UseCases/Telco_5G_QOE_Analysis/Telco_5G_QOE_Analysis_SQL.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-06-23T06:50:45.180615Z",
     "start_time": "2023-06-23T06:50:45.160411Z"
    }
   },
   "source": [
    "<header>\n",
    "   <p  style='font-size:36px;font-family:Arial; color:#F0F0F0; background-color: #00233c; padding-left: 20pt; padding-top: 20pt;padding-bottom: 10pt; padding-right: 20pt;'>\n",
    "       5G Quality of Experience using ClearScape Analytics\n",
    "  <br>\n",
    "       <img id=\"teradata-logo\" src=\"https://storage.googleapis.com/clearscape_analytics_demo_data/DEMO_Logo/teradata.svg\" alt=\"Teradata\" style=\"width: 125px; height: auto; margin-top: 20pt;\">\n",
    "    </p>\n",
    "</header>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-06-23T06:50:45.180615Z",
     "start_time": "2023-06-23T06:50:45.160411Z"
    }
   },
   "source": [
    "<p style = 'font-size:20px;font-family:Arial;color:#00233C'><b>Introduction</b></p>\n",
    "    \n",
    "<p style = 'font-size:16px;font-family:Arial;color:#00233C'>Due to the exponential increase in 5G cellular network users, providing extensive coverage and ensuring maximum Quality of Experience (QoE) has become a significant challenge for global telecommunications. The demand for high-speed connectivity, low latency, and massive data transfer poses obstacles in delivering seamless 5G coverage across diverse geographical areas. Furthermore, transitioning from 4G/LTE to 5G technology introduces complexities in terms of compatibility and network integration. Addressing these challenges is essential to meet the evolving expectations of end-users and ensure optimal QoE.<br>\n",
    "To tackle these challenges, an analysis of Key Performance Indicators (KPIs) and channel level metrics is conducted to understand their impact on user's perceived QoE. Moreover, we gain valuable insights by investigating YouTube streaming QoE, KPIs, using commercial 4G and 5G cellular networks under different mobility and static scenarios. The demo shows the power of Vantage by utilizing its In-DB analytics capabilities to address a critical challenge in telecommunication industry: <br>Predicting quality of experience (QoE) in 5G networks."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-06-23T06:50:45.180615Z",
     "start_time": "2023-06-23T06:50:45.160411Z"
    }
   },
   "source": [
    "<hr style=\"height:2px;border:none;background-color:#00233C;\">\n",
    "<p style = 'font-size:20px;font-family:Arial;color:#00233C'><b>1. Connect to Vantage and explore the dataset</b></p>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p style = 'font-size:16px;font-family:Arial;color:#00233C'>After executing this cell you will be prompted to provide the password. Enter your password, press the Enter key, then <b>use down arrow</b> to go to next cell.</p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%connect local, hidewarnings=true"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "Set query_band='DEMO=Telco_5G_QOE_Analysis_SQL.ipynb;' update for session;"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p style = 'font-size:18px;font-family:Arial;color:#00233C'><b>Getting Data for This Demo</b></p>\n",
    "<p style = 'font-size:16px;font-family:Arial;color:#00233C'>    \n",
    "We have provided data for this demo on cloud storage. You have the option of either running the demo using foreign tables to access the data without using any storage on your environment or downloading the data to local storage which may yield somewhat faster execution, but will consume some of your available storage.\n",
    "<br> There are two statements in the following cell -- one is commented out. You may switch the location of your data's storage by changing the comment string.</p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "call get_data('DEMO_5G_cloud');    -- takes about 20 seconds, estimated space: 0 MB\n",
    "--call get_data('DEMO_5G_local');     -- takes about 40 seconds, estimated space: 1MB"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p style = 'font-size:16px;font-family:Arial;color:#00233C'>Next is an optional step – if you want to see status of databases/tables created and space used.</p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "call space_report();  -- optional, takes about 10 seconds"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<hr style=\"height:2px;border:none;background-color:#00233C;\">\n",
    "<p style = 'font-size:20px;font-family:Arial;color:#00233C'><b>2. Exploring source data</b></p>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-06-23T06:50:45.180615Z",
     "start_time": "2023-06-23T06:50:45.160411Z"
    }
   },
   "source": [
    "<p style = 'font-size:16px;font-family:Arial;color:#00233C'> <b>5G Quality of Experience Data <br><br>CLM</b>: Refers to measurements and indicators that provide information about the quality and characteristics of the wireless communication channel between a base station (eNodeB/gNB) and a user equipment (UE) device. These metrics help in assessing the channel conditions and performance of the wireless link. Channel level metrics with 1-second granularity collected using Gnetrack Pro under different scenarios includes CQI, SNR, RSRP, RSRQ, Download & Upload bitrate, Cellid, etc. <br><b>QoE</b>: YouTube Quality of Experience information collected after every 1-second, i.e., Current quality, Video bytes downloaded, Loaded percentage. <br><b>Events</b>: YouTube player events, i.e., Stall, Quality Shift, Paused, Played, Ended<br></p>"
   ]
  },
  {
   "attachments": {
    "Dataset%20Desc%20Vr2.png": {
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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![Dataset%20Desc%20Vr2.png](attachment:Dataset%20Desc%20Vr2.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-07-20T07:35:42.525884Z",
     "start_time": "2023-07-20T07:35:42.178395Z"
    }
   },
   "source": [
    "<p style = 'font-size:16px;font-family:Arial;color:#00233C'><b>Data Collection</b>The KPIs (CQI, SNR, RSRP, RSRQ) etc on 4G and 5G cell networks were collected for different scenarios. The channel level metrics were captured using G Network Pro which is a wireless network monitor. Using both android and iphone the youtube video was played, paused and queued. The buffering information and download bit rate was also captured. Four experiments that were conducted were mobility, pedestrian user that was walking outdoor and indoor sitting.</p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-07-20T07:35:42.525884Z",
     "start_time": "2023-07-20T07:35:42.178395Z"
    }
   },
   "outputs": [],
   "source": [
    "select top 10 * from Demo_5G.YouTube_QoE;"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-07-20T07:35:45.168021Z",
     "start_time": "2023-07-20T07:35:44.827965Z"
    }
   },
   "outputs": [],
   "source": [
    "select top 10 * from Demo_5G.YouTube_Player_Events;"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-07-20T07:35:47.076982Z",
     "start_time": "2023-07-20T07:35:46.718894Z"
    }
   },
   "outputs": [],
   "source": [
    "select top 10 * from Demo_5G.Channel_Level_Metrics;"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "<hr style=\"height:2px;border:none;background-color:#00233C;\">\n",
    "<p style = 'font-size:20px;font-family:Arial;color:#00233C'><b>3. Data Preparation</b></p>\n",
    "<p style = 'font-size:16px;font-family:Arial;color:#00233C'>Let us first create a combined table of Youtube QoE and Channel level metrics.</p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-07-17T17:17:31.743773Z",
     "start_time": "2023-07-17T17:17:31.159048Z"
    },
    "hidden": true
   },
   "outputs": [],
   "source": [
    "CREATE TABLE CLM_QOE AS (\n",
    "    SELECT \n",
    "        QoE.\"Times\" AS QoE_Time, \n",
    "        QoE.\"Dates\" AS QoE_Date, \n",
    "        QoE.\"Eid\" AS  Eid, \n",
    "        QoE.\"Video_Bytes_Downloaded\",\n",
    "        QoE.\"Video\",\n",
    "        QoE.\"Quality\",\n",
    "        QoE.\"Loaded_Percentage\",\n",
    "        CLM.\"Longitude\",\n",
    "        CLM.\"Latitude\",\n",
    "        CLM.\"NetworkTech\",\n",
    "        CLM.\"NetworkMode\",\n",
    "        CLM.\"Level\",\n",
    "        CLM.\"Qual\",\n",
    "        CLM.\"SNR\",\n",
    "        CLM.\"CQI\",\n",
    "        CLM.\"LTERSSI\",\n",
    "        CLM.\"DL_bitrate\",\n",
    "        CLM.\"UL_bitrate\",\n",
    "        CLM.\"Altitude\",\n",
    "        CLM.\"Height\",\n",
    "        CLM.\"State\",\n",
    "        CLM.\"Event\",\n",
    "        CLM.\"SecondCell_RSRP\",\n",
    "        CLM.\"SecondCell_RSRQ\",\n",
    "        CLM.\"SecondCell_SNR\",\n",
    "        CLM.\"Experiment_Type\",    \n",
    "        QoE.\"Dates\" || ' ' ||QoE.\"Times\" AS DateTime\n",
    "    FROM Demo_5G.YouTube_QoE QoE\n",
    "    INNER JOIN Demo_5G.Channel_Level_Metrics CLM\n",
    "        ON QoE.\"Times\" = CLM.\"Times\"\n",
    "        AND QoE.\"Dates\" = CLM.\"Dates\"\n",
    "        AND QoE.\"Eid\" = CLM.\"Eid\"\n",
    ") WITH DATA NO PRIMARY INDEX;"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-07-20T07:37:35.765210Z",
     "start_time": "2023-07-20T07:37:34.692683Z"
    },
    "hidden": true
   },
   "outputs": [],
   "source": [
    "select top 10 * from CLM_QOE;"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-07-17T17:24:36.857721Z",
     "start_time": "2023-07-17T17:24:36.502848Z"
    },
    "hidden": true
   },
   "outputs": [],
   "source": [
    "SELECT COUNT(*) AS total_rows FROM CLM_QOE;"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "<hr style=\"height:2px;border:none;background-color:#00233C;\">\n",
    "<p style = 'font-size:20px;font-family:Arial;color:#00233C'><b>4. Data Exploration</b></p> "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "<p style = 'font-size:18px;font-family:Arial;color:#00233C'><b>TD_CategoricalSummary</b></p>\n",
    "<p style = 'font-size:16px;font-family:Arial;color:#00233C'><b>TD_CATEGORICALSUMMARY </b>function   displays the distinct values and their counts for each specified input table column</p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-06-23T13:57:08.469790Z",
     "start_time": "2023-06-23T13:57:07.312559Z"
    },
    "hidden": true,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "SELECT * FROM TD_CategoricalSummary (\n",
    "ON CLM_QOE AS InputTable\n",
    "USING\n",
    "    TargetColumns ('Experiment_Type','NetworkTech','NetworkMode','Event','State','Eid') \n",
    ") AS dt;"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "<p style = 'font-size:18px;font-family:Arial;color:#00233C'><b>TD_ColumnSummary</b></p>\n",
    "<p style = 'font-size:16px;font-family:Arial;color:#00233C'><b>TD_ColumnSummary </b>function  displays Column name, datatype and other demographics like count of NULLs etc for each specified input table column</p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-06-23T14:04:02.110162Z",
     "start_time": "2023-06-23T14:04:01.155146Z"
    },
    "hidden": true,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "SELECT * FROM TD_ColumnSummary (\n",
    "  ON CLM_QOE AS InputTable\n",
    "  USING\n",
    "  TargetColumns ('Longitude','Latitude','LTERSSI','Level')\n",
    ") AS dt;"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<hr style=\"height:1px;border:none;background-color:#00233C;\">\n",
    "<p style = 'font-size:18px;font-family:Arial;color:#00233C'><b>4.1 Exploratory Data Analysis using TD_Plot</b></p> "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p style = 'font-size:16px;font-family:Arial;color:#00233C'><b>TD_Plot</b> provides the ability to generate charts. The generated charts can be in the JPG, PNG, or SVG formats. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p style = 'font-size:18px;font-family:Arial;color:#00233C'><b>\n",
    "    Bar Chart - Video Quality Distribution</b></p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-07-14T21:18:47.082440Z",
     "start_time": "2023-07-14T21:18:32.665310Z"
    },
    "hidden": true
   },
   "outputs": [],
   "source": [
    "CREATE MULTISET VOLATILE TABLE BAR_Graph_Videos_4G AS (\n",
    "    SELECT Video, Quality,Video_Bytes_Downloaded,\n",
    "        AVG(Loaded_Percentage) OVER (PARTITION BY Quality) AS Average_Loaded_Percentage, \n",
    "        Eid, DateTime,\n",
    "        CASE \n",
    "            WHEN Quality = 'hd2160' THEN 1\n",
    "            WHEN Quality = 'hd1440' THEN 2\n",
    "            WHEN Quality = 'hd1080' THEN 3\n",
    "            WHEN Quality = 'hd720' THEN 4\n",
    "            WHEN Quality IN ('unknown', 'medium', 'large') THEN 5\n",
    "        END AS Quality_Code,\n",
    "        1 AS series\n",
    "    FROM CLM_QOE\n",
    "    WHERE Eid LIKE '4%'\n",
    "    GROUP BY Video, Quality, Video_Bytes_Downloaded, Loaded_Percentage, Eid, DateTime\n",
    ") WITH DATA\n",
    "On Commit Preserve Rows;"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p style = 'font-size:16px;font-family:Arial;color:#00233C'>In table BAR_Graph_Videos_4G we have the metrics for 4G videos</p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-07-14T21:21:18.044056Z",
     "start_time": "2023-07-14T21:21:06.803069Z"
    },
    "hidden": true
   },
   "outputs": [],
   "source": [
    "CREATE MULTISET VOLATILE TABLE BAR_Graph_Videos_5G AS (\n",
    "    SELECT Video, Quality,Video_Bytes_Downloaded, \n",
    "        AVG(Loaded_Percentage) OVER (PARTITION BY Quality) AS Average_Loaded_Percentage, \n",
    "        Eid, DateTime,\n",
    "        CASE \n",
    "            WHEN Quality = 'hd2160' THEN 1\n",
    "            WHEN Quality = 'hd1440' THEN 2\n",
    "            WHEN Quality = 'hd1080' THEN 3\n",
    "            WHEN Quality = 'hd720' THEN 4\n",
    "            WHEN Quality IN ('unknown', 'medium', 'large') THEN 5\n",
    "        END AS Quality_Code,\n",
    "        1 AS series\n",
    "    FROM CLM_QOE\n",
    "    WHERE Eid LIKE '5%'\n",
    "    GROUP BY Video, Quality, Video_Bytes_Downloaded,Loaded_Percentage, Eid, DateTime\n",
    ") WITH DATA\n",
    "On Commit Preserve Rows;\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p style = 'font-size:16px;font-family:Arial;color:#00233C'>In table BAR_Graph_Videos_5G we have the metrics for 5G videos</p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-07-18T08:18:59.077711Z",
     "start_time": "2023-07-18T08:18:57.663481Z"
    },
    "hidden": true
   },
   "outputs": [],
   "source": [
    "EXECUTE FUNCTION INTO VOLATILE ART(barGraph)\n",
    "TD_Plot\n",
    "(\n",
    "    SERIES_SPEC\n",
    "    (\n",
    "        TABLE_NAME(BAR_Graph_Videos_4G),\n",
    "        ROW_AXIS(SEQUENCE(Quality_Code)),\n",
    "        SERIES_ID(Quality),\n",
    "        PAYLOAD\n",
    "        (\n",
    "           FIELDS(\"Average_Loaded_Percentage\"),\n",
    "           CONTENT(REAL)\n",
    "        )\n",
    "    ) where Quality_Code IN (1,2,3,4),\n",
    "    \n",
    "    \n",
    "    SERIES_SPEC\n",
    "    (\n",
    "        TABLE_NAME(BAR_Graph_Videos_5G),\n",
    "        ROW_AXIS(SEQUENCE(Quality_Code)),\n",
    "        SERIES_ID(Quality),\n",
    "        PAYLOAD\n",
    "        (\n",
    "           FIELDS(\"Average_Loaded_Percentage\"),\n",
    "           CONTENT(REAL)\n",
    "        )\n",
    "    ) where Quality_Code IN (1,2,3,4),\n",
    "    \n",
    "    \n",
    "    \n",
    "    FUNC_PARAMS\n",
    "    (\n",
    "        LAYOUT(2,1),\n",
    "        WIDTH(900),\n",
    "        HEIGHT(600),\n",
    "        TITLE('Video Quality Distribution'),\n",
    "        PLOTS[\n",
    "            (\n",
    "                ID(1),\n",
    "                CELL(1,1),\n",
    "                TITLE ('Video Quality Distribution on 4G Smartphone'),\n",
    "                TYPE('bar'),\n",
    "                LEGEND('best'),\n",
    "                XLABEL('Quality Threshold'),\n",
    "                YLABEL('Average_Loaded_Percentage')\n",
    "            ),\n",
    "            \n",
    "             (\n",
    "                ID(2),\n",
    "                CELL(2,1),\n",
    "                TITLE ('Video Quality Distribution on 5G Smartphone'),\n",
    "                TYPE('bar'),\n",
    "                LEGEND('best'),\n",
    "                XLABEL('Quality Threshold'),\n",
    "                YLABEL('Average_Loaded_Percentage')\n",
    "            )\n",
    "        ]\n",
    "    )\n",
    ");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-07-18T08:19:01.452807Z",
     "start_time": "2023-07-18T08:19:00.780940Z"
    },
    "hidden": true,
    "tags": []
   },
   "outputs": [],
   "source": [
    "Select * from barGraph;"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p style = 'font-size:16px;font-family:Arial;color:#00233C'> <b><i>* Click on the cell in the IMAGE column to select it, then right click on the cell and select View Image.</b></i> <br>The image should look like below.</p>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<center><img src=\"images/bargraph.png\"/></center>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-07-14T21:46:38.831942Z",
     "start_time": "2023-07-14T21:46:38.808716Z"
    },
    "hidden": true
   },
   "source": [
    "<p style = 'font-size:16px;font-family:Arial;color:#00233C'>From the above Bar Chart we can infer that</p>\n",
    "<ul style = 'font-size:16px;font-family:Arial;color:#00233C'>\n",
    "<li> hd1440 consistently shows higher average loaded percentages on both 4G and 5G smartphones. </li>\n",
    "<li> hd1440 provides a better user experience in terms of loading time compared to other quality categories on both networks.</li>\n",
    "<li> These findings also suggest that network performance can vary for different video quality categories on different networks.</li> \n",
    "    <li> Optimal quality choices may depend on the smartphone's network capabilities.</li></ul>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-07-09T18:13:58.874618Z",
     "start_time": "2023-07-09T18:13:58.871672Z"
    },
    "heading_collapsed": true
   },
   "source": [
    "<p style = 'font-size:18px;font-family:Arial;color:#00233C'><b>Scatter Plot - 5G Vs 4G Mobility and Indoor Stalling Events</b></p>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p style = 'font-size:16px;font-family:Arial;color:#00233C'> Let us create subsets of 4G & 5G data for our analysis</p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-07-15T13:11:59.411028Z",
     "start_time": "2023-07-15T13:11:59.034779Z"
    },
    "hidden": true
   },
   "outputs": [],
   "source": [
    "CREATE MULTISET VOLATILE TABLE \"5g_time_stall_Mobility\" AS (\n",
    "    SELECT Video, Quality, Status, TimeStall, Eid, Dates,\n",
    "           CAST(TO_TIMESTAMP(Dates || ' ' || Times, 'DD/MM/YYYY HH24:MI:SS') AS TIMESTAMP(6)) AS Date_Time, Category\n",
    "    FROM Demo_5G.YouTube_Player_Events\n",
    "    WHERE SUBSTRING(Eid FROM 1 FOR 1) = '5'\n",
    "    AND SUBSTRING(Eid FROM 2 FOR 1) = 'M'\n",
    "    AND Quality IN ('hd2160,', 'hd1080,', 'hd1440,', 'hd720,')\n",
    ") WITH DATA\n",
    "On Commit Preserve Rows;"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-07-15T13:12:08.779989Z",
     "start_time": "2023-07-15T13:12:08.272557Z"
    },
    "hidden": true
   },
   "outputs": [],
   "source": [
    "CREATE MULTISET VOLATILE TABLE \"4g_time_stall_Mobility\" AS (\n",
    "    SELECT Video, Quality, Status, TimeStall, Eid, Dates,\n",
    "           CAST(TO_TIMESTAMP(Dates || ' ' || Times, 'DD/MM/YYYY HH24:MI:SS') AS TIMESTAMP(6)) AS Date_Time, Category\n",
    "    FROM Demo_5G.YouTube_Player_Events\n",
    "    WHERE SUBSTRING(Eid FROM 1 FOR 1) = '4'\n",
    "    AND SUBSTRING(Eid FROM 2 FOR 1) = 'M'\n",
    "    AND Quality IN ('hd2160,', 'hd1080,', 'hd1440,', 'hd720,')\n",
    ") WITH DATA               \n",
    "On Commit Preserve Rows;"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-07-15T13:12:18.741058Z",
     "start_time": "2023-07-15T13:12:18.358739Z"
    },
    "hidden": true
   },
   "outputs": [],
   "source": [
    "CREATE MULTISET VOLATILE TABLE \"4g_time_stall_Indoor\" AS (\n",
    "    SELECT Video, Quality, Status, TimeStall, Eid, Dates,\n",
    "           CAST(TO_TIMESTAMP(Dates || ' ' || Times, 'DD/MM/YYYY HH24:MI:SS') AS TIMESTAMP(6)) AS Date_Time, Category\n",
    "    FROM Demo_5G.YouTube_Player_Events\n",
    "    WHERE SUBSTRING(Eid FROM 1 FOR 1) = '4'\n",
    "    AND SUBSTRING(Eid FROM 2 FOR 1) = 'I'\n",
    "    AND Quality IN ('hd2160,', 'hd1080,', 'hd1440,', 'hd720,')\n",
    ") WITH DATA\n",
    "On Commit Preserve Rows;"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-07-15T13:12:28.772038Z",
     "start_time": "2023-07-15T13:12:28.419119Z"
    },
    "hidden": true
   },
   "outputs": [],
   "source": [
    "CREATE MULTISET VOLATILE TABLE \"5g_time_stall_Indoor\" AS (\n",
    "    SELECT Video, Quality, Status, TimeStall, Eid, Dates,\n",
    "           CAST(TO_TIMESTAMP(Dates || ' ' || Times, 'DD/MM/YYYY HH24:MI:SS') AS TIMESTAMP(6)) AS Date_Time, Category\n",
    "    FROM Demo_5G.YouTube_Player_Events\n",
    "    WHERE SUBSTRING(Eid FROM 1 FOR 1) = '5'\n",
    "    AND SUBSTRING(Eid FROM 2 FOR 1) = 'I'\n",
    "    AND Quality IN ('hd2160,', 'hd1080,', 'hd1440,', 'hd720,')\n",
    ") WITH DATA\n",
    "On Commit Preserve Rows;"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p style = 'font-size:16px;font-family:Arial;color:#00233C'>Let us check if our timeseries is regular or irregular series.<br>Irregular time series are data sequences where the observations or data points are not uniformly spaced in time. This may happen due to missing data or uneven data collection."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "WITH time_diffs AS (\n",
    "  SELECT\n",
    "    Eid,\n",
    "    MIN(Times) AS Min_Times,\n",
    "    MAX(Times) AS Max_Times,\n",
    "    Max_Times - LAG(Max_Times) OVER (ORDER BY Max_Times) AS Time_Diff\n",
    "  FROM Demo_5G.YouTube_Player_Events\n",
    "  GROUP BY Eid\n",
    ")\n",
    "SELECT\n",
    "  CASE\n",
    "    WHEN COUNT(DISTINCT Time_Diff) > 1 THEN 'Irregular'\n",
    "    ELSE 'Regular' \n",
    "  END AS Time_Series_Type\n",
    "FROM time_diffs;"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p style = 'font-size:16px;font-family:Arial;color:#00233C'> Our series is irregular, first let us make the series regular by resampling our data."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-07-15T12:13:47.169701Z",
     "start_time": "2023-07-15T12:13:47.167023Z"
    },
    "hidden": true
   },
   "source": [
    "<p style = 'font-size:18px;font-family:Arial;color:#00233C'><b>TD_Resample</b></p>\n",
    "<p style = 'font-size:16px;font-family:Arial;color:#00233C'>\n",
    "    <b>TD_Resample</b> function Transforms converts irregular time series into a regular time series and performs linear interpolation.TD_Resample can also be used to alter the sampling interval for a time series.<br>Finding the minimum date, so that we can re-sample the dates from this date onwards which will convert irregular time series into a regular time series."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-07-15T13:16:16.541361Z",
     "start_time": "2023-07-15T13:16:16.198630Z"
    },
    "hidden": true
   },
   "outputs": [],
   "source": [
    "SELECT MIN(Date_Time) AS Min_Date,\n",
    "       MAX(Date_Time) AS Max_Date,\n",
    "       COUNT(*) AS Total_Count,\n",
    "       COUNT(DISTINCT Date_Time) AS Unique_Count,\n",
    "       CASE\n",
    "           WHEN COUNT(*) <> COUNT(DISTINCT Date_Time) THEN 'Repetitions Detected'\n",
    "           ELSE 'No Repetitions Found'\n",
    "       END AS Repetitions_Status\n",
    "FROM \"5g_time_stall_Mobility\";\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-07-15T13:01:33.181003Z",
     "start_time": "2023-07-15T13:01:32.832447Z"
    },
    "hidden": true
   },
   "outputs": [],
   "source": [
    "SELECT MIN(Date_Time) AS Min_Date,\n",
    "       MAX(Date_Time) AS Max_Date,\n",
    "       COUNT(*) AS Total_Count,\n",
    "       COUNT(DISTINCT Date_Time) AS Unique_Count,\n",
    "       CASE\n",
    "           WHEN COUNT(*) <> COUNT(DISTINCT Date_Time) THEN 'Repetitions Detected'\n",
    "           ELSE 'No Repetitions Found'\n",
    "       END AS Repetitions_Status\n",
    "FROM \"4g_time_stall_Mobility\";\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-07-15T13:12:39.744808Z",
     "start_time": "2023-07-15T13:12:39.407574Z"
    },
    "hidden": true
   },
   "outputs": [],
   "source": [
    "SELECT MIN(Date_Time) AS Min_Date,\n",
    "       MAX(Date_Time) AS Max_Date,\n",
    "       COUNT(*) AS Total_Count,\n",
    "       COUNT(DISTINCT Date_Time) AS Unique_Count,\n",
    "       CASE\n",
    "           WHEN COUNT(*) <> COUNT(DISTINCT Date_Time) THEN 'Repetitions Detected'\n",
    "           ELSE 'No Repetitions Found'\n",
    "       END AS Repetitions_Status\n",
    "FROM \"5g_time_stall_Indoor\";\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-07-15T13:12:40.697532Z",
     "start_time": "2023-07-15T13:12:40.360491Z"
    },
    "hidden": true
   },
   "outputs": [],
   "source": [
    "SELECT MIN(Date_Time) AS Min_Date,\n",
    "       MAX(Date_Time) AS Max_Date,\n",
    "       COUNT(*) AS Total_Count,\n",
    "       COUNT(DISTINCT Date_Time) AS Unique_Count,\n",
    "       CASE\n",
    "           WHEN COUNT(*) <> COUNT(DISTINCT Date_Time) THEN 'Repetitions Detected'\n",
    "           ELSE 'No Repetitions Found'\n",
    "       END AS Repetitions_Status\n",
    "FROM \"4g_time_stall_Indoor\";\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "<p style = 'font-size:16px;font-family:Arial;color:#00233C'>Using the resample function to convert irregular time series into a regular time series "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-07-17T07:18:34.702771Z",
     "start_time": "2023-07-17T07:18:24.062271Z"
    },
    "hidden": true
   },
   "outputs": [],
   "source": [
    "EXECUTE FUNCTION INTO VOLATILE ART(\"4g_resampled_time_stall_Mobility\")\n",
    "\n",
    "TD_RESAMPLE(\n",
    "    SERIES_SPEC(\n",
    "        TABLE_NAME(\"4g_time_stall_Mobility\"),\n",
    "        ROW_AXIS(timecode(date_time)),\n",
    "        SERIES_ID(Quality),\n",
    "        PAYLOAD(FIELDS(TimeStall), CONTENT(REAL)\n",
    "     )\n",
    "    ),\n",
    "    FUNC_PARAMS(\n",
    "        TIMECODE(\n",
    "            START_VALUE(TIMESTAMP '2022-07-09 04:01:34'), \n",
    "            DURATION(minutes(1))\n",
    "        ),\n",
    "        INTERPOLATE(LINEAR)\n",
    "    )\n",
    ");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-07-17T07:18:44.539007Z",
     "start_time": "2023-07-17T07:18:35.442421Z"
    },
    "hidden": true
   },
   "outputs": [],
   "source": [
    "EXECUTE FUNCTION INTO VOLATILE ART(\"5g_resampled_time_stall_Mobility\")\n",
    "\n",
    "TD_RESAMPLE(\n",
    "    SERIES_SPEC(\n",
    "        TABLE_NAME(\"5g_time_stall_Mobility\"),\n",
    "        ROW_AXIS(timecode(date_time)),\n",
    "        SERIES_ID(Quality),\n",
    "        PAYLOAD(FIELDS(TimeStall), CONTENT(REAL)\n",
    "     )\n",
    "    ),\n",
    "    FUNC_PARAMS(\n",
    "        TIMECODE(\n",
    "            START_VALUE(TIMESTAMP '2022-07-09 03:58:12'), \n",
    "            DURATION(minutes(1))\n",
    "        ),\n",
    "        INTERPOLATE(LINEAR)\n",
    "    )\n",
    ");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-07-17T07:18:56.094453Z",
     "start_time": "2023-07-17T07:18:44.542728Z"
    },
    "hidden": true
   },
   "outputs": [],
   "source": [
    "EXECUTE FUNCTION INTO VOLATILE ART(\"5g_resampled_time_stall_Indoor\")\n",
    "\n",
    "TD_RESAMPLE(\n",
    "    SERIES_SPEC(\n",
    "        TABLE_NAME(\"5g_time_stall_Indoor\"),\n",
    "        ROW_AXIS(timecode(date_time)),\n",
    "        SERIES_ID(Quality),\n",
    "        PAYLOAD(FIELDS(TimeStall), CONTENT(REAL)\n",
    "     )\n",
    "    ),\n",
    "    FUNC_PARAMS(\n",
    "        TIMECODE(\n",
    "            START_VALUE(TIMESTAMP '2022-08-09 15:24:44'), \n",
    "            DURATION(minutes(1))\n",
    "        ),\n",
    "        INTERPOLATE(LINEAR)\n",
    "    )\n",
    ");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-07-17T07:19:07.714862Z",
     "start_time": "2023-07-17T07:18:56.096903Z"
    },
    "hidden": true
   },
   "outputs": [],
   "source": [
    "EXECUTE FUNCTION INTO VOLATILE ART(\"4g_resampled_time_stall_Indoor\")\n",
    "\n",
    "TD_RESAMPLE(\n",
    "    SERIES_SPEC(\n",
    "        TABLE_NAME(\"4g_time_stall_Indoor\"),\n",
    "        ROW_AXIS(timecode(date_time)),\n",
    "        SERIES_ID(Quality),\n",
    "        PAYLOAD(FIELDS(TimeStall), CONTENT(REAL)\n",
    "     )\n",
    "    ),\n",
    "    FUNC_PARAMS(\n",
    "        TIMECODE(\n",
    "            START_VALUE(TIMESTAMP '2022-08-09 15:31:35'), \n",
    "            DURATION(minutes(1))\n",
    "        ),\n",
    "        INTERPOLATE(LINEAR)\n",
    "    )\n",
    ");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-07-17T07:22:19.842112Z",
     "start_time": "2023-07-17T07:20:18.055921Z"
    },
    "hidden": true,
    "tags": []
   },
   "outputs": [],
   "source": [
    "EXECUTE FUNCTION INTO VOLATILE ART(scatter_graph_timestall)\n",
    "TD_Plot\n",
    "(\n",
    "    SERIES_SPEC\n",
    "    (\n",
    "        TABLE_NAME(\"4g_resampled_time_stall_Mobility\"),\n",
    "        ROW_AXIS(TIMECODE(ROW_I)),\n",
    "        SERIES_ID(Quality),\n",
    "        PAYLOAD\n",
    "        (\n",
    "           FIELDS(TimeStall),\n",
    "           CONTENT(REAL)\n",
    "        )\n",
    "    ),\n",
    "    \n",
    "    SERIES_SPEC\n",
    "    (\n",
    "        TABLE_NAME(\"5g_resampled_time_stall_Mobility\"),\n",
    "        ROW_AXIS(TIMECODE(ROW_I)),\n",
    "        SERIES_ID(Quality),\n",
    "        PAYLOAD\n",
    "        (\n",
    "           FIELDS(TimeStall),\n",
    "           CONTENT(REAL)\n",
    "        )\n",
    "    ),\n",
    "    \n",
    "\n",
    "     SERIES_SPEC\n",
    "    (\n",
    "        TABLE_NAME(\"4g_resampled_time_stall_Indoor\"),\n",
    "        ROW_AXIS(TIMECODE(ROW_I)),\n",
    "        SERIES_ID(Quality),\n",
    "        PAYLOAD\n",
    "        (\n",
    "           FIELDS(TimeStall),\n",
    "           CONTENT(REAL)\n",
    "        )\n",
    "    ),\n",
    "    \n",
    "    SERIES_SPEC\n",
    "    (\n",
    "        TABLE_NAME(\"5g_resampled_time_stall_Indoor\"),\n",
    "        ROW_AXIS(TIMECODE(ROW_I)),\n",
    "        SERIES_ID(Quality),\n",
    "        PAYLOAD\n",
    "        (\n",
    "           FIELDS(TimeStall),\n",
    "           CONTENT(REAL)\n",
    "        )\n",
    "    ),\n",
    "    \n",
    "    FUNC_PARAMS\n",
    "    (\n",
    "        LAYOUT(4, 1),\n",
    "        WIDTH(1500),\n",
    "        HEIGHT(600),\n",
    "        TITLE('SCATTER GRAPH'),\n",
    "        PLOTS[\n",
    "            (\n",
    "                ID(1),\n",
    "                CELL(1, 1),\n",
    "                TITLE ('4G Stalling on Video Resolutions Mobility'),\n",
    "                TYPE('scatter'),\n",
    "                LEGEND('best'),\n",
    "                XLABEL('TIME SERIES'),\n",
    "                YLABEL('Stalling Time in minutes')\n",
    "            ),\n",
    "            \n",
    "            (\n",
    "                ID(2),\n",
    "                CELL(2, 1),\n",
    "                TITLE ('5G Stalling on Video Resolutions Mobility'),\n",
    "                TYPE('scatter'),\n",
    "                LEGEND('best'),\n",
    "                XLABEL('TIME SERIES'),\n",
    "                YLABEL('Stalling Time in minutes')\n",
    "            ),\n",
    "            \n",
    "            (\n",
    "                ID(3),\n",
    "                CELL(3, 1),\n",
    "                TITLE ('4G Stalling on Video Resolutions Indoor'),\n",
    "                TYPE('scatter'),\n",
    "                LEGEND('best'),\n",
    "                XLABEL('TIME SERIES'),\n",
    "                YLABEL('Stalling Time in minutes')\n",
    "            ),\n",
    "            \n",
    "            (\n",
    "                ID(4),\n",
    "                CELL(4, 1),\n",
    "                TITLE ('5G Stalling on Video Resolutions Indoor'),\n",
    "                TYPE('scatter'),\n",
    "                LEGEND('best'),\n",
    "                XLABEL('TIME SERIES'),\n",
    "                YLABEL('Stalling Time in minutes')\n",
    "            )\n",
    "        ]\n",
    "    )\n",
    ");\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-07-17T07:22:20.728602Z",
     "start_time": "2023-07-17T07:22:20.314538Z"
    },
    "hidden": true
   },
   "outputs": [],
   "source": [
    "select * from scatter_graph_timestall;"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p style = 'font-size:16px;font-family:Arial;color:#00233C'> <b><i>* Click on the cell in the IMAGE column to select it, then right click on the cell and select View Image.</b></i> <br>The image should look like below."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<center><img src=\"images/scatter.png\"/></center>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-07-15T13:52:16.239796Z",
     "start_time": "2023-07-15T13:52:16.226490Z"
    }
   },
   "source": [
    "<p style = 'font-size:16px;font-family:Arial;color:#00233C'>From the above Scatter Plot we can infer that</p>\n",
    "<ul style = 'font-size:16px;font-family:Arial;color:#00233C'>\n",
    "<li> 4G on mobility, hd1440 exhibited the highest stalling duration over time, while hd720 experienced the least stalling. On 5G in mobility, hd2160 had brief but high stalling, while hd1440 showed less stalling but played for longer durations. </li>\n",
    "<li>Shifting to indoor settings on 4G, hd2160 initially had the highest stalling and hd1440 had minimal stalling.</li>\n",
    "<li>Indoor scenario on 5G, hd2160 had persistent stalling, hd1440 had the least stalling, and both hd1080 and hd720 showed minimal interruptions.</li>\n",
    " <li> This indicates that higher video qualities tend to experience more stalling, especially in challenging network conditions, while lower qualities offer smoother playback</li></ul>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "<p style = 'font-size:18px;font-family:Arial;color:#00233C'><b>Line Graph - 5G vs 4G ( CQI, RSRP, RSRQ, SNR)</b></p>\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-07-15T16:59:33.656377Z",
     "start_time": "2023-07-15T16:59:33.173561Z"
    },
    "hidden": true
   },
   "outputs": [],
   "source": [
    "CREATE MULTISET VOLATILE TABLE CLM_5G_CQI AS (\n",
    "  SELECT\n",
    "    CQI,\n",
    "    Experiment_Type,\n",
    "    Experiment_Count,\n",
    "    CASE\n",
    "      WHEN MAX(Experiment_Count_Standardized) OVER (PARTITION BY CQI) = MIN(Experiment_Count_Standardized) OVER (PARTITION BY CQI) THEN 0 -- Handle division by zero\n",
    "      ELSE (Experiment_Count_Standardized - MIN(Experiment_Count_Standardized) OVER (PARTITION BY CQI)) / NULLIF(MAX(Experiment_Count_Standardized) OVER (PARTITION BY CQI) - MIN(Experiment_Count_Standardized) OVER (PARTITION BY CQI), 0)\n",
    "    END AS Experiment_Count_Standardized_Range\n",
    "  FROM (\n",
    "    SELECT\n",
    "      CQI,\n",
    "      Experiment_Type,\n",
    "      Experiment_Count,\n",
    "      COALESCE((Experiment_Count - AVG(Experiment_Count) OVER (PARTITION BY CQI)) / NULLIF(STDDEV_POP(Experiment_Count) OVER (PARTITION BY CQI), 0), 0) AS Experiment_Count_Standardized\n",
    "    FROM (\n",
    "      SELECT\n",
    "        CQI,\n",
    "        Experiment_Type,\n",
    "        COUNT(*) AS Experiment_Count\n",
    "      FROM CLM_QOE\n",
    "      WHERE NetworkTech = '5G'\n",
    "      GROUP BY CQI, Experiment_Type\n",
    "    ) t\n",
    "  ) t2\n",
    ") WITH DATA\n",
    "on commit preserve rows;"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-07-15T16:59:35.735013Z",
     "start_time": "2023-07-15T16:59:35.278378Z"
    },
    "code_folding": [
     3
    ],
    "hidden": true
   },
   "outputs": [],
   "source": [
    "CREATE  MULTISET VOLATILE TABLE CLM_4G_CQI AS (\n",
    "  SELECT\n",
    "    CQI,\n",
    "    Experiment_Type,\n",
    "    Experiment_Count,\n",
    "    CASE\n",
    "      WHEN MAX(Experiment_Count_Standardized) OVER (PARTITION BY CQI) = MIN(Experiment_Count_Standardized) OVER (PARTITION BY CQI) THEN 0 -- Handle division by zero\n",
    "      ELSE (Experiment_Count_Standardized - MIN(Experiment_Count_Standardized) OVER (PARTITION BY CQI)) / NULLIF(MAX(Experiment_Count_Standardized) OVER (PARTITION BY CQI) - MIN(Experiment_Count_Standardized) OVER (PARTITION BY CQI), 0)\n",
    "    END AS Experiment_Count_Standardized_Range\n",
    "  FROM (\n",
    "    SELECT\n",
    "      CQI,\n",
    "      Experiment_Type,\n",
    "      Experiment_Count,\n",
    "      COALESCE((Experiment_Count - AVG(Experiment_Count) OVER (PARTITION BY CQI)) / NULLIF(STDDEV_POP(Experiment_Count) OVER (PARTITION BY CQI), 0), 0) AS Experiment_Count_Standardized\n",
    "    FROM (\n",
    "      SELECT\n",
    "        CQI,\n",
    "        Experiment_Type,\n",
    "        COUNT(*) AS Experiment_Count\n",
    "      FROM CLM_QOE\n",
    "      WHERE NetworkTech = '4G'\n",
    "      GROUP BY CQI, Experiment_Type\n",
    "    ) t\n",
    "  ) t2\n",
    ") WITH DATA\n",
    "on commit preserve rows;"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-07-15T17:53:07.423126Z",
     "start_time": "2023-07-15T17:53:06.951204Z"
    },
    "hidden": true
   },
   "outputs": [],
   "source": [
    "CREATE MULTISET VOLATILE TABLE CLM_5G_Level AS (\n",
    "  SELECT\n",
    "    Level,\n",
    "    Experiment_Type,\n",
    "    Experiment_Count,\n",
    "    CASE\n",
    "      WHEN MAX(Experiment_Count_Standardized) OVER (PARTITION BY Level) = MIN(Experiment_Count_Standardized) OVER (PARTITION BY Level) THEN 0 -- Handle division by zero\n",
    "      ELSE (Experiment_Count_Standardized - MIN(Experiment_Count_Standardized) OVER (PARTITION BY Level)) / NULLIF(MAX(Experiment_Count_Standardized) OVER (PARTITION BY Level) - MIN(Experiment_Count_Standardized) OVER (PARTITION BY Level), 0)\n",
    "    END AS Experiment_Count_Standardized_Range\n",
    "  FROM (\n",
    "    SELECT\n",
    "      Level,\n",
    "      Experiment_Type,\n",
    "      Experiment_Count,\n",
    "      COALESCE((Experiment_Count - AVG(Experiment_Count) OVER (PARTITION BY Level)) / NULLIF(STDDEV_POP(Experiment_Count) OVER (PARTITION BY Level), 0), 0) AS Experiment_Count_Standardized\n",
    "    FROM (\n",
    "      SELECT\n",
    "        Level,\n",
    "        Experiment_Type,\n",
    "        COUNT(*) AS Experiment_Count\n",
    "      FROM CLM_QOE\n",
    "      WHERE NetworkTech = '5G'\n",
    "      GROUP BY Level, Experiment_Type\n",
    "    ) t\n",
    "  ) t2\n",
    ") WITH DATA\n",
    "on commit preserve rows;\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-07-15T17:53:17.521691Z",
     "start_time": "2023-07-15T17:53:17.073833Z"
    },
    "hidden": true
   },
   "outputs": [],
   "source": [
    "CREATE MULTISET VOLATILE TABLE CLM_4G_Level AS (\n",
    "  SELECT\n",
    "    Level,\n",
    "    Experiment_Type,\n",
    "    Experiment_Count,\n",
    "    CASE\n",
    "      WHEN MAX(Experiment_Count_Standardized) OVER (PARTITION BY Level) = MIN(Experiment_Count_Standardized) OVER (PARTITION BY Level) THEN 0 -- Handle division by zero\n",
    "      ELSE (Experiment_Count_Standardized - MIN(Experiment_Count_Standardized) OVER (PARTITION BY Level)) / NULLIF(MAX(Experiment_Count_Standardized) OVER (PARTITION BY Level) - MIN(Experiment_Count_Standardized) OVER (PARTITION BY Level), 0)\n",
    "    END AS Experiment_Count_Standardized_Range\n",
    "  FROM (\n",
    "    SELECT\n",
    "      Level,\n",
    "      Experiment_Type,\n",
    "      Experiment_Count,\n",
    "      COALESCE((Experiment_Count - AVG(Experiment_Count) OVER (PARTITION BY Level)) / NULLIF(STDDEV_POP(Experiment_Count) OVER (PARTITION BY Level), 0), 0) AS Experiment_Count_Standardized\n",
    "    FROM (\n",
    "      SELECT\n",
    "        Level,\n",
    "        Experiment_Type,\n",
    "        COUNT(*) AS Experiment_Count\n",
    "      FROM CLM_QOE\n",
    "      WHERE NetworkTech = '4G'\n",
    "      GROUP BY Level, Experiment_Type\n",
    "    ) t\n",
    "  ) t2\n",
    ") WITH DATA\n",
    "on commit preserve rows;\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-07-15T17:54:05.618185Z",
     "start_time": "2023-07-15T17:54:05.066431Z"
    },
    "hidden": true,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "EXECUTE FUNCTION INTO VOLATILE ART(Benchamrking_CLM)\n",
    "TD_Plot\n",
    "(\n",
    "    SERIES_SPEC\n",
    "    (\n",
    "        TABLE_NAME(CLM_5G_CQI),\n",
    "        ROW_AXIS(SEQUENCE(CQI)),\n",
    "        SERIES_ID(Experiment_Type),\n",
    "        PAYLOAD\n",
    "        (\n",
    "           FIELDS(Experiment_Count_Standardized_Range),\n",
    "           CONTENT(REAL)\n",
    "        )\n",
    "    ),\n",
    "    \n",
    "    SERIES_SPEC\n",
    "    (\n",
    "        TABLE_NAME(CLM_4G_CQI ),\n",
    "        ROW_AXIS(SEQUENCE(CQI)),\n",
    "        SERIES_ID(Experiment_Type),\n",
    "        PAYLOAD\n",
    "        (\n",
    "           FIELDS(Experiment_Count_Standardized_Range),\n",
    "           CONTENT(REAL))\n",
    "    \n",
    "    ),\n",
    "    \n",
    "    SERIES_SPEC\n",
    "    (\n",
    "        TABLE_NAME(CLM_5G_Level ),\n",
    "        ROW_AXIS(SEQUENCE(Level)),\n",
    "        SERIES_ID(Experiment_Type),\n",
    "        PAYLOAD\n",
    "        (\n",
    "           FIELDS(Experiment_Count_Standardized_Range),\n",
    "           CONTENT(REAL))\n",
    "    \n",
    "    ),\n",
    "    SERIES_SPEC\n",
    "    (\n",
    "        TABLE_NAME(CLM_4G_Level ),\n",
    "        ROW_AXIS(SEQUENCE(Level)),\n",
    "        SERIES_ID(Experiment_Type),\n",
    "        PAYLOAD\n",
    "        (\n",
    "           FIELDS(Experiment_Count_Standardized_Range),\n",
    "           CONTENT(REAL))\n",
    "    \n",
    "    ),\n",
    "    \n",
    "    \n",
    "    FUNC_PARAMS\n",
    "    (\n",
    "        LAYOUT(2,2),\n",
    "        WIDTH(1000),\n",
    "        HEIGHT(800),\n",
    "        TITLE('4G Vs 5G Channel Level Metric Performance '),\n",
    "        PLOTS[\n",
    "            (\n",
    "                ID(1),\n",
    "                CELL(1,1),\n",
    "                TYPE('line'),\n",
    "                Title('5G CQI'),\n",
    "                MARKER('o'),\n",
    "                LEGEND('best'),\n",
    "                XLABEL('CQI'),\n",
    "                YLABEL('Frequecny')\n",
    "            ),(\n",
    "                ID(2),\n",
    "                CELL(1,2),\n",
    "                TYPE('line'),\n",
    "                Title('4G CQI'),\n",
    "                MARKER('o'),\n",
    "                LEGEND('best'),\n",
    "                XLABEL('CQI'),\n",
    "                YLABEL('Frequecny')\n",
    "            ),\n",
    "            \n",
    "            (\n",
    "                ID(3),\n",
    "                CELL(2,1),\n",
    "                TYPE('line'),\n",
    "                Title('5G RSRP'),\n",
    "                MARKER('o'),\n",
    "                LEGEND('best'),\n",
    "                XLABEL('RSRP'),\n",
    "                YLABEL('Frequecny')\n",
    "            ),\n",
    "            (\n",
    "                ID(4),\n",
    "                CELL(2,2),\n",
    "                TYPE('line'),\n",
    "                Title('4G RSRP'),\n",
    "                MARKER('o'),\n",
    "                LEGEND('best'),\n",
    "                XLABEL('RSRP'),\n",
    "                YLABEL('Frequecny')\n",
    "            )\n",
    "\n",
    "        ]\n",
    "    )\n",
    ");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-07-15T17:54:08.147336Z",
     "start_time": "2023-07-15T17:54:07.731698Z"
    },
    "hidden": true
   },
   "outputs": [],
   "source": [
    "select * from Benchamrking_CLM;"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "<p style = 'font-size:16px;font-family:Arial;color:#00233C'> <b><i>* Click on the cell in the IMAGE column to select it, then right click on the cell and select View Image.</b></i> <br>The image should look like below."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<center><img src=\"images/line.png\"/></center>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "<p style = 'font-size:16px;font-family:Arial;color:#00233C'>From the above line chart we can infer that\n",
    "<ul style = 'font-size:16px;font-family:Arial;color:#00233C'>\n",
    "<li> The spread-out distribution in the 5G and 4G CQI graphs indicates that the CQI values vary across different experiments, higher values indicating better signal quality.</li>\n",
    "<li>In the case of RSRP, the presence of frequent spikes in both the 5G and 4G RSRP graphs suggests significant fluctuations in signal strength. The spikes indicate instances where the RSRP values deviate from the average or expected levels. Negative values of RSRP indicating relatively weaker signal power.</li>\n",
    "<li>Overall, these observations provide insights into the performance of CQI and RSRP metrics across different experiments and network technologies. The variations and patterns in CQI and RSRP values reflect the changing channel conditions, signal quality, and signal power, which can impact the overall network performance and user experience.</li>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<hr style=\"height:2px;border:none;background-color:#00233C;\">\n",
    "<p style = 'font-size:20px;font-family:Arial;color:#00233C'><b>5. Feature Engineering and Selection</b></p> \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p style = 'font-size:18px;font-family:Arial;color:#00233C'><b>\n",
    "    Target Variable</b></p>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<ul style = 'font-size:16px;font-family:Arial;color:#00233C'>\n",
    "<li>To identify a user's quality of experience using youtube video statistics we derive \"load rate\", a metric can be used to represent how smooth the video is playing. </li>\n",
    "    <li>If the load rate will is low there will be more buffering time and in turn will impact user's overall viewing  experience and vice versa</li>\n",
    "    </ul>\n",
    " <ul style = 'font-size:16px;font-family:Arial;color:#00233C'> Derive Features Include  \n",
    "    <li><b>Load_rate</b>  = loaded percentage/ (current time)-(start time of the experiment) i.e Duration of Experiment</li>\n",
    "    <li><b>Rate_category</b> = (Less Then 0.3 = Low),  (Between  0.3 and 0.5 = Medium), (Above 0.5 - High)</li>\n",
    "    <li><b>Distance</b> = Total Haversine Distance for EIDs </li></ul>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-07-18T08:07:05.985250Z",
     "start_time": "2023-07-18T08:07:04.972623Z"
    }
   },
   "outputs": [],
   "source": [
    "CREATE MULTISET VOLATILE TABLE CLM_ADS AS (\n",
    "  SELECT dt.*,  \n",
    "    CASE\n",
    "      WHEN dt.load_rate >= 0 AND dt.load_rate <= 0.30 THEN 'low'\n",
    "      WHEN dt.load_rate >= 0.30 AND dt.load_rate < 0.50 THEN 'med'\n",
    "      ELSE 'high'\n",
    "    END AS rate_category\n",
    "    \n",
    "  FROM (\n",
    "    select CLM_QOE.*,  \n",
    "     TO_TIMESTAMP(CLM_QOE.\"DateTime\",'DD/MM/YYYY HH24:MI:SS') AS OTHER_TIME,\n",
    "     MIN(TO_TIMESTAMP(CLM_QOE.\"DateTime\",'DD/MM/YYYY HH24:MI:SS')) \n",
    "     OVER (PARTITION BY CLM_QOE.\"Eid\") AS MIN_TIME,\n",
    "     (OTHER_TIME - MIN_TIME) HOUR(2) TO SECOND AS TIME_DIFF,\n",
    "     CAST(EXTRACT(HOUR  FROM TIME_DIFF) * 3600\n",
    "     + EXTRACT(MINUTE FROM TIME_DIFF) * 60\n",
    "      + EXTRACT(SECOND FROM TIME_DIFF) AS INT) AS TIME_DIFF_SECS,\n",
    "      CLM_QOE.Loaded_Percentage / NULLIFZERO (TIME_DIFF_SECS) AS LOAD_RATE\n",
    "      FROM CLM_QOE\n",
    "  ) AS dt\n",
    ") WITH DATA\n",
    "On commit preserve rows;"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-07-20T19:34:10.274735Z",
     "start_time": "2023-07-20T19:34:07.662373Z"
    }
   },
   "outputs": [],
   "source": [
    "select top 10 * from CLM_ADS;"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-07-18T08:10:07.315637Z",
     "start_time": "2023-07-18T08:10:04.246069Z"
    },
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "SELECT * FROM TD_CategoricalSummary (\n",
    "ON CLM_ADS AS InputTable\n",
    "USING\n",
    "    TargetColumns ('rate_category') \n",
    ") AS dt;"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p style = 'font-size:16px;font-family:Arial;color:#00233C'>Rate_Category column is our target variable for the models we'll create in next steps. We will classify our test data in one of the above category(cluster).</p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%chart x=DistinctValue,y=DistinctValueCount,labelx=\"QoE Categories\", labely=\"count of ids\",type=bar,title=\"Number of Ids in each QoE Cluster\",height= 400,width=400"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "jp-MarkdownHeadingCollapsed": true,
    "tags": []
   },
   "source": [
    "<p style = 'font-size:18px;font-family:Arial;color:#00233C'><b>\n",
    "    Data Preparation</b></p>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p style = 'font-size:16px;font-family:Arial;color:#00233C'>Before we can use the data in models we will perform some data preparation steps.</p>\n",
    "<p style = 'font-size:16px;font-family:Arial;color:#00233C'><b>Label Encoding </b></p>\n",
    "<p style = 'font-size:16px;font-family:Arial;color:#00233C'><b>TD_OrdinalEncodingFit </b>function identifies distinct categorical values from an input table or a user-defined list and returns the distinct categorical values along with the ordinal value for each category.The <b>TD_OrdinalEncodingTransform</b> function maps the categorical value to a specified ordinal value using the TD_OrdinalEncodingFit output.</p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-07-20T08:05:35.302392Z",
     "start_time": "2023-07-20T08:05:34.644043Z"
    }
   },
   "outputs": [],
   "source": [
    "CREATE MULTISET VOLATILE TABLE CLM_ADS_ENCODED AS (\n",
    "SELECT * FROM TD_OrdinalEncodingFit (\n",
    "ON CLM_ADS AS InputTable\n",
    "USING\n",
    "TargetColumn('Quality','NetworkTech','NetworkMode','Experiment_Type','rate_category')\n",
    "Approach ('AUTO')\n",
    "StartValue (0,0,0,0,0)\n",
    "DefaultValue (-1,-1,-1,-1,-1)\n",
    ") AS dt\n",
    ") WITH DATA\n",
    "On commit preserve rows;"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-07-20T12:05:10.641164Z",
     "start_time": "2023-07-20T12:05:10.273298Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "select top 10 * from CLM_ADS_ENCODED;"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-07-20T08:07:42.483853Z",
     "start_time": "2023-07-20T08:07:41.858756Z"
    }
   },
   "outputs": [],
   "source": [
    "CREATE MULTISET VOLATILE TABLE CLM_ADS_TRANSFORMED AS (\n",
    "SELECT * FROM TD_OrdinalEncodingTransform (\n",
    "ON CLM_ADS as InputTable\n",
    "ON CLM_ADS_ENCODED as FitTable Dimension\n",
    "USING\n",
    "ACCUMULATE('QoE_Time','QoE_Date','Eid','Video_Bytes_Downloaded','Video','Loaded_Percentage', 'Longitude','Latitude','Level','Qual','SNR','CQI', 'LTERSSI', 'DL_bitrate','UL_bitrate','Altitude','Height','State','EVENT','SecondCell_RSRP','SecondCell_RSRQ','SecondCell_SNR', 'DateTime','OTHER_TIME', 'MIN_TIME', 'TIME_DIFF','TIME_DIFF_SECS','Load_Rate')\n",
    ") AS dt\n",
    ") WITH DATA\n",
    "On commit preserve rows;\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-07-20T08:33:20.359212Z",
     "start_time": "2023-07-20T08:33:20.042214Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "select top 10 * from CLM_ADS_TRANSFORMED;"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p style = 'font-size:16px;font-family:Arial;color:#00233C'><b>TD_TrainTestSplit </b>The function randomly divides the data sets into train and test subsets to evaluate machine learning algorithms and validation processes. The first subset is used to train the model. The second subset is used to make predictions and compare the predictions to actual values. </p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-07-20T08:38:27.273670Z",
     "start_time": "2023-07-20T08:33:46.888440Z"
    }
   },
   "outputs": [],
   "source": [
    "CREATE MULTISET TABLE CLM_ADS_TEST AS (\n",
    "select * from TD_TrainTestSplit (\n",
    "ON CLM_ADS_TRANSFORMED AS InputTable\n",
    "USING\n",
    "TrainSize(0.7)\n",
    "TestSize(0.3)\n",
    ")as dt WHERE TD_isTrainRow = 0\n",
    ") WITH DATA;"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-07-20T09:21:17.006811Z",
     "start_time": "2023-07-20T08:55:44.169004Z"
    },
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "CREATE MULTISET TABLE CLM_ADS_TRAIN AS (\n",
    "select * from TD_TrainTestSplit (\n",
    "ON CLM_ADS_TRANSFORMED AS InputTable\n",
    "USING\n",
    "TrainSize(0.7)\n",
    "TestSize(0.3)\n",
    ")as dt WHERE TD_isTrainRow = 1\n",
    ") WITH DATA;"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "select rate_category, count(*) from CLM_ADS_TEST group by 1;"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "select rate_category, count(*) as counts from CLM_ADS_TEST group by 1;"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%chart x=rate_category,y=counts,labelx=\"QoE Categories\", labely=\"count of ids\",type=bar,title=\"Number of Ids in each QoE Cluster in Test data\",height= 400,width=400"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<hr style=\"height:2px;border:none;background-color:#00233C;\">\n",
    "<p style = 'font-size:20px;font-family:Arial;color:#00233C'><b>6. Model Training and Evaluation </b> </p>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p style = 'font-size:18px;font-family:Arial;color:#00233C'><b>\n",
    "    6.1 Decision Forest</b></p>\n",
    "<p style = 'font-size:16px;font-family:Arial;color:#00233C'><b>TD_DecisionForest</b> function is an ensemble algorithm used for classification and regression predictive modeling problems. It is an extension of bootstrap aggregation (bagging) of decision trees. \n",
    "<p style = 'font-size:16px;font-family:Arial;color:#00233C'>This function takes the training data as input, as well as the following function parameters</p>\n",
    "    <ul style = 'font-size:16px;font-family:Arial;color:#00233C'>\n",
    "        <li>InputColumns; list or range of columns used as features (we used an ordinal reference of columns 2:217)</li>\n",
    "        <li>ResponseColumn; the dependent or target value (we used “class”, the first column)</li>\n",
    "        <li>TreeType; either CLASSIFICATION or REGRESSION</li>\n",
    "    <li>Other hyperparameter values detailed in the documentation</li>\n",
    "        </ul>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p style = 'font-size:16px;font-family:Arial;color:#00233C'> <b><i>* Below step creates model.  Due to the limited resources of this VM, processing this step may take up to 4 minutes to run.</b></i>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-07-20T10:38:18.260747Z",
     "start_time": "2023-07-20T10:19:20.485637Z"
    }
   },
   "outputs": [],
   "source": [
    "CREATE MULTISET TABLE CLM_ADS_MDL_DF AS (\n",
    "select * from TD_DecisionForest (\n",
    "ON CLM_ADS_TRAIN AS InputTable PARTITION BY ANY\n",
    "USING\n",
    "   InputColumns ('Level','Qual','SNR','CQI','LTERSSI','DL_bitrate','UL_bitrate','Altitude','Height','Quality','NetworkTech','NetworkMode','Experiment_Type')\n",
    "   ResponseColumn('rate_category')\n",
    "   MaxDepth (50)\n",
    "   MinNodeSize (20)\n",
    "   NumTrees (100)\n",
    "   ModelType('classification')\n",
    ") as dt\n",
    ") WITH DATA\n",
    ";"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<hr style=\"height:1px;border:none;background-color:#00233C;\">\n",
    "<p style = 'font-size:18px;font-family:Arial;color:#00233C'><b>\n",
    "    6.2 XGboost</b></p>\n",
    "<p style = 'font-size:16px;font-family:Arial;color:#00233C'>\n",
    " The <b>TD_XGBoost </b>is an implementation of the gradient boosted decision tree designed for speed and performance. In gradient boosting, each iteration fits a model to the residuals (errors) of the previous iteration to correct the errors made by existing models. The predicted residual is multiplied by this learning rate and then added to the previous prediction. Models are added sequentially until no further improvements can be made. It is called gradient boosting because it uses a gradient descent algorithm to minimize the loss when adding new models.</p>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "<p style = 'font-size:16px;font-family:Arial;color:#00233C'> <b><i>* Below step creates a model. Due to the limited resources of this VM, processing this step may take up to 1 minute to run.</b></i>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-07-20T11:14:05.840053Z",
     "start_time": "2023-07-20T11:13:46.895183Z"
    }
   },
   "outputs": [],
   "source": [
    "CREATE MULTISET TABLE CLM_ADS_MDL_XGB AS (\n",
    "select * from TD_XGBoost (\n",
    "ON CLM_ADS_TRAIN AS InputTable PARTITION BY ANY\n",
    "USING\n",
    "   InputColumns ('Level','Qual','SNR','CQI','LTERSSI','DL_bitrate','UL_bitrate','Altitude','Height','Quality','NetworkTech','NetworkMode','Experiment_Type')\n",
    "   ResponseColumn('rate_category')\n",
    "   MaxDepth (50)\n",
    "   MinNodeSize (20)    \n",
    "   ModelType('classification')\n",
    ") as dt\n",
    ") WITH DATA;"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<hr style=\"height:2px;border:none;background-color:#00233C;\">\n",
    "<p style = 'font-size:20px;font-family:Arial;color:#00233C'><b>\n",
    "    7. Macro Model - Combined</b></p>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p style = 'font-size:16px;font-family:Arial;color:#00233C'> Run predictions based on the models created above."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p style = 'font-size:16px;font-family:Arial;color:#00233C'><b>Decision Forest</b></p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "CREATE MULTISET VOLATILE TABLE CLM_ADS_PRED_DF AS (\n",
    "SELECT * FROM TD_DecisionForestPredict (\n",
    "ON CLM_ADS_TEST AS InputTable PARTITION BY ANY\n",
    "ON CLM_ADS_MDL_DF AS ModelTable DIMENSION\n",
    "USING\n",
    "  IdColumn ('DateTime')\n",
    "  Accumulate('rate_category')\n",
    ") AS dt\n",
    ") WITH DATA\n",
    "ON COMMIT PRESERVE ROWS;"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "select top 10* from CLM_ADS_PRED_DF;"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "select prediction as rate_category, count(*) as counts from CLM_ADS_PRED_DF group by 1;"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%chart x=rate_category,y=counts,labelx=\"QoE Categories\", labely=\"count of ids\",type=bar,title=\"Number of Ids in each QoE Cluster in Test data by Decision Forest Model\",height= 400,width=400"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p style = 'font-size:16px;font-family:Arial;color:#00233C'>We can compare the counts categorized by DF model and the test data category counts.</p>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p style = 'font-size:16px;font-family:Arial;color:#00233C'><b>XG-boost</b></p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-07-20T11:15:32.254795Z",
     "start_time": "2023-07-20T11:15:18.413929Z"
    }
   },
   "outputs": [],
   "source": [
    "CREATE MULTISET VOLATILE TABLE CLM_ADS_PRED_XGB AS (\n",
    "SELECT * FROM TD_XGBoostPredict (\n",
    "ON CLM_ADS_TEST AS InputTable PARTITION BY ANY\n",
    "ON CLM_ADS_MDL_XGB AS ModelTable DIMENSION ORDER BY task_index, tree_num, iter, class_num, \n",
    "tree_order\n",
    "USING\n",
    "  IdColumn ('DateTime')\n",
    "  Accumulate('rate_category')\n",
    "    ModelType('Classification')\n",
    ") AS dt\n",
    ") WITH DATA\n",
    "ON COMMIT PRESERVE ROWS;"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "select top 10* from CLM_ADS_PRED_XGB;"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "select prediction as rate_category, count(*) as counts from CLM_ADS_PRED_XGB group by 1;"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%chart x=rate_category,y=counts,labelx=\"QoE Categories\", labely=\"count of ids\",type=bar,title=\"Number of Ids in each QoE Cluster in Test data by XGB Model\",height= 400,width=400"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p style = 'font-size:16px;font-family:Arial;color:#00233C'>We can compare the counts categorized by DF model and the test data category counts.</p>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<hr style=\"height:2px;border:none;background-color:#00233C;\">\n",
    "<p style = 'font-size:20px;font-family:Arial;color:#00233C'><b>\n",
    "    8. Model Evaluation</b></p>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p style = 'font-size:16px;font-family:Arial;color:#00233C'><b>TD_ClassificationEvaluator</b> In classification problems, a confusion matrix is used to visualize the performance of a classifier. The confusion matrix contains predicted labels represented across the row-axis and actual labels represented across the column-axis. Each cell in the confusion matrix corresponds to the count of occurrences of labels in the test data. TD_ClassificationEvaluator functions creates the confusion matrix. </p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "SELECT * from TD_CLASSIFICATIONEVALUATOR(\n",
    "ON  CLM_ADS_PRED_DF AS InputTable\n",
    "USING\n",
    "Labels(0,1,2)\n",
    "ObservationColumn('rate_category')\n",
    "PredictionColumn ('prediction')\n",
    ") as dt;"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-07-20T11:15:35.431392Z",
     "start_time": "2023-07-20T11:15:33.786829Z"
    }
   },
   "outputs": [],
   "source": [
    "SELECT * from TD_CLASSIFICATIONEVALUATOR(\n",
    "ON  CLM_ADS_PRED_XGB AS InputTable\n",
    "USING\n",
    "Labels(0,1,2)\n",
    "ObservationColumn('rate_category')\n",
    "PredictionColumn ('prediction')\n",
    ") as dt;"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p style = 'font-size:16px;font-family:Arial;color:#00233C'>The above output shows recall, and F1-score values of confusion matrix.</p>\n",
    "<table style = 'font-size:16px;font-family:Arial;color:#00233C'>\n",
    "  <tr>\n",
    "    <th>Column</th>\n",
    "    <th>Description</th>\n",
    "  </tr>\n",
    "  <tr>\n",
    "    <td>Precision</td>\n",
    "    <td>The positive predictive value. Refers to the fraction of relevant instances among\n",
    "the total retrieved instances.\n",
    "        Precision answers the following question: what proportion of predicted Positives is truly Positive? \n",
    "        Precision = (TP)/(TP+FP)</td>\n",
    "  </tr>\n",
    "  <tr>\n",
    "    <td>Recall</td>\n",
    "    <td>Refers to the fraction of relevant instances retrieved over the total amount of\n",
    "relevant instances. Recall answers a different question: what proportion of actual Positives is correctly classified?\n",
    "Recall = (TP)/(TP+FN)</td>\n",
    "  </tr>\n",
    "  <tr>\n",
    "    <td>F1</td>\n",
    "    <td>F1 score, defined as the harmonic mean of the precision and recall and is a number between 0 and 1. F1 score maintains a balance between the precision and recall for your classifier.                                         \n",
    "                      F1 = 2*(precision*recall/precision+recall)</td>\n",
    "  </tr>\n",
    "  <tr>\n",
    "    <td>Support</td>\n",
    "    <td>The number of times a label displays in the Observation Column.</td>\n",
    "  </tr>\n",
    "</table>\n",
    "<p style = 'font-size:16px;font-family:Arial;color:#00233C'>**TP:- True Positive , FP :- False Positive, TN :- True Negative , FN :- False Negative</p>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p style = 'font-size:20px;font-family:Arial;color:#00233C'>\n",
    "Feature selection using GLM</p>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p style = 'font-size:16px;font-family:Arial;color:#00233C'>We can use GLM for chosing the significant features "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "CREATE VOLATILE TABLE TD_Glm_FeatureSelection AS(\n",
    "SELECT * FROM TD_GLM (\n",
    "ON CLM_ADS_TRANSFORMED\n",
    "USING\n",
    "InputColumns('Level','Qual','SNR','CQI','LTERSSI','DL_bitrate','UL_bitrate','Altitude','Height','Quality','NetworkTech','NetworkMode','Experiment_Type')\n",
    "ResponseColumn('rate_category')\n",
    "MaxIterNum(100)\n",
    "RegularizationLambda(0.027)\n",
    "Alpha(0)\n",
    "IterNumNoChange(50)\n",
    "Intercept('true')\n",
    ") AS dt\n",
    ") WITH DATA\n",
    "ON COMMIT PRESERVE ROWS;"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "select * from TD_Glm_FeatureSelection order by attribute;"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p style = 'font-size:16px;font-family:Arial;color:#00233C'>We can see the estimates column to see its significance of the attribute on the model.  </p>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<hr style=\"height:2px;border:none;background-color:#00233C;\">\n",
    "<p style = 'font-size:20px;font-family:Arial;color:#00233C'><b>\n",
    "    9. Micro-models (dividing the dataset on experiment types)</b></p>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p style = 'font-size:16px;font-family:Arial;color:#00233C'>Let us divide our dataset based on experiment types."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "CREATE MULTISET VOLATILE TABLE CLM_ADS_INDOOR AS (\n",
    "select * from CLM_ADS_TRANSFORMED where Experiment_Type = 0\n",
    ") WITH DATA\n",
    "ON COMMIT PRESERVE ROWS;"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "CREATE MULTISET VOLATILE TABLE CLM_ADS_MOBILITY AS (\n",
    "select * from CLM_ADS_TRANSFORMED where Experiment_Type = 1\n",
    ") WITH DATA\n",
    "ON COMMIT PRESERVE ROWS;"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "CREATE MULTISET VOLATILE TABLE CLM_ADS_OUTDOOR AS (\n",
    "select * from CLM_ADS_TRANSFORMED where Experiment_Type = 2\n",
    ") WITH DATA\n",
    "ON COMMIT PRESERVE ROWS;"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "CREATE MULTISET VOLATILE TABLE CLM_ADS_PEDESTRIAN AS (\n",
    "select * from CLM_ADS_TRANSFORMED where Experiment_Type = 3\n",
    ") WITH DATA\n",
    "ON COMMIT PRESERVE ROWS;"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p style = 'font-size:20px;font-family:Arial;color:#00233C'>Indoor</p>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p style = 'font-size:16px;font-family:Arial;color:#00233C'>Now let's create train and test dataset for experiment type =0 i.e Indoor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "CREATE MULTISET VOLATILE TABLE INDOOR_TRAIN AS (\n",
    "select * from TD_TrainTestSplit (\n",
    "ON CLM_ADS_INDOOR AS InputTable\n",
    "USING\n",
    "TrainSize(0.7)\n",
    "TestSize(0.3)\n",
    ")as dt WHERE TD_isTrainRow = 1\n",
    ") WITH DATA\n",
    "ON COMMIT PRESERVE ROWS;"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "CREATE MULTISET VOLATILE TABLE INDOOR_TEST AS (\n",
    "select * from TD_TrainTestSplit (\n",
    "ON CLM_ADS_INDOOR AS InputTable\n",
    "USING\n",
    "TrainSize(0.7)\n",
    "TestSize(0.3)\n",
    ")as dt WHERE TD_isTrainRow = 0\n",
    ") WITH DATA\n",
    "ON COMMIT PRESERVE ROWS;"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3 style = 'font-size:16px;font-family:Arial;color:#00233C'>Model Creation for Indoor</h3>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p style = 'font-size:16px;font-family:Arial;color:#00233C'>As we have seen earlier in macro models that Decision Forest model gave better results, hence we will use Decision forest for micro models."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "<p style = 'font-size:16px;font-family:Arial;color:#00233C'> <b><i>* Below step creates a model. Due to the limited resources of this VM, processing this step may take up to 2 minutes to run.</b></i>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "CREATE MULTISET VOLATILE TABLE INDOOR_MDL AS (\n",
    "select * from TD_DecisionForest (\n",
    "ON INDOOR_TRAIN AS InputTable PARTITION BY ANY\n",
    "USING\n",
    "   InputColumns ('Level','Qual','SNR','CQI','LTERSSI','DL_bitrate','UL_bitrate','Altitude','Height','Quality','NetworkTech','NetworkMode')\n",
    "   ResponseColumn('rate_category')\n",
    "   MaxDepth (50)\n",
    "   MinNodeSize (20)\n",
    "   NumTrees (100)\n",
    "   ModelType ('classification')\n",
    ") as dt\n",
    ") WITH DATA\n",
    "ON COMMIT PRESERVE ROWS;"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3 style = 'font-size:16px;font-family:Arial;color:#00233C'>Prediction for Indoor</h3>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "CREATE MULTISET VOLATILE TABLE INDOOR_PRED AS (\n",
    "SELECT * FROM TD_DecisionForestPredict (\n",
    "ON INDOOR_TEST AS InputTable PARTITION BY ANY\n",
    "ON INDOOR_MDL AS ModelTable DIMENSION\n",
    "USING\n",
    "  IdColumn ('DateTime')\n",
    "  Accumulate('rate_category')\n",
    ") AS dt\n",
    ") WITH DATA\n",
    "ON COMMIT PRESERVE ROWS;"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "select top 10* from INDOOR_PRED;"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3 style = 'font-size:16px;font-family:Arial;color:#00233C'>Model Evaluation for Indoor</h3>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "SELECT * from TD_CLASSIFICATIONEVALUATOR(\n",
    "ON  INDOOR_PRED AS InputTable\n",
    "USING\n",
    "Labels(0,1,2)\n",
    "ObservationColumn('rate_category')\n",
    "PredictionColumn ('prediction')\n",
    ") as dt;"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p style = 'font-size:20px;font-family:Arial;color:#00233C'>Outdoor</p>\n",
    "<p style = 'font-size:16px;font-family:Arial;color:#00233C'>Now let's create train and test dataset for experiment type =1 i.e Outdoor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "CREATE MULTISET VOLATILE TABLE OUTDOOR_TRAIN AS (\n",
    "select * from TD_TrainTestSplit (\n",
    "ON CLM_ADS_OUTDOOR AS InputTable\n",
    "USING\n",
    "TrainSize(0.7)\n",
    "TestSize(0.3)\n",
    ")as dt WHERE TD_isTrainRow = 1\n",
    ") WITH DATA\n",
    "ON COMMIT PRESERVE ROWS;"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "CREATE MULTISET VOLATILE TABLE OUTDOOR_TEST AS (\n",
    "select * from TD_TrainTestSplit (\n",
    "ON CLM_ADS_OUTDOOR AS InputTable\n",
    "USING\n",
    "TrainSize(0.7)\n",
    "TestSize(0.3)\n",
    ")as dt WHERE TD_isTrainRow = 0\n",
    ") WITH DATA\n",
    "ON COMMIT PRESERVE ROWS;"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3 style = 'font-size:16px;font-family:Arial;color:#00233C'>Model Creation for Outdoor</h3>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "<p style = 'font-size:16px;font-family:Arial;color:#00233C'> <b><i>* Below step creates a model. Due to the limited resources of this VM, processing this step may take up to 2 minute to run.</b></i>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "CREATE MULTISET VOLATILE TABLE OUTDOOR_MDL AS (\n",
    "select * from TD_DecisionForest (\n",
    "ON OUTDOOR_TRAIN AS InputTable PARTITION BY ANY\n",
    "USING\n",
    "   InputColumns ('Level','Qual','SNR','CQI','LTERSSI','DL_bitrate','UL_bitrate','Altitude','Height','Quality','NetworkTech','NetworkMode')\n",
    "   ResponseColumn('rate_category')\n",
    "   MaxDepth (50)\n",
    "   MinNodeSize (20)\n",
    "   NumTrees (100)\n",
    "   ModelType ('classification')\n",
    ") as dt\n",
    ") WITH DATA\n",
    "ON COMMIT PRESERVE ROWS;"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3 style = 'font-size:16px;font-family:Arial;color:#00233C'>Predicition for Outdoor</h3>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "CREATE MULTISET VOLATILE TABLE OUTDOOR_PRED AS (\n",
    "SELECT * FROM TD_DecisionForestPredict (\n",
    "ON OUTDOOR_TEST AS InputTable PARTITION BY ANY\n",
    "ON OUTDOOR_MDL AS ModelTable DIMENSION\n",
    "USING\n",
    "  IdColumn ('DateTime')\n",
    "  Accumulate('rate_category')\n",
    ") AS dt\n",
    ") WITH DATA\n",
    "ON COMMIT PRESERVE ROWS;"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "select top 10* from OUTDOOR_PRED;"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3 style = 'font-size:16px;font-family:Arial;color:#00233C'>Model Evaluation for Outdoor</h3>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "SELECT * from TD_CLASSIFICATIONEVALUATOR(\n",
    "ON  OUTDOOR_PRED AS InputTable\n",
    "USING\n",
    "Labels(0,1,2)\n",
    "ObservationColumn('rate_category')\n",
    "PredictionColumn ('prediction')\n",
    ") as dt;\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p style = 'font-size:20px;font-family:Arial;color:#00233C'>Mobility</p>\n",
    "<p style = 'font-size:16px;font-family:Arial;color:#00233C'>Now let's create train and test dataset for experiment type =2 i.e Mobility"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "CREATE MULTISET VOLATILE TABLE MOBILITY_TRAIN AS (\n",
    "select * from TD_TrainTestSplit (\n",
    "ON CLM_ADS_MOBILITY AS InputTable\n",
    "USING\n",
    "TrainSize(0.7)\n",
    "TestSize(0.3)\n",
    ")as dt WHERE TD_isTrainRow = 1\n",
    ") WITH DATA\n",
    "ON COMMIT PRESERVE ROWS;"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "CREATE VOLATILE TABLE MOBILITY_TEST AS (\n",
    "select * from TD_TrainTestSplit (\n",
    "ON CLM_ADS_MOBILITY AS InputTable\n",
    "USING\n",
    "TrainSize(0.7)\n",
    "TestSize(0.3)\n",
    ")as dt WHERE TD_isTrainRow = 0\n",
    ") WITH DATA\n",
    "ON COMMIT PRESERVE ROWS;"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3 style = 'font-size:16px;font-family:Arial;color:#00233C'>Model Creation for Mobility</h3>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "<p style = 'font-size:16px;font-family:Arial;color:#00233C'> <b><i>* Below step creates a model. Due to the limited resources of this VM, processing this step may take up to 12 minutes to run.</b></i>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "CREATE MULTISET VOLATILE TABLE MOBILITY_MDL AS (\n",
    "select * from TD_DecisionForest (\n",
    "ON MOBILITY_TRAIN AS InputTable PARTITION BY ANY\n",
    "USING\n",
    "   InputColumns ('Level','Qual','SNR','CQI','LTERSSI','DL_bitrate','UL_bitrate','Altitude','Height','Quality','NetworkTech','NetworkMode')\n",
    "   ResponseColumn('rate_category')\n",
    "   MaxDepth (50)\n",
    "   MinNodeSize (20)\n",
    "   NumTrees (100)\n",
    "   ModelType ('classification')\n",
    ") as dt\n",
    ") WITH DATA\n",
    "ON COMMIT PRESERVE ROWS;"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3 style = 'font-size:16px;font-family:Arial;color:#00233C'>Predicition for Mobility</h3>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "CREATE MULTISET VOLATILE TABLE MOBILITY_PRED AS (\n",
    "SELECT * FROM TD_DecisionForestPredict (\n",
    "ON MOBILITY_TEST AS InputTable PARTITION BY ANY\n",
    "ON MOBILITY_MDL AS ModelTable DIMENSION\n",
    "USING\n",
    "  IdColumn ('DateTime')\n",
    "  Accumulate('rate_category')\n",
    ") AS dt\n",
    ") WITH DATA\n",
    "ON COMMIT PRESERVE ROWS;"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "select top 10* from MOBILITY_PRED;"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3 style = 'font-size:16px;font-family:Arial;color:#00233C'>Model Evaluation for Mobility</h3>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "SELECT * from TD_CLASSIFICATIONEVALUATOR(\n",
    "ON  MOBILITY_PRED AS InputTable\n",
    "USING\n",
    "Labels(0,1,2)\n",
    "ObservationColumn('rate_category')\n",
    "PredictionColumn ('prediction')\n",
    ") as dt;"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p style = 'font-size:20px;font-family:Arial;color:#00233C'>Pedestrian</p>\n",
    "<p style = 'font-size:16px;font-family:Arial;color:#00233C'>Now let's create train and test dataset for experiment type =3 i.e Pedestrian"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "CREATE MULTISET VOLATILE TABLE PEDESTRIAN_TRAIN AS (\n",
    "select * from TD_TrainTestSplit (\n",
    "ON CLM_ADS_PEDESTRIAN AS InputTable\n",
    "USING\n",
    "TrainSize(0.7)\n",
    "TestSize(0.3)\n",
    ")as dt WHERE TD_isTrainRow = 1\n",
    ") WITH DATA\n",
    "ON COMMIT PRESERVE ROWS;"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "CREATE MULTISET VOLATILE TABLE PEDESTRIAN_TEST AS (\n",
    "select * from TD_TrainTestSplit (\n",
    "ON CLM_ADS_PEDESTRIAN AS InputTable\n",
    "USING\n",
    "TrainSize(0.7)\n",
    "TestSize(0.3)\n",
    ")as dt WHERE TD_isTrainRow = 0\n",
    ") WITH DATA\n",
    "ON COMMIT PRESERVE ROWS;"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3 style = 'font-size:16px;font-family:Arial;color:#00233C'>Model Creation for Pedestrian</h3>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p style = 'font-size:16px;font-family:Arial;color:#00233C'> <b><i>* Below step creates model.  Due to limited resources of this VM, processing may up to 3 minutes to complete.</b></i>    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "CREATE VOLATILE TABLE PEDESTRIAN_MDL AS (\n",
    "select * from TD_DecisionForest (\n",
    "ON PEDESTRIAN_TRAIN AS InputTable PARTITION BY ANY\n",
    "USING\n",
    "   InputColumns ('Level','Qual','SNR','CQI','LTERSSI','DL_bitrate','UL_bitrate','Altitude','Height','Quality','NetworkTech','NetworkMode')\n",
    "   ResponseColumn('rate_category')\n",
    "   MaxDepth (50)\n",
    "   MinNodeSize (20)\n",
    "   NumTrees (100)\n",
    "   ModelType ('classification')\n",
    ") as dt\n",
    ") WITH DATA\n",
    "ON COMMIT PRESERVE ROWS;"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3 style = 'font-size:16px;font-family:Arial;color:#00233C'>Predicition for Pedestrian</h3>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "CREATE MULTISET VOLATILE TABLE PEDESTRIAN_PRED AS (\n",
    "SELECT * FROM TD_DecisionForestPredict (\n",
    "ON PEDESTRIAN_TEST AS InputTable PARTITION BY ANY\n",
    "ON PEDESTRIAN_MDL AS ModelTable DIMENSION\n",
    "USING\n",
    "  IdColumn ('DateTime')\n",
    "  Accumulate('rate_category')\n",
    ") AS dt\n",
    ") WITH DATA\n",
    "ON COMMIT PRESERVE ROWS;"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "select top 10* from PEDESTRIAN_PRED;"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3 style = 'font-size:16px;font-family:Arial;color:#00233C'>Model Evaluation for Pedestrian</h3>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "SELECT * from TD_CLASSIFICATIONEVALUATOR(\n",
    "ON  PEDESTRIAN_PRED AS InputTable\n",
    "USING\n",
    "Labels(0,1,2)\n",
    "ObservationColumn('rate_category')\n",
    "PredictionColumn ('prediction')\n",
    ") as dt;\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<hr style=\"height:2px;border:none;background-color:#00233C;\">\n",
    "<p style = 'font-size:20px;font-family:Arial;color:#00233C'><b>10. Conclusion </b> </p>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p style = 'font-size:16px;font-family:Arial;color:#00233C'> We have seen that for macro model Decision Forest is performing better than the XGBoost model. Also pridicition of micro models is better than the macro model."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<hr style=\"height:2px;border:none;background-color:#00233C;\">\n",
    "<p style = 'font-size:20px;font-family:Arial;color:#00233C'><b>11. Cleanup</b> </p>\n",
    "<p style = 'font-size:18px;font-family:Arial;color:#00233C'><b>Worktables</b></p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "DROP TABLE CLM_QOE;"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "DROP TABLE CLM_ADS_TEST;"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "DROP TABLE CLM_ADS_TRAIN;"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "DROP TABLE CLM_ADS_MDL_DF;"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "DROP TABLE CLM_ADS_MDL_XGB;"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p style = 'font-size:18px;font-family:Arial;color:#00233C'><b>Database and Tables</b></p>\n",
    "<p style = 'font-size:16px;font-family:Arial;color:#00233C'>The following code will clean up tables and databases created above.</p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "call remove_data('DEMO_5G');-- takes about 5 seconds, optional if you want to use the data later"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p style = 'font-size:20px;font-family:Arial;color:#00233C'><b>Reference Links:</b></p>\n",
    "<ul style = 'font-size:16px;font-family:Arial;color:#00233C'>\n",
    "        <li>Teradata Analytic Function Reference:\n",
    "        <a href = 'https://docs.teradata.com/r/Teradata-VantageCloud-Lake/Analyzing-Your-Data/Analytics-Database-Analytic-Functions/Analytics-Database-Analytic-Functions-Overview'>\n",
    "        https://docs.teradata.com/r/Teradata-VantageCloud-Lake/Analyzing-Your-Data/Analytics-Database-Analytic-Functions/Analytics-Database-Analytic-Functions-Overview</a></li>\n",
    "     <li>TD_Plot\n",
    "        <a href = 'https://docs.teradata.com/search/all?query=TD_PLOT&content-lang=en-US'>\n",
    "       TD_Plot</a></li>\n",
    "         <li>Unbounded Array Framework\n",
    "        <a href = 'https://docs.teradata.com/search/all?query=Unbounded+Array+Framework+&content-lang=en-US'>\n",
    "       UAF</a></li>\n",
    "  \n",
    "</ul>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-06-23T12:51:47.048577Z",
     "start_time": "2023-06-23T12:51:47.044596Z"
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   },
   "source": [
    "<footer style=\"padding-bottom:35px; background:#f9f9f9; border-bottom:3px solid #00233C\">\n",
    "    <div style=\"float:left;margin-top:14px\">ClearScape Analytics™</div>\n",
    "    <div style=\"float:right;\">\n",
    "        <div style=\"float:left; margin-top:14px\">\n",
    "            Copyright © Teradata Corporation - 2023. All Rights Reserved\n",
    "        </div>\n",
    "    </div>\n",
    "</footer>"
   ]
  }
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