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/*
* Copyright 2019-2024 Amazon.com, Inc. or its affiliates. All Rights Reserved.
*
* Licensed under the Apache License, Version 2.0 (the "License"). You may not use this file except in compliance with
* the License. A copy of the License is located at
*
* http://aws.amazon.com/apache2.0
*
* or in the "license" file accompanying this file. This file is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR
* CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions
* and limitations under the License.
*/
package com.amazonaws.services.forecast.model;
import java.io.Serializable;
import javax.annotation.Generated;
import com.amazonaws.protocol.StructuredPojo;
import com.amazonaws.protocol.ProtocolMarshaller;
/**
*
* Provides information about the method used to transform attributes.
*
*
* The following is an example using the RETAIL domain:
*
*
* {
*
*
* "AttributeName": "demand",
*
*
* "Transformations": {"aggregation": "sum", "middlefill": "zero", "backfill": "zero"}
*
*
* }
*
*
* @see AWS API
* Documentation
*/
@Generated("com.amazonaws:aws-java-sdk-code-generator")
public class AttributeConfig implements Serializable, Cloneable, StructuredPojo {
/**
*
* The name of the attribute as specified in the schema. Amazon Forecast supports the target field of the target
* time series and the related time series datasets. For example, for the RETAIL domain, the target is
* demand.
*
*/
private String attributeName;
/**
*
* The method parameters (key-value pairs), which are a map of override parameters. Specify these parameters to
* override the default values. Related Time Series attributes do not accept aggregation parameters.
*
*
* The following list shows the parameters and their valid values for the "filling" featurization method for a
* Target Time Series dataset. Default values are bolded.
*
*
* -
*
* aggregation: sum, avg, first, min, max
*
*
* -
*
* frontfill: none
*
*
* -
*
* middlefill: zero, nan (not a number), value, median,
* mean, min, max
*
*
* -
*
* backfill: zero, nan, value, median, mean,
* min, max
*
*
*
*
* The following list shows the parameters and their valid values for a Related Time Series featurization
* method (there are no defaults):
*
*
* -
*
* middlefill: zero, value, median, mean,
* min, max
*
*
* -
*
* backfill: zero, value, median, mean,
* min, max
*
*
* -
*
* futurefill: zero, value, median, mean,
* min, max
*
*
*
*
* To set a filling method to a specific value, set the fill parameter to value and define the value in
* a corresponding _value parameter. For example, to set backfilling to a value of 2, include the
* following: "backfill": "value" and "backfill_value":"2".
*
*/
private java.util.Map transformations;
/**
*
* The name of the attribute as specified in the schema. Amazon Forecast supports the target field of the target
* time series and the related time series datasets. For example, for the RETAIL domain, the target is
* demand.
*
*
* @param attributeName
* The name of the attribute as specified in the schema. Amazon Forecast supports the target field of the
* target time series and the related time series datasets. For example, for the RETAIL domain, the target is
* demand.
*/
public void setAttributeName(String attributeName) {
this.attributeName = attributeName;
}
/**
*
* The name of the attribute as specified in the schema. Amazon Forecast supports the target field of the target
* time series and the related time series datasets. For example, for the RETAIL domain, the target is
* demand.
*
*
* @return The name of the attribute as specified in the schema. Amazon Forecast supports the target field of the
* target time series and the related time series datasets. For example, for the RETAIL domain, the target
* is demand.
*/
public String getAttributeName() {
return this.attributeName;
}
/**
*
* The name of the attribute as specified in the schema. Amazon Forecast supports the target field of the target
* time series and the related time series datasets. For example, for the RETAIL domain, the target is
* demand.
*
*
* @param attributeName
* The name of the attribute as specified in the schema. Amazon Forecast supports the target field of the
* target time series and the related time series datasets. For example, for the RETAIL domain, the target is
* demand.
* @return Returns a reference to this object so that method calls can be chained together.
*/
public AttributeConfig withAttributeName(String attributeName) {
setAttributeName(attributeName);
return this;
}
/**
*
* The method parameters (key-value pairs), which are a map of override parameters. Specify these parameters to
* override the default values. Related Time Series attributes do not accept aggregation parameters.
*
*
* The following list shows the parameters and their valid values for the "filling" featurization method for a
* Target Time Series dataset. Default values are bolded.
*
*
* -
*
* aggregation: sum, avg, first, min, max
*
*
* -
*
* frontfill: none
*
*
* -
*
* middlefill: zero, nan (not a number), value, median,
* mean, min, max
*
*
* -
*
* backfill: zero, nan, value, median, mean,
* min, max
*
*
*
*
* The following list shows the parameters and their valid values for a Related Time Series featurization
* method (there are no defaults):
*
*
* -
*
* middlefill: zero, value, median, mean,
* min, max
*
*
* -
*
* backfill: zero, value, median, mean,
* min, max
*
*
* -
*
* futurefill: zero, value, median, mean,
* min, max
*
*
*
*
* To set a filling method to a specific value, set the fill parameter to value and define the value in
* a corresponding _value parameter. For example, to set backfilling to a value of 2, include the
* following: "backfill": "value" and "backfill_value":"2".
*
*
* @return The method parameters (key-value pairs), which are a map of override parameters. Specify these parameters
* to override the default values. Related Time Series attributes do not accept aggregation parameters.
*
* The following list shows the parameters and their valid values for the "filling" featurization method for
* a Target Time Series dataset. Default values are bolded.
*
*
* -
*
* aggregation: sum, avg, first, min,
* max
*
*
* -
*
* frontfill: none
*
*
* -
*
* middlefill: zero, nan (not a number), value,
* median, mean, min, max
*
*
* -
*
* backfill: zero, nan, value, median,
* mean, min, max
*
*
*
*
* The following list shows the parameters and their valid values for a Related Time Series
* featurization method (there are no defaults):
*
*
* -
*
* middlefill: zero, value, median, mean,
* min, max
*
*
* -
*
* backfill: zero, value, median, mean,
* min, max
*
*
* -
*
* futurefill: zero, value, median, mean,
* min, max
*
*
*
*
* To set a filling method to a specific value, set the fill parameter to value and define the
* value in a corresponding _value parameter. For example, to set backfilling to a value of 2,
* include the following: "backfill": "value" and "backfill_value":"2".
*/
public java.util.Map getTransformations() {
return transformations;
}
/**
*
* The method parameters (key-value pairs), which are a map of override parameters. Specify these parameters to
* override the default values. Related Time Series attributes do not accept aggregation parameters.
*
*
* The following list shows the parameters and their valid values for the "filling" featurization method for a
* Target Time Series dataset. Default values are bolded.
*
*
* -
*
* aggregation: sum, avg, first, min, max
*
*
* -
*
* frontfill: none
*
*
* -
*
* middlefill: zero, nan (not a number), value, median,
* mean, min, max
*
*
* -
*
* backfill: zero, nan, value, median, mean,
* min, max
*
*
*
*
* The following list shows the parameters and their valid values for a Related Time Series featurization
* method (there are no defaults):
*
*
* -
*
* middlefill: zero, value, median, mean,
* min, max
*
*
* -
*
* backfill: zero, value, median, mean,
* min, max
*
*
* -
*
* futurefill: zero, value, median, mean,
* min, max
*
*
*
*
* To set a filling method to a specific value, set the fill parameter to value and define the value in
* a corresponding _value parameter. For example, to set backfilling to a value of 2, include the
* following: "backfill": "value" and "backfill_value":"2".
*
*
* @param transformations
* The method parameters (key-value pairs), which are a map of override parameters. Specify these parameters
* to override the default values. Related Time Series attributes do not accept aggregation parameters.
*
* The following list shows the parameters and their valid values for the "filling" featurization method for
* a Target Time Series dataset. Default values are bolded.
*
*
* -
*
* aggregation: sum, avg, first, min,
* max
*
*
* -
*
* frontfill: none
*
*
* -
*
* middlefill: zero, nan (not a number), value,
* median, mean, min, max
*
*
* -
*
* backfill: zero, nan, value, median,
* mean, min, max
*
*
*
*
* The following list shows the parameters and their valid values for a Related Time Series
* featurization method (there are no defaults):
*
*
* -
*
* middlefill: zero, value, median, mean,
* min, max
*
*
* -
*
* backfill: zero, value, median, mean,
* min, max
*
*
* -
*
* futurefill: zero, value, median, mean,
* min, max
*
*
*
*
* To set a filling method to a specific value, set the fill parameter to value and define the
* value in a corresponding _value parameter. For example, to set backfilling to a value of 2,
* include the following: "backfill": "value" and "backfill_value":"2".
*/
public void setTransformations(java.util.Map transformations) {
this.transformations = transformations;
}
/**
*
* The method parameters (key-value pairs), which are a map of override parameters. Specify these parameters to
* override the default values. Related Time Series attributes do not accept aggregation parameters.
*
*
* The following list shows the parameters and their valid values for the "filling" featurization method for a
* Target Time Series dataset. Default values are bolded.
*
*
* -
*
* aggregation: sum, avg, first, min, max
*
*
* -
*
* frontfill: none
*
*
* -
*
* middlefill: zero, nan (not a number), value, median,
* mean, min, max
*
*
* -
*
* backfill: zero, nan, value, median, mean,
* min, max
*
*
*
*
* The following list shows the parameters and their valid values for a Related Time Series featurization
* method (there are no defaults):
*
*
* -
*
* middlefill: zero, value, median, mean,
* min, max
*
*
* -
*
* backfill: zero, value, median, mean,
* min, max
*
*
* -
*
* futurefill: zero, value, median, mean,
* min, max
*
*
*
*
* To set a filling method to a specific value, set the fill parameter to value and define the value in
* a corresponding _value parameter. For example, to set backfilling to a value of 2, include the
* following: "backfill": "value" and "backfill_value":"2".
*
*
* @param transformations
* The method parameters (key-value pairs), which are a map of override parameters. Specify these parameters
* to override the default values. Related Time Series attributes do not accept aggregation parameters.
*
* The following list shows the parameters and their valid values for the "filling" featurization method for
* a Target Time Series dataset. Default values are bolded.
*
*
* -
*
* aggregation: sum, avg, first, min,
* max
*
*
* -
*
* frontfill: none
*
*
* -
*
* middlefill: zero, nan (not a number), value,
* median, mean, min, max
*
*
* -
*
* backfill: zero, nan, value, median,
* mean, min, max
*
*
*
*
* The following list shows the parameters and their valid values for a Related Time Series
* featurization method (there are no defaults):
*
*
* -
*
* middlefill: zero, value, median, mean,
* min, max
*
*
* -
*
* backfill: zero, value, median, mean,
* min, max
*
*
* -
*
* futurefill: zero, value, median, mean,
* min, max
*
*
*
*
* To set a filling method to a specific value, set the fill parameter to value and define the
* value in a corresponding _value parameter. For example, to set backfilling to a value of 2,
* include the following: "backfill": "value" and "backfill_value":"2".
* @return Returns a reference to this object so that method calls can be chained together.
*/
public AttributeConfig withTransformations(java.util.Map transformations) {
setTransformations(transformations);
return this;
}
/**
* Add a single Transformations entry
*
* @see AttributeConfig#withTransformations
* @returns a reference to this object so that method calls can be chained together.
*/
public AttributeConfig addTransformationsEntry(String key, String value) {
if (null == this.transformations) {
this.transformations = new java.util.HashMap();
}
if (this.transformations.containsKey(key))
throw new IllegalArgumentException("Duplicated keys (" + key.toString() + ") are provided.");
this.transformations.put(key, value);
return this;
}
/**
* Removes all the entries added into Transformations.
*
* @return Returns a reference to this object so that method calls can be chained together.
*/
public AttributeConfig clearTransformationsEntries() {
this.transformations = null;
return this;
}
/**
* 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.
*
* @return A string representation of this object.
*
* @see java.lang.Object#toString()
*/
@Override
public String toString() {
StringBuilder sb = new StringBuilder();
sb.append("{");
if (getAttributeName() != null)
sb.append("AttributeName: ").append(getAttributeName()).append(",");
if (getTransformations() != null)
sb.append("Transformations: ").append(getTransformations());
sb.append("}");
return sb.toString();
}
@Override
public boolean equals(Object obj) {
if (this == obj)
return true;
if (obj == null)
return false;
if (obj instanceof AttributeConfig == false)
return false;
AttributeConfig other = (AttributeConfig) obj;
if (other.getAttributeName() == null ^ this.getAttributeName() == null)
return false;
if (other.getAttributeName() != null && other.getAttributeName().equals(this.getAttributeName()) == false)
return false;
if (other.getTransformations() == null ^ this.getTransformations() == null)
return false;
if (other.getTransformations() != null && other.getTransformations().equals(this.getTransformations()) == false)
return false;
return true;
}
@Override
public int hashCode() {
final int prime = 31;
int hashCode = 1;
hashCode = prime * hashCode + ((getAttributeName() == null) ? 0 : getAttributeName().hashCode());
hashCode = prime * hashCode + ((getTransformations() == null) ? 0 : getTransformations().hashCode());
return hashCode;
}
@Override
public AttributeConfig clone() {
try {
return (AttributeConfig) super.clone();
} catch (CloneNotSupportedException e) {
throw new IllegalStateException("Got a CloneNotSupportedException from Object.clone() " + "even though we're Cloneable!", e);
}
}
@com.amazonaws.annotation.SdkInternalApi
@Override
public void marshall(ProtocolMarshaller protocolMarshaller) {
com.amazonaws.services.forecast.model.transform.AttributeConfigMarshaller.getInstance().marshall(this, protocolMarshaller);
}
}