com.amazonaws.services.sagemaker.model.DataProcessing Maven / Gradle / Ivy
Show all versions of aws-java-sdk-sagemaker Show documentation
/*
* Copyright 2019-2024 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.sagemaker.model;
import java.io.Serializable;
import javax.annotation.Generated;
import com.amazonaws.protocol.StructuredPojo;
import com.amazonaws.protocol.ProtocolMarshaller;
/**
*
* The data structure used to specify the data to be used for inference in a batch transform job and to associate the
* data that is relevant to the prediction results in the output. The input filter provided allows you to exclude input
* data that is not needed for inference in a batch transform job. The output filter provided allows you to include
* input data relevant to interpreting the predictions in the output from the job. For more information, see Associate Prediction
* Results with their Corresponding Input Records.
*
*
* @see AWS API
* Documentation
*/
@Generated("com.amazonaws:aws-java-sdk-code-generator")
public class DataProcessing implements Serializable, Cloneable, StructuredPojo {
/**
*
* A JSONPath expression used to select a portion of the input data to pass to the algorithm. Use the
* InputFilter parameter to exclude fields, such as an ID column, from the input. If you want SageMaker
* to pass the entire input dataset to the algorithm, accept the default value $.
*
*
* Examples: "$", "$[1:]", "$.features"
*
*/
private String inputFilter;
/**
*
* A JSONPath expression used to select a portion of the joined dataset to save in the output file for a batch
* transform job. If you want SageMaker to store the entire input dataset in the output file, leave the default
* value, $. If you specify indexes that aren't within the dimension size of the joined dataset, you
* get an error.
*
*
* Examples: "$", "$[0,5:]", "$['id','SageMakerOutput']"
*
*/
private String outputFilter;
/**
*
* Specifies the source of the data to join with the transformed data. The valid values are None and
* Input. The default value is None, which specifies not to join the input with the
* transformed data. If you want the batch transform job to join the original input data with the transformed data,
* set JoinSource to Input. You can specify OutputFilter as an additional
* filter to select a portion of the joined dataset and store it in the output file.
*
*
* For JSON or JSONLines objects, such as a JSON array, SageMaker adds the transformed data to the input JSON object
* in an attribute called SageMakerOutput. The joined result for JSON must be a key-value pair object.
* If the input is not a key-value pair object, SageMaker creates a new JSON file. In the new JSON file, and the
* input data is stored under the SageMakerInput key and the results are stored in
* SageMakerOutput.
*
*
* For CSV data, SageMaker takes each row as a JSON array and joins the transformed data with the input by appending
* each transformed row to the end of the input. The joined data has the original input data followed by the
* transformed data and the output is a CSV file.
*
*
* For information on how joining in applied, see Workflow for Associating Inferences with Input Records.
*
*/
private String joinSource;
/**
*
* A JSONPath expression used to select a portion of the input data to pass to the algorithm. Use the
* InputFilter parameter to exclude fields, such as an ID column, from the input. If you want SageMaker
* to pass the entire input dataset to the algorithm, accept the default value $.
*
*
* Examples: "$", "$[1:]", "$.features"
*
*
* @param inputFilter
* A JSONPath expression used to select a portion of the input data to pass to the algorithm. Use the
* InputFilter parameter to exclude fields, such as an ID column, from the input. If you want
* SageMaker to pass the entire input dataset to the algorithm, accept the default value $.
*
* Examples: "$", "$[1:]", "$.features"
*/
public void setInputFilter(String inputFilter) {
this.inputFilter = inputFilter;
}
/**
*
* A JSONPath expression used to select a portion of the input data to pass to the algorithm. Use the
* InputFilter parameter to exclude fields, such as an ID column, from the input. If you want SageMaker
* to pass the entire input dataset to the algorithm, accept the default value $.
*
*
* Examples: "$", "$[1:]", "$.features"
*
*
* @return A JSONPath expression used to select a portion of the input data to pass to the algorithm. Use the
* InputFilter parameter to exclude fields, such as an ID column, from the input. If you want
* SageMaker to pass the entire input dataset to the algorithm, accept the default value $.
*
* Examples: "$", "$[1:]", "$.features"
*/
public String getInputFilter() {
return this.inputFilter;
}
/**
*
* A JSONPath expression used to select a portion of the input data to pass to the algorithm. Use the
* InputFilter parameter to exclude fields, such as an ID column, from the input. If you want SageMaker
* to pass the entire input dataset to the algorithm, accept the default value $.
*
*
* Examples: "$", "$[1:]", "$.features"
*
*
* @param inputFilter
* A JSONPath expression used to select a portion of the input data to pass to the algorithm. Use the
* InputFilter parameter to exclude fields, such as an ID column, from the input. If you want
* SageMaker to pass the entire input dataset to the algorithm, accept the default value $.
*
* Examples: "$", "$[1:]", "$.features"
* @return Returns a reference to this object so that method calls can be chained together.
*/
public DataProcessing withInputFilter(String inputFilter) {
setInputFilter(inputFilter);
return this;
}
/**
*
* A JSONPath expression used to select a portion of the joined dataset to save in the output file for a batch
* transform job. If you want SageMaker to store the entire input dataset in the output file, leave the default
* value, $. If you specify indexes that aren't within the dimension size of the joined dataset, you
* get an error.
*
*
* Examples: "$", "$[0,5:]", "$['id','SageMakerOutput']"
*
*
* @param outputFilter
* A JSONPath expression used to select a portion of the joined dataset to save in the output file for a
* batch transform job. If you want SageMaker to store the entire input dataset in the output file, leave the
* default value, $. If you specify indexes that aren't within the dimension size of the joined
* dataset, you get an error.
*
* Examples: "$", "$[0,5:]", "$['id','SageMakerOutput']"
*/
public void setOutputFilter(String outputFilter) {
this.outputFilter = outputFilter;
}
/**
*
* A JSONPath expression used to select a portion of the joined dataset to save in the output file for a batch
* transform job. If you want SageMaker to store the entire input dataset in the output file, leave the default
* value, $. If you specify indexes that aren't within the dimension size of the joined dataset, you
* get an error.
*
*
* Examples: "$", "$[0,5:]", "$['id','SageMakerOutput']"
*
*
* @return A JSONPath expression used to select a portion of the joined dataset to save in the output file for a
* batch transform job. If you want SageMaker to store the entire input dataset in the output file, leave
* the default value, $. If you specify indexes that aren't within the dimension size of the
* joined dataset, you get an error.
*
* Examples: "$", "$[0,5:]", "$['id','SageMakerOutput']"
*/
public String getOutputFilter() {
return this.outputFilter;
}
/**
*
* A JSONPath expression used to select a portion of the joined dataset to save in the output file for a batch
* transform job. If you want SageMaker to store the entire input dataset in the output file, leave the default
* value, $. If you specify indexes that aren't within the dimension size of the joined dataset, you
* get an error.
*
*
* Examples: "$", "$[0,5:]", "$['id','SageMakerOutput']"
*
*
* @param outputFilter
* A JSONPath expression used to select a portion of the joined dataset to save in the output file for a
* batch transform job. If you want SageMaker to store the entire input dataset in the output file, leave the
* default value, $. If you specify indexes that aren't within the dimension size of the joined
* dataset, you get an error.
*
* Examples: "$", "$[0,5:]", "$['id','SageMakerOutput']"
* @return Returns a reference to this object so that method calls can be chained together.
*/
public DataProcessing withOutputFilter(String outputFilter) {
setOutputFilter(outputFilter);
return this;
}
/**
*
* Specifies the source of the data to join with the transformed data. The valid values are None and
* Input. The default value is None, which specifies not to join the input with the
* transformed data. If you want the batch transform job to join the original input data with the transformed data,
* set JoinSource to Input. You can specify OutputFilter as an additional
* filter to select a portion of the joined dataset and store it in the output file.
*
*
* For JSON or JSONLines objects, such as a JSON array, SageMaker adds the transformed data to the input JSON object
* in an attribute called SageMakerOutput. The joined result for JSON must be a key-value pair object.
* If the input is not a key-value pair object, SageMaker creates a new JSON file. In the new JSON file, and the
* input data is stored under the SageMakerInput key and the results are stored in
* SageMakerOutput.
*
*
* For CSV data, SageMaker takes each row as a JSON array and joins the transformed data with the input by appending
* each transformed row to the end of the input. The joined data has the original input data followed by the
* transformed data and the output is a CSV file.
*
*
* For information on how joining in applied, see Workflow for Associating Inferences with Input Records.
*
*
* @param joinSource
* Specifies the source of the data to join with the transformed data. The valid values are None
* and Input. The default value is None, which specifies not to join the input with
* the transformed data. If you want the batch transform job to join the original input data with the
* transformed data, set JoinSource to Input. You can specify
* OutputFilter as an additional filter to select a portion of the joined dataset and store it
* in the output file.
*
* For JSON or JSONLines objects, such as a JSON array, SageMaker adds the transformed data to the input JSON
* object in an attribute called SageMakerOutput. The joined result for JSON must be a key-value
* pair object. If the input is not a key-value pair object, SageMaker creates a new JSON file. In the new
* JSON file, and the input data is stored under the SageMakerInput key and the results are
* stored in SageMakerOutput.
*
*
* For CSV data, SageMaker takes each row as a JSON array and joins the transformed data with the input by
* appending each transformed row to the end of the input. The joined data has the original input data
* followed by the transformed data and the output is a CSV file.
*
*
* For information on how joining in applied, see Workflow for Associating Inferences with Input Records.
* @see JoinSource
*/
public void setJoinSource(String joinSource) {
this.joinSource = joinSource;
}
/**
*
* Specifies the source of the data to join with the transformed data. The valid values are None and
* Input. The default value is None, which specifies not to join the input with the
* transformed data. If you want the batch transform job to join the original input data with the transformed data,
* set JoinSource to Input. You can specify OutputFilter as an additional
* filter to select a portion of the joined dataset and store it in the output file.
*
*
* For JSON or JSONLines objects, such as a JSON array, SageMaker adds the transformed data to the input JSON object
* in an attribute called SageMakerOutput. The joined result for JSON must be a key-value pair object.
* If the input is not a key-value pair object, SageMaker creates a new JSON file. In the new JSON file, and the
* input data is stored under the SageMakerInput key and the results are stored in
* SageMakerOutput.
*
*
* For CSV data, SageMaker takes each row as a JSON array and joins the transformed data with the input by appending
* each transformed row to the end of the input. The joined data has the original input data followed by the
* transformed data and the output is a CSV file.
*
*
* For information on how joining in applied, see Workflow for Associating Inferences with Input Records.
*
*
* @return Specifies the source of the data to join with the transformed data. The valid values are
* None and Input. The default value is None, which specifies not to
* join the input with the transformed data. If you want the batch transform job to join the original input
* data with the transformed data, set JoinSource to Input. You can specify
* OutputFilter as an additional filter to select a portion of the joined dataset and store it
* in the output file.
*
* For JSON or JSONLines objects, such as a JSON array, SageMaker adds the transformed data to the input
* JSON object in an attribute called SageMakerOutput. The joined result for JSON must be a
* key-value pair object. If the input is not a key-value pair object, SageMaker creates a new JSON file. In
* the new JSON file, and the input data is stored under the SageMakerInput key and the results
* are stored in SageMakerOutput.
*
*
* For CSV data, SageMaker takes each row as a JSON array and joins the transformed data with the input by
* appending each transformed row to the end of the input. The joined data has the original input data
* followed by the transformed data and the output is a CSV file.
*
*
* For information on how joining in applied, see Workflow for Associating Inferences with Input Records.
* @see JoinSource
*/
public String getJoinSource() {
return this.joinSource;
}
/**
*
* Specifies the source of the data to join with the transformed data. The valid values are None and
* Input. The default value is None, which specifies not to join the input with the
* transformed data. If you want the batch transform job to join the original input data with the transformed data,
* set JoinSource to Input. You can specify OutputFilter as an additional
* filter to select a portion of the joined dataset and store it in the output file.
*
*
* For JSON or JSONLines objects, such as a JSON array, SageMaker adds the transformed data to the input JSON object
* in an attribute called SageMakerOutput. The joined result for JSON must be a key-value pair object.
* If the input is not a key-value pair object, SageMaker creates a new JSON file. In the new JSON file, and the
* input data is stored under the SageMakerInput key and the results are stored in
* SageMakerOutput.
*
*
* For CSV data, SageMaker takes each row as a JSON array and joins the transformed data with the input by appending
* each transformed row to the end of the input. The joined data has the original input data followed by the
* transformed data and the output is a CSV file.
*
*
* For information on how joining in applied, see Workflow for Associating Inferences with Input Records.
*
*
* @param joinSource
* Specifies the source of the data to join with the transformed data. The valid values are None
* and Input. The default value is None, which specifies not to join the input with
* the transformed data. If you want the batch transform job to join the original input data with the
* transformed data, set JoinSource to Input. You can specify
* OutputFilter as an additional filter to select a portion of the joined dataset and store it
* in the output file.
*
* For JSON or JSONLines objects, such as a JSON array, SageMaker adds the transformed data to the input JSON
* object in an attribute called SageMakerOutput. The joined result for JSON must be a key-value
* pair object. If the input is not a key-value pair object, SageMaker creates a new JSON file. In the new
* JSON file, and the input data is stored under the SageMakerInput key and the results are
* stored in SageMakerOutput.
*
*
* For CSV data, SageMaker takes each row as a JSON array and joins the transformed data with the input by
* appending each transformed row to the end of the input. The joined data has the original input data
* followed by the transformed data and the output is a CSV file.
*
*
* For information on how joining in applied, see Workflow for Associating Inferences with Input Records.
* @return Returns a reference to this object so that method calls can be chained together.
* @see JoinSource
*/
public DataProcessing withJoinSource(String joinSource) {
setJoinSource(joinSource);
return this;
}
/**
*
* Specifies the source of the data to join with the transformed data. The valid values are None and
* Input. The default value is None, which specifies not to join the input with the
* transformed data. If you want the batch transform job to join the original input data with the transformed data,
* set JoinSource to Input. You can specify OutputFilter as an additional
* filter to select a portion of the joined dataset and store it in the output file.
*
*
* For JSON or JSONLines objects, such as a JSON array, SageMaker adds the transformed data to the input JSON object
* in an attribute called SageMakerOutput. The joined result for JSON must be a key-value pair object.
* If the input is not a key-value pair object, SageMaker creates a new JSON file. In the new JSON file, and the
* input data is stored under the SageMakerInput key and the results are stored in
* SageMakerOutput.
*
*
* For CSV data, SageMaker takes each row as a JSON array and joins the transformed data with the input by appending
* each transformed row to the end of the input. The joined data has the original input data followed by the
* transformed data and the output is a CSV file.
*
*
* For information on how joining in applied, see Workflow for Associating Inferences with Input Records.
*
*
* @param joinSource
* Specifies the source of the data to join with the transformed data. The valid values are None
* and Input. The default value is None, which specifies not to join the input with
* the transformed data. If you want the batch transform job to join the original input data with the
* transformed data, set JoinSource to Input. You can specify
* OutputFilter as an additional filter to select a portion of the joined dataset and store it
* in the output file.
*
* For JSON or JSONLines objects, such as a JSON array, SageMaker adds the transformed data to the input JSON
* object in an attribute called SageMakerOutput. The joined result for JSON must be a key-value
* pair object. If the input is not a key-value pair object, SageMaker creates a new JSON file. In the new
* JSON file, and the input data is stored under the SageMakerInput key and the results are
* stored in SageMakerOutput.
*
*
* For CSV data, SageMaker takes each row as a JSON array and joins the transformed data with the input by
* appending each transformed row to the end of the input. The joined data has the original input data
* followed by the transformed data and the output is a CSV file.
*
*
* For information on how joining in applied, see Workflow for Associating Inferences with Input Records.
* @return Returns a reference to this object so that method calls can be chained together.
* @see JoinSource
*/
public DataProcessing withJoinSource(JoinSource joinSource) {
this.joinSource = joinSource.toString();
return this;
}
/**
* 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.
*
* @return A string representation of this object.
*
* @see java.lang.Object#toString()
*/
@Override
public String toString() {
StringBuilder sb = new StringBuilder();
sb.append("{");
if (getInputFilter() != null)
sb.append("InputFilter: ").append(getInputFilter()).append(",");
if (getOutputFilter() != null)
sb.append("OutputFilter: ").append(getOutputFilter()).append(",");
if (getJoinSource() != null)
sb.append("JoinSource: ").append(getJoinSource());
sb.append("}");
return sb.toString();
}
@Override
public boolean equals(Object obj) {
if (this == obj)
return true;
if (obj == null)
return false;
if (obj instanceof DataProcessing == false)
return false;
DataProcessing other = (DataProcessing) obj;
if (other.getInputFilter() == null ^ this.getInputFilter() == null)
return false;
if (other.getInputFilter() != null && other.getInputFilter().equals(this.getInputFilter()) == false)
return false;
if (other.getOutputFilter() == null ^ this.getOutputFilter() == null)
return false;
if (other.getOutputFilter() != null && other.getOutputFilter().equals(this.getOutputFilter()) == false)
return false;
if (other.getJoinSource() == null ^ this.getJoinSource() == null)
return false;
if (other.getJoinSource() != null && other.getJoinSource().equals(this.getJoinSource()) == false)
return false;
return true;
}
@Override
public int hashCode() {
final int prime = 31;
int hashCode = 1;
hashCode = prime * hashCode + ((getInputFilter() == null) ? 0 : getInputFilter().hashCode());
hashCode = prime * hashCode + ((getOutputFilter() == null) ? 0 : getOutputFilter().hashCode());
hashCode = prime * hashCode + ((getJoinSource() == null) ? 0 : getJoinSource().hashCode());
return hashCode;
}
@Override
public DataProcessing clone() {
try {
return (DataProcessing) super.clone();
} catch (CloneNotSupportedException e) {
throw new IllegalStateException("Got a CloneNotSupportedException from Object.clone() " + "even though we're Cloneable!", e);
}
}
@com.amazonaws.annotation.SdkInternalApi
@Override
public void marshall(ProtocolMarshaller protocolMarshaller) {
com.amazonaws.services.sagemaker.model.transform.DataProcessingMarshaller.getInstance().marshall(this, protocolMarshaller);
}
}