Class AutoMlForecastingInputs (3.13.0)

Version 3.13.0

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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)

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)

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)

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.

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()

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.

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.

repeated .google.cloud.aiplatform.v1beta1.schema.trainingjob.definition.AutoMlForecastingInputs.Transformation transformations = 4;

Parameter

Name	Description
index	int

Returns

Type	Description
AutoMlForecastingInputs.Transformation

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.

getUnknownFields()

public final UnknownFieldSet getUnknownFields()

Returns

Type	Description
UnknownFieldSet

Overrides

GeneratedMessageV3.getUnknownFields()

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.

hashCode()

public int hashCode()

Returns

Type	Description
int

Overrides

AbstractMessage.hashCode()

internalGetFieldAccessorTable()

protected GeneratedMessageV3.FieldAccessorTable internalGetFieldAccessorTable()

Returns

Type	Description
FieldAccessorTable

Overrides

GeneratedMessageV3.internalGetFieldAccessorTable()

isInitialized()

public final boolean isInitialized()

Returns

Type	Description
boolean

Overrides

GeneratedMessageV3.isInitialized()

newBuilderForType()

public AutoMlForecastingInputs.Builder newBuilderForType()

Returns

Type	Description
AutoMlForecastingInputs.Builder

newBuilderForType(GeneratedMessageV3.BuilderParent parent)

protected AutoMlForecastingInputs.Builder newBuilderForType(GeneratedMessageV3.BuilderParent parent)

Parameter

Name	Description
parent	BuilderParent

Returns

Type	Description
AutoMlForecastingInputs.Builder

Overrides

GeneratedMessageV3.newBuilderForType(GeneratedMessageV3.BuilderParent parent)

newInstance(GeneratedMessageV3.UnusedPrivateParameter unused)

protected Object newInstance(GeneratedMessageV3.UnusedPrivateParameter unused)

Parameter

Name	Description
unused	UnusedPrivateParameter

Returns

Type	Description
Object

Overrides

GeneratedMessageV3.newInstance(GeneratedMessageV3.UnusedPrivateParameter unused)

toBuilder()

public AutoMlForecastingInputs.Builder toBuilder()

Returns

Type	Description
AutoMlForecastingInputs.Builder

writeTo(CodedOutputStream output)

public void writeTo(CodedOutputStream output)

Parameter

Name	Description
output	CodedOutputStream

Overrides

GeneratedMessageV3.writeTo(CodedOutputStream output)

Exceptions

Type	Description
IOException