Adaptive Syntax Filter

A Python research package implementing an adaptive Kalman–EM algorithm for learning time-varying syntax rules in behavioral sequences, with applications to canary song analysis and real-time behavioral monitoring.

Research Group: Neural Syntax Lab at the Weizmann Institute of Science

🎯 Research Overview

Canary songs exhibit rich, history-dependent phrase sequencing whose syntax rules drift on timescales from a few songs to several weeks. This package presents an adaptive Kalman–EM algorithm that learns:

• Block-diagonal state-transition matrix F and control vector u
• Process variance Sigma and initial logit vector x0
• Time-varying logit vectors that govern soft-max transition probabilities between song phrases

By unifying Bayes' rule, the Chapman–Kolmogorov equation, and a soft-max observation model, the filter–smoother achieves sub-song latency and detects syntax changes across multiple timescales—an essential step toward real-time behavioral monitoring in songbirds.

Core Research Question: Can a state-space filtering approach accurately characterize canary-syntax dynamics across timescales from minutes to weeks?

🚀 Features

• Adaptive Kalman Filtering: Real-time parameter estimation with block-diagonal dynamics
• Higher-Order Markov Models: Support for 1st through 5th order Markov chains
• Comprehensive Testing: Full test suite with 300+ tests
• Research Pipeline: Complete experimental framework with visualization
• Jupyter Integration: Notebooks for experimentation and analysis
• Modular Architecture: Clean separation of core algorithms, data processing, and visualization

📦 Installation

Prerequisites

• Python 3.11 or higher
• pip package manager

Install from Source

# Clone the repository
git clone https://github.com/Nadav-Elami/adaptive-syntax-filter.git
cd adaptive-syntax-filter

# Install in development mode
pip install -e .

# Install development dependencies (optional)
pip install -e ".[dev]"

Quick Verification

# Run the test suite
pytest

# Run a quick demo
python cli.py run --config configs/minimal.yml

🎮 Quick Start

Basic Usage

import numpy as np
from adaptive_syntax_filter.core import EMAlgorithm, StateSpaceManager
from adaptive_syntax_filter.data import SequenceGenerator

# Create a simple first-order Markov model
alphabet = ['<', 'a', 'b', '>']
state_manager = StateSpaceManager(alphabet_size=len(alphabet), markov_order=1)

# Generate synthetic data
generator = SequenceGenerator(alphabet, order=1)
sequences = generator.generate_sequences(n_sequences=10, max_length=20)

# Initialize and run EM algorithm
em = EMAlgorithm(state_manager, max_iterations=10)
results = em.fit(sequences)

print(f"Converged: {results.final_params}")

Command Line Interface

# Run with default configuration
python cli.py run --config configs/minimal.yml

# Run with custom parameters
python cli.py run --config configs/higher_order.yml

# Export results
python export_cli.py --experiment_id my_experiment

📁 Project Structure

adaptive-syntax-filter/
├── src/adaptive_syntax_filter/     # Core package
│   ├── core/                       # EM algorithm, Kalman filter
│   ├── data/                       # Data generation, constraints
│   ├── viz/                        # Visualization tools
│   └── config/                     # Configuration management
├── tests/                          # Comprehensive test suite
├── configs/                        # Experiment configurations
├── notebooks/                      # Jupyter notebooks
├── documentation/                  # Complete documentation
├── results/                        # Experiment results
└── cli.py                         # Command line interface

🔬 Research Components

Core Algorithms

• EM Algorithm: Expectation-maximization for parameter estimation
• Kalman Filter: Forward filtering and RTS smoothing
• State Space Management: Higher-order Markov model support
• Observation Model: Soft-max probability computation

Data Processing

• Sequence Generation: Synthetic birdsong data generation
• Constraint System: Syntax rule enforcement
• Temporal Evolution: Time-varying parameter models
• Dataset Builder: Research dataset creation

Visualization

• Logit Evolution: Parameter trajectory visualization
• Probability Evolution: Transition probability analysis
• Performance Assessment: Algorithm evaluation tools
• Publication Figures: Publication-ready plots

📊 Configuration

The package uses YAML configuration files for experiments:

experiment_id: my_experiment

data:
  alphabet: ['<', 'a', 'b', 'c', '>']
  order: 2
  n_sequences: 100
  max_length: 25
  evolution_type: linear

em:
  max_iterations: 20
  tolerance: 1e-4
  regularization_lambda: 0.001
  damping_factor: 0.1
  adaptive_damping: true

🧪 Testing

# Run all tests
pytest

# Run with coverage
pytest --cov=src/adaptive_syntax_filter

# Run specific test categories
pytest tests/test_core/
pytest tests/test_data/
pytest tests/test_viz/

📚 Documentation

• Algorithm Documentation: Mathematical foundations and implementation details
• Research Guide: How to conduct experiments
• Development Guide: Contributing guidelines

🎯 Research Applications

Birdsong Analysis

• Real-time syntax rule detection
• Multi-timescale evolution tracking
• Behavioral monitoring systems

General Sequence Analysis

• Time-varying Markov models
• Adaptive filtering applications
• Behavioral pattern recognition

🤝 Contributing

We welcome contributions! Please see our Development Guide for details on:

• Code style and standards
• Testing requirements
• Documentation guidelines
• Research contribution process

📄 License

This project is licensed under the MIT License - see the LICENSE file for details.

🙏 Acknowledgments

• Neural Syntax Lab at the Weizmann Institute of Science
• Research collaborators and advisors
• Open source community

📞 Contact

• Maintainer: Nadav Elami
• GitHub: Nadav-Elami
• Repository: https://github.com/Nadav-Elami/adaptive-syntax-filter
• Issues: https://github.com/Nadav-Elami/adaptive-syntax-filter/issues
• Research Group: Neural Syntax Lab

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Note: This is a research package. For production use, additional validation and testing may be required.