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/*
* 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;
/**
*
* Represents the output of a GetMLModel operation, and provides detailed
* information about a MLModel.
*
*/
public class GetMLModelResult implements Serializable, Cloneable {
/**
*
* The MLModel ID which is same as the MLModelId in the
* request.
*
*/
private String mLModelId;
/**
*
* The ID of the training DataSource.
*
*/
private String trainingDataSourceId;
/**
*
* The AWS user account from which the MLModel was created. The
* account type can be either an AWS root account or an AWS Identity and
* Access Management (IAM) user account.
*
*/
private String createdByIamUser;
/**
*
* The time that the MLModel was created. The time is expressed
* in epoch time.
*
*/
private java.util.Date createdAt;
/**
*
* The time of the most recent edit to the MLModel. The time is
* expressed in epoch time.
*
*/
private java.util.Date lastUpdatedAt;
/**
*
* A user-supplied name or description of the MLModel.
*
*/
private String name;
/**
*
* The current status of the MLModel. This element can have one
* of the following values:
*
*
* -
PENDING - Amazon Machine Learning (Amazon ML) submitted
* a request to describe a MLModel.
* -
INPROGRESS - The request is processing.
* -
FAILED - The request did not run to completion. It is
* not usable.
* -
COMPLETED - The request completed successfully.
* -
DELETED - The MLModel is marked as deleted.
* It is not usable.
*
*/
private String status;
private Long sizeInBytes;
/**
*
* The current endpoint of the MLModel
*
*/
private RealtimeEndpointInfo endpointInfo;
/**
*
* A list of the training parameters in the MLModel. The list
* is implemented as a map of key/value pairs.
*
*
* The following is the current set of training parameters:
*
*
* -
*
* sgd.l1RegularizationAmount - Coefficient regularization L1
* norm. It controls overfitting the data by penalizing large coefficients.
* This tends to drive coefficients to zero, resulting in a sparse feature
* set. If you use this parameter, specify a small value, such as 1.0E-04 or
* 1.0E-08.
*
*
* The value is a double that ranges from 0 to MAX_DOUBLE. The default is
* not to use L1 normalization. The parameter cannot be used when
* L2 is specified. Use this parameter sparingly.
*
*
* -
*
* sgd.l2RegularizationAmount - Coefficient regularization L2
* norm. It controls overfitting the data by penalizing large coefficients.
* This tends to drive coefficients to small, nonzero values. If you use
* this parameter, specify a small value, such as 1.0E-04 or 1.0E-08.
*
*
* The value is a double that ranges from 0 to MAX_DOUBLE. The default is
* not to use L2 normalization. This parameter cannot be used when
* L1 is specified. Use this parameter sparingly.
*
*
* -
*
* sgd.maxPasses - The number of times that the training
* process traverses the observations to build the MLModel. The
* value is an integer that ranges from 1 to 10000. The default value is 10.
*
*
* -
*
* sgd.maxMLModelSizeInBytes - The maximum allowed size of the
* model. Depending on the input data, the model size might affect
* performance.
*
*
* The value is an integer that ranges from 100000 to 2147483648. The
* default value is 33554432.
*
*
*
*/
private com.amazonaws.internal.SdkInternalMap trainingParameters;
/**
*
* The location of the data file or directory in Amazon Simple Storage
* Service (Amazon S3).
*
*/
private String inputDataLocationS3;
/**
*
* Identifies the MLModel category. The following are the
* available types:
*
*
* - REGRESSION -- Produces a numeric result. For example,
* "What listing price should a house have?"
* - BINARY -- Produces one of two possible results. For example,
* "Is this an e-commerce website?"
* - MULTICLASS -- Produces more than two possible results. For example,
* "Is this a HIGH, LOW or MEDIUM risk trade?"
*
*/
private String mLModelType;
/**
*
* The scoring threshold is used in binary classification
* MLModels, and marks the boundary between a positive
* prediction and a negative prediction.
*
*
* Output values greater than or equal to the threshold receive a positive
* result from the MLModel, such as true. Output values less
* than the threshold receive a negative response from the MLModel, such as
* false.
*
*/
private Float scoreThreshold;
/**
*
* The time of the most recent edit to the ScoreThreshold. The
* time is expressed in epoch time.
*
*/
private java.util.Date scoreThresholdLastUpdatedAt;
/**
*
* A link to the file that contains logs of the CreateMLModel
* operation.
*
*/
private String logUri;
/**
*
* Description of the most recent details about accessing the
* MLModel.
*
*/
private String message;
/**
*
* The recipe to use when training the MLModel. The
* Recipe provides detailed information about the observation
* data to use during training, as well as manipulations to perform on the
* observation data during training.
*
* Note
*
* This parameter is provided as part of the verbose format.
*
*
* The schema used by all of the data files referenced by the
* DataSource.
*
* Note
*
* This parameter is provided as part of the verbose format.
*
*
* The MLModel ID which is same as the MLModelId in the
* request.
*
*
* @param mLModelId
* The MLModel ID which is same as the MLModelId in the
* request.
*/
public void setMLModelId(String mLModelId) {
this.mLModelId = mLModelId;
}
/**
*
* The MLModel ID which is same as the MLModelId in the
* request.
*
*
* @return The MLModel ID which is same as the MLModelId in the
* request.
*/
public String getMLModelId() {
return this.mLModelId;
}
/**
*
* The MLModel ID which is same as the MLModelId in the
* request.
*
*
* @param mLModelId
* The MLModel ID which is same as the MLModelId 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;
}
/**
*
* The ID of the training DataSource.
*
*
* @param trainingDataSourceId
* The ID of the training DataSource.
*/
public void setTrainingDataSourceId(String trainingDataSourceId) {
this.trainingDataSourceId = trainingDataSourceId;
}
/**
*
* The ID of the training DataSource.
*
*
* @return The ID of the training DataSource.
*/
public String getTrainingDataSourceId() {
return this.trainingDataSourceId;
}
/**
*
* The ID of the training DataSource.
*
*
* @param trainingDataSourceId
* The ID of the training DataSource.
* @return Returns a reference to this object so that method calls can be
* chained together.
*/
public GetMLModelResult withTrainingDataSourceId(String trainingDataSourceId) {
setTrainingDataSourceId(trainingDataSourceId);
return this;
}
/**
*
* The AWS user account from which the MLModel was created. The
* account type can be either an AWS root account or an AWS Identity and
* Access Management (IAM) user account.
*
*
* @param createdByIamUser
* The AWS user account from which the MLModel was
* created. The account type can be either an AWS root account or an
* AWS Identity and Access Management (IAM) user account.
*/
public void setCreatedByIamUser(String createdByIamUser) {
this.createdByIamUser = createdByIamUser;
}
/**
*
* The AWS user account from which the MLModel was created. The
* account type can be either an AWS root account or an AWS Identity and
* Access Management (IAM) user account.
*
*
* @return The AWS user account from which the MLModel was
* created. The account type can be either an AWS root account or an
* AWS Identity and Access Management (IAM) user account.
*/
public String getCreatedByIamUser() {
return this.createdByIamUser;
}
/**
*
* The AWS user account from which the MLModel was created. The
* account type can be either an AWS root account or an AWS Identity and
* Access Management (IAM) user account.
*
*
* @param createdByIamUser
* The AWS user account from which the MLModel was
* created. The account type can be either an AWS root account or an
* AWS Identity and Access Management (IAM) user account.
* @return Returns a reference to this object so that method calls can be
* chained together.
*/
public GetMLModelResult withCreatedByIamUser(String createdByIamUser) {
setCreatedByIamUser(createdByIamUser);
return this;
}
/**
*
* The time that the MLModel was created. The time is expressed
* in epoch time.
*
*
* @param createdAt
* The time that the MLModel was created. The time is
* expressed in epoch time.
*/
public void setCreatedAt(java.util.Date createdAt) {
this.createdAt = createdAt;
}
/**
*
* The time that the MLModel was created. The time is expressed
* in epoch time.
*
*
* @return The time that the MLModel was created. The time is
* expressed in epoch time.
*/
public java.util.Date getCreatedAt() {
return this.createdAt;
}
/**
*
* The time that the MLModel was created. The time is expressed
* in epoch time.
*
*
* @param createdAt
* The time that the MLModel was created. The time is
* expressed in epoch time.
* @return Returns a reference to this object so that method calls can be
* chained together.
*/
public GetMLModelResult withCreatedAt(java.util.Date createdAt) {
setCreatedAt(createdAt);
return this;
}
/**
*
* The time of the most recent edit to the MLModel. The time is
* expressed in epoch time.
*
*
* @param lastUpdatedAt
* The time of the most recent edit to the MLModel. The
* time is expressed in epoch time.
*/
public void setLastUpdatedAt(java.util.Date lastUpdatedAt) {
this.lastUpdatedAt = lastUpdatedAt;
}
/**
*
* The time of the most recent edit to the MLModel. The time is
* expressed in epoch time.
*
*
* @return The time of the most recent edit to the MLModel. The
* time is expressed in epoch time.
*/
public java.util.Date getLastUpdatedAt() {
return this.lastUpdatedAt;
}
/**
*
* The time of the most recent edit to the MLModel. The time is
* expressed in epoch time.
*
*
* @param lastUpdatedAt
* The time of the most recent edit to the MLModel. The
* time is expressed in epoch time.
* @return Returns a reference to this object so that method calls can be
* chained together.
*/
public GetMLModelResult withLastUpdatedAt(java.util.Date lastUpdatedAt) {
setLastUpdatedAt(lastUpdatedAt);
return this;
}
/**
*
* A user-supplied name or description of the MLModel.
*
*
* @param name
* A user-supplied name or description of the MLModel.
*/
public void setName(String name) {
this.name = name;
}
/**
*
* A user-supplied name or description of the MLModel.
*
*
* @return A user-supplied name or description of the MLModel.
*/
public String getName() {
return this.name;
}
/**
*
* A user-supplied name or description of the MLModel.
*
*
* @param name
* A user-supplied name or description of the MLModel.
* @return Returns a reference to this object so that method calls can be
* chained together.
*/
public GetMLModelResult withName(String name) {
setName(name);
return this;
}
/**
*
* The current status of the MLModel. This element can have one
* of the following values:
*
*
* -
PENDING - Amazon Machine Learning (Amazon ML) submitted
* a request to describe a MLModel.
* -
INPROGRESS - The request is processing.
* -
FAILED - The request did not run to completion. It is
* not usable.
* -
COMPLETED - The request completed successfully.
* -
DELETED - The MLModel is marked as deleted.
* It is not usable.
*
*
* @param status
* The current status of the MLModel. This element can
* have one of the following values:
*
* -
PENDING - Amazon Machine Learning (Amazon ML)
* submitted a request to describe a MLModel.
* -
INPROGRESS - The request is processing.
* -
FAILED - The request did not run to completion.
* It is not usable.
* -
COMPLETED - The request completed successfully.
* -
DELETED - The MLModel is marked as
* deleted. It is not usable.
* @see EntityStatus
*/
public void setStatus(String status) {
this.status = status;
}
/**
*
* The current status of the MLModel. This element can have one
* of the following values:
*
*
* -
PENDING - Amazon Machine Learning (Amazon ML) submitted
* a request to describe a MLModel.
* -
INPROGRESS - The request is processing.
* -
FAILED - The request did not run to completion. It is
* not usable.
* -
COMPLETED - The request completed successfully.
* -
DELETED - The MLModel is marked as deleted.
* It is not usable.
*
*
* @return The current status of the MLModel. This element can
* have one of the following values:
*
* -
PENDING - Amazon Machine Learning (Amazon ML)
* submitted a request to describe a MLModel.
* -
INPROGRESS - The request is processing.
* -
FAILED - The request did not run to completion.
* It is not usable.
* -
COMPLETED - The request completed successfully.
* -
DELETED - The MLModel is marked as
* deleted. It is not usable.
* @see EntityStatus
*/
public String getStatus() {
return this.status;
}
/**
*
* The current status of the MLModel. This element can have one
* of the following values:
*
*
* -
PENDING - Amazon Machine Learning (Amazon ML) submitted
* a request to describe a MLModel.
* -
INPROGRESS - The request is processing.
* -
FAILED - The request did not run to completion. It is
* not usable.
* -
COMPLETED - The request completed successfully.
* -
DELETED - The MLModel is marked as deleted.
* It is not usable.
*
*
* @param status
* The current status of the MLModel. This element can
* have one of the following values:
*
* -
PENDING - Amazon Machine Learning (Amazon ML)
* submitted a request to describe a MLModel.
* -
INPROGRESS - The request is processing.
* -
FAILED - The request did not run to completion.
* It is not usable.
* -
COMPLETED - The request completed successfully.
* -
DELETED - The MLModel is marked as
* deleted. It is not usable.
* @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;
}
/**
*
* The current status of the MLModel. This element can have one
* of the following values:
*
*
* -
PENDING - Amazon Machine Learning (Amazon ML) submitted
* a request to describe a MLModel.
* -
INPROGRESS - The request is processing.
* -
FAILED - The request did not run to completion. It is
* not usable.
* -
COMPLETED - The request completed successfully.
* -
DELETED - The MLModel is marked as deleted.
* It is not usable.
*
*
* @param status
* The current status of the MLModel. This element can
* have one of the following values:
*
* -
PENDING - Amazon Machine Learning (Amazon ML)
* submitted a request to describe a MLModel.
* -
INPROGRESS - The request is processing.
* -
FAILED - The request did not run to completion.
* It is not usable.
* -
COMPLETED - The request completed successfully.
* -
DELETED - The MLModel is marked as
* deleted. It is not usable.
* @see EntityStatus
*/
public void setStatus(EntityStatus status) {
this.status = status.toString();
}
/**
*
* The current status of the MLModel. This element can have one
* of the following values:
*
*
* -
PENDING - Amazon Machine Learning (Amazon ML) submitted
* a request to describe a MLModel.
* -
INPROGRESS - The request is processing.
* -
FAILED - The request did not run to completion. It is
* not usable.
* -
COMPLETED - The request completed successfully.
* -
DELETED - The MLModel is marked as deleted.
* It is not usable.
*
*
* @param status
* The current status of the MLModel. This element can
* have one of the following values:
*
* -
PENDING - Amazon Machine Learning (Amazon ML)
* submitted a request to describe a MLModel.
* -
INPROGRESS - The request is processing.
* -
FAILED - The request did not run to completion.
* It is not usable.
* -
COMPLETED - The request completed successfully.
* -
DELETED - The MLModel is marked as
* deleted. It is not usable.
* @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;
}
/**
*
* The current endpoint of the MLModel
*
*
* @param endpointInfo
* The current endpoint of the MLModel
*/
public void setEndpointInfo(RealtimeEndpointInfo endpointInfo) {
this.endpointInfo = endpointInfo;
}
/**
*
* The current endpoint of the MLModel
*
*
* @return The current endpoint of the MLModel
*/
public RealtimeEndpointInfo getEndpointInfo() {
return this.endpointInfo;
}
/**
*
* The current endpoint of the MLModel
*
*
* @param endpointInfo
* The current endpoint of the MLModel
* @return Returns a reference to this object so that method calls can be
* chained together.
*/
public GetMLModelResult withEndpointInfo(RealtimeEndpointInfo endpointInfo) {
setEndpointInfo(endpointInfo);
return this;
}
/**
*
* A list of the training parameters in the MLModel. The list
* is implemented as a map of key/value pairs.
*
*
* The following is the current set of training parameters:
*
*
* -
*
* sgd.l1RegularizationAmount - Coefficient regularization L1
* norm. It controls overfitting the data by penalizing large coefficients.
* This tends to drive coefficients to zero, resulting in a sparse feature
* set. If you use this parameter, specify a small value, such as 1.0E-04 or
* 1.0E-08.
*
*
* The value is a double that ranges from 0 to MAX_DOUBLE. The default is
* not to use L1 normalization. The parameter cannot be used when
* L2 is specified. Use this parameter sparingly.
*
*
* -
*
* sgd.l2RegularizationAmount - Coefficient regularization L2
* norm. It controls overfitting the data by penalizing large coefficients.
* This tends to drive coefficients to small, nonzero values. If you use
* this parameter, specify a small value, such as 1.0E-04 or 1.0E-08.
*
*
* The value is a double that ranges from 0 to MAX_DOUBLE. The default is
* not to use L2 normalization. This parameter cannot be used when
* L1 is specified. Use this parameter sparingly.
*
*
* -
*
* sgd.maxPasses - The number of times that the training
* process traverses the observations to build the MLModel. The
* value is an integer that ranges from 1 to 10000. The default value is 10.
*
*
* -
*
* sgd.maxMLModelSizeInBytes - The maximum allowed size of the
* model. Depending on the input data, the model size might affect
* performance.
*
*
* The value is an integer that ranges from 100000 to 2147483648. The
* default value is 33554432.
*
*
*
*
* @return A list of the training parameters in the MLModel.
* The list is implemented as a map of key/value pairs.
*
* The following is the current set of training parameters:
*
*
* -
*
* sgd.l1RegularizationAmount - Coefficient
* regularization L1 norm. It controls overfitting the data by
* penalizing large coefficients. This tends to drive coefficients
* to zero, resulting in a sparse feature set. If you use this
* parameter, specify a small value, such as 1.0E-04 or 1.0E-08.
*
*
* The value is a double that ranges from 0 to MAX_DOUBLE. The
* default is not to use L1 normalization. The parameter cannot be
* used when L2 is specified. Use this parameter
* sparingly.
*
*
* -
*
* sgd.l2RegularizationAmount - Coefficient
* regularization L2 norm. It controls overfitting the data by
* penalizing large coefficients. This tends to drive coefficients
* to small, nonzero values. If you use this parameter, specify a
* small value, such as 1.0E-04 or 1.0E-08.
*
*
* The value is a double that ranges from 0 to MAX_DOUBLE. The
* default is not to use L2 normalization. This parameter cannot be
* used when L1 is specified. Use this parameter
* sparingly.
*
*
* -
*
* sgd.maxPasses - The number of times that the
* training process traverses the observations to build the
* MLModel. The value is an integer that ranges from 1
* to 10000. The default value is 10.
*
*
* -
*
* sgd.maxMLModelSizeInBytes - The maximum allowed size
* of the model. Depending on the input data, the model size might
* affect performance.
*
*
* The value is an integer that ranges from 100000 to 2147483648.
* The default value is 33554432.
*
*
*/
public java.util.Map getTrainingParameters() {
if (trainingParameters == null) {
trainingParameters = new com.amazonaws.internal.SdkInternalMap();
}
return trainingParameters;
}
/**
*
* A list of the training parameters in the MLModel. The list
* is implemented as a map of key/value pairs.
*
*
* The following is the current set of training parameters:
*
*
* -
*
* sgd.l1RegularizationAmount - Coefficient regularization L1
* norm. It controls overfitting the data by penalizing large coefficients.
* This tends to drive coefficients to zero, resulting in a sparse feature
* set. If you use this parameter, specify a small value, such as 1.0E-04 or
* 1.0E-08.
*
*
* The value is a double that ranges from 0 to MAX_DOUBLE. The default is
* not to use L1 normalization. The parameter cannot be used when
* L2 is specified. Use this parameter sparingly.
*
*
* -
*
* sgd.l2RegularizationAmount - Coefficient regularization L2
* norm. It controls overfitting the data by penalizing large coefficients.
* This tends to drive coefficients to small, nonzero values. If you use
* this parameter, specify a small value, such as 1.0E-04 or 1.0E-08.
*
*
* The value is a double that ranges from 0 to MAX_DOUBLE. The default is
* not to use L2 normalization. This parameter cannot be used when
* L1 is specified. Use this parameter sparingly.
*
*
* -
*
* sgd.maxPasses - The number of times that the training
* process traverses the observations to build the MLModel. The
* value is an integer that ranges from 1 to 10000. The default value is 10.
*
*
* -
*
* sgd.maxMLModelSizeInBytes - The maximum allowed size of the
* model. Depending on the input data, the model size might affect
* performance.
*
*
* The value is an integer that ranges from 100000 to 2147483648. The
* default value is 33554432.
*
*
*
*
* @param trainingParameters
* A list of the training parameters in the MLModel. The
* list is implemented as a map of key/value pairs.
*
* The following is the current set of training parameters:
*
*
* -
*
* sgd.l1RegularizationAmount - Coefficient
* regularization L1 norm. It controls overfitting the data by
* penalizing large coefficients. This tends to drive coefficients to
* zero, resulting in a sparse feature set. If you use this
* parameter, specify a small value, such as 1.0E-04 or 1.0E-08.
*
*
* The value is a double that ranges from 0 to MAX_DOUBLE. The
* default is not to use L1 normalization. The parameter cannot be
* used when L2 is specified. Use this parameter
* sparingly.
*
*
* -
*
* sgd.l2RegularizationAmount - Coefficient
* regularization L2 norm. It controls overfitting the data by
* penalizing large coefficients. This tends to drive coefficients to
* small, nonzero values. If you use this parameter, specify a small
* value, such as 1.0E-04 or 1.0E-08.
*
*
* The value is a double that ranges from 0 to MAX_DOUBLE. The
* default is not to use L2 normalization. This parameter cannot be
* used when L1 is specified. Use this parameter
* sparingly.
*
*
* -
*
* sgd.maxPasses - The number of times that the training
* process traverses the observations to build the
* MLModel. The value is an integer that ranges from 1
* to 10000. The default value is 10.
*
*
* -
*
* sgd.maxMLModelSizeInBytes - The maximum allowed size
* of the model. Depending on the input data, the model size might
* affect performance.
*
*
* The value is an integer that ranges from 100000 to 2147483648. The
* default value is 33554432.
*
*
*/
public void setTrainingParameters(
java.util.Map trainingParameters) {
this.trainingParameters = trainingParameters == null ? null
: new com.amazonaws.internal.SdkInternalMap(
trainingParameters);
}
/**
*
* A list of the training parameters in the MLModel. The list
* is implemented as a map of key/value pairs.
*
*
* The following is the current set of training parameters:
*
*
* -
*
* sgd.l1RegularizationAmount - Coefficient regularization L1
* norm. It controls overfitting the data by penalizing large coefficients.
* This tends to drive coefficients to zero, resulting in a sparse feature
* set. If you use this parameter, specify a small value, such as 1.0E-04 or
* 1.0E-08.
*
*
* The value is a double that ranges from 0 to MAX_DOUBLE. The default is
* not to use L1 normalization. The parameter cannot be used when
* L2 is specified. Use this parameter sparingly.
*
*
* -
*
* sgd.l2RegularizationAmount - Coefficient regularization L2
* norm. It controls overfitting the data by penalizing large coefficients.
* This tends to drive coefficients to small, nonzero values. If you use
* this parameter, specify a small value, such as 1.0E-04 or 1.0E-08.
*
*
* The value is a double that ranges from 0 to MAX_DOUBLE. The default is
* not to use L2 normalization. This parameter cannot be used when
* L1 is specified. Use this parameter sparingly.
*
*
* -
*
* sgd.maxPasses - The number of times that the training
* process traverses the observations to build the MLModel. The
* value is an integer that ranges from 1 to 10000. The default value is 10.
*
*
* -
*
* sgd.maxMLModelSizeInBytes - The maximum allowed size of the
* model. Depending on the input data, the model size might affect
* performance.
*
*
* The value is an integer that ranges from 100000 to 2147483648. The
* default value is 33554432.
*
*
*
*
* @param trainingParameters
* A list of the training parameters in the MLModel. The
* list is implemented as a map of key/value pairs.
*
* The following is the current set of training parameters:
*
*
* -
*
* sgd.l1RegularizationAmount - Coefficient
* regularization L1 norm. It controls overfitting the data by
* penalizing large coefficients. This tends to drive coefficients to
* zero, resulting in a sparse feature set. If you use this
* parameter, specify a small value, such as 1.0E-04 or 1.0E-08.
*
*
* The value is a double that ranges from 0 to MAX_DOUBLE. The
* default is not to use L1 normalization. The parameter cannot be
* used when L2 is specified. Use this parameter
* sparingly.
*
*
* -
*
* sgd.l2RegularizationAmount - Coefficient
* regularization L2 norm. It controls overfitting the data by
* penalizing large coefficients. This tends to drive coefficients to
* small, nonzero values. If you use this parameter, specify a small
* value, such as 1.0E-04 or 1.0E-08.
*
*
* The value is a double that ranges from 0 to MAX_DOUBLE. The
* default is not to use L2 normalization. This parameter cannot be
* used when L1 is specified. Use this parameter
* sparingly.
*
*
* -
*
* sgd.maxPasses - The number of times that the training
* process traverses the observations to build the
* MLModel. The value is an integer that ranges from 1
* to 10000. The default value is 10.
*
*
* -
*
* sgd.maxMLModelSizeInBytes - The maximum allowed size
* of the model. Depending on the input data, the model size might
* affect performance.
*
*
* The value is an integer that ranges from 100000 to 2147483648. The
* default value is 33554432.
*
*
* @return Returns a reference to this object so that method calls can be
* chained together.
*/
public GetMLModelResult withTrainingParameters(
java.util.Map trainingParameters) {
setTrainingParameters(trainingParameters);
return this;
}
public GetMLModelResult addTrainingParametersEntry(String key, String value) {
if (null == this.trainingParameters) {
this.trainingParameters = new com.amazonaws.internal.SdkInternalMap();
}
if (this.trainingParameters.containsKey(key))
throw new IllegalArgumentException("Duplicated keys ("
+ key.toString() + ") are provided.");
this.trainingParameters.put(key, value);
return this;
}
/**
* Removes all the entries added into TrainingParameters. <p> Returns a
* reference to this object so that method calls can be chained together.
*/
public GetMLModelResult clearTrainingParametersEntries() {
this.trainingParameters = null;
return this;
}
/**
*
* The location of the data file or directory in Amazon Simple Storage
* Service (Amazon S3).
*
*
* @param inputDataLocationS3
* The location of the data file or directory in Amazon Simple
* Storage Service (Amazon S3).
*/
public void setInputDataLocationS3(String inputDataLocationS3) {
this.inputDataLocationS3 = inputDataLocationS3;
}
/**
*
* The location of the data file or directory in Amazon Simple Storage
* Service (Amazon S3).
*
*
* @return The location of the data file or directory in Amazon Simple
* Storage Service (Amazon S3).
*/
public String getInputDataLocationS3() {
return this.inputDataLocationS3;
}
/**
*
* The location of the data file or directory in Amazon Simple Storage
* Service (Amazon S3).
*
*
* @param inputDataLocationS3
* The location of the data file or directory in Amazon Simple
* Storage Service (Amazon S3).
* @return Returns a reference to this object so that method calls can be
* chained together.
*/
public GetMLModelResult withInputDataLocationS3(String inputDataLocationS3) {
setInputDataLocationS3(inputDataLocationS3);
return this;
}
/**
*
* Identifies the MLModel category. The following are the
* available types:
*
*
* - REGRESSION -- Produces a numeric result. For example,
* "What listing price should a house have?"
* - BINARY -- Produces one of two possible results. For example,
* "Is this an e-commerce website?"
* - MULTICLASS -- Produces more than two possible results. For example,
* "Is this a HIGH, LOW or MEDIUM risk trade?"
*
*
* @param mLModelType
* Identifies the MLModel category. The following are
* the available types:
*
* - REGRESSION -- Produces a numeric result. For example,
* "What listing price should a house have?"
* - BINARY -- Produces one of two possible results. For example,
* "Is this an e-commerce website?"
* - MULTICLASS -- Produces more than two possible results. For
* example, "Is this a HIGH, LOW or MEDIUM risk trade?"
* @see MLModelType
*/
public void setMLModelType(String mLModelType) {
this.mLModelType = mLModelType;
}
/**
*
* Identifies the MLModel category. The following are the
* available types:
*
*
* - REGRESSION -- Produces a numeric result. For example,
* "What listing price should a house have?"
* - BINARY -- Produces one of two possible results. For example,
* "Is this an e-commerce website?"
* - MULTICLASS -- Produces more than two possible results. For example,
* "Is this a HIGH, LOW or MEDIUM risk trade?"
*
*
* @return Identifies the MLModel category. The following are
* the available types:
*
* - REGRESSION -- Produces a numeric result. For example,
* "What listing price should a house have?"
* - BINARY -- Produces one of two possible results. For example,
* "Is this an e-commerce website?"
* - MULTICLASS -- Produces more than two possible results. For
* example, "Is this a HIGH, LOW or MEDIUM risk trade?"
* @see MLModelType
*/
public String getMLModelType() {
return this.mLModelType;
}
/**
*
* Identifies the MLModel category. The following are the
* available types:
*
*
* - REGRESSION -- Produces a numeric result. For example,
* "What listing price should a house have?"
* - BINARY -- Produces one of two possible results. For example,
* "Is this an e-commerce website?"
* - MULTICLASS -- Produces more than two possible results. For example,
* "Is this a HIGH, LOW or MEDIUM risk trade?"
*
*
* @param mLModelType
* Identifies the MLModel category. The following are
* the available types:
*
* - REGRESSION -- Produces a numeric result. For example,
* "What listing price should a house have?"
* - BINARY -- Produces one of two possible results. For example,
* "Is this an e-commerce website?"
* - MULTICLASS -- Produces more than two possible results. For
* example, "Is this a HIGH, LOW or MEDIUM risk trade?"
* @return Returns a reference to this object so that method calls can be
* chained together.
* @see MLModelType
*/
public GetMLModelResult withMLModelType(String mLModelType) {
setMLModelType(mLModelType);
return this;
}
/**
*
* Identifies the MLModel category. The following are the
* available types:
*
*
* - REGRESSION -- Produces a numeric result. For example,
* "What listing price should a house have?"
* - BINARY -- Produces one of two possible results. For example,
* "Is this an e-commerce website?"
* - MULTICLASS -- Produces more than two possible results. For example,
* "Is this a HIGH, LOW or MEDIUM risk trade?"
*
*
* @param mLModelType
* Identifies the MLModel category. The following are
* the available types:
*
* - REGRESSION -- Produces a numeric result. For example,
* "What listing price should a house have?"
* - BINARY -- Produces one of two possible results. For example,
* "Is this an e-commerce website?"
* - MULTICLASS -- Produces more than two possible results. For
* example, "Is this a HIGH, LOW or MEDIUM risk trade?"
* @see MLModelType
*/
public void setMLModelType(MLModelType mLModelType) {
this.mLModelType = mLModelType.toString();
}
/**
*
* Identifies the MLModel category. The following are the
* available types:
*
*
* - REGRESSION -- Produces a numeric result. For example,
* "What listing price should a house have?"
* - BINARY -- Produces one of two possible results. For example,
* "Is this an e-commerce website?"
* - MULTICLASS -- Produces more than two possible results. For example,
* "Is this a HIGH, LOW or MEDIUM risk trade?"
*
*
* @param mLModelType
* Identifies the MLModel category. The following are
* the available types:
*
* - REGRESSION -- Produces a numeric result. For example,
* "What listing price should a house have?"
* - BINARY -- Produces one of two possible results. For example,
* "Is this an e-commerce website?"
* - MULTICLASS -- Produces more than two possible results. For
* example, "Is this a HIGH, LOW or MEDIUM risk trade?"
* @return Returns a reference to this object so that method calls can be
* chained together.
* @see MLModelType
*/
public GetMLModelResult withMLModelType(MLModelType mLModelType) {
setMLModelType(mLModelType);
return this;
}
/**
*
* The scoring threshold is used in binary classification
* MLModels, and marks the boundary between a positive
* prediction and a negative prediction.
*
*
* Output values greater than or equal to the threshold receive a positive
* result from the MLModel, such as true. Output values less
* than the threshold receive a negative response from the MLModel, such as
* false.
*
*
* @param scoreThreshold
* The scoring threshold is used in binary classification
* MLModels, and marks the boundary between a positive
* prediction and a negative prediction.
*
* Output values greater than or equal to the threshold receive a
* positive result from the MLModel, such as true.
* Output values less than the threshold receive a negative response
* from the MLModel, such as false.
*/
public void setScoreThreshold(Float scoreThreshold) {
this.scoreThreshold = scoreThreshold;
}
/**
*
* The scoring threshold is used in binary classification
* MLModels, and marks the boundary between a positive
* prediction and a negative prediction.
*
*
* Output values greater than or equal to the threshold receive a positive
* result from the MLModel, such as true. Output values less
* than the threshold receive a negative response from the MLModel, such as
* false.
*
*
* @return The scoring threshold is used in binary classification
* MLModels, and marks the boundary between a positive
* prediction and a negative prediction.
*
* Output values greater than or equal to the threshold receive a
* positive result from the MLModel, such as true.
* Output values less than the threshold receive a negative response
* from the MLModel, such as false.
*/
public Float getScoreThreshold() {
return this.scoreThreshold;
}
/**
*
* The scoring threshold is used in binary classification
* MLModels, and marks the boundary between a positive
* prediction and a negative prediction.
*
*
* Output values greater than or equal to the threshold receive a positive
* result from the MLModel, such as true. Output values less
* than the threshold receive a negative response from the MLModel, such as
* false.
*
*
* @param scoreThreshold
* The scoring threshold is used in binary classification
* MLModels, and marks the boundary between a positive
* prediction and a negative prediction.
*
* Output values greater than or equal to the threshold receive a
* positive result from the MLModel, such as true.
* Output values less than the threshold receive a negative response
* from the MLModel, such as false.
* @return Returns a reference to this object so that method calls can be
* chained together.
*/
public GetMLModelResult withScoreThreshold(Float scoreThreshold) {
setScoreThreshold(scoreThreshold);
return this;
}
/**
*
* The time of the most recent edit to the ScoreThreshold. The
* time is expressed in epoch time.
*
*
* @param scoreThresholdLastUpdatedAt
* The time of the most recent edit to the
* ScoreThreshold. The time is expressed in epoch time.
*/
public void setScoreThresholdLastUpdatedAt(
java.util.Date scoreThresholdLastUpdatedAt) {
this.scoreThresholdLastUpdatedAt = scoreThresholdLastUpdatedAt;
}
/**
*
* The time of the most recent edit to the ScoreThreshold. The
* time is expressed in epoch time.
*
*
* @return The time of the most recent edit to the
* ScoreThreshold. The time is expressed in epoch time.
*/
public java.util.Date getScoreThresholdLastUpdatedAt() {
return this.scoreThresholdLastUpdatedAt;
}
/**
*
* The time of the most recent edit to the ScoreThreshold. The
* time is expressed in epoch time.
*
*
* @param scoreThresholdLastUpdatedAt
* The time of the most recent edit to the
* ScoreThreshold. The time is expressed in epoch time.
* @return Returns a reference to this object so that method calls can be
* chained together.
*/
public GetMLModelResult withScoreThresholdLastUpdatedAt(
java.util.Date scoreThresholdLastUpdatedAt) {
setScoreThresholdLastUpdatedAt(scoreThresholdLastUpdatedAt);
return this;
}
/**
*
* A link to the file that contains logs of the CreateMLModel
* operation.
*
*
* @param logUri
* A link to the file that contains logs of the
* CreateMLModel operation.
*/
public void setLogUri(String logUri) {
this.logUri = logUri;
}
/**
*
* A link to the file that contains logs of the CreateMLModel
* operation.
*
*
* @return A link to the file that contains logs of the
* CreateMLModel operation.
*/
public String getLogUri() {
return this.logUri;
}
/**
*
* A link to the file that contains logs of the CreateMLModel
* operation.
*
*
* @param logUri
* A link to the file that contains logs of the
* CreateMLModel operation.
* @return Returns a reference to this object so that method calls can be
* chained together.
*/
public GetMLModelResult withLogUri(String logUri) {
setLogUri(logUri);
return this;
}
/**
*
* Description of the most recent details about accessing the
* MLModel.
*
*
* @param message
* Description of the most recent details about accessing the
* MLModel.
*/
public void setMessage(String message) {
this.message = message;
}
/**
*
* Description of the most recent details about accessing the
* MLModel.
*
*
* @return Description of the most recent details about accessing the
* MLModel.
*/
public String getMessage() {
return this.message;
}
/**
*
* Description of the most recent details about accessing the
* MLModel.
*
*
* @param message
* Description of the most recent details about accessing the
* MLModel.
* @return Returns a reference to this object so that method calls can be
* chained together.
*/
public GetMLModelResult withMessage(String message) {
setMessage(message);
return this;
}
/**
*
* The recipe to use when training the MLModel. The
* Recipe provides detailed information about the observation
* data to use during training, as well as manipulations to perform on the
* observation data during training.
*
* Note
*
* This parameter is provided as part of the verbose format.
*
* MLModel. The
* Recipe provides detailed information about the
* observation data to use during training, as well as manipulations
* to perform on the observation data during training.
* Note
*
* This parameter is provided as part of the verbose format.
*
*/
public void setRecipe(String recipe) {
this.recipe = recipe;
}
/**
*
* The recipe to use when training the MLModel. The
* Recipe provides detailed information about the observation
* data to use during training, as well as manipulations to perform on the
* observation data during training.
*
* Note
*
* This parameter is provided as part of the verbose format.
*
* MLModel. The
* Recipe provides detailed information about the
* observation data to use during training, as well as manipulations
* to perform on the observation data during training.
* Note
*
* This parameter is provided as part of the verbose format.
*
*/
public String getRecipe() {
return this.recipe;
}
/**
*
* The recipe to use when training the MLModel. The
* Recipe provides detailed information about the observation
* data to use during training, as well as manipulations to perform on the
* observation data during training.
*
* Note
*
* This parameter is provided as part of the verbose format.
*
* MLModel. The
* Recipe provides detailed information about the
* observation data to use during training, as well as manipulations
* to perform on the observation data during training.
* Note
*
* This parameter is provided as part of the verbose format.
*
* @return Returns a reference to this object so that method calls can be
* chained together.
*/
public GetMLModelResult withRecipe(String recipe) {
setRecipe(recipe);
return this;
}
/**
*
* The schema used by all of the data files referenced by the
* DataSource.
*
* Note
*
* This parameter is provided as part of the verbose format.
*
* DataSource. Note
*
* This parameter is provided as part of the verbose format.
*
*/
public void setSchema(String schema) {
this.schema = schema;
}
/**
*
* The schema used by all of the data files referenced by the
* DataSource.
*
* Note
*
* This parameter is provided as part of the verbose format.
*
* DataSource. Note
*
* This parameter is provided as part of the verbose format.
*
*/
public String getSchema() {
return this.schema;
}
/**
*
* The schema used by all of the data files referenced by the
* DataSource.
*
* Note
*
* This parameter is provided as part of the verbose format.
*
* DataSource. Note
*
* This parameter is provided as part of the verbose format.
*
* @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: " + 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 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;
}
@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 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);
}
}
}