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reset

Reset incremental one-class SVM model

Since R2023b

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    Syntax

    Mdl = reset(Mdl)

    Description

    Mdl = reset(Mdl) returns the incremental model Mdl with reset learned parameters. If any hyperparameters of Mdl are estimated during incremental training, the reset function resets these hyperparameters as well. reset always preserves the Mdl.NumPredictors property.

    example

    Examples

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    Open Live Script

    Create a default one-class support vector machine (SVM) model for incremental anomaly detection using the stochastic gradient descent (SGD) solver. Specify to standardize the predictor data. Reset the model after incremental training and see which parameters are reset.

    IncrementalMdl = incrementalOneClassSVM(Solver="sgd",StandardizeData=true)
    IncrementalMdl = 
  incrementalOneClassSVM

                    IsWarm: 0
     ContaminationFraction: 0
            ScoreThreshold: 0
    NumExpansionDimensions: 0
                    Lambda: 1.0000e-05
               KernelScale: 1

    IncrementalMdl is an incrementalOneClassSVM model object. All its properties are read-only. By default, the software sets the anomaly contamination fraction to 0 and the score threshold to 0.

    IncrementalMdl must be fit to data before you can use it to perform any other operations.

    Load Data

    Load the 1994 census data stored in census1994.mat. The data set consists of demographic data from the US Census Bureau.

    load census1994.mat

    The fit function of incrementalOneClassSVM does not support categorical predictors and does not use observations with missing values. Remove missing values in the data to reduce memory consumption and speed up training. Remove the categorical predictors. 

    adultdata = rmmissing(adultdata);
adultdata = removevars(adultdata,["workClass","education","marital_status", ...
    "occupation","relationship","race","sex","native_country","salary"]);

    Fit Incremental Model

    Fit the incremental model IncrementalMdl to the data by using the fit function. To simulate a data stream, fit the model in chunks of 100 observations at a time. At each iteration:

    • Process 100 observations.

    • Overwrite the previous incremental model with a new one fitted to the incoming observations.

    n = numel(adultdata(:,1));
numObsPerChunk = 100;
nchunk = floor(n/numObsPerChunk);

% Incremental fitting
rng("default"); % For reproducibility
for j = 1:nchunk
    ibegin = min(n,numObsPerChunk*(j-1) + 1);
    iend = min(n,numObsPerChunk*j);
    idx = ibegin:iend;    
    IncrementalMdl = fit(IncrementalMdl,adultdata(idx,:));
end

    Display all the properties of the trained model object IncrementalMdl. 

    details(IncrementalMdl);
      incrementalOneClassSVM with properties:

                KernelScale: 1
                     Lambda: 1.0000e-05
     NumExpansionDimensions: 256
              SolverOptions: [1x1 struct]
                     Solver: 'sgd'
                 FittedLoss: 'hinge'
                         Mu: [37.9400 1.9217e+05 10.1980 567.7170 102.5340 40.7060]
                      Sigma: [12.8905 1.0789e+05 2.5006 2.4309e+03 431.7485 11.7970]
           EstimationPeriod: 1000
                     IsWarm: 1
      ContaminationFraction: 0
    NumTrainingObservations: 29100
              NumPredictors: 6
             ScoreThreshold: 2.0912
          ScoreWarmupPeriod: 0
             PredictorNames: {'age'  'fnlwgt'  'education_num'  'capital_gain'  'capital_loss'  'hours_per_week'}
            ScoreWindowSize: 1000

    Reset Incremental Model

    Reset the learned parameters by using the reset function, and compare them to the previous model to see which parameters are reset. 

    newMdl = reset(IncrementalMdl);
details(newMdl)
      incrementalOneClassSVM with properties:

                KernelScale: 1
                     Lambda: 1.0000e-05
     NumExpansionDimensions: 256
              SolverOptions: [1x1 struct]
                     Solver: 'sgd'
                 FittedLoss: 'hinge'
                         Mu: [0 0 0 0 0 0]
                      Sigma: [1 1 1 1 1 1]
           EstimationPeriod: 1000
                     IsWarm: 0
      ContaminationFraction: 0
    NumTrainingObservations: 0
              NumPredictors: 6
             ScoreThreshold: 0
          ScoreWarmupPeriod: 0
             PredictorNames: {'age'  'fnlwgt'  'education_num'  'capital_gain'  'capital_loss'  'hours_per_week'}
            ScoreWindowSize: 1000

    The reset function resets the warmup status of the model (IsWarm = 0), the score threshold, the number of training observations, and the estimated hyperparameters (Mu and Sigma).

    Input Arguments

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    Incremental one-class SVM model, specified as an incrementalOneClassSVM model object. You can create Mdl directly or by converting a supported, traditionally trained machine learning model using the incrementalLearner function. For more details, see the incrementalOneClassSVM object page.

    Version History

    Introduced in R2023b

    See Also

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