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verticapy.machine_learning.memmodel.cluster.Clustering

class verticapy.machine_learning.memmodel.cluster.Clustering(clusters: list | ndarray, p: int = 2, clusters_names: list | ndarray | None = None)

InMemoryModel implementation of clustering algorithms.

Note

This is base class for all in-memory implementations of clustering algorithms viz. KMeans, NearestCentroid , BisectingKMeans and KPrototypes

Parameters

clusters: ArrayLike

ArrayLike of the model’s cluster centers.

p: int, optional

The p corresponding to one of the p-distances.

clusters_names: ArrayLike, optional

Names of the clusters.

Attributes

Attributes are identical to the input parameters, followed by an underscore (‘_’).

Examples

This is a base class. To see a comprehensive example specific to your class of interest, please refer to that particular class.

__init__(clusters: list | ndarray, p: int = 2, clusters_names: list | ndarray | None = None) → None

Methods

__init__(clusters[, p, clusters_names])
get_attributes()	Returns the model attributes.
predict(X)	Predicts clusters using the input matrix.
predict_proba(X)	Predicts the probability of each input to belong to the model clusters.
predict_proba_sql(X)	Returns the SQL code needed to deploy the model probabilities.
predict_sql(X)	Returns the SQL code needed to deploy the model using its attributes.
set_attributes(**kwargs)	Sets the model attributes.
transform(X)	Transforms and returns the distance to each cluster.
transform_sql(X)	Transforms and returns the SQL distance to each cluster.

Attributes

object_type

Must be overridden in child class