public static final class AutoMlTablesInputs.Builder extends com.google.protobuf.GeneratedMessageV3.Builder<AutoMlTablesInputs.Builder> implements AutoMlTablesInputsOrBuilder
google.cloud.aiplatform.v1.schema.trainingjob.definition.AutoMlTablesInputs| Modifier and Type | Method and Description |
|---|---|
AutoMlTablesInputs.Builder |
addAdditionalExperiments(String value)
Additional experiment flags for the Tables training pipeline.
|
AutoMlTablesInputs.Builder |
addAdditionalExperimentsBytes(com.google.protobuf.ByteString value)
Additional experiment flags for the Tables training pipeline.
|
AutoMlTablesInputs.Builder |
addAllAdditionalExperiments(Iterable<String> values)
Additional experiment flags for the Tables training pipeline.
|
AutoMlTablesInputs.Builder |
addAllTransformations(Iterable<? extends AutoMlTablesInputs.Transformation> values)
Each transformation will apply transform function to given input column.
|
AutoMlTablesInputs.Builder |
addRepeatedField(com.google.protobuf.Descriptors.FieldDescriptor field,
Object value) |
AutoMlTablesInputs.Builder |
addTransformations(AutoMlTablesInputs.Transformation.Builder builderForValue)
Each transformation will apply transform function to given input column.
|
AutoMlTablesInputs.Builder |
addTransformations(AutoMlTablesInputs.Transformation value)
Each transformation will apply transform function to given input column.
|
AutoMlTablesInputs.Builder |
addTransformations(int index,
AutoMlTablesInputs.Transformation.Builder builderForValue)
Each transformation will apply transform function to given input column.
|
AutoMlTablesInputs.Builder |
addTransformations(int index,
AutoMlTablesInputs.Transformation value)
Each transformation will apply transform function to given input column.
|
AutoMlTablesInputs.Transformation.Builder |
addTransformationsBuilder()
Each transformation will apply transform function to given input column.
|
AutoMlTablesInputs.Transformation.Builder |
addTransformationsBuilder(int index)
Each transformation will apply transform function to given input column.
|
AutoMlTablesInputs |
build() |
AutoMlTablesInputs |
buildPartial() |
AutoMlTablesInputs.Builder |
clear() |
AutoMlTablesInputs.Builder |
clearAdditionalExperiments()
Additional experiment flags for the Tables training pipeline.
|
AutoMlTablesInputs.Builder |
clearAdditionalOptimizationObjectiveConfig() |
AutoMlTablesInputs.Builder |
clearDisableEarlyStopping()
Use the entire training budget.
|
AutoMlTablesInputs.Builder |
clearExportEvaluatedDataItemsConfig()
Configuration for exporting test set predictions to a BigQuery table.
|
AutoMlTablesInputs.Builder |
clearField(com.google.protobuf.Descriptors.FieldDescriptor field) |
AutoMlTablesInputs.Builder |
clearOneof(com.google.protobuf.Descriptors.OneofDescriptor oneof) |
AutoMlTablesInputs.Builder |
clearOptimizationObjective()
Objective function the model is optimizing towards.
|
AutoMlTablesInputs.Builder |
clearOptimizationObjectivePrecisionValue()
Required when optimization_objective is "maximize-recall-at-precision".
|
AutoMlTablesInputs.Builder |
clearOptimizationObjectiveRecallValue()
Required when optimization_objective is "maximize-precision-at-recall".
|
AutoMlTablesInputs.Builder |
clearPredictionType()
The type of prediction the Model is to produce.
|
AutoMlTablesInputs.Builder |
clearTargetColumn()
The column name of the target column that the model is to predict.
|
AutoMlTablesInputs.Builder |
clearTrainBudgetMilliNodeHours()
Required.
|
AutoMlTablesInputs.Builder |
clearTransformations()
Each transformation will apply transform function to given input column.
|
AutoMlTablesInputs.Builder |
clearWeightColumnName()
Column name that should be used as the weight column.
|
AutoMlTablesInputs.Builder |
clone() |
String |
getAdditionalExperiments(int index)
Additional experiment flags for the Tables training pipeline.
|
com.google.protobuf.ByteString |
getAdditionalExperimentsBytes(int index)
Additional experiment flags for the Tables training pipeline.
|
int |
getAdditionalExperimentsCount()
Additional experiment flags for the Tables training pipeline.
|
com.google.protobuf.ProtocolStringList |
getAdditionalExperimentsList()
Additional experiment flags for the Tables training pipeline.
|
AutoMlTablesInputs.AdditionalOptimizationObjectiveConfigCase |
getAdditionalOptimizationObjectiveConfigCase() |
AutoMlTablesInputs |
getDefaultInstanceForType() |
static com.google.protobuf.Descriptors.Descriptor |
getDescriptor() |
com.google.protobuf.Descriptors.Descriptor |
getDescriptorForType() |
boolean |
getDisableEarlyStopping()
Use the entire training budget.
|
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.
|
String |
getOptimizationObjective()
Objective function the model is optimizing towards.
|
com.google.protobuf.ByteString |
getOptimizationObjectiveBytes()
Objective function the model is optimizing towards.
|
float |
getOptimizationObjectivePrecisionValue()
Required when optimization_objective is "maximize-recall-at-precision".
|
float |
getOptimizationObjectiveRecallValue()
Required when optimization_objective is "maximize-precision-at-recall".
|
String |
getPredictionType()
The type of prediction the Model is to produce.
|
com.google.protobuf.ByteString |
getPredictionTypeBytes()
The type of prediction the Model is to produce.
|
String |
getTargetColumn()
The column name of the target column that the model is to predict.
|
com.google.protobuf.ByteString |
getTargetColumnBytes()
The column name of the target column that the model is to predict.
|
long |
getTrainBudgetMilliNodeHours()
Required.
|
AutoMlTablesInputs.Transformation |
getTransformations(int index)
Each transformation will apply transform function to given input column.
|
AutoMlTablesInputs.Transformation.Builder |
getTransformationsBuilder(int index)
Each transformation will apply transform function to given input column.
|
List<AutoMlTablesInputs.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<AutoMlTablesInputs.Transformation> |
getTransformationsList()
Each transformation will apply transform function to given input column.
|
AutoMlTablesInputs.TransformationOrBuilder |
getTransformationsOrBuilder(int index)
Each transformation will apply transform function to given input column.
|
List<? extends AutoMlTablesInputs.TransformationOrBuilder> |
getTransformationsOrBuilderList()
Each transformation will apply transform function to given input column.
|
String |
getWeightColumnName()
Column name that should be used as the weight column.
|
com.google.protobuf.ByteString |
getWeightColumnNameBytes()
Column name that should be used as the weight column.
|
boolean |
hasExportEvaluatedDataItemsConfig()
Configuration for exporting test set predictions to a BigQuery table.
|
boolean |
hasOptimizationObjectivePrecisionValue()
Required when optimization_objective is "maximize-recall-at-precision".
|
boolean |
hasOptimizationObjectiveRecallValue()
Required when optimization_objective is "maximize-precision-at-recall".
|
protected com.google.protobuf.GeneratedMessageV3.FieldAccessorTable |
internalGetFieldAccessorTable() |
boolean |
isInitialized() |
AutoMlTablesInputs.Builder |
mergeExportEvaluatedDataItemsConfig(ExportEvaluatedDataItemsConfig value)
Configuration for exporting test set predictions to a BigQuery table.
|
AutoMlTablesInputs.Builder |
mergeFrom(AutoMlTablesInputs other) |
AutoMlTablesInputs.Builder |
mergeFrom(com.google.protobuf.CodedInputStream input,
com.google.protobuf.ExtensionRegistryLite extensionRegistry) |
AutoMlTablesInputs.Builder |
mergeFrom(com.google.protobuf.Message other) |
AutoMlTablesInputs.Builder |
mergeUnknownFields(com.google.protobuf.UnknownFieldSet unknownFields) |
AutoMlTablesInputs.Builder |
removeTransformations(int index)
Each transformation will apply transform function to given input column.
|
AutoMlTablesInputs.Builder |
setAdditionalExperiments(int index,
String value)
Additional experiment flags for the Tables training pipeline.
|
AutoMlTablesInputs.Builder |
setDisableEarlyStopping(boolean value)
Use the entire training budget.
|
AutoMlTablesInputs.Builder |
setExportEvaluatedDataItemsConfig(ExportEvaluatedDataItemsConfig.Builder builderForValue)
Configuration for exporting test set predictions to a BigQuery table.
|
AutoMlTablesInputs.Builder |
setExportEvaluatedDataItemsConfig(ExportEvaluatedDataItemsConfig value)
Configuration for exporting test set predictions to a BigQuery table.
|
AutoMlTablesInputs.Builder |
setField(com.google.protobuf.Descriptors.FieldDescriptor field,
Object value) |
AutoMlTablesInputs.Builder |
setOptimizationObjective(String value)
Objective function the model is optimizing towards.
|
AutoMlTablesInputs.Builder |
setOptimizationObjectiveBytes(com.google.protobuf.ByteString value)
Objective function the model is optimizing towards.
|
AutoMlTablesInputs.Builder |
setOptimizationObjectivePrecisionValue(float value)
Required when optimization_objective is "maximize-recall-at-precision".
|
AutoMlTablesInputs.Builder |
setOptimizationObjectiveRecallValue(float value)
Required when optimization_objective is "maximize-precision-at-recall".
|
AutoMlTablesInputs.Builder |
setPredictionType(String value)
The type of prediction the Model is to produce.
|
AutoMlTablesInputs.Builder |
setPredictionTypeBytes(com.google.protobuf.ByteString value)
The type of prediction the Model is to produce.
|
AutoMlTablesInputs.Builder |
setRepeatedField(com.google.protobuf.Descriptors.FieldDescriptor field,
int index,
Object value) |
AutoMlTablesInputs.Builder |
setTargetColumn(String value)
The column name of the target column that the model is to predict.
|
AutoMlTablesInputs.Builder |
setTargetColumnBytes(com.google.protobuf.ByteString value)
The column name of the target column that the model is to predict.
|
AutoMlTablesInputs.Builder |
setTrainBudgetMilliNodeHours(long value)
Required.
|
AutoMlTablesInputs.Builder |
setTransformations(int index,
AutoMlTablesInputs.Transformation.Builder builderForValue)
Each transformation will apply transform function to given input column.
|
AutoMlTablesInputs.Builder |
setTransformations(int index,
AutoMlTablesInputs.Transformation value)
Each transformation will apply transform function to given input column.
|
AutoMlTablesInputs.Builder |
setUnknownFields(com.google.protobuf.UnknownFieldSet unknownFields) |
AutoMlTablesInputs.Builder |
setWeightColumnName(String value)
Column name that should be used as the weight column.
|
AutoMlTablesInputs.Builder |
setWeightColumnNameBytes(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<AutoMlTablesInputs.Builder>public AutoMlTablesInputs.Builder clear()
clear in interface com.google.protobuf.Message.Builderclear in interface com.google.protobuf.MessageLite.Builderclear in class com.google.protobuf.GeneratedMessageV3.Builder<AutoMlTablesInputs.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<AutoMlTablesInputs.Builder>public AutoMlTablesInputs getDefaultInstanceForType()
getDefaultInstanceForType in interface com.google.protobuf.MessageLiteOrBuildergetDefaultInstanceForType in interface com.google.protobuf.MessageOrBuilderpublic AutoMlTablesInputs build()
build in interface com.google.protobuf.Message.Builderbuild in interface com.google.protobuf.MessageLite.Builderpublic AutoMlTablesInputs buildPartial()
buildPartial in interface com.google.protobuf.Message.BuilderbuildPartial in interface com.google.protobuf.MessageLite.Builderpublic AutoMlTablesInputs.Builder clone()
clone in interface com.google.protobuf.Message.Builderclone in interface com.google.protobuf.MessageLite.Builderclone in class com.google.protobuf.GeneratedMessageV3.Builder<AutoMlTablesInputs.Builder>public AutoMlTablesInputs.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<AutoMlTablesInputs.Builder>public AutoMlTablesInputs.Builder clearField(com.google.protobuf.Descriptors.FieldDescriptor field)
clearField in interface com.google.protobuf.Message.BuilderclearField in class com.google.protobuf.GeneratedMessageV3.Builder<AutoMlTablesInputs.Builder>public AutoMlTablesInputs.Builder clearOneof(com.google.protobuf.Descriptors.OneofDescriptor oneof)
clearOneof in interface com.google.protobuf.Message.BuilderclearOneof in class com.google.protobuf.GeneratedMessageV3.Builder<AutoMlTablesInputs.Builder>public AutoMlTablesInputs.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<AutoMlTablesInputs.Builder>public AutoMlTablesInputs.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<AutoMlTablesInputs.Builder>public AutoMlTablesInputs.Builder mergeFrom(com.google.protobuf.Message other)
mergeFrom in interface com.google.protobuf.Message.BuildermergeFrom in class com.google.protobuf.AbstractMessage.Builder<AutoMlTablesInputs.Builder>public AutoMlTablesInputs.Builder mergeFrom(AutoMlTablesInputs other)
public final boolean isInitialized()
isInitialized in interface com.google.protobuf.MessageLiteOrBuilderisInitialized in class com.google.protobuf.GeneratedMessageV3.Builder<AutoMlTablesInputs.Builder>public AutoMlTablesInputs.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<AutoMlTablesInputs.Builder>IOExceptionpublic AutoMlTablesInputs.AdditionalOptimizationObjectiveConfigCase getAdditionalOptimizationObjectiveConfigCase()
getAdditionalOptimizationObjectiveConfigCase in interface AutoMlTablesInputsOrBuilderpublic AutoMlTablesInputs.Builder clearAdditionalOptimizationObjectiveConfig()
public boolean hasOptimizationObjectiveRecallValue()
Required when optimization_objective is "maximize-precision-at-recall". Must be between 0 and 1, inclusive.
float optimization_objective_recall_value = 5;hasOptimizationObjectiveRecallValue in interface AutoMlTablesInputsOrBuilderpublic float getOptimizationObjectiveRecallValue()
Required when optimization_objective is "maximize-precision-at-recall". Must be between 0 and 1, inclusive.
float optimization_objective_recall_value = 5;getOptimizationObjectiveRecallValue in interface AutoMlTablesInputsOrBuilderpublic AutoMlTablesInputs.Builder setOptimizationObjectiveRecallValue(float value)
Required when optimization_objective is "maximize-precision-at-recall". Must be between 0 and 1, inclusive.
float optimization_objective_recall_value = 5;value - The optimizationObjectiveRecallValue to set.public AutoMlTablesInputs.Builder clearOptimizationObjectiveRecallValue()
Required when optimization_objective is "maximize-precision-at-recall". Must be between 0 and 1, inclusive.
float optimization_objective_recall_value = 5;public boolean hasOptimizationObjectivePrecisionValue()
Required when optimization_objective is "maximize-recall-at-precision". Must be between 0 and 1, inclusive.
float optimization_objective_precision_value = 6;hasOptimizationObjectivePrecisionValue in interface AutoMlTablesInputsOrBuilderpublic float getOptimizationObjectivePrecisionValue()
Required when optimization_objective is "maximize-recall-at-precision". Must be between 0 and 1, inclusive.
float optimization_objective_precision_value = 6;getOptimizationObjectivePrecisionValue in interface AutoMlTablesInputsOrBuilderpublic AutoMlTablesInputs.Builder setOptimizationObjectivePrecisionValue(float value)
Required when optimization_objective is "maximize-recall-at-precision". Must be between 0 and 1, inclusive.
float optimization_objective_precision_value = 6;value - The optimizationObjectivePrecisionValue to set.public AutoMlTablesInputs.Builder clearOptimizationObjectivePrecisionValue()
Required when optimization_objective is "maximize-recall-at-precision". Must be between 0 and 1, inclusive.
float optimization_objective_precision_value = 6;public String getPredictionType()
The type of prediction the Model is to produce.
"classification" - Predict one out of multiple target values is
picked for each row.
"regression" - Predict a value based on its relation to other values.
This type is available only to columns that contain
semantically numeric values, i.e. integers or floating
point number, even if stored as e.g. strings.
string prediction_type = 1;getPredictionType in interface AutoMlTablesInputsOrBuilderpublic com.google.protobuf.ByteString getPredictionTypeBytes()
The type of prediction the Model is to produce.
"classification" - Predict one out of multiple target values is
picked for each row.
"regression" - Predict a value based on its relation to other values.
This type is available only to columns that contain
semantically numeric values, i.e. integers or floating
point number, even if stored as e.g. strings.
string prediction_type = 1;getPredictionTypeBytes in interface AutoMlTablesInputsOrBuilderpublic AutoMlTablesInputs.Builder setPredictionType(String value)
The type of prediction the Model is to produce.
"classification" - Predict one out of multiple target values is
picked for each row.
"regression" - Predict a value based on its relation to other values.
This type is available only to columns that contain
semantically numeric values, i.e. integers or floating
point number, even if stored as e.g. strings.
string prediction_type = 1;value - The predictionType to set.public AutoMlTablesInputs.Builder clearPredictionType()
The type of prediction the Model is to produce.
"classification" - Predict one out of multiple target values is
picked for each row.
"regression" - Predict a value based on its relation to other values.
This type is available only to columns that contain
semantically numeric values, i.e. integers or floating
point number, even if stored as e.g. strings.
string prediction_type = 1;public AutoMlTablesInputs.Builder setPredictionTypeBytes(com.google.protobuf.ByteString value)
The type of prediction the Model is to produce.
"classification" - Predict one out of multiple target values is
picked for each row.
"regression" - Predict a value based on its relation to other values.
This type is available only to columns that contain
semantically numeric values, i.e. integers or floating
point number, even if stored as e.g. strings.
string prediction_type = 1;value - The bytes for predictionType to set.public String getTargetColumn()
The column name of the target column that the model is to predict.
string target_column = 2;getTargetColumn in interface AutoMlTablesInputsOrBuilderpublic com.google.protobuf.ByteString getTargetColumnBytes()
The column name of the target column that the model is to predict.
string target_column = 2;getTargetColumnBytes in interface AutoMlTablesInputsOrBuilderpublic AutoMlTablesInputs.Builder setTargetColumn(String value)
The column name of the target column that the model is to predict.
string target_column = 2;value - The targetColumn to set.public AutoMlTablesInputs.Builder clearTargetColumn()
The column name of the target column that the model is to predict.
string target_column = 2;public AutoMlTablesInputs.Builder setTargetColumnBytes(com.google.protobuf.ByteString value)
The column name of the target column that the model is to predict.
string target_column = 2;value - The bytes for targetColumn to set.public List<AutoMlTablesInputs.Transformation> getTransformationsList()
Each transformation will apply transform function to given input column. And the result will be used for training. When creating transformation for BigQuery Struct column, the column should be flattened using "." as the delimiter.
repeated .google.cloud.aiplatform.v1.schema.trainingjob.definition.AutoMlTablesInputs.Transformation transformations = 3;
getTransformationsList in interface AutoMlTablesInputsOrBuilderpublic 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.v1.schema.trainingjob.definition.AutoMlTablesInputs.Transformation transformations = 3;
getTransformationsCount in interface AutoMlTablesInputsOrBuilderpublic AutoMlTablesInputs.Transformation getTransformations(int index)
Each transformation will apply transform function to given input column. And the result will be used for training. When creating transformation for BigQuery Struct column, the column should be flattened using "." as the delimiter.
repeated .google.cloud.aiplatform.v1.schema.trainingjob.definition.AutoMlTablesInputs.Transformation transformations = 3;
getTransformations in interface AutoMlTablesInputsOrBuilderpublic AutoMlTablesInputs.Builder setTransformations(int index, AutoMlTablesInputs.Transformation value)
Each transformation will apply transform function to given input column. And the result will be used for training. When creating transformation for BigQuery Struct column, the column should be flattened using "." as the delimiter.
repeated .google.cloud.aiplatform.v1.schema.trainingjob.definition.AutoMlTablesInputs.Transformation transformations = 3;
public AutoMlTablesInputs.Builder setTransformations(int index, AutoMlTablesInputs.Transformation.Builder builderForValue)
Each transformation will apply transform function to given input column. And the result will be used for training. When creating transformation for BigQuery Struct column, the column should be flattened using "." as the delimiter.
repeated .google.cloud.aiplatform.v1.schema.trainingjob.definition.AutoMlTablesInputs.Transformation transformations = 3;
public AutoMlTablesInputs.Builder addTransformations(AutoMlTablesInputs.Transformation value)
Each transformation will apply transform function to given input column. And the result will be used for training. When creating transformation for BigQuery Struct column, the column should be flattened using "." as the delimiter.
repeated .google.cloud.aiplatform.v1.schema.trainingjob.definition.AutoMlTablesInputs.Transformation transformations = 3;
public AutoMlTablesInputs.Builder addTransformations(int index, AutoMlTablesInputs.Transformation value)
Each transformation will apply transform function to given input column. And the result will be used for training. When creating transformation for BigQuery Struct column, the column should be flattened using "." as the delimiter.
repeated .google.cloud.aiplatform.v1.schema.trainingjob.definition.AutoMlTablesInputs.Transformation transformations = 3;
public AutoMlTablesInputs.Builder addTransformations(AutoMlTablesInputs.Transformation.Builder builderForValue)
Each transformation will apply transform function to given input column. And the result will be used for training. When creating transformation for BigQuery Struct column, the column should be flattened using "." as the delimiter.
repeated .google.cloud.aiplatform.v1.schema.trainingjob.definition.AutoMlTablesInputs.Transformation transformations = 3;
public AutoMlTablesInputs.Builder addTransformations(int index, AutoMlTablesInputs.Transformation.Builder builderForValue)
Each transformation will apply transform function to given input column. And the result will be used for training. When creating transformation for BigQuery Struct column, the column should be flattened using "." as the delimiter.
repeated .google.cloud.aiplatform.v1.schema.trainingjob.definition.AutoMlTablesInputs.Transformation transformations = 3;
public AutoMlTablesInputs.Builder addAllTransformations(Iterable<? extends AutoMlTablesInputs.Transformation> values)
Each transformation will apply transform function to given input column. And the result will be used for training. When creating transformation for BigQuery Struct column, the column should be flattened using "." as the delimiter.
repeated .google.cloud.aiplatform.v1.schema.trainingjob.definition.AutoMlTablesInputs.Transformation transformations = 3;
public AutoMlTablesInputs.Builder clearTransformations()
Each transformation will apply transform function to given input column. And the result will be used for training. When creating transformation for BigQuery Struct column, the column should be flattened using "." as the delimiter.
repeated .google.cloud.aiplatform.v1.schema.trainingjob.definition.AutoMlTablesInputs.Transformation transformations = 3;
public AutoMlTablesInputs.Builder removeTransformations(int index)
Each transformation will apply transform function to given input column. And the result will be used for training. When creating transformation for BigQuery Struct column, the column should be flattened using "." as the delimiter.
repeated .google.cloud.aiplatform.v1.schema.trainingjob.definition.AutoMlTablesInputs.Transformation transformations = 3;
public AutoMlTablesInputs.Transformation.Builder getTransformationsBuilder(int index)
Each transformation will apply transform function to given input column. And the result will be used for training. When creating transformation for BigQuery Struct column, the column should be flattened using "." as the delimiter.
repeated .google.cloud.aiplatform.v1.schema.trainingjob.definition.AutoMlTablesInputs.Transformation transformations = 3;
public AutoMlTablesInputs.TransformationOrBuilder getTransformationsOrBuilder(int index)
Each transformation will apply transform function to given input column. And the result will be used for training. When creating transformation for BigQuery Struct column, the column should be flattened using "." as the delimiter.
repeated .google.cloud.aiplatform.v1.schema.trainingjob.definition.AutoMlTablesInputs.Transformation transformations = 3;
getTransformationsOrBuilder in interface AutoMlTablesInputsOrBuilderpublic List<? extends AutoMlTablesInputs.TransformationOrBuilder> getTransformationsOrBuilderList()
Each transformation will apply transform function to given input column. And the result will be used for training. When creating transformation for BigQuery Struct column, the column should be flattened using "." as the delimiter.
repeated .google.cloud.aiplatform.v1.schema.trainingjob.definition.AutoMlTablesInputs.Transformation transformations = 3;
getTransformationsOrBuilderList in interface AutoMlTablesInputsOrBuilderpublic AutoMlTablesInputs.Transformation.Builder addTransformationsBuilder()
Each transformation will apply transform function to given input column. And the result will be used for training. When creating transformation for BigQuery Struct column, the column should be flattened using "." as the delimiter.
repeated .google.cloud.aiplatform.v1.schema.trainingjob.definition.AutoMlTablesInputs.Transformation transformations = 3;
public AutoMlTablesInputs.Transformation.Builder addTransformationsBuilder(int index)
Each transformation will apply transform function to given input column. And the result will be used for training. When creating transformation for BigQuery Struct column, the column should be flattened using "." as the delimiter.
repeated .google.cloud.aiplatform.v1.schema.trainingjob.definition.AutoMlTablesInputs.Transformation transformations = 3;
public List<AutoMlTablesInputs.Transformation.Builder> getTransformationsBuilderList()
Each transformation will apply transform function to given input column. And the result will be used for training. When creating transformation for BigQuery Struct column, the column should be flattened using "." as the delimiter.
repeated .google.cloud.aiplatform.v1.schema.trainingjob.definition.AutoMlTablesInputs.Transformation transformations = 3;
public String getOptimizationObjective()
Objective function the model is optimizing towards. The training process
creates a model that maximizes/minimizes the value of the objective
function over the validation set.
The supported optimization objectives depend on the prediction type.
If the field is not set, a default objective function is used.
classification (binary):
"maximize-au-roc" (default) - Maximize the area under the receiver
operating characteristic (ROC) curve.
"minimize-log-loss" - Minimize log loss.
"maximize-au-prc" - Maximize the area under the precision-recall curve.
"maximize-precision-at-recall" - Maximize precision for a specified
recall value.
"maximize-recall-at-precision" - Maximize recall for a specified
precision value.
classification (multi-class):
"minimize-log-loss" (default) - Minimize log loss.
regression:
"minimize-rmse" (default) - Minimize root-mean-squared error (RMSE).
"minimize-mae" - Minimize mean-absolute error (MAE).
"minimize-rmsle" - Minimize root-mean-squared log error (RMSLE).
string optimization_objective = 4;getOptimizationObjective in interface AutoMlTablesInputsOrBuilderpublic com.google.protobuf.ByteString getOptimizationObjectiveBytes()
Objective function the model is optimizing towards. The training process
creates a model that maximizes/minimizes the value of the objective
function over the validation set.
The supported optimization objectives depend on the prediction type.
If the field is not set, a default objective function is used.
classification (binary):
"maximize-au-roc" (default) - Maximize the area under the receiver
operating characteristic (ROC) curve.
"minimize-log-loss" - Minimize log loss.
"maximize-au-prc" - Maximize the area under the precision-recall curve.
"maximize-precision-at-recall" - Maximize precision for a specified
recall value.
"maximize-recall-at-precision" - Maximize recall for a specified
precision value.
classification (multi-class):
"minimize-log-loss" (default) - Minimize log loss.
regression:
"minimize-rmse" (default) - Minimize root-mean-squared error (RMSE).
"minimize-mae" - Minimize mean-absolute error (MAE).
"minimize-rmsle" - Minimize root-mean-squared log error (RMSLE).
string optimization_objective = 4;getOptimizationObjectiveBytes in interface AutoMlTablesInputsOrBuilderpublic AutoMlTablesInputs.Builder setOptimizationObjective(String value)
Objective function the model is optimizing towards. The training process
creates a model that maximizes/minimizes the value of the objective
function over the validation set.
The supported optimization objectives depend on the prediction type.
If the field is not set, a default objective function is used.
classification (binary):
"maximize-au-roc" (default) - Maximize the area under the receiver
operating characteristic (ROC) curve.
"minimize-log-loss" - Minimize log loss.
"maximize-au-prc" - Maximize the area under the precision-recall curve.
"maximize-precision-at-recall" - Maximize precision for a specified
recall value.
"maximize-recall-at-precision" - Maximize recall for a specified
precision value.
classification (multi-class):
"minimize-log-loss" (default) - Minimize log loss.
regression:
"minimize-rmse" (default) - Minimize root-mean-squared error (RMSE).
"minimize-mae" - Minimize mean-absolute error (MAE).
"minimize-rmsle" - Minimize root-mean-squared log error (RMSLE).
string optimization_objective = 4;value - The optimizationObjective to set.public AutoMlTablesInputs.Builder clearOptimizationObjective()
Objective function the model is optimizing towards. The training process
creates a model that maximizes/minimizes the value of the objective
function over the validation set.
The supported optimization objectives depend on the prediction type.
If the field is not set, a default objective function is used.
classification (binary):
"maximize-au-roc" (default) - Maximize the area under the receiver
operating characteristic (ROC) curve.
"minimize-log-loss" - Minimize log loss.
"maximize-au-prc" - Maximize the area under the precision-recall curve.
"maximize-precision-at-recall" - Maximize precision for a specified
recall value.
"maximize-recall-at-precision" - Maximize recall for a specified
precision value.
classification (multi-class):
"minimize-log-loss" (default) - Minimize log loss.
regression:
"minimize-rmse" (default) - Minimize root-mean-squared error (RMSE).
"minimize-mae" - Minimize mean-absolute error (MAE).
"minimize-rmsle" - Minimize root-mean-squared log error (RMSLE).
string optimization_objective = 4;public AutoMlTablesInputs.Builder setOptimizationObjectiveBytes(com.google.protobuf.ByteString value)
Objective function the model is optimizing towards. The training process
creates a model that maximizes/minimizes the value of the objective
function over the validation set.
The supported optimization objectives depend on the prediction type.
If the field is not set, a default objective function is used.
classification (binary):
"maximize-au-roc" (default) - Maximize the area under the receiver
operating characteristic (ROC) curve.
"minimize-log-loss" - Minimize log loss.
"maximize-au-prc" - Maximize the area under the precision-recall curve.
"maximize-precision-at-recall" - Maximize precision for a specified
recall value.
"maximize-recall-at-precision" - Maximize recall for a specified
precision value.
classification (multi-class):
"minimize-log-loss" (default) - Minimize log loss.
regression:
"minimize-rmse" (default) - Minimize root-mean-squared error (RMSE).
"minimize-mae" - Minimize mean-absolute error (MAE).
"minimize-rmsle" - Minimize root-mean-squared log error (RMSLE).
string optimization_objective = 4;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 = 7;getTrainBudgetMilliNodeHours in interface AutoMlTablesInputsOrBuilderpublic AutoMlTablesInputs.Builder setTrainBudgetMilliNodeHours(long value)
Required. The train budget of creating this model, expressed in milli node hours i.e. 1,000 value in this field means 1 node hour. The training cost of the model will not exceed this budget. The final cost will be attempted to be close to the budget, though may end up being (even) noticeably smaller - at the backend's discretion. This especially may happen when further model training ceases to provide any improvements. If the budget is set to a value known to be insufficient to train a model for the given dataset, the training won't be attempted and will error. The train budget must be between 1,000 and 72,000 milli node hours, inclusive.
int64 train_budget_milli_node_hours = 7;value - The trainBudgetMilliNodeHours to set.public AutoMlTablesInputs.Builder clearTrainBudgetMilliNodeHours()
Required. The train budget of creating this model, expressed in milli node hours i.e. 1,000 value in this field means 1 node hour. The training cost of the model will not exceed this budget. The final cost will be attempted to be close to the budget, though may end up being (even) noticeably smaller - at the backend's discretion. This especially may happen when further model training ceases to provide any improvements. If the budget is set to a value known to be insufficient to train a model for the given dataset, the training won't be attempted and will error. The train budget must be between 1,000 and 72,000 milli node hours, inclusive.
int64 train_budget_milli_node_hours = 7;public boolean getDisableEarlyStopping()
Use the entire training budget. This disables the early stopping feature. By default, the early stopping feature is enabled, which means that AutoML Tables might stop training before the entire training budget has been used.
bool disable_early_stopping = 8;getDisableEarlyStopping in interface AutoMlTablesInputsOrBuilderpublic AutoMlTablesInputs.Builder setDisableEarlyStopping(boolean value)
Use the entire training budget. This disables the early stopping feature. By default, the early stopping feature is enabled, which means that AutoML Tables might stop training before the entire training budget has been used.
bool disable_early_stopping = 8;value - The disableEarlyStopping to set.public AutoMlTablesInputs.Builder clearDisableEarlyStopping()
Use the entire training budget. This disables the early stopping feature. By default, the early stopping feature is enabled, which means that AutoML Tables might stop training before the entire training budget has been used.
bool disable_early_stopping = 8;public String getWeightColumnName()
Column name that should be used as the weight column. Higher values in this column give more importance to the row during model training. The column must have numeric values between 0 and 10000 inclusively; 0 means the row is ignored for training. If weight column field is not set, then all rows are assumed to have equal weight of 1.
string weight_column_name = 9;getWeightColumnName in interface AutoMlTablesInputsOrBuilderpublic com.google.protobuf.ByteString getWeightColumnNameBytes()
Column name that should be used as the weight column. Higher values in this column give more importance to the row during model training. The column must have numeric values between 0 and 10000 inclusively; 0 means the row is ignored for training. If weight column field is not set, then all rows are assumed to have equal weight of 1.
string weight_column_name = 9;getWeightColumnNameBytes in interface AutoMlTablesInputsOrBuilderpublic AutoMlTablesInputs.Builder setWeightColumnName(String value)
Column name that should be used as the weight column. Higher values in this column give more importance to the row during model training. The column must have numeric values between 0 and 10000 inclusively; 0 means the row is ignored for training. If weight column field is not set, then all rows are assumed to have equal weight of 1.
string weight_column_name = 9;value - The weightColumnName to set.public AutoMlTablesInputs.Builder clearWeightColumnName()
Column name that should be used as the weight column. Higher values in this column give more importance to the row during model training. The column must have numeric values between 0 and 10000 inclusively; 0 means the row is ignored for training. If weight column field is not set, then all rows are assumed to have equal weight of 1.
string weight_column_name = 9;public AutoMlTablesInputs.Builder setWeightColumnNameBytes(com.google.protobuf.ByteString value)
Column name that should be used as the weight column. Higher values in this column give more importance to the row during model training. The column must have numeric values between 0 and 10000 inclusively; 0 means the row is ignored for training. If weight column field is not set, then all rows are assumed to have equal weight of 1.
string weight_column_name = 9;value - The bytes for weightColumnName to set.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.v1.schema.trainingjob.definition.ExportEvaluatedDataItemsConfig export_evaluated_data_items_config = 10;
hasExportEvaluatedDataItemsConfig in interface AutoMlTablesInputsOrBuilderpublic 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.v1.schema.trainingjob.definition.ExportEvaluatedDataItemsConfig export_evaluated_data_items_config = 10;
getExportEvaluatedDataItemsConfig in interface AutoMlTablesInputsOrBuilderpublic AutoMlTablesInputs.Builder setExportEvaluatedDataItemsConfig(ExportEvaluatedDataItemsConfig value)
Configuration for exporting test set predictions to a BigQuery table. If this configuration is absent, then the export is not performed.
.google.cloud.aiplatform.v1.schema.trainingjob.definition.ExportEvaluatedDataItemsConfig export_evaluated_data_items_config = 10;
public AutoMlTablesInputs.Builder setExportEvaluatedDataItemsConfig(ExportEvaluatedDataItemsConfig.Builder builderForValue)
Configuration for exporting test set predictions to a BigQuery table. If this configuration is absent, then the export is not performed.
.google.cloud.aiplatform.v1.schema.trainingjob.definition.ExportEvaluatedDataItemsConfig export_evaluated_data_items_config = 10;
public AutoMlTablesInputs.Builder mergeExportEvaluatedDataItemsConfig(ExportEvaluatedDataItemsConfig value)
Configuration for exporting test set predictions to a BigQuery table. If this configuration is absent, then the export is not performed.
.google.cloud.aiplatform.v1.schema.trainingjob.definition.ExportEvaluatedDataItemsConfig export_evaluated_data_items_config = 10;
public AutoMlTablesInputs.Builder clearExportEvaluatedDataItemsConfig()
Configuration for exporting test set predictions to a BigQuery table. If this configuration is absent, then the export is not performed.
.google.cloud.aiplatform.v1.schema.trainingjob.definition.ExportEvaluatedDataItemsConfig export_evaluated_data_items_config = 10;
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.v1.schema.trainingjob.definition.ExportEvaluatedDataItemsConfig export_evaluated_data_items_config = 10;
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.v1.schema.trainingjob.definition.ExportEvaluatedDataItemsConfig export_evaluated_data_items_config = 10;
getExportEvaluatedDataItemsConfigOrBuilder in interface AutoMlTablesInputsOrBuilderpublic com.google.protobuf.ProtocolStringList getAdditionalExperimentsList()
Additional experiment flags for the Tables training pipeline.
repeated string additional_experiments = 11;getAdditionalExperimentsList in interface AutoMlTablesInputsOrBuilderpublic int getAdditionalExperimentsCount()
Additional experiment flags for the Tables training pipeline.
repeated string additional_experiments = 11;getAdditionalExperimentsCount in interface AutoMlTablesInputsOrBuilderpublic String getAdditionalExperiments(int index)
Additional experiment flags for the Tables training pipeline.
repeated string additional_experiments = 11;getAdditionalExperiments in interface AutoMlTablesInputsOrBuilderindex - The index of the element to return.public com.google.protobuf.ByteString getAdditionalExperimentsBytes(int index)
Additional experiment flags for the Tables training pipeline.
repeated string additional_experiments = 11;getAdditionalExperimentsBytes in interface AutoMlTablesInputsOrBuilderindex - The index of the value to return.public AutoMlTablesInputs.Builder setAdditionalExperiments(int index, String value)
Additional experiment flags for the Tables training pipeline.
repeated string additional_experiments = 11;index - The index to set the value at.value - The additionalExperiments to set.public AutoMlTablesInputs.Builder addAdditionalExperiments(String value)
Additional experiment flags for the Tables training pipeline.
repeated string additional_experiments = 11;value - The additionalExperiments to add.public AutoMlTablesInputs.Builder addAllAdditionalExperiments(Iterable<String> values)
Additional experiment flags for the Tables training pipeline.
repeated string additional_experiments = 11;values - The additionalExperiments to add.public AutoMlTablesInputs.Builder clearAdditionalExperiments()
Additional experiment flags for the Tables training pipeline.
repeated string additional_experiments = 11;public AutoMlTablesInputs.Builder addAdditionalExperimentsBytes(com.google.protobuf.ByteString value)
Additional experiment flags for the Tables training pipeline.
repeated string additional_experiments = 11;value - The bytes of the additionalExperiments to add.public final AutoMlTablesInputs.Builder setUnknownFields(com.google.protobuf.UnknownFieldSet unknownFields)
setUnknownFields in interface com.google.protobuf.Message.BuildersetUnknownFields in class com.google.protobuf.GeneratedMessageV3.Builder<AutoMlTablesInputs.Builder>public final AutoMlTablesInputs.Builder mergeUnknownFields(com.google.protobuf.UnknownFieldSet unknownFields)
mergeUnknownFields in interface com.google.protobuf.Message.BuildermergeUnknownFields in class com.google.protobuf.GeneratedMessageV3.Builder<AutoMlTablesInputs.Builder>Copyright © 2025 Google LLC. All rights reserved.