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desdeo-brb

A trainable Belief Rule-Based (BRB) inference system for Python.

desdeo-brb implements the RIMER methodology for Belief Rule-Based systems 12 with a scikit-learn-compatible API, multiple optimization backends, and first-class explainability.

Features

  • Trainable BRB models with MSE or custom loss objectives
  • scikit-learn-style fit() / predict() API
  • Multiple training backends: NumPy (SLSQP, trust-constr, DE), JAX (L-BFGS-B with autodiff), and Pyomo (IPOPT with exact Hessians)
  • Multi-start optimization for non-convex problems
  • Adaptive referential value training
  • Human-readable rule descriptions and prediction explanations
  • Extensive test suite reproducing paper benchmarks

Quick example

import numpy as np
from desdeo_brb import BRBModel

# Define a BRB model for f(x) = x * sin(x^2)
prv = [np.array([0.0, 0.5, 1.0, 1.5, 2.0, 2.5, 3.0])]
crv = np.array([-2.5, -1.0, 1.0, 2.0, 3.0])
model = BRBModel(prv, crv, initial_rule_fn=lambda x: x[0] * np.sin(x[0] ** 2))

# Train with multiple restarts for reliable convergence
X_train = np.linspace(0, 3, 1000).reshape(-1, 1)
y_train = X_train[:, 0] * np.sin(X_train[:, 0] ** 2)
model.fit(X_train, y_train, n_restarts=10)

# Predict
y_pred = model.predict_values(np.array([[1.5]]))

# Explain the prediction
print(model.explain(np.array([[1.5]])))

Next steps

  1. Jian-Bo Yang, Jun Liu, Jin Wang, How-Sing Sii, and Hong-Wei Wang. Belief rule-base inference methodology using the evidential reasoning approach – rimer. IEEE Transactions on Systems, Man, and Cybernetics – Part A: Systems and Humans, 36(2):266–285, 2006. doi:10.1109/TSMCA.2005.851270. ↩

  2. Yu-Wang Chen, Jian-Bo Yang, Dong-Ling Xu, Zhi-Jie Zhou, and Da-Wei Tang. Inference analysis and adaptive training for belief rule based systems. Expert Systems with Applications, 38(10):12845–12860, 2011. doi:10.1016/j.eswa.2011.04.077. ↩