matilda-cli 0.1.0

MATILDA - TGD Rule Discovery Algorithm CLI

MATILDA: Tuple-Generating Dependencies Discovery

MATILDA (Mining Approximate Tuple-Generating Dependencies in Large Databases) is a research-grade command-line tool for automatically discovering Tuple-Generating Dependencies (TGDs) in relational databases with a modern, beautiful CLI interface.

✨ New: Modern CLI Interface

MATILDA now features a gorgeous, colorful interface powered by Rich:

• 🎨 Beautiful ASCII art banner with MATILDA branding
• 📊 Live progress bars with spinners and time tracking
• 📋 Elegant tables for displaying discovered rules
• 🎯 Color-coded panels for configuration and results
• ✨ Status indicators with emojis for better UX

Try it: python demo_cli.py for a quick preview!

See CLI_MODERNIZATION.md for full details.

🎓 Academic Context

This tool was developed as part of post-doctoral research in database theory and knowledge discovery. It implements novel algorithms for mining logical rules from relational data, with applications in:

• Data Quality: Detecting inconsistencies and integrity violations
• Schema Design: Understanding implicit constraints and relationships
• Knowledge Extraction: Discovering hidden patterns in enterprise databases
• Database Reverse Engineering: Reconstructing business logic from legacy systems

✨ Key Features

• 🔍 Automated TGD Discovery: Extracts logical rules without manual specification
• 📊 Confidence Metrics: Ranks discovered rules by support and accuracy
• 🗄️ Multi-Database Support: Works with SQLite, MySQL, PostgreSQL via SQLAlchemy
• ⚡ Efficient Pruning: Uses constraint graphs and heuristics to scale to large schemas
• 📈 MLflow Integration: Optional experiment tracking for research workflows
• 🛡️ Resource Management: Built-in memory limits and timeout controls
• 📝 Comprehensive Reporting: Generates JSON and Markdown outputs

📦 Installation

Quick Install

pip install -e .

From Source

git clone https://github.com/Fran-cois/MATILDA.git
cd matilda_cli
pip install -e .

With MLflow Support

pip install -e ".[mlflow]"

Development Installation

pip install -e ".[dev]"

🎯 Quick Start

Demo Mode (Quickest Way to Try MATILDA)

# Run with pre-configured university demo database
matilda --demo imperfect_database

This will:

• ✅ Auto-create a demo SQLite database with 50 students, 81 enrollments
• 🔍 Run TGD discovery on realistic data with built-in violations
• 📊 Generate reports in results/

See DEMO_GUIDE.md for details.

Basic Usage

# Run with default config.yaml
matilda

# Run with specific config
matilda --config /path/to/config.yaml

Configuration

Create a config.yaml file:

monitor:
  memory_threshold: 16106127360  # 15GB
  timeout: 3600  # 1 hour

database:
  path: "data/db/"
  name: "database.db"

logging:
  log_dir: "logs"

results:
  output_dir: "results"

mlflow:
  use: false

📖 Usage Examples

Quick Demo with Fake Database

Try MATILDA instantly with our pre-built university database:

# Generate the demo database
python scripts/generate_fake_university_db.py

# Run the beautiful CLI demo
python ai_doc/demo_cli.py

# Inspect the database
python scripts/inspect_university_db.py

# Run MATILDA on the demo database
python -m matilda_cli --database data/university.db

The fake database contains:

• 5 departments (CS, Math, Physics, Business, Engineering)
• 10 professors
• 15 students
• 13 courses
• 42 enrollments + teaching assignments + advisor relationships
• 113 total tuples across 7 tables

See data/README.md for more details.

Example 1: Basic Discovery

# Run with default configuration
matilda --config config.yaml

Sample Output:

INFO - Discovered 47 rules
INFO - Top rule: student(X, Y) ∧ enrollment(Y, Z) → course(Z, W) [support: 156, confidence: 0.94]

Example 2: Custom Parameters

# config.yaml
monitor:
  memory_threshold: 16106127360  # 15GB
  timeout: 3600  # 1 hour

database:
  path: "data/databases/"
  name: "university.db"

algorithm:
  nb_occurrence: 5      # Minimum support
  max_table: 4          # Max tables per rule
  max_vars: 8           # Max variables per rule

logging:
  log_dir: "logs"

results:
  output_dir: "results"

Example 3: With MLflow Experiment Tracking

# Install with MLflow support
pip install -e ".[mlflow]"

# Configure MLflow in config.yaml
cat > config.yaml << EOF
mlflow:
  use: true
  tracking_uri: "http://localhost:5000"
  experiment_name: "MATILDA_University_DB"
  
database:
  path: "data/"
  name: "university.db"
EOF

# Start MLflow server (separate terminal)
mlflow server --host 127.0.0.1 --port 5000

# Run MATILDA with tracking
matilda --config config.yaml

Example 4: Programmatic Usage

from pathlib import Path
from matilda_cli.database.alchemy_utility import AlchemyUtility
from matilda_cli.algorithms.matilda import MATILDA

# Connect to database
db_path = Path("data/university.db")
db = AlchemyUtility(db_path)

# Configure algorithm
settings = {
    "nb_occurrence": 3,
    "max_table": 3,
    "max_vars": 6
}

# Discover rules
matilda = MATILDA(db, settings)
for rule in matilda.discover_rules():
    print(f"{rule} [support: {rule.support}, confidence: {rule.confidence:.2f}]")

# Cleanup
db.close()

🔧 Configuration Options

Complete Configuration Reference

# Monitor Settings
monitor:
  memory_threshold: 16106127360  # Maximum memory usage (bytes, default: 15GB)
  timeout: 3600                   # Maximum execution time (seconds, default: 1h)

# Database Settings  
database:
  path: "data/databases/"         # Directory containing database files
  name: "database.db"             # Database filename

# Algorithm Settings
algorithm:
  nb_occurrence: 3                # Minimum rule support (default: 3)
  max_table: 3                    # Maximum tables per rule (default: 3)
  max_vars: 6                     # Maximum variables per rule (default: 6)

# Logging Settings
logging:
  log_dir: "logs"                 # Directory for log files
  level: "INFO"                   # Log level: DEBUG, INFO, WARNING, ERROR

# Results Settings
results:
  output_dir: "results"           # Directory for output files
  
# MLflow Settings (optional)
mlflow:
  use: false                      # Enable/disable MLflow tracking
  tracking_uri: "http://localhost:5000"  # MLflow server URL
  experiment_name: "MATILDA Discovery"   # Experiment name

Algorithm Parameters Tuning Guide

Parameter	Effect	Recommendation
nb_occurrence	Higher = more general rules, fewer results	Start with 3-5 for exploration
max_table	Higher = more complex rules, slower execution	3 for most cases, 4-5 for complex schemas
max_vars	Higher = more expressive rules, larger search space	6 is balanced, increase to 8-10 for research

📊 Output Format

MATILDA generates three types of output:

1. JSON Rules File

{
  "rules": [
    {
      "body": ["student(X, Y)", "enrollment(Y, Z)"],
      "head": ["course(Z, W)"],
      "support": 156,
      "confidence": 0.94,
      "accuracy": 0.94,
      "tgd_string": "student(X, Y) ∧ enrollment(Y, Z) → course(Z, W)"
    }
  ],
  "metadata": {
    "database": "university.db",
    "total_rules": 47,
    "execution_time": "12.3s"
  }
}

2. Markdown Report

Auto-generated summary with:

• Execution statistics
• Top 5 rules by confidence
• Database schema overview
• Timestamps and configuration

3. Detailed Logs

2025-11-18 10:15:23 - INFO - Starting MATILDA discovery
2025-11-18 10:15:24 - INFO - Loaded database: university.db (3 tables, 12 attributes)
2025-11-18 10:15:25 - INFO - Generated 45 candidate rules
2025-11-18 10:15:26 - INFO - Pruned 12 rules (support threshold)
2025-11-18 10:15:27 - INFO - Discovered 47 valid TGDs

🛠️ Development

Project Architecture

matilda_cli/
├── matilda_cli/              # Main package
│   ├── __init__.py
│   ├── __main__.py           # CLI entry point
│   ├── algorithms/           # MATILDA algorithm implementation
│   │   ├── matilda.py        # Main algorithm class
│   │   └── MATILDA/          # Core discovery logic
│   │       ├── tgd_discovery.py       # TGD mining algorithms
│   │       ├── constraint_graph.py    # Graph-based representation
│   │       └── candidate_rule_chains.py  # Rule generation
│   ├── database/             # Database utilities
│   │   ├── alchemy_utility.py         # SQLAlchemy interface
│   │   ├── query_utility.py           # Query optimization
│   │   └── data_exporter.py           # Export utilities
│   └── utils/                # Shared utilities
│       ├── config_loader.py           # YAML configuration
│       ├── rules.py                   # Rule data structures
│       └── monitor.py                 # Resource monitoring
├── tests/                    # Comprehensive test suite
│   ├── algorithms/           # Algorithm tests (61 tests passing)
│   ├── database/             # Database utilities tests
│   ├── fixtures/             # Test databases and fixtures
│   └── benchmarks/           # Performance benchmarks
├── scripts/                  # Development utilities
│   ├── check_tests.py        # Test integrity verification
│   ├── run_benchmarks.py     # Performance benchmarking
│   └── generate_test_report.py  # Test coverage reports
├── docs/                     # Documentation (auto-generated)
├── setup.py                  # Package configuration
├── pyproject.toml            # Modern Python packaging
└── pytest.ini                # Test configuration

Running Tests

# Run all tests
pytest tests/

# Run with coverage
pytest --cov=matilda_cli tests/

# Run specific test categories
pytest tests/algorithms/         # Algorithm tests
pytest tests/database/           # Database tests
pytest -m benchmark tests/       # Performance benchmarks only

# Run tests with detailed output
pytest -v --tb=short tests/

Test Suite Status

• Total Tests: 61 ✅
• Coverage: 45%
• Test Categories:
  • Unit Tests: 35 tests
  • Integration Tests: 20 tests
  • Benchmarks: 6 tests
• Databases: 3 test schemas (university, employee, retail)

Code Quality Tools

# Type checking
mypy matilda_cli/

# Code formatting
black matilda_cli/ tests/

# Linting
ruff check matilda_cli/

# Run all checks
./scripts/check_tests.py

📋 Requirements

• Python: 3.10 or higher (required for type hints and modern language features)
• Core Dependencies:
  • SQLAlchemy >= 2.0
  • PyYAML >= 6.0
  • psutil >= 5.9
  • tqdm >= 4.66
  • colorama >= 0.4.6
• Optional:
  • mlflow >= 2.0 (for experiment tracking)
  • pytest >= 7.0 (for development)

📚 Documentation

• Quick Start Guide - Get started in 5 minutes
• Testing Guide - Comprehensive testing documentation
• Test Status - Current test suite status
• API Documentation - Full API reference (auto-generated)

🤝 Contributing

Contributions are welcome! This is an academic research project, so please:

1. Fork the repository
2. Create a feature branch (git checkout -b feature/amazing-feature)
3. Add tests for new functionality
4. Ensure all tests pass (pytest tests/)
5. Document your changes
6. Commit with clear messages (git commit -m 'Add amazing feature')
7. Push to your branch (git push origin feature/amazing-feature)
8. Open a Pull Request

Development Setup

# Clone repository
git clone https://github.com/Fran-cois/MATILDA.git
cd matilda_cli

# Create virtual environment
python -m venv .venv
source .venv/bin/activate  # On Windows: .venv\Scripts\activate

# Install in development mode with all extras
pip install -e ".[dev,mlflow]"

# Verify installation
matilda --help
pytest tests/

Related Datasets

Test databases are based on schemas from:

• Zenodo Repository: DOI 10.5281/zenodo.17644035

🙏 Acknowledgments

• Database Theory Community: For foundational work on TGDs and dependencies
• SQLAlchemy Project: For the excellent database abstraction layer
• Open Source Contributors: For tools and libraries that made this possible
• Academic Reviewers: For feedback on algorithm design and implementation

🗺️ Roadmap

Current Version (0.1.0)

• ✅ Core TGD discovery algorithm
• ✅ SQLite, MySQL, PostgreSQL support
• ✅ MLflow integration
• ✅ Comprehensive test suite

Planned Features (0.2.0)

• 🔄 Parallel rule discovery
• 🔄 Interactive rule refinement
• 🔄 Web-based visualization
• 🔄 Incremental discovery for large databases

Future Research Directions

• 🔮 Probabilistic TGDs
• 🔮 Temporal dependency discovery
• 🔮 Multi-database federation
• 🔮 Machine learning-guided pruning

🏆 Academic Impact

This tool is designed to support:

• PhD Students: As a baseline for dependency discovery research
• Database Researchers: For experimental evaluation and comparison
• Industry Practitioners: For data quality assessment and schema understanding
• Educators: As a teaching tool for database theory concepts