vastorbit.machine_learning.memmodel.ensemble.GradientBoostingRegressor

class vastorbit.machine_learning.memmodel.ensemble.GradientBoostingRegressor(trees: list[BinaryTreeRegressor], mean: float = 0.0, eta: float = 1.0)

InMemoryModel implementation of the GradientBoosting regressor algorithm.

Parameters:

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

• mean (float, optional) – Average of the response column.

• eta (float, optional) – Learning rate.

Variables:

• input (Attributes are identical to the)

• underscore (parameters, followed by an)

• ('_').

Examples

Initalization

A model is an ensemble of multiple binary tree regressors. 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, 11, 23],
)
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 GradientBoostingRegressor model.

from vastorbit.machine_learning.memmodel.ensemble import GradientBoostingRegressor

model_gbr = GradientBoostingRegressor(
    trees = [model1, model2, model3],
    mean = 2.5,
    eta = 0.9,
)

Note

We have used mean that represents average of the response column and eta that represents learning rate of GradientBoosting regressor model. Both are optional parameters.

Create a dataset.

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

Making In-Memory Predictions

Use predict() method to do predictions.

model_gbr.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_gbr.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_gbr.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], mean: float = 0.0, eta: float = 1.0) → None

Methods

__init__(trees[, mean, eta])
get_attributes()	Returns the model attributes.
plot_tree([pic_path, tree_id])	Draws the input tree.
predict(X)	Predicts using the GradientBoostingRegressor 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