vastorbit.machine_learning.memmodel.ensemble.RandomForestRegressor

class vastorbit.machine_learning.memmodel.ensemble.RandomForestRegressor(trees: list[BinaryTreeRegressor])

InMemoryModel implementation of the random forest regressor algorithm.

Parameters:

trees (list[BinaryTreeRegressor]) – list of BinaryTrees for regression.

Variables:

• input (Attributes are identical to the)

• underscore (parameters, followed by an)

• ('_').

Examples

Initalization

A Random Forest Regressor model is an ensemble of multiple binary tree regressor models. In this example, we will create three BinaryTreeRegressor models:

from vastorbit.machine_learning.memmodel.tree import BinaryTreeRegressor

model1 = BinaryTreeRegressor(
    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, 3.0, 11.0, 23.5],
)
model2 = BinaryTreeRegressor(
    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, 12, 56],
)
model3 = BinaryTreeRegressor(
    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, 1, 3, 6],
)

Now we will use above models to create RandomForestRegressor model.

from vastorbit.machine_learning.memmodel.ensemble import RandomForestRegressor

model_rfr = RandomForestRegressor(trees = [model1, model2, model3])

Create a dataset.

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

Making In-Memory Predictions

Use predict() method to do predictions.

model_rfr.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_rfr.predict_sql(cnames)

Hint

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

Drawing Trees

Use plot_tree() method to draw the input tree.

model_rfr.plot_tree(tree_id = 0)

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__(trees: list[BinaryTreeRegressor]) → None

Methods

__init__(trees)
get_attributes()	Returns the model attributes.
plot_tree([pic_path, tree_id])	Draws the input tree.
predict(X)	Predicts using the Random Forest regressor model.
predict_sql(X)	Returns the SQL code needed to deploy the model.
set_attributes(**kwargs)	Sets the model attributes.

Attributes

object_type

Must be overridden in child class