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Toolbox for inferring psychological embeddings.

Project description

PsiZ logo

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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

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.

Project details


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Files for psiz, version 0.5.1
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