Model Accuracy

Iris is the genus of flower which contains 3 species: setosa, versicolor, and virginica. This demo is based on iris classification model based on flower properties like sepal length, sepal width, petal length, and petal width. The species are also the classes that will be used for the classification. Here we will:

  • Set up a metrics server for the pre-existing iris classifier deployment

  • Send a request to get an iris classification

  • Send feedback requests to gather accuracy metrics

This demo requires Knative installation on the cluster as the metrics server will be installed as a kservice. Also, the metrics server only works with classification models in this version.

These model metrics are only supported for pre-existing Seldon Core 1 deployments with the Seldon inference protocol. Seldon now recommends adopting the industry-standard Open Inference Protocol (OIP) in preference to the Seldon protocol. Seldon has a separate module to support the calculation of model performance metrics with the Open Inference Protocol. Please contact your customer success representative to learn more.

Setup Metrics Server

  1. Use the pre-existing iris classifier deployment with the Seldon inference protocol

  2. From the Dashboard page, click on Add in the Metrics Server panel:

    • Detector Name: multiclass

    • Storage URI: adserver.cm_models.multiclass_numeric.MultiClassNumeric

    • Storage Secret: Leave empty as we are using a public bucket

    • Reply URL: Leave as default value, http://seldon-request-logger.seldon-logs

    metrics server

Make Predictions

Run a single prediction using the ndarray payload format. Make a couple of these requests at random using the predict tool in the UI.

  1. Go to the Predict page.

  2. Paste in the following JSON data for the prediction, and click the Predict button:

    {
  "data": {
    "names": [
      "Sepal length",
      "Sepal width",
      "Petal length",
      "Petal Width"
    ],
    "ndarray": [
      [
        6.8,
        2.8,
        4.8,
        1.4
      ]
    ]
  }
}

  3. Inspect the response, to see that the class with the highest confidence is the second of the iris dataset, versicolor.

    {
  "data": {
    "names": [
      "t:0",
      "t:1",
      "t:2"
    ],
    "ndarray": [
      [
         0.008074020139119268,
         0.7781601484223357,
         0.21376583143854502
      ]
    ]
  },
  "meta": {
    "requestPath": {
    "iris-accuracy-container": "seldonio/sklearnserver:1.18.2"
    }
  }
}

Send Feedback

As we saw the prediction response was versicolor (the second class of iris). In numeric form the response is,

{
  "data": {
    "ndarray": [
      1
    ]
  }
}

A feedback request consists of the returned value in a response (response.data on line 5) and a truth value (truth.data on line 12), for that data. For example, a true-positive feedback request looks like this:

{
  "response": {
    "data": {
      "ndarray": [
        1
      ]
    }
  },
  "truth": {
    "data": {
      "ndarray": [
        1
      ]
    }
  }
}

We'll send several feedback requests to the metrics server, that we set-up earlier, for various scenarios.

  1. Set-up the environment variable(s) for the request:

    export CLUSTER_IP=$(kubectl -n istio-system get service istio-ingressgateway -o jsonpath='{.status.loadBalancer.ingress[0].ip}')

    (Recommended) You may also need to set-up for the Authorization header:

    1. Return to the predict page, and paste in the prediction, as you did above

    2. Click the Copy as curl button, and copy the value that looks like Bearer <authorization header>

    Copying aithorization header

    1. Set the environment variable:

    export AUTH_TOKEN="<paste the authorization value>"

  2. Scenario 1: True-positive:

    Request without Authorization header:

    curl -k \
    -H "Content-Type: application/json" \
    https://$CLUSTER_IP/seldon/seldon/iris-accuracy/api/v0.1/feedback \
    -d '{"response":{"data":{"ndarray":[1]}},"truth":{"data":{"ndarray":[1]}}}'

    Request with Authorization header:

    curl -k \
    -H "Content-Type: application/json" \
    -H "Authorization: $AUTH_TOKEN" \
    https://$CLUSTER_IP/seldon/seldon/iris-accuracy/api/v0.1/feedback \
    -d '{"response":{"data":{"ndarray":[1]}},"truth":{"data":{"ndarray":[1]}}}'

    Response:

    {
  "data": {
     "tensor": {
     "shape": [
         0
       ]
      }
    },
    "meta": {
     "requestPath": {
       "iris-accuracy-container": 1.18.2
    }
  }
}

  3. Scenario 2: False-positive:

    The difference from the previous scenario is the in the truth value:

    -   -d '{"response":{"data":{"ndarray":[1]}},"truth":{"data":{"ndarray":[1]}}}'
+   -d '{"response":{"data":{"ndarray":[1]}},"truth":{"data":{"ndarray":[0]}}}'

    Request without Authorization header:

    curl -k \
    -H "Content-Type: application/json" \
    https://$CLUSTER_IP/seldon/seldon/iris-accuracy/api/v0.1/feedback \
    -d '{"response":{"data":{"ndarray":[1]}},"truth":{"data":{"ndarray":[0]}}}'

    Request with Authorization header:

    curl -k \
    -H "Content-Type: application/json" \
    -H "Authorization: $AUTH_TOKEN" \
    https://$CLUSTER_IP/seldon/seldon/iris-accuracy/api/v0.1/feedback \
    -d '{"response":{"data":{"ndarray":[1]}},"truth":{"data":{"ndarray":[0]}}}'

    Response:

    {
  "data": {
     "tensor": {
     "shape": [
         0
       ]
      }
    },
    "meta": {
     "requestPath": {
       "iris-accuracy-container": 1.18.2
    }
  }
}

Monitor accuracy metrics on the Monitor Screen

Having done a prediction, metrics will begin to become available.

  1. Go to the monitor screen's Prediction Accuracy tab to view all the metrics.

  2. Set the time range to view the metrics, using the "From Time" and "To Time" selectors.

  3. You can see metrics like accuracy, precision, recall and specificity here. Notice the drop in accuracy metrics after the false feedback was received.

Submit batch feedback using Batch Processor component

Now we will submit a feedback as a batch using the Batch Processor component.

We will use two files, each containing 10k feedback instances:

  • 90% success rate feedback file

762KB
feedback-input-90.txt
Open

  • 40% success rate feedback file

762KB
feedback-input-40.txt
Open

  1. We need to upload the files to MinIO's data bucket. For details on interacting with MinIO UI please see Batch Demo.

  2. Once the files are in the MinIO bucket, go to the Batch Jobs screen using either the navigation bar on the left side or with the button on the model dashboard. For each of feedback-input-90.txt and feedback-input-40.txt files, submit a batch job. Wait for the first to complete before proceeding to the next. Submit batch request using the following form values:

    • Input Data Location: minio://data/feedback-input-40.txt

    • Output Data Location: minio://data/output-data-40-{{workflow.name}}.txt

    • Number of Workers: 15

    • Number of Retries: 3

    • Batch Size: 1

    • Minimum Batch Wait Interval (sec): 0

    • Method: Feedback

    • Transport Protocol: REST

    • Input Data Type: Raw Data

    • Object Store Secret Name: minio-bucket-envvars

    • Resources: Leave defaults

    metrics server

  3. Now go to the monitor view and observe how metrics value evolve over time.

Troubleshooting

If you experience issues with this demo, see the troubleshooting docs and also the Knative or Elasticsearch sections.

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