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verticapy.machine_learning.memmodel.ensemble.XGBClassifier.predict_proba_sql

XGBClassifier.predict_proba_sql(X: list | ndarray) → list[str]

Returns the SQL code needed to deploy the model using its attributes.

Parameters

X: list | numpy.array

The names or values of the input predictors.

Returns

str

SQL code.

Examples

Import the required modules and create many BinaryTreeClassifier.

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"],
)

Let’s create a 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,
)

Let’s use the following column names:

cnames = ["sex", "fare"]

Get the SQL code needed to deploy the model.

model_xgbc.predict_proba_sql(cnames)
Out[8]: 
["(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)))))))"]

Note

Refer to XGBClassifier for more information about the different methods and usages.