Class TablesModelMetadata.Builder (2.2.3)

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public static final class TablesModelMetadata.Builder extends GeneratedMessageV3.Builder<TablesModelMetadata.Builder> implements TablesModelMetadataOrBuilder

Model metadata specific to AutoML Tables.

Protobuf type google.cloud.automl.v1beta1.TablesModelMetadata

Inheritance

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

Implements

TablesModelMetadataOrBuilder

Inherited Members

AbstractMessage.Builder.findInitializationErrors()

AbstractMessage.Builder.getInitializationErrorString()

AbstractMessage.Builder.internalMergeFrom(AbstractMessageLite)

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.mergeDelimitedFrom(InputStream)

AbstractMessageLite.Builder.mergeDelimitedFrom(InputStream,ExtensionRegistryLite)

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)

Static Methods

getDescriptor()

public static final Descriptors.Descriptor getDescriptor()

Returns

Type	Description
Descriptor

Methods

addAllInputFeatureColumnSpecs(Iterable<? extends ColumnSpec> values)

public TablesModelMetadata.Builder addAllInputFeatureColumnSpecs(Iterable<? extends ColumnSpec> values)

Column specs of the dataset's primary table's columns, on which the model is trained and which are used as the input for predictions. The target_column as well as, according to dataset's state upon model creation, weight_column, and ml_use_column must never be included here. Only 3 fields are used:

• name - May be set on CreateModel, if set only the columns specified are used, otherwise all primary table's columns (except the ones listed above) are used for the training and prediction input.
• display_name - Output only.
• data_type - Output only.

repeated .google.cloud.automl.v1beta1.ColumnSpec input_feature_column_specs = 3;

Parameter

Name	Description
values	Iterable<? extends com.google.cloud.automl.v1beta1.ColumnSpec>

Returns

Type	Description
TablesModelMetadata.Builder

addAllTablesModelColumnInfo(Iterable<? extends TablesModelColumnInfo> values)

public TablesModelMetadata.Builder addAllTablesModelColumnInfo(Iterable<? extends TablesModelColumnInfo> values)

Output only. Auxiliary information for each of the input_feature_column_specs with respect to this particular model.

repeated .google.cloud.automl.v1beta1.TablesModelColumnInfo tables_model_column_info = 5;

Parameter

Name	Description
values	Iterable<? extends com.google.cloud.automl.v1beta1.TablesModelColumnInfo>

Returns

Type	Description
TablesModelMetadata.Builder

addInputFeatureColumnSpecs(ColumnSpec value)

public TablesModelMetadata.Builder addInputFeatureColumnSpecs(ColumnSpec value)

Column specs of the dataset's primary table's columns, on which the model is trained and which are used as the input for predictions. The target_column as well as, according to dataset's state upon model creation, weight_column, and ml_use_column must never be included here. Only 3 fields are used:

• name - May be set on CreateModel, if set only the columns specified are used, otherwise all primary table's columns (except the ones listed above) are used for the training and prediction input.
• display_name - Output only.
• data_type - Output only.

repeated .google.cloud.automl.v1beta1.ColumnSpec input_feature_column_specs = 3;

Parameter

Name	Description
value	ColumnSpec

Returns

Type	Description
TablesModelMetadata.Builder

addInputFeatureColumnSpecs(ColumnSpec.Builder builderForValue)

public TablesModelMetadata.Builder addInputFeatureColumnSpecs(ColumnSpec.Builder builderForValue)

Column specs of the dataset's primary table's columns, on which the model is trained and which are used as the input for predictions. The target_column as well as, according to dataset's state upon model creation, weight_column, and ml_use_column must never be included here. Only 3 fields are used:

• name - May be set on CreateModel, if set only the columns specified are used, otherwise all primary table's columns (except the ones listed above) are used for the training and prediction input.
• display_name - Output only.
• data_type - Output only.

repeated .google.cloud.automl.v1beta1.ColumnSpec input_feature_column_specs = 3;

Parameter

Name	Description
builderForValue	ColumnSpec.Builder

Returns

Type	Description
TablesModelMetadata.Builder

addInputFeatureColumnSpecs(int index, ColumnSpec value)

public TablesModelMetadata.Builder addInputFeatureColumnSpecs(int index, ColumnSpec value)

Column specs of the dataset's primary table's columns, on which the model is trained and which are used as the input for predictions. The target_column as well as, according to dataset's state upon model creation, weight_column, and ml_use_column must never be included here. Only 3 fields are used:

• name - May be set on CreateModel, if set only the columns specified are used, otherwise all primary table's columns (except the ones listed above) are used for the training and prediction input.
• display_name - Output only.
• data_type - Output only.

repeated .google.cloud.automl.v1beta1.ColumnSpec input_feature_column_specs = 3;

Parameters

Name	Description
index	int
value	ColumnSpec

Returns

Type	Description
TablesModelMetadata.Builder

addInputFeatureColumnSpecs(int index, ColumnSpec.Builder builderForValue)

public TablesModelMetadata.Builder addInputFeatureColumnSpecs(int index, ColumnSpec.Builder builderForValue)

Column specs of the dataset's primary table's columns, on which the model is trained and which are used as the input for predictions. The target_column as well as, according to dataset's state upon model creation, weight_column, and ml_use_column must never be included here. Only 3 fields are used:

• name - May be set on CreateModel, if set only the columns specified are used, otherwise all primary table's columns (except the ones listed above) are used for the training and prediction input.
• display_name - Output only.
• data_type - Output only.

repeated .google.cloud.automl.v1beta1.ColumnSpec input_feature_column_specs = 3;

Parameters

Name	Description
index	int
builderForValue	ColumnSpec.Builder

Returns

Type	Description
TablesModelMetadata.Builder

addInputFeatureColumnSpecsBuilder()

public ColumnSpec.Builder addInputFeatureColumnSpecsBuilder()

Column specs of the dataset's primary table's columns, on which the model is trained and which are used as the input for predictions. The target_column as well as, according to dataset's state upon model creation, weight_column, and ml_use_column must never be included here. Only 3 fields are used:

• name - May be set on CreateModel, if set only the columns specified are used, otherwise all primary table's columns (except the ones listed above) are used for the training and prediction input.
• display_name - Output only.
• data_type - Output only.

repeated .google.cloud.automl.v1beta1.ColumnSpec input_feature_column_specs = 3;

Returns

Type	Description
ColumnSpec.Builder

addInputFeatureColumnSpecsBuilder(int index)

public ColumnSpec.Builder addInputFeatureColumnSpecsBuilder(int index)

Column specs of the dataset's primary table's columns, on which the model is trained and which are used as the input for predictions. The target_column as well as, according to dataset's state upon model creation, weight_column, and ml_use_column must never be included here. Only 3 fields are used:

• name - May be set on CreateModel, if set only the columns specified are used, otherwise all primary table's columns (except the ones listed above) are used for the training and prediction input.
• display_name - Output only.
• data_type - Output only.

repeated .google.cloud.automl.v1beta1.ColumnSpec input_feature_column_specs = 3;

Parameter

Name	Description
index	int

Returns

Type	Description
ColumnSpec.Builder

addRepeatedField(Descriptors.FieldDescriptor field, Object value)

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

Parameters

Name	Description
field	FieldDescriptor
value	Object

Returns

Type	Description
TablesModelMetadata.Builder

Overrides

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

addTablesModelColumnInfo(TablesModelColumnInfo value)

public TablesModelMetadata.Builder addTablesModelColumnInfo(TablesModelColumnInfo value)

Output only. Auxiliary information for each of the input_feature_column_specs with respect to this particular model.

repeated .google.cloud.automl.v1beta1.TablesModelColumnInfo tables_model_column_info = 5;

Parameter

Name	Description
value	TablesModelColumnInfo

Returns

Type	Description
TablesModelMetadata.Builder

addTablesModelColumnInfo(TablesModelColumnInfo.Builder builderForValue)

public TablesModelMetadata.Builder addTablesModelColumnInfo(TablesModelColumnInfo.Builder builderForValue)

Output only. Auxiliary information for each of the input_feature_column_specs with respect to this particular model.

repeated .google.cloud.automl.v1beta1.TablesModelColumnInfo tables_model_column_info = 5;

Parameter

Name	Description
builderForValue	TablesModelColumnInfo.Builder

Returns

Type	Description
TablesModelMetadata.Builder

addTablesModelColumnInfo(int index, TablesModelColumnInfo value)

public TablesModelMetadata.Builder addTablesModelColumnInfo(int index, TablesModelColumnInfo value)

Output only. Auxiliary information for each of the input_feature_column_specs with respect to this particular model.

repeated .google.cloud.automl.v1beta1.TablesModelColumnInfo tables_model_column_info = 5;

Parameters

Name	Description
index	int
value	TablesModelColumnInfo

Returns

Type	Description
TablesModelMetadata.Builder

addTablesModelColumnInfo(int index, TablesModelColumnInfo.Builder builderForValue)

public TablesModelMetadata.Builder addTablesModelColumnInfo(int index, TablesModelColumnInfo.Builder builderForValue)

Output only. Auxiliary information for each of the input_feature_column_specs with respect to this particular model.

repeated .google.cloud.automl.v1beta1.TablesModelColumnInfo tables_model_column_info = 5;

Parameters

Name	Description
index	int
builderForValue	TablesModelColumnInfo.Builder

Returns

Type	Description
TablesModelMetadata.Builder

addTablesModelColumnInfoBuilder()

public TablesModelColumnInfo.Builder addTablesModelColumnInfoBuilder()

Output only. Auxiliary information for each of the input_feature_column_specs with respect to this particular model.

repeated .google.cloud.automl.v1beta1.TablesModelColumnInfo tables_model_column_info = 5;

Returns

Type	Description
TablesModelColumnInfo.Builder

addTablesModelColumnInfoBuilder(int index)

public TablesModelColumnInfo.Builder addTablesModelColumnInfoBuilder(int index)

Output only. Auxiliary information for each of the input_feature_column_specs with respect to this particular model.

repeated .google.cloud.automl.v1beta1.TablesModelColumnInfo tables_model_column_info = 5;

Parameter

Name	Description
index	int

Returns

Type	Description
TablesModelColumnInfo.Builder

build()

public TablesModelMetadata build()

Returns

Type	Description
TablesModelMetadata

buildPartial()

public TablesModelMetadata buildPartial()

Returns

Type	Description
TablesModelMetadata

clear()

public TablesModelMetadata.Builder clear()

Returns

Type	Description
TablesModelMetadata.Builder

Overrides

GeneratedMessageV3.Builder<BuilderType>.clear()

clearAdditionalOptimizationObjectiveConfig()

public TablesModelMetadata.Builder clearAdditionalOptimizationObjectiveConfig()

Returns

Type	Description
TablesModelMetadata.Builder

clearDisableEarlyStopping()

public TablesModelMetadata.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 = 12;

Returns

Type	Description
TablesModelMetadata.Builder	This builder for chaining.

clearField(Descriptors.FieldDescriptor field)

public TablesModelMetadata.Builder clearField(Descriptors.FieldDescriptor field)

Parameter

Name	Description
field	FieldDescriptor

Returns

Type	Description
TablesModelMetadata.Builder

Overrides

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

clearInputFeatureColumnSpecs()

public TablesModelMetadata.Builder clearInputFeatureColumnSpecs()

Column specs of the dataset's primary table's columns, on which the model is trained and which are used as the input for predictions. The target_column as well as, according to dataset's state upon model creation, weight_column, and ml_use_column must never be included here. Only 3 fields are used:

• name - May be set on CreateModel, if set only the columns specified are used, otherwise all primary table's columns (except the ones listed above) are used for the training and prediction input.
• display_name - Output only.
• data_type - Output only.

repeated .google.cloud.automl.v1beta1.ColumnSpec input_feature_column_specs = 3;

Returns

Type	Description
TablesModelMetadata.Builder

clearOneof(Descriptors.OneofDescriptor oneof)

public TablesModelMetadata.Builder clearOneof(Descriptors.OneofDescriptor oneof)

Parameter

Name	Description
oneof	OneofDescriptor

Returns

Type	Description
TablesModelMetadata.Builder

Overrides

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

clearOptimizationObjective()

public TablesModelMetadata.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
TablesModelMetadata.Builder	This builder for chaining.

clearOptimizationObjectivePrecisionValue()

public TablesModelMetadata.Builder clearOptimizationObjectivePrecisionValue()

Required when optimization_objective is "MAXIMIZE_RECALL_AT_PRECISION". Must be between 0 and 1, inclusive.

float optimization_objective_precision_value = 18;

Returns

Type	Description
TablesModelMetadata.Builder	This builder for chaining.

clearOptimizationObjectiveRecallValue()

public TablesModelMetadata.Builder clearOptimizationObjectiveRecallValue()

Required when optimization_objective is "MAXIMIZE_PRECISION_AT_RECALL". Must be between 0 and 1, inclusive.

float optimization_objective_recall_value = 17;

Returns

Type	Description
TablesModelMetadata.Builder	This builder for chaining.

clearTablesModelColumnInfo()

public TablesModelMetadata.Builder clearTablesModelColumnInfo()

Output only. Auxiliary information for each of the input_feature_column_specs with respect to this particular model.

repeated .google.cloud.automl.v1beta1.TablesModelColumnInfo tables_model_column_info = 5;

Returns

Type	Description
TablesModelMetadata.Builder

clearTargetColumnSpec()

public TablesModelMetadata.Builder clearTargetColumnSpec()

Column spec of the dataset's primary table's column the model is predicting. Snapshotted when model creation started. Only 3 fields are used: name - May be set on CreateModel, if it's not then the ColumnSpec corresponding to the current target_column_spec_id of the dataset the model is trained from is used. If neither is set, CreateModel will error. display_name - Output only. data_type - Output only.

.google.cloud.automl.v1beta1.ColumnSpec target_column_spec = 2;

Returns

Type	Description
TablesModelMetadata.Builder

clearTrainBudgetMilliNodeHours()

public TablesModelMetadata.Builder clearTrainBudgetMilliNodeHours()

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

int64 train_budget_milli_node_hours = 6;

Returns

Type	Description
TablesModelMetadata.Builder	This builder for chaining.

clearTrainCostMilliNodeHours()

public TablesModelMetadata.Builder clearTrainCostMilliNodeHours()

Output only. The actual training cost of the model, expressed in milli node hours, i.e. 1,000 value in this field means 1 node hour. Guaranteed to not exceed the train budget.

int64 train_cost_milli_node_hours = 7;

Returns

Type	Description
TablesModelMetadata.Builder	This builder for chaining.

clone()

public TablesModelMetadata.Builder clone()

Returns

Type	Description
TablesModelMetadata.Builder

Overrides

GeneratedMessageV3.Builder<BuilderType>.clone()

getAdditionalOptimizationObjectiveConfigCase()

public TablesModelMetadata.AdditionalOptimizationObjectiveConfigCase getAdditionalOptimizationObjectiveConfigCase()

Returns

Type	Description
TablesModelMetadata.AdditionalOptimizationObjectiveConfigCase

getDefaultInstanceForType()

public TablesModelMetadata getDefaultInstanceForType()

Returns

Type	Description
TablesModelMetadata

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 = 12;

Returns

Type	Description
boolean	The disableEarlyStopping.

getInputFeatureColumnSpecs(int index)

public ColumnSpec getInputFeatureColumnSpecs(int index)

Column specs of the dataset's primary table's columns, on which the model is trained and which are used as the input for predictions. The target_column as well as, according to dataset's state upon model creation, weight_column, and ml_use_column must never be included here. Only 3 fields are used:

• name - May be set on CreateModel, if set only the columns specified are used, otherwise all primary table's columns (except the ones listed above) are used for the training and prediction input.
• display_name - Output only.
• data_type - Output only.

repeated .google.cloud.automl.v1beta1.ColumnSpec input_feature_column_specs = 3;

Parameter

Name	Description
index	int

Returns

Type	Description
ColumnSpec

getInputFeatureColumnSpecsBuilder(int index)

public ColumnSpec.Builder getInputFeatureColumnSpecsBuilder(int index)

Column specs of the dataset's primary table's columns, on which the model is trained and which are used as the input for predictions. The target_column as well as, according to dataset's state upon model creation, weight_column, and ml_use_column must never be included here. Only 3 fields are used:

• name - May be set on CreateModel, if set only the columns specified are used, otherwise all primary table's columns (except the ones listed above) are used for the training and prediction input.
• display_name - Output only.
• data_type - Output only.

repeated .google.cloud.automl.v1beta1.ColumnSpec input_feature_column_specs = 3;

Parameter

Name	Description
index	int

Returns

Type	Description
ColumnSpec.Builder

getInputFeatureColumnSpecsBuilderList()

public List<ColumnSpec.Builder> getInputFeatureColumnSpecsBuilderList()

Column specs of the dataset's primary table's columns, on which the model is trained and which are used as the input for predictions. The target_column as well as, according to dataset's state upon model creation, weight_column, and ml_use_column must never be included here. Only 3 fields are used:

• name - May be set on CreateModel, if set only the columns specified are used, otherwise all primary table's columns (except the ones listed above) are used for the training and prediction input.
• display_name - Output only.
• data_type - Output only.

repeated .google.cloud.automl.v1beta1.ColumnSpec input_feature_column_specs = 3;

Returns

Type	Description
List<Builder>

getInputFeatureColumnSpecsCount()

public int getInputFeatureColumnSpecsCount()

Column specs of the dataset's primary table's columns, on which the model is trained and which are used as the input for predictions. The target_column as well as, according to dataset's state upon model creation, weight_column, and ml_use_column must never be included here. Only 3 fields are used:

• name - May be set on CreateModel, if set only the columns specified are used, otherwise all primary table's columns (except the ones listed above) are used for the training and prediction input.
• display_name - Output only.
• data_type - Output only.

repeated .google.cloud.automl.v1beta1.ColumnSpec input_feature_column_specs = 3;

Returns

Type	Description
int

getInputFeatureColumnSpecsList()

public List<ColumnSpec> getInputFeatureColumnSpecsList()

Column specs of the dataset's primary table's columns, on which the model is trained and which are used as the input for predictions. The target_column as well as, according to dataset's state upon model creation, weight_column, and ml_use_column must never be included here. Only 3 fields are used:

• name - May be set on CreateModel, if set only the columns specified are used, otherwise all primary table's columns (except the ones listed above) are used for the training and prediction input.
• display_name - Output only.
• data_type - Output only.

repeated .google.cloud.automl.v1beta1.ColumnSpec input_feature_column_specs = 3;

Returns

Type	Description
List<ColumnSpec>

getInputFeatureColumnSpecsOrBuilder(int index)

public ColumnSpecOrBuilder getInputFeatureColumnSpecsOrBuilder(int index)

Column specs of the dataset's primary table's columns, on which the model is trained and which are used as the input for predictions. The target_column as well as, according to dataset's state upon model creation, weight_column, and ml_use_column must never be included here. Only 3 fields are used:

• name - May be set on CreateModel, if set only the columns specified are used, otherwise all primary table's columns (except the ones listed above) are used for the training and prediction input.
• display_name - Output only.
• data_type - Output only.

repeated .google.cloud.automl.v1beta1.ColumnSpec input_feature_column_specs = 3;

Parameter

Name	Description
index	int

Returns

Type	Description
ColumnSpecOrBuilder

getInputFeatureColumnSpecsOrBuilderList()

public List<? extends ColumnSpecOrBuilder> getInputFeatureColumnSpecsOrBuilderList()

Column specs of the dataset's primary table's columns, on which the model is trained and which are used as the input for predictions. The target_column as well as, according to dataset's state upon model creation, weight_column, and ml_use_column must never be included here. Only 3 fields are used:

• name - May be set on CreateModel, if set only the columns specified are used, otherwise all primary table's columns (except the ones listed above) are used for the training and prediction input.
• display_name - Output only.
• data_type - Output only.

repeated .google.cloud.automl.v1beta1.ColumnSpec input_feature_column_specs = 3;

Returns

Type	Description
List<? extends com.google.cloud.automl.v1beta1.ColumnSpecOrBuilder>

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 = 18;

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 = 17;

Returns

Type	Description
float	The optimizationObjectiveRecallValue.

getTablesModelColumnInfo(int index)

public TablesModelColumnInfo getTablesModelColumnInfo(int index)

Output only. Auxiliary information for each of the input_feature_column_specs with respect to this particular model.

repeated .google.cloud.automl.v1beta1.TablesModelColumnInfo tables_model_column_info = 5;

Parameter

Name	Description
index	int

Returns

Type	Description
TablesModelColumnInfo

getTablesModelColumnInfoBuilder(int index)

public TablesModelColumnInfo.Builder getTablesModelColumnInfoBuilder(int index)

Output only. Auxiliary information for each of the input_feature_column_specs with respect to this particular model.

repeated .google.cloud.automl.v1beta1.TablesModelColumnInfo tables_model_column_info = 5;

Parameter

Name	Description
index	int

Returns

Type	Description
TablesModelColumnInfo.Builder

getTablesModelColumnInfoBuilderList()

public List<TablesModelColumnInfo.Builder> getTablesModelColumnInfoBuilderList()

Output only. Auxiliary information for each of the input_feature_column_specs with respect to this particular model.

repeated .google.cloud.automl.v1beta1.TablesModelColumnInfo tables_model_column_info = 5;

Returns

Type	Description
List<Builder>

getTablesModelColumnInfoCount()

public int getTablesModelColumnInfoCount()

Output only. Auxiliary information for each of the input_feature_column_specs with respect to this particular model.

repeated .google.cloud.automl.v1beta1.TablesModelColumnInfo tables_model_column_info = 5;

Returns

Type	Description
int

getTablesModelColumnInfoList()

public List<TablesModelColumnInfo> getTablesModelColumnInfoList()

Output only. Auxiliary information for each of the input_feature_column_specs with respect to this particular model.

repeated .google.cloud.automl.v1beta1.TablesModelColumnInfo tables_model_column_info = 5;

Returns

Type	Description
List<TablesModelColumnInfo>

getTablesModelColumnInfoOrBuilder(int index)

public TablesModelColumnInfoOrBuilder getTablesModelColumnInfoOrBuilder(int index)

Output only. Auxiliary information for each of the input_feature_column_specs with respect to this particular model.

repeated .google.cloud.automl.v1beta1.TablesModelColumnInfo tables_model_column_info = 5;

Parameter

Name	Description
index	int

Returns

Type	Description
TablesModelColumnInfoOrBuilder

getTablesModelColumnInfoOrBuilderList()

public List<? extends TablesModelColumnInfoOrBuilder> getTablesModelColumnInfoOrBuilderList()

Output only. Auxiliary information for each of the input_feature_column_specs with respect to this particular model.

repeated .google.cloud.automl.v1beta1.TablesModelColumnInfo tables_model_column_info = 5;

Returns

Type	Description
List<? extends com.google.cloud.automl.v1beta1.TablesModelColumnInfoOrBuilder>

getTargetColumnSpec()

public ColumnSpec getTargetColumnSpec()

Column spec of the dataset's primary table's column the model is predicting. Snapshotted when model creation started. Only 3 fields are used: name - May be set on CreateModel, if it's not then the ColumnSpec corresponding to the current target_column_spec_id of the dataset the model is trained from is used. If neither is set, CreateModel will error. display_name - Output only. data_type - Output only.

.google.cloud.automl.v1beta1.ColumnSpec target_column_spec = 2;

Returns

Type	Description
ColumnSpec	The targetColumnSpec.

getTargetColumnSpecBuilder()

public ColumnSpec.Builder getTargetColumnSpecBuilder()

Column spec of the dataset's primary table's column the model is predicting. Snapshotted when model creation started. Only 3 fields are used: name - May be set on CreateModel, if it's not then the ColumnSpec corresponding to the current target_column_spec_id of the dataset the model is trained from is used. If neither is set, CreateModel will error. display_name - Output only. data_type - Output only.

.google.cloud.automl.v1beta1.ColumnSpec target_column_spec = 2;

Returns

Type	Description
ColumnSpec.Builder

getTargetColumnSpecOrBuilder()

public ColumnSpecOrBuilder getTargetColumnSpecOrBuilder()

Column spec of the dataset's primary table's column the model is predicting. Snapshotted when model creation started. Only 3 fields are used: name - May be set on CreateModel, if it's not then the ColumnSpec corresponding to the current target_column_spec_id of the dataset the model is trained from is used. If neither is set, CreateModel will error. display_name - Output only. data_type - Output only.

.google.cloud.automl.v1beta1.ColumnSpec target_column_spec = 2;

Returns

Type	Description
ColumnSpecOrBuilder

getTrainBudgetMilliNodeHours()

public long getTrainBudgetMilliNodeHours()

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

int64 train_budget_milli_node_hours = 6;

Returns

Type	Description
long	The trainBudgetMilliNodeHours.

getTrainCostMilliNodeHours()

public long getTrainCostMilliNodeHours()

Output only. The actual training cost of the model, expressed in milli node hours, i.e. 1,000 value in this field means 1 node hour. Guaranteed to not exceed the train budget.

int64 train_cost_milli_node_hours = 7;

Returns

Type	Description
long	The trainCostMilliNodeHours.

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 = 18;

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 = 17;

Returns

Type	Description
boolean	Whether the optimizationObjectiveRecallValue field is set.

hasTargetColumnSpec()

public boolean hasTargetColumnSpec()

Column spec of the dataset's primary table's column the model is predicting. Snapshotted when model creation started. Only 3 fields are used: name - May be set on CreateModel, if it's not then the ColumnSpec corresponding to the current target_column_spec_id of the dataset the model is trained from is used. If neither is set, CreateModel will error. display_name - Output only. data_type - Output only.

.google.cloud.automl.v1beta1.ColumnSpec target_column_spec = 2;

Returns

Type	Description
boolean	Whether the targetColumnSpec 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()

mergeFrom(TablesModelMetadata other)

public TablesModelMetadata.Builder mergeFrom(TablesModelMetadata other)

Parameter

Name	Description
other	TablesModelMetadata

Returns

Type	Description
TablesModelMetadata.Builder

mergeFrom(CodedInputStream input, ExtensionRegistryLite extensionRegistry)

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

Parameters

Name	Description
input	CodedInputStream
extensionRegistry	ExtensionRegistryLite

Returns

Type	Description
TablesModelMetadata.Builder

Overrides

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

Exceptions

Type	Description
IOException

mergeFrom(Message other)

public TablesModelMetadata.Builder mergeFrom(Message other)

Parameter

Name	Description
other	Message

Returns

Type	Description
TablesModelMetadata.Builder

Overrides

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

mergeTargetColumnSpec(ColumnSpec value)

public TablesModelMetadata.Builder mergeTargetColumnSpec(ColumnSpec value)

Column spec of the dataset's primary table's column the model is predicting. Snapshotted when model creation started. Only 3 fields are used: name - May be set on CreateModel, if it's not then the ColumnSpec corresponding to the current target_column_spec_id of the dataset the model is trained from is used. If neither is set, CreateModel will error. display_name - Output only. data_type - Output only.

.google.cloud.automl.v1beta1.ColumnSpec target_column_spec = 2;

Parameter

Name	Description
value	ColumnSpec

Returns

Type	Description
TablesModelMetadata.Builder

mergeUnknownFields(UnknownFieldSet unknownFields)

public final TablesModelMetadata.Builder mergeUnknownFields(UnknownFieldSet unknownFields)

Parameter

Name	Description
unknownFields	UnknownFieldSet

Returns

Type	Description
TablesModelMetadata.Builder

Overrides

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

removeInputFeatureColumnSpecs(int index)

public TablesModelMetadata.Builder removeInputFeatureColumnSpecs(int index)

Column specs of the dataset's primary table's columns, on which the model is trained and which are used as the input for predictions. The target_column as well as, according to dataset's state upon model creation, weight_column, and ml_use_column must never be included here. Only 3 fields are used:

• name - May be set on CreateModel, if set only the columns specified are used, otherwise all primary table's columns (except the ones listed above) are used for the training and prediction input.
• display_name - Output only.
• data_type - Output only.

repeated .google.cloud.automl.v1beta1.ColumnSpec input_feature_column_specs = 3;

Parameter

Name	Description
index	int

Returns

Type	Description
TablesModelMetadata.Builder

removeTablesModelColumnInfo(int index)

public TablesModelMetadata.Builder removeTablesModelColumnInfo(int index)

Output only. Auxiliary information for each of the input_feature_column_specs with respect to this particular model.

repeated .google.cloud.automl.v1beta1.TablesModelColumnInfo tables_model_column_info = 5;

Parameter

Name	Description
index	int

Returns

Type	Description
TablesModelMetadata.Builder

setDisableEarlyStopping(boolean value)

public TablesModelMetadata.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 = 12;

Parameter

Name	Description
value	boolean The disableEarlyStopping to set.

Returns

Type	Description
TablesModelMetadata.Builder	This builder for chaining.

setField(Descriptors.FieldDescriptor field, Object value)

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

Parameters

Name	Description
field	FieldDescriptor
value	Object

Returns

Type	Description
TablesModelMetadata.Builder

Overrides

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

setInputFeatureColumnSpecs(int index, ColumnSpec value)

public TablesModelMetadata.Builder setInputFeatureColumnSpecs(int index, ColumnSpec value)

Column specs of the dataset's primary table's columns, on which the model is trained and which are used as the input for predictions. The target_column as well as, according to dataset's state upon model creation, weight_column, and ml_use_column must never be included here. Only 3 fields are used:

• name - May be set on CreateModel, if set only the columns specified are used, otherwise all primary table's columns (except the ones listed above) are used for the training and prediction input.
• display_name - Output only.
• data_type - Output only.

repeated .google.cloud.automl.v1beta1.ColumnSpec input_feature_column_specs = 3;

Parameters

Name	Description
index	int
value	ColumnSpec

Returns

Type	Description
TablesModelMetadata.Builder

setInputFeatureColumnSpecs(int index, ColumnSpec.Builder builderForValue)

public TablesModelMetadata.Builder setInputFeatureColumnSpecs(int index, ColumnSpec.Builder builderForValue)

Column specs of the dataset's primary table's columns, on which the model is trained and which are used as the input for predictions. The target_column as well as, according to dataset's state upon model creation, weight_column, and ml_use_column must never be included here. Only 3 fields are used:

• name - May be set on CreateModel, if set only the columns specified are used, otherwise all primary table's columns (except the ones listed above) are used for the training and prediction input.
• display_name - Output only.
• data_type - Output only.

repeated .google.cloud.automl.v1beta1.ColumnSpec input_feature_column_specs = 3;

Parameters

Name	Description
index	int
builderForValue	ColumnSpec.Builder

Returns

Type	Description
TablesModelMetadata.Builder

setOptimizationObjective(String value)

public TablesModelMetadata.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
TablesModelMetadata.Builder	This builder for chaining.

setOptimizationObjectiveBytes(ByteString value)

public TablesModelMetadata.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
TablesModelMetadata.Builder	This builder for chaining.

setOptimizationObjectivePrecisionValue(float value)

public TablesModelMetadata.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 = 18;

Parameter

Name	Description
value	float The optimizationObjectivePrecisionValue to set.

Returns

Type	Description
TablesModelMetadata.Builder	This builder for chaining.

setOptimizationObjectiveRecallValue(float value)

public TablesModelMetadata.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 = 17;

Parameter

Name	Description
value	float The optimizationObjectiveRecallValue to set.

Returns

Type	Description
TablesModelMetadata.Builder	This builder for chaining.

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

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

Parameters

Name	Description
field	FieldDescriptor
index	int
value	Object

Returns

Type	Description
TablesModelMetadata.Builder

Overrides

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

setTablesModelColumnInfo(int index, TablesModelColumnInfo value)

public TablesModelMetadata.Builder setTablesModelColumnInfo(int index, TablesModelColumnInfo value)

Output only. Auxiliary information for each of the input_feature_column_specs with respect to this particular model.

repeated .google.cloud.automl.v1beta1.TablesModelColumnInfo tables_model_column_info = 5;

Parameters

Name	Description
index	int
value	TablesModelColumnInfo

Returns

Type	Description
TablesModelMetadata.Builder

setTablesModelColumnInfo(int index, TablesModelColumnInfo.Builder builderForValue)

public TablesModelMetadata.Builder setTablesModelColumnInfo(int index, TablesModelColumnInfo.Builder builderForValue)

Output only. Auxiliary information for each of the input_feature_column_specs with respect to this particular model.

repeated .google.cloud.automl.v1beta1.TablesModelColumnInfo tables_model_column_info = 5;

Parameters

Name	Description
index	int
builderForValue	TablesModelColumnInfo.Builder

Returns

Type	Description
TablesModelMetadata.Builder

setTargetColumnSpec(ColumnSpec value)

public TablesModelMetadata.Builder setTargetColumnSpec(ColumnSpec value)

Column spec of the dataset's primary table's column the model is predicting. Snapshotted when model creation started. Only 3 fields are used: name - May be set on CreateModel, if it's not then the ColumnSpec corresponding to the current target_column_spec_id of the dataset the model is trained from is used. If neither is set, CreateModel will error. display_name - Output only. data_type - Output only.

.google.cloud.automl.v1beta1.ColumnSpec target_column_spec = 2;

Parameter

Name	Description
value	ColumnSpec

Returns

Type	Description
TablesModelMetadata.Builder

setTargetColumnSpec(ColumnSpec.Builder builderForValue)

public TablesModelMetadata.Builder setTargetColumnSpec(ColumnSpec.Builder builderForValue)

Column spec of the dataset's primary table's column the model is predicting. Snapshotted when model creation started. Only 3 fields are used: name - May be set on CreateModel, if it's not then the ColumnSpec corresponding to the current target_column_spec_id of the dataset the model is trained from is used. If neither is set, CreateModel will error. display_name - Output only. data_type - Output only.

.google.cloud.automl.v1beta1.ColumnSpec target_column_spec = 2;

Parameter

Name	Description
builderForValue	ColumnSpec.Builder

Returns

Type	Description
TablesModelMetadata.Builder

setTrainBudgetMilliNodeHours(long value)

public TablesModelMetadata.Builder setTrainBudgetMilliNodeHours(long value)

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

int64 train_budget_milli_node_hours = 6;

Parameter

Name	Description
value	long The trainBudgetMilliNodeHours to set.

Returns

Type	Description
TablesModelMetadata.Builder	This builder for chaining.

setTrainCostMilliNodeHours(long value)

public TablesModelMetadata.Builder setTrainCostMilliNodeHours(long value)

Output only. The actual training cost of the model, expressed in milli node hours, i.e. 1,000 value in this field means 1 node hour. Guaranteed to not exceed the train budget.

int64 train_cost_milli_node_hours = 7;

Parameter

Name	Description
value	long The trainCostMilliNodeHours to set.

Returns

Type	Description
TablesModelMetadata.Builder	This builder for chaining.

setUnknownFields(UnknownFieldSet unknownFields)

public final TablesModelMetadata.Builder setUnknownFields(UnknownFieldSet unknownFields)

Parameter

Name	Description
unknownFields	UnknownFieldSet

Returns

Type	Description
TablesModelMetadata.Builder

Overrides

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