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Data monitoring tables

Adaptive Decision Manager (ADM) data mart tables contain data snapshots of all the models in ADM. You can use this data mart for ADM reporting and monitoring in the Reports section of the Analytics Center portal. When you need to customize ADM model reports to serve your business needs, you need to understand all the properties that are part of the PR_DATA_DM_ADMMART_MDL_FACT and PR_DATA_DM_ADMMART_PRED_FACT tables.

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
PropertyDescription
pxApplicationThe name of the application.
pyAppliesToClassThe Applies To class of the Adaptive Model rule with which the model is associated.
pyModelIDA unique identifier for each model.
pyConfigurationNameThe name of the Adaptive Model rule with which the model is associated.
pySnapshotTimeThe time when the snapshot was taken.
pyIssueThe default model identifier that defines the model context in your application. For more information, see Model context.
pyGroupThe default model identifier that defines the model context in your application.
pyNameThe 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.
pyChannelThe default model identifier that defines the model context in your application.
pyDirectionThe default model identifier that defines the model context in your application.
pyTreatmentThe default model identifier that defines the model context in your application.
pyPerformanceThe performance of each ADM model.
pySuccessRateNot used in ADM reports.
pyResponseCountThe number of all responses for each model.
pxObjClassNot used in ADM reports.
pzInsKeyNot used in ADM reports.
pxInsNameNot used in ADM reports.
pxSaveDateTimeNot used in ADM reports.
pxCommitDateTimeNot used in ADM reports.
pyExtensionNot used in ADM reports.
pyActivePredictorsThe number of active predictors for each model.
pyTotalPredictorsThe number of all predictors for each model.
pyNegativesThe number of negative responses for each model.
pyPositivesThe number of positive responses for each model.
pyRelativeNegativesThe difference between the number of negative responses for each model and the number of negative responses for each model in the last snapshot.
pyRelativePositivesThe difference between the number of positive responses for each model and the number of negative responses for each model in the last snapshot.
pyRelativeResponseCountThe difference between the number of all responses for each model and the number of all responses for each model in the last snapshot.
pyMemoryNot used in ADM reports.
pyPerformanceThresholdNot used in ADM reports.
pyCorrelationThresholdNot used in ADM 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
PropertyDescription
pxCommitDateTimeNot used in ADM reports.
pxSaveDateTimeNot used in ADM reports.
pyModelIDA unique identifier for each model.
pxObjClassNot used in ADM reports.
pzInsKeyNot used in ADM reports.
pxInsNameNot used in ADM reports.
pyPredictorNameThe name of the predictor.
pyContentsThe overall range of the predictor.
pyPerformanceThe performance of the predictor.
pyPositivesThe number of positive responses for the predictor per model.
pyNegativesThe number of negative responses for the predictor per model
pyTypeThe type of predictornumeric or symbolic.
pyTotalBinsThe number of bins for the predictor per model.
pyResponseCountThe number of responses for the predictor per model.
pyRelativePositivesThe difference between the number of positive responses for the predictor and the number of positive responses for the predictor in the last snapshot.
pyRelativeNegativesThe difference between the number of negative responses for the predictor and the number of negative responses for the predictor in the last snapshot.
pyRelativeResponseCountThe difference between the number of all responses for the predictor and the number of all responses for the predictor in the last snapshot.
pyBinNegativesThe number of negative responses for the predictor per model in the bin.
pyBinPositivesThe 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.
pyBinNegativesPercentageThe percentage of negative responses in the bin out of all responses for the predictor.
pyBinPositivesPercentageThe percentage of positive responses in the bin out of all responses for the predictor.
pyBinSymbolThe actual range of the bin.
pyBinLowerBoundThe lower bound of the bin. This property applies to numeric predictors.
pyBinUpperBoundThe upper bound of the bin. This property applies to numeric predictors.
pyRelativeBinPositivesThe 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.
pyRelativeBinNegativesThe 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.
pyBinResponseCountThe total number of responses for the predictor in the bin that is measured per model.
pyRelativeBinResponseCountThe 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.
pyBinResponseCountPercentageThe percentage of responses falling under a particular bin for a predictor that is measured per model.
pySnapShotTimeThe time when the snapshot was taken.
pyBinIndexAn 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.
pyLiftBehavior in the predictor bin divided by the overall behavior.
pyZRatioThe number of standard deviations in the behavior of the predictor bin differs from the overall behavior.
pyEntryTypeAn indication whether it is an active or inactive predictor. Active predictors are the ones that are used by the model.
pyExtensionNot used in ADM reports.
pyGroupIndex

Not used in ADM reports.

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

pyCorrelationPredictorNot used in ADM reports.

Properties in the PR_DATA_DM_BINARY_DISTRIBUTION table (scoring models)

For scoring 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
PropertyDescription
pxApplicationThe name of the application.
pyRulesetNameThe name of the ruleset to which the Predictive Model rule belongs.
pyRulesetVersionThe version of the ruleset to which the Predictive Model rule belongs.
pyModelIDA unique identifier for each model.
pyFactoryKeyA key that corresponds to the version information in the ADM factory data set (internal audit usage).
pySnapshotTimeThe time when the snapshot was taken.
pySnapshotIDAn identifier for the snapshot.
pyBinIDA unique identifier for each bin that is created.
pyBinLabelA label for each bin with ranges.
pyBinUpperThe upper boundary of the bin.
pyBinLowerThe lower boundary of the bin.
pyPredictorNameThe name of the predictor for which the statistic is stored.
pyCountThe total number of responses for the bin.
pyPositiveCountThe number of positive responses.
pyNegativeCountThe number of negative responses.
pyDataUsageThe stage of the model building process at which the data was derivedtraining, predicted, or observed.
pyMetricTypeThe metric type for the binbinary distribution or side-by-side distribution.
pyBehaviorThe behavior of the bin, usually the percentage of positive responses.
pyPercentageThe percentage of responses in the bin.
pxObjClassNot used in ADM reports.
pzInsKeyNot used in ADM reports.
pxInsNameNot used in ADM reports.
pxSaveDateTimeNot used in ADM reports.
pxCreateDateTimeNot used in ADM reports.
pxUpdateDateTimeNot used in ADM reports.
pxCommitDateTimeNot used in ADM reports.

Properties in the PR_DATA_DM_CONTINGENCYTABLE table (categorical models)

For categorical 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
PropertyDescription
pxApplicationThe name of the application.
pyRulesetNameThe name of the ruleset to which the Predictive Model rule belongs.
pyRulesetVersionThe version of the ruleset to which the Predictive Model rule belongs.
pyModelIDA unique identifier for each model.
pyFactoryKeyA key that corresponds to the version information in the ADM factory data set (internal audit usage).
pySnapshotTimeThe time when the snapshot was taken.
pySnapshotIDAn identifier for the snapshot.
pyIdentifierAn identifier for the category combination.
pyBinLabelA label for each bin with ranges.
pyPredictedCategoryThe category at the make-decision time.
pyActualCategoryA category that was retrieved from the response.
pyDataUsageThe stage of the model building process at which the data was derivedtraining, predicted, or observed.
pyDistributionTypeThe type of distribution that is storedresidual values or actual values.
pyCountThe number of values that are observed in each bin.
pxObjClassNot used in ADM reports.
pzInsKeyNot used in ADM reports.
pxInsNameNot used in ADM reports.
pxSaveDateTimeNot used in ADM reports.
pxCreateDateTimeNot used in ADM reports.
pxUpdateDateTimeNot used in ADM reports.
pxCommitDateTimeNot used in ADM reports.

Properties in the PR_DATA_DM_HISTOGRAM table (continuous models)

For continuous models, the residual distribution shows the model performance (observed vs. actual outcome). The distribution of residual values in the form of numerical bins facilitates the interpretation of data.

The bins are used to calculate the RMSE and MAE values for the model.

pr_data_dm_histogram table properties
PropertyDescription
pxApplicationThe name of the application.
pyRulesetNameThe name of the ruleset to which the Predictive Model rule belongs.
pyRulesetVersionThe version of the ruleset to which the Predictive Model rule belongs.
pyModelIDA unique identifier for each model.
pyFactoryKeyA key that corresponds to the version information in the ADM factory data set (internal audit usage).
pySnapshotTimeThe time when the snapshot was taken.
pySnapshotIDAn identifier for the snapshot.
pyBinIDA unique identifier for each bin that is created.
pyBinLabelA label for each bin with ranges.
pyBinUpperThe upper boundary of the bin.
pyBinLowerThe lower boundary of the bin.
pyDataUsageThe stage of the model building process at which the data was derivedtraining, predicted, or observed.
pyDistributionTypeThe type of distribution that is storedresidual values or actual values.
pyCountThe total number of responses for the bin.
pyMinimumThe minimum value in the bin.
pyMaximumThe maximum value in the bin.
pyAverageThe average value in the bin. The mininum, maximum, and average values help describe the distribution of data in the bin.
pxObjClassNot used in ADM reports.
pzInsKeyNot used in ADM reports.
pxInsNameNot used in ADM reports.
pxSaveDateTimeNot used in ADM reports.
pxCreateDateTimeNot used in ADM reports.
pxUpdateDateTimeNot used in ADM reports.
pxCommitDateTimeNot used in ADM reports.

Properties in the PR_DATA_DM_SNAPSHOTS table (snapshot summary)

For each statistic snapshot, a set of meta and non-meta values is stored. That data explains a snapshot or a model. This meta information is captured in a separate table and is used by all the statistic monitors.

One-time model information 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
PropertyDescription
pxApplicationThe name of the application.
pyRulesetNameThe name of the ruleset to which the Predictive Model rule belongs.
pyRulesetVersionThe version of the ruleset to which the Predictive Model rule belongs.
pyModelIDA unique identifier for each model.
pyFactoryKeyA key that corresponds to the version information in the ADM factory data set (internal audit usage).
pySnapshotTimeThe time when the snapshot was taken.
pySnapshotIDAn identifier for the snapshot.
pySnapshotDayThe day when the snapshot was taken.
pyValueThe value of the (meta) property.
pyLabelThe label of the category.
pySnapshotTypeThe frequency of taking the snapshotdaily, weekly, monthly, and so on.
pyDataUsageThe stage of the model building process at which the data was derivedtraining, predicted, or observed.
pyIdentifierThe identifier of the category for the snapshot.
pyNameThe name of the (meta) property.
pyDistributionTypeThe type of distribution that is storedresidual values or actual values.
pyCountThe total number of responses for the bin.
pxObjClassNot used in ADM reports.
pzInsKeyNot used in ADM reports.
pxInsNameNot used in ADM reports.
pxSaveDateTimeNot used in ADM reports.
pxCreateDateTimeNot used in ADM reports.
pxUpdateDateTimeNot used in ADM reports.
pxCommitDateTimeNot used in ADM reports.

 

Published October 3, 2017 — Updated April 16, 2019

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