effector


Nameeffector JSON
Version 0.0.272 PyPI version JSON
download
home_pageNone
SummaryA Python library for global and regional effects
upload_time2024-05-05 20:52:13
maintainerNone
docs_urlNone
authorNone
requires_python>=3.7
licenseMIT License
keywords explainability machine learning deep learning interpretability feature effect
VCS
bugtrack_url
requirements No requirements were recorded.
Travis-CI No Travis.
coveralls test coverage No coveralls.
            # 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"
}
        
Elapsed time: 0.21981s