com.amazonaws.services.forecast.model.EvaluationParameters Maven / Gradle / Ivy
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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;
/**
*
* Parameters that define how to split a dataset into training data and testing data, and the number of iterations to
* perform. These parameters are specified in the predefined algorithms but you can override them in the
* CreatePredictor request.
*
*
* @see AWS API
* Documentation
*/
@Generated("com.amazonaws:aws-java-sdk-code-generator")
public class EvaluationParameters implements Serializable, Cloneable, StructuredPojo {
/**
*
* The number of times to split the input data. The default is 1. Valid values are 1 through 5.
*
*/
private Integer numberOfBacktestWindows;
/**
*
* The point from the end of the dataset where you want to split the data for model training and testing
* (evaluation). Specify the value as the number of data points. The default is the value of the forecast horizon.
* BackTestWindowOffset can be used to mimic a past virtual forecast start date. This value must be
* greater than or equal to the forecast horizon and less than half of the TARGET_TIME_SERIES dataset length.
*
*
* ForecastHorizon <= BackTestWindowOffset < 1/2 * TARGET_TIME_SERIES dataset length
*
*/
private Integer backTestWindowOffset;
/**
*
* The number of times to split the input data. The default is 1. Valid values are 1 through 5.
*
*
* @param numberOfBacktestWindows
* The number of times to split the input data. The default is 1. Valid values are 1 through 5.
*/
public void setNumberOfBacktestWindows(Integer numberOfBacktestWindows) {
this.numberOfBacktestWindows = numberOfBacktestWindows;
}
/**
*
* The number of times to split the input data. The default is 1. Valid values are 1 through 5.
*
*
* @return The number of times to split the input data. The default is 1. Valid values are 1 through 5.
*/
public Integer getNumberOfBacktestWindows() {
return this.numberOfBacktestWindows;
}
/**
*
* The number of times to split the input data. The default is 1. Valid values are 1 through 5.
*
*
* @param numberOfBacktestWindows
* The number of times to split the input data. The default is 1. Valid values are 1 through 5.
* @return Returns a reference to this object so that method calls can be chained together.
*/
public EvaluationParameters withNumberOfBacktestWindows(Integer numberOfBacktestWindows) {
setNumberOfBacktestWindows(numberOfBacktestWindows);
return this;
}
/**
*
* The point from the end of the dataset where you want to split the data for model training and testing
* (evaluation). Specify the value as the number of data points. The default is the value of the forecast horizon.
* BackTestWindowOffset can be used to mimic a past virtual forecast start date. This value must be
* greater than or equal to the forecast horizon and less than half of the TARGET_TIME_SERIES dataset length.
*
*
* ForecastHorizon <= BackTestWindowOffset < 1/2 * TARGET_TIME_SERIES dataset length
*
*
* @param backTestWindowOffset
* The point from the end of the dataset where you want to split the data for model training and testing
* (evaluation). Specify the value as the number of data points. The default is the value of the forecast
* horizon. BackTestWindowOffset can be used to mimic a past virtual forecast start date. This
* value must be greater than or equal to the forecast horizon and less than half of the TARGET_TIME_SERIES
* dataset length.
*
* ForecastHorizon <= BackTestWindowOffset < 1/2 * TARGET_TIME_SERIES dataset
* length
*/
public void setBackTestWindowOffset(Integer backTestWindowOffset) {
this.backTestWindowOffset = backTestWindowOffset;
}
/**
*
* The point from the end of the dataset where you want to split the data for model training and testing
* (evaluation). Specify the value as the number of data points. The default is the value of the forecast horizon.
* BackTestWindowOffset can be used to mimic a past virtual forecast start date. This value must be
* greater than or equal to the forecast horizon and less than half of the TARGET_TIME_SERIES dataset length.
*
*
* ForecastHorizon <= BackTestWindowOffset < 1/2 * TARGET_TIME_SERIES dataset length
*
*
* @return The point from the end of the dataset where you want to split the data for model training and testing
* (evaluation). Specify the value as the number of data points. The default is the value of the forecast
* horizon. BackTestWindowOffset can be used to mimic a past virtual forecast start date. This
* value must be greater than or equal to the forecast horizon and less than half of the TARGET_TIME_SERIES
* dataset length.
*
* ForecastHorizon <= BackTestWindowOffset < 1/2 * TARGET_TIME_SERIES
* dataset length
*/
public Integer getBackTestWindowOffset() {
return this.backTestWindowOffset;
}
/**
*
* The point from the end of the dataset where you want to split the data for model training and testing
* (evaluation). Specify the value as the number of data points. The default is the value of the forecast horizon.
* BackTestWindowOffset can be used to mimic a past virtual forecast start date. This value must be
* greater than or equal to the forecast horizon and less than half of the TARGET_TIME_SERIES dataset length.
*
*
* ForecastHorizon <= BackTestWindowOffset < 1/2 * TARGET_TIME_SERIES dataset length
*
*
* @param backTestWindowOffset
* The point from the end of the dataset where you want to split the data for model training and testing
* (evaluation). Specify the value as the number of data points. The default is the value of the forecast
* horizon. BackTestWindowOffset can be used to mimic a past virtual forecast start date. This
* value must be greater than or equal to the forecast horizon and less than half of the TARGET_TIME_SERIES
* dataset length.
*
* ForecastHorizon <= BackTestWindowOffset < 1/2 * TARGET_TIME_SERIES dataset
* length
* @return Returns a reference to this object so that method calls can be chained together.
*/
public EvaluationParameters withBackTestWindowOffset(Integer backTestWindowOffset) {
setBackTestWindowOffset(backTestWindowOffset);
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 (getNumberOfBacktestWindows() != null)
sb.append("NumberOfBacktestWindows: ").append(getNumberOfBacktestWindows()).append(",");
if (getBackTestWindowOffset() != null)
sb.append("BackTestWindowOffset: ").append(getBackTestWindowOffset());
sb.append("}");
return sb.toString();
}
@Override
public boolean equals(Object obj) {
if (this == obj)
return true;
if (obj == null)
return false;
if (obj instanceof EvaluationParameters == false)
return false;
EvaluationParameters other = (EvaluationParameters) obj;
if (other.getNumberOfBacktestWindows() == null ^ this.getNumberOfBacktestWindows() == null)
return false;
if (other.getNumberOfBacktestWindows() != null && other.getNumberOfBacktestWindows().equals(this.getNumberOfBacktestWindows()) == false)
return false;
if (other.getBackTestWindowOffset() == null ^ this.getBackTestWindowOffset() == null)
return false;
if (other.getBackTestWindowOffset() != null && other.getBackTestWindowOffset().equals(this.getBackTestWindowOffset()) == false)
return false;
return true;
}
@Override
public int hashCode() {
final int prime = 31;
int hashCode = 1;
hashCode = prime * hashCode + ((getNumberOfBacktestWindows() == null) ? 0 : getNumberOfBacktestWindows().hashCode());
hashCode = prime * hashCode + ((getBackTestWindowOffset() == null) ? 0 : getBackTestWindowOffset().hashCode());
return hashCode;
}
@Override
public EvaluationParameters clone() {
try {
return (EvaluationParameters) 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.EvaluationParametersMarshaller.getInstance().marshall(this, protocolMarshaller);
}
}