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plotDriftStatus

Plot p-values and confidence intervals for variables tested for data drift

    Description

    example

    plotDriftStatus(DDiagnostics) plots the estimated p-value of the permutation test for each variable specified for drift detection in the call to detectdrift, as well as the confidence interval for each estimated p-value, using error bars. The function also plots the warning and drift thresholds as well and color-codes the p-values with their confidence intervals according to their drift status.

    If you set the value of EstimatePValues to false in the call to detectdrift, then plotDriftStatus does not generate a plot and, instead, returns a warning.

    example

    plotDriftStatus(DDiagnostics,Variables=variables) plots the drift status for the variables specified by variables.

    plotDriftStatus(ax,___) plots on the axes ax instead of gca, using any of the input argument combinations in the previous syntaxes.

    EB = plotDriftStatus(___) creates an error bar plot and returns an array of ErrorBar objects EB. Use EB to inspect and modify the properties of the error bars. To learn more, see ErrorBar Properties.

    example

    [EB,CL] = plotDriftStatus(___) additionally returns an array of ConstantLine objects CL for the drift and warning threshold values. Use CL is an array of ConstantLine objects. Use CL to inspect and modify the properties of the lines. For more information, see ConstantLine Properties.

    Examples

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    Generate baseline and target data with three variables, where the distribution parameters of the second and third variables change for target data.

    rng('default') % For reproducibility
    baseline = [normrnd(0,1,100,1),wblrnd(1.1,1,100,1),betarnd(1,2,100,1)];
    target = [normrnd(0,1,100,1),wblrnd(1.2,2,100,1),betarnd(1.7,2.8,100,1)];

    Perform permutation testing for all variables to check for any drift between the baseline and target data.

    DDiagnostics = detectdrift(baseline,target)
    DDiagnostics = 
      DriftDiagnostics
    
                  VariableNames: ["x1"    "x2"    "x3"]
           CategoricalVariables: []
                    DriftStatus: ["Stable"    "Drift"    "Warning"]
                        PValues: [0.3850 0.0050 0.0910]
            ConfidenceIntervals: [2×3 double]
        MultipleTestDriftStatus: "Drift"
                 DriftThreshold: 0.0500
               WarningThreshold: 0.1000
    
    
      Properties, Methods
    
    

    Display the 95% confidence intervals for the estimated p-values.

    DDiagnostics.ConfidenceIntervals
    ans = 2×3
    
        0.3547    0.0016    0.0739
        0.4160    0.0116    0.1106
    
    

    Plot the drift status for all three variables.

    plotDriftStatus(DDiagnostics)

    plotDriftStatus plots the confidence intervals for the estimated p-values, using error bars. The function also compares the confidence bounds against the drift and warning thresholds, and indicates the drift status of each variable using different colors. The lower confidence bound of the p-value for the first variable is higher than the warning threshold. Therefore, the drift status for the first variable is Stable, indicated by the color blue. The lower confidence bound of the p-value for the third variable is lower than the warning threshold, but higher than the drift threshold. Therefore, the drift status for the third variable is Warning, and is indicated by the color yellow. The upper confidence bound of the p-value for the second variable is lower than the drift threshold. Therefore, the drift status for the third variable is Drift and is indicated by the color orange.

    Load the sample data.

    load humanactivity

    For details on the data set, enter Description at the command line.

    Assign the first 250 observations as baseline data and the next 250 as target data for the first 15 variables.

    baseline = feat(1:250,1:15);
    target = feat(251:500,1:15);

    Test for drift on all variables.

    DDiagnostics = detectdrift(baseline,target)
    DDiagnostics = 
      DriftDiagnostics
    
                  VariableNames: ["x1"    "x2"    "x3"    "x4"    "x5"    "x6"    "x7"    "x8"    "x9"    "x10"    "x11"    "x12"    "x13"    "x14"    "x15"]
           CategoricalVariables: []
                    DriftStatus: ["Drift"    "Drift"    "Drift"    "Drift"    "Drift"    "Drift"    "Drift"    "Stable"    "Stable"    "Drift"    "Stable"    "Stable"    "Drift"    "Stable"    "Warning"]
                        PValues: [1.0000e-03 1.0000e-03 1.0000e-03 1.0000e-03 1.0000e-03 1.0000e-03 1.0000e-03 0.8630 0.7260 1.0000e-03 0.4960 0.2490 1.0000e-03 0.5740 0.0940]
            ConfidenceIntervals: [2×15 double]
        MultipleTestDriftStatus: "Drift"
                 DriftThreshold: 0.0500
               WarningThreshold: 0.1000
    
    
      Properties, Methods
    
    

    Display the 95% confidence intervals of the p-values for variables 10 to 15.

    DDiagnostics.ConfidenceIntervals(:,10:15)
    ans = 2×6
    
        0.0000    0.4646    0.2225    0.0000    0.5427    0.0766
        0.0056    0.5275    0.2770    0.0056    0.6049    0.1138
    
    

    Plot the drift status for variables 10 to 15.

    plotDriftStatus(DDiagnostics,Variables=(10:15))

    Load the sample data.

    load humanactivity

    For details on the data set, enter Description at the command line.

    Assign the first 250 observations as baseline data and the next 250 as target data for the first 15 variables.

    baseline = feat(1:250,1:15);
    target = feat(251:500,1:15);

    Test for drift on all variables.

    DDiagnostics = detectdrift(baseline,target);

    Plot the drift status for all variables and return the ErrorBar and ConstantLine objects.

    [EB,CL] = plotDriftStatus(DDiagnostics)

    EB = 
      3×1 ErrorBar array:
    
      ErrorBar    (Stable)
      ErrorBar    (Warning)
      ErrorBar    (Drift)
    
    
    CL = 
      2×1 ConstantLine array:
    
      ConstantLine
      ConstantLine
    
    

    EB is an array of ErrorBar objects and CL is an array of ConstantLine objects. You can change the appearance of the plot by accessing the properties of these objects. Change the color of the error bars and markers for status Stable to green. Change the color of the drift threshold line, error bars, and markers for the status Drift to magenta.

    EB(1).Color = [0 1 0];
    EB(1).MarkerFaceColor = [0 1 0];
    EB(1).MarkerEdgeColor = [0 1 0];
    EB(3).Color = [1 0 1];
    EB(3).MarkerFaceColor = [1 0 1];
    EB(3).MarkerEdgeColor = [1 0 1];
    CL(2).Color = [1 0 1];

    You can also access and modify properties by double-clicking EB or CL in the Workspace to open and use the Property Inspector.

    Input Arguments

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    Diagnostics of the permutation testing for drift detection, specified as a DriftDiagnostics object returned by detectdrift.

    List of variables for which to plot the drift status, specified as a string array, a cell array of character vectors, or a list of integer indices.

    Example: Variables=["x1","x3"]

    Example: Variables=(1,3)

    Data Types: single | double | char | string

    Axes for plotDriftStatus to plot into, specified as an Axes or UIAxes object. If you do not specify ax, then plotDriftStatus creates the plot using the current axes. For more information on creating an axes object, see axes and uiaxes.

    Output Arguments

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    Error bars showing the confidence intervals for the estimated p-values in the plot, returned as a 3-by-1 array of ErrorBar objects. Use EB to inspect and adjust the properties of the error bars. To learn more about the properties of the ErrorBar object, see ErrorBar Properties.

    Lines showing the drift and warning threshold values in the plot, returned as a 2-by-1 array of ConstantLine objects. Use CL to inspect and adjust the properties of the error bars.

    Version History

    Introduced in R2022a