Add text explainer notebook (kubeflow#355)

* Add text explainer notebook example * Fix formatting
magdalenakuhn17 · Sep 24, 2019 · 4c647d9 · 4c647d9
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diff --git a/docs/samples/explanation/alibi/moviesentiment/README.md b/docs/samples/explanation/alibi/moviesentiment/README.md
@@ -199,3 +199,6 @@ make train
 
 You can then store the `model.joblib` in a bucket accessible from your Kubernetes cluster.
 
+## Run on Notebook
+
+You can also run this example on [notebook](./kfserving_text_explainer.ipynb)
diff --git a/docs/samples/explanation/alibi/moviesentiment/kfserving_text_explainer.ipynb b/docs/samples/explanation/alibi/moviesentiment/kfserving_text_explainer.ipynb
@@ -0,0 +1,252 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Sample for KFService Explainer"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The notebook shows how to use KFServing SDK to create KFService with explainer, predictor."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 59,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from kubernetes import client\n",
+    "\n",
+    "from kfserving import KFServingClient\n",
+    "from kfserving import constants\n",
+    "from kfserving import V1alpha2EndpointSpec\n",
+    "from kfserving import V1alpha2PredictorSpec\n",
+    "from kfserving import V1alpha2ExplainerSpec\n",
+    "from kfserving import V1alpha2AlibiExplainerSpec\n",
+    "from kfserving import V1alpha2SKLearnSpec\n",
+    "from kfserving import V1alpha2KFServiceSpec\n",
+    "from kfserving import V1alpha2KFService\n",
+    "from kubernetes.client import V1ResourceRequirements\n",
+    "from kubernetes import client, config\n",
+    "import kubernetes.client\n",
+    "import os\n",
+    "import requests \n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Define KFService with Explainer"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Add predictor and explainer on the endpoint spec"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "api_version = constants.KFSERVING_GROUP + '/' + constants.KFSERVING_VERSION\n",
+    "default_endpoint_spec = V1alpha2EndpointSpec(\n",
+    "                          predictor=V1alpha2PredictorSpec(\n",
+    "                            min_replicas=1,\n",
+    "                            sklearn=V1alpha2SKLearnSpec(\n",
+    "                              storage_uri='gs://seldon-models/sklearn/moviesentiment',\n",
+    "                              resources=V1ResourceRequirements(\n",
+    "                                  requests={'cpu': '100m', 'memory': '1Gi'},\n",
+    "                                  limits={'cpu': '100m', 'memory': '1Gi'}))),\n",
+    "                          explainer=V1alpha2ExplainerSpec(\n",
+    "                            min_replicas=1, \n",
+    "                            alibi=V1alpha2AlibiExplainerSpec(\n",
+    "                              type='AnchorText',\n",
+    "                              resources=V1ResourceRequirements(\n",
+    "                                  requests={'cpu': '100m', 'memory': '2Gi'},\n",
+    "                                  limits={'cpu': '100m', 'memory': '2Gi'}))))\n",
+    "    \n",
+    "kfsvc = V1alpha2KFService(api_version=api_version,\n",
+    "                          kind=constants.KFSERVING_KIND,\n",
+    "                          metadata=client.V1ObjectMeta(\n",
+    "                              name='moviesentiment', namespace='kubeflow'),\n",
+    "                          spec=V1alpha2KFServiceSpec(default=default_endpoint_spec))\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Create KFService"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Call KFServingClient to create KFService."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "KFServing = KFServingClient()\n",
+    "KFServing.create(kfsvc)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Check the KFService"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 60,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "NAME                 READY      DEFAULT_TRAFFIC CANARY_TRAFFIC  URL                                               \n",
+      "moviesentiment       True                   100                 http://moviesentiment-predict.kubeflow.example.com\n"
+     ]
+    }
+   ],
+   "source": [
+    "KFServing.get('moviesentiment', namespace='kubeflow', watch=True, timeout_seconds=120)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Predict the sentiment for the sentence"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 61,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "34.67.11.82\n"
+     ]
+    }
+   ],
+   "source": [
+    "api_instance = kubernetes.client.CoreV1Api(kubernetes.client.ApiClient())\n",
+    "service = api_instance.read_namespaced_service(\"istio-ingressgateway\", \"istio-system\", exact='true')\n",
+    "cluster_ip = service.status.load_balancer.ingress[0].ip\n",
+    "print(cluster_ip)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 62,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "b'{\"predictions\": [0]}'\n",
+      "b'{\"predictions\": [1]}'\n"
+     ]
+    }
+   ],
+   "source": [
+    "url = \"http://\" + cluster_ip + \"/v1/models/moviesentiment:predict\"\n",
+    "headers = {'Host': 'moviesentiment-predict.kubeflow.example.com'}\n",
+    "response = requests.post(url, '{\"instances\":[\"This is a bad book .\"]}', headers=headers)\n",
+    "print(response.content)\n",
+    "response = requests.post(url, '{\"instances\":[\"This is a good book .\"]}', headers=headers)\n",
+    "print(response.content)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Explain the Prediction"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 58,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "b'{\"names\": [\"bad\"], \"precision\": 1.0, \"coverage\": 0.5007, \"raw\": {\"feature\": [3], \"mean\": [1.0], \"precision\": [1.0], \"coverage\": [0.5007], \"examples\": [{\"covered\": [[\"This is a bad book UNK\"], [\"UNK is UNK bad book UNK\"], [\"UNK is UNK bad book .\"], [\"This UNK UNK bad book UNK\"], [\"This is UNK bad UNK .\"], [\"UNK UNK UNK bad book .\"], [\"UNK is a bad UNK UNK\"], [\"UNK UNK a bad UNK .\"], [\"UNK UNK a bad book UNK\"], [\"UNK is UNK bad book .\"]], \"covered_true\": [[\"UNK is UNK bad UNK UNK\"], [\"UNK is UNK bad UNK UNK\"], [\"UNK is UNK bad book UNK\"], [\"This UNK UNK bad book UNK\"], [\"UNK UNK a bad book .\"], [\"This is UNK bad UNK UNK\"], [\"UNK UNK UNK bad UNK UNK\"], [\"This is UNK bad UNK .\"], [\"This is UNK bad UNK .\"], [\"This UNK a bad UNK .\"]], \"covered_false\": [], \"uncovered_true\": [], \"uncovered_false\": []}], \"all_precision\": 0, \"num_preds\": 1000101, \"names\": [\"bad\"], \"positions\": [10], \"instance\": \"This is a bad book .\", \"prediction\": 0}}'\n",
+      "b'{\"names\": [\"good\"], \"precision\": 1.0, \"coverage\": 0.5007, \"raw\": {\"feature\": [3], \"mean\": [1.0], \"precision\": [1.0], \"coverage\": [0.5007], \"examples\": [{\"covered\": [[\"This is a good book .\"], [\"This is a good book UNK\"], [\"UNK is UNK good book UNK\"], [\"This UNK UNK good UNK UNK\"], [\"UNK UNK a good book UNK\"], [\"This UNK a good UNK UNK\"], [\"UNK is a good book UNK\"], [\"UNK is a good UNK UNK\"], [\"This is UNK good book .\"], [\"UNK UNK UNK good UNK UNK\"]], \"covered_true\": [[\"UNK UNK UNK good book UNK\"], [\"UNK is a good book .\"], [\"UNK is a good book UNK\"], [\"UNK is UNK good book .\"], [\"UNK UNK UNK good book .\"], [\"UNK is a good book .\"], [\"UNK is UNK good UNK UNK\"], [\"UNK UNK UNK good UNK .\"], [\"This UNK a good UNK UNK\"], [\"This is a good UNK .\"]], \"covered_false\": [], \"uncovered_true\": [], \"uncovered_false\": []}], \"all_precision\": 0, \"num_preds\": 1000001, \"names\": [\"good\"], \"positions\": [10], \"instance\": \"This is a good book .\", \"prediction\": 1}}'\n"
+     ]
+    }
+   ],
+   "source": [
+    "url = \"http://\" + cluster_ip + \"/v1/models/moviesentiment:explain\"\n",
+    "headers = {'Host': 'moviesentiment-explain.kubeflow.example.com'}\n",
+    "response = requests.post(url, '{\"instances\":[\"This is a bad book .\"]}', headers=headers)\n",
+    "print(response.content)\n",
+    "response = requests.post(url, '{\"instances\":[\"This is a good book .\"]}', headers=headers)\n",
+    "print(response.content)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Delete the KFService"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "KFServing.delete('moviesentiment', namespace='kubeflow')\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}