PyTEAP 0.1.2

PyTEAP: A Python implementation of Toolbox for Emotion Analysis using Physiological signals (TEAP).

PyTEAP

PyTEAP is a Python implementation of Toolbox for Emotion Analysis using Physiological signals (TEAP).

This package intends to reimplement TEAP, originally written in MATLAB, in Python, to enable interoperation with other Python packages.

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Installation

To use PyTEAP, you can either clone this repository:

$ git clone https://github.com/cheulyop/PyTEAP.git
$ cd PyTEAP

or install it via pip:

$ pip install PyTEAP

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Baseline classification on DEAP

As the primary goal of this package is to provide a toolbox for processing physiological signals for emotion analysis, this package includes a script to perform simple baseline classification on DEAP dataset.

There are two ways you can do baseline classification on DEAP with PyTEAP:

1. Clone this repository and run the script for baseline classification.

$ python baseline.py --root '/path/to/deap_root'

Running baseline.py will load raw datafiles from the root directory, preprocess features and target labels, perform baseline classification with four simple classifiers: 1) Gaussian Naive Bayes, 2) random voting assuming a uniform distribution between classes, 3) majority voting, 4) class ratio voting, and finally print a table showing performance of each classifier, measured with accuracy score, balanced accuracy score, and F1-score.

2. Or, install PyTEAP via pip as shown above, and use its modules and functions as you want.

The below table shows the results of baseline classification with seed=0.

	Gaussian NB	Random voting	Majority voting	Class ratio voting
Acc.	0.508594	0.496094	0.553125	0.489063
Balanced acc.	0.513142	0.494228	0.500000	0.484621
F1-score	0.513712	0.528224	0.712272	0.531533

* You must have access to DEAP dataset, in particular a preprocessed data in Python format to perform above baseline classification. Please contact DEAP maintainers if you need access to the dataset.