Cloud AI Platform v1 API - Class AutoMlTablesInputs (2.23.0) 

public sealed class AutoMlTablesInputs : IMessage<AutoMlTablesInputs>, IEquatable<AutoMlTablesInputs>, IDeepCloneable<AutoMlTablesInputs>, IBufferMessage, IMessage

Inheritance

object > AutoMlTablesInputs

Namespace

Google.Cloud.AIPlatform.V1.Schema.TrainingJob.Definition

Assembly

Google.Cloud.AIPlatform.V1.dll

Constructors

AutoMlTablesInputs()

public AutoMlTablesInputs()

AutoMlTablesInputs(AutoMlTablesInputs)

public AutoMlTablesInputs(AutoMlTablesInputs other)
Parameter
NameDescription
otherAutoMlTablesInputs

Properties

AdditionalExperiments

public RepeatedField<string> AdditionalExperiments { get; }

Additional experiment flags for the Tables training pipeline.

Property Value
TypeDescription
RepeatedFieldstring

AdditionalOptimizationObjectiveConfigCase

public AutoMlTablesInputs.AdditionalOptimizationObjectiveConfigOneofCase AdditionalOptimizationObjectiveConfigCase { get; }
Property Value
TypeDescription
AutoMlTablesInputsAdditionalOptimizationObjectiveConfigOneofCase

DisableEarlyStopping

public bool DisableEarlyStopping { get; set; }

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.

Property Value
TypeDescription
bool

ExportEvaluatedDataItemsConfig

public ExportEvaluatedDataItemsConfig ExportEvaluatedDataItemsConfig { get; set; }

Configuration for exporting test set predictions to a BigQuery table. If this configuration is absent, then the export is not performed.

Property Value
TypeDescription
ExportEvaluatedDataItemsConfig

HasOptimizationObjectivePrecisionValue

public bool HasOptimizationObjectivePrecisionValue { get; }

Gets whether the "optimization_objective_precision_value" field is set

Property Value
TypeDescription
bool

HasOptimizationObjectiveRecallValue

public bool HasOptimizationObjectiveRecallValue { get; }

Gets whether the "optimization_objective_recall_value" field is set

Property Value
TypeDescription
bool

OptimizationObjective

public string OptimizationObjective { get; set; }

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).

Property Value
TypeDescription
string

OptimizationObjectivePrecisionValue

public float OptimizationObjectivePrecisionValue { get; set; }

Required when optimization_objective is "maximize-recall-at-precision". Must be between 0 and 1, inclusive.

Property Value
TypeDescription
float

OptimizationObjectiveRecallValue

public float OptimizationObjectiveRecallValue { get; set; }

Required when optimization_objective is "maximize-precision-at-recall". Must be between 0 and 1, inclusive.

Property Value
TypeDescription
float

PredictionType

public string PredictionType { get; set; }

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.

Property Value
TypeDescription
string

TargetColumn

public string TargetColumn { get; set; }

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

Property Value
TypeDescription
string

TrainBudgetMilliNodeHours

public long TrainBudgetMilliNodeHours { get; set; }

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.

Property Value
TypeDescription
long

Transformations

public RepeatedField<AutoMlTablesInputs.Types.Transformation> Transformations { get; }

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.

Property Value
TypeDescription
RepeatedFieldAutoMlTablesInputsTypesTransformation

WeightColumnName

public string WeightColumnName { get; set; }

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.

Property Value
TypeDescription
string