Overview

pyRuleAnalyzer is a Python-based tool designed to support rule extraction, analysis, and simplification from tree-based artificial intelligence models. It provides a comprehensive pipeline to generate interpretable models, remove redundancies, and evaluate model accuracy and interpretability.

It currently supports three scikit-learn algorithms:

• Decision Tree (DecisionTreeClassifier)

• Random Forest (RandomForestClassifier)

• Gradient Boosting Decision Trees (GradientBoostingClassifier)

Key functionalities include:

• Extracting decision rules from tree-based models.

• Identifying and removing redundant and duplicate rules (intra-tree and inter-tree).

• High-performance vectorized batch prediction via compiled numpy arrays (with optional C acceleration).

• Evaluating initial and simplified models through classification accuracy, confusion matrices, and interpretability metrics.

• Computing the Structural Complexity Score (SCS) and interpretability metrics to assess model complexity.

• Exporting standalone classifiers to Python (.py), binary (.bin), and C header (.h) formats.

• Saving, loading, and applying rule-based classifiers on new datasets.

• Interactive terminal-based rule editing for manual refinement with domain knowledge.