com.amazonaws.services.forecast.model.DescribePredictorResult Maven / Gradle / Ivy
Show all versions of aws-java-sdk-forecast Show documentation
/*
* Copyright 2015-2020 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;
/**
*
* @see AWS API
* Documentation
*/
@Generated("com.amazonaws:aws-java-sdk-code-generator")
public class DescribePredictorResult extends com.amazonaws.AmazonWebServiceResult implements Serializable, Cloneable {
/**
*
* The ARN of the predictor.
*
*/
private String predictorArn;
/**
*
* The name of the predictor.
*
*/
private String predictorName;
/**
*
* The Amazon Resource Name (ARN) of the algorithm used for model training.
*
*/
private String algorithmArn;
/**
*
* The number of time-steps of the forecast. The forecast horizon is also called the prediction length.
*
*/
private Integer forecastHorizon;
/**
*
* Whether the predictor is set to perform AutoML.
*
*/
private Boolean performAutoML;
/**
*
* Whether the predictor is set to perform hyperparameter optimization (HPO).
*
*/
private Boolean performHPO;
/**
*
* The default training parameters or overrides selected during model training. If using the AutoML algorithm or if
* HPO is turned on while using the DeepAR+ algorithms, the optimized values for the chosen hyperparameters are
* returned. For more information, see aws-forecast-choosing-recipes.
*
*/
private java.util.Map trainingParameters;
/**
*
* Used to override the default evaluation parameters of the specified algorithm. Amazon Forecast evaluates a
* predictor by splitting a dataset into training data and testing data. The evaluation parameters define how to
* perform the split and the number of iterations.
*
*/
private EvaluationParameters evaluationParameters;
/**
*
* The hyperparameter override values for the algorithm.
*
*/
private HyperParameterTuningJobConfig hPOConfig;
/**
*
* Describes the dataset group that contains the data to use to train the predictor.
*
*/
private InputDataConfig inputDataConfig;
/**
*
* The featurization configuration.
*
*/
private FeaturizationConfig featurizationConfig;
/**
*
* An AWS Key Management Service (KMS) key and the AWS Identity and Access Management (IAM) role that Amazon
* Forecast can assume to access the key.
*
*/
private EncryptionConfig encryptionConfig;
/**
*
* Details on the the status and results of the backtests performed to evaluate the accuracy of the predictor. You
* specify the number of backtests to perform when you call the operation.
*
*/
private PredictorExecutionDetails predictorExecutionDetails;
/**
*
* An array of the ARNs of the dataset import jobs used to import training data for the predictor.
*
*/
private java.util.List datasetImportJobArns;
/**
*
* When PerformAutoML is specified, the ARN of the chosen algorithm.
*
*/
private java.util.List autoMLAlgorithmArns;
/**
*
* The status of the predictor. States include:
*
*
* -
*
* ACTIVE
*
*
* -
*
* CREATE_PENDING, CREATE_IN_PROGRESS, CREATE_FAILED
*
*
* -
*
* DELETE_PENDING, DELETE_IN_PROGRESS, DELETE_FAILED
*
*
* -
*
* UPDATE_PENDING, UPDATE_IN_PROGRESS, UPDATE_FAILED
*
*
*
*
*
* The Status of the predictor must be ACTIVE before you can use the predictor to create a
* forecast.
*
*
*/
private String status;
/**
*
* If an error occurred, an informational message about the error.
*
*/
private String message;
/**
*
* When the model training task was created.
*
*/
private java.util.Date creationTime;
/**
*
* Initially, the same as CreationTime (when the status is CREATE_PENDING). This value is
* updated when training starts (when the status changes to CREATE_IN_PROGRESS), and when training has
* completed (when the status changes to ACTIVE) or fails (when the status changes to
* CREATE_FAILED).
*
*/
private java.util.Date lastModificationTime;
/**
*
* The ARN of the predictor.
*
*
* @param predictorArn
* The ARN of the predictor.
*/
public void setPredictorArn(String predictorArn) {
this.predictorArn = predictorArn;
}
/**
*
* The ARN of the predictor.
*
*
* @return The ARN of the predictor.
*/
public String getPredictorArn() {
return this.predictorArn;
}
/**
*
* The ARN of the predictor.
*
*
* @param predictorArn
* The ARN of the predictor.
* @return Returns a reference to this object so that method calls can be chained together.
*/
public DescribePredictorResult withPredictorArn(String predictorArn) {
setPredictorArn(predictorArn);
return this;
}
/**
*
* The name of the predictor.
*
*
* @param predictorName
* The name of the predictor.
*/
public void setPredictorName(String predictorName) {
this.predictorName = predictorName;
}
/**
*
* The name of the predictor.
*
*
* @return The name of the predictor.
*/
public String getPredictorName() {
return this.predictorName;
}
/**
*
* The name of the predictor.
*
*
* @param predictorName
* The name of the predictor.
* @return Returns a reference to this object so that method calls can be chained together.
*/
public DescribePredictorResult withPredictorName(String predictorName) {
setPredictorName(predictorName);
return this;
}
/**
*
* The Amazon Resource Name (ARN) of the algorithm used for model training.
*
*
* @param algorithmArn
* The Amazon Resource Name (ARN) of the algorithm used for model training.
*/
public void setAlgorithmArn(String algorithmArn) {
this.algorithmArn = algorithmArn;
}
/**
*
* The Amazon Resource Name (ARN) of the algorithm used for model training.
*
*
* @return The Amazon Resource Name (ARN) of the algorithm used for model training.
*/
public String getAlgorithmArn() {
return this.algorithmArn;
}
/**
*
* The Amazon Resource Name (ARN) of the algorithm used for model training.
*
*
* @param algorithmArn
* The Amazon Resource Name (ARN) of the algorithm used for model training.
* @return Returns a reference to this object so that method calls can be chained together.
*/
public DescribePredictorResult withAlgorithmArn(String algorithmArn) {
setAlgorithmArn(algorithmArn);
return this;
}
/**
*
* The number of time-steps of the forecast. The forecast horizon is also called the prediction length.
*
*
* @param forecastHorizon
* The number of time-steps of the forecast. The forecast horizon is also called the prediction length.
*/
public void setForecastHorizon(Integer forecastHorizon) {
this.forecastHorizon = forecastHorizon;
}
/**
*
* The number of time-steps of the forecast. The forecast horizon is also called the prediction length.
*
*
* @return The number of time-steps of the forecast. The forecast horizon is also called the prediction length.
*/
public Integer getForecastHorizon() {
return this.forecastHorizon;
}
/**
*
* The number of time-steps of the forecast. The forecast horizon is also called the prediction length.
*
*
* @param forecastHorizon
* The number of time-steps of the forecast. The forecast horizon is also called the prediction length.
* @return Returns a reference to this object so that method calls can be chained together.
*/
public DescribePredictorResult withForecastHorizon(Integer forecastHorizon) {
setForecastHorizon(forecastHorizon);
return this;
}
/**
*
* Whether the predictor is set to perform AutoML.
*
*
* @param performAutoML
* Whether the predictor is set to perform AutoML.
*/
public void setPerformAutoML(Boolean performAutoML) {
this.performAutoML = performAutoML;
}
/**
*
* Whether the predictor is set to perform AutoML.
*
*
* @return Whether the predictor is set to perform AutoML.
*/
public Boolean getPerformAutoML() {
return this.performAutoML;
}
/**
*
* Whether the predictor is set to perform AutoML.
*
*
* @param performAutoML
* Whether the predictor is set to perform AutoML.
* @return Returns a reference to this object so that method calls can be chained together.
*/
public DescribePredictorResult withPerformAutoML(Boolean performAutoML) {
setPerformAutoML(performAutoML);
return this;
}
/**
*
* Whether the predictor is set to perform AutoML.
*
*
* @return Whether the predictor is set to perform AutoML.
*/
public Boolean isPerformAutoML() {
return this.performAutoML;
}
/**
*
* Whether the predictor is set to perform hyperparameter optimization (HPO).
*
*
* @param performHPO
* Whether the predictor is set to perform hyperparameter optimization (HPO).
*/
public void setPerformHPO(Boolean performHPO) {
this.performHPO = performHPO;
}
/**
*
* Whether the predictor is set to perform hyperparameter optimization (HPO).
*
*
* @return Whether the predictor is set to perform hyperparameter optimization (HPO).
*/
public Boolean getPerformHPO() {
return this.performHPO;
}
/**
*
* Whether the predictor is set to perform hyperparameter optimization (HPO).
*
*
* @param performHPO
* Whether the predictor is set to perform hyperparameter optimization (HPO).
* @return Returns a reference to this object so that method calls can be chained together.
*/
public DescribePredictorResult withPerformHPO(Boolean performHPO) {
setPerformHPO(performHPO);
return this;
}
/**
*
* Whether the predictor is set to perform hyperparameter optimization (HPO).
*
*
* @return Whether the predictor is set to perform hyperparameter optimization (HPO).
*/
public Boolean isPerformHPO() {
return this.performHPO;
}
/**
*
* The default training parameters or overrides selected during model training. If using the AutoML algorithm or if
* HPO is turned on while using the DeepAR+ algorithms, the optimized values for the chosen hyperparameters are
* returned. For more information, see aws-forecast-choosing-recipes.
*
*
* @return The default training parameters or overrides selected during model training. If using the AutoML
* algorithm or if HPO is turned on while using the DeepAR+ algorithms, the optimized values for the chosen
* hyperparameters are returned. For more information, see aws-forecast-choosing-recipes.
*/
public java.util.Map getTrainingParameters() {
return trainingParameters;
}
/**
*
* The default training parameters or overrides selected during model training. If using the AutoML algorithm or if
* HPO is turned on while using the DeepAR+ algorithms, the optimized values for the chosen hyperparameters are
* returned. For more information, see aws-forecast-choosing-recipes.
*
*
* @param trainingParameters
* The default training parameters or overrides selected during model training. If using the AutoML algorithm
* or if HPO is turned on while using the DeepAR+ algorithms, the optimized values for the chosen
* hyperparameters are returned. For more information, see aws-forecast-choosing-recipes.
*/
public void setTrainingParameters(java.util.Map trainingParameters) {
this.trainingParameters = trainingParameters;
}
/**
*
* The default training parameters or overrides selected during model training. If using the AutoML algorithm or if
* HPO is turned on while using the DeepAR+ algorithms, the optimized values for the chosen hyperparameters are
* returned. For more information, see aws-forecast-choosing-recipes.
*
*
* @param trainingParameters
* The default training parameters or overrides selected during model training. If using the AutoML algorithm
* or if HPO is turned on while using the DeepAR+ algorithms, the optimized values for the chosen
* hyperparameters are returned. For more information, see aws-forecast-choosing-recipes.
* @return Returns a reference to this object so that method calls can be chained together.
*/
public DescribePredictorResult withTrainingParameters(java.util.Map trainingParameters) {
setTrainingParameters(trainingParameters);
return this;
}
public DescribePredictorResult addTrainingParametersEntry(String key, String value) {
if (null == this.trainingParameters) {
this.trainingParameters = new java.util.HashMap();
}
if (this.trainingParameters.containsKey(key))
throw new IllegalArgumentException("Duplicated keys (" + key.toString() + ") are provided.");
this.trainingParameters.put(key, value);
return this;
}
/**
* Removes all the entries added into TrainingParameters.
*
* @return Returns a reference to this object so that method calls can be chained together.
*/
public DescribePredictorResult clearTrainingParametersEntries() {
this.trainingParameters = null;
return this;
}
/**
*
* Used to override the default evaluation parameters of the specified algorithm. Amazon Forecast evaluates a
* predictor by splitting a dataset into training data and testing data. The evaluation parameters define how to
* perform the split and the number of iterations.
*
*
* @param evaluationParameters
* Used to override the default evaluation parameters of the specified algorithm. Amazon Forecast evaluates a
* predictor by splitting a dataset into training data and testing data. The evaluation parameters define how
* to perform the split and the number of iterations.
*/
public void setEvaluationParameters(EvaluationParameters evaluationParameters) {
this.evaluationParameters = evaluationParameters;
}
/**
*
* Used to override the default evaluation parameters of the specified algorithm. Amazon Forecast evaluates a
* predictor by splitting a dataset into training data and testing data. The evaluation parameters define how to
* perform the split and the number of iterations.
*
*
* @return Used to override the default evaluation parameters of the specified algorithm. Amazon Forecast evaluates
* a predictor by splitting a dataset into training data and testing data. The evaluation parameters define
* how to perform the split and the number of iterations.
*/
public EvaluationParameters getEvaluationParameters() {
return this.evaluationParameters;
}
/**
*
* Used to override the default evaluation parameters of the specified algorithm. Amazon Forecast evaluates a
* predictor by splitting a dataset into training data and testing data. The evaluation parameters define how to
* perform the split and the number of iterations.
*
*
* @param evaluationParameters
* Used to override the default evaluation parameters of the specified algorithm. Amazon Forecast evaluates a
* predictor by splitting a dataset into training data and testing data. The evaluation parameters define how
* to perform the split and the number of iterations.
* @return Returns a reference to this object so that method calls can be chained together.
*/
public DescribePredictorResult withEvaluationParameters(EvaluationParameters evaluationParameters) {
setEvaluationParameters(evaluationParameters);
return this;
}
/**
*
* The hyperparameter override values for the algorithm.
*
*
* @param hPOConfig
* The hyperparameter override values for the algorithm.
*/
public void setHPOConfig(HyperParameterTuningJobConfig hPOConfig) {
this.hPOConfig = hPOConfig;
}
/**
*
* The hyperparameter override values for the algorithm.
*
*
* @return The hyperparameter override values for the algorithm.
*/
public HyperParameterTuningJobConfig getHPOConfig() {
return this.hPOConfig;
}
/**
*
* The hyperparameter override values for the algorithm.
*
*
* @param hPOConfig
* The hyperparameter override values for the algorithm.
* @return Returns a reference to this object so that method calls can be chained together.
*/
public DescribePredictorResult withHPOConfig(HyperParameterTuningJobConfig hPOConfig) {
setHPOConfig(hPOConfig);
return this;
}
/**
*
* Describes the dataset group that contains the data to use to train the predictor.
*
*
* @param inputDataConfig
* Describes the dataset group that contains the data to use to train the predictor.
*/
public void setInputDataConfig(InputDataConfig inputDataConfig) {
this.inputDataConfig = inputDataConfig;
}
/**
*
* Describes the dataset group that contains the data to use to train the predictor.
*
*
* @return Describes the dataset group that contains the data to use to train the predictor.
*/
public InputDataConfig getInputDataConfig() {
return this.inputDataConfig;
}
/**
*
* Describes the dataset group that contains the data to use to train the predictor.
*
*
* @param inputDataConfig
* Describes the dataset group that contains the data to use to train the predictor.
* @return Returns a reference to this object so that method calls can be chained together.
*/
public DescribePredictorResult withInputDataConfig(InputDataConfig inputDataConfig) {
setInputDataConfig(inputDataConfig);
return this;
}
/**
*
* The featurization configuration.
*
*
* @param featurizationConfig
* The featurization configuration.
*/
public void setFeaturizationConfig(FeaturizationConfig featurizationConfig) {
this.featurizationConfig = featurizationConfig;
}
/**
*
* The featurization configuration.
*
*
* @return The featurization configuration.
*/
public FeaturizationConfig getFeaturizationConfig() {
return this.featurizationConfig;
}
/**
*
* The featurization configuration.
*
*
* @param featurizationConfig
* The featurization configuration.
* @return Returns a reference to this object so that method calls can be chained together.
*/
public DescribePredictorResult withFeaturizationConfig(FeaturizationConfig featurizationConfig) {
setFeaturizationConfig(featurizationConfig);
return this;
}
/**
*
* An AWS Key Management Service (KMS) key and the AWS Identity and Access Management (IAM) role that Amazon
* Forecast can assume to access the key.
*
*
* @param encryptionConfig
* An AWS Key Management Service (KMS) key and the AWS Identity and Access Management (IAM) role that Amazon
* Forecast can assume to access the key.
*/
public void setEncryptionConfig(EncryptionConfig encryptionConfig) {
this.encryptionConfig = encryptionConfig;
}
/**
*
* An AWS Key Management Service (KMS) key and the AWS Identity and Access Management (IAM) role that Amazon
* Forecast can assume to access the key.
*
*
* @return An AWS Key Management Service (KMS) key and the AWS Identity and Access Management (IAM) role that Amazon
* Forecast can assume to access the key.
*/
public EncryptionConfig getEncryptionConfig() {
return this.encryptionConfig;
}
/**
*
* An AWS Key Management Service (KMS) key and the AWS Identity and Access Management (IAM) role that Amazon
* Forecast can assume to access the key.
*
*
* @param encryptionConfig
* An AWS Key Management Service (KMS) key and the AWS Identity and Access Management (IAM) role that Amazon
* Forecast can assume to access the key.
* @return Returns a reference to this object so that method calls can be chained together.
*/
public DescribePredictorResult withEncryptionConfig(EncryptionConfig encryptionConfig) {
setEncryptionConfig(encryptionConfig);
return this;
}
/**
*
* Details on the the status and results of the backtests performed to evaluate the accuracy of the predictor. You
* specify the number of backtests to perform when you call the operation.
*
*
* @param predictorExecutionDetails
* Details on the the status and results of the backtests performed to evaluate the accuracy of the
* predictor. You specify the number of backtests to perform when you call the operation.
*/
public void setPredictorExecutionDetails(PredictorExecutionDetails predictorExecutionDetails) {
this.predictorExecutionDetails = predictorExecutionDetails;
}
/**
*
* Details on the the status and results of the backtests performed to evaluate the accuracy of the predictor. You
* specify the number of backtests to perform when you call the operation.
*
*
* @return Details on the the status and results of the backtests performed to evaluate the accuracy of the
* predictor. You specify the number of backtests to perform when you call the operation.
*/
public PredictorExecutionDetails getPredictorExecutionDetails() {
return this.predictorExecutionDetails;
}
/**
*
* Details on the the status and results of the backtests performed to evaluate the accuracy of the predictor. You
* specify the number of backtests to perform when you call the operation.
*
*
* @param predictorExecutionDetails
* Details on the the status and results of the backtests performed to evaluate the accuracy of the
* predictor. You specify the number of backtests to perform when you call the operation.
* @return Returns a reference to this object so that method calls can be chained together.
*/
public DescribePredictorResult withPredictorExecutionDetails(PredictorExecutionDetails predictorExecutionDetails) {
setPredictorExecutionDetails(predictorExecutionDetails);
return this;
}
/**
*
* An array of the ARNs of the dataset import jobs used to import training data for the predictor.
*
*
* @return An array of the ARNs of the dataset import jobs used to import training data for the predictor.
*/
public java.util.List getDatasetImportJobArns() {
return datasetImportJobArns;
}
/**
*
* An array of the ARNs of the dataset import jobs used to import training data for the predictor.
*
*
* @param datasetImportJobArns
* An array of the ARNs of the dataset import jobs used to import training data for the predictor.
*/
public void setDatasetImportJobArns(java.util.Collection datasetImportJobArns) {
if (datasetImportJobArns == null) {
this.datasetImportJobArns = null;
return;
}
this.datasetImportJobArns = new java.util.ArrayList(datasetImportJobArns);
}
/**
*
* An array of the ARNs of the dataset import jobs used to import training data for the predictor.
*
*
* NOTE: This method appends the values to the existing list (if any). Use
* {@link #setDatasetImportJobArns(java.util.Collection)} or {@link #withDatasetImportJobArns(java.util.Collection)}
* if you want to override the existing values.
*
*
* @param datasetImportJobArns
* An array of the ARNs of the dataset import jobs used to import training data for the predictor.
* @return Returns a reference to this object so that method calls can be chained together.
*/
public DescribePredictorResult withDatasetImportJobArns(String... datasetImportJobArns) {
if (this.datasetImportJobArns == null) {
setDatasetImportJobArns(new java.util.ArrayList(datasetImportJobArns.length));
}
for (String ele : datasetImportJobArns) {
this.datasetImportJobArns.add(ele);
}
return this;
}
/**
*
* An array of the ARNs of the dataset import jobs used to import training data for the predictor.
*
*
* @param datasetImportJobArns
* An array of the ARNs of the dataset import jobs used to import training data for the predictor.
* @return Returns a reference to this object so that method calls can be chained together.
*/
public DescribePredictorResult withDatasetImportJobArns(java.util.Collection datasetImportJobArns) {
setDatasetImportJobArns(datasetImportJobArns);
return this;
}
/**
*
* When PerformAutoML is specified, the ARN of the chosen algorithm.
*
*
* @return When PerformAutoML is specified, the ARN of the chosen algorithm.
*/
public java.util.List getAutoMLAlgorithmArns() {
return autoMLAlgorithmArns;
}
/**
*
* When PerformAutoML is specified, the ARN of the chosen algorithm.
*
*
* @param autoMLAlgorithmArns
* When PerformAutoML is specified, the ARN of the chosen algorithm.
*/
public void setAutoMLAlgorithmArns(java.util.Collection autoMLAlgorithmArns) {
if (autoMLAlgorithmArns == null) {
this.autoMLAlgorithmArns = null;
return;
}
this.autoMLAlgorithmArns = new java.util.ArrayList(autoMLAlgorithmArns);
}
/**
*
* When PerformAutoML is specified, the ARN of the chosen algorithm.
*
*
* NOTE: This method appends the values to the existing list (if any). Use
* {@link #setAutoMLAlgorithmArns(java.util.Collection)} or {@link #withAutoMLAlgorithmArns(java.util.Collection)}
* if you want to override the existing values.
*
*
* @param autoMLAlgorithmArns
* When PerformAutoML is specified, the ARN of the chosen algorithm.
* @return Returns a reference to this object so that method calls can be chained together.
*/
public DescribePredictorResult withAutoMLAlgorithmArns(String... autoMLAlgorithmArns) {
if (this.autoMLAlgorithmArns == null) {
setAutoMLAlgorithmArns(new java.util.ArrayList(autoMLAlgorithmArns.length));
}
for (String ele : autoMLAlgorithmArns) {
this.autoMLAlgorithmArns.add(ele);
}
return this;
}
/**
*
* When PerformAutoML is specified, the ARN of the chosen algorithm.
*
*
* @param autoMLAlgorithmArns
* When PerformAutoML is specified, the ARN of the chosen algorithm.
* @return Returns a reference to this object so that method calls can be chained together.
*/
public DescribePredictorResult withAutoMLAlgorithmArns(java.util.Collection autoMLAlgorithmArns) {
setAutoMLAlgorithmArns(autoMLAlgorithmArns);
return this;
}
/**
*
* The status of the predictor. States include:
*
*
* -
*
* ACTIVE
*
*
* -
*
* CREATE_PENDING, CREATE_IN_PROGRESS, CREATE_FAILED
*
*
* -
*
* DELETE_PENDING, DELETE_IN_PROGRESS, DELETE_FAILED
*
*
* -
*
* UPDATE_PENDING, UPDATE_IN_PROGRESS, UPDATE_FAILED
*
*
*
*
*
* The Status of the predictor must be ACTIVE before you can use the predictor to create a
* forecast.
*
*
*
* @param status
* The status of the predictor. States include:
*
* -
*
* ACTIVE
*
*
* -
*
* CREATE_PENDING, CREATE_IN_PROGRESS, CREATE_FAILED
*
*
* -
*
* DELETE_PENDING, DELETE_IN_PROGRESS, DELETE_FAILED
*
*
* -
*
* UPDATE_PENDING, UPDATE_IN_PROGRESS, UPDATE_FAILED
*
*
*
*
*
* The Status of the predictor must be ACTIVE before you can use the predictor to
* create a forecast.
*
*/
public void setStatus(String status) {
this.status = status;
}
/**
*
* The status of the predictor. States include:
*
*
* -
*
* ACTIVE
*
*
* -
*
* CREATE_PENDING, CREATE_IN_PROGRESS, CREATE_FAILED
*
*
* -
*
* DELETE_PENDING, DELETE_IN_PROGRESS, DELETE_FAILED
*
*
* -
*
* UPDATE_PENDING, UPDATE_IN_PROGRESS, UPDATE_FAILED
*
*
*
*
*
* The Status of the predictor must be ACTIVE before you can use the predictor to create a
* forecast.
*
*
*
* @return The status of the predictor. States include:
*
* -
*
* ACTIVE
*
*
* -
*
* CREATE_PENDING, CREATE_IN_PROGRESS, CREATE_FAILED
*
*
* -
*
* DELETE_PENDING, DELETE_IN_PROGRESS, DELETE_FAILED
*
*
* -
*
* UPDATE_PENDING, UPDATE_IN_PROGRESS, UPDATE_FAILED
*
*
*
*
*
* The Status of the predictor must be ACTIVE before you can use the predictor to
* create a forecast.
*
*/
public String getStatus() {
return this.status;
}
/**
*
* The status of the predictor. States include:
*
*
* -
*
* ACTIVE
*
*
* -
*
* CREATE_PENDING, CREATE_IN_PROGRESS, CREATE_FAILED
*
*
* -
*
* DELETE_PENDING, DELETE_IN_PROGRESS, DELETE_FAILED
*
*
* -
*
* UPDATE_PENDING, UPDATE_IN_PROGRESS, UPDATE_FAILED
*
*
*
*
*
* The Status of the predictor must be ACTIVE before you can use the predictor to create a
* forecast.
*
*
*
* @param status
* The status of the predictor. States include:
*
* -
*
* ACTIVE
*
*
* -
*
* CREATE_PENDING, CREATE_IN_PROGRESS, CREATE_FAILED
*
*
* -
*
* DELETE_PENDING, DELETE_IN_PROGRESS, DELETE_FAILED
*
*
* -
*
* UPDATE_PENDING, UPDATE_IN_PROGRESS, UPDATE_FAILED
*
*
*
*
*
* The Status of the predictor must be ACTIVE before you can use the predictor to
* create a forecast.
*
* @return Returns a reference to this object so that method calls can be chained together.
*/
public DescribePredictorResult withStatus(String status) {
setStatus(status);
return this;
}
/**
*
* If an error occurred, an informational message about the error.
*
*
* @param message
* If an error occurred, an informational message about the error.
*/
public void setMessage(String message) {
this.message = message;
}
/**
*
* If an error occurred, an informational message about the error.
*
*
* @return If an error occurred, an informational message about the error.
*/
public String getMessage() {
return this.message;
}
/**
*
* If an error occurred, an informational message about the error.
*
*
* @param message
* If an error occurred, an informational message about the error.
* @return Returns a reference to this object so that method calls can be chained together.
*/
public DescribePredictorResult withMessage(String message) {
setMessage(message);
return this;
}
/**
*
* When the model training task was created.
*
*
* @param creationTime
* When the model training task was created.
*/
public void setCreationTime(java.util.Date creationTime) {
this.creationTime = creationTime;
}
/**
*
* When the model training task was created.
*
*
* @return When the model training task was created.
*/
public java.util.Date getCreationTime() {
return this.creationTime;
}
/**
*
* When the model training task was created.
*
*
* @param creationTime
* When the model training task was created.
* @return Returns a reference to this object so that method calls can be chained together.
*/
public DescribePredictorResult withCreationTime(java.util.Date creationTime) {
setCreationTime(creationTime);
return this;
}
/**
*
* Initially, the same as CreationTime (when the status is CREATE_PENDING). This value is
* updated when training starts (when the status changes to CREATE_IN_PROGRESS), and when training has
* completed (when the status changes to ACTIVE) or fails (when the status changes to
* CREATE_FAILED).
*
*
* @param lastModificationTime
* Initially, the same as CreationTime (when the status is CREATE_PENDING). This
* value is updated when training starts (when the status changes to CREATE_IN_PROGRESS), and
* when training has completed (when the status changes to ACTIVE) or fails (when the status
* changes to CREATE_FAILED).
*/
public void setLastModificationTime(java.util.Date lastModificationTime) {
this.lastModificationTime = lastModificationTime;
}
/**
*
* Initially, the same as CreationTime (when the status is CREATE_PENDING). This value is
* updated when training starts (when the status changes to CREATE_IN_PROGRESS), and when training has
* completed (when the status changes to ACTIVE) or fails (when the status changes to
* CREATE_FAILED).
*
*
* @return Initially, the same as CreationTime (when the status is CREATE_PENDING). This
* value is updated when training starts (when the status changes to CREATE_IN_PROGRESS), and
* when training has completed (when the status changes to ACTIVE) or fails (when the status
* changes to CREATE_FAILED).
*/
public java.util.Date getLastModificationTime() {
return this.lastModificationTime;
}
/**
*
* Initially, the same as CreationTime (when the status is CREATE_PENDING). This value is
* updated when training starts (when the status changes to CREATE_IN_PROGRESS), and when training has
* completed (when the status changes to ACTIVE) or fails (when the status changes to
* CREATE_FAILED).
*
*
* @param lastModificationTime
* Initially, the same as CreationTime (when the status is CREATE_PENDING). This
* value is updated when training starts (when the status changes to CREATE_IN_PROGRESS), and
* when training has completed (when the status changes to ACTIVE) or fails (when the status
* changes to CREATE_FAILED).
* @return Returns a reference to this object so that method calls can be chained together.
*/
public DescribePredictorResult withLastModificationTime(java.util.Date lastModificationTime) {
setLastModificationTime(lastModificationTime);
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 (getPredictorArn() != null)
sb.append("PredictorArn: ").append(getPredictorArn()).append(",");
if (getPredictorName() != null)
sb.append("PredictorName: ").append(getPredictorName()).append(",");
if (getAlgorithmArn() != null)
sb.append("AlgorithmArn: ").append(getAlgorithmArn()).append(",");
if (getForecastHorizon() != null)
sb.append("ForecastHorizon: ").append(getForecastHorizon()).append(",");
if (getPerformAutoML() != null)
sb.append("PerformAutoML: ").append(getPerformAutoML()).append(",");
if (getPerformHPO() != null)
sb.append("PerformHPO: ").append(getPerformHPO()).append(",");
if (getTrainingParameters() != null)
sb.append("TrainingParameters: ").append(getTrainingParameters()).append(",");
if (getEvaluationParameters() != null)
sb.append("EvaluationParameters: ").append(getEvaluationParameters()).append(",");
if (getHPOConfig() != null)
sb.append("HPOConfig: ").append(getHPOConfig()).append(",");
if (getInputDataConfig() != null)
sb.append("InputDataConfig: ").append(getInputDataConfig()).append(",");
if (getFeaturizationConfig() != null)
sb.append("FeaturizationConfig: ").append(getFeaturizationConfig()).append(",");
if (getEncryptionConfig() != null)
sb.append("EncryptionConfig: ").append(getEncryptionConfig()).append(",");
if (getPredictorExecutionDetails() != null)
sb.append("PredictorExecutionDetails: ").append(getPredictorExecutionDetails()).append(",");
if (getDatasetImportJobArns() != null)
sb.append("DatasetImportJobArns: ").append(getDatasetImportJobArns()).append(",");
if (getAutoMLAlgorithmArns() != null)
sb.append("AutoMLAlgorithmArns: ").append(getAutoMLAlgorithmArns()).append(",");
if (getStatus() != null)
sb.append("Status: ").append(getStatus()).append(",");
if (getMessage() != null)
sb.append("Message: ").append(getMessage()).append(",");
if (getCreationTime() != null)
sb.append("CreationTime: ").append(getCreationTime()).append(",");
if (getLastModificationTime() != null)
sb.append("LastModificationTime: ").append(getLastModificationTime());
sb.append("}");
return sb.toString();
}
@Override
public boolean equals(Object obj) {
if (this == obj)
return true;
if (obj == null)
return false;
if (obj instanceof DescribePredictorResult == false)
return false;
DescribePredictorResult other = (DescribePredictorResult) obj;
if (other.getPredictorArn() == null ^ this.getPredictorArn() == null)
return false;
if (other.getPredictorArn() != null && other.getPredictorArn().equals(this.getPredictorArn()) == false)
return false;
if (other.getPredictorName() == null ^ this.getPredictorName() == null)
return false;
if (other.getPredictorName() != null && other.getPredictorName().equals(this.getPredictorName()) == false)
return false;
if (other.getAlgorithmArn() == null ^ this.getAlgorithmArn() == null)
return false;
if (other.getAlgorithmArn() != null && other.getAlgorithmArn().equals(this.getAlgorithmArn()) == 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.getPerformAutoML() == null ^ this.getPerformAutoML() == null)
return false;
if (other.getPerformAutoML() != null && other.getPerformAutoML().equals(this.getPerformAutoML()) == false)
return false;
if (other.getPerformHPO() == null ^ this.getPerformHPO() == null)
return false;
if (other.getPerformHPO() != null && other.getPerformHPO().equals(this.getPerformHPO()) == false)
return false;
if (other.getTrainingParameters() == null ^ this.getTrainingParameters() == null)
return false;
if (other.getTrainingParameters() != null && other.getTrainingParameters().equals(this.getTrainingParameters()) == false)
return false;
if (other.getEvaluationParameters() == null ^ this.getEvaluationParameters() == null)
return false;
if (other.getEvaluationParameters() != null && other.getEvaluationParameters().equals(this.getEvaluationParameters()) == false)
return false;
if (other.getHPOConfig() == null ^ this.getHPOConfig() == null)
return false;
if (other.getHPOConfig() != null && other.getHPOConfig().equals(this.getHPOConfig()) == false)
return false;
if (other.getInputDataConfig() == null ^ this.getInputDataConfig() == null)
return false;
if (other.getInputDataConfig() != null && other.getInputDataConfig().equals(this.getInputDataConfig()) == false)
return false;
if (other.getFeaturizationConfig() == null ^ this.getFeaturizationConfig() == null)
return false;
if (other.getFeaturizationConfig() != null && other.getFeaturizationConfig().equals(this.getFeaturizationConfig()) == 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.getPredictorExecutionDetails() == null ^ this.getPredictorExecutionDetails() == null)
return false;
if (other.getPredictorExecutionDetails() != null && other.getPredictorExecutionDetails().equals(this.getPredictorExecutionDetails()) == false)
return false;
if (other.getDatasetImportJobArns() == null ^ this.getDatasetImportJobArns() == null)
return false;
if (other.getDatasetImportJobArns() != null && other.getDatasetImportJobArns().equals(this.getDatasetImportJobArns()) == false)
return false;
if (other.getAutoMLAlgorithmArns() == null ^ this.getAutoMLAlgorithmArns() == null)
return false;
if (other.getAutoMLAlgorithmArns() != null && other.getAutoMLAlgorithmArns().equals(this.getAutoMLAlgorithmArns()) == false)
return false;
if (other.getStatus() == null ^ this.getStatus() == null)
return false;
if (other.getStatus() != null && other.getStatus().equals(this.getStatus()) == false)
return false;
if (other.getMessage() == null ^ this.getMessage() == null)
return false;
if (other.getMessage() != null && other.getMessage().equals(this.getMessage()) == false)
return false;
if (other.getCreationTime() == null ^ this.getCreationTime() == null)
return false;
if (other.getCreationTime() != null && other.getCreationTime().equals(this.getCreationTime()) == false)
return false;
if (other.getLastModificationTime() == null ^ this.getLastModificationTime() == null)
return false;
if (other.getLastModificationTime() != null && other.getLastModificationTime().equals(this.getLastModificationTime()) == false)
return false;
return true;
}
@Override
public int hashCode() {
final int prime = 31;
int hashCode = 1;
hashCode = prime * hashCode + ((getPredictorArn() == null) ? 0 : getPredictorArn().hashCode());
hashCode = prime * hashCode + ((getPredictorName() == null) ? 0 : getPredictorName().hashCode());
hashCode = prime * hashCode + ((getAlgorithmArn() == null) ? 0 : getAlgorithmArn().hashCode());
hashCode = prime * hashCode + ((getForecastHorizon() == null) ? 0 : getForecastHorizon().hashCode());
hashCode = prime * hashCode + ((getPerformAutoML() == null) ? 0 : getPerformAutoML().hashCode());
hashCode = prime * hashCode + ((getPerformHPO() == null) ? 0 : getPerformHPO().hashCode());
hashCode = prime * hashCode + ((getTrainingParameters() == null) ? 0 : getTrainingParameters().hashCode());
hashCode = prime * hashCode + ((getEvaluationParameters() == null) ? 0 : getEvaluationParameters().hashCode());
hashCode = prime * hashCode + ((getHPOConfig() == null) ? 0 : getHPOConfig().hashCode());
hashCode = prime * hashCode + ((getInputDataConfig() == null) ? 0 : getInputDataConfig().hashCode());
hashCode = prime * hashCode + ((getFeaturizationConfig() == null) ? 0 : getFeaturizationConfig().hashCode());
hashCode = prime * hashCode + ((getEncryptionConfig() == null) ? 0 : getEncryptionConfig().hashCode());
hashCode = prime * hashCode + ((getPredictorExecutionDetails() == null) ? 0 : getPredictorExecutionDetails().hashCode());
hashCode = prime * hashCode + ((getDatasetImportJobArns() == null) ? 0 : getDatasetImportJobArns().hashCode());
hashCode = prime * hashCode + ((getAutoMLAlgorithmArns() == null) ? 0 : getAutoMLAlgorithmArns().hashCode());
hashCode = prime * hashCode + ((getStatus() == null) ? 0 : getStatus().hashCode());
hashCode = prime * hashCode + ((getMessage() == null) ? 0 : getMessage().hashCode());
hashCode = prime * hashCode + ((getCreationTime() == null) ? 0 : getCreationTime().hashCode());
hashCode = prime * hashCode + ((getLastModificationTime() == null) ? 0 : getLastModificationTime().hashCode());
return hashCode;
}
@Override
public DescribePredictorResult clone() {
try {
return (DescribePredictorResult) super.clone();
} catch (CloneNotSupportedException e) {
throw new IllegalStateException("Got a CloneNotSupportedException from Object.clone() " + "even though we're Cloneable!", e);
}
}
}