reset
Syntax
Description
returns the incremental drift-aware model Mdl
= reset(Mdl
)Mdl
after
resetting the learned parameters of Mdl.BaseLearner
and
Mdl.DriftDetector
. If any hyperparameters of
Mdl.BaseLearner
are estimated during incremental training, the
reset
function resets these hyperparameters as well.
reset
always preserves the
Mdl.BaseLearner.Numpredictors
property.
For incremental classification models, reset
always preserves the
Mdl.BaseLearner.ClassNames
property and resets
Mdl.BaseLearner.Prior
if prior class probabilities are
"empirical"
.
The fit
function
internally calls reset
during incremental drift-aware learning.
reset
is suitable for using in custom workflows.
Examples
Reset Incremental Drift-Aware Model
Create the random concept data and the concept drift generator using the helper functions HelperRegrGenerator
and HelperConceptDriftGenerator
, respectively.
concept1 = HelperRegrGenerator(NumFeatures=100,NonZeroFeatures=[1,20,40,50,55], ... FeatureCoefficients=[4,5,10,-2,-6],NoiseStd=1.1,TableOutput=false); concept2 = HelperRegrGenerator(NumFeatures=100,NonZeroFeatures=[10,20,45,56,80], ... FeatureCoefficients=[4,5,10,-2,-6],NoiseStd=1.1,TableOutput=false); driftGenerator = HelperConceptDriftGenerator(concept1,concept2,15000,1000);
HelperRegrGenerator
generates streaming data using features and feature coefficients for regression specified in the call to the function. At each step, the function samples the predictors from a normal distribution. Then, the function computes the response using the feature coefficients and predictor values and adding a random noise from a normal distribution with mean zero and specified noise standard deviation. The software returns the data in matrices for using in incremental learners.
HelperConceptDriftGenerator
establishes the concept drift. The object uses a sigmoid function 1./(1+exp(-4*(numobservations-position)./width))
to decide the probability of choosing the first stream when generating data [3]. In this case, the position argument is 15000 and the width argument is 1000. As the number of observations exceeds the position value minus half of the width, the probability of sampling from the first stream when generating data decreases. The sigmoid function allows a smooth transition from one stream to the other. Larger width values indicate a larger transition period where both streams are approximately equally likely to be selected.
Initiate an incremental drift-aware model for regression as follows:
Create an incremental linear model for regression. Specify the linear regression model type and solver type.
Initiate an incremental concept drift detector that uses the Hoeffding's Bounds Drift Detection Method with moving average (HDDMA).
Using the incremental linear model and the concept drift detector, instantiate an incremental drift-aware model. Specify the training period as 6000 observations.
baseMdl = incrementalRegressionLinear(Learner="leastsquares",Solver="sgd",EstimationPeriod=1000,Standardize=false); dd = incrementalConceptDriftDetector("hddma",Alternative="greater",InputType="continuous",WarmupPeriod=1000); idal = incrementalDriftAwareLearner(baseMdl,DriftDetector=dd,TrainingPeriod=6000);
Preallocate the number of variables in each chunk and number of iterations for creating a stream of data.
numObsPerChunk = 10; numIterations = 1000;
Preallocate the variable for storing the regression error.
ce = array2table(zeros(numIterations,2),VariableNames=["Cumulative" "Window"]);
Simulate a data stream with incoming chunks of 10 observations each and perform incremental drift-aware learning. At each iteration:
Simulate predictor data and labels, and update the drift generator using the helper function
hgenerate
.Call
updateMetricsAndFit
to update the performance metrics and fit the incremental drift-aware model to the incoming data.Track the regression error for visualization purposes.
rng(12); % For reproducibility for j = 1:numIterations % Generate data [driftGenerator,X,Y] = hgenerate(driftGenerator,numObsPerChunk); % Update performance metrics and fit the model idal = updateMetricsAndFit(idal,X,Y); % Record regression error ce{j,:} = idal.Metrics{"MeanSquaredError",:}; end
Plot the cumulative and per window regression error. Mark the warmup plus estimation period.
h = plot(ce.Variables); xlim([0 numIterations]) ylabel("Mean Squared Error") xlabel("Iteration") xline((idal.MetricsWarmupPeriod+idal.BaseLearner.EstimationPeriod)/numObsPerChunk,"g-.","Estimation Period+Warmup Period",LineWidth=1.5) legend(h,ce.Properties.VariableNames) legend(h,Location="best")
Display the incremental drift-aware learner and the current metrics values.
idal
idal = incrementalDriftAwareLearner IsWarm: 1 Metrics: [1x2 table] BaseLearner: [1x1 incrementalRegressionLinear] DriftDetector: [1x1 HoeffdingDriftDetectionMethod] IsTraining: 0
idal.Metrics
ans=1×2 table
Cumulative Window
__________ ______
MeanSquaredError 2.0976 1.826
Display the base learner, and the drift detector.
idal.BaseLearner
ans = incrementalRegressionLinear IsWarm: 1 Metrics: [1x2 table] ResponseTransform: 'none' Beta: [100x1 double] Bias: -0.0793 Learner: 'leastsquares'
idal.BaseLearner.Beta
ans = 100×1
4.0221
0.0492
0.0046
0.0529
-0.0818
-0.1161
0.0307
-0.0669
-0.0103
0.0159
⋮
Display the drift detector.
idal.DriftDetector
ans = HoeffdingDriftDetectionMethod PreviousDriftStatus: 'Stable' DriftStatus: 'Stable' IsWarm: 1 NumTrainingObservations: 7900 Alternative: 'greater' InputType: 'continuous' TestMethod: 'average'
Reset the incremental drift-aware learner. Display the model and the metrics property.
idal = reset(idal)
idal = incrementalDriftAwareLearner IsWarm: 0 Metrics: [1x2 table] BaseLearner: [1x1 incrementalRegressionLinear] DriftDetector: [1x1 HoeffdingDriftDetectionMethod] IsTraining: 1
idal.Metrics
ans=1×2 table
Cumulative Window
__________ ______
MeanSquaredError NaN NaN
The metrics are reset to NaN
values. The software will wait until idal.BaseLearner.EstimationPeriod
+idal.BaseLearner.MetricsWarmUpPeriod
observations have passed before computing the metrics again.
Resetting the model resets the base learner and the underlying drift detector as well.
Display the base learner.
idal.BaseLearner
ans = incrementalRegressionLinear IsWarm: 0 Metrics: [1x2 table] ResponseTransform: 'none' Beta: [100x1 double] Bias: 0 Learner: 'leastsquares'
The Bias
parameter is also reset to 0.
Display the Beta
property of the base learner.
idal.BaseLearner.Beta
ans = 100×1
0
0
0
0
0
0
0
0
0
0
⋮
Coefficient values are all set to 0.
Display the drift detector.
idal.DriftDetector
ans = HoeffdingDriftDetectionMethod PreviousDriftStatus: 'Stable' DriftStatus: 'Stable' IsWarm: 0 NumTrainingObservations: 0 Alternative: 'greater' InputType: 'continuous' TestMethod: 'average'
IsWarm
property and the number of training observations are both set to 0.
You can use the dot notation to explore how other properties of the drift-aware learner, the base learner, and the drift detector change after a reset.
Input Arguments
Mdl
— Incremental drift-aware learning model
incrementalDriftAwareLearner
model object
Incremental drift-aware learning model fit to streaming data, specified as an incrementalDriftAwareLearner
model object. You can create
Mdl
using the incrementalDriftAwareLearner
function. For more details, see the object reference page.
Output Arguments
Mdl
— Updated incremental drift-aware learning model
incrementalDriftAwareLearner
model object
Updated incremental drift-aware learning model, returned as an incremental learning
model object of the same data type as the input model Mdl
,
incrementalDriftAwareLearner
.
References
[1] Barros, Roberto S.M. , et al. "RDDM: Reactive drift detection method." Expert Systems with Applications. vol. 90, Dec. 2017, pp. 344-55. https://doi.org/10.1016/j.eswa.2017.08.023.
[2] Bifet, Albert, et al. "New Ensemble Methods for Evolving Data Streams." Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM Press, 2009, p. 139. https://doi.org/10.1145/1557019.1557041.
[3] Gama, João, et al. "Learning with drift detection". Advances in Artificial Intelligence – SBIA 2004, edited by Ana L. C. Bazzan and Sofiane Labidi, vol. 3171, Springer Berlin Heidelberg, 2004, pp. 286–95. https://doi.org/10.1007/978-3-540-28645-5_29.
Version History
Introduced in R2022b
See Also
fit
| incrementalDriftAwareLearner
| updateMetrics
| updateMetricsAndFit
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