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https://github.com/ap6yc/metaicvi.jl
A Julia implementation of the Meta-ICVI method as a separate package.
https://github.com/ap6yc/metaicvi.jl
Last synced: about 1 month ago
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A Julia implementation of the Meta-ICVI method as a separate package.
- Host: GitHub
- URL: https://github.com/ap6yc/metaicvi.jl
- Owner: AP6YC
- License: mit
- Created: 2021-08-25T18:45:35.000Z (over 3 years ago)
- Default Branch: develop
- Last Pushed: 2024-09-09T01:20:28.000Z (4 months ago)
- Last Synced: 2024-11-19T03:08:43.486Z (about 2 months ago)
- Language: Julia
- Size: 459 KB
- Stars: 2
- Watchers: 1
- Forks: 0
- Open Issues: 3
-
Metadata Files:
- Readme: README.md
- License: LICENSE
- Code of conduct: CODE_OF_CONDUCT.md
- Citation: CITATION.cff
Awesome Lists containing this project
README
# MetaICVI
A Julia implementation of the Meta-ICVI method as a standalone package.
Please see the official [documentation][docs-dev-url] for usage and contribution guidelines.
| **Documentation** | **Coverage** | **CI Status** | **Releases** |
|:-----------------:|:------------:|:-------------:|:------------:|
| [![Dev][docs-dev-img]][docs-dev-url] | [![Codecov][codecov-img]][codecov-url] | [![CI Status][ci-img]][ci-url] | [![Zenodo][zenodo-img]][zenodo-url] |
| [![Stable][docs-stable-img]][docs-stable-url] | [![Coveralls][coveralls-img]][coveralls-url] | [![Documentation][doc-status-img]][doc-status-url] | [![version][version-img]][version-url] |
| **Dependents** | **Issues** | **JuliaHub Status** | **Downloads** |
| [![deps][deps-img]][deps-url] | [![GitHubIssues][issues-img]][issues-url] | [![JuliaHub][pkgeval-img]][pkgeval-url] | [![Downloads][downloads-img]][downloads-url] |
[version-img]: https://juliahub.com/docs/General/MetaICVI/stable/version.svg
[version-url]: https://juliahub.com/ui/Packages/General/MetaICVI
[deps-img]: https://juliahub.com/docs/General/MetaICVI/stable/deps.svg
[deps-url]: https://juliahub.com/ui/Packages/General/MetaICVI?t=2
[downloads-img]: https://shields.io/endpoint?url=https://pkgs.genieframework.com/api/v1/badge/MetaICVI
[downloads-url]: https://pkgs.genieframework.com?packages=MetaICVI
[issues-img]: https://img.shields.io/github/issues/AP6YC/MetaICVI.jl
[issues-url]: https://github.com/AP6YC/MetaICVI.jl/issues
[zenodo-img]: https://zenodo.org/badge/DOI/10.5281/zenodo.5633988.svg
[zenodo-url]: https://doi.org/10.5281/zenodo.5633988
[docs-stable-img]: https://img.shields.io/badge/docs-stable-blue.svg
[docs-stable-url]: https://AP6YC.github.io/MetaICVI.jl/stable
[docs-dev-img]: https://img.shields.io/badge/docs-dev-blue.svg
[docs-dev-url]: https://AP6YC.github.io/MetaICVI.jl/dev
[doc-status-img]: https://github.com/AP6YC/MetaICVI.jl/actions/workflows/Documentation.yml/badge.svg
[doc-status-url]: https://github.com/AP6YC/MetaICVI.jl/actions/workflows/Documentation.yml
[ci-img]: https://github.com/AP6YC/MetaICVI.jl/workflows/CI/badge.svg
[ci-url]: https://github.com/AP6YC/MetaICVI.jl/actions
[codecov-img]: https://codecov.io/gh/AP6YC/MetaICVI.jl/branch/main/graph/badge.svg
[codecov-url]: https://codecov.io/gh/AP6YC/MetaICVI.jl
[pkgeval-img]: https://juliahub.com/docs/MetaICVI/pkgeval.svg
[pkgeval-url]: https://juliahub.com/ui/Packages/MetaICVI/N0cWm
[issues-url]: https://github.com/AP6YC/MetaICVI.jl/issues
## Table of Contents
- [MetaICVI](#metaicvi)
- [Table of Contents](#table-of-contents)
- [Usage](#usage)
- [Installation](#installation)
- [Basic Usage](#basic-usage)
- [Advanced Usage](#advanced-usage)
- [Contributing](#contributing)
- [Acknowledgements](#acknowledgements)
- [Authors](#authors)
- [License](#license)
- [Citation](#citation)
## Usage
### Installation
You must install `PyCallJLD.jl` alongside `MetaICVI.jl` for correct classifier module loading and saving.
This is because the `ScikitLearn.jl` dependency requires saving/loading with the `JLD.jl` package on `PyCall.jl` objects, and PyCallJLD correctly loads the serialized object definitions into the current workspace.
Otherwise, the classifier is loaded a memory block wrapped in a PyObject type, breaking inference and other operations.
Both `PyCallJLD.jl` and `MetaICVI.jl` are distributed as Julia packages, available on [JuliaHub](https://juliahub.com/).
Their installation followa the usual Julia package installation procedure, and they can both be installed simultaneously interactively:
```julia-repl
julia> ]
(@v1.9) pkg> add PyCallJLD MetaICVI
```
or programmatically:
```julia
using Pkg
Pkg.add("PyCallJLD")
Pkg.add("MetaICVI")
```
You may also get the most recent changes directly from the GitHub repository with:
```julia-repl
julia> ]
(@v1.9) pkg> add https://github.com/AP6YC/MetaICVI.jl
```
or programmatically, also with the GitHub link:
```julia
using Pkg
Pkg.add("https://github.com/AP6YC/MetaICVI.jl")
```
### Basic Usage
First, load both `PyCallJLD` and `MetaICVI` with
```julia
using PyCallJLD, MetaICVI
```
Then, create a MetaICVI module with the default constructor
```julia
metaicvi = MetaICVIModule()
```
and retrieve the MetaICVI value iteratively with
```julia
get_metaicvi(metaicvi, sample, label)
```
where `sample` is a real-valued vector and `label` is an integer.
### Advanced Usage
After loading both `PyCallJLD` and `MetaICVI`
```julia
using PyCallJLD, MetaICVI
```
you can specify the MetaICVI options with
```julia
opts = MetaICVIOpts(
classifier_selection = :SGDClassifier,
classifier_opts = (loss="log_loss", max_iter=30),
icvi_window = 5,
correlation_window = 5,
n_rocket = 5,
rocket_file = "data/models/rocket.jld2",
classifier_file = "data/models/classifier.jld",
display = true,
fail_on_missing = false
)
metaicvi = MetaICVIModule(opts)
```
The options are
- `classifier_selection`: a symbol for a linear classifier from `ScikitLearn.jl` (only used if you are creating and training a new classifier).
- `classifier_opts`: the options passed to the classifier during instantiation (also only used if creating and training a new classifier).
- `icvi_window`: the number of ICVI criterion values to compute rank correlation across.
- `correlation_window`: the number of correlations to compute rocket features across.
- `rocket_file`: filename of a saved RocketModule.
- `classifier_file`: filename of a saved linear classifier.
- `display`: boolean flag for logging info.
- `fail_on_missing`: boolean flag for crashing if missing rocket and/or classifier files.
## Contributing
Please raise an [issue][issues-url].
## Acknowledgements
### Authors
- Sasha Petrenko
### License
This software is developed by the Applied Computational Intelligence Laboratory (ACIL) of the Missouri University of Science and Technology (S&T) under the supervision of Teledyne Technologies for the DARPA L2M program.
Read the [License](LICENSE).
### Citation
This project has a [citation file](CITATION.cff) file that generates citation information for the package and corresponding JOSS paper, which can be accessed at the "Cite this repository button" under the "About" section of the GitHub page.
You may also cite this repository with the following BibTeX entry:
```bibtex
@article{Melton2022,
author = "Niklas Melton and Sasha Petrenko and Donald Wunsch",
title = "{Meta-iCVIs: Ensemble Validity Metrics for Concise Labeling of Correct, Under- or Over-Partitioning in Streaming Clustering}",
year = "2022",
month = "12",
url = "https://doi.org/10.36227/techrxiv.21685214",
doi = "10.36227/techrxiv.21685214"
}
```