Feedback

×

• Feedback
• Was this page helpful?*
  • Yes
  • No
• Do you have any additional feedback?
  • Yes
  • No
• Additional Feedback

verticapy.machine_learning.memmodel.ensemble.XGBClassifier

class verticapy.machine_learning.memmodel.ensemble.XGBClassifier(trees: list[BinaryTreeRegressor], logodds: list | ndarray, classes: list | ndarray | None = None, learning_rate: float = 1.0)

InMemoryModel implementation of the XGBoost classifier algorithm.

Parameters

trees: list[BinaryTreeRegressor]

list of BinaryTree for regression.

logodds: ArrayLike[float], optional

ArrayLike of the logodds of the response classes.

classes: ArrayLike, optional

The model’s classes.

learning_rate: float, optional

Learning rate.

Attributes

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

Examples

Initalization

A XGBoostClassifier 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 XGBClassifier model.

from verticapy.machine_learning.memmodel.ensemble import XGBClassifier

model_xgbc = XGBClassifier(
    trees = [model1, model2, model3],
    classes = ["a", "b", "c"],
    logodds = [0.1, 0.12, 0.15],
    learning_rate = 0.1,
)

Note

We have used logodds that represents logodds of the response column and learning_rate that represents learning rate of XGBoost 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_xgbc.predict(data)
Out[8]: array(['a', 'b', 'c'], dtype='<U1')

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

model_xgbc.predict_proba(data)
Out[9]: 
array([[0.34318847, 0.32840576, 0.32840576],
       [0.32393829, 0.34024456, 0.33581715],
       [0.32394919, 0.33138502, 0.34466579]])

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_xgbc.predict_sql(cnames)
Out[11]: "CASE WHEN sex IS NULL OR fare IS NULL THEN NULL WHEN (1 / (1 + EXP(- (0.15 + 0.1 * ((CASE WHEN sex = 'female' THEN (CASE WHEN fare < 30 THEN 0.1 ELSE 0.6 END) ELSE 0.1 END) + (CASE WHEN sex = 'female' THEN (CASE WHEN fare < 30 THEN 0.2 ELSE 0.6 END) ELSE 0.1 END) + (CASE WHEN sex = 'female' THEN (CASE WHEN fare < 30 THEN 0.6 ELSE 0.3 END) ELSE 0.2 END)))))) / ((1 / (1 + EXP(- (0.1 + 0.1 * ((CASE WHEN sex = 'female' THEN (CASE WHEN fare < 30 THEN 0.1 ELSE 0.2 END) ELSE 0.8 END) + (CASE WHEN sex = 'female' THEN (CASE WHEN fare < 30 THEN 0.3 ELSE 0.2 END) ELSE 0.7 END) + (CASE WHEN sex = 'female' THEN (CASE WHEN fare < 30 THEN 0.2 ELSE 0.2 END) ELSE 0.4 END)))))) + (1 / (1 + EXP(- (0.12 + 0.1 * ((CASE WHEN sex = 'female' THEN (CASE WHEN fare < 30 THEN 0.8 ELSE 0.2 END) ELSE 0.1 END) + (CASE WHEN sex = 'female' THEN (CASE WHEN fare < 30 THEN 0.5 ELSE 0.2 END) ELSE 0.2 END) + (CASE WHEN sex = 'female' THEN (CASE WHEN fare < 30 THEN 0.2 ELSE 0.5 END) ELSE 0.4 END)))))) + (1 / (1 + EXP(- (0.15 + 0.1 * ((CASE WHEN sex = 'female' THEN (CASE WHEN fare < 30 THEN 0.1 ELSE 0.6 END) ELSE 0.1 END) + (CASE WHEN sex = 'female' THEN (CASE WHEN fare < 30 THEN 0.2 ELSE 0.6 END) ELSE 0.1 END) + (CASE WHEN sex = 'female' THEN (CASE WHEN fare < 30 THEN 0.6 ELSE 0.3 END) ELSE 0.2 END))))))) >= (1 / (1 + EXP(- (0.1 + 0.1 * ((CASE WHEN sex = 'female' THEN (CASE WHEN fare < 30 THEN 0.1 ELSE 0.2 END) ELSE 0.8 END) + (CASE WHEN sex = 'female' THEN (CASE WHEN fare < 30 THEN 0.3 ELSE 0.2 END) ELSE 0.7 END) + (CASE WHEN sex = 'female' THEN (CASE WHEN fare < 30 THEN 0.2 ELSE 0.2 END) ELSE 0.4 END)))))) / ((1 / (1 + EXP(- (0.1 + 0.1 * ((CASE WHEN sex = 'female' THEN (CASE WHEN fare < 30 THEN 0.1 ELSE 0.2 END) ELSE 0.8 END) + (CASE WHEN sex = 'female' THEN (CASE WHEN fare < 30 THEN 0.3 ELSE 0.2 END) ELSE 0.7 END) + (CASE WHEN sex = 'female' THEN (CASE WHEN fare < 30 THEN 0.2 ELSE 0.2 END) ELSE 0.4 END)))))) + (1 / (1 + EXP(- (0.12 + 0.1 * ((CASE WHEN sex = 'female' THEN (CASE WHEN fare < 30 THEN 0.8 ELSE 0.2 END) ELSE 0.1 END) + (CASE WHEN sex = 'female' THEN (CASE WHEN fare < 30 THEN 0.5 ELSE 0.2 END) ELSE 0.2 END) + (CASE WHEN sex = 'female' THEN (CASE WHEN fare < 30 THEN 0.2 ELSE 0.5 END) ELSE 0.4 END)))))) + (1 / (1 + EXP(- (0.15 + 0.1 * ((CASE WHEN sex = 'female' THEN (CASE WHEN fare < 30 THEN 0.1 ELSE 0.6 END) ELSE 0.1 END) + (CASE WHEN sex = 'female' THEN (CASE WHEN fare < 30 THEN 0.2 ELSE 0.6 END) ELSE 0.1 END) + (CASE WHEN sex = 'female' THEN (CASE WHEN fare < 30 THEN 0.6 ELSE 0.3 END) ELSE 0.2 END))))))) AND (1 / (1 + EXP(- (0.15 + 0.1 * ((CASE WHEN sex = 'female' THEN (CASE WHEN fare < 30 THEN 0.1 ELSE 0.6 END) ELSE 0.1 END) + (CASE WHEN sex = 'female' THEN (CASE WHEN fare < 30 THEN 0.2 ELSE 0.6 END) ELSE 0.1 END) + (CASE WHEN sex = 'female' THEN (CASE WHEN fare < 30 THEN 0.6 ELSE 0.3 END) ELSE 0.2 END)))))) / ((1 / (1 + EXP(- (0.1 + 0.1 * ((CASE WHEN sex = 'female' THEN (CASE WHEN fare < 30 THEN 0.1 ELSE 0.2 END) ELSE 0.8 END) + (CASE WHEN sex = 'female' THEN (CASE WHEN fare < 30 THEN 0.3 ELSE 0.2 END) ELSE 0.7 END) + (CASE WHEN sex = 'female' THEN (CASE WHEN fare < 30 THEN 0.2 ELSE 0.2 END) ELSE 0.4 END)))))) + (1 / (1 + EXP(- (0.12 + 0.1 * ((CASE WHEN sex = 'female' THEN (CASE WHEN fare < 30 THEN 0.8 ELSE 0.2 END) ELSE 0.1 END) + (CASE WHEN sex = 'female' THEN (CASE WHEN fare < 30 THEN 0.5 ELSE 0.2 END) ELSE 0.2 END) + (CASE WHEN sex = 'female' THEN (CASE WHEN fare < 30 THEN 0.2 ELSE 0.5 END) ELSE 0.4 END)))))) + (1 / (1 + EXP(- (0.15 + 0.1 * ((CASE WHEN sex = 'female' THEN (CASE WHEN fare < 30 THEN 0.1 ELSE 0.6 END) ELSE 0.1 END) + (CASE WHEN sex = 'female' THEN (CASE WHEN fare < 30 THEN 0.2 ELSE 0.6 END) ELSE 0.1 END) + (CASE WHEN sex = 'female' THEN (CASE WHEN fare < 30 THEN 0.6 ELSE 0.3 END) ELSE 0.2 END))))))) >= (1 / (1 + EXP(- (0.12 + 0.1 * ((CASE WHEN sex = 'female' THEN (CASE WHEN fare < 30 THEN 0.8 ELSE 0.2 END) ELSE 0.1 END) + (CASE WHEN sex = 'female' THEN (CASE WHEN fare < 30 THEN 0.5 ELSE 0.2 END) ELSE 0.2 END) + (CASE WHEN sex = 'female' THEN (CASE WHEN fare < 30 THEN 0.2 ELSE 0.5 END) ELSE 0.4 END)))))) / ((1 / (1 + EXP(- (0.1 + 0.1 * ((CASE WHEN sex = 'female' THEN (CASE WHEN fare < 30 THEN 0.1 ELSE 0.2 END) ELSE 0.8 END) + (CASE WHEN sex = 'female' THEN (CASE WHEN fare < 30 THEN 0.3 ELSE 0.2 END) ELSE 0.7 END) + (CASE WHEN sex = 'female' THEN (CASE WHEN fare < 30 THEN 0.2 ELSE 0.2 END) ELSE 0.4 END)))))) + (1 / (1 + EXP(- (0.12 + 0.1 * ((CASE WHEN sex = 'female' THEN (CASE WHEN fare < 30 THEN 0.8 ELSE 0.2 END) ELSE 0.1 END) + (CASE WHEN sex = 'female' THEN (CASE WHEN fare < 30 THEN 0.5 ELSE 0.2 END) ELSE 0.2 END) + (CASE WHEN sex = 'female' THEN (CASE WHEN fare < 30 THEN 0.2 ELSE 0.5 END) ELSE 0.4 END)))))) + (1 / (1 + EXP(- (0.15 + 0.1 * ((CASE WHEN sex = 'female' THEN (CASE WHEN fare < 30 THEN 0.1 ELSE 0.6 END) ELSE 0.1 END) + (CASE WHEN sex = 'female' THEN (CASE WHEN fare < 30 THEN 0.2 ELSE 0.6 END) ELSE 0.1 END) + (CASE WHEN sex = 'female' THEN (CASE WHEN fare < 30 THEN 0.6 ELSE 0.3 END) ELSE 0.2 END))))))) THEN 'c' WHEN (1 / (1 + EXP(- (0.12 + 0.1 * ((CASE WHEN sex = 'female' THEN (CASE WHEN fare < 30 THEN 0.8 ELSE 0.2 END) ELSE 0.1 END) + (CASE WHEN sex = 'female' THEN (CASE WHEN fare < 30 THEN 0.5 ELSE 0.2 END) ELSE 0.2 END) + (CASE WHEN sex = 'female' THEN (CASE WHEN fare < 30 THEN 0.2 ELSE 0.5 END) ELSE 0.4 END)))))) / ((1 / (1 + EXP(- (0.1 + 0.1 * ((CASE WHEN sex = 'female' THEN (CASE WHEN fare < 30 THEN 0.1 ELSE 0.2 END) ELSE 0.8 END) + (CASE WHEN sex = 'female' THEN (CASE WHEN fare < 30 THEN 0.3 ELSE 0.2 END) ELSE 0.7 END) + (CASE WHEN sex = 'female' THEN (CASE WHEN fare < 30 THEN 0.2 ELSE 0.2 END) ELSE 0.4 END)))))) + (1 / (1 + EXP(- (0.12 + 0.1 * ((CASE WHEN sex = 'female' THEN (CASE WHEN fare < 30 THEN 0.8 ELSE 0.2 END) ELSE 0.1 END) + (CASE WHEN sex = 'female' THEN (CASE WHEN fare < 30 THEN 0.5 ELSE 0.2 END) ELSE 0.2 END) + (CASE WHEN sex = 'female' THEN (CASE WHEN fare < 30 THEN 0.2 ELSE 0.5 END) ELSE 0.4 END)))))) + (1 / (1 + EXP(- (0.15 + 0.1 * ((CASE WHEN sex = 'female' THEN (CASE WHEN fare < 30 THEN 0.1 ELSE 0.6 END) ELSE 0.1 END) + (CASE WHEN sex = 'female' THEN (CASE WHEN fare < 30 THEN 0.2 ELSE 0.6 END) ELSE 0.1 END) + (CASE WHEN sex = 'female' THEN (CASE WHEN fare < 30 THEN 0.6 ELSE 0.3 END) ELSE 0.2 END))))))) >= (1 / (1 + EXP(- (0.1 + 0.1 * ((CASE WHEN sex = 'female' THEN (CASE WHEN fare < 30 THEN 0.1 ELSE 0.2 END) ELSE 0.8 END) + (CASE WHEN sex = 'female' THEN (CASE WHEN fare < 30 THEN 0.3 ELSE 0.2 END) ELSE 0.7 END) + (CASE WHEN sex = 'female' THEN (CASE WHEN fare < 30 THEN 0.2 ELSE 0.2 END) ELSE 0.4 END)))))) / ((1 / (1 + EXP(- (0.1 + 0.1 * ((CASE WHEN sex = 'female' THEN (CASE WHEN fare < 30 THEN 0.1 ELSE 0.2 END) ELSE 0.8 END) + (CASE WHEN sex = 'female' THEN (CASE WHEN fare < 30 THEN 0.3 ELSE 0.2 END) ELSE 0.7 END) + (CASE WHEN sex = 'female' THEN (CASE WHEN fare < 30 THEN 0.2 ELSE 0.2 END) ELSE 0.4 END)))))) + (1 / (1 + EXP(- (0.12 + 0.1 * ((CASE WHEN sex = 'female' THEN (CASE WHEN fare < 30 THEN 0.8 ELSE 0.2 END) ELSE 0.1 END) + (CASE WHEN sex = 'female' THEN (CASE WHEN fare < 30 THEN 0.5 ELSE 0.2 END) ELSE 0.2 END) + (CASE WHEN sex = 'female' THEN (CASE WHEN fare < 30 THEN 0.2 ELSE 0.5 END) ELSE 0.4 END)))))) + (1 / (1 + EXP(- (0.15 + 0.1 * ((CASE WHEN sex = 'female' THEN (CASE WHEN fare < 30 THEN 0.1 ELSE 0.6 END) ELSE 0.1 END) + (CASE WHEN sex = 'female' THEN (CASE WHEN fare < 30 THEN 0.2 ELSE 0.6 END) ELSE 0.1 END) + (CASE WHEN sex = 'female' THEN (CASE WHEN fare < 30 THEN 0.6 ELSE 0.3 END) ELSE 0.2 END))))))) 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_xgbc.predict_proba_sql(cnames)
Out[12]: 
["(1 / (1 + EXP(- (0.1 + 0.1 * ((CASE WHEN sex = 'female' THEN (CASE WHEN fare < 30 THEN 0.1 ELSE 0.2 END) ELSE 0.8 END) + (CASE WHEN sex = 'female' THEN (CASE WHEN fare < 30 THEN 0.3 ELSE 0.2 END) ELSE 0.7 END) + (CASE WHEN sex = 'female' THEN (CASE WHEN fare < 30 THEN 0.2 ELSE 0.2 END) ELSE 0.4 END)))))) / ((1 / (1 + EXP(- (0.1 + 0.1 * ((CASE WHEN sex = 'female' THEN (CASE WHEN fare < 30 THEN 0.1 ELSE 0.2 END) ELSE 0.8 END) + (CASE WHEN sex = 'female' THEN (CASE WHEN fare < 30 THEN 0.3 ELSE 0.2 END) ELSE 0.7 END) + (CASE WHEN sex = 'female' THEN (CASE WHEN fare < 30 THEN 0.2 ELSE 0.2 END) ELSE 0.4 END)))))) + (1 / (1 + EXP(- (0.12 + 0.1 * ((CASE WHEN sex = 'female' THEN (CASE WHEN fare < 30 THEN 0.8 ELSE 0.2 END) ELSE 0.1 END) + (CASE WHEN sex = 'female' THEN (CASE WHEN fare < 30 THEN 0.5 ELSE 0.2 END) ELSE 0.2 END) + (CASE WHEN sex = 'female' THEN (CASE WHEN fare < 30 THEN 0.2 ELSE 0.5 END) ELSE 0.4 END)))))) + (1 / (1 + EXP(- (0.15 + 0.1 * ((CASE WHEN sex = 'female' THEN (CASE WHEN fare < 30 THEN 0.1 ELSE 0.6 END) ELSE 0.1 END) + (CASE WHEN sex = 'female' THEN (CASE WHEN fare < 30 THEN 0.2 ELSE 0.6 END) ELSE 0.1 END) + (CASE WHEN sex = 'female' THEN (CASE WHEN fare < 30 THEN 0.6 ELSE 0.3 END) ELSE 0.2 END)))))))",
 "(1 / (1 + EXP(- (0.12 + 0.1 * ((CASE WHEN sex = 'female' THEN (CASE WHEN fare < 30 THEN 0.8 ELSE 0.2 END) ELSE 0.1 END) + (CASE WHEN sex = 'female' THEN (CASE WHEN fare < 30 THEN 0.5 ELSE 0.2 END) ELSE 0.2 END) + (CASE WHEN sex = 'female' THEN (CASE WHEN fare < 30 THEN 0.2 ELSE 0.5 END) ELSE 0.4 END)))))) / ((1 / (1 + EXP(- (0.1 + 0.1 * ((CASE WHEN sex = 'female' THEN (CASE WHEN fare < 30 THEN 0.1 ELSE 0.2 END) ELSE 0.8 END) + (CASE WHEN sex = 'female' THEN (CASE WHEN fare < 30 THEN 0.3 ELSE 0.2 END) ELSE 0.7 END) + (CASE WHEN sex = 'female' THEN (CASE WHEN fare < 30 THEN 0.2 ELSE 0.2 END) ELSE 0.4 END)))))) + (1 / (1 + EXP(- (0.12 + 0.1 * ((CASE WHEN sex = 'female' THEN (CASE WHEN fare < 30 THEN 0.8 ELSE 0.2 END) ELSE 0.1 END) + (CASE WHEN sex = 'female' THEN (CASE WHEN fare < 30 THEN 0.5 ELSE 0.2 END) ELSE 0.2 END) + (CASE WHEN sex = 'female' THEN (CASE WHEN fare < 30 THEN 0.2 ELSE 0.5 END) ELSE 0.4 END)))))) + (1 / (1 + EXP(- (0.15 + 0.1 * ((CASE WHEN sex = 'female' THEN (CASE WHEN fare < 30 THEN 0.1 ELSE 0.6 END) ELSE 0.1 END) + (CASE WHEN sex = 'female' THEN (CASE WHEN fare < 30 THEN 0.2 ELSE 0.6 END) ELSE 0.1 END) + (CASE WHEN sex = 'female' THEN (CASE WHEN fare < 30 THEN 0.6 ELSE 0.3 END) ELSE 0.2 END)))))))",
 "(1 / (1 + EXP(- (0.15 + 0.1 * ((CASE WHEN sex = 'female' THEN (CASE WHEN fare < 30 THEN 0.1 ELSE 0.6 END) ELSE 0.1 END) + (CASE WHEN sex = 'female' THEN (CASE WHEN fare < 30 THEN 0.2 ELSE 0.6 END) ELSE 0.1 END) + (CASE WHEN sex = 'female' THEN (CASE WHEN fare < 30 THEN 0.6 ELSE 0.3 END) ELSE 0.2 END)))))) / ((1 / (1 + EXP(- (0.1 + 0.1 * ((CASE WHEN sex = 'female' THEN (CASE WHEN fare < 30 THEN 0.1 ELSE 0.2 END) ELSE 0.8 END) + (CASE WHEN sex = 'female' THEN (CASE WHEN fare < 30 THEN 0.3 ELSE 0.2 END) ELSE 0.7 END) + (CASE WHEN sex = 'female' THEN (CASE WHEN fare < 30 THEN 0.2 ELSE 0.2 END) ELSE 0.4 END)))))) + (1 / (1 + EXP(- (0.12 + 0.1 * ((CASE WHEN sex = 'female' THEN (CASE WHEN fare < 30 THEN 0.8 ELSE 0.2 END) ELSE 0.1 END) + (CASE WHEN sex = 'female' THEN (CASE WHEN fare < 30 THEN 0.5 ELSE 0.2 END) ELSE 0.2 END) + (CASE WHEN sex = 'female' THEN (CASE WHEN fare < 30 THEN 0.2 ELSE 0.5 END) ELSE 0.4 END)))))) + (1 / (1 + EXP(- (0.15 + 0.1 * ((CASE WHEN sex = 'female' THEN (CASE WHEN fare < 30 THEN 0.1 ELSE 0.6 END) ELSE 0.1 END) + (CASE WHEN sex = 'female' THEN (CASE WHEN fare < 30 THEN 0.2 ELSE 0.6 END) ELSE 0.1 END) + (CASE WHEN sex = 'female' THEN (CASE WHEN fare < 30 THEN 0.6 ELSE 0.3 END) ELSE 0.2 END)))))))"]

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_xgbc.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 :ref:`chart_gallery.tree`_

__init__(trees: list[BinaryTreeRegressor], logodds: list | ndarray, classes: list | ndarray | None = None, learning_rate: float = 1.0) → None

Methods

__init__(trees, logodds[, 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