Close popover
Swapnil Kashyap (SwapnilK7859)
British Telecom

British Telecom
SwapnilK7859 Member since 2021 2 posts
British Telecom
Posted: January 14, 2021
Last activity: January 21, 2021

Reference Adaptive Model propensity in strategy via java function

Hi Community,

I am looking at ways to reference adaptive model propensity using a java function, from within a strategy. The reason is to have models created but not connected in the strategy, instead to call up and use the propensity result for further use. This way the models can be switched, say using a decision data attribute depending upon the proposition being served up.

I have looked at Activity: Example call to pxGetPredictionCase Pega-DecisionEngine:07-10-33, and understand the call to the pxGetPredictionCase activity. The first step in this example call is to "Create inputs page for model in DMSample initial data" step which creates pyModelIdentifiers & pySerializedADMInputs with dummy data (for inputsPageName, see further)

I want to have these referred from the actual model. As an example, an adaptive model called ADM001 has been created with 60 predictors.

pxGetPredictionCase has 4 parameters: ruleName, appliesTo, inputsPageName & resultsPageName

Creating the inputs page is tricky and I would appreciate any help I can get.

I have had people suggesting to drop this approach but for the purpose of doing something differently I would like to attempt it. Happy to hear thoughts (& solutions) for both sides though.

Pega Decision Strategy Manager 7.3.1 Decision Management Communications and Media Decisioning Consultant Senior System Architect