Class AutoMlTablesInputs (3.20.0)

public final class AutoMlTablesInputs extends GeneratedMessageV3 implements AutoMlTablesInputsOrBuilder

Protobuf type google.cloud.aiplatform.v1.schema.trainingjob.definition.AutoMlTablesInputs

Static Fields

ADDITIONAL_EXPERIMENTS_FIELD_NUMBER

public static final int ADDITIONAL_EXPERIMENTS_FIELD_NUMBER
Field Value
TypeDescription
int

DISABLE_EARLY_STOPPING_FIELD_NUMBER

public static final int DISABLE_EARLY_STOPPING_FIELD_NUMBER
Field Value
TypeDescription
int

EXPORT_EVALUATED_DATA_ITEMS_CONFIG_FIELD_NUMBER

public static final int EXPORT_EVALUATED_DATA_ITEMS_CONFIG_FIELD_NUMBER
Field Value
TypeDescription
int

OPTIMIZATION_OBJECTIVE_FIELD_NUMBER

public static final int OPTIMIZATION_OBJECTIVE_FIELD_NUMBER
Field Value
TypeDescription
int

OPTIMIZATION_OBJECTIVE_PRECISION_VALUE_FIELD_NUMBER

public static final int OPTIMIZATION_OBJECTIVE_PRECISION_VALUE_FIELD_NUMBER
Field Value
TypeDescription
int

OPTIMIZATION_OBJECTIVE_RECALL_VALUE_FIELD_NUMBER

public static final int OPTIMIZATION_OBJECTIVE_RECALL_VALUE_FIELD_NUMBER
Field Value
TypeDescription
int

PREDICTION_TYPE_FIELD_NUMBER

public static final int PREDICTION_TYPE_FIELD_NUMBER
Field Value
TypeDescription
int

TARGET_COLUMN_FIELD_NUMBER

public static final int TARGET_COLUMN_FIELD_NUMBER
Field Value
TypeDescription
int

TRAIN_BUDGET_MILLI_NODE_HOURS_FIELD_NUMBER

public static final int TRAIN_BUDGET_MILLI_NODE_HOURS_FIELD_NUMBER
Field Value
TypeDescription
int

TRANSFORMATIONS_FIELD_NUMBER

public static final int TRANSFORMATIONS_FIELD_NUMBER
Field Value
TypeDescription
int

WEIGHT_COLUMN_NAME_FIELD_NUMBER

public static final int WEIGHT_COLUMN_NAME_FIELD_NUMBER
Field Value
TypeDescription
int

Static Methods

getDefaultInstance()

public static AutoMlTablesInputs getDefaultInstance()
Returns
TypeDescription
AutoMlTablesInputs

getDescriptor()

public static final Descriptors.Descriptor getDescriptor()
Returns
TypeDescription
Descriptor

newBuilder()

public static AutoMlTablesInputs.Builder newBuilder()
Returns
TypeDescription
AutoMlTablesInputs.Builder

newBuilder(AutoMlTablesInputs prototype)

public static AutoMlTablesInputs.Builder newBuilder(AutoMlTablesInputs prototype)
Parameter
NameDescription
prototypeAutoMlTablesInputs
Returns
TypeDescription
AutoMlTablesInputs.Builder

parseDelimitedFrom(InputStream input)

public static AutoMlTablesInputs parseDelimitedFrom(InputStream input)
Parameter
NameDescription
inputInputStream
Returns
TypeDescription
AutoMlTablesInputs
Exceptions
TypeDescription
IOException

parseDelimitedFrom(InputStream input, ExtensionRegistryLite extensionRegistry)

public static AutoMlTablesInputs parseDelimitedFrom(InputStream input, ExtensionRegistryLite extensionRegistry)
Parameters
NameDescription
inputInputStream
extensionRegistryExtensionRegistryLite
Returns
TypeDescription
AutoMlTablesInputs
Exceptions
TypeDescription
IOException

parseFrom(byte[] data)

public static AutoMlTablesInputs parseFrom(byte[] data)
Parameter
NameDescription
databyte[]
Returns
TypeDescription
AutoMlTablesInputs
Exceptions
TypeDescription
InvalidProtocolBufferException

parseFrom(byte[] data, ExtensionRegistryLite extensionRegistry)

public static AutoMlTablesInputs parseFrom(byte[] data, ExtensionRegistryLite extensionRegistry)
Parameters
NameDescription
databyte[]
extensionRegistryExtensionRegistryLite
Returns
TypeDescription
AutoMlTablesInputs
Exceptions
TypeDescription
InvalidProtocolBufferException

parseFrom(ByteString data)

public static AutoMlTablesInputs parseFrom(ByteString data)
Parameter
NameDescription
dataByteString
Returns
TypeDescription
AutoMlTablesInputs
Exceptions
TypeDescription
InvalidProtocolBufferException

parseFrom(ByteString data, ExtensionRegistryLite extensionRegistry)

public static AutoMlTablesInputs parseFrom(ByteString data, ExtensionRegistryLite extensionRegistry)
Parameters
NameDescription
dataByteString
extensionRegistryExtensionRegistryLite
Returns
TypeDescription
AutoMlTablesInputs
Exceptions
TypeDescription
InvalidProtocolBufferException

parseFrom(CodedInputStream input)

public static AutoMlTablesInputs parseFrom(CodedInputStream input)
Parameter
NameDescription
inputCodedInputStream
Returns
TypeDescription
AutoMlTablesInputs
Exceptions
TypeDescription
IOException

parseFrom(CodedInputStream input, ExtensionRegistryLite extensionRegistry)

public static AutoMlTablesInputs parseFrom(CodedInputStream input, ExtensionRegistryLite extensionRegistry)
Parameters
NameDescription
inputCodedInputStream
extensionRegistryExtensionRegistryLite
Returns
TypeDescription
AutoMlTablesInputs
Exceptions
TypeDescription
IOException

parseFrom(InputStream input)

public static AutoMlTablesInputs parseFrom(InputStream input)
Parameter
NameDescription
inputInputStream
Returns
TypeDescription
AutoMlTablesInputs
Exceptions
TypeDescription
IOException

parseFrom(InputStream input, ExtensionRegistryLite extensionRegistry)

public static AutoMlTablesInputs parseFrom(InputStream input, ExtensionRegistryLite extensionRegistry)
Parameters
NameDescription
inputInputStream
extensionRegistryExtensionRegistryLite
Returns
TypeDescription
AutoMlTablesInputs
Exceptions
TypeDescription
IOException

parseFrom(ByteBuffer data)

public static AutoMlTablesInputs parseFrom(ByteBuffer data)
Parameter
NameDescription
dataByteBuffer
Returns
TypeDescription
AutoMlTablesInputs
Exceptions
TypeDescription
InvalidProtocolBufferException

parseFrom(ByteBuffer data, ExtensionRegistryLite extensionRegistry)

public static AutoMlTablesInputs parseFrom(ByteBuffer data, ExtensionRegistryLite extensionRegistry)
Parameters
NameDescription
dataByteBuffer
extensionRegistryExtensionRegistryLite
Returns
TypeDescription
AutoMlTablesInputs
Exceptions
TypeDescription
InvalidProtocolBufferException

parser()

public static Parser<AutoMlTablesInputs> parser()
Returns
TypeDescription
Parser<AutoMlTablesInputs>

Methods

equals(Object obj)

public boolean equals(Object obj)
Parameter
NameDescription
objObject
Returns
TypeDescription
boolean
Overrides

getAdditionalExperiments(int index)

public String getAdditionalExperiments(int index)

Additional experiment flags for the Tables training pipeline.

repeated string additional_experiments = 11;

Parameter
NameDescription
indexint

The index of the element to return.

Returns
TypeDescription
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;

Parameter
NameDescription
indexint

The index of the value to return.

Returns
TypeDescription
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;

Returns
TypeDescription
int

The count of additionalExperiments.

getAdditionalExperimentsList()

public ProtocolStringList getAdditionalExperimentsList()

Additional experiment flags for the Tables training pipeline.

repeated string additional_experiments = 11;

Returns
TypeDescription
ProtocolStringList

A list containing the additionalExperiments.

getAdditionalOptimizationObjectiveConfigCase()

public AutoMlTablesInputs.AdditionalOptimizationObjectiveConfigCase getAdditionalOptimizationObjectiveConfigCase()
Returns
TypeDescription
AutoMlTablesInputs.AdditionalOptimizationObjectiveConfigCase

getDefaultInstanceForType()

public AutoMlTablesInputs getDefaultInstanceForType()
Returns
TypeDescription
AutoMlTablesInputs

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;

Returns
TypeDescription
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;

Returns
TypeDescription
ExportEvaluatedDataItemsConfig

The exportEvaluatedDataItemsConfig.

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;

Returns
TypeDescription
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;

Returns
TypeDescription
String

The optimizationObjective.

getOptimizationObjectiveBytes()

public ByteString getOptimizationObjectiveBytes()

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

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

classification (binary): "maximize-au-roc" (default) - Maximize the area under the receiver operating characteristic (ROC) curve. "minimize-log-loss" - Minimize log loss. "maximize-au-prc" - Maximize the area under the precision-recall curve. "maximize-precision-at-recall" - Maximize precision for a specified recall value. "maximize-recall-at-precision" - Maximize recall for a specified precision value.

classification (multi-class): "minimize-log-loss" (default) - Minimize log loss.

regression: "minimize-rmse" (default) - Minimize root-mean-squared error (RMSE). "minimize-mae" - Minimize mean-absolute error (MAE). "minimize-rmsle" - Minimize root-mean-squared log error (RMSLE).

string optimization_objective = 4;

Returns
TypeDescription
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;

Returns
TypeDescription
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;

Returns
TypeDescription
float

The optimizationObjectiveRecallValue.

getParserForType()

public Parser<AutoMlTablesInputs> getParserForType()
Returns
TypeDescription
Parser<AutoMlTablesInputs>
Overrides

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;

Returns
TypeDescription
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;

Returns
TypeDescription
ByteString

The bytes for predictionType.

getSerializedSize()

public int getSerializedSize()
Returns
TypeDescription
int
Overrides

getTargetColumn()

public String getTargetColumn()

The column name of the target column that the model is to predict.

string target_column = 2;

Returns
TypeDescription
String

The targetColumn.

getTargetColumnBytes()

public ByteString getTargetColumnBytes()

The column name of the target column that the model is to predict.

string target_column = 2;

Returns
TypeDescription
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;

Returns
TypeDescription
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;

Parameter
NameDescription
indexint
Returns
TypeDescription
AutoMlTablesInputs.Transformation

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;

Returns
TypeDescription
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;

Returns
TypeDescription
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;

Parameter
NameDescription
indexint
Returns
TypeDescription
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;

Returns
TypeDescription
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;

Returns
TypeDescription
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;

Returns
TypeDescription
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;

Returns
TypeDescription
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;

Returns
TypeDescription
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;

Returns
TypeDescription
boolean

Whether the optimizationObjectiveRecallValue field is set.

hashCode()

public int hashCode()
Returns
TypeDescription
int
Overrides

internalGetFieldAccessorTable()

protected GeneratedMessageV3.FieldAccessorTable internalGetFieldAccessorTable()
Returns
TypeDescription
FieldAccessorTable
Overrides

isInitialized()

public final boolean isInitialized()
Returns
TypeDescription
boolean
Overrides

newBuilderForType()

public AutoMlTablesInputs.Builder newBuilderForType()
Returns
TypeDescription
AutoMlTablesInputs.Builder

newBuilderForType(GeneratedMessageV3.BuilderParent parent)

protected AutoMlTablesInputs.Builder newBuilderForType(GeneratedMessageV3.BuilderParent parent)
Parameter
NameDescription
parentBuilderParent
Returns
TypeDescription
AutoMlTablesInputs.Builder
Overrides

newInstance(GeneratedMessageV3.UnusedPrivateParameter unused)

protected Object newInstance(GeneratedMessageV3.UnusedPrivateParameter unused)
Parameter
NameDescription
unusedUnusedPrivateParameter
Returns
TypeDescription
Object
Overrides

toBuilder()

public AutoMlTablesInputs.Builder toBuilder()
Returns
TypeDescription
AutoMlTablesInputs.Builder

writeTo(CodedOutputStream output)

public void writeTo(CodedOutputStream output)
Parameter
NameDescription
outputCodedOutputStream
Overrides
Exceptions
TypeDescription
IOException