detectdrift

Update drift detector states and drift status with new data

Since R2022a

Syntax

IncCDDetector = detectdrift(IncCDDetector,X)

IncCDDetector = detectdrift(IncCDDetector,X,Weights=W)

Description

IncCDDetector = detectdrift(IncCDDetector,X) updates the internal statistics and the drift status with new data in X and returns the modified detector IncCDDetector.

detectdrift updates the drift status only if the IsWarm property is true (or 1).

IncCDDetector = detectdrift(IncCDDetector,X,Weights=W) updates IncCDDetector also using the observation weights in W.

You can use the Weights argument for the Drift Detection Method (DDM) only. Specify "DDM" for the DetectionMethod argument in the call to incrementalConceptDriftDetector.

Examples

Perform Drift Detection on Data Stream

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:

• Monitor for drift using the new data with detectdrift.

• Track and record the drift status for visualization purposes.

• When a drift is detected, reset the incremental concept drift detector by using the function reset.

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")

Input Arguments

IncCDDetector — Incremental concept drift detector

DriftDetectionMethod | HoeffdingDriftDetectionMethod

Incremental concept drift detector, specified as either DriftDetectionMethod or HoeffdingDriftDetectionMethod object. For more information on these objects and their properties, see the corresponding reference pages.

X — Input data

n-by-1 vector of real numbers | logical vector | vector of 0s and 1s

Input data, specified as an n-by-1 vector of real numbers, where n is the number of observations.

• If the InputType value in the call to incrementalConceptDriftDetector is "continuous", then X must be a vector of real-valued numbers.

• If the InputType value in the call to incrementalConceptDriftDetector is "binary", then X can be a logical vector or vector of 0s and 1s.

Data Types: single | double | logical

W — Observation weights

n-by-1 vector of real numbers

Observation weights, specified as an n-by-1 vector of real numbers, where n is the number of observations. W must have the same number of elements as X.

You cannot use the Weights argument for the Hoeffding's Bounds Drift Detection Method using exponentially weighted moving averages (HDDMW). To use observation weights, specify "ddm" or "hddma" as the DetectionMethod in the call to incrementalConceptDriftDetector.

Data Types: single | double

Version History

Introduced in R2022a