Chilkat Online Tools

phpAx / Cognite API v1 / Create entity matcher model

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<?php

// 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 new COM('Chilkat_9_5_0.Chilkat.Http')
$http = new COM("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 new COM('Chilkat_9_5_0.Chilkat.JsonObject')
$json = new COM("Chilkat.JsonObject");
$json->UpdateNewArray('trueMatches[0].');
$json->UpdateNewArray('trueMatches[1].');
$json->UpdateString('externalId','in irure amet');
$json->UpdateString('name','minim');
$json->UpdateString('description','aliqua ipsum culpa eiusmod');
$json->UpdateString('featureType','simple');
$json->UpdateString('matchFields[0].source','name');
$json->UpdateString('matchFields[0].target','name');
$json->UpdateString('classifier','randomforest');
$json->UpdateBool('ignoreMissingFields',0);

$http->SetRequestHeader('content-type','application/json');
$http->SetRequestHeader('api-key','{{api-key}}');

// resp is a Chilkat.HttpResponse
$resp = $http->PostJson3('https://domain.com/api/v1/projects/{{project}}/context/entitymatching','application/json',$json);
if ($http->LastMethodSuccess == 0) {
    print $http->LastErrorText . "\n";
    exit;
}

print $resp->StatusCode . "\n";
print $resp->BodyStr . "\n";


?>

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}"
    }
  }
}