vastorbit.machine_learning.memmodel.cluster.BisectingKMeans¶

class vastorbit.machine_learning.memmodel.cluster.BisectingKMeans(clusters: Annotated[list | ndarray, 'Array Like Structure'], children_left: Annotated[list | ndarray, 'Array Like Structure'], children_right: Annotated[list | ndarray, 'Array Like Structure'], cluster_size: Annotated[list | ndarray, 'Array Like Structure'] | None = None, cluster_score: Annotated[list | ndarray, 'Array Like Structure'] | None = None, p: int = 2)¶

InMemoryModel implementation of BisectingKMeans.

Parameters:

clusters (ArrayLike) – list of the model’s cluster centers.
children_left (ArrayLike) – A list of node IDs, where children_left[i] is the node ID of the left child of node i.
children_right (ArrayLike) – A list of node IDs, where children_right[i] is the node ID of the right child of node i.
cluster_size (ArrayLike) – A list of sizes, where cluster_size[i] is the number of elements in node i.
cluster_score (ArrayLike) – A list of scores, where cluster_score[i] is the score for internal node i. The score is the ratio between the within -cluster sum of squares of the node and the total within-cluster sum of squares.
p (int, optional) – The p corresponding to one of the p-distances.

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 BisectingKMeans

A BisectingKMeans model is defined by its clusters centroids, left and right child node id’s of given node. In this example, we will use the following:

clusters = [
    [0.5, 0.6],
    [1, 2],
    [100, 200],
    [10, 700],
    [-100, -200],
]
children_left = [1, 3, None, None, None]
children_right = [2, 4, None, None, None]

Let’s create a BisectingKMeans model.

model_bkm = BisectingKMeans(clusters, children_left, children_right)

Create a dataset.

data = [[2, 3]]

Making In-Memory Predictions

Use predict() method to do predictions.

model_bkm.predict(data)[0]

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

model_bkm.predict_proba(data)

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

model_bkm.transform(data)

Use to_graphviz() method to generate code for a Graphviz tree.

model_bkm.to_graphviz()

Use plot_tree() method to draw the input tree.

model_bkm.plot_tree()

../_images/machine_learning_cluster_bisecting_kmeans.png

Note

plot_tree() requires the Graphviz module.

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_bkm.predict_sql(cnames)

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

model_bkm.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_bkm.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'], children_left: Annotated[list | ndarray, 'Array Like Structure'], children_right: Annotated[list | ndarray, 'Array Like Structure'], cluster_size: Annotated[list | ndarray, 'Array Like Structure'] | None = None, cluster_score: Annotated[list | ndarray, 'Array Like Structure'] | None = None, p: int = 2) → None¶

Methods

`__init__`(clusters, children_left, children_right)
`get_attributes`()	Returns the model attributes.
`plot_tree`([pic_path])	Draws the input tree.
`predict`(X)	Predicts using the `BisectingKMeans` model.
`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 `BisectingKMeans` model using its attributes.
`set_attributes`(**kwargs)	Sets the model attributes.
`to_graphviz`([round_score, percent, ...])	Returns the code for a Graphviz tree.
`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