kmeans.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### sklearn.cluster.KMeans\n",
    "- class sklearn.cluster.KMeans(n_clusters=8, *, init='k-means++', n_init='warn', max_iter=300, tol=0.0001, verbose=0, random_state=None, copy_x=True, algorithm='lloyd')[source]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import scale\n",
    "from sklearn.datasets import load_iris\n",
    "from sklearn.cluster import KMeans\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sepal length (cm)</th>\n",
       "      <th>sepal width (cm)</th>\n",
       "      <th>petal length (cm)</th>\n",
       "      <th>petal width (cm)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   sepal length (cm)  sepal width (cm)  petal length (cm)  petal width (cm)\n",
       "0                5.1               3.5                1.4               0.2\n",
       "1                4.9               3.0                1.4               0.2\n",
       "2                4.7               3.2                1.3               0.2"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris = load_iris()\n",
    "irisDF = pd.DataFrame(data=iris.data, columns = iris.feature_names)\n",
    "irisDF.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KMeans(n_clusters=3, random_state=0)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kmeans = KMeans(n_clusters=3, random_state=0)\n",
    "kmeans.fit(irisDF)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "irisDF['cluster']=kmeans.labels_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "irisDF['target']=iris.target"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "target  cluster\n",
       "0       1          50\n",
       "1       0          48\n",
       "        2           2\n",
       "2       0          14\n",
       "        2          36\n",
       "Name: sepal length (cm), dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris_result = irisDF.groupby(['target','cluster'])['sepal length (cm)'].count()\n",
    "iris_result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sepal length (cm)</th>\n",
       "      <th>sepal width (cm)</th>\n",
       "      <th>petal length (cm)</th>\n",
       "      <th>petal width (cm)</th>\n",
       "      <th>cluster</th>\n",
       "      <th>target</th>\n",
       "      <th>pca_x</th>\n",
       "      <th>pca_y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>-2.684126</td>\n",
       "      <td>0.319397</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>-2.714142</td>\n",
       "      <td>-0.177001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>-2.888991</td>\n",
       "      <td>-0.144949</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.6</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>-2.745343</td>\n",
       "      <td>-0.318299</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>-2.728717</td>\n",
       "      <td>0.326755</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   sepal length (cm)  sepal width (cm)  petal length (cm)  petal width (cm)  \\\n",
       "0                5.1               3.5                1.4               0.2   \n",
       "1                4.9               3.0                1.4               0.2   \n",
       "2                4.7               3.2                1.3               0.2   \n",
       "3                4.6               3.1                1.5               0.2   \n",
       "4                5.0               3.6                1.4               0.2   \n",
       "\n",
       "   cluster  target     pca_x     pca_y  \n",
       "0        1       0 -2.684126  0.319397  \n",
       "1        1       0 -2.714142 -0.177001  \n",
       "2        1       0 -2.888991 -0.144949  \n",
       "3        1       0 -2.745343 -0.318299  \n",
       "4        1       0 -2.728717  0.326755  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.decomposition import PCA\n",
    "\n",
    "pca = PCA(n_components=2)\n",
    "pca_transformed = pca.fit_transform(iris.data)\n",
    "\n",
    "print(pca_transformed)\n",
    "irisDF['pca_x'] = pca_transformed[:,0]\n",
    "irisDF['pca_y'] = pca_transformed[:,1]\n",
    "irisDF.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "marker0_ind = irisDF[irisDF['cluster']==0].index\n",
    "marker1_ind = irisDF[irisDF['cluster']==1].index\n",
    "marker2_ind = irisDF[irisDF['cluster']==2].index\n",
    "\n",
    "plt.scatter(x=irisDF.loc[marker0_ind, 'pca_x'], y=irisDF.loc[marker0_ind, 'pca_y'] , marker='o')\n",
    "plt.scatter(x=irisDF.loc[marker1_ind, 'pca_x'], y=irisDF.loc[marker1_ind, 'pca_y'] , marker='s')\n",
    "plt.scatter(x=irisDF.loc[marker2_ind, 'pca_x'], y=irisDF.loc[marker2_ind, 'pca_y'] , marker='^')\n",
    "\n",
    "plt.xlabel('PCA_1')\n",
    "plt.ylabel('PCA_2')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import silhouette_samples, silhouette_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.5528190123564095\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<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>sepal length (cm)</th>\n",
       "      <th>sepal width (cm)</th>\n",
       "      <th>petal length (cm)</th>\n",
       "      <th>petal width (cm)</th>\n",
       "      <th>cluster</th>\n",
       "      <th>target</th>\n",
       "      <th>pca_x</th>\n",
       "      <th>pca_y</th>\n",
       "      <th>silhouette_coeff</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>-2.684126</td>\n",
       "      <td>0.319397</td>\n",
       "      <td>0.852955</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>-2.714142</td>\n",
       "      <td>-0.177001</td>\n",
       "      <td>0.815495</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>-2.888991</td>\n",
       "      <td>-0.144949</td>\n",
       "      <td>0.829315</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.6</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>-2.745343</td>\n",
       "      <td>-0.318299</td>\n",
       "      <td>0.805014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>-2.728717</td>\n",
       "      <td>0.326755</td>\n",
       "      <td>0.849302</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   sepal length (cm)  sepal width (cm)  petal length (cm)  petal width (cm)  \\\n",
       "0                5.1               3.5                1.4               0.2   \n",
       "1                4.9               3.0                1.4               0.2   \n",
       "2                4.7               3.2                1.3               0.2   \n",
       "3                4.6               3.1                1.5               0.2   \n",
       "4                5.0               3.6                1.4               0.2   \n",
       "\n",
       "   cluster  target     pca_x     pca_y  silhouette_coeff  \n",
       "0        1       0 -2.684126  0.319397          0.852955  \n",
       "1        1       0 -2.714142 -0.177001          0.815495  \n",
       "2        1       0 -2.888991 -0.144949          0.829315  \n",
       "3        1       0 -2.745343 -0.318299          0.805014  \n",
       "4        1       0 -2.728717  0.326755          0.849302  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "score_samples = silhouette_samples(iris.data, irisDF['cluster'])\n",
    "\n",
    "irisDF['silhouette_coeff'] = score_samples\n",
    "\n",
    "average_score = silhouette_score(iris.data, irisDF['cluster'])\n",
    "print(average_score)\n",
    "irisDF.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<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>sepal length (cm)</th>\n",
       "      <th>sepal width (cm)</th>\n",
       "      <th>petal length (cm)</th>\n",
       "      <th>petal width (cm)</th>\n",
       "      <th>cluster</th>\n",
       "      <th>target</th>\n",
       "      <th>pca_x</th>\n",
       "      <th>pca_y</th>\n",
       "      <th>silhouette_coeff</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>145</th>\n",
       "      <td>6.7</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.2</td>\n",
       "      <td>2.3</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1.944110</td>\n",
       "      <td>0.187532</td>\n",
       "      <td>0.425136</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>146</th>\n",
       "      <td>6.3</td>\n",
       "      <td>2.5</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.9</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1.527167</td>\n",
       "      <td>-0.375317</td>\n",
       "      <td>0.104171</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>147</th>\n",
       "      <td>6.5</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.2</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1.764346</td>\n",
       "      <td>0.078859</td>\n",
       "      <td>0.314930</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>148</th>\n",
       "      <td>6.2</td>\n",
       "      <td>3.4</td>\n",
       "      <td>5.4</td>\n",
       "      <td>2.3</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1.900942</td>\n",
       "      <td>0.116628</td>\n",
       "      <td>0.352454</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149</th>\n",
       "      <td>5.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.1</td>\n",
       "      <td>1.8</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1.390189</td>\n",
       "      <td>-0.282661</td>\n",
       "      <td>0.185442</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     sepal length (cm)  sepal width (cm)  petal length (cm)  petal width (cm)  \\\n",
       "145                6.7               3.0                5.2               2.3   \n",
       "146                6.3               2.5                5.0               1.9   \n",
       "147                6.5               3.0                5.2               2.0   \n",
       "148                6.2               3.4                5.4               2.3   \n",
       "149                5.9               3.0                5.1               1.8   \n",
       "\n",
       "     cluster  target     pca_x     pca_y  silhouette_coeff  \n",
       "145        2       2  1.944110  0.187532          0.425136  \n",
       "146        0       2  1.527167 -0.375317          0.104171  \n",
       "147        2       2  1.764346  0.078859          0.314930  \n",
       "148        2       2  1.900942  0.116628          0.352454  \n",
       "149        0       2  1.390189 -0.282661          0.185442  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "irisDF.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "cluster\n",
       "0    0.417320\n",
       "1    0.798140\n",
       "2    0.451105\n",
       "Name: silhouette_coeff, dtype: float64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "irisDF.groupby('cluster')['silhouette_coeff'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "### 여러개의 클러스터링 갯수를 List로 입력 받아 각각의 실루엣 계수를 면적으로 시각화한 함수 작성\n",
    "def visualize_silhouette(cluster_lists, X_features): \n",
    "\n",
    "    from sklearn.datasets import make_blobs\n",
    "    from sklearn.cluster import KMeans\n",
    "    from sklearn.metrics import silhouette_samples, silhouette_score\n",
    "\n",
    "    import matplotlib.pyplot as plt\n",
    "    import matplotlib.cm as cm\n",
    "    import math\n",
    "\n",
    "    # 입력값으로 클러스터링 갯수들을 리스트로 받아서, 각 갯수별로 클러스터링을 적용하고 실루엣 개수를 구함\n",
    "    n_cols = len(cluster_lists)\n",
    "\n",
    "    # plt.subplots()으로 리스트에 기재된 클러스터링 수만큼의 sub figures를 가지는 axs 생성 \n",
    "    fig, axs = plt.subplots(figsize=(4*n_cols, 4), nrows=1, ncols=n_cols)\n",
    "\n",
    "    # 리스트에 기재된 클러스터링 갯수들을 차례로 iteration 수행하면서 실루엣 개수 시각화\n",
    "    for ind, n_cluster in enumerate(cluster_lists):\n",
    "\n",
    "        # KMeans 클러스터링 수행하고, 실루엣 스코어와 개별 데이터의 실루엣 값 계산. \n",
    "        clusterer = KMeans(n_clusters = n_cluster, max_iter=500, random_state=0)\n",
    "        cluster_labels = clusterer.fit_predict(X_features)\n",
    "\n",
    "        sil_avg = silhouette_score(X_features, cluster_labels)\n",
    "        sil_values = silhouette_samples(X_features, cluster_labels)\n",
    "\n",
    "        y_lower = 10\n",
    "        axs[ind].set_title('Number of Cluster : '+ str(n_cluster)+'\\n' \\\n",
    "                          'Silhouette Score :' + str(round(sil_avg,3)) )\n",
    "        axs[ind].set_xlabel(\"The silhouette coefficient values\")\n",
    "        axs[ind].set_ylabel(\"Cluster label\")\n",
    "        axs[ind].set_xlim([-0.1, 1])\n",
    "        axs[ind].set_ylim([0, len(X_features) + (n_cluster + 1) * 10])\n",
    "        axs[ind].set_yticks([])  # Clear the yaxis labels / ticks\n",
    "        axs[ind].set_xticks([0, 0.2, 0.4, 0.6, 0.8, 1])\n",
    "\n",
    "        # 클러스터링 갯수별로 fill_betweenx( )형태의 막대 그래프 표현. \n",
    "        for i in range(n_cluster):\n",
    "            ith_cluster_sil_values = sil_values[cluster_labels==i]\n",
    "            ith_cluster_sil_values.sort()\n",
    "\n",
    "            size_cluster_i = ith_cluster_sil_values.shape[0]\n",
    "            y_upper = y_lower + size_cluster_i\n",
    "\n",
    "            color = cm.nipy_spectral(float(i) / n_cluster)\n",
    "            axs[ind].fill_betweenx(np.arange(y_lower, y_upper), 0, ith_cluster_sil_values, \\\n",
    "                                facecolor=color, edgecolor=color, alpha=0.7)\n",
    "            axs[ind].text(-0.05, y_lower + 0.5 * size_cluster_i, str(i))\n",
    "            y_lower = y_upper + 10\n",
    "\n",
    "        axs[ind].axvline(x=sil_avg, color=\"red\", linestyle=\"--\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 클러스터 수 변화시키면서 random 데이터 실루엣 계수 분포 시각화\n",
    "\n",
    "# make_blobs 을 통해 clustering 을 위한 4개의 클러스터 중심의 500개 2차원 데이터 셋 생성  \n",
    "from sklearn.datasets import make_blobs\n",
    "X, y = make_blobs(n_samples=500, n_features=2, centers=4, cluster_std=1, \\\n",
    "                  center_box=(-10.0, 10.0), shuffle=True, random_state=1)  \n",
    "\n",
    "# cluster 개수를 2개, 3개, 4개, 5개 일때의 클러스터별 실루엣 계수 평균값을 시각화 \n",
    "visualize_silhouette([ 2, 3, 4, 5], X)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "iris = load_iris()\n",
    "visualize_silhouette([2, 3, 4, 5], iris.data)"
   ]
  }
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