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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.sagemaker.model;
import java.io.Serializable;
import javax.annotation.Generated;
import com.amazonaws.protocol.StructuredPojo;
import com.amazonaws.protocol.ProtocolMarshaller;
/**
*
* The collection of settings used by an AutoML job V2 for the time-series forecasting problem type.
*
*
* @see AWS API Documentation
*/
@Generated("com.amazonaws:aws-java-sdk-code-generator")
public class TimeSeriesForecastingJobConfig implements Serializable, Cloneable, StructuredPojo {
/**
*
* A URL to the Amazon S3 data source containing additional selected features that complement the target, itemID,
* timestamp, and grouped columns set in TimeSeriesConfig. When not provided, the AutoML job V2
* includes all the columns from the original dataset that are not already declared in TimeSeriesConfig
* . If provided, the AutoML job V2 only considers these additional columns as a complement to the ones declared in
* TimeSeriesConfig.
*
*
* You can input FeatureAttributeNames (optional) in JSON format as shown below:
*
*
* { "FeatureAttributeNames":["col1", "col2", ...] }.
*
*
* You can also specify the data type of the feature (optional) in the format shown below:
*
*
* { "FeatureDataTypes":{"col1":"numeric", "col2":"categorical" ... } }
*
*
* Autopilot supports the following data types: numeric, categorical, text,
* and datetime.
*
*
*
* These column keys must not include any column set in TimeSeriesConfig.
*
*
*/
private String featureSpecificationS3Uri;
private AutoMLJobCompletionCriteria completionCriteria;
/**
*
* 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. The value of
* a frequency must not overlap with the next larger frequency. For example, you must use a frequency of
* 1H instead of 60min.
*
*
* 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
*
*
*
*/
private String forecastFrequency;
/**
*
* The number of time-steps that the model predicts. The forecast horizon is also called the prediction length. The
* maximum forecast horizon is the lesser of 500 time-steps or 1/4 of the time-steps in the dataset.
*
*/
private Integer forecastHorizon;
/**
*
* The quantiles used to train the model for forecasts at a specified quantile. You can specify quantiles from
* 0.01 (p1) to 0.99 (p99), by increments of 0.01 or higher. Up to five forecast quantiles
* can be specified. When ForecastQuantiles is not provided, the AutoML job uses the quantiles p10,
* p50, and p90 as default.
*
*/
private java.util.List forecastQuantiles;
/**
*
* The transformations modifying specific attributes of the time-series, such as filling strategies for missing
* values.
*
*/
private TimeSeriesTransformations transformations;
/**
*
* The collection of components that defines the time-series.
*
*/
private TimeSeriesConfig timeSeriesConfig;
/**
*
* The collection of holiday featurization attributes used to incorporate national holiday information into your
* forecasting model.
*
*/
private java.util.List holidayConfig;
private CandidateGenerationConfig candidateGenerationConfig;
/**
*
* A URL to the Amazon S3 data source containing additional selected features that complement the target, itemID,
* timestamp, and grouped columns set in TimeSeriesConfig. When not provided, the AutoML job V2
* includes all the columns from the original dataset that are not already declared in TimeSeriesConfig
* . If provided, the AutoML job V2 only considers these additional columns as a complement to the ones declared in
* TimeSeriesConfig.
*
*
* You can input FeatureAttributeNames (optional) in JSON format as shown below:
*
*
* { "FeatureAttributeNames":["col1", "col2", ...] }.
*
*
* You can also specify the data type of the feature (optional) in the format shown below:
*
*
* { "FeatureDataTypes":{"col1":"numeric", "col2":"categorical" ... } }
*
*
* Autopilot supports the following data types: numeric, categorical, text,
* and datetime.
*
*
*
* These column keys must not include any column set in TimeSeriesConfig.
*
*
*
* @param featureSpecificationS3Uri
* A URL to the Amazon S3 data source containing additional selected features that complement the target,
* itemID, timestamp, and grouped columns set in TimeSeriesConfig. When not provided, the AutoML
* job V2 includes all the columns from the original dataset that are not already declared in
* TimeSeriesConfig. If provided, the AutoML job V2 only considers these additional columns as a
* complement to the ones declared in TimeSeriesConfig.
*
* You can input FeatureAttributeNames (optional) in JSON format as shown below:
*
*
* { "FeatureAttributeNames":["col1", "col2", ...] }.
*
*
* You can also specify the data type of the feature (optional) in the format shown below:
*
*
* { "FeatureDataTypes":{"col1":"numeric", "col2":"categorical" ... } }
*
*
* Autopilot supports the following data types: numeric, categorical,
* text, and datetime.
*
*
*
* These column keys must not include any column set in TimeSeriesConfig.
*
*/
public void setFeatureSpecificationS3Uri(String featureSpecificationS3Uri) {
this.featureSpecificationS3Uri = featureSpecificationS3Uri;
}
/**
*
* A URL to the Amazon S3 data source containing additional selected features that complement the target, itemID,
* timestamp, and grouped columns set in TimeSeriesConfig. When not provided, the AutoML job V2
* includes all the columns from the original dataset that are not already declared in TimeSeriesConfig
* . If provided, the AutoML job V2 only considers these additional columns as a complement to the ones declared in
* TimeSeriesConfig.
*
*
* You can input FeatureAttributeNames (optional) in JSON format as shown below:
*
*
* { "FeatureAttributeNames":["col1", "col2", ...] }.
*
*
* You can also specify the data type of the feature (optional) in the format shown below:
*
*
* { "FeatureDataTypes":{"col1":"numeric", "col2":"categorical" ... } }
*
*
* Autopilot supports the following data types: numeric, categorical, text,
* and datetime.
*
*
*
* These column keys must not include any column set in TimeSeriesConfig.
*
*
*
* @return A URL to the Amazon S3 data source containing additional selected features that complement the target,
* itemID, timestamp, and grouped columns set in TimeSeriesConfig. When not provided, the
* AutoML job V2 includes all the columns from the original dataset that are not already declared in
* TimeSeriesConfig. If provided, the AutoML job V2 only considers these additional columns as
* a complement to the ones declared in TimeSeriesConfig.
*
* You can input FeatureAttributeNames (optional) in JSON format as shown below:
*
*
* { "FeatureAttributeNames":["col1", "col2", ...] }.
*
*
* You can also specify the data type of the feature (optional) in the format shown below:
*
*
* { "FeatureDataTypes":{"col1":"numeric", "col2":"categorical" ... } }
*
*
* Autopilot supports the following data types: numeric, categorical,
* text, and datetime.
*
*
*
* These column keys must not include any column set in TimeSeriesConfig.
*
*/
public String getFeatureSpecificationS3Uri() {
return this.featureSpecificationS3Uri;
}
/**
*
* A URL to the Amazon S3 data source containing additional selected features that complement the target, itemID,
* timestamp, and grouped columns set in TimeSeriesConfig. When not provided, the AutoML job V2
* includes all the columns from the original dataset that are not already declared in TimeSeriesConfig
* . If provided, the AutoML job V2 only considers these additional columns as a complement to the ones declared in
* TimeSeriesConfig.
*
*
* You can input FeatureAttributeNames (optional) in JSON format as shown below:
*
*
* { "FeatureAttributeNames":["col1", "col2", ...] }.
*
*
* You can also specify the data type of the feature (optional) in the format shown below:
*
*
* { "FeatureDataTypes":{"col1":"numeric", "col2":"categorical" ... } }
*
*
* Autopilot supports the following data types: numeric, categorical, text,
* and datetime.
*
*
*
* These column keys must not include any column set in TimeSeriesConfig.
*
*
*
* @param featureSpecificationS3Uri
* A URL to the Amazon S3 data source containing additional selected features that complement the target,
* itemID, timestamp, and grouped columns set in TimeSeriesConfig. When not provided, the AutoML
* job V2 includes all the columns from the original dataset that are not already declared in
* TimeSeriesConfig. If provided, the AutoML job V2 only considers these additional columns as a
* complement to the ones declared in TimeSeriesConfig.
*
* You can input FeatureAttributeNames (optional) in JSON format as shown below:
*
*
* { "FeatureAttributeNames":["col1", "col2", ...] }.
*
*
* You can also specify the data type of the feature (optional) in the format shown below:
*
*
* { "FeatureDataTypes":{"col1":"numeric", "col2":"categorical" ... } }
*
*
* Autopilot supports the following data types: numeric, categorical,
* text, and datetime.
*
*
*
* These column keys must not include any column set in TimeSeriesConfig.
*
* @return Returns a reference to this object so that method calls can be chained together.
*/
public TimeSeriesForecastingJobConfig withFeatureSpecificationS3Uri(String featureSpecificationS3Uri) {
setFeatureSpecificationS3Uri(featureSpecificationS3Uri);
return this;
}
/**
* @param completionCriteria
*/
public void setCompletionCriteria(AutoMLJobCompletionCriteria completionCriteria) {
this.completionCriteria = completionCriteria;
}
/**
* @return
*/
public AutoMLJobCompletionCriteria getCompletionCriteria() {
return this.completionCriteria;
}
/**
* @param completionCriteria
* @return Returns a reference to this object so that method calls can be chained together.
*/
public TimeSeriesForecastingJobConfig withCompletionCriteria(AutoMLJobCompletionCriteria completionCriteria) {
setCompletionCriteria(completionCriteria);
return this;
}
/**
*
* 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. The value of
* a frequency must not overlap with the next larger frequency. For example, you must use a frequency of
* 1H instead of 60min.
*
*
* 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
*
*
*
*
* @param 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. The value of a frequency must not overlap with the next larger frequency. For example, you must
* use a frequency of 1H instead of 60min.
*
*
* 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
*
*
*/
public void setForecastFrequency(String forecastFrequency) {
this.forecastFrequency = 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. The value of
* a frequency must not overlap with the next larger frequency. For example, you must use a frequency of
* 1H instead of 60min.
*
*
* 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
*
*
*
*
* @return 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. The value of a frequency must not overlap with the next larger frequency. For example, you must
* use a frequency of 1H instead of 60min.
*
*
* 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
*
*
*/
public String getForecastFrequency() {
return this.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. The value of
* a frequency must not overlap with the next larger frequency. For example, you must use a frequency of
* 1H instead of 60min.
*
*
* 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
*
*
*
*
* @param 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. The value of a frequency must not overlap with the next larger frequency. For example, you must
* use a frequency of 1H instead of 60min.
*
*
* 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
*
*
* @return Returns a reference to this object so that method calls can be chained together.
*/
public TimeSeriesForecastingJobConfig withForecastFrequency(String forecastFrequency) {
setForecastFrequency(forecastFrequency);
return this;
}
/**
*
* The number of time-steps that the model predicts. The forecast horizon is also called the prediction length. The
* maximum forecast horizon is the lesser of 500 time-steps or 1/4 of the time-steps in the dataset.
*
*
* @param forecastHorizon
* The number of time-steps that the model predicts. The forecast horizon is also called the prediction
* length. The maximum forecast horizon is the lesser of 500 time-steps or 1/4 of the time-steps in the
* dataset.
*/
public void setForecastHorizon(Integer forecastHorizon) {
this.forecastHorizon = forecastHorizon;
}
/**
*
* The number of time-steps that the model predicts. The forecast horizon is also called the prediction length. The
* maximum forecast horizon is the lesser of 500 time-steps or 1/4 of the time-steps in the dataset.
*
*
* @return The number of time-steps that the model predicts. The forecast horizon is also called the prediction
* length. The maximum forecast horizon is the lesser of 500 time-steps or 1/4 of the time-steps in the
* dataset.
*/
public Integer getForecastHorizon() {
return this.forecastHorizon;
}
/**
*
* The number of time-steps that the model predicts. The forecast horizon is also called the prediction length. The
* maximum forecast horizon is the lesser of 500 time-steps or 1/4 of the time-steps in the dataset.
*
*
* @param forecastHorizon
* The number of time-steps that the model predicts. The forecast horizon is also called the prediction
* length. The maximum forecast horizon is the lesser of 500 time-steps or 1/4 of the time-steps in the
* dataset.
* @return Returns a reference to this object so that method calls can be chained together.
*/
public TimeSeriesForecastingJobConfig withForecastHorizon(Integer forecastHorizon) {
setForecastHorizon(forecastHorizon);
return this;
}
/**
*
* The quantiles used to train the model for forecasts at a specified quantile. You can specify quantiles from
* 0.01 (p1) to 0.99 (p99), by increments of 0.01 or higher. Up to five forecast quantiles
* can be specified. When ForecastQuantiles is not provided, the AutoML job uses the quantiles p10,
* p50, and p90 as default.
*
*
* @return The quantiles used to train the model for forecasts at a specified quantile. You can specify quantiles
* from 0.01 (p1) to 0.99 (p99), by increments of 0.01 or higher. Up to five
* forecast quantiles can be specified. When ForecastQuantiles is not provided, the AutoML job
* uses the quantiles p10, p50, and p90 as default.
*/
public java.util.List getForecastQuantiles() {
return forecastQuantiles;
}
/**
*
* The quantiles used to train the model for forecasts at a specified quantile. You can specify quantiles from
* 0.01 (p1) to 0.99 (p99), by increments of 0.01 or higher. Up to five forecast quantiles
* can be specified. When ForecastQuantiles is not provided, the AutoML job uses the quantiles p10,
* p50, and p90 as default.
*
*
* @param forecastQuantiles
* The quantiles used to train the model for forecasts at a specified quantile. You can specify quantiles
* from 0.01 (p1) to 0.99 (p99), by increments of 0.01 or higher. Up to five
* forecast quantiles can be specified. When ForecastQuantiles is not provided, the AutoML job
* uses the quantiles p10, p50, and p90 as default.
*/
public void setForecastQuantiles(java.util.Collection forecastQuantiles) {
if (forecastQuantiles == null) {
this.forecastQuantiles = null;
return;
}
this.forecastQuantiles = new java.util.ArrayList(forecastQuantiles);
}
/**
*
* The quantiles used to train the model for forecasts at a specified quantile. You can specify quantiles from
* 0.01 (p1) to 0.99 (p99), by increments of 0.01 or higher. Up to five forecast quantiles
* can be specified. When ForecastQuantiles is not provided, the AutoML job uses the quantiles p10,
* p50, and p90 as default.
*
*
* NOTE: This method appends the values to the existing list (if any). Use
* {@link #setForecastQuantiles(java.util.Collection)} or {@link #withForecastQuantiles(java.util.Collection)} if
* you want to override the existing values.
*
*
* @param forecastQuantiles
* The quantiles used to train the model for forecasts at a specified quantile. You can specify quantiles
* from 0.01 (p1) to 0.99 (p99), by increments of 0.01 or higher. Up to five
* forecast quantiles can be specified. When ForecastQuantiles is not provided, the AutoML job
* uses the quantiles p10, p50, and p90 as default.
* @return Returns a reference to this object so that method calls can be chained together.
*/
public TimeSeriesForecastingJobConfig withForecastQuantiles(String... forecastQuantiles) {
if (this.forecastQuantiles == null) {
setForecastQuantiles(new java.util.ArrayList(forecastQuantiles.length));
}
for (String ele : forecastQuantiles) {
this.forecastQuantiles.add(ele);
}
return this;
}
/**
*
* The quantiles used to train the model for forecasts at a specified quantile. You can specify quantiles from
* 0.01 (p1) to 0.99 (p99), by increments of 0.01 or higher. Up to five forecast quantiles
* can be specified. When ForecastQuantiles is not provided, the AutoML job uses the quantiles p10,
* p50, and p90 as default.
*
*
* @param forecastQuantiles
* The quantiles used to train the model for forecasts at a specified quantile. You can specify quantiles
* from 0.01 (p1) to 0.99 (p99), by increments of 0.01 or higher. Up to five
* forecast quantiles can be specified. When ForecastQuantiles is not provided, the AutoML job
* uses the quantiles p10, p50, and p90 as default.
* @return Returns a reference to this object so that method calls can be chained together.
*/
public TimeSeriesForecastingJobConfig withForecastQuantiles(java.util.Collection forecastQuantiles) {
setForecastQuantiles(forecastQuantiles);
return this;
}
/**
*
* The transformations modifying specific attributes of the time-series, such as filling strategies for missing
* values.
*
*
* @param transformations
* The transformations modifying specific attributes of the time-series, such as filling strategies for
* missing values.
*/
public void setTransformations(TimeSeriesTransformations transformations) {
this.transformations = transformations;
}
/**
*
* The transformations modifying specific attributes of the time-series, such as filling strategies for missing
* values.
*
*
* @return The transformations modifying specific attributes of the time-series, such as filling strategies for
* missing values.
*/
public TimeSeriesTransformations getTransformations() {
return this.transformations;
}
/**
*
* The transformations modifying specific attributes of the time-series, such as filling strategies for missing
* values.
*
*
* @param transformations
* The transformations modifying specific attributes of the time-series, such as filling strategies for
* missing values.
* @return Returns a reference to this object so that method calls can be chained together.
*/
public TimeSeriesForecastingJobConfig withTransformations(TimeSeriesTransformations transformations) {
setTransformations(transformations);
return this;
}
/**
*
* The collection of components that defines the time-series.
*
*
* @param timeSeriesConfig
* The collection of components that defines the time-series.
*/
public void setTimeSeriesConfig(TimeSeriesConfig timeSeriesConfig) {
this.timeSeriesConfig = timeSeriesConfig;
}
/**
*
* The collection of components that defines the time-series.
*
*
* @return The collection of components that defines the time-series.
*/
public TimeSeriesConfig getTimeSeriesConfig() {
return this.timeSeriesConfig;
}
/**
*
* The collection of components that defines the time-series.
*
*
* @param timeSeriesConfig
* The collection of components that defines the time-series.
* @return Returns a reference to this object so that method calls can be chained together.
*/
public TimeSeriesForecastingJobConfig withTimeSeriesConfig(TimeSeriesConfig timeSeriesConfig) {
setTimeSeriesConfig(timeSeriesConfig);
return this;
}
/**
*
* The collection of holiday featurization attributes used to incorporate national holiday information into your
* forecasting model.
*
*
* @return The collection of holiday featurization attributes used to incorporate national holiday information into
* your forecasting model.
*/
public java.util.List getHolidayConfig() {
return holidayConfig;
}
/**
*
* The collection of holiday featurization attributes used to incorporate national holiday information into your
* forecasting model.
*
*
* @param holidayConfig
* The collection of holiday featurization attributes used to incorporate national holiday information into
* your forecasting model.
*/
public void setHolidayConfig(java.util.Collection holidayConfig) {
if (holidayConfig == null) {
this.holidayConfig = null;
return;
}
this.holidayConfig = new java.util.ArrayList(holidayConfig);
}
/**
*
* The collection of holiday featurization attributes used to incorporate national holiday information into your
* forecasting model.
*
*
* NOTE: This method appends the values to the existing list (if any). Use
* {@link #setHolidayConfig(java.util.Collection)} or {@link #withHolidayConfig(java.util.Collection)} if you want
* to override the existing values.
*
*
* @param holidayConfig
* The collection of holiday featurization attributes used to incorporate national holiday information into
* your forecasting model.
* @return Returns a reference to this object so that method calls can be chained together.
*/
public TimeSeriesForecastingJobConfig withHolidayConfig(HolidayConfigAttributes... holidayConfig) {
if (this.holidayConfig == null) {
setHolidayConfig(new java.util.ArrayList(holidayConfig.length));
}
for (HolidayConfigAttributes ele : holidayConfig) {
this.holidayConfig.add(ele);
}
return this;
}
/**
*
* The collection of holiday featurization attributes used to incorporate national holiday information into your
* forecasting model.
*
*
* @param holidayConfig
* The collection of holiday featurization attributes used to incorporate national holiday information into
* your forecasting model.
* @return Returns a reference to this object so that method calls can be chained together.
*/
public TimeSeriesForecastingJobConfig withHolidayConfig(java.util.Collection holidayConfig) {
setHolidayConfig(holidayConfig);
return this;
}
/**
* @param candidateGenerationConfig
*/
public void setCandidateGenerationConfig(CandidateGenerationConfig candidateGenerationConfig) {
this.candidateGenerationConfig = candidateGenerationConfig;
}
/**
* @return
*/
public CandidateGenerationConfig getCandidateGenerationConfig() {
return this.candidateGenerationConfig;
}
/**
* @param candidateGenerationConfig
* @return Returns a reference to this object so that method calls can be chained together.
*/
public TimeSeriesForecastingJobConfig withCandidateGenerationConfig(CandidateGenerationConfig candidateGenerationConfig) {
setCandidateGenerationConfig(candidateGenerationConfig);
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 (getFeatureSpecificationS3Uri() != null)
sb.append("FeatureSpecificationS3Uri: ").append(getFeatureSpecificationS3Uri()).append(",");
if (getCompletionCriteria() != null)
sb.append("CompletionCriteria: ").append(getCompletionCriteria()).append(",");
if (getForecastFrequency() != null)
sb.append("ForecastFrequency: ").append(getForecastFrequency()).append(",");
if (getForecastHorizon() != null)
sb.append("ForecastHorizon: ").append(getForecastHorizon()).append(",");
if (getForecastQuantiles() != null)
sb.append("ForecastQuantiles: ").append(getForecastQuantiles()).append(",");
if (getTransformations() != null)
sb.append("Transformations: ").append(getTransformations()).append(",");
if (getTimeSeriesConfig() != null)
sb.append("TimeSeriesConfig: ").append(getTimeSeriesConfig()).append(",");
if (getHolidayConfig() != null)
sb.append("HolidayConfig: ").append(getHolidayConfig()).append(",");
if (getCandidateGenerationConfig() != null)
sb.append("CandidateGenerationConfig: ").append(getCandidateGenerationConfig());
sb.append("}");
return sb.toString();
}
@Override
public boolean equals(Object obj) {
if (this == obj)
return true;
if (obj == null)
return false;
if (obj instanceof TimeSeriesForecastingJobConfig == false)
return false;
TimeSeriesForecastingJobConfig other = (TimeSeriesForecastingJobConfig) obj;
if (other.getFeatureSpecificationS3Uri() == null ^ this.getFeatureSpecificationS3Uri() == null)
return false;
if (other.getFeatureSpecificationS3Uri() != null && other.getFeatureSpecificationS3Uri().equals(this.getFeatureSpecificationS3Uri()) == false)
return false;
if (other.getCompletionCriteria() == null ^ this.getCompletionCriteria() == null)
return false;
if (other.getCompletionCriteria() != null && other.getCompletionCriteria().equals(this.getCompletionCriteria()) == false)
return false;
if (other.getForecastFrequency() == null ^ this.getForecastFrequency() == null)
return false;
if (other.getForecastFrequency() != null && other.getForecastFrequency().equals(this.getForecastFrequency()) == false)
return false;
if (other.getForecastHorizon() == null ^ this.getForecastHorizon() == null)
return false;
if (other.getForecastHorizon() != null && other.getForecastHorizon().equals(this.getForecastHorizon()) == false)
return false;
if (other.getForecastQuantiles() == null ^ this.getForecastQuantiles() == null)
return false;
if (other.getForecastQuantiles() != null && other.getForecastQuantiles().equals(this.getForecastQuantiles()) == false)
return false;
if (other.getTransformations() == null ^ this.getTransformations() == null)
return false;
if (other.getTransformations() != null && other.getTransformations().equals(this.getTransformations()) == false)
return false;
if (other.getTimeSeriesConfig() == null ^ this.getTimeSeriesConfig() == null)
return false;
if (other.getTimeSeriesConfig() != null && other.getTimeSeriesConfig().equals(this.getTimeSeriesConfig()) == false)
return false;
if (other.getHolidayConfig() == null ^ this.getHolidayConfig() == null)
return false;
if (other.getHolidayConfig() != null && other.getHolidayConfig().equals(this.getHolidayConfig()) == false)
return false;
if (other.getCandidateGenerationConfig() == null ^ this.getCandidateGenerationConfig() == null)
return false;
if (other.getCandidateGenerationConfig() != null && other.getCandidateGenerationConfig().equals(this.getCandidateGenerationConfig()) == false)
return false;
return true;
}
@Override
public int hashCode() {
final int prime = 31;
int hashCode = 1;
hashCode = prime * hashCode + ((getFeatureSpecificationS3Uri() == null) ? 0 : getFeatureSpecificationS3Uri().hashCode());
hashCode = prime * hashCode + ((getCompletionCriteria() == null) ? 0 : getCompletionCriteria().hashCode());
hashCode = prime * hashCode + ((getForecastFrequency() == null) ? 0 : getForecastFrequency().hashCode());
hashCode = prime * hashCode + ((getForecastHorizon() == null) ? 0 : getForecastHorizon().hashCode());
hashCode = prime * hashCode + ((getForecastQuantiles() == null) ? 0 : getForecastQuantiles().hashCode());
hashCode = prime * hashCode + ((getTransformations() == null) ? 0 : getTransformations().hashCode());
hashCode = prime * hashCode + ((getTimeSeriesConfig() == null) ? 0 : getTimeSeriesConfig().hashCode());
hashCode = prime * hashCode + ((getHolidayConfig() == null) ? 0 : getHolidayConfig().hashCode());
hashCode = prime * hashCode + ((getCandidateGenerationConfig() == null) ? 0 : getCandidateGenerationConfig().hashCode());
return hashCode;
}
@Override
public TimeSeriesForecastingJobConfig clone() {
try {
return (TimeSeriesForecastingJobConfig) 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.sagemaker.model.transform.TimeSeriesForecastingJobConfigMarshaller.getInstance().marshall(this, protocolMarshaller);
}
}