path: root/python-text-explainability.spec

%global _empty_manifest_terminate_build 0
Name:		python-text-explainability
Version:	0.7.0
Release:	1
Summary:	Generic explainability architecture for text machine learning models
License:	GNU LGPL v3
URL:		https://text-explainability.readthedocs.io/
Source0:	https://mirrors.nju.edu.cn/pypi/web/packages/a7/ec/731e27ff33b8ab47144b59300ce151a496151f4bb6c98ac55286b3ef24fc/text_explainability-0.7.0.tar.gz
BuildArch:	noarch

Requires:	python3-instancelib
Requires:	python3-genbase
Requires:	python3-scikit-learn
Requires:	python3-plotly
Requires:	python3-sentence-transformers
Requires:	python3-scikit-learn-extra
Requires:	python3-imodels
Requires:	python3-genbase-test-helpers
Requires:	python3-fastcountvectorizer

%description
`text_explainability` provides a **generic architecture** from which well-known state-of-the-art explainability approaches for text can be composed. This modular architecture allows components to be swapped out and combined, to **quickly develop new types of explainability approaches** for (natural language) text, or to **improve a plethora of approaches by improving a single module**.
Several example methods are included, which provide **local explanations** (_explaining the prediction of a single instance_, e.g. `LIME` and `SHAP`) or **global explanations** (_explaining the dataset, or model behavior on the dataset_, e.g. `TokenFrequency` and `MMDCritic`). By replacing the default modules (e.g. local data generation, global data sampling or improved embedding methods), these methods can be improved upon or new methods can be introduced.
© Marcel Robeer, 2021
## Quick tour
**Local explanation**: explain a models' prediction on a given sample, self-provided or from a dataset.
```python
from text_explainability import LIME, LocalTree
# Define sample to explain
sample = 'Explain why this is positive and not negative!'
# LIME explanation (local feature importance)
LIME().explain(sample, model).scores
# List of local rules, extracted from tree
LocalTree().explain(sample, model).rules
``` 
**Global explanation**: explain the whole dataset (e.g. train set, test set), and what they look like for the ground-truth or predicted labels.
```python
from text_explainability import import_data, TokenFrequency, MMDCritic
# Import dataset
env = import_data('./datasets/test.csv', data_cols=['fulltext'], label_cols=['label'])
# Top-k most frequent tokens per label
TokenFrequency(env.dataset).explain(labelprovider=env.labels, explain_model=False, k=3)
# 2 prototypes and 1 criticisms for the dataset
MMDCritic(env.dataset)(n_prototypes=2, n_criticisms=1)
```
## Installation
See the [installation](docs/INSTALLATION.md) instructions for an extended installation guide.
| Method | Instructions |
|--------|--------------|
| `pip` | Install from [PyPI](https://pypi.org/project/text-explainability/) via `pip3 install text_explainability`. To speed up the explanation generation process use `pip3 install text_explainability[fast]`. |
| Local | Clone this repository and install via `pip3 install -e .` or locally run `python3 setup.py install`.
## Documentation
Full documentation of the latest version is provided at [https://text-explainability.readthedocs.io/](https://text-explainability.readthedocs.io/).
## Example usage
See [example usage](example_usage.md) to see an example of how the package can be used, or run the lines in `example_usage.py` to do explore it interactively.
## Explanation methods included
`text_explainability` includes methods for model-agnostic _local explanation_ and _global explanation_. Each of these methods can be fully customized to fit the explainees' needs.
| Type | Explanation method | Description | Paper/link |
|------|--------------------|-------------|-------|
| *Local explanation* | `LIME` | Calculate feature attribution with _Local Intepretable Model-Agnostic Explanations_ (LIME). | [[Ribeiro2016](https://paperswithcode.com/method/lime)], [interpretable-ml/lime](https://christophm.github.io/interpretable-ml-book/lime.html) |
| |  `KernelSHAP` | Calculate feature attribution with _Shapley Additive Explanations_ (SHAP). | [[Lundberg2017](https://paperswithcode.com/paper/a-unified-approach-to-interpreting-model)], [interpretable-ml/shap](https://christophm.github.io/interpretable-ml-book/shap.html) |
| |  `LocalTree` | Fit a local decision tree around a single decision. | [[Guidotti2018](https://paperswithcode.com/paper/local-rule-based-explanations-of-black-box)] |
| | `LocalRules` | Fit a local sparse set of label-specific rules using `SkopeRules`. | [github/skope-rules](https://github.com/scikit-learn-contrib/skope-rules) |
| |  `FoilTree` | Fit a local contrastive/counterfactual decision tree around a single decision. | [[Robeer2018](https://github.com/MarcelRobeer/ContrastiveExplanation)] |
| | `BayLIME` | Bayesian extension of LIME for include prior knowledge and more consistent explanations. | [[Zhao201](https://paperswithcode.com/paper/baylime-bayesian-local-interpretable-model)] |
| *Global explanation* | `TokenFrequency` | Show the top-_k_ number of tokens for each ground-truth or predicted label. |
| |  `TokenInformation` | Show the top-_k_ token mutual information for a dataset or model. | [wikipedia/mutual_information](https://en.wikipedia.org/wiki/Mutual_information) |
| | `KMedoids` | Embed instances and find top-_n_ prototypes (can also be performed for each label using `LabelwiseKMedoids`). | [interpretable-ml/prototypes](https://christophm.github.io/interpretable-ml-book/proto.html) |
| | `MMDCritic` | Embed instances and find top-_n_ prototypes and top-_n_ criticisms (can also be performed for each label using `LabelwiseMMDCritic`). | [[Kim2016](https://papers.nips.cc/paper/2016/hash/5680522b8e2bb01943234bce7bf84534-Abstract.html)], [interpretable-ml/prototypes](https://christophm.github.io/interpretable-ml-book/proto.html) |
## Releases
`text_explainability` is officially released through [PyPI](https://pypi.org/project/text-explainability/).
See [CHANGELOG.md](CHANGELOG.md) for a full overview of the changes for each version.
## Extensions
<a href="https://marcelrobeer.github.io/text_sensitivity/" target="_blank"><img src="https://git.science.uu.nl/m.j.robeer/text_sensitivity/-/raw/main/img/TextLogo-Logo_large_sensitivity.png" alt="T_xt sensitivity logo" width="200px"></a><p>`text_explainability` can be extended to also perform _sensitivity testing_, checking for machine learning model robustness and fairness. The `text_sensitivity` package is available through [PyPI](https://pypi.org/project/text-sensitivity/) and fully documented at [https://text-sensitivity.rtfd.io/](https://text-sensitivity.rtfd.io/).</p>
## Citation
```bibtex
@misc{text_explainability,
  title = {Python package text\_explainability},
  author = {Marcel Robeer},
  howpublished = {\url{https://git.science.uu.nl/m.j.robeer/text_explainability}},
  year = {2021}
}
```
## Maintenance
### Contributors
- [Marcel Robeer](https://www.uu.nl/staff/MJRobeer) (`@m.j.robeer`)
- [Michiel Bron](https://www.uu.nl/staff/MPBron) (`@mpbron-phd`)
### Todo
Tasks yet to be done:
* Implement local post-hoc explanations:
    - Implement Anchors
* Implement global post-hoc explanations:
    - Representative subset
* Add support for regression models
* More complex data augmentation
    - Top-k replacement (e.g. according to LM / WordNet)
    - Tokens to exclude from being changed
    - Bag-of-words style replacements
* Add rule-based return type
* Write more tests
## Credits
- Florian Gardin, Ronan Gautier, Nicolas Goix, Bibi Ndiaye and Jean-Matthieu Schertzer. _[Skope-rules](https://github.com/scikit-learn-contrib/skope-rules)_. 2020.
- Riccardo Guidotti, Anna Monreale, Salvatore Ruggieri, Dino Pedreschi, Franco Turini and Fosca Gianotti. _[Local Rule-Based Explanations of Black Box Decision Systems](https://paperswithcode.com/paper/local-rule-based-explanations-of-black-box)_. 2018.
- Been Kim, Rajiv Khanna and Oluwasanmi O. Koyejo. [Examples are not Enough, Learn to Criticize! Criticism for Interpretability](https://papers.nips.cc/paper/2016/hash/5680522b8e2bb01943234bce7bf84534-Abstract.html). _Advances in Neural Information Processing Systems (NIPS 2016)_. 2016.
- Scott Lundberg and Su-In Lee. [A Unified Approach to Interpreting Model Predictions](https://paperswithcode.com/paper/a-unified-approach-to-interpreting-model). _31st Conference on Neural Information Processing Systems (NIPS 2017)_. 2017.
- Christoph Molnar. _[Interpretable Machine Learning: A Guide for Making Black Box Models Explainable](https://christophm.github.io/interpretable-ml-book/)_. 2021.
- Marco Tulio Ribeiro, Sameer Singh and Carlos Guestrin. ["Why Should I Trust You?": Explaining the Predictions of Any Classifier](https://paperswithcode.com/method/lime). _Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics (NAACL 2016)_. 2016.
- Marco Tulio Ribeiro, Sameer Singh and Carlos Guestrin. [Anchors: High-Precision Model-Agnostic Explanations](https://github.com/marcotcr/anchor). _AAAI Conference on Artificial Intelligence (AAAI)_. 2018.
- Jasper van der Waa, Marcel Robeer, Jurriaan van Diggelen, Matthieu Brinkhuis and Mark Neerincx. ["Contrastive Explanations with Local Foil Trees"](https://github.com/MarcelRobeer/ContrastiveExplanation). _2018 Workshop on Human Interpretability in Machine Learning (WHI 2018)_. 2018.

%package -n python3-text-explainability
Summary:	Generic explainability architecture for text machine learning models
Provides:	python-text-explainability
BuildRequires:	python3-devel
BuildRequires:	python3-setuptools
BuildRequires:	python3-pip
%description -n python3-text-explainability
`text_explainability` provides a **generic architecture** from which well-known state-of-the-art explainability approaches for text can be composed. This modular architecture allows components to be swapped out and combined, to **quickly develop new types of explainability approaches** for (natural language) text, or to **improve a plethora of approaches by improving a single module**.
Several example methods are included, which provide **local explanations** (_explaining the prediction of a single instance_, e.g. `LIME` and `SHAP`) or **global explanations** (_explaining the dataset, or model behavior on the dataset_, e.g. `TokenFrequency` and `MMDCritic`). By replacing the default modules (e.g. local data generation, global data sampling or improved embedding methods), these methods can be improved upon or new methods can be introduced.
&copy; Marcel Robeer, 2021
## Quick tour
**Local explanation**: explain a models' prediction on a given sample, self-provided or from a dataset.
```python
from text_explainability import LIME, LocalTree
# Define sample to explain
sample = 'Explain why this is positive and not negative!'
# LIME explanation (local feature importance)
LIME().explain(sample, model).scores
# List of local rules, extracted from tree
LocalTree().explain(sample, model).rules
``` 
**Global explanation**: explain the whole dataset (e.g. train set, test set), and what they look like for the ground-truth or predicted labels.
```python
from text_explainability import import_data, TokenFrequency, MMDCritic
# Import dataset
env = import_data('./datasets/test.csv', data_cols=['fulltext'], label_cols=['label'])
# Top-k most frequent tokens per label
TokenFrequency(env.dataset).explain(labelprovider=env.labels, explain_model=False, k=3)
# 2 prototypes and 1 criticisms for the dataset
MMDCritic(env.dataset)(n_prototypes=2, n_criticisms=1)
```
## Installation
See the [installation](docs/INSTALLATION.md) instructions for an extended installation guide.
| Method | Instructions |
|--------|--------------|
| `pip` | Install from [PyPI](https://pypi.org/project/text-explainability/) via `pip3 install text_explainability`. To speed up the explanation generation process use `pip3 install text_explainability[fast]`. |
| Local | Clone this repository and install via `pip3 install -e .` or locally run `python3 setup.py install`.
## Documentation
Full documentation of the latest version is provided at [https://text-explainability.readthedocs.io/](https://text-explainability.readthedocs.io/).
## Example usage
See [example usage](example_usage.md) to see an example of how the package can be used, or run the lines in `example_usage.py` to do explore it interactively.
## Explanation methods included
`text_explainability` includes methods for model-agnostic _local explanation_ and _global explanation_. Each of these methods can be fully customized to fit the explainees' needs.
| Type | Explanation method | Description | Paper/link |
|------|--------------------|-------------|-------|
| *Local explanation* | `LIME` | Calculate feature attribution with _Local Intepretable Model-Agnostic Explanations_ (LIME). | [[Ribeiro2016](https://paperswithcode.com/method/lime)], [interpretable-ml/lime](https://christophm.github.io/interpretable-ml-book/lime.html) |
| |  `KernelSHAP` | Calculate feature attribution with _Shapley Additive Explanations_ (SHAP). | [[Lundberg2017](https://paperswithcode.com/paper/a-unified-approach-to-interpreting-model)], [interpretable-ml/shap](https://christophm.github.io/interpretable-ml-book/shap.html) |
| |  `LocalTree` | Fit a local decision tree around a single decision. | [[Guidotti2018](https://paperswithcode.com/paper/local-rule-based-explanations-of-black-box)] |
| | `LocalRules` | Fit a local sparse set of label-specific rules using `SkopeRules`. | [github/skope-rules](https://github.com/scikit-learn-contrib/skope-rules) |
| |  `FoilTree` | Fit a local contrastive/counterfactual decision tree around a single decision. | [[Robeer2018](https://github.com/MarcelRobeer/ContrastiveExplanation)] |
| | `BayLIME` | Bayesian extension of LIME for include prior knowledge and more consistent explanations. | [[Zhao201](https://paperswithcode.com/paper/baylime-bayesian-local-interpretable-model)] |
| *Global explanation* | `TokenFrequency` | Show the top-_k_ number of tokens for each ground-truth or predicted label. |
| |  `TokenInformation` | Show the top-_k_ token mutual information for a dataset or model. | [wikipedia/mutual_information](https://en.wikipedia.org/wiki/Mutual_information) |
| | `KMedoids` | Embed instances and find top-_n_ prototypes (can also be performed for each label using `LabelwiseKMedoids`). | [interpretable-ml/prototypes](https://christophm.github.io/interpretable-ml-book/proto.html) |
| | `MMDCritic` | Embed instances and find top-_n_ prototypes and top-_n_ criticisms (can also be performed for each label using `LabelwiseMMDCritic`). | [[Kim2016](https://papers.nips.cc/paper/2016/hash/5680522b8e2bb01943234bce7bf84534-Abstract.html)], [interpretable-ml/prototypes](https://christophm.github.io/interpretable-ml-book/proto.html) |
## Releases
`text_explainability` is officially released through [PyPI](https://pypi.org/project/text-explainability/).
See [CHANGELOG.md](CHANGELOG.md) for a full overview of the changes for each version.
## Extensions
<a href="https://marcelrobeer.github.io/text_sensitivity/" target="_blank"><img src="https://git.science.uu.nl/m.j.robeer/text_sensitivity/-/raw/main/img/TextLogo-Logo_large_sensitivity.png" alt="T_xt sensitivity logo" width="200px"></a><p>`text_explainability` can be extended to also perform _sensitivity testing_, checking for machine learning model robustness and fairness. The `text_sensitivity` package is available through [PyPI](https://pypi.org/project/text-sensitivity/) and fully documented at [https://text-sensitivity.rtfd.io/](https://text-sensitivity.rtfd.io/).</p>
## Citation
```bibtex
@misc{text_explainability,
  title = {Python package text\_explainability},
  author = {Marcel Robeer},
  howpublished = {\url{https://git.science.uu.nl/m.j.robeer/text_explainability}},
  year = {2021}
}
```
## Maintenance
### Contributors
- [Marcel Robeer](https://www.uu.nl/staff/MJRobeer) (`@m.j.robeer`)
- [Michiel Bron](https://www.uu.nl/staff/MPBron) (`@mpbron-phd`)
### Todo
Tasks yet to be done:
* Implement local post-hoc explanations:
    - Implement Anchors
* Implement global post-hoc explanations:
    - Representative subset
* Add support for regression models
* More complex data augmentation
    - Top-k replacement (e.g. according to LM / WordNet)
    - Tokens to exclude from being changed
    - Bag-of-words style replacements
* Add rule-based return type
* Write more tests
## Credits
- Florian Gardin, Ronan Gautier, Nicolas Goix, Bibi Ndiaye and Jean-Matthieu Schertzer. _[Skope-rules](https://github.com/scikit-learn-contrib/skope-rules)_. 2020.
- Riccardo Guidotti, Anna Monreale, Salvatore Ruggieri, Dino Pedreschi, Franco Turini and Fosca Gianotti. _[Local Rule-Based Explanations of Black Box Decision Systems](https://paperswithcode.com/paper/local-rule-based-explanations-of-black-box)_. 2018.
- Been Kim, Rajiv Khanna and Oluwasanmi O. Koyejo. [Examples are not Enough, Learn to Criticize! Criticism for Interpretability](https://papers.nips.cc/paper/2016/hash/5680522b8e2bb01943234bce7bf84534-Abstract.html). _Advances in Neural Information Processing Systems (NIPS 2016)_. 2016.
- Scott Lundberg and Su-In Lee. [A Unified Approach to Interpreting Model Predictions](https://paperswithcode.com/paper/a-unified-approach-to-interpreting-model). _31st Conference on Neural Information Processing Systems (NIPS 2017)_. 2017.
- Christoph Molnar. _[Interpretable Machine Learning: A Guide for Making Black Box Models Explainable](https://christophm.github.io/interpretable-ml-book/)_. 2021.
- Marco Tulio Ribeiro, Sameer Singh and Carlos Guestrin. ["Why Should I Trust You?": Explaining the Predictions of Any Classifier](https://paperswithcode.com/method/lime). _Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics (NAACL 2016)_. 2016.
- Marco Tulio Ribeiro, Sameer Singh and Carlos Guestrin. [Anchors: High-Precision Model-Agnostic Explanations](https://github.com/marcotcr/anchor). _AAAI Conference on Artificial Intelligence (AAAI)_. 2018.
- Jasper van der Waa, Marcel Robeer, Jurriaan van Diggelen, Matthieu Brinkhuis and Mark Neerincx. ["Contrastive Explanations with Local Foil Trees"](https://github.com/MarcelRobeer/ContrastiveExplanation). _2018 Workshop on Human Interpretability in Machine Learning (WHI 2018)_. 2018.

%package help
Summary:	Development documents and examples for text-explainability
Provides:	python3-text-explainability-doc
%description help
`text_explainability` provides a **generic architecture** from which well-known state-of-the-art explainability approaches for text can be composed. This modular architecture allows components to be swapped out and combined, to **quickly develop new types of explainability approaches** for (natural language) text, or to **improve a plethora of approaches by improving a single module**.
Several example methods are included, which provide **local explanations** (_explaining the prediction of a single instance_, e.g. `LIME` and `SHAP`) or **global explanations** (_explaining the dataset, or model behavior on the dataset_, e.g. `TokenFrequency` and `MMDCritic`). By replacing the default modules (e.g. local data generation, global data sampling or improved embedding methods), these methods can be improved upon or new methods can be introduced.
&copy; Marcel Robeer, 2021
## Quick tour
**Local explanation**: explain a models' prediction on a given sample, self-provided or from a dataset.
```python
from text_explainability import LIME, LocalTree
# Define sample to explain
sample = 'Explain why this is positive and not negative!'
# LIME explanation (local feature importance)
LIME().explain(sample, model).scores
# List of local rules, extracted from tree
LocalTree().explain(sample, model).rules
``` 
**Global explanation**: explain the whole dataset (e.g. train set, test set), and what they look like for the ground-truth or predicted labels.
```python
from text_explainability import import_data, TokenFrequency, MMDCritic
# Import dataset
env = import_data('./datasets/test.csv', data_cols=['fulltext'], label_cols=['label'])
# Top-k most frequent tokens per label
TokenFrequency(env.dataset).explain(labelprovider=env.labels, explain_model=False, k=3)
# 2 prototypes and 1 criticisms for the dataset
MMDCritic(env.dataset)(n_prototypes=2, n_criticisms=1)
```
## Installation
See the [installation](docs/INSTALLATION.md) instructions for an extended installation guide.
| Method | Instructions |
|--------|--------------|
| `pip` | Install from [PyPI](https://pypi.org/project/text-explainability/) via `pip3 install text_explainability`. To speed up the explanation generation process use `pip3 install text_explainability[fast]`. |
| Local | Clone this repository and install via `pip3 install -e .` or locally run `python3 setup.py install`.
## Documentation
Full documentation of the latest version is provided at [https://text-explainability.readthedocs.io/](https://text-explainability.readthedocs.io/).
## Example usage
See [example usage](example_usage.md) to see an example of how the package can be used, or run the lines in `example_usage.py` to do explore it interactively.
## Explanation methods included
`text_explainability` includes methods for model-agnostic _local explanation_ and _global explanation_. Each of these methods can be fully customized to fit the explainees' needs.
| Type | Explanation method | Description | Paper/link |
|------|--------------------|-------------|-------|
| *Local explanation* | `LIME` | Calculate feature attribution with _Local Intepretable Model-Agnostic Explanations_ (LIME). | [[Ribeiro2016](https://paperswithcode.com/method/lime)], [interpretable-ml/lime](https://christophm.github.io/interpretable-ml-book/lime.html) |
| |  `KernelSHAP` | Calculate feature attribution with _Shapley Additive Explanations_ (SHAP). | [[Lundberg2017](https://paperswithcode.com/paper/a-unified-approach-to-interpreting-model)], [interpretable-ml/shap](https://christophm.github.io/interpretable-ml-book/shap.html) |
| |  `LocalTree` | Fit a local decision tree around a single decision. | [[Guidotti2018](https://paperswithcode.com/paper/local-rule-based-explanations-of-black-box)] |
| | `LocalRules` | Fit a local sparse set of label-specific rules using `SkopeRules`. | [github/skope-rules](https://github.com/scikit-learn-contrib/skope-rules) |
| |  `FoilTree` | Fit a local contrastive/counterfactual decision tree around a single decision. | [[Robeer2018](https://github.com/MarcelRobeer/ContrastiveExplanation)] |
| | `BayLIME` | Bayesian extension of LIME for include prior knowledge and more consistent explanations. | [[Zhao201](https://paperswithcode.com/paper/baylime-bayesian-local-interpretable-model)] |
| *Global explanation* | `TokenFrequency` | Show the top-_k_ number of tokens for each ground-truth or predicted label. |
| |  `TokenInformation` | Show the top-_k_ token mutual information for a dataset or model. | [wikipedia/mutual_information](https://en.wikipedia.org/wiki/Mutual_information) |
| | `KMedoids` | Embed instances and find top-_n_ prototypes (can also be performed for each label using `LabelwiseKMedoids`). | [interpretable-ml/prototypes](https://christophm.github.io/interpretable-ml-book/proto.html) |
| | `MMDCritic` | Embed instances and find top-_n_ prototypes and top-_n_ criticisms (can also be performed for each label using `LabelwiseMMDCritic`). | [[Kim2016](https://papers.nips.cc/paper/2016/hash/5680522b8e2bb01943234bce7bf84534-Abstract.html)], [interpretable-ml/prototypes](https://christophm.github.io/interpretable-ml-book/proto.html) |
## Releases
`text_explainability` is officially released through [PyPI](https://pypi.org/project/text-explainability/).
See [CHANGELOG.md](CHANGELOG.md) for a full overview of the changes for each version.
## Extensions
<a href="https://marcelrobeer.github.io/text_sensitivity/" target="_blank"><img src="https://git.science.uu.nl/m.j.robeer/text_sensitivity/-/raw/main/img/TextLogo-Logo_large_sensitivity.png" alt="T_xt sensitivity logo" width="200px"></a><p>`text_explainability` can be extended to also perform _sensitivity testing_, checking for machine learning model robustness and fairness. The `text_sensitivity` package is available through [PyPI](https://pypi.org/project/text-sensitivity/) and fully documented at [https://text-sensitivity.rtfd.io/](https://text-sensitivity.rtfd.io/).</p>
## Citation
```bibtex
@misc{text_explainability,
  title = {Python package text\_explainability},
  author = {Marcel Robeer},
  howpublished = {\url{https://git.science.uu.nl/m.j.robeer/text_explainability}},
  year = {2021}
}
```
## Maintenance
### Contributors
- [Marcel Robeer](https://www.uu.nl/staff/MJRobeer) (`@m.j.robeer`)
- [Michiel Bron](https://www.uu.nl/staff/MPBron) (`@mpbron-phd`)
### Todo
Tasks yet to be done:
* Implement local post-hoc explanations:
    - Implement Anchors
* Implement global post-hoc explanations:
    - Representative subset
* Add support for regression models
* More complex data augmentation
    - Top-k replacement (e.g. according to LM / WordNet)
    - Tokens to exclude from being changed
    - Bag-of-words style replacements
* Add rule-based return type
* Write more tests
## Credits
- Florian Gardin, Ronan Gautier, Nicolas Goix, Bibi Ndiaye and Jean-Matthieu Schertzer. _[Skope-rules](https://github.com/scikit-learn-contrib/skope-rules)_. 2020.
- Riccardo Guidotti, Anna Monreale, Salvatore Ruggieri, Dino Pedreschi, Franco Turini and Fosca Gianotti. _[Local Rule-Based Explanations of Black Box Decision Systems](https://paperswithcode.com/paper/local-rule-based-explanations-of-black-box)_. 2018.
- Been Kim, Rajiv Khanna and Oluwasanmi O. Koyejo. [Examples are not Enough, Learn to Criticize! Criticism for Interpretability](https://papers.nips.cc/paper/2016/hash/5680522b8e2bb01943234bce7bf84534-Abstract.html). _Advances in Neural Information Processing Systems (NIPS 2016)_. 2016.
- Scott Lundberg and Su-In Lee. [A Unified Approach to Interpreting Model Predictions](https://paperswithcode.com/paper/a-unified-approach-to-interpreting-model). _31st Conference on Neural Information Processing Systems (NIPS 2017)_. 2017.
- Christoph Molnar. _[Interpretable Machine Learning: A Guide for Making Black Box Models Explainable](https://christophm.github.io/interpretable-ml-book/)_. 2021.
- Marco Tulio Ribeiro, Sameer Singh and Carlos Guestrin. ["Why Should I Trust You?": Explaining the Predictions of Any Classifier](https://paperswithcode.com/method/lime). _Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics (NAACL 2016)_. 2016.
- Marco Tulio Ribeiro, Sameer Singh and Carlos Guestrin. [Anchors: High-Precision Model-Agnostic Explanations](https://github.com/marcotcr/anchor). _AAAI Conference on Artificial Intelligence (AAAI)_. 2018.
- Jasper van der Waa, Marcel Robeer, Jurriaan van Diggelen, Matthieu Brinkhuis and Mark Neerincx. ["Contrastive Explanations with Local Foil Trees"](https://github.com/MarcelRobeer/ContrastiveExplanation). _2018 Workshop on Human Interpretability in Machine Learning (WHI 2018)_. 2018.

%prep
%autosetup -n text-explainability-0.7.0

%build
%py3_build

%install
%py3_install
install -d -m755 %{buildroot}/%{_pkgdocdir}
if [ -d doc ]; then cp -arf doc %{buildroot}/%{_pkgdocdir}; fi
if [ -d docs ]; then cp -arf docs %{buildroot}/%{_pkgdocdir}; fi
if [ -d example ]; then cp -arf example %{buildroot}/%{_pkgdocdir}; fi
if [ -d examples ]; then cp -arf examples %{buildroot}/%{_pkgdocdir}; fi
pushd %{buildroot}
if [ -d usr/lib ]; then
	find usr/lib -type f -printf "/%h/%f\n" >> filelist.lst
fi
if [ -d usr/lib64 ]; then
	find usr/lib64 -type f -printf "/%h/%f\n" >> filelist.lst
fi
if [ -d usr/bin ]; then
	find usr/bin -type f -printf "/%h/%f\n" >> filelist.lst
fi
if [ -d usr/sbin ]; then
	find usr/sbin -type f -printf "/%h/%f\n" >> filelist.lst
fi
touch doclist.lst
if [ -d usr/share/man ]; then
	find usr/share/man -type f -printf "/%h/%f.gz\n" >> doclist.lst
fi
popd
mv %{buildroot}/filelist.lst .
mv %{buildroot}/doclist.lst .

%files -n python3-text-explainability -f filelist.lst
%dir %{python3_sitelib}/*

%files help -f doclist.lst
%{_docdir}/*

%changelog
* Tue May 30 2023 Python_Bot <Python_Bot@openeuler.org> - 0.7.0-1
- Package Spec generated