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public static final class AutoMlForecastingInputs.Builder extends GeneratedMessageV3.Builder<AutoMlForecastingInputs.Builder> implements AutoMlForecastingInputsOrBuilderProtobuf type
google.cloud.aiplatform.v1beta1.schema.trainingjob.definition.AutoMlForecastingInputs
Inheritance
Object > AbstractMessageLite.Builder<MessageType,BuilderType> > AbstractMessage.Builder<BuilderType> > GeneratedMessageV3.Builder > AutoMlForecastingInputs.BuilderImplements
AutoMlForecastingInputsOrBuilderStatic Methods
getDescriptor()
public static final Descriptors.Descriptor getDescriptor()| Type | Description |
| Descriptor |
Methods
addAdditionalExperiments(String value)
public AutoMlForecastingInputs.Builder addAdditionalExperiments(String value)Additional experiment flags for the time series forcasting training.
repeated string additional_experiments = 25;
| Name | Description |
| value | StringThe additionalExperiments to add. |
| Type | Description |
| AutoMlForecastingInputs.Builder | This builder for chaining. |
addAdditionalExperimentsBytes(ByteString value)
public AutoMlForecastingInputs.Builder addAdditionalExperimentsBytes(ByteString value)Additional experiment flags for the time series forcasting training.
repeated string additional_experiments = 25;
| Name | Description |
| value | ByteStringThe bytes of the additionalExperiments to add. |
| Type | Description |
| AutoMlForecastingInputs.Builder | This builder for chaining. |
addAllAdditionalExperiments(Iterable<String> values)
public AutoMlForecastingInputs.Builder addAllAdditionalExperiments(Iterable<String> values)Additional experiment flags for the time series forcasting training.
repeated string additional_experiments = 25;
| Name | Description |
| values | Iterable<String>The additionalExperiments to add. |
| Type | Description |
| AutoMlForecastingInputs.Builder | This builder for chaining. |
addAllAvailableAtForecastColumns(Iterable<String> values)
public AutoMlForecastingInputs.Builder addAllAvailableAtForecastColumns(Iterable<String> values)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;
| Name | Description |
| values | Iterable<String>The availableAtForecastColumns to add. |
| Type | Description |
| AutoMlForecastingInputs.Builder | This builder for chaining. |
addAllQuantiles(Iterable<? extends Double> values)
public AutoMlForecastingInputs.Builder addAllQuantiles(Iterable<? extends Double> values) 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;
| Name | Description |
| values | Iterable<? extends java.lang.Double>The quantiles to add. |
| Type | Description |
| AutoMlForecastingInputs.Builder | This builder for chaining. |
addAllTimeSeriesAttributeColumns(Iterable<String> values)
public AutoMlForecastingInputs.Builder addAllTimeSeriesAttributeColumns(Iterable<String> values)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;
| Name | Description |
| values | Iterable<String>The timeSeriesAttributeColumns to add. |
| Type | Description |
| AutoMlForecastingInputs.Builder | This builder for chaining. |
addAllTransformations(Iterable<? extends AutoMlForecastingInputs.Transformation> values)
public AutoMlForecastingInputs.Builder addAllTransformations(Iterable<? extends AutoMlForecastingInputs.Transformation> values)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;
| Name | Description |
| values | Iterable<? extends com.google.cloud.aiplatform.v1beta1.schema.trainingjob.definition.AutoMlForecastingInputs.Transformation> |
| Type | Description |
| AutoMlForecastingInputs.Builder |
addAllUnavailableAtForecastColumns(Iterable<String> values)
public AutoMlForecastingInputs.Builder addAllUnavailableAtForecastColumns(Iterable<String> values)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;
| Name | Description |
| values | Iterable<String>The unavailableAtForecastColumns to add. |
| Type | Description |
| AutoMlForecastingInputs.Builder | This builder for chaining. |
addAvailableAtForecastColumns(String value)
public AutoMlForecastingInputs.Builder addAvailableAtForecastColumns(String value)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;
| Name | Description |
| value | StringThe availableAtForecastColumns to add. |
| Type | Description |
| AutoMlForecastingInputs.Builder | This builder for chaining. |
addAvailableAtForecastColumnsBytes(ByteString value)
public AutoMlForecastingInputs.Builder addAvailableAtForecastColumnsBytes(ByteString value)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;
| Name | Description |
| value | ByteStringThe bytes of the availableAtForecastColumns to add. |
| Type | Description |
| AutoMlForecastingInputs.Builder | This builder for chaining. |
addQuantiles(double value)
public AutoMlForecastingInputs.Builder addQuantiles(double value) 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;
| Name | Description |
| value | doubleThe quantiles to add. |
| Type | Description |
| AutoMlForecastingInputs.Builder | This builder for chaining. |
addRepeatedField(Descriptors.FieldDescriptor field, Object value)
public AutoMlForecastingInputs.Builder addRepeatedField(Descriptors.FieldDescriptor field, Object value)| Name | Description |
| field | FieldDescriptor |
| value | Object |
| Type | Description |
| AutoMlForecastingInputs.Builder |
addTimeSeriesAttributeColumns(String value)
public AutoMlForecastingInputs.Builder addTimeSeriesAttributeColumns(String value)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;
| Name | Description |
| value | StringThe timeSeriesAttributeColumns to add. |
| Type | Description |
| AutoMlForecastingInputs.Builder | This builder for chaining. |
addTimeSeriesAttributeColumnsBytes(ByteString value)
public AutoMlForecastingInputs.Builder addTimeSeriesAttributeColumnsBytes(ByteString value)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;
| Name | Description |
| value | ByteStringThe bytes of the timeSeriesAttributeColumns to add. |
| Type | Description |
| AutoMlForecastingInputs.Builder | This builder for chaining. |
addTransformations(AutoMlForecastingInputs.Transformation value)
public AutoMlForecastingInputs.Builder addTransformations(AutoMlForecastingInputs.Transformation value)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;
| Name | Description |
| value | AutoMlForecastingInputs.Transformation |
| Type | Description |
| AutoMlForecastingInputs.Builder |
addTransformations(AutoMlForecastingInputs.Transformation.Builder builderForValue)
public AutoMlForecastingInputs.Builder addTransformations(AutoMlForecastingInputs.Transformation.Builder builderForValue)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;
| Name | Description |
| builderForValue | AutoMlForecastingInputs.Transformation.Builder |
| Type | Description |
| AutoMlForecastingInputs.Builder |
addTransformations(int index, AutoMlForecastingInputs.Transformation value)
public AutoMlForecastingInputs.Builder addTransformations(int index, AutoMlForecastingInputs.Transformation value)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;
| Name | Description |
| index | int |
| value | AutoMlForecastingInputs.Transformation |
| Type | Description |
| AutoMlForecastingInputs.Builder |
addTransformations(int index, AutoMlForecastingInputs.Transformation.Builder builderForValue)
public AutoMlForecastingInputs.Builder addTransformations(int index, AutoMlForecastingInputs.Transformation.Builder builderForValue)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;
| Name | Description |
| index | int |
| builderForValue | AutoMlForecastingInputs.Transformation.Builder |
| Type | Description |
| AutoMlForecastingInputs.Builder |
addTransformationsBuilder()
public AutoMlForecastingInputs.Transformation.Builder addTransformationsBuilder()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;
| Type | Description |
| AutoMlForecastingInputs.Transformation.Builder |
addTransformationsBuilder(int index)
public AutoMlForecastingInputs.Transformation.Builder addTransformationsBuilder(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;
| Name | Description |
| index | int |
| Type | Description |
| AutoMlForecastingInputs.Transformation.Builder |
addUnavailableAtForecastColumns(String value)
public AutoMlForecastingInputs.Builder addUnavailableAtForecastColumns(String value)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;
| Name | Description |
| value | StringThe unavailableAtForecastColumns to add. |
| Type | Description |
| AutoMlForecastingInputs.Builder | This builder for chaining. |
addUnavailableAtForecastColumnsBytes(ByteString value)
public AutoMlForecastingInputs.Builder addUnavailableAtForecastColumnsBytes(ByteString value)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;
| Name | Description |
| value | ByteStringThe bytes of the unavailableAtForecastColumns to add. |
| Type | Description |
| AutoMlForecastingInputs.Builder | This builder for chaining. |
build()
public AutoMlForecastingInputs build()| Type | Description |
| AutoMlForecastingInputs |
buildPartial()
public AutoMlForecastingInputs buildPartial()| Type | Description |
| AutoMlForecastingInputs |
clear()
public AutoMlForecastingInputs.Builder clear()| Type | Description |
| AutoMlForecastingInputs.Builder |
clearAdditionalExperiments()
public AutoMlForecastingInputs.Builder clearAdditionalExperiments()Additional experiment flags for the time series forcasting training.
repeated string additional_experiments = 25;
| Type | Description |
| AutoMlForecastingInputs.Builder | This builder for chaining. |
clearAvailableAtForecastColumns()
public AutoMlForecastingInputs.Builder clearAvailableAtForecastColumns()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;
| Type | Description |
| AutoMlForecastingInputs.Builder | This builder for chaining. |
clearContextWindow()
public AutoMlForecastingInputs.Builder clearContextWindow() 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;
| Type | Description |
| AutoMlForecastingInputs.Builder | This builder for chaining. |
clearDataGranularity()
public AutoMlForecastingInputs.Builder clearDataGranularity()Expected difference in time granularity between rows in the data.
.google.cloud.aiplatform.v1beta1.schema.trainingjob.definition.AutoMlForecastingInputs.Granularity data_granularity = 22;
| Type | Description |
| AutoMlForecastingInputs.Builder |
clearExportEvaluatedDataItemsConfig()
public AutoMlForecastingInputs.Builder clearExportEvaluatedDataItemsConfig()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;
| Type | Description |
| AutoMlForecastingInputs.Builder |
clearField(Descriptors.FieldDescriptor field)
public AutoMlForecastingInputs.Builder clearField(Descriptors.FieldDescriptor field)| Name | Description |
| field | FieldDescriptor |
| Type | Description |
| AutoMlForecastingInputs.Builder |
clearForecastHorizon()
public AutoMlForecastingInputs.Builder clearForecastHorizon() 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;
| Type | Description |
| AutoMlForecastingInputs.Builder | This builder for chaining. |
clearOneof(Descriptors.OneofDescriptor oneof)
public AutoMlForecastingInputs.Builder clearOneof(Descriptors.OneofDescriptor oneof)| Name | Description |
| oneof | OneofDescriptor |
| Type | Description |
| AutoMlForecastingInputs.Builder |
clearOptimizationObjective()
public AutoMlForecastingInputs.Builder clearOptimizationObjective()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;
| Type | Description |
| AutoMlForecastingInputs.Builder | This builder for chaining. |
clearQuantiles()
public AutoMlForecastingInputs.Builder clearQuantiles() 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;
| Type | Description |
| AutoMlForecastingInputs.Builder | This builder for chaining. |
clearTargetColumn()
public AutoMlForecastingInputs.Builder clearTargetColumn()The name of the column that the model is to predict.
string target_column = 1;
| Type | Description |
| AutoMlForecastingInputs.Builder | This builder for chaining. |
clearTimeColumn()
public AutoMlForecastingInputs.Builder clearTimeColumn()The name of the column that identifies time order in the time series.
string time_column = 3;
| Type | Description |
| AutoMlForecastingInputs.Builder | This builder for chaining. |
clearTimeSeriesAttributeColumns()
public AutoMlForecastingInputs.Builder clearTimeSeriesAttributeColumns()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;
| Type | Description |
| AutoMlForecastingInputs.Builder | This builder for chaining. |
clearTimeSeriesIdentifierColumn()
public AutoMlForecastingInputs.Builder clearTimeSeriesIdentifierColumn()The name of the column that identifies the time series.
string time_series_identifier_column = 2;
| Type | Description |
| AutoMlForecastingInputs.Builder | This builder for chaining. |
clearTrainBudgetMilliNodeHours()
public AutoMlForecastingInputs.Builder clearTrainBudgetMilliNodeHours()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;
| Type | Description |
| AutoMlForecastingInputs.Builder | This builder for chaining. |
clearTransformations()
public AutoMlForecastingInputs.Builder clearTransformations()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;
| Type | Description |
| AutoMlForecastingInputs.Builder |
clearUnavailableAtForecastColumns()
public AutoMlForecastingInputs.Builder clearUnavailableAtForecastColumns()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;
| Type | Description |
| AutoMlForecastingInputs.Builder | This builder for chaining. |
clearValidationOptions()
public AutoMlForecastingInputs.Builder clearValidationOptions()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;
| Type | Description |
| AutoMlForecastingInputs.Builder | This builder for chaining. |
clearWeightColumn()
public AutoMlForecastingInputs.Builder clearWeightColumn()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;
| Type | Description |
| AutoMlForecastingInputs.Builder | This builder for chaining. |
clone()
public AutoMlForecastingInputs.Builder clone()| Type | Description |
| AutoMlForecastingInputs.Builder |
getAdditionalExperiments(int index)
public String getAdditionalExperiments(int index)Additional experiment flags for the time series forcasting training.
repeated string additional_experiments = 25;
| Name | Description |
| index | intThe index of the element to return. |
| Type | Description |
| String | The additionalExperiments at the given index. |
getAdditionalExperimentsBytes(int index)
public ByteString getAdditionalExperimentsBytes(int index)Additional experiment flags for the time series forcasting training.
repeated string additional_experiments = 25;
| Name | Description |
| index | intThe index of the value to return. |
| Type | Description |
| ByteString | The bytes of the additionalExperiments at the given index. |
getAdditionalExperimentsCount()
public int getAdditionalExperimentsCount()Additional experiment flags for the time series forcasting training.
repeated string additional_experiments = 25;
| Type | Description |
| int | The count of additionalExperiments. |
getAdditionalExperimentsList()
public ProtocolStringList getAdditionalExperimentsList()Additional experiment flags for the time series forcasting training.
repeated string additional_experiments = 25;
| Type | Description |
| ProtocolStringList | A list containing the additionalExperiments. |
getAvailableAtForecastColumns(int index)
public 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;
| Name | Description |
| index | intThe index of the element to return. |
| Type | Description |
| String | The availableAtForecastColumns at the given index. |
getAvailableAtForecastColumnsBytes(int index)
public 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;
| Name | Description |
| index | intThe index of the value to return. |
| Type | Description |
| ByteString | The bytes of the availableAtForecastColumns at the given index. |
getAvailableAtForecastColumnsCount()
public 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;
| Type | Description |
| int | The count of availableAtForecastColumns. |
getAvailableAtForecastColumnsList()
public ProtocolStringList 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;
| Type | Description |
| ProtocolStringList | A list containing the availableAtForecastColumns. |
getContextWindow()
public 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;
| Type | Description |
| long | The contextWindow. |
getDataGranularity()
public 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;
| Type | Description |
| AutoMlForecastingInputs.Granularity | The dataGranularity. |
getDataGranularityBuilder()
public AutoMlForecastingInputs.Granularity.Builder getDataGranularityBuilder()Expected difference in time granularity between rows in the data.
.google.cloud.aiplatform.v1beta1.schema.trainingjob.definition.AutoMlForecastingInputs.Granularity data_granularity = 22;
| Type | Description |
| AutoMlForecastingInputs.Granularity.Builder |
getDataGranularityOrBuilder()
public 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;
| Type | Description |
| AutoMlForecastingInputs.GranularityOrBuilder |
getDefaultInstanceForType()
public AutoMlForecastingInputs getDefaultInstanceForType()| Type | Description |
| AutoMlForecastingInputs |
getDescriptorForType()
public Descriptors.Descriptor getDescriptorForType()| Type | Description |
| Descriptor |
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.v1beta1.schema.trainingjob.definition.ExportEvaluatedDataItemsConfig export_evaluated_data_items_config = 15;
| Type | Description |
| ExportEvaluatedDataItemsConfig | The exportEvaluatedDataItemsConfig. |
getExportEvaluatedDataItemsConfigBuilder()
public ExportEvaluatedDataItemsConfig.Builder getExportEvaluatedDataItemsConfigBuilder()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;
| Type | Description |
| ExportEvaluatedDataItemsConfig.Builder |
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.v1beta1.schema.trainingjob.definition.ExportEvaluatedDataItemsConfig export_evaluated_data_items_config = 15;
| Type | Description |
| ExportEvaluatedDataItemsConfigOrBuilder |
getForecastHorizon()
public 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;
| Type | Description |
| long | The forecastHorizon. |
getOptimizationObjective()
public 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;
| Type | Description |
| String | The optimizationObjective. |
getOptimizationObjectiveBytes()
public 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;
| Type | Description |
| ByteString | The bytes for optimizationObjective. |
getQuantiles(int index)
public 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;
| Name | Description |
| index | intThe index of the element to return. |
| Type | Description |
| double | The quantiles at the given index. |
getQuantilesCount()
public 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;
| Type | Description |
| int | The count of quantiles. |
getQuantilesList()
public 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;
| Type | Description |
| List<Double> | A list containing the quantiles. |
getTargetColumn()
public String getTargetColumn()The name of the column that the model is to predict.
string target_column = 1;
| Type | Description |
| String | The targetColumn. |
getTargetColumnBytes()
public ByteString getTargetColumnBytes()The name of the column that the model is to predict.
string target_column = 1;
| Type | Description |
| ByteString | The bytes for targetColumn. |
getTimeColumn()
public String getTimeColumn()The name of the column that identifies time order in the time series.
string time_column = 3;
| Type | Description |
| String | The timeColumn. |
getTimeColumnBytes()
public ByteString getTimeColumnBytes()The name of the column that identifies time order in the time series.
string time_column = 3;
| Type | Description |
| ByteString | The bytes for timeColumn. |
getTimeSeriesAttributeColumns(int index)
public 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;
| Name | Description |
| index | intThe index of the element to return. |
| Type | Description |
| String | The timeSeriesAttributeColumns at the given index. |
getTimeSeriesAttributeColumnsBytes(int index)
public 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;
| Name | Description |
| index | intThe index of the value to return. |
| Type | Description |
| ByteString | The bytes of the timeSeriesAttributeColumns at the given index. |
getTimeSeriesAttributeColumnsCount()
public 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;
| Type | Description |
| int | The count of timeSeriesAttributeColumns. |
getTimeSeriesAttributeColumnsList()
public ProtocolStringList 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;
| Type | Description |
| ProtocolStringList | A list containing the timeSeriesAttributeColumns. |
getTimeSeriesIdentifierColumn()
public String getTimeSeriesIdentifierColumn()The name of the column that identifies the time series.
string time_series_identifier_column = 2;
| Type | Description |
| String | The timeSeriesIdentifierColumn. |
getTimeSeriesIdentifierColumnBytes()
public ByteString getTimeSeriesIdentifierColumnBytes()The name of the column that identifies the time series.
string time_series_identifier_column = 2;
| Type | Description |
| ByteString | The bytes for timeSeriesIdentifierColumn. |
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 = 6;
| Type | Description |
| long | The trainBudgetMilliNodeHours. |
getTransformations(int index)
public 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;
| Name | Description |
| index | int |
| Type | Description |
| AutoMlForecastingInputs.Transformation |
getTransformationsBuilder(int index)
public AutoMlForecastingInputs.Transformation.Builder getTransformationsBuilder(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;
| Name | Description |
| index | int |
| Type | Description |
| AutoMlForecastingInputs.Transformation.Builder |
getTransformationsBuilderList()
public List<AutoMlForecastingInputs.Transformation.Builder> getTransformationsBuilderList()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;
| Type | Description |
| List<Builder> |
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.v1beta1.schema.trainingjob.definition.AutoMlForecastingInputs.Transformation transformations = 4;
| Type | Description |
| int |
getTransformationsList()
public 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;
| Type | Description |
| List<Transformation> |
getTransformationsOrBuilder(int index)
public 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;
| Name | Description |
| index | int |
| Type | Description |
| AutoMlForecastingInputs.TransformationOrBuilder |
getTransformationsOrBuilderList()
public 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;
| Type | Description |
| List<? extends com.google.cloud.aiplatform.v1beta1.schema.trainingjob.definition.AutoMlForecastingInputs.TransformationOrBuilder> |
getUnavailableAtForecastColumns(int index)
public 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;
| Name | Description |
| index | intThe index of the element to return. |
| Type | Description |
| String | The unavailableAtForecastColumns at the given index. |
getUnavailableAtForecastColumnsBytes(int index)
public 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;
| Name | Description |
| index | intThe index of the value to return. |
| Type | Description |
| ByteString | The bytes of the unavailableAtForecastColumns at the given index. |
getUnavailableAtForecastColumnsCount()
public 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;
| Type | Description |
| int | The count of unavailableAtForecastColumns. |
getUnavailableAtForecastColumnsList()
public ProtocolStringList 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;
| Type | Description |
| ProtocolStringList | A list containing the unavailableAtForecastColumns. |
getValidationOptions()
public 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;
| Type | Description |
| String | The validationOptions. |
getValidationOptionsBytes()
public 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;
| Type | Description |
| ByteString | The bytes for validationOptions. |
getWeightColumn()
public 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;
| Type | Description |
| String | The weightColumn. |
getWeightColumnBytes()
public 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;
| Type | Description |
| ByteString | The bytes for weightColumn. |
hasDataGranularity()
public boolean hasDataGranularity()Expected difference in time granularity between rows in the data.
.google.cloud.aiplatform.v1beta1.schema.trainingjob.definition.AutoMlForecastingInputs.Granularity data_granularity = 22;
| Type | Description |
| boolean | Whether the dataGranularity field is set. |
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.v1beta1.schema.trainingjob.definition.ExportEvaluatedDataItemsConfig export_evaluated_data_items_config = 15;
| Type | Description |
| boolean | Whether the exportEvaluatedDataItemsConfig field is set. |
internalGetFieldAccessorTable()
protected GeneratedMessageV3.FieldAccessorTable internalGetFieldAccessorTable()| Type | Description |
| FieldAccessorTable |
isInitialized()
public final boolean isInitialized()| Type | Description |
| boolean |
mergeDataGranularity(AutoMlForecastingInputs.Granularity value)
public AutoMlForecastingInputs.Builder mergeDataGranularity(AutoMlForecastingInputs.Granularity value)Expected difference in time granularity between rows in the data.
.google.cloud.aiplatform.v1beta1.schema.trainingjob.definition.AutoMlForecastingInputs.Granularity data_granularity = 22;
| Name | Description |
| value | AutoMlForecastingInputs.Granularity |
| Type | Description |
| AutoMlForecastingInputs.Builder |
mergeExportEvaluatedDataItemsConfig(ExportEvaluatedDataItemsConfig value)
public AutoMlForecastingInputs.Builder mergeExportEvaluatedDataItemsConfig(ExportEvaluatedDataItemsConfig value)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;
| Name | Description |
| value | ExportEvaluatedDataItemsConfig |
| Type | Description |
| AutoMlForecastingInputs.Builder |
mergeFrom(AutoMlForecastingInputs other)
public AutoMlForecastingInputs.Builder mergeFrom(AutoMlForecastingInputs other)| Name | Description |
| other | AutoMlForecastingInputs |
| Type | Description |
| AutoMlForecastingInputs.Builder |
mergeFrom(CodedInputStream input, ExtensionRegistryLite extensionRegistry)
public AutoMlForecastingInputs.Builder mergeFrom(CodedInputStream input, ExtensionRegistryLite extensionRegistry)| Name | Description |
| input | CodedInputStream |
| extensionRegistry | ExtensionRegistryLite |
| Type | Description |
| AutoMlForecastingInputs.Builder |
| Type | Description |
| IOException |
mergeFrom(Message other)
public AutoMlForecastingInputs.Builder mergeFrom(Message other)| Name | Description |
| other | Message |
| Type | Description |
| AutoMlForecastingInputs.Builder |
mergeUnknownFields(UnknownFieldSet unknownFields)
public final AutoMlForecastingInputs.Builder mergeUnknownFields(UnknownFieldSet unknownFields)| Name | Description |
| unknownFields | UnknownFieldSet |
| Type | Description |
| AutoMlForecastingInputs.Builder |
removeTransformations(int index)
public AutoMlForecastingInputs.Builder removeTransformations(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;
| Name | Description |
| index | int |
| Type | Description |
| AutoMlForecastingInputs.Builder |
setAdditionalExperiments(int index, String value)
public AutoMlForecastingInputs.Builder setAdditionalExperiments(int index, String value)Additional experiment flags for the time series forcasting training.
repeated string additional_experiments = 25;
| Name | Description |
| index | intThe index to set the value at. |
| value | StringThe additionalExperiments to set. |
| Type | Description |
| AutoMlForecastingInputs.Builder | This builder for chaining. |
setAvailableAtForecastColumns(int index, String value)
public AutoMlForecastingInputs.Builder setAvailableAtForecastColumns(int index, String value)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;
| Name | Description |
| index | intThe index to set the value at. |
| value | StringThe availableAtForecastColumns to set. |
| Type | Description |
| AutoMlForecastingInputs.Builder | This builder for chaining. |
setContextWindow(long value)
public AutoMlForecastingInputs.Builder setContextWindow(long value) 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;
| Name | Description |
| value | longThe contextWindow to set. |
| Type | Description |
| AutoMlForecastingInputs.Builder | This builder for chaining. |
setDataGranularity(AutoMlForecastingInputs.Granularity value)
public AutoMlForecastingInputs.Builder setDataGranularity(AutoMlForecastingInputs.Granularity value)Expected difference in time granularity between rows in the data.
.google.cloud.aiplatform.v1beta1.schema.trainingjob.definition.AutoMlForecastingInputs.Granularity data_granularity = 22;
| Name | Description |
| value | AutoMlForecastingInputs.Granularity |
| Type | Description |
| AutoMlForecastingInputs.Builder |
setDataGranularity(AutoMlForecastingInputs.Granularity.Builder builderForValue)
public AutoMlForecastingInputs.Builder setDataGranularity(AutoMlForecastingInputs.Granularity.Builder builderForValue)Expected difference in time granularity between rows in the data.
.google.cloud.aiplatform.v1beta1.schema.trainingjob.definition.AutoMlForecastingInputs.Granularity data_granularity = 22;
| Name | Description |
| builderForValue | AutoMlForecastingInputs.Granularity.Builder |
| Type | Description |
| AutoMlForecastingInputs.Builder |
setExportEvaluatedDataItemsConfig(ExportEvaluatedDataItemsConfig value)
public AutoMlForecastingInputs.Builder setExportEvaluatedDataItemsConfig(ExportEvaluatedDataItemsConfig value)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;
| Name | Description |
| value | ExportEvaluatedDataItemsConfig |
| Type | Description |
| AutoMlForecastingInputs.Builder |
setExportEvaluatedDataItemsConfig(ExportEvaluatedDataItemsConfig.Builder builderForValue)
public AutoMlForecastingInputs.Builder setExportEvaluatedDataItemsConfig(ExportEvaluatedDataItemsConfig.Builder builderForValue)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;
| Name | Description |
| builderForValue | ExportEvaluatedDataItemsConfig.Builder |
| Type | Description |
| AutoMlForecastingInputs.Builder |
setField(Descriptors.FieldDescriptor field, Object value)
public AutoMlForecastingInputs.Builder setField(Descriptors.FieldDescriptor field, Object value)| Name | Description |
| field | FieldDescriptor |
| value | Object |
| Type | Description |
| AutoMlForecastingInputs.Builder |
setForecastHorizon(long value)
public AutoMlForecastingInputs.Builder setForecastHorizon(long value) 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;
| Name | Description |
| value | longThe forecastHorizon to set. |
| Type | Description |
| AutoMlForecastingInputs.Builder | This builder for chaining. |
setOptimizationObjective(String value)
public AutoMlForecastingInputs.Builder setOptimizationObjective(String value)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;
| Name | Description |
| value | StringThe optimizationObjective to set. |
| Type | Description |
| AutoMlForecastingInputs.Builder | This builder for chaining. |
setOptimizationObjectiveBytes(ByteString value)
public AutoMlForecastingInputs.Builder setOptimizationObjectiveBytes(ByteString value)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;
| Name | Description |
| value | ByteStringThe bytes for optimizationObjective to set. |
| Type | Description |
| AutoMlForecastingInputs.Builder | This builder for chaining. |
setQuantiles(int index, double value)
public AutoMlForecastingInputs.Builder setQuantiles(int index, double value) 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;
| Name | Description |
| index | intThe index to set the value at. |
| value | doubleThe quantiles to set. |
| Type | Description |
| AutoMlForecastingInputs.Builder | This builder for chaining. |
setRepeatedField(Descriptors.FieldDescriptor field, int index, Object value)
public AutoMlForecastingInputs.Builder setRepeatedField(Descriptors.FieldDescriptor field, int index, Object value)| Name | Description |
| field | FieldDescriptor |
| index | int |
| value | Object |
| Type | Description |
| AutoMlForecastingInputs.Builder |
setTargetColumn(String value)
public AutoMlForecastingInputs.Builder setTargetColumn(String value)The name of the column that the model is to predict.
string target_column = 1;
| Name | Description |
| value | StringThe targetColumn to set. |
| Type | Description |
| AutoMlForecastingInputs.Builder | This builder for chaining. |
setTargetColumnBytes(ByteString value)
public AutoMlForecastingInputs.Builder setTargetColumnBytes(ByteString value)The name of the column that the model is to predict.
string target_column = 1;
| Name | Description |
| value | ByteStringThe bytes for targetColumn to set. |
| Type | Description |
| AutoMlForecastingInputs.Builder | This builder for chaining. |
setTimeColumn(String value)
public AutoMlForecastingInputs.Builder setTimeColumn(String value)The name of the column that identifies time order in the time series.
string time_column = 3;
| Name | Description |
| value | StringThe timeColumn to set. |
| Type | Description |
| AutoMlForecastingInputs.Builder | This builder for chaining. |
setTimeColumnBytes(ByteString value)
public AutoMlForecastingInputs.Builder setTimeColumnBytes(ByteString value)The name of the column that identifies time order in the time series.
string time_column = 3;
| Name | Description |
| value | ByteStringThe bytes for timeColumn to set. |
| Type | Description |
| AutoMlForecastingInputs.Builder | This builder for chaining. |
setTimeSeriesAttributeColumns(int index, String value)
public AutoMlForecastingInputs.Builder setTimeSeriesAttributeColumns(int index, String value)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;
| Name | Description |
| index | intThe index to set the value at. |
| value | StringThe timeSeriesAttributeColumns to set. |
| Type | Description |
| AutoMlForecastingInputs.Builder | This builder for chaining. |
setTimeSeriesIdentifierColumn(String value)
public AutoMlForecastingInputs.Builder setTimeSeriesIdentifierColumn(String value)The name of the column that identifies the time series.
string time_series_identifier_column = 2;
| Name | Description |
| value | StringThe timeSeriesIdentifierColumn to set. |
| Type | Description |
| AutoMlForecastingInputs.Builder | This builder for chaining. |
setTimeSeriesIdentifierColumnBytes(ByteString value)
public AutoMlForecastingInputs.Builder setTimeSeriesIdentifierColumnBytes(ByteString value)The name of the column that identifies the time series.
string time_series_identifier_column = 2;
| Name | Description |
| value | ByteStringThe bytes for timeSeriesIdentifierColumn to set. |
| Type | Description |
| AutoMlForecastingInputs.Builder | This builder for chaining. |
setTrainBudgetMilliNodeHours(long value)
public AutoMlForecastingInputs.Builder setTrainBudgetMilliNodeHours(long value)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;
| Name | Description |
| value | longThe trainBudgetMilliNodeHours to set. |
| Type | Description |
| AutoMlForecastingInputs.Builder | This builder for chaining. |
setTransformations(int index, AutoMlForecastingInputs.Transformation value)
public AutoMlForecastingInputs.Builder setTransformations(int index, AutoMlForecastingInputs.Transformation value)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;
| Name | Description |
| index | int |
| value | AutoMlForecastingInputs.Transformation |
| Type | Description |
| AutoMlForecastingInputs.Builder |
setTransformations(int index, AutoMlForecastingInputs.Transformation.Builder builderForValue)
public AutoMlForecastingInputs.Builder setTransformations(int index, AutoMlForecastingInputs.Transformation.Builder builderForValue)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;
| Name | Description |
| index | int |
| builderForValue | AutoMlForecastingInputs.Transformation.Builder |
| Type | Description |
| AutoMlForecastingInputs.Builder |
setUnavailableAtForecastColumns(int index, String value)
public AutoMlForecastingInputs.Builder setUnavailableAtForecastColumns(int index, String value)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;
| Name | Description |
| index | intThe index to set the value at. |
| value | StringThe unavailableAtForecastColumns to set. |
| Type | Description |
| AutoMlForecastingInputs.Builder | This builder for chaining. |
setUnknownFields(UnknownFieldSet unknownFields)
public final AutoMlForecastingInputs.Builder setUnknownFields(UnknownFieldSet unknownFields)| Name | Description |
| unknownFields | UnknownFieldSet |
| Type | Description |
| AutoMlForecastingInputs.Builder |
setValidationOptions(String value)
public AutoMlForecastingInputs.Builder setValidationOptions(String value)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;
| Name | Description |
| value | StringThe validationOptions to set. |
| Type | Description |
| AutoMlForecastingInputs.Builder | This builder for chaining. |
setValidationOptionsBytes(ByteString value)
public AutoMlForecastingInputs.Builder setValidationOptionsBytes(ByteString value)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;
| Name | Description |
| value | ByteStringThe bytes for validationOptions to set. |
| Type | Description |
| AutoMlForecastingInputs.Builder | This builder for chaining. |
setWeightColumn(String value)
public AutoMlForecastingInputs.Builder setWeightColumn(String value)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;
| Name | Description |
| value | StringThe weightColumn to set. |
| Type | Description |
| AutoMlForecastingInputs.Builder | This builder for chaining. |
setWeightColumnBytes(ByteString value)
public AutoMlForecastingInputs.Builder setWeightColumnBytes(ByteString value)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;
| Name | Description |
| value | ByteStringThe bytes for weightColumn to set. |
| Type | Description |
| AutoMlForecastingInputs.Builder | This builder for chaining. |