Interface AutoMlForecastingInputsOrBuilder (3.16.0)

Version 3.16.0

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public interface AutoMlForecastingInputsOrBuilder extends MessageOrBuilder

Implements

MessageOrBuilder

Methods

getAdditionalExperiments(int index)

public abstract String getAdditionalExperiments(int index)

Additional experiment flags for the time series forcasting training.

repeated string additional_experiments = 25;

Parameter
Name	Description
index	int The index of the element to return.

Returns
Type	Description
String	The additionalExperiments at the given index.

getAdditionalExperimentsBytes(int index)

public abstract ByteString getAdditionalExperimentsBytes(int index)

Additional experiment flags for the time series forcasting training.

repeated string additional_experiments = 25;

Parameter
Name	Description
index	int The index of the value to return.

Returns
Type	Description
ByteString	The bytes of the additionalExperiments at the given index.

getAdditionalExperimentsCount()

public abstract int getAdditionalExperimentsCount()

Additional experiment flags for the time series forcasting training.

repeated string additional_experiments = 25;

Returns
Type	Description
int	The count of additionalExperiments.

getAdditionalExperimentsList()

public abstract List<String> getAdditionalExperimentsList()

Additional experiment flags for the time series forcasting training.

repeated string additional_experiments = 25;

Returns
Type	Description
List<String>	A list containing the additionalExperiments.

getAvailableAtForecastColumns(int index)

public abstract String getAvailableAtForecastColumns(int index)

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.

repeated string available_at_forecast_columns = 21;

Parameter
Name	Description
index	int The index of the element to return.

Returns
Type	Description
String	The availableAtForecastColumns at the given index.

getAvailableAtForecastColumnsBytes(int index)

public abstract ByteString getAvailableAtForecastColumnsBytes(int index)

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.

repeated string available_at_forecast_columns = 21;

Parameter
Name	Description
index	int The index of the value to return.

Returns
Type	Description
ByteString	The bytes of the availableAtForecastColumns at the given index.

getAvailableAtForecastColumnsCount()

public abstract int getAvailableAtForecastColumnsCount()

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.

repeated string available_at_forecast_columns = 21;

Returns
Type	Description
int	The count of availableAtForecastColumns.

getAvailableAtForecastColumnsList()

public abstract List<String> getAvailableAtForecastColumnsList()

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.

repeated string available_at_forecast_columns = 21;

Returns
Type	Description
List<String>	A list containing the availableAtForecastColumns.

getContextWindow()

public abstract long getContextWindow()

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.

int64 context_window = 24;

Returns
Type	Description
long	The contextWindow.

getDataGranularity()

public abstract AutoMlForecastingInputs.Granularity getDataGranularity()

Expected difference in time granularity between rows in the data.

.google.cloud.aiplatform.v1beta1.schema.trainingjob.definition.AutoMlForecastingInputs.Granularity data_granularity = 22;

Returns
Type	Description
AutoMlForecastingInputs.Granularity	The dataGranularity.

getDataGranularityOrBuilder()

public abstract AutoMlForecastingInputs.GranularityOrBuilder getDataGranularityOrBuilder()

Expected difference in time granularity between rows in the data.

.google.cloud.aiplatform.v1beta1.schema.trainingjob.definition.AutoMlForecastingInputs.Granularity data_granularity = 22;

Returns
Type	Description
AutoMlForecastingInputs.GranularityOrBuilder

getExportEvaluatedDataItemsConfig()

public abstract 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.v1beta1.schema.trainingjob.definition.ExportEvaluatedDataItemsConfig export_evaluated_data_items_config = 15;

Returns
Type	Description
ExportEvaluatedDataItemsConfig	The exportEvaluatedDataItemsConfig.

getExportEvaluatedDataItemsConfigOrBuilder()

public abstract 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.v1beta1.schema.trainingjob.definition.ExportEvaluatedDataItemsConfig export_evaluated_data_items_config = 15;

Returns
Type	Description
ExportEvaluatedDataItemsConfigOrBuilder

getForecastHorizon()

public abstract long getForecastHorizon()

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.

int64 forecast_horizon = 23;

Returns
Type	Description
long	The forecastHorizon.

getOptimizationObjective()

public abstract String getOptimizationObjective()

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.

string optimization_objective = 5;

Returns
Type	Description
String	The optimizationObjective.

getOptimizationObjectiveBytes()

public abstract ByteString getOptimizationObjectiveBytes()

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.

string optimization_objective = 5;

Returns
Type	Description
ByteString	The bytes for optimizationObjective.

getQuantiles(int index)

public abstract double getQuantiles(int index)

Quantiles to use for minimize-quantile-loss optimization_objective. 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.

repeated double quantiles = 16;

Parameter
Name	Description
index	int The index of the element to return.

Returns
Type	Description
double	The quantiles at the given index.

getQuantilesCount()

public abstract int getQuantilesCount()

Quantiles to use for minimize-quantile-loss optimization_objective. 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.

repeated double quantiles = 16;

Returns
Type	Description
int	The count of quantiles.

getQuantilesList()

public abstract List<Double> getQuantilesList()

Quantiles to use for minimize-quantile-loss optimization_objective. 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.

repeated double quantiles = 16;

Returns
Type	Description
List<Double>	A list containing the quantiles.

getTargetColumn()

public abstract String getTargetColumn()

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

string target_column = 1;

Returns
Type	Description
String	The targetColumn.

getTargetColumnBytes()

public abstract ByteString getTargetColumnBytes()

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

string target_column = 1;

Returns
Type	Description
ByteString	The bytes for targetColumn.

getTimeColumn()

public abstract String getTimeColumn()

The name of the column that identifies time order in the time series.

string time_column = 3;

Returns
Type	Description
String	The timeColumn.

getTimeColumnBytes()

public abstract ByteString getTimeColumnBytes()

The name of the column that identifies time order in the time series.

string time_column = 3;

Returns
Type	Description
ByteString	The bytes for timeColumn.

getTimeSeriesAttributeColumns(int index)

public abstract String getTimeSeriesAttributeColumns(int index)

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.

repeated string time_series_attribute_columns = 19;

Parameter
Name	Description
index	int The index of the element to return.

Returns
Type	Description
String	The timeSeriesAttributeColumns at the given index.

getTimeSeriesAttributeColumnsBytes(int index)

public abstract ByteString getTimeSeriesAttributeColumnsBytes(int index)

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.

repeated string time_series_attribute_columns = 19;

Parameter
Name	Description
index	int The index of the value to return.

Returns
Type	Description
ByteString	The bytes of the timeSeriesAttributeColumns at the given index.

getTimeSeriesAttributeColumnsCount()

public abstract int getTimeSeriesAttributeColumnsCount()

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.

repeated string time_series_attribute_columns = 19;

Returns
Type	Description
int	The count of timeSeriesAttributeColumns.

getTimeSeriesAttributeColumnsList()

public abstract List<String> getTimeSeriesAttributeColumnsList()

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.

repeated string time_series_attribute_columns = 19;

Returns
Type	Description
List<String>	A list containing the timeSeriesAttributeColumns.

getTimeSeriesIdentifierColumn()

public abstract String getTimeSeriesIdentifierColumn()

The name of the column that identifies the time series.

string time_series_identifier_column = 2;

Returns
Type	Description
String	The timeSeriesIdentifierColumn.

getTimeSeriesIdentifierColumnBytes()

public abstract ByteString getTimeSeriesIdentifierColumnBytes()

The name of the column that identifies the time series.

string time_series_identifier_column = 2;

Returns
Type	Description
ByteString	The bytes for timeSeriesIdentifierColumn.

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
Type	Description
long	The trainBudgetMilliNodeHours.

getTransformations(int index)

public abstract AutoMlForecastingInputs.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.v1beta1.schema.trainingjob.definition.AutoMlForecastingInputs.Transformation transformations = 4;

Parameter
Name	Description
index	int

Returns
Type	Description
AutoMlForecastingInputs.Transformation

getTransformationsCount()

public abstract 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.v1beta1.schema.trainingjob.definition.AutoMlForecastingInputs.Transformation transformations = 4;

Returns
Type	Description
int

getTransformationsList()

public abstract List<AutoMlForecastingInputs.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.v1beta1.schema.trainingjob.definition.AutoMlForecastingInputs.Transformation transformations = 4;

Returns
Type	Description
List<Transformation>

getTransformationsOrBuilder(int index)

public abstract AutoMlForecastingInputs.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.v1beta1.schema.trainingjob.definition.AutoMlForecastingInputs.Transformation transformations = 4;

Parameter
Name	Description
index	int

Returns
Type	Description
AutoMlForecastingInputs.TransformationOrBuilder

getTransformationsOrBuilderList()

public abstract List<? extends AutoMlForecastingInputs.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.v1beta1.schema.trainingjob.definition.AutoMlForecastingInputs.Transformation transformations = 4;

Returns
Type	Description
List<? extends com.google.cloud.aiplatform.v1beta1.schema.trainingjob.definition.AutoMlForecastingInputs.TransformationOrBuilder>

getUnavailableAtForecastColumns(int index)

public abstract String getUnavailableAtForecastColumns(int index)

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.

repeated string unavailable_at_forecast_columns = 20;

Parameter
Name	Description
index	int The index of the element to return.

Returns
Type	Description
String	The unavailableAtForecastColumns at the given index.

getUnavailableAtForecastColumnsBytes(int index)

public abstract ByteString getUnavailableAtForecastColumnsBytes(int index)

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.

repeated string unavailable_at_forecast_columns = 20;

Parameter
Name	Description
index	int The index of the value to return.

Returns
Type	Description
ByteString	The bytes of the unavailableAtForecastColumns at the given index.

getUnavailableAtForecastColumnsCount()

public abstract int getUnavailableAtForecastColumnsCount()

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.

repeated string unavailable_at_forecast_columns = 20;

Returns
Type	Description
int	The count of unavailableAtForecastColumns.

getUnavailableAtForecastColumnsList()

public abstract List<String> getUnavailableAtForecastColumnsList()

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.

repeated string unavailable_at_forecast_columns = 20;

Returns
Type	Description
List<String>	A list containing the unavailableAtForecastColumns.

getValidationOptions()

public abstract String getValidationOptions()

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

string validation_options = 17;

Returns
Type	Description
String	The validationOptions.

getValidationOptionsBytes()

public abstract ByteString getValidationOptionsBytes()

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

string validation_options = 17;

Returns
Type	Description
ByteString	The bytes for validationOptions.

getWeightColumn()

public abstract String getWeightColumn()

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 = 7;

Returns
Type	Description
String	The weightColumn.

getWeightColumnBytes()

public abstract ByteString getWeightColumnBytes()

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 = 7;

Returns
Type	Description
ByteString	The bytes for weightColumn.

hasDataGranularity()

public abstract boolean hasDataGranularity()

Expected difference in time granularity between rows in the data.

.google.cloud.aiplatform.v1beta1.schema.trainingjob.definition.AutoMlForecastingInputs.Granularity data_granularity = 22;

Returns
Type	Description
boolean	Whether the dataGranularity field is set.

hasExportEvaluatedDataItemsConfig()

public abstract 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.v1beta1.schema.trainingjob.definition.ExportEvaluatedDataItemsConfig export_evaluated_data_items_config = 15;

Returns
Type	Description
boolean	Whether the exportEvaluatedDataItemsConfig field is set.