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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 {
/**
* The MLModel ID which is same as the MLModelId in the
* request.
*
* Constraints:
* Length: 1 - 64
* Pattern: [a-zA-Z0-9_.-]+
*/
private String mLModelId;
/**
* The ID of the training DataSource.
*
* Constraints:
* Length: 1 - 64
* Pattern: [a-zA-Z0-9_.-]+
*/
private String trainingDataSourceId;
/**
* The AWS user account from which the MLModel was created.
* The account type can be either an AWS root account or an AWS Identity
* and Access Management (IAM) user account.
*
* Constraints:
* Pattern: arn:aws:iam::[0-9]+:((user/.+)|(root))
*/
private String createdByIamUser;
/**
* The time that the MLModel was created. The time is
* expressed in epoch time.
*/
private java.util.Date createdAt;
/**
* The time of the most recent edit to the MLModel. The time
* is expressed in epoch time.
*/
private java.util.Date lastUpdatedAt;
/**
* A user-supplied name or description of the MLModel.
*
* Constraints:
* Length: 0 - 1024
*/
private String name;
/**
* The current status of 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.
*
* Constraints:
* Allowed Values: PENDING, INPROGRESS, FAILED, COMPLETED, DELETED
*/
private String status;
/**
* Long integer type that is a 64-bit signed number.
*/
private Long sizeInBytes;
/**
* The current endpoint of the MLModel
*/
private RealtimeEndpointInfo endpointInfo;
/**
* A list of the training parameters in the MLModel. The
* list is implemented as a map of key/value pairs.
The following is
* the current set of training parameters:
* sgd.l1RegularizationAmount - Coefficient
* regularization L1 norm. It controls overfitting the data by penalizing
* large coefficients. This tends to drive coefficients to zero,
* resulting in a sparse feature set. If you use this parameter, specify
* a small value, such as 1.0E-04 or 1.0E-08.
The value is a double
* that ranges from 0 to MAX_DOUBLE. The default is not to use L1
* normalization. The parameter cannot be used when L2 is
* specified. Use this parameter sparingly.
sgd.l2RegularizationAmount - Coefficient
* regularization L2 norm. It controls overfitting the data by penalizing
* large coefficients. This tends to drive coefficients to small, nonzero
* values. If you use this parameter, specify a small value, such as
* 1.0E-04 or 1.0E-08.
The 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 java.util.Map trainingParameters;
/**
* The location of the data file or directory in Amazon Simple Storage
* Service (Amazon S3).
*
* Constraints:
* Length: 0 - 2048
* Pattern: 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?"
*
* Constraints:
* Allowed Values: REGRESSION, BINARY, MULTICLASS
*/
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.
*
* Constraints:
* Length: 0 - 10240
*/
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.
* Constraints:
* Length: 0 - 131071
*/
private String recipe;
/**
* The schema used by all of the data files referenced by the
* DataSource. Note This parameter is
* provided as part of the verbose format.
* Constraints:
* Length: 0 - 131071
*/
private String schema;
/**
* The MLModel ID which is same as the MLModelId in the
* request.
*
* Constraints:
* Length: 1 - 64
* Pattern: [a-zA-Z0-9_.-]+
*
* @return The MLModel ID which is same as the MLModelId in the
* request.
*/
public String getMLModelId() {
return mLModelId;
}
/**
* The MLModel ID which is same as the MLModelId in the
* request.
*
* Constraints:
* Length: 1 - 64
* Pattern: [a-zA-Z0-9_.-]+
*
* @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.
*
* Returns a reference to this object so that method calls can be chained together.
*
* Constraints:
* Length: 1 - 64
* Pattern: [a-zA-Z0-9_.-]+
*
* @param mLModelId The MLModel ID which is same as the MLModelId in the
* request.
*
* @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;
}
/**
* The ID of the training DataSource.
*
* Constraints:
* Length: 1 - 64
* Pattern: [a-zA-Z0-9_.-]+
*
* @return The ID of the training DataSource.
*/
public String getTrainingDataSourceId() {
return trainingDataSourceId;
}
/**
* The ID of the training DataSource.
*
* Constraints:
* Length: 1 - 64
* Pattern: [a-zA-Z0-9_.-]+
*
* @param trainingDataSourceId The ID of the training DataSource.
*/
public void setTrainingDataSourceId(String trainingDataSourceId) {
this.trainingDataSourceId = trainingDataSourceId;
}
/**
* The ID of the training DataSource.
*
* Returns a reference to this object so that method calls can be chained together.
*
* Constraints:
* Length: 1 - 64
* Pattern: [a-zA-Z0-9_.-]+
*
* @param trainingDataSourceId The ID of the training DataSource.
*
* @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;
}
/**
* The AWS user account from which the MLModel was created.
* The account type can be either an AWS root account or an AWS Identity
* and Access Management (IAM) user account.
*
* Constraints:
* Pattern: arn:aws:iam::[0-9]+:((user/.+)|(root))
*
* @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 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.
*
* Constraints:
* Pattern: arn:aws:iam::[0-9]+:((user/.+)|(root))
*
* @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.
*
* Returns a reference to this object so that method calls can be chained together.
*
* Constraints:
* Pattern: arn:aws:iam::[0-9]+:((user/.+)|(root))
*
* @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 A reference to this updated object so that method calls can be chained
* together.
*/
public GetMLModelResult withCreatedByIamUser(String createdByIamUser) {
this.createdByIamUser = createdByIamUser;
return this;
}
/**
* 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 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.
*/
public void setCreatedAt(java.util.Date createdAt) {
this.createdAt = createdAt;
}
/**
* The time that the MLModel was created. The time is
* expressed in epoch time.
*
* Returns a reference to this object so that method calls can be chained together.
*
* @param createdAt The time that the MLModel was created. The time is
* expressed in epoch time.
*
* @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;
}
/**
* 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 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.
*/
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.
*
* Returns a reference to this object so that method calls can be chained together.
*
* @param lastUpdatedAt The time of the most recent edit to the MLModel. The time
* is expressed in epoch time.
*
* @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;
}
/**
* A user-supplied name or description of the MLModel.
*
* Constraints:
* Length: 0 - 1024
*
* @return A user-supplied name or description of the MLModel.
*/
public String getName() {
return name;
}
/**
* A user-supplied name or description of the MLModel.
*
* Constraints:
* Length: 0 - 1024
*
* @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.
*
* Returns a reference to this object so that method calls can be chained together.
*
* Constraints:
* Length: 0 - 1024
*
* @param name A user-supplied name or description of the MLModel.
*
* @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;
}
/**
* 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.
*
* Constraints:
* Allowed Values: PENDING, INPROGRESS, FAILED, COMPLETED, DELETED
*
* @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 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.
*
* Constraints:
* Allowed Values: PENDING, INPROGRESS, FAILED, COMPLETED, DELETED
*
* @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.
*
* Returns a reference to this object so that method calls can be chained together.
*
* Constraints:
* Allowed Values: PENDING, INPROGRESS, FAILED, COMPLETED, DELETED
*
* @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 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;
}
/**
* 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.
*
* Constraints:
* Allowed Values: PENDING, INPROGRESS, FAILED, COMPLETED, DELETED
*
* @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.
*
* Returns a reference to this object so that method calls can be chained together.
*
* Constraints:
* Allowed Values: PENDING, INPROGRESS, FAILED, COMPLETED, DELETED
*
* @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 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;
}
/**
* Long integer type that is a 64-bit signed number.
*
* @return Long integer type that is a 64-bit signed number.
*/
public Long getSizeInBytes() {
return sizeInBytes;
}
/**
* Long integer type that is a 64-bit signed number.
*
* @param sizeInBytes Long integer type that is a 64-bit signed number.
*/
public void setSizeInBytes(Long sizeInBytes) {
this.sizeInBytes = sizeInBytes;
}
/**
* Long integer type that is a 64-bit signed number.
*
* Returns a reference to this object so that method calls can be chained together.
*
* @param sizeInBytes Long integer type that is a 64-bit signed number.
*
* @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;
}
/**
* The current endpoint of the MLModel
*
* @return The current endpoint of the MLModel
*/
public RealtimeEndpointInfo getEndpointInfo() {
return endpointInfo;
}
/**
* 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
*
* Returns a reference to this object so that method calls can be chained together.
*
* @param endpointInfo The current endpoint of the MLModel
*
* @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;
}
/**
* 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 java.util.HashMap();
}
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;
}
/**
* 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.
*
* Returns a reference to this object so that method calls can be chained together.
*
* @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 A reference to this updated object so that method calls can be chained
* together.
*/
public GetMLModelResult withTrainingParameters(java.util.Map trainingParameters) {
setTrainingParameters(trainingParameters);
return this;
}
/**
* A list of the training parameters in the MLModel. The
* list is implemented as a map of key/value pairs. The following is
* the current set of training parameters:
* sgd.l1RegularizationAmount - Coefficient
* regularization L1 norm. It controls overfitting the data by penalizing
* large coefficients. This tends to drive coefficients to zero,
* resulting in a sparse feature set. If you use this parameter, specify
* a small value, such as 1.0E-04 or 1.0E-08.
The value is a double
* that ranges from 0 to MAX_DOUBLE. The default is not to use L1
* normalization. The parameter cannot be used when L2 is
* specified. Use this parameter sparingly.
sgd.l2RegularizationAmount - Coefficient
* regularization L2 norm. It controls overfitting the data by penalizing
* large coefficients. This tends to drive coefficients to small, nonzero
* values. If you use this parameter, specify a small value, such as
* 1.0E-04 or 1.0E-08.
The 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.
*
* 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.
*/
public GetMLModelResult addTrainingParametersEntry(String key, String value) {
if (null == this.trainingParameters) {
this.trainingParameters = new java.util.HashMap();
}
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.
*
* 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).
*
* Constraints:
* Length: 0 - 2048
* Pattern: s3://([^/]+)(/.*)?
*
* @return The location of the data file or directory in Amazon Simple Storage
* Service (Amazon S3).
*/
public String getInputDataLocationS3() {
return inputDataLocationS3;
}
/**
* The location of the data file or directory in Amazon Simple Storage
* Service (Amazon S3).
*
* Constraints:
* Length: 0 - 2048
* Pattern: 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).
*
* Returns a reference to this object so that method calls can be chained together.
*
* Constraints:
* Length: 0 - 2048
* Pattern: s3://([^/]+)(/.*)?
*
* @param inputDataLocationS3 The location of the data file or directory in Amazon Simple Storage
* Service (Amazon S3).
*
* @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;
}
/**
* 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?"
*
* Constraints:
* Allowed Values: REGRESSION, BINARY, MULTICLASS
*
* @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 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?"
*
* Constraints:
* Allowed Values: REGRESSION, BINARY, MULTICLASS
*
* @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?"
*
* Returns a reference to this object so that method calls can be chained together.
*
* Constraints:
* Allowed Values: REGRESSION, BINARY, MULTICLASS
*
* @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 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;
}
/**
* 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?"
*
* Constraints:
* Allowed Values: REGRESSION, BINARY, MULTICLASS
*
* @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?"
*
* Returns a reference to this object so that method calls can be chained together.
*
* Constraints:
* Allowed Values: REGRESSION, BINARY, MULTICLASS
*
* @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 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;
}
/**
* 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 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.
*/
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.
*
* Returns a reference to this object so that method calls can be chained together.
*
* @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 A reference to this updated object so that method calls can be chained
* together.
*/
public GetMLModelResult withScoreThreshold(Float scoreThreshold) {
this.scoreThreshold = scoreThreshold;
return this;
}
/**
* 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 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.
*/
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.
*
* Returns a reference to this object so that method calls can be chained together.
*
* @param scoreThresholdLastUpdatedAt The time of the most recent edit to the ScoreThreshold.
* The time is expressed in epoch time.
*
* @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;
}
/**
* 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 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.
*/
public void setLogUri(String logUri) {
this.logUri = logUri;
}
/**
* A link to the file that contains logs of the
* CreateMLModel operation.
*
* Returns a reference to this object so that method calls can be chained together.
*
* @param logUri A link to the file that contains logs of the
* CreateMLModel operation.
*
* @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;
}
/**
* Description of the most recent details about accessing the
* MLModel.
*
* Constraints:
* Length: 0 - 10240
*
* @return Description of the most recent details about accessing the
* MLModel.
*/
public String getMessage() {
return message;
}
/**
* Description of the most recent details about accessing the
* MLModel.
*
* Constraints:
* Length: 0 - 10240
*
* @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.
*
* Returns a reference to this object so that method calls can be chained together.
*
* Constraints:
* Length: 0 - 10240
*
* @param message Description of the most recent details about accessing the
* MLModel.
*
* @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;
}
/**
* 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.
* Constraints:
* Length: 0 - 131071
*
* @return 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.
* Constraints:
* Length: 0 - 131071
*
* @param 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.
* Returns a reference to this object so that method calls can be chained together.
*
* Constraints:
* Length: 0 - 131071
*
* @param 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.
DataSource. Note This parameter is
* provided as part of the verbose format.
* Constraints:
* Length: 0 - 131071
*
* @return 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.
* Constraints:
* Length: 0 - 131071
*
* @param 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.
* Returns a reference to this object so that method calls can be chained together.
*
* Constraints:
* Length: 0 - 131071
*
* @param 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.