verticapy.machine_learning.memmodel.cluster.Clustering.predict_proba_sql#
- Clustering.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[18]: ['(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.