Aula 19/2023_Q1_PLN_Notebook_13.ipynb

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      "source": [
        "# **Processamento de Linguagem Natural [2023.Q1]**\n",
        "Prof. Alexandre Donizeti Alves"
      ]
    },
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      "source": [
        "## **Similaridade de Textos**\n",
        "---\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Similaridade de Textos** refere-se à medida de quão semelhantes são dois textos. Essa medida pode ser usada para comparar diferentes documentos, avaliar a originalidade de um texto ou detectar plágio."
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        "Há muitas aplicações práticas para a similaridade de textos, incluindo:\n",
        "\n",
        "   * Detecção de plágio: a similaridade de textos pode ser usada para detectar plágio em trabalhos acadêmicos, teses, artigos ou outros tipos de documentos. A comparação da similaridade entre um documento e uma base de dados de textos pode indicar se há evidência de plágio.\n",
        "\n",
        "   * Recomendação de conteúdo: a similaridade de textos pode ser usada para recomendar conteúdo a usuários com base em suas preferências. Por exemplo, se um usuário gosta de um determinado tipo de artigo ou livro, a similaridade de texto pode ser usada para recomendar conteúdo semelhante.\n",
        "\n",
        "   * Busca de informações: a similaridade de textos pode ser usada para ajudar na busca de informações. Por exemplo, se um usuário procura informações sobre um determinado tópico, a similaridade de texto pode ser usada para encontrar documentos semelhantes que possam fornecer informações relevantes.\n",
        "\n",
        "   * Análise de sentimentos: a similaridade de textos pode ser usada para analisar sentimentos em comentários ou avaliações de usuários. A comparação da similaridade entre as opiniões de diferentes usuários pode indicar tendências ou opiniões gerais sobre um produto ou serviço.\n",
        "\n",
        "   * Classificação de documentos: a similaridade de textos pode ser usada para classificar documentos em diferentes categorias com base em seu conteúdo. Por exemplo, se uma empresa tem um grande volume de documentos, a similaridade de texto pode ser usada para classificar esses documentos de acordo com suas categorias ou temas.\n",
        "\n",
        "   * Identificação de fraudes: a similaridade de textos pode ser usada para identificar fraudes ou atividades suspeitas em documentos financeiros ou transações bancárias. A comparação da similaridade entre documentos pode ajudar a identificar possíveis irregularidades ou padrões suspeitos.\n",
        "\n",
        "   * Análise de concorrência: a similaridade de textos pode ser usada para analisar a concorrência em um mercado ou setor específico. A comparação da similaridade entre os sites, publicidades e materiais promocionais de diferentes empresas pode ajudar a identificar tendências e estratégias de marketing.\n",
        "\n",
        "   * Resumos automáticos: a similaridade de textos pode ser usada para gerar resumos automáticos de documentos longos. A comparação da similaridade entre diferentes seções de um documento pode ajudar a identificar as partes mais relevantes e importantes para incluir no resumo."
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      "source": [
        "Algumas das principais vantagens da similaridade de textos são:\n",
        "\n",
        "   * Eficiência: a análise de similaridade de textos pode ser realizada de forma rápida e eficiente, especialmente quando utilizamos técnicas computacionais. Isso significa que podemos processar grandes volumes de dados de forma mais eficiente do que se estivéssemos fazendo uma análise manual.\n",
        "\n",
        "   * Precisão: a similaridade de textos pode fornecer resultados precisos e confiáveis. As técnicas utilizadas na análise de similaridade de textos são projetadas para fornecer uma medida objetiva de quão semelhantes são dois textos.\n",
        "\n",
        "   * Flexibilidade: as técnicas de similaridade de textos podem ser aplicadas a diferentes tipos de documentos, desde artigos científicos até avaliações de produtos. Isso significa que podemos aplicar essas técnicas em diversas áreas de negócios e em diferentes contextos."
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      "source": [
        "Apesar de suas vantagens, a similaridade de textos também apresenta alguns problemas e limitações, tais como:\n",
        "\n",
        "   * Interpretação de contexto: as técnicas de similaridade de textos são baseadas em cálculos matemáticos, e muitas vezes não levam em consideração o contexto do texto. Isso significa que duas frases com palavras semelhantes podem ser consideradas semelhantes, mesmo que tenham significados completamente diferentes.\n",
        "\n",
        "   * Complexidade do idioma: a similaridade de textos pode ser afetada pela complexidade do idioma utilizado nos documentos. Por exemplo, documentos técnicos e acadêmicos podem conter terminologia específica que pode afetar a precisão dos resultados de similaridade.\n",
        "\n",
        "   * Variabilidade do texto: a similaridade de textos pode ser afetada pela variabilidade do texto. Por exemplo, uma pequena mudança em uma palavra ou frase pode levar a resultados diferentes de similaridade.\n",
        "\n",
        "   * Dependência de dados: a similaridade de textos depende de um conjunto de dados de referência para comparar os documentos. Isso significa que a precisão da análise de similaridade pode ser afetada pela qualidade e tamanho desse conjunto de dados.\n",
        "\n",
        "   * Dependência de parâmetros: as técnicas de similaridade de textos exigem que certos parâmetros sejam definidos para a análise. A seleção de parâmetros inadequados pode afetar a precisão dos resultados de similaridade.\n",
        "\n",
        "   * Problemas de privacidade: a análise de similaridade de textos pode envolver o processamento de grandes volumes de dados sensíveis. Isso pode levantar questões de privacidade e segurança, especialmente se esses dados incluem informações pessoais ou confidenciais."
      ],
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      "source": [
        "Existem várias técnicas para medir a similaridade de textos, algumas das quais são:\n",
        "\n",
        "   * Similaridade de Hamming: essa técnica é usada para medir a distância entre duas strings de caracteres. Ela conta o número de posições em que duas strings diferem e divide pelo comprimento total da string.\n",
        "\n",
        "   * Similaridade de Jaccard: essa técnica é baseada na análise da interseção e união de conjuntos de palavras em dois documentos. A similaridade de Jaccard é a razão da interseção de palavras pelo número total de palavras nos dois documentos.\n",
        "\n",
        "   * Distância euclidiana: essa técnica calcula a distância euclidiana entre os vetores de palavras em dois documentos. Essa distância é calculada tomando a raiz quadrada da soma dos quadrados das diferenças entre as contagens de palavras em cada vetor.\n",
        "\n",
        "   * Similaridade de cosseno: esta é uma das técnicas mais comuns usadas para medir a similaridade de textos. Ele usa o cosseno do ângulo entre dois vetores para calcular a semelhança entre eles. O vetor pode ser formado a partir da contagem de palavras em cada documento.   \n",
        "\n",
        "Essas são apenas algumas das técnicas comuns usadas para medir a similaridade de textos. Dependendo do caso de uso, outras técnicas podem ser mais apropriadas."
      ],
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      "source": [
        "**Similaridade de Hamming**"
      ],
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      "source": [
        "A **distância de Hamming** é uma métrica que mede a diferença entre duas sequências de igual comprimento, contando o número de posições em que essas sequências diferem. \n",
        "\n",
        "Para calcular a distância de Hamming entre duas sequências, é necessário comparar cada posição correspondente nas duas sequências e contar o número de posições em que os valores são diferentes. \n",
        "\n",
        "A distância de Hamming é uma métrica muito simples e útil para detectar erros em transmissões de dados, onde pode ser usada para detectar a presença de bits errados ou perdidos. "
      ],
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      "source": [
        "def distancia_hamming(palavra1, palavra2):\n",
        "    \"\"\"Calcula a distância de Hamming entre duas palavras.\"\"\"\n",
        "    if len(palavra1) != len(palavra2):\n",
        "        raise ValueError(\"As palavras precisam ter o mesmo comprimento.\")\n",
        "    return sum(c1 != c2 for c1, c2 in zip(palavra1, palavra2))\n",
        "\n",
        "# Exemplo de uso:\n",
        "palavra1 = \"gato\"\n",
        "palavra2 = \"gata\"\n",
        "\n",
        "distancia = distancia_hamming(palavra1, palavra2)\n",
        "print(f\"A distância de Hamming entre '{palavra1}' e '{palavra2}' é {distancia}.\")"
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          "text": [
            "A distância de Hamming entre 'gato' e 'gata' é 1.\n"
          ]
        }
      ]
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      "source": [
        "Neste exemplo, a função `distancia_hamming` recebe duas palavras `'palavra1'` e `'palavra2'` e calcula a distância de Hamming entre elas. A função compara cada letra nas duas palavras e conta o número de posições em que elas diferem. Em seguida, a distância de Hamming é retornada. O código também inclui um exemplo de uso da função, onde a distância de Hamming entre as palavras `\"gato\"` e `\"gata\"` é calculada e impressa."
      ],
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      "cell_type": "markdown",
      "source": [
        "A distância de Hamming é uma medida discreta que representa o número de posições em que as duas sequências são diferentes. No entanto, é possível calcular uma `\"distância de Hamming normalizada\"` dividindo a distância de Hamming pelo comprimento das sequências, o que resulta em um valor entre 0 e 1 que indica a proporção de posições em que as duas sequências são diferentes. Esse valor pode ser interpretado como uma medida de dissimilaridade ou `distância relativa` entre as duas sequências."
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      "source": [
        "def hamming_distance_normalized(s1, s2):\n",
        "    \"\"\"Calcula a distância de Hamming normalizada entre duas sequências.\"\"\"\n",
        "    if len(s1) != len(s2):\n",
        "        raise ValueError(\"As sequências precisam ter o mesmo comprimento.\")\n",
        "    d = sum(c1 != c2 for c1, c2 in zip(s1, s2))\n",
        "    return d / len(s1)\n",
        "\n",
        "# Exemplo de uso:\n",
        "s1 = \"abcd\"\n",
        "s2 = \"abed\"\n",
        "\n",
        "distancia = hamming_distance_normalized(s1, s2)\n",
        "print(f\"A distância de Hamming normalizada entre '{s1}' e '{s2}' é {distancia:.2f}.\")"
      ],
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          "text": [
            "A distância de Hamming normalizada entre 'abcd' e 'abed' é 0.25.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Similaridade de Jaccard**"
      ],
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    {
      "cell_type": "markdown",
      "source": [
        "A **Similaridade de Jaccard** é uma medida de similaridade entre dois conjuntos que avalia a proporção de elementos que são compartilhados entre eles. A similaridade de Jaccard é definida como a interseção do conjunto dividida pela união dos conjuntos."
      ],
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      "cell_type": "markdown",
      "source": [
        "A fórmula da similaridade de Jaccard é a seguinte:\n",
        "\n",
        "> `J(A, B) = |A ∩ B| / |A ∪ B|`\n",
        "\n",
        "onde A e B são dois conjuntos, A ∩ B é a interseção de A e B (ou seja, o conjunto de elementos que estão em ambos os conjuntos) e A ∪ B é a união de A e B (ou seja, o conjunto de elementos que estão em pelo menos um dos conjuntos)."
      ],
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    },
    {
      "cell_type": "markdown",
      "source": [
        "O resultado da similaridade de Jaccard é um valor entre 0 e 1, em que 0 significa que não há elementos compartilhados entre os conjuntos e 1 significa que os conjuntos são idênticos. Quanto maior a similaridade de Jaccard, mais semelhantes são os conjuntos."
      ],
      "metadata": {
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      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "A similaridade de Jaccard é comumente usada em áreas como mineração de dados, aprendizado de máquina e PLN para comparar a similaridade entre conjuntos de itens. "
      ],
      "metadata": {
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    {
      "cell_type": "markdown",
      "source": [
        "Aqui está um exemplo simples de como calcular a similaridade de Jaccard entre dois conjuntos:"
      ],
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    {
      "cell_type": "code",
      "source": [
        "def jaccard_similarity(set1, set2):\n",
        "    \"\"\"\n",
        "    Calcula a similaridade de Jaccard entre dois conjuntos.\n",
        "    \"\"\"\n",
        "    intersection = set1.intersection(set2)\n",
        "    union = set1.union(set2)\n",
        "    return len(intersection) / len(union)\n",
        "\n",
        "# Exemplo de uso\n",
        "fruits1 = set(['maçã', 'banana', 'laranja', 'morango'])\n",
        "fruits2 = set(['maçã', 'uva', 'kiwi'])\n",
        "\n",
        "print(\"A similaridade de Jaccard entre os conjuntos é:\", jaccard_similarity(fruits1, fruits2))"
      ],
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          "text": [
            "A similaridade de Jaccard entre os conjuntos é: 0.16666666666666666\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Aqui está um exemplo de como usar a similaridade de Jaccard para comparar duas frases:"
      ],
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    {
      "cell_type": "code",
      "source": [
        "def jaccard_similarity(text1, text2):\n",
        "    \"\"\"\n",
        "    Calcula a similaridade de Jaccard entre duas frases.\n",
        "    \"\"\"\n",
        "    # Transforma as frases em conjuntos de palavras únicas\n",
        "    set1 = set(text1.split())\n",
        "    set2 = set(text2.split())\n",
        "    \n",
        "    # Calcula a interseção e a união dos conjuntos de palavras\n",
        "    intersection = set1.intersection(set2)\n",
        "    union = set1.union(set2)\n",
        "    \n",
        "    # Retorna a similaridade de Jaccard\n",
        "    return len(intersection) / len(union)\n",
        "\n",
        "# Exemplo de uso\n",
        "text1 = \"O gato está dormindo.\"\n",
        "text2 = \"O cachorro está acordado.\"\n",
        "\n",
        "similarity = jaccard_similarity(text1, text2)\n",
        "print(\"A similaridade de Jaccard entre as frases é:\", similarity)"
      ],
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          "text": [
            "A similaridade de Jaccard entre as frases é: 0.3333333333333333\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Distância Euclidiana**"
      ],
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    {
      "cell_type": "markdown",
      "source": [
        "A **distância euclidiana** é uma medida de distância entre dois pontos em um espaço n-dimensional, que pode ser usada para calcular a distância entre dois vetores de características que representam dois textos. A distância euclidiana é calculada como a raiz quadrada da soma dos quadrados das diferenças entre as coordenadas dos vetores.\n",
        "\n",
        "Para aplicar a distância euclidiana à similaridade de textos, primeiro é necessário representar cada texto como um vetor de características. Existem várias maneiras de fazer isso, como representar cada palavra como uma dimensão do vetor e a contagem de ocorrências de cada palavra como o valor da coordenada, ou usar técnicas de embedding para representar cada palavra como um vetor contínuo de dimensão fixa.\n",
        "\n",
        "Suponha que temos dois textos representados como vetores de características `x` e `y`, cada um com `n` dimensões. A distância euclidiana entre os vetores `x` e `y` é dada pela seguinte fórmula:\n",
        "\n",
        "> `d(x, y) = sqrt((x1 - y1)^2 + (x2 - y2)^2 + ... + (xn - yn)^2)`\n",
        "\n",
        "Onde `x1, x2, ..., xn` são as coordenadas do vetor `x` e `y1, y2, ..., yn` são as coordenadas do vetor `y`. A raiz quadrada é usada para obter uma medida de distância positiva.\n",
        "\n"
      ],
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    {
      "cell_type": "markdown",
      "source": [
        "Para calcular a similaridade de textos usando a distância euclidiana, podemos usar a fórmula inversa da distância, que é dada por:\n",
        "\n",
        "> `s(x, y) = 1 / (1 + d(x, y))`\n",
        "\n",
        "onde `s(x, y)` é a similaridade entre os textos x e y. A fórmula acima retorna um valor entre 0 e 1, onde 0 indica que os textos são totalmente diferentes e 1 indica que os textos são idênticos. O uso da fórmula inversa da distância garante que quanto menor a distância euclidiana entre os vetores, maior será a similaridade entre os textos."
      ],
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    },
    {
      "cell_type": "markdown",
      "source": [
        "Segue abaixo um exemplo de código para calcular a similaridade de dois textos utilizando a distância euclidiana:"
      ],
      "metadata": {
        "id": "06Qo3igNMf-R"
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    },
    {
      "cell_type": "code",
      "source": [
        "import math\n",
        "\n",
        "def distancia_euclidiana(vetor1, vetor2):\n",
        "    # Calcula a distância euclidiana entre os dois vetores\n",
        "    distancia = math.sqrt(sum([(vetor1[i]-vetor2[i])**2 for i in range(len(vetor1))]))\n",
        "    return distancia\n",
        "\n",
        "def similaridade_euclidiana(texto1, texto2):\n",
        "    # Calcula a similaridade entre os dois textos utilizando a distância euclidiana\n",
        "    palavras = list(set(texto1.split() + texto2.split()))\n",
        "    vetor1 = [texto1.split().count(palavra) for palavra in palavras]\n",
        "    vetor2 = [texto2.split().count(palavra) for palavra in palavras]\n",
        "    distancia = distancia_euclidiana(vetor1, vetor2)\n",
        "    similaridade = 1 / (1 + distancia)\n",
        "    return similaridade\n",
        "\n",
        "# Exemplo de uso da função\n",
        "texto1 = 'O gato comeu o rato'\n",
        "texto2 = 'O cachorro comeu a ração'\n",
        "\n",
        "similaridade = similaridade_euclidiana(texto1, texto2)\n",
        "print('A similaridade entre os textos é: ', similaridade)"
      ],
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        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "A similaridade entre os textos é:  0.28989794855663564\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Similaridade de Cosseno**"
      ],
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    },
    {
      "cell_type": "markdown",
      "source": [
        "A **similaridade de cosseno** é uma medida de similaridade entre dois vetores, muito utilizada na área de PLN para comparar a similaridade entre documentos.\n",
        "\n",
        "A ideia por trás da similaridade de cosseno é comparar o ângulo formado pelos vetores no espaço de n dimensões onde n é o número de termos distintos presentes nos documentos. A similaridade é calculada a partir do cosseno do ângulo entre esses vetores. Se o ângulo entre dois vetores for pequeno, então os vetores são considerados semelhantes e a similaridade de cosseno será próxima de 1. Se o ângulo for próximo a 90 graus, então os vetores são considerados dissimilares e a similaridade de cosseno será próxima de 0.\n",
        "\n"
      ],
      "metadata": {
        "id": "iseo5Jw3OmtJ"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Em resumo, a similaridade de cosseno mede a semelhança entre dois vetores através do cosseno do ângulo formado entre eles. Quando aplicada a documentos, cada documento é representado como um vetor de termos, e a similaridade de cosseno é utilizada para comparar a similaridade entre esses vetores."
      ],
      "metadata": {
        "id": "LlBpssgSPA-J"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "`TF-IDF` é uma medida de peso que atribui maior importância aos termos que aparecem com frequência em um documento, mas que aparecem raramente em outros documentos. `TF-IDF` é calculado multiplicando a frequência de um termo no documento (TF) pelo inverso da frequência desse termo em todo o conjunto de documentos (IDF).\n",
        "\n",
        "A similaridade entre dois textos é então calculada utilizando a pontuação `TF-IDF` dos termos que aparecem em ambos os textos. Existem várias formas de calcular a similaridade usando `TF-IDF`, como a similaridade de cosseno, que mede a similaridade entre dois vetores de pesos de termos `TF-IDF`."
      ],
      "metadata": {
        "id": "6IUZdlRyRTaI"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Aqui está um exemplo de código para calcular a similaridade de cosseno entre dois textos usando a biblioteca `scikit-learn` e a técnica `TF-IDF`:"
      ],
      "metadata": {
        "id": "qlIOcGBTPzz3"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.feature_extraction.text import TfidfVectorizer\n",
        "from sklearn.metrics.pairwise import cosine_similarity\n",
        "\n",
        "# Textos de exemplo\n",
        "text1 = \"O Brasil é um país localizado na América do Sul\"\n",
        "text2 = \"O Japão é um país localizado na Ásia\"\n",
        "\n",
        "# Vetorização dos textos usando TF-IDF\n",
        "vectorizer = TfidfVectorizer()\n",
        "tfidf = vectorizer.fit_transform([text1, text2])\n",
        "\n",
        "# Cálculo da similaridade de cosseno\n",
        "cos_sim = cosine_similarity(tfidf[0], tfidf[1])\n",
        "\n",
        "print(cos_sim)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yLruhifgQDwT",
        "outputId": "c628b1b1-dbfd-4e5a-b671-38d7faf43f87"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[0.41120706]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Uma outra biblioteca que pode ser usada para cálculo de similaridade de cosseno é a biblioteca `numpy`. Aqui está um exemplo de código usando `numpy` para calcular a similaridade de cosseno entre dois vetores de texto:"
      ],
      "metadata": {
        "id": "jxYruVj6SiTQ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "from sklearn.feature_extraction.text import TfidfVectorizer\n",
        "\n",
        "# exemplo de dois textos para calcular a similaridade de cosseno\n",
        "text1 = \"O Brasil é um país localizado na América do Sul\"\n",
        "text2 = \"O Japão é um país localizado na Ásia\"\n",
        "\n",
        "# vetorização dos textos usando tf-idf\n",
        "vectorizer = TfidfVectorizer()\n",
        "vectorized_texts = vectorizer.fit_transform([text1, text2]).toarray()\n",
        "\n",
        "# cálculo da similaridade de cosseno\n",
        "cosine_similarity = np.dot(vectorized_texts[0], vectorized_texts[1]) / (np.linalg.norm(vectorized_texts[0]) * np.linalg.norm(vectorized_texts[1]))\n",
        "\n",
        "print(\"Similaridade de cosseno entre os textos: \", cosine_similarity)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "knCEV0f4SVOU",
        "outputId": "5330140c-0eb3-418f-f7b2-451893601920"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Similaridade de cosseno entre os textos:  0.41120705506761873\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Sistema para recomendar filmes**"
      ],
      "metadata": {
        "id": "v57beHb0UPDX"
      }
    },
    {
      "cell_type": "markdown",
      "source": [],
      "metadata": {
        "id": "MhDNjzpoUXty"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "\n",
        "filmes = [\n",
        "    {\n",
        "        \"titulo\": \"O Poderoso Chefão\",\n",
        "        \"descricao\": \"A saga da família Corleone e sua ascensão ao poder no mundo do crime organizado.\"\n",
        "    },\n",
        "    {\n",
        "        \"titulo\": \"O Poderoso Chefão: Parte II\",\n",
        "        \"descricao\": \"A continuação da saga da família Corleone, com um foco maior na ascensão de Michael como líder da família.\"\n",
        "    },\n",
        "    {\n",
        "        \"titulo\": \"Batman: O Cavaleiro das Trevas\",\n",
        "        \"descricao\": \"Batman enfrenta o Coringa, um dos seus maiores inimigos, em uma batalha pelo destino de Gotham City.\"\n",
        "    },\n",
        "    {\n",
        "        \"titulo\": \"12 Homens e uma Sentença\",\n",
        "        \"descricao\": \"Doze homens precisam chegar a um veredito unânime em um julgamento de homicídio, mas nem todos concordam com a culpabilidade do réu.\"\n",
        "    },\n",
        "    {\n",
        "        \"titulo\": \"A Lista de Schindler\",\n",
        "        \"descricao\": \"A história real de um empresário alemão que salvou a vida de mais de mil judeus durante o Holocausto.\"\n",
        "    },\n",
        "    {\n",
        "        \"titulo\": \"O Senhor dos Anéis: O Retorno do Rei\",\n",
        "        \"descricao\": \"O épico final da jornada de Frodo e seus amigos para destruir o Anel do Poder e salvar a Terra-Média da escuridão.\"\n",
        "    },\n",
        "    {\n",
        "        \"titulo\": \"Interestelar\",\n",
        "        \"descricao\": \"Um grupo de astronautas é enviado em uma missão para encontrar um novo lar para a humanidade, em meio a um futuro sombrio para o planeta Terra.\"\n",
        "    },\n",
        "    {\n",
        "        \"titulo\": \"Jurassic Park\",\n",
        "        \"descricao\": \"Um bilionário decide criar um parque temático com dinossauros vivos, mas as coisas saem do controle quando os dinossauros escapam e começam a atacar os visitantes.\"\n",
        "    },\n",
        "    {\n",
        "        \"titulo\": \"Pulp Fiction\",\n",
        "        \"descricao\": \"Uma série de histórias interligadas que giram em torno de gangsters, boxeadores, assassinos e outras figuras marginais, todas com um estilo único de diálogo e narrativa.\"\n",
        "    },\n",
        "    {\n",
        "        \"titulo\": \"Cidade de Deus\",\n",
        "        \"descricao\": \"A vida dos jovens moradores da favela Cidade de Deus, no Rio de Janeiro, que são atraídos para o mundo do crime enquanto tentam sobreviver em um ambiente hostil.\"\n",
        "    },\n",
        "    {\n",
        "        \"titulo\": \"Forrest Gump: O Contador de Histórias\",\n",
        "        \"descricao\": \"A vida de Forrest Gump, um homem simples e bondoso que se envolve em alguns dos eventos mais importantes da história recente dos Estados Unidos, enquanto tenta reencontrar seu grande amor de infância.\"\n",
        "    },\n",
        "    {\n",
        "        \"titulo\": \"O Grande Hotel Budapeste\",\n",
        "        \"descricao\": \"As aventuras do gerente de um hotel em um período conturbado da história europeia.\"\n",
        "    },\n",
        "    {\n",
        "        \"titulo\": \"Whiplash: Em Busca da Perfeição\",\n",
        "        \"descricao\": \"Um jovem baterista tenta se tornar o melhor do mundo, mas precisa lidar com o treinador exigente e abusivo.\"\n",
        "    },\n",
        "    {\n",
        "        \"titulo\": \"A Viagem de Chihiro\",\n",
        "        \"descricao\": \"Uma menina de dez anos descobre um mundo mágico e cheio de aventuras enquanto procura um meio de salvar seus pais transformados em porcos.\"\n",
        "    },\n",
        "    {\n",
        "        \"titulo\": \"O Regresso\",\n",
        "        \"descricao\": \"Um caçador é deixado à beira da morte após ser atacado por um urso e precisa lutar pela sobrevivência em uma jornada épica através do oeste selvagem americano.\"\n",
        "    },\n",
        "    {\n",
        "        \"titulo\": \"A Invenção de Hugo Cabret\",\n",
        "        \"descricao\": \"Um órfão que vive escondido em uma estação de trem em Paris descobre um segredo incrível e inesperado relacionado a um inventor famoso.\"\n",
        "    },\n",
        "    {\n",
        "        \"titulo\": \"O Labirinto do Fauno\",\n",
        "        \"descricao\": \"Em um mundo pós-guerra na Espanha, uma jovem descobre um mundo mágico e perigoso enquanto luta para sobreviver em meio ao regime franquista.\"\n",
        "    },\n",
        "    {\n",
        "        \"titulo\": \"A Origem\",\n",
        "        \"descricao\": \"Um ladrão de sonhos é contratado para implantar uma ideia na mente de um empresário rico, mas precisa lidar com desafios cada vez maiores e mais perigosos.\"\n",
        "    },\n",
        "    {\n",
        "        \"titulo\": \"De Volta para o Futuro\",\n",
        "        \"descricao\": \"Um jovem é enviado de volta no tempo para tentar evitar que seus pais sejam separados e acaba enfrentando diversos desafios e paradoxos temporais.\"\n",
        "    },\n",
        "    {\n",
        "        \"titulo\": \"O Iluminado\",\n",
        "        \"descricao\": \"Um escritor em crise se muda para um hotel isolado nas montanhas para trabalhar em seu novo livro, mas acaba enlouquecendo em meio a visões horripilantes e estranhos acontecimentos.\"\n",
        "    },\n",
        "    {\n",
        "        \"titulo\": \"Os Incríveis\",\n",
        "        \"descricao\": \"Uma família de super-heróis aposentados precisa lidar com os desafios da vida cotidiana enquanto tenta salvar o mundo de um vilão perigoso.\"\n",
        "    },\n",
        "    {\n",
        "        \"titulo\": \"Clube da Luta\",\n",
        "        \"descricao\": \"Um homem comum se envolve em um clube clandestino de luta e acaba perdendo o controle de sua vida em meio a uma conspiração sinistra.\"\n",
        "    },\n",
        "    {\n",
        "        \"titulo\": \"Um Sonho de Liberdade\",\n",
        "        \"descricao\": \"A história de um homem condenado à prisão perpétua por um crime que não cometeu e sua luta para sobreviver e encontrar a liberdade em meio a um sistema opressivo.\"\n",
        "    },\n",
        "    {\n",
        "        \"titulo\": \"A Vida é Bela\",\n",
        "        \"descricao\": \"A história de um pai que usa sua imaginação para fazer com que seu filho não perceba que está em um campo de concentração durante a Segunda Guerra Mundial.\"\n",
        "    },\n",
        "    {\n",
        "        \"titulo\": \"Titanic\",\n",
        "        \"descricao\": \"Uma história de amor entre um passageiro da primeira classe e uma jovem da terceira classe a bordo do Titanic, que naufragou em sua viagem inaugural.\"\n",
        "    },\n",
        "    {\n",
        "        \"titulo\": \"O Senhor dos Anéis: A Sociedade do Anel\",\n",
        "        \"descricao\": \"A primeira parte da saga de Frodo e seus amigos em sua jornada para destruir o Um Anel, que pode destruir a Terra-média se cair nas mãos erradas.\"\n",
        "    },\n",
        "    {\n",
        "        \"titulo\": \"Blade Runner\",\n",
        "        \"descricao\": \"No futuro distópico de 2019, um ex-policial é chamado de volta para a ação para caçar e eliminar quatro replicantes rebeldes.\"\n",
        "    },\n",
        "    {\n",
        "        \"titulo\": \"Indiana Jones e os Caçadores da Arca Perdida\",\n",
        "        \"descricao\": \"Indiana Jones parte em uma aventura para encontrar a Arca da Aliança antes dos nazistas, que acreditam que ela pode conceder poderes divinos.\"\n",
        "    },\n",
        "    {\n",
        "        \"titulo\": \"Matrix\",\n",
        "        \"descricao\": \"Um programador descobre que a realidade que ele conhece é uma simulação criada por máquinas, e junta-se a um grupo de rebeldes para tentar libertar a humanidade.\"\n",
        "    },\n",
        "    {\n",
        "        \"titulo\": \"O Sexto Sentido\",\n",
        "        \"descricao\": \"Um psicólogo infantil tenta ajudar um menino que afirma ver pessoas mortas, mas acaba descobrindo algo surpreendente sobre si mesmo.\"\n",
        "    },\n",
        "    {\n",
        "        \"titulo\": \"Toy Story\",\n",
        "        \"descricao\": \"Um cowboy de brinquedo precisa lidar com a chegada de um novo brinquedo em sua casa.\"\n",
        "    },\n",
        "    {\n",
        "        \"titulo\": \"Moana\",\n",
        "        \"descricao\": \"Uma jovem aventureira sai em busca de uma ilha mítica para salvar seu povo.\"\n",
        "    },\n",
        "    {\n",
        "        \"titulo\": \"Frozen\",\n",
        "        \"descricao\": \"Duas irmãs precisam lidar com os desafios e descobertas de suas habilidades especiais em um reino congelado.\"\n",
        "    },\n",
        "    {\n",
        "        \"titulo\": \"A Pequena Sereia\",\n",
        "        \"descricao\": \"Uma sereia sonha em se tornar humana e se apaixona por um príncipe.\"\n",
        "    },\n",
        "    {\n",
        "        \"titulo\": \"Procurando Nemo\",\n",
        "        \"descricao\": \"Um pai peixe sai em busca de seu filho perdido no oceano com a ajuda de uma peixinha amiga.\"\n",
        "    },\n",
        "    {\n",
        "        \"titulo\": \"A Era do Gelo\",\n",
        "        \"descricao\": \"Uma turma de animais pré-históricos tenta sobreviver a uma era glacial.\"\n",
        "    },\n",
        "    {\n",
        "        \"titulo\": \"O Rei Leão\",\n",
        "        \"descricao\": \"Um jovem leão precisa enfrentar seu tio malvado para assumir o trono de seu pai.\"\n",
        "    },\n",
        "    {\n",
        "        \"titulo\": \"Mulan\",\n",
        "        \"descricao\": \"Uma jovem chinesa se disfarça de homem para lutar na guerra em lugar de seu pai doente.\"\n",
        "    },\n",
        "    {\n",
        "        \"titulo\": \"Aladdin\",\n",
        "        \"descricao\": \"Um jovem ladrão se apaixona por uma princesa e usa um gênio para conquistá-la.\"\n",
        "    },\n",
        "    {\n",
        "        \"titulo\": \"Wall-E\",\n",
        "        \"descricao\": \"Um robô solitário é enviado à Terra para limpar o planeta, mas acaba descobrindo o amor.\"\n",
        "    },\n",
        "    {\n",
        "        \"titulo\": \"Monstros S.A.\",\n",
        "        \"descricao\": \"Monstros que trabalham assustando crianças descobrem que uma delas pode ser muito especial.\"\n",
        "    },\n",
        "    {\n",
        "        \"titulo\": \"Zootopia\",\n",
        "        \"descricao\": \"Uma coelha policial e um raposo malandro unem forças para desvendar um mistério na cidade dos animais.\"\n",
        "    },\n",
        "    {\n",
        "        \"titulo\": \"Como Treinar o Seu Dragão\",\n",
        "        \"descricao\": \"Um jovem viking faz amizade com um dragão e precisa provar que eles podem conviver em paz.\"\n",
        "    },\n",
        "    {\n",
        "        \"titulo\": \"O Grilo Feliz\",\n",
        "        \"descricao\": \"Um grilo sonhador parte em uma aventura para encontrar a namorada perdida.\"\n",
        "    },\n",
        "    {\n",
        "        \"titulo\": \"Coraline e o Mundo Secreto\",\n",
        "        \"descricao\": \"Uma garotinha descobre uma porta secreta que leva a uma versão alternativa de sua vida.\"\n",
        "    },\n",
        "    {\n",
        "        \"titulo\": \"Divertida Mente\",\n",
        "        \"descricao\": \"As emoções de uma menina entram em conflito quando ela precisa se mudar para outra cidade.\"\n",
        "    },\n",
        "    {\n",
        "        \"titulo\": \"O Castelo Animado\",\n",
        "        \"descricao\": \"Uma jovem é transformada em uma velha e encontra abrigo em um castelo mágico.\"\n",
        "    },\n",
        "    {\n",
        "        \"titulo\": \"Os Croods\",\n",
        "        \"descricao\": \"Uma família pré-histórica precisa enfrentar o mundo lá fora quando sua caverna é destruída.\"\n",
        "    },\n",
        "    {\n",
        "        \"titulo\": \"Velozes e Furiosos 9\",       \n",
        "        \"descricao\": \"Após um tempo fora de cena, Dom e sua equipe precisam enfrentar o irmão mais novo de Dom, que busca vingança pelo passado.\"   \n",
        "    },  \n",
        "    { \n",
        "        \"titulo\": \"John Wick 3: Parabellum\",        \n",
        "        \"descricao\": \"Após ser banido da organização de assassinos, John Wick precisa lutar pela sobrevivência contra um exército de assassinos contratados.\"\n",
        "    },    \n",
        "    { \n",
        "        \"titulo\": \"Missão Impossível: Efeito Fallout\",        \n",
        "        \"descricao\": \"Ethan Hunt e sua equipe precisam recuperar três esferas de plutônio antes que caiam nas mãos de uma organização criminosa.\"  \n",
        "    },   \n",
        "    {        \n",
        "         \"titulo\": \"Kill Bill: Volume 1\",        \n",
        "          \"descricao\": \"A Noiva acorda de um coma após quatro anos e busca vingança contra seus antigos colegas de trabalho que tentaram matá-la.\"    \n",
        "    },  \n",
        "    {        \n",
        "         \"titulo\": \"Duro de Matar\",        \n",
        "         \"descricao\": \"O policial John McClane tenta salvar sua esposa e outros reféns em um prédio ocupado por terroristas.\"    \n",
        "    },    \n",
        "    {        \n",
        "         \"titulo\": \"Rambo: Programado para Matar\",        \n",
        "         \"descricao\": \"O ex-combatente John Rambo é contratado para resgatar um grupo de reféns no Vietnã, mas acaba sendo traído por seus superiores.\"    \n",
        "    },   \n",
        "    {        \n",
        "         \"titulo\": \"Mad Max: Estrada da Fúria\",        \n",
        "         \"descricao\": \"Max Rockatansky se une a uma guerreira para fugir de um líder tirano e ajudar um grupo de mulheres a escapar de seu domínio.\"    \n",
        "    },    \n",
        "    {        \n",
        "         \"titulo\": \"O Profissional\",        \n",
        "         \"descricao\": \"Um assassino profissional acaba se apegando a uma garota de 12 anos que perdeu a família e precisa protegê-la de seus inimigos.\"    \n",
        "    },    \n",
        "    {        \n",
        "         \"titulo\": \"Operação Invasão\",        \n",
        "         \"descricao\": \"Uma equipe da polícia de Jacarta invade um prédio cheio de criminosos e precisa lutar pela sobrevivência contra todos os inimigos.\"    \n",
        "    },\n",
        "    {        \n",
        "          \"titulo\": \"A Casa Assombrada\",        \n",
        "       \"descricao\": \"Uma família se muda para uma casa nova, mas descobre que o local é assombrado por espíritos malignos. Eles tentam desvendar o mistério por trás da casa antes que seja tarde demais.\"    \n",
        "     },    \n",
        "     {        \n",
        "          \"titulo\": \"A Possessão\",        \n",
        "          \"descricao\": \"Uma garota começa a agir de forma estranha depois que encontra uma caixa antiga em um brechó. Logo, sua família descobre que ela está possuída por um demônio e precisa encontrar uma forma de exorcizá-la.\"    \n",
        "     },    \n",
        "     {        \n",
        "           \"titulo\": \"Sobrenatural\",       \n",
        "           \"descricao\": \"Uma família se muda para uma casa nova e começa a ser assombrada por espíritos malignos. Eles descobrem que a casa foi construída em cima de um cemitério indígena e precisam encontrar uma forma de expulsar os espíritos.\"   \n",
        "     },    \n",
        "     {        \n",
        "           \"titulo\": \"O Exorcista\",        \n",
        "           \"descricao\": \"Uma menina é possuída por um demônio e uma mãe desesperada pede ajuda a um padre para exorcizá-la. O padre enfrenta seus próprios demônios interiores enquanto tenta salvar a garota.\"   \n",
        "     },\n",
        "     {\n",
        "        \"titulo\": \"O Chamado\",\n",
        "        \"descricao\": \"Uma jornalista investiga uma misteriosa fita de vídeo que supostamente causa a morte de quem a assiste sete dias depois. Ela descobre uma conexão com uma lenda urbana e precisa desvendar o mistério antes que seja tarde demais.\"\n",
        "     },\n",
        "     {\n",
        "        \"titulo\": \"O Exorcismo de Emily Rose\",\n",
        "        \"descricao\": \"Um padre é acusado de negligência quando uma jovem morre durante um exorcismo. Ele contrata uma advogada para defendê-lo no tribunal, onde são revelados detalhes chocantes sobre o caso.\"\n",
        "     },\n",
        "     {\n",
        "        \"titulo\": \"A Entidade\",\n",
        "        \"descricao\": \"Um escritor de livros sobre crimes reais se muda com a família para uma casa onde ocorreu um assassinato. Ele descobre uma caixa de filmes caseiros que mostram o assassinato de outras famílias, levando-o a investigar o caso por conta própria.\"\n",
        "     },\n",
        "     {\n",
        "        \"titulo\": \"A Bruxa de Blair\",\n",
        "        \"descricao\": \"Um grupo de documentaristas se perde na floresta enquanto investiga a lenda da Bruxa de Blair. Conforme a noite avança, coisas estranhas começam a acontecer e eles percebem que não estão sozinhos na floresta.\"\n",
        "     },\n",
        "     {\n",
        "        \"titulo\": \"O Ritual\",\n",
        "        \"descricao\": \"Um padre cético é enviado para investigar um suposto milagre em uma igreja na Romênia. Ele descobre que a igreja esconde um segredo sombrio e precisa enfrentar seus próprios demônios interiores para salvar a si mesmo e aos outros.\"\n",
        "     },\n",
        "     {\n",
        "        \"titulo\": \"A Autópsia de Jane Doe\",\n",
        "        \"descricao\": \"Um pai e filho legistas investigam o corpo de uma mulher desconhecida que foi encontrada morta em uma casa. Conforme eles realizam a autópsia, descobrem coisas estranhas e aterrorizantes sobre a mulher.\"\n",
        "     },\n",
        "     {\n",
        "        \"titulo\": \"A Morte do Demônio\",\n",
        "        \"descricao\": \"Um grupo de amigos viaja para uma cabana na floresta e descobre um livro antigo que invoca demônios. Eles lutam pela sobrevivência quando os demônios começam a possuí-los um por um.\"\n",
        "     },\n",
        "     {\n",
        "        \"titulo\": \"Os Estranhos\",\n",
        "        \"descricao\": \"Um casal passa a noite em uma casa isolada na floresta e são atormentados por três estranhos mascarados. Eles lutam pela sobrevivência enquanto tentam descobrir por que os estranhos estão fazendo isso com eles.\"\n",
        "     },\n",
        "     {\n",
        "        \"titulo\": \"A Órfã\",\n",
        "        \"descricao\": \"Um casal adota uma menina órfã de nove anos e descobre que ela não é quem aparenta ser. Conforme eventos estranhos começam a ocorrer na casa, eles percebem que a menina esconde um segredo sinistro.\"\n",
        "     },  \n",
        "     {\n",
        "        \"titulo\": \"A Fuga\",\n",
        "        \"descricao\": \"Um grupo de prisioneiros planeja a fuga da prisão mais segura do mundo, enfrentando diversos obstáculos e perigos em sua jornada.\"\n",
        "     },\n",
        "     {\n",
        "        \"titulo\": \"O Desafio Final\",\n",
        "        \"descricao\": \"Dois irmãos rivais competem em um torneio de artes marciais, onde o vencedor ganha o controle de um império milionário de lutas clandestinas.\"\n",
        "     },\n",
        "     {\n",
        "       \"titulo\": \"O Ataque dos Gigantes\",\n",
        "       \"descricao\": \"Uma cidade é invadida por gigantes misteriosos que devoram os habitantes sem piedade. Um grupo de sobreviventes precisa encontrar uma forma de deter os monstros e salvar a população.\"\n",
        "    }\n",
        "]\n",
        "\n",
        "df = pd.DataFrame(filmes)\n",
        "df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "q8k4_R5oUbTj",
        "outputId": "6603d187-3f89-47be-bfa6-3b1e0e361b95"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
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              "                            titulo  \\\n",
              "0                O Poderoso Chefão   \n",
              "1      O Poderoso Chefão: Parte II   \n",
              "2   Batman: O Cavaleiro das Trevas   \n",
              "3         12 Homens e uma Sentença   \n",
              "4             A Lista de Schindler   \n",
              "..                             ...   \n",
              "68                    Os Estranhos   \n",
              "69                          A Órfã   \n",
              "70                          A Fuga   \n",
              "71                 O Desafio Final   \n",
              "72           O Ataque dos Gigantes   \n",
              "\n",
              "                                            descricao  \n",
              "0   A saga da família Corleone e sua ascensão ao p...  \n",
              "1   A continuação da saga da família Corleone, com...  \n",
              "2   Batman enfrenta o Coringa, um dos seus maiores...  \n",
              "3   Doze homens precisam chegar a um veredito unân...  \n",
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              "..                                                ...  \n",
              "68  Um casal passa a noite em uma casa isolada na ...  \n",
              "69  Um casal adota uma menina órfã de nove anos e ...  \n",
              "70  Um grupo de prisioneiros planeja a fuga da pri...  \n",
              "71  Dois irmãos rivais competem em um torneio de a...  \n",
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        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "duplicatas = df[df.duplicated(['titulo'], keep=False)]\n",
        "print(duplicatas)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ItpdSfEAaMvY",
        "outputId": "e09b657b-68e0-4706-b78d-994a8a91eb26"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Empty DataFrame\n",
            "Columns: [titulo, descricao]\n",
            "Index: []\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Para criar uma função que recomende títulos de filmes baseado na descrição semelhante usando similaridade de textos, podemos utilizar `TF-IDF` para representar cada descrição em um vetor numérico, e depois calcular a similaridade entre as descrições usando a distância do cosseno."
      ],
      "metadata": {
        "id": "NBGAjRvAfQaQ"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Para isso, podemos seguir os seguintes passos:\n",
        "\n",
        "   1. Importar as bibliotecas necessárias: `pandas` para manipulação de dataframes, `scikit-learn` para o cálculo do `TF-IDF` e da similaridade de cosseno, e `numpy` para operações numéricas.\n",
        "\n",
        "   2. Criar uma função que recebe como entrada um título de filme e um dataframe contendo títulos e descrições de filmes, e retorna uma lista com os títulos de filmes recomendados com base na similaridade de descrições.\n",
        "\n",
        "   3. Na função, usar o título de entrada para selecionar a descrição correspondente no dataframe.\n",
        "\n",
        "   4. Usar o `TF-IDFVectorizer` da biblioteca `scikit-learn` para calcular a representação `TF-IDF` de todas as descrições no dataframe.\n",
        "\n",
        "   5. Calcular a matriz de similaridade de cosseno entre as descrições usando a função `cosine_similarity` da biblioteca `scikit-learn`.\n",
        "\n",
        "   6. Selecionar as linhas da matriz de similaridade correspondentes à descrição do título de entrada e ordená-las em ordem decrescente de similaridade.\n",
        "\n",
        "   7. Selecionar os títulos correspondentes às linhas selecionadas e adicioná-los a uma lista de recomendações.\n",
        "\n",
        "   8. Retornar a lista de recomendações."
      ],
      "metadata": {
        "id": "rzfq6U8pfXqH"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Segue abaixo uma possível implementação dessa função:"
      ],
      "metadata": {
        "id": "QHSTuk0Af20m"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import nltk\n",
        "\n",
        "nltk.download('stopwords')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "K-v8J4gYjEsf",
        "outputId": "873b5440-554b-4a91-ff99-f9cfc1438b1d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[nltk_data] Downloading package stopwords to /root/nltk_data...\n",
            "[nltk_data]   Unzipping corpora/stopwords.zip.\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "True"
            ]
          },
          "metadata": {},
          "execution_count": 100
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.feature_extraction.text import TfidfVectorizer\n",
        "from sklearn.metrics.pairwise import cosine_similarity\n",
        "\n",
        "def recomendar_filmes(titulo, df, n=5):\n",
        "    # seleciona a descrição correspondente ao título de entrada\n",
        "    descricao = df[df['titulo'] == titulo]['descricao'].values[0]\n",
        "\n",
        "    stopwords = nltk.corpus.stopwords.words('portuguese')\n",
        "\n",
        "    # calcula a representação TF-IDF de todas as descrições, desconsiderando as stopwords\n",
        "    vectorizer = TfidfVectorizer(stop_words=stopwords)\n",
        "    tfidf_matrix = vectorizer.fit_transform(df['descricao'])\n",
        "    \n",
        "    # calcula a matriz de similaridade de cosseno entre as descrições\n",
        "    sim_matrix = cosine_similarity(tfidf_matrix)\n",
        "    \n",
        "    # seleciona as linhas correspondentes à descrição do título de entrada\n",
        "    idx = df[df['titulo'] == titulo].index[0]\n",
        "    sim_scores = list(enumerate(sim_matrix[idx]))\n",
        "    \n",
        "    # ordena as linhas em ordem decrescente de similaridade\n",
        "    sim_scores = sorted(sim_scores, key=lambda x: x[1], reverse=True)\n",
        "    \n",
        "    # seleciona os títulos correspondentes às linhas selecionadas e adiciona a uma lista de recomendações\n",
        "    recommendations = []\n",
        "    for i in range(1, n+1):\n",
        "        title = df.iloc[sim_scores[i][0]]['titulo']\n",
        "        sim = sim_scores[i][1]\n",
        "        recommendations.append((title, sim))\n",
        "    \n",
        "    return recommendations"
      ],
      "metadata": {
        "id": "nYBTcYr-f7-N"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Essa função recebe três argumentos: o título de entrada, um dataframe contendo títulos e descrições de filmes, e o número de filmes a serem recomendados (padrão é 5)."
      ],
      "metadata": {
        "id": "wPzL7v36gK01"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Exemplo de uso\n",
        "recomendacoes = recomendar_filmes('O Poderoso Chefão', df)\n",
        "\n",
        "# título do filme e similaridade\n",
        "for recomendacao in recomendacoes:\n",
        "  print(recomendacao)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "x6SG4z_YgMqw",
        "outputId": "ce1c1759-7b3d-42c2-8475-3e6498dd7d10"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "('O Poderoso Chefão: Parte II', 0.44902209884419697)\n",
            "('Os Croods', 0.14124251772470747)\n",
            "('Cidade de Deus', 0.13436861734749356)\n",
            "('Os Incríveis', 0.11424855205813159)\n",
            "('O Labirinto do Fauno', 0.11265977274830816)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Somente o título dos filmes\n",
        "for recomendacao in recomendacoes:\n",
        "  print(recomendacao[0])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dJm1d_vIihmW",
        "outputId": "d18df39c-d722-4d16-fa90-53951444f5ba"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "O Poderoso Chefão: Parte II\n",
            "Os Croods\n",
            "Cidade de Deus\n",
            "Os Incríveis\n",
            "O Labirinto do Fauno\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Scikit-learn**"
      ],
      "metadata": {
        "id": "EZf7Ez5QkyGT"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "`Scikit-learn` é uma biblioteca em Python de código aberto para aprendizado de máquina. Ela inclui uma ampla gama de algoritmos de classificação, regressão e clustering, bem como pré-processamento de dados e ferramentas de validação de modelo. A `Scikit-learn` é uma das bibliotecas mais populares para aprendizado de máquina em Python e é amplamente utilizada na indústria e em pesquisas acadêmicas."
      ],
      "metadata": {
        "id": "LVAYFLHwwYJV"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Aqui está um exemplo básico de como usar o `TfidfVectorizer` e `cosine_similarity` do `scikit-learn` para calcular a similaridade entre textos:"
      ],
      "metadata": {
        "id": "-u3WzhbAoD38"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.feature_extraction.text import TfidfVectorizer\n",
        "from sklearn.metrics.pairwise import cosine_similarity\n",
        "\n",
        "# lista de exemplos de textos\n",
        "textos = [\n",
        "    \"Este é um exemplo de texto.\",\n",
        "    \"Este é outro exemplo de texto.\",\n",
        "    \"Este é um terceiro exemplo de texto.\"\n",
        "]\n",
        "\n",
        "# instanciando o vetorizador TF-IDF\n",
        "tfidf = TfidfVectorizer()\n",
        "\n",
        "# vetorizando os textos\n",
        "tfidf_matrix = tfidf.fit_transform(textos)\n",
        "\n",
        "# calculando a similaridade de cosseno entre todos os pares de textos\n",
        "sim_matrix = cosine_similarity(tfidf_matrix)\n",
        "\n",
        "# exibindo a matriz de similaridade\n",
        "print(sim_matrix)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "z28kl5rJk17z",
        "outputId": "483fd287-cf16-418a-de49-3c026759bb15"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[1.         0.64172385 0.81468989]\n",
            " [0.64172385 1.         0.52280594]\n",
            " [0.81468989 0.52280594 1.        ]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Aqui está um exemplo de como usar o `CountVectorizer` e o `cosine_similarity` do `scikit-learn` para encontrar a similaridade entre documentos. "
      ],
      "metadata": {
        "id": "PWfp5EhwqAq0"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Inicialmente, criamos uma função para retornar uma lista de tuplas, onde cada tupla contém o documento mais similar e sua pontuação de similaridade."
      ],
      "metadata": {
        "id": "Zvb0S9ZNqgP4"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.feature_extraction.text import CountVectorizer\n",
        "from sklearn.metrics.pairwise import cosine_similarity\n",
        "\n",
        "def encontrar_documentos_similares(input_doc, docs, n=3):\n",
        "    # Vetorizando os documentos\n",
        "    vectorizer = CountVectorizer()\n",
        "    doc_vectors = vectorizer.fit_transform(docs)\n",
        "\n",
        "    # Encontrando os documentos mais similares\n",
        "    input_vector = vectorizer.transform([input_doc])\n",
        "    similarity_scores = cosine_similarity(input_vector, doc_vectors)\n",
        "\n",
        "    # Obtendo o índice dos documentos mais similares\n",
        "    most_similar_docs_idx = similarity_scores.argsort()[0][::-1][:n]\n",
        "\n",
        "    # Criando lista de tuplas com o documento mais similar e a pontuação de similaridade\n",
        "    similar_docs = [(docs[idx], similarity_scores[0][idx]) for idx in most_similar_docs_idx]\n",
        "\n",
        "    return similar_docs"
      ],
      "metadata": {
        "id": "ectHsfP4pHzs"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Podemos usá-la da seguinte maneira:"
      ],
      "metadata": {
        "id": "u0LzwHLJqqH3"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "docs = [\n",
        "    \"O Brasil é um país localizado na América do Sul\",\n",
        "    \"A língua oficial do Brasil é o Português\",\n",
        "    \"O carnaval do Brasil é conhecido em todo o mundo\",\n",
        "    \"A Amazônia é a maior floresta tropical do mundo\",\n",
        "    \"O futebol é o esporte mais popular do Brasil\",\n",
        "    \"O Cristo Redentor é um dos pontos turísticos mais famosos do Brasil\",\n",
        "]\n",
        "\n",
        "input_doc = \"O Brasil é um país com uma grande diversidade cultural\"\n",
        "similar_docs = encontrar_documentos_similares(input_doc, docs, n=3)\n",
        "\n",
        "print(\"Documentos mais similares:\")\n",
        "for doc, score in similar_docs:\n",
        "    print(\"\\n\", doc)\n",
        "    print(\"Pontuação de similaridade:\", score)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-48FkbRFpMot",
        "outputId": "be811678-6b30-4611-f1a1-e0760d1c3857"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Documentos mais similares:\n",
            "\n",
            " O Brasil é um país localizado na América do Sul\n",
            "Pontuação de similaridade: 0.6123724356957945\n",
            "\n",
            " O Cristo Redentor é um dos pontos turísticos mais famosos do Brasil\n",
            "Pontuação de similaridade: 0.3651483716701108\n",
            "\n",
            " A língua oficial do Brasil é o Português\n",
            "Pontuação de similaridade: 0.25819888974716115\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "`TfidfVectorizer` e `CountVectorizer` são duas classes do `scikit-learn` que permitem vetorizar textos, transformando-os em representações numéricas que podem ser usadas como entrada para algoritmos de aprendizado de máquina.\n",
        "\n",
        "A principal diferença entre elas é que o `CountVectorizer` simplesmente conta o número de ocorrências de cada palavra em cada documento, enquanto o `TfidfVectorizer` pondera a importância de cada palavra em relação ao corpus de documentos.\n",
        "\n",
        "O `TfidfVectorizer` calcula a frequência inversa do documento (IDF) para cada palavra, que é um indicador da raridade da palavra no corpus. Em seguida, o `TfidfVectorizer` multiplica a contagem de cada palavra em cada documento pelo IDF correspondente para obter uma pontuação `TF-IDF` para cada palavra.\n",
        "\n",
        "Assim, palavras que aparecem frequentemente em um determinado documento, mas raramente em outros documentos do corpus, têm uma pontuação `TF-IDF` mais alta do que palavras que aparecem com a mesma frequência em todos os documentos. Isso pode ajudar a destacar palavras-chave importantes para cada documento, enquanto reduz o impacto de palavras comuns que não acrescentam muito valor semântico."
      ],
      "metadata": {
        "id": "06QxTwj3q-Bm"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Ambas as técnicas, `TfidfVectorizer` e `CountVectorizer`, podem ser usadas para calcular a similaridade de textos. No entanto, a escolha entre as duas técnicas depende do objetivo do seu projeto e do tipo de texto que você está lidando.\n",
        "\n",
        "O `CountVectorizer` é uma técnica mais simples que calcula a frequência de ocorrência de cada palavra em um texto. Ele é útil quando se deseja identificar a frequência de palavras em um texto, sem levar em conta a importância relativa dessas palavras em relação ao texto como um todo. O `CountVectorizer` pode ser usado para comparar documentos com base na frequência de ocorrência de palavras comuns.\n",
        "\n",
        "O `TfidfVectorizer` é uma técnica mais avançada que leva em consideração a importância relativa das palavras em um texto. Ele calcula o peso de cada palavra com base na sua frequência de ocorrência no texto e na frequência em que a palavra ocorre em todo o conjunto de documentos. Isso significa que palavras que aparecem com frequência em um documento, mas não em outros, recebem uma pontuação maior do que palavras que aparecem com frequência em todos os documentos. O `TfidfVectorizer` é útil quando se deseja identificar palavras-chave importantes em um texto ou quando se deseja comparar documentos com base na importância relativa das palavras.\n",
        "\n",
        "Em resumo, use o `CountVectorizer` se quiser uma comparação simples da frequência de palavras em textos. Use o `TfidfVectorizer` se quiser uma comparação mais sofisticada que leve em consideração a importância relativa das palavras nos textos."
      ],
      "metadata": {
        "id": "cKidmqaVreuL"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**spaCy**"
      ],
      "metadata": {
        "id": "eSzDFS6Br0Lq"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "`spaCy` é uma biblioteca Python de PLN de código aberto. Ela inclui modelos pré-treinados para várias línguas, incluindo português, inglês, espanhol, alemão, francês, entre outras."
      ],
      "metadata": {
        "id": "kVI8ZHA5v3t7"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "![image.png](data:image/png;base64,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)"
      ],
      "metadata": {
        "id": "zohtbZADs1ux"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "É importante ressaltar que, para usar o `spaCy` em **português**, é necessário instalar o modelo correspondente. Você pode fazer isso executando o seguinte comando:"
      ],
      "metadata": {
        "id": "v30VJySDuKzs"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!python -m spacy download pt_core_news_lg"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "imW06EQisgF1",
        "outputId": "653208b4-bfa7-44a1-c029-5395e1bb16e1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "2023-04-09 15:46:14.389689: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT\n",
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting pt-core-news-lg==3.5.0\n",
            "  Downloading https://github.com/explosion/spacy-models/releases/download/pt_core_news_lg-3.5.0/pt_core_news_lg-3.5.0-py3-none-any.whl (568.2 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m568.2/568.2 MB\u001b[0m \u001b[31m2.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: spacy<3.6.0,>=3.5.0 in /usr/local/lib/python3.9/dist-packages (from pt-core-news-lg==3.5.0) (3.5.1)\n",
            "Requirement already satisfied: preshed<3.1.0,>=3.0.2 in /usr/local/lib/python3.9/dist-packages (from spacy<3.6.0,>=3.5.0->pt-core-news-lg==3.5.0) (3.0.8)\n",
            "Requirement already satisfied: catalogue<2.1.0,>=2.0.6 in /usr/local/lib/python3.9/dist-packages (from spacy<3.6.0,>=3.5.0->pt-core-news-lg==3.5.0) (2.0.8)\n",
            "Requirement already satisfied: cymem<2.1.0,>=2.0.2 in /usr/local/lib/python3.9/dist-packages (from spacy<3.6.0,>=3.5.0->pt-core-news-lg==3.5.0) (2.0.7)\n",
            "Requirement already satisfied: pathy>=0.10.0 in /usr/local/lib/python3.9/dist-packages (from spacy<3.6.0,>=3.5.0->pt-core-news-lg==3.5.0) (0.10.1)\n",
            "Requirement already satisfied: spacy-legacy<3.1.0,>=3.0.11 in /usr/local/lib/python3.9/dist-packages (from spacy<3.6.0,>=3.5.0->pt-core-news-lg==3.5.0) (3.0.12)\n",
            "Requirement already satisfied: setuptools in /usr/local/lib/python3.9/dist-packages (from spacy<3.6.0,>=3.5.0->pt-core-news-lg==3.5.0) (67.6.1)\n",
            "Requirement already satisfied: typer<0.8.0,>=0.3.0 in /usr/local/lib/python3.9/dist-packages (from spacy<3.6.0,>=3.5.0->pt-core-news-lg==3.5.0) (0.7.0)\n",
            "Requirement already satisfied: numpy>=1.15.0 in /usr/local/lib/python3.9/dist-packages (from spacy<3.6.0,>=3.5.0->pt-core-news-lg==3.5.0) (1.22.4)\n",
            "Requirement already satisfied: smart-open<7.0.0,>=5.2.1 in /usr/local/lib/python3.9/dist-packages (from spacy<3.6.0,>=3.5.0->pt-core-news-lg==3.5.0) (6.3.0)\n",
            "Requirement already satisfied: wasabi<1.2.0,>=0.9.1 in /usr/local/lib/python3.9/dist-packages (from spacy<3.6.0,>=3.5.0->pt-core-news-lg==3.5.0) (1.1.1)\n",
            "Requirement already satisfied: langcodes<4.0.0,>=3.2.0 in /usr/local/lib/python3.9/dist-packages (from spacy<3.6.0,>=3.5.0->pt-core-news-lg==3.5.0) (3.3.0)\n",
            "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.9/dist-packages (from spacy<3.6.0,>=3.5.0->pt-core-news-lg==3.5.0) (23.0)\n",
            "Requirement already satisfied: spacy-loggers<2.0.0,>=1.0.0 in /usr/local/lib/python3.9/dist-packages (from spacy<3.6.0,>=3.5.0->pt-core-news-lg==3.5.0) (1.0.4)\n",
            "Requirement already satisfied: murmurhash<1.1.0,>=0.28.0 in /usr/local/lib/python3.9/dist-packages (from spacy<3.6.0,>=3.5.0->pt-core-news-lg==3.5.0) (1.0.9)\n",
            "Requirement already satisfied: thinc<8.2.0,>=8.1.8 in /usr/local/lib/python3.9/dist-packages (from spacy<3.6.0,>=3.5.0->pt-core-news-lg==3.5.0) (8.1.9)\n",
            "Requirement already satisfied: srsly<3.0.0,>=2.4.3 in /usr/local/lib/python3.9/dist-packages (from spacy<3.6.0,>=3.5.0->pt-core-news-lg==3.5.0) (2.4.6)\n",
            "Requirement already satisfied: jinja2 in /usr/local/lib/python3.9/dist-packages (from spacy<3.6.0,>=3.5.0->pt-core-news-lg==3.5.0) (3.1.2)\n",
            "Requirement already satisfied: pydantic!=1.8,!=1.8.1,<1.11.0,>=1.7.4 in /usr/local/lib/python3.9/dist-packages (from spacy<3.6.0,>=3.5.0->pt-core-news-lg==3.5.0) (1.10.7)\n",
            "Requirement already satisfied: requests<3.0.0,>=2.13.0 in /usr/local/lib/python3.9/dist-packages (from spacy<3.6.0,>=3.5.0->pt-core-news-lg==3.5.0) (2.27.1)\n",
            "Requirement already satisfied: tqdm<5.0.0,>=4.38.0 in /usr/local/lib/python3.9/dist-packages (from spacy<3.6.0,>=3.5.0->pt-core-news-lg==3.5.0) (4.65.0)\n",
            "Requirement already satisfied: typing-extensions>=4.2.0 in /usr/local/lib/python3.9/dist-packages (from pydantic!=1.8,!=1.8.1,<1.11.0,>=1.7.4->spacy<3.6.0,>=3.5.0->pt-core-news-lg==3.5.0) (4.5.0)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.9/dist-packages (from requests<3.0.0,>=2.13.0->spacy<3.6.0,>=3.5.0->pt-core-news-lg==3.5.0) (2022.12.7)\n",
            "Requirement already satisfied: charset-normalizer~=2.0.0 in /usr/local/lib/python3.9/dist-packages (from requests<3.0.0,>=2.13.0->spacy<3.6.0,>=3.5.0->pt-core-news-lg==3.5.0) (2.0.12)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.9/dist-packages (from requests<3.0.0,>=2.13.0->spacy<3.6.0,>=3.5.0->pt-core-news-lg==3.5.0) (3.4)\n",
            "Requirement already satisfied: urllib3<1.27,>=1.21.1 in /usr/local/lib/python3.9/dist-packages (from requests<3.0.0,>=2.13.0->spacy<3.6.0,>=3.5.0->pt-core-news-lg==3.5.0) (1.26.15)\n",
            "Requirement already satisfied: blis<0.8.0,>=0.7.8 in /usr/local/lib/python3.9/dist-packages (from thinc<8.2.0,>=8.1.8->spacy<3.6.0,>=3.5.0->pt-core-news-lg==3.5.0) (0.7.9)\n",
            "Requirement already satisfied: confection<1.0.0,>=0.0.1 in /usr/local/lib/python3.9/dist-packages (from thinc<8.2.0,>=8.1.8->spacy<3.6.0,>=3.5.0->pt-core-news-lg==3.5.0) (0.0.4)\n",
            "Requirement already satisfied: click<9.0.0,>=7.1.1 in /usr/local/lib/python3.9/dist-packages (from typer<0.8.0,>=0.3.0->spacy<3.6.0,>=3.5.0->pt-core-news-lg==3.5.0) (8.1.3)\n",
            "Requirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.9/dist-packages (from jinja2->spacy<3.6.0,>=3.5.0->pt-core-news-lg==3.5.0) (2.1.2)\n",
            "Installing collected packages: pt-core-news-lg\n",
            "Successfully installed pt-core-news-lg-3.5.0\n",
            "\u001b[38;5;2m✔ Download and installation successful\u001b[0m\n",
            "You can now load the package via spacy.load('pt_core_news_lg')\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import spacy\n",
        "\n",
        "# Carregando o modelo do Spacy para a língua portuguesa\n",
        "nlp = spacy.load('pt_core_news_lg')\n",
        "\n",
        "# Definindo os textos para cálculo da similaridade\n",
        "texto1 = \"O Brasil é conhecido pelo seu futebol.\"\n",
        "texto2 = \"O futebol é um esporte muito popular no Brasil.\"\n",
        "\n",
        "# Criando objetos Doc para cada texto\n",
        "doc1 = nlp(texto1)\n",
        "doc2 = nlp(texto2)\n",
        "\n",
        "# Calculando a similaridade entre os textos\n",
        "similaridade = doc1.similarity(doc2)\n",
        "\n",
        "# Imprimindo a similaridade\n",
        "print(f\"A similaridade entre os textos é: {similaridade:.2f}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_oK29W9lsGQi",
        "outputId": "283caf1d-294f-4257-99be-9a947befd984"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "A similaridade entre os textos é: 0.90\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Para usar o `spaCy` em **inglês**, também é necessário instalar o modelo correspondente. Você pode fazer isso executando o seguinte comando:"
      ],
      "metadata": {
        "id": "j92NoRa2uU2T"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!python -m spacy download en_core_web_md"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GAOp0t5Pto3O",
        "outputId": "1c63fa40-482a-49db-9fc7-5439d6d6457e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "2023-04-09 15:52:06.141389: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT\n",
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting en-core-web-md==3.5.0\n",
            "  Downloading https://github.com/explosion/spacy-models/releases/download/en_core_web_md-3.5.0/en_core_web_md-3.5.0-py3-none-any.whl (42.8 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m42.8/42.8 MB\u001b[0m \u001b[31m15.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: spacy<3.6.0,>=3.5.0 in /usr/local/lib/python3.9/dist-packages (from en-core-web-md==3.5.0) (3.5.1)\n",
            "Requirement already satisfied: tqdm<5.0.0,>=4.38.0 in /usr/local/lib/python3.9/dist-packages (from spacy<3.6.0,>=3.5.0->en-core-web-md==3.5.0) (4.65.0)\n",
            "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.9/dist-packages (from spacy<3.6.0,>=3.5.0->en-core-web-md==3.5.0) (23.0)\n",
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            "Requirement already satisfied: spacy-legacy<3.1.0,>=3.0.11 in /usr/local/lib/python3.9/dist-packages (from spacy<3.6.0,>=3.5.0->en-core-web-md==3.5.0) (3.0.12)\n",
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            "Requirement already satisfied: numpy>=1.15.0 in /usr/local/lib/python3.9/dist-packages (from spacy<3.6.0,>=3.5.0->en-core-web-md==3.5.0) (1.22.4)\n",
            "Requirement already satisfied: pydantic!=1.8,!=1.8.1,<1.11.0,>=1.7.4 in /usr/local/lib/python3.9/dist-packages (from spacy<3.6.0,>=3.5.0->en-core-web-md==3.5.0) (1.10.7)\n",
            "Requirement already satisfied: spacy-loggers<2.0.0,>=1.0.0 in /usr/local/lib/python3.9/dist-packages (from spacy<3.6.0,>=3.5.0->en-core-web-md==3.5.0) (1.0.4)\n",
            "Requirement already satisfied: preshed<3.1.0,>=3.0.2 in /usr/local/lib/python3.9/dist-packages (from spacy<3.6.0,>=3.5.0->en-core-web-md==3.5.0) (3.0.8)\n",
            "Requirement already satisfied: jinja2 in /usr/local/lib/python3.9/dist-packages (from spacy<3.6.0,>=3.5.0->en-core-web-md==3.5.0) (3.1.2)\n",
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            "Requirement already satisfied: catalogue<2.1.0,>=2.0.6 in /usr/local/lib/python3.9/dist-packages (from spacy<3.6.0,>=3.5.0->en-core-web-md==3.5.0) (2.0.8)\n",
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            "Requirement already satisfied: wasabi<1.2.0,>=0.9.1 in /usr/local/lib/python3.9/dist-packages (from spacy<3.6.0,>=3.5.0->en-core-web-md==3.5.0) (1.1.1)\n",
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            "Requirement already satisfied: urllib3<1.27,>=1.21.1 in /usr/local/lib/python3.9/dist-packages (from requests<3.0.0,>=2.13.0->spacy<3.6.0,>=3.5.0->en-core-web-md==3.5.0) (1.26.15)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.9/dist-packages (from requests<3.0.0,>=2.13.0->spacy<3.6.0,>=3.5.0->en-core-web-md==3.5.0) (2022.12.7)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.9/dist-packages (from requests<3.0.0,>=2.13.0->spacy<3.6.0,>=3.5.0->en-core-web-md==3.5.0) (3.4)\n",
            "Requirement already satisfied: confection<1.0.0,>=0.0.1 in /usr/local/lib/python3.9/dist-packages (from thinc<8.2.0,>=8.1.8->spacy<3.6.0,>=3.5.0->en-core-web-md==3.5.0) (0.0.4)\n",
            "Requirement already satisfied: blis<0.8.0,>=0.7.8 in /usr/local/lib/python3.9/dist-packages (from thinc<8.2.0,>=8.1.8->spacy<3.6.0,>=3.5.0->en-core-web-md==3.5.0) (0.7.9)\n",
            "Requirement already satisfied: click<9.0.0,>=7.1.1 in /usr/local/lib/python3.9/dist-packages (from typer<0.8.0,>=0.3.0->spacy<3.6.0,>=3.5.0->en-core-web-md==3.5.0) (8.1.3)\n",
            "Requirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.9/dist-packages (from jinja2->spacy<3.6.0,>=3.5.0->en-core-web-md==3.5.0) (2.1.2)\n",
            "\u001b[38;5;2m✔ Download and installation successful\u001b[0m\n",
            "You can now load the package via spacy.load('en_core_web_md')\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import spacy\n",
        "\n",
        "# Carregando modelo pré-treinado do Spacy\n",
        "nlp = spacy.load(\"en_core_web_md\")\n",
        "\n",
        "# Processando os textos com o modelo carregado\n",
        "doc1 = nlp(u'I love pets.')\n",
        "doc2 = nlp(u'I hate pets')\n",
        "\n",
        "# Calculando a similaridade entre os textos\n",
        "print(doc1.similarity(doc2));"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DUV6isZ3tQ0l",
        "outputId": "ab8319ca-fe73-4b63-eea3-43f225c29e0e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.9041243947777828\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Uma outra abordagem comum é utilizar o modelo pré-treinado de linguagem do `spaCy` para obter as representações vetoriais das palavras e, em seguida, calcular a similaridade de cosseno entre os vetores das frases.\n",
        "\n",
        "Segue um exemplo de código que utiliza essa abordagem:"
      ],
      "metadata": {
        "id": "yXsJXfjovlRf"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import spacy\n",
        "from sklearn.metrics.pairwise import cosine_similarity\n",
        "from typing import List, Tuple\n",
        "\n",
        "def spacy_similarity(docs: List[str], input_doc: str, top_n: int = 3) -> List[Tuple[str, float]]:\n",
        "    # Carregando o modelo de linguagem do Spacy para o idioma português\n",
        "    nlp = spacy.load('pt_core_news_lg')\n",
        "\n",
        "    # Processando os documentos com o modelo do Spacy e obtendo as representações vetoriais\n",
        "    doc_vectors = [nlp(doc).vector for doc in docs]\n",
        "\n",
        "    # Obtendo a representação vetorial do documento de entrada\n",
        "    input_vector = nlp(input_doc).vector.reshape(1, -1)\n",
        "\n",
        "    # Calculando a similaridade entre o documento de entrada e os documentos existentes\n",
        "    similarity_scores = cosine_similarity(input_vector, doc_vectors)\n",
        "\n",
        "    # Obtendo o índice dos documentos mais similares\n",
        "    most_similar_docs_idx = similarity_scores.argsort()[0][::-1][:top_n]\n",
        "\n",
        "    # Retornando a lista de documentos mais similares e seus scores de similaridade\n",
        "    return [(docs[idx], similarity_scores[0][idx]) for idx in most_similar_docs_idx]"
      ],
      "metadata": {
        "id": "P3Wa9makvHld"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Definindo alguns documentos\n",
        "docs = [\n",
        "    \"O Brasil é um país localizado na América do Sul\",\n",
        "    \"A língua oficial do Brasil é o Português\",\n",
        "    \"O carnaval do Brasil é conhecido em todo o mundo\",\n",
        "    \"A Amazônia é a maior floresta tropical do mundo\",\n",
        "    \"O futebol é o esporte mais popular do Brasil\",\n",
        "    \"O Cristo Redentor é um dos pontos turísticos mais famosos do Brasil\",\n",
        "]\n",
        "\n",
        "# Testando a função\n",
        "input_doc = \"O Brasil é um país com uma grande diversidade cultural\"\n",
        "spacy_similarity(docs, input_doc)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UBTe-73avS5a",
        "outputId": "f44df6b2-fa58-4490-9294-b35e3af929d2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[('O futebol é o esporte mais popular do Brasil', 0.83132064),\n",
              " ('O Brasil é um país localizado na América do Sul', 0.82746077),\n",
              " ('O carnaval do Brasil é conhecido em todo o mundo', 0.824836)]"
            ]
          },
          "metadata": {},
          "execution_count": 154
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**BERT**"
      ],
      "metadata": {
        "id": "QE_Sdqoqwri2"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install sentence-transformers"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tLBSTEdNxPMJ",
        "outputId": "f8f56c1a-7af3-450e-acdb-b580c3b3729b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting sentence-transformers\n",
            "  Downloading sentence-transformers-2.2.2.tar.gz (85 kB)\n",
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            "  Downloading huggingface_hub-0.13.4-py3-none-any.whl (200 kB)\n",
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            "Requirement already satisfied: mpmath>=0.19 in /usr/local/lib/python3.9/dist-packages (from sympy->torch>=1.6.0->sentence-transformers) (1.3.0)\n",
            "Building wheels for collected packages: sentence-transformers\n",
            "  Building wheel for sentence-transformers (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for sentence-transformers: filename=sentence_transformers-2.2.2-py3-none-any.whl size=125942 sha256=5c2984350c96dd84fa48f23848f3f2dac650d8df7e5b7f627975cc14305a857c\n",
            "  Stored in directory: /root/.cache/pip/wheels/71/67/06/162a3760c40d74dd40bc855d527008d26341c2b0ecf3e8e11f\n",
            "Successfully built sentence-transformers\n",
            "Installing collected packages: tokenizers, sentencepiece, huggingface-hub, transformers, sentence-transformers\n",
            "Successfully installed huggingface-hub-0.13.4 sentence-transformers-2.2.2 sentencepiece-0.1.97 tokenizers-0.13.3 transformers-4.27.4\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "O modelo `all-MiniLM-L6-v2` é uma versão pré-treinada da arquitetura MiniLM, desenvolvida pela Microsoft. Essa arquitetura é uma variação mais leve e compacta do modelo BERT, projetada para ser mais eficiente em termos de memória e tempo de treinamento. O modelo `all-MiniLM-L6-v2` foi treinado em um conjunto de dados multilíngue e pode ser usado para tarefas de PLN em vários idiomas."
      ],
      "metadata": {
        "id": "tA2bR7Pf0BPn"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sentence_transformers import SentenceTransformer\n",
        "from scipy.spatial.distance import cosine\n",
        "\n",
        "# Carregar o modelo all-MiniLM-L6-v2 pré-treinado\n",
        "model = SentenceTransformer('all-MiniLM-L6-v2')\n",
        "\n",
        "# Exemplo de textos a serem comparados\n",
        "text1 = \"O Brasil é um país localizado na América do Sul\"\n",
        "text2 = \"A língua oficial do Brasil é o Português\"\n",
        "\n",
        "# Codificar os textos em vetores de alta dimensão usando o modelo\n",
        "vector1 = model.encode(text1, convert_to_tensor=True)\n",
        "vector2 = model.encode(text2, convert_to_tensor=True)\n",
        "\n",
        "# Calcular a similaridade usando a distância do cosseno\n",
        "similarity = 1 - cosine(vector1, vector2)\n",
        "\n",
        "print(\"Similaridade:\", similarity)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "K3f83gPR0SY8",
        "outputId": "809b09d4-be76-4a6a-ae02-19358d7abdb9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Similaridade: 0.6013340950012207\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Aqui está um outro exemplo de código:"
      ],
      "metadata": {
        "id": "57XKHdnO1oOC"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sentence_transformers import SentenceTransformer, util\n",
        "\n",
        "# Carregando o modelo pré-treinado\n",
        "model = SentenceTransformer('all-MiniLM-L6-v2')\n",
        "\n",
        "# 2 listas of sentenças\n",
        "sentences1 = ['The cat sits outside',\n",
        "             'A man is playing guitar',\n",
        "             'The new movie is awesome']\n",
        "\n",
        "sentences2 = ['The dog plays in the garden',\n",
        "              'A woman watches TV',\n",
        "              'The new movie is so great']\n",
        "\n",
        "# Obter as representações vetoriais das frases\n",
        "embeddings1 = model.encode(sentences1, convert_to_tensor=True)\n",
        "embeddings2 = model.encode(sentences2, convert_to_tensor=True)\n",
        "\n",
        "# Calcular a similaridade \n",
        "cosine_scores = util.cos_sim(embeddings1, embeddings2)\n",
        "\n",
        "# Imprimir o resultado\n",
        "for i in range(len(sentences1)):\n",
        "    print(\"{}      \\t {} \\t Score: {:.4f}\".format(sentences1[i], sentences2[i], cosine_scores[i][i]))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jgLyvBBbzScc",
        "outputId": "27016efc-49db-4666-db86-71e80a9ea299"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The cat sits outside      \t The dog plays in the garden \t Score: 0.2838\n",
            "A man is playing guitar      \t A woman watches TV \t Score: -0.0327\n",
            "The new movie is awesome      \t The new movie is so great \t Score: 0.8939\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "O modelo `distilbert-base-nli-stsb-mean-tokens` é um modelo pré-treinado do `Transformer` que utiliza a arquitetura do BERT (*Bidirectional Encoder Representations from Transformers*) para criar representações de texto que são úteis para tarefas de PLN, como classificação de texto, análise de sentimento e similaridade de texto. Ele é pré-treinado usando o processo de Masked Language Model (MLM) e emparelhamento de texto, utilizando o conjunto de dados do GLUE (*General Language Understanding Evaluation*).\n",
        "\n",
        "O modelo é específico para a tarefa de avaliação de similaridade de texto SemEval-2012 STS (S*emantic Textual Similarity*) e é treinado usando a métrica de correlação de Pearson. Ele é chamado de `distilbert-base-nli-stsb-mean-tokens` porque utiliza uma versão menor do modelo BERT chamada DistilBERT (uma versão mais leve do BERT) e as representações de texto são obtidas pela agregação da saída do token [CLS] (com a média) para as duas sequências de entrada (pares de frases) fornecidas."
      ],
      "metadata": {
        "id": "ZD2GLQG31wBD"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sentence_transformers import SentenceTransformer, util\n",
        "\n",
        "# Carregando o modelo pré-treinado\n",
        "model = SentenceTransformer('distilbert-base-nli-stsb-mean-tokens')\n",
        "\n",
        "# Duas frases de exemplo\n",
        "sentence1 = \"O Brasil é um país localizado na América do Sul\"\n",
        "sentence2 = \"O Japão é um país localizado na Ásia\"\n",
        "\n",
        "# Obter as representações vetoriais das frases\n",
        "embeddings1 = model.encode(sentence1, convert_to_tensor=True)\n",
        "embeddings2 = model.encode(sentence2, convert_to_tensor=True)\n",
        "\n",
        "# Calcular a similaridade usando a função cosine_similarity\n",
        "cosine_scores = util.pytorch_cos_sim(embeddings1, embeddings2)\n",
        "\n",
        "# Imprimir o resultado\n",
        "print(\"Similaridade: \", cosine_scores.item())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pn6OEb2L0ivq",
        "outputId": "244e3c7b-38b2-44fd-d7de-82cec4969e98"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Similaridade:  0.4515385031700134\n"
          ]
        }
      ]
    }
  ]
}