Class AutoMlTablesInputs.Builder (2.4.0)

Version 2.4.0

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public static final class AutoMlTablesInputs.Builder extends GeneratedMessageV3.Builder<AutoMlTablesInputs.Builder> implements AutoMlTablesInputsOrBuilder

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

Inheritance

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

Implements

AutoMlTablesInputsOrBuilder

Inherited Members

AbstractMessage.Builder.findInitializationErrors()

AbstractMessage.Builder.getInitializationErrorString()

AbstractMessage.Builder.internalMergeFrom(AbstractMessageLite)

AbstractMessage.Builder.mergeDelimitedFrom(InputStream)

AbstractMessage.Builder.mergeDelimitedFrom(InputStream,ExtensionRegistryLite)

AbstractMessage.Builder.mergeFrom(byte[])

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

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

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

AbstractMessage.Builder.mergeFrom(ByteString)

AbstractMessage.Builder.mergeFrom(ByteString,ExtensionRegistryLite)

AbstractMessage.Builder.mergeFrom(CodedInputStream)

AbstractMessage.Builder.mergeFrom(CodedInputStream,ExtensionRegistryLite)

AbstractMessage.Builder.mergeFrom(Message)

AbstractMessage.Builder.mergeFrom(InputStream)

AbstractMessage.Builder.mergeFrom(InputStream,ExtensionRegistryLite)

AbstractMessage.Builder.newUninitializedMessageException(Message)

AbstractMessage.Builder.toString()

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

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

AbstractMessageLite.Builder.internalMergeFrom(MessageType)

AbstractMessageLite.Builder.mergeFrom(MessageLite)

AbstractMessageLite.Builder.newUninitializedMessageException(MessageLite)

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

GeneratedMessageV3.Builder.clear()

GeneratedMessageV3.Builder.clearField(Descriptors.FieldDescriptor)

GeneratedMessageV3.Builder.clearOneof(Descriptors.OneofDescriptor)

GeneratedMessageV3.Builder.clone()

GeneratedMessageV3.Builder.getAllFields()

GeneratedMessageV3.Builder.getDescriptorForType()

GeneratedMessageV3.Builder.getField(Descriptors.FieldDescriptor)

GeneratedMessageV3.Builder.getFieldBuilder(Descriptors.FieldDescriptor)

GeneratedMessageV3.Builder.getOneofFieldDescriptor(Descriptors.OneofDescriptor)

GeneratedMessageV3.Builder.getParentForChildren()

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

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

GeneratedMessageV3.Builder.getRepeatedFieldCount(Descriptors.FieldDescriptor)

GeneratedMessageV3.Builder.getUnknownFields()

GeneratedMessageV3.Builder.hasField(Descriptors.FieldDescriptor)

GeneratedMessageV3.Builder.hasOneof(Descriptors.OneofDescriptor)

GeneratedMessageV3.Builder.internalGetFieldAccessorTable()

GeneratedMessageV3.Builder.internalGetMapField(int)

GeneratedMessageV3.Builder.internalGetMutableMapField(int)

GeneratedMessageV3.Builder.isClean()

GeneratedMessageV3.Builder.isInitialized()

GeneratedMessageV3.Builder.markClean()

GeneratedMessageV3.Builder.mergeUnknownFields(UnknownFieldSet)

GeneratedMessageV3.Builder.newBuilderForField(Descriptors.FieldDescriptor)

GeneratedMessageV3.Builder.onBuilt()

GeneratedMessageV3.Builder.onChanged()

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

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

GeneratedMessageV3.Builder.setUnknownFields(UnknownFieldSet)

GeneratedMessageV3.Builder.setUnknownFieldsProto3(UnknownFieldSet)

Object.equals(Object)

Object.finalize()

Object.getClass()

Object.hashCode()

Object.notify()

Object.notifyAll()

Object.wait()

Object.wait(long)

Object.wait(long,int)

Methods

addAdditionalExperiments(String value)

public AutoMlTablesInputs.Builder addAdditionalExperiments(String value)

Additional experiment flags for the Tables training pipeline.

repeated string additional_experiments = 11;

Parameter

Name	Description
value	String The additionalExperiments to add.

Returns

Type	Description
AutoMlTablesInputs.Builder	This builder for chaining.

addAdditionalExperimentsBytes(ByteString value)

public AutoMlTablesInputs.Builder addAdditionalExperimentsBytes(ByteString value)

Additional experiment flags for the Tables training pipeline.

repeated string additional_experiments = 11;

Parameter

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

Returns

Type	Description
AutoMlTablesInputs.Builder	This builder for chaining.

addAllAdditionalExperiments(Iterable<String> values)

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

Additional experiment flags for the Tables training pipeline.

repeated string additional_experiments = 11;

Parameter

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

Returns

Type	Description
AutoMlTablesInputs.Builder	This builder for chaining.

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

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.v1beta1.schema.trainingjob.definition.AutoMlTablesInputs.Transformation transformations = 3;

Parameter

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

Returns

Type	Description
AutoMlTablesInputs.Builder

addRepeatedField(Descriptors.FieldDescriptor field, Object value)

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

Parameters

Name	Description
field	FieldDescriptor
value	Object

Returns

Type	Description
AutoMlTablesInputs.Builder

Overrides

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

addTransformations(AutoMlTablesInputs.Transformation value)

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.v1beta1.schema.trainingjob.definition.AutoMlTablesInputs.Transformation transformations = 3;

Parameter

Name	Description
value	AutoMlTablesInputs.Transformation

Returns

Type	Description
AutoMlTablesInputs.Builder

addTransformations(AutoMlTablesInputs.Transformation.Builder builderForValue)

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.v1beta1.schema.trainingjob.definition.AutoMlTablesInputs.Transformation transformations = 3;

Parameter

Name	Description
builderForValue	AutoMlTablesInputs.Transformation.Builder

Returns

Type	Description
AutoMlTablesInputs.Builder

addTransformations(int index, AutoMlTablesInputs.Transformation value)

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.v1beta1.schema.trainingjob.definition.AutoMlTablesInputs.Transformation transformations = 3;

Parameters

Name	Description
index	int
value	AutoMlTablesInputs.Transformation

Returns

Type	Description
AutoMlTablesInputs.Builder

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

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.v1beta1.schema.trainingjob.definition.AutoMlTablesInputs.Transformation transformations = 3;

Parameters

Name	Description
index	int
builderForValue	AutoMlTablesInputs.Transformation.Builder

Returns

Type	Description
AutoMlTablesInputs.Builder

addTransformationsBuilder()

public 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.v1beta1.schema.trainingjob.definition.AutoMlTablesInputs.Transformation transformations = 3;

Returns

Type	Description
AutoMlTablesInputs.Transformation.Builder

addTransformationsBuilder(int index)

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.v1beta1.schema.trainingjob.definition.AutoMlTablesInputs.Transformation transformations = 3;

Parameter

Name	Description
index	int

Returns

Type	Description
AutoMlTablesInputs.Transformation.Builder

build()

public AutoMlTablesInputs build()

Returns

Type	Description
AutoMlTablesInputs

buildPartial()

public AutoMlTablesInputs buildPartial()

Returns

Type	Description
AutoMlTablesInputs

clear()

public AutoMlTablesInputs.Builder clear()

Returns

Type	Description
AutoMlTablesInputs.Builder

Overrides

GeneratedMessageV3.Builder<BuilderType>.clear()

clearAdditionalExperiments()

public AutoMlTablesInputs.Builder clearAdditionalExperiments()

Additional experiment flags for the Tables training pipeline.

repeated string additional_experiments = 11;

Returns

Type	Description
AutoMlTablesInputs.Builder	This builder for chaining.

clearAdditionalOptimizationObjectiveConfig()

public AutoMlTablesInputs.Builder clearAdditionalOptimizationObjectiveConfig()

Returns

Type	Description
AutoMlTablesInputs.Builder

clearDisableEarlyStopping()

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;

Returns

Type	Description
AutoMlTablesInputs.Builder	This builder for chaining.

clearExportEvaluatedDataItemsConfig()

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.v1beta1.schema.trainingjob.definition.ExportEvaluatedDataItemsConfig export_evaluated_data_items_config = 10;

Returns

Type	Description
AutoMlTablesInputs.Builder

clearField(Descriptors.FieldDescriptor field)

public AutoMlTablesInputs.Builder clearField(Descriptors.FieldDescriptor field)

Parameter

Name	Description
field	FieldDescriptor

Returns

Type	Description
AutoMlTablesInputs.Builder

Overrides

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

clearOneof(Descriptors.OneofDescriptor oneof)

public AutoMlTablesInputs.Builder clearOneof(Descriptors.OneofDescriptor oneof)

Parameter

Name	Description
oneof	OneofDescriptor

Returns

Type	Description
AutoMlTablesInputs.Builder

Overrides

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

clearOptimizationObjective()

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;

Returns

Type	Description
AutoMlTablesInputs.Builder	This builder for chaining.

clearOptimizationObjectivePrecisionValue()

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;

Returns

Type	Description
AutoMlTablesInputs.Builder	This builder for chaining.

clearOptimizationObjectiveRecallValue()

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;

Returns

Type	Description
AutoMlTablesInputs.Builder	This builder for chaining.

clearPredictionType()

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;

Returns

Type	Description
AutoMlTablesInputs.Builder	This builder for chaining.

clearTargetColumn()

public AutoMlTablesInputs.Builder clearTargetColumn()

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

string target_column = 2;

Returns

Type	Description
AutoMlTablesInputs.Builder	This builder for chaining.

clearTrainBudgetMilliNodeHours()

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;

Returns

Type	Description
AutoMlTablesInputs.Builder	This builder for chaining.

clearTransformations()

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.v1beta1.schema.trainingjob.definition.AutoMlTablesInputs.Transformation transformations = 3;

Returns

Type	Description
AutoMlTablesInputs.Builder

clearWeightColumnName()

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;

Returns

Type	Description
AutoMlTablesInputs.Builder	This builder for chaining.

clone()

public AutoMlTablesInputs.Builder clone()

Returns

Type	Description
AutoMlTablesInputs.Builder

Overrides

GeneratedMessageV3.Builder<BuilderType>.clone()

getAdditionalExperiments(int index)

public String getAdditionalExperiments(int index)

Additional experiment flags for the Tables training pipeline.

repeated string additional_experiments = 11;

Parameter

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

Returns

Type	Description
String	The additionalExperiments at the given index.

getAdditionalExperimentsBytes(int index)

public ByteString getAdditionalExperimentsBytes(int index)

Additional experiment flags for the Tables training pipeline.

repeated string additional_experiments = 11;

Parameter

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

Returns

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

getAdditionalExperimentsCount()

public int getAdditionalExperimentsCount()

Additional experiment flags for the Tables training pipeline.

repeated string additional_experiments = 11;

Returns

Type	Description
int	The count of additionalExperiments.

getAdditionalExperimentsList()

public ProtocolStringList getAdditionalExperimentsList()

Additional experiment flags for the Tables training pipeline.

repeated string additional_experiments = 11;

Returns

Type	Description
ProtocolStringList	A list containing the additionalExperiments.

getAdditionalOptimizationObjectiveConfigCase()

public AutoMlTablesInputs.AdditionalOptimizationObjectiveConfigCase getAdditionalOptimizationObjectiveConfigCase()

Returns

Type	Description
AutoMlTablesInputs.AdditionalOptimizationObjectiveConfigCase

getDefaultInstanceForType()

public AutoMlTablesInputs getDefaultInstanceForType()

Returns

Type	Description
AutoMlTablesInputs

getDescriptor()

public static final Descriptors.Descriptor getDescriptor()

Returns

Type	Description
Descriptor

getDescriptorForType()

public Descriptors.Descriptor getDescriptorForType()

Returns

Type	Description
Descriptor

Overrides

GeneratedMessageV3.Builder<BuilderType>.getDescriptorForType()

getDisableEarlyStopping()

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;

Returns

Type	Description
boolean	The disableEarlyStopping.

getExportEvaluatedDataItemsConfig()

public ExportEvaluatedDataItemsConfig getExportEvaluatedDataItemsConfig()

Configuration for exporting test set predictions to a BigQuery table. If this configuration is absent, then the export is not performed.

.google.cloud.aiplatform.v1beta1.schema.trainingjob.definition.ExportEvaluatedDataItemsConfig export_evaluated_data_items_config = 10;

Returns

Type	Description
ExportEvaluatedDataItemsConfig	The exportEvaluatedDataItemsConfig.

getExportEvaluatedDataItemsConfigBuilder()

public ExportEvaluatedDataItemsConfig.Builder getExportEvaluatedDataItemsConfigBuilder()

Configuration for exporting test set predictions to a BigQuery table. If this configuration is absent, then the export is not performed.

.google.cloud.aiplatform.v1beta1.schema.trainingjob.definition.ExportEvaluatedDataItemsConfig export_evaluated_data_items_config = 10;

Returns

Type	Description
ExportEvaluatedDataItemsConfig.Builder

getExportEvaluatedDataItemsConfigOrBuilder()

public ExportEvaluatedDataItemsConfigOrBuilder getExportEvaluatedDataItemsConfigOrBuilder()

Configuration for exporting test set predictions to a BigQuery table. If this configuration is absent, then the export is not performed.

.google.cloud.aiplatform.v1beta1.schema.trainingjob.definition.ExportEvaluatedDataItemsConfig export_evaluated_data_items_config = 10;

Returns

Type	Description
ExportEvaluatedDataItemsConfigOrBuilder

getOptimizationObjective()

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;

Returns

Type	Description
String	The optimizationObjective.

getOptimizationObjectiveBytes()

public 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;

Returns

Type	Description
ByteString	The bytes for optimizationObjective.

getOptimizationObjectivePrecisionValue()

public float getOptimizationObjectivePrecisionValue()

Required when optimization_objective is "maximize-recall-at-precision". Must be between 0 and 1, inclusive.

float optimization_objective_precision_value = 6;

Returns

Type	Description
float	The optimizationObjectivePrecisionValue.

getOptimizationObjectiveRecallValue()

public float getOptimizationObjectiveRecallValue()

Required when optimization_objective is "maximize-precision-at-recall". Must be between 0 and 1, inclusive.

float optimization_objective_recall_value = 5;

Returns

Type	Description
float	The optimizationObjectiveRecallValue.

getPredictionType()

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;

Returns

Type	Description
String	The predictionType.

getPredictionTypeBytes()

public 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;

Returns

Type	Description
ByteString	The bytes for predictionType.

getTargetColumn()

public String getTargetColumn()

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

string target_column = 2;

Returns

Type	Description
String	The targetColumn.

getTargetColumnBytes()

public ByteString getTargetColumnBytes()

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

string target_column = 2;

Returns

Type	Description
ByteString	The bytes for targetColumn.

getTrainBudgetMilliNodeHours()

public long getTrainBudgetMilliNodeHours()

Required. The train budget of creating this model, expressed in milli node hours i.e. 1,000 value in this field means 1 node hour. The training cost of the model will not exceed this budget. The final cost will be attempted to be close to the budget, though may end up being (even) noticeably smaller - at the backend's discretion. This especially may happen when further model training ceases to provide any improvements. If the budget is set to a value known to be insufficient to train a model for the given dataset, the training won't be attempted and will error. The train budget must be between 1,000 and 72,000 milli node hours, inclusive.

int64 train_budget_milli_node_hours = 7;

Returns

Type	Description
long	The trainBudgetMilliNodeHours.

getTransformations(int index)

public 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.v1beta1.schema.trainingjob.definition.AutoMlTablesInputs.Transformation transformations = 3;

Parameter

Name	Description
index	int

Returns

Type	Description
AutoMlTablesInputs.Transformation

getTransformationsBuilder(int index)

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.v1beta1.schema.trainingjob.definition.AutoMlTablesInputs.Transformation transformations = 3;

Parameter

Name	Description
index	int

Returns

Type	Description
AutoMlTablesInputs.Transformation.Builder

getTransformationsBuilderList()

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.v1beta1.schema.trainingjob.definition.AutoMlTablesInputs.Transformation transformations = 3;

Returns

Type	Description
List<Builder>

getTransformationsCount()

public int getTransformationsCount()

Each transformation will apply transform function to given input column. And the result will be used for training. When creating transformation for BigQuery Struct column, the column should be flattened using "." as the delimiter.

repeated .google.cloud.aiplatform.v1beta1.schema.trainingjob.definition.AutoMlTablesInputs.Transformation transformations = 3;

Returns

Type	Description
int

getTransformationsList()

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.v1beta1.schema.trainingjob.definition.AutoMlTablesInputs.Transformation transformations = 3;

Returns

Type	Description
List<Transformation>

getTransformationsOrBuilder(int index)

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.v1beta1.schema.trainingjob.definition.AutoMlTablesInputs.Transformation transformations = 3;

Parameter

Name	Description
index	int

Returns

Type	Description
AutoMlTablesInputs.TransformationOrBuilder

getTransformationsOrBuilderList()

public 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.v1beta1.schema.trainingjob.definition.AutoMlTablesInputs.Transformation transformations = 3;

Returns

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

getWeightColumnName()

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;

Returns

Type	Description
String	The weightColumnName.

getWeightColumnNameBytes()

public 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;

Returns

Type	Description
ByteString	The bytes for weightColumnName.

hasExportEvaluatedDataItemsConfig()

public boolean hasExportEvaluatedDataItemsConfig()

Configuration for exporting test set predictions to a BigQuery table. If this configuration is absent, then the export is not performed.

.google.cloud.aiplatform.v1beta1.schema.trainingjob.definition.ExportEvaluatedDataItemsConfig export_evaluated_data_items_config = 10;

Returns

Type	Description
boolean	Whether the exportEvaluatedDataItemsConfig field is set.

hasOptimizationObjectivePrecisionValue()

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;

Returns

Type	Description
boolean	Whether the optimizationObjectivePrecisionValue field is set.

hasOptimizationObjectiveRecallValue()

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;

Returns

Type	Description
boolean	Whether the optimizationObjectiveRecallValue field is set.

internalGetFieldAccessorTable()

protected GeneratedMessageV3.FieldAccessorTable internalGetFieldAccessorTable()

Returns

Type	Description
FieldAccessorTable

Overrides

GeneratedMessageV3.Builder<BuilderType>.internalGetFieldAccessorTable()

isInitialized()

public final boolean isInitialized()

Returns

Type	Description
boolean

Overrides

GeneratedMessageV3.Builder<BuilderType>.isInitialized()

mergeExportEvaluatedDataItemsConfig(ExportEvaluatedDataItemsConfig value)

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.v1beta1.schema.trainingjob.definition.ExportEvaluatedDataItemsConfig export_evaluated_data_items_config = 10;

Parameter

Name	Description
value	ExportEvaluatedDataItemsConfig

Returns

Type	Description
AutoMlTablesInputs.Builder

mergeFrom(AutoMlTablesInputs other)

public AutoMlTablesInputs.Builder mergeFrom(AutoMlTablesInputs other)

Parameter

Name	Description
other	AutoMlTablesInputs

Returns

Type	Description
AutoMlTablesInputs.Builder

mergeFrom(CodedInputStream input, ExtensionRegistryLite extensionRegistry)

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

Parameters

Name	Description
input	CodedInputStream
extensionRegistry	ExtensionRegistryLite

Returns

Type	Description
AutoMlTablesInputs.Builder

Overrides

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

Exceptions

Type	Description
IOException

mergeFrom(Message other)

public AutoMlTablesInputs.Builder mergeFrom(Message other)

Parameter

Name	Description
other	Message

Returns

Type	Description
AutoMlTablesInputs.Builder

Overrides

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

mergeUnknownFields(UnknownFieldSet unknownFields)

public final AutoMlTablesInputs.Builder mergeUnknownFields(UnknownFieldSet unknownFields)

Parameter

Name	Description
unknownFields	UnknownFieldSet

Returns

Type	Description
AutoMlTablesInputs.Builder

Overrides

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

removeTransformations(int index)

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.v1beta1.schema.trainingjob.definition.AutoMlTablesInputs.Transformation transformations = 3;

Parameter

Name	Description
index	int

Returns

Type	Description
AutoMlTablesInputs.Builder

setAdditionalExperiments(int index, String value)

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

Additional experiment flags for the Tables training pipeline.

repeated string additional_experiments = 11;

Parameters

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

Returns

Type	Description
AutoMlTablesInputs.Builder	This builder for chaining.

setDisableEarlyStopping(boolean value)

public 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;

Parameter

Name	Description
value	boolean The disableEarlyStopping to set.

Returns

Type	Description
AutoMlTablesInputs.Builder	This builder for chaining.

setExportEvaluatedDataItemsConfig(ExportEvaluatedDataItemsConfig value)

public 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.v1beta1.schema.trainingjob.definition.ExportEvaluatedDataItemsConfig export_evaluated_data_items_config = 10;

Parameter

Name	Description
value	ExportEvaluatedDataItemsConfig

Returns

Type	Description
AutoMlTablesInputs.Builder

setExportEvaluatedDataItemsConfig(ExportEvaluatedDataItemsConfig.Builder builderForValue)

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.v1beta1.schema.trainingjob.definition.ExportEvaluatedDataItemsConfig export_evaluated_data_items_config = 10;

Parameter

Name	Description
builderForValue	ExportEvaluatedDataItemsConfig.Builder

Returns

Type	Description
AutoMlTablesInputs.Builder

setField(Descriptors.FieldDescriptor field, Object value)

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

Parameters

Name	Description
field	FieldDescriptor
value	Object

Returns

Type	Description
AutoMlTablesInputs.Builder

Overrides

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

setOptimizationObjective(String value)

public 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;

Parameter

Name	Description
value	String The optimizationObjective to set.

Returns

Type	Description
AutoMlTablesInputs.Builder	This builder for chaining.

setOptimizationObjectiveBytes(ByteString value)

public AutoMlTablesInputs.Builder setOptimizationObjectiveBytes(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;

Parameter

Name	Description
value	ByteString The bytes for optimizationObjective to set.

Returns

Type	Description
AutoMlTablesInputs.Builder	This builder for chaining.

setOptimizationObjectivePrecisionValue(float value)

public 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;

Parameter

Name	Description
value	float The optimizationObjectivePrecisionValue to set.

Returns

Type	Description
AutoMlTablesInputs.Builder	This builder for chaining.

setOptimizationObjectiveRecallValue(float value)

public 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;

Parameter

Name	Description
value	float The optimizationObjectiveRecallValue to set.

Returns

Type	Description
AutoMlTablesInputs.Builder	This builder for chaining.

setPredictionType(String value)

public 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;

Parameter

Name	Description
value	String The predictionType to set.

Returns

Type	Description
AutoMlTablesInputs.Builder	This builder for chaining.

setPredictionTypeBytes(ByteString value)

public AutoMlTablesInputs.Builder setPredictionTypeBytes(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;

Parameter

Name	Description
value	ByteString The bytes for predictionType to set.

Returns

Type	Description
AutoMlTablesInputs.Builder	This builder for chaining.

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

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

Parameters

Name	Description
field	FieldDescriptor
index	int
value	Object

Returns

Type	Description
AutoMlTablesInputs.Builder

Overrides

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

setTargetColumn(String value)

public AutoMlTablesInputs.Builder setTargetColumn(String value)

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

string target_column = 2;

Parameter

Name	Description
value	String The targetColumn to set.

Returns

Type	Description
AutoMlTablesInputs.Builder	This builder for chaining.

setTargetColumnBytes(ByteString value)

public AutoMlTablesInputs.Builder setTargetColumnBytes(ByteString value)

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

string target_column = 2;

Parameter

Name	Description
value	ByteString The bytes for targetColumn to set.

Returns

Type	Description
AutoMlTablesInputs.Builder	This builder for chaining.

setTrainBudgetMilliNodeHours(long value)

public 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;

Parameter

Name	Description
value	long The trainBudgetMilliNodeHours to set.

Returns

Type	Description
AutoMlTablesInputs.Builder	This builder for chaining.

setTransformations(int index, AutoMlTablesInputs.Transformation value)

public 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.v1beta1.schema.trainingjob.definition.AutoMlTablesInputs.Transformation transformations = 3;

Parameters

Name	Description
index	int
value	AutoMlTablesInputs.Transformation

Returns

Type	Description
AutoMlTablesInputs.Builder

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

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.v1beta1.schema.trainingjob.definition.AutoMlTablesInputs.Transformation transformations = 3;

Parameters

Name	Description
index	int
builderForValue	AutoMlTablesInputs.Transformation.Builder

Returns

Type	Description
AutoMlTablesInputs.Builder

setUnknownFields(UnknownFieldSet unknownFields)

public final AutoMlTablesInputs.Builder setUnknownFields(UnknownFieldSet unknownFields)

Parameter

Name	Description
unknownFields	UnknownFieldSet

Returns

Type	Description
AutoMlTablesInputs.Builder

Overrides

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

setWeightColumnName(String value)

public 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;

Parameter

Name	Description
value	String The weightColumnName to set.

Returns

Type	Description
AutoMlTablesInputs.Builder	This builder for chaining.

setWeightColumnNameBytes(ByteString value)

public AutoMlTablesInputs.Builder setWeightColumnNameBytes(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;

Parameter

Name	Description
value	ByteString The bytes for weightColumnName to set.

Returns

Type	Description
AutoMlTablesInputs.Builder	This builder for chaining.