An_attention_based_hybrid_architecture.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OCHYwLyyCf3k"
      },
      "source": [
        "Implementation of the paper proposed by Ghosh et al. entitled\n",
        "*\"An attention-based hybrid architecture with explainability for depressive social media text detection in Bangla\"* published in 2023.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8EFwSONyD758",
        "outputId": "eaf6c7c5-3284-457a-eeba-c745a45e4e73"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting keras_preprocessing\n",
            "  Downloading Keras_Preprocessing-1.1.2-py2.py3-none-any.whl (42 kB)\n",
            "\u001b[?25l     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/42.6 kB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0m\r\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m42.6/42.6 kB\u001b[0m \u001b[31m2.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: numpy>=1.9.1 in /usr/local/lib/python3.10/dist-packages (from keras_preprocessing) (1.22.4)\n",
            "Requirement already satisfied: six>=1.9.0 in /usr/local/lib/python3.10/dist-packages (from keras_preprocessing) (1.16.0)\n",
            "Installing collected packages: keras_preprocessing\n",
            "Successfully installed keras_preprocessing-1.1.2\n"
          ]
        }
      ],
      "source": [
        "! pip install keras_preprocessing"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Bb3qf3seD2FB",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "f70736ac-b67f-452b-d228-51703385d7b9"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[nltk_data] Downloading package punkt to /root/nltk_data...\n",
            "[nltk_data]   Unzipping tokenizers/punkt.zip.\n",
            "[nltk_data] Downloading package stopwords to /root/nltk_data...\n",
            "[nltk_data]   Unzipping corpora/stopwords.zip.\n"
          ]
        }
      ],
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "from gensim.models import Word2Vec\n",
        "import matplotlib.pyplot as plt\n",
        "import tensorflow as tf\n",
        "# from tensorflow.keras.layers import *\n",
        "# from tensorflow.keras.models import *\n",
        "from nltk.tokenize import word_tokenize\n",
        "from keras_preprocessing.sequence import pad_sequences\n",
        "from keras.preprocessing.text import Tokenizer\n",
        "from nltk.corpus import stopwords\n",
        "from sklearn.model_selection import train_test_split\n",
        "import keras.backend as K\n",
        "import gensim\n",
        "from sklearn.metrics import classification_report, accuracy_score, precision_score, recall_score, f1_score\n",
        "\n",
        "import nltk\n",
        "nltk.download('punkt')\n",
        "nltk.download('stopwords')\n",
        "from nltk.corpus import stopwords\n",
        "\n",
        "import warnings\n",
        "warnings.filterwarnings(action = 'ignore')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "eQ_Q0xPuEMeR"
      },
      "outputs": [],
      "source": [
        "#Preprocessing function\n",
        "def preprocessing(data_frame):\n",
        "    ## Preprocessing\n",
        "    # Removing URLs whithin the tweets\n",
        "    data_frame[\"Text\"] = data_frame[\"Text\"].str.replace(r'\\s*https?://\\S+(\\s+|$)', ' ').str.strip()\n",
        "    # Removing emails, hashtags and punctuations\n",
        "    data_frame['Text'] = data_frame[\"Text\"].str.replace(r'\\S*@\\S*\\s?', ' ').str.strip()\n",
        "    data_frame['Text'] = data_frame['Text'].str.replace(r'#\\S*\\s?', ' ').str.strip()\n",
        "    data_frame['Text'] = data_frame['Text'].str.replace(r'[^\\w\\s]+', ' ').str.strip()\n",
        "\n",
        "    # # Removing stopwords\n",
        "    stop = stopwords.words('english')\n",
        "    data_frame['Text'].apply(lambda x: [item for item in str(x) if item not in stop])\n",
        "\n",
        "    # Removing newline characters\n",
        "    data_frame['Text'] = data_frame['Text'].str.rstrip()\n",
        "\n",
        "    # Tokenizing Posts and counting the length of each post\n",
        "    data_frame['Tokens'] = data_frame.apply(lambda row: word_tokenize(str(row['Text'])), axis=1)\n",
        "    data_frame['Length'] = data_frame.apply(lambda row: len(row['Tokens']), axis=1)\n",
        "\n",
        "    return data_frame"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Kaxvssh_An3w"
      },
      "source": [
        "# Data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "fZjHbBHHAZfs"
      },
      "outputs": [],
      "source": [
        "DS_Path = \"/Datasets\""
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Y3Hj8CPuAo6F"
      },
      "source": [
        "### Twitter 1"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "UGPiPa9oMuCT",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
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        "outputId": "cbc5925b-8ac0-4ba5-aa4e-495bbd4afc86"
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              "                                                   Text  Label  \\\n",
              "0     my life is meaningless i just want to end my l...      1   \n",
              "1     muttering i wanna die to myself daily for a fe...      1   \n",
              "2     work slave i really feel like my only purpose ...      1   \n",
              "3     i did something on the 2 of october i overdose...      1   \n",
              "4     i feel like no one cares i just want to die ma...      1   \n",
              "...                                                 ...    ...   \n",
              "9114  have you ever laid on your bed at night and cr...      1   \n",
              "9115  the fault the blame the pain s still there i m...      1   \n",
              "9116  stop asking me to trust you when i m still cou...      1   \n",
              "9117  i never know how to handle sadness crying make...      1   \n",
              "9118  when cancer takes a life we blame cancer depre...      1   \n",
              "\n",
              "                                                 Tokens  Length  \n",
              "0     [my, life, is, meaningless, i, just, want, to,...      79  \n",
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              "9114  [have, you, ever, laid, on, your, bed, at, nig...      33  \n",
              "9115  [the, fault, the, blame, the, pain, s, still, ...      20  \n",
              "9116  [stop, asking, me, to, trust, you, when, i, m,...      22  \n",
              "9117  [i, never, know, how, to, handle, sadness, cry...      12  \n",
              "9118  [when, cancer, takes, a, life, we, blame, canc...      21  \n",
              "\n",
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              "      <td>21</td>\n",
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          },
          "metadata": {},
          "execution_count": 14
        }
      ],
      "source": [
        "# ## Preparing the data\n",
        "# ## Twitter 10000\n",
        "Twitter_path = DS_Path + \"/Twitter/twitter-suicidal_data_10000.csv\"\n",
        "df = pd.read_csv(Twitter_path, encoding='latin-1')\n",
        "df = df.rename(columns={'tweet': 'Text', 'intention': 'Label'})\n",
        "df = preprocessing(df)\n",
        "df\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nSPSlQHKEz7O"
      },
      "source": [
        "### Twitter 2"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "IWVMl5bCE1R5",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "ebf4c050-3bcd-43d3-db03-fe7832c50a73"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                                    Text  Label  \\\n",
              "0      to go to treatment if they are actively suicid...      1   \n",
              "1      Thy  should be putting these kids in stabiliza...      1   \n",
              "2                   Might want to talk to her work again      0   \n",
              "3      Just a heads head up she think she going goofy...      0   \n",
              "4      I donâ t believe in religious dogma at all  Fo...      0   \n",
              "...                                                  ...    ...   \n",
              "17137  Make sure you are happy in real life  Not just...      0   \n",
              "17138  With one of our keyrings you will never be alo...      0   \n",
              "17139              Always here if you ever need a chat ð      0   \n",
              "17140  Itâ s not selfish  Suicide is usually caused b...      1   \n",
              "17141  Donâ t forget that by supporting us it gives u...      0   \n",
              "\n",
              "                                                  Tokens  Length  \n",
              "0      [to, go, to, treatment, if, they, are, activel...      41  \n",
              "1      [Thy, should, be, putting, these, kids, in, st...      49  \n",
              "2          [Might, want, to, talk, to, her, work, again]       8  \n",
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              "\n",
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              "\n",
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              "          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",
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              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ],
      "source": [
        "twitter_path = DS_Path + \"Twitter/suicidal-tendency-tweets.csv\"\n",
        "df = pd.read_csv(twitter_path, encoding='latin-1', usecols=['tweet', 'intention'], nrows = 17142)\n",
        "df = df.rename(columns={'tweet': 'Text', 'intention': 'Label'})\n",
        "\n",
        "df = preprocessing(df)\n",
        "df"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "BGA9DwlNE-f-",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "733391b7-b9c9-43a4-94ec-b9b0ddcde577"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "3754 13388\n"
          ]
        }
      ],
      "source": [
        "print(list(df['Label']).count(1), list(df['Label']).count(0))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KJueHyMVArJ3"
      },
      "source": [
        "## Reddit SNS"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "RhaDCILz4Z10",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a287bd1d-4b9a-4480-a316-4f47e40c32e2"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "                                                    Text  Label  \\\n",
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              "                                                     [key], {});\n",
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              "\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",
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              "    </div>\n",
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            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ],
      "source": [
        "# ## Preparing the data\n",
        "\n",
        "Reddit_path = DS_Path + \"/Reddit_non suicide  suicide/Suicide_Detection.csv\"\n",
        "\n",
        "## reading the abovementioned datasets\n",
        "# df_r = pd.read_csv(Reddit_path, encoding='latin-1', usecols=['text', 'class'])\n",
        "df_r = pd.read_csv(Reddit_path, encoding='latin-1', usecols=['text', 'class'], nrows = 20000)\n",
        "df_r = df_r.rename(columns={'text': 'Text', 'class': 'Label'})\n",
        "\n",
        "df = preprocessing(df_r)\n",
        "\n",
        "label_dict = {'suicide': 1, 'non-suicide': 0}\n",
        "df['Label'] = df['Label'].apply(lambda row: label_dict[row])\n",
        "df['Label']\n",
        "\n",
        "df"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "JQCrp8lQBT6R",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e46e94eb-44c0-48aa-f87f-4cc8665ff5cb"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "9865 10135\n"
          ]
        }
      ],
      "source": [
        "print(list(df['Label']).count(1), list(df['Label']).count(0))"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Reddit _ SD"
      ],
      "metadata": {
        "id": "6vNphMAprMDx"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
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          "height": 424
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              "            + ' to learn more about interactive tables.';\n",
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            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ],
      "source": [
        "## Preparing the data\n",
        "reddit_path = DS_Path + \"/Reddit/reddit_depression_suicidewatch.csv\"\n",
        "\n",
        "df = pd.read_csv(reddit_path, encoding='latin-1')\n",
        "df = df.rename(columns={'text':'Text', 'label':'Label'})\n",
        "label_dict = {'depression': 0, 'SuicideWatch': 1}\n",
        "df['Label'] = df['Label'].apply(lambda row: label_dict[row])\n",
        "df = preprocessing(df)\n",
        "df"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Reddit_SD (Positive Suicide Samples) + Facebook (Negative samples)"
      ],
      "metadata": {
        "id": "0rCTpp9cgSKg"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "## Preparing the data\n",
        "reddit_path = DS_Path + \"/Reddit/reddit_depression_suicidewatch.csv\"\n",
        "\n",
        "df = pd.read_csv(reddit_path, encoding='latin-1')\n",
        "df = df.rename(columns={'text':'Text', 'label':'Label'})\n",
        "label_dict = {'depression': 0, 'SuicideWatch': 1}\n",
        "df['Label'] = df['Label'].apply(lambda row: label_dict[row])\n",
        "\n",
        "df.drop(df[df['Label'] <1].index, inplace = True)\n",
        "df.reset_index(drop=True)\n",
        "\n",
        "## Adding negative samples\n",
        "\n",
        "zero_sampels = DS_Path + \"/Facebook/100_000_samples.csv\"\n",
        "\n",
        "df_zero = pd.read_csv(zero_sampels, encoding='latin-1', nrows=df.shape[0])\n",
        "df_zero = df_zero.rename(columns={'message':'Text'})\n",
        "df_zero = df_zero.assign(Label=0)"
      ],
      "metadata": {
        "id": "zgqWIDv-rMD0"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
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        "id": "YfIzwS1rtKHq",
        "outputId": "920f4291-eafa-4a1e-d639-1ca2c85f7fe5"
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              "        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>Text</th>\n",
              "      <th>Label</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>I am so exhausted of this. Just when I think I...</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>I am 20 year old with some good friends but I ...</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>it is looming around the corner again. It alwa...</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>there is.....foodAnd other things I will be ju...</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>I am on zoloft and focalin and it is changed m...</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20353</th>\n",
              "      <td>I am sorry I am sorry i made studying as Joke ...</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20354</th>\n",
              "      <td>Being told to end my life by the only person I...</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20356</th>\n",
              "      <td>Last week I did the most disgusting thing by c...</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20357</th>\n",
              "      <td>I do not see a way out of my head and a way to...</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20358</th>\n",
              "      <td>I took 50mg of seroquel a few hours after I dr...</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>9992 rows × 2 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-64574a33-e4a2-41c1-99e1-ff3a8e7d27ba')\"\n",
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              "\n",
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              "      async function quickchart(key) {\n",
              "        const containerElement = document.querySelector('#' + key);\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
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              "\n",
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              "    google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "}\n",
              "\n",
              "        displayQuickchartButton(document);\n",
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              "      display:flex;\n",
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              "\n",
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              "      border: none;\n",
              "      border-radius: 50%;\n",
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              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
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              "\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",
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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",
              "      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-64574a33-e4a2-41c1-99e1-ff3a8e7d27ba 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-64574a33-e4a2-41c1-99e1-ff3a8e7d27ba');\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": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_zero"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "hH8KLreDuVb7",
        "outputId": "98177f89-1898-46c7-db02-431069c4df13"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                                   Text  Label\n",
              "0     This was a waste of taxpayer dollars. Save you...      0\n",
              "1     About time. We should have never been spending...      0\n",
              "2     There's a lot more important things that our m...      0\n",
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              "4     Republicans live to bully citizens and limit o...      0\n",
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              "9989  US has the right to do whatever they want & if...      0\n",
              "9990     And North Korea condemns this. Lol. Sanctions.      0\n",
              "9991                              Oh no not Seattle!!!!      0\n",
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              "[9992 rows x 2 columns]"
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              "\n",
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              "  [theme=dark] .colab-df-quickchart:hover {\n",
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              "\n",
              "    <script>\n",
              "      async function quickchart(key) {\n",
              "        const containerElement = document.querySelector('#' + key);\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
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              "    </script>\n",
              "\n",
              "      <script>\n",
              "\n",
              "function displayQuickchartButton(domScope) {\n",
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              "  quickchartButtonEl.style.display =\n",
              "    google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "}\n",
              "\n",
              "        displayQuickchartButton(document);\n",
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              "\n",
              "    .colab-df-convert {\n",
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              "      border-radius: 50%;\n",
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              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\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",
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              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-21177419-ccea-40c4-a2dd-4988a4fefdf2 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-21177419-ccea-40c4-a2dd-4988a4fefdf2');\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": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df = pd.concat([df, df_zero])"
      ],
      "metadata": {
        "id": "SlgZ_lyzuoI9"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.reset_index(drop=True)"
      ],
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          "base_uri": "https://localhost:8080/",
          "height": 424
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        "id": "hvqS4yhVwZG8",
        "outputId": "923e9d98-691c-43db-bfcd-bf2dad32fa9e"
      },
      "execution_count": null,
      "outputs": [
        {
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              "                                                    Text  Label\n",
              "0      I am so exhausted of this. Just when I think I...      1\n",
              "1      I am 20 year old with some good friends but I ...      1\n",
              "2      it is looming around the corner again. It alwa...      1\n",
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              "19982     And North Korea condemns this. Lol. Sanctions.      0\n",
              "19983                              Oh no not Seattle!!!!      0\n",
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              "\n",
              "      <script>\n",
              "\n",
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              "\n",
              "        displayQuickchartButton(document);\n",
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              "\n",
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              "          document.querySelector('#df-bed7be1d-83ce-450e-b3a4-a2a7867bb359 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-bed7be1d-83ce-450e-b3a4-a2a7867bb359');\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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            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df = preprocessing(df)"
      ],
      "metadata": {
        "id": "e6Z-pkDOyWIs"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_e3KxycdDOoo"
      },
      "source": [
        "# Word Embeddings"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Downloading Pre-trained Models"
      ],
      "metadata": {
        "id": "B13Rgd6tQq4b"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LlP3qOp1NXRf",
        "outputId": "5f9343fe-d6cf-4415-bbaf-3ea6e8b8528b"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "dict_keys(['corpora', 'models'])\n",
            "__testing_word2vec-matrix-synopsis (-1 records): [THIS IS ONLY FOR TESTING] Word vecrors ...\n",
            "conceptnet-numberbatch-17-06-300 (1917247 records): ConceptNet Numberbatch consists of state...\n",
            "fasttext-wiki-news-subwords-300 (999999 records): 1 million word vectors trained on Wikipe...\n",
            "glove-twitter-100 (1193514 records): Pre-trained vectors based on  2B tweets,...\n",
            "glove-twitter-200 (1193514 records): Pre-trained vectors based on 2B tweets, ...\n",
            "glove-twitter-25 (1193514 records): Pre-trained vectors based on 2B tweets, ...\n",
            "glove-twitter-50 (1193514 records): Pre-trained vectors based on 2B tweets, ...\n",
            "glove-wiki-gigaword-100 (400000 records): Pre-trained vectors based on Wikipedia 2...\n",
            "glove-wiki-gigaword-200 (400000 records): Pre-trained vectors based on Wikipedia 2...\n",
            "glove-wiki-gigaword-300 (400000 records): Pre-trained vectors based on Wikipedia 2...\n",
            "glove-wiki-gigaword-50 (400000 records): Pre-trained vectors based on Wikipedia 2...\n",
            "word2vec-google-news-300 (3000000 records): Pre-trained vectors trained on a part of...\n",
            "word2vec-ruscorpora-300 (184973 records): Word2vec Continuous Skipgram vectors tra...\n"
          ]
        }
      ],
      "source": [
        "import gensim.downloader as api\n",
        "\n",
        "import json\n",
        "info = api.info()\n",
        "print(info.keys())\n",
        "\n",
        "for model_name, model_data in sorted(info['models'].items()):\n",
        "    print(\n",
        "        '%s (%d records): %s' % (\n",
        "            model_name,\n",
        "            model_data.get('num_records', -1),\n",
        "            model_data['description'][:40] + '...',\n",
        "        )\n",
        "    )"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "w89ATOQEK704",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "2e8fff50-139b-4231-838f-e80442e4c5a6"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[=================================================-] 99.8% 956.5/958.4MB downloaded\n"
          ]
        }
      ],
      "source": [
        "embeddings = {0: \"word2vec-google-news-300\", 1: \"fasttext-wiki-news-subwords-300\", 2: \"glove-twitter-100\"}\n",
        "embedding_format = 1\n",
        "\n",
        "embedding_model = api.load(embeddings[embedding_format])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "JTwcHT-dNycz",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a154c3ab-2bd5-41b1-d7c3-c576572757e3"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[('kills', 0.7860026955604553), ('killing', 0.7399880290031433), ('destroy', 0.7350039482116699), ('maim', 0.7278950810432434), ('decapitate', 0.7270759344100952), ('slay', 0.7220808267593384), ('killy', 0.721636176109314), ('kill-', 0.7112785577774048), ('kill.', 0.705057680606842), ('killed', 0.7015096545219421)]\n"
          ]
        }
      ],
      "source": [
        "similar_words = embedding_model.most_similar('kill')\n",
        "print(similar_words)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "O5_qjtHmT0Av"
      },
      "outputs": [],
      "source": [
        "vocab = embedding_model.key_to_index"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "uKbF8XNEUfxH",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "fe7b1087-0b0b-4842-b742-7309b66c7f4e"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The total number of words are :  999999\n",
            "The no of key-value pairs :  999999\n",
            "maximum length =  9360\n",
            "pad_shape =  (20363, 9360)\n",
            "(35627, 300)\n"
          ]
        }
      ],
      "source": [
        "## Creating the embedding matrix\n",
        "\n",
        "# vocab = wd2vc_model.wv.vocab ## gensim 3\n",
        "vocab = embedding_model.key_to_index\n",
        "print(\"The total number of words are : \", len(vocab))\n",
        "vocab = list(vocab.keys())\n",
        "\n",
        "word_vec_dict = {}\n",
        "for word in vocab:\n",
        "    word_vec_dict[word] = embedding_model.get_vector(word)\n",
        "print(\"The no of key-value pairs : \", len(word_vec_dict))  # should come equal to vocab size\n",
        "\n",
        "max_length = max(df['Length'])\n",
        "print(\"maximum length = \", max_length)\n",
        "\n",
        "tok = Tokenizer()\n",
        "tok.fit_on_texts(df['Tokens'])\n",
        "vocab_size = len(tok.word_index) + 1\n",
        "\n",
        "encd_rev = tok.texts_to_sequences(df['Tokens'])\n",
        "\n",
        "embed_dim = 300\n",
        "pad_rev = pad_sequences(encd_rev, maxlen=max_length, padding='post')\n",
        "print(\"pad_shape = \", pad_rev.shape)\n",
        "\n",
        "# the embedding matrix\n",
        "embed_matrix = np.zeros(shape=(vocab_size, embed_dim))\n",
        "print(embed_matrix.shape)\n",
        "for word, i in tok.word_index.items():\n",
        "    embed_vector = word_vec_dict.get(word)\n",
        "    if embed_vector is not None:\n",
        "        embed_matrix[i] = embed_vector"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cqLFWv2FU_Yk"
      },
      "source": [
        "## Train_Validation_Test data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Qb3c26CHU9ep",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "9cf2afbb-5883-4983-aabd-b9cdf1dc77a7"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Train =  (13032, 9360) (13032,)\n",
            "Test =  (4073, 9360) (4073,)\n",
            "Validation =  (3258, 9360) (3258,)\n"
          ]
        }
      ],
      "source": [
        "## Spliting the data\n",
        "X, X_test, y, y_test = train_test_split(pad_rev, df['Label'], shuffle=True, test_size=0.2, random_state=42)\n",
        "\n",
        "X_train, X_val, y_train, y_val = train_test_split(X, y, test_size=0.2, random_state=33)\n",
        "\n",
        "print(\"Train = \", X_train.shape, y_train.shape)\n",
        "print(\"Test = \", X_test.shape, y_test.shape)\n",
        "print(\"Validation = \",X_val.shape, y_val.shape)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MXlQhEOIVRCk"
      },
      "source": [
        "# Attention_based hybrid BiLSTM - CNN model"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "class LuongAttention(tf.keras.Model):\n",
        "    def __init__(self, rnn_size, attention_func):\n",
        "        super(LuongAttention, self).__init__()\n",
        "        self.attention_func = attention_func\n",
        "\n",
        "        if attention_func not in ['dot', 'general', 'concat']:\n",
        "            raise ValueError(\n",
        "                'Unknown attention score function! Must be either dot, general or concat.')\n",
        "\n",
        "        if attention_func == 'general':\n",
        "            # General score function\n",
        "            self.wa = tf.keras.layers.Dense(rnn_size)\n",
        "        elif attention_func == 'concat':\n",
        "            # Concat score function\n",
        "            self.wa = tf.keras.layers.Dense(rnn_size, activation='tanh')\n",
        "            self.va = tf.keras.layers.Dense(1)\n",
        "\n",
        "    def call(self, decoder_output, encoder_output):\n",
        "        if self.attention_func == 'dot':\n",
        "            # Dot score function: decoder_output (dot) encoder_output\n",
        "            # decoder_output has shape: (batch_size, 1, rnn_size)\n",
        "            # encoder_output has shape: (batch_size, max_len, rnn_size)\n",
        "            # => score has shape: (batch_size, 1, max_len)\n",
        "            score = tf.matmul(decoder_output, encoder_output, transpose_b=True)\n",
        "        elif self.attention_func == 'general':\n",
        "            # General score function: decoder_output (dot) (Wa (dot) encoder_output)\n",
        "            # decoder_output has shape: (batch_size, 1, rnn_size)\n",
        "            # encoder_output has shape: (batch_size, max_len, rnn_size)\n",
        "            # => score has shape: (batch_size, 1, max_len)\n",
        "            score = tf.matmul(decoder_output, self.wa(\n",
        "                encoder_output), transpose_b=True)\n",
        "        elif self.attention_func == 'concat':\n",
        "            # Concat score function: va (dot) tanh(Wa (dot) concat(decoder_output + encoder_output))\n",
        "            # Decoder output must be broadcasted to encoder output's shape first\n",
        "            decoder_output = tf.tile(\n",
        "                decoder_output, [1, encoder_output.shape[1], 1])\n",
        "\n",
        "            # Concat => Wa => va\n",
        "            # (batch_size, max_len, 2 * rnn_size) => (batch_size, max_len, rnn_size) => (batch_size, max_len, 1)\n",
        "            score = self.va(\n",
        "                self.wa(tf.concat((decoder_output, encoder_output), axis=-1)))\n",
        "\n",
        "            # Transpose score vector to have the same shape as other two above\n",
        "            # (batch_size, max_len, 1) => (batch_size, 1, max_len)\n",
        "            score = tf.transpose(score, [0, 2, 1])\n",
        "\n",
        "        # alignment a_t = softmax(score)\n",
        "        alignment = tf.nn.softmax(score, axis=2)\n",
        "\n",
        "        # context vector c_t is the weighted average sum of encoder output\n",
        "        context = tf.matmul(alignment, encoder_output)\n",
        "\n",
        "        return context, alignment"
      ],
      "metadata": {
        "id": "iduAUBpUGTh7"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "yr2FkGVMV5cu",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e58c7502-d208-4fd3-aa10-8ca7e3b42cbc"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "conv shape (None, 9358, 50)\n",
            "before output shape (None, 50)\n"
          ]
        }
      ],
      "source": [
        "# from tensorflow.keras.layers import Bidirectional, LSTM, Dense, Input, Embedding, Conv1D, GlobalMaxPooling1D, Dropout\n",
        "from tensorflow.keras.layers import Bidirectional, Dense, Input, Embedding, Conv1D, GlobalMaxPooling1D, Dropout\n",
        "from tensorflow.compat.v1.keras.layers import CuDNNLSTM\n",
        "from tensorflow.keras import regularizers\n",
        "from tensorflow.keras.models import *\n",
        "\n",
        "Attention = LuongAttention(20, 'dot')\n",
        "\n",
        "def hybrid_model(kernel_size = 3, input_dim = vocab_size, output_dim=100, max_length = None, emb_matrix = None):\n",
        "\n",
        "    inp = Input(shape=(max_length,))\n",
        "    x = Embedding(input_dim=input_dim,\n",
        "                  output_dim=output_dim,\n",
        "                  input_length=max_length,\n",
        "                  weights = [emb_matrix],\n",
        "                  trainable = False)(inp)\n",
        "\n",
        "    x = Bidirectional(CuDNNLSTM(100, stateful=False, return_sequences = True))(x)\n",
        "    # x = tf.keras.activations.sigmoid(x)\n",
        "    x = CuDNNLSTM(100, stateful=False, return_sequences = True)(x)\n",
        "    # x = tf.keras.activations.sigmoid(x)\n",
        "\n",
        "    x = Conv1D(50, kernel_size, activation='relu')(x)\n",
        "\n",
        "    print(\"conv shape\", x.shape)\n",
        "    x = Attention(x,x)[0]\n",
        "    x = Dense(50, activation=\"relu\", kernel_regularizer=regularizers.L1L2(l1=1e-5, l2=1e-4))(x)\n",
        "    # x = tf.keras.activations.relu(x)\n",
        "    # regularizer = regularizers.L1L2(l1=1e-5, l2=1e-4)\n",
        "    # regularizer(x)\n",
        "\n",
        "    x = GlobalMaxPooling1D()(x)\n",
        "    x = Dense(250, activation=\"relu\")(x)\n",
        "    x = Dropout(0.5)(x)\n",
        "    x = Dense(50, activation=\"relu\")(x)\n",
        "    x = Dropout(0.5)(x)\n",
        "    print(\"before output shape\", x.shape)\n",
        "    outp = Dense(2, activation=\"sigmoid\")(x)\n",
        "    # outp = Dense(1, activation=\"sigmoid\")(x)\n",
        "\n",
        "    model = Model(inputs=inp, outputs=outp)\n",
        "    model.compile( loss = 'sparse_categorical_crossentropy', optimizer = 'adam', metrics = ['accuracy'])\n",
        "\n",
        "    return model\n",
        "\n",
        "model= hybrid_model(kernel_size = 3, input_dim = vocab_size, output_dim=embed_dim, max_length = max_length, emb_matrix = embed_matrix)\n",
        "\n",
        "# model.summary()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "9zRRoy4owVo6"
      },
      "outputs": [],
      "source": [
        "epochs = 30\n",
        "batch_size = 32\n",
        "r = model.fit(X_train, y_train, epochs=epochs, batch_size=batch_size, validation_data=(X_val, y_val))"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Prediction"
      ],
      "metadata": {
        "id": "vHHivCIVwdAU"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#Prediction on the test data\n",
        "pred = model.predict(X_test)\n",
        "print(pred)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "8d26367f-b6dc-4d64-a21f-4d08faeae4b8",
        "id": "3eUqXngpwdAU"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "125/125 [==============================] - 55s 438ms/step\n",
            "[[9.8512417e-01 3.6647186e-02]\n",
            " [9.8512417e-01 3.6647186e-02]\n",
            " [9.8512417e-01 3.6647186e-02]\n",
            " ...\n",
            " [9.8512417e-01 3.6647186e-02]\n",
            " [9.8512417e-01 3.6647186e-02]\n",
            " [1.7490360e-05 9.9888724e-01]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred = np.zeros(len(pred))\n",
        "for item in range(len(pred)):\n",
        "  y_pred[item] = list(pred[item]).index(max(pred[item]))\n",
        "  # y_pred[item] = 0 if pred[item]<0.5 else 1\n",
        "\n",
        "print(y_pred)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a56d6f61-4f78-49a4-e619-d7beea199aca",
        "id": "uhFI5w8PwdAU"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[0. 0. 0. ... 0. 0. 1.]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import sklearn\n",
        "\n",
        "report = sklearn.metrics.classification_report(y_test, y_pred)\n",
        "print(report)\n",
        "accuracy = sklearn.metrics.accuracy_score(y_test, y_pred)\n",
        "precision = sklearn.metrics.precision_score(y_test, y_pred)\n",
        "recall = sklearn.metrics.recall_score(y_test, y_pred)\n",
        "f1score = sklearn.metrics.f1_score(y_test, y_pred)\n",
        "print(\"-----------------------\")\n",
        "print(accuracy)\n",
        "print(precision)\n",
        "print(recall)\n",
        "print(f1score)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "1c66c972-1018-4c49-d6f2-e86ddecd3fb3",
        "id": "hP-aJiUiwdAV"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.99      0.92      0.96      2002\n",
            "           1       0.93      0.99      0.96      1995\n",
            "\n",
            "    accuracy                           0.96      3997\n",
            "   macro avg       0.96      0.96      0.96      3997\n",
            "weighted avg       0.96      0.96      0.96      3997\n",
            "\n",
            "-----------------------\n",
            "0.9582186639979985\n",
            "0.9279026217228464\n",
            "0.993483709273183\n",
            "0.9595739530380054\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Reddit+FB"
      ],
      "metadata": {
        "id": "MBUAN5inwRUJ"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "8PU0gMsa3pS6",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "54c72fa0-46e4-4cd3-e9d2-aef1080283fe"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/15\n",
            "800/800 [==============================] - 688s 860ms/step - loss: 0.1723 - accuracy: 0.9462 - val_loss: 0.0968 - val_accuracy: 0.9700\n",
            "Epoch 2/15\n",
            "800/800 [==============================] - 634s 792ms/step - loss: 0.1205 - accuracy: 0.9630 - val_loss: 0.1116 - val_accuracy: 0.9543\n",
            "Epoch 3/15\n",
            "800/800 [==============================] - 633s 791ms/step - loss: 0.0887 - accuracy: 0.9728 - val_loss: 0.0792 - val_accuracy: 0.9750\n",
            "Epoch 4/15\n",
            "800/800 [==============================] - 632s 790ms/step - loss: 0.0726 - accuracy: 0.9786 - val_loss: 0.0669 - val_accuracy: 0.9778\n",
            "Epoch 5/15\n",
            "800/800 [==============================] - 597s 746ms/step - loss: 0.0661 - accuracy: 0.9793 - val_loss: 0.0704 - val_accuracy: 0.9709\n",
            "Epoch 6/15\n",
            "800/800 [==============================] - 630s 788ms/step - loss: 0.0630 - accuracy: 0.9798 - val_loss: 0.0941 - val_accuracy: 0.9675\n",
            "Epoch 7/15\n",
            "800/800 [==============================] - 597s 746ms/step - loss: 0.0519 - accuracy: 0.9823 - val_loss: 0.0518 - val_accuracy: 0.9825\n",
            "Epoch 8/15\n",
            "800/800 [==============================] - 629s 786ms/step - loss: 0.0456 - accuracy: 0.9851 - val_loss: 0.0618 - val_accuracy: 0.9825\n",
            "Epoch 9/15\n",
            "800/800 [==============================] - 629s 786ms/step - loss: 0.0436 - accuracy: 0.9853 - val_loss: 0.0587 - val_accuracy: 0.9806\n",
            "Epoch 10/15\n",
            "800/800 [==============================] - 629s 787ms/step - loss: 0.0457 - accuracy: 0.9855 - val_loss: 0.0552 - val_accuracy: 0.9794\n",
            "Epoch 11/15\n",
            "800/800 [==============================] - 629s 786ms/step - loss: 0.0403 - accuracy: 0.9879 - val_loss: 0.0546 - val_accuracy: 0.9844\n",
            "Epoch 12/15\n",
            "800/800 [==============================] - 628s 785ms/step - loss: 0.0352 - accuracy: 0.9890 - val_loss: 0.0898 - val_accuracy: 0.9744\n",
            "Epoch 13/15\n",
            "800/800 [==============================] - 627s 784ms/step - loss: 0.0275 - accuracy: 0.9917 - val_loss: 0.1137 - val_accuracy: 0.9759\n",
            "Epoch 14/15\n",
            "800/800 [==============================] - 626s 783ms/step - loss: 0.0309 - accuracy: 0.9911 - val_loss: 0.0688 - val_accuracy: 0.9825\n",
            "Epoch 15/15\n",
            "800/800 [==============================] - 629s 786ms/step - loss: 0.0314 - accuracy: 0.9935 - val_loss: 0.1697 - val_accuracy: 0.9528\n"
          ]
        }
      ],
      "source": [
        "epochs = 15\n",
        "batch_size = 16\n",
        "r = model.fit(X_train, y_train, epochs=epochs, batch_size=batch_size, validation_data=(X_val, y_val))"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model.save('/trained_model_Reddit_Facebook.h5')"
      ],
      "metadata": {
        "id": "W205ymi3uluR"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Prediction"
      ],
      "metadata": {
        "id": "U3k2t1W3Ydi6"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#Prediction on the test data\n",
        "pred = model.predict(X_test)\n",
        "print(pred)"
      ],
      "metadata": {
        "id": "pds1OThhmMSW",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "8d26367f-b6dc-4d64-a21f-4d08faeae4b8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "125/125 [==============================] - 55s 438ms/step\n",
            "[[9.8512417e-01 3.6647186e-02]\n",
            " [9.8512417e-01 3.6647186e-02]\n",
            " [9.8512417e-01 3.6647186e-02]\n",
            " ...\n",
            " [9.8512417e-01 3.6647186e-02]\n",
            " [9.8512417e-01 3.6647186e-02]\n",
            " [1.7490360e-05 9.9888724e-01]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred = np.zeros(len(pred))\n",
        "for item in range(len(pred)):\n",
        "  y_pred[item] = list(pred[item]).index(max(pred[item]))\n",
        "  # y_pred[item] = 0 if pred[item]<0.5 else 1\n",
        "\n",
        "print(y_pred)"
      ],
      "metadata": {
        "id": "qhsfpqj0mPoG",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a56d6f61-4f78-49a4-e619-d7beea199aca"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[0. 0. 0. ... 0. 0. 1.]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import sklearn\n",
        "\n",
        "report = sklearn.metrics.classification_report(y_test, y_pred)\n",
        "print(report)\n",
        "accuracy = sklearn.metrics.accuracy_score(y_test, y_pred)\n",
        "precision = sklearn.metrics.precision_score(y_test, y_pred)\n",
        "recall = sklearn.metrics.recall_score(y_test, y_pred)\n",
        "f1score = sklearn.metrics.f1_score(y_test, y_pred)\n",
        "print(\"-----------------------\")\n",
        "print(accuracy)\n",
        "print(precision)\n",
        "print(recall)\n",
        "print(f1score)"
      ],
      "metadata": {
        "id": "xm-zXpTmmXs_",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "1c66c972-1018-4c49-d6f2-e86ddecd3fb3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.99      0.92      0.96      2002\n",
            "           1       0.93      0.99      0.96      1995\n",
            "\n",
            "    accuracy                           0.96      3997\n",
            "   macro avg       0.96      0.96      0.96      3997\n",
            "weighted avg       0.96      0.96      0.96      3997\n",
            "\n",
            "-----------------------\n",
            "0.9582186639979985\n",
            "0.9279026217228464\n",
            "0.993483709273183\n",
            "0.9595739530380054\n"
          ]
        }
      ]
    }
  ],
  "metadata": {
    "accelerator": "GPU",
    "colab": {
      "collapsed_sections": [
        "Y3Hj8CPuAo6F",
        "nSPSlQHKEz7O",
        "KJueHyMVArJ3"
      ],
      "provenance": [],
      "gpuType": "T4"
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}