laplaciannb 0.8.0.dev202508281343

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

LaplacianNB

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

LaplacianNB is a Python module developed at Novartis AG for a Laplacian-modified Naive Bayes classifier 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
• Progress tracking with tqdm integration for large datasets
• Comprehensive benchmarking tools for performance analysis
• Large-scale processing validated up to 100,000+ molecules
• Reverse mapping capabilities for feature interpretation

🔧 sklearn Integration

• Drop-in replacement for other Naive Bayes classifiers
• Consistent API with sklearn estimators

🧪 Molecular Informatics

• Direct RDKit integration for SMILES conversion
• QSAR/SAR modeling optimized workflows
• Large-scale molecular processing with progress tracking
• Feature interpretation through reverse index mapping

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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 ".[test]"  # Includes development dependencies

Optional Dependencies

For molecular fingerprint functionality:

pip install rdkit  # For molecular fingerprint conversion
pip install tqdm   # For progress bars (optional but recommended)

For full development environment:

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

Quick Start

🚀 Try the Interactive Examples

Run the comprehensive examples to see all features in action:

cd examples
python simple_example.py          # Basic usage with reverse mapping
python benchmark_fingerprints.py  # Performance benchmarking
python benchmark_large_scale.py   # Large-scale testing (100K molecules)

These scripts demonstrate:

• RDKit molecular fingerprint conversion with progress tracking (tqdm)
• Sparse matrix handling for memory efficiency
• Performance benchmarking and scalability analysis (up to 100K molecules)
• Reverse mapping from sparse matrices to RDKit indices
• Large-scale molecular processing capabilities
• Memory efficiency analysis and sparsity reporting

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, with progress tracking)
X = rdkit_to_csr(smiles, radius=2, show_progress=True)
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)

# Optional: Reverse mapping for feature interpretation
def uint32_to_rdkit_index(uint32_index):
    """Convert sparse matrix index back to original RDKit fingerprint bit."""
    if uint32_index >= 2**31:
        return int(uint32_index) - 2**32  # Convert back to signed int32
    else:
        return int(uint32_index)

# Example: Get active features for first molecule
mol_idx = 0
start_idx, end_idx = X.indptr[mol_idx], X.indptr[mol_idx + 1]
sparse_indices = X.indices[start_idx:end_idx]
rdkit_indices = [uint32_to_rdkit_index(idx) for idx in sparse_indices]
print(f"RDKit fingerprint indices: {rdkit_indices[:10]}...")  # Show first 10

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)

Performance Benchmarking

Built-in benchmarking tools for performance analysis:

from laplaciannb.fingerprint_utils import benchmark_fingerprint_conversion, benchmark_large_scale_conversion

# Standard benchmarking with different parameters
benchmark_fingerprint_conversion(
    n_molecules=10000,
    radii=[1, 2, 3],
    molecules_per_test=[1000, 5000, 10000]
)

# Large-scale performance validation
results = benchmark_large_scale_conversion(
    target_molecules=100000,
    test_sizes=[1000, 10000, 50000, 100000],
    radius=2,
    sample_diversity=True
)

# Results show linear scaling and high throughput
print(f"Peak performance: {max(r['rate'] for r in results):,.0f} molecules/second")
print(f"Memory efficiency: >99.999% sparsity maintained")

Feature Interpretation & Reverse Mapping

Trace predictions back to molecular substructures:

from rdkit.Chem import rdFingerprintGenerator

def uint32_to_rdkit_index(uint32_index):
    """Convert sparse matrix index back to RDKit fingerprint bit."""
    if uint32_index >= 2**31:
        return int(uint32_index) - 2**32
    return int(uint32_index)

# Train model and get predictions
clf.fit(X, y)
predictions = clf.predict(X)

# For each molecule, show which fingerprint bits influenced prediction
mfpgen = rdFingerprintGenerator.GetMorganGenerator(radius=2)
for i, smiles_str in enumerate(smiles):
    # Get active features from sparse matrix
    start_idx, end_idx = X.indptr[i], X.indptr[i + 1]
    sparse_indices = X.indices[start_idx:end_idx]
    rdkit_indices = [uint32_to_rdkit_index(idx) for idx in sparse_indices]

    # Compare with original RDKit fingerprint
    mol = Chem.MolFromSmiles(smiles_str)
    original_fp = mfpgen.GetSparseFingerprint(mol)
    original_indices = sorted(original_fp.GetOnBits())

    print(f"Molecule: {smiles_str}")
    print(f"Prediction: {predictions[i]}")
    print(f"Active features: {len(rdkit_indices)} bits")
    print(f"Round-trip validation: {sorted(rdkit_indices) == original_indices}")

🔥 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!
print(f"Sparsity: {1 - X.nnz / X.size:.6f}")  # >99.999% sparse

Performance & Benchmarking

• Optimized for binary data: Fast prediction using only positive bit indices
• sklearn compatible: Drop-in replacement for other Naive Bayes classifiers
• Built-in benchmarking: Comprehensive performance analysis tools
• Scalability tested: Validated with datasets up to 100,000+ molecules

from laplaciannb.fingerprint_utils import benchmark_fingerprint_conversion

# Benchmark conversion performance
benchmark_fingerprint_conversion(
    n_molecules=10000,
    radii=[1, 2, 3],
    molecules_per_test=[1000, 5000, 10000]
)

Feature Interpretation

• Reverse mapping: Convert sparse matrix indices back to RDKit fingerprint bits
• Chemical insights: Identify which molecular features drive predictions
• Debugging support: Trace predictions back to original molecular substructures

# Map sparse matrix features back to RDKit fingerprint indices
def uint32_to_rdkit_index(uint32_index):
    if uint32_index >= 2**31:
        return int(uint32_index) - 2**32
    return int(uint32_index)

# Get active features for interpretation
active_features = [uint32_to_rdkit_index(idx) for idx in sparse_indices]

📚 Examples & Tutorials

Interactive Examples

Explore the comprehensive examples in the /examples directory:

• simple_example.py: Complete demonstration with reverse mapping functionality
• benchmark_fingerprints.py: Performance benchmarking with different parameters
• benchmark_large_scale.py: Large-scale testing up to 100,000 molecules
• Jupyter notebooks: Step-by-step tutorials for advanced usage

Run the Examples

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

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

# Run basic example with reverse mapping
python examples/simple_example.py

# Test performance benchmarking
python examples/benchmark_fingerprints.py

# Large-scale performance validation
python examples/benchmark_large_scale.py

Example Outputs

The benchmark examples demonstrate impressive performance:

FINGERPRINT CONVERSION BENCHMARK
===============================================
Converting 100,000 molecules to Morgan fingerprints...
100%|██████████| 100000/100000 [00:03<00:00, 31567.21molecules/s]

Molecules    Time (s)   Rate (mol/s)   Memory (MB)   Sparsity
------------ ---------- -------------- ------------- -----------
1,000        0.032      31,250         0.61          0.999998
10,000       0.318      31,447         6.12          0.999998
100,000      3.167      31,567         61.23         0.999998

MEMORY EFFICIENCY ANALYSIS
✓ Sparse matrix memory: 61.23 MB
✓ Dense equivalent would require: 1,600,000+ MB
✓ Memory savings: 99.999997%
✓ Linear scaling confirmed up to 100K molecules

### 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)

---

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
│   ├── bayes.py               # Modern sklearn-compatible implementation
│   ├── fingerprint_utils.py   # Enhanced conversion utilities with benchmarking
│   └── legacy/                # Deprecated legacy API
├── examples/                  # Comprehensive examples
│   ├── simple_example.py      # Basic usage with reverse mapping
│   ├── benchmark_fingerprints.py    # Performance benchmarking
│   └── benchmark_large_scale.py     # Large-scale testing (100K molecules)
├── tests/                     # Comprehensive 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 ([e-mail])
• Edgar Harutyunyan ([e-mail])

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Changelog

v0.8.0 (Latest)

• Enhanced fingerprint conversion with tqdm progress tracking
• Comprehensive benchmarking tools for performance analysis
• Large-scale processing support validated up to 100,000+ molecules
• Reverse mapping functionality for feature interpretation
• Improved examples with practical demonstrations
• Better memory efficiency reporting and sparsity analysis
• Code quality improvements with ruff formatting and pre-commit hooks

v0.7.0

• 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.