Issue Using Nupic Anomaly Output

Engineering NuPIC
1

Hi,

I’m using Nupic Anomaly Output plotter (https://github.com/numenta/nupic/blob/master/examples/opf/clients/hotgym/anomaly/one_gym/nupic_anomaly_output.py) from the hotgym folder and I’m not sure if I’m using it correctly. It seems to be putting the predictions on top of my observed values (which often makes it look like I’m getting perfect predictions because after a point, nupic will usually give you the previous value as its prediction.

My text output looks like (here the observed value is the value at time t, and predicted is the value predicted for time t :

initializing 3001
observed: 49 predicted: 37.0
observed: 37 predicted: 37.0
observed: 51 predicted: 51.0
observed: 53 predicted: 51.6
observed: 52 predicted: 51.72
observed: 57 predicted: 61.0
observed: 61 predicted: 61.0
observed: 61 predicted: 83.0
observed: 83 predicted: 74.0
observed: 74 predicted: 76.0
observed: 76 predicted: 65.0
observed: 65 predicted: 61.0
observed: 61 predicted: 82.0
observed: 82 predicted: 99.0
observed: 99 predicted: 89.0
observed: 89 predicted: 75.0
observed: 75 predicted: 52.0
observed: 52 predicted: 82.0
observed: 82 predicted: 71.0
observed: 71 predicted: 57.0

But my plot looks like this:

anomaly_plot
anomaly_plot.png2500×1342 148 KB

I had to add plt.pause(0.0001) after plt.draw() in order to make the animation work.

I’m using matplotlib 2.2.2 and nupic 1.0.3

My code is here:

threshold = 0.99995
def run_model(coll, data, location, si, ds):
    model = ModelFactory.create(MODEL_PARAMS)
    model.enableInference({'predictedField': 'measured_flow'})
    anomaly_likelihood_helper = anomaly_likelihood.AnomalyLikelihood()
    output = nupic_anomaly_output.NuPICPlotOutput('3001')
    last = None
    for row in data:
        to_process = make_input(row)
        result = model.run(to_process)
        raw_anomaly_score = result.inferences['anomalyScore']
        likelihood = anomaly_likelihood_helper.anomalyProbability(to_process['measured_flow'], raw_anomaly_score,
                                                                  to_process['timestamp'])
        pred = result.inferences["multiStepBestPredictions"][1]
        if last:
            output.write(to_process['timestamp'], to_process['measured_flow'], pred, raw_anomaly_score)
            print("observed:", last, "predicted:", pred)
        last = to_process['measured_flow']
        if likelihood >= threshold:
            print("Anonaly Detected!")
            doc = ({'intersection': '3001',
                    'algorithm': 'HTM',
                    'datetime': to_process['timestamp'],
                    'other': {'likelihood': likelihood, 'score': raw_anomaly_score}})
            print(doc)

Is this a bug or am I using the plotter wrong?

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2

Are you using the inference shifter?

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3

me too, today I tried this example, but I can’t understand two things:
1- why in this example we don’t use the SDRclassifeir and why we use inference shifter
2- and how I choose anomaly threshold, slidingWindowSize, learningPeriod, historicWindowSize
Anomaly (slidingWindowSize =???)
AnomalyLikelihood (learningPeriod=???, historicWindowSize=???)
In my case I detect anomaly in ECG signal

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4

Usually just so data displays better in charts (predictions are aligned with ground truth).

For the other questions, start with NAB model params and adjust slowly to see what happens.

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5

Thanks, silly of me to miss that.

As an aside, I got tripped up by this (and I suspect other people will too), that when you want to detect anomalies, you need to provide an encoder with:

"_classifierInput": {
     "classifierOnly": True,
     "type": "RandomDistributedScalarEncoder",
     "resolution": 81,
     "fieldname": "field_name",
     "name": "_classifierInput"
 },

Is this correct? It seems like some optimised model params omit this even though they are labelled as anomaly detectors: https://github.com/numenta/nupic/blob/master/src/nupic/frameworks/opf/common_models/anomaly_params_random_encoder/best_single_metric_anomaly_params_cpp.json

  • Is it possible to add documentation about this special encoder?
  • Can it be different from the encoder for your predicted field?
  • What are the implications if it’s different?
  • Trying to get anomaly scores without it doesn’t indicate any error

Maybe something could be added to the quick start guide regarding this.

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