| Name | meta-optimizer-mdt-test JSON |
| Version |
0.1.1
JSON |
| download |
| home_page | https://github.com/example/meta_optimizer |
| Summary | Meta-Optimizer Framework for optimization, meta-learning, explainability, and drift detection |
| upload_time | 2025-03-05 05:13:22 |
| maintainer | None |
| docs_url | None |
| author | MDT Team |
| requires_python | >=3.8 |
| license | MIT License
Copyright (c) 2025 Blair Dupre
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.
|
| keywords |
optimization
meta-learning
explainability
drift-detection
hyperparameter-tuning
|
| VCS |
 |
| bugtrack_url |
|
| requirements |
No requirements were recorded.
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# Adaptive Optimization Framework with Meta-Learning and Drift Detection
A comprehensive framework for optimization, meta-learning, drift detection, and model explainability designed for solving complex optimization problems and adapting to changing environments.
[](./LICENSE)
## Overview
This framework provides a complete suite of tools for optimization problems with a special focus on adaptivity, explainability, and robustness. The system leverages meta-learning techniques to select the most appropriate optimization algorithm based on problem characteristics and historical performance, while also detecting and adapting to concept drift in the underlying optimization landscape.
## Key Features
### Optimization Components
- **Multiple Optimization Algorithms**: Includes implementations of Differential Evolution, Evolution Strategy, Ant Colony Optimization, and Grey Wolf Optimization
- **Meta-Optimizer**: Automatically selects the best optimizer for a given problem using machine learning techniques
- **Parameter Adaptation**: Algorithms adapt their parameters during optimization to improve performance
- **Robust Error Handling**: Comprehensive validation and error handling to gracefully manage edge cases
### Explainability and Analysis
- **Optimizer Explainability**: Visualizes and explains optimizer behavior, decision processes, and performance
- **Model Explainability**: Supports SHAP, LIME, and Feature Importance for model interpretability
- **Visualization Tools**: Comprehensive visualization suite for analyzing optimization results
- **Performance Analysis**: Tools for benchmarking and comparing optimizers
### Drift Detection and Adaptation
- **Concept Drift Detection**: Monitors and detects changes in the optimization landscape
- **Adaptation Strategies**: Automatically adapts to changing conditions
- **Drift Visualization**: Tools for visualizing drift patterns and adaptations
### Framework Infrastructure
- **Modular Design**: Components can be used independently or together
- **Extensible Architecture**: Easy to add new optimizers, explainers, or drift detectors
- **Comprehensive CLI**: Command-line interface for all framework features
- **Robust Testing**: Extensive test suite to ensure reliability
## Installation
### Prerequisites
- Python 3.8+
- pip package manager
### Setup
1. Clone the repository:
```bash
git clone https://github.com/yourusername/adaptive-optimization-framework.git
cd adaptive-optimization-framework
```
2. Create and activate a virtual environment:
```bash
python -m venv venv
source venv/bin/activate # On Windows: venv\Scripts\activate
```
3. Install dependencies:
```bash
pip install -r requirements.txt
```
## Quick Start
### Basic Optimization
```bash
python main.py --optimize --summary
```
### Meta-Learning
```bash
python main.py --meta --method bayesian --summary
```
### Drift Detection
```bash
python main.py --drift --drift-window 50 --summary
```
### Explainability
```bash
python main.py --explain --explainer shap --explain-plots --summary
```
### Optimizer Explainability
```bash
python main.py --explain-optimizer --optimizer-type differential_evolution --summary
```
## Command-Line Interface
The framework provides a comprehensive command-line interface. For a complete list of options, see:
```bash
python main.py --help
```
For detailed documentation on all command-line options, see [Command-Line Interface Documentation](./docs/command_line_interface.md).
## Project Structure
```
├── benchmarking/ # Tools for benchmarking optimizers
├── command_test_results/ # Test results for CLI commands
├── docs/ # Detailed documentation
├── drift_detection/ # Drift detection algorithms
├── evaluation/ # Framework evaluation tools
├── examples/ # Example usage scripts
├── explainability/ # Explainability tools
├── meta/ # Meta-learning components
├── models/ # ML model implementations
├── optimizers/ # Optimization algorithms
├── tests/ # Test suite
├── utils/ # Utility functions
├── visualization/ # Visualization components
├── main.py # Main entry point
└── requirements.txt # Dependencies
```
## Documentation
Comprehensive documentation is available in the `docs` directory:
- [Framework Architecture](./docs/framework_architecture.md) - Overview of system architecture
- [Component Integration](./docs/component_integration.md) - How components work together
- [Command-Line Interface](./docs/command_line_interface.md) - Command-line options
- [Explainability Guide](./docs/explainability_guide.md) - Guide to explainability features
- [Model Explainability](./docs/model_explainability.md) - Model explanation features
- [Optimizer Explainability](./docs/optimizer_explainability.md) - Optimizer explanation features
- [Testing Guide](./docs/testing_guide.md) - Guide to testing the framework
- [Examples](./docs/examples.md) - Example usage scenarios
## Advanced Usage
For more advanced usage examples, please refer to the [Examples](./docs/examples.md) documentation or check the `examples/` directory.
### Meta-Learning with Drift Detection
```python
from meta.meta_learner import MetaLearner
from drift_detection.drift_detector import DriftDetector
# Initialize components
meta_learner = MetaLearner(method='bayesian')
drift_detector = DriftDetector(window_size=50)
# Integrate components
meta_learner.add_drift_detector(drift_detector)
# Run optimization with adaptation
results = meta_learner.optimize(objective_function, max_evaluations=1000)
```
### Optimizer Explainability
```python
from optimizers.optimizer_factory import OptimizerFactory
from explainability.optimizer_explainer import OptimizerExplainer
# Create optimizer
factory = OptimizerFactory()
optimizer = factory.create_optimizer('differential_evolution')
# Run optimization
optimizer.run(objective_function)
# Create explainer
explainer = OptimizerExplainer(optimizer)
# Generate explanations
explanation = explainer.explain()
explainer.plot('convergence')
explainer.plot('parameter_adaptation')
```
## Contributing
Contributions are welcome! Please feel free to submit a Pull Request.
1. Fork the repository
2. Create your feature branch (`git checkout -b feature/amazing-feature`)
3. Commit your changes (`git commit -m 'Add some amazing feature'`)
4. Push to the branch (`git push origin feature/amazing-feature`)
5. Open a Pull Request
## License
This project is licensed under the MIT License - see the [LICENSE](./LICENSE) file for details.
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"description": "# Adaptive Optimization Framework with Meta-Learning and Drift Detection\n\nA comprehensive framework for optimization, meta-learning, drift detection, and model explainability designed for solving complex optimization problems and adapting to changing environments.\n\n[](./LICENSE)\n\n## Overview\n\nThis framework provides a complete suite of tools for optimization problems with a special focus on adaptivity, explainability, and robustness. The system leverages meta-learning techniques to select the most appropriate optimization algorithm based on problem characteristics and historical performance, while also detecting and adapting to concept drift in the underlying optimization landscape.\n\n## Key Features\n\n### Optimization Components\n\n- **Multiple Optimization Algorithms**: Includes implementations of Differential Evolution, Evolution Strategy, Ant Colony Optimization, and Grey Wolf Optimization\n- **Meta-Optimizer**: Automatically selects the best optimizer for a given problem using machine learning techniques\n- **Parameter Adaptation**: Algorithms adapt their parameters during optimization to improve performance\n- **Robust Error Handling**: Comprehensive validation and error handling to gracefully manage edge cases\n\n### Explainability and Analysis\n\n- **Optimizer Explainability**: Visualizes and explains optimizer behavior, decision processes, and performance\n- **Model Explainability**: Supports SHAP, LIME, and Feature Importance for model interpretability\n- **Visualization Tools**: Comprehensive visualization suite for analyzing optimization results\n- **Performance Analysis**: Tools for benchmarking and comparing optimizers\n\n### Drift Detection and Adaptation\n\n- **Concept Drift Detection**: Monitors and detects changes in the optimization landscape\n- **Adaptation Strategies**: Automatically adapts to changing conditions\n- **Drift Visualization**: Tools for visualizing drift patterns and adaptations\n\n### Framework Infrastructure\n\n- **Modular Design**: Components can be used independently or together\n- **Extensible Architecture**: Easy to add new optimizers, explainers, or drift detectors\n- **Comprehensive CLI**: Command-line interface for all framework features\n- **Robust Testing**: Extensive test suite to ensure reliability\n\n## Installation\n\n### Prerequisites\n\n- Python 3.8+\n- pip package manager\n\n### Setup\n\n1. Clone the repository:\n ```bash\n git clone https://github.com/yourusername/adaptive-optimization-framework.git\n cd adaptive-optimization-framework\n ```\n\n2. Create and activate a virtual environment:\n ```bash\n python -m venv venv\n source venv/bin/activate # On Windows: venv\\Scripts\\activate\n ```\n\n3. Install dependencies:\n ```bash\n pip install -r requirements.txt\n ```\n\n## Quick Start\n\n### Basic Optimization\n\n```bash\npython main.py --optimize --summary\n```\n\n### Meta-Learning\n\n```bash\npython main.py --meta --method bayesian --summary\n```\n\n### Drift Detection\n\n```bash\npython main.py --drift --drift-window 50 --summary\n```\n\n### Explainability\n\n```bash\npython main.py --explain --explainer shap --explain-plots --summary\n```\n\n### Optimizer Explainability\n\n```bash\npython main.py --explain-optimizer --optimizer-type differential_evolution --summary\n```\n\n## Command-Line Interface\n\nThe framework provides a comprehensive command-line interface. For a complete list of options, see:\n\n```bash\npython main.py --help\n```\n\nFor detailed documentation on all command-line options, see [Command-Line Interface Documentation](./docs/command_line_interface.md).\n\n## Project Structure\n\n```\n\u251c\u2500\u2500 benchmarking/ # Tools for benchmarking optimizers\n\u251c\u2500\u2500 command_test_results/ # Test results for CLI commands\n\u251c\u2500\u2500 docs/ # Detailed documentation\n\u251c\u2500\u2500 drift_detection/ # Drift detection algorithms\n\u251c\u2500\u2500 evaluation/ # Framework evaluation tools\n\u251c\u2500\u2500 examples/ # Example usage scripts\n\u251c\u2500\u2500 explainability/ # Explainability tools\n\u251c\u2500\u2500 meta/ # Meta-learning components\n\u251c\u2500\u2500 models/ # ML model implementations\n\u251c\u2500\u2500 optimizers/ # Optimization algorithms\n\u251c\u2500\u2500 tests/ # Test suite\n\u251c\u2500\u2500 utils/ # Utility functions\n\u251c\u2500\u2500 visualization/ # Visualization components\n\u251c\u2500\u2500 main.py # Main entry point\n\u2514\u2500\u2500 requirements.txt # Dependencies\n```\n\n## Documentation\n\nComprehensive documentation is available in the `docs` directory:\n\n- [Framework Architecture](./docs/framework_architecture.md) - Overview of system architecture\n- [Component Integration](./docs/component_integration.md) - How components work together\n- [Command-Line Interface](./docs/command_line_interface.md) - Command-line options\n- [Explainability Guide](./docs/explainability_guide.md) - Guide to explainability features\n- [Model Explainability](./docs/model_explainability.md) - Model explanation features\n- [Optimizer Explainability](./docs/optimizer_explainability.md) - Optimizer explanation features\n- [Testing Guide](./docs/testing_guide.md) - Guide to testing the framework\n- [Examples](./docs/examples.md) - Example usage scenarios\n\n## Advanced Usage\n\nFor more advanced usage examples, please refer to the [Examples](./docs/examples.md) documentation or check the `examples/` directory.\n\n### Meta-Learning with Drift Detection\n\n```python\nfrom meta.meta_learner import MetaLearner\nfrom drift_detection.drift_detector import DriftDetector\n\n# Initialize components\nmeta_learner = MetaLearner(method='bayesian')\ndrift_detector = DriftDetector(window_size=50)\n\n# Integrate components\nmeta_learner.add_drift_detector(drift_detector)\n\n# Run optimization with adaptation\nresults = meta_learner.optimize(objective_function, max_evaluations=1000)\n```\n\n### Optimizer Explainability\n\n```python\nfrom optimizers.optimizer_factory import OptimizerFactory\nfrom explainability.optimizer_explainer import OptimizerExplainer\n\n# Create optimizer\nfactory = OptimizerFactory()\noptimizer = factory.create_optimizer('differential_evolution')\n\n# Run optimization\noptimizer.run(objective_function)\n\n# Create explainer\nexplainer = OptimizerExplainer(optimizer)\n\n# Generate explanations\nexplanation = explainer.explain()\nexplainer.plot('convergence')\nexplainer.plot('parameter_adaptation')\n```\n\n## Contributing\n\nContributions are welcome! Please feel free to submit a Pull Request.\n\n1. Fork the repository\n2. Create your feature branch (`git checkout -b feature/amazing-feature`)\n3. Commit your changes (`git commit -m 'Add some amazing feature'`)\n4. Push to the branch (`git push origin feature/amazing-feature`)\n5. Open a Pull Request\n\n## License\n\nThis project is licensed under the MIT License - see the [LICENSE](./LICENSE) file for details.\n",
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