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updateMetricsAndFit

Update performance metrics in kernel incremental learning model given new data and train model

    Description

    Given streaming data, updateMetricsAndFit first evaluates the performance of a configured incremental learning model for kernel regression (incrementalRegressionKernel object) or binary kernel classification (incrementalClassificationKernel object) by calling updateMetrics on incoming data. Then updateMetricsAndFit fits the model to that data by calling fit. In other words, updateMetricsAndFit performs prequential evaluation because it treats each incoming chunk of data as a test set, and tracks performance metrics measured cumulatively and over a specified window [1].

    updateMetricsAndFit provides a simple way to update model performance metrics and train the model on each chunk of data. Alternatively, you can perform the operations separately by calling updateMetrics and then fit, which allows for more flexibility (for example, you can decide whether you need to train the model based on its performance on a chunk of data).

    example

    Mdl = updateMetricsAndFit(Mdl,X,Y) returns an incremental learning model Mdl, which is the input incremental learning model Mdl with the following modifications:

    1. updateMetricsAndFit measures the model performance on the incoming predictor and response data, X and Y respectively. When the input model is warm (Mdl.IsWarm is true), updateMetricsAndFit overwrites previously computed metrics, stored in the Metrics property, with the new values. Otherwise, updateMetricsAndFit stores NaN values in Metrics instead.

    2. updateMetricsAndFit fits the modified model to the incoming data by following this procedure:

      1. Initialize the solver with the configurations and model parameters of the input model Mdl.

      2. Fit the model to the data, and store the updated model parameters and configurations in the output model Mdl.

    The input and output models have the same data type.

    example

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

    Examples

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    Create an incremental kernel model for binary classification by calling incrementalClassificationKernel directly. Track the model performance and fit the model to streaming data in one call by using updateMetricsAndFit.

    Create a default incremental kernel model for binary classification.

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

    Mdl is an incrementalClassificationKernel 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 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;

    Fit the incremental model to the training data by using the updateMetricsAndFit function. At each iteration:

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

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

    • Store the cumulative metrics, window metrics, 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 = updateMetricsAndFit(Mdl,X(idx,:),Y(idx));
        ce{j,:} = Mdl.Metrics{"ClassificationError",:};
        numtrainobs(j) = Mdl.NumTrainingObservations; 
    end

    Mdl is an incrementalClassificationKernel 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 observations, and then fits the model to those observations.

    To see how the number of observations and performance metrics evolve during training, plot them 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")
    xline((Mdl.EstimationPeriod + Mdl.MetricsWarmupPeriod)/numObsPerChunk,"--");
    legend(ce.Properties.VariableNames)
    xlabel(t,"Iteration")

    Figure contains 2 axes objects. Axes object 1 contains an object of type line. Axes object 2 contains 3 objects of type line, constantline. These objects represent Cumulative, Window.

    The plot suggests that updateMetricsAndFit does the following:

    • Fit the model during all incremental learning iterations.

    • Compute the performance metrics after the metrics warm-up period only.

    • Compute the cumulative metrics during each iteration.

    • Compute the window metrics after processing 200 observations (4 iterations).

    Train a kernel regression model by using fitrkernel, and convert it to an incremental learner by using incrementalLearner. Track the model performance, and fit the model to streaming data in one call by using updateMetricsAndFit. Specify the observation weights when you call updateMetricsAndFit.

    Load and Preprocess Data

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

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

    Suppose that the data collected from Manhattan (BOROUGH = 1) was collected using a new method that doubles its quality. Create a weight variable that attributes 2 to observations collected from Manhattan, and 1 to all other observations.

    n = size(NYCHousing2015,1);
    NYCHousing2015.W = ones(n,1) + (NYCHousing2015.BOROUGH == 1);

    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}];

    Train Kernel Regression Model

    Fit a kernel regression model to a random sample of half the data.

    idxtt = randsample([true false],n,true);
    TTMdl = fitrkernel(X(idxtt,:),Y(idxtt),Weights=NYCHousing2015.W(idxtt))
    TTMdl = 
      RegressionKernel
                  ResponseName: 'Y'
                       Learner: 'svm'
        NumExpansionDimensions: 2048
                   KernelScale: 1
                        Lambda: 2.1977e-05
                 BoxConstraint: 1
                       Epsilon: 0.0547
    
    
      Properties, Methods
    
    

    TTMdl is a RegressionKernel model object representing a traditionally trained kernel regression model.

    Convert Trained Model

    Convert the traditionally trained kernel regression model to a model for incremental learning.

    IncrementalMdl = incrementalLearner(TTMdl)
    IncrementalMdl = 
      incrementalRegressionKernel
    
                        IsWarm: 1
                       Metrics: [1x2 table]
             ResponseTransform: 'none'
        NumExpansionDimensions: 2048
                   KernelScale: 1
    
    
      Properties, Methods
    
    

    IncrementalMdl is an incrementalRegressionKernel model object. All its properties are read-only.

    Track Performance Metrics and Fit Model

    Perform incremental learning on the rest of the data by using the updateMetricsAndFit function. At each iteration:

    1. Simulate a data stream by processing a chunk of 500 observations.

    2. Call updateMetricsAndFit to update the cumulative and window epsilon insensitive loss of the model given the incoming chunk of observations, and then fit the model to the data. Overwrite the previous incremental model with a new one. Specify the observation weights.

    3. Store the losses.

    % Preallocation
    idxil = ~idxtt;
    nil = sum(idxil);
    numObsPerChunk = 500;
    nchunk = floor(nil/numObsPerChunk);
    ei = array2table(zeros(nchunk,2),VariableNames=["Cumulative","Window"]);
    Xil = X(idxil,:);
    Yil = Y(idxil);
    Wil = NYCHousing2015.W(idxil);
    
    % Incremental fitting
    for j = 1:nchunk
        ibegin = min(nil,numObsPerChunk*(j-1) + 1);
        iend   = min(nil,numObsPerChunk*j);
        idx = ibegin:iend;
        IncrementalMdl = updateMetricsAndFit(IncrementalMdl,Xil(idx,:),Yil(idx), ...
            Weights=Wil(idx));
        ei{j,:} = IncrementalMdl.Metrics{"EpsilonInsensitiveLoss",:};
    end

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

    Plot a trace plot of the performance metrics.

    plot(ei.Variables)
    xlim([0 nchunk])
    ylabel("Epsilon Insensitive Loss")
    legend(ei.Properties.VariableNames)
    xlabel("Iteration")

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

    The cumulative loss gradually changes with each iteration (chunk of 500 observations), whereas the window loss jumps. Because the metrics window is 200 by default, updateMetricsAndFit measures the performance based on the latest 200 observations in each 500 observation chunk.

    Input Arguments

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    Incremental learning model whose performance is measured and then the model is fit to data, 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, updateMetricsAndFit does not track the performance of the model. 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, updateMetricsAndFit 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, updateMetricsAndFit issues an error.

    • updateMetricsAndFit 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:

    • updateMetricsAndFit 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, updateMetricsAndFit 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. updateMetricsAndFit 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, updateMetricsAndFit ignores the observation. Consequently, updateMetricsAndFit uses fewer than n observations to compute the model performance and create an updated model, where n is the number of observations in X.

    • The chunk size n and the stochastic gradient descent (SGD) hyperparameter mini-batch size (Mdl.SolverOptions.BatchSize) can be different values, and n does not have to be an exact multiple of the mini-batch size. updateMetricsAndFit uses the BatchSize observations when it applies SGD for each learning cycle. The number of observations in the last mini-batch for the last learning cycle can be less than or equal to Mdl.SolverOptions.BatchSize.

    Output Arguments

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    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.

    When you call updateMetricsAndFit, the following conditions apply:

    • If the model is not warm, updateMetricsAndFit does not compute performance metrics. As a result, the Metrics property of Mdl remains completely composed of NaN values. For more details, see Performance Metrics.

    • If Mdl.EstimationPeriod > 0, updateMetricsAndFit estimates hyperparameters using the first Mdl.EstimationPeriod observations passed to it; the function does not train the input model using that data. However, if an incoming chunk of n observations is greater than or equal to the number of observations remaining in the estimation period m, updateMetricsAndFit estimates hyperparameters using the first nm observations, and fits the input model to the remaining m observations. Consequently, the software updates model parameters, hyperparameter properties, and recordkeeping properties such as NumTrainingObservations.

    For classification problems, if the ClassNames property of the input model Mdl is an empty array, updateMetricsAndFit sets the ClassNames property of the output model Mdl to unique(Y).

    Algorithms

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    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), updateMetricsAndFit 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 updateMetricsAndFit.

    References

    [1] Bifet, Albert, Ricard Gavaldá, Geoffrey Holmes, and Bernhard Pfahringer. Machine Learning for Data Streams with Practical Example in MOA. Cambridge, MA: The MIT Press, 2007.

    Version History

    Introduced in R2022a