Interface TablesModelMetadataOrBuilder

public interface TablesModelMetadataOrBuilder extends MessageOrBuilder

Implements

MessageOrBuilder

Methods

getAdditionalOptimizationObjectiveConfigCase()

public abstract TablesModelMetadata.AdditionalOptimizationObjectiveConfigCase getAdditionalOptimizationObjectiveConfigCase()
Returns
TypeDescription
TablesModelMetadata.AdditionalOptimizationObjectiveConfigCase

getDisableEarlyStopping()

public abstract 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
TypeDescription
boolean

The disableEarlyStopping.

getInputFeatureColumnSpecs(int index)

public abstract 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
NameDescription
indexint
Returns
TypeDescription
ColumnSpec

getInputFeatureColumnSpecsCount()

public abstract 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
TypeDescription
int

getInputFeatureColumnSpecsList()

public abstract 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
TypeDescription
List<ColumnSpec>

getInputFeatureColumnSpecsOrBuilder(int index)

public abstract 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
NameDescription
indexint
Returns
TypeDescription
ColumnSpecOrBuilder

getInputFeatureColumnSpecsOrBuilderList()

public abstract 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
TypeDescription
List<? extends com.google.cloud.automl.v1beta1.ColumnSpecOrBuilder>

getOptimizationObjective()

public abstract 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
TypeDescription
String

The optimizationObjective.

getOptimizationObjectiveBytes()

public abstract 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
TypeDescription
ByteString

The bytes for optimizationObjective.

getOptimizationObjectivePrecisionValue()

public abstract 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
TypeDescription
float

The optimizationObjectivePrecisionValue.

getOptimizationObjectiveRecallValue()

public abstract 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
TypeDescription
float

The optimizationObjectiveRecallValue.

getTablesModelColumnInfo(int index)

public abstract 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
NameDescription
indexint
Returns
TypeDescription
TablesModelColumnInfo

getTablesModelColumnInfoCount()

public abstract 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
TypeDescription
int

getTablesModelColumnInfoList()

public abstract 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
TypeDescription
List<TablesModelColumnInfo>

getTablesModelColumnInfoOrBuilder(int index)

public abstract 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
NameDescription
indexint
Returns
TypeDescription
TablesModelColumnInfoOrBuilder

getTablesModelColumnInfoOrBuilderList()

public abstract 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
TypeDescription
List<? extends com.google.cloud.automl.v1beta1.TablesModelColumnInfoOrBuilder>

getTargetColumnSpec()

public abstract 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
TypeDescription
ColumnSpec

The targetColumnSpec.

getTargetColumnSpecOrBuilder()

public abstract 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
TypeDescription
ColumnSpecOrBuilder

getTrainBudgetMilliNodeHours()

public abstract 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
TypeDescription
long

The trainBudgetMilliNodeHours.

getTrainCostMilliNodeHours()

public abstract 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
TypeDescription
long

The trainCostMilliNodeHours.

hasOptimizationObjectivePrecisionValue()

public abstract 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
TypeDescription
boolean

Whether the optimizationObjectivePrecisionValue field is set.

hasOptimizationObjectiveRecallValue()

public abstract 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
TypeDescription
boolean

Whether the optimizationObjectiveRecallValue field is set.

hasTargetColumnSpec()

public abstract 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
TypeDescription
boolean

Whether the targetColumnSpec field is set.