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

Class AutoMlTablesInputs.Builder

java.lang.Object
com.google.protobuf.AbstractMessageLite.Builder
com.google.protobuf.AbstractMessage.Builder<BuilderT>
com.google.protobuf.GeneratedMessage.Builder<AutoMlTablesInputs.Builder>
com.google.cloud.aiplatform.v1.schema.trainingjob.definition.AutoMlTablesInputs.Builder
All Implemented Interfaces:
AutoMlTablesInputsOrBuilder, com.google.protobuf.Message.Builder, com.google.protobuf.MessageLite.Builder, com.google.protobuf.MessageLiteOrBuilder, com.google.protobuf.MessageOrBuilder, Cloneable
Enclosing class:
AutoMlTablesInputs
public static final class AutoMlTablesInputs.Builder extends com.google.protobuf.GeneratedMessage.Builder<AutoMlTablesInputs.Builder> implements AutoMlTablesInputsOrBuilder
Protobuf type google.cloud.aiplatform.v1.schema.trainingjob.definition.AutoMlTablesInputs

  • Method Details

    • getDescriptor

      public static final com.google.protobuf.Descriptors.Descriptor getDescriptor()

    • internalGetFieldAccessorTable

      protected com.google.protobuf.GeneratedMessage.FieldAccessorTable internalGetFieldAccessorTable()
      Specified by:
      internalGetFieldAccessorTable in class com.google.protobuf.GeneratedMessage.Builder<AutoMlTablesInputs.Builder>

    • clear

      public AutoMlTablesInputs.Builder clear()
      Specified by:
      clear in interface com.google.protobuf.Message.Builder
      Specified by:
      clear in interface com.google.protobuf.MessageLite.Builder
      Overrides:
      clear in class com.google.protobuf.GeneratedMessage.Builder<AutoMlTablesInputs.Builder>

    • getDescriptorForType

      public com.google.protobuf.Descriptors.Descriptor getDescriptorForType()
      Specified by:
      getDescriptorForType in interface com.google.protobuf.Message.Builder
      Specified by:
      getDescriptorForType in interface com.google.protobuf.MessageOrBuilder
      Overrides:
      getDescriptorForType in class com.google.protobuf.GeneratedMessage.Builder<AutoMlTablesInputs.Builder>

    • getDefaultInstanceForType

      public AutoMlTablesInputs getDefaultInstanceForType()
      Specified by:
      getDefaultInstanceForType in interface com.google.protobuf.MessageLiteOrBuilder
      Specified by:
      getDefaultInstanceForType in interface com.google.protobuf.MessageOrBuilder

    • build

      public AutoMlTablesInputs build()
      Specified by:
      build in interface com.google.protobuf.Message.Builder
      Specified by:
      build in interface com.google.protobuf.MessageLite.Builder

    • buildPartial

      public AutoMlTablesInputs buildPartial()
      Specified by:
      buildPartial in interface com.google.protobuf.Message.Builder
      Specified by:
      buildPartial in interface com.google.protobuf.MessageLite.Builder

    • mergeFrom

      public AutoMlTablesInputs.Builder mergeFrom(com.google.protobuf.Message other)
      Specified by:
      mergeFrom in interface com.google.protobuf.Message.Builder
      Overrides:
      mergeFrom in class com.google.protobuf.AbstractMessage.Builder<AutoMlTablesInputs.Builder>

    • mergeFrom

      public AutoMlTablesInputs.Builder mergeFrom(AutoMlTablesInputs other)

    • isInitialized

      public final boolean isInitialized()
      Specified by:
      isInitialized in interface com.google.protobuf.MessageLiteOrBuilder
      Overrides:
      isInitialized in class com.google.protobuf.GeneratedMessage.Builder<AutoMlTablesInputs.Builder>

    • mergeFrom

      public AutoMlTablesInputs.Builder mergeFrom(com.google.protobuf.CodedInputStream input, com.google.protobuf.ExtensionRegistryLite extensionRegistry) throws IOException
      Specified by:
      mergeFrom in interface com.google.protobuf.Message.Builder
      Specified by:
      mergeFrom in interface com.google.protobuf.MessageLite.Builder
      Overrides:
      mergeFrom in class com.google.protobuf.AbstractMessage.Builder<AutoMlTablesInputs.Builder>
      Throws:
      IOException

    • getAdditionalOptimizationObjectiveConfigCase

      public AutoMlTablesInputs.AdditionalOptimizationObjectiveConfigCase getAdditionalOptimizationObjectiveConfigCase()
      Specified by:
      getAdditionalOptimizationObjectiveConfigCase in interface AutoMlTablesInputsOrBuilder

    • clearAdditionalOptimizationObjectiveConfig

      public AutoMlTablesInputs.Builder clearAdditionalOptimizationObjectiveConfig()

    • hasOptimizationObjectiveRecallValue

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

    • getOptimizationObjectiveRecallValue

      public float getOptimizationObjectiveRecallValue()
       Required when optimization_objective is "maximize-precision-at-recall".
 Must be between 0 and 1, inclusive.
      float optimization_objective_recall_value = 5;
      Specified by:
      getOptimizationObjectiveRecallValue in interface AutoMlTablesInputsOrBuilder
      Returns:
      The optimizationObjectiveRecallValue.

    • setOptimizationObjectiveRecallValue

      public AutoMlTablesInputs.Builder setOptimizationObjectiveRecallValue(float value)
       Required when optimization_objective is "maximize-precision-at-recall".
 Must be between 0 and 1, inclusive.
      float optimization_objective_recall_value = 5;
      Parameters:
      value - The optimizationObjectiveRecallValue to set.
      Returns:
      This builder for chaining.

    • clearOptimizationObjectiveRecallValue

      public AutoMlTablesInputs.Builder clearOptimizationObjectiveRecallValue()
       Required when optimization_objective is "maximize-precision-at-recall".
 Must be between 0 and 1, inclusive.
      float optimization_objective_recall_value = 5;
      Returns:
      This builder for chaining.

    • hasOptimizationObjectivePrecisionValue

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

    • getOptimizationObjectivePrecisionValue

      public float getOptimizationObjectivePrecisionValue()
       Required when optimization_objective is "maximize-recall-at-precision".
 Must be between 0 and 1, inclusive.
      float optimization_objective_precision_value = 6;
      Specified by:
      getOptimizationObjectivePrecisionValue in interface AutoMlTablesInputsOrBuilder
      Returns:
      The optimizationObjectivePrecisionValue.

    • setOptimizationObjectivePrecisionValue

      public AutoMlTablesInputs.Builder setOptimizationObjectivePrecisionValue(float value)
       Required when optimization_objective is "maximize-recall-at-precision".
 Must be between 0 and 1, inclusive.
      float optimization_objective_precision_value = 6;
      Parameters:
      value - The optimizationObjectivePrecisionValue to set.
      Returns:
      This builder for chaining.

    • clearOptimizationObjectivePrecisionValue

      public AutoMlTablesInputs.Builder clearOptimizationObjectivePrecisionValue()
       Required when optimization_objective is "maximize-recall-at-precision".
 Must be between 0 and 1, inclusive.
      float optimization_objective_precision_value = 6;
      Returns:
      This builder for chaining.

    • getPredictionType

      public 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;
      Specified by:
      getPredictionType in interface AutoMlTablesInputsOrBuilder
      Returns:
      The predictionType.

    • getPredictionTypeBytes

      public 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;
      Specified by:
      getPredictionTypeBytes in interface AutoMlTablesInputsOrBuilder
      Returns:
      The bytes for predictionType.

    • setPredictionType

      public AutoMlTablesInputs.Builder setPredictionType(String value)
       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;
      Parameters:
      value - The predictionType to set.
      Returns:
      This builder for chaining.

    • clearPredictionType

      public AutoMlTablesInputs.Builder clearPredictionType()
       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:
      This builder for chaining.

    • setPredictionTypeBytes

      public AutoMlTablesInputs.Builder setPredictionTypeBytes(com.google.protobuf.ByteString value)
       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;
      Parameters:
      value - The bytes for predictionType to set.
      Returns:
      This builder for chaining.

    • getTargetColumn

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

    • getTargetColumnBytes

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

    • setTargetColumn

      public AutoMlTablesInputs.Builder setTargetColumn(String value)
       The column name of the target column that the model is to predict.
      string target_column = 2;
      Parameters:
      value - The targetColumn to set.
      Returns:
      This builder for chaining.

    • clearTargetColumn

      public AutoMlTablesInputs.Builder clearTargetColumn()
       The column name of the target column that the model is to predict.
      string target_column = 2;
      Returns:
      This builder for chaining.

    • setTargetColumnBytes

      public AutoMlTablesInputs.Builder setTargetColumnBytes(com.google.protobuf.ByteString value)
       The column name of the target column that the model is to predict.
      string target_column = 2;
      Parameters:
      value - The bytes for targetColumn to set.
      Returns:
      This builder for chaining.

    • getTransformationsList

      public 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;
      Specified by:
      getTransformationsList in interface AutoMlTablesInputsOrBuilder

    • getTransformationsCount

      public 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;
      Specified by:
      getTransformationsCount in interface AutoMlTablesInputsOrBuilder

    • getTransformations

      public 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;
      Specified by:
      getTransformations in interface AutoMlTablesInputsOrBuilder

    • setTransformations

      public AutoMlTablesInputs.Builder setTransformations(int index, AutoMlTablesInputs.Transformation value)
       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;

    • setTransformations

      public AutoMlTablesInputs.Builder setTransformations(int index, AutoMlTablesInputs.Transformation.Builder builderForValue)
       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;

    • addTransformations

      public AutoMlTablesInputs.Builder addTransformations(AutoMlTablesInputs.Transformation value)
       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;

    • addTransformations

      public AutoMlTablesInputs.Builder addTransformations(int index, AutoMlTablesInputs.Transformation value)
       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;

    • addTransformations

      public AutoMlTablesInputs.Builder addTransformations(AutoMlTablesInputs.Transformation.Builder builderForValue)
       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;

    • addTransformations

      public AutoMlTablesInputs.Builder addTransformations(int index, AutoMlTablesInputs.Transformation.Builder builderForValue)
       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;

    • addAllTransformations

      public AutoMlTablesInputs.Builder addAllTransformations(Iterable<? extends AutoMlTablesInputs.Transformation> values)
       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;

    • clearTransformations

      public AutoMlTablesInputs.Builder clearTransformations()
       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;

    • removeTransformations

      public AutoMlTablesInputs.Builder removeTransformations(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;

    • getTransformationsBuilder

      public AutoMlTablesInputs.Transformation.Builder getTransformationsBuilder(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;

    • getTransformationsOrBuilder

      public 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;
      Specified by:
      getTransformationsOrBuilder in interface AutoMlTablesInputsOrBuilder

    • getTransformationsOrBuilderList

      public 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;
      Specified by:
      getTransformationsOrBuilderList in interface AutoMlTablesInputsOrBuilder

    • addTransformationsBuilder

      public AutoMlTablesInputs.Transformation.Builder addTransformationsBuilder()
       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;

    • addTransformationsBuilder

      public AutoMlTablesInputs.Transformation.Builder addTransformationsBuilder(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;

    • getTransformationsBuilderList

      public List<AutoMlTablesInputs.Transformation.Builder> getTransformationsBuilderList()
       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

      public 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;
      Specified by:
      getOptimizationObjective in interface AutoMlTablesInputsOrBuilder
      Returns:
      The optimizationObjective.

    • getOptimizationObjectiveBytes

      public 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;
      Specified by:
      getOptimizationObjectiveBytes in interface AutoMlTablesInputsOrBuilder
      Returns:
      The bytes for optimizationObjective.

    • setOptimizationObjective

      public AutoMlTablesInputs.Builder setOptimizationObjective(String value)
       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;
      Parameters:
      value - The optimizationObjective to set.
      Returns:
      This builder for chaining.

    • clearOptimizationObjective

      public AutoMlTablesInputs.Builder clearOptimizationObjective()
       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:
      This builder for chaining.

    • setOptimizationObjectiveBytes

      public AutoMlTablesInputs.Builder setOptimizationObjectiveBytes(com.google.protobuf.ByteString value)
       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;
      Parameters:
      value - The bytes for optimizationObjective to set.
      Returns:
      This builder for chaining.

    • getTrainBudgetMilliNodeHours

      public 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;
      Specified by:
      getTrainBudgetMilliNodeHours in interface AutoMlTablesInputsOrBuilder
      Returns:
      The trainBudgetMilliNodeHours.

    • setTrainBudgetMilliNodeHours

      public AutoMlTablesInputs.Builder setTrainBudgetMilliNodeHours(long value)
       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;
      Parameters:
      value - The trainBudgetMilliNodeHours to set.
      Returns:
      This builder for chaining.

    • clearTrainBudgetMilliNodeHours

      public AutoMlTablesInputs.Builder clearTrainBudgetMilliNodeHours()
       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:
      This builder for chaining.

    • getDisableEarlyStopping

      public 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;
      Specified by:
      getDisableEarlyStopping in interface AutoMlTablesInputsOrBuilder
      Returns:
      The disableEarlyStopping.

    • setDisableEarlyStopping

      public AutoMlTablesInputs.Builder setDisableEarlyStopping(boolean value)
       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;
      Parameters:
      value - The disableEarlyStopping to set.
      Returns:
      This builder for chaining.

    • clearDisableEarlyStopping

      public AutoMlTablesInputs.Builder clearDisableEarlyStopping()
       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:
      This builder for chaining.

    • getWeightColumnName

      public 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;
      Specified by:
      getWeightColumnName in interface AutoMlTablesInputsOrBuilder
      Returns:
      The weightColumnName.

    • getWeightColumnNameBytes

      public 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;
      Specified by:
      getWeightColumnNameBytes in interface AutoMlTablesInputsOrBuilder
      Returns:
      The bytes for weightColumnName.

    • setWeightColumnName

      public AutoMlTablesInputs.Builder setWeightColumnName(String value)
       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;
      Parameters:
      value - The weightColumnName to set.
      Returns:
      This builder for chaining.

    • clearWeightColumnName

      public AutoMlTablesInputs.Builder clearWeightColumnName()
       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:
      This builder for chaining.

    • setWeightColumnNameBytes

      public AutoMlTablesInputs.Builder setWeightColumnNameBytes(com.google.protobuf.ByteString value)
       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;
      Parameters:
      value - The bytes for weightColumnName to set.
      Returns:
      This builder for chaining.

    • hasExportEvaluatedDataItemsConfig

      public 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;
      Specified by:
      hasExportEvaluatedDataItemsConfig in interface AutoMlTablesInputsOrBuilder
      Returns:
      Whether the exportEvaluatedDataItemsConfig field is set.

    • getExportEvaluatedDataItemsConfig

      public 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;
      Specified by:
      getExportEvaluatedDataItemsConfig in interface AutoMlTablesInputsOrBuilder
      Returns:
      The exportEvaluatedDataItemsConfig.

    • setExportEvaluatedDataItemsConfig

      public AutoMlTablesInputs.Builder setExportEvaluatedDataItemsConfig(ExportEvaluatedDataItemsConfig value)
       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;

    • setExportEvaluatedDataItemsConfig

      public AutoMlTablesInputs.Builder setExportEvaluatedDataItemsConfig(ExportEvaluatedDataItemsConfig.Builder builderForValue)
       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;

    • mergeExportEvaluatedDataItemsConfig

      public AutoMlTablesInputs.Builder mergeExportEvaluatedDataItemsConfig(ExportEvaluatedDataItemsConfig value)
       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;

    • clearExportEvaluatedDataItemsConfig

      public AutoMlTablesInputs.Builder clearExportEvaluatedDataItemsConfig()
       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;

    • getExportEvaluatedDataItemsConfigBuilder

      public ExportEvaluatedDataItemsConfig.Builder getExportEvaluatedDataItemsConfigBuilder()
       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;

    • getExportEvaluatedDataItemsConfigOrBuilder

      public 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;
      Specified by:
      getExportEvaluatedDataItemsConfigOrBuilder in interface AutoMlTablesInputsOrBuilder

    • getAdditionalExperimentsList

      public com.google.protobuf.ProtocolStringList getAdditionalExperimentsList()
       Additional experiment flags for the Tables training pipeline.
      repeated string additional_experiments = 11;
      Specified by:
      getAdditionalExperimentsList in interface AutoMlTablesInputsOrBuilder
      Returns:
      A list containing the additionalExperiments.

    • getAdditionalExperimentsCount

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

    • getAdditionalExperiments

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

    • getAdditionalExperimentsBytes

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

    • setAdditionalExperiments

      public AutoMlTablesInputs.Builder setAdditionalExperiments(int index, String value)
       Additional experiment flags for the Tables training pipeline.
      repeated string additional_experiments = 11;
      Parameters:
      index - The index to set the value at.
      value - The additionalExperiments to set.
      Returns:
      This builder for chaining.

    • addAdditionalExperiments

      public AutoMlTablesInputs.Builder addAdditionalExperiments(String value)
       Additional experiment flags for the Tables training pipeline.
      repeated string additional_experiments = 11;
      Parameters:
      value - The additionalExperiments to add.
      Returns:
      This builder for chaining.

    • addAllAdditionalExperiments

      public AutoMlTablesInputs.Builder addAllAdditionalExperiments(Iterable<String> values)
       Additional experiment flags for the Tables training pipeline.
      repeated string additional_experiments = 11;
      Parameters:
      values - The additionalExperiments to add.
      Returns:
      This builder for chaining.

    • clearAdditionalExperiments

      public AutoMlTablesInputs.Builder clearAdditionalExperiments()
       Additional experiment flags for the Tables training pipeline.
      repeated string additional_experiments = 11;
      Returns:
      This builder for chaining.

    • addAdditionalExperimentsBytes

      public AutoMlTablesInputs.Builder addAdditionalExperimentsBytes(com.google.protobuf.ByteString value)
       Additional experiment flags for the Tables training pipeline.
      repeated string additional_experiments = 11;
      Parameters:
      value - The bytes of the additionalExperiments to add.
      Returns:
      This builder for chaining.