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/*
* Copyright 2019-2024 Amazon.com, Inc. or its affiliates. All Rights Reserved.
*
* Licensed under the Apache License, Version 2.0 (the "License"). You may not use this file except in compliance with
* the License. A copy of the License is located at
*
* http://aws.amazon.com/apache2.0
*
* or in the "license" file accompanying this file. This file is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR
* CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions
* and limitations under the License.
*/
package com.amazonaws.services.machinelearning.model;
import java.io.Serializable;
import javax.annotation.Generated;
import com.amazonaws.protocol.StructuredPojo;
import com.amazonaws.protocol.ProtocolMarshaller;
/**
*
* Represents the output of a GetMLModel operation.
*
*
* The content consists of the detailed metadata and the current status of the MLModel.
*
*/
@Generated("com.amazonaws:aws-java-sdk-code-generator")
public class MLModel implements Serializable, Cloneable, StructuredPojo {
/**
*
* The ID assigned to the MLModel at creation.
*
*/
private String mLModelId;
/**
*
* The ID of the training DataSource. The CreateMLModel operation uses the
* TrainingDataSourceId.
*
*/
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.
*
*/
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.
*
*/
private String name;
/**
*
* The current status of an MLModel. This element can have one of the following values:
*
*
* -
*
* PENDING - Amazon Machine Learning (Amazon ML) submitted a request to create an MLModel.
*
*
* -
*
* INPROGRESS - The creation process is underway.
*
*
* -
*
* FAILED - The request to create an MLModel didn't run to completion. The model isn't
* usable.
*
*
* -
*
* COMPLETED - The creation process completed successfully.
*
*
* -
*
* DELETED - The MLModel is marked as deleted. It isn't usable.
*
*
*
*/
private String status;
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:
*
*
* -
*
* 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 the 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.
*
*
* -
*
* sgd.l1RegularizationAmount - The coefficient regularization L1 norm, which controls overfitting the
* data by penalizing large coefficients. This parameter tends to drive coefficients to zero, resulting in 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, which 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.
*
*
*
*/
private com.amazonaws.internal.SdkInternalMap trainingParameters;
/**
*
* The location of the data file or directory in Amazon Simple Storage Service (Amazon S3).
*
*/
private String inputDataLocationS3;
/**
*
* The algorithm used to train the MLModel. The following algorithm is supported:
*
*
* -
*
* SGD -- Stochastic gradient descent. The goal of SGD is to minimize the gradient of the
* loss function.
*
*
*
*/
private String algorithm;
/**
*
* 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 a child-friendly web site?".
*
*
* -
*
* MULTICLASS - Produces one of several possible results. For example,
* "Is this a HIGH-, LOW-, or MEDIUM-risk trade?".
*
*
*
*/
private String mLModelType;
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 description of the most recent details about accessing the MLModel.
*
*/
private String message;
private Long computeTime;
private java.util.Date finishedAt;
private java.util.Date startedAt;
/**
*
* The ID assigned to the MLModel at creation.
*
*
* @param mLModelId
* The ID assigned to the MLModel at creation.
*/
public void setMLModelId(String mLModelId) {
this.mLModelId = mLModelId;
}
/**
*
* The ID assigned to the MLModel at creation.
*
*
* @return The ID assigned to the MLModel at creation.
*/
public String getMLModelId() {
return this.mLModelId;
}
/**
*
* The ID assigned to the MLModel at creation.
*
*
* @param mLModelId
* The ID assigned to the MLModel at creation.
* @return Returns a reference to this object so that method calls can be chained together.
*/
public MLModel withMLModelId(String mLModelId) {
setMLModelId(mLModelId);
return this;
}
/**
*
* The ID of the training DataSource. The CreateMLModel operation uses the
* TrainingDataSourceId.
*
*
* @param trainingDataSourceId
* The ID of the training DataSource. The CreateMLModel operation uses the
* TrainingDataSourceId.
*/
public void setTrainingDataSourceId(String trainingDataSourceId) {
this.trainingDataSourceId = trainingDataSourceId;
}
/**
*
* The ID of the training DataSource. The CreateMLModel operation uses the
* TrainingDataSourceId.
*
*
* @return The ID of the training DataSource. The CreateMLModel operation uses the
* TrainingDataSourceId.
*/
public String getTrainingDataSourceId() {
return this.trainingDataSourceId;
}
/**
*
* The ID of the training DataSource. The CreateMLModel operation uses the
* TrainingDataSourceId.
*
*
* @param trainingDataSourceId
* The ID of the training DataSource. The CreateMLModel operation uses the
* TrainingDataSourceId.
* @return Returns a reference to this object so that method calls can be chained together.
*/
public MLModel withTrainingDataSourceId(String trainingDataSourceId) {
setTrainingDataSourceId(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.
*
*
* @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.
*
*
* @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 this.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.
*
*
* @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 Returns a reference to this object so that method calls can be chained together.
*/
public MLModel withCreatedByIamUser(String createdByIamUser) {
setCreatedByIamUser(createdByIamUser);
return this;
}
/**
*
* 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.
*
*
* @return The time that the MLModel was created. The time is expressed in epoch time.
*/
public java.util.Date getCreatedAt() {
return this.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.
* @return Returns a reference to this object so that method calls can be chained together.
*/
public MLModel withCreatedAt(java.util.Date createdAt) {
setCreatedAt(createdAt);
return this;
}
/**
*
* 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.
*
*
* @return The time of the most recent edit to the MLModel. The time is expressed in epoch time.
*/
public java.util.Date getLastUpdatedAt() {
return this.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.
* @return Returns a reference to this object so that method calls can be chained together.
*/
public MLModel withLastUpdatedAt(java.util.Date lastUpdatedAt) {
setLastUpdatedAt(lastUpdatedAt);
return this;
}
/**
*
* A user-supplied name or description of the MLModel.
*
*
* @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.
*
*
* @return A user-supplied name or description of the MLModel.
*/
public String getName() {
return this.name;
}
/**
*
* A user-supplied name or description of the MLModel.
*
*
* @param name
* A user-supplied name or description of the MLModel.
* @return Returns a reference to this object so that method calls can be chained together.
*/
public MLModel withName(String name) {
setName(name);
return this;
}
/**
*
* The current status of an MLModel. This element can have one of the following values:
*
*
* -
*
* PENDING - Amazon Machine Learning (Amazon ML) submitted a request to create an MLModel.
*
*
* -
*
* INPROGRESS - The creation process is underway.
*
*
* -
*
* FAILED - The request to create an MLModel didn't run to completion. The model isn't
* usable.
*
*
* -
*
* COMPLETED - The creation process completed successfully.
*
*
* -
*
* DELETED - The MLModel is marked as deleted. It isn't usable.
*
*
*
*
* @param status
* The current status of an MLModel. This element can have one of the following values:
*
* -
*
* PENDING - Amazon Machine Learning (Amazon ML) submitted a request to create an
* MLModel.
*
*
* -
*
* INPROGRESS - The creation process is underway.
*
*
* -
*
* FAILED - The request to create an MLModel didn't run to completion. The model
* isn't usable.
*
*
* -
*
* COMPLETED - The creation process completed successfully.
*
*
* -
*
* DELETED - The MLModel is marked as deleted. It isn't usable.
*
*
* @see EntityStatus
*/
public void setStatus(String status) {
this.status = status;
}
/**
*
* The current status of an MLModel. This element can have one of the following values:
*
*
* -
*
* PENDING - Amazon Machine Learning (Amazon ML) submitted a request to create an MLModel.
*
*
* -
*
* INPROGRESS - The creation process is underway.
*
*
* -
*
* FAILED - The request to create an MLModel didn't run to completion. The model isn't
* usable.
*
*
* -
*
* COMPLETED - The creation process completed successfully.
*
*
* -
*
* DELETED - The MLModel is marked as deleted. It isn't usable.
*
*
*
*
* @return The current status of an MLModel. This element can have one of the following values:
*
* -
*
* PENDING - Amazon Machine Learning (Amazon ML) submitted a request to create an
* MLModel.
*
*
* -
*
* INPROGRESS - The creation process is underway.
*
*
* -
*
* FAILED - The request to create an MLModel didn't run to completion. The model
* isn't usable.
*
*
* -
*
* COMPLETED - The creation process completed successfully.
*
*
* -
*
* DELETED - The MLModel is marked as deleted. It isn't usable.
*
*
* @see EntityStatus
*/
public String getStatus() {
return this.status;
}
/**
*
* The current status of an MLModel. This element can have one of the following values:
*
*
* -
*
* PENDING - Amazon Machine Learning (Amazon ML) submitted a request to create an MLModel.
*
*
* -
*
* INPROGRESS - The creation process is underway.
*
*
* -
*
* FAILED - The request to create an MLModel didn't run to completion. The model isn't
* usable.
*
*
* -
*
* COMPLETED - The creation process completed successfully.
*
*
* -
*
* DELETED - The MLModel is marked as deleted. It isn't usable.
*
*
*
*
* @param status
* The current status of an MLModel. This element can have one of the following values:
*
* -
*
* PENDING - Amazon Machine Learning (Amazon ML) submitted a request to create an
* MLModel.
*
*
* -
*
* INPROGRESS - The creation process is underway.
*
*
* -
*
* FAILED - The request to create an MLModel didn't run to completion. The model
* isn't usable.
*
*
* -
*
* COMPLETED - The creation process completed successfully.
*
*
* -
*
* DELETED - The MLModel is marked as deleted. It isn't usable.
*
*
* @return Returns a reference to this object so that method calls can be chained together.
* @see EntityStatus
*/
public MLModel withStatus(String status) {
setStatus(status);
return this;
}
/**
*
* The current status of an MLModel. This element can have one of the following values:
*
*
* -
*
* PENDING - Amazon Machine Learning (Amazon ML) submitted a request to create an MLModel.
*
*
* -
*
* INPROGRESS - The creation process is underway.
*
*
* -
*
* FAILED - The request to create an MLModel didn't run to completion. The model isn't
* usable.
*
*
* -
*
* COMPLETED - The creation process completed successfully.
*
*
* -
*
* DELETED - The MLModel is marked as deleted. It isn't usable.
*
*
*
*
* @param status
* The current status of an MLModel. This element can have one of the following values:
*
* -
*
* PENDING - Amazon Machine Learning (Amazon ML) submitted a request to create an
* MLModel.
*
*
* -
*
* INPROGRESS - The creation process is underway.
*
*
* -
*
* FAILED - The request to create an MLModel didn't run to completion. The model
* isn't usable.
*
*
* -
*
* COMPLETED - The creation process completed successfully.
*
*
* -
*
* DELETED - The MLModel is marked as deleted. It isn't usable.
*
*
* @see EntityStatus
*/
public void setStatus(EntityStatus status) {
withStatus(status);
}
/**
*
* The current status of an MLModel. This element can have one of the following values:
*
*
* -
*
* PENDING - Amazon Machine Learning (Amazon ML) submitted a request to create an MLModel.
*
*
* -
*
* INPROGRESS - The creation process is underway.
*
*
* -
*
* FAILED - The request to create an MLModel didn't run to completion. The model isn't
* usable.
*
*
* -
*
* COMPLETED - The creation process completed successfully.
*
*
* -
*
* DELETED - The MLModel is marked as deleted. It isn't usable.
*
*
*
*
* @param status
* The current status of an MLModel. This element can have one of the following values:
*
* -
*
* PENDING - Amazon Machine Learning (Amazon ML) submitted a request to create an
* MLModel.
*
*
* -
*
* INPROGRESS - The creation process is underway.
*
*
* -
*
* FAILED - The request to create an MLModel didn't run to completion. The model
* isn't usable.
*
*
* -
*
* COMPLETED - The creation process completed successfully.
*
*
* -
*
* DELETED - The MLModel is marked as deleted. It isn't usable.
*
*
* @return Returns a reference to this object so that method calls can be chained together.
* @see EntityStatus
*/
public MLModel withStatus(EntityStatus status) {
this.status = status.toString();
return this;
}
/**
* @param sizeInBytes
*/
public void setSizeInBytes(Long sizeInBytes) {
this.sizeInBytes = sizeInBytes;
}
/**
* @return
*/
public Long getSizeInBytes() {
return this.sizeInBytes;
}
/**
* @param sizeInBytes
* @return Returns a reference to this object so that method calls can be chained together.
*/
public MLModel withSizeInBytes(Long sizeInBytes) {
setSizeInBytes(sizeInBytes);
return this;
}
/**
*
* 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.
*
*
* @return The current endpoint of the MLModel.
*/
public RealtimeEndpointInfo getEndpointInfo() {
return this.endpointInfo;
}
/**
*
* The current endpoint of the MLModel.
*
*
* @param endpointInfo
* The current endpoint of the MLModel.
* @return Returns a reference to this object so that method calls can be chained together.
*/
public MLModel withEndpointInfo(RealtimeEndpointInfo endpointInfo) {
setEndpointInfo(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:
*
*
* -
*
* 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 the 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.
*
*
* -
*
* sgd.l1RegularizationAmount - The coefficient regularization L1 norm, which controls overfitting the
* data by penalizing large coefficients. This parameter tends to drive coefficients to zero, resulting in 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, which 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 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 the 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.
*
*
* -
*
* sgd.l1RegularizationAmount - The coefficient regularization L1 norm, which controls
* overfitting the data by penalizing large coefficients. This parameter tends to drive coefficients to
* zero, resulting in 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, which 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 java.util.Map getTrainingParameters() {
if (trainingParameters == null) {
trainingParameters = new com.amazonaws.internal.SdkInternalMap();
}
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 the 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.
*
*
* -
*
* sgd.l1RegularizationAmount - The coefficient regularization L1 norm, which controls overfitting the
* data by penalizing large coefficients. This parameter tends to drive coefficients to zero, resulting in 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, which 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 the 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.
*
*
* -
*
* sgd.l1RegularizationAmount - The coefficient regularization L1 norm, which controls
* overfitting the data by penalizing large coefficients. This parameter tends to drive coefficients to zero,
* resulting in 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, which 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 == null ? null : new com.amazonaws.internal.SdkInternalMap(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 the 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.
*
*
* -
*
* sgd.l1RegularizationAmount - The coefficient regularization L1 norm, which controls overfitting the
* data by penalizing large coefficients. This parameter tends to drive coefficients to zero, resulting in 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, which 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 the 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.
*
*
* -
*
* sgd.l1RegularizationAmount - The coefficient regularization L1 norm, which controls
* overfitting the data by penalizing large coefficients. This parameter tends to drive coefficients to zero,
* resulting in 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, which 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 Returns a reference to this object so that method calls can be chained together.
*/
public MLModel withTrainingParameters(java.util.Map trainingParameters) {
setTrainingParameters(trainingParameters);
return this;
}
/**
* Add a single TrainingParameters entry
*
* @see MLModel#withTrainingParameters
* @returns a reference to this object so that method calls can be chained together.
*/
public MLModel addTrainingParametersEntry(String key, String value) {
if (null == this.trainingParameters) {
this.trainingParameters = new com.amazonaws.internal.SdkInternalMap();
}
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 MLModel clearTrainingParametersEntries() {
this.trainingParameters = null;
return this;
}
/**
*
* The location of the data file or directory in Amazon Simple Storage Service (Amazon 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).
*
*
* @return The location of the data file or directory in Amazon Simple Storage Service (Amazon S3).
*/
public String getInputDataLocationS3() {
return this.inputDataLocationS3;
}
/**
*
* The location of the data file or directory in Amazon Simple Storage Service (Amazon S3).
*
*
* @param inputDataLocationS3
* The location of the data file or directory in Amazon Simple Storage Service (Amazon S3).
* @return Returns a reference to this object so that method calls can be chained together.
*/
public MLModel withInputDataLocationS3(String inputDataLocationS3) {
setInputDataLocationS3(inputDataLocationS3);
return this;
}
/**
*
* The algorithm used to train the MLModel. The following algorithm is supported:
*
*
* -
*
* SGD -- Stochastic gradient descent. The goal of SGD is to minimize the gradient of the
* loss function.
*
*
*
*
* @param algorithm
* The algorithm used to train the MLModel. The following algorithm is supported:
*
* -
*
* SGD -- Stochastic gradient descent. The goal of SGD is to minimize the gradient
* of the loss function.
*
*
* @see Algorithm
*/
public void setAlgorithm(String algorithm) {
this.algorithm = algorithm;
}
/**
*
* The algorithm used to train the MLModel. The following algorithm is supported:
*
*
* -
*
* SGD -- Stochastic gradient descent. The goal of SGD is to minimize the gradient of the
* loss function.
*
*
*
*
* @return The algorithm used to train the MLModel. The following algorithm is supported:
*
* -
*
* SGD -- Stochastic gradient descent. The goal of SGD is to minimize the gradient
* of the loss function.
*
*
* @see Algorithm
*/
public String getAlgorithm() {
return this.algorithm;
}
/**
*
* The algorithm used to train the MLModel. The following algorithm is supported:
*
*
* -
*
* SGD -- Stochastic gradient descent. The goal of SGD is to minimize the gradient of the
* loss function.
*
*
*
*
* @param algorithm
* The algorithm used to train the MLModel. The following algorithm is supported:
*
* -
*
* SGD -- Stochastic gradient descent. The goal of SGD is to minimize the gradient
* of the loss function.
*
*
* @return Returns a reference to this object so that method calls can be chained together.
* @see Algorithm
*/
public MLModel withAlgorithm(String algorithm) {
setAlgorithm(algorithm);
return this;
}
/**
*
* The algorithm used to train the MLModel. The following algorithm is supported:
*
*
* -
*
* SGD -- Stochastic gradient descent. The goal of SGD is to minimize the gradient of the
* loss function.
*
*
*
*
* @param algorithm
* The algorithm used to train the MLModel. The following algorithm is supported:
*
* -
*
* SGD -- Stochastic gradient descent. The goal of SGD is to minimize the gradient
* of the loss function.
*
*
* @see Algorithm
*/
public void setAlgorithm(Algorithm algorithm) {
withAlgorithm(algorithm);
}
/**
*
* The algorithm used to train the MLModel. The following algorithm is supported:
*
*
* -
*
* SGD -- Stochastic gradient descent. The goal of SGD is to minimize the gradient of the
* loss function.
*
*
*
*
* @param algorithm
* The algorithm used to train the MLModel. The following algorithm is supported:
*
* -
*
* SGD -- Stochastic gradient descent. The goal of SGD is to minimize the gradient
* of the loss function.
*
*
* @return Returns a reference to this object so that method calls can be chained together.
* @see Algorithm
*/
public MLModel withAlgorithm(Algorithm algorithm) {
this.algorithm = algorithm.toString();
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 a child-friendly web site?".
*
*
* -
*
* MULTICLASS - Produces one of several possible results. For example,
* "Is this a HIGH-, LOW-, or MEDIUM-risk trade?".
*
*
*
*
* @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 a child-friendly web site?".
*
*
* -
*
* 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 a child-friendly web site?".
*
*
* -
*
* MULTICLASS - Produces one of several possible results. For example,
* "Is this a HIGH-, LOW-, or MEDIUM-risk trade?".
*
*
*
*
* @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 a child-friendly web site?".
*
*
* -
*
* MULTICLASS - Produces one of several possible results. For example,
* "Is this a HIGH-, LOW-, or MEDIUM-risk trade?".
*
*
* @see MLModelType
*/
public String getMLModelType() {
return this.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 a child-friendly web site?".
*
*
* -
*
* MULTICLASS - Produces one of several possible results. For example,
* "Is this a HIGH-, LOW-, or MEDIUM-risk trade?".
*
*
*
*
* @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 a child-friendly web site?".
*
*
* -
*
* MULTICLASS - Produces one of several possible results. For example,
* "Is this a HIGH-, LOW-, or MEDIUM-risk trade?".
*
*
* @return Returns a reference to this object so that method calls can be chained together.
* @see MLModelType
*/
public MLModel withMLModelType(String mLModelType) {
setMLModelType(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 a child-friendly web site?".
*
*
* -
*
* MULTICLASS - Produces one of several possible results. For example,
* "Is this a HIGH-, LOW-, or MEDIUM-risk trade?".
*
*
*
*
* @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 a child-friendly web site?".
*
*
* -
*
* 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) {
withMLModelType(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 a child-friendly web site?".
*
*
* -
*
* MULTICLASS - Produces one of several possible results. For example,
* "Is this a HIGH-, LOW-, or MEDIUM-risk trade?".
*
*
*
*
* @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 a child-friendly web site?".
*
*
* -
*
* MULTICLASS - Produces one of several possible results. For example,
* "Is this a HIGH-, LOW-, or MEDIUM-risk trade?".
*
*
* @return Returns a reference to this object so that method calls can be chained together.
* @see MLModelType
*/
public MLModel withMLModelType(MLModelType mLModelType) {
this.mLModelType = mLModelType.toString();
return this;
}
/**
* @param scoreThreshold
*/
public void setScoreThreshold(Float scoreThreshold) {
this.scoreThreshold = scoreThreshold;
}
/**
* @return
*/
public Float getScoreThreshold() {
return this.scoreThreshold;
}
/**
* @param scoreThreshold
* @return Returns a reference to this object so that method calls can be chained together.
*/
public MLModel withScoreThreshold(Float scoreThreshold) {
setScoreThreshold(scoreThreshold);
return this;
}
/**
*
* 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.
*
*
* @return The time of the most recent edit to the ScoreThreshold. The time is expressed in epoch time.
*/
public java.util.Date getScoreThresholdLastUpdatedAt() {
return this.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.
* @return Returns a reference to this object so that method calls can be chained together.
*/
public MLModel withScoreThresholdLastUpdatedAt(java.util.Date scoreThresholdLastUpdatedAt) {
setScoreThresholdLastUpdatedAt(scoreThresholdLastUpdatedAt);
return this;
}
/**
*
* A description of the most recent details about accessing the MLModel.
*
*
* @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.
*
*
* @return A description of the most recent details about accessing the MLModel.
*/
public String getMessage() {
return this.message;
}
/**
*
* A description of the most recent details about accessing the MLModel.
*
*
* @param message
* A description of the most recent details about accessing the MLModel.
* @return Returns a reference to this object so that method calls can be chained together.
*/
public MLModel withMessage(String message) {
setMessage(message);
return this;
}
/**
* @param computeTime
*/
public void setComputeTime(Long computeTime) {
this.computeTime = computeTime;
}
/**
* @return
*/
public Long getComputeTime() {
return this.computeTime;
}
/**
* @param computeTime
* @return Returns a reference to this object so that method calls can be chained together.
*/
public MLModel withComputeTime(Long computeTime) {
setComputeTime(computeTime);
return this;
}
/**
* @param finishedAt
*/
public void setFinishedAt(java.util.Date finishedAt) {
this.finishedAt = finishedAt;
}
/**
* @return
*/
public java.util.Date getFinishedAt() {
return this.finishedAt;
}
/**
* @param finishedAt
* @return Returns a reference to this object so that method calls can be chained together.
*/
public MLModel withFinishedAt(java.util.Date finishedAt) {
setFinishedAt(finishedAt);
return this;
}
/**
* @param startedAt
*/
public void setStartedAt(java.util.Date startedAt) {
this.startedAt = startedAt;
}
/**
* @return
*/
public java.util.Date getStartedAt() {
return this.startedAt;
}
/**
* @param startedAt
* @return Returns a reference to this object so that method calls can be chained together.
*/
public MLModel withStartedAt(java.util.Date startedAt) {
setStartedAt(startedAt);
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 (getMLModelId() != null)
sb.append("MLModelId: ").append(getMLModelId()).append(",");
if (getTrainingDataSourceId() != null)
sb.append("TrainingDataSourceId: ").append(getTrainingDataSourceId()).append(",");
if (getCreatedByIamUser() != null)
sb.append("CreatedByIamUser: ").append(getCreatedByIamUser()).append(",");
if (getCreatedAt() != null)
sb.append("CreatedAt: ").append(getCreatedAt()).append(",");
if (getLastUpdatedAt() != null)
sb.append("LastUpdatedAt: ").append(getLastUpdatedAt()).append(",");
if (getName() != null)
sb.append("Name: ").append(getName()).append(",");
if (getStatus() != null)
sb.append("Status: ").append(getStatus()).append(",");
if (getSizeInBytes() != null)
sb.append("SizeInBytes: ").append(getSizeInBytes()).append(",");
if (getEndpointInfo() != null)
sb.append("EndpointInfo: ").append(getEndpointInfo()).append(",");
if (getTrainingParameters() != null)
sb.append("TrainingParameters: ").append(getTrainingParameters()).append(",");
if (getInputDataLocationS3() != null)
sb.append("InputDataLocationS3: ").append(getInputDataLocationS3()).append(",");
if (getAlgorithm() != null)
sb.append("Algorithm: ").append(getAlgorithm()).append(",");
if (getMLModelType() != null)
sb.append("MLModelType: ").append(getMLModelType()).append(",");
if (getScoreThreshold() != null)
sb.append("ScoreThreshold: ").append(getScoreThreshold()).append(",");
if (getScoreThresholdLastUpdatedAt() != null)
sb.append("ScoreThresholdLastUpdatedAt: ").append(getScoreThresholdLastUpdatedAt()).append(",");
if (getMessage() != null)
sb.append("Message: ").append(getMessage()).append(",");
if (getComputeTime() != null)
sb.append("ComputeTime: ").append(getComputeTime()).append(",");
if (getFinishedAt() != null)
sb.append("FinishedAt: ").append(getFinishedAt()).append(",");
if (getStartedAt() != null)
sb.append("StartedAt: ").append(getStartedAt());
sb.append("}");
return sb.toString();
}
@Override
public boolean equals(Object obj) {
if (this == obj)
return true;
if (obj == null)
return false;
if (obj instanceof MLModel == false)
return false;
MLModel other = (MLModel) 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.getAlgorithm() == null ^ this.getAlgorithm() == null)
return false;
if (other.getAlgorithm() != null && other.getAlgorithm().equals(this.getAlgorithm()) == 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.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;
return true;
}
@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 + ((getAlgorithm() == null) ? 0 : getAlgorithm().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 + ((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());
return hashCode;
}
@Override
public MLModel clone() {
try {
return (MLModel) super.clone();
} catch (CloneNotSupportedException e) {
throw new IllegalStateException("Got a CloneNotSupportedException from Object.clone() " + "even though we're Cloneable!", e);
}
}
@com.amazonaws.annotation.SdkInternalApi
@Override
public void marshall(ProtocolMarshaller protocolMarshaller) {
com.amazonaws.services.machinelearning.model.transform.MLModelMarshaller.getInstance().marshall(this, protocolMarshaller);
}
}