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plotHistogram

Plot histogram of a variable specified for data drift detection

    Description

    example

    plotHistogram(DDiagnostics) plots a histogram of the baseline and target data for the variable with the lowest p-value computed by the detectdrift function.

    If you set the value of EstimatePValues to false in the call to detectdrift, then plotHistogram displays NaN for the p-value and the drift status.

    plotHistogram(DDiagnostics,Variable=variable) plots the histogram of the baseline and target data for the variable specified by variable.

    example

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

    example

    H = plotHistogram(___) plots the histogram and returns an array of Histogram objects in H. Use H to inspect and modify the properties of the histogram. For more information, see Histogram 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 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 histogram for the default variable.

    plotHistogram(DDiagnostics)

    By default, plotHistogram plots a histogram of the baseline and target data for the variable with the lowest p-value. The function also displays the p-value and the drift status for the variable.

    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 statistic 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 histograms for all three variables in a tiled layout.

    tiledlayout(3,1);
    ax1 = nexttile;
    plotHistogram(DDiagnostics,ax1,Variable="x1")
    ax2 = nexttile;
    plotHistogram(DDiagnostics,ax2,Variable="x2")
    ax3 = nexttile;
    plotHistogram(DDiagnostics,ax3,Variable="x3")

    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 histogram for the first variable and return the Histogram object.

    H = plotHistogram(DDiagnostics,Variable=1)

    H = 
      2×1 Bar array:
    
      Bar    (Baseline)
      Bar    (Target)
    
    

    Change the color of the histogram bars for the baseline data.

    H(1).FaceColor = [1 0 1];

    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 histogram, specified as a string, a character vector, or an integer index.

    Example: Variable="x2"

    Example: Variable=2

    Data Types: single | double | char | string

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

    Algorithms

    • For categorical data, detectdrift adds a 0.5 correction factor to the histogram bin counts for each bin to handle empty bins (categories). This is equivalent to the assumption that the parameter p, probability that value of the variable would be in that category, has the prior distribution Beta(0.5,0.5), (Jeffreys prior assumption for the distribution parameter).

    • plotHistogram treats a variable as ordinal for visualization purposes in these cases:

      • The variable is ordinal in either the baseline data or the target data, and the categories from both the baseline data and the target data are the same.

      • The variable is ordinal in either the baseline data or the target data, and the categories of the other data set are a subset of the ordinal data.

      • The variable is ordinal in both the baseline data and the target data, and categories from either data set are a subset of the other.

    • If a variable is ordinal, plotHistogram preserves the order of the bin names.

    Version History

    Introduced in R2022a