slmetric.config.Classification Class

R2026b

Namespace: slmetric.config

(To be removed) Specify categorical metric data ranges

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The Metrics Dashboard app, model metrics, slmetric API, and corresponding customizations will be removed in a future release. For more information, see Migrating from Metrics Dashboard to Model Design Dashboards.

Description

Use the slmetric.config.Classification class to classify metric data ranges as Compliant, Warning, and NonCompliant. The Metrics Dashboard indicates the range that your metric data falls under.

Construction

For an slmetric.config.Threshold object, there must be one slmetric.config.Classification object corresponding to the Compliant range. There can be only one compliant range. You can specify multiple slmetric.config.Classification objects corresponding to Warning and Noncompliant ranges.

By default, threshold objects contain an slmetric.config.Classification object with a Compliant range of -inf to inf. To add additional classification objects, use the slmetric.config.Classification.addClassification method.

Properties

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You can classify metric data values into these three categories:

  • Compliant — Metric data that is in an acceptable range.

  • Warning — Metric data that requires review.

  • Noncompliant — Metric data that requires you to modify your model.

This property is read/write.

Data Types: char

For each slmetric.metric.config.Classification object, specify the properties of the slmetric.config.MetricRange object. This property is read/write.

Examples

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Use the slmetric.config packaged classes to add threshold information to the Metrics Dashboard. You can add thresholds that define metric data ranges for these three categories:

  • Compliant — Metric data that is an acceptable range.

  • Warning — Metric data that requires review.

  • Noncompliant — Metric data that requires you to modify your model.

Create an slmetric.config.Configuration object.

CONF = slmetric.config.Configuration.new('name', 'Config');

Get the default slmetric.config.ThresholdConfiguration object in CONF.

TC = getThresholdConfigurations(CONF);

Add an slmetric.config.Threshold object to the slmetric.config.ThresholdConfiguration object. This threshold is for the mathworks.metrics.SimulinkBlockCount metric and the Value property of the slmetric.metric.Results object.

T = addThreshold(TC, 'mathworks.metrics.SimulinkBlockCount', 'Value');

An slmetric.config.Threshold object contains a default slmetric.config.Classification object that corresponds to the Compliant category. Use the slmetric.metric.MetricRange class to specify metric values for the Compliant metric range.

C = getClassifications(T); % default classification is Compliant
C.Range.Start = 5;
C.Range.IncludeStart = 0;
C.Range.End = 100;
C.Range.IncludeEnd = 0;

These values specify that a compliant range is a block count from 5 to 100. This range does not include the values 5 and 100.

Specify values for the Warning metric range.

C = addClassification(T,'Warning');
C.Range.Start = -inf;
C.Range.IncludeStart = 0;
C.Range.End = 5;
C.Range.IncludeEnd = 1

These values specify that a warning is a block count between -inf and 5. This range does not include -inf. It does include 5.

Specify values for the NonCompliant metric range.

C = addClassification(T,'NonCompliant');
C.Range.Start = 100;
C.Range.IncludeStart = 1;
C.Range.End = inf;
C.Range.IncludeEnd = 0;

These values specify that a block count greater than 100 is noncompliant. This range includes 100. It does not include inf.

Use the validate method to validate the metric ranges corresponding to the thresholds in the slmetric.config.ThresholdConfiguration object.

validate(T)

If the ranges are not valid, you get an error message. In this example, the ranges are valid.

Save the changes to the configuration file. Use the slmetric.config.setActiveConfiguration function to activate this configuration for the metric engine to use.

configName = 'Config.xml';
save(CONF,'FileName', configName);
slmetric.config.setActiveConfiguration(fullfile(pwd, configName));

You can now run the Metrics Dashboard with this custom configuration on a model.

Version History

Introduced in R2018b

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See Also

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