instacart.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from sklearn.decomposition import PCA\n",
    "from sklearn.cluster import KMeans\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.metrics import silhouette_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# 读取四张表的数据\n",
    "prior = pd.read_csv(\"./data/instacart/order_products__prior.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "products = pd.read_csv(\"./data/instacart/products.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "orders = pd.read_csv(\"./data/instacart/orders.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "aisles = pd.read_csv(\"./data/instacart/aisles.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# 合并四张表到一张表  （用户-物品类别）\n",
    "_mg = pd.merge(prior, products, on=['product_id', 'product_id'])\n",
    "_mg = pd.merge(_mg, orders, on=['order_id', 'order_id'])\n",
    "mt = pd.merge(_mg, aisles, on=['aisle_id', 'aisle_id'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>order_id</th>\n",
       "      <th>product_id</th>\n",
       "      <th>add_to_cart_order</th>\n",
       "      <th>reordered</th>\n",
       "      <th>product_name</th>\n",
       "      <th>aisle_id</th>\n",
       "      <th>department_id</th>\n",
       "      <th>user_id</th>\n",
       "      <th>eval_set</th>\n",
       "      <th>order_number</th>\n",
       "      <th>order_dow</th>\n",
       "      <th>order_hour_of_day</th>\n",
       "      <th>days_since_prior_order</th>\n",
       "      <th>aisle</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>2</td>\n",
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       "      <td>1</td>\n",
       "      <td>Organic Egg Whites</td>\n",
       "      <td>86</td>\n",
       "      <td>16</td>\n",
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       "      <td>16</td>\n",
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       "      <td>16</td>\n",
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       "      <td>prior</td>\n",
       "      <td>11</td>\n",
       "      <td>6</td>\n",
       "      <td>8</td>\n",
       "      <td>10.0</td>\n",
       "      <td>eggs</td>\n",
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       "      <td>1</td>\n",
       "      <td>Organic Egg Whites</td>\n",
       "      <td>86</td>\n",
       "      <td>16</td>\n",
       "      <td>58707</td>\n",
       "      <td>prior</td>\n",
       "      <td>21</td>\n",
       "      <td>6</td>\n",
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       "      <td>8.0</td>\n",
       "      <td>eggs</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>390</td>\n",
       "      <td>33120</td>\n",
       "      <td>28</td>\n",
       "      <td>1</td>\n",
       "      <td>Organic Egg Whites</td>\n",
       "      <td>86</td>\n",
       "      <td>16</td>\n",
       "      <td>166654</td>\n",
       "      <td>prior</td>\n",
       "      <td>48</td>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "      <td>9.0</td>\n",
       "      <td>eggs</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>537</td>\n",
       "      <td>33120</td>\n",
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       "      <td>1</td>\n",
       "      <td>Organic Egg Whites</td>\n",
       "      <td>86</td>\n",
       "      <td>16</td>\n",
       "      <td>180135</td>\n",
       "      <td>prior</td>\n",
       "      <td>15</td>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>3.0</td>\n",
       "      <td>eggs</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>582</td>\n",
       "      <td>33120</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>Organic Egg Whites</td>\n",
       "      <td>86</td>\n",
       "      <td>16</td>\n",
       "      <td>193223</td>\n",
       "      <td>prior</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>19</td>\n",
       "      <td>10.0</td>\n",
       "      <td>eggs</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>608</td>\n",
       "      <td>33120</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>Organic Egg Whites</td>\n",
       "      <td>86</td>\n",
       "      <td>16</td>\n",
       "      <td>91030</td>\n",
       "      <td>prior</td>\n",
       "      <td>11</td>\n",
       "      <td>3</td>\n",
       "      <td>21</td>\n",
       "      <td>12.0</td>\n",
       "      <td>eggs</td>\n",
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       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>623</td>\n",
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       "      <td>16</td>\n",
       "      <td>37804</td>\n",
       "      <td>prior</td>\n",
       "      <td>63</td>\n",
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       "      <td>12</td>\n",
       "      <td>3.0</td>\n",
       "      <td>eggs</td>\n",
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       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>689</td>\n",
       "      <td>33120</td>\n",
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       "      <td>1</td>\n",
       "      <td>Organic Egg Whites</td>\n",
       "      <td>86</td>\n",
       "      <td>16</td>\n",
       "      <td>108932</td>\n",
       "      <td>prior</td>\n",
       "      <td>16</td>\n",
       "      <td>1</td>\n",
       "      <td>13</td>\n",
       "      <td>3.0</td>\n",
       "      <td>eggs</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   order_id  product_id  add_to_cart_order  reordered        product_name  \\\n",
       "0         2       33120                  1          1  Organic Egg Whites   \n",
       "1        26       33120                  5          0  Organic Egg Whites   \n",
       "2       120       33120                 13          0  Organic Egg Whites   \n",
       "3       327       33120                  5          1  Organic Egg Whites   \n",
       "4       390       33120                 28          1  Organic Egg Whites   \n",
       "5       537       33120                  2          1  Organic Egg Whites   \n",
       "6       582       33120                  7          1  Organic Egg Whites   \n",
       "7       608       33120                  5          1  Organic Egg Whites   \n",
       "8       623       33120                  1          1  Organic Egg Whites   \n",
       "9       689       33120                  4          1  Organic Egg Whites   \n",
       "\n",
       "   aisle_id  department_id  user_id eval_set  order_number  order_dow  \\\n",
       "0        86             16   202279    prior             3          5   \n",
       "1        86             16   153404    prior             2          0   \n",
       "2        86             16    23750    prior            11          6   \n",
       "3        86             16    58707    prior            21          6   \n",
       "4        86             16   166654    prior            48          0   \n",
       "5        86             16   180135    prior            15          2   \n",
       "6        86             16   193223    prior             6          2   \n",
       "7        86             16    91030    prior            11          3   \n",
       "8        86             16    37804    prior            63          3   \n",
       "9        86             16   108932    prior            16          1   \n",
       "\n",
       "   order_hour_of_day  days_since_prior_order aisle  \n",
       "0                  9                     8.0  eggs  \n",
       "1                 16                     7.0  eggs  \n",
       "2                  8                    10.0  eggs  \n",
       "3                  9                     8.0  eggs  \n",
       "4                 12                     9.0  eggs  \n",
       "5                  8                     3.0  eggs  \n",
       "6                 19                    10.0  eggs  \n",
       "7                 21                    12.0  eggs  \n",
       "8                 12                     3.0  eggs  \n",
       "9                 13                     3.0  eggs  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mt.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# 交叉表（特殊的分组工具）\n",
    "cross = pd.crosstab(mt['user_id'], mt['aisle'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
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       "      <th>aisle</th>\n",
       "      <th>air fresheners candles</th>\n",
       "      <th>asian foods</th>\n",
       "      <th>baby accessories</th>\n",
       "      <th>baby bath body care</th>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</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>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\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>...</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>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10 rows × 134 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "aisle    air fresheners candles  asian foods  baby accessories  \\\n",
       "user_id                                                          \n",
       "1                             0            0                 0   \n",
       "2                             0            3                 0   \n",
       "3                             0            0                 0   \n",
       "4                             0            0                 0   \n",
       "5                             0            2                 0   \n",
       "6                             0            0                 0   \n",
       "7                             0            0                 0   \n",
       "8                             0            1                 0   \n",
       "9                             0            0                 0   \n",
       "10                            0            1                 0   \n",
       "\n",
       "aisle    baby bath body care  baby food formula  bakery desserts  \\\n",
       "user_id                                                            \n",
       "1                          0                  0                0   \n",
       "2                          0                  0                0   \n",
       "3                          0                  0                0   \n",
       "4                          0                  0                0   \n",
       "5                          0                  0                0   \n",
       "6                          0                  0                0   \n",
       "7                          0                  0                0   \n",
       "8                          0                  0                0   \n",
       "9                          0                  6                0   \n",
       "10                         0                  0                0   \n",
       "\n",
       "aisle    baking ingredients  baking supplies decor  beauty  beers coolers  \\\n",
       "user_id                                                                     \n",
       "1                         0                      0       0              0   \n",
       "2                         2                      0       0              0   \n",
       "3                         0                      0       0              0   \n",
       "4                         0                      0       0              0   \n",
       "5                         0                      0       0              0   \n",
       "6                         0                      0       0              0   \n",
       "7                         2                      0       0              0   \n",
       "8                         1                      0       0              0   \n",
       "9                         2                      0       0              0   \n",
       "10                        0                      0       0              0   \n",
       "\n",
       "aisle     ...    spreads  tea  tofu meat alternatives  tortillas flat bread  \\\n",
       "user_id   ...                                                                 \n",
       "1         ...          1    0                       0                     0   \n",
       "2         ...          3    1                       1                     0   \n",
       "3         ...          4    1                       0                     0   \n",
       "4         ...          0    0                       0                     1   \n",
       "5         ...          0    0                       0                     0   \n",
       "6         ...          0    0                       0                     0   \n",
       "7         ...          0    0                       0                     0   \n",
       "8         ...          0    0                       0                     0   \n",
       "9         ...          0    0                       0                     0   \n",
       "10        ...          0    0                       0                     0   \n",
       "\n",
       "aisle    trail mix snack mix  trash bags liners  vitamins supplements  \\\n",
       "user_id                                                                 \n",
       "1                          0                  0                     0   \n",
       "2                          0                  0                     0   \n",
       "3                          0                  0                     0   \n",
       "4                          0                  0                     0   \n",
       "5                          0                  0                     0   \n",
       "6                          0                  0                     0   \n",
       "7                          0                  0                     0   \n",
       "8                          0                  0                     0   \n",
       "9                          0                  0                     0   \n",
       "10                         0                  0                     0   \n",
       "\n",
       "aisle    water seltzer sparkling water  white wines  yogurt  \n",
       "user_id                                                      \n",
       "1                                    0            0       1  \n",
       "2                                    2            0      42  \n",
       "3                                    2            0       0  \n",
       "4                                    1            0       0  \n",
       "5                                    0            0       3  \n",
       "6                                    0            0       0  \n",
       "7                                    0            0       5  \n",
       "8                                    0            0       0  \n",
       "9                                    2            0      19  \n",
       "10                                   0            0       2  \n",
       "\n",
       "[10 rows x 134 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cross.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# 进行主成分分析\n",
    "pca = PCA(n_components=0.9)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "data = pca.fit_transform(cross)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(500, 27)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 把样本数量减少\n",
    "x = data[:500]\n",
    "x.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# 假设用户一共分为四个类别\n",
    "km = KMeans(n_clusters=4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KMeans(algorithm='auto', copy_x=True, init='k-means++', max_iter=300,\n",
       "    n_clusters=4, n_init=10, n_jobs=1, precompute_distances='auto',\n",
       "    random_state=None, tol=0.0001, verbose=0)"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "km.fit(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "predict = km.predict(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 3, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 2, 1, 1, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 1, 1, 1, 1, 1,\n",
       "       1, 2, 1, 1, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 1, 2, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 3, 1, 2, 1, 1, 1, 2, 1, 1, 1, 1, 1, 2, 2, 1, 1, 1, 1, 1, 1, 1, 2,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 1, 1, 1, 1, 1, 2, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 1, 1, 1, 2, 1, 2, 1, 1, 1,\n",
       "       1, 1, 0, 1, 1, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 3, 1, 1, 1, 2, 1, 1, 1,\n",
       "       1, 2, 2, 2, 1, 2, 1, 1, 1, 1, 1, 1, 2, 1, 1, 1, 1, 2, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 3, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 2, 1, 2, 2, 1, 1, 1, 1, 1, 1, 1, 0, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 2, 1,\n",
       "       3, 1, 1, 1, 2, 1, 1, 1, 1, 1, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 2, 1,\n",
       "       1, 1, 1, 1, 1, 1, 2, 1, 1, 1, 1, 2, 1, 1, 1, 1, 1, 3, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 2, 1, 1, 2, 2, 1, 2, 1, 1, 1, 1, 1, 1,\n",
       "       1, 3, 1, 2, 2, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 1,\n",
       "       1, 1, 2, 2, 1, 1, 1, 1, 1, 1, 1, 2, 2, 1, 1, 1, 1], dtype=int32)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10a519978>"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 显示聚类的结果\n",
    "plt.figure(figsize=(10,10))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x109527978>"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 建立四个颜色的列表\n",
    "colored = ['orange', 'green', 'blue', 'purple']\n",
    "colr = [colored[i] for i in predict]\n",
    "plt.scatter(x[:, 1], x[:, 20], color=colr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x109539c50>"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plt.xlabel(\"1\")\n",
    "plt.ylabel(\"20\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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92QxoNYCnLniK+sn1T/a3QKTCKe5yp0pwiIhUYF3rdWVgq4FM3zg9L1FzO92k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\nSZKkDP1/ErUsgifYq+4AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10a519978>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.61023872302956417"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 评判聚类效果，轮廓系数\n",
    "silhouette_score(x, predict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.0"
  }
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