Mushroom_Classification_kmean.ipynb

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      "name": "Mushroom Classification kmean.ipynb",
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      "source": [
        "#Mushroom Classification Using K-mean Clustering\n",
        "Xingsheng Wei"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "The purpose of this project is for me to learn and practice k-mean clustering, and see how it deal with catogorized data by using one-hot encoding, so the learning algorithm is written without using any machine learning libraries.\n",
        "\n",
        "The practice problem and data is from Kaggle: Mushroom Classification\n",
        "\n",
        "https://www.kaggle.com/uciml/mushroom-classification \n"
      ],
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        "id": "Wy_pskwR9x3p"
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      "source": [
        "%matplotlib notebook\n",
        "import numpy as np\n",
        "import pandas as pd \n",
        "import matplotlib.pyplot as plt\n",
        "from sklearn.metrics.cluster import adjusted_rand_score"
      ],
      "execution_count": 20,
      "outputs": []
    },
    {
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      "metadata": {
        "id": "19XnTrcCtLbm"
      },
      "source": [
        "## Prepare the Data"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "917UF7ba3qGi"
      },
      "source": [
        "Load data."
      ]
    },
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        "outputId": "02e552d7-59f2-4f7e-d6ca-6f6d0acfd8e7"
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      "source": [
        "data = pd.read_csv(\"https://raw.githubusercontent.com/XingshengWei233/Mushroom_Classification_Clustering/main/mushrooms.csv\")\n",
        "data_origin=data.copy()\n",
        "data.head()"
      ],
      "execution_count": 21,
      "outputs": [
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              "      <th></th>\n",
              "      <th>class</th>\n",
              "      <th>cap-shape</th>\n",
              "      <th>cap-surface</th>\n",
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            "text/plain": [
              "  class cap-shape cap-surface  ... spore-print-color population habitat\n",
              "0     p         x           s  ...                 k          s       u\n",
              "1     e         x           s  ...                 n          n       g\n",
              "2     e         b           s  ...                 n          n       m\n",
              "3     p         x           y  ...                 k          s       u\n",
              "4     e         x           s  ...                 n          a       g\n",
              "\n",
              "[5 rows x 23 columns]"
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      "cell_type": "markdown",
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      "source": [
        "Check if any category has no distinct values."
      ]
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      "source": [
        "data.nunique()"
      ],
      "execution_count": 22,
      "outputs": [
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          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "class                        2\n",
              "cap-shape                    6\n",
              "cap-surface                  4\n",
              "cap-color                   10\n",
              "bruises                      2\n",
              "odor                         9\n",
              "gill-attachment              2\n",
              "gill-spacing                 2\n",
              "gill-size                    2\n",
              "gill-color                  12\n",
              "stalk-shape                  2\n",
              "stalk-root                   5\n",
              "stalk-surface-above-ring     4\n",
              "stalk-surface-below-ring     4\n",
              "stalk-color-above-ring       9\n",
              "stalk-color-below-ring       9\n",
              "veil-type                    1\n",
              "veil-color                   4\n",
              "ring-number                  3\n",
              "ring-type                    5\n",
              "spore-print-color            9\n",
              "population                   6\n",
              "habitat                      7\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 22
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      "source": [
        "Drop the category with no distinct value, which is meaningless for clustering. \\\\\n",
        "Drop the 'class' column from the data used for clustering since clustering is an unsupervised learning process."
      ]
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        "\n",
        "dataWithClass = data.drop(['veil-type'],axis=1)\n",
        "data = data.drop(['veil-type','class'],axis=1) #class is to be classify, veil-type has only one unique value which is not useful\n",
        "data.head()"
      ],
      "execution_count": 23,
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              "      <td>w</td>\n",
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              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-fc623d62-e269-41a8-bd5a-e6b37483ec65')\"\n",
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              "  <style>\n",
              "    .colab-df-container {\n",
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              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "          const element = document.querySelector('#df-fc623d62-e269-41a8-bd5a-e6b37483ec65');\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",
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              "            + ' to learn more about interactive tables.';\n",
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              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "  cap-shape cap-surface cap-color  ... spore-print-color population habitat\n",
              "0         x           s         n  ...                 k          s       u\n",
              "1         x           s         y  ...                 n          n       g\n",
              "2         b           s         w  ...                 n          n       m\n",
              "3         x           y         w  ...                 k          s       u\n",
              "4         x           s         g  ...                 n          a       g\n",
              "\n",
              "[5 rows x 21 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "iEY8ymIi4vAI"
      },
      "source": [
        "Perform one-hot encoding in order to use kmean method."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 352
        },
        "id": "zSVcvRSiSWoe",
        "outputId": "b6362264-e850-4782-afec-d8835147053e"
      },
      "source": [
        "data = pd.get_dummies(data)\n",
        "dataWithClass = pd.get_dummies(dataWithClass)\n",
        "dataWithClass.head()"
      ],
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "      <th></th>\n",
              "      <th>class_e</th>\n",
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              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 118 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-84fde430-1171-42d7-938d-f93a5d00a86e')\"\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-84fde430-1171-42d7-938d-f93a5d00a86e 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-84fde430-1171-42d7-938d-f93a5d00a86e');\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",
              "  "
            ],
            "text/plain": [
              "   class_e  class_p  cap-shape_b  ...  habitat_p  habitat_u  habitat_w\n",
              "0        0        1            0  ...          0          1          0\n",
              "1        1        0            0  ...          0          0          0\n",
              "2        1        0            1  ...          0          0          0\n",
              "3        0        1            0  ...          0          1          0\n",
              "4        1        0            0  ...          0          0          0\n",
              "\n",
              "[5 rows x 118 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KGDaKDg8435y"
      },
      "source": [
        "Convert datas into numpy arrays."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gBap8VVxuCFb"
      },
      "source": [
        "dataWithClass = np.array(dataWithClass)\n",
        "data = np.array(data.values.tolist())"
      ],
      "execution_count": 25,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eyM89gAEHBbZ"
      },
      "source": [
        "Data is ready."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "F-9sqQRLtC-p"
      },
      "source": [
        "## Perform Clustering Operation"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Rk8c4NLn6dHs"
      },
      "source": [
        "Put the size of data in parameters."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0uw22sHVLGUl"
      },
      "source": [
        "dataLen = len(data) #number of mushroom datas\n",
        "nCol = np.shape(data)[1] #number of catogory columns"
      ],
      "execution_count": 26,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EmBJYiyL6hIv"
      },
      "source": [
        "Define function that measure the euclidean distance from a data row to a centroid."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "y0CpAdPYBKfN"
      },
      "source": [
        "def distToCentroid(dataRow,centroid):\n",
        "  diff = dataRow-centroid\n",
        "  dist = np.linalg.norm(diff)\n",
        "  return dist"
      ],
      "execution_count": 27,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1AKkQTEc6yIp"
      },
      "source": [
        "Define function that assign a data row to cluster 0 or cluster 1 (Since the mushroom needs to be classify to \"poisonous\" or \"edible\", so there are 2 clusters)."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oBABvXk8Cq9j"
      },
      "source": [
        "def assignIndividual(dataRow,centroid0,centroid1): #assign to cluster 0 or 1\n",
        "  dist_to_0 = distToCentroid(dataRow,centroid0)\n",
        "  dist_to_1 = distToCentroid(dataRow,centroid1)\n",
        "  if dist_to_0<=dist_to_1:\n",
        "    return 0 \n",
        "  if dist_to_0>dist_to_1:\n",
        "    return 1"
      ],
      "execution_count": 28,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "b6EWgUsF7TSi"
      },
      "source": [
        "Define function that assign a data array with multiple rows with a 1D assignment array."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XovqI43SKoIb"
      },
      "source": [
        "def assignData(data,centroid0,centroid1):\n",
        "  assignment = []\n",
        "  for i in range(np.shape(data)[0]):\n",
        "    assignment.append(assignIndividual(data[i],centroid0,centroid1))\n",
        "  return assignment"
      ],
      "execution_count": 29,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4qNuG2Vq7ntL"
      },
      "source": [
        "Perform clustering.\n",
        "The method to evaluate how well the clusters fit the data is to use the adjusted rand index.\n",
        "Since I noticed that the quality of clusters depends highly on the "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PVqnL1d2M_MB"
      },
      "source": [
        "bestARI = 0\n",
        "totARI = []\n",
        "for n in range(10): #perform clustering multiple time to find the best one\n",
        "  #print('n:');print(n)\n",
        "\n",
        "  #randomly generate centroids\n",
        "  r0 = np.random.randint(dataLen)\n",
        "  r1 = np.random.randint(dataLen)\n",
        "  centroid0 = data[r0,:]\n",
        "  centroid1 = data[r1,:]\n",
        "\n",
        "  #assign each row (each mushroom) to cluster 0 or cluster 1\n",
        "  assignment = np.array(assignData(data,centroid0,centroid1))\n",
        "\n",
        "  #re-generate centroids if all mushroom are assigned to the same cluster\n",
        "  while sum(assignment) == 0 or sum(assignment) == dataLen: \n",
        "    for i in range(nCol):\n",
        "      centroid0[i] = np.random.randint(2)\n",
        "      centroid1[i] = np.random.randint(2)\n",
        "    assignment = np.array(assignData(data,centroid0,centroid1))\n",
        "  assignmentPrev = abs(assignment-1) #make initial assignment Prev different\n",
        "\n",
        "  #LEARNING LOOP to get the clusters\n",
        "  while sum(abs(assignment-assignmentPrev))!= 0: #loop until it makes no progress\n",
        "    assignmentPrev = assignment\n",
        "    data0 = []\n",
        "    data1 = []\n",
        "    for i in range(dataLen):\n",
        "      if assignment[i] == 0:\n",
        "        data0.append(data[i]) #put all data assigned to cluster 0 together\n",
        "      if assignment[i] == 1:\n",
        "        data1.append(data[i]) #put all data assigned to cluster 1 together\n",
        "    data0 = np.array(data0) \n",
        "    data1 = np.array(data1)\n",
        "    centroid0 = np.mean(data0,axis=0) #get centroid from data in cluster 0\n",
        "    centroid1 = np.mean(data1,axis=0) #get centroid from data in cluster 1\n",
        "    assignment = np.array(assignData(data,centroid0,centroid1)) #re-assign each data row to cluster 0 or 1\n",
        "    centroidDist = distToCentroid(centroid0,centroid1)\n",
        "\n",
        "  #keep the clusters with the best Adjusted Rand Index\n",
        "  classLabel = dataWithClass[:,0].T\n",
        "  ARI = adjusted_rand_score(assignment, classLabel)\n",
        "  if ARI>bestARI:\n",
        "    bestARI = ARI\n",
        "    bestCentroid0 = centroid0\n",
        "    bestCentroid1 = centroid1\n",
        "    bestAssignment = assignment\n",
        "\n",
        "  totARI.append(ARI)"
      ],
      "execution_count": 30,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Result"
      ],
      "metadata": {
        "id": "Wlca1BaIrZfd"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "print('bestARI:');print(bestARI)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jp3Wv-lordp0",
        "outputId": "641b6bbe-c2f8-4027-98c7-676f07d76326"
      },
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "bestARI:\n",
            "0.615123948541104\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "i6Heh-SC7yru"
      },
      "source": [
        "Make a histogram to show how the clusters' ARI distributed resulting from random initial centroids (from the loop above)."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8lh1y2O6Jsbr",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 279
        },
        "outputId": "5546d849-8ac6-44ee-806c-a584565ae76b"
      },
      "source": [
        "%matplotlib inline\n",
        "totARI = np.transpose(totARI)\n",
        "plt.hist(totARI, density=True, bins=30)\n",
        "plt.ylabel('Percentage Occur')\n",
        "plt.xlabel('ARI');"
      ],
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Cross tabulate the number of mushroom in each of the classes and in each of the clusters."
      ],
      "metadata": {
        "id": "qgIHJQBrmxDb"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "data_origin['cluster'] = bestAssignment\n",
        "ct = pd.crosstab(data_origin['class'],data_origin['cluster'])\n",
        "print(ct)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mgJZJ72-cwI2",
        "outputId": "90cecef3-92e0-491e-840b-f685b460e856"
      },
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "cluster     0     1\n",
            "class              \n",
            "e          60  4148\n",
            "p        3100   816\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "err = (ct[0][1]+ct[1][0])/dataLen\n",
        "print('% of mushrooms in wrong clusters:');print(err)\n",
        "print('% of mushrooms in right clusters:');print(1-err)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EhWMtTTjpTXd",
        "outputId": "39f9eb28-6b4d-46cd-df41-1f5b99e43e12"
      },
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "% of mushrooms in wrong clusters:\n",
            "0.8921713441654358\n",
            "% of mushrooms in right clusters:\n",
            "0.10782865583456425\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Conclusion"
      ],
      "metadata": {
        "id": "XEDH7ub5ruTn"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "The result of each k-mean clustering operation is highly depended on the randomly selected initial centroids. Performing clustering for multiple times can help finding a good set of clusters. The best set of clusters found in this problem groups 89% of data correctly."
      ],
      "metadata": {
        "id": "LcjH-WnrpSeZ"
      }
    }
  ]
}