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

Class AutoMlTablesInputs.Builder

    • Method Detail

      • getDescriptor

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

      • internalGetFieldAccessorTable

        protected com.google.protobuf.GeneratedMessageV3.FieldAccessorTable internalGetFieldAccessorTable()
        Specified by:
        internalGetFieldAccessorTable in class com.google.protobuf.GeneratedMessageV3.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.GeneratedMessageV3.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.GeneratedMessageV3.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

      • clone

        public AutoMlTablesInputs.Builder clone()
        Specified by:
        clone in interface com.google.protobuf.Message.Builder
        Specified by:
        clone in interface com.google.protobuf.MessageLite.Builder
        Overrides:
        clone in class com.google.protobuf.GeneratedMessageV3.Builder<AutoMlTablesInputs.Builder>

      • setField

        public AutoMlTablesInputs.Builder setField​(com.google.protobuf.Descriptors.FieldDescriptor field,
                                           Object value)
        Specified by:
        setField in interface com.google.protobuf.Message.Builder
        Overrides:
        setField in class com.google.protobuf.GeneratedMessageV3.Builder<AutoMlTablesInputs.Builder>

      • clearField

        public AutoMlTablesInputs.Builder clearField​(com.google.protobuf.Descriptors.FieldDescriptor field)
        Specified by:
        clearField in interface com.google.protobuf.Message.Builder
        Overrides:
        clearField in class com.google.protobuf.GeneratedMessageV3.Builder<AutoMlTablesInputs.Builder>

      • clearOneof

        public AutoMlTablesInputs.Builder clearOneof​(com.google.protobuf.Descriptors.OneofDescriptor oneof)
        Specified by:
        clearOneof in interface com.google.protobuf.Message.Builder
        Overrides:
        clearOneof in class com.google.protobuf.GeneratedMessageV3.Builder<AutoMlTablesInputs.Builder>

      • setRepeatedField

        public AutoMlTablesInputs.Builder setRepeatedField​(com.google.protobuf.Descriptors.FieldDescriptor field,
                                                   int index,
                                                   Object value)
        Specified by:
        setRepeatedField in interface com.google.protobuf.Message.Builder
        Overrides:
        setRepeatedField in class com.google.protobuf.GeneratedMessageV3.Builder<AutoMlTablesInputs.Builder>

      • addRepeatedField

        public AutoMlTablesInputs.Builder addRepeatedField​(com.google.protobuf.Descriptors.FieldDescriptor field,
                                                   Object value)
        Specified by:
        addRepeatedField in interface com.google.protobuf.Message.Builder
        Overrides:
        addRepeatedField in class com.google.protobuf.GeneratedMessageV3.Builder<AutoMlTablesInputs.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>

      • isInitialized

        public final boolean isInitialized()
        Specified by:
        isInitialized in interface com.google.protobuf.MessageLiteOrBuilder
        Overrides:
        isInitialized in class com.google.protobuf.GeneratedMessageV3.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

      • 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.v1beta1.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.v1beta1.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.v1beta1.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.v1beta1.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.v1beta1.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.v1beta1.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.v1beta1.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.v1beta1.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.v1beta1.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.v1beta1.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.v1beta1.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.v1beta1.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.v1beta1.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.v1beta1.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.v1beta1.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.v1beta1.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.v1beta1.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.v1beta1.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.v1beta1.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.v1beta1.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.v1beta1.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.v1beta1.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.v1beta1.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.v1beta1.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.v1beta1.schema.trainingjob.definition.ExportEvaluatedDataItemsConfig export_evaluated_data_items_config = 10;

      • 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.

      • setUnknownFields

        public final AutoMlTablesInputs.Builder setUnknownFields​(com.google.protobuf.UnknownFieldSet unknownFields)
        Specified by:
        setUnknownFields in interface com.google.protobuf.Message.Builder
        Overrides:
        setUnknownFields in class com.google.protobuf.GeneratedMessageV3.Builder<AutoMlTablesInputs.Builder>

      • mergeUnknownFields

        public final AutoMlTablesInputs.Builder mergeUnknownFields​(com.google.protobuf.UnknownFieldSet unknownFields)
        Specified by:
        mergeUnknownFields in interface com.google.protobuf.Message.Builder
        Overrides:
        mergeUnknownFields in class com.google.protobuf.GeneratedMessageV3.Builder<AutoMlTablesInputs.Builder>