Class AutoMlForecastingInputs (3.52.0) 

public final class AutoMlForecastingInputs extends GeneratedMessageV3 implements AutoMlForecastingInputsOrBuilder

Protobuf type google.cloud.aiplatform.v1beta1.schema.trainingjob.definition.AutoMlForecastingInputs

Inheritance

Object > AbstractMessageLite<MessageType,BuilderType> > AbstractMessage > GeneratedMessageV3 > AutoMlForecastingInputs

Static Fields

ADDITIONAL_EXPERIMENTS_FIELD_NUMBER

public static final int ADDITIONAL_EXPERIMENTS_FIELD_NUMBER
Field Value
Type Description
int

AVAILABLE_AT_FORECAST_COLUMNS_FIELD_NUMBER

public static final int AVAILABLE_AT_FORECAST_COLUMNS_FIELD_NUMBER
Field Value
Type Description
int

CONTEXT_WINDOW_FIELD_NUMBER

public static final int CONTEXT_WINDOW_FIELD_NUMBER
Field Value
Type Description
int

DATA_GRANULARITY_FIELD_NUMBER

public static final int DATA_GRANULARITY_FIELD_NUMBER
Field Value
Type Description
int

EXPORT_EVALUATED_DATA_ITEMS_CONFIG_FIELD_NUMBER

public static final int EXPORT_EVALUATED_DATA_ITEMS_CONFIG_FIELD_NUMBER
Field Value
Type Description
int

FORECAST_HORIZON_FIELD_NUMBER

public static final int FORECAST_HORIZON_FIELD_NUMBER
Field Value
Type Description
int

OPTIMIZATION_OBJECTIVE_FIELD_NUMBER

public static final int OPTIMIZATION_OBJECTIVE_FIELD_NUMBER
Field Value
Type Description
int

QUANTILES_FIELD_NUMBER

public static final int QUANTILES_FIELD_NUMBER
Field Value
Type Description
int

TARGET_COLUMN_FIELD_NUMBER

public static final int TARGET_COLUMN_FIELD_NUMBER
Field Value
Type Description
int

TIME_COLUMN_FIELD_NUMBER

public static final int TIME_COLUMN_FIELD_NUMBER
Field Value
Type Description
int

TIME_SERIES_ATTRIBUTE_COLUMNS_FIELD_NUMBER

public static final int TIME_SERIES_ATTRIBUTE_COLUMNS_FIELD_NUMBER
Field Value
Type Description
int

TIME_SERIES_IDENTIFIER_COLUMN_FIELD_NUMBER

public static final int TIME_SERIES_IDENTIFIER_COLUMN_FIELD_NUMBER
Field Value
Type Description
int

TRAIN_BUDGET_MILLI_NODE_HOURS_FIELD_NUMBER

public static final int TRAIN_BUDGET_MILLI_NODE_HOURS_FIELD_NUMBER
Field Value
Type Description
int

TRANSFORMATIONS_FIELD_NUMBER

public static final int TRANSFORMATIONS_FIELD_NUMBER
Field Value
Type Description
int

UNAVAILABLE_AT_FORECAST_COLUMNS_FIELD_NUMBER

public static final int UNAVAILABLE_AT_FORECAST_COLUMNS_FIELD_NUMBER
Field Value
Type Description
int

VALIDATION_OPTIONS_FIELD_NUMBER

public static final int VALIDATION_OPTIONS_FIELD_NUMBER
Field Value
Type Description
int

WEIGHT_COLUMN_FIELD_NUMBER

public static final int WEIGHT_COLUMN_FIELD_NUMBER
Field Value
Type Description
int

Static Methods

getDefaultInstance()

public static AutoMlForecastingInputs getDefaultInstance()
Returns
Type Description
AutoMlForecastingInputs

getDescriptor()

public static final Descriptors.Descriptor getDescriptor()
Returns
Type Description
Descriptor

newBuilder()

public static AutoMlForecastingInputs.Builder newBuilder()
Returns
Type Description
AutoMlForecastingInputs.Builder

newBuilder(AutoMlForecastingInputs prototype)

public static AutoMlForecastingInputs.Builder newBuilder(AutoMlForecastingInputs prototype)
Parameter
Name Description
prototype AutoMlForecastingInputs
Returns
Type Description
AutoMlForecastingInputs.Builder

parseDelimitedFrom(InputStream input)

public static AutoMlForecastingInputs parseDelimitedFrom(InputStream input)
Parameter
Name Description
input InputStream
Returns
Type Description
AutoMlForecastingInputs
Exceptions
Type Description
IOException

parseDelimitedFrom(InputStream input, ExtensionRegistryLite extensionRegistry)

public static AutoMlForecastingInputs parseDelimitedFrom(InputStream input, ExtensionRegistryLite extensionRegistry)
Parameters
Name Description
input InputStream
extensionRegistry ExtensionRegistryLite
Returns
Type Description
AutoMlForecastingInputs
Exceptions
Type Description
IOException

parseFrom(byte[] data)

public static AutoMlForecastingInputs parseFrom(byte[] data)
Parameter
Name Description
data byte[]
Returns
Type Description
AutoMlForecastingInputs
Exceptions
Type Description
InvalidProtocolBufferException

parseFrom(byte[] data, ExtensionRegistryLite extensionRegistry)

public static AutoMlForecastingInputs parseFrom(byte[] data, ExtensionRegistryLite extensionRegistry)
Parameters
Name Description
data byte[]
extensionRegistry ExtensionRegistryLite
Returns
Type Description
AutoMlForecastingInputs
Exceptions
Type Description
InvalidProtocolBufferException

parseFrom(ByteString data)

public static AutoMlForecastingInputs parseFrom(ByteString data)
Parameter
Name Description
data ByteString
Returns
Type Description
AutoMlForecastingInputs
Exceptions
Type Description
InvalidProtocolBufferException

parseFrom(ByteString data, ExtensionRegistryLite extensionRegistry)

public static AutoMlForecastingInputs parseFrom(ByteString data, ExtensionRegistryLite extensionRegistry)
Parameters
Name Description
data ByteString
extensionRegistry ExtensionRegistryLite
Returns
Type Description
AutoMlForecastingInputs
Exceptions
Type Description
InvalidProtocolBufferException

parseFrom(CodedInputStream input)

public static AutoMlForecastingInputs parseFrom(CodedInputStream input)
Parameter
Name Description
input CodedInputStream
Returns
Type Description
AutoMlForecastingInputs
Exceptions
Type Description
IOException

parseFrom(CodedInputStream input, ExtensionRegistryLite extensionRegistry)

public static AutoMlForecastingInputs parseFrom(CodedInputStream input, ExtensionRegistryLite extensionRegistry)
Parameters
Name Description
input CodedInputStream
extensionRegistry ExtensionRegistryLite
Returns
Type Description
AutoMlForecastingInputs
Exceptions
Type Description
IOException

parseFrom(InputStream input)

public static AutoMlForecastingInputs parseFrom(InputStream input)
Parameter
Name Description
input InputStream
Returns
Type Description
AutoMlForecastingInputs
Exceptions
Type Description
IOException

parseFrom(InputStream input, ExtensionRegistryLite extensionRegistry)

public static AutoMlForecastingInputs parseFrom(InputStream input, ExtensionRegistryLite extensionRegistry)
Parameters
Name Description
input InputStream
extensionRegistry ExtensionRegistryLite
Returns
Type Description
AutoMlForecastingInputs
Exceptions
Type Description
IOException

parseFrom(ByteBuffer data)

public static AutoMlForecastingInputs parseFrom(ByteBuffer data)
Parameter
Name Description
data ByteBuffer
Returns
Type Description
AutoMlForecastingInputs
Exceptions
Type Description
InvalidProtocolBufferException

parseFrom(ByteBuffer data, ExtensionRegistryLite extensionRegistry)

public static AutoMlForecastingInputs parseFrom(ByteBuffer data, ExtensionRegistryLite extensionRegistry)
Parameters
Name Description
data ByteBuffer
extensionRegistry ExtensionRegistryLite
Returns
Type Description
AutoMlForecastingInputs
Exceptions
Type Description
InvalidProtocolBufferException

parser()

public static Parser<AutoMlForecastingInputs> parser()
Returns
Type Description
Parser<AutoMlForecastingInputs>

Methods

equals(Object obj)

public boolean equals(Object obj)
Parameter
Name Description
obj Object
Returns
Type Description
boolean
Overrides

getAdditionalExperiments(int index)

public 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 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 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 ProtocolStringList getAdditionalExperimentsList()

Additional experiment flags for the time series forcasting training.

repeated string additional_experiments = 25;

Returns
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;

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 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 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 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;

Returns
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;

Returns
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;

Returns
Type Description
AutoMlForecastingInputs.Granularity

The dataGranularity.

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;

Returns
Type Description
AutoMlForecastingInputs.GranularityOrBuilder

getDefaultInstanceForType()

public AutoMlForecastingInputs getDefaultInstanceForType()
Returns
Type Description
AutoMlForecastingInputs

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;

Returns
Type Description
ExportEvaluatedDataItemsConfig

The exportEvaluatedDataItemsConfig.

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;

Returns
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;

Returns
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;

Returns
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;

Returns
Type Description
ByteString

The bytes for optimizationObjective.

getParserForType()

public Parser<AutoMlForecastingInputs> getParserForType()
Returns
Type Description
Parser<AutoMlForecastingInputs>
Overrides

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;

Parameter
Name Description
index int

The index of the element to return.
Returns
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;

Returns
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;

Returns
Type Description
List<Double>

A list containing the quantiles.

getSerializedSize()

public int getSerializedSize()
Returns
Type Description
int
Overrides

getTargetColumn()

public 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 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 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 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 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 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 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 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;

Returns
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;

Returns
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;

Returns
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;

Returns
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;

Parameter
Name Description
index int
Returns
Type Description
AutoMlForecastingInputs.Transformation

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;

Returns
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;

Returns
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;

Parameter
Name Description
index int
Returns
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;

Returns
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;

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 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 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 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;

Returns
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;

Returns
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;

Returns
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;

Returns
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;

Returns
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;

Returns
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;

Returns
Type Description
boolean

Whether the exportEvaluatedDataItemsConfig field is set.

hashCode()

public int hashCode()
Returns
Type Description
int
Overrides

internalGetFieldAccessorTable()

protected GeneratedMessageV3.FieldAccessorTable internalGetFieldAccessorTable()
Returns
Type Description
FieldAccessorTable
Overrides

isInitialized()

public final boolean isInitialized()
Returns
Type Description
boolean
Overrides

newBuilderForType()

public AutoMlForecastingInputs.Builder newBuilderForType()
Returns
Type Description
AutoMlForecastingInputs.Builder

newBuilderForType(GeneratedMessageV3.BuilderParent parent)

protected AutoMlForecastingInputs.Builder newBuilderForType(GeneratedMessageV3.BuilderParent parent)
Parameter
Name Description
parent BuilderParent
Returns
Type Description
AutoMlForecastingInputs.Builder
Overrides

newInstance(GeneratedMessageV3.UnusedPrivateParameter unused)

protected Object newInstance(GeneratedMessageV3.UnusedPrivateParameter unused)
Parameter
Name Description
unused UnusedPrivateParameter
Returns
Type Description
Object
Overrides

toBuilder()

public AutoMlForecastingInputs.Builder toBuilder()
Returns
Type Description
AutoMlForecastingInputs.Builder

writeTo(CodedOutputStream output)

public void writeTo(CodedOutputStream output)
Parameter
Name Description
output CodedOutputStream
Overrides
Exceptions
Type Description
IOException