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   /*
    * Copyright 2010-2015 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;
  

Represents the output of a GetMLModel operation.

The content consists of the detailed metadata and the current status of the MLModel .

  
  public class MLModel implements SerializableCloneable {

    
The ID assigned to the MLModel at creation.

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

  
      private String mLModelId;

    
The ID of the training DataSource. The CreateMLModel operation uses the TrainingDataSourceId.

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: 0 - 1024

  
      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 did not run to completion. It is not usable.
  • COMPLETED - The creation process completed successfully.
  • DELETED - The MLModel is marked as deleted. It is not usable.

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:

  • sgd.l1RegularizationAmount - 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, specify a small value, such as 1.0E-04 or 1.0E-08.

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

  • sgd.l2RegularizationAmount - 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, specify a small value, such as 1.0E-04 or 1.0E-08.

    The valus is a double that ranges from 0 to MAX_DOUBLE. The default is not to use L2 normalization. This cannot be used when L1 is specified. Use this parameter sparingly.

  • sgd.maxPasses - 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.maxMLModelSizeInBytes - Maximum allowed size of the model. Depending on the input data, the model size might affect performance.

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

 
     private java.util.Map<String,StringtrainingParameters;

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

Constraints:
Length: 0 - 2048
Pattern: 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.

Constraints:
Allowed Values: sgd

 
     private String algorithm;

    
Identifies the MLModel category. The following are the available types:
  • REGRESSION - Produces a numeric result. For example, "What listing price should a house have?".
  • BINARY - Produces one of two possible results. For example, "Is this a child-friendly web site?".
  • MULTICLASS - Produces more than two possible results. For example, "Is this a HIGH, LOW or MEDIUM risk trade?".

Constraints:
Allowed Values: REGRESSION, BINARY, MULTICLASS

 
     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.

Constraints:
Length: 0 - 10240

 
     private String message;

    
The ID assigned to the MLModel at creation.

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

Returns:
The ID assigned to the MLModel at creation.
 
     public String getMLModelId() {
         return ;
     }
    
    
The ID assigned to the MLModel at creation.

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

Parameters:
mLModelId The ID assigned to the MLModel at creation.
 
     public void setMLModelId(String mLModelId) {
         this. = mLModelId;
     }
    
    
The ID assigned to the MLModel at creation.

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

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

Parameters:
mLModelId The ID assigned to the MLModel at creation.
Returns:
A reference to this updated object so that method calls can be chained together.
 
     public MLModel withMLModelId(String mLModelId) {
         this. = mLModelId;
         return this;
     }

    
The ID of the training DataSource. The CreateMLModel operation uses the TrainingDataSourceId.

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

Returns:
The ID of the training DataSource. The CreateMLModel operation uses the TrainingDataSourceId.
 
     public String getTrainingDataSourceId() {
         return ;
     }
    
    
The ID of the training DataSource. The CreateMLModel operation uses the TrainingDataSourceId.

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

Parameters:
trainingDataSourceId The ID of the training DataSource. The CreateMLModel operation uses the TrainingDataSourceId.
 
     public void setTrainingDataSourceId(String trainingDataSourceId) {
         this. = trainingDataSourceId;
     }
    
    
The ID of the training DataSource. The CreateMLModel operation uses the TrainingDataSourceId.

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

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

Parameters:
trainingDataSourceId The ID of the training DataSource. The CreateMLModel operation uses the TrainingDataSourceId.
Returns:
A reference to this updated object so that method calls can be chained together.
 
     public MLModel withTrainingDataSourceId(String trainingDataSourceId) {
         this. = 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))

Returns:
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 ;
     }
    
    
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))

Parameters:
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;
     }
    
    
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))

Parameters:
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 updated object so that method calls can be chained together.
 
     public MLModel withCreatedByIamUser(String createdByIamUser) {
         this. = createdByIamUser;
         return this;
     }

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

Returns:
The time that the MLModel was created. The time is expressed in epoch time.
 
     public java.util.Date getCreatedAt() {
         return ;
     }
    
    
The time that the MLModel was created. The time is expressed in epoch time.

Parameters:
createdAt The time that the MLModel was created. The time is expressed in epoch time.
 
     public void setCreatedAt(java.util.Date createdAt) {
         this. = 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.

Parameters:
createdAt The time that the MLModel was created. The time is expressed in epoch time.
Returns:
A reference to this updated object so that method calls can be chained together.
 
     public MLModel withCreatedAt(java.util.Date createdAt) {
         this. = createdAt;
         return this;
     }

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

Returns:
The time of the most recent edit to the MLModel. The time is expressed in epoch time.
 
     public java.util.Date getLastUpdatedAt() {
         return ;
     }
    
    
The time of the most recent edit to the MLModel. The time is expressed in epoch time.

Parameters:
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;
     }
    
    
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.

Parameters:
lastUpdatedAt The time of the most recent edit to the MLModel. The time is expressed in epoch time.
Returns:
A reference to this updated object so that method calls can be chained together.
 
     public MLModel withLastUpdatedAt(java.util.Date lastUpdatedAt) {
         this. = lastUpdatedAt;
         return this;
     }

    
A user-supplied name or description of the MLModel.

Constraints:
Length: 0 - 1024

Returns:
A user-supplied name or description of the MLModel.
 
     public String getName() {
         return ;
     }
    
    
A user-supplied name or description of the MLModel.

Constraints:
Length: 0 - 1024

Parameters:
name A user-supplied name or description of the MLModel.
 
     public void setName(String name) {
         this. = 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: 0 - 1024

Parameters:
name A user-supplied name or description of the MLModel.
Returns:
A reference to this updated object so that method calls can be chained together.
 
     public MLModel withName(String name) {
         this. = 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 did not run to completion. It is not usable.
  • COMPLETED - The creation process completed successfully.
  • DELETED - The MLModel is marked as deleted. It is not usable.

Constraints:
Allowed Values: PENDING, INPROGRESS, FAILED, COMPLETED, DELETED

Returns:
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 did not run to completion. It is not usable.
  • COMPLETED - The creation process completed successfully.
  • DELETED - The MLModel is marked as deleted. It is not usable.
See also:
EntityStatus
 
     public String getStatus() {
         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 did not run to completion. It is not usable.
  • COMPLETED - The creation process completed successfully.
  • DELETED - The MLModel is marked as deleted. It is not usable.

Constraints:
Allowed Values: PENDING, INPROGRESS, FAILED, COMPLETED, DELETED

Parameters:
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 did not run to completion. It is not usable.
  • COMPLETED - The creation process completed successfully.
  • DELETED - The MLModel is marked as deleted. It is not usable.
See also:
EntityStatus
 
     public void setStatus(String status) {
         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 did not run to completion. It is not usable.
  • COMPLETED - The creation process completed successfully.
  • DELETED - The MLModel is marked as deleted. It is not usable.

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

Constraints:
Allowed Values: PENDING, INPROGRESS, FAILED, COMPLETED, DELETED

Parameters:
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 did not run to completion. It is not usable.
  • COMPLETED - The creation process completed successfully.
  • DELETED - The MLModel is marked as deleted. It is not usable.
Returns:
A reference to this updated object so that method calls can be chained together.
See also:
EntityStatus
 
     public MLModel withStatus(String status) {
         this. = 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 did not run to completion. It is not usable.
  • COMPLETED - The creation process completed successfully.
  • DELETED - The MLModel is marked as deleted. It is not usable.

Constraints:
Allowed Values: PENDING, INPROGRESS, FAILED, COMPLETED, DELETED

Parameters:
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 did not run to completion. It is not usable.
  • COMPLETED - The creation process completed successfully.
  • DELETED - The MLModel is marked as deleted. It is not usable.
See also:
EntityStatus
 
     public void setStatus(EntityStatus status) {
         this. = status.toString();
     }
    
    
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 did not run to completion. It is not usable.
  • COMPLETED - The creation process completed successfully.
  • DELETED - The MLModel is marked as deleted. It is not usable.

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

Constraints:
Allowed Values: PENDING, INPROGRESS, FAILED, COMPLETED, DELETED

Parameters:
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 did not run to completion. It is not usable.
  • COMPLETED - The creation process completed successfully.
  • DELETED - The MLModel is marked as deleted. It is not usable.
Returns:
A reference to this updated object so that method calls can be chained together.
See also:
EntityStatus
 
     public MLModel withStatus(EntityStatus status) {
         this. = status.toString();
         return this;
     }

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

Returns:
Long integer type that is a 64-bit signed number.
 
     public Long getSizeInBytes() {
         return ;
     }
    
    
Long integer type that is a 64-bit signed number.

Parameters:
sizeInBytes Long integer type that is a 64-bit signed number.
 
     public void setSizeInBytes(Long sizeInBytes) {
         this. = 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.

Parameters:
sizeInBytes Long integer type that is a 64-bit signed number.
Returns:
A reference to this updated object so that method calls can be chained together.
 
     public MLModel withSizeInBytes(Long sizeInBytes) {
         this. = sizeInBytes;
         return this;
     }

    
The current endpoint of the MLModel.

Returns:
The current endpoint of the MLModel.
 
         return ;
     }
    
    
The current endpoint of the MLModel.

Parameters:
endpointInfo The current endpoint of the MLModel.
 
     public void setEndpointInfo(RealtimeEndpointInfo endpointInfo) {
         this. = endpointInfo;
     }
    
    
The current endpoint of the MLModel.

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

Parameters:
endpointInfo The current endpoint of the MLModel.
Returns:
A reference to this updated object so that method calls can be chained together.
 
     public MLModel withEndpointInfo(RealtimeEndpointInfo endpointInfo) {
         this. = 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.l1RegularizationAmount - 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, specify a small value, such as 1.0E-04 or 1.0E-08.

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

  • sgd.l2RegularizationAmount - 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, specify a small value, such as 1.0E-04 or 1.0E-08.

    The valus is a double that ranges from 0 to MAX_DOUBLE. The default is not to use L2 normalization. This cannot be used when L1 is specified. Use this parameter sparingly.

  • sgd.maxPasses - 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.maxMLModelSizeInBytes - Maximum allowed size of the model. Depending on the input data, the model size might affect performance.

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

Returns:
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.l1RegularizationAmount - 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, specify a small value, such as 1.0E-04 or 1.0E-08.

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

  • sgd.l2RegularizationAmount - 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, specify a small value, such as 1.0E-04 or 1.0E-08.

    The valus is a double that ranges from 0 to MAX_DOUBLE. The default is not to use L2 normalization. This cannot be used when L1 is specified. Use this parameter sparingly.

  • sgd.maxPasses - 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.maxMLModelSizeInBytes - Maximum allowed size of the model. Depending on the input data, the model size might affect performance.

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

 
         
         if ( == null) {
              = new java.util.HashMap<String,String>();
         }
         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.l1RegularizationAmount - 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, specify a small value, such as 1.0E-04 or 1.0E-08.

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

  • sgd.l2RegularizationAmount - 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, specify a small value, such as 1.0E-04 or 1.0E-08.

    The valus is a double that ranges from 0 to MAX_DOUBLE. The default is not to use L2 normalization. This cannot be used when L1 is specified. Use this parameter sparingly.

  • sgd.maxPasses - 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.maxMLModelSizeInBytes - Maximum allowed size of the model. Depending on the input data, the model size might affect performance.

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

Parameters:
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.l1RegularizationAmount - 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, specify a small value, such as 1.0E-04 or 1.0E-08.

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

  • sgd.l2RegularizationAmount - 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, specify a small value, such as 1.0E-04 or 1.0E-08.

    The valus is a double that ranges from 0 to MAX_DOUBLE. The default is not to use L2 normalization. This cannot be used when L1 is specified. Use this parameter sparingly.

  • sgd.maxPasses - 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.maxMLModelSizeInBytes - Maximum allowed size of the model. Depending on the input data, the model size might affect performance.

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

 
     public void setTrainingParameters(java.util.Map<String,StringtrainingParameters) {
         this. = 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.l1RegularizationAmount - 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, specify a small value, such as 1.0E-04 or 1.0E-08.

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

  • sgd.l2RegularizationAmount - 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, specify a small value, such as 1.0E-04 or 1.0E-08.

    The valus is a double that ranges from 0 to MAX_DOUBLE. The default is not to use L2 normalization. This cannot be used when L1 is specified. Use this parameter sparingly.

  • sgd.maxPasses - 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.maxMLModelSizeInBytes - Maximum allowed size of the model. Depending on the input data, the model size might affect performance.

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

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

Parameters:
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.l1RegularizationAmount - 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, specify a small value, such as 1.0E-04 or 1.0E-08.

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

  • sgd.l2RegularizationAmount - 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, specify a small value, such as 1.0E-04 or 1.0E-08.

    The valus is a double that ranges from 0 to MAX_DOUBLE. The default is not to use L2 normalization. This cannot be used when L1 is specified. Use this parameter sparingly.

  • sgd.maxPasses - 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.maxMLModelSizeInBytes - Maximum allowed size of the model. Depending on the input data, the model size might affect performance.

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

Returns:
A reference to this updated object so that method calls can be chained together.
 
     public MLModel withTrainingParameters(java.util.Map<String,StringtrainingParameters) {
         setTrainingParameters(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.l1RegularizationAmount - 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, specify a small value, such as 1.0E-04 or 1.0E-08.

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

  • sgd.l2RegularizationAmount - 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, specify a small value, such as 1.0E-04 or 1.0E-08.

    The valus is a double that ranges from 0 to MAX_DOUBLE. The default is not to use L2 normalization. This cannot be used when L1 is specified. Use this parameter sparingly.

  • sgd.maxPasses - 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.maxMLModelSizeInBytes - Maximum allowed size of the model. Depending on the input data, the model size might affect performance.

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

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.

Parameters:
key The key of the entry to be added into TrainingParameters.
value The corresponding value of the entry to be added into TrainingParameters.
 
   public MLModel addTrainingParametersEntry(String keyString value) {
     if (null == this.) {
       this. = new java.util.HashMap<String,String>();
     }
     if (this..containsKey(key))
       throw new IllegalArgumentException("Duplicated keys (" + key.toString() + ") are provided.");
     this..put(keyvalue);
     return this;
   }

  
Removes all the entries added into TrainingParameters.

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

 
     this. = null;
     return this;
   }
  
    
The location of the data file or directory in Amazon Simple Storage Service (Amazon S3).

Constraints:
Length: 0 - 2048
Pattern: s3://([^/]+)(/.*)?

Returns:
The location of the data file or directory in Amazon Simple Storage Service (Amazon S3).
 
     public String getInputDataLocationS3() {
         return ;
     }
    
    
The location of the data file or directory in Amazon Simple Storage Service (Amazon S3).

Constraints:
Length: 0 - 2048
Pattern: s3://([^/]+)(/.*)?

Parameters:
inputDataLocationS3 The location of the data file or directory in Amazon Simple Storage Service (Amazon S3).
 
     public void setInputDataLocationS3(String inputDataLocationS3) {
         this. = 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: 0 - 2048
Pattern: s3://([^/]+)(/.*)?

Parameters:
inputDataLocationS3 The location of the data file or directory in Amazon Simple Storage Service (Amazon S3).
Returns:
A reference to this updated object so that method calls can be chained together.
 
     public MLModel withInputDataLocationS3(String inputDataLocationS3) {
         this. = 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.

Constraints:
Allowed Values: sgd

Returns:
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 also:
Algorithm
    public String getAlgorithm() {
        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.

Constraints:
Allowed Values: sgd

Parameters:
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 also:
Algorithm
    public void setAlgorithm(String algorithm) {
        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.

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

Constraints:
Allowed Values: sgd

Parameters:
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.
Returns:
A reference to this updated object so that method calls can be chained together.
See also:
Algorithm
    public MLModel withAlgorithm(String algorithm) {
        this. = 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.

Constraints:
Allowed Values: sgd

Parameters:
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 also:
Algorithm
    public void setAlgorithm(Algorithm algorithm) {
        this. = algorithm.toString();
    }
    
    
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.

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

Constraints:
Allowed Values: sgd

Parameters:
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.
Returns:
A reference to this updated object so that method calls can be chained together.
See also:
Algorithm
    public MLModel withAlgorithm(Algorithm algorithm) {
        this. = algorithm.toString();
        return this;
    }

    
Identifies the MLModel category. The following are the available types:
  • REGRESSION - Produces a numeric result. For example, "What listing price should a house have?".
  • BINARY - Produces one of two possible results. For example, "Is this a child-friendly web site?".
  • MULTICLASS - Produces more than two possible results. For example, "Is this a HIGH, LOW or MEDIUM risk trade?".

Constraints:
Allowed Values: REGRESSION, BINARY, MULTICLASS

Returns:
Identifies the MLModel category. The following are the available types:
  • REGRESSION - Produces a numeric result. For example, "What listing price should a house have?".
  • BINARY - Produces one of two possible results. For example, "Is this a child-friendly web site?".
  • MULTICLASS - Produces more than two possible results. For example, "Is this a HIGH, LOW or MEDIUM risk trade?".
See also:
MLModelType
    public String getMLModelType() {
        return ;
    }
    
    
Identifies the MLModel category. The following are the available types:
  • REGRESSION - Produces a numeric result. For example, "What listing price should a house have?".
  • BINARY - Produces one of two possible results. For example, "Is this a child-friendly web site?".
  • MULTICLASS - Produces more than two possible results. For example, "Is this a HIGH, LOW or MEDIUM risk trade?".

Constraints:
Allowed Values: REGRESSION, BINARY, MULTICLASS

Parameters:
mLModelType Identifies the MLModel category. The following are the available types:
  • REGRESSION - Produces a numeric result. For example, "What listing price should a house have?".
  • BINARY - Produces one of two possible results. For example, "Is this a child-friendly web site?".
  • MULTICLASS - Produces more than two possible results. For example, "Is this a HIGH, LOW or MEDIUM risk trade?".
See also:
MLModelType
    public void setMLModelType(String mLModelType) {
        this. = mLModelType;
    }
    
    
Identifies the MLModel category. The following are the available types:
  • REGRESSION - Produces a numeric result. For example, "What listing price should a house have?".
  • BINARY - Produces one of two possible results. For example, "Is this a child-friendly web site?".
  • MULTICLASS - Produces more than two 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

Parameters:
mLModelType Identifies the MLModel category. The following are the available types:
  • REGRESSION - Produces a numeric result. For example, "What listing price should a house have?".
  • BINARY - Produces one of two possible results. For example, "Is this a child-friendly web site?".
  • MULTICLASS - Produces more than two possible results. For example, "Is this a HIGH, LOW or MEDIUM risk trade?".
Returns:
A reference to this updated object so that method calls can be chained together.
See also:
MLModelType
    public MLModel withMLModelType(String mLModelType) {
        this. = mLModelType;
        return this;
    }

    
Identifies the MLModel category. The following are the available types:
  • REGRESSION - Produces a numeric result. For example, "What listing price should a house have?".
  • BINARY - Produces one of two possible results. For example, "Is this a child-friendly web site?".
  • MULTICLASS - Produces more than two possible results. For example, "Is this a HIGH, LOW or MEDIUM risk trade?".

Constraints:
Allowed Values: REGRESSION, BINARY, MULTICLASS

Parameters:
mLModelType Identifies the MLModel category. The following are the available types:
  • REGRESSION - Produces a numeric result. For example, "What listing price should a house have?".
  • BINARY - Produces one of two possible results. For example, "Is this a child-friendly web site?".
  • MULTICLASS - Produces more than two possible results. For example, "Is this a HIGH, LOW or MEDIUM risk trade?".
See also:
MLModelType
    public void setMLModelType(MLModelType mLModelType) {
        this. = mLModelType.toString();
    }
    
    
Identifies the MLModel category. The following are the available types:
  • REGRESSION - Produces a numeric result. For example, "What listing price should a house have?".
  • BINARY - Produces one of two possible results. For example, "Is this a child-friendly web site?".
  • MULTICLASS - Produces more than two 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

Parameters:
mLModelType Identifies the MLModel category. The following are the available types:
  • REGRESSION - Produces a numeric result. For example, "What listing price should a house have?".
  • BINARY - Produces one of two possible results. For example, "Is this a child-friendly web site?".
  • MULTICLASS - Produces more than two possible results. For example, "Is this a HIGH, LOW or MEDIUM risk trade?".
Returns:
A reference to this updated object so that method calls can be chained together.
See also:
MLModelType
    public MLModel withMLModelType(MLModelType mLModelType) {
        this. = mLModelType.toString();
        return this;
    }

    
Returns the value of the ScoreThreshold property for this object.

Returns:
The value of the ScoreThreshold property for this object.
    public Float getScoreThreshold() {
        return ;
    }
    
    
Sets the value of the ScoreThreshold property for this object.

Parameters:
scoreThreshold The new value for the ScoreThreshold property for this object.
    public void setScoreThreshold(Float scoreThreshold) {
        this. = scoreThreshold;
    }
    
    
Sets the value of the ScoreThreshold property for this object.

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

Parameters:
scoreThreshold The new value for the ScoreThreshold property for this object.
Returns:
A reference to this updated object so that method calls can be chained together.
    public MLModel withScoreThreshold(Float scoreThreshold) {
        this. = scoreThreshold;
        return this;
    }

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

Returns:
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.

Parameters:
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;
    }
    
    
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.

Parameters:
scoreThresholdLastUpdatedAt The time of the most recent edit to the ScoreThreshold. The time is expressed in epoch time.
Returns:
A reference to this updated object so that method calls can be chained together.
    public MLModel withScoreThresholdLastUpdatedAt(java.util.Date scoreThresholdLastUpdatedAt) {
        this. = scoreThresholdLastUpdatedAt;
        return this;
    }

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

Constraints:
Length: 0 - 10240

Returns:
A description of the most recent details about accessing the MLModel.
    public String getMessage() {
        return ;
    }
    
    
A description of the most recent details about accessing the MLModel.

Constraints:
Length: 0 - 10240

Parameters:
message A description of the most recent details about accessing the MLModel.
    public void setMessage(String message) {
        this. = 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: 0 - 10240

Parameters:
message A description of the most recent details about accessing the MLModel.
Returns:
A reference to this updated object so that method calls can be chained together.
    public MLModel withMessage(String message) {
        this. = message;
        return this;
    }

    
Returns a string representation of this object; useful for testing and debugging.

Returns:
A string representation of this object.
See also:
java.lang.Object.toString()
    @Override
    public String toString() {
        StringBuilder sb = new StringBuilder();
        sb.append("{");
        if (getMLModelId() != nullsb.append("MLModelId: " + getMLModelId() + ",");
        if (getTrainingDataSourceId() != nullsb.append("TrainingDataSourceId: " + getTrainingDataSourceId() + ",");
        if (getCreatedByIamUser() != nullsb.append("CreatedByIamUser: " + getCreatedByIamUser() + ",");
        if (getCreatedAt() != nullsb.append("CreatedAt: " + getCreatedAt() + ",");
        if (getLastUpdatedAt() != nullsb.append("LastUpdatedAt: " + getLastUpdatedAt() + ",");
        if (getName() != nullsb.append("Name: " + getName() + ",");
        if (getStatus() != nullsb.append("Status: " + getStatus() + ",");
        if (getSizeInBytes() != nullsb.append("SizeInBytes: " + getSizeInBytes() + ",");
        if (getEndpointInfo() != nullsb.append("EndpointInfo: " + getEndpointInfo() + ",");
        if (getTrainingParameters() != nullsb.append("TrainingParameters: " + getTrainingParameters() + ",");
        if (getInputDataLocationS3() != nullsb.append("InputDataLocationS3: " + getInputDataLocationS3() + ",");
        if (getAlgorithm() != nullsb.append("Algorithm: " + getAlgorithm() + ",");
        if (getMLModelType() != nullsb.append("MLModelType: " + getMLModelType() + ",");
        if (getScoreThreshold() != nullsb.append("ScoreThreshold: " + getScoreThreshold() + ",");
        if (getScoreThresholdLastUpdatedAt() != nullsb.append("ScoreThresholdLastUpdatedAt: " + getScoreThresholdLastUpdatedAt() + ",");
        if (getMessage() != nullsb.append("Message: " + getMessage() );
        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 + ((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()); 
        return hashCode;
    }
    
    @Override
    public boolean equals(Object obj) {
        if (this == objreturn true;
        if (obj == nullreturn false;
        if (obj instanceof MLModel == falsereturn false;
        MLModel other = (MLModel)obj;
        
        if (other.getMLModelId() == null ^ this.getMLModelId() == nullreturn false;
        if (other.getMLModelId() != null && other.getMLModelId().equals(this.getMLModelId()) == falsereturn false
        if (other.getTrainingDataSourceId() == null ^ this.getTrainingDataSourceId() == nullreturn false;
        if (other.getTrainingDataSourceId() != null && other.getTrainingDataSourceId().equals(this.getTrainingDataSourceId()) == falsereturn false
        if (other.getCreatedByIamUser() == null ^ this.getCreatedByIamUser() == nullreturn false;
        if (other.getCreatedByIamUser() != null && other.getCreatedByIamUser().equals(this.getCreatedByIamUser()) == falsereturn false
        if (other.getCreatedAt() == null ^ this.getCreatedAt() == nullreturn false;
        if (other.getCreatedAt() != null && other.getCreatedAt().equals(this.getCreatedAt()) == falsereturn false
        if (other.getLastUpdatedAt() == null ^ this.getLastUpdatedAt() == nullreturn false;
        if (other.getLastUpdatedAt() != null && other.getLastUpdatedAt().equals(this.getLastUpdatedAt()) == falsereturn false
        if (other.getName() == null ^ this.getName() == nullreturn false;
        if (other.getName() != null && other.getName().equals(this.getName()) == falsereturn false
        if (other.getStatus() == null ^ this.getStatus() == nullreturn false;
        if (other.getStatus() != null && other.getStatus().equals(this.getStatus()) == falsereturn false
        if (other.getSizeInBytes() == null ^ this.getSizeInBytes() == nullreturn false;
        if (other.getSizeInBytes() != null && other.getSizeInBytes().equals(this.getSizeInBytes()) == falsereturn false
        if (other.getEndpointInfo() == null ^ this.getEndpointInfo() == nullreturn false;
        if (other.getEndpointInfo() != null && other.getEndpointInfo().equals(this.getEndpointInfo()) == falsereturn false
        if (other.getTrainingParameters() == null ^ this.getTrainingParameters() == nullreturn false;
        if (other.getTrainingParameters() != null && other.getTrainingParameters().equals(this.getTrainingParameters()) == falsereturn false
        if (other.getInputDataLocationS3() == null ^ this.getInputDataLocationS3() == nullreturn false;
        if (other.getInputDataLocationS3() != null && other.getInputDataLocationS3().equals(this.getInputDataLocationS3()) == falsereturn false
        if (other.getAlgorithm() == null ^ this.getAlgorithm() == nullreturn false;
        if (other.getAlgorithm() != null && other.getAlgorithm().equals(this.getAlgorithm()) == falsereturn false
        if (other.getMLModelType() == null ^ this.getMLModelType() == nullreturn false;
        if (other.getMLModelType() != null && other.getMLModelType().equals(this.getMLModelType()) == falsereturn false
        if (other.getScoreThreshold() == null ^ this.getScoreThreshold() == nullreturn false;
        if (other.getScoreThreshold() != null && other.getScoreThreshold().equals(this.getScoreThreshold()) == falsereturn false
        if (other.getScoreThresholdLastUpdatedAt() == null ^ this.getScoreThresholdLastUpdatedAt() == nullreturn false;
        if (other.getScoreThresholdLastUpdatedAt() != null && other.getScoreThresholdLastUpdatedAt().equals(this.getScoreThresholdLastUpdatedAt()) == falsereturn false
        if (other.getMessage() == null ^ this.getMessage() == nullreturn false;
        if (other.getMessage() != null && other.getMessage().equals(this.getMessage()) == falsereturn false
        return true;
    }
    
    @Override
    public MLModel clone() {
        try {
            return (MLModelsuper.clone();
        
        } catch (CloneNotSupportedException e) {
            throw new IllegalStateException(
                    "Got a CloneNotSupportedException from Object.clone() "
                    + "even though we're Cloneable!",
                    e);
        }
        
    }
}
    
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