You can efficiently train adaptive models in bulk by using a data flow with a
dedicated decision strategy. This method can be useful if you have a historical data set
or simulated data and you want to perform further analysis, testing, or
Adaptive models do not require historical or sample data as the Adaptive Decision Manager
(ADM) typically starts without any data and quickly updates the models. However, you can
train your adaptive models with historical or sample customer responses. The use of
previous results enables the ADM server to create models that can predict behavior more
Adaptive models are Pega Platform scoring models that can capture
and analyze customer responses in real time. Adaptive models identify the most suitable
proposition for a customer or determine the proposition that the customer is most likely
The system creates adaptive models when you run a decision strategy that contains an
Adaptive Model component (strategy shape). For each proposition referenced in the
strategy, the system creates one adaptive model. You can view the models on the Model
Management landing page in Dev Studio.
This tutorial explores the following use case:
A company that serves a large number of customers wants to improve its method for
identifying the best proposition for each customer. The company can make several
propositions and has already acquired some information about its customers and
interaction data in an external database. The CustomerResponse database column contains
historical responses for a strategy in which each customer is presented with a group of
propositions and can choose only one (Insurance1, Insurance2, or Insurance3). When a
customer accepts a proposition, the system automatically rejects the other two.
Sample database fragment with customer information and recorded response
The company needs a solution that takes the customer interaction data, feeds the data to
adaptive models, and trains the models simultaneously. Adaptive models that are trained
with this information can determine with improved accuracy which proposition a customer
is most likely to accept.
To support training adaptive models in bulk, you must create and configure two rules. The
first rule is your decision strategy that references the propositions that you want to offer to your
customers. This strategy must also reference an adaptive model rule that learns
from each recorded customer response. The second rule that you must create is a data
flow. The purpose of this data flow is to upload historical or sample data
from a dedicated data set into the adaptive model data set.
Note: This tutorial takes approximately 60 minutes to complete.
Before you begin: Verify that your historical customer response data is stored
in a dedicated data set.
Creating a strategy for training adaptive models in bulk
First, you must create a strategy that references multiple propositions for
training adaptive models in bulk. This strategy must import proposition data and feed it
to an adaptive model that trains with each received response.
For each proposition in the strategy, the system creates an adaptive model that you can
view on the Model Management landing page in Dev Studio. For
example, if your decision strategy references 50 propositions, the system creates 50
In the header of Dev Studio, click CreateDecisionStrategy.
On the strategy canvas, configure importing propositions to the strategy (for
example, by using the Proposition Data strategy shape).
You can use the single case test run to verify whether the proposition data is
imported as expected.
If the channel dimension or subject ID information for the strategy are not
already configured, specify the channel dimension and subject ID settings:
On the strategy canvas, right-click, and then click EnrichmentSet Property.
Right-click the Set Property shape, and then click
Set the .pyDirection property to the direction
that you use in the interaction with the customer.
Set the .pyChannel property to the channel that
you use in the interaction with the customer.
Note: The configuration of the channel dimension settings ensures that
the correct adaptive models are trained.
Set the .pySubjectID property. Set this property
only if .pySubjectID is not already the class key
of the Applies-To class of the strategy.
Note: In a typical decision strategy, the key of the Applies-To class is
set to either Customer or Subject ID and is used as the
.pySubjectID property in the strategy
results. In a bulk response upload scenario like this one, the data
model might not contain a Subject ID, or the Applies-To class might
not have the Subject ID set. In this case, verify that the
.pySubjectID property is set to a non-empty
Configure an Adaptive Model strategy shape:
On the strategy canvas, right-click, and then click Decision AnalyticsAdaptive Model.
Right-click the Adaptive Model shape, and then click
In the Adaptive Model field, specify the
adaptive model rule that you want to use in the strategy. Use one of
your existing adaptive model rules.
On the Outcomes tab, verify that you set the
correct outcome values for the adaptive model rule.
The outcome values in the model must match the outcome values returned
by the strategy (for example, Accept and
Configure the strategy so that it updates adaptive models with historical or
On the strategy canvas, right-click, and then click EnrichmentSet Property. Place the Set Property shape after the Adaptive Model
Right-click the Set Property shape, and then click
In the Define action, target, and source section,
click Add Item.
In the Action field, select
In the Target field, enter
In the Source field, set the adaptive model
outcome based on the database field that contains historical or sample
customer response.For example: @if(.pyName == Primary.CustomerResponse, "Accept",
This example expression is valid only if proposition names are the
same as historical or sample responses. The expression states that if
the name of the proposition matches the value in the CustomerResponse
database column, then the adaptive model outcome is Accept. Otherwise,
the adaptive model outcome is Reject.Note: When you introduce more
complex expressions such as
you can have multiple adaptive models learn from responses to their
corresponding propositions and from responses to other propositions.
You can develop a powerful pattern in which adaptive models learn
more effectively and are more accurate.
With complex expressions,
you can train adaptive models in scenarios where your database
contains multiple fields where customer responses are captured.
Consider the following expression: @if(
@contains(Primary.RESPONSE_POSITIVE, .pyName) , "Accept",
@if ( @contains(Primary.RESPONSE_NEGATIVE,
.pyName),"Decline", "None"). In this example,
the RESPONSE_POSITIVE database field contains positive customer
responses and the RESPONSE_NEGATIVE database field contains
negative customer responses.
Connect the strategy shapes.
Save the strategy,
Run the strategy by clicking ActionsRun.
Result: The Model Management landing page is populated with adaptive
models. The number of adaptive models created is equal to the number of propositions
referenced in the strategy. The initial performance value is 50% for each adaptive
Creating a data flow for training adaptive models in bulk
After you create your decision strategy, you must create a data flow that uploads
your historical or sample data to each adaptive model that was created on the Model
Management landing page. The source of the data flow is always the data set with
customer interaction data.
The data flow must reference the decision strategy that trains the adaptive models that
you want to improve. The destination of the data flow is always the Adaptive Decision
Manager (ADM) server (pxAdaptiveAnalytics). If the class of the
data set and the decision strategy referenced in this data flow do not match, you must
convert the properties from the data set class to the strategy class.
In the header of Dev Studio, click CreateData ModelData Flow.
To configure the Source shape, right-click the shape, and then click
Reference the data set with historical or sample outcomes that you want
to use to train your adaptive models.
Use the Preview option to preview the data set
and test the connection.
Optional: If the data set with historical or sample data and the referenced strategy are
configured in different classes, configure a Convert shape:
Right-click the green plus sign, and then click
Convert to add the shape to the data
Right-click the Convert shape, and then click
In the Convert to field, enter the name of the
class where the strategy for adaptive model training is located.
Configure the property mapping to propagate properties from the source
to the target class.
Configure a Decision Strategy shape:
Right-click the green plus sign, and then click Decision
Strategy to add the shape to the data flow.
Right-click the Decision Strategy shape, and then click
In the Strategy field, enter the name of the
strategy that you prepared for adaptive model training.
Select Manage learning cycle or outcome
Select and configure the Store adaptive inputs and strategy
Configure the Destination shape:
Display the shape properties.
In the Destination field, click Data
In the Data set field, enter
Save the data flow.
Run the data flow to train multiple adaptive models:
In the header of the data flow tab, click ActionsRun.
In the Data Flow Test Run window, click
Activate to run the data flow.
You can view the progress of the data flow on the progress bar. You
can also run the data flow from the Data Flows landing page.
What to do next: After you complete this tutorial, you can view the
increased performance of adaptive models on the Model Management landing page.