DriftDetectionMethod
Description
DriftDetectionMethod
model object represents an incremental
concept drift detector that uses the Drift Detection Method [1]. After creating the object,
you can use the detectdrift
object
function to update the statistics and check for any drift in the concept data (for example,
failure rate, regression loss, and so on).
DriftDetectionMethod
is suitable for incremental concept drift detection. For
drift detection on raw data, see detectdrift
for
batch drift detection.
Creation
You can create DriftDetectionMethod
by specifying the
DetectionMethod
argument as "ddm"
in the call to
incrementalConceptDriftDetector
.
Properties
Alternative
— Type of alternative hypothesis
'greater'
(default) | 'less'
Type of alternative hypothesis for determining the drift status, specified as either
'greater'
or 'less'
.
Data Types: char
DriftDetected
— Flag indicating whether software detects drift
1
| 0
This property is read-only.
Flag indicating whether software detects drift or not, specified as either
1
or 0
. Value of 1
means
DriftStatus
is 'Drift'
.
Data Types: logical
DriftStatus
— Current drift status
'Stable'
| 'Warning'
| 'Drift'
This property is read-only.
Current drift status, specified as 'Stable'
,
'Warning'
, or 'Drift'
. You can see the
transition in the drift status by comparing DriftStatus
and
PreviousDriftStaus
.
Data Types: char
DriftThreshold
— Number of standard deviations for drift limit
nonnegative scalar value
This property is read-only.
Number of standard deviations for drift limit, specified as a nonnegative scalar
value. This is the number of standard deviations the overall test statistic can be away
from the optimal test statistic before the software sets DriftStatus
to 'Drift'
.
Data Types: double
InputType
— Type of input data
'binary'
(default) | 'continuous'
This property is read-only.
Type of input data, specified as either 'binary'
or
'continuous'
.
Data Types: char
IsWarm
— Flag indicating whether warmup period is over
1
| 0
This property is read-only.
Flag indicating whether the warmup period is over or not, specified as
1
(true
) or
0
(false
).
Data Types: logical
Mean
— Weighted average of all input data
numeric value
This property is read-only.
Weighted average of all input data used for training the drift detector, specified as a numeric value.
Data Types: double
NumTrainingObservations
— Number of observations used for training
nonnegative integer value
This property is read-only.
Number of observations used for training the drift detector, specified as a nonnegative integer value.
Data Types: double
OptimalMean
— Optimal weighted average
numeric value
Optimal weighted average detectdrift
observes up to the most
current data point, specified as a numeric value.
detectdrift
updates
the OptimalMean
and OptimalStandardDeviation
under any
of these conditions:
When
Alternative
is'greater'
andMean
+StandardDeviation
is less than or equal toOptimalMean
+OptimalStandardDeviation
.When
Alternative
is'less'
andMean
-StandardDeviation
is greater than or equal toOptimalMean
-OptimalStandardDeviation
.
Data Types: double
OptimalStandardDeviation
— Optimal weighted standard deviation
numeric value
This property is read-only.
Optimal weighted standard deviation detectdrift
observes up to
the most current data point, specified as a numeric value.
detectdrift
updates
the OptimalMean
and OptimalStandardDeviation
under any
of these conditions:
When
Alternative
is'greater'
andMean
+StandardDeviation
is less than or equal toOptimalMean
+OptimalStandardDeviation
.When
Alternative
is'less'
andMean
-StandardDeviation
is greater than or equal toOptimalMean
-OptimalStandardDeviation
.
Data Types: double
PreviousDriftStatus
— Drift status prior to the latest training
'Stable'
| 'Warning'
| 'Drift'
This property is read-only.
Drift status prior to the latest training using the most recent batch of data,
specified as 'Stable'
, 'Warning'
, or
'Drift'
. You can see the transition in the drift status by
comparing DriftStatus
and
PreviousDriftStaus
.
Data Types: char
StandardDeviation
— Weighted standard deviation of all input data
numeric value
This property is read-only.
Weighted standard deviation of all input data used for training the drift detector, specified as a numeric value.
Data Types: double
WarmupPeriod
— Number of observations for drift detector warmup
nonnegative integer value
This property is read-only.
Number of observations for drift detector warmup, specified as a nonnegative integer.
Data Types: double
WarningDetected
— Flag indicating whether there is warning
1
| 0
This property is read-only.
Flag indicating whether there is warning or not, specified as either
1
or 0
. Value of 1
means
DriftStatus
is 'Warning'
.
Data Types: logical
WarningThreshold
— Number of standard deviations for warning limit
nonnegative scalar value
This property is read-only.
Number of standard deviations for warning limit, specified as a nonnegative scalar
value. This is the number of standard deviations the overall test statistic can be away
from the optimal test statistic before the software sets DriftStatus
to 'Warning'
.
Data Types: double
Object Functions
detectdrift | Update drift detector states and drift status with new data |
reset | Reset incremental concept drift detector |
Examples
Monitor Data Stream for Potential Drift
Initiate the concept drift detector using the Drift Detection Method (DDM).
incCDDetector = incrementalConceptDriftDetector("ddm");
Create a random stream such that for the first 1000 observations, failure rate is 0.1 and after 1000 observations, failure rate increases to 0.6.
rng(1234) % For reproducibility numObservations = 3000; switchPeriod = 1000; for i = 1:numObservations if i <= switchPeriod failurerate = 0.1; else failurerate = 0.6; end X(i) = rand()<failurerate; % Value 1 represents failure end
Preallocate variables for tracking drift status.
status = zeros(numObservations,1); statusname = strings(numObservations,1);
Continuously feed the data to the drift detector and perform incremental drift detection. At each iteration:
Update statistics of the drift detector and monitor for drift using the new data point with
detectdrift
. (Note:detectdrift
checks for drift after the warmup period.)Track and record the drift status for visualization purposes.
When a drift is detected, reset the incremental concept drift detector by using
reset
.
for i = 1:numObservations incCDDetector = detectdrift(incCDDetector,X(i)); statusname(i) = string(incCDDetector.DriftStatus); if incCDDetector.DriftDetected status(i) = 2; incCDDetector = reset(incCDDetector); % If drift detected, reset the detector sprintf("Drift detected at Observation #%d. Detector reset.",i) elseif incCDDetector.WarningDetected status(i) = 1; else status(i) = 0; end end
ans = "Drift detected at Observation #1078. Detector reset."
After the change in the failure rate at observation number 1000, detectdrift
detects the shift at observation number 1078.
Plot the drift status versus the observation number.
gscatter(1:numObservations,status,statusname,'gyr','*',4,'on',"Observation number","Drift status")
Monitor Data Stream for Decrease in Failure Rate
Initiate the concept drift detector using the Drift Detection Method (DDM).
incCDDetector = incrementalConceptDriftDetector("ddm",Alternative="less",WarmupPeriod=100);
Create a random stream such that for the first 1000 observations, failure rate is 0.4 and after 1000 failure rate decreases to 0.1.
rng(1234) % For reproducibility numObservations = 3000; switchPeriod = 1000; for i = 1:numObservations if i <= switchPeriod failurerate = 0.4; else failurerate = 0.125; end X(i) = rand()<failurerate; % Value 1 represents failure end
Preallocate variables for tracking drift status and the optimal mean and optimal standard deviation value.
optmean = zeros(numObservations,1); optstddev = zeros(numObservations,1); status = zeros(numObservations,1); statusname = strings(numObservations,1);
Continuously feed the data to the drift detector and monitor for any potential change. Record the drift status for visualization purposes.
for i = 1:numObservations incCDDetector = detectdrift(incCDDetector,X(i)); statusname(i) = string(incCDDetector.DriftStatus); optmean(i) = incCDDetector.OptimalMean; optstddev(i) = incCDDetector.OptimalStandardDeviation; if incCDDetector.DriftDetected status(i) = 2; incCDDetector = reset(incCDDetector); % If drift detected, reset the detector sprintf("Drift detected at Observation #%d. Detector reset.",i) elseif incCDDetector.WarningDetected status(i) = 1; else status(i) = 0; end end
ans = "Drift detected at Observation #1107. Detector reset."
After the change in the failure rate at observation number 1000, detectdrift
detects the shift at observation number 1096.
Plot the change in the optimal mean and optimal standard deviation.
tiledlayout(2,1); ax1 = nexttile; plot(ax1,1:numObservations,optmean) ax2 = nexttile; plot(ax2,1:numObservations,optstddev)
Plot the drift status versus the observation number.
figure(); gscatter(1:numObservations,status,statusname,'gyr','*',4,'on',"Observation number","Drift status")
detectdrift
concludes on a warning status for multiple observations before it decides on a drift.
References
[1] Gama, Joao, Pedro Medas, Gladys Castillo, and Pedro P. Rodrigues. “Learning with drift detection.“ In Brazilian symposium on artificial intelligence, pp. 286-295. Berlin, Heidelberg: Springer. 2004, September.
Version History
Introduced in R2022a
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