TP2/7506R_TP2_GRUPO02_ENTREGA_N4.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/kikiymini/7506R-1C2024-GRUPO02/blob/main/TP2/7506R_TP2_GRUPO2_Red_Neuronal.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GDDSr-SglU1G"
      },
      "source": [
        "# <center>Organizaci&oacute;n de Datos</center>\n",
        "\n",
        "#### <center>C&aacute;tedra Ing. Rodriguez, Juan Manuel </center>\n",
        "\n",
        "## <center>Trabajo Práctico 2: Críticas cinematográficas</center>\n",
        "\n",
        "### <center> Grupo 2</center>\n",
        "\n",
        "## Integrantes:\n",
        "\n",
        "*   Aramayo Carolina\n",
        "*   Utrera Maximo Damian\n",
        "*   Villalba Ana Daniela\n",
        "*   Fiorilo Roy\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yQzgLrPLld-Z"
      },
      "source": [
        "# Importación de librerias"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VvM4hVZ9uFpL",
        "outputId": "d39df891-5db0-4dd5-eafe-3eb171290d77"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[nltk_data] Downloading package stopwords to /root/nltk_data...\n",
            "[nltk_data]   Unzipping corpora/stopwords.zip.\n",
            "[nltk_data] Downloading package punkt to /root/nltk_data...\n",
            "[nltk_data]   Unzipping tokenizers/punkt.zip.\n",
            "[nltk_data] Downloading package omw-1.4 to /root/nltk_data...\n",
            "[nltk_data] Downloading package wordnet to /root/nltk_data...\n",
            "[nltk_data] Downloading package averaged_perceptron_tagger to\n",
            "[nltk_data]     /root/nltk_data...\n",
            "[nltk_data]   Unzipping taggers/averaged_perceptron_tagger.zip.\n"
          ]
        }
      ],
      "source": [
        "from google.colab import drive\n",
        "\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "from matplotlib import pyplot as plt\n",
        "import seaborn as sns\n",
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "# Tokens y preprocesamiento\n",
        "import nltk\n",
        "from nltk import word_tokenize\n",
        "from nltk.corpus import stopwords\n",
        "from nltk import pos_tag\n",
        "from nltk.corpus import wordnet\n",
        "from collections import Counter\n",
        "import spacy\n",
        "from nltk.corpus import stopwords\n",
        "\n",
        "# nltk\n",
        "nltk.download('stopwords')\n",
        "nltk.download('punkt')\n",
        "nltk.download('omw-1.4')\n",
        "nltk.download('wordnet')\n",
        "nltk.download('averaged_perceptron_tagger')\n",
        "\n",
        "# Modelos\n",
        "import joblib\n",
        "from sklearn.model_selection import cross_val_score, KFold, RandomizedSearchCV,StratifiedKFold\n",
        "from sklearn.metrics import confusion_matrix, classification_report, f1_score, make_scorer\n",
        "\n",
        "from sklearn.preprocessing import LabelEncoder\n",
        "\n",
        "import tensorflow as tf\n",
        "from tensorflow.keras.preprocessing.text import Tokenizer\n",
        "from tensorflow.keras.preprocessing.sequence import pad_sequences\n",
        "from tensorflow import keras\n",
        "from keras.preprocessing.text import one_hot\n",
        "from keras.preprocessing.text import text_to_word_sequence\n",
        "from keras import layers\n",
        "\n",
        "import warnings\n",
        "warnings.filterwarnings(\"ignore\")\n",
        "\n",
        "RAND_SEED = 42"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "IJSQ_EQMWC-9"
      },
      "outputs": [],
      "source": [
        "# !pip install keras==2.12.0\n",
        "# !pip uninstall tensorflow -y\n",
        "# !pip install tensorflow==2.12.0"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZCstguaruDzD"
      },
      "source": [
        "# Lectura de archivo\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MZ-i4EbmuIVX"
      },
      "source": [
        "### Desde google drive"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9wJ_ERJKwzAQ",
        "outputId": "78faa45c-e585-401e-dee9-29c2214c9280"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ],
      "source": [
        "drive.mount('/content/drive')\n",
        "root_path = \"/content/drive/MyDrive/7506R-1C2024-GRUPO02/TP2/\""
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Aaf7SBZluLlI"
      },
      "source": [
        "### Desde maquina local"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "gPBHdD8KladB"
      },
      "outputs": [],
      "source": [
        "# Si se esta trabajando en local, descomentar esta celta y comentar la de arriba\n",
        "# root_path = \"./\""
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "DMJR5Rizlduf"
      },
      "outputs": [],
      "source": [
        "train_path = root_path + 'Dataset/train.csv'\n",
        "test_path = root_path + 'Dataset/test.csv'\n",
        "normalizado_path = root_path + 'Dataset/sentimientos_limpios.csv'\n",
        "sample_path = root_path + 'Dataset/sample_solution.csv'\n",
        "ds_train = pd.read_csv(train_path)\n",
        "ds_test = pd.read_csv(test_path)\n",
        "sample = pd.read_csv(sample_path)\n",
        "ds_normalizado = pd.read_csv(normalizado_path)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RlY6pAgdvbe5"
      },
      "source": [
        "# Visualizamos los datasets"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cUWg64kmAV8w",
        "outputId": "dd701e23-1b58-4d8c-9445-170d4d3c9ebd"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 50000 entries, 0 to 49999\n",
            "Data columns (total 3 columns):\n",
            " #   Column       Non-Null Count  Dtype \n",
            "---  ------       --------------  ----- \n",
            " 0   ID           50000 non-null  int64 \n",
            " 1   review_es    50000 non-null  object\n",
            " 2   sentimiento  50000 non-null  object\n",
            "dtypes: int64(1), object(2)\n",
            "memory usage: 1.1+ MB\n",
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 8599 entries, 0 to 8598\n",
            "Data columns (total 2 columns):\n",
            " #   Column     Non-Null Count  Dtype \n",
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            " 0   ID         8599 non-null   int64 \n",
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            "dtypes: int64(1), object(1)\n",
            "memory usage: 134.5+ KB\n",
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 50000 entries, 0 to 49999\n",
            "Data columns (total 4 columns):\n",
            " #   Column       Non-Null Count  Dtype \n",
            "---  ------       --------------  ----- \n",
            " 0   ID           50000 non-null  int64 \n",
            " 1   review_es    50000 non-null  object\n",
            " 2   sentimiento  50000 non-null  object\n",
            " 3   review_norm  50000 non-null  object\n",
            "dtypes: int64(1), object(3)\n",
            "memory usage: 1.5+ MB\n"
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      "source": [
        "ds_train.info()\n",
        "ds_test.info()\n",
        "ds_normalizado.info()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
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              "          ID                                          review_es sentimiento\n",
              "0          0  Uno de los otros críticos ha mencionado que de...    positivo\n",
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              "49995  49995  Pensé que esta película hizo un buen trabajo a...    positivo\n",
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              "49999  49999  Nadie espera que las películas de Star Trek se...    negativo\n",
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              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
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              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-b24bed3f-4726-4285-acc7-a1411c78d479 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "  <div id=\"id_83580ece-b23c-4517-8623-10b8af680591\">\n",
              "    <style>\n",
              "      .colab-df-generate {\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-generate: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-generate {\n",
              "        background-color: #3B4455;\n",
              "        fill: #D2E3FC;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate: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",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('ds_train')\"\n",
              "            title=\"Generate code using this dataframe.\"\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=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
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              "      (() => {\n",
              "      const buttonEl =\n",
              "        document.querySelector('#id_83580ece-b23c-4517-8623-10b8af680591 button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('ds_train');\n",
              "      }\n",
              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "ds_train",
              "summary": "{\n  \"name\": \"ds_train\",\n  \"rows\": 50000,\n  \"fields\": [\n    {\n      \"column\": \"ID\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 14433,\n        \"min\": 0,\n        \"max\": 49999,\n        \"num_unique_values\": 50000,\n        \"samples\": [\n          33553,\n          9427,\n          199\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"review_es\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 49599,\n        \"samples\": [\n          \"Dos a\\u00f1os despu\\u00e9s del \\u00e9xito de 'avi\\u00f3n', Jim Abrahams y Jerry y David Zucker crearon esta brillante comedia protagonizada por la Gran Leslie Nielsen, ya que el detective video simple 'Frank Drebin'. Tambi\\u00e9n en el elenco fue Alan North como 'Capit\\u00e1n Ed Hocken', Ed Williams como 'Ted Olsen' y William Duell como 'Johnny The Shoe Shine Boy'. '\\u00a1Escuadr\\u00f3n de la polic\\u00eda!' Destac\\u00f3 chistes sin verg\\u00fcenza y gags visuales inteligentes jugando en el fondo. Cada episodio concluir\\u00eda con un marco de congelaci\\u00f3n simulado en el que los caracteres en el marco se encuentran completamente quietos. \\u00a1Una de las mejores secuencias de 'congelamiento' de 'congelamiento' vieron a uno de los personajes que vierte caf\\u00e9 en una taza mientras est\\u00e1 de pie, lo que hace que la taza se desborde! Las estrellas invitadas fueron asesinadas en los t\\u00edtulos de apertura, \\u00a1uno incluido Georg Stanford Brown siendo aplastado por una caja fuerte! A pesar de obtener revisiones positivas y mucho cr\\u00edtico, '\\u00a1Escuadr\\u00f3n!' Solo dur\\u00f3 seis episodios antes de ser cancelados. Esto no signific\\u00f3 que el final, sin embargo, cinco a\\u00f1os despu\\u00e9s, el espect\\u00e1culo se transfiri\\u00f3 a la pantalla grande para la primera en la trilog\\u00eda de las pel\\u00edculas \\\"desnudas de la pistola\\\".\",\n          \"He le\\u00eddo los comentarios de KERCHING ON IMDB, y francamente, no he visto una revisi\\u00f3n positiva, hasta ahora.En realidad me gusta a Kerching.Kerching es sobre un adolescente llamado Taj Lewis, para hacer \\u00a3 1,000,000 para su madre, establece un sitio web llamado Rudeboy.El sitio web ofrece muchas cosas interesantes, aunque solo TAJ, y sus 2 amigos, Danny y Seymour pueden evitar que TAJ se exponga.Encuentro este espect\\u00e1culo bastante divertido y me gusta verlo.La actuaci\\u00f3n es O.K., pero definitivamente puede ser mejor, los personajes son divertidos, especialmente a Danny Spooner, el amigo de Taj, su estupidez es lo que lo hace divertido.y el otro amigo de Taj seymour.Tambi\\u00e9n Carlton, el due\\u00f1o de la cafeter\\u00eda llamada \\\"El fr\\u00edo\\\".Nunca lo vemos, solo lo escuchamos.El hermano menor de Taj, Omar, y su hermana mayor, Missy tambi\\u00e9n es divertida.Y la mejor amiga de Missy Kareesha.Muchas veces, Taj's casi se ha expuesto, casi.Adem\\u00e1s de la comedia, el drama en kerching, como la vida del amor y la p\\u00e9rdida de seres queridos hace que este programa sea excelente.\",\n          \"Una historia sobre el amor y el odio, la tragedia y la felicidad, y sobre todo, la amistad establecida en el momento muy interesante de la guerra civil estadounidense. Te interesa interesado en la historia, te involucra emocionalmente y te hace febrilmente esperar el pr\\u00f3ximo episodio., el casting era espl\\u00e9ndido.Muchas superestrellas aparecen en los camafeos cortos, los roles principales son jugados por una gran variedad de monumentos talentosos: Kirstie Alley y Terri Garber se deben mencionar aqu\\u00ed: este es simplemente un excelente ejemplo para una producci\\u00f3n de televisi\\u00f3n como deber\\u00eda ser. No se olvide de los pura.Lovelity of Wendy Kilbourne retratando constanza :-)\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"sentimiento\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"negativo\",\n          \"positivo\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 7
        }
      ],
      "source": [
        "ds_train"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kl2l-4k1E01V",
        "outputId": "3791e332-02e7-43b7-a37a-375a0c47dbca"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "          ID                                          review_es sentimiento  \\\n",
              "0          0  Uno de los otros críticos ha mencionado que de...    positivo   \n",
              "1          1  Una pequeña pequeña producción.La técnica de f...    positivo   \n",
              "2          2  Pensé que esta era una manera maravillosa de p...    positivo   \n",
              "3          3  Básicamente, hay una familia donde un niño peq...    negativo   \n",
              "4          4  El \"amor en el tiempo\" de Petter Mattei es una...    positivo   \n",
              "...      ...                                                ...         ...   \n",
              "49995  49995  Pensé que esta película hizo un buen trabajo a...    positivo   \n",
              "49996  49996  Mala parcela, mal diálogo, mala actuación, dir...    negativo   \n",
              "49997  49997  Soy católica enseñada en escuelas primarias pa...    negativo   \n",
              "49998  49998  Voy a tener que estar en desacuerdo con el com...    negativo   \n",
              "49999  49999  Nadie espera que las películas de Star Trek se...    negativo   \n",
              "\n",
              "                                             review_norm  \n",
              "0      critico haber mencionar despues ver solo oz ep...  \n",
              "1      pequeno pequeno produccion tecnica filmacion s...  \n",
              "2      pensar ser manera maravilloso pasar tiempo fin...  \n",
              "3      basicamente haber familia nino pequeno jake pe...  \n",
              "4      amor tiempo petter mattei ser pelicula visualm...  \n",
              "...                                                  ...  \n",
              "49995  pensar pelicula hacer buen trabajo derecha ser...  \n",
              "49996  mala parcela mal dialogo malo actuacion direcc...  \n",
              "49997  ser catolico ensenado escuela primario parroqu...  \n",
              "49998  ir tener desacuerdo comentario anterior lado m...  \n",
              "49999  nadie esperar pelicula star trek ser alto art ...  \n",
              "\n",
              "[50000 rows x 4 columns]"
            ],
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              "      <th>49998</th>\n",
              "      <td>49998</td>\n",
              "      <td>Voy a tener que estar en desacuerdo con el com...</td>\n",
              "      <td>negativo</td>\n",
              "      <td>ir tener desacuerdo comentario anterior lado m...</td>\n",
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              "    <tr>\n",
              "      <th>49999</th>\n",
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              "      <td>Nadie espera que las películas de Star Trek se...</td>\n",
              "      <td>negativo</td>\n",
              "      <td>nadie esperar pelicula star trek ser alto art ...</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>50000 rows × 4 columns</p>\n",
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              "\n",
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              "      display:flex;\n",
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              "\n",
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              "      height: 32px;\n",
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              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "ds_normalizado",
              "summary": "{\n  \"name\": \"ds_normalizado\",\n  \"rows\": 50000,\n  \"fields\": [\n    {\n      \"column\": \"ID\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 14433,\n        \"min\": 0,\n        \"max\": 49999,\n        \"num_unique_values\": 50000,\n        \"samples\": [\n          33553,\n          9427,\n          199\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"review_es\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 49599,\n        \"samples\": [\n          \"Dos a\\u00f1os despu\\u00e9s del \\u00e9xito de 'avi\\u00f3n', Jim Abrahams y Jerry y David Zucker crearon esta brillante comedia protagonizada por la Gran Leslie Nielsen, ya que el detective video simple 'Frank Drebin'. Tambi\\u00e9n en el elenco fue Alan North como 'Capit\\u00e1n Ed Hocken', Ed Williams como 'Ted Olsen' y William Duell como 'Johnny The Shoe Shine Boy'. '\\u00a1Escuadr\\u00f3n de la polic\\u00eda!' Destac\\u00f3 chistes sin verg\\u00fcenza y gags visuales inteligentes jugando en el fondo. Cada episodio concluir\\u00eda con un marco de congelaci\\u00f3n simulado en el que los caracteres en el marco se encuentran completamente quietos. \\u00a1Una de las mejores secuencias de 'congelamiento' de 'congelamiento' vieron a uno de los personajes que vierte caf\\u00e9 en una taza mientras est\\u00e1 de pie, lo que hace que la taza se desborde! Las estrellas invitadas fueron asesinadas en los t\\u00edtulos de apertura, \\u00a1uno incluido Georg Stanford Brown siendo aplastado por una caja fuerte! A pesar de obtener revisiones positivas y mucho cr\\u00edtico, '\\u00a1Escuadr\\u00f3n!' Solo dur\\u00f3 seis episodios antes de ser cancelados. Esto no signific\\u00f3 que el final, sin embargo, cinco a\\u00f1os despu\\u00e9s, el espect\\u00e1culo se transfiri\\u00f3 a la pantalla grande para la primera en la trilog\\u00eda de las pel\\u00edculas \\\"desnudas de la pistola\\\".\",\n          \"He le\\u00eddo los comentarios de KERCHING ON IMDB, y francamente, no he visto una revisi\\u00f3n positiva, hasta ahora.En realidad me gusta a Kerching.Kerching es sobre un adolescente llamado Taj Lewis, para hacer \\u00a3 1,000,000 para su madre, establece un sitio web llamado Rudeboy.El sitio web ofrece muchas cosas interesantes, aunque solo TAJ, y sus 2 amigos, Danny y Seymour pueden evitar que TAJ se exponga.Encuentro este espect\\u00e1culo bastante divertido y me gusta verlo.La actuaci\\u00f3n es O.K., pero definitivamente puede ser mejor, los personajes son divertidos, especialmente a Danny Spooner, el amigo de Taj, su estupidez es lo que lo hace divertido.y el otro amigo de Taj seymour.Tambi\\u00e9n Carlton, el due\\u00f1o de la cafeter\\u00eda llamada \\\"El fr\\u00edo\\\".Nunca lo vemos, solo lo escuchamos.El hermano menor de Taj, Omar, y su hermana mayor, Missy tambi\\u00e9n es divertida.Y la mejor amiga de Missy Kareesha.Muchas veces, Taj's casi se ha expuesto, casi.Adem\\u00e1s de la comedia, el drama en kerching, como la vida del amor y la p\\u00e9rdida de seres queridos hace que este programa sea excelente.\",\n          \"Una historia sobre el amor y el odio, la tragedia y la felicidad, y sobre todo, la amistad establecida en el momento muy interesante de la guerra civil estadounidense. Te interesa interesado en la historia, te involucra emocionalmente y te hace febrilmente esperar el pr\\u00f3ximo episodio., el casting era espl\\u00e9ndido.Muchas superestrellas aparecen en los camafeos cortos, los roles principales son jugados por una gran variedad de monumentos talentosos: Kirstie Alley y Terri Garber se deben mencionar aqu\\u00ed: este es simplemente un excelente ejemplo para una producci\\u00f3n de televisi\\u00f3n como deber\\u00eda ser. No se olvide de los pura.Lovelity of Wendy Kilbourne retratando constanza :-)\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"sentimiento\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"negativo\",\n          \"positivo\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"review_norm\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 49595,\n        \"samples\": [\n          \"pensar pelicula ser excelente jon foster ser mejor actor favorito ser perfecto skakel michealencontre respecto ser excelente actuar disfraz produccion direccion fotografia guion musica etc subehas ser advertir martha moxley haber contar historia pelicula ser golpear muerte violento skakel micheal micheal hacer celo hermano tommy martha rechazar llevar tommy afortunadamente embargo atrapar ano despues ser condenar ano vida prision aunque creer deber haber ser condenar vida natural posibilidad parolekudos reparto tripulacion cineasta dos pulgar lejos\",\n          \"sugerencia pelicula realmente destinado ser comedia si productor director actor estilista cabello realmente deber elegir carreraahora directriz presentacion requerir minimo lineascomo mundo poder agregar mas estohmm dejamir ver oh si comienzo pelicula ser bastante hilarante escena aterrizaje choque lastima avion golpear camion petrolero grupo marciano haber escapar resto obviamente ocultando decidir hacer cargo planeta mundo hacer mejor pelicula ahora haber ser gran comienzo t\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 8
        }
      ],
      "source": [
        "ds_normalizado"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YrXa8bzno_t7"
      },
      "source": [
        "Dividimos el dataset de train en x_train e y_train (la primera tendra el texto y la segunda las labels).\n",
        "\n",
        "Adicionalmente no incluiremos la columna ID ya que no es necesaria para nuestro proposito de entrenamiento."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "T3Pz1qvoo_fM"
      },
      "outputs": [],
      "source": [
        "x_train_text_completo = ds_train[\"review_es\"]\n",
        "y_train_completo = ds_train[\"sentimiento\"]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "kyfAlA4_FHwM"
      },
      "outputs": [],
      "source": [
        "x_train_text_norm = ds_normalizado[\"review_norm\"]\n",
        "y_train_norm = ds_normalizado[\"sentimiento\"]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1ZGZoNbcviQL",
        "outputId": "d9ecc98e-6426-46d1-ab18-f0fd7fbdb22b"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "         ID                                          review_es\n",
              "0     60000  La mayor virtud de esta película es su existen...\n",
              "1     60001  No soy un experto cinéfilo, pero pocas veces m...\n",
              "2     60002  Si no eres un incondicional del humor estilo T...\n",
              "3     60003  No sé qué está pasando, si la gente se deja ll...\n",
              "4     60004  Pero cuando amanece,y me quedo solo,siento en ...\n",
              "...     ...                                                ...\n",
              "8594  68594  Buena no, lo siguiente. Por fin un film serio ...\n",
              "8595  68595  Me esperaba mucho, pero que mucho, más.Guión m...\n",
              "8596  68596  De mal cuerpo como sensación al finalizar, de ...\n",
              "8597  68597  Los que han añadido comentarios os lo han dich...\n",
              "8598  68598  Fui a ver esta película de cine con entusiasmo...\n",
              "\n",
              "[8599 rows x 2 columns]"
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              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-30c75674-4421-4e50-8594-96bc4aa686e0 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "  <div id=\"id_a39cb2d8-57bc-4fc7-9b8c-6d8094e08ab9\">\n",
              "    <style>\n",
              "      .colab-df-generate {\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-generate: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-generate {\n",
              "        background-color: #3B4455;\n",
              "        fill: #D2E3FC;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate: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",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('ds_test')\"\n",
              "            title=\"Generate code using this dataframe.\"\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=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "    <script>\n",
              "      (() => {\n",
              "      const buttonEl =\n",
              "        document.querySelector('#id_a39cb2d8-57bc-4fc7-9b8c-6d8094e08ab9 button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('ds_test');\n",
              "      }\n",
              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "ds_test",
              "summary": "{\n  \"name\": \"ds_test\",\n  \"rows\": 8599,\n  \"fields\": [\n    {\n      \"column\": \"ID\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2482,\n        \"min\": 60000,\n        \"max\": 68598,\n        \"num_unique_values\": 8599,\n        \"samples\": [\n          62499,\n          66723,\n          63944\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"review_es\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 8599,\n        \"samples\": [\n          \"Amenabar acert\\u00f3 de pleno con esta emotiva y dram\\u00e1tica historia. Como siempre en sus pel\\u00edculas, gran fotograf\\u00eda y banda sonora. Brillante Bardem y muy buenos secundarios. Totalmente recomendable, a no ser que s\\u00f3lo te guste el cine de acci\\u00f3n, si es as\\u00ed olv\\u00eddate.\",\n          \"Una de las obras espa\\u00f1olas m\\u00e1s exitosas del siglo XXI se trata en realidad de un coproducci\\u00f3n con Estados Unidos y M\\u00e9xico, de forma muy similar a Los Otros pero en esta ocasi\\u00f3n con actores nacionales. El resultado de esta producci\\u00f3n es el de una historia interesante, magn\\u00edficamente ambientada pero que flojea a partir de su mitad. El contraste entre la crueldad y la inhumanidad en la posguerra (aparcando debates pol\\u00edticos sobre su enfoque) y el mundo de fantas\\u00eda que s\\u00f3lo es capaz de ver la ni\\u00f1a resulta art\\u00edsticamente muy atractivo y diferente a lo que el cine espa\\u00f1ol nos tiene acostumbrados, pero el gui\\u00f3n resulta simple y pierde ritmo en su desarrollo.Sin embargo, las limitaciones argumentales son superadas por unas destacables interpretaciones de sus personajes. En especial de Sergi L\\u00f3pez, encarnando a un capit\\u00e1n despiadado, incapaz de sentir amor y obsesionado con eliminar a los que cree sus enemigos. Tambi\\u00e9n es digno de mencionar el trabajo siempre efectivo de Maribel Verd\\u00fa y las sorpresas de Doug Jones en el papel del fauno y la por aqu\\u00e9l entonces peque\\u00f1a Ivana Baquero, que ejecuta probablemente el mejor papel de su carrera hasta la fecha de la presente cr\\u00edtica.Sorprendentemente, la pel\\u00edcula se llev\\u00f3... \\u00a1tres Oscars! Y candidata para otros tres. Parece claro que para volver a triunfar en la gala de oro del cine hay que introducir recursos que admiren los americanos y adem\\u00e1s juntarnos con ellos.\",\n          \"Creo que se sobrevalora esta cinta, si bien es justo decir que la producci\\u00f3n es notable y un Luis Tosar no decepciona pero pienso que la trama empieza demasiado r\\u00e1pido no le da tiempo al espectador a hacerse con los personajes, los secundarios est\\u00e1n como fuera de lugar, alguno de ellos no veo que sean demasiado relevantes para la trama, est\\u00e1n como de paso, parece ser que los personajes principales arrastren el argumento,  creo que se ha desperdiciado un argumento muy original y se ha quedado en un intento bastante flojo.\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 11
        }
      ],
      "source": [
        "ds_test"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IGXF2ESLqFR2"
      },
      "source": [
        "Creamos el dataset de x_test sin incluir el id"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "EWVFwP9JqK-6"
      },
      "outputs": [],
      "source": [
        "x_test_text = ds_test[\"review_es\"]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cCvNYz6zpT9S"
      },
      "source": [
        "# Preprocesamiento"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "J3IUR3SjQ_ep"
      },
      "source": [
        "## Division de train y validation\n",
        "\n",
        "Dividimos el training set en training y validating sets"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "1EUYD4uWo0sU"
      },
      "outputs": [],
      "source": [
        "x_train_text, x_validation_text, y_train, y_validation = train_test_split(\n",
        "    ds_normalizado['review_norm'], ds_normalizado[\"sentimiento\"],\n",
        "    stratify=ds_normalizado[\"sentimiento\"],\n",
        "    test_size=0.20,\n",
        "    random_state=RAND_SEED\n",
        "    )"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "p3QxP7yhoWcS"
      },
      "source": [
        "## Normalizacion"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zd9swgrXpl5r"
      },
      "source": [
        "## Vectorizacion"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PdlbIp4EBQVM"
      },
      "source": [
        "### Utils\n",
        "\n",
        "Creacion de algunas funciones de utilidad"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "1w58Vv8VBWlt"
      },
      "outputs": [],
      "source": [
        "def plot_confussion_matrix(true, pred):\n",
        "  #Creo matriz de confusión\n",
        "  tabla = confusion_matrix(true, pred)\n",
        "\n",
        "  #Grafico matriz de confusión\n",
        "  sns.heatmap(tabla, cmap='Blues',annot=True,fmt='g')\n",
        "  plt.xlabel('Predicciones')\n",
        "  plt.ylabel('Verdaderos')\n",
        "\n",
        "  #Reporte\n",
        "  print(classification_report(true, pred))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "IJd51iWFDGZ1"
      },
      "outputs": [],
      "source": [
        "def build_results(pred):\n",
        "  res = ds_test.copy()\n",
        "  res.drop(columns=[\"review_es\"], inplace=True)\n",
        "  res['sentimiento'] = pred\n",
        "  return res"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "tciD9NNPDm-C"
      },
      "outputs": [],
      "source": [
        "def print_positive_negatives(sentimientos):\n",
        "  total_positives = (sentimientos == \"positivo\").sum()\n",
        "  total_negatives = (sentimientos == \"negativo\").sum()\n",
        "  print(f\"Positives: {total_positives}\\nNegatives: {total_negatives}\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NpUY6QIKMgv3"
      },
      "source": [
        "# Red Neuronal"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "r0aw_9wExnDw"
      },
      "source": [
        "## Modelos"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xXYMffruxqQI"
      },
      "source": [
        "Para esta red neuronal utilizamos la reducción de palabras, pues requerimos la menor cantidad de neuronas posibles para realizar la red. Si tenemos muchas neuronas conectadas vamos a generar overfiting."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4z-w4HasYsEQ",
        "outputId": "b0513501-36c2-4d6e-e8f3-18c684f94427"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "50000"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ],
      "source": [
        "reviews = ds_normalizado['review_norm'].tolist()\n",
        "len(reviews)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "df_rME7eLQ9C"
      },
      "source": [
        "Como tengo 50000 palabras debo deshacerme de varias. Decido mantener el 25% de estas palabras"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "WxSFomAJLbQL"
      },
      "outputs": [],
      "source": [
        "#Tokenización y secuenciación de texto\n",
        "max_words = 12500\n",
        "max_longitud_secuencia = 500\n",
        "tokenizer = Tokenizer(num_words=max_words)\n",
        "tokenizer.fit_on_texts(reviews)\n",
        "\n",
        "#Obtencion del diccionario de palabras a índices\n",
        "word_index = tokenizer.word_index\n",
        "\n",
        "#Secuencias de tokens\n",
        "sequences = tokenizer.texts_to_sequences(reviews)\n",
        "sequences_prueba = tokenizer.texts_to_sequences(ds_test['review_es'].tolist())\n",
        "\n",
        "data = pad_sequences(sequences, maxlen=max_longitud_secuencia)\n",
        "test_padded = pad_sequences(sequences_prueba, maxlen=max_longitud_secuencia)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ROWJyjHlMMY_",
        "outputId": "cecf164b-be55-4b97-c8f4-4a2c7c806ca4"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[    0,     0,     0, ...,   238,     8,   389],\n",
              "       [    0,     0,     0, ...,  1329,    27,    54],\n",
              "       [    0,     0,     0, ...,    34,     5,   129],\n",
              "       ...,\n",
              "       [    0,     0,     0, ...,   364,  5918,  3662],\n",
              "       [    0,     0,     0, ..., 10341,     7,  2437],\n",
              "       [    0,     0,     0, ...,  1218,   413,     2]], dtype=int32)"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ],
      "source": [
        "data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yeWAIB5EMNmP",
        "outputId": "a109aa85-034c-4728-b809-775f9bc57392"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[   0,    0,    0, ...,    6,    6,  125],\n",
              "       [   0,    0,    0, ..., 2584,  383,  776],\n",
              "       [   0,    0,    0, ..., 1171,  558,  558],\n",
              "       ...,\n",
              "       [   0,    0,    0, ..., 2746,  226, 2111],\n",
              "       [   0,    0,    0, ..., 2860, 1041,  524],\n",
              "       [   0,    0,    0, ...,   10, 9678,  125]], dtype=int32)"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ],
      "source": [
        "test_padded"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8WHrRR_z_YZz"
      },
      "source": [
        "Mapeo los sentimientos positivos y negativos."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Em8mpA4TyQKN"
      },
      "outputs": [],
      "source": [
        "ds_normalizado['sentimiento'] = ds_normalizado['sentimiento'].map(lambda x: 1 if x == 'positivo' else 0)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "CpR2BHe_O9kB",
        "outputId": "504b1021-9157-49a4-9088-839a39924a8e"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "          ID                                          review_es  sentimiento  \\\n",
              "0          0  Uno de los otros críticos ha mencionado que de...            1   \n",
              "1          1  Una pequeña pequeña producción.La técnica de f...            1   \n",
              "2          2  Pensé que esta era una manera maravillosa de p...            1   \n",
              "3          3  Básicamente, hay una familia donde un niño peq...            0   \n",
              "4          4  El \"amor en el tiempo\" de Petter Mattei es una...            1   \n",
              "...      ...                                                ...          ...   \n",
              "49995  49995  Pensé que esta película hizo un buen trabajo a...            1   \n",
              "49996  49996  Mala parcela, mal diálogo, mala actuación, dir...            0   \n",
              "49997  49997  Soy católica enseñada en escuelas primarias pa...            0   \n",
              "49998  49998  Voy a tener que estar en desacuerdo con el com...            0   \n",
              "49999  49999  Nadie espera que las películas de Star Trek se...            0   \n",
              "\n",
              "                                             review_norm  \n",
              "0      critico haber mencionar despues ver solo oz ep...  \n",
              "1      pequeno pequeno produccion tecnica filmacion s...  \n",
              "2      pensar ser manera maravilloso pasar tiempo fin...  \n",
              "3      basicamente haber familia nino pequeno jake pe...  \n",
              "4      amor tiempo petter mattei ser pelicula visualm...  \n",
              "...                                                  ...  \n",
              "49995  pensar pelicula hacer buen trabajo derecha ser...  \n",
              "49996  mala parcela mal dialogo malo actuacion direcc...  \n",
              "49997  ser catolico ensenado escuela primario parroqu...  \n",
              "49998  ir tener desacuerdo comentario anterior lado m...  \n",
              "49999  nadie esperar pelicula star trek ser alto art ...  \n",
              "\n",
              "[50000 rows x 4 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-6f744d2f-86e4-413e-add2-879007e8c49d\" 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>ID</th>\n",
              "      <th>review_es</th>\n",
              "      <th>sentimiento</th>\n",
              "      <th>review_norm</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>Uno de los otros críticos ha mencionado que de...</td>\n",
              "      <td>1</td>\n",
              "      <td>critico haber mencionar despues ver solo oz ep...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>Una pequeña pequeña producción.La técnica de f...</td>\n",
              "      <td>1</td>\n",
              "      <td>pequeno pequeno produccion tecnica filmacion s...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2</td>\n",
              "      <td>Pensé que esta era una manera maravillosa de p...</td>\n",
              "      <td>1</td>\n",
              "      <td>pensar ser manera maravilloso pasar tiempo fin...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>3</td>\n",
              "      <td>Básicamente, hay una familia donde un niño peq...</td>\n",
              "      <td>0</td>\n",
              "      <td>basicamente haber familia nino pequeno jake pe...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>4</td>\n",
              "      <td>El \"amor en el tiempo\" de Petter Mattei es una...</td>\n",
              "      <td>1</td>\n",
              "      <td>amor tiempo petter mattei ser pelicula visualm...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>49995</th>\n",
              "      <td>49995</td>\n",
              "      <td>Pensé que esta película hizo un buen trabajo a...</td>\n",
              "      <td>1</td>\n",
              "      <td>pensar pelicula hacer buen trabajo derecha ser...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>49996</th>\n",
              "      <td>49996</td>\n",
              "      <td>Mala parcela, mal diálogo, mala actuación, dir...</td>\n",
              "      <td>0</td>\n",
              "      <td>mala parcela mal dialogo malo actuacion direcc...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>49997</th>\n",
              "      <td>49997</td>\n",
              "      <td>Soy católica enseñada en escuelas primarias pa...</td>\n",
              "      <td>0</td>\n",
              "      <td>ser catolico ensenado escuela primario parroqu...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>49998</th>\n",
              "      <td>49998</td>\n",
              "      <td>Voy a tener que estar en desacuerdo con el com...</td>\n",
              "      <td>0</td>\n",
              "      <td>ir tener desacuerdo comentario anterior lado m...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>49999</th>\n",
              "      <td>49999</td>\n",
              "      <td>Nadie espera que las películas de Star Trek se...</td>\n",
              "      <td>0</td>\n",
              "      <td>nadie esperar pelicula star trek ser alto art ...</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>50000 rows × 4 columns</p>\n",
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              "\n",
              "      .colab-df-generate:hover {\n",
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              "      [theme=dark] .colab-df-generate {\n",
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              "      [theme=dark] .colab-df-generate:hover {\n",
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              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('ds_normalizado')\"\n",
              "            title=\"Generate code using this dataframe.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
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              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('ds_normalizado');\n",
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              "\n",
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            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "ds_normalizado",
              "summary": "{\n  \"name\": \"ds_normalizado\",\n  \"rows\": 50000,\n  \"fields\": [\n    {\n      \"column\": \"ID\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 14433,\n        \"min\": 0,\n        \"max\": 49999,\n        \"num_unique_values\": 50000,\n        \"samples\": [\n          33553,\n          9427,\n          199\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"review_es\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 49599,\n        \"samples\": [\n          \"Dos a\\u00f1os despu\\u00e9s del \\u00e9xito de 'avi\\u00f3n', Jim Abrahams y Jerry y David Zucker crearon esta brillante comedia protagonizada por la Gran Leslie Nielsen, ya que el detective video simple 'Frank Drebin'. Tambi\\u00e9n en el elenco fue Alan North como 'Capit\\u00e1n Ed Hocken', Ed Williams como 'Ted Olsen' y William Duell como 'Johnny The Shoe Shine Boy'. '\\u00a1Escuadr\\u00f3n de la polic\\u00eda!' Destac\\u00f3 chistes sin verg\\u00fcenza y gags visuales inteligentes jugando en el fondo. Cada episodio concluir\\u00eda con un marco de congelaci\\u00f3n simulado en el que los caracteres en el marco se encuentran completamente quietos. \\u00a1Una de las mejores secuencias de 'congelamiento' de 'congelamiento' vieron a uno de los personajes que vierte caf\\u00e9 en una taza mientras est\\u00e1 de pie, lo que hace que la taza se desborde! Las estrellas invitadas fueron asesinadas en los t\\u00edtulos de apertura, \\u00a1uno incluido Georg Stanford Brown siendo aplastado por una caja fuerte! A pesar de obtener revisiones positivas y mucho cr\\u00edtico, '\\u00a1Escuadr\\u00f3n!' Solo dur\\u00f3 seis episodios antes de ser cancelados. Esto no signific\\u00f3 que el final, sin embargo, cinco a\\u00f1os despu\\u00e9s, el espect\\u00e1culo se transfiri\\u00f3 a la pantalla grande para la primera en la trilog\\u00eda de las pel\\u00edculas \\\"desnudas de la pistola\\\".\",\n          \"He le\\u00eddo los comentarios de KERCHING ON IMDB, y francamente, no he visto una revisi\\u00f3n positiva, hasta ahora.En realidad me gusta a Kerching.Kerching es sobre un adolescente llamado Taj Lewis, para hacer \\u00a3 1,000,000 para su madre, establece un sitio web llamado Rudeboy.El sitio web ofrece muchas cosas interesantes, aunque solo TAJ, y sus 2 amigos, Danny y Seymour pueden evitar que TAJ se exponga.Encuentro este espect\\u00e1culo bastante divertido y me gusta verlo.La actuaci\\u00f3n es O.K., pero definitivamente puede ser mejor, los personajes son divertidos, especialmente a Danny Spooner, el amigo de Taj, su estupidez es lo que lo hace divertido.y el otro amigo de Taj seymour.Tambi\\u00e9n Carlton, el due\\u00f1o de la cafeter\\u00eda llamada \\\"El fr\\u00edo\\\".Nunca lo vemos, solo lo escuchamos.El hermano menor de Taj, Omar, y su hermana mayor, Missy tambi\\u00e9n es divertida.Y la mejor amiga de Missy Kareesha.Muchas veces, Taj's casi se ha expuesto, casi.Adem\\u00e1s de la comedia, el drama en kerching, como la vida del amor y la p\\u00e9rdida de seres queridos hace que este programa sea excelente.\",\n          \"Una historia sobre el amor y el odio, la tragedia y la felicidad, y sobre todo, la amistad establecida en el momento muy interesante de la guerra civil estadounidense. Te interesa interesado en la historia, te involucra emocionalmente y te hace febrilmente esperar el pr\\u00f3ximo episodio., el casting era espl\\u00e9ndido.Muchas superestrellas aparecen en los camafeos cortos, los roles principales son jugados por una gran variedad de monumentos talentosos: Kirstie Alley y Terri Garber se deben mencionar aqu\\u00ed: este es simplemente un excelente ejemplo para una producci\\u00f3n de televisi\\u00f3n como deber\\u00eda ser. No se olvide de los pura.Lovelity of Wendy Kilbourne retratando constanza :-)\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"sentimiento\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"review_norm\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 49595,\n        \"samples\": [\n          \"pensar pelicula ser excelente jon foster ser mejor actor favorito ser perfecto skakel michealencontre respecto ser excelente actuar disfraz produccion direccion fotografia guion musica etc subehas ser advertir martha moxley haber contar historia pelicula ser golpear muerte violento skakel micheal micheal hacer celo hermano tommy martha rechazar llevar tommy afortunadamente embargo atrapar ano despues ser condenar ano vida prision aunque creer deber haber ser condenar vida natural posibilidad parolekudos reparto tripulacion cineasta dos pulgar lejos\",\n          \"sugerencia pelicula realmente destinado ser comedia si productor director actor estilista cabello realmente deber elegir carreraahora directriz presentacion requerir minimo lineascomo mundo poder agregar mas estohmm dejamir ver oh si comienzo pelicula ser bastante hilarante escena aterrizaje choque lastima avion golpear camion petrolero grupo marciano haber escapar resto obviamente ocultando decidir hacer cargo planeta mundo hacer mejor pelicula ahora haber ser gran comienzo t\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 22
        }
      ],
      "source": [
        "ds_normalizado"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "u_j3ls1gPEaN"
      },
      "outputs": [],
      "source": [
        "df_trabajo_x = ds_normalizado['review_norm']\n",
        "\n",
        "df_trabajo_y = ds_normalizado['sentimiento'].copy()\n",
        "\n",
        "x_train_rnn, x_test_rnn, y_train_rnn, y_test_rnn = train_test_split(data,\n",
        "                                                    df_trabajo_y,\n",
        "                                                    test_size=0.30,\n",
        "                                                    random_state=12,\n",
        "                                                    shuffle=True\n",
        "                                                    )"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "TDAJRo-NRxFY"
      },
      "outputs": [],
      "source": [
        "training_padded = np.array(x_train_rnn)\n",
        "testing_padded = np.array(x_test_rnn)\n",
        "testing_prueba_padded = np.array(test_padded)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "9QMy8NARRyp-"
      },
      "outputs": [],
      "source": [
        "\n",
        "sequences = training_padded"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eaY4FCZdySHo"
      },
      "source": [
        "Los mejores hiperparametros fueron:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "6FekUCYOV0PD",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "60b2d1f0-6221-4a4c-a041-c666923b1998"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " embedding (Embedding)       (None, None, 128)         1600000   \n",
            "                                                                 \n",
            " conv1d (Conv1D)             (None, None, 128)         49280     \n",
            "                                                                 \n",
            " gru (GRU)                   (None, None, 128)         99072     \n",
            "                                                                 \n",
            " gru_1 (GRU)                 (None, 128)               99072     \n",
            "                                                                 \n",
            " dropout (Dropout)           (None, 128)               0         \n",
            "                                                                 \n",
            " dense (Dense)               (None, 1)                 129       \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 1847553 (7.05 MB)\n",
            "Trainable params: 1847553 (7.05 MB)\n",
            "Non-trainable params: 0 (0.00 Byte)\n",
            "_________________________________________________________________\n"
          ]
        }
      ],
      "source": [
        "import tensorflow as tf\n",
        "from tensorflow.keras.models import Sequential\n",
        "from tensorflow.keras.layers import Conv1D, GRU, Dropout, Dense, Embedding\n",
        "\n",
        "# Parámetros del modelo\n",
        "vocab_size = max_words  # Increased vocab_size to encompass all tokens\n",
        "embed_size = 128\n",
        "conv_filters = 128\n",
        "\n",
        "# Definición del modelo\n",
        "model = Sequential([\n",
        "    Embedding(vocab_size,embed_size,input_shape=[None]),\n",
        "    Conv1D(filters=conv_filters, kernel_size=3, activation='relu'),\n",
        "    GRU(128, return_sequences=True),\n",
        "    GRU(128),\n",
        "    Dropout(0.2),\n",
        "    Dense(1, activation='sigmoid')\n",
        "])\n",
        "\n",
        "# Compilación del modelo\n",
        "model.compile(\n",
        "    optimizer=tf.keras.optimizers.Adam(learning_rate=0.001, beta_1=0.9, beta_2=0.999),\n",
        "    loss='binary_crossentropy',\n",
        "    metrics=['AUC']\n",
        ")\n",
        "\n",
        "# Resumen del modelo\n",
        "model.summary()"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "class AccuracyEarlyStopping(tf.keras.callbacks.Callback):\n",
        "    def __init__(self, target_accuracy):\n",
        "        super(AccuracyEarlyStopping, self).__init__()\n",
        "        self.target_accuracy = target_accuracy\n",
        "\n",
        "    def on_epoch_end(self, epoch, logs=None):\n",
        "        pass  # Override to avoid on_epoch_end behavior\n",
        "\n",
        "    def on_batch_end(self, batch, logs=None):\n",
        "        current_accuracy = logs.get('accuracy')\n",
        "        if current_accuracy is not None:\n",
        "          if current_accuracy >= self.target_accuracy:\n",
        "              print(f\"\\nReached {self.target_accuracy} accuracy. Stopping training.\")\n",
        "              self.model.stop_training = True\n",
        "early_stopping_callback = AccuracyEarlyStopping(target_accuracy=0.984) # and adjust batch size?"
      ],
      "metadata": {
        "id": "4Vlsnpf5Hta-"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "w6mgcufOZz_Z",
        "outputId": "613f7b1e-8d1a-4fd7-a0e2-26e65683db0c"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/4\n",
            "1094/1094 [==============================] - 1483s 1s/step - loss: 0.0452 - auc: 0.9977\n",
            "Epoch 2/4\n",
            "1094/1094 [==============================] - 1457s 1s/step - loss: 0.0292 - auc: 0.9988\n",
            "Epoch 3/4\n",
            "1094/1094 [==============================] - 1451s 1s/step - loss: 0.0245 - auc: 0.9992\n",
            "Epoch 4/4\n",
            "1094/1094 [==============================] - 1442s 1s/step - loss: 0.0214 - auc: 0.9993\n"
          ]
        }
      ],
      "source": [
        "# Verificar si el archivo del modelo existe\n",
        "import os\n",
        "model_file = root_path + 'Models/red_neuronal_prueba_v2.pkl'\n",
        "if os.path.exists(model_file):\n",
        "    # Cargar el modelo desde el archivo\n",
        "    model = joblib.load(model_file)\n",
        "else:\n",
        "    # Entrenar el modelo y guardar\n",
        "    model.fit(sequences, y_train_rnn, epochs=4, callbacks=[early_stopping_callback])\n",
        "    joblib.dump(model, model_file)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "A-RTCN2taGMh",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "5e93b7ff-6a1f-4a0f-8f75-f1d6ed1953a3"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "469/469 [==============================] - 155s 325ms/step\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[0],\n",
              "       [1],\n",
              "       [1],\n",
              "       ...,\n",
              "       [0],\n",
              "       [0],\n",
              "       [1]])"
            ]
          },
          "metadata": {},
          "execution_count": 31
        }
      ],
      "source": [
        "y_pred = model.predict(testing_padded)\n",
        "\n",
        "y_predic_cat_ej1 = np.where(y_pred>0.5,1,0)\n",
        "\n",
        "y_predic_cat_ej1"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "csHdPQxdaLAa",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 616
        },
        "outputId": "73d16020-c8ef-44f3-e381-01426d9190b2"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.86      0.86      0.86      7534\n",
            "           1       0.86      0.86      0.86      7466\n",
            "\n",
            "    accuracy                           0.86     15000\n",
            "   macro avg       0.86      0.86      0.86     15000\n",
            "weighted avg       0.86      0.86      0.86     15000\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "ds_validacion=pd.DataFrame(y_predic_cat_ej1,y_test_rnn).reset_index()\n",
        "ds_validacion.columns=['y_pred','y_real']\n",
        "\n",
        "print(classification_report(y_test_rnn,y_predic_cat_ej1))\n",
        "\n",
        "tabla=pd.crosstab(ds_validacion.y_pred, ds_validacion.y_real)\n",
        "grf=sns.heatmap(tabla,annot=True, cmap = 'Blues', fmt='g')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "piZCx1XkaNET",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "c1b8ddbf-2f76-4dca-94eb-062584e22654"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "269/269 [==============================] - 84s 310ms/step\n"
          ]
        }
      ],
      "source": [
        "y_pred_final = model.predict(testing_prueba_padded)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "dmFE1pHuaOZa",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "ca7c1e18-1899-4db2-b039-bc9a7d3b1ba2"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0, 0, 0, ..., 1, 1, 0])"
            ]
          },
          "metadata": {},
          "execution_count": 55
        }
      ],
      "source": [
        "y_pred_final = np.where(y_pred_final>0.5,1,0)\n",
        "y_pred_final = y_pred_final.ravel()\n",
        "y_pred_final"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Q8O7Dd_TaPmr",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 363
        },
        "outputId": "0bc9e6c0-5ce3-4d3b-e733-ef865d469bce"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      id  sentimiento\n",
              "0  60000            0\n",
              "1  60001            0\n",
              "2  60002            0\n",
              "3  60003            1\n",
              "4  60004            0\n",
              "5  60005            1\n",
              "6  60006            0\n",
              "7  60007            0\n",
              "8  60008            1\n",
              "9  60009            1"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-1548c441-6147-4888-bc30-aa45bc5fbd9f\" 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>id</th>\n",
              "      <th>sentimiento</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>60000</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>60001</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>60002</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>60003</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>60004</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>60005</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>60006</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>60007</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>60008</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>60009</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-1548c441-6147-4888-bc30-aa45bc5fbd9f')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\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",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\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-1548c441-6147-4888-bc30-aa45bc5fbd9f 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-1548c441-6147-4888-bc30-aa45bc5fbd9f');\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",
              "\n",
              "\n",
              "<div id=\"df-c2d4a204-4313-487a-974b-6e3011bed5db\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-c2d4a204-4313-487a-974b-6e3011bed5db')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
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              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-c2d4a204-4313-487a-974b-6e3011bed5db button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "ds_resultados",
              "summary": "{\n  \"name\": \"ds_resultados\",\n  \"rows\": 8599,\n  \"fields\": [\n    {\n      \"column\": \"id\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2482,\n        \"min\": 60000,\n        \"max\": 68598,\n        \"num_unique_values\": 8599,\n        \"samples\": [\n          62499,\n          66723,\n          63944\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"sentimiento\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 57
        }
      ],
      "source": [
        "ds_resultados=pd.DataFrame({'id': ds_test['ID'], 'sentimiento': y_pred_final})\n",
        "ds_resultados.head(10)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "-0zrU_DgaRPS"
      },
      "outputs": [],
      "source": [
        "ds_resultados['sentimiento'] = ds_resultados['sentimiento'].replace({1: 'positivo', 0: 'negativo'})"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "hz9w2of6aUJh"
      },
      "outputs": [],
      "source": [
        "ds_resultados.to_csv(root_path + 'Dataset/red_neuronal_GRU2.csv', index=False)"
      ]
    }
  ],
  "metadata": {
    "colab": {
      "provenance": [],
      "toc_visible": true,
      "include_colab_link": true
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}