AplicacionNLTK.ipynb

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    "colab": {
      "name": "Code.ipynb",
      "provenance": []
    },
    "accelerator": "GPU"
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "F_svjsx4ArJt",
        "colab_type": "text"
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      "source": [
        "<table class=\"tfo-notebook-buttons\" align=\"left\">\n",
        "  <td>\n",
        "    <a target=\"_blank\" href=\"https://colab.research.google.com/github/JuliethLopez/chatbot_simpsons/blob/master/Code.ipynb\"><img src=\"https://www.tensorflow.org/images/colab_logo_32px.png\" />Run in Google Colab</a>\n",
        "  </td>\n",
        "  <td>\n",
        "    <a target=\"_blank\" href=\"https://github.com/JuliethLopez/chatbot_simpsons/blob/master/Code.ipynb\"><img src=\"https://www.tensorflow.org/images/GitHub-Mark-32px.png\" />View source on GitHub</a>\n",
        "  </td>\n",
        "</table>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xvzz_D3vY1eB",
        "colab_type": "text"
      },
      "source": [
        "#Preprocesamiento de los datos"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "U3GrTNw5A81t",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#!pip install tensorflow-text\n",
        "#nltk.download()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NsEvj6MeAyGZ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import csv\n",
        "import tensorflow as tf\n",
        "import tensorflow_text as text\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import nltk"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NaXJ4jYLBDYZ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 112
        },
        "outputId": "28c42832-0631-4fb7-e015-9d5addd44e68"
      },
      "source": [
        "#cargamos los datos\n",
        "train = pd.read_csv(\"https://meli-data-challenge.s3.amazonaws.com/train.csv.gz\",encoding = 'utf-8', header=0)\n",
        "train.head(2)"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>title</th>\n",
              "      <th>label_quality</th>\n",
              "      <th>language</th>\n",
              "      <th>category</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Hidrolavadora Lavor One 120 Bar 1700w  Bomba A...</td>\n",
              "      <td>unreliable</td>\n",
              "      <td>spanish</td>\n",
              "      <td>ELECTRIC_PRESSURE_WASHERS</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Placa De Sonido - Behringer Umc22</td>\n",
              "      <td>unreliable</td>\n",
              "      <td>spanish</td>\n",
              "      <td>SOUND_CARDS</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
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            ],
            "text/plain": [
              "                                               title  ...                   category\n",
              "0  Hidrolavadora Lavor One 120 Bar 1700w  Bomba A...  ...  ELECTRIC_PRESSURE_WASHERS\n",
              "1                  Placa De Sonido - Behringer Umc22  ...                SOUND_CARDS\n",
              "\n",
              "[2 rows x 4 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 3
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    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KaPXbl6HXwtu",
        "colab_type": "text"
      },
      "source": [
        "## Intento 1\n",
        "Tratamos de cambiar la base de datos de formato cvs a txt desde python. Sin embargo no pudimos obtener resultados muy precisos y no pudimos aplicar muchas de las funciones del *introNLTK*."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "56uoiPemBIeT",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#seleccionamos los datos en español y los ponemos en minuscula\n",
        "train_df = train[train['language'] == 'spanish']\n",
        "train_df = train_df.applymap(lambda s:s.lower() if type(s) == str else s)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hUkXoBzWB1wX",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 363
        },
        "outputId": "6d8ecb3c-f66b-4854-fe39-5d568eb177e8"
      },
      "source": [
        "#seleccionamos 10 datos para probar algunas funciones\n",
        "train_df_10 = train_df.head(10)\n",
        "train_df_10"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>title</th>\n",
              "      <th>label_quality</th>\n",
              "      <th>language</th>\n",
              "      <th>category</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>hidrolavadora lavor one 120 bar 1700w  bomba a...</td>\n",
              "      <td>unreliable</td>\n",
              "      <td>spanish</td>\n",
              "      <td>electric_pressure_washers</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>placa de sonido - behringer umc22</td>\n",
              "      <td>unreliable</td>\n",
              "      <td>spanish</td>\n",
              "      <td>sound_cards</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>flashes led pestañas luminoso falso pestañas p...</td>\n",
              "      <td>unreliable</td>\n",
              "      <td>spanish</td>\n",
              "      <td>false_eyelashes</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>gatito lunchera neoprene</td>\n",
              "      <td>unreliable</td>\n",
              "      <td>spanish</td>\n",
              "      <td>lunchboxes</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>rosario contador de billetes uv / mg detecta f...</td>\n",
              "      <td>unreliable</td>\n",
              "      <td>spanish</td>\n",
              "      <td>bill_counters</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>battery pack huawei 13000mah cuerpo de metal 2...</td>\n",
              "      <td>unreliable</td>\n",
              "      <td>spanish</td>\n",
              "      <td>portable_cellphone_chargers</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23</th>\n",
              "      <td>portón de chapa 3 hojas mtr 2.50 sin marco</td>\n",
              "      <td>unreliable</td>\n",
              "      <td>spanish</td>\n",
              "      <td>garage_doors</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24</th>\n",
              "      <td>a4404 multi puertos usb rápido cargador cabeza...</td>\n",
              "      <td>unreliable</td>\n",
              "      <td>spanish</td>\n",
              "      <td>label_makers</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>26</th>\n",
              "      <td>base simil cemento - 30 cm x 5 mm</td>\n",
              "      <td>unreliable</td>\n",
              "      <td>spanish</td>\n",
              "      <td>cake_toppers</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>30</th>\n",
              "      <td>maquina para sublimar estampar goldex 60x40</td>\n",
              "      <td>unreliable</td>\n",
              "      <td>spanish</td>\n",
              "      <td>screen_printers</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                                                title  ...                     category\n",
              "0   hidrolavadora lavor one 120 bar 1700w  bomba a...  ...    electric_pressure_washers\n",
              "1                   placa de sonido - behringer umc22  ...                  sound_cards\n",
              "4   flashes led pestañas luminoso falso pestañas p...  ...              false_eyelashes\n",
              "9                           gatito lunchera neoprene   ...                   lunchboxes\n",
              "11  rosario contador de billetes uv / mg detecta f...  ...                bill_counters\n",
              "18  battery pack huawei 13000mah cuerpo de metal 2...  ...  portable_cellphone_chargers\n",
              "23         portón de chapa 3 hojas mtr 2.50 sin marco  ...                 garage_doors\n",
              "24  a4404 multi puertos usb rápido cargador cabeza...  ...                 label_makers\n",
              "26                  base simil cemento - 30 cm x 5 mm  ...                 cake_toppers\n",
              "30        maquina para sublimar estampar goldex 60x40  ...              screen_printers\n",
              "\n",
              "[10 rows x 4 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MxPnx5qtB8jv",
        "colab_type": "code",
        "outputId": "56d1a589-85eb-494a-960f-73a27782a10a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "#\n",
        "tokenizer = text.WhitespaceTokenizer() #se extraen los tokens de una cadena de palabras u oraciones sin espacios en blanco\n",
        "tokens = tokenizer.tokenize(train_df_10)\n",
        "tokens.to_list()"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow/python/util/dispatch.py:180: batch_gather (from tensorflow.python.ops.array_ops) is deprecated and will be removed after 2017-10-25.\n",
            "Instructions for updating:\n",
            "`tf.batch_gather` is deprecated, please use `tf.gather` with `batch_dims=-1` instead.\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[[[b'hidrolavadora',\n",
              "   b'lavor',\n",
              "   b'one',\n",
              "   b'120',\n",
              "   b'bar',\n",
              "   b'1700w',\n",
              "   b'bomba',\n",
              "   b'aluminio',\n",
              "   b'italia'],\n",
              "  [b'unreliable'],\n",
              "  [b'spanish'],\n",
              "  [b'electric_pressure_washers']],\n",
              " [[b'placa', b'de', b'sonido', b'-', b'behringer', b'umc22'],\n",
              "  [b'unreliable'],\n",
              "  [b'spanish'],\n",
              "  [b'sound_cards']],\n",
              " [[b'flashes',\n",
              "   b'led',\n",
              "   b'pesta\\xc3\\xb1as',\n",
              "   b'luminoso',\n",
              "   b'falso',\n",
              "   b'pesta\\xc3\\xb1as',\n",
              "   b'para',\n",
              "   b'partido'],\n",
              "  [b'unreliable'],\n",
              "  [b'spanish'],\n",
              "  [b'false_eyelashes']],\n",
              " [[b'gatito', b'lunchera', b'neoprene'],\n",
              "  [b'unreliable'],\n",
              "  [b'spanish'],\n",
              "  [b'lunchboxes']],\n",
              " [[b'rosario',\n",
              "   b'contador',\n",
              "   b'de',\n",
              "   b'billetes',\n",
              "   b'uv',\n",
              "   b'/',\n",
              "   b'mg',\n",
              "   b'detecta',\n",
              "   b'falsos',\n",
              "   b'nuevo'],\n",
              "  [b'unreliable'],\n",
              "  [b'spanish'],\n",
              "  [b'bill_counters']],\n",
              " [[b'battery',\n",
              "   b'pack',\n",
              "   b'huawei',\n",
              "   b'13000mah',\n",
              "   b'cuerpo',\n",
              "   b'de',\n",
              "   b'metal',\n",
              "   b'2',\n",
              "   b'puertos',\n",
              "   b'usb'],\n",
              "  [b'unreliable'],\n",
              "  [b'spanish'],\n",
              "  [b'portable_cellphone_chargers']],\n",
              " [[b'port\\xc3\\xb3n',\n",
              "   b'de',\n",
              "   b'chapa',\n",
              "   b'3',\n",
              "   b'hojas',\n",
              "   b'mtr',\n",
              "   b'2.50',\n",
              "   b'sin',\n",
              "   b'marco'],\n",
              "  [b'unreliable'],\n",
              "  [b'spanish'],\n",
              "  [b'garage_doors']],\n",
              " [[b'a4404',\n",
              "   b'multi',\n",
              "   b'puertos',\n",
              "   b'usb',\n",
              "   b'r\\xc3\\xa1pido',\n",
              "   b'cargador',\n",
              "   b'cabeza',\n",
              "   b'r\\xc3\\xa1pido',\n",
              "   b'carg'],\n",
              "  [b'unreliable'],\n",
              "  [b'spanish'],\n",
              "  [b'label_makers']],\n",
              " [[b'base', b'simil', b'cemento', b'-', b'30', b'cm', b'x', b'5', b'mm'],\n",
              "  [b'unreliable'],\n",
              "  [b'spanish'],\n",
              "  [b'cake_toppers']],\n",
              " [[b'maquina', b'para', b'sublimar', b'estampar', b'goldex', b'60x40'],\n",
              "  [b'unreliable'],\n",
              "  [b'spanish'],\n",
              "  [b'screen_printers']]]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yEJYcrkXGwip",
        "colab_type": "code",
        "outputId": "00b0f770-838e-47c3-d613-8d889b2758bc",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "cuatrigrama = text.ngrams(tokens, 4, reduction_type=text.Reduction.STRING_JOIN)\n",
        "cuatrigrama = cuatrigrama.to_list()\n",
        "type(cuatrigrama)"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "list"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KLEYrkNwHKWL",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 364
        },
        "outputId": "f1824fd1-92d6-4c72-d52b-e66d161b3d54"
      },
      "source": [
        "#grafica con los cuatrigramas\n",
        "freq = nltk.FreqDist(cuatrigrama)\n",
        "freq.plot(20, cumulative=False, title = \"4-grama\")"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "error",
          "ename": "TypeError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-8-fc57f4032ef5>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;31m#grafica con los cuatrigramas\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mfreq\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnltk\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mFreqDist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcuatrigrama\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      3\u001b[0m \u001b[0mfreq\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m20\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcumulative\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtitle\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m\"4-grama\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/nltk/probability.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, samples)\u001b[0m\n\u001b[1;32m    106\u001b[0m         \u001b[0;34m:\u001b[0m\u001b[0mtype\u001b[0m \u001b[0msamples\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mSequence\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    107\u001b[0m         \"\"\"\n\u001b[0;32m--> 108\u001b[0;31m         \u001b[0mCounter\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__init__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msamples\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    109\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    110\u001b[0m         \u001b[0;31m# Cached number of samples in this FreqDist\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/lib/python3.6/collections/__init__.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(*args, **kwds)\u001b[0m\n\u001b[1;32m    533\u001b[0m             \u001b[0;32mraise\u001b[0m \u001b[0mTypeError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'expected at most 1 arguments, got %d'\u001b[0m \u001b[0;34m%\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    534\u001b[0m         \u001b[0msuper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mCounter\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__init__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 535\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    536\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    537\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__missing__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/nltk/probability.py\u001b[0m in \u001b[0;36mupdate\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m    144\u001b[0m         \"\"\"\n\u001b[1;32m    145\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_N\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 146\u001b[0;31m         \u001b[0msuper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mFreqDist\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    147\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    148\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0msetdefault\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mval\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/lib/python3.6/collections/__init__.py\u001b[0m in \u001b[0;36mupdate\u001b[0;34m(*args, **kwds)\u001b[0m\n\u001b[1;32m    620\u001b[0m                     \u001b[0msuper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mCounter\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0miterable\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;31m# fast path when counter is empty\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    621\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 622\u001b[0;31m                 \u001b[0m_count_elements\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0miterable\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    623\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mkwds\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    624\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mTypeError\u001b[0m: unhashable type: 'list'"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rAHE3hO9ZTUI",
        "colab_type": "text"
      },
      "source": [
        "### Intento 2\n",
        "Si bien se pueden aplicar las funciones de introNLTK, subir el archivo txt de la base completa tarda demasiado tiempo. Hay que encontrar otra forma, pues ni la 1 y la 2 nos sirven del todo."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RGXTbKfgSKIA",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "786d3eaf-4983-473e-ffeb-e1436cc97cf4"
      },
      "source": [
        "len(train_df)"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "10000000"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "o3s8WYfjaJIY",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "6c06f1f0-ed53-4bfa-b737-a923d8522c63"
      },
      "source": [
        "#subconjunto de datos\n",
        "train_data = train.iloc[:2000] #seleccionar 2000 filas\n",
        "train_data = train[train['language'] == 'spanish'] #seleccionar las entradas en español\n",
        "train_data = train.drop(['label_quality','language'], axis=1) #eliminar columna label_quality\n",
        "title = train.iloc[:,0]\n",
        "category = train.iloc[:,1]\n",
        "\n",
        "len(train_data)"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "20000000"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PRXUwxp-apkp",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#exportar csv\n",
        "title.to_csv(r'title.csv', index = False, header=True)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dkDt7bTKatoR",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#importar txt\n",
        "f = open('title.txt','r',errors = 'ignore')\n",
        "title_txt = f.read()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jqpF8QwXa6Ln",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 323
        },
        "outputId": "5e80d241-8364-4e89-b21d-d20ca526a4fe"
      },
      "source": [
        "#mayusculas a minusculas\n",
        "title_txt = title_txt.lower()# se pasa a minusculas\n",
        "\n",
        "sent_tokens = nltk.sent_tokenize(title_txt)#tokens por frases\n",
        "word_tokens = nltk.word_tokenize(title_txt)#tokens por palabras\n",
        "\n",
        "#distribucion de frecuencia de las palabras usando nltk\n",
        "freq = nltk.FreqDist(word_tokens)\n",
        "\n",
        "#grafico de la distribución de frecuencias\n",
        "freq.plot(20, cumulative=False, title = \"Tokens sin signos de puntiación\")"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ykyaoesEbEAq",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 323
        },
        "outputId": "054872b3-5af2-4448-b90d-757f1ab53f8e"
      },
      "source": [
        "from nltk.tokenize import word_tokenize, regexp_tokenize\n",
        "\n",
        "#utilizamos una fucnion mas precisa para los tokens, se eliminan algunos signos de puntuación\n",
        "tokens = regexp_tokenize(title_txt, pattern=\"\\w+\")\n",
        "\n",
        "#distribucion de frecuencia de las palabras usando nltk\n",
        "freq = nltk.FreqDist(tokens)\n",
        "\n",
        "#grafico de la distribución de frecuencias\n",
        "freq.plot(20, cumulative=False, title = \"Tokens sin signos de puntiación\")"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_VpG5Efrbc9y",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#palabras de parada\n",
        "from nltk.corpus import stopwords\n",
        "sr = stopwords.words('spanish')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aZv6rAcjcjM5",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#limpiamos los tokens\n",
        "clean_tokens = tokens[:]\n",
        "\n",
        "for token in tokens:\n",
        "    if token in stopwords.words('spanish'):\n",
        "        clean_tokens.remove(token)\n",
        "\n",
        "freq = nltk.FreqDist(clean_tokens)\n",
        "\n",
        "#for key,val in freq.items():\n",
        "#    print (str(key) + ':' + str(val))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "arH62XwPcoy8",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 316
        },
        "outputId": "1f613995-d443-487b-87e6-8585b20ce935"
      },
      "source": [
        "freq.plot(40,cumulative=False)"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "b7K_I3PIdO2o",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}