public static final class AutoMlForecastingInputs.Builder extends com.google.protobuf.GeneratedMessageV3.Builder<AutoMlForecastingInputs.Builder> implements AutoMlForecastingInputsOrBuilder
google.cloud.aiplatform.v1beta1.schema.trainingjob.definition.AutoMlForecastingInputs| Modifier and Type | Method and Description |
|---|---|
AutoMlForecastingInputs.Builder |
addAdditionalExperiments(String value)
Additional experiment flags for the time series forcasting training.
|
AutoMlForecastingInputs.Builder |
addAdditionalExperimentsBytes(com.google.protobuf.ByteString value)
Additional experiment flags for the time series forcasting training.
|
AutoMlForecastingInputs.Builder |
addAllAdditionalExperiments(Iterable<String> values)
Additional experiment flags for the time series forcasting training.
|
AutoMlForecastingInputs.Builder |
addAllAvailableAtForecastColumns(Iterable<String> values)
Names of columns that are available and provided when a forecast
is requested.
|
AutoMlForecastingInputs.Builder |
addAllQuantiles(Iterable<? extends Double> values)
Quantiles to use for minimize-quantile-loss `optimization_objective`.
|
AutoMlForecastingInputs.Builder |
addAllTimeSeriesAttributeColumns(Iterable<String> values)
Column names that should be used as attribute columns.
|
AutoMlForecastingInputs.Builder |
addAllTransformations(Iterable<? extends AutoMlForecastingInputs.Transformation> values)
Each transformation will apply transform function to given input column.
|
AutoMlForecastingInputs.Builder |
addAllUnavailableAtForecastColumns(Iterable<String> values)
Names of columns that are unavailable when a forecast is requested.
|
AutoMlForecastingInputs.Builder |
addAvailableAtForecastColumns(String value)
Names of columns that are available and provided when a forecast
is requested.
|
AutoMlForecastingInputs.Builder |
addAvailableAtForecastColumnsBytes(com.google.protobuf.ByteString value)
Names of columns that are available and provided when a forecast
is requested.
|
AutoMlForecastingInputs.Builder |
addQuantiles(double value)
Quantiles to use for minimize-quantile-loss `optimization_objective`.
|
AutoMlForecastingInputs.Builder |
addRepeatedField(com.google.protobuf.Descriptors.FieldDescriptor field,
Object value) |
AutoMlForecastingInputs.Builder |
addTimeSeriesAttributeColumns(String value)
Column names that should be used as attribute columns.
|
AutoMlForecastingInputs.Builder |
addTimeSeriesAttributeColumnsBytes(com.google.protobuf.ByteString value)
Column names that should be used as attribute columns.
|
AutoMlForecastingInputs.Builder |
addTransformations(AutoMlForecastingInputs.Transformation.Builder builderForValue)
Each transformation will apply transform function to given input column.
|
AutoMlForecastingInputs.Builder |
addTransformations(AutoMlForecastingInputs.Transformation value)
Each transformation will apply transform function to given input column.
|
AutoMlForecastingInputs.Builder |
addTransformations(int index,
AutoMlForecastingInputs.Transformation.Builder builderForValue)
Each transformation will apply transform function to given input column.
|
AutoMlForecastingInputs.Builder |
addTransformations(int index,
AutoMlForecastingInputs.Transformation value)
Each transformation will apply transform function to given input column.
|
AutoMlForecastingInputs.Transformation.Builder |
addTransformationsBuilder()
Each transformation will apply transform function to given input column.
|
AutoMlForecastingInputs.Transformation.Builder |
addTransformationsBuilder(int index)
Each transformation will apply transform function to given input column.
|
AutoMlForecastingInputs.Builder |
addUnavailableAtForecastColumns(String value)
Names of columns that are unavailable when a forecast is requested.
|
AutoMlForecastingInputs.Builder |
addUnavailableAtForecastColumnsBytes(com.google.protobuf.ByteString value)
Names of columns that are unavailable when a forecast is requested.
|
AutoMlForecastingInputs |
build() |
AutoMlForecastingInputs |
buildPartial() |
AutoMlForecastingInputs.Builder |
clear() |
AutoMlForecastingInputs.Builder |
clearAdditionalExperiments()
Additional experiment flags for the time series forcasting training.
|
AutoMlForecastingInputs.Builder |
clearAvailableAtForecastColumns()
Names of columns that are available and provided when a forecast
is requested.
|
AutoMlForecastingInputs.Builder |
clearContextWindow()
The amount of time into the past training and prediction data is used
for model training and prediction respectively.
|
AutoMlForecastingInputs.Builder |
clearDataGranularity()
Expected difference in time granularity between rows in the data.
|
AutoMlForecastingInputs.Builder |
clearExportEvaluatedDataItemsConfig()
Configuration for exporting test set predictions to a BigQuery table.
|
AutoMlForecastingInputs.Builder |
clearField(com.google.protobuf.Descriptors.FieldDescriptor field) |
AutoMlForecastingInputs.Builder |
clearForecastHorizon()
The amount of time into the future for which forecasted values for the
target are returned.
|
AutoMlForecastingInputs.Builder |
clearOneof(com.google.protobuf.Descriptors.OneofDescriptor oneof) |
AutoMlForecastingInputs.Builder |
clearOptimizationObjective()
Objective function the model is optimizing towards.
|
AutoMlForecastingInputs.Builder |
clearQuantiles()
Quantiles to use for minimize-quantile-loss `optimization_objective`.
|
AutoMlForecastingInputs.Builder |
clearTargetColumn()
The name of the column that the model is to predict.
|
AutoMlForecastingInputs.Builder |
clearTimeColumn()
The name of the column that identifies time order in the time series.
|
AutoMlForecastingInputs.Builder |
clearTimeSeriesAttributeColumns()
Column names that should be used as attribute columns.
|
AutoMlForecastingInputs.Builder |
clearTimeSeriesIdentifierColumn()
The name of the column that identifies the time series.
|
AutoMlForecastingInputs.Builder |
clearTrainBudgetMilliNodeHours()
Required.
|
AutoMlForecastingInputs.Builder |
clearTransformations()
Each transformation will apply transform function to given input column.
|
AutoMlForecastingInputs.Builder |
clearUnavailableAtForecastColumns()
Names of columns that are unavailable when a forecast is requested.
|
AutoMlForecastingInputs.Builder |
clearValidationOptions()
Validation options for the data validation component.
|
AutoMlForecastingInputs.Builder |
clearWeightColumn()
Column name that should be used as the weight column.
|
AutoMlForecastingInputs.Builder |
clone() |
String |
getAdditionalExperiments(int index)
Additional experiment flags for the time series forcasting training.
|
com.google.protobuf.ByteString |
getAdditionalExperimentsBytes(int index)
Additional experiment flags for the time series forcasting training.
|
int |
getAdditionalExperimentsCount()
Additional experiment flags for the time series forcasting training.
|
com.google.protobuf.ProtocolStringList |
getAdditionalExperimentsList()
Additional experiment flags for the time series forcasting training.
|
String |
getAvailableAtForecastColumns(int index)
Names of columns that are available and provided when a forecast
is requested.
|
com.google.protobuf.ByteString |
getAvailableAtForecastColumnsBytes(int index)
Names of columns that are available and provided when a forecast
is requested.
|
int |
getAvailableAtForecastColumnsCount()
Names of columns that are available and provided when a forecast
is requested.
|
com.google.protobuf.ProtocolStringList |
getAvailableAtForecastColumnsList()
Names of columns that are available and provided when a forecast
is requested.
|
long |
getContextWindow()
The amount of time into the past training and prediction data is used
for model training and prediction respectively.
|
AutoMlForecastingInputs.Granularity |
getDataGranularity()
Expected difference in time granularity between rows in the data.
|
AutoMlForecastingInputs.Granularity.Builder |
getDataGranularityBuilder()
Expected difference in time granularity between rows in the data.
|
AutoMlForecastingInputs.GranularityOrBuilder |
getDataGranularityOrBuilder()
Expected difference in time granularity between rows in the data.
|
AutoMlForecastingInputs |
getDefaultInstanceForType() |
static com.google.protobuf.Descriptors.Descriptor |
getDescriptor() |
com.google.protobuf.Descriptors.Descriptor |
getDescriptorForType() |
ExportEvaluatedDataItemsConfig |
getExportEvaluatedDataItemsConfig()
Configuration for exporting test set predictions to a BigQuery table.
|
ExportEvaluatedDataItemsConfig.Builder |
getExportEvaluatedDataItemsConfigBuilder()
Configuration for exporting test set predictions to a BigQuery table.
|
ExportEvaluatedDataItemsConfigOrBuilder |
getExportEvaluatedDataItemsConfigOrBuilder()
Configuration for exporting test set predictions to a BigQuery table.
|
long |
getForecastHorizon()
The amount of time into the future for which forecasted values for the
target are returned.
|
String |
getOptimizationObjective()
Objective function the model is optimizing towards.
|
com.google.protobuf.ByteString |
getOptimizationObjectiveBytes()
Objective function the model is optimizing towards.
|
double |
getQuantiles(int index)
Quantiles to use for minimize-quantile-loss `optimization_objective`.
|
int |
getQuantilesCount()
Quantiles to use for minimize-quantile-loss `optimization_objective`.
|
List<Double> |
getQuantilesList()
Quantiles to use for minimize-quantile-loss `optimization_objective`.
|
String |
getTargetColumn()
The name of the column that the model is to predict.
|
com.google.protobuf.ByteString |
getTargetColumnBytes()
The name of the column that the model is to predict.
|
String |
getTimeColumn()
The name of the column that identifies time order in the time series.
|
com.google.protobuf.ByteString |
getTimeColumnBytes()
The name of the column that identifies time order in the time series.
|
String |
getTimeSeriesAttributeColumns(int index)
Column names that should be used as attribute columns.
|
com.google.protobuf.ByteString |
getTimeSeriesAttributeColumnsBytes(int index)
Column names that should be used as attribute columns.
|
int |
getTimeSeriesAttributeColumnsCount()
Column names that should be used as attribute columns.
|
com.google.protobuf.ProtocolStringList |
getTimeSeriesAttributeColumnsList()
Column names that should be used as attribute columns.
|
String |
getTimeSeriesIdentifierColumn()
The name of the column that identifies the time series.
|
com.google.protobuf.ByteString |
getTimeSeriesIdentifierColumnBytes()
The name of the column that identifies the time series.
|
long |
getTrainBudgetMilliNodeHours()
Required.
|
AutoMlForecastingInputs.Transformation |
getTransformations(int index)
Each transformation will apply transform function to given input column.
|
AutoMlForecastingInputs.Transformation.Builder |
getTransformationsBuilder(int index)
Each transformation will apply transform function to given input column.
|
List<AutoMlForecastingInputs.Transformation.Builder> |
getTransformationsBuilderList()
Each transformation will apply transform function to given input column.
|
int |
getTransformationsCount()
Each transformation will apply transform function to given input column.
|
List<AutoMlForecastingInputs.Transformation> |
getTransformationsList()
Each transformation will apply transform function to given input column.
|
AutoMlForecastingInputs.TransformationOrBuilder |
getTransformationsOrBuilder(int index)
Each transformation will apply transform function to given input column.
|
List<? extends AutoMlForecastingInputs.TransformationOrBuilder> |
getTransformationsOrBuilderList()
Each transformation will apply transform function to given input column.
|
String |
getUnavailableAtForecastColumns(int index)
Names of columns that are unavailable when a forecast is requested.
|
com.google.protobuf.ByteString |
getUnavailableAtForecastColumnsBytes(int index)
Names of columns that are unavailable when a forecast is requested.
|
int |
getUnavailableAtForecastColumnsCount()
Names of columns that are unavailable when a forecast is requested.
|
com.google.protobuf.ProtocolStringList |
getUnavailableAtForecastColumnsList()
Names of columns that are unavailable when a forecast is requested.
|
String |
getValidationOptions()
Validation options for the data validation component.
|
com.google.protobuf.ByteString |
getValidationOptionsBytes()
Validation options for the data validation component.
|
String |
getWeightColumn()
Column name that should be used as the weight column.
|
com.google.protobuf.ByteString |
getWeightColumnBytes()
Column name that should be used as the weight column.
|
boolean |
hasDataGranularity()
Expected difference in time granularity between rows in the data.
|
boolean |
hasExportEvaluatedDataItemsConfig()
Configuration for exporting test set predictions to a BigQuery table.
|
protected com.google.protobuf.GeneratedMessageV3.FieldAccessorTable |
internalGetFieldAccessorTable() |
boolean |
isInitialized() |
AutoMlForecastingInputs.Builder |
mergeDataGranularity(AutoMlForecastingInputs.Granularity value)
Expected difference in time granularity between rows in the data.
|
AutoMlForecastingInputs.Builder |
mergeExportEvaluatedDataItemsConfig(ExportEvaluatedDataItemsConfig value)
Configuration for exporting test set predictions to a BigQuery table.
|
AutoMlForecastingInputs.Builder |
mergeFrom(AutoMlForecastingInputs other) |
AutoMlForecastingInputs.Builder |
mergeFrom(com.google.protobuf.CodedInputStream input,
com.google.protobuf.ExtensionRegistryLite extensionRegistry) |
AutoMlForecastingInputs.Builder |
mergeFrom(com.google.protobuf.Message other) |
AutoMlForecastingInputs.Builder |
mergeUnknownFields(com.google.protobuf.UnknownFieldSet unknownFields) |
AutoMlForecastingInputs.Builder |
removeTransformations(int index)
Each transformation will apply transform function to given input column.
|
AutoMlForecastingInputs.Builder |
setAdditionalExperiments(int index,
String value)
Additional experiment flags for the time series forcasting training.
|
AutoMlForecastingInputs.Builder |
setAvailableAtForecastColumns(int index,
String value)
Names of columns that are available and provided when a forecast
is requested.
|
AutoMlForecastingInputs.Builder |
setContextWindow(long value)
The amount of time into the past training and prediction data is used
for model training and prediction respectively.
|
AutoMlForecastingInputs.Builder |
setDataGranularity(AutoMlForecastingInputs.Granularity.Builder builderForValue)
Expected difference in time granularity between rows in the data.
|
AutoMlForecastingInputs.Builder |
setDataGranularity(AutoMlForecastingInputs.Granularity value)
Expected difference in time granularity between rows in the data.
|
AutoMlForecastingInputs.Builder |
setExportEvaluatedDataItemsConfig(ExportEvaluatedDataItemsConfig.Builder builderForValue)
Configuration for exporting test set predictions to a BigQuery table.
|
AutoMlForecastingInputs.Builder |
setExportEvaluatedDataItemsConfig(ExportEvaluatedDataItemsConfig value)
Configuration for exporting test set predictions to a BigQuery table.
|
AutoMlForecastingInputs.Builder |
setField(com.google.protobuf.Descriptors.FieldDescriptor field,
Object value) |
AutoMlForecastingInputs.Builder |
setForecastHorizon(long value)
The amount of time into the future for which forecasted values for the
target are returned.
|
AutoMlForecastingInputs.Builder |
setOptimizationObjective(String value)
Objective function the model is optimizing towards.
|
AutoMlForecastingInputs.Builder |
setOptimizationObjectiveBytes(com.google.protobuf.ByteString value)
Objective function the model is optimizing towards.
|
AutoMlForecastingInputs.Builder |
setQuantiles(int index,
double value)
Quantiles to use for minimize-quantile-loss `optimization_objective`.
|
AutoMlForecastingInputs.Builder |
setRepeatedField(com.google.protobuf.Descriptors.FieldDescriptor field,
int index,
Object value) |
AutoMlForecastingInputs.Builder |
setTargetColumn(String value)
The name of the column that the model is to predict.
|
AutoMlForecastingInputs.Builder |
setTargetColumnBytes(com.google.protobuf.ByteString value)
The name of the column that the model is to predict.
|
AutoMlForecastingInputs.Builder |
setTimeColumn(String value)
The name of the column that identifies time order in the time series.
|
AutoMlForecastingInputs.Builder |
setTimeColumnBytes(com.google.protobuf.ByteString value)
The name of the column that identifies time order in the time series.
|
AutoMlForecastingInputs.Builder |
setTimeSeriesAttributeColumns(int index,
String value)
Column names that should be used as attribute columns.
|
AutoMlForecastingInputs.Builder |
setTimeSeriesIdentifierColumn(String value)
The name of the column that identifies the time series.
|
AutoMlForecastingInputs.Builder |
setTimeSeriesIdentifierColumnBytes(com.google.protobuf.ByteString value)
The name of the column that identifies the time series.
|
AutoMlForecastingInputs.Builder |
setTrainBudgetMilliNodeHours(long value)
Required.
|
AutoMlForecastingInputs.Builder |
setTransformations(int index,
AutoMlForecastingInputs.Transformation.Builder builderForValue)
Each transformation will apply transform function to given input column.
|
AutoMlForecastingInputs.Builder |
setTransformations(int index,
AutoMlForecastingInputs.Transformation value)
Each transformation will apply transform function to given input column.
|
AutoMlForecastingInputs.Builder |
setUnavailableAtForecastColumns(int index,
String value)
Names of columns that are unavailable when a forecast is requested.
|
AutoMlForecastingInputs.Builder |
setUnknownFields(com.google.protobuf.UnknownFieldSet unknownFields) |
AutoMlForecastingInputs.Builder |
setValidationOptions(String value)
Validation options for the data validation component.
|
AutoMlForecastingInputs.Builder |
setValidationOptionsBytes(com.google.protobuf.ByteString value)
Validation options for the data validation component.
|
AutoMlForecastingInputs.Builder |
setWeightColumn(String value)
Column name that should be used as the weight column.
|
AutoMlForecastingInputs.Builder |
setWeightColumnBytes(com.google.protobuf.ByteString value)
Column name that should be used as the weight column.
|
getAllFields, getField, getFieldBuilder, getOneofFieldDescriptor, getParentForChildren, getRepeatedField, getRepeatedFieldBuilder, getRepeatedFieldCount, getUnknownFields, getUnknownFieldSetBuilder, hasField, hasOneof, internalGetMapField, internalGetMapFieldReflection, internalGetMutableMapField, internalGetMutableMapFieldReflection, isClean, markClean, mergeUnknownLengthDelimitedField, mergeUnknownVarintField, newBuilderForField, onBuilt, onChanged, parseUnknownField, setUnknownFieldSetBuilder, setUnknownFieldsProto3findInitializationErrors, getInitializationErrorString, internalMergeFrom, mergeFrom, mergeFrom, mergeFrom, mergeFrom, mergeFrom, mergeFrom, mergeFrom, mergeFrom, mergeFrom, newUninitializedMessageException, toStringaddAll, addAll, mergeDelimitedFrom, mergeDelimitedFrom, newUninitializedMessageExceptionequals, finalize, getClass, hashCode, notify, notifyAll, wait, wait, waitpublic static final com.google.protobuf.Descriptors.Descriptor getDescriptor()
protected com.google.protobuf.GeneratedMessageV3.FieldAccessorTable internalGetFieldAccessorTable()
internalGetFieldAccessorTable in class com.google.protobuf.GeneratedMessageV3.Builder<AutoMlForecastingInputs.Builder>public AutoMlForecastingInputs.Builder clear()
clear in interface com.google.protobuf.Message.Builderclear in interface com.google.protobuf.MessageLite.Builderclear in class com.google.protobuf.GeneratedMessageV3.Builder<AutoMlForecastingInputs.Builder>public com.google.protobuf.Descriptors.Descriptor getDescriptorForType()
getDescriptorForType in interface com.google.protobuf.Message.BuildergetDescriptorForType in interface com.google.protobuf.MessageOrBuildergetDescriptorForType in class com.google.protobuf.GeneratedMessageV3.Builder<AutoMlForecastingInputs.Builder>public AutoMlForecastingInputs getDefaultInstanceForType()
getDefaultInstanceForType in interface com.google.protobuf.MessageLiteOrBuildergetDefaultInstanceForType in interface com.google.protobuf.MessageOrBuilderpublic AutoMlForecastingInputs build()
build in interface com.google.protobuf.Message.Builderbuild in interface com.google.protobuf.MessageLite.Builderpublic AutoMlForecastingInputs buildPartial()
buildPartial in interface com.google.protobuf.Message.BuilderbuildPartial in interface com.google.protobuf.MessageLite.Builderpublic AutoMlForecastingInputs.Builder clone()
clone in interface com.google.protobuf.Message.Builderclone in interface com.google.protobuf.MessageLite.Builderclone in class com.google.protobuf.GeneratedMessageV3.Builder<AutoMlForecastingInputs.Builder>public AutoMlForecastingInputs.Builder setField(com.google.protobuf.Descriptors.FieldDescriptor field, Object value)
setField in interface com.google.protobuf.Message.BuildersetField in class com.google.protobuf.GeneratedMessageV3.Builder<AutoMlForecastingInputs.Builder>public AutoMlForecastingInputs.Builder clearField(com.google.protobuf.Descriptors.FieldDescriptor field)
clearField in interface com.google.protobuf.Message.BuilderclearField in class com.google.protobuf.GeneratedMessageV3.Builder<AutoMlForecastingInputs.Builder>public AutoMlForecastingInputs.Builder clearOneof(com.google.protobuf.Descriptors.OneofDescriptor oneof)
clearOneof in interface com.google.protobuf.Message.BuilderclearOneof in class com.google.protobuf.GeneratedMessageV3.Builder<AutoMlForecastingInputs.Builder>public AutoMlForecastingInputs.Builder setRepeatedField(com.google.protobuf.Descriptors.FieldDescriptor field, int index, Object value)
setRepeatedField in interface com.google.protobuf.Message.BuildersetRepeatedField in class com.google.protobuf.GeneratedMessageV3.Builder<AutoMlForecastingInputs.Builder>public AutoMlForecastingInputs.Builder addRepeatedField(com.google.protobuf.Descriptors.FieldDescriptor field, Object value)
addRepeatedField in interface com.google.protobuf.Message.BuilderaddRepeatedField in class com.google.protobuf.GeneratedMessageV3.Builder<AutoMlForecastingInputs.Builder>public AutoMlForecastingInputs.Builder mergeFrom(com.google.protobuf.Message other)
mergeFrom in interface com.google.protobuf.Message.BuildermergeFrom in class com.google.protobuf.AbstractMessage.Builder<AutoMlForecastingInputs.Builder>public AutoMlForecastingInputs.Builder mergeFrom(AutoMlForecastingInputs other)
public final boolean isInitialized()
isInitialized in interface com.google.protobuf.MessageLiteOrBuilderisInitialized in class com.google.protobuf.GeneratedMessageV3.Builder<AutoMlForecastingInputs.Builder>public AutoMlForecastingInputs.Builder mergeFrom(com.google.protobuf.CodedInputStream input, com.google.protobuf.ExtensionRegistryLite extensionRegistry) throws IOException
mergeFrom in interface com.google.protobuf.Message.BuildermergeFrom in interface com.google.protobuf.MessageLite.BuildermergeFrom in class com.google.protobuf.AbstractMessage.Builder<AutoMlForecastingInputs.Builder>IOExceptionpublic String getTargetColumn()
The name of the column that the model is to predict.
string target_column = 1;getTargetColumn in interface AutoMlForecastingInputsOrBuilderpublic com.google.protobuf.ByteString getTargetColumnBytes()
The name of the column that the model is to predict.
string target_column = 1;getTargetColumnBytes in interface AutoMlForecastingInputsOrBuilderpublic AutoMlForecastingInputs.Builder setTargetColumn(String value)
The name of the column that the model is to predict.
string target_column = 1;value - The targetColumn to set.public AutoMlForecastingInputs.Builder clearTargetColumn()
The name of the column that the model is to predict.
string target_column = 1;public AutoMlForecastingInputs.Builder setTargetColumnBytes(com.google.protobuf.ByteString value)
The name of the column that the model is to predict.
string target_column = 1;value - The bytes for targetColumn to set.public String getTimeSeriesIdentifierColumn()
The name of the column that identifies the time series.
string time_series_identifier_column = 2;getTimeSeriesIdentifierColumn in interface AutoMlForecastingInputsOrBuilderpublic com.google.protobuf.ByteString getTimeSeriesIdentifierColumnBytes()
The name of the column that identifies the time series.
string time_series_identifier_column = 2;getTimeSeriesIdentifierColumnBytes in interface AutoMlForecastingInputsOrBuilderpublic AutoMlForecastingInputs.Builder setTimeSeriesIdentifierColumn(String value)
The name of the column that identifies the time series.
string time_series_identifier_column = 2;value - The timeSeriesIdentifierColumn to set.public AutoMlForecastingInputs.Builder clearTimeSeriesIdentifierColumn()
The name of the column that identifies the time series.
string time_series_identifier_column = 2;public AutoMlForecastingInputs.Builder setTimeSeriesIdentifierColumnBytes(com.google.protobuf.ByteString value)
The name of the column that identifies the time series.
string time_series_identifier_column = 2;value - The bytes for timeSeriesIdentifierColumn to set.public String getTimeColumn()
The name of the column that identifies time order in the time series.
string time_column = 3;getTimeColumn in interface AutoMlForecastingInputsOrBuilderpublic com.google.protobuf.ByteString getTimeColumnBytes()
The name of the column that identifies time order in the time series.
string time_column = 3;getTimeColumnBytes in interface AutoMlForecastingInputsOrBuilderpublic AutoMlForecastingInputs.Builder setTimeColumn(String value)
The name of the column that identifies time order in the time series.
string time_column = 3;value - The timeColumn to set.public AutoMlForecastingInputs.Builder clearTimeColumn()
The name of the column that identifies time order in the time series.
string time_column = 3;public AutoMlForecastingInputs.Builder setTimeColumnBytes(com.google.protobuf.ByteString value)
The name of the column that identifies time order in the time series.
string time_column = 3;value - The bytes for timeColumn to set.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;
getTransformationsList in interface AutoMlForecastingInputsOrBuilderpublic 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;
getTransformationsCount in interface AutoMlForecastingInputsOrBuilderpublic 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;
getTransformations in interface AutoMlForecastingInputsOrBuilderpublic 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;
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;
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;
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;
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;
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;
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;
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;
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;
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;
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;
getTransformationsOrBuilder in interface AutoMlForecastingInputsOrBuilderpublic 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;
getTransformationsOrBuilderList in interface AutoMlForecastingInputsOrBuilderpublic 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;
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;
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;
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;getOptimizationObjective in interface AutoMlForecastingInputsOrBuilderpublic com.google.protobuf.ByteString getOptimizationObjectiveBytes()
Objective function the model is optimizing towards. The training process
creates a model that optimizes the value of the objective
function over the validation set.
The supported optimization objectives:
* "minimize-rmse" (default) - Minimize root-mean-squared error (RMSE).
* "minimize-mae" - Minimize mean-absolute error (MAE).
* "minimize-rmsle" - Minimize root-mean-squared log error (RMSLE).
* "minimize-rmspe" - Minimize root-mean-squared percentage error (RMSPE).
* "minimize-wape-mae" - Minimize the combination of weighted absolute
percentage error (WAPE) and mean-absolute-error (MAE).
* "minimize-quantile-loss" - Minimize the quantile loss at the quantiles
defined in `quantiles`.
string optimization_objective = 5;getOptimizationObjectiveBytes in interface AutoMlForecastingInputsOrBuilderpublic 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;value - The optimizationObjective to set.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;public AutoMlForecastingInputs.Builder setOptimizationObjectiveBytes(com.google.protobuf.ByteString value)
Objective function the model is optimizing towards. The training process
creates a model that optimizes the value of the objective
function over the validation set.
The supported optimization objectives:
* "minimize-rmse" (default) - Minimize root-mean-squared error (RMSE).
* "minimize-mae" - Minimize mean-absolute error (MAE).
* "minimize-rmsle" - Minimize root-mean-squared log error (RMSLE).
* "minimize-rmspe" - Minimize root-mean-squared percentage error (RMSPE).
* "minimize-wape-mae" - Minimize the combination of weighted absolute
percentage error (WAPE) and mean-absolute-error (MAE).
* "minimize-quantile-loss" - Minimize the quantile loss at the quantiles
defined in `quantiles`.
string optimization_objective = 5;value - The bytes for optimizationObjective to set.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;getTrainBudgetMilliNodeHours in interface AutoMlForecastingInputsOrBuilderpublic 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;value - The trainBudgetMilliNodeHours to set.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;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;getWeightColumn in interface AutoMlForecastingInputsOrBuilderpublic com.google.protobuf.ByteString getWeightColumnBytes()
Column name that should be used as the weight column. Higher values in this column give more importance to the row during model training. The column must have numeric values between 0 and 10000 inclusively; 0 means the row is ignored for training. If weight column field is not set, then all rows are assumed to have equal weight of 1.
string weight_column = 7;getWeightColumnBytes in interface AutoMlForecastingInputsOrBuilderpublic 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;value - The weightColumn to set.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;public AutoMlForecastingInputs.Builder setWeightColumnBytes(com.google.protobuf.ByteString value)
Column name that should be used as the weight column. Higher values in this column give more importance to the row during model training. The column must have numeric values between 0 and 10000 inclusively; 0 means the row is ignored for training. If weight column field is not set, then all rows are assumed to have equal weight of 1.
string weight_column = 7;value - The bytes for weightColumn to set.public com.google.protobuf.ProtocolStringList getTimeSeriesAttributeColumnsList()
Column names that should be used as attribute columns. The value of these columns does not vary as a function of time. For example, store ID or item color.
repeated string time_series_attribute_columns = 19;getTimeSeriesAttributeColumnsList in interface AutoMlForecastingInputsOrBuilderpublic 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;getTimeSeriesAttributeColumnsCount in interface AutoMlForecastingInputsOrBuilderpublic 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;getTimeSeriesAttributeColumns in interface AutoMlForecastingInputsOrBuilderindex - The index of the element to return.public com.google.protobuf.ByteString getTimeSeriesAttributeColumnsBytes(int index)
Column names that should be used as attribute columns. The value of these columns does not vary as a function of time. For example, store ID or item color.
repeated string time_series_attribute_columns = 19;getTimeSeriesAttributeColumnsBytes in interface AutoMlForecastingInputsOrBuilderindex - The index of the value to return.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;index - The index to set the value at.value - The timeSeriesAttributeColumns to set.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;value - The timeSeriesAttributeColumns to add.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;values - The timeSeriesAttributeColumns to add.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;public AutoMlForecastingInputs.Builder addTimeSeriesAttributeColumnsBytes(com.google.protobuf.ByteString value)
Column names that should be used as attribute columns. The value of these columns does not vary as a function of time. For example, store ID or item color.
repeated string time_series_attribute_columns = 19;value - The bytes of the timeSeriesAttributeColumns to add.public com.google.protobuf.ProtocolStringList getUnavailableAtForecastColumnsList()
Names of columns that are unavailable when a forecast is requested. This column contains information for the given entity (identified by the time_series_identifier_column) that is unknown before the forecast For example, actual weather on a given day.
repeated string unavailable_at_forecast_columns = 20;getUnavailableAtForecastColumnsList in interface AutoMlForecastingInputsOrBuilderpublic 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;getUnavailableAtForecastColumnsCount in interface AutoMlForecastingInputsOrBuilderpublic 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;getUnavailableAtForecastColumns in interface AutoMlForecastingInputsOrBuilderindex - The index of the element to return.public com.google.protobuf.ByteString getUnavailableAtForecastColumnsBytes(int index)
Names of columns that are unavailable when a forecast is requested. This column contains information for the given entity (identified by the time_series_identifier_column) that is unknown before the forecast For example, actual weather on a given day.
repeated string unavailable_at_forecast_columns = 20;getUnavailableAtForecastColumnsBytes in interface AutoMlForecastingInputsOrBuilderindex - The index of the value to return.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;index - The index to set the value at.value - The unavailableAtForecastColumns to set.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;value - The unavailableAtForecastColumns to add.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;values - The unavailableAtForecastColumns to add.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;public AutoMlForecastingInputs.Builder addUnavailableAtForecastColumnsBytes(com.google.protobuf.ByteString value)
Names of columns that are unavailable when a forecast is requested. This column contains information for the given entity (identified by the time_series_identifier_column) that is unknown before the forecast For example, actual weather on a given day.
repeated string unavailable_at_forecast_columns = 20;value - The bytes of the unavailableAtForecastColumns to add.public com.google.protobuf.ProtocolStringList getAvailableAtForecastColumnsList()
Names of columns that are available and provided when a forecast is requested. These columns contain information for the given entity (identified by the time_series_identifier_column column) that is known at forecast. For example, predicted weather for a specific day.
repeated string available_at_forecast_columns = 21;getAvailableAtForecastColumnsList in interface AutoMlForecastingInputsOrBuilderpublic 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;getAvailableAtForecastColumnsCount in interface AutoMlForecastingInputsOrBuilderpublic 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;getAvailableAtForecastColumns in interface AutoMlForecastingInputsOrBuilderindex - The index of the element to return.public com.google.protobuf.ByteString getAvailableAtForecastColumnsBytes(int index)
Names of columns that are available and provided when a forecast is requested. These columns contain information for the given entity (identified by the time_series_identifier_column column) that is known at forecast. For example, predicted weather for a specific day.
repeated string available_at_forecast_columns = 21;getAvailableAtForecastColumnsBytes in interface AutoMlForecastingInputsOrBuilderindex - The index of the value to return.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;index - The index to set the value at.value - The availableAtForecastColumns to set.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;value - The availableAtForecastColumns to add.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;values - The availableAtForecastColumns to add.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;public AutoMlForecastingInputs.Builder addAvailableAtForecastColumnsBytes(com.google.protobuf.ByteString value)
Names of columns that are available and provided when a forecast is requested. These columns contain information for the given entity (identified by the time_series_identifier_column column) that is known at forecast. For example, predicted weather for a specific day.
repeated string available_at_forecast_columns = 21;value - The bytes of the availableAtForecastColumns to add.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;
hasDataGranularity in interface AutoMlForecastingInputsOrBuilderpublic 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;
getDataGranularity in interface AutoMlForecastingInputsOrBuilderpublic 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;
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;
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;
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;
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;
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;
getDataGranularityOrBuilder in interface AutoMlForecastingInputsOrBuilderpublic 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;getForecastHorizon in interface AutoMlForecastingInputsOrBuilderpublic 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;value - The forecastHorizon to set.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;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;getContextWindow in interface AutoMlForecastingInputsOrBuilderpublic 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;value - The contextWindow to set.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;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;
hasExportEvaluatedDataItemsConfig in interface AutoMlForecastingInputsOrBuilderpublic 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;
getExportEvaluatedDataItemsConfig in interface AutoMlForecastingInputsOrBuilderpublic 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;
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;
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;
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;
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;
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;
getExportEvaluatedDataItemsConfigOrBuilder in interface AutoMlForecastingInputsOrBuilderpublic 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;getQuantilesList in interface AutoMlForecastingInputsOrBuilderpublic 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;getQuantilesCount in interface AutoMlForecastingInputsOrBuilderpublic 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;getQuantiles in interface AutoMlForecastingInputsOrBuilderindex - The index of the element to return.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;index - The index to set the value at.value - The quantiles to set.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;value - The quantiles to add.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;values - The quantiles to add.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;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;getValidationOptions in interface AutoMlForecastingInputsOrBuilderpublic com.google.protobuf.ByteString getValidationOptionsBytes()
Validation options for the data validation component. The available options
are:
* "fail-pipeline" - default, will validate against the validation and
fail the pipeline if it fails.
* "ignore-validation" - ignore the results of the validation and continue
string validation_options = 17;getValidationOptionsBytes in interface AutoMlForecastingInputsOrBuilderpublic 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;value - The validationOptions to set.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;public AutoMlForecastingInputs.Builder setValidationOptionsBytes(com.google.protobuf.ByteString value)
Validation options for the data validation component. The available options
are:
* "fail-pipeline" - default, will validate against the validation and
fail the pipeline if it fails.
* "ignore-validation" - ignore the results of the validation and continue
string validation_options = 17;value - The bytes for validationOptions to set.public com.google.protobuf.ProtocolStringList getAdditionalExperimentsList()
Additional experiment flags for the time series forcasting training.
repeated string additional_experiments = 25;getAdditionalExperimentsList in interface AutoMlForecastingInputsOrBuilderpublic int getAdditionalExperimentsCount()
Additional experiment flags for the time series forcasting training.
repeated string additional_experiments = 25;getAdditionalExperimentsCount in interface AutoMlForecastingInputsOrBuilderpublic String getAdditionalExperiments(int index)
Additional experiment flags for the time series forcasting training.
repeated string additional_experiments = 25;getAdditionalExperiments in interface AutoMlForecastingInputsOrBuilderindex - The index of the element to return.public com.google.protobuf.ByteString getAdditionalExperimentsBytes(int index)
Additional experiment flags for the time series forcasting training.
repeated string additional_experiments = 25;getAdditionalExperimentsBytes in interface AutoMlForecastingInputsOrBuilderindex - The index of the value to return.public AutoMlForecastingInputs.Builder setAdditionalExperiments(int index, String value)
Additional experiment flags for the time series forcasting training.
repeated string additional_experiments = 25;index - The index to set the value at.value - The additionalExperiments to set.public AutoMlForecastingInputs.Builder addAdditionalExperiments(String value)
Additional experiment flags for the time series forcasting training.
repeated string additional_experiments = 25;value - The additionalExperiments to add.public AutoMlForecastingInputs.Builder addAllAdditionalExperiments(Iterable<String> values)
Additional experiment flags for the time series forcasting training.
repeated string additional_experiments = 25;values - The additionalExperiments to add.public AutoMlForecastingInputs.Builder clearAdditionalExperiments()
Additional experiment flags for the time series forcasting training.
repeated string additional_experiments = 25;public AutoMlForecastingInputs.Builder addAdditionalExperimentsBytes(com.google.protobuf.ByteString value)
Additional experiment flags for the time series forcasting training.
repeated string additional_experiments = 25;value - The bytes of the additionalExperiments to add.public final AutoMlForecastingInputs.Builder setUnknownFields(com.google.protobuf.UnknownFieldSet unknownFields)
setUnknownFields in interface com.google.protobuf.Message.BuildersetUnknownFields in class com.google.protobuf.GeneratedMessageV3.Builder<AutoMlForecastingInputs.Builder>public final AutoMlForecastingInputs.Builder mergeUnknownFields(com.google.protobuf.UnknownFieldSet unknownFields)
mergeUnknownFields in interface com.google.protobuf.Message.BuildermergeUnknownFields in class com.google.protobuf.GeneratedMessageV3.Builder<AutoMlForecastingInputs.Builder>Copyright © 2024 Google LLC. All rights reserved.