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 Amazon SageMaker k-nearest-neighbor algorithm.

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.

content-type: application/json

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

content-type: application/jsonlines

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

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 = {}
    },
]

accept: application/json

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

accept: application/jsonlines

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

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]
    }
  ]
}

content-type: application/x-recordio-protobuf

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

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
            }
        }
    }
]

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)