Class XGBoostModel (1.26.0)

Version latest

• 1.26.0 (latest)
• 1.25.0
• 1.24.0
• 1.22.0
• 1.21.0
• 1.20.0
• 1.19.0
• 1.18.0
• 1.17.0
• 1.16.0
• 1.15.0
• 1.14.0
• 1.13.0
• 1.12.0
• 1.11.1
• 1.10.0
• 1.9.0
• 1.8.0
• 1.7.0
• 1.6.0
• 1.5.0
• 1.4.0
• 1.3.0
• 1.2.0
• 1.1.0
• 1.0.0
• 0.26.0
• 0.25.0
• 0.24.0
• 0.23.0
• 0.22.0
• 0.21.0
• 0.20.1
• 0.19.2
• 0.18.0
• 0.17.0
• 0.16.0
• 0.15.0
• 0.14.1
• 0.13.0
• 0.12.0
• 0.11.0
• 0.10.0
• 0.9.0
• 0.8.0
• 0.7.0
• 0.6.0
• 0.5.0
• 0.4.0
• 0.3.0
• 0.2.0

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

Imported XGBoost model.

Warning: Imported XGBoost models have the several limitations. See: https://cloud.google.com/bigquery/docs/reference/standard-sql/bigqueryml-syntax-create-xgboost#limitations

Parameters
Name	Description
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
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,
        pandas.core.frame.DataFrame,
        pandas.core.series.Series,
    ]
) -> bigframes.dataframe.DataFrame

Predict the result from input DataFrame.

Parameter
Name	Description
X	bigframes.dataframe.DataFrame or bigframes.series.Series or pandas.core.frame.DataFrame or pandas.core.series.Series Input DataFrame or Series. Schema is defined by the model.

Returns
Type	Description
bigframes.dataframe.DataFrame	Output 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 the 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
Name	Description
vertex_ai_model_id	Optional[str], default None Optional string id as model id in Vertex. If not set, will 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
Name	Description
model_name	str The name of the model.
replace	bool, default False Determine whether to replace if the model already exists. Default to False.

Returns
Type	Description
XGBoostModel	Saved model.