vastorbit.machine_learning.memmodel.ensemble.RandomForestClassifier

class vastorbit.machine_learning.memmodel.ensemble.RandomForestClassifier(trees: list[BinaryTreeClassifier], classes: Annotated[list | ndarray, 'Array Like Structure'] | None = None)

InMemoryModel implementation of the random forest classifier algorithm.

Parameters:

• trees (list[BinaryTreeClassifier]) – list of BinaryTree for classification.

• classes (ArrayLike, optional) – The model’s classes.

Variables:

• input (Attributes are identical to the)

• underscore (parameters, followed by an)

• ('_').

Examples

Initalization

A Random Forest Classifier model is an ensemble of multiple binary tree classifier models. In this example, we will create three BinaryTreeClassifier models:

from vastorbit.machine_learning.memmodel.tree import BinaryTreeClassifier

model1 = BinaryTreeClassifier(
    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, [0.8, 0.1, 0.1], [0.1, 0.8, 0.1], [0.2, 0.2, 0.6]],
    classes = ["a", "b", "c"],
)
model2 = BinaryTreeClassifier(
    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, [0.7, 0.2, 0.1], [0.3, 0.5, 0.2], [0.2, 0.2, 0.6]],
    classes = ["a", "b", "c"],
)
model3 = BinaryTreeClassifier(
    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, [0.4, 0.4, 0.2], [0.2, 0.2, 0.6], [0.2, 0.5, 0.3]],
    classes = ["a", "b", "c"],
)

Now we will use above models to create RandomForestClassifier model.

from vastorbit.machine_learning.memmodel.ensemble import RandomForestClassifier

model_rfc = RandomForestClassifier(
    trees = [model1, model2, model3],
    classes = ["a", "b", "c"],
)

Create a dataset.

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

Making In-Memory Predictions

Use predict() method to do predictions.

model_rfc.predict(data)

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

model_rfc.predict_proba(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_rfc.predict_sql(cnames)

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

model_rfc.predict_proba_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_rfc.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[BinaryTreeClassifier], classes: Annotated[list | ndarray, 'Array Like Structure'] | None = None) → None

Methods

__init__(trees[, classes])
get_attributes()	Returns the model attributes.
plot_tree([pic_path, tree_id])	Draws the input tree.
predict(X)	Predicts using the input matrix.
predict_proba(X)	Computes the model's probabilites using the input matrix.
predict_proba_sql(X)	Returns the SQL code needed to deploy the model using its attributes.
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