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plotPermutationResults

Plot histogram of permutation results for a variable specified for data drift detection

Since R2022a

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    Syntax

    plotPermutationResults(DDiagnostics)
    plotPermutationResults(DDiagnostics,Variable=variable)
    plotPermutationResults(ax,___)
    H = plotPermutationResults(___)
    [H,CL] = plotPermutationResults(___)

    Description

    example

    plotPermutationResults(DDiagnostics) plots the histogram of metric values computed by the driftdetect function during permutation testing for the variable with the lowest p-value.

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

    example

    plotPermutationResults(DDiagnostics,Variable=variable) plots the histogram for the variable specified by variable.

    example

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

    H = plotPermutationResults(___) plots the histogram and returns an array of Histogram objects H for the metric values computed during permutation testing. Use H to inspect and modify the properties of the histogram. For more information, see Histogram Properties.

    example

    [H,CL] = plotPermutationResults(___) additionally returns a ConstantLine object CL for the metric threshold value. Use CL to inspect and modify the properties of the line. For more information, see ConstantLine Properties.

    Examples

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    Open Live Script

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

    Plot the permutation results for the default variable.

    plotPermutationResults(DDiagnostics)

    By default, plotPermutationResults plots a histogram of the metric values computed in permutation testing for the variable with the lowest p-value, which is x2 in this case. The function includes the metric threshold value (the initial metric value computed by detectdrift using the baseline and target data) on the histogram, so you can see the values that are greater than or equal to the threshold. plotPermutationResults also displays the p-value and the drift status for the variable, and the metric that you specify to use for permutation testing in the call to detectdrift. In this example, no metric is specified, so detectdrift uses the default metric (Wasserstein) for continuous variables.

    Open Live Script

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

    DDiagnostics = detectdrift(baseline,target,ContinuousMetric="energy")
    DDiagnostics = 
  DriftDiagnostics

              VariableNames: ["x1"    "x2"    "x3"]
       CategoricalVariables: []
                DriftStatus: ["Stable"    "Drift"    "Warning"]
                    PValues: [0.3790 0.0110 0.0820]
        ConfidenceIntervals: [2×3 double]
    MultipleTestDriftStatus: "Drift"
             DriftThreshold: 0.0500
           WarningThreshold: 0.1000


  Properties, Methods

    Display the 95% confidence bounds for the p-values.

    DDiagnostics.ConfidenceIntervals
    ans = 2×3

    0.3488    0.0055    0.0657
    0.4099    0.0196    0.1008

    Plot the permutation results for the third variable.

    plotPermutationResults(DDiagnostics,Variable=3)

    Open Live Script

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

    DDiagnostics = detectdrift(baseline,target,ContinuousMetric="energy")
    DDiagnostics = 
  DriftDiagnostics

              VariableNames: ["x1"    "x2"    "x3"]
       CategoricalVariables: []
                DriftStatus: ["Stable"    "Drift"    "Warning"]
                    PValues: [0.3790 0.0110 0.0820]
        ConfidenceIntervals: [2x3 double]
    MultipleTestDriftStatus: "Drift"
             DriftThreshold: 0.0500
           WarningThreshold: 0.1000

    Plot the permutation results for variables x1 and x2 in a tiled layout.

    tiledlayout(2,1);
ax1 = nexttile;
plotPermutationResults(DDiagnostics,ax1,Variable="x1")
ax2 = nexttile;
plotPermutationResults(DDiagnostics,ax2,Variable="x2")

    Figure contains 2 axes objects. Axes object 1 with title Permutation Results for x1, xlabel Energy Metric Values, ylabel Distribution (%) contains 3 objects of type histogram, constantline. These objects represent $<$ 0.15234, $\geq$ 0.15234. Axes object 2 with title Permutation Results for x2, xlabel Energy Metric Values, ylabel Distribution (%) contains 3 objects of type histogram, constantline. These objects represent $<$ 0.28619, $\geq$ 0.28619.

    Plot the permutation results for variables x1 and x3 in a tiled layout.

    tiledlayout(2,1);
ax1 = nexttile;
plotPermutationResults(DDiagnostics,ax1,Variable="x1")
ax3= nexttile;
plotPermutationResults(DDiagnostics,ax3,Variable="x3")

    Figure contains 2 axes objects. Axes object 1 with title Permutation Results for x1, xlabel Energy Metric Values, ylabel Distribution (%) contains 3 objects of type histogram, constantline. These objects represent $<$ 0.15234, $\geq$ 0.15234. Axes object 2 with title Permutation Results for x3, xlabel Energy Metric Values, ylabel Distribution (%) contains 3 objects of type histogram, constantline. These objects represent $<$ 0.10117, $\geq$ 0.10117.

    Open Live Script

    Generate baseline and target data with three variables, where the distribution parameters of the second and third variables change for the 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. Use the Energy distance as the metric.

    DDiagnostics = detectdrift(baseline,target,ContinuousMetric="energy")
    DDiagnostics = 
  DriftDiagnostics

              VariableNames: ["x1"    "x2"    "x3"]
       CategoricalVariables: []
                DriftStatus: ["Stable"    "Drift"    "Warning"]
                    PValues: [0.3790 0.0110 0.0820]
        ConfidenceIntervals: [2×3 double]
    MultipleTestDriftStatus: "Drift"
             DriftThreshold: 0.0500
           WarningThreshold: 0.1000


  Properties, Methods

    Plot the permutation results for the third variable. 

    [H,CL] = plotPermutationResults(DDiagnostics,Variable=3)

    H = 
  2×1 Histogram array:

  Histogram
  Histogram


    CL = 
  ConstantLine with properties:

    InterceptAxis: 'x'
            Value: 0.1012
            Color: [0.1500 0.1500 0.1500]
        LineStyle: ':'
        LineWidth: 3
            Label: ''
      DisplayName: ''

  Show all properties

    Change the histogram bar colors to blue and the threshold line color to red.

    H(1).FaceColor = "b";
CL.Color = "r";

    You can also access and modify properties by double-clicking H 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.

    Variable for which to plot the permutation results, specified as a string, character vector, or integer index.

    Example: Variable="x2"

    Example: Variable=2

    Data Types: single | double | char | string

    Axes on which to plot, specified as an Axes or UIAxes object. If you do not specify ax, then plotPermutationResults 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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    Histogram of metric values computed during permutation testing, returned as a 2-by-1 array of Histogram objects. Use H to inspect and adjust the properties of the histogram. For more information on the Histogram object properties, see Histogram Properties.

    Line showing the metric threshold value in the plot, returned as a ConstantLine object. Use CL to inspect and modify the properties of the line.

    Version History

    Introduced in R2022a

    See Also

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