com.google.cloud.aiplatform.v1beta1.schema.trainingjob.definition

Interface AutoMlForecastingInputsOrBuilder

All Superinterfaces:

com.google.protobuf.MessageLiteOrBuilder, com.google.protobuf.MessageOrBuilder

All Known Implementing Classes:

AutoMlForecastingInputs, AutoMlForecastingInputs.Builder

public interface AutoMlForecastingInputsOrBuilder
extends com.google.protobuf.MessageOrBuilder

Method Summary

Modifier and Type	Method and Description
String	getAdditionalExperiments(int index) Additional experiment flags for the time series forcasting training.
com.google.protobuf.ByteString	getAdditionalExperimentsBytes(int index) Additional experiment flags for the time series forcasting training.
int	getAdditionalExperimentsCount() Additional experiment flags for the time series forcasting training.
List<String>	getAdditionalExperimentsList() Additional experiment flags for the time series forcasting training.
String	getAvailableAtForecastColumns(int index) Names of columns that are available and provided when a forecast is requested.
com.google.protobuf.ByteString	getAvailableAtForecastColumnsBytes(int index) Names of columns that are available and provided when a forecast is requested.
int	getAvailableAtForecastColumnsCount() Names of columns that are available and provided when a forecast is requested.
List<String>	getAvailableAtForecastColumnsList() Names of columns that are available and provided when a forecast is requested.
long	getContextWindow() The amount of time into the past training and prediction data is used for model training and prediction respectively.
AutoMlForecastingInputs.Granularity	getDataGranularity() Expected difference in time granularity between rows in the data.
AutoMlForecastingInputs.GranularityOrBuilder	getDataGranularityOrBuilder() Expected difference in time granularity between rows in the data.
ExportEvaluatedDataItemsConfig	getExportEvaluatedDataItemsConfig() Configuration for exporting test set predictions to a BigQuery table.
ExportEvaluatedDataItemsConfigOrBuilder	getExportEvaluatedDataItemsConfigOrBuilder() Configuration for exporting test set predictions to a BigQuery table.
long	getForecastHorizon() The amount of time into the future for which forecasted values for the target are returned.
String	getOptimizationObjective() Objective function the model is optimizing towards.
com.google.protobuf.ByteString	getOptimizationObjectiveBytes() Objective function the model is optimizing towards.
double	getQuantiles(int index) Quantiles to use for minimize-quantile-loss `optimization_objective`.
int	getQuantilesCount() Quantiles to use for minimize-quantile-loss `optimization_objective`.
List<Double>	getQuantilesList() Quantiles to use for minimize-quantile-loss `optimization_objective`.
String	getTargetColumn() The name of the column that the model is to predict.
com.google.protobuf.ByteString	getTargetColumnBytes() The name of the column that the model is to predict.
String	getTimeColumn() The name of the column that identifies time order in the time series.
com.google.protobuf.ByteString	getTimeColumnBytes() The name of the column that identifies time order in the time series.
String	getTimeSeriesAttributeColumns(int index) Column names that should be used as attribute columns.
com.google.protobuf.ByteString	getTimeSeriesAttributeColumnsBytes(int index) Column names that should be used as attribute columns.
int	getTimeSeriesAttributeColumnsCount() Column names that should be used as attribute columns.
List<String>	getTimeSeriesAttributeColumnsList() Column names that should be used as attribute columns.
String	getTimeSeriesIdentifierColumn() The name of the column that identifies the time series.
com.google.protobuf.ByteString	getTimeSeriesIdentifierColumnBytes() The name of the column that identifies the time series.
long	getTrainBudgetMilliNodeHours() Required.
AutoMlForecastingInputs.Transformation	getTransformations(int index) Each transformation will apply transform function to given input column.
int	getTransformationsCount() Each transformation will apply transform function to given input column.
List<AutoMlForecastingInputs.Transformation>	getTransformationsList() Each transformation will apply transform function to given input column.
AutoMlForecastingInputs.TransformationOrBuilder	getTransformationsOrBuilder(int index) Each transformation will apply transform function to given input column.
List<? extends AutoMlForecastingInputs.TransformationOrBuilder>	getTransformationsOrBuilderList() Each transformation will apply transform function to given input column.
String	getUnavailableAtForecastColumns(int index) Names of columns that are unavailable when a forecast is requested.
com.google.protobuf.ByteString	getUnavailableAtForecastColumnsBytes(int index) Names of columns that are unavailable when a forecast is requested.
int	getUnavailableAtForecastColumnsCount() Names of columns that are unavailable when a forecast is requested.
List<String>	getUnavailableAtForecastColumnsList() Names of columns that are unavailable when a forecast is requested.
String	getValidationOptions() Validation options for the data validation component.
com.google.protobuf.ByteString	getValidationOptionsBytes() Validation options for the data validation component.
String	getWeightColumn() Column name that should be used as the weight column.
com.google.protobuf.ByteString	getWeightColumnBytes() Column name that should be used as the weight column.
boolean	hasDataGranularity() Expected difference in time granularity between rows in the data.
boolean	hasExportEvaluatedDataItemsConfig() Configuration for exporting test set predictions to a BigQuery table.

Methods inherited from interface com.google.protobuf.MessageOrBuilder

findInitializationErrors, getAllFields, getDefaultInstanceForType, getDescriptorForType, getField, getInitializationErrorString, getOneofFieldDescriptor, getRepeatedField, getRepeatedFieldCount, getUnknownFields, hasField, hasOneof

Methods inherited from interface com.google.protobuf.MessageLiteOrBuilder

isInitialized

Method Detail

getTargetColumn

String getTargetColumn()

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

string target_column = 1;

Returns:

The targetColumn.

getTargetColumnBytes

com.google.protobuf.ByteString getTargetColumnBytes()

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

string target_column = 1;

Returns:

The bytes for targetColumn.

getTimeSeriesIdentifierColumn

String getTimeSeriesIdentifierColumn()

 The name of the column that identifies the time series.
 

string time_series_identifier_column = 2;

Returns:

The timeSeriesIdentifierColumn.

getTimeSeriesIdentifierColumnBytes

com.google.protobuf.ByteString getTimeSeriesIdentifierColumnBytes()

 The name of the column that identifies the time series.
 

string time_series_identifier_column = 2;

Returns:

The bytes for timeSeriesIdentifierColumn.

getTimeColumn

String getTimeColumn()

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

string time_column = 3;

Returns:

The timeColumn.

getTimeColumnBytes

com.google.protobuf.ByteString getTimeColumnBytes()

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

string time_column = 3;

Returns:

The bytes for timeColumn.

getTransformationsList

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;

getTransformations

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;

getTransformationsCount

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;

getTransformationsOrBuilderList

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;

getTransformationsOrBuilder

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;

getOptimizationObjective

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:

The optimizationObjective.

getOptimizationObjectiveBytes

com.google.protobuf.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:

The bytes for optimizationObjective.

getTrainBudgetMilliNodeHours

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:

The trainBudgetMilliNodeHours.

getWeightColumn

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:

The weightColumn.

getWeightColumnBytes

com.google.protobuf.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:

The bytes for weightColumn.

getTimeSeriesAttributeColumnsList

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:

A list containing the timeSeriesAttributeColumns.

getTimeSeriesAttributeColumnsCount

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:

The count of timeSeriesAttributeColumns.

getTimeSeriesAttributeColumns

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;

Parameters:

index - The index of the element to return.

Returns:

The timeSeriesAttributeColumns at the given index.

getTimeSeriesAttributeColumnsBytes

com.google.protobuf.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;

Parameters:

index - The index of the value to return.

Returns:

The bytes of the timeSeriesAttributeColumns at the given index.

getUnavailableAtForecastColumnsList

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:

A list containing the unavailableAtForecastColumns.

getUnavailableAtForecastColumnsCount

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:

The count of unavailableAtForecastColumns.

getUnavailableAtForecastColumns

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;

Parameters:

index - The index of the element to return.

Returns:

The unavailableAtForecastColumns at the given index.

getUnavailableAtForecastColumnsBytes

com.google.protobuf.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;

Parameters:

index - The index of the value to return.

Returns:

The bytes of the unavailableAtForecastColumns at the given index.

getAvailableAtForecastColumnsList

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:

A list containing the availableAtForecastColumns.

getAvailableAtForecastColumnsCount

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:

The count of availableAtForecastColumns.

getAvailableAtForecastColumns

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;

Parameters:

index - The index of the element to return.

Returns:

The availableAtForecastColumns at the given index.

getAvailableAtForecastColumnsBytes

com.google.protobuf.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;

Parameters:

index - The index of the value to return.

Returns:

The bytes of the availableAtForecastColumns at the given index.

hasDataGranularity

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:

Whether the dataGranularity field is set.

getDataGranularity

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:

The dataGranularity.

getDataGranularityOrBuilder

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;

getForecastHorizon

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:

The forecastHorizon.

getContextWindow

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:

The contextWindow.

hasExportEvaluatedDataItemsConfig

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:

Whether the exportEvaluatedDataItemsConfig field is set.

getExportEvaluatedDataItemsConfig

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:

The exportEvaluatedDataItemsConfig.

getExportEvaluatedDataItemsConfigOrBuilder

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;

getQuantilesList

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:

A list containing the quantiles.

getQuantilesCount

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:

The count of quantiles.

getQuantiles

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;

Parameters:

index - The index of the element to return.

Returns:

The quantiles at the given index.

getValidationOptions

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:

The validationOptions.

getValidationOptionsBytes

com.google.protobuf.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:

The bytes for validationOptions.

getAdditionalExperimentsList

List<String> getAdditionalExperimentsList()

 Additional experiment flags for the time series forcasting training.
 

repeated string additional_experiments = 25;

Returns:

A list containing the additionalExperiments.

getAdditionalExperimentsCount

int getAdditionalExperimentsCount()

 Additional experiment flags for the time series forcasting training.
 

repeated string additional_experiments = 25;

Returns:

The count of additionalExperiments.

getAdditionalExperiments

String getAdditionalExperiments(int index)

 Additional experiment flags for the time series forcasting training.
 

repeated string additional_experiments = 25;

Parameters:

index - The index of the element to return.

Returns:

The additionalExperiments at the given index.

getAdditionalExperimentsBytes

com.google.protobuf.ByteString getAdditionalExperimentsBytes(int index)

 Additional experiment flags for the time series forcasting training.
 

repeated string additional_experiments = 25;

Parameters:

index - The index of the value to return.

Returns:

The bytes of the additionalExperiments at the given index.