word2vec_BiLSTM.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "fd769af5",
   "metadata": {},
   "source": [
    "# Preparação dos dados"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "16d72509",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>time</th>\n",
       "      <th>title</th>\n",
       "      <th>text</th>\n",
       "      <th>link</th>\n",
       "      <th>club</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>10/11/2022</td>\n",
       "      <td>19h55</td>\n",
       "      <td>Convocados do Uruguai para a Copa do Mundo 202...</td>\n",
       "      <td>O técnico Diego Alonso divulgou a lista dos 2...</td>\n",
       "      <td>https://ge.globo.com/futebol/copa-do-mundo/not...</td>\n",
       "      <td>athletico-pr</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>22/12/2017</td>\n",
       "      <td>10h04</td>\n",
       "      <td>Conmebol divulga tabela detalhada da pré e da ...</td>\n",
       "      <td>Argentinos são destaques no caminho dos brasi...</td>\n",
       "      <td>https://globoesporte.globo.com/futebol/liberta...</td>\n",
       "      <td>athletico-pr</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>11/02/2018</td>\n",
       "      <td>08h00</td>\n",
       "      <td>Com assistência, Demethryus recebe elogios no ...</td>\n",
       "      <td>O meia Demethryus conseguiu aproveitar bem a ...</td>\n",
       "      <td>https://globoesporte.globo.com/pr/futebol/time...</td>\n",
       "      <td>athletico-pr</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>18/12/2020</td>\n",
       "      <td>12h48</td>\n",
       "      <td>Athletico confirma fim de contrato com Welling...</td>\n",
       "      <td>A reportagem do ge já havia adiantado que as ...</td>\n",
       "      <td>https://globoesporte.globo.com/pr/futebol/time...</td>\n",
       "      <td>athletico-pr</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>13/09/2018</td>\n",
       "      <td>22h17</td>\n",
       "      <td>Alô, cartoleiros! Atlético-PR passa limpo cont...</td>\n",
       "      <td>O atacante Marcelo Cirino, desfalque já nos ú...</td>\n",
       "      <td>https://globoesporte.globo.com/pr/futebol/time...</td>\n",
       "      <td>athletico-pr</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         date   time                                              title  \\\n",
       "0  10/11/2022  19h55  Convocados do Uruguai para a Copa do Mundo 202...   \n",
       "1  22/12/2017  10h04  Conmebol divulga tabela detalhada da pré e da ...   \n",
       "2  11/02/2018  08h00  Com assistência, Demethryus recebe elogios no ...   \n",
       "3  18/12/2020  12h48  Athletico confirma fim de contrato com Welling...   \n",
       "4  13/09/2018  22h17  Alô, cartoleiros! Atlético-PR passa limpo cont...   \n",
       "\n",
       "                                                text  \\\n",
       "0   O técnico Diego Alonso divulgou a lista dos 2...   \n",
       "1   Argentinos são destaques no caminho dos brasi...   \n",
       "2   O meia Demethryus conseguiu aproveitar bem a ...   \n",
       "3   A reportagem do ge já havia adiantado que as ...   \n",
       "4   O atacante Marcelo Cirino, desfalque já nos ú...   \n",
       "\n",
       "                                                link          club  \n",
       "0  https://ge.globo.com/futebol/copa-do-mundo/not...  athletico-pr  \n",
       "1  https://globoesporte.globo.com/futebol/liberta...  athletico-pr  \n",
       "2  https://globoesporte.globo.com/pr/futebol/time...  athletico-pr  \n",
       "3  https://globoesporte.globo.com/pr/futebol/time...  athletico-pr  \n",
       "4  https://globoesporte.globo.com/pr/futebol/time...  athletico-pr  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "# Lê o arquivo .csv\n",
    "df = pd.read_csv('ge_news.csv')\n",
    "\n",
    "# Exibe as primeiras linhas\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "4750c7b4",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Removendo simultaneamente as colunas de índice 0, 1, 4 (date, time and link)\n",
    "column_indices = [0, 1, 4]\n",
    "df.drop([df.columns[i] for i in column_indices], axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "4190d3fe",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>title</th>\n",
       "      <th>text</th>\n",
       "      <th>club</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Convocados do Uruguai para a Copa do Mundo 202...</td>\n",
       "      <td>O técnico Diego Alonso divulgou a lista dos 2...</td>\n",
       "      <td>athletico-pr</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Conmebol divulga tabela detalhada da pré e da ...</td>\n",
       "      <td>Argentinos são destaques no caminho dos brasi...</td>\n",
       "      <td>athletico-pr</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Com assistência, Demethryus recebe elogios no ...</td>\n",
       "      <td>O meia Demethryus conseguiu aproveitar bem a ...</td>\n",
       "      <td>athletico-pr</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Athletico confirma fim de contrato com Welling...</td>\n",
       "      <td>A reportagem do ge já havia adiantado que as ...</td>\n",
       "      <td>athletico-pr</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Alô, cartoleiros! Atlético-PR passa limpo cont...</td>\n",
       "      <td>O atacante Marcelo Cirino, desfalque já nos ú...</td>\n",
       "      <td>athletico-pr</td>\n",
       "    </tr>\n",
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      "text/plain": [
       "                                               title  \\\n",
       "0  Convocados do Uruguai para a Copa do Mundo 202...   \n",
       "1  Conmebol divulga tabela detalhada da pré e da ...   \n",
       "2  Com assistência, Demethryus recebe elogios no ...   \n",
       "3  Athletico confirma fim de contrato com Welling...   \n",
       "4  Alô, cartoleiros! Atlético-PR passa limpo cont...   \n",
       "\n",
       "                                                text          club  \n",
       "0   O técnico Diego Alonso divulgou a lista dos 2...  athletico-pr  \n",
       "1   Argentinos são destaques no caminho dos brasi...  athletico-pr  \n",
       "2   O meia Demethryus conseguiu aproveitar bem a ...  athletico-pr  \n",
       "3   A reportagem do ge já havia adiantado que as ...  athletico-pr  \n",
       "4   O atacante Marcelo Cirino, desfalque já nos ú...  athletico-pr  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "e7b3299a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Fundindo colunas 'title' e 'text' para a coluna 'title_and_text'\n",
    "df['data'] = df['title'] + ' ' + df['text']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "e8db8b90",
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>title</th>\n",
       "      <th>text</th>\n",
       "      <th>club</th>\n",
       "      <th>data</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Convocados do Uruguai para a Copa do Mundo 202...</td>\n",
       "      <td>O técnico Diego Alonso divulgou a lista dos 2...</td>\n",
       "      <td>athletico-pr</td>\n",
       "      <td>Convocados do Uruguai para a Copa do Mundo 202...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Conmebol divulga tabela detalhada da pré e da ...</td>\n",
       "      <td>Argentinos são destaques no caminho dos brasi...</td>\n",
       "      <td>athletico-pr</td>\n",
       "      <td>Conmebol divulga tabela detalhada da pré e da ...</td>\n",
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       "      <th>2</th>\n",
       "      <td>Com assistência, Demethryus recebe elogios no ...</td>\n",
       "      <td>O meia Demethryus conseguiu aproveitar bem a ...</td>\n",
       "      <td>athletico-pr</td>\n",
       "      <td>Com assistência, Demethryus recebe elogios no ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Athletico confirma fim de contrato com Welling...</td>\n",
       "      <td>A reportagem do ge já havia adiantado que as ...</td>\n",
       "      <td>athletico-pr</td>\n",
       "      <td>Athletico confirma fim de contrato com Welling...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Alô, cartoleiros! Atlético-PR passa limpo cont...</td>\n",
       "      <td>O atacante Marcelo Cirino, desfalque já nos ú...</td>\n",
       "      <td>athletico-pr</td>\n",
       "      <td>Alô, cartoleiros! Atlético-PR passa limpo cont...</td>\n",
       "    </tr>\n",
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      "text/plain": [
       "                                               title  \\\n",
       "0  Convocados do Uruguai para a Copa do Mundo 202...   \n",
       "1  Conmebol divulga tabela detalhada da pré e da ...   \n",
       "2  Com assistência, Demethryus recebe elogios no ...   \n",
       "3  Athletico confirma fim de contrato com Welling...   \n",
       "4  Alô, cartoleiros! Atlético-PR passa limpo cont...   \n",
       "\n",
       "                                                text          club  \\\n",
       "0   O técnico Diego Alonso divulgou a lista dos 2...  athletico-pr   \n",
       "1   Argentinos são destaques no caminho dos brasi...  athletico-pr   \n",
       "2   O meia Demethryus conseguiu aproveitar bem a ...  athletico-pr   \n",
       "3   A reportagem do ge já havia adiantado que as ...  athletico-pr   \n",
       "4   O atacante Marcelo Cirino, desfalque já nos ú...  athletico-pr   \n",
       "\n",
       "                                                data  \n",
       "0  Convocados do Uruguai para a Copa do Mundo 202...  \n",
       "1  Conmebol divulga tabela detalhada da pré e da ...  \n",
       "2  Com assistência, Demethryus recebe elogios no ...  \n",
       "3  Athletico confirma fim de contrato com Welling...  \n",
       "4  Alô, cartoleiros! Atlético-PR passa limpo cont...  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "389e2ace",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import LabelEncoder\n",
    "\n",
    "# Cria um objeto LabelEncoder\n",
    "le = LabelEncoder()\n",
    "\n",
    "# Treinando o codificador de rótulos, transformando o nome do clube em um identificador numérico\n",
    "df['label'] = le.fit_transform(df['club'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "6fb405d5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array(['athletico-pr', 'atletico-go', 'atletico-mg', 'bahia',\n",
       "        'bragantino', 'botafogo', 'ceara', 'corinthians', 'coritiba',\n",
       "        'flamengo', 'fluminense', 'fortaleza', 'goias', 'gremio',\n",
       "        'internacional', 'palmeiras', 'santos', 'sao-paulo', 'sport',\n",
       "        'vasco', 'cruzeiro'], dtype=object),\n",
       " array([ 0,  1,  2,  3,  5,  4,  6,  7,  8, 10, 11, 12, 13, 14, 15, 16, 17,\n",
       "        18, 19, 20,  9]))"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['club'].unique(), df['label'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "5e04bd12",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Removendo as colunas originais, não precisamos mais delas\n",
    "column_indices = [0, 1, 2]\n",
    "df.drop([df.columns[i] for i in column_indices], axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "6c41ada1",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>data</th>\n",
       "      <th>label</th>\n",
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       "      <td>Convocados do Uruguai para a Copa do Mundo 202...</td>\n",
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      "text/plain": [
       "                                                data  label\n",
       "0  Convocados do Uruguai para a Copa do Mundo 202...      0\n",
       "1  Conmebol divulga tabela detalhada da pré e da ...      0\n",
       "2  Com assistência, Demethryus recebe elogios no ...      0\n",
       "3  Athletico confirma fim de contrato com Welling...      0\n",
       "4  Alô, cartoleiros! Atlético-PR passa limpo cont...      0"
      ]
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     "execution_count": 21,
     "metadata": {},
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   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "13a347bd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Conta quantos elementos (notícias) temos para cada categoria\n",
    "category_counts = df['label'].value_counts()\n",
    "\n",
    "# Plota a distribuição das categorias\n",
    "plt.figure(figsize=(12, 6))\n",
    "category_counts.plot(kind='bar')\n",
    "plt.xlabel('Categories')\n",
    "plt.ylabel('Number of Articles')\n",
    "plt.title('Distribution of Categories in the Dataset')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b67b64c5",
   "metadata": {},
   "source": [
    "# Pré-processamento"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "4ea6c46b",
   "metadata": {},
   "outputs": [],
   "source": [
    "import string\n",
    "import spacy\n",
    "import unicodedata\n",
    "from tqdm import tqdm\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "# Habilita o suporte do tqdm para métodos de progressão do pandas (como progress_apply)\n",
    "# Com isso, é possível ver barras de progresso em operções feitas no Pandas\n",
    "tqdm.pandas()\n",
    "\n",
    "# Carrega o modelo de linguagem 'pt_core_news_lg' do spaCy para processamento de texto em português\n",
    "# Desabilita os componentes 'parser' e 'ner', já que não são necessários para a lematização\n",
    "spacy_nlp_model = spacy.load(\"pt_core_news_lg\", disable=[\"parser\", \"ner\"])\n",
    "\n",
    "def preprocess_data(df, text_column):\n",
    "    \"\"\"\n",
    "    Realiza o pré-processamento dos dados de texto em um DataFrame do Pandas.\n",
    "    Remove pontuação, números e palavras comuns (stop words), converte para mínusculas, \n",
    "    remove acentos e símbolos diversos, e aplica lematização.\n",
    "    \"\"\"\n",
    "    \n",
    "    # Cria uma tabela de tradução que mapeia cada caractere de pontuação do módulo `string.punctuation` \n",
    "    # para `None` (ou seja, remove-os do texto).\n",
    "    print(\"Removendo pontuação...\")\n",
    "    translator = str.maketrans('', '', string.punctuation) # Só usamos o terceiro parâmetro, que mapeia ocorrências de string.punctuation para None\n",
    "    df[text_column] = df[text_column].progress_apply(lambda x: x.translate(translator))\n",
    "    \n",
    "    print(\"Removendo números...\")\n",
    "    translator = str.maketrans('', '', string.digits)\n",
    "    df[text_column] = df[text_column].progress_apply(lambda x: x.translate(translator))\n",
    "    \n",
    "    print(\"Removendo acentos e símbolos diversos...\")\n",
    "    def remove_accents_and_symbols(text):\n",
    "        try:\n",
    "            # Normaliza a string para a forma NFKD e mantém apenas caracteres que não são diacríticos\n",
    "            # nem combinam caracteres com diacríticos\n",
    "            return ''.join(c for c in unicodedata.normalize('NFKD', text) if not unicodedata.combining(c) and unicodedata.category(c) != 'Mn')\n",
    "        except TypeError:\n",
    "            # Se não for possível normalizar um caractere, retorna uma string vazia\n",
    "            return ''\n",
    "\n",
    "    # Aplica a função 'remove_accents_and_symbols' na coluna 'text_column'\n",
    "    df[text_column] = df[text_column].progress_apply(lambda x: remove_accents_and_symbols(x))\n",
    "    \n",
    "    print(\"Convertendo para minúsculas...\")\n",
    "    df[text_column] = df[text_column].progress_apply(lambda x: x.lower())\n",
    "\n",
    "    print(\"Computando Lematização...\")\n",
    "    def lemmatize_text(doc):\n",
    "        return [token.lemma_ for token in doc if not token.is_stop]\n",
    "\n",
    "    n_chunks = 10  # Ajuste esse valor de acordo com o tamanho da base de dados e a memória disponível no sistema\n",
    "    chunks = np.array_split(df, n_chunks) # Divide o dataframe em várias partes\n",
    "\n",
    "    processed_chunks = []\n",
    "    for i, chunk in enumerate(chunks):\n",
    "        print(f\"Processando segmento {i + 1} de {n_chunks}\")\n",
    "        processed_chunk = chunk.copy() # Cria uma cópia para realizar o processamento\n",
    "        \n",
    "        #  Aplica a função 'lemmatize_text' a cada documento processado pelo spaCy (usando 'spacy_nlp_model.pipe') e\n",
    "        # atribui os resultados (uma lista de palavras lematizadas) à coluna 'text_column' do DataFrame 'processed_chunk'.\n",
    "        # O tqdm é utilizado para exibir uma barra de progresso durante o processamento dos documentos.\n",
    "        processed_chunk[text_column] = [lemmatize_text(doc) for doc in tqdm(spacy_nlp_model.pipe(chunk[text_column].astype(str), batch_size=100, disable=['parser', 'ner']), total=len(chunk[text_column]))]\n",
    "\n",
    "        # Junta as partes em uma lista, para formar o dataframe final\n",
    "        processed_chunks.append(processed_chunk)\n",
    "\n",
    "    concatenated_df = pd.concat(processed_chunks) # Concatenar os DataFrames processados\n",
    "    df[text_column] = concatenated_df[text_column] # Atribuir a coluna 'data' processada de volta ao dataframe original\n",
    "    \n",
    "    print(\"Remover tokens com espaços vazios...\")\n",
    "    df['data'] = df['data'].progress_apply(lambda x: [token for token in x if token.strip()])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "f05948d7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Removendo pontuação...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|█████████████████████████████████| 191005/191005 [00:23<00:00, 7986.63it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Removendo números...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|█████████████████████████████████| 191005/191005 [00:22<00:00, 8533.09it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Removendo acentos e símbolos diversos...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|█████████████████████████████████| 191005/191005 [01:06<00:00, 2852.68it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Convertendo para minúsculas...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|███████████████████████████████| 191005/191005 [00:01<00:00, 113617.66it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Computando Lematização...\n",
      "Processando segmento 1 de 10\n"
     ]
    },
    {
     "name": "stderr",
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     ]
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    {
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     "text": [
      "Processando segmento 2 de 10\n"
     ]
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     "text": [
      "Processando segmento 3 de 10\n"
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      "Processando segmento 5 de 10\n"
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      "Processando segmento 6 de 10\n"
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      "Processando segmento 7 de 10\n"
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     "output_type": "stream",
     "text": [
      "Processando segmento 8 de 10\n"
     ]
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     "output_type": "stream",
     "text": [
      "Processando segmento 9 de 10\n"
     ]
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    {
     "name": "stderr",
     "output_type": "stream",
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     "output_type": "stream",
     "text": [
      "Processando segmento 10 de 10\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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      "100%|█████████████████████████████████████| 19100/19100 [03:58<00:00, 79.96it/s]\n"
     ]
    }
   ],
   "source": [
    "preprocess_data(df, 'data') # Realiza o pré-processamento"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "0997a5c6",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# Salvar em formato pickle\n",
    "df.to_pickle(\"ge_df.pkl\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "73d44330",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0         [convocar, uruguai, copa, mundo, lista, tecnic...\n",
       "1         [conmebol, divulgar, tabela, detalhar, pre, fa...\n",
       "2         [assistencia, demethryus, receber, elogio, atl...\n",
       "3         [athletico, confirmar, contrato, wellington, n...\n",
       "4         [alo, cartoleiro, atleticopr, passar, limpo, c...\n",
       "                                ...                        \n",
       "191000    [mano, trabalho, tecnico, bruno, Silva, entors...\n",
       "191001    [apos, eliminacao, cruzeiro, copa, Brasil, per...\n",
       "191002    [coleta, sangue, jogador, cruzeiro, acordar, j...\n",
       "191003    [cruzeiro, encerrar, preparacao, enfrentar, gu...\n",
       "191004    [cruzeiro, ceder, direito, economico, Mauricio...\n",
       "Name: data, Length: 191005, dtype: object"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['data']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "942bea8d",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "\n",
    "# Carregar dataframe salvo em formato pickle\n",
    "df = pd.read_pickle(\"ge_df.pkl\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "479c5d8f",
   "metadata": {},
   "source": [
    "# Treinando modelo Word2Vec\n",
    "\n",
    "## Definição\n",
    "\n",
    "O Word2Vec é um algoritmo de aprendizado de máquina não supervisionado que tem como objetivo aprender representações vetoriais (também conhecidas como incorporações ou embeddings) para palavras em um texto. Essas representações vetoriais capturam informações semânticas e sintáticas sobre as palavras e permitem que elas sejam usadas em tarefas de Processamento de Linguagem Natural (PLN), como análise de sentimentos, agrupamento de documentos, classificação de texto e detecção de similaridade entre palavras.\n",
    "\n",
    "O Word2Vec foi desenvolvido por Tomas Mikolov e sua equipe no Google e é baseado em duas arquiteturas diferentes: Continuous Bag-of-Words (CBOW) e Skip-Gram. Ambas as arquiteturas usam redes neurais de uma única camada oculta para aprender as representações vetoriais das palavras.\n",
    "\n",
    "## Tipos de Word2Vec\n",
    "### Continuous Bag-of-Words (CBOW)\n",
    "\n",
    "No CBOW, o modelo tenta prever uma palavra com base no contexto em que ela aparece. O contexto é representado como um conjunto de palavras vizinhas (palavras antes e depois da palavra alvo). O objetivo do treinamento é aprender os pesos da rede neural de forma que, dado o contexto, o modelo seja capaz de prever a palavra alvo com a maior probabilidade possível.\n",
    "\n",
    "### Skip-Gram\n",
    "\n",
    "No Skip-Gram, a abordagem é invertida em comparação com o CBOW. O modelo tenta prever o contexto (palavras vizinhas) a partir de uma palavra alvo. Durante o treinamento, o modelo aprende os pesos da rede neural de forma que, dada uma palavra alvo, ele possa prever as palavras no contexto com a maior probabilidade possível.\n",
    "\n",
    "## Exemplos\n",
    "\n",
    "Vamos supor que temos a seguinte frase: \"O gato pulou sobre o cachorro\". Com um tamanho de janela de contexto de 2 (uma palavra antes e uma depois), o Word2Vec geraria os seguintes pares de treinamento para a arquitetura CBOW:\n",
    "\n",
    "    ([O, pulou], gato)\n",
    "    ([gato, sobre], pulou)\n",
    "    ([pulou, o], sobre)\n",
    "    ([sobre, cachorro], o)\n",
    "\n",
    "E para a arquitetura Skip-Gram:\n",
    "\n",
    "    (gato, O)\n",
    "    (gato, pulou)\n",
    "    (pulou, gato)\n",
    "    (pulou, sobre)\n",
    "    (sobre, pulou)\n",
    "    (sobre, o)\n",
    "\n",
    "Ao treinar o modelo Word2Vec nessas arquiteturas, obtemos representações vetoriais das palavras que preservam a semântica e a sintaxe. Por exemplo, palavras semanticamente semelhantes, como \"gato\" e \"cachorro\", terão representações vetoriais próximas, no espaço vetorial."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "2bb15c8a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Importar a classe Word2Vec da biblioteca gensim, que permite criar e treinar modelos de incorporação de palavras (word embeddings) usando o algoritmo Word2Vec\n",
    "from gensim.models import Word2Vec\n",
    "\n",
    "# Importar a classe CallbackAny2Vec da biblioteca gensim, que fornece uma base para criar funções de retorno de chamada (callbacks) personalizadas \n",
    "# durante o treinamento de modelos Word2Vec (ou outros modelos baseados em Any2Vec)\n",
    "from gensim.models.callbacks import CallbackAny2Vec"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "1c60aac8",
   "metadata": {},
   "outputs": [],
   "source": [
    "class SimpleTextCallback(CallbackAny2Vec):\n",
    "    def __init__(self, total_epochs):\n",
    "        self.epoch = 0  # Inicializa o contador de épocas\n",
    "        self.total_epochs = total_epochs  # Armazena o número total de épocas para exibição\n",
    "\n",
    "    def on_epoch_end(self, model):\n",
    "        self.epoch += 1  # Incrementa o contador de épocas\n",
    "        print(f\"Época {self.epoch} de {self.total_epochs}...\") # Imprime uma mensagem informando o número da época atual e o total de épocas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "58cda673",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Converte a coluna 'data' do DataFrame em uma lista e armazena na variável preprocessed_articles, para ser usada no word2vec\n",
    "preprocessed_articles = df['data'].tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "57b48c11",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Época 1 de 10.\n",
      "Época 2 de 10.\n",
      "Época 3 de 10.\n",
      "Época 4 de 10.\n",
      "Época 5 de 10.\n",
      "Época 6 de 10.\n",
      "Época 7 de 10.\n",
      "Época 8 de 10.\n",
      "Época 9 de 10.\n",
      "Época 10 de 10.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(374541884, 397113750)"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Instancia o modelo Word2Vec com os seguintes parâmetros:\n",
    "# vector_size=100: Dimensão do vetor de palavras gerado\n",
    "# window=5: Tamanho da janela de contexto ao redor de cada palavra\n",
    "# min_count=1: Ignora palavras com frequência total menor que 1\n",
    "# workers=4: Número de threads a serem usadas para treinar o modelo\n",
    "word2vec_model = Word2Vec(vector_size=100, window=5, min_count=1, workers=4) #Se adicionar sg=0, muda pra CBOW\n",
    "\n",
    "# Constrói o vocabulário com base nos artigos pré-processados\n",
    "word2vec_model.build_vocab(preprocessed_articles)\n",
    "\n",
    "# Define o número total de épocas para o treinamento do modelo\n",
    "total_epochs = 10\n",
    "\n",
    "# Instancia o callback que exibe o progresso do treinamento após cada época\n",
    "callback = SimpleTextCallback(total_epochs)\n",
    "\n",
    "# Treina o modelo Word2Vec nos artigos pré-processados, utilizando o número total de exemplos e épocas definidos\n",
    "word2vec_model.train(preprocessed_articles, total_examples=word2vec_model.corpus_count, \n",
    "                     epochs=total_epochs, callbacks=[callback])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "f186d88c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Salva o modelo Word2Vec para um arquivo\n",
    "word2vec_model.save(\"word2vec_model.model\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "1a4a7465",
   "metadata": {},
   "outputs": [],
   "source": [
    "from gensim.models import Word2Vec\n",
    "\n",
    "# Carrega o modelo Word2Vec para um arquivo\n",
    "word2vec_model = Word2Vec.load(\"word2vec_model.model\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "0b433193",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('fluminense', 0.8631806373596191),\n",
       " ('palmeira', 0.7947038412094116),\n",
       " ('atleticomg', 0.7925401329994202),\n",
       " ('gremio', 0.7683544754981995),\n",
       " ('botafogo', 0.756887674331665),\n",
       " ('vasco', 0.7453044056892395),\n",
       " ('corinthians', 0.7275006175041199),\n",
       " ('corinthian', 0.7189180850982666),\n",
       " ('internacional', 0.7003942728042603),\n",
       " ('Vasco', 0.6960463523864746)]"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Encontra palavras mais similares de acordo com a palavra alvo\n",
    "word2vec_model.wv.most_similar('flamengo')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "f672fdbd",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Cria dois dicionários para mapear as palavras aos seus respectivos índices e vice-versa.\n",
    "# Isso é feito para facilitar a conversão entre palavras e índices durante a criação de sequências\n",
    "# numéricas que representam os textos. Essas sequências numéricas serão usadas como entrada para\n",
    "# modelos de aprendizado de máquina, como redes neurais.\n",
    "# Exemplo: \n",
    "# Suponha que o vocabulário seja ['casa', 'carro', 'jardim'], então os dicionários serão:\n",
    "# word_to_index = {'casa': 1, 'carro': 2, 'jardim': 3}\n",
    "# index_to_word = {1: 'casa', 2: 'carro', 3: 'jardim'}\n",
    "\n",
    "# 'word_to_index' é um dicionário que mapeia cada palavra ao seu índice correspondente.\n",
    "word_to_index = {}\n",
    "\n",
    "# 'index_to_word' é um dicionário que mapeia cada índice à palavra correspondente.\n",
    "index_to_word = {}\n",
    "\n",
    "# Itera sobre a lista de palavras únicas obtida do modelo Word2Vec\n",
    "for i, word in enumerate(word2vec_model.wv.index_to_key):\n",
    "    # Atribui a palavra ao índice i + 1 no dicionário 'word_to_index'.\n",
    "    # Os índices começam em 1 para reservar o índice 0 para preenchimento (padding) quando necessário.\n",
    "    word_to_index[word] = i + 1\n",
    "    \n",
    "    # Atribui o índice i + 1 à palavra no dicionário 'index_to_word'.\n",
    "    index_to_word[i + 1] = word"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "d6ffe380",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|████████████████████████████████| 191005/191005 [00:08<00:00, 22110.70it/s]\n"
     ]
    }
   ],
   "source": [
    "from tqdm import tqdm\n",
    "\n",
    "# 'sequences' é uma lista que armazenará as sequências numéricas correspondentes aos artigos pré-processados.\n",
    "sequences = []\n",
    "\n",
    "# Itera sobre os artigos pré-processados.\n",
    "for tokens in tqdm(preprocessed_articles):\n",
    "    # 'sequence' é uma lista temporária que armazenará a sequência numérica para o artigo atual.\n",
    "    sequence = []\n",
    "    \n",
    "    # Itera sobre os tokens (palavras) no artigo atual.\n",
    "    for token in tokens:\n",
    "        # Verifica se o token atual está presente no dicionário 'word_to_index'.\n",
    "        if token in word_to_index:\n",
    "            # Se o token estiver presente, adiciona o índice correspondente à lista 'sequence'.\n",
    "            sequence.append(word_to_index[token])\n",
    "    \n",
    "    # Após processar todos os tokens do artigo atual, adiciona a sequência numérica completa à lista 'sequences'.\n",
    "    sequences.append(sequence)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "dc88873c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "convocar uruguai copa mundo lista tecnico diego Alonso divulgar lista convocar uruguai copa mundo idolo veterano atacante cavani suarez puxar elenco celeste arrascaeta flamengo jovem valverde Real Madrid darwin nunez Liverpool simbolo renovacao Alonso optar manter zagueiro ronald Araujo Barcelona lista defensor recuperar lesao coxa nao jogar setembro expectativa inicial so poder voltar jogar anuncio convocar uruguai copa mundo alem arrascaeta jogador atuar Brasil chamar diego Alonso lateral varela flamengo atacante canobbio Athleticopr conhecer torcida brasileiro tambem estao lista veterano zagueiro diego Godin exatleticomg atualmente velez sarsfield lateral Matias vina expalmeir atualmente Roma uruguai terminar preparacao copa mundo abu dhabi emirado Arabes delegacao desembarcar quintafeira ida catar ocorrer proximo dia celeste h copa Portugal coreia Sul ganar estreia sulcoreano dia estadio cidade educacao Luis suarez Arrascaeta Sergio rochet estao lista uruguai — fotor divulgacao\n"
     ]
    }
   ],
   "source": [
    "# Utiliza uma compreensão de lista para converter a sequência numérica do primeiro artigo em uma lista de palavras.\n",
    "# Para cada índice 'i' na sequência numérica 'sequences[0]', obtém a palavra correspondente no dicionário 'index_to_word'.\n",
    "# A compreensão de lista retorna uma lista de palavras.\n",
    "word_list = [index_to_word[i] for i in sequences[0]]\n",
    "\n",
    "# Usa o método 'join()' para combinar as palavras da lista 'word_list' em uma única string.\n",
    "# As palavras são separadas por um espaço em branco.\n",
    "text = \" \".join(word_list)\n",
    "\n",
    "# Imprime o texto reconstruído a partir da sequência numérica.\n",
    "print(text)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "c75614eb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean sequence length:  207.9075155100652\n",
      "Maximum sequence length:  4967\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "# Calcula o comprimento de cada sequência em 'sequences' e armazena os resultados na lista 'sequence_lengths'\n",
    "sequence_lengths = [len(seq) for seq in sequences]\n",
    "\n",
    "# Calcula a média dos comprimentos das sequências usando a função 'mean' do numpy\n",
    "mean_length = np.mean(sequence_lengths)\n",
    "\n",
    "# Obtém o comprimento máximo das sequências usando a função 'max' do Python\n",
    "max_length = max(sequence_lengths)\n",
    "\n",
    "# Imprime a média e o comprimento máximo das sequências\n",
    "print(\"Mean sequence length: \", mean_length)\n",
    "print(\"Maximum sequence length: \", max_length)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "8ab7d458",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from tensorflow.keras.preprocessing.sequence import pad_sequences\n",
    "\n",
    "# Calcula o percentil 95 dos comprimentos das sequências (preserva 95% das sequências)\n",
    "max_length = int(np.percentile(sequence_lengths, 95))\n",
    "\n",
    "# Realiza o preenchimento das sequências com base no novo 'max_length'\n",
    "padded_sequences = pad_sequences(sequences, maxlen=max_length, padding='post')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "bbf394bb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "21"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['label'].nunique() # Retorna o número de categorias únicas (classes) presentes na coluna 'label'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "f7ce97f5",
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow.keras.utils as ku\n",
    "\n",
    "# Converte rótulos para one-hot encoding\n",
    "num_classes = df['label'].nunique()\n",
    "labels = ku.to_categorical(df['label'], num_classes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "22e8993e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1., 0., 0., ..., 0., 0., 0.],\n",
       "       [1., 0., 0., ..., 0., 0., 0.],\n",
       "       [1., 0., 0., ..., 0., 0., 0.],\n",
       "       ...,\n",
       "       [0., 0., 0., ..., 0., 0., 0.],\n",
       "       [0., 0., 0., ..., 0., 0., 0.],\n",
       "       [0., 0., 0., ..., 0., 0., 0.]], dtype=float32)"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "685a5668",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "# Divida os dados em um conjunto de treino (70%) e um conjunto temporário (30%)\n",
    "X_train, X_temp, y_train, y_temp = train_test_split(padded_sequences, labels, test_size=0.3)\n",
    "\n",
    "# Divida o conjunto temporário em conjuntos de validação (15%) e teste (15%)\n",
    "X_val, X_test, y_val, y_test = train_test_split(X_temp, y_temp, test_size=0.5)  # 0.5 * 0.3 = 0.15"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c412f308",
   "metadata": {},
   "source": [
    "# Treinamento da BiLSTM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "fb4e108c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Num GPUs:  1\n"
     ]
    }
   ],
   "source": [
    "import tensorflow as tf\n",
    "print(\"Num GPUs: \", len(tf.config.list_physical_devices('GPU')))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e768cd68",
   "metadata": {},
   "source": [
    "## Tipos de LSTM que existem:\n",
    "\n",
    "### Vanilla LSTM:\n",
    "\n",
    "A LSTM (Long Short-Term Memory) Vanilla é o modelo mais simples de LSTM, sendo uma rede neural recorrente que é capaz de lidar com dependências temporais de longo prazo. Uma das principais vantagens da Vanilla LSTM é sua facilidade de implementação e interpretação, o que torna o modelo adequado para tarefas simples de processamento de linguagem natural. No entanto, ela pode ser limitada em tarefas mais complexas e em sequências muito longas, pois a rede pode sofrer de problemas envolvendo gradientes (desvanecimento ou explosão de gradientes).\n",
    "\n",
    "### Stacked LSTM\n",
    "\n",
    "A Stacked LSTM é uma extensão da Vanilla LSTM que permite a criação de redes neurais mais profundas, com duas ou mais camadas LSTM. Com isso, é possível extrair features mais complexas e capturar padrões mais sutis nos dados. A principal vantagem da Stacked LSTM é sua capacidade de lidar com tarefas de processamento de linguagem natural mais complexas, mas ela também pode sofrer de problemas ligados a gradientes.\n",
    "\n",
    "### BiLSTM\n",
    "\n",
    "A BiLSTM (Bidirectional LSTM) é um tipo de rede neural que usa duas LSTMs, uma que processa a sequência de entrada na ordem normal e outra que processa a sequência na ordem inversa. A principal vantagem da BiLSTM é que ela pode capturar informações contextuais tanto do passado quanto do futuro, o que é importante para muitas tarefas de processamento de linguagem natural, como a análise de sentimento e a identificação de entidades nomeadas. No entanto, a BiLSTM é computacionalmente mais complexa do que a Vanilla LSTM e a Stacked LSTM, o que pode tornar seu treinamento mais demorado e exigir mais recursos computacionais."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "996575d5",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-04-02 20:14:58.281638: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations:  AVX2 FMA\n",
      "To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\n",
      "2023-04-02 20:14:58.282233: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
      "2023-04-02 20:14:58.282435: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
      "2023-04-02 20:14:58.282557: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
      "2023-04-02 20:14:58.634779: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
      "2023-04-02 20:14:58.634973: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
      "2023-04-02 20:14:58.635101: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
      "2023-04-02 20:14:58.635204: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1613] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 7065 MB memory:  -> device: 0, name: NVIDIA GeForce GTX 1070 Ti, pci bus id: 0000:01:00.0, compute capability: 6.1\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/40\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-04-02 20:15:05.126632: I tensorflow/compiler/xla/stream_executor/cuda/cuda_dnn.cc:428] Loaded cuDNN version 8100\n",
      "2023-04-02 20:15:05.476145: I tensorflow/compiler/xla/service/service.cc:173] XLA service 0x7ff5c24f1670 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\n",
      "2023-04-02 20:15:05.476166: I tensorflow/compiler/xla/service/service.cc:181]   StreamExecutor device (0): NVIDIA GeForce GTX 1070 Ti, Compute Capability 6.1\n",
      "2023-04-02 20:15:05.479862: I tensorflow/compiler/mlir/tensorflow/utils/dump_mlir_util.cc:268] disabling MLIR crash reproducer, set env var `MLIR_CRASH_REPRODUCER_DIRECTORY` to enable.\n",
      "2023-04-02 20:15:05.570629: I tensorflow/compiler/jit/xla_compilation_cache.cc:477] Compiled cluster using XLA!  This line is logged at most once for the lifetime of the process.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2090/2090 [==============================] - 376s 177ms/step - loss: 1.8630 - accuracy: 0.4956 - val_loss: 1.2249 - val_accuracy: 0.7072\n",
      "Epoch 2/40\n",
      "2090/2090 [==============================] - 335s 160ms/step - loss: 1.0516 - accuracy: 0.7506 - val_loss: 1.3083 - val_accuracy: 0.6998\n",
      "Epoch 3/40\n",
      "2090/2090 [==============================] - 332s 159ms/step - loss: 0.9012 - accuracy: 0.7856 - val_loss: 0.9163 - val_accuracy: 0.7725\n",
      "Epoch 4/40\n",
      "2090/2090 [==============================] - 329s 157ms/step - loss: 0.7657 - accuracy: 0.8072 - val_loss: 0.8540 - val_accuracy: 0.7815\n",
      "Epoch 5/40\n",
      "2090/2090 [==============================] - 328s 157ms/step - loss: 0.6900 - accuracy: 0.8189 - val_loss: 0.8132 - val_accuracy: 0.7844\n",
      "Epoch 6/40\n",
      "2090/2090 [==============================] - 328s 157ms/step - loss: 0.6408 - accuracy: 0.8260 - val_loss: 0.8250 - val_accuracy: 0.7845\n",
      "Epoch 7/40\n",
      "2090/2090 [==============================] - 329s 157ms/step - loss: 0.6046 - accuracy: 0.8317 - val_loss: 0.8447 - val_accuracy: 0.7748\n",
      "Epoch 8/40\n",
      "2090/2090 [==============================] - 327s 157ms/step - loss: 0.5784 - accuracy: 0.8360 - val_loss: 0.8287 - val_accuracy: 0.7806\n",
      "Epoch 9/40\n",
      "2090/2090 [==============================] - 328s 157ms/step - loss: 0.5517 - accuracy: 0.8404 - val_loss: 0.8243 - val_accuracy: 0.7779\n",
      "Epoch 10/40\n",
      "2090/2090 [==============================] - 328s 157ms/step - loss: 0.5298 - accuracy: 0.8425 - val_loss: 0.8270 - val_accuracy: 0.7800\n"
     ]
    }
   ],
   "source": [
    "from tensorflow.keras.models import Sequential\n",
    "from tensorflow.keras.layers import Embedding, LSTM, Bidirectional, Dense, Dropout\n",
    "from tensorflow.keras.callbacks import EarlyStopping\n",
    "\n",
    "# Cria modelo BiLSTM\n",
    "model = Sequential()\n",
    "model.add(Embedding(input_dim=len(word2vec_model.wv.key_to_index) + 1, \n",
    "                    output_dim=word2vec_model.vector_size, input_length=max_length))\n",
    "model.add(Bidirectional(LSTM(units=128, return_sequences=True, \n",
    "                             kernel_regularizer=tf.keras.regularizers.l2(0.001))))\n",
    "model.add(Dropout(0.5))\n",
    "model.add(Bidirectional(LSTM(units=64, return_sequences=True, \n",
    "                             kernel_regularizer=tf.keras.regularizers.l2(0.001))))\n",
    "model.add(Dropout(0.5))\n",
    "model.add(Bidirectional(LSTM(units=32, kernel_regularizer=tf.keras.regularizers.l2(0.001))))\n",
    "model.add(Dense(units=len(labels[0]), activation='softmax'))\n",
    "\n",
    "# Chamada ao Early Stopping\n",
    "early_stopping = EarlyStopping(monitor='val_loss', patience=5)\n",
    "\n",
    "# Compila modelo\n",
    "model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])\n",
    "\n",
    "# Treina modelo\n",
    "history = model.fit(X_train, y_train, epochs=40, batch_size=64, validation_data=(X_val, y_val), callbacks=[early_stopping], verbose=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "5bb969e9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Plota a perda (loss) e a perda de validação (val_loss) durante o treinamento\n",
    "plt.plot(history.history['loss'], label='Training Loss')\n",
    "plt.plot(history.history['val_loss'], label='Validation Loss')\n",
    "plt.title('Loss and Validation Loss')\n",
    "plt.xlabel('Epoch')\n",
    "plt.ylabel('Loss')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "56470378",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Salva arquivos em discos que podemos carregar sem treinar a rede neural novamente\n",
    "model.save('ge_BiLSTM.h5')\n",
    "np.save(\"X_test_BiLSTM.npy\", X_test)\n",
    "np.save(\"y_test_BiLSTM.npy\", y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "2b5298f8",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-04-03 18:44:43.326206: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations:  AVX2 FMA\n",
      "To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\n",
      "2023-04-03 18:44:43.973901: W tensorflow/compiler/xla/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libnvinfer.so.7'; dlerror: libnvinfer.so.7: cannot open shared object file: No such file or directory; LD_LIBRARY_PATH: :/home/lucas/miniconda3/envs/tf/lib/\n",
      "2023-04-03 18:44:43.973963: W tensorflow/compiler/xla/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libnvinfer_plugin.so.7'; dlerror: libnvinfer_plugin.so.7: cannot open shared object file: No such file or directory; LD_LIBRARY_PATH: :/home/lucas/miniconda3/envs/tf/lib/\n",
      "2023-04-03 18:44:43.973969: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Cannot dlopen some TensorRT libraries. If you would like to use Nvidia GPU with TensorRT, please make sure the missing libraries mentioned above are installed properly.\n",
      "2023-04-03 18:44:44.703159: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
      "2023-04-03 18:44:44.707719: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
      "2023-04-03 18:44:44.707946: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
      "2023-04-03 18:44:44.708269: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations:  AVX2 FMA\n",
      "To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\n",
      "2023-04-03 18:44:44.708795: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
      "2023-04-03 18:44:44.708919: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
      "2023-04-03 18:44:44.709031: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
      "2023-04-03 18:44:45.120032: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
      "2023-04-03 18:44:45.120205: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
      "2023-04-03 18:44:45.120325: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
      "2023-04-03 18:44:45.120425: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1613] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 6947 MB memory:  -> device: 0, name: NVIDIA GeForce GTX 1070 Ti, pci bus id: 0000:01:00.0, compute capability: 6.1\n"
     ]
    }
   ],
   "source": [
    "from tensorflow.keras.models import load_model\n",
    "import numpy as np\n",
    "\n",
    "# Carrega arquivos pertinentes ao treinamento da rede neural e X_test e y_test (usados na avaliação de resultados)\n",
    "model = load_model('ge_BiLSTM.h5')\n",
    "X_test = np.load(\"X_test_BiLSTM.npy\")\n",
    "y_test = np.load(\"y_test_BiLSTM.npy\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "990000c8",
   "metadata": {},
   "source": [
    "# Avaliação de Resultados"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "0fa18b7f",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-04-03 18:44:48.574271: I tensorflow/compiler/xla/stream_executor/cuda/cuda_dnn.cc:428] Loaded cuDNN version 8100\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Acurácia final: 0.7745279669761658\n"
     ]
    }
   ],
   "source": [
    "loss, accuracy = model.evaluate(X_test, y_test, verbose=0)\n",
    "print('Acurácia final:', accuracy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "eb3237d5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "896/896 [==============================] - 41s 45ms/step\n"
     ]
    }
   ],
   "source": [
    "# Fazer as previsões do modelo para o conjunto de teste\n",
    "y_pred = model.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "fe519cbf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1232,    3,    4,   33,    1,   19,    6,   28,   56,    0,   44,\n",
       "          23,   27,    3,   18,   21,   27,    2,   26,    4,    1],\n",
       "       [  16,  418,    4,   24,    3,    9,    5,   18,    0,    0,   11,\n",
       "           8,   16,   54,    1,    1,    4,    2,   19,    4,    4],\n",
       "       [  22,    0, 1168,   28,    5,    9,    6,   22,    2,   43,   24,\n",
       "          21,   16,    4,    6,    8,   38,    8,   22,   10,    4],\n",
       "       [  13,    0,    3,  807,    3,   11,    9,   11,    5,    1,   15,\n",
       "          10,   38,    3,    1,    6,    7,    2,   16,   17,    2],\n",
       "       [  22,    5,   14,   37, 1109,    0,   15,   19,    7,    6,   86,\n",
       "          53,   15,    4,    2,   11,   69,    9,   37,    9,    8],\n",
       "       [  15,    2,    5,   19,    1,  391,    7,   24,    2,    2,    3,\n",
       "           3,   14,    0,    3,    1,    6,    0,   13,   11,    4],\n",
       "       [  11,    2,    4,   28,    5,    5,  619,   12,    3,    4,   10,\n",
       "          14,  125,    1,    0,    2,   10,    0,   14,   12,    1],\n",
       "       [  30,    4,   28,   33,   17,   14,   10, 1823,    1,    5,   94,\n",
       "          26,   21,    3,   17,   22,   65,   17,   68,   15,    8],\n",
       "       [  80,    0,    6,   17,    4,   14,    6,    8,  626,    2,    7,\n",
       "           5,   14,    1,    1,    9,    6,    0,   13,    5,    5],\n",
       "       [  10,    4,   45,   17,    5,    6,   13,   13,    6, 1201,   22,\n",
       "          30,   15,    5,   15,    7,   29,    9,   10,   21,    3],\n",
       "       [  23,   10,   18,   42,   32,    5,   10,   33,    4,    3, 1793,\n",
       "          92,   16,    4,   22,   21,   74,   28,   48,    8,   17],\n",
       "       [  28,    4,    8,   37,   31,    5,    9,   18,    0,    0,  120,\n",
       "        1323,   23,    5,    5,   17,   33,   10,   31,    9,   10],\n",
       "       [  19,    2,    1,   39,    1,    3,   40,   11,    0,    0,   24,\n",
       "          16,  756,    1,    3,    2,   12,    2,   27,   18,    0],\n",
       "       [   8,   40,    8,   35,    4,    4,    6,   16,    8,    4,   12,\n",
       "          21,   19,  491,    3,   11,    9,    3,   14,    5,    1],\n",
       "       [  15,    1,    9,   31,    3,    2,    7,   24,    3,   12,   44,\n",
       "          22,   22,    2, 1252,   94,   31,   11,   42,   14,    3],\n",
       "       [  24,    3,   16,   43,    8,   10,   12,   34,    0,    4,   26,\n",
       "          41,   37,    2,   69, 1559,   24,    5,   35,    8,    4],\n",
       "       [  36,    0,   11,   40,    2,   14,    7,   65,    3,    2,   53,\n",
       "          26,   17,    4,   10,   15, 1462,    9,   53,    8,    2],\n",
       "       [  19,    1,    5,   13,    7,    1,    8,   47,    1,    6,   30,\n",
       "          26,   16,    4,    6,   12,   43,  965,   42,    5,    2],\n",
       "       [  22,    3,    7,   28,    7,   12,   13,   76,    3,    3,   45,\n",
       "          37,   23,    3,   24,   23,   42,   10, 1724,   12,    4],\n",
       "       [   9,    1,    3,   21,    1,    2,    5,   11,    0,    1,   21,\n",
       "          11,   24,    0,    7,    0,   10,    0,   23,  743,    2],\n",
       "       [   5,    2,    7,   39,   18,    2,   13,   10,    2,    1,   58,\n",
       "          32,    7,    4,    5,    9,    8,    1,   12,   11,  729]])"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Transformar as previsões de volta em rótulos\n",
    "y_pred_labels = np.argmax(y_pred, axis=1)\n",
    "y_true_labels = np.argmax(y_test, axis=1)\n",
    "\n",
    "# Calcular a matriz de confusão\n",
    "from sklearn.metrics import confusion_matrix\n",
    "conf_mat = confusion_matrix(y_true_labels, y_pred_labels)\n",
    "\n",
    "conf_mat"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "6a477aaa",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "\n",
    "# Lê o arquivo .csv\n",
    "df = pd.read_pickle(\"ge_df.pkl\")\n",
    "\n",
    "# Total de classes (times de futebol)\n",
    "classes = df['label'].nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "770139f8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x1000 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Define o tamanho da figura\n",
    "plt.figure(figsize=(15, 10))\n",
    "\n",
    "# Define mapa de cores\n",
    "cmap = plt.cm.Blues\n",
    "\n",
    "# Plota matriz de confusão\n",
    "plt.imshow(conf_mat, cmap=cmap)\n",
    "plt.colorbar()\n",
    "\n",
    "# Adiciona rótulos a matriz\n",
    "plt.xticks([int(i) for i in range(21)], [str(i) for i in range(21)])\n",
    "plt.yticks([int(i) for i in range(21)], [str(i) for i in range(21)])\n",
    "\n",
    "for i in range(classes):\n",
    "    for j in range(classes):\n",
    "        plt.text(j, i, str(conf_mat[i, j]), ha='center', va='center', color='white' if conf_mat[i, j] > len(y_true_labels)/ classes else 'black')\n",
    "\n",
    "# Adiciona rótulos dos eixos\n",
    "plt.xlabel('Predicted Class')\n",
    "plt.ylabel('True Class')\n",
    "\n",
    "# Mostra o plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "4c9288ae",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Class '0': Precision=0.7426, Recall=0.7807, F1-Score=0.7612\n",
      "Class '1': Precision=0.8277, Recall=0.6731, F1-Score=0.7425\n",
      "Class '2': Precision=0.8501, Recall=0.7967, F1-Score=0.8225\n",
      "Class '3': Precision=0.5719, Recall=0.8235, F1-Score=0.6750\n",
      "Class '4': Precision=0.8753, Recall=0.7215, F1-Score=0.7910\n",
      "Class '5': Precision=0.7268, Recall=0.7433, F1-Score=0.7350\n",
      "Class '6': Precision=0.7494, Recall=0.7018, F1-Score=0.7248\n",
      "Class '7': Precision=0.7848, Recall=0.7854, F1-Score=0.7851\n",
      "Class '8': Precision=0.8552, Recall=0.7551, F1-Score=0.8020\n",
      "Class '9': Precision=0.9238, Recall=0.8082, F1-Score=0.8622\n",
      "Class '10': Precision=0.7054, Recall=0.7785, F1-Score=0.7401\n",
      "Class '11': Precision=0.7190, Recall=0.7665, F1-Score=0.7420\n",
      "Class '12': Precision=0.5995, Recall=0.7738, F1-Score=0.6756\n",
      "Class '13': Precision=0.8211, Recall=0.6801, F1-Score=0.7439\n",
      "Class '14': Precision=0.8517, Recall=0.7616, F1-Score=0.8041\n",
      "Class '15': Precision=0.8422, Recall=0.7938, F1-Score=0.8173\n",
      "Class '16': Precision=0.7277, Recall=0.7950, F1-Score=0.7599\n",
      "Class '17': Precision=0.8829, Recall=0.7665, F1-Score=0.8206\n",
      "Class '18': Precision=0.7532, Recall=0.8128, F1-Score=0.7819\n",
      "Class '19': Precision=0.7829, Recall=0.8302, F1-Score=0.8059\n",
      "Class '20': Precision=0.8956, Recall=0.7477, F1-Score=0.8150\n",
      "Average F1-Score: 0.7718\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "from sklearn.metrics import precision_score, recall_score, f1_score\n",
    "\n",
    "# Calcula precisão, recall e f1-score para cada classe\n",
    "precisions = []\n",
    "recalls = []\n",
    "f1_scores = []\n",
    "for i in range(classes):\n",
    "    precision = precision_score(y_true=y_true_labels == i, y_pred=y_pred_labels == i, zero_division=0)\n",
    "    recall = recall_score(y_true=y_true_labels == i, y_pred=y_pred_labels == i, zero_division=0)\n",
    "    f1 = f1_score(y_true=y_true_labels == i, y_pred=y_pred_labels == i, zero_division=0)\n",
    "    precisions.append(precision)\n",
    "    recalls.append(recall)\n",
    "    f1_scores.append(f1)\n",
    "    \n",
    "# Printa resultados\n",
    "for i in range(classes):\n",
    "    print(f\"Class '{i}': Precision={precisions[i]:.4f}, Recall={recalls[i]:.4f}, F1-Score={f1_scores[i]:.4f}\")\n",
    "\n",
    "# Calcula e printa a média dos f1-scores\n",
    "f1_average = np.mean(f1_scores)\n",
    "print(f\"Average F1-Score: {f1_average:.4f}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8991ad3a",
   "metadata": {},
   "source": [
    "# Teste com um exemplo real (Flamengo)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "59257bae",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Removendo pontuação...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|███████████████████████████████████████████| 1/1 [00:00<00:00, 3167.90it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Removendo números...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|███████████████████████████████████████████| 1/1 [00:00<00:00, 3123.09it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Removendo acentos e símbolos diversos...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|███████████████████████████████████████████| 1/1 [00:00<00:00, 2059.06it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Convertendo para minúsculas...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|███████████████████████████████████████████| 1/1 [00:00<00:00, 4804.47it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Computando Lematização...\n",
      "Processando segmento 1 de 10\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|█████████████████████████████████████████████| 1/1 [00:00<00:00, 81.07it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Processando segmento 2 de 10\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "0it [00:00, ?it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Processando segmento 3 de 10\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "0it [00:00, ?it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Processando segmento 4 de 10\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "0it [00:00, ?it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Processando segmento 5 de 10\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "0it [00:00, ?it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Processando segmento 6 de 10\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "0it [00:00, ?it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Processando segmento 7 de 10\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "0it [00:00, ?it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Processando segmento 8 de 10\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "0it [00:00, ?it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Processando segmento 9 de 10\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "0it [00:00, ?it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Processando segmento 10 de 10\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "0it [00:00, ?it/s]\n",
      "100%|██████████████████████████████████████████| 1/1 [00:00<00:00, 10894.30it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1/1 [==============================] - 0s 30ms/step\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "# Criei um dataframe simples com uma notícia só para aplicarmos a função preprocess_data\n",
    "\n",
    "import pandas as pd\n",
    "from nltk.tokenize import word_tokenize\n",
    "\n",
    "value = \"\"\"Santos diz que grupo do Flamengo na Libertadores é difícil e mira tetracampeonato \n",
    "\n",
    "Flamengo desfruta de dias livres para treinar com foco na final do Campeonato Carioca, mas também já mira a estreia na Libertadores. A estreia na competição continental acontece na próxima quarta-feira, entre as finais do Estadual. \n",
    "\n",
    "O primeiro jogo será em Quito, no Equador, contra o Aucas, e o goleiro Santos comentou o desejo do Rubro-Negro em conquistar o tetracampeonato na Libertadores. \n",
    "\n",
    "- A expectativa é sempre muito boa para que essa competição comece. É a principal competição do nosso continente. O grupo é muito difícil, a gente sabe a dificuldade que é a Libertadores. Cada time teve a sua dificuldade para se classificar. A gente está vivendo uma expectativa boa para a estreia, queremos fazer uma boa competição em busca do objetivo que é ser campeão mais uma vez. \n",
    "\n",
    "Mas, antes de focar na estreia da Libertadores, o Flamengo disputa a primeira partida da final com o Fluminense, no sábado. Para o goleiro, o tempo para treinar em meio ao calendário apertado do futebol brasileiro é algo a ser valorizado.\n",
    "\n",
    "- Semana muito boa com essa pausa para treinarmos. Estamos muito focados nessa decisão. Os treinos estão com muita intensidade e a gente tem aproveitado o máximo possível para chegarmos preparados.\n",
    "\n",
    "O Flamengo é o atual campeão da Libertadores e luta pelo tetracampeonato. No Carioca, o Rubro-Negro tenta a revanche em cima do Fluminense, que terminou campeão na temporada de 2022. O segundo e decisivo jogo do Estadual acontece no domingo, dia 9.\n",
    "\"\"\"\n",
    "\n",
    "# Crie um DataFrame com uma linha e a coluna 'data'\n",
    "df_predict = pd.DataFrame(data={'data': [value]})\n",
    "\n",
    "# Faz pré-processamento\n",
    "preprocess_data(df_predict, 'data')\n",
    "\n",
    "# Conversão dos dados para serem usados no modelo (rede neural)\n",
    "preprocessed_articles = df_predict['data'].tolist()\n",
    "\n",
    "sequences_test = []\n",
    "for tokens in tqdm(preprocessed_articles):\n",
    "    sequence = []\n",
    "    for token in tokens:\n",
    "        if token in word_to_index:\n",
    "            sequence.append(word_to_index[token])\n",
    "    sequences_test.append(sequence)\n",
    "    \n",
    "padded_example = pad_sequences(sequences_test, maxlen=max_length, padding='post')\n",
    "\n",
    "# Fazer a previsão usando o modelo\n",
    "predictions = model.predict(padded_example)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "9cef0370",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1.2887026e-04, 1.4834206e-04, 2.7329827e-06, 4.2924832e-05,\n",
       "        6.7785433e-05, 8.0610221e-08, 3.4387465e-06, 9.3827301e-05,\n",
       "        4.4890638e-07, 8.2399842e-05, 9.8701096e-01, 1.5721966e-03,\n",
       "        7.8381699e-06, 8.0580330e-06, 8.6841224e-05, 1.4176588e-05,\n",
       "        6.0303926e-05, 1.0499354e-02, 2.4728452e-06, 1.3093569e-07,\n",
       "        1.6671167e-04]], dtype=float32)"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "db511063",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Classe prevista: 10\n"
     ]
    }
   ],
   "source": [
    "# Identificar a classe com a maior probabilidade\n",
    "predicted_class = np.argmax(predictions)\n",
    "\n",
    "# Exibir a classe prevista\n",
    "print(f\"Classe prevista: {predicted_class}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8921872d",
   "metadata": {},
   "source": [
    "# Transformando a classe numérica em texto, de volta"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "0b6ce4c6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Classe original: flamengo\n"
     ]
    }
   ],
   "source": [
    "from sklearn.preprocessing import LabelEncoder\n",
    "\n",
    "# Lê o arquivo .csv\n",
    "df = pd.read_csv('ge_news.csv')\n",
    "\n",
    "# Cria um objeto LabelEncoder\n",
    "le = LabelEncoder()\n",
    "\n",
    "# Transforma os rótulos de times de futebol para variáveis categóricas\n",
    "df['label'] = le.fit_transform(df['club'])\n",
    "\n",
    "original_class = le.inverse_transform([predicted_class]) # Transforma a classe predita de variável categórica para o nome do clube, de volta\n",
    "print(f\"Classe original: {original_class[0]}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "964b398b",
   "metadata": {},
   "source": [
    "# Teste com um exemplo real (Corinthians)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "a2c812fa",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Removendo pontuação...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|███████████████████████████████████████████| 1/1 [00:00<00:00, 3070.50it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Removendo números...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|███████████████████████████████████████████| 1/1 [00:00<00:00, 3463.50it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Removendo acentos e símbolos diversos...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|███████████████████████████████████████████| 1/1 [00:00<00:00, 2121.55it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Convertendo para minúsculas...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|███████████████████████████████████████████| 1/1 [00:00<00:00, 3489.44it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Computando Lematização...\n",
      "Processando segmento 1 de 10\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|█████████████████████████████████████████████| 1/1 [00:00<00:00, 95.85it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Processando segmento 2 de 10\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "0it [00:00, ?it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Processando segmento 3 de 10\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "0it [00:00, ?it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Processando segmento 4 de 10\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "0it [00:00, ?it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Processando segmento 5 de 10\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "0it [00:00, ?it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Processando segmento 6 de 10\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "0it [00:00, ?it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Processando segmento 7 de 10\n"
     ]
    },
    {
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      "1/1 [==============================] - 0s 41ms/step\n"
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    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "# Criei um dataframe simples e com uma notícia só apenas por conta da função preprocess_data\n",
    "\n",
    "import pandas as pd\n",
    "\n",
    "value = \"\"\"Nova camisa do Corinthians: vazam imagens de supostos uniformes para 2023; veja fotos\n",
    "\n",
    "Imagens de supostas novas camisas do Corinthians para 2023 vazaram nas redes sociais nessa quarta-feira. Foram divulgadas fotos do que seriam os modelos 1 e 2 do uniforme do Timão.\n",
    "\n",
    "Uma loja de um shopping na Zona Sul de São Paulo já está comercializando as novas camisas branca e listrada. A reportagem do ge entrou em contato com o estabelecimento e teve a confirmação de um funcionário de que as peças já estão à venda por R$ 300 (modelo de torcedor). Confira as imagens abaixo:\n",
    "\n",
    "As supostas novas camisas do Corinthians para 2023 possui detalhes na cor vermelha ao redor da logomarca da Nike, fornecedora do material esportivo do clube.\n",
    "\n",
    "Os uniformes alvinegros para este ano homenagearão a Democracia Corinthiana, movimento organizado na década de 1980. CLIQUE AQUI e saiba mais sobre o tema.\n",
    "\n",
    "Usualmente, o Corinthians lança seus novos uniformes principais entre o fim de abril e o início de maio. Já a terceira camisa costuma ser estreada em setembro, mês de aniversário do clube. \n",
    "\n",
    "O Timão volta a campo na próxima quinta-feira, na estreia da Libertadores, contra o Liverpool-URU, às 19h, no estádio Centenário de Montevidéu. \n",
    "\"\"\"\n",
    "\n",
    "# Crie um DataFrame com uma linha e a coluna 'data'\n",
    "df_predict = pd.DataFrame(data={'data': [value]})\n",
    "\n",
    "# Faz pré-processamento\n",
    "preprocess_data(df_predict, 'data')\n",
    "\n",
    "# Conversão dos dados para serem usados no modelo (rede neural)\n",
    "preprocessed_articles = df_predict['data'].tolist()\n",
    "\n",
    "sequences_test = []\n",
    "for tokens in tqdm(preprocessed_articles):\n",
    "    sequence = []\n",
    "    for token in tokens:\n",
    "        if token in word_to_index:\n",
    "            sequence.append(word_to_index[token])\n",
    "    sequences_test.append(sequence)\n",
    "    \n",
    "padded_example = pad_sequences(sequences_test, maxlen=max_length, padding='post')\n",
    "\n",
    "# Fazer a previsão usando o modelo\n",
    "predictions = model.predict(padded_example)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "166a8495",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Classe prevista: 7\n"
     ]
    }
   ],
   "source": [
    "# Identificar a classe com a maior probabilidade\n",
    "predicted_class = np.argmax(predictions)\n",
    "\n",
    "# Exibir a classe prevista\n",
    "print(f\"Classe prevista: {predicted_class}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "4fd0e782",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Classe original: corinthians\n"
     ]
    }
   ],
   "source": [
    "from sklearn.preprocessing import LabelEncoder\n",
    "\n",
    "# Lê o arquivo .csv\n",
    "df = pd.read_csv('ge_news.csv')\n",
    "\n",
    "# Cria um objeto LabelEncoder\n",
    "le = LabelEncoder()\n",
    "\n",
    "# Transforma os rótulos de times de futebol para variáveis categóricas\n",
    "df['label'] = le.fit_transform(df['club'])\n",
    "\n",
    "original_class = le.inverse_transform([predicted_class]) # Transforma a classe predita de variável categórica para o nome do clube, de volta\n",
    "print(f\"Classe original: {original_class[0]}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "53dd00bc",
   "metadata": {},
   "source": [
    "## Referências\n",
    "\n",
    "* https://www.kaggle.com/datasets/lgmoneda/ge-soccer-clubs-news\n",
    "* https://medium.com/@everton.tomalok/word2vec-e-sua-import%C3%A2ncia-na-etapa-de-pr%C3%A9-processamento-d0813acfc8ab\n",
    "* https://www.youtube.com/watch?v=hQwFeIupNP0\n",
    "* https://www.youtube.com/watch?v=ISPId9Lhc1g\n",
    "* https://jalammar.github.io/illustrated-word2vec/\n",
    "* https://towardsdatascience.com/a-beginners-guide-to-word-embedding-with-gensim-word2vec-model-5970fa56cc92"
   ]
  }
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