Class GoogleCloudAiplatformV1beta1SchemaTrainingjobDefinitionAutoMlForecastingInputs

Inheritance
object
GoogleCloudAiplatformV1beta1SchemaTrainingjobDefinitionAutoMlForecastingInputs
Implements
Inherited Members
Namespace: Google.Apis.Aiplatform.v1beta1.Data
Assembly: Google.Apis.Aiplatform.v1beta1.dll
Syntax
public class GoogleCloudAiplatformV1beta1SchemaTrainingjobDefinitionAutoMlForecastingInputs : IDirectResponseSchema

Properties

AdditionalExperiments

Additional experiment flags for the time series forcasting training.

Declaration
[JsonProperty("additionalExperiments")]
public virtual IList<string> AdditionalExperiments { get; set; }
Property Value
Type Description
IList<string>

AvailableAtForecastColumns

Names of columns that are available and provided when a forecast is requested. These columns contain information for the given entity (identified by the time_series_identifier_column column) that is known at forecast. For example, predicted weather for a specific day.

Declaration
[JsonProperty("availableAtForecastColumns")]
public virtual IList<string> AvailableAtForecastColumns { get; set; }
Property Value
Type Description
IList<string>

ContextWindow

The amount of time into the past training and prediction data is used for model training and prediction respectively. Expressed in number of units defined by the data_granularity field.

Declaration
[JsonProperty("contextWindow")]
public virtual long? ContextWindow { get; set; }
Property Value
Type Description
long?

DataGranularity

Expected difference in time granularity between rows in the data.

Declaration
[JsonProperty("dataGranularity")]
public virtual GoogleCloudAiplatformV1beta1SchemaTrainingjobDefinitionAutoMlForecastingInputsGranularity DataGranularity { get; set; }
Property Value
Type Description
GoogleCloudAiplatformV1beta1SchemaTrainingjobDefinitionAutoMlForecastingInputsGranularity

ETag

The ETag of the item.

Declaration
public virtual string ETag { get; set; }
Property Value
Type Description
string

EnableProbabilisticInference

If probabilistic inference is enabled, the model will fit a distribution that captures the uncertainty of a prediction. At inference time, the predictive distribution is used to make a point prediction that minimizes the optimization objective. For example, the mean of a predictive distribution is the point prediction that minimizes RMSE loss. If quantiles are specified, then the quantiles of the distribution are also returned. The optimization objective cannot be minimize-quantile-loss.

Declaration
[JsonProperty("enableProbabilisticInference")]
public virtual bool? EnableProbabilisticInference { get; set; }
Property Value
Type Description
bool?

ExportEvaluatedDataItemsConfig

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

Declaration
[JsonProperty("exportEvaluatedDataItemsConfig")]
public virtual GoogleCloudAiplatformV1beta1SchemaTrainingjobDefinitionExportEvaluatedDataItemsConfig ExportEvaluatedDataItemsConfig { get; set; }
Property Value
Type Description
GoogleCloudAiplatformV1beta1SchemaTrainingjobDefinitionExportEvaluatedDataItemsConfig

ForecastHorizon

The amount of time into the future for which forecasted values for the target are returned. Expressed in number of units defined by the data_granularity field.

Declaration
[JsonProperty("forecastHorizon")]
public virtual long? ForecastHorizon { get; set; }
Property Value
Type Description
long?

HierarchyConfig

Configuration that defines the hierarchical relationship of time series and parameters for hierarchical forecasting strategies.

Declaration
[JsonProperty("hierarchyConfig")]
public virtual GoogleCloudAiplatformV1beta1SchemaTrainingjobDefinitionHierarchyConfig HierarchyConfig { get; set; }
Property Value
Type Description
GoogleCloudAiplatformV1beta1SchemaTrainingjobDefinitionHierarchyConfig

HolidayRegions

The geographical region based on which the holiday effect is applied in modeling by adding holiday categorical array feature that include all holidays matching the date. This option only allowed when data_granularity is day. By default, holiday effect modeling is disabled. To turn it on, specify the holiday region using this option.

Declaration
[JsonProperty("holidayRegions")]
public virtual IList<string> HolidayRegions { get; set; }
Property Value
Type Description
IList<string>

OptimizationObjective

Objective function the model is optimizing towards. The training process creates a model that optimizes the value of the objective function over the validation set. The supported optimization objectives: * "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). * "minimize-rmspe" - Minimize root-mean-squared percentage error (RMSPE). * "minimize-wape-mae" - Minimize the combination of weighted absolute percentage error (WAPE) and mean-absolute-error (MAE). * "minimize-quantile-loss" - Minimize the quantile loss at the quantiles defined in quantiles. * "minimize-mape" - Minimize the mean absolute percentage error.

Declaration
[JsonProperty("optimizationObjective")]
public virtual string OptimizationObjective { get; set; }
Property Value
Type Description
string

Quantiles

Quantiles to use for minimize-quantile-loss optimization_objective, or for probabilistic inference. Up to 5 quantiles are allowed of values between 0 and 1, exclusive. Required if the value of optimization_objective is minimize-quantile-loss. Represents the percent quantiles to use for that objective. Quantiles must be unique.

Declaration
[JsonProperty("quantiles")]
public virtual IList<double?> Quantiles { get; set; }
Property Value
Type Description
IList<double?>

TargetColumn

The name of the column that the Model is to predict values for. This column must be unavailable at forecast.

Declaration
[JsonProperty("targetColumn")]
public virtual string TargetColumn { get; set; }
Property Value
Type Description
string

TimeColumn

The name of the column that identifies time order in the time series. This column must be available at forecast.

Declaration
[JsonProperty("timeColumn")]
public virtual string TimeColumn { get; set; }
Property Value
Type Description
string

TimeSeriesAttributeColumns

Column names that should be used as attribute columns. The value of these columns does not vary as a function of time. For example, store ID or item color.

Declaration
[JsonProperty("timeSeriesAttributeColumns")]
public virtual IList<string> TimeSeriesAttributeColumns { get; set; }
Property Value
Type Description
IList<string>

TimeSeriesIdentifierColumn

The name of the column that identifies the time series.

Declaration
[JsonProperty("timeSeriesIdentifierColumn")]
public virtual string TimeSeriesIdentifierColumn { get; set; }
Property Value
Type Description
string

TrainBudgetMilliNodeHours

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.

Declaration
[JsonProperty("trainBudgetMilliNodeHours")]
public virtual long? TrainBudgetMilliNodeHours { get; set; }
Property Value
Type Description
long?

Transformations

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.

Declaration
[JsonProperty("transformations")]
public virtual IList<GoogleCloudAiplatformV1beta1SchemaTrainingjobDefinitionAutoMlForecastingInputsTransformation> Transformations { get; set; }
Property Value
Type Description
IList<GoogleCloudAiplatformV1beta1SchemaTrainingjobDefinitionAutoMlForecastingInputsTransformation>

UnavailableAtForecastColumns

Names of columns that are unavailable when a forecast is requested. This column contains information for the given entity (identified by the time_series_identifier_column) that is unknown before the forecast For example, actual weather on a given day.

Declaration
[JsonProperty("unavailableAtForecastColumns")]
public virtual IList<string> UnavailableAtForecastColumns { get; set; }
Property Value
Type Description
IList<string>

ValidationOptions

Validation options for the data validation component. The available options are: * "fail-pipeline" - default, will validate against the validation and fail the pipeline if it fails. * "ignore-validation" - ignore the results of the validation and continue

Declaration
[JsonProperty("validationOptions")]
public virtual string ValidationOptions { get; set; }
Property Value
Type Description
string

WeightColumn

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.

Declaration
[JsonProperty("weightColumn")]
public virtual string WeightColumn { get; set; }
Property Value
Type Description
string

WindowConfig

Config containing strategy for generating sliding windows.

Declaration
[JsonProperty("windowConfig")]
public virtual GoogleCloudAiplatformV1beta1SchemaTrainingjobDefinitionWindowConfig WindowConfig { get; set; }
Property Value
Type Description
GoogleCloudAiplatformV1beta1SchemaTrainingjobDefinitionWindowConfig

Implements