Amazon SageMaker
Developer Guide

K-NN Request and Response Formats

INPUT: CSV

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

content-type: application/json

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

INPUT: JSONLINES

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

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

accept: application/json

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

OUTPUT: JSONLINES

accept: application/jsonlines

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

OUTPUT: VERBOSE JSON

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

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

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 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)