Class VertexAIModel (0.26.0)

Version 0.26.0

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• 0.24.0
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• 0.22.0
• 0.21.0
• 0.20.1
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• 0.18.0
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• 0.16.0
• 0.15.0
• 0.14.1
• 0.13.0
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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
Name	Description
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
Name	Description
deep	bool, default True Default True. If True, will return the parameters for this estimator and contained subobjects that are estimators.

Returns
Type	Description
Dictionary	A 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
Name	Description
X	bigframes.dataframe.DataFrame or bigframes.series.Series Input DataFrame or Series, which needs to comply with the input parameter of the model.

Returns
Type	Description
bigframes.dataframe.DataFrame	DataFrame of shape (n_samples, n_input_columns + n_prediction_columns). Returns predicted values.