/*
* Copyright 2012-2017 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;
/**
* <p>
* Represents the output of a <code>GetMLModel</code> operation, and provides detailed information about a
* <code>MLModel</code>.
* </p>
*/
@Generated("com.amazonaws:aws-java-sdk-code-generator")
public class GetMLModelResult extends com.amazonaws.AmazonWebServiceResult<com.amazonaws.ResponseMetadata> implements Serializable, Cloneable {
/**
* <p>
* The MLModel ID<?oxy_insert_start author="annbech" timestamp="20160328T151251-0700">,<?oxy_insert_end> which is
* same as the <code>MLModelId</code> in the request.
* </p>
*/
private String mLModelId;
/**
* <p>
* The ID of the training <code>DataSource</code>.
* </p>
*/
private String trainingDataSourceId;
/**
* <p>
* The AWS user account from which the <code>MLModel</code> was created. The account type can be either an AWS root
* account or an AWS Identity and Access Management (IAM) user account.
* </p>
*/
private String createdByIamUser;
/**
* <p>
* The time that the <code>MLModel</code> was created. The time is expressed in epoch time.
* </p>
*/
private java.util.Date createdAt;
/**
* <p>
* The time of the most recent edit to the <code>MLModel</code>. The time is expressed in epoch time.
* </p>
*/
private java.util.Date lastUpdatedAt;
/**
* <p>
* A user-supplied name or description of the <code>MLModel</code>.
* </p>
*/
private String name;
/**
* <p>
* The current status of the <code>MLModel</code>. This element can have one of the following values:
* </p>
* <ul>
* <li> <code>PENDING</code> - Amazon Machine Learning (Amazon ML) submitted a request to describe a
* <code>MLModel</code>.</li>
* <li> <code>INPROGRESS</code> - The request is processing.</li>
* <li> <code>FAILED</code> - The request did not run to completion. The ML model isn't usable.</li>
* <li> <code>COMPLETED</code> - The request completed successfully.</li>
* <li> <code>DELETED</code> - The <code>MLModel</code> is marked as deleted. It isn't usable.</li>
* </ul>
*/
private String status;
private Long sizeInBytes;
/**
* <p>
* The current endpoint of the <code>MLModel</code>
* </p>
*/
private RealtimeEndpointInfo endpointInfo;
/**
* <p>
* A list of the training parameters in the <code>MLModel</code>. The list is implemented as a map of key-value
* pairs.
* </p>
* <p>
* The following is the current set of training parameters:
* </p>
* <ul>
* <li>
* <p>
* <code>sgd.maxMLModelSizeInBytes</code> - The maximum allowed size of the model. Depending on the input data, the
* size of the model might affect its performance.
* </p>
* <p>
* The value is an integer that ranges from <code>100000</code> to <code>2147483648</code>. The default value is
* <code>33554432</code>.
* </p>
* </li>
* <li>
* <p>
* <code>sgd.maxPasses</code> - The number of times that the training process traverses the observations to build
* the <code>MLModel</code>. The value is an integer that ranges from <code>1</code> to <code>10000</code>. The
* default value is <code>10</code>.
* </p>
* </li>
* <li>
* <p>
* <code>sgd.shuffleType</code> - Whether Amazon ML shuffles the training data. Shuffling data improves a model's
* ability to find the optimal solution for a variety of data types. The valid values are <code>auto</code> and
* <code>none</code>. The default value is <code>none</code>. We strongly recommend that you shuffle your data.
* </p>
* </li>
* <li>
* <p>
* <code>sgd.l1RegularizationAmount</code> - The coefficient regularization L1 norm. It controls overfitting the
* data by penalizing large coefficients. This tends to drive coefficients to zero, resulting in a sparse feature
* set. If you use this parameter, start by specifying a small value, such as <code>1.0E-08</code>.
* </p>
* <p>
* The value is a double that ranges from <code>0</code> to <code>MAX_DOUBLE</code>. The default is to not use L1
* normalization. This parameter can't be used when <code>L2</code> is specified. Use this parameter sparingly.
* </p>
* </li>
* <li>
* <p>
* <code>sgd.l2RegularizationAmount</code> - The coefficient regularization L2 norm. It controls overfitting the
* data by penalizing large coefficients. This tends to drive coefficients to small, nonzero values. If you use this
* parameter, start by specifying a small value, such as <code>1.0E-08</code>.
* </p>
* <p>
* The value is a double that ranges from <code>0</code> to <code>MAX_DOUBLE</code>. The default is to not use L2
* normalization. This parameter can't be used when <code>L1</code> is specified. Use this parameter sparingly.
* </p>
* </li>
* </ul>
*/
private com.amazonaws.internal.SdkInternalMap<String, String> trainingParameters;
/**
* <p>
* The location of the data file or directory in Amazon Simple Storage Service (Amazon S3).
* </p>
*/
private String inputDataLocationS3;
/**
* <p>
* Identifies the <code>MLModel</code> category. The following are the available types:
* </p>
* <ul>
* <li>REGRESSION -- Produces a numeric result. For example, "What price should a house be listed at?"</li>
* <li>BINARY -- Produces one of two possible results. For example, "Is this an e-commerce website?"</li>
* <li>MULTICLASS -- Produces one of several possible results. For example,
* "Is this a HIGH, LOW or MEDIUM risk trade?"</li>
* </ul>
*/
private String mLModelType;
/**
* <p>
* The scoring threshold is used in binary classification <code>MLModel</code><?oxy_insert_start author="laurama"
* timestamp="20160329T114851-0700"> <?oxy_insert_end>models. It marks the boundary between a positive prediction
* and a negative prediction.
* </p>
* <p>
* Output values greater than or equal to the threshold receive a positive result from the MLModel, such as
* <code>true</code>. Output values less than the threshold receive a negative response from the MLModel, such as
* <code>false</code>.
* </p>
*/
private Float scoreThreshold;
/**
* <p>
* The time of the most recent edit to the <code>ScoreThreshold</code>. The time is expressed in epoch time.
* </p>
*/
private java.util.Date scoreThresholdLastUpdatedAt;
/**
* <p>
* A link to the file that contains logs of the <code>CreateMLModel</code> operation.
* </p>
*/
private String logUri;
/**
* <p>
* A description of the most recent details about accessing the <code>MLModel</code>.
* </p>
*/
private String message;
/**
* <p>
* The approximate CPU time in milliseconds that Amazon Machine Learning spent processing the <code>MLModel</code>,
* normalized and scaled on computation resources. <code>ComputeTime</code> is only available if the
* <code>MLModel</code> is in the <code>COMPLETED</code> state.
* </p>
*/
private Long computeTime;
/**
* <p>
* The epoch time when Amazon Machine Learning marked the <code>MLModel</code> as <code>COMPLETED</code> or
* <code>FAILED</code>. <code>FinishedAt</code> is only available when the <code>MLModel</code> is in the
* <code>COMPLETED</code> or <code>FAILED</code> state.
* </p>
*/
private java.util.Date finishedAt;
/**
* <p>
* The epoch time when Amazon Machine Learning marked the <code>MLModel</code> as <code>INPROGRESS</code>.
* <code>StartedAt</code> isn't available if the <code>MLModel</code> is in the <code>PENDING</code> state.
* </p>
*/
private java.util.Date startedAt;
/**
* <p>
* The recipe to use when training the <code>MLModel</code>. The <code>Recipe</code> provides detailed information
* about the observation data to use during training, and manipulations to perform on the observation data during
* training.
* </p>
* <note><title>Note</title>
* <p>
* This parameter is provided as part of the verbose format.
* </p>
* </note>
*/
private String recipe;
/**
* <p>
* The schema used by all of the data files referenced by the <code>DataSource</code>.
* </p>
* <note><title>Note</title>
* <p>
* This parameter is provided as part of the verbose format.
* </p>
* </note>
*/
private String schema;
/**
* <p>
* The MLModel ID<?oxy_insert_start author="annbech" timestamp="20160328T151251-0700">,<?oxy_insert_end> which is
* same as the <code>MLModelId</code> in the request.
* </p>
*
* @param mLModelId
* The MLModel ID<?oxy_insert_start author="annbech" timestamp="20160328T151251-0700">,<?oxy_insert_end>
* which is same as the <code>MLModelId</code> in the request.
*/
public void setMLModelId(String mLModelId) {
this.mLModelId = mLModelId;
}
/**
* <p>
* The MLModel ID<?oxy_insert_start author="annbech" timestamp="20160328T151251-0700">,<?oxy_insert_end> which is
* same as the <code>MLModelId</code> in the request.
* </p>
*
* @return The MLModel ID<?oxy_insert_start author="annbech" timestamp="20160328T151251-0700">,<?oxy_insert_end>
* which is same as the <code>MLModelId</code> in the request.
*/
public String getMLModelId() {
return this.mLModelId;
}
/**
* <p>
* The MLModel ID<?oxy_insert_start author="annbech" timestamp="20160328T151251-0700">,<?oxy_insert_end> which is
* same as the <code>MLModelId</code> in the request.
* </p>
*
* @param mLModelId
* The MLModel ID<?oxy_insert_start author="annbech" timestamp="20160328T151251-0700">,<?oxy_insert_end>
* which is same as the <code>MLModelId</code> in the request.
* @return Returns a reference to this object so that method calls can be chained together.
*/
public GetMLModelResult withMLModelId(String mLModelId) {
setMLModelId(mLModelId);
return this;
}
/**
* <p>
* The ID of the training <code>DataSource</code>.
* </p>
*
* @param trainingDataSourceId
* The ID of the training <code>DataSource</code>.
*/
public void setTrainingDataSourceId(String trainingDataSourceId) {
this.trainingDataSourceId = trainingDataSourceId;
}
/**
* <p>
* The ID of the training <code>DataSource</code>.
* </p>
*
* @return The ID of the training <code>DataSource</code>.
*/
public String getTrainingDataSourceId() {
return this.trainingDataSourceId;
}
/**
* <p>
* The ID of the training <code>DataSource</code>.
* </p>
*
* @param trainingDataSourceId
* The ID of the training <code>DataSource</code>.
* @return Returns a reference to this object so that method calls can be chained together.
*/
public GetMLModelResult withTrainingDataSourceId(String trainingDataSourceId) {
setTrainingDataSourceId(trainingDataSourceId);
return this;
}
/**
* <p>
* The AWS user account from which the <code>MLModel</code> was created. The account type can be either an AWS root
* account or an AWS Identity and Access Management (IAM) user account.
* </p>
*
* @param createdByIamUser
* The AWS user account from which the <code>MLModel</code> 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;
}
/**
* <p>
* The AWS user account from which the <code>MLModel</code> was created. The account type can be either an AWS root
* account or an AWS Identity and Access Management (IAM) user account.
* </p>
*
* @return The AWS user account from which the <code>MLModel</code> 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;
}
/**
* <p>
* The AWS user account from which the <code>MLModel</code> was created. The account type can be either an AWS root
* account or an AWS Identity and Access Management (IAM) user account.
* </p>
*
* @param createdByIamUser
* The AWS user account from which the <code>MLModel</code> 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 GetMLModelResult withCreatedByIamUser(String createdByIamUser) {
setCreatedByIamUser(createdByIamUser);
return this;
}
/**
* <p>
* The time that the <code>MLModel</code> was created. The time is expressed in epoch time.
* </p>
*
* @param createdAt
* The time that the <code>MLModel</code> was created. The time is expressed in epoch time.
*/
public void setCreatedAt(java.util.Date createdAt) {
this.createdAt = createdAt;
}
/**
* <p>
* The time that the <code>MLModel</code> was created. The time is expressed in epoch time.
* </p>
*
* @return The time that the <code>MLModel</code> was created. The time is expressed in epoch time.
*/
public java.util.Date getCreatedAt() {
return this.createdAt;
}
/**
* <p>
* The time that the <code>MLModel</code> was created. The time is expressed in epoch time.
* </p>
*
* @param createdAt
* The time that the <code>MLModel</code> 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 GetMLModelResult withCreatedAt(java.util.Date createdAt) {
setCreatedAt(createdAt);
return this;
}
/**
* <p>
* The time of the most recent edit to the <code>MLModel</code>. The time is expressed in epoch time.
* </p>
*
* @param lastUpdatedAt
* The time of the most recent edit to the <code>MLModel</code>. The time is expressed in epoch time.
*/
public void setLastUpdatedAt(java.util.Date lastUpdatedAt) {
this.lastUpdatedAt = lastUpdatedAt;
}
/**
* <p>
* The time of the most recent edit to the <code>MLModel</code>. The time is expressed in epoch time.
* </p>
*
* @return The time of the most recent edit to the <code>MLModel</code>. The time is expressed in epoch time.
*/
public java.util.Date getLastUpdatedAt() {
return this.lastUpdatedAt;
}
/**
* <p>
* The time of the most recent edit to the <code>MLModel</code>. The time is expressed in epoch time.
* </p>
*
* @param lastUpdatedAt
* The time of the most recent edit to the <code>MLModel</code>. The time is expressed in epoch time.
* @return Returns a reference to this object so that method calls can be chained together.
*/
public GetMLModelResult withLastUpdatedAt(java.util.Date lastUpdatedAt) {
setLastUpdatedAt(lastUpdatedAt);
return this;
}
/**
* <p>
* A user-supplied name or description of the <code>MLModel</code>.
* </p>
*
* @param name
* A user-supplied name or description of the <code>MLModel</code>.
*/
public void setName(String name) {
this.name = name;
}
/**
* <p>
* A user-supplied name or description of the <code>MLModel</code>.
* </p>
*
* @return A user-supplied name or description of the <code>MLModel</code>.
*/
public String getName() {
return this.name;
}
/**
* <p>
* A user-supplied name or description of the <code>MLModel</code>.
* </p>
*
* @param name
* A user-supplied name or description of the <code>MLModel</code>.
* @return Returns a reference to this object so that method calls can be chained together.
*/
public GetMLModelResult withName(String name) {
setName(name);
return this;
}
/**
* <p>
* The current status of the <code>MLModel</code>. This element can have one of the following values:
* </p>
* <ul>
* <li> <code>PENDING</code> - Amazon Machine Learning (Amazon ML) submitted a request to describe a
* <code>MLModel</code>.</li>
* <li> <code>INPROGRESS</code> - The request is processing.</li>
* <li> <code>FAILED</code> - The request did not run to completion. The ML model isn't usable.</li>
* <li> <code>COMPLETED</code> - The request completed successfully.</li>
* <li> <code>DELETED</code> - The <code>MLModel</code> is marked as deleted. It isn't usable.</li>
* </ul>
*
* @param status
* The current status of the <code>MLModel</code>. This element can have one of the following values:</p>
* <ul>
* <li> <code>PENDING</code> - Amazon Machine Learning (Amazon ML) submitted a request to describe a
* <code>MLModel</code>.</li>
* <li> <code>INPROGRESS</code> - The request is processing.</li>
* <li> <code>FAILED</code> - The request did not run to completion. The ML model isn't usable.</li>
* <li> <code>COMPLETED</code> - The request completed successfully.</li>
* <li> <code>DELETED</code> - The <code>MLModel</code> is marked as deleted. It isn't usable.</li>
* @see EntityStatus
*/
public void setStatus(String status) {
this.status = status;
}
/**
* <p>
* The current status of the <code>MLModel</code>. This element can have one of the following values:
* </p>
* <ul>
* <li> <code>PENDING</code> - Amazon Machine Learning (Amazon ML) submitted a request to describe a
* <code>MLModel</code>.</li>
* <li> <code>INPROGRESS</code> - The request is processing.</li>
* <li> <code>FAILED</code> - The request did not run to completion. The ML model isn't usable.</li>
* <li> <code>COMPLETED</code> - The request completed successfully.</li>
* <li> <code>DELETED</code> - The <code>MLModel</code> is marked as deleted. It isn't usable.</li>
* </ul>
*
* @return The current status of the <code>MLModel</code>. This element can have one of the following values:</p>
* <ul>
* <li> <code>PENDING</code> - Amazon Machine Learning (Amazon ML) submitted a request to describe a
* <code>MLModel</code>.</li>
* <li> <code>INPROGRESS</code> - The request is processing.</li>
* <li> <code>FAILED</code> - The request did not run to completion. The ML model isn't usable.</li>
* <li> <code>COMPLETED</code> - The request completed successfully.</li>
* <li> <code>DELETED</code> - The <code>MLModel</code> is marked as deleted. It isn't usable.</li>
* @see EntityStatus
*/
public String getStatus() {
return this.status;
}
/**
* <p>
* The current status of the <code>MLModel</code>. This element can have one of the following values:
* </p>
* <ul>
* <li> <code>PENDING</code> - Amazon Machine Learning (Amazon ML) submitted a request to describe a
* <code>MLModel</code>.</li>
* <li> <code>INPROGRESS</code> - The request is processing.</li>
* <li> <code>FAILED</code> - The request did not run to completion. The ML model isn't usable.</li>
* <li> <code>COMPLETED</code> - The request completed successfully.</li>
* <li> <code>DELETED</code> - The <code>MLModel</code> is marked as deleted. It isn't usable.</li>
* </ul>
*
* @param status
* The current status of the <code>MLModel</code>. This element can have one of the following values:</p>
* <ul>
* <li> <code>PENDING</code> - Amazon Machine Learning (Amazon ML) submitted a request to describe a
* <code>MLModel</code>.</li>
* <li> <code>INPROGRESS</code> - The request is processing.</li>
* <li> <code>FAILED</code> - The request did not run to completion. The ML model isn't usable.</li>
* <li> <code>COMPLETED</code> - The request completed successfully.</li>
* <li> <code>DELETED</code> - The <code>MLModel</code> is marked as deleted. It isn't usable.</li>
* @return Returns a reference to this object so that method calls can be chained together.
* @see EntityStatus
*/
public GetMLModelResult withStatus(String status) {
setStatus(status);
return this;
}
/**
* <p>
* The current status of the <code>MLModel</code>. This element can have one of the following values:
* </p>
* <ul>
* <li> <code>PENDING</code> - Amazon Machine Learning (Amazon ML) submitted a request to describe a
* <code>MLModel</code>.</li>
* <li> <code>INPROGRESS</code> - The request is processing.</li>
* <li> <code>FAILED</code> - The request did not run to completion. The ML model isn't usable.</li>
* <li> <code>COMPLETED</code> - The request completed successfully.</li>
* <li> <code>DELETED</code> - The <code>MLModel</code> is marked as deleted. It isn't usable.</li>
* </ul>
*
* @param status
* The current status of the <code>MLModel</code>. This element can have one of the following values:</p>
* <ul>
* <li> <code>PENDING</code> - Amazon Machine Learning (Amazon ML) submitted a request to describe a
* <code>MLModel</code>.</li>
* <li> <code>INPROGRESS</code> - The request is processing.</li>
* <li> <code>FAILED</code> - The request did not run to completion. The ML model isn't usable.</li>
* <li> <code>COMPLETED</code> - The request completed successfully.</li>
* <li> <code>DELETED</code> - The <code>MLModel</code> is marked as deleted. It isn't usable.</li>
* @see EntityStatus
*/
public void setStatus(EntityStatus status) {
this.status = status.toString();
}
/**
* <p>
* The current status of the <code>MLModel</code>. This element can have one of the following values:
* </p>
* <ul>
* <li> <code>PENDING</code> - Amazon Machine Learning (Amazon ML) submitted a request to describe a
* <code>MLModel</code>.</li>
* <li> <code>INPROGRESS</code> - The request is processing.</li>
* <li> <code>FAILED</code> - The request did not run to completion. The ML model isn't usable.</li>
* <li> <code>COMPLETED</code> - The request completed successfully.</li>
* <li> <code>DELETED</code> - The <code>MLModel</code> is marked as deleted. It isn't usable.</li>
* </ul>
*
* @param status
* The current status of the <code>MLModel</code>. This element can have one of the following values:</p>
* <ul>
* <li> <code>PENDING</code> - Amazon Machine Learning (Amazon ML) submitted a request to describe a
* <code>MLModel</code>.</li>
* <li> <code>INPROGRESS</code> - The request is processing.</li>
* <li> <code>FAILED</code> - The request did not run to completion. The ML model isn't usable.</li>
* <li> <code>COMPLETED</code> - The request completed successfully.</li>
* <li> <code>DELETED</code> - The <code>MLModel</code> is marked as deleted. It isn't usable.</li>
* @return Returns a reference to this object so that method calls can be chained together.
* @see EntityStatus
*/
public GetMLModelResult withStatus(EntityStatus status) {
setStatus(status);
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 GetMLModelResult withSizeInBytes(Long sizeInBytes) {
setSizeInBytes(sizeInBytes);
return this;
}
/**
* <p>
* The current endpoint of the <code>MLModel</code>
* </p>
*
* @param endpointInfo
* The current endpoint of the <code>MLModel</code>
*/
public void setEndpointInfo(RealtimeEndpointInfo endpointInfo) {
this.endpointInfo = endpointInfo;
}
/**
* <p>
* The current endpoint of the <code>MLModel</code>
* </p>
*
* @return The current endpoint of the <code>MLModel</code>
*/
public RealtimeEndpointInfo getEndpointInfo() {
return this.endpointInfo;
}
/**
* <p>
* The current endpoint of the <code>MLModel</code>
* </p>
*
* @param endpointInfo
* The current endpoint of the <code>MLModel</code>
* @return Returns a reference to this object so that method calls can be chained together.
*/
public GetMLModelResult withEndpointInfo(RealtimeEndpointInfo endpointInfo) {
setEndpointInfo(endpointInfo);
return this;
}
/**
* <p>
* A list of the training parameters in the <code>MLModel</code>. The list is implemented as a map of key-value
* pairs.
* </p>
* <p>
* The following is the current set of training parameters:
* </p>
* <ul>
* <li>
* <p>
* <code>sgd.maxMLModelSizeInBytes</code> - The maximum allowed size of the model. Depending on the input data, the
* size of the model might affect its performance.
* </p>
* <p>
* The value is an integer that ranges from <code>100000</code> to <code>2147483648</code>. The default value is
* <code>33554432</code>.
* </p>
* </li>
* <li>
* <p>
* <code>sgd.maxPasses</code> - The number of times that the training process traverses the observations to build
* the <code>MLModel</code>. The value is an integer that ranges from <code>1</code> to <code>10000</code>. The
* default value is <code>10</code>.
* </p>
* </li>
* <li>
* <p>
* <code>sgd.shuffleType</code> - Whether Amazon ML shuffles the training data. Shuffling data improves a model's
* ability to find the optimal solution for a variety of data types. The valid values are <code>auto</code> and
* <code>none</code>. The default value is <code>none</code>. We strongly recommend that you shuffle your data.
* </p>
* </li>
* <li>
* <p>
* <code>sgd.l1RegularizationAmount</code> - The coefficient regularization L1 norm. It controls overfitting the
* data by penalizing large coefficients. This tends to drive coefficients to zero, resulting in a sparse feature
* set. If you use this parameter, start by specifying a small value, such as <code>1.0E-08</code>.
* </p>
* <p>
* The value is a double that ranges from <code>0</code> to <code>MAX_DOUBLE</code>. The default is to not use L1
* normalization. This parameter can't be used when <code>L2</code> is specified. Use this parameter sparingly.
* </p>
* </li>
* <li>
* <p>
* <code>sgd.l2RegularizationAmount</code> - The coefficient regularization L2 norm. It controls overfitting the
* data by penalizing large coefficients. This tends to drive coefficients to small, nonzero values. If you use this
* parameter, start by specifying a small value, such as <code>1.0E-08</code>.
* </p>
* <p>
* The value is a double that ranges from <code>0</code> to <code>MAX_DOUBLE</code>. The default is to not use L2
* normalization. This parameter can't be used when <code>L1</code> is specified. Use this parameter sparingly.
* </p>
* </li>
* </ul>
*
* @return A list of the training parameters in the <code>MLModel</code>. The list is implemented as a map of
* key-value pairs.</p>
* <p>
* The following is the current set of training parameters:
* </p>
* <ul>
* <li>
* <p>
* <code>sgd.maxMLModelSizeInBytes</code> - The maximum allowed size of the model. Depending on the input
* data, the size of the model might affect its performance.
* </p>
* <p>
* The value is an integer that ranges from <code>100000</code> to <code>2147483648</code>. The default
* value is <code>33554432</code>.
* </p>
* </li>
* <li>
* <p>
* <code>sgd.maxPasses</code> - The number of times that the training process traverses the observations to
* build the <code>MLModel</code>. The value is an integer that ranges from <code>1</code> to
* <code>10000</code>. The default value is <code>10</code>.
* </p>
* </li>
* <li>
* <p>
* <code>sgd.shuffleType</code> - Whether Amazon ML shuffles the training data. Shuffling data improves a
* model's ability to find the optimal solution for a variety of data types. The valid values are
* <code>auto</code> and <code>none</code>. The default value is <code>none</code>. We strongly recommend
* that you shuffle your data.
* </p>
* </li>
* <li>
* <p>
* <code>sgd.l1RegularizationAmount</code> - The coefficient regularization L1 norm. It controls overfitting
* the data by penalizing large coefficients. This tends to drive coefficients to zero, resulting in a
* sparse feature set. If you use this parameter, start by specifying a small value, such as
* <code>1.0E-08</code>.
* </p>
* <p>
* The value is a double that ranges from <code>0</code> to <code>MAX_DOUBLE</code>. The default is to not
* use L1 normalization. This parameter can't be used when <code>L2</code> is specified. Use this parameter
* sparingly.
* </p>
* </li>
* <li>
* <p>
* <code>sgd.l2RegularizationAmount</code> - The coefficient regularization L2 norm. It controls overfitting
* the data by penalizing large coefficients. This tends to drive coefficients to small, nonzero values. If
* you use this parameter, start by specifying a small value, such as <code>1.0E-08</code>.
* </p>
* <p>
* The value is a double that ranges from <code>0</code> to <code>MAX_DOUBLE</code>. The default is to not
* use L2 normalization. This parameter can't be used when <code>L1</code> is specified. Use this parameter
* sparingly.
* </p>
* </li>
*/
public java.util.Map<String, String> getTrainingParameters() {
if (trainingParameters == null) {
trainingParameters = new com.amazonaws.internal.SdkInternalMap<String, String>();
}
return trainingParameters;
}
/**
* <p>
* A list of the training parameters in the <code>MLModel</code>. The list is implemented as a map of key-value
* pairs.
* </p>
* <p>
* The following is the current set of training parameters:
* </p>
* <ul>
* <li>
* <p>
* <code>sgd.maxMLModelSizeInBytes</code> - The maximum allowed size of the model. Depending on the input data, the
* size of the model might affect its performance.
* </p>
* <p>
* The value is an integer that ranges from <code>100000</code> to <code>2147483648</code>. The default value is
* <code>33554432</code>.
* </p>
* </li>
* <li>
* <p>
* <code>sgd.maxPasses</code> - The number of times that the training process traverses the observations to build
* the <code>MLModel</code>. The value is an integer that ranges from <code>1</code> to <code>10000</code>. The
* default value is <code>10</code>.
* </p>
* </li>
* <li>
* <p>
* <code>sgd.shuffleType</code> - Whether Amazon ML shuffles the training data. Shuffling data improves a model's
* ability to find the optimal solution for a variety of data types. The valid values are <code>auto</code> and
* <code>none</code>. The default value is <code>none</code>. We strongly recommend that you shuffle your data.
* </p>
* </li>
* <li>
* <p>
* <code>sgd.l1RegularizationAmount</code> - The coefficient regularization L1 norm. It controls overfitting the
* data by penalizing large coefficients. This tends to drive coefficients to zero, resulting in a sparse feature
* set. If you use this parameter, start by specifying a small value, such as <code>1.0E-08</code>.
* </p>
* <p>
* The value is a double that ranges from <code>0</code> to <code>MAX_DOUBLE</code>. The default is to not use L1
* normalization. This parameter can't be used when <code>L2</code> is specified. Use this parameter sparingly.
* </p>
* </li>
* <li>
* <p>
* <code>sgd.l2RegularizationAmount</code> - The coefficient regularization L2 norm. It controls overfitting the
* data by penalizing large coefficients. This tends to drive coefficients to small, nonzero values. If you use this
* parameter, start by specifying a small value, such as <code>1.0E-08</code>.
* </p>
* <p>
* The value is a double that ranges from <code>0</code> to <code>MAX_DOUBLE</code>. The default is to not use L2
* normalization. This parameter can't be used when <code>L1</code> is specified. Use this parameter sparingly.
* </p>
* </li>
* </ul>
*
* @param trainingParameters
* A list of the training parameters in the <code>MLModel</code>. The list is implemented as a map of
* key-value pairs.</p>
* <p>
* The following is the current set of training parameters:
* </p>
* <ul>
* <li>
* <p>
* <code>sgd.maxMLModelSizeInBytes</code> - The maximum allowed size of the model. Depending on the input
* data, the size of the model might affect its performance.
* </p>
* <p>
* The value is an integer that ranges from <code>100000</code> to <code>2147483648</code>. The default value
* is <code>33554432</code>.
* </p>
* </li>
* <li>
* <p>
* <code>sgd.maxPasses</code> - The number of times that the training process traverses the observations to
* build the <code>MLModel</code>. The value is an integer that ranges from <code>1</code> to
* <code>10000</code>. The default value is <code>10</code>.
* </p>
* </li>
* <li>
* <p>
* <code>sgd.shuffleType</code> - Whether Amazon ML shuffles the training data. Shuffling data improves a
* model's ability to find the optimal solution for a variety of data types. The valid values are
* <code>auto</code> and <code>none</code>. The default value is <code>none</code>. We strongly recommend
* that you shuffle your data.
* </p>
* </li>
* <li>
* <p>
* <code>sgd.l1RegularizationAmount</code> - The coefficient regularization L1 norm. It controls overfitting
* the data by penalizing large coefficients. This tends to drive coefficients to zero, resulting in a sparse
* feature set. If you use this parameter, start by specifying a small value, such as <code>1.0E-08</code>.
* </p>
* <p>
* The value is a double that ranges from <code>0</code> to <code>MAX_DOUBLE</code>. The default is to not
* use L1 normalization. This parameter can't be used when <code>L2</code> is specified. Use this parameter
* sparingly.
* </p>
* </li>
* <li>
* <p>
* <code>sgd.l2RegularizationAmount</code> - The coefficient regularization L2 norm. It controls overfitting
* the data by penalizing large coefficients. This tends to drive coefficients to small, nonzero values. If
* you use this parameter, start by specifying a small value, such as <code>1.0E-08</code>.
* </p>
* <p>
* The value is a double that ranges from <code>0</code> to <code>MAX_DOUBLE</code>. The default is to not
* use L2 normalization. This parameter can't be used when <code>L1</code> is specified. Use this parameter
* sparingly.
* </p>
* </li>
*/
public void setTrainingParameters(java.util.Map<String, String> trainingParameters) {
this.trainingParameters = trainingParameters == null ? null : new com.amazonaws.internal.SdkInternalMap<String, String>(trainingParameters);
}
/**
* <p>
* A list of the training parameters in the <code>MLModel</code>. The list is implemented as a map of key-value
* pairs.
* </p>
* <p>
* The following is the current set of training parameters:
* </p>
* <ul>
* <li>
* <p>
* <code>sgd.maxMLModelSizeInBytes</code> - The maximum allowed size of the model. Depending on the input data, the
* size of the model might affect its performance.
* </p>
* <p>
* The value is an integer that ranges from <code>100000</code> to <code>2147483648</code>. The default value is
* <code>33554432</code>.
* </p>
* </li>
* <li>
* <p>
* <code>sgd.maxPasses</code> - The number of times that the training process traverses the observations to build
* the <code>MLModel</code>. The value is an integer that ranges from <code>1</code> to <code>10000</code>. The
* default value is <code>10</code>.
* </p>
* </li>
* <li>
* <p>
* <code>sgd.shuffleType</code> - Whether Amazon ML shuffles the training data. Shuffling data improves a model's
* ability to find the optimal solution for a variety of data types. The valid values are <code>auto</code> and
* <code>none</code>. The default value is <code>none</code>. We strongly recommend that you shuffle your data.
* </p>
* </li>
* <li>
* <p>
* <code>sgd.l1RegularizationAmount</code> - The coefficient regularization L1 norm. It controls overfitting the
* data by penalizing large coefficients. This tends to drive coefficients to zero, resulting in a sparse feature
* set. If you use this parameter, start by specifying a small value, such as <code>1.0E-08</code>.
* </p>
* <p>
* The value is a double that ranges from <code>0</code> to <code>MAX_DOUBLE</code>. The default is to not use L1
* normalization. This parameter can't be used when <code>L2</code> is specified. Use this parameter sparingly.
* </p>
* </li>
* <li>
* <p>
* <code>sgd.l2RegularizationAmount</code> - The coefficient regularization L2 norm. It controls overfitting the
* data by penalizing large coefficients. This tends to drive coefficients to small, nonzero values. If you use this
* parameter, start by specifying a small value, such as <code>1.0E-08</code>.
* </p>
* <p>
* The value is a double that ranges from <code>0</code> to <code>MAX_DOUBLE</code>. The default is to not use L2
* normalization. This parameter can't be used when <code>L1</code> is specified. Use this parameter sparingly.
* </p>
* </li>
* </ul>
*
* @param trainingParameters
* A list of the training parameters in the <code>MLModel</code>. The list is implemented as a map of
* key-value pairs.</p>
* <p>
* The following is the current set of training parameters:
* </p>
* <ul>
* <li>
* <p>
* <code>sgd.maxMLModelSizeInBytes</code> - The maximum allowed size of the model. Depending on the input
* data, the size of the model might affect its performance.
* </p>
* <p>
* The value is an integer that ranges from <code>100000</code> to <code>2147483648</code>. The default value
* is <code>33554432</code>.
* </p>
* </li>
* <li>
* <p>
* <code>sgd.maxPasses</code> - The number of times that the training process traverses the observations to
* build the <code>MLModel</code>. The value is an integer that ranges from <code>1</code> to
* <code>10000</code>. The default value is <code>10</code>.
* </p>
* </li>
* <li>
* <p>
* <code>sgd.shuffleType</code> - Whether Amazon ML shuffles the training data. Shuffling data improves a
* model's ability to find the optimal solution for a variety of data types. The valid values are
* <code>auto</code> and <code>none</code>. The default value is <code>none</code>. We strongly recommend
* that you shuffle your data.
* </p>
* </li>
* <li>
* <p>
* <code>sgd.l1RegularizationAmount</code> - The coefficient regularization L1 norm. It controls overfitting
* the data by penalizing large coefficients. This tends to drive coefficients to zero, resulting in a sparse
* feature set. If you use this parameter, start by specifying a small value, such as <code>1.0E-08</code>.
* </p>
* <p>
* The value is a double that ranges from <code>0</code> to <code>MAX_DOUBLE</code>. The default is to not
* use L1 normalization. This parameter can't be used when <code>L2</code> is specified. Use this parameter
* sparingly.
* </p>
* </li>
* <li>
* <p>
* <code>sgd.l2RegularizationAmount</code> - The coefficient regularization L2 norm. It controls overfitting
* the data by penalizing large coefficients. This tends to drive coefficients to small, nonzero values. If
* you use this parameter, start by specifying a small value, such as <code>1.0E-08</code>.
* </p>
* <p>
* The value is a double that ranges from <code>0</code> to <code>MAX_DOUBLE</code>. The default is to not
* use L2 normalization. This parameter can't be used when <code>L1</code> is specified. Use this parameter
* sparingly.
* </p>
* </li>
* @return Returns a reference to this object so that method calls can be chained together.
*/
public GetMLModelResult withTrainingParameters(java.util.Map<String, String> trainingParameters) {
setTrainingParameters(trainingParameters);
return this;
}
public GetMLModelResult addTrainingParametersEntry(String key, String value) {
if (null == this.trainingParameters) {
this.trainingParameters = new com.amazonaws.internal.SdkInternalMap<String, String>();
}
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 GetMLModelResult clearTrainingParametersEntries() {
this.trainingParameters = null;
return this;
}
/**
* <p>
* The location of the data file or directory in Amazon Simple Storage Service (Amazon S3).
* </p>
*
* @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;
}
/**
* <p>
* The location of the data file or directory in Amazon Simple Storage Service (Amazon S3).
* </p>
*
* @return The location of the data file or directory in Amazon Simple Storage Service (Amazon S3).
*/
public String getInputDataLocationS3() {
return this.inputDataLocationS3;
}
/**
* <p>
* The location of the data file or directory in Amazon Simple Storage Service (Amazon S3).
* </p>
*
* @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 GetMLModelResult withInputDataLocationS3(String inputDataLocationS3) {
setInputDataLocationS3(inputDataLocationS3);
return this;
}
/**
* <p>
* Identifies the <code>MLModel</code> category. The following are the available types:
* </p>
* <ul>
* <li>REGRESSION -- Produces a numeric result. For example, "What price should a house be listed at?"</li>
* <li>BINARY -- Produces one of two possible results. For example, "Is this an e-commerce website?"</li>
* <li>MULTICLASS -- Produces one of several possible results. For example,
* "Is this a HIGH, LOW or MEDIUM risk trade?"</li>
* </ul>
*
* @param mLModelType
* Identifies the <code>MLModel</code> category. The following are the available types: </p>
* <ul>
* <li>REGRESSION -- Produces a numeric result. For example, "What price should a house be listed at?"</li>
* <li>BINARY -- Produces one of two possible results. For example, "Is this an e-commerce website?"</li>
* <li>MULTICLASS -- Produces one of several possible results. For example,
* "Is this a HIGH, LOW or MEDIUM risk trade?"</li>
* @see MLModelType
*/
public void setMLModelType(String mLModelType) {
this.mLModelType = mLModelType;
}
/**
* <p>
* Identifies the <code>MLModel</code> category. The following are the available types:
* </p>
* <ul>
* <li>REGRESSION -- Produces a numeric result. For example, "What price should a house be listed at?"</li>
* <li>BINARY -- Produces one of two possible results. For example, "Is this an e-commerce website?"</li>
* <li>MULTICLASS -- Produces one of several possible results. For example,
* "Is this a HIGH, LOW or MEDIUM risk trade?"</li>
* </ul>
*
* @return Identifies the <code>MLModel</code> category. The following are the available types: </p>
* <ul>
* <li>REGRESSION -- Produces a numeric result. For example, "What price should a house be listed at?"</li>
* <li>BINARY -- Produces one of two possible results. For example, "Is this an e-commerce website?"</li>
* <li>MULTICLASS -- Produces one of several possible results. For example,
* "Is this a HIGH, LOW or MEDIUM risk trade?"</li>
* @see MLModelType
*/
public String getMLModelType() {
return this.mLModelType;
}
/**
* <p>
* Identifies the <code>MLModel</code> category. The following are the available types:
* </p>
* <ul>
* <li>REGRESSION -- Produces a numeric result. For example, "What price should a house be listed at?"</li>
* <li>BINARY -- Produces one of two possible results. For example, "Is this an e-commerce website?"</li>
* <li>MULTICLASS -- Produces one of several possible results. For example,
* "Is this a HIGH, LOW or MEDIUM risk trade?"</li>
* </ul>
*
* @param mLModelType
* Identifies the <code>MLModel</code> category. The following are the available types: </p>
* <ul>
* <li>REGRESSION -- Produces a numeric result. For example, "What price should a house be listed at?"</li>
* <li>BINARY -- Produces one of two possible results. For example, "Is this an e-commerce website?"</li>
* <li>MULTICLASS -- Produces one of several possible results. For example,
* "Is this a HIGH, LOW or MEDIUM risk trade?"</li>
* @return Returns a reference to this object so that method calls can be chained together.
* @see MLModelType
*/
public GetMLModelResult withMLModelType(String mLModelType) {
setMLModelType(mLModelType);
return this;
}
/**
* <p>
* Identifies the <code>MLModel</code> category. The following are the available types:
* </p>
* <ul>
* <li>REGRESSION -- Produces a numeric result. For example, "What price should a house be listed at?"</li>
* <li>BINARY -- Produces one of two possible results. For example, "Is this an e-commerce website?"</li>
* <li>MULTICLASS -- Produces one of several possible results. For example,
* "Is this a HIGH, LOW or MEDIUM risk trade?"</li>
* </ul>
*
* @param mLModelType
* Identifies the <code>MLModel</code> category. The following are the available types: </p>
* <ul>
* <li>REGRESSION -- Produces a numeric result. For example, "What price should a house be listed at?"</li>
* <li>BINARY -- Produces one of two possible results. For example, "Is this an e-commerce website?"</li>
* <li>MULTICLASS -- Produces one of several possible results. For example,
* "Is this a HIGH, LOW or MEDIUM risk trade?"</li>
* @see MLModelType
*/
public void setMLModelType(MLModelType mLModelType) {
this.mLModelType = mLModelType.toString();
}
/**
* <p>
* Identifies the <code>MLModel</code> category. The following are the available types:
* </p>
* <ul>
* <li>REGRESSION -- Produces a numeric result. For example, "What price should a house be listed at?"</li>
* <li>BINARY -- Produces one of two possible results. For example, "Is this an e-commerce website?"</li>
* <li>MULTICLASS -- Produces one of several possible results. For example,
* "Is this a HIGH, LOW or MEDIUM risk trade?"</li>
* </ul>
*
* @param mLModelType
* Identifies the <code>MLModel</code> category. The following are the available types: </p>
* <ul>
* <li>REGRESSION -- Produces a numeric result. For example, "What price should a house be listed at?"</li>
* <li>BINARY -- Produces one of two possible results. For example, "Is this an e-commerce website?"</li>
* <li>MULTICLASS -- Produces one of several possible results. For example,
* "Is this a HIGH, LOW or MEDIUM risk trade?"</li>
* @return Returns a reference to this object so that method calls can be chained together.
* @see MLModelType
*/
public GetMLModelResult withMLModelType(MLModelType mLModelType) {
setMLModelType(mLModelType);
return this;
}
/**
* <p>
* The scoring threshold is used in binary classification <code>MLModel</code><?oxy_insert_start author="laurama"
* timestamp="20160329T114851-0700"> <?oxy_insert_end>models. It marks the boundary between a positive prediction
* and a negative prediction.
* </p>
* <p>
* Output values greater than or equal to the threshold receive a positive result from the MLModel, such as
* <code>true</code>. Output values less than the threshold receive a negative response from the MLModel, such as
* <code>false</code>.
* </p>
*
* @param scoreThreshold
* The scoring threshold is used in binary classification <code>MLModel</code><?oxy_insert_start
* author="laurama" timestamp="20160329T114851-0700"> <?oxy_insert_end>models. It marks the boundary between
* a positive prediction and a negative prediction.</p>
* <p>
* Output values greater than or equal to the threshold receive a positive result from the MLModel, such as
* <code>true</code>. Output values less than the threshold receive a negative response from the MLModel,
* such as <code>false</code>.
*/
public void setScoreThreshold(Float scoreThreshold) {
this.scoreThreshold = scoreThreshold;
}
/**
* <p>
* The scoring threshold is used in binary classification <code>MLModel</code><?oxy_insert_start author="laurama"
* timestamp="20160329T114851-0700"> <?oxy_insert_end>models. It marks the boundary between a positive prediction
* and a negative prediction.
* </p>
* <p>
* Output values greater than or equal to the threshold receive a positive result from the MLModel, such as
* <code>true</code>. Output values less than the threshold receive a negative response from the MLModel, such as
* <code>false</code>.
* </p>
*
* @return The scoring threshold is used in binary classification <code>MLModel</code><?oxy_insert_start
* author="laurama" timestamp="20160329T114851-0700"> <?oxy_insert_end>models. It marks the boundary between
* a positive prediction and a negative prediction.</p>
* <p>
* Output values greater than or equal to the threshold receive a positive result from the MLModel, such as
* <code>true</code>. Output values less than the threshold receive a negative response from the MLModel,
* such as <code>false</code>.
*/
public Float getScoreThreshold() {
return this.scoreThreshold;
}
/**
* <p>
* The scoring threshold is used in binary classification <code>MLModel</code><?oxy_insert_start author="laurama"
* timestamp="20160329T114851-0700"> <?oxy_insert_end>models. It marks the boundary between a positive prediction
* and a negative prediction.
* </p>
* <p>
* Output values greater than or equal to the threshold receive a positive result from the MLModel, such as
* <code>true</code>. Output values less than the threshold receive a negative response from the MLModel, such as
* <code>false</code>.
* </p>
*
* @param scoreThreshold
* The scoring threshold is used in binary classification <code>MLModel</code><?oxy_insert_start
* author="laurama" timestamp="20160329T114851-0700"> <?oxy_insert_end>models. It marks the boundary between
* a positive prediction and a negative prediction.</p>
* <p>
* Output values greater than or equal to the threshold receive a positive result from the MLModel, such as
* <code>true</code>. Output values less than the threshold receive a negative response from the MLModel,
* such as <code>false</code>.
* @return Returns a reference to this object so that method calls can be chained together.
*/
public GetMLModelResult withScoreThreshold(Float scoreThreshold) {
setScoreThreshold(scoreThreshold);
return this;
}
/**
* <p>
* The time of the most recent edit to the <code>ScoreThreshold</code>. The time is expressed in epoch time.
* </p>
*
* @param scoreThresholdLastUpdatedAt
* The time of the most recent edit to the <code>ScoreThreshold</code>. The time is expressed in epoch time.
*/
public void setScoreThresholdLastUpdatedAt(java.util.Date scoreThresholdLastUpdatedAt) {
this.scoreThresholdLastUpdatedAt = scoreThresholdLastUpdatedAt;
}
/**
* <p>
* The time of the most recent edit to the <code>ScoreThreshold</code>. The time is expressed in epoch time.
* </p>
*
* @return The time of the most recent edit to the <code>ScoreThreshold</code>. The time is expressed in epoch time.
*/
public java.util.Date getScoreThresholdLastUpdatedAt() {
return this.scoreThresholdLastUpdatedAt;
}
/**
* <p>
* The time of the most recent edit to the <code>ScoreThreshold</code>. The time is expressed in epoch time.
* </p>
*
* @param scoreThresholdLastUpdatedAt
* The time of the most recent edit to the <code>ScoreThreshold</code>. The time is expressed in epoch time.
* @return Returns a reference to this object so that method calls can be chained together.
*/
public GetMLModelResult withScoreThresholdLastUpdatedAt(java.util.Date scoreThresholdLastUpdatedAt) {
setScoreThresholdLastUpdatedAt(scoreThresholdLastUpdatedAt);
return this;
}
/**
* <p>
* A link to the file that contains logs of the <code>CreateMLModel</code> operation.
* </p>
*
* @param logUri
* A link to the file that contains logs of the <code>CreateMLModel</code> operation.
*/
public void setLogUri(String logUri) {
this.logUri = logUri;
}
/**
* <p>
* A link to the file that contains logs of the <code>CreateMLModel</code> operation.
* </p>
*
* @return A link to the file that contains logs of the <code>CreateMLModel</code> operation.
*/
public String getLogUri() {
return this.logUri;
}
/**
* <p>
* A link to the file that contains logs of the <code>CreateMLModel</code> operation.
* </p>
*
* @param logUri
* A link to the file that contains logs of the <code>CreateMLModel</code> operation.
* @return Returns a reference to this object so that method calls can be chained together.
*/
public GetMLModelResult withLogUri(String logUri) {
setLogUri(logUri);
return this;
}
/**
* <p>
* A description of the most recent details about accessing the <code>MLModel</code>.
* </p>
*
* @param message
* A description of the most recent details about accessing the <code>MLModel</code>.
*/
public void setMessage(String message) {
this.message = message;
}
/**
* <p>
* A description of the most recent details about accessing the <code>MLModel</code>.
* </p>
*
* @return A description of the most recent details about accessing the <code>MLModel</code>.
*/
public String getMessage() {
return this.message;
}
/**
* <p>
* A description of the most recent details about accessing the <code>MLModel</code>.
* </p>
*
* @param message
* A description of the most recent details about accessing the <code>MLModel</code>.
* @return Returns a reference to this object so that method calls can be chained together.
*/
public GetMLModelResult withMessage(String message) {
setMessage(message);
return this;
}
/**
* <p>
* The approximate CPU time in milliseconds that Amazon Machine Learning spent processing the <code>MLModel</code>,
* normalized and scaled on computation resources. <code>ComputeTime</code> is only available if the
* <code>MLModel</code> is in the <code>COMPLETED</code> state.
* </p>
*
* @param computeTime
* The approximate CPU time in milliseconds that Amazon Machine Learning spent processing the
* <code>MLModel</code>, normalized and scaled on computation resources. <code>ComputeTime</code> is only
* available if the <code>MLModel</code> is in the <code>COMPLETED</code> state.
*/
public void setComputeTime(Long computeTime) {
this.computeTime = computeTime;
}
/**
* <p>
* The approximate CPU time in milliseconds that Amazon Machine Learning spent processing the <code>MLModel</code>,
* normalized and scaled on computation resources. <code>ComputeTime</code> is only available if the
* <code>MLModel</code> is in the <code>COMPLETED</code> state.
* </p>
*
* @return The approximate CPU time in milliseconds that Amazon Machine Learning spent processing the
* <code>MLModel</code>, normalized and scaled on computation resources. <code>ComputeTime</code> is only
* available if the <code>MLModel</code> is in the <code>COMPLETED</code> state.
*/
public Long getComputeTime() {
return this.computeTime;
}
/**
* <p>
* The approximate CPU time in milliseconds that Amazon Machine Learning spent processing the <code>MLModel</code>,
* normalized and scaled on computation resources. <code>ComputeTime</code> is only available if the
* <code>MLModel</code> is in the <code>COMPLETED</code> state.
* </p>
*
* @param computeTime
* The approximate CPU time in milliseconds that Amazon Machine Learning spent processing the
* <code>MLModel</code>, normalized and scaled on computation resources. <code>ComputeTime</code> is only
* available if the <code>MLModel</code> is in the <code>COMPLETED</code> state.
* @return Returns a reference to this object so that method calls can be chained together.
*/
public GetMLModelResult withComputeTime(Long computeTime) {
setComputeTime(computeTime);
return this;
}
/**
* <p>
* The epoch time when Amazon Machine Learning marked the <code>MLModel</code> as <code>COMPLETED</code> or
* <code>FAILED</code>. <code>FinishedAt</code> is only available when the <code>MLModel</code> is in the
* <code>COMPLETED</code> or <code>FAILED</code> state.
* </p>
*
* @param finishedAt
* The epoch time when Amazon Machine Learning marked the <code>MLModel</code> as <code>COMPLETED</code> or
* <code>FAILED</code>. <code>FinishedAt</code> is only available when the <code>MLModel</code> is in the
* <code>COMPLETED</code> or <code>FAILED</code> state.
*/
public void setFinishedAt(java.util.Date finishedAt) {
this.finishedAt = finishedAt;
}
/**
* <p>
* The epoch time when Amazon Machine Learning marked the <code>MLModel</code> as <code>COMPLETED</code> or
* <code>FAILED</code>. <code>FinishedAt</code> is only available when the <code>MLModel</code> is in the
* <code>COMPLETED</code> or <code>FAILED</code> state.
* </p>
*
* @return The epoch time when Amazon Machine Learning marked the <code>MLModel</code> as <code>COMPLETED</code> or
* <code>FAILED</code>. <code>FinishedAt</code> is only available when the <code>MLModel</code> is in the
* <code>COMPLETED</code> or <code>FAILED</code> state.
*/
public java.util.Date getFinishedAt() {
return this.finishedAt;
}
/**
* <p>
* The epoch time when Amazon Machine Learning marked the <code>MLModel</code> as <code>COMPLETED</code> or
* <code>FAILED</code>. <code>FinishedAt</code> is only available when the <code>MLModel</code> is in the
* <code>COMPLETED</code> or <code>FAILED</code> state.
* </p>
*
* @param finishedAt
* The epoch time when Amazon Machine Learning marked the <code>MLModel</code> as <code>COMPLETED</code> or
* <code>FAILED</code>. <code>FinishedAt</code> is only available when the <code>MLModel</code> is in the
* <code>COMPLETED</code> or <code>FAILED</code> state.
* @return Returns a reference to this object so that method calls can be chained together.
*/
public GetMLModelResult withFinishedAt(java.util.Date finishedAt) {
setFinishedAt(finishedAt);
return this;
}
/**
* <p>
* The epoch time when Amazon Machine Learning marked the <code>MLModel</code> as <code>INPROGRESS</code>.
* <code>StartedAt</code> isn't available if the <code>MLModel</code> is in the <code>PENDING</code> state.
* </p>
*
* @param startedAt
* The epoch time when Amazon Machine Learning marked the <code>MLModel</code> as <code>INPROGRESS</code>.
* <code>StartedAt</code> isn't available if the <code>MLModel</code> is in the <code>PENDING</code> state.
*/
public void setStartedAt(java.util.Date startedAt) {
this.startedAt = startedAt;
}
/**
* <p>
* The epoch time when Amazon Machine Learning marked the <code>MLModel</code> as <code>INPROGRESS</code>.
* <code>StartedAt</code> isn't available if the <code>MLModel</code> is in the <code>PENDING</code> state.
* </p>
*
* @return The epoch time when Amazon Machine Learning marked the <code>MLModel</code> as <code>INPROGRESS</code>.
* <code>StartedAt</code> isn't available if the <code>MLModel</code> is in the <code>PENDING</code> state.
*/
public java.util.Date getStartedAt() {
return this.startedAt;
}
/**
* <p>
* The epoch time when Amazon Machine Learning marked the <code>MLModel</code> as <code>INPROGRESS</code>.
* <code>StartedAt</code> isn't available if the <code>MLModel</code> is in the <code>PENDING</code> state.
* </p>
*
* @param startedAt
* The epoch time when Amazon Machine Learning marked the <code>MLModel</code> as <code>INPROGRESS</code>.
* <code>StartedAt</code> isn't available if the <code>MLModel</code> is in the <code>PENDING</code> state.
* @return Returns a reference to this object so that method calls can be chained together.
*/
public GetMLModelResult withStartedAt(java.util.Date startedAt) {
setStartedAt(startedAt);
return this;
}
/**
* <p>
* The recipe to use when training the <code>MLModel</code>. The <code>Recipe</code> provides detailed information
* about the observation data to use during training, and manipulations to perform on the observation data during
* training.
* </p>
* <note><title>Note</title>
* <p>
* This parameter is provided as part of the verbose format.
* </p>
* </note>
*
* @param recipe
* The recipe to use when training the <code>MLModel</code>. The <code>Recipe</code> provides detailed
* information about the observation data to use during training, and manipulations to perform on the
* observation data during training.</p> <note><title>Note</title>
* <p>
* This parameter is provided as part of the verbose format.
* </p>
*/
public void setRecipe(String recipe) {
this.recipe = recipe;
}
/**
* <p>
* The recipe to use when training the <code>MLModel</code>. The <code>Recipe</code> provides detailed information
* about the observation data to use during training, and manipulations to perform on the observation data during
* training.
* </p>
* <note><title>Note</title>
* <p>
* This parameter is provided as part of the verbose format.
* </p>
* </note>
*
* @return The recipe to use when training the <code>MLModel</code>. The <code>Recipe</code> provides detailed
* information about the observation data to use during training, and manipulations to perform on the
* observation data during training.</p> <note><title>Note</title>
* <p>
* This parameter is provided as part of the verbose format.
* </p>
*/
public String getRecipe() {
return this.recipe;
}
/**
* <p>
* The recipe to use when training the <code>MLModel</code>. The <code>Recipe</code> provides detailed information
* about the observation data to use during training, and manipulations to perform on the observation data during
* training.
* </p>
* <note><title>Note</title>
* <p>
* This parameter is provided as part of the verbose format.
* </p>
* </note>
*
* @param recipe
* The recipe to use when training the <code>MLModel</code>. The <code>Recipe</code> provides detailed
* information about the observation data to use during training, and manipulations to perform on the
* observation data during training.</p> <note><title>Note</title>
* <p>
* This parameter is provided as part of the verbose format.
* </p>
* @return Returns a reference to this object so that method calls can be chained together.
*/
public GetMLModelResult withRecipe(String recipe) {
setRecipe(recipe);
return this;
}
/**
* <p>
* The schema used by all of the data files referenced by the <code>DataSource</code>.
* </p>
* <note><title>Note</title>
* <p>
* This parameter is provided as part of the verbose format.
* </p>
* </note>
*
* @param schema
* The schema used by all of the data files referenced by the <code>DataSource</code>.</p>
* <note><title>Note</title>
* <p>
* This parameter is provided as part of the verbose format.
* </p>
*/
public void setSchema(String schema) {
this.schema = schema;
}
/**
* <p>
* The schema used by all of the data files referenced by the <code>DataSource</code>.
* </p>
* <note><title>Note</title>
* <p>
* This parameter is provided as part of the verbose format.
* </p>
* </note>
*
* @return The schema used by all of the data files referenced by the <code>DataSource</code>.</p>
* <note><title>Note</title>
* <p>
* This parameter is provided as part of the verbose format.
* </p>
*/
public String getSchema() {
return this.schema;
}
/**
* <p>
* The schema used by all of the data files referenced by the <code>DataSource</code>.
* </p>
* <note><title>Note</title>
* <p>
* This parameter is provided as part of the verbose format.
* </p>
* </note>
*
* @param schema
* The schema used by all of the data files referenced by the <code>DataSource</code>.</p>
* <note><title>Note</title>
* <p>
* This parameter is provided as part of the verbose format.
* </p>
* @return Returns a reference to this object so that method calls can be chained together.
*/
public GetMLModelResult withSchema(String schema) {
setSchema(schema);
return this;
}
/**
* Returns a string representation of this object; useful for testing and debugging.
*
* @return A string representation of this object.
*
* @see java.lang.Object#toString()
*/
@Override
public String toString() {
StringBuilder sb = new StringBuilder();
sb.append("{");
if (getMLModelId() != null)
sb.append("MLModelId: ").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 (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 (getLogUri() != null)
sb.append("LogUri: ").append(getLogUri()).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()).append(",");
if (getRecipe() != null)
sb.append("Recipe: ").append(getRecipe()).append(",");
if (getSchema() != null)
sb.append("Schema: ").append(getSchema());
sb.append("}");
return sb.toString();
}
@Override
public boolean equals(Object obj) {
if (this == obj)
return true;
if (obj == null)
return false;
if (obj instanceof GetMLModelResult == false)
return false;
GetMLModelResult other = (GetMLModelResult) obj;
if (other.getMLModelId() == null ^ this.getMLModelId() == null)
return false;
if (other.getMLModelId() != null && other.getMLModelId().equals(this.getMLModelId()) == false)
return false;
if (other.getTrainingDataSourceId() == null ^ this.getTrainingDataSourceId() == null)
return false;
if (other.getTrainingDataSourceId() != null && other.getTrainingDataSourceId().equals(this.getTrainingDataSourceId()) == false)
return false;
if (other.getCreatedByIamUser() == null ^ this.getCreatedByIamUser() == null)
return false;
if (other.getCreatedByIamUser() != null && other.getCreatedByIamUser().equals(this.getCreatedByIamUser()) == false)
return false;
if (other.getCreatedAt() == null ^ this.getCreatedAt() == null)
return false;
if (other.getCreatedAt() != null && other.getCreatedAt().equals(this.getCreatedAt()) == false)
return false;
if (other.getLastUpdatedAt() == null ^ this.getLastUpdatedAt() == null)
return false;
if (other.getLastUpdatedAt() != null && other.getLastUpdatedAt().equals(this.getLastUpdatedAt()) == false)
return false;
if (other.getName() == null ^ this.getName() == null)
return false;
if (other.getName() != null && other.getName().equals(this.getName()) == false)
return false;
if (other.getStatus() == null ^ this.getStatus() == null)
return false;
if (other.getStatus() != null && other.getStatus().equals(this.getStatus()) == false)
return false;
if (other.getSizeInBytes() == null ^ this.getSizeInBytes() == null)
return false;
if (other.getSizeInBytes() != null && other.getSizeInBytes().equals(this.getSizeInBytes()) == false)
return false;
if (other.getEndpointInfo() == null ^ this.getEndpointInfo() == null)
return false;
if (other.getEndpointInfo() != null && other.getEndpointInfo().equals(this.getEndpointInfo()) == false)
return false;
if (other.getTrainingParameters() == null ^ this.getTrainingParameters() == null)
return false;
if (other.getTrainingParameters() != null && other.getTrainingParameters().equals(this.getTrainingParameters()) == false)
return false;
if (other.getInputDataLocationS3() == null ^ this.getInputDataLocationS3() == null)
return false;
if (other.getInputDataLocationS3() != null && other.getInputDataLocationS3().equals(this.getInputDataLocationS3()) == false)
return false;
if (other.getMLModelType() == null ^ this.getMLModelType() == null)
return false;
if (other.getMLModelType() != null && other.getMLModelType().equals(this.getMLModelType()) == false)
return false;
if (other.getScoreThreshold() == null ^ this.getScoreThreshold() == null)
return false;
if (other.getScoreThreshold() != null && other.getScoreThreshold().equals(this.getScoreThreshold()) == false)
return false;
if (other.getScoreThresholdLastUpdatedAt() == null ^ this.getScoreThresholdLastUpdatedAt() == null)
return false;
if (other.getScoreThresholdLastUpdatedAt() != null && other.getScoreThresholdLastUpdatedAt().equals(this.getScoreThresholdLastUpdatedAt()) == false)
return false;
if (other.getLogUri() == null ^ this.getLogUri() == null)
return false;
if (other.getLogUri() != null && other.getLogUri().equals(this.getLogUri()) == false)
return false;
if (other.getMessage() == null ^ this.getMessage() == null)
return false;
if (other.getMessage() != null && other.getMessage().equals(this.getMessage()) == false)
return false;
if (other.getComputeTime() == null ^ this.getComputeTime() == null)
return false;
if (other.getComputeTime() != null && other.getComputeTime().equals(this.getComputeTime()) == false)
return false;
if (other.getFinishedAt() == null ^ this.getFinishedAt() == null)
return false;
if (other.getFinishedAt() != null && other.getFinishedAt().equals(this.getFinishedAt()) == false)
return false;
if (other.getStartedAt() == null ^ this.getStartedAt() == null)
return false;
if (other.getStartedAt() != null && other.getStartedAt().equals(this.getStartedAt()) == false)
return false;
if (other.getRecipe() == null ^ this.getRecipe() == null)
return false;
if (other.getRecipe() != null && other.getRecipe().equals(this.getRecipe()) == false)
return false;
if (other.getSchema() == null ^ this.getSchema() == null)
return false;
if (other.getSchema() != null && other.getSchema().equals(this.getSchema()) == false)
return false;
return true;
}
@Override
public int hashCode() {
final int prime = 31;
int hashCode = 1;
hashCode = prime * hashCode + ((getMLModelId() == null) ? 0 : getMLModelId().hashCode());
hashCode = prime * hashCode + ((getTrainingDataSourceId() == null) ? 0 : getTrainingDataSourceId().hashCode());
hashCode = prime * hashCode + ((getCreatedByIamUser() == null) ? 0 : getCreatedByIamUser().hashCode());
hashCode = prime * hashCode + ((getCreatedAt() == null) ? 0 : getCreatedAt().hashCode());
hashCode = prime * hashCode + ((getLastUpdatedAt() == null) ? 0 : getLastUpdatedAt().hashCode());
hashCode = prime * hashCode + ((getName() == null) ? 0 : getName().hashCode());
hashCode = prime * hashCode + ((getStatus() == null) ? 0 : getStatus().hashCode());
hashCode = prime * hashCode + ((getSizeInBytes() == null) ? 0 : getSizeInBytes().hashCode());
hashCode = prime * hashCode + ((getEndpointInfo() == null) ? 0 : getEndpointInfo().hashCode());
hashCode = prime * hashCode + ((getTrainingParameters() == null) ? 0 : getTrainingParameters().hashCode());
hashCode = prime * hashCode + ((getInputDataLocationS3() == null) ? 0 : getInputDataLocationS3().hashCode());
hashCode = prime * hashCode + ((getMLModelType() == null) ? 0 : getMLModelType().hashCode());
hashCode = prime * hashCode + ((getScoreThreshold() == null) ? 0 : getScoreThreshold().hashCode());
hashCode = prime * hashCode + ((getScoreThresholdLastUpdatedAt() == null) ? 0 : getScoreThresholdLastUpdatedAt().hashCode());
hashCode = prime * hashCode + ((getLogUri() == null) ? 0 : getLogUri().hashCode());
hashCode = prime * hashCode + ((getMessage() == null) ? 0 : getMessage().hashCode());
hashCode = prime * hashCode + ((getComputeTime() == null) ? 0 : getComputeTime().hashCode());
hashCode = prime * hashCode + ((getFinishedAt() == null) ? 0 : getFinishedAt().hashCode());
hashCode = prime * hashCode + ((getStartedAt() == null) ? 0 : getStartedAt().hashCode());
hashCode = prime * hashCode + ((getRecipe() == null) ? 0 : getRecipe().hashCode());
hashCode = prime * hashCode + ((getSchema() == null) ? 0 : getSchema().hashCode());
return hashCode;
}
@Override
public GetMLModelResult clone() {
try {
return (GetMLModelResult) super.clone();
} catch (CloneNotSupportedException e) {
throw new IllegalStateException("Got a CloneNotSupportedException from Object.clone() " + "even though we're Cloneable!", e);
}
}
}