GlassBoxAI-RandomForest

Random Forest Suite

GPU-Accelerated Random Forest Implementations with Formal Verification

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Overview

GlassBoxAI-RandomForest is a comprehensive, production-ready Random Forest implementation suite featuring:

• Multiple GPU backends: CUDA and OpenCL acceleration
• Multiple language implementations: C++ and Rust
• Facade pattern architecture: Clean API separation with deep introspection capabilities
• Formal verification: Kani-verified Rust implementation for memory safety guarantees
• CISA/NSA Secure by Design compliance: Built following government cybersecurity standards

This project demonstrates enterprise-grade software engineering practices including comprehensive testing, formal verification, cross-platform compatibility, and security-first development.

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Table of Contents

1. Features
2. Architecture
3. File Structure
4. Prerequisites
5. Installation & Compilation
6. CLI Reference
   • Standard Random Forest Commands
   • Facade Random Forest Commands
7. Testing
8. Formal Verification with Kani
9. CISA/NSA Compliance
10. License
11. Author

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Features

Core Capabilities

Feature	Description
Classification	Multi-class classification with majority voting
Regression	Continuous value prediction with mean aggregation
Split Criteria	Gini impurity, Entropy, MSE, Variance Reduction
Bootstrap Sampling	Random sampling with replacement for tree diversity
Feature Subsampling	Random feature selection at each split
Out-of-Bag Error	Built-in validation using OOB samples
Feature Importance	Impurity-based feature ranking
Model Persistence	Binary serialization for model save/load

GPU Acceleration

Backend	Implementation	Performance
CUDA	Native CUDA kernels	Optimal for NVIDIA GPUs
OpenCL	Cross-platform GPU	AMD, Intel, NVIDIA support

Safety & Security

Feature	Technology
Memory Safety	Rust ownership model
Formal Verification	Kani proof harnesses
Bounds Checking	Verified array access
Input Validation	CLI argument validation

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Architecture

┌─────────────────────────────────────────────────────────────────────┐
│                      GlassBoxAI-RandomForest                        │
├─────────────────────────────────────────────────────────────────────┤
│                                                                     │
│  ┌─────────────┐  ┌─────────────┐  ┌─────────────────────────────┐  │
│  │   C++ CUDA  │  │ C++ OpenCL  │  │         Rust CUDA           │  │
│  ├─────────────┤  ├─────────────┤  ├─────────────────────────────┤  │
│  │ • random_   │  │ • random_   │  │ • rust_cuda/                │  │
│  │   forest.cu │  │   forest_   │  │ • facaded_rust_cuda/        │  │
│  │ • facaded_  │  │   opencl.cpp│  │   └─ kani/ (Formal          │  │
│  │   random_   │  │ • facaded_  │  │      Verification)          │  │
│  │   forest.cu │  │   random_   │  │                             │  │
│  │             │  │   forest_   │  │                             │  │
│  │             │  │   opencl.cpp│  │                             │  │
│  └─────────────┘  └─────────────┘  └─────────────────────────────┘  │
│                                                                     │
│  ┌─────────────────────────────────────────────────────────────────┐│
│  │                     Shared Features                             ││
│  │  • Consistent CLI interface across all implementations          ││
│  │  • Binary compatible model formats                              ││
│  │  • Comprehensive test suites                                    ││
│  └─────────────────────────────────────────────────────────────────┘│
│                                                                     │
└─────────────────────────────────────────────────────────────────────┘

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File Structure

GlassBoxAI-RandomForest/
│
├── random_forest.cu                  # C++ CUDA Random Forest implementation
├── random_forest_opencl.cpp          # C++ OpenCL Random Forest implementation
├── facaded_random_forest.cu          # C++ CUDA Random Forest with Facade pattern
├── facaded_random_forest_opencl.cpp  # C++ OpenCL Random Forest with Facade pattern
│
├── rust_cuda/                        # Rust CUDA Random Forest implementation
│   ├── Cargo.toml
│   └── src/
│       └── main.rs
│
├── facaded_rust_cuda/                # Rust CUDA Random Forest with Facade pattern
│   ├── Cargo.toml
│   └── src/
│       ├── main.rs
│       └── kani/                     # Kani proof harnesses
│
├── random_forest_cpp_tests.sh        # C++ test suite
├── random_forest_opencl_tests.sh     # OpenCL test suite
│
├── license.md                        # MIT License
└── README.md                         # This file

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Prerequisites

Required

Dependency	Version	Purpose
GCC/G++	11+	C++ compilation
CUDA Toolkit	12.0+	CUDA compilation
Rust	1.75+	Rust compilation

Optional

Dependency	Version	Purpose
OpenCL SDK	3.0	OpenCL compilation
Kani	0.67+	Formal verification

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Installation & Compilation

C++ CUDA Implementation

# Standard Random Forest
nvcc -O2 -o forest_cuda random_forest.cu

# Facade Random Forest
nvcc -O2 -o forest_facade_cuda facaded_random_forest.cu

C++ OpenCL Implementation

# Standard Random Forest
g++ -O2 -std=c++14 -o forest_opencl random_forest_opencl.cpp -lOpenCL

# Facade Random Forest
g++ -O2 -std=c++14 -o forest_facade_opencl facaded_random_forest_opencl.cpp -lOpenCL

Rust CUDA Implementation

# Standard Random Forest
cd rust_cuda
cargo build --release

# Facade Random Forest
cd facaded_rust_cuda
cargo build --release

Build All

# Build everything
nvcc -O2 -o forest_cuda random_forest.cu
nvcc -O2 -o forest_facade_cuda facaded_random_forest.cu
g++ -O2 -std=c++14 -o forest_opencl random_forest_opencl.cpp -lOpenCL
g++ -O2 -std=c++14 -o forest_facade_opencl facaded_random_forest_opencl.cpp -lOpenCL
(cd rust_cuda && cargo build --release)
(cd facaded_rust_cuda && cargo build --release)

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CLI Reference

Standard Random Forest Commands

The standard Random Forest implementations provide core ensemble learning functionality.

Usage

forest_cuda <command> [options]
forest_opencl <command> [options]
rust_cuda/target/release/forest_cuda <command> [options]

Commands

Command	Description
create	Create a new Random Forest model
train	Train a Random Forest model
predict	Make predictions with a trained model
info	Display model information
help	Show help message

Create Options

Option	Description
--trees=N	Number of trees (default: 100)
--max-depth=N	Maximum tree depth (default: 10)
--min-leaf=N	Minimum samples per leaf (default: 1)
--min-split=N	Minimum samples to split (default: 2)
--max-features=N	Maximum features per split (default: sqrt(n))
--criterion=C	Split criterion: gini, entropy, mse, variancereduction
--task=T	Task type: classification, regression
--save=FILE	Save model to file (required)

Train Options

Option	Description
--model=FILE	Model file to load (required)
--data=FILE	Training data CSV file (required)
--save=FILE	Save trained model to file (required)

Predict Options

Option	Description
--model=FILE	Model file to load (required)
--data=FILE	Data file for predictions (required)
--output=FILE	Save predictions to file (optional)

Standard Examples

# Create a new model
forest_cuda create --trees=50 --max-depth=15 --save=model.bin

# Train the model
forest_cuda train --model=model.bin --data=train.csv --save=model_trained.bin

# Make predictions
forest_cuda predict --model=model_trained.bin --data=test.csv --output=predictions.csv

# Get model information
forest_cuda info --model=model_trained.bin

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Facade Random Forest Commands

The facade implementations provide deep introspection and tree manipulation capabilities.

Usage

forest_facade_cuda <command> [options]
forest_facade_opencl <command> [options]
facaded_rust_cuda/target/release/forest_facade <command> [options]

Core Commands

Command	Description
create	Create a new empty forest model
train	Train a random forest model
predict	Make predictions using a trained model
evaluate	Evaluate model on test data
info	Show forest hyperparameters
gpu-info	Show GPU device information
help	Show help message

Tree Inspection Commands

Command	Description
inspect-tree	Inspect tree structure and nodes
tree-depth	Get depth of a specific tree
tree-nodes	Get node count of a specific tree
tree-leaves	Get leaf count of a specific tree
node-details	Get details of a specific node

Tree Manipulation Commands

Command	Description
add-tree	Add a new tree to the forest
remove-tree	Remove a tree from the forest
replace-tree	Replace a tree with new bootstrap sample
retrain-tree	Retrain a specific tree
prune-tree	Prune subtree at specified node
modify-split	Modify split threshold at node
modify-leaf	Modify leaf prediction value
convert-to-leaf	Convert node to leaf

Feature Control Commands

Command	Description
enable-feature	Enable a feature for predictions
disable-feature	Disable a feature for predictions
reset-features	Reset all feature filters
feature-usage	Show feature usage summary
importance	Show feature importances

Aggregation Commands

Command	Description
set-aggregation	Set prediction aggregation method
get-aggregation	Get current aggregation method
set-weight	Set weight for specific tree
get-weight	Get weight of specific tree
reset-weights	Reset all tree weights to 1.0

Performance Analysis Commands

Command	Description
oob-summary	Show OOB error summary per tree
track-sample	Track which trees influence a sample
metrics	Calculate accuracy/MSE/F1 etc.
misclassified	Highlight misclassified samples
worst-trees	Find trees with highest error

Facade Options

Option	Description
--input=<file>	Training input data (CSV)
--target=<file>	Training targets (CSV)
--data=<file>	Test/prediction data (CSV)
--model=<file>	Model file (default: forest.bin)
--output=<file>	Output predictions file
--trees=<n>	Number of trees (default: 100)
--depth=<n>	Max tree depth (default: 10)
--tree=<id>	Tree ID for operations
--node=<id>	Node ID for operations
--feature=<id>	Feature ID for operations
--weight=<val>	Tree weight (0.0-1.0)
--aggregation=<method>	majority, weighted, mean, weighted-mean

Facade Examples

# Create and train forest
forest_facade_cuda create --trees=100 --depth=10 --model=rf.bin
forest_facade_cuda train --input=data.csv --target=labels.csv --model=rf.bin

# Make predictions and evaluate
forest_facade_cuda predict --data=test.csv --model=rf.bin --output=preds.csv
forest_facade_cuda evaluate --data=test.csv --model=rf.bin

# Tree inspection
forest_facade_cuda inspect-tree --tree=5 --model=rf.bin
forest_facade_cuda tree-depth --tree=5 --model=rf.bin

# Feature analysis
forest_facade_cuda feature-usage --model=rf.bin
forest_facade_cuda importance --model=rf.bin

# Tree manipulation
forest_facade_cuda add-tree --model=rf.bin
forest_facade_cuda remove-tree --tree=5 --model=rf.bin
forest_facade_cuda disable-feature --feature=3 --model=rf.bin

# Aggregation control
forest_facade_cuda set-aggregation --aggregation=weighted-mean --model=rf.bin
forest_facade_cuda set-weight --tree=5 --weight=1.5 --model=rf.bin

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Testing

Running All Tests

# Run C++ tests
./random_forest_cpp_tests.sh

# Run OpenCL tests
./random_forest_opencl_tests.sh

# Run Rust tests
cd rust_cuda && cargo test
cd facaded_rust_cuda && cargo test

Test Categories

Each test suite covers:

Category	Tests
Help & Usage	Command-line interface verification
Model Creation	Various hyperparameter configurations
Hyperparameters	Trees, depth, split criteria, task types
Model Info	Metadata retrieval
Save & Load	Model persistence
Introspection	Tree, node, feature inspection
Error Handling	Invalid input handling
Cross-Implementation	API compatibility
Train & Predict	End-to-end workflows

Test Output Example

=========================================
Random Forest Test Suite
=========================================

Group: Help & Usage
Test 1: Help command... PASS
Test 2: --help flag... PASS
Test 3: -h flag... PASS
...

=========================================
Test Summary
=========================================
Total tests: 50
Passed: 50
Failed: 0

All tests passed!

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Formal Verification with Kani

Overview

The Rust Facade implementation includes Kani formal verification proofs that mathematically prove the absence of certain classes of bugs. This goes beyond traditional testing to provide mathematical guarantees about code correctness.

Verification Categories

Category	Description
Strict Bound Checks	Array/collection indexing safety
Pointer Validity	Slice-to-pointer conversion safety
No-Panic Guarantee	Enum and command handling safety
Integer Overflow Prevention	Tree size, dimension calculations
Division-by-Zero Exclusion	Bootstrap sampling, feature selection
Global State Consistency	Training mode state tracking
Input Sanitization Bounds	Loop iteration limits
Memory Leak Prevention	Vector allocation bounds
Floating-Point Sanity	NaN/Infinity prevention

Key Kani Proofs

Bound Checking Proofs

• verify_tree_indexing ✓
• verify_node_indexing ✓
• verify_feature_indexing ✓

Overflow Prevention Proofs

• verify_tree_size_no_overflow ✓
• verify_bootstrap_no_overflow ✓
• verify_feature_selection_no_overflow ✓

Safety Proofs

• verify_criterion_type_no_panic ✓
• verify_task_type_no_panic ✓
• verify_command_parsing_no_panic ✓
• verify_gini_no_nan ✓

Running Kani Verification

# CLI Version
cd facaded_rust_cuda
cargo kani

# Run specific proof
cargo kani --harness verify_tree_indexing

Why Formal Verification Matters

Traditional testing can only verify specific test cases. Formal verification with Kani:

• Exhaustively checks all possible inputs within defined bounds
• Mathematically proves absence of panics, buffer overflows, and undefined behavior
• Catches edge cases that random testing might miss
• Provides cryptographic-level assurance for safety-critical code

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CISA/NSA Compliance

Secure by Design

This project follows CISA (Cybersecurity and Infrastructure Security Agency) and NSA (National Security Agency) Secure by Design principles:

Principle	Implementation
Memory Safety	Rust ownership model eliminates buffer overflows, use-after-free, and data races
Formal Verification	Kani proofs mathematically verify absence of critical bugs
Input Validation	All CLI inputs validated before processing
Defense in Depth	Multiple layers of safety (language, compiler, runtime checks)
Secure Defaults	Safe default configurations throughout
Transparency	Open source with full code visibility

Compliance Checklist

• Memory-safe language (Rust implementation)
• Static analysis (Rust compiler + Clippy)
• Formal verification (Kani proof harnesses)
• Comprehensive testing (Unit tests + integration tests)
• Bounds checking (Verified array access)
• Input validation (CLI argument parsing)
• No unsafe code in critical paths (Where possible)
• Documentation (Inline docs + README)
• Version control (Git)
• License clarity (MIT License)

Attestation

This codebase has been developed following secure software development lifecycle (SSDLC) practices and demonstrates:

• Formal verification proofs passed (Kani proofs)
• Zero warnings compilation across all implementations
• Consistent API across all language/backend combinations
• Production-ready code quality

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License

MIT License

Copyright (c) 2025 Matthew Abbott

Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.

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Author

Matthew Abbott
Email: mattbachg@gmail.com

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Built with precision. Verified with rigor. Secured by design.