# Effector
[Documenation](https://xai-effector.github.io/) | [Global Effect](https://xai-effector.github.io/global_effect_intro/) | [Regional Effect](https://xai-effector.github.io/regional_effect_intro/) | [API](https://xai-effector.github.io/api/) | [Tutorials](https://xai-effector.github.io/)
`Effector` is a python package for global and regional effect analysis.
---
![using effector](docs/docs/static/effector_intro.gif)
---
### Installation
`Effector` is compatible with `Python 3.7+`. We recommend to first create a virtual environment with `conda`:
```bash
conda create -n effector python=3.11
conda activate effector
```
and then install `Effector` via `pip`:
```bash
pip install effector
```
If you want to also run the Tutorial notebooks, add some more dependencies to the environment:
```bash
pip install -r requirements-dev.txt
```
## Methods and Publications
### Methods
`Effector` implements the following methods:
| Method | Global Effect | Regional Effect | Paper |
|----------|-----------------------------------------------------------|-------------------------------------------------------------------------------|-----------------------------------------------------------------------------------------------------------------------------------------------------|
| PDP | [`PDP`](./api/#effector.global_effect_pdp.PDP) | [`RegionalPDP`](./api/#effector.regional_effect_pdp.RegionalPDP) | [PDP](https://projecteuclid.org/euclid.aos/1013203451), [ICE](https://arxiv.org/abs/1309.6392), [GAGDET-PD](https://arxiv.org/pdf/2306.00541.pdf) |
| d-PDP | [`DerPDP`](./api/#effector.global_effect_pdp.DerPDP) | [`RegionalDerPDP`](./api/#effector.regional_effect_pdp.RegionalDerPDP) | [d-PDP, d-ICE](https://arxiv.org/abs/1309.6392) |
| ALE | [`ALE`](./api/#effector.global_effect_ale.ALE) | [`RegionalALE`](./api/#effector.regional_effect_ale.RegionalALE) | [ALE](https://academic.oup.com/jrsssb/article/82/4/1059/7056085), [GAGDET-ALE](https://arxiv.org/pdf/2306.00541.pdf) |
| RHALE | [`RHALE`](./api/#effector.global_effect_ale.RHALE) | [`RegionalRHALE`](./api/#effector.regional_effect_ale.RegionalRHALE) | [RHALE](https://ebooks.iospress.nl/doi/10.3233/FAIA230354), [DALE](https://proceedings.mlr.press/v189/gkolemis23a/gkolemis23a.pdf) |
| SHAP-DP | [`ShapDP`](./api/#effector.global_effect_shap.ShapDP) | [`RegionalShapDP`](./api/#effector.regional_effect_shap.RegionalShapDP) | [SHAP](https://papers.nips.cc/paper/7062-a-unified-approach-to-interpreting-model-predictions), [GAGDET-DP](https://arxiv.org/pdf/2306.00541.pdf) |
Raw data
{
"_id": null,
"home_page": null,
"name": "effector",
"maintainer": null,
"docs_url": null,
"requires_python": ">=3.7",
"maintainer_email": "Julia Herbinger <julia.herbinger@gmail.com>, Christos Diou <cdiou@hua.gr>, Giuseppe Casalicchio <giuseppe.casalicchio@gmail.com>",
"keywords": "explainability, machine learning, deep learning, interpretability, feature effect",
"author": null,
"author_email": "Vasilis Gkolemis <ntipakos@gmail.com>",
"download_url": "https://files.pythonhosted.org/packages/b7/74/0c2811259fcd52ebdde9fa9da4eb58e78794a608892e57c1a93607cb16cb/effector-0.0.272.tar.gz",
"platform": null,
"description": "# Effector\n\n[Documenation](https://xai-effector.github.io/) | [Global Effect](https://xai-effector.github.io/global_effect_intro/) | [Regional Effect](https://xai-effector.github.io/regional_effect_intro/) | [API](https://xai-effector.github.io/api/) | [Tutorials](https://xai-effector.github.io/)\n\n`Effector` is a python package for global and regional effect analysis.\n\n---\n\n![using effector](docs/docs/static/effector_intro.gif)\n\n---\n### Installation\n\n`Effector` is compatible with `Python 3.7+`. We recommend to first create a virtual environment with `conda`:\n\n```bash\nconda create -n effector python=3.11\nconda activate effector\n```\n\nand then install `Effector` via `pip`:\n\n```bash\npip install effector\n```\n\nIf you want to also run the Tutorial notebooks, add some more dependencies to the environment:\n\n```bash\npip install -r requirements-dev.txt\n```\n\n## Methods and Publications\n\n### Methods\n\n`Effector` implements the following methods:\n\n| Method | Global Effect | Regional Effect | Paper | \n|----------|-----------------------------------------------------------|-------------------------------------------------------------------------------|-----------------------------------------------------------------------------------------------------------------------------------------------------|\n| PDP | [`PDP`](./api/#effector.global_effect_pdp.PDP) | [`RegionalPDP`](./api/#effector.regional_effect_pdp.RegionalPDP) | [PDP](https://projecteuclid.org/euclid.aos/1013203451), [ICE](https://arxiv.org/abs/1309.6392), [GAGDET-PD](https://arxiv.org/pdf/2306.00541.pdf) |\n| d-PDP | [`DerPDP`](./api/#effector.global_effect_pdp.DerPDP) | [`RegionalDerPDP`](./api/#effector.regional_effect_pdp.RegionalDerPDP) | [d-PDP, d-ICE](https://arxiv.org/abs/1309.6392) | \n| ALE | [`ALE`](./api/#effector.global_effect_ale.ALE) | [`RegionalALE`](./api/#effector.regional_effect_ale.RegionalALE) | [ALE](https://academic.oup.com/jrsssb/article/82/4/1059/7056085), [GAGDET-ALE](https://arxiv.org/pdf/2306.00541.pdf) | \n| RHALE | [`RHALE`](./api/#effector.global_effect_ale.RHALE) | [`RegionalRHALE`](./api/#effector.regional_effect_ale.RegionalRHALE) | [RHALE](https://ebooks.iospress.nl/doi/10.3233/FAIA230354), [DALE](https://proceedings.mlr.press/v189/gkolemis23a/gkolemis23a.pdf) |\n| SHAP-DP | [`ShapDP`](./api/#effector.global_effect_shap.ShapDP) | [`RegionalShapDP`](./api/#effector.regional_effect_shap.RegionalShapDP) | [SHAP](https://papers.nips.cc/paper/7062-a-unified-approach-to-interpreting-model-predictions), [GAGDET-DP](https://arxiv.org/pdf/2306.00541.pdf) |\n",
"bugtrack_url": null,
"license": "MIT License",
"summary": "A Python library for global and regional effects",
"version": "0.0.272",
"project_urls": {
"documentation": "https://xai-effector.github.io",
"source": "https://github.com/givasile/effector",
"tracker": "https://github.com/givasile/effector/issues"
},
"split_keywords": [
"explainability",
" machine learning",
" deep learning",
" interpretability",
" feature effect"
],
"urls": [
{
"comment_text": "",
"digests": {
"blake2b_256": "eab17b1e18c3d5ecec937e22049f10b064896e071c137dc8b22a748d4736a6a1",
"md5": "fd04040a66ab57c26ec834f730c41264",
"sha256": "f2476899d0fef1bf2aa7b7d2a75f8e567d1224a8ab0401ff075acbe4e3eae3cc"
},
"downloads": -1,
"filename": "effector-0.0.272-py3-none-any.whl",
"has_sig": false,
"md5_digest": "fd04040a66ab57c26ec834f730c41264",
"packagetype": "bdist_wheel",
"python_version": "py3",
"requires_python": ">=3.7",
"size": 59708,
"upload_time": "2024-05-05T20:52:11",
"upload_time_iso_8601": "2024-05-05T20:52:11.288795Z",
"url": "https://files.pythonhosted.org/packages/ea/b1/7b1e18c3d5ecec937e22049f10b064896e071c137dc8b22a748d4736a6a1/effector-0.0.272-py3-none-any.whl",
"yanked": false,
"yanked_reason": null
},
{
"comment_text": "",
"digests": {
"blake2b_256": "b7740c2811259fcd52ebdde9fa9da4eb58e78794a608892e57c1a93607cb16cb",
"md5": "9144638453af42d27829c72650411977",
"sha256": "0a91814656dd9d3661d11af2b0b3b3481d1dd995904f2888e61cf2252eeec5d1"
},
"downloads": -1,
"filename": "effector-0.0.272.tar.gz",
"has_sig": false,
"md5_digest": "9144638453af42d27829c72650411977",
"packagetype": "sdist",
"python_version": "source",
"requires_python": ">=3.7",
"size": 9436764,
"upload_time": "2024-05-05T20:52:13",
"upload_time_iso_8601": "2024-05-05T20:52:13.235066Z",
"url": "https://files.pythonhosted.org/packages/b7/74/0c2811259fcd52ebdde9fa9da4eb58e78794a608892e57c1a93607cb16cb/effector-0.0.272.tar.gz",
"yanked": false,
"yanked_reason": null
}
],
"upload_time": "2024-05-05 20:52:13",
"github": true,
"gitlab": false,
"bitbucket": false,
"codeberg": false,
"github_user": "givasile",
"github_project": "effector",
"travis_ci": false,
"coveralls": false,
"github_actions": true,
"lcname": "effector"
}