Sentiment_Analysis___an_introduction_to_machine_learning_algorithms.ipynb

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  "metadata": {
    "colab": {
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      "mount_file_id": "1lr_k2rdwO0cJ8IJHjOFXMBF0l78AYy0S",
      "authorship_tag": "ABX9TyMSS+BPU27HgU9sxMIcJEny",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/SampMark/Machine-Learning/blob/main/Sentiment_Analysis___an_introduction_to_machine_learning_algorithms.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Classificação: Análise de Sentimentos/Opiniões de Usuários**\n",
        "\n",
        "### **Objetivo**\n",
        "Este laboratório tem por objetivo construir um **sistema de análise de sentimentos/opiniões**, utilizando aprendizado de máquina para classificar avaliações de produtos e/ou serviços como **positivas (+1)** ou **negativas (-1)**. O foco principal é treinar e avaliar diferentes classificadores lineares, analisando sua precisão e capacidade de generalização.\n",
        "\n",
        "## **✅ O projeto envolve as seguintes etapas**:\n",
        "1. **Carregamento dos dados**: Importação e processamento do conjunto de dados de avaliações de produtos.\n",
        "2. **Pré-processamento**: Transformação dos textos em representações numéricas (_Bag-of-Words_), com remoção de palavras irrelevantes (_stopwords_).\n",
        "3. **Treinamento dos modelos**: Implementação e treinamento de três algoritmos de classificação:\n",
        "   - **Perceptron**: Algoritmo linear básico.\n",
        "   - **Perceptron Médio**: Variante que suaviza os pesos ao longo do tempo.\n",
        "   - **Pegasos**: Um método baseado em *Support Vector Machines (SVM)* com otimização por *Stochastic Gradient Descent*.\n",
        "4. **Avaliação dos Modelos**: Comparação de desempenho no conjunto de validação e teste.\n",
        "5. **Otimização de Hiperparâmetros**: Ajuste fino dos parâmetros para obter melhor desempenho.\n",
        "6. **Visualização de Resultados**: Geração de gráficos para melhor compreensão do comportamento dos modelos.\n",
        "\n",
        "## **✅ Funcionamento Geral**\n",
        "O laboratório segue uma **estrutura modular**, organizada em diferentes classes e funções. Isso facilita a manutenção do código e a reutilização das funções para futuras análises.\n",
        "\n",
        "O _pipeline_ geral é o seguinte:\n",
        "\n",
        "1. **Montagem do Google Drive** (para carregar os arquivos do dataset).\n",
        "2. **Carregamento dos dados** a partir de repositório no GitHub usando `DatasetLoader`.\n",
        "3. **Pré-processamento dos textos** e conversão para vetores numéricos com `Preprocessing`.\n",
        "4. **Treinamento dos modelos** (Perceptron, Perceptron Médio, Pegasos).\n",
        "5. **Avaliação dos modelos** em conjunto de validação e teste.\n",
        "6. **Ajuste de hiperparâmetros** para otimizar os modelos.\n",
        "7. **Visualização e interpretação dos resultados**.\n",
        "\n",
        "## **✅ Estrutura do Código**\n",
        "O código tem uma estrutura **modular** e **orientado a objetos**, com as seguintes classes e funções principais:\n",
        "\n",
        "### **1. Manipulação dos Dados**\n",
        "- **Classe `DatasetLoader`**\n",
        "  - Responsável por carregar os arquivos do dataset.\n",
        "  - Pode descompactar arquivos ZIP (se necessário).\n",
        "  - Extrai textos e rótulos das avaliações.\n",
        "\n",
        "- **Classe `Preprocessing`**\n",
        "  - Gera representações numéricas (*Bag-of-Words*).\n",
        "  - Remove *stopwords* (se habilitado).\n",
        "  - Transforma textos em vetores de features.\n",
        "\n",
        "### **2. Modelos de Classificação**\n",
        "- **Classe `BaseClassifier`**\n",
        "  - Define a estrutura básica para classificadores lineares.\n",
        "  - Implementa `predict()` e `evaluate()`.\n",
        "\n",
        "- **Classe `PerceptronClassifier`**\n",
        "  - Implementa o algoritmo Perceptron padrão.\n",
        "  - Atualiza os pesos iterativamente com base nos erros.\n",
        "\n",
        "- **Classe `AveragePerceptronClassifier`**\n",
        "  - Variante que acumula e usa a média dos pesos ao longo das iterações.\n",
        "\n",
        "- **Classe `PegasosClassifier`**\n",
        "  - Implementa o algoritmo Pegasos (*SVM linear com SGD*).\n",
        "  - Usa regularização para evitar overfitting.\n",
        "\n",
        "### **3. Treinamento e Avaliação**\n",
        "- **Classe `ModelTrainer`**\n",
        "  - Realiza treinamento de modelos.\n",
        "  - Permite afinar hiperparâmetros (`T` e `L`).\n",
        "  - Avalia a acurácia nos conjuntos de treino e validação.\n",
        "\n",
        "### **4. Otimização de Hiperparâmetros**\n",
        "- **Funções para ajuste de hiperparâmetros**\n",
        "  - `tune_parameter()`: Busca o melhor valor de `T` e `L`.\n",
        "  - `plot_tune_results()`: Gera gráficos para visualizar os resultados.\n",
        "\n",
        "### **5. Visualização de Resultados**\n",
        "- **Gráficos de desempenho**\n",
        "  - Acurácia do Perceptron vs Épocas (`T`).\n",
        "  - Acurácia do Pegasos vs Regularização (`λ`).\n",
        "  - Comparação entre modelos.\n",
        "\n",
        "---\n",
        "\n",
        "Em linhas gerais, este laboratório proporciona uma visão prática de aprendizado de máquina aplicado à análise de sentimentos/opiniões, cobrindo desde a **preparação dos dados** até a **implementação de modelos** e **avaliação de desempenho**.\n",
        "\n",
        "Com efeito, é possível entender não apenas **como construir um classificador de sentimentos/opiniões**, mas também **como avaliar e melhorar modelos de aprendizado de máquina**.\n",
        "\n",
        "---\n"
      ],
      "metadata": {
        "id": "Z7AYRF07oTYt"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "r-ZAyfAMZMsY"
      },
      "outputs": [],
      "source": [
        "# @title Importação e instalação de bibliotecas\n",
        "import os\n",
        "import csv\n",
        "import sys\n",
        "import string\n",
        "import random\n",
        "import requests\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "from google.colab import drive\n",
        "\n",
        "# Para garantir reprodutibilidade para experimentos\n",
        "np.random.seed(42)\n",
        "random.seed(42)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "drive.mount('/content/drive')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CMest5ejZlOu",
        "outputId": "e5fd7ab2-6b6f-4e66-c37c-a5cdeacf44ce"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# @title Download do DataSet disponível no GitHub\n",
        "\n",
        "urls = {\n",
        "    \"200.txt\": \"https://raw.githubusercontent.com/SampMark/Sentiment-Analysis/refs/heads/main/200.txt\",\n",
        "    \"4000.txt\": \"https://raw.githubusercontent.com/SampMark/Sentiment-Analysis/refs/heads/main/4000.txt\",\n",
        "    \"stopwords.txt\": \"https://raw.githubusercontent.com/SampMark/Sentiment-Analysis/refs/heads/main/stopwords.txt\",\n",
        "    \"reviews_test.tsv\": \"https://raw.githubusercontent.com/SampMark/Sentiment-Analysis/af8d87fee38cd2844e349d734613822ff9d33a5a/reviews_test.tsv\",\n",
        "    \"reviews_train.tsv\": \"https://raw.githubusercontent.com/SampMark/Sentiment-Analysis/af8d87fee38cd2844e349d734613822ff9d33a5a/reviews_train.tsv\",\n",
        "    \"reviews_val.tsv\": \"https://raw.githubusercontent.com/SampMark/Sentiment-Analysis/af8d87fee38cd2844e349d734613822ff9d33a5a/reviews_val.tsv\",\n",
        "    \"toy_data.tsv\": \"https://raw.githubusercontent.com/SampMark/Sentiment-Analysis/af8d87fee38cd2844e349d734613822ff9d33a5a/toy_data.tsv\"\n",
        "}\n",
        "\n",
        "for filename, url in urls.items():\n",
        "    r = requests.get(url)\n",
        "    with open(filename, 'wb') as f:\n",
        "        f.write(r.content)\n",
        "    print(f\"Baixado: {filename}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yfIEAISUaBqN",
        "outputId": "d8ed76c8-60ad-485b-a648-54f98ab4bed9"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Baixado: 200.txt\n",
            "Baixado: 4000.txt\n",
            "Baixado: stopwords.txt\n",
            "Baixado: reviews_test.tsv\n",
            "Baixado: reviews_train.tsv\n",
            "Baixado: reviews_val.tsv\n",
            "Baixado: toy_data.tsv\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# @title Caminho dos arquivos baixados no Drive\n",
        "for filename in os.listdir():\n",
        "    if os.path.isfile(filename):\n",
        "        print(f\"Arquivo: {filename}, Caminho completo: {os.path.abspath(filename)}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bHQ7QkMab5Zv",
        "outputId": "8e0d581a-e8d8-4082-e1ac-213dfeca740a"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Arquivo: reviews_val.tsv, Caminho completo: /content/reviews_val.tsv\n",
            "Arquivo: stopwords.txt, Caminho completo: /content/stopwords.txt\n",
            "Arquivo: 200.txt, Caminho completo: /content/200.txt\n",
            "Arquivo: reviews_train.tsv, Caminho completo: /content/reviews_train.tsv\n",
            "Arquivo: 4000.txt, Caminho completo: /content/4000.txt\n",
            "Arquivo: toy_data.tsv, Caminho completo: /content/toy_data.tsv\n",
            "Arquivo: reviews_test.tsv, Caminho completo: /content/reviews_test.tsv\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Classe `DatasetLoader`**\n",
        "\n",
        "A classe `DatasetLoader` gerencia o carregamento dos arquivos de dados, permitindo:\n",
        "- **Descompactar** os arquivos que compõem um Dataset (quando for o caso), para um diretório de destino.\n",
        "- **Ler** os arquivos do tipo TSV de reviews (treino, validação, teste) e extrair as colunas relevantes (texto e rótulo de sentimento).\n",
        "- Retornar os textos e rótulos prontos para uso.\n",
        "\n",
        "---\n",
        "\n"
      ],
      "metadata": {
        "id": "yDcO7in1rGRQ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# @title Carregamento do DataSet\n",
        "class DatasetLoader:\n",
        "    def __init__(self, base_path='/content', zip_path=None):\n",
        "        \"\"\"\n",
        "        Classe para carregar datasets de análise de sentimentos.\n",
        "        - base_path: diretório base onde os arquivos TSV estão ou serão extraídos.\n",
        "        - zip_path: caminho opcional para um arquivo .zip contendo o dataset.\n",
        "        \"\"\"\n",
        "        self.base_path = base_path\n",
        "        # Se um arquivo zip for fornecido, descompacta no diretório base\n",
        "        if zip_path:\n",
        "            self._extract_zip(zip_path, base_path)\n",
        "\n",
        "    def _extract_zip(self, zip_path, extract_to):\n",
        "        \"\"\"Descompacta um arquivo zip para o diretório especificado.\"\"\"\n",
        "        import zipfile\n",
        "        if not os.path.exists(extract_to):\n",
        "            os.makedirs(extract_to)\n",
        "        with zipfile.ZipFile(zip_path, 'r') as zip_ref:\n",
        "            zip_ref.extractall(extract_to)\n",
        "        print(f\"Dataset descompactado em: {extract_to}\")\n",
        "\n",
        "    def load_data(self, filename, extras=False):\n",
        "        \"\"\"\n",
        "        Carrega um arquivo TSV de reviews e retorna uma lista de dicionários\n",
        "        (ou tuplas de texto e sentimento).\n",
        "        - filename: nome do arquivo (ou caminho relativo a base_path) a ser carregado.\n",
        "        - extras: se True, mantém campos extras (como produto, usuário, etc.),\n",
        "                  se False, carrega apenas texto e sentimento.\n",
        "        \"\"\"\n",
        "        path = os.path.join(self.base_path, filename)\n",
        "        data = []\n",
        "        # Define quais campos manter e quais são numéricos\n",
        "        basic_fields = {'sentiment', 'text'}\n",
        "        numeric_fields = {'sentiment', 'helpfulY', 'helpfulN'}\n",
        "        # Abre o arquivo TSV com encoding apropriado\n",
        "        with open(path, encoding='latin1') as f:\n",
        "            reader = csv.DictReader(f, delimiter='\\t')\n",
        "            for row in reader:\n",
        "                if not extras:\n",
        "                    # Remove campos não básicos se extras=False\n",
        "                    for field in list(row.keys()):\n",
        "                        if field not in basic_fields:\n",
        "                            row.pop(field, None)\n",
        "                # Converte campos numéricos para int\n",
        "                for field in numeric_fields:\n",
        "                    if field in row and row[field] != '':\n",
        "                        row[field] = int(row[field])\n",
        "                data.append(row)\n",
        "        print(f\"{len(data)} registros carregados de {filename}.\")\n",
        "        return data\n",
        "\n",
        "    def load_split(self, split='train'):\n",
        "        \"\"\"\n",
        "        Carrega os dados do conjunto especificado (train, val ou test) e retorna\n",
        "        duas listas: textos e rótulos.\n",
        "        \"\"\"\n",
        "        file_map = {\n",
        "            'train': 'reviews_train.tsv',\n",
        "            'val': 'reviews_val.tsv',\n",
        "            'test': 'reviews_test.tsv'\n",
        "        }\n",
        "        if split not in file_map:\n",
        "            raise ValueError(\"Split inválido. Use 'train', 'val' ou 'test'.\")\n",
        "        # Carrega o arquivo correspondente\n",
        "        data = self.load_data(file_map[split], extras=False)\n",
        "        # Separa textos e rótulos do sentimento\n",
        "        texts = [sample['text'] for sample in data]\n",
        "        labels = [sample['sentiment'] for sample in data]\n",
        "        return texts, labels"
      ],
      "metadata": {
        "id": "FONt-8eua8lk"
      },
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# @title Verificação do carregamento do DataSet para uso...\n",
        "loader = DatasetLoader(base_path='/content', zip_path=None)\n",
        "train_texts, train_labels = loader.load_split('train')\n",
        "val_texts, val_labels = loader.load_split('val')\n",
        "test_texts, test_labels = loader.load_split('test')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Xym5c6yNbFGT",
        "outputId": "109debc4-db87-4b13-8f74-8e06df435a87"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "4000 registros carregados de reviews_train.tsv.\n",
            "500 registros carregados de reviews_val.tsv.\n",
            "500 registros carregados de reviews_test.tsv.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# @title **Pré-processamento dos Dados**\n",
        "class Preprocessing:\n",
        "    def __init__(self, remove_stopwords=True, binarize=True):\n",
        "        \"\"\"\n",
        "        Classe para pré-processar textos em vetores de features.\n",
        "        - remove_stopwords: se True, remove stopwords do vocabulário.\n",
        "        - binarize: se True, usa representação binária (presença/ausência de palavra).\n",
        "        \"\"\"\n",
        "        self.remove_stopwords = remove_stopwords\n",
        "        self.binarize = binarize\n",
        "        self.stopwords = set()\n",
        "        self.vocabulary = {}  # mapeamento palavra -> índice\n",
        "        # Carrega stopwords de arquivo se necessário\n",
        "        if self.remove_stopwords:\n",
        "            try:\n",
        "                with open(\"stopwords.txt\", \"r\", encoding=\"utf-8\") as f:\n",
        "                    for line in f:\n",
        "                        word = line.strip()\n",
        "                        if word:\n",
        "                            self.stopwords.add(word)\n",
        "            except FileNotFoundError:\n",
        "                print(\"Aviso: 'stopwords.txt' não encontrado. Processando sem remover stopwords.\")\n",
        "\n",
        "    def _tokenize(self, text):\n",
        "        \"\"\"\n",
        "        Quebra um texto em tokens/palavras, separando pontuação e números como tokens próprios.\n",
        "        Retorna a lista de palavras em minúsculas.\n",
        "        \"\"\"\n",
        "        # Substitui cada pontuação ou dígito por espaço + símbolo + espaço, para isolar tokens\n",
        "        for char in string.punctuation + string.digits:\n",
        "            text = text.replace(char, f\" {char} \")\n",
        "        # Converte para minúsculas e separa por espaços\n",
        "        tokens = text.lower().split()\n",
        "        return tokens\n",
        "\n",
        "    def fit(self, texts):\n",
        "        \"\"\"\n",
        "        Constroi o vocabulário (dicionário de palavra->índice) a partir de uma lista de textos.\n",
        "        \"\"\"\n",
        "        self.vocabulary = {}\n",
        "        for text in texts:\n",
        "            words = self._tokenize(text)\n",
        "            for w in words:\n",
        "                # Se remove_stopwords estiver habilitado, ignora stopwords\n",
        "                if self.remove_stopwords and w in self.stopwords:\n",
        "                    continue\n",
        "                if w not in self.vocabulary:\n",
        "                    # Atribui o próximo índice disponível à nova palavra\n",
        "                    self.vocabulary[w] = len(self.vocabulary)\n",
        "        print(f\"Vocabulário construído com {len(self.vocabulary)} palavras (features).\")\n",
        "        return self.vocabulary\n",
        "\n",
        "    def transform(self, texts):\n",
        "        \"\"\"\n",
        "        Transforma uma lista de textos em uma matriz de features NumPy utilizando o vocabulário.\n",
        "        Retorna uma matriz de shape (n_textos, n_features).\n",
        "        \"\"\"\n",
        "        n_texts = len(texts)\n",
        "        n_features = len(self.vocabulary)\n",
        "        # Inicializa matriz de zeros\n",
        "        feature_matrix = np.zeros((n_texts, n_features), dtype=np.float64)\n",
        "        for i, text in enumerate(texts):\n",
        "            words = self._tokenize(text)\n",
        "            for w in words:\n",
        "                if self.remove_stopwords and w in self.stopwords:\n",
        "                    continue\n",
        "                if w in self.vocabulary:\n",
        "                    j = self.vocabulary[w]  # índice da palavra no vocabulário\n",
        "                    if self.binarize:\n",
        "                        feature_matrix[i, j] = 1.0  # Marca presença da palavra\n",
        "                    else:\n",
        "                        feature_matrix[i, j] += 1.0  # Conta ocorrências da palavra\n",
        "        return feature_matrix"
      ],
      "metadata": {
        "id": "nW90-c-ac-xp"
      },
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Exemplo de uso:\n",
        "preproc = Preprocessing(remove_stopwords=True, binarize=True)\n",
        "vocab = preproc.fit(train_texts)       # constroi dicionário a partir dos textos de treino\n",
        "X_train = preproc.transform(train_texts)\n",
        "X_val   = preproc.transform(val_texts)\n",
        "X_test  = preproc.transform(test_texts)\n",
        "print(\"Tamanho de X_train:\", X_train.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nLxkcTQEdFsm",
        "outputId": "76600bfb-7028-47cf-adb8-ebec20cf058c"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Vocabulário construído com 13108 palavras (features).\n",
            "Tamanho de X_train: (4000, 13108)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# @title **Modelos de Classificação**\n",
        "class BaseClassifier:\n",
        "    def __init__(self):\n",
        "        # Inicializa os parâmetros do modelo (serão ajustados no treinamento)\n",
        "        self.theta = None   # vetor de pesos\n",
        "        self.theta0 = None  # bias (termo independente)\n",
        "\n",
        "    def predict(self, X):\n",
        "        \"\"\"\n",
        "        Realiza previsão (+1 ou -1) para cada exemplo em X, usando o modelo treinado.\n",
        "        \"\"\"\n",
        "        if self.theta is None or self.theta0 is None:\n",
        "            raise Exception(\"Modelo não treinado. Chame train() antes de predict().\")\n",
        "        # Calcula as pontuações lineares: theta . x + theta0\n",
        "        scores = X.dot(self.theta) + self.theta0\n",
        "        # Classifica como +1 se score > 0, caso contrário -1\n",
        "        # (Aqui, consideramos score=0 como classe -1 por convenção)\n",
        "        preds = np.where(scores > 0, 1, -1)\n",
        "        return preds\n",
        "\n",
        "    def evaluate(self, X, y):\n",
        "        \"\"\"\n",
        "        Calcula a acurácia do modelo (fração de acertos) em relação aos rótulos verdadeiros y.\n",
        "        \"\"\"\n",
        "        preds = self.predict(X)\n",
        "        # Converte y para array numpy para facilitar comparação\n",
        "        y_array = np.array(y)\n",
        "        accuracy = (preds == y_array).mean()\n",
        "        return accuracy\n",
        "\n",
        "    def train(self, X, y, **kwargs):\n",
        "        \"\"\"\n",
        "        Método de treinamento (a ser implementado nas subclasses específicas).\n",
        "        \"\"\"\n",
        "        raise NotImplementedError(\"Este método deve ser implementado na subclasse.\")"
      ],
      "metadata": {
        "id": "OOJpSQcxdXxU"
      },
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# @title **Algoritmo Perceptron**\n",
        "class PerceptronClassifier(BaseClassifier):\n",
        "    def __init__(self):\n",
        "        super().__init__()  # inicializa theta e theta0 como None\n",
        "\n",
        "    def train(self, X, y, T=1):\n",
        "        \"\"\"\n",
        "        Treina o classificador Perceptron.\n",
        "        - X: matriz de features (n_examples x n_features)\n",
        "        - y: vetor de rótulos (+1, -1) de tamanho n_examples\n",
        "        - T: número de épocas (iterações sobre todo o conjunto de treinamento)\n",
        "        \"\"\"\n",
        "        n_samples, n_features = X.shape\n",
        "        # Inicializa parâmetros\n",
        "        self.theta = np.zeros(n_features)\n",
        "        self.theta0 = 0.0\n",
        "        # Realiza T épocas de treinamento\n",
        "        for _ in range(T):\n",
        "            # Embaralha a ordem dos exemplos para cada época (melhora convergência)\n",
        "            indices = np.random.permutation(n_samples)\n",
        "            for i in indices:\n",
        "                x_i = X[i]\n",
        "                y_i = y[i]\n",
        "                # Checa se o exemplo está misclassificado (y_i * (theta·x_i + theta0) <= 0)\n",
        "                if y_i * (np.dot(self.theta, x_i) + self.theta0) <= 0:\n",
        "                    # Atualiza os parâmetros\n",
        "                    self.theta += y_i * x_i\n",
        "                    self.theta0 += y_i\n",
        "        return self.theta, self.theta0  # retorna os parâmetros aprendidos (opcional)\n",
        "\n",
        "print(\"Treinando o Perceptron...\")\n",
        "perceptron = PerceptronClassifier()\n",
        "perceptron.train(X_train, train_labels, T=10)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GgZ6PlxkdqE9",
        "outputId": "ab5607c7-6a47-47a4-fed5-621f05d91437"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Treinando o Perceptron...\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(array([  3., -11.,  -4., ...,   1.,   1.,   1.]), -4.0)"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# @title **Perceptron Médio (_Average Perceptron_)**\n",
        "\n",
        "class AveragePerceptronClassifier(BaseClassifier):\n",
        "    def __init__(self):\n",
        "        super().__init__()\n",
        "\n",
        "    def train(self, X, y, T=1):\n",
        "        \"\"\"\n",
        "        Treina o Perceptron Médio por T épocas.\n",
        "        \"\"\"\n",
        "        n_samples, n_features = X.shape\n",
        "        # Inicializa parâmetros\n",
        "        self.theta = np.zeros(n_features)\n",
        "        self.theta0 = 0.0\n",
        "        # Variáveis para somar os parâmetros em cada atualização\n",
        "        sum_theta = np.zeros(n_features)\n",
        "        sum_theta0 = 0.0\n",
        "        count = 0  # contador de updates (ou de instâncias processadas)\n",
        "        for _ in range(T):\n",
        "            indices = np.random.permutation(n_samples)\n",
        "            for i in indices:\n",
        "                x_i = X[i]\n",
        "                y_i = y[i]\n",
        "                if y_i * (np.dot(self.theta, x_i) + self.theta0) <= 0:\n",
        "                    # Atualiza (igual ao Perceptron padrão)\n",
        "                    self.theta += y_i * x_i\n",
        "                    self.theta0 += y_i\n",
        "                # Acumula os parâmetros atuais em sum_theta/ sum_theta0\n",
        "                sum_theta += self.theta\n",
        "                sum_theta0 += self.theta0\n",
        "                count += 1\n",
        "        # Calcula a média dos parâmetros\n",
        "        if count > 0:\n",
        "            self.theta = sum_theta / count\n",
        "            self.theta0 = sum_theta0 / count\n",
        "        # Retorna os parâmetros médios\n",
        "        return self.theta, self.theta0\n",
        "\n",
        "print(\"Treinando o Perceptron Médio...\")\n",
        "avg_perceptron = AveragePerceptronClassifier()\n",
        "avg_perceptron.train(X_train, train_labels, T=10)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zhzNUsFteNH0",
        "outputId": "a8a81a56-186c-4e4a-bbc4-338b9b5b166a"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Treinando o Perceptron Médio...\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(array([ 2.640825, -8.288675, -2.47505 , ...,  1.533975,  1.533975,\n",
              "         1.533975]),\n",
              " -0.4136)"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# @title **Pegasos (SVM Linear via SGD)**\n",
        "\n",
        "class PegasosClassifier(BaseClassifier):\n",
        "    def __init__(self):\n",
        "        super().__init__()\n",
        "\n",
        "    def train(self, X, y, T=1, L=0.01):\n",
        "        \"\"\"\n",
        "        Treina o classificador Pegasos (SVM linear) por T épocas.\n",
        "        - T: número de épocas (passadas completas pelos dados)\n",
        "        - L: lambda de regularização (parâmetro do Pegasos)\n",
        "        \"\"\"\n",
        "        n_samples, n_features = X.shape\n",
        "        # Inicializa parâmetros\n",
        "        self.theta = np.zeros(n_features)\n",
        "        self.theta0 = 0.0\n",
        "        # Contador de iterações para calcular eta\n",
        "        t = 1\n",
        "        for _ in range(T):\n",
        "            indices = np.random.permutation(n_samples)\n",
        "            for i in indices:\n",
        "                x_i = X[i]\n",
        "                y_i = y[i]\n",
        "                # Calcula a margem\n",
        "                margin = y_i * (np.dot(self.theta, x_i) + self.theta0)\n",
        "                # Calcula taxa de aprendizado para esta iteração\n",
        "                eta = 1.0 / np.sqrt(t)\n",
        "                if margin <= 1:\n",
        "                    # Violou margem: atualiza theta e theta0 com gradiente de hinge loss\n",
        "                    self.theta = (1 - eta * L) * self.theta + eta * y_i * x_i\n",
        "                    self.theta0 += eta * y_i\n",
        "                else:\n",
        "                    # Não violou margem: só aplica regularização (decaimento)\n",
        "                    self.theta = (1 - eta * L) * self.theta\n",
        "                    # theta0 não muda\n",
        "                t += 1\n",
        "        return self.theta, self.theta0\n",
        "print(\"Treinando o Pegasos...\")\n",
        "pegasos = PegasosClassifier()\n",
        "pegasos.train(X_train, train_labels, T=10, L=0.01)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kJFJ714velNL",
        "outputId": "36e7f428-8cd9-4583-b4f7-a16925072664"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Treinando o Pegasos...\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(array([ 0.02999411, -0.26543663, -0.02150188, ...,  0.02419352,\n",
              "         0.02419352,  0.02419352]),\n",
              " 0.036655683474547514)"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# @title **Treinamento e Avaliação**\n",
        "# Configuração dos dados\n",
        "loader = DatasetLoader(base_path='/content', zip_path=None)\n",
        "train_texts, train_labels = loader.load_split('train')\n",
        "val_texts, val_labels = loader.load_split('val')\n",
        "test_texts, test_labels = loader.load_split('test')\n",
        "\n",
        "preproc = Preprocessing(remove_stopwords=True, binarize=True)\n",
        "preproc.fit(train_texts)\n",
        "X_train = preproc.transform(train_texts)\n",
        "X_val   = preproc.transform(val_texts)\n",
        "X_test  = preproc.transform(test_texts)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "J7jVxbQAfSVx",
        "outputId": "4e25c4f3-33b6-4152-e660-4e0c8e078288"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "4000 registros carregados de reviews_train.tsv.\n",
            "500 registros carregados de reviews_val.tsv.\n",
            "500 registros carregados de reviews_test.tsv.\n",
            "Vocabulário construído com 13108 palavras (features).\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# @title **Treinando os modelos com um hiperparâmetro base**\n",
        "# Instancia os classificadores\n",
        "perc = PerceptronClassifier()\n",
        "avg_perc = AveragePerceptronClassifier()\n",
        "peg = PegasosClassifier()\n",
        "\n",
        "# Treina cada modelo com hiperparâmetros iniciais\n",
        "perc.train(X_train, train_labels, T=10)\n",
        "avg_perc.train(X_train, train_labels, T=10)\n",
        "peg.train(X_train, train_labels, T=10, L=0.01)\n",
        "\n",
        "# Avalia acurácia em treino e validação para cada modelo\n",
        "print(f\"Perceptron - Acurácia treino: {perc.evaluate(X_train, train_labels)*100:.2f}%, validação: {perc.evaluate(X_val, val_labels)*100:.2f}%\")\n",
        "print(f\"Perceptron Médio - Acurácia treino: {avg_perc.evaluate(X_train, train_labels)*100:.2f}%, validação: {avg_perc.evaluate(X_val, val_labels)*100:.2f}%\")\n",
        "print(f\"Pegasos - Acurácia treino: {peg.evaluate(X_train, train_labels)*100:.2f}%, validação: {peg.evaluate(X_val, val_labels)*100:.2f}%\")\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "O46D873XfqTI",
        "outputId": "beb87526-a8ce-4190-e755-a5f6a99773ef"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Perceptron - Acurácia treino: 98.92%, validação: 76.20%\n",
            "Perceptron Médio - Acurácia treino: 98.95%, validação: 76.40%\n",
            "Pegasos - Acurácia treino: 91.17%, validação: 79.00%\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# @title **Otimização de Hiperparâmetros**\n",
        "\n",
        "class ModelTrainer:\n",
        "    def __init__(self):\n",
        "        pass  # nenhuma propriedade fixa necessária\n",
        "\n",
        "    def tune_parameter(self, ModelClass, param_name, param_values, X_train, y_train, X_val, y_val, **kwargs):\n",
        "        \"\"\"\n",
        "        Testa diferentes valores de um hiperparâmetro para um dado modelo e retorna o melhor.\n",
        "        - ModelClass: a classe do modelo (PerceptronClassifier, etc.)\n",
        "        - param_name: nome do parâmetro a tunar (string exatamente como o nome do argumento em train())\n",
        "        - param_values: lista de valores a experimentar\n",
        "        - X_train, y_train: dados de treinamento\n",
        "        - X_val, y_val: dados de validação\n",
        "        - kwargs: outros hiperparâmetros fixos a manter (ex: fixar L enquanto varia T, ou vice-versa)\n",
        "\n",
        "        Retorna: uma tupla (best_value, train_accuracies, val_accuracies)\n",
        "        \"\"\"\n",
        "        best_value = None\n",
        "        best_val_acc = -1\n",
        "        train_acc_list = []\n",
        "        val_acc_list = []\n",
        "        for val in param_values:\n",
        "            # Instancia um novo modelo para cada valor testado (começa \"do zero\")\n",
        "            model = ModelClass()\n",
        "            # Prepara os argumentos de treinamento: combinando o param sendo tunado com outros fixos\n",
        "            train_args = {**kwargs}\n",
        "            train_args[param_name] = val\n",
        "            # Treina o modelo\n",
        "            model.train(X_train, y_train, **train_args)\n",
        "            # Avalia no treino e validação\n",
        "            train_acc = model.evaluate(X_train, y_train)\n",
        "            val_acc = model.evaluate(X_val, y_val)\n",
        "            train_acc_list.append(train_acc)\n",
        "            val_acc_list.append(val_acc)\n",
        "            # Verifica se é o melhor até agora\n",
        "            if val_acc > best_val_acc:\n",
        "                best_val_acc = val_acc\n",
        "                best_value = val\n",
        "        # Converte listas de acurácias para numpy arrays para fácil manipulação (opcional)\n",
        "        train_acc_list = np.array(train_acc_list)\n",
        "        val_acc_list = np.array(val_acc_list)\n",
        "        print(f\"Tuning {param_name}: valores testados = {param_values}\")\n",
        "        print(f\"Acurácias de validação = {[f'{acc*100:.4f}%' for acc in val_acc_list]}\")\n",
        "        print(f\"Melhor {param_name} = {best_value} com acurácia de validação = {best_val_acc*100:.4f}%\")\n",
        "        return best_value, train_acc_list, val_acc_list"
      ],
      "metadata": {
        "id": "qCHZ96x6fv0B"
      },
      "execution_count": 40,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# @title **Utilização da Classe `ModelTrainer` para encontrar os melhores hiperparâmetros**\n",
        "\n",
        "trainer = ModelTrainer()\n",
        "\n",
        "# Definindo grades de valores para testar\n",
        "Ts = [1, 5, 10, 15, 25, 50]         # candidatos para número de épocas\n",
        "Ls = [0.001, 0.01, 0.1, 1, 10]      # candidatos para lambda Pegasos\n",
        "\n",
        "# Tuning do Perceptron (T)\n",
        "best_T_perc, perc_train_acc, perc_val_acc = trainer.tune_parameter(\n",
        "    PerceptronClassifier, 'T', Ts, X_train, train_labels, X_val, val_labels\n",
        ")\n",
        "\n",
        "# Tuning do Perceptron Médio (T)\n",
        "best_T_avg, avg_train_acc, avg_val_acc = trainer.tune_parameter(\n",
        "    AveragePerceptronClassifier, 'T', Ts, X_train, train_labels, X_val, val_labels\n",
        ")\n",
        "\n",
        "# Tuning do Pegasos:\n",
        "# Primeiro tunar T com lambda fixo (e.g., 0.01)\n",
        "fixed_L = 0.01\n",
        "best_T_peg, peg_train_acc_T, peg_val_acc_T = trainer.tune_parameter(\n",
        "    PegasosClassifier, 'T', Ts, X_train, train_labels, X_val, val_labels, L=fixed_L\n",
        ")\n",
        "# Agora tunar lambda L com T fixo no melhor valor obtido\n",
        "best_L_peg, peg_train_acc_L, peg_val_acc_L = trainer.tune_parameter(\n",
        "    PegasosClassifier, 'L', Ls, X_train, train_labels, X_val, val_labels, T=best_T_peg\n",
        ")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "S9TUinFshP0X",
        "outputId": "c42b6126-3d5b-4421-ace9-3dce5f172127"
      },
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Tuning T: valores testados = [1, 5, 10, 15, 25, 50]\n",
            "Acurácias de validação = ['72.0000%', '74.0000%', '76.8000%', '76.8000%', '76.6000%', '76.0000%']\n",
            "Melhor T = 10 com acurácia de validação = 76.8000%\n",
            "Tuning T: valores testados = [1, 5, 10, 15, 25, 50]\n",
            "Acurácias de validação = ['75.0000%', '76.4000%', '79.8000%', '76.4000%', '77.0000%', '73.8000%']\n",
            "Melhor T = 10 com acurácia de validação = 79.8000%\n",
            "Tuning T: valores testados = [1, 5, 10, 15, 25, 50]\n",
            "Acurácias de validação = ['76.0000%', '79.8000%', '79.4000%', '79.4000%', '79.6000%', '79.2000%']\n",
            "Melhor T = 5 com acurácia de validação = 79.8000%\n",
            "Tuning L: valores testados = [0.001, 0.01, 0.1, 1, 10]\n",
            "Acurácias de validação = ['78.4000%', '78.8000%', '71.4000%', '51.8000%', '51.8000%']\n",
            "Melhor L = 0.01 com acurácia de validação = 78.8000%\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# @title **Avaliação no Conjunto de Teste**\n",
        "\n",
        "# Escolhe o modelo final - vamos usar Pegasos com hiperparâmetros otimizados neste exemplo\n",
        "final_model = PegasosClassifier()\n",
        "final_model.train(X_train, train_labels, T=best_T_peg, L=best_L_peg)\n",
        "test_accuracy = final_model.evaluate(X_test, test_labels)\n",
        "print(f\"Acurácia no conjunto de TESTE com Pegasos (T={best_T_peg}, λ={best_L_peg}): {test_accuracy*100:.4f}%\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fgI9QZvYtxX-",
        "outputId": "4c9f0aaf-f1f7-4d5b-c027-2e3d24df626e"
      },
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Acurácia no conjunto de TESTE com Pegasos (T=5, λ=0.01): 80.0000%\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Visualização dos Resultados**\n",
        "\n",
        "Agora, vamos **visualizar graficamente** os resultados dos experimentos de hiperparâmetro que fizemos, para nos ajudar na interpretação do comportamento dos modelos e a justificar a escolha dos hiperparâmetros.\n",
        "\n",
        "A seguir, serão gerados um gráfico para cada caso, mostrando a curva de acurácia de **treinamento** e **validação** em função do hiperparâmetro. Desta forma, é possível analisar claramente as tendências de _overfitting_ ou _underfitting_.\n",
        "\n",
        "1. **Perceptron:** Acurácia vs Número de Épocas (T).\n",
        "2. **Perceptron Médio:** Acurácia vs Número de Épocas (T).\n",
        "3. **Pegasos:** Acurácia vs Número de Épocas (T) com λ fixo (no valor inicial usado, ex: 0.01).\n",
        "4. **Pegasos:** Acurácia vs Lambda (λ) com T fixo (no melhor valor encontrado de T).\n",
        "\n",
        "---"
      ],
      "metadata": {
        "id": "HIBxHbL5uM6S"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# @title **Plotando os modelos num grid 2x2**\n",
        "plt.figure(figsize=(14, 10))  # Tamanho total da figura\n",
        "\n",
        "# 1. Perceptron: Acurácia vs T\n",
        "plt.subplot(2, 2, 1)  # Posição 1 no grid 2x2\n",
        "plt.plot(Ts, perc_train_acc * 100, 'o-', label='Treino')\n",
        "plt.plot(Ts, perc_val_acc * 100, 'o-', label='Validação')\n",
        "plt.title('Perceptron - Acurácia vs Épocas (T)')\n",
        "plt.xlabel('Número de Épocas (T)')\n",
        "plt.ylabel('Acurácia (%)')\n",
        "plt.legend()\n",
        "plt.grid(True)\n",
        "\n",
        "# 2. Perceptron Médio: Acurácia vs T\n",
        "plt.subplot(2, 2, 2)  # Posição 2 no grid 2x2\n",
        "plt.plot(Ts, avg_train_acc * 100, 'o-', label='Treino')\n",
        "plt.plot(Ts, avg_val_acc * 100, 'o-', label='Validação')\n",
        "plt.title('Perceptron Médio - Acurácia vs Épocas (T)')\n",
        "plt.xlabel('Número de Épocas (T)')\n",
        "plt.ylabel('Acurácia (%)')\n",
        "plt.legend()\n",
        "plt.grid(True)\n",
        "\n",
        "# 3. Pegasos: Acurácia vs T (λ fixo = 0.01)\n",
        "plt.subplot(2, 2, 3)  # Posição 3 no grid 2x2\n",
        "plt.plot(Ts, peg_train_acc_T * 100, 'o-', label='Treino')\n",
        "plt.plot(Ts, peg_val_acc_T * 100, 'o-', label='Validação')\n",
        "plt.title(f'Pegasos - Acurácia vs Épocas (T) (λ={fixed_L})')\n",
        "plt.xlabel('Número de Épocas (T)')\n",
        "plt.ylabel('Acurácia (%)')\n",
        "plt.legend()\n",
        "plt.grid(True)\n",
        "\n",
        "# 4. Pegasos: Acurácia vs Lambda (T fixo = {best_T_peg})\n",
        "plt.subplot(2, 2, 4)  # Posição 4 no grid 2x2\n",
        "plt.plot(Ls, peg_train_acc_L * 100, 'o-', label='Treino')\n",
        "plt.plot(Ls, peg_val_acc_L * 100, 'o-', label='Validação')\n",
        "plt.title(f'Pegasos - Acurácia vs Regularização (λ) (T={best_T_peg})')\n",
        "plt.xlabel('Lambda (λ)')\n",
        "plt.ylabel('Acurácia (%)')\n",
        "plt.xscale('log')  # Escala logarítmica para visualizar melhor a variação de λ\n",
        "plt.legend()\n",
        "plt.grid(True)\n",
        "\n",
        "# Ajustar layout para evitar sobreposição\n",
        "plt.tight_layout()\n",
        "\n",
        "# Exibir a figura com todos os gráficos\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 611
        },
        "id": "l5Prb2UsiMEk",
        "outputId": "7e0b9c0a-acae-4086-a8f2-2eb7d8908dcd"
      },
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1400x1000 with 4 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **Visualização dos Gráficos Separadamente**\n",
        "\n",
        "---\n",
        "\n"
      ],
      "metadata": {
        "id": "E3F9LRJdiiY4"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# 1. Perceptron: Acurácia vs T\n",
        "plt.figure(figsize=(6,4))\n",
        "plt.plot(Ts, perc_train_acc*100, 'o-', label='Treino')\n",
        "plt.plot(Ts, perc_val_acc*100, 'o-', label='Validação')\n",
        "plt.title('Perceptron - Acurácia vs Épocas (T)')\n",
        "plt.xlabel('Número de Épocas (T)')\n",
        "plt.ylabel('Acurácia (%)')\n",
        "plt.legend()\n",
        "plt.grid(True)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 415
        },
        "id": "_Eg9hdEPiWxi",
        "outputId": "2baeacec-8751-44e6-dcd7-f5f36a4bd798"
      },
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 600x400 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 2. Perceptron Médio: Acurácia vs T\n",
        "plt.figure(figsize=(6,4))\n",
        "plt.plot(Ts, avg_train_acc*100, 'o-', label='Treino')\n",
        "plt.plot(Ts, avg_val_acc*100, 'o-', label='Validação')\n",
        "plt.title('Perceptron Médio - Acurácia vs Épocas (T)')\n",
        "plt.xlabel('Número de Épocas (T)')\n",
        "plt.ylabel('Acurácia (%)')\n",
        "plt.legend()\n",
        "plt.grid(True)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 415
        },
        "id": "NKcvrviniZ4U",
        "outputId": "0f4fc4ac-4814-4445-9b0b-6fadedba7c53"
      },
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 600x400 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 3. Pegasos: Acurácia vs T (λ fixo = 0.01)\n",
        "plt.figure(figsize=(6,4))\n",
        "plt.plot(Ts, peg_train_acc_T*100, 'o-', label='Treino')\n",
        "plt.plot(Ts, peg_val_acc_T*100, 'o-', label='Validação')\n",
        "plt.title(f'Pegasos - Acurácia vs Épocas (T) (λ={fixed_L})')\n",
        "plt.xlabel('Número de Épocas (T)')\n",
        "plt.ylabel('Acurácia (%)')\n",
        "plt.legend()\n",
        "plt.grid(True)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 415
        },
        "id": "iVkIiRJvic5Q",
        "outputId": "7736dfc2-43d9-4e79-9d40-bea2ecf3c063"
      },
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 600x400 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 4. Pegasos: Acurácia vs Lambda (T fixo = {best_T_peg})\n",
        "plt.figure(figsize=(6,4))\n",
        "plt.plot(Ls, peg_train_acc_L*100, 'o-', label='Treino')\n",
        "plt.plot(Ls, peg_val_acc_L*100, 'o-', label='Validação')\n",
        "plt.title(f'Pegasos - Acurácia vs Regularização (λ) (T={best_T_peg})')\n",
        "plt.xlabel('Lambda (λ)')\n",
        "plt.ylabel('Acurácia (%)')\n",
        "plt.xscale('log')  # escala logarítmica para visualizar melhor a variação de λ\n",
        "plt.legend()\n",
        "plt.grid(True)\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 415
        },
        "id": "VC-DK69phvV0",
        "outputId": "317ed124-75f8-4d9a-984a-219b7582303e"
      },
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 600x400 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    }
  ]
}