vastorbit.machine_learning.memmodel.tree.BinaryTreeRegressor

class vastorbit.machine_learning.memmodel.tree.BinaryTreeRegressor(children_left: Annotated[list | ndarray, 'Array Like Structure'], children_right: Annotated[list | ndarray, 'Array Like Structure'], feature: Annotated[list | ndarray, 'Array Like Structure'], threshold: Annotated[list | ndarray, 'Array Like Structure'], value: Annotated[list | ndarray, 'Array Like Structure'])

InMemoryModel implementation of binary trees for regression.

Parameters:

• 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.

• feature (ArrayLike) – A list of features, where feature[i] is the feature to split on for the internal node i.

• threshold (ArrayLike) – A list of thresholds, where threshold[i] is the threshold for the internal node i.

• value (ArrayLike) – Contains the constant prediction value of each node. If used for classification and return_proba is set to True, each element of the list must be a sublist with the probabilities of each class.

Variables:

• input (Attributes are identical to the)

• underscore (parameters, followed by an)

• ('_').

Examples

Initalization

Import the required module.

from vastorbit.machine_learning.memmodel.tree import BinaryTreeRegressor

A BinaryTreeRegressor model is defined by its left and right child node id’s, feature and threshold value to split a node. Final values at leaf nodes are also required.

Let’s create a BinaryTreeRegressor model:

from vastorbit.machine_learning.memmodel.tree import BinaryTreeRegressor

# Different Attributes
children_left = [1, 3, None, None, None]
children_right = [2, 4, None, None, None]
feature = [0, 1, None, None, None]
threshold = ["female", 30, None, None, None]
value = [None, None, 3, 11, 1993]

# Building the Model
model_btr = BinaryTreeRegressor(
    children_left = children_left,
    children_right = children_right,
    feature = feature,
    threshold = threshold,
    value = value,
)

Create a dataset.

data = [["male", 100], ["female", 20], ["female", 50]]

Making In-Memory Predictions

Use predict() method to do predictions.

model_btr.predict(data)

Deploy SQL Code

Let’s use the following column names:

cnames = ["sex", "fare"]

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

model_btr.predict_sql(cnames)

Hint

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

Drawing Tree

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

model_btr.to_graphviz()

Use plot_tree() method to draw the input tree.

model_btr.plot_tree()

Important

plot_tree() requires the Graphviz module.

Note

The above example is a very basic one. For other more detailed examples and customization options, please see Machine Learning - Tree Plots

__init__(children_left: Annotated[list | ndarray, 'Array Like Structure'], children_right: Annotated[list | ndarray, 'Array Like Structure'], feature: Annotated[list | ndarray, 'Array Like Structure'], threshold: Annotated[list | ndarray, 'Array Like Structure'], value: Annotated[list | ndarray, 'Array Like Structure']) → None

Methods

__init__(children_left, children_right, ...)
get_attributes()	Returns the model attributes.
plot_tree([pic_path])	Draws the input tree.
predict(X)	Predicts using the BinaryTree model.
predict_proba(X)	Returns the model probabilities.
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.
set_attributes(**kwargs)	Sets the model attributes.
to_graphviz([feature_names, classes_color, ...])	Returns the code for a Graphviz tree.

Attributes

object_type

Must be overridden in child class