Toolbox for inferring psychological embeddings.
Project description
WARNING: This package is pre-release and the API is not stable. All APIs are subject to change and all releases are alpha.
What's in a name?
The name PsiZ (pronounced like the word size, /sʌɪz/) is meant to serve as shorthand for the term psychological embedding. The greek letter Psi is often used to represent the field of psychology and the matrix variable Z is often used in machine learning to denote a latent feature space.
Purpose
PsiZ provides the computational tools to infer psychological representations from human behavior (i.e., a psychological embedding). It integrates cognitive theory with contemporary computational methods.
Installation
There are two different ways to install: PyPI or git. Installing via git has the advantage of including examples and tests in the cloned repository.
Using PyPI
$ pip install psiz
You can optionally install pytest
necessary for running package tests:
$ pip install psiz[test]
Using git
# Clone the PsiZ repository from GitHub to your local machine.
$ git clone https://github.com/roads/psiz.git
# Use `pip` to install the cloned repository.
$ pip install /local/path/to/psiz
Notes:
- PsiZ depends on TensorFlow. Please see the TF compatibility matrix for supported Python and CUDA versions for each version of TensorFlow.
- PsiZ versions 0.5.0 and older must be installed using git clone and editable mode (
pip install -e /local/path/to/psiz
). - You can install specific releases:
- using PyPI:
pip install 'psiz==0.5.1'
- using git:
git clone https://github.com/roads/psiz.git --branch v0.5.1
- using PyPI:
Resources
Attribution
If you use PsiZ in your work please cite one of the following:
@InProceedings{Roads_Love_2021:CVPR,
title = {Enriching ImageNet with Human Similarity Judgments and Psychological Embeddings},
author = {Brett D. Roads and Bradley C. Love},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
year = {2021},
month = {6},
pages = {3547--3557}
}
@Article{Roads_Mozer_2019:BRM,
title = {Obtaining psychological embeddings through joint kernel and metric learning},
author = {Brett D. Roads and Michael C. Mozer},
journal = {Behavior Research Methods},
year = {2019},
volume = {51},
pages = {2180–-2193},
doi = {10.3758/s13428-019-01285-3}
}
Contribution Guidelines
If you would like to contribute please see the contributing guidelines.
This project uses a Code of Conduct adapted from the Contributor Covenant version 2.0, available at https://www.contributor-covenant.org/version/2/0/code_of_conduct.html.
Licence
This project is licensed under the Apache Licence 2.0 - see LICENSE file for details.
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