psolr 0.1.0

Metaheuristic-Optimized Logistic Regression (PSO-LR / MOLR)

PSO-LR: Metaheuristic-Optimized Logistic Regression (MOLR)

PSO-LR is an open-source, sklearn-compatible implementation of
Metaheuristic-Optimized Logistic Regression (MOLR), where model parameters are estimated using Particle Swarm Optimization (PSO) instead of gradient-based solvers.

This framework preserves the classical interpretability of logistic regression while improving robustness, global convergence behavior, and stability in noisy or ill-conditioned datasets.

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Motivation

Traditional logistic regression relies on gradient-based optimization (LBFGS, Newton-CG, SGD). These methods can struggle when data exhibits:

• Multicollinearity
• Noisy or sparse signals
• Poor conditioning
• Strong regularization requirements

PSO-LR replaces gradient descent with a population-based global optimizer, making it suitable for:

• Marketing analytics
• Choice modeling
• Behavioral modeling
• Econometrics
• High-noise real-world datasets

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Key Features

• Binary & Multinomial Logistic Regression
• Particle Swarm Optimization (PSO)
• Early stopping for efficiency
• L1 / L2 regularization
• Hard coefficient constraints
• Fully sklearn-compatible API
• Interpretable coefficients & odds ratios
• Domain-independent design

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Mathematical Formulation

The logistic model remains unchanged:

[ P(y=1|x) = \sigma(x^\top \beta) ]

The regularized negative log-likelihood is optimized using PSO:

[ \min_{\beta} ; -\mathcal{L}(\beta) + \lambda \Omega(\beta) ]

PSO searches the parameter space globally without requiring gradients, ensuring robust convergence.

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Installation

pip install psolr