verticapy.machine_learning.memmodel.ensemble.RandomForestClassifier#

class verticapy.machine_learning.memmodel.ensemble.RandomForestClassifier(trees: list[BinaryTreeClassifier], classes: list | ndarray | 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.

Attributes#

Attributes are identical to the input parameters, followed by an underscore (‘_’).

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 verticapy.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 verticapy.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)
Out[8]: array(['a', 'b', 'c'], dtype='<U1')

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

model_rfc.predict_proba(data)
Out[9]: 
array([[1.        , 0.        , 0.        ],
       [0.        , 0.66666667, 0.33333333],
       [0.        , 0.33333333, 0.66666667]])

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)
Out[11]: "CASE WHEN sex IS NULL OR fare IS NULL THEN NULL WHEN ((CASE WHEN sex = 'female' THEN (CASE WHEN fare < 30 THEN 0.0 ELSE 1.0 END) ELSE 0.0 END) + (CASE WHEN sex = 'female' THEN (CASE WHEN fare < 30 THEN 0.0 ELSE 1.0 END) ELSE 0.0 END) + (CASE WHEN sex = 'female' THEN (CASE WHEN fare < 30 THEN 1.0 ELSE 0.0 END) ELSE 0.0 END)) / 3 >= ((CASE WHEN sex = 'female' THEN (CASE WHEN fare < 30 THEN 0.0 ELSE 0.0 END) ELSE 1.0 END) + (CASE WHEN sex = 'female' THEN (CASE WHEN fare < 30 THEN 0.0 ELSE 0.0 END) ELSE 1.0 END) + (CASE WHEN sex = 'female' THEN (CASE WHEN fare < 30 THEN 0.0 ELSE 0.0 END) ELSE 1.0 END)) / 3 AND ((CASE WHEN sex = 'female' THEN (CASE WHEN fare < 30 THEN 0.0 ELSE 1.0 END) ELSE 0.0 END) + (CASE WHEN sex = 'female' THEN (CASE WHEN fare < 30 THEN 0.0 ELSE 1.0 END) ELSE 0.0 END) + (CASE WHEN sex = 'female' THEN (CASE WHEN fare < 30 THEN 1.0 ELSE 0.0 END) ELSE 0.0 END)) / 3 >= ((CASE WHEN sex = 'female' THEN (CASE WHEN fare < 30 THEN 1.0 ELSE 0.0 END) ELSE 0.0 END) + (CASE WHEN sex = 'female' THEN (CASE WHEN fare < 30 THEN 1.0 ELSE 0.0 END) ELSE 0.0 END) + (CASE WHEN sex = 'female' THEN (CASE WHEN fare < 30 THEN 0.0 ELSE 1.0 END) ELSE 0.0 END)) / 3 THEN 'c' WHEN ((CASE WHEN sex = 'female' THEN (CASE WHEN fare < 30 THEN 1.0 ELSE 0.0 END) ELSE 0.0 END) + (CASE WHEN sex = 'female' THEN (CASE WHEN fare < 30 THEN 1.0 ELSE 0.0 END) ELSE 0.0 END) + (CASE WHEN sex = 'female' THEN (CASE WHEN fare < 30 THEN 0.0 ELSE 1.0 END) ELSE 0.0 END)) / 3 >= ((CASE WHEN sex = 'female' THEN (CASE WHEN fare < 30 THEN 0.0 ELSE 0.0 END) ELSE 1.0 END) + (CASE WHEN sex = 'female' THEN (CASE WHEN fare < 30 THEN 0.0 ELSE 0.0 END) ELSE 1.0 END) + (CASE WHEN sex = 'female' THEN (CASE WHEN fare < 30 THEN 0.0 ELSE 0.0 END) ELSE 1.0 END)) / 3 THEN 'b' ELSE 'a' END"

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)
Out[12]: 
["((CASE WHEN sex = 'female' THEN (CASE WHEN fare < 30 THEN 0.0 ELSE 0.0 END) ELSE 1.0 END) + (CASE WHEN sex = 'female' THEN (CASE WHEN fare < 30 THEN 0.0 ELSE 0.0 END) ELSE 1.0 END) + (CASE WHEN sex = 'female' THEN (CASE WHEN fare < 30 THEN 0.0 ELSE 0.0 END) ELSE 1.0 END)) / 3",
 "((CASE WHEN sex = 'female' THEN (CASE WHEN fare < 30 THEN 1.0 ELSE 0.0 END) ELSE 0.0 END) + (CASE WHEN sex = 'female' THEN (CASE WHEN fare < 30 THEN 1.0 ELSE 0.0 END) ELSE 0.0 END) + (CASE WHEN sex = 'female' THEN (CASE WHEN fare < 30 THEN 0.0 ELSE 1.0 END) ELSE 0.0 END)) / 3",
 "((CASE WHEN sex = 'female' THEN (CASE WHEN fare < 30 THEN 0.0 ELSE 1.0 END) ELSE 0.0 END) + (CASE WHEN sex = 'female' THEN (CASE WHEN fare < 30 THEN 0.0 ELSE 1.0 END) ELSE 0.0 END) + (CASE WHEN sex = 'female' THEN (CASE WHEN fare < 30 THEN 1.0 ELSE 0.0 END) ELSE 0.0 END)) / 3"]

Hint

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

Drawing Trees

Use plot_tree() method to draw the input tree.

model_rfc.plot_tree(tree_id = 0)

../_images/machine_learning_memmodel_ensemble_rfclassifier.png

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 :ref:`chart_gallery.tree`_

__init__(trees: list[BinaryTreeClassifier], classes: list | ndarray | 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