com.amazonaws.services.lookoutequipment.model

Class CreateModelRequest

java.lang.Object

com.amazonaws.AmazonWebServiceRequest

com.amazonaws.services.lookoutequipment.model.CreateModelRequest

All Implemented Interfaces:

HandlerContextAware, ReadLimitInfo, Serializable, Cloneable

@Generated(value="com.amazonaws:aws-java-sdk-code-generator")
public class CreateModelRequest
extends AmazonWebServiceRequest
implements Serializable, Cloneable

See Also:

AWS API Documentation, Serialized Form

Field Summary

Fields inherited from class com.amazonaws.AmazonWebServiceRequest

NOOP

Constructor Summary

Constructors

Constructor and Description
CreateModelRequest()

Method Summary

Modifier and Type	Method and Description
CreateModelRequest	clone() Creates a shallow clone of this object for all fields except the handler context.
boolean	equals(Object obj)
String	getClientToken() A unique identifier for the request.
DataPreProcessingConfiguration	getDataPreProcessingConfiguration() The configuration is the TargetSamplingRate, which is the sampling rate of the data after post processing by Amazon Lookout for Equipment.
String	getDatasetName() The name of the dataset for the ML model being created.
DatasetSchema	getDatasetSchema() The data schema for the ML model being created.
Date	getEvaluationDataEndTime() Indicates the time reference in the dataset that should be used to end the subset of evaluation data for the ML model.
Date	getEvaluationDataStartTime() Indicates the time reference in the dataset that should be used to begin the subset of evaluation data for the ML model.
LabelsInputConfiguration	getLabelsInputConfiguration() The input configuration for the labels being used for the ML model that's being created.
String	getModelName() The name for the ML model to be created.
String	getOffCondition() Indicates that the asset associated with this sensor has been shut off.
String	getRoleArn() The Amazon Resource Name (ARN) of a role with permission to access the data source being used to create the ML model.
String	getServerSideKmsKeyId() Provides the identifier of the KMS key used to encrypt model data by Amazon Lookout for Equipment.
List<Tag>	getTags() Any tags associated with the ML model being created.
Date	getTrainingDataEndTime() Indicates the time reference in the dataset that should be used to end the subset of training data for the ML model.
Date	getTrainingDataStartTime() Indicates the time reference in the dataset that should be used to begin the subset of training data for the ML model.
int	hashCode()
void	setClientToken(String clientToken) A unique identifier for the request.
void	setDataPreProcessingConfiguration(DataPreProcessingConfiguration dataPreProcessingConfiguration) The configuration is the TargetSamplingRate, which is the sampling rate of the data after post processing by Amazon Lookout for Equipment.
void	setDatasetName(String datasetName) The name of the dataset for the ML model being created.
void	setDatasetSchema(DatasetSchema datasetSchema) The data schema for the ML model being created.
void	setEvaluationDataEndTime(Date evaluationDataEndTime) Indicates the time reference in the dataset that should be used to end the subset of evaluation data for the ML model.
void	setEvaluationDataStartTime(Date evaluationDataStartTime) Indicates the time reference in the dataset that should be used to begin the subset of evaluation data for the ML model.
void	setLabelsInputConfiguration(LabelsInputConfiguration labelsInputConfiguration) The input configuration for the labels being used for the ML model that's being created.
void	setModelName(String modelName) The name for the ML model to be created.
void	setOffCondition(String offCondition) Indicates that the asset associated with this sensor has been shut off.
void	setRoleArn(String roleArn) The Amazon Resource Name (ARN) of a role with permission to access the data source being used to create the ML model.
void	setServerSideKmsKeyId(String serverSideKmsKeyId) Provides the identifier of the KMS key used to encrypt model data by Amazon Lookout for Equipment.
void	setTags(Collection<Tag> tags) Any tags associated with the ML model being created.
void	setTrainingDataEndTime(Date trainingDataEndTime) Indicates the time reference in the dataset that should be used to end the subset of training data for the ML model.
void	setTrainingDataStartTime(Date trainingDataStartTime) Indicates the time reference in the dataset that should be used to begin the subset of training data for the ML model.
String	toString() Returns a string representation of this object.
CreateModelRequest	withClientToken(String clientToken) A unique identifier for the request.
CreateModelRequest	withDataPreProcessingConfiguration(DataPreProcessingConfiguration dataPreProcessingConfiguration) The configuration is the TargetSamplingRate, which is the sampling rate of the data after post processing by Amazon Lookout for Equipment.
CreateModelRequest	withDatasetName(String datasetName) The name of the dataset for the ML model being created.
CreateModelRequest	withDatasetSchema(DatasetSchema datasetSchema) The data schema for the ML model being created.
CreateModelRequest	withEvaluationDataEndTime(Date evaluationDataEndTime) Indicates the time reference in the dataset that should be used to end the subset of evaluation data for the ML model.
CreateModelRequest	withEvaluationDataStartTime(Date evaluationDataStartTime) Indicates the time reference in the dataset that should be used to begin the subset of evaluation data for the ML model.
CreateModelRequest	withLabelsInputConfiguration(LabelsInputConfiguration labelsInputConfiguration) The input configuration for the labels being used for the ML model that's being created.
CreateModelRequest	withModelName(String modelName) The name for the ML model to be created.
CreateModelRequest	withOffCondition(String offCondition) Indicates that the asset associated with this sensor has been shut off.
CreateModelRequest	withRoleArn(String roleArn) The Amazon Resource Name (ARN) of a role with permission to access the data source being used to create the ML model.
CreateModelRequest	withServerSideKmsKeyId(String serverSideKmsKeyId) Provides the identifier of the KMS key used to encrypt model data by Amazon Lookout for Equipment.
CreateModelRequest	withTags(Collection<Tag> tags) Any tags associated with the ML model being created.
CreateModelRequest	withTags(Tag... tags) Any tags associated with the ML model being created.
CreateModelRequest	withTrainingDataEndTime(Date trainingDataEndTime) Indicates the time reference in the dataset that should be used to end the subset of training data for the ML model.
CreateModelRequest	withTrainingDataStartTime(Date trainingDataStartTime) Indicates the time reference in the dataset that should be used to begin the subset of training data for the ML model.

Methods inherited from class com.amazonaws.AmazonWebServiceRequest

addHandlerContext, getCloneRoot, getCloneSource, getCustomQueryParameters, getCustomRequestHeaders, getGeneralProgressListener, getHandlerContext, getReadLimit, getRequestClientOptions, getRequestCredentials, getRequestCredentialsProvider, getRequestMetricCollector, getSdkClientExecutionTimeout, getSdkRequestTimeout, putCustomQueryParameter, putCustomRequestHeader, setGeneralProgressListener, setRequestCredentials, setRequestCredentialsProvider, setRequestMetricCollector, setSdkClientExecutionTimeout, setSdkRequestTimeout, withGeneralProgressListener, withRequestCredentialsProvider, withRequestMetricCollector, withSdkClientExecutionTimeout, withSdkRequestTimeout

Methods inherited from class java.lang.Object

getClass, notify, notifyAll, wait, wait, wait

Constructor Detail

CreateModelRequest

public CreateModelRequest()

Method Detail

setModelName

public void setModelName(String modelName)

The name for the ML model to be created.

Parameters:

modelName - The name for the ML model to be created.

getModelName

public String getModelName()

The name for the ML model to be created.

Returns:

The name for the ML model to be created.

withModelName

public CreateModelRequest withModelName(String modelName)

The name for the ML model to be created.

Parameters:

modelName - The name for the ML model to be created.

Returns:

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

setDatasetName

public void setDatasetName(String datasetName)

The name of the dataset for the ML model being created.

Parameters:

datasetName - The name of the dataset for the ML model being created.

getDatasetName

public String getDatasetName()

The name of the dataset for the ML model being created.

Returns:

The name of the dataset for the ML model being created.

withDatasetName

public CreateModelRequest withDatasetName(String datasetName)

The name of the dataset for the ML model being created.

Parameters:

datasetName - The name of the dataset for the ML model being created.

Returns:

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

setDatasetSchema

public void setDatasetSchema(DatasetSchema datasetSchema)

The data schema for the ML model being created.

Parameters:

datasetSchema - The data schema for the ML model being created.

getDatasetSchema

public DatasetSchema getDatasetSchema()

The data schema for the ML model being created.

Returns:

The data schema for the ML model being created.

withDatasetSchema

public CreateModelRequest withDatasetSchema(DatasetSchema datasetSchema)

The data schema for the ML model being created.

Parameters:

datasetSchema - The data schema for the ML model being created.

Returns:

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

setLabelsInputConfiguration

public void setLabelsInputConfiguration(LabelsInputConfiguration labelsInputConfiguration)

The input configuration for the labels being used for the ML model that's being created.

Parameters:

labelsInputConfiguration - The input configuration for the labels being used for the ML model that's being created.

getLabelsInputConfiguration

public LabelsInputConfiguration getLabelsInputConfiguration()

The input configuration for the labels being used for the ML model that's being created.

Returns:

The input configuration for the labels being used for the ML model that's being created.

withLabelsInputConfiguration

public CreateModelRequest withLabelsInputConfiguration(LabelsInputConfiguration labelsInputConfiguration)

The input configuration for the labels being used for the ML model that's being created.

Parameters:

labelsInputConfiguration - The input configuration for the labels being used for the ML model that's being created.

Returns:

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

setClientToken

public void setClientToken(String clientToken)

A unique identifier for the request. If you do not set the client request token, Amazon Lookout for Equipment generates one.

Parameters:

clientToken - A unique identifier for the request. If you do not set the client request token, Amazon Lookout for Equipment generates one.

getClientToken

public String getClientToken()

A unique identifier for the request. If you do not set the client request token, Amazon Lookout for Equipment generates one.

Returns:

A unique identifier for the request. If you do not set the client request token, Amazon Lookout for Equipment generates one.

withClientToken

public CreateModelRequest withClientToken(String clientToken)

A unique identifier for the request. If you do not set the client request token, Amazon Lookout for Equipment generates one.

Parameters:

clientToken - A unique identifier for the request. If you do not set the client request token, Amazon Lookout for Equipment generates one.

Returns:

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

setTrainingDataStartTime

public void setTrainingDataStartTime(Date trainingDataStartTime)

Indicates the time reference in the dataset that should be used to begin the subset of training data for the ML model.

Parameters:

trainingDataStartTime - Indicates the time reference in the dataset that should be used to begin the subset of training data for the ML model.

getTrainingDataStartTime

public Date getTrainingDataStartTime()

Indicates the time reference in the dataset that should be used to begin the subset of training data for the ML model.

Returns:

Indicates the time reference in the dataset that should be used to begin the subset of training data for the ML model.

withTrainingDataStartTime

public CreateModelRequest withTrainingDataStartTime(Date trainingDataStartTime)

Indicates the time reference in the dataset that should be used to begin the subset of training data for the ML model.

Parameters:

trainingDataStartTime - Indicates the time reference in the dataset that should be used to begin the subset of training data for the ML model.

Returns:

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

setTrainingDataEndTime

public void setTrainingDataEndTime(Date trainingDataEndTime)

Indicates the time reference in the dataset that should be used to end the subset of training data for the ML model.

Parameters:

trainingDataEndTime - Indicates the time reference in the dataset that should be used to end the subset of training data for the ML model.

getTrainingDataEndTime

public Date getTrainingDataEndTime()

Indicates the time reference in the dataset that should be used to end the subset of training data for the ML model.

Returns:

Indicates the time reference in the dataset that should be used to end the subset of training data for the ML model.

withTrainingDataEndTime

public CreateModelRequest withTrainingDataEndTime(Date trainingDataEndTime)

Indicates the time reference in the dataset that should be used to end the subset of training data for the ML model.

Parameters:

trainingDataEndTime - Indicates the time reference in the dataset that should be used to end the subset of training data for the ML model.

Returns:

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

setEvaluationDataStartTime

public void setEvaluationDataStartTime(Date evaluationDataStartTime)

Indicates the time reference in the dataset that should be used to begin the subset of evaluation data for the ML model.

Parameters:

evaluationDataStartTime - Indicates the time reference in the dataset that should be used to begin the subset of evaluation data for the ML model.

getEvaluationDataStartTime

public Date getEvaluationDataStartTime()

Indicates the time reference in the dataset that should be used to begin the subset of evaluation data for the ML model.

Returns:

Indicates the time reference in the dataset that should be used to begin the subset of evaluation data for the ML model.

withEvaluationDataStartTime

public CreateModelRequest withEvaluationDataStartTime(Date evaluationDataStartTime)

Indicates the time reference in the dataset that should be used to begin the subset of evaluation data for the ML model.

Parameters:

evaluationDataStartTime - Indicates the time reference in the dataset that should be used to begin the subset of evaluation data for the ML model.

Returns:

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

setEvaluationDataEndTime

public void setEvaluationDataEndTime(Date evaluationDataEndTime)

Indicates the time reference in the dataset that should be used to end the subset of evaluation data for the ML model.

Parameters:

evaluationDataEndTime - Indicates the time reference in the dataset that should be used to end the subset of evaluation data for the ML model.

getEvaluationDataEndTime

public Date getEvaluationDataEndTime()

Indicates the time reference in the dataset that should be used to end the subset of evaluation data for the ML model.

Returns:

Indicates the time reference in the dataset that should be used to end the subset of evaluation data for the ML model.

withEvaluationDataEndTime

public CreateModelRequest withEvaluationDataEndTime(Date evaluationDataEndTime)

Indicates the time reference in the dataset that should be used to end the subset of evaluation data for the ML model.

Parameters:

evaluationDataEndTime - Indicates the time reference in the dataset that should be used to end the subset of evaluation data for the ML model.

Returns:

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

setRoleArn

public void setRoleArn(String roleArn)

The Amazon Resource Name (ARN) of a role with permission to access the data source being used to create the ML model.

Parameters:

roleArn - The Amazon Resource Name (ARN) of a role with permission to access the data source being used to create the ML model.

getRoleArn

public String getRoleArn()

The Amazon Resource Name (ARN) of a role with permission to access the data source being used to create the ML model.

Returns:

The Amazon Resource Name (ARN) of a role with permission to access the data source being used to create the ML model.

withRoleArn

public CreateModelRequest withRoleArn(String roleArn)

The Amazon Resource Name (ARN) of a role with permission to access the data source being used to create the ML model.

Parameters:

roleArn - The Amazon Resource Name (ARN) of a role with permission to access the data source being used to create the ML model.

Returns:

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

setDataPreProcessingConfiguration

public void setDataPreProcessingConfiguration(DataPreProcessingConfiguration dataPreProcessingConfiguration)

The configuration is the TargetSamplingRate, which is the sampling rate of the data after post processing by Amazon Lookout for Equipment. For example, if you provide data that has been collected at a 1 second level and you want the system to resample the data at a 1 minute rate before training, the TargetSamplingRate is 1 minute.

When providing a value for the TargetSamplingRate, you must attach the prefix "PT" to the rate you want. The value for a 1 second rate is therefore PT1S, the value for a 15 minute rate is PT15M, and the value for a 1 hour rate is PT1H

Parameters:

dataPreProcessingConfiguration - The configuration is the TargetSamplingRate, which is the sampling rate of the data after post processing by Amazon Lookout for Equipment. For example, if you provide data that has been collected at a 1 second level and you want the system to resample the data at a 1 minute rate before training, the TargetSamplingRate is 1 minute.

When providing a value for the TargetSamplingRate, you must attach the prefix "PT" to the rate you want. The value for a 1 second rate is therefore PT1S, the value for a 15 minute rate is PT15M, and the value for a 1 hour rate is PT1H

getDataPreProcessingConfiguration

public DataPreProcessingConfiguration getDataPreProcessingConfiguration()

The configuration is the TargetSamplingRate, which is the sampling rate of the data after post processing by Amazon Lookout for Equipment. For example, if you provide data that has been collected at a 1 second level and you want the system to resample the data at a 1 minute rate before training, the TargetSamplingRate is 1 minute.

When providing a value for the TargetSamplingRate, you must attach the prefix "PT" to the rate you want. The value for a 1 second rate is therefore PT1S, the value for a 15 minute rate is PT15M, and the value for a 1 hour rate is PT1H

Returns:

The configuration is the TargetSamplingRate, which is the sampling rate of the data after post processing by Amazon Lookout for Equipment. For example, if you provide data that has been collected at a 1 second level and you want the system to resample the data at a 1 minute rate before training, the TargetSamplingRate is 1 minute.

When providing a value for the TargetSamplingRate, you must attach the prefix "PT" to the rate you want. The value for a 1 second rate is therefore PT1S, the value for a 15 minute rate is PT15M, and the value for a 1 hour rate is PT1H

withDataPreProcessingConfiguration

public CreateModelRequest withDataPreProcessingConfiguration(DataPreProcessingConfiguration dataPreProcessingConfiguration)

The configuration is the TargetSamplingRate, which is the sampling rate of the data after post processing by Amazon Lookout for Equipment. For example, if you provide data that has been collected at a 1 second level and you want the system to resample the data at a 1 minute rate before training, the TargetSamplingRate is 1 minute.

When providing a value for the TargetSamplingRate, you must attach the prefix "PT" to the rate you want. The value for a 1 second rate is therefore PT1S, the value for a 15 minute rate is PT15M, and the value for a 1 hour rate is PT1H

Parameters:

dataPreProcessingConfiguration - The configuration is the TargetSamplingRate, which is the sampling rate of the data after post processing by Amazon Lookout for Equipment. For example, if you provide data that has been collected at a 1 second level and you want the system to resample the data at a 1 minute rate before training, the TargetSamplingRate is 1 minute.

When providing a value for the TargetSamplingRate, you must attach the prefix "PT" to the rate you want. The value for a 1 second rate is therefore PT1S, the value for a 15 minute rate is PT15M, and the value for a 1 hour rate is PT1H

Returns:

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

setServerSideKmsKeyId

public void setServerSideKmsKeyId(String serverSideKmsKeyId)

Provides the identifier of the KMS key used to encrypt model data by Amazon Lookout for Equipment.

Parameters:

serverSideKmsKeyId - Provides the identifier of the KMS key used to encrypt model data by Amazon Lookout for Equipment.

getServerSideKmsKeyId

public String getServerSideKmsKeyId()

Provides the identifier of the KMS key used to encrypt model data by Amazon Lookout for Equipment.

Returns:

Provides the identifier of the KMS key used to encrypt model data by Amazon Lookout for Equipment.

withServerSideKmsKeyId

public CreateModelRequest withServerSideKmsKeyId(String serverSideKmsKeyId)

Provides the identifier of the KMS key used to encrypt model data by Amazon Lookout for Equipment.

Parameters:

serverSideKmsKeyId - Provides the identifier of the KMS key used to encrypt model data by Amazon Lookout for Equipment.

Returns:

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

getTags

public List<Tag> getTags()

Any tags associated with the ML model being created.

Returns:

Any tags associated with the ML model being created.

setTags

public void setTags(Collection<Tag> tags)

Any tags associated with the ML model being created.

Parameters:

tags - Any tags associated with the ML model being created.

withTags

public CreateModelRequest withTags(Tag... tags)

Any tags associated with the ML model being created.

NOTE: This method appends the values to the existing list (if any). Use setTags(java.util.Collection) or withTags(java.util.Collection) if you want to override the existing values.

Parameters:

tags - Any tags associated with the ML model being created.

Returns:

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

withTags

public CreateModelRequest withTags(Collection<Tag> tags)

Any tags associated with the ML model being created.

Parameters:

tags - Any tags associated with the ML model being created.

Returns:

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

setOffCondition

public void setOffCondition(String offCondition)

Indicates that the asset associated with this sensor has been shut off. As long as this condition is met, Lookout for Equipment will not use data from this asset for training, evaluation, or inference.

Parameters:

offCondition - Indicates that the asset associated with this sensor has been shut off. As long as this condition is met, Lookout for Equipment will not use data from this asset for training, evaluation, or inference.

getOffCondition

public String getOffCondition()

Indicates that the asset associated with this sensor has been shut off. As long as this condition is met, Lookout for Equipment will not use data from this asset for training, evaluation, or inference.

Returns:

Indicates that the asset associated with this sensor has been shut off. As long as this condition is met, Lookout for Equipment will not use data from this asset for training, evaluation, or inference.

withOffCondition

public CreateModelRequest withOffCondition(String offCondition)

Indicates that the asset associated with this sensor has been shut off. As long as this condition is met, Lookout for Equipment will not use data from this asset for training, evaluation, or inference.

Parameters:

offCondition - Indicates that the asset associated with this sensor has been shut off. As long as this condition is met, Lookout for Equipment will not use data from this asset for training, evaluation, or inference.

Returns:

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

toString

public String toString()

Returns a string representation of this object. This is useful for testing and debugging. Sensitive data will be redacted from this string using a placeholder value.

Overrides:

toString in class Object

Returns:

A string representation of this object.

See Also:

Object.toString()

equals

public boolean equals(Object obj)

Overrides:

equals in class Object

hashCode

public int hashCode()

Overrides:

hashCode in class Object

clone

public CreateModelRequest clone()

Description copied from class: AmazonWebServiceRequest

Creates a shallow clone of this object for all fields except the handler context. Explicitly does not clone the deep structure of the other fields in the message.

Overrides:

clone in class AmazonWebServiceRequest

See Also:

Object.clone()