Duplicate_questions_Detection.ipynb

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  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Duplicate questions Detection",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "NFZW2AsxBjXX",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4-OUQtxZtTMv",
        "colab_type": "text"
      },
      "source": [
        "### Imporitng the required libraries"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ee8xJZ6GD6E7",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 89
        },
        "outputId": "b769fa6f-8162-40ea-e7d2-bc24a3f6d0a9"
      },
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import tensorflow as tf\n",
        "import tensorflow_hub as hub\n",
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "import os\n",
        "import pandas as pd\n",
        "import re\n",
        "import seaborn as sns\n",
        "import keras.layers as layers\n",
        "from keras.models import Model\n",
        "from keras import backend as K\n",
        "np.random.seed(10)"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/statsmodels/tools/_testing.py:19: FutureWarning: pandas.util.testing is deprecated. Use the functions in the public API at pandas.testing instead.\n",
            "  import pandas.util.testing as tm\n",
            "Using TensorFlow backend.\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Eir2E8FWta-4",
        "colab_type": "text"
      },
      "source": [
        "### Mounting the drive for the source file and reading the data in dataframe"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "80EbqphG_LMc",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 55
        },
        "outputId": "758d5eec-77fa-4749-a96c-8b0949d2f01d"
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KIRN2RlR_pg2",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "f355607e-a3b1-4477-89e4-4b3d7bf9a902"
      },
      "source": [
        "df = pd.read_csv('/content/drive/My Drive/quora_duplicate_questions.tsv',sep='\\t')\n",
        "df.shape"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(404290, 6)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NJjISuR5Husu",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 294
        },
        "outputId": "1b7f73f6-ea3e-4bfd-b396-40b2b178b6e7"
      },
      "source": [
        "df.describe()\n"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
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              "    .dataframe tbody tr th:only-of-type {\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>id</th>\n",
              "      <th>qid1</th>\n",
              "      <th>qid2</th>\n",
              "      <th>is_duplicate</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>404290.000000</td>\n",
              "      <td>404290.000000</td>\n",
              "      <td>404290.000000</td>\n",
              "      <td>404290.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>202144.500000</td>\n",
              "      <td>217243.942418</td>\n",
              "      <td>220955.655337</td>\n",
              "      <td>0.369198</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>116708.614502</td>\n",
              "      <td>157751.700002</td>\n",
              "      <td>159903.182629</td>\n",
              "      <td>0.482588</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>101072.250000</td>\n",
              "      <td>74437.500000</td>\n",
              "      <td>74727.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>202144.500000</td>\n",
              "      <td>192182.000000</td>\n",
              "      <td>197052.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>303216.750000</td>\n",
              "      <td>346573.500000</td>\n",
              "      <td>354692.500000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>404289.000000</td>\n",
              "      <td>537932.000000</td>\n",
              "      <td>537933.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                  id           qid1           qid2   is_duplicate\n",
              "count  404290.000000  404290.000000  404290.000000  404290.000000\n",
              "mean   202144.500000  217243.942418  220955.655337       0.369198\n",
              "std    116708.614502  157751.700002  159903.182629       0.482588\n",
              "min         0.000000       1.000000       2.000000       0.000000\n",
              "25%    101072.250000   74437.500000   74727.000000       0.000000\n",
              "50%    202144.500000  192182.000000  197052.000000       0.000000\n",
              "75%    303216.750000  346573.500000  354692.500000       1.000000\n",
              "max    404289.000000  537932.000000  537933.000000       1.000000"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3lD1lBpRJPxi",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 905
        },
        "outputId": "5bb35472-b64a-41d3-864b-58ff804e572c"
      },
      "source": [
        "df.head(20)"
      ],
      "execution_count": 6,
      "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>id</th>\n",
              "      <th>qid1</th>\n",
              "      <th>qid2</th>\n",
              "      <th>question1</th>\n",
              "      <th>question2</th>\n",
              "      <th>is_duplicate</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>What is the step by step guide to invest in sh...</td>\n",
              "      <td>What is the step by step guide to invest in sh...</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>4</td>\n",
              "      <td>What is the story of Kohinoor (Koh-i-Noor) Dia...</td>\n",
              "      <td>What would happen if the Indian government sto...</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2</td>\n",
              "      <td>5</td>\n",
              "      <td>6</td>\n",
              "      <td>How can I increase the speed of my internet co...</td>\n",
              "      <td>How can Internet speed be increased by hacking...</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>3</td>\n",
              "      <td>7</td>\n",
              "      <td>8</td>\n",
              "      <td>Why am I mentally very lonely? How can I solve...</td>\n",
              "      <td>Find the remainder when [math]23^{24}[/math] i...</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>4</td>\n",
              "      <td>9</td>\n",
              "      <td>10</td>\n",
              "      <td>Which one dissolve in water quikly sugar, salt...</td>\n",
              "      <td>Which fish would survive in salt water?</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>5</td>\n",
              "      <td>11</td>\n",
              "      <td>12</td>\n",
              "      <td>Astrology: I am a Capricorn Sun Cap moon and c...</td>\n",
              "      <td>I'm a triple Capricorn (Sun, Moon and ascendan...</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>6</td>\n",
              "      <td>13</td>\n",
              "      <td>14</td>\n",
              "      <td>Should I buy tiago?</td>\n",
              "      <td>What keeps childern active and far from phone ...</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>7</td>\n",
              "      <td>15</td>\n",
              "      <td>16</td>\n",
              "      <td>How can I be a good geologist?</td>\n",
              "      <td>What should I do to be a great geologist?</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>8</td>\n",
              "      <td>17</td>\n",
              "      <td>18</td>\n",
              "      <td>When do you use シ instead of し?</td>\n",
              "      <td>When do you use \"&amp;\" instead of \"and\"?</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>9</td>\n",
              "      <td>19</td>\n",
              "      <td>20</td>\n",
              "      <td>Motorola (company): Can I hack my Charter Moto...</td>\n",
              "      <td>How do I hack Motorola DCX3400 for free internet?</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>10</td>\n",
              "      <td>21</td>\n",
              "      <td>22</td>\n",
              "      <td>Method to find separation of slits using fresn...</td>\n",
              "      <td>What are some of the things technicians can te...</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>11</td>\n",
              "      <td>23</td>\n",
              "      <td>24</td>\n",
              "      <td>How do I read and find my YouTube comments?</td>\n",
              "      <td>How can I see all my Youtube comments?</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>12</td>\n",
              "      <td>25</td>\n",
              "      <td>26</td>\n",
              "      <td>What can make Physics easy to learn?</td>\n",
              "      <td>How can you make physics easy to learn?</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>13</td>\n",
              "      <td>27</td>\n",
              "      <td>28</td>\n",
              "      <td>What was your first sexual experience like?</td>\n",
              "      <td>What was your first sexual experience?</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>14</td>\n",
              "      <td>29</td>\n",
              "      <td>30</td>\n",
              "      <td>What are the laws to change your status from a...</td>\n",
              "      <td>What are the laws to change your status from a...</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>15</td>\n",
              "      <td>31</td>\n",
              "      <td>32</td>\n",
              "      <td>What would a Trump presidency mean for current...</td>\n",
              "      <td>How will a Trump presidency affect the student...</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>16</td>\n",
              "      <td>33</td>\n",
              "      <td>34</td>\n",
              "      <td>What does manipulation mean?</td>\n",
              "      <td>What does manipulation means?</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>17</td>\n",
              "      <td>35</td>\n",
              "      <td>36</td>\n",
              "      <td>Why do girls want to be friends with the guy t...</td>\n",
              "      <td>How do guys feel after rejecting a girl?</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>18</td>\n",
              "      <td>37</td>\n",
              "      <td>38</td>\n",
              "      <td>Why are so many Quora users posting questions ...</td>\n",
              "      <td>Why do people ask Quora questions which can be...</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>19</td>\n",
              "      <td>39</td>\n",
              "      <td>40</td>\n",
              "      <td>Which is the best digital marketing institutio...</td>\n",
              "      <td>Which is the best digital marketing institute ...</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "    id  qid1  ...                                          question2 is_duplicate\n",
              "0    0     1  ...  What is the step by step guide to invest in sh...            0\n",
              "1    1     3  ...  What would happen if the Indian government sto...            0\n",
              "2    2     5  ...  How can Internet speed be increased by hacking...            0\n",
              "3    3     7  ...  Find the remainder when [math]23^{24}[/math] i...            0\n",
              "4    4     9  ...            Which fish would survive in salt water?            0\n",
              "5    5    11  ...  I'm a triple Capricorn (Sun, Moon and ascendan...            1\n",
              "6    6    13  ...  What keeps childern active and far from phone ...            0\n",
              "7    7    15  ...          What should I do to be a great geologist?            1\n",
              "8    8    17  ...              When do you use \"&\" instead of \"and\"?            0\n",
              "9    9    19  ...  How do I hack Motorola DCX3400 for free internet?            0\n",
              "10  10    21  ...  What are some of the things technicians can te...            0\n",
              "11  11    23  ...             How can I see all my Youtube comments?            1\n",
              "12  12    25  ...            How can you make physics easy to learn?            1\n",
              "13  13    27  ...             What was your first sexual experience?            1\n",
              "14  14    29  ...  What are the laws to change your status from a...            0\n",
              "15  15    31  ...  How will a Trump presidency affect the student...            1\n",
              "16  16    33  ...                      What does manipulation means?            1\n",
              "17  17    35  ...           How do guys feel after rejecting a girl?            0\n",
              "18  18    37  ...  Why do people ask Quora questions which can be...            1\n",
              "19  19    39  ...  Which is the best digital marketing institute ...            0\n",
              "\n",
              "[20 rows x 6 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-MLmEtSRtyiN",
        "colab_type": "text"
      },
      "source": [
        "## Exploratory Data Analysis"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MBiTaRIJt1Nv",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 437
        },
        "outputId": "54ae18e3-ea3b-4873-a936-01fa06095ba7"
      },
      "source": [
        "print('Total number of question pairs for training: {}'.format(len(df)))\n",
        "print('Duplicate pairs: {}%'.format(round(df['is_duplicate'].mean()*100, 2)))\n",
        "qids = pd.Series(df['qid1'].tolist() + df['qid2'].tolist())\n",
        "\n",
        "print('Total number of questions in the training data: {}'.format(len(\n",
        "    np.unique(qids))))\n",
        "\n",
        "print('Number of questions that appear multiple times: {}'.format(np.sum(qids.value_counts() > 1)))\n",
        "\n",
        "\n",
        "plt.figure(figsize=(12, 5))\n",
        "plt.hist(qids.value_counts(), bins=50)\n",
        "plt.yscale('log', nonposy='clip')\n",
        "plt.title('Log-Histogram of question appearance counts')\n",
        "plt.xlabel('Number of occurences of question')\n",
        "plt.ylabel('Number of questions')\n",
        "print()"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Total number of question pairs for training: 404290\n",
            "Duplicate pairs: 36.92%\n",
            "Total number of questions in the training data: 537933\n",
            "Number of questions that appear multiple times: 111780\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 864x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rMwa-CV4tr9c",
        "colab_type": "text"
      },
      "source": [
        "### Dropping the Null values\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xscdzLB0JSjd",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 431
        },
        "outputId": "5de44290-0b43-4a7d-dbcf-49bf69383056"
      },
      "source": [
        "null = df.isnull().sum()\n",
        "print(null)\n",
        "df = df.dropna(subset = ['question1', 'question2'])\n",
        "\n",
        "pd.value_counts(df['is_duplicate']).plot.bar()\n",
        "plt.xlabel('Values')\n",
        "plt.ylabel('Count')\n",
        "plt.title('Class Distribution')"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "id              0\n",
            "qid1            0\n",
            "qid2            0\n",
            "question1       1\n",
            "question2       2\n",
            "is_duplicate    0\n",
            "dtype: int64\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Class Distribution')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        },
        {
          "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": {
        "id": "Anr6fJ2wFL4a",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "4c50c9cb-882b-4a10-c27a-f407621c59bf"
      },
      "source": [
        "# !pip install tensorflow==1.15\n",
        "# !pip install \"tensorflow_hub>=0.6.0\"\n",
        "# !pip3 install tensorflow_text==1.15"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Collecting tensorflow==1.15\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/3f/98/5a99af92fb911d7a88a0005ad55005f35b4c1ba8d75fba02df726cd936e6/tensorflow-1.15.0-cp36-cp36m-manylinux2010_x86_64.whl (412.3MB)\n",
            "\u001b[K     |████████████████████████████████| 412.3MB 37kB/s \n",
            "\u001b[?25hRequirement already satisfied: astor>=0.6.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow==1.15) (0.8.1)\n",
            "Requirement already satisfied: wrapt>=1.11.1 in /usr/local/lib/python3.6/dist-packages (from tensorflow==1.15) (1.12.1)\n",
            "Requirement already satisfied: termcolor>=1.1.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow==1.15) (1.1.0)\n",
            "Requirement already satisfied: opt-einsum>=2.3.2 in /usr/local/lib/python3.6/dist-packages (from tensorflow==1.15) (3.2.1)\n",
            "Collecting gast==0.2.2\n",
            "  Downloading https://files.pythonhosted.org/packages/4e/35/11749bf99b2d4e3cceb4d55ca22590b0d7c2c62b9de38ac4a4a7f4687421/gast-0.2.2.tar.gz\n",
            "Requirement already satisfied: numpy<2.0,>=1.16.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow==1.15) (1.18.5)\n",
            "Requirement already satisfied: wheel>=0.26 in /usr/local/lib/python3.6/dist-packages (from tensorflow==1.15) (0.34.2)\n",
            "Collecting tensorboard<1.16.0,>=1.15.0\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/1e/e9/d3d747a97f7188f48aa5eda486907f3b345cd409f0a0850468ba867db246/tensorboard-1.15.0-py3-none-any.whl (3.8MB)\n",
            "\u001b[K     |████████████████████████████████| 3.8MB 35.7MB/s \n",
            "\u001b[?25hRequirement already satisfied: google-pasta>=0.1.6 in /usr/local/lib/python3.6/dist-packages (from tensorflow==1.15) (0.2.0)\n",
            "Requirement already satisfied: keras-preprocessing>=1.0.5 in /usr/local/lib/python3.6/dist-packages (from tensorflow==1.15) (1.1.2)\n",
            "Requirement already satisfied: six>=1.10.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow==1.15) (1.12.0)\n",
            "Requirement already satisfied: grpcio>=1.8.6 in /usr/local/lib/python3.6/dist-packages (from tensorflow==1.15) (1.30.0)\n",
            "Requirement already satisfied: absl-py>=0.7.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow==1.15) (0.9.0)\n",
            "Requirement already satisfied: keras-applications>=1.0.8 in /usr/local/lib/python3.6/dist-packages (from tensorflow==1.15) (1.0.8)\n",
            "Requirement already satisfied: protobuf>=3.6.1 in /usr/local/lib/python3.6/dist-packages (from tensorflow==1.15) (3.10.0)\n",
            "Collecting tensorflow-estimator==1.15.1\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/de/62/2ee9cd74c9fa2fa450877847ba560b260f5d0fb70ee0595203082dafcc9d/tensorflow_estimator-1.15.1-py2.py3-none-any.whl (503kB)\n",
            "\u001b[K     |████████████████████████████████| 512kB 38.9MB/s \n",
            "\u001b[?25hRequirement already satisfied: werkzeug>=0.11.15 in /usr/local/lib/python3.6/dist-packages (from tensorboard<1.16.0,>=1.15.0->tensorflow==1.15) (1.0.1)\n",
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            "Building wheels for collected packages: gast\n",
            "  Building wheel for gast (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for gast: filename=gast-0.2.2-cp36-none-any.whl size=7540 sha256=aa8628afb99dfaf51337568b2e3ed34de07b89944739f8bbd86162da8f9cec98\n",
            "  Stored in directory: /root/.cache/pip/wheels/5c/2e/7e/a1d4d4fcebe6c381f378ce7743a3ced3699feb89bcfbdadadd\n",
            "Successfully built gast\n",
            "\u001b[31mERROR: tensorflow-probability 0.10.0 has requirement gast>=0.3.2, but you'll have gast 0.2.2 which is incompatible.\u001b[0m\n",
            "Installing collected packages: gast, tensorboard, tensorflow-estimator, tensorflow\n",
            "  Found existing installation: gast 0.3.3\n",
            "    Uninstalling gast-0.3.3:\n",
            "      Successfully uninstalled gast-0.3.3\n",
            "  Found existing installation: tensorboard 2.2.2\n",
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            "  Found existing installation: tensorflow-estimator 2.2.0\n",
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            "      Successfully uninstalled tensorflow-estimator-2.2.0\n",
            "  Found existing installation: tensorflow 2.2.0\n",
            "    Uninstalling tensorflow-2.2.0:\n",
            "      Successfully uninstalled tensorflow-2.2.0\n",
            "Successfully installed gast-0.2.2 tensorboard-1.15.0 tensorflow-1.15.0 tensorflow-estimator-1.15.1\n",
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            "Collecting tensorflow_text==1.15\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/44/61/9569353ce3efb8ee818d0d2e2f67afbb19e1e3a56eba3a93986e92949003/tensorflow_text-1.15.0-cp36-cp36m-manylinux1_x86_64.whl (9.1MB)\n",
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            "Requirement already satisfied: importlib-metadata; python_version < \"3.8\" in /usr/local/lib/python3.6/dist-packages (from markdown>=2.6.8->tensorboard<1.16.0,>=1.15.0->tensorflow<1.16,>=1.15.0->tensorflow_text==1.15) (1.6.1)\n",
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            "Installing collected packages: tensorflow-text\n",
            "Successfully installed tensorflow-text-1.15.0\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "O7zpuLthujTw",
        "colab_type": "text"
      },
      "source": [
        "## Using Universal Sentence Encoder - For creating embedding of text at sentence level"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eDvXeP3bAi09",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import tensorflow as tf\n",
        "import tensorflow_hub as hub\n",
        "module_url = \"https://tfhub.dev/google/universal-sentence-encoder-large/3\"\n",
        "embed = hub.Module(module_url)\n",
        "\n",
        "# creating a method for embedding and will using method for every input layer \n",
        "def UniversalEmbedding(x):\n",
        "    return embed(tf.squeeze(tf.cast(x, tf.string)), signature=\"default\", as_dict=True)[\"default\"]\n",
        " \n"
      ],
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9ywhyFN5GgGa",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "DROPOUT = 0.1"
      ],
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WbXVIhUHuuXR",
        "colab_type": "text"
      },
      "source": [
        "### Building the neural network"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "shVOz9OrEiXg",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "77b48aab-39ef-422f-e3b0-16ac48fb96bc"
      },
      "source": [
        "# Taking the question1 as input and ceating a embedding for each question before feed it to neural network\n",
        "q1 = layers.Input(shape=(1,), dtype=tf.string)\n",
        "embedding_q1 = layers.Lambda(UniversalEmbedding, output_shape=(512,))(q1)\n",
        "# Taking the question2 and doing the same thing mentioned above, using the lambda function\n",
        "q2 = layers.Input(shape=(1,), dtype=tf.string)\n",
        "embedding_q2 = layers.Lambda(UniversalEmbedding, output_shape=(512,))(q2)\n",
        "\n",
        "# Concatenating the both input layer\n",
        "merged = layers.concatenate([embedding_q1, embedding_q2])\n",
        "merged = layers.Dense(200, activation='relu')(merged)\n",
        "merged = layers.Dropout(DROPOUT)(merged)\n",
        "\n",
        "merged = layers.BatchNormalization()(merged)\n",
        "merged = layers.Dense(200, activation='relu')(merged)\n",
        "merged = layers.Dropout(DROPOUT)(merged)\n",
        "\n",
        "merged = layers.BatchNormalization()(merged)\n",
        "merged = layers.Dense(200, activation='relu')(merged)\n",
        "merged = layers.Dropout(DROPOUT)(merged)\n",
        "\n",
        "merged = layers.BatchNormalization()(merged)\n",
        "merged = layers.Dense(200, activation='relu')(merged)\n",
        "merged = layers.Dropout(DROPOUT)(merged)\n",
        "\n",
        "merged = layers.BatchNormalization()(merged)\n",
        "pred = layers.Dense(2, activation='sigmoid')(merged)\n",
        "model = Model(inputs=[q1,q2], outputs=pred)\n",
        "model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])\n",
        "model.summary()"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "INFO:tensorflow:Saver not created because there are no variables in the graph to restore\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:tensorflow:Saver not created because there are no variables in the graph to restore\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:tensorflow:Saver not created because there are no variables in the graph to restore\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:tensorflow:Saver not created because there are no variables in the graph to restore\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow_core/python/ops/resource_variable_ops.py:1630: calling BaseResourceVariable.__init__ (from tensorflow.python.ops.resource_variable_ops) with constraint is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "If using Keras pass *_constraint arguments to layers.\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow_core/python/ops/resource_variable_ops.py:1630: calling BaseResourceVariable.__init__ (from tensorflow.python.ops.resource_variable_ops) with constraint is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "If using Keras pass *_constraint arguments to layers.\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow_core/python/ops/nn_impl.py:183: where (from tensorflow.python.ops.array_ops) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Use tf.where in 2.0, which has the same broadcast rule as np.where\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow_core/python/ops/nn_impl.py:183: where (from tensorflow.python.ops.array_ops) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Use tf.where in 2.0, which has the same broadcast rule as np.where\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Model: \"model_1\"\n",
            "__________________________________________________________________________________________________\n",
            "Layer (type)                    Output Shape         Param #     Connected to                     \n",
            "==================================================================================================\n",
            "input_1 (InputLayer)            (None, 1)            0                                            \n",
            "__________________________________________________________________________________________________\n",
            "input_2 (InputLayer)            (None, 1)            0                                            \n",
            "__________________________________________________________________________________________________\n",
            "lambda_1 (Lambda)               (None, 512)          0           input_1[0][0]                    \n",
            "__________________________________________________________________________________________________\n",
            "lambda_2 (Lambda)               (None, 512)          0           input_2[0][0]                    \n",
            "__________________________________________________________________________________________________\n",
            "concatenate_1 (Concatenate)     (None, 1024)         0           lambda_1[0][0]                   \n",
            "                                                                 lambda_2[0][0]                   \n",
            "__________________________________________________________________________________________________\n",
            "dense_1 (Dense)                 (None, 200)          205000      concatenate_1[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "dropout_1 (Dropout)             (None, 200)          0           dense_1[0][0]                    \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_1 (BatchNor (None, 200)          800         dropout_1[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "dense_2 (Dense)                 (None, 200)          40200       batch_normalization_1[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "dropout_2 (Dropout)             (None, 200)          0           dense_2[0][0]                    \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_2 (BatchNor (None, 200)          800         dropout_2[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "dense_3 (Dense)                 (None, 200)          40200       batch_normalization_2[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "dropout_3 (Dropout)             (None, 200)          0           dense_3[0][0]                    \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_3 (BatchNor (None, 200)          800         dropout_3[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "dense_4 (Dense)                 (None, 200)          40200       batch_normalization_3[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "dropout_4 (Dropout)             (None, 200)          0           dense_4[0][0]                    \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_4 (BatchNor (None, 200)          800         dropout_4[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "dense_5 (Dense)                 (None, 2)            402         batch_normalization_4[0][0]      \n",
            "==================================================================================================\n",
            "Total params: 329,202\n",
            "Trainable params: 327,602\n",
            "Non-trainable params: 1,600\n",
            "__________________________________________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "in7LCAt5pX_Z",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Pushing all the strings to a list and converting to nparray\n",
        "q1= df.question1.tolist()\n",
        "q2= df.question2.tolist()\n",
        "labels= df.is_duplicate.tolist()"
      ],
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1zvie2yku3pD",
        "colab_type": "text"
      },
      "source": [
        "### Preparing the train and test data using Sklearn library"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "R85vpRPKByAC",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "X1 = df['question1']\n",
        "X2 = df['question2']\n",
        "y = df['is_duplicate']\n",
        "X1_train, X1_test,X2_train, X2_test, y_train, y_test = train_test_split(X1, X2, y, test_size=0.2, random_state=42)\n",
        "\n",
        "train_q1 = X1_train.tolist()\n",
        "train_q1 = np.array(train_q1, dtype=object)[:, np.newaxis]\n",
        "train_q2 = X2_train.tolist()\n",
        "train_q2 = np.array(train_q2, dtype=object)[:, np.newaxis]\n",
        "\n",
        "train_labels = np.asarray(pd.get_dummies(y_train), dtype = np.int8)\n",
        "\n",
        "test_q1 = X1_test.tolist()\n",
        "test_q1 = np.array(test_q1, dtype=object)[:, np.newaxis]\n",
        "test_q2 = X2_test.tolist()\n",
        "test_q2 = np.array(test_q2, dtype=object)[:, np.newaxis]\n",
        "\n",
        "test_labels = np.asarray(pd.get_dummies(y_test), dtype = np.int8)\n",
        "\n"
      ],
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tsqXBt_KvEgH",
        "colab_type": "text"
      },
      "source": [
        "### Training the model "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SO2GRdB6CVDf",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from keras.callbacks import ModelCheckpoint\n",
        "\n",
        "with tf.Session() as session:\n",
        "  K.set_session(session)\n",
        "  session.run(tf.global_variables_initializer())\n",
        "  session.run(tf.tables_initializer())\n",
        "  \n",
        "  filepath=\"/content/drive/My Drive/Quora_Duplicate/model-{epoch:02d}.hdf5\"\n",
        "  checkpoint = ModelCheckpoint(filepath, monitor='val_loss', verbose=0, save_best_only=False, save_weights_only=True, mode='auto', period=1)\n",
        "  callbacks_list = [checkpoint]\n",
        "\n",
        "  model.load_weights('/content/drive/My Drive/Quora_Duplicate/model-10.hdf5')\n",
        "  history = model.fit([train_q1, train_q2], \n",
        "            # train_labels,\n",
        "            initial_epoch=10,\n",
        "            validation_data=([test_q1, test_q2], test_labels),\n",
        "            epochs=11,\n",
        "            batch_size=512, callbacks=callbacks_list)\n",
        "  model.save('/content/drive/My Drive/Quora_Duplicate/final_model.h5')"
      ],
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5duOBYUdvQVh",
        "colab_type": "text"
      },
      "source": [
        "### Predicting the model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bWp3WNTTIH8q",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 87
        },
        "outputId": "347ccba2-29de-44bd-ceac-5759aeff57bf"
      },
      "source": [
        "import numpy as np\n",
        "import tensorflow as tf\n",
        "from keras.models import load_model\n",
        "from keras import backend as K\n",
        "q1 = input(\"Enter Question 1 (with/without question mark): \")\n",
        "q2 = input(\"Enter Question 2 (with/without question mark): \") \n",
        "q1 = np.array([[q1],[q1]])\n",
        "q2 = np.array([[q2],[q2]])\n",
        "\n",
        "\n",
        "# Using the same tensorflow session for embedding the test string\n",
        "with tf.Session() as session:\n",
        "  K.set_session(session)\n",
        "  session.run(tf.global_variables_initializer())\n",
        "  session.run(tf.tables_initializer())\n",
        "  # Loading the save weights\n",
        "  model.load_weights('/content/drive/My Drive/Quora_Duplicate/final_model.hdf5')  \n",
        "  predicts = model.predict([q1, q2], verbose=0)\n",
        "  predict_logits = predicts.argmax(axis=1)\n",
        "  print(\"----FINAL RESULT----\")\n",
        "  if(predict_logits[0] == 1):\n",
        "    print(\"****Questions are Similar****\")\n",
        "  else:\n",
        "    print(\"****Questions are not Similar****\")\n",
        "  "
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Enter Question 1 (with/without question mark): What is your age?\n",
            "Enter Question 2 (with/without question mark): How old are you?\n",
            "----FINAL RESULT----\n",
            "****Questions are Similar****\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_xGJRaz5EvYf",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}