Classic ASP / Cognite API v1 / Create entity matcher model
Back to Collection Items
<html>
<head>
<meta http-equiv="Content-Type" content="text/html; charset=utf-8">
</head>
<body>
<%
' This example assumes the Chilkat API to have been previously unlocked.
' See Global Unlock Sample for sample code.
' For versions of Chilkat < 10.0.0, use CreateObject("Chilkat_9_5_0.Http")
set http = Server.CreateObject("Chilkat.Http")
' Use this online tool to generate code from sample JSON: Generate Code to Create JSON
' The following JSON is sent in the request body.
' {
' "sources": [
' {},
' {}
' ],
' "targets": [
' {},
' {}
' ],
' "trueMatches": [
' [
' ],
' [
' ]
' ],
' "externalId": "in irure amet",
' "name": "minim",
' "description": "aliqua ipsum culpa eiusmod",
' "featureType": "simple",
' "matchFields": [
' {
' "source": "name",
' "target": "name"
' }
' ],
' "classifier": "randomforest",
' "ignoreMissingFields": false
' }
' For versions of Chilkat < 10.0.0, use CreateObject("Chilkat_9_5_0.JsonObject")
set json = Server.CreateObject("Chilkat.JsonObject")
success = json.UpdateNewArray("trueMatches[0].")
success = json.UpdateNewArray("trueMatches[1].")
success = json.UpdateString("externalId","in irure amet")
success = json.UpdateString("name","minim")
success = json.UpdateString("description","aliqua ipsum culpa eiusmod")
success = json.UpdateString("featureType","simple")
success = json.UpdateString("matchFields[0].source","name")
success = json.UpdateString("matchFields[0].target","name")
success = json.UpdateString("classifier","randomforest")
success = json.UpdateBool("ignoreMissingFields",0)
http.SetRequestHeader "content-type","application/json"
http.SetRequestHeader "api-key","{{api-key}}"
' resp is a Chilkat.HttpResponse
Set resp = http.PostJson3("https://domain.com/api/v1/projects/{{project}}/context/entitymatching","application/json",json)
If (http.LastMethodSuccess = 0) Then
Response.Write "<pre>" & Server.HTMLEncode( http.LastErrorText) & "</pre>"
Response.End
End If
Response.Write "<pre>" & Server.HTMLEncode( resp.StatusCode) & "</pre>"
Response.Write "<pre>" & Server.HTMLEncode( resp.BodyStr) & "</pre>"
%>
</body>
</html>
Curl Command
curl -X POST
-H "api-key: {{api-key}}"
-H "content-type: application/json"
-d '{
"sources": [
{},
{}
],
"targets": [
{},
{}
],
"trueMatches": [
[],
[]
],
"externalId": "in irure amet",
"name": "minim",
"description": "aliqua ipsum culpa eiusmod",
"featureType": "simple",
"matchFields": [
{
"source": "name",
"target": "name"
}
],
"classifier": "randomforest",
"ignoreMissingFields": false
}'
https://domain.com/api/v1/projects/{{project}}/context/entitymatching
Postman Collection Item JSON
{
"id": "entityMatchingCreate",
"name": "Create entity matcher model",
"request": {
"url": {
"host": "{{baseUrl}}",
"path": [
"api",
"v1",
"projects",
"{{project}}",
"context",
"entitymatching"
],
"query": [
],
"variable": [
]
},
"method": "POST",
"header": [
{
"key": "api-key",
"value": "{{api-key}}",
"description": "An admin can create API keys in the Cognite console."
},
{
"key": "content-type",
"value": "application/json"
}
],
"description": "Note: All users on this CDF subscription with assets read-all and entitymatching read-all and write-all capabilities in the project, are able to access the data sent to this endpoint. Train a model that predicts matches between entities (for example, time series names to asset names). This is also known as fuzzy joining. If there are no trueMatches (labeled data), you train a static (unsupervised) model, otherwise a machine learned (supervised) model is trained.",
"body": {
"mode": "raw",
"raw": "{\n \"sources\": [\n {},\n {}\n ],\n \"targets\": [\n {},\n {}\n ],\n \"trueMatches\": [\n [],\n []\n ],\n \"externalId\": \"in irure amet\",\n \"name\": \"minim\",\n \"description\": \"aliqua ipsum culpa eiusmod\",\n \"featureType\": \"simple\",\n \"matchFields\": [\n {\n \"source\": \"name\",\n \"target\": \"name\"\n }\n ],\n \"classifier\": \"randomforest\",\n \"ignoreMissingFields\": false\n}"
}
}
}