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verticapy.machine_learning.memmodel.cluster.NearestCentroid.predict_proba_sql

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

Returns the SQL code needed to deploy the model probabilities.

Parameters

X: ArrayLike

The names or values of the input predictors.

Returns

list

SQL code.

Examples

Import the required module.

from verticapy.machine_learning.memmodel.cluster import KMeans

We will use the following attributes:

clusters = [[0.5, 0.6], [1, 2], [100, 200]]

p = 2

Let’s create a model.

model_km = KMeans(clusters, p)

Let’s use the following column names:

cnames = ['col1', 'col2']

Get the SQL code needed to deploy the model.

model_km.predict_proba_sql(cnames)
Out[36]: 
['(CASE WHEN POWER(POWER(col1 - 0.5, 2) + POWER(col2 - 0.6, 2), 1 / 2) = 0 THEN 1.0 ELSE 1 / (POWER(POWER(col1 - 0.5, 2) + POWER(col2 - 0.6, 2), 1 / 2)) / (1 / (POWER(POWER(col1 - 0.5, 2) + POWER(col2 - 0.6, 2), 1 / 2)) + 1 / (POWER(POWER(col1 - 1.0, 2) + POWER(col2 - 2.0, 2), 1 / 2)) + 1 / (POWER(POWER(col1 - 100.0, 2) + POWER(col2 - 200.0, 2), 1 / 2))) END)',
 '(CASE WHEN POWER(POWER(col1 - 1.0, 2) + POWER(col2 - 2.0, 2), 1 / 2) = 0 THEN 1.0 ELSE 1 / (POWER(POWER(col1 - 1.0, 2) + POWER(col2 - 2.0, 2), 1 / 2)) / (1 / (POWER(POWER(col1 - 0.5, 2) + POWER(col2 - 0.6, 2), 1 / 2)) + 1 / (POWER(POWER(col1 - 1.0, 2) + POWER(col2 - 2.0, 2), 1 / 2)) + 1 / (POWER(POWER(col1 - 100.0, 2) + POWER(col2 - 200.0, 2), 1 / 2))) END)',
 '(CASE WHEN POWER(POWER(col1 - 100.0, 2) + POWER(col2 - 200.0, 2), 1 / 2) = 0 THEN 1.0 ELSE 1 / (POWER(POWER(col1 - 100.0, 2) + POWER(col2 - 200.0, 2), 1 / 2)) / (1 / (POWER(POWER(col1 - 0.5, 2) + POWER(col2 - 0.6, 2), 1 / 2)) + 1 / (POWER(POWER(col1 - 1.0, 2) + POWER(col2 - 2.0, 2), 1 / 2)) + 1 / (POWER(POWER(col1 - 100.0, 2) + POWER(col2 - 200.0, 2), 1 / 2))) END)']

Important

For this example, a specific model is utilized, and it may not correspond exactly to the model you are working with. To see a comprehensive example specific to your class of interest, please refer to that particular class.