aind-dynamic-foraging-models's Introduction

aind-dynamic-foraging-models

Overview

Descriptive and generative behavioral models for the dynamic foraging task.

Logistic regression: code, notebook

Installation

To install the software, run

pip install aind-dynamic-foraging-models

To develop the code, clone the repo to your local machine, and run

pip install -e .[dev]

Contributing

Linters and testing

There are several libraries used to run linters, check documentation, and run tests.

Please test your changes using the coverage library, which will run the tests and log a coverage report:

coverage run -m unittest discover && coverage report

Use interrogate to check that modules, methods, etc. have been documented thoroughly:

interrogate .

Use flake8 to check that code is up to standards (no unused imports, etc.):

flake8 .

Use black to automatically format the code into PEP standards:

black .

Use isort to automatically sort import statements:

isort .

Pull requests

For internal members, please create a branch. For external members, please fork the repository and open a pull request from the fork. We'll primarily use Angular style for commit messages. Roughly, they should follow the pattern:

<type>(<scope>): <short summary>

where scope (optional) describes the packages affected by the code changes and type (mandatory) is one of:

build: Changes that affect build tools or external dependencies (example scopes: pyproject.toml, setup.py)
ci: Changes to our CI configuration files and scripts (examples: .github/workflows/ci.yml)
docs: Documentation only changes
feat: A new feature
fix: A bugfix
perf: A code change that improves performance
refactor: A code change that neither fixes a bug nor adds a feature
test: Adding missing tests or correcting existing tests

Semantic Release

The table below, from semantic release, shows which commit message gets you which release type when semantic-release runs (using the default configuration):

Commit message	Release type
`fix(pencil): stop graphite breaking when too much pressure applied`	~~Patch~~ Fix Release, Default release
`feat(pencil): add 'graphiteWidth' option`	~~Minor~~ Feature Release
`perf(pencil): remove graphiteWidth option` `BREAKING CHANGE: The graphiteWidth option has been removed.` `The default graphite width of 10mm is always used for performance reasons.`	~~Major~~ Breaking Release (Note that the `BREAKING CHANGE:` token must be in the footer of the commit)

Documentation

To generate the rst files source files for documentation, run

sphinx-apidoc -o doc_template/source/ src

Then to create the documentation HTML files, run

sphinx-build -b html doc_template/source/ doc_template/build/html

More info on sphinx installation can be found here.

aind-dynamic-foraging-models's People

Contributors

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aind-dynamic-foraging-models's Issues

Refactor: isolate gymnasium env and task (block structure)

          I decided to refactor the code so that `agent`, `environment`, and `task` are fully disentangled.

Here are some ideas,

We code all AIND behavioral tasks (including future VR foraging / force foraging) using gymnasium in a shared library aind-behavior-gym. For dynamic foraging, we can further separate the block structure logic to a separate library called aind-dynamic-foraging-reward-schedule.
Both aind-behavior-gym and the behavioral GUI that trains animal call the same task class in reward-schedule , so that our artificial agents and animals are doing exactly the same task.
On the other hand, we can have different agent libraries that perform the task in the gym. Could be either Po-Chen's meta-rl agents, my Q-learning models, Lukasz's Bayesian agents, or Ulises's RNNs.
If we were to fit the agents to animal behavior, we can write the fitting functions inside the agent libraries since fitting should not rely on the exact task.