/* * Copyright 2010-2021 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.machinelearning.model; import java.io.Serializable; /** *

* Represents the output of a GetMLModel operation, and provides * detailed information about a MLModel. *

*/ public class GetMLModelResult implements Serializable { /** *

* The MLModel ID, which is same as the MLModelId in the * request. *

*

* Constraints:
* Length: 1 - 64
* Pattern: [a-zA-Z0-9_.-]+
*/ private String mLModelId; /** *

* The ID of the training DataSource. *

*

* Constraints:
* Length: 1 - 64
* Pattern: [a-zA-Z0-9_.-]+
*/ private String trainingDataSourceId; /** *

* The AWS user account from which the MLModel was created. The * account type can be either an AWS root account or an AWS Identity and * Access Management (IAM) user account. *

*

* Constraints:
* Pattern: arn:aws:iam::[0-9]+:((user/.+)|(root))
*/ private String createdByIamUser; /** *

* The time that the MLModel was created. The time is expressed * in epoch time. *

*/ private java.util.Date createdAt; /** *

* The time of the most recent edit to the MLModel. The time is * expressed in epoch time. *

*/ private java.util.Date lastUpdatedAt; /** *

* A user-supplied name or description of the MLModel. *

*

* Constraints:
* Length: - 1024
*/ private String name; /** *

* The current status of the MLModel. This element can have one * of the following values: *

* *

* Constraints:
* Allowed Values: PENDING, INPROGRESS, FAILED, COMPLETED, DELETED */ private String status; /** *

* Long integer type that is a 64-bit signed number. *

*/ private Long sizeInBytes; /** *

* The current endpoint of the MLModel *

*/ private RealtimeEndpointInfo endpointInfo; /** *

* A list of the training parameters in the MLModel. The list * is implemented as a map of key-value pairs. *

*

* The following is the current set of training parameters: *

* */ private java.util.Map trainingParameters = new java.util.HashMap(); /** *

* The location of the data file or directory in Amazon Simple Storage * Service (Amazon S3). *

*

* Constraints:
* Length: - 2048
* Pattern: s3://([^/]+)(/.*)?
*/ private String inputDataLocationS3; /** *

* Identifies the MLModel category. The following are the * available types: *

* *

* Constraints:
* Allowed Values: REGRESSION, BINARY, MULTICLASS */ private String mLModelType; /** *

* The scoring threshold is used in binary classification * MLModel models. It marks the boundary between a positive * prediction and a negative prediction. *

*

* Output values greater than or equal to the threshold receive a positive * result from the MLModel, such as true. Output values less * than the threshold receive a negative response from the MLModel, such as * false. *

*/ private Float scoreThreshold; /** *

* The time of the most recent edit to the ScoreThreshold. The * time is expressed in epoch time. *

*/ private java.util.Date scoreThresholdLastUpdatedAt; /** *

* A link to the file that contains logs of the CreateMLModel * operation. *

*/ private String logUri; /** *

* A description of the most recent details about accessing the * MLModel. *

*

* Constraints:
* Length: - 10240
*/ private String message; /** *

* The approximate CPU time in milliseconds that Amazon Machine Learning * spent processing the MLModel, normalized and scaled on * computation resources. ComputeTime is only available if the * MLModel is in the COMPLETED state. *

*/ private Long computeTime; /** *

* The epoch time when Amazon Machine Learning marked the * MLModel as COMPLETED or FAILED. * FinishedAt is only available when the MLModel * is in the COMPLETED or FAILED state. *

*/ private java.util.Date finishedAt; /** *

* The epoch time when Amazon Machine Learning marked the * MLModel as INPROGRESS. StartedAt * isn't available if the MLModel is in the * PENDING state. *

*/ private java.util.Date startedAt; /** *

* The recipe to use when training the MLModel. The * Recipe provides detailed information about the observation * data to use during training, and manipulations to perform on the * observation data during training. *

*

* Note: This parameter is provided as part of the verbose format. *

*

* Constraints:
* Length: - 131071
*/ private String recipe; /** *

* The schema used by all of the data files referenced by the * DataSource. *

*

* Note: This parameter is provided as part of the verbose format. *

*

* Constraints:
* Length: - 131071
*/ private String schema; /** *

* The MLModel ID, which is same as the MLModelId in the * request. *

*

* Constraints:
* Length: 1 - 64
* Pattern: [a-zA-Z0-9_.-]+
* * @return

* The MLModel ID, which is same as the MLModelId in * the request. *

*/ public String getMLModelId() { return mLModelId; } /** *

* The MLModel ID, which is same as the MLModelId in the * request. *

*

* Constraints:
* Length: 1 - 64
* Pattern: [a-zA-Z0-9_.-]+
* * @param mLModelId

* The MLModel ID, which is same as the MLModelId in * the request. *

*/ public void setMLModelId(String mLModelId) { this.mLModelId = mLModelId; } /** *

* The MLModel ID, which is same as the MLModelId in the * request. *

*

* Returns a reference to this object so that method calls can be chained * together. *

* Constraints:
* Length: 1 - 64
* Pattern: [a-zA-Z0-9_.-]+
* * @param mLModelId

* The MLModel ID, which is same as the MLModelId in * the request. *

* @return A reference to this updated object so that method calls can be * chained together. */ public GetMLModelResult withMLModelId(String mLModelId) { this.mLModelId = mLModelId; return this; } /** *

* The ID of the training DataSource. *

*

* Constraints:
* Length: 1 - 64
* Pattern: [a-zA-Z0-9_.-]+
* * @return

* The ID of the training DataSource. *

*/ public String getTrainingDataSourceId() { return trainingDataSourceId; } /** *

* The ID of the training DataSource. *

*

* Constraints:
* Length: 1 - 64
* Pattern: [a-zA-Z0-9_.-]+
* * @param trainingDataSourceId

* The ID of the training DataSource. *

*/ public void setTrainingDataSourceId(String trainingDataSourceId) { this.trainingDataSourceId = trainingDataSourceId; } /** *

* The ID of the training DataSource. *

*

* Returns a reference to this object so that method calls can be chained * together. *

* Constraints:
* Length: 1 - 64
* Pattern: [a-zA-Z0-9_.-]+
* * @param trainingDataSourceId

* The ID of the training DataSource. *

* @return A reference to this updated object so that method calls can be * chained together. */ public GetMLModelResult withTrainingDataSourceId(String trainingDataSourceId) { this.trainingDataSourceId = trainingDataSourceId; return this; } /** *

* The AWS user account from which the MLModel was created. The * account type can be either an AWS root account or an AWS Identity and * Access Management (IAM) user account. *

*

* Constraints:
* Pattern: arn:aws:iam::[0-9]+:((user/.+)|(root))
* * @return

* The AWS user account from which the MLModel was * created. The account type can be either an AWS root account or an * AWS Identity and Access Management (IAM) user account. *

*/ public String getCreatedByIamUser() { return createdByIamUser; } /** *

* The AWS user account from which the MLModel was created. The * account type can be either an AWS root account or an AWS Identity and * Access Management (IAM) user account. *

*

* Constraints:
* Pattern: arn:aws:iam::[0-9]+:((user/.+)|(root))
* * @param createdByIamUser

* The AWS user account from which the MLModel was * created. The account type can be either an AWS root account or * an AWS Identity and Access Management (IAM) user account. *

*/ public void setCreatedByIamUser(String createdByIamUser) { this.createdByIamUser = createdByIamUser; } /** *

* The AWS user account from which the MLModel was created. The * account type can be either an AWS root account or an AWS Identity and * Access Management (IAM) user account. *

*

* Returns a reference to this object so that method calls can be chained * together. *

* Constraints:
* Pattern: arn:aws:iam::[0-9]+:((user/.+)|(root))
* * @param createdByIamUser

* The AWS user account from which the MLModel was * created. The account type can be either an AWS root account or * an AWS Identity and Access Management (IAM) user account. *

* @return A reference to this updated object so that method calls can be * chained together. */ public GetMLModelResult withCreatedByIamUser(String createdByIamUser) { this.createdByIamUser = createdByIamUser; return this; } /** *

* The time that the MLModel was created. The time is expressed * in epoch time. *

* * @return

* The time that the MLModel was created. The time is * expressed in epoch time. *

*/ public java.util.Date getCreatedAt() { return createdAt; } /** *

* The time that the MLModel was created. The time is expressed * in epoch time. *

* * @param createdAt

* The time that the MLModel was created. The time * is expressed in epoch time. *

*/ public void setCreatedAt(java.util.Date createdAt) { this.createdAt = createdAt; } /** *

* The time that the MLModel was created. The time is expressed * in epoch time. *

*

* Returns a reference to this object so that method calls can be chained * together. * * @param createdAt

* The time that the MLModel was created. The time * is expressed in epoch time. *

* @return A reference to this updated object so that method calls can be * chained together. */ public GetMLModelResult withCreatedAt(java.util.Date createdAt) { this.createdAt = createdAt; return this; } /** *

* The time of the most recent edit to the MLModel. The time is * expressed in epoch time. *

* * @return

* The time of the most recent edit to the MLModel. The * time is expressed in epoch time. *

*/ public java.util.Date getLastUpdatedAt() { return lastUpdatedAt; } /** *

* The time of the most recent edit to the MLModel. The time is * expressed in epoch time. *

* * @param lastUpdatedAt

* The time of the most recent edit to the MLModel. * The time is expressed in epoch time. *

*/ public void setLastUpdatedAt(java.util.Date lastUpdatedAt) { this.lastUpdatedAt = lastUpdatedAt; } /** *

* The time of the most recent edit to the MLModel. The time is * expressed in epoch time. *

*

* Returns a reference to this object so that method calls can be chained * together. * * @param lastUpdatedAt

* The time of the most recent edit to the MLModel. * The time is expressed in epoch time. *

* @return A reference to this updated object so that method calls can be * chained together. */ public GetMLModelResult withLastUpdatedAt(java.util.Date lastUpdatedAt) { this.lastUpdatedAt = lastUpdatedAt; return this; } /** *

* A user-supplied name or description of the MLModel. *

*

* Constraints:
* Length: - 1024
* * @return

* A user-supplied name or description of the MLModel. *

*/ public String getName() { return name; } /** *

* A user-supplied name or description of the MLModel. *

*

* Constraints:
* Length: - 1024
* * @param name

* A user-supplied name or description of the * MLModel. *

*/ public void setName(String name) { this.name = name; } /** *

* A user-supplied name or description of the MLModel. *

*

* Returns a reference to this object so that method calls can be chained * together. *

* Constraints:
* Length: - 1024
* * @param name

* A user-supplied name or description of the * MLModel. *

* @return A reference to this updated object so that method calls can be * chained together. */ public GetMLModelResult withName(String name) { this.name = name; return this; } /** *

* The current status of the MLModel. This element can have one * of the following values: *

* *

* Constraints:
* Allowed Values: PENDING, INPROGRESS, FAILED, COMPLETED, DELETED * * @return

* The current status of the MLModel. This element can * have one of the following values: *

* * @see EntityStatus */ public String getStatus() { return status; } /** *

* The current status of the MLModel. This element can have one * of the following values: *

* *

* Constraints:
* Allowed Values: PENDING, INPROGRESS, FAILED, COMPLETED, DELETED * * @param status

* The current status of the MLModel. This element * can have one of the following values: *

* * @see EntityStatus */ public void setStatus(String status) { this.status = status; } /** *

* The current status of the MLModel. This element can have one * of the following values: *

* *

* Returns a reference to this object so that method calls can be chained * together. *

* Constraints:
* Allowed Values: PENDING, INPROGRESS, FAILED, COMPLETED, DELETED * * @param status

* The current status of the MLModel. This element * can have one of the following values: *

* * @return A reference to this updated object so that method calls can be * chained together. * @see EntityStatus */ public GetMLModelResult withStatus(String status) { this.status = status; return this; } /** *

* The current status of the MLModel. This element can have one * of the following values: *

* *

* Constraints:
* Allowed Values: PENDING, INPROGRESS, FAILED, COMPLETED, DELETED * * @param status

* The current status of the MLModel. This element * can have one of the following values: *

* * @see EntityStatus */ public void setStatus(EntityStatus status) { this.status = status.toString(); } /** *

* The current status of the MLModel. This element can have one * of the following values: *

* *

* Returns a reference to this object so that method calls can be chained * together. *

* Constraints:
* Allowed Values: PENDING, INPROGRESS, FAILED, COMPLETED, DELETED * * @param status

* The current status of the MLModel. This element * can have one of the following values: *

* * @return A reference to this updated object so that method calls can be * chained together. * @see EntityStatus */ public GetMLModelResult withStatus(EntityStatus status) { this.status = status.toString(); return this; } /** *

* Long integer type that is a 64-bit signed number. *

* * @return

* Long integer type that is a 64-bit signed number. *

*/ public Long getSizeInBytes() { return sizeInBytes; } /** *

* Long integer type that is a 64-bit signed number. *

* * @param sizeInBytes

* Long integer type that is a 64-bit signed number. *

*/ public void setSizeInBytes(Long sizeInBytes) { this.sizeInBytes = sizeInBytes; } /** *

* Long integer type that is a 64-bit signed number. *

*

* Returns a reference to this object so that method calls can be chained * together. * * @param sizeInBytes

* Long integer type that is a 64-bit signed number. *

* @return A reference to this updated object so that method calls can be * chained together. */ public GetMLModelResult withSizeInBytes(Long sizeInBytes) { this.sizeInBytes = sizeInBytes; return this; } /** *

* The current endpoint of the MLModel *

* * @return

* The current endpoint of the MLModel *

*/ public RealtimeEndpointInfo getEndpointInfo() { return endpointInfo; } /** *

* The current endpoint of the MLModel *

* * @param endpointInfo

* The current endpoint of the MLModel *

*/ public void setEndpointInfo(RealtimeEndpointInfo endpointInfo) { this.endpointInfo = endpointInfo; } /** *

* The current endpoint of the MLModel *

*

* Returns a reference to this object so that method calls can be chained * together. * * @param endpointInfo

* The current endpoint of the MLModel *

* @return A reference to this updated object so that method calls can be * chained together. */ public GetMLModelResult withEndpointInfo(RealtimeEndpointInfo endpointInfo) { this.endpointInfo = endpointInfo; return this; } /** *

* A list of the training parameters in the MLModel. The list * is implemented as a map of key-value pairs. *

*

* The following is the current set of training parameters: *

* * * @return

* A list of the training parameters in the MLModel. * The list is implemented as a map of key-value pairs. *

*

* The following is the current set of training parameters: *

* */ public java.util.Map getTrainingParameters() { return trainingParameters; } /** *

* A list of the training parameters in the MLModel. The list * is implemented as a map of key-value pairs. *

*

* The following is the current set of training parameters: *

*
    *
  • *

    * sgd.maxMLModelSizeInBytes - The maximum allowed size of the * model. Depending on the input data, the size of the model might affect * its performance. *

    *

    * The value is an integer that ranges from 100000 to * 2147483648. The default value is 33554432. *

    *
    • *
  • *

    * sgd.maxPasses - The number of times that the training * process traverses the observations to build the MLModel. The * value is an integer that ranges from 1 to 10000 * . The default value is 10. *

    *
    • *
  • *

    * sgd.shuffleType - Whether Amazon ML shuffles the training * data. Shuffling data improves a model's ability to find the optimal * solution for a variety of data types. The valid values are * auto and none. The default value is * none. We strongly recommend that you shuffle your data. *

    *
    • *
  • *

    * sgd.l1RegularizationAmount - The coefficient regularization * L1 norm. It controls overfitting the data by penalizing large * coefficients. This tends to drive coefficients to zero, resulting in a * sparse feature set. If you use this parameter, start by specifying a * small value, such as 1.0E-08. *

    *

    * The value is a double that ranges from 0 to * MAX_DOUBLE. The default is to not use L1 normalization. This * parameter can't be used when L2 is specified. Use this * parameter sparingly. *

    *
    • *
  • *

    * sgd.l2RegularizationAmount - The coefficient regularization * L2 norm. It controls overfitting the data by penalizing large * coefficients. This tends to drive coefficients to small, nonzero values. * If you use this parameter, start by specifying a small value, such as * 1.0E-08. *

    *

    * The value is a double that ranges from 0 to * MAX_DOUBLE. The default is to not use L2 normalization. This * parameter can't be used when L1 is specified. Use this * parameter sparingly. *

    *
    • *
* * @param trainingParameters

* A list of the training parameters in the MLModel. * The list is implemented as a map of key-value pairs. *

*

* The following is the current set of training parameters: *

*
    *
  • *

    * sgd.maxMLModelSizeInBytes - The maximum allowed * size of the model. Depending on the input data, the size of * the model might affect its performance. *

    *

    * The value is an integer that ranges from 100000 * to 2147483648. The default value is * 33554432. *

    *
    • *
  • *

    * sgd.maxPasses - The number of times that the * training process traverses the observations to build the * MLModel. The value is an integer that ranges from * 1 to 10000. The default value is * 10. *

    *
    • *
  • *

    * sgd.shuffleType - Whether Amazon ML shuffles the * training data. Shuffling data improves a model's ability to * find the optimal solution for a variety of data types. The * valid values are auto and none. The * default value is none. We strongly recommend that * you shuffle your data. *

    *
    • *
  • *

    * sgd.l1RegularizationAmount - The coefficient * regularization L1 norm. It controls overfitting the data by * penalizing large coefficients. This tends to drive * coefficients to zero, resulting in a sparse feature set. If * you use this parameter, start by specifying a small value, * such as 1.0E-08. *

    *

    * The value is a double that ranges from 0 to * MAX_DOUBLE. The default is to not use L1 * normalization. This parameter can't be used when * L2 is specified. Use this parameter sparingly. *

    *
    • *
  • *

    * sgd.l2RegularizationAmount - The coefficient * regularization L2 norm. It controls overfitting the data by * penalizing large coefficients. This tends to drive * coefficients to small, nonzero values. If you use this * parameter, start by specifying a small value, such as * 1.0E-08. *

    *

    * The value is a double that ranges from 0 to * MAX_DOUBLE. The default is to not use L2 * normalization. This parameter can't be used when * L1 is specified. Use this parameter sparingly. *

    *
    • *
*/ public void setTrainingParameters(java.util.Map trainingParameters) { this.trainingParameters = trainingParameters; } /** *

* A list of the training parameters in the MLModel. The list * is implemented as a map of key-value pairs. *

*

* The following is the current set of training parameters: *

*
    *
  • *

    * sgd.maxMLModelSizeInBytes - The maximum allowed size of the * model. Depending on the input data, the size of the model might affect * its performance. *

    *

    * The value is an integer that ranges from 100000 to * 2147483648. The default value is 33554432. *

    *
    • *
  • *

    * sgd.maxPasses - The number of times that the training * process traverses the observations to build the MLModel. The * value is an integer that ranges from 1 to 10000 * . The default value is 10. *

    *
    • *
  • *

    * sgd.shuffleType - Whether Amazon ML shuffles the training * data. Shuffling data improves a model's ability to find the optimal * solution for a variety of data types. The valid values are * auto and none. The default value is * none. We strongly recommend that you shuffle your data. *

    *
    • *
  • *

    * sgd.l1RegularizationAmount - The coefficient regularization * L1 norm. It controls overfitting the data by penalizing large * coefficients. This tends to drive coefficients to zero, resulting in a * sparse feature set. If you use this parameter, start by specifying a * small value, such as 1.0E-08. *

    *

    * The value is a double that ranges from 0 to * MAX_DOUBLE. The default is to not use L1 normalization. This * parameter can't be used when L2 is specified. Use this * parameter sparingly. *

    *
    • *
  • *

    * sgd.l2RegularizationAmount - The coefficient regularization * L2 norm. It controls overfitting the data by penalizing large * coefficients. This tends to drive coefficients to small, nonzero values. * If you use this parameter, start by specifying a small value, such as * 1.0E-08. *

    *

    * The value is a double that ranges from 0 to * MAX_DOUBLE. The default is to not use L2 normalization. This * parameter can't be used when L1 is specified. Use this * parameter sparingly. *

    *
    • *
*

* Returns a reference to this object so that method calls can be chained * together. * * @param trainingParameters

* A list of the training parameters in the MLModel. * The list is implemented as a map of key-value pairs. *

*

* The following is the current set of training parameters: *

*
    *
  • *

    * sgd.maxMLModelSizeInBytes - The maximum allowed * size of the model. Depending on the input data, the size of * the model might affect its performance. *

    *

    * The value is an integer that ranges from 100000 * to 2147483648. The default value is * 33554432. *

    *
    • *
  • *

    * sgd.maxPasses - The number of times that the * training process traverses the observations to build the * MLModel. The value is an integer that ranges from * 1 to 10000. The default value is * 10. *

    *
    • *
  • *

    * sgd.shuffleType - Whether Amazon ML shuffles the * training data. Shuffling data improves a model's ability to * find the optimal solution for a variety of data types. The * valid values are auto and none. The * default value is none. We strongly recommend that * you shuffle your data. *

    *
    • *
  • *

    * sgd.l1RegularizationAmount - The coefficient * regularization L1 norm. It controls overfitting the data by * penalizing large coefficients. This tends to drive * coefficients to zero, resulting in a sparse feature set. If * you use this parameter, start by specifying a small value, * such as 1.0E-08. *

    *

    * The value is a double that ranges from 0 to * MAX_DOUBLE. The default is to not use L1 * normalization. This parameter can't be used when * L2 is specified. Use this parameter sparingly. *

    *
    • *
  • *

    * sgd.l2RegularizationAmount - The coefficient * regularization L2 norm. It controls overfitting the data by * penalizing large coefficients. This tends to drive * coefficients to small, nonzero values. If you use this * parameter, start by specifying a small value, such as * 1.0E-08. *

    *

    * The value is a double that ranges from 0 to * MAX_DOUBLE. The default is to not use L2 * normalization. This parameter can't be used when * L1 is specified. Use this parameter sparingly. *

    *
    • *
* @return A reference to this updated object so that method calls can be * chained together. */ public GetMLModelResult withTrainingParameters(java.util.Map trainingParameters) { this.trainingParameters = trainingParameters; return this; } /** *

* A list of the training parameters in the MLModel. The list * is implemented as a map of key-value pairs. *

*

* The following is the current set of training parameters: *

*
    *
  • *

    * sgd.maxMLModelSizeInBytes - The maximum allowed size of the * model. Depending on the input data, the size of the model might affect * its performance. *

    *

    * The value is an integer that ranges from 100000 to * 2147483648. The default value is 33554432. *

    *
    • *
  • *

    * sgd.maxPasses - The number of times that the training * process traverses the observations to build the MLModel. The * value is an integer that ranges from 1 to 10000 * . The default value is 10. *

    *
    • *
  • *

    * sgd.shuffleType - Whether Amazon ML shuffles the training * data. Shuffling data improves a model's ability to find the optimal * solution for a variety of data types. The valid values are * auto and none. The default value is * none. We strongly recommend that you shuffle your data. *

    *
    • *
  • *

    * sgd.l1RegularizationAmount - The coefficient regularization * L1 norm. It controls overfitting the data by penalizing large * coefficients. This tends to drive coefficients to zero, resulting in a * sparse feature set. If you use this parameter, start by specifying a * small value, such as 1.0E-08. *

    *

    * The value is a double that ranges from 0 to * MAX_DOUBLE. The default is to not use L1 normalization. This * parameter can't be used when L2 is specified. Use this * parameter sparingly. *

    *
    • *
  • *

    * sgd.l2RegularizationAmount - The coefficient regularization * L2 norm. It controls overfitting the data by penalizing large * coefficients. This tends to drive coefficients to small, nonzero values. * If you use this parameter, start by specifying a small value, such as * 1.0E-08. *

    *

    * The value is a double that ranges from 0 to * MAX_DOUBLE. The default is to not use L2 normalization. This * parameter can't be used when L1 is specified. Use this * parameter sparingly. *

    *
    • *
*

* The method adds a new key-value pair into TrainingParameters parameter, * and returns a reference to this object so that method calls can be * chained together. * * @param key The key of the entry to be added into TrainingParameters. * @param value The corresponding value of the entry to be added into * TrainingParameters. * @return A reference to this updated object so that method calls can be * chained together. */ public GetMLModelResult 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. *

* Returns a reference to this object so that method calls can be chained * together. */ public GetMLModelResult clearTrainingParametersEntries() { this.trainingParameters = null; return this; } /** *

* The location of the data file or directory in Amazon Simple Storage * Service (Amazon S3). *

*

* Constraints:
* Length: - 2048
* Pattern: s3://([^/]+)(/.*)?
* * @return

* The location of the data file or directory in Amazon Simple * Storage Service (Amazon S3). *

*/ public String getInputDataLocationS3() { return inputDataLocationS3; } /** *

* The location of the data file or directory in Amazon Simple Storage * Service (Amazon S3). *

*

* Constraints:
* Length: - 2048
* Pattern: s3://([^/]+)(/.*)?
* * @param inputDataLocationS3

* The location of the data file or directory in Amazon Simple * Storage Service (Amazon S3). *

*/ public void setInputDataLocationS3(String inputDataLocationS3) { this.inputDataLocationS3 = inputDataLocationS3; } /** *

* The location of the data file or directory in Amazon Simple Storage * Service (Amazon S3). *

*

* Returns a reference to this object so that method calls can be chained * together. *

* Constraints:
* Length: - 2048
* Pattern: s3://([^/]+)(/.*)?
* * @param inputDataLocationS3

* The location of the data file or directory in Amazon Simple * Storage Service (Amazon S3). *

* @return A reference to this updated object so that method calls can be * chained together. */ public GetMLModelResult withInputDataLocationS3(String inputDataLocationS3) { this.inputDataLocationS3 = inputDataLocationS3; return this; } /** *

* Identifies the MLModel category. The following are the * available types: *

*
    *
  • *

    * REGRESSION -- Produces a numeric result. For example, * "What price should a house be listed at?" *

    *
    • *
  • *

    * BINARY -- Produces one of two possible results. For example, * "Is this an e-commerce website?" *

    *
    • *
  • *

    * MULTICLASS -- Produces one of several possible results. For example, * "Is this a HIGH, LOW or MEDIUM risk trade?" *

    *
    • *
*

* Constraints:
* Allowed Values: REGRESSION, BINARY, MULTICLASS * * @return

* Identifies the MLModel category. The following are * the available types: *

*
    *
  • *

    * REGRESSION -- Produces a numeric result. For example, * "What price should a house be listed at?" *

    *
    • *
  • *

    * BINARY -- Produces one of two possible results. For example, * "Is this an e-commerce website?" *

    *
    • *
  • *

    * MULTICLASS -- Produces one of several possible results. For * example, "Is this a HIGH, LOW or MEDIUM risk trade?" *

    *
    • *
* @see MLModelType */ public String getMLModelType() { return mLModelType; } /** *

* Identifies the MLModel category. The following are the * available types: *

*
    *
  • *

    * REGRESSION -- Produces a numeric result. For example, * "What price should a house be listed at?" *

    *
    • *
  • *

    * BINARY -- Produces one of two possible results. For example, * "Is this an e-commerce website?" *

    *
    • *
  • *

    * MULTICLASS -- Produces one of several possible results. For example, * "Is this a HIGH, LOW or MEDIUM risk trade?" *

    *
    • *
*

* Constraints:
* Allowed Values: REGRESSION, BINARY, MULTICLASS * * @param mLModelType

* Identifies the MLModel category. The following * are the available types: *

*
    *
  • *

    * REGRESSION -- Produces a numeric result. For example, * "What price should a house be listed at?" *

    *
    • *
  • *

    * BINARY -- Produces one of two possible results. For example, * "Is this an e-commerce website?" *

    *
    • *
  • *

    * MULTICLASS -- Produces one of several possible results. For * example, "Is this a HIGH, LOW or MEDIUM risk trade?" *

    *
    • *
* @see MLModelType */ public void setMLModelType(String mLModelType) { this.mLModelType = mLModelType; } /** *

* Identifies the MLModel category. The following are the * available types: *

*
    *
  • *

    * REGRESSION -- Produces a numeric result. For example, * "What price should a house be listed at?" *

    *
    • *
  • *

    * BINARY -- Produces one of two possible results. For example, * "Is this an e-commerce website?" *

    *
    • *
  • *

    * MULTICLASS -- Produces one of several possible results. For example, * "Is this a HIGH, LOW or MEDIUM risk trade?" *

    *
    • *
*

* Returns a reference to this object so that method calls can be chained * together. *

* Constraints:
* Allowed Values: REGRESSION, BINARY, MULTICLASS * * @param mLModelType

* Identifies the MLModel category. The following * are the available types: *

*
    *
  • *

    * REGRESSION -- Produces a numeric result. For example, * "What price should a house be listed at?" *

    *
    • *
  • *

    * BINARY -- Produces one of two possible results. For example, * "Is this an e-commerce website?" *

    *
    • *
  • *

    * MULTICLASS -- Produces one of several possible results. For * example, "Is this a HIGH, LOW or MEDIUM risk trade?" *

    *
    • *
* @return A reference to this updated object so that method calls can be * chained together. * @see MLModelType */ public GetMLModelResult withMLModelType(String mLModelType) { this.mLModelType = mLModelType; return this; } /** *

* Identifies the MLModel category. The following are the * available types: *

*
    *
  • *

    * REGRESSION -- Produces a numeric result. For example, * "What price should a house be listed at?" *

    *
    • *
  • *

    * BINARY -- Produces one of two possible results. For example, * "Is this an e-commerce website?" *

    *
    • *
  • *

    * MULTICLASS -- Produces one of several possible results. For example, * "Is this a HIGH, LOW or MEDIUM risk trade?" *

    *
    • *
*

* Constraints:
* Allowed Values: REGRESSION, BINARY, MULTICLASS * * @param mLModelType

* Identifies the MLModel category. The following * are the available types: *

*
    *
  • *

    * REGRESSION -- Produces a numeric result. For example, * "What price should a house be listed at?" *

    *
    • *
  • *

    * BINARY -- Produces one of two possible results. For example, * "Is this an e-commerce website?" *

    *
    • *
  • *

    * MULTICLASS -- Produces one of several possible results. For * example, "Is this a HIGH, LOW or MEDIUM risk trade?" *

    *
    • *
* @see MLModelType */ public void setMLModelType(MLModelType mLModelType) { this.mLModelType = mLModelType.toString(); } /** *

* Identifies the MLModel category. The following are the * available types: *

*
    *
  • *

    * REGRESSION -- Produces a numeric result. For example, * "What price should a house be listed at?" *

    *
    • *
  • *

    * BINARY -- Produces one of two possible results. For example, * "Is this an e-commerce website?" *

    *
    • *
  • *

    * MULTICLASS -- Produces one of several possible results. For example, * "Is this a HIGH, LOW or MEDIUM risk trade?" *

    *
    • *
*

* Returns a reference to this object so that method calls can be chained * together. *

* Constraints:
* Allowed Values: REGRESSION, BINARY, MULTICLASS * * @param mLModelType

* Identifies the MLModel category. The following * are the available types: *

*
    *
  • *

    * REGRESSION -- Produces a numeric result. For example, * "What price should a house be listed at?" *

    *
    • *
  • *

    * BINARY -- Produces one of two possible results. For example, * "Is this an e-commerce website?" *

    *
    • *
  • *

    * MULTICLASS -- Produces one of several possible results. For * example, "Is this a HIGH, LOW or MEDIUM risk trade?" *

    *
    • *
* @return A reference to this updated object so that method calls can be * chained together. * @see MLModelType */ public GetMLModelResult withMLModelType(MLModelType mLModelType) { this.mLModelType = mLModelType.toString(); return this; } /** *

* The scoring threshold is used in binary classification * MLModel models. It marks the boundary between a positive * prediction and a negative prediction. *

*

* Output values greater than or equal to the threshold receive a positive * result from the MLModel, such as true. Output values less * than the threshold receive a negative response from the MLModel, such as * false. *

* * @return

* The scoring threshold is used in binary classification * MLModel models. It marks the boundary between a * positive prediction and a negative prediction. *

*

* Output values greater than or equal to the threshold receive a * positive result from the MLModel, such as true. * Output values less than the threshold receive a negative response * from the MLModel, such as false. *

*/ public Float getScoreThreshold() { return scoreThreshold; } /** *

* The scoring threshold is used in binary classification * MLModel models. It marks the boundary between a positive * prediction and a negative prediction. *

*

* Output values greater than or equal to the threshold receive a positive * result from the MLModel, such as true. Output values less * than the threshold receive a negative response from the MLModel, such as * false. *

* * @param scoreThreshold

* The scoring threshold is used in binary classification * MLModel models. It marks the boundary between a * positive prediction and a negative prediction. *

*

* Output values greater than or equal to the threshold receive a * positive result from the MLModel, such as true. * Output values less than the threshold receive a negative * response from the MLModel, such as false. *

*/ public void setScoreThreshold(Float scoreThreshold) { this.scoreThreshold = scoreThreshold; } /** *

* The scoring threshold is used in binary classification * MLModel models. It marks the boundary between a positive * prediction and a negative prediction. *

*

* Output values greater than or equal to the threshold receive a positive * result from the MLModel, such as true. Output values less * than the threshold receive a negative response from the MLModel, such as * false. *

*

* Returns a reference to this object so that method calls can be chained * together. * * @param scoreThreshold

* The scoring threshold is used in binary classification * MLModel models. It marks the boundary between a * positive prediction and a negative prediction. *

*

* Output values greater than or equal to the threshold receive a * positive result from the MLModel, such as true. * Output values less than the threshold receive a negative * response from the MLModel, such as false. *

* @return A reference to this updated object so that method calls can be * chained together. */ public GetMLModelResult withScoreThreshold(Float scoreThreshold) { this.scoreThreshold = scoreThreshold; return this; } /** *

* The time of the most recent edit to the ScoreThreshold. The * time is expressed in epoch time. *

* * @return

* The time of the most recent edit to the * ScoreThreshold. The time is expressed in epoch time. *

*/ public java.util.Date getScoreThresholdLastUpdatedAt() { return scoreThresholdLastUpdatedAt; } /** *

* The time of the most recent edit to the ScoreThreshold. The * time is expressed in epoch time. *

* * @param scoreThresholdLastUpdatedAt

* The time of the most recent edit to the * ScoreThreshold. The time is expressed in epoch * time. *

*/ public void setScoreThresholdLastUpdatedAt(java.util.Date scoreThresholdLastUpdatedAt) { this.scoreThresholdLastUpdatedAt = scoreThresholdLastUpdatedAt; } /** *

* The time of the most recent edit to the ScoreThreshold. The * time is expressed in epoch time. *

*

* Returns a reference to this object so that method calls can be chained * together. * * @param scoreThresholdLastUpdatedAt

* The time of the most recent edit to the * ScoreThreshold. The time is expressed in epoch * time. *

* @return A reference to this updated object so that method calls can be * chained together. */ public GetMLModelResult withScoreThresholdLastUpdatedAt( java.util.Date scoreThresholdLastUpdatedAt) { this.scoreThresholdLastUpdatedAt = scoreThresholdLastUpdatedAt; return this; } /** *

* A link to the file that contains logs of the CreateMLModel * operation. *

* * @return

* A link to the file that contains logs of the * CreateMLModel operation. *

*/ public String getLogUri() { return logUri; } /** *

* A link to the file that contains logs of the CreateMLModel * operation. *

* * @param logUri

* A link to the file that contains logs of the * CreateMLModel operation. *

*/ public void setLogUri(String logUri) { this.logUri = logUri; } /** *

* A link to the file that contains logs of the CreateMLModel * operation. *

*

* Returns a reference to this object so that method calls can be chained * together. * * @param logUri

* A link to the file that contains logs of the * CreateMLModel operation. *

* @return A reference to this updated object so that method calls can be * chained together. */ public GetMLModelResult withLogUri(String logUri) { this.logUri = logUri; return this; } /** *

* A description of the most recent details about accessing the * MLModel. *

*

* Constraints:
* Length: - 10240
* * @return

* A description of the most recent details about accessing the * MLModel. *

*/ public String getMessage() { return message; } /** *

* A description of the most recent details about accessing the * MLModel. *

*

* Constraints:
* Length: - 10240
* * @param message

* A description of the most recent details about accessing the * MLModel. *

*/ public void setMessage(String message) { this.message = message; } /** *

* A description of the most recent details about accessing the * MLModel. *

*

* Returns a reference to this object so that method calls can be chained * together. *

* Constraints:
* Length: - 10240
* * @param message

* A description of the most recent details about accessing the * MLModel. *

* @return A reference to this updated object so that method calls can be * chained together. */ public GetMLModelResult withMessage(String message) { this.message = message; return this; } /** *

* The approximate CPU time in milliseconds that Amazon Machine Learning * spent processing the MLModel, normalized and scaled on * computation resources. ComputeTime is only available if the * MLModel is in the COMPLETED state. *

* * @return

* The approximate CPU time in milliseconds that Amazon Machine * Learning spent processing the MLModel, normalized * and scaled on computation resources. ComputeTime is * only available if the MLModel is in the * COMPLETED state. *

*/ public Long getComputeTime() { return computeTime; } /** *

* The approximate CPU time in milliseconds that Amazon Machine Learning * spent processing the MLModel, normalized and scaled on * computation resources. ComputeTime is only available if the * MLModel is in the COMPLETED state. *

* * @param computeTime

* The approximate CPU time in milliseconds that Amazon Machine * Learning spent processing the MLModel, normalized * and scaled on computation resources. ComputeTime * is only available if the MLModel is in the * COMPLETED state. *

*/ public void setComputeTime(Long computeTime) { this.computeTime = computeTime; } /** *

* The approximate CPU time in milliseconds that Amazon Machine Learning * spent processing the MLModel, normalized and scaled on * computation resources. ComputeTime is only available if the * MLModel is in the COMPLETED state. *

*

* Returns a reference to this object so that method calls can be chained * together. * * @param computeTime

* The approximate CPU time in milliseconds that Amazon Machine * Learning spent processing the MLModel, normalized * and scaled on computation resources. ComputeTime * is only available if the MLModel is in the * COMPLETED state. *

* @return A reference to this updated object so that method calls can be * chained together. */ public GetMLModelResult withComputeTime(Long computeTime) { this.computeTime = computeTime; return this; } /** *

* The epoch time when Amazon Machine Learning marked the * MLModel as COMPLETED or FAILED. * FinishedAt is only available when the MLModel * is in the COMPLETED or FAILED state. *

* * @return

* The epoch time when Amazon Machine Learning marked the * MLModel as COMPLETED or * FAILED. FinishedAt is only available * when the MLModel is in the COMPLETED or * FAILED state. *

*/ public java.util.Date getFinishedAt() { return finishedAt; } /** *

* The epoch time when Amazon Machine Learning marked the * MLModel as COMPLETED or FAILED. * FinishedAt is only available when the MLModel * is in the COMPLETED or FAILED state. *

* * @param finishedAt

* The epoch time when Amazon Machine Learning marked the * MLModel as COMPLETED or * FAILED. FinishedAt is only available * when the MLModel is in the COMPLETED * or FAILED state. *

*/ public void setFinishedAt(java.util.Date finishedAt) { this.finishedAt = finishedAt; } /** *

* The epoch time when Amazon Machine Learning marked the * MLModel as COMPLETED or FAILED. * FinishedAt is only available when the MLModel * is in the COMPLETED or FAILED state. *

*

* Returns a reference to this object so that method calls can be chained * together. * * @param finishedAt

* The epoch time when Amazon Machine Learning marked the * MLModel as COMPLETED or * FAILED. FinishedAt is only available * when the MLModel is in the COMPLETED * or FAILED state. *

* @return A reference to this updated object so that method calls can be * chained together. */ public GetMLModelResult withFinishedAt(java.util.Date finishedAt) { this.finishedAt = finishedAt; return this; } /** *

* The epoch time when Amazon Machine Learning marked the * MLModel as INPROGRESS. StartedAt * isn't available if the MLModel is in the * PENDING state. *

* * @return

* The epoch time when Amazon Machine Learning marked the * MLModel as INPROGRESS. * StartedAt isn't available if the * MLModel is in the PENDING state. *

*/ public java.util.Date getStartedAt() { return startedAt; } /** *

* The epoch time when Amazon Machine Learning marked the * MLModel as INPROGRESS. StartedAt * isn't available if the MLModel is in the * PENDING state. *

* * @param startedAt

* The epoch time when Amazon Machine Learning marked the * MLModel as INPROGRESS. * StartedAt isn't available if the * MLModel is in the PENDING state. *

*/ public void setStartedAt(java.util.Date startedAt) { this.startedAt = startedAt; } /** *

* The epoch time when Amazon Machine Learning marked the * MLModel as INPROGRESS. StartedAt * isn't available if the MLModel is in the * PENDING state. *

*

* Returns a reference to this object so that method calls can be chained * together. * * @param startedAt

* The epoch time when Amazon Machine Learning marked the * MLModel as INPROGRESS. * StartedAt isn't available if the * MLModel is in the PENDING state. *

* @return A reference to this updated object so that method calls can be * chained together. */ public GetMLModelResult withStartedAt(java.util.Date startedAt) { this.startedAt = startedAt; return this; } /** *

* The recipe to use when training the MLModel. The * Recipe provides detailed information about the observation * data to use during training, and manipulations to perform on the * observation data during training. *

*

* Note: This parameter is provided as part of the verbose format. *

*

* Constraints:
* Length: - 131071
* * @return

* The recipe to use when training the MLModel. The * Recipe provides detailed information about the * observation data to use during training, and manipulations to * perform on the observation data during training. *

*

* Note: This parameter is provided as part of the verbose * format. *

*/ public String getRecipe() { return recipe; } /** *

* The recipe to use when training the MLModel. The * Recipe provides detailed information about the observation * data to use during training, and manipulations to perform on the * observation data during training. *

*

* Note: This parameter is provided as part of the verbose format. *

*

* Constraints:
* Length: - 131071
* * @param recipe

* The recipe to use when training the MLModel. The * Recipe provides detailed information about the * observation data to use during training, and manipulations to * perform on the observation data during training. *

*

* Note: This parameter is provided as part of the verbose * format. *

*/ public void setRecipe(String recipe) { this.recipe = recipe; } /** *

* The recipe to use when training the MLModel. The * Recipe provides detailed information about the observation * data to use during training, and manipulations to perform on the * observation data during training. *

*

* Note: This parameter is provided as part of the verbose format. *

*

* Returns a reference to this object so that method calls can be chained * together. *

* Constraints:
* Length: - 131071
* * @param recipe

* The recipe to use when training the MLModel. The * Recipe provides detailed information about the * observation data to use during training, and manipulations to * perform on the observation data during training. *

*

* Note: This parameter is provided as part of the verbose * format. *

* @return A reference to this updated object so that method calls can be * chained together. */ public GetMLModelResult withRecipe(String recipe) { this.recipe = recipe; return this; } /** *

* The schema used by all of the data files referenced by the * DataSource. *

*

* Note: This parameter is provided as part of the verbose format. *

*

* Constraints:
* Length: - 131071
* * @return

* The schema used by all of the data files referenced by the * DataSource. *

*

* Note: This parameter is provided as part of the verbose * format. *

*/ public String getSchema() { return schema; } /** *

* The schema used by all of the data files referenced by the * DataSource. *

*

* Note: This parameter is provided as part of the verbose format. *

*

* Constraints:
* Length: - 131071
* * @param schema

* The schema used by all of the data files referenced by the * DataSource. *

*

* Note: This parameter is provided as part of the verbose * format. *

*/ public void setSchema(String schema) { this.schema = schema; } /** *

* The schema used by all of the data files referenced by the * DataSource. *

*

* Note: This parameter is provided as part of the verbose format. *

*

* Returns a reference to this object so that method calls can be chained * together. *

* Constraints:
* Length: - 131071
* * @param schema

* The schema used by all of the data files referenced by the * DataSource. *

*

* Note: This parameter is provided as part of the verbose * format. *

* @return A reference to this updated object so that method calls can be * chained together. */ public GetMLModelResult withSchema(String schema) { this.schema = schema; return this; } /** * Returns a string representation of this object; useful for testing and * debugging. * * @return A string representation of this object. * @see java.lang.Object#toString() */ @Override public String toString() { StringBuilder sb = new StringBuilder(); sb.append("{"); if (getMLModelId() != null) sb.append("MLModelId: " + getMLModelId() + ","); if (getTrainingDataSourceId() != null) sb.append("TrainingDataSourceId: " + getTrainingDataSourceId() + ","); if (getCreatedByIamUser() != null) sb.append("CreatedByIamUser: " + getCreatedByIamUser() + ","); if (getCreatedAt() != null) sb.append("CreatedAt: " + getCreatedAt() + ","); if (getLastUpdatedAt() != null) sb.append("LastUpdatedAt: " + getLastUpdatedAt() + ","); if (getName() != null) sb.append("Name: " + getName() + ","); if (getStatus() != null) sb.append("Status: " + getStatus() + ","); if (getSizeInBytes() != null) sb.append("SizeInBytes: " + getSizeInBytes() + ","); if (getEndpointInfo() != null) sb.append("EndpointInfo: " + getEndpointInfo() + ","); if (getTrainingParameters() != null) sb.append("TrainingParameters: " + getTrainingParameters() + ","); if (getInputDataLocationS3() != null) sb.append("InputDataLocationS3: " + getInputDataLocationS3() + ","); if (getMLModelType() != null) sb.append("MLModelType: " + getMLModelType() + ","); if (getScoreThreshold() != null) sb.append("ScoreThreshold: " + getScoreThreshold() + ","); if (getScoreThresholdLastUpdatedAt() != null) sb.append("ScoreThresholdLastUpdatedAt: " + getScoreThresholdLastUpdatedAt() + ","); if (getLogUri() != null) sb.append("LogUri: " + getLogUri() + ","); if (getMessage() != null) sb.append("Message: " + getMessage() + ","); if (getComputeTime() != null) sb.append("ComputeTime: " + getComputeTime() + ","); if (getFinishedAt() != null) sb.append("FinishedAt: " + getFinishedAt() + ","); if (getStartedAt() != null) sb.append("StartedAt: " + getStartedAt() + ","); if (getRecipe() != null) sb.append("Recipe: " + getRecipe() + ","); if (getSchema() != null) sb.append("Schema: " + getSchema()); sb.append("}"); return sb.toString(); } @Override public int hashCode() { final int prime = 31; int hashCode = 1; hashCode = prime * hashCode + ((getMLModelId() == null) ? 0 : getMLModelId().hashCode()); hashCode = prime * hashCode + ((getTrainingDataSourceId() == null) ? 0 : getTrainingDataSourceId().hashCode()); hashCode = prime * hashCode + ((getCreatedByIamUser() == null) ? 0 : getCreatedByIamUser().hashCode()); hashCode = prime * hashCode + ((getCreatedAt() == null) ? 0 : getCreatedAt().hashCode()); hashCode = prime * hashCode + ((getLastUpdatedAt() == null) ? 0 : getLastUpdatedAt().hashCode()); hashCode = prime * hashCode + ((getName() == null) ? 0 : getName().hashCode()); hashCode = prime * hashCode + ((getStatus() == null) ? 0 : getStatus().hashCode()); hashCode = prime * hashCode + ((getSizeInBytes() == null) ? 0 : getSizeInBytes().hashCode()); hashCode = prime * hashCode + ((getEndpointInfo() == null) ? 0 : getEndpointInfo().hashCode()); hashCode = prime * hashCode + ((getTrainingParameters() == null) ? 0 : getTrainingParameters().hashCode()); hashCode = prime * hashCode + ((getInputDataLocationS3() == null) ? 0 : getInputDataLocationS3().hashCode()); hashCode = prime * hashCode + ((getMLModelType() == null) ? 0 : getMLModelType().hashCode()); hashCode = prime * hashCode + ((getScoreThreshold() == null) ? 0 : getScoreThreshold().hashCode()); hashCode = prime * hashCode + ((getScoreThresholdLastUpdatedAt() == null) ? 0 : getScoreThresholdLastUpdatedAt().hashCode()); hashCode = prime * hashCode + ((getLogUri() == null) ? 0 : getLogUri().hashCode()); hashCode = prime * hashCode + ((getMessage() == null) ? 0 : getMessage().hashCode()); hashCode = prime * hashCode + ((getComputeTime() == null) ? 0 : getComputeTime().hashCode()); hashCode = prime * hashCode + ((getFinishedAt() == null) ? 0 : getFinishedAt().hashCode()); hashCode = prime * hashCode + ((getStartedAt() == null) ? 0 : getStartedAt().hashCode()); hashCode = prime * hashCode + ((getRecipe() == null) ? 0 : getRecipe().hashCode()); hashCode = prime * hashCode + ((getSchema() == null) ? 0 : getSchema().hashCode()); return hashCode; } @Override public boolean equals(Object obj) { if (this == obj) return true; if (obj == null) return false; if (obj instanceof GetMLModelResult == false) return false; GetMLModelResult other = (GetMLModelResult) obj; if (other.getMLModelId() == null ^ this.getMLModelId() == null) return false; if (other.getMLModelId() != null && other.getMLModelId().equals(this.getMLModelId()) == false) return false; if (other.getTrainingDataSourceId() == null ^ this.getTrainingDataSourceId() == null) return false; if (other.getTrainingDataSourceId() != null && other.getTrainingDataSourceId().equals(this.getTrainingDataSourceId()) == false) return false; if (other.getCreatedByIamUser() == null ^ this.getCreatedByIamUser() == null) return false; if (other.getCreatedByIamUser() != null && other.getCreatedByIamUser().equals(this.getCreatedByIamUser()) == false) return false; if (other.getCreatedAt() == null ^ this.getCreatedAt() == null) return false; if (other.getCreatedAt() != null && other.getCreatedAt().equals(this.getCreatedAt()) == false) return false; if (other.getLastUpdatedAt() == null ^ this.getLastUpdatedAt() == null) return false; if (other.getLastUpdatedAt() != null && other.getLastUpdatedAt().equals(this.getLastUpdatedAt()) == false) return false; if (other.getName() == null ^ this.getName() == null) return false; if (other.getName() != null && other.getName().equals(this.getName()) == 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.getSizeInBytes() == null ^ this.getSizeInBytes() == null) return false; if (other.getSizeInBytes() != null && other.getSizeInBytes().equals(this.getSizeInBytes()) == false) return false; if (other.getEndpointInfo() == null ^ this.getEndpointInfo() == null) return false; if (other.getEndpointInfo() != null && other.getEndpointInfo().equals(this.getEndpointInfo()) == 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.getInputDataLocationS3() == null ^ this.getInputDataLocationS3() == null) return false; if (other.getInputDataLocationS3() != null && other.getInputDataLocationS3().equals(this.getInputDataLocationS3()) == false) return false; if (other.getMLModelType() == null ^ this.getMLModelType() == null) return false; if (other.getMLModelType() != null && other.getMLModelType().equals(this.getMLModelType()) == false) return false; if (other.getScoreThreshold() == null ^ this.getScoreThreshold() == null) return false; if (other.getScoreThreshold() != null && other.getScoreThreshold().equals(this.getScoreThreshold()) == false) return false; if (other.getScoreThresholdLastUpdatedAt() == null ^ this.getScoreThresholdLastUpdatedAt() == null) return false; if (other.getScoreThresholdLastUpdatedAt() != null && other.getScoreThresholdLastUpdatedAt().equals( this.getScoreThresholdLastUpdatedAt()) == false) return false; if (other.getLogUri() == null ^ this.getLogUri() == null) return false; if (other.getLogUri() != null && other.getLogUri().equals(this.getLogUri()) == 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.getComputeTime() == null ^ this.getComputeTime() == null) return false; if (other.getComputeTime() != null && other.getComputeTime().equals(this.getComputeTime()) == false) return false; if (other.getFinishedAt() == null ^ this.getFinishedAt() == null) return false; if (other.getFinishedAt() != null && other.getFinishedAt().equals(this.getFinishedAt()) == false) return false; if (other.getStartedAt() == null ^ this.getStartedAt() == null) return false; if (other.getStartedAt() != null && other.getStartedAt().equals(this.getStartedAt()) == false) return false; if (other.getRecipe() == null ^ this.getRecipe() == null) return false; if (other.getRecipe() != null && other.getRecipe().equals(this.getRecipe()) == false) return false; if (other.getSchema() == null ^ this.getSchema() == null) return false; if (other.getSchema() != null && other.getSchema().equals(this.getSchema()) == false) return false; return true; } }