ADBI_Project.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "ADBI Project",
      "version": "0.3.2",
      "provenance": [],
      "collapsed_sections": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/rachit-shah/Apparent-Age-Prediction-using-CNN/blob/master/ADBI_Project.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "metadata": {
        "id": "4odqspY75XqD",
        "colab_type": "code",
        "outputId": "da79fa8c-9ed4-4620-eb38-ef51f9635b8c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 124
        }
      },
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/gdrive')"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Go to this URL in a browser: https://accounts.google.com/o/oauth2/auth?client_id=947318989803-6bn6qk8qdgf4n4g3pfee6491hc0brc4i.apps.googleusercontent.com&redirect_uri=urn%3Aietf%3Awg%3Aoauth%3A2.0%3Aoob&scope=email%20https%3A%2F%2Fwww.googleapis.com%2Fauth%2Fdocs.test%20https%3A%2F%2Fwww.googleapis.com%2Fauth%2Fdrive%20https%3A%2F%2Fwww.googleapis.com%2Fauth%2Fdrive.photos.readonly%20https%3A%2F%2Fwww.googleapis.com%2Fauth%2Fpeopleapi.readonly&response_type=code\n",
            "\n",
            "Enter your authorization code:\n",
            "··········\n",
            "Mounted at /content/gdrive\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "z8c7Ay-J7Qwh",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "!cp gdrive/'My Drive'/bbc-fulltext.zip .\n",
        "!cp gdrive/'My Drive'/glove.6B.zip ."
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "3jL_ajgI7Fug",
        "colab_type": "code",
        "outputId": "617f0171-266f-4e7a-c932-de82ac0d6094",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 38587
        }
      },
      "cell_type": "code",
      "source": [
        "!unzip bbc-fulltext.zip"
      ],
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Archive:  bbc-fulltext.zip\n",
            "   creating: bbc/\n",
            "   creating: bbc/business/\n",
            "  inflating: bbc/business/001.txt    \n",
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          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "eUI9Bh1W7UUR",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "!rm bbc/README.TXT"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "ZofUmlTHfIlu",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 760
        },
        "outputId": "2488df54-482f-4145-ba4d-87077b72acd6"
      },
      "cell_type": "code",
      "source": [
        "!ls bbc/entertainment/"
      ],
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "001.txt  044.txt  087.txt  130.txt  173.txt  216.txt  259.txt  302.txt\t345.txt\n",
            "002.txt  045.txt  088.txt  131.txt  174.txt  217.txt  260.txt  303.txt\t346.txt\n",
            "003.txt  046.txt  089.txt  132.txt  175.txt  218.txt  261.txt  304.txt\t347.txt\n",
            "004.txt  047.txt  090.txt  133.txt  176.txt  219.txt  262.txt  305.txt\t348.txt\n",
            "005.txt  048.txt  091.txt  134.txt  177.txt  220.txt  263.txt  306.txt\t349.txt\n",
            "006.txt  049.txt  092.txt  135.txt  178.txt  221.txt  264.txt  307.txt\t350.txt\n",
            "007.txt  050.txt  093.txt  136.txt  179.txt  222.txt  265.txt  308.txt\t351.txt\n",
            "008.txt  051.txt  094.txt  137.txt  180.txt  223.txt  266.txt  309.txt\t352.txt\n",
            "009.txt  052.txt  095.txt  138.txt  181.txt  224.txt  267.txt  310.txt\t353.txt\n",
            "010.txt  053.txt  096.txt  139.txt  182.txt  225.txt  268.txt  311.txt\t354.txt\n",
            "011.txt  054.txt  097.txt  140.txt  183.txt  226.txt  269.txt  312.txt\t355.txt\n",
            "012.txt  055.txt  098.txt  141.txt  184.txt  227.txt  270.txt  313.txt\t356.txt\n",
            "013.txt  056.txt  099.txt  142.txt  185.txt  228.txt  271.txt  314.txt\t357.txt\n",
            "014.txt  057.txt  100.txt  143.txt  186.txt  229.txt  272.txt  315.txt\t358.txt\n",
            "015.txt  058.txt  101.txt  144.txt  187.txt  230.txt  273.txt  316.txt\t359.txt\n",
            "016.txt  059.txt  102.txt  145.txt  188.txt  231.txt  274.txt  317.txt\t360.txt\n",
            "017.txt  060.txt  103.txt  146.txt  189.txt  232.txt  275.txt  318.txt\t361.txt\n",
            "018.txt  061.txt  104.txt  147.txt  190.txt  233.txt  276.txt  319.txt\t362.txt\n",
            "019.txt  062.txt  105.txt  148.txt  191.txt  234.txt  277.txt  320.txt\t363.txt\n",
            "020.txt  063.txt  106.txt  149.txt  192.txt  235.txt  278.txt  321.txt\t364.txt\n",
            "021.txt  064.txt  107.txt  150.txt  193.txt  236.txt  279.txt  322.txt\t365.txt\n",
            "022.txt  065.txt  108.txt  151.txt  194.txt  237.txt  280.txt  323.txt\t366.txt\n",
            "023.txt  066.txt  109.txt  152.txt  195.txt  238.txt  281.txt  324.txt\t367.txt\n",
            "024.txt  067.txt  110.txt  153.txt  196.txt  239.txt  282.txt  325.txt\t368.txt\n",
            "025.txt  068.txt  111.txt  154.txt  197.txt  240.txt  283.txt  326.txt\t369.txt\n",
            "026.txt  069.txt  112.txt  155.txt  198.txt  241.txt  284.txt  327.txt\t370.txt\n",
            "027.txt  070.txt  113.txt  156.txt  199.txt  242.txt  285.txt  328.txt\t371.txt\n",
            "028.txt  071.txt  114.txt  157.txt  200.txt  243.txt  286.txt  329.txt\t372.txt\n",
            "029.txt  072.txt  115.txt  158.txt  201.txt  244.txt  287.txt  330.txt\t373.txt\n",
            "030.txt  073.txt  116.txt  159.txt  202.txt  245.txt  288.txt  331.txt\t374.txt\n",
            "031.txt  074.txt  117.txt  160.txt  203.txt  246.txt  289.txt  332.txt\t375.txt\n",
            "032.txt  075.txt  118.txt  161.txt  204.txt  247.txt  290.txt  333.txt\t376.txt\n",
            "033.txt  076.txt  119.txt  162.txt  205.txt  248.txt  291.txt  334.txt\t377.txt\n",
            "034.txt  077.txt  120.txt  163.txt  206.txt  249.txt  292.txt  335.txt\t378.txt\n",
            "035.txt  078.txt  121.txt  164.txt  207.txt  250.txt  293.txt  336.txt\t379.txt\n",
            "036.txt  079.txt  122.txt  165.txt  208.txt  251.txt  294.txt  337.txt\t380.txt\n",
            "037.txt  080.txt  123.txt  166.txt  209.txt  252.txt  295.txt  338.txt\t381.txt\n",
            "038.txt  081.txt  124.txt  167.txt  210.txt  253.txt  296.txt  339.txt\t382.txt\n",
            "039.txt  082.txt  125.txt  168.txt  211.txt  254.txt  297.txt  340.txt\t383.txt\n",
            "040.txt  083.txt  126.txt  169.txt  212.txt  255.txt  298.txt  341.txt\t384.txt\n",
            "041.txt  084.txt  127.txt  170.txt  213.txt  256.txt  299.txt  342.txt\t385.txt\n",
            "042.txt  085.txt  128.txt  171.txt  214.txt  257.txt  300.txt  343.txt\t386.txt\n",
            "043.txt  086.txt  129.txt  172.txt  215.txt  258.txt  301.txt  344.txt\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "WdYfGej57VGK",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import os\n",
        "%matplotlib inline\n",
        "import nltk\n",
        "nltk.download('popular')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "xi2U3lg-gZd3",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 173
        },
        "outputId": "06646b24-fc06-46b6-df7a-38b1233b040c"
      },
      "cell_type": "code",
      "source": [
        "!unzip glove.6B.zip\n",
        "!ls"
      ],
      "execution_count": 285,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Archive:  glove.6B.zip\n",
            "  inflating: glove.6B.50d.txt        \n",
            "  inflating: glove.6B.100d.txt       \n",
            "  inflating: glove.6B.200d.txt       \n",
            "  inflating: glove.6B.300d.txt       \n",
            "2pageSessions.csv  glove.6B.100d.txt  glove.6B.zip\t\t readme.txt\n",
            "bbc\t\t   glove.6B.200d.txt  __MACOSX\t\t\t sample_data\n",
            "bbc-fulltext.zip   glove.6B.300d.txt  NewsAggregatorDataset.zip\n",
            "gdrive\t\t   glove.6B.50d.txt   newsCorpora.csv\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "Ew62XD1v790p",
        "colab_type": "code",
        "outputId": "4934efca-bc0b-4a67-8c90-fab852c25e60",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 198
        }
      },
      "cell_type": "code",
      "source": [
        "#Read Data\n",
        "categories = ['business','entertainment','politics','sport','tech']\n",
        "df = pd.DataFrame([],columns=['category','id','text'])\n",
        "for cat in categories:\n",
        "  for file in os.listdir(\"bbc/\"+cat):\n",
        "      if file.endswith(\".txt\"):\n",
        "          filepath = os.path.join(\"bbc/\"+cat, file)\n",
        "          text = open(filepath,'r', errors='ignore').read()\n",
        "          s = pd.Series([cat,int(filepath.split('/')[-1][:-4]),text],index=['category','id','text'])\n",
        "          df = df.append(s,ignore_index=True)\n",
        "\n",
        "df.head()\n",
        "          \n",
        "      \n"
      ],
      "execution_count": 247,
      "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>category</th>\n",
              "      <th>id</th>\n",
              "      <th>text</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>business</td>\n",
              "      <td>182</td>\n",
              "      <td>Weak data buffets French economy\\n\\nA batch of...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>business</td>\n",
              "      <td>449</td>\n",
              "      <td>Wal-Mart fights back at accusers\\n\\nTwo big US...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>business</td>\n",
              "      <td>403</td>\n",
              "      <td>WorldCom bosses' $54m payout\\n\\nTen former dir...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>business</td>\n",
              "      <td>223</td>\n",
              "      <td>Gaming firm to sell UK dog tracks\\n\\nSix UK gr...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>business</td>\n",
              "      <td>481</td>\n",
              "      <td>Christmas sales worst since 1981\\n\\nUK retail ...</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   category   id                                               text\n",
              "0  business  182  Weak data buffets French economy\\n\\nA batch of...\n",
              "1  business  449  Wal-Mart fights back at accusers\\n\\nTwo big US...\n",
              "2  business  403  WorldCom bosses' $54m payout\\n\\nTen former dir...\n",
              "3  business  223  Gaming firm to sell UK dog tracks\\n\\nSix UK gr...\n",
              "4  business  481  Christmas sales worst since 1981\\n\\nUK retail ..."
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 247
        }
      ]
    },
    {
      "metadata": {
        "id": "uZXIyNwn8tT3",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 55
        },
        "outputId": "e3858aa7-13f7-4253-afc5-83dccb029a0f"
      },
      "cell_type": "code",
      "source": [
        "'''\n",
        "#WordCloud\n",
        "\n",
        "from wordcloud import WordCloud\n",
        "import matplotlib.pyplot as plt\n",
        "from sklearn.feature_extraction import text \n",
        "from wordcloud import WordCloud, STOPWORDS\n",
        "cat = df['category'].unique()\n",
        "\n",
        "for i in range(0,len(cat)):\n",
        "    words = ' '.join(df.loc[df['category']==cat[i], 'text'])\n",
        "\n",
        "    wordcloud = WordCloud( \n",
        "                          stopwords=STOPWORDS,\n",
        "                          background_color='white',\n",
        "                          width=800,\n",
        "                          height=400\n",
        "                ).generate(words)\n",
        "    print(cat[i])\n",
        "    plt.figure(figsize=(10, 5))\n",
        "    plt.imshow(wordcloud)\n",
        "    plt.axis('off')\n",
        "    plt.show()\n",
        "\n",
        "words = ' '.join(df.loc[:, 'text'])\n",
        "\n",
        "wordcloud = WordCloud( \n",
        "                      stopwords=STOPWORDS,\n",
        "                      background_color='white',\n",
        "                      width=800,\n",
        "                      height=400\n",
        "            ).generate(words)\n",
        "print(\"ALL Categories:\")\n",
        "plt.figure(figsize=(10, 5))\n",
        "plt.imshow(wordcloud)\n",
        "plt.axis('off')\n",
        "plt.show()\n",
        "'''"
      ],
      "execution_count": 243,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'\\n#WordCloud\\n\\nfrom wordcloud import WordCloud\\nimport matplotlib.pyplot as plt\\nfrom sklearn.feature_extraction import text \\nfrom wordcloud import WordCloud, STOPWORDS\\ncat = df[\\'category\\'].unique()\\n\\nfor i in range(0,len(cat)):\\n    words = \\' \\'.join(df.loc[df[\\'category\\']==cat[i], \\'text\\'])\\n\\n    wordcloud = WordCloud( \\n                          stopwords=STOPWORDS,\\n                          background_color=\\'white\\',\\n                          width=800,\\n                          height=400\\n                ).generate(words)\\n    print(cat[i])\\n    plt.figure(figsize=(10, 5))\\n    plt.imshow(wordcloud)\\n    plt.axis(\\'off\\')\\n    plt.show()\\n\\nwords = \\' \\'.join(df.loc[:, \\'text\\'])\\n\\nwordcloud = WordCloud( \\n                      stopwords=STOPWORDS,\\n                      background_color=\\'white\\',\\n                      width=800,\\n                      height=400\\n            ).generate(words)\\nprint(\"ALL Categories:\")\\nplt.figure(figsize=(10, 5))\\nplt.imshow(wordcloud)\\nplt.axis(\\'off\\')\\nplt.show()\\n'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 243
        }
      ]
    },
    {
      "metadata": {
        "id": "MEExPwFpr-46",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "JR2fsHlbr_Tj",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "## Label Encoder\n",
        "#### Use inverse_transform at the end after predicting"
      ]
    },
    {
      "metadata": {
        "id": "385fGoXNldB0",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 138
        },
        "outputId": "497a6f3c-1dcb-44a7-e5da-6544606e2d82"
      },
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import LabelEncoder\n",
        "import pickle\n",
        "import numpy as np\n",
        "from sklearn.model_selection import train_test_split\n",
        "y = df['category']\n",
        "X = df.drop(['category','id'],axis=1)\n",
        "X_train, X_test, y_train, y_test  = train_test_split(X,y,stratify=y, test_size=0.2, random_state=123)\n",
        "X_train = X_train.reset_index(drop=True)\n",
        "X_test = X_test.reset_index(drop=True)\n",
        "\n",
        "print('before: %s ...' %y_train[:5])\n",
        "\n",
        "le = LabelEncoder()\n",
        "le.fit(y_train)\n",
        "y_train = le.transform(y_train)\n",
        "\n",
        "print('after: %s ...' %y_train)"
      ],
      "execution_count": 336,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "before: 1508       sport\n",
            "1908        tech\n",
            "2104        tech\n",
            "2019        tech\n",
            "1245    politics\n",
            "Name: category, dtype: object ...\n",
            "after: [3 4 4 ... 1 0 2] ...\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "n9jqvQtYDhR0",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 155
        },
        "outputId": "1ac0482f-62e0-47a1-bc81-0ee158071732"
      },
      "cell_type": "code",
      "source": [
        "print(np.bincount(y_train))\n",
        "print(y.value_counts())\n",
        "\n",
        "print(le.inverse_transform([i for i in range(5)]))"
      ],
      "execution_count": 337,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[408 309 333 409 321]\n",
            "sport            511\n",
            "business         510\n",
            "politics         417\n",
            "tech             401\n",
            "entertainment    386\n",
            "Name: category, dtype: int64\n",
            "['business' 'entertainment' 'politics' 'sport' 'tech']\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "1e6g_BjOsZ3i",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        ""
      ]
    },
    {
      "metadata": {
        "id": "_3Iv6MC3s26i",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "def preprocess(X):\n",
        "  return X.map(lambda x: x.lower().split(\"\\n\")).map(lambda x: [y.split(\". \") for y in x]).map(lambda x: [i.replace('\\'','') for sl in x for i in sl if i is not ''])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "6EgQMq_CtK8d",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1088
        },
        "outputId": "81f3164b-7cea-4775-8fa1-f4d70e29ca17"
      },
      "cell_type": "code",
      "source": [
        "X_train['text'] = preprocess(X_train['text'])\n",
        "X_train['text']"
      ],
      "execution_count": 339,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0       [tigers wary of farrell gamble, leicester say ...\n",
              "1       [supercomputer breaks speed record, the us is ...\n",
              "2       [anti-tremor mouse stops pc shakes, a special ...\n",
              "3       [who do you think you are?, the real danger is...\n",
              "4       [blair defends terror law changes, the prime m...\n",
              "5       [csa chief who quit still in job, the head of ...\n",
              "6       [us bank boss hails genius smith, us federal r...\n",
              "7       [argentina closes $102.6bn debt swap, argentin...\n",
              "8       [the gaming world in 2005, if you have finishe...\n",
              "9       [visa decision every 11 minutes, visa processi...\n",
              "10      [scots suffer another injury blow, scotlands b...\n",
              "11      [blairs hope for blunkett return, the events l...\n",
              "12      [manics in charge of bbc 6 music, the manic st...\n",
              "13      [wenger steps up row, arsene wenger has steppe...\n",
              "14      [hospital suspends no welsh plan, an english h...\n",
              "15      [baa support ahead of court battle, uk airport...\n",
              "16      [african double in edinburgh, world 5000m cham...\n",
              "17      [hearts 2-1 livingston, hearts wrapped up thei...\n",
              "18      [eastwoods baby scoops top oscars, clint eastw...\n",
              "19      [broadband steams ahead in the us, more and mo...\n",
              "20      [italy 8-38 wales, wales secured their first a...\n",
              "21      [trade gap narrows as exports rise, the uks tr...\n",
              "22      [john peel replacement show begins, the perman...\n",
              "23      [china ripe for media explosion, asia is set t...\n",
              "24      [moody joins up with england, lewis moody has ...\n",
              "25      [regiments group in poll move, a regiments cam...\n",
              "26      [cup holders man utd visit everton, holders ma...\n",
              "27      [court mulls file-sharing future, judges at th...\n",
              "28      [warnings on woeful wi-fi security, companies ...\n",
              "29      [vera drake scoops film award, oscar hopefuls ...\n",
              "                              ...                        \n",
              "1750    [satellite mapping aids darfur relief, aid wor...\n",
              "1751    [thousands join strike in wales, thousands of ...\n",
              "1752    [campbell to extend sprint career, darren camp...\n",
              "1753    [world leaders gather to face uncertainty, mor...\n",
              "1754    [mobile tv tipped as one to watch, scandinavia...\n",
              "1755    [mumbai bombs movie postponed, the release of ...\n",
              "1756    [t-mobile bets on pocket office, t-mobile has ...\n",
              "1757    [youssou ndour wins music prize, senegalese mu...\n",
              "1758    [gm pays $2bn to evade fiat buyout, general mo...\n",
              "1759    [nuclear strike key terror risk, the uk and us...\n",
              "1760    [ferguson rues failure to cut gap, boss sir al...\n",
              "1761    [gamers snap up new sony psp, gamers have boug...\n",
              "1762    [computer grid to help the world, your compute...\n",
              "1763    [local net tv takes off in austria, an austria...\n",
              "1764    [straw backs ending china embargo, uk foreign ...\n",
              "1765    [clarke faces id cards rebellion, charles clar...\n",
              "1766    [student inequality exposed, teenagers from we...\n",
              "1767    [fuming robinson blasts officials, england coa...\n",
              "1768    [stars shine on bafta red carpet, hollywood st...\n",
              "1769    [apple laptop is greatest gadget, the apple po...\n",
              "1770    [kewell waits as injury drags on, liverpool wi...\n",
              "1771    [broadband in the uk growing fast, high-speed ...\n",
              "1772    [warning over tsunami aid website, net users a...\n",
              "1773    [controversial film tops festival, a controver...\n",
              "1774    [business fears over sluggish eu economy, as e...\n",
              "1775    [napster offers rented music to go, music down...\n",
              "1776    [uk plan to deport terror suspects, deals are ...\n",
              "1777    [rem concerts blighted by illness, us rock ban...\n",
              "1778    [healthsouth ex-boss goes on trial, the former...\n",
              "1779    [cherie accused of attacking bush, cherie blai...\n",
              "Name: text, Length: 1780, dtype: object"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 339
        }
      ]
    },
    {
      "metadata": {
        "id": "Wme2RkYu2UR0",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "#Stop Words and Lemmatization\n",
        "import nltk.corpus\n",
        "import string\n",
        "from nltk.tokenize import word_tokenize,sent_tokenize\n",
        "from nltk.stem import WordNetLemmatizer   \n",
        "lemmatizer = WordNetLemmatizer()\n",
        "stopw = nltk.corpus.stopwords.words('english')\n",
        "punct = string.punctuation\n",
        "punct = word_tokenize(punct)\n",
        "punct += ['.','``','...','\\'s','--','-','n\\'t','\\'']\n",
        "stopw += punct\n",
        "def token_stop(text):\n",
        "    global stopw\n",
        "    global lemmatizer\n",
        "    words = word_tokenize(text)\n",
        "    filtered = [lemmatizer.lemmatize(w) for w in words if not w in stopw]\n",
        "    return filtered\n",
        "  \n",
        "X_train['text'] = X_train['text'].map(lambda x: [token_stop(i) for i in x]).map(lambda x: [i for sl in x for i in sl])\n",
        "  "
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "sLe602IIEqV6",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "064cf693-5a03-454c-f1a8-f31ad1ddc2c2"
      },
      "cell_type": "code",
      "source": [
        "texts = np.array(X_train['text'])\n",
        "MAX_SEQUENCE_LENGTH = 1000\n",
        "MAX_NB_WORDS = 20000\n",
        "EMBEDDING_DIM = 100\n",
        "VALIDATION_SPLIT = 0.2\n",
        "embeddings_index = {}\n",
        "with open('glove.6B.100d.txt') as f:\n",
        "    for line in f:\n",
        "        word, coefs = line.split(maxsplit=1)\n",
        "        coefs = np.fromstring(coefs, 'f', sep=' ')\n",
        "        embeddings_index[word] = coefs\n",
        "\n",
        "print('Found %s word vectors.' % len(embeddings_index))"
      ],
      "execution_count": 341,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Found 400000 word vectors.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "9thZOjdY_Sl6",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 124
        },
        "outputId": "572dc7f1-0cb3-4cd9-f481-561147731ab6"
      },
      "cell_type": "code",
      "source": [
        "from keras.preprocessing.text import Tokenizer\n",
        "from keras.preprocessing.sequence import pad_sequences\n",
        "from keras.utils import to_categorical\n",
        "from keras.layers import Dense, Input, GlobalMaxPooling1D\n",
        "from keras.layers import Conv1D, MaxPooling1D, Embedding, Flatten\n",
        "from keras.models import Model\n",
        "from keras.initializers import Constant\n",
        "\n",
        "tokenizer = Tokenizer(nb_words=MAX_NB_WORDS)\n",
        "tokenizer.fit_on_texts(texts)\n",
        "sequences = tokenizer.texts_to_sequences(texts)\n",
        "\n",
        "word_index = tokenizer.word_index\n",
        "print('Found %s unique tokens.' % len(word_index))\n",
        "\n",
        "data = pad_sequences(sequences, maxlen=MAX_SEQUENCE_LENGTH)\n",
        "\n",
        "labels = to_categorical(np.asarray(y_train))\n",
        "print('Shape of data tensor:', data.shape)\n",
        "print('Shape of label tensor:', labels.shape)\n",
        "\n",
        "# split the data into a training set and a validation set\n",
        "indices = np.arange(data.shape[0])\n",
        "np.random.shuffle(indices)\n",
        "data = data[indices]\n",
        "labels = labels[indices]\n",
        "nb_validation_samples = int(VALIDATION_SPLIT * data.shape[0])\n",
        "\n",
        "xtrain = data[:-nb_validation_samples]\n",
        "ytrain = labels[:-nb_validation_samples]\n",
        "xval = data[-nb_validation_samples:]\n",
        "yval = labels[-nb_validation_samples:]"
      ],
      "execution_count": 342,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/keras_preprocessing/text.py:178: UserWarning: The `nb_words` argument in `Tokenizer` has been renamed `num_words`.\n",
            "  warnings.warn('The `nb_words` argument in `Tokenizer` '\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Found 28405 unique tokens.\n",
            "Shape of data tensor: (1780, 1000)\n",
            "Shape of label tensor: (1780, 5)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "VofHdiAqIRqc",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "embedding_matrix = np.zeros((len(word_index) + 1, EMBEDDING_DIM))\n",
        "for word, i in word_index.items():\n",
        "    embedding_vector = embeddings_index.get(word)\n",
        "    if embedding_vector is not None:\n",
        "        # words not found in embedding index will be all-zeros.\n",
        "        embedding_matrix[i] = embedding_vector"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "o3yooq2DI-dT",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "from keras.layers import Embedding\n",
        "\n",
        "embedding_layer = Embedding(len(word_index) + 1,\n",
        "                            EMBEDDING_DIM,\n",
        "                            weights=[embedding_matrix],\n",
        "                            input_length=MAX_SEQUENCE_LENGTH,\n",
        "                            trainable=False)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "iuZyVOsKKD6T",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "6303e62c-b026-4fe6-d576-e3c3279e4300"
      },
      "cell_type": "code",
      "source": [
        "labels_index = {}\n",
        "for i in range(5):\n",
        "  name = le.inverse_transform([i])[0]\n",
        "  labels_index[name] = i\n",
        "labels_index"
      ],
      "execution_count": 345,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'business': 0, 'entertainment': 1, 'politics': 2, 'sport': 3, 'tech': 4}"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 345
        }
      ]
    },
    {
      "metadata": {
        "id": "53s9TZ_mI_XY",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 397
        },
        "outputId": "100de4a1-67a2-456b-c745-7e2e41f0f4a7"
      },
      "cell_type": "code",
      "source": [
        "\n",
        "sequence_input = Input(shape=(MAX_SEQUENCE_LENGTH,), dtype='int32')\n",
        "embedded_sequences = embedding_layer(sequence_input)\n",
        "x = Conv1D(128, 5, activation='relu')(embedded_sequences)\n",
        "x = MaxPooling1D(5)(x)\n",
        "x = Conv1D(128, 5, activation='relu')(x)\n",
        "x = MaxPooling1D(5)(x)\n",
        "x = Conv1D(128, 5, activation='relu')(x)\n",
        "x = MaxPooling1D(35)(x)  # global max pooling\n",
        "x = Flatten()(x)\n",
        "x = Dense(128, activation='relu')(x)\n",
        "preds = Dense(len(labels_index), activation='softmax')(x)\n",
        "\n",
        "model = Model(sequence_input, preds)\n",
        "model.compile(loss='categorical_crossentropy',\n",
        "              optimizer='rmsprop',\n",
        "              metrics=['acc'])\n",
        "\n",
        "# happy learning!\n",
        "model.fit(xtrain, ytrain, validation_data=(xval, yval),\n",
        "          epochs=10, batch_size=128)"
      ],
      "execution_count": 346,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Train on 1424 samples, validate on 356 samples\n",
            "Epoch 1/10\n",
            "1424/1424 [==============================] - 16s 11ms/step - loss: 1.5685 - acc: 0.4157 - val_loss: 1.0281 - val_acc: 0.5646\n",
            "Epoch 2/10\n",
            "1424/1424 [==============================] - 15s 11ms/step - loss: 0.6512 - acc: 0.7549 - val_loss: 0.5209 - val_acc: 0.8287\n",
            "Epoch 3/10\n",
            "1424/1424 [==============================] - 15s 11ms/step - loss: 0.3451 - acc: 0.8792 - val_loss: 0.1592 - val_acc: 0.9635\n",
            "Epoch 4/10\n",
            "1424/1424 [==============================] - 15s 11ms/step - loss: 0.2251 - acc: 0.9171 - val_loss: 0.1292 - val_acc: 0.9719\n",
            "Epoch 5/10\n",
            "1424/1424 [==============================] - 15s 11ms/step - loss: 0.2498 - acc: 0.9192 - val_loss: 0.1375 - val_acc: 0.9691\n",
            "Epoch 6/10\n",
            "1424/1424 [==============================] - 15s 11ms/step - loss: 0.0790 - acc: 0.9726 - val_loss: 0.9254 - val_acc: 0.7331\n",
            "Epoch 7/10\n",
            "1424/1424 [==============================] - 15s 11ms/step - loss: 0.1661 - acc: 0.9417 - val_loss: 0.0825 - val_acc: 0.9747\n",
            "Epoch 8/10\n",
            "1424/1424 [==============================] - 15s 11ms/step - loss: 0.1104 - acc: 0.9649 - val_loss: 0.1365 - val_acc: 0.9551\n",
            "Epoch 9/10\n",
            "1424/1424 [==============================] - 15s 11ms/step - loss: 0.0356 - acc: 0.9923 - val_loss: 0.0977 - val_acc: 0.9719\n",
            "Epoch 10/10\n",
            "1424/1424 [==============================] - 15s 11ms/step - loss: 0.0906 - acc: 0.9740 - val_loss: 0.1329 - val_acc: 0.9635\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.callbacks.History at 0x7f467e25c4a8>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 346
        }
      ]
    },
    {
      "metadata": {
        "id": "1sM8QeKfJgRr",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 262
        },
        "outputId": "e60e3881-41f6-494a-e299-9c826bc65ea1"
      },
      "cell_type": "code",
      "source": [
        "X_test['text'] = preprocess(X_test['text'])\n",
        "X_test['text'] = X_test['text'].map(lambda x: [token_stop(i) for i in x]).map(lambda x: [i for sl in x for i in sl])\n",
        "test_text = np.array(X_test['text'])\n",
        "tokenizer = Tokenizer(nb_words=MAX_NB_WORDS)\n",
        "tokenizer.fit_on_texts(test_text)\n",
        "sequences = tokenizer.texts_to_sequences(test_text)\n",
        "\n",
        "word_index = tokenizer.word_index\n",
        "print('Found %s unique tokens.' % len(word_index))\n",
        "\n",
        "data = pad_sequences(sequences, maxlen=MAX_SEQUENCE_LENGTH)\n",
        "\n",
        "print('Shape of data tensor:', data.shape)\n",
        "print('Shape of label tensor:', labels.shape)\n",
        "prediction = model.predict(data)\n",
        "prediction"
      ],
      "execution_count": 348,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/keras_preprocessing/text.py:178: UserWarning: The `nb_words` argument in `Tokenizer` has been renamed `num_words`.\n",
            "  warnings.warn('The `nb_words` argument in `Tokenizer` '\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Found 14490 unique tokens.\n",
            "Shape of data tensor: (445, 1000)\n",
            "Shape of label tensor: (1780, 5)\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[0.43026203, 0.07141124, 0.02702082, 0.00890647, 0.46239948],\n",
              "       [0.80635977, 0.02241753, 0.05791005, 0.0098392 , 0.10347339],\n",
              "       [0.7553198 , 0.06388112, 0.16078942, 0.00230318, 0.01770654],\n",
              "       ...,\n",
              "       [0.19572994, 0.47767732, 0.06225896, 0.00856006, 0.25577372],\n",
              "       [0.82450974, 0.01964773, 0.06259804, 0.04207886, 0.05116564],\n",
              "       [0.9717814 , 0.00268447, 0.01266349, 0.00190804, 0.01096253]],\n",
              "      dtype=float32)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 348
        }
      ]
    },
    {
      "metadata": {
        "id": "u_z5eMImMj57",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "89b4160f-0302-4760-8ca1-110a6f4c09da"
      },
      "cell_type": "code",
      "source": [
        "count = 0\n",
        "t = 0\n",
        "for pred in prediction:\n",
        "  p = pred.argmax()\n",
        "  name = le.inverse_transform([p])\n",
        "  if name == y_test.loc[count]:\n",
        "    t+=1\n",
        "  count+=1\n",
        "    \n",
        "print(t/count)"
      ],
      "execution_count": 356,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0.22696629213483147\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "m3jq0075L7Iq",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "preds = le.inverse_transform(prediction)\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "ZG11nLJFJ0UB",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "y_test = y_test.reset_index(drop=True)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "ADFpnkxANf32",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}