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+%global _empty_manifest_terminate_build 0
+Name:		python-alibi
+Version:	0.9.1
+Release:	1
+Summary:	Algorithms for monitoring and explaining machine learning models
+License:	Apache 2.0
+URL:		https://github.com/SeldonIO/alibi
+Source0:	https://mirrors.nju.edu.cn/pypi/web/packages/d6/23/3be3695d2b8e4f373af03c9894bf8eb15435b47d139747565cd684ed2bd4/alibi-0.9.1.tar.gz
+BuildArch:	noarch
+
+Requires:	python3-numpy
+Requires:	python3-pandas
+Requires:	python3-scikit-learn
+Requires:	python3-spacy[lookups]
+Requires:	python3-blis
+Requires:	python3-scikit-image
+Requires:	python3-requests
+Requires:	python3-Pillow
+Requires:	python3-attrs
+Requires:	python3-scipy
+Requires:	python3-matplotlib
+Requires:	python3-typing-extensions
+Requires:	python3-dill
+Requires:	python3-transformers
+Requires:	python3-tqdm
+Requires:	python3-ray
+Requires:	python3-shap
+Requires:	python3-numba
+Requires:	python3-tensorflow
+Requires:	python3-torch
+Requires:	python3-ray
+Requires:	python3-shap
+Requires:	python3-numba
+Requires:	python3-tensorflow
+Requires:	python3-torch
+
+%description
+[Alibi](https://docs.seldon.io/projects/alibi) is an open source Python library aimed at machine learning model inspection and interpretation.
+The focus of the library is to provide high-quality implementations of black-box, white-box, local and global
+explanation methods for classification and regression models.
+*  [Documentation](https://docs.seldon.io/projects/alibi/en/stable/)
+If you're interested in outlier detection, concept drift or adversarial instance detection, check out our sister project [alibi-detect](https://github.com/SeldonIO/alibi-detect).
+<table>
+  <tr valign="top">
+    <td width="50%" >
+        <a href="https://docs.seldon.io/projects/alibi/en/stable/examples/anchor_image_imagenet.html">
+            <br>
+            <b>Anchor explanations for images</b>
+            <br>
+            <br>
+            <img src="https://github.com/SeldonIO/alibi/raw/master/doc/source/_static/anchor_image.png">
+        </a>
+    </td>
+    <td width="50%">
+        <a href="https://docs.seldon.io/projects/alibi/en/stable/examples/integrated_gradients_imdb.html">
+            <br>
+            <b>Integrated Gradients for text</b>
+            <br>
+            <br>
+            <img src="https://github.com/SeldonIO/alibi/raw/master/doc/source/_static/ig_text.png">
+        </a>
+    </td>
+  </tr>
+  <tr valign="top">
+    <td width="50%">
+        <a href="https://docs.seldon.io/projects/alibi/en/stable/methods/CFProto.html">
+            <br>
+            <b>Counterfactual examples</b>
+            <br>
+            <br>
+            <img src="https://github.com/SeldonIO/alibi/raw/master/doc/source/_static/cf.png">
+        </a>
+    </td>
+    <td width="50%">
+        <a href="https://docs.seldon.io/projects/alibi/en/stable/methods/ALE.html">
+            <br>
+            <b>Accumulated Local Effects</b>
+            <br>
+            <br>
+            <img src="https://github.com/SeldonIO/alibi/raw/master/doc/source/_static/ale.png">
+        </a>
+    </td>
+  </tr>
+</table>
+## Table of Contents
+* [Installation and Usage](#installation-and-usage)
+* [Supported Methods](#supported-methods)
+  * [Model Explanations](#model-explanations)
+  * [Model Confidence](#model-confidence)
+  * [Prototypes](#prototypes)
+  * [References and Examples](#references-and-examples)
+* [Citations](#citations)
+## Installation and Usage
+Alibi can be installed from:
+- PyPI or GitHub source (with `pip`)
+- Anaconda (with `conda`/`mamba`)
+### With pip
+- Alibi can be installed from [PyPI](https://pypi.org/project/alibi):
+  ```bash
+  pip install alibi
+  ```
+- Alternatively, the development version can be installed:
+  ```bash
+  pip install git+https://github.com/SeldonIO/alibi.git 
+  ```
+- To take advantage of distributed computation of explanations, install `alibi` with `ray`:
+  ```bash
+  pip install alibi[ray]
+  ```
+- For SHAP support, install `alibi` as follows:
+  ```bash
+  pip install alibi[shap]
+  ```
+### With conda 
+To install from [conda-forge](https://conda-forge.org/) it is recommended to use [mamba](https://mamba.readthedocs.io/en/stable/), 
+which can be installed to the *base* conda enviroment with:
+```bash
+conda install mamba -n base -c conda-forge
+```
+- For the standard Alibi install:
+  ```bash
+  mamba install -c conda-forge alibi
+  ```
+- For distributed computing support:
+  ```bash
+  mamba install -c conda-forge alibi ray
+  ```
+- For SHAP support:
+  ```bash
+  mamba install -c conda-forge alibi shap
+  ```
+### Usage
+The alibi explanation API takes inspiration from `scikit-learn`, consisting of distinct initialize,
+fit and explain steps. We will use the [AnchorTabular](https://docs.seldon.io/projects/alibi/en/stable/methods/Anchors.html)
+explainer to illustrate the API:
+```python
+from alibi.explainers import AnchorTabular
+# initialize and fit explainer by passing a prediction function and any other required arguments
+explainer = AnchorTabular(predict_fn, feature_names=feature_names, category_map=category_map)
+explainer.fit(X_train)
+# explain an instance
+explanation = explainer.explain(x)
+```
+The explanation returned is an `Explanation` object with attributes `meta` and `data`. `meta` is a dictionary
+containing the explainer metadata and any hyperparameters and `data` is a dictionary containing everything
+related to the computed explanation. For example, for the Anchor algorithm the explanation can be accessed
+via `explanation.data['anchor']` (or `explanation.anchor`). The exact details of available fields varies
+from method to method so we encourage the reader to become familiar with the
+[types of methods supported](https://docs.seldon.io/projects/alibi/en/stable/overview/algorithms.html).
+## Supported Methods
+The following tables summarize the possible use cases for each method.
+### Model Explanations
+| Method                                                                                                       |    Models    |     Explanations      | Classification | Regression | Tabular | Text | Images | Categorical features | Train set required | Distributed |
+|:-------------------------------------------------------------------------------------------------------------|:------------:|:---------------------:|:--------------:|:----------:|:-------:|:----:|:------:|:--------------------:|:------------------:|:-----------:|
+| [ALE](https://docs.seldon.io/projects/alibi/en/stable/methods/ALE.html)                                      |      BB      |        global         |       ✔        |     ✔      |    ✔    |      |        |                      |                    |             |
+| [Partial Dependence](https://docs.seldon.io/projects/alibi/en/stable/methods/PartialDependence.html)         |    BB WB     |        global         |       ✔        |     ✔      |    ✔    |      |        |          ✔           |                    |             |
+| [PD Variance](https://docs.seldon.io/projects/alibi/en/stable/methods/PartialDependenceVariance.html)        |    BB WB     |        global         |       ✔        |     ✔      |    ✔    |      |        |          ✔           |                    |             |
+| [Permutation Importance](https://docs.seldon.io/projects/alibi/en/stable/methods/PermutationImportance.html) |      BB      |        global         |       ✔        |     ✔      |    ✔    |      |        |          ✔           |                    |             |
+| [Anchors](https://docs.seldon.io/projects/alibi/en/stable/methods/Anchors.html)                              |      BB      |         local         |       ✔        |            |    ✔    |  ✔   |   ✔    |          ✔           |    For Tabular     |             |
+| [CEM](https://docs.seldon.io/projects/alibi/en/stable/methods/CEM.html)                                      | BB* TF/Keras |         local         |       ✔        |            |    ✔    |      |   ✔    |                      |      Optional      |             |
+| [Counterfactuals](https://docs.seldon.io/projects/alibi/en/stable/methods/CF.html)                           | BB* TF/Keras |         local         |       ✔        |            |    ✔    |      |   ✔    |                      |         No         |             |
+| [Prototype Counterfactuals](https://docs.seldon.io/projects/alibi/en/stable/methods/CFProto.html)            | BB* TF/Keras |         local         |       ✔        |            |    ✔    |      |   ✔    |          ✔           |      Optional      |             |
+| [Counterfactuals with RL](https://docs.seldon.io/projects/alibi/en/stable/methods/CFRL.html)                 |      BB      |         local         |       ✔        |            |    ✔    |      |   ✔    |          ✔           |         ✔          |             |
+| [Integrated Gradients](https://docs.seldon.io/projects/alibi/en/stable/methods/IntegratedGradients.html)     |   TF/Keras   |         local         |       ✔        |     ✔      |    ✔    |  ✔   |   ✔    |          ✔           |      Optional      |             |
+| [Kernel SHAP](https://docs.seldon.io/projects/alibi/en/stable/methods/KernelSHAP.html)                       |      BB      | local <br></br>global |       ✔        |     ✔      |    ✔    |      |        |          ✔           |         ✔          |      ✔      |
+| [Tree SHAP](https://docs.seldon.io/projects/alibi/en/stable/methods/TreeSHAP.html)                           |      WB      | local <br></br>global |       ✔        |     ✔      |    ✔    |      |        |          ✔           |      Optional      |             |
+| [Similarity explanations](https://docs.seldon.io/projects/alibi/en/stable/methods/Similarity.html)           |      WB      |         local         |       ✔        |     ✔      |    ✔    |  ✔   |   ✔    |          ✔           |         ✔          |             |
+### Model Confidence
+These algorithms provide **instance-specific** scores measuring the model confidence for making a
+particular prediction.
+|Method|Models|Classification|Regression|Tabular|Text|Images|Categorical Features|Train set required|
+|:---|:---|:---:|:---:|:---:|:---:|:---:|:---:|:---|
+|[Trust Scores](https://docs.seldon.io/projects/alibi/en/stable/methods/TrustScores.html)|BB|✔| |✔|✔(1)|✔(2)| |Yes|
+|[Linearity Measure](https://docs.seldon.io/projects/alibi/en/stable/methods/LinearityMeasure.html)|BB|✔|✔|✔| |✔| |Optional|
+Key:
+ - **BB** - black-box (only require a prediction function)
+ - **BB\*** - black-box but assume model is differentiable
+ - **WB** - requires white-box model access. There may be limitations on models supported
+ - **TF/Keras** - TensorFlow models via the Keras API
+ - **Local** - instance specific explanation, why was this prediction made?
+ - **Global** - explains the model with respect to a set of instances
+ - **(1)** -  depending on model
+ - **(2)** -  may require dimensionality reduction
+### Prototypes
+These algorithms provide a **distilled** view of the dataset and help construct a 1-KNN **interpretable** classifier.
+|Method|Classification|Regression|Tabular|Text|Images|Categorical Features|Train set labels|
+|:-----|:-------------|:---------|:------|:---|:-----|:-------------------|:---------------|
+|[ProtoSelect](https://docs.seldon.io/projects/alibi/en/latest/methods/ProtoSelect.html)|✔| |✔|✔|✔|✔| Optional       |
+## References and Examples
+- Accumulated Local Effects (ALE, [Apley and Zhu, 2016](https://arxiv.org/abs/1612.08468))
+  - [Documentation](https://docs.seldon.io/projects/alibi/en/stable/methods/ALE.html)
+  - Examples:
+    [California housing dataset](https://docs.seldon.io/projects/alibi/en/stable/examples/ale_regression_california.html),
+    [Iris dataset](https://docs.seldon.io/projects/alibi/en/stable/examples/ale_classification.html)
+- Partial Dependence ([J.H. Friedman, 2001](https://projecteuclid.org/journals/annals-of-statistics/volume-29/issue-5/Greedy-function-approximation-A-gradient-boostingmachine/10.1214/aos/1013203451.full))
+  - [Documentation](https://docs.seldon.io/projects/alibi/en/stable/methods/PartialDependence.html)
+  - Examples:
+    [Bike rental](https://docs.seldon.io/projects/alibi/en/stable/examples/pdp_regression_bike.html)
+- Partial Dependence Variance([Greenwell et al., 2018](https://arxiv.org/abs/1805.04755))
+  - [Documentation](https://docs.seldon.io/projects/alibi/en/stable/methods/PartialDependenceVariance.html)
+  - Examples:
+    [Friedman’s regression problem](https://docs.seldon.io/projects/alibi/en/stable/examples/pd_variance_regression_friedman.html)
+- Permutation Importance([Breiman, 2001](https://link.springer.com/article/10.1023/A:1010933404324); [Fisher et al., 2018](https://arxiv.org/abs/1801.01489))
+  - [Documentation](https://docs.seldon.io/projects/alibi/en/stable/methods/PermutationImportance.html)
+  - Examples:
+    [Who's Going to Leave Next?](https://docs.seldon.io/projects/alibi/en/stable/examples/permutation_importance_classification_leave.html)
+- Anchor explanations ([Ribeiro et al., 2018](https://homes.cs.washington.edu/~marcotcr/aaai18.pdf))
+  - [Documentation](https://docs.seldon.io/projects/alibi/en/stable/methods/Anchors.html)
+  - Examples:
+    [income prediction](https://docs.seldon.io/projects/alibi/en/stable/examples/anchor_tabular_adult.html),
+    [Iris dataset](https://docs.seldon.io/projects/alibi/en/stable/examples/anchor_tabular_iris.html),
+    [movie sentiment classification](https://docs.seldon.io/projects/alibi/en/stable/examples/anchor_text_movie.html),
+    [ImageNet](https://docs.seldon.io/projects/alibi/en/stable/examples/anchor_image_imagenet.html),
+    [fashion MNIST](https://docs.seldon.io/projects/alibi/en/stable/examples/anchor_image_fashion_mnist.html)
+- Contrastive Explanation Method (CEM, [Dhurandhar et al., 2018](https://papers.nips.cc/paper/7340-explanations-based-on-the-missing-towards-contrastive-explanations-with-pertinent-negatives))
+  - [Documentation](https://docs.seldon.io/projects/alibi/en/stable/methods/CEM.html)
+  - Examples: [MNIST](https://docs.seldon.io/projects/alibi/en/stable/examples/cem_mnist.html),
+    [Iris dataset](https://docs.seldon.io/projects/alibi/en/stable/examples/cem_iris.html)
+- Counterfactual Explanations (extension of
+  [Wachter et al., 2017](https://arxiv.org/abs/1711.00399))
+  - [Documentation](https://docs.seldon.io/projects/alibi/en/stable/methods/CF.html)
+  - Examples: 
+    [MNIST](https://docs.seldon.io/projects/alibi/en/stable/examples/cf_mnist.html)
+- Counterfactual Explanations Guided by Prototypes ([Van Looveren and Klaise, 2019](https://arxiv.org/abs/1907.02584))
+  - [Documentation](https://docs.seldon.io/projects/alibi/en/stable/methods/CFProto.html)
+  - Examples:
+    [MNIST](https://docs.seldon.io/projects/alibi/en/stable/examples/cfproto_mnist.html),
+    [California housing dataset](https://docs.seldon.io/projects/alibi/en/stable/examples/cfproto_housing.html),
+    [Adult income (one-hot)](https://docs.seldon.io/projects/alibi/en/stable/examples/cfproto_cat_adult_ohe.html),
+    [Adult income (ordinal)](https://docs.seldon.io/projects/alibi/en/stable/examples/cfproto_cat_adult_ord.html)
+- Model-agnostic Counterfactual Explanations via RL([Samoilescu et al., 2021](https://arxiv.org/abs/2106.02597))
+  - [Documentation](https://docs.seldon.io/projects/alibi/en/stable/methods/CFRL.html)
+  - Examples:
+    [MNIST](https://docs.seldon.io/projects/alibi/en/stable/examples/cfrl_mnist.html),
+    [Adult income](https://docs.seldon.io/projects/alibi/en/stable/examples/cfrl_adult.html)
+- Integrated Gradients ([Sundararajan et al., 2017](https://arxiv.org/abs/1703.01365))
+  - [Documentation](https://docs.seldon.io/projects/alibi/en/stable/methods/IntegratedGradients.html),
+  - Examples:
+    [MNIST example](https://docs.seldon.io/projects/alibi/en/stable/examples/integrated_gradients_mnist.html),
+    [Imagenet example](https://docs.seldon.io/projects/alibi/en/stable/examples/integrated_gradients_imagenet.html),
+    [IMDB example](https://docs.seldon.io/projects/alibi/en/stable/examples/integrated_gradients_imdb.html).
+- Kernel Shapley Additive Explanations ([Lundberg et al., 2017](https://papers.nips.cc/paper/7062-a-unified-approach-to-interpreting-model-predictions))
+  - [Documentation](https://docs.seldon.io/projects/alibi/en/stable/methods/KernelSHAP.html)
+  - Examples:
+    [SVM with continuous data](https://docs.seldon.io/projects/alibi/en/stable/examples/kernel_shap_wine_intro.html),
+    [multinomial logistic regression with continous data](https://docs.seldon.io/projects/alibi/en/stable/examples/kernel_shap_wine_lr.html),
+    [handling categorical variables](https://docs.seldon.io/projects/alibi/en/stable/examples/kernel_shap_adult_lr.html)
+- Tree Shapley Additive Explanations ([Lundberg et al., 2020](https://www.nature.com/articles/s42256-019-0138-9))
+  - [Documentation](https://docs.seldon.io/projects/alibi/en/stable/methods/TreeSHAP.html)
+  - Examples:
+    [Interventional (adult income, xgboost)](https://docs.seldon.io/projects/alibi/en/stable/examples/interventional_tree_shap_adult_xgb.html),
+    [Path-dependent (adult income, xgboost)](https://docs.seldon.io/projects/alibi/en/stable/examples/path_dependent_tree_shap_adult_xgb.html)
+- Trust Scores ([Jiang et al., 2018](https://arxiv.org/abs/1805.11783))
+  - [Documentation](https://docs.seldon.io/projects/alibi/en/stable/methods/TrustScores.html)
+  - Examples:
+    [MNIST](https://docs.seldon.io/projects/alibi/en/stable/examples/trustscore_mnist.html),
+    [Iris dataset](https://docs.seldon.io/projects/alibi/en/stable/examples/trustscore_mnist.html)
+- Linearity Measure
+  - [Documentation](https://docs.seldon.io/projects/alibi/en/stable/methods/LinearityMeasure.html)
+  - Examples:
+    [Iris dataset](https://docs.seldon.io/projects/alibi/en/stable/examples/linearity_measure_iris.html),
+    [fashion MNIST](https://docs.seldon.io/projects/alibi/en/stable/examples/linearity_measure_fashion_mnist.html)
+- ProtoSelect
+  - [Documentation](https://docs.seldon.io/projects/alibi/en/latest/methods/ProtoSelect.html)
+  - Examples:
+    [Adult Census & CIFAR10](https://docs.seldon.io/projects/alibi/en/latest/examples/protoselect_adult_cifar10.html)
+- Similarity explanations
+  - [Documentation](https://docs.seldon.io/projects/alibi/en/stable/methods/Similarity.html)
+  - Examples:
+    [20 news groups dataset](https://docs.seldon.io/projects/alibi/en/stable/examples/similarity_explanations_20ng.html),
+    [ImageNet dataset](https://docs.seldon.io/projects/alibi/en/stable/examples/similarity_explanations_imagenet.html),
+    [MNIST dataset](https://docs.seldon.io/projects/alibi/en/stable/examples/similarity_explanations_mnist.html)
+## Citations
+If you use alibi in your research, please consider citing it.
+BibTeX entry:
+```
+@article{JMLR:v22:21-0017,
+  author  = {Janis Klaise and Arnaud Van Looveren and Giovanni Vacanti and Alexandru Coca},
+  title   = {Alibi Explain: Algorithms for Explaining Machine Learning Models},
+  journal = {Journal of Machine Learning Research},
+  year    = {2021},
+  volume  = {22},
+  number  = {181},
+  pages   = {1-7},
+  url     = {http://jmlr.org/papers/v22/21-0017.html}
+}
+```
+
+%package -n python3-alibi
+Summary:	Algorithms for monitoring and explaining machine learning models
+Provides:	python-alibi
+BuildRequires:	python3-devel
+BuildRequires:	python3-setuptools
+BuildRequires:	python3-pip
+%description -n python3-alibi
+[Alibi](https://docs.seldon.io/projects/alibi) is an open source Python library aimed at machine learning model inspection and interpretation.
+The focus of the library is to provide high-quality implementations of black-box, white-box, local and global
+explanation methods for classification and regression models.
+*  [Documentation](https://docs.seldon.io/projects/alibi/en/stable/)
+If you're interested in outlier detection, concept drift or adversarial instance detection, check out our sister project [alibi-detect](https://github.com/SeldonIO/alibi-detect).
+<table>
+  <tr valign="top">
+    <td width="50%" >
+        <a href="https://docs.seldon.io/projects/alibi/en/stable/examples/anchor_image_imagenet.html">
+            <br>
+            <b>Anchor explanations for images</b>
+            <br>
+            <br>
+            <img src="https://github.com/SeldonIO/alibi/raw/master/doc/source/_static/anchor_image.png">
+        </a>
+    </td>
+    <td width="50%">
+        <a href="https://docs.seldon.io/projects/alibi/en/stable/examples/integrated_gradients_imdb.html">
+            <br>
+            <b>Integrated Gradients for text</b>
+            <br>
+            <br>
+            <img src="https://github.com/SeldonIO/alibi/raw/master/doc/source/_static/ig_text.png">
+        </a>
+    </td>
+  </tr>
+  <tr valign="top">
+    <td width="50%">
+        <a href="https://docs.seldon.io/projects/alibi/en/stable/methods/CFProto.html">
+            <br>
+            <b>Counterfactual examples</b>
+            <br>
+            <br>
+            <img src="https://github.com/SeldonIO/alibi/raw/master/doc/source/_static/cf.png">
+        </a>
+    </td>
+    <td width="50%">
+        <a href="https://docs.seldon.io/projects/alibi/en/stable/methods/ALE.html">
+            <br>
+            <b>Accumulated Local Effects</b>
+            <br>
+            <br>
+            <img src="https://github.com/SeldonIO/alibi/raw/master/doc/source/_static/ale.png">
+        </a>
+    </td>
+  </tr>
+</table>
+## Table of Contents
+* [Installation and Usage](#installation-and-usage)
+* [Supported Methods](#supported-methods)
+  * [Model Explanations](#model-explanations)
+  * [Model Confidence](#model-confidence)
+  * [Prototypes](#prototypes)
+  * [References and Examples](#references-and-examples)
+* [Citations](#citations)
+## Installation and Usage
+Alibi can be installed from:
+- PyPI or GitHub source (with `pip`)
+- Anaconda (with `conda`/`mamba`)
+### With pip
+- Alibi can be installed from [PyPI](https://pypi.org/project/alibi):
+  ```bash
+  pip install alibi
+  ```
+- Alternatively, the development version can be installed:
+  ```bash
+  pip install git+https://github.com/SeldonIO/alibi.git 
+  ```
+- To take advantage of distributed computation of explanations, install `alibi` with `ray`:
+  ```bash
+  pip install alibi[ray]
+  ```
+- For SHAP support, install `alibi` as follows:
+  ```bash
+  pip install alibi[shap]
+  ```
+### With conda 
+To install from [conda-forge](https://conda-forge.org/) it is recommended to use [mamba](https://mamba.readthedocs.io/en/stable/), 
+which can be installed to the *base* conda enviroment with:
+```bash
+conda install mamba -n base -c conda-forge
+```
+- For the standard Alibi install:
+  ```bash
+  mamba install -c conda-forge alibi
+  ```
+- For distributed computing support:
+  ```bash
+  mamba install -c conda-forge alibi ray
+  ```
+- For SHAP support:
+  ```bash
+  mamba install -c conda-forge alibi shap
+  ```
+### Usage
+The alibi explanation API takes inspiration from `scikit-learn`, consisting of distinct initialize,
+fit and explain steps. We will use the [AnchorTabular](https://docs.seldon.io/projects/alibi/en/stable/methods/Anchors.html)
+explainer to illustrate the API:
+```python
+from alibi.explainers import AnchorTabular
+# initialize and fit explainer by passing a prediction function and any other required arguments
+explainer = AnchorTabular(predict_fn, feature_names=feature_names, category_map=category_map)
+explainer.fit(X_train)
+# explain an instance
+explanation = explainer.explain(x)
+```
+The explanation returned is an `Explanation` object with attributes `meta` and `data`. `meta` is a dictionary
+containing the explainer metadata and any hyperparameters and `data` is a dictionary containing everything
+related to the computed explanation. For example, for the Anchor algorithm the explanation can be accessed
+via `explanation.data['anchor']` (or `explanation.anchor`). The exact details of available fields varies
+from method to method so we encourage the reader to become familiar with the
+[types of methods supported](https://docs.seldon.io/projects/alibi/en/stable/overview/algorithms.html).
+## Supported Methods
+The following tables summarize the possible use cases for each method.
+### Model Explanations
+| Method                                                                                                       |    Models    |     Explanations      | Classification | Regression | Tabular | Text | Images | Categorical features | Train set required | Distributed |
+|:-------------------------------------------------------------------------------------------------------------|:------------:|:---------------------:|:--------------:|:----------:|:-------:|:----:|:------:|:--------------------:|:------------------:|:-----------:|
+| [ALE](https://docs.seldon.io/projects/alibi/en/stable/methods/ALE.html)                                      |      BB      |        global         |       ✔        |     ✔      |    ✔    |      |        |                      |                    |             |
+| [Partial Dependence](https://docs.seldon.io/projects/alibi/en/stable/methods/PartialDependence.html)         |    BB WB     |        global         |       ✔        |     ✔      |    ✔    |      |        |          ✔           |                    |             |
+| [PD Variance](https://docs.seldon.io/projects/alibi/en/stable/methods/PartialDependenceVariance.html)        |    BB WB     |        global         |       ✔        |     ✔      |    ✔    |      |        |          ✔           |                    |             |
+| [Permutation Importance](https://docs.seldon.io/projects/alibi/en/stable/methods/PermutationImportance.html) |      BB      |        global         |       ✔        |     ✔      |    ✔    |      |        |          ✔           |                    |             |
+| [Anchors](https://docs.seldon.io/projects/alibi/en/stable/methods/Anchors.html)                              |      BB      |         local         |       ✔        |            |    ✔    |  ✔   |   ✔    |          ✔           |    For Tabular     |             |
+| [CEM](https://docs.seldon.io/projects/alibi/en/stable/methods/CEM.html)                                      | BB* TF/Keras |         local         |       ✔        |            |    ✔    |      |   ✔    |                      |      Optional      |             |
+| [Counterfactuals](https://docs.seldon.io/projects/alibi/en/stable/methods/CF.html)                           | BB* TF/Keras |         local         |       ✔        |            |    ✔    |      |   ✔    |                      |         No         |             |
+| [Prototype Counterfactuals](https://docs.seldon.io/projects/alibi/en/stable/methods/CFProto.html)            | BB* TF/Keras |         local         |       ✔        |            |    ✔    |      |   ✔    |          ✔           |      Optional      |             |
+| [Counterfactuals with RL](https://docs.seldon.io/projects/alibi/en/stable/methods/CFRL.html)                 |      BB      |         local         |       ✔        |            |    ✔    |      |   ✔    |          ✔           |         ✔          |             |
+| [Integrated Gradients](https://docs.seldon.io/projects/alibi/en/stable/methods/IntegratedGradients.html)     |   TF/Keras   |         local         |       ✔        |     ✔      |    ✔    |  ✔   |   ✔    |          ✔           |      Optional      |             |
+| [Kernel SHAP](https://docs.seldon.io/projects/alibi/en/stable/methods/KernelSHAP.html)                       |      BB      | local <br></br>global |       ✔        |     ✔      |    ✔    |      |        |          ✔           |         ✔          |      ✔      |
+| [Tree SHAP](https://docs.seldon.io/projects/alibi/en/stable/methods/TreeSHAP.html)                           |      WB      | local <br></br>global |       ✔        |     ✔      |    ✔    |      |        |          ✔           |      Optional      |             |
+| [Similarity explanations](https://docs.seldon.io/projects/alibi/en/stable/methods/Similarity.html)           |      WB      |         local         |       ✔        |     ✔      |    ✔    |  ✔   |   ✔    |          ✔           |         ✔          |             |
+### Model Confidence
+These algorithms provide **instance-specific** scores measuring the model confidence for making a
+particular prediction.
+|Method|Models|Classification|Regression|Tabular|Text|Images|Categorical Features|Train set required|
+|:---|:---|:---:|:---:|:---:|:---:|:---:|:---:|:---|
+|[Trust Scores](https://docs.seldon.io/projects/alibi/en/stable/methods/TrustScores.html)|BB|✔| |✔|✔(1)|✔(2)| |Yes|
+|[Linearity Measure](https://docs.seldon.io/projects/alibi/en/stable/methods/LinearityMeasure.html)|BB|✔|✔|✔| |✔| |Optional|
+Key:
+ - **BB** - black-box (only require a prediction function)
+ - **BB\*** - black-box but assume model is differentiable
+ - **WB** - requires white-box model access. There may be limitations on models supported
+ - **TF/Keras** - TensorFlow models via the Keras API
+ - **Local** - instance specific explanation, why was this prediction made?
+ - **Global** - explains the model with respect to a set of instances
+ - **(1)** -  depending on model
+ - **(2)** -  may require dimensionality reduction
+### Prototypes
+These algorithms provide a **distilled** view of the dataset and help construct a 1-KNN **interpretable** classifier.
+|Method|Classification|Regression|Tabular|Text|Images|Categorical Features|Train set labels|
+|:-----|:-------------|:---------|:------|:---|:-----|:-------------------|:---------------|
+|[ProtoSelect](https://docs.seldon.io/projects/alibi/en/latest/methods/ProtoSelect.html)|✔| |✔|✔|✔|✔| Optional       |
+## References and Examples
+- Accumulated Local Effects (ALE, [Apley and Zhu, 2016](https://arxiv.org/abs/1612.08468))
+  - [Documentation](https://docs.seldon.io/projects/alibi/en/stable/methods/ALE.html)
+  - Examples:
+    [California housing dataset](https://docs.seldon.io/projects/alibi/en/stable/examples/ale_regression_california.html),
+    [Iris dataset](https://docs.seldon.io/projects/alibi/en/stable/examples/ale_classification.html)
+- Partial Dependence ([J.H. Friedman, 2001](https://projecteuclid.org/journals/annals-of-statistics/volume-29/issue-5/Greedy-function-approximation-A-gradient-boostingmachine/10.1214/aos/1013203451.full))
+  - [Documentation](https://docs.seldon.io/projects/alibi/en/stable/methods/PartialDependence.html)
+  - Examples:
+    [Bike rental](https://docs.seldon.io/projects/alibi/en/stable/examples/pdp_regression_bike.html)
+- Partial Dependence Variance([Greenwell et al., 2018](https://arxiv.org/abs/1805.04755))
+  - [Documentation](https://docs.seldon.io/projects/alibi/en/stable/methods/PartialDependenceVariance.html)
+  - Examples:
+    [Friedman’s regression problem](https://docs.seldon.io/projects/alibi/en/stable/examples/pd_variance_regression_friedman.html)
+- Permutation Importance([Breiman, 2001](https://link.springer.com/article/10.1023/A:1010933404324); [Fisher et al., 2018](https://arxiv.org/abs/1801.01489))
+  - [Documentation](https://docs.seldon.io/projects/alibi/en/stable/methods/PermutationImportance.html)
+  - Examples:
+    [Who's Going to Leave Next?](https://docs.seldon.io/projects/alibi/en/stable/examples/permutation_importance_classification_leave.html)
+- Anchor explanations ([Ribeiro et al., 2018](https://homes.cs.washington.edu/~marcotcr/aaai18.pdf))
+  - [Documentation](https://docs.seldon.io/projects/alibi/en/stable/methods/Anchors.html)
+  - Examples:
+    [income prediction](https://docs.seldon.io/projects/alibi/en/stable/examples/anchor_tabular_adult.html),
+    [Iris dataset](https://docs.seldon.io/projects/alibi/en/stable/examples/anchor_tabular_iris.html),
+    [movie sentiment classification](https://docs.seldon.io/projects/alibi/en/stable/examples/anchor_text_movie.html),
+    [ImageNet](https://docs.seldon.io/projects/alibi/en/stable/examples/anchor_image_imagenet.html),
+    [fashion MNIST](https://docs.seldon.io/projects/alibi/en/stable/examples/anchor_image_fashion_mnist.html)
+- Contrastive Explanation Method (CEM, [Dhurandhar et al., 2018](https://papers.nips.cc/paper/7340-explanations-based-on-the-missing-towards-contrastive-explanations-with-pertinent-negatives))
+  - [Documentation](https://docs.seldon.io/projects/alibi/en/stable/methods/CEM.html)
+  - Examples: [MNIST](https://docs.seldon.io/projects/alibi/en/stable/examples/cem_mnist.html),
+    [Iris dataset](https://docs.seldon.io/projects/alibi/en/stable/examples/cem_iris.html)
+- Counterfactual Explanations (extension of
+  [Wachter et al., 2017](https://arxiv.org/abs/1711.00399))
+  - [Documentation](https://docs.seldon.io/projects/alibi/en/stable/methods/CF.html)
+  - Examples: 
+    [MNIST](https://docs.seldon.io/projects/alibi/en/stable/examples/cf_mnist.html)
+- Counterfactual Explanations Guided by Prototypes ([Van Looveren and Klaise, 2019](https://arxiv.org/abs/1907.02584))
+  - [Documentation](https://docs.seldon.io/projects/alibi/en/stable/methods/CFProto.html)
+  - Examples:
+    [MNIST](https://docs.seldon.io/projects/alibi/en/stable/examples/cfproto_mnist.html),
+    [California housing dataset](https://docs.seldon.io/projects/alibi/en/stable/examples/cfproto_housing.html),
+    [Adult income (one-hot)](https://docs.seldon.io/projects/alibi/en/stable/examples/cfproto_cat_adult_ohe.html),
+    [Adult income (ordinal)](https://docs.seldon.io/projects/alibi/en/stable/examples/cfproto_cat_adult_ord.html)
+- Model-agnostic Counterfactual Explanations via RL([Samoilescu et al., 2021](https://arxiv.org/abs/2106.02597))
+  - [Documentation](https://docs.seldon.io/projects/alibi/en/stable/methods/CFRL.html)
+  - Examples:
+    [MNIST](https://docs.seldon.io/projects/alibi/en/stable/examples/cfrl_mnist.html),
+    [Adult income](https://docs.seldon.io/projects/alibi/en/stable/examples/cfrl_adult.html)
+- Integrated Gradients ([Sundararajan et al., 2017](https://arxiv.org/abs/1703.01365))
+  - [Documentation](https://docs.seldon.io/projects/alibi/en/stable/methods/IntegratedGradients.html),
+  - Examples:
+    [MNIST example](https://docs.seldon.io/projects/alibi/en/stable/examples/integrated_gradients_mnist.html),
+    [Imagenet example](https://docs.seldon.io/projects/alibi/en/stable/examples/integrated_gradients_imagenet.html),
+    [IMDB example](https://docs.seldon.io/projects/alibi/en/stable/examples/integrated_gradients_imdb.html).
+- Kernel Shapley Additive Explanations ([Lundberg et al., 2017](https://papers.nips.cc/paper/7062-a-unified-approach-to-interpreting-model-predictions))
+  - [Documentation](https://docs.seldon.io/projects/alibi/en/stable/methods/KernelSHAP.html)
+  - Examples:
+    [SVM with continuous data](https://docs.seldon.io/projects/alibi/en/stable/examples/kernel_shap_wine_intro.html),
+    [multinomial logistic regression with continous data](https://docs.seldon.io/projects/alibi/en/stable/examples/kernel_shap_wine_lr.html),
+    [handling categorical variables](https://docs.seldon.io/projects/alibi/en/stable/examples/kernel_shap_adult_lr.html)
+- Tree Shapley Additive Explanations ([Lundberg et al., 2020](https://www.nature.com/articles/s42256-019-0138-9))
+  - [Documentation](https://docs.seldon.io/projects/alibi/en/stable/methods/TreeSHAP.html)
+  - Examples:
+    [Interventional (adult income, xgboost)](https://docs.seldon.io/projects/alibi/en/stable/examples/interventional_tree_shap_adult_xgb.html),
+    [Path-dependent (adult income, xgboost)](https://docs.seldon.io/projects/alibi/en/stable/examples/path_dependent_tree_shap_adult_xgb.html)
+- Trust Scores ([Jiang et al., 2018](https://arxiv.org/abs/1805.11783))
+  - [Documentation](https://docs.seldon.io/projects/alibi/en/stable/methods/TrustScores.html)
+  - Examples:
+    [MNIST](https://docs.seldon.io/projects/alibi/en/stable/examples/trustscore_mnist.html),
+    [Iris dataset](https://docs.seldon.io/projects/alibi/en/stable/examples/trustscore_mnist.html)
+- Linearity Measure
+  - [Documentation](https://docs.seldon.io/projects/alibi/en/stable/methods/LinearityMeasure.html)
+  - Examples:
+    [Iris dataset](https://docs.seldon.io/projects/alibi/en/stable/examples/linearity_measure_iris.html),
+    [fashion MNIST](https://docs.seldon.io/projects/alibi/en/stable/examples/linearity_measure_fashion_mnist.html)
+- ProtoSelect
+  - [Documentation](https://docs.seldon.io/projects/alibi/en/latest/methods/ProtoSelect.html)
+  - Examples:
+    [Adult Census & CIFAR10](https://docs.seldon.io/projects/alibi/en/latest/examples/protoselect_adult_cifar10.html)
+- Similarity explanations
+  - [Documentation](https://docs.seldon.io/projects/alibi/en/stable/methods/Similarity.html)
+  - Examples:
+    [20 news groups dataset](https://docs.seldon.io/projects/alibi/en/stable/examples/similarity_explanations_20ng.html),
+    [ImageNet dataset](https://docs.seldon.io/projects/alibi/en/stable/examples/similarity_explanations_imagenet.html),
+    [MNIST dataset](https://docs.seldon.io/projects/alibi/en/stable/examples/similarity_explanations_mnist.html)
+## Citations
+If you use alibi in your research, please consider citing it.
+BibTeX entry:
+```
+@article{JMLR:v22:21-0017,
+  author  = {Janis Klaise and Arnaud Van Looveren and Giovanni Vacanti and Alexandru Coca},
+  title   = {Alibi Explain: Algorithms for Explaining Machine Learning Models},
+  journal = {Journal of Machine Learning Research},
+  year    = {2021},
+  volume  = {22},
+  number  = {181},
+  pages   = {1-7},
+  url     = {http://jmlr.org/papers/v22/21-0017.html}
+}
+```
+
+%package help
+Summary:	Development documents and examples for alibi
+Provides:	python3-alibi-doc
+%description help
+[Alibi](https://docs.seldon.io/projects/alibi) is an open source Python library aimed at machine learning model inspection and interpretation.
+The focus of the library is to provide high-quality implementations of black-box, white-box, local and global
+explanation methods for classification and regression models.
+*  [Documentation](https://docs.seldon.io/projects/alibi/en/stable/)
+If you're interested in outlier detection, concept drift or adversarial instance detection, check out our sister project [alibi-detect](https://github.com/SeldonIO/alibi-detect).
+<table>
+  <tr valign="top">
+    <td width="50%" >
+        <a href="https://docs.seldon.io/projects/alibi/en/stable/examples/anchor_image_imagenet.html">
+            <br>
+            <b>Anchor explanations for images</b>
+            <br>
+            <br>
+            <img src="https://github.com/SeldonIO/alibi/raw/master/doc/source/_static/anchor_image.png">
+        </a>
+    </td>
+    <td width="50%">
+        <a href="https://docs.seldon.io/projects/alibi/en/stable/examples/integrated_gradients_imdb.html">
+            <br>
+            <b>Integrated Gradients for text</b>
+            <br>
+            <br>
+            <img src="https://github.com/SeldonIO/alibi/raw/master/doc/source/_static/ig_text.png">
+        </a>
+    </td>
+  </tr>
+  <tr valign="top">
+    <td width="50%">
+        <a href="https://docs.seldon.io/projects/alibi/en/stable/methods/CFProto.html">
+            <br>
+            <b>Counterfactual examples</b>
+            <br>
+            <br>
+            <img src="https://github.com/SeldonIO/alibi/raw/master/doc/source/_static/cf.png">
+        </a>
+    </td>
+    <td width="50%">
+        <a href="https://docs.seldon.io/projects/alibi/en/stable/methods/ALE.html">
+            <br>
+            <b>Accumulated Local Effects</b>
+            <br>
+            <br>
+            <img src="https://github.com/SeldonIO/alibi/raw/master/doc/source/_static/ale.png">
+        </a>
+    </td>
+  </tr>
+</table>
+## Table of Contents
+* [Installation and Usage](#installation-and-usage)
+* [Supported Methods](#supported-methods)
+  * [Model Explanations](#model-explanations)
+  * [Model Confidence](#model-confidence)
+  * [Prototypes](#prototypes)
+  * [References and Examples](#references-and-examples)
+* [Citations](#citations)
+## Installation and Usage
+Alibi can be installed from:
+- PyPI or GitHub source (with `pip`)
+- Anaconda (with `conda`/`mamba`)
+### With pip
+- Alibi can be installed from [PyPI](https://pypi.org/project/alibi):
+  ```bash
+  pip install alibi
+  ```
+- Alternatively, the development version can be installed:
+  ```bash
+  pip install git+https://github.com/SeldonIO/alibi.git 
+  ```
+- To take advantage of distributed computation of explanations, install `alibi` with `ray`:
+  ```bash
+  pip install alibi[ray]
+  ```
+- For SHAP support, install `alibi` as follows:
+  ```bash
+  pip install alibi[shap]
+  ```
+### With conda 
+To install from [conda-forge](https://conda-forge.org/) it is recommended to use [mamba](https://mamba.readthedocs.io/en/stable/), 
+which can be installed to the *base* conda enviroment with:
+```bash
+conda install mamba -n base -c conda-forge
+```
+- For the standard Alibi install:
+  ```bash
+  mamba install -c conda-forge alibi
+  ```
+- For distributed computing support:
+  ```bash
+  mamba install -c conda-forge alibi ray
+  ```
+- For SHAP support:
+  ```bash
+  mamba install -c conda-forge alibi shap
+  ```
+### Usage
+The alibi explanation API takes inspiration from `scikit-learn`, consisting of distinct initialize,
+fit and explain steps. We will use the [AnchorTabular](https://docs.seldon.io/projects/alibi/en/stable/methods/Anchors.html)
+explainer to illustrate the API:
+```python
+from alibi.explainers import AnchorTabular
+# initialize and fit explainer by passing a prediction function and any other required arguments
+explainer = AnchorTabular(predict_fn, feature_names=feature_names, category_map=category_map)
+explainer.fit(X_train)
+# explain an instance
+explanation = explainer.explain(x)
+```
+The explanation returned is an `Explanation` object with attributes `meta` and `data`. `meta` is a dictionary
+containing the explainer metadata and any hyperparameters and `data` is a dictionary containing everything
+related to the computed explanation. For example, for the Anchor algorithm the explanation can be accessed
+via `explanation.data['anchor']` (or `explanation.anchor`). The exact details of available fields varies
+from method to method so we encourage the reader to become familiar with the
+[types of methods supported](https://docs.seldon.io/projects/alibi/en/stable/overview/algorithms.html).
+## Supported Methods
+The following tables summarize the possible use cases for each method.
+### Model Explanations
+| Method                                                                                                       |    Models    |     Explanations      | Classification | Regression | Tabular | Text | Images | Categorical features | Train set required | Distributed |
+|:-------------------------------------------------------------------------------------------------------------|:------------:|:---------------------:|:--------------:|:----------:|:-------:|:----:|:------:|:--------------------:|:------------------:|:-----------:|
+| [ALE](https://docs.seldon.io/projects/alibi/en/stable/methods/ALE.html)                                      |      BB      |        global         |       ✔        |     ✔      |    ✔    |      |        |                      |                    |             |
+| [Partial Dependence](https://docs.seldon.io/projects/alibi/en/stable/methods/PartialDependence.html)         |    BB WB     |        global         |       ✔        |     ✔      |    ✔    |      |        |          ✔           |                    |             |
+| [PD Variance](https://docs.seldon.io/projects/alibi/en/stable/methods/PartialDependenceVariance.html)        |    BB WB     |        global         |       ✔        |     ✔      |    ✔    |      |        |          ✔           |                    |             |
+| [Permutation Importance](https://docs.seldon.io/projects/alibi/en/stable/methods/PermutationImportance.html) |      BB      |        global         |       ✔        |     ✔      |    ✔    |      |        |          ✔           |                    |             |
+| [Anchors](https://docs.seldon.io/projects/alibi/en/stable/methods/Anchors.html)                              |      BB      |         local         |       ✔        |            |    ✔    |  ✔   |   ✔    |          ✔           |    For Tabular     |             |
+| [CEM](https://docs.seldon.io/projects/alibi/en/stable/methods/CEM.html)                                      | BB* TF/Keras |         local         |       ✔        |            |    ✔    |      |   ✔    |                      |      Optional      |             |
+| [Counterfactuals](https://docs.seldon.io/projects/alibi/en/stable/methods/CF.html)                           | BB* TF/Keras |         local         |       ✔        |            |    ✔    |      |   ✔    |                      |         No         |             |
+| [Prototype Counterfactuals](https://docs.seldon.io/projects/alibi/en/stable/methods/CFProto.html)            | BB* TF/Keras |         local         |       ✔        |            |    ✔    |      |   ✔    |          ✔           |      Optional      |             |
+| [Counterfactuals with RL](https://docs.seldon.io/projects/alibi/en/stable/methods/CFRL.html)                 |      BB      |         local         |       ✔        |            |    ✔    |      |   ✔    |          ✔           |         ✔          |             |
+| [Integrated Gradients](https://docs.seldon.io/projects/alibi/en/stable/methods/IntegratedGradients.html)     |   TF/Keras   |         local         |       ✔        |     ✔      |    ✔    |  ✔   |   ✔    |          ✔           |      Optional      |             |
+| [Kernel SHAP](https://docs.seldon.io/projects/alibi/en/stable/methods/KernelSHAP.html)                       |      BB      | local <br></br>global |       ✔        |     ✔      |    ✔    |      |        |          ✔           |         ✔          |      ✔      |
+| [Tree SHAP](https://docs.seldon.io/projects/alibi/en/stable/methods/TreeSHAP.html)                           |      WB      | local <br></br>global |       ✔        |     ✔      |    ✔    |      |        |          ✔           |      Optional      |             |
+| [Similarity explanations](https://docs.seldon.io/projects/alibi/en/stable/methods/Similarity.html)           |      WB      |         local         |       ✔        |     ✔      |    ✔    |  ✔   |   ✔    |          ✔           |         ✔          |             |
+### Model Confidence
+These algorithms provide **instance-specific** scores measuring the model confidence for making a
+particular prediction.
+|Method|Models|Classification|Regression|Tabular|Text|Images|Categorical Features|Train set required|
+|:---|:---|:---:|:---:|:---:|:---:|:---:|:---:|:---|
+|[Trust Scores](https://docs.seldon.io/projects/alibi/en/stable/methods/TrustScores.html)|BB|✔| |✔|✔(1)|✔(2)| |Yes|
+|[Linearity Measure](https://docs.seldon.io/projects/alibi/en/stable/methods/LinearityMeasure.html)|BB|✔|✔|✔| |✔| |Optional|
+Key:
+ - **BB** - black-box (only require a prediction function)
+ - **BB\*** - black-box but assume model is differentiable
+ - **WB** - requires white-box model access. There may be limitations on models supported
+ - **TF/Keras** - TensorFlow models via the Keras API
+ - **Local** - instance specific explanation, why was this prediction made?
+ - **Global** - explains the model with respect to a set of instances
+ - **(1)** -  depending on model
+ - **(2)** -  may require dimensionality reduction
+### Prototypes
+These algorithms provide a **distilled** view of the dataset and help construct a 1-KNN **interpretable** classifier.
+|Method|Classification|Regression|Tabular|Text|Images|Categorical Features|Train set labels|
+|:-----|:-------------|:---------|:------|:---|:-----|:-------------------|:---------------|
+|[ProtoSelect](https://docs.seldon.io/projects/alibi/en/latest/methods/ProtoSelect.html)|✔| |✔|✔|✔|✔| Optional       |
+## References and Examples
+- Accumulated Local Effects (ALE, [Apley and Zhu, 2016](https://arxiv.org/abs/1612.08468))
+  - [Documentation](https://docs.seldon.io/projects/alibi/en/stable/methods/ALE.html)
+  - Examples:
+    [California housing dataset](https://docs.seldon.io/projects/alibi/en/stable/examples/ale_regression_california.html),
+    [Iris dataset](https://docs.seldon.io/projects/alibi/en/stable/examples/ale_classification.html)
+- Partial Dependence ([J.H. Friedman, 2001](https://projecteuclid.org/journals/annals-of-statistics/volume-29/issue-5/Greedy-function-approximation-A-gradient-boostingmachine/10.1214/aos/1013203451.full))
+  - [Documentation](https://docs.seldon.io/projects/alibi/en/stable/methods/PartialDependence.html)
+  - Examples:
+    [Bike rental](https://docs.seldon.io/projects/alibi/en/stable/examples/pdp_regression_bike.html)
+- Partial Dependence Variance([Greenwell et al., 2018](https://arxiv.org/abs/1805.04755))
+  - [Documentation](https://docs.seldon.io/projects/alibi/en/stable/methods/PartialDependenceVariance.html)
+  - Examples:
+    [Friedman’s regression problem](https://docs.seldon.io/projects/alibi/en/stable/examples/pd_variance_regression_friedman.html)
+- Permutation Importance([Breiman, 2001](https://link.springer.com/article/10.1023/A:1010933404324); [Fisher et al., 2018](https://arxiv.org/abs/1801.01489))
+  - [Documentation](https://docs.seldon.io/projects/alibi/en/stable/methods/PermutationImportance.html)
+  - Examples:
+    [Who's Going to Leave Next?](https://docs.seldon.io/projects/alibi/en/stable/examples/permutation_importance_classification_leave.html)
+- Anchor explanations ([Ribeiro et al., 2018](https://homes.cs.washington.edu/~marcotcr/aaai18.pdf))
+  - [Documentation](https://docs.seldon.io/projects/alibi/en/stable/methods/Anchors.html)
+  - Examples:
+    [income prediction](https://docs.seldon.io/projects/alibi/en/stable/examples/anchor_tabular_adult.html),
+    [Iris dataset](https://docs.seldon.io/projects/alibi/en/stable/examples/anchor_tabular_iris.html),
+    [movie sentiment classification](https://docs.seldon.io/projects/alibi/en/stable/examples/anchor_text_movie.html),
+    [ImageNet](https://docs.seldon.io/projects/alibi/en/stable/examples/anchor_image_imagenet.html),
+    [fashion MNIST](https://docs.seldon.io/projects/alibi/en/stable/examples/anchor_image_fashion_mnist.html)
+- Contrastive Explanation Method (CEM, [Dhurandhar et al., 2018](https://papers.nips.cc/paper/7340-explanations-based-on-the-missing-towards-contrastive-explanations-with-pertinent-negatives))
+  - [Documentation](https://docs.seldon.io/projects/alibi/en/stable/methods/CEM.html)
+  - Examples: [MNIST](https://docs.seldon.io/projects/alibi/en/stable/examples/cem_mnist.html),
+    [Iris dataset](https://docs.seldon.io/projects/alibi/en/stable/examples/cem_iris.html)
+- Counterfactual Explanations (extension of
+  [Wachter et al., 2017](https://arxiv.org/abs/1711.00399))
+  - [Documentation](https://docs.seldon.io/projects/alibi/en/stable/methods/CF.html)
+  - Examples: 
+    [MNIST](https://docs.seldon.io/projects/alibi/en/stable/examples/cf_mnist.html)
+- Counterfactual Explanations Guided by Prototypes ([Van Looveren and Klaise, 2019](https://arxiv.org/abs/1907.02584))
+  - [Documentation](https://docs.seldon.io/projects/alibi/en/stable/methods/CFProto.html)
+  - Examples:
+    [MNIST](https://docs.seldon.io/projects/alibi/en/stable/examples/cfproto_mnist.html),
+    [California housing dataset](https://docs.seldon.io/projects/alibi/en/stable/examples/cfproto_housing.html),
+    [Adult income (one-hot)](https://docs.seldon.io/projects/alibi/en/stable/examples/cfproto_cat_adult_ohe.html),
+    [Adult income (ordinal)](https://docs.seldon.io/projects/alibi/en/stable/examples/cfproto_cat_adult_ord.html)
+- Model-agnostic Counterfactual Explanations via RL([Samoilescu et al., 2021](https://arxiv.org/abs/2106.02597))
+  - [Documentation](https://docs.seldon.io/projects/alibi/en/stable/methods/CFRL.html)
+  - Examples:
+    [MNIST](https://docs.seldon.io/projects/alibi/en/stable/examples/cfrl_mnist.html),
+    [Adult income](https://docs.seldon.io/projects/alibi/en/stable/examples/cfrl_adult.html)
+- Integrated Gradients ([Sundararajan et al., 2017](https://arxiv.org/abs/1703.01365))
+  - [Documentation](https://docs.seldon.io/projects/alibi/en/stable/methods/IntegratedGradients.html),
+  - Examples:
+    [MNIST example](https://docs.seldon.io/projects/alibi/en/stable/examples/integrated_gradients_mnist.html),
+    [Imagenet example](https://docs.seldon.io/projects/alibi/en/stable/examples/integrated_gradients_imagenet.html),
+    [IMDB example](https://docs.seldon.io/projects/alibi/en/stable/examples/integrated_gradients_imdb.html).
+- Kernel Shapley Additive Explanations ([Lundberg et al., 2017](https://papers.nips.cc/paper/7062-a-unified-approach-to-interpreting-model-predictions))
+  - [Documentation](https://docs.seldon.io/projects/alibi/en/stable/methods/KernelSHAP.html)
+  - Examples:
+    [SVM with continuous data](https://docs.seldon.io/projects/alibi/en/stable/examples/kernel_shap_wine_intro.html),
+    [multinomial logistic regression with continous data](https://docs.seldon.io/projects/alibi/en/stable/examples/kernel_shap_wine_lr.html),
+    [handling categorical variables](https://docs.seldon.io/projects/alibi/en/stable/examples/kernel_shap_adult_lr.html)
+- Tree Shapley Additive Explanations ([Lundberg et al., 2020](https://www.nature.com/articles/s42256-019-0138-9))
+  - [Documentation](https://docs.seldon.io/projects/alibi/en/stable/methods/TreeSHAP.html)
+  - Examples:
+    [Interventional (adult income, xgboost)](https://docs.seldon.io/projects/alibi/en/stable/examples/interventional_tree_shap_adult_xgb.html),
+    [Path-dependent (adult income, xgboost)](https://docs.seldon.io/projects/alibi/en/stable/examples/path_dependent_tree_shap_adult_xgb.html)
+- Trust Scores ([Jiang et al., 2018](https://arxiv.org/abs/1805.11783))
+  - [Documentation](https://docs.seldon.io/projects/alibi/en/stable/methods/TrustScores.html)
+  - Examples:
+    [MNIST](https://docs.seldon.io/projects/alibi/en/stable/examples/trustscore_mnist.html),
+    [Iris dataset](https://docs.seldon.io/projects/alibi/en/stable/examples/trustscore_mnist.html)
+- Linearity Measure
+  - [Documentation](https://docs.seldon.io/projects/alibi/en/stable/methods/LinearityMeasure.html)
+  - Examples:
+    [Iris dataset](https://docs.seldon.io/projects/alibi/en/stable/examples/linearity_measure_iris.html),
+    [fashion MNIST](https://docs.seldon.io/projects/alibi/en/stable/examples/linearity_measure_fashion_mnist.html)
+- ProtoSelect
+  - [Documentation](https://docs.seldon.io/projects/alibi/en/latest/methods/ProtoSelect.html)
+  - Examples:
+    [Adult Census & CIFAR10](https://docs.seldon.io/projects/alibi/en/latest/examples/protoselect_adult_cifar10.html)
+- Similarity explanations
+  - [Documentation](https://docs.seldon.io/projects/alibi/en/stable/methods/Similarity.html)
+  - Examples:
+    [20 news groups dataset](https://docs.seldon.io/projects/alibi/en/stable/examples/similarity_explanations_20ng.html),
+    [ImageNet dataset](https://docs.seldon.io/projects/alibi/en/stable/examples/similarity_explanations_imagenet.html),
+    [MNIST dataset](https://docs.seldon.io/projects/alibi/en/stable/examples/similarity_explanations_mnist.html)
+## Citations
+If you use alibi in your research, please consider citing it.
+BibTeX entry:
+```
+@article{JMLR:v22:21-0017,
+  author  = {Janis Klaise and Arnaud Van Looveren and Giovanni Vacanti and Alexandru Coca},
+  title   = {Alibi Explain: Algorithms for Explaining Machine Learning Models},
+  journal = {Journal of Machine Learning Research},
+  year    = {2021},
+  volume  = {22},
+  number  = {181},
+  pages   = {1-7},
+  url     = {http://jmlr.org/papers/v22/21-0017.html}
+}
+```
+
+%prep
+%autosetup -n alibi-0.9.1
+
+%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-alibi -f filelist.lst
+%dir %{python3_sitelib}/*
+
+%files help -f doclist.lst
+%{_docdir}/*
+
+%changelog
+* Tue Apr 11 2023 Python_Bot <Python_Bot@openeuler.org> - 0.9.1-1
+- Package Spec generated
diff --git a/sources b/sources
new file mode 100644
index 0000000..5580a23
--- /dev/null
+++ b/sources
@@ -0,0 +1 @@
+e85efefef0c63e0765887fccab9948b6  alibi-0.9.1.tar.gz