Class TablesModelMetadata.Builder (2.34.0)

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)

com.google.protobuf.GeneratedMessageV3.Builder.getUnknownFieldSetBuilder()

GeneratedMessageV3.Builder.getUnknownFields()

GeneratedMessageV3.Builder.hasField(Descriptors.FieldDescriptor)

GeneratedMessageV3.Builder.hasOneof(Descriptors.OneofDescriptor)

GeneratedMessageV3.Builder.internalGetFieldAccessorTable()

GeneratedMessageV3.Builder.internalGetMapField(int)

com.google.protobuf.GeneratedMessageV3.Builder.internalGetMapFieldReflection(int)

GeneratedMessageV3.Builder.internalGetMutableMapField(int)

com.google.protobuf.GeneratedMessageV3.Builder.internalGetMutableMapFieldReflection(int)

GeneratedMessageV3.Builder.isClean()

GeneratedMessageV3.Builder.isInitialized()

GeneratedMessageV3.Builder.markClean()

GeneratedMessageV3.Builder.mergeUnknownFields(UnknownFieldSet)

com.google.protobuf.GeneratedMessageV3.Builder.mergeUnknownLengthDelimitedField(int,com.google.protobuf.ByteString)

com.google.protobuf.GeneratedMessageV3.Builder.mergeUnknownVarintField(int,int)

GeneratedMessageV3.Builder.newBuilderForField(Descriptors.FieldDescriptor)

GeneratedMessageV3.Builder.onBuilt()

GeneratedMessageV3.Builder.onChanged()

com.google.protobuf.GeneratedMessageV3.Builder.parseUnknownField(com.google.protobuf.CodedInputStream,com.google.protobuf.ExtensionRegistryLite,int)

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

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

com.google.protobuf.GeneratedMessageV3.Builder.setUnknownFieldSetBuilder(com.google.protobuf.UnknownFieldSet.Builder)

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()

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_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_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()

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

Returns
Type	Description
`ColumnSpec`	The targetColumnSpec.

getTargetColumnSpecBuilder()

public ColumnSpec.Builder getTargetColumnSpecBuilder()

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

Returns
Type	Description
`ColumnSpec.Builder`

getTargetColumnSpecOrBuilder()

public ColumnSpecOrBuilder getTargetColumnSpecOrBuilder()

.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.

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()

.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)

.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)

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_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_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)

.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)

.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.

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)