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public static final class AutoMlTablesInputs.Builder extends GeneratedMessageV3.Builder<AutoMlTablesInputs.Builder> implements AutoMlTablesInputsOrBuilderProtobuf type
google.cloud.aiplatform.v1.schema.trainingjob.definition.AutoMlTablesInputs
Inheritance
Object > AbstractMessageLite.Builder<MessageType,BuilderType> > AbstractMessage.Builder<BuilderType> > GeneratedMessageV3.Builder > AutoMlTablesInputs.BuilderImplements
AutoMlTablesInputsOrBuilderStatic Methods
getDescriptor()
public static final Descriptors.Descriptor getDescriptor()| Type | Description |
Descriptor |
Methods
addAdditionalExperiments(String value)
public AutoMlTablesInputs.Builder addAdditionalExperiments(String value)Additional experiment flags for the Tables training pipeline.
repeated string additional_experiments = 11;
| Name | Description |
value | StringThe additionalExperiments to add. |
| Type | Description |
AutoMlTablesInputs.Builder | This builder for chaining. |
addAdditionalExperimentsBytes(ByteString value)
public AutoMlTablesInputs.Builder addAdditionalExperimentsBytes(ByteString value)Additional experiment flags for the Tables training pipeline.
repeated string additional_experiments = 11;
| Name | Description |
value | ByteStringThe bytes of the additionalExperiments to add. |
| Type | Description |
AutoMlTablesInputs.Builder | This builder for chaining. |
addAllAdditionalExperiments(Iterable<String> values)
public AutoMlTablesInputs.Builder addAllAdditionalExperiments(Iterable<String> values)Additional experiment flags for the Tables training pipeline.
repeated string additional_experiments = 11;
| Name | Description |
values | Iterable<String>The additionalExperiments to add. |
| Type | Description |
AutoMlTablesInputs.Builder | This builder for chaining. |
addAllTransformations(Iterable<? extends AutoMlTablesInputs.Transformation> values)
public AutoMlTablesInputs.Builder addAllTransformations(Iterable<? extends AutoMlTablesInputs.Transformation> values)Each transformation will apply transform function to given input column. And the result will be used for training. When creating transformation for BigQuery Struct column, the column should be flattened using "." as the delimiter.
repeated .google.cloud.aiplatform.v1.schema.trainingjob.definition.AutoMlTablesInputs.Transformation transformations = 3;
| Name | Description |
values | Iterable<? extends com.google.cloud.aiplatform.v1.schema.trainingjob.definition.AutoMlTablesInputs.Transformation> |
| Type | Description |
AutoMlTablesInputs.Builder |
addRepeatedField(Descriptors.FieldDescriptor field, Object value)
public AutoMlTablesInputs.Builder addRepeatedField(Descriptors.FieldDescriptor field, Object value)| Name | Description |
field | FieldDescriptor |
value | Object |
| Type | Description |
AutoMlTablesInputs.Builder |
addTransformations(AutoMlTablesInputs.Transformation value)
public AutoMlTablesInputs.Builder addTransformations(AutoMlTablesInputs.Transformation value)Each transformation will apply transform function to given input column. And the result will be used for training. When creating transformation for BigQuery Struct column, the column should be flattened using "." as the delimiter.
repeated .google.cloud.aiplatform.v1.schema.trainingjob.definition.AutoMlTablesInputs.Transformation transformations = 3;
| Name | Description |
value | AutoMlTablesInputs.Transformation |
| Type | Description |
AutoMlTablesInputs.Builder |
addTransformations(AutoMlTablesInputs.Transformation.Builder builderForValue)
public AutoMlTablesInputs.Builder addTransformations(AutoMlTablesInputs.Transformation.Builder builderForValue)Each transformation will apply transform function to given input column. And the result will be used for training. When creating transformation for BigQuery Struct column, the column should be flattened using "." as the delimiter.
repeated .google.cloud.aiplatform.v1.schema.trainingjob.definition.AutoMlTablesInputs.Transformation transformations = 3;
| Name | Description |
builderForValue | AutoMlTablesInputs.Transformation.Builder |
| Type | Description |
AutoMlTablesInputs.Builder |
addTransformations(int index, AutoMlTablesInputs.Transformation value)
public AutoMlTablesInputs.Builder addTransformations(int index, AutoMlTablesInputs.Transformation value)Each transformation will apply transform function to given input column. And the result will be used for training. When creating transformation for BigQuery Struct column, the column should be flattened using "." as the delimiter.
repeated .google.cloud.aiplatform.v1.schema.trainingjob.definition.AutoMlTablesInputs.Transformation transformations = 3;
| Name | Description |
index | int |
value | AutoMlTablesInputs.Transformation |
| Type | Description |
AutoMlTablesInputs.Builder |
addTransformations(int index, AutoMlTablesInputs.Transformation.Builder builderForValue)
public AutoMlTablesInputs.Builder addTransformations(int index, AutoMlTablesInputs.Transformation.Builder builderForValue)Each transformation will apply transform function to given input column. And the result will be used for training. When creating transformation for BigQuery Struct column, the column should be flattened using "." as the delimiter.
repeated .google.cloud.aiplatform.v1.schema.trainingjob.definition.AutoMlTablesInputs.Transformation transformations = 3;
| Name | Description |
index | int |
builderForValue | AutoMlTablesInputs.Transformation.Builder |
| Type | Description |
AutoMlTablesInputs.Builder |
addTransformationsBuilder()
public AutoMlTablesInputs.Transformation.Builder addTransformationsBuilder()Each transformation will apply transform function to given input column. And the result will be used for training. When creating transformation for BigQuery Struct column, the column should be flattened using "." as the delimiter.
repeated .google.cloud.aiplatform.v1.schema.trainingjob.definition.AutoMlTablesInputs.Transformation transformations = 3;
| Type | Description |
AutoMlTablesInputs.Transformation.Builder |
addTransformationsBuilder(int index)
public AutoMlTablesInputs.Transformation.Builder addTransformationsBuilder(int index)Each transformation will apply transform function to given input column. And the result will be used for training. When creating transformation for BigQuery Struct column, the column should be flattened using "." as the delimiter.
repeated .google.cloud.aiplatform.v1.schema.trainingjob.definition.AutoMlTablesInputs.Transformation transformations = 3;
| Name | Description |
index | int |
| Type | Description |
AutoMlTablesInputs.Transformation.Builder |
build()
public AutoMlTablesInputs build()| Type | Description |
AutoMlTablesInputs |
buildPartial()
public AutoMlTablesInputs buildPartial()| Type | Description |
AutoMlTablesInputs |
clear()
public AutoMlTablesInputs.Builder clear()| Type | Description |
AutoMlTablesInputs.Builder |
clearAdditionalExperiments()
public AutoMlTablesInputs.Builder clearAdditionalExperiments()Additional experiment flags for the Tables training pipeline.
repeated string additional_experiments = 11;
| Type | Description |
AutoMlTablesInputs.Builder | This builder for chaining. |
clearAdditionalOptimizationObjectiveConfig()
public AutoMlTablesInputs.Builder clearAdditionalOptimizationObjectiveConfig()| Type | Description |
AutoMlTablesInputs.Builder |
clearDisableEarlyStopping()
public AutoMlTablesInputs.Builder clearDisableEarlyStopping()Use the entire training budget. This disables the early stopping feature. By default, the early stopping feature is enabled, which means that AutoML Tables might stop training before the entire training budget has been used.
bool disable_early_stopping = 8;
| Type | Description |
AutoMlTablesInputs.Builder | This builder for chaining. |
clearExportEvaluatedDataItemsConfig()
public AutoMlTablesInputs.Builder clearExportEvaluatedDataItemsConfig()Configuration for exporting test set predictions to a BigQuery table. If this configuration is absent, then the export is not performed.
.google.cloud.aiplatform.v1.schema.trainingjob.definition.ExportEvaluatedDataItemsConfig export_evaluated_data_items_config = 10;
| Type | Description |
AutoMlTablesInputs.Builder |
clearField(Descriptors.FieldDescriptor field)
public AutoMlTablesInputs.Builder clearField(Descriptors.FieldDescriptor field)| Name | Description |
field | FieldDescriptor |
| Type | Description |
AutoMlTablesInputs.Builder |
clearOneof(Descriptors.OneofDescriptor oneof)
public AutoMlTablesInputs.Builder clearOneof(Descriptors.OneofDescriptor oneof)| Name | Description |
oneof | OneofDescriptor |
| Type | Description |
AutoMlTablesInputs.Builder |
clearOptimizationObjective()
public AutoMlTablesInputs.Builder clearOptimizationObjective()Objective function the model is optimizing towards. The training process creates a model that maximizes/minimizes the value of the objective function over the validation set. The supported optimization objectives depend on the prediction type. If the field is not set, a default objective function is used. classification (binary): "maximize-au-roc" (default) - Maximize the area under the receiver operating characteristic (ROC) curve. "minimize-log-loss" - Minimize log loss. "maximize-au-prc" - Maximize the area under the precision-recall curve. "maximize-precision-at-recall" - Maximize precision for a specified recall value. "maximize-recall-at-precision" - Maximize recall for a specified precision value. classification (multi-class): "minimize-log-loss" (default) - Minimize log loss. regression: "minimize-rmse" (default) - Minimize root-mean-squared error (RMSE). "minimize-mae" - Minimize mean-absolute error (MAE). "minimize-rmsle" - Minimize root-mean-squared log error (RMSLE).
string optimization_objective = 4;
| Type | Description |
AutoMlTablesInputs.Builder | This builder for chaining. |
clearOptimizationObjectivePrecisionValue()
public AutoMlTablesInputs.Builder clearOptimizationObjectivePrecisionValue()Required when optimization_objective is "maximize-recall-at-precision". Must be between 0 and 1, inclusive.
float optimization_objective_precision_value = 6;
| Type | Description |
AutoMlTablesInputs.Builder | This builder for chaining. |
clearOptimizationObjectiveRecallValue()
public AutoMlTablesInputs.Builder clearOptimizationObjectiveRecallValue()Required when optimization_objective is "maximize-precision-at-recall". Must be between 0 and 1, inclusive.
float optimization_objective_recall_value = 5;
| Type | Description |
AutoMlTablesInputs.Builder | This builder for chaining. |
clearPredictionType()
public AutoMlTablesInputs.Builder clearPredictionType()The type of prediction the Model is to produce. "classification" - Predict one out of multiple target values is picked for each row. "regression" - Predict a value based on its relation to other values. This type is available only to columns that contain semantically numeric values, i.e. integers or floating point number, even if stored as e.g. strings.
string prediction_type = 1;
| Type | Description |
AutoMlTablesInputs.Builder | This builder for chaining. |
clearTargetColumn()
public AutoMlTablesInputs.Builder clearTargetColumn()The column name of the target column that the model is to predict.
string target_column = 2;
| Type | Description |
AutoMlTablesInputs.Builder | This builder for chaining. |
clearTrainBudgetMilliNodeHours()
public AutoMlTablesInputs.Builder clearTrainBudgetMilliNodeHours()Required. The train budget of creating this model, expressed in milli node hours i.e. 1,000 value in this field means 1 node hour. The training cost of the model will not exceed this budget. The final cost will be attempted to be close to the budget, though may end up being (even) noticeably smaller - at the backend's discretion. This especially may happen when further model training ceases to provide any improvements. If the budget is set to a value known to be insufficient to train a model for the given dataset, the training won't be attempted and will error. The train budget must be between 1,000 and 72,000 milli node hours, inclusive.
int64 train_budget_milli_node_hours = 7;
| Type | Description |
AutoMlTablesInputs.Builder | This builder for chaining. |
clearTransformations()
public AutoMlTablesInputs.Builder clearTransformations()Each transformation will apply transform function to given input column. And the result will be used for training. When creating transformation for BigQuery Struct column, the column should be flattened using "." as the delimiter.
repeated .google.cloud.aiplatform.v1.schema.trainingjob.definition.AutoMlTablesInputs.Transformation transformations = 3;
| Type | Description |
AutoMlTablesInputs.Builder |
clearWeightColumnName()
public AutoMlTablesInputs.Builder clearWeightColumnName()Column name that should be used as the weight column. Higher values in this column give more importance to the row during model training. The column must have numeric values between 0 and 10000 inclusively; 0 means the row is ignored for training. If weight column field is not set, then all rows are assumed to have equal weight of 1.
string weight_column_name = 9;
| Type | Description |
AutoMlTablesInputs.Builder | This builder for chaining. |
clone()
public AutoMlTablesInputs.Builder clone()| Type | Description |
AutoMlTablesInputs.Builder |
getAdditionalExperiments(int index)
public String getAdditionalExperiments(int index)Additional experiment flags for the Tables training pipeline.
repeated string additional_experiments = 11;
| Name | Description |
index | intThe index of the element to return. |
| Type | Description |
String | The additionalExperiments at the given index. |
getAdditionalExperimentsBytes(int index)
public ByteString getAdditionalExperimentsBytes(int index)Additional experiment flags for the Tables training pipeline.
repeated string additional_experiments = 11;
| Name | Description |
index | intThe index of the value to return. |
| Type | Description |
ByteString | The bytes of the additionalExperiments at the given index. |
getAdditionalExperimentsCount()
public int getAdditionalExperimentsCount()Additional experiment flags for the Tables training pipeline.
repeated string additional_experiments = 11;
| Type | Description |
int | The count of additionalExperiments. |
getAdditionalExperimentsList()
public ProtocolStringList getAdditionalExperimentsList()Additional experiment flags for the Tables training pipeline.
repeated string additional_experiments = 11;
| Type | Description |
ProtocolStringList | A list containing the additionalExperiments. |
getAdditionalOptimizationObjectiveConfigCase()
public AutoMlTablesInputs.AdditionalOptimizationObjectiveConfigCase getAdditionalOptimizationObjectiveConfigCase()| Type | Description |
AutoMlTablesInputs.AdditionalOptimizationObjectiveConfigCase |
getDefaultInstanceForType()
public AutoMlTablesInputs getDefaultInstanceForType()| Type | Description |
AutoMlTablesInputs |
getDescriptorForType()
public Descriptors.Descriptor getDescriptorForType()| Type | Description |
Descriptor |
getDisableEarlyStopping()
public boolean getDisableEarlyStopping()Use the entire training budget. This disables the early stopping feature. By default, the early stopping feature is enabled, which means that AutoML Tables might stop training before the entire training budget has been used.
bool disable_early_stopping = 8;
| Type | Description |
boolean | The disableEarlyStopping. |
getExportEvaluatedDataItemsConfig()
public ExportEvaluatedDataItemsConfig getExportEvaluatedDataItemsConfig()Configuration for exporting test set predictions to a BigQuery table. If this configuration is absent, then the export is not performed.
.google.cloud.aiplatform.v1.schema.trainingjob.definition.ExportEvaluatedDataItemsConfig export_evaluated_data_items_config = 10;
| Type | Description |
ExportEvaluatedDataItemsConfig | The exportEvaluatedDataItemsConfig. |
getExportEvaluatedDataItemsConfigBuilder()
public ExportEvaluatedDataItemsConfig.Builder getExportEvaluatedDataItemsConfigBuilder()Configuration for exporting test set predictions to a BigQuery table. If this configuration is absent, then the export is not performed.
.google.cloud.aiplatform.v1.schema.trainingjob.definition.ExportEvaluatedDataItemsConfig export_evaluated_data_items_config = 10;
| Type | Description |
ExportEvaluatedDataItemsConfig.Builder |
getExportEvaluatedDataItemsConfigOrBuilder()
public ExportEvaluatedDataItemsConfigOrBuilder getExportEvaluatedDataItemsConfigOrBuilder()Configuration for exporting test set predictions to a BigQuery table. If this configuration is absent, then the export is not performed.
.google.cloud.aiplatform.v1.schema.trainingjob.definition.ExportEvaluatedDataItemsConfig export_evaluated_data_items_config = 10;
| Type | Description |
ExportEvaluatedDataItemsConfigOrBuilder |
getOptimizationObjective()
public String getOptimizationObjective()Objective function the model is optimizing towards. The training process creates a model that maximizes/minimizes the value of the objective function over the validation set. The supported optimization objectives depend on the prediction type. If the field is not set, a default objective function is used. classification (binary): "maximize-au-roc" (default) - Maximize the area under the receiver operating characteristic (ROC) curve. "minimize-log-loss" - Minimize log loss. "maximize-au-prc" - Maximize the area under the precision-recall curve. "maximize-precision-at-recall" - Maximize precision for a specified recall value. "maximize-recall-at-precision" - Maximize recall for a specified precision value. classification (multi-class): "minimize-log-loss" (default) - Minimize log loss. regression: "minimize-rmse" (default) - Minimize root-mean-squared error (RMSE). "minimize-mae" - Minimize mean-absolute error (MAE). "minimize-rmsle" - Minimize root-mean-squared log error (RMSLE).
string optimization_objective = 4;
| 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;
| Type | Description |
ByteString | The bytes for optimizationObjective. |
getOptimizationObjectivePrecisionValue()
public float getOptimizationObjectivePrecisionValue()Required when optimization_objective is "maximize-recall-at-precision". Must be between 0 and 1, inclusive.
float optimization_objective_precision_value = 6;
| Type | Description |
float | The optimizationObjectivePrecisionValue. |
getOptimizationObjectiveRecallValue()
public float getOptimizationObjectiveRecallValue()Required when optimization_objective is "maximize-precision-at-recall". Must be between 0 and 1, inclusive.
float optimization_objective_recall_value = 5;
| Type | Description |
float | The optimizationObjectiveRecallValue. |
getPredictionType()
public String getPredictionType()The type of prediction the Model is to produce. "classification" - Predict one out of multiple target values is picked for each row. "regression" - Predict a value based on its relation to other values. This type is available only to columns that contain semantically numeric values, i.e. integers or floating point number, even if stored as e.g. strings.
string prediction_type = 1;
| Type | Description |
String | The predictionType. |
getPredictionTypeBytes()
public ByteString getPredictionTypeBytes()The type of prediction the Model is to produce. "classification" - Predict one out of multiple target values is picked for each row. "regression" - Predict a value based on its relation to other values. This type is available only to columns that contain semantically numeric values, i.e. integers or floating point number, even if stored as e.g. strings.
string prediction_type = 1;
| Type | Description |
ByteString | The bytes for predictionType. |
getTargetColumn()
public String getTargetColumn()The column name of the target column that the model is to predict.
string target_column = 2;
| Type | Description |
String | The targetColumn. |
getTargetColumnBytes()
public ByteString getTargetColumnBytes()The column name of the target column that the model is to predict.
string target_column = 2;
| Type | Description |
ByteString | The bytes for targetColumn. |
getTrainBudgetMilliNodeHours()
public long getTrainBudgetMilliNodeHours()Required. The train budget of creating this model, expressed in milli node hours i.e. 1,000 value in this field means 1 node hour. The training cost of the model will not exceed this budget. The final cost will be attempted to be close to the budget, though may end up being (even) noticeably smaller - at the backend's discretion. This especially may happen when further model training ceases to provide any improvements. If the budget is set to a value known to be insufficient to train a model for the given dataset, the training won't be attempted and will error. The train budget must be between 1,000 and 72,000 milli node hours, inclusive.
int64 train_budget_milli_node_hours = 7;
| Type | Description |
long | The trainBudgetMilliNodeHours. |
getTransformations(int index)
public AutoMlTablesInputs.Transformation getTransformations(int index)Each transformation will apply transform function to given input column. And the result will be used for training. When creating transformation for BigQuery Struct column, the column should be flattened using "." as the delimiter.
repeated .google.cloud.aiplatform.v1.schema.trainingjob.definition.AutoMlTablesInputs.Transformation transformations = 3;
| Name | Description |
index | int |
| Type | Description |
AutoMlTablesInputs.Transformation |
getTransformationsBuilder(int index)
public AutoMlTablesInputs.Transformation.Builder getTransformationsBuilder(int index)Each transformation will apply transform function to given input column. And the result will be used for training. When creating transformation for BigQuery Struct column, the column should be flattened using "." as the delimiter.
repeated .google.cloud.aiplatform.v1.schema.trainingjob.definition.AutoMlTablesInputs.Transformation transformations = 3;
| Name | Description |
index | int |
| Type | Description |
AutoMlTablesInputs.Transformation.Builder |
getTransformationsBuilderList()
public List<AutoMlTablesInputs.Transformation.Builder> getTransformationsBuilderList()Each transformation will apply transform function to given input column. And the result will be used for training. When creating transformation for BigQuery Struct column, the column should be flattened using "." as the delimiter.
repeated .google.cloud.aiplatform.v1.schema.trainingjob.definition.AutoMlTablesInputs.Transformation transformations = 3;
| Type | Description |
List<Builder> |
getTransformationsCount()
public int getTransformationsCount()Each transformation will apply transform function to given input column. And the result will be used for training. When creating transformation for BigQuery Struct column, the column should be flattened using "." as the delimiter.
repeated .google.cloud.aiplatform.v1.schema.trainingjob.definition.AutoMlTablesInputs.Transformation transformations = 3;
| Type | Description |
int |
getTransformationsList()
public List<AutoMlTablesInputs.Transformation> getTransformationsList()Each transformation will apply transform function to given input column. And the result will be used for training. When creating transformation for BigQuery Struct column, the column should be flattened using "." as the delimiter.
repeated .google.cloud.aiplatform.v1.schema.trainingjob.definition.AutoMlTablesInputs.Transformation transformations = 3;
| Type | Description |
List<Transformation> |
getTransformationsOrBuilder(int index)
public AutoMlTablesInputs.TransformationOrBuilder getTransformationsOrBuilder(int index)Each transformation will apply transform function to given input column. And the result will be used for training. When creating transformation for BigQuery Struct column, the column should be flattened using "." as the delimiter.
repeated .google.cloud.aiplatform.v1.schema.trainingjob.definition.AutoMlTablesInputs.Transformation transformations = 3;
| Name | Description |
index | int |
| Type | Description |
AutoMlTablesInputs.TransformationOrBuilder |
getTransformationsOrBuilderList()
public List<? extends AutoMlTablesInputs.TransformationOrBuilder> getTransformationsOrBuilderList()Each transformation will apply transform function to given input column. And the result will be used for training. When creating transformation for BigQuery Struct column, the column should be flattened using "." as the delimiter.
repeated .google.cloud.aiplatform.v1.schema.trainingjob.definition.AutoMlTablesInputs.Transformation transformations = 3;
| Type | Description |
List<? extends com.google.cloud.aiplatform.v1.schema.trainingjob.definition.AutoMlTablesInputs.TransformationOrBuilder> |
getWeightColumnName()
public String getWeightColumnName()Column name that should be used as the weight column. Higher values in this column give more importance to the row during model training. The column must have numeric values between 0 and 10000 inclusively; 0 means the row is ignored for training. If weight column field is not set, then all rows are assumed to have equal weight of 1.
string weight_column_name = 9;
| Type | Description |
String | The weightColumnName. |
getWeightColumnNameBytes()
public ByteString getWeightColumnNameBytes()Column name that should be used as the weight column. Higher values in this column give more importance to the row during model training. The column must have numeric values between 0 and 10000 inclusively; 0 means the row is ignored for training. If weight column field is not set, then all rows are assumed to have equal weight of 1.
string weight_column_name = 9;
| Type | Description |
ByteString | The bytes for weightColumnName. |
hasExportEvaluatedDataItemsConfig()
public boolean hasExportEvaluatedDataItemsConfig()Configuration for exporting test set predictions to a BigQuery table. If this configuration is absent, then the export is not performed.
.google.cloud.aiplatform.v1.schema.trainingjob.definition.ExportEvaluatedDataItemsConfig export_evaluated_data_items_config = 10;
| Type | Description |
boolean | Whether the exportEvaluatedDataItemsConfig field is set. |
hasOptimizationObjectivePrecisionValue()
public boolean hasOptimizationObjectivePrecisionValue()Required when optimization_objective is "maximize-recall-at-precision". Must be between 0 and 1, inclusive.
float optimization_objective_precision_value = 6;
| Type | Description |
boolean | Whether the optimizationObjectivePrecisionValue field is set. |
hasOptimizationObjectiveRecallValue()
public boolean hasOptimizationObjectiveRecallValue()Required when optimization_objective is "maximize-precision-at-recall". Must be between 0 and 1, inclusive.
float optimization_objective_recall_value = 5;
| Type | Description |
boolean | Whether the optimizationObjectiveRecallValue field is set. |
internalGetFieldAccessorTable()
protected GeneratedMessageV3.FieldAccessorTable internalGetFieldAccessorTable()| Type | Description |
FieldAccessorTable |
isInitialized()
public final boolean isInitialized()| Type | Description |
boolean |
mergeExportEvaluatedDataItemsConfig(ExportEvaluatedDataItemsConfig value)
public AutoMlTablesInputs.Builder mergeExportEvaluatedDataItemsConfig(ExportEvaluatedDataItemsConfig value)Configuration for exporting test set predictions to a BigQuery table. If this configuration is absent, then the export is not performed.
.google.cloud.aiplatform.v1.schema.trainingjob.definition.ExportEvaluatedDataItemsConfig export_evaluated_data_items_config = 10;
| Name | Description |
value | ExportEvaluatedDataItemsConfig |
| Type | Description |
AutoMlTablesInputs.Builder |
mergeFrom(AutoMlTablesInputs other)
public AutoMlTablesInputs.Builder mergeFrom(AutoMlTablesInputs other)| Name | Description |
other | AutoMlTablesInputs |
| Type | Description |
AutoMlTablesInputs.Builder |
mergeFrom(CodedInputStream input, ExtensionRegistryLite extensionRegistry)
public AutoMlTablesInputs.Builder mergeFrom(CodedInputStream input, ExtensionRegistryLite extensionRegistry)| Name | Description |
input | CodedInputStream |
extensionRegistry | ExtensionRegistryLite |
| Type | Description |
AutoMlTablesInputs.Builder |
| Type | Description |
IOException |
mergeFrom(Message other)
public AutoMlTablesInputs.Builder mergeFrom(Message other)| Name | Description |
other | Message |
| Type | Description |
AutoMlTablesInputs.Builder |
mergeUnknownFields(UnknownFieldSet unknownFields)
public final AutoMlTablesInputs.Builder mergeUnknownFields(UnknownFieldSet unknownFields)| Name | Description |
unknownFields | UnknownFieldSet |
| Type | Description |
AutoMlTablesInputs.Builder |
removeTransformations(int index)
public AutoMlTablesInputs.Builder removeTransformations(int index)Each transformation will apply transform function to given input column. And the result will be used for training. When creating transformation for BigQuery Struct column, the column should be flattened using "." as the delimiter.
repeated .google.cloud.aiplatform.v1.schema.trainingjob.definition.AutoMlTablesInputs.Transformation transformations = 3;
| Name | Description |
index | int |
| Type | Description |
AutoMlTablesInputs.Builder |
setAdditionalExperiments(int index, String value)
public AutoMlTablesInputs.Builder setAdditionalExperiments(int index, String value)Additional experiment flags for the Tables training pipeline.
repeated string additional_experiments = 11;
| Name | Description |
index | intThe index to set the value at. |
value | StringThe additionalExperiments to set. |
| Type | Description |
AutoMlTablesInputs.Builder | This builder for chaining. |
setDisableEarlyStopping(boolean value)
public AutoMlTablesInputs.Builder setDisableEarlyStopping(boolean value)Use the entire training budget. This disables the early stopping feature. By default, the early stopping feature is enabled, which means that AutoML Tables might stop training before the entire training budget has been used.
bool disable_early_stopping = 8;
| Name | Description |
value | booleanThe disableEarlyStopping to set. |
| Type | Description |
AutoMlTablesInputs.Builder | This builder for chaining. |
setExportEvaluatedDataItemsConfig(ExportEvaluatedDataItemsConfig value)
public AutoMlTablesInputs.Builder setExportEvaluatedDataItemsConfig(ExportEvaluatedDataItemsConfig value)Configuration for exporting test set predictions to a BigQuery table. If this configuration is absent, then the export is not performed.
.google.cloud.aiplatform.v1.schema.trainingjob.definition.ExportEvaluatedDataItemsConfig export_evaluated_data_items_config = 10;
| Name | Description |
value | ExportEvaluatedDataItemsConfig |
| Type | Description |
AutoMlTablesInputs.Builder |
setExportEvaluatedDataItemsConfig(ExportEvaluatedDataItemsConfig.Builder builderForValue)
public AutoMlTablesInputs.Builder setExportEvaluatedDataItemsConfig(ExportEvaluatedDataItemsConfig.Builder builderForValue)Configuration for exporting test set predictions to a BigQuery table. If this configuration is absent, then the export is not performed.
.google.cloud.aiplatform.v1.schema.trainingjob.definition.ExportEvaluatedDataItemsConfig export_evaluated_data_items_config = 10;
| Name | Description |
builderForValue | ExportEvaluatedDataItemsConfig.Builder |
| Type | Description |
AutoMlTablesInputs.Builder |
setField(Descriptors.FieldDescriptor field, Object value)
public AutoMlTablesInputs.Builder setField(Descriptors.FieldDescriptor field, Object value)| Name | Description |
field | FieldDescriptor |
value | Object |
| Type | Description |
AutoMlTablesInputs.Builder |
setOptimizationObjective(String value)
public AutoMlTablesInputs.Builder setOptimizationObjective(String value)Objective function the model is optimizing towards. The training process creates a model that maximizes/minimizes the value of the objective function over the validation set. The supported optimization objectives depend on the prediction type. If the field is not set, a default objective function is used. classification (binary): "maximize-au-roc" (default) - Maximize the area under the receiver operating characteristic (ROC) curve. "minimize-log-loss" - Minimize log loss. "maximize-au-prc" - Maximize the area under the precision-recall curve. "maximize-precision-at-recall" - Maximize precision for a specified recall value. "maximize-recall-at-precision" - Maximize recall for a specified precision value. classification (multi-class): "minimize-log-loss" (default) - Minimize log loss. regression: "minimize-rmse" (default) - Minimize root-mean-squared error (RMSE). "minimize-mae" - Minimize mean-absolute error (MAE). "minimize-rmsle" - Minimize root-mean-squared log error (RMSLE).
string optimization_objective = 4;
| Name | Description |
value | StringThe optimizationObjective to set. |
| Type | Description |
AutoMlTablesInputs.Builder | This builder for chaining. |
setOptimizationObjectiveBytes(ByteString value)
public AutoMlTablesInputs.Builder setOptimizationObjectiveBytes(ByteString value)Objective function the model is optimizing towards. The training process creates a model that maximizes/minimizes the value of the objective function over the validation set. The supported optimization objectives depend on the prediction type. If the field is not set, a default objective function is used. classification (binary): "maximize-au-roc" (default) - Maximize the area under the receiver operating characteristic (ROC) curve. "minimize-log-loss" - Minimize log loss. "maximize-au-prc" - Maximize the area under the precision-recall curve. "maximize-precision-at-recall" - Maximize precision for a specified recall value. "maximize-recall-at-precision" - Maximize recall for a specified precision value. classification (multi-class): "minimize-log-loss" (default) - Minimize log loss. regression: "minimize-rmse" (default) - Minimize root-mean-squared error (RMSE). "minimize-mae" - Minimize mean-absolute error (MAE). "minimize-rmsle" - Minimize root-mean-squared log error (RMSLE).
string optimization_objective = 4;
| Name | Description |
value | ByteStringThe bytes for optimizationObjective to set. |
| Type | Description |
AutoMlTablesInputs.Builder | This builder for chaining. |
setOptimizationObjectivePrecisionValue(float value)
public AutoMlTablesInputs.Builder setOptimizationObjectivePrecisionValue(float value)Required when optimization_objective is "maximize-recall-at-precision". Must be between 0 and 1, inclusive.
float optimization_objective_precision_value = 6;
| Name | Description |
value | floatThe optimizationObjectivePrecisionValue to set. |
| Type | Description |
AutoMlTablesInputs.Builder | This builder for chaining. |
setOptimizationObjectiveRecallValue(float value)
public AutoMlTablesInputs.Builder setOptimizationObjectiveRecallValue(float value)Required when optimization_objective is "maximize-precision-at-recall". Must be between 0 and 1, inclusive.
float optimization_objective_recall_value = 5;
| Name | Description |
value | floatThe optimizationObjectiveRecallValue to set. |
| Type | Description |
AutoMlTablesInputs.Builder | This builder for chaining. |
setPredictionType(String value)
public AutoMlTablesInputs.Builder setPredictionType(String value)The type of prediction the Model is to produce. "classification" - Predict one out of multiple target values is picked for each row. "regression" - Predict a value based on its relation to other values. This type is available only to columns that contain semantically numeric values, i.e. integers or floating point number, even if stored as e.g. strings.
string prediction_type = 1;
| Name | Description |
value | StringThe predictionType to set. |
| Type | Description |
AutoMlTablesInputs.Builder | This builder for chaining. |
setPredictionTypeBytes(ByteString value)
public AutoMlTablesInputs.Builder setPredictionTypeBytes(ByteString value)The type of prediction the Model is to produce. "classification" - Predict one out of multiple target values is picked for each row. "regression" - Predict a value based on its relation to other values. This type is available only to columns that contain semantically numeric values, i.e. integers or floating point number, even if stored as e.g. strings.
string prediction_type = 1;
| Name | Description |
value | ByteStringThe bytes for predictionType to set. |
| Type | Description |
AutoMlTablesInputs.Builder | This builder for chaining. |
setRepeatedField(Descriptors.FieldDescriptor field, int index, Object value)
public AutoMlTablesInputs.Builder setRepeatedField(Descriptors.FieldDescriptor field, int index, Object value)| Name | Description |
field | FieldDescriptor |
index | int |
value | Object |
| Type | Description |
AutoMlTablesInputs.Builder |
setTargetColumn(String value)
public AutoMlTablesInputs.Builder setTargetColumn(String value)The column name of the target column that the model is to predict.
string target_column = 2;
| Name | Description |
value | StringThe targetColumn to set. |
| Type | Description |
AutoMlTablesInputs.Builder | This builder for chaining. |
setTargetColumnBytes(ByteString value)
public AutoMlTablesInputs.Builder setTargetColumnBytes(ByteString value)The column name of the target column that the model is to predict.
string target_column = 2;
| Name | Description |
value | ByteStringThe bytes for targetColumn to set. |
| Type | Description |
AutoMlTablesInputs.Builder | This builder for chaining. |
setTrainBudgetMilliNodeHours(long value)
public AutoMlTablesInputs.Builder setTrainBudgetMilliNodeHours(long value)Required. The train budget of creating this model, expressed in milli node hours i.e. 1,000 value in this field means 1 node hour. The training cost of the model will not exceed this budget. The final cost will be attempted to be close to the budget, though may end up being (even) noticeably smaller - at the backend's discretion. This especially may happen when further model training ceases to provide any improvements. If the budget is set to a value known to be insufficient to train a model for the given dataset, the training won't be attempted and will error. The train budget must be between 1,000 and 72,000 milli node hours, inclusive.
int64 train_budget_milli_node_hours = 7;
| Name | Description |
value | longThe trainBudgetMilliNodeHours to set. |
| Type | Description |
AutoMlTablesInputs.Builder | This builder for chaining. |
setTransformations(int index, AutoMlTablesInputs.Transformation value)
public AutoMlTablesInputs.Builder setTransformations(int index, AutoMlTablesInputs.Transformation value)Each transformation will apply transform function to given input column. And the result will be used for training. When creating transformation for BigQuery Struct column, the column should be flattened using "." as the delimiter.
repeated .google.cloud.aiplatform.v1.schema.trainingjob.definition.AutoMlTablesInputs.Transformation transformations = 3;
| Name | Description |
index | int |
value | AutoMlTablesInputs.Transformation |
| Type | Description |
AutoMlTablesInputs.Builder |
setTransformations(int index, AutoMlTablesInputs.Transformation.Builder builderForValue)
public AutoMlTablesInputs.Builder setTransformations(int index, AutoMlTablesInputs.Transformation.Builder builderForValue)Each transformation will apply transform function to given input column. And the result will be used for training. When creating transformation for BigQuery Struct column, the column should be flattened using "." as the delimiter.
repeated .google.cloud.aiplatform.v1.schema.trainingjob.definition.AutoMlTablesInputs.Transformation transformations = 3;
| Name | Description |
index | int |
builderForValue | AutoMlTablesInputs.Transformation.Builder |
| Type | Description |
AutoMlTablesInputs.Builder |
setUnknownFields(UnknownFieldSet unknownFields)
public final AutoMlTablesInputs.Builder setUnknownFields(UnknownFieldSet unknownFields)| Name | Description |
unknownFields | UnknownFieldSet |
| Type | Description |
AutoMlTablesInputs.Builder |
setWeightColumnName(String value)
public AutoMlTablesInputs.Builder setWeightColumnName(String value)Column name that should be used as the weight column. Higher values in this column give more importance to the row during model training. The column must have numeric values between 0 and 10000 inclusively; 0 means the row is ignored for training. If weight column field is not set, then all rows are assumed to have equal weight of 1.
string weight_column_name = 9;
| Name | Description |
value | StringThe weightColumnName to set. |
| Type | Description |
AutoMlTablesInputs.Builder | This builder for chaining. |
setWeightColumnNameBytes(ByteString value)
public AutoMlTablesInputs.Builder setWeightColumnNameBytes(ByteString value)Column name that should be used as the weight column. Higher values in this column give more importance to the row during model training. The column must have numeric values between 0 and 10000 inclusively; 0 means the row is ignored for training. If weight column field is not set, then all rows are assumed to have equal weight of 1.
string weight_column_name = 9;
| Name | Description |
value | ByteStringThe bytes for weightColumnName to set. |
| Type | Description |
AutoMlTablesInputs.Builder | This builder for chaining. |