GPALexp 0.1.2

A package for incorporating Gaussian Process Active Learning (GPAL) in psychology experiments.

GPALexp

GPALexp is a Python package implementation of Gaussian Process Active Learning (GPAL, Chang et al., 2021).

What is GPAL?

GPAL is a nonparametric Bayesian optimization technique that can approximate a wide range of underlying continuous functions.

GPAL enables us to optimize experimental stimuli and obtain maximal information regarding each participant, in the most efficient way.

What are the benefits of using GPALexp?

GPALexp built-in functions can readily be incorporated in existing Python experiment codes, thereby efficiently capture varying patterns of individual data.

Since GPALexp has integrated a long sequence of executions required to conduct GPAL into 3 functions, we can easily apply GPAL optimization in the existing experiment codes.

This will help us effectively discover underlying functions of individual data in a concise manner.

Features of GPALexp

• Adaptive Design Selection with internal functions: GPRInstance(), argsConstructor(), and gpal_optimize()
• Various built-in plotting functions for visualization
• Supports GPAL optimization for arbitrary number of feature stimuli
• Example code for 1D GPAL optimization with 1D Number-Line Task (Lee et al., 2022)

Installation

GPALexp is built upon Python 3.10.18 and other libraries including numpy, pandas, scipy, and scikit-learn.
Note that the only thing requried in advance is Python 3.10, since other libraries will automatically be installed during the installation process.

# Installing from PyPI
pip install gpalexp

# Installing directly from github (developmental version)
TBD

GPALexp Wiki

We've provided explanatory materials in the github Wiki of this repository.
Please refer to this Wiki page for further details.

Contacts

If there are any things that the maintainer should be noticed (bug reports, update requests, questions, future suggestions, etc), please feel free to contact Junyup Kim ([e-mail]).