rbp-engine

Relevance-Based Prediction (RBP) Engine for Python: NumPy-friendly bindings to the RBP prediction engine from Cambridge Sports Analytics.

Relevance-based prediction (RBP) is a model-free prediction routine that forms a prediction as a weighted average of observed outcomes in which the weights are based on a statistic called relevance. Relevance is composed of similarity and informativeness which are both measured as Mahalanobis distances. RBP serves as a favorable alternative to conventional prediction methods such as linear regression analysis because it addresses complex dynamics that are beyond the reach of linear regression analysis. It also serves as a favorable alternative to AI models because RBP can extract as much information from complex datasets as AI models but more efficiently and with full transparency.

pip install rbp-engine

rbp-engine 2.0.0 is the Python interface product. It has the same call surface as 1.3.4. The jump marks the split from the math crate: this package can version on its own. The bundled native library is still rbp-math-lib 1.3.4 (rbp_engine_version() / rbp-license-info --viewlicense). Later 2.x releases may change Python helpers without a new math build.

Quick start

import numpy as np
from rbp_engine import predict_grid, GridOptions, relevance, similarity, info_x, info_theta, relevance_metrics

# y: outcomes (N,)
# X: attributes (N, K)
# theta: circumstances (K,) — the single case you want a prediction for
out = predict_grid(y, X, theta, GridOptions())
print(float(out.yhat[0]))   # fit-weighted composite across attribute × observation calibrations

# Relevance-based scores without a full prediction
r = relevance(X, theta)       # relevance of each training row to the circumstances
s = similarity(X, theta)      # similarity (Mahalanobis) of each row to theta
ix = info_x(X)                # informativeness of each row vs the sample mean
it = info_theta(X, theta)     # informativeness of the circumstances vs the sample mean

# Or, all four in one native pass:
m = relevance_metrics(X, theta)
m.relevance, m.similarity, m.info_x, m.info_theta

For most analysis work, start with predict_grid. It evaluates combinations of attributes and observation subsets, then forms one composite prediction (yhat of length 1) weighted by adjusted fit.

Use relevance, similarity, info_x, and info_theta when you need scores without running a full prediction.

Built-in help includes paste-ready examples:

help(predict_grid)
help(GridOptions)
help(PredictionResults)

Core functions

Supported platforms

Installs with pip on Python 3.10+. Nothing to compile, no extra system libraries.

If your platform isn't listed, pip will report that no matching distribution was found.

Requires numpy>=1.24.

License

rbp-engine is commercial software and requires a license from Cambridge Sports Analytics. Contact prediction@csanalytics.io to get started.

After install, rbp-license-info is available in the same environment (activate your virtual environment if you use one):

rbp-license-info                             # request code, or status if already licensed
rbp-license-info --setlicense 'RBP-LIC-1:…'  # install the token you receive
rbp-license-info --viewlicense               # license status plus engine path/version/ABI

For site or server deployments, contact Cambridge Sports Analytics, setup differs from the workstation flow above.

(c) 2026 Cambridge Prediction Analytics, LLC. All rights reserved.