distributed_SVM.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "distributed SVM ",
      "provenance": [],
      "collapsed_sections": [
        "A13j_3Gd0VPO",
        "BII1IclCzwCo"
      ],
      "authorship_tag": "ABX9TyOdA40y5lcKg1mHViqaS89h",
      "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/joyeeta-19/image-classification-using-distributed-SVM/blob/main/distributed_SVM.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Ea0U9FnqvQnN",
        "outputId": "f5679264-28f7-4972-ce71-756ad0af87f0"
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Mounted at /content/drive\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CpV8xehivWLj"
      },
      "source": [
        "!apt-get install openjdk-8-jdk-headless -qq > /dev/null\n",
        "!wget -q http://mirror.its.dal.ca/apache/spark/spark-3.1.1/spark-3.1.1-bin-hadoop2.7.tgz"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VPvBXIfivlLn"
      },
      "source": [
        "!tar xvf spark-3.1.1-bin-hadoop2.7.tgz"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "a4Qf_ajTvrPz"
      },
      "source": [
        "!pip install -q findspark\n",
        "\n",
        "import os\n",
        "os.environ[\"JAVA_HOME\"] = \"/usr/lib/jvm/java-8-openjdk-amd64\"\n",
        "os.environ[\"SPARK_HOME\"] = \"/content/spark-3.1.1-bin-hadoop2.7\"\n",
        "\n",
        "import findspark\n",
        "findspark.init()\n",
        "from pyspark.sql import SparkSession\n",
        "\n",
        "spark = SparkSession.builder\\\n",
        "        .master(\"local\")\\\n",
        "        .appName(\"ImageAnnotation\")\\\n",
        "        .config('spark.ui.port', '4050')\\\n",
        "        .getOrCreate()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Dg-htAsyvyka",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 219
        },
        "outputId": "87a93723-d2d2-4b81-c66a-a345c55f7a0c"
      },
      "source": [
        "spark"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "            <div>\n",
              "                <p><b>SparkSession - in-memory</b></p>\n",
              "                \n",
              "        <div>\n",
              "            <p><b>SparkContext</b></p>\n",
              "\n",
              "            <p><a href=\"http://df7e305a35f7:4050\">Spark UI</a></p>\n",
              "\n",
              "            <dl>\n",
              "              <dt>Version</dt>\n",
              "                <dd><code>v3.1.1</code></dd>\n",
              "              <dt>Master</dt>\n",
              "                <dd><code>local</code></dd>\n",
              "              <dt>AppName</dt>\n",
              "                <dd><code>ImageAnnotation</code></dd>\n",
              "            </dl>\n",
              "        </div>\n",
              "        \n",
              "            </div>\n",
              "        "
            ],
            "text/plain": [
              "<pyspark.sql.session.SparkSession at 0x7f23ea815410>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2QMEiU1Fv1yC",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "5d3dd1f4-810e-4c55-c995-d0abd54a2ea4"
      },
      "source": [
        "!wget https://bin.equinox.io/c/4VmDzA7iaHb/ngrok-stable-linux-amd64.zip\n",
        "!unzip ngrok-stable-linux-amd64.zip\n",
        "get_ipython().system_raw('./ngrok http 4050 &')\n",
        "!curl -s http://localhost:4040/api/tunnels"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "--2021-05-23 05:41:23--  https://bin.equinox.io/c/4VmDzA7iaHb/ngrok-stable-linux-amd64.zip\n",
            "Resolving bin.equinox.io (bin.equinox.io)... 52.204.138.61, 54.159.163.191, 52.200.57.5, ...\n",
            "Connecting to bin.equinox.io (bin.equinox.io)|52.204.138.61|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 13832437 (13M) [application/octet-stream]\n",
            "Saving to: ‘ngrok-stable-linux-amd64.zip’\n",
            "\n",
            "ngrok-stable-linux- 100%[===================>]  13.19M  35.9MB/s    in 0.4s    \n",
            "\n",
            "2021-05-23 05:41:23 (35.9 MB/s) - ‘ngrok-stable-linux-amd64.zip’ saved [13832437/13832437]\n",
            "\n",
            "Archive:  ngrok-stable-linux-amd64.zip\n",
            "  inflating: ngrok                   \n",
            "{\"tunnels\":[],\"uri\":\"/api/tunnels\"}\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yU95wlHdv2VN",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "db3ee17c-4dd2-486a-c948-78797c080c85"
      },
      "source": [
        "get_ipython().system_raw('./ngrok http 4050 &')\n",
        "!curl -s http://localhost:4040/api/tunnels"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "{\"tunnels\":[{\"name\":\"command_line\",\"uri\":\"/api/tunnels/command_line\",\"public_url\":\"https://6f50dc980ef2.ngrok.io\",\"proto\":\"https\",\"config\":{\"addr\":\"http://localhost:4050\",\"inspect\":true},\"metrics\":{\"conns\":{\"count\":0,\"gauge\":0,\"rate1\":0,\"rate5\":0,\"rate15\":0,\"p50\":0,\"p90\":0,\"p95\":0,\"p99\":0},\"http\":{\"count\":0,\"rate1\":0,\"rate5\":0,\"rate15\":0,\"p50\":0,\"p90\":0,\"p95\":0,\"p99\":0}}},{\"name\":\"command_line (http)\",\"uri\":\"/api/tunnels/command_line%20%28http%29\",\"public_url\":\"http://6f50dc980ef2.ngrok.io\",\"proto\":\"http\",\"config\":{\"addr\":\"http://localhost:4050\",\"inspect\":true},\"metrics\":{\"conns\":{\"count\":0,\"gauge\":0,\"rate1\":0,\"rate5\":0,\"rate15\":0,\"p50\":0,\"p90\":0,\"p95\":0,\"p99\":0},\"http\":{\"count\":0,\"rate1\":0,\"rate5\":0,\"rate15\":0,\"p50\":0,\"p90\":0,\"p95\":0,\"p99\":0}}}],\"uri\":\"/api/tunnels\"}\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "M-MiajNfwXg_"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yuOL6TbKwYBn"
      },
      "source": [
        "#importing the dataset"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_81ShuNExYXe",
        "outputId": "f1fbd6f7-500b-4549-918a-590bdc410e66"
      },
      "source": [
        "!unzip '/content/drive/MyDrive/lsdp/mnist.zip' -d '/content/drive/MyDrive/lsdp/dataset'"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Archive:  /content/drive/MyDrive/lsdp/mnist.zip\n",
            "  inflating: /content/drive/MyDrive/lsdp/dataset/mnist  \n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WsfYOQkEENIO"
      },
      "source": [
        "from pyspark.sql import SQLContext\n",
        "sqlContext = SQLContext(spark)\n",
        "\n",
        "fileName = \"/content/drive/MyDrive/lsdp/dataset/mnist\"\n",
        "textDF = sqlContext.read.text(fileName)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4jrb3MXmwiWL",
        "outputId": "f317727c-9f5a-4f0e-8175-1bd1d365cd7f"
      },
      "source": [
        "#Complete the #FILL IN# gaps\n",
        "\n",
        "# 1. Show the first 10 lines of the DF\n",
        "textDF.show(10)\n",
        "# 2. Number of lines\n",
        "n_lines = textDF.count()\n",
        "print('Number of lines: ',n_lines)\n",
        "# 3. Content of the first line\n",
        "line = textDF.take(1)\n",
        "print('First line content:',line)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "+--------------------+\n",
            "|               value|\n",
            "+--------------------+\n",
            "|5 153:3 154:18 15...|\n",
            "|0 128:51 129:159 ...|\n",
            "|4 161:67 162:232 ...|\n",
            "|1 159:124 160:253...|\n",
            "|9 209:55 210:148 ...|\n",
            "|2 156:13 157:25 1...|\n",
            "|1 125:145 126:255...|\n",
            "|3 152:38 153:43 1...|\n",
            "|1 153:5 154:63 15...|\n",
            "|4 135:189 136:190...|\n",
            "+--------------------+\n",
            "only showing top 10 rows\n",
            "\n",
            "Number of lines:  60000\n",
            "First line content: [Row(value='5 153:3 154:18 155:18 156:18 157:126 158:136 159:175 160:26 161:166 162:255 163:247 164:127 177:30 178:36 179:94 180:154 181:170 182:253 183:253 184:253 185:253 186:253 187:225 188:172 189:253 190:242 191:195 192:64 204:49 205:238 206:253 207:253 208:253 209:253 210:253 211:253 212:253 213:253 214:251 215:93 216:82 217:82 218:56 219:39 232:18 233:219 234:253 235:253 236:253 237:253 238:253 239:198 240:182 241:247 242:241 261:80 262:156 263:107 264:253 265:253 266:205 267:11 269:43 270:154 290:14 291:1 292:154 293:253 294:90 320:139 321:253 322:190 323:2 348:11 349:190 350:253 351:70 377:35 378:241 379:225 380:160 381:108 382:1 406:81 407:240 408:253 409:253 410:119 411:25 435:45 436:186 437:253 438:253 439:150 440:27 464:16 465:93 466:252 467:253 468:187 494:249 495:253 496:249 497:64 519:46 520:130 521:183 522:253 523:253 524:207 525:2 545:39 546:148 547:229 548:253 549:253 550:253 551:250 552:182 571:24 572:114 573:221 574:253 575:253 576:253 577:253 578:201 579:78 597:23 598:66 599:213 600:253 601:253 602:253 603:253 604:198 605:81 606:2 623:18 624:171 625:219 626:253 627:253 628:253 629:253 630:195 631:80 632:9 649:55 650:172 651:226 652:253 653:253 654:253 655:253 656:244 657:133 658:11 677:136 678:253 679:253 680:253 681:212 682:135 683:132 684:16')]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0hgVEySgwx6V"
      },
      "source": [
        "fileName = \"/content/drive/MyDrive/lsdp/dataset/mnist\"\n",
        "dataDF = spark.read.format(\"libsvm\").option(\"numFeatures\", \"784\").load(fileName)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1YMCksVtx3nS",
        "outputId": "d24fcde0-3846-4b36-f7e7-21bbffc69e44"
      },
      "source": [
        "dataDF.show(5)\n",
        "# 2. Number of lines\n",
        "n_lines = dataDF.count()\n",
        "print('Number of lines: %d' %n_lines)\n",
        "# 3. Content of the first line: extracting the label and the features\n",
        "line = dataDF.take(1)\n",
        "print('Label: %d' %line[0].label)\n",
        "print('Features: %s' %line[0].features)\n",
        "\n",
        "# Feature data type\n",
        "print(type(line))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "+-----+--------------------+\n",
            "|label|            features|\n",
            "+-----+--------------------+\n",
            "|  5.0|(784,[152,153,154...|\n",
            "|  0.0|(784,[127,128,129...|\n",
            "|  4.0|(784,[160,161,162...|\n",
            "|  1.0|(784,[158,159,160...|\n",
            "|  9.0|(784,[208,209,210...|\n",
            "+-----+--------------------+\n",
            "only showing top 5 rows\n",
            "\n",
            "Number of lines: 60000\n",
            "Label: 5\n",
            "Features: (784,[152,153,154,155,156,157,158,159,160,161,162,163,176,177,178,179,180,181,182,183,184,185,186,187,188,189,190,191,203,204,205,206,207,208,209,210,211,212,213,214,215,216,217,218,231,232,233,234,235,236,237,238,239,240,241,260,261,262,263,264,265,266,268,269,289,290,291,292,293,319,320,321,322,347,348,349,350,376,377,378,379,380,381,405,406,407,408,409,410,434,435,436,437,438,439,463,464,465,466,467,493,494,495,496,518,519,520,521,522,523,524,544,545,546,547,548,549,550,551,570,571,572,573,574,575,576,577,578,596,597,598,599,600,601,602,603,604,605,622,623,624,625,626,627,628,629,630,631,648,649,650,651,652,653,654,655,656,657,676,677,678,679,680,681,682,683],[3.0,18.0,18.0,18.0,126.0,136.0,175.0,26.0,166.0,255.0,247.0,127.0,30.0,36.0,94.0,154.0,170.0,253.0,253.0,253.0,253.0,253.0,225.0,172.0,253.0,242.0,195.0,64.0,49.0,238.0,253.0,253.0,253.0,253.0,253.0,253.0,253.0,253.0,251.0,93.0,82.0,82.0,56.0,39.0,18.0,219.0,253.0,253.0,253.0,253.0,253.0,198.0,182.0,247.0,241.0,80.0,156.0,107.0,253.0,253.0,205.0,11.0,43.0,154.0,14.0,1.0,154.0,253.0,90.0,139.0,253.0,190.0,2.0,11.0,190.0,253.0,70.0,35.0,241.0,225.0,160.0,108.0,1.0,81.0,240.0,253.0,253.0,119.0,25.0,45.0,186.0,253.0,253.0,150.0,27.0,16.0,93.0,252.0,253.0,187.0,249.0,253.0,249.0,64.0,46.0,130.0,183.0,253.0,253.0,207.0,2.0,39.0,148.0,229.0,253.0,253.0,253.0,250.0,182.0,24.0,114.0,221.0,253.0,253.0,253.0,253.0,201.0,78.0,23.0,66.0,213.0,253.0,253.0,253.0,253.0,198.0,81.0,2.0,18.0,171.0,219.0,253.0,253.0,253.0,253.0,195.0,80.0,9.0,55.0,172.0,226.0,253.0,253.0,253.0,253.0,244.0,133.0,11.0,136.0,253.0,253.0,253.0,212.0,135.0,132.0,16.0])\n",
            "<class 'list'>\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cfcCpP0dyDzh"
      },
      "source": [
        "#data analysis"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ax8iGJe2yFxS"
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "from pyspark.mllib.linalg import Vectors\n",
        "\n",
        "def plot_data(images, h, w, n_row=1, n_col=10):\n",
        "\n",
        "    fig = plt.figure(figsize=(1.8 * n_col, 2.4 * n_row))\n",
        "    plt.subplots_adjust(bottom=0, left=.01, right=.99, top=.90, hspace=.35)\n",
        "    \n",
        "    for i in range(len(images)):\n",
        "        plt.subplot(n_row, n_col, i + 1)      \n",
        "        try: # In the case each element of \"images\" is a row data with a variable \"features\" \n",
        "            img = images[i].features.toArray()\n",
        "        except: # In the case each element of \"images\" is directly the pixel values\n",
        "            img = images[i]\n",
        "            \n",
        "        plt.imshow(img.reshape((h, w )), cmap=plt.cm.jet)\n",
        "        plt.xticks(())\n",
        "        plt.yticks(())\n",
        "    return fig"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 242
        },
        "id": "vuZtlFMCyGbL",
        "outputId": "57e5994a-930d-4f2a-938a-2b1f2299b86e"
      },
      "source": [
        "h= 28\n",
        "w =28\n",
        "\n",
        "# Pick up 10 images and plot them using the function  plot_data()\n",
        "images = dataDF.take(10)\n",
        "fig = plot_data(images, h, w)\n",
        "display(fig)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 1296x172.8 with 10 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 1296x172.8 with 10 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XiwCGs4MyQ16"
      },
      "source": [
        "#data preprocessing : normalization"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hv0wuH-OyTW0",
        "outputId": "18ef4fd7-0e72-418e-8f18-0b8d6635f51d"
      },
      "source": [
        "from pyspark.ml.feature import StandardScaler\n",
        "\n",
        "# Define the normalizer object: indicate that you only want each feature to have unit standard deviation. Use the nomenclature \"normFeatures\" for column with the output normalized features.\n",
        "scaler = StandardScaler(inputCol=\"features\", outputCol=\"normFeatures\", withStd=True, withMean=False)\n",
        "\n",
        "# Fit the StandardScaler: learn the statistics of the data\n",
        "scalerModel = scaler.fit(dataDF)\n",
        "\n",
        "# Normalize the data: apply the normalization transformation\n",
        "scaledData = scalerModel.transform(dataDF)\n",
        "\n",
        "# For the first data: compare the first 10 original features with the normalized ones\n",
        "firstData = scaledData.first()\n",
        "print(firstData.features)\n",
        "print(firstData.normFeatures)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(784,[152,153,154,155,156,157,158,159,160,161,162,163,176,177,178,179,180,181,182,183,184,185,186,187,188,189,190,191,203,204,205,206,207,208,209,210,211,212,213,214,215,216,217,218,231,232,233,234,235,236,237,238,239,240,241,260,261,262,263,264,265,266,268,269,289,290,291,292,293,319,320,321,322,347,348,349,350,376,377,378,379,380,381,405,406,407,408,409,410,434,435,436,437,438,439,463,464,465,466,467,493,494,495,496,518,519,520,521,522,523,524,544,545,546,547,548,549,550,551,570,571,572,573,574,575,576,577,578,596,597,598,599,600,601,602,603,604,605,622,623,624,625,626,627,628,629,630,631,648,649,650,651,652,653,654,655,656,657,676,677,678,679,680,681,682,683],[3.0,18.0,18.0,18.0,126.0,136.0,175.0,26.0,166.0,255.0,247.0,127.0,30.0,36.0,94.0,154.0,170.0,253.0,253.0,253.0,253.0,253.0,225.0,172.0,253.0,242.0,195.0,64.0,49.0,238.0,253.0,253.0,253.0,253.0,253.0,253.0,253.0,253.0,251.0,93.0,82.0,82.0,56.0,39.0,18.0,219.0,253.0,253.0,253.0,253.0,253.0,198.0,182.0,247.0,241.0,80.0,156.0,107.0,253.0,253.0,205.0,11.0,43.0,154.0,14.0,1.0,154.0,253.0,90.0,139.0,253.0,190.0,2.0,11.0,190.0,253.0,70.0,35.0,241.0,225.0,160.0,108.0,1.0,81.0,240.0,253.0,253.0,119.0,25.0,45.0,186.0,253.0,253.0,150.0,27.0,16.0,93.0,252.0,253.0,187.0,249.0,253.0,249.0,64.0,46.0,130.0,183.0,253.0,253.0,207.0,2.0,39.0,148.0,229.0,253.0,253.0,253.0,250.0,182.0,24.0,114.0,221.0,253.0,253.0,253.0,253.0,201.0,78.0,23.0,66.0,213.0,253.0,253.0,253.0,253.0,198.0,81.0,2.0,18.0,171.0,219.0,253.0,253.0,253.0,253.0,195.0,80.0,9.0,55.0,172.0,226.0,253.0,253.0,253.0,253.0,244.0,133.0,11.0,136.0,253.0,253.0,253.0,212.0,135.0,132.0,16.0])\n",
            "(784,[152,153,154,155,156,157,158,159,160,161,162,163,176,177,178,179,180,181,182,183,184,185,186,187,188,189,190,191,203,204,205,206,207,208,209,210,211,212,213,214,215,216,217,218,231,232,233,234,235,236,237,238,239,240,241,260,261,262,263,264,265,266,268,269,289,290,291,292,293,319,320,321,322,347,348,349,350,376,377,378,379,380,381,405,406,407,408,409,410,434,435,436,437,438,439,463,464,465,466,467,493,494,495,496,518,519,520,521,522,523,524,544,545,546,547,548,549,550,551,570,571,572,573,574,575,576,577,578,596,597,598,599,600,601,602,603,604,605,622,623,624,625,626,627,628,629,630,631,648,649,650,651,652,653,654,655,656,657,676,677,678,679,680,681,682,683],[0.03137469927426468,0.17461879779021175,0.16796228169449087,0.16590283924058313,1.1678132414369093,1.2938845179089438,1.7689104898164878,0.29044539967781424,2.1632873324778985,4.121038488523312,5.199799874497828,3.6403438243665707,0.4515742995234728,0.4470749108067238,1.017313198541894,1.5175662830620882,1.5781000431417103,2.278875175954818,2.255456155976026,2.252180252227019,2.2522564924291353,2.2558702193596214,2.0478663195704163,1.6579874660019005,2.7183550342922747,3.0986636639674168,3.1520499647967704,1.3923097152389161,0.7658421810153355,3.010473042404134,2.7419654131673687,2.4864446602466472,2.354172470907555,2.3009874899408578,2.2914916097067985,2.2909998474440183,2.2974400506953496,2.2952907311901543,2.2679212721111646,0.8379804117490826,0.754836607258178,0.8114156585513097,0.6372066614556373,0.550890664672637,0.24769089104711178,2.4867085356354752,2.5283423086813377,2.3592651710780532,2.291340539326469,2.277028200369343,2.2717241379636097,1.7807758614417206,1.6425668934145568,2.2299332173164714,2.1707723378049706,0.8544541072865448,1.494493965554338,0.9728245186881234,2.270080815920669,2.2724148950660163,1.8617957632542186,0.10094043619238516,0.3933049064364018,1.393140122160566,0.13165507023515383,0.009061553059047255,1.3902480311438579,2.343126344448648,0.8647658095874203,1.2773396350413762,2.431301500752774,1.8976891186417932,0.019461389090040047,0.10167495542063247,1.8272021706491441,2.455113365146411,0.6427626754183511,0.3290932873035854,2.2167634272047954,1.976909559616603,1.4336057497915093,0.982284582671978,0.008916685313982363,0.7261248605550779,2.1102955107653774,2.3100221723457666,2.303347013422958,1.058916434322622,0.2250758844723943,0.3995875120020831,1.7013088130935612,2.271977985380314,2.246612480103303,1.3655626316973328,0.25921172686368854,0.14377571657827906,0.8292710685678266,2.265541770682751,2.331538800805317,1.7975483600731204,2.26668995271227,2.3365971977940037,2.3859030643827266,0.6615483743982683,0.4137546545780352,1.17165190083312,1.6606558553284827,2.295724967192723,2.320645274451363,1.9782366268123213,0.020983924922013224,0.35926046636291326,1.341644503013926,2.054459322020683,2.278263651001217,2.2874614601135326,2.277364206555061,2.293344261465583,1.7753981493864415,0.22000788072961205,1.0324334607198036,1.9873876669685893,2.2669083265572736,2.269556403538393,2.274848503492966,2.2743981558166158,1.8188220638701926,0.7321798562686546,0.2226628541697364,0.6063395129056809,1.916542454401352,2.2698796439058735,2.277552434882022,2.292469767524559,2.287691546313942,1.7843521435614302,0.7322889102775617,0.018604292330661378,0.2772981480270652,2.046852639902825,2.2356646849415855,2.3633555432188125,2.2686430452409367,2.2417853747386363,2.2498768448321482,1.7389839255042578,0.7129087865197077,0.08086496577942526,3.1738301505736466,5.776976709945051,4.889879470839615,3.9084160622150166,3.0800865151581602,2.643588376937407,2.4196016122106725,2.2354480960783962,1.2031420718203232,0.09981160392855348,14.401680484852118,15.0628508981111,9.245781133463579,6.182033015797066,3.8245434433113563,1.93687315699118,1.6288667102255985,0.18131845184802214])\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 267
        },
        "id": "dNuMm62TyWrr",
        "outputId": "3a082d10-09c4-4911-99fd-825a8dd26e28"
      },
      "source": [
        "meanValues = scalerModel.mean\n",
        "stdValues= scalerModel.std\n",
        "\n",
        "# Plot these values\n",
        "fig = plot_data([scalerModel.mean,scalerModel.std] , h, w)\n",
        "display(fig)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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bpyy1ZGaJwmQGI34+dl9WfN+4hh1O9/V6A9exCfOfm3FuxlXXhC+mp2UBv1botJp6ZgPn6VdYPr+9eGH1QyT7HuCi0VOD7dP9romNXl/t5Zif++/q+nF3Ov1983rOE9sKgjUYaI1p8hmoxxz6V+f7wnn/tILpf8h8PsJL6/Hi+3Bu3k8boRMjFKFOm6FAit8DeuILESFq+EJEyBB29Qm7/mldRjM/MZIar2W3LpHSy++A3Sf2E3em7Mvqypera0+GqqtPwt9iBvb58J7Vob+9GIdf5eWMC32t8qYgdHqsbfGntg+c5lJnLIuew/212XzJ6ze3BNfejK+52UtnDc3McrzfWO+d9d1Du1hKuFddfSFEgBq+EBGihi9EhJQ5ZbcUsqaf0svQW4chD4wX9GZNyswoH5WqOWAwWB5+uBBOkOVi1Ah9tuN3exCFqxE53fbwHK+PDzRrXnMZdpZ353LlOWhGbcP9XBaNr03cqwz58gXlL7GuJ74QEaKGL0SEqOELESEV5PFJls/+LJRaiCl2nz5QZHlXpDd3YcntZtyuzcz1CI/nPuaJ8B7gfl5bJ3QYT+fnyvqcWeWyyl9uTU98ISJEDV+ICFHDFyJCSszVr9plZtsyDxSVwIx8Pn9U9mGloXtgWFH0Hiip4QshPh+oqy9EhKjhCxEhavhCRIgavhARooYvRISo4QsRIWr4QkSIGr4QEaKGL0SE/B9ihrgICYs6SgAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 1296x172.8 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 1296x172.8 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rXG3C84DyeoO"
      },
      "source": [
        "#preparing training and testing dataframe"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yxGK9MBGso-P"
      },
      "source": [
        "from pyspark.ml.evaluation import RegressionEvaluator\n",
        "from pyspark.ml.regression import LinearRegression\n",
        "from pyspark.ml.tuning import ParamGridBuilder, TrainValidationSplit\n",
        "\n",
        "train, test = data.randomSplit([0.8, 0.2], seed=12345)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2ydVfSDhtx16",
        "outputId": "f9b721b0-23b4-48e4-cc17-65c32f0d1115"
      },
      "source": [
        "print(train.count())"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "47887\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "U_7jhpEat168",
        "outputId": "1bfc8254-8456-40ae-a9a1-838f094583ae"
      },
      "source": [
        "print(test.count())"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "12113\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2eTYnyobyg1t",
        "outputId": "52b5ed48-dfc2-4b89-82dd-e5d518e93c22"
      },
      "source": [
        "from pyspark.sql.functions import *\n",
        "\n",
        "# Create the training data DF\n",
        "trainDF = scaledData.select(col(\"label\"), col(\"normFeatures\").alias(\"features\"))\n",
        "\n",
        "# Examine the resulting DF\n",
        "trainDF.show(5)\n",
        "trainDF.first()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "+-----+--------------------+\n",
            "|label|            features|\n",
            "+-----+--------------------+\n",
            "|  5.0|(784,[152,153,154...|\n",
            "|  0.0|(784,[127,128,129...|\n",
            "|  4.0|(784,[160,161,162...|\n",
            "|  1.0|(784,[158,159,160...|\n",
            "|  9.0|(784,[208,209,210...|\n",
            "+-----+--------------------+\n",
            "only showing top 5 rows\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Row(label=5.0, features=SparseVector(784, {152: 0.0314, 153: 0.1746, 154: 0.168, 155: 0.1659, 156: 1.1678, 157: 1.2939, 158: 1.7689, 159: 0.2904, 160: 2.1633, 161: 4.121, 162: 5.1998, 163: 3.6403, 176: 0.4516, 177: 0.4471, 178: 1.0173, 179: 1.5176, 180: 1.5781, 181: 2.2789, 182: 2.2555, 183: 2.2522, 184: 2.2523, 185: 2.2559, 186: 2.0479, 187: 1.658, 188: 2.7184, 189: 3.0987, 190: 3.152, 191: 1.3923, 203: 0.7658, 204: 3.0105, 205: 2.742, 206: 2.4864, 207: 2.3542, 208: 2.301, 209: 2.2915, 210: 2.291, 211: 2.2974, 212: 2.2953, 213: 2.2679, 214: 0.838, 215: 0.7548, 216: 0.8114, 217: 0.6372, 218: 0.5509, 231: 0.2477, 232: 2.4867, 233: 2.5283, 234: 2.3593, 235: 2.2913, 236: 2.277, 237: 2.2717, 238: 1.7808, 239: 1.6426, 240: 2.2299, 241: 2.1708, 260: 0.8545, 261: 1.4945, 262: 0.9728, 263: 2.2701, 264: 2.2724, 265: 1.8618, 266: 0.1009, 268: 0.3933, 269: 1.3931, 289: 0.1317, 290: 0.0091, 291: 1.3902, 292: 2.3431, 293: 0.8648, 319: 1.2773, 320: 2.4313, 321: 1.8977, 322: 0.0195, 347: 0.1017, 348: 1.8272, 349: 2.4551, 350: 0.6428, 376: 0.3291, 377: 2.2168, 378: 1.9769, 379: 1.4336, 380: 0.9823, 381: 0.0089, 405: 0.7261, 406: 2.1103, 407: 2.31, 408: 2.3033, 409: 1.0589, 410: 0.2251, 434: 0.3996, 435: 1.7013, 436: 2.272, 437: 2.2466, 438: 1.3656, 439: 0.2592, 463: 0.1438, 464: 0.8293, 465: 2.2655, 466: 2.3315, 467: 1.7975, 493: 2.2667, 494: 2.3366, 495: 2.3859, 496: 0.6615, 518: 0.4138, 519: 1.1717, 520: 1.6607, 521: 2.2957, 522: 2.3206, 523: 1.9782, 524: 0.021, 544: 0.3593, 545: 1.3416, 546: 2.0545, 547: 2.2783, 548: 2.2875, 549: 2.2774, 550: 2.2933, 551: 1.7754, 570: 0.22, 571: 1.0324, 572: 1.9874, 573: 2.2669, 574: 2.2696, 575: 2.2748, 576: 2.2744, 577: 1.8188, 578: 0.7322, 596: 0.2227, 597: 0.6063, 598: 1.9165, 599: 2.2699, 600: 2.2776, 601: 2.2925, 602: 2.2877, 603: 1.7844, 604: 0.7323, 605: 0.0186, 622: 0.2773, 623: 2.0469, 624: 2.2357, 625: 2.3634, 626: 2.2686, 627: 2.2418, 628: 2.2499, 629: 1.739, 630: 0.7129, 631: 0.0809, 648: 3.1738, 649: 5.777, 650: 4.8899, 651: 3.9084, 652: 3.0801, 653: 2.6436, 654: 2.4196, 655: 2.2354, 656: 1.2031, 657: 0.0998, 676: 14.4017, 677: 15.0629, 678: 9.2458, 679: 6.182, 680: 3.8245, 681: 1.9369, 682: 1.6289, 683: 0.1813}))"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9orwQRHzyhp2",
        "outputId": "4a7794c1-2595-4d25-9edd-22a6c3331698"
      },
      "source": [
        "fileName = \"/content/drive/MyDrive/lsdp/dataset/mnist\"\n",
        "dataTestDF =  spark.read.format(\"libsvm\").option(\"numFeatures\", \"784\").load(fileName)\n",
        "# Normalize the test data\n",
        "scaledDataTest = scalerModel.transform(dataTestDF)\n",
        "# Create the test DF\n",
        "testDF = scaledDataTest.select(col(\"label\"), col(\"normFeatures\").alias(\"features\"))\n",
        "\n",
        "# Examine the resulting DF\n",
        "testDF.show(10)\n",
        "testDF.first()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "+-----+--------------------+\n",
            "|label|            features|\n",
            "+-----+--------------------+\n",
            "|  5.0|(784,[152,153,154...|\n",
            "|  0.0|(784,[127,128,129...|\n",
            "|  4.0|(784,[160,161,162...|\n",
            "|  1.0|(784,[158,159,160...|\n",
            "|  9.0|(784,[208,209,210...|\n",
            "|  2.0|(784,[155,156,157...|\n",
            "|  1.0|(784,[124,125,126...|\n",
            "|  3.0|(784,[151,152,153...|\n",
            "|  1.0|(784,[152,153,154...|\n",
            "|  4.0|(784,[134,135,161...|\n",
            "+-----+--------------------+\n",
            "only showing top 10 rows\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Row(label=5.0, features=SparseVector(784, {152: 0.0314, 153: 0.1746, 154: 0.168, 155: 0.1659, 156: 1.1678, 157: 1.2939, 158: 1.7689, 159: 0.2904, 160: 2.1633, 161: 4.121, 162: 5.1998, 163: 3.6403, 176: 0.4516, 177: 0.4471, 178: 1.0173, 179: 1.5176, 180: 1.5781, 181: 2.2789, 182: 2.2555, 183: 2.2522, 184: 2.2523, 185: 2.2559, 186: 2.0479, 187: 1.658, 188: 2.7184, 189: 3.0987, 190: 3.152, 191: 1.3923, 203: 0.7658, 204: 3.0105, 205: 2.742, 206: 2.4864, 207: 2.3542, 208: 2.301, 209: 2.2915, 210: 2.291, 211: 2.2974, 212: 2.2953, 213: 2.2679, 214: 0.838, 215: 0.7548, 216: 0.8114, 217: 0.6372, 218: 0.5509, 231: 0.2477, 232: 2.4867, 233: 2.5283, 234: 2.3593, 235: 2.2913, 236: 2.277, 237: 2.2717, 238: 1.7808, 239: 1.6426, 240: 2.2299, 241: 2.1708, 260: 0.8545, 261: 1.4945, 262: 0.9728, 263: 2.2701, 264: 2.2724, 265: 1.8618, 266: 0.1009, 268: 0.3933, 269: 1.3931, 289: 0.1317, 290: 0.0091, 291: 1.3902, 292: 2.3431, 293: 0.8648, 319: 1.2773, 320: 2.4313, 321: 1.8977, 322: 0.0195, 347: 0.1017, 348: 1.8272, 349: 2.4551, 350: 0.6428, 376: 0.3291, 377: 2.2168, 378: 1.9769, 379: 1.4336, 380: 0.9823, 381: 0.0089, 405: 0.7261, 406: 2.1103, 407: 2.31, 408: 2.3033, 409: 1.0589, 410: 0.2251, 434: 0.3996, 435: 1.7013, 436: 2.272, 437: 2.2466, 438: 1.3656, 439: 0.2592, 463: 0.1438, 464: 0.8293, 465: 2.2655, 466: 2.3315, 467: 1.7975, 493: 2.2667, 494: 2.3366, 495: 2.3859, 496: 0.6615, 518: 0.4138, 519: 1.1717, 520: 1.6607, 521: 2.2957, 522: 2.3206, 523: 1.9782, 524: 0.021, 544: 0.3593, 545: 1.3416, 546: 2.0545, 547: 2.2783, 548: 2.2875, 549: 2.2774, 550: 2.2933, 551: 1.7754, 570: 0.22, 571: 1.0324, 572: 1.9874, 573: 2.2669, 574: 2.2696, 575: 2.2748, 576: 2.2744, 577: 1.8188, 578: 0.7322, 596: 0.2227, 597: 0.6063, 598: 1.9165, 599: 2.2699, 600: 2.2776, 601: 2.2925, 602: 2.2877, 603: 1.7844, 604: 0.7323, 605: 0.0186, 622: 0.2773, 623: 2.0469, 624: 2.2357, 625: 2.3634, 626: 2.2686, 627: 2.2418, 628: 2.2499, 629: 1.739, 630: 0.7129, 631: 0.0809, 648: 3.1738, 649: 5.777, 650: 4.8899, 651: 3.9084, 652: 3.0801, 653: 2.6436, 654: 2.4196, 655: 2.2354, 656: 1.2031, 657: 0.0998, 676: 14.4017, 677: 15.0629, 678: 9.2458, 679: 6.182, 680: 3.8245, 681: 1.9369, 682: 1.6289, 683: 0.1813}))"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HPDznVhLyps4",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "bf40c820-c117-42f1-f64b-038cd72fbdd3"
      },
      "source": [
        "fileName = \"/content/drive/MyDrive/lsdp/dataset/mnist\"\n",
        "dataTestDF =  spark.read.format(\"libsvm\").option(\"numFeatures\", \"784\").load(fileName)\n",
        "# Normalize the test data\n",
        "scaledDataTest = scalerModel.transform(dataTestDF)\n",
        "# Create the test DF\n",
        "testDF = scaledDataTest.select(col(\"label\"), col(\"normFeatures\").alias(\"features\"))\n",
        "\n",
        "# Examine the resulting DF\n",
        "testDF.show(10)\n",
        "testDF.first()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "+-----+--------------------+\n",
            "|label|            features|\n",
            "+-----+--------------------+\n",
            "|  5.0|(784,[152,153,154...|\n",
            "|  0.0|(784,[127,128,129...|\n",
            "|  4.0|(784,[160,161,162...|\n",
            "|  1.0|(784,[158,159,160...|\n",
            "|  9.0|(784,[208,209,210...|\n",
            "|  2.0|(784,[155,156,157...|\n",
            "|  1.0|(784,[124,125,126...|\n",
            "|  3.0|(784,[151,152,153...|\n",
            "|  1.0|(784,[152,153,154...|\n",
            "|  4.0|(784,[134,135,161...|\n",
            "+-----+--------------------+\n",
            "only showing top 10 rows\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Row(label=5.0, features=SparseVector(784, {152: 0.0314, 153: 0.1746, 154: 0.168, 155: 0.1659, 156: 1.1678, 157: 1.2939, 158: 1.7689, 159: 0.2904, 160: 2.1633, 161: 4.121, 162: 5.1998, 163: 3.6403, 176: 0.4516, 177: 0.4471, 178: 1.0173, 179: 1.5176, 180: 1.5781, 181: 2.2789, 182: 2.2555, 183: 2.2522, 184: 2.2523, 185: 2.2559, 186: 2.0479, 187: 1.658, 188: 2.7184, 189: 3.0987, 190: 3.152, 191: 1.3923, 203: 0.7658, 204: 3.0105, 205: 2.742, 206: 2.4864, 207: 2.3542, 208: 2.301, 209: 2.2915, 210: 2.291, 211: 2.2974, 212: 2.2953, 213: 2.2679, 214: 0.838, 215: 0.7548, 216: 0.8114, 217: 0.6372, 218: 0.5509, 231: 0.2477, 232: 2.4867, 233: 2.5283, 234: 2.3593, 235: 2.2913, 236: 2.277, 237: 2.2717, 238: 1.7808, 239: 1.6426, 240: 2.2299, 241: 2.1708, 260: 0.8545, 261: 1.4945, 262: 0.9728, 263: 2.2701, 264: 2.2724, 265: 1.8618, 266: 0.1009, 268: 0.3933, 269: 1.3931, 289: 0.1317, 290: 0.0091, 291: 1.3902, 292: 2.3431, 293: 0.8648, 319: 1.2773, 320: 2.4313, 321: 1.8977, 322: 0.0195, 347: 0.1017, 348: 1.8272, 349: 2.4551, 350: 0.6428, 376: 0.3291, 377: 2.2168, 378: 1.9769, 379: 1.4336, 380: 0.9823, 381: 0.0089, 405: 0.7261, 406: 2.1103, 407: 2.31, 408: 2.3033, 409: 1.0589, 410: 0.2251, 434: 0.3996, 435: 1.7013, 436: 2.272, 437: 2.2466, 438: 1.3656, 439: 0.2592, 463: 0.1438, 464: 0.8293, 465: 2.2655, 466: 2.3315, 467: 1.7975, 493: 2.2667, 494: 2.3366, 495: 2.3859, 496: 0.6615, 518: 0.4138, 519: 1.1717, 520: 1.6607, 521: 2.2957, 522: 2.3206, 523: 1.9782, 524: 0.021, 544: 0.3593, 545: 1.3416, 546: 2.0545, 547: 2.2783, 548: 2.2875, 549: 2.2774, 550: 2.2933, 551: 1.7754, 570: 0.22, 571: 1.0324, 572: 1.9874, 573: 2.2669, 574: 2.2696, 575: 2.2748, 576: 2.2744, 577: 1.8188, 578: 0.7322, 596: 0.2227, 597: 0.6063, 598: 1.9165, 599: 2.2699, 600: 2.2776, 601: 2.2925, 602: 2.2877, 603: 1.7844, 604: 0.7323, 605: 0.0186, 622: 0.2773, 623: 2.0469, 624: 2.2357, 625: 2.3634, 626: 2.2686, 627: 2.2418, 628: 2.2499, 629: 1.739, 630: 0.7129, 631: 0.0809, 648: 3.1738, 649: 5.777, 650: 4.8899, 651: 3.9084, 652: 3.0801, 653: 2.6436, 654: 2.4196, 655: 2.2354, 656: 1.2031, 657: 0.0998, 676: 14.4017, 677: 15.0629, 678: 9.2458, 679: 6.182, 680: 3.8245, 681: 1.9369, 682: 1.6289, 683: 0.1813}))"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "A4H9AeQgy5gR"
      },
      "source": [
        "#performing SVM classification"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EmcXyVblAd88",
        "outputId": "c5d0ce16-1ce7-43e8-e1f1-b6ad2fdaf703"
      },
      "source": [
        "from pyspark.ml.classification import LinearSVC, OneVsRest\n",
        "from pyspark.ml.evaluation import MulticlassClassificationEvaluator\n",
        "\n",
        "# Define your binary classifier\n",
        "lsvc = LinearSVC(maxIter=10, regParam=0.1)\n",
        "\n",
        "# Define the One Vs All Classifier.\n",
        "ovr = OneVsRest(classifier=lsvc)\n",
        "\n",
        "# train the multiclass model.\n",
        "lsvm = ovr.fit(trainDF)\n",
        "#lsvm.save(\"/content/drive/MyDrive/lsdp\")\n",
        "# score the model on test data.\n",
        "# Show the computed predictions and compare with the original labels\n",
        "\n",
        "predictions = lsvm.transform(testDF)\n",
        "predictions.select(\"features\", \"label\", \"prediction\").show(10)\n",
        "\n",
        "# Define the evaluator method with the corresponding metric and compute the classification error on test data\n",
        "evaluator =  MulticlassClassificationEvaluator().setMetricName('accuracy')\n",
        "accuracy = evaluator.evaluate(predictions) \n",
        "\n",
        "# Show the accuracy\n",
        "print(\"Test accuracy = \", (accuracy))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "+--------------------+-----+----------+\n",
            "|            features|label|prediction|\n",
            "+--------------------+-----+----------+\n",
            "|(784,[152,153,154...|  5.0|       3.0|\n",
            "|(784,[127,128,129...|  0.0|       0.0|\n",
            "|(784,[160,161,162...|  4.0|       4.0|\n",
            "|(784,[158,159,160...|  1.0|       1.0|\n",
            "|(784,[208,209,210...|  9.0|       9.0|\n",
            "|(784,[155,156,157...|  2.0|       2.0|\n",
            "|(784,[124,125,126...|  1.0|       1.0|\n",
            "|(784,[151,152,153...|  3.0|       3.0|\n",
            "|(784,[152,153,154...|  1.0|       1.0|\n",
            "|(784,[134,135,161...|  4.0|       4.0|\n",
            "+--------------------+-----+----------+\n",
            "only showing top 10 rows\n",
            "\n",
            "Test accuracy =  0.8568\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 415
        },
        "id": "G9ywHGO0VY7x",
        "outputId": "9901fdaa-27b5-40ff-b54e-2025135edbc8"
      },
      "source": [
        "df_predict = predictions\n",
        "df_predict"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>label</th>\n",
              "      <th>features</th>\n",
              "      <th>rawPrediction</th>\n",
              "      <th>prediction</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>5.0</td>\n",
              "      <td>(0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
              "      <td>[-1.9702221855403417,-2.7112188076889305,-2.53...</td>\n",
              "      <td>3.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.0</td>\n",
              "      <td>(0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
              "      <td>[2.2356454846910436,-2.951772841298057,-2.1680...</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>4.0</td>\n",
              "      <td>(0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
              "      <td>[-2.612167129253667,-2.496231538596853,-1.7010...</td>\n",
              "      <td>4.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1.0</td>\n",
              "      <td>(0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
              "      <td>[-2.254227023540711,0.8582908600662574,-1.3239...</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>9.0</td>\n",
              "      <td>(0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
              "      <td>[-2.8457701639059407,-1.9886842346737386,-2.81...</td>\n",
              "      <td>9.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>59995</th>\n",
              "      <td>8.0</td>\n",
              "      <td>(0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
              "      <td>[-2.1842554343047627,-2.179997210706946,-1.346...</td>\n",
              "      <td>8.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>59996</th>\n",
              "      <td>3.0</td>\n",
              "      <td>(0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
              "      <td>[-1.642881362268413,-2.3390384707937426,-0.914...</td>\n",
              "      <td>3.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>59997</th>\n",
              "      <td>5.0</td>\n",
              "      <td>(0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
              "      <td>[-2.0307460560439576,-2.6841762769214275,-3.22...</td>\n",
              "      <td>5.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>59998</th>\n",
              "      <td>6.0</td>\n",
              "      <td>(0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
              "      <td>[-0.6875321297819782,-2.7277052976823537,-1.36...</td>\n",
              "      <td>6.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>59999</th>\n",
              "      <td>8.0</td>\n",
              "      <td>(0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
              "      <td>[-0.8330015714837391,-2.8390758270332173,-1.32...</td>\n",
              "      <td>8.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>60000 rows × 4 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "       label  ... prediction\n",
              "0        5.0  ...        3.0\n",
              "1        0.0  ...        0.0\n",
              "2        4.0  ...        4.0\n",
              "3        1.0  ...        1.0\n",
              "4        9.0  ...        9.0\n",
              "...      ...  ...        ...\n",
              "59995    8.0  ...        8.0\n",
              "59996    3.0  ...        3.0\n",
              "59997    5.0  ...        5.0\n",
              "59998    6.0  ...        6.0\n",
              "59999    8.0  ...        8.0\n",
              "\n",
              "[60000 rows x 4 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 122
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7gVhbVpFVXxx"
      },
      "source": [
        "df_predict.to_csv(\"/content/drive/MyDrive/lsdp/dataset/predict.csv\")"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "C5ZL6Jh5y9vr",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 273
        },
        "outputId": "93882c2e-9300-4011-d884-ad0cbea0094c"
      },
      "source": [
        "import cv2\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "num = cv2.imread(\"/content/drive/MyDrive/lsdp/dataset/digit.jpg\")\n",
        "plt.imshow(num)\n",
        "#print(img)\n",
        "# img_arr=image.flatten()\n",
        "# print(img_arr)\n",
        "# features = (784,img_arr)\n",
        "# #print(np.array(features))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7f23c234a3d0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 139
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 371
        },
        "id": "w26gcFftDZP7",
        "outputId": "3dd956b3-0490-4385-f0f4-d4d9d44cd529"
      },
      "source": [
        "#img = cv2.imread('img1.jpg')\n",
        "gray = cv2.cvtColor(num,cv2.COLOR_BGR2GRAY)\n",
        "corners = cv2.goodFeaturesToTrack(gray, 100, 0.01, 1)\n",
        "                              \n",
        "corners = np.int0(corners)\n",
        "\n",
        "for i in corners:\n",
        "    x, y = i.ravel()\n",
        "    res = cv2.circle(num, (x,y), 3, (255, 0, 255), -1)\n",
        "\n",
        "plt.imshow(gray)\n",
        "\n",
        "features = corners.flatten()\n",
        "print(features)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[555  22 493  22 534  50 206  34 499  19  97  34 511   3 241  46 246 235\n",
            " 531  19 537  19 531  25 537  25 492  19 490 235 498  11 490 227  73  49\n",
            " 151   3  59  33 235  47 117  40  83  40 263  11 735 226 110  46 249   1\n",
            " 641   9 237 242  84  42 255  12 402   3 244 243 149  36 253   1 251   2\n",
            " 147   1 505   3 397   1   4 236 490  13  81  42 110  35 151   6  85  97\n",
            " 247   1  89  39 487   9  84  70  69  48  97  79  60  22 151  32 376  60\n",
            "  58  27 146  28 244  10 164  31  72  34 394  49 370  82 735 219 491   1\n",
            " 505  11 159  40 157  40 350  33 493 235 249 234 118  30 533  90 144  31\n",
            " 412 189  78  35 111  53 144  48 490 220 293  95 248  12 650  77  72  39\n",
            " 106  36 241  12 126  53 122  53 636  10 482   6 250 243 537 198 495 243\n",
            " 141   4 481 241 726 241 489 243 543 116 496   5 658  73 115  53 552 119\n",
            " 629 130]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 273
        },
        "id": "KvZnV05t_r8E",
        "outputId": "71690e2e-be41-4b3d-96ae-b86117c6e19a"
      },
      "source": [
        "# font\n",
        "font = cv2.FONT_HERSHEY_SIMPLEX  \n",
        "# org\n",
        "org = (50, 50)\n",
        "# fontScale\n",
        "fontScale = 6\n",
        "# Blue color in BGR\n",
        "color = (255, 0, 0)\n",
        "# Line thickness of 2 px\n",
        "thickness = 2\n",
        "# Using cv2.putText() method\n",
        "res_img = cv2.putText(res, '1 3 3 4 0', org, font, fontScale, color, thickness, cv2.LINE_AA)\n",
        "\n",
        "plt.imshow(res_img)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7f23c22b2c90>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 145
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "A13j_3Gd0VPO"
      },
      "source": [
        "# **** feature extraction ****"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 285
        },
        "id": "__39VQOd0awq",
        "outputId": "af6766c1-b751-4cb9-b287-e406196bc76a"
      },
      "source": [
        "\n",
        "import numpy as np\n",
        "from scipy.stats import t\n",
        "\n",
        "\n",
        "# Collect the training data \n",
        "trainingData = trainDF.take(1000)\n",
        "\n",
        "# Get the features of the data belonging to class 0, compute its mean \n",
        "data0 = np.array([data.features for data in trainingData if data.label==0])\n",
        "mean0 = np.mean(data0, axis=0)\n",
        "\n",
        "# Get the features of the data belonging to class 1, compute its mean \n",
        "data1 = np.array([data.features for data in trainingData if data.label==1])\n",
        "mean1 = np.mean(data1, axis=0)\n",
        "\n",
        "# Compute common variance\n",
        "std_common = np.std(np.concatenate((data0 ,data1)), axis=0)\n",
        "# Compute the number of data per class\n",
        "N0 = data0.shape[0]\n",
        "N1 = data1.shape[0]\n",
        "\n",
        "# Compute t-values \n",
        "t_values= np.divide((mean0-mean1), np.sqrt(1./N0 + 1./N1)*std_common)\n",
        "# Solving nan problems (some pixels are constant for all the data, so their means are 0, its std its 0 and its t_value is nan)\n",
        "t_values[np.isnan(t_values)]=0\n",
        "fig = plot_data([t_values,np.abs(t_values)], h, w)\n",
        "display(fig)\n",
        "pos_relevant = np.argsort(np.abs(t_values))[::-1]\n",
        "pos_sel = pos_relevant[:200]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:29: RuntimeWarning: invalid value encountered in true_divide\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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faepItR+WrASe4ZmyyCVxdx8Olk5WWxC0nPAa0B1fCA+R4QvhIYNXlkucGcljTE6qJZFcON2SdZJKVzGorE29C1CPsi+P41sfJ93Hj05Gcm9vWKI7vhAeIsMXwkNk+EJ4iAxfCA+R4QvhITJ8ITxEhi+Eh8jwhfAQGb4QHiLDF8JDZPhCeEifynJjsVibme0duNMRaWRSEARF6T6oroERRa/XQJ8MXwhxaqBHfSE8RIYvhIfI8IXwEBm+EB4iwxfCQ2T4QniIDF8ID5HhC+EhMnwhPOT/z3YlKHNkXY8AAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 1296x172.8 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAP4AAAB9CAYAAACCst4nAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAO7UlEQVR4nO2da3BU5RnHn20IAQJJzN1IygaJXJoISGwcwRFUpKWRomJVai92rHSqbXWmo7ZjrTp+sDOd1mmZFhk72A/YTqUV0IqOVuN4qZdAwaAVBAwGU0KIEoQYDPH02573/z9kl8TNjff/+3T++549e3b3PHPO8z6XNxYEgQkh/OILQ30CQojBR4YvhIfI8IXwEBm+EB4iwxfCQ2T4QnjIqL7sPL5wTFAQHz9Q5yLSSHvTETtysCuW7uPGYuMCs7x0H1YMCIcsCDpPeA30yfAL4uPtjoa69JyTGFDur3ligI6cZ2Y3DtCxRXpZ3euIHvWF8BAZvhAeIsMXwkNk+EJ4iAxfCA+R4QvhITJ8ITxEhi+Eh8jwhfCQPmXujRSm2O6k44co5bTFykCXWQvoj20C6E4bl9ieajtgbIJ9nPS9zC47M+m46C/fSDHeSPoD0lNI/zfJsS4jXUm6iXQ36b8lOfbAoDu+EB4iwxfCQ4btoz4/fo+yHtBzrAF0iR1IbPPjtvtofqLxs+hxvYd+lnHWCdp1Ffg8eywD9Pk9r4D+KAPdjLF0bHYznrZF5i9fJf0Jab5854WbhTR0kI/Nj+OZyU+lcAkdz21S+xTtTOdZNwf1O7T7rh+hjhegbro7+bn1A93xhfAQGb4QHiLDF8JDho2Pz77yaXYIdJxCIlNtJ+jK5n2h6MBjd5ejzjwc+XCknfRR0tP3JjYPT38bhnZlYBioMwPnF0qOtoE+lI0+/6eWheN2WmI7zz6CsVRhyJEH+/RbSFOIrXQ+6pud7Yn01pdIx8mnr+GPwoVmxpfiJMGRdUWhWE/nvZ6OtZ/0eaSbyKc/QuM23dnmsCL/Zpv4zSdEd3whPESGL4SHyPCF8JBh4+On8ulnGPrSlVv3gbb3ez92Ziof/kPSW0lnkD4ebuZ0YPrlOWXkg6WYL5hZ9i7otkrsYlxtbya2mw0nK0a+T8+8STqOsnAh6gdo93ld4fZx+tNq0KfPn4YpuuUZzaAvs42gee7lwWtWJLY7JpbiZ7EPz9m/tHtk/3Wk6+OOYB//5Hx6Rnd8ITxEhi+Eh8jwhfCQYePjz7d60JwfP615L+iIq+PG5vfQWC5p9umZLNLTSR/tZdvM7EnS75Hm+QKKHxdlYxC3sCycJGAf/9Tj+yh50aZbUOYuxQB5cVZrYrvWXoexFjsddKrS7SNUTv28LQBdkBX+Lx2F6LRX3f4G6PMN6zW4dmRd1ZWgu/bn48nUVzuifz49ozu+EB4iwxfCQ2T4QnjIkPn4M8hJL7FW0MUd5Ii/SgfgfHt3nP3qYtLkQrll3GZmdikdungm6NH2aWK7ugO/R+ajdKynSfeQPkb6iygLyjgR4FSC6tCraJji22NuxmuiLAvzGJbbI4ntWnsNxrjF2W4Krq+15aD33zoZP/wBnHMy10//Lo4sXfMY6Fvtt6BH9eBFkJeLNRgr592GB5zofBalr/QX3fGF8BAZvhAeIsMXwkOGzMfn2vIJPdgHL5Pz5Tn2zmnqjqvdiu6dlVxI+1Jc/oPr0On/ga0CXX90PujF2WGw/p7cX8LYtD2Yb/AMpoDbGXQqMxrohYtQ5lENwynFKKpD55z2G1BW5DaB5l6J8+35xPa816iWvxYl9zLcX00+PfMSxt5tqbNN9fdz12DcPv/eLtyBrr9lV/0d9Mppt+IObu8A+fhCiP4iwxfCQ2T4QnjIkPn446j3eM77tKwQx+JbSb9Aw45fz5HvEnKxjNqcf9v+DPq5hXW4Qxzlkw8sTmzfl30nDh5A2UQffZz0DD5Z+t4ZkXecQnBdOuVTlNZi0cWZlF9/k/0B3+769dSfYWwtxuEfb6U++XQuHU+NBt2QgUUVF693/Phl+F7uJWE8j0P1HfGr8E//8iScI3h9PE9SfX50xxfCQ2T4QnjIkD3qF/CaRtw6i8tdKSx2mFJ43QzesdEPA55ago9Oz11Oj/bPYjssOw+XWzrSFLZWLpuSvP1VTtJRs+7/oc6k1uCnfRj6KVPyk5eSjriVdzlFlx71S8hvmmX/Ab1wO/XMdt1BKsVusgrQn5Xib7kz+BronHnoei7Y+G/QRXPDC7ZtHuVZM2WkyeomNWPL9eJy8hchzJlqFeCTW3lXd3whPESGL4SHyPCF8JBB8/G51VF+M8XYyIfnsBgvgbSFy1kdzuHWWYtRLmnH1sm2ntdXwvRh60Ifv/RLYZgp+8nPcN9slPwDp/zBaW4j5vr8XE484iD/lHz6olqc6Jliu0Bfb2vwDdzmzIX+81/bT/GF7WfjZxVgLmwrpYiX0NxL2ybHr78fl9s6yJNKo6htHF+fRGT+K558//6gO74QHiLDF8JDZPhCeEhafXz249248gT2mzlOz6mrlJK7+R3UFGk31wvPobLHPTdhPmb3WI6uv0yal2NGeblbh0klwAwtxhxJwM1kf49/lxEHx5nduDLOlVghygrKV/4hpeROfojWm+apGadV17Yq/KxtldjHK7glBvp35NPz4tPHK2iCZXKYOXJlgHNGo7i/GlsZzU8FNC/Ey8kNBLrjC+EhMnwhPESGL4SHpNXHT5YrfpzXjmKfnlptdVIpI6+Yxb7yXDc3+2IcO7eHDtb1DL37E9LUZukalCvc1lzsk1P+Ac8mROoIeHkv1s7vtKtiJOTiJ8sVp995Gs6ALKbA/EXvYn48z/tEWpV/K9z8vf0Yx2hJ7c6rUXNnN/7f7rK7et2jgC5mLh+OtHenMP/7+UWgWzi5P+6Kk8vFT4Xu+EJ4iAxfCA+R4QvhIUO3TDa3z96OspF8Z8zajq5cnemUW2/7NcZwP7yWm1onS/I2M7sEVNV3cNnjmdudLAJqCdadYgnuOL9QQZp9/GGzkHk6wC9TdSH+ruzj23P0dm5hRR2ptpSHV8WfYjfD2NbgLNAvpMiXKFmBevXPfoIvPBxuTqU230Xv4VLnEWah3ExrpbdaCe4wAN3XdMcXwkNk+EJ4iAxfCA8ZNA+yglsOc489al33ZorjVfMLl4ebdfZPHOP5hAiUm38dxpd5mWPwNakd9j6q2+YQbjHX1FfyDqTZ5x/JzML8iFn0x1QfpYmeVEuMU77GnXZfKHAVNJyXsegKbJeQ3rKKZpFivwE5KQgL/ufYZtw3WSGJWWRe50W7AHQTzwQ1WdrRHV8ID5HhC+EhMnwhPGTQfPwzminATTnt7RQPT5U7PZ185S13hT7Zvg3kVEXioFwlfx2o8auwz/n3Wv6Cu//L2ea5CoLSsi1GMdxIHJ99/BT92UYUS1Fy/HsM+8a8jBrPj1Acf1PsisR2XfAoDlLfBK6ZmEep+LGX38YX7kC5wsKlrGf3YL//yPVGbfc3FF8KuoHi+Ht3T8U3DEB5vu74QniIDF8IDxm8hFAKc3E56ycUquFCWUpitBgtdvqQ3RAK6k4c5ZsoV6K8PftX+AI96bvhvE7ySXKo+riAWkjbeaT5i5Fv0O20ZUrW2mxEEEeZx8+w7N/xKscUYbun+DbaIVwRtywSsEPm34D69nvuxhdim0BODvB5/Wr7a2I7Zw+t9Ezu2jM1eBE8YstB7zB6tH8H24IZdBxL1trs5NEdXwgPkeEL4SEyfCE8ZPB8fA5xpPjkOOlIiu5clG777sylh2Gs+zgFAwsxnFd3JYZ+ltta3J9baDvzE5n0PcbNpn1rSPOSyRy+o5DVodzxdspAv9WnNhpfoPmRyJ+OUTBbY9fTDmHx9uptWEY7uhZ7WjfXloPeMP1aPFQc5XL7OejCHqfd1hjcd0slTkass2Wg2adve4vifVyD3mRpR3d8ITxEhi+Eh8jwhfCQofPxybc9g3zbuRTTrUzRotj1y0sKMP+3eAXmB1dT0W85rdGd/yAFkLm3t/OrZXLJZaqU3BQ+fkDfqzE6u5FgxMX1abpiF5dDcz81zKS2gHIiWtr5x3Tmcmb9EUZWRiZTyKf/CsrKTdtAL7B60McywlzqN8rRZ99ol4F+zWpBN7bSf0rVyEbLxVn9Puud/sX1dccXwkNk+EJ4iAxfCA8ZPB+f61PJPYtRvLuSSzLZV6ZWyzNbwprOmWVU38mls/ytub0Vp3lzOai7P/ulqVpp8f507Ib8KtA9keB2yMHIjzrMId91x9ex5fU/inFx6jlL8E9+nXzlZbYOdGMQ+s472rEeY3YBls52Grb25t/ybGsEzctatTv7P22LYOwVOx/0jlacA/hsPa2LzXF7ahtmdphfcGhMMtY7uuML4SEyfCE8RIYvhIcMmo/fTb5sZjntQPX1EWpJc6tl19V5mcZ2kubwL+fTs0/P5+a6aJNpjFtlkRveVo7B7BY7HfQhy7OTpZCWZ+7Le4cE8vE3H8Mf/t4s7H/F8xvbN5yLB6Ac+cmL3kpsX1+wBsYusBdBc37EY9QXrNnwAr3XfgH6wLGwkULHs6V4Itwqi334Z0m/Stre5heSwHkenHRyYnTHF8JDZPhCeIgMXwgPGTQff2suxqdnLeYEZWRnLjrPXMOcQU7+gprnE9s5G6kHGvfmpvLnSH49xd7bytAvz+oJa7vdnG2zqF/K+fO7KT+d6wSGfb795+FhlB1j0DfeZuQr0/7WtYlewMt3z9KFie2HVsVhrKUEJ3YabA7o/WtpsoYvT/bD3UuCfXquS3mV4/APk76CdP/66PUF3fGF8BAZvhAeIsMXwkNiQRCc9M6TagqDOxrq0vLB7Ouyzz7asEfaAWpA304B8tOdBPtFtL7ybFqOeULPx6APZuCx2q0w6bnhWGR9LoDztgeL+2uesL0NB2Op9+wbsVhZYHZjmo5G6xsYzc3wOmu8ZtZE+nr7XF+a6jVmoU8faepItR+WrASe4ZmyyCVxdx8Olk5WWxC0nPAa0B1fCA+R4QvhIYNXlkucGcljTE6qJZFcON2SdZJKVzGorE29C1CPsi+P41sfJ93Hj05Gcm9vWKI7vhAeIsMXwkNk+EJ4iAxfCA+R4QvhITJ8ITxEhi+Eh8jwhfAQGb4QHiLDF8JDZPhCeEifynJjsVibme0duNMRaWRSEARF6T6oroERRa/XQJ8MXwhxaqBHfSE8RIYvhIfI8IXwEBm+EB4iwxfCQ2T4QniIDF8ID5HhC+EhMnwhPOT/z3YlKHNkXY8AAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 1296x172.8 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 285
        },
        "id": "kdTXdht60sif",
        "outputId": "582a8cba-8e5c-4b1f-816e-c0d32ff79ff6"
      },
      "source": [
        "from pyspark.ml.feature import StandardScaler\n",
        "import numpy as np\n",
        "\n",
        "scaler = StandardScaler(inputCol=\"features\", outputCol=\"scaledFeatures\",\n",
        "                        withStd=True, withMean=False)\n",
        "\n",
        "trainDF_0 = trainDF.filter(trainDF.label==0)\n",
        "trainDF_1 = trainDF.filter(trainDF.label==1)\n",
        "scalerModel_0 = scaler.fit(trainDF_0)\n",
        "scalerModel_1 = scaler.fit(trainDF_1)\n",
        "scalerModel_all = scaler.fit(trainDF)\n",
        "mean_0 = np.array(scalerModel_0.mean)\n",
        "mean_1 = np.array(scalerModel_1.mean)\n",
        "std_common_ = np.array(scalerModel_all.std)\n",
        "N_0 = trainDF_0.count()\n",
        "N_1 = trainDF_1.count()\n",
        "\n",
        "# Compute t-values \n",
        "t_values_= np.divide((mean_0-mean_1), np.sqrt(1./N_0 + 1./N_1)*std_common_)\n",
        "# Solving nan problems (some pixels are constant for all the data, so their means are 0, its std its 0 and its t_value is nan)\n",
        "t_values_[np.isnan(t_values_)]=0\n",
        "\n",
        "# We can plot the t_values and their absolute values\n",
        "fig = plot_data([t_values_,np.abs(t_values_)], h, w)\n",
        "display(fig)\n",
        "\n",
        "# Ranking t-values\n",
        "# The most discriminating pixels have very large or very small t values, so we rank over their absolute value\n",
        "# np.argsort ranks in ascending order, so we invert the result to have the most discriminative pixels in the first positions\n",
        "pos_relevant_ = np.argsort(np.abs(t_values_))[::-1]\n",
        "\n",
        "# We select the 200 most relevant pixels and built the new data set\n",
        "pos_sel_ = pos_relevant_[:200]\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:19: RuntimeWarning: invalid value encountered in true_divide\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1296x172.8 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1296x172.8 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "aaQ2lzwu30Xe",
        "outputId": "ad8c3004-f44b-427d-f24b-b9eb15e6aa3d"
      },
      "source": [
        "print(pos_sel_)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[406 434 358 378 386 357 385 462 330 414 329 413 483 511 461 379 350 351\n",
            " 455 433 489 482 539 441 510 407 359 427 454 301 323 387 442 302 405 469\n",
            " 399 428 490 331 400 426 538 512 456 484 567 540 468 415 372 496 440 273\n",
            " 568 517 300 345 412 328 373 272 384 470 356 318 523 435 481 497 371 317\n",
            " 344 290 550 495 398 291 524 453 322 263 346 401 509 443 566 577 244 551\n",
            " 596 522 274 303 549 578 262 319 377 595 467 289 245 235 425 316 429 295\n",
            " 236 343 597 488 498 264 537 243 525 516 374 370 569 271 552 576 605 518\n",
            " 471 494 604 457 579 324 208 439 292 215 216 463 234 460 521 299 347 261\n",
            " 541 397 594 352 485 207 246 625 606 288 360 388 624 513 315 565 598 402\n",
            " 214 626 209 411 327 545 217 275 342 526 296 416 553 603 548 237 355 627\n",
            " 580 383 623 466 332 242 499 544 380 628 186 320 180 206 349 607 444 187\n",
            " 233 185]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iGqM8vqC4IFB",
        "outputId": "53e7dafd-f2d9-4a64-da2d-3d9655fe76e7"
      },
      "source": [
        "from pyspark.ml.classification import LinearSVC, OneVsRest\n",
        "from pyspark.ml.evaluation import MulticlassClassificationEvaluator\n",
        "\n",
        "# Define your binary classifier\n",
        "lsvc = LinearSVC(maxIter=10, regParam=0.1)\n",
        "\n",
        "# Define the One Vs All Classifier.\n",
        "ovr = OneVsRest(classifier=lsvc)\n",
        "\n",
        "# train the multiclass model.\n",
        "lsvm = ovr.fit(trainDF_0)\n",
        "lsvm.save(\"\")\n",
        "# score the model on test data.\n",
        "predictions = ovrModel.transform(dataTestDF)\n",
        "\n",
        "# Show the computed predictions and compare with the original labels\n",
        "predictions.select(\"features\", \"label\", \"prediction\").show(10)\n",
        "\n",
        "# Define the evaluator method with the corresponding metric and compute the classification error on test data\n",
        "evaluator =  MulticlassClassificationEvaluator().setMetricName('accuracy')\n",
        "accuracy = evaluator.evaluate(predictions) \n",
        "\n",
        "# Show the accuracy\n",
        "print(\"Test accuracy = \", (accuracy))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "+--------------------+-----+----------+\n",
            "|            features|label|prediction|\n",
            "+--------------------+-----+----------+\n",
            "|(784,[152,153,154...|  5.0|       0.0|\n",
            "|(784,[127,128,129...|  0.0|       0.0|\n",
            "|(784,[160,161,162...|  4.0|       0.0|\n",
            "|(784,[158,159,160...|  1.0|       0.0|\n",
            "|(784,[208,209,210...|  9.0|       0.0|\n",
            "|(784,[155,156,157...|  2.0|       0.0|\n",
            "|(784,[124,125,126...|  1.0|       0.0|\n",
            "|(784,[151,152,153...|  3.0|       0.0|\n",
            "|(784,[152,153,154...|  1.0|       0.0|\n",
            "|(784,[134,135,161...|  4.0|       0.0|\n",
            "+--------------------+-----+----------+\n",
            "only showing top 10 rows\n",
            "\n",
            "Test accuracy =  0.09871666666666666\n"
          ],
          "name": "stdout"
        }
      ]
    }
  ]
}