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.

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✨ Features

🔬 Core Algorithm

• Laplacian-modified Naive Bayes with enhanced smoothing for sparse data
• Optimized for binary/boolean features using bit index representation
• Fast prediction leveraging only positive bit indices
• Robust handling of unseen features and classes

🚀 Performance & Scalability

• Memory-efficient sparse matrix support for massive feature spaces (2^32 features)
• Lossless RDKit fingerprint conversion with bit reinterpretation
• Automatic sparsity detection and optimization
• Parallel processing compatible with joblib

🔧 sklearn Integration

• Full sklearn ecosystem compatibility (pipelines, cross-validation, grid search)
• Drop-in replacement for other Naive Bayes classifiers
• Consistent API with sklearn estimators
• Custom transformers for molecular data preprocessing

🧪 Molecular Informatics

• Direct RDKit integration for SMILES conversion
• Morgan fingerprint support with configurable radius
• Chemical space analysis capabilities
• QSAR/SAR modeling optimized workflows

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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 with examples:

git clone https://github.com/rdkit/laplaciannb.git
cd laplaciannb
pip install -e ".[dev]"  # Includes development dependencies

Optional Dependencies

For molecular fingerprint functionality:

pip install rdkit  # For molecular fingerprint conversion

For full development environment:

pip install laplaciannb[dev]  # Includes testing, linting, and examples

Quick Start

🚀 Try the Interactive Example

Run the comprehensive quickstart example to see all features in action:

cd examples
python quickstart_example.py

This script demonstrates:

• RDKit molecular fingerprint conversion
• Sparse matrix handling for memory efficiency
• scikit-learn ecosystem integration
• Performance comparisons with other classifiers
• Memory efficiency demonstrations

Recommended Usage (Modern sklearn-compatible API)

For molecular data with RDKit:

from laplaciannb import LaplacianNB
from laplaciannb.fingerprint_utils import rdkit_to_csr

# Sample molecular data (SMILES strings)
smiles = [
    "CCO",                              # Ethanol
    "CC(=O)OC1=CC=CC=C1C(=O)O",        # Aspirin
    "CC(C)CC1=CC=C(C=C1)C(C)C(=O)O"    # Ibuprofen
]
y = [0, 1, 1]  # Activity labels

# Convert to sparse CSR matrix (memory efficient)
X = rdkit_to_csr(smiles, radius=2)
print(f"Matrix shape: {X.shape}")  # (3, 4294967296)
print(f"Sparsity: {1 - X.nnz / (X.shape[0] * X.shape[1]):.6f}")

# Train classifier
clf = LaplacianNB(alpha=1.0)
clf.fit(X, y)

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

For general binary/boolean data:

import numpy as np
from scipy.sparse import csr_matrix
from laplaciannb import LaplacianNB

# Create sparse binary matrix directly
row = [0, 0, 1, 1, 2, 2]
col = [1, 5, 2, 6, 1, 3]
data = [1, 1, 1, 1, 1, 1]
X = csr_matrix((data, (row, col)), shape=(3, 10), dtype=np.bool_)
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

Full Pipeline Example:

from sklearn.pipeline import Pipeline
from sklearn.model_selection import GridSearchCV, cross_val_score
from sklearn.base import BaseEstimator, TransformerMixin
from laplaciannb import LaplacianNB
from laplaciannb.fingerprint_utils import rdkit_to_csr

# Custom transformer for pipelines
class RDKitFingerprintTransformer(BaseEstimator, TransformerMixin):
    def __init__(self, radius=2):
        self.radius = radius

    def fit(self, X, y=None):
        return self

    def transform(self, X):
        return rdkit_to_csr(X, radius=self.radius)

# Create pipeline
pipeline = Pipeline([
    ('fingerprints', RDKitFingerprintTransformer(radius=2)),
    ('classifier', LaplacianNB(alpha=1.0))
])

# Grid search
param_grid = {
    'classifier__alpha': [0.1, 1.0, 10.0],
    'fingerprints__radius': [1, 2, 3]
}
grid_search = GridSearchCV(pipeline, param_grid, cv=5)
grid_search.fit(smiles_data, y)  # Use SMILES directly in pipeline

# Cross-validation
cv_scores = cross_val_score(pipeline, smiles_data, y, cv=5)
print(f"CV Accuracy: {cv_scores.mean():.3f} (+/- {cv_scores.std() * 2:.3f})")

# Direct sparse matrix usage (for pre-converted data)
X_sparse = rdkit_to_csr(smiles_data, radius=2)
clf = LaplacianNB(alpha=1.0)
scores = cross_val_score(clf, X_sparse, y, cv=5)

🔥 Key Features & Advantages

Memory Efficiency

• Sparse matrix support: Handle 2^32 feature spaces with minimal memory
• Lossless fingerprint conversion: Convert RDKit fingerprints without data loss
• Automatic sparsity detection: Works seamlessly with both sparse and dense data

# Handle massive feature spaces efficiently
X = rdkit_to_csr(smiles_list, radius=2)  # Shape: (n_samples, 4294967296)
print(f"Memory usage: {X.data.nbytes / 1024**2:.1f} MB")  # Only a few MB!

Performance

• Optimized for binary data: Fast prediction using only positive bit indices
• sklearn compatible: Drop-in replacement for other Naive Bayes classifiers
• Parallel processing: Supports joblib parallelization

Molecular Informatics

• RDKit integration: Direct conversion from molecular structures
• Flexible fingerprints: Support for Morgan, MACCS, and custom fingerprints
• Chemical space analysis: Ideal for QSAR/SAR modeling

📚 Examples & Tutorials

Interactive Examples

Explore the comprehensive examples in the /examples directory:

• quickstart_example.py: Complete demonstration with molecular data
• basic_usage_tutorial.ipynb: Step-by-step Jupyter notebook
• sklearn_integration_tutorial.ipynb: Advanced sklearn integration
• bayes_tutorial.ipynb: Deep dive into Naive Bayes concepts

Run the Quickstart

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

# Install with examples
pip install -e ".[dev]"

# Run comprehensive example
python examples/quickstart_example.py

Example Outputs

The quickstart example demonstrates:

BASIC LAPLACIANNB USAGE
Matrix shape: (10, 4294967296)
Matrix sparsity: 0.999998
Training completed in 0.002 seconds
Test Accuracy: 1.000

SPARSE MATRIX EFFICIENCY
Radius   Features     Sparsity   Train Time   Accuracy
1        4,294,967,296 0.999999   0.001       1.000
2        4,294,967,296 0.999998   0.002       1.000
3        4,294,967,296 0.999997   0.003       1.000

MEMORY EFFICIENCY
Sparse matrix memory: 0.12 MB
Dense equivalent would require 40,000+ MB!
✓ Designed specifically for extremely sparse binary features

### 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.

```python
# 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)

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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

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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}")

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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

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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

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Authors & Maintainers

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

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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

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License

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