Sequence reset happening?

I’m training a model for anomaly detection. The data is as follows

neuron_id,value
int,float
S,
6921,0.300356
6921,0.069099
6921,0.316716
6921,0.167633
6921,0.100218

The neuron_id field serves as a sequence identifier hence the S flag on the second line. The neuron_id value changes once every 50 lines to mark the beginning of a new sequence. How can I check that sequence reset is actually happening when the neuron_id value changes?

I was thinking that by printing the model at each step I would see the sequenceReset field going from 0.0 to 1.0 but this not the case. Here is the model output when the transition is supposed to happen (neuron_id going from 6921 to 53):

ModelResult(	predictionNumber=49
	rawInput={'value': 0.004258, 'neuron_id': 53}
	sensorInput=SensorInput(	dataRow=(0.004258,)
	dataDict={'neuron_id': 53, 'value': 0.004258}
	dataEncodings=[array([ 1.,  1.,  1., ...,  0.,  0.,  0.], dtype=float32)]
	sequenceReset=0.0
	category=-1
)
	inferences={'multiStepPredictions': {1: {0.08826500000000001: 0.0026059335204494951, 0.1321598: 0.0028300311198308369, 1.564562: 0.0026570105870779375, 0.779939: 0.0028050158965931883, 0.261934: 0.0027851742030226227, 0.076092: 0.0026463728372261518, 0.004258: 0.90575743768592765, 0.756751: 0.002573166054106916}}, 'multiStepBucketLikelihoods': {1: {0: 0.90575743768592765, 8: 0.0026463728372261518, 9: 0.0026059335204494951, 76: 0.002573166054106916, 13: 0.0028300311198308369, 78: 0.0028050158965931883, 26: 0.0027851742030226227, 156: 0.0026570105870779375}}, 'multiStepBestPredictions': {1: 0.004258}, 'anomalyLabel': '[]', 'anomalyScore': 1.0}
	metrics=None
	predictedFieldIdx=0
	predictedFieldName=value
	classifierInput=ClassifierInput(	dataRow=0.004258
	bucketIndex=0
)
)
ModelResult(	predictionNumber=50
	rawInput={'value': 0.005851, 'neuron_id': 53}
	sensorInput=SensorInput(	dataRow=(0.005851,)
	dataDict={'neuron_id': 53, 'value': 0.005851}
	dataEncodings=[array([ 0.,  1.,  1., ...,  0.,  0.,  0.], dtype=float32)]
	sequenceReset=0.0
	category=-1
)
	inferences={'multiStepPredictions': {1: {0.0627962: 0.0055714714736493946, 0.005851: 0.86610401636359924, 0.261934: 0.011780366887778429, 3.217478: 0.008001018185725078, 0.076092: 0.0090889321537235071, 0.336359: 0.004160104491713979, 0.0412563: 0.010323617261193156, 0.316716: 0.0042960821749932062}}, 'multiStepBucketLikelihoods': {1: {32: 0.0042960821749932062, 1: 0.86610401636359924, 34: 0.004160104491713979, 4: 0.010323617261193156, 6: 0.0055714714736493946, 8: 0.0090889321537235071, 322: 0.008001018185725078, 26: 0.011780366887778429}}, 'multiStepBestPredictions': {1: 0.005851}, 'anomalyLabel': '[]', 'anomalyScore': 0.1}
	metrics=None
	predictedFieldIdx=0
	predictedFieldName=value
	classifierInput=ClassifierInput(	dataRow=0.005851
	bucketIndex=1
)
)

Look into modelResult.sensorInput.sequenceReset and see if it is different from modelResult.sequenceReset. They should be the same, but we haven’t use this property for our OPF code for many years, so I’m not sure you can depend on it.