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 2.3.0 is the Python interface product. It exposes min_k, required_attributes, and attribute_groups on grid search. 2.2.0 added math_lib_version(). 2.1.0 renamed predict to predict_psr; use predict_grid for analysis. 2.0.0 was the packaging split from the math crate (same call surface as the old 1.3.4 wheels). 2.0.1 bundled math 1.3.5. This package versions on its own. The math crate is rbp-math-lib 1.3.7 (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.