amgd-optimizer 1.0.3

Adaptive Momentum Gradient Descent for Regularized Poisson Regression

AMGD_Optimizer: Adaptive Momentum Gradient Descent for Regularized Poisson Regression

A high-performance Python package implementing the Adaptive Momentum Gradient Descent (AMGD) algorithm for regularized Poisson regression. AMGD provides superior performance for sparse, high-dimensional count data modeling.

🚀 Key Features

• 56.6% reduction in Mean Absolute Error compared to AdaGrad
• 2.7% improvement over Adam optimizer
• 35.29% sparsity achievement through effective feature selection
• Novel adaptive soft-thresholding for direct L1 penalty handling
• Scikit-learn compatible API for seamless integration
• Comprehensive validation with statistical significance testing

📊 Performance Highlights

Metric	AMGD	Adam	AdaGrad	Improvement
MAE	3.016	3.101	6.945	-56.6% vs AdaGrad
RMSE	3.885	4.001	7.653	-49.2% vs AdaGrad
Sparsity	35.29%	11.76%	0.00%	+200% vs Adam

Results from ecological dataset with 61,345 observations and 17 features

🛠️ Installation

From PyPI (recommended)

pip install amgd-optimizer

From Source

git clone https://github.com/elbakari01/amgd_optimizer.git
cd amgd-optimizer
pip install -e .

Development Installation

git clone https://github.com/elbakari01/amgd_optimizer.git
cd amgd-optimizer
pip install -e ".[dev,docs,examples]"

🏃‍♂️ Quick Start

Basic Usage

import numpy as np
from amgd-optimizer import AMGDPoissonRegressor

# Generate sample count data
np.random.seed(42)
X = np.random.randn(1000, 20)
y = np.random.poisson(np.exp(X @ np.random.randn(20) * 0.1))

# Fit AMGD model
model = AMGDPoissonRegressor(
    alpha=0.01,           # Regularization strength
    l1_ratio=0.7,         # 70% L1, 30% L2 penalty
    max_iter=1000
)

model.fit(X, y)
predictions = model.predict(X)

# Check results
print(f"Sparsity: {model.get_sparsity():.2%}")
print(f"Selected features: {np.sum(np.abs(model.coef_) > 1e-8)}")
print(f"Converged in: {model.n_iter_} iterations")

Feature Selection Example

from amgd-optimizer import AMGDPoissonRegressor
import matplotlib.pyplot as plt

# Fit with different regularization strengths
alphas = [0.001, 0.01, 0.1, 1.0]
sparsities = []

for alpha in alphas:
    model = AMGDPoissonRegressor(alpha=alpha, l1_ratio=1.0)  # Pure L1
    model.fit(X, y)
    sparsities.append(model.get_sparsity())
    print(f"α={alpha}: {model.get_sparsity():.1%} sparsity, "
          f"{np.sum(np.abs(model.coef_) > 1e-8)} features selected")

# Plot sparsity vs regularization
plt.figure(figsize=(8, 5))
plt.semilogx(alphas, sparsities, 'o-')
plt.xlabel('Regularization Strength (α)')
plt.ylabel('Sparsity Ratio')
plt.title('Feature Selection with AMGD')
plt.grid(True, alpha=0.3)
plt.show()

Comparison with Other Optimizers

from amgd-optimizer import compare_optimizers

# Compare AMGD against other methods
results = compare_optimizers(X, y, methods=['amgd'], alpha=0.01)

for method, metrics in results.items():
    print(f"{method.upper()}:")
    print(f"  MAE: {metrics['mae']:.4f}")
    print(f"  RMSE: {metrics['rmse']:.4f}")
    print(f"  Sparsity: {metrics['sparsity']:.2%}")
    print(f"  Iterations: {metrics['n_iter']}")

🔬 Advanced Usage

Custom Optimization Parameters

from amgd-optimizer import AMGDOptimizer

# Low-level optimizer access for custom applications
optimizer = AMGDOptimizer(
    learning_rate=0.01,
    momentum_beta1=0.9,      # First moment decay
    momentum_beta2=0.999,    # Second moment decay
    lambda1=0.01,            # L1 regularization
    lambda2=0.001,           # L2 regularization
    penalty='elasticnet',
    decay_rate=1e-4,         # Learning rate decay
    gradient_clip=5.0,       # Gradient clipping threshold
    max_iter=1000,
    tol=1e-6
)

optimizer.fit(X, y)
coefficients = optimizer.coef_

Cross-Validation and Model Selection

from sklearn.model_selection import GridSearchCV
from amgd-optimizer import AMGDPoissonRegressor

# Hyperparameter tuning
param_grid = {
    'alpha': [0.001, 0.01, 0.1, 1.0],
    'l1_ratio': [0.1, 0.5, 0.7, 0.9],
    'learning_rate': [0.001, 0.01, 0.1]
}

model = AMGDPoissonRegressor(max_iter=500)
grid_search = GridSearchCV(
    model, 
    param_grid, 
    cv=5, 
    scoring='neg_mean_absolute_error',
    n_jobs=-1
)

grid_search.fit(X, y)
print(f"Best parameters: {grid_search.best_params_}")
print(f"Best score: {-grid_search.best_score_:.4f}")

Monitoring Convergence

# Fit with verbose output
model = AMGDPoissonRegressor(alpha=0.01, verbose=True, max_iter=1000)
model.fit(X, y)

# Plot convergence history
model.plot_convergence()
plt.show()

# Get detailed convergence info
info = model.get_convergence_info()
print(f"Converged: {info['converged']}")
print(f"Final loss: {info['final_loss']:.6f}")

🧮 Algorithm Details

AMGD integrates three key innovations:

1. Adaptive Learning Rate Decay

αₜ = α / (1 + ηt)

2. Momentum Updates with Bias Correction

mₜ = ζ₁mₜ₋₁ + (1 - ζ₁)∇f(βₜ)
vₜ = ζ₂vₜ₋₁ + (1 - ζ₂)(∇f(βₜ))²
m̂ₜ = mₜ / (1 - ζ₁ᵗ)
v̂ₜ = vₜ / (1 - ζ₂ᵗ)

3. Adaptive Soft-Thresholding

βⱼ ← sign(βⱼ) · max(|βⱼ| - αₜλ/(|βⱼ| + ε), 0)

This adaptive thresholding is the key innovation, providing coefficient-dependent regularization that preserves large coefficients while aggressively shrinking small ones.

📈 Benchmarks

Ecological Dataset (n=61,345, p=17)

Algorithm	MAE	RMSE	MPD	Sparsity	Iterations
AMGD	3.016	3.885	2.185	35.29%	~300
Adam	3.101	4.001	2.249	11.76%	~1000
AdaGrad	6.945	7.653	11.507	0.00%	>1000
GLMNet	9.007	9.554	29.394	0.00%	~500

Statistical Significance

All improvements are statistically significant (p < 0.0001) with large effect sizes (Cohen's d: -9.46 to -713.03).

🎯 Use Cases

AMGD is particularly effective for:

• High-dimensional count data (genomics, network analysis)
• Sparse modeling requiring feature selection
• Ecological modeling (species counts, biodiversity indices)
• Epidemiological studies (disease incidence rates)
• Marketing analytics (customer behavior counts)
• Quality control (defect counts, failure rates)

📚 Documentation

• Full Documentation: https://amgd-optimizer.readthedocs.io/
• API Reference: https://amgd-optimizer.readthedocs.io/en/latest/api.html
• Examples: https://github.com/yourusername/amgd-optimizer/tree/main/examples
• Paper: "Adaptive Momentum Gradient Descent: A New Algorithm in Regularized Poisson Regression"

🤝 Contributing

We welcome contributions! Please see our Contributing Guide for details.

Development Setup

git clone https://github.com/elbakari01/amgd_optimizer
cd amgd-optimizer
pip install -e ".[dev]"
pre-commit install

Running Tests

pytest tests/ -v --cov=amgd-optimizer

📄 Citation

If you use AMGD in your research, please cite:

@article{bakari2024amgd,
  title={Adaptive Momentum Gradient Descent: A New Algorithm in Regularized Poisson Regression},
  author={Bakari, Ibrahim and Özkale, M. Revan},
  journal={Journal Name},
  year={2024},
  publisher={Publisher}
}

@software{amgd_optimizer,
  title={AMGD-optimizer: Adaptive Momentum Gradient Descent for Regularized Poisson Regression},
  author={Bakari, Ibrahim},
  url={https://github.com/yourusername/amgd-optimizer},
  version={0.1.0},
  year={2024}
}

📞 Support

• Issues: GitHub Issues
• Discussions: GitHub Discussions
• Email: [e-mail]

📜 License

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

🙏 Acknowledgments

• Çukurova University - Department of Statistics
• Research Community - For valuable feedback and suggestions
• Scikit-learn - For the API design inspiration

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AMGD-optimizer: Making sparse Poisson regression fast and effective

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