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k-NN Request and Response Formats

All Amazon SageMaker built-in algorithms adhere to the common input inference format described in Common Data Formats - Inference. This topic contains a list of the available output formats for the SageMaker 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)