Package com.google.cloud.aiplatform.v1beta1.schema.trainingjob.definition

Class AutoMlForecastingInputs.Builder

java.lang.Object
com.google.protobuf.AbstractMessageLite.Builder
com.google.protobuf.AbstractMessage.Builder<BuilderT>
com.google.protobuf.GeneratedMessage.Builder<AutoMlForecastingInputs.Builder>
com.google.cloud.aiplatform.v1beta1.schema.trainingjob.definition.AutoMlForecastingInputs.Builder
All Implemented Interfaces:
AutoMlForecastingInputsOrBuilder, com.google.protobuf.Message.Builder, com.google.protobuf.MessageLite.Builder, com.google.protobuf.MessageLiteOrBuilder, com.google.protobuf.MessageOrBuilder, Cloneable
Enclosing class:
AutoMlForecastingInputs
public static final class AutoMlForecastingInputs.Builder extends com.google.protobuf.GeneratedMessage.Builder<AutoMlForecastingInputs.Builder> implements AutoMlForecastingInputsOrBuilder
Protobuf type google.cloud.aiplatform.v1beta1.schema.trainingjob.definition.AutoMlForecastingInputs

  • 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<AutoMlForecastingInputs.Builder>

    • clear

      public AutoMlForecastingInputs.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<AutoMlForecastingInputs.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<AutoMlForecastingInputs.Builder>

    • getDefaultInstanceForType

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

    • build

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

    • buildPartial

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

    • mergeFrom

      public AutoMlForecastingInputs.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<AutoMlForecastingInputs.Builder>

    • mergeFrom

      public AutoMlForecastingInputs.Builder mergeFrom(AutoMlForecastingInputs other)

    • isInitialized

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

    • mergeFrom

      public AutoMlForecastingInputs.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<AutoMlForecastingInputs.Builder>
      Throws:
      IOException

    • getTargetColumn

      public String getTargetColumn()
       The name of the column that the model is to predict.
      string target_column = 1;
      Specified by:
      getTargetColumn in interface AutoMlForecastingInputsOrBuilder
      Returns:
      The targetColumn.

    • getTargetColumnBytes

      public com.google.protobuf.ByteString getTargetColumnBytes()
       The name of the column that the model is to predict.
      string target_column = 1;
      Specified by:
      getTargetColumnBytes in interface AutoMlForecastingInputsOrBuilder
      Returns:
      The bytes for targetColumn.

    • setTargetColumn

      public AutoMlForecastingInputs.Builder setTargetColumn(String value)
       The name of the column that the model is to predict.
      string target_column = 1;
      Parameters:
      value - The targetColumn to set.
      Returns:
      This builder for chaining.

    • clearTargetColumn

      public AutoMlForecastingInputs.Builder clearTargetColumn()
       The name of the column that the model is to predict.
      string target_column = 1;
      Returns:
      This builder for chaining.

    • setTargetColumnBytes

      public AutoMlForecastingInputs.Builder setTargetColumnBytes(com.google.protobuf.ByteString value)
       The name of the column that the model is to predict.
      string target_column = 1;
      Parameters:
      value - The bytes for targetColumn to set.
      Returns:
      This builder for chaining.

    • getTimeSeriesIdentifierColumn

      public String getTimeSeriesIdentifierColumn()
       The name of the column that identifies the time series.
      string time_series_identifier_column = 2;
      Specified by:
      getTimeSeriesIdentifierColumn in interface AutoMlForecastingInputsOrBuilder
      Returns:
      The timeSeriesIdentifierColumn.

    • getTimeSeriesIdentifierColumnBytes

      public com.google.protobuf.ByteString getTimeSeriesIdentifierColumnBytes()
       The name of the column that identifies the time series.
      string time_series_identifier_column = 2;
      Specified by:
      getTimeSeriesIdentifierColumnBytes in interface AutoMlForecastingInputsOrBuilder
      Returns:
      The bytes for timeSeriesIdentifierColumn.

    • setTimeSeriesIdentifierColumn

      public AutoMlForecastingInputs.Builder setTimeSeriesIdentifierColumn(String value)
       The name of the column that identifies the time series.
      string time_series_identifier_column = 2;
      Parameters:
      value - The timeSeriesIdentifierColumn to set.
      Returns:
      This builder for chaining.

    • clearTimeSeriesIdentifierColumn

      public AutoMlForecastingInputs.Builder clearTimeSeriesIdentifierColumn()
       The name of the column that identifies the time series.
      string time_series_identifier_column = 2;
      Returns:
      This builder for chaining.

    • setTimeSeriesIdentifierColumnBytes

      public AutoMlForecastingInputs.Builder setTimeSeriesIdentifierColumnBytes(com.google.protobuf.ByteString value)
       The name of the column that identifies the time series.
      string time_series_identifier_column = 2;
      Parameters:
      value - The bytes for timeSeriesIdentifierColumn to set.
      Returns:
      This builder for chaining.

    • getTimeColumn

      public String getTimeColumn()
       The name of the column that identifies time order in the time series.
      string time_column = 3;
      Specified by:
      getTimeColumn in interface AutoMlForecastingInputsOrBuilder
      Returns:
      The timeColumn.

    • getTimeColumnBytes

      public com.google.protobuf.ByteString getTimeColumnBytes()
       The name of the column that identifies time order in the time series.
      string time_column = 3;
      Specified by:
      getTimeColumnBytes in interface AutoMlForecastingInputsOrBuilder
      Returns:
      The bytes for timeColumn.

    • setTimeColumn

      public AutoMlForecastingInputs.Builder setTimeColumn(String value)
       The name of the column that identifies time order in the time series.
      string time_column = 3;
      Parameters:
      value - The timeColumn to set.
      Returns:
      This builder for chaining.

    • clearTimeColumn

      public AutoMlForecastingInputs.Builder clearTimeColumn()
       The name of the column that identifies time order in the time series.
      string time_column = 3;
      Returns:
      This builder for chaining.

    • setTimeColumnBytes

      public AutoMlForecastingInputs.Builder setTimeColumnBytes(com.google.protobuf.ByteString value)
       The name of the column that identifies time order in the time series.
      string time_column = 3;
      Parameters:
      value - The bytes for timeColumn to set.
      Returns:
      This builder for chaining.

    • getTransformationsList

      public List<AutoMlForecastingInputs.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.v1beta1.schema.trainingjob.definition.AutoMlForecastingInputs.Transformation transformations = 4;
      Specified by:
      getTransformationsList in interface AutoMlForecastingInputsOrBuilder

    • 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.v1beta1.schema.trainingjob.definition.AutoMlForecastingInputs.Transformation transformations = 4;
      Specified by:
      getTransformationsCount in interface AutoMlForecastingInputsOrBuilder

    • getTransformations

      public AutoMlForecastingInputs.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.v1beta1.schema.trainingjob.definition.AutoMlForecastingInputs.Transformation transformations = 4;
      Specified by:
      getTransformations in interface AutoMlForecastingInputsOrBuilder

    • setTransformations

      public AutoMlForecastingInputs.Builder setTransformations(int index, AutoMlForecastingInputs.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.v1beta1.schema.trainingjob.definition.AutoMlForecastingInputs.Transformation transformations = 4;

    • setTransformations

      public AutoMlForecastingInputs.Builder setTransformations(int index, AutoMlForecastingInputs.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.v1beta1.schema.trainingjob.definition.AutoMlForecastingInputs.Transformation transformations = 4;

    • addTransformations

      public AutoMlForecastingInputs.Builder addTransformations(AutoMlForecastingInputs.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.v1beta1.schema.trainingjob.definition.AutoMlForecastingInputs.Transformation transformations = 4;

    • addTransformations

      public AutoMlForecastingInputs.Builder addTransformations(int index, AutoMlForecastingInputs.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.v1beta1.schema.trainingjob.definition.AutoMlForecastingInputs.Transformation transformations = 4;

    • addTransformations

      public AutoMlForecastingInputs.Builder addTransformations(AutoMlForecastingInputs.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.v1beta1.schema.trainingjob.definition.AutoMlForecastingInputs.Transformation transformations = 4;

    • addTransformations

      public AutoMlForecastingInputs.Builder addTransformations(int index, AutoMlForecastingInputs.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.v1beta1.schema.trainingjob.definition.AutoMlForecastingInputs.Transformation transformations = 4;

    • addAllTransformations

      public AutoMlForecastingInputs.Builder addAllTransformations(Iterable<? extends AutoMlForecastingInputs.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.v1beta1.schema.trainingjob.definition.AutoMlForecastingInputs.Transformation transformations = 4;

    • clearTransformations

      public AutoMlForecastingInputs.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.v1beta1.schema.trainingjob.definition.AutoMlForecastingInputs.Transformation transformations = 4;

    • removeTransformations

      public AutoMlForecastingInputs.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.v1beta1.schema.trainingjob.definition.AutoMlForecastingInputs.Transformation transformations = 4;

    • getTransformationsBuilder

      public AutoMlForecastingInputs.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.v1beta1.schema.trainingjob.definition.AutoMlForecastingInputs.Transformation transformations = 4;

    • getTransformationsOrBuilder

      public AutoMlForecastingInputs.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.v1beta1.schema.trainingjob.definition.AutoMlForecastingInputs.Transformation transformations = 4;
      Specified by:
      getTransformationsOrBuilder in interface AutoMlForecastingInputsOrBuilder

    • getTransformationsOrBuilderList

      public List<? extends AutoMlForecastingInputs.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.v1beta1.schema.trainingjob.definition.AutoMlForecastingInputs.Transformation transformations = 4;
      Specified by:
      getTransformationsOrBuilderList in interface AutoMlForecastingInputsOrBuilder

    • addTransformationsBuilder

      public AutoMlForecastingInputs.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.v1beta1.schema.trainingjob.definition.AutoMlForecastingInputs.Transformation transformations = 4;

    • addTransformationsBuilder

      public AutoMlForecastingInputs.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.v1beta1.schema.trainingjob.definition.AutoMlForecastingInputs.Transformation transformations = 4;

    • getTransformationsBuilderList

      public List<AutoMlForecastingInputs.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.v1beta1.schema.trainingjob.definition.AutoMlForecastingInputs.Transformation transformations = 4;

    • getOptimizationObjective

      public String getOptimizationObjective()
       Objective function the model is optimizing towards. The training process
 creates a model that optimizes the value of the objective
 function over the validation set.

 The supported optimization objectives:

 * "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).

 * "minimize-rmspe" - Minimize root-mean-squared percentage error (RMSPE).

 * "minimize-wape-mae" - Minimize the combination of weighted absolute
 percentage error (WAPE) and mean-absolute-error (MAE).

 * "minimize-quantile-loss" - Minimize the quantile loss at the quantiles
 defined in `quantiles`.
      string optimization_objective = 5;
      Specified by:
      getOptimizationObjective in interface AutoMlForecastingInputsOrBuilder
      Returns:
      The optimizationObjective.

    • getOptimizationObjectiveBytes

      public com.google.protobuf.ByteString getOptimizationObjectiveBytes()
       Objective function the model is optimizing towards. The training process
 creates a model that optimizes the value of the objective
 function over the validation set.

 The supported optimization objectives:

 * "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).

 * "minimize-rmspe" - Minimize root-mean-squared percentage error (RMSPE).

 * "minimize-wape-mae" - Minimize the combination of weighted absolute
 percentage error (WAPE) and mean-absolute-error (MAE).

 * "minimize-quantile-loss" - Minimize the quantile loss at the quantiles
 defined in `quantiles`.
      string optimization_objective = 5;
      Specified by:
      getOptimizationObjectiveBytes in interface AutoMlForecastingInputsOrBuilder
      Returns:
      The bytes for optimizationObjective.

    • setOptimizationObjective

      public AutoMlForecastingInputs.Builder setOptimizationObjective(String value)
       Objective function the model is optimizing towards. The training process
 creates a model that optimizes the value of the objective
 function over the validation set.

 The supported optimization objectives:

 * "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).

 * "minimize-rmspe" - Minimize root-mean-squared percentage error (RMSPE).

 * "minimize-wape-mae" - Minimize the combination of weighted absolute
 percentage error (WAPE) and mean-absolute-error (MAE).

 * "minimize-quantile-loss" - Minimize the quantile loss at the quantiles
 defined in `quantiles`.
      string optimization_objective = 5;
      Parameters:
      value - The optimizationObjective to set.
      Returns:
      This builder for chaining.

    • clearOptimizationObjective

      public AutoMlForecastingInputs.Builder clearOptimizationObjective()
       Objective function the model is optimizing towards. The training process
 creates a model that optimizes the value of the objective
 function over the validation set.

 The supported optimization objectives:

 * "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).

 * "minimize-rmspe" - Minimize root-mean-squared percentage error (RMSPE).

 * "minimize-wape-mae" - Minimize the combination of weighted absolute
 percentage error (WAPE) and mean-absolute-error (MAE).

 * "minimize-quantile-loss" - Minimize the quantile loss at the quantiles
 defined in `quantiles`.
      string optimization_objective = 5;
      Returns:
      This builder for chaining.

    • setOptimizationObjectiveBytes

      public AutoMlForecastingInputs.Builder setOptimizationObjectiveBytes(com.google.protobuf.ByteString value)
       Objective function the model is optimizing towards. The training process
 creates a model that optimizes the value of the objective
 function over the validation set.

 The supported optimization objectives:

 * "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).

 * "minimize-rmspe" - Minimize root-mean-squared percentage error (RMSPE).

 * "minimize-wape-mae" - Minimize the combination of weighted absolute
 percentage error (WAPE) and mean-absolute-error (MAE).

 * "minimize-quantile-loss" - Minimize the quantile loss at the quantiles
 defined in `quantiles`.
      string optimization_objective = 5;
      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 = 6;
      Specified by:
      getTrainBudgetMilliNodeHours in interface AutoMlForecastingInputsOrBuilder
      Returns:
      The trainBudgetMilliNodeHours.

    • setTrainBudgetMilliNodeHours

      public AutoMlForecastingInputs.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 = 6;
      Parameters:
      value - The trainBudgetMilliNodeHours to set.
      Returns:
      This builder for chaining.

    • clearTrainBudgetMilliNodeHours

      public AutoMlForecastingInputs.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 = 6;
      Returns:
      This builder for chaining.

    • getWeightColumn

      public String getWeightColumn()
       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 = 7;
      Specified by:
      getWeightColumn in interface AutoMlForecastingInputsOrBuilder
      Returns:
      The weightColumn.

    • getWeightColumnBytes

      public com.google.protobuf.ByteString getWeightColumnBytes()
       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 = 7;
      Specified by:
      getWeightColumnBytes in interface AutoMlForecastingInputsOrBuilder
      Returns:
      The bytes for weightColumn.

    • setWeightColumn

      public AutoMlForecastingInputs.Builder setWeightColumn(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 = 7;
      Parameters:
      value - The weightColumn to set.
      Returns:
      This builder for chaining.

    • clearWeightColumn

      public AutoMlForecastingInputs.Builder clearWeightColumn()
       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 = 7;
      Returns:
      This builder for chaining.

    • setWeightColumnBytes

      public AutoMlForecastingInputs.Builder setWeightColumnBytes(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 = 7;
      Parameters:
      value - The bytes for weightColumn to set.
      Returns:
      This builder for chaining.

    • getTimeSeriesAttributeColumnsList

      public com.google.protobuf.ProtocolStringList getTimeSeriesAttributeColumnsList()
       Column names that should be used as attribute columns.
 The value of these columns does not vary as a function of time.
 For example, store ID or item color.
      repeated string time_series_attribute_columns = 19;
      Specified by:
      getTimeSeriesAttributeColumnsList in interface AutoMlForecastingInputsOrBuilder
      Returns:
      A list containing the timeSeriesAttributeColumns.

    • getTimeSeriesAttributeColumnsCount

      public int getTimeSeriesAttributeColumnsCount()
       Column names that should be used as attribute columns.
 The value of these columns does not vary as a function of time.
 For example, store ID or item color.
      repeated string time_series_attribute_columns = 19;
      Specified by:
      getTimeSeriesAttributeColumnsCount in interface AutoMlForecastingInputsOrBuilder
      Returns:
      The count of timeSeriesAttributeColumns.

    • getTimeSeriesAttributeColumns

      public String getTimeSeriesAttributeColumns(int index)
       Column names that should be used as attribute columns.
 The value of these columns does not vary as a function of time.
 For example, store ID or item color.
      repeated string time_series_attribute_columns = 19;
      Specified by:
      getTimeSeriesAttributeColumns in interface AutoMlForecastingInputsOrBuilder
      Parameters:
      index - The index of the element to return.
      Returns:
      The timeSeriesAttributeColumns at the given index.

    • getTimeSeriesAttributeColumnsBytes

      public com.google.protobuf.ByteString getTimeSeriesAttributeColumnsBytes(int index)
       Column names that should be used as attribute columns.
 The value of these columns does not vary as a function of time.
 For example, store ID or item color.
      repeated string time_series_attribute_columns = 19;
      Specified by:
      getTimeSeriesAttributeColumnsBytes in interface AutoMlForecastingInputsOrBuilder
      Parameters:
      index - The index of the value to return.
      Returns:
      The bytes of the timeSeriesAttributeColumns at the given index.

    • setTimeSeriesAttributeColumns

      public AutoMlForecastingInputs.Builder setTimeSeriesAttributeColumns(int index, String value)
       Column names that should be used as attribute columns.
 The value of these columns does not vary as a function of time.
 For example, store ID or item color.
      repeated string time_series_attribute_columns = 19;
      Parameters:
      index - The index to set the value at.
      value - The timeSeriesAttributeColumns to set.
      Returns:
      This builder for chaining.

    • addTimeSeriesAttributeColumns

      public AutoMlForecastingInputs.Builder addTimeSeriesAttributeColumns(String value)
       Column names that should be used as attribute columns.
 The value of these columns does not vary as a function of time.
 For example, store ID or item color.
      repeated string time_series_attribute_columns = 19;
      Parameters:
      value - The timeSeriesAttributeColumns to add.
      Returns:
      This builder for chaining.

    • addAllTimeSeriesAttributeColumns

      public AutoMlForecastingInputs.Builder addAllTimeSeriesAttributeColumns(Iterable<String> values)
       Column names that should be used as attribute columns.
 The value of these columns does not vary as a function of time.
 For example, store ID or item color.
      repeated string time_series_attribute_columns = 19;
      Parameters:
      values - The timeSeriesAttributeColumns to add.
      Returns:
      This builder for chaining.

    • clearTimeSeriesAttributeColumns

      public AutoMlForecastingInputs.Builder clearTimeSeriesAttributeColumns()
       Column names that should be used as attribute columns.
 The value of these columns does not vary as a function of time.
 For example, store ID or item color.
      repeated string time_series_attribute_columns = 19;
      Returns:
      This builder for chaining.

    • addTimeSeriesAttributeColumnsBytes

      public AutoMlForecastingInputs.Builder addTimeSeriesAttributeColumnsBytes(com.google.protobuf.ByteString value)
       Column names that should be used as attribute columns.
 The value of these columns does not vary as a function of time.
 For example, store ID or item color.
      repeated string time_series_attribute_columns = 19;
      Parameters:
      value - The bytes of the timeSeriesAttributeColumns to add.
      Returns:
      This builder for chaining.

    • getUnavailableAtForecastColumnsList

      public com.google.protobuf.ProtocolStringList getUnavailableAtForecastColumnsList()
       Names of columns that are unavailable when a forecast is requested.
 This column contains information for the given entity (identified
 by the time_series_identifier_column) that is unknown before the forecast
 For example, actual weather on a given day.
      repeated string unavailable_at_forecast_columns = 20;
      Specified by:
      getUnavailableAtForecastColumnsList in interface AutoMlForecastingInputsOrBuilder
      Returns:
      A list containing the unavailableAtForecastColumns.

    • getUnavailableAtForecastColumnsCount

      public int getUnavailableAtForecastColumnsCount()
       Names of columns that are unavailable when a forecast is requested.
 This column contains information for the given entity (identified
 by the time_series_identifier_column) that is unknown before the forecast
 For example, actual weather on a given day.
      repeated string unavailable_at_forecast_columns = 20;
      Specified by:
      getUnavailableAtForecastColumnsCount in interface AutoMlForecastingInputsOrBuilder
      Returns:
      The count of unavailableAtForecastColumns.

    • getUnavailableAtForecastColumns

      public String getUnavailableAtForecastColumns(int index)
       Names of columns that are unavailable when a forecast is requested.
 This column contains information for the given entity (identified
 by the time_series_identifier_column) that is unknown before the forecast
 For example, actual weather on a given day.
      repeated string unavailable_at_forecast_columns = 20;
      Specified by:
      getUnavailableAtForecastColumns in interface AutoMlForecastingInputsOrBuilder
      Parameters:
      index - The index of the element to return.
      Returns:
      The unavailableAtForecastColumns at the given index.

    • getUnavailableAtForecastColumnsBytes

      public com.google.protobuf.ByteString getUnavailableAtForecastColumnsBytes(int index)
       Names of columns that are unavailable when a forecast is requested.
 This column contains information for the given entity (identified
 by the time_series_identifier_column) that is unknown before the forecast
 For example, actual weather on a given day.
      repeated string unavailable_at_forecast_columns = 20;
      Specified by:
      getUnavailableAtForecastColumnsBytes in interface AutoMlForecastingInputsOrBuilder
      Parameters:
      index - The index of the value to return.
      Returns:
      The bytes of the unavailableAtForecastColumns at the given index.

    • setUnavailableAtForecastColumns

      public AutoMlForecastingInputs.Builder setUnavailableAtForecastColumns(int index, String value)
       Names of columns that are unavailable when a forecast is requested.
 This column contains information for the given entity (identified
 by the time_series_identifier_column) that is unknown before the forecast
 For example, actual weather on a given day.
      repeated string unavailable_at_forecast_columns = 20;
      Parameters:
      index - The index to set the value at.
      value - The unavailableAtForecastColumns to set.
      Returns:
      This builder for chaining.

    • addUnavailableAtForecastColumns

      public AutoMlForecastingInputs.Builder addUnavailableAtForecastColumns(String value)
       Names of columns that are unavailable when a forecast is requested.
 This column contains information for the given entity (identified
 by the time_series_identifier_column) that is unknown before the forecast
 For example, actual weather on a given day.
      repeated string unavailable_at_forecast_columns = 20;
      Parameters:
      value - The unavailableAtForecastColumns to add.
      Returns:
      This builder for chaining.

    • addAllUnavailableAtForecastColumns

      public AutoMlForecastingInputs.Builder addAllUnavailableAtForecastColumns(Iterable<String> values)
       Names of columns that are unavailable when a forecast is requested.
 This column contains information for the given entity (identified
 by the time_series_identifier_column) that is unknown before the forecast
 For example, actual weather on a given day.
      repeated string unavailable_at_forecast_columns = 20;
      Parameters:
      values - The unavailableAtForecastColumns to add.
      Returns:
      This builder for chaining.

    • clearUnavailableAtForecastColumns

      public AutoMlForecastingInputs.Builder clearUnavailableAtForecastColumns()
       Names of columns that are unavailable when a forecast is requested.
 This column contains information for the given entity (identified
 by the time_series_identifier_column) that is unknown before the forecast
 For example, actual weather on a given day.
      repeated string unavailable_at_forecast_columns = 20;
      Returns:
      This builder for chaining.

    • addUnavailableAtForecastColumnsBytes

      public AutoMlForecastingInputs.Builder addUnavailableAtForecastColumnsBytes(com.google.protobuf.ByteString value)
       Names of columns that are unavailable when a forecast is requested.
 This column contains information for the given entity (identified
 by the time_series_identifier_column) that is unknown before the forecast
 For example, actual weather on a given day.
      repeated string unavailable_at_forecast_columns = 20;
      Parameters:
      value - The bytes of the unavailableAtForecastColumns to add.
      Returns:
      This builder for chaining.

    • getAvailableAtForecastColumnsList

      public com.google.protobuf.ProtocolStringList getAvailableAtForecastColumnsList()
       Names of columns that are available and provided when a forecast
 is requested. These columns
 contain information for the given entity (identified by the
 time_series_identifier_column column) that is known at forecast.
 For example, predicted weather for a specific day.
      repeated string available_at_forecast_columns = 21;
      Specified by:
      getAvailableAtForecastColumnsList in interface AutoMlForecastingInputsOrBuilder
      Returns:
      A list containing the availableAtForecastColumns.

    • getAvailableAtForecastColumnsCount

      public int getAvailableAtForecastColumnsCount()
       Names of columns that are available and provided when a forecast
 is requested. These columns
 contain information for the given entity (identified by the
 time_series_identifier_column column) that is known at forecast.
 For example, predicted weather for a specific day.
      repeated string available_at_forecast_columns = 21;
      Specified by:
      getAvailableAtForecastColumnsCount in interface AutoMlForecastingInputsOrBuilder
      Returns:
      The count of availableAtForecastColumns.

    • getAvailableAtForecastColumns

      public String getAvailableAtForecastColumns(int index)
       Names of columns that are available and provided when a forecast
 is requested. These columns
 contain information for the given entity (identified by the
 time_series_identifier_column column) that is known at forecast.
 For example, predicted weather for a specific day.
      repeated string available_at_forecast_columns = 21;
      Specified by:
      getAvailableAtForecastColumns in interface AutoMlForecastingInputsOrBuilder
      Parameters:
      index - The index of the element to return.
      Returns:
      The availableAtForecastColumns at the given index.

    • getAvailableAtForecastColumnsBytes

      public com.google.protobuf.ByteString getAvailableAtForecastColumnsBytes(int index)
       Names of columns that are available and provided when a forecast
 is requested. These columns
 contain information for the given entity (identified by the
 time_series_identifier_column column) that is known at forecast.
 For example, predicted weather for a specific day.
      repeated string available_at_forecast_columns = 21;
      Specified by:
      getAvailableAtForecastColumnsBytes in interface AutoMlForecastingInputsOrBuilder
      Parameters:
      index - The index of the value to return.
      Returns:
      The bytes of the availableAtForecastColumns at the given index.

    • setAvailableAtForecastColumns

      public AutoMlForecastingInputs.Builder setAvailableAtForecastColumns(int index, String value)
       Names of columns that are available and provided when a forecast
 is requested. These columns
 contain information for the given entity (identified by the
 time_series_identifier_column column) that is known at forecast.
 For example, predicted weather for a specific day.
      repeated string available_at_forecast_columns = 21;
      Parameters:
      index - The index to set the value at.
      value - The availableAtForecastColumns to set.
      Returns:
      This builder for chaining.

    • addAvailableAtForecastColumns

      public AutoMlForecastingInputs.Builder addAvailableAtForecastColumns(String value)
       Names of columns that are available and provided when a forecast
 is requested. These columns
 contain information for the given entity (identified by the
 time_series_identifier_column column) that is known at forecast.
 For example, predicted weather for a specific day.
      repeated string available_at_forecast_columns = 21;
      Parameters:
      value - The availableAtForecastColumns to add.
      Returns:
      This builder for chaining.

    • addAllAvailableAtForecastColumns

      public AutoMlForecastingInputs.Builder addAllAvailableAtForecastColumns(Iterable<String> values)
       Names of columns that are available and provided when a forecast
 is requested. These columns
 contain information for the given entity (identified by the
 time_series_identifier_column column) that is known at forecast.
 For example, predicted weather for a specific day.
      repeated string available_at_forecast_columns = 21;
      Parameters:
      values - The availableAtForecastColumns to add.
      Returns:
      This builder for chaining.

    • clearAvailableAtForecastColumns

      public AutoMlForecastingInputs.Builder clearAvailableAtForecastColumns()
       Names of columns that are available and provided when a forecast
 is requested. These columns
 contain information for the given entity (identified by the
 time_series_identifier_column column) that is known at forecast.
 For example, predicted weather for a specific day.
      repeated string available_at_forecast_columns = 21;
      Returns:
      This builder for chaining.

    • addAvailableAtForecastColumnsBytes

      public AutoMlForecastingInputs.Builder addAvailableAtForecastColumnsBytes(com.google.protobuf.ByteString value)
       Names of columns that are available and provided when a forecast
 is requested. These columns
 contain information for the given entity (identified by the
 time_series_identifier_column column) that is known at forecast.
 For example, predicted weather for a specific day.
      repeated string available_at_forecast_columns = 21;
      Parameters:
      value - The bytes of the availableAtForecastColumns to add.
      Returns:
      This builder for chaining.

    • hasDataGranularity

      public boolean hasDataGranularity()
       Expected difference in time granularity between rows in the data.
      .google.cloud.aiplatform.v1beta1.schema.trainingjob.definition.AutoMlForecastingInputs.Granularity data_granularity = 22;
      Specified by:
      hasDataGranularity in interface AutoMlForecastingInputsOrBuilder
      Returns:
      Whether the dataGranularity field is set.

    • getDataGranularity

      public AutoMlForecastingInputs.Granularity getDataGranularity()
       Expected difference in time granularity between rows in the data.
      .google.cloud.aiplatform.v1beta1.schema.trainingjob.definition.AutoMlForecastingInputs.Granularity data_granularity = 22;
      Specified by:
      getDataGranularity in interface AutoMlForecastingInputsOrBuilder
      Returns:
      The dataGranularity.

    • setDataGranularity

      public AutoMlForecastingInputs.Builder setDataGranularity(AutoMlForecastingInputs.Granularity value)
       Expected difference in time granularity between rows in the data.
      .google.cloud.aiplatform.v1beta1.schema.trainingjob.definition.AutoMlForecastingInputs.Granularity data_granularity = 22;

    • setDataGranularity

      public AutoMlForecastingInputs.Builder setDataGranularity(AutoMlForecastingInputs.Granularity.Builder builderForValue)
       Expected difference in time granularity between rows in the data.
      .google.cloud.aiplatform.v1beta1.schema.trainingjob.definition.AutoMlForecastingInputs.Granularity data_granularity = 22;

    • mergeDataGranularity

      public AutoMlForecastingInputs.Builder mergeDataGranularity(AutoMlForecastingInputs.Granularity value)
       Expected difference in time granularity between rows in the data.
      .google.cloud.aiplatform.v1beta1.schema.trainingjob.definition.AutoMlForecastingInputs.Granularity data_granularity = 22;

    • clearDataGranularity

      public AutoMlForecastingInputs.Builder clearDataGranularity()
       Expected difference in time granularity between rows in the data.
      .google.cloud.aiplatform.v1beta1.schema.trainingjob.definition.AutoMlForecastingInputs.Granularity data_granularity = 22;

    • getDataGranularityBuilder

      public AutoMlForecastingInputs.Granularity.Builder getDataGranularityBuilder()
       Expected difference in time granularity between rows in the data.
      .google.cloud.aiplatform.v1beta1.schema.trainingjob.definition.AutoMlForecastingInputs.Granularity data_granularity = 22;

    • getDataGranularityOrBuilder

      public AutoMlForecastingInputs.GranularityOrBuilder getDataGranularityOrBuilder()
       Expected difference in time granularity between rows in the data.
      .google.cloud.aiplatform.v1beta1.schema.trainingjob.definition.AutoMlForecastingInputs.Granularity data_granularity = 22;
      Specified by:
      getDataGranularityOrBuilder in interface AutoMlForecastingInputsOrBuilder

    • getForecastHorizon

      public long getForecastHorizon()
       The amount of time into the future for which forecasted values for the
 target are returned. Expressed in number of units defined by the
 `data_granularity` field.
      int64 forecast_horizon = 23;
      Specified by:
      getForecastHorizon in interface AutoMlForecastingInputsOrBuilder
      Returns:
      The forecastHorizon.

    • setForecastHorizon

      public AutoMlForecastingInputs.Builder setForecastHorizon(long value)
       The amount of time into the future for which forecasted values for the
 target are returned. Expressed in number of units defined by the
 `data_granularity` field.
      int64 forecast_horizon = 23;
      Parameters:
      value - The forecastHorizon to set.
      Returns:
      This builder for chaining.

    • clearForecastHorizon

      public AutoMlForecastingInputs.Builder clearForecastHorizon()
       The amount of time into the future for which forecasted values for the
 target are returned. Expressed in number of units defined by the
 `data_granularity` field.
      int64 forecast_horizon = 23;
      Returns:
      This builder for chaining.

    • getContextWindow

      public long getContextWindow()
       The amount of time into the past training and prediction data is used
 for model training and prediction respectively. Expressed in number of
 units defined by the `data_granularity` field.
      int64 context_window = 24;
      Specified by:
      getContextWindow in interface AutoMlForecastingInputsOrBuilder
      Returns:
      The contextWindow.

    • setContextWindow

      public AutoMlForecastingInputs.Builder setContextWindow(long value)
       The amount of time into the past training and prediction data is used
 for model training and prediction respectively. Expressed in number of
 units defined by the `data_granularity` field.
      int64 context_window = 24;
      Parameters:
      value - The contextWindow to set.
      Returns:
      This builder for chaining.

    • clearContextWindow

      public AutoMlForecastingInputs.Builder clearContextWindow()
       The amount of time into the past training and prediction data is used
 for model training and prediction respectively. Expressed in number of
 units defined by the `data_granularity` field.
      int64 context_window = 24;
      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.v1beta1.schema.trainingjob.definition.ExportEvaluatedDataItemsConfig export_evaluated_data_items_config = 15;
      Specified by:
      hasExportEvaluatedDataItemsConfig in interface AutoMlForecastingInputsOrBuilder
      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.v1beta1.schema.trainingjob.definition.ExportEvaluatedDataItemsConfig export_evaluated_data_items_config = 15;
      Specified by:
      getExportEvaluatedDataItemsConfig in interface AutoMlForecastingInputsOrBuilder
      Returns:
      The exportEvaluatedDataItemsConfig.

    • setExportEvaluatedDataItemsConfig

      public AutoMlForecastingInputs.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.v1beta1.schema.trainingjob.definition.ExportEvaluatedDataItemsConfig export_evaluated_data_items_config = 15;

    • setExportEvaluatedDataItemsConfig

      public AutoMlForecastingInputs.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.v1beta1.schema.trainingjob.definition.ExportEvaluatedDataItemsConfig export_evaluated_data_items_config = 15;

    • mergeExportEvaluatedDataItemsConfig

      public AutoMlForecastingInputs.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.v1beta1.schema.trainingjob.definition.ExportEvaluatedDataItemsConfig export_evaluated_data_items_config = 15;

    • clearExportEvaluatedDataItemsConfig

      public AutoMlForecastingInputs.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.v1beta1.schema.trainingjob.definition.ExportEvaluatedDataItemsConfig export_evaluated_data_items_config = 15;

    • 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.v1beta1.schema.trainingjob.definition.ExportEvaluatedDataItemsConfig export_evaluated_data_items_config = 15;

    • 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.v1beta1.schema.trainingjob.definition.ExportEvaluatedDataItemsConfig export_evaluated_data_items_config = 15;
      Specified by:
      getExportEvaluatedDataItemsConfigOrBuilder in interface AutoMlForecastingInputsOrBuilder

    • getQuantilesList

      public List<Double> getQuantilesList()
       Quantiles to use for minimize-quantile-loss `optimization_objective`. Up to
 5 quantiles are allowed of values between 0 and 1, exclusive. Required if
 the value of optimization_objective is minimize-quantile-loss. Represents
 the percent quantiles to use for that objective. Quantiles must be unique.
      repeated double quantiles = 16;
      Specified by:
      getQuantilesList in interface AutoMlForecastingInputsOrBuilder
      Returns:
      A list containing the quantiles.

    • getQuantilesCount

      public int getQuantilesCount()
       Quantiles to use for minimize-quantile-loss `optimization_objective`. Up to
 5 quantiles are allowed of values between 0 and 1, exclusive. Required if
 the value of optimization_objective is minimize-quantile-loss. Represents
 the percent quantiles to use for that objective. Quantiles must be unique.
      repeated double quantiles = 16;
      Specified by:
      getQuantilesCount in interface AutoMlForecastingInputsOrBuilder
      Returns:
      The count of quantiles.

    • getQuantiles

      public double getQuantiles(int index)
       Quantiles to use for minimize-quantile-loss `optimization_objective`. Up to
 5 quantiles are allowed of values between 0 and 1, exclusive. Required if
 the value of optimization_objective is minimize-quantile-loss. Represents
 the percent quantiles to use for that objective. Quantiles must be unique.
      repeated double quantiles = 16;
      Specified by:
      getQuantiles in interface AutoMlForecastingInputsOrBuilder
      Parameters:
      index - The index of the element to return.
      Returns:
      The quantiles at the given index.

    • setQuantiles

      public AutoMlForecastingInputs.Builder setQuantiles(int index, double value)
       Quantiles to use for minimize-quantile-loss `optimization_objective`. Up to
 5 quantiles are allowed of values between 0 and 1, exclusive. Required if
 the value of optimization_objective is minimize-quantile-loss. Represents
 the percent quantiles to use for that objective. Quantiles must be unique.
      repeated double quantiles = 16;
      Parameters:
      index - The index to set the value at.
      value - The quantiles to set.
      Returns:
      This builder for chaining.

    • addQuantiles

      public AutoMlForecastingInputs.Builder addQuantiles(double value)
       Quantiles to use for minimize-quantile-loss `optimization_objective`. Up to
 5 quantiles are allowed of values between 0 and 1, exclusive. Required if
 the value of optimization_objective is minimize-quantile-loss. Represents
 the percent quantiles to use for that objective. Quantiles must be unique.
      repeated double quantiles = 16;
      Parameters:
      value - The quantiles to add.
      Returns:
      This builder for chaining.

    • addAllQuantiles

      public AutoMlForecastingInputs.Builder addAllQuantiles(Iterable<? extends Double> values)
       Quantiles to use for minimize-quantile-loss `optimization_objective`. Up to
 5 quantiles are allowed of values between 0 and 1, exclusive. Required if
 the value of optimization_objective is minimize-quantile-loss. Represents
 the percent quantiles to use for that objective. Quantiles must be unique.
      repeated double quantiles = 16;
      Parameters:
      values - The quantiles to add.
      Returns:
      This builder for chaining.

    • clearQuantiles

      public AutoMlForecastingInputs.Builder clearQuantiles()
       Quantiles to use for minimize-quantile-loss `optimization_objective`. Up to
 5 quantiles are allowed of values between 0 and 1, exclusive. Required if
 the value of optimization_objective is minimize-quantile-loss. Represents
 the percent quantiles to use for that objective. Quantiles must be unique.
      repeated double quantiles = 16;
      Returns:
      This builder for chaining.

    • getValidationOptions

      public String getValidationOptions()
       Validation options for the data validation component. The available options
 are:

 * "fail-pipeline" - default, will validate against the validation and
 fail the pipeline if it fails.

 * "ignore-validation" - ignore the results of the validation and continue
      string validation_options = 17;
      Specified by:
      getValidationOptions in interface AutoMlForecastingInputsOrBuilder
      Returns:
      The validationOptions.

    • getValidationOptionsBytes

      public com.google.protobuf.ByteString getValidationOptionsBytes()
       Validation options for the data validation component. The available options
 are:

 * "fail-pipeline" - default, will validate against the validation and
 fail the pipeline if it fails.

 * "ignore-validation" - ignore the results of the validation and continue
      string validation_options = 17;
      Specified by:
      getValidationOptionsBytes in interface AutoMlForecastingInputsOrBuilder
      Returns:
      The bytes for validationOptions.

    • setValidationOptions

      public AutoMlForecastingInputs.Builder setValidationOptions(String value)
       Validation options for the data validation component. The available options
 are:

 * "fail-pipeline" - default, will validate against the validation and
 fail the pipeline if it fails.

 * "ignore-validation" - ignore the results of the validation and continue
      string validation_options = 17;
      Parameters:
      value - The validationOptions to set.
      Returns:
      This builder for chaining.

    • clearValidationOptions

      public AutoMlForecastingInputs.Builder clearValidationOptions()
       Validation options for the data validation component. The available options
 are:

 * "fail-pipeline" - default, will validate against the validation and
 fail the pipeline if it fails.

 * "ignore-validation" - ignore the results of the validation and continue
      string validation_options = 17;
      Returns:
      This builder for chaining.

    • setValidationOptionsBytes

      public AutoMlForecastingInputs.Builder setValidationOptionsBytes(com.google.protobuf.ByteString value)
       Validation options for the data validation component. The available options
 are:

 * "fail-pipeline" - default, will validate against the validation and
 fail the pipeline if it fails.

 * "ignore-validation" - ignore the results of the validation and continue
      string validation_options = 17;
      Parameters:
      value - The bytes for validationOptions to set.
      Returns:
      This builder for chaining.

    • getAdditionalExperimentsList

      public com.google.protobuf.ProtocolStringList getAdditionalExperimentsList()
       Additional experiment flags for the time series forcasting training.
      repeated string additional_experiments = 25;
      Specified by:
      getAdditionalExperimentsList in interface AutoMlForecastingInputsOrBuilder
      Returns:
      A list containing the additionalExperiments.

    • getAdditionalExperimentsCount

      public int getAdditionalExperimentsCount()
       Additional experiment flags for the time series forcasting training.
      repeated string additional_experiments = 25;
      Specified by:
      getAdditionalExperimentsCount in interface AutoMlForecastingInputsOrBuilder
      Returns:
      The count of additionalExperiments.

    • getAdditionalExperiments

      public String getAdditionalExperiments(int index)
       Additional experiment flags for the time series forcasting training.
      repeated string additional_experiments = 25;
      Specified by:
      getAdditionalExperiments in interface AutoMlForecastingInputsOrBuilder
      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 time series forcasting training.
      repeated string additional_experiments = 25;
      Specified by:
      getAdditionalExperimentsBytes in interface AutoMlForecastingInputsOrBuilder
      Parameters:
      index - The index of the value to return.
      Returns:
      The bytes of the additionalExperiments at the given index.

    • setAdditionalExperiments

      public AutoMlForecastingInputs.Builder setAdditionalExperiments(int index, String value)
       Additional experiment flags for the time series forcasting training.
      repeated string additional_experiments = 25;
      Parameters:
      index - The index to set the value at.
      value - The additionalExperiments to set.
      Returns:
      This builder for chaining.

    • addAdditionalExperiments

      public AutoMlForecastingInputs.Builder addAdditionalExperiments(String value)
       Additional experiment flags for the time series forcasting training.
      repeated string additional_experiments = 25;
      Parameters:
      value - The additionalExperiments to add.
      Returns:
      This builder for chaining.

    • addAllAdditionalExperiments

      public AutoMlForecastingInputs.Builder addAllAdditionalExperiments(Iterable<String> values)
       Additional experiment flags for the time series forcasting training.
      repeated string additional_experiments = 25;
      Parameters:
      values - The additionalExperiments to add.
      Returns:
      This builder for chaining.

    • clearAdditionalExperiments

      public AutoMlForecastingInputs.Builder clearAdditionalExperiments()
       Additional experiment flags for the time series forcasting training.
      repeated string additional_experiments = 25;
      Returns:
      This builder for chaining.

    • addAdditionalExperimentsBytes

      public AutoMlForecastingInputs.Builder addAdditionalExperimentsBytes(com.google.protobuf.ByteString value)
       Additional experiment flags for the time series forcasting training.
      repeated string additional_experiments = 25;
      Parameters:
      value - The bytes of the additionalExperiments to add.
      Returns:
      This builder for chaining.