desafio_5_nicolasFreyer.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kBLpTr7plguX"
      },
      "source": [
        "<img src=\"https://github.com/hernancontigiani/ceia_memorias_especializacion/raw/master/Figures/logoFIUBA.jpg\" width=\"500\" align=\"center\">\n",
        "\n",
        "\n",
        "# Procesamiento de lenguaje natural\n",
        "## Sentiment analysis con Embeddings + LSTM"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9W6nuajhlqZD"
      },
      "source": [
        "### Objetivo\n",
        "El objetivo es utilizar las críticas de compradores de ropa para que el sistema determine la evaluación del comprador y su crítica (cuantas estrellas le asigna al producto)."
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install --upgrade --no-cache-dir gdown --quiet"
      ],
      "metadata": {
        "id": "i6zvzv3qZ6xS"
      },
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hCpOVzJdl8_p"
      },
      "source": [
        "import numpy as np\n",
        "import random\n",
        "import io\n",
        "import pickle\n",
        "import pandas as pd\n",
        "\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "\n",
        "import tensorflow as tf\n",
        "from tensorflow import keras\n",
        "from tensorflow.keras import layers\n",
        "\n",
        "from keras.utils.np_utils import to_categorical\n",
        "from tensorflow.keras.utils import pad_sequences\n",
        "from keras.models import Sequential\n",
        "from keras.layers import Dense\n",
        "from keras.layers import LSTM\n",
        "from keras.layers import Embedding"
      ],
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8UPeRkrAmbF3"
      },
      "source": [
        "### Datos\n",
        "Utilizaremos como dataset críticas de compradores de ropa (eCommerce) los cuales puntuaron a cada prenda con un puntaje de 1 a 5 estrellas.\\\n",
        "Referencia del dataset: [LINK](https://www.kaggle.com/nicapotato/womens-ecommerce-clothing-reviews/version/1)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "C7jLvTU3lSyL",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "0ff10a07-8039-4f59-f03e-b27320887db3"
      },
      "source": [
        "# Descargar la carpeta de dataset\n",
        "import os\n",
        "import gdown\n",
        "if os.access('clothing_ecommerce_reviews.csv', os.F_OK) is False:\n",
        "    url = 'https://drive.google.com/uc?id=1Urn1UFSrodN5BuW6-sc_igtaySGRwhV8'\n",
        "    output = 'clothing_ecommerce_reviews.csv'\n",
        "    gdown.download(url, output, quiet=False)\n",
        "else:\n",
        "    print(\"El dataset ya se encuentra descargado\")"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Downloading...\n",
            "From: https://drive.google.com/uc?id=1Urn1UFSrodN5BuW6-sc_igtaySGRwhV8\n",
            "To: /content/clothing_ecommerce_reviews.csv\n",
            "100%|██████████| 8.48M/8.48M [00:00<00:00, 23.5MB/s]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "o-SV1P3dnD1J",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 354
        },
        "outputId": "1d8b9465-a67c-46dd-d8fe-54f5c44bd004"
      },
      "source": [
        "# Armar el dataset\n",
        "df = pd.read_csv('clothing_ecommerce_reviews.csv')\n",
        "df.drop(columns = ['Unnamed: 0'], inplace = True)\n",
        "df.head()"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Clothing ID  Age                    Title  \\\n",
              "0          767   33                      NaN   \n",
              "1         1080   34                      NaN   \n",
              "2         1077   60  Some major design flaws   \n",
              "3         1049   50         My favorite buy!   \n",
              "4          847   47         Flattering shirt   \n",
              "\n",
              "                                         Review Text  Rating  Recommended IND  \\\n",
              "0  Absolutely wonderful - silky and sexy and comf...       4                1   \n",
              "1  Love this dress!  it's sooo pretty.  i happene...       5                1   \n",
              "2  I had such high hopes for this dress and reall...       3                0   \n",
              "3  I love, love, love this jumpsuit. it's fun, fl...       5                1   \n",
              "4  This shirt is very flattering to all due to th...       5                1   \n",
              "\n",
              "   Positive Feedback Count   Division Name Department Name Class Name  \n",
              "0                        0       Initmates        Intimate  Intimates  \n",
              "1                        4         General         Dresses    Dresses  \n",
              "2                        0         General         Dresses    Dresses  \n",
              "3                        0  General Petite         Bottoms      Pants  \n",
              "4                        6         General            Tops    Blouses  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-d8b014ad-8905-44d3-b057-ad605b9d3c62\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <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>Clothing ID</th>\n",
              "      <th>Age</th>\n",
              "      <th>Title</th>\n",
              "      <th>Review Text</th>\n",
              "      <th>Rating</th>\n",
              "      <th>Recommended IND</th>\n",
              "      <th>Positive Feedback Count</th>\n",
              "      <th>Division Name</th>\n",
              "      <th>Department Name</th>\n",
              "      <th>Class Name</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>767</td>\n",
              "      <td>33</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Absolutely wonderful - silky and sexy and comf...</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>Initmates</td>\n",
              "      <td>Intimate</td>\n",
              "      <td>Intimates</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1080</td>\n",
              "      <td>34</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Love this dress!  it's sooo pretty.  i happene...</td>\n",
              "      <td>5</td>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "      <td>General</td>\n",
              "      <td>Dresses</td>\n",
              "      <td>Dresses</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1077</td>\n",
              "      <td>60</td>\n",
              "      <td>Some major design flaws</td>\n",
              "      <td>I had such high hopes for this dress and reall...</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>General</td>\n",
              "      <td>Dresses</td>\n",
              "      <td>Dresses</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1049</td>\n",
              "      <td>50</td>\n",
              "      <td>My favorite buy!</td>\n",
              "      <td>I love, love, love this jumpsuit. it's fun, fl...</td>\n",
              "      <td>5</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>General Petite</td>\n",
              "      <td>Bottoms</td>\n",
              "      <td>Pants</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>847</td>\n",
              "      <td>47</td>\n",
              "      <td>Flattering shirt</td>\n",
              "      <td>This shirt is very flattering to all due to th...</td>\n",
              "      <td>5</td>\n",
              "      <td>1</td>\n",
              "      <td>6</td>\n",
              "      <td>General</td>\n",
              "      <td>Tops</td>\n",
              "      <td>Blouses</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-d8b014ad-8905-44d3-b057-ad605b9d3c62')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-d8b014ad-8905-44d3-b057-ad605b9d3c62 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-d8b014ad-8905-44d3-b057-ad605b9d3c62');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "q-OwSePKm-FK"
      },
      "source": [
        "### 1 - Limpieza de datos\n",
        "Alumno:\n",
        "- Del dataset unicamente utilizar las columnas \"Review Text\" y \"Rating.\n",
        "- Tranformar el rating 1-5 a una escala numérica de 0 a 4.\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-hc7-AmYnPC3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "outputId": "65999aec-a66f-4a50-9962-17ddef361afd"
      },
      "source": [
        "df_reviews = df.loc[:, ['Review Text', 'Rating']].dropna()\n",
        "df_reviews['Rating'] = df_reviews['Rating'] - 1\n",
        "df_reviews.head()"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                         Review Text  Rating\n",
              "0  Absolutely wonderful - silky and sexy and comf...       3\n",
              "1  Love this dress!  it's sooo pretty.  i happene...       4\n",
              "2  I had such high hopes for this dress and reall...       2\n",
              "3  I love, love, love this jumpsuit. it's fun, fl...       4\n",
              "4  This shirt is very flattering to all due to th...       4"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-6fb0ead2-e8b8-4d87-a807-9231f1d4bb4c\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <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>Review Text</th>\n",
              "      <th>Rating</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Absolutely wonderful - silky and sexy and comf...</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Love this dress!  it's sooo pretty.  i happene...</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>I had such high hopes for this dress and reall...</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>I love, love, love this jumpsuit. it's fun, fl...</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>This shirt is very flattering to all due to th...</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-6fb0ead2-e8b8-4d87-a807-9231f1d4bb4c')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-6fb0ead2-e8b8-4d87-a807-9231f1d4bb4c button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-6fb0ead2-e8b8-4d87-a807-9231f1d4bb4c');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_reviews.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cBp-5Urx8Wmm",
        "outputId": "b378d402-759e-4ea1-907b-b67edbef086c"
      },
      "execution_count": 78,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Int64Index: 22641 entries, 0 to 23485\n",
            "Data columns (total 2 columns):\n",
            " #   Column       Non-Null Count  Dtype \n",
            "---  ------       --------------  ----- \n",
            " 0   Review Text  22641 non-null  object\n",
            " 1   Rating       22641 non-null  int64 \n",
            "dtypes: int64(1), object(1)\n",
            "memory usage: 1.0+ MB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "len(df_reviews)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xQaaEfktyKP4",
        "outputId": "3f33cfdc-d3fb-4ea9-9748-8f337f8fc48f"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "22641"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_reviews['Rating'].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "V1zF8kqrxj_T",
        "outputId": "38b6895d-945c-4a07-9314-031e369dd057"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "4    12540\n",
              "3     4908\n",
              "2     2823\n",
              "1     1549\n",
              "0      821\n",
              "Name: Rating, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_reviews['Rating'].value_counts()/len(df_reviews)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tmkUr9zFyOXL",
        "outputId": "d4566c47-a6a5-41f0-a56d-0ac639ae05d4"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "4    0.553862\n",
              "3    0.216775\n",
              "2    0.124685\n",
              "1    0.068416\n",
              "0    0.036262\n",
              "Name: Rating, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gVJ_RVi4o1h3",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "322bd145-ae40-4ac4-d342-2fbe7b4a8291"
      },
      "source": [
        "# Alumno: tomar la columna de las review y almacenarlo todo en un vector numpy de reviews\n",
        "type(df_reviews['Review Text'].values)"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "numpy.ndarray"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "reviews = df_reviews['Review Text'].values"
      ],
      "metadata": {
        "id": "Qje9cXZA0KW-"
      },
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4nT5Un_co65Q",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "7a6715b3-e410-4537-9d4b-2cc8406fc264"
      },
      "source": [
        "# Alumno: Cuantas reviews (rows) hay para evaluar?\n",
        "len(reviews)"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "22641"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HP5uN9tqpHu_"
      },
      "source": [
        "# Alumno: Concatenar todas las reviews para armar el corpus\n",
        "\n",
        "corpus = ' '.join(reviews)"
      ],
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FEzmePgdpf74",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "f6865d61-b1ab-4eb2-b9e7-3c6e10e5364d"
      },
      "source": [
        "# Alumno: ¿Cuál es la longitud de ese corpus?\n",
        "len(corpus)"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "7011643"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MYeJLdDmpvOe"
      },
      "source": [
        "# Alumno: Utilizar \"text_to_word_sequence\" para separar las palabras en tokens\n",
        "# recordar que text_to_word_sequence automaticamente quita los signos de puntuacion y pasa el texto a lowercase\n",
        "from keras.preprocessing.text import text_to_word_sequence\n",
        "\n",
        "tokens=text_to_word_sequence(corpus)"
      ],
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "M6L-fnWAp_lA",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "66cb3d6d-4595-4a3d-fe00-546959ba6a34"
      },
      "source": [
        "# Alumno: Dar un vistazo a los primeros 20 tokens/palabras\n",
        "tokens[:20]"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['absolutely',\n",
              " 'wonderful',\n",
              " 'silky',\n",
              " 'and',\n",
              " 'sexy',\n",
              " 'and',\n",
              " 'comfortable',\n",
              " 'love',\n",
              " 'this',\n",
              " 'dress',\n",
              " \"it's\",\n",
              " 'sooo',\n",
              " 'pretty',\n",
              " 'i',\n",
              " 'happened',\n",
              " 'to',\n",
              " 'find',\n",
              " 'it',\n",
              " 'in',\n",
              " 'a']"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-8QgwwMUqG0d",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "aa338cba-8019-40bd-fbe9-a2b3a605c8df"
      },
      "source": [
        "# Alumno: ¿Cuántos tokens/palabras hay?\n",
        "len(tokens)"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1372203"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TFukNZdOsZ8_"
      },
      "source": [
        "# Alumno: Tokenizar las palabras con el Tokenizer de Keras\n",
        "# Definir una máxima cantidad de palabras a utilizar:\n",
        "# num_words --> the maximum number of words to keep, based on word frequency.\n",
        "# Only the most common num_words-1 words will be kept.\n",
        "from keras.preprocessing.text import Tokenizer\n",
        "num_words = 2000\n",
        "vocab_size = num_words\n",
        "\n",
        "tokenizer = Tokenizer(num_words=2000) \n",
        "tokenizer.fit_on_texts(tokens) \n"
      ],
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JnR1tlqZy94X",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "cb994197-a906-42ce-8703-e21a6cbeecc2"
      },
      "source": [
        "# Alumno: Obtener el diccionario de palabra (word) a índice\n",
        "# y observar la cantidad total del vocabulario\n",
        "\n",
        "word_index = tokenizer.word_index\n",
        "len(word_index)"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "14847"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AvWzzSretQXf"
      },
      "source": [
        "# Alumno: Convertir las palabras/tokens a números\n",
        "tokens_nums = tokenizer.texts_to_sequences(reviews)"
      ],
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "tokens_nums[0]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KJUgrqZWSis-",
        "outputId": "4bebf4c2-3afd-4cf6-b795-fb66297d1724"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[253, 532, 917, 3, 662, 3, 68]"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "za73M5SRtbrP",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "70e68516-02cf-4d0b-8dd3-ba8c2a4c26af"
      },
      "source": [
        "# Alumno: Determinar cual es la oración más larga\n",
        "max(len(s) for s in tokens_nums)"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "115"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oCkO9Wc9tls1"
      },
      "source": [
        "# Alumno: Realizar padding de las sentencias al mismo tamaño\n",
        "# tomando de referencia la máxima sentencia\n",
        "from tensorflow.keras.utils import pad_sequences\n",
        "maxlen = 115\n",
        "\n",
        "X = pad_sequences(tokens_nums, padding='pre', maxlen=maxlen)\n"
      ],
      "execution_count": 78,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kGHHabVdt_aa",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "da57984f-9b4a-4bb6-d225-30d64ded27c2"
      },
      "source": [
        "# Alumno: Observar las dimensiones de la variable input\n",
        "X.shape"
      ],
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(22641, 115)"
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def one_hot_encode(arr):\n",
        "    n_values = np.max(arr) + 1\n",
        "    return np.eye(n_values)[arr]"
      ],
      "metadata": {
        "id": "-9YcDWqX931k"
      },
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df_reviews['Rating'].head(5)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "e35_76am96B_",
        "outputId": "fd3a30d5-e7ba-46fd-b2e1-50a0ed5bd865"
      },
      "execution_count": 87,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    3\n",
              "1    4\n",
              "2    2\n",
              "3    4\n",
              "4    4\n",
              "Name: Rating, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 87
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "one_hot_encode(df_reviews['Rating'].head(5))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZD-xCQ6A-CtV",
        "outputId": "e0ef1dfc-8f48-4e26-e69a-0ab24d54cd71"
      },
      "execution_count": 88,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[0., 0., 0., 1., 0.],\n",
              "       [0., 0., 0., 0., 1.],\n",
              "       [0., 0., 1., 0., 0.],\n",
              "       [0., 0., 0., 0., 1.],\n",
              "       [0., 0., 0., 0., 1.]])"
            ]
          },
          "metadata": {},
          "execution_count": 88
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "llVM-tzQo9_F"
      },
      "source": [
        "# Alumno tomar la columna rating y alcemacenarla en una variable \"y\" transformada a oneHotEncoding\n",
        "# Su shape debe ser equivalente la cantidad de rows del corpus y a la cantidad\n",
        "# de clases que se deseen predecir (en este ejemplo son 5)\n",
        "\n",
        "y  = one_hot_encode(df_reviews['Rating'])"
      ],
      "execution_count": 81,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "y.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OTj4a_wN2YJM",
        "outputId": "201456eb-b8b5-47e9-cf33-49b30fe17fd9"
      },
      "execution_count": 90,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(22641, 5)"
            ]
          },
          "metadata": {},
          "execution_count": 90
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Rmz9A6n4uK4V"
      },
      "source": [
        "# Alumno: Dividir los datos en train y test\n",
        "from sklearn.model_selection import train_test_split"
      ],
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Para reducir el desbalanceo voy a achicar mi conjunto de entrenamiento eliminando de manera random instancias de las clases mas pobladas"
      ],
      "metadata": {
        "id": "7RI_RVCvTmHc"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "type(X)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-EJldde8QmVN",
        "outputId": "17123e02-b839-4ec5-af6d-f331536f84ea"
      },
      "execution_count": 138,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "numpy.ndarray"
            ]
          },
          "metadata": {},
          "execution_count": 138
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "type(df_reviews['Rating'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "haBlc9HjQwt9",
        "outputId": "b1842fb9-d9dc-41e0-96bb-1b74aea51d1b"
      },
      "execution_count": 140,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "pandas.core.series.Series"
            ]
          },
          "metadata": {},
          "execution_count": 140
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_nuevo  = pd.DataFrame(X)"
      ],
      "metadata": {
        "id": "g-XO61WORl3T"
      },
      "execution_count": 79,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "ys = [f'y_{i}' for i in range(5)]\n",
        "for i, col in enumerate(ys):\n",
        "    df_nuevo[col] = y[:, i]"
      ],
      "metadata": {
        "id": "4mEdOlwJRtie"
      },
      "execution_count": 82,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df_nuevo.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "03nt3ZWAQ_4H",
        "outputId": "5578f9f2-5c33-4d6d-c89d-84f6c8018f73"
      },
      "execution_count": 145,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(22641, 120)"
            ]
          },
          "metadata": {},
          "execution_count": 145
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_nuevo['Rating'] = df_reviews['Rating']"
      ],
      "metadata": {
        "id": "Y0YvpOrDSVSG"
      },
      "execution_count": 83,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "for i in range(5):\n",
        "  print(len(df_nuevo[df_nuevo['Rating']== i ]['Rating']) /df_nuevo.shape[0])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UjRizFOVTsn0",
        "outputId": "92d49973-d2ea-4cb0-e546-eb9c08bd7040"
      },
      "execution_count": 158,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.035201625369904156\n",
            "0.06550064043107637\n",
            "0.11991519809195707\n",
            "0.20802968066781502\n",
            "0.5350470385583675\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "cantidad_4 = int(cantidad_4 / 5)\n",
        "cantidad_4"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iZrDilewUmi0",
        "outputId": "16395c31-2de9-4614-b0c7-e98d4b632783"
      },
      "execution_count": 91,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2422"
            ]
          },
          "metadata": {},
          "execution_count": 91
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sample_to_remove = df_nuevo[df_nuevo['Rating'] == 4].sample(n=len(df_nuevo[df_nuevo['Rating']== 4 ]['Rating']) - cantidad_4, random_state=42)"
      ],
      "metadata": {
        "id": "EyK1COvgQCBe"
      },
      "execution_count": 92,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df_nuevo = df_nuevo.drop(sample_to_remove.index)"
      ],
      "metadata": {
        "id": "WSPOfB0NVHf-"
      },
      "execution_count": 93,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df_nuevo.shape[0]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4sZZerjSWpyO",
        "outputId": "929fcdcd-68c5-44be-e6ce-9ba31e9d5db8"
      },
      "execution_count": 94,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "12949"
            ]
          },
          "metadata": {},
          "execution_count": 94
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "for i in range(5):\n",
        "  print(len(df_nuevo[df_nuevo['Rating']== i ]['Rating']) /df_nuevo.shape[0])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "AbyJZPE-VL92",
        "outputId": "dab96936-2dfc-4ef2-afb3-f2a161103ce1"
      },
      "execution_count": 95,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.061549154374855204\n",
            "0.11452621824079079\n",
            "0.20966870028573634\n",
            "0.3637346513244266\n",
            "0.1870414703838134\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "cantidad_3 = len(df_nuevo[df_nuevo['Rating']== 3 ]['Rating'])\n",
        "quinto_de_3 = int(cantidad_3 * 0.3)\n",
        "sample_to_remove = df_nuevo[df_nuevo['Rating'] == 3].sample(n=quinto_de_3, random_state=42)\n",
        "df_nuevo = df_nuevo.drop(sample_to_remove.index)\n",
        "df_nuevo.shape[0]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5vSSWdxzW1Eq",
        "outputId": "d9a41b83-2714-438d-ab98-fa2056aed9be"
      },
      "execution_count": 98,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "10877"
            ]
          },
          "metadata": {},
          "execution_count": 98
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "for i in range(5):\n",
        "  print(len(df_nuevo[df_nuevo['Rating']== i ]['Rating']) /df_nuevo.shape[0])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eAlWU81uXo79",
        "outputId": "5d3e91ef-9dd7-4f3d-879d-09fb495529d7"
      },
      "execution_count": 99,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.07327388066562471\n",
            "0.1363427415647697\n",
            "0.24960926726119334\n",
            "0.24253010940516687\n",
            "0.2226716925622874\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Emparejé bastante el desbalanceo"
      ],
      "metadata": {
        "id": "f5s5_ULtXuIH"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df_nuevo.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 235
        },
        "id": "gIB-k8ruXyFe",
        "outputId": "6fa1ae1a-5aec-42cf-8751-23a44274f193"
      },
      "execution_count": 168,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   0  1  2  3  4  5  6  7  8  9  ...  111   112   113  114  y_0  y_1  y_2  \\\n",
              "0  0  0  0  0  0  0  0  0  0  0  ...    3   662     3   68  0.0  0.0  0.0   \n",
              "2  0  0  0  0  0  0  0  0  0  0  ...    1   469     5  688  0.0  0.0  1.0   \n",
              "3  0  0  0  0  0  0  0  0  0  0  ...  533    10    34  210  0.0  0.0  0.0   \n",
              "5  0  0  0  0  0  0  0  0  0  0  ...    2   358     7   18  0.0  1.0  0.0   \n",
              "7  0  0  0  0  0  0  0  0  0  0  ...    9  1354  1689   22  0.0  0.0  0.0   \n",
              "\n",
              "   y_3  y_4  Rating  \n",
              "0  1.0  0.0     3.0  \n",
              "2  0.0  0.0     2.0  \n",
              "3  0.0  1.0     4.0  \n",
              "5  0.0  0.0     1.0  \n",
              "7  1.0  0.0     3.0  \n",
              "\n",
              "[5 rows x 121 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-b8708063-1d9d-4738-a76c-5b60cd0748fc\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <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>0</th>\n",
              "      <th>1</th>\n",
              "      <th>2</th>\n",
              "      <th>3</th>\n",
              "      <th>4</th>\n",
              "      <th>5</th>\n",
              "      <th>6</th>\n",
              "      <th>7</th>\n",
              "      <th>8</th>\n",
              "      <th>9</th>\n",
              "      <th>...</th>\n",
              "      <th>111</th>\n",
              "      <th>112</th>\n",
              "      <th>113</th>\n",
              "      <th>114</th>\n",
              "      <th>y_0</th>\n",
              "      <th>y_1</th>\n",
              "      <th>y_2</th>\n",
              "      <th>y_3</th>\n",
              "      <th>y_4</th>\n",
              "      <th>Rating</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>3</td>\n",
              "      <td>662</td>\n",
              "      <td>3</td>\n",
              "      <td>68</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>3.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>1</td>\n",
              "      <td>469</td>\n",
              "      <td>5</td>\n",
              "      <td>688</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>533</td>\n",
              "      <td>10</td>\n",
              "      <td>34</td>\n",
              "      <td>210</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>4.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>2</td>\n",
              "      <td>358</td>\n",
              "      <td>7</td>\n",
              "      <td>18</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>9</td>\n",
              "      <td>1354</td>\n",
              "      <td>1689</td>\n",
              "      <td>22</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>3.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 121 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-b8708063-1d9d-4738-a76c-5b60cd0748fc')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-b8708063-1d9d-4738-a76c-5b60cd0748fc button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-b8708063-1d9d-4738-a76c-5b60cd0748fc');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 168
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X = df_nuevo.iloc[:, :115]\n",
        "X"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "Bs5yVy-0ZK3S",
        "outputId": "7965ca12-fcca-4cab-b527-a1d50023c7d8"
      },
      "execution_count": 100,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       0    1    2    3    4    5    6    7    8    9    ...  105  106  107  \\\n",
              "2        0    0    0    0    0    0    0    0    0    0  ...   15    1  151   \n",
              "3        0    0    0    0    0    0    0    0    0    0  ...  183    2   32   \n",
              "5        0    0    0    0    0    0    0    0    0    0  ...   39  131   19   \n",
              "7        0    0    0    0    0    0    0    0    0    0  ...  187   47    6   \n",
              "8        0    0    0    0    0    0    0    0    0    0  ...   58    3  385   \n",
              "...    ...  ...  ...  ...  ...  ...  ...  ...  ...  ...  ...  ...  ...  ...   \n",
              "22632    0    0    0    0    0    0    0    0    0    0  ...  205  290   43   \n",
              "22634    0    0    0    0    0    0    0    0    0    0  ...  764    3  790   \n",
              "22635    0    0    0    0    0    0    0    0    0    0  ...    4  335  510   \n",
              "22636    0    0    0    0    0    0    0    0    0    0  ...   11    3   89   \n",
              "22639    0    0    0    0    0    0    0    0    0    0  ...   11    5    3   \n",
              "\n",
              "       108  109  110  111   112   113   114  \n",
              "2      475  830  313    1   469     5   688  \n",
              "3        5    2  115  533    10    34   210  \n",
              "5      102   11   31    2   358     7    18  \n",
              "7      455  450   62    9  1354  1689    22  \n",
              "8       14    1  236  103  1323    12   133  \n",
              "...    ...  ...  ...  ...   ...   ...   ...  \n",
              "22632   49   40  216    8    24    50   840  \n",
              "22634    1   83   34    2    65     8   261  \n",
              "22635   71    4  148  948    26    10     1  \n",
              "22636    4   23   58  120     3    47  1156  \n",
              "22639    2  219    5   84   129   206   185  \n",
              "\n",
              "[10877 rows x 115 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-b7bdb46b-4e1c-4828-9161-d1775676cf21\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <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>0</th>\n",
              "      <th>1</th>\n",
              "      <th>2</th>\n",
              "      <th>3</th>\n",
              "      <th>4</th>\n",
              "      <th>5</th>\n",
              "      <th>6</th>\n",
              "      <th>7</th>\n",
              "      <th>8</th>\n",
              "      <th>9</th>\n",
              "      <th>...</th>\n",
              "      <th>105</th>\n",
              "      <th>106</th>\n",
              "      <th>107</th>\n",
              "      <th>108</th>\n",
              "      <th>109</th>\n",
              "      <th>110</th>\n",
              "      <th>111</th>\n",
              "      <th>112</th>\n",
              "      <th>113</th>\n",
              "      <th>114</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>15</td>\n",
              "      <td>1</td>\n",
              "      <td>151</td>\n",
              "      <td>475</td>\n",
              "      <td>830</td>\n",
              "      <td>313</td>\n",
              "      <td>1</td>\n",
              "      <td>469</td>\n",
              "      <td>5</td>\n",
              "      <td>688</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>183</td>\n",
              "      <td>2</td>\n",
              "      <td>32</td>\n",
              "      <td>5</td>\n",
              "      <td>2</td>\n",
              "      <td>115</td>\n",
              "      <td>533</td>\n",
              "      <td>10</td>\n",
              "      <td>34</td>\n",
              "      <td>210</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>39</td>\n",
              "      <td>131</td>\n",
              "      <td>19</td>\n",
              "      <td>102</td>\n",
              "      <td>11</td>\n",
              "      <td>31</td>\n",
              "      <td>2</td>\n",
              "      <td>358</td>\n",
              "      <td>7</td>\n",
              "      <td>18</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>187</td>\n",
              "      <td>47</td>\n",
              "      <td>6</td>\n",
              "      <td>455</td>\n",
              "      <td>450</td>\n",
              "      <td>62</td>\n",
              "      <td>9</td>\n",
              "      <td>1354</td>\n",
              "      <td>1689</td>\n",
              "      <td>22</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>58</td>\n",
              "      <td>3</td>\n",
              "      <td>385</td>\n",
              "      <td>14</td>\n",
              "      <td>1</td>\n",
              "      <td>236</td>\n",
              "      <td>103</td>\n",
              "      <td>1323</td>\n",
              "      <td>12</td>\n",
              "      <td>133</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22632</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>205</td>\n",
              "      <td>290</td>\n",
              "      <td>43</td>\n",
              "      <td>49</td>\n",
              "      <td>40</td>\n",
              "      <td>216</td>\n",
              "      <td>8</td>\n",
              "      <td>24</td>\n",
              "      <td>50</td>\n",
              "      <td>840</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22634</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>764</td>\n",
              "      <td>3</td>\n",
              "      <td>790</td>\n",
              "      <td>1</td>\n",
              "      <td>83</td>\n",
              "      <td>34</td>\n",
              "      <td>2</td>\n",
              "      <td>65</td>\n",
              "      <td>8</td>\n",
              "      <td>261</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22635</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>4</td>\n",
              "      <td>335</td>\n",
              "      <td>510</td>\n",
              "      <td>71</td>\n",
              "      <td>4</td>\n",
              "      <td>148</td>\n",
              "      <td>948</td>\n",
              "      <td>26</td>\n",
              "      <td>10</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22636</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>11</td>\n",
              "      <td>3</td>\n",
              "      <td>89</td>\n",
              "      <td>4</td>\n",
              "      <td>23</td>\n",
              "      <td>58</td>\n",
              "      <td>120</td>\n",
              "      <td>3</td>\n",
              "      <td>47</td>\n",
              "      <td>1156</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22639</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>11</td>\n",
              "      <td>5</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>219</td>\n",
              "      <td>5</td>\n",
              "      <td>84</td>\n",
              "      <td>129</td>\n",
              "      <td>206</td>\n",
              "      <td>185</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>10877 rows × 115 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-b7bdb46b-4e1c-4828-9161-d1775676cf21')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-b7bdb46b-4e1c-4828-9161-d1775676cf21 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-b7bdb46b-4e1c-4828-9161-d1775676cf21');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 100
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y = df_nuevo.iloc[:, 115:120]\n",
        "y"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "YluhS3XxZvMU",
        "outputId": "0499fe5d-085b-4044-f1f1-d7b131a5c068"
      },
      "execution_count": 101,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       y_0  y_1  y_2  y_3  y_4\n",
              "2      0.0  0.0  1.0  0.0  0.0\n",
              "3      0.0  0.0  0.0  0.0  1.0\n",
              "5      0.0  1.0  0.0  0.0  0.0\n",
              "7      0.0  0.0  0.0  1.0  0.0\n",
              "8      0.0  0.0  0.0  0.0  1.0\n",
              "...    ...  ...  ...  ...  ...\n",
              "22632  0.0  0.0  0.0  1.0  0.0\n",
              "22634  1.0  0.0  0.0  0.0  0.0\n",
              "22635  0.0  0.0  0.0  0.0  1.0\n",
              "22636  0.0  0.0  0.0  0.0  1.0\n",
              "22639  0.0  0.0  1.0  0.0  0.0\n",
              "\n",
              "[10877 rows x 5 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-1073ba81-dc0d-4f06-81aa-7f867adc6087\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <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>y_0</th>\n",
              "      <th>y_1</th>\n",
              "      <th>y_2</th>\n",
              "      <th>y_3</th>\n",
              "      <th>y_4</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22632</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22634</th>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22635</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22636</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22639</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>10877 rows × 5 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-1073ba81-dc0d-4f06-81aa-7f867adc6087')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-1073ba81-dc0d-4f06-81aa-7f867adc6087 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-1073ba81-dc0d-4f06-81aa-7f867adc6087');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 101
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Ahora separo en train y test"
      ],
      "metadata": {
        "id": "esQyoYYxZ3ql"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.33, random_state=42 )"
      ],
      "metadata": {
        "id": "gPHzFS0lZ7Wh"
      },
      "execution_count": 102,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NpbQHExL6OTu"
      },
      "source": [
        "### 2 - Entrenar el modelo con Embeddings + LSTM"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NUkuWBsM6cx3"
      },
      "source": [
        "# Alumno: Entrene su modelo con LSTM entrenando sus propios embeddings\n",
        "# o utilizando embeddings pre-entrenados.\n",
        "from keras.models import Sequential\n",
        "from keras.layers import LSTM\n",
        "from keras.layers import Embedding\n",
        "from keras.layers import Dropout"
      ],
      "execution_count": 27,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import os\n",
        "import gdown\n",
        "if os.access('fasttext.pkl', os.F_OK) is False:\n",
        "    url = 'https://drive.google.com/uc?id=1KU5qmAYh3LATMvVgocFDfW-PK3prm1WU&export=download'\n",
        "    output = 'fasttext.pkl'\n",
        "    gdown.download(url, output, quiet=False)\n",
        "else:\n",
        "    print(\"Los embeddings fasttext.pkl ya están descargados\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ot5J5l5RGUUZ",
        "outputId": "14010e63-7d2a-48d5-fd6e-76b042096054"
      },
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Downloading...\n",
            "From (uriginal): https://drive.google.com/uc?id=1KU5qmAYh3LATMvVgocFDfW-PK3prm1WU&export=download\n",
            "From (redirected): https://drive.google.com/uc?id=1KU5qmAYh3LATMvVgocFDfW-PK3prm1WU&export=download&confirm=t&uuid=960fc076-325a-46cc-af4e-8498530c83f6\n",
            "To: /content/fasttext.pkl\n",
            "100%|██████████| 2.88G/2.88G [00:12<00:00, 222MB/s]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import logging\n",
        "import os\n",
        "from pathlib import Path\n",
        "from io import StringIO\n",
        "import pickle\n",
        "\n",
        "class WordsEmbeddings(object):\n",
        "    logger = logging.getLogger(__name__)\n",
        "\n",
        "    def __init__(self):\n",
        "        # load the embeddings\n",
        "        words_embedding_pkl = Path(self.PKL_PATH)\n",
        "        if not words_embedding_pkl.is_file():\n",
        "            words_embedding_txt = Path(self.WORD_TO_VEC_MODEL_TXT_PATH)\n",
        "            assert words_embedding_txt.is_file(), 'Words embedding not available'\n",
        "            embeddings = self.convert_model_to_pickle()\n",
        "        else:\n",
        "            embeddings = self.load_model_from_pickle()\n",
        "        self.embeddings = embeddings\n",
        "        # build the vocabulary hashmap\n",
        "        index = np.arange(self.embeddings.shape[0])\n",
        "        # Dicctionarios para traducir de embedding a IDX de la palabra\n",
        "        self.word2idx = dict(zip(self.embeddings['word'], index))\n",
        "        self.idx2word = dict(zip(index, self.embeddings['word']))\n",
        "\n",
        "    def get_words_embeddings(self, words):\n",
        "        words_idxs = self.words2idxs(words)\n",
        "        return self.embeddings[words_idxs]['embedding']\n",
        "\n",
        "    def words2idxs(self, words):\n",
        "        return np.array([self.word2idx.get(word, -1) for word in words])\n",
        "\n",
        "    def idxs2words(self, idxs):\n",
        "        return np.array([self.idx2word.get(idx, '-1') for idx in idxs])\n",
        "\n",
        "    def load_model_from_pickle(self):\n",
        "        self.logger.debug(\n",
        "            'loading words embeddings from pickle {}'.format(\n",
        "                self.PKL_PATH\n",
        "            )\n",
        "        )\n",
        "        max_bytes = 2**28 - 1 # 256MB\n",
        "        bytes_in = bytearray(0)\n",
        "        input_size = os.path.getsize(self.PKL_PATH)\n",
        "        with open(self.PKL_PATH, 'rb') as f_in:\n",
        "            for _ in range(0, input_size, max_bytes):\n",
        "                bytes_in += f_in.read(max_bytes)\n",
        "        embeddings = pickle.loads(bytes_in)\n",
        "        self.logger.debug('words embeddings loaded')\n",
        "        return embeddings\n",
        "\n",
        "    def convert_model_to_pickle(self):\n",
        "        # create a numpy strctured array:\n",
        "        # word     embedding\n",
        "        # U50      np.float32[]\n",
        "        # word_1   a, b, c\n",
        "        # word_2   d, e, f\n",
        "        # ...\n",
        "        # word_n   g, h, i\n",
        "        self.logger.debug(\n",
        "            'converting and loading words embeddings from text file {}'.format(\n",
        "                self.WORD_TO_VEC_MODEL_TXT_PATH\n",
        "            )\n",
        "        )\n",
        "        structure = [('word', np.dtype('U' + str(self.WORD_MAX_SIZE))),\n",
        "                     ('embedding', np.float32, (self.N_FEATURES,))]\n",
        "        structure = np.dtype(structure)\n",
        "        # load numpy array from disk using a generator\n",
        "        with open(self.WORD_TO_VEC_MODEL_TXT_PATH, encoding=\"utf8\") as words_embeddings_txt:\n",
        "            embeddings_gen = (\n",
        "                (line.split()[0], line.split()[1:]) for line in words_embeddings_txt\n",
        "                if len(line.split()[1:]) == self.N_FEATURES\n",
        "            )\n",
        "            embeddings = np.fromiter(embeddings_gen, structure)\n",
        "        # add a null embedding\n",
        "        null_embedding = np.array(\n",
        "            [('null_embedding', np.zeros((self.N_FEATURES,), dtype=np.float32))],\n",
        "            dtype=structure\n",
        "        )\n",
        "        embeddings = np.concatenate([embeddings, null_embedding])\n",
        "        # dump numpy array to disk using pickle\n",
        "        max_bytes = 2**28 - 1 # # 256MB\n",
        "        bytes_out = pickle.dumps(embeddings, protocol=pickle.HIGHEST_PROTOCOL)\n",
        "        with open(self.PKL_PATH, 'wb') as f_out:\n",
        "            for idx in range(0, len(bytes_out), max_bytes):\n",
        "                f_out.write(bytes_out[idx:idx+max_bytes])\n",
        "        self.logger.debug('words embeddings loaded')\n",
        "        return embeddings\n",
        "\n",
        "\n",
        "class GloveEmbeddings(WordsEmbeddings):\n",
        "    WORD_TO_VEC_MODEL_TXT_PATH = 'glove.twitter.27B.50d.txt'\n",
        "    PKL_PATH = 'gloveembedding.pkl'\n",
        "    N_FEATURES = 50\n",
        "    WORD_MAX_SIZE = 60\n",
        "\n",
        "\n",
        "class FasttextEmbeddings(WordsEmbeddings):\n",
        "    WORD_TO_VEC_MODEL_TXT_PATH = 'cc.en.300.vec'\n",
        "    PKL_PATH = 'fasttext.pkl'\n",
        "    N_FEATURES = 300\n",
        "    WORD_MAX_SIZE = 60"
      ],
      "metadata": {
        "id": "gnFtWEhCkr_Z"
      },
      "execution_count": 29,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model_fasttext = FasttextEmbeddings()"
      ],
      "metadata": {
        "id": "sMG6aoZ4m0AI"
      },
      "execution_count": 30,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Crear la Embedding matrix\n",
        "\n",
        "print('preparing embedding matrix...')\n",
        "embed_dim = 300 # fasttext\n",
        "words_not_found = []\n",
        "\n",
        "# word_index provieen del tokenizer\n",
        "\n",
        "nb_words = min(num_words, len(corpus)) # vocab_size\n",
        "embedding_matrix = np.zeros((nb_words, embed_dim))\n",
        "for word, i in word_index.items():\n",
        "    if i >= nb_words:\n",
        "        continue\n",
        "    embedding_vector = model_fasttext.get_words_embeddings(word)[0]\n",
        "    if (embedding_vector is not None) and len(embedding_vector) > 0:\n",
        "        embedding_matrix[i] = embedding_vector\n",
        "    else:\n",
        "        # words not found in embedding index will be all-zeros.\n",
        "        words_not_found.append(word)\n",
        "        print(word)\n",
        "\n",
        "print('number of null word embeddings:', np.sum(np.sum(embedding_matrix, axis=1) == 0))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jYqs-m2mk4Ze",
        "outputId": "7ebe6c11-553a-4253-91f4-5185a6a099ab"
      },
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "preparing embedding matrix...\n",
            "number of null word embeddings: 2\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "words_not_found"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "N1na0V6UnOgc",
        "outputId": "ea59004d-40c8-46f7-bc26-66da410b6288"
      },
      "execution_count": 180,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[]"
            ]
          },
          "metadata": {},
          "execution_count": 180
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model = Sequential()\n",
        "\n",
        "model.add(Embedding(input_dim = vocab_size,\n",
        "                    output_dim = embed_dim,\n",
        "                    input_length = 115,\n",
        "                    weights = [embedding_matrix],\n",
        "                    trainable =False))\n",
        "\n",
        "model.add(LSTM(64, return_sequences = True))\n",
        "model.add(Dropout(0.2))\n",
        "model.add(LSTM(64, return_sequences = False))\n",
        "model.add(Dropout(0.2))\n",
        "model.add(Dense(32, activation = 'relu'))\n",
        "model.add(Dropout(0.2))\n",
        "model.add(Dense(5, activation = 'softmax'))\n",
        "\n",
        "model.compile(optimizer =\"adam\", \n",
        "              loss = \"binary_crossentropy\", \n",
        "              metrics=['accuracy', ]) \n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "YRJ6aFcLnggF"
      },
      "execution_count": 32,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "hist = model.fit(X_train, y_train, epochs = 7, validation_split =0.2)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dhhbl-9hs45b",
        "outputId": "2f810634-4791-4ccb-d31e-5db8358aa9bc"
      },
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/7\n",
            "229/229 [==============================] - 14s 16ms/step - loss: 0.4242 - accuracy: 0.5336 - val_loss: 0.3985 - val_accuracy: 0.5487\n",
            "Epoch 2/7\n",
            "229/229 [==============================] - 3s 12ms/step - loss: 0.4068 - accuracy: 0.5497 - val_loss: 0.3952 - val_accuracy: 0.5487\n",
            "Epoch 3/7\n",
            "229/229 [==============================] - 4s 15ms/step - loss: 0.3980 - accuracy: 0.5502 - val_loss: 0.3884 - val_accuracy: 0.5487\n",
            "Epoch 4/7\n",
            "229/229 [==============================] - 3s 12ms/step - loss: 0.3918 - accuracy: 0.5506 - val_loss: 0.3836 - val_accuracy: 0.5487\n",
            "Epoch 5/7\n",
            "229/229 [==============================] - 3s 12ms/step - loss: 0.3879 - accuracy: 0.5505 - val_loss: 0.3834 - val_accuracy: 0.5487\n",
            "Epoch 6/7\n",
            "229/229 [==============================] - 3s 12ms/step - loss: 0.3848 - accuracy: 0.5505 - val_loss: 0.3771 - val_accuracy: 0.5487\n",
            "Epoch 7/7\n",
            "229/229 [==============================] - 4s 16ms/step - loss: 0.3790 - accuracy: 0.5507 - val_loss: 0.3757 - val_accuracy: 0.5487\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X_test[:1].shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lycPmoSr4b-K",
        "outputId": "8892aad6-ed75-4c63-b9db-ed98c6f263a8"
      },
      "execution_count": 60,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1, 115)"
            ]
          },
          "metadata": {},
          "execution_count": 60
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "r = np.zeros(5) # a.size if a is a numpy array\n",
        "r[np.argmax(model.predict(X_test[:1]))]=1\n",
        "r"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jFnVgxBC81hv",
        "outputId": "4f091825-564d-4acb-ae65-91543400947c"
      },
      "execution_count": 63,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1/1 [==============================] - 0s 84ms/step\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0., 0., 0., 0., 1.])"
            ]
          },
          "metadata": {},
          "execution_count": 63
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_test[:1]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 80
        },
        "id": "OD3pWkDz6rCs",
        "outputId": "fa41ec96-9c02-4a38-dd6a-b73f5254454e"
      },
      "execution_count": 64,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       y_0  y_1  y_2  y_3  y_4\n",
              "12760  0.0  0.0  0.0  1.0  0.0"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-3eca7f8d-dda7-4580-ac52-9589f85285e5\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <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>y_0</th>\n",
              "      <th>y_1</th>\n",
              "      <th>y_2</th>\n",
              "      <th>y_3</th>\n",
              "      <th>y_4</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>12760</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-3eca7f8d-dda7-4580-ac52-9589f85285e5')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-3eca7f8d-dda7-4580-ac52-9589f85285e5 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-3eca7f8d-dda7-4580-ac52-9589f85285e5');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 64
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import multilabel_confusion_matrix\n",
        "from sklearn.metrics import confusion_matrix"
      ],
      "metadata": {
        "id": "tfNv3sUx5Z7P"
      },
      "execution_count": 39,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "y_prediction = model.predict(X_test)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "O5KeescS-cj9",
        "outputId": "64b5ae50-eef8-41cb-8488-aae1071a27e0"
      },
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "141/141 [==============================] - 1s 5ms/step\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_prediction.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pdgizUWU-nLM",
        "outputId": "1d53b3bc-06b4-4ea4-ec49-dda4a46b0d11"
      },
      "execution_count": 66,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(4496, 5)"
            ]
          },
          "metadata": {},
          "execution_count": 66
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "binary_predictions = np.zeros_like(y_prediction)"
      ],
      "metadata": {
        "id": "8k8xyIlZ-sPn"
      },
      "execution_count": 36,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "binary_predictions.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wSlzMAZL-u15",
        "outputId": "06aa3336-1128-499c-9be1-fd8039363bc2"
      },
      "execution_count": 68,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(4496, 5)"
            ]
          },
          "metadata": {},
          "execution_count": 68
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "for i in range(4496):\n",
        "  binary_predictions[i][np.argmax(y_prediction[i])] = 1"
      ],
      "metadata": {
        "id": "kZx1W13c-5VI"
      },
      "execution_count": 37,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "todos = True\n",
        "for i in range(4496):\n",
        "  todos = todos * (sum(binary_predictions[i] == 1))\n",
        "todos"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9CEqB3qEAFyc",
        "outputId": "8e2b7580-18f0-4743-aab1-37446741b736"
      },
      "execution_count": 81,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1"
            ]
          },
          "metadata": {},
          "execution_count": 81
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "import itertools\n"
      ],
      "metadata": {
        "id": "o4OpXmb0GhxI"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Create confusion matrix and normalizes it over predicted (columns)\n",
        "result = multilabel_confusion_matrix(y_test, binary_predictions)\n",
        "result"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yXM3N4z72cQt",
        "outputId": "5bcf8b15-166f-4437-ec70-c84948da27d7"
      },
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[[4321,    0],\n",
              "        [ 175,    0]],\n",
              "\n",
              "       [[4211,    0],\n",
              "        [ 285,    0]],\n",
              "\n",
              "       [[3925,    0],\n",
              "        [ 571,    0]],\n",
              "\n",
              "       [[3512,    0],\n",
              "        [ 984,    0]],\n",
              "\n",
              "       [[   0, 2015],\n",
              "        [   0, 2481]]])"
            ]
          },
          "metadata": {},
          "execution_count": 40
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "result.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9PSbWihOUZCF",
        "outputId": "e3236854-0ef6-44cc-b469-dad494cc4b4a"
      },
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(5, 2, 2)"
            ]
          },
          "metadata": {},
          "execution_count": 41
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model = Sequential()\n",
        "\n",
        "model.add(Embedding(input_dim = vocab_size,\n",
        "                    output_dim = embed_dim,\n",
        "                    input_length = 115,\n",
        "                    weights = [embedding_matrix],\n",
        "                    trainable =False))\n",
        "\n",
        "model.add(LSTM(64, return_sequences = True))\n",
        "model.add(Dropout(0.2))\n",
        "model.add(LSTM(128, return_sequences = False))\n",
        "model.add(Dropout(0.2))\n",
        "model.add(Dense(32, activation = 'relu'))\n",
        "model.add(Dropout(0.2))\n",
        "model.add(Dense(5, activation = 'softmax'))\n",
        "\n",
        "model.compile(optimizer =\"adam\", \n",
        "              loss = \"binary_crossentropy\", \n",
        "              metrics=['accuracy' ]) \n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "nLfB26YJUuru"
      },
      "execution_count": 42,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "hist = model.fit(X_train, y_train, epochs = 7, validation_split =0.2)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "46c75bb3-4d78-4a66-fb4a-a56cdab9c298",
        "id": "tjRL_-A7Uuru"
      },
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/7\n",
            "229/229 [==============================] - 9s 17ms/step - loss: 0.4240 - accuracy: 0.5420 - val_loss: 0.3985 - val_accuracy: 0.5487\n",
            "Epoch 2/7\n",
            "229/229 [==============================] - 3s 12ms/step - loss: 0.4079 - accuracy: 0.5498 - val_loss: 0.3943 - val_accuracy: 0.5487\n",
            "Epoch 3/7\n",
            "229/229 [==============================] - 3s 12ms/step - loss: 0.3992 - accuracy: 0.5499 - val_loss: 0.3907 - val_accuracy: 0.5487\n",
            "Epoch 4/7\n",
            "229/229 [==============================] - 4s 16ms/step - loss: 0.3929 - accuracy: 0.5501 - val_loss: 0.3820 - val_accuracy: 0.5487\n",
            "Epoch 5/7\n",
            "229/229 [==============================] - 3s 13ms/step - loss: 0.3865 - accuracy: 0.5502 - val_loss: 0.3787 - val_accuracy: 0.5487\n",
            "Epoch 6/7\n",
            "229/229 [==============================] - 3s 12ms/step - loss: 0.3811 - accuracy: 0.5513 - val_loss: 0.3790 - val_accuracy: 0.5487\n",
            "Epoch 7/7\n",
            "229/229 [==============================] - 3s 12ms/step - loss: 0.3762 - accuracy: 0.5498 - val_loss: 0.3724 - val_accuracy: 0.5515\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_prediction = model.predict(X_test)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "8df8a039-fb5b-451c-eff9-4eb96243aa36",
        "id": "dhXBQlYXUuru"
      },
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "141/141 [==============================] - 1s 5ms/step\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "binary_predictions = np.zeros_like(y_prediction)"
      ],
      "metadata": {
        "id": "KVk_4CkCUuru"
      },
      "execution_count": 45,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "for i in range(4496):\n",
        "  binary_predictions[i][np.argmax(y_prediction[i])] = 1"
      ],
      "metadata": {
        "id": "KIXTQShyUuru"
      },
      "execution_count": 46,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Create confusion matrix and normalizes it over predicted (columns)\n",
        "result = multilabel_confusion_matrix(y_test, binary_predictions)\n",
        "result"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3RZfWZ4zVE6L",
        "outputId": "833f16eb-031a-452a-f37b-ad285f866d97"
      },
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[[4321,    0],\n",
              "        [ 175,    0]],\n",
              "\n",
              "       [[4211,    0],\n",
              "        [ 285,    0]],\n",
              "\n",
              "       [[3917,    8],\n",
              "        [ 561,   10]],\n",
              "\n",
              "       [[3512,    0],\n",
              "        [ 984,    0]],\n",
              "\n",
              "       [[  17, 1998],\n",
              "        [   1, 2480]]])"
            ]
          },
          "metadata": {},
          "execution_count": 47
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model = Sequential()\n",
        "\n",
        "model.add(Embedding(input_dim = vocab_size,\n",
        "                    output_dim = embed_dim,\n",
        "                    input_length = 115,\n",
        "                    weights = [embedding_matrix],\n",
        "                    trainable =False))\n",
        "\n",
        "model.add(LSTM(128, return_sequences = True))\n",
        "model.add(Dropout(0.2))\n",
        "model.add(LSTM(512, return_sequences = True))\n",
        "model.add(Dropout(0.2))\n",
        "model.add(LSTM(64, return_sequences = False))\n",
        "model.add(Dropout(0.2))\n",
        "model.add(Dense(32, activation = 'relu'))\n",
        "model.add(Dropout(0.2))\n",
        "model.add(Dense(5, activation = 'softmax'))\n",
        "\n",
        "model.compile(optimizer =\"adam\", \n",
        "              loss = \"binary_crossentropy\", \n",
        "              metrics=['accuracy']) \n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "cv0tS1asVNrV"
      },
      "execution_count": 69,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "hist = model.fit(X_train, y_train, epochs = 15, validation_split =0.2)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "fbf8f9d7-b0da-451f-cb3e-2cec11634b0f",
        "id": "iQjPnjNeVNrW"
      },
      "execution_count": 70,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/15\n",
            "229/229 [==============================] - 14s 37ms/step - loss: 0.4221 - accuracy: 0.5286 - val_loss: 0.3987 - val_accuracy: 0.5487\n",
            "Epoch 2/15\n",
            "229/229 [==============================] - 7s 29ms/step - loss: 0.4097 - accuracy: 0.5499 - val_loss: 0.3983 - val_accuracy: 0.5487\n",
            "Epoch 3/15\n",
            "229/229 [==============================] - 7s 30ms/step - loss: 0.4057 - accuracy: 0.5505 - val_loss: 0.3981 - val_accuracy: 0.5487\n",
            "Epoch 4/15\n",
            "229/229 [==============================] - 7s 30ms/step - loss: 0.4015 - accuracy: 0.5505 - val_loss: 0.3925 - val_accuracy: 0.5487\n",
            "Epoch 5/15\n",
            "229/229 [==============================] - 7s 30ms/step - loss: 0.3930 - accuracy: 0.5505 - val_loss: 0.3830 - val_accuracy: 0.5487\n",
            "Epoch 6/15\n",
            "229/229 [==============================] - 7s 30ms/step - loss: 0.3857 - accuracy: 0.5505 - val_loss: 0.3796 - val_accuracy: 0.5487\n",
            "Epoch 7/15\n",
            "229/229 [==============================] - 7s 29ms/step - loss: 0.3820 - accuracy: 0.5505 - val_loss: 0.3839 - val_accuracy: 0.5487\n",
            "Epoch 8/15\n",
            "229/229 [==============================] - 7s 30ms/step - loss: 0.3773 - accuracy: 0.5497 - val_loss: 0.3748 - val_accuracy: 0.5487\n",
            "Epoch 9/15\n",
            "229/229 [==============================] - 7s 29ms/step - loss: 0.3716 - accuracy: 0.5531 - val_loss: 0.3723 - val_accuracy: 0.5635\n",
            "Epoch 10/15\n",
            "229/229 [==============================] - 7s 30ms/step - loss: 0.3676 - accuracy: 0.5561 - val_loss: 0.3701 - val_accuracy: 0.5575\n",
            "Epoch 11/15\n",
            "229/229 [==============================] - 7s 29ms/step - loss: 0.3625 - accuracy: 0.5581 - val_loss: 0.3713 - val_accuracy: 0.5586\n",
            "Epoch 12/15\n",
            "229/229 [==============================] - 7s 30ms/step - loss: 0.3580 - accuracy: 0.5605 - val_loss: 0.3717 - val_accuracy: 0.5602\n",
            "Epoch 13/15\n",
            "229/229 [==============================] - 7s 29ms/step - loss: 0.3528 - accuracy: 0.5643 - val_loss: 0.3759 - val_accuracy: 0.5581\n",
            "Epoch 14/15\n",
            "229/229 [==============================] - 7s 29ms/step - loss: 0.3466 - accuracy: 0.5684 - val_loss: 0.3812 - val_accuracy: 0.5575\n",
            "Epoch 15/15\n",
            "229/229 [==============================] - 7s 32ms/step - loss: 0.3396 - accuracy: 0.5758 - val_loss: 0.3847 - val_accuracy: 0.5646\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_prediction = model.predict(X_test)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "bffb2e33-1b72-4506-d476-332663121c11",
        "id": "DyLIo3aSVNrW"
      },
      "execution_count": 71,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "141/141 [==============================] - 2s 11ms/step\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "binary_predictions = np.zeros_like(y_prediction)"
      ],
      "metadata": {
        "id": "sh41iOAzVNrW"
      },
      "execution_count": 72,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "for i in range(4496):\n",
        "  binary_predictions[i][np.argmax(y_prediction[i])] = 1"
      ],
      "metadata": {
        "id": "O0urdBFmVNrW"
      },
      "execution_count": 73,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Create confusion matrix and normalizes it over predicted (columns)\n",
        "result = multilabel_confusion_matrix(y_test, binary_predictions)\n",
        "result"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "70ab1bc6-5dfc-4a4f-c7f9-6cf355614a93",
        "id": "e_5cycsBVNrW"
      },
      "execution_count": 74,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[[4321,    0],\n",
              "        [ 175,    0]],\n",
              "\n",
              "       [[4211,    0],\n",
              "        [ 285,    0]],\n",
              "\n",
              "       [[3604,  321],\n",
              "        [ 418,  153]],\n",
              "\n",
              "       [[3467,   45],\n",
              "        [ 975,    9]],\n",
              "\n",
              "       [[ 413, 1602],\n",
              "        [ 115, 2366]]])"
            ]
          },
          "metadata": {},
          "execution_count": 74
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "balancee el dataset un poco mas"
      ],
      "metadata": {
        "id": "sGvhnqg2aAPj"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model = Sequential()\n",
        "\n",
        "model.add(Embedding(input_dim = vocab_size,\n",
        "                    output_dim = embed_dim,\n",
        "                    input_length = 115,\n",
        "                    weights = [embedding_matrix],\n",
        "                    trainable =False))\n",
        "\n",
        "model.add(LSTM(64, return_sequences = True))\n",
        "model.add(Dropout(0.2))\n",
        "model.add(LSTM(64, return_sequences = False))\n",
        "model.add(Dropout(0.2))\n",
        "#model.add(LSTM(64, return_sequences = False))\n",
        "#model.add(Dropout(0.2))\n",
        "model.add(Dense(128, activation = 'relu'))\n",
        "model.add(Dropout(0.2))\n",
        "model.add(Dense(5, activation = 'softmax'))\n",
        "\n",
        "model.compile(optimizer =\"adam\", \n",
        "              loss = \"binary_crossentropy\", \n",
        "              metrics=['accuracy']) \n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "-ASuR256aFwv"
      },
      "execution_count": 118,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "hist = model.fit(X_train, y_train, epochs = 15, validation_split =0.2)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "3b22ed71-23c8-438b-b5a9-e9aba7d3566e",
        "id": "AMwPqLiXaFww"
      },
      "execution_count": 119,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/15\n",
            "183/183 [==============================] - 8s 17ms/step - loss: 0.4230 - accuracy: 0.5347 - val_loss: 0.4004 - val_accuracy: 0.5487\n",
            "Epoch 2/15\n",
            "183/183 [==============================] - 2s 12ms/step - loss: 0.4056 - accuracy: 0.5490 - val_loss: 0.3972 - val_accuracy: 0.5487\n",
            "Epoch 3/15\n",
            "183/183 [==============================] - 3s 16ms/step - loss: 0.3994 - accuracy: 0.5490 - val_loss: 0.3911 - val_accuracy: 0.5487\n",
            "Epoch 4/15\n",
            "183/183 [==============================] - 2s 13ms/step - loss: 0.3918 - accuracy: 0.5493 - val_loss: 0.3938 - val_accuracy: 0.5487\n",
            "Epoch 5/15\n",
            "183/183 [==============================] - 2s 13ms/step - loss: 0.3857 - accuracy: 0.5510 - val_loss: 0.3802 - val_accuracy: 0.5528\n",
            "Epoch 6/15\n",
            "183/183 [==============================] - 2s 12ms/step - loss: 0.3823 - accuracy: 0.5516 - val_loss: 0.4032 - val_accuracy: 0.5473\n",
            "Epoch 7/15\n",
            "183/183 [==============================] - 2s 12ms/step - loss: 0.3782 - accuracy: 0.5519 - val_loss: 0.3763 - val_accuracy: 0.5549\n",
            "Epoch 8/15\n",
            "183/183 [==============================] - 3s 16ms/step - loss: 0.3722 - accuracy: 0.5558 - val_loss: 0.3750 - val_accuracy: 0.5521\n",
            "Epoch 9/15\n",
            "183/183 [==============================] - 2s 12ms/step - loss: 0.3707 - accuracy: 0.5565 - val_loss: 0.3719 - val_accuracy: 0.5549\n",
            "Epoch 10/15\n",
            "183/183 [==============================] - 2s 12ms/step - loss: 0.3672 - accuracy: 0.5562 - val_loss: 0.3763 - val_accuracy: 0.5501\n",
            "Epoch 11/15\n",
            "183/183 [==============================] - 2s 12ms/step - loss: 0.3626 - accuracy: 0.5600 - val_loss: 0.3851 - val_accuracy: 0.5398\n",
            "Epoch 12/15\n",
            "183/183 [==============================] - 2s 13ms/step - loss: 0.3588 - accuracy: 0.5634 - val_loss: 0.3704 - val_accuracy: 0.5562\n",
            "Epoch 13/15\n",
            "183/183 [==============================] - 3s 17ms/step - loss: 0.3564 - accuracy: 0.5685 - val_loss: 0.3708 - val_accuracy: 0.5562\n",
            "Epoch 14/15\n",
            "183/183 [==============================] - 2s 12ms/step - loss: 0.3520 - accuracy: 0.5716 - val_loss: 0.3689 - val_accuracy: 0.5583\n",
            "Epoch 15/15\n",
            "183/183 [==============================] - 2s 12ms/step - loss: 0.3459 - accuracy: 0.5776 - val_loss: 0.3693 - val_accuracy: 0.5494\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_prediction = model.predict(X_test)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b1de65aa-ed20-428e-93fd-bdfc643a4a3d",
        "id": "6JgO6gX9aFww"
      },
      "execution_count": 120,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "113/113 [==============================] - 1s 5ms/step\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "binary_predictions = np.zeros_like(y_prediction)"
      ],
      "metadata": {
        "id": "y6jV4hijaFwx"
      },
      "execution_count": 121,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "for i in range(3590):\n",
        "  binary_predictions[i][np.argmax(y_prediction[i])] = 1"
      ],
      "metadata": {
        "id": "x33EWuDgaFwx"
      },
      "execution_count": 123,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Create confusion matrix and normalizes it over predicted (columns)\n",
        "result = multilabel_confusion_matrix(y_test, binary_predictions)\n",
        "result"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "8068ac79-4337-49ac-d70f-5b072a98563b",
        "id": "9EK-PFhZaFwx"
      },
      "execution_count": 124,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[[3482,    0],\n",
              "        [ 108,    0]],\n",
              "\n",
              "       [[3346,    0],\n",
              "        [ 244,    0]],\n",
              "\n",
              "       [[2809,  349],\n",
              "        [ 295,  137]],\n",
              "\n",
              "       [[2701,   82],\n",
              "        [ 771,   36]],\n",
              "\n",
              "       [[ 459, 1132],\n",
              "        [ 145, 1854]]])"
            ]
          },
          "metadata": {},
          "execution_count": 124
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Luego de haber probado distintas combinaciones entre redes y tamaños del dataset con el fin de obtener un dataset mas balanceado, no pude obtener ningún resultado considerabalmente positivo. Es necesario de mayor cantidad de datos para poder mejorar la solución a este problema de clasificación multiclases."
      ],
      "metadata": {
        "id": "R5l2R3GmcM4F"
      }
    }
  ]
}