java.lang.Object
- software.amazon.awssdk.services.forecast.model.FeaturizationConfig

All Implemented Interfaces:

Serializable, SdkPojo, ToCopyableBuilder<FeaturizationConfig.Builder,FeaturizationConfig>
```
@Generated("software.amazon.awssdk:codegen")
public final class FeaturizationConfig
extends Object
implements SdkPojo, Serializable, ToCopyableBuilder<FeaturizationConfig.Builder,FeaturizationConfig>
```
This object belongs to the CreatePredictor operation. If you created your predictor with CreateAutoPredictor, see AttributeConfig.

In a CreatePredictor operation, the specified algorithm trains a model using the specified dataset group. You can optionally tell the operation to modify data fields prior to training a model. These modifications are referred to as featurization.

You define featurization using the FeaturizationConfig object. You specify an array of transformations, one for each field that you want to featurize. You then include the FeaturizationConfig object in your CreatePredictor request. Amazon Forecast applies the featurization to the TARGET_TIME_SERIES and RELATED_TIME_SERIES datasets before model training.

You can create multiple featurization configurations. For example, you might call the CreatePredictor operation twice by specifying different featurization configurations.

See Also:

Serialized Form

Nested Class Summary

Nested Classes
Modifier and Type Class Description

static interface FeaturizationConfig.Builder

Method Summary

All Methods Static Methods Instance Methods Concrete Methods
Modifier and Type	Method	Description
`static FeaturizationConfig.Builder`	`builder()`
`boolean`	`equals(Object obj)`
`boolean`	`equalsBySdkFields(Object obj)`
`List<Featurization>`	`featurizations()`	An array of featurization (transformation) information for the fields of a dataset.
`List<String>`	`forecastDimensions()`	An array of dimension (field) names that specify how to group the generated forecast.
`String`	`forecastFrequency()`	The frequency of predictions in a forecast.
`<T> Optional<T>`	`getValueForField(String fieldName, Class<T> clazz)`
`boolean`	`hasFeaturizations()`	For responses, this returns true if the service returned a value for the Featurizations property.
`boolean`	`hasForecastDimensions()`	For responses, this returns true if the service returned a value for the ForecastDimensions property.
`int`	`hashCode()`
`List<SdkField<?>>`	`sdkFields()`
`static Class<? extends FeaturizationConfig.Builder>`	`serializableBuilderClass()`
`FeaturizationConfig.Builder`	`toBuilder()`
`String`	`toString()`	Returns a string representation of this object.

Methods inherited from class java.lang.Object
clone, finalize, getClass, notify, notifyAll, wait, wait, wait

Methods inherited from interface software.amazon.awssdk.utils.builder.ToCopyableBuilder
copy

- Method Detail
  - forecastFrequency
```
public final String forecastFrequency()
```
    The frequency of predictions in a forecast.
    
    Valid intervals are an integer followed by Y (Year), M (Month), W (Week), D (Day), H (Hour), and min (Minute). For example, "1D" indicates every day and "15min" indicates every 15 minutes. You cannot specify a value that would overlap with the next larger frequency. That means, for example, you cannot specify a frequency of 60 minutes, because that is equivalent to 1 hour. The valid values for each frequency are the following:
    
    Minute - 1-59
    
    Hour - 1-23
    
    Day - 1-6
    
    Week - 1-4
    
    Month - 1-11
    
    Year - 1
    
    Thus, if you want every other week forecasts, specify "2W". Or, if you want quarterly forecasts, you specify "3M".
    
    The frequency must be greater than or equal to the TARGET_TIME_SERIES dataset frequency.
    
    When a RELATED_TIME_SERIES dataset is provided, the frequency must be equal to the TARGET_TIME_SERIES dataset frequency.
    Returns:
    
    The frequency of predictions in a forecast.
    
    Valid intervals are an integer followed by Y (Year), M (Month), W (Week), D (Day), H (Hour), and min (Minute). For example, "1D" indicates every day and "15min" indicates every 15 minutes. You cannot specify a value that would overlap with the next larger frequency. That means, for example, you cannot specify a frequency of 60 minutes, because that is equivalent to 1 hour. The valid values for each frequency are the following:
    
    Minute - 1-59
    
    Hour - 1-23
    
    Day - 1-6
    
    Week - 1-4
    
    Month - 1-11
    
    Year - 1
    
    Thus, if you want every other week forecasts, specify "2W". Or, if you want quarterly forecasts, you specify "3M".
    
    The frequency must be greater than or equal to the TARGET_TIME_SERIES dataset frequency.
    
    When a RELATED_TIME_SERIES dataset is provided, the frequency must be equal to the TARGET_TIME_SERIES dataset frequency.
  - hasForecastDimensions
```
public final boolean hasForecastDimensions()
```
    For responses, this returns true if the service returned a value for the ForecastDimensions property. This DOES NOT check that the value is non-empty (for which, you should check the isEmpty() method on the property). This is useful because the SDK will never return a null collection or map, but you may need to differentiate between the service returning nothing (or null) and the service returning an empty collection or map. For requests, this returns true if a value for the property was specified in the request builder, and false if a value was not specified.
  - forecastDimensions
```
public final List<String> forecastDimensions()
```
    An array of dimension (field) names that specify how to group the generated forecast.
    
    For example, suppose that you are generating a forecast for item sales across all of your stores, and your dataset contains a store_id field. If you want the sales forecast for each item by store, you would specify store_id as the dimension.
    
    All forecast dimensions specified in the TARGET_TIME_SERIES dataset don't need to be specified in the CreatePredictor request. All forecast dimensions specified in the RELATED_TIME_SERIES dataset must be specified in the CreatePredictor request.
    
    Attempts to modify the collection returned by this method will result in an UnsupportedOperationException.
    
    This method will never return null. If you would like to know whether the service returned this field (so that you can differentiate between null and empty), you can use the hasForecastDimensions() method.
    
    Returns:
    
    An array of dimension (field) names that specify how to group the generated forecast.
    
    For example, suppose that you are generating a forecast for item sales across all of your stores, and your dataset contains a store_id field. If you want the sales forecast for each item by store, you would specify store_id as the dimension.
    
    All forecast dimensions specified in the TARGET_TIME_SERIES dataset don't need to be specified in the CreatePredictor request. All forecast dimensions specified in the RELATED_TIME_SERIES dataset must be specified in the CreatePredictor request.
  - hasFeaturizations
```
public final boolean hasFeaturizations()
```
    For responses, this returns true if the service returned a value for the Featurizations property. This DOES NOT check that the value is non-empty (for which, you should check the isEmpty() method on the property). This is useful because the SDK will never return a null collection or map, but you may need to differentiate between the service returning nothing (or null) and the service returning an empty collection or map. For requests, this returns true if a value for the property was specified in the request builder, and false if a value was not specified.
  - featurizations
```
public final List<Featurization> featurizations()
```
    An array of featurization (transformation) information for the fields of a dataset.
    
    Attempts to modify the collection returned by this method will result in an UnsupportedOperationException.
    
    This method will never return null. If you would like to know whether the service returned this field (so that you can differentiate between null and empty), you can use the hasFeaturizations() method.
    
    Returns:
    
    An array of featurization (transformation) information for the fields of a dataset.
  - toBuilder
```
public FeaturizationConfig.Builder toBuilder()
```
    Specified by:
    
    toBuilder in interface ToCopyableBuilder<FeaturizationConfig.Builder,FeaturizationConfig>
  - builder
```
public static FeaturizationConfig.Builder builder()
```
  - serializableBuilderClass
```
public static Class<? extends FeaturizationConfig.Builder> serializableBuilderClass()
```
  - hashCode
```
public final int hashCode()
```
    Overrides:
    
    hashCode in class Object
  - equals
```
public final boolean equals(Object obj)
```
    Overrides:
    
    equals in class Object
  - equalsBySdkFields
```
public final boolean equalsBySdkFields(Object obj)
```
    Specified by:
    
    equalsBySdkFields in interface SdkPojo
  - toString
```
public final String toString()
```
    Returns a string representation of this object. This is useful for testing and debugging. Sensitive data will be redacted from this string using a placeholder value.
    
    Overrides:
    
    toString in class Object
  - getValueForField
```
public final <T> Optional<T> getValueForField(String fieldName,
                                              Class<T> clazz)
```
  - sdkFields
```
public final List<SdkField<?>> sdkFields()
```
    Specified by:
    
    sdkFields in interface SdkPojo

Class FeaturizationConfig

Nested Class Summary

Method Summary

Methods inherited from class java.lang.Object

Methods inherited from interface software.amazon.awssdk.utils.builder.ToCopyableBuilder

Method Detail

forecastFrequency

hasForecastDimensions

forecastDimensions

hasFeaturizations

featurizations

toBuilder

builder

serializableBuilderClass

hashCode

equals

equalsBySdkFields

toString

getValueForField

sdkFields