307DBSCAN.ipynb

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      "source": [
        "This training material is developed by Dr. Yang Song at UNC Wilmington.\n",
        "\n",
        "This work is supported by NSF #2230046. You can use this material for non-commercial purposes. But please retain proper attribution by keeping this content.\n",
        "\n",
        "### Assume that Dr. Yang Song is a poor phd student, and he cannot say no to his advisor...\n",
        "\n",
        "Dr. Song has a clutering problem to solve, and he sampled 20% of the data for testing purpuse. He has build the model below with `t4.8k_sample.csv`.\n",
        "\n",
        "Full dataset is [here](https://drive.google.com/file/d/1Te8tCSFKzSVSTJmVtZEMr8m0CPdeZewo/view?usp=sharing) and the sampled dataset is [here](https://drive.google.com/file/d/11WQT9PUmmJ_af2CS8UOYFaC1AVGxqFy5/view?usp=sharing).\n",
        "\n",
        "Below is his finished code, and you don't need to make any changes."
      ],
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        "id": "A92m65Pue-rd"
      }
    },
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      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
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        },
        "id": "WNKQOvrCYkbq",
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      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import numpy as np"
      ],
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        "id": "bsgkphjeYxms"
      },
      "execution_count": null,
      "outputs": []
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      "source": [
        "# The datafile in google drive root/Colab Notebooks/data/t4.8k_sample.txt\n",
        "# for the full data, we can read it like below -\n",
        "# df = pd.read_csv(\"data/t4.8k.txt\", sep=\" \", names=['x','y'])\n",
        "df = pd.read_csv('/content/drive/My Drive/Colab Notebooks/data/t4.8k_sample.csv')\n",
        "df.head()"
      ],
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      "outputs": [
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              "            x           y\n",
              "0   61.886002  159.761993\n",
              "1  335.018005  114.391998\n",
              "2  232.563995  260.798004\n",
              "3  491.881989  177.149994\n",
              "4  199.119003  108.886002"
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        "df.shape"
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              "(1499, 2)"
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          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.cluster import DBSCAN\n",
        "# This is a model Dr. Song tuned with the sampled model.\n",
        "db = DBSCAN(eps=15, min_samples=15, metric='euclidean')\n",
        "labels = db.fit_predict(df)"
      ],
      "metadata": {
        "id": "ahQ81M4dY6mt"
      },
      "execution_count": null,
      "outputs": []
    },
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      "cell_type": "code",
      "source": [
        "df[\"cluster_lables\"] = labels\n",
        "df.head(2)"
      ],
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          "data": {
            "text/plain": [
              "            x           y  cluster_lables\n",
              "0   61.886002  159.761993              19\n",
              "1  335.018005  114.391998               0"
            ],
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              "            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",
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          "data": {
            "text/plain": [
              "-1     374\n",
              " 9     106\n",
              " 2      84\n",
              " 5      82\n",
              " 1      71\n",
              " 0      63\n",
              " 13     62\n",
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              " 8      40\n",
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              " 20     17\n",
              " 28     17\n",
              " 14     16\n",
              " 27     12\n",
              " 25     10\n",
              " 23     10\n",
              "Name: cluster_lables, dtype: int64"
            ]
          },
          "metadata": {},
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        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# On HPC, this line should be changed, too!\n",
        "df.to_csv(\"/content/drive/My Drive/Colab Notebooks/data/t4.8k_output.csv\")"
      ],
      "metadata": {
        "id": "e1Jx9Pu-Z0SL"
      },
      "execution_count": null,
      "outputs": []
    }
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}