Package com.google.cloud.automl.v1beta1

Interface TablesModelMetadataOrBuilder

All Superinterfaces:

com.google.protobuf.MessageLiteOrBuilder, com.google.protobuf.MessageOrBuilder

All Known Implementing Classes:

TablesModelMetadata, TablesModelMetadata.Builder

public interface TablesModelMetadataOrBuilder
extends com.google.protobuf.MessageOrBuilder

Method Summary

Modifier and Type	Method	Description
TablesModelMetadata.AdditionalOptimizationObjectiveConfigCase	getAdditionalOptimizationObjectiveConfigCase()
boolean	getDisableEarlyStopping()	Use the entire training budget.
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.
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.
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.
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.
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.
String	getOptimizationObjective()	Objective function the model is optimizing towards.
com.google.protobuf.ByteString	getOptimizationObjectiveBytes()	Objective function the model is optimizing towards.
float	getOptimizationObjectivePrecisionValue()	Required when optimization_objective is "MAXIMIZE_RECALL_AT_PRECISION".
float	getOptimizationObjectiveRecallValue()	Required when optimization_objective is "MAXIMIZE_PRECISION_AT_RECALL".
TablesModelColumnInfo	getTablesModelColumnInfo(int index)	Output only.
int	getTablesModelColumnInfoCount()	Output only.
List<TablesModelColumnInfo>	getTablesModelColumnInfoList()	Output only.
TablesModelColumnInfoOrBuilder	getTablesModelColumnInfoOrBuilder(int index)	Output only.
List<? extends TablesModelColumnInfoOrBuilder>	getTablesModelColumnInfoOrBuilderList()	Output only.
ColumnSpec	getTargetColumnSpec()	Column spec of the dataset's primary table's column the model is predicting.
ColumnSpecOrBuilder	getTargetColumnSpecOrBuilder()	Column spec of the dataset's primary table's column the model is predicting.
long	getTrainBudgetMilliNodeHours()	Required.
long	getTrainCostMilliNodeHours()	Output only.
boolean	hasOptimizationObjectivePrecisionValue()	Required when optimization_objective is "MAXIMIZE_RECALL_AT_PRECISION".
boolean	hasOptimizationObjectiveRecallValue()	Required when optimization_objective is "MAXIMIZE_PRECISION_AT_RECALL".
boolean	hasTargetColumnSpec()	Column spec of the dataset's primary table's column the model is predicting.

Methods inherited from interface com.google.protobuf.MessageLiteOrBuilder

isInitialized

Methods inherited from interface com.google.protobuf.MessageOrBuilder

findInitializationErrors, getAllFields, getDefaultInstanceForType, getDescriptorForType, getField, getInitializationErrorString, getOneofFieldDescriptor, getRepeatedField, getRepeatedFieldCount, getUnknownFields, hasField, hasOneof

Method Detail

hasOptimizationObjectiveRecallValue

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:

Whether the optimizationObjectiveRecallValue field is set.

getOptimizationObjectiveRecallValue

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:

The optimizationObjectiveRecallValue.

hasOptimizationObjectivePrecisionValue

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:

Whether the optimizationObjectivePrecisionValue field is set.

getOptimizationObjectivePrecisionValue

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:

The optimizationObjectivePrecisionValue.

hasTargetColumnSpec

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:

Whether the targetColumnSpec field is set.

getTargetColumnSpec

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:

The targetColumnSpec.

getTargetColumnSpecOrBuilder

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;

getInputFeatureColumnSpecsList

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][google.cloud.automl.v1beta1.TablesModelMetadata.target_column_spec]
 as well as, according to dataset's state upon model creation,

 [weight_column][google.cloud.automl.v1beta1.TablesDatasetMetadata.weight_column_spec_id],
 and

 [ml_use_column][google.cloud.automl.v1beta1.TablesDatasetMetadata.ml_use_column_spec_id]
 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;

getInputFeatureColumnSpecs

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][google.cloud.automl.v1beta1.TablesModelMetadata.target_column_spec]
 as well as, according to dataset's state upon model creation,

 [weight_column][google.cloud.automl.v1beta1.TablesDatasetMetadata.weight_column_spec_id],
 and

 [ml_use_column][google.cloud.automl.v1beta1.TablesDatasetMetadata.ml_use_column_spec_id]
 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;

getInputFeatureColumnSpecsCount

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][google.cloud.automl.v1beta1.TablesModelMetadata.target_column_spec]
 as well as, according to dataset's state upon model creation,

 [weight_column][google.cloud.automl.v1beta1.TablesDatasetMetadata.weight_column_spec_id],
 and

 [ml_use_column][google.cloud.automl.v1beta1.TablesDatasetMetadata.ml_use_column_spec_id]
 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;

getInputFeatureColumnSpecsOrBuilderList

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][google.cloud.automl.v1beta1.TablesModelMetadata.target_column_spec]
 as well as, according to dataset's state upon model creation,

 [weight_column][google.cloud.automl.v1beta1.TablesDatasetMetadata.weight_column_spec_id],
 and

 [ml_use_column][google.cloud.automl.v1beta1.TablesDatasetMetadata.ml_use_column_spec_id]
 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;

getInputFeatureColumnSpecsOrBuilder

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][google.cloud.automl.v1beta1.TablesModelMetadata.target_column_spec]
 as well as, according to dataset's state upon model creation,

 [weight_column][google.cloud.automl.v1beta1.TablesDatasetMetadata.weight_column_spec_id],
 and

 [ml_use_column][google.cloud.automl.v1beta1.TablesDatasetMetadata.ml_use_column_spec_id]
 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;

getOptimizationObjective

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:

The optimizationObjective.

getOptimizationObjectiveBytes

com.google.protobuf.ByteString getOptimizationObjectiveBytes()

 Objective function the model is optimizing towards. The training process
 creates a model that maximizes/minimizes the value of the objective
 function over the validation set.

 The supported optimization objectives depend on the prediction type.
 If the field is not set, a default objective function is used.

 CLASSIFICATION_BINARY:
   "MAXIMIZE_AU_ROC" (default) - Maximize the area under the receiver
                                 operating characteristic (ROC) curve.
   "MINIMIZE_LOG_LOSS" - Minimize log loss.
   "MAXIMIZE_AU_PRC" - Maximize the area under the precision-recall curve.
   "MAXIMIZE_PRECISION_AT_RECALL" - Maximize precision for a specified
                                   recall value.
   "MAXIMIZE_RECALL_AT_PRECISION" - Maximize recall for a specified
                                    precision value.

 CLASSIFICATION_MULTI_CLASS :
   "MINIMIZE_LOG_LOSS" (default) - Minimize log loss.


 REGRESSION:
   "MINIMIZE_RMSE" (default) - Minimize root-mean-squared error (RMSE).
   "MINIMIZE_MAE" - Minimize mean-absolute error (MAE).
   "MINIMIZE_RMSLE" - Minimize root-mean-squared log error (RMSLE).
 

string optimization_objective = 4;

Returns:

The bytes for optimizationObjective.

getTablesModelColumnInfoList

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;

getTablesModelColumnInfo

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;

getTablesModelColumnInfoCount

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;

getTablesModelColumnInfoOrBuilderList

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;

getTablesModelColumnInfoOrBuilder

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;

getTrainBudgetMilliNodeHours

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:

The trainBudgetMilliNodeHours.

getTrainCostMilliNodeHours

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:

The trainCostMilliNodeHours.

getDisableEarlyStopping

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:

The disableEarlyStopping.

getAdditionalOptimizationObjectiveConfigCase

TablesModelMetadata.AdditionalOptimizationObjectiveConfigCase getAdditionalOptimizationObjectiveConfigCase()