viz-3a-iris-classification.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[Home](https://walterra.github.io/jupyter2kibana/)\n",
    "\n",
    "*To make sure the Vega charts render correctly, view the notebook not from the Github repo but the published website here: \n",
    "https://walterra.github.io/jupyter2kibana/viz-3a-iris-classification.html*\n",
    "\n",
    "# viz-3a-iris-classification.ipynb\n",
    "\n",
    "This notebook demonstrates how small multiples of stacked bar chart histograms can be used to evaluate machine learning results, in this case comparing original values to predicted categorizations."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import datetime\n",
    "import altair as alt\n",
    "import eland as ed\n",
    "from elasticsearch import Elasticsearch\n",
    "import elastic_transport\n",
    "import logging\n",
    "import json\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import urllib3\n",
    "import warnings\n",
    "\n",
    "alt.data_transformers.disable_max_rows()\n",
    "logging.getLogger(\"elastic_transport\").setLevel(logging.ERROR)\n",
    "\n",
    "# Suppress insecure SSL connection warnings\n",
    "# In dev environments with the `verify_certs=False`\n",
    "# you might want to reduce those warnings.\n",
    "urllib3.disable_warnings(urllib3.exceptions.InsecureRequestWarning)\n",
    "urllib3.disable_warnings(elastic_transport.SecurityWarning)\n",
    "\n",
    "# For rendering the notebook to HTML hide all warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 150 entries, 0 to 149\n",
      "Data columns (total 5 columns):\n",
      " #   Column       Non-Null Count  Dtype  \n",
      "---  ------       --------------  -----  \n",
      " 0   sepalLength  150 non-null    float64\n",
      " 1   sepalWidth   150 non-null    float64\n",
      " 2   petalLength  150 non-null    float64\n",
      " 3   petalWidth   150 non-null    float64\n",
      " 4   species      150 non-null    object \n",
      "dtypes: float64(4), object(1)\n",
      "memory usage: 6.0+ KB\n"
     ]
    }
   ],
   "source": [
    "from vega_datasets import data\n",
    "pd_df = data.iris()\n",
    "pd_df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The next cell takes the iris dataset from VEGA's examples and pushes it to an Elasticsearch index using `eland`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['petalLength', 'petalWidth', 'sepalLength', 'sepalWidth', 'species'], dtype='object')"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "with open('config.json') as config_file:\n",
    "  es_config = json.load(config_file)\n",
    "\n",
    "# First instantiate an 'Elasticsearch' instance with the supplied config\n",
    "es = Elasticsearch(\n",
    "    hosts=[es_config['es_client']],\n",
    "    basic_auth=[\n",
    "        es_config['user'],\n",
    "        es_config['password']\n",
    "    ],\n",
    "    # Only in development environments with self signed certificates fall back to use `verify_certs=False`\n",
    "    verify_certs=False\n",
    ")\n",
    "\n",
    "ed_df = ed.pandas_to_eland(\n",
    "    pd_df.dropna(),\n",
    "    es,\n",
    "    'eland_iris',\n",
    "    es_if_exists=\"replace\",\n",
    "    es_refresh=True\n",
    ")\n",
    "ed_df.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Before you can run the next cell, you need to run a Machine Learning job doing classification in Elasticsearch using the following configuration. It will write the results to an index `iris_classification_3` which we can then access in the following cell.\n",
    "\n",
    "```json\n",
    "{\n",
    "  \"id\": \"iris_classification_3\",\n",
    "  \"create_time\": 1604478053294,\n",
    "  \"version\": \"8.0.0\",\n",
    "  \"description\": \"\",\n",
    "  \"source\": {\n",
    "    \"index\": [\n",
    "      \"eland_iris\"\n",
    "    ],\n",
    "    \"query\": {\n",
    "      \"match_all\": {}\n",
    "    }\n",
    "  },\n",
    "  \"dest\": {\n",
    "    \"index\": \"iris_classification_3\",\n",
    "    \"results_field\": \"ml\"\n",
    "  },\n",
    "  \"analysis\": {\n",
    "    \"classification\": {\n",
    "      \"dependent_variable\": \"species\",\n",
    "      \"num_top_feature_importance_values\": 10,\n",
    "      \"class_assignment_objective\": \"maximize_minimum_recall\",\n",
    "      \"num_top_classes\": 2,\n",
    "      \"prediction_field_name\": \"species_prediction\",\n",
    "      \"training_percent\": 80,\n",
    "      \"randomize_seed\": -2245814554146464500\n",
    "    }\n",
    "  },\n",
    "  \"analyzed_fields\": {\n",
    "    \"includes\": [],\n",
    "    \"excludes\": []\n",
    "  },\n",
    "  \"model_memory_limit\": \"100mb\",\n",
    "  \"allow_lazy_start\": false,\n",
    "  \"max_num_threads\": 1\n",
    "}\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>ml.feature_importance</th>\n",
       "      <th>ml.feature_importance.classes</th>\n",
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       "      <th>ml.feature_importance.classes.importance</th>\n",
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       "      <td>0.2</td>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>setosa</td>\n",
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       "      <th>3</th>\n",
       "      <td>[{'feature_name': 'petalLength', 'classes': [{...</td>\n",
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      "text/plain": [
       "                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                 ml.feature_importance  \\\n",
       "0  [{'feature_name': 'petalLength', 'classes': [{'importance': 1.9195933129452092, 'class_name': 'setosa'}, {'importance': -0.16847830043742174, 'class_name': 'versicolor'}, {'importance': -1.7511150125077808, 'class_name': 'virginica'}]}, {'feature_name': 'petalWidth', 'classes': [{'importance': 2.5946200982288277, 'class_name': 'setosa'}, {'importance': -1.0545938330543287, 'class_name': 'versicolor'}, {'importance': -1.5400262651744983, 'class_name': 'virginica'}]}, {'feature_name': 'sepalLength', 'classes': [{'importance': 0.30580175378472185, 'class_name': 'setosa'}, {'importance': 0.023857376650292576, 'class_name': 'versicolor'}, {'importance': -0.3296591304350174, 'class_name': 'virginica'}]}]   \n",
       "1      [{'feature_name': 'petalLength', 'classes': [{'importance': 1.7992885966665033, 'class_name': 'setosa'}, {'importance': -0.23087498515276883, 'class_name': 'versicolor'}, {'importance': -1.568413611513728, 'class_name': 'virginica'}]}, {'feature_name': 'petalWidth', 'classes': [{'importance': 2.5946200982288277, 'class_name': 'setosa'}, {'importance': -1.0545938330543287, 'class_name': 'versicolor'}, {'importance': -1.5400262651744983, 'class_name': 'virginica'}]}, {'feature_name': 'sepalLength', 'classes': [{'importance': 1.0051498156803778, 'class_name': 'setosa'}, {'importance': -2.4838658800155033, 'class_name': 'versicolor'}, {'importance': 1.4787160643351178, 'class_name': 'virginica'}]}]   \n",
       "2      [{'feature_name': 'petalLength', 'classes': [{'importance': 1.7992885966665033, 'class_name': 'setosa'}, {'importance': -0.23087498515276883, 'class_name': 'versicolor'}, {'importance': -1.568413611513728, 'class_name': 'virginica'}]}, {'feature_name': 'petalWidth', 'classes': [{'importance': 2.5946200982288277, 'class_name': 'setosa'}, {'importance': -1.0545938330543287, 'class_name': 'versicolor'}, {'importance': -1.5400262651744983, 'class_name': 'virginica'}]}, {'feature_name': 'sepalLength', 'classes': [{'importance': 1.0051498156803778, 'class_name': 'setosa'}, {'importance': -2.4838658800155033, 'class_name': 'versicolor'}, {'importance': 1.4787160643351178, 'class_name': 'virginica'}]}]   \n",
       "3      [{'feature_name': 'petalLength', 'classes': [{'importance': 1.7992885966665033, 'class_name': 'setosa'}, {'importance': -0.23087498515276883, 'class_name': 'versicolor'}, {'importance': -1.568413611513728, 'class_name': 'virginica'}]}, {'feature_name': 'petalWidth', 'classes': [{'importance': 2.5946200982288277, 'class_name': 'setosa'}, {'importance': -1.0545938330543287, 'class_name': 'versicolor'}, {'importance': -1.5400262651744983, 'class_name': 'virginica'}]}, {'feature_name': 'sepalLength', 'classes': [{'importance': 1.0051498156803778, 'class_name': 'setosa'}, {'importance': -2.4838658800155033, 'class_name': 'versicolor'}, {'importance': 1.4787160643351178, 'class_name': 'virginica'}]}]   \n",
       "4     [{'feature_name': 'petalLength', 'classes': [{'importance': 1.9195933129452092, 'class_name': 'setosa'}, {'importance': -0.16847830043742174, 'class_name': 'versicolor'}, {'importance': -1.7511150125077808, 'class_name': 'virginica'}]}, {'feature_name': 'petalWidth', 'classes': [{'importance': 2.5946200982288277, 'class_name': 'setosa'}, {'importance': -1.0545938330543287, 'class_name': 'versicolor'}, {'importance': -1.5400262651744983, 'class_name': 'virginica'}]}, {'feature_name': 'sepalLength', 'classes': [{'importance': 0.5650545049327028, 'class_name': 'setosa'}, {'importance': -0.8748598175078091, 'class_name': 'versicolor'}, {'importance': 0.3098053125750979, 'class_name': 'virginica'}]}]   \n",
       "\n",
       "  ml.feature_importance.classes ml.feature_importance.classes.class_name  \\\n",
       "0                           NaN                                      NaN   \n",
       "1                           NaN                                      NaN   \n",
       "2                           NaN                                      NaN   \n",
       "3                           NaN                                      NaN   \n",
       "4                           NaN                                      NaN   \n",
       "\n",
       "   ml.feature_importance.classes.importance  \\\n",
       "0                                       NaN   \n",
       "1                                       NaN   \n",
       "2                                       NaN   \n",
       "3                                       NaN   \n",
       "4                                       NaN   \n",
       "\n",
       "  ml.feature_importance.feature_name  ml.is_training  \\\n",
       "0                                NaN            True   \n",
       "1                                NaN            True   \n",
       "2                                NaN            True   \n",
       "3                                NaN            True   \n",
       "4                                NaN            True   \n",
       "\n",
       "   ml.prediction_probability  ml.prediction_score ml.species_prediction  \\\n",
       "0                   0.993851             0.171765                setosa   \n",
       "1                   0.997786             0.172445                setosa   \n",
       "2                   0.997786             0.172445                setosa   \n",
       "3                   0.997786             0.172445                setosa   \n",
       "4                   0.997510             0.172397                setosa   \n",
       "\n",
       "                                                                                                                                                                                                                                                                                                                           ml.top_classes  \\\n",
       "0  [{'class_probability': 0.9938514843172568, 'class_score': 0.17176486324514342, 'class_name': 'setosa'}, {'class_probability': 0.005670491194662051, 'class_score': 0.005670491194662051, 'class_name': 'versicolor'}, {'class_probability': 0.00047802448808118193, 'class_score': 0.00043442879541487925, 'class_name': 'virginica'}]   \n",
       "1  [{'class_probability': 0.9977861220921344, 'class_score': 0.17244487683870896, 'class_name': 'setosa'}, {'class_probability': 0.0019697190670708814, 'class_score': 0.0017900812676943875, 'class_name': 'virginica'}, {'class_probability': 0.0002441588407948145, 'class_score': 0.0002441588407948145, 'class_name': 'versicolor'}]   \n",
       "2  [{'class_probability': 0.9977861220921344, 'class_score': 0.17244487683870896, 'class_name': 'setosa'}, {'class_probability': 0.0019697190670708814, 'class_score': 0.0017900812676943875, 'class_name': 'virginica'}, {'class_probability': 0.0002441588407948145, 'class_score': 0.0002441588407948145, 'class_name': 'versicolor'}]   \n",
       "3  [{'class_probability': 0.9977861220921344, 'class_score': 0.17244487683870896, 'class_name': 'setosa'}, {'class_probability': 0.0019697190670708814, 'class_score': 0.0017900812676943875, 'class_name': 'virginica'}, {'class_probability': 0.0002441588407948145, 'class_score': 0.0002441588407948145, 'class_name': 'versicolor'}]   \n",
       "4    [{'class_probability': 0.997510480108977, 'class_score': 0.1723972383250188, 'class_name': 'setosa'}, {'class_probability': 0.0017877902616310534, 'class_score': 0.0017877902616310534, 'class_name': 'versicolor'}, {'class_probability': 0.0007017296293917948, 'class_score': 0.0006377320936575842, 'class_name': 'virginica'}]   \n",
       "\n",
       "  ml.top_classes.class_name  ml.top_classes.class_probability  \\\n",
       "0                       NaN                               NaN   \n",
       "1                       NaN                               NaN   \n",
       "2                       NaN                               NaN   \n",
       "3                       NaN                               NaN   \n",
       "4                       NaN                               NaN   \n",
       "\n",
       "   ml.top_classes.class_score  ml__incremental_id  petalLength  petalWidth  \\\n",
       "0                         NaN                   0          1.4         0.2   \n",
       "1                         NaN                   1          1.4         0.2   \n",
       "2                         NaN                   2          1.3         0.2   \n",
       "3                         NaN                   3          1.5         0.2   \n",
       "4                         NaN                   4          1.4         0.2   \n",
       "\n",
       "   sepalLength  sepalWidth species  \n",
       "0          5.1         3.5  setosa  \n",
       "1          4.9         3.0  setosa  \n",
       "2          4.7         3.2  setosa  \n",
       "3          4.6         3.1  setosa  \n",
       "4          5.0         3.6  setosa  \n",
       "\n",
       "[5 rows x 19 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "index_name='iris_classification_3'\n",
    "ed_df = ed.DataFrame(es, index_name)\n",
    "ed_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
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       "\n",
       "    function maybeLoadScript(lib, version) {\n",
       "      var key = `${lib.replace(\"-\", \"\")}_version`;\n",
       "      return (VEGA_DEBUG[key] == version) ?\n",
       "        Promise.resolve(paths[lib]) :\n",
       "        new Promise(function(resolve, reject) {\n",
       "          var s = document.createElement('script');\n",
       "          document.getElementsByTagName(\"head\")[0].appendChild(s);\n",
       "          s.async = true;\n",
       "          s.onload = () => {\n",
       "            VEGA_DEBUG[key] = version;\n",
       "            return resolve(paths[lib]);\n",
       "          };\n",
       "          s.onerror = () => reject(`Error loading script: ${paths[lib]}`);\n",
       "          s.src = paths[lib];\n",
       "        });\n",
       "    }\n",
       "\n",
       "    function showError(err) {\n",
       "      outputDiv.innerHTML = `<div class=\"error\" style=\"color:red;\">${err}</div>`;\n",
       "      throw err;\n",
       "    }\n",
       "\n",
       "    function displayChart(vegaEmbed) {\n",
       "      vegaEmbed(outputDiv, spec, embedOpt)\n",
       "        .catch(err => showError(`Javascript Error: ${err.message}<br>This usually means there's a typo in your chart specification. See the javascript console for the full traceback.`));\n",
       "    }\n",
       "\n",
       "    if(typeof define === \"function\" && define.amd) {\n",
       "      requirejs.config({paths});\n",
       "      require([\"vega-embed\"], displayChart, err => showError(`Error loading script: ${err.message}`));\n",
       "    } else {\n",
       "      maybeLoadScript(\"vega\", \"5\")\n",
       "        .then(() => maybeLoadScript(\"vega-lite\", \"4.17.0\"))\n",
       "        .then(() => maybeLoadScript(\"vega-embed\", \"6\"))\n",
       "        .catch(showError)\n",
       "        .then(() => displayChart(vegaEmbed));\n",
       "    }\n",
       "  })({\"config\": {\"view\": {\"continuousWidth\": 400, \"continuousHeight\": 300}}, \"concat\": [{\"mark\": \"bar\", \"encoding\": {\"color\": {\"field\": \"species\", \"type\": \"nominal\"}, \"tooltip\": [{\"bin\": true, \"field\": \"value\", \"title\": \"x\", \"type\": \"quantitative\"}, {\"aggregate\": \"count\", \"title\": \"y\", \"type\": \"quantitative\"}], \"x\": {\"bin\": true, \"field\": \"value\", \"title\": \"\", \"type\": \"quantitative\"}, \"y\": {\"aggregate\": \"count\", \"title\": \"\", \"type\": \"quantitative\"}}, \"height\": 150, \"title\": \"petalLength\", \"transform\": [{\"calculate\": \"datum._source.ml.species_prediction\", \"as\": \"species_prediction\"}, {\"calculate\": \"datum._source.species\", \"as\": \"species\"}, {\"calculate\": \"datum._source.petalLength\", \"as\": \"petalLength\"}, {\"calculate\": \"datum._source.petalWidth\", \"as\": \"petalWidth\"}, {\"calculate\": \"datum._source.sepalLength\", \"as\": \"sepalLength\"}, {\"calculate\": \"datum._source.sepalWidth\", \"as\": \"sepalWidth\"}, {\"fold\": [\"petalLength\", \"petalWidth\", \"sepalLength\", \"sepalWidth\"], \"as\": [\"attribute\", \"value\"]}, {\"filter\": \"(datum.attribute === 'petalLength')\"}], \"width\": 150}, {\"mark\": \"bar\", \"encoding\": {\"color\": {\"field\": \"species\", \"type\": \"nominal\"}, \"tooltip\": [{\"bin\": true, \"field\": \"value\", \"title\": \"x\", \"type\": \"quantitative\"}, {\"aggregate\": \"count\", \"title\": \"y\", \"type\": \"quantitative\"}], \"x\": {\"bin\": true, \"field\": \"value\", \"title\": \"\", \"type\": \"quantitative\"}, \"y\": {\"aggregate\": \"count\", \"title\": \"\", \"type\": \"quantitative\"}}, \"height\": 150, \"title\": \"petalWidth\", \"transform\": [{\"calculate\": \"datum._source.ml.species_prediction\", \"as\": \"species_prediction\"}, {\"calculate\": \"datum._source.species\", \"as\": \"species\"}, {\"calculate\": \"datum._source.petalLength\", \"as\": \"petalLength\"}, {\"calculate\": \"datum._source.petalWidth\", \"as\": \"petalWidth\"}, {\"calculate\": \"datum._source.sepalLength\", \"as\": \"sepalLength\"}, {\"calculate\": \"datum._source.sepalWidth\", \"as\": \"sepalWidth\"}, {\"fold\": [\"petalLength\", \"petalWidth\", \"sepalLength\", \"sepalWidth\"], \"as\": [\"attribute\", \"value\"]}, {\"filter\": \"(datum.attribute === 'petalWidth')\"}], \"width\": 150}, {\"mark\": \"bar\", \"encoding\": {\"color\": {\"field\": \"species\", \"type\": \"nominal\"}, \"tooltip\": [{\"bin\": true, \"field\": \"value\", \"title\": \"x\", \"type\": \"quantitative\"}, {\"aggregate\": \"count\", \"title\": \"y\", \"type\": \"quantitative\"}], \"x\": {\"bin\": true, \"field\": \"value\", \"title\": \"\", \"type\": \"quantitative\"}, \"y\": {\"aggregate\": \"count\", \"title\": \"\", \"type\": \"quantitative\"}}, \"height\": 150, \"title\": \"sepalLength\", \"transform\": [{\"calculate\": \"datum._source.ml.species_prediction\", \"as\": \"species_prediction\"}, {\"calculate\": \"datum._source.species\", \"as\": \"species\"}, {\"calculate\": \"datum._source.petalLength\", \"as\": \"petalLength\"}, {\"calculate\": \"datum._source.petalWidth\", \"as\": \"petalWidth\"}, {\"calculate\": \"datum._source.sepalLength\", \"as\": \"sepalLength\"}, {\"calculate\": \"datum._source.sepalWidth\", \"as\": \"sepalWidth\"}, {\"fold\": [\"petalLength\", \"petalWidth\", \"sepalLength\", \"sepalWidth\"], \"as\": [\"attribute\", \"value\"]}, {\"filter\": \"(datum.attribute === 'sepalLength')\"}], \"width\": 150}, {\"mark\": \"bar\", \"encoding\": {\"color\": {\"field\": \"species\", \"type\": \"nominal\"}, \"tooltip\": [{\"bin\": true, \"field\": \"value\", \"title\": \"x\", \"type\": \"quantitative\"}, {\"aggregate\": \"count\", \"title\": \"y\", \"type\": \"quantitative\"}], \"x\": {\"bin\": true, \"field\": \"value\", \"title\": \"\", \"type\": \"quantitative\"}, \"y\": {\"aggregate\": \"count\", \"title\": \"\", \"type\": \"quantitative\"}}, \"height\": 150, \"title\": \"sepalWidth\", \"transform\": [{\"calculate\": \"datum._source.ml.species_prediction\", \"as\": \"species_prediction\"}, {\"calculate\": \"datum._source.species\", \"as\": \"species\"}, {\"calculate\": \"datum._source.petalLength\", \"as\": \"petalLength\"}, {\"calculate\": \"datum._source.petalWidth\", \"as\": \"petalWidth\"}, {\"calculate\": \"datum._source.sepalLength\", \"as\": \"sepalLength\"}, {\"calculate\": \"datum._source.sepalWidth\", \"as\": \"sepalWidth\"}, {\"fold\": [\"petalLength\", \"petalWidth\", \"sepalLength\", \"sepalWidth\"], \"as\": [\"attribute\", \"value\"]}, {\"filter\": \"(datum.attribute === 'sepalWidth')\"}], \"width\": 150}], \"columns\": 2, \"data\": {\"url\": \"https://walterra.github.io/jupyter2kibana/data/iris_classification.json\", \"format\": {\"property\": \"hits.hits\", \"type\": \"json\"}}, \"resolve\": {\"axis\": {\"x\": \"independent\", \"y\": \"independent\"}, \"scale\": {\"x\": \"independent\", \"y\": \"independent\"}}, \"$schema\": \"https://vega.github.io/schema/vega-lite/v4.17.0.json\"}, {\"mode\": \"vega-lite\"});\n",
       "</script>"
      ],
      "text/plain": [
       "alt.ConcatChart(...)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Note: To create the Vega spec using Altair we reference ES via URL first. This will only work\n",
    "# for non-secured ES instances. If your ES instance runs using SSL and/or authentication the chart\n",
    "# in this cell will render empty. You can still save the visualization in Kibana correctly in the\n",
    "# next cell because there the URL gets replaced with an Elasticsearch query\n",
    "# to be used via the Kibana Vega plugin.\n",
    "\n",
    "# WARNING:\n",
    "# Do the following approach using a proxy only for demo purposes in a development environment.\n",
    "# It will expose a secured ES instance unsecured!\n",
    "# To make this work for demo purposes run the nodejs based proxy in a separate terminal like this:\n",
    "# NODE_TLS_REJECT_UNAUTHORIZED='0' node proxy\n",
    "\n",
    "# URL as ES endpoint\n",
    "# url = 'http://localhost:9220/'+index_name+'/_search?size=1000'\n",
    "\n",
    "# URL static fallback\n",
    "url = 'https://walterra.github.io/jupyter2kibana/data/iris_classification.json'\n",
    "\n",
    "url_data = alt.Data(url=url, format=alt.DataFormat(property='hits.hits',type='json'))\n",
    "\n",
    "fields = ['petalLength', 'petalWidth', 'sepalLength', 'sepalWidth']\n",
    "\n",
    "rename_dict = dict((a, 'datum._source.'+a) for a in fields)\n",
    "\n",
    "def small_multiples(split='species'):\n",
    "    url_chart = alt.Chart(url_data).transform_calculate(\n",
    "        species_prediction='datum._source.ml.species_prediction',\n",
    "        species='datum._source.species'\n",
    "    ).transform_calculate(**rename_dict).transform_fold(\n",
    "        fields,\n",
    "        as_=['attribute', 'value']\n",
    "    ).mark_bar().encode(\n",
    "        alt.X('value:Q', title='', bin=True),\n",
    "        alt.Y('count()', title=''),\n",
    "        tooltip=[\n",
    "            alt.Tooltip('value:Q', bin=True, title='x'),\n",
    "            alt.Tooltip('count()', title='y')\n",
    "        ],\n",
    "        color=split+':N'\n",
    "    ).properties(\n",
    "        width=150,\n",
    "        height=150\n",
    "    )\n",
    "\n",
    "    url_charts = alt.ConcatChart(\n",
    "        concat=[\n",
    "          url_chart.transform_filter(alt.datum.attribute == attribute).properties(title=attribute)\n",
    "          for attribute in sorted(['petalLength', 'petalWidth', 'sepalLength', 'sepalWidth'])\n",
    "        ],\n",
    "        columns=2\n",
    "    ).resolve_axis(\n",
    "        x='independent',\n",
    "        y='independent'\n",
    "    ).resolve_scale(\n",
    "        x='independent', \n",
    "        y='independent'\n",
    "    )\n",
    "    \n",
    "    return url_charts\n",
    "\n",
    "chart_raw = small_multiples('species')\n",
    "chart_raw"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "<div id=\"altair-viz-d1d75083bebb40e49428a394bdd7cd0d\"></div>\n",
       "<script type=\"text/javascript\">\n",
       "  var VEGA_DEBUG = (typeof VEGA_DEBUG == \"undefined\") ? {} : VEGA_DEBUG;\n",
       "  (function(spec, embedOpt){\n",
       "    let outputDiv = document.currentScript.previousElementSibling;\n",
       "    if (outputDiv.id !== \"altair-viz-d1d75083bebb40e49428a394bdd7cd0d\") {\n",
       "      outputDiv = document.getElementById(\"altair-viz-d1d75083bebb40e49428a394bdd7cd0d\");\n",
       "    }\n",
       "    const paths = {\n",
       "      \"vega\": \"https://cdn.jsdelivr.net/npm//vega@5?noext\",\n",
       "      \"vega-lib\": \"https://cdn.jsdelivr.net/npm//vega-lib?noext\",\n",
       "      \"vega-lite\": \"https://cdn.jsdelivr.net/npm//vega-lite@4.17.0?noext\",\n",
       "      \"vega-embed\": \"https://cdn.jsdelivr.net/npm//vega-embed@6?noext\",\n",
       "    };\n",
       "\n",
       "    function maybeLoadScript(lib, version) {\n",
       "      var key = `${lib.replace(\"-\", \"\")}_version`;\n",
       "      return (VEGA_DEBUG[key] == version) ?\n",
       "        Promise.resolve(paths[lib]) :\n",
       "        new Promise(function(resolve, reject) {\n",
       "          var s = document.createElement('script');\n",
       "          document.getElementsByTagName(\"head\")[0].appendChild(s);\n",
       "          s.async = true;\n",
       "          s.onload = () => {\n",
       "            VEGA_DEBUG[key] = version;\n",
       "            return resolve(paths[lib]);\n",
       "          };\n",
       "          s.onerror = () => reject(`Error loading script: ${paths[lib]}`);\n",
       "          s.src = paths[lib];\n",
       "        });\n",
       "    }\n",
       "\n",
       "    function showError(err) {\n",
       "      outputDiv.innerHTML = `<div class=\"error\" style=\"color:red;\">${err}</div>`;\n",
       "      throw err;\n",
       "    }\n",
       "\n",
       "    function displayChart(vegaEmbed) {\n",
       "      vegaEmbed(outputDiv, spec, embedOpt)\n",
       "        .catch(err => showError(`Javascript Error: ${err.message}<br>This usually means there's a typo in your chart specification. See the javascript console for the full traceback.`));\n",
       "    }\n",
       "\n",
       "    if(typeof define === \"function\" && define.amd) {\n",
       "      requirejs.config({paths});\n",
       "      require([\"vega-embed\"], displayChart, err => showError(`Error loading script: ${err.message}`));\n",
       "    } else {\n",
       "      maybeLoadScript(\"vega\", \"5\")\n",
       "        .then(() => maybeLoadScript(\"vega-lite\", \"4.17.0\"))\n",
       "        .then(() => maybeLoadScript(\"vega-embed\", \"6\"))\n",
       "        .catch(showError)\n",
       "        .then(() => displayChart(vegaEmbed));\n",
       "    }\n",
       "  })({\"config\": {\"view\": {\"continuousWidth\": 400, \"continuousHeight\": 300}}, \"concat\": [{\"mark\": \"bar\", \"encoding\": {\"color\": {\"field\": \"species_prediction\", \"type\": \"nominal\"}, \"tooltip\": [{\"bin\": true, \"field\": \"value\", \"title\": \"x\", \"type\": \"quantitative\"}, {\"aggregate\": \"count\", \"title\": \"y\", \"type\": \"quantitative\"}], \"x\": {\"bin\": true, \"field\": \"value\", \"title\": \"\", \"type\": \"quantitative\"}, \"y\": {\"aggregate\": \"count\", \"title\": \"\", \"type\": \"quantitative\"}}, \"height\": 150, \"title\": \"petalLength\", \"transform\": [{\"calculate\": \"datum._source.ml.species_prediction\", \"as\": \"species_prediction\"}, {\"calculate\": \"datum._source.species\", \"as\": \"species\"}, {\"calculate\": \"datum._source.petalLength\", \"as\": \"petalLength\"}, {\"calculate\": \"datum._source.petalWidth\", \"as\": \"petalWidth\"}, {\"calculate\": \"datum._source.sepalLength\", \"as\": \"sepalLength\"}, {\"calculate\": \"datum._source.sepalWidth\", \"as\": \"sepalWidth\"}, {\"fold\": [\"petalLength\", \"petalWidth\", \"sepalLength\", \"sepalWidth\"], \"as\": [\"attribute\", \"value\"]}, {\"filter\": \"(datum.attribute === 'petalLength')\"}], \"width\": 150}, {\"mark\": \"bar\", \"encoding\": {\"color\": {\"field\": \"species_prediction\", \"type\": \"nominal\"}, \"tooltip\": [{\"bin\": true, \"field\": \"value\", \"title\": \"x\", \"type\": \"quantitative\"}, {\"aggregate\": \"count\", \"title\": \"y\", \"type\": \"quantitative\"}], \"x\": {\"bin\": true, \"field\": \"value\", \"title\": \"\", \"type\": \"quantitative\"}, \"y\": {\"aggregate\": \"count\", \"title\": \"\", \"type\": \"quantitative\"}}, \"height\": 150, \"title\": \"petalWidth\", \"transform\": [{\"calculate\": \"datum._source.ml.species_prediction\", \"as\": \"species_prediction\"}, {\"calculate\": \"datum._source.species\", \"as\": \"species\"}, {\"calculate\": \"datum._source.petalLength\", \"as\": \"petalLength\"}, {\"calculate\": \"datum._source.petalWidth\", \"as\": \"petalWidth\"}, {\"calculate\": \"datum._source.sepalLength\", \"as\": \"sepalLength\"}, {\"calculate\": \"datum._source.sepalWidth\", \"as\": \"sepalWidth\"}, {\"fold\": [\"petalLength\", \"petalWidth\", \"sepalLength\", \"sepalWidth\"], \"as\": [\"attribute\", \"value\"]}, {\"filter\": \"(datum.attribute === 'petalWidth')\"}], \"width\": 150}, {\"mark\": \"bar\", \"encoding\": {\"color\": {\"field\": \"species_prediction\", \"type\": \"nominal\"}, \"tooltip\": [{\"bin\": true, \"field\": \"value\", \"title\": \"x\", \"type\": \"quantitative\"}, {\"aggregate\": \"count\", \"title\": \"y\", \"type\": \"quantitative\"}], \"x\": {\"bin\": true, \"field\": \"value\", \"title\": \"\", \"type\": \"quantitative\"}, \"y\": {\"aggregate\": \"count\", \"title\": \"\", \"type\": \"quantitative\"}}, \"height\": 150, \"title\": \"sepalLength\", \"transform\": [{\"calculate\": \"datum._source.ml.species_prediction\", \"as\": \"species_prediction\"}, {\"calculate\": \"datum._source.species\", \"as\": \"species\"}, {\"calculate\": \"datum._source.petalLength\", \"as\": \"petalLength\"}, {\"calculate\": \"datum._source.petalWidth\", \"as\": \"petalWidth\"}, {\"calculate\": \"datum._source.sepalLength\", \"as\": \"sepalLength\"}, {\"calculate\": \"datum._source.sepalWidth\", \"as\": \"sepalWidth\"}, {\"fold\": [\"petalLength\", \"petalWidth\", \"sepalLength\", \"sepalWidth\"], \"as\": [\"attribute\", \"value\"]}, {\"filter\": \"(datum.attribute === 'sepalLength')\"}], \"width\": 150}, {\"mark\": \"bar\", \"encoding\": {\"color\": {\"field\": \"species_prediction\", \"type\": \"nominal\"}, \"tooltip\": [{\"bin\": true, \"field\": \"value\", \"title\": \"x\", \"type\": \"quantitative\"}, {\"aggregate\": \"count\", \"title\": \"y\", \"type\": \"quantitative\"}], \"x\": {\"bin\": true, \"field\": \"value\", \"title\": \"\", \"type\": \"quantitative\"}, \"y\": {\"aggregate\": \"count\", \"title\": \"\", \"type\": \"quantitative\"}}, \"height\": 150, \"title\": \"sepalWidth\", \"transform\": [{\"calculate\": \"datum._source.ml.species_prediction\", \"as\": \"species_prediction\"}, {\"calculate\": \"datum._source.species\", \"as\": \"species\"}, {\"calculate\": \"datum._source.petalLength\", \"as\": \"petalLength\"}, {\"calculate\": \"datum._source.petalWidth\", \"as\": \"petalWidth\"}, {\"calculate\": \"datum._source.sepalLength\", \"as\": \"sepalLength\"}, {\"calculate\": \"datum._source.sepalWidth\", \"as\": \"sepalWidth\"}, {\"fold\": [\"petalLength\", \"petalWidth\", \"sepalLength\", \"sepalWidth\"], \"as\": [\"attribute\", \"value\"]}, {\"filter\": \"(datum.attribute === 'sepalWidth')\"}], \"width\": 150}], \"columns\": 2, \"data\": {\"url\": \"https://walterra.github.io/jupyter2kibana/data/iris_classification.json\", \"format\": {\"property\": \"hits.hits\", \"type\": \"json\"}}, \"resolve\": {\"axis\": {\"x\": \"independent\", \"y\": \"independent\"}, \"scale\": {\"x\": \"independent\", \"y\": \"independent\"}}, \"$schema\": \"https://vega.github.io/schema/vega-lite/v4.17.0.json\"}, {\"mode\": \"vega-lite\"});\n",
       "</script>"
      ],
      "text/plain": [
       "alt.ConcatChart(...)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "chart_prediction = small_multiples('species_prediction')\n",
    "chart_prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "<div id=\"altair-viz-9a4500e8e3924a048a1e9fe784af52bc\"></div>\n",
       "<script type=\"text/javascript\">\n",
       "  var VEGA_DEBUG = (typeof VEGA_DEBUG == \"undefined\") ? {} : VEGA_DEBUG;\n",
       "  (function(spec, embedOpt){\n",
       "    let outputDiv = document.currentScript.previousElementSibling;\n",
       "    if (outputDiv.id !== \"altair-viz-9a4500e8e3924a048a1e9fe784af52bc\") {\n",
       "      outputDiv = document.getElementById(\"altair-viz-9a4500e8e3924a048a1e9fe784af52bc\");\n",
       "    }\n",
       "    const paths = {\n",
       "      \"vega\": \"https://cdn.jsdelivr.net/npm//vega@5?noext\",\n",
       "      \"vega-lib\": \"https://cdn.jsdelivr.net/npm//vega-lib?noext\",\n",
       "      \"vega-lite\": \"https://cdn.jsdelivr.net/npm//vega-lite@4.17.0?noext\",\n",
       "      \"vega-embed\": \"https://cdn.jsdelivr.net/npm//vega-embed@6?noext\",\n",
       "    };\n",
       "\n",
       "    function maybeLoadScript(lib, version) {\n",
       "      var key = `${lib.replace(\"-\", \"\")}_version`;\n",
       "      return (VEGA_DEBUG[key] == version) ?\n",
       "        Promise.resolve(paths[lib]) :\n",
       "        new Promise(function(resolve, reject) {\n",
       "          var s = document.createElement('script');\n",
       "          document.getElementsByTagName(\"head\")[0].appendChild(s);\n",
       "          s.async = true;\n",
       "          s.onload = () => {\n",
       "            VEGA_DEBUG[key] = version;\n",
       "            return resolve(paths[lib]);\n",
       "          };\n",
       "          s.onerror = () => reject(`Error loading script: ${paths[lib]}`);\n",
       "          s.src = paths[lib];\n",
       "        });\n",
       "    }\n",
       "\n",
       "    function showError(err) {\n",
       "      outputDiv.innerHTML = `<div class=\"error\" style=\"color:red;\">${err}</div>`;\n",
       "      throw err;\n",
       "    }\n",
       "\n",
       "    function displayChart(vegaEmbed) {\n",
       "      vegaEmbed(outputDiv, spec, embedOpt)\n",
       "        .catch(err => showError(`Javascript Error: ${err.message}<br>This usually means there's a typo in your chart specification. See the javascript console for the full traceback.`));\n",
       "    }\n",
       "\n",
       "    if(typeof define === \"function\" && define.amd) {\n",
       "      requirejs.config({paths});\n",
       "      require([\"vega-embed\"], displayChart, err => showError(`Error loading script: ${err.message}`));\n",
       "    } else {\n",
       "      maybeLoadScript(\"vega\", \"5\")\n",
       "        .then(() => maybeLoadScript(\"vega-lite\", \"4.17.0\"))\n",
       "        .then(() => maybeLoadScript(\"vega-embed\", \"6\"))\n",
       "        .catch(showError)\n",
       "        .then(() => displayChart(vegaEmbed));\n",
       "    }\n",
       "  })({\"config\": {\"view\": {\"continuousWidth\": 400, \"continuousHeight\": 300}}, \"data\": {\"url\": \"https://walterra.github.io/jupyter2kibana/data/iris_classification.json\", \"format\": {\"property\": \"hits.hits\", \"type\": \"json\"}}, \"mark\": {\"type\": \"circle\", \"size\": 30}, \"encoding\": {\"color\": {\"field\": \"species\", \"type\": \"nominal\"}, \"tooltip\": [{\"field\": \"species\", \"type\": \"nominal\"}], \"x\": {\"field\": \"petalLength\", \"type\": \"quantitative\"}, \"y\": {\"field\": \"petalWidth\", \"type\": \"quantitative\"}}, \"transform\": [{\"calculate\": \"datum._source.ml.species_prediction\", \"as\": \"species_prediction\"}, {\"calculate\": \"datum._source.species\", \"as\": \"species\"}, {\"calculate\": \"datum._source.petalLength\", \"as\": \"petalLength\"}, {\"calculate\": \"datum._source.petalWidth\", \"as\": \"petalWidth\"}, {\"calculate\": \"datum._source.sepalLength\", \"as\": \"sepalLength\"}, {\"calculate\": \"datum._source.sepalWidth\", \"as\": \"sepalWidth\"}], \"$schema\": \"https://vega.github.io/schema/vega-lite/v4.17.0.json\"}, {\"mode\": \"vega-lite\"});\n",
       "</script>"
      ],
      "text/plain": [
       "alt.Chart(...)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "alt.Chart(url_data).transform_calculate(\n",
    "    species_prediction='datum._source.ml.species_prediction',\n",
    "    species='datum._source.species'\n",
    ").transform_calculate(**rename_dict).mark_circle(size=30).encode(\n",
    "    x='petalLength:Q',\n",
    "    y='petalWidth:Q',\n",
    "    color='species:N',\n",
    "    tooltip=['species:N']\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Response [409]>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from kibana_vega_util import saveVegaLiteVis\n",
    "\n",
    "saveVegaLiteVis(\n",
    "    index_name,\n",
    "    'iris-histogram-raw-1',\n",
    "    chart_raw,\n",
    "    resultSize=10000,\n",
    "    timeField=False,\n",
    "    # Only in development environments with self signed certificates fall back to use `verify=False`\n",
    "    verify=False\n",
    ")\n",
    "saveVegaLiteVis(\n",
    "    index_name,\n",
    "    'iris-histogram-prediction-1',\n",
    "    chart_prediction,\n",
    "    resultSize=10000,\n",
    "    timeField=False,\n",
    "    # Only in development environments with self signed certificates fall back to use `verify=False`\n",
    "    verify=False\n",
    ")"
   ]
  }
 ],
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