vastorbit.machine_learning.memmodel.ensemble.IsolationForest

class vastorbit.machine_learning.memmodel.ensemble.IsolationForest(trees: list[BinaryTreeAnomaly])

InMemoryModel implementation of the isolation forest algorithm.

Parameters:

trees (list[BinaryTreeAnomaly]) – list of BinaryTree for anomaly detection.

Variables:

• input (Attributes are identical to the)

• underscore (parameters, followed by an)

• ('_').

Examples

Initalization

An Isolation Forest model is an ensemble of multiple binary tree anomaly models. In this example, we will create three BinaryTreeAnomaly models:

from vastorbit.machine_learning.memmodel.tree import BinaryTreeAnomaly

model1 = BinaryTreeAnomaly(
    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, [2, 10], [3, 4], [7, 8]],
    psy = 100,
)
model2 = BinaryTreeAnomaly(
    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, 11], [2, 5], [5, 10]],
    psy = 100,
)
model3 = BinaryTreeAnomaly(
    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, 9], [1, 6], [8, 7]],
    psy = 100,
)

Now we will use above models to create IsolationForest model.

from vastorbit.machine_learning.memmodel.ensemble import IsolationForest

model_isf = IsolationForest(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_isf.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_isf.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_isf.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[BinaryTreeAnomaly]) → 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 IsolationForest 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