verticapy.machine_learning.vertica.neighbors.KernelDensity.export_models#

static KernelDensity.export_models(name: str, path: str, kind: Literal['pmml', 'vertica', 'vertica_models', 'tensorflow', 'tf', None] | None = None) → bool#

Exports machine learning models.

Parameters#

name: str

Model’s name.

path: str

Absolute path of an output directory to store the exported models.

Warning

This function operates solely on the server side and is not accessible locally. The path provided should match the location where the file(s) will be exported on the server.

kind: str, optional

One of the following:

pmml
vertica
tensorflow

Returns#

bool: True if the model was successfully exported.

Examples#

Let’s consider we’ve fitted a Vertica model named ‘my_model’. It is stored in the ‘my_schema’ schema and available in the Database.

You can export it easily:

from verticapy.machine_learning.vertica.base import VerticaModel

VerticaModel.export_models(
    name = 'my_schema.my_model',
    kind = 'pmml', # Pick up the export type.
)

Warning

This function operates solely on the server side and is not accessible locally.