Political_Fake_News.ipynb

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        "<a href=\"https://colab.research.google.com/github/Yash0330/Political-Fake-News/blob/master/Political_Fake_News.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
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        "colab_type": "code",
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        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 101
        }
      },
      "source": [
        "%tensorflow_version 2.0"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "`%tensorflow_version` only switches the major version: 1.x or 2.x.\n",
            "You set: `2.0`. This will be interpreted as: `2.x`.\n",
            "\n",
            "\n",
            "TensorFlow 2.x selected.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ggYnucEeU-2I",
        "colab_type": "code",
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        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "import tensorflow as tf\n",
        "print(tf.__version__)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "2.1.0\n"
          ],
          "name": "stdout"
        }
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        "colab": {
          "base_uri": "https://localhost:8080/",
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      },
      "source": [
        "!pip3 install -q ktrain"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\u001b[K     |████████████████████████████████| 25.2MB 41.0MB/s \n",
            "\u001b[K     |████████████████████████████████| 6.7MB 51.3MB/s \n",
            "\u001b[K     |████████████████████████████████| 10.1MB 44.4MB/s \n",
            "\u001b[K     |████████████████████████████████| 983kB 59.7MB/s \n",
            "\u001b[K     |████████████████████████████████| 245kB 42.4MB/s \n",
            "\u001b[K     |████████████████████████████████| 1.8MB 62.3MB/s \n",
            "\u001b[K     |████████████████████████████████| 501kB 57.7MB/s \n",
            "\u001b[K     |████████████████████████████████| 3.7MB 56.2MB/s \n",
            "\u001b[K     |████████████████████████████████| 1.0MB 51.1MB/s \n",
            "\u001b[K     |████████████████████████████████| 870kB 54.6MB/s \n",
            "\u001b[?25h  Building wheel for ktrain (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Building wheel for keras-bert (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Building wheel for langdetect (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Building wheel for networkx (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Building wheel for seqeval (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Building wheel for syntok (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Building wheel for keras-transformer (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Building wheel for sacremoses (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Building wheel for keras-pos-embd (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Building wheel for keras-multi-head (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Building wheel for keras-layer-normalization (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Building wheel for keras-position-wise-feed-forward (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Building wheel for keras-embed-sim (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Building wheel for keras-self-attention (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "\u001b[31mERROR: google-colab 1.0.0 has requirement google-auth~=1.7.2, but you'll have google-auth 1.11.2 which is incompatible.\u001b[0m\n",
            "\u001b[31mERROR: google-colab 1.0.0 has requirement pandas~=0.25.0; python_version >= \"3.0\", but you'll have pandas 1.0.1 which is incompatible.\u001b[0m\n",
            "\u001b[31mERROR: google-colab 1.0.0 has requirement requests~=2.21.0, but you'll have requests 2.23.0 which is incompatible.\u001b[0m\n",
            "\u001b[31mERROR: google-colab 1.0.0 has requirement six~=1.12.0, but you'll have six 1.14.0 which is incompatible.\u001b[0m\n",
            "\u001b[31mERROR: albumentations 0.1.12 has requirement imgaug<0.2.7,>=0.2.5, but you'll have imgaug 0.2.9 which is incompatible.\u001b[0m\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0PJZgoOIVMVF",
        "colab_type": "code",
        "outputId": "a5271a09-0508-4abd-f907-9cb15432dc6a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7edqaEQyVMYk",
        "colab_type": "code",
        "outputId": "f9dfb2b8-28f6-49d6-8c7e-563e7f510169",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "%cd 'drive/My Drive/bert1'"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/content/drive/My Drive/bert1\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Hc3h23ojWCZf",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import pandas as pd\n",
        "from tqdm import tqdm_notebook"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hkJs4VCdVF_o",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "train = pd.read_csv('t.csv')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6LWV344rVGCB",
        "colab_type": "code",
        "outputId": "007e8b6e-77b5-4bfe-851f-88872eeeccb3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 402
        }
      },
      "source": [
        "train"
      ],
      "execution_count": 0,
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      },
      "source": [
        "from sklearn.model_selection import train_test_split"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_tdlexj-pvBM",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "X_train, X_test = train_test_split(train, test_size=0.18, random_state=42)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oxTteDvgpvDk",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "X_val, X_test = train_test_split(X_test, test_size=0.5, random_state=42)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3MPTJfK0qJ9y",
        "colab_type": "code",
        "outputId": "15d260dc-92a2-4cbc-89f9-1628353fb174",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "print('train size:',X_train.shape,',validation size:',X_val.shape,',test size:',X_test.shape)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "train size: (18973, 5) ,validation size: (2082, 5) ,test size: (2083, 5)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1tQFEi7buFRX",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "x_train = X_train[\"concatenate\"].tolist()\n",
        "y_train = X_train[\"label\"].tolist()\n",
        "x_val = X_val[\"concatenate\"].tolist()\n",
        "y_val = X_val[\"label\"].tolist()\n",
        "x_test = X_test[\"concatenate\"].tolist()\n",
        "y_test = X_test[\"label\"].tolist()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "myhob_olv3TC",
        "colab_type": "code",
        "outputId": "46ea526d-0c0d-4297-b215-f8bb1cd04593",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "import ktrain\n",
        "from ktrain import text\n",
        "MODEL_NAME = 'bert-large-uncased'"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "using Keras version: 2.2.4-tf\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "P-jER1ZMwen3",
        "colab_type": "code",
        "outputId": "2338983f-16e5-41d5-c675-9b7f22136333",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 50
        }
      },
      "source": [
        "t = text.Transformer(MODEL_NAME, maxlen=500, classes=[0,1])"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/ktrain/text/preprocessor.py:324: UserWarning: The class_names argument is replacing the classes argument. Please update your code.\n",
            "  warnings.warn('The class_names argument is replacing the classes argument. Please update your code.')\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "22S2I7Irwesj",
        "colab_type": "code",
        "outputId": "4ef47914-56bf-4554-92f0-4d640cb0f40c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 118
        }
      },
      "source": [
        "trn = t.preprocess_train(x_train, y_train)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "preprocessing train...\n",
            "language: en\n",
            "train sequence lengths:\n",
            "\tmean : 94\n",
            "\t95percentile : 161\n",
            "\t99percentile : 205\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              ""
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uesH6vwLwlFp",
        "colab_type": "code",
        "outputId": "c554892e-b41a-497e-a931-15eafcd01e6b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 118
        }
      },
      "source": [
        "val = t.preprocess_train(x_val, y_val)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "preprocessing train...\n",
            "language: en\n",
            "train sequence lengths:\n",
            "\tmean : 94\n",
            "\t95percentile : 161\n",
            "\t99percentile : 200\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              ""
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AQ5oKMrlwewD",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "model = t.get_classifier()\n",
        "learner = ktrain.get_learner(model, train_data=trn, val_data=val, batch_size=8)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5HHDGMVfVF9U",
        "colab_type": "code",
        "outputId": "a8f14303-d552-4691-e4b5-5c8f7c83cd20",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 202
        }
      },
      "source": [
        "learner.fit_onecycle(1e-5, 3)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\n",
            "\n",
            "begin training using onecycle policy with max lr of 1e-05...\n",
            "Train for 2372 steps, validate for 261 steps\n",
            "Epoch 1/3\n",
            "2372/2372 [==============================] - 1161s 490ms/step - loss: 0.6256 - accuracy: 0.6351 - val_loss: 0.5080 - val_accuracy: 0.7344\n",
            "Epoch 2/3\n",
            "2372/2372 [==============================] - 1155s 487ms/step - loss: 0.4375 - accuracy: 0.7798 - val_loss: 0.3676 - val_accuracy: 0.8074\n",
            "Epoch 3/3\n",
            "2372/2372 [==============================] - 1154s 487ms/step - loss: 0.3027 - accuracy: 0.8523 - val_loss: 0.3575 - val_accuracy: 0.8333\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tensorflow.python.keras.callbacks.History at 0x7fc1516945f8>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 42
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SyqavPPupnI6",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "predictor = ktrain.get_predictor(learner.model, preproc=t)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LePWCnez_qOV",
        "colab_type": "code",
        "outputId": "0d72d751-5397-4507-88ff-d859a3a64521",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 54
        }
      },
      "source": [
        "statement = 'Says Barack Obama complained that Democrats continue to ignore blacks except at election time.'\n",
        "description = 'Great America Alliance said that Obama complained that Democrats continue to ignore blacks except at election time. \\\n",
        "              Obama did say the words used in the ad, but they weren’t his.He was quoting a Chicago barber talking about the days before African-Americans in Chicago helped elect the city’s first black mayor. \\\n",
        "              The full text from Obama’s book shows that he was arguing that voting can make a difference. Nothing in the section suggests that he thinks Democrats today only pay attention to blacks at election time.'\n",
        "test = statement + '<>' + description\n",
        "print(test)    "
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Says Barack Obama complained that Democrats continue to ignore blacks except at election time.<>Great America Alliance said that Obama complained that Democrats continue to ignore blacks except at election time.               Obama did say the words used in the ad, but they weren’t his.He was quoting a Chicago barber talking about the days before African-Americans in Chicago helped elect the city’s first black mayor.               The full text from Obama’s book shows that he was arguing that voting can make a difference. Nothing in the section suggests that he thinks Democrats today only pay attention to blacks at election time.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9y-BhMA-pnNZ",
        "colab_type": "code",
        "outputId": "769ed30c-5ce6-4b63-d6c2-a86d245ca7ef",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "if predictor.predict(test) is 0: \n",
        "  print('False')  # indeed it is false\n",
        "else:\n",
        "  print('True')"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              ""
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "False\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "J10A14d8pnLU",
        "colab_type": "code",
        "outputId": "aed6fd87-6001-4cbc-8bf5-61cdd83aaf99",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "predictor.predict_proba(test)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              ""
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0.99607974, 0.00392029], dtype=float32)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 51
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4IHAQOMeVGEV",
        "colab_type": "code",
        "outputId": "0a948fd5-edfd-4200-8222-3914730e1bf8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "predictor.get_classes()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[0, 1]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 52
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ueN1jGELzLBW",
        "colab_type": "code",
        "outputId": "7f6a7cc8-aa9a-4220-bcc4-c4ac57433c07",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17
        }
      },
      "source": [
        "pred = predictor.predict(x_test)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              ""
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eHcna751DSp-",
        "colab_type": "code",
        "outputId": "bf154f19-f4b3-408f-c5d0-6946a2766471",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "j = 0\n",
        "s = 0\n",
        "for i in pred:\n",
        "  if(i==y_test[j]):\n",
        "    s+=1\n",
        "  j+=1\n",
        "print('The Accuracy of the test set is',s/2083*100)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "The Accuracy of the test set is 80.31685069611137\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7pJ8JlzWxU1i",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "predictor.save('my_distilbert_predictor')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mtekUrEVxU4G",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "reloaded_predictor = ktrain.load_predictor('my_distilbert_predictor')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9lXeY1cSHPMM",
        "colab_type": "text"
      },
      "source": [
        "### **Prediction**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ipBEpxSpxU6G",
        "colab_type": "code",
        "cellView": "form",
        "outputId": "b9fe95f3-d715-4f8d-b620-052d39b7fe06",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 166,
          "referenced_widgets": [
            "8fb97266fa8b4687a59caf783d5336ac",
            "5727d2d4af66401eaff4d6d2673eedab",
            "2e86d4c2627c46d0ba9056cfc6b22f0c",
            "8c9ed6ac0d314ca282f570074de0d735"
          ]
        }
      },
      "source": [
        "#@title Prediction\n",
        "statement = 'some person in campus has reported symptom of COVID 19 virus. ' #@param {type:\"string\"}\n",
        "description = 'So far no person (student / faculty member / staff) in the campus has reported any symptom of COVID 19 virus. ' #@param {type:\"string\"}\n",
        "test = statement + '<>' + description\n",
        "import ipywidgets as widgets\n",
        "from IPython.display import display\n",
        "button = widgets.Button(description=\"Enter\")\n",
        "output = widgets.Output()\n",
        "\n",
        "def on_button_clicked(b):\n",
        "  # Display the message within the output widget.\n",
        "  with output:\n",
        "    if reloaded_predictor.predict(test) is 0: \n",
        "      print('False')  \n",
        "    else:\n",
        "      print('True')\n",
        "\n",
        "button.on_click(on_button_clicked)\n",
        "display(button, output)\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "8fb97266fa8b4687a59caf783d5336ac",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "Button(description='Enter', style=ButtonStyle())"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "8c9ed6ac0d314ca282f570074de0d735",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "Output()"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nO2nH1-LxU8d",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}