open-rating-engine

An insurance/banking rating engine for Python. You describe a pricing algorithm as a set of typed layers; the engine builds a dependency graph (DAG) from them and scores policies through it. It also ships price optimisers that run on top of a pipeline.

A pipeline is an MLflow PythonModel, so you log, version, load and serve it with standard MLflow tooling.

Install

pip install open-rating-engine          # core: pipeline, layers, optimisers
pip install "open-rating-engine[all]"   # plus every optional extra

Optional dependencies are imported lazily, so import open_rating_engine works on a core-only install.

Imports

The headline names (the pipeline, every layer class, the layer exceptions and apply_aging) are available from the top-level package. Everything else lives in its subpackage:

from open_rating_engine import PyfuncPricingPipeline, RatingTablesLayer, apply_aging
from open_rating_engine.optimization import PriceOptimizerRatingFactor
from open_rating_engine.rating_engine.binning import thermometer_column_names

Package __init__ modules load their names on first use, so import open_rating_engine is near-instant. You pay for mlflow only when you touch the pipeline or a layer. Each module's __all__ defines its public API.

Rating engine

PyfuncPricingPipeline (open_rating_engine.rating_engine.pricing_pipeline):

• add_parameter() / remove_parameter() register and remove layers.
• Layers are topologically sorted, and cycles are reported. Independent column calculations run in parallel waves. Structural layers (joins, drops, renames, aggregations) run in sequence so that tables stay consistent.
• predict(context, model_input) is the MLflow-compatible scoring entry point.
• load_context(context) expects every artifact in context.artifacts to be a local file path. The engine fetches nothing remote, so download artifacts from blob storage or a model registry before building the context.
• profile_predict() returns how long each layer took. plot_dag() draws the graph.

Pipeline state is a dict of named DataFrames. Each layer declares what it reads (depends_on) and what it writes, and the pipeline builds the DAG from those declarations.

Layer types (open_rating_engine.rating_engine.pricing_layers), all extending BasePricingLayer:

open_rating_engine.rating_engine.binning holds the thermometer (cumulative) encoding. Training and scoring both use it, so they produce identical columns.

Price optimisation

open_rating_engine.optimization:

Helpers: simulate_adjustment_grid, make_demand_emulation, make_log_multiplier_penalty and make_log_multiplier_regularizer.

Multi-period optimisers age the book each period with open_rating_engine.data_processing.data_aging.apply_aging.

Development

pip install -e ".[all,test]"
pytest

License

Apache License 2.0. See LICENSE.