Class AutoMlForecastingInputs.Builder (3.31.0)

Version 3.31.0

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public static final class AutoMlForecastingInputs.Builder extends GeneratedMessageV3.Builder<AutoMlForecastingInputs.Builder> implements AutoMlForecastingInputsOrBuilder

Protobuf type google.cloud.aiplatform.v1beta1.schema.trainingjob.definition.AutoMlForecastingInputs

Inheritance

Object > AbstractMessageLite.Builder<MessageType,BuilderType> > AbstractMessage.Builder<BuilderType> > GeneratedMessageV3.Builder > AutoMlForecastingInputs.Builder

Implements

AutoMlForecastingInputsOrBuilder

Inherited Members

AbstractMessage.Builder.findInitializationErrors()

AbstractMessage.Builder.getInitializationErrorString()

AbstractMessage.Builder.internalMergeFrom(AbstractMessageLite)

AbstractMessage.Builder.mergeFrom(byte[])

AbstractMessage.Builder.mergeFrom(byte[],ExtensionRegistryLite)

AbstractMessage.Builder.mergeFrom(byte[],int,int)

AbstractMessage.Builder.mergeFrom(byte[],int,int,ExtensionRegistryLite)

AbstractMessage.Builder.mergeFrom(ByteString)

AbstractMessage.Builder.mergeFrom(ByteString,ExtensionRegistryLite)

AbstractMessage.Builder.mergeFrom(CodedInputStream)

AbstractMessage.Builder.mergeFrom(CodedInputStream,ExtensionRegistryLite)

AbstractMessage.Builder.mergeFrom(Message)

AbstractMessage.Builder.mergeFrom(InputStream)

AbstractMessage.Builder.mergeFrom(InputStream,ExtensionRegistryLite)

AbstractMessage.Builder.newUninitializedMessageException(Message)

AbstractMessage.Builder.toString()

AbstractMessageLite.Builder.<T>addAll(Iterable<T>,Collection<? super T>)

AbstractMessageLite.Builder.<T>addAll(Iterable<T>,List<? super T>)

AbstractMessageLite.Builder.internalMergeFrom(MessageType)

AbstractMessageLite.Builder.mergeDelimitedFrom(InputStream)

AbstractMessageLite.Builder.mergeDelimitedFrom(InputStream,ExtensionRegistryLite)

AbstractMessageLite.Builder.mergeFrom(MessageLite)

AbstractMessageLite.Builder.newUninitializedMessageException(MessageLite)

GeneratedMessageV3.Builder.addRepeatedField(Descriptors.FieldDescriptor,Object)

GeneratedMessageV3.Builder.clear()

GeneratedMessageV3.Builder.clearField(Descriptors.FieldDescriptor)

GeneratedMessageV3.Builder.clearOneof(Descriptors.OneofDescriptor)

GeneratedMessageV3.Builder.clone()

GeneratedMessageV3.Builder.getAllFields()

GeneratedMessageV3.Builder.getDescriptorForType()

GeneratedMessageV3.Builder.getField(Descriptors.FieldDescriptor)

GeneratedMessageV3.Builder.getFieldBuilder(Descriptors.FieldDescriptor)

GeneratedMessageV3.Builder.getOneofFieldDescriptor(Descriptors.OneofDescriptor)

GeneratedMessageV3.Builder.getParentForChildren()

GeneratedMessageV3.Builder.getRepeatedField(Descriptors.FieldDescriptor,int)

GeneratedMessageV3.Builder.getRepeatedFieldBuilder(Descriptors.FieldDescriptor,int)

GeneratedMessageV3.Builder.getRepeatedFieldCount(Descriptors.FieldDescriptor)

com.google.protobuf.GeneratedMessageV3.Builder.getUnknownFieldSetBuilder()

GeneratedMessageV3.Builder.getUnknownFields()

GeneratedMessageV3.Builder.hasField(Descriptors.FieldDescriptor)

GeneratedMessageV3.Builder.hasOneof(Descriptors.OneofDescriptor)

GeneratedMessageV3.Builder.internalGetFieldAccessorTable()

GeneratedMessageV3.Builder.internalGetMapField(int)

GeneratedMessageV3.Builder.internalGetMutableMapField(int)

GeneratedMessageV3.Builder.isClean()

GeneratedMessageV3.Builder.isInitialized()

GeneratedMessageV3.Builder.markClean()

GeneratedMessageV3.Builder.mergeUnknownFields(UnknownFieldSet)

com.google.protobuf.GeneratedMessageV3.Builder.mergeUnknownLengthDelimitedField(int,com.google.protobuf.ByteString)

com.google.protobuf.GeneratedMessageV3.Builder.mergeUnknownVarintField(int,int)

GeneratedMessageV3.Builder.newBuilderForField(Descriptors.FieldDescriptor)

GeneratedMessageV3.Builder.onBuilt()

GeneratedMessageV3.Builder.onChanged()

com.google.protobuf.GeneratedMessageV3.Builder.parseUnknownField(com.google.protobuf.CodedInputStream,com.google.protobuf.ExtensionRegistryLite,int)

GeneratedMessageV3.Builder.setField(Descriptors.FieldDescriptor,Object)

GeneratedMessageV3.Builder.setRepeatedField(Descriptors.FieldDescriptor,int,Object)

com.google.protobuf.GeneratedMessageV3.Builder.setUnknownFieldSetBuilder(com.google.protobuf.UnknownFieldSet.Builder)

GeneratedMessageV3.Builder.setUnknownFields(UnknownFieldSet)

GeneratedMessageV3.Builder.setUnknownFieldsProto3(UnknownFieldSet)

Object.equals(Object)

Object.finalize()

Object.getClass()

Object.hashCode()

Object.notify()

Object.notifyAll()

Object.wait()

Object.wait(long)

Object.wait(long,int)

Static Methods

getDescriptor()

public static final Descriptors.Descriptor getDescriptor()

Returns
Type	Description
Descriptor

Methods

addAdditionalExperiments(String value)

public AutoMlForecastingInputs.Builder addAdditionalExperiments(String value)

Additional experiment flags for the time series forcasting training.

repeated string additional_experiments = 25;

Parameter
Name	Description
value	String The additionalExperiments to add.

Returns
Type	Description
AutoMlForecastingInputs.Builder	This builder for chaining.

addAdditionalExperimentsBytes(ByteString value)

public AutoMlForecastingInputs.Builder addAdditionalExperimentsBytes(ByteString value)

Additional experiment flags for the time series forcasting training.

repeated string additional_experiments = 25;

Parameter
Name	Description
value	ByteString The bytes of the additionalExperiments to add.

Returns
Type	Description
AutoMlForecastingInputs.Builder	This builder for chaining.

addAllAdditionalExperiments(Iterable<String> values)

public AutoMlForecastingInputs.Builder addAllAdditionalExperiments(Iterable<String> values)

Additional experiment flags for the time series forcasting training.

repeated string additional_experiments = 25;

Parameter
Name	Description
values	Iterable<String> The additionalExperiments to add.

Returns
Type	Description
AutoMlForecastingInputs.Builder	This builder for chaining.

addAllAvailableAtForecastColumns(Iterable<String> values)

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;

Parameter
Name	Description
values	Iterable<String> The availableAtForecastColumns to add.

Returns
Type	Description
AutoMlForecastingInputs.Builder	This builder for chaining.

addAllQuantiles(Iterable<? extends Double> values)

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;

Parameter
Name	Description
values	Iterable<? extends java.lang.Double> The quantiles to add.

Returns
Type	Description
AutoMlForecastingInputs.Builder	This builder for chaining.

addAllTimeSeriesAttributeColumns(Iterable<String> values)

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;

Parameter
Name	Description
values	Iterable<String> The timeSeriesAttributeColumns to add.

Returns
Type	Description
AutoMlForecastingInputs.Builder	This builder for chaining.

addAllTransformations(Iterable<? extends AutoMlForecastingInputs.Transformation> values)

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;

Parameter
Name	Description
values	Iterable<? extends com.google.cloud.aiplatform.v1beta1.schema.trainingjob.definition.AutoMlForecastingInputs.Transformation>

Returns
Type	Description
AutoMlForecastingInputs.Builder

addAllUnavailableAtForecastColumns(Iterable<String> values)

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;

Parameter
Name	Description
values	Iterable<String> The unavailableAtForecastColumns to add.

Returns
Type	Description
AutoMlForecastingInputs.Builder	This builder for chaining.

addAvailableAtForecastColumns(String value)

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;

Parameter
Name	Description
value	String The availableAtForecastColumns to add.

Returns
Type	Description
AutoMlForecastingInputs.Builder	This builder for chaining.

addAvailableAtForecastColumnsBytes(ByteString value)

public AutoMlForecastingInputs.Builder addAvailableAtForecastColumnsBytes(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;

Parameter
Name	Description
value	ByteString The bytes of the availableAtForecastColumns to add.

Returns
Type	Description
AutoMlForecastingInputs.Builder	This builder for chaining.

addQuantiles(double value)

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;

Parameter
Name	Description
value	double The quantiles to add.

Returns
Type	Description
AutoMlForecastingInputs.Builder	This builder for chaining.

addRepeatedField(Descriptors.FieldDescriptor field, Object value)

public AutoMlForecastingInputs.Builder addRepeatedField(Descriptors.FieldDescriptor field, Object value)

Parameters
Name	Description
field	FieldDescriptor
value	Object

Returns
Type	Description
AutoMlForecastingInputs.Builder

Overrides

GeneratedMessageV3.Builder<BuilderType>.addRepeatedField(Descriptors.FieldDescriptor field, Object value)

addTimeSeriesAttributeColumns(String value)

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;

Parameter
Name	Description
value	String The timeSeriesAttributeColumns to add.

Returns
Type	Description
AutoMlForecastingInputs.Builder	This builder for chaining.

addTimeSeriesAttributeColumnsBytes(ByteString value)

public AutoMlForecastingInputs.Builder addTimeSeriesAttributeColumnsBytes(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;

Parameter
Name	Description
value	ByteString The bytes of the timeSeriesAttributeColumns to add.

Returns
Type	Description
AutoMlForecastingInputs.Builder	This builder for chaining.

addTransformations(AutoMlForecastingInputs.Transformation value)

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;

Parameter
Name	Description
value	AutoMlForecastingInputs.Transformation

Returns
Type	Description
AutoMlForecastingInputs.Builder

addTransformations(AutoMlForecastingInputs.Transformation.Builder builderForValue)

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;

Parameter
Name	Description
builderForValue	AutoMlForecastingInputs.Transformation.Builder

Returns
Type	Description
AutoMlForecastingInputs.Builder

addTransformations(int index, AutoMlForecastingInputs.Transformation value)

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;

Parameters
Name	Description
index	int
value	AutoMlForecastingInputs.Transformation

Returns
Type	Description
AutoMlForecastingInputs.Builder

addTransformations(int index, AutoMlForecastingInputs.Transformation.Builder builderForValue)

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;

Parameters
Name	Description
index	int
builderForValue	AutoMlForecastingInputs.Transformation.Builder

Returns
Type	Description
AutoMlForecastingInputs.Builder

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;

Returns
Type	Description
AutoMlForecastingInputs.Transformation.Builder

addTransformationsBuilder(int index)

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;

Parameter
Name	Description
index	int

Returns
Type	Description
AutoMlForecastingInputs.Transformation.Builder

addUnavailableAtForecastColumns(String value)

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;

Parameter
Name	Description
value	String The unavailableAtForecastColumns to add.

Returns
Type	Description
AutoMlForecastingInputs.Builder	This builder for chaining.

addUnavailableAtForecastColumnsBytes(ByteString value)

public AutoMlForecastingInputs.Builder addUnavailableAtForecastColumnsBytes(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;

Parameter
Name	Description
value	ByteString The bytes of the unavailableAtForecastColumns to add.

Returns
Type	Description
AutoMlForecastingInputs.Builder	This builder for chaining.

build()

public AutoMlForecastingInputs build()

Returns
Type	Description
AutoMlForecastingInputs

buildPartial()

public AutoMlForecastingInputs buildPartial()

Returns
Type	Description
AutoMlForecastingInputs

clear()

public AutoMlForecastingInputs.Builder clear()

Returns
Type	Description
AutoMlForecastingInputs.Builder

Overrides

GeneratedMessageV3.Builder<BuilderType>.clear()

clearAdditionalExperiments()

public AutoMlForecastingInputs.Builder clearAdditionalExperiments()

Additional experiment flags for the time series forcasting training.

repeated string additional_experiments = 25;

Returns
Type	Description
AutoMlForecastingInputs.Builder	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
Type	Description
AutoMlForecastingInputs.Builder	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
Type	Description
AutoMlForecastingInputs.Builder	This builder for chaining.

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;

Returns
Type	Description
AutoMlForecastingInputs.Builder

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;

Returns
Type	Description
AutoMlForecastingInputs.Builder

clearField(Descriptors.FieldDescriptor field)

public AutoMlForecastingInputs.Builder clearField(Descriptors.FieldDescriptor field)

Parameter
Name	Description
field	FieldDescriptor

Returns
Type	Description
AutoMlForecastingInputs.Builder

Overrides

GeneratedMessageV3.Builder<BuilderType>.clearField(Descriptors.FieldDescriptor field)

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
Type	Description
AutoMlForecastingInputs.Builder	This builder for chaining.

clearOneof(Descriptors.OneofDescriptor oneof)

public AutoMlForecastingInputs.Builder clearOneof(Descriptors.OneofDescriptor oneof)

Parameter
Name	Description
oneof	OneofDescriptor

Returns
Type	Description
AutoMlForecastingInputs.Builder

Overrides

GeneratedMessageV3.Builder<BuilderType>.clearOneof(Descriptors.OneofDescriptor oneof)

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
Type	Description
AutoMlForecastingInputs.Builder	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
Type	Description
AutoMlForecastingInputs.Builder	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
Type	Description
AutoMlForecastingInputs.Builder	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
Type	Description
AutoMlForecastingInputs.Builder	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
Type	Description
AutoMlForecastingInputs.Builder	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
Type	Description
AutoMlForecastingInputs.Builder	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
Type	Description
AutoMlForecastingInputs.Builder	This builder for chaining.

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;

Returns
Type	Description
AutoMlForecastingInputs.Builder

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
Type	Description
AutoMlForecastingInputs.Builder	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
Type	Description
AutoMlForecastingInputs.Builder	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
Type	Description
AutoMlForecastingInputs.Builder	This builder for chaining.

clone()

public AutoMlForecastingInputs.Builder clone()

Returns
Type	Description
AutoMlForecastingInputs.Builder

Overrides

GeneratedMessageV3.Builder<BuilderType>.clone()

getAdditionalExperiments(int index)

public String getAdditionalExperiments(int index)

Additional experiment flags for the time series forcasting training.

repeated string additional_experiments = 25;

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

Returns
Type	Description
String	The additionalExperiments at the given index.

getAdditionalExperimentsBytes(int index)

public ByteString getAdditionalExperimentsBytes(int index)

Additional experiment flags for the time series forcasting training.

repeated string additional_experiments = 25;

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

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

getAdditionalExperimentsCount()

public int getAdditionalExperimentsCount()

Additional experiment flags for the time series forcasting training.

repeated string additional_experiments = 25;

Returns
Type	Description
int	The count of additionalExperiments.

getAdditionalExperimentsList()

public ProtocolStringList getAdditionalExperimentsList()

Additional experiment flags for the time series forcasting training.

repeated string additional_experiments = 25;

Returns
Type	Description
ProtocolStringList	A list containing the additionalExperiments.

getAvailableAtForecastColumns(int index)

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;

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

Returns
Type	Description
String	The availableAtForecastColumns at the given index.

getAvailableAtForecastColumnsBytes(int index)

public 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;

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

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

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;

Returns
Type	Description
int	The count of availableAtForecastColumns.

getAvailableAtForecastColumnsList()

public 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;

Returns
Type	Description
ProtocolStringList	A list containing the availableAtForecastColumns.

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;

Returns
Type	Description
long	The contextWindow.

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;

Returns
Type	Description
AutoMlForecastingInputs.Granularity	The dataGranularity.

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;

Returns
Type	Description
AutoMlForecastingInputs.Granularity.Builder

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;

Returns
Type	Description
AutoMlForecastingInputs.GranularityOrBuilder

getDefaultInstanceForType()

public AutoMlForecastingInputs getDefaultInstanceForType()

Returns
Type	Description
AutoMlForecastingInputs

getDescriptorForType()

public Descriptors.Descriptor getDescriptorForType()

Returns
Type	Description
Descriptor

Overrides

GeneratedMessageV3.Builder<BuilderType>.getDescriptorForType()

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;

Returns
Type	Description
ExportEvaluatedDataItemsConfig	The exportEvaluatedDataItemsConfig.

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;

Returns
Type	Description
ExportEvaluatedDataItemsConfig.Builder

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;

Returns
Type	Description
ExportEvaluatedDataItemsConfigOrBuilder

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;

Returns
Type	Description
long	The forecastHorizon.

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;

Returns
Type	Description
String	The optimizationObjective.

getOptimizationObjectiveBytes()

public 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;

Returns
Type	Description
ByteString	The bytes for optimizationObjective.

getQuantiles(int index)

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;

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

Returns
Type	Description
double	The quantiles at the given index.

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;

Returns
Type	Description
int	The count of quantiles.

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;

Returns
Type	Description
List<Double>	A list containing the quantiles.

getTargetColumn()

public String getTargetColumn()

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

string target_column = 1;

Returns
Type	Description
String	The targetColumn.

getTargetColumnBytes()

public ByteString getTargetColumnBytes()

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

string target_column = 1;

Returns
Type	Description
ByteString	The bytes for targetColumn.

getTimeColumn()

public String getTimeColumn()

The name of the column that identifies time order in the time series.

string time_column = 3;

Returns
Type	Description
String	The timeColumn.

getTimeColumnBytes()

public ByteString getTimeColumnBytes()

The name of the column that identifies time order in the time series.

string time_column = 3;

Returns
Type	Description
ByteString	The bytes for timeColumn.

getTimeSeriesAttributeColumns(int index)

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;

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

Returns
Type	Description
String	The timeSeriesAttributeColumns at the given index.

getTimeSeriesAttributeColumnsBytes(int index)

public 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;

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

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

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;

Returns
Type	Description
int	The count of timeSeriesAttributeColumns.

getTimeSeriesAttributeColumnsList()

public 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;

Returns
Type	Description
ProtocolStringList	A list containing the timeSeriesAttributeColumns.

getTimeSeriesIdentifierColumn()

public String getTimeSeriesIdentifierColumn()

The name of the column that identifies the time series.

string time_series_identifier_column = 2;

Returns
Type	Description
String	The timeSeriesIdentifierColumn.

getTimeSeriesIdentifierColumnBytes()

public ByteString getTimeSeriesIdentifierColumnBytes()

The name of the column that identifies the time series.

string time_series_identifier_column = 2;

Returns
Type	Description
ByteString	The bytes for timeSeriesIdentifierColumn.

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;

Returns
Type	Description
long	The trainBudgetMilliNodeHours.

getTransformations(int index)

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;

Parameter
Name	Description
index	int

Returns
Type	Description
AutoMlForecastingInputs.Transformation

getTransformationsBuilder(int index)

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;

Parameter
Name	Description
index	int

Returns
Type	Description
AutoMlForecastingInputs.Transformation.Builder

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;

Returns
Type	Description
List<Builder>

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;

Returns
Type	Description
int

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;

Returns
Type	Description
List<Transformation>

getTransformationsOrBuilder(int index)

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;

Parameter
Name	Description
index	int

Returns
Type	Description
AutoMlForecastingInputs.TransformationOrBuilder

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;

Returns
Type	Description
List<? extends com.google.cloud.aiplatform.v1beta1.schema.trainingjob.definition.AutoMlForecastingInputs.TransformationOrBuilder>

getUnavailableAtForecastColumns(int index)

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;

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

Returns
Type	Description
String	The unavailableAtForecastColumns at the given index.

getUnavailableAtForecastColumnsBytes(int index)

public 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;

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

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

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;

Returns
Type	Description
int	The count of unavailableAtForecastColumns.

getUnavailableAtForecastColumnsList()

public 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;

Returns
Type	Description
ProtocolStringList	A list containing the unavailableAtForecastColumns.

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;

Returns
Type	Description
String	The validationOptions.

getValidationOptionsBytes()

public 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;

Returns
Type	Description
ByteString	The bytes for validationOptions.

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;

Returns
Type	Description
String	The weightColumn.

getWeightColumnBytes()

public 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;

Returns
Type	Description
ByteString	The bytes for weightColumn.

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;

Returns
Type	Description
boolean	Whether the dataGranularity field is set.

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;

Returns
Type	Description
boolean	Whether the exportEvaluatedDataItemsConfig field is set.

internalGetFieldAccessorTable()

protected GeneratedMessageV3.FieldAccessorTable internalGetFieldAccessorTable()

Returns
Type	Description
FieldAccessorTable

Overrides

GeneratedMessageV3.Builder<BuilderType>.internalGetFieldAccessorTable()

isInitialized()

public final boolean isInitialized()

Returns
Type	Description
boolean

Overrides

GeneratedMessageV3.Builder<BuilderType>.isInitialized()

mergeDataGranularity(AutoMlForecastingInputs.Granularity value)

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;

Parameter
Name	Description
value	AutoMlForecastingInputs.Granularity

Returns
Type	Description
AutoMlForecastingInputs.Builder

mergeExportEvaluatedDataItemsConfig(ExportEvaluatedDataItemsConfig value)

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;

Parameter
Name	Description
value	ExportEvaluatedDataItemsConfig

Returns
Type	Description
AutoMlForecastingInputs.Builder

mergeFrom(AutoMlForecastingInputs other)

public AutoMlForecastingInputs.Builder mergeFrom(AutoMlForecastingInputs other)

Parameter
Name	Description
other	AutoMlForecastingInputs

Returns
Type	Description
AutoMlForecastingInputs.Builder

mergeFrom(CodedInputStream input, ExtensionRegistryLite extensionRegistry)

public AutoMlForecastingInputs.Builder mergeFrom(CodedInputStream input, ExtensionRegistryLite extensionRegistry)

Parameters
Name	Description
input	CodedInputStream
extensionRegistry	ExtensionRegistryLite

Returns
Type	Description
AutoMlForecastingInputs.Builder

Overrides

AbstractMessage.Builder<BuilderType>.mergeFrom(CodedInputStream input, ExtensionRegistryLite extensionRegistry)

Exceptions
Type	Description
IOException

mergeFrom(Message other)

public AutoMlForecastingInputs.Builder mergeFrom(Message other)

Parameter
Name	Description
other	Message

Returns
Type	Description
AutoMlForecastingInputs.Builder

Overrides

AbstractMessage.Builder<BuilderType>.mergeFrom(Message other)

mergeUnknownFields(UnknownFieldSet unknownFields)

public final AutoMlForecastingInputs.Builder mergeUnknownFields(UnknownFieldSet unknownFields)

Parameter
Name	Description
unknownFields	UnknownFieldSet

Returns
Type	Description
AutoMlForecastingInputs.Builder

Overrides

GeneratedMessageV3.Builder<BuilderType>.mergeUnknownFields(UnknownFieldSet unknownFields)

removeTransformations(int index)

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;

Parameter
Name	Description
index	int

Returns
Type	Description
AutoMlForecastingInputs.Builder

setAdditionalExperiments(int index, String value)

public AutoMlForecastingInputs.Builder setAdditionalExperiments(int index, String value)

Additional experiment flags for the time series forcasting training.

repeated string additional_experiments = 25;

Parameters
Name	Description
index	int The index to set the value at.
value	String The additionalExperiments to set.

Returns
Type	Description
AutoMlForecastingInputs.Builder	This builder for chaining.

setAvailableAtForecastColumns(int index, String value)

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
Name	Description
index	int The index to set the value at.
value	String The availableAtForecastColumns to set.

Returns
Type	Description
AutoMlForecastingInputs.Builder	This builder for chaining.

setContextWindow(long value)

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;

Parameter
Name	Description
value	long The contextWindow to set.

Returns
Type	Description
AutoMlForecastingInputs.Builder	This builder for chaining.

setDataGranularity(AutoMlForecastingInputs.Granularity value)

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;

Parameter
Name	Description
value	AutoMlForecastingInputs.Granularity

Returns
Type	Description
AutoMlForecastingInputs.Builder

setDataGranularity(AutoMlForecastingInputs.Granularity.Builder builderForValue)

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;

Parameter
Name	Description
builderForValue	AutoMlForecastingInputs.Granularity.Builder

Returns
Type	Description
AutoMlForecastingInputs.Builder

setExportEvaluatedDataItemsConfig(ExportEvaluatedDataItemsConfig value)

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;

Parameter
Name	Description
value	ExportEvaluatedDataItemsConfig

Returns
Type	Description
AutoMlForecastingInputs.Builder

setExportEvaluatedDataItemsConfig(ExportEvaluatedDataItemsConfig.Builder builderForValue)

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;

Parameter
Name	Description
builderForValue	ExportEvaluatedDataItemsConfig.Builder

Returns
Type	Description
AutoMlForecastingInputs.Builder

setField(Descriptors.FieldDescriptor field, Object value)

public AutoMlForecastingInputs.Builder setField(Descriptors.FieldDescriptor field, Object value)

Parameters
Name	Description
field	FieldDescriptor
value	Object

Returns
Type	Description
AutoMlForecastingInputs.Builder

Overrides

GeneratedMessageV3.Builder<BuilderType>.setField(Descriptors.FieldDescriptor field, Object value)

setForecastHorizon(long value)

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;

Parameter
Name	Description
value	long The forecastHorizon to set.

Returns
Type	Description
AutoMlForecastingInputs.Builder	This builder for chaining.

setOptimizationObjective(String value)

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;

Parameter
Name	Description
value	String The optimizationObjective to set.

Returns
Type	Description
AutoMlForecastingInputs.Builder	This builder for chaining.

setOptimizationObjectiveBytes(ByteString value)

public AutoMlForecastingInputs.Builder setOptimizationObjectiveBytes(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;

Parameter
Name	Description
value	ByteString The bytes for optimizationObjective to set.

Returns
Type	Description
AutoMlForecastingInputs.Builder	This builder for chaining.

setQuantiles(int index, double value)

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
Name	Description
index	int The index to set the value at.
value	double The quantiles to set.

Returns
Type	Description
AutoMlForecastingInputs.Builder	This builder for chaining.

setRepeatedField(Descriptors.FieldDescriptor field, int index, Object value)

public AutoMlForecastingInputs.Builder setRepeatedField(Descriptors.FieldDescriptor field, int index, Object value)

Parameters
Name	Description
field	FieldDescriptor
index	int
value	Object

Returns
Type	Description
AutoMlForecastingInputs.Builder

Overrides

GeneratedMessageV3.Builder<BuilderType>.setRepeatedField(Descriptors.FieldDescriptor field, int index, Object value)

setTargetColumn(String value)

public AutoMlForecastingInputs.Builder setTargetColumn(String value)

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

string target_column = 1;

Parameter
Name	Description
value	String The targetColumn to set.

Returns
Type	Description
AutoMlForecastingInputs.Builder	This builder for chaining.

setTargetColumnBytes(ByteString value)

public AutoMlForecastingInputs.Builder setTargetColumnBytes(ByteString value)

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

string target_column = 1;

Parameter
Name	Description
value	ByteString The bytes for targetColumn to set.

Returns
Type	Description
AutoMlForecastingInputs.Builder	This builder for chaining.

setTimeColumn(String value)

public AutoMlForecastingInputs.Builder setTimeColumn(String value)

The name of the column that identifies time order in the time series.

string time_column = 3;

Parameter
Name	Description
value	String The timeColumn to set.

Returns
Type	Description
AutoMlForecastingInputs.Builder	This builder for chaining.

setTimeColumnBytes(ByteString value)

public AutoMlForecastingInputs.Builder setTimeColumnBytes(ByteString value)

The name of the column that identifies time order in the time series.

string time_column = 3;

Parameter
Name	Description
value	ByteString The bytes for timeColumn to set.

Returns
Type	Description
AutoMlForecastingInputs.Builder	This builder for chaining.

setTimeSeriesAttributeColumns(int index, String value)

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
Name	Description
index	int The index to set the value at.
value	String The timeSeriesAttributeColumns to set.

Returns
Type	Description
AutoMlForecastingInputs.Builder	This builder for chaining.

setTimeSeriesIdentifierColumn(String value)

public AutoMlForecastingInputs.Builder setTimeSeriesIdentifierColumn(String value)

The name of the column that identifies the time series.

string time_series_identifier_column = 2;

Parameter
Name	Description
value	String The timeSeriesIdentifierColumn to set.

Returns
Type	Description
AutoMlForecastingInputs.Builder	This builder for chaining.

setTimeSeriesIdentifierColumnBytes(ByteString value)

public AutoMlForecastingInputs.Builder setTimeSeriesIdentifierColumnBytes(ByteString value)

The name of the column that identifies the time series.

string time_series_identifier_column = 2;

Parameter
Name	Description
value	ByteString The bytes for timeSeriesIdentifierColumn to set.

Returns
Type	Description
AutoMlForecastingInputs.Builder	This builder for chaining.

setTrainBudgetMilliNodeHours(long value)

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;

Parameter
Name	Description
value	long The trainBudgetMilliNodeHours to set.

Returns
Type	Description
AutoMlForecastingInputs.Builder	This builder for chaining.

setTransformations(int index, AutoMlForecastingInputs.Transformation value)

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;

Parameters
Name	Description
index	int
value	AutoMlForecastingInputs.Transformation

Returns
Type	Description
AutoMlForecastingInputs.Builder

setTransformations(int index, AutoMlForecastingInputs.Transformation.Builder builderForValue)

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;

Parameters
Name	Description
index	int
builderForValue	AutoMlForecastingInputs.Transformation.Builder

Returns
Type	Description
AutoMlForecastingInputs.Builder

setUnavailableAtForecastColumns(int index, String value)

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
Name	Description
index	int The index to set the value at.
value	String The unavailableAtForecastColumns to set.

Returns
Type	Description
AutoMlForecastingInputs.Builder	This builder for chaining.

setUnknownFields(UnknownFieldSet unknownFields)

public final AutoMlForecastingInputs.Builder setUnknownFields(UnknownFieldSet unknownFields)

Parameter
Name	Description
unknownFields	UnknownFieldSet

Returns
Type	Description
AutoMlForecastingInputs.Builder

Overrides

GeneratedMessageV3.Builder<BuilderType>.setUnknownFields(UnknownFieldSet unknownFields)

setValidationOptions(String value)

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;

Parameter
Name	Description
value	String The validationOptions to set.

Returns
Type	Description
AutoMlForecastingInputs.Builder	This builder for chaining.

setValidationOptionsBytes(ByteString value)

public AutoMlForecastingInputs.Builder setValidationOptionsBytes(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;

Parameter
Name	Description
value	ByteString The bytes for validationOptions to set.

Returns
Type	Description
AutoMlForecastingInputs.Builder	This builder for chaining.

setWeightColumn(String value)

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;

Parameter
Name	Description
value	String The weightColumn to set.

Returns
Type	Description
AutoMlForecastingInputs.Builder	This builder for chaining.

setWeightColumnBytes(ByteString value)

public AutoMlForecastingInputs.Builder setWeightColumnBytes(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;

Parameter
Name	Description
value	ByteString The bytes for weightColumn to set.

Returns
Type	Description
AutoMlForecastingInputs.Builder	This builder for chaining.