Implement Incremental Learning Using Succinct Workflow
This example shows how to use the succinct workflow to implement incremental learning for binary classification with prequential evaluation. Specifically, this example does the following:
Create a default incremental learning model for binary classification.
Simulate a data stream using a for loop, which feeds small chunks of observations to the incremental learning algorithm.
For each chunk, use
updateMetricsAndFitto measure the model performance given the incoming data, and then fit the model to that data.
Create Default Model Object
Mdl = incrementalClassificationLinear()
Mdl =
incrementalClassificationLinear
IsWarm: 0
Metrics: [1×2 table]
ClassNames: [1×0 double]
ScoreTransform: 'none'
Beta: [0×1 double]
Bias: 0
Learner: 'svm'
Properties, Methods
Mdl is an incrementalClassificationLinear model object. All its properties are read-only.
Mdl must be fit to data before you can use it to perform any other operations.
Load and Preprocess Data
Load the human activity data set. Randomly shuffle the data.
load humanactivity n = numel(actid); rng(1); % For reproducibility idx = randsample(n,n); X = feat(idx,:); Y = actid(idx);
For details on the data set, enter Description at the command line.
Responses can be one of five classes: Sitting, Standing, Walking, Running, or Dancing. Dichotomize the response by identifying whether the subject is moving (actid > 2).
Y = Y > 2;
Implement Incremental Learning
Fit the incremental model to the training data by using the updateMetricsAndfit function. Simulate a data stream by processing chunks of 50 observations at a time. At each iteration:
Process 50 observations.
Overwrite the previous incremental model with a new one fitted to the incoming observation.
Store , the cumulative metrics, and the window metrics to see how they evolve during incremental learning.
% Preallocation numObsPerChunk = 50; nchunk = floor(n/numObsPerChunk); ce = array2table(zeros(nchunk,2),'VariableNames',["Cumulative" "Window"]); beta1 = zeros(nchunk,1); % Incremental fitting for j = 1:nchunk ibegin = min(n,numObsPerChunk*(j-1) + 1); iend = min(n,numObsPerChunk*j); idx = ibegin:iend; Mdl = updateMetricsAndFit(Mdl,X(idx,:),Y(idx)); ce{j,:} = Mdl.Metrics{"ClassificationError",:}; beta1(j + 1) = Mdl.Beta(1); end
IncrementalMdl is an incrementalClassificationLinear model object trained on all the data in the stream. During incremental learning and after the model is warmed up, updateMetricsAndFit checks the performance of the model on the incoming observation, and then fits the model to that observation.
Inspect Model Evolution
To see how the performance metrics and evolved during training, plot them on separate subplots.
figure; subplot(2,1,1) plot(beta1) ylabel('\beta_1') xlim([0 nchunk]); xline(Mdl.EstimationPeriod/numObsPerChunk,'r-.'); subplot(2,1,2) h = plot(ce.Variables); xlim([0 nchunk]); ylabel('Classification Error') xline(Mdl.EstimationPeriod/numObsPerChunk,'r-.'); xline((Mdl.EstimationPeriod + Mdl.MetricsWarmupPeriod)/numObsPerChunk,'g-.'); legend(h,ce.Properties.VariableNames) xlabel('Iteration')
The plot suggests that updateMetricsAndFit does the following:
Fit during all incremental learning iterations
Compute performance metrics after the metrics warm-up period only.
Compute the cumulative metrics during each iteration.
Compute the window metrics after processing 500 observations.