Class VertexAIModel (0.18.0) 

VertexAIModel(
    endpoint: str,
    input: typing.Mapping[str, str],
    output: typing.Mapping[str, str],
    session: typing.Optional[bigframes.session.Session] = None,
    connection_name: typing.Optional[str] = None,
)

Remote model from a Vertex AI https endpoint. User must specify https endpoint, input schema and output schema. How to deploy a model in Vertex AI https://cloud.google.com/bigquery/docs/bigquery-ml-remote-model-tutorial#Deploy-Model-on-Vertex-AI.

Parameters

NameDescription
endpoint str

Vertex AI https endpoint.

input Mapping

Input schema: {column_name: column_type}. Supported types are "bool", "string", "int64", "float64", "array

output Mapping

Output label schema: {column_name: column_type}. Supported the same types as the input.

session bigframes.Session or None

BQ session to create the model. If None, use the global default session.

connection_name str or None

Connection to connect with remote service. str of the format <PROJECT_NUMBER/PROJECT_ID>.

Methods

__repr__

__repr__()

Print the estimator's constructor with all non-default parameter values

get_params

get_params(deep: bool = True) -> typing.Dict[str, typing.Any]

Get parameters for this estimator.

Parameter
NameDescription
deep bool, default True

Default True. If True, will return the parameters for this estimator and contained subobjects that are estimators.

Returns
TypeDescription
DictionaryA dictionary of parameter names mapped to their values.

predict

predict(
    X: typing.Union[bigframes.dataframe.DataFrame, bigframes.series.Series]
) -> bigframes.dataframe.DataFrame

Predict the result from the input DataFrame.

Parameter
NameDescription
X bigframes.dataframe.DataFrame or bigframes.series.Series

Input DataFrame or Series, which needs to comply with the input parameter of the model.

Returns
TypeDescription
bigframes.dataframe.DataFrameDataFrame of shape (n_samples, n_input_columns + n_prediction_columns). Returns predicted values.