blackbox2c 0.2.1

Convert ML models to optimized code for embedded systems

BlackBox2C

Convert scikit-learn models to native embedded code — C, C++, Arduino, MicroPython

BlackBox2C converts any trained scikit-learn model into a minimal if-else decision tree in your target language. The generated code has zero runtime dependencies, runs on any microcontroller with a C compiler, and fits in a few hundred bytes of FLASH.

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How It Works

1. Surrogate extraction — A lightweight DecisionTree is trained to mimic any black-box model (Random Forest, SVM, MLP, etc.) by generating synthetic boundary samples and labeling them with the original model's predictions.
2. Rule optimization — Redundant branches are pruned and similar leaves are merged to minimize code size.
3. Code generation — The optimized tree is serialized as a pure if-else function in the target language.

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Supported Models and Targets

Input models	Output formats
Any scikit-learn estimator with predict()	Pure C (C99)
Decision Tree, Random Forest, SVM, MLP...	C++11 (class + namespace)
Classification and Regression tasks	Arduino (.h with PROGMEM)
	MicroPython (.py module)

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Installation

pip install blackbox2c

Requirements: Python 3.8+, NumPy >= 1.21, scikit-learn >= 1.0.

Tip: Use a virtual environment to keep your project isolated:

# python -m venv .venv && source .venv/bin/activate  # Linux/macOS
python -m venv .venv && .venv\Scripts\activate     # Windows
pip install blackbox2c

For development (from source):

git clone https://github.com/AxelSkrauba/BlackBox2C.git
cd BlackBox2C
pip install -e ".[dev]"

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Quick Start

Classification

from sklearn.ensemble import RandomForestClassifier
from sklearn.datasets import load_iris
from blackbox2c import convert

iris = load_iris()
model = RandomForestClassifier(n_estimators=50, random_state=42)
model.fit(iris.data, iris.target)

# Convert to C (default target)
c_code = convert(
    model,
    iris.data,
    feature_names=list(iris.feature_names),
    class_names=list(iris.target_names),
    max_depth=5,
)
print(c_code)

Generated output:

/*
 * Auto-generated C code by BlackBox2C
 *   - Input features: 4
 *   - Output classes: 3
 *   - Precision: 8-bit
 */
#include <stdint.h>

#define setosa 0
#define versicolor 1
#define virginica 2

uint8_t predict(float features[4]) {
    if (features[2] <= 2.449999f) {
        return 0;
    } else {
        if (features[3] <= 1.750000f) {
            return 1;
        } else {
            return 2;
        }
    }
}

Export to Other Formats

# Arduino .ino file
arduino_code = convert(model, iris.data, target='arduino')

# C++ class
cpp_code = convert(model, iris.data, target='cpp')

# MicroPython module
mp_code = convert(model, iris.data, target='micropython')

Regression

from sklearn.ensemble import GradientBoostingRegressor
from sklearn.datasets import load_diabetes
from blackbox2c import convert

data = load_diabetes()
model = GradientBoostingRegressor(random_state=42)
model.fit(data.data, data.target)

c_code = convert(model, data.data, max_depth=5)
# Generates: float predict(float features[10]) { ... }

Feature Analysis

from blackbox2c.analysis import FeatureSensitivityAnalyzer

analyzer = FeatureSensitivityAnalyzer(n_repeats=10, random_state=42)
results = analyzer.analyze(model, X_train, y_train, feature_names=feature_names)
print(results.summary())

# Get top 3 most important features by index
top3 = results.get_top_features(3)

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Configuration

from blackbox2c import Converter, ConversionConfig

config = ConversionConfig(
    max_depth=5,             # Surrogate tree depth (1-10, default 5)
    optimize_rules='medium', # 'low' | 'medium' | 'high' | 'qm' | 'bdd' | 'auto'
    qm_max_literals=12,      # Hard cap on literals processed by QM (v0.2+)
    bdd_max_literals=24,     # Hard cap on literals processed by BDD (v0.2+)
    use_fixed_point=False,   # Use integer arithmetic instead of float
    precision=8,             # Bit width for fixed-point: 8 | 16 | 32
    function_name='predict', # Name of the generated function
    n_samples=10000,         # Synthetic samples for surrogate training
    feature_threshold=None,  # Auto-select N most important features
    memory_budget_kb=None,   # Auto-tune params to fit a KB budget
)

converter = Converter(config)
code = converter.convert(model, X_train, target='arduino')
metrics = converter.get_metrics()
# {'fidelity': 0.97, 'complexity': {...}, 'size_estimate': {...}}

Rule optimization levels

Level	Algorithm	Notes
low	No post-processing.	Returns the surrogate tree as-is.
medium	Prune internal nodes whose direct children are same-class leaves.	Default. Backward-compatible with v0.1.
high	medium + merge sibling leaves with very similar class distributions.	Backward-compatible with v0.1.
qm	Multi-valued Quine-McCluskey boolean minimisation lifted to continuous splits. (v0.2)	Classification only. Capped by qm_max_literals.
bdd	Frequency-ordered Reduced Ordered BDD rebuilt as a tree. (v0.2)	Classification only. Capped by bdd_max_literals.
auto	Runs every applicable optimiser plus the no-op baseline; returns the smallest. (v0.2)	Recommended default if you only care about code size.

Advanced levels (qm, bdd, auto) preserve 100 % functional equivalence with the surrogate tree (verified by the test suite). On regression they emit a single UserWarning and fall back to the legacy 'high' path. Best-case savings on the benchmark (benchmarks/results/v0.2.md): −47 % FLASH on Iris + RandomForest. See notebooks/07_advanced_optimization.ipynb for a guided walkthrough.

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CLI

# Convert a pickled model to C
blackbox2c convert model.pkl X_train.npy -o output.c

# Export to Arduino
blackbox2c convert model.pkl X_train.npy -t arduino -o predict.h

# Analyze feature importance
blackbox2c analyze model.pkl X_train.npy --top-n 5

# Export a decision tree directly (no surrogate extraction)
blackbox2c export model.pkl -f cpp -o predictor.hpp

# Help
blackbox2c --help
blackbox2c convert --help

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Benchmarks

python benchmarks/benchmark_classic_datasets.py --output results.md

Covers Iris, Wine, Diabetes, and California Housing with Decision Trees, Random Forests, SVMs, and Neural Networks. Metrics: fidelity, estimated FLASH size, tree depth, conversion time.

Note: Code size figures are estimates from BlackBox2C's built-in size estimator, not measurements on real hardware.

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Project Structure

blackbox2c/
├── blackbox2c/
│   ├── __init__.py      # Public API: convert(), Converter, ConversionConfig
│   ├── converter.py     # Main orchestration pipeline
│   ├── config.py        # ConversionConfig dataclass
│   ├── surrogate.py     # Surrogate tree extraction
│   ├── codegen.py       # C code generation
│   ├── optimizer.py     # Rule pruning and merging
│   ├── exporters.py     # C++, Arduino, MicroPython exporters
│   ├── analysis.py      # Feature sensitivity analysis
│   └── cli.py           # Command-line interface
├── tests/               # 182 tests, >91% coverage
├── notebooks/           # Jupyter notebook examples (runnable on Colab)
├── benchmarks/          # Classic dataset benchmarks
├── examples/            # Script-based end-to-end examples
└── docs/                # MkDocs documentation source

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Comparison with Alternatives

Feature	BlackBox2C	emlearn	MicroMLGen	TFLite Micro
Any sklearn model	✅	⚠️ Trees only	⚠️ Trees only	❌ TF only
Pure if-else output	✅	✅	✅	❌
C++ / Arduino / MicroPython	✅	⚠️ Partial	❌	⚠️ Partial
Feature selection built-in	✅	❌	❌	❌
Memory budget control	✅	❌	❌	⚠️
Zero runtime dependencies	✅	✅	✅	❌

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Roadmap (v0.2)

• Quine-McCluskey and BDD rule optimization
• Hardware-validated benchmarks on real MCUs
• Quantization-aware training integration

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License

MIT — see LICENSE.

Contributing

Issues and PRs welcome at github.com/AxelSkrauba/BlackBox2C.