Plagiarism_detection_in_computer_programming_assignments__before_sampling (1).ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "# ***Plagiarism Detection before Sampling process***"
      ],
      "metadata": {
        "id": "noPw5nylkLWz"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "*Initiliasing all the libraries in the beginning*"
      ],
      "metadata": {
        "id": "qJcPqekXrHvC"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# !pip install fast_ml\n",
        "# from fast_ml.model_development import train_valid_test_split\n",
        "!pip install -q -U keras-tuner\n",
        "!pip install visualkeras"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "s3n1iGmqoytt",
        "outputId": "ace0f2bb-fd40-4811-860c-2299ed093e48"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[?25l\r\u001b[K     |██▌                             | 10 kB 13.6 MB/s eta 0:00:01\r\u001b[K     |█████                           | 20 kB 17.4 MB/s eta 0:00:01\r\u001b[K     |███████▍                        | 30 kB 21.1 MB/s eta 0:00:01\r\u001b[K     |█████████▉                      | 40 kB 21.7 MB/s eta 0:00:01\r\u001b[K     |████████████▎                   | 51 kB 21.2 MB/s eta 0:00:01\r\u001b[K     |██████████████▊                 | 61 kB 23.2 MB/s eta 0:00:01\r\u001b[K     |█████████████████▏              | 71 kB 6.4 MB/s eta 0:00:01\r\u001b[K     |███████████████████▋            | 81 kB 6.3 MB/s eta 0:00:01\r\u001b[K     |██████████████████████          | 92 kB 7.0 MB/s eta 0:00:01\r\u001b[K     |████████████████████████▌       | 102 kB 7.6 MB/s eta 0:00:01\r\u001b[K     |███████████████████████████     | 112 kB 7.6 MB/s eta 0:00:01\r\u001b[K     |█████████████████████████████▍  | 122 kB 7.6 MB/s eta 0:00:01\r\u001b[K     |███████████████████████████████▉| 133 kB 7.6 MB/s eta 0:00:01\r\u001b[K     |████████████████████████████████| 133 kB 7.6 MB/s \n",
            "\u001b[?25hLooking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting visualkeras\n",
            "  Downloading visualkeras-0.0.2-py3-none-any.whl (12 kB)\n",
            "Requirement already satisfied: pillow>=6.2.0 in /usr/local/lib/python3.7/dist-packages (from visualkeras) (7.1.2)\n",
            "Collecting aggdraw>=1.3.11\n",
            "  Downloading aggdraw-1.3.14-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (992 kB)\n",
            "\u001b[K     |████████████████████████████████| 992 kB 5.2 MB/s \n",
            "\u001b[?25hRequirement already satisfied: numpy>=1.18.1 in /usr/local/lib/python3.7/dist-packages (from visualkeras) (1.21.6)\n",
            "Installing collected packages: aggdraw, visualkeras\n",
            "Successfully installed aggdraw-1.3.14 visualkeras-0.0.2\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": true,
        "id": "qgofza2N1n35"
      },
      "outputs": [],
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import sklearn.metrics\n",
        "import tensorflow as tf\n",
        "from tensorflow import keras\n",
        "import keras_tuner as kt\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.utils import shuffle\n",
        "from sklearn.preprocessing import MinMaxScaler\n",
        "import re\n",
        "import imblearn\n",
        "import collections\n",
        "import seaborn as sns"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "outputs": [],
      "source": [
        "from collections import Counter\n",
        "from imblearn.over_sampling import RandomOverSampler\n",
        "from tensorflow.keras.models import load_model\n",
        "from tensorflow.keras.models import Sequential\n",
        "from tensorflow.keras.layers import Activation, Dense, BatchNormalization,Dropout\n",
        "from tensorflow.keras.optimizers import Adam,SGD\n",
        "from tensorflow.keras import regularizers,layers\n",
        "from tensorflow.keras.callbacks import EarlyStopping,ReduceLROnPlateau,LearningRateScheduler,ModelCheckpoint\n",
        "from tensorflow.keras.losses import BinaryCrossentropy,SparseCategoricalCrossentropy,CategoricalCrossentropy\n",
        "from keras.models import model_from_json\n",
        "from matplotlib import pyplot as plt\n"
      ],
      "metadata": {
        "pycharm": {
          "name": "#%%\n"
        },
        "id": "IEAV9wOy1n38"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "***Geting the feature dataset and label using pandas***"
      ],
      "metadata": {
        "id": "IHsMDuWbs3YR"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "  df=pd.read_csv('/content/sample_data/plagiarism_student_dataset_features.csv',index_col=0)\n",
        "  df.index.names=[None]\n",
        "  print(df.dtypes)\n",
        "  df.describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 632
        },
        "id": "1jYkpMtMMiZg",
        "outputId": "3d8710bb-b3b3-442e-b3a2-f1f935061a9c"
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      "execution_count": null,
      "outputs": [
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            "remove_lines             float64\n",
            "cps                      float64\n",
            "paste_lines              float64\n",
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            "average_test_results     float64\n",
            "avg_paste                float64\n",
            "dtype: object\n"
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              "         total_time      compiled  compiled_successfully    ran_tests  \\\n",
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              "\n",
              "       test_results       modified      add_lines        change  \\\n",
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              "mean     615.199636   19741.609107   15007.514699  20594.829073   \n",
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              "min        0.000000       1.000000       1.000000      1.000000   \n",
              "25%      175.750000     218.500000    1696.500000   6155.250000   \n",
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              "max     8574.000000  113231.000000  521578.000000  98826.000000   \n",
              "\n",
              "        remove_lines          cps    paste_lines    max_pastes  \\\n",
              "count    1972.000000  2240.000000    1915.000000   1915.000000   \n",
              "mean     2564.556288     0.038880   10881.229243    239.545692   \n",
              "std     14440.750302     0.604302   34453.639701    724.122757   \n",
              "min         1.000000     0.000000       6.000000      1.000000   \n",
              "25%       269.000000     0.001328    1057.000000     64.000000   \n",
              "50%      1044.500000     0.003528    3967.000000    139.000000   \n",
              "75%      2240.000000     0.007138    8576.000000    263.000000   \n",
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              "\n",
              "       average_test_results    avg_paste  \n",
              "count           1648.000000  1915.000000  \n",
              "mean               1.277133     5.251724  \n",
              "std                1.059974    11.343774  \n",
              "min                0.000000     1.000000  \n",
              "25%                0.614717     2.606246  \n",
              "50%                1.029409     3.864455  \n",
              "75%                1.615406     5.552023  \n",
              "max               10.000000   432.557784  "
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              "      <td>423.561568</td>\n",
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              "      <th>25%</th>\n",
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              "      <td>245.000000</td>\n",
              "      <td>160.000000</td>\n",
              "      <td>151.000000</td>\n",
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          "metadata": {},
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    {
      "cell_type": "code",
      "source": [
        "label_A=pd.read_csv('/content/sample_data/label_for_plagiarism_dataset_stat_dynamic.csv',index_col=0)\n",
        "label_A.index.names=[None]\n",
        "label_A.describe()"
      ],
      "metadata": {
        "colab": {
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          "height": 300
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        "id": "SMY-xjnsM48o",
        "outputId": "4af2b649-8d66-4937-ff8b-04e2785baa43"
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      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "             label\n",
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              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
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              "      \n",
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              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
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              "\n",
              "    .colab-df-convert {\n",
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              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
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              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
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              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-4e1b6b9e-cb80-46f3-b2f5-b4cb5b631f8b button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-4e1b6b9e-cb80-46f3-b2f5-b4cb5b631f8b');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "label_list = label_A['label'].tolist()"
      ],
      "metadata": {
        "id": "yMFaVlkJniHh"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(label_list[:10])\n",
        "print(len(label_list))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pbut3_NSoXUc",
        "outputId": "d2f6f542-81c5-4f8f-c8d6-587bf2bfeadb"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[0, 0, 1, 1, 0, 0, 1, 0, 0, 0]\n",
            "2339\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['label']=label_list\n",
        "df.fillna(0,inplace=True)\n",
        "# df.dropna(inplace = True)\n",
        "trainA=df.iloc[:,:-1]\n",
        "label_A=df.iloc[:,-1]"
      ],
      "metadata": {
        "id": "ZMl6SbE9olOt"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "trainA.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 270
        },
        "id": "_KcFjwlsqf-d",
        "outputId": "84192233-85a9-47fc-8c57-a5f726fe6200"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                   total_time  compiled  compiled_successfully  ran_tests  \\\n",
              "A2016\\student1013   6894752.0    1441.0                 1116.0      689.0   \n",
              "A2016\\student1016     37440.0       0.0                    0.0        0.0   \n",
              "A2016\\student1021   9363152.0    2108.0                 1278.0      787.0   \n",
              "A2016\\student1029   6949656.0    1775.0                 1201.0      852.0   \n",
              "A2016\\student1066   2784368.0     338.0                  223.0      449.0   \n",
              "\n",
              "                   test_results  modified  add_lines   change  remove_lines  \\\n",
              "A2016\\student1013        1349.0   38441.0    12365.0  32597.0        2023.0   \n",
              "A2016\\student1016           0.0       1.0        0.0      0.0           8.0   \n",
              "A2016\\student1021        1605.0   58550.0    19160.0  49003.0        6105.0   \n",
              "A2016\\student1029         754.0   36461.0    15605.0  30332.0        2683.0   \n",
              "A2016\\student1066         810.0   13924.0     6140.0  12169.0         369.0   \n",
              "\n",
              "                        cps  paste_lines  max_pastes  average_test_results  \\\n",
              "A2016\\student1013  0.003436       5202.0       120.0              1.957910   \n",
              "A2016\\student1016  0.000000          0.0         0.0              0.000000   \n",
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              "A2016\\student1066  0.004970       3988.0       139.0              1.804009   \n",
              "\n",
              "                   avg_paste  \n",
              "A2016\\student1013   4.553441  \n",
              "A2016\\student1016   0.000000  \n",
              "A2016\\student1021   5.147455  \n",
              "A2016\\student1029   4.840774  \n",
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            ],
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              "      <td>2784368.0</td>\n",
              "      <td>338.0</td>\n",
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              "      <td>449.0</td>\n",
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              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-d7062d71-0124-4a2f-82ce-1a0837f89f56')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
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              "      border-radius: 50%;\n",
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              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
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              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-d7062d71-0124-4a2f-82ce-1a0837f89f56 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-d7062d71-0124-4a2f-82ce-1a0837f89f56');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "label_A.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "f0Z_i-rTTSsz",
        "outputId": "6b63df2b-211c-45ab-df50-3c7f411c10f0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "A2016\\student1013    0\n",
              "A2016\\student1016    0\n",
              "A2016\\student1021    1\n",
              "A2016\\student1029    1\n",
              "A2016\\student1066    0\n",
              "Name: label, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "***The count of plagiarised and not plagiarised dataset available to us which is skewed, as the count of not plagiarised is greater than the count of plagiarised***"
      ],
      "metadata": {
        "id": "FmnTaEYitAni"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "sns.countplot(label_A)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 353
        },
        "id": "IhZejAP9tCbw",
        "outputId": "1168bc93-b468-49f6-d840-77e707e75f0e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
            "  FutureWarning\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f58a09e4390>"
            ]
          },
          "metadata": {},
          "execution_count": 13
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "***Scaling the values of features between 0 to 1 for better results and classification***"
      ],
      "metadata": {
        "id": "rMTYHJ8q4VBv"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "scaler=MinMaxScaler(feature_range=(0,1))\n",
        "train_A=scaler.fit_transform(trainA)"
      ],
      "metadata": {
        "id": "uPkJ8hmRIfxb"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(train_A.dtype)\n",
        "print(train_A.shape)\n",
        "print(train_A)"
      ],
      "metadata": {
        "id": "DXtI8bfLKoxn",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "9f96f80b-77eb-454e-b5a0-a50f0f4af39b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "float64\n",
            "(2339, 14)\n",
            "[[3.00070017e-01 1.34384034e-01 1.52417372e-01 ... 6.00000000e-03\n",
            "  1.95791002e-01 1.05267808e-02]\n",
            " [1.62944533e-03 0.00000000e+00 0.00000000e+00 ... 0.00000000e+00\n",
            "  0.00000000e+00 0.00000000e+00]\n",
            " [4.07498512e-01 1.96586776e-01 1.74542475e-01 ... 8.10000000e-03\n",
            "  2.03939009e-01 1.19000408e-02]\n",
            " ...\n",
            " [5.37605544e-01 5.28676676e-01 4.87298552e-01 ... 1.16000000e-02\n",
            "  6.28982528e-02 1.11948382e-02]\n",
            " [3.69758748e-04 0.00000000e+00 0.00000000e+00 ... 1.00000000e-04\n",
            "  0.00000000e+00 3.39974979e-03]\n",
            " [2.87237439e-01 1.54714166e-01 1.35618683e-01 ... 1.62500000e-02\n",
            "  1.02476780e-01 1.58131852e-02]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "label_A=label_A.to_numpy()\n",
        "print(label_A.dtype)\n",
        "# label_A=label_A.astype(float)\n",
        "print(label_A.shape)\n",
        "print(label_A)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sfF641gqM4JQ",
        "outputId": "6e2d0f10-bdbf-41f4-e7d2-59568c9e5da1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "int64\n",
            "(2339,)\n",
            "[0 0 1 ... 0 0 0]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.plot(train_A)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 515
        },
        "id": "7Un4spmctOqN",
        "outputId": "2ca3b20e-9704-47d8-df7d-d3ca9cbe1847"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7f58a032e650>,\n",
              " <matplotlib.lines.Line2D at 0x7f58a032e810>,\n",
              " <matplotlib.lines.Line2D at 0x7f58a032e9d0>,\n",
              " <matplotlib.lines.Line2D at 0x7f58a032eb90>,\n",
              " <matplotlib.lines.Line2D at 0x7f58a032ed50>,\n",
              " <matplotlib.lines.Line2D at 0x7f58a032ef10>,\n",
              " <matplotlib.lines.Line2D at 0x7f58a0337190>,\n",
              " <matplotlib.lines.Line2D at 0x7f58a0337350>,\n",
              " <matplotlib.lines.Line2D at 0x7f58a032ed10>,\n",
              " <matplotlib.lines.Line2D at 0x7f58a032ef50>,\n",
              " <matplotlib.lines.Line2D at 0x7f58a032e610>,\n",
              " <matplotlib.lines.Line2D at 0x7f58a0337990>,\n",
              " <matplotlib.lines.Line2D at 0x7f58a0337b50>,\n",
              " <matplotlib.lines.Line2D at 0x7f58a0337d10>]"
            ]
          },
          "metadata": {},
          "execution_count": 17
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "***Splitting the dataset into training and testing with a train_size split of 0.8***"
      ],
      "metadata": {
        "id": "0Tt-LsnC4Zua"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "x_train,x_test,y_train,y_test= train_test_split(train_A,label_A,train_size=0.8)"
      ],
      "metadata": {
        "id": "i7l9ytP3TdYY"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(x_train.shape)\n",
        "print(y_train.shape)\n",
        "print(x_test.shape)\n",
        "print(y_test.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZCm49h-JajoB",
        "outputId": "2d47693e-6a80-4a02-c2e8-fa85ef3d7f2c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(1871, 14)\n",
            "(1871,)\n",
            "(468, 14)\n",
            "(468,)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "***The callbacks are defined here to stop training the model when there is no change in the parameter we wish to monitor***"
      ],
      "metadata": {
        "id": "gD66gOM44iy_"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "custom_early_stopping = EarlyStopping(\n",
        "    monitor='val_accuracy', \n",
        "    patience=5, \n",
        "    min_delta=0, \n",
        "    mode='max',\n",
        "    restore_best_weights=True\n",
        "\n",
        ")\n",
        "custom_early_stopping_loss = EarlyStopping(\n",
        "    monitor='val_loss', \n",
        "    patience=5, \n",
        "    min_delta=0, \n",
        "    mode='min',\n",
        "    restore_best_weights=True\n",
        ")\n",
        "custom_early_stopping_recall=EarlyStopping(\n",
        "    monitor='val_recall', \n",
        "    patience=5, \n",
        "    min_delta=0, \n",
        "    mode='max',\n",
        "    restore_best_weights=True\n",
        ")\n",
        "\n",
        "# reduce_lr callback\n",
        "reduce_lr = ReduceLROnPlateau(\n",
        "    monitor='val_accuracy', \n",
        "    factor=0.2,   \n",
        "    patience=2, \n",
        "    min_lr=0.001,\n",
        "    verbose=2\n",
        ")\n",
        "# lr_decay callback\n",
        "def lr_decay(epoch, lr):\n",
        "    if epoch != 0 and epoch % 5 == 0:\n",
        "        return lr * 0.2\n",
        "    return lr\n",
        "\n",
        "# modelcheckpoint callback\n",
        "filepath = 'my_best_model_acc{val_accuracy:.2f}.hdf5'\n",
        "checkpoint = ModelCheckpoint(filepath=filepath, \n",
        "                             monitor='val_accuracy',\n",
        "                             verbose=1, \n",
        "                             save_best_only=True,\n",
        "                             mode='max')"
      ],
      "metadata": {
        "id": "a7j4MOOzm8d2"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "***A sequential model is built in the function using keras tuner which selects the best model for the inputs based on the range of layers given(2-40) and number of neurons between 16-512 with a step size of 32 and also the learning rate. It selects the best hyperparameters based on the input dataset provided***"
      ],
      "metadata": {
        "id": "_nY_mJ1hBQbj"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "outputs": [],
      "source": [
        "def build_model(hp):\n",
        "  model=keras.Sequential()\n",
        "  \n",
        "  for layer in range(hp.Int('num_layers',2,30)):\n",
        "    model.add(layers.Dense(units=hp.Int('units' + str(layer),\n",
        "                                            min_value=16,\n",
        "                                            max_value=512,\n",
        "                                            step=32),\n",
        "                               activation='relu')),\n",
        "    # model.add(layers.BatchNormalization(axis=1)),\n",
        "    # model.add(layers.Dropout(rate=hp.Float('dropout_1',min_value=0.1,max_value=1.0,step=0.05))),\n",
        "  model.add(layers.Dense(units=1,activation='sigmoid'))\n",
        "  model.compile(optimizer=SGD(hp.Choice('learning_rate',[1e-2, 1e-3, 1e-4,1e-5,1e-6,1e-7])),\n",
        "                              loss=keras.losses.BinaryCrossentropy(),metrics=['accuracy',tf.keras.metrics.Precision(),tf.keras.metrics.Recall()])\n",
        "  return model "
      ],
      "metadata": {
        "pycharm": {
          "name": "#%%\n"
        },
        "id": "CJ_0REee1n39"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "tuner = kt.RandomSearch(\n",
        "    build_model,\n",
        "    objective=kt.Objective(\"val_accuracy\",direction=\"max\"),\n",
        "    max_trials=3,\n",
        "    executions_per_trial=5,\n",
        "    # directory='/Users/rhuthuhegde/Desktop/Others/Final Year Project Files/project',\n",
        "    directory='project2',\n",
        "    overwrite=True,\n",
        "    project_name='Plagiarism Detection')"
      ],
      "metadata": {
        "id": "VkgKIzCT2d6j"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# build_model.summary()\n",
        "tuner.search_space_summary()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1QCIvQRHbvyy",
        "outputId": "4bc5c793-654f-4d09-eba5-3d3945a2c269"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Search space summary\n",
            "Default search space size: 4\n",
            "num_layers (Int)\n",
            "{'default': None, 'conditions': [], 'min_value': 2, 'max_value': 30, 'step': 1, 'sampling': None}\n",
            "units0 (Int)\n",
            "{'default': None, 'conditions': [], 'min_value': 16, 'max_value': 512, 'step': 32, 'sampling': None}\n",
            "units1 (Int)\n",
            "{'default': None, 'conditions': [], 'min_value': 16, 'max_value': 512, 'step': 32, 'sampling': None}\n",
            "learning_rate (Choice)\n",
            "{'default': 0.01, 'conditions': [], 'values': [0.01, 0.001, 0.0001, 1e-05, 1e-06, 1e-07], 'ordered': True}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "tuner.search(x_train,\n",
        "          y_train,\n",
        "          epochs=100,\n",
        "          validation_split=0.25,\n",
        "          callbacks=[custom_early_stopping_recall,reduce_lr]\n",
        "                )"
      ],
      "metadata": {
        "id": "MncBvyyUAjya",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "9e9f56ec-b795-4332-b126-43a6e2e5c08c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Trial 3 Complete [00h 00m 52s]\n",
            "val_accuracy: 0.6547008574008941\n",
            "\n",
            "Best val_accuracy So Far: 0.6940170764923096\n",
            "Total elapsed time: 00h 02m 59s\n",
            "INFO:tensorflow:Oracle triggered exit\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "tuner.results_summary()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "U9cjk4cbeuk7",
        "outputId": "b3fb2428-0f34-41cb-e255-93e75bd99fbc"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Results summary\n",
            "Results in project2/Plagiarism Detection\n",
            "Showing 10 best trials\n",
            "<keras_tuner.engine.objective.Objective object at 0x7f58a02aa650>\n",
            "Trial summary\n",
            "Hyperparameters:\n",
            "num_layers: 4\n",
            "units0: 272\n",
            "units1: 144\n",
            "learning_rate: 1e-05\n",
            "units2: 240\n",
            "units3: 304\n",
            "units4: 240\n",
            "units5: 112\n",
            "units6: 48\n",
            "units7: 112\n",
            "units8: 80\n",
            "units9: 272\n",
            "units10: 336\n",
            "units11: 240\n",
            "units12: 304\n",
            "units13: 208\n",
            "units14: 176\n",
            "Score: 0.6940170764923096\n",
            "Trial summary\n",
            "Hyperparameters:\n",
            "num_layers: 9\n",
            "units0: 432\n",
            "units1: 272\n",
            "learning_rate: 1e-07\n",
            "units2: 144\n",
            "units3: 400\n",
            "units4: 336\n",
            "units5: 176\n",
            "units6: 432\n",
            "units7: 112\n",
            "units8: 432\n",
            "units9: 112\n",
            "units10: 368\n",
            "units11: 368\n",
            "units12: 400\n",
            "units13: 144\n",
            "units14: 144\n",
            "Score: 0.6547008574008941\n",
            "Trial summary\n",
            "Hyperparameters:\n",
            "num_layers: 15\n",
            "units0: 432\n",
            "units1: 432\n",
            "learning_rate: 1e-07\n",
            "units2: 16\n",
            "units3: 16\n",
            "units4: 16\n",
            "units5: 16\n",
            "units6: 16\n",
            "units7: 16\n",
            "units8: 16\n",
            "units9: 16\n",
            "units10: 16\n",
            "units11: 16\n",
            "units12: 16\n",
            "units13: 16\n",
            "units14: 16\n",
            "Score: 0.626068377494812\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "***The model with best validation accuracy is chosen after searching it using RandomSearch***"
      ],
      "metadata": {
        "id": "Y2JdQQyfBVGY"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "best_hps=tuner.get_best_hyperparameters(num_trials=1)[0]\n",
        "print(best_hps.get('num_layers'))\n",
        "print(best_hps.get('learning_rate'))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NDfe9gexgAzL",
        "outputId": "50ef43d9-c9ae-405e-c047-d271822489a0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "4\n",
            "1e-05\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "***The best model is built and fit on the dataset. The model is also retrained to get better accuracy and recall values***"
      ],
      "metadata": {
        "id": "ozv_TkPmBYHn"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model = tuner.hypermodel.build(best_hps)\n",
        "history = model.fit(x_train, y_train, epochs=100, validation_split=0.25,callbacks=[custom_early_stopping,reduce_lr])\n",
        "# val_acc_per_epoch=history.history['val_loss']\n",
        "val_acc_per_epoch = history.history['val_accuracy']\n",
        "best_epoch = val_acc_per_epoch.index(min(val_acc_per_epoch)) + 1\n",
        "print('Best epoch: %d' % (best_epoch,))"
      ],
      "metadata": {
        "id": "TOGna7WQPHYR",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "59c67cb4-87c5-4b9b-c09f-6868da6b2fce"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/100\n",
            "44/44 [==============================] - 2s 14ms/step - loss: 0.6915 - accuracy: 0.6935 - precision_1: 0.1500 - recall_1: 0.0232 - val_loss: 0.6917 - val_accuracy: 0.7073 - val_precision_1: 0.1667 - val_recall_1: 0.0155 - lr: 1.0000e-05\n",
            "Epoch 2/100\n",
            "44/44 [==============================] - 0s 5ms/step - loss: 0.6915 - accuracy: 0.7028 - precision_1: 0.1915 - recall_1: 0.0232 - val_loss: 0.6917 - val_accuracy: 0.7137 - val_precision_1: 0.1429 - val_recall_1: 0.0078 - lr: 1.0000e-05\n",
            "Epoch 3/100\n",
            "44/44 [==============================] - 0s 5ms/step - loss: 0.6914 - accuracy: 0.7042 - precision_1: 0.2000 - recall_1: 0.0232 - val_loss: 0.6916 - val_accuracy: 0.7137 - val_precision_1: 0.1429 - val_recall_1: 0.0078 - lr: 1.0000e-05\n",
            "Epoch 4/100\n",
            "44/44 [==============================] - 0s 5ms/step - loss: 0.6914 - accuracy: 0.7071 - precision_1: 0.2195 - recall_1: 0.0232 - val_loss: 0.6916 - val_accuracy: 0.7158 - val_precision_1: 0.1667 - val_recall_1: 0.0078 - lr: 1.0000e-05\n",
            "Epoch 5/100\n",
            "44/44 [==============================] - 0s 5ms/step - loss: 0.6914 - accuracy: 0.7099 - precision_1: 0.2286 - recall_1: 0.0206 - val_loss: 0.6915 - val_accuracy: 0.7179 - val_precision_1: 0.2000 - val_recall_1: 0.0078 - lr: 1.0000e-05\n",
            "Epoch 6/100\n",
            "44/44 [==============================] - 0s 5ms/step - loss: 0.6913 - accuracy: 0.7128 - precision_1: 0.2581 - recall_1: 0.0206 - val_loss: 0.6915 - val_accuracy: 0.7179 - val_precision_1: 0.2000 - val_recall_1: 0.0078 - lr: 1.0000e-05\n",
            "Epoch 7/100\n",
            "44/44 [==============================] - 0s 5ms/step - loss: 0.6913 - accuracy: 0.7142 - precision_1: 0.2759 - recall_1: 0.0206 - val_loss: 0.6915 - val_accuracy: 0.7222 - val_precision_1: 0.3333 - val_recall_1: 0.0078 - lr: 1.0000e-05\n",
            "Epoch 8/100\n",
            "44/44 [==============================] - 0s 6ms/step - loss: 0.6912 - accuracy: 0.7128 - precision_1: 0.2222 - recall_1: 0.0155 - val_loss: 0.6914 - val_accuracy: 0.7222 - val_precision_1: 0.3333 - val_recall_1: 0.0078 - lr: 1.0000e-05\n",
            "Epoch 9/100\n",
            "44/44 [==============================] - 0s 5ms/step - loss: 0.6912 - accuracy: 0.7142 - precision_1: 0.2400 - recall_1: 0.0155 - val_loss: 0.6914 - val_accuracy: 0.7222 - val_precision_1: 0.3333 - val_recall_1: 0.0078 - lr: 1.0000e-05\n",
            "Epoch 10/100\n",
            "44/44 [==============================] - 0s 5ms/step - loss: 0.6911 - accuracy: 0.7149 - precision_1: 0.2273 - recall_1: 0.0129 - val_loss: 0.6913 - val_accuracy: 0.7222 - val_precision_1: 0.3333 - val_recall_1: 0.0078 - lr: 1.0000e-05\n",
            "Epoch 11/100\n",
            "44/44 [==============================] - 0s 6ms/step - loss: 0.6911 - accuracy: 0.7163 - precision_1: 0.2222 - recall_1: 0.0103 - val_loss: 0.6913 - val_accuracy: 0.7222 - val_precision_1: 0.3333 - val_recall_1: 0.0078 - lr: 1.0000e-05\n",
            "Epoch 12/100\n",
            "44/44 [==============================] - 0s 6ms/step - loss: 0.6910 - accuracy: 0.7192 - precision_1: 0.2500 - recall_1: 0.0077 - val_loss: 0.6912 - val_accuracy: 0.7222 - val_precision_1: 0.3333 - val_recall_1: 0.0078 - lr: 1.0000e-05\n",
            "Best epoch: 1\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "hypermodel = tuner.hypermodel.build(best_hps)\n",
        "\n",
        "# Retrain the model\n",
        "hypermodel.fit(x_train, y_train, epochs=best_epoch, validation_split=0.2,callbacks=[reduce_lr,custom_early_stopping],shuffle=True)"
      ],
      "metadata": {
        "id": "dx2Pfc8u4mFG",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "f975f0cd-6981-4304-82e4-9d34e1c00f65"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "47/47 [==============================] - 1s 13ms/step - loss: 0.6933 - accuracy: 0.5127 - precision_2: 0.2451 - recall_2: 0.3639 - val_loss: 0.6932 - val_accuracy: 0.5733 - val_precision_2: 0.2623 - val_recall_2: 0.3137 - lr: 1.0000e-05\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.callbacks.History at 0x7f5898a5ff50>"
            ]
          },
          "metadata": {},
          "execution_count": 69
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from statistics import median, mode\n",
        "eval_result = hypermodel.evaluate(x_test, y_test)\n",
        "eval_result_train=hypermodel.evaluate(x_train,y_train)\n",
        "eval_result\n",
        "print(\"[test loss, test accuracy,test precision,test recall]:\", eval_result)# print(predictions.dtype)\n",
        "predictions=hypermodel.predict(x_test)\n",
        "\n",
        "print(sum(predictions)/len(predictions))\n",
        "print(median(predictions))\n",
        "val=min(sum(predictions)/len(predictions),median(predictions))\n",
        "predictions"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hUO07Hryv2_1",
        "outputId": "5c32b9e5-10b1-4fdc-87e2-cdf35d86d25f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "15/15 [==============================] - 0s 2ms/step - loss: 0.6933 - accuracy: 0.5641 - precision_2: 0.3228 - recall_2: 0.3446\n",
            "59/59 [==============================] - 0s 2ms/step - loss: 0.6933 - accuracy: 0.5639 - precision_2: 0.2704 - recall_2: 0.3404\n",
            "[test loss, test accuracy,test precision,test recall]: [0.6932815313339233, 0.5641025900840759, 0.32278481125831604, 0.3445945978164673]\n",
            "[0.49992266]\n",
            "[0.49995512]\n"
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        {
          "output_type": "execute_result",
          "data": {
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            ]
          },
          "metadata": {},
          "execution_count": 70
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "*Here is the graph showing the performance of the training and testing data after fitting the model on the dataset after first try*"
      ],
      "metadata": {
        "id": "Oo-vZKROBfJR"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "plt.plot(eval_result,label=\"validation\")\n",
        "plt.plot(eval_result_train,label=\"train\")\n",
        "plt.legend(ncol=2)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 283
        },
        "id": "F9rOXKwwLUeH",
        "outputId": "f4f32bf6-0974-46a9-a0d2-c6dcc5880231"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7f5899fba2d0>"
            ]
          },
          "metadata": {},
          "execution_count": 71
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(predictions.shape)\n",
        "seq_predictions=np.transpose(predictions)[0]  # transformation to get (n,)\n",
        "print(seq_predictions.shape)  # now the shape is (n,)\n",
        "# Applying transformation to get binary values predictions with 0.5 as thresold\n",
        "seq_predictions = list(map(lambda x: 0 if x< val else 1, seq_predictions))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mAzYnui_G-Od",
        "outputId": "2698db8d-410a-4446-abf1-a386abcdbd2c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(468, 1)\n",
            "(468,)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "loss,acc,pre,rec=hypermodel.evaluate(x_train,y_train)\n",
        "print(acc)\n",
        "loss,acc,pre,rec=hypermodel.evaluate(x_test,y_test)\n",
        "print(acc)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "l3agUFonJtw4",
        "outputId": "b5bb1a6c-c23f-488a-89dd-5a65fb6e2d9b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "59/59 [==============================] - 0s 2ms/step - loss: 0.6933 - accuracy: 0.5639 - precision_2: 0.2704 - recall_2: 0.3404\n",
            "0.5638695955276489\n",
            "15/15 [==============================] - 0s 3ms/step - loss: 0.6933 - accuracy: 0.5641 - precision_2: 0.3228 - recall_2: 0.3446\n",
            "0.5641025900840759\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# save the file in json\n",
        "model_json = hypermodel.to_json()\n",
        "with open(\"model_acctest_71.json\", \"w\") as json_file:\n",
        "    json_file.write(model_json)\n",
        "# serialize weights to HDF5\n",
        "hypermodel.save_weights(\"model_acctest_71.h5\")\n",
        "print(\"Saved model to disk\")"
      ],
      "metadata": {
        "id": "9o4y0dkZStOm",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "9d49afeb-b797-43a2-d480-a71caebc9ce2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Saved model to disk\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# load the model from json\n",
        "json_file = open('model_acctest_71.json', 'r')\n",
        "loaded_model_json = json_file.read()\n",
        "json_file.close()\n",
        "loaded_model = model_from_json(loaded_model_json)\n",
        "# load weights into new model\n",
        "loaded_model.load_weights(\"model_acctest_71.h5\")\n",
        "print(\"Loaded model from disk\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Sk0xcqsYMos6",
        "outputId": "17aee2dc-4338-4aea-86e7-75ea21c83423"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Loaded model from disk\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "***The saved and loaded model is compiled and evaluated again on the training and test dataset to give us the following results***"
      ],
      "metadata": {
        "id": "N4isUZouBlJf"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# loading the model from disk and calculating the recall and accuracy\n",
        "loaded_model.compile(loss='binary_crossentropy', optimizer='SGD', metrics=['accuracy',keras.metrics.Recall(),keras.metrics.AUC()])\n",
        "score = loaded_model.evaluate(x_test, y_test, verbose=0)\n",
        "score_train=loaded_model.evaluate(x_train,y_train,verbose=0)\n",
        "print(\"For testing data\")\n",
        "print(\"%s: %.2f%%\" % (loaded_model.metrics_names[1], score[1]*100))\n",
        "print(\"%s: %.2f%%\" % (loaded_model.metrics_names[2], score[2]*100))\n",
        "print(\"%s: %.2f%%\" % (loaded_model.metrics_names[3], score[3]*100))\n",
        "print(\"For training data\")\n",
        "print(\"%s: %.2f%%\" % (loaded_model.metrics_names[1], score_train[1]*100))\n",
        "print(\"%s: %.2f%%\" % (loaded_model.metrics_names[2], score_train[2]*100))\n",
        "print(\"%s: %.2f%%\" % (loaded_model.metrics_names[3], score_train[3]*100))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sGXEoXkfMtiz",
        "outputId": "eb96c035-6e16-49d8-a0d2-bb73dfa008aa"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "For testing data\n",
            "accuracy: 68.38%\n",
            "recall_7: 0.00%\n",
            "auc_4: 50.00%\n",
            "For training data\n",
            "accuracy: 72.37%\n",
            "recall_7: 0.00%\n",
            "auc_4: 50.00%\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.plot(score,label=\"test\")\n",
        "plt.plot(score_train,label=\"train\")\n",
        "plt.legend(ncol=2)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 283
        },
        "id": "0S0_FmuyM2_A",
        "outputId": "6319b164-49ca-4bf5-faaa-381fdc2645a7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7f58985c9c50>"
            ]
          },
          "metadata": {},
          "execution_count": 106
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "***The loaded model is compiled and fit again to give us better results of accuracy and recall which is 68.4 and 91.1 respectively after the sampling process***"
      ],
      "metadata": {
        "id": "EVxyMnGpBvhg"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# retrain the model\n",
        "loaded_model.fit(x_train,y_train,epochs=100,validation_split=0.2,use_multiprocessing=True,callbacks=[custom_early_stopping,reduce_lr])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uOpRzXd4M7yJ",
        "outputId": "34cdc62e-82f1-4d8c-c828-4943556a0807"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/100\n",
            "47/47 [==============================] - 2s 6ms/step - loss: 0.6698 - accuracy: 0.7226 - recall_7: 0.0000e+00 - auc_4: 0.4903 - val_loss: 0.6481 - val_accuracy: 0.7280 - val_recall_7: 0.0000e+00 - val_auc_4: 0.5000 - lr: 0.0100\n",
            "Epoch 2/100\n",
            "47/47 [==============================] - 0s 5ms/step - loss: 0.6359 - accuracy: 0.7226 - recall_7: 0.0000e+00 - auc_4: 0.5007 - val_loss: 0.6218 - val_accuracy: 0.7280 - val_recall_7: 0.0000e+00 - val_auc_4: 0.5000 - lr: 0.0100\n",
            "Epoch 3/100\n",
            "32/47 [===================>..........] - ETA: 0s - loss: 0.6132 - accuracy: 0.7334 - recall_7: 0.0000e+00 - auc_4: 0.5081\n",
            "Epoch 3: ReduceLROnPlateau reducing learning rate to 0.0019999999552965165.\n",
            "47/47 [==============================] - 0s 4ms/step - loss: 0.6163 - accuracy: 0.7226 - recall_7: 0.0000e+00 - auc_4: 0.4853 - val_loss: 0.6065 - val_accuracy: 0.7280 - val_recall_7: 0.0000e+00 - val_auc_4: 0.5000 - lr: 0.0100\n",
            "Epoch 4/100\n",
            "47/47 [==============================] - 0s 4ms/step - loss: 0.6083 - accuracy: 0.7226 - recall_7: 0.0000e+00 - auc_4: 0.5114 - val_loss: 0.6042 - val_accuracy: 0.7280 - val_recall_7: 0.0000e+00 - val_auc_4: 0.5000 - lr: 0.0020\n",
            "Epoch 5/100\n",
            "33/47 [====================>.........] - ETA: 0s - loss: 0.6023 - accuracy: 0.7301 - recall_7: 0.0000e+00 - auc_4: 0.4996\n",
            "Epoch 5: ReduceLROnPlateau reducing learning rate to 0.001.\n",
            "47/47 [==============================] - 0s 4ms/step - loss: 0.6063 - accuracy: 0.7226 - recall_7: 0.0000e+00 - auc_4: 0.4933 - val_loss: 0.6022 - val_accuracy: 0.7280 - val_recall_7: 0.0000e+00 - val_auc_4: 0.5000 - lr: 0.0020\n",
            "Epoch 6/100\n",
            "47/47 [==============================] - 0s 5ms/step - loss: 0.6050 - accuracy: 0.7226 - recall_7: 0.0000e+00 - auc_4: 0.4793 - val_loss: 0.6013 - val_accuracy: 0.7280 - val_recall_7: 0.0000e+00 - val_auc_4: 0.5000 - lr: 0.0010\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.callbacks.History at 0x7f5899f835d0>"
            ]
          },
          "metadata": {},
          "execution_count": 107
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# save the file in json\n",
        "model_json = hypermodel.to_json()\n",
        "with open(\"model_acctest_71.json\", \"w\") as json_file:\n",
        "    json_file.write(model_json)\n",
        "# serialize weights to HDF5\n",
        "hypermodel.save_weights(\"model_acctest_71.h5\")\n",
        "print(\"Saved model to disk\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Vm8lzO2_Ny5J",
        "outputId": "1a3f0879-cc8c-4e0d-f0b1-361f8d45a230"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Saved model to disk\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# load the model from json\n",
        "json_file = open('model_acctest_71.json', 'r')\n",
        "loaded_model_json = json_file.read()\n",
        "json_file.close()\n",
        "loaded_model = model_from_json(loaded_model_json)\n",
        "# load weights into new model\n",
        "loaded_model.load_weights(\"model_acctest_71.h5\")\n",
        "print(\"Loaded model from disk\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_C_yRHcfNzIP",
        "outputId": "6e0fa45e-14a4-4f2c-d3f1-453e6d06b6a1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Loaded model from disk\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# loading the model from disk and calculating the recall and accuracy\n",
        "loaded_model.compile(loss='binary_crossentropy', optimizer='SGD', metrics=['accuracy',keras.metrics.Recall(),keras.metrics.AUC()])\n",
        "score = loaded_model.evaluate(x_test, y_test, verbose=0)\n",
        "score_train=loaded_model.evaluate(x_train,y_train,verbose=0)\n",
        "print(\"For testing data\")\n",
        "print(\"%s: %.2f%%\" % (loaded_model.metrics_names[1], score[1]*100))\n",
        "print(\"%s: %.2f%%\" % (loaded_model.metrics_names[2], score[2]*100))\n",
        "print(\"%s: %.2f%%\" % (loaded_model.metrics_names[3], score[3]*100))\n",
        "print(\"For training data\")\n",
        "print(\"%s: %.2f%%\" % (loaded_model.metrics_names[1], score_train[1]*100))\n",
        "print(\"%s: %.2f%%\" % (loaded_model.metrics_names[2], score_train[2]*100))\n",
        "print(\"%s: %.2f%%\" % (loaded_model.metrics_names[3], score_train[3]*100))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YZrSKG1UNzTV",
        "outputId": "85a8cce0-c32d-4c2a-d197-ce2523370a5d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "For testing data\n",
            "accuracy: 56.41%\n",
            "recall_8: 34.46%\n",
            "auc_5: 48.40%\n",
            "For training data\n",
            "accuracy: 56.39%\n",
            "recall_8: 34.04%\n",
            "auc_5: 50.09%\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "*The performance graph after fitting the model 2nd time for better performance*\n"
      ],
      "metadata": {
        "id": "Qm1m0WR6B4Uo"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "plt.plot(score,label=\"test\")\n",
        "plt.plot(score_train,label=\"train\")\n",
        "plt.legend(ncol=2)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 283
        },
        "id": "xeRoUyi8Nzd-",
        "outputId": "399445f8-28b3-4061-a9f2-3499bf6fd0a0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7f589804f6d0>"
            ]
          },
          "metadata": {},
          "execution_count": 111
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "***Visualizing the neural network based on the density of each layer***"
      ],
      "metadata": {
        "id": "GzP82V3GB8RY"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import visualkeras\n",
        "visualkeras.layered_view(loaded_model).show() # display using your system viewer\n",
        "visualkeras.layered_view(loaded_model, to_file='output.png') # write to disk\n",
        "visualkeras.layered_view(loaded_model, to_file='output.png').show() # write and show\n",
        "\n",
        "visualkeras.layered_view(loaded_model, legend=True, draw_volume=False,spacing=30)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 68
        },
        "id": "fmpnShZqNzoJ",
        "outputId": "1f7e8d8c-ef29-4e6f-b648-b2dea57843b6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<PIL.Image.Image image mode=RGBA size=262x51 at 0x7F58983B0ED0>"
            ],
            "image/png": "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\n"
          },
          "metadata": {},
          "execution_count": 112
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from statistics import median, mode\n",
        "eval_result = loaded_model.evaluate(x_test, y_test)\n",
        "eval_result\n",
        "print(\"[test loss, test accuracy,test precision,test recall]:\", eval_result)\n",
        "predictions=loaded_model.predict(x_test)\n",
        "\n",
        "print(sum(predictions)/len(predictions))\n",
        "print(median(predictions))\n",
        "val=min(sum(predictions)/len(predictions),median(predictions))\n",
        "predictions"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_cIzdJrENzxU",
        "outputId": "1c976bd6-7a74-4a23-a202-d94ebb86f6d3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "15/15 [==============================] - 0s 3ms/step - loss: 0.6933 - accuracy: 0.5641 - recall_8: 0.3446 - auc_5: 0.4840\n",
            "[test loss, test accuracy,test precision,test recall]: [0.6932815313339233, 0.5641025900840759, 0.3445945978164673, 0.4839949309825897]\n",
            "[0.49992266]\n",
            "[0.49995512]\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
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              "       [0.49871156]], dtype=float32)"
            ]
          },
          "metadata": {},
          "execution_count": 113
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(predictions.shape)\n",
        "seq_predictions=np.transpose(predictions)[0]  # transformation to get (n,)\n",
        "print(seq_predictions.shape)  # now the shape is (n,)\n",
        "# Applying transformation to get binary values predictions with 0.5 as thresold\n",
        "seq_predictions = list(map(lambda x: 0 if x< val else 1, seq_predictions))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3RGSCJPuNz7A",
        "outputId": "32ebb26a-5abf-4c9e-fe08-f60c504fe020"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(468, 1)\n",
            "(468,)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "***The working of the dataset on the testing data and comparing it with its label gives us the confusion matrix which has a better recall. Recall is an important factor as higher recall implies that the model can predict plagiarised assignments almost 90% of the times***"
      ],
      "metadata": {
        "id": "_btRjcaQCAtv"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "%matplotlib inline\n",
        "from sklearn.metrics import confusion_matrix\n",
        "import itertools\n",
        "import matplotlib.pyplot as plt"
      ],
      "metadata": {
        "id": "devPsSkth2jC"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "cm = confusion_matrix(y_test,seq_predictions)"
      ],
      "metadata": {
        "id": "qSeTvY5tOFrN"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def plot_confusion_matrix(cm, classes,\n",
        "                        normalize=False,\n",
        "                        title='Confusion matrix',\n",
        "                        cmap=plt.cm.Blues):\n",
        "    \"\"\"\n",
        "    This function prints and plots the confusion matrix.\n",
        "    Normalization can be applied by setting `normalize=True`.\n",
        "    \"\"\"\n",
        "    plt.imshow(cm, interpolation='nearest', cmap=cmap)\n",
        "    plt.title(title)\n",
        "    plt.colorbar()\n",
        "    tick_marks = np.arange(len(classes))\n",
        "    plt.xticks(tick_marks, classes, rotation=45)\n",
        "    plt.yticks(tick_marks, classes)\n",
        "\n",
        "    if normalize:\n",
        "        cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\n",
        "        print(\"Normalized confusion matrix\")\n",
        "    else:\n",
        "        print('Confusion matrix, without normalization')\n",
        "\n",
        "    print(cm)\n",
        "\n",
        "    thresh = cm.max() / 2.\n",
        "    for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\n",
        "        plt.text(j, i, cm[i, j],\n",
        "            horizontalalignment=\"center\",\n",
        "            color=\"white\" if cm[i, j] > thresh else \"black\")\n",
        "\n",
        "    plt.tight_layout()\n",
        "    plt.ylabel('True label')\n",
        "    plt.xlabel('Predicted label')"
      ],
      "metadata": {
        "id": "_NbXE3kbOF4Q"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "cm_plot_labels = ['No Plagiarism','Plagiarism']\n",
        "plot_confusion_matrix(cm=cm, classes=cm_plot_labels, title='Confusion Matrix')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 365
        },
        "id": "cXSvnFgVOGVQ",
        "outputId": "36f11d28-5e87-485e-de07-14d7bba6d05f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Confusion matrix, without normalization\n",
            "[[104 216]\n",
            " [ 88  60]]\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"Accuracy\",sklearn.metrics.accuracy_score(y_test,seq_predictions))\n",
        "print(\"Recall\",sklearn.metrics.recall_score(y_test,seq_predictions))\n",
        "print(\"ROC AUC\",sklearn.metrics.roc_auc_score(y_test,seq_predictions))\n",
        "print(\"F1\",sklearn.metrics.f1_score(y_test,seq_predictions))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sAocl-OCSqHS",
        "outputId": "fe3aab06-627d-4d78-802e-d2c9f48cbe68"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy 0.3504273504273504\n",
            "Recall 0.40540540540540543\n",
            "ROC AUC 0.3652027027027027\n",
            "F1 0.2830188679245283\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "***Testing on the B2017 dataset to generate the output***"
      ],
      "metadata": {
        "id": "j9QgK4KnCKqh"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from numpy import asarray\n",
        "# values=[\n",
        "# 7216624.0,2786.0,1682.0,810.0,499.0,30029.0,18276.0,24492.0,1321.0,0.00542927,10928.0,469.0,0.616049383,3.585377013    \n",
        "#    ]\n",
        "test_2017=pd.read_csv('/content/sample_data/plagiarism_student_dataset_test_B2017.csv',index_col=0)\n",
        "test_2017.index.names=[None]\n",
        "test_2017.fillna(0,inplace=True)\n",
        "test_2017.describe()\n",
        "test_2017=scaler.fit_transform(test_2017)\n",
        "# values=np.array(values).reshape(-1,1)\n",
        "# values=scaler.fit_transform(values)\n",
        "# val=(max(values)+min(values))/2\n",
        "# print(val)\n",
        "# print(values)\n",
        "# input_array = np.array([1,2,3,4,5,6,7,8,9,10,11,12,13,14])\n",
        "# values = np.expand_dims(values,axis=0)\n",
        "label=loaded_model.predict(test_2017)\n",
        "val=min(sum(label)/len(label),median(label))\n",
        "print(label)\n",
        "for i in label:\n",
        "  print(\"Plagiarised\" if i>val else \"Non Plagiarised\")\n",
        "print(val)\n",
        "print(label.dtype)\n",
        "# sns.countplot(label)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CaZbXSsWOGhE",
        "outputId": "179660e1-ed5e-4736-e5ab-c82e3ee57412"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
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            "Plagiarised\n",
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            "Non Plagiarised\n",
            "Non Plagiarised\n",
            "Plagiarised\n",
            "Non Plagiarised\n",
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            "Plagiarised\n",
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            "Plagiarised\n",
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            "Plagiarised\n",
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            "Plagiarised\n",
            "Plagiarised\n",
            "Plagiarised\n",
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            "Plagiarised\n",
            "Plagiarised\n",
            "Non Plagiarised\n",
            "Plagiarised\n",
            "Non Plagiarised\n",
            "Plagiarised\n",
            "Plagiarised\n",
            "Plagiarised\n",
            "Plagiarised\n",
            "Plagiarised\n",
            "Non Plagiarised\n",
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            "Plagiarised\n",
            "Plagiarised\n",
            "Non Plagiarised\n",
            "Plagiarised\n",
            "Plagiarised\n",
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            "Plagiarised\n",
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            "Non Plagiarised\n",
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            "Plagiarised\n",
            "Plagiarised\n",
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            "Plagiarised\n",
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            "Plagiarised\n",
            "Non Plagiarised\n",
            "Non Plagiarised\n",
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            "Non Plagiarised\n",
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            "Plagiarised\n",
            "Non Plagiarised\n",
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            "Non Plagiarised\n",
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            "Plagiarised\n",
            "Non Plagiarised\n",
            "Non Plagiarised\n",
            "Plagiarised\n",
            "Plagiarised\n",
            "Plagiarised\n",
            "Non Plagiarised\n",
            "Non Plagiarised\n",
            "Non Plagiarised\n",
            "Non Plagiarised\n",
            "Plagiarised\n",
            "Plagiarised\n",
            "Non Plagiarised\n",
            "Non Plagiarised\n",
            "Plagiarised\n",
            "Non Plagiarised\n",
            "Non Plagiarised\n",
            "Non Plagiarised\n",
            "Non Plagiarised\n",
            "Plagiarised\n",
            "Non Plagiarised\n",
            "Plagiarised\n",
            "Plagiarised\n",
            "Non Plagiarised\n",
            "Plagiarised\n",
            "Non Plagiarised\n",
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            "Plagiarised\n",
            "Plagiarised\n",
            "Plagiarised\n",
            "Plagiarised\n",
            "Plagiarised\n",
            "Plagiarised\n",
            "Plagiarised\n",
            "Plagiarised\n",
            "Plagiarised\n",
            "Plagiarised\n",
            "Plagiarised\n",
            "Plagiarised\n",
            "Non Plagiarised\n",
            "Non Plagiarised\n",
            "Plagiarised\n",
            "Non Plagiarised\n",
            "Plagiarised\n",
            "Plagiarised\n",
            "Non Plagiarised\n",
            "Non Plagiarised\n",
            "Plagiarised\n",
            "Plagiarised\n",
            "Non Plagiarised\n",
            "Non Plagiarised\n",
            "Plagiarised\n",
            "Non Plagiarised\n",
            "Non Plagiarised\n",
            "Non Plagiarised\n",
            "Plagiarised\n",
            "Non Plagiarised\n",
            "Non Plagiarised\n",
            "Plagiarised\n",
            "Plagiarised\n",
            "Non Plagiarised\n",
            "Non Plagiarised\n",
            "Plagiarised\n",
            "Plagiarised\n",
            "Plagiarised\n",
            "Plagiarised\n",
            "Plagiarised\n",
            "Non Plagiarised\n",
            "Plagiarised\n",
            "Plagiarised\n",
            "Non Plagiarised\n",
            "Non Plagiarised\n",
            "Non Plagiarised\n",
            "Non Plagiarised\n",
            "Plagiarised\n",
            "Plagiarised\n",
            "Non Plagiarised\n",
            "Plagiarised\n",
            "Non Plagiarised\n",
            "Non Plagiarised\n",
            "Non Plagiarised\n",
            "Plagiarised\n",
            "Non Plagiarised\n",
            "Plagiarised\n",
            "Non Plagiarised\n",
            "[0.49957645]\n",
            "float32\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# ***In conclusion we can say that the model after sampling performs better than the model before sampling with better recall and accuracy***"
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