Class AutoMlForecastingInputs (3.31.0)

public final class AutoMlForecastingInputs extends GeneratedMessageV3 implements AutoMlForecastingInputsOrBuilder

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

Inheritance

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

Implements

AutoMlForecastingInputsOrBuilder

Inherited Members

AbstractMessage.equals(Object)

AbstractMessage.findInitializationErrors()

AbstractMessage.getInitializationErrorString()

AbstractMessage.hashBoolean(boolean)

AbstractMessage.hashCode()

AbstractMessage.hashEnum(Internal.EnumLite)

AbstractMessage.hashEnumList(List<? extends Internal.EnumLite>)

AbstractMessage.hashFields(int,Map<Descriptors.FieldDescriptor,Object>)

AbstractMessage.hashLong(long)

AbstractMessage.toString()

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

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

AbstractMessageLite.checkByteStringIsUtf8(ByteString)

AbstractMessageLite.toByteArray()

AbstractMessageLite.toByteString()

AbstractMessageLite.writeDelimitedTo(OutputStream)

AbstractMessageLite.writeTo(OutputStream)

com.google.protobuf.GeneratedMessageV3.<ListT>makeMutableCopy(ListT)

com.google.protobuf.GeneratedMessageV3.<ListT>makeMutableCopy(ListT,int)

GeneratedMessageV3.<M>parseDelimitedWithIOException(Parser<M>,InputStream)

GeneratedMessageV3.<M>parseDelimitedWithIOException(Parser<M>,InputStream,ExtensionRegistryLite)

GeneratedMessageV3.<M>parseWithIOException(Parser<M>,CodedInputStream)

GeneratedMessageV3.<M>parseWithIOException(Parser<M>,CodedInputStream,ExtensionRegistryLite)

GeneratedMessageV3.<M>parseWithIOException(Parser<M>,InputStream)

GeneratedMessageV3.<M>parseWithIOException(Parser<M>,InputStream,ExtensionRegistryLite)

com.google.protobuf.GeneratedMessageV3.<T>emptyList(java.lang.Class<T>)

GeneratedMessageV3.<V>serializeBooleanMapTo(CodedOutputStream,MapField<Boolean,V>,MapEntry<Boolean,V>,int)

GeneratedMessageV3.<V>serializeIntegerMapTo(CodedOutputStream,MapField<Integer,V>,MapEntry<Integer,V>,int)

GeneratedMessageV3.<V>serializeLongMapTo(CodedOutputStream,MapField<Long,V>,MapEntry<Long,V>,int)

GeneratedMessageV3.<V>serializeStringMapTo(CodedOutputStream,MapField<String,V>,MapEntry<String,V>,int)

GeneratedMessageV3.canUseUnsafe()

GeneratedMessageV3.computeStringSize(int,Object)

GeneratedMessageV3.computeStringSizeNoTag(Object)

GeneratedMessageV3.emptyBooleanList()

GeneratedMessageV3.emptyDoubleList()

GeneratedMessageV3.emptyFloatList()

GeneratedMessageV3.emptyIntList()

GeneratedMessageV3.emptyLongList()

GeneratedMessageV3.getAllFields()

GeneratedMessageV3.getDescriptorForType()

GeneratedMessageV3.getField(Descriptors.FieldDescriptor)

GeneratedMessageV3.getOneofFieldDescriptor(Descriptors.OneofDescriptor)

GeneratedMessageV3.getParserForType()

GeneratedMessageV3.getRepeatedField(Descriptors.FieldDescriptor,int)

GeneratedMessageV3.getRepeatedFieldCount(Descriptors.FieldDescriptor)

GeneratedMessageV3.getSerializedSize()

GeneratedMessageV3.getUnknownFields()

GeneratedMessageV3.hasField(Descriptors.FieldDescriptor)

GeneratedMessageV3.hasOneof(Descriptors.OneofDescriptor)

GeneratedMessageV3.internalGetFieldAccessorTable()

GeneratedMessageV3.internalGetMapField(int)

GeneratedMessageV3.isInitialized()

GeneratedMessageV3.isStringEmpty(Object)

GeneratedMessageV3.makeExtensionsImmutable()

GeneratedMessageV3.mergeFromAndMakeImmutableInternal(CodedInputStream,ExtensionRegistryLite)

GeneratedMessageV3.mutableCopy(Internal.BooleanList)

GeneratedMessageV3.mutableCopy(Internal.DoubleList)

GeneratedMessageV3.mutableCopy(Internal.FloatList)

GeneratedMessageV3.mutableCopy(Internal.IntList)

GeneratedMessageV3.mutableCopy(Internal.LongList)

GeneratedMessageV3.newBooleanList()

GeneratedMessageV3.newBuilderForType(AbstractMessage.BuilderParent)

GeneratedMessageV3.newBuilderForType(GeneratedMessageV3.BuilderParent)

GeneratedMessageV3.newDoubleList()

GeneratedMessageV3.newFloatList()

GeneratedMessageV3.newInstance(GeneratedMessageV3.UnusedPrivateParameter)

GeneratedMessageV3.newIntList()

GeneratedMessageV3.newLongList()

GeneratedMessageV3.parseUnknownField(CodedInputStream,UnknownFieldSet.Builder,ExtensionRegistryLite,int)

GeneratedMessageV3.parseUnknownFieldProto3(CodedInputStream,UnknownFieldSet.Builder,ExtensionRegistryLite,int)

GeneratedMessageV3.writeReplace()

GeneratedMessageV3.writeString(CodedOutputStream,int,Object)

GeneratedMessageV3.writeStringNoTag(CodedOutputStream,Object)

GeneratedMessageV3.writeTo(CodedOutputStream)

Object.clone()

Object.finalize()

Object.getClass()

Object.notify()

Object.notifyAll()

Object.wait()

Object.wait(long)

Object.wait(long,int)

Static Fields

ADDITIONAL_EXPERIMENTS_FIELD_NUMBER

public static final int ADDITIONAL_EXPERIMENTS_FIELD_NUMBER

Field Value
Type	Description
`int`

AVAILABLE_AT_FORECAST_COLUMNS_FIELD_NUMBER

public static final int AVAILABLE_AT_FORECAST_COLUMNS_FIELD_NUMBER

Field Value
Type	Description
`int`

CONTEXT_WINDOW_FIELD_NUMBER

public static final int CONTEXT_WINDOW_FIELD_NUMBER

Field Value
Type	Description
`int`

DATA_GRANULARITY_FIELD_NUMBER

public static final int DATA_GRANULARITY_FIELD_NUMBER

Field Value
Type	Description
`int`

EXPORT_EVALUATED_DATA_ITEMS_CONFIG_FIELD_NUMBER

public static final int EXPORT_EVALUATED_DATA_ITEMS_CONFIG_FIELD_NUMBER

Field Value
Type	Description
`int`

FORECAST_HORIZON_FIELD_NUMBER

public static final int FORECAST_HORIZON_FIELD_NUMBER

Field Value
Type	Description
`int`

OPTIMIZATION_OBJECTIVE_FIELD_NUMBER

public static final int OPTIMIZATION_OBJECTIVE_FIELD_NUMBER

Field Value
Type	Description
`int`

QUANTILES_FIELD_NUMBER

public static final int QUANTILES_FIELD_NUMBER

Field Value
Type	Description
`int`

TARGET_COLUMN_FIELD_NUMBER

public static final int TARGET_COLUMN_FIELD_NUMBER

Field Value
Type	Description
`int`

TIME_COLUMN_FIELD_NUMBER

public static final int TIME_COLUMN_FIELD_NUMBER

Field Value
Type	Description
`int`

TIME_SERIES_ATTRIBUTE_COLUMNS_FIELD_NUMBER

public static final int TIME_SERIES_ATTRIBUTE_COLUMNS_FIELD_NUMBER

Field Value
Type	Description
`int`

TIME_SERIES_IDENTIFIER_COLUMN_FIELD_NUMBER

public static final int TIME_SERIES_IDENTIFIER_COLUMN_FIELD_NUMBER

Field Value
Type	Description
`int`

TRAIN_BUDGET_MILLI_NODE_HOURS_FIELD_NUMBER

public static final int TRAIN_BUDGET_MILLI_NODE_HOURS_FIELD_NUMBER

Field Value
Type	Description
`int`

TRANSFORMATIONS_FIELD_NUMBER

public static final int TRANSFORMATIONS_FIELD_NUMBER

Field Value
Type	Description
`int`

UNAVAILABLE_AT_FORECAST_COLUMNS_FIELD_NUMBER

public static final int UNAVAILABLE_AT_FORECAST_COLUMNS_FIELD_NUMBER

Field Value
Type	Description
`int`

VALIDATION_OPTIONS_FIELD_NUMBER

public static final int VALIDATION_OPTIONS_FIELD_NUMBER

Field Value
Type	Description
`int`

WEIGHT_COLUMN_FIELD_NUMBER

public static final int WEIGHT_COLUMN_FIELD_NUMBER

Field Value
Type	Description
`int`

Static Methods

getDefaultInstance()

public static AutoMlForecastingInputs getDefaultInstance()

Returns
Type	Description
`AutoMlForecastingInputs`

getDescriptor()

public static final Descriptors.Descriptor getDescriptor()

Returns
Type	Description
`Descriptor`

newBuilder()

public static AutoMlForecastingInputs.Builder newBuilder()

Returns
Type	Description
`AutoMlForecastingInputs.Builder`

newBuilder(AutoMlForecastingInputs prototype)

public static AutoMlForecastingInputs.Builder newBuilder(AutoMlForecastingInputs prototype)

Parameter
Name	Description
`prototype`	`AutoMlForecastingInputs`

Returns
Type	Description
`AutoMlForecastingInputs.Builder`

parseDelimitedFrom(InputStream input)

public static AutoMlForecastingInputs parseDelimitedFrom(InputStream input)

Parameter
Name	Description
`input`	`InputStream`

Returns
Type	Description
`AutoMlForecastingInputs`

Exceptions
Type	Description
`IOException`

parseDelimitedFrom(InputStream input, ExtensionRegistryLite extensionRegistry)

public static AutoMlForecastingInputs parseDelimitedFrom(InputStream input, ExtensionRegistryLite extensionRegistry)

Parameters
Name	Description
`input`	`InputStream`
`extensionRegistry`	`ExtensionRegistryLite`

Returns
Type	Description
`AutoMlForecastingInputs`

Exceptions
Type	Description
`IOException`

parseFrom(byte[] data)

public static AutoMlForecastingInputs parseFrom(byte[] data)

Parameter
Name	Description
`data`	`byte[]`

Returns
Type	Description
`AutoMlForecastingInputs`

Exceptions
Type	Description
`InvalidProtocolBufferException`

parseFrom(byte[] data, ExtensionRegistryLite extensionRegistry)

public static AutoMlForecastingInputs parseFrom(byte[] data, ExtensionRegistryLite extensionRegistry)

Parameters
Name	Description
`data`	`byte[]`
`extensionRegistry`	`ExtensionRegistryLite`

Returns
Type	Description
`AutoMlForecastingInputs`

Exceptions
Type	Description
`InvalidProtocolBufferException`

parseFrom(ByteString data)

public static AutoMlForecastingInputs parseFrom(ByteString data)

Parameter
Name	Description
`data`	`ByteString`

Returns
Type	Description
`AutoMlForecastingInputs`

Exceptions
Type	Description
`InvalidProtocolBufferException`

parseFrom(ByteString data, ExtensionRegistryLite extensionRegistry)

public static AutoMlForecastingInputs parseFrom(ByteString data, ExtensionRegistryLite extensionRegistry)

Parameters
Name	Description
`data`	`ByteString`
`extensionRegistry`	`ExtensionRegistryLite`

Returns
Type	Description
`AutoMlForecastingInputs`

Exceptions
Type	Description
`InvalidProtocolBufferException`

parseFrom(CodedInputStream input)

public static AutoMlForecastingInputs parseFrom(CodedInputStream input)

Parameter
Name	Description
`input`	`CodedInputStream`

Returns
Type	Description
`AutoMlForecastingInputs`

Exceptions
Type	Description
`IOException`

parseFrom(CodedInputStream input, ExtensionRegistryLite extensionRegistry)

public static AutoMlForecastingInputs parseFrom(CodedInputStream input, ExtensionRegistryLite extensionRegistry)

Parameters
Name	Description
`input`	`CodedInputStream`
`extensionRegistry`	`ExtensionRegistryLite`

Returns
Type	Description
`AutoMlForecastingInputs`

Exceptions
Type	Description
`IOException`

parseFrom(InputStream input)

public static AutoMlForecastingInputs parseFrom(InputStream input)

Parameter
Name	Description
`input`	`InputStream`

Returns
Type	Description
`AutoMlForecastingInputs`

Exceptions
Type	Description
`IOException`

parseFrom(InputStream input, ExtensionRegistryLite extensionRegistry)

public static AutoMlForecastingInputs parseFrom(InputStream input, ExtensionRegistryLite extensionRegistry)

Parameters
Name	Description
`input`	`InputStream`
`extensionRegistry`	`ExtensionRegistryLite`

Returns
Type	Description
`AutoMlForecastingInputs`

Exceptions
Type	Description
`IOException`

parseFrom(ByteBuffer data)

public static AutoMlForecastingInputs parseFrom(ByteBuffer data)

Parameter
Name	Description
`data`	`ByteBuffer`

Returns
Type	Description
`AutoMlForecastingInputs`

Exceptions
Type	Description
`InvalidProtocolBufferException`

parseFrom(ByteBuffer data, ExtensionRegistryLite extensionRegistry)

public static AutoMlForecastingInputs parseFrom(ByteBuffer data, ExtensionRegistryLite extensionRegistry)

Parameters
Name	Description
`data`	`ByteBuffer`
`extensionRegistry`	`ExtensionRegistryLite`

Returns
Type	Description
`AutoMlForecastingInputs`

Exceptions
Type	Description
`InvalidProtocolBufferException`

parser()

public static Parser<AutoMlForecastingInputs> parser()

Returns
Type	Description
`Parser<AutoMlForecastingInputs>`

Methods

equals(Object obj)

public boolean equals(Object obj)

Parameter
Name	Description
`obj`	`Object`

Returns
Type	Description
`boolean`

Overrides

AbstractMessage.equals(Object other)

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)

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()

repeated string available_at_forecast_columns = 21;

Returns
Type	Description
`int`	The count of availableAtForecastColumns.

getAvailableAtForecastColumnsList()

public ProtocolStringList getAvailableAtForecastColumnsList()

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.

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`

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.

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.

getParserForType()

public Parser<AutoMlForecastingInputs> getParserForType()

Returns
Type	Description
`Parser<AutoMlForecastingInputs>`

Overrides

GeneratedMessageV3.getParserForType()

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()

repeated double quantiles = 16;

Returns
Type	Description
`int`	The count of quantiles.

getQuantilesList()

public List<Double> getQuantilesList()

repeated double quantiles = 16;

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

getSerializedSize()

public int getSerializedSize()

Returns
Type	Description
`int`

Overrides

GeneratedMessageV3.getSerializedSize()

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.