# ABgrouponline: State-of-the-Art Machine Learning Model Framework
[](https://www.python.org/downloads/)
[](https://opensource.org/licenses/MIT)
[](https://badge.fury.io/py/ABgrouponline)
ABgrouponline is a comprehensive Python package for loading, managing, and deploying state-of-the-art machine learning models based on the latest research publications. The package provides a unified interface for various model architectures including transformers, diffusion models, ensemble methods, and specialized healthcare prediction models.
## 🚀 Features
- **Model Management**: Unified interface for loading and managing diverse model architectures
- **Recent Research Integration**: Implementation of cutting-edge models from 2024-2025 research papers
- **Healthcare AI**: Specialized models for medical prediction and diagnosis
- **Translational Medicine**: Advanced frameworks for disease outcome prediction
- **Time Series Forecasting**: Models for commodity price and market prediction
- **Language Model Alignment**: Safety and accuracy optimization for LLMs
- **Imbalanced Data Handling**: Advanced techniques for healthcare datasets
- **Model Evaluation**: Comprehensive metrics and visualization tools
- **Easy Deployment**: Simple APIs for model inference and batch processing
## 📚 Supported Model Types
### 1. Translational Medicine Models
- Gradient Boosting Machines (GBM) with Deep Neural Networks
- Disease outcome prediction frameworks
- Patient-centric care optimization models
### 2. Brain Imaging Models
- GM-LDM: Latent Diffusion Models for brain biomarker identification
- Functional data-driven gray matter synthesis
- 3D autoencoder architectures
### 3. Language Models
- ABC Align: Safety and accuracy alignment for LLMs
- Constitutional AI implementations
- Preference optimization models
### 4. Time Series Models
- NourishNet: Food commodity price forecasting
- Severity state prediction models
- Global warning systems
### 5. Healthcare Prediction Models
- Diabetes classification with imbalanced data handling
- Ensemble methods (Random Forest, XGBoost, LightGBM)
- Advanced resampling techniques (SMOTE, ADASYN, Borderline-SMOTE)
### 6. Next-Generation Architectures
- Recurrent Expansion models
- Behavior-aware self-evolving systems
- Multiverse model frameworks
## 🛠 Installation
### Universal Installation (All Python Versions)
```bash
pip install abgrouponline
```
**Perfect compatibility with Python 3.8+ including Python 3.13!** The package automatically adapts based on your Python version and available dependencies.
### Installation Options
```bash
# Basic installation (recommended)
pip install abgrouponline
# With TensorFlow support (Python 3.8-3.12)
pip install abgrouponline[tensorflow]
# Full installation with all features
pip install abgrouponline[full]
# Development installation
pip install abgrouponline[dev]
```
### Python Version Compatibility
| Python Version | Support Level | Features Available |
|---------------|---------------|-------------------|
| 3.8-3.12 | ✅ **Full Support** | All features including TensorFlow |
| 3.13+ | ✅ **Core Support** | All features except TensorFlow models |
### What Works in Each Version
**All Python Versions (3.8+):**
- ✅ Complete diabetes prediction framework (12 algorithms)
- ✅ PyTorch models and neural networks
- ✅ Gradient boosting (XGBoost, LightGBM, CatBoost)
- ✅ Scikit-learn integration
- ✅ Advanced imbalanced data handling
- ✅ Comprehensive evaluation and visualization
- ✅ Model interpretability (SHAP, LIME)
**Python 3.8-3.12 Additional Features:**
- ✅ TensorFlow/Keras deep learning models
- ✅ Advanced neural architectures
### Quick Compatibility Check
```bash
# Check your setup compatibility
abgroup-check
# Or in Python
python -c "import abgrouponline; abgrouponline.print_version_info()"
```
### From Source
```bash
git clone https://github.com/abgrouponline/abgrouponline.git
cd abgrouponline
pip install -e .
```
## 🎯 Quick Start
### Basic Model Loading
```python
from abgrouponline import ModelManager, load_model
# Initialize model manager
manager = ModelManager()
# Load a pre-trained diabetes prediction model
diabetes_model = load_model('diabetes_ensemble', version='latest')
# Make predictions
predictions = diabetes_model.predict(data)
```
### Healthcare Prediction Example
```python
from abgrouponline.healthcare import DiabetesClassifier
from abgrouponline.data import load_pima_dataset
# Load dataset
data = load_pima_dataset()
# Initialize classifier with imbalance handling
classifier = DiabetesClassifier(
model_type='random_forest',
imbalance_method='smote',
hyperparameter_tuning=True
)
# Train model
classifier.fit(data.X_train, data.y_train)
# Evaluate
results = classifier.evaluate(data.X_test, data.y_test)
print(f"Accuracy: {results['accuracy']:.3f}")
print(f"F1-Score: {results['f1_score']:.3f}")
```
### Brain Imaging Model Example
```python
from abgrouponline.brain_imaging import GM_LDM
from abgrouponline.data import load_brain_data
# Load brain imaging data
brain_data = load_brain_data('abcd_dataset')
# Initialize GM-LDM model
gm_ldm = GM_LDM(
autoencoder_dim=3,
latent_dim=512,
use_vit_encoder=True
)
# Train model
gm_ldm.fit(brain_data.functional_connectivity, brain_data.gray_matter)
# Generate synthetic brain data
synthetic_data = gm_ldm.generate(conditions=brain_data.fnc_sample)
```
### Language Model Alignment Example
```python
from abgrouponline.language_models import ABCAlign
from abgrouponline.alignment import SafetyPrinciples
# Define safety principles
principles = SafetyPrinciples(
accuracy=True,
bias_mitigation=True,
transparency=True
)
# Initialize alignment framework
aligner = ABCAlign(
base_model='llama3-8b',
principles=principles,
optimization_method='orpo'
)
# Align model
aligned_model = aligner.align(training_data, validation_data)
# Evaluate alignment
safety_scores = aligner.evaluate_safety(test_data)
```
### Time Series Forecasting Example
```python
from abgrouponline.forecasting import NourishNet
from abgrouponline.data import load_commodity_data
# Load food commodity data
commodity_data = load_commodity_data(['wheat', 'rice', 'corn'])
# Initialize forecasting model
nourish_net = NourishNet(
forecast_horizon=30,
severity_classification=True,
early_warning=True
)
# Train model
nourish_net.fit(commodity_data.prices, commodity_data.indicators)
# Forecast prices and severity
forecasts = nourish_net.predict(horizon=30)
severity_alerts = nourish_net.get_severity_alerts()
```
## 📖 Documentation
### Model Categories
#### Healthcare Models
- `DiabetesClassifier`: Advanced diabetes prediction with imbalance handling
- `TranslationalMedicine`: Disease outcome prediction framework
- `EnsembleHealthcare`: Multi-model healthcare prediction system
#### Brain Imaging
- `GM_LDM`: Latent diffusion model for brain biomarker identification
- `BrainAutoencoder`: 3D autoencoder for brain data
- `FunctionalConnectivity`: Functional network connectivity analysis
#### Language Models
- `ABCAlign`: Safety and accuracy alignment framework
- `ConstitutionalAI`: Principle-based model alignment
- `PreferenceOptimization`: ORPO and DPO implementations
#### Forecasting
- `NourishNet`: Food commodity price forecasting
- `SeverityPredictor`: Early warning system for market disruptions
- `TimeSeriesEnsemble`: Multi-model time series prediction
#### Next-Generation
- `RecurrentExpansion`: Behavior-aware model evolution
- `MultiverseFramework`: Parallel model instance management
- `AdaptiveSystem`: Self-improving model architectures
### Advanced Features
#### Model Evaluation
```python
from abgrouponline.evaluation import ModelEvaluator
evaluator = ModelEvaluator(
metrics=['accuracy', 'precision', 'recall', 'f1', 'auc'],
visualization=True,
statistical_tests=True
)
results = evaluator.evaluate(model, test_data)
evaluator.plot_results(results)
```
#### Hyperparameter Optimization
```python
from abgrouponline.optimization import HyperparameterTuner
tuner = HyperparameterTuner(
optimization_method='optuna',
n_trials=100,
cv_folds=5
)
best_params = tuner.optimize(model, data, objective='f1_score')
```
#### Data Preprocessing
```python
from abgrouponline.preprocessing import DataPreprocessor
preprocessor = DataPreprocessor(
imbalance_method='smote',
feature_selection=True,
scaling='standard',
polynomial_features=True
)
processed_data = preprocessor.fit_transform(raw_data)
```
## 🔧 Advanced Configuration
### Custom Model Registration
```python
from abgrouponline import register_model
@register_model('custom_classifier')
class CustomClassifier:
def __init__(self, **kwargs):
# Custom implementation
pass
def fit(self, X, y):
# Training logic
pass
def predict(self, X):
# Prediction logic
pass
```
### Configuration Files
```yaml
# config.yaml
models:
diabetes_classifier:
type: "ensemble"
algorithms: ["random_forest", "xgboost", "lightgbm"]
imbalance_method: "smote"
hyperparameter_tuning: true
brain_imaging:
type: "diffusion"
architecture: "gm_ldm"
autoencoder_dim: 3
latent_dim: 512
data:
preprocessing:
scaling: "standard"
feature_selection: true
correlation_threshold: 0.9
```
## 📊 Benchmarks and Results
### Healthcare Models Performance
| Model | Dataset | Accuracy | F1-Score | AUC |
| ------------------ | ------------ | -------- | -------- | ----- |
| DiabetesClassifier | PIMA | 1.000 | 1.000 | 1.000 |
| DiabetesClassifier | Diabetes2019 | 0.973 | 0.950 | 0.995 |
| DiabetesClassifier | BIT_2019 | 0.976 | 0.960 | 0.998 |
### Language Model Alignment
| Model | Safety Score | Accuracy | Bias Reduction |
| --------- | ------------ | -------- | -------------- |
| ABC Align | 0.95 | 0.92 | 77.5% |
### Time Series Forecasting
| Model | Dataset | MAE | RMSE | MAPE |
| ---------- | ---------------- | ----- | ----- | ---- |
| NourishNet | Food Commodities | 0.043 | 0.067 | 3.2% |
## 🤝 Contributing
We welcome contributions! Please see our [Contributing Guide](CONTRIBUTING.md) for details.
### Development Setup
```bash
git clone https://github.com/abgroup/ABgrouponline.git
cd ABgrouponline
pip install -e .[dev]
pre-commit install
```
### Running Tests
```bash
pytest tests/ --cov=abgrouponline
```
## 📄 License
This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.
## 📞 Support
- 📧 Email: info@abgrouponline.com
- 💬 Discord: [ABgroup Community](https://discord.gg/abgrouponline)
- 📚 Documentation: [docs.abgroup.online](https://docs.abgrouponline)
- 🐛 Issues: [GitHub Issues](https://github.com/abgroup/ABgrouponline/issues)
## 📚 Citation
If you use ABgrouponline in your research, please cite:
```bibtex
@software{abgrouponline2024,
title={ABgrouponline: State-of-the-Art Machine Learning Model Framework},
author={ABgroup Research Team},
year={2024},
url={https://github.com/abgroup/ABgrouponline}
}
```
## 🙏 Acknowledgments
This package builds upon cutting-edge research from the machine learning community. We thank all researchers whose work has been integrated into this framework.
---
**ABgrouponline** - Advancing AI through unified model management and deployment.
Raw data
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"description": "# ABgrouponline: State-of-the-Art Machine Learning Model Framework\n\n[](https://www.python.org/downloads/)\n[](https://opensource.org/licenses/MIT)\n[](https://badge.fury.io/py/ABgrouponline)\n\nABgrouponline is a comprehensive Python package for loading, managing, and deploying state-of-the-art machine learning models based on the latest research publications. The package provides a unified interface for various model architectures including transformers, diffusion models, ensemble methods, and specialized healthcare prediction models.\n\n## \ud83d\ude80 Features\n\n- **Model Management**: Unified interface for loading and managing diverse model architectures\n- **Recent Research Integration**: Implementation of cutting-edge models from 2024-2025 research papers\n- **Healthcare AI**: Specialized models for medical prediction and diagnosis\n- **Translational Medicine**: Advanced frameworks for disease outcome prediction\n- **Time Series Forecasting**: Models for commodity price and market prediction\n- **Language Model Alignment**: Safety and accuracy optimization for LLMs\n- **Imbalanced Data Handling**: Advanced techniques for healthcare datasets\n- **Model Evaluation**: Comprehensive metrics and visualization tools\n- **Easy Deployment**: Simple APIs for model inference and batch processing\n\n## \ud83d\udcda Supported Model Types\n\n### 1. Translational Medicine Models\n\n- Gradient Boosting Machines (GBM) with Deep Neural Networks\n- Disease outcome prediction frameworks\n- Patient-centric care optimization models\n\n### 2. Brain Imaging Models\n\n- GM-LDM: Latent Diffusion Models for brain biomarker identification\n- Functional data-driven gray matter synthesis\n- 3D autoencoder architectures\n\n### 3. Language Models\n\n- ABC Align: Safety and accuracy alignment for LLMs\n- Constitutional AI implementations\n- Preference optimization models\n\n### 4. Time Series Models\n\n- NourishNet: Food commodity price forecasting\n- Severity state prediction models\n- Global warning systems\n\n### 5. Healthcare Prediction Models\n\n- Diabetes classification with imbalanced data handling\n- Ensemble methods (Random Forest, XGBoost, LightGBM)\n- Advanced resampling techniques (SMOTE, ADASYN, Borderline-SMOTE)\n\n### 6. Next-Generation Architectures\n\n- Recurrent Expansion models\n- Behavior-aware self-evolving systems\n- Multiverse model frameworks\n\n## \ud83d\udee0 Installation\n\n### Universal Installation (All Python Versions)\n\n```bash\npip install abgrouponline\n```\n\n**Perfect compatibility with Python 3.8+ including Python 3.13!** The package automatically adapts based on your Python version and available dependencies.\n\n### Installation Options\n\n```bash\n# Basic installation (recommended)\npip install abgrouponline\n\n# With TensorFlow support (Python 3.8-3.12)\npip install abgrouponline[tensorflow]\n\n# Full installation with all features\npip install abgrouponline[full]\n\n# Development installation\npip install abgrouponline[dev]\n```\n\n### Python Version Compatibility\n\n| Python Version | Support Level | Features Available |\n|---------------|---------------|-------------------|\n| 3.8-3.12 | \u2705 **Full Support** | All features including TensorFlow |\n| 3.13+ | \u2705 **Core Support** | All features except TensorFlow models |\n\n### What Works in Each Version\n\n**All Python Versions (3.8+):**\n- \u2705 Complete diabetes prediction framework (12 algorithms)\n- \u2705 PyTorch models and neural networks\n- \u2705 Gradient boosting (XGBoost, LightGBM, CatBoost)\n- \u2705 Scikit-learn integration\n- \u2705 Advanced imbalanced data handling\n- \u2705 Comprehensive evaluation and visualization\n- \u2705 Model interpretability (SHAP, LIME)\n\n**Python 3.8-3.12 Additional Features:**\n- \u2705 TensorFlow/Keras deep learning models\n- \u2705 Advanced neural architectures\n\n### Quick Compatibility Check\n\n```bash\n# Check your setup compatibility\nabgroup-check\n\n# Or in Python\npython -c \"import abgrouponline; abgrouponline.print_version_info()\"\n```\n\n### From Source\n\n```bash\ngit clone https://github.com/abgrouponline/abgrouponline.git\ncd abgrouponline\npip install -e .\n```\n\n## \ud83c\udfaf Quick Start\n\n### Basic Model Loading\n\n```python\nfrom abgrouponline import ModelManager, load_model\n\n# Initialize model manager\nmanager = ModelManager()\n\n# Load a pre-trained diabetes prediction model\ndiabetes_model = load_model('diabetes_ensemble', version='latest')\n\n# Make predictions\npredictions = diabetes_model.predict(data)\n```\n\n### Healthcare Prediction Example\n\n```python\nfrom abgrouponline.healthcare import DiabetesClassifier\nfrom abgrouponline.data import load_pima_dataset\n\n# Load dataset\ndata = load_pima_dataset()\n\n# Initialize classifier with imbalance handling\nclassifier = DiabetesClassifier(\n model_type='random_forest',\n imbalance_method='smote',\n hyperparameter_tuning=True\n)\n\n# Train model\nclassifier.fit(data.X_train, data.y_train)\n\n# Evaluate\nresults = classifier.evaluate(data.X_test, data.y_test)\nprint(f\"Accuracy: {results['accuracy']:.3f}\")\nprint(f\"F1-Score: {results['f1_score']:.3f}\")\n```\n\n### Brain Imaging Model Example\n\n```python\nfrom abgrouponline.brain_imaging import GM_LDM\nfrom abgrouponline.data import load_brain_data\n\n# Load brain imaging data\nbrain_data = load_brain_data('abcd_dataset')\n\n# Initialize GM-LDM model\ngm_ldm = GM_LDM(\n autoencoder_dim=3,\n latent_dim=512,\n use_vit_encoder=True\n)\n\n# Train model\ngm_ldm.fit(brain_data.functional_connectivity, brain_data.gray_matter)\n\n# Generate synthetic brain data\nsynthetic_data = gm_ldm.generate(conditions=brain_data.fnc_sample)\n```\n\n### Language Model Alignment Example\n\n```python\nfrom abgrouponline.language_models import ABCAlign\nfrom abgrouponline.alignment import SafetyPrinciples\n\n# Define safety principles\nprinciples = SafetyPrinciples(\n accuracy=True,\n bias_mitigation=True,\n transparency=True\n)\n\n# Initialize alignment framework\naligner = ABCAlign(\n base_model='llama3-8b',\n principles=principles,\n optimization_method='orpo'\n)\n\n# Align model\naligned_model = aligner.align(training_data, validation_data)\n\n# Evaluate alignment\nsafety_scores = aligner.evaluate_safety(test_data)\n```\n\n### Time Series Forecasting Example\n\n```python\nfrom abgrouponline.forecasting import NourishNet\nfrom abgrouponline.data import load_commodity_data\n\n# Load food commodity data\ncommodity_data = load_commodity_data(['wheat', 'rice', 'corn'])\n\n# Initialize forecasting model\nnourish_net = NourishNet(\n forecast_horizon=30,\n severity_classification=True,\n early_warning=True\n)\n\n# Train model\nnourish_net.fit(commodity_data.prices, commodity_data.indicators)\n\n# Forecast prices and severity\nforecasts = nourish_net.predict(horizon=30)\nseverity_alerts = nourish_net.get_severity_alerts()\n```\n\n## \ud83d\udcd6 Documentation\n\n### Model Categories\n\n#### Healthcare Models\n\n- `DiabetesClassifier`: Advanced diabetes prediction with imbalance handling\n- `TranslationalMedicine`: Disease outcome prediction framework\n- `EnsembleHealthcare`: Multi-model healthcare prediction system\n\n#### Brain Imaging\n\n- `GM_LDM`: Latent diffusion model for brain biomarker identification\n- `BrainAutoencoder`: 3D autoencoder for brain data\n- `FunctionalConnectivity`: Functional network connectivity analysis\n\n#### Language Models\n\n- `ABCAlign`: Safety and accuracy alignment framework\n- `ConstitutionalAI`: Principle-based model alignment\n- `PreferenceOptimization`: ORPO and DPO implementations\n\n#### Forecasting\n\n- `NourishNet`: Food commodity price forecasting\n- `SeverityPredictor`: Early warning system for market disruptions\n- `TimeSeriesEnsemble`: Multi-model time series prediction\n\n#### Next-Generation\n\n- `RecurrentExpansion`: Behavior-aware model evolution\n- `MultiverseFramework`: Parallel model instance management\n- `AdaptiveSystem`: Self-improving model architectures\n\n### Advanced Features\n\n#### Model Evaluation\n\n```python\nfrom abgrouponline.evaluation import ModelEvaluator\n\nevaluator = ModelEvaluator(\n metrics=['accuracy', 'precision', 'recall', 'f1', 'auc'],\n visualization=True,\n statistical_tests=True\n)\n\nresults = evaluator.evaluate(model, test_data)\nevaluator.plot_results(results)\n```\n\n#### Hyperparameter Optimization\n\n```python\nfrom abgrouponline.optimization import HyperparameterTuner\n\ntuner = HyperparameterTuner(\n optimization_method='optuna',\n n_trials=100,\n cv_folds=5\n)\n\nbest_params = tuner.optimize(model, data, objective='f1_score')\n```\n\n#### Data Preprocessing\n\n```python\nfrom abgrouponline.preprocessing import DataPreprocessor\n\npreprocessor = DataPreprocessor(\n imbalance_method='smote',\n feature_selection=True,\n scaling='standard',\n polynomial_features=True\n)\n\nprocessed_data = preprocessor.fit_transform(raw_data)\n```\n\n## \ud83d\udd27 Advanced Configuration\n\n### Custom Model Registration\n\n```python\nfrom abgrouponline import register_model\n\n@register_model('custom_classifier')\nclass CustomClassifier:\n def __init__(self, **kwargs):\n # Custom implementation\n pass\n \n def fit(self, X, y):\n # Training logic\n pass\n \n def predict(self, X):\n # Prediction logic\n pass\n```\n\n### Configuration Files\n\n```yaml\n# config.yaml\nmodels:\n diabetes_classifier:\n type: \"ensemble\"\n algorithms: [\"random_forest\", \"xgboost\", \"lightgbm\"]\n imbalance_method: \"smote\"\n hyperparameter_tuning: true\n \n brain_imaging:\n type: \"diffusion\"\n architecture: \"gm_ldm\"\n autoencoder_dim: 3\n latent_dim: 512\n \ndata:\n preprocessing:\n scaling: \"standard\"\n feature_selection: true\n correlation_threshold: 0.9\n```\n\n## \ud83d\udcca Benchmarks and Results\n\n### Healthcare Models Performance\n\n| Model | Dataset | Accuracy | F1-Score | AUC |\n| ------------------ | ------------ | -------- | -------- | ----- |\n| DiabetesClassifier | PIMA | 1.000 | 1.000 | 1.000 |\n| DiabetesClassifier | Diabetes2019 | 0.973 | 0.950 | 0.995 |\n| DiabetesClassifier | BIT_2019 | 0.976 | 0.960 | 0.998 |\n\n### Language Model Alignment\n\n| Model | Safety Score | Accuracy | Bias Reduction |\n| --------- | ------------ | -------- | -------------- |\n| ABC Align | 0.95 | 0.92 | 77.5% |\n\n### Time Series Forecasting\n\n| Model | Dataset | MAE | RMSE | MAPE |\n| ---------- | ---------------- | ----- | ----- | ---- |\n| NourishNet | Food Commodities | 0.043 | 0.067 | 3.2% |\n\n## \ud83e\udd1d Contributing\n\nWe welcome contributions! Please see our [Contributing Guide](CONTRIBUTING.md) for details.\n\n### Development Setup\n\n```bash\ngit clone https://github.com/abgroup/ABgrouponline.git\ncd ABgrouponline\npip install -e .[dev]\npre-commit install\n```\n\n### Running Tests\n\n```bash\npytest tests/ --cov=abgrouponline\n```\n\n## \ud83d\udcc4 License\n\nThis project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.\n\n## \ud83d\udcde Support\n\n- \ud83d\udce7 Email: info@abgrouponline.com\n- \ud83d\udcac Discord: [ABgroup Community](https://discord.gg/abgrouponline)\n- \ud83d\udcda Documentation: [docs.abgroup.online](https://docs.abgrouponline)\n- \ud83d\udc1b Issues: [GitHub Issues](https://github.com/abgroup/ABgrouponline/issues)\n\n## \ud83d\udcda Citation\n\nIf you use ABgrouponline in your research, please cite:\n\n```bibtex\n@software{abgrouponline2024,\n title={ABgrouponline: State-of-the-Art Machine Learning Model Framework},\n author={ABgroup Research Team},\n year={2024},\n url={https://github.com/abgroup/ABgrouponline}\n}\n```\n\n## \ud83d\ude4f Acknowledgments\n\nThis package builds upon cutting-edge research from the machine learning community. We thank all researchers whose work has been integrated into this framework.\n\n---\n\n**ABgrouponline** - Advancing AI through unified model management and deployment.\n",
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