Foxpro / Cognite API v1 / Create entity matcher model
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LOCAL loHttp
LOCAL lnSuccess
LOCAL loJson
LOCAL loResp
* 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')
loHttp = 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')
loJson = CreateObject('Chilkat.JsonObject')
loJson.UpdateNewArray("trueMatches[0].")
loJson.UpdateNewArray("trueMatches[1].")
loJson.UpdateString("externalId","in irure amet")
loJson.UpdateString("name","minim")
loJson.UpdateString("description","aliqua ipsum culpa eiusmod")
loJson.UpdateString("featureType","simple")
loJson.UpdateString("matchFields[0].source","name")
loJson.UpdateString("matchFields[0].target","name")
loJson.UpdateString("classifier","randomforest")
loJson.UpdateBool("ignoreMissingFields",0)
loHttp.SetRequestHeader("content-type","application/json")
loHttp.SetRequestHeader("api-key","{{api-key}}")
loResp = loHttp.PostJson3("https://domain.com/api/v1/projects/{{project}}/context/entitymatching","application/json",loJson)
IF (loHttp.LastMethodSuccess = 0) THEN
? loHttp.LastErrorText
RELEASE loHttp
RELEASE loJson
CANCEL
ENDIF
? STR(loResp.StatusCode)
? loResp.BodyStr
RELEASE loResp
RELEASE loHttp
RELEASE loJson
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}"
}
}
}