LaplacianNB

Naive Bayes classifier for Laplacian-modified models
Efficient, scikit-learn compatible, and designed for binary/boolean data

LaplacianNB is a Python module developed at Novartis AG for a Naive Bayes classifier for Laplacian-modified models, based on the scikit-learn Naive Bayes implementation.

This classifier is ideal for binary/boolean data, using only the indices of positive bits for efficient prediction. The algorithm was first implemented in Pipeline Pilot and KNIME.

The package includes both a modern sklearn-compatible implementation (recommended) and a legacy version for backward compatibility.

Features

• Modern sklearn-compatible implementation with full ecosystem integration
• Optimized for binary/boolean data with fast prediction using indices of positive bits
• RDKit fingerprint conversion utilities for molecular data
• Support for sparse and dense data formats
• Memory-efficient sparse matrix handling
• Lightweight and easy to integrate

Installation

Stable Release

Install the latest stable release from PyPI:

pip install laplaciannb

Development Version

Get the latest features with development releases:

pip install --pre laplaciannb

From Source

For the latest development version:

git clone https://github.com/rdkit/laplaciannb.git
cd laplaciannb
pip install -e .

Quick Start

Recommended Usage (Modern sklearn-compatible API)

import numpy as np
from laplaciannb import LaplacianNB
from laplaciannb.fingerprint_utils import convert_fingerprints

# Convert fingerprint data to sklearn format
fingerprints = [
    {1, 5, 10, 15},      # Fingerprint as set of bit indices
    {2, 6, 11, 16},      # Each set represents active bits
    {1, 3, 7, 12}
]
X = convert_fingerprints(fingerprints, n_bits=20)
y = [0, 1, 0]

# Train and predict
clf = LaplacianNB(alpha=1.0)
clf.fit(X, y)
predictions = clf.predict(X)
probabilities = clf.predict_proba(X)

sklearn Ecosystem Integration

from sklearn.pipeline import Pipeline
from sklearn.model_selection import GridSearchCV, cross_val_score
from laplaciannb import LaplacianNB, FingerprintTransformer

# Create pipeline
pipeline = Pipeline([
    ('fingerprints', FingerprintTransformer(n_bits=2048)),
    ('classifier', LaplacianNB())
])

# Grid search
param_grid = {
    'classifier__alpha': [0.1, 1.0, 10.0],
    'fingerprints__output_format': ['csr', 'dense']
}
grid_search = GridSearchCV(pipeline, param_grid, cv=5)
grid_search.fit(fingerprints, y)

# Cross-validation
cv_scores = cross_val_score(pipeline, fingerprints, y, cv=5)

Legacy Usage (Deprecated)

⚠️ DEPRECATION NOTICE: The legacy API is deprecated and will be removed in a future release. Please migrate to the modern sklearn-compatible API above.

# For backward compatibility only - will show deprecation warnings
import warnings
warnings.filterwarnings("ignore", category=FutureWarning)

from laplaciannb.legacy import LaplacianNB as LegacyLaplacianNB

# Legacy format (sets of bit indices)
X_sets = np.array([{1, 5, 10}, {2, 6, 11}, {1, 3, 7}], dtype=object)
y = [0, 1, 0]

clf = LegacyLaplacianNB(alpha=1.0)
clf.fit(X_sets, y)
predictions = clf.predict(X_sets)

Migration Guide

Migrating from legacy to modern implementation is easy:

1. Update imports:

   # Before (deprecated)
   from laplaciannb.legacy import LaplacianNB
   
   # After (recommended)
   from laplaciannb import LaplacianNB
   from laplaciannb.fingerprint_utils import convert_fingerprints
   

2. Convert input data:

   # Convert fingerprint sets to sklearn format
   X = convert_fingerprints(your_fingerprint_sets, n_bits=your_size)
   

3. Same API for basic usage:

   clf = LaplacianNB(alpha=1.0)
   clf.fit(X, y)
   predictions = clf.predict(X)
   

📖 Detailed migration instructions: MIGRATION_GUIDE.md 📅 Deprecation timeline: DEPRECATION_TIMELINE.md

Basic Usage with LaplacianNB

import numpy as np
from laplaciannb import LaplacianNB

# Create sample data (sets of positive bit indices)
X = np.array([
    {1, 5, 10, 15},      # Sample 1: bits 1,5,10,15 are on
    {2, 6, 11, 16},      # Sample 2: bits 2,6,11,16 are on
    {1, 3, 7, 12},       # Sample 3: bits 1,3,7,12 are on
], dtype=object)
y = np.array([0, 1, 0])  # Class labels

# Train the classifier
clf = LaplacianNB()
clf.fit(X, y)

# Make predictions
predictions = clf.predict(X)
probabilities = clf.predict_proba(X)

RDKit Fingerprint Integration

from rdkit import Chem
from rdkit.Chem import AllChem
from laplaciannb import LaplacianNB, convert_fingerprints

# Generate molecular fingerprints
molecules = [Chem.MolFromSmiles(smi) for smi in ['CCO', 'CC', 'CCC']]
fingerprints = [AllChem.GetMorganFingerprintAsBitVect(mol, 2) for mol in molecules]

# Convert to sklearn-compatible format
X = convert_fingerprints(fingerprints, output_format='csr')
y = [0, 1, 0]

# Train classifier
clf = LaplacianNB()
clf.fit(X, y)

Advanced Fingerprint Conversion

from laplaciannb import RDKitFingerprintConverter

# Create converter with custom settings
converter = RDKitFingerprintConverter(
    n_bits=2048,
    output_format='auto',  # Automatically choose sparse/dense
    dtype=np.float32
)

# Convert fingerprints
X_dense = converter.to_dense(fingerprints)
X_sparse = converter.to_csr(fingerprints)

# Get statistics
stats = converter.get_statistics(fingerprints)
print(f"Sparsity: {stats['sparsity']:.2%}")
print(f"Average on-bits: {stats['avg_on_bits']:.1f}")

Development

Contributing

We welcome contributions! Please see our development setup:

# Clone the repository
git clone https://github.com/rdkit/laplaciannb.git
cd laplaciannb

# Install in development mode with test dependencies
pip install -e .[test]

# Install pre-commit hooks
pre-commit install

# Run tests
pytest tests/

# Run quality checks
pre-commit run --all-files

CI/CD Pipeline

• Code Quality: Ruff linting and formatting
• Testing: Multi-Python version testing with coverage
• Security: Bandit security scanning
• Auto-publishing: Development versions on merge to develop
• Dependency Management: Dependabot for automated updates

Project Structure

laplaciannb/
├── src/laplaciannb/           # Main package
│   ├── LaplacianNB_new.py     # Modern implementation
│   ├── fingerprint_utils.py   # Conversion utilities
│   └── legacy/                # Deprecated legacy API
├── tests/                     # Test suite
├── .github/                   # CI/CD workflows
└── docs/                      # Documentation

Literature

Nidhi; Glick, M.; Davies, J. W.; Jenkins, J. L. Prediction of biological targets
for compounds using multiple-category Bayesian models trained on chemogenomics
databases. J. Chem. Inf. Model. 2006, 46, 1124– 1133,
https://doi.org/10.1021/ci060003g

Lam PY, Kutchukian P, Anand R, et al. Cyp1 inhibition prevents doxorubicin-induced cardiomyopathy
in a zebrafish heart-failure model. Chem Bio Chem. 2020:cbic.201900741.
https://doi.org/10.1002/cbic.201900741

Authors & Maintainers

• Bartosz Baranowski (bartosz.baranowski@novartis.com)
• Edgar Harutyunyan (edgar.harutyunyan_ext@novartis.com)

Changelog

v0.7.0 (Latest)

• Sklearn integration handling standard sklearn input allowing for full integration with sklearn framework
• Enhanced deprecation strategy with comprehensive migration support
• Legacy input detection in new version with helpful error messages
• Dependabot configuration for automated dependency updates

v0.6.1

• Fixes for scikit-learn 1.7, rdkit 2025+ compatibility
• Move to uv build system

v0.6.0

• Move to pdm build system

v0.5.0

• Initial public release

License

This project is licensed under the BSD 3-Clause License. See the LICENSE file for details.