* 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"
*
* 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']"
*
* 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"
*
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"
*
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"
*
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']"
*
$. If you specify indexes that aren't within the dimension size of the joined
* dataset, you get an error.
*
* Examples: "$", "$[0,5:]", "$['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']"
*
$. If you specify indexes that aren't within the dimension size of the
* joined dataset, you get an error.
*
* Examples: "$", "$[0,5:]", "$['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']"
*
$. If you specify indexes that aren't within the dimension size of the joined
* dataset, you get an error.
*
* Examples: "$", "$[0,5:]", "$['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 areNone
* 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 areNone
* 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 areNone
* 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); } }