| Name | lemon-explainer-research-use JSON |
| Version |
1.0.0
JSON |
| download |
| home_page | None |
| Summary | lemon-explainer package updated for research usages. |
| upload_time | 2024-06-15 08:14:37 |
| maintainer | Mohammad Amin Dadgar |
| docs_url | None |
| author | Dennis Collaris |
| requires_python | None |
| license | None |
| keywords |
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| VCS |
|
| bugtrack_url |
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| requirements |
No requirements were recorded.
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<img width="250" src="https://explaining.ml/images/lemon-logo.png" />
[](https://badge.fury.io/py/lemon_explainer)
**LEMON** is a technique to explain why predictions of machine learning models are made. It does so by providing feature contribution: a score for each feature that indicates how much it contributed to the final prediction. More precisely, it shows the sensitivity of the feature: a small change in an important feature's value results in a relatively large change in prediction. It is similar to the popular [LIME](https://github.com/marcotcr/lime) explanation technique, but is more faithful to the reference model, especially for larger datasets.
[Website ↗](https://explaining.ml/lemon)
[Academic paper ↗](https://link.springer.com/chapter/10.1007/978-3-031-30047-9_7)
## Installation
To install use pip:
```
$ pip install lemon-explainer
```
## Example
A minimal working example is shown below:
```python
import numpy as np
import pandas as pd
from sklearn.datasets import load_iris
from sklearn.ensemble import RandomForestClassifier
from lemon import LemonExplainer
# Load dataset
data = load_iris(as_frame=True)
X = data.data
y = pd.Series(np.array(data.target_names)[data.target])
# Train complex model
clf = RandomForestClassifier()
clf.fit(X, y)
# Explain instance
explainer = LemonExplainer(X, radius_max=0.5)
instance = X.iloc[-1, :]
explanation = explainer.explain_instance(instance, clf.predict_proba)[0]
explanation.show_in_notebook()
```
## Development
For a development installation (requires npm or yarn),
```
$ git clone https://github.com/iamDecode/lemon.git
$ cd lemon
```
You may want to (create and) activate a virtual environment:
```
$ python3 -m venv venv
$ source venv/bin/activate
```
Install requirements:
```
$ pip install -r requirements.txt
```
And run the tests with:
```
$ pytest .
```
## Approximate distance kernel LIME
If you prefer to use a Gaussian distance kernel as used in LIME, we can approximate this behavior with:
```python
from lemon import LemonExplainer, gaussian_kernel
from scipy.special import gammainccinv
DIMENSIONS = X.shape[1]
KERNEL_SIZE = np.sqrt(DIMENSIONS) * .75 # kernel size as used in LIME
# Obtain a distance kernel very close to LIME's gaussian kernel, see the paper for details.
p = 0.999
radius = KERNEL_SIZE * np.sqrt(2 * gammainccinv(DIMENSIONS / 2, (1 - p)))
kernel = lambda x: gaussian_kernel(x, KERNEL_SIZE)
explainer = LemonExplainer(X, distance_kernel=kernel, radius_max=radius)
```
This behavior is as close as possible to LIME, but still yields more faithful explanations due to LEMON's improved sampling technique. Read the paper for more details about this approach.
## Citation
If you want to refer to our explanation technique, please cite our paper using the following BibTeX entry:
```bibtex
@inproceedings{collaris2023lemon,
title={{LEMON}: Alternative Sampling for More Faithful Explanation Through Local Surrogate Models},
author={Collaris, Dennis and Gajane, Pratik and Jorritsma, Joost and van Wijk, Jarke J and Pechenizkiy, Mykola},
booktitle={Advances in Intelligent Data Analysis XXI: 21st International Symposium on Intelligent Data Analysis (IDA 2023)},
pages={77--90},
year={2023},
organization={Springer}
}
```
## License
This project is licensed under the BSD 2-Clause License - see the [LICENSE](LICENSE) file for details.
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"description": "<img width=\"250\" src=\"https://explaining.ml/images/lemon-logo.png\" />\n\n[](https://badge.fury.io/py/lemon_explainer)\n\n**LEMON** is a technique to explain why predictions of machine learning models are made. It does so by providing feature contribution: a score for each feature that indicates how much it contributed to the final prediction. More precisely, it shows the sensitivity of the feature: a small change in an important feature's value results in a relatively large change in prediction. It is similar to the popular [LIME](https://github.com/marcotcr/lime) explanation technique, but is more faithful to the reference model, especially for larger datasets. \n\n[Website \u2197](https://explaining.ml/lemon)\n[Academic paper \u2197](https://link.springer.com/chapter/10.1007/978-3-031-30047-9_7)\n\n\n## Installation\n\nTo install use pip:\n\n```\n$ pip install lemon-explainer\n```\n\n## Example\nA minimal working example is shown below:\n\n```python\nimport numpy as np\nimport pandas as pd\nfrom sklearn.datasets import load_iris\nfrom sklearn.ensemble import RandomForestClassifier\nfrom lemon import LemonExplainer\n\n# Load dataset\ndata = load_iris(as_frame=True)\nX = data.data\ny = pd.Series(np.array(data.target_names)[data.target])\n\n# Train complex model\nclf = RandomForestClassifier()\nclf.fit(X, y)\n\n# Explain instance\nexplainer = LemonExplainer(X, radius_max=0.5)\ninstance = X.iloc[-1, :]\nexplanation = explainer.explain_instance(instance, clf.predict_proba)[0]\nexplanation.show_in_notebook()\n```\n\n## Development\n\nFor a development installation (requires npm or yarn),\n\n```\n$ git clone https://github.com/iamDecode/lemon.git\n$ cd lemon\n```\n\nYou may want to (create and) activate a virtual environment:\n\n```\n$ python3 -m venv venv\n$ source venv/bin/activate\n```\n\nInstall requirements:\n\n```\n$ pip install -r requirements.txt\n```\n\nAnd run the tests with:\n\n```\n$ pytest .\n```\n\n## Approximate distance kernel LIME\n\nIf you prefer to use a Gaussian distance kernel as used in LIME, we can approximate this behavior with:\n\n```python\nfrom lemon import LemonExplainer, gaussian_kernel\nfrom scipy.special import gammainccinv\n\nDIMENSIONS = X.shape[1]\nKERNEL_SIZE = np.sqrt(DIMENSIONS) * .75 # kernel size as used in LIME\n\n# Obtain a distance kernel very close to LIME's gaussian kernel, see the paper for details.\np = 0.999\nradius = KERNEL_SIZE * np.sqrt(2 * gammainccinv(DIMENSIONS / 2, (1 - p)))\nkernel = lambda x: gaussian_kernel(x, KERNEL_SIZE)\n\nexplainer = LemonExplainer(X, distance_kernel=kernel, radius_max=radius)\n```\n\nThis behavior is as close as possible to LIME, but still yields more faithful explanations due to LEMON's improved sampling technique. Read the paper for more details about this approach.\n\n## Citation\n\nIf you want to refer to our explanation technique, please cite our paper using the following BibTeX entry:\n\n```bibtex\n@inproceedings{collaris2023lemon,\n title={{LEMON}: Alternative Sampling for More Faithful Explanation Through Local Surrogate Models},\n author={Collaris, Dennis and Gajane, Pratik and Jorritsma, Joost and van Wijk, Jarke J and Pechenizkiy, Mykola},\n booktitle={Advances in Intelligent Data Analysis XXI: 21st International Symposium on Intelligent Data Analysis (IDA 2023)},\n pages={77--90},\n year={2023},\n organization={Springer}\n}\n```\n\n## License\n\nThis project is licensed under the BSD 2-Clause License - see the [LICENSE](LICENSE) file for details.\n",
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