com.amazonaws.services.forecast.model.CreateAutoPredictorRequest Maven / Gradle / Ivy
/*
* 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.AmazonWebServiceRequest;
/**
*
* @see AWS API
* Documentation
*/
@Generated("com.amazonaws:aws-java-sdk-code-generator")
public class CreateAutoPredictorRequest extends com.amazonaws.AmazonWebServiceRequest implements Serializable, Cloneable {
/**
*
* A unique name for the predictor
*
*/
private String predictorName;
/**
*
* 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 TARGET_TIME_SERIES dataset length. If
* you are retraining an existing AutoPredictor, then the maximum forecast horizon is the lesser of 500 time-steps
* or 1/3 of the TARGET_TIME_SERIES dataset length.
*
*
* If you are upgrading to an AutoPredictor or retraining an existing AutoPredictor, you cannot update the forecast
* horizon parameter. You can meet this requirement by providing longer time-series in the dataset.
*
*/
private Integer forecastHorizon;
/**
*
* The forecast types used to train a predictor. You can specify up to five forecast types. Forecast types can be
* quantiles from 0.01 to 0.99, by increments of 0.01 or higher. You can also specify the mean forecast with
* mean.
*
*/
private java.util.List forecastTypes;
/**
*
* An array of dimension (field) names that specify how to group the generated forecast.
*
*
* For example, if you are generating forecasts for item sales across all your stores, and your dataset contains a
* store_id field, you would specify store_id as a dimension to group sales forecasts for
* each store.
*
*/
private java.util.List forecastDimensions;
/**
*
* 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 RELATED_TIME_SERIES dataset
* frequency.
*
*/
private String forecastFrequency;
/**
*
* The data configuration for your dataset group and any additional datasets.
*
*/
private DataConfig dataConfig;
private EncryptionConfig encryptionConfig;
/**
*
* The ARN of the predictor to retrain or upgrade. This parameter is only used when retraining or upgrading a
* predictor. When creating a new predictor, do not specify a value for this parameter.
*
*
* When upgrading or retraining a predictor, only specify values for the ReferencePredictorArn and
* PredictorName. The value for PredictorName must be a unique predictor name.
*
*/
private String referencePredictorArn;
/**
*
* The accuracy metric used to optimize the predictor.
*
*/
private String optimizationMetric;
/**
*
* Create an Explainability resource for the predictor.
*
*/
private Boolean explainPredictor;
/**
*
* Optional metadata to help you categorize and organize your predictors. Each tag consists of a key and an optional
* value, both of which you define. Tag keys and values are case sensitive.
*
*
* The following restrictions apply to tags:
*
*
* -
*
* For each resource, each tag key must be unique and each tag key must have one value.
*
*
* -
*
* Maximum number of tags per resource: 50.
*
*
* -
*
* Maximum key length: 128 Unicode characters in UTF-8.
*
*
* -
*
* Maximum value length: 256 Unicode characters in UTF-8.
*
*
* -
*
* Accepted characters: all letters and numbers, spaces representable in UTF-8, and + - = . _ : / @. If your tagging
* schema is used across other services and resources, the character restrictions of those services also apply.
*
*
* -
*
* Key prefixes cannot include any upper or lowercase combination of aws: or AWS:. Values
* can have this prefix. If a tag value has aws as its prefix but the key does not, Forecast considers
* it to be a user tag and will count against the limit of 50 tags. Tags with only the key prefix of
* aws do not count against your tags per resource limit. You cannot edit or delete tag keys with this
* prefix.
*
*
*
*/
private java.util.List tags;
/**
*
* The configuration details for predictor monitoring. Provide a name for the monitor resource to enable predictor
* monitoring.
*
*
* Predictor monitoring allows you to see how your predictor's performance changes over time. For more information,
* see Predictor Monitoring.
*
*/
private MonitorConfig monitorConfig;
/**
*
* The time boundary Forecast uses to align and aggregate any data that doesn't align with your forecast frequency.
* Provide the unit of time and the time boundary as a key value pair. For more information on specifying a time
* boundary, see Specifying a
* Time Boundary. If you don't provide a time boundary, Forecast uses a set of Default Time
* Boundaries.
*
*/
private TimeAlignmentBoundary timeAlignmentBoundary;
/**
*
* A unique name for the predictor
*
*
* @param predictorName
* A unique name for the predictor
*/
public void setPredictorName(String predictorName) {
this.predictorName = predictorName;
}
/**
*
* A unique name for the predictor
*
*
* @return A unique name for the predictor
*/
public String getPredictorName() {
return this.predictorName;
}
/**
*
* A unique name for the predictor
*
*
* @param predictorName
* A unique name for the predictor
* @return Returns a reference to this object so that method calls can be chained together.
*/
public CreateAutoPredictorRequest withPredictorName(String predictorName) {
setPredictorName(predictorName);
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 TARGET_TIME_SERIES dataset length. If
* you are retraining an existing AutoPredictor, then the maximum forecast horizon is the lesser of 500 time-steps
* or 1/3 of the TARGET_TIME_SERIES dataset length.
*
*
* If you are upgrading to an AutoPredictor or retraining an existing AutoPredictor, you cannot update the forecast
* horizon parameter. You can meet this requirement by providing longer time-series 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 TARGET_TIME_SERIES dataset
* length. If you are retraining an existing AutoPredictor, then the maximum forecast horizon is the lesser
* of 500 time-steps or 1/3 of the TARGET_TIME_SERIES dataset length.
*
*
* If you are upgrading to an AutoPredictor or retraining an existing AutoPredictor, you cannot update the
* forecast horizon parameter. You can meet this requirement by providing longer time-series 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 TARGET_TIME_SERIES dataset length. If
* you are retraining an existing AutoPredictor, then the maximum forecast horizon is the lesser of 500 time-steps
* or 1/3 of the TARGET_TIME_SERIES dataset length.
*
*
* If you are upgrading to an AutoPredictor or retraining an existing AutoPredictor, you cannot update the forecast
* horizon parameter. You can meet this requirement by providing longer time-series 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 TARGET_TIME_SERIES dataset
* length. If you are retraining an existing AutoPredictor, then the maximum forecast horizon is the lesser
* of 500 time-steps or 1/3 of the TARGET_TIME_SERIES dataset length.
*
*
* If you are upgrading to an AutoPredictor or retraining an existing AutoPredictor, you cannot update the
* forecast horizon parameter. You can meet this requirement by providing longer time-series 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 TARGET_TIME_SERIES dataset length. If
* you are retraining an existing AutoPredictor, then the maximum forecast horizon is the lesser of 500 time-steps
* or 1/3 of the TARGET_TIME_SERIES dataset length.
*
*
* If you are upgrading to an AutoPredictor or retraining an existing AutoPredictor, you cannot update the forecast
* horizon parameter. You can meet this requirement by providing longer time-series 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 TARGET_TIME_SERIES dataset
* length. If you are retraining an existing AutoPredictor, then the maximum forecast horizon is the lesser
* of 500 time-steps or 1/3 of the TARGET_TIME_SERIES dataset length.
*
*
* If you are upgrading to an AutoPredictor or retraining an existing AutoPredictor, you cannot update the
* forecast horizon parameter. You can meet this requirement by providing longer time-series in the dataset.
* @return Returns a reference to this object so that method calls can be chained together.
*/
public CreateAutoPredictorRequest withForecastHorizon(Integer forecastHorizon) {
setForecastHorizon(forecastHorizon);
return this;
}
/**
*
* The forecast types used to train a predictor. You can specify up to five forecast types. Forecast types can be
* quantiles from 0.01 to 0.99, by increments of 0.01 or higher. You can also specify the mean forecast with
* mean.
*
*
* @return The forecast types used to train a predictor. You can specify up to five forecast types. Forecast types
* can be quantiles from 0.01 to 0.99, by increments of 0.01 or higher. You can also specify the mean
* forecast with mean.
*/
public java.util.List getForecastTypes() {
return forecastTypes;
}
/**
*
* The forecast types used to train a predictor. You can specify up to five forecast types. Forecast types can be
* quantiles from 0.01 to 0.99, by increments of 0.01 or higher. You can also specify the mean forecast with
* mean.
*
*
* @param forecastTypes
* The forecast types used to train a predictor. You can specify up to five forecast types. Forecast types
* can be quantiles from 0.01 to 0.99, by increments of 0.01 or higher. You can also specify the mean
* forecast with mean.
*/
public void setForecastTypes(java.util.Collection forecastTypes) {
if (forecastTypes == null) {
this.forecastTypes = null;
return;
}
this.forecastTypes = new java.util.ArrayList(forecastTypes);
}
/**
*
* The forecast types used to train a predictor. You can specify up to five forecast types. Forecast types can be
* quantiles from 0.01 to 0.99, by increments of 0.01 or higher. You can also specify the mean forecast with
* mean.
*
*
* NOTE: This method appends the values to the existing list (if any). Use
* {@link #setForecastTypes(java.util.Collection)} or {@link #withForecastTypes(java.util.Collection)} if you want
* to override the existing values.
*
*
* @param forecastTypes
* The forecast types used to train a predictor. You can specify up to five forecast types. Forecast types
* can be quantiles from 0.01 to 0.99, by increments of 0.01 or higher. You can also specify the mean
* forecast with mean.
* @return Returns a reference to this object so that method calls can be chained together.
*/
public CreateAutoPredictorRequest withForecastTypes(String... forecastTypes) {
if (this.forecastTypes == null) {
setForecastTypes(new java.util.ArrayList(forecastTypes.length));
}
for (String ele : forecastTypes) {
this.forecastTypes.add(ele);
}
return this;
}
/**
*
* The forecast types used to train a predictor. You can specify up to five forecast types. Forecast types can be
* quantiles from 0.01 to 0.99, by increments of 0.01 or higher. You can also specify the mean forecast with
* mean.
*
*
* @param forecastTypes
* The forecast types used to train a predictor. You can specify up to five forecast types. Forecast types
* can be quantiles from 0.01 to 0.99, by increments of 0.01 or higher. You can also specify the mean
* forecast with mean.
* @return Returns a reference to this object so that method calls can be chained together.
*/
public CreateAutoPredictorRequest withForecastTypes(java.util.Collection forecastTypes) {
setForecastTypes(forecastTypes);
return this;
}
/**
*
* An array of dimension (field) names that specify how to group the generated forecast.
*
*
* For example, if you are generating forecasts for item sales across all your stores, and your dataset contains a
* store_id field, you would specify store_id as a dimension to group sales forecasts for
* each store.
*
*
* @return An array of dimension (field) names that specify how to group the generated forecast.
*
* For example, if you are generating forecasts for item sales across all your stores, and your dataset
* contains a store_id field, you would specify store_id as a dimension to group
* sales forecasts for each store.
*/
public java.util.List getForecastDimensions() {
return forecastDimensions;
}
/**
*
* An array of dimension (field) names that specify how to group the generated forecast.
*
*
* For example, if you are generating forecasts for item sales across all your stores, and your dataset contains a
* store_id field, you would specify store_id as a dimension to group sales forecasts for
* each store.
*
*
* @param forecastDimensions
* An array of dimension (field) names that specify how to group the generated forecast.
*
* For example, if you are generating forecasts for item sales across all your stores, and your dataset
* contains a store_id field, you would specify store_id as a dimension to group
* sales forecasts for each store.
*/
public void setForecastDimensions(java.util.Collection forecastDimensions) {
if (forecastDimensions == null) {
this.forecastDimensions = null;
return;
}
this.forecastDimensions = new java.util.ArrayList(forecastDimensions);
}
/**
*
* An array of dimension (field) names that specify how to group the generated forecast.
*
*
* For example, if you are generating forecasts for item sales across all your stores, and your dataset contains a
* store_id field, you would specify store_id as a dimension to group sales forecasts for
* each store.
*
*
* NOTE: This method appends the values to the existing list (if any). Use
* {@link #setForecastDimensions(java.util.Collection)} or {@link #withForecastDimensions(java.util.Collection)} if
* you want to override the existing values.
*
*
* @param forecastDimensions
* An array of dimension (field) names that specify how to group the generated forecast.
*
* For example, if you are generating forecasts for item sales across all your stores, and your dataset
* contains a store_id field, you would specify store_id as a dimension to group
* sales forecasts for each store.
* @return Returns a reference to this object so that method calls can be chained together.
*/
public CreateAutoPredictorRequest withForecastDimensions(String... forecastDimensions) {
if (this.forecastDimensions == null) {
setForecastDimensions(new java.util.ArrayList(forecastDimensions.length));
}
for (String ele : forecastDimensions) {
this.forecastDimensions.add(ele);
}
return this;
}
/**
*
* An array of dimension (field) names that specify how to group the generated forecast.
*
*
* For example, if you are generating forecasts for item sales across all your stores, and your dataset contains a
* store_id field, you would specify store_id as a dimension to group sales forecasts for
* each store.
*
*
* @param forecastDimensions
* An array of dimension (field) names that specify how to group the generated forecast.
*
* For example, if you are generating forecasts for item sales across all your stores, and your dataset
* contains a store_id field, you would specify store_id as a dimension to group
* sales forecasts for each store.
* @return Returns a reference to this object so that method calls can be chained together.
*/
public CreateAutoPredictorRequest withForecastDimensions(java.util.Collection forecastDimensions) {
setForecastDimensions(forecastDimensions);
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. 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 RELATED_TIME_SERIES dataset
* frequency.
*
*
* @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. 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 RELATED_TIME_SERIES
* dataset frequency.
*/
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. 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 RELATED_TIME_SERIES dataset
* frequency.
*
*
* @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. 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 RELATED_TIME_SERIES
* dataset frequency.
*/
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. 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 RELATED_TIME_SERIES dataset
* frequency.
*
*
* @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. 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 RELATED_TIME_SERIES
* dataset frequency.
* @return Returns a reference to this object so that method calls can be chained together.
*/
public CreateAutoPredictorRequest withForecastFrequency(String forecastFrequency) {
setForecastFrequency(forecastFrequency);
return this;
}
/**
*
* The data configuration for your dataset group and any additional datasets.
*
*
* @param dataConfig
* The data configuration for your dataset group and any additional datasets.
*/
public void setDataConfig(DataConfig dataConfig) {
this.dataConfig = dataConfig;
}
/**
*
* The data configuration for your dataset group and any additional datasets.
*
*
* @return The data configuration for your dataset group and any additional datasets.
*/
public DataConfig getDataConfig() {
return this.dataConfig;
}
/**
*
* The data configuration for your dataset group and any additional datasets.
*
*
* @param dataConfig
* The data configuration for your dataset group and any additional datasets.
* @return Returns a reference to this object so that method calls can be chained together.
*/
public CreateAutoPredictorRequest withDataConfig(DataConfig dataConfig) {
setDataConfig(dataConfig);
return this;
}
/**
* @param encryptionConfig
*/
public void setEncryptionConfig(EncryptionConfig encryptionConfig) {
this.encryptionConfig = encryptionConfig;
}
/**
* @return
*/
public EncryptionConfig getEncryptionConfig() {
return this.encryptionConfig;
}
/**
* @param encryptionConfig
* @return Returns a reference to this object so that method calls can be chained together.
*/
public CreateAutoPredictorRequest withEncryptionConfig(EncryptionConfig encryptionConfig) {
setEncryptionConfig(encryptionConfig);
return this;
}
/**
*
* The ARN of the predictor to retrain or upgrade. This parameter is only used when retraining or upgrading a
* predictor. When creating a new predictor, do not specify a value for this parameter.
*
*
* When upgrading or retraining a predictor, only specify values for the ReferencePredictorArn and
* PredictorName. The value for PredictorName must be a unique predictor name.
*
*
* @param referencePredictorArn
* The ARN of the predictor to retrain or upgrade. This parameter is only used when retraining or upgrading a
* predictor. When creating a new predictor, do not specify a value for this parameter.
*
* When upgrading or retraining a predictor, only specify values for the ReferencePredictorArn
* and PredictorName. The value for PredictorName must be a unique predictor name.
*/
public void setReferencePredictorArn(String referencePredictorArn) {
this.referencePredictorArn = referencePredictorArn;
}
/**
*
* The ARN of the predictor to retrain or upgrade. This parameter is only used when retraining or upgrading a
* predictor. When creating a new predictor, do not specify a value for this parameter.
*
*
* When upgrading or retraining a predictor, only specify values for the ReferencePredictorArn and
* PredictorName. The value for PredictorName must be a unique predictor name.
*
*
* @return The ARN of the predictor to retrain or upgrade. This parameter is only used when retraining or upgrading
* a predictor. When creating a new predictor, do not specify a value for this parameter.
*
* When upgrading or retraining a predictor, only specify values for the ReferencePredictorArn
* and PredictorName. The value for PredictorName must be a unique predictor name.
*/
public String getReferencePredictorArn() {
return this.referencePredictorArn;
}
/**
*
* The ARN of the predictor to retrain or upgrade. This parameter is only used when retraining or upgrading a
* predictor. When creating a new predictor, do not specify a value for this parameter.
*
*
* When upgrading or retraining a predictor, only specify values for the ReferencePredictorArn and
* PredictorName. The value for PredictorName must be a unique predictor name.
*
*
* @param referencePredictorArn
* The ARN of the predictor to retrain or upgrade. This parameter is only used when retraining or upgrading a
* predictor. When creating a new predictor, do not specify a value for this parameter.
*
* When upgrading or retraining a predictor, only specify values for the ReferencePredictorArn
* and PredictorName. The value for PredictorName must be a unique predictor name.
* @return Returns a reference to this object so that method calls can be chained together.
*/
public CreateAutoPredictorRequest withReferencePredictorArn(String referencePredictorArn) {
setReferencePredictorArn(referencePredictorArn);
return this;
}
/**
*
* The accuracy metric used to optimize the predictor.
*
*
* @param optimizationMetric
* The accuracy metric used to optimize the predictor.
* @see OptimizationMetric
*/
public void setOptimizationMetric(String optimizationMetric) {
this.optimizationMetric = optimizationMetric;
}
/**
*
* The accuracy metric used to optimize the predictor.
*
*
* @return The accuracy metric used to optimize the predictor.
* @see OptimizationMetric
*/
public String getOptimizationMetric() {
return this.optimizationMetric;
}
/**
*
* The accuracy metric used to optimize the predictor.
*
*
* @param optimizationMetric
* The accuracy metric used to optimize the predictor.
* @return Returns a reference to this object so that method calls can be chained together.
* @see OptimizationMetric
*/
public CreateAutoPredictorRequest withOptimizationMetric(String optimizationMetric) {
setOptimizationMetric(optimizationMetric);
return this;
}
/**
*
* The accuracy metric used to optimize the predictor.
*
*
* @param optimizationMetric
* The accuracy metric used to optimize the predictor.
* @return Returns a reference to this object so that method calls can be chained together.
* @see OptimizationMetric
*/
public CreateAutoPredictorRequest withOptimizationMetric(OptimizationMetric optimizationMetric) {
this.optimizationMetric = optimizationMetric.toString();
return this;
}
/**
*
* Create an Explainability resource for the predictor.
*
*
* @param explainPredictor
* Create an Explainability resource for the predictor.
*/
public void setExplainPredictor(Boolean explainPredictor) {
this.explainPredictor = explainPredictor;
}
/**
*
* Create an Explainability resource for the predictor.
*
*
* @return Create an Explainability resource for the predictor.
*/
public Boolean getExplainPredictor() {
return this.explainPredictor;
}
/**
*
* Create an Explainability resource for the predictor.
*
*
* @param explainPredictor
* Create an Explainability resource for the predictor.
* @return Returns a reference to this object so that method calls can be chained together.
*/
public CreateAutoPredictorRequest withExplainPredictor(Boolean explainPredictor) {
setExplainPredictor(explainPredictor);
return this;
}
/**
*
* Create an Explainability resource for the predictor.
*
*
* @return Create an Explainability resource for the predictor.
*/
public Boolean isExplainPredictor() {
return this.explainPredictor;
}
/**
*
* Optional metadata to help you categorize and organize your predictors. Each tag consists of a key and an optional
* value, both of which you define. Tag keys and values are case sensitive.
*
*
* The following restrictions apply to tags:
*
*
* -
*
* For each resource, each tag key must be unique and each tag key must have one value.
*
*
* -
*
* Maximum number of tags per resource: 50.
*
*
* -
*
* Maximum key length: 128 Unicode characters in UTF-8.
*
*
* -
*
* Maximum value length: 256 Unicode characters in UTF-8.
*
*
* -
*
* Accepted characters: all letters and numbers, spaces representable in UTF-8, and + - = . _ : / @. If your tagging
* schema is used across other services and resources, the character restrictions of those services also apply.
*
*
* -
*
* Key prefixes cannot include any upper or lowercase combination of aws: or AWS:. Values
* can have this prefix. If a tag value has aws as its prefix but the key does not, Forecast considers
* it to be a user tag and will count against the limit of 50 tags. Tags with only the key prefix of
* aws do not count against your tags per resource limit. You cannot edit or delete tag keys with this
* prefix.
*
*
*
*
* @return Optional metadata to help you categorize and organize your predictors. Each tag consists of a key and an
* optional value, both of which you define. Tag keys and values are case sensitive.
*
* The following restrictions apply to tags:
*
*
* -
*
* For each resource, each tag key must be unique and each tag key must have one value.
*
*
* -
*
* Maximum number of tags per resource: 50.
*
*
* -
*
* Maximum key length: 128 Unicode characters in UTF-8.
*
*
* -
*
* Maximum value length: 256 Unicode characters in UTF-8.
*
*
* -
*
* Accepted characters: all letters and numbers, spaces representable in UTF-8, and + - = . _ : / @. If your
* tagging schema is used across other services and resources, the character restrictions of those services
* also apply.
*
*
* -
*
* Key prefixes cannot include any upper or lowercase combination of aws: or AWS:.
* Values can have this prefix. If a tag value has aws as its prefix but the key does not,
* Forecast considers it to be a user tag and will count against the limit of 50 tags. Tags with only the
* key prefix of aws do not count against your tags per resource limit. You cannot edit or
* delete tag keys with this prefix.
*
*
*/
public java.util.List getTags() {
return tags;
}
/**
*
* Optional metadata to help you categorize and organize your predictors. Each tag consists of a key and an optional
* value, both of which you define. Tag keys and values are case sensitive.
*
*
* The following restrictions apply to tags:
*
*
* -
*
* For each resource, each tag key must be unique and each tag key must have one value.
*
*
* -
*
* Maximum number of tags per resource: 50.
*
*
* -
*
* Maximum key length: 128 Unicode characters in UTF-8.
*
*
* -
*
* Maximum value length: 256 Unicode characters in UTF-8.
*
*
* -
*
* Accepted characters: all letters and numbers, spaces representable in UTF-8, and + - = . _ : / @. If your tagging
* schema is used across other services and resources, the character restrictions of those services also apply.
*
*
* -
*
* Key prefixes cannot include any upper or lowercase combination of aws: or AWS:. Values
* can have this prefix. If a tag value has aws as its prefix but the key does not, Forecast considers
* it to be a user tag and will count against the limit of 50 tags. Tags with only the key prefix of
* aws do not count against your tags per resource limit. You cannot edit or delete tag keys with this
* prefix.
*
*
*
*
* @param tags
* Optional metadata to help you categorize and organize your predictors. Each tag consists of a key and an
* optional value, both of which you define. Tag keys and values are case sensitive.
*
* The following restrictions apply to tags:
*
*
* -
*
* For each resource, each tag key must be unique and each tag key must have one value.
*
*
* -
*
* Maximum number of tags per resource: 50.
*
*
* -
*
* Maximum key length: 128 Unicode characters in UTF-8.
*
*
* -
*
* Maximum value length: 256 Unicode characters in UTF-8.
*
*
* -
*
* Accepted characters: all letters and numbers, spaces representable in UTF-8, and + - = . _ : / @. If your
* tagging schema is used across other services and resources, the character restrictions of those services
* also apply.
*
*
* -
*
* Key prefixes cannot include any upper or lowercase combination of aws: or AWS:.
* Values can have this prefix. If a tag value has aws as its prefix but the key does not,
* Forecast considers it to be a user tag and will count against the limit of 50 tags. Tags with only the key
* prefix of aws do not count against your tags per resource limit. You cannot edit or delete
* tag keys with this prefix.
*
*
*/
public void setTags(java.util.Collection tags) {
if (tags == null) {
this.tags = null;
return;
}
this.tags = new java.util.ArrayList(tags);
}
/**
*
* Optional metadata to help you categorize and organize your predictors. Each tag consists of a key and an optional
* value, both of which you define. Tag keys and values are case sensitive.
*
*
* The following restrictions apply to tags:
*
*
* -
*
* For each resource, each tag key must be unique and each tag key must have one value.
*
*
* -
*
* Maximum number of tags per resource: 50.
*
*
* -
*
* Maximum key length: 128 Unicode characters in UTF-8.
*
*
* -
*
* Maximum value length: 256 Unicode characters in UTF-8.
*
*
* -
*
* Accepted characters: all letters and numbers, spaces representable in UTF-8, and + - = . _ : / @. If your tagging
* schema is used across other services and resources, the character restrictions of those services also apply.
*
*
* -
*
* Key prefixes cannot include any upper or lowercase combination of aws: or AWS:. Values
* can have this prefix. If a tag value has aws as its prefix but the key does not, Forecast considers
* it to be a user tag and will count against the limit of 50 tags. Tags with only the key prefix of
* aws do not count against your tags per resource limit. You cannot edit or delete tag keys with this
* prefix.
*
*
*
*
* NOTE: This method appends the values to the existing list (if any). Use
* {@link #setTags(java.util.Collection)} or {@link #withTags(java.util.Collection)} if you want to override the
* existing values.
*
*
* @param tags
* Optional metadata to help you categorize and organize your predictors. Each tag consists of a key and an
* optional value, both of which you define. Tag keys and values are case sensitive.
*
* The following restrictions apply to tags:
*
*
* -
*
* For each resource, each tag key must be unique and each tag key must have one value.
*
*
* -
*
* Maximum number of tags per resource: 50.
*
*
* -
*
* Maximum key length: 128 Unicode characters in UTF-8.
*
*
* -
*
* Maximum value length: 256 Unicode characters in UTF-8.
*
*
* -
*
* Accepted characters: all letters and numbers, spaces representable in UTF-8, and + - = . _ : / @. If your
* tagging schema is used across other services and resources, the character restrictions of those services
* also apply.
*
*
* -
*
* Key prefixes cannot include any upper or lowercase combination of aws: or AWS:.
* Values can have this prefix. If a tag value has aws as its prefix but the key does not,
* Forecast considers it to be a user tag and will count against the limit of 50 tags. Tags with only the key
* prefix of aws do not count against your tags per resource limit. You cannot edit or delete
* tag keys with this prefix.
*
*
* @return Returns a reference to this object so that method calls can be chained together.
*/
public CreateAutoPredictorRequest withTags(Tag... tags) {
if (this.tags == null) {
setTags(new java.util.ArrayList(tags.length));
}
for (Tag ele : tags) {
this.tags.add(ele);
}
return this;
}
/**
*
* Optional metadata to help you categorize and organize your predictors. Each tag consists of a key and an optional
* value, both of which you define. Tag keys and values are case sensitive.
*
*
* The following restrictions apply to tags:
*
*
* -
*
* For each resource, each tag key must be unique and each tag key must have one value.
*
*
* -
*
* Maximum number of tags per resource: 50.
*
*
* -
*
* Maximum key length: 128 Unicode characters in UTF-8.
*
*
* -
*
* Maximum value length: 256 Unicode characters in UTF-8.
*
*
* -
*
* Accepted characters: all letters and numbers, spaces representable in UTF-8, and + - = . _ : / @. If your tagging
* schema is used across other services and resources, the character restrictions of those services also apply.
*
*
* -
*
* Key prefixes cannot include any upper or lowercase combination of aws: or AWS:. Values
* can have this prefix. If a tag value has aws as its prefix but the key does not, Forecast considers
* it to be a user tag and will count against the limit of 50 tags. Tags with only the key prefix of
* aws do not count against your tags per resource limit. You cannot edit or delete tag keys with this
* prefix.
*
*
*
*
* @param tags
* Optional metadata to help you categorize and organize your predictors. Each tag consists of a key and an
* optional value, both of which you define. Tag keys and values are case sensitive.
*
* The following restrictions apply to tags:
*
*
* -
*
* For each resource, each tag key must be unique and each tag key must have one value.
*
*
* -
*
* Maximum number of tags per resource: 50.
*
*
* -
*
* Maximum key length: 128 Unicode characters in UTF-8.
*
*
* -
*
* Maximum value length: 256 Unicode characters in UTF-8.
*
*
* -
*
* Accepted characters: all letters and numbers, spaces representable in UTF-8, and + - = . _ : / @. If your
* tagging schema is used across other services and resources, the character restrictions of those services
* also apply.
*
*
* -
*
* Key prefixes cannot include any upper or lowercase combination of aws: or AWS:.
* Values can have this prefix. If a tag value has aws as its prefix but the key does not,
* Forecast considers it to be a user tag and will count against the limit of 50 tags. Tags with only the key
* prefix of aws do not count against your tags per resource limit. You cannot edit or delete
* tag keys with this prefix.
*
*
* @return Returns a reference to this object so that method calls can be chained together.
*/
public CreateAutoPredictorRequest withTags(java.util.Collection tags) {
setTags(tags);
return this;
}
/**
*
* The configuration details for predictor monitoring. Provide a name for the monitor resource to enable predictor
* monitoring.
*
*
* Predictor monitoring allows you to see how your predictor's performance changes over time. For more information,
* see Predictor Monitoring.
*
*
* @param monitorConfig
* The configuration details for predictor monitoring. Provide a name for the monitor resource to enable
* predictor monitoring.
*
* Predictor monitoring allows you to see how your predictor's performance changes over time. For more
* information, see Predictor Monitoring.
*/
public void setMonitorConfig(MonitorConfig monitorConfig) {
this.monitorConfig = monitorConfig;
}
/**
*
* The configuration details for predictor monitoring. Provide a name for the monitor resource to enable predictor
* monitoring.
*
*
* Predictor monitoring allows you to see how your predictor's performance changes over time. For more information,
* see Predictor Monitoring.
*
*
* @return The configuration details for predictor monitoring. Provide a name for the monitor resource to enable
* predictor monitoring.
*
* Predictor monitoring allows you to see how your predictor's performance changes over time. For more
* information, see Predictor Monitoring.
*/
public MonitorConfig getMonitorConfig() {
return this.monitorConfig;
}
/**
*
* The configuration details for predictor monitoring. Provide a name for the monitor resource to enable predictor
* monitoring.
*
*
* Predictor monitoring allows you to see how your predictor's performance changes over time. For more information,
* see Predictor Monitoring.
*
*
* @param monitorConfig
* The configuration details for predictor monitoring. Provide a name for the monitor resource to enable
* predictor monitoring.
*
* Predictor monitoring allows you to see how your predictor's performance changes over time. For more
* information, see Predictor Monitoring.
* @return Returns a reference to this object so that method calls can be chained together.
*/
public CreateAutoPredictorRequest withMonitorConfig(MonitorConfig monitorConfig) {
setMonitorConfig(monitorConfig);
return this;
}
/**
*
* The time boundary Forecast uses to align and aggregate any data that doesn't align with your forecast frequency.
* Provide the unit of time and the time boundary as a key value pair. For more information on specifying a time
* boundary, see Specifying a
* Time Boundary. If you don't provide a time boundary, Forecast uses a set of Default Time
* Boundaries.
*
*
* @param timeAlignmentBoundary
* The time boundary Forecast uses to align and aggregate any data that doesn't align with your forecast
* frequency. Provide the unit of time and the time boundary as a key value pair. For more information on
* specifying a time boundary, see Specifying a Time Boundary. If you don't provide a time boundary, Forecast uses a set of Default Time Boundaries.
*/
public void setTimeAlignmentBoundary(TimeAlignmentBoundary timeAlignmentBoundary) {
this.timeAlignmentBoundary = timeAlignmentBoundary;
}
/**
*
* The time boundary Forecast uses to align and aggregate any data that doesn't align with your forecast frequency.
* Provide the unit of time and the time boundary as a key value pair. For more information on specifying a time
* boundary, see Specifying a
* Time Boundary. If you don't provide a time boundary, Forecast uses a set of Default Time
* Boundaries.
*
*
* @return The time boundary Forecast uses to align and aggregate any data that doesn't align with your forecast
* frequency. Provide the unit of time and the time boundary as a key value pair. For more information on
* specifying a time boundary, see Specifying a Time Boundary. If you don't provide a time boundary, Forecast uses a set of Default Time Boundaries.
*/
public TimeAlignmentBoundary getTimeAlignmentBoundary() {
return this.timeAlignmentBoundary;
}
/**
*
* The time boundary Forecast uses to align and aggregate any data that doesn't align with your forecast frequency.
* Provide the unit of time and the time boundary as a key value pair. For more information on specifying a time
* boundary, see Specifying a
* Time Boundary. If you don't provide a time boundary, Forecast uses a set of Default Time
* Boundaries.
*
*
* @param timeAlignmentBoundary
* The time boundary Forecast uses to align and aggregate any data that doesn't align with your forecast
* frequency. Provide the unit of time and the time boundary as a key value pair. For more information on
* specifying a time boundary, see Specifying a Time Boundary. If you don't provide a time boundary, Forecast uses a set of Default Time Boundaries.
* @return Returns a reference to this object so that method calls can be chained together.
*/
public CreateAutoPredictorRequest withTimeAlignmentBoundary(TimeAlignmentBoundary timeAlignmentBoundary) {
setTimeAlignmentBoundary(timeAlignmentBoundary);
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 (getPredictorName() != null)
sb.append("PredictorName: ").append(getPredictorName()).append(",");
if (getForecastHorizon() != null)
sb.append("ForecastHorizon: ").append(getForecastHorizon()).append(",");
if (getForecastTypes() != null)
sb.append("ForecastTypes: ").append(getForecastTypes()).append(",");
if (getForecastDimensions() != null)
sb.append("ForecastDimensions: ").append(getForecastDimensions()).append(",");
if (getForecastFrequency() != null)
sb.append("ForecastFrequency: ").append(getForecastFrequency()).append(",");
if (getDataConfig() != null)
sb.append("DataConfig: ").append(getDataConfig()).append(",");
if (getEncryptionConfig() != null)
sb.append("EncryptionConfig: ").append(getEncryptionConfig()).append(",");
if (getReferencePredictorArn() != null)
sb.append("ReferencePredictorArn: ").append(getReferencePredictorArn()).append(",");
if (getOptimizationMetric() != null)
sb.append("OptimizationMetric: ").append(getOptimizationMetric()).append(",");
if (getExplainPredictor() != null)
sb.append("ExplainPredictor: ").append(getExplainPredictor()).append(",");
if (getTags() != null)
sb.append("Tags: ").append(getTags()).append(",");
if (getMonitorConfig() != null)
sb.append("MonitorConfig: ").append(getMonitorConfig()).append(",");
if (getTimeAlignmentBoundary() != null)
sb.append("TimeAlignmentBoundary: ").append(getTimeAlignmentBoundary());
sb.append("}");
return sb.toString();
}
@Override
public boolean equals(Object obj) {
if (this == obj)
return true;
if (obj == null)
return false;
if (obj instanceof CreateAutoPredictorRequest == false)
return false;
CreateAutoPredictorRequest other = (CreateAutoPredictorRequest) obj;
if (other.getPredictorName() == null ^ this.getPredictorName() == null)
return false;
if (other.getPredictorName() != null && other.getPredictorName().equals(this.getPredictorName()) == 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.getForecastTypes() == null ^ this.getForecastTypes() == null)
return false;
if (other.getForecastTypes() != null && other.getForecastTypes().equals(this.getForecastTypes()) == false)
return false;
if (other.getForecastDimensions() == null ^ this.getForecastDimensions() == null)
return false;
if (other.getForecastDimensions() != null && other.getForecastDimensions().equals(this.getForecastDimensions()) == 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.getDataConfig() == null ^ this.getDataConfig() == null)
return false;
if (other.getDataConfig() != null && other.getDataConfig().equals(this.getDataConfig()) == false)
return false;
if (other.getEncryptionConfig() == null ^ this.getEncryptionConfig() == null)
return false;
if (other.getEncryptionConfig() != null && other.getEncryptionConfig().equals(this.getEncryptionConfig()) == false)
return false;
if (other.getReferencePredictorArn() == null ^ this.getReferencePredictorArn() == null)
return false;
if (other.getReferencePredictorArn() != null && other.getReferencePredictorArn().equals(this.getReferencePredictorArn()) == false)
return false;
if (other.getOptimizationMetric() == null ^ this.getOptimizationMetric() == null)
return false;
if (other.getOptimizationMetric() != null && other.getOptimizationMetric().equals(this.getOptimizationMetric()) == false)
return false;
if (other.getExplainPredictor() == null ^ this.getExplainPredictor() == null)
return false;
if (other.getExplainPredictor() != null && other.getExplainPredictor().equals(this.getExplainPredictor()) == false)
return false;
if (other.getTags() == null ^ this.getTags() == null)
return false;
if (other.getTags() != null && other.getTags().equals(this.getTags()) == false)
return false;
if (other.getMonitorConfig() == null ^ this.getMonitorConfig() == null)
return false;
if (other.getMonitorConfig() != null && other.getMonitorConfig().equals(this.getMonitorConfig()) == false)
return false;
if (other.getTimeAlignmentBoundary() == null ^ this.getTimeAlignmentBoundary() == null)
return false;
if (other.getTimeAlignmentBoundary() != null && other.getTimeAlignmentBoundary().equals(this.getTimeAlignmentBoundary()) == false)
return false;
return true;
}
@Override
public int hashCode() {
final int prime = 31;
int hashCode = 1;
hashCode = prime * hashCode + ((getPredictorName() == null) ? 0 : getPredictorName().hashCode());
hashCode = prime * hashCode + ((getForecastHorizon() == null) ? 0 : getForecastHorizon().hashCode());
hashCode = prime * hashCode + ((getForecastTypes() == null) ? 0 : getForecastTypes().hashCode());
hashCode = prime * hashCode + ((getForecastDimensions() == null) ? 0 : getForecastDimensions().hashCode());
hashCode = prime * hashCode + ((getForecastFrequency() == null) ? 0 : getForecastFrequency().hashCode());
hashCode = prime * hashCode + ((getDataConfig() == null) ? 0 : getDataConfig().hashCode());
hashCode = prime * hashCode + ((getEncryptionConfig() == null) ? 0 : getEncryptionConfig().hashCode());
hashCode = prime * hashCode + ((getReferencePredictorArn() == null) ? 0 : getReferencePredictorArn().hashCode());
hashCode = prime * hashCode + ((getOptimizationMetric() == null) ? 0 : getOptimizationMetric().hashCode());
hashCode = prime * hashCode + ((getExplainPredictor() == null) ? 0 : getExplainPredictor().hashCode());
hashCode = prime * hashCode + ((getTags() == null) ? 0 : getTags().hashCode());
hashCode = prime * hashCode + ((getMonitorConfig() == null) ? 0 : getMonitorConfig().hashCode());
hashCode = prime * hashCode + ((getTimeAlignmentBoundary() == null) ? 0 : getTimeAlignmentBoundary().hashCode());
return hashCode;
}
@Override
public CreateAutoPredictorRequest clone() {
return (CreateAutoPredictorRequest) super.clone();
}
}