spark_regression.ipynb

{
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  "metadata": {
    "colab": {
      "provenance": [],
      "collapsed_sections": [
        "GuuFSunM_rL2",
        "Y3vw_quGSgrW",
        "yC40UHIKPJEL",
        "w8cVWRNoPOS6",
        "mfFBQ0FLcolo",
        "IJNKSScPcsDS",
        "Y159J8TDc3wS",
        "7M2kWg3dc6FT",
        "WII-UNCzc9qJ",
        "iHJyHMhUdCRZ",
        "XBFVWWuafLBL",
        "CyuLCFRJsvW3",
        "M7kLbAFLszN3",
        "MYRPnQhTRdOn",
        "iHd3YFPHO1SV",
        "k5Kb-o82T0oQ",
        "Obzrn0jTVslw"
      ],
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/harnalashok/hadoop/blob/main/spark_regression.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4q64Z8IYb8fr"
      },
      "source": [
        "# Last amended: 27th March 2023\n",
        "# Myfolder: github/hadoop\n",
        "# Objectives:\n",
        "#             i)  Learn to perform regression using pyspark\n",
        "# Ref: https://towardsdatascience.com/building-a-linear-regression-with-pyspark-and-mllib-d065c3ba246a\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GuuFSunM_rL2"
      },
      "source": [
        "# Spark Reference API\n",
        "a. [Quickstart](https://spark.apache.org/docs/latest/quick-start.html) <br>\n",
        "b. Dataframe [APIs list](https://spark.apache.org/docs/latest/api/python/reference/pyspark.sql/dataframe.html) at a glance<br>\n",
        "c. Pandas [API](https://spark.apache.org/docs/latest/api/python/reference/pyspark.pandas/index.html) on spark <br>\n",
        "c. ALso look at useful [this source code](https://spark.apache.org/docs/latest/api/python/_modules/pyspark/sql/functions.html) of functions that has examples<br>\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Y3vw_quGSgrW"
      },
      "source": [
        "# A. Full spark install\n",
        "Installs `pyspark (spark-3.3.2-bin-hadoop3)` and `koalas`.<br>\n",
        "Takes less than a minute to install"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yC40UHIKPJEL"
      },
      "source": [
        "### 1.0 Libraries"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HBVUrlkidyaE"
      },
      "source": [
        "# 1.0 How to set environment variable\n",
        "import os  \n",
        "import time  "
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "w8cVWRNoPOS6"
      },
      "source": [
        "## 2.0 Define some functions"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mfFBQ0FLcolo"
      },
      "source": [
        "#### ssh_install()"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mgS9HNCyR7C0"
      },
      "source": [
        "# 2.0 Function to install ssh client and sshd (Server)\n",
        "def ssh_install():\n",
        "  print(\"\\n--1. Download and install ssh server----\\n\")\n",
        "  ! sudo apt-get remove openssh-client openssh-server\n",
        "  ! sudo apt install openssh-client openssh-server\n",
        "  \n",
        "  print(\"\\n--2. Restart ssh server----\\n\")\n",
        "  ! service ssh restart"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IJNKSScPcsDS"
      },
      "source": [
        "#### Java install"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BsFu84PSR9jR"
      },
      "source": [
        "# 3.0 Function to download and install java 8\n",
        "def install_java():\n",
        "  ! rm -rf /usr/java\n",
        "\n",
        "  print(\"\\n--Download and install Java 8----\\n\")\n",
        "  !apt-get install -y openjdk-8-jdk-headless -qq > /dev/null        # install openjdk\n",
        "  os.environ[\"JAVA_HOME\"] = \"/usr/lib/jvm/java-8-openjdk-amd64\"     # set environment variable\n",
        "\n",
        "  !update-alternatives --set java /usr/lib/jvm/java-8-openjdk-amd64/jre/bin/java\n",
        "  !update-alternatives --set javac /usr/lib/jvm/java-8-openjdk-amd64/bin/javac\n",
        "  \n",
        "  !mkdir -p /usr/java\n",
        "  ! ln -s \"/usr/lib/jvm/java-8-openjdk-amd64\"  \"/usr/java\"\n",
        "  ! mv \"/usr/java/java-8-openjdk-amd64\"  \"/usr/java/latest\"\n",
        "  \n",
        "  !java -version       #check java version\n",
        "  !javac -version"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Y159J8TDc3wS"
      },
      "source": [
        "#### setup ssh passphrase"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EOFbfw7n0Pps"
      },
      "source": [
        "# 6.0 Function tp setup ssh passphrase\n",
        "def set_keys():\n",
        "  print(\"\\n---22. Generate SSH keys----\\n\")\n",
        "  ! cd ~ ; pwd \n",
        "  ! cd ~ ; ssh-keygen -t rsa -P '' -f ~/.ssh/id_rsa\n",
        "  ! cd ~ ; cat ~/.ssh/id_rsa.pub >> ~/.ssh/authorized_keys\n",
        "  ! cd ~ ; chmod 0600 ~/.ssh/authorized_keys\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7M2kWg3dc6FT"
      },
      "source": [
        "#### Set environment"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CRSn9XAV4rsR"
      },
      "source": [
        "# 7.0 Function to set up environmental variables\n",
        "def set_env():\n",
        "  print(\"\\n---23. Set Environment variables----\\n\")\n",
        "  # 'export' command does not work in colab\n",
        "  # https://stackoverflow.com/a/57240319\n",
        "  os.environ[\"JAVA_HOME\"] = \"/usr/lib/jvm/java-8-openjdk-amd64\"     #set environment variable\n",
        "  os.environ[\"JRE_HOME\"] = \"/usr/lib/jvm/java-8-openjdk-amd64/jre\"   \n",
        "  "
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WII-UNCzc9qJ"
      },
      "source": [
        "#### function to install prerequisites\n",
        "java and ssh<br>\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oh1Mi0rHFpkU"
      },
      "source": [
        "# 8.0 Function to call all functions\n",
        "def install_components():\n",
        "  print(\"\\n--Install java----\\n\")\n",
        "  ssh_install()\n",
        "  install_java()  \n",
        "  #set_keys()\n",
        "  set_env()\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "iHJyHMhUdCRZ"
      },
      "source": [
        "## 3.0 Install components\n",
        "Start downloading, install and configure. Takes around 2 minutes<br>\n",
        "Your <u>input *'y'* is required </u>at one place while overwriting earlier ssh keys"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "77YQikvsJiTm"
      },
      "source": [
        "# 9.0 Start installation\n",
        "start = time.time()\n",
        "install_components()\n",
        "end = time.time()\n",
        "print(\"\\n---Time taken----\\n\")\n",
        "print((end- start)/60)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XBFVWWuafLBL"
      },
      "source": [
        "## 4.0 Install spark\n",
        "koalas will also be installed"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CyuLCFRJsvW3"
      },
      "source": [
        "### Define functions"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kk3BP0OfYpT1"
      },
      "source": [
        "`findspark`: PySpark isn't on `sys.path` by default, but that doesn't mean it can't be used as a regular library. You can address this by either symlinking pyspark into your site-packages, or adding `pyspark` to `sys.path` at runtime. `findspark` does the latter."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "opLGqtPRfM_5"
      },
      "source": [
        "# 1.0 Function to download and unzip spark\n",
        "def spark_koalas_install():\n",
        "  print(\"\\n--1.1 Install findspark----\\n\")\n",
        "  !pip install -q findspark\n",
        "\n",
        "  #print(\"\\n--1.2 Install databricks Koalas----\\n\")\n",
        "  #!pip install koalas\n",
        "  \n",
        "  # This download link NEEDS TO BE CHECKED AGAIN\n",
        "  print(\"\\n--1.3 Download Apache tar.gz----\\n\")\n",
        "            \n",
        "  ! wget -c https://dlcdn.apache.org/spark/spark-3.3.2/spark-3.3.2-bin-hadoop3.tgz \n",
        "\n",
        "  print(\"\\n--1.4 Transfer downloaded content and unzip tar.gz----\\n\")\n",
        "  !  mv /content/spark*   /opt/\n",
        "  ! tar -xzf /opt/spark-3.3.2-bin-hadoop3.tgz  --directory /opt/\n",
        "\n",
        "  print(\"\\n--1.5 Check folder for files----\\n\")\n",
        "  ! ls -la /opt\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ebXfvQ1qiQHz"
      },
      "source": [
        "# 1.1 Function to set environment\n",
        "def set_spark_env():\n",
        "  print(\"\\n---2. Set Environment variables----\\n\")\n",
        "  os.environ[\"JAVA_HOME\"] = \"/usr/lib/jvm/java-8-openjdk-amd64\" \n",
        "  os.environ[\"JRE_HOME\"] = \"/usr/lib/jvm/java-8-openjdk-amd64/jre\" \n",
        "  os.environ[\"SPARK_HOME\"] = \"/opt/spark-3.3.2-bin-hadoop3\" \n",
        "  os.environ[\"SPARK_CONF_DIR\"] = \"/opt/spark-3.3.2-bin-hadoop3/conf\"     \n",
        "  os.environ[\"LD_LIBRARY_PATH\"] += \":/opt/spark-3.3.2-bin-hadoop3/lib/native\"\n",
        "  os.environ[\"PATH\"] += \":/opt/spark-3.3.2-bin-hadoop3/bin:/opt/spark-3.3.2-bin-hadoop3/sbin\"\n",
        "  print(\"\\n---2.1. Check Environment variables----\\n\")\n",
        "  # Check\n",
        "  ! echo $PATH\n",
        "  ! echo $LD_LIBRARY_PATH"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qUsZggDHj13U"
      },
      "source": [
        "# 1.2 Function to configure spark \n",
        "def spark_conf():\n",
        "  print(\"\\n---3. Configure spark to access hadoop----\\n\")\n",
        "  !mv /opt/spark-3.3.2-bin-hadoop3/conf/spark-env.sh.template  /opt/spark-3.3.2-bin-hadoop3/conf/spark-env.sh\n",
        "  #!echo \"HADOOP_CONF_DIR=/opt/hadoop-3.2.2/etc/hadoop/\" >> /opt/spark-3.1.2-bin-hadoop3.2/conf/spark-env.sh\n",
        "  print(\"\\n---3.1 Check ----\\n\")\n",
        "  #!cat /opt/spark-3.1.1-bin-hadoop3.2/conf/spark-env.sh"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "M7kLbAFLszN3"
      },
      "source": [
        "### Install spark\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "z_eaLhtPktHJ"
      },
      "source": [
        "# 2.0 Call all the three functions\n",
        "def install_spark():\n",
        "  spark_koalas_install()\n",
        "  set_spark_env()\n",
        "  spark_conf()\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "emaHs1XxRt5z",
        "outputId": "d44eba72-6268-47d0-82ed-85d27281aed4",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "# 2.1 \n",
        "install_spark()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "--1.1 Install findspark----\n",
            "\n",
            "\n",
            "--1.2 Install databricks Koalas----\n",
            "\n",
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting koalas\n",
            "  Downloading koalas-1.8.2-py3-none-any.whl (390 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m390.8/390.8 KB\u001b[0m \u001b[31m7.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: pyarrow>=0.10 in /usr/local/lib/python3.9/dist-packages (from koalas) (9.0.0)\n",
            "Requirement already satisfied: pandas>=0.23.2 in /usr/local/lib/python3.9/dist-packages (from koalas) (1.4.4)\n",
            "Requirement already satisfied: numpy>=1.14 in /usr/local/lib/python3.9/dist-packages (from koalas) (1.22.4)\n",
            "Requirement already satisfied: python-dateutil>=2.8.1 in /usr/local/lib/python3.9/dist-packages (from pandas>=0.23.2->koalas) (2.8.2)\n",
            "Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.9/dist-packages (from pandas>=0.23.2->koalas) (2022.7.1)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.9/dist-packages (from python-dateutil>=2.8.1->pandas>=0.23.2->koalas) (1.16.0)\n",
            "Installing collected packages: koalas\n",
            "Successfully installed koalas-1.8.2\n",
            "\n",
            "--1.3 Download Apache tar.gz----\n",
            "\n",
            "--2023-03-24 11:22:54--  https://dlcdn.apache.org/spark/spark-3.3.2/spark-3.3.2-bin-hadoop3.tgz\n",
            "Resolving dlcdn.apache.org (dlcdn.apache.org)... 151.101.2.132, 2a04:4e42::644\n",
            "Connecting to dlcdn.apache.org (dlcdn.apache.org)|151.101.2.132|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 299360284 (285M) [application/x-gzip]\n",
            "Saving to: ‘spark-3.3.2-bin-hadoop3.tgz’\n",
            "\n",
            "spark-3.3.2-bin-had 100%[===================>] 285.49M   198MB/s    in 1.4s    \n",
            "\n",
            "2023-03-24 11:22:55 (198 MB/s) - ‘spark-3.3.2-bin-hadoop3.tgz’ saved [299360284/299360284]\n",
            "\n",
            "\n",
            "--1.4 Transfer downloaded content and unzip tar.gz----\n",
            "\n",
            "\n",
            "--1.5 Check folder for files----\n",
            "\n",
            "total 292368\n",
            "drwxr-xr-x  1 root root      4096 Mar 24 11:22 .\n",
            "drwxr-xr-x  1 root root      4096 Mar 24 11:21 ..\n",
            "drwxr-xr-x  1 root root      4096 Mar 22 13:51 google\n",
            "drwxr-xr-x  1 root root      4096 Mar 22 13:30 nvidia\n",
            "drwxr-xr-x 13  501 1000      4096 Feb 10 20:40 spark-3.3.2-bin-hadoop3\n",
            "-rw-r--r--  1 root root 299360284 Feb 10 21:28 spark-3.3.2-bin-hadoop3.tgz\n",
            "\n",
            "---2. Set Environment variables----\n",
            "\n",
            "\n",
            "---2.1. Check Environment variables----\n",
            "\n",
            "/opt/bin:/usr/local/nvidia/bin:/usr/local/cuda/bin:/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin:/tools/node/bin:/tools/google-cloud-sdk/bin:/opt/spark-3.3.2-bin-hadoop3/bin:/opt/spark-3.3.2-bin-hadoop3/sbin\n",
            "/usr/local/nvidia/lib:/usr/local/nvidia/lib64:/opt/spark-3.3.2-bin-hadoop3/lib/native\n",
            "\n",
            "---3. Configure spark to access hadoop----\n",
            "\n",
            "\n",
            "---3.1 Check ----\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MYRPnQhTRdOn"
      },
      "source": [
        "# B. Call Essential libraries\n",
        "We do not import `pandas` but `pyspark.pandas` as `ps`."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AiO4shcjRjNO",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "f62f62c6-5155-4b58-8a64-383ef8c4e7d0"
      },
      "source": [
        "# 3.0 Just call some libraries to test\n",
        "import numpy as np\n",
        "import os\n",
        "import time \n",
        "\n",
        "# 3.1 Get spark in sys.path\n",
        "import findspark\n",
        "findspark.init()\n",
        "\n",
        "# 3.2 Call other spark libraries\n",
        "#     Just to test\n",
        "import pyspark.pandas as ps\n",
        "from pyspark.sql import SparkSession"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/opt/spark-3.3.2-bin-hadoop3/python/pyspark/pandas/__init__.py:49: UserWarning: 'PYARROW_IGNORE_TIMEZONE' environment variable was not set. It is required to set this environment variable to '1' in both driver and executor sides if you use pyarrow>=2.0.0. pandas-on-Spark will set it for you but it does not work if there is a Spark context already launched.\n",
            "  warnings.warn(\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "i7lb_6xCwrZm"
      },
      "source": [
        "# 3.3\n",
        "from IPython.core.interactiveshell import InteractiveShell\n",
        "InteractiveShell.ast_node_interactivity = \"all\""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VCOPgKGO60_I",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "c235b9e6-8e23-4803-d5f7-0323f689785c"
      },
      "source": [
        "# 3.4 Increase cell width to display wide columnar output\n",
        "from IPython.core.display import display, HTML\n",
        "display(HTML(\"<style>.container { width:100% !important; }</style>\"))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "<style>.container { width:100% !important; }</style>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "iHd3YFPHO1SV"
      },
      "source": [
        "# C. Build spark session\n",
        "You can modify spark driver/executor memory here<br>\n",
        "SparkSession name is `spark`<br>\n",
        "databricks.koalas is imported as `ks`\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "k5Kb-o82T0oQ"
      },
      "source": [
        "## Modifying spark configuraion\n",
        "Increase driver and executor memory"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3M6d5xfNJSwx",
        "outputId": "9994a5c8-35d0-4450-df3e-1aa4010bb1f3",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "# 4.0 Check template file\n",
        "! cat /opt/spark-3.3.2-bin-hadoop3/conf/spark-defaults.conf.template"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "#\n",
            "# Licensed to the Apache Software Foundation (ASF) under one or more\n",
            "# contributor license agreements.  See the NOTICE file distributed with\n",
            "# this work for additional information regarding copyright ownership.\n",
            "# The ASF licenses this file to You under the Apache License, Version 2.0\n",
            "# (the \"License\"); you may not use this file except in compliance with\n",
            "# the License.  You may obtain a copy of the License at\n",
            "#\n",
            "#    http://www.apache.org/licenses/LICENSE-2.0\n",
            "#\n",
            "# Unless required by applicable law or agreed to in writing, software\n",
            "# distributed under the License is distributed on an \"AS IS\" BASIS,\n",
            "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
            "# See the License for the specific language governing permissions and\n",
            "# limitations under the License.\n",
            "#\n",
            "\n",
            "# Default system properties included when running spark-submit.\n",
            "# This is useful for setting default environmental settings.\n",
            "\n",
            "# Example:\n",
            "# spark.master                     spark://master:7077\n",
            "# spark.eventLog.enabled           true\n",
            "# spark.eventLog.dir               hdfs://namenode:8021/directory\n",
            "# spark.serializer                 org.apache.spark.serializer.KryoSerializer\n",
            "# spark.driver.memory              5g\n",
            "# spark.executor.extraJavaOptions  -XX:+PrintGCDetails -Dkey=value -Dnumbers=\"one two three\"\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SXEkJxzyNYn4"
      },
      "source": [
        "# 4.1 Create spark-defaults.conf \n",
        "! cp /opt/spark-3.3.2-bin-hadoop3/conf/spark-defaults.conf.template  /opt/spark-3.3.2-bin-hadoop3/conf/spark-defaults.conf\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oZD3i2MXNacY"
      },
      "source": [
        "# 4.2 Amend properties\n",
        "! echo \"spark.driver.memory 6g\" >> /opt/spark-3.3.2-bin-hadoop3/conf/spark-defaults.conf\n",
        "! echo \"spark.executor.memory 3g\" >> /opt/spark-3.3.2-bin-hadoop3/conf/spark-defaults.conf"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JtFbJ5UEObIg",
        "outputId": "0df39948-8b9c-4d08-9446-36a2076ffb40",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "# 4.3 Check now\n",
        "! cat /opt/spark-3.3.2-bin-hadoop3/conf/spark-defaults.conf"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "#\n",
            "# Licensed to the Apache Software Foundation (ASF) under one or more\n",
            "# contributor license agreements.  See the NOTICE file distributed with\n",
            "# this work for additional information regarding copyright ownership.\n",
            "# The ASF licenses this file to You under the Apache License, Version 2.0\n",
            "# (the \"License\"); you may not use this file except in compliance with\n",
            "# the License.  You may obtain a copy of the License at\n",
            "#\n",
            "#    http://www.apache.org/licenses/LICENSE-2.0\n",
            "#\n",
            "# Unless required by applicable law or agreed to in writing, software\n",
            "# distributed under the License is distributed on an \"AS IS\" BASIS,\n",
            "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
            "# See the License for the specific language governing permissions and\n",
            "# limitations under the License.\n",
            "#\n",
            "\n",
            "# Default system properties included when running spark-submit.\n",
            "# This is useful for setting default environmental settings.\n",
            "\n",
            "# Example:\n",
            "# spark.master                     spark://master:7077\n",
            "# spark.eventLog.enabled           true\n",
            "# spark.eventLog.dir               hdfs://namenode:8021/directory\n",
            "# spark.serializer                 org.apache.spark.serializer.KryoSerializer\n",
            "# spark.driver.memory              5g\n",
            "# spark.executor.extraJavaOptions  -XX:+PrintGCDetails -Dkey=value -Dnumbers=\"one two three\"\n",
            "spark.driver.memory 6g\n",
            "spark.executor.memory 3g\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Obzrn0jTVslw"
      },
      "source": [
        "## Stop and start SparkSession"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JDGr6SWsOqox"
      },
      "source": [
        "# 5.0 Build spark session:\n",
        "# Stop spark, if started\n",
        "#spark.stop()\n",
        "# 5.1 Now start spark\n",
        "spark = SparkSession. \\\n",
        "                    builder. \\\n",
        "                    master(\"local[*]\"). \\\n",
        "                    appName(\"myexpt\"). \\\n",
        "                    getOrCreate()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dFOjGwWiaNZH",
        "outputId": "1a8c2989-5228-44da-90ab-2f2ac58cbd31",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "sc = spark.sparkContext\n",
        "spark.sparkContext.getConf().getAll()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[('spark.driver.extraJavaOptions',\n",
              "  '-XX:+IgnoreUnrecognizedVMOptions --add-opens=java.base/java.lang=ALL-UNNAMED --add-opens=java.base/java.lang.invoke=ALL-UNNAMED --add-opens=java.base/java.lang.reflect=ALL-UNNAMED --add-opens=java.base/java.io=ALL-UNNAMED --add-opens=java.base/java.net=ALL-UNNAMED --add-opens=java.base/java.nio=ALL-UNNAMED --add-opens=java.base/java.util=ALL-UNNAMED --add-opens=java.base/java.util.concurrent=ALL-UNNAMED --add-opens=java.base/java.util.concurrent.atomic=ALL-UNNAMED --add-opens=java.base/sun.nio.ch=ALL-UNNAMED --add-opens=java.base/sun.nio.cs=ALL-UNNAMED --add-opens=java.base/sun.security.action=ALL-UNNAMED --add-opens=java.base/sun.util.calendar=ALL-UNNAMED --add-opens=java.security.jgss/sun.security.krb5=ALL-UNNAMED'),\n",
              " ('spark.executor.memory', '3g'),\n",
              " ('spark.driver.host', '901a60fc5863'),\n",
              " ('spark.driver.port', '42563'),\n",
              " ('spark.app.name', 'myexpt'),\n",
              " ('spark.executor.id', 'driver'),\n",
              " ('spark.app.startTime', '1679657021251'),\n",
              " ('spark.driver.memory', '6g'),\n",
              " ('spark.rdd.compress', 'True'),\n",
              " ('spark.executor.extraJavaOptions',\n",
              "  '-XX:+IgnoreUnrecognizedVMOptions --add-opens=java.base/java.lang=ALL-UNNAMED --add-opens=java.base/java.lang.invoke=ALL-UNNAMED --add-opens=java.base/java.lang.reflect=ALL-UNNAMED --add-opens=java.base/java.io=ALL-UNNAMED --add-opens=java.base/java.net=ALL-UNNAMED --add-opens=java.base/java.nio=ALL-UNNAMED --add-opens=java.base/java.util=ALL-UNNAMED --add-opens=java.base/java.util.concurrent=ALL-UNNAMED --add-opens=java.base/java.util.concurrent.atomic=ALL-UNNAMED --add-opens=java.base/sun.nio.ch=ALL-UNNAMED --add-opens=java.base/sun.nio.cs=ALL-UNNAMED --add-opens=java.base/sun.security.action=ALL-UNNAMED --add-opens=java.base/sun.util.calendar=ALL-UNNAMED --add-opens=java.security.jgss/sun.security.krb5=ALL-UNNAMED'),\n",
              " ('spark.serializer.objectStreamReset', '100'),\n",
              " ('spark.master', 'local[*]'),\n",
              " ('spark.submit.pyFiles', ''),\n",
              " ('spark.submit.deployMode', 'client'),\n",
              " ('spark.app.id', 'local-1679657023749'),\n",
              " ('spark.ui.showConsoleProgress', 'true'),\n",
              " ('spark.app.submitTime', '1679657020966')]"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jimCCywmPJmm",
        "outputId": "0b8850db-7205-492a-ed5b-5dcf026b5ea9"
      },
      "source": [
        "# 5.2\n",
        "print(spark.sparkContext._conf.getAll())"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[('spark.driver.extraJavaOptions', '-XX:+IgnoreUnrecognizedVMOptions --add-opens=java.base/java.lang=ALL-UNNAMED --add-opens=java.base/java.lang.invoke=ALL-UNNAMED --add-opens=java.base/java.lang.reflect=ALL-UNNAMED --add-opens=java.base/java.io=ALL-UNNAMED --add-opens=java.base/java.net=ALL-UNNAMED --add-opens=java.base/java.nio=ALL-UNNAMED --add-opens=java.base/java.util=ALL-UNNAMED --add-opens=java.base/java.util.concurrent=ALL-UNNAMED --add-opens=java.base/java.util.concurrent.atomic=ALL-UNNAMED --add-opens=java.base/sun.nio.ch=ALL-UNNAMED --add-opens=java.base/sun.nio.cs=ALL-UNNAMED --add-opens=java.base/sun.security.action=ALL-UNNAMED --add-opens=java.base/sun.util.calendar=ALL-UNNAMED --add-opens=java.security.jgss/sun.security.krb5=ALL-UNNAMED'), ('spark.executor.memory', '3g'), ('spark.driver.host', '901a60fc5863'), ('spark.driver.port', '42563'), ('spark.app.name', 'myexpt'), ('spark.executor.id', 'driver'), ('spark.app.startTime', '1679657021251'), ('spark.driver.memory', '6g'), ('spark.rdd.compress', 'True'), ('spark.executor.extraJavaOptions', '-XX:+IgnoreUnrecognizedVMOptions --add-opens=java.base/java.lang=ALL-UNNAMED --add-opens=java.base/java.lang.invoke=ALL-UNNAMED --add-opens=java.base/java.lang.reflect=ALL-UNNAMED --add-opens=java.base/java.io=ALL-UNNAMED --add-opens=java.base/java.net=ALL-UNNAMED --add-opens=java.base/java.nio=ALL-UNNAMED --add-opens=java.base/java.util=ALL-UNNAMED --add-opens=java.base/java.util.concurrent=ALL-UNNAMED --add-opens=java.base/java.util.concurrent.atomic=ALL-UNNAMED --add-opens=java.base/sun.nio.ch=ALL-UNNAMED --add-opens=java.base/sun.nio.cs=ALL-UNNAMED --add-opens=java.base/sun.security.action=ALL-UNNAMED --add-opens=java.base/sun.util.calendar=ALL-UNNAMED --add-opens=java.security.jgss/sun.security.krb5=ALL-UNNAMED'), ('spark.serializer.objectStreamReset', '100'), ('spark.master', 'local[*]'), ('spark.submit.pyFiles', ''), ('spark.submit.deployMode', 'client'), ('spark.app.id', 'local-1679657023749'), ('spark.ui.showConsoleProgress', 'true'), ('spark.app.submitTime', '1679657020966')]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "L6XYg1IGs17n"
      },
      "source": [
        "# D. Mount gdrive"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 1.0\n",
        "from google.colab import drive\n",
        "drive.mount('/gdrive')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HqXmp8A3XLWu",
        "outputId": "f7f54fa0-c048-4ac0-efba-9fcc4f73e4b2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /gdrive\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Read Data and explore"
      ],
      "metadata": {
        "id": "dAQCFVyTmYfR"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# 1.1 List path to folder containg csv file. \n",
        "#      Note '/' at the end:\n",
        "\n",
        "pathToFolder = \"/gdrive/MyDrive/Colab_data_files/learning_spark/advertising_data/\""
      ],
      "metadata": {
        "id": "7SixHUkoXSZn"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# 1.2 Read data:\n",
        "df = ps.read_csv(pathToFolder + \"advertising.csv\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4wL9BsFXXraE",
        "outputId": "a738deb4-ada9-4092-fb0c-dff47cee5669"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/opt/spark-3.3.2-bin-hadoop3/python/pyspark/pandas/utils.py:975: PandasAPIOnSparkAdviceWarning: If `index_col` is not specified for `read_csv`, the default index is attached which can cause additional overhead.\n",
            "  warnings.warn(message, PandasAPIOnSparkAdviceWarning)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 2.0 Explore data:\n",
        "df.shape\n",
        "\n",
        "print(\"\\n=====\\n\")\n",
        "\n",
        "df.head()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 259
        },
        "id": "ulRJHbnWYEuU",
        "outputId": "225d7bc2-82bb-41a9-b28a-03f203504c42"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(200, 4)"
            ]
          },
          "metadata": {},
          "execution_count": 28
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "=====\n",
            "\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      TV  Radio  Newspaper  Sales\n",
              "0  230.1   37.8       69.2   22.1\n",
              "1   44.5   39.3       45.1   10.4\n",
              "2   17.2   45.9       69.3   12.0\n",
              "3  151.5   41.3       58.5   16.5\n",
              "4  180.8   10.8       58.4   17.9"
            ],
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>TV</th>\n",
              "      <th>Radio</th>\n",
              "      <th>Newspaper</th>\n",
              "      <th>Sales</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>230.1</td>\n",
              "      <td>37.8</td>\n",
              "      <td>69.2</td>\n",
              "      <td>22.1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>44.5</td>\n",
              "      <td>39.3</td>\n",
              "      <td>45.1</td>\n",
              "      <td>10.4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>17.2</td>\n",
              "      <td>45.9</td>\n",
              "      <td>69.3</td>\n",
              "      <td>12.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>151.5</td>\n",
              "      <td>41.3</td>\n",
              "      <td>58.5</td>\n",
              "      <td>16.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>180.8</td>\n",
              "      <td>10.8</td>\n",
              "      <td>58.4</td>\n",
              "      <td>17.9</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ]
          },
          "metadata": {},
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Plot few graphs\n",
        "See [here](https://spark.apache.org/docs/3.2.1/api/python/reference/pyspark.pandas/frame.html#plotting) for what all graphs are available."
      ],
      "metadata": {
        "id": "h7kTpkqdZfF8"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Density graph of Sales\n",
        "See [here](https://spark.apache.org/docs/3.2.1/api/python/reference/pyspark.pandas/api/pyspark.pandas.DataFrame.plot.density.html#pyspark.pandas.DataFrame.plot.density) for density graph"
      ],
      "metadata": {
        "id": "BtAi77VJhUkh"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "What are density graphs?<br>\n",
        "See [Wikipedia](https://en.wikipedia.org/wiki/Kernel_density_estimation#:~:text=In%20statistics%2C%20kernel%20density%20estimation,based%20on%20kernels%20as%20weights.)<br>\n",
        "Kernel density estimates are closely related to histograms, but can be endowed with properties such as smoothness or continuity by using a suitable kernel. The diagram below based on these 6 data points illustrates this relationship:"
      ],
      "metadata": {
        "id": "dUtUWxwNgi_S"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        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)"
      ],
      "metadata": {
        "id": "4BIvURWGgfni"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "For the histogram, first, the horizontal axis is divided into sub-intervals or bins which cover the range of the data: In this case, six bins each of width 2. Whenever a data point falls inside this interval, a box of height 1/12 is placed there. If more than one data point falls inside the same bin, the boxes are stacked on top of each other.\n",
        "\n",
        "For the kernel density estimate, normal kernels with variance 2.25 (indicated by the red dashed lines) are placed on each of the data points xi. The kernels are summed to make the kernel density estimate (solid blue curve). The smoothness of the kernel density estimate (compared to the discreteness of the histogram) illustrates how kernel density estimates converge faster to the true underlying density for continuous random variables.["
      ],
      "metadata": {
        "id": "eTOpGP_egRa6"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        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)"
      ],
      "metadata": {
        "id": "USdomFvyf95a"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Comparison of the histogram (left) and kernel density estimate (right) constructed using the same data. The six individual kernels are the red dashed curves, the kernel density estimate the blue curves. The data points are the rug plot on the horizontal axis."
      ],
      "metadata": {
        "id": "raNeQli4gGCg"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# 2.1 Look at the distribution of Sales:\n",
        "#     Is it symmetric or skewed?\n",
        "#     'bw_method' determines smoothness of graph:\n",
        "\n",
        "df['Sales'].plot.density(bw_method = 0.3)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "id": "q5i-O9_YYiIZ",
        "outputId": "0d76de7e-295a-49f9-96b9-e2acc3ad5a0c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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    {
      "cell_type": "code",
      "source": [
        "# 2.1.1 More smoothed graph:\n",
        "df['Sales'].plot.density(bw_method = 3.0)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "id": "5hVqH2_DhAkK",
        "outputId": "aa38b600-f0cd-4fc5-c971-b7e5cd8ffcba"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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          "metadata": {}
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      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## What is VectorAssembler?"
      ],
      "metadata": {
        "id": "UklZ-UQTtY1G"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "![image.png](data:image/png;base64,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Hzdfa0Ly9hmtf67CsfRlUO3Lyx0Bfo+KJpnrGeGa+NqxsGtiPUXzbGuvCpMoX31pny/ryTnr2Mxx5eaZusrKww8Pdwz2vIEAv/UzK28UXm4PHqy/6dRdBePNOQ4pw15tqXg3Zrlz6jlmobatm6/igY0DB4snN6+xXgrbbuXOgwlv100x93mVyTON3/LZtTvKPp+oUJlzUq9ghtY+3ZjfnHx9nEXP6fFhjvy2Z4PecNTtaWP6Vo+E7fNG+zwEyojh6VGoSSaPrRjjuFnBlJF170So2et3a9WLjkmfi4fo8XWrGtuLkL6enT+ddaxVnh/dGHu1BCeT9+f/eHPTvN9jgIrDzrE+rVRV8xvBMJnXN0tJuXOl759O5jo8vM5u88LqQqvfNtMZpDigXyf89INpS+9fpV9tflRzHX3S0ElAd8Y4bvRXWhXjyxZw4TjPV2ndSmd9PUrPvGDVI2HvIbXymClDayZ1t79hfLpV5ZHagCyQyLVMfdmr0xuvOiZEOw3X475zHd7eNrZfzvbWCoq/GvLEuIs6XK7+V9lpw7vnSMSnr0LGW/2KVGgHgrpc7VgJYM8BHYnUnn94zDFWy1P/KyIHVFqn/stTwTXBiWdvdeTjt9RXV4UG+C17q6Ivc3viiPYGMkCeiq4On+BOX9/CUzBFm7fY8xxW0MjSMceue02+dPx7H71R1YI5hHRK7zg4Ijf61Yde0SOOwJynYKI2bIwwR2XVHeF1v/ewHnlmd+JjuBvm62f+vmR4vrDGys6bSD61A5Cm3Xj6J2Z/buzbGIC0mL/pcvkQ3zGG9MWPv6I3Ej3H9nZLxpn838nu03kat3vr489pw6Sw41yf4r51qsYY56pU9pjj5q7Lx2mN1b+51pEnzHYjzRFVrVmpXYm8qzJhKeonp3Os55pE+vap1arlK431SKuX3SgydkyVxr54SuYLLhzWy886A/z+uVtzLp31islLo79vzqXZbnDuFu0BQf5M9PGJkMHXbTcXBivicLkDNH3WEMfPq3UwUifHNCxljomdoqnzNakgeiPaddxczQ00dKaS2V4V933cVkf6iXuGqIuzMkwX09lZ/3Iw6Nz1xrs194bodyd5Bk7R/WOCx//Ra7tU06Szc1h7dwfPmeeGGZo+MEbHo9dNpqG20xbr/VF2MntYA9qFKs6P1cEyg7jVjoB8p+Gaa657xKegLFYevXdUMND1aYXe2G+nA+p0/KijgRw2TiO62ekIuo67WyWOmw5O7z0cMdCYSvW/d95dO0TF/xLj7rduozTX2QE4a/JCs5d7bM182FlFwxx34+55K8Y7ghpUvbPaThvDhquok532c6WcHlP5murgtek1UQ/fFeNJgA55Gv3A3Roe+IWTevn1FHQca7aqbE/wOvUcP19zh0S++9LHlIuSJ0ID5qvXVybUgU9pXVv7C61y7HfBpKUqiZW/TN5e9H1HmxRpQFNfqfWbHQOZQXdr0YTesa/Jkgcdkz2mzVm5NQMGoPEV3TE3Zn3pGTTBlFf7g9/A6VpU7KgfwoWX4f2HElpiy5cv7ipUbrR8Eb7dPYlt101x99nkt+KQfY48aV2/zznZ210lc0ybE7V85Kjotlscy9PGW+LVDQ3a9UqFPrI/yTNEDy+Jvc8Fd83XNEcZiXTzR+0rK1QZKM6dNe3Be1TkmEQN5xlonX/HYN8MVtcndINNY19hdA/7YxPh57hBNUdSdY4zq2+WsnLnRt8/bTwquuvRyKs02br+yzgV22lL5f5E2t3G7c4dFqO/ZXFjjONKf6WVMb5sPjeOs/aAygOb9Kh41hQVxLhroVfxxJAytCWhMpRZErqWrpQ9M8509FXzx42L8VSYaW/vmBESaE2svkqFHOUX9rbTxqlD+qjJuMurg3uC4638awYEnz7u1V9DA2OvX6smQpmt3fdrO2V4CpXvvInfrxXrv14Di4JzBqeivZe/wZwD/zXproLAMVSren+wnDrVH/xtsO3vVOT4G7809S2sAMKSKKvHWHKLVHyb41yfPazadHdRmyON4+DI3OhXn1Hl+q2ObRZq0QPj1StGPd113EI947x5YM8KvdrkbpQIOo3S/XcXRa8brbHytPCnhHM07YczVBg+3xIQoS9+NJE+fl/NN9cm1nZ7TXC2ceY436xIYUA8dWOM2pdXBm/OM8c198Epyo92/axzfJ/j5nJvtcp3p6rwpa6fnL6xnlsS7NunUquWrzTWI61edqPI5DFVmvriqZovOLfzFa129Avjzd168seHzt2izSPIn4G855Lr3noKhqvkplGaZH918zS966/GVN6BhqXTOE27KV6jmqfrxxTaaePQb3UwanDPkqfpM2N1pKOren66rhtxU8Jf87Y27WzV7/6Fo7MzQNOnxKvIPCq4wfGEpXeXqsNHZufzVPTAQj1tOpnW1zMT42+zTz/nHZjHdDwbBmVOg6fGHtBa6qpU7hjEDb1jQtzlczwDh2p0oLPh1a7q8MbwL/LGzF/huqvolmCen1QQZS2rFPpTQ3Kd4a6Dvh3cv5v66gvBU5acVs6HudcOd7wfMfrkifVuouo9dtooHtx0P10pp4d26VXHsobFE29Sr3j1UJchGu3oRJ0+dCzuJHNsXlVXOJ5o9wxRyW3RllZ06DZK00Y7ytverXoj7qoCza9rI6nd5WgbNESTYgWfbbnDvuHo3DYd0Jz+5dbAKx58Addp0ZdCD8gdoMmTHDeUnK1Q+bvNLTTp0ldDi+K1pZ31D/1Df6d41NA45yOsDHuP6bQjj0eWp5LvxcsXmdZGJZKXw/f5RMRz4bnylkAd+fSSezQi6sSQrVcfOe+9qDqR5hNxtlpvOe78zr/t2yqKV0g69NWIbzkm/8Nv/jhv2ubyYP/PM2yGbnMM4qPpOmaKJjkmA3a9uiv+TXNm23H7Cm6d44zqm6Wu3LnR90+bbjdpVrxXEnXsrXxnfvx9Xfx2N5HtGm6McVzpr7Sy9ja+TCU3jtP790V6PNBuPajbBsbOYU3K0NGTLey7plti19KdMX2D6aknoeMAjZgUzPuTvvL5FgQfk9O13wBHoOGgDn5gJ/0uHFb1O3ZaORo60Nlv76vCIf6z1aBde4OT6o3O6KNDjnJ8VT9FjvG3Yv2XX6ihgX8synv5rT6Uf8zZ7XrNnTjE/iAzdnDc+B3g1ZGa4NyDZ0ihIzBjS1Pfonhm7ACCpVc/R91hzsHxj+1kxkrnODgKN/rVJ3bp1b122ii4baqGxp1yylHBhAkaan+yyuH6igSelB1SFP+VIJf3DQagLd2+qaHBRz8jC+uLV/8+gRN84y0aHeNhm0Z5Gm3qyGAZSWaFpjhSNca4cEBvOFZ26Fo8Jf5x5Q3V6EF22qjacyQ1dX+q+snpHOu5JcG+fUq1ZvlKZz3S2mU3ikweU6WnL56q+YKwh+pMWzstkbnb/l+Vs1eBto0gfwby/H0Xx+CuTq9uPxC7Qu80QMUxl7kxg0HnncnWwCaByZLcKwc4KoPqpoPMEJ2VG/WOMbc57+Y2HMvWxZQ/IKThbHIHaMc8FVxbqEL7q6BbnAq/LejypTgNbviS7KYzdHWMu7QD+ppzaSeN0zXhQdXOuuhSO2l5xwzy4jz53vWGYJ6fNmuIo8y44+I8ZwP6rrbsiDOS72UaXUe5DHndQTJaOx92ulYjHAHxLe9EmjwxHaQ9v9YWO20Fi4sGhe+nO+X0+N4qxyDFnKOI7xhrqs8NZrDjn7zrl6s/299vlguHVeWoYzX4awm/Nyp/5DcdE00nVbUv3s0kqaxrT6rGuQTpoEHqH28AZOnQV9dPC05+FuQ6BwZ1qn7bManY7XoNTDDv5w4Z5bh5wKvyfYndGZvpvpjjrCP7Kr9P8mX4z3HvXu4sTyLXLqMklpe7XBovYm/yzuXBOrLw2t5pXMK5ebz796jSTjedpI+u68BvBustazUZ58zTIeed6NLoCDdaRWTK81Dne9mOV6km3k0l1vv97GTaZVTfLHXlLvV9/zTq1EVfjDuuyFPXr9jJRCW0XTfGOC6NK1oZ48vmcuc4Pd3MzwN1Wegyu21TItfSrTG9GWc6/u3aX/xCNTEjNzkqGB/M+2l9NUQvZ6DBq72HwgL1tdXa5c+OnqEqDItW518VHHDXvncgdLx94ZiOOJ4GLCzsE+G4Wrn+M32SwsF22iiP8JScsw/VdXB/c8yDAjeke3cfiLAS2DHVBoIsHhUPjvCe6TT1LTz+JZ/bkrSOgyNzo18d+kqIPI0YnNg21WVoyMMEvjxpp1ukY1jf25TfZKeXvP/XTsTi+bydiOOqr4Y+eX4oZmOeuFSNMZx1pdni0GsSuPHE5J2Q1VRStepdqvrJ6RzruSWhc9EKXCpfaa1HWrvsRpTZY6r09MVTNV/wgWqdKyKbtjahuVu0KwT5M1H+cN3maFE+2v6gxpasNZVvnbzNWaak7phqHE8PDO13uZ2KI6+H/sFOWj76fWZNVgV9YBoOO2l0LegdXLYulg7d1ctxKtL+JF+WOn7IcfdYp77KT/BGzG6XOTrMh04oNIafo6IbHcuCeg+rdPp0lW6u1vEMeUzFeqI9uHSjV1XPl2jq0q2q/qB5g9Hs4VHh4OATEnrn1zp43k4HeHXw3XfttPmLG8zgvkmHw41y6tVpZ73UrZ96JjgZnHvVuODk3bQW3iRi6tj/7RhwFRU4lkSLp1efkPckVR1v7iMMzXD+hI47CmL+lYkGR0MnPycNcwxWLpwMWRLVMyiJAVLHvurvuGPaW5ttT6nBFR0SHLBmkVMnjtgpS2Hk5XIjCbl5bFzI02CnjzveJydTlq5MuBZSrwLnJNFhHfm9nUT6pLrvn4H+JvEsmThXxjhtdFzB+LJ52stxZgS3yp4V4HD0VT+t0MwJc7R+x2GddvTfM0JYX7j2/WMh9/OF3Nw8eECTG048joB3kxUkag84VtrK0cB+kQbxrV3/edR/UPD2mKbv5XeOOXM0+rq+UqdCXe9/+tZ64i/8NWLHD2pX4Dxcq8IEnnxFEjJgHOxGv/rDDxyBqWivtojIoz7mHAR4W3vlNJdYZT6DV5epP+J8nWd/9U+w6e7aw/mL6b128frJjPWyT0bWI2ktu8RqUqbupGqdbW2/GK9DRbtFkD8TmQqt+EfOd1RJ52q3avHs6fr6Td/R1HnLtGnrbtWeSHBUGvIORqnyse9EXAa/yde/LNEm+2/cVHTXKr39xmsJfz3uvAvRYiq74HLTpvOz+b7Ix9Pka7pKk7lp7cIZHX+3QpseeVBTJ0/WyAjbHPt807vN2xbr3fl20nJ2q6ZGOA+RvsavOGb/UWSeITND3j0kb53KVy/RxFtv0shb52jxU5tVuf+Y6ltrMia3SCVLRqmn/dGaZKjdvVaz75ys626arNkLy7RlxwEdd3sHWyEfegZ9LXiDg/dd7dobdowhd/B7It9V7Eo5PaPP/mAnLZ1y9EU7mVZ/qJPj1hf1vDSR1S388tTVOZZM5wD5T2dClmq7KCcFwdRP60Kuc+GlCd4F5NNZXS+zk5YmNwMBifGeOqzKTWVaXDJdU8ePj1CvPJiW/k005+qdA9VcfSEFd2F/9rGzje2trjHez9jEl3s4njjNsmBUW+mbpbrv3164McZJ17gi3drZ+DJl0nWc9cdUvXWDls8z7datkdotk7/Cg5dtjYtlr9etCzW/wJH5zx/T6mX3afzo8Rp7+4NavqZCVYdOZsBNVZ31D4WOvvOeAzoS2Kc61bwXbJ8jvRpNnfo4gtihr1mrP+oMekUJNmRA/WctLRtYzSD8vfzOMWdgJYMcFQ7xP53foPKq0Db/3NFDwScgE121jHmfxGXAODj1/eo6nXYGQS/vobirYDt0udTxcIuy4ZULzZGnXlfYScvZBv3JTmaCUyec5bNaC8Y3LcMRvxYE14TINO12rJe1MrUeSWPZzbYxVSb3xf9wIqytTWaeFe0FQf5M1W2UHt/4kEoKwpaN8Taodv9ulT2/TFP/1VQ6N83S4vW7ddx5lzZS64I551tLNfWmyZq4cKXKdh5Q7akzaXs3X/uRo4K712nzPUOavDPpnGlsKys2aPG8ORprTcbMLVP53vR3TD0DZ2jjj0tU3CNsSuX8GVW/W6nlyx7URNMZ+Pr4+1S29YDqUzlR1Jr5sGOhhgbe8eg15z5sUmN/lbYEYvzXami8dxm55St5id0Z6qqclATsWoMbHcXc3ATXawRSoa5a60tMHTz5Pi1eU6nK2jrVns3woOjlebrITqZMJ0/bv7O7LfbN6PvDbeSxzHP2mMqXTtfXb52j2c9v1pb9pt1qtTua27AOeRr9+DqtmNBXXUIzv+rrDmjLppWaN3uWvv4v4zVz4QZVHXf3HbWxWO9kD+7iAX3kf7j57BHHU+qFUV5RlqeCa4L9eedy9x/WOsZvBf3UJ1PHK3kDVBSIhBxW9UHHtXAswe25sUgF9koGuVcXBW4MOP3OwZCn/4/sD06LFw2K9IoCB+Z9WigDxsFu9KvNeDZSaYPDqXrKSTq1h7FeW5Op9QhlNzv64hesHqtDJr72Aq2OIH8m6zRAty5/Sa+ve0jziwtVYBryJs6fVOXGZZo4YbpKtx4LLfRouQt1Kp83WVOfrwpZGiW3S54Krx0efIeX/VVc0IrvRG0jut54j1ZsWacN909U8VWdIwzEvaqvqVTp/OkaWbJW1el+n1SP4Zr74816/YV7NHfYAPWMMJD1nj2sTc8/qLHFc7R+TwqW82/1fOhR/8HXBjql3h1VqnHcwFD73q5A3eO5cXiEpfrTJNH3ULkqL+S9n1nFhY7iRZ2oE5Ee3r0rNeH2JVrtrCQ9OcrvNkDFYXXkpJtMn8r+lVbn8ehv7GTKXNxZX7CTbVJb7pvR94fbyGOZ41SF5k2Yo9LddY5z7FHPbr01fFRoPTbppuEqytb+ZabokKOCSY9q25ZVWnHXOA3vFendz17VvLtZ86abNmZphY63xsz35f0c76KtU9XBxrFkyHvH+31V/aPkh14DiwKvIfPurrafYj9sjsuX8MkvHJABN0ZH010Fg4Pt9i7H+4Jr364MrEYw+irHUsbWjQH+lQlOvaW9gVXfncedp6KrY9zQzLxPCmTAONiNfnWXnARfaQekSVsf67VF1COZib442hCC/FnA022ARs9cqBWbN+vt11Zpw+MlTYOL1tLmz9+n722KvSS65dbSyMvgx/tqskx+hkp2+f/A193+Zd78zCB/xX0qrfFX9R7lD5mhNZte0+ubVunpJSWOd3g1fk0e0sP+3Xai3wxti3Qu437NiB1g6dBZvYaN19zH1+n1/7lZr//4UT0eYTLGWmZ09veWqaoVXgCWe/kQFd//kDa+9pp+teU5rbl/hiZd2z306RBrKcgFc7T8nZYE+jMjH3quHe5Yst/5vsPDqvql/wkLj4oHh5ejyFJXTh0y4l1wx3Tc+QqBLOLGsm0ffcxScEiDugotWFgRXA6uY29NW7JKv3rtJa1Z95DmhtWRvvdu2r/a6tx4LcUHdfrMTrY97aNvluq+f3vhxhjHlf5KBmB82TwpO87zB7T63pWqClRleRp916O+cc/GdU9q0fdD67Fps8ZraDLrvLYBrpU9c64Lxk3RolUv6VfmfG974SEtmjpEQ0MGcdZr2VZq6rzNOp7uJfytJfed76KtbSx/tfuDUfr8a2IE6fMLNdR/KGerVGM91v7pMdUEVubwaGC/7nY6ttaq//ILhwbG/MEbFU6q5h3/mDN8JYPuGjjUX8ZOqqraHn/UOY67U5EKor4PmXmf1MiAcbAb/WozRm79OYbM82d/cbFk9JPlo7QiUj0V9+tRjc7ULkqbHuu1URlUj7RW2c24MVU29cU7egI3cALREOTPNh3z1Ouq4Y3BxS2btXnJKAVfL2cGJ2uWaUvgzmVbWGXgPees0duAv80NuSPus4a/2KkWqt+ll18LBmd7jnlQKx4YpfzMve09DXL1BefJTse7tzp4lNujr4r8kzGbHtX8wY53vX26WwterDK5v/V4crsrf9goTVvynLa9tk5r7ipyvLv/jLY8skbV5+2PycqUfNhhgK6/0V/ZNKj8bXvJx+MHtcs/eeK5QddfZafDuVJOO+uiL9tJi9erP9vJtPpynpzvOPusPpklPut03Plepyu6p+/Jmi92Dm0b/q+daImL8xx532SP/y+ZG1zMuThqJy39kntvGdqv2p9vcAzO+mr+C09q0rV58mToMmbWU2FBf9GfUxBAuCjkvX11+iyZZbbD3jnd8ysZHHRrj32z5vT92ws3xjhujSsyGePL6Fw6znO7N2t9YDWyzrp1yZOaP66vctvz8putUfbMOLPL5QM0/NZ79PCmzfrVuoWadHlwutv7wQaVbjlpf0qXPOUPdDwlfvSETlvL1vvfRa8cDR0YI0jfoa8KB9tp0x84eNSMS37vbOf7q7/zPehOmVL/9Rug0XZSZw+r1orZ1x1QlT96O+h6FYY9Tddr8HD5XtFvVO054lt+2HvkUOC4PUMKAz9vgnmf5suAcXDq+9V56voVO2k5VadkFo+s/73zxri+6nWpnWxTwt433i1Pybwi3m2ev3WutHFO/9HcubgM0m7Gem1GptYjaSy7GT6myqq+uGnnnPcpfvRxMvOsaC8I8mczMyjteu0MPbNouGPy4aR27Ql7crJXX/nfUWaper+NPfHTqbf6OyJBtQcPpeSdMuf2VmmXnZZ667abB2Tw3anpkqOeVzo6hKcO6H+n+3bELn01+oFHNb+f/dnwvlmtg+l+yiKaDp2VP26+npjpWELQu1tVNXY6SZmUDwuGjQrUNad/WeV7quL4O5X20xVS19FDA+9GbMKVcmrqQOcA5YMPdLw1BnB5vfUPjoviXFYyruNHtNdOWop6pfGpkI491MtxTaqPpGBJ3g7d1ctRNk/XHEv8juXzx1TrmOjx5KfxhgdkMedqIsawWzQ6w+8O6dajj52yON632wJdTV8vWA0d1MFkqqFDwffVWpMIfZwTEhmm3ffNEu37txdujHFcGldkDcaXoVw5zgZV7z5gp43Lx+lmZ1C3vcqAsufpVqhpj8/XrY4Acs2vfhtYIj5devVxhE2tJzZPHdbewBPpw1UUNVpt8aj/oODfV+4/rPqPHfn28gHKj7bcbKbUfx0HqHCQnbZucDjYoPqqXYEgVcR36/fqr6H+49rzlqrN+XKufhCyvH8Y5n1aIAPGwW70qy+73JFf/DeaJMSrI4fshyEsnt7q1RbjqWHj9rQ+qJCAkDpU1apNIltmqvYy1mtLMrIeSWfZzegxVZb1xfO6K9/R1vpXeQKcCPK3hBt39n9aqdLJ0zXV/irdEf/uHM+gIY73tpkuTPjyyB17K98ZdCn/RfOfKs5IprIrdNR2e7fqjRR07L3nnHchm+235t1cGfS0TK9+hY7O5QG9+suWP91QsyaY56dO3qC48fAOefraMOdA/YROu/lu/poNjv2brk0JBOy7Dh4aMinY3GXLMyof9ivSaP/kydlKVR06qb27/MeVo9HXRZ88caucOt87Kb2rqr2JlY3jG6fruhE3NX7dubVlk3cd+qpoSPDYvDsqE65ja1//eeAmCescFV3tWKXCdaHvvNQ7v9bBhPb7pLZMts+d+Zq9xZm381R4nePpokO/0FsJXudzuyu0xU5beX301bHyUyytsOIIWpFX/+F8ksHzeTuRuTxXDdJwO23dTf/GnsTaUe/uZcF6a0SZqp03t1lPvQWqIa+2vJngCjcXDmvXVkcZ7lWkgkx6FCdMRrWJqeJG37+9cGWM405/JWGMLzOLK8d5Tv/hnOV04x3SSfDkOic1z8jbatfRjbJ3RruWBvP+1KWV8W8+zS3U9UPstKUVlkX2XNnPMY48ptqdH8gfovE9kR6n7cvt/9Xg3x88prccTwR2Hdg3pI4J1cr1X0CO+g8KjgN2HapW9R5/wKOvhhZFinaY8dgIf142v7//sGqr/fVn+PL+odpk3yJdMmAc7Ea/ukuh6Q/baetGk/LdCc551e9S+U47bXiGDIi+gkQm8ib2pK13769Vbqctw/tFWx6kdXj6OOvQBq2vaN2VP1PC1bGeR19w3vxl6sSsP18ZIK31SEaW3UzpU0SSWX3x+PqqILBKk7HzrbY57kKLEORviW6Xh7zTNf6d/Q2q3hm8Rziii/N00ad1qj3V+FW+87fx73S6EFqZe5pc1TxdP8rxDhPvbi1fcyCxRvvUbm3amoKnO13mfMLYCkCVPVmR2NOj5w5oy/pq1cd9CjyBCY8Ldfr1TsfddqnkRl5rrquGapLz6Yb1z6oyobnlBtVs2qDqCMH4bn/fOZDna09VqDqBIPqf/29orvwbNwfjX+6iiwL7V6dX/UvVx3LBG1puOkSfWEhcK+dDaxD/z/7JkwYzWPh3VVnvebRYT5U4JkEjcaWc5jtuPDBnfMv61+K/O9Ma6PwimGk9V/aOMdmVCI/633BDcBtWHbshgWtQV6HV5Y5JpYHjNCLNr3fML3I8qWf2e3UiS5IeqtSrgZcdevQPYbcVdx3yDceg2lznFbvjt2PnD+jl9Y47aTuN0uhrm1tmctS1l2PCOpEVR05VaZfzjmZkrwQmJbx7djue2GoFnfrrawPttFGz3tSlcQvJGVXtCD6Rpn69dZmz3etYqBE3BvO9982VejmBPF1fsVbrHTdJDL15aAvrw3Rq5TYxVVzp+7cX7oxx3B9XxMD4MsOk4TgTuRnRxX5K7qU9HPm9TtVH4twE4mK9mvqy11kXdT4TyPu1u3erOoENhryvNhVDuGRdPEBFgaffjmnVK4k9kR6Q5/j7U7/Qq+8ED6ioj3PJ56Zatf5z6HL1VwNBDe/uFVq1x/7Q7asqiPJEo/Nd/uUvr9Qb/vFKv6+qvzOAFVMb6VukTQaMg93oV/co0mjH3EbN+jXaFbcgeFXz8kbHGCNHk0YVtUoV0mw7XlF5YJwfhcn/b7yy29G2DTDn33mzWAbIK9IIR56wxkXr9yfUcuv0mxtUXuu88SdDuDrW66Fezijy3sNx59RafTydDdJZj2Ro2c2UPkVcrdwXj8+jwiFDgvnAtLWb/mf8QMzpql2Bm0TR9hHkbwlr6RHHSzFOl6+NWame3rJE83bE6ywM0NDRjkp271qt2hl7oH16+yuOpx/NwK9/04Ff7g0TNM1xt95Hrz2kxVtjB3O8tVu14M5lKnt+jqYsrtTpdFWuzVEwTiWDgs2et2at7n2uWudi7fOnVSq78yEt37hE3ypZqxrnk4BGl6/0djSkdSp7piL6OTh7TFvmlag04Qq/i7o5lq3Rx3WxJ9tcyWvN1KGvbp7heJrfe1iP3LvStyReVBfMIGrZHH1vzWbNnjzHdLBD963L4BscTws1aPXqDToe64SYBv/VnzkmPc1g/x/cfPLw4tBBwukta7UpZse/QTWvbnU0pnkq6t+8O9PdzYfJyx8aDArX7twdWDax6z8XxX2qxI1y2iQ/Ht+gBc/HmHy90KDq50u1OnCzSXeVjHFM3jqEvt/vpE7H6I16Bo7T9OALbPXR5lIt3x2j7j53WOt/tDb4HnF11rRJw5suPem2sGtSs/4Ble2NkbfPVqvsoa36yP6oXrdo9FV22i/vBk26KZjfvXue1eKNMSa5zWBj12PLHe/D8mj4jHHx81MMva4MXXFk3cYYecI6pnvNtbA/Iglf7BwySXD8D/GfDk29vJBXRGjnSq2KkYfrdy7TlAWmT2N/jie3yyV2ynJCn/1/drJFOmv4pHHqaX+yBmkLFsao243TW0q1eI8/F3tUfPPQsKX1PCq4ZaKKAhn/jFY/tExVMVa5sergH648HCwbF4/TpBvSuZpIo2Tq2kxrE1PDnb5/MtzJ5+nhyhjHjf5KohhfZpzUH6dpt64I5i+dekXPOZ+yC3OudrPmTU+in5Js22y9ksCxO7vWv6KaaOMwXz/6PvfqVRfKXsFQ5wTzAa1auTv2pPGpCm1yPEGnwVGeoLvg1bmzDeYrag+zBZxPv5l/57z/34j9RHqQc7WuM/oo0Bfoq/5Xxvn71qz/nJzL75vz7O+3dR3cP+S9tCGuKlKxveveD44FVgfMv2ZAzOWIs7NvkeR8UlKS23brj4Pd6FfnacTUUY5tVmvx0s0x56hOv7lcj7wWPG7PoJm62XnXXlY4rNJ7Y40fGnR803Itd+T/rmPGa2gmrdXvE5YnTD24fuFDKo/5FLE5ts0Paspjm1VaMkulOxJ4+CGt3Bzr5Sj/asdqjGcrtHp7jADiCdPHsfo29sdU8fraVPvLjaY17dJZj2Ro2XWjTxHofzV+eWNtKyqX++Iu8Fz7DU1yjEeqVtwXc+723J6VWvCio/yjzSPI3yLddf0ox4SH16pUS7Vl78lAg+Q9W6fj71aorGS8xq9IrHDl3zJTwwN1TYO2PDJds5+qUM2J0L/21h8zhXqOpjwf2miPHuyoqPw69NWkf5voeDLEq13Pz9HUBRtUfSK0UvTWNW53rKlMd9l3MZ8+m9jSL62ns4bePzcwqLOOr/Y10yDMLlPlobrQ4zt3UjVm4DF1cqk21TWeOa81OA8PKFlPrDsqUG/NSk0wf7Pl3WOqtxuT+g8OqHL9Ek2dMEfLa8KvrvkctbG5RF2drw7bu1FlWw+r3gzgreXi6uvDK2p38lpz5d5wjx4e5mio6yo0+7tzVLbDOgb7mxavGZDv3epbInTeDnMdfN87o/8IPy9dhmvSeEdn89BmTb1ziTbtNOfaeSCmMfdtzzT4mwKdF4+GT7wh+mA/JcIHCYdVNneOFq/freMhO2gO78QBbVk6R9/b7uyYTdSI5u6gq/mwGZxL9gfkaPTQRAIMLpRTI3fYDM11Tixsf9BXf1V9cCa4TZN36j/YrfWzJ2u249r0vGmGRke5NuHv91v3bIVqzb54Tb4+V2e2bf+kUZ6G3zslZNC1Zel0zVxWqVpnHrHy8LtrNXvCfVr9QfD7PcfM1W1xVkJwh7kms0L3e9N8c45WhOVtU26P7zT7/V1TLgNlr7MmzbpJvZpcEzMAnTRXtwbyrVdV601bNc9qb0LPRf2hClM/lGjBO47yUjBF04e1LNDoGThcxY58+tH2h/S958wx2XnJqpvOnThslxvnMSEpHXuol2MS8HT5Sq1+12qXTDmx6iZXJr7D5en6Mc6bOvx52JQ9079pHPydVO3OxvZx7CO7gzep+MVY3s7zla84JvcbtP65lY39JnOM586e0Tlnm5eMfrfo/jGOm2H8dbtp10PqQl+bMl0THO26VUYmD4lw133eKM29w/G+xk93a95ku212XorzJ1Vt9fPmblWwyeisW++9pUU31zRXUnVtprWJKeJK3z8JruXzdHBljONOfyUxjC8zjgvHWXCDY9LXbK/q+RJNXbpV1R/U2e2Wucb7K7VpwXSzrQ2OYJjNlM0/28kmkm2bw54O1KcV+t68laYfbed132SqaUd3bNDiyaH96NRzoeyFTTCf3rlM3zHbKw9rbwN93TvNsQdOUWdNG1fk6GPYLhzWpskTNHL8d8zXeE14NsHVHZLQq19/O+WQxBPpzqfaAxK6Mb416z+nvip0LAPfKM7r4UxZdS4d3yhHQwc6gleRZGXfItn5pGQku+0MGAe70K/2XDUxbJsbNHH6g9pk6tOQbdYfVrm1zceqgmMMT1/NnTUk/Tfwp4Jv/DBdpZurzdjZbuNMn9w3l1IyWRPXO/oHnkKVTBjQtK7JBP2m6OFJjrJ//oBK75yuxeurddoZQfbPE1nHtvqAfUNLgz47n4GvgHNxrBe6GqPVL7nP5IEDwXN13soD1Sp/ao5G/qvzJp1UOaBVvja18WtxRfynlLNBWuuRjCy7LvQpPn1Tix15ZdV++/tJcrUv7gbTx7ntXuc+O+Zu/dfb6rObds4qp99aUKHalJdTZLLP/dWw02gOM8Bbf7vppCYYIMjNy9MX6uoCd7zdWvqaShxPCPt5967UzIVJFkhPb00rXapJBREmfm3N2a7HdCSeeWGGCsJalvqt92ns8/6luPpq/k8f1egoS6eFqKvQvNvN4Nn+WHTXKj0+LpE/TMCpCi0wA3P/pEpCOg7Qohce0nDn3co27/61mrlga4Lny2MGkL21a29webJo19fifWeZRi52LpXjMOYhvX132NPFruS1AyobYToY9qeI/240F+pUuaBEixN8B3ojc47uLtPDYyJc72ZtzwwKB9+jJx4Yoq7NnkxI/Byc3v6gpjzr7/gn6OIiPfzEfA2NkL8S5WY+bA7rffYT1zs63Z3Gac3mKfHfE+WX4nLqc+6AVt/5kNbbncFExM0756u0fHyptkTc5CiteMPUi/YnP2/tZi2YG6EDGEPPG+br2XuK1CXKfjS7rk1C8m1DZ43+waOaf0OMHfnUnL/vmfOXRADdUzBRa5aMV69oI5kk2o9zby7R2MeqI9ex4Tx5yv9inWr9+9pvhrY9ParJUz/NuRZu/U269iWe05tnafzqyE86hF+fZv37Jm9eN7/C/hDtb0z7MT/x9sOTN0CFHQ6oyv90bJTr3eiktkyfpeX+V5OEaOE5bEa757l8nFY8burbqKP9BlNHL9FU5yA+rs4qfuBJzR0S7eaa5vQVkvibJOtat9rEmmdv0szt9ocodbyb5c6tvn9iksnndSqfPT34FGPM8hMUcn4j/k3ztuuXyjFOgBv9lUQwvrQ/tbCOjaaZY9HU5jGvalaU6Htb6hKrq02+GtrrgGOZ0Mh1lF8ybbNP/W4t/u4yVSa0Mx7l5+Wots4f7I98nVp0LVNd9poxTrCOs2D8Q3pmmiOg4rd/pb4+r8Jx7Qr18OaFwSfPU+HUVs2evDZkmdX8Sc9pzYQ4AWs/q229ybSt9keLx7TFv0p0rN9a9Z+D9c70ry/dbX8yPCbfv2byfYxxf3P+xuJK38LUGcE+bIJj8iT+Jun5pCQ0Z9tujIOT4ka/2l6Rct6bSdzcZAXmli9UcX60dtPlfnVAIn8T+juFN46SZ2dFgtews8mfz5n8GeU4E2pr3T4XDap5bo6+91qCba2PaeNuelAr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      ],
      "metadata": {
        "id": "QZVDQQDQtcae"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## VectorAssemble data now"
      ],
      "metadata": {
        "id": "xY4oVqLnt-Wf"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# 3.0 Import VectorAssembler:\n",
        "from pyspark.ml.feature import VectorAssembler"
      ],
      "metadata": {
        "id": "2a48743CiIXR"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# 3.1 Transform psypark pandas dataframe to pyspark dataframe:\n",
        "spark_df = df.to_spark()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9q0qLDSPjMlx",
        "outputId": "7a64d892-692c-48f6-ec57-a2a23638d2b0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/opt/spark-3.3.2-bin-hadoop3/python/pyspark/pandas/utils.py:975: PandasAPIOnSparkAdviceWarning:\n",
            "\n",
            "If `index_col` is not specified for `to_spark`, the existing index is lost when converting to Spark DataFrame.\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 3.1.1\n",
        "type(spark_df)\n",
        "\n",
        "print(\"\\n==========\\n\")\n",
        "\n",
        "# 3.1.2\n",
        "spark_df.dtypes"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PA1oXTjltrEf",
        "outputId": "ddf7b941-62d6-4210-fe6f-951c82c8b269"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "pyspark.sql.dataframe.DataFrame"
            ]
          },
          "metadata": {},
          "execution_count": 68
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "==========\n",
            "\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[('TV', 'double'),\n",
              " ('Radio', 'double'),\n",
              " ('Newspaper', 'double'),\n",
              " ('Sales', 'double')]"
            ]
          },
          "metadata": {},
          "execution_count": 68
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 3.1.3\n",
        "spark_df.show(5)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lKmHYAaJt5rn",
        "outputId": "d159cfe4-ed17-41be-90e7-fb5ef3ad8ad1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "+-----+-----+---------+-----+\n",
            "|   TV|Radio|Newspaper|Sales|\n",
            "+-----+-----+---------+-----+\n",
            "|230.1| 37.8|     69.2| 22.1|\n",
            "| 44.5| 39.3|     45.1| 10.4|\n",
            "| 17.2| 45.9|     69.3| 12.0|\n",
            "|151.5| 41.3|     58.5| 16.5|\n",
            "|180.8| 10.8|     58.4| 17.9|\n",
            "+-----+-----+---------+-----+\n",
            "only showing top 5 rows\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 4.0 Instantiate VectorAssembler:\n",
        "vectorAssembler = VectorAssembler(\n",
        "                                   inputCols = ['TV',\t'Radio',\t'Newspaper'],\n",
        "                                   outputCol = 'features'\n",
        "                                  )"
      ],
      "metadata": {
        "id": "vVGxqM2liNIV"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# 4.1 Transform spark_df.\n",
        "#     Note that THERE IS NO fitting:\n",
        "\n",
        "spark_df = vectorAssembler.transform(spark_df)"
      ],
      "metadata": {
        "id": "lt2nGc5_ieJ6"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# 4.2 A column 'features' gets added:\n",
        "\n",
        "spark_df.show(3)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UKoJz36JjzcJ",
        "outputId": "9bd952b3-1e7c-451b-bb44-e63eef2134af"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "+-----+-----+---------+-----+-----------------+\n",
            "|   TV|Radio|Newspaper|Sales|         features|\n",
            "+-----+-----+---------+-----+-----------------+\n",
            "|230.1| 37.8|     69.2| 22.1|[230.1,37.8,69.2]|\n",
            "| 44.5| 39.3|     45.1| 10.4| [44.5,39.3,45.1]|\n",
            "| 17.2| 45.9|     69.3| 12.0| [17.2,45.9,69.3]|\n",
            "+-----+-----+---------+-----+-----------------+\n",
            "only showing top 3 rows\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 4.2.1 Check dtypes:\n",
        "#       Note the dtype of 'features' column:\n",
        "\n",
        "spark_df.dtypes"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rW0Sm1xLuYcW",
        "outputId": "b4a28ee5-7cd1-4d02-f197-8f93f2eab1c4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[('TV', 'double'),\n",
              " ('Radio', 'double'),\n",
              " ('Newspaper', 'double'),\n",
              " ('Sales', 'double'),\n",
              " ('features', 'vector')]"
            ]
          },
          "metadata": {},
          "execution_count": 78
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Split data in train/test"
      ],
      "metadata": {
        "id": "XkORiwVSuEsP"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# 5.0 Split spark_df into train/test:\n",
        "\n",
        "splits = spark_df.randomSplit([0.7, 0.3])\n",
        "type(splits)   # list\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "quUf1BRakCxh",
        "outputId": "967e93f8-e759-4b83-ef6e-1d2a753030af"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "list"
            ]
          },
          "metadata": {},
          "execution_count": 79
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 5.1\n",
        "train_df = splits[0]\n",
        "test_df = splits[1]"
      ],
      "metadata": {
        "id": "jI-Tnb7qu1nt"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# 5.2\n",
        "train_df.show(3)\n",
        "print(\"\\n==========\\n\")\n",
        "train_df.count()\n",
        "print(\"\\n==========\\n\")\n",
        "test_df.count()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "oXQMtpwHkIJ7",
        "outputId": "d3b87555-2b0a-47b2-c497-f29532030f4f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "+---+-----+---------+-----+---------------+\n",
            "| TV|Radio|Newspaper|Sales|       features|\n",
            "+---+-----+---------+-----+---------------+\n",
            "|4.1| 11.6|      5.7|  3.2| [4.1,11.6,5.7]|\n",
            "|5.4| 29.9|      9.4|  5.3| [5.4,29.9,9.4]|\n",
            "|7.3| 28.1|     41.4|  5.5|[7.3,28.1,41.4]|\n",
            "+---+-----+---------+-----+---------------+\n",
            "only showing top 3 rows\n",
            "\n",
            "\n",
            "==========\n",
            "\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "137"
            ]
          },
          "metadata": {},
          "execution_count": 83
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "==========\n",
            "\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "63"
            ]
          },
          "metadata": {},
          "execution_count": 83
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Build Regression model"
      ],
      "metadata": {
        "id": "Czr9WrIiuKG9"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Elastic net includes both L1 and L2 regularisation as special instances when set properly. For example, a Lasso model is created by training a linear regression model with the elastic net parameter α set to 1. If α set to 0, on the other hand, the trained model is reduced to a ridge regression model. (Refer [here](https://www.edureka.co/community/170787/how-can-import-elastic-net-lasso-and-ridge-regression-pyspark))<br>\n",
        "\n",
        "Where:\n",
        "\n",
        "elasticNetParam is equivalent to α and regParam is equivalent to λ."
      ],
      "metadata": {
        "id": "LwKx2MvKx1FA"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# 6.0 Import LinearRegression library:\n",
        "\n",
        "from pyspark.ml.regression import LinearRegression"
      ],
      "metadata": {
        "id": "uqXozdrakbEP"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# 6.1 \n",
        "lr = LinearRegression(\n",
        "                      featuresCol = 'features',\n",
        "                      labelCol='Sales',\n",
        "                      maxIter=10,\n",
        "                      regParam=0.3,        # alpha of elasticNet\n",
        "                      elasticNetParam=0.8  # lambda of elasticNet\n",
        "                      )\n"
      ],
      "metadata": {
        "id": "kInhEH9ZkYvp"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "lr_model = lr.fit(train_df)\n"
      ],
      "metadata": {
        "id": "69MR6NLSkdn0"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"Coefficients: \" + str(lr_model.coefficients))\n",
        "print(\"Intercept: \" + str(lr_model.intercept))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZIgUgFdDkYfg",
        "outputId": "c8dea3e4-7d16-42aa-b876-a2ba1cfbffc7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Coefficients: [0.05116260864177558,0.09587468660492526,0.0]\n",
            "Intercept: 5.409379051533406\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "trainingSummary = lr_model.summary"
      ],
      "metadata": {
        "id": "EiHkJy1sraz8"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"RMSE: %f\" % trainingSummary.rootMeanSquaredError)\n",
        "print(\"r2: %f\" % trainingSummary.r2)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YIlj8xYjkotb",
        "outputId": "fe95ae23-bf08-43ce-b0aa-40b7b6e4313d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "RMSE: 1.715038\n",
            "r2: 0.893853\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Predictions"
      ],
      "metadata": {
        "id": "QVeS_yIIuPDY"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "lr_predictions = lr_model.transform(test_df)"
      ],
      "metadata": {
        "id": "PMBzyul9rftW"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "lr_predictions.select(\"prediction\",\"Sales\",\"features\").show(5)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LEm4I3DUriBL",
        "outputId": "97f26ca2-5ab7-4336-e92a-7e0042de23cd"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "+------------------+-----+----------------+\n",
            "|        prediction|Sales|        features|\n",
            "+------------------+-----+----------------+\n",
            "| 6.731292111581819|  3.2|  [4.1,11.6,5.7]|\n",
            "| 8.476944788216768|  5.5| [7.3,28.1,41.4]|\n",
            "|  10.5427659216977|  7.2| [8.7,48.9,75.0]|\n",
            "|10.463750942214649|  8.7|[16.9,43.7,89.4]|\n",
            "| 8.451716793325666|  7.0|[18.8,21.7,50.4]|\n",
            "+------------------+-----+----------------+\n",
            "only showing top 5 rows\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from pyspark.ml.evaluation import RegressionEvaluator"
      ],
      "metadata": {
        "id": "TQcpGS9UrqxF"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "lr_evaluator = RegressionEvaluator(\n",
        "                                    predictionCol=\"prediction\", \\\n",
        "                                    labelCol=\"Sales\",\n",
        "                                    metricName=\"r2\"\n",
        "                                    )\n",
        "\n",
        "print(\"R Squared (R2) on test data = %g\" % lr_evaluator.evaluate(lr_predictions))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nZ-mCdz1l3ou",
        "outputId": "d631a870-0ef3-4768-e044-05fb739e90df"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "R Squared (R2) on test data = 0.908742\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "test_result = lr_model.evaluate(test_df)\n",
        "print(\"Root Mean Squared Error (RMSE) on test data = %g\" % test_result.rootMeanSquaredError)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "to8rwFClmUix",
        "outputId": "0da5a38d-a3f7-4383-97af-6ab7035adad0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Root Mean Squared Error (RMSE) on test data = 1.5922\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"numIterations: %d\" % trainingSummary.totalIterations)\n",
        "print(\"objectiveHistory: %s\" % str(trainingSummary.objectiveHistory))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "u3srpon0mXeo",
        "outputId": "5238f389-bcec-443d-e6f0-c6968410c7b7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "numIterations: 9\n",
            "objectiveHistory: [0.4999999999999991, 0.4147579187824404, 0.10718909283289998, 0.10686427065214876, 0.10683207474984857, 0.10683051286154353, 0.10683048729171055, 0.10683048688079023, 0.10683048687409152, 0.10683048687398267]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "trainingSummary.residuals.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "E2dqXpxor85Q",
        "outputId": "1aa059dd-fd12-4206-cd57-08933a378e81"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "+--------------------+\n",
            "|           residuals|\n",
            "+--------------------+\n",
            "|  -7.641830467137689|\n",
            "| -3.2523102676862594|\n",
            "| -2.9379727078708484|\n",
            "| -2.7469364397782874|\n",
            "| -1.2507143277230197|\n",
            "| -2.2457575083639236|\n",
            "| -0.8179590993826364|\n",
            "|  -2.009133002623156|\n",
            "|  -0.782462135252139|\n",
            "|  1.3099759646619837|\n",
            "| -1.9300779625663793|\n",
            "| -0.8262035410542046|\n",
            "| -1.3359286448626566|\n",
            "| -0.7392473816712073|\n",
            "| -0.7922506370405422|\n",
            "| -0.5430658202319743|\n",
            "| -0.6575399741885519|\n",
            "| -0.9582546103549472|\n",
            "|   1.620481485769501|\n",
            "|-0.11852704208745735|\n",
            "+--------------------+\n",
            "only showing top 20 rows\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "predictions = lr_model.transform(test_df)\n",
        "predictions.select(\"prediction\",\"MV\",\"features\").show()"
      ],
      "metadata": {
        "id": "8xF1ksI2majY"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}