verticapy.machine_learning.memmodel.decomposition.SVD.transform_sql#

SVD.transform_sql(X: list | ndarray) → list[str]#

Transforms and returns the SQL needed to deploy the SVD model.

Parameters#

Import the required module.

from verticapy.machine_learning.memmodel.decomposition import SVD

We will use the following attributes:

vectors = [
    [0.4, 0.5],
    [0.3, 0.2],
]


values = [0.1, 0.3]

Let’s create a model.

model_svd = SVD(vectors, values)

Let’s use the following column names:

cnames = ['col1', 'col2']

Get the SQL code needed to deploy the model.

model_svd.transform_sql(cnames)
Out[6]: ['col1 * 0.4 / 0.1 + col2 * 0.3 / 0.1', 'col1 * 0.5 / 0.3 + col2 * 0.2 / 0.3']

Note

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