LSTM_Sarcasm.ipynb

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            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
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            "Requirement already satisfied: typing-extensions in /usr/local/lib/python3.7/dist-packages (from kiwisolver>=1.0.1->matplotlib>=1.4.0->scikit-plot) (4.1.1)\n",
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      "source": [
        "import numpy as np # linear algebra\n",
        "import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n",
        "import tensorflow as tf\n",
        "import matplotlib.pyplot as plt\n",
        "import scikitplot as skplt\n",
        "import seaborn as sns\n",
        "from sklearn import metrics\n",
        "from keras.models import Sequential\n",
        "from keras.layers import LSTM, Dense, Bidirectional, Embedding, Flatten, SpatialDropout1D\n",
        "from keras.initializers import Constant\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.metrics import plot_confusion_matrix\n",
        "from sklearn.metrics import confusion_matrix\n",
        "\n",
        "import os\n",
        "for dirname, _, filenames in os.walk('/kaggle/input'):\n",
        "    for filename in filenames:\n",
        "        print(os.path.join(dirname, filename))\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import json\n",
        "data = [json.loads(line) for \n",
        "        line in open(\"/content/Sarcasm_Headlines_Dataset.json\", 'r')]\n"
      ],
      "metadata": {
        "id": "1i5ijuhiq0NN"
      },
      "execution_count": 31,
      "outputs": []
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        "new_df = pd.DataFrame.from_dict(data) "
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      "execution_count": 32,
      "outputs": []
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              "                                        article_link  \\\n",
              "0  https://www.huffingtonpost.com/entry/versace-b...   \n",
              "1  https://www.huffingtonpost.com/entry/roseanne-...   \n",
              "2  https://local.theonion.com/mom-starting-to-fea...   \n",
              "3  https://politics.theonion.com/boehner-just-wan...   \n",
              "4  https://www.huffingtonpost.com/entry/jk-rowlin...   \n",
              "\n",
              "                                            headline  is_sarcastic  \n",
              "0  former versace store clerk sues over secret 'b...             0  \n",
              "1  the 'roseanne' revival catches up to our thorn...             0  \n",
              "2  mom starting to fear son's web series closest ...             1  \n",
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              "                                            headline  is_sarcastic\n",
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              "                                            headline  is_sarcastic  \\\n",
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              "4  j.k. rowling wishes snape happy birthday in th...             0   \n",
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              "   headline_len  \n",
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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>headline</th>\n",
              "      <th>is_sarcastic</th>\n",
              "      <th>headline_len</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>former versace store clerk sues over secret 'b...</td>\n",
              "      <td>0</td>\n",
              "      <td>78</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>the 'roseanne' revival catches up to our thorn...</td>\n",
              "      <td>0</td>\n",
              "      <td>84</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>mom starting to fear son's web series closest ...</td>\n",
              "      <td>1</td>\n",
              "      <td>79</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>boehner just wants wife to listen, not come up...</td>\n",
              "      <td>1</td>\n",
              "      <td>84</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>j.k. rowling wishes snape happy birthday in th...</td>\n",
              "      <td>0</td>\n",
              "      <td>64</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-77e76e38-8a64-4da6-85d7-67c344da2362')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      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",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-77e76e38-8a64-4da6-85d7-67c344da2362 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-77e76e38-8a64-4da6-85d7-67c344da2362');\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": 37
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "max_features = 10000\n",
        "maxlen = 25\n",
        "embedding_size = 200"
      ],
      "metadata": {
        "id": "Qp7lk4rNr9NQ"
      },
      "execution_count": 38,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "top_k = 10000\n",
        "tokenizer = tf.keras.preprocessing.text.Tokenizer(num_words=top_k,\n",
        "                                                  oov_token=\"<unk>\",\n",
        "                                                  filters='!\"#$%&()*+.,-/:;=?@[\\]^_`{|}~ ')\n",
        "tokenizer.fit_on_texts(new_df2['headline'])"
      ],
      "metadata": {
        "id": "Mcqzag40r_7v"
      },
      "execution_count": 39,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "tokenizer.word_index['<pad>'] = 0\n",
        "tokenizer.index_word[0] = '<pad>'"
      ],
      "metadata": {
        "id": "4xPkE-WCsDXh"
      },
      "execution_count": 40,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "train_seqs = tokenizer.texts_to_sequences(new_df2['headline'])"
      ],
      "metadata": {
        "id": "DXxd5Ws_sF97"
      },
      "execution_count": 41,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X = tf.keras.preprocessing.sequence.pad_sequences(train_seqs, padding='post',maxlen=maxlen)"
      ],
      "metadata": {
        "id": "5mIOHWjCsJMd"
      },
      "execution_count": 42,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "Y = new_df2[\"is_sarcastic\"].values"
      ],
      "metadata": {
        "id": "caeaLK0usMNL"
      },
      "execution_count": 43,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(type(Y))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xtmkkXSnsQG5",
        "outputId": "560baad6-be44-46f0-dbfa-47e87f3a1898"
      },
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'numpy.ndarray'>\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.countplot(x='is_sarcastic',data=new_df2);"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 280
        },
        "id": "IcksM1oKsSP3",
        "outputId": "0fde96d4-338b-4fcd-9413-36fc4759e842"
      },
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "tokenizer.word_index"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QQXk3cAPsWxd",
        "outputId": "2ade8dab-cc12-40a9-e16a-8ccbd0e30837"
      },
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'<unk>': 1,\n",
              " 'to': 2,\n",
              " 'of': 3,\n",
              " 'the': 4,\n",
              " 'in': 5,\n",
              " 'for': 6,\n",
              " 'a': 7,\n",
              " 'on': 8,\n",
              " 'and': 9,\n",
              " 'with': 10,\n",
              " 'is': 11,\n",
              " 'new': 12,\n",
              " 'trump': 13,\n",
              " 'man': 14,\n",
              " 'from': 15,\n",
              " 'at': 16,\n",
              " 'about': 17,\n",
              " 'you': 18,\n",
              " 'this': 19,\n",
              " 'by': 20,\n",
              " 'after': 21,\n",
              " 'up': 22,\n",
              " 'out': 23,\n",
              " 'be': 24,\n",
              " 'how': 25,\n",
              " 'as': 26,\n",
              " 'it': 27,\n",
              " 'that': 28,\n",
              " 'not': 29,\n",
              " 'are': 30,\n",
              " 'your': 31,\n",
              " 'his': 32,\n",
              " 'what': 33,\n",
              " 'he': 34,\n",
              " 'all': 35,\n",
              " 'just': 36,\n",
              " 'who': 37,\n",
              " 'has': 38,\n",
              " 'will': 39,\n",
              " 'more': 40,\n",
              " 'one': 41,\n",
              " 'into': 42,\n",
              " 'report': 43,\n",
              " 'year': 44,\n",
              " 'why': 45,\n",
              " 'have': 46,\n",
              " 'area': 47,\n",
              " 'over': 48,\n",
              " 'donald': 49,\n",
              " 'u': 50,\n",
              " 'day': 51,\n",
              " 'says': 52,\n",
              " 's': 53,\n",
              " 'can': 54,\n",
              " 'first': 55,\n",
              " 'woman': 56,\n",
              " 'time': 57,\n",
              " 'like': 58,\n",
              " 'her': 59,\n",
              " \"trump's\": 60,\n",
              " 'old': 61,\n",
              " 'no': 62,\n",
              " 'get': 63,\n",
              " 'off': 64,\n",
              " 'an': 65,\n",
              " 'life': 66,\n",
              " 'people': 67,\n",
              " 'obama': 68,\n",
              " 'now': 69,\n",
              " 'house': 70,\n",
              " 'still': 71,\n",
              " \"'\": 72,\n",
              " 'women': 73,\n",
              " 'make': 74,\n",
              " 'was': 75,\n",
              " 'than': 76,\n",
              " 'white': 77,\n",
              " 'back': 78,\n",
              " 'my': 79,\n",
              " 'i': 80,\n",
              " 'clinton': 81,\n",
              " 'down': 82,\n",
              " 'if': 83,\n",
              " '5': 84,\n",
              " 'when': 85,\n",
              " 'world': 86,\n",
              " 'could': 87,\n",
              " 'we': 88,\n",
              " 'their': 89,\n",
              " 'before': 90,\n",
              " 'americans': 91,\n",
              " 'way': 92,\n",
              " 'do': 93,\n",
              " 'family': 94,\n",
              " 'most': 95,\n",
              " 'gop': 96,\n",
              " 'they': 97,\n",
              " 'study': 98,\n",
              " 'school': 99,\n",
              " \"it's\": 100,\n",
              " 'black': 101,\n",
              " 'best': 102,\n",
              " 'years': 103,\n",
              " 'bill': 104,\n",
              " 'should': 105,\n",
              " '3': 106,\n",
              " 'him': 107,\n",
              " 'would': 108,\n",
              " 'so': 109,\n",
              " 'police': 110,\n",
              " 'only': 111,\n",
              " 'watch': 112,\n",
              " 'american': 113,\n",
              " 'really': 114,\n",
              " 'being': 115,\n",
              " 'but': 116,\n",
              " 'last': 117,\n",
              " 'know': 118,\n",
              " '10': 119,\n",
              " \"can't\": 120,\n",
              " 'death': 121,\n",
              " 'home': 122,\n",
              " 'during': 123,\n",
              " 'video': 124,\n",
              " 'finds': 125,\n",
              " 'state': 126,\n",
              " 'or': 127,\n",
              " 'president': 128,\n",
              " 'health': 129,\n",
              " 'going': 130,\n",
              " 'say': 131,\n",
              " 'show': 132,\n",
              " 'nation': 133,\n",
              " 'good': 134,\n",
              " 'things': 135,\n",
              " 'hillary': 136,\n",
              " \"'the\": 137,\n",
              " 'may': 138,\n",
              " '2': 139,\n",
              " 'against': 140,\n",
              " 'campaign': 141,\n",
              " 'every': 142,\n",
              " 'she': 143,\n",
              " 'love': 144,\n",
              " 'mom': 145,\n",
              " 'need': 146,\n",
              " 'big': 147,\n",
              " 'right': 148,\n",
              " 'party': 149,\n",
              " 'gets': 150,\n",
              " '000': 151,\n",
              " 'too': 152,\n",
              " 'getting': 153,\n",
              " 'these': 154,\n",
              " 'kids': 155,\n",
              " 'some': 156,\n",
              " 'parents': 157,\n",
              " 'work': 158,\n",
              " 'court': 159,\n",
              " 'little': 160,\n",
              " 'change': 161,\n",
              " 'take': 162,\n",
              " 'high': 163,\n",
              " 'makes': 164,\n",
              " 'self': 165,\n",
              " 'our': 166,\n",
              " 'calls': 167,\n",
              " 'john': 168,\n",
              " 'other': 169,\n",
              " 'news': 170,\n",
              " 'through': 171,\n",
              " \"doesn't\": 172,\n",
              " 'while': 173,\n",
              " \"here's\": 174,\n",
              " 'never': 175,\n",
              " 'child': 176,\n",
              " 'gay': 177,\n",
              " 'dead': 178,\n",
              " 'look': 179,\n",
              " 'election': 180,\n",
              " 'want': 181,\n",
              " 'own': 182,\n",
              " '4': 183,\n",
              " \"don't\": 184,\n",
              " 'see': 185,\n",
              " 'takes': 186,\n",
              " 'america': 187,\n",
              " '7': 188,\n",
              " 'local': 189,\n",
              " 'real': 190,\n",
              " 'where': 191,\n",
              " 'next': 192,\n",
              " 'stop': 193,\n",
              " 'even': 194,\n",
              " 'its': 195,\n",
              " \"he's\": 196,\n",
              " 'war': 197,\n",
              " 'college': 198,\n",
              " 'go': 199,\n",
              " '6': 200,\n",
              " \"nation's\": 201,\n",
              " 'sex': 202,\n",
              " 'bush': 203,\n",
              " 'made': 204,\n",
              " 'plan': 205,\n",
              " 'office': 206,\n",
              " 'again': 207,\n",
              " 'guy': 208,\n",
              " 'two': 209,\n",
              " 'dad': 210,\n",
              " 'another': 211,\n",
              " 'around': 212,\n",
              " 'dog': 213,\n",
              " 'got': 214,\n",
              " '1': 215,\n",
              " 'million': 216,\n",
              " 'ever': 217,\n",
              " 'week': 218,\n",
              " 'baby': 219,\n",
              " 'debate': 220,\n",
              " 'thing': 221,\n",
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              " 'supporters': 912,\n",
              " 'google': 913,\n",
              " 'football': 914,\n",
              " 'voice': 915,\n",
              " 'easy': 916,\n",
              " 'teens': 917,\n",
              " 'card': 918,\n",
              " 'kerry': 919,\n",
              " 'wait': 920,\n",
              " 'try': 921,\n",
              " 'throws': 922,\n",
              " 'tour': 923,\n",
              " 'pregnant': 924,\n",
              " 'pizza': 925,\n",
              " 'dying': 926,\n",
              " 'press': 927,\n",
              " 'chicken': 928,\n",
              " 'urges': 929,\n",
              " 'reveal': 930,\n",
              " 'simple': 931,\n",
              " 'green': 932,\n",
              " 'economy': 933,\n",
              " 'problems': 934,\n",
              " 'culture': 935,\n",
              " 'lgbtq': 936,\n",
              " 'asking': 937,\n",
              " 'ebola': 938,\n",
              " 'robert': 939,\n",
              " 'learn': 940,\n",
              " 'performance': 941,\n",
              " 'album': 942,\n",
              " 'church': 943,\n",
              " 'begins': 944,\n",
              " 'officer': 945,\n",
              " 'shop': 946,\n",
              " 'poor': 947,\n",
              " 'uses': 948,\n",
              " 'plane': 949,\n",
              " 'families': 950,\n",
              " 'harassment': 951,\n",
              " 'picture': 952,\n",
              " 'jobs': 953,\n",
              " 'fails': 954,\n",
              " 'sean': 955,\n",
              " 'voter': 956,\n",
              " 'beauty': 957,\n",
              " 'demand': 958,\n",
              " 'doctor': 959,\n",
              " \"we're\": 960,\n",
              " 'spot': 961,\n",
              " 'shares': 962,\n",
              " 'leads': 963,\n",
              " 'hilarious': 964,\n",
              " 'suggests': 965,\n",
              " 'rally': 966,\n",
              " 'results': 967,\n",
              " 'ideas': 968,\n",
              " '18': 969,\n",
              " 'jenner': 970,\n",
              " 'arrested': 971,\n",
              " 'male': 972,\n",
              " 'fuck': 973,\n",
              " 'leaving': 974,\n",
              " 'address': 975,\n",
              " 'rest': 976,\n",
              " 'receives': 977,\n",
              " 'amid': 978,\n",
              " 'epa': 979,\n",
              " 'deadly': 980,\n",
              " 'netflix': 981,\n",
              " 'desperate': 982,\n",
              " 'planet': 983,\n",
              " 'cnn': 984,\n",
              " 'marijuana': 985,\n",
              " 'quietly': 986,\n",
              " 'action': 987,\n",
              " 'website': 988,\n",
              " 'pick': 989,\n",
              " 'explains': 990,\n",
              " 'table': 991,\n",
              " 'energy': 992,\n",
              " 'users': 993,\n",
              " 'feeling': 994,\n",
              " 'sales': 995,\n",
              " 'colbert': 996,\n",
              " 'apparently': 997,\n",
              " \"let's\": 998,\n",
              " 'amazing': 999,\n",
              " 'went': 1000,\n",
              " ...}"
            ]
          },
          "metadata": {},
          "execution_count": 46
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "num_words = len(tokenizer.word_index) + 1\n",
        "print(num_words)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "H5AMW1lIsdlq",
        "outputId": "554f3864-85f7-48f0-ed59-a488311ebf60"
      },
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "29660\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "EMBEDDING_FILE = '/content/glove.6B.200d.txt'"
      ],
      "metadata": {
        "id": "lbJ-HIW1seV1"
      },
      "execution_count": 48,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "embeddings = {}\n",
        "for o in open(EMBEDDING_FILE):\n",
        "    word = o.split(\" \")[0]\n",
        "    embd = o.split(\" \")[1:]\n",
        "    embd = np.asarray(embd, dtype='float32')\n",
        "    embeddings[word] = embd"
      ],
      "metadata": {
        "id": "ZNyeNZt23aYp"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "embedding_matrix = np.zeros((num_words, 200))\n",
        "\n",
        "for word, i in tokenizer.word_index.items():\n",
        "\tembedding_vector = embeddings.get(word)\n",
        "\tif embedding_vector is not None:\n",
        "\t\tembedding_matrix[i] = embedding_vector\n"
      ],
      "metadata": {
        "id": "b50l1LB34udE"
      },
      "execution_count": 54,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "lstm_out = 196\n",
        "\n",
        "# define the model\n",
        "model = Sequential()\n",
        "model.add(Embedding(num_words,\n",
        "                    embedding_size,\n",
        "                    embeddings_initializer=Constant(embedding_matrix),\n",
        "                    input_length=maxlen,\n",
        "                    trainable=False))\n",
        "model.add(SpatialDropout1D(0.4))\n",
        "model.add(Bidirectional(LSTM(lstm_out, dropout=0.2, recurrent_dropout=0.2)))\n",
        "model.add(Flatten())\n",
        "model.add(Dense(1, activation='sigmoid'))"
      ],
      "metadata": {
        "id": "Xwp4zLAQ4wwu"
      },
      "execution_count": 56,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# compile the model\n",
        "model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])\n",
        "# summarize the model\n",
        "print(model.summary())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fI7mC4aV48gA",
        "outputId": "3f18c594-9bd6-4330-d05c-b5e336115382"
      },
      "execution_count": 57,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " embedding (Embedding)       (None, 25, 200)           5932000   \n",
            "                                                                 \n",
            " spatial_dropout1d (SpatialD  (None, 25, 200)          0         \n",
            " ropout1D)                                                       \n",
            "                                                                 \n",
            " bidirectional (Bidirectiona  (None, 392)              622496    \n",
            " l)                                                              \n",
            "                                                                 \n",
            " flatten (Flatten)           (None, 392)               0         \n",
            "                                                                 \n",
            " dense (Dense)               (None, 1)                 393       \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 6,554,889\n",
            "Trainable params: 622,889\n",
            "Non-trainable params: 5,932,000\n",
            "_________________________________________________________________\n",
            "None\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X_train, X_test, Y_train, Y_test = train_test_split(X,Y, test_size = 0.20, random_state = 42)\n",
        "print(X_train.shape,Y_train.shape)\n",
        "print(X_test.shape,Y_test.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YFVZJd814_QT",
        "outputId": "4a5e95c3-0142-4a51-8fcb-07bb40fed409"
      },
      "execution_count": 58,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(21367, 25) (21367,)\n",
            "(5342, 25) (5342,)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "batch_size = 64\n",
        "history = model.fit(X_train, Y_train, epochs = 10, batch_size=batch_size, \n",
        "                    verbose = 1, validation_split=0.1)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ngQrUIbj5Ald",
        "outputId": "0d272504-0da3-4e63-a9d2-96816e90ee8e"
      },
      "execution_count": 59,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/10\n",
            "301/301 [==============================] - 164s 513ms/step - loss: 0.5521 - accuracy: 0.7124 - val_loss: 0.4435 - val_accuracy: 0.7946\n",
            "Epoch 2/10\n",
            "301/301 [==============================] - 155s 515ms/step - loss: 0.4536 - accuracy: 0.7822 - val_loss: 0.3750 - val_accuracy: 0.8386\n",
            "Epoch 3/10\n",
            "301/301 [==============================] - 153s 509ms/step - loss: 0.3990 - accuracy: 0.8142 - val_loss: 0.3484 - val_accuracy: 0.8409\n",
            "Epoch 4/10\n",
            "301/301 [==============================] - 156s 518ms/step - loss: 0.3628 - accuracy: 0.8330 - val_loss: 0.3291 - val_accuracy: 0.8563\n",
            "Epoch 5/10\n",
            "301/301 [==============================] - 153s 507ms/step - loss: 0.3401 - accuracy: 0.8500 - val_loss: 0.3080 - val_accuracy: 0.8591\n",
            "Epoch 6/10\n",
            "301/301 [==============================] - 153s 509ms/step - loss: 0.3162 - accuracy: 0.8603 - val_loss: 0.2977 - val_accuracy: 0.8708\n",
            "Epoch 7/10\n",
            "301/301 [==============================] - 164s 544ms/step - loss: 0.3028 - accuracy: 0.8686 - val_loss: 0.2893 - val_accuracy: 0.8718\n",
            "Epoch 8/10\n",
            "301/301 [==============================] - 166s 550ms/step - loss: 0.2815 - accuracy: 0.8757 - val_loss: 0.2845 - val_accuracy: 0.8751\n",
            "Epoch 9/10\n",
            "301/301 [==============================] - 183s 610ms/step - loss: 0.2683 - accuracy: 0.8863 - val_loss: 0.2867 - val_accuracy: 0.8732\n",
            "Epoch 10/10\n",
            "301/301 [==============================] - 158s 524ms/step - loss: 0.2523 - accuracy: 0.8924 - val_loss: 0.2839 - val_accuracy: 0.8760\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.plot(history.history['accuracy'])\n",
        "plt.plot(history.history['val_accuracy'])\n",
        "plt.title('model accuracy')\n",
        "plt.ylabel('accuracy')\n",
        "plt.xlabel('epoch')\n",
        "plt.legend(['train', 'validation'], loc='upper left')\n",
        "plt.show();"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "nFPj-4WF-DrZ",
        "outputId": "2b1a9e5c-d8b1-4ada-c029-4dd4abd8087c"
      },
      "execution_count": 60,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.plot(history.history['loss'])\n",
        "plt.plot(history.history['val_loss'])\n",
        "plt.title('model loss')\n",
        "plt.ylabel('loss')\n",
        "plt.xlabel('epoch')\n",
        "plt.legend(['train', 'validation'], loc='upper left')\n",
        "plt.show();"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "eMhWptiC-Oqg",
        "outputId": "d8cae08e-2443-44f1-8359-10f809f6bfc4"
      },
      "execution_count": 61,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "loss, accuracy = model.evaluate(X_test, Y_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cp6jNYZD-Q7k",
        "outputId": "1207cfca-504d-4dfb-f20f-fb98e1487fb2"
      },
      "execution_count": 62,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "167/167 [==============================] - 8s 45ms/step - loss: 0.3001 - accuracy: 0.8751\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(f'My test loss is {loss*100:.2f}% and test accuracy is {accuracy*100:.2f}%')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YPfPXU_V-Tts",
        "outputId": "89dac1d0-1640-4982-c295-bd9f798a01e6"
      },
      "execution_count": 63,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "My test loss is 30.01% and test accuracy is 87.51%\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def model_Evaluate(model,test_data):\n",
        "    # Predict values for Test dataset\n",
        "    y_pred = model.predict(test_data)\n",
        "    # Print the evaluation metrics for the dataset.\n",
        "    print(classification_report(y_test, y_pred))\n",
        "    # Compute and plot the Confusion matrix\n",
        "    cf_matrix = confusion_matrix(y_test, y_pred)\n",
        "    categories = ['Positive','Negative']\n",
        "    group_names = ['True Pos','False Neg','True Neg','False Pos']\n",
        "    group_percentages = ['{0:.2%}'.format(value) for value in cf_matrix.flatten() / np.sum(cf_matrix)]\n",
        "    labels = [f'{v1}n{v2}' for v1, v2 in zip(group_names,group_percentages)]\n",
        "    labels = np.asarray(labels).reshape(2,2)\n",
        "    sns.heatmap(cf_matrix, annot = labels, cmap = 'crest',fmt = '',\n",
        "    xticklabels = categories, yticklabels = categories)\n",
        "    plt.xlabel(\"Predicted values\", fontdict = {'size':14}, labelpad = 5)\n",
        "    plt.ylabel(\"Actual values\" , fontdict = {'size':14}, labelpad = 5)\n",
        "    plt.title (\"Confusion Matrix\", fontdict = {'size':18}, pad = 10)"
      ],
      "metadata": {
        "id": "frme8oBd-bgH"
      },
      "execution_count": 66,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def make_confusion_matrix(cf,\n",
        "                          group_names=None,\n",
        "                          categories='auto',\n",
        "                          count=True,\n",
        "                          percent=True,\n",
        "                          cbar=True,\n",
        "                          xyticks=True,\n",
        "                          xyplotlabels=True,\n",
        "                          sum_stats=True,\n",
        "                          figsize=None,\n",
        "                          cmap='Blues',\n",
        "                          title=None):\n",
        "\n",
        "    # CODE TO GENERATE TEXT INSIDE EACH SQUARE\n",
        "    blanks = ['' for i in range(cf.size)]\n",
        "\n",
        "    if group_names and len(group_names)==cf.size:\n",
        "        group_labels = [\"{}\\n\".format(value) for value in group_names]\n",
        "    else:\n",
        "        group_labels = blanks\n",
        "\n",
        "    if count:\n",
        "        group_counts = [\"{0:0.0f}\\n\".format(value) for value in cf.flatten()]\n",
        "    else:\n",
        "        group_counts = blanks\n",
        "\n",
        "    if percent:\n",
        "        group_percentages = [\"{0:.2%}\".format(value) for value in cf.flatten()/np.sum(cf)]\n",
        "    else:\n",
        "        group_percentages = blanks\n",
        "\n",
        "    box_labels = [f\"{v1}{v2}{v3}\".strip() for v1, v2, v3 in zip(group_labels,group_counts,group_percentages)]\n",
        "    box_labels = np.asarray(box_labels).reshape(cf.shape[0],cf.shape[1])\n",
        "\n",
        "\n",
        "    # CODE TO GENERATE SUMMARY STATISTICS & TEXT FOR SUMMARY STATS\n",
        "    if sum_stats:\n",
        "        #Accuracy is sum of diagonal divided by total observations\n",
        "        accuracy  = np.trace(cf) / float(np.sum(cf))\n",
        "\n",
        "        #if it is a binary confusion matrix, show some more stats\n",
        "        if len(cf)==2:\n",
        "            #Metrics for Binary Confusion Matrices\n",
        "            precision = cf[1,1] / sum(cf[:,1])\n",
        "            recall    = cf[1,1] / sum(cf[1,:])\n",
        "            f1_score  = 2*precision*recall / (precision + recall)\n",
        "            stats_text = \"\\n\\nAccuracy={:0.3f}\\nPrecision={:0.3f}\\nRecall={:0.3f}\\nF1 Score={:0.3f}\".format(\n",
        "                accuracy,precision,recall,f1_score)\n",
        "        else:\n",
        "            stats_text = \"\\n\\nAccuracy={:0.3f}\".format(accuracy)\n",
        "    else:\n",
        "        stats_text = \"\"\n",
        "\n",
        "\n",
        "    # SET FIGURE PARAMETERS ACCORDING TO OTHER ARGUMENTS\n",
        "    if figsize==None:\n",
        "        #Get default figure size if not set\n",
        "        figsize = plt.rcParams.get('figure.figsize')\n",
        "\n",
        "    if xyticks==False:\n",
        "        #Do not show categories if xyticks is False\n",
        "        categories=False\n",
        "\n",
        "\n",
        "    # MAKE THE HEATMAP VISUALIZATION\n",
        "    plt.figure(figsize=figsize)\n",
        "    sns.heatmap(cf,annot=box_labels,fmt=\"\",cmap=cmap,cbar=cbar,xticklabels=categories,yticklabels=categories)\n",
        "\n",
        "    if xyplotlabels:\n",
        "        plt.ylabel('True label')\n",
        "        plt.xlabel('Predicted label' + stats_text)\n",
        "    else:\n",
        "        plt.xlabel(stats_text)\n",
        "    \n",
        "    if title:\n",
        "        plt.title(title)"
      ],
      "metadata": {
        "id": "b5O5IlO9A-1R"
      },
      "execution_count": 73,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "cf_matrix = confusion_matrix(Y_test, (model.predict(X_test) > 0.5).astype(\"int32\"))\n",
        "labels = ['True Neg','False Pos','False Neg','True Pos']\n",
        "categories = ['Non Sarcastic','Sarcastic']\n",
        "make_confusion_matrix(cf_matrix, \n",
        "                      group_names=labels,\n",
        "                      categories=categories, \n",
        "                      cmap='viridis')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 354
        },
        "id": "6SAqN6e2BDPT",
        "outputId": "addd74af-b97b-42f0-8b7d-3594bc3f40af"
      },
      "execution_count": 75,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "167/167 [==============================] - 12s 69ms/step\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "np.set_printoptions(precision=2)\n",
        "y_pred_proba = (model.predict(X_test))\n",
        "fpr, tpr, _ = metrics.roc_curve(Y_test,  y_pred_proba)\n",
        "auc = metrics.roc_auc_score(Y_test, y_pred_proba)\n",
        "plt.plot(fpr,tpr,label=\"Sarcasm data, auc=\"+str(auc*100)+\"%\")\n",
        "plt.legend(loc=4)\n",
        "plt.show();"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "id": "NUgTyl7JCKdm",
        "outputId": "53eb25dd-dd7e-4b8b-8511-adbbddcd4668"
      },
      "execution_count": 82,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "167/167 [==============================] - 8s 49ms/step\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!jupyter nbconvert to -html \"\""
      ],
      "metadata": {
        "id": "5vSZIxMFU-ud"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}