Artificial Immune Systems Package

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Introduction

AISP (Artificial Immune Systems Package) is a Python package of immunoinspired techniques that apply metaphors from the vertebrate immune system to pattern recognition and optimization tasks. Conceived as an open-source package of artificial immune systems, AISP emerged from a research project initiated in 2022 at the Instituto Federal do Norte de Minas Gerais - Campus Salinas (IFNMG - Salinas). Its distribution is governed by the GNU Lesser General Public License v3.0 (LGPLv3).

What can you do with AISP?

AISP provides implementations of bio-inspired algorithms for:

• Anomaly detection: Identify abnormal patterns in data.
• Classification: Classify data with multiple classes.
• Optmization: Find optimal solutions for objective functions.
• Clustering: Group data without supervision.

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Implemented Algorithms

Negative Selection (aisp.nsa)

• BNSA - Binary Negative Selection Algorithm
• RNSA - Real-Valued Negative Selection Algorithm

Clonal Selection (aisp.csa)

• AIRS - Artificial Immune Recognition System
• CLONALG - Clonal Selection Algorithm

Immune Network Theory (aisp.ina)

• AiNet - Artificial Immune Network for clustering and data compression

Module in Development

Danger Theory (aisp.dta)

• DCA - Dendritic Cell Algorithm (planned)

API overview

All algorithms follow a simple and consistent interface:

• fit(X, y, verbose: bool = True): trains the model for classification tasks.
• fit(X, verbose: bool = True): trains the model for clustering tasks.
• predict(X): makes predictions based on new data.
• optimize(max_iters: int =..., n_iter_no_change: int =..., verbose: bool = True): run the optimization algorithms