tree='auto', distance_threshold=None, compute_distances=False, handle=None, verbose=False, n_neighbors=10, output_type=None, **kwargs)

Bases: dasf.ml.cluster.classifier.ClusterClassifier

Agglomerative Clustering

Recursively merges the pair of clusters that minimally increases a given linkage distance.

Parameters

n_clustersint or None, default=2

The number of clusters to find. It must be None if distance_threshold is not None.

affinitystr or callable, default=’euclidean’

Metric used to compute the linkage. Can be “euclidean”, “l1”, “l2”, “manhattan”, “cosine”, or “precomputed”. If linkage is “ward”, only “euclidean” is accepted. If “precomputed”, a distance matrix (instead of a similarity matrix) is needed as input for the fit method.

memorystr or object with the joblib.Memory interface, default=None

Used to cache the output of the computation of the tree. By default, no caching is done. If a string is given, it is the path to the caching directory.

connectivityarray-like or callable, default=None

Connectivity matrix. Defines for each sample the neighboring samples following a given structure of the data. This can be a connectivity matrix itself or a callable that transforms the data into a connectivity matrix, such as derived from kneighbors_graph. Default is None, i.e, the hierarchical clustering algorithm is unstructured.

compute_full_tree‘auto’ or bool, default=’auto’

Stop early the construction of the tree at n_clusters. This is useful to decrease computation time if the number of clusters is not small compared to the number of samples. This option is useful only when specifying a connectivity matrix. Note also that when varying the number of clusters and using caching, it may be advantageous to compute the full tree. It must be True if distance_threshold is not None. By default compute_full_tree is “auto”, which is equivalent to True when distance_threshold is not None or that n_clusters is inferior to the maximum between 100 or 0.02 * n_samples. Otherwise, “auto” is equivalent to False.

linkage{‘ward’, ‘complete’, ‘average’, ‘single’}, default=’ward’

Which linkage criterion to use. The linkage criterion determines which distance to use between sets of observation. The algorithm will merge the pairs of cluster that minimize this criterion.

‘ward’ minimizes the variance of the clusters being merged.
‘average’ uses the average of the distances of each observation of the two sets.
‘complete’ or ‘maximum’ linkage uses the maximum distances between all observations of the two sets.
‘single’ uses the minimum of the distances between all observations of the two sets.

Added in version 0.20: Added the ‘single’ option

distance_thresholdfloat, default=None

The linkage distance threshold above which, clusters will not be merged. If not None, n_clusters must be None and compute_full_tree must be True.

Added in version 0.21.

compute_distancesbool, default=False

Computes distances between clusters even if distance_threshold is not used. This can be used to make dendrogram visualization, but introduces a computational and memory overhead.

Added in version 0.24.

n_neighborsint, default = 15

The number of neighbors to compute when connectivity = “knn”

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.

Examples

>>> from dasf.ml.cluster import AgglomerativeClustering
>>> import numpy as np
>>> X = np.array([[1, 2], [1, 4], [1, 0],
...               [4, 2], [4, 4], [4, 0]])
>>> clustering = AgglomerativeClustering().fit(X)
>>> clustering
AgglomerativeClustering()

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

A generic constructor method.

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

Fit without validation using CPU only.

Parameters

Xndarray of shape (n_samples, n_features) or (n_samples, n_samples): Training instances to cluster, or distances between instances if affinity='precomputed'.

Returns

selfobject: Returns the fitted instance.

_fit_gpu(X, y=None, convert_dtype=True)[source]

Fit without validation using GPU only.

Parameters

Xndarray of shape (n_samples, n_features) or (n_samples, n_samples): Training instances to cluster, or distances between instances if affinity='precomputed'.

Returns

selfobject: Returns the fitted instance.

_fit_predict_cpu(X, y=None)[source]

Fit and return the result of each sample’s clustering assignment using CPU only.

In addition to fitting, this method also return the result of the clustering assignment for each sample in the training set.

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 affinity='precomputed'.
yIgnored: Not used, present here for API consistency by convention.

Returns

labelsndarray of shape (n_samples,): Cluster labels.

_fit_predict_gpu(X, y=None)[source]

Fit and return the result of each sample’s clustering assignment using GPU only.

In addition to fitting, this method also return the result of the clustering assignment for each sample in the training set.

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 affinity='precomputed'.
yIgnored: Not used, present here for API consistency by convention.

Returns

labelsndarray of shape (n_samples,): Cluster labels.