Class AutoMlTablesInputs (1.18.0)

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AutoMlTablesInputs(mapping=None, *, ignore_unknown_fields=False, **kwargs)

This message has oneof_ fields (mutually exclusive fields). For each oneof, at most one member field can be set at the same time. Setting any member of the oneof automatically clears all other members.

.. _oneof: https://proto-plus-python.readthedocs.io/en/stable/fields.html#oneofs-mutually-exclusive-fields

Attributes

NameDescription
optimization_objective_recall_value float
Required when optimization_objective is "maximize-precision-at-recall". Must be between 0 and 1, inclusive. This field is a member of `oneof`_ ``additional_optimization_objective_config``.
optimization_objective_precision_value float
Required when optimization_objective is "maximize-recall-at-precision". Must be between 0 and 1, inclusive. This field is a member of `oneof`_ ``additional_optimization_objective_config``.
prediction_type str
The type of prediction the Model is to produce. "classification" - Predict one out of multiple target values is picked for each row. "regression" - Predict a value based on its relation to other values. This type is available only to columns that contain semantically numeric values, i.e. integers or floating point number, even if stored as e.g. strings.
target_column str
The column name of the target column that the model is to predict.
transformations Sequence[google.cloud.aiplatform.v1beta1.schema.trainingjob.definition_v1beta1.types.AutoMlTablesInputs.Transformation]
Each transformation will apply transform function to given input column. And the result will be used for training. When creating transformation for BigQuery Struct column, the column should be flattened using "." as the delimiter.
optimization_objective str
Objective function the model is optimizing towards. The training process creates a model that maximizes/minimizes the value of the objective function over the validation set. The supported optimization objectives depend on the prediction type. If the field is not set, a default objective function is used. classification (binary): "maximize-au-roc" (default) - Maximize the area under the receiver operating characteristic (ROC) curve. "minimize-log-loss" - Minimize log loss. "maximize-au-prc" - Maximize the area under the precision-recall curve. "maximize-precision-at-recall" - Maximize precision for a specified recall value. "maximize-recall-at-precision" - Maximize recall for a specified precision value. classification (multi-class): "minimize-log-loss" (default) - Minimize log loss. regression: "minimize-rmse" (default) - Minimize root-mean-squared error (RMSE). "minimize-mae" - Minimize mean-absolute error (MAE). "minimize-rmsle" - Minimize root-mean-squared log error (RMSLE).
train_budget_milli_node_hours int
Required. The train budget of creating this model, expressed in milli node hours i.e. 1,000 value in this field means 1 node hour. The training cost of the model will not exceed this budget. The final cost will be attempted to be close to the budget, though may end up being (even) noticeably smaller - at the backend's discretion. This especially may happen when further model training ceases to provide any improvements. If the budget is set to a value known to be insufficient to train a model for the given dataset, the training won't be attempted and will error. The train budget must be between 1,000 and 72,000 milli node hours, inclusive.
disable_early_stopping bool
Use the entire training budget. This disables the early stopping feature. By default, the early stopping feature is enabled, which means that AutoML Tables might stop training before the entire training budget has been used.
weight_column_name str
Column name that should be used as the weight column. Higher values in this column give more importance to the row during model training. The column must have numeric values between 0 and 10000 inclusively; 0 means the row is ignored for training. If weight column field is not set, then all rows are assumed to have equal weight of 1.
export_evaluated_data_items_config google.cloud.aiplatform.v1beta1.schema.trainingjob.definition_v1beta1.types.ExportEvaluatedDataItemsConfig
Configuration for exporting test set predictions to a BigQuery table. If this configuration is absent, then the export is not performed.
additional_experiments Sequence[str]
Additional experiment flags for the Tables training pipeline.

Inheritance

builtins.object > proto.message.Message > AutoMlTablesInputs

Classes

Transformation

Transformation(mapping=None, *, ignore_unknown_fields=False, **kwargs)

This message has oneof_ fields (mutually exclusive fields). For each oneof, at most one member field can be set at the same time. Setting any member of the oneof automatically clears all other members.

.. _oneof: https://proto-plus-python.readthedocs.io/en/stable/fields.html#oneofs-mutually-exclusive-fields

Methods

AutoMlTablesInputs

AutoMlTablesInputs(mapping=None, *, ignore_unknown_fields=False, **kwargs)

This message has oneof_ fields (mutually exclusive fields). For each oneof, at most one member field can be set at the same time. Setting any member of the oneof automatically clears all other members.

.. _oneof: https://proto-plus-python.readthedocs.io/en/stable/fields.html#oneofs-mutually-exclusive-fields