notebooks/assignments/Week1Notebook.ipynb

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 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Scope of Notebook\n",
    "\n",
    "The goal of this notebook is to showcase how you can generate a synthetic dataset through building a schema and fieldgroups and then use the Data Distiller to do exploratory data analysis.\n",
    "The steps we executed in this notebook include\n",
    "- Create FieldGroup\n",
    "- Create Profile and Experience Event Schema\n",
    "- Create Profile and Experience Event Schema Descriptor\n",
    "- Generate Systhetic Data After Data Exploration\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![EndToEndDesign](../media/CMLE-Notebooks-Week1-Workflow.jpeg)"
   ]
  },
  {
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   "source": [
    "# Setup\n",
    "\n",
    "This notebook requires some configuration data to properly authenticate to your Adobe Experience Platform instance. You should be able to find all the values required above by following the Setup section of the **README**.\n",
    "\n",
    "The next cell will be looking for your configuration file under your **ADOBE_HOME** path to fetch the values used throughout this notebook. See more details in the Setup section of the **README** to understand how to create your configuration file."
   ]
  },
  {
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   "source": [
    "In order to generate synthetic data we'll need to first create a schema and a dataset using the [aepp Python library](https://github.com/pitchmuc/aepp), this library is a set of python function modules which are calling AEP APIs.  Please see the following reference [guide](https://developer.adobe.com/experience-platform-apis/) for the underlying APIs. Same flow can achieved through UI which are powdered by AEP APIs.  For the AEP UI workflow please click [here](https://experienceleague.adobe.com/docs/experience-platform/xdm/tutorials/create-schema-ui.html?lang=en)."
   ]
  },
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   "execution_count": 2,
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   "source": [
    "import os\n",
    "\n",
    "from configparser import ConfigParser\n",
    "\n",
    "if \"ADOBE_HOME\" not in os.environ:\n",
    "    raise Exception(\"ADOBE_HOME environment variable needs to be set.\")\n",
    "\n",
    "config = ConfigParser()\n",
    "config_path = os.path.join(os.environ[\"ADOBE_HOME\"], \"conf\", \"config.ini\")\n",
    "\n",
    "if not os.path.exists(config_path):\n",
    "    raise Exception(f\"Looking for configuration under {config_path} but config not found, please verify path\")\n",
    "\n",
    "config.read(config_path)\n",
    "  \n",
    "ims_org_id = config.get(\"Platform\", \"ims_org_id\")\n",
    "environment = config.get(\"Platform\", \"environment\")\n",
    "sandbox_name = config.get(\"Platform\", \"sandbox_name\")\n",
    "client_id = config.get(\"Authentication\", \"client_id\")\n",
    "client_secret = config.get(\"Authentication\", \"client_secret\")\n",
    "scopes = config.get(\"Authentication\", \"scopes\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To ensure uniqueness of resources created as part of this notebook, we are using your local username to include in each of the resource titles to avoid conflicts."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import getpass\n",
    "import re\n",
    "username=getpass.getuser()\n",
    "unique_id = s = re.sub(\"[^0-9a-zA-Z]+\", \"_\", username)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Before we run anything, make sure to install the following required libraries for this notebook. They are all publicly available libraries and the latest version should work fine."
   ]
  },
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   "execution_count": 4,
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     "text": [
      "Requirement already satisfied: mmh3 in /usr/local/lib/python3.10/site-packages (3.0.0)\n",
      "\u001b[33mWARNING: You are using pip version 22.0.2; however, version 23.0.1 is available.\n",
      "You should consider upgrading via the '/usr/local/opt/python@3.10/bin/python3.10 -m pip install --upgrade pip' command.\u001b[0m\u001b[33m\n",
      "\u001b[0mRequirement already satisfied: rstr in /usr/local/lib/python3.10/site-packages (3.2.0)\n",
      "\u001b[33mWARNING: You are using pip version 22.0.2; however, version 23.0.1 is available.\n",
      "You should consider upgrading via the '/usr/local/opt/python@3.10/bin/python3.10 -m pip install --upgrade pip' command.\u001b[0m\u001b[33m\n",
      "\u001b[0mRequirement already satisfied: aepp in /usr/local/lib/python3.10/site-packages (0.2.7)\n",
      "Requirement already satisfied: PyJWT[crypto] in /usr/local/lib/python3.10/site-packages (from aepp) (1.7.1)\n",
      "Requirement already satisfied: pathlib2 in /usr/local/lib/python3.10/site-packages (from aepp) (2.3.7.post1)\n",
      "Requirement already satisfied: pandas in /Users/cmenguy/Library/Python/3.10/lib/python/site-packages (from aepp) (1.4.3)\n",
      "Requirement already satisfied: pathlib in /usr/local/lib/python3.10/site-packages (from aepp) (1.0.1)\n",
      "Requirement already satisfied: requests in /usr/local/lib/python3.10/site-packages (from aepp) (2.27.1)\n",
      "Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.10/site-packages (from pandas->aepp) (2021.3)\n",
      "Requirement already satisfied: numpy>=1.21.0 in /Users/cmenguy/Library/Python/3.10/lib/python/site-packages (from pandas->aepp) (1.23.1)\n",
      "Requirement already satisfied: python-dateutil>=2.8.1 in /usr/local/lib/python3.10/site-packages (from pandas->aepp) (2.8.2)\n",
      "Requirement already satisfied: six in /usr/local/lib/python3.10/site-packages (from pathlib2->aepp) (1.16.0)\n",
      "Requirement already satisfied: cryptography>=1.4 in /usr/local/lib/python3.10/site-packages (from PyJWT[crypto]->aepp) (36.0.1)\n",
      "Requirement already satisfied: urllib3<1.27,>=1.21.1 in /usr/local/lib/python3.10/site-packages (from requests->aepp) (1.26.8)\n",
      "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/site-packages (from requests->aepp) (3.3)\n",
      "Requirement already satisfied: charset-normalizer~=2.0.0 in /usr/local/lib/python3.10/site-packages (from requests->aepp) (2.0.12)\n",
      "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/site-packages (from requests->aepp) (2021.10.8)\n",
      "Requirement already satisfied: cffi>=1.12 in /usr/local/lib/python3.10/site-packages (from cryptography>=1.4->PyJWT[crypto]->aepp) (1.15.0)\n",
      "Requirement already satisfied: pycparser in /usr/local/lib/python3.10/site-packages (from cffi>=1.12->cryptography>=1.4->PyJWT[crypto]->aepp) (2.21)\n",
      "\u001b[33mWARNING: You are using pip version 22.0.2; however, version 23.0.1 is available.\n",
      "You should consider upgrading via the '/usr/local/opt/python@3.10/bin/python3.10 -m pip install --upgrade pip' command.\u001b[0m\u001b[33m\n",
      "\u001b[0mRequirement already satisfied: pygresql in /usr/local/lib/python3.10/site-packages (5.2.4)\n",
      "\u001b[33mWARNING: You are using pip version 22.0.2; however, version 23.0.1 is available.\n",
      "You should consider upgrading via the '/usr/local/opt/python@3.10/bin/python3.10 -m pip install --upgrade pip' command.\u001b[0m\u001b[33m\n",
      "\u001b[0m"
     ]
    }
   ],
   "source": [
    "!pip install mmh3\n",
    "!pip install rstr\n",
    "!pip install aepp\n",
    "!pip install pygresql==5.2.5\n",
    "!pip install plotly\n",
    "!pip install —upgrade nbformat"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Some utility functions that will be used throughout this notebook:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_ui_link(tenant_id, resource_type, resource_id):\n",
    "    if environment == \"prod\":\n",
    "        prefix = f\"https://experience.adobe.com\"\n",
    "    else:\n",
    "        prefix = f\"https://experience-{environment}.adobe.com\"\n",
    "    return f\"{prefix}/#/@{tenant_id}/sname:{sandbox_name}/platform/{resource_type}/{resource_id}\""
   ]
  },
  {
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   "source": [
    "We will now need to configure the aepp library and setup authentication credentials. For this please setup the following pieces of information. For information about how you can get these, please refer to the `Setup` section of the **Readme**:\n",
    "- Client ID\n",
    "- Client secret"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
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   "source": [
    "import aepp\n",
    "aepp.configure(\n",
    "  org_id=ims_org_id,\n",
    "  secret=client_secret,\n",
    "  scopes=scopes,\n",
    "  client_id=client_id,\n",
    "  environment=environment,\n",
    "  sandbox=sandbox_name\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 1. Generating Synthetic Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1.1 Setting up schemas"
   ]
  },
  {
   "cell_type": "markdown",
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   "source": [
    "We will now create the schema to support our synthetic data. We need a few fields which will be included in the synthetic data:\n",
    "\n",
    "Direct Marketing information\n",
    "Web details\n",
    "Identity information\n",
    "These are already provided in your AEP instance as default field groups, so we'll be leveraging that for creation below."
   ]
  },
  {
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   },
   "source": [
    "We first print out the tenantId, this represents our ims org name."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
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   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'cloudmlecosystem'"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from aepp import schema\n",
    "schema_conn = schema.Schema()\n",
    "schema_conn.sandbox\n",
    "tenant_id = schema_conn.getTenantId()\n",
    "tenant_id"
   ]
  },
  {
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   "source": [
    "### 1.1.1 Creating the Experience Event field group\n",
    "\n",
    "Our goal is to create schemas for the Profile and Experience Events and fieldgroups in these schemas, a fieldgroup allows us to define and query segments around the profile and experience events, conceptually a fieldgroup allows us to gather together a set of fields to represent data in our segments"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
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   },
   "outputs": [],
   "source": [
    "fieldgroup_res = schema_conn.createFieldGroup({\n",
    "  \t\"type\": \"object\",\n",
    "\t\"title\": f\"[CMLE] [Week1] Exp Event related to user propensity subscription (created by {username})\",\n",
    "\t\"description\": \"This mixin is used to define a propensity score that can be assigned to a given profile and associated experience events.\",\n",
    "\t\"allOf\": [{\n",
    "\t\t\"$ref\": \"#/definitions/customFields\"\n",
    "\t}],\n",
    "\t\"meta:containerId\": \"tenant\",\n",
    "\t\"meta:resourceType\": \"mixins\",\n",
    "\t\"meta:xdmType\": \"object\",\n",
    "\t\"definitions\": {\n",
    "      \"customFields\": {\n",
    "        \"type\": \"object\",\n",
    "        \"properties\": {\n",
    "          f\"_{tenant_id}\": {\n",
    "            \"type\": \"object\",\n",
    "            \"properties\": {\n",
    "              \"userid\": {\n",
    "                \"title\": \"User ID\",\n",
    "                \"description\": \"This refers to the user having a propensity towards an outcome.\",\n",
    "                \"type\": \"string\"\n",
    "              }\n",
    "            }\n",
    "          }\n",
    "        }\n",
    "      }\n",
    "\t},\n",
    "\t\"meta:intendedToExtend\": [\"https://ns.adobe.com/xdm/context/experienceevent\"]\n",
    "})\n",
    "\n",
    "fieldgroup_res\n",
    "fieldgroup_id = fieldgroup_res['$id']\n"
   ]
  },
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   "execution_count": 8,
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   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "https://experience-stage.adobe.com/#/@cloudmlecosystem/sname:bugbash-pod-a/platform/schema/mixin/browse/https%3A%2F%2Fns.adobe.com%2Fcloudmlecosystem%2Fmixins%2Fc402d85d6dc7058e588fbffc1d0d02d7fe667726bed6c080\n"
     ]
    }
   ],
   "source": [
    "import urllib.parse\n",
    "fieldgroup_link = get_ui_link(tenant_id, \"schema/mixin/browse\", urllib.parse.quote(fieldgroup_id, safe=\"a\"))\n",
    "print(fieldgroup_link)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![AEP-FieldGroup](../media/CMLE-Notebooks-Week1-FieldGroup.jpeg)"
   ]
  },
  {
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   },
   "source": [
    "## 1.1.2 Creating the Profile field group"
   ]
  },
  {
   "cell_type": "markdown",
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    }
   },
   "source": [
    "Creating a profile fieldgroup for assigning a propensity score into it"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'$id': 'https://ns.adobe.com/cloudmlecosystem/mixins/70d2e7dbd03a4ba05eacc0c5c0e8b8f6328de076f7002969',\n",
       " 'meta:altId': '_cloudmlecosystem.mixins.70d2e7dbd03a4ba05eacc0c5c0e8b8f6328de076f7002969',\n",
       " 'meta:resourceType': 'mixins',\n",
       " 'version': '1.0',\n",
       " 'title': '[CMLE] [Week1] Profile Fieldgroup associated with user propensity subscription (created by saikatkanjilal)',\n",
       " 'type': 'object',\n",
       " 'description': 'This mixin is used to define a propensity score that can be assigned to a given profile and associated experience events.',\n",
       " 'definitions': {'customFields': {'type': 'object',\n",
       "   'properties': {'_cloudmlecosystem': {'type': 'object',\n",
       "     'properties': {'userid': {'title': 'User ID',\n",
       "       'description': 'This refers to the user having a propensity towards an outcome.',\n",
       "       'type': 'string',\n",
       "       'meta:xdmType': 'string'}},\n",
       "     'meta:xdmType': 'object'}},\n",
       "   'meta:xdmType': 'object'}},\n",
       " 'allOf': [{'$ref': '#/definitions/customFields',\n",
       "   'type': 'object',\n",
       "   'meta:xdmType': 'object'}],\n",
       " 'refs': [],\n",
       " 'imsOrg': '3ADF23C463D98F640A494032@AdobeOrg',\n",
       " 'meta:extensible': True,\n",
       " 'meta:abstract': True,\n",
       " 'meta:intendedToExtend': ['https://ns.adobe.com/xdm/context/profile'],\n",
       " 'meta:xdmType': 'object',\n",
       " 'meta:registryMetadata': {'repo:createdDate': 1679451481023,\n",
       "  'repo:lastModifiedDate': 1679451481023,\n",
       "  'xdm:createdClientId': '35e6e4d205274c4ca1418805ac41153b',\n",
       "  'xdm:lastModifiedClientId': '35e6e4d205274c4ca1418805ac41153b',\n",
       "  'xdm:createdUserId': 'D94C245363DBF9770A494005@techacct.adobe.com',\n",
       "  'xdm:lastModifiedUserId': 'D94C245363DBF9770A494005@techacct.adobe.com',\n",
       "  'eTag': 'ae9ba30dbd0811eb2684fc6eb224a63a55dbb3e6bfe7bdf501828de6e4fd584d',\n",
       "  'meta:globalLibVersion': '1.40.2'},\n",
       " 'meta:containerId': '2e62b46d-b7f6-4b0c-a2b4-6db7f65b0cf5',\n",
       " 'meta:sandboxId': '2e62b46d-b7f6-4b0c-a2b4-6db7f65b0cf5',\n",
       " 'meta:sandboxType': 'development',\n",
       " 'meta:tenantNamespace': '_cloudmlecosystem'}"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fieldgroup_res_profile = schema_conn.createFieldGroup({\n",
    "  \t\"type\": \"object\",\n",
    "\t\"title\": f\"[CMLE] [Week1] Profile Fieldgroup associated with user propensity subscription (created by {username})\",\n",
    "\t\"description\": \"This mixin is used to define a propensity score that can be assigned to a given profile and associated experience events.\",\n",
    "\t\"allOf\": [{\n",
    "\t\t\"$ref\": \"#/definitions/customFields\"\n",
    "\t}],\n",
    "\t\"meta:containerId\": \"tenant\",\n",
    "\t\"meta:resourceType\": \"mixins\",\n",
    "\t\"meta:xdmType\": \"object\",\n",
    "\t\"definitions\": {\n",
    "      \"customFields\": {\n",
    "        \"type\": \"object\",\n",
    "        \"properties\": {\n",
    "          f\"_{tenant_id}\": {\n",
    "            \"type\": \"object\",\n",
    "            \"properties\": {\n",
    "              \"userid\": {\n",
    "                \"title\": \"User ID\",\n",
    "                \"description\": \"This refers to the user having a propensity towards an outcome.\",\n",
    "                \"type\": \"string\"\n",
    "              }\n",
    "            }\n",
    "          }\n",
    "        }\n",
    "      }\n",
    "\t},\n",
    "\t\"meta:intendedToExtend\": [\"https://ns.adobe.com/xdm/context/profile\"]\n",
    "})\n",
    "\n",
    "fieldgroup_res_profile"
   ]
  },
  {
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   "execution_count": 15,
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   "outputs": [],
   "source": [
    "fieldgroup_id_profile=fieldgroup_res_profile[\"$id\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "application/vnd.databricks.v1+cell": {
     "cellMetadata": {},
     "inputWidgets": {},
     "nuid": "ff751e32-819b-4cae-9005-a2145e262898",
     "showTitle": false,
     "title": ""
    }
   },
   "source": [
    "## 1.1.3 Creating the ExperienceEvent schema\n",
    "\n",
    "Now create the experience event schema and the descriptor, schema descriptors are tenant-level metadata,  unique to your IMS Organization and all descriptor operations take place in the tenant container."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "application/vnd.databricks.v1+cell": {
     "cellMetadata": {},
     "inputWidgets": {},
     "nuid": "9f263fc3-86d2-4366-96b8-6973ebc9e2d3",
     "showTitle": false,
     "title": ""
    }
   },
   "outputs": [],
   "source": [
    "schema_res = schema_conn.createExperienceEventSchema(\n",
    "  name=f\"[CMLE] [Week1] Notebook with ecid added and Synthetic Event Schema (created by {username})\",\n",
    "  mixinIds=[\n",
    "    fieldgroup_id,\n",
    "    \"https://ns.adobe.com/xdm/context/experienceevent-directmarketing\",\n",
    "    \"https://ns.adobe.com/xdm/context/experienceevent-web\"\n",
    "  ],\n",
    "  description=\"Profile Schema generated by CMLE for synthetic events\",\n",
    ")\n",
    "schema_res\n",
    "schema_experience_event_id = schema_res['$id']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "https://experience-stage.adobe.com/#/@cloudmlecosystem/sname:bugbash-pod-a/platform/schema/mixin/browse/https%3A%2F%2Fns.adobe.com%2Fcloudmlecosystem%2Fschemas%2F70e2392745a94ad72a1270a52722c54596ec416028ae836f\n"
     ]
    }
   ],
   "source": [
    "import urllib.parse\n",
    "schema_link = get_ui_link(tenant_id, \"schema/mixin/browse\", urllib.parse.quote(schema_experience_event_id, safe=\"a\"))\n",
    "print(schema_link)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![AEP-Schema](../media/CMLE-Notebooks-Week1-Schema.jpeg)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "application/vnd.databricks.v1+cell": {
     "cellMetadata": {},
     "inputWidgets": {},
     "nuid": "f91bea3e-28ff-4f3a-9495-d9ecc606ee42",
     "showTitle": false,
     "title": ""
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Schema ID: https://ns.adobe.com/cloudmlecosystem/schemas/70e2392745a94ad72a1270a52722c54596ec416028ae836f\n",
      "Schema Alt ID: _cloudmlecosystem.schemas.70e2392745a94ad72a1270a52722c54596ec416028ae836f\n"
     ]
    }
   ],
   "source": [
    "schema_id = schema_res[\"$id\"]\n",
    "schema_alt_id = schema_res[\"meta:altId\"]\n",
    "\n",
    "print(f\"Schema ID: {schema_id}\")\n",
    "print(f\"Schema Alt ID: {schema_alt_id}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1.1.4 Creating the Experience Event identity descriptor\n",
    "Now create the experience event schema and the descriptor, schema descriptors are tenant-level metadata,  unique to your IMS Organization and all descriptor operations take place in the tenant container.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "application/vnd.databricks.v1+cell": {
     "cellMetadata": {},
     "inputWidgets": {},
     "nuid": "8a394873-4165-4cf2-b5ca-1cc6691e22a6",
     "showTitle": false,
     "title": ""
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'@id': 'a9e850e8afe98d52e40deb34b2d6437713e770ea9603eb0a',\n",
       " '@type': 'xdm:descriptorIdentity',\n",
       " 'xdm:sourceSchema': 'https://ns.adobe.com/cloudmlecosystem/schemas/70e2392745a94ad72a1270a52722c54596ec416028ae836f',\n",
       " 'xdm:sourceVersion': 1,\n",
       " 'xdm:sourceProperty': '/_cloudmlecosystem/userid',\n",
       " 'imsOrg': '3ADF23C463D98F640A494032@AdobeOrg',\n",
       " 'version': '1',\n",
       " 'xdm:namespace': 'ECID',\n",
       " 'xdm:property': 'xdm:id',\n",
       " 'xdm:isPrimary': True,\n",
       " 'meta:containerId': '2e62b46d-b7f6-4b0c-a2b4-6db7f65b0cf5',\n",
       " 'meta:sandboxId': '2e62b46d-b7f6-4b0c-a2b4-6db7f65b0cf5',\n",
       " 'meta:sandboxType': 'development'}"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "identity_type = \"ECID\"\n",
    "descriptor_res = schema_conn.createDescriptor(\n",
    "  descriptorObj = {\n",
    "    \"@type\": \"xdm:descriptorIdentity\",\n",
    "    \"xdm:sourceSchema\": schema_id,\n",
    "    \"xdm:sourceVersion\": 1,\n",
    "    \"xdm:sourceProperty\": f\"/_{tenant_id}/userid\",\n",
    "    \"xdm:namespace\": identity_type,\n",
    "    \"xdm:property\": \"xdm:id\",\n",
    "    \"xdm:isPrimary\": True\n",
    "  }\n",
    ")\n",
    "descriptor_res"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1.1.5 Creating the Profile Schema\n",
    "Now create the profile schema and the descriptor, schema descriptors are tenant-level metadata,  unique to your IMS Organization and all descriptor operations take place in the tenant container."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "application/vnd.databricks.v1+cell": {
     "cellMetadata": {
      "byteLimit": 2048000,
      "rowLimit": 10000
     },
     "inputWidgets": {},
     "nuid": "bdcc22de-9baf-4892-ad8f-e260a46b46b6",
     "showTitle": false,
     "title": ""
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'$id': 'https://ns.adobe.com/cloudmlecosystem/schemas/2e3b704e0b7a435587ba4348d09c3e543ef65b178ba78e23',\n",
       " 'meta:altId': '_cloudmlecosystem.schemas.2e3b704e0b7a435587ba4348d09c3e543ef65b178ba78e23',\n",
       " 'meta:resourceType': 'schemas',\n",
       " 'version': '1.0',\n",
       " 'title': '[CMLE] [Week1] Notebook with ecid added and Profile Schema (created by saikatkanjilal)',\n",
       " 'type': 'object',\n",
       " 'description': 'Profile Schema generated by CMLEs',\n",
       " 'allOf': [{'$ref': 'https://ns.adobe.com/xdm/context/profile',\n",
       "   'type': 'object',\n",
       "   'meta:xdmType': 'object'},\n",
       "  {'$ref': 'https://ns.adobe.com/cloudmlecosystem/mixins/70d2e7dbd03a4ba05eacc0c5c0e8b8f6328de076f7002969',\n",
       "   'type': 'object',\n",
       "   'meta:xdmType': 'object'}],\n",
       " 'refs': ['https://ns.adobe.com/cloudmlecosystem/mixins/70d2e7dbd03a4ba05eacc0c5c0e8b8f6328de076f7002969',\n",
       "  'https://ns.adobe.com/xdm/context/profile'],\n",
       " 'imsOrg': '3ADF23C463D98F640A494032@AdobeOrg',\n",
       " 'additionalInfo': {'numberOfIdentities': 0,\n",
       "  'numberOfRelationShips': 0,\n",
       "  'classTitle': 'XDM Individual Profile',\n",
       "  'behavior': 'Record',\n",
       "  'hasRelationShip': False,\n",
       "  'profileEnabled': False},\n",
       " 'meta:extensible': False,\n",
       " 'meta:abstract': False,\n",
       " 'meta:extends': ['https://ns.adobe.com/xdm/common/auditable',\n",
       "  'https://ns.adobe.com/xdm/data/record',\n",
       "  'https://ns.adobe.com/cloudmlecosystem/mixins/70d2e7dbd03a4ba05eacc0c5c0e8b8f6328de076f7002969',\n",
       "  'https://ns.adobe.com/xdm/context/profile'],\n",
       " 'meta:xdmType': 'object',\n",
       " 'meta:registryMetadata': {'repo:createdDate': 1679451665137,\n",
       "  'repo:lastModifiedDate': 1679451665137,\n",
       "  'xdm:createdClientId': '35e6e4d205274c4ca1418805ac41153b',\n",
       "  'xdm:lastModifiedClientId': '35e6e4d205274c4ca1418805ac41153b',\n",
       "  'xdm:createdUserId': 'D94C245363DBF9770A494005@techacct.adobe.com',\n",
       "  'xdm:lastModifiedUserId': 'D94C245363DBF9770A494005@techacct.adobe.com',\n",
       "  'eTag': '62c61e622660122fe3d118659e3b707b0736dbe173b7edd770bf6c3dae459ae0',\n",
       "  'meta:globalLibVersion': '1.40.2'},\n",
       " 'meta:class': 'https://ns.adobe.com/xdm/context/profile',\n",
       " 'meta:containerId': '2e62b46d-b7f6-4b0c-a2b4-6db7f65b0cf5',\n",
       " 'meta:sandboxId': '2e62b46d-b7f6-4b0c-a2b4-6db7f65b0cf5',\n",
       " 'meta:sandboxType': 'development',\n",
       " 'meta:tenantNamespace': '_cloudmlecosystem'}"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "schema_res_profile = schema_conn.createProfileSchema(\n",
    "  name=f\"[CMLE] [Week1] Notebook with ecid added and Profile Schema (created by {username})\",\n",
    "  mixinIds=[\n",
    "    fieldgroup_id_profile\n",
    "  ],\n",
    "  description=\"Profile Schema generated by CMLEs\",\n",
    ")\n",
    "schema_res_profile"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "application/vnd.databricks.v1+cell": {
     "cellMetadata": {
      "byteLimit": 2048000,
      "rowLimit": 10000
     },
     "inputWidgets": {},
     "nuid": "a0e6a7e6-9c4b-40d4-9975-b676059dc93d",
     "showTitle": false,
     "title": ""
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'_cloudmlecosystem.schemas.2e3b704e0b7a435587ba4348d09c3e543ef65b178ba78e23'"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "schema_profile_id=schema_res_profile[\"$id\"]\n",
    "schema_alt_profile_id = schema_res_profile[\"meta:altId\"]\n",
    "schema_alt_profile_id"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "application/vnd.databricks.v1+cell": {
     "cellMetadata": {},
     "inputWidgets": {},
     "nuid": "346dbe9e-44bf-4d6d-bd04-ccca4b2c3f7c",
     "showTitle": false,
     "title": ""
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'@id': 'a9e850e8afe98d52e40deb34b2d6437713e770ea9603eb0a',\n",
       " '@type': 'xdm:descriptorIdentity',\n",
       " 'xdm:sourceSchema': 'https://ns.adobe.com/cloudmlecosystem/schemas/70e2392745a94ad72a1270a52722c54596ec416028ae836f',\n",
       " 'xdm:sourceVersion': 1,\n",
       " 'xdm:sourceProperty': '/_cloudmlecosystem/userid',\n",
       " 'imsOrg': '3ADF23C463D98F640A494032@AdobeOrg',\n",
       " 'version': '1',\n",
       " 'xdm:namespace': 'ECID',\n",
       " 'xdm:property': 'xdm:id',\n",
       " 'xdm:isPrimary': True,\n",
       " 'meta:containerId': '2e62b46d-b7f6-4b0c-a2b4-6db7f65b0cf5',\n",
       " 'meta:sandboxId': '2e62b46d-b7f6-4b0c-a2b4-6db7f65b0cf5',\n",
       " 'meta:sandboxType': 'development'}"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "identity_type = \"ECID\"\n",
    "descriptor_profile_res = schema_conn.createDescriptor(\n",
    "  descriptorObj = {\n",
    "    \"@type\": \"xdm:descriptorIdentity\",\n",
    "    \"xdm:sourceSchema\": schema_id,\n",
    "    \"xdm:sourceVersion\": 1,\n",
    "    \"xdm:sourceProperty\": f\"/_{tenant_id}/userid\",\n",
    "    \"xdm:namespace\": identity_type,\n",
    "    \"xdm:property\": \"xdm:id\",\n",
    "    \"xdm:isPrimary\": True\n",
    "  }\n",
    ")\n",
    "descriptor_res"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "application/vnd.databricks.v1+cell": {
     "cellMetadata": {},
     "inputWidgets": {},
     "nuid": "b2816c41-158a-4da6-a809-8c35391a6165",
     "showTitle": false,
     "title": ""
    }
   },
   "source": [
    "### 1.1.6 Enabling Schemas for Unified Profile"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "application/vnd.databricks.v1+cell": {
     "cellMetadata": {},
     "inputWidgets": {},
     "nuid": "4c92185a-1cad-45c2-9805-efbb073b2dff",
     "showTitle": false,
     "title": ""
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'$id': 'https://ns.adobe.com/cloudmlecosystem/schemas/70e2392745a94ad72a1270a52722c54596ec416028ae836f',\n",
       " 'meta:altId': '_cloudmlecosystem.schemas.70e2392745a94ad72a1270a52722c54596ec416028ae836f',\n",
       " 'meta:resourceType': 'schemas',\n",
       " 'version': '1.1',\n",
       " 'title': '[CMLE] [Week1] Notebook with ecid added and Synthetic Event Schema (created by saikatkanjilal)',\n",
       " 'type': 'object',\n",
       " 'description': 'Profile Schema generated by CMLE for synthetic events',\n",
       " 'allOf': [{'$ref': 'https://ns.adobe.com/xdm/context/experienceevent',\n",
       "   'type': 'object',\n",
       "   'meta:xdmType': 'object'},\n",
       "  {'$ref': 'https://ns.adobe.com/cloudmlecosystem/mixins/c402d85d6dc7058e588fbffc1d0d02d7fe667726bed6c080',\n",
       "   'type': 'object',\n",
       "   'meta:xdmType': 'object'},\n",
       "  {'$ref': 'https://ns.adobe.com/xdm/context/experienceevent-directmarketing',\n",
       "   'type': 'object',\n",
       "   'meta:xdmType': 'object'},\n",
       "  {'$ref': 'https://ns.adobe.com/xdm/context/experienceevent-web',\n",
       "   'type': 'object',\n",
       "   'meta:xdmType': 'object'}],\n",
       " 'refs': ['https://ns.adobe.com/cloudmlecosystem/mixins/c402d85d6dc7058e588fbffc1d0d02d7fe667726bed6c080',\n",
       "  'https://ns.adobe.com/xdm/context/experienceevent-directmarketing',\n",
       "  'https://ns.adobe.com/xdm/context/experienceevent-web',\n",
       "  'https://ns.adobe.com/xdm/context/experienceevent'],\n",
       " 'required': ['@id', 'xdm:timestamp'],\n",
       " 'imsOrg': '3ADF23C463D98F640A494032@AdobeOrg',\n",
       " 'additionalInfo': {'numberOfIdentities': 1,\n",
       "  'numberOfRelationShips': 0,\n",
       "  'classTitle': 'XDM ExperienceEvent',\n",
       "  'hasPrimaryIdentity': 'true',\n",
       "  'primaryIdentityNamespace': 'ECID',\n",
       "  'hasRelationShip': False,\n",
       "  'profileEnabled': True},\n",
       " 'meta:extensible': False,\n",
       " 'meta:abstract': False,\n",
       " 'meta:extends': ['https://ns.adobe.com/xdm/data/time-series',\n",
       "  'https://ns.adobe.com/cloudmlecosystem/mixins/c402d85d6dc7058e588fbffc1d0d02d7fe667726bed6c080',\n",
       "  'https://ns.adobe.com/xdm/context/experienceevent-directmarketing',\n",
       "  'https://ns.adobe.com/xdm/context/identitymap',\n",
       "  'https://ns.adobe.com/xdm/context/experienceevent-web',\n",
       "  'https://ns.adobe.com/xdm/context/experienceevent'],\n",
       " 'meta:xdmType': 'object',\n",
       " 'meta:registryMetadata': {'repo:createdDate': 1679451499853,\n",
       "  'repo:lastModifiedDate': 1679451687725,\n",
       "  'xdm:createdClientId': '35e6e4d205274c4ca1418805ac41153b',\n",
       "  'xdm:lastModifiedClientId': '35e6e4d205274c4ca1418805ac41153b',\n",
       "  'xdm:createdUserId': 'D94C245363DBF9770A494005@techacct.adobe.com',\n",
       "  'xdm:lastModifiedUserId': 'D94C245363DBF9770A494005@techacct.adobe.com',\n",
       "  'eTag': '283d09105605250dd390ce9851569f82fa38bc6fd1945c6be042d9ca7628c89b',\n",
       "  'meta:globalLibVersion': '1.40.2'},\n",
       " 'meta:class': 'https://ns.adobe.com/xdm/context/experienceevent',\n",
       " 'meta:containerId': '2e62b46d-b7f6-4b0c-a2b4-6db7f65b0cf5',\n",
       " 'meta:sandboxId': '2e62b46d-b7f6-4b0c-a2b4-6db7f65b0cf5',\n",
       " 'meta:sandboxType': 'development',\n",
       " 'meta:tenantNamespace': '_cloudmlecosystem',\n",
       " 'meta:immutableTags': ['union'],\n",
       " 'meta:descriptorStatus': {'result': []}}"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "enable_res = schema_conn.enableSchemaForRealTime(schema_alt_id)\n",
    "enable_res"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "application/vnd.databricks.v1+cell": {
     "cellMetadata": {
      "byteLimit": 2048000,
      "rowLimit": 10000
     },
     "inputWidgets": {},
     "nuid": "ac0de747-b14b-4af3-8ff6-503ddb50d014",
     "showTitle": false,
     "title": ""
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'$id': 'https://ns.adobe.com/cloudmlecosystem/schemas/2e3b704e0b7a435587ba4348d09c3e543ef65b178ba78e23',\n",
       " 'meta:altId': '_cloudmlecosystem.schemas.2e3b704e0b7a435587ba4348d09c3e543ef65b178ba78e23',\n",
       " 'meta:resourceType': 'schemas',\n",
       " 'version': '1.1',\n",
       " 'title': '[CMLE] [Week1] Notebook with ecid added and Profile Schema (created by saikatkanjilal)',\n",
       " 'type': 'object',\n",
       " 'description': 'Profile Schema generated by CMLEs',\n",
       " 'allOf': [{'$ref': 'https://ns.adobe.com/xdm/context/profile',\n",
       "   'type': 'object',\n",
       "   'meta:xdmType': 'object'},\n",
       "  {'$ref': 'https://ns.adobe.com/cloudmlecosystem/mixins/70d2e7dbd03a4ba05eacc0c5c0e8b8f6328de076f7002969',\n",
       "   'type': 'object',\n",
       "   'meta:xdmType': 'object'}],\n",
       " 'refs': ['https://ns.adobe.com/cloudmlecosystem/mixins/70d2e7dbd03a4ba05eacc0c5c0e8b8f6328de076f7002969',\n",
       "  'https://ns.adobe.com/xdm/context/profile'],\n",
       " 'imsOrg': '3ADF23C463D98F640A494032@AdobeOrg',\n",
       " 'additionalInfo': {'numberOfIdentities': 0,\n",
       "  'numberOfRelationShips': 0,\n",
       "  'classTitle': 'XDM Individual Profile',\n",
       "  'behavior': 'Record',\n",
       "  'hasRelationShip': False,\n",
       "  'profileEnabled': True},\n",
       " 'meta:extensible': False,\n",
       " 'meta:abstract': False,\n",
       " 'meta:extends': ['https://ns.adobe.com/xdm/common/auditable',\n",
       "  'https://ns.adobe.com/xdm/data/record',\n",
       "  'https://ns.adobe.com/cloudmlecosystem/mixins/70d2e7dbd03a4ba05eacc0c5c0e8b8f6328de076f7002969',\n",
       "  'https://ns.adobe.com/xdm/context/profile'],\n",
       " 'meta:xdmType': 'object',\n",
       " 'meta:registryMetadata': {'repo:createdDate': 1679451665137,\n",
       "  'repo:lastModifiedDate': 1679451719126,\n",
       "  'xdm:createdClientId': '35e6e4d205274c4ca1418805ac41153b',\n",
       "  'xdm:lastModifiedClientId': '35e6e4d205274c4ca1418805ac41153b',\n",
       "  'xdm:createdUserId': 'D94C245363DBF9770A494005@techacct.adobe.com',\n",
       "  'xdm:lastModifiedUserId': 'D94C245363DBF9770A494005@techacct.adobe.com',\n",
       "  'eTag': '3ba44da736605b3ea13c89894391806ae8f891e7baa5a884f84baf4130a7f767',\n",
       "  'meta:globalLibVersion': '1.40.2'},\n",
       " 'meta:class': 'https://ns.adobe.com/xdm/context/profile',\n",
       " 'meta:containerId': '2e62b46d-b7f6-4b0c-a2b4-6db7f65b0cf5',\n",
       " 'meta:sandboxId': '2e62b46d-b7f6-4b0c-a2b4-6db7f65b0cf5',\n",
       " 'meta:sandboxType': 'development',\n",
       " 'meta:tenantNamespace': '_cloudmlecosystem',\n",
       " 'meta:immutableTags': ['union'],\n",
       " 'meta:descriptorStatus': {'result': []}}"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "enable_res_profile = schema_conn.enableSchemaForRealTime(schema_alt_profile_id)\n",
    "enable_res_profile"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1.2 Setting up datasets"
   ]
  },
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    }
   },
   "source": [
    "### 1.2.1 Creating the Experience Event dataset"
   ]
  },
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   "execution_count": 24,
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     "title": ""
    }
   },
   "outputs": [],
   "source": [
    "from aepp import catalog\n",
    "cat_conn = catalog.Catalog()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
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    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['@/dataSets/641a666b4081f11b98e54efd']"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset_res = cat_conn.createDataSets(\n",
    "  name=f\"[CMLE] [Week1] Notebook with ecid used Dataset (created by {username})\",\n",
    "  schemaId=schema_experience_event_id,\n",
    ")\n",
    "dataset_res"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
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     "nuid": "f70a32fd-7768-417e-b72b-7d6fc1652d58",
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    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'641a666b4081f11b98e54efd'"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset_id = dataset_res[0].split(\"/\")[-1]\n",
    "dataset_id"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "https://experience-stage.adobe.com/#/@cloudmlecosystem/sname:bugbash-pod-a/platform/dataset/browse/641a666b4081f11b98e54efd\n"
     ]
    }
   ],
   "source": [
    "dataset_link = get_ui_link(tenant_id, \"dataset/browse\", dataset_id)\n",
    "print(dataset_link)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![AEPDataset](../media/CMLE-Notebooks-Week1-DataSet.jpeg)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "application/vnd.databricks.v1+cell": {
     "cellMetadata": {},
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     "nuid": "3de60a11-9d14-4e59-a036-3c8722b544a9",
     "showTitle": false,
     "title": ""
    }
   },
   "source": [
    "### 1.2.2 Enabling the dataset for Unified Profile\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
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    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['@/dataSets/641a666b4081f11b98e54efd']"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cat_conn.enableDatasetProfile(dataset_id)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-block alert-warning\">\n",
    "<b>Note:</b> After you do this step please go in the UI and click on the link above, if the profile toggle is not enabled please manually toggle the profile on\n",
    "</div>"
   ]
  },
  {
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     "cellMetadata": {},
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    }
   },
   "source": [
    "The dataset ID printed above is where we will be uploading all the synthetic data, and what we will use as the basis for querying the data and building our ML model down the line.   Here is the [link](https://experience-stage.adobe.com/#/@cloudmlecosystem/sname:prod/platform/dataset/browse?hideSystemDatasets=true&limit=50&page=1&sortDescending=1&sortField=created) to the UI where the dataset is located"
   ]
  },
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   "source": [
    "# 2. Statistical Simulation\n",
    "\n",
    "The first step is to create a detailed simulation that will allow a reasonable propensity model to be built. \n",
    "\n",
    "Our goal in this task will be to create a propensity model for \"subscription\" events.\n",
    "\n",
    "A subscription event will be defined as an event where a `web.formFilledOut` event is recorded, with a specific \n",
    "These will be events where a customer subscribes to the desired plan.\n",
    "\n",
    "In order to incorporate our custom experience events we replace the identityMap with the tenantId\n",
    "\n",
    "## 2.1 EventTypes and their contribution to propensity\n",
    "\n",
    "We will allow for several types of experience events to be received for each user.\n",
    "For each user, we will create a \"generative\" model of subscription as follows:\n",
    "\n",
    "1. We sample randomly from a poisson distribution for the number of advertising impressions, webPageViews, emailsSent. **These events can happen at random times over a 10 week interval.**\n",
    "2. For each of these \"base\" exposure events, we then have a corresponding conversion:\n",
    "    - If an advertising impression occurs, we then allow for an advertising click to happen, with a certain probability.  \n",
    "    - If a web Page view occurs, then linkClicks, productViews, purchases, propositionDisplays, Interacts and Dismisses can all occur.\n",
    "    - For an Email Sent, opens and clicks can then also occur. \n",
    "    \n",
    "3. After all these base events have been generated, we then have a timeseries of events for each user. Each of the timeseries events affects the user's propensity to subscribe. After each event the user then has a certain probability of subscribing. The subscription is then evaluated with a Bernoulli draw - if the user subscribes, no further subscription evaluations are made. If the subscription does not happen, the subscription possibility will continue to be evaluated. \n",
    "\n",
    "4. Extra - if more than 10 advertising impressions, or 5 emails are sent, the user churns, and no more events for that user are generated."
   ]
  },
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   "source": [
    "import random, string\n",
    "import uuid\n",
    "from datetime import datetime, timedelta\n",
    "import mmh3\n",
    "import rstr\n",
    "from random import randrange"
   ]
  },
  {
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   "execution_count": 31,
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   "source": [
    "#define some events and dependencies between the events\n",
    "advertising_events = {\n",
    " \n",
    "    #eventType          : (weeklyAverageOccurrence, propensityDelta, [(field_to_replace, value)], timeInHoursFromDependent)\n",
    "    \"advertising.clicks\": (0.01,                    0.002,            [(\"advertising/clicks/value\", 1.0)], 0.5) , \n",
    "    \"advertising.impressions\": (0.1, 0.001, [(\"advertising/impressions/value\", 1.0)], 0),\n",
    "\n",
    "    \"web.webpagedetails.pageViews\": (0.1, 0.005, [(\"web/webPageDetails/pageViews/value\", 1.0)], 0.1),\n",
    "    \"web.webinteraction.linkClicks\": (0.05, 0.005, [(\"web/webInteraction/linkClicks/value\", 1.0)], 0.1),\n",
    "   \n",
    "    \n",
    "    \"commerce.productViews\": (0.05, 0.005, [(\"commerce/productViews/value\", 1.0)], 0.2),\n",
    "    \"commerce.purchases\": (0.01, 0.1, [(\"commerce/purchases/value\", 1.0)], 1),\n",
    "    \n",
    "    \n",
    "    \"decisioning.propositionDisplay\": (0.05, 0.005, [(\"_experience/decisioning/propositionEventType/display\", 1)], 0.1),\n",
    "    \"decisioning.propositionInteract\": (0.01, 0.1, [(\"_experience/decisioning/propositionEventType/interact\", 1)], 0.05),\n",
    "    \"decisioning.propositionDismiss\": (0.01, -0.2, [(\"_experience/decisioning/propositionEventType/dismiss\", 1)], 0.05),\n",
    "\n",
    "    \n",
    "    \"directMarketing.emailOpened\": (0.2, 0.02, [(\"directMarketing/opens/value\", 1.0)], 24),\n",
    "    \"directMarketing.emailClicked\": (0.05, 0.1, [(\"directMarketing/clicks/value\", 1.0)], 0.5),\n",
    "    \"directMarketing.emailSent\": (0.5, 0.005, [(\"directMarketing/sends/value\", 1.0)], 0),\n",
    "    \n",
    "    \"web.formFilledOut\": (0.0, 0.0, [(\"web/webPageDetails/name\", \"subscriptionForm\")], 0),\n",
    "\n",
    "}\n",
    "\n",
    "event_dependencies = {\n",
    "    \"advertising.impressions\": [\"advertising.clicks\"],\n",
    "    \"directMarketing.emailSent\": [\"directMarketing.emailOpened\"],\n",
    "    \"directMarketing.emailOpened\": [\"directMarketing.emailClicked\"],\n",
    "    \"directMarketing.emailClicked\": [\"web.webpagedetails.pageViews\"],\n",
    "    \"web.webpagedetails.pageViews\": [\"web.webinteraction.linkClicks\", \"commerce.productViews\", \"decisioning.propositionDisplay\"],\n",
    "    \"commerce.productViews\": [\"commerce.purchases\"],\n",
    "    \"decisioning.propositionDisplay\": [\"decisioning.propositionInteract\", \"decisioning.propositionDismiss\"]\n",
    "    \n",
    "}"
   ]
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   "source": [
    "# we define some utility functions to be used later\n",
    "\n",
    "import numpy as np\n",
    "from datetime import datetime\n",
    "import math\n",
    "\n",
    "\n",
    "def random_date(start, end):\n",
    "    \"\"\"\n",
    "    This function will return a random datetime between two datetime \n",
    "    objects.\n",
    "    \"\"\"\n",
    "    delta = end - start\n",
    "    int_delta = (delta.days * 24 * 60 * 60) + delta.seconds\n",
    "    random_second = randrange(int_delta)\n",
    "    return start + timedelta(seconds=random_second)\n",
    "\n",
    "def create_data_for_n_users(n_users, first_user):\n",
    "  \n",
    "  N_USERS = n_users\n",
    "  FIRST_USER = first_user\n",
    "  \n",
    "  N_WEEKS = 10\n",
    "  GLOBAL_START_DATE = datetime.now() - timedelta(weeks=12)\n",
    "  GLOBAL_END_DATE = GLOBAL_START_DATE + timedelta(weeks=N_WEEKS)\n",
    "\n",
    "  events = []\n",
    "\n",
    "  for user in range(N_USERS):\n",
    "        user_id = FIRST_USER + user\n",
    "        user_events = []\n",
    "        base_events = {}\n",
    "        for event_type in [\"advertising.impressions\", \"web.webpagedetails.pageViews\", \"directMarketing.emailSent\"]:\n",
    "            n_events = np.random.poisson(advertising_events[event_type][0] * N_WEEKS)\n",
    "            times = []\n",
    "            for _ in range(n_events):\n",
    "                #times.append(random_date(GLOBAL_START_DATE, GLOBAL_END_DATE)\n",
    "                times.append(random_date(GLOBAL_START_DATE, GLOBAL_END_DATE).isoformat())\n",
    "\n",
    "            base_events[event_type] = times\n",
    "\n",
    "        for event_type, dependent_event_types in event_dependencies.items():\n",
    "\n",
    "            if event_type in base_events:\n",
    "                #for each originating event\n",
    "                for event_time in base_events[event_type]:\n",
    "                    #Look for possible later on events\n",
    "                    for dependent_event in dependent_event_types:\n",
    "                                n_events = np.random.poisson(advertising_events[dependent_event][0] * N_WEEKS)\n",
    "                                times = []\n",
    "                                for _ in range(n_events):\n",
    "                                    #times.append(event_time + timedelta(hours = np.random.exponential(advertising_events[dependent_event][3])))\n",
    "                                    new_time = datetime.fromisoformat(event_time) + timedelta(hours = np.random.exponential(advertising_events[dependent_event][3]))\n",
    "                                    times.append(new_time.isoformat())\n",
    "                                base_events[dependent_event] = times\n",
    "\n",
    "\n",
    "        for event_type, times in base_events.items():\n",
    "            for time in times:\n",
    "                user_events.append({\"userId\": user_id, \"eventType\": event_type, \"timestamp\": time})\n",
    "\n",
    "        user_events = sorted(user_events, key = lambda x: (x[\"userId\"], x[\"timestamp\"]))\n",
    "\n",
    "\n",
    "        cumulative_probability = 0.001\n",
    "        subscribed = False\n",
    "        for event in user_events:\n",
    "            cumulative_probability = min(1.0, max(cumulative_probability + advertising_events[event[\"eventType\"]][1], 0))\n",
    "            event[\"subscriptionPropensity\"] = cumulative_probability\n",
    "            if subscribed == False and \"directMarketing\" not in event[\"eventType\"] and \"advertising\" not in event[\"eventType\"]:\n",
    "                subscribed = np.random.binomial(1, cumulative_probability) > 0\n",
    "                if subscribed:\n",
    "                    subscriptiontime = (datetime.fromisoformat(event[\"timestamp\"]) + timedelta(seconds = 60)).isoformat()\n",
    "                    #subscriptiontime = event[\"timestamp\"] + timedelta(seconds = 60)\n",
    "                    user_events.append({\"userId\": user_id, \"eventType\": \"web.formFilledOut\",  \"timestamp\": subscriptiontime})\n",
    "            event[\"subscribed\"] = subscribed\n",
    "        user_events = sorted(user_events, key = lambda x: (x[\"userId\"], x[\"timestamp\"]))\n",
    "\n",
    "        events = events + user_events\n",
    "  return events"
   ]
  },
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   "source": [
    "#utility functions continued\n",
    "def normalize_ecid(ecid_part):\n",
    "    ecid_part_str = str(abs(ecid_part))\n",
    "    if len(ecid_part_str) != 19:\n",
    "        ecid_part_str = \"\".join([str(x) for x in range(\n",
    "            0, 19 - len(ecid_part_str))]) + ecid_part_str\n",
    "    return ecid_part_str\n",
    "\n",
    "\n",
    "def get_ecid(email):\n",
    "    \"\"\"\n",
    "    The ECID must be two valid 19 digit longs concatenated together\n",
    "    \"\"\"\n",
    "    ecidpart1, ecidpart2 = mmh3.hash64(email)\n",
    "    ecid1, ecid2 = (normalize_ecid(ecidpart1), normalize_ecid(ecidpart2))\n",
    "    return ecid1 + ecid2"
   ]
  },
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   "source": [
    "# define the different types of events\n",
    "def create_email_event(user_id, event_type, timestamp):\n",
    "  \"\"\"\n",
    "  Combines previous methods to create various type of email events\n",
    "  \"\"\"\n",
    "  \n",
    "  if event_type == \"directMarketing.emailSent\":\n",
    "    directMarketing = {\"emailDelivered\": {\"value\": 1.0}, \n",
    "                       \"sends\": {\"value\": 1.0}, \n",
    "                       \"emailVisitorID\": user_id,\n",
    "                       \"hashedEmail\": ''.join(random.choices(string.ascii_letters + string.digits, k=10)),\n",
    "                       \"messageID\": str(uuid.uuid4()),\n",
    "                      }\n",
    "  elif event_type == \"directMarketing.emailOpened\":\n",
    "    directMarketing = {\"offerOpens\": {\"value\": 1.0}, \n",
    "                     \"opens\": {\"value\": 1.0}, \n",
    "                     \"emailVisitorID\": user_id,\n",
    "                     \"messageID\": str(uuid.uuid4()),\n",
    "                    }\n",
    "  elif event_type == \"directMarketing.emailClicked\":\n",
    "    directMarketing = {\"clicks\": {\"value\": 1.0}, \n",
    "                     \"offerOpens\": {\"value\": 1.0}, \n",
    "                     \"emailVisitorID\": user_id,\n",
    "                     \"messageID\": str(uuid.uuid4()),\n",
    "                    }\n",
    "  return {\n",
    "    \"directMarketing\": directMarketing,\n",
    "    \"web\": None,\n",
    "    \"_id\": str(uuid.uuid4()),\n",
    "    \"eventMergeId\": None,\n",
    "    \"eventType\": event_type,\n",
    "    f\"_{tenant_id}\": {\"userid\":get_ecid(user_id)},\n",
    "    \"producedBy\": \"databricks-synthetic\",\n",
    "    \"timestamp\": timestamp\n",
    "  }\n",
    "  \n",
    "def create_web_event(user_id, event_type, timestamp):\n",
    "  \"\"\"\n",
    "  Combines previous methods to creat various type of web events\n",
    "  \"\"\"\n",
    "  url = f\"http://www.{''.join(random.choices(string.ascii_letters + string.digits, k=5))}.com\"\n",
    "  ref_url = f\"http://www.{''.join(random.choices(string.ascii_letters + string.digits, k=5))}.com\"\n",
    "  name = ''.join(random.choices(string.ascii_letters + string.digits, k=5))\n",
    "  isHomePage = random.choice([True, False])\n",
    "  server = ''.join(random.choices(string.ascii_letters + string.digits, k=10))\n",
    "  site_section = ''.join(random.choices(string.ascii_letters, k=2))\n",
    "  view_name = ''.join(random.choices(string.ascii_letters, k=3))\n",
    "  region = ''.join(random.choices(string.ascii_letters + string.digits, k=5))\n",
    "  interaction_type = random.choice([\"download\", \"exit\", \"other\"])\n",
    "  web_referrer = random.choice([\"internal\", \"external\", \"search_engine\", \"email\", \"social\", \"unknown\", \"usenet\", \"typed_bookmarked\"])\n",
    "  base_web = {\"webInteraction\": {\"linkClicks\": {\"value\": 0.0}, \n",
    "                                 \"URL\": url, \n",
    "                                 \"name\": name,\n",
    "                                \"region\": region,\n",
    "                                \"type\": interaction_type},\n",
    "              \"webPageDetails\": {\"pageViews\": {\"value\": 1.0},\n",
    "                                 \"URL\": url,\n",
    "                                 \"isErrorPage\": False,\n",
    "                                 #\"isHomepage\": isHomePage,\n",
    "                                 \"name\": name,\n",
    "                                 \"server\": server,\n",
    "                                 \"siteSection\": site_section,\n",
    "                                 \"viewName\": view_name\n",
    "                                },\n",
    "              \"webReferrer\": {\n",
    "                \"URL\": ref_url,\n",
    "                \"type\": web_referrer\n",
    "              }\n",
    "             }\n",
    "  if event_type in [\"advertising.clicks\", \"commerce.purchases\", \"web.webinteraction.linkClicks\", \"web.formFilledOut\", \n",
    "                   \"decisioning.propositionInteract\", \"decisioning.propositionDismiss\"]:\n",
    "    base_web[\"webInteraction\"][\"linkClicks\"][\"value\"] = 1.0\n",
    "\n",
    "  return {\n",
    "    \"directMarketing\": None,\n",
    "    \"web\": base_web,\n",
    "    \"_id\": str(uuid.uuid4()),\n",
    "    \"eventMergeId\": None,\n",
    "    \"eventType\": event_type,\n",
    "    f\"_{tenant_id}\": {\"userid\":get_ecid(user_id)},\n",
    "    \"producedBy\": \"databricks-synthetic\",\n",
    "    \"timestamp\": timestamp\n",
    "  }\n",
    "    \n",
    "    \n",
    "    \n",
    "def create_xdm_event(user_id, event_type, timestamp):\n",
    "  \"\"\"\n",
    "  The final 'event factory' method that converts an event into an XDM event\n",
    "  \"\"\"\n",
    "  if \"directMarketing\" in event_type:\n",
    "    return create_email_event(user_id, event_type, timestamp)\n",
    "  else: \n",
    "    return create_web_event(user_id, event_type, timestamp)"
   ]
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    }
   },
   "source": [
    "## 2.2 Batch ingestion "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "application/vnd.databricks.v1+cell": {
     "cellMetadata": {
      "byteLimit": 2048000,
      "rowLimit": 10000
     },
     "inputWidgets": {},
     "nuid": "4b94c34c-c3fc-44ee-8b31-e7e207b4ec78",
     "showTitle": false,
     "title": ""
    }
   },
   "outputs": [],
   "source": [
    "num_batches = 10\n",
    "batch_size = 10000"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "application/vnd.databricks.v1+cell": {
     "cellMetadata": {},
     "inputWidgets": {},
     "nuid": "2a371094-1106-4550-881d-4df12b8c3ce5",
     "showTitle": false,
     "title": ""
    }
   },
   "source": [
    "Now we're going to use the ingestion APIs using the same `aepp` library. Ingesting a batch of data is a multi-step process:\n",
    "1. Create an empty batch to initialize the connection\n",
    "2. Upload some data using the same batch ID we just created\n",
    "3. Mark the batch as completed successfully\n",
    "\n",
    "For more details about batch ingestion you can find API details [here](https://experienceleague.adobe.com/docs/experience-platform/ingestion/batch/overview.html?lang=en)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "application/vnd.databricks.v1+cell": {
     "cellMetadata": {
      "byteLimit": 2048000,
      "rowLimit": 10000
     },
     "inputWidgets": {},
     "nuid": "6fe7d22a-0f2f-4ad3-9d46-04b21391589d",
     "showTitle": false,
     "title": ""
    }
   },
   "outputs": [],
   "source": [
    "from aepp import ingestion\n",
    "\n",
    "ingest_conn = ingestion.DataIngestion()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "application/vnd.databricks.v1+cell": {
     "cellMetadata": {
      "byteLimit": 2048000,
      "rowLimit": 10000
     },
     "inputWidgets": {},
     "nuid": "6660aa28-0ca3-487c-aea1-9f54539ce43e",
     "showTitle": false,
     "title": ""
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Processing batch 1/10 with ID 01GW3JKCNFMB1TSMY21VGPV98K\n",
      "Processing batch 2/10 with ID 01GW3JNJKT7X432G6P5RJ77G7M\n",
      "Processing batch 3/10 with ID 01GW3JQX5WERH4SS7VPD44DVW8\n",
      "Processing batch 4/10 with ID 01GW3JSZA1VYKAK7TQTP55P0PH\n",
      "Processing batch 5/10 with ID 01GW3JW4D8NM4FZWSFT3G3HCND\n",
      "Processing batch 6/10 with ID 01GW3JYGE3HKZBNJ9YKYANCA38\n",
      "Processing batch 7/10 with ID 01GW3K0PDMDTG0T46DW27TZYJV\n",
      "Processing batch 8/10 with ID 01GW3K2ZMCP7NPDQ56C9M28B6Q\n",
      "Processing batch 9/10 with ID 01GW3K5ECDAHRSXW37CS424RMY\n",
      "Processing batch 10/10 with ID 01GW3K7RDTQ0GMZNNJ0G6PKMYH\n"
     ]
    }
   ],
   "source": [
    "batch_ids = []\n",
    "for batch_index in range(num_batches):\n",
    "  first_user_id_for_batch = batch_index * batch_size\n",
    "  batch_events = create_data_for_n_users(batch_size, first_user_id_for_batch)\n",
    "  batch_data = [create_xdm_event(f\"synthetic-user-{x['userId']}@adobe.com\", x[\"eventType\"], x[\"timestamp\"]) for x in batch_events]\n",
    "  \n",
    "  # Initialize batch creation\n",
    "  batch_res = ingest_conn.createBatch(\n",
    "    datasetId = dataset_id\n",
    "  )\n",
    "  #print(str(batch_res))\n",
    "  batch_id = batch_res[\"id\"]\n",
    "  print(f\"Processing batch {batch_index + 1}/{num_batches} with ID {batch_id}\")\n",
    "  \n",
    "  # Upload XDM data\n",
    "  file_path = f\"batch-synthetic-{batch_id}\"\n",
    "  ingest_conn.uploadSmallFile(\n",
    "    batchId = batch_id,\n",
    "    datasetId = dataset_id,\n",
    "    filePath = batch_id,\n",
    "    data = batch_data\n",
    "  )\n",
    "  \n",
    "  # Complete the batch\n",
    "  ingest_conn.uploadSmallFileFinish(\n",
    "    batchId = batch_id\n",
    "  )\n",
    "  \n",
    "  # Store the batch ID to check status\n",
    "  batch_ids.append(batch_id)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "application/vnd.databricks.v1+cell": {
     "cellMetadata": {},
     "inputWidgets": {},
     "nuid": "308a979b-ef82-4aa6-ac24-3860bd3ff619",
     "showTitle": false,
     "title": ""
    }
   },
   "source": [
    "**Note**: Batches are ingested asynchronously in AEP. It may take some time for all the data generated here to be available in your dataset depending on how your AEP organization has been provisioned. You can check ingestion status for all your batches in [the dataset page of your AEP UI](https://experience.adobe.com/#/@TENANT/sname:SANDBOX/platform/dataset/browse/DATASETID)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "application/vnd.databricks.v1+cell": {
     "cellMetadata": {},
     "inputWidgets": {},
     "nuid": "cdb87863-e017-43f2-98ef-3dfbee7147f7",
     "showTitle": false,
     "title": ""
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Remaining batches being ingested: 1\n",
      "All batches have been ingested\n"
     ]
    }
   ],
   "source": [
    "from aepp import catalog\n",
    "\n",
    "import time\n",
    "\n",
    "cat_conn = catalog.Catalog()\n",
    "\n",
    "all_ingested = False\n",
    "while not all_ingested:\n",
    "  incomplete_batches = cat_conn.getBatches(\n",
    "    limit=min(100, num_batches),\n",
    "    n_results=num_batches,\n",
    "    output=\"dataframe\",\n",
    "    dataSet=dataset_id,\n",
    "    status=\"staging\"\n",
    "  )\n",
    "  \n",
    "  num_incomplete_batches = len(incomplete_batches)\n",
    "  if num_incomplete_batches == 0:\n",
    "    print(\"All batches have been ingested\")\n",
    "    all_ingested = True\n",
    "  else:\n",
    "    print(f\"Remaining batches being ingested: {num_incomplete_batches}\")\n",
    "    time.sleep(30)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "application/vnd.databricks.v1+cell": {
     "cellMetadata": {},
     "inputWidgets": {},
     "nuid": "33ed6b1f-e0fa-4453-86ed-e06d39e63afc",
     "showTitle": false,
     "title": ""
    }
   },
   "source": [
    "# 3. Data Exploration for Propensity Models\n",
    "\n",
    "In this notebook, we connect to the query service using the aepp library, and examine the data that we have uploaded. \n",
    "\n",
    "We do the following steps:\n",
    "\n",
    "- Connect to query service using the aepp configuration parameters\n",
    "- Discover the schema of data, and explore a few rows\n",
    "- Compute basic statistics\n",
    "- Examine correlations among features, to inform feature creation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "application/vnd.databricks.v1+cell": {
     "cellMetadata": {},
     "inputWidgets": {},
     "nuid": "069804cb-e9dc-4201-a266-7676c4c63627",
     "showTitle": false,
     "title": ""
    }
   },
   "source": [
    "## 3.1 Interactive Session with Data Distiller"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Every dataset ID in the Adobe Experience Platform is tied to a table name in Query Service world. We can easily get the table name by doing a lookup on the dataset ID and extracting the table name from the tags:\n",
    "We will be using the dataset created in 1.2.1, please make sure dataset_id is set with the correct value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'cmle_week1_new_notebook_with_ecid_used_dataset_created_by_saikat'"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from aepp import catalog\n",
    "\n",
    "cat_conn = catalog.Catalog()\n",
    "\n",
    "dataset_info = cat_conn.getDataSet(dataset_id)\n",
    "table_name = dataset_info[dataset_id][\"tags\"][\"adobe/pqs/table\"][0]\n",
    "table_name"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "When you set the connection to the query service object you'll setup a connection to the actual table you need to connect to, this will be faster and use up less resources from the query service API"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "application/vnd.databricks.v1+cell": {
     "cellMetadata": {},
     "inputWidgets": {},
     "nuid": "c0b34fab-a2b1-4b2c-8ce0-623e0bda8084",
     "showTitle": false,
     "title": ""
    }
   },
   "outputs": [],
   "source": [
    "from aepp import queryservice\n",
    "\n",
    "qs_conn = queryservice.QueryService().connection()\n",
    "qs_conn[\"dbname\"] = f\"{sandbox_name}:{table_name}\"\n",
    "qs_cursor = queryservice.InteractiveQuery(qs_conn)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "application/vnd.databricks.v1+cell": {
     "cellMetadata": {},
     "inputWidgets": {},
     "nuid": "70c34366-7c3f-42f2-9adf-993d3bcecff7",
     "showTitle": false,
     "title": ""
    }
   },
   "source": [
    "## 3.2 Querying the dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can use the interactive session created just before to issue any kinds of queries to Query Service. As an example here we simply select all the fields in our synthetic data table."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "application/vnd.databricks.v1+cell": {
     "cellMetadata": {},
     "inputWidgets": {},
     "nuid": "eee551bc-bcf7-40af-abc3-0c6a152d072a",
     "showTitle": false,
     "title": ""
    }
   },
   "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",
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       "    }\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>_aamds</th>\n",
       "      <th>_id</th>\n",
       "      <th>directMarketing</th>\n",
       "      <th>eventMergeId</th>\n",
       "      <th>eventType</th>\n",
       "      <th>producedBy</th>\n",
       "      <th>timestamp</th>\n",
       "      <th>web</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>(65243412659796291646255339364107433273)</td>\n",
       "      <td>e5134037-8569-4aad-bbef-279bb831808f</td>\n",
       "      <td>(NULL,\"(1.0)\",synthetic-user-5429@adobe.com,MD...</td>\n",
       "      <td>None</td>\n",
       "      <td>directMarketing.emailSent</td>\n",
       "      <td>databricks-synthetic</td>\n",
       "      <td>2023-01-21 00:01:41.534</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>(49844234781406159997650770259889908940)</td>\n",
       "      <td>e1793fdc-4ac7-49c6-9aa3-e0867aa0ec55</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>commerce.productViews</td>\n",
       "      <td>databricks-synthetic</td>\n",
       "      <td>2023-01-21 00:02:15.916</td>\n",
       "      <td>(\"(http://www.SxTqn.com,\"(0.0)\",t91jg,n7Gcl,ex...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>(12020984676794453631540776783207087622)</td>\n",
       "      <td>78ee3b46-c3fd-4058-8041-f7fe15180268</td>\n",
       "      <td>(NULL,\"(1.0)\",synthetic-user-7954@adobe.com,re...</td>\n",
       "      <td>None</td>\n",
       "      <td>directMarketing.emailSent</td>\n",
       "      <td>databricks-synthetic</td>\n",
       "      <td>2023-01-21 00:02:34.534</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>(21690903347635878125817940858837590243)</td>\n",
       "      <td>d46358de-d9bf-4bc7-861a-cc3275203fde</td>\n",
       "      <td>(NULL,\"(1.0)\",synthetic-user-3552@adobe.com,5N...</td>\n",
       "      <td>None</td>\n",
       "      <td>directMarketing.emailSent</td>\n",
       "      <td>databricks-synthetic</td>\n",
       "      <td>2023-01-21 00:02:36.534</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>(53459861322107555163196673979056455929)</td>\n",
       "      <td>5af2c15c-ec23-4a41-ac3d-1dab9fe9a057</td>\n",
       "      <td>(NULL,\"(1.0)\",synthetic-user-5914@adobe.com,YB...</td>\n",
       "      <td>None</td>\n",
       "      <td>directMarketing.emailSent</td>\n",
       "      <td>databricks-synthetic</td>\n",
       "      <td>2023-01-21 00:05:18.534</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                     _aamds  \\\n",
       "0  (65243412659796291646255339364107433273)   \n",
       "1  (49844234781406159997650770259889908940)   \n",
       "2  (12020984676794453631540776783207087622)   \n",
       "3  (21690903347635878125817940858837590243)   \n",
       "4  (53459861322107555163196673979056455929)   \n",
       "\n",
       "                                    _id  \\\n",
       "0  e5134037-8569-4aad-bbef-279bb831808f   \n",
       "1  e1793fdc-4ac7-49c6-9aa3-e0867aa0ec55   \n",
       "2  78ee3b46-c3fd-4058-8041-f7fe15180268   \n",
       "3  d46358de-d9bf-4bc7-861a-cc3275203fde   \n",
       "4  5af2c15c-ec23-4a41-ac3d-1dab9fe9a057   \n",
       "\n",
       "                                     directMarketing eventMergeId  \\\n",
       "0  (NULL,\"(1.0)\",synthetic-user-5429@adobe.com,MD...         None   \n",
       "1                                               None         None   \n",
       "2  (NULL,\"(1.0)\",synthetic-user-7954@adobe.com,re...         None   \n",
       "3  (NULL,\"(1.0)\",synthetic-user-3552@adobe.com,5N...         None   \n",
       "4  (NULL,\"(1.0)\",synthetic-user-5914@adobe.com,YB...         None   \n",
       "\n",
       "                   eventType            producedBy                timestamp  \\\n",
       "0  directMarketing.emailSent  databricks-synthetic  2023-01-21 00:01:41.534   \n",
       "1      commerce.productViews  databricks-synthetic  2023-01-21 00:02:15.916   \n",
       "2  directMarketing.emailSent  databricks-synthetic  2023-01-21 00:02:34.534   \n",
       "3  directMarketing.emailSent  databricks-synthetic  2023-01-21 00:02:36.534   \n",
       "4  directMarketing.emailSent  databricks-synthetic  2023-01-21 00:05:18.534   \n",
       "\n",
       "                                                 web  \n",
       "0                                               None  \n",
       "1  (\"(http://www.SxTqn.com,\"(0.0)\",t91jg,n7Gcl,ex...  \n",
       "2                                               None  \n",
       "3                                               None  \n",
       "4                                               None  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sample_experience_event_query = f'''SELECT * FROM {table_name} LIMIT 5'''\n",
    "qs_cursor.query(sample_experience_event_query)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "application/vnd.databricks.v1+cell": {
     "cellMetadata": {},
     "inputWidgets": {},
     "nuid": "a356d2d3-6931-4f7e-8286-ce74cf870f4f",
     "showTitle": false,
     "title": ""
    },
    "nteract": {
     "transient": {
      "deleting": false
     }
    }
   },
   "source": [
    "## 3.3 Querying Complex Fields\n",
    "\n",
    "Let's sample some of the fields in our dataset - we have different types, some are timestamps, some are just primitives like strings, and then some are complex nested XDM structures. Let's see what we get when we query it as-is: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "application/vnd.databricks.v1+cell": {
     "cellMetadata": {},
     "inputWidgets": {},
     "nuid": "62c856f3-b608-42e5-96b5-a1d10dfddd3c",
     "showTitle": false,
     "title": ""
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "\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>directMarketing</th>\n",
       "      <th>_id</th>\n",
       "      <th>eventType</th>\n",
       "      <th>timestamp</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>(NULL,\"(1.0)\",synthetic-user-72328@adobe.com,h...</td>\n",
       "      <td>fbf988a7-deb1-4dfa-b6a6-0f22a1e479e8</td>\n",
       "      <td>directMarketing.emailSent</td>\n",
       "      <td>2023-01-21 00:01:18.909</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>(NULL,NULL,synthetic-user-74797@adobe.com,NULL...</td>\n",
       "      <td>04bf1142-325b-4faf-b028-dcecb0b38f1b</td>\n",
       "      <td>directMarketing.emailOpened</td>\n",
       "      <td>2023-01-21 00:01:30.281</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>(NULL,\"(1.0)\",synthetic-user-78676@adobe.com,z...</td>\n",
       "      <td>5127f038-ad0b-40b6-9689-d5e17c5b6589</td>\n",
       "      <td>directMarketing.emailSent</td>\n",
       "      <td>2023-01-21 00:01:34.909</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>None</td>\n",
       "      <td>2dd27960-2ee6-4926-a1ff-940a19be7b42</td>\n",
       "      <td>advertising.impressions</td>\n",
       "      <td>2023-01-21 00:02:35.909</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>(NULL,NULL,synthetic-user-72648@adobe.com,NULL...</td>\n",
       "      <td>c4439186-05dd-4b21-9867-424af166c1e6</td>\n",
       "      <td>directMarketing.emailOpened</td>\n",
       "      <td>2023-01-21 00:03:13.375</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                     directMarketing  \\\n",
       "0  (NULL,\"(1.0)\",synthetic-user-72328@adobe.com,h...   \n",
       "1  (NULL,NULL,synthetic-user-74797@adobe.com,NULL...   \n",
       "2  (NULL,\"(1.0)\",synthetic-user-78676@adobe.com,z...   \n",
       "3                                               None   \n",
       "4  (NULL,NULL,synthetic-user-72648@adobe.com,NULL...   \n",
       "\n",
       "                                    _id                    eventType  \\\n",
       "0  fbf988a7-deb1-4dfa-b6a6-0f22a1e479e8    directMarketing.emailSent   \n",
       "1  04bf1142-325b-4faf-b028-dcecb0b38f1b  directMarketing.emailOpened   \n",
       "2  5127f038-ad0b-40b6-9689-d5e17c5b6589    directMarketing.emailSent   \n",
       "3  2dd27960-2ee6-4926-a1ff-940a19be7b42      advertising.impressions   \n",
       "4  c4439186-05dd-4b21-9867-424af166c1e6  directMarketing.emailOpened   \n",
       "\n",
       "                 timestamp  \n",
       "0  2023-01-21 00:01:18.909  \n",
       "1  2023-01-21 00:01:30.281  \n",
       "2  2023-01-21 00:01:34.909  \n",
       "3  2023-01-21 00:02:35.909  \n",
       "4  2023-01-21 00:03:13.375  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "schema_query= f'''SELECT directMarketing, _id, eventType, timestamp FROM {table_name} LIMIT 5'''\n",
    "df = qs_cursor.query(schema_query, output=\"dataframe\")\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As we can see when looking at the complex nested field, it's pretty hard to make sense of what this data is and the underlying structure:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df[\"directMarketing\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'(NULL,\"(1.0)\",synthetic-user-72328@adobe.com,h0etCqXVFg,db076fce-9c9a-4bab-ae63-25eb91e12de1,NULL,NULL,\"(1.0)\")'"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"directMarketing\"].iloc[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let's run the same query again but with a twist: we can set the `auto_to_json` flag to be true - this configuration ensures that complex structures are automatically converted into a json form so that the field names can be queried."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "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",
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       "    }\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>_id</th>\n",
       "      <th>eventType</th>\n",
       "      <th>timestamp</th>\n",
       "      <th>directMarketing</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>fbf988a7-deb1-4dfa-b6a6-0f22a1e479e8</td>\n",
       "      <td>directMarketing.emailSent</td>\n",
       "      <td>2023-01-21 00:01:18.909</td>\n",
       "      <td>{\"emailDelivered\":{\"value\":1.0},\"emailVisitorI...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>04bf1142-325b-4faf-b028-dcecb0b38f1b</td>\n",
       "      <td>directMarketing.emailOpened</td>\n",
       "      <td>2023-01-21 00:01:30.281</td>\n",
       "      <td>{\"emailVisitorID\":\"synthetic-user-74797@adobe....</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5127f038-ad0b-40b6-9689-d5e17c5b6589</td>\n",
       "      <td>directMarketing.emailSent</td>\n",
       "      <td>2023-01-21 00:01:34.909</td>\n",
       "      <td>{\"emailDelivered\":{\"value\":1.0},\"emailVisitorI...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2dd27960-2ee6-4926-a1ff-940a19be7b42</td>\n",
       "      <td>advertising.impressions</td>\n",
       "      <td>2023-01-21 00:02:35.909</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>c4439186-05dd-4b21-9867-424af166c1e6</td>\n",
       "      <td>directMarketing.emailOpened</td>\n",
       "      <td>2023-01-21 00:03:13.375</td>\n",
       "      <td>{\"emailVisitorID\":\"synthetic-user-72648@adobe....</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                    _id                    eventType  \\\n",
       "0  fbf988a7-deb1-4dfa-b6a6-0f22a1e479e8    directMarketing.emailSent   \n",
       "1  04bf1142-325b-4faf-b028-dcecb0b38f1b  directMarketing.emailOpened   \n",
       "2  5127f038-ad0b-40b6-9689-d5e17c5b6589    directMarketing.emailSent   \n",
       "3  2dd27960-2ee6-4926-a1ff-940a19be7b42      advertising.impressions   \n",
       "4  c4439186-05dd-4b21-9867-424af166c1e6  directMarketing.emailOpened   \n",
       "\n",
       "                 timestamp                                    directMarketing  \n",
       "0  2023-01-21 00:01:18.909  {\"emailDelivered\":{\"value\":1.0},\"emailVisitorI...  \n",
       "1  2023-01-21 00:01:30.281  {\"emailVisitorID\":\"synthetic-user-74797@adobe....  \n",
       "2  2023-01-21 00:01:34.909  {\"emailDelivered\":{\"value\":1.0},\"emailVisitorI...  \n",
       "3  2023-01-21 00:02:35.909                                               None  \n",
       "4  2023-01-21 00:03:13.375  {\"emailVisitorID\":\"synthetic-user-72648@adobe....  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "schema_query= f\"\"\"\n",
    "SET auto_to_json=true; \n",
    "\n",
    "SELECT directMarketing, _id, eventType, timestamp \n",
    "FROM {table_name} \n",
    "LIMIT 5\"\"\"\n",
    "df = qs_cursor.query(schema_query, output=\"dataframe\")\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As we can see, the data is now much easier to digest, and we can see clearly the underlying structure along with the different field names."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'{\"emailDelivered\":{\"value\":1.0},\"emailVisitorID\":\"synthetic-user-72328@adobe.com\",\"hashedEmail\":\"h0etCqXVFg\",\"messageID\":\"db076fce-9c9a-4bab-ae63-25eb91e12de1\",\"sends\":{\"value\":1.0}}'"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"directMarketing\"].iloc[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'emailDelivered': {'value': 1.0},\n",
       " 'emailVisitorID': 'synthetic-user-72328@adobe.com',\n",
       " 'hashedEmail': 'h0etCqXVFg',\n",
       " 'messageID': 'db076fce-9c9a-4bab-ae63-25eb91e12de1',\n",
       " 'sends': {'value': 1.0}}"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import json\n",
    "\n",
    "json.loads(df[\"directMarketing\"].iloc[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "application/vnd.databricks.v1+cell": {
     "cellMetadata": {},
     "inputWidgets": {},
     "nuid": "9ef944d7-9ac6-4946-ad9c-37f8d930b9bf",
     "showTitle": false,
     "title": ""
    },
    "nteract": {
     "transient": {
      "deleting": false
     }
    }
   },
   "source": [
    "## 3.4 Manually get some basic statistics\n",
    "\n",
    "Let's look at the number of rows, number of profiles in our synthetic dataset as an example of basic computations that can be done with Query Service:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "application/vnd.databricks.v1+cell": {
     "cellMetadata": {},
     "inputWidgets": {},
     "nuid": "6a9a9bc1-808f-4c8c-a446-545de9f46bc3",
     "showTitle": false,
     "title": ""
    },
    "gather": {
     "logged": 1667866562768
    },
    "jupyter": {
     "outputs_hidden": false,
     "source_hidden": false
    },
    "nteract": {
     "transient": {
      "deleting": false
     }
    }
   },
   "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>totalRows</th>\n",
       "      <th>distinctUsers</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1064337</td>\n",
       "      <td>1064337</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   totalRows  distinctUsers\n",
       "0    1064337        1064337"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "basic_statistics_query = f\"\"\"\n",
    "SELECT\n",
    "    COUNT(_id) as \"totalRows\",  \n",
    "    COUNT(DISTINCT _id) as \"distinctUsers\" \n",
    "FROM {table_name}\"\"\"\n",
    "df = qs_cursor.query(basic_statistics_query, output=\"dataframe\")\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "application/vnd.databricks.v1+cell": {
     "cellMetadata": {},
     "inputWidgets": {},
     "nuid": "c8254ae0-3017-4390-b79a-4d68b9e62c0f",
     "showTitle": false,
     "title": ""
    }
   },
   "source": [
    "## 3.5 Create a sampled version of the table\n",
    "\n",
    "If our dataset is too big, or we simply don't need to get exact numbers for our queries, we can use the [sampling functionality](https://experienceleague.adobe.com/docs/experience-platform/query/sql/dataset-samples.html?lang=en) available in Query Service. This happens in multiple steps:\n",
    "- First we have to **analyze** the table to create an actual sample with a specific sampling ratio.\n",
    "- Then we can query the actual sample created which will automatically extrapolate the numbers to the full dataset.\n",
    "\n",
    "As an example below, this is how we start by analyzing the table and creating a 5% sample:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "# A sampling rate of 10 is 100% in Query Service, so for 5% we have to use 0.5\n",
    "sampling_rate = 0.5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "analyze_table_query=f\"\"\"\n",
    "SET aqp=true; \n",
    "\n",
    "ANALYZE TABLE {table_name} TABLESAMPLE SAMPLERATE {sampling_rate}\"\"\"\n",
    "qs_cursor.query(analyze_table_query, output=\"raw\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If we want to see all the different samples available for our table, we can use the `sample_meta` function which will have an entry for each sample pointing to that sample's dataset ID and table name as well as the recorded sampling ratio."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "application/vnd.databricks.v1+cell": {
     "cellMetadata": {},
     "inputWidgets": {},
     "nuid": "f744d60d-303c-461a-bbca-fc6abc6619c0",
     "showTitle": false,
     "title": ""
    }
   },
   "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>sample_table_name</th>\n",
       "      <th>sample_dataset_id</th>\n",
       "      <th>parent_dataset_id</th>\n",
       "      <th>sample_type</th>\n",
       "      <th>sampling_rate</th>\n",
       "      <th>filter_condition_on_source_dataset</th>\n",
       "      <th>sample_num_rows</th>\n",
       "      <th>created</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>cmle_week1_new_notebook_with_ecid_used_dataset...</td>\n",
       "      <td>643426f164d2b81c05d79e6d</td>\n",
       "      <td>642c452bb871e21c0669cba8</td>\n",
       "      <td>uniform</td>\n",
       "      <td>0.5</td>\n",
       "      <td></td>\n",
       "      <td>5412</td>\n",
       "      <td>10/04/2023 03:10:41</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                   sample_table_name  \\\n",
       "0  cmle_week1_new_notebook_with_ecid_used_dataset...   \n",
       "\n",
       "          sample_dataset_id         parent_dataset_id sample_type  \\\n",
       "0  643426f164d2b81c05d79e6d  642c452bb871e21c0669cba8     uniform   \n",
       "\n",
       "   sampling_rate filter_condition_on_source_dataset  sample_num_rows  \\\n",
       "0            0.5                                                5412   \n",
       "\n",
       "               created  \n",
       "0  10/04/2023 03:10:41  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sampled_version_of_table_query = f'''SELECT sample_meta('{table_name}')'''\n",
    "\n",
    "df_samples = qs_cursor.query(sampled_version_of_table_query, output=\"dataframe\")\n",
    "df_samples"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So now let's compare what happens when we run the same query on both the original table, and our 5% sample. We're using a very simple query to just do a `COUNT` to compare both the accuracy of the result, and also how much faster it is."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 1.29 ms, sys: 1.12 ms, total: 2.41 ms\n",
      "Wall time: 4.48 s\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count(1)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1066272</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   count(1)\n",
       "0   1066272"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%time\n",
    "count_query=f'''SELECT count(*) from {table_name}'''\n",
    "df = qs_cursor.query(count_query, output=\"dataframe\")\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To query the sample we get the sampling table name from the metadata above, and then query it directly and multiply the results by the sampling ratio to get an estimate."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'cmle_week1_new_notebook_with_ecid_used_dataset_cre_sample_0_5'"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sample_table_name = df_samples[df_samples[\"sampling_rate\"] == sampling_rate][\"sample_table_name\"].iloc[0]\n",
    "sample_table_name"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Approximate count: 1079600.0 using 5.0% sample\n",
      "CPU times: user 1.65 ms, sys: 1.23 ms, total: 2.87 ms\n",
      "Wall time: 3.08 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "count_query=f'''SELECT count(*) as cnt from {sample_table_name}'''\n",
    "df = qs_cursor.query(count_query, output=\"dataframe\")\n",
    "approx_count = df[\"cnt\"].iloc[0] / (sampling_rate / 100)\n",
    "print(f\"Approximate count: {approx_count} using {sampling_rate *10}% sample\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-block alert-warning\">\n",
    "<b>Note:</b>\n",
    "    \n",
    "You can also query the latest sample from that dataset by using `SELECT * from {table_name} WITHAPPROXIMATE` however it is not advised to do aggregation queries or joins with that since this is only a uniform random sample.\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see that the results are pretty accurate <1% error, and also the runtime is reduced by at least 20%, so using samples are a good choice for featurization data if we have a ML model that is not necessarily data-hungry."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "application/vnd.databricks.v1+cell": {
     "cellMetadata": {},
     "inputWidgets": {},
     "nuid": "272c9e84-9321-469e-9c9d-032fe45f4357",
     "showTitle": false,
     "title": ""
    }
   },
   "source": [
    "# 4. Analyzing the data\n",
    "\n",
    "Let's now analyze the data by creating a few visualizations using some commonly requested questions. \n",
    "\n",
    "## 4.1 Email Funnel Analysis\n",
    "\n",
    "Let's look at the funnel of how many users actually fill out the webForm. A funnel analysis is a method of understanding the steps required to reach an outcome on a website and how many users get through each of those steps."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "application/vnd.databricks.v1+cell": {
     "cellMetadata": {},
     "inputWidgets": {},
     "nuid": "6627674c-a074-49f0-80cd-327e58be1f30",
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     "title": ""
    }
   },
   "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",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>eventType</th>\n",
       "      <th>distinctUsers</th>\n",
       "      <th>distinctEvents</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>directMarketing.emailSent</td>\n",
       "      <td>500970</td>\n",
       "      <td>500970</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>directMarketing.emailOpened</td>\n",
       "      <td>198474</td>\n",
       "      <td>198474</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>advertising.impressions</td>\n",
       "      <td>100246</td>\n",
       "      <td>100246</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>web.webpagedetails.pageViews</td>\n",
       "      <td>99389</td>\n",
       "      <td>99389</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>directMarketing.emailClicked</td>\n",
       "      <td>42431</td>\n",
       "      <td>42431</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>decisioning.propositionDisplay</td>\n",
       "      <td>31639</td>\n",
       "      <td>31639</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>commerce.productViews</td>\n",
       "      <td>31545</td>\n",
       "      <td>31545</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>web.webinteraction.linkClicks</td>\n",
       "      <td>31501</td>\n",
       "      <td>31501</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>web.formFilledOut</td>\n",
       "      <td>14841</td>\n",
       "      <td>14841</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>advertising.clicks</td>\n",
       "      <td>6376</td>\n",
       "      <td>6376</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>decisioning.propositionDismiss</td>\n",
       "      <td>2554</td>\n",
       "      <td>2554</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>decisioning.propositionInteract</td>\n",
       "      <td>2540</td>\n",
       "      <td>2540</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>commerce.purchases</td>\n",
       "      <td>2538</td>\n",
       "      <td>2538</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                          eventType  distinctUsers  distinctEvents\n",
       "0         directMarketing.emailSent         500970          500970\n",
       "1       directMarketing.emailOpened         198474          198474\n",
       "2           advertising.impressions         100246          100246\n",
       "3      web.webpagedetails.pageViews          99389           99389\n",
       "4      directMarketing.emailClicked          42431           42431\n",
       "5    decisioning.propositionDisplay          31639           31639\n",
       "6             commerce.productViews          31545           31545\n",
       "7     web.webinteraction.linkClicks          31501           31501\n",
       "8                 web.formFilledOut          14841           14841\n",
       "9                advertising.clicks           6376            6376\n",
       "10   decisioning.propositionDismiss           2554            2554\n",
       "11  decisioning.propositionInteract           2540            2540\n",
       "12               commerce.purchases           2538            2538"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "simple_funnel_analysis_query = f'''SELECT eventType, COUNT(DISTINCT _id) as \"distinctUsers\",COUNT(_id) as \"distinctEvents\" FROM {table_name} GROUP BY eventType ORDER BY distinctUsers DESC'''\n",
    "funnel_df = qs_cursor.query(simple_funnel_analysis_query, output=\"dataframe\")\n",
    "funnel_df"
   ]
  },
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     "title": ""
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     "transient": {
      "deleting": false
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   },
   "source": [
    "Here, we launch a query to get number of users in each phase"
   ]
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     "title": ""
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import plotly.express as px\n",
    "\n",
    "email_funnel_events = [\"directMarketing.emailSent\", \"directMarketing.emailOpened\", \"directMarketing.emailClicked\", \"web.formFilledOut\"]\n",
    "email_funnel_df = funnel_df[funnel_df[\"eventType\"].isin(email_funnel_events)]\n",
    "\n",
    "fig = px.funnel(email_funnel_df, y='eventType', x='distinctUsers')\n",
    "fig.show()"
   ]
  },
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   "cell_type": "markdown",
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   "source": [
    "## 4.2 Event correlation\n",
    "Now, we analyze the correlation between various types of events. \n",
    "\n",
    "We'll look for which events predict the `web.formFilledOut` outcome. \n",
    "\n",
    "To do this, we must execute a more complex join query."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
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    {
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       "        text-align: right;\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>eventType_First</th>\n",
       "      <th>eventType_Later</th>\n",
       "      <th>distinctUsers</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>directMarketing.emailSent</td>\n",
       "      <td>directMarketing.emailSent</td>\n",
       "      <td>99319</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>directMarketing.emailOpened</td>\n",
       "      <td>directMarketing.emailOpened</td>\n",
       "      <td>85822</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>directMarketing.emailSent</td>\n",
       "      <td>directMarketing.emailOpened</td>\n",
       "      <td>85822</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>advertising.impressions</td>\n",
       "      <td>advertising.impressions</td>\n",
       "      <td>63322</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>directMarketing.emailOpened</td>\n",
       "      <td>directMarketing.emailSent</td>\n",
       "      <td>63169</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>164</th>\n",
       "      <td>commerce.purchases</td>\n",
       "      <td>web.webinteraction.linkClicks</td>\n",
       "      <td>46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>165</th>\n",
       "      <td>commerce.purchases</td>\n",
       "      <td>decisioning.propositionDisplay</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>166</th>\n",
       "      <td>commerce.purchases</td>\n",
       "      <td>directMarketing.emailClicked</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>167</th>\n",
       "      <td>commerce.purchases</td>\n",
       "      <td>decisioning.propositionDismiss</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>168</th>\n",
       "      <td>commerce.purchases</td>\n",
       "      <td>decisioning.propositionInteract</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>169 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                 eventType_First                  eventType_Later  \\\n",
       "0      directMarketing.emailSent        directMarketing.emailSent   \n",
       "1    directMarketing.emailOpened      directMarketing.emailOpened   \n",
       "2      directMarketing.emailSent      directMarketing.emailOpened   \n",
       "3        advertising.impressions          advertising.impressions   \n",
       "4    directMarketing.emailOpened        directMarketing.emailSent   \n",
       "..                           ...                              ...   \n",
       "164           commerce.purchases    web.webinteraction.linkClicks   \n",
       "165           commerce.purchases   decisioning.propositionDisplay   \n",
       "166           commerce.purchases     directMarketing.emailClicked   \n",
       "167           commerce.purchases   decisioning.propositionDismiss   \n",
       "168           commerce.purchases  decisioning.propositionInteract   \n",
       "\n",
       "     distinctUsers  \n",
       "0            99319  \n",
       "1            85822  \n",
       "2            85822  \n",
       "3            63322  \n",
       "4            63169  \n",
       "..             ...  \n",
       "164             46  \n",
       "165             32  \n",
       "166             25  \n",
       "167             10  \n",
       "168              9  \n",
       "\n",
       "[169 rows x 3 columns]"
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   ],
   "source": [
    "event_correlation_query=f'''SELECT  eventType_First, eventType_Later, COUNT(DISTINCT userId) as \"distinctUsers\"\n",
    "FROM \n",
    "    (\n",
    "        SELECT a.eventType as eventType_First, \n",
    "                b.eventType as eventType_Later, \n",
    "                a._{tenant_id}.userid as userID \n",
    "        FROM {table_name} a\n",
    "        JOIN {table_name} b\n",
    "        ON a._{tenant_id}.userid = b._{tenant_id}.userid\n",
    "        WHERE a.timestamp <= b.timestamp\n",
    "    )\n",
    "GROUP BY eventType_First, eventType_Later\n",
    "ORDER BY distinctUsers DESC'''\n",
    "event_correlation_df = qs_cursor.query(event_correlation_query, output=\"dataframe\")\n",
    "event_correlation_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we join the results of this correlation to obtain a coocurrence matrix which we can then display to get a visual feel of which events are likely to be occurring together."
   ]
  },
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    "coocc_matrix = event_correlation_df\n",
    "individual_counts = funnel_df\n",
    "cocc_with_individual = coocc_matrix.merge(individual_counts, left_on=\"eventType_First\", right_on=\"eventType\")\n",
    "cocc_with_individual[\"probability\"] = cocc_with_individual[\"distinctUsers_x\"]/ cocc_with_individual[\"distinctUsers_y\"]"
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      "/var/folders/4_/y52rp_4d21sdgtx662w561dm0000gq/T/ipykernel_15483/402706120.py:3: FutureWarning:\n",
      "\n",
      "In a future version of pandas all arguments of DataFrame.pivot will be keyword-only.\n",
      "\n"
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       "<Axes: xlabel='eventType_Later', ylabel='eventType_First'>"
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\n",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "\n",
    "pivoted = cocc_with_individual.pivot(\"eventType_First\", \"eventType_Later\", \"probability\")\n",
    "sns.heatmap(pivoted)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "application/vnd.databricks.v1+cell": {
     "cellMetadata": {},
     "inputWidgets": {},
     "nuid": "f05daa1f-a4f0-477a-bd38-01879c308a2c",
     "showTitle": false,
     "title": ""
    }
   },
   "source": [
    "## 4.3 A more robust correlation calculation\n",
    "\n",
    "Let's use an in-built feature of the query service (the Spark functions) to get a better handle of correlations between various eventTypes. We'll usee the `corr` function - which computes pearson correlation coefficients between a given eventType, and the target eventType"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "application/vnd.databricks.v1+cell": {
     "cellMetadata": {},
     "inputWidgets": {},
     "nuid": "a471826d-84dc-4261-b6eb-7f45a77649cd",
     "showTitle": false,
     "title": ""
    }
   },
   "source": [
    "#### Run on the full dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "application/vnd.databricks.v1+cell": {
     "cellMetadata": {},
     "inputWidgets": {},
     "nuid": "822365c5-3140-4ad4-bc67-4ac8f51cf775",
     "showTitle": false,
     "title": ""
    }
   },
   "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>webFormsFilled_totalUsers</th>\n",
       "      <th>advertisingClicks_totalUsers</th>\n",
       "      <th>productViews_totalUsers</th>\n",
       "      <th>productPurchases_totalUsers</th>\n",
       "      <th>propositionDismisses_totaUsers</th>\n",
       "      <th>propositionDisplays_totaUsers</th>\n",
       "      <th>propositionInteracts_totalUsers</th>\n",
       "      <th>emailClicks_totalUsers</th>\n",
       "      <th>emailOpens_totalUsers</th>\n",
       "      <th>webLinksClicks_totalUsers</th>\n",
       "      <th>...</th>\n",
       "      <th>webForms_advertisingClicks</th>\n",
       "      <th>webForms_productViews</th>\n",
       "      <th>webForms_productPurchases</th>\n",
       "      <th>webForms_propositionDismisses</th>\n",
       "      <th>webForms_propositionInteracts</th>\n",
       "      <th>webForms_emailClicks</th>\n",
       "      <th>webForms_emailOpens</th>\n",
       "      <th>webForms_emailSends</th>\n",
       "      <th>webForms_webLinkClicks</th>\n",
       "      <th>webForms_webPageViews</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>14841</td>\n",
       "      <td>6376</td>\n",
       "      <td>31545</td>\n",
       "      <td>0</td>\n",
       "      <td>2554</td>\n",
       "      <td>31639</td>\n",
       "      <td>2540</td>\n",
       "      <td>42431</td>\n",
       "      <td>198474</td>\n",
       "      <td>31501</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.006571</td>\n",
       "      <td>0.228957</td>\n",
       "      <td>None</td>\n",
       "      <td>0.049756</td>\n",
       "      <td>0.132518</td>\n",
       "      <td>0.246498</td>\n",
       "      <td>0.094207</td>\n",
       "      <td>0.027688</td>\n",
       "      <td>0.21801</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   webFormsFilled_totalUsers  advertisingClicks_totalUsers  \\\n",
       "0                      14841                          6376   \n",
       "\n",
       "   productViews_totalUsers  productPurchases_totalUsers  \\\n",
       "0                    31545                            0   \n",
       "\n",
       "   propositionDismisses_totaUsers  propositionDisplays_totaUsers  \\\n",
       "0                            2554                          31639   \n",
       "\n",
       "   propositionInteracts_totalUsers  emailClicks_totalUsers  \\\n",
       "0                             2540                   42431   \n",
       "\n",
       "   emailOpens_totalUsers  webLinksClicks_totalUsers  ...  \\\n",
       "0                 198474                      31501  ...   \n",
       "\n",
       "   webForms_advertisingClicks  webForms_productViews  \\\n",
       "0                   -0.006571               0.228957   \n",
       "\n",
       "   webForms_productPurchases  webForms_propositionDismisses  \\\n",
       "0                       None                       0.049756   \n",
       "\n",
       "  webForms_propositionInteracts  webForms_emailClicks  webForms_emailOpens  \\\n",
       "0                      0.132518              0.246498             0.094207   \n",
       "\n",
       "   webForms_emailSends  webForms_webLinkClicks  webForms_webPageViews  \n",
       "0             0.027688                 0.21801                   None  \n",
       "\n",
       "[1 rows x 22 columns]"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "large_correlation_query=f'''\n",
    "SELECT SUM(webFormsFilled) as webFormsFilled_totalUsers,\n",
    "       SUM(advertisingClicks) as advertisingClicks_totalUsers,\n",
    "       SUM(productViews) as productViews_totalUsers,\n",
    "       SUM(productPurchases) as productPurchases_totalUsers,\n",
    "       SUM(propositionDismisses) as propositionDismisses_totaUsers,\n",
    "       SUM(propositionDisplays) as propositionDisplays_totaUsers,\n",
    "       SUM(propositionInteracts) as propositionInteracts_totalUsers,\n",
    "       SUM(emailClicks) as emailClicks_totalUsers,\n",
    "       SUM(emailOpens) as emailOpens_totalUsers,\n",
    "       SUM(webLinkClicks) as webLinksClicks_totalUsers,\n",
    "       SUM(webPageViews) as webPageViews_totalusers,\n",
    "       corr(webFormsFilled, emailOpens) as webForms_EmalOpens,\n",
    "       corr(webFormsFilled, advertisingClicks) as webForms_advertisingClicks,\n",
    "       corr(webFormsFilled, productViews) as webForms_productViews,\n",
    "       corr(webFormsFilled, productPurchases) as webForms_productPurchases,\n",
    "       corr(webFormsFilled, propositionDismisses) as webForms_propositionDismisses,\n",
    "       corr(webFormsFilled, propositionInteracts) as webForms_propositionInteracts,\n",
    "       corr(webFormsFilled, emailClicks) as webForms_emailClicks,\n",
    "       corr(webFormsFilled, emailOpens) as webForms_emailOpens,\n",
    "       corr(webFormsFilled, emailSends) as webForms_emailSends,\n",
    "       corr(webFormsFilled, webLinkClicks) as webForms_webLinkClicks,\n",
    "       corr(webFormsFilled, webPageViews) as webForms_webPageViews\n",
    "FROM(\n",
    "    SELECT _{tenant_id}.userid as userID,\n",
    "            SUM(CASE WHEN eventType='web.formFilledOut' THEN 1 ELSE 0 END) as webFormsFilled,\n",
    "            SUM(CASE WHEN eventType='advertising.clicks' THEN 1 ELSE 0 END) as advertisingClicks,\n",
    "            SUM(CASE WHEN eventType='commerce.productViews' THEN 1 ELSE 0 END) as productViews,\n",
    "            SUM(CASE WHEN eventType='commerce.productPurchases' THEN 1 ELSE 0 END) as productPurchases,\n",
    "            SUM(CASE WHEN eventType='decisioning.propositionDismiss' THEN 1 ELSE 0 END) as propositionDismisses,\n",
    "            SUM(CASE WHEN eventType='decisioning.propositionDisplay' THEN 1 ELSE 0 END) as propositionDisplays,\n",
    "            SUM(CASE WHEN eventType='decisioning.propositionInteract' THEN 1 ELSE 0 END) as propositionInteracts,\n",
    "            SUM(CASE WHEN eventType='directMarketing.emailClicked' THEN 1 ELSE 0 END) as emailClicks,\n",
    "            SUM(CASE WHEN eventType='directMarketing.emailOpened' THEN 1 ELSE 0 END) as emailOpens,\n",
    "            SUM(CASE WHEN eventType='directMarketing.emailSent' THEN 1 ELSE 0 END) as emailSends,\n",
    "            SUM(CASE WHEN eventType='web.webinteraction.linkClicks' THEN 1 ELSE 0 END) as webLinkClicks,\n",
    "            SUM(CASE WHEN eventType='web.webinteraction.pageViews' THEN 1 ELSE 0 END) as webPageViews\n",
    "    FROM {table_name}\n",
    "    GROUP BY userId\n",
    ")\n",
    "'''\n",
    "large_correlation_df = qs_cursor.query(large_correlation_query, output=\"dataframe\")\n",
    "large_correlation_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "application/vnd.databricks.v1+cell": {
     "cellMetadata": {},
     "inputWidgets": {},
     "nuid": "90daab0e-20f3-4557-a344-a604c79ba745",
     "showTitle": false,
     "title": ""
    },
    "gather": {
     "logged": 1667866445864
    }
   },
   "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>variable</th>\n",
       "      <th>value</th>\n",
       "      <th>feature</th>\n",
       "      <th>pearsonCorrelation</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>webForms_EmalOpens</td>\n",
       "      <td>0.094207</td>\n",
       "      <td>EmalOpens</td>\n",
       "      <td>0.094207</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>webForms_advertisingClicks</td>\n",
       "      <td>-0.006571</td>\n",
       "      <td>advertisingClicks</td>\n",
       "      <td>-0.006571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>webForms_productViews</td>\n",
       "      <td>0.228957</td>\n",
       "      <td>productViews</td>\n",
       "      <td>0.228957</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>webForms_productPurchases</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>productPurchases</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>webForms_propositionDismisses</td>\n",
       "      <td>0.049756</td>\n",
       "      <td>propositionDismisses</td>\n",
       "      <td>0.049756</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>webForms_propositionInteracts</td>\n",
       "      <td>0.132518</td>\n",
       "      <td>propositionInteracts</td>\n",
       "      <td>0.132518</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>webForms_emailClicks</td>\n",
       "      <td>0.246498</td>\n",
       "      <td>emailClicks</td>\n",
       "      <td>0.246498</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>webForms_emailOpens</td>\n",
       "      <td>0.094207</td>\n",
       "      <td>emailOpens</td>\n",
       "      <td>0.094207</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>webForms_emailSends</td>\n",
       "      <td>0.027688</td>\n",
       "      <td>emailSends</td>\n",
       "      <td>0.027688</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>webForms_webLinkClicks</td>\n",
       "      <td>0.218010</td>\n",
       "      <td>webLinkClicks</td>\n",
       "      <td>0.218010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>webForms_webPageViews</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>webPageViews</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         variable     value               feature  \\\n",
       "0              webForms_EmalOpens  0.094207             EmalOpens   \n",
       "1      webForms_advertisingClicks -0.006571     advertisingClicks   \n",
       "2           webForms_productViews  0.228957          productViews   \n",
       "3       webForms_productPurchases  0.000000      productPurchases   \n",
       "4   webForms_propositionDismisses  0.049756  propositionDismisses   \n",
       "5   webForms_propositionInteracts  0.132518  propositionInteracts   \n",
       "6            webForms_emailClicks  0.246498           emailClicks   \n",
       "7             webForms_emailOpens  0.094207            emailOpens   \n",
       "8             webForms_emailSends  0.027688            emailSends   \n",
       "9          webForms_webLinkClicks  0.218010         webLinkClicks   \n",
       "10          webForms_webPageViews  0.000000          webPageViews   \n",
       "\n",
       "    pearsonCorrelation  \n",
       "0             0.094207  \n",
       "1            -0.006571  \n",
       "2             0.228957  \n",
       "3             0.000000  \n",
       "4             0.049756  \n",
       "5             0.132518  \n",
       "6             0.246498  \n",
       "7             0.094207  \n",
       "8             0.027688  \n",
       "9             0.218010  \n",
       "10            0.000000  "
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cols = large_correlation_df.columns\n",
    "corrdf = large_correlation_df[[col for col in cols if (\"webForms_\"  in col)]].melt()\n",
    "corrdf[\"feature\"] = corrdf[\"variable\"].apply(lambda x: x.replace(\"webForms_\", \"\"))\n",
    "corrdf[\"pearsonCorrelation\"] = corrdf[\"value\"]\n",
    "\n",
    "corrdf.fillna(0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "application/vnd.databricks.v1+cell": {
     "cellMetadata": {},
     "inputWidgets": {},
     "nuid": "040f70cd-2df9-4b05-bd4f-64bafeb5b541",
     "showTitle": false,
     "title": ""
    }
   },
   "source": [
    "Let's visualize the results:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "application/vnd.databricks.v1+cell": {
     "cellMetadata": {},
     "inputWidgets": {},
     "nuid": "4cef1e7c-df03-4060-b4e4-2673194810ae",
     "showTitle": false,
     "title": ""
    },
    "gather": {
     "logged": 1667866449127
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Pearson Correlation of Events with the outcome event')"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 500x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "fig, ax = plt.subplots(figsize=(5,10))\n",
    "sns.barplot(data=corrdf.fillna(0), y=\"feature\", x=\"pearsonCorrelation\")\n",
    "ax.set_title(\"Pearson Correlation of Events with the outcome event\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "application/vnd.databricks.v1+cell": {
     "cellMetadata": {},
     "inputWidgets": {},
     "nuid": "277dceed-66c7-4b34-9f57-90334de32a00",
     "showTitle": false,
     "title": ""
    }
   },
   "source": [
    "<div class=\"alert alert-block alert-success\">\n",
    "    <b>Conclusion</b>\n",
    "With this information, we now have a hypothesis on necessary features to use in our model.\n",
    "We will use the number of these various event types, as well as the recency of each event type as features \n",
    "for the model. The next step is to create these \"featurized\" datasets\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "## 4.4 Saving the synthetic dataset to the configuration"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now that we got everything working, we just need to save the `dataset_id` variable in the original configuration file, so we can refer to it in the following weekly assignments. To do that, execute the code below:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "config.set(\"Platform\", \"dataset_id\", created_dataset_id)\n",
    "\n",
    "with open(config_path, \"w\") as configfile:\n",
    "    config.write(configfile)"
   ]
  }
 ],
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