news_headline_classification_GRU.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "news_headline_classification_GRU.ipynb",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "hLBEUCrh06rG",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "X5xYrCLy1JXP",
        "colab_type": "text"
      },
      "source": [
        "#Libraries"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_qVBJO_31PRk",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 74
        },
        "outputId": "4c7929c5-68a3-42ea-ccc8-a8aa2392f820"
      },
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "%matplotlib inline\n",
        "import pandas as pd\n",
        "import seaborn as sns\n",
        "import re,nltk,json\n",
        "import tensorflow as tf\n",
        "from tensorflow import keras\n",
        "from tensorflow.keras import regularizers\n",
        "from tensorflow.keras.preprocessing.sequence import pad_sequences\n",
        "from keras import models\n",
        "from keras import layers\n",
        "from tensorflow.keras.layers import LSTM,GRU\n",
        "from tensorflow.keras.models import load_model\n",
        "from sklearn.metrics import confusion_matrix\n",
        "from sklearn.metrics import classification_report \n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.metrics import accuracy_score,precision_score,recall_score,f1_score,roc_auc_score\n",
        "from sklearn.metrics import average_precision_score,roc_auc_score, roc_curve, precision_recall_curve\n",
        "from sklearn.preprocessing import LabelEncoder\n",
        "from tensorflow.keras.preprocessing.text import Tokenizer\n",
        "np.random.seed(42)\n",
        "class color: # Text style\n",
        "   PURPLE = '\\033[95m'\n",
        "   CYAN = '\\033[96m'\n",
        "   DARKCYAN = '\\033[36m'\n",
        "   BLUE = '\\033[94m'\n",
        "   GREEN = '\\033[92m'\n",
        "   YELLOW = '\\033[93m'\n",
        "   RED = '\\033[91m'\n",
        "   BOLD = '\\033[1m'\n",
        "   UNDERLINE = '\\033[4m'\n",
        "   END = '\\033[0m'\n",
        "# dataset path\n",
        "path = '/content/drive/My Drive/Colab Notebooks/NLP Projects/News Headline Classification/'"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/statsmodels/tools/_testing.py:19: FutureWarning: pandas.util.testing is deprecated. Use the functions in the public API at pandas.testing instead.\n",
            "  import pandas.util.testing as tm\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VYgeRkab1q3c",
        "colab_type": "text"
      },
      "source": [
        "#Data Preparation"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "D_1JzSZn1u8t",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 347
        },
        "outputId": "205a2695-cc75-4ed5-fc4c-2939b9d448da"
      },
      "source": [
        "data = pd.read_csv(path+'headlines.csv',encoding='utf-8')\n",
        "print(f'Total number of headlines: {len(data)}')\n",
        "sns.set(font_scale=1.4)\n",
        "data['category'].value_counts().plot(kind='barh', figsize=(6, 4))\n",
        "plt.xlabel(\"Number of Headlines\", labelpad=12)\n",
        "plt.ylabel(\"Category\", labelpad=12)\n",
        "plt.yticks(rotation = 45)\n",
        "plt.title(\"Dataset Distribution\", y=1.02);"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Total number of headlines: 136811\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RaJzhzHZ25LY",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "25b9f9e7-3c20-4b1f-8ad0-093e30be63b1"
      },
      "source": [
        "data.columns"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['headline', 'category', 'newspaper name'], dtype='object')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 104
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TVjNVcuC2lyQ",
        "colab_type": "text"
      },
      "source": [
        "#Data Cleaning"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wY1Def2a2s7l",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "fcc51ba2-eff5-4101-9151-cbc6a7d30cde"
      },
      "source": [
        "# Cleaning Data [Remove unncessary symbols]\n",
        "def cleaning_data(row):\n",
        "      headlines = re.sub('[^\\u0980-\\u09FF]',' ',str(row)) #removing unnecessary punctuation\n",
        "      return headlines\n",
        "# Apply the function into the dataframe\n",
        "data['cleaned'] = data['headline'].apply(cleaning_data)  \n",
        "\n",
        "# print some cleaned reviews from the dataset\n",
        "sample_data = [2000,5000,10000,20000,30000,35000,40000,45000,50000,60000,65000,70000,75000,80000,100000]\n",
        "for i in sample_data:\n",
        "  print('Original: ',data.headline[i],'\\nCleaned:',\n",
        "           data.cleaned[i],'\\n','Category:-- ',data.category[i],'\\n')   "
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Original:  ক্ষমা চেয়েও মুক্তি পেলেন না পরিচালক গাজী মাহবুব \n",
            "Cleaned: ক্ষমা চেয়েও মুক্তি পেলেন না পরিচালক গাজী মাহবুব \n",
            " Category:--  Amusement \n",
            "\n",
            "Original:  ব্র্যান্ডউইথের ব্যবহার ৮০০ জিবিপিএস ছাড়িয়ে \n",
            "Cleaned: ব্র্যান্ডউইথের ব্যবহার ৮০০ জিবিপিএস ছাড়িয়ে \n",
            " Category:--  IT \n",
            "\n",
            "Original:  জামিনে মুক্তি পেলেন ছাত্রদল সভাপতি \n",
            "Cleaned: জামিনে মুক্তি পেলেন ছাত্রদল সভাপতি \n",
            " Category:--  politics \n",
            "\n",
            "Original:  দ. কোরিয়ায় ১০০টি খালি কফিন পাঠিয়েছে যুক্তরাষ্ট্র \n",
            "Cleaned: দ  কোরিয়ায় ১০০টি খালি কফিন পাঠিয়েছে যুক্তরাষ্ট্র \n",
            " Category:--  International \n",
            "\n",
            "Original:  ফ্লোরিডায় হামলাকারী ‘মানসিকভাবে অসুস্থ’: ট্রাম্প \n",
            "Cleaned: ফ্লোরিডায় হামলাকারী  মানসিকভাবে অসুস্থ   ট্রাম্প \n",
            " Category:--  International \n",
            "\n",
            "Original:  সেরাটা দিতে পারলে সিরিজ জিতবে বাংলাদেশ: মাশরাফি \n",
            "Cleaned: সেরাটা দিতে পারলে সিরিজ জিতবে বাংলাদেশ  মাশরাফি \n",
            " Category:--  sports \n",
            "\n",
            "Original:  সাকিব ফেরালেন শাই হোপকে \n",
            "Cleaned: সাকিব ফেরালেন শাই হোপকে \n",
            " Category:--  sports \n",
            "\n",
            "Original:  কংগ্রেস সভাপতির পদ থেকে রাহুল গান্ধীর পদত্যাগ \n",
            "Cleaned: কংগ্রেস সভাপতির পদ থেকে রাহুল গান্ধীর পদত্যাগ \n",
            " Category:--  International \n",
            "\n",
            "Original:  তৃতীয়-চতুর্থ শ্রেণির নিয়োগও হবে পিএসসির মাধ্যমে \n",
            "Cleaned: তৃতীয় চতুর্থ শ্রেণির নিয়োগও হবে পিএসসির মাধ্যমে \n",
            " Category:--  national \n",
            "\n",
            "Original:  নূরজাহান আমের ওজন আড়াই কেজি \n",
            "Cleaned: নূরজাহান আমের ওজন আড়াই কেজি \n",
            " Category:--  International \n",
            "\n",
            "Original:  ইন্টারনেট বলছে, ‘উফ্ ভাবিজি’ \n",
            "Cleaned: ইন্টারনেট বলছে   উফ্ ভাবিজি  \n",
            " Category:--  International \n",
            "\n",
            "Original:  ইমরানের আহ্বানে মোদির সাড়া, বৈঠকে বসবেন দুই দেশের পররাষ্ট্রমন্ত্রীরা \n",
            "Cleaned: ইমরানের আহ্বানে মোদির সাড়া  বৈঠকে বসবেন দুই দেশের পররাষ্ট্রমন্ত্রীরা \n",
            " Category:--  International \n",
            "\n",
            "Original:  ইয়েমেনে ড্রোন হামলায় ১০ আল-কায়েদা সদস্য নিহত \n",
            "Cleaned: ইয়েমেনে ড্রোন হামলায় ১০ আল কায়েদা সদস্য নিহত \n",
            " Category:--  International \n",
            "\n",
            "Original:  অবশেষে তালিবানের ওপর প্রভাবের কথা স্বীকার করল পাকিস্তান \n",
            "Cleaned: অবশেষে তালিবানের ওপর প্রভাবের কথা স্বীকার করল পাকিস্তান \n",
            " Category:--  International \n",
            "\n",
            "Original:  কমার্স কলেজের বার্ষিক ক্রীড়া \n",
            "Cleaned: কমার্স কলেজের বার্ষিক ক্রীড়া \n",
            " Category:--  sports \n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hPrg1wPCEydF",
        "colab_type": "text"
      },
      "source": [
        "#Remove Low Length Data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8JZZPNtRE4Hc",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 72
        },
        "outputId": "82d01c32-2f62-4ac2-caa3-9fec2c8a7405"
      },
      "source": [
        "# Length of each headlines\n",
        "data['length'] = data['cleaned'].apply(lambda x:len(x.split()))\n",
        "# Remove the headlines with least words\n",
        "dataset = data.loc[data.length>2]\n",
        "dataset = dataset.reset_index(drop = True)\n",
        "print(\"After Cleaning:\",\"\\nRemoved {} Small Headlines\".format(len(data)-len(dataset)),\n",
        "      \"\\nTotal Headlines:\",len(dataset))"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "After Cleaning: \n",
            "Removed 4098 Small Headlines \n",
            "Total Headlines: 132713\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IWcMzeMN4dm4",
        "colab_type": "text"
      },
      "source": [
        "#Dataset Analysis"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "D1z7CDlt4nl3",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "74aefbeb-be06-4968-c3f8-bad354e81931"
      },
      "source": [
        "def data_summary(dataset):\n",
        "    \n",
        "    \"\"\"\n",
        "    This function will print the summary of the headlines and words distribution in the dataset. \n",
        "    \n",
        "    Args:\n",
        "        dataset: list of cleaned sentences   \n",
        "        \n",
        "    Returns:\n",
        "        Number of documnets per class: int \n",
        "        Number of words per class: int\n",
        "        Number of unique words per class: int\n",
        "    \"\"\"\n",
        "    documents = []\n",
        "    words = []\n",
        "    u_words = []\n",
        "    total_u_words = [word.strip().lower() for t in list(dataset.cleaned) for word in t.strip().split()]\n",
        "    class_label= [k for k,v in dataset.category.value_counts().to_dict().items()]\n",
        "  # find word list\n",
        "    for label in class_label: \n",
        "        word_list = [word.strip().lower() for t in list(dataset[dataset.category==label].cleaned) for word in t.strip().split()]\n",
        "        counts = dict()\n",
        "        for word in word_list:\n",
        "                counts[word] = counts.get(word, 0)+1\n",
        "        # sort the dictionary of word list  \n",
        "        ordered = sorted(counts.items(), key= lambda item: item[1],reverse = True)\n",
        "        # Documents per class\n",
        "        documents.append(len(list(dataset[dataset.category==label].cleaned)))\n",
        "        # Total Word per class\n",
        "        words.append(len(word_list))\n",
        "        # Unique words per class \n",
        "        u_words.append(len(np.unique(word_list)))\n",
        "       \n",
        "        print(\"\\nClass Name : \",label)\n",
        "        print(\"Number of Documents:{}\".format(len(list(dataset[dataset.category==label].cleaned))))  \n",
        "        print(\"Number of Words:{}\".format(len(word_list))) \n",
        "        print(\"Number of Unique Words:{}\".format(len(np.unique(word_list)))) \n",
        "        print(\"Most Frequent Words:\\n\")\n",
        "        for k,v in ordered[:10]:\n",
        "              print(\"{}\\t{}\".format(k,v))\n",
        "    print(\"Total Number of Unique Words:{}\".format(len(np.unique(total_u_words))))           \n",
        "   \n",
        "    return documents,words,u_words,class_label\n",
        "\n",
        "#call the fucntion\n",
        "documents,words,u_words,class_names = data_summary(dataset)    \n"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Class Name :  International\n",
            "Number of Documents:47885\n",
            "Number of Words:307354\n",
            "Number of Unique Words:28710\n",
            "Most Frequent Words:\n",
            "\n",
            "নিহত\t3398\n",
            "না\t2133\n",
            "নিয়ে\t1634\n",
            "ট্রাম্প\t1472\n",
            "মার্কিন\t1434\n",
            "ও\t1342\n",
            "থেকে\t1332\n",
            "ভারতের\t1212\n",
            "যুক্তরাষ্ট্র\t1208\n",
            "ভারত\t1192\n",
            "\n",
            "Class Name :  sports\n",
            "Number of Documents:30831\n",
            "Number of Words:152852\n",
            "Number of Unique Words:18581\n",
            "Most Frequent Words:\n",
            "\n",
            "বাংলাদেশ\t1581\n",
            "না\t1122\n",
            "জয়\t883\n",
            "বাংলাদেশের\t873\n",
            "শুরু\t782\n",
            "নিয়ে\t689\n",
            "সাকিব\t672\n",
            "ভারত\t619\n",
            "শেষ\t603\n",
            "দল\t573\n",
            "\n",
            "Class Name :  national\n",
            "Number of Documents:24557\n",
            "Number of Words:158042\n",
            "Number of Unique Words:20710\n",
            "Most Frequent Words:\n",
            "\n",
            "না\t1444\n",
            "হবে\t1292\n",
            "ও\t1215\n",
            "প্রধানমন্ত্রী\t1003\n",
            "আজ\t752\n",
            "থেকে\t617\n",
            "কাদের\t613\n",
            "খালেদা\t566\n",
            "বিএনপি\t557\n",
            "নিয়ে\t556\n",
            "\n",
            "Class Name :  Amusement\n",
            "Number of Documents:16067\n",
            "Number of Words:98582\n",
            "Number of Unique Words:16622\n",
            "Most Frequent Words:\n",
            "\n",
            "নতুন\t1158\n",
            "নিয়ে\t1074\n",
            "ও\t1003\n",
            "গান\t683\n",
            "ভিডিও\t517\n",
            "না\t484\n",
            "নাটক\t469\n",
            "খান\t461\n",
            "চলচ্চিত্র\t416\n",
            "আজ\t412\n",
            "\n",
            "Class Name :  politics\n",
            "Number of Documents:10577\n",
            "Number of Words:75657\n",
            "Number of Unique Words:10398\n",
            "Most Frequent Words:\n",
            "\n",
            "খালেদা\t1260\n",
            "বিএনপি\t918\n",
            "বিএনপির\t907\n",
            "না\t880\n",
            "কাদের\t861\n",
            "আ\t821\n",
            "জিয়ার\t820\n",
            "লীগের\t589\n",
            "হবে\t492\n",
            "লীগ\t477\n",
            "\n",
            "Class Name :  IT\n",
            "Number of Documents:2796\n",
            "Number of Words:17692\n",
            "Number of Unique Words:5528\n",
            "Most Frequent Words:\n",
            "\n",
            "নতুন\t167\n",
            "ফেসবুক\t165\n",
            "ও\t143\n",
            "স্মার্টফোন\t107\n",
            "নিয়ে\t95\n",
            "থেকে\t94\n",
            "শুরু\t86\n",
            "ডিজিটাল\t80\n",
            "জন্য\t79\n",
            "মোবাইল\t75\n",
            "Total Number of Unique Words:57490\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "o2R8rfbI5CDi",
        "colab_type": "text"
      },
      "source": [
        "#Summary Visualization"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YiBBy7UM5F9G",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 489
        },
        "outputId": "ff6e8e07-93a1-4466-ccf5-bbbaa63d9eb4"
      },
      "source": [
        "data_matrix = pd.DataFrame({'Total Documents':documents,\n",
        "                            'Total Words':words,\n",
        "                            'Unique Words':u_words,\n",
        "                            'Class Names':class_names})\n",
        "df = pd.melt(data_matrix, id_vars=\"Class Names\", var_name=\"Category\", value_name=\"Values\")\n",
        "plt.figure(figsize=(8, 6))\n",
        "ax = plt.subplot()\n",
        "\n",
        "sns.barplot(data=df,x='Class Names', y='Values' ,hue='Category')\n",
        "ax.set_xlabel('Class Names') \n",
        "ax.set_title('Data Statistics')\n",
        "\n",
        "ax.xaxis.set_ticklabels(class_names, rotation=45);"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 576x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ffzR1D915ixn",
        "colab_type": "text"
      },
      "source": [
        "#Headline Length Distribution"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HYKLhO9l5qZz",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 369
        },
        "outputId": "6bb17859-68bb-4f62-bc63-4a6d65fde8d5"
      },
      "source": [
        "# Calculate the Review of each of the Review\n",
        "dataset['HeadlineLength'] = dataset.cleaned.apply(lambda x:len(x.split()))\n",
        "frequency = dict()\n",
        "for i in dataset.HeadlineLength:\n",
        "    frequency[i] = frequency.get(i, 0)+1\n",
        "\n",
        "plt.bar(frequency.keys(), frequency.values(), color =\"b\")\n",
        "plt.xlim(1, 20)\n",
        "# in this notbook color is not working but it should work.\n",
        "plt.xlabel('Length of the Headlines')\n",
        "plt.ylabel('Frequency')\n",
        "plt.title('Length-Frequency Distribution')\n",
        "plt.show()  \n",
        "print(f\"Maximum Length of a headline: {max(dataset.HeadlineLength)}\")\n",
        "print(f\"Minimum Length of a headline: {min(dataset.HeadlineLength)}\")\n",
        "print(f\"Average Length of a headline: {round(np.mean(dataset.HeadlineLength),0)}\")"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Maximum Length of a headline: 21\n",
            "Minimum Length of a headline: 3\n",
            "Average Length of a headline: 6.0\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Tm8jDENf68Nb",
        "colab_type": "text"
      },
      "source": [
        "#Lable Encoding and Dataset Splitting"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nn36LUQH7AI_",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "                                      #==================================================\n",
        "                                       ################# Label Encoding Function #########\n",
        "                                       #==================================================\n",
        "\n",
        "def label_encoding(category,bool):\n",
        "    \"\"\"\n",
        "    This function will return the encoded labels in array format. \n",
        "    \n",
        "    Args:\n",
        "        category: series of class names(str)\n",
        "        bool: boolean (True or False)\n",
        "        \n",
        "    Returns:\n",
        "        labels: numpy array \n",
        "    \"\"\"\n",
        "    le = LabelEncoder()\n",
        "    le.fit(category)\n",
        "    encoded_labels = le.transform(category)\n",
        "    labels = np.array(encoded_labels) # Converting into numpy array\n",
        "    class_names =le.classes_ ## Define the class names again\n",
        "    if bool == True:\n",
        "        print(\"\\n\\t\\t\\t===== Label Encoding =====\",\"\\nClass Names:-->\",le.classes_)\n",
        "        for i in sample_data:\n",
        "            print(category[i],' ', encoded_labels[i],'\\n')\n",
        "\n",
        "    return labels\n",
        "\n",
        "\n",
        "\n",
        "                           #===========================================================\n",
        "                           ################# Dataset Splitting Function ###############\n",
        "                           #=========================================================== \n",
        "\n",
        "def dataset_split(headlines,category):\n",
        "    \"\"\"\n",
        "    This function will return the splitted (90%-10%-10%) feature vector . \n",
        "    \n",
        "    Args:\n",
        "        headlines: sequenced headlines \n",
        "        category: encoded lables (array) \n",
        "        \n",
        "    Returns:\n",
        "        X_train: training data \n",
        "        X_valid: validation data\n",
        "        X_test : testing feature vector \n",
        "        y_train: training encoded labels (array) \n",
        "        y_valid: training encoded labels (array) \n",
        "        y_test : testing encoded labels (array) \n",
        "    \"\"\"\n",
        "\n",
        "    X,X_test,y,y_test = train_test_split(headlines,category,train_size = 0.9,\n",
        "                                                  test_size = 0.1,random_state =0)\n",
        "    X_train,X_valid,y_train,y_valid = train_test_split(X,y,train_size = 0.8,\n",
        "                                                  test_size = 0.2,random_state =0)\n",
        "    print(color.BOLD+\"\\nDataset Distribution:\\n\"+color.END)\n",
        "    print(\"\\tSet Name\",\"\\t\\tSize\")\n",
        "    print(\"\\t========\\t\\t======\")\n",
        "\n",
        "    print(\"\\tFull\\t\\t\\t\",len(headlines),\n",
        "        \"\\n\\tTraining\\t\\t\",len(X_train),\n",
        "        \"\\n\\tTest\\t\\t\\t\",len(X_test),\n",
        "        \"\\n\\tValidation\\t\\t\",len(X_valid))\n",
        "  \n",
        "    return X_train,X_valid,X_test,y_train,y_valid,y_test\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bDoeCWymdvQX",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 617
        },
        "outputId": "34867218-4a5b-4774-f28d-2e658f188ba0"
      },
      "source": [
        "labels = label_encoding(dataset.category,True)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\n",
            "\t\t\t===== Label Encoding ===== \n",
            "Class Names:--> ['Amusement' 'IT' 'International' 'national' 'politics' 'sports']\n",
            "Amusement   0 \n",
            "\n",
            "IT   1 \n",
            "\n",
            "politics   4 \n",
            "\n",
            "International   2 \n",
            "\n",
            "International   2 \n",
            "\n",
            "sports   5 \n",
            "\n",
            "sports   5 \n",
            "\n",
            "International   2 \n",
            "\n",
            "national   3 \n",
            "\n",
            "International   2 \n",
            "\n",
            "International   2 \n",
            "\n",
            "International   2 \n",
            "\n",
            "International   2 \n",
            "\n",
            "International   2 \n",
            "\n",
            "Amusement   0 \n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CGxZ2er08gPr",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 181
        },
        "outputId": "2b098c18-5394-4888-bb3c-2db25f2cde92"
      },
      "source": [
        "X_train,X_valid,X_test,y_train,y_valid,y_test = dataset_split(dataset.headline,labels)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\u001b[1m\n",
            "Dataset Distribution:\n",
            "\u001b[0m\n",
            "\tSet Name \t\tSize\n",
            "\t========\t\t======\n",
            "\tFull\t\t\t 132713 \n",
            "\tTraining\t\t 95552 \n",
            "\tTest\t\t\t 13272 \n",
            "\tValidation\t\t 23889\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NQOHalFi9iMa",
        "colab_type": "text"
      },
      "source": [
        "#Tokenization"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "viKvAq5T9k37",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "vocab_size = 57000\n",
        "embedding_dim = 64\n",
        "max_length = 21\n",
        "trunc_type='post'\n",
        "padding_type='post'\n",
        "oov_tok = \"<OOV>\"\n",
        "\n",
        "def padded_headlines(original,encoded,padded):\n",
        "  '''\n",
        "  print the samples padded headlines\n",
        "  '''\n",
        "  print(color.BOLD+\"\\n\\t\\t\\t====== Encoded Sequences ======\"+color.END,\"\\n\")  \n",
        "  print(original,\"\\n\",encoded) \n",
        "  print(color.BOLD+\"\\n\\t\\t\\t====== Paded Sequences ======\\n\"+color.END,original,\"\\n\",padded)  "
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "b292GeAZ9uYE",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Train Data Tokenization\n",
        "tokenizer = Tokenizer(num_words = vocab_size, oov_token=oov_tok)\n",
        "tokenizer.fit_on_texts(X_train)\n",
        "word_index = tokenizer.word_index\n",
        "train_sequences = tokenizer.texts_to_sequences(X_train)\n",
        "train_padded = pad_sequences(train_sequences, padding=padding_type, maxlen=max_length)\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2iDecox7-b0I",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 726
        },
        "outputId": "08e63730-5eb6-4358-ff56-fbbf67e24340"
      },
      "source": [
        " #============================== Tokenizer Info =================================\n",
        "(word_counts,word_docs,word_index,document_count) = (tokenizer.word_counts,\n",
        "                                                       tokenizer.word_docs,\n",
        "                                                       tokenizer.word_index,\n",
        "                                                       tokenizer.document_count)\n",
        "def tokenizer_info(mylist,bool):\n",
        "  ordered = sorted(mylist.items(), key= lambda item: item[1],reverse = bool)\n",
        "  for w,c in ordered[:10]:\n",
        "    print(w,\"\\t\",c)\n",
        "  #=============================== Print all the information =========================\n",
        "print(color.BOLD+\"\\t\\t\\t====== Tokenizer Info ======\"+color.END)   \n",
        "print(\"Words --> Counts:\")\n",
        "tokenizer_info(word_counts,bool =True )\n",
        "print(\"\\nWords --> Documents:\")\n",
        "tokenizer_info(word_docs,bool =True )\n",
        "print(\"\\nWords --> Index:\")\n",
        "tokenizer_info(word_index,bool =True )    \n",
        "print(\"\\nTotal Documents -->\",document_count)\n",
        "print(f\"Found {len(word_index)} unique tokens\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\u001b[1m\t\t\t====== Tokenizer Info ======\u001b[0m\n",
            "Words --> Counts:\n",
            "না \t 4125\n",
            "নিয়ে \t 3213\n",
            "ও \t 3201\n",
            "নিহত \t 2683\n",
            "নতুন \t 2288\n",
            "হবে \t 2193\n",
            "থেকে \t 2165\n",
            "বাংলাদেশ \t 1741\n",
            "সঙ্গে \t 1692\n",
            "করে \t 1510\n",
            "\n",
            "Words --> Documents:\n",
            "না \t 4031\n",
            "নিয়ে \t 3204\n",
            "ও \t 3173\n",
            "নিহত \t 2681\n",
            "নতুন \t 2273\n",
            "হবে \t 2182\n",
            "থেকে \t 2162\n",
            "বাংলাদেশ \t 1737\n",
            "সঙ্গে \t 1684\n",
            "করে \t 1499\n",
            "\n",
            "Words --> Index:\n",
            "মিসিসিপিতে \t 55055\n",
            "ইয়ামেনি \t 55054\n",
            "ওকিনাওয়ায় \t 55053\n",
            "শনাক্তকরণের \t 55052\n",
            "আবিষ্কৃত \t 55051\n",
            "বেলজীয় \t 55050\n",
            "পুজদেমনকে \t 55049\n",
            "‘গান’ \t 55048\n",
            "বেস \t 55047\n",
            "ইনস্ট্রুমেন্টাল \t 55046\n",
            "\n",
            "Total Documents --> 95552\n",
            "Found 55055 unique tokens\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0zUwx2Mk-9aF",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 199
        },
        "outputId": "a9c6b5f6-c8be-4909-f2c1-762f8f7659ac"
      },
      "source": [
        "padded_headlines(X_train[10],train_sequences[10],train_padded[10]) "
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\u001b[1m\n",
            "\t\t\t====== Encoded Sequences ======\u001b[0m \n",
            "\n",
            "মোদির পাশে তৈমুর! \n",
            " [4172, 2216, 6238, 301, 2629, 5925]\n",
            "\u001b[1m\n",
            "\t\t\t====== Paded Sequences ======\n",
            "\u001b[0m মোদির পাশে তৈমুর! \n",
            " [4172 2216 6238  301 2629 5925    0    0    0    0    0    0    0    0\n",
            "    0    0    0    0    0    0    0]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FHONzUstCPfd",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 199
        },
        "outputId": "4d443639-20b5-43e6-d09e-4c0b46a622df"
      },
      "source": [
        "# Validation Data Tokenization\n",
        "validation_sequences = tokenizer.texts_to_sequences(X_valid)\n",
        "validation_padded = pad_sequences(validation_sequences, padding=padding_type , maxlen=max_length)\n",
        "padded_headlines(X_valid[61569],validation_sequences[1],validation_padded[1]) \n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\u001b[1m\n",
            "\t\t\t====== Encoded Sequences ======\u001b[0m \n",
            "\n",
            "জেলবন্দি তামিলদের মুক্তি দিতে পারেন রাজাপক্ষে \n",
            " [1, 1410, 161, 18585, 4123, 2124, 2521, 2, 851]\n",
            "\u001b[1m\n",
            "\t\t\t====== Paded Sequences ======\n",
            "\u001b[0m জেলবন্দি তামিলদের মুক্তি দিতে পারেন রাজাপক্ষে \n",
            " [    1  1410   161 18585  4123  2124  2521     2   851     0     0     0\n",
            "     0     0     0     0     0     0     0     0     0]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-FQT_IuTBocX",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 199
        },
        "outputId": "f77e87b2-0d1a-4cc3-bb51-d3233a3f3260"
      },
      "source": [
        "# Test Data Tokenization\n",
        "test_sequences = tokenizer.texts_to_sequences(X_test)\n",
        "test_padded = pad_sequences(test_sequences, padding=padding_type , maxlen=max_length)\n",
        "padded_headlines(X_test[100],test_sequences[100],test_padded[100]) "
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\u001b[1m\n",
            "\t\t\t====== Encoded Sequences ======\u001b[0m \n",
            "\n",
            "দেখতে পারেন শ্রীদেবীর সেরা ৪ ছবি (ভিডিও) \n",
            " [822, 466, 778, 54443]\n",
            "\u001b[1m\n",
            "\t\t\t====== Paded Sequences ======\n",
            "\u001b[0m দেখতে পারেন শ্রীদেবীর সেরা ৪ ছবি (ভিডিও) \n",
            " [  822   466   778 54443     0     0     0     0     0     0     0     0\n",
            "     0     0     0     0     0     0     0     0     0]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "shAO46yDGNV9",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Labels Tokenization\n",
        "#label_tokenizer = Tokenizer()\n",
        "#label_tokenizer.fit_on_texts(dataset.category)\n",
        "\n",
        "train_label_seq = y_train\n",
        "valid_label_seq = y_valid\n",
        "testing_label_seq = y_test\n",
        "\n",
        "#print(train_label_seq.shape)\n",
        "#print(valid_label_seq.shape)\n",
        "#print(testing_label_seq.shape)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JCuh3RC3G9h3",
        "colab_type": "text"
      },
      "source": [
        "#Model Defination"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TfdblA64HI8B",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 345
        },
        "outputId": "37a2d97c-66f3-43c7-d29a-0b90c6ce645d"
      },
      "source": [
        "keras.backend.clear_session()\n",
        "accuracy_threshold = 0.97\n",
        "vocab_size = 57000\n",
        "embedding_dim = 64\n",
        "max_length = 21\n",
        "num_category = 6\n",
        "\n",
        "class myCallback(keras.callbacks.Callback):\n",
        "  def on_epoch_end(self, epoch, logs={}):\n",
        "      if(logs.get('accuracy')>accuracy_threshold):\n",
        "        print(\"\\nReached %2.2f%% accuracy so we will stop trianing\" % (accuracy_threshold*100))\n",
        "        self.model.stop_training = True\n",
        "\n",
        "acc_callback = myCallback()\n",
        "# Saved the Best Model\n",
        "filepath = path+\"Model.h5\"\n",
        "checkpoint = keras.callbacks.ModelCheckpoint(filepath, monitor='val_accuracy', verbose=2, save_best_only=True, \n",
        "                                             save_weights_only=False, mode='max')\n",
        "callback_list = [acc_callback, checkpoint] \n",
        "model = tf.keras.Sequential([\n",
        "    tf.keras.layers.Embedding(vocab_size, embedding_dim, input_length=max_length),\n",
        "    tf.keras.layers.Bidirectional(GRU(64,dropout=0.2)),\n",
        "    tf.keras.layers.Dense(24, activation='relu'),\n",
        "    tf.keras.layers.Flatten(),\n",
        "    tf.keras.layers.Dense(num_category, activation='softmax')\n",
        "])\n",
        "model.compile(loss='sparse_categorical_crossentropy',optimizer='adam',metrics=['accuracy'])\n",
        "model.summary()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "embedding (Embedding)        (None, 21, 64)            3648000   \n",
            "_________________________________________________________________\n",
            "bidirectional (Bidirectional (None, 128)               49920     \n",
            "_________________________________________________________________\n",
            "dense (Dense)                (None, 24)                3096      \n",
            "_________________________________________________________________\n",
            "flatten (Flatten)            (None, 24)                0         \n",
            "_________________________________________________________________\n",
            "dense_1 (Dense)              (None, 6)                 150       \n",
            "=================================================================\n",
            "Total params: 3,701,166\n",
            "Trainable params: 3,701,166\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gySWitpfHf33",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 583
        },
        "outputId": "b3d88976-2c44-4825-8b28-b7463efec63b"
      },
      "source": [
        "num_epochs = 10\n",
        "batch = 64\n",
        "history = model.fit(train_padded, train_label_seq, \n",
        "                    epochs=num_epochs,\n",
        "                    batch_size = batch,\n",
        "                    validation_data=(validation_padded, valid_label_seq), \n",
        "                    verbose=1,\n",
        "                    callbacks = callback_list)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/10\n",
            "1493/1493 [==============================] - ETA: 0s - loss: 0.6987 - accuracy: 0.7449\n",
            "Epoch 00001: val_accuracy improved from -inf to 0.82574, saving model to /content/drive/My Drive/Colab Notebooks/NLP Projects/News Headline Classification/Model.h5\n",
            "1493/1493 [==============================] - 52s 35ms/step - loss: 0.6987 - accuracy: 0.7449 - val_loss: 0.4903 - val_accuracy: 0.8257\n",
            "Epoch 2/10\n",
            "1492/1493 [============================>.] - ETA: 0s - loss: 0.3350 - accuracy: 0.8804\n",
            "Epoch 00002: val_accuracy improved from 0.82574 to 0.83771, saving model to /content/drive/My Drive/Colab Notebooks/NLP Projects/News Headline Classification/Model.h5\n",
            "1493/1493 [==============================] - 55s 37ms/step - loss: 0.3350 - accuracy: 0.8804 - val_loss: 0.4596 - val_accuracy: 0.8377\n",
            "Epoch 3/10\n",
            "1493/1493 [==============================] - ETA: 0s - loss: 0.2152 - accuracy: 0.9235\n",
            "Epoch 00003: val_accuracy did not improve from 0.83771\n",
            "1493/1493 [==============================] - 55s 37ms/step - loss: 0.2152 - accuracy: 0.9235 - val_loss: 0.5129 - val_accuracy: 0.8318\n",
            "Epoch 4/10\n",
            "1493/1493 [==============================] - ETA: 0s - loss: 0.1581 - accuracy: 0.9439\n",
            "Epoch 00004: val_accuracy did not improve from 0.83771\n",
            "1493/1493 [==============================] - 51s 34ms/step - loss: 0.1581 - accuracy: 0.9439 - val_loss: 0.5298 - val_accuracy: 0.8317\n",
            "Epoch 5/10\n",
            "1493/1493 [==============================] - ETA: 0s - loss: 0.1222 - accuracy: 0.9562\n",
            "Epoch 00005: val_accuracy did not improve from 0.83771\n",
            "1493/1493 [==============================] - 52s 35ms/step - loss: 0.1222 - accuracy: 0.9562 - val_loss: 0.5864 - val_accuracy: 0.8306\n",
            "Epoch 6/10\n",
            "1493/1493 [==============================] - ETA: 0s - loss: 0.0965 - accuracy: 0.9650\n",
            "Epoch 00006: val_accuracy did not improve from 0.83771\n",
            "1493/1493 [==============================] - 54s 36ms/step - loss: 0.0965 - accuracy: 0.9650 - val_loss: 0.6754 - val_accuracy: 0.8241\n",
            "Epoch 7/10\n",
            "1493/1493 [==============================] - ETA: 0s - loss: 0.0806 - accuracy: 0.9707\n",
            "Reached 97.00% accuracy so we will stop trianing\n",
            "\n",
            "Epoch 00007: val_accuracy did not improve from 0.83771\n",
            "1493/1493 [==============================] - 51s 34ms/step - loss: 0.0806 - accuracy: 0.9707 - val_loss: 0.7095 - val_accuracy: 0.8247\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ixYs6bQ4oBCH",
        "colab_type": "text"
      },
      "source": [
        "##Confusion Matrix"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "U5GRIl25qF_b",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 509
        },
        "outputId": "dc6553aa-f3c4-40ac-8fba-a1c54ede4a96"
      },
      "source": [
        "from sklearn.metrics import classification_report, confusion_matrix\n",
        "# load the Saved model from directory\n",
        "model = load_model(path+\"Model.h5\")\n",
        "predictions = model.predict(test_padded)\n",
        "y_pred = np.argmax(predictions, axis=1)\n",
        "\n",
        "cm = confusion_matrix(testing_label_seq, y_pred) \n",
        "\n",
        "# Transform to df for easier plotting\n",
        "cm_df = pd.DataFrame(cm,\n",
        "                     \n",
        "                     index = ['Amusement' ,'IT' ,'International', 'National', 'Politics', 'Sports'], \n",
        "                     columns = ['Amusement' ,'IT' ,'International', 'National', 'Politics', 'Sports'])\n",
        "\n",
        "plt.figure(figsize=(8,6))\n",
        "sns.heatmap(cm_df, annot=True,cmap=\"YlGnBu\", fmt='g')\n",
        "plt.title('GRU \\nAccuracy: {0:.2f}'.format(accuracy_score(testing_label_seq, y_pred)*100))\n",
        "plt.ylabel('True label')\n",
        "plt.xlabel('Predicted label')\n",
        "plt.xticks(rotation = 45)\n",
        "plt.yticks(rotation = 45)\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 576x432 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kJMjeeagoG-9",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 318
        },
        "outputId": "eef1732b-eb12-48fa-95fb-63aebc69affc"
      },
      "source": [
        "report = pd.DataFrame(classification_report(y_true = testing_label_seq, y_pred = y_pred, output_dict=True)).transpose()\n",
        "report = report.rename(index={'0': 'Amusement','1':'IT','2':'International','3':'National','4':'Politics','5':'Sports'})\n",
        "report[['precision','recall','f1-score']]=report[['precision','recall','f1-score']].apply(lambda x: round(x*100,2))\n",
        "report"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>precision</th>\n",
              "      <th>recall</th>\n",
              "      <th>f1-score</th>\n",
              "      <th>support</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Amusement</th>\n",
              "      <td>87.17</td>\n",
              "      <td>86.95</td>\n",
              "      <td>87.06</td>\n",
              "      <td>1617.00000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>IT</th>\n",
              "      <td>65.83</td>\n",
              "      <td>55.24</td>\n",
              "      <td>60.08</td>\n",
              "      <td>286.00000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>International</th>\n",
              "      <td>88.61</td>\n",
              "      <td>90.44</td>\n",
              "      <td>89.51</td>\n",
              "      <td>4852.00000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>National</th>\n",
              "      <td>72.46</td>\n",
              "      <td>65.39</td>\n",
              "      <td>68.74</td>\n",
              "      <td>2398.00000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Politics</th>\n",
              "      <td>66.03</td>\n",
              "      <td>72.30</td>\n",
              "      <td>69.02</td>\n",
              "      <td>1054.00000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Sports</th>\n",
              "      <td>91.11</td>\n",
              "      <td>93.61</td>\n",
              "      <td>92.34</td>\n",
              "      <td>3065.00000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>accuracy</th>\n",
              "      <td>84.02</td>\n",
              "      <td>84.02</td>\n",
              "      <td>84.02</td>\n",
              "      <td>0.84019</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>macro avg</th>\n",
              "      <td>78.53</td>\n",
              "      <td>77.32</td>\n",
              "      <td>77.79</td>\n",
              "      <td>13272.00000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>weighted avg</th>\n",
              "      <td>83.81</td>\n",
              "      <td>84.02</td>\n",
              "      <td>83.85</td>\n",
              "      <td>13272.00000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "               precision  recall  f1-score      support\n",
              "Amusement          87.17   86.95     87.06   1617.00000\n",
              "IT                 65.83   55.24     60.08    286.00000\n",
              "International      88.61   90.44     89.51   4852.00000\n",
              "National           72.46   65.39     68.74   2398.00000\n",
              "Politics           66.03   72.30     69.02   1054.00000\n",
              "Sports             91.11   93.61     92.34   3065.00000\n",
              "accuracy           84.02   84.02     84.02      0.84019\n",
              "macro avg          78.53   77.32     77.79  13272.00000\n",
              "weighted avg       83.81   84.02     83.85  13272.00000"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 63
        }
      ]
    }
  ]
}