{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Tce3stUlHN0L"
      },
      "source": [
        "##### Copyright 2020 The TensorFlow IO Authors."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "cellView": "form",
        "id": "tuOe1ymfHZPu"
      },
      "outputs": [],
      "source": [
        "#@title Licensed under the Apache License, Version 2.0 (the \"License\");\n",
        "# you may not use this file except in compliance with the License.\n",
        "# You may obtain a copy of the License at\n",
        "#\n",
        "# https://www.apache.org/licenses/LICENSE-2.0\n",
        "#\n",
        "# Unless required by applicable law or agreed to in writing, software\n",
        "# distributed under the License is distributed on an \"AS IS\" BASIS,\n",
        "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
        "# See the License for the specific language governing permissions and\n",
        "# limitations under the License."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qFdPvlXBOdUN"
      },
      "source": [
        "# Streaming structured data from Elasticsearch using Tensorflow-IO"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MfBg1C5NB3X0"
      },
      "source": [
        "<table class=\"tfo-notebook-buttons\" align=\"left\">\n",
        "  <td>\n",
        "    <a target=\"_blank\" href=\"https://www.tensorflow.org/io/tutorials/elasticsearch\"><img src=\"https://www.tensorflow.org/images/tf_logo_32px.png\" />View on TensorFlow.org</a>\n",
        "  </td>\n",
        "  <td>\n",
        "    <a target=\"_blank\" href=\"https://colab.research.google.com/github/tensorflow/io/blob/master/docs/tutorials/elasticsearch.ipynb\"><img src=\"https://www.tensorflow.org/images/colab_logo_32px.png\" />Run in Google Colab</a>\n",
        "  </td>\n",
        "  <td>\n",
        "    <a target=\"_blank\" href=\"https://github.com/tensorflow/io/blob/master/docs/tutorials/elasticsearch.ipynb\"><img src=\"https://www.tensorflow.org/images/GitHub-Mark-32px.png\" />View source on GitHub</a>\n",
        "  </td>\n",
        "      <td>\n",
        "    <a href=\"https://storage.googleapis.com/tensorflow_docs/io/docs/tutorials/elasticsearch.ipynb\"><img src=\"https://www.tensorflow.org/images/download_logo_32px.png\" />Download notebook</a>\n",
        "  </td>\n",
        "</table>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xHxb-dlhMIzW"
      },
      "source": [
        "## Overview\n",
        "\n",
        "This tutorial focuses on streaming data from an [Elasticsearch](https://www.elastic.co/guide/en/elasticsearch/reference/current/elasticsearch-intro.html) cluster into a `tf.data.Dataset` which is then used in conjunction with `tf.keras` for training and inference.\n",
        "\n",
        "Elasticseach is primarily a distributed search engine which supports storing structured, unstructured, geospatial, numeric data etc. For the purpose of this tutorial, a dataset with structured records is utilized.\n",
        "\n",
        "**NOTE:** A basic understanding of [elasticsearch storage](https://www.elastic.co/guide/en/elasticsearch/reference/current/documents-indices.html) will help you in following the tutorial with ease."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MUXex9ctTuDB"
      },
      "source": [
        "## Setup packages\n",
        "\n",
        "The `elasticsearch` package is utilized for preparing and storing the data within elasticsearch indices for demonstration purposes only. In real-world production clusters with numerous nodes, the cluster might be receiving the data from connectors like logstash etc.\n",
        "\n",
        "Once the data is available in the elasticsearch cluster, only `tensorflow-io` is required to stream the data into the models.\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "upgCc3gXybsA"
      },
      "source": [
        "### Install the required tensorflow-io and elasticsearch packages"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "id": "48B9eAMMhAgw"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Requirement already satisfied: tensorflow-io in /usr/local/lib/python3.6/dist-packages (0.16.0)\n",
            "Requirement already satisfied: tensorflow<2.4.0,>=2.3.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow-io) (2.3.0)\n",
            "Requirement already satisfied: keras-preprocessing<1.2,>=1.1.1 in /usr/local/lib/python3.6/dist-packages (from tensorflow<2.4.0,>=2.3.0->tensorflow-io) (1.1.2)\n",
            "Requirement already satisfied: numpy<1.19.0,>=1.16.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow<2.4.0,>=2.3.0->tensorflow-io) (1.18.5)\n",
            "Requirement already satisfied: scipy==1.4.1 in /usr/local/lib/python3.6/dist-packages (from tensorflow<2.4.0,>=2.3.0->tensorflow-io) (1.4.1)\n",
            "Requirement already satisfied: absl-py>=0.7.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow<2.4.0,>=2.3.0->tensorflow-io) (0.10.0)\n",
            "Requirement already satisfied: opt-einsum>=2.3.2 in /usr/local/lib/python3.6/dist-packages (from tensorflow<2.4.0,>=2.3.0->tensorflow-io) (3.3.0)\n",
            "Requirement already satisfied: h5py<2.11.0,>=2.10.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow<2.4.0,>=2.3.0->tensorflow-io) (2.10.0)\n",
            "Requirement already satisfied: grpcio>=1.8.6 in /usr/local/lib/python3.6/dist-packages (from tensorflow<2.4.0,>=2.3.0->tensorflow-io) (1.33.2)\n",
            "Requirement already satisfied: wheel>=0.26 in /usr/local/lib/python3.6/dist-packages (from tensorflow<2.4.0,>=2.3.0->tensorflow-io) (0.35.1)\n",
            "Requirement already satisfied: astunparse==1.6.3 in /usr/local/lib/python3.6/dist-packages (from tensorflow<2.4.0,>=2.3.0->tensorflow-io) (1.6.3)\n",
            "Requirement already satisfied: six>=1.12.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow<2.4.0,>=2.3.0->tensorflow-io) (1.15.0)\n",
            "Requirement already satisfied: google-pasta>=0.1.8 in /usr/local/lib/python3.6/dist-packages (from tensorflow<2.4.0,>=2.3.0->tensorflow-io) (0.2.0)\n",
            "Requirement already satisfied: termcolor>=1.1.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow<2.4.0,>=2.3.0->tensorflow-io) (1.1.0)\n",
            "Requirement already satisfied: tensorboard<3,>=2.3.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow<2.4.0,>=2.3.0->tensorflow-io) (2.3.0)\n",
            "Requirement already satisfied: tensorflow-estimator<2.4.0,>=2.3.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow<2.4.0,>=2.3.0->tensorflow-io) (2.3.0)\n",
            "Requirement already satisfied: wrapt>=1.11.1 in /usr/local/lib/python3.6/dist-packages (from tensorflow<2.4.0,>=2.3.0->tensorflow-io) (1.12.1)\n",
            "Requirement already satisfied: protobuf>=3.9.2 in /usr/local/lib/python3.6/dist-packages (from tensorflow<2.4.0,>=2.3.0->tensorflow-io) (3.12.4)\n",
            "Requirement already satisfied: gast==0.3.3 in /usr/local/lib/python3.6/dist-packages (from tensorflow<2.4.0,>=2.3.0->tensorflow-io) (0.3.3)\n",
            "Requirement already satisfied: requests<3,>=2.21.0 in /usr/local/lib/python3.6/dist-packages (from tensorboard<3,>=2.3.0->tensorflow<2.4.0,>=2.3.0->tensorflow-io) (2.23.0)\n",
            "Requirement already satisfied: tensorboard-plugin-wit>=1.6.0 in /usr/local/lib/python3.6/dist-packages (from tensorboard<3,>=2.3.0->tensorflow<2.4.0,>=2.3.0->tensorflow-io) (1.7.0)\n",
            "Requirement already satisfied: werkzeug>=0.11.15 in /usr/local/lib/python3.6/dist-packages (from tensorboard<3,>=2.3.0->tensorflow<2.4.0,>=2.3.0->tensorflow-io) (1.0.1)\n",
            "Requirement already satisfied: setuptools>=41.0.0 in /usr/local/lib/python3.6/dist-packages (from tensorboard<3,>=2.3.0->tensorflow<2.4.0,>=2.3.0->tensorflow-io) (50.3.2)\n",
            "Requirement already satisfied: google-auth<2,>=1.6.3 in /usr/local/lib/python3.6/dist-packages (from tensorboard<3,>=2.3.0->tensorflow<2.4.0,>=2.3.0->tensorflow-io) (1.17.2)\n",
            "Requirement already satisfied: markdown>=2.6.8 in /usr/local/lib/python3.6/dist-packages (from tensorboard<3,>=2.3.0->tensorflow<2.4.0,>=2.3.0->tensorflow-io) (3.3.3)\n",
            "Requirement already satisfied: google-auth-oauthlib<0.5,>=0.4.1 in /usr/local/lib/python3.6/dist-packages (from tensorboard<3,>=2.3.0->tensorflow<2.4.0,>=2.3.0->tensorflow-io) (0.4.2)\n",
            "Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.6/dist-packages (from requests<3,>=2.21.0->tensorboard<3,>=2.3.0->tensorflow<2.4.0,>=2.3.0->tensorflow-io) (1.24.3)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.6/dist-packages (from requests<3,>=2.21.0->tensorboard<3,>=2.3.0->tensorflow<2.4.0,>=2.3.0->tensorflow-io) (2020.6.20)\n",
            "Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.6/dist-packages (from requests<3,>=2.21.0->tensorboard<3,>=2.3.0->tensorflow<2.4.0,>=2.3.0->tensorflow-io) (3.0.4)\n",
            "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.6/dist-packages (from requests<3,>=2.21.0->tensorboard<3,>=2.3.0->tensorflow<2.4.0,>=2.3.0->tensorflow-io) (2.10)\n",
            "Requirement already satisfied: pyasn1-modules>=0.2.1 in /usr/local/lib/python3.6/dist-packages (from google-auth<2,>=1.6.3->tensorboard<3,>=2.3.0->tensorflow<2.4.0,>=2.3.0->tensorflow-io) (0.2.8)\n",
            "Requirement already satisfied: cachetools<5.0,>=2.0.0 in /usr/local/lib/python3.6/dist-packages (from google-auth<2,>=1.6.3->tensorboard<3,>=2.3.0->tensorflow<2.4.0,>=2.3.0->tensorflow-io) (4.1.1)\n",
            "Requirement already satisfied: rsa<5,>=3.1.4; python_version >= \"3\" in /usr/local/lib/python3.6/dist-packages (from google-auth<2,>=1.6.3->tensorboard<3,>=2.3.0->tensorflow<2.4.0,>=2.3.0->tensorflow-io) (4.6)\n",
            "Requirement already satisfied: importlib-metadata; python_version < \"3.8\" in /usr/local/lib/python3.6/dist-packages (from markdown>=2.6.8->tensorboard<3,>=2.3.0->tensorflow<2.4.0,>=2.3.0->tensorflow-io) (2.0.0)\n",
            "Requirement already satisfied: requests-oauthlib>=0.7.0 in /usr/local/lib/python3.6/dist-packages (from google-auth-oauthlib<0.5,>=0.4.1->tensorboard<3,>=2.3.0->tensorflow<2.4.0,>=2.3.0->tensorflow-io) (1.3.0)\n",
            "Requirement already satisfied: pyasn1<0.5.0,>=0.4.6 in /usr/local/lib/python3.6/dist-packages (from pyasn1-modules>=0.2.1->google-auth<2,>=1.6.3->tensorboard<3,>=2.3.0->tensorflow<2.4.0,>=2.3.0->tensorflow-io) (0.4.8)\n",
            "Requirement already satisfied: zipp>=0.5 in /usr/local/lib/python3.6/dist-packages (from importlib-metadata; python_version < \"3.8\"->markdown>=2.6.8->tensorboard<3,>=2.3.0->tensorflow<2.4.0,>=2.3.0->tensorflow-io) (3.4.0)\n",
            "Requirement already satisfied: oauthlib>=3.0.0 in /usr/local/lib/python3.6/dist-packages (from requests-oauthlib>=0.7.0->google-auth-oauthlib<0.5,>=0.4.1->tensorboard<3,>=2.3.0->tensorflow<2.4.0,>=2.3.0->tensorflow-io) (3.1.0)\n",
            "Requirement already satisfied: elasticsearch in /usr/local/lib/python3.6/dist-packages (7.9.1)\n",
            "Requirement already satisfied: urllib3>=1.21.1 in /usr/local/lib/python3.6/dist-packages (from elasticsearch) (1.24.3)\n",
            "Requirement already satisfied: certifi in /usr/local/lib/python3.6/dist-packages (from elasticsearch) (2020.6.20)\n"
          ]
        }
      ],
      "source": [
        "!pip install tensorflow-io\n",
        "!pip install elasticsearch\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gjrZNJQRJP-U"
      },
      "source": [
        "### Import packages"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "id": "m6KXZuTBWgRm"
      },
      "outputs": [],
      "source": [
        "import os\n",
        "import time\n",
        "from sklearn.model_selection import train_test_split\n",
        "from elasticsearch import Elasticsearch\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import tensorflow as tf\n",
        "from tensorflow.keras import layers\n",
        "from tensorflow.keras.layers.experimental import preprocessing\n",
        "import tensorflow_io as tfio"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eCgO11GTJaTj"
      },
      "source": [
        "### Validate tf and tfio imports"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "id": "dX74RKfZ_TdF"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "tensorflow-io version: 0.16.0\n",
            "tensorflow version: 2.3.0\n"
          ]
        }
      ],
      "source": [
        "print(\"tensorflow-io version: {}\".format(tfio.__version__))\n",
        "print(\"tensorflow version: {}\".format(tf.__version__))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yZmI7l_GykcW"
      },
      "source": [
        "## Download and setup the Elasticsearch instance\n",
        "\n",
        "For demo purposes, the open-source version of the elasticsearch package is used.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "id": "YUj0878jPyz7"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "elasticsearch-oss-7.9.2-linux-x86_64.tar.gz: OK\n"
          ]
        }
      ],
      "source": [
        "%%bash\n",
        "\n",
        "wget -q https://artifacts.elastic.co/downloads/elasticsearch/elasticsearch-oss-7.9.2-linux-x86_64.tar.gz\n",
        "wget -q https://artifacts.elastic.co/downloads/elasticsearch/elasticsearch-oss-7.9.2-linux-x86_64.tar.gz.sha512\n",
        "tar -xzf elasticsearch-oss-7.9.2-linux-x86_64.tar.gz\n",
        "sudo chown -R daemon:daemon elasticsearch-7.9.2/\n",
        "shasum -a 512 -c elasticsearch-oss-7.9.2-linux-x86_64.tar.gz.sha512 "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vAzfu_WiEs4F"
      },
      "source": [
        "Run the instance as a daemon process"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "id": "n9ujlunrWgRx"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Starting job # 0 in a separate thread.\n"
          ]
        }
      ],
      "source": [
        "%%bash --bg\n",
        "\n",
        "sudo -H -u daemon elasticsearch-7.9.2/bin/elasticsearch"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "id": "XyUa9r6MgWtW"
      },
      "outputs": [],
      "source": [
        "# Sleep for few seconds to let the instance start.\n",
        "time.sleep(20)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "f6qxCdypE1DD"
      },
      "source": [
        "Once the instance has been started, grep for `elasticsearch` in the processes list to confirm the availability."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "id": "48LqMJ1BEHm5"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "root         144     142  0 21:24 ?        00:00:00 sudo -H -u daemon elasticsearch-7.9.2/bin/elasticsearch\n",
            "daemon       145     144 86 21:24 ?        00:00:17 /content/elasticsearch-7.9.2/jdk/bin/java -Xshare:auto -Des.networkaddress.cache.ttl=60 -Des.networkaddress.cache.negative.ttl=10 -XX:+AlwaysPreTouch -Xss1m -Djava.awt.headless=true -Dfile.encoding=UTF-8 -Djna.nosys=true -XX:-OmitStackTraceInFastThrow -XX:+ShowCodeDetailsInExceptionMessages -Dio.netty.noUnsafe=true -Dio.netty.noKeySetOptimization=true -Dio.netty.recycler.maxCapacityPerThread=0 -Dio.netty.allocator.numDirectArenas=0 -Dlog4j.shutdownHookEnabled=false -Dlog4j2.disable.jmx=true -Djava.locale.providers=SPI,COMPAT -Xms1g -Xmx1g -XX:+UseG1GC -XX:G1ReservePercent=25 -XX:InitiatingHeapOccupancyPercent=30 -Djava.io.tmpdir=/tmp/elasticsearch-16913031424109346409 -XX:+HeapDumpOnOutOfMemoryError -XX:HeapDumpPath=data -XX:ErrorFile=logs/hs_err_pid%p.log -Xlog:gc*,gc+age=trace,safepoint:file=logs/gc.log:utctime,pid,tags:filecount=32,filesize=64m -XX:MaxDirectMemorySize=536870912 -Des.path.home=/content/elasticsearch-7.9.2 -Des.path.conf=/content/elasticsearch-7.9.2/config -Des.distribution.flavor=oss -Des.distribution.type=tar -Des.bundled_jdk=true -cp /content/elasticsearch-7.9.2/lib/* org.elasticsearch.bootstrap.Elasticsearch\n",
            "root         382     380  0 21:24 ?        00:00:00 grep elasticsearch\n"
          ]
        }
      ],
      "source": [
        "%%bash\n",
        "\n",
        "ps -ef | grep elasticsearch"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wBuRpiyf_kNS"
      },
      "source": [
        "query the base endpoint to retrieve information about the cluster."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "id": "ILyohKWQ_XQS"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "{\n",
            "  \"name\" : \"d1bc7d054c69\",\n",
            "  \"cluster_name\" : \"elasticsearch\",\n",
            "  \"cluster_uuid\" : \"P8YXfKqYS-OS3k9CdMmlsw\",\n",
            "  \"version\" : {\n",
            "    \"number\" : \"7.9.2\",\n",
            "    \"build_flavor\" : \"oss\",\n",
            "    \"build_type\" : \"tar\",\n",
            "    \"build_hash\" : \"d34da0ea4a966c4e49417f2da2f244e3e97b4e6e\",\n",
            "    \"build_date\" : \"2020-09-23T00:45:33.626720Z\",\n",
            "    \"build_snapshot\" : false,\n",
            "    \"lucene_version\" : \"8.6.2\",\n",
            "    \"minimum_wire_compatibility_version\" : \"6.8.0\",\n",
            "    \"minimum_index_compatibility_version\" : \"6.0.0-beta1\"\n",
            "  },\n",
            "  \"tagline\" : \"You Know, for Search\"\n",
            "}\n"
          ]
        }
      ],
      "source": [
        "%%bash\n",
        "\n",
        "curl -sX GET \"localhost:9200/\""
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4CfKVmCvwcL7"
      },
      "source": [
        "### Explore the dataset\n",
        "\n",
        "For the purpose of this tutorial, lets download the [PetFinder](https://www.kaggle.com/c/petfinder-adoption-prediction) dataset and feed the data into elasticsearch manually. The goal of this classification problem is predict if the pet will be adopted or not.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "id": "XkXyocIdKRSB"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Downloading data from http://storage.googleapis.com/download.tensorflow.org/data/petfinder-mini.zip\n",
            "1671168/1668792 [==============================] - 0s 0us/step\n"
          ]
        }
      ],
      "source": [
        "dataset_url = 'http://storage.googleapis.com/download.tensorflow.org/data/petfinder-mini.zip'\n",
        "csv_file = 'datasets/petfinder-mini/petfinder-mini.csv'\n",
        "tf.keras.utils.get_file('petfinder_mini.zip', dataset_url,\n",
        "                        extract=True, cache_dir='.')\n",
        "pf_df = pd.read_csv(csv_file)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "id": "nC-yt_c9u0sH"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Type</th>\n",
              "      <th>Age</th>\n",
              "      <th>Breed1</th>\n",
              "      <th>Gender</th>\n",
              "      <th>Color1</th>\n",
              "      <th>Color2</th>\n",
              "      <th>MaturitySize</th>\n",
              "      <th>FurLength</th>\n",
              "      <th>Vaccinated</th>\n",
              "      <th>Sterilized</th>\n",
              "      <th>Health</th>\n",
              "      <th>Fee</th>\n",
              "      <th>Description</th>\n",
              "      <th>PhotoAmt</th>\n",
              "      <th>AdoptionSpeed</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Cat</td>\n",
              "      <td>3</td>\n",
              "      <td>Tabby</td>\n",
              "      <td>Male</td>\n",
              "      <td>Black</td>\n",
              "      <td>White</td>\n",
              "      <td>Small</td>\n",
              "      <td>Short</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>Healthy</td>\n",
              "      <td>100</td>\n",
              "      <td>Nibble is a 3+ month old ball of cuteness. He ...</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Cat</td>\n",
              "      <td>1</td>\n",
              "      <td>Domestic Medium Hair</td>\n",
              "      <td>Male</td>\n",
              "      <td>Black</td>\n",
              "      <td>Brown</td>\n",
              "      <td>Medium</td>\n",
              "      <td>Medium</td>\n",
              "      <td>Not Sure</td>\n",
              "      <td>Not Sure</td>\n",
              "      <td>Healthy</td>\n",
              "      <td>0</td>\n",
              "      <td>I just found it alone yesterday near my apartm...</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Dog</td>\n",
              "      <td>1</td>\n",
              "      <td>Mixed Breed</td>\n",
              "      <td>Male</td>\n",
              "      <td>Brown</td>\n",
              "      <td>White</td>\n",
              "      <td>Medium</td>\n",
              "      <td>Medium</td>\n",
              "      <td>Yes</td>\n",
              "      <td>No</td>\n",
              "      <td>Healthy</td>\n",
              "      <td>0</td>\n",
              "      <td>Their pregnant mother was dumped by her irresp...</td>\n",
              "      <td>7</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Dog</td>\n",
              "      <td>4</td>\n",
              "      <td>Mixed Breed</td>\n",
              "      <td>Female</td>\n",
              "      <td>Black</td>\n",
              "      <td>Brown</td>\n",
              "      <td>Medium</td>\n",
              "      <td>Short</td>\n",
              "      <td>Yes</td>\n",
              "      <td>No</td>\n",
              "      <td>Healthy</td>\n",
              "      <td>150</td>\n",
              "      <td>Good guard dog, very alert, active, obedience ...</td>\n",
              "      <td>8</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Dog</td>\n",
              "      <td>1</td>\n",
              "      <td>Mixed Breed</td>\n",
              "      <td>Male</td>\n",
              "      <td>Black</td>\n",
              "      <td>No Color</td>\n",
              "      <td>Medium</td>\n",
              "      <td>Short</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>Healthy</td>\n",
              "      <td>0</td>\n",
              "      <td>This handsome yet cute boy is up for adoption....</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "  Type  Age  ... PhotoAmt AdoptionSpeed\n",
              "0  Cat    3  ...        1             2\n",
              "1  Cat    1  ...        2             0\n",
              "2  Dog    1  ...        7             3\n",
              "3  Dog    4  ...        8             2\n",
              "4  Dog    1  ...        3             2\n",
              "\n",
              "[5 rows x 15 columns]"
            ]
          },
          "execution_count": 11,
          "metadata": {
            "tags": []
          },
          "output_type": "execute_result"
        }
      ],
      "source": [
        "pf_df.head()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FTFL8nmnGVOc"
      },
      "source": [
        "For the purpose of the tutorial, modifications are made to the label column.\n",
        "0 will indicate the pet was not adopted, and 1 will indicate that it was.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "id": "c6Cg22bU0-na"
      },
      "outputs": [],
      "source": [
        "# In the original dataset \"4\" indicates the pet was not adopted.\n",
        "pf_df['target'] = np.where(pf_df['AdoptionSpeed']==4, 0, 1)\n",
        "\n",
        "# Drop un-used columns.\n",
        "pf_df = pf_df.drop(columns=['AdoptionSpeed', 'Description'])\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "id": "klnNOM5oGtH1"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "(11537, 14)"
            ]
          },
          "execution_count": 13,
          "metadata": {
            "tags": []
          },
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Number of datapoints and columns\n",
        "len(pf_df), len(pf_df.columns)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tF5K9xtmlT2P"
      },
      "source": [
        "### Split the dataset\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "id": "n-ku_X0Wld59"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Number of training samples:  8075\n",
            "Number of testing sample:  3462\n"
          ]
        }
      ],
      "source": [
        "train_df, test_df = train_test_split(pf_df, test_size=0.3, shuffle=True)\n",
        "print(\"Number of training samples: \",len(train_df))\n",
        "print(\"Number of testing sample: \",len(test_df))\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wwP5U4GqmhoL"
      },
      "source": [
        "### Store the train and test data in elasticsearch indices\n",
        "\n",
        "Storing the data in the local elasticsearch cluster simulates an environment for continuous remote data retrieval for training and inference purposes."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "id": "YhwFImSqncLE"
      },
      "outputs": [],
      "source": [
        "ES_NODES = \"http://localhost:9200\"\n",
        "\n",
        "def prepare_es_data(index, doc_type, df):\n",
        "  records = df.to_dict(orient=\"records\")\n",
        "  es_data = []\n",
        "  for idx, record in enumerate(records):\n",
        "    meta_dict = {\n",
        "          \"index\": {\n",
        "              \"_index\": index, \n",
        "              \"_type\": doc_type, \n",
        "              \"_id\": idx\n",
        "          }\n",
        "      }\n",
        "    es_data.append(meta_dict)\n",
        "    es_data.append(record)\n",
        "\n",
        "  return es_data\n",
        "\n",
        "def index_es_data(index, es_data):\n",
        "  es_client = Elasticsearch(hosts = [ES_NODES])\n",
        "  if es_client.indices.exists(index):\n",
        "      print(\"deleting the '{}' index.\".format(index))\n",
        "      res = es_client.indices.delete(index=index)\n",
        "      print(\"Response from server: {}\".format(res))\n",
        "\n",
        "  print(\"creating the '{}' index.\".format(index))\n",
        "  res = es_client.indices.create(index=index)\n",
        "  print(\"Response from server: {}\".format(res))\n",
        "\n",
        "  print(\"bulk index the data\")\n",
        "  res = es_client.bulk(index=index, body=es_data, refresh = True)\n",
        "  print(\"Errors: {}, Num of records indexed: {}\".format(res[\"errors\"], len(res[\"items\"])))\n",
        "  "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "id": "4wBiwCRBNGAu"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "creating the 'train' index.\n",
            "Response from server: {'acknowledged': True, 'shards_acknowledged': True, 'index': 'train'}\n",
            "bulk index the data\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/elasticsearch/connection/base.py:190: ElasticsearchDeprecationWarning: [types removal] Specifying types in bulk requests is deprecated.\n",
            "  warnings.warn(message, category=ElasticsearchDeprecationWarning)\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Errors: False, Num of records indexed: 8075\n",
            "creating the 'test' index.\n",
            "Response from server: {'acknowledged': True, 'shards_acknowledged': True, 'index': 'test'}\n",
            "bulk index the data\n",
            "Errors: False, Num of records indexed: 3462\n"
          ]
        }
      ],
      "source": [
        "train_es_data = prepare_es_data(index=\"train\", doc_type=\"pet\", df=train_df)\n",
        "test_es_data = prepare_es_data(index=\"test\", doc_type=\"pet\", df=test_df)\n",
        "\n",
        "index_es_data(index=\"train\", es_data=train_es_data)\n",
        "time.sleep(3)\n",
        "index_es_data(index=\"test\", es_data=test_es_data)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2mOrfOYrHpQj"
      },
      "source": [
        "## Prepare tfio datasets\n",
        "\n",
        "Once the data is available in the cluster, only `tensorflow-io` is required to stream the data from the indices. The `elasticsearch.ElasticsearchIODataset` class is utilized for this purpose. The class inherits from `tf.data.Dataset` and thus exposes all the useful functionalities of `tf.data.Dataset` out of the box.\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "58q52py93jEf"
      },
      "source": [
        "### Training dataset\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "id": "HHOcitbW2_d1"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Connection successful: http://localhost:9200/_cluster/health\n"
          ]
        }
      ],
      "source": [
        "BATCH_SIZE=32\n",
        "HEADERS = {\"Content-Type\": \"application/json\"}\n",
        "\n",
        "train_ds = tfio.experimental.elasticsearch.ElasticsearchIODataset(\n",
        "        nodes=[ES_NODES],\n",
        "        index=\"train\",\n",
        "        doc_type=\"pet\",\n",
        "        headers=HEADERS\n",
        "    )\n",
        "\n",
        "# Prepare a tuple of (features, label)\n",
        "train_ds = train_ds.map(lambda v: (v, v.pop(\"target\")))\n",
        "train_ds = train_ds.batch(BATCH_SIZE)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Me0zgeCQIsKH"
      },
      "source": [
        "### Testing dataset"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "id": "2R-I9hUgIcXR"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Connection successful: http://localhost:9200/_cluster/health\n"
          ]
        }
      ],
      "source": [
        "test_ds = tfio.experimental.elasticsearch.ElasticsearchIODataset(\n",
        "        nodes=[ES_NODES],\n",
        "        index=\"test\",\n",
        "        doc_type=\"pet\",\n",
        "        headers=HEADERS\n",
        "    )\n",
        "\n",
        "# Prepare a tuple of (features, label)\n",
        "test_ds = test_ds.map(lambda v: (v, v.pop(\"target\")))\n",
        "test_ds = test_ds.batch(BATCH_SIZE)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7fAC5HDERL4-"
      },
      "source": [
        "### Define the keras preprocessing layers\n",
        "\n",
        "As per the [structured data tutorial](https://www.tensorflow.org/tutorials/structured_data/preprocessing_layers), it is recommended to use the [Keras Preprocessing Layers](https://www.tensorflow.org/api_docs/python/tf/keras/layers/experimental/preprocessing) as they are more intuitive, and can be easily integrated with the models. However, the standard [feature_columns](https://www.tensorflow.org/api_docs/python/tf/feature_column) can also be used.\n",
        "\n",
        "For a better understanding of the `preprocessing_layers` in classifying structured data, please refer to the [structured data tutorial](https://www.tensorflow.org/tutorials/structured_data/preprocessing_layers)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {
        "id": "CBzR7Li4SaQS"
      },
      "outputs": [],
      "source": [
        "def get_normalization_layer(name, dataset):\n",
        "  # Create a Normalization layer for our feature.\n",
        "  normalizer = preprocessing.Normalization()\n",
        "\n",
        "  # Prepare a Dataset that only yields our feature.\n",
        "  feature_ds = dataset.map(lambda x, y: x[name])\n",
        "\n",
        "  # Learn the statistics of the data.\n",
        "  normalizer.adapt(feature_ds)\n",
        "\n",
        "  return normalizer\n",
        "\n",
        "def get_category_encoding_layer(name, dataset, dtype, max_tokens=None):\n",
        "  # Create a StringLookup layer which will turn strings into integer indices\n",
        "  if dtype == 'string':\n",
        "    index = preprocessing.StringLookup(max_tokens=max_tokens)\n",
        "  else:\n",
        "    index = preprocessing.IntegerLookup(max_values=max_tokens)\n",
        "\n",
        "  # Prepare a Dataset that only yields our feature\n",
        "  feature_ds = dataset.map(lambda x, y: x[name])\n",
        "\n",
        "  # Learn the set of possible values and assign them a fixed integer index.\n",
        "  index.adapt(feature_ds)\n",
        "\n",
        "  # Create a Discretization for our integer indices.\n",
        "  encoder = preprocessing.CategoryEncoding(max_tokens=index.vocab_size())\n",
        "\n",
        "  # Prepare a Dataset that only yields our feature.\n",
        "  feature_ds = feature_ds.map(index)\n",
        "\n",
        "  # Learn the space of possible indices.\n",
        "  encoder.adapt(feature_ds)\n",
        "\n",
        "  # Apply one-hot encoding to our indices. The lambda function captures the\n",
        "  # layer so you can use them, or include them in the functional model later.\n",
        "  return lambda feature: encoder(index(feature))\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4s9c7e2hbIET"
      },
      "source": [
        "Fetch a batch and observe the features of a sample record. This will help in defining the keras preprocessing layers for training the `tf.keras` model."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {
        "id": "CRukoDPKbKqu"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "{'Age': 2,\n",
              " 'Breed1': b'Tabby',\n",
              " 'Color1': b'Black',\n",
              " 'Color2': b'Cream',\n",
              " 'Fee': 0,\n",
              " 'FurLength': b'Short',\n",
              " 'Gender': b'Male',\n",
              " 'Health': b'Healthy',\n",
              " 'MaturitySize': b'Small',\n",
              " 'PhotoAmt': 4,\n",
              " 'Sterilized': b'No',\n",
              " 'Type': b'Cat',\n",
              " 'Vaccinated': b'No'}"
            ]
          },
          "execution_count": 20,
          "metadata": {
            "tags": []
          },
          "output_type": "execute_result"
        }
      ],
      "source": [
        "ds_iter = iter(train_ds)\n",
        "features, label = next(ds_iter)\n",
        "{key: value.numpy()[0] for key,value in features.items()}"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LI0Mmp_dT7yu"
      },
      "source": [
        "Choose a subset of features."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "metadata": {
        "id": "M0X9LEKoUfbU"
      },
      "outputs": [],
      "source": [
        "all_inputs = []\n",
        "encoded_features = []\n",
        "\n",
        "# Numeric features.\n",
        "for header in ['PhotoAmt', 'Fee']:\n",
        "  numeric_col = tf.keras.Input(shape=(1,), name=header)\n",
        "  normalization_layer = get_normalization_layer(header, train_ds)\n",
        "  encoded_numeric_col = normalization_layer(numeric_col)\n",
        "  all_inputs.append(numeric_col)\n",
        "  encoded_features.append(encoded_numeric_col)\n",
        "\n",
        "# Categorical features encoded as string.\n",
        "categorical_cols = ['Type', 'Color1', 'Color2', 'Gender', 'MaturitySize',\n",
        "                    'FurLength', 'Vaccinated', 'Sterilized', 'Health', 'Breed1']\n",
        "for header in categorical_cols:\n",
        "  categorical_col = tf.keras.Input(shape=(1,), name=header, dtype='string')\n",
        "  encoding_layer = get_category_encoding_layer(header, train_ds, dtype='string',\n",
        "                                               max_tokens=5)\n",
        "  encoded_categorical_col = encoding_layer(categorical_col)\n",
        "  all_inputs.append(categorical_col)\n",
        "  encoded_features.append(encoded_categorical_col)  "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "x84lZJY164RI"
      },
      "source": [
        "## Build, compile and train the model\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "metadata": {
        "id": "uuHtpAMqLqmv"
      },
      "outputs": [],
      "source": [
        "# Set the parameters\n",
        "\n",
        "OPTIMIZER=\"adam\"\n",
        "LOSS=tf.keras.losses.BinaryCrossentropy(from_logits=True)\n",
        "METRICS=['accuracy']\n",
        "EPOCHS=10\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 23,
      "metadata": {
        "id": "7lBmxxuj63jZ"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "execution_count": 23,
          "metadata": {
            "tags": []
          },
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Convert the feature columns into a tf.keras layer\n",
        "all_features = tf.keras.layers.concatenate(encoded_features)\n",
        "\n",
        "# design/build the model\n",
        "x = tf.keras.layers.Dense(32, activation=\"relu\")(all_features)\n",
        "x = tf.keras.layers.Dropout(0.5)(x)\n",
        "x = tf.keras.layers.Dense(64, activation=\"relu\")(x)\n",
        "x = tf.keras.layers.Dropout(0.5)(x)\n",
        "output = tf.keras.layers.Dense(1)(x)\n",
        "model = tf.keras.Model(all_inputs, output)\n",
        "\n",
        "tf.keras.utils.plot_model(model, rankdir='LR', show_shapes=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 24,
      "metadata": {
        "id": "LTDFVxpSLfXI"
      },
      "outputs": [],
      "source": [
        "# compile the model\n",
        "model.compile(optimizer=OPTIMIZER, loss=LOSS, metrics=METRICS)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 25,
      "metadata": {
        "id": "SIJMg-saLgeR"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epoch 1/10\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/functional.py:543: UserWarning: Input dict contained keys ['Age'] which did not match any model input. They will be ignored by the model.\n",
            "  [n for n in tensors.keys() if n not in ref_input_names])\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "253/253 [==============================] - 4s 14ms/step - loss: 0.6169 - accuracy: 0.6042\n",
            "Epoch 2/10\n",
            "253/253 [==============================] - 4s 14ms/step - loss: 0.5634 - accuracy: 0.6937\n",
            "Epoch 3/10\n",
            "253/253 [==============================] - 4s 15ms/step - loss: 0.5573 - accuracy: 0.6981\n",
            "Epoch 4/10\n",
            "253/253 [==============================] - 4s 15ms/step - loss: 0.5528 - accuracy: 0.7087\n",
            "Epoch 5/10\n",
            "253/253 [==============================] - 4s 14ms/step - loss: 0.5512 - accuracy: 0.7173\n",
            "Epoch 6/10\n",
            "253/253 [==============================] - 4s 15ms/step - loss: 0.5456 - accuracy: 0.7219\n",
            "Epoch 7/10\n",
            "253/253 [==============================] - 4s 15ms/step - loss: 0.5397 - accuracy: 0.7283\n",
            "Epoch 8/10\n",
            "253/253 [==============================] - 4s 14ms/step - loss: 0.5385 - accuracy: 0.7331\n",
            "Epoch 9/10\n",
            "253/253 [==============================] - 4s 15ms/step - loss: 0.5355 - accuracy: 0.7326\n",
            "Epoch 10/10\n",
            "253/253 [==============================] - 4s 15ms/step - loss: 0.5412 - accuracy: 0.7321\n"
          ]
        },
        {
          "data": {
            "text/plain": [
              "<tensorflow.python.keras.callbacks.History at 0x7f5c235112e8>"
            ]
          },
          "execution_count": 25,
          "metadata": {
            "tags": []
          },
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# fit the model\n",
        "model.fit(train_ds, epochs=EPOCHS)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XYJW8za2qm4c"
      },
      "source": [
        "## Infer on the test data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 26,
      "metadata": {
        "id": "6hMtIe1X215P"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/functional.py:543: UserWarning: Input dict contained keys ['Age'] which did not match any model input. They will be ignored by the model.\n",
            "  [n for n in tensors.keys() if n not in ref_input_names])\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "109/109 [==============================] - 2s 15ms/step - loss: 0.5344 - accuracy: 0.7421\n",
            "test loss, test acc: [0.534355640411377, 0.7420566082000732]\n"
          ]
        }
      ],
      "source": [
        "res = model.evaluate(test_ds)\n",
        "print(\"test loss, test acc:\", res)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2SvFjOJcdRyO"
      },
      "source": [
        "Note: Since the goal of this tutorial is to demonstrate Tensorflow-IO's capability to stream data from elasticsearch and train `tf.keras` models directly, improving the accuracy of the models is out of the current scope. However, the user can explore the dataset and play around with the feature columns and model architectures to get a better classification performance."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "P8QAS_3k1y3u"
      },
      "source": [
        "## References:\n",
        "\n",
        "- [Elasticsearch](https://www.elastic.co/guide/en/elasticsearch/reference/current/targz.html)\n",
        "\n",
        "- [PetFinder Dataset](https://www.kaggle.com/c/petfinder-adoption-prediction)\n",
        "\n",
        "- [Classify Structured Data using Keras](https://www.tensorflow.org/tutorials/structured_data/preprocessing_layers#create_compile_and_train_the_model)\n"
      ]
    }
  ],
  "metadata": {
    "accelerator": "GPU",
    "colab": {
      "collapsed_sections": [],
      "name": "elasticsearch.ipynb",
      "toc_visible": true
    },
    "kernelspec": {
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