DL.ipynb

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  "metadata": {
    "colab": {
      "name": "DL.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "authorship_tag": "ABX9TyNza54UGH99BRn7UK1z9kR2",
      "include_colab_link": true
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      "name": "python"
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    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/SohaliBaisla/Hindi-Fake-News-Fact-Checker/blob/main/DL.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "lgkyipO2G5B2"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.multiclass import OneVsRestClassifier\n",
        "from sklearn.metrics import classification_report\n",
        "from sklearn.feature_extraction.text import TfidfVectorizer\n",
        "from sklearn.model_selection import train_test_split\n",
        "size=[0.1,0.2,0.3,0.4]\n",
        "import numpy as np\n",
        "import keras\n",
        "from keras.datasets import mnist\n",
        "from keras.models import Sequential\n",
        "from keras.layers import CuDNNLSTM, Dense, Dropout, LSTM\n",
        "from tensorflow.keras.optimizers import Adam\n",
        "from keras.callbacks import ModelCheckpoint, EarlyStopping\n",
        "from keras.layers import Embedding"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "drive.mount(\"/content/gdrive\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "x3RPuSVCG5qj",
        "outputId": "15c63a6e-308a-40f7-d87b-4e5e2c318230"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Drive already mounted at /content/gdrive; to attempt to forcibly remount, call drive.mount(\"/content/gdrive\", force_remount=True).\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install inltk\n",
        "from inltk.inltk import setup\n",
        "#setup('hi')\n",
        "from inltk.inltk import tokenize"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2vFt4cxmHAUO",
        "outputId": "335a6f32-7ce0-4f60-906c-1a22786726e9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
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          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yleLHjiRHA4Y",
        "outputId": "c7cde9a0-0ca7-4782-c98a-367604d99e09"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df=pd.read_csv(\"/content/drive/MyDrive/Dataset/acm_datasetFinalUsed.csv\")\n",
        "df.columns =['heading', 'text', 'link','output']\n",
        "df=df.drop(['heading','link'], axis = 1)\n",
        "df = df.dropna(axis=0, how='any', thresh=None, subset=None, inplace=False)\n",
        "df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "jE0eNTq7HQh8",
        "outputId": "027803cf-e6ee-4d8b-c98b-52501946a010"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "      <td>तस्वीर रिवर्स सर्च हमें 'जागरण' 24 सितंबर 2019...</td>\n",
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              "                                                   text  output\n",
              "0     हैशटैग तस्वीर शेयर ओवैसी पोस्टर कालिख लगी देखी...       1\n",
              "1     हैशटैग तस्वीर शेयर ओवैसी पोस्टर कालिख लगी देखी...       1\n",
              "2     हैशटैग तस्वीर शेयर ओवैसी पोस्टर कालिख लगी देखी...       1\n",
              "3     तस्वीर रिवर्स सर्च हमें 'जागरण' 24 सितंबर 2019...       0\n",
              "4                         मामले और=_खबरें प्रकाशित थीं.       0\n",
              "...                                                 ...     ...\n",
              "2201  हालांकि पक्षियों टॉवरों से==_टकराकर मौत खासकर ...       0\n",
              "2202  दावा भारत पक्षियों मौत ट्रायल है, बेबुनियाद भी...       0\n",
              "2203  सत्ता को आईना दिखाने वाली पत्रकारिता का कॉरपोर...       0\n",
              "2204                              तस्वीर==_फे़सबुक शेयर       1\n",
              "2205  ऑल्ट न्यूज़ तस्वीर ‘’ कीवर्ड रिवर्स इमेज सर्च ह...       0\n",
              "\n",
              "[2206 rows x 2 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import files\n",
        "uploaded = files.upload()\n",
        "file1 = open(\"final_stopwords.txt\",\"r+\")\n",
        "Lines = file1.readlines()\n",
        "stopword=[]\n",
        "for line in Lines:\n",
        "  stopword.append(line)\n",
        "\n",
        "from google.colab import files\n",
        "uploaded = files.upload()\n",
        "file1 = open(\"final_stopwords2.txt\",\"r+\")\n",
        "Lines = file1.readlines()\n",
        "stopword3=[]\n",
        "for line in Lines:\n",
        "  stopword.append(line)\n",
        "stopword2 = ['','.','!','?', 'अर्थात', 'कुछ', 'तेरी', 'साबुत', 'अपनि', 'हूं', 'काफि', 'यिह', 'जा' ,'दे', 'देकर' ,'रह', 'कह' , 'कर' , 'कहा', 'बात' , 'जिन्हों', 'किर', 'कोई', 'हे', 'कोन', 'रहा', 'सब', 'सो', 'तक', 'इंहें', 'इसकि', 'अपनी', 'दबारा', 'सभि', 'होते', 'भीतर', 'निचे', 'घर', 'उन्हें', 'उन्ह' , 'मेरे' , 'था', 'व', 'इसमें', 'उसी', 'बिलकुल', 'होति', 'गया', 'सकता', 'अपना', 'लिये', 'उसका', 'पर', 'दवारा', 'गए', 'है', 'कितना', 'भि', 'लिए', 'वुह ', 'ना', 'किसि', 'परन्तु', 'किन्हें', 'बहुत', 'भी', 'तुम्हारे', 'निहायत', 'उन्हीं', 'वहिं', 'हैं', 'उन्हों', 'इतयादि','यहाँ', 'तब', 'पूरा', 'क्योंकि', 'कौनसा', 'आप', 'हुअ', 'ऐसे', 'एस', 'कारण', 'अप', 'पहले', 'तुम', 'जेसा', 'तिस', 'लेकिन', 'कहते', 'मगर', 'करता', 'संग', 'सभी', 'जीधर', 'किंहों', 'हि', 'द्वारा', 'हुआ', 'तू', 'जिंहें', 'उसने', 'पास', 'वहां', 'वह', 'किंहें', 'इंहों', 'मुझ', 'कुल', 'तिंहों', 'का', 'मेरी', 'तेरे', 'उनके', 'क्या', 'जहाँ', 'काफ़ी', 'वर्ग', 'वरग','बही', 'ये', 'जिस', 'इसि', 'हुई', 'साम्हने', 'नहिं', 'जैसे', 'वहीं', 'दिया', 'अभी', 'यहि', 'वग़ैरह', 'उनकि', 'न', 'जा','बनि', 'हें', 'यिह ', 'उन', 'को', 'तिन्हों', 'उन्होंने', 'तुझे', 'उसे', 'होने', 'इन्हीं', 'थे', 'उंहिं', 'अपने', 'में', 'फिर','यही', 'नीचे', 'होती', 'तिसे', 'हम', 'यदि', 'सारा', 'कर', 'सकते', 'कोइ', 'और', 'जिंहों', 'तिंहें', 'दूसरे', 'जब', 'रहे','अत', 'मानो', 'जिन', 'बाद', 'उनका', 'किया', 'या', 'उनकी', 'कौन', 'ऐसा', 'सबसे', 'अनुसार', 'दुसरे', 'इन', 'अदि','जिसे', 'उसकी', 'इत्यादि', 'करना', 'यहां', 'हुए', 'तेरा', 'आदि', 'पर  ', 'वाले', 'कहता', 'किन्हों', 'किसे', 'जिन्हें', 'मे','होता', 'करने', 'साभ', 'अभि', 'उसको', 'कई', 'बनी', 'के', 'इन्हें', 'वहाँ', 'कोनसा', 'कइ', 'इनका', 'थि', 'बाला','ऱ्वासा', 'हो', 'उंहें', 'दुसरा', 'वे', 'भितर', 'जेसे', 'एवं', 'अंदर', 'दो', 'साथ', 'करें', 'जिधर', 'तरह', 'उसि', 'इस', 'एसे', 'तिन', 'नहीं', 'से','न','उनको', 'किस', 'किसी', 'इसी', 'मैं', 'यह', 'हुइ', 'ले', 'कि', 'की', 'इसलिये', 'रवासा', 'ने', 'जैसा', 'वह ', 'तिन्हें', 'वुह', 'उस', 'उंहों', 'वगेरह', 'उसके', 'मुझे', 'करते', 'जितना', 'जहां', 'इन्हों', 'इसके', 'होना', 'इसका', 'इंहिं', 'एक', 'जो', 'पे', 'ही', 'तो', 'थी', 'रखें', 'इसे', 'इन ', 'के', 'बहि', 'पुरा', 'ओर', 'इसकी']\n",
        "stopword=stopword+stopword2+stopword3"
      ],
      "metadata": {
        "colab": {
          "resources": {
            "http://localhost:8080/nbextensions/google.colab/files.js": {
              "data": "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",
              "ok": true,
              "headers": [
                [
                  "content-type",
                  "application/javascript"
                ]
              ],
              "status": 200,
              "status_text": ""
            }
          },
          "base_uri": "https://localhost:8080/",
          "height": 121
        },
        "id": "_vrBHCAwHbEm",
        "outputId": "8b1481be-10e1-4efa-db16-a4a9a0125f5c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "     <input type=\"file\" id=\"files-24ac34e2-fc49-4aee-ba6b-0a4cd5b80727\" name=\"files[]\" multiple disabled\n",
              "        style=\"border:none\" />\n",
              "     <output id=\"result-24ac34e2-fc49-4aee-ba6b-0a4cd5b80727\">\n",
              "      Upload widget is only available when the cell has been executed in the\n",
              "      current browser session. Please rerun this cell to enable.\n",
              "      </output>\n",
              "      <script src=\"/nbextensions/google.colab/files.js\"></script> "
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Saving final_stopwords.txt to final_stopwords.txt\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "     <input type=\"file\" id=\"files-fe2bc536-7cfa-486c-b6be-a26ffee5b2a2\" name=\"files[]\" multiple disabled\n",
              "        style=\"border:none\" />\n",
              "     <output id=\"result-fe2bc536-7cfa-486c-b6be-a26ffee5b2a2\">\n",
              "      Upload widget is only available when the cell has been executed in the\n",
              "      current browser session. Please rerun this cell to enable.\n",
              "      </output>\n",
              "      <script src=\"/nbextensions/google.colab/files.js\"></script> "
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Saving final_stopwords2.txt to final_stopwords2.txt\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "for m in size:\n",
        "      x_train, x_test = train_test_split(df, test_size=m)\n",
        "\n",
        "      x_tr=[]\n",
        "      for i in x_train['text'].astype(str):\n",
        "        if i not in stopword:\n",
        "          x_tr.append(i)\n",
        "      x_ts=[]\n",
        "      for i in x_test['text'].astype(str):\n",
        "        if i not in stopword:\n",
        "          x_ts.append(i)\n",
        "\n",
        "\n",
        "      y_train=x_train['output']\n",
        "\n",
        "      y_test=x_test['output']\n",
        "      \n",
        "      from sklearn.feature_extraction.text import TfidfVectorizer\n",
        "      vectorizer=TfidfVectorizer()\n",
        "\n",
        "      x_train=vectorizer.fit_transform(x_train['text'].astype(str))\n",
        " \n",
        "      x_test=vectorizer.transform(x_test['text'].astype(str))\n",
        "\n",
        "      \n",
        "      x_test = x_test.toarray()\n",
        "      x_train = x_train.toarray()\n",
        "      \n",
        "      #DL-----------------------------------------------------\n",
        "      \n",
        "      MAX_NB_WORDS = 50000\n",
        "      EMBEDDING_DIM = 100\n",
        "      model = Sequential()\n",
        "      from keras.callbacks import ModelCheckpoint, EarlyStopping\n",
        "      from keras.layers import Embedding\n",
        "      model.add(Embedding(MAX_NB_WORDS, EMBEDDING_DIM, input_length=x_train.shape[1]))\n",
        "      model.add(Dropout(0.2))\n",
        "      model.add(LSTM(100, dropout=0.2, recurrent_dropout=0.2))\n",
        "      model.add(Dense(1, activation='sigmoid'))\n",
        "      model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])\n",
        "\n",
        "      epochs = 100\n",
        "      batch_size = 64\n",
        "\n",
        "      history = model.fit(x_train, y_train, epochs=epochs, batch_size=batch_size,callbacks=[EarlyStopping(monitor='accuracy', patience=3, min_delta=0.0001)])\n",
        "      accr = model.evaluate(x_test,y_test)\n",
        "      #accuracy.append(accr[1])\n",
        "      print('Test set\\n  Loss: {:0.3f}\\n  Accuracy: {:0.3f}'.format(accr[0],accr[1]))\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uj0DVWOOHimu",
        "outputId": "6f7fc3b4-430b-49e6-f9d4-6e5a13aad814"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/100\n",
            "32/32 [==============================] - 205s 6s/step - loss: 0.0000e+00 - accuracy: 0.5622\n",
            "Epoch 2/100\n",
            "32/32 [==============================] - 201s 6s/step - loss: 0.0000e+00 - accuracy: 0.5698\n",
            "Epoch 3/100\n",
            "32/32 [==============================] - 200s 6s/step - loss: 0.0000e+00 - accuracy: 0.5698\n",
            "Epoch 4/100\n",
            "32/32 [==============================] - 201s 6s/step - loss: 0.0000e+00 - accuracy: 0.5698\n",
            "Epoch 5/100\n",
            "32/32 [==============================] - 199s 6s/step - loss: 0.0000e+00 - accuracy: 0.5698\n",
            "7/7 [==============================] - 4s 529ms/step - loss: 0.0000e+00 - accuracy: 0.6471\n",
            "Test set\n",
            "  Loss: 0.000\n",
            "  Accuracy: 0.647\n",
            "Epoch 1/100\n",
            "28/28 [==============================] - 175s 6s/step - loss: 0.0000e+00 - accuracy: 0.5794\n",
            "Epoch 2/100\n",
            "28/28 [==============================] - 179s 6s/step - loss: 0.0000e+00 - accuracy: 0.5788\n",
            "Epoch 3/100\n",
            "28/28 [==============================] - 174s 6s/step - loss: 0.0000e+00 - accuracy: 0.5788\n",
            "Epoch 4/100\n",
            "28/28 [==============================] - 173s 6s/step - loss: 0.0000e+00 - accuracy: 0.5788\n",
            "14/14 [==============================] - 8s 521ms/step - loss: 0.0000e+00 - accuracy: 0.5724\n",
            "Test set\n",
            "  Loss: 0.000\n",
            "  Accuracy: 0.572\n",
            "Epoch 1/100\n",
            "25/25 [==============================] - 150s 6s/step - loss: 0.0000e+00 - accuracy: 0.5797\n",
            "Epoch 2/100\n",
            "25/25 [==============================] - 147s 6s/step - loss: 0.0000e+00 - accuracy: 0.5861\n",
            "Epoch 3/100\n",
            "25/25 [==============================] - 147s 6s/step - loss: 0.0000e+00 - accuracy: 0.5861\n",
            "Epoch 4/100\n",
            "25/25 [==============================] - 148s 6s/step - loss: 0.0000e+00 - accuracy: 0.5861\n",
            "Epoch 5/100\n",
            "25/25 [==============================] - 147s 6s/step - loss: 0.0000e+00 - accuracy: 0.5861\n",
            "21/21 [==============================] - 11s 497ms/step - loss: 0.0000e+00 - accuracy: 0.5574\n",
            "Test set\n",
            "  Loss: 0.000\n",
            "  Accuracy: 0.557\n",
            "Epoch 1/100\n",
            "21/21 [==============================] - 124s 6s/step - loss: 0.0000e+00 - accuracy: 0.5843\n",
            "Epoch 2/100\n",
            "21/21 [==============================] - 122s 6s/step - loss: 0.0000e+00 - accuracy: 0.5850\n",
            "Epoch 3/100\n",
            "21/21 [==============================] - 123s 6s/step - loss: 0.0000e+00 - accuracy: 0.5850\n",
            "Epoch 4/100\n",
            "21/21 [==============================] - 123s 6s/step - loss: 0.0000e+00 - accuracy: 0.5850\n",
            "Epoch 5/100\n",
            "21/21 [==============================] - 122s 6s/step - loss: 0.0000e+00 - accuracy: 0.5850\n",
            "28/28 [==============================] - 15s 490ms/step - loss: 0.0000e+00 - accuracy: 0.5663\n",
            "Test set\n",
            "  Loss: 0.000\n",
            "  Accuracy: 0.566\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "4J7fC7yQLA2q"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}