Package com.google.cloud.aiplatform.v1.schema.trainingjob.definition

Interface AutoMlTablesInputsOrBuilder

All Superinterfaces:
com.google.protobuf.MessageLiteOrBuilder, com.google.protobuf.MessageOrBuilder
All Known Implementing Classes:
AutoMlTablesInputs, AutoMlTablesInputs.Builder
@Generated public interface AutoMlTablesInputsOrBuilder extends com.google.protobuf.MessageOrBuilder

  • Method Details

    • hasOptimizationObjectiveRecallValue

      boolean hasOptimizationObjectiveRecallValue()
       Required when optimization_objective is "maximize-precision-at-recall".
 Must be between 0 and 1, inclusive.
      float optimization_objective_recall_value = 5;
      Returns:
      Whether the optimizationObjectiveRecallValue field is set.

    • getOptimizationObjectiveRecallValue

      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:
      The optimizationObjectiveRecallValue.

    • hasOptimizationObjectivePrecisionValue

      boolean hasOptimizationObjectivePrecisionValue()
       Required when optimization_objective is "maximize-recall-at-precision".
 Must be between 0 and 1, inclusive.
      float optimization_objective_precision_value = 6;
      Returns:
      Whether the optimizationObjectivePrecisionValue field is set.

    • getOptimizationObjectivePrecisionValue

      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:
      The optimizationObjectivePrecisionValue.

    • getPredictionType

      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:
      The predictionType.

    • getPredictionTypeBytes

      com.google.protobuf.ByteString getPredictionTypeBytes()
       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:
      The bytes for predictionType.

    • getTargetColumn

      String getTargetColumn()
       The column name of the target column that the model is to predict.
      string target_column = 2;
      Returns:
      The targetColumn.

    • getTargetColumnBytes

      com.google.protobuf.ByteString getTargetColumnBytes()
       The column name of the target column that the model is to predict.
      string target_column = 2;
      Returns:
      The bytes for targetColumn.

    • getTransformationsList

      List<AutoMlTablesInputs.Transformation> getTransformationsList()
       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.
      repeated .google.cloud.aiplatform.v1.schema.trainingjob.definition.AutoMlTablesInputs.Transformation transformations = 3;

    • getTransformations

      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.
      repeated .google.cloud.aiplatform.v1.schema.trainingjob.definition.AutoMlTablesInputs.Transformation transformations = 3;

    • getTransformationsCount

      int getTransformationsCount()
       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.
      repeated .google.cloud.aiplatform.v1.schema.trainingjob.definition.AutoMlTablesInputs.Transformation transformations = 3;

    • getTransformationsOrBuilderList

      List<? extends AutoMlTablesInputs.TransformationOrBuilder> getTransformationsOrBuilderList()
       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.
      repeated .google.cloud.aiplatform.v1.schema.trainingjob.definition.AutoMlTablesInputs.Transformation transformations = 3;

    • getTransformationsOrBuilder

      AutoMlTablesInputs.TransformationOrBuilder getTransformationsOrBuilder(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.
      repeated .google.cloud.aiplatform.v1.schema.trainingjob.definition.AutoMlTablesInputs.Transformation transformations = 3;

    • getOptimizationObjective

      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:
      The optimizationObjective.

    • getOptimizationObjectiveBytes

      com.google.protobuf.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 (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:
      The bytes for optimizationObjective.

    • getTrainBudgetMilliNodeHours

      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:
      The trainBudgetMilliNodeHours.

    • getDisableEarlyStopping

      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:
      The disableEarlyStopping.

    • getWeightColumnName

      String getWeightColumnName()
       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.
      string weight_column_name = 9;
      Returns:
      The weightColumnName.

    • getWeightColumnNameBytes

      com.google.protobuf.ByteString getWeightColumnNameBytes()
       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.
      string weight_column_name = 9;
      Returns:
      The bytes for weightColumnName.

    • hasExportEvaluatedDataItemsConfig

      boolean hasExportEvaluatedDataItemsConfig()
       Configuration for exporting test set predictions to a BigQuery table. If
 this configuration is absent, then the export is not performed.
      .google.cloud.aiplatform.v1.schema.trainingjob.definition.ExportEvaluatedDataItemsConfig export_evaluated_data_items_config = 10;
      Returns:
      Whether the exportEvaluatedDataItemsConfig field is set.

    • getExportEvaluatedDataItemsConfig

      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.v1.schema.trainingjob.definition.ExportEvaluatedDataItemsConfig export_evaluated_data_items_config = 10;
      Returns:
      The exportEvaluatedDataItemsConfig.

    • getExportEvaluatedDataItemsConfigOrBuilder

      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.v1.schema.trainingjob.definition.ExportEvaluatedDataItemsConfig export_evaluated_data_items_config = 10;

    • getAdditionalExperimentsList

      List<String> getAdditionalExperimentsList()
       Additional experiment flags for the Tables training pipeline.
      repeated string additional_experiments = 11;
      Returns:
      A list containing the additionalExperiments.

    • getAdditionalExperimentsCount

      int getAdditionalExperimentsCount()
       Additional experiment flags for the Tables training pipeline.
      repeated string additional_experiments = 11;
      Returns:
      The count of additionalExperiments.

    • getAdditionalExperiments

      String getAdditionalExperiments(int index)
       Additional experiment flags for the Tables training pipeline.
      repeated string additional_experiments = 11;
      Parameters:
      index - The index of the element to return.
      Returns:
      The additionalExperiments at the given index.

    • getAdditionalExperimentsBytes

      com.google.protobuf.ByteString getAdditionalExperimentsBytes(int index)
       Additional experiment flags for the Tables training pipeline.
      repeated string additional_experiments = 11;
      Parameters:
      index - The index of the value to return.
      Returns:
      The bytes of the additionalExperiments at the given index.

    • getAdditionalOptimizationObjectiveConfigCase

      AutoMlTablesInputs.AdditionalOptimizationObjectiveConfigCase getAdditionalOptimizationObjectiveConfigCase()