XY_dataset(DBScan_with_scratch).ipynb

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      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import random\n",
        "import seaborn as sns"
      ]
    },
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      "source": [
        "df = pd.read_excel(\"/content/XY_dataset.xlsx\")\n",
        "df.head()"
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              "    X  Y\n",
              "0  13  7\n",
              "1   1  0\n",
              "2   1  1\n",
              "3  12  8\n",
              "4   3  1"
            ],
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              "      <td>8</td>\n",
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        "EPSI = 2\n",
        "MIN_POINTS = 4"
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        "fig, ax = plt.subplots(figsize=(16, 8))\n",
        "sns.scatterplot(df['X'], df['Y'], ax=ax)\n",
        "plt.show()"
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          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.8/dist-packages/seaborn/_decorators.py:36: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
            "  warnings.warn(\n"
          ]
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          "data": {
            "text/plain": [
              "<Figure size 1152x576 with 1 Axes>"
            ],
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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "class Point(object):\n",
        "    def __init__(self, coordinate):\n",
        "        self.coordinate = coordinate\n",
        "        self.cluster_idx = None\n",
        "        \n",
        "    def is_clustered(self):\n",
        "        return self.cluster_idx is not None\n",
        "    \n",
        "    def cluster(self, cluster_idx):\n",
        "        self.cluster_idx = cluster_idx\n",
        "\n",
        "\n",
        "class DBSCAN(object):\n",
        "    def __init__(self, epsi, min_points):\n",
        "        self.epsi = epsi\n",
        "        self.min_points = min_points\n",
        "    \n",
        "    @staticmethod\n",
        "    def _compute_distance(x1, x2):\n",
        "        return np.sum((x1 - x2)**2)**0.5\n",
        "\n",
        "    def _find_neighbor_indices(self, core_point, other_points):\n",
        "        neighbor_indices = []\n",
        "        \n",
        "        for idx in range(len(other_points)):\n",
        "            if self._compute_distance(core_point.coordinate, other_points[idx].coordinate) <= self.epsi:\n",
        "                neighbor_indices.append(idx)\n",
        "                \n",
        "        return neighbor_indices\n",
        "\n",
        "    def fit(self, df, feature_cols=['X', 'Y']):\n",
        "        points = []\n",
        "        for idx, row in df.loc[:, feature_cols].iterrows():\n",
        "            points.append(Point(row.to_numpy()))\n",
        "                    \n",
        "        free_point_indices = set(range(len(points)))\n",
        "\n",
        "        core_point_indices = set()\n",
        "        cluster_count = 1\n",
        "\n",
        "        while len(free_point_indices) > 0:\n",
        "            if len(core_point_indices) == 0:\n",
        "                start_idx = random.choice(list(free_point_indices))\n",
        "                core_point_indices.add(start_idx)\n",
        "                free_point_indices.remove(start_idx)\n",
        "                points[start_idx].cluster(cluster_count)\n",
        "\n",
        "            while len(core_point_indices) > 0:\n",
        "                picked_core_point_idx = core_point_indices.pop()\n",
        "                \n",
        "                neighbor_indices = self._find_neighbor_indices(points[picked_core_point_idx], points)\n",
        "                \n",
        "                if len(neighbor_indices) >= (self.min_points - 1):\n",
        "                    for neighbor_idx in neighbor_indices:\n",
        "                        if neighbor_idx in free_point_indices:\n",
        "                            points[neighbor_idx].cluster(cluster_count)\n",
        "                            if neighbor_idx in free_point_indices:\n",
        "                                core_point_indices.add(neighbor_idx)\n",
        "                                free_point_indices.remove(neighbor_idx)\n",
        "                    \n",
        "            cluster_count += 1\n",
        "                    \n",
        "        return points"
      ],
      "metadata": {
        "id": "F4T8Pr4PwlPH"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model = DBSCAN(epsi=EPSI, min_points=MIN_POINTS)\n",
        "points = model.fit(df)"
      ],
      "metadata": {
        "id": "d_ptlK_HxEvC"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "x = []\n",
        "y = []\n",
        "cluster = []\n",
        "\n",
        "for point in points:\n",
        "    _x, _y = point.coordinate\n",
        "    x.append(_x)\n",
        "    y.append(_y)\n",
        "    cluster.append(point.cluster_idx)"
      ],
      "metadata": {
        "id": "DwlqNg8w7ZYm"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "new_df = pd.DataFrame({\n",
        "    'X Axis': x,\n",
        "    'Y Axis': y,\n",
        "    'Cluster': cluster\n",
        "})\n",
        "new_df"
      ],
      "metadata": {
        "id": "S--gepKk7zrg",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 676
        },
        "outputId": "bd9205df-707a-4d37-ab7a-530bc98a2da9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    X Axis  Y Axis  Cluster\n",
              "0       13       7        3\n",
              "1        1       0        2\n",
              "2        1       1        2\n",
              "3       12       8        3\n",
              "4        3       1        2\n",
              "5       12       9        3\n",
              "6        0       1        2\n",
              "7        3       2        2\n",
              "8       11       7        3\n",
              "9        6       4        1\n",
              "10       7       5        1\n",
              "11       6       2        1\n",
              "12       8       3        1\n",
              "13       7       6        1\n",
              "14       7       4        1\n",
              "15       6       3        1\n",
              "16       2       2        2\n",
              "17      11       9        3\n",
              "18       3       0        2\n",
              "19       0       0        2"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-e32fe80d-f71b-4102-90a1-dff8c3ea6421\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>X Axis</th>\n",
              "      <th>Y Axis</th>\n",
              "      <th>Cluster</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>13</td>\n",
              "      <td>7</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>12</td>\n",
              "      <td>8</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>12</td>\n",
              "      <td>9</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>11</td>\n",
              "      <td>7</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>6</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>7</td>\n",
              "      <td>5</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>6</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>8</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>7</td>\n",
              "      <td>6</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>7</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>6</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>11</td>\n",
              "      <td>9</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-e32fe80d-f71b-4102-90a1-dff8c3ea6421')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\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",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      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",
              "    [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-e32fe80d-f71b-4102-90a1-dff8c3ea6421 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-e32fe80d-f71b-4102-90a1-dff8c3ea6421');\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",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 329
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "fig, ax = plt.subplots(figsize=(16, 8))\n",
        "sns.scatterplot(data=new_df, x='X Axis', y='Y Axis', hue='Cluster', palette='brg_r', ax=ax)\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "09YEKqxp74TW",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 497
        },
        "outputId": "c3b730e0-056f-415d-dc67-a61302e5f0bd"
      },
      "execution_count": 332,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1152x576 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "vv1hwj1Pzfd3"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}