notebook_detector_de_lingua.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "13e89fe2",
   "metadata": {},
   "source": [
    "# Detector de língua"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5a1f84f8",
   "metadata": {},
   "source": [
    "## Objetivo\n",
    "\n",
    "Elaborar uma estratégia e construa um sistema que seja capaz de responder a qual idioma uma frase qualquer pertence.\n",
    "\n",
    "Por Exemplo:\n",
    "\n",
    "`eu gosto de café` → `por`\n",
    "\n",
    "`I like coffee`→ `eng`"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3bcc448c",
   "metadata": {},
   "source": [
    "## Bibliotecas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "a4fda78f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# manipulação de dados\n",
    "import pandas as pd\n",
    "\n",
    "# manipulação de texto\n",
    "\n",
    "# modelo\n",
    "from sklearn.feature_extraction.text import CountVectorizer\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.ensemble import GradientBoostingClassifier\n",
    "from sklearn.metrics import accuracy_score, confusion_matrix\n",
    "from tensorflow import keras\n",
    "from tensorflow.keras import layers\n",
    "from tensorflow.keras.callbacks import EarlyStopping\n",
    "\n",
    "# exportar o modelo\n",
    "import joblib"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "837152d3",
   "metadata": {},
   "source": [
    "## Base de Dados\n",
    "\n",
    "Dessa vez encontrei uma base de dados contendo frases em seus respectivos idiomas Afrikaans, English e Nederlands (africano, inglês e holandês). Queria achar alguma em português mas não consegui.\n",
    "\n",
    "Disponível no seguinte repositório do github: https://github.com/rolandgem/LanguageClassification/tree/main"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ebc8acd6",
   "metadata": {},
   "source": [
    "## Leitura dos dados "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "dd26aae4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>text</th>\n",
       "      <th>language</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Ship shape and Bristol fashion</td>\n",
       "      <td>English</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Know the ropes</td>\n",
       "      <td>English</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Graveyard shift</td>\n",
       "      <td>English</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Milk of human kindness</td>\n",
       "      <td>English</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Touch with a barge-pole - Wouldn't</td>\n",
       "      <td>English</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                 text language\n",
       "0      Ship shape and Bristol fashion  English\n",
       "1                      Know the ropes  English\n",
       "2                     Graveyard shift  English\n",
       "3              Milk of human kindness  English\n",
       "4  Touch with a barge-pole - Wouldn't  English"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('lang_data.csv')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "dfe1febe",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>text</th>\n",
       "      <th>language</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Ship shape and Bristol fashion</td>\n",
       "      <td>English</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Know the ropes</td>\n",
       "      <td>English</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Graveyard shift</td>\n",
       "      <td>English</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Milk of human kindness</td>\n",
       "      <td>English</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Touch with a barge-pole - Wouldn't</td>\n",
       "      <td>English</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2834</th>\n",
       "      <td>Daar’s ‘n geurtjie aan.</td>\n",
       "      <td>Afrikaans</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2835</th>\n",
       "      <td>Men's evil manners live in brass; their virtue...</td>\n",
       "      <td>English</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2836</th>\n",
       "      <td>Go-faster</td>\n",
       "      <td>English</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2837</th>\n",
       "      <td>Red tape</td>\n",
       "      <td>English</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2838</th>\n",
       "      <td>In a pickle</td>\n",
       "      <td>English</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2839 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                   text   language\n",
       "0                        Ship shape and Bristol fashion    English\n",
       "1                                        Know the ropes    English\n",
       "2                                       Graveyard shift    English\n",
       "3                                Milk of human kindness    English\n",
       "4                    Touch with a barge-pole - Wouldn't    English\n",
       "...                                                 ...        ...\n",
       "2834                            Daar’s ‘n geurtjie aan.  Afrikaans\n",
       "2835  Men's evil manners live in brass; their virtue...    English\n",
       "2836                                          Go-faster    English\n",
       "2837                                           Red tape    English\n",
       "2838                                        In a pickle    English\n",
       "\n",
       "[2839 rows x 2 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "121428ba",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2839, 2)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "c41ec5ca",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "84"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.duplicated().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "22178c56",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.drop_duplicates(inplace=True)\n",
    "df.duplicated().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "71300dfb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "text        3\n",
       "language    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "e49a1e55",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "text        0\n",
       "language    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dropna(inplace=True)\n",
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "896107ec",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2752, 2)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "d304f5ac",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "English       74.382267\n",
       "Afrikaans     23.183140\n",
       "Nederlands     2.434593\n",
       "Name: language, dtype: float64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "percentual_lingua = df['language'].value_counts(normalize=True) * 100\n",
    "percentual_lingua\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4bb4862a",
   "metadata": {},
   "source": [
    "**Temos muito pouca amostra de textos em holandês**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6cf83926",
   "metadata": {},
   "source": [
    "## Preparação dos textos"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4370f6e4",
   "metadata": {},
   "source": [
    "### Stop Words\n",
    "\n",
    "Aqui teremos que separar as 3 línguas e usar pacotes para remover as stop words dos 3 idiomas distintos."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "26a076de",
   "metadata": {},
   "outputs": [],
   "source": [
    "df1 = df.copy()\n",
    "\n",
    "df_africano = df1[df1['language'] == 'Afrikaans']\n",
    "df_ingles = df1[df1['language'] == 'English']\n",
    "df_holandes = df1[df1['language'] == 'Nederlands']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e92f9f6f",
   "metadata": {},
   "source": [
    "Encontrei os stop words no repositório do github -> https://github.com/6/stopwords-json?tab=readme-ov-file\n",
    "\n",
    "Tentei usar o pacote nltk, mas acho que eles não tem para africanos e holandeses então resolvi usar o que tem todos no mesmo lugar."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "6709106c",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Ben-Hur\\AppData\\Local\\Temp\\ipykernel_15004\\399999869.py:12: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df_africano['text'] = df_africano['text'].apply(remover_stopwords)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>text</th>\n",
       "      <th>language</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Sy kan altyd my battery natpiepie.</td>\n",
       "      <td>Afrikaans</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Piet Pompies van Soetmelksvlei is nie van hier...</td>\n",
       "      <td>Afrikaans</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>Die lewe is 10 % wat met ons gebeur en 90 % on...</td>\n",
       "      <td>Afrikaans</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>Saam met ons, rondom ons, in ons en by ons is ...</td>\n",
       "      <td>Afrikaans</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>Vroeg uit die bed, maak die beursie vet!</td>\n",
       "      <td>Afrikaans</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                 text   language\n",
       "5                  Sy kan altyd my battery natpiepie.  Afrikaans\n",
       "16  Piet Pompies van Soetmelksvlei is nie van hier...  Afrikaans\n",
       "28  Die lewe is 10 % wat met ons gebeur en 90 % on...  Afrikaans\n",
       "31  Saam met ons, rondom ons, in ons en by ons is ...  Afrikaans\n",
       "35           Vroeg uit die bed, maak die beursie vet!  Afrikaans"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Começando com os africanos\n",
    "with open('stop_words/afrikaans_stopwords.txt', 'r', encoding='utf-8') as arquivo:\n",
    "    stopwords_lista = arquivo.read().splitlines()\n",
    "\n",
    "# Função para remover as stop words do texto\n",
    "def remover_stopwords(texto):\n",
    "    palavras = texto.split()\n",
    "    palavras_sem_stopwords = [palavra for palavra in palavras if palavra.lower() not in stopwords_lista]\n",
    "    return ' '.join(palavras_sem_stopwords)\n",
    "\n",
    "\n",
    "df_africano['text'] = df_africano['text'].apply(remover_stopwords)\n",
    "\n",
    "df_africano.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "e0af827e",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Ben-Hur\\AppData\\Local\\Temp\\ipykernel_15004\\3200920810.py:12: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df_ingles['text'] = df_ingles['text'].apply(remover_stopwords)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>text</th>\n",
       "      <th>language</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Ship shape and Bristol fashion</td>\n",
       "      <td>English</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Know the ropes</td>\n",
       "      <td>English</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Graveyard shift</td>\n",
       "      <td>English</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Milk of human kindness</td>\n",
       "      <td>English</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Touch with a barge-pole - Wouldn't</td>\n",
       "      <td>English</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                 text language\n",
       "0      Ship shape and Bristol fashion  English\n",
       "1                      Know the ropes  English\n",
       "2                     Graveyard shift  English\n",
       "3              Milk of human kindness  English\n",
       "4  Touch with a barge-pole - Wouldn't  English"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Inglês\n",
    "with open('stop_words/english_stopwords.txt', 'r', encoding='utf-8') as arquivo:\n",
    "    stopwords_lista = arquivo.read().splitlines()\n",
    "\n",
    "# Função para remover as stop words do texto\n",
    "def remover_stopwords(texto):\n",
    "    palavras = texto.split()\n",
    "    palavras_sem_stopwords = [palavra for palavra in palavras if palavra.lower() not in stopwords_lista]\n",
    "    return ' '.join(palavras_sem_stopwords)\n",
    "\n",
    "\n",
    "df_ingles['text'] = df_ingles['text'].apply(remover_stopwords)\n",
    "\n",
    "df_ingles.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "15668047",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Ben-Hur\\AppData\\Local\\Temp\\ipykernel_15004\\3793866697.py:12: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df_holandes['text'] = df_holandes['text'].apply(remover_stopwords)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>text</th>\n",
       "      <th>language</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>64</th>\n",
       "      <td>Je moet geen oude schoenen weggooien, voordat ...</td>\n",
       "      <td>Nederlands</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>93</th>\n",
       "      <td>Wie zaait, zal oogsten.</td>\n",
       "      <td>Nederlands</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>119</th>\n",
       "      <td>Wie het eerst komt, het eerst maalt.</td>\n",
       "      <td>Nederlands</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>143</th>\n",
       "      <td>Gedane zaken nemen geen keer.</td>\n",
       "      <td>Nederlands</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>170</th>\n",
       "      <td>Aan alles komt een eind.</td>\n",
       "      <td>Nederlands</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                  text    language\n",
       "64   Je moet geen oude schoenen weggooien, voordat ...  Nederlands\n",
       "93                             Wie zaait, zal oogsten.  Nederlands\n",
       "119               Wie het eerst komt, het eerst maalt.  Nederlands\n",
       "143                      Gedane zaken nemen geen keer.  Nederlands\n",
       "170                           Aan alles komt een eind.  Nederlands"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Holandês\n",
    "with open('stop_words/nederlands_stopwords.txt', 'r', encoding='utf-8') as arquivo:\n",
    "    stopwords_lista = arquivo.read().splitlines()\n",
    "\n",
    "# Função para remover as stop words do texto\n",
    "def remover_stopwords(texto):\n",
    "    palavras = texto.split()\n",
    "    palavras_sem_stopwords = [palavra for palavra in palavras if palavra.lower() not in stopwords_lista]\n",
    "    return ' '.join(palavras_sem_stopwords)\n",
    "\n",
    "\n",
    "df_holandes['text'] = df_holandes['text'].apply(remover_stopwords)\n",
    "\n",
    "df_holandes.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8697294c",
   "metadata": {},
   "source": [
    "### Juntandos os dataframes de volta"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "276b948d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>text</th>\n",
       "      <th>language</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Sy kan altyd my battery natpiepie.</td>\n",
       "      <td>Afrikaans</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Piet Pompies van Soetmelksvlei is nie van hier...</td>\n",
       "      <td>Afrikaans</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>Die lewe is 10 % wat met ons gebeur en 90 % on...</td>\n",
       "      <td>Afrikaans</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>Saam met ons, rondom ons, in ons en by ons is ...</td>\n",
       "      <td>Afrikaans</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>Vroeg uit die bed, maak die beursie vet!</td>\n",
       "      <td>Afrikaans</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2516</th>\n",
       "      <td>De laatste druppel doet de emmer overlopen.</td>\n",
       "      <td>Nederlands</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2751</th>\n",
       "      <td>Kleine potjes hebben grote oren.</td>\n",
       "      <td>Nederlands</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2776</th>\n",
       "      <td>Schijn bedriegt.</td>\n",
       "      <td>Nederlands</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2797</th>\n",
       "      <td>Iets is beter dan niets.</td>\n",
       "      <td>Nederlands</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2826</th>\n",
       "      <td>Waar rook is, is vuur.</td>\n",
       "      <td>Nederlands</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2752 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                   text    language\n",
       "5                    Sy kan altyd my battery natpiepie.   Afrikaans\n",
       "16    Piet Pompies van Soetmelksvlei is nie van hier...   Afrikaans\n",
       "28    Die lewe is 10 % wat met ons gebeur en 90 % on...   Afrikaans\n",
       "31    Saam met ons, rondom ons, in ons en by ons is ...   Afrikaans\n",
       "35             Vroeg uit die bed, maak die beursie vet!   Afrikaans\n",
       "...                                                 ...         ...\n",
       "2516        De laatste druppel doet de emmer overlopen.  Nederlands\n",
       "2751                   Kleine potjes hebben grote oren.  Nederlands\n",
       "2776                                   Schijn bedriegt.  Nederlands\n",
       "2797                           Iets is beter dan niets.  Nederlands\n",
       "2826                             Waar rook is, is vuur.  Nederlands\n",
       "\n",
       "[2752 rows x 2 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_stop = pd.concat([df_africano, df_ingles, df_holandes])\n",
    "df_stop"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "5504d6e2",
   "metadata": {},
   "outputs": [],
   "source": [
    "#df_stop.to_csv('stop_words.csv', index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8d5b2ce5",
   "metadata": {},
   "source": [
    "## Representação Bag of Word"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "17f7c474",
   "metadata": {},
   "outputs": [],
   "source": [
    "countvec = CountVectorizer(ngram_range = (1,2))\n",
    "countvec_data = countvec.fit_transform(df_stop['text'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "a25998c5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>10</th>\n",
       "      <th>10 wat</th>\n",
       "      <th>1500s</th>\n",
       "      <th>1500s folk</th>\n",
       "      <th>1ste</th>\n",
       "      <th>1ste graad</th>\n",
       "      <th>22</th>\n",
       "      <th>90</th>\n",
       "      <th>90 ons</th>\n",
       "      <th>99</th>\n",
       "      <th>...</th>\n",
       "      <th>zwijgen is</th>\n",
       "      <th>àugur</th>\n",
       "      <th>àugur well</th>\n",
       "      <th>één</th>\n",
       "      <th>één ding</th>\n",
       "      <th>één klap</th>\n",
       "      <th>één paard</th>\n",
       "      <th>één rede</th>\n",
       "      <th>één vogel</th>\n",
       "      <th>language</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\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>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>Afrikaans</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</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>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>Afrikaans</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</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>Afrikaans</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</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>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>Afrikaans</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</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>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>Afrikaans</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 13376 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    10  10 wat  1500s  1500s folk  1ste  1ste graad  22  90  90 ons  99  ...  \\\n",
       "5    0       0      0           0     0           0   0   0       0   0  ...   \n",
       "16   0       0      0           0     0           0   0   0       0   0  ...   \n",
       "28   1       1      0           0     0           0   0   1       1   0  ...   \n",
       "31   0       0      0           0     0           0   0   0       0   0  ...   \n",
       "35   0       0      0           0     0           0   0   0       0   0  ...   \n",
       "\n",
       "    zwijgen is  àugur  àugur well  één  één ding  één klap  één paard  \\\n",
       "5            0      0           0    0         0         0          0   \n",
       "16           0      0           0    0         0         0          0   \n",
       "28           0      0           0    0         0         0          0   \n",
       "31           0      0           0    0         0         0          0   \n",
       "35           0      0           0    0         0         0          0   \n",
       "\n",
       "    één rede  één vogel   language  \n",
       "5          0          0  Afrikaans  \n",
       "16         0          0  Afrikaans  \n",
       "28         0          0  Afrikaans  \n",
       "31         0          0  Afrikaans  \n",
       "35         0          0  Afrikaans  \n",
       "\n",
       "[5 rows x 13376 columns]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Conversão da matriz esparsa para DataFrame\n",
    "df_bow = pd.DataFrame(countvec_data.toarray(), \n",
    "                      columns=countvec.get_feature_names_out())\n",
    "df_bow.index = df_stop.index\n",
    "df_bow = df_bow.join(df_stop[['language']], how='left')\n",
    "df_bow.head()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4ef7e140",
   "metadata": {},
   "source": [
    "# Criação do modelo "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "089a44c5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>10</th>\n",
       "      <th>10 wat</th>\n",
       "      <th>1500s</th>\n",
       "      <th>1500s folk</th>\n",
       "      <th>1ste</th>\n",
       "      <th>1ste graad</th>\n",
       "      <th>22</th>\n",
       "      <th>90</th>\n",
       "      <th>90 ons</th>\n",
       "      <th>99</th>\n",
       "      <th>...</th>\n",
       "      <th>zwijgen is</th>\n",
       "      <th>àugur</th>\n",
       "      <th>àugur well</th>\n",
       "      <th>één</th>\n",
       "      <th>één ding</th>\n",
       "      <th>één klap</th>\n",
       "      <th>één paard</th>\n",
       "      <th>één rede</th>\n",
       "      <th>één vogel</th>\n",
       "      <th>language</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\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>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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</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>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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</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>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</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>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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</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>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",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 13376 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    10  10 wat  1500s  1500s folk  1ste  1ste graad  22  90  90 ons  99  ...  \\\n",
       "5    0       0      0           0     0           0   0   0       0   0  ...   \n",
       "16   0       0      0           0     0           0   0   0       0   0  ...   \n",
       "28   1       1      0           0     0           0   0   1       1   0  ...   \n",
       "31   0       0      0           0     0           0   0   0       0   0  ...   \n",
       "35   0       0      0           0     0           0   0   0       0   0  ...   \n",
       "\n",
       "    zwijgen is  àugur  àugur well  één  één ding  één klap  één paard  \\\n",
       "5            0      0           0    0         0         0          0   \n",
       "16           0      0           0    0         0         0          0   \n",
       "28           0      0           0    0         0         0          0   \n",
       "31           0      0           0    0         0         0          0   \n",
       "35           0      0           0    0         0         0          0   \n",
       "\n",
       "    één rede  één vogel  language  \n",
       "5          0          0         0  \n",
       "16         0          0         0  \n",
       "28         0          0         0  \n",
       "31         0          0         0  \n",
       "35         0          0         0  \n",
       "\n",
       "[5 rows x 13376 columns]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "language_map = {'Afrikaans': 0, 'English':1, 'Nederlands':2}\n",
    "df_bow['language'] = df_bow['language'].map(language_map)\n",
    "df_bow.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1695ba50",
   "metadata": {},
   "source": [
    "## Definição do target e features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "67400e7d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Target \n",
    "y = df_bow['language']\n",
    "\n",
    "# Features\n",
    "x = df_bow.drop('language', axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "51c2724f",
   "metadata": {},
   "source": [
    "## Divisão treino e teste"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "bd64dd1a",
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train, x_test, y_train, y_test = train_test_split(x, \n",
    "                                                    y, \n",
    "                                                    test_size=0.30, \n",
    "                                                    random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "c6f8436c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((1926, 13375), (826, 13375), (1926,), (826,))"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(x_train.shape, x_test.shape, y_train.shape, y_test.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ae940fba",
   "metadata": {},
   "source": [
    "## Criação de treinamento do modelo "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "5cc878be",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GradientBoostingClassifier(random_state=42, subsample=0.5)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GradientBoostingClassifier</label><div class=\"sk-toggleable__content\"><pre>GradientBoostingClassifier(random_state=42, subsample=0.5)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "GradientBoostingClassifier(random_state=42, subsample=0.5)"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "modelo = GradientBoostingClassifier(n_estimators=100,\n",
    "                                        subsample=0.5,\n",
    "                                        random_state=42)\n",
    "modelo.fit(x_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "8669f0a7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 0, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1,\n",
       "       1, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 0, 1,\n",
       "       1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 1,\n",
       "       1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1,\n",
       "       0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       0, 0, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0,\n",
       "       1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 0, 1, 1, 1, 1,\n",
       "       1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 2, 1,\n",
       "       0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 0, 0, 1, 1, 1, 0, 1, 1,\n",
       "       1, 0, 1, 1, 1, 0, 1, 0, 0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       0, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 0, 0, 1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 0, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 0,\n",
       "       1, 1, 1, 1, 0, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0,\n",
       "       0, 0, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 0, 0, 0, 1, 0, 1, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1,\n",
       "       1, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 1,\n",
       "       0, 0, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 0, 1, 0, 1, 0, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 0, 1, 1, 0, 0, 0, 1, 1, 1, 0,\n",
       "       1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       0, 0, 1, 0, 2, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1,\n",
       "       1, 0, 0, 1, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 0, 0,\n",
       "       1, 0, 1, 1, 1, 1, 0, 2, 1, 0, 1, 1, 1, 0, 1, 1, 0, 0, 0, 0, 0, 1,\n",
       "       1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0,\n",
       "       1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0,\n",
       "       0, 1, 0, 1, 1, 2, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 1,\n",
       "       1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 0, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1], dtype=int64)"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred = modelo.predict(x_test)\n",
    "y_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "8c24b81a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Acurácia: 0.9297820823244553\n"
     ]
    }
   ],
   "source": [
    "accuracy = accuracy_score(y_test, y_pred)\n",
    "print(\"Acurácia:\", accuracy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "2fa22838",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[153,  40,   0],\n",
       "       [  1, 610,   0],\n",
       "       [  0,  17,   5]], dtype=int64)"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "confusion_matrix(y_test, y_pred)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "78f3844c",
   "metadata": {},
   "source": [
    "# Exportando o modelo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "982681b1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['modelo.pkl']"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Exportando o modelo para um arquivo\n",
    "joblib.dump(modelo, 'modelo.pkl')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0837ebe1",
   "metadata": {},
   "source": [
    "# Testando o modelo com frases inputadas "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "43f8a7f6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Digite uma frase: alo\n",
      "A frase foi classificada como: [1]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Ben-Hur\\anaconda3\\lib\\site-packages\\sklearn\\base.py:439: UserWarning: X does not have valid feature names, but GradientBoostingClassifier was fitted with feature names\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import joblib\n",
    "from sklearn.feature_extraction.text import CountVectorizer\n",
    "\n",
    "\n",
    "\n",
    "# Carregando os stop words\n",
    "df_stop = pd.read_csv('stop_words.csv')\n",
    "\n",
    "# Carregando o modelo a partir do arquivo\n",
    "modelo = joblib.load('modelo.pkl')\n",
    "\n",
    "\n",
    "frase = input(\"Digite uma frase: \")\n",
    "\n",
    "# Ajustar o CountVectorizer\n",
    "countvec = CountVectorizer(ngram_range=(1, 2))\n",
    "countvec.fit(df_stop['text'])\n",
    "\n",
    "# Vetorização da frase de teste usando o mesmo CountVectorizer ajustado\n",
    "frase_vetorizada = countvec.transform([frase])\n",
    "\n",
    "# Fazer a previsão\n",
    "classe_prevista = modelo.predict(frase_vetorizada)\n",
    "print(\"A frase foi classificada como:\", classe_prevista)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0c11db38",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "38aa21bd",
   "metadata": {},
   "source": [
    "# Testando novos modelos de Redes Neurais\n",
    "\n",
    "Como eu já tinha uma base de dados boa, não preciso ir atrás de outra, texto já está preparado, base de treino e teste dividido agora é só treinar o modelo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "b30a6122",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_3\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " dense_9 (Dense)             (None, 10)                133760    \n",
      "                                                                 \n",
      " dropout_6 (Dropout)         (None, 10)                0         \n",
      "                                                                 \n",
      " dense_10 (Dense)            (None, 8)                 88        \n",
      "                                                                 \n",
      " dropout_7 (Dropout)         (None, 8)                 0         \n",
      "                                                                 \n",
      " dense_11 (Dense)            (None, 3)                 27        \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 133,875\n",
      "Trainable params: 133,875\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "# Inicialização do modelo_rnao\n",
    "modelo_rna = keras.Sequential()\n",
    "\n",
    "# 1a camada oculta\n",
    "modelo_rna.add(layers.Dense(10, activation='tanh', input_dim=x_train.shape[1]))\n",
    "modelo_rna.add(layers.Dropout(0.2))\n",
    "\n",
    "# 2a camada oculta\n",
    "modelo_rna.add(layers.Dense(8, activation='tanh'))\n",
    "modelo_rna.add(layers.Dropout(0.1))\n",
    "\n",
    "# camada de saída\n",
    "modelo_rna.add(layers.Dense(3, activation='softmax'))\n",
    "\n",
    "# Compilando o modelo_rnao\n",
    "modelo_rna.compile(loss='sparse_categorical_crossentropy', optimizer='adam', metrics=['accuracy'])\n",
    "\n",
    "# Exibindo o resumo do modelo_rnao\n",
    "modelo_rna.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "54d68f81",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/100\n",
      "55/55 [==============================] - 1s 6ms/step - loss: 0.9222 - accuracy: 0.7421 - val_loss: 0.7159 - val_accuracy: 0.9275\n",
      "Epoch 2/100\n",
      "55/55 [==============================] - 0s 4ms/step - loss: 0.5834 - accuracy: 0.8898 - val_loss: 0.4123 - val_accuracy: 0.9534\n",
      "Epoch 3/100\n",
      "55/55 [==============================] - 0s 4ms/step - loss: 0.3406 - accuracy: 0.9406 - val_loss: 0.2399 - val_accuracy: 0.9689\n",
      "Epoch 4/100\n",
      "55/55 [==============================] - 0s 4ms/step - loss: 0.1974 - accuracy: 0.9752 - val_loss: 0.1606 - val_accuracy: 0.9793\n",
      "Epoch 5/100\n",
      "55/55 [==============================] - 0s 4ms/step - loss: 0.1311 - accuracy: 0.9833 - val_loss: 0.1194 - val_accuracy: 0.9793\n",
      "Epoch 6/100\n",
      "55/55 [==============================] - 0s 4ms/step - loss: 0.0896 - accuracy: 0.9890 - val_loss: 0.0960 - val_accuracy: 0.9793\n",
      "Epoch 7/100\n",
      "55/55 [==============================] - 0s 4ms/step - loss: 0.0685 - accuracy: 0.9937 - val_loss: 0.0809 - val_accuracy: 0.9793\n",
      "Epoch 8/100\n",
      "55/55 [==============================] - 0s 4ms/step - loss: 0.0504 - accuracy: 0.9971 - val_loss: 0.0709 - val_accuracy: 0.9793\n",
      "Epoch 9/100\n",
      "55/55 [==============================] - 0s 4ms/step - loss: 0.0405 - accuracy: 0.9977 - val_loss: 0.0640 - val_accuracy: 0.9793\n",
      "Epoch 10/100\n",
      "55/55 [==============================] - 0s 4ms/step - loss: 0.0328 - accuracy: 0.9988 - val_loss: 0.0588 - val_accuracy: 0.9793\n",
      "Epoch 11/100\n",
      "55/55 [==============================] - 0s 3ms/step - loss: 0.0293 - accuracy: 0.9988 - val_loss: 0.0550 - val_accuracy: 0.9793\n",
      "Epoch 12/100\n",
      "55/55 [==============================] - 0s 3ms/step - loss: 0.0228 - accuracy: 1.0000 - val_loss: 0.0515 - val_accuracy: 0.9845\n",
      "Epoch 13/100\n",
      "55/55 [==============================] - 0s 3ms/step - loss: 0.0201 - accuracy: 1.0000 - val_loss: 0.0491 - val_accuracy: 0.9793\n",
      "Epoch 14/100\n",
      "55/55 [==============================] - 0s 3ms/step - loss: 0.0159 - accuracy: 1.0000 - val_loss: 0.0475 - val_accuracy: 0.9793\n",
      "Epoch 15/100\n",
      "55/55 [==============================] - 0s 4ms/step - loss: 0.0143 - accuracy: 1.0000 - val_loss: 0.0459 - val_accuracy: 0.9793\n",
      "Epoch 16/100\n",
      "55/55 [==============================] - 0s 4ms/step - loss: 0.0138 - accuracy: 1.0000 - val_loss: 0.0441 - val_accuracy: 0.9793\n",
      "Epoch 17/100\n",
      "55/55 [==============================] - 0s 3ms/step - loss: 0.0119 - accuracy: 1.0000 - val_loss: 0.0436 - val_accuracy: 0.9793\n",
      "Epoch 18/100\n",
      "55/55 [==============================] - 0s 3ms/step - loss: 0.0107 - accuracy: 0.9994 - val_loss: 0.0426 - val_accuracy: 0.9793\n",
      "Epoch 19/100\n",
      "55/55 [==============================] - 0s 4ms/step - loss: 0.0094 - accuracy: 1.0000 - val_loss: 0.0421 - val_accuracy: 0.9793\n",
      "Epoch 20/100\n",
      "55/55 [==============================] - 0s 3ms/step - loss: 0.0080 - accuracy: 1.0000 - val_loss: 0.0414 - val_accuracy: 0.9793\n",
      "Epoch 21/100\n",
      "55/55 [==============================] - 0s 4ms/step - loss: 0.0081 - accuracy: 1.0000 - val_loss: 0.0410 - val_accuracy: 0.9793\n",
      "Epoch 22/100\n",
      "55/55 [==============================] - 0s 3ms/step - loss: 0.0070 - accuracy: 1.0000 - val_loss: 0.0403 - val_accuracy: 0.9793\n",
      "Epoch 23/100\n",
      "55/55 [==============================] - 0s 3ms/step - loss: 0.0071 - accuracy: 1.0000 - val_loss: 0.0404 - val_accuracy: 0.9793\n",
      "Epoch 24/100\n",
      "55/55 [==============================] - 0s 4ms/step - loss: 0.0058 - accuracy: 1.0000 - val_loss: 0.0403 - val_accuracy: 0.9793\n",
      "Epoch 25/100\n",
      "55/55 [==============================] - 0s 3ms/step - loss: 0.0054 - accuracy: 1.0000 - val_loss: 0.0412 - val_accuracy: 0.9793\n",
      "Epoch 26/100\n",
      "55/55 [==============================] - 0s 4ms/step - loss: 0.0054 - accuracy: 1.0000 - val_loss: 0.0414 - val_accuracy: 0.9793\n",
      "Epoch 27/100\n",
      "55/55 [==============================] - 0s 4ms/step - loss: 0.0059 - accuracy: 1.0000 - val_loss: 0.0404 - val_accuracy: 0.9793\n",
      "Epoch 28/100\n",
      "55/55 [==============================] - 0s 4ms/step - loss: 0.0042 - accuracy: 1.0000 - val_loss: 0.0403 - val_accuracy: 0.9793\n",
      "Epoch 29/100\n",
      "55/55 [==============================] - 0s 3ms/step - loss: 0.0044 - accuracy: 0.9994 - val_loss: 0.0398 - val_accuracy: 0.9793\n",
      "Epoch 30/100\n",
      "55/55 [==============================] - 0s 4ms/step - loss: 0.0043 - accuracy: 1.0000 - val_loss: 0.0393 - val_accuracy: 0.9793\n",
      "Epoch 31/100\n",
      "55/55 [==============================] - 0s 3ms/step - loss: 0.0036 - accuracy: 1.0000 - val_loss: 0.0393 - val_accuracy: 0.9793\n",
      "Epoch 32/100\n",
      "55/55 [==============================] - 0s 4ms/step - loss: 0.0042 - accuracy: 0.9994 - val_loss: 0.0389 - val_accuracy: 0.9793\n",
      "Epoch 33/100\n",
      "55/55 [==============================] - 0s 4ms/step - loss: 0.0039 - accuracy: 1.0000 - val_loss: 0.0384 - val_accuracy: 0.9845\n",
      "Epoch 34/100\n",
      "55/55 [==============================] - 0s 4ms/step - loss: 0.0035 - accuracy: 1.0000 - val_loss: 0.0389 - val_accuracy: 0.9793\n",
      "Epoch 35/100\n",
      "55/55 [==============================] - 0s 4ms/step - loss: 0.0025 - accuracy: 1.0000 - val_loss: 0.0387 - val_accuracy: 0.9793\n",
      "Epoch 36/100\n",
      "55/55 [==============================] - 0s 4ms/step - loss: 0.0029 - accuracy: 1.0000 - val_loss: 0.0386 - val_accuracy: 0.9845\n",
      "Epoch 37/100\n",
      "55/55 [==============================] - 0s 3ms/step - loss: 0.0028 - accuracy: 1.0000 - val_loss: 0.0376 - val_accuracy: 0.9845\n",
      "Epoch 38/100\n",
      "55/55 [==============================] - 0s 4ms/step - loss: 0.0027 - accuracy: 1.0000 - val_loss: 0.0377 - val_accuracy: 0.9793\n",
      "Epoch 39/100\n",
      "55/55 [==============================] - 0s 3ms/step - loss: 0.0022 - accuracy: 1.0000 - val_loss: 0.0378 - val_accuracy: 0.9793\n",
      "Epoch 40/100\n",
      "55/55 [==============================] - 0s 4ms/step - loss: 0.0026 - accuracy: 1.0000 - val_loss: 0.0374 - val_accuracy: 0.9845\n",
      "Epoch 41/100\n",
      "55/55 [==============================] - 0s 4ms/step - loss: 0.0029 - accuracy: 0.9994 - val_loss: 0.0388 - val_accuracy: 0.9845\n",
      "Epoch 42/100\n",
      "55/55 [==============================] - 0s 4ms/step - loss: 0.0023 - accuracy: 1.0000 - val_loss: 0.0405 - val_accuracy: 0.9793\n",
      "Epoch 43/100\n",
      "55/55 [==============================] - 0s 4ms/step - loss: 0.0020 - accuracy: 1.0000 - val_loss: 0.0403 - val_accuracy: 0.9845\n",
      "Epoch 44/100\n",
      "55/55 [==============================] - 0s 4ms/step - loss: 0.0025 - accuracy: 1.0000 - val_loss: 0.0391 - val_accuracy: 0.9845\n",
      "Epoch 45/100\n",
      "55/55 [==============================] - 0s 4ms/step - loss: 0.0022 - accuracy: 1.0000 - val_loss: 0.0394 - val_accuracy: 0.9845\n",
      "Epoch 46/100\n",
      "55/55 [==============================] - 0s 4ms/step - loss: 0.0018 - accuracy: 1.0000 - val_loss: 0.0395 - val_accuracy: 0.9845\n",
      "Epoch 47/100\n",
      "55/55 [==============================] - 0s 4ms/step - loss: 0.0022 - accuracy: 1.0000 - val_loss: 0.0387 - val_accuracy: 0.9793\n",
      "Epoch 48/100\n",
      "55/55 [==============================] - 0s 4ms/step - loss: 0.0020 - accuracy: 1.0000 - val_loss: 0.0382 - val_accuracy: 0.9793\n",
      "Epoch 49/100\n",
      "55/55 [==============================] - 0s 4ms/step - loss: 0.0016 - accuracy: 1.0000 - val_loss: 0.0386 - val_accuracy: 0.9845\n",
      "Epoch 50/100\n",
      "55/55 [==============================] - 0s 4ms/step - loss: 0.0019 - accuracy: 1.0000 - val_loss: 0.0396 - val_accuracy: 0.9845\n",
      "Epoch 51/100\n",
      "55/55 [==============================] - 0s 4ms/step - loss: 0.0015 - accuracy: 1.0000 - val_loss: 0.0393 - val_accuracy: 0.9845\n",
      "Epoch 52/100\n",
      "55/55 [==============================] - 0s 4ms/step - loss: 0.0017 - accuracy: 0.9994 - val_loss: 0.0379 - val_accuracy: 0.9845\n",
      "Epoch 53/100\n",
      "55/55 [==============================] - 0s 4ms/step - loss: 0.0013 - accuracy: 1.0000 - val_loss: 0.0384 - val_accuracy: 0.9845\n",
      "Epoch 54/100\n",
      "55/55 [==============================] - 0s 3ms/step - loss: 0.0020 - accuracy: 0.9994 - val_loss: 0.0372 - val_accuracy: 0.9793\n",
      "Epoch 55/100\n",
      "55/55 [==============================] - 0s 4ms/step - loss: 0.0013 - accuracy: 1.0000 - val_loss: 0.0381 - val_accuracy: 0.9845\n",
      "Epoch 56/100\n",
      "55/55 [==============================] - 0s 4ms/step - loss: 0.0013 - accuracy: 1.0000 - val_loss: 0.0386 - val_accuracy: 0.9845\n",
      "Epoch 57/100\n",
      "55/55 [==============================] - 0s 4ms/step - loss: 0.0012 - accuracy: 1.0000 - val_loss: 0.0391 - val_accuracy: 0.9845\n",
      "Epoch 58/100\n",
      "55/55 [==============================] - 0s 4ms/step - loss: 0.0011 - accuracy: 1.0000 - val_loss: 0.0400 - val_accuracy: 0.9845\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 59/100\n",
      "55/55 [==============================] - 0s 3ms/step - loss: 0.0013 - accuracy: 1.0000 - val_loss: 0.0403 - val_accuracy: 0.9845\n",
      "Epoch 60/100\n",
      "55/55 [==============================] - 0s 4ms/step - loss: 0.0011 - accuracy: 1.0000 - val_loss: 0.0403 - val_accuracy: 0.9845\n",
      "Epoch 61/100\n",
      "55/55 [==============================] - 0s 3ms/step - loss: 0.0012 - accuracy: 1.0000 - val_loss: 0.0401 - val_accuracy: 0.9845\n",
      "Epoch 62/100\n",
      "55/55 [==============================] - 0s 3ms/step - loss: 0.0013 - accuracy: 1.0000 - val_loss: 0.0406 - val_accuracy: 0.9845\n",
      "Epoch 63/100\n",
      "55/55 [==============================] - 0s 3ms/step - loss: 9.8411e-04 - accuracy: 1.0000 - val_loss: 0.0404 - val_accuracy: 0.9845\n",
      "Epoch 64/100\n",
      "55/55 [==============================] - 0s 4ms/step - loss: 7.6744e-04 - accuracy: 1.0000 - val_loss: 0.0407 - val_accuracy: 0.9845\n",
      "Epoch 65/100\n",
      "55/55 [==============================] - 0s 4ms/step - loss: 7.9765e-04 - accuracy: 1.0000 - val_loss: 0.0408 - val_accuracy: 0.9845\n",
      "Epoch 66/100\n",
      "55/55 [==============================] - 0s 3ms/step - loss: 8.9803e-04 - accuracy: 1.0000 - val_loss: 0.0398 - val_accuracy: 0.9845\n",
      "Epoch 67/100\n",
      "55/55 [==============================] - 0s 4ms/step - loss: 9.0259e-04 - accuracy: 1.0000 - val_loss: 0.0395 - val_accuracy: 0.9845\n",
      "Epoch 68/100\n",
      "55/55 [==============================] - 0s 3ms/step - loss: 0.0011 - accuracy: 1.0000 - val_loss: 0.0399 - val_accuracy: 0.9845\n",
      "Epoch 69/100\n",
      "55/55 [==============================] - 0s 3ms/step - loss: 7.4337e-04 - accuracy: 1.0000 - val_loss: 0.0397 - val_accuracy: 0.9845\n",
      "Epoch 70/100\n",
      "55/55 [==============================] - 0s 3ms/step - loss: 7.5184e-04 - accuracy: 1.0000 - val_loss: 0.0392 - val_accuracy: 0.9845\n",
      "Epoch 71/100\n",
      "55/55 [==============================] - 0s 3ms/step - loss: 7.8894e-04 - accuracy: 1.0000 - val_loss: 0.0395 - val_accuracy: 0.9845\n",
      "Epoch 72/100\n",
      "55/55 [==============================] - 0s 4ms/step - loss: 7.5609e-04 - accuracy: 1.0000 - val_loss: 0.0381 - val_accuracy: 0.9845\n",
      "Epoch 73/100\n",
      "55/55 [==============================] - 0s 3ms/step - loss: 0.0012 - accuracy: 1.0000 - val_loss: 0.0348 - val_accuracy: 0.9845\n",
      "Epoch 74/100\n",
      "55/55 [==============================] - 0s 4ms/step - loss: 0.0015 - accuracy: 0.9994 - val_loss: 0.0364 - val_accuracy: 0.9845\n",
      "Epoch 75/100\n",
      "55/55 [==============================] - 0s 4ms/step - loss: 9.5368e-04 - accuracy: 1.0000 - val_loss: 0.0345 - val_accuracy: 0.9845\n",
      "Epoch 76/100\n",
      "55/55 [==============================] - 0s 3ms/step - loss: 0.0015 - accuracy: 0.9994 - val_loss: 0.0332 - val_accuracy: 0.9845\n",
      "Epoch 77/100\n",
      "55/55 [==============================] - 0s 3ms/step - loss: 7.0451e-04 - accuracy: 1.0000 - val_loss: 0.0335 - val_accuracy: 0.9845\n",
      "Epoch 78/100\n",
      "55/55 [==============================] - 0s 3ms/step - loss: 7.4950e-04 - accuracy: 1.0000 - val_loss: 0.0342 - val_accuracy: 0.9896\n",
      "Epoch 79/100\n",
      "55/55 [==============================] - 0s 3ms/step - loss: 6.2044e-04 - accuracy: 1.0000 - val_loss: 0.0358 - val_accuracy: 0.9845\n",
      "Epoch 80/100\n",
      "55/55 [==============================] - 0s 4ms/step - loss: 7.7555e-04 - accuracy: 1.0000 - val_loss: 0.0351 - val_accuracy: 0.9845\n",
      "Epoch 81/100\n",
      "55/55 [==============================] - 0s 3ms/step - loss: 0.0018 - accuracy: 0.9994 - val_loss: 0.0331 - val_accuracy: 0.9845\n",
      "Epoch 82/100\n",
      "55/55 [==============================] - 0s 3ms/step - loss: 6.5272e-04 - accuracy: 1.0000 - val_loss: 0.0334 - val_accuracy: 0.9896\n",
      "Epoch 83/100\n",
      "55/55 [==============================] - 0s 3ms/step - loss: 9.7522e-04 - accuracy: 1.0000 - val_loss: 0.0357 - val_accuracy: 0.9845\n",
      "Epoch 84/100\n",
      "55/55 [==============================] - 0s 4ms/step - loss: 7.1553e-04 - accuracy: 1.0000 - val_loss: 0.0344 - val_accuracy: 0.9845\n",
      "Epoch 85/100\n",
      "55/55 [==============================] - 0s 3ms/step - loss: 5.6302e-04 - accuracy: 1.0000 - val_loss: 0.0350 - val_accuracy: 0.9845\n",
      "Epoch 86/100\n",
      "55/55 [==============================] - 0s 3ms/step - loss: 5.0703e-04 - accuracy: 1.0000 - val_loss: 0.0351 - val_accuracy: 0.9845\n",
      "Epoch 87/100\n",
      "55/55 [==============================] - 0s 3ms/step - loss: 6.8082e-04 - accuracy: 1.0000 - val_loss: 0.0350 - val_accuracy: 0.9845\n",
      "Epoch 88/100\n",
      "55/55 [==============================] - 0s 3ms/step - loss: 9.9313e-04 - accuracy: 0.9994 - val_loss: 0.0344 - val_accuracy: 0.9896\n",
      "Epoch 89/100\n",
      "55/55 [==============================] - 0s 3ms/step - loss: 7.0684e-04 - accuracy: 1.0000 - val_loss: 0.0336 - val_accuracy: 0.9896\n",
      "Epoch 90/100\n",
      "55/55 [==============================] - 0s 3ms/step - loss: 5.5794e-04 - accuracy: 1.0000 - val_loss: 0.0331 - val_accuracy: 0.9896\n",
      "Epoch 91/100\n",
      "55/55 [==============================] - 0s 4ms/step - loss: 5.8758e-04 - accuracy: 1.0000 - val_loss: 0.0336 - val_accuracy: 0.9896\n",
      "Epoch 92/100\n",
      "55/55 [==============================] - 0s 4ms/step - loss: 4.3699e-04 - accuracy: 1.0000 - val_loss: 0.0346 - val_accuracy: 0.9896\n",
      "Epoch 93/100\n",
      "55/55 [==============================] - 0s 4ms/step - loss: 5.2244e-04 - accuracy: 1.0000 - val_loss: 0.0356 - val_accuracy: 0.9845\n",
      "Epoch 94/100\n",
      "55/55 [==============================] - 0s 4ms/step - loss: 3.4056e-04 - accuracy: 1.0000 - val_loss: 0.0358 - val_accuracy: 0.9845\n",
      "Epoch 95/100\n",
      "55/55 [==============================] - 0s 4ms/step - loss: 0.0013 - accuracy: 0.9994 - val_loss: 0.0359 - val_accuracy: 0.9845\n",
      "Epoch 96/100\n",
      "55/55 [==============================] - 0s 4ms/step - loss: 4.8914e-04 - accuracy: 1.0000 - val_loss: 0.0360 - val_accuracy: 0.9845\n",
      "Epoch 97/100\n",
      "55/55 [==============================] - 0s 4ms/step - loss: 3.4531e-04 - accuracy: 1.0000 - val_loss: 0.0358 - val_accuracy: 0.9845\n",
      "Epoch 98/100\n",
      "55/55 [==============================] - 0s 4ms/step - loss: 5.1048e-04 - accuracy: 1.0000 - val_loss: 0.0349 - val_accuracy: 0.9845\n",
      "Epoch 99/100\n",
      "55/55 [==============================] - 0s 4ms/step - loss: 4.9593e-04 - accuracy: 1.0000 - val_loss: 0.0364 - val_accuracy: 0.9845\n",
      "Epoch 100/100\n",
      "55/55 [==============================] - 0s 4ms/step - loss: 3.2342e-04 - accuracy: 1.0000 - val_loss: 0.0367 - val_accuracy: 0.9845\n"
     ]
    }
   ],
   "source": [
    "historico = modelo_rna.fit(x=x_train, \n",
    "                           y=y_train,\n",
    "                           epochs=100,\n",
    "                           batch_size=32,\n",
    "                           validation_split=0.1,\n",
    "                           verbose=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "40505b40",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Acurácia do modelo: 97.09 %\n"
     ]
    }
   ],
   "source": [
    "# Avaliação do modelo\n",
    "resultado_teste = modelo_rna.evaluate(x_test, y_test, verbose=0)\n",
    "\n",
    "# Acurácia\n",
    "acuracia = resultado_teste[1]\n",
    "print(\"Acurácia do modelo:\", round((acuracia*100),2), \"%\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "dbe913d2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 800x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Pacotes gráficos\n",
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Acurácia do teste\n",
    "acuracia_teste = historico.history['val_accuracy']\n",
    "\n",
    "# Gráfico com a evolução do treinamento\n",
    "plt.figure(figsize=(8, 8))\n",
    "plt.style.use('ggplot')\n",
    "plt.plot(acuracia_teste)\n",
    "plt.title('Desempenho do Modelo no Conjunto de Teste')\n",
    "plt.ylabel('Acurácia')\n",
    "plt.xlabel('Épocas')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bc676790",
   "metadata": {},
   "source": [
    "**Acho que estamos próximo de um overfit, vamos ajustar as épocas ao testar os parametros**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4c130d89",
   "metadata": {},
   "source": [
    "###  Melhorando os hiperparamentros"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "4f2b5ab3",
   "metadata": {},
   "outputs": [],
   "source": [
    "def treina_rna(x_train, y_train, x_test, y_test, nnc1, nnc2):\n",
    "    # Definição do Modelo RNA\n",
    "    model = keras.Sequential([\n",
    "        layers.Dense(nnc1, activation='relu', input_dim=x_train.shape[1]),\n",
    "        layers.Dense(nnc2, activation='relu'),\n",
    "        layers.Dense(3, activation='softmax')  # Ajuste para 3 classes de saída\n",
    "    ])\n",
    "\n",
    "    # Compilação do Modelo\n",
    "    model.compile(loss='sparse_categorical_crossentropy', \n",
    "                  optimizer='adam',\n",
    "                  metrics=['accuracy'])\n",
    "\n",
    "    # Treinamento do Modelo RNA\n",
    "    historico = model.fit(x=x_train, \n",
    "                          y=y_train,\n",
    "                          epochs=50,  # Reduzindo para 50 épocas\n",
    "                          batch_size=32,\n",
    "                          validation_split=0.1,\n",
    "                          verbose=0)\n",
    "\n",
    "    # Avaliação do Desempenho do Modelo RNA no conjunto de teste\n",
    "    _, metric_test = model.evaluate(x_test, y_test, verbose=0)\n",
    "\n",
    "    return model, metric_test\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "69fc1a6c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Treinando Modelo 0\n",
      "Treinando Modelo 1\n",
      "Treinando Modelo 2\n",
      "Treinando Modelo 3\n",
      "Treinando Modelo 4\n",
      "Treinando Modelo 5\n",
      "Treinando Modelo 6\n",
      "Treinando Modelo 7\n",
      "Treinando Modelo 8\n",
      "Treinando Modelo 9\n",
      "Treinando Modelo 10\n",
      "Treinando Modelo 11\n",
      "Treinando Modelo 12\n",
      "Treinando Modelo 13\n",
      "Treinando Modelo 14\n",
      "Treinando Modelo 15\n",
      "Wall time: 2min 37s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "lst_models = list()\n",
    "lst_nnc1 = list()\n",
    "lst_nnc2 = list()\n",
    "acc_test = list()\n",
    "\n",
    "id_model = 0\n",
    "for nnc1 in range(8, 12):\n",
    "    for nnc2 in range(8, 12):\n",
    "        print(f'Treinando Modelo {id_model}')\n",
    "        lst_nnc1.append(nnc1)\n",
    "        lst_nnc2.append(nnc2)\n",
    "        modelo, acc = treina_rna(x_train, y_train, \n",
    "                                 x_test, y_test,\n",
    "                                 nnc1, nnc2)\n",
    "        lst_models.append(modelo)\n",
    "        acc_test.append(acc)\n",
    "        id_model += 1\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "70e48be4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Num Neuronios C1</th>\n",
       "      <th>Num Neuronios C2</th>\n",
       "      <th>Accuracy Test</th>\n",
       "      <th>Modelo</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>0.976998</td>\n",
       "      <td>&lt;keras.engine.sequential.Sequential object at ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10</td>\n",
       "      <td>9</td>\n",
       "      <td>0.970944</td>\n",
       "      <td>&lt;keras.engine.sequential.Sequential object at ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>0.967312</td>\n",
       "      <td>&lt;keras.engine.sequential.Sequential object at ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>9</td>\n",
       "      <td>10</td>\n",
       "      <td>0.967312</td>\n",
       "      <td>&lt;keras.engine.sequential.Sequential object at ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>11</td>\n",
       "      <td>11</td>\n",
       "      <td>0.966102</td>\n",
       "      <td>&lt;keras.engine.sequential.Sequential object at ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>11</td>\n",
       "      <td>9</td>\n",
       "      <td>0.964891</td>\n",
       "      <td>&lt;keras.engine.sequential.Sequential object at ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>0.963680</td>\n",
       "      <td>&lt;keras.engine.sequential.Sequential object at ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>8</td>\n",
       "      <td>9</td>\n",
       "      <td>0.963680</td>\n",
       "      <td>&lt;keras.engine.sequential.Sequential object at ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>10</td>\n",
       "      <td>8</td>\n",
       "      <td>0.963680</td>\n",
       "      <td>&lt;keras.engine.sequential.Sequential object at ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>8</td>\n",
       "      <td>10</td>\n",
       "      <td>0.962470</td>\n",
       "      <td>&lt;keras.engine.sequential.Sequential object at ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>8</td>\n",
       "      <td>11</td>\n",
       "      <td>0.962470</td>\n",
       "      <td>&lt;keras.engine.sequential.Sequential object at ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>9</td>\n",
       "      <td>11</td>\n",
       "      <td>0.960048</td>\n",
       "      <td>&lt;keras.engine.sequential.Sequential object at ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>11</td>\n",
       "      <td>8</td>\n",
       "      <td>0.960048</td>\n",
       "      <td>&lt;keras.engine.sequential.Sequential object at ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>11</td>\n",
       "      <td>10</td>\n",
       "      <td>0.958838</td>\n",
       "      <td>&lt;keras.engine.sequential.Sequential object at ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>9</td>\n",
       "      <td>8</td>\n",
       "      <td>0.956416</td>\n",
       "      <td>&lt;keras.engine.sequential.Sequential object at ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>10</td>\n",
       "      <td>11</td>\n",
       "      <td>0.955206</td>\n",
       "      <td>&lt;keras.engine.sequential.Sequential object at ...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Num Neuronios C1  Num Neuronios C2  Accuracy Test  \\\n",
       "10                10                10       0.976998   \n",
       "9                 10                 9       0.970944   \n",
       "5                  9                 9       0.967312   \n",
       "6                  9                10       0.967312   \n",
       "15                11                11       0.966102   \n",
       "13                11                 9       0.964891   \n",
       "0                  8                 8       0.963680   \n",
       "1                  8                 9       0.963680   \n",
       "8                 10                 8       0.963680   \n",
       "2                  8                10       0.962470   \n",
       "3                  8                11       0.962470   \n",
       "7                  9                11       0.960048   \n",
       "12                11                 8       0.960048   \n",
       "14                11                10       0.958838   \n",
       "4                  9                 8       0.956416   \n",
       "11                10                11       0.955206   \n",
       "\n",
       "                                               Modelo  \n",
       "10  <keras.engine.sequential.Sequential object at ...  \n",
       "9   <keras.engine.sequential.Sequential object at ...  \n",
       "5   <keras.engine.sequential.Sequential object at ...  \n",
       "6   <keras.engine.sequential.Sequential object at ...  \n",
       "15  <keras.engine.sequential.Sequential object at ...  \n",
       "13  <keras.engine.sequential.Sequential object at ...  \n",
       "0   <keras.engine.sequential.Sequential object at ...  \n",
       "1   <keras.engine.sequential.Sequential object at ...  \n",
       "8   <keras.engine.sequential.Sequential object at ...  \n",
       "2   <keras.engine.sequential.Sequential object at ...  \n",
       "3   <keras.engine.sequential.Sequential object at ...  \n",
       "7   <keras.engine.sequential.Sequential object at ...  \n",
       "12  <keras.engine.sequential.Sequential object at ...  \n",
       "14  <keras.engine.sequential.Sequential object at ...  \n",
       "4   <keras.engine.sequential.Sequential object at ...  \n",
       "11  <keras.engine.sequential.Sequential object at ...  "
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "busca_rna = pd.DataFrame(list(zip(lst_nnc1, lst_nnc2, acc_test, lst_models)), \n",
    "                        columns=['Num Neuronios C1',\n",
    "                                 'Num Neuronios C2',\n",
    "                                 'Accuracy Test',\n",
    "                                 'Modelo'\n",
    "                                ])\n",
    "\n",
    "busca_rna.sort_values('Accuracy Test', ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d4fd3f81",
   "metadata": {},
   "source": [
    "**O modelo 10 é o melhor pela acurácia (não esqueci que tem outras métricas) dentre os modelos testados**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "1b8f2ef5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<keras.engine.sequential.Sequential at 0x2b9283db7f0>"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "modelo_selecionado = busca_rna.loc[10]['Modelo']\n",
    "modelo_selecionado"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "374b755b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['modelo_redes_neurais.pkl']"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Exportando o modelo para um arquivo\n",
    "joblib.dump(modelo_selecionado, 'modelo_redes_neurais.pkl')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "c3b6b82f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Digite uma frase (ou 'exit' para sair): inglês\n",
      "inglês -> Inglês\n",
      "Digite uma frase (ou 'exit' para sair): africaner\n",
      "africaner -> Inglês\n",
      "Digite uma frase (ou 'exit' para sair): holandes\n",
      "holandes -> Inglês\n",
      "Digite uma frase (ou 'exit' para sair): exit\n",
      "Programa encerrado.\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import joblib\n",
    "from sklearn.feature_extraction.text import CountVectorizer\n",
    "import tensorflow as tf\n",
    "\n",
    "# Carregando os stop words\n",
    "df_stop = pd.read_csv('stop_words.csv')\n",
    "\n",
    "# Carregando o modelo a partir do arquivo\n",
    "modelo = joblib.load('modelo_redes_neurais.pkl')\n",
    "\n",
    "# Mapeamento das classes\n",
    "mapa_classes = {\n",
    "    0: 'Africaner',\n",
    "    1: 'Inglês',\n",
    "    2: 'Holandês'\n",
    "}\n",
    "\n",
    "# Ajustar o CountVectorizer\n",
    "countvec = CountVectorizer(ngram_range=(1, 2))\n",
    "countvec.fit(df_stop['text'])\n",
    "\n",
    "# Função de previsão eficiente usando TensorFlow\n",
    "def prever_classe(frase_vetorizada):\n",
    "    return modelo.predict(frase_vetorizada, verbose=0)\n",
    "\n",
    "while True:\n",
    "    frase = input(\"Digite uma frase (ou 'exit' para sair): \")\n",
    "    if frase.lower() == 'exit':\n",
    "        print(\"Programa encerrado.\")\n",
    "        break\n",
    "\n",
    "    # Vetorização da frase de teste usando o mesmo CountVectorizer ajustado\n",
    "    frase_vetorizada = countvec.transform([frase])\n",
    "\n",
    "    # Converter a matriz esparsa em um tensor\n",
    "    frase_vetorizada_tensor = tf.convert_to_tensor(frase_vetorizada.toarray())\n",
    "\n",
    "    # Fazer a previsão\n",
    "    classe_prevista_prob = prever_classe(frase_vetorizada_tensor)\n",
    "    classe_prevista = tf.argmax(classe_prevista_prob, axis=1).numpy()[0]\n",
    "    classe_prevista_nome = mapa_classes[classe_prevista]\n",
    "    print(frase, \"->\", classe_prevista_nome)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a497a328",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "51eb0d2b",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "022e6cc3",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2c23ad5e",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7c1679ff",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1375dd66",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f02a3f37",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1be0e9ca",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e01baeb1",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "37bb6beb",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6f8c3445",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "86a71fe0",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "46ed8d6b",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "be6bcd03",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}