# Associate Prediction Results with Input Records
Associate Prediction Results with Input

When making predictions on a large dataset, you can exclude attributes that aren't needed for prediction. After the predictions have been made, you can associate some of the excluded attributes with those predictions or with other input data in your report. By using batch transform to perform these data processing steps, you can often eliminate additional preprocessing or postprocessing. You can use input files in JSON and CSV format only. 

**Topics**
+ [

## Workflow for Associating Inferences with Input Records
](#batch-transform-data-processing-workflow)
+ [

## Use Data Processing in Batch Transform Jobs
](#batch-transform-data-processing-steps)
+ [

## Supported JSONPath Operators
](#data-processing-operators)
+ [

## Batch Transform Examples
](#batch-transform-data-processing-examples)

## Workflow for Associating Inferences with Input Records
Data Processing Workflow

The following diagram shows the workflow for associating inferences with input records.

![\[The workflow for associating inferences with input records.\]](http://docs.amazonaws.cn/en_us/sagemaker/latest/dg/images/batch-transform-data-processing.png)


To associate inferences with input data, there are three main steps:

1. Filter the input data that is not needed for inference before passing the input data to the batch transform job. Use the [https://docs.amazonaws.cn/sagemaker/latest/APIReference/API_CreateTransformJob.html#SageMaker-Type-DataProcessing-InputFilter                             ](https://docs.amazonaws.cn/sagemaker/latest/APIReference/API_CreateTransformJob.html#SageMaker-Type-DataProcessing-InputFilter                             ) parameter to determine which attributes to use as input for the model.

1. Associate the input data with the inference results. Use the [https://docs.amazonaws.cn/sagemaker/latest/APIReference/API_CreateTransformJob.html#SageMaker-Type-DataProcessing-JoinSource                         ](https://docs.amazonaws.cn/sagemaker/latest/APIReference/API_CreateTransformJob.html#SageMaker-Type-DataProcessing-JoinSource                         ) parameter to combine the input data with the inference.

1. Filter the joined data to retain the inputs that are needed to provide context for interpreting the predictions in the reports. Use [https://docs.amazonaws.cn/sagemaker/latest/APIReference/API_CreateTransformJob.html#SageMaker-Type-DataProcessing-OutputFilter                             ](https://docs.amazonaws.cn/sagemaker/latest/APIReference/API_CreateTransformJob.html#SageMaker-Type-DataProcessing-OutputFilter                             ) to store the specified portion of the joined dataset in the output file.

## Use Data Processing in Batch Transform Jobs
Use Data Processing in Batch Transform

When creating a batch transform job with [https://docs.amazonaws.cn/sagemaker/latest/APIReference/API_CreateTransformJob.html](https://docs.amazonaws.cn/sagemaker/latest/APIReference/API_CreateTransformJob.html) to process data:

1. Specify the portion of the input to pass to the model with the `InputFilter` parameter in the `DataProcessing` data structure. 

1. Join the raw input data with the transformed data with the `JoinSource` parameter.

1. Specify which portion of the joined input and transformed data from the batch transform job to include in the output file with the `OutputFilter` parameter.

1.  Choose either JSON- or CSV-formatted files for input: 
   + For JSON- or JSON Lines-formatted input files, SageMaker AI either adds the `SageMakerOutput` attribute to the input file or creates a new JSON output file with the `SageMakerInput` and `SageMakerOutput` attributes. For more information, see [https://docs.amazonaws.cn/sagemaker/latest/APIReference/API_DataProcessing.html](https://docs.amazonaws.cn/sagemaker/latest/APIReference/API_DataProcessing.html). 
   + For CSV-formatted input files, the joined input data is followed by the transformed data and the output is a CSV file.

If you use an algorithm with the `DataProcessing` structure, it must support your chosen format for *both* input and output files. For example, with the [https://docs.amazonaws.cn/sagemaker/latest/APIReference/API_TransformOutput.html](https://docs.amazonaws.cn/sagemaker/latest/APIReference/API_TransformOutput.html) field of the `CreateTransformJob` API, you must set both the [https://docs.amazonaws.cn/sagemaker/latest/APIReference/API_Channel.html#SageMaker-Type-Channel-ContentType](https://docs.amazonaws.cn/sagemaker/latest/APIReference/API_Channel.html#SageMaker-Type-Channel-ContentType) and [https://docs.amazonaws.cn/sagemaker/latest/APIReference/API_TransformOutput.html#SageMaker-Type-TransformOutput-Accept](https://docs.amazonaws.cn/sagemaker/latest/APIReference/API_TransformOutput.html#SageMaker-Type-TransformOutput-Accept) parameters to one of the following values: `text/csv`, `application/json`, or `application/jsonlines`. The syntax for specifying columns in a CSV file and specifying attributes in a JSON file are different. Using the wrong syntax causes an error. For more information, see [Batch Transform Examples](#batch-transform-data-processing-examples). For more information about input and output file formats for built-in algorithms, see [Built-in algorithms and pretrained models in Amazon SageMaker](algos.md).

The record delimiters for the input and output must also be consistent with your chosen file input. The [https://docs.amazonaws.cn/sagemaker/latest/APIReference/API_TransformInput.html#SageMaker-Type-TransformInput-SplitType](https://docs.amazonaws.cn/sagemaker/latest/APIReference/API_TransformInput.html#SageMaker-Type-TransformInput-SplitType) parameter indicates how to split the records in the input dataset. The [https://docs.amazonaws.cn/sagemaker/latest/APIReference/API_TransformOutput.html#SageMaker-Type-TransformOutput-AssembleWith                     ](https://docs.amazonaws.cn/sagemaker/latest/APIReference/API_TransformOutput.html#SageMaker-Type-TransformOutput-AssembleWith                     ) parameter indicates how to reassemble the records for the output. If you set input and output formats to `text/csv`, you must also set the `SplitType` and `AssembleWith` parameters to `line`. If you set the input and output formats to `application/jsonlines`, you can set both `SplitType` and `AssembleWith` to `line`.

For CSV files, you cannot use embedded newline characters. For JSON files, the attribute name `SageMakerOutput` is reserved for output. The JSON input file can't have an attribute with this name. If it does, the data in the input file might be overwritten. 

## Supported JSONPath Operators
Supported JSONPath operators

To filter and join the input data and inference, use a JSONPath subexpression. SageMaker AI supports only a subset of the defined JSONPath operators. The following table lists the supported JSONPath operators. For CSV data, each row is taken as a JSON array, so only index based JSONPaths can be applied, e.g. `$[0]`, `$[1:]`. CSV data should also follow [RFC format](https://tools.ietf.org/html/rfc4180).


| JSONPath Operator | Description | Example | 
| --- | --- | --- | 
| \$1 |  The root element to a query. This operator is required at the beginning of all path expressions.  | \$1 | 
| .<name> |  A dot-notated child element.  |  `$.id`  | 
| \$1 |  A wildcard. Use in place of an attribute name or numeric value.  |  `$.id.*`  | 
| ['<name>' (,'<name>')] |  A bracket-notated element or multiple child elements.  |  `$['id','SageMakerOutput']`  | 
| [<number> (,<number>)] |  An index or array of indexes. Negative index values are also supported. A `-1` index refers to the last element in an array.  |  `$[1]` , `$[1,3,5]`  | 
| [<start>:<end>] |  An array slice operator.  The array slice() method extracts a section of an array and returns a new array. If you omit *<start>*, SageMaker AI uses the first element of the array. If you omit *<end>*, SageMaker AI uses the last element of the array.  |  `$[2:5]`, `$[:5]`, `$[2:]`  | 

When using the bracket-notation to specify multiple child elements of a given field, additional nesting of children within brackets is not supported. For example, `$.field1.['child1','child2']` is supported while `$.field1.['child1','child2.grandchild']` is not. 

For more information about JSONPath operators, see [JsonPath](https://github.com/json-path/JsonPath) on GitHub.

## Batch Transform Examples
Examples

The following examples show some common ways to join input data with prediction results.

**Topics**
+ [

### Example: Output Only Inferences
](#batch-transform-data-processing-example-default)
+ [

### Example: Output Inferences Joined with Input Data
](#batch-transform-data-processing-example-all)
+ [

### Example: Output Inferences Joined with Input Data and Exclude the ID Column from the Input (CSV)
](#batch-transform-data-processing-example-select-csv)
+ [

### Example: Output Inferences Joined with an ID Column and Exclude the ID Column from the Input (CSV)
](#batch-transform-data-processing-example-select-json)

### Example: Output Only Inferences
Output Only Inferences

By default, the [https://docs.amazonaws.cn/sagemaker/latest/APIReference/API_CreateTransformJob.html#SageMaker-CreateTransformJob-request-DataProcessing](https://docs.amazonaws.cn/sagemaker/latest/APIReference/API_CreateTransformJob.html#SageMaker-CreateTransformJob-request-DataProcessing) parameter doesn't join inference results with input. It outputs only the inference results.

If you want to explicitly specify to not join results with input, use the [Amazon SageMaker Python SDK](https://sagemaker.readthedocs.io/en/stable) and specify the following settings in a transformer call.

```
sm_transformer = sagemaker.transformer.Transformer(…)
sm_transformer.transform(…, input_filter="$", join_source= "None", output_filter="$")
```

To output inferences using the Amazon SDK for Python, add the following code to your CreateTransformJob request. The following code mimics the default behavior.

```
{
    "DataProcessing": {
        "InputFilter": "$",
        "JoinSource": "None",
        "OutputFilter": "$"
    }
}
```

### Example: Output Inferences Joined with Input Data
Join Input with Inferences

If you're using the [Amazon SageMaker Python SDK](https://sagemaker.readthedocs.io/en/stable) to combine the input data with the inferences in the output file, specify the `assemble_with` and `accept` parameters when initializing the transformer object. When you use the transform call, specify `Input` for the `join_source` parameter, and specify the `split_type` and `content_type` parameters as well. The `split_type` parameter must have the same value as `assemble_with`, and the `content_type` parameter must have the same value as `accept`. For more information about the parameters and their accepted values, see the [Transformer](https://sagemaker.readthedocs.io/en/stable/api/inference/transformer.html#sagemaker.transformer.Transformer) page in the *Amazon SageMaker AI Python SDK*.

```
sm_transformer = sagemaker.transformer.Transformer(…, assemble_with="Line", accept="text/csv")
sm_transformer.transform(…, join_source="Input", split_type="Line", content_type="text/csv")
```

If you're using the Amazon SDK for Python (Boto 3), join all input data with the inference by adding the following code to your [https://docs.amazonaws.cn/sagemaker/latest/APIReference/API_CreateTransformJob.html](https://docs.amazonaws.cn/sagemaker/latest/APIReference/API_CreateTransformJob.html) request. The values for `Accept` and `ContentType` must match, and the values for `AssembleWith` and `SplitType` must also match.

```
{
    "DataProcessing": {
        "JoinSource": "Input"
    },
    "TransformOutput": {
        "Accept": "text/csv",
        "AssembleWith": "Line"
    },
    "TransformInput": {
        "ContentType": "text/csv",
        "SplitType": "Line"
    }
}
```

For JSON or JSON Lines input files, the results are in the `SageMakerOutput` key in the input JSON file. For example, if the input is a JSON file that contains the key-value pair `{"key":1}`, the data transform result might be `{"label":1}`.

SageMaker AI stores both in the input file in the `SageMakerInput` key.

```
{
    "key":1,
    "SageMakerOutput":{"label":1}
}
```

**Note**  
The joined result for JSON must be a key-value pair object. If the input isn't a key-value pair object, SageMaker AI creates a new JSON file. In the new JSON file, the input data is stored in the `SageMakerInput` key and the results are stored as the `SageMakerOutput` value.

For a CSV file, for example, if the record is `[1,2,3]`, and the label result is `[1]`, then the output file would contain `[1,2,3,1]`.

### Example: Output Inferences Joined with Input Data and Exclude the ID Column from the Input (CSV)
Add ID Column to Inference (CSV)

If you are using the [Amazon SageMaker Python SDK](https://sagemaker.readthedocs.io/en/stable) to join your input data with the inference output while excluding an ID column from the transformer input, specify the same parameters from the preceding example as well as a JSONPath subexpression for the `input_filter` in your transformer call. For example, if your input data includes five columns and the first one is the ID column, use the following transform request to select all columns except the ID column as features. The transformer still outputs all of the input columns joined with the inferences. For more information about the parameters and their accepted values, see the [Transformer](https://sagemaker.readthedocs.io/en/stable/api/inference/transformer.html#sagemaker.transformer.Transformer) page in the *Amazon SageMaker AI Python SDK*.

```
sm_transformer = sagemaker.transformer.Transformer(…, assemble_with="Line", accept="text/csv")
sm_transformer.transform(…, split_type="Line", content_type="text/csv", input_filter="$[1:]", join_source="Input")
```

If you are using the Amazon SDK for Python (Boto 3), add the following code to your `[ CreateTransformJob](https://docs.amazonaws.cn/sagemaker/latest/APIReference/API_CreateTransformJob.html)` request.

```
{
    "DataProcessing": {
        "InputFilter": "$[1:]",
        "JoinSource": "Input"
    },
    "TransformOutput": {
        "Accept": "text/csv",
        "AssembleWith": "Line"
    },
    "TransformInput": {
        "ContentType": "text/csv",
        "SplitType": "Line"
    }
}
```

To specify columns in SageMaker AI, use the index of the array elements. The first column is index 0, the second column is index 1, and the sixth column is index 5.

To exclude the first column from the input, set `[InputFilter](https://docs.amazonaws.cn/sagemaker/latest/APIReference/API_CreateTransformJob.html#SageMaker-Type-DataProcessing-InputFilter )` to `"$[1:]"`. The colon (`:`) tells SageMaker AI to include all of the elements between two values, inclusive. For example, `$[1:4]` specifies the second through fifth columns.

If you omit the number after the colon, for example, `[5:]`, the subset includes all columns from the 6th column through the last column. If you omit the number before the colon, for example, `[:5]`, the subset includes all columns from the first column (index 0) through the sixth column.

### Example: Output Inferences Joined with an ID Column and Exclude the ID Column from the Input (CSV)
Add ID Column to Inference (CSV)

If you are using the [Amazon SageMaker Python SDK](https://sagemaker.readthedocs.io/en/stable), you can specify the output to join only specific input columns (such as the ID column) with the inferences by specifying the `output_filter` in the transformer call. The `output_filter` uses a JSONPath subexpression to specify which columns to return as output after joining the input data with the inference results. The following request shows how you can make predictions while excluding an ID column and then join the ID column with the inferences. Note that in the following example, the last column (`-1`) of the output contains the inferences. If you are using JSON files, SageMaker AI stores the inference results in the attribute `SageMakerOutput`. For more information about the parameters and their accepted values, see the [Transformer](https://sagemaker.readthedocs.io/en/stable/api/inference/transformer.html#sagemaker.transformer.Transformer) page in the *Amazon SageMaker AI Python SDK*.

```
sm_transformer = sagemaker.transformer.Transformer(…, assemble_with="Line", accept="text/csv")
sm_transformer.transform(…, split_type="Line", content_type="text/csv", input_filter="$[1:]", join_source="Input", output_filter="$[0,-1]")
```

If you are using the Amazon SDK for Python (Boto 3), join only the ID column with the inferences by adding the following code to your [https://docs.amazonaws.cn/sagemaker/latest/APIReference/API_CreateTransformJob.html](https://docs.amazonaws.cn/sagemaker/latest/APIReference/API_CreateTransformJob.html) request.

```
{
    "DataProcessing": {
        "InputFilter": "$[1:]",
        "JoinSource": "Input",
        "OutputFilter": "$[0,-1]"
    },
    "TransformOutput": {
        "Accept": "text/csv",
        "AssembleWith": "Line"
    },
    "TransformInput": {
        "ContentType": "text/csv",
        "SplitType": "Line"
    }
}
```

**Warning**  
If you are using a JSON-formatted input file, the file can't contain the attribute name `SageMakerOutput`. This attribute name is reserved for the inferences in the output file. If your JSON-formatted input file contains an attribute with this name, values in the input file might be overwritten with the inference.