# k-NN Request and Response Formats
Inference Formats

All Amazon SageMaker AI built-in algorithms adhere to the common input inference format described in [Common Data Formats - Inference](https://docs.aws.amazon.com/sagemaker/latest/dg/cdf-inference.html). This topic contains a list of the available output formats for the SageMaker AI k-nearest-neighbor algorithm.

## INPUT: CSV Request Format


content-type: text/csv

```
1.2,1.3,9.6,20.3
```

This accepts a `label_size` or encoding parameter. It assumes a `label_size` of 0 and a utf-8 encoding.

## INPUT: JSON Request Format


content-type: application/json

```
{
  "instances": [
    {"data": {"features": {"values": [-3, -1, -4, 2]}}},
    {"features": [3.0, 0.1, 0.04, 0.002]}]
}
```

## INPUT: JSONLINES Request Format


content-type: application/jsonlines

```
{"features": [1.5, 16.0, 14.0, 23.0]}
{"data": {"features": {"values": [1.5, 16.0, 14.0, 23.0]}}
```

## INPUT: RECORDIO Request Format


content-type: application/x-recordio-protobuf

```
[
    Record = {
        features = {
            'values': {
                values: [-3, -1, -4, 2]  # float32
            }
        },
        label = {}
    },
    Record = {
        features = {
            'values': {
                values: [3.0, 0.1, 0.04, 0.002]  # float32
            }
        },
        label = {}
    },
]
```

## OUTPUT: JSON Response Format


accept: application/json

```
{
  "predictions": [
    {"predicted_label": 0.0},
    {"predicted_label": 2.0}
  ]
}
```

## OUTPUT: JSONLINES Response Format


accept: application/jsonlines

```
{"predicted_label": 0.0}
{"predicted_label": 2.0}
```

## OUTPUT: VERBOSE JSON Response Format


In verbose mode, the API provides the search results with the distances vector sorted from smallest to largest, with corresponding elements in the labels vector. In this example, k is set to 3.

accept: application/json; verbose=true

```
{
  "predictions": [
    {
        "predicted_label": 0.0,
        "distances": [3.11792408, 3.89746071, 6.32548437],
        "labels": [0.0, 1.0, 0.0]
    },
    {
        "predicted_label": 2.0,
        "distances": [1.08470316, 3.04917915, 5.25393973],
        "labels": [2.0, 2.0, 0.0]
    }
  ]
}
```

## OUTPUT: RECORDIO-PROTOBUF Response Format


content-type: application/x-recordio-protobuf

```
[
    Record = {
        features = {},
        label = {
            'predicted_label': {
                values: [0.0]  # float32
            }
        }
    },
    Record = {
        features = {},
        label = {
            'predicted_label': {
                values: [2.0]  # float32
            }
        }
    }
]
```

## OUTPUT: VERBOSE RECORDIO-PROTOBUF Response Format


In verbose mode, the API provides the search results with the distances vector sorted from smallest to largest, with corresponding elements in the labels vector. In this example, k is set to 3.

accept: application/x-recordio-protobuf; verbose=true

```
[
    Record = {
        features = {},
        label = {
            'predicted_label': {
                values: [0.0]  # float32
            },
            'distances': {
                values: [3.11792408, 3.89746071, 6.32548437]  # float32
            },
            'labels': {
                values: [0.0, 1.0, 0.0]  # float32
            }
        }
    },
    Record = {
        features = {},
        label = {
            'predicted_label': {
                values: [0.0]  # float32
            },
            'distances': {
                values: [1.08470316, 3.04917915, 5.25393973]  # float32
            },
            'labels': {
                values: [2.0, 2.0, 0.0]  # float32
            }
        }
    }
]
```

## SAMPLE OUTPUT for the k-NN Algorithm


For regressor tasks:

```
[06/08/2018 20:15:33 INFO 140026520049408] #test_score (algo-1) : ('mse', 0.013333333333333334)
```

For classifier tasks:

```
[06/08/2018 20:15:46 INFO 140285487171328] #test_score (algo-1) : ('accuracy', 0.98666666666666669)
```