dbscan.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "# Adaptado por Bruno Menezes (https://github.com/brunoleomenezes) e será utilizado apenas para fins acadêmicos."
      ],
      "metadata": {
        "id": "dUWFqPBBXDq8"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "S_Djb_RSOP6C",
        "outputId": "36479c4a-9837-4395-967c-d084416ed7b1"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Requirement already satisfied: matplotlib in /usr/local/lib/python3.7/dist-packages (3.2.2)\n",
            "Requirement already satisfied: numpy>=1.11 in /usr/local/lib/python3.7/dist-packages (from matplotlib) (1.21.6)\n",
            "Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib) (3.0.9)\n",
            "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib) (1.4.4)\n",
            "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.7/dist-packages (from matplotlib) (0.11.0)\n",
            "Requirement already satisfied: python-dateutil>=2.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib) (2.8.2)\n",
            "Requirement already satisfied: typing-extensions in /usr/local/lib/python3.7/dist-packages (from kiwisolver>=1.0.1->matplotlib) (4.1.1)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.7/dist-packages (from python-dateutil>=2.1->matplotlib) (1.15.0)\n"
          ]
        }
      ],
      "source": [
        "!pip install matplotlib\n",
        "\n",
        "\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "from sklearn import preprocessing\n",
        "import plotly.graph_objects as go\n",
        "from sklearn.cluster import DBSCAN"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dados_cru = \"/content/Wine.csv\"\n",
        "dados = pd.read_csv(dados_cru)"
      ],
      "metadata": {
        "id": "Jv1xfy3sOely"
      },
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "labels_df = dados['Customer_Segment']\n",
        "dados = dados.drop(columns = 'Customer_Segment')"
      ],
      "metadata": {
        "id": "TX4_Cj65Rz1H"
      },
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "dados = dados.rename(columns = {'Alcohol' : 'Alcool', 'Ash' : 'Po', 'Ash_Alcalinity': 'Alcalinidade_po',\n",
        "                          'Magnesium' : 'Magnesio','Total_Phenols' : 'Total_Fenois',\n",
        "                          'Nonflavanoid_Phenols':'Fenois_nao_flavalonoides','Color_Intensity':'Intensidade_de_cor'})\n",
        "\n",
        "\n",
        "colunas = dados.columns"
      ],
      "metadata": {
        "id": "zla2w081SrEw"
      },
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "id": "-pbxkNsSOP6D"
      },
      "outputs": [],
      "source": [
        "dbscan = DBSCAN(eps = 1.31, min_samples = 15, metric = 'manhattan')"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "min_and_max_scaler = preprocessing.MinMaxScaler()\n",
        "transf_dados = min_and_max_scaler.fit(dados)"
      ],
      "metadata": {
        "id": "ol252KgEQiYT"
      },
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "type(dados)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SLzpg56VUoL4",
        "outputId": "9f9e714e-01b5-4d45-befb-fd07539d9e44"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "pandas.core.frame.DataFrame"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "transf_dados"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gcXJvSXRUlnE",
        "outputId": "7c2e41d9-7f42-40df-df66-a056094244fe"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "MinMaxScaler()"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tyoSSFMrOP6E",
        "outputId": "ceb559de-b942-4fd0-90a7-ed8a20813b1b"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DBSCAN(eps=1.31, metric='manhattan', min_samples=15)"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ],
      "source": [
        "dbscan.fit(dados)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FPUKqmkkOP6E",
        "outputId": "78ad125a-d539-457c-ac12-32577f7e7e85"
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          },
          "metadata": {},
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      ],
      "source": [
        "dbscan.labels_"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
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        "id": "J5VShHPAOP6F",
        "outputId": "6e8debfa-960d-4bbf-c647-9158e4ea8a51"
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          },
          "metadata": {},
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      ],
      "source": [
        "dbscan = DBSCAN(eps = 0.01, min_samples = 15, metric = 'manhattan')\n",
        "dbscan.fit(dados)\n",
        "dbscan.labels_"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
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        "id": "fhNdGl4wOP6G",
        "outputId": "fa8373f2-9bee-40eb-bd01-d0fecb42b082"
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          "output_type": "execute_result",
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          },
          "metadata": {},
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        }
      ],
      "source": [
        "dbscan = DBSCAN(eps = 10, min_samples = 15, metric = 'manhattan')\n",
        "dbscan.fit(dados)\n",
        "dbscan.labels_"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
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        "id": "HHLQct_oOP6G",
        "outputId": "4c04f61e-70ea-4c92-d837-a7ce659f6d9c"
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          },
          "metadata": {},
          "execution_count": 13
        }
      ],
      "source": [
        "dbscan = DBSCAN(eps = 1.31, min_samples = 1, metric = 'manhattan')\n",
        "dbscan.fit(dados)\n",
        "dbscan.labels_"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nFqwTJIkOP6J",
        "outputId": "2b1aed16-dd40-46b6-d87c-1e11b778e60d"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[  1   2   3   4   5   6   7   8   9  10  11  12  13  14  15  16  17  18\n",
            "  19  20  21  22  23  24  25  26  27  28  29  30  31  32  33  34  35  36\n",
            "  37  38  39  40  41  42  43  44  45  46  47  48  49  50  51  52  53  54\n",
            "  55  56  57  58  59  60  61  62  63  64  65  66  67  68  69  70  71  72\n",
            "  73  74  75  76  77  78  79  80  81  82  83  84  85  86  87  88  89  90\n",
            "  91  92  93  94  95  96  97  98  99 100 101 102 103 104 105 106 107 108\n",
            " 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126\n",
            " 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144\n",
            " 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162\n",
            " 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178]\n"
          ]
        }
      ],
      "source": [
        "labels = dbscan.labels_+1\n",
        "print(labels)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "id": "ZwU9l3qGOP6J",
        "outputId": "db8453e0-db48-44fc-d6c8-6ad231352753"
      },
      "outputs": [
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      "source": [
        "fig = go.Figure()\n",
        "fig.add_trace(go.Scatter3d(x=dados['Intensidade_de_cor'],\n",
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