Class XGBoostModel (0.23.0) 

XGBoostModel(
    model_path: str,
    *,
    input: typing.Mapping[str, str] = {},
    output: typing.Mapping[str, str] = {},
    session: typing.Optional[bigframes.session.Session] = None
)

Imported XGBoost model.

Parameters

NameDescription
model_path str

Cloud Storage path that holds the model files.

input Dict, default None

Specify the model input schema information when you create the XGBoost model. The input should be the format of {field_name: field_type}. Input is optional only if feature_names and feature_types are both specified in the model file. Supported types are "bool", "string", "int64", "float64", "array

output Dict, default None

Specify the model output schema information when you create the XGBoost model. The input should be the format of {field_name: field_type}. Output is optional only if feature_names and feature_types are both specified in the model file. Supported types are "bool", "string", "int64", "float64", "array

session BigQuery Session

BQ session to create the model

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 input DataFrame.

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

Input DataFrame or Series, schema is defined by the model.

Returns
TypeDescription
bigframes.dataframe.DataFrameOutput DataFrame, schema is defined by the model.

register

register(vertex_ai_model_id: typing.Optional[str] = None) -> bigframes.ml.base._T

Register the model to Vertex AI.

After register, go to Google Cloud Console (https://console.cloud.google.com/vertex-ai/models) to manage the model registries. Refer to https://cloud.google.com/vertex-ai/docs/model-registry/introduction for more options.

Parameter
NameDescription
vertex_ai_model_id Optional[str], default None

optional string id as model id in Vertex. If not set, will by default to 'bigframes_{bq_model_id}'. Vertex Ai model id will be truncated to 63 characters due to its limitation.

to_gbq

to_gbq(
    model_name: str, replace: bool = False
) -> bigframes.ml.imported.XGBoostModel

Save the model to BigQuery.

Parameters
NameDescription
model_name str

the name of the model.

replace bool, default False

whether to replace if the model already exists. Default to False.

Returns
TypeDescription
XGBoostModelsaved model.