Merge pull request #3092 from cliveseldon/3053_alibi_docs

Update alibi docs

doc/source/analytics/explainers.md

@@ -7,6 +7,21 @@ Seldon provides model explanations using its [Alibi](https://github.com/SeldonIO
 
 We provide [an example notebook](../examples/explainer_examples.html) showing how to deploy an explainer for Tabular, Text and Image models.
 
+
+## Available Methods
+
+Seldon Core supports a subset of the methods currently available in [Alibi](https://github.com/SeldonIO/alibi). Presently this the following:
+
+
+| Method | Explainer Key | Example |
+|--------|---------------|---------||
+| [Anchor Tabular](https://docs.seldon.io/projects/alibi/en/latest/methods/Anchors.html) | `AnchorTabular` | [Example](https://docs.seldon.io/projects/seldon-core/en/latest/examples/explainer_examples.html#Income-Prediction-Model-with-Anchors-Explainer) |
+| [Anchor Text](https://docs.seldon.io/projects/alibi/en/latest/methods/Anchors.html) | `AnchorText` | [Example](https://docs.seldon.io/projects/seldon-core/en/latest/examples/explainer_examples.html#Movie-Sentiment-Model) |
+| [Anchor Images](https://docs.seldon.io/projects/alibi/en/latest/methods/Anchors.html) | `AnchorImages` | [Example](https://docs.seldon.io/projects/seldon-core/en/latest/examples/explainer_examples.html#Tensorflow-CIFAR10-Model) |
+| [kernel Shap](https://docs.seldon.io/projects/alibi/en/latest/methods/KernelSHAP.html) | `KernelShap` | [Example](https://docs.seldon.io/projects/seldon-core/en/latest/examples/explainer_examples.html#Wine-Prediction-Model-with-Shap-Explainer) |
+| [Integrated Gradients](https://docs.seldon.io/projects/alibi/en/latest/methods/IntegratedGradients.html) | `IntegratedGradients` | [Example](https://docs.seldon.io/projects/seldon-core/en/latest/examples/explainer_examples.html#MNIST-Model-with-Integrated-Gradients-Explainer) |
+| [Tree Shap](https://docs.seldon.io/projects/alibi/en/latest/methods/TreeSHAP.html) | `TreeShap` | [Example](https://docs.seldon.io/projects/seldon-core/en/latest/examples/explainer_examples.html#XGBoost-Model-with-TreeShap-Explainer) |
+
 ## Creating your explainer
 
 For Alibi explainers that need to be trained you should

doc/source/examples/notebooks.rst

@@ -83,7 +83,7 @@ Advanced Machine Learning Insights
    :titlesonly:
 
    Real time monitoring of statistical metrics <feedback_reward_custom_metrics>
-   Tabular, Text and Image Model Explainers <explainer_examples>
+   Model Explainers <explainer_examples>
    Outlier Detection on CIFAR10 <outlier_cifar10>
 
 Batch Processing with Seldon Core

notebooks/explainer_examples.ipynb

@@ -5,29 +5,31 @@
    "metadata": {},
    "source": [
     "# Example Model Explanations with Seldon\n",
+    "\n",
     "Seldon core supports various out-of-the-box explainers that leverage the [Alibi ML Expalinability](https://github.com/SeldonIO/alibi) open source library.\n",
     "\n",
     "In this notebook we show how you can use the pre-packaged explainer functionality that simplifies the creation of advanced AI model explainers.\n",
     "\n",
     "Seldon provides the following out-of-the-box pre-packaged explainers:\n",
-    "* Anchor Tabular Explainer \n",
-    "    * A black box Explainer that uses the [anchor technique](https://docs.seldon.io/projects/alibi/en/latest/methods/Anchors.html) for tabular data\n",
-    "    * It basically answers the question of what are the most \"powerul\" or \"important\" features in a tabular prediction\n",
-    "* Anchor Image Explainer\n",
-    "    * A black box Explainer that uses the [anchor technique](https://docs.seldon.io/projects/alibi/en/latest/methods/Anchors.html) for image data\n",
-    "    * It basically answers the question of what are the most \"powerul\" or \"important\" pixels in an image prediction\n",
-    "* Anchor Text Explainer\n",
-    "    * A black box Explainer that uses the [anchor technique](https://docs.seldon.io/projects/alibi/en/latest/methods/Anchors.html) for text data\n",
-    "    * It basically answers the question of what are the most \"powerul\" or \"important\" tokens in a text prediction\n",
-    "* Kernel Shap Explainer\n",
-    "    * A black box Explainer that uses the [kernel shap technique](https://docs.seldon.io/projects/alibi/en/latest/methods/Anchors.html) for tabular data\n",
-    "    * It provides postive and negative feature attributions that contributed to the predictions\n",
-    "* Integrated Gradient Explainer\n",
-    "    * A white box explainer that uses the [Integrated Gradients technique](https://docs.seldon.io/projects/alibi/en/latest/methods/Anchors.html) for Keras models\n",
-    "    * It provides importance values for each feature\n",
-    "* Tree Shap Explainer\n",
-    "    * A white box explainer that uses the [TreeShap technqiue](https://docs.seldon.io/projects/alibi/en/latest/methods/Anchors.html) for tree based models\n",
-    "    * It provides positive and negative feature attributions that contributed to the predictions"
+    "\n",
+    " - Anchor Tabular Explainer \n",
+    "    - A black box Explainer that uses the [anchor technique](https://docs.seldon.io/projects/alibi/en/latest/methods/Anchors.html) for tabular data\n",
+    "    - It basically answers the question of what are the most \"powerul\" or \"important\" features in a tabular  prediction\n",
+    " - Anchor Image Explainer\n",
+    "    - A black box Explainer that uses the [anchor technique](https://docs.seldon.io/projects/alibi/en/latest/methods/Anchors.html) for image data\n",
+    "    - It basically answers the question of what are the most \"powerul\" or \"important\" pixels in an image prediction\n",
+    " - Anchor Text Explainer\n",
+    "    - A black box Explainer that uses the [anchor technique](https://docs.seldon.io/projects/alibi/en/latest/methods/Anchors.html) for text data\n",
+    "    - It basically answers the question of what are the most \"powerul\" or \"important\" tokens in a text  prediction\n",
+    " - Kernel Shap Explainer\n",
+    "    - A black box Explainer that uses the [kernel shap technique](https://docs.seldon.io/projects/alibi/en/latest/methods/Anchors.html) for tabular data\n",
+    "    - It provides postive and negative feature attributions that contributed to the predictions\n",
+    " - Integrated Gradient Explainer\n",
+    "    - A white box explainer that uses the [Integrated Gradients technique](https://docs.seldon.io/projects/alibi/en/latest/methods/Anchors.html) for Keras models\n",
+    "    - It provides importance values for each feature\n",
+    " - Tree Shap Explainer\n",
+    "    - A white box explainer that uses the [TreeShap technique](https://docs.seldon.io/projects/alibi/en/latest/methods/Anchors.html) for tree based models\n",
+    "    - It provides positive and negative feature attributions that contributed to the predictions"
    ]
   },
   {
@@ -1614,7 +1616,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.9"
+   "version": "3.6.8"
   },
   "varInspector": {
    "cols": {