PMML Scorecard generator
Python client for submitting PMML ScoreCard models and queries against them to OpenScoring
PMML ScoreCards: http://dmg.org/pmml/v4-2-1/Scorecard.html for additional background
OpenScoring REST API: https://github.com/jpmml/openscoring Demo instance: http://openscoring-ncoghlan.rhcloud.com/openscoring/ Demo git repo: https://github.com/ncoghlan/openscoring-openshift
Command line invocation:
pyscorecard input_spec.json pmml_output_dir
Output PMML file names are generated based on a combination of “model_name” and “param_grid” entries as described below.
In the Python API, scorecard.pmml_scorecard generates PMML scorecard definitions from a JSON-compatible input mapping.
See examples/risk_example.json (input) and examples/risk_example.xml (output)
All ScoreCards produce a single predicted risk score and up to 3 reason codes:
Generated ScoreCards are also currently all hardcoded to use the “pointsAbove” reason code algorithm, the “min” baseline score algorithm, 0 as the initial score for the overall scorecard evaluation and 1 as the baseline score for each individual characteristic (this ensures that characteristics achieving a partial score of 0 are never reported as reason codes for the overall risk scoring).
The input format is a JSON mapping with the following fields:
Predicates can be defined as either a single string, or as a sequence of such strings. Each string predicate is of the form “OP value”, with the data field named in the characteristic definition being the implied left hand side of the operation. Predicate sequences are implicitly and’ed together to define the overall criterion to be met for that attribute. Predicate values may start with $ to indicate a grid parameter - these will be substituted with the appropriate value for the scorecard currently being generated.
Permitted operations are == for data fields with the categorical optype, and ==, <, <=, >=, and > for data fields with the ordinal or continuous optype.