Trigger Scenario Execution

Execute Scenario

To execute the scenario, run the following.

%python-pgx

if run_flag == True:
    asc.asc_cleanup4rerun()

asc.execute_scenario()

Execution Status

You can verify the execution status for each run date using the Execution Status paragraph.

Figure 4-10 Execution Status

Description of "Figure 4-10 Execution Status"

There are two types of execution status as follows:

Current Executions: It shows only executions which are currently running in the notebook python session.
All Executions: It shows current executions along with executions from the previous runs/sessions for the same definition id and version.

Detailed Execution Status

If it is True then it gives detailed status of runs for each Job Ids. It shows error in case execution is failed, start time, end time and execution time for each job.

Note:

Scenario having multiple Job Ids, each Job Id status must have COMPLETED, then only execution for given run date would be considered as COMPLETED. Otherwise, it will be marked as FAILED.

Error log will display the error occurred in paragraph from the Utility Scenario notebook.

Upon successful completion of each run, the post processing step can be completed as described below. This moves the generated events into a table in the schema which can be retrieved for further analysis.

Re-run/Update Flag and All Execution status

The user may want to execute the failed run dates again, considering the post-processing which was not yet executed for the current definition id and version. The run dates status in All Execution Status would be updated with the latest status of the run dates, whether the run_flag is True or False.

Only after executing post-processing, the asc_runid_lookup table gets entry for the current definition id and version.

Users must use the Re-run/Update flag is set to True for making any updates to existing run dates or the addition of any new run dates to the existing definition and version in the asc_runid_lookup table.

Upon completion of each run, only the COMPLETED run dates will be considered for the post processing step, which is described below. This moves the generated events into a table in the schema, which can be retrieved for further analysis.

Show Available Scenario Bindings

To show available scenario bindings, execute the following.

%python-pgx

asc.show_scenario_bindings()

Set Expression for Tunable Parameters

To set expression for tunable parameters, you must specify the parameters to be analyzed. This must be expressed in the same form as the scenario logic.

Note:

For many scenarios, the available bindings include both a Base and Func versions of a parameter. In such cases, determine the Curr_Type the scenario has been configured to use. If it has been set to F, the Func version of the parameter should be used; otherwise, the Base version should be used.

To set expression for tunable parameters, execute the following.

%python-pgx
tunable_parameters = "TOTAL_DEPOSIT_AMOUNT | TOTAL_WITHDRAWAL_AMOUNT"
setattr(asc,'tunable_parameters',tunable_parameters)

Note:

Using setattr method, the expression for tunable parameters is stored into class object for PreProd analysis.

Post Execution Activity

To perform post processing activity, execute the following.

%python-pgx

asc.scenario_post_processing()

This API perform the post scenario execution activities as follows:

Creating a unique asc_run_id in the ASC_RUNID_LOOKUP table.
Loading ATL alerts for given run_id in the ASC_INVESTIGATED_ENTITIES table.
Loading scenario data for given Run Dates into ASC_EVENT_MASTER table.

Review Results

You can review the results of scenario execution. The summary of the event volumes is generated as shown below.

Event Volume

You are provided with multiple groups options across run dates as follows:

RUN_DATE: Aggregate alerts by different run dates.
EVENT_TAG: Aggregate alerts by event tag (ATL or BTL).
RUN_DATE_AND_EVENT_TAG: Aggregate alerts by RUN DATE and EVENT TAG.
JURISDICTION_AND_RUN_DATE: Aggregate alerts by JURISDICTION and RUN_DATE.
RUN_DATE_AND_THRESHOLD_SET_ID: Aggregate alerts by RUN DATE and THRESHOLD SET ID.
EVENT_TAG_AND_THRESHOLD_SET_ID: Aggregate alerts by EVENT_TAG and THRESHOLD SET ID.
THRESHOLD_SET_ID: Aggregate alerts by THRESHOLD SET ID.
ALL: Aggregate alerts by all inputs together (by SEGMENT_ID, RUN_DATE, EVENT_TAG).

Figure 4-11 Event Volume

Description of "Figure 4-11 Event Volume"

asc.save_object()

Save Object

It saves the current ASC object in DB tracking table for later use in ATL, BTL, Impact and PreProd Analysis notebooks.

To save object, execute the following:

%python-pgx

asc.save_object()