CLiMB.exploratory package

Submodules

CLiMB.exploratory.DBSCANExploratory module

class CLiMB.exploratory.DBSCANExploratory.DBSCANExploratory(eps=0.5, min_samples=5)[source]

Bases: ExploratoryClusteringBase

DBSCAN for exploratory clustering

fit_predict(X)[source]

Perform DBSCAN clustering on X.

Parameters:

Xarray-like of shape (n_samples, n_features): The input samples to cluster.

Returns:

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

get_name()[source]: Returns the name of the clustering algorithm

get_parameters()[source]: Returns the parameters of the clustering algorithm

CLiMB.exploratory.HDBSCANExploratory module

class CLiMB.exploratory.HDBSCANExploratory.HDBSCANExploratory(min_cluster_size=5, min_samples=None)[source]

Bases: ExploratoryClusteringBase

HDBSCAN for exploratory clustering

fit_predict(X)[source]

Perform HDBSCAN clustering on X.

Parameters:

Xarray-like of shape (n_samples, n_features): The input samples to cluster.

Returns:

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

get_name()[source]: Returns the name of the clustering algorithm

get_parameters()[source]: Returns the parameters of the clustering algorithm

CLiMB.exploratory.OPTICSExploratory module

class CLiMB.exploratory.OPTICSExploratory.OPTICSExploratory(min_samples=5)[source]

Bases: ExploratoryClusteringBase

OPTICS for exploratory clustering

fit_predict(X)[source]

Perform OPTICS clustering on X.

Parameters:

Xarray-like of shape (n_samples, n_features): The input samples to cluster.

Returns:

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

get_name()[source]: Returns the name of the clustering algorithm

get_parameters()[source]: Returns the parameters of the clustering algorithm

Module contents

class CLiMB.exploratory.ExploratoryClusteringBase[source]

Bases: ABC

Base abstract class for exploratory clustering algorithms

abstract fit_predict(X)[source]

Fit the model and return cluster labels

Parameters:

Xarray-like of shape (n_samples, n_features): The input samples to cluster.

Returns:

labelsndarray of shape (n_samples,): Cluster labels for each point.

abstract get_name()[source]: Returns the name of the clustering algorithm

abstract get_parameters()[source]: Returns the parameters of the clustering algorithm