Proyecto_Final_R_Emp.ipynb

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      "cell_type": "markdown",
      "metadata": {
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        "<a href=\"https://colab.research.google.com/github/rafa-cc/Proyecto-Final-R/blob/main/Proyecto_Final_R_Emp.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install -U kaleido"
      ],
      "metadata": {
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        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "f2f02d2d-975d-4e2d-ca79-404c366063a3"
      },
      "execution_count": null,
      "outputs": [
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          "name": "stdout",
          "text": [
            "Collecting kaleido\n",
            "  Downloading kaleido-0.2.1-py2.py3-none-manylinux1_x86_64.whl (79.9 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m79.9/79.9 MB\u001b[0m \u001b[31m9.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hInstalling collected packages: kaleido\n",
            "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
            "lida 0.0.10 requires fastapi, which is not installed.\n",
            "lida 0.0.10 requires python-multipart, which is not installed.\n",
            "lida 0.0.10 requires uvicorn, which is not installed.\u001b[0m\u001b[31m\n",
            "\u001b[0mSuccessfully installed kaleido-0.2.1\n"
          ]
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    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import statistics\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.svm import SVC\n",
        "from sklearn.metrics import accuracy_score\n",
        "import plotly.graph_objects as go\n",
        "from plotly.io import write_image"
      ],
      "metadata": {
        "id": "fFHPFbY3Jq2r"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# __Código de thema para gráficos__"
      ],
      "metadata": {
        "id": "wxyZZM48H_Gt"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Definir un tema personalizado para el gráfico en Plotly\n",
        "tam_titulo = 22\n",
        "tam_ejes = 14\n",
        "tam_tics_legend = 10\n",
        "gris_600 = '#272727'\n",
        "gris_500 = '#595959'\n",
        "gris_100 = '#F5F5F5'\n",
        "\n",
        "my_custom_theme = {\n",
        "    'layout': {\n",
        "        'title_font': {\n",
        "            'family': 'Inter semibold',\n",
        "            'size': tam_titulo,\n",
        "            'color': gris_600\n",
        "        },\n",
        "        'title_x': 0.075,  # Centrar el título horizontalmente\n",
        "        'title_y': 0.925,\n",
        "        'xaxis': {\n",
        "            'showgrid' : True, 'gridwidth' : 2, 'gridcolor' : 'White',\n",
        "            'title_font': {\n",
        "                'family': 'Inter semibold',\n",
        "                'size': tam_ejes,\n",
        "                'color': gris_600\n",
        "            },\n",
        "            'tickfont': {\n",
        "                'family': 'Raleway',\n",
        "                'size': tam_tics_legend,\n",
        "                'color': gris_500\n",
        "            },\n",
        "            'title_standoff': 5\n",
        "        },\n",
        "        'yaxis': {\n",
        "            'showgrid' : True, 'gridwidth' : 2, 'gridcolor' : 'White',\n",
        "            'title_font': {\n",
        "                'family': 'Inter semibold',\n",
        "                'size': tam_ejes,\n",
        "                'color': gris_600\n",
        "            },\n",
        "            'tickfont': {\n",
        "                'family': 'Raleway',\n",
        "                'size': tam_tics_legend,\n",
        "                'color': gris_500\n",
        "            },\n",
        "            'title_standoff': 5\n",
        "        },\n",
        "        'legend': {\n",
        "            'font': {\n",
        "                'family': 'Raleway',\n",
        "                'size': tam_tics_legend,\n",
        "                'color': gris_500\n",
        "            },\n",
        "            'title_font': {\n",
        "                'family': 'Raleway',\n",
        "                'size': tam_tics_legend,\n",
        "                'color': gris_500\n",
        "            },\n",
        "            #'y': 1.02,  # Ajustar la posición vertical de la leyenda\n",
        "            #'x': 0.08,   # Centrar la leyenda horizontalmente\n",
        "            #'xanchor': 'center',  # Anclaje horizontal al centro\n",
        "            #'yanchor': 'bottom',\n",
        "            'bgcolor': gris_100,\n",
        "            'tracegroupgap': 20\n",
        "        },\n",
        "        'width': 21 * 37.7952756,  # Convertir de cm a píxeles (1 cm = 37.7952756 píxeles)\n",
        "        'height': 13 * 37.7952756,\n",
        "        'margin': {'l': 1 * 37.7952756, 'r': 1.5 * 37.7952756, 't': 2.5 * 37.7952756, 'b': 1 * 37.7952756},\n",
        "        'plot_bgcolor': gris_100,\n",
        "    }\n",
        "}"
      ],
      "metadata": {
        "id": "tpQhv0BvH9a-"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# __Clasificación de los datos__"
      ],
      "metadata": {
        "id": "uM6nnWhHIhY5"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Cargar los datos guardados en el análisis hecho con R de la sección _Análisis de empaquetado_\n",
        "df_py = pd.read_csv('https://raw.githubusercontent.com/rafa-cc/Proyecto-Final-R/main/data_emp.csv')\n",
        "df_py.head()"
      ],
      "metadata": {
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        "id": "Mb8082lgJ12r",
        "outputId": "a7d0cd29-0de8-4786-e289-b74510076d7c"
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              "   Length  Width  Height\n",
              "0     0.1    5.1     6.2\n",
              "1     0.2    4.4     6.8\n",
              "2     0.3    0.3     0.7\n",
              "3     0.3    0.3     0.7\n",
              "4     0.3    2.9     4.3"
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              "      <th>0</th>\n",
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      "source": [
        "Los datos se normalizan respecto a la mediana pues al final lo que caracteriza a cada forma es la distancia de los valores extremos respecto a ese punto medio, por ello fue importane ordenar por filas de menor a mayor cuando se manipuló con `R`"
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        "def normal(triada):\n",
        "    # Calcular la mediana de la triada\n",
        "    med = statistics.median(triada)\n",
        "\n",
        "    # Normalizar cada número en la triada respecto a la mediana. Eliminar el segundo elemento pues siempre será 1\n",
        "    triada_normalizada = [round(x / med, 2) for i, x in enumerate(triada) if i != 1]\n",
        "\n",
        "    return triada_normalizada"
      ],
      "metadata": {
        "id": "N2GyGF-0hB4U"
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      "execution_count": null,
      "outputs": []
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      "cell_type": "code",
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        "# Crear una columna con una lista de las dimensiones normalizadas\n",
        "df_py['normal'] = df_py.apply(normal, axis=1)\n",
        "df_py.head(n=2)"
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              "   Length  Width  Height        normal\n",
              "0     0.1    5.1     6.2  [0.02, 1.22]\n",
              "1     0.2    4.4     6.8  [0.05, 1.55]"
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              "      <td>0.1</td>\n",
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              "      <td>6.2</td>\n",
              "      <td>[0.02, 1.22]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.2</td>\n",
              "      <td>4.4</td>\n",
              "      <td>6.8</td>\n",
              "      <td>[0.05, 1.55]</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-a208f177-75ba-493d-85f5-3ae896dae7cb')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
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              "      border-radius: 50%;\n",
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              "      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",
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              "\n",
              "    .colab-df-buttons div {\n",
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              "\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-a208f177-75ba-493d-85f5-3ae896dae7cb 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-a208f177-75ba-493d-85f5-3ae896dae7cb');\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",
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              "  </div>\n",
              "\n",
              "\n",
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              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-4d93bf33-1e32-4873-aabd-2ce9e5c020b2')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
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              "    </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",
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              "      --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",
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              "    30% {\n",
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              "      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-4d93bf33-1e32-4873-aabd-2ce9e5c020b2 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": 6
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "El modelo se entrena con los valores posibles que pueden tener las formas de los objetos. _Por ejemplo un cubo puede tener su mínimo entre $\\sf[0.66,1]$ y su máximo entre $\\sf[1,1.33]$_. Entonces se colocan algunas combinaciones para que el modelo considere ese rango de opciones."
      ],
      "metadata": {
        "id": "YMYFDBlOVPBv"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Ejemplo de datos etiquetados para entrenar el modelo\n",
        "data = [\n",
        "    ((0.64, 1.0), 'Cubo'),\n",
        "    ((1.0, 1.0), 'Cubo'),\n",
        "    ((1.0, 1.33), 'Cubo'),\n",
        "    ((0.64, 1.33), 'Cubo'),\n",
        "    ((0.88, 1.0), 'Cubo'),\n",
        "    ((1.0, 1.0), 'Cubo'),\n",
        "    ((1.0, 1.15), 'Cubo'),\n",
        "    ((0.88, 1.15), 'Cubo'),\n",
        "    ((0.88, 1.33), 'Cubo'),\n",
        "    ((0.64, 1.11), 'Cubo'),\n",
        "    ((0.7, 1.2), 'Cubo'),\n",
        "\n",
        "    ((0.11, 1.0), 'Pizza'),\n",
        "    ((0.33, 1.0), 'Pizza'),\n",
        "    ((0.4, 1.0), 'Pizza'),\n",
        "    ((0.11, 1.33), 'Pizza'),\n",
        "    ((0.33, 1.33), 'Pizza'),\n",
        "    ((0.4, 1.33), 'Pizza'),\n",
        "    ((0.4, 1.11), 'Pizza'),\n",
        "    ((0.33, 1.11), 'Pizza'),\n",
        "    ((0.11, 1.11), 'Pizza'),\n",
        "    ((0.45, 1.0), 'Pizza'),\n",
        "    ((0.45, 1.11), 'Pizza'),\n",
        "\n",
        "    ((1.0, 2.0), 'Leche'),\n",
        "    ((1.0, 1.66), 'Leche'),\n",
        "    ((1.0, 1.4), 'Leche'),\n",
        "    ((0.88,2.0), 'Leche'),\n",
        "    ((0.88,1.66), 'Leche'),\n",
        "    ((0.88,1.4), 'Leche'),\n",
        "    ((0.66,2.0), 'Leche'),\n",
        "    ((0.66,1.66), 'Leche'),\n",
        "    ((0.66,1.4), 'Leche'),\n",
        "\n",
        "    ((0.5,2.11), 'Estaca'),\n",
        "    ((0.5,2.66), 'Estaca'),\n",
        "    ((0.5,2.4), 'Estaca'),\n",
        "    ((1.0,2.11), 'Estaca'),\n",
        "    ((1.0,2.66), 'Estaca'),\n",
        "    ((1.0,2.4), 'Estaca'),\n",
        "    ((1.0,2.88), 'Estaca'),\n",
        "    ((1.0,3), 'Estaca'),\n",
        "    ((1.0,4), 'Estaca'),\n",
        "    ((0.88, 2.0), 'Estaca'),\n",
        "    ((0.88, 2.66), 'Estaca'),\n",
        "    ((0.88, 2.4), 'Estaca'),\n",
        "    ((0.66, 2.0), 'Estaca'),\n",
        "    ((0.66, 2.66), 'Estaca'),\n",
        "    ((0.66, 2.4), 'Estaca'),\n",
        "\n",
        "    ((0.11,1.4), 'Tabla'),\n",
        "    ((0.11,1.6), 'Tabla'),\n",
        "    ((0.11,1.8), 'Tabla'),\n",
        "    ((0.11,2.0), 'Tabla'),\n",
        "    ((0.11,2.4), 'Tabla'),\n",
        "    ((0.11,2.6), 'Tabla'),\n",
        "    ((0.33,1.4), 'Tabla'),\n",
        "    ((0.33,1.6), 'Tabla'),\n",
        "    ((0.33,1.8), 'Tabla'),\n",
        "    ((0.33,2.0), 'Tabla'),\n",
        "    ((0.33,2.4), 'Tabla'),\n",
        "    ((0.33,2.6), 'Tabla'),\n",
        "    ((0.4,1.4), 'Tabla'),\n",
        "    ((0.4,1.6), 'Tabla'),\n",
        "    ((0.4,1.8), 'Tabla'),\n",
        "    ((0.4,2.0), 'Tabla'),\n",
        "    ((0.4,2.4), 'Tabla'),\n",
        "\n",
        "    ((0.6, 1.2), 'Desco'),\n",
        "    ((0.6, 1.33), 'Desco'),\n",
        "    ((0.6, 1.4), 'Desco'),\n",
        "    ((0.6, 1.6), 'Desco'),\n",
        "    ((0.6, 2.0), 'Desco'),\n",
        "    ((0.55,1.0), 'Desco'),\n",
        "    ((0.55,1.2), 'Desco'),\n",
        "    ((0.55,1.33), 'Desco'),\n",
        "    ((0.55,1.4), 'Desco'),\n",
        "    ((0.55,1.4), 'Desco'),\n",
        "    ((0.55,1.6), 'Desco'),\n",
        "    ((0.55,2.0), 'Desco'),\n",
        "]\n",
        "\n",
        "# Separar triadas y etiquetas\n",
        "X = [entry[0] for entry in data]\n",
        "y = [entry[1] for entry in data]\n",
        "\n",
        "# Dividir el conjunto de datos de manera 'aleatoria' (se usó la misma semilla en R)\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=127)\n",
        "\n",
        "# Crear y entrenar el modelo\n",
        "model = SVC()\n",
        "model.fit(X_train, y_train)\n",
        "\n",
        "# Evaluar el modelo\n",
        "y_pred = model.predict(X_test)\n",
        "accuracy = accuracy_score(y_test, y_pred)\n",
        "print(f'Precisión del modelo: {accuracy}')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vu7GFX-oee2K",
        "outputId": "6e36197f-ae60-418f-ef54-2d2433864b86"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Precisión del modelo: 0.7333333333333333\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Crear una función para aplicar el modelo a cada triada\n",
        "def clasificar_triada(triada):\n",
        "    etiqueta = model.predict([triada])[0]\n",
        "    return etiqueta\n",
        "\n",
        "# Aplicar la función a la columna 'normal' y crear una nueva columna 'etiqueta'\n",
        "df_py['etiqueta'] = df_py['normal'].apply(clasificar_triada)"
      ],
      "metadata": {
        "id": "DRwiiCna_tRR"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# __Comparación de Clasificaciones__"
      ],
      "metadata": {
        "id": "kc15sLTygYDS"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Todo el proceso anterior se hizo tambien pero con R\n",
        "df_r = pd.read_csv('https://raw.githubusercontent.com/rafa-cc/Proyecto-Final-R/main/data__emp_class_R.csv')"
      ],
      "metadata": {
        "id": "RAasCyZR882d"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Se calcula la distancia al origen pues, si se considera el origen como una esquina de la caja, el punto será la esquina opuesta y esto es directamente proporcional al volumen de la caja (_entre más se aleja el punto de la caja, más grande es ésta_)"
      ],
      "metadata": {
        "id": "q80vphaiWVcu"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Crear la columna 'distance' las cuales son la distancia al origen de cada punto\n",
        "df_py['distance'] = round(np.sqrt(df_py['Length']**2 + df_py['Width']**2 + df_py['Height']**2),2)\n",
        "\n",
        "# Filtrar los puntos con ditancia menor o igual a 21 pues ahi se encuentra el 95% de los datos\n",
        "df_temp = df_py.query('distance <= 21')"
      ],
      "metadata": {
        "id": "ov0EXiydJw_K"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Crear la columna 'distance' las cuales son la distancia al origen de cada punto\n",
        "df_r['distance'] = round(np.sqrt(df_r['Length']**2 + df_r['Width']**2 + df_r['Height']**2),2)\n",
        "\n",
        "# Filtrar los puntos con ditancia menor o igual a 21 pues ahi se encuentra el 95% de los datos\n",
        "df_temp_2 = df_r.query('distance <= 21')"
      ],
      "metadata": {
        "id": "VFIaMkzNgjoe"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Comprobación de que el cuantil 95 corresponde a la distancia 21"
      ],
      "metadata": {
        "id": "h9ZsDm-IhHmA"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "print(round(df_py['distance'].quantile(0.95),1))\n",
        "\n",
        "fig = go.Figure()\n",
        "\n",
        "fig.add_trace(go.Histogram(x=df_py['distance'],marker_color='#7D58EB'))\n",
        "\n",
        "# Líneas verticales para la media\n",
        "fig.add_shape(\n",
        "    go.layout.Shape(\n",
        "        type=\"line\",\n",
        "        x0=df_py['distance'].mean(),\n",
        "        x1=df_py['distance'].mean(),\n",
        "        y0=0,\n",
        "        y1=178,\n",
        "        line=dict(color=\"#E71224\", width=2.5),\n",
        "    )\n",
        ")\n",
        "\n",
        "# Línea punteada que representa el cuantil 95\n",
        "fig.add_shape(\n",
        "    go.layout.Shape(\n",
        "        type=\"line\",\n",
        "        x0=df_py['distance'].quantile(0.95),\n",
        "        x1=df_py['distance'].quantile(0.95),\n",
        "        y0=0,\n",
        "        y1=178,\n",
        "        line=dict(color=\"#FFC114\", width=2.5, dash=\"dash\"),\n",
        "    )\n",
        ")\n",
        "\n",
        "fig.update_layout(\n",
        "    title='Histograma de Distribución <br>de la Distancia al Origen',\n",
        "    xaxis=dict(title='Cantidad de datos'),\n",
        "    yaxis=dict(title='Frecuencia'),\n",
        "    width= 21 * 37.7952756, height= 13 * 37.7952756,\n",
        "    margin={'l': 1 * 37.7952756, 'r': 1.5 * 37.7952756, 't': 2.5 * 37.7952756, 'b': 1 * 37.7952756}\n",
        ")\n",
        "fig.update_layout(template=my_custom_theme)\n",
        "\n",
        "fig.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 526
        },
        "id": "1gjgmiFCFZZR",
        "outputId": "287bb619-9005-440a-e636-902095a78bff"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "20.5\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<html>\n",
              "<head><meta charset=\"utf-8\" /></head>\n",
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          "metadata": {}
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      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Ahora se puede ver cómo los datos fueron categorizados y cada etiqueta forma una especie de línea.\n",
        "\n",
        "> 💡 Notar como el conjunto de datos parece una pirámide."
      ],
      "metadata": {
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    {
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      "source": [
        "fig = go.Figure()\n",
        "\n",
        "for cluster in df_temp['etiqueta'].unique():\n",
        "    df_cluster = df_temp[df_temp['etiqueta'] == cluster]\n",
        "    fig.add_trace(go.Scatter3d(\n",
        "        x=df_cluster['Length'],\n",
        "        y=df_cluster['Width'],\n",
        "        z=df_cluster['Height'],\n",
        "        mode='markers',\n",
        "        marker=dict(\n",
        "            size=1,\n",
        "            opacity=0.9\n",
        "        ),\n",
        "        name=f'Cluster {cluster}',\n",
        "        hovertemplate='Length: %{x}<br>Width: %{y}<br>Height: %{z}'\n",
        "    ))\n",
        "\n",
        "fig.update_layout(title='Datos Categorizados con Python',\n",
        "    scene=dict(\n",
        "      xaxis_title='Length',\n",
        "      yaxis_title='Width',\n",
        "      zaxis_title='Height',\n",
        "      xaxis=dict(backgroundcolor=gris_100, color=gris_500,title_font=dict(family='Raleway', size=12),tickfont=dict(family='Raleway', size=12)),\n",
        "      yaxis=dict(backgroundcolor=gris_100, color=gris_500,title_font=dict(family='Raleway', size=12),tickfont=dict(family='Raleway', size=12)),\n",
        "      zaxis=dict(backgroundcolor=gris_100, color=gris_500,title_font=dict(family='Raleway', size=12),tickfont=dict(family='Raleway', size=12)),\n",
        "))\n",
        "fig.update_layout(template=my_custom_theme)\n",
        "\n",
        "fig.show()"
      ],
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          "height": 508
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        "outputId": "35086ad4-77fd-49d3-e46e-23009c00cfb2"
      },
      "execution_count": null,
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Categorizados con Python\"}},                        {\"responsive\": true}                    ).then(function(){\n",
              "                            \n",
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              "            console.log([gd, 'removed!']);\n",
              "            Plotly.purge(gd);\n",
              "            observer.disconnect();\n",
              "        }}\n",
              "}});\n",
              "\n",
              "// Listen for the removal of the full notebook cells\n",
              "var notebookContainer = gd.closest('#notebook-container');\n",
              "if (notebookContainer) {{\n",
              "    x.observe(notebookContainer, {childList: true});\n",
              "}}\n",
              "\n",
              "// Listen for the clearing of the current output cell\n",
              "var outputEl = gd.closest('.output');\n",
              "if (outputEl) {{\n",
              "    x.observe(outputEl, {childList: true});\n",
              "}}\n",
              "\n",
              "                        })                };                            </script>        </div>\n",
              "</body>\n",
              "</html>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Guardar la figura como archivo HTML interactivo\n",
        "fig.write_html('ctg_py.html')"
      ],
      "metadata": {
        "id": "2qJiulKSONMY"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Se recategorizan los datos que calculó mal el modelo (_categorias **Pizza** y **cubo**_)"
      ],
      "metadata": {
        "id": "aAJgokcekli0"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Los datos que estén a la izquierda el plano y = 5x - 6 y sean categoria 'Pizza' se reasignan los valores\n",
        "indices = df_temp.loc[(df_temp['etiqueta'] == 'Pizza') & (((df_temp['Width'] + 6)/df_temp['Length']) < 5)].index\n",
        "df_temp.loc[indices, 'etiqueta'] = 'Desco'\n",
        "\n",
        "# Asignar los empaques pequeños directamente en la categoria 'Cubo'\n",
        "indices = df_temp.loc[(df_temp['distance'] <= 4)].index\n",
        "df_temp.loc[indices, 'etiqueta'] = 'Cubo'"
      ],
      "metadata": {
        "id": "Knrcj5VKVmbv"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Los datos que estén a la izquierda el plano y = 1.37x y sean categoria 'Desco' se reasignan los valores\n",
        "indices = df_temp.loc[(df_temp['etiqueta'] == 'Desco') & ((df_temp['Width']/df_temp['Length']) < 1.37)].index\n",
        "df_temp.loc[indices, 'etiqueta'] = 'Cubo'"
      ],
      "metadata": {
        "id": "gEcsKys2ZFQd"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "fig = go.Figure()\n",
        "\n",
        "for cluster in df_temp['etiqueta'].unique():\n",
        "    df_cluster = df_temp[df_temp['etiqueta'] == cluster]\n",
        "    fig.add_trace(go.Scatter3d(\n",
        "        x=df_cluster['Length'],\n",
        "        y=df_cluster['Width'],\n",
        "        z=df_cluster['Height'],\n",
        "        mode='markers',\n",
        "        marker=dict(\n",
        "            size=1,\n",
        "            opacity=0.9\n",
        "        ),\n",
        "        name=f'Cluster {cluster}',\n",
        "        hovertemplate='Length: %{x}<br>Width: %{y}<br>Height: %{z}'\n",
        "    ))\n",
        "\n",
        "fig.update_layout(title='Datos Recategorizados con Python',\n",
        "    scene=dict(\n",
        "      xaxis_title='Length',\n",
        "      yaxis_title='Width',\n",
        "      zaxis_title='Height',\n",
        "      xaxis=dict(backgroundcolor=gris_100, color=gris_500,title_font=dict(family='Raleway', size=12),tickfont=dict(family='Raleway', size=12)),\n",
        "      yaxis=dict(backgroundcolor=gris_100, color=gris_500,title_font=dict(family='Raleway', size=12),tickfont=dict(family='Raleway', size=12)),\n",
        "      zaxis=dict(backgroundcolor=gris_100, color=gris_500,title_font=dict(family='Raleway', size=12),tickfont=dict(family='Raleway', size=12)),\n",
        "))\n",
        "fig.update_layout(template=my_custom_theme)\n",
        "\n",
        "fig.show()"
      ],
      "metadata": {
        "id": "Tj9j60bqkhAh",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 508
        },
        "outputId": "0b47655a-a95b-4c9f-dbd6-75cb46acdb7d"
      },
      "execution_count": null,
      "outputs": [
        {
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    {
      "cell_type": "code",
      "source": [
        "# Guardar la figura como archivo HTML interactivo\n",
        "fig.write_html('rectg_py.html')"
      ],
      "metadata": {
        "id": "ZxvqofonPAsK"
      },
      "execution_count": null,
      "outputs": []
    },
    {
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      "source": [
        "df_temp.head()"
      ],
      "metadata": {
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          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "outputId": "7f767971-8f36-4983-a515-0b053e6a0acb"
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      "execution_count": null,
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              "   Length  Width  Height        normal etiqueta  distance\n",
              "0     0.1    5.1     6.2  [0.02, 1.22]    Pizza      8.03\n",
              "1     0.2    4.4     6.8  [0.05, 1.55]    Tabla      8.10\n",
              "2     0.3    0.3     0.7   [1.0, 2.33]     Cubo      0.82\n",
              "3     0.3    0.3     0.7   [1.0, 2.33]     Cubo      0.82\n",
              "4     0.3    2.9     4.3   [0.1, 1.48]    Tabla      5.20"
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              "\n",
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            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Eliminar la columna 'normal' porque si no el csv no se genera\n",
        "df_temp = df_temp.drop('normal', axis=1)\n",
        "# Guardar el dataframe para regresar al análisis con R\n",
        "df_temp.to_csv('data_emp_class.csv',index=False)"
      ],
      "metadata": {
        "id": "GC2RpwBoOjKf"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "fig = go.Figure()\n",
        "\n",
        "for cluster in df_temp_2['etiqueta'].unique():\n",
        "    df_cluster = df_temp_2[df_temp_2['etiqueta'] == cluster]\n",
        "    fig.add_trace(go.Scatter3d(\n",
        "        x=df_cluster['Length'],\n",
        "        y=df_cluster['Width'],\n",
        "        z=df_cluster['Height'],\n",
        "        mode='markers',\n",
        "        marker=dict(\n",
        "            size=1,\n",
        "            opacity=0.9\n",
        "        ),\n",
        "        name=f'Cluster {cluster}',\n",
        "        hovertemplate='Length: %{x}<br>Width: %{y}<br>Height: %{z}'\n",
        "    ))\n",
        "\n",
        "fig.update_layout(title='Datos Categorizados con R',\n",
        "    scene=dict(\n",
        "      xaxis_title='Length',\n",
        "      yaxis_title='Width',\n",
        "      zaxis_title='Height',\n",
        "      xaxis=dict(backgroundcolor=gris_100, color=gris_500,title_font=dict(family='Raleway', size=12),tickfont=dict(family='Raleway', size=12)),\n",
        "      yaxis=dict(backgroundcolor=gris_100, color=gris_500,title_font=dict(family='Raleway', size=12),tickfont=dict(family='Raleway', size=12)),\n",
        "      zaxis=dict(backgroundcolor=gris_100, color=gris_500,title_font=dict(family='Raleway', size=12),tickfont=dict(family='Raleway', size=12)),\n",
        "))\n",
        "fig.update_layout(template=my_custom_theme)\n",
        "\n",
        "fig.show()"
      ],
      "metadata": {
        "id": "snnSJFIshir7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 508
        },
        "outputId": "e95d059f-cbe9-40eb-8338-81ade16865b9"
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      "execution_count": null,
      "outputs": [
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              "                            \n",
              "var gd = document.getElementById('648623ce-ea64-465c-9f50-6d5ccc77448d');\n",
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      "cell_type": "code",
      "source": [
        "# Guardar la figura como archivo HTML interactivo\n",
        "fig.write_html('ctg_r.html')"
      ],
      "metadata": {
        "id": "dd2CSMHdPDLV"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Se recategorizan los datos que calculó mal el modelo (_categoria **Tabla**_)"
      ],
      "metadata": {
        "id": "5G17ccOSi3Pa"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df_temp_2.head()"
      ],
      "metadata": {
        "id": "x0n65ArBm9Um",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "outputId": "b4e59071-6eca-4645-8db9-cb61c01bed80"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Length  Width  Height etiqueta  distance\n",
              "0     0.1    5.1     6.2    Pizza      8.03\n",
              "1     0.2    4.4     6.8    Tabla      8.10\n",
              "2     0.3    0.3     0.7   Estaca      0.82\n",
              "3     0.3    0.3     0.7   Estaca      0.82\n",
              "4     0.3    2.9     4.3    Tabla      5.20"
            ],
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              "      <th>0</th>\n",
              "      <td>0.1</td>\n",
              "      <td>5.1</td>\n",
              "      <td>6.2</td>\n",
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              "      <td>Estaca</td>\n",
              "      <td>0.82</td>\n",
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              "    <tr>\n",
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              "  @keyframes spin {\n",
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              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
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              "        const charts = await google.colab.kernel.invokeFunction(\n",
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              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
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              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Los datps que estén arriba del plano z = 4.5y - 1 y sean categoria 'Tabla' se reasignan los valores\n",
        "indices = df_temp_2.loc[(df_temp_2['etiqueta'] == 'Tabla') & (((df_temp_2['Height'] + 1)/df_temp_2['Width']) > 4)].index\n",
        "df_temp_2.loc[indices, 'etiqueta'] = 'Estaca'\n",
        "\n",
        "# Asignar los empaques pequeños directamente en la categoria 'Cubo'\n",
        "indices = df_temp_2.loc[(df_temp_2['distance'] <= 4)].index\n",
        "df_temp_2.loc[indices, 'etiqueta'] = 'Cubo'"
      ],
      "metadata": {
        "id": "uSLldVvWjXwd"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "fig = go.Figure()\n",
        "\n",
        "for cluster in df_temp_2['etiqueta'].unique():\n",
        "    df_cluster = df_temp_2[df_temp_2['etiqueta'] == cluster]\n",
        "    fig.add_trace(go.Scatter3d(\n",
        "        x=df_cluster['Length'],\n",
        "        y=df_cluster['Width'],\n",
        "        z=df_cluster['Height'],\n",
        "        mode='markers',\n",
        "        marker=dict(\n",
        "            size=1,\n",
        "            opacity=0.9\n",
        "        ),\n",
        "        name=f'Cluster {cluster}',\n",
        "        hovertemplate='Length: %{x}<br>Width: %{y}<br>Height: %{z}'\n",
        "    ))\n",
        "\n",
        "fig.update_layout(title='Datos Recategorizados con R',\n",
        "    scene=dict(\n",
        "      xaxis_title='Length',\n",
        "      yaxis_title='Width',\n",
        "      zaxis_title='Height',\n",
        "      xaxis=dict(backgroundcolor=gris_100, color=gris_500,title_font=dict(family='Raleway', size=12),tickfont=dict(family='Raleway', size=12)),\n",
        "      yaxis=dict(backgroundcolor=gris_100, color=gris_500,title_font=dict(family='Raleway', size=12),tickfont=dict(family='Raleway', size=12)),\n",
        "      zaxis=dict(backgroundcolor=gris_100, color=gris_500,title_font=dict(family='Raleway', size=12),tickfont=dict(family='Raleway', size=12)),\n",
        "))\n",
        "fig.update_layout(template=my_custom_theme)\n",
        "\n",
        "fig.show()"
      ],
      "metadata": {
        "id": "lH2ledRrkT_D",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 508
        },
        "outputId": "7da341b4-8086-4c83-be5c-81c0fda37d66"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Guardar la figura como archivo HTML interactivo\n",
        "fig.write_html('rctg_r.html')"
      ],
      "metadata": {
        "id": "Y257wYgvPGWj"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Guardar el dataframe para regresar al análisis con R\n",
        "df_temp_2.to_csv('data_emp_reclass_R.csv',index=False)"
      ],
      "metadata": {
        "id": "Ha8YPAQhqkP5"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# __Medidas de las Cajas__"
      ],
      "metadata": {
        "id": "SgFUyV_Tcsar"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df_temp_2 = df_temp_2[df_temp_2['etiqueta']=='Leche']"
      ],
      "metadata": {
        "id": "KKT3FmN5Klpo"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.cluster import KMeans\n",
        "# Definir el número de clusters\n",
        "k = 5\n",
        "\n",
        "# Crear el modelo KMeans\n",
        "kmeans = KMeans(n_clusters=k)\n",
        "\n",
        "# Ajustar el modelo a los datos\n",
        "kmeans.fit(df_temp_2[['Length', 'Width', 'Height']])\n",
        "\n",
        "# Agregar las etiquetas de los clusters al DataFrame\n",
        "df_temp_2['cluster'] = kmeans.labels_"
      ],
      "metadata": {
        "id": "uPno37y0Ka4B",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "ce53b687-80b0-4ad9-c836-a308b482d118"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning:\n",
            "\n",
            "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",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "fig = go.Figure()\n",
        "\n",
        "for cluster in df_temp_2['cluster'].unique():\n",
        "    df_cluster = df_temp_2[df_temp_2['cluster'] == cluster]\n",
        "    fig.add_trace(go.Scatter3d(\n",
        "        x=df_cluster['Length'],\n",
        "        y=df_cluster['Width'],\n",
        "        z=df_cluster['Height'],\n",
        "        mode='markers',\n",
        "        marker=dict(\n",
        "            size=1,\n",
        "            opacity=0.9\n",
        "        ),\n",
        "        name=f'Distancia {cluster}',\n",
        "        hovertemplate='Length: %{x}<br>Width: %{y}<br>Height: %{z}'\n",
        "    ))\n",
        "\n",
        "fig.update_layout(title='Clusters de la Categoría -Leche-',\n",
        "    scene=dict(\n",
        "      xaxis_title='Length',\n",
        "      yaxis_title='Width',\n",
        "      zaxis_title='Height',\n",
        "      xaxis=dict(backgroundcolor=gris_100, color=gris_500,title_font=dict(family='Raleway', size=12),tickfont=dict(family='Raleway', size=12)),\n",
        "      yaxis=dict(backgroundcolor=gris_100, color=gris_500,title_font=dict(family='Raleway', size=12),tickfont=dict(family='Raleway', size=12)),\n",
        "      zaxis=dict(backgroundcolor=gris_100, color=gris_500,title_font=dict(family='Raleway', size=12),tickfont=dict(family='Raleway', size=12)),\n",
        "))\n",
        "fig.update_layout(template=my_custom_theme)\n",
        "\n",
        "fig.show()"
      ],
      "metadata": {
        "id": "bKfHbxBYLbwA",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 508
        },
        "outputId": "d1995440-0be5-457d-fb05-19b24f37dbd3"
      },
      "execution_count": null,
      "outputs": [
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