Anomaly metadata

Anomaly metadata

Get metadata about a trained anomaly block. Use the impulse blocks to find the learnId.

GEThttps://studio.edgeimpulse.com/v1/api/{projectId}/training/anomaly/{learnId}/metadata

Path parameters

projectId*integer

Project ID

learnId*integer

Learn Block ID, use the impulse functions to retrieve the ID

Response

200

OK

Body

application/json

success*boolean

Whether the operation succeeded

errorstring

Optional error description (set if 'success' was false)

created*string (date-time)

Date when the model was trained

scale*array of number

Scale input for StandardScaler. Values are scaled like this (where ix is axis index): input[ix] = (input[ix] - mean[ix]) / scale[ix];

mean*array of number

Mean input for StandardScaler. Values are scaled like this (where ix is axis index): input[ix] = (input[ix] - mean[ix]) / scale[ix];

clusters*array of object

Trained K-means clusters

axes*array of integer

Which axes were included during training (by index)

Example: "`[ 0, 11, 22 ]`"

defaultMinimumConfidenceRatingnumber

Default minimum confidence rating required before tagging as anomaly, based on scores of training data (GMM only).

availableModelTypesarray of KerasModelTypeEnum (enum)

The types of model that are available

recommendedModelTypeKerasModelTypeEnum (enum)

int8float32akidarequiresRetrain

modelValidationMetricsarray of KerasModelMetadataMetrics (object)

Metrics for each of the available model types

hasTrainedModelboolean

Request

JavaScriptCurlPython

const response = await fetch('https://studio.edgeimpulse.com/v1/api/{projectId}/training/anomaly/{learnId}/metadata', {
    method: 'GET',
    headers: {},
});
const data = await response.json();

Response

200

{
  "success": false,
  "error": "text",
  "created": "2024-12-22T05:53:20.522Z",
  "scale": [
    0
  ],
  "mean": [
    0
  ],
  "clusters": [
    {
      "center": [
        0
      ],
      "maxError": 0
    }
  ],
  "axes": "`[ 0, 11, 22 ]`",
  "defaultMinimumConfidenceRating": 0,
  "availableModelTypes": [
    "int8"
  ],
  "recommendedModelType": "int8",
  "modelValidationMetrics": [
    {
      "type": "int8",
      "loss": 0,
      "accuracy": 0,
      "confusionMatrix": [
        [
          31,
          1,
          0
        ],
        [
          2,
          27,
          3
        ],
        [
          1,
          0,
          39
        ]
      ],
      "onDevicePerformance": [
        {
          "mcu": "text",
          "name": "text",
          "isDefault": false,
          "latency": 0,
          "tflite": {
            "ramRequired": 0,
            "romRequired": 0,
            "arenaSize": 0,
            "modelSize": 0
          },
          "eon": {
            "ramRequired": 0,
            "romRequired": 0,
            "arenaSize": 0,
            "modelSize": 0
          },
          "eon_ram_optimized": {
            "ramRequired": 0,
            "romRequired": 0,
            "arenaSize": 0,
            "modelSize": 0
          },
          "customMetrics": [
            {
              "name": "text",
              "value": "text"
            }
          ]
        }
      ],
      "predictions": [
        {
          "sampleId": 0,
          "startMs": 0,
          "endMs": 0,
          "label": "text",
          "prediction": "text",
          "predictionCorrect": false,
          "f1Score": 0,
          "anomalyScores": [
            [
              0
            ]
          ]
        }
      ],
      "visualization": "featureExplorer",
      "isSupportedOnMcu": false,
      "mcuSupportError": "text",
      "profilingJobId": 0,
      "profilingJobFailed": false,
      "additionalMetrics": [
        {
          "name": "text",
          "value": "text",
          "fullPrecisionValue": 0,
          "tooltipText": "text",
          "link": "text"
        }
      ]
    }
  ],
  "hasTrainedModel": false
}