Table of Contents

Database tables for monitoring models

Database tables contain monitoring information for adaptive and predictive models. This data is used to populate the charts in the Monitor tab of every model rule in Dev Studio and additional reports in the Actions > Reports section of Prediction Studio.

The tables are transparently designed to contain monitoring data that you can easily use to create your custom reports in Pega Platform™ or any other reporting tool. Before you customize model reports to serve your business needs, learn what properties you can find in these tables.

For more information about how the Adaptive Decision Manager service captures and populates that monitoring data, see Configuring the Adaptive Decision Manager service.

Properties in the PR_DATA_DM_ADMMART_MDL_FACT table

Properties with the py prefix are defined on the Data-Decision-ADM-ModelSnapshot class.

pr_data_dm_admmart_mdl_fact table properties
Property Description
pxApplication The name of the application.
pyAppliesToClass The Applies To class of the Adaptive Model rule with which the model is associated.
pyModelID A unique identifier for each model.
pyConfigurationName The name of the Adaptive Model rule with which the model is associated.
pySnapshotTime The time when the snapshot was taken.
pyIssue The default model identifier that defines the model context in your application. For more information, see Model context.
pyGroup The default model identifier that defines the model context in your application.
pyName The default model identifier. When the context of the models is different from the default hierarchy, this property contains the context values in the JSON format.
pyChannel The default model identifier that defines the model context in your application.
pyDirection The default model identifier that defines the model context in your application.
pyTreatment The default model identifier that defines the model context in your application.
pyPerformance The performance of each ADM model.
pySuccessRate Not used in reports.
pyResponseCount The number of all responses for each model.
pxObjClass Not used in reports.
pzInsKey Not used in reports.
pxInsName Not used in reports.
pxSaveDateTime Not used in reports.
pxCommitDateTime Not used in reports.
pyExtension Not used in reports.
pyActivePredictors The number of active predictors for each model.
pyTotalPredictors The number of all predictors for each model.
pyNegatives The number of negative responses for each model.
pyPositives The number of positive responses for each model.
pyRelativeNegatives The difference between the number of negative responses for each model and the number of negative responses for each model in the last snapshot.
pyRelativePositives The difference between the number of positive responses for each model and the number of negative responses for each model in the last snapshot.
pyRelativeResponseCount The difference between the number of all responses for each model and the number of all responses for each model in the last snapshot.
pyMemory Not used in reports.
pyPerformanceThreshold Not used in reports.
pyCorrelationThreshold Not used in reports.

Properties in the PR_DATA_DM_ADMMART_PRED_FACT table

Properties with the py prefix are defined on the Data-Decision-ADM-PredictorBinningSnapshot class.

pr_data_dm_admmart_pred_fact table properties
Property Description
pxCommitDateTime Not used in reports.
pxSaveDateTime Not used in reports.
pyModelID A unique identifier for each model.
pxObjClass Not used in reports.
pzInsKey Not used in reports.
pxInsName Not used in reports.
pyPredictorName The name of the predictor.
pyContents The overall range of the predictor.
pyPerformance The performance of the predictor.
pyPositives The number of positive responses for the predictor per model.
pyNegatives The number of negative responses for the predictor per model
pyType The type of predictornumeric or symbolic.
pyTotalBins The number of bins for the predictor per model.
pyResponseCount The number of responses for the predictor per model.
pyRelativePositives The difference between the number of positive responses for the predictor and the number of positive responses for the predictor in the last snapshot.
pyRelativeNegatives The difference between the number of negative responses for the predictor and the number of negative responses for the predictor in the last snapshot.
pyRelativeResponseCount The difference between the number of all responses for the predictor and the number of all responses for the predictor in the last snapshot.
pyBinNegatives The number of negative responses for the predictor per model in the bin.
pyBinPositives The number of positive responses for the predictor per model in the bin.
pyBinType

The type of bin:

  • Equibehavior a bin type for the normal bins. Bin ranges have the same values within one bin.
  • Missing a bin type for empty values.
  • Residual a bin type for symbols that did not fit in any of the normal bins because, for example, they occur relatively infrequently. This bin type applies to symbolic fields.
pyBinNegativesPercentage The percentage of negative responses in the bin out of all responses for the predictor.
pyBinPositivesPercentage The percentage of positive responses in the bin out of all responses for the predictor.
pyBinSymbol The actual range of the bin.
pyBinLowerBound The lower bound of the bin. This property applies to numeric predictors.
pyBinUpperBound The upper bound of the bin. This property applies to numeric predictors.
pyRelativeBinPositives The difference between the number of positive responses for the predictor in the bin that is measured per model and the number of positive responses for the predictor in the bin from the last snapshot.
pyRelativeBinNegatives The difference between the number of negative responses for the predictor in the bin that is measured per model and the number of negative responses for the predictor in the bin from the last snapshot.
pyBinResponseCount The total number of responses for the predictor in the bin that is measured per model.
pyRelativeBinResponseCount The difference between the number of all responses for the predictor in the bin that is measured per model and the number of all responses for the predictor in the bin from the last snapshot.
pyBinResponseCountPercentage The percentage of responses falling under a particular bin for a predictor that is measured per model.
pySnapShotTime The time when the snapshot was taken.
pyBinIndex An index assigned to a predictor bin entry. For example, when a model contains the AGE predictor that has 10 bins, then we have 10 entries in the table and the pyBinIndex for each entry is: 1, 2, 3, 4, 5, 6, 7, 8, 9, 10.
pyLift Behavior in the predictor bin divided by the overall behavior.
pyZRatio The number of standard deviations in the behavior of the predictor bin differs from the overall behavior.
pyEntryType An indication whether it is an active or inactive predictor. Active predictors are the ones that are used by the model.
pyExtension Not used in reports.
pyGroupIndex

Not used in reports.

This property cannot be compared between different model versions (updates) because it is just an index.

pyCorrelationPredictor Not used in reports.

Properties in the PR_DATA_DM_BINARY_DISTRIBUTION table (binary outcome predictive models)

For binary outcome models, the count of the positives and negatives is stored in a granular set of bins which are used to calculate the AUC and the ROC curves. The score distribution and the observed responses overlay occur during training.

pr_data_dm_binary_distribution table properties
Property Description
pxApplication The name of the application.
pyRulesetName The name of the ruleset to which the Predictive Model rule belongs.
pyRulesetVersion The version of the ruleset to which the Predictive Model rule belongs.
pyModelID A unique identifier for each model.
pyFactoryKey A key that corresponds to the version information in the ADM factory data set (internal audit usage).
pySnapshotTime The time when the snapshot was taken.
pySnapshotID An identifier for the snapshot.
pyBinID A unique identifier for each bin that is created.
pyBinLabel A label for each bin with ranges.
pyBinUpper The upper boundary of the bin.
pyBinLower The lower boundary of the bin.
pyPredictorName The name of the predictor for which the statistic is stored.
pyCount The total number of responses for the bin.
pyPositiveCount The number of positive responses.
pyNegativeCount The number of negative responses.
pyDataUsage The stage of the model building process at which the data was derivedtraining, predicted, or observed.
pyMetricType The metric type for the binbinary distribution or side-by-side distribution.
pyBehavior The behavior of the bin, usually the percentage of positive responses.
pyPercentage The percentage of responses in the bin.
pxObjClass Not used in reports.
pzInsKey Not used in reports.
pxInsName Not used in reports.
pxSaveDateTime Not used in reports.
pxCreateDateTime Not used in reports.
pxUpdateDateTime Not used in reports.
pxCommitDateTime Not used in reports.

Properties in the PR_DATA_DM_CONTINGENCYTABLE table (categorical outcome predictive models)

For categorical outcome models, the confusion matrix of responses is the main statistic. Each cell in the confusion matrix is stored as a record in the database table.

The confusion matrix is used to calculate the performance values, such as the F-statistic for the model or the accuracy for the classes.

pr_data_dm_contingencytable properties
Property Description
pxApplication The name of the application.
pyRulesetName The name of the ruleset to which the Predictive Model rule belongs.
pyRulesetVersion The version of the ruleset to which the Predictive Model rule belongs.
pyModelID A unique identifier for each model.
pyFactoryKey A key that corresponds to the version information in the ADM factory data set (internal audit usage).
pySnapshotTime The time when the snapshot was taken.
pySnapshotID An identifier for the snapshot.
pyIdentifier An identifier for the category combination.
pyBinLabel A label for each bin with ranges.
pyPredictedCategory The category at the make-decision time.
pyActualCategory A category that was retrieved from the response.
pyDataUsage The stage of the model building process at which the data was derivedtraining, predicted, or observed.
pyDistributionType The type of distribution that is storedresidual values or actual values.
pyCount The number of values that are observed in each bin.
pxObjClass Not used in reports.
pzInsKey Not used in reports.
pxInsName Not used in reports.
pxSaveDateTime Not used in reports.
pxCreateDateTime Not used in reports.
pxUpdateDateTime Not used in reports.
pxCommitDateTime Not used in reports.

Properties in the PR_DATA_DM_HISTOGRAM table (continuous outcome models)

For continuous outcome models, the difference between the predicted outcome and the actual outcome is used to measure the performance. The distribution of these residual values is stored in bins of equal interval size. The Information that is gathered in the bins is used to calculate the root-mean-square error (RMSE) and mean absolute error (MAE) performance statistics for the model.

pr_data_dm_histogram table properties
Property Description
pxApplication The name of the application.
pyRulesetName The name of the ruleset to which the Predictive Model rule belongs.
pyRulesetVersion The version of the ruleset to which the Predictive Model rule belongs.
pyModelID A unique identifier for each model.
pyFactoryKey A key that corresponds to the version information in the ADM factory data set (internal audit usage).
pySnapshotTime The time when the snapshot was taken.
pySnapshotID An identifier for the snapshot.
pyBinID A unique identifier for each bin that is created.
pyBinLabel A label for each bin with ranges.
pyBinUpper The upper boundary of the bin.
pyBinLower The lower boundary of the bin.
pyDataUsage The stage of the model building process at which the data was derivedtraining, predicted, or observed.
pyDistributionType The type of distribution that is storedresidual values or actual values.
pyCount The total number of responses for the bin.
pyMinimum The minimum value in the bin.
pyMaximum The maximum value in the bin.
pyAverage The average value in the bin. The minimum, maximum, and average values help describe the distribution of data in the bin.
pxObjClass Not used in reports.
pzInsKey Not used in reports.
pxInsName Not used in reports.
pxSaveDateTime Not used in reports.
pxCreateDateTime Not used in reports.
pxUpdateDateTime Not used in reports.
pxCommitDateTime Not used in reports.

Properties in the PR_DATA_DM_SNAPSHOTS table (snapshot summary)

The monitoring information that is stored in the monitoring data mart contains data that is related to the same point in time; that collection of monitoring data is called a snapshot. This table contains one record per snapshot per model.

This information can be linked to data in other tables that contain more detailed, binned information that is used to calculate the performance statistics.

One-time model data may consist of the following details:

  • The number of responses in the snapshot
  • The performance of the model during the snapshot
  • The user that triggered the snapshot
pr_data_dm_snapshots table properties
Property Description
pxApplication The name of the application.
pyRulesetName The name of the ruleset to which the Predictive Model rule belongs.
pyRulesetVersion The version of the ruleset to which the Predictive Model rule belongs.
pyModelID A unique identifier for each model.
pyFactoryKey A key that corresponds to the version information in the ADM factory data set (internal audit usage).
pySnapshotTime The time when the snapshot was taken.
pySnapshotID An identifier for the snapshot.
pySnapshotDay The day when the snapshot was taken.
pyValue The value of the (meta) property.
pyLabel The label of the category.
pySnapshotType The frequency of taking the snapshotdaily, weekly, monthly, and so on.
pyDataUsage The stage of the model building process at which the data was derivedtraining, predicted, or observed.
pyIdentifier The identifier of the category for the snapshot.
pyName The name of the (meta) property.
pyDistributionType The type of distribution that is storedresidual values or actual values.
pyCount The total number of responses for the bin.
pxObjClass Not used in reports.
pzInsKey Not used in reports.
pxInsName Not used in reports.
pxSaveDateTime Not used in reports.
pxCreateDateTime Not used in reports.
pxUpdateDateTime Not used in reports.
pxCommitDateTime Not used in reports.

Properties in the PR_DATA_DM_NOTIFICATION table

This table contains Prediction Studio notifications that inform about sudden drops in predictive performance, models with low performance, or other issues with models.

pr_data_dm_notification properties
Property Description
pzinskey Not used in reports.
pxobjclass Not used in reports.
pxinsname Not used in reports.
pxsavedatetime Not used in reports.
pxcommitdatetime Not used in reports.
pylabel The name of the model for which the notification is generated.
pyrulesetname The name of the ruleset to which the model rule belongs.
pyrulesetversion The version of the ruleset to which the model rule belongs.
pynotificationtype The objective on which the notification is generated. For example, notifications can be based on Performance/Response count.
pxcreatedatetime Not used in reports.
pxupdatedatetime Not used in reports.
pytriggertime The time when the notification is generated.
pxcreateoperator Not used in reports.
pyconfigid An identifier that indicates the model insName or model ID for adaptive models.
pycontext The values of model identifiers that define the model context for an Adaptive Model rule.
pydescription The notification message that is visible in the user interface.
pymodelid A unique identifier of the model rule.
pymodelreferencekey A reference to the models of a single Adaptive Model rule.
pymodeltype The type of model for which the notification is generated (predictive or adaptive).
pynotificationtypeid A unique ID for each notification type.
pypriority The priority for each notification type.
pystatus The status that indicates whether the user has read the notification.

 

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