Interface AutoMlTablesInputsOrBuilder (3.33.0)

public interface AutoMlTablesInputsOrBuilder extends MessageOrBuilder

Implements

MessageOrBuilder

Methods

getAdditionalExperiments(int index)

public abstract String getAdditionalExperiments(int index)

Additional experiment flags for the Tables training pipeline.

repeated string additional_experiments = 11;

Parameter
Name	Description
`index`	`int` The index of the element to return.

Returns
Type	Description
`String`	The additionalExperiments at the given index.

getAdditionalExperimentsBytes(int index)

public abstract ByteString getAdditionalExperimentsBytes(int index)

Additional experiment flags for the Tables training pipeline.

repeated string additional_experiments = 11;

Parameter
Name	Description
`index`	`int` The index of the value to return.

Returns
Type	Description
`ByteString`	The bytes of the additionalExperiments at the given index.

getAdditionalExperimentsCount()

public abstract int getAdditionalExperimentsCount()

Additional experiment flags for the Tables training pipeline.

repeated string additional_experiments = 11;

Returns
Type	Description
`int`	The count of additionalExperiments.

getAdditionalExperimentsList()

public abstract List<String> getAdditionalExperimentsList()

Additional experiment flags for the Tables training pipeline.

repeated string additional_experiments = 11;

Returns
Type	Description
`List<String>`	A list containing the additionalExperiments.

getAdditionalOptimizationObjectiveConfigCase()

public abstract AutoMlTablesInputs.AdditionalOptimizationObjectiveConfigCase getAdditionalOptimizationObjectiveConfigCase()

Returns
Type	Description
`AutoMlTablesInputs.AdditionalOptimizationObjectiveConfigCase`

getDisableEarlyStopping()

public abstract boolean getDisableEarlyStopping()

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.

bool disable_early_stopping = 8;

Returns
Type	Description
`boolean`	The disableEarlyStopping.

getExportEvaluatedDataItemsConfig()

public abstract ExportEvaluatedDataItemsConfig getExportEvaluatedDataItemsConfig()

Configuration for exporting test set predictions to a BigQuery table. If this configuration is absent, then the export is not performed.

.google.cloud.aiplatform.v1beta1.schema.trainingjob.definition.ExportEvaluatedDataItemsConfig export_evaluated_data_items_config = 10;

Returns
Type	Description
`ExportEvaluatedDataItemsConfig`	The exportEvaluatedDataItemsConfig.

getExportEvaluatedDataItemsConfigOrBuilder()

public abstract ExportEvaluatedDataItemsConfigOrBuilder getExportEvaluatedDataItemsConfigOrBuilder()

Configuration for exporting test set predictions to a BigQuery table. If this configuration is absent, then the export is not performed.

.google.cloud.aiplatform.v1beta1.schema.trainingjob.definition.ExportEvaluatedDataItemsConfig export_evaluated_data_items_config = 10;

Returns
Type	Description
`ExportEvaluatedDataItemsConfigOrBuilder`

getOptimizationObjective()

public abstract String getOptimizationObjective()

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).

string optimization_objective = 4;

Returns
Type	Description
`String`	The optimizationObjective.

getOptimizationObjectiveBytes()

public abstract ByteString getOptimizationObjectiveBytes()

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 (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).

string optimization_objective = 4;

Returns
Type	Description
`ByteString`	The bytes for optimizationObjective.

getOptimizationObjectivePrecisionValue()

public abstract float getOptimizationObjectivePrecisionValue()

Required when optimization_objective is "maximize-recall-at-precision". Must be between 0 and 1, inclusive.

float optimization_objective_precision_value = 6;

Returns
Type	Description
`float`	The optimizationObjectivePrecisionValue.

getOptimizationObjectiveRecallValue()

public abstract float getOptimizationObjectiveRecallValue()

Required when optimization_objective is "maximize-precision-at-recall". Must be between 0 and 1, inclusive.

float optimization_objective_recall_value = 5;

Returns
Type	Description
`float`	The optimizationObjectiveRecallValue.

getPredictionType()

public abstract String getPredictionType()

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.

string prediction_type = 1;

Returns
Type	Description
`String`	The predictionType.

getPredictionTypeBytes()

public abstract ByteString getPredictionTypeBytes()

string prediction_type = 1;

Returns
Type	Description
`ByteString`	The bytes for predictionType.

getTargetColumn()

public abstract String getTargetColumn()

The column name of the target column that the model is to predict.

string target_column = 2;

Returns
Type	Description
`String`	The targetColumn.

getTargetColumnBytes()

public abstract ByteString getTargetColumnBytes()

The column name of the target column that the model is to predict.

string target_column = 2;

Returns
Type	Description
`ByteString`	The bytes for targetColumn.

getTrainBudgetMilliNodeHours()

public abstract long getTrainBudgetMilliNodeHours()

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.

int64 train_budget_milli_node_hours = 7;

Returns
Type	Description
`long`	The trainBudgetMilliNodeHours.

getTransformations(int index)

public abstract AutoMlTablesInputs.Transformation getTransformations(int index)

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.