detectdrift

Create a random stream such that the first 1000 observations come from a normal distribution with mean 2 and standard deviation 0.75 and the next 1000 come from a normal distribution with mean 4 and standard deviation 1. In an incremental drift detection application, access to data stream and model update would happen consecutively. One would not collect the data first and then feed into the model. However, for the purpose of clarification, this example demonstrates the simulation of data separately.

rng(1234) % For reproducibility
numObservations = 3000;
switchPeriod1 = 1000;
X = zeros([numObservations 1]);

for i = 1:numObservations
   if i <= switchPeriod1
      X(i) = normrnd(2,0.75);
   else
      X(i) = normrnd(4,1);
   end
end

Initiate the incremental concept drift detector. Utilize the Hoeffding's bound method with exponential moving average method (EWMA). Specify the input type as continuous, a warmup of 50 observations, and an estimation period of 50 observations.

incCDDetector = incrementalConceptDriftDetector("hddmw",InputType="continuous", ...
          WarmupPeriod=50,EstimationPeriod=50)

incCDDetector = 
  HoeffdingDriftDetectionMethod

        PreviousDriftStatus: 'Stable'
                DriftStatus: 'Stable'
                     IsWarm: 0
    NumTrainingObservations: 0
                Alternative: 'greater'
                  InputType: 'continuous'
                 TestMethod: 'ewma'

incDDetector is a HoeffdingDriftDetectionMethod object. When you first create the object, properties such as DriftStatus, IsWarm, CutMean, and NumTrainingObservations are at their initial state. detectdrift updates them as you feed the data incrementally and monitor for drift.

Preallocate the batch size and the variables to record drift status and statistics.

status = zeros([numObservations 1]);
statusname = strings([numObservations 1]);

Simulate the data stream of one observation at a time and perform incremental drift detection. At each iteration:

for i = 1:numObservations
    
    incCDDetector = detectdrift(incCDDetector,X(i));
    
    if incCDDetector.DriftDetected
        status(i) = 2;
        statusname(i) = string(incCDDetector.DriftStatus);
        incCDDetector = reset(incCDDetector); % If drift detected, reset the detector
        sprintf("Drift detected at observation #%d. Detector reset.",i)
    elseif incCDDetector.WarningDetected
        status(i) = 1;
        statusname(i) = string(incCDDetector.DriftStatus);
        sprintf("Warning detected at observation #%d.",i)
    else 
        status(i) = 0;
        statusname(i) = string(incCDDetector.DriftStatus);
    end      
end

ans = 
"Warning detected at observation #1019."

ans = 
"Warning detected at observation #1020."

ans = 
"Warning detected at observation #1021."

ans = 
"Warning detected at observation #1022."

ans = 
"Drift detected at observation #1023. Detector reset."

Plot the drift status versus the data observation number.

gscatter(1:numObservations,status,statusname,'gyr','*',5,'on',"Number of observations","Drift status")