vastorbit.machine_learning.memmodel.cluster.KPrototypes¶

class vastorbit.machine_learning.memmodel.cluster.KPrototypes(clusters: Annotated[list | ndarray, 'Array Like Structure'], p: int = 2, gamma: float = 1.0, is_categorical: Annotated[list | ndarray, 'Array Like Structure'] | None = None)¶

InMemoryModel implementation of KPrototypes.

Parameters:

clusters (ArrayLike) – list of the model’s cluster centers.
p (int, optional) – The p corresponding to one of the p-distances.
gamma (float, optional) – Weighting factor for categorical columns. This determines relative importance of numerical and categorical attributes.
is_categorical (list | numpy.array, optional) – ArrayLike of booleans to indicate whether X[idx] is a categorical variable, where True indicates categorical and False numerical. If empty, all the variables are considered categorical.
note:: (..) – KPrototypes algorithm allows you to use categorical variables directly without the need to encode them.

Variables:

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

Examples

Initalization

Import the required module.

from vastorbit.machine_learning.memmodel.cluster import KPrototypes

A KPrototypes model is defined by its cluster centroids. Optionally you can also provide p value, gamma and provide information about categorical variables. In this example, we will use the following:

clusters = [
    [0.5, 'high'],
    [1, 'low'],
    [100, 'high'],
]
p = 2
gamma = 1.0
is_categorical = [0, 1]

Let’s create a KPrototypes model.

model_kp = KPrototypes(clusters, p, gamma, is_categorical)

Create a dataset.

data = [[2, 'low']]

Making In-Memory Predictions

Use predict() method to do predictions.

model_kp.predict(data)[0]

Note

KPrototypes assigns a cluster id to identify each cluster. In this example, cluster with centroid [0.5, 'high'] will have id = 0, with centroid [1,'low'] will have id = 1 and so on. predict() method returns the id of the predicted cluster.

Use predict_proba() method to compute the predicted probabilities for each cluster.

model_kp.predict_proba(data)

Use transform() method to compute the distance from each cluster.

model_kp.transform(data)

Deploy SQL Code

Let’s use the following column names:

cnames = ['col1', 'col2']

Use predict_sql() method to get the SQL code needed to deploy the model using its attributes.

model_kp.predict_sql(cnames)

Use predict_proba_sql() method to get the SQL code needed to deploy the model that computes predicted probabilities.

model_kp.predict_proba_sql(cnames)

Use transform_sql() method to get the SQL code needed to deploy the model that computes distance from each cluster.

model_kp.transform_sql(cnames)

Hint

This object can be pickled and used in any in-memory environment, just like scikit-learn models.

__init__(clusters: Annotated[list | ndarray, 'Array Like Structure'], p: int = 2, gamma: float = 1.0, is_categorical: Annotated[list | ndarray, 'Array Like Structure'] | None = None) → None¶

Methods

`__init__`(clusters[, p, gamma, is_categorical])
`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