4.2_KMeans_Alimento.ipynb

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        "# K-Means Clustering\n",
        "\n",
        "O K-Means é um algoritmo de aprendizado de máquina não supervisionado que agrupa dados em k grupos distintos. É considerado não supervisionado porque não possui uma variável alvo definida que identifica o grupo.\n",
        "\n",
        "\n",
        "Os tipos de clusters mais convencionais são:\n",
        "\n",
        "**Clusters particionais**: dividem os dados em subconjuntos mutuamente exclusivos, de modo que cada ponto de dados pertence a exatamente um cluster. Um exemplo de algoritmo de clusterização particional é o K-Means;\n",
        "\n",
        "**Clusters hierárquicos**: criam uma hierarquia de clusters, que podem ser representados por uma árvore chamada dendrograma. Os clusters podem ser obtidos cortando a árvore em diferentes níveis. Um exemplo de algoritmo de clusterização hierárquica é o Agglomerative Clustering;\n",
        "\n",
        "**Clusters baseados em densidade**: agrupam os dados com base na densidade dos pontos de dados, de modo que os clusters são formados por regiões de alta densidade separadas por regiões de baixa densidade. Um exemplo de algoritmo de clusterização baseado em densidade é o DBSCAN (veja o arquivo 4.1-DBSCAN_temperatura.ipynb deste repositório).\n",
        "\n",
        "De forma rápida, o algoritmo de CLustering K-Means funciona da seguinte forma:\n",
        "\n",
        "1.   Escolhe-se um número k de clusters a serem formados.\n",
        "2.   Inicializa-se k centroides (pontos que representam o centro de cada cluster) aleatoriamente ou usando algum critério.\n",
        "3.   Atribui-se cada ponto de dados ao cluster cujo centroide é o mais próximo, usando alguma medida de distância (por exemplo, a distância euclidiana).\n",
        "4.   Atualiza-se o centroide de cada cluster como sendo a média dos pontos de dados atribuídos a ele.\n",
        "5.   Repete-se os passos 3 e 4 até que os centroides não mudem mais ou até que se atinja um número máximo de iterações.\n",
        "\n",
        "Um dos desafios ao usar o K-Means é determinar o número ideal de clusters (quantidade de K que serão agrupados os dados). Isso pode ser feito usando métodos como o Método do Cotovelo (Elbow Method) ou Silhouette Score, que avaliam a qualidade dos clusters para diferentes valores de K."
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      "source": [
        "#!pip install -U scikit-learn\n",
        "#!pip show scikit-learn\n",
        "\n",
        "from sklearn.cluster import KMeans\n",
        "import matplotlib.pyplot as plt\n",
        "import pandas as pd\n",
        "import numpy as np"
      ]
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      "source": [
        "Para começar o experimento, separamos 2 grupos de alimentos obtidos a partir da tabela TACO da Unicamp.\n",
        "Site da TACO com os dados originais: http://www.unicamp.br/nepa/taco/tabela.php?ativo=tabela\n",
        "\n",
        "Estes dados estão nos 2 datasets: Alimentos e Alimentos2. Em ambos arquivos limitamos a informação nutricional para conter apenas Lipídeos e Ferro dos alimentos, para facilitar a didática e explicação do algoritmo."
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        "dados = pd.read_csv(\"https://raw.githubusercontent.com/diegonogare/MachineLearning/main/alimentos.csv\", sep=';')\n",
        "#dados = pd.read_csv(\"https://raw.githubusercontent.com/diegonogare/MachineLearning/main/alimentos2.csv\", sep=';')"
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              "        Lipídeos      Ferro\n",
              "count  28.000000  28.000000\n",
              "mean    8.114286   1.417857\n",
              "std     9.945992   1.514389\n",
              "min     0.000000   0.000000\n",
              "25%     0.650000   0.275000\n",
              "50%     3.350000   0.700000\n",
              "75%    13.700000   2.400000\n",
              "max    33.500000   5.700000"
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            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 28 entries, 0 to 27\n",
            "Data columns (total 3 columns):\n",
            " #   Column                   Non-Null Count  Dtype  \n",
            "---  ------                   --------------  -----  \n",
            " 0   Descrição dos alimentos  28 non-null     object \n",
            " 1   Lipídeos                 28 non-null     float64\n",
            " 2   Ferro                    28 non-null     float64\n",
            "dtypes: float64(2), object(1)\n",
            "memory usage: 800.0+ bytes\n"
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              "    Descrição dos alimentos  Lipídeos  Ferro\n",
              "0    Queijo. minas. frescal      20.2    0.9\n",
              "1  Queijo. minas. meia cura      24.6    0.2\n",
              "2          Queijo. mozarela      25.2    0.3\n",
              "3          Queijo. parmesão      33.5    0.5\n",
              "4      Queijo. pasteurizado      27.4    0.3"
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              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-e9c1c735-7122-48fd-b8b9-8b4885c991c5 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-e9c1c735-7122-48fd-b8b9-8b4885c991c5');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-516823f9-bcc7-415a-8429-4604c56b7eec\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-516823f9-bcc7-415a-8429-4604c56b7eec')\"\n",
              "            title=\"Suggest charts.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-516823f9-bcc7-415a-8429-4604c56b7eec button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Repare que, ao gerar um gráfico de dispersão dos dados, não são simples de se separar visualmente. A proposta é exatamente essa, encontrar grupos similares de dados a partir das distâncias entre si."
      ],
      "metadata": {
        "id": "ENnfRPri0wkg"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "plt.scatter(dados['Lipídeos'],dados['Ferro'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 447
        },
        "id": "Q8so47k0stJZ",
        "outputId": "42a7d88f-7c5a-4dab-99f5-c410e9cac25a"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.collections.PathCollection at 0x7b6132085420>"
            ]
          },
          "metadata": {},
          "execution_count": 6
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "A construção dos clusters é relativamente simples. Neste exemplo, informamos ao algoritmo a quantidade de K que queremos (que é a quantidade de clusters que os dados deverão ser agrupados) e quais são os dados que serão utilizados para fazer os agrupamentos.\n",
        "\n",
        "Repare que passamos apenas os dados de Lipídeo e Ferro para o algoritmo fazer o treinamento, e a partir disso, teremos os resultados armazenados na variável cluster."
      ],
      "metadata": {
        "id": "oiPuXjqc1AfA"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "cluster = KMeans(n_clusters=4).fit(dados[['Lipídeos','Ferro']])\n",
        "#cluster = KMeans(n_clusters=5).fit(dados[['Lipídeos','Ferro']])\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sRjeWUdPtPEi",
        "outputId": "ea845b3b-770d-451c-a841-f7e3bd1bee9e"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
            "  warnings.warn(\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Ao imprimir a variável cluster, nos é retornado a quantidade de grupos distintos que o algoritmo utilizou. Ao explorar um pouco mais, trazendo os *labels_* desta variável cluster é possível entender em qual cluster aquela observação (linha dos dados) foi atribuída. Também é possível entender quais foram as posições finais dos centróides de cada um dos clusters ao imprimir o *cluster_centers_* da variável cluster."
      ],
      "metadata": {
        "id": "nZQFBJLr1YCD"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "print(cluster)\n",
        "print(cluster.labels_)\n",
        "print(cluster.cluster_centers_)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Sr0UCt70vhdx",
        "outputId": "ed3ac566-6560-4834-a98c-2faa5fdb4e52"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "KMeans(n_clusters=4)\n",
            "[3 1 1 1 1 3 3 3 3 0 0 2 2 2 2 2 2 2 2 2 2 0 0 0 0 0 0 0]\n",
            "[[ 3.91111111  3.01111111]\n",
            " [27.675       0.325     ]\n",
            " [ 0.34        0.23      ]\n",
            " [15.58        1.8       ]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dados2=dados\n",
        "dados2['clusters'] = cluster.labels_\n",
        "\n",
        "dados2.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "LY2a3RZh5WwR",
        "outputId": "41e56a41-c297-413d-86a0-48a7d01db5bc"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    Descrição dos alimentos  Lipídeos  Ferro  clusters\n",
              "0    Queijo. minas. frescal      20.2    0.9         3\n",
              "1  Queijo. minas. meia cura      24.6    0.2         1\n",
              "2          Queijo. mozarela      25.2    0.3         1\n",
              "3          Queijo. parmesão      33.5    0.5         1\n",
              "4      Queijo. pasteurizado      27.4    0.3         1"
            ],
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              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-5b59f329-816c-48d4-849d-db3ce4602646 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-5b59f329-816c-48d4-849d-db3ce4602646');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-5b531e6c-29f0-4b5b-81ae-83d551021244\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-5b531e6c-29f0-4b5b-81ae-83d551021244')\"\n",
              "            title=\"Suggest charts.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-5b531e6c-29f0-4b5b-81ae-83d551021244 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Veja que agora, ao imprimir os mesmos dados de antes, porém modificando o atributo de impressão cor reflita o cluster que foi atribuído a cada observação, que os dados próximos entre si estão encaixados em um mesmo cluster e dados mais afastados ficam em outro cluster."
      ],
      "metadata": {
        "id": "YxDGfM6m2Mxh"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "cores = {0:'cornflowerblue',1:'slateblue',2:'lightseagreen',3:'coral',4:'hotpink'}\n",
        "\n",
        "for grupo in dados2['clusters'].unique():\n",
        "    tmp = dados2[dados2['clusters'] == grupo]\n",
        "    plt.scatter(tmp['Lipídeos'], tmp['Ferro'], color=cores[grupo] )\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 430
        },
        "id": "SlqvHxfC7oQ1",
        "outputId": "86f2892b-ac74-4f40-b52c-5cbe540251a1"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Lembram-se que era possível retornar a posição final de cada centróide dentro de um cluster. Com base nisso é possível retornar a posição deles também de forma gráfica."
      ],
      "metadata": {
        "id": "lD3NGhXp2iaA"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "plt.scatter(cluster.cluster_centers_[:, 0], cluster.cluster_centers_[:, 1], color='black', marker='^')\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 447
        },
        "id": "9OP0eskpKNJz",
        "outputId": "20cf2b85-5503-4393-aea0-71d976a9832e"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.collections.PathCollection at 0x7b612d69baf0>"
            ]
          },
          "metadata": {},
          "execution_count": 11
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Fazendo a predição de um novo ponto\n",
        "\n",
        "Agora imagine a situação que você terá um novo alimento para encaixar em um destes grupos. Para isso, é necessário pegar o modelo treinado anteriormente e informar os dados de entrada do novo alimento para descobrir a qual grupo ele faz parte.\n",
        "\n",
        "Repare que a nova observação (nova linha de dados) não tem nenhuma indicação de grupo ou qualquer relação que a faça se encaixar em um dos clustes existentes.\n",
        "\n",
        "Veja o ponto preto no grafico, essa é a posição relativa ao novo dado que estamos querendo predizer."
      ],
      "metadata": {
        "id": "cJKqHsWf2wIy"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "Novo_Dado_Lipideo = [13]\n",
        "Novo_Dado_Ferro = [3]\n",
        "Novo_Dado = {'Lipideo':Novo_Dado_Lipideo, 'Ferro':Novo_Dado_Ferro }\n",
        "df = pd.DataFrame(Novo_Dado)\n",
        "\n",
        "for grupo in dados2['clusters'].unique():\n",
        "    tmp = dados2[dados2['clusters'] == grupo]\n",
        "    plt.scatter(tmp['Lipídeos'], tmp['Ferro'], color=cores[grupo] )\n",
        "\n",
        "plt.scatter(df['Lipideo'], df['Ferro'], c='black')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 447
        },
        "id": "aNYX06lky1Lr",
        "outputId": "de52a30b-2e13-4b74-d11d-a1ea1e5a1068"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.collections.PathCollection at 0x7b612d72a890>"
            ]
          },
          "metadata": {},
          "execution_count": 12
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Utilizando a função *Predict* do modelo treinado, é possível informar os valores de entrada da nova observação e retornar a qual cluster ela faz parte.\n",
        "O valor retornado é declarado a qual cluster esse elemento faz parte (lembrando que é um calculo de aproximação)."
      ],
      "metadata": {
        "id": "57CLXSGZ3dDH"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "novo_valor = cluster.predict( [ df.iloc[0] ])\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tNJLrMF0zdpo",
        "outputId": "e2981946-fad0-466e-e52e-2be991245b26"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but KMeans was fitted with feature names\n",
            "  warnings.warn(\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Ao chamar o *Transform* é possível ver a distância calculada entre a nova observação e os centróides do algoritmo treinado. Veja que são apresentados os valores da distância, e o menos valor é exatamente qual cluster foi definido;"
      ],
      "metadata": {
        "id": "DE9sS3DZ3yJO"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "valor_Transform = cluster.transform([ df.iloc[0][0:2] ])\n",
        "print(valor_Transform)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7w1ky3OkM0m-",
        "outputId": "cff160a5-f402-4892-e6bf-24bdb45cef68"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[ 9.08889568 14.91681099 12.95949459  2.84541737]]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but KMeans was fitted with feature names\n",
            "  warnings.warn(\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(novo_valor)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mBRZnC4r0EMZ",
        "outputId": "4d344211-9695-48d9-8fa7-64617d1557b4"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[3]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['clusters'] = novo_valor\n",
        "\n",
        "print(df)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XQP1yFuH4cye",
        "outputId": "6bd6b0f2-ec2b-469d-e90d-10006255fe8f"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "   Lipideo  Ferro  clusters\n",
            "0       13      3         3\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Repare que, agora, ao imprimir os pontos iniciais utilizados, mais o ponto do novo cluster utilizando o mesmo critério de impressão de cores, o novo dado será adicionado ao cluster informado no Predict. Inclusive com os centróides também demarcados nessa impressão do gráfico."
      ],
      "metadata": {
        "id": "lKC5P-kl4Ikp"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "cores = {0:'deepskyblue',1:'blueviolet',2:'lightseagreen',3:'coral',4:'hotpink'}\n",
        "for grupo in dados2['clusters'].unique():\n",
        "    tmp = dados2[dados2['clusters'] == grupo]\n",
        "    plt.scatter(tmp['Lipídeos'], tmp['Ferro'], color=cores[grupo] )\n",
        "\n",
        "plt.scatter(df['Lipideo'], df['Ferro'], color=cores[df.iloc[0]['clusters']])\n",
        "\n",
        "plt.scatter(cluster.cluster_centers_[:, 0], cluster.cluster_centers_[:, 1], color='black', marker='^')\n",
        "\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 447
        },
        "id": "S41A_bWx0H2i",
        "outputId": "171fef6b-c4af-494f-f1bc-ff285a7371f9"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.collections.PathCollection at 0x7b612d5d7070>"
            ]
          },
          "metadata": {},
          "execution_count": 17
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    }
  ]
}