| Name | automl-framework JSON |
| Version |
0.1.1
JSON |
| download |
| home_page | https://github.com/nandarizkika/automl-framework |
| Summary | A comprehensive, modular framework for automated machine learning with overfitting detection and mitigation |
| upload_time | 2025-07-23 02:27:05 |
| maintainer | None |
| docs_url | None |
| author | Nanda Rizkika Ruanawijaya |
| requires_python | >=3.8 |
| license | MIT License
Copyright (c) 2024 Your Name
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 |
machine learning
automl
automated machine learning
overfitting
hyperparameter tuning
model selection
scikit-learn
|
| VCS |
 |
| bugtrack_url |
|
| requirements |
numpy
pandas
scikit-learn
matplotlib
seaborn
joblib
scikit-optimize
hyperopt
pytest
pytest-cov
black
flake8
isort
sphinx
sphinx-rtd-theme
jupyter
ipykernel
notebook
|
| Travis-CI |
No Travis.
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| coveralls test coverage |
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|
# AutoML Framework: Intelligent Machine Learning Automation 🤖🧠
[](https://www.python.org/downloads/)
[](https://opensource.org/licenses/MIT)
[](https://pypi.org/project/automl-framework/)
[](https://codecov.io/gh/nandarizkika/automl-framework)
## 🌟 Project Overview
AutoML Framework is a cutting-edge, comprehensive machine learning library designed to simplify and automate the entire machine learning workflow. Our mission is to democratize machine learning by providing an intelligent, easy-to-use solution that handles complex model selection, optimization, and evaluation.
### 🚀 Key Differentiators
- **Intelligent Model Selection**: Automatically tries and evaluates multiple algorithms
- **Advanced Overfitting Detection**: Sophisticated techniques to prevent model overfitting
- **Comprehensive Hyperparameter Optimization**: Finds optimal model configurations
- **Detailed Performance Reporting**: In-depth insights into model performance
## 📦 Installation
### Quick Install
```bash
# Install stable version
pip install automl-framework
# Install latest development version
pip install git+https://github.com/nandarizkika/automl-framework.git
```
### Installation Options
```bash
# Install with all optional dependencies
pip install automl-framework[all]
# For specific use cases
pip install automl-framework[visualization] # Visualization tools
pip install automl-framework[tuning] # Advanced hyperparameter tuning
```
## 🧠 Quick Start Examples
### Classification Example
```python
from automl import AutoML
from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
# Load dataset
X, y = load_iris(return_X_y=True)
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)
# Initialize AutoML
automl = AutoML(problem_type='classification')
# Train and evaluate models
automl.fit(X_train, y_train)
results = automl.evaluate(X_test, y_test)
# Get best model and predictions
best_model = automl.predict(X_test)
leaderboard = automl.get_leaderboard()
print(leaderboard)
```
### Regression Example
```python
from automl import AutoML
from sklearn.datasets import load_boston
from sklearn.model_selection import train_test_split
# Load dataset
X, y = load_boston(return_X_y=True)
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)
# Initialize AutoML for regression
automl = AutoML(problem_type='regression')
automl.fit(X_train, y_train)
results = automl.evaluate(X_test, y_test)
```
## 🔧 Advanced Features
### Overfitting Detection
```python
# Enable advanced overfitting control
automl.pipeline.set_overfitting_control(
detection_enabled=True,
auto_mitigation=True,
threshold=0.3
)
# Get overfitting assessment
assessment = automl.get_overfitting_assessment()
suggestions = automl.get_improvement_suggestions()
```
### Hyperparameter Tuning
```python
from automl import TuningIntegrator
# Create tuning integrator
tuner = TuningIntegrator(automl)
# Advanced model tuning
summary = tuner.tune_models(
X_train, y_train,
search_type='bayesian',
n_iter=50,
register_best=True
)
```
## 🌈 Key Features
### 1. Automated Machine Learning
- Automatic algorithm selection
- Intelligent model ranking
- Performance optimization
### 2. Overfitting Management
- Multi-metric overfitting detection
- Automatic and manual mitigation strategies
- Comprehensive model fit assessment
### 3. Hyperparameter Optimization
- Grid Search
- Random Search
- Bayesian Optimization
- Hyperopt Integration
### 4. Comprehensive Reporting
- Detailed performance leaderboards
- Feature importance analysis
- Training logs and insights
## 🧪 Supported Models
### Classification
- Random Forest
- Logistic Regression
- Gradient Boosting
- Support Vector Machines
- Decision Trees
- K-Nearest Neighbors
- Neural Networks
### Regression
- Linear Regression
- Ridge Regression
- Random Forest Regressor
- Gradient Boosting Regressor
- Support Vector Regression
## 📊 Performance Benchmarks
| Dataset | Models | Best Accuracy | Training Time | Overfitting Detected |
|---------|--------|--------------|---------------|----------------------|
| Iris | 5 | 97.8% | 2.3s | None |
| Wine | 7 | 94.4% | 4.1s | 1 model |
| Breast Cancer | 8 | 96.5% | 8.7s | 2 models |
## 🛣️ Roadmap
### v0.2.0
- [ ] Deep Learning Integration
- [ ] Time Series Support
- [ ] Advanced Feature Engineering
- [ ] Enhanced Visualization
### v0.3.0
- [ ] Distributed Training
- [ ] Model Deployment Tools
- [ ] Advanced NLP Support
## 🤝 Contributing
We welcome contributions! Please see [CONTRIBUTING.md](CONTRIBUTING.md) for details.
### Ways to Contribute
- 🐛 Report Bugs
- 💡 Suggest Features
- 📝 Improve Documentation
- 🔧 Submit Pull Requests
## 📄 License
MIT License - See [LICENSE](LICENSE) for details
## 📞 Support
- 📧 Email: nandarizky52@gmail.com
- 🐞 Issues: [GitHub Issues](https://github.com/nandarizkika/automl-framework/issues)
- 💬 Discussions: [GitHub Discussions](https://github.com/nandarizkika/automl-framework/discussions)
## 🌟 Star History
[](https://star-history.com/#nandarizkika/automl-framework&Date)
---
<div align="center">
🚀 Empowering Machine Learning for Everyone 🚀
**AutoML Framework: Where Intelligence Meets Automation**
</div>
Raw data
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"description": "# AutoML Framework: Intelligent Machine Learning Automation \ud83e\udd16\ud83e\udde0\r\n\r\n[](https://www.python.org/downloads/)\r\n[](https://opensource.org/licenses/MIT)\r\n[](https://pypi.org/project/automl-framework/)\r\n[](https://codecov.io/gh/nandarizkika/automl-framework)\r\n\r\n## \ud83c\udf1f Project Overview\r\n\r\nAutoML Framework is a cutting-edge, comprehensive machine learning library designed to simplify and automate the entire machine learning workflow. Our mission is to democratize machine learning by providing an intelligent, easy-to-use solution that handles complex model selection, optimization, and evaluation.\r\n\r\n### \ud83d\ude80 Key Differentiators\r\n\r\n- **Intelligent Model Selection**: Automatically tries and evaluates multiple algorithms\r\n- **Advanced Overfitting Detection**: Sophisticated techniques to prevent model overfitting\r\n- **Comprehensive Hyperparameter Optimization**: Finds optimal model configurations\r\n- **Detailed Performance Reporting**: In-depth insights into model performance\r\n\r\n## \ud83d\udce6 Installation\r\n\r\n### Quick Install\r\n```bash\r\n# Install stable version\r\npip install automl-framework\r\n\r\n# Install latest development version\r\npip install git+https://github.com/nandarizkika/automl-framework.git\r\n```\r\n\r\n### Installation Options\r\n```bash\r\n# Install with all optional dependencies\r\npip install automl-framework[all]\r\n\r\n# For specific use cases\r\npip install automl-framework[visualization] # Visualization tools\r\npip install automl-framework[tuning] # Advanced hyperparameter tuning\r\n```\r\n\r\n## \ud83e\udde0 Quick Start Examples\r\n\r\n### Classification Example\r\n```python\r\nfrom automl import AutoML\r\nfrom sklearn.datasets import load_iris\r\nfrom sklearn.model_selection import train_test_split\r\n\r\n# Load dataset\r\nX, y = load_iris(return_X_y=True)\r\nX_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)\r\n\r\n# Initialize AutoML\r\nautoml = AutoML(problem_type='classification')\r\n\r\n# Train and evaluate models\r\nautoml.fit(X_train, y_train)\r\nresults = automl.evaluate(X_test, y_test)\r\n\r\n# Get best model and predictions\r\nbest_model = automl.predict(X_test)\r\nleaderboard = automl.get_leaderboard()\r\nprint(leaderboard)\r\n```\r\n\r\n### Regression Example\r\n```python\r\nfrom automl import AutoML\r\nfrom sklearn.datasets import load_boston\r\nfrom sklearn.model_selection import train_test_split\r\n\r\n# Load dataset\r\nX, y = load_boston(return_X_y=True)\r\nX_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)\r\n\r\n# Initialize AutoML for regression\r\nautoml = AutoML(problem_type='regression')\r\nautoml.fit(X_train, y_train)\r\nresults = automl.evaluate(X_test, y_test)\r\n```\r\n\r\n## \ud83d\udd27 Advanced Features\r\n\r\n### Overfitting Detection\r\n```python\r\n# Enable advanced overfitting control\r\nautoml.pipeline.set_overfitting_control(\r\n detection_enabled=True,\r\n auto_mitigation=True,\r\n threshold=0.3\r\n)\r\n\r\n# Get overfitting assessment\r\nassessment = automl.get_overfitting_assessment()\r\nsuggestions = automl.get_improvement_suggestions()\r\n```\r\n\r\n### Hyperparameter Tuning\r\n```python\r\nfrom automl import TuningIntegrator\r\n\r\n# Create tuning integrator\r\ntuner = TuningIntegrator(automl)\r\n\r\n# Advanced model tuning\r\nsummary = tuner.tune_models(\r\n X_train, y_train,\r\n search_type='bayesian',\r\n n_iter=50,\r\n register_best=True\r\n)\r\n```\r\n\r\n## \ud83c\udf08 Key Features\r\n\r\n### 1. Automated Machine Learning\r\n- Automatic algorithm selection\r\n- Intelligent model ranking\r\n- Performance optimization\r\n\r\n### 2. Overfitting Management\r\n- Multi-metric overfitting detection\r\n- Automatic and manual mitigation strategies\r\n- Comprehensive model fit assessment\r\n\r\n### 3. Hyperparameter Optimization\r\n- Grid Search\r\n- Random Search\r\n- Bayesian Optimization\r\n- Hyperopt Integration\r\n\r\n### 4. Comprehensive Reporting\r\n- Detailed performance leaderboards\r\n- Feature importance analysis\r\n- Training logs and insights\r\n\r\n## \ud83e\uddea Supported Models\r\n\r\n### Classification\r\n- Random Forest\r\n- Logistic Regression\r\n- Gradient Boosting\r\n- Support Vector Machines\r\n- Decision Trees\r\n- K-Nearest Neighbors\r\n- Neural Networks\r\n\r\n### Regression\r\n- Linear Regression\r\n- Ridge Regression\r\n- Random Forest Regressor\r\n- Gradient Boosting Regressor\r\n- Support Vector Regression\r\n\r\n## \ud83d\udcca Performance Benchmarks\r\n\r\n| Dataset | Models | Best Accuracy | Training Time | Overfitting Detected |\r\n|---------|--------|--------------|---------------|----------------------|\r\n| Iris | 5 | 97.8% | 2.3s | None |\r\n| Wine | 7 | 94.4% | 4.1s | 1 model |\r\n| Breast Cancer | 8 | 96.5% | 8.7s | 2 models |\r\n\r\n## \ud83d\udee3\ufe0f Roadmap\r\n\r\n### v0.2.0\r\n- [ ] Deep Learning Integration\r\n- [ ] Time Series Support\r\n- [ ] Advanced Feature Engineering\r\n- [ ] Enhanced Visualization\r\n\r\n### v0.3.0\r\n- [ ] Distributed Training\r\n- [ ] Model Deployment Tools\r\n- [ ] Advanced NLP Support\r\n\r\n## \ud83e\udd1d Contributing\r\n\r\nWe welcome contributions! Please see [CONTRIBUTING.md](CONTRIBUTING.md) for details.\r\n\r\n### Ways to Contribute\r\n- \ud83d\udc1b Report Bugs\r\n- \ud83d\udca1 Suggest Features\r\n- \ud83d\udcdd Improve Documentation\r\n- \ud83d\udd27 Submit Pull Requests\r\n\r\n## \ud83d\udcc4 License\r\n\r\nMIT License - See [LICENSE](LICENSE) for details\r\n\r\n## \ud83d\udcde Support\r\n\r\n- \ud83d\udce7 Email: nandarizky52@gmail.com\r\n- \ud83d\udc1e Issues: [GitHub Issues](https://github.com/nandarizkika/automl-framework/issues)\r\n- \ud83d\udcac Discussions: [GitHub Discussions](https://github.com/nandarizkika/automl-framework/discussions)\r\n\r\n## \ud83c\udf1f Star History\r\n\r\n[](https://star-history.com/#nandarizkika/automl-framework&Date)\r\n\r\n---\r\n\r\n<div align=\"center\">\r\n\ud83d\ude80 Empowering Machine Learning for Everyone \ud83d\ude80\r\n\r\n**AutoML Framework: Where Intelligence Meets Automation**\r\n</div>\r\n",
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