spark4_clustering.ipynb

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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "international-owner"
      },
      "source": [
        "# Clustering using pyspark"
      ],
      "id": "international-owner"
    },
    {
      "cell_type": "code",
      "source": [
        "!apt-get install openjdk-8-jdk-headless -qq > /dev/null # install Java8\n",
        "!wget -q https://archive.apache.org/dist/spark/spark-3.1.1/spark-3.1.1-bin-hadoop2.7.tgz # download spark 3.1.1\n",
        "!tar xf spark-3.1.1-bin-hadoop2.7.tgz # unzip it\n",
        "!pip install -q findspark # install findspark\n",
        "!pip install pyspark==3.1.1 # install pyspark\n",
        "!ls\n",
        "\n",
        "import os\n",
        "os.environ[\"JAVA_HOME\"] = \"/usr/lib/jvm/java-8-openjdk-amd64\"\n",
        "os.environ[\"SPARK_HOME\"] = \"/content/spark-3.1.1-bin-hadoop2.7\"\n",
        "\n",
        "import findspark\n",
        "findspark.init()\n",
        "\n",
        "import seaborn as sns\n",
        "import pyspark.sql.functions as F\n",
        "from pyspark.ml.linalg import Vectors\n",
        "from pyspark.ml import feature\n",
        "from pyspark.ml import clustering\n",
        "from pyspark.sql import SparkSession\n",
        "spark = SparkSession.builder.master(\"local[*]\").getOrCreate()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ob-7BtiFjNMC",
        "outputId": "239a7a4c-99d4-4b5b-d7b9-6814c4934d9a"
      },
      "id": "ob-7BtiFjNMC",
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: pyspark==3.1.1 in /usr/local/lib/python3.10/dist-packages (3.1.1)\n",
            "Requirement already satisfied: py4j==0.10.9 in /usr/local/lib/python3.10/dist-packages (from pyspark==3.1.1) (0.10.9)\n",
            "clustering_dataset.csv\tspark-3.1.1-bin-hadoop2.7      spark-3.1.1-bin-hadoop2.7.tgz.1\n",
            "sample_data\t\tspark-3.1.1-bin-hadoop2.7.tgz  spark-3.1.1-bin-hadoop2.7.tgz.2\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "instrumental-carolina"
      },
      "source": [
        "df = spark.read.csv(\"/content/clustering_dataset.csv\", header = True, inferSchema = True)"
      ],
      "id": "instrumental-carolina",
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "quarterly-donna",
        "outputId": "95760201-c19b-4ac6-e523-f11230fd9c7a"
      },
      "source": [
        "df.count()"
      ],
      "id": "quarterly-donna",
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "75"
            ]
          },
          "metadata": {},
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "catholic-climate",
        "outputId": "a9690db0-ae13-4656-92a4-5cffb341fdb8"
      },
      "source": [
        "df.printSchema()"
      ],
      "id": "catholic-climate",
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "root\n",
            " |-- col1: integer (nullable = true)\n",
            " |-- col2: integer (nullable = true)\n",
            " |-- col3: integer (nullable = true)\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ecological-corrections",
        "outputId": "06ae5e03-e352-4ef1-c9b0-6518032aab9b"
      },
      "source": [
        "df.take(3)"
      ],
      "id": "ecological-corrections",
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[Row(col1=7, col2=4, col3=1),\n",
              " Row(col1=7, col2=7, col3=9),\n",
              " Row(col1=7, col2=9, col3=6)]"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "following-biology",
        "outputId": "ef14e49a-1af2-4eb5-80ab-377eecdad68d"
      },
      "source": [
        "df.show()"
      ],
      "id": "following-biology",
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "+----+----+----+\n",
            "|col1|col2|col3|\n",
            "+----+----+----+\n",
            "|   7|   4|   1|\n",
            "|   7|   7|   9|\n",
            "|   7|   9|   6|\n",
            "|   1|   6|   5|\n",
            "|   6|   7|   7|\n",
            "|   7|   9|   4|\n",
            "|   7|  10|   6|\n",
            "|   7|   8|   2|\n",
            "|   8|   3|   8|\n",
            "|   4|  10|   5|\n",
            "|   7|   4|   5|\n",
            "|   7|   8|   4|\n",
            "|   2|   5|   1|\n",
            "|   2|   6|   2|\n",
            "|   2|   3|   8|\n",
            "|   3|   9|   1|\n",
            "|   4|   2|   9|\n",
            "|   1|   7|   1|\n",
            "|   6|   2|   3|\n",
            "|   4|   1|   9|\n",
            "+----+----+----+\n",
            "only showing top 20 rows\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "enhanced-viewer",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "98bcb67a-5e90-4181-e0a2-4ae8e3ef753d"
      },
      "source": [
        "vector_assembler = feature.VectorAssembler(inputCols = ['col1', 'col2', 'col3'],\n",
        "                                           outputCol = 'features')\n",
        "df2 = vector_assembler.transform(df)\n",
        "df2.show()"
      ],
      "id": "enhanced-viewer",
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "+----+----+----+--------------+\n",
            "|col1|col2|col3|      features|\n",
            "+----+----+----+--------------+\n",
            "|   7|   4|   1| [7.0,4.0,1.0]|\n",
            "|   7|   7|   9| [7.0,7.0,9.0]|\n",
            "|   7|   9|   6| [7.0,9.0,6.0]|\n",
            "|   1|   6|   5| [1.0,6.0,5.0]|\n",
            "|   6|   7|   7| [6.0,7.0,7.0]|\n",
            "|   7|   9|   4| [7.0,9.0,4.0]|\n",
            "|   7|  10|   6|[7.0,10.0,6.0]|\n",
            "|   7|   8|   2| [7.0,8.0,2.0]|\n",
            "|   8|   3|   8| [8.0,3.0,8.0]|\n",
            "|   4|  10|   5|[4.0,10.0,5.0]|\n",
            "|   7|   4|   5| [7.0,4.0,5.0]|\n",
            "|   7|   8|   4| [7.0,8.0,4.0]|\n",
            "|   2|   5|   1| [2.0,5.0,1.0]|\n",
            "|   2|   6|   2| [2.0,6.0,2.0]|\n",
            "|   2|   3|   8| [2.0,3.0,8.0]|\n",
            "|   3|   9|   1| [3.0,9.0,1.0]|\n",
            "|   4|   2|   9| [4.0,2.0,9.0]|\n",
            "|   1|   7|   1| [1.0,7.0,1.0]|\n",
            "|   6|   2|   3| [6.0,2.0,3.0]|\n",
            "|   4|   1|   9| [4.0,1.0,9.0]|\n",
            "+----+----+----+--------------+\n",
            "only showing top 20 rows\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bwF5kvS4ZXU8"
      },
      "source": [
        "### K-means clustering"
      ],
      "id": "bwF5kvS4ZXU8"
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "roman-senate"
      },
      "source": [
        "kmeans = clustering.KMeans(seed = 10).setK(3)\n",
        "kmeans_model = kmeans.fit(df2)"
      ],
      "id": "roman-senate",
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "generous-identification",
        "outputId": "b767a0b1-e03f-41ad-869b-d97293c4d58f"
      },
      "source": [
        "kmeans_model.clusterCenters()"
      ],
      "id": "generous-identification",
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[array([35.88461538, 31.46153846, 34.42307692]),\n",
              " array([5.12, 5.84, 4.84]),\n",
              " array([80.        , 79.20833333, 78.29166667])]"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 36
        },
        "id": "anticipated-chester",
        "outputId": "5ecd4bf2-a9c8-4aac-b0bd-fd8136a2a930"
      },
      "source": [
        "kmeans_model.getDistanceMeasure()"
      ],
      "id": "anticipated-chester",
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'euclidean'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "current-accordance",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "acf44a3e-f640-4fa3-e6e0-c5b88b1cfd76"
      },
      "source": [
        "df2_cluster = kmeans_model.transform(df2)\n",
        "df2_cluster.show(n = 10)"
      ],
      "id": "current-accordance",
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "+----+----+----+--------------+----------+\n",
            "|col1|col2|col3|      features|prediction|\n",
            "+----+----+----+--------------+----------+\n",
            "|   7|   4|   1| [7.0,4.0,1.0]|         1|\n",
            "|   7|   7|   9| [7.0,7.0,9.0]|         1|\n",
            "|   7|   9|   6| [7.0,9.0,6.0]|         1|\n",
            "|   1|   6|   5| [1.0,6.0,5.0]|         1|\n",
            "|   6|   7|   7| [6.0,7.0,7.0]|         1|\n",
            "|   7|   9|   4| [7.0,9.0,4.0]|         1|\n",
            "|   7|  10|   6|[7.0,10.0,6.0]|         1|\n",
            "|   7|   8|   2| [7.0,8.0,2.0]|         1|\n",
            "|   8|   3|   8| [8.0,3.0,8.0]|         1|\n",
            "|   4|  10|   5|[4.0,10.0,5.0]|         1|\n",
            "+----+----+----+--------------+----------+\n",
            "only showing top 10 rows\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "unlike-weekly",
        "outputId": "4213550e-a0d2-46d6-8a67-f42b5eeadbf0"
      },
      "source": [
        "df2_cluster.drop('features').show(n = 10)"
      ],
      "id": "unlike-weekly",
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "+----+----+----+----------+\n",
            "|col1|col2|col3|prediction|\n",
            "+----+----+----+----------+\n",
            "|   7|   4|   1|         1|\n",
            "|   7|   7|   9|         1|\n",
            "|   7|   9|   6|         1|\n",
            "|   1|   6|   5|         1|\n",
            "|   6|   7|   7|         1|\n",
            "|   7|   9|   4|         1|\n",
            "|   7|  10|   6|         1|\n",
            "|   7|   8|   2|         1|\n",
            "|   8|   3|   8|         1|\n",
            "|   4|  10|   5|         1|\n",
            "+----+----+----+----------+\n",
            "only showing top 10 rows\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import seaborn as sns"
      ],
      "metadata": {
        "id": "AjxGjhqBm4p_"
      },
      "id": "AjxGjhqBm4p_",
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "1mIA1PkMZefM",
        "outputId": "67240354-dc03-4616-cc4d-8d1f87829754"
      },
      "source": [
        "df2p = df2_cluster.drop('features').toPandas()\n",
        "df2p.head()"
      ],
      "id": "1mIA1PkMZefM",
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   col1  col2  col3  prediction\n",
              "0     7     4     1           1\n",
              "1     7     7     9           1\n",
              "2     7     9     6           1\n",
              "3     1     6     5           1\n",
              "4     6     7     7           1"
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              "     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-c320362c-79a4-47e6-892a-91552a85af41 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df2p",
              "summary": "{\n  \"name\": \"df2p\",\n  \"rows\": 75,\n  \"fields\": [\n    {\n      \"column\": \"col1\",\n      \"properties\": {\n        \"dtype\": \"int32\",\n        \"num_unique_values\": 44,\n        \"samples\": [\n          81,\n          85,\n          68\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"col2\",\n      \"properties\": {\n        \"dtype\": \"int32\",\n        \"num_unique_values\": 46,\n        \"samples\": [\n          60,\n          24,\n          44\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"col3\",\n      \"properties\": {\n        \"dtype\": \"int32\",\n        \"num_unique_values\": 46,\n        \"samples\": [\n          100,\n          31,\n          37\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"prediction\",\n      \"properties\": {\n        \"dtype\": \"int32\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          1,\n          0,\n          2\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 466
        },
        "id": "xMRkyze8abVh",
        "outputId": "1f1f316c-4ff6-415e-92d1-d909058ef515"
      },
      "source": [
        "df2p['prediction'] = df2p.prediction.astype('str')\n",
        "sns.scatterplot(data = df2p, x = 'col1', y = 'col2', hue = 'prediction')"
      ],
      "id": "xMRkyze8abVh",
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: xlabel='col1', ylabel='col2'>"
            ]
          },
          "metadata": {},
          "execution_count": 15
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "conscious-characterization"
      },
      "source": [
        "### Bisecting K-means clustering"
      ],
      "id": "conscious-characterization"
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "complex-delta"
      },
      "source": [
        "bkmeans = clustering.BisectingKMeans(seed = 10).setK(3)\n",
        "bkmodel = bkmeans.fit(df2)"
      ],
      "id": "complex-delta",
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "contrary-potential",
        "outputId": "f50769d2-e9ba-428a-c8a1-d6c548bf0e45"
      },
      "source": [
        "bkmodel.clusterCenters()"
      ],
      "id": "contrary-potential",
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[array([5.12, 5.84, 4.84]),\n",
              " array([35.88461538, 31.46153846, 34.42307692]),\n",
              " array([80.        , 79.20833333, 78.29166667])]"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "noticed-atmosphere",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "4f68765e-8513-413a-f660-e2ba9aab55fb"
      },
      "source": [
        "df2_cluster_bisect = bkmodel.transform(df2)\n",
        "df2_cluster_bisect.show(n = 10)"
      ],
      "id": "noticed-atmosphere",
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "+----+----+----+--------------+----------+\n",
            "|col1|col2|col3|      features|prediction|\n",
            "+----+----+----+--------------+----------+\n",
            "|   7|   4|   1| [7.0,4.0,1.0]|         0|\n",
            "|   7|   7|   9| [7.0,7.0,9.0]|         0|\n",
            "|   7|   9|   6| [7.0,9.0,6.0]|         0|\n",
            "|   1|   6|   5| [1.0,6.0,5.0]|         0|\n",
            "|   6|   7|   7| [6.0,7.0,7.0]|         0|\n",
            "|   7|   9|   4| [7.0,9.0,4.0]|         0|\n",
            "|   7|  10|   6|[7.0,10.0,6.0]|         0|\n",
            "|   7|   8|   2| [7.0,8.0,2.0]|         0|\n",
            "|   8|   3|   8| [8.0,3.0,8.0]|         0|\n",
            "|   4|  10|   5|[4.0,10.0,5.0]|         0|\n",
            "+----+----+----+--------------+----------+\n",
            "only showing top 10 rows\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "7LemC1J-a8p6",
        "outputId": "5054c53e-f2e5-429c-8ffd-c37bd064d9db"
      },
      "source": [
        "df2p = df2_cluster_bisect.drop('features').toPandas()\n",
        "df2p.head()"
      ],
      "id": "7LemC1J-a8p6",
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   col1  col2  col3  prediction\n",
              "0     7     4     1           0\n",
              "1     7     7     9           0\n",
              "2     7     9     6           0\n",
              "3     1     6     5           0\n",
              "4     6     7     7           0"
            ],
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              "    [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-c6b45175-4fc3-4a29-a494-4c3f5f886582 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-c6b45175-4fc3-4a29-a494-4c3f5f886582');\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-966c2654-22ac-4f79-96d8-2a6b2e14d2af\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-966c2654-22ac-4f79-96d8-2a6b2e14d2af')\"\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-966c2654-22ac-4f79-96d8-2a6b2e14d2af button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df2p",
              "summary": "{\n  \"name\": \"df2p\",\n  \"rows\": 75,\n  \"fields\": [\n    {\n      \"column\": \"col1\",\n      \"properties\": {\n        \"dtype\": \"int32\",\n        \"num_unique_values\": 44,\n        \"samples\": [\n          81,\n          85,\n          68\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"col2\",\n      \"properties\": {\n        \"dtype\": \"int32\",\n        \"num_unique_values\": 46,\n        \"samples\": [\n          60,\n          24,\n          44\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"col3\",\n      \"properties\": {\n        \"dtype\": \"int32\",\n        \"num_unique_values\": 46,\n        \"samples\": [\n          100,\n          31,\n          37\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"prediction\",\n      \"properties\": {\n        \"dtype\": \"int32\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          0,\n          1,\n          2\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 466
        },
        "id": "9yRA_boma8p9",
        "outputId": "1110b31f-4ca7-40fe-ce96-a713561fb3a2"
      },
      "source": [
        "df2p['prediction'] = df2p.prediction.astype('str')\n",
        "sns.scatterplot(data = df2p, x = 'col1', y = 'col2', hue = 'prediction')"
      ],
      "id": "9yRA_boma8p9",
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: xlabel='col1', ylabel='col2'>"
            ]
          },
          "metadata": {},
          "execution_count": 20
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    }
  ]
}