`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/).
## 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. © 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`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/).
## 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. © 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`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/).
## 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