/*
* Copyright 2010-2016 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;
/**
* <p>
* Represents the output of a <code>GetMLModel</code> operation, and provides
* detailed information about a <code>MLModel</code>.
* </p>
*/
public class GetMLModelResult implements Serializable {
/**
* <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>
* <p>
* <b>Constraints:</b><br/>
* <b>Length: </b>1 - 64<br/>
* <b>Pattern: </b>[a-zA-Z0-9_.-]+<br/>
*/
private String mLModelId;
/**
* <p>
* The ID of the training <code>DataSource</code>.
* </p>
* <p>
* <b>Constraints:</b><br/>
* <b>Length: </b>1 - 64<br/>
* <b>Pattern: </b>[a-zA-Z0-9_.-]+<br/>
*/
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>
* <p>
* <b>Constraints:</b><br/>
* <b>Pattern: </b>arn:aws:iam::[0-9]+:((user/.+)|(root))<br/>
*/
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>
* <p>
* <b>Constraints:</b><br/>
* <b>Length: </b> - 1024<br/>
*/
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>
* <p>
* <b>Constraints:</b><br/>
* <b>Allowed Values: </b>PENDING, INPROGRESS, FAILED, COMPLETED, DELETED
*/
private String status;
/**
* <p>
* Long integer type that is a 64-bit signed number.
* </p>
*/
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 java.util.Map<String, String> trainingParameters = new java.util.HashMap<String, String>();
/**
* <p>
* The location of the data file or directory in Amazon Simple Storage
* Service (Amazon S3).
* </p>
* <p>
* <b>Constraints:</b><br/>
* <b>Length: </b> - 2048<br/>
* <b>Pattern: </b>s3://([^/]+)(/.*)?<br/>
*/
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>
* <p>
* <b>Constraints:</b><br/>
* <b>Allowed Values: </b>REGRESSION, BINARY, MULTICLASS
*/
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>
* <p>
* <b>Constraints:</b><br/>
* <b>Length: </b> - 10240<br/>
*/
private String message;
/**
* <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>
* <p>
* <b>Constraints:</b><br/>
* <b>Length: </b> - 131071<br/>
*/
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>
* <p>
* <b>Constraints:</b><br/>
* <b>Length: </b> - 131071<br/>
*/
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>
* <p>
* <b>Constraints:</b><br/>
* <b>Length: </b>1 - 64<br/>
* <b>Pattern: </b>[a-zA-Z0-9_.-]+<br/>
*
* @return <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>
*/
public String getMLModelId() {
return 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>
* <p>
* <b>Constraints:</b><br/>
* <b>Length: </b>1 - 64<br/>
* <b>Pattern: </b>[a-zA-Z0-9_.-]+<br/>
*
* @param 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>
*/
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>
* <p>
* Returns a reference to this object so that method calls can be chained
* together.
* <p>
* <b>Constraints:</b><br/>
* <b>Length: </b>1 - 64<br/>
* <b>Pattern: </b>[a-zA-Z0-9_.-]+<br/>
*
* @param 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 A reference to this updated object so that method calls can be
* chained together.
*/
public GetMLModelResult withMLModelId(String mLModelId) {
this.mLModelId = mLModelId;
return this;
}
/**
* <p>
* The ID of the training <code>DataSource</code>.
* </p>
* <p>
* <b>Constraints:</b><br/>
* <b>Length: </b>1 - 64<br/>
* <b>Pattern: </b>[a-zA-Z0-9_.-]+<br/>
*
* @return <p>
* The ID of the training <code>DataSource</code>.
* </p>
*/
public String getTrainingDataSourceId() {
return trainingDataSourceId;
}
/**
* <p>
* The ID of the training <code>DataSource</code>.
* </p>
* <p>
* <b>Constraints:</b><br/>
* <b>Length: </b>1 - 64<br/>
* <b>Pattern: </b>[a-zA-Z0-9_.-]+<br/>
*
* @param trainingDataSourceId <p>
* The ID of the training <code>DataSource</code>.
* </p>
*/
public void setTrainingDataSourceId(String trainingDataSourceId) {
this.trainingDataSourceId = trainingDataSourceId;
}
/**
* <p>
* The ID of the training <code>DataSource</code>.
* </p>
* <p>
* Returns a reference to this object so that method calls can be chained
* together.
* <p>
* <b>Constraints:</b><br/>
* <b>Length: </b>1 - 64<br/>
* <b>Pattern: </b>[a-zA-Z0-9_.-]+<br/>
*
* @param trainingDataSourceId <p>
* The ID of the training <code>DataSource</code>.
* </p>
* @return A reference to this updated object so that method calls can be
* chained together.
*/
public GetMLModelResult withTrainingDataSourceId(String trainingDataSourceId) {
this.trainingDataSourceId = trainingDataSourceId;
return this;
}
/**
* <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>
* <p>
* <b>Constraints:</b><br/>
* <b>Pattern: </b>arn:aws:iam::[0-9]+:((user/.+)|(root))<br/>
*
* @return <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>
*/
public String getCreatedByIamUser() {
return 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>
* <p>
* <b>Constraints:</b><br/>
* <b>Pattern: </b>arn:aws:iam::[0-9]+:((user/.+)|(root))<br/>
*
* @param 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>
*/
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>
* <p>
* Returns a reference to this object so that method calls can be chained
* together.
* <p>
* <b>Constraints:</b><br/>
* <b>Pattern: </b>arn:aws:iam::[0-9]+:((user/.+)|(root))<br/>
*
* @param 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 A reference to this updated object so that method calls can be
* chained together.
*/
public GetMLModelResult withCreatedByIamUser(String createdByIamUser) {
this.createdByIamUser = createdByIamUser;
return this;
}
/**
* <p>
* The time that the <code>MLModel</code> was created. The time is expressed
* in epoch time.
* </p>
*
* @return <p>
* The time that the <code>MLModel</code> was created. The time is
* expressed in epoch time.
* </p>
*/
public java.util.Date getCreatedAt() {
return createdAt;
}
/**
* <p>
* The time that the <code>MLModel</code> was created. The time is expressed
* in epoch time.
* </p>
*
* @param createdAt <p>
* The time that the <code>MLModel</code> was created. The time
* is expressed in epoch time.
* </p>
*/
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>
* <p>
* Returns a reference to this object so that method calls can be chained
* together.
*
* @param createdAt <p>
* The time that the <code>MLModel</code> was created. The time
* is expressed in epoch time.
* </p>
* @return A reference to this updated object so that method calls can be
* chained together.
*/
public GetMLModelResult withCreatedAt(java.util.Date createdAt) {
this.createdAt = createdAt;
return this;
}
/**
* <p>
* The time of the most recent edit to the <code>MLModel</code>. The time is
* expressed in epoch time.
* </p>
*
* @return <p>
* The time of the most recent edit to the <code>MLModel</code>. The
* time is expressed in epoch time.
* </p>
*/
public java.util.Date getLastUpdatedAt() {
return lastUpdatedAt;
}
/**
* <p>
* The time of the most recent edit to the <code>MLModel</code>. The time is
* expressed in epoch time.
* </p>
*
* @param lastUpdatedAt <p>
* The time of the most recent edit to the <code>MLModel</code>.
* The time is expressed in epoch time.
* </p>
*/
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>
* <p>
* Returns a reference to this object so that method calls can be chained
* together.
*
* @param lastUpdatedAt <p>
* The time of the most recent edit to the <code>MLModel</code>.
* The time is expressed in epoch time.
* </p>
* @return A reference to this updated object so that method calls can be
* chained together.
*/
public GetMLModelResult withLastUpdatedAt(java.util.Date lastUpdatedAt) {
this.lastUpdatedAt = lastUpdatedAt;
return this;
}
/**
* <p>
* A user-supplied name or description of the <code>MLModel</code>.
* </p>
* <p>
* <b>Constraints:</b><br/>
* <b>Length: </b> - 1024<br/>
*
* @return <p>
* A user-supplied name or description of the <code>MLModel</code>.
* </p>
*/
public String getName() {
return name;
}
/**
* <p>
* A user-supplied name or description of the <code>MLModel</code>.
* </p>
* <p>
* <b>Constraints:</b><br/>
* <b>Length: </b> - 1024<br/>
*
* @param name <p>
* A user-supplied name or description of the
* <code>MLModel</code>.
* </p>
*/
public void setName(String name) {
this.name = name;
}
/**
* <p>
* A user-supplied name or description of the <code>MLModel</code>.
* </p>
* <p>
* Returns a reference to this object so that method calls can be chained
* together.
* <p>
* <b>Constraints:</b><br/>
* <b>Length: </b> - 1024<br/>
*
* @param name <p>
* A user-supplied name or description of the
* <code>MLModel</code>.
* </p>
* @return A reference to this updated object so that method calls can be
* chained together.
*/
public GetMLModelResult withName(String name) {
this.name = name;
return this;
}
/**
* <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>
* <p>
* <b>Constraints:</b><br/>
* <b>Allowed Values: </b>PENDING, INPROGRESS, FAILED, COMPLETED, DELETED
*
* @return <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>
* @see EntityStatus
*/
public String getStatus() {
return 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>
* <p>
* <b>Constraints:</b><br/>
* <b>Allowed Values: </b>PENDING, INPROGRESS, FAILED, COMPLETED, DELETED
*
* @param 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>
* @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>
* <p>
* Returns a reference to this object so that method calls can be chained
* together.
* <p>
* <b>Constraints:</b><br/>
* <b>Allowed Values: </b>PENDING, INPROGRESS, FAILED, COMPLETED, DELETED
*
* @param 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 A reference to this updated object so that method calls can be
* chained together.
* @see EntityStatus
*/
public GetMLModelResult withStatus(String status) {
this.status = status;
return this;
}
/**
* <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>
* <p>
* <b>Constraints:</b><br/>
* <b>Allowed Values: </b>PENDING, INPROGRESS, FAILED, COMPLETED, DELETED
*
* @param 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>
* @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>
* <p>
* Returns a reference to this object so that method calls can be chained
* together.
* <p>
* <b>Constraints:</b><br/>
* <b>Allowed Values: </b>PENDING, INPROGRESS, FAILED, COMPLETED, DELETED
*
* @param 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 A reference to this updated object so that method calls can be
* chained together.
* @see EntityStatus
*/
public GetMLModelResult withStatus(EntityStatus status) {
this.status = status.toString();
return this;
}
/**
* <p>
* Long integer type that is a 64-bit signed number.
* </p>
*
* @return <p>
* Long integer type that is a 64-bit signed number.
* </p>
*/
public Long getSizeInBytes() {
return sizeInBytes;
}
/**
* <p>
* Long integer type that is a 64-bit signed number.
* </p>
*
* @param sizeInBytes <p>
* Long integer type that is a 64-bit signed number.
* </p>
*/
public void setSizeInBytes(Long sizeInBytes) {
this.sizeInBytes = sizeInBytes;
}
/**
* <p>
* Long integer type that is a 64-bit signed number.
* </p>
* <p>
* Returns a reference to this object so that method calls can be chained
* together.
*
* @param sizeInBytes <p>
* Long integer type that is a 64-bit signed number.
* </p>
* @return A reference to this updated object so that method calls can be
* chained together.
*/
public GetMLModelResult withSizeInBytes(Long sizeInBytes) {
this.sizeInBytes = sizeInBytes;
return this;
}
/**
* <p>
* The current endpoint of the <code>MLModel</code>
* </p>
*
* @return <p>
* The current endpoint of the <code>MLModel</code>
* </p>
*/
public RealtimeEndpointInfo getEndpointInfo() {
return endpointInfo;
}
/**
* <p>
* The current endpoint of the <code>MLModel</code>
* </p>
*
* @param endpointInfo <p>
* The current endpoint of the <code>MLModel</code>
* </p>
*/
public void setEndpointInfo(RealtimeEndpointInfo endpointInfo) {
this.endpointInfo = endpointInfo;
}
/**
* <p>
* The current endpoint of the <code>MLModel</code>
* </p>
* <p>
* Returns a reference to this object so that method calls can be chained
* together.
*
* @param endpointInfo <p>
* The current endpoint of the <code>MLModel</code>
* </p>
* @return A reference to this updated object so that method calls can be
* chained together.
*/
public GetMLModelResult withEndpointInfo(RealtimeEndpointInfo endpointInfo) {
this.endpointInfo = endpointInfo;
return this;
}
/**
* <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 <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>
*/
public java.util.Map<String, String> getTrainingParameters() {
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 <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>
*/
public void setTrainingParameters(java.util.Map<String, String> trainingParameters) {
this.trainingParameters = 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>
* <p>
* Returns a reference to this object so that method calls can be chained
* together.
*
* @param 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>
* @return A reference to this updated object so that method calls can be
* chained together.
*/
public GetMLModelResult withTrainingParameters(java.util.Map<String, String> trainingParameters) {
this.trainingParameters = trainingParameters;
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>
* <p>
* The method adds a new key-value pair into TrainingParameters parameter,
* and returns a reference to this object so that method calls can be
* chained together.
*
* @param key The key of the entry to be added into TrainingParameters.
* @param value The corresponding value of the entry to be added into
* TrainingParameters.
* @return A reference to this updated object so that method calls can be
* chained together.
*/
public GetMLModelResult addTrainingParametersEntry(String key, String value) {
if (null == this.trainingParameters) {
this.trainingParameters = new java.util.HashMap<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.
* <p>
* 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>
* <p>
* <b>Constraints:</b><br/>
* <b>Length: </b> - 2048<br/>
* <b>Pattern: </b>s3://([^/]+)(/.*)?<br/>
*
* @return <p>
* The location of the data file or directory in Amazon Simple
* Storage Service (Amazon S3).
* </p>
*/
public String getInputDataLocationS3() {
return inputDataLocationS3;
}
/**
* <p>
* The location of the data file or directory in Amazon Simple Storage
* Service (Amazon S3).
* </p>
* <p>
* <b>Constraints:</b><br/>
* <b>Length: </b> - 2048<br/>
* <b>Pattern: </b>s3://([^/]+)(/.*)?<br/>
*
* @param inputDataLocationS3 <p>
* The location of the data file or directory in Amazon Simple
* Storage Service (Amazon S3).
* </p>
*/
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>
* <p>
* Returns a reference to this object so that method calls can be chained
* together.
* <p>
* <b>Constraints:</b><br/>
* <b>Length: </b> - 2048<br/>
* <b>Pattern: </b>s3://([^/]+)(/.*)?<br/>
*
* @param inputDataLocationS3 <p>
* The location of the data file or directory in Amazon Simple
* Storage Service (Amazon S3).
* </p>
* @return A reference to this updated object so that method calls can be
* chained together.
*/
public GetMLModelResult withInputDataLocationS3(String inputDataLocationS3) {
this.inputDataLocationS3 = inputDataLocationS3;
return this;
}
/**
* <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>
* <p>
* <b>Constraints:</b><br/>
* <b>Allowed Values: </b>REGRESSION, BINARY, MULTICLASS
*
* @return <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>
* @see MLModelType
*/
public String getMLModelType() {
return 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>
* <p>
* <b>Constraints:</b><br/>
* <b>Allowed Values: </b>REGRESSION, BINARY, MULTICLASS
*
* @param 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>
* @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>
* <p>
* Returns a reference to this object so that method calls can be chained
* together.
* <p>
* <b>Constraints:</b><br/>
* <b>Allowed Values: </b>REGRESSION, BINARY, MULTICLASS
*
* @param 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 A reference to this updated object so that method calls can be
* chained together.
* @see MLModelType
*/
public GetMLModelResult withMLModelType(String mLModelType) {
this.mLModelType = mLModelType;
return this;
}
/**
* <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>
* <p>
* <b>Constraints:</b><br/>
* <b>Allowed Values: </b>REGRESSION, BINARY, MULTICLASS
*
* @param 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>
* @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>
* <p>
* Returns a reference to this object so that method calls can be chained
* together.
* <p>
* <b>Constraints:</b><br/>
* <b>Allowed Values: </b>REGRESSION, BINARY, MULTICLASS
*
* @param 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 A reference to this updated object so that method calls can be
* chained together.
* @see MLModelType
*/
public GetMLModelResult withMLModelType(MLModelType mLModelType) {
this.mLModelType = mLModelType.toString();
return this;
}
/**
* <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 <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>
*/
public Float getScoreThreshold() {
return 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 <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>
*/
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>
* <p>
* Returns a reference to this object so that method calls can be chained
* together.
*
* @param 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 A reference to this updated object so that method calls can be
* chained together.
*/
public GetMLModelResult withScoreThreshold(Float scoreThreshold) {
this.scoreThreshold = scoreThreshold;
return this;
}
/**
* <p>
* The time of the most recent edit to the <code>ScoreThreshold</code>. The
* time is expressed in epoch time.
* </p>
*
* @return <p>
* The time of the most recent edit to the
* <code>ScoreThreshold</code>. The time is expressed in epoch time.
* </p>
*/
public java.util.Date getScoreThresholdLastUpdatedAt() {
return scoreThresholdLastUpdatedAt;
}
/**
* <p>
* The time of the most recent edit to the <code>ScoreThreshold</code>. The
* time is expressed in epoch time.
* </p>
*
* @param scoreThresholdLastUpdatedAt <p>
* The time of the most recent edit to the
* <code>ScoreThreshold</code>. The time is expressed in epoch
* time.
* </p>
*/
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>
* <p>
* Returns a reference to this object so that method calls can be chained
* together.
*
* @param scoreThresholdLastUpdatedAt <p>
* The time of the most recent edit to the
* <code>ScoreThreshold</code>. The time is expressed in epoch
* time.
* </p>
* @return A reference to this updated object so that method calls can be
* chained together.
*/
public GetMLModelResult withScoreThresholdLastUpdatedAt(
java.util.Date scoreThresholdLastUpdatedAt) {
this.scoreThresholdLastUpdatedAt = scoreThresholdLastUpdatedAt;
return this;
}
/**
* <p>
* A link to the file that contains logs of the <code>CreateMLModel</code>
* operation.
* </p>
*
* @return <p>
* A link to the file that contains logs of the
* <code>CreateMLModel</code> operation.
* </p>
*/
public String getLogUri() {
return logUri;
}
/**
* <p>
* A link to the file that contains logs of the <code>CreateMLModel</code>
* operation.
* </p>
*
* @param logUri <p>
* A link to the file that contains logs of the
* <code>CreateMLModel</code> operation.
* </p>
*/
public void setLogUri(String logUri) {
this.logUri = logUri;
}
/**
* <p>
* A link to the file that contains logs of the <code>CreateMLModel</code>
* operation.
* </p>
* <p>
* Returns a reference to this object so that method calls can be chained
* together.
*
* @param logUri <p>
* A link to the file that contains logs of the
* <code>CreateMLModel</code> operation.
* </p>
* @return A reference to this updated object so that method calls can be
* chained together.
*/
public GetMLModelResult withLogUri(String logUri) {
this.logUri = logUri;
return this;
}
/**
* <p>
* A description of the most recent details about accessing the
* <code>MLModel</code>.
* </p>
* <p>
* <b>Constraints:</b><br/>
* <b>Length: </b> - 10240<br/>
*
* @return <p>
* A description of the most recent details about accessing the
* <code>MLModel</code>.
* </p>
*/
public String getMessage() {
return message;
}
/**
* <p>
* A description of the most recent details about accessing the
* <code>MLModel</code>.
* </p>
* <p>
* <b>Constraints:</b><br/>
* <b>Length: </b> - 10240<br/>
*
* @param message <p>
* A description of the most recent details about accessing the
* <code>MLModel</code>.
* </p>
*/
public void setMessage(String message) {
this.message = message;
}
/**
* <p>
* A description of the most recent details about accessing the
* <code>MLModel</code>.
* </p>
* <p>
* Returns a reference to this object so that method calls can be chained
* together.
* <p>
* <b>Constraints:</b><br/>
* <b>Length: </b> - 10240<br/>
*
* @param message <p>
* A description of the most recent details about accessing the
* <code>MLModel</code>.
* </p>
* @return A reference to this updated object so that method calls can be
* chained together.
*/
public GetMLModelResult withMessage(String message) {
this.message = message;
return this;
}
/**
* <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>
* <p>
* <b>Constraints:</b><br/>
* <b>Length: </b> - 131071<br/>
*
* @return <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>
*/
public String getRecipe() {
return 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>
* <p>
* <b>Constraints:</b><br/>
* <b>Length: </b> - 131071<br/>
*
* @param 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>
*/
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>
* <p>
* Returns a reference to this object so that method calls can be chained
* together.
* <p>
* <b>Constraints:</b><br/>
* <b>Length: </b> - 131071<br/>
*
* @param 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 A reference to this updated object so that method calls can be
* chained together.
*/
public GetMLModelResult withRecipe(String recipe) {
this.recipe = 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>
* <p>
* <b>Constraints:</b><br/>
* <b>Length: </b> - 131071<br/>
*
* @return <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>
*/
public String getSchema() {
return 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>
* <p>
* <b>Constraints:</b><br/>
* <b>Length: </b> - 131071<br/>
*
* @param 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>
*/
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>
* <p>
* Returns a reference to this object so that method calls can be chained
* together.
* <p>
* <b>Constraints:</b><br/>
* <b>Length: </b> - 131071<br/>
*
* @param 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 A reference to this updated object so that method calls can be
* chained together.
*/
public GetMLModelResult withSchema(String schema) {
this.schema = schema;
return this;
}
/**
* Returns a string representation of this object; useful for testing and
* debugging.
*
* @return A string representation of this object.
* @see java.lang.Object#toString()
*/
@Override
public String toString() {
StringBuilder sb = new StringBuilder();
sb.append("{");
if (getMLModelId() != null)
sb.append("MLModelId: " + getMLModelId() + ",");
if (getTrainingDataSourceId() != null)
sb.append("TrainingDataSourceId: " + getTrainingDataSourceId() + ",");
if (getCreatedByIamUser() != null)
sb.append("CreatedByIamUser: " + getCreatedByIamUser() + ",");
if (getCreatedAt() != null)
sb.append("CreatedAt: " + getCreatedAt() + ",");
if (getLastUpdatedAt() != null)
sb.append("LastUpdatedAt: " + getLastUpdatedAt() + ",");
if (getName() != null)
sb.append("Name: " + getName() + ",");
if (getStatus() != null)
sb.append("Status: " + getStatus() + ",");
if (getSizeInBytes() != null)
sb.append("SizeInBytes: " + getSizeInBytes() + ",");
if (getEndpointInfo() != null)
sb.append("EndpointInfo: " + getEndpointInfo() + ",");
if (getTrainingParameters() != null)
sb.append("TrainingParameters: " + getTrainingParameters() + ",");
if (getInputDataLocationS3() != null)
sb.append("InputDataLocationS3: " + getInputDataLocationS3() + ",");
if (getMLModelType() != null)
sb.append("MLModelType: " + getMLModelType() + ",");
if (getScoreThreshold() != null)
sb.append("ScoreThreshold: " + getScoreThreshold() + ",");
if (getScoreThresholdLastUpdatedAt() != null)
sb.append("ScoreThresholdLastUpdatedAt: " + getScoreThresholdLastUpdatedAt() + ",");
if (getLogUri() != null)
sb.append("LogUri: " + getLogUri() + ",");
if (getMessage() != null)
sb.append("Message: " + getMessage() + ",");
if (getRecipe() != null)
sb.append("Recipe: " + getRecipe() + ",");
if (getSchema() != null)
sb.append("Schema: " + getSchema());
sb.append("}");
return sb.toString();
}
@Override
public int hashCode() {
final int prime = 31;
int hashCode = 1;
hashCode = prime * hashCode + ((getMLModelId() == null) ? 0 : getMLModelId().hashCode());
hashCode = prime * hashCode
+ ((getTrainingDataSourceId() == null) ? 0 : getTrainingDataSourceId().hashCode());
hashCode = prime * hashCode
+ ((getCreatedByIamUser() == null) ? 0 : getCreatedByIamUser().hashCode());
hashCode = prime * hashCode + ((getCreatedAt() == null) ? 0 : getCreatedAt().hashCode());
hashCode = prime * hashCode
+ ((getLastUpdatedAt() == null) ? 0 : getLastUpdatedAt().hashCode());
hashCode = prime * hashCode + ((getName() == null) ? 0 : getName().hashCode());
hashCode = prime * hashCode + ((getStatus() == null) ? 0 : getStatus().hashCode());
hashCode = prime * hashCode
+ ((getSizeInBytes() == null) ? 0 : getSizeInBytes().hashCode());
hashCode = prime * hashCode
+ ((getEndpointInfo() == null) ? 0 : getEndpointInfo().hashCode());
hashCode = prime * hashCode
+ ((getTrainingParameters() == null) ? 0 : getTrainingParameters().hashCode());
hashCode = prime * hashCode
+ ((getInputDataLocationS3() == null) ? 0 : getInputDataLocationS3().hashCode());
hashCode = prime * hashCode
+ ((getMLModelType() == null) ? 0 : getMLModelType().hashCode());
hashCode = prime * hashCode
+ ((getScoreThreshold() == null) ? 0 : getScoreThreshold().hashCode());
hashCode = prime
* hashCode
+ ((getScoreThresholdLastUpdatedAt() == null) ? 0
: getScoreThresholdLastUpdatedAt().hashCode());
hashCode = prime * hashCode + ((getLogUri() == null) ? 0 : getLogUri().hashCode());
hashCode = prime * hashCode + ((getMessage() == null) ? 0 : getMessage().hashCode());
hashCode = prime * hashCode + ((getRecipe() == null) ? 0 : getRecipe().hashCode());
hashCode = prime * hashCode + ((getSchema() == null) ? 0 : getSchema().hashCode());
return hashCode;
}
@Override
public boolean equals(Object obj) {
if (this == obj)
return true;
if (obj == null)
return false;
if (obj instanceof GetMLModelResult == false)
return false;
GetMLModelResult other = (GetMLModelResult) obj;
if (other.getMLModelId() == null ^ this.getMLModelId() == null)
return false;
if (other.getMLModelId() != null
&& other.getMLModelId().equals(this.getMLModelId()) == false)
return false;
if (other.getTrainingDataSourceId() == null ^ this.getTrainingDataSourceId() == null)
return false;
if (other.getTrainingDataSourceId() != null
&& other.getTrainingDataSourceId().equals(this.getTrainingDataSourceId()) == false)
return false;
if (other.getCreatedByIamUser() == null ^ this.getCreatedByIamUser() == null)
return false;
if (other.getCreatedByIamUser() != null
&& other.getCreatedByIamUser().equals(this.getCreatedByIamUser()) == false)
return false;
if (other.getCreatedAt() == null ^ this.getCreatedAt() == null)
return false;
if (other.getCreatedAt() != null
&& other.getCreatedAt().equals(this.getCreatedAt()) == false)
return false;
if (other.getLastUpdatedAt() == null ^ this.getLastUpdatedAt() == null)
return false;
if (other.getLastUpdatedAt() != null
&& other.getLastUpdatedAt().equals(this.getLastUpdatedAt()) == false)
return false;
if (other.getName() == null ^ this.getName() == null)
return false;
if (other.getName() != null && other.getName().equals(this.getName()) == false)
return false;
if (other.getStatus() == null ^ this.getStatus() == null)
return false;
if (other.getStatus() != null && other.getStatus().equals(this.getStatus()) == false)
return false;
if (other.getSizeInBytes() == null ^ this.getSizeInBytes() == null)
return false;
if (other.getSizeInBytes() != null
&& other.getSizeInBytes().equals(this.getSizeInBytes()) == false)
return false;
if (other.getEndpointInfo() == null ^ this.getEndpointInfo() == null)
return false;
if (other.getEndpointInfo() != null
&& other.getEndpointInfo().equals(this.getEndpointInfo()) == false)
return false;
if (other.getTrainingParameters() == null ^ this.getTrainingParameters() == null)
return false;
if (other.getTrainingParameters() != null
&& other.getTrainingParameters().equals(this.getTrainingParameters()) == false)
return false;
if (other.getInputDataLocationS3() == null ^ this.getInputDataLocationS3() == null)
return false;
if (other.getInputDataLocationS3() != null
&& other.getInputDataLocationS3().equals(this.getInputDataLocationS3()) == false)
return false;
if (other.getMLModelType() == null ^ this.getMLModelType() == null)
return false;
if (other.getMLModelType() != null
&& other.getMLModelType().equals(this.getMLModelType()) == false)
return false;
if (other.getScoreThreshold() == null ^ this.getScoreThreshold() == null)
return false;
if (other.getScoreThreshold() != null
&& other.getScoreThreshold().equals(this.getScoreThreshold()) == false)
return false;
if (other.getScoreThresholdLastUpdatedAt() == null
^ this.getScoreThresholdLastUpdatedAt() == null)
return false;
if (other.getScoreThresholdLastUpdatedAt() != null
&& other.getScoreThresholdLastUpdatedAt().equals(
this.getScoreThresholdLastUpdatedAt()) == false)
return false;
if (other.getLogUri() == null ^ this.getLogUri() == null)
return false;
if (other.getLogUri() != null && other.getLogUri().equals(this.getLogUri()) == false)
return false;
if (other.getMessage() == null ^ this.getMessage() == null)
return false;
if (other.getMessage() != null && other.getMessage().equals(this.getMessage()) == false)
return false;
if (other.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;
}
}