Main Content

updateMetrics

Update performance metrics in kernel incremental learning model given new data

    Description

    Given streaming data, updateMetrics measures the performance of a configured incremental learning model for kernel regression (incrementalRegressionKernel object) or binary kernel classification (incrementalClassificationKernel object). updateMetrics stores the performance metrics in the output model.

    updateMetrics allows for flexible incremental learning. After you call the function to update model performance metrics on an incoming chunk of data, you can perform other actions before you train the model to the data. For example, you can decide whether you need to train the model based on its performance on a chunk of data. Alternatively, you can both update model performance metrics and train the model on the data as it arrives, in one call, by using the updateMetricsAndFit function.

    To measure the model performance on a specified batch of data, call loss instead.

    example

    Mdl = updateMetrics(Mdl,X,Y) returns an incremental learning model Mdl, which is the input incremental learning model Mdl modified to contain the model performance metrics on the incoming predictor and response data, X and Y respectively.

    When the input model is warm (Mdl.IsWarm is true), updateMetrics overwrites previously computed metrics, stored in the Metrics property, with the new values. Otherwise, updateMetrics stores NaN values in Metrics instead.

    The input and output models have the same data type.

    example

    Mdl = updateMetrics(Mdl,X,Y,Weights=weights) also sets observation weights.

    Examples

    collapse all

    Train a kernel model for binary classification by using fitckernel, convert it to an incremental learner, and then track its performance to streaming data.

    Load and Preprocess Data

    Load the human activity data set. Randomly shuffle the data.

    load humanactivity
    rng(1) % For reproducibility
    n = numel(actid);
    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;

    Train Kernel Model for Binary Classification

    Fit a kernel model for binary classification to a random sample of half the data.

    idxtt = randsample([true false],n,true);
    TTMdl = fitckernel(X(idxtt,:),Y(idxtt))
    TTMdl = 
      ClassificationKernel
                  ResponseName: 'Y'
                    ClassNames: [0 1]
                       Learner: 'svm'
        NumExpansionDimensions: 2048
                   KernelScale: 1
                        Lambda: 8.2967e-05
                 BoxConstraint: 1
    
    
      Properties, Methods
    
    

    TTMdl is a ClassificationKernel model object representing a traditionally trained kernel model for binary classification.

    Convert Trained Model

    Convert the traditionally trained classification model to a model for incremental learning.

    IncrementalMdl = incrementalLearner(TTMdl)
    IncrementalMdl = 
      incrementalClassificationKernel
    
                        IsWarm: 1
                       Metrics: [1x2 table]
                    ClassNames: [0 1]
                ScoreTransform: 'none'
        NumExpansionDimensions: 2048
                   KernelScale: 1
    
    
      Properties, Methods
    
    

    IncrementalMdl is an incrementalClassificationKernel model. The model display shows that the model is warm (IsWarm is 1). Therefore, updateMetrics can track model performance metrics given data.

    Track Performance Metrics

    Track the model performance on the rest of the data by using the updateMetrics function. Simulate a data stream by processing 50 observations at a time. At each iteration:

    1. Call updateMetrics to update the cumulative and window classification error of the model given the incoming chunk of observations. Overwrite the previous incremental model to update the losses in the Metrics property. Note that the function does not fit the model to the chunk of data—the chunk is "new" data for the model.

    2. Store the classification error and number of training observations.

    % Preallocation
    idxil = ~idxtt;
    nil = sum(idxil);
    numObsPerChunk = 50;
    nchunk = floor(nil/numObsPerChunk);
    ce = array2table(zeros(nchunk,2),VariableNames=["Cumulative","Window"]);
    numtrainobs = [zeros(nchunk,1)];
    Xil = X(idxil,:);
    Yil = Y(idxil);
    
    % Incremental fitting
    for j = 1:nchunk
        ibegin = min(nil,numObsPerChunk*(j-1) + 1);
        iend   = min(nil,numObsPerChunk*j);
        idx = ibegin:iend;
        IncrementalMdl = updateMetrics(IncrementalMdl,Xil(idx,:),Yil(idx));
        ce{j,:} = IncrementalMdl.Metrics{"ClassificationError",:};
        numtrainobs(j) = IncrementalMdl.NumTrainingObservations; 
    end

    IncrementalMdl is an incrementalClassificationKernel model object that has tracked the model performance to observations in the data stream.

    Plot a trace plot of the number of training observations and the performance metrics on separate tiles.

    t = tiledlayout(2,1);
    nexttile
    plot(numtrainobs)
    ylabel("Number of Training Observations")
    xlim([0 nchunk])
    nexttile
    plot(ce.Variables)
    xlim([0 nchunk])
    ylabel("Classification Error")
    legend(ce.Properties.VariableNames,Location="best")
    xlabel(t,"Iteration")

    Figure contains 2 axes objects. Axes object 1 contains an object of type line. Axes object 2 contains 2 objects of type line. These objects represent Cumulative, Window.

    The cumulative loss is stable, whereas the window loss jumps. The number of training observations does not change because updateMetrics does not fit the model to the data.

    Configure incremental learning options for an incrementalClassificationKernel model object when you call the incrementalClassificationKernel function. Track the model performance on streaming data, and fit the model to the data. Specify observation weights when you call incremental learning functions.

    Create an incremental kernel model for binary classification. Specify an estimation period of 5000 observations and the stochastic gradient descent (SGD) solver.

    Mdl = incrementalClassificationKernel(EstimationPeriod=5000,Solver="sgd")
    Mdl = 
      incrementalClassificationKernel
    
                        IsWarm: 0
                       Metrics: [1x2 table]
                    ClassNames: [1x0 double]
                ScoreTransform: 'none'
        NumExpansionDimensions: 0
                   KernelScale: 1
    
    
      Properties, Methods
    
    

    Mdl is an incrementalClassificationKernel model. All its properties are read-only.

    The model display shows that the model is not warm (IsWarm is 0). Determine the size of the metrics warm-up period by displaying model properties.

    mwp = Mdl.MetricsWarmupPeriod
    mwp = 1000
    

    Determine the number of observations that incremental fitting functions, such as fit, must process before measuring the performance of the model.

    numObsBeforeMetrics = Mdl.MetricsWarmupPeriod + Mdl.EstimationPeriod
    numObsBeforeMetrics = 6000
    

    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;

    Suppose that the data collected when the subject was not moving (Y = false) has double the quality than when the subject was moving. Create a weight variable that attributes 2 to observations collected from a still subject, and 1 to a moving subject.

    W = ones(n,1) + ~Y;

    Perform incremental learning. At each iteration:

    • Simulate a data stream by processing a chunk of 50 observations.

    • Measure model performance metrics on the incoming chunk using updateMetrics. Specify the corresponding observation weights and overwrite the input model.

    • Fit the model to the incoming chunk by using the fit function. Specify the corresponding observation weights and overwrite the input model.

    • Store the misclassification error rate and number of training observations to see how they evolve during incremental learning.

    % Preallocation
    numObsPerChunk = 50;
    nchunk = floor(n/numObsPerChunk);
    ce = array2table(zeros(nchunk,2),VariableNames=["Cumulative","Window"]);
    numtrainobs = [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 = updateMetrics(Mdl,X(idx,:),Y(idx),Weights=W(idx));
        ce{j,:} = Mdl.Metrics{"ClassificationError",:};
        Mdl = fit(Mdl,X(idx,:),Y(idx),Weights=W(idx));
        numtrainobs(j) = Mdl.NumTrainingObservations; 
    end

    Mdl is an incrementalClassificationKernel model object trained on all the data in the stream.

    Plot a trace plot of the number of training observations and the performance metrics on separate tiles.

    t = tiledlayout(2,1);
    nexttile
    plot(numtrainobs)
    ylabel("Number of Training Observations")
    xline(Mdl.EstimationPeriod/numObsPerChunk,'-.')
    xlim([0 nchunk])
    nexttile
    plot(ce.Variables)
    ylabel('ClassificationError')
    xline(Mdl.EstimationPeriod/numObsPerChunk,'-.')
    xline(numObsBeforeMetrics/numObsPerChunk,'--')
    xlim([0 nchunk]);
    legend(ce.Properties.VariableNames,Location="best")
    xlabel(t,'Iteration')

    Figure contains 2 axes objects. Axes object 1 contains 2 objects of type line, constantline. Axes object 2 contains 4 objects of type line, constantline. These objects represent Cumulative, Window.

    mdlIsWarm = numObsBeforeMetrics/numObsPerChunk
    mdlIsWarm = 120
    

    The plot suggests that fit does not fit the model to the data or update the parameters until after the estimation period. Also, updateMetrics does not track the classification error until after the estimation and metrics warm-up periods (120 chunks).

    Incrementally train a kernel regression model only when its performance degrades.

    Load and shuffle the 2015 NYC housing data set. For more details on the data, see NYC Open Data.

    load NYCHousing2015
    
    rng(1) % For reproducibility
    n = size(NYCHousing2015,1);
    shuffidx = randsample(n,n);
    NYCHousing2015 = NYCHousing2015(shuffidx,:);

    Extract the response variable SALEPRICE from the table. For numerical stability, scale SALEPRICE by 1e6.

    Y = NYCHousing2015.SALEPRICE/1e6;
    NYCHousing2015.SALEPRICE = [];

    To reduce computational cost for this example, remove the NEIGHBORHOOD column, which contains a categorical variable with 254 categories.

    NYCHousing2015.NEIGHBORHOOD = [];

    Create dummy variable matrices from the other categorical predictors.

    catvars = ["BOROUGH","BUILDINGCLASSCATEGORY"];
    dumvarstbl = varfun(@(x)dummyvar(categorical(x)),NYCHousing2015, ...
        InputVariables=catvars);
    dumvarmat = table2array(dumvarstbl);
    NYCHousing2015(:,catvars) = [];

    Treat all other numeric variables in the table as predictors of sales price. Concatenate the matrix of dummy variables to the rest of the predictor data.

    idxnum = varfun(@isnumeric,NYCHousing2015,OutputFormat="uniform");
    X = [dumvarmat NYCHousing2015{:,idxnum}];

    Configure a kernel regression model for incremental learning so that it does not have an estimation or metrics warm-up period. Specify a metrics window size of 1000. Prepare the model for updateMetrics by fitting it to the first 100 observations.

    Mdl = incrementalRegressionKernel(EstimationPeriod=0, ...
        MetricsWarmupPeriod=0,MetricsWindowSize=1000);
    initobs = 100;
    Mdl = fit(Mdl,X(1:initobs,:),Y(1:initobs));

    Mdl is an incrementalRegressionKernel model object.

    Perform incremental learning, with conditional fitting, by following this procedure for each iteration:

    • Simulate a data stream by processing a chunk of 100 observations at a time.

    • Update the model performance by computing the epsilon insensitive loss, within a 200 observation window.

    • Fit the model to the chunk of data only when the loss more than doubles from the minimum loss experienced.

    • When tracking performance and fitting, overwrite the previous incremental model.

    • Store the epsilon insensitive loss and number of training observations to see how they evolve during training.

    • Track when fit trains the model.

    % Preallocation
    numObsPerChunk = 100;
    nchunk = floor((n - initobs)/numObsPerChunk);
    ei = array2table(nan(nchunk,2),VariableNames=["Cumulative","Window"]);
    numtrainobs = zeros(nchunk,1);
    trained = false(nchunk,1);
    
    % Incremental fitting
    for j = 1:nchunk
        ibegin = min(n,numObsPerChunk*(j-1) + 1 + initobs);
        iend   = min(n,numObsPerChunk*j + initobs);
        idx = ibegin:iend;
        Mdl = updateMetrics(Mdl,X(idx,:),Y(idx));
        ei{j,:} = Mdl.Metrics{"EpsilonInsensitiveLoss",:};
        minei = min(ei{:,2});
        pdiffloss = (ei{j,2} - minei)/minei*100;
        if pdiffloss > 100
            Mdl = fit(Mdl,X(idx,:),Y(idx));
            trained(j) = true;
        end    
        numtrainobs(j) = Mdl.NumTrainingObservations;
    end

    Mdl is an incrementalRegressionKernel model object trained on all the data in the stream.

    To see how the number of training observations and model performance evolve during training, plot them on separate tiles.

    t = tiledlayout(2,1);
    nexttile
    plot(numtrainobs)
    hold on
    plot(find(trained),numtrainobs(trained),"r.")
    xlim([0 nchunk])
    ylabel("Number of Training Observations")
    legend("Number of Training Observations","Training occurs",Location="best")
    hold off
    nexttile
    plot(ei.Variables)
    xlim([0 nchunk])
    ylabel("Epsilon Insensitive Loss")
    legend(ei.Properties.VariableNames)
    xlabel(t,"Iteration")

    Figure contains 2 axes objects. Axes object 1 contains 2 objects of type line. These objects represent Number of Training Observations, Training occurs. Axes object 2 contains 2 objects of type line. These objects represent Cumulative, Window.

    The trace plot of the number of training observations shows periods of constant values, during which the loss does not double from the minimum experienced.

    Input Arguments

    collapse all

    Incremental learning model whose performance is measured, specified as an incrementalClassificationKernel or incrementalRegressionKernel 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 corresponding reference page.

    If Mdl.IsWarm is false, updateMetrics does not track the performance of the model. You must fit Mdl to Mdl.EstimationPeriod + Mdl.MetricsWarmupPeriod observations by passing Mdl and the data to fit before updateMetrics can track performance metrics. For more details, see Performance Metrics.

    Chunk of predictor data, specified as a floating-point matrix of n observations and Mdl.NumPredictors predictor variables.

    The length of the observation labels Y and the number of observations in X must be equal; Y(j) is the label of observation j (row) in X.

    Note

    • If Mdl.NumPredictors = 0, updateMetrics infers the number of predictors from X, and sets the corresponding property of the output model. Otherwise, if the number of predictor variables in the streaming data changes from Mdl.NumPredictors, updateMetrics issues an error.

    • updateMetrics supports only floating-point input predictor data. If your input data includes categorical data, you must prepare an encoded version of the categorical data. Use dummyvar to convert each categorical variable to a numeric matrix of dummy variables. Then, concatenate all dummy variable matrices and any other numeric predictors. For more details, see Dummy Variables.

    Data Types: single | double

    Chunk of responses (labels), specified as a categorical, character, or string array, a logical or floating-point vector, or a cell array of character vectors for classification problems; or a floating-point vector for regression problems.

    The length of the observation labels Y and the number of observations in X must be equal; Y(j) is the label of observation j (row) in X.

    For classification problems:

    • updateMetrics supports binary classification only.

    • When the ClassNames property of the input model Mdl is nonempty, the following conditions apply:

      • If Y contains a label that is not a member of Mdl.ClassNames, updateMetrics issues an error.

      • The data type of Y and Mdl.ClassNames must be the same.

    Data Types: char | string | cell | categorical | logical | single | double

    Chunk of observation weights, specified as a floating-point vector of positive values. updateMetrics weighs the observations in X with the corresponding values in weights. The size of weights must equal n, the number of observations in X.

    By default, weights is ones(n,1).

    For more details, including normalization schemes, see Observation Weights.

    Data Types: double | single

    Note

    If an observation (predictor or label) or weight contains at least one missing (NaN) value, updateMetrics ignores the observation. Consequently, updateMetrics uses fewer than n observations to compute the model performance, where n is the number of observations in X.

    Output Arguments

    collapse all

    Updated incremental learning model, returned as an incremental learning model object of the same data type as the input model Mdl, either incrementalClassificationKernel or incrementalRegressionKernel.

    If the model is not warm, updateMetrics does not compute performance metrics. As a result, the Metrics property of Mdl remains completely composed of NaN values. If the model is warm, updateMetrics computes the cumulative and window performance metrics on the new data X and Y, and overwrites the corresponding elements of Mdl.Metrics. All other properties of the input model Mdl carry over to the output model Mdl. For more details, see Performance Metrics.

    Tips

    • Unlike traditional training, incremental learning might not have a separate test (holdout) set. Therefore, to treat each incoming chunk of data as a test set, pass the incremental model and each incoming chunk to updateMetrics before training the model on the same data using fit.

    Algorithms

    collapse all

    Performance Metrics

    • updateMetrics and updateMetricsAndFit track model performance metrics, specified by the row labels of the table in Mdl.Metrics, from new data only when the incremental model is warm (IsWarm property is true). An incremental model is warm after fit or updateMetricsAndFit fits the incremental model to Mdl.MetricsWarmupPeriod observations, which is the metrics warm-up period.

      If Mdl.EstimationPeriod > 0, the fit and updateMetricsAndFit functions estimate hyperparameters before fitting the model to data. Therefore, the functions must process an additional EstimationPeriod observations before the model starts the metrics warm-up period.

    • The Mdl.Metrics property stores two forms of each performance metric as variables (columns) of a table, Cumulative and Window, with individual metrics in rows. When the incremental model is warm, updateMetrics and updateMetricsAndFit update the metrics at the following frequencies:

      • Cumulative — The functions compute cumulative metrics since the start of model performance tracking. The functions update metrics every time you call the functions and base the calculation on the entire supplied data set.

      • Window — The functions compute metrics based on all observations within a window determined by the Mdl.MetricsWindowSize property. Mdl.MetricsWindowSize also determines the frequency at which the software updates Window metrics. For example, if Mdl.MetricsWindowSize is 20, the functions compute metrics based on the last 20 observations in the supplied data (X((end – 20 + 1):end,:) and Y((end – 20 + 1):end)).

        Incremental functions that track performance metrics within a window use the following process:

        1. Store a buffer of length Mdl.MetricsWindowSize for each specified metric, and store a buffer of observation weights.

        2. Populate elements of the metrics buffer with the model performance based on batches of incoming observations, and store corresponding observation weights in the weights buffer.

        3. When the buffer is filled, overwrite Mdl.Metrics.Window with the weighted average performance in the metrics window. If the buffer is overfilled when the function processes a batch of observations, the latest incoming Mdl.MetricsWindowSize observations enter the buffer, and the earliest observations are removed from the buffer. For example, suppose Mdl.MetricsWindowSize is 20, the metrics buffer has 10 values from a previously processed batch, and 15 values are incoming. To compose the length 20 window, the function uses the measurements from the 15 incoming observations and the latest 5 measurements from the previous batch.

    • The software omits an observation with a NaN prediction (score for classification and response for regression) when computing the Cumulative and Window performance metric values.

    Observation Weights

    For classification problems, if the prior class probability distribution is known (in other words, the prior distribution is not empirical), updateMetrics normalizes observation weights to sum to the prior class probabilities in the respective classes. This action implies that observation weights are the respective prior class probabilities by default.

    For regression problems or if the prior class probability distribution is empirical, the software normalizes the specified observation weights to sum to 1 each time you call updateMetrics.

    Version History

    Introduced in R2022a