verticapy.machine_learning.memmodel.cluster.Clustering¶

class verticapy.machine_learning.memmodel.cluster.Clustering(clusters: Annotated[list | ndarray, 'Array Like Structure'], p: int = 2, clusters_names: Annotated[list | ndarray, 'Array Like Structure'] | 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: Annotated[list | ndarray, 'Array Like Structure'], p: int = 2, clusters_names: Annotated[list | ndarray, 'Array Like Structure'] | 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