{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 获取数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import pandas as pd\n",
    "def load_housing_data(housing_path = './'):\n",
    "    csv_path = os.path.join(housing_path, 'housing.csv')\n",
    "    return pd.read_csv(csv_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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>longitude</th>\n",
       "      <th>latitude</th>\n",
       "      <th>housing_median_age</th>\n",
       "      <th>total_rooms</th>\n",
       "      <th>total_bedrooms</th>\n",
       "      <th>population</th>\n",
       "      <th>households</th>\n",
       "      <th>median_income</th>\n",
       "      <th>median_house_value</th>\n",
       "      <th>ocean_proximity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-122.23</td>\n",
       "      <td>37.88</td>\n",
       "      <td>41.0</td>\n",
       "      <td>880.0</td>\n",
       "      <td>129.0</td>\n",
       "      <td>322.0</td>\n",
       "      <td>126.0</td>\n",
       "      <td>8.3252</td>\n",
       "      <td>452600.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-122.22</td>\n",
       "      <td>37.86</td>\n",
       "      <td>21.0</td>\n",
       "      <td>7099.0</td>\n",
       "      <td>1106.0</td>\n",
       "      <td>2401.0</td>\n",
       "      <td>1138.0</td>\n",
       "      <td>8.3014</td>\n",
       "      <td>358500.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-122.24</td>\n",
       "      <td>37.85</td>\n",
       "      <td>52.0</td>\n",
       "      <td>1467.0</td>\n",
       "      <td>190.0</td>\n",
       "      <td>496.0</td>\n",
       "      <td>177.0</td>\n",
       "      <td>7.2574</td>\n",
       "      <td>352100.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-122.25</td>\n",
       "      <td>37.85</td>\n",
       "      <td>52.0</td>\n",
       "      <td>1274.0</td>\n",
       "      <td>235.0</td>\n",
       "      <td>558.0</td>\n",
       "      <td>219.0</td>\n",
       "      <td>5.6431</td>\n",
       "      <td>341300.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-122.25</td>\n",
       "      <td>37.85</td>\n",
       "      <td>52.0</td>\n",
       "      <td>1627.0</td>\n",
       "      <td>280.0</td>\n",
       "      <td>565.0</td>\n",
       "      <td>259.0</td>\n",
       "      <td>3.8462</td>\n",
       "      <td>342200.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   longitude  latitude  housing_median_age  total_rooms  total_bedrooms  \\\n",
       "0    -122.23     37.88                41.0        880.0           129.0   \n",
       "1    -122.22     37.86                21.0       7099.0          1106.0   \n",
       "2    -122.24     37.85                52.0       1467.0           190.0   \n",
       "3    -122.25     37.85                52.0       1274.0           235.0   \n",
       "4    -122.25     37.85                52.0       1627.0           280.0   \n",
       "\n",
       "   population  households  median_income  median_house_value ocean_proximity  \n",
       "0       322.0       126.0         8.3252            452600.0        NEAR BAY  \n",
       "1      2401.0      1138.0         8.3014            358500.0        NEAR BAY  \n",
       "2       496.0       177.0         7.2574            352100.0        NEAR BAY  \n",
       "3       558.0       219.0         5.6431            341300.0        NEAR BAY  \n",
       "4       565.0       259.0         3.8462            342200.0        NEAR BAY  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing = load_housing_data()\n",
    "housing.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(20640, 10)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 20640 entries, 0 to 20639\n",
      "Data columns (total 10 columns):\n",
      " #   Column              Non-Null Count  Dtype  \n",
      "---  ------              --------------  -----  \n",
      " 0   longitude           20640 non-null  float64\n",
      " 1   latitude            20640 non-null  float64\n",
      " 2   housing_median_age  20640 non-null  float64\n",
      " 3   total_rooms         20640 non-null  float64\n",
      " 4   total_bedrooms      20433 non-null  float64\n",
      " 5   population          20640 non-null  float64\n",
      " 6   households          20640 non-null  float64\n",
      " 7   median_income       20640 non-null  float64\n",
      " 8   median_house_value  20640 non-null  float64\n",
      " 9   ocean_proximity     20640 non-null  object \n",
      "dtypes: float64(9), object(1)\n",
      "memory usage: 1.6+ MB\n"
     ]
    }
   ],
   "source": [
    "housing.info()  # 查看数据集的简单描述， 得到每个属性类型和非空值数据量"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ocean_proximity\n",
       "<1H OCEAN     9136\n",
       "INLAND        6551\n",
       "NEAR OCEAN    2658\n",
       "NEAR BAY      2290\n",
       "ISLAND           5\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#  total_bedrooms  20433 有一部分空值\n",
    "housing['ocean_proximity'].value_counts()  # 查看多少种分类  ，5 种分类，获得每种分类下有少区域"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "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>longitude</th>\n",
       "      <th>latitude</th>\n",
       "      <th>housing_median_age</th>\n",
       "      <th>total_rooms</th>\n",
       "      <th>total_bedrooms</th>\n",
       "      <th>population</th>\n",
       "      <th>households</th>\n",
       "      <th>median_income</th>\n",
       "      <th>median_house_value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>20640.000000</td>\n",
       "      <td>20640.000000</td>\n",
       "      <td>20640.000000</td>\n",
       "      <td>20640.000000</td>\n",
       "      <td>20433.000000</td>\n",
       "      <td>20640.000000</td>\n",
       "      <td>20640.000000</td>\n",
       "      <td>20640.000000</td>\n",
       "      <td>20640.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>-119.569704</td>\n",
       "      <td>35.631861</td>\n",
       "      <td>28.639486</td>\n",
       "      <td>2635.763081</td>\n",
       "      <td>537.870553</td>\n",
       "      <td>1425.476744</td>\n",
       "      <td>499.539680</td>\n",
       "      <td>3.870671</td>\n",
       "      <td>206855.816909</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>2.003532</td>\n",
       "      <td>2.135952</td>\n",
       "      <td>12.585558</td>\n",
       "      <td>2181.615252</td>\n",
       "      <td>421.385070</td>\n",
       "      <td>1132.462122</td>\n",
       "      <td>382.329753</td>\n",
       "      <td>1.899822</td>\n",
       "      <td>115395.615874</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-124.350000</td>\n",
       "      <td>32.540000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.499900</td>\n",
       "      <td>14999.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>-121.800000</td>\n",
       "      <td>33.930000</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>1447.750000</td>\n",
       "      <td>296.000000</td>\n",
       "      <td>787.000000</td>\n",
       "      <td>280.000000</td>\n",
       "      <td>2.563400</td>\n",
       "      <td>119600.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>-118.490000</td>\n",
       "      <td>34.260000</td>\n",
       "      <td>29.000000</td>\n",
       "      <td>2127.000000</td>\n",
       "      <td>435.000000</td>\n",
       "      <td>1166.000000</td>\n",
       "      <td>409.000000</td>\n",
       "      <td>3.534800</td>\n",
       "      <td>179700.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>-118.010000</td>\n",
       "      <td>37.710000</td>\n",
       "      <td>37.000000</td>\n",
       "      <td>3148.000000</td>\n",
       "      <td>647.000000</td>\n",
       "      <td>1725.000000</td>\n",
       "      <td>605.000000</td>\n",
       "      <td>4.743250</td>\n",
       "      <td>264725.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>-114.310000</td>\n",
       "      <td>41.950000</td>\n",
       "      <td>52.000000</td>\n",
       "      <td>39320.000000</td>\n",
       "      <td>6445.000000</td>\n",
       "      <td>35682.000000</td>\n",
       "      <td>6082.000000</td>\n",
       "      <td>15.000100</td>\n",
       "      <td>500001.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          longitude      latitude  housing_median_age   total_rooms  \\\n",
       "count  20640.000000  20640.000000        20640.000000  20640.000000   \n",
       "mean    -119.569704     35.631861           28.639486   2635.763081   \n",
       "std        2.003532      2.135952           12.585558   2181.615252   \n",
       "min     -124.350000     32.540000            1.000000      2.000000   \n",
       "25%     -121.800000     33.930000           18.000000   1447.750000   \n",
       "50%     -118.490000     34.260000           29.000000   2127.000000   \n",
       "75%     -118.010000     37.710000           37.000000   3148.000000   \n",
       "max     -114.310000     41.950000           52.000000  39320.000000   \n",
       "\n",
       "       total_bedrooms    population    households  median_income  \\\n",
       "count    20433.000000  20640.000000  20640.000000   20640.000000   \n",
       "mean       537.870553   1425.476744    499.539680       3.870671   \n",
       "std        421.385070   1132.462122    382.329753       1.899822   \n",
       "min          1.000000      3.000000      1.000000       0.499900   \n",
       "25%        296.000000    787.000000    280.000000       2.563400   \n",
       "50%        435.000000   1166.000000    409.000000       3.534800   \n",
       "75%        647.000000   1725.000000    605.000000       4.743250   \n",
       "max       6445.000000  35682.000000   6082.000000      15.000100   \n",
       "\n",
       "       median_house_value  \n",
       "count        20640.000000  \n",
       "mean        206855.816909  \n",
       "std         115395.615874  \n",
       "min          14999.000000  \n",
       "25%         119600.000000  \n",
       "50%         179700.000000  \n",
       "75%         264725.000000  \n",
       "max         500001.000000  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing.describe()  # 显示数值属性摘要"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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AiIBDFQAAAAAAAAAAAAAAABFwqAIAAAAAAAAAAAAAACACDlUAAAAAAAAAAAAAAABEwKEKAAAAAAAAAAAAAACACDhUAQAAAAAAAAAAAAAAEAGHKgAAAAAAAAAAAAAAACLgUAUAAAAAAAAAAAAAAEAEHKoAAAAAAAAAAAAAAACIgEMVAAAAAAAAAAAAAAAAEf4FvwcC0PCfGFEAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 2000x1500 with 9 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "housing.hist(bins = 50, figsize = (20, 15))\n",
    "plt.show()\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 房龄和房价被设定了上限\n",
    "   1. 设置了上限的区域，重新收集标签值\n",
    "   2. 把设置了上限的区域数据移除\n",
    "   \n",
    "### 重尾 头高尾长\n",
    "   * 转换数据，把数据形状变成偏钟形分布（正太分布， 平均值为0，标准差为1）\n",
    "### 收入特征\n",
    "   * 数据提供的上游证实 是年薪 单位万美元， 提前对特征进行缩放是正常的，需要得知数据是如何缩放的"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 创建训练集和测试集\n",
    "   * 训练集用于模型训练 占整个数据集的80% ，测试占20%"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "def split_train_test(data, test_radio):\n",
    "    np.random.seed(42)\n",
    "    indices = np.random.permutation(len(data))  # 对原来的数组进行重新洗牌，随机打乱原来的元素顺序\n",
    "    test_set_size = int(len(data) * test_radio)\n",
    "    test_indices = indices[:test_set_size]\n",
    "    train_indices = indices[test_set_size:]\n",
    "    return data.iloc[train_indices],  data.iloc[test_indices]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_set, test_set =  split_train_test(housing, 0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "16512"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(train_set)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4128"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(test_set)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 对样本设置唯一的标识符，对标识符取hash值， 去hash的最后一个字节， 值小于等于51 ，256*20%，放入测试集\n",
    "# hash值相同，对象不一定相同，hash不同，对象一定不同 \n",
    "\n",
    "import hashlib\n",
    "\n",
    "def test_set_check(identifier, test_radio, hash = hashlib.md5):\n",
    "    return hash(np.int64(identifier)).digest()[-1] < 256 * test_radio  # 返回摘要，作为二进制数据字符串\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "def split_train_test_by_id(data, test_ratio, id_column):\n",
    "    ids = data[id_column]\n",
    "    in_test_set = ids.apply(lambda id_:test_set_check(id_, test_ratio))\n",
    "    return data.loc[~in_test_set], data.loc[in_test_set]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>longitude</th>\n",
       "      <th>latitude</th>\n",
       "      <th>housing_median_age</th>\n",
       "      <th>total_rooms</th>\n",
       "      <th>total_bedrooms</th>\n",
       "      <th>population</th>\n",
       "      <th>households</th>\n",
       "      <th>median_income</th>\n",
       "      <th>median_house_value</th>\n",
       "      <th>ocean_proximity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
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       "      <td>37.88</td>\n",
       "      <td>41.0</td>\n",
       "      <td>880.0</td>\n",
       "      <td>129.0</td>\n",
       "      <td>322.0</td>\n",
       "      <td>126.0</td>\n",
       "      <td>8.3252</td>\n",
       "      <td>452600.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-122.22</td>\n",
       "      <td>37.86</td>\n",
       "      <td>21.0</td>\n",
       "      <td>7099.0</td>\n",
       "      <td>1106.0</td>\n",
       "      <td>2401.0</td>\n",
       "      <td>1138.0</td>\n",
       "      <td>8.3014</td>\n",
       "      <td>358500.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-122.24</td>\n",
       "      <td>37.85</td>\n",
       "      <td>52.0</td>\n",
       "      <td>1467.0</td>\n",
       "      <td>190.0</td>\n",
       "      <td>496.0</td>\n",
       "      <td>177.0</td>\n",
       "      <td>7.2574</td>\n",
       "      <td>352100.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-122.25</td>\n",
       "      <td>37.85</td>\n",
       "      <td>52.0</td>\n",
       "      <td>1274.0</td>\n",
       "      <td>235.0</td>\n",
       "      <td>558.0</td>\n",
       "      <td>219.0</td>\n",
       "      <td>5.6431</td>\n",
       "      <td>341300.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-122.25</td>\n",
       "      <td>37.85</td>\n",
       "      <td>52.0</td>\n",
       "      <td>1627.0</td>\n",
       "      <td>280.0</td>\n",
       "      <td>565.0</td>\n",
       "      <td>259.0</td>\n",
       "      <td>3.8462</td>\n",
       "      <td>342200.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   longitude  latitude  housing_median_age  total_rooms  total_bedrooms  \\\n",
       "0    -122.23     37.88                41.0        880.0           129.0   \n",
       "1    -122.22     37.86                21.0       7099.0          1106.0   \n",
       "2    -122.24     37.85                52.0       1467.0           190.0   \n",
       "3    -122.25     37.85                52.0       1274.0           235.0   \n",
       "4    -122.25     37.85                52.0       1627.0           280.0   \n",
       "\n",
       "   population  households  median_income  median_house_value ocean_proximity  \n",
       "0       322.0       126.0         8.3252            452600.0        NEAR BAY  \n",
       "1      2401.0      1138.0         8.3014            358500.0        NEAR BAY  \n",
       "2       496.0       177.0         7.2574            352100.0        NEAR BAY  \n",
       "3       558.0       219.0         5.6431            341300.0        NEAR BAY  \n",
       "4       565.0       259.0         3.8462            342200.0        NEAR BAY  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "housing_with_id = housing.reset_index()  # 使用行索引作为标识符 ID"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>index</th>\n",
       "      <th>longitude</th>\n",
       "      <th>latitude</th>\n",
       "      <th>housing_median_age</th>\n",
       "      <th>total_rooms</th>\n",
       "      <th>total_bedrooms</th>\n",
       "      <th>population</th>\n",
       "      <th>households</th>\n",
       "      <th>median_income</th>\n",
       "      <th>median_house_value</th>\n",
       "      <th>ocean_proximity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <td>129.0</td>\n",
       "      <td>322.0</td>\n",
       "      <td>126.0</td>\n",
       "      <td>8.3252</td>\n",
       "      <td>452600.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>-122.22</td>\n",
       "      <td>37.86</td>\n",
       "      <td>21.0</td>\n",
       "      <td>7099.0</td>\n",
       "      <td>1106.0</td>\n",
       "      <td>2401.0</td>\n",
       "      <td>1138.0</td>\n",
       "      <td>8.3014</td>\n",
       "      <td>358500.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>-122.24</td>\n",
       "      <td>37.85</td>\n",
       "      <td>52.0</td>\n",
       "      <td>1467.0</td>\n",
       "      <td>190.0</td>\n",
       "      <td>496.0</td>\n",
       "      <td>177.0</td>\n",
       "      <td>7.2574</td>\n",
       "      <td>352100.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>-122.25</td>\n",
       "      <td>37.85</td>\n",
       "      <td>52.0</td>\n",
       "      <td>1274.0</td>\n",
       "      <td>235.0</td>\n",
       "      <td>558.0</td>\n",
       "      <td>219.0</td>\n",
       "      <td>5.6431</td>\n",
       "      <td>341300.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>-122.25</td>\n",
       "      <td>37.85</td>\n",
       "      <td>52.0</td>\n",
       "      <td>1627.0</td>\n",
       "      <td>280.0</td>\n",
       "      <td>565.0</td>\n",
       "      <td>259.0</td>\n",
       "      <td>3.8462</td>\n",
       "      <td>342200.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   index  longitude  latitude  housing_median_age  total_rooms  \\\n",
       "0      0    -122.23     37.88                41.0        880.0   \n",
       "1      1    -122.22     37.86                21.0       7099.0   \n",
       "2      2    -122.24     37.85                52.0       1467.0   \n",
       "3      3    -122.25     37.85                52.0       1274.0   \n",
       "4      4    -122.25     37.85                52.0       1627.0   \n",
       "\n",
       "   total_bedrooms  population  households  median_income  median_house_value  \\\n",
       "0           129.0       322.0       126.0         8.3252            452600.0   \n",
       "1          1106.0      2401.0      1138.0         8.3014            358500.0   \n",
       "2           190.0       496.0       177.0         7.2574            352100.0   \n",
       "3           235.0       558.0       219.0         5.6431            341300.0   \n",
       "4           280.0       565.0       259.0         3.8462            342200.0   \n",
       "\n",
       "  ocean_proximity  \n",
       "0        NEAR BAY  \n",
       "1        NEAR BAY  \n",
       "2        NEAR BAY  \n",
       "3        NEAR BAY  \n",
       "4        NEAR BAY  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing_with_id.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_set, test_set = split_train_test_by_id(housing_with_id, 0.2, 'index')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>longitude</th>\n",
       "      <th>latitude</th>\n",
       "      <th>housing_median_age</th>\n",
       "      <th>total_rooms</th>\n",
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       "      <th>households</th>\n",
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       "      <td>129.0</td>\n",
       "      <td>322.0</td>\n",
       "      <td>126.0</td>\n",
       "      <td>8.3252</td>\n",
       "      <td>452600.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>-122.22</td>\n",
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       "      <td>1106.0</td>\n",
       "      <td>2401.0</td>\n",
       "      <td>1138.0</td>\n",
       "      <td>8.3014</td>\n",
       "      <td>358500.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>-122.24</td>\n",
       "      <td>37.85</td>\n",
       "      <td>52.0</td>\n",
       "      <td>1467.0</td>\n",
       "      <td>190.0</td>\n",
       "      <td>496.0</td>\n",
       "      <td>177.0</td>\n",
       "      <td>7.2574</td>\n",
       "      <td>352100.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>-122.25</td>\n",
       "      <td>37.85</td>\n",
       "      <td>52.0</td>\n",
       "      <td>1274.0</td>\n",
       "      <td>235.0</td>\n",
       "      <td>558.0</td>\n",
       "      <td>219.0</td>\n",
       "      <td>5.6431</td>\n",
       "      <td>341300.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>6</td>\n",
       "      <td>-122.25</td>\n",
       "      <td>37.84</td>\n",
       "      <td>52.0</td>\n",
       "      <td>2535.0</td>\n",
       "      <td>489.0</td>\n",
       "      <td>1094.0</td>\n",
       "      <td>514.0</td>\n",
       "      <td>3.6591</td>\n",
       "      <td>299200.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   index  longitude  latitude  housing_median_age  total_rooms  \\\n",
       "0      0    -122.23     37.88                41.0        880.0   \n",
       "1      1    -122.22     37.86                21.0       7099.0   \n",
       "2      2    -122.24     37.85                52.0       1467.0   \n",
       "3      3    -122.25     37.85                52.0       1274.0   \n",
       "6      6    -122.25     37.84                52.0       2535.0   \n",
       "\n",
       "   total_bedrooms  population  households  median_income  median_house_value  \\\n",
       "0           129.0       322.0       126.0         8.3252            452600.0   \n",
       "1          1106.0      2401.0      1138.0         8.3014            358500.0   \n",
       "2           190.0       496.0       177.0         7.2574            352100.0   \n",
       "3           235.0       558.0       219.0         5.6431            341300.0   \n",
       "6           489.0      1094.0       514.0         3.6591            299200.0   \n",
       "\n",
       "  ocean_proximity  \n",
       "0        NEAR BAY  \n",
       "1        NEAR BAY  \n",
       "2        NEAR BAY  \n",
       "3        NEAR BAY  \n",
       "6        NEAR BAY  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_set.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 基于行索引，不能删除和中间插入数据，只能末尾插入，否则行索引会变\n",
    "# 寻找稳定特征来创建唯一标识符，"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "housing_with_id['id'] = housing['longitude'] * 1000 + housing['latitude']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>id</th>\n",
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       "      <th>0</th>\n",
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       "      <td>126.0</td>\n",
       "      <td>8.3252</td>\n",
       "      <td>452600.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "      <td>-122192.12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
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       "      <td>7099.0</td>\n",
       "      <td>1106.0</td>\n",
       "      <td>2401.0</td>\n",
       "      <td>1138.0</td>\n",
       "      <td>8.3014</td>\n",
       "      <td>358500.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "      <td>-122182.14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>-122.24</td>\n",
       "      <td>37.85</td>\n",
       "      <td>52.0</td>\n",
       "      <td>1467.0</td>\n",
       "      <td>190.0</td>\n",
       "      <td>496.0</td>\n",
       "      <td>177.0</td>\n",
       "      <td>7.2574</td>\n",
       "      <td>352100.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "      <td>-122202.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>-122.25</td>\n",
       "      <td>37.85</td>\n",
       "      <td>52.0</td>\n",
       "      <td>1274.0</td>\n",
       "      <td>235.0</td>\n",
       "      <td>558.0</td>\n",
       "      <td>219.0</td>\n",
       "      <td>5.6431</td>\n",
       "      <td>341300.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "      <td>-122212.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>-122.25</td>\n",
       "      <td>37.85</td>\n",
       "      <td>52.0</td>\n",
       "      <td>1627.0</td>\n",
       "      <td>280.0</td>\n",
       "      <td>565.0</td>\n",
       "      <td>259.0</td>\n",
       "      <td>3.8462</td>\n",
       "      <td>342200.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "      <td>-122212.15</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   index  longitude  latitude  housing_median_age  total_rooms  \\\n",
       "0      0    -122.23     37.88                41.0        880.0   \n",
       "1      1    -122.22     37.86                21.0       7099.0   \n",
       "2      2    -122.24     37.85                52.0       1467.0   \n",
       "3      3    -122.25     37.85                52.0       1274.0   \n",
       "4      4    -122.25     37.85                52.0       1627.0   \n",
       "\n",
       "   total_bedrooms  population  households  median_income  median_house_value  \\\n",
       "0           129.0       322.0       126.0         8.3252            452600.0   \n",
       "1          1106.0      2401.0      1138.0         8.3014            358500.0   \n",
       "2           190.0       496.0       177.0         7.2574            352100.0   \n",
       "3           235.0       558.0       219.0         5.6431            341300.0   \n",
       "4           280.0       565.0       259.0         3.8462            342200.0   \n",
       "\n",
       "  ocean_proximity         id  \n",
       "0        NEAR BAY -122192.12  \n",
       "1        NEAR BAY -122182.14  \n",
       "2        NEAR BAY -122202.15  \n",
       "3        NEAR BAY -122212.15  \n",
       "4        NEAR BAY -122212.15  "
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing_with_id.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_set, test_set = split_train_test_by_id(housing_with_id, 0.2, 'id')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\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>index</th>\n",
       "      <th>longitude</th>\n",
       "      <th>latitude</th>\n",
       "      <th>housing_median_age</th>\n",
       "      <th>total_rooms</th>\n",
       "      <th>total_bedrooms</th>\n",
       "      <th>population</th>\n",
       "      <th>households</th>\n",
       "      <th>median_income</th>\n",
       "      <th>median_house_value</th>\n",
       "      <th>ocean_proximity</th>\n",
       "      <th>id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>-122.23</td>\n",
       "      <td>37.88</td>\n",
       "      <td>41.0</td>\n",
       "      <td>880.0</td>\n",
       "      <td>129.0</td>\n",
       "      <td>322.0</td>\n",
       "      <td>126.0</td>\n",
       "      <td>8.3252</td>\n",
       "      <td>452600.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "      <td>-122192.12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>-122.22</td>\n",
       "      <td>37.86</td>\n",
       "      <td>21.0</td>\n",
       "      <td>7099.0</td>\n",
       "      <td>1106.0</td>\n",
       "      <td>2401.0</td>\n",
       "      <td>1138.0</td>\n",
       "      <td>8.3014</td>\n",
       "      <td>358500.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "      <td>-122182.14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>-122.24</td>\n",
       "      <td>37.85</td>\n",
       "      <td>52.0</td>\n",
       "      <td>1467.0</td>\n",
       "      <td>190.0</td>\n",
       "      <td>496.0</td>\n",
       "      <td>177.0</td>\n",
       "      <td>7.2574</td>\n",
       "      <td>352100.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "      <td>-122202.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>-122.25</td>\n",
       "      <td>37.85</td>\n",
       "      <td>52.0</td>\n",
       "      <td>1274.0</td>\n",
       "      <td>235.0</td>\n",
       "      <td>558.0</td>\n",
       "      <td>219.0</td>\n",
       "      <td>5.6431</td>\n",
       "      <td>341300.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "      <td>-122212.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>-122.25</td>\n",
       "      <td>37.85</td>\n",
       "      <td>52.0</td>\n",
       "      <td>1627.0</td>\n",
       "      <td>280.0</td>\n",
       "      <td>565.0</td>\n",
       "      <td>259.0</td>\n",
       "      <td>3.8462</td>\n",
       "      <td>342200.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "      <td>-122212.15</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   index  longitude  latitude  housing_median_age  total_rooms  \\\n",
       "0      0    -122.23     37.88                41.0        880.0   \n",
       "1      1    -122.22     37.86                21.0       7099.0   \n",
       "2      2    -122.24     37.85                52.0       1467.0   \n",
       "3      3    -122.25     37.85                52.0       1274.0   \n",
       "4      4    -122.25     37.85                52.0       1627.0   \n",
       "\n",
       "   total_bedrooms  population  households  median_income  median_house_value  \\\n",
       "0           129.0       322.0       126.0         8.3252            452600.0   \n",
       "1          1106.0      2401.0      1138.0         8.3014            358500.0   \n",
       "2           190.0       496.0       177.0         7.2574            352100.0   \n",
       "3           235.0       558.0       219.0         5.6431            341300.0   \n",
       "4           280.0       565.0       259.0         3.8462            342200.0   \n",
       "\n",
       "  ocean_proximity         id  \n",
       "0        NEAR BAY -122192.12  \n",
       "1        NEAR BAY -122182.14  \n",
       "2        NEAR BAY -122202.15  \n",
       "3        NEAR BAY -122212.15  \n",
       "4        NEAR BAY -122212.15  "
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_set.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "train_set, test_set = train_test_split(housing, test_size = 0.2, random_state = 42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "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>longitude</th>\n",
       "      <th>latitude</th>\n",
       "      <th>housing_median_age</th>\n",
       "      <th>total_rooms</th>\n",
       "      <th>total_bedrooms</th>\n",
       "      <th>population</th>\n",
       "      <th>households</th>\n",
       "      <th>median_income</th>\n",
       "      <th>median_house_value</th>\n",
       "      <th>ocean_proximity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>14196</th>\n",
       "      <td>-117.03</td>\n",
       "      <td>32.71</td>\n",
       "      <td>33.0</td>\n",
       "      <td>3126.0</td>\n",
       "      <td>627.0</td>\n",
       "      <td>2300.0</td>\n",
       "      <td>623.0</td>\n",
       "      <td>3.2596</td>\n",
       "      <td>103000.0</td>\n",
       "      <td>NEAR OCEAN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8267</th>\n",
       "      <td>-118.16</td>\n",
       "      <td>33.77</td>\n",
       "      <td>49.0</td>\n",
       "      <td>3382.0</td>\n",
       "      <td>787.0</td>\n",
       "      <td>1314.0</td>\n",
       "      <td>756.0</td>\n",
       "      <td>3.8125</td>\n",
       "      <td>382100.0</td>\n",
       "      <td>NEAR OCEAN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17445</th>\n",
       "      <td>-120.48</td>\n",
       "      <td>34.66</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1897.0</td>\n",
       "      <td>331.0</td>\n",
       "      <td>915.0</td>\n",
       "      <td>336.0</td>\n",
       "      <td>4.1563</td>\n",
       "      <td>172600.0</td>\n",
       "      <td>NEAR OCEAN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14265</th>\n",
       "      <td>-117.11</td>\n",
       "      <td>32.69</td>\n",
       "      <td>36.0</td>\n",
       "      <td>1421.0</td>\n",
       "      <td>367.0</td>\n",
       "      <td>1418.0</td>\n",
       "      <td>355.0</td>\n",
       "      <td>1.9425</td>\n",
       "      <td>93400.0</td>\n",
       "      <td>NEAR OCEAN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2271</th>\n",
       "      <td>-119.80</td>\n",
       "      <td>36.78</td>\n",
       "      <td>43.0</td>\n",
       "      <td>2382.0</td>\n",
       "      <td>431.0</td>\n",
       "      <td>874.0</td>\n",
       "      <td>380.0</td>\n",
       "      <td>3.5542</td>\n",
       "      <td>96500.0</td>\n",
       "      <td>INLAND</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       longitude  latitude  housing_median_age  total_rooms  total_bedrooms  \\\n",
       "14196    -117.03     32.71                33.0       3126.0           627.0   \n",
       "8267     -118.16     33.77                49.0       3382.0           787.0   \n",
       "17445    -120.48     34.66                 4.0       1897.0           331.0   \n",
       "14265    -117.11     32.69                36.0       1421.0           367.0   \n",
       "2271     -119.80     36.78                43.0       2382.0           431.0   \n",
       "\n",
       "       population  households  median_income  median_house_value  \\\n",
       "14196      2300.0       623.0         3.2596            103000.0   \n",
       "8267       1314.0       756.0         3.8125            382100.0   \n",
       "17445       915.0       336.0         4.1563            172600.0   \n",
       "14265      1418.0       355.0         1.9425             93400.0   \n",
       "2271        874.0       380.0         3.5542             96500.0   \n",
       "\n",
       "      ocean_proximity  \n",
       "14196      NEAR OCEAN  \n",
       "8267       NEAR OCEAN  \n",
       "17445      NEAR OCEAN  \n",
       "14265      NEAR OCEAN  \n",
       "2271           INLAND  "
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_set.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 从数据可视化中探索数据"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 分层采样\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 抽样偏差：调查公司给1000个人来调研几个问题，不会在电话簿中随机查找1000人，他们视图确保1000人代表全体人口，美国人，51.3女性，48.7男性\n",
    "# 你的调查应该维持这一比例，513名女性，487男性，这就是分层采样，人口划分为均匀的子集，每个子集称为一层，然后从每层中抽取正确的实例数量"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "housing['median_income'].hist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 希望测试集能够代表整个数据集中各种不同类型的收入"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "   1. 创建一个收入类别属性 \n",
    "   2. 不应该数据分层太多，但每一层应该有足够的数据量\n",
    "   3. 将收入中位数处以1.5，限制收入类别数量，使用ceil取整，得到离散类别，将大于5的列别合并为类别5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "housing['income_cat'] = np.ceil(housing['median_income']/1.5)\n",
    "housing['income_cat'].head(20)\n",
    "\n",
    "housing['income_cat'].where(housing['income_cat'] < 5, 5.0, inplace = True )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     5.0\n",
       "1     5.0\n",
       "2     5.0\n",
       "3     4.0\n",
       "4     3.0\n",
       "5     3.0\n",
       "6     3.0\n",
       "7     3.0\n",
       "8     2.0\n",
       "9     3.0\n",
       "10    3.0\n",
       "11    3.0\n",
       "12    3.0\n",
       "13    2.0\n",
       "14    2.0\n",
       "15    2.0\n",
       "16    2.0\n",
       "17    2.0\n",
       "18    2.0\n",
       "19    2.0\n",
       "Name: income_cat, dtype: float64"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing['income_cat'].head(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "housing['income_cat'] = pd.cut(housing['median_income'], bins=[0., 1.5, 3.0, 4.5, 6., np.inf], labels = [1,2, 3, 4, 5]) # 把连续值转换成类别标签  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     5\n",
       "1     5\n",
       "2     5\n",
       "3     4\n",
       "4     3\n",
       "5     3\n",
       "6     3\n",
       "7     3\n",
       "8     2\n",
       "9     3\n",
       "10    3\n",
       "11    3\n",
       "12    3\n",
       "13    2\n",
       "14    2\n",
       "15    2\n",
       "16    2\n",
       "17    2\n",
       "18    2\n",
       "19    2\n",
       "Name: income_cat, dtype: category\n",
       "Categories (5, int64): [1 < 2 < 3 < 4 < 5]"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing['income_cat'].head(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "income_cat\n",
       "3    7236\n",
       "2    6581\n",
       "4    3639\n",
       "5    2362\n",
       "1     822\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing['income_cat'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 收入类别直方图\n",
    "housing['income_cat'].hist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 根据收入类别进行分层采样\n",
    "from sklearn.model_selection import StratifiedShuffleSplit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "split = StratifiedShuffleSplit(n_splits=1, test_size = 0.2, random_state = 42 )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "for  train_index, test_index in split.split(housing, housing['income_cat']):\n",
    "    strat_train_set = housing.loc[train_index]\n",
    "    strat_test_set = housing.loc[test_index]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(16512, 11)"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "strat_train_set.shape\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "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>longitude</th>\n",
       "      <th>latitude</th>\n",
       "      <th>housing_median_age</th>\n",
       "      <th>total_rooms</th>\n",
       "      <th>total_bedrooms</th>\n",
       "      <th>population</th>\n",
       "      <th>households</th>\n",
       "      <th>median_income</th>\n",
       "      <th>median_house_value</th>\n",
       "      <th>ocean_proximity</th>\n",
       "      <th>income_cat</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>12655</th>\n",
       "      <td>-121.46</td>\n",
       "      <td>38.52</td>\n",
       "      <td>29.0</td>\n",
       "      <td>3873.0</td>\n",
       "      <td>797.0</td>\n",
       "      <td>2237.0</td>\n",
       "      <td>706.0</td>\n",
       "      <td>2.1736</td>\n",
       "      <td>72100.0</td>\n",
       "      <td>INLAND</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15502</th>\n",
       "      <td>-117.23</td>\n",
       "      <td>33.09</td>\n",
       "      <td>7.0</td>\n",
       "      <td>5320.0</td>\n",
       "      <td>855.0</td>\n",
       "      <td>2015.0</td>\n",
       "      <td>768.0</td>\n",
       "      <td>6.3373</td>\n",
       "      <td>279600.0</td>\n",
       "      <td>NEAR OCEAN</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2908</th>\n",
       "      <td>-119.04</td>\n",
       "      <td>35.37</td>\n",
       "      <td>44.0</td>\n",
       "      <td>1618.0</td>\n",
       "      <td>310.0</td>\n",
       "      <td>667.0</td>\n",
       "      <td>300.0</td>\n",
       "      <td>2.8750</td>\n",
       "      <td>82700.0</td>\n",
       "      <td>INLAND</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14053</th>\n",
       "      <td>-117.13</td>\n",
       "      <td>32.75</td>\n",
       "      <td>24.0</td>\n",
       "      <td>1877.0</td>\n",
       "      <td>519.0</td>\n",
       "      <td>898.0</td>\n",
       "      <td>483.0</td>\n",
       "      <td>2.2264</td>\n",
       "      <td>112500.0</td>\n",
       "      <td>NEAR OCEAN</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20496</th>\n",
       "      <td>-118.70</td>\n",
       "      <td>34.28</td>\n",
       "      <td>27.0</td>\n",
       "      <td>3536.0</td>\n",
       "      <td>646.0</td>\n",
       "      <td>1837.0</td>\n",
       "      <td>580.0</td>\n",
       "      <td>4.4964</td>\n",
       "      <td>238300.0</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1481</th>\n",
       "      <td>-122.04</td>\n",
       "      <td>37.96</td>\n",
       "      <td>28.0</td>\n",
       "      <td>1207.0</td>\n",
       "      <td>252.0</td>\n",
       "      <td>724.0</td>\n",
       "      <td>252.0</td>\n",
       "      <td>3.6964</td>\n",
       "      <td>165700.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18125</th>\n",
       "      <td>-122.03</td>\n",
       "      <td>37.33</td>\n",
       "      <td>23.0</td>\n",
       "      <td>4221.0</td>\n",
       "      <td>671.0</td>\n",
       "      <td>1782.0</td>\n",
       "      <td>641.0</td>\n",
       "      <td>7.4863</td>\n",
       "      <td>412300.0</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5830</th>\n",
       "      <td>-118.31</td>\n",
       "      <td>34.20</td>\n",
       "      <td>36.0</td>\n",
       "      <td>1692.0</td>\n",
       "      <td>263.0</td>\n",
       "      <td>778.0</td>\n",
       "      <td>278.0</td>\n",
       "      <td>5.0865</td>\n",
       "      <td>349600.0</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17989</th>\n",
       "      <td>-121.95</td>\n",
       "      <td>37.27</td>\n",
       "      <td>17.0</td>\n",
       "      <td>1330.0</td>\n",
       "      <td>271.0</td>\n",
       "      <td>408.0</td>\n",
       "      <td>258.0</td>\n",
       "      <td>1.7171</td>\n",
       "      <td>181300.0</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4861</th>\n",
       "      <td>-118.28</td>\n",
       "      <td>34.02</td>\n",
       "      <td>29.0</td>\n",
       "      <td>515.0</td>\n",
       "      <td>229.0</td>\n",
       "      <td>2690.0</td>\n",
       "      <td>217.0</td>\n",
       "      <td>0.4999</td>\n",
       "      <td>500001.0</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20615</th>\n",
       "      <td>-121.54</td>\n",
       "      <td>39.08</td>\n",
       "      <td>23.0</td>\n",
       "      <td>1076.0</td>\n",
       "      <td>216.0</td>\n",
       "      <td>724.0</td>\n",
       "      <td>197.0</td>\n",
       "      <td>2.3598</td>\n",
       "      <td>57500.0</td>\n",
       "      <td>INLAND</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11493</th>\n",
       "      <td>-117.99</td>\n",
       "      <td>33.69</td>\n",
       "      <td>12.0</td>\n",
       "      <td>2480.0</td>\n",
       "      <td>858.0</td>\n",
       "      <td>1441.0</td>\n",
       "      <td>788.0</td>\n",
       "      <td>1.6705</td>\n",
       "      <td>350000.0</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12386</th>\n",
       "      <td>-116.39</td>\n",
       "      <td>33.69</td>\n",
       "      <td>10.0</td>\n",
       "      <td>11659.0</td>\n",
       "      <td>2007.0</td>\n",
       "      <td>2186.0</td>\n",
       "      <td>1083.0</td>\n",
       "      <td>6.9833</td>\n",
       "      <td>238800.0</td>\n",
       "      <td>INLAND</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2167</th>\n",
       "      <td>-119.83</td>\n",
       "      <td>36.78</td>\n",
       "      <td>30.0</td>\n",
       "      <td>3162.0</td>\n",
       "      <td>640.0</td>\n",
       "      <td>1660.0</td>\n",
       "      <td>639.0</td>\n",
       "      <td>2.8359</td>\n",
       "      <td>80300.0</td>\n",
       "      <td>INLAND</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14562</th>\n",
       "      <td>-117.22</td>\n",
       "      <td>32.83</td>\n",
       "      <td>31.0</td>\n",
       "      <td>2558.0</td>\n",
       "      <td>512.0</td>\n",
       "      <td>1164.0</td>\n",
       "      <td>492.0</td>\n",
       "      <td>3.4318</td>\n",
       "      <td>200400.0</td>\n",
       "      <td>NEAR OCEAN</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17821</th>\n",
       "      <td>-121.88</td>\n",
       "      <td>37.37</td>\n",
       "      <td>14.0</td>\n",
       "      <td>6016.0</td>\n",
       "      <td>1404.0</td>\n",
       "      <td>3258.0</td>\n",
       "      <td>1316.0</td>\n",
       "      <td>3.5745</td>\n",
       "      <td>333700.0</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7667</th>\n",
       "      <td>-118.08</td>\n",
       "      <td>33.93</td>\n",
       "      <td>39.0</td>\n",
       "      <td>1478.0</td>\n",
       "      <td>324.0</td>\n",
       "      <td>1127.0</td>\n",
       "      <td>320.0</td>\n",
       "      <td>3.5250</td>\n",
       "      <td>158000.0</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10779</th>\n",
       "      <td>-117.91</td>\n",
       "      <td>33.65</td>\n",
       "      <td>17.0</td>\n",
       "      <td>1328.0</td>\n",
       "      <td>377.0</td>\n",
       "      <td>762.0</td>\n",
       "      <td>344.0</td>\n",
       "      <td>2.2222</td>\n",
       "      <td>276800.0</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9390</th>\n",
       "      <td>-122.53</td>\n",
       "      <td>37.90</td>\n",
       "      <td>44.0</td>\n",
       "      <td>2846.0</td>\n",
       "      <td>551.0</td>\n",
       "      <td>1232.0</td>\n",
       "      <td>537.0</td>\n",
       "      <td>3.8839</td>\n",
       "      <td>327200.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11063</th>\n",
       "      <td>-117.86</td>\n",
       "      <td>33.79</td>\n",
       "      <td>42.0</td>\n",
       "      <td>1024.0</td>\n",
       "      <td>191.0</td>\n",
       "      <td>483.0</td>\n",
       "      <td>187.0</td>\n",
       "      <td>4.1050</td>\n",
       "      <td>194500.0</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       longitude  latitude  housing_median_age  total_rooms  total_bedrooms  \\\n",
       "12655    -121.46     38.52                29.0       3873.0           797.0   \n",
       "15502    -117.23     33.09                 7.0       5320.0           855.0   \n",
       "2908     -119.04     35.37                44.0       1618.0           310.0   \n",
       "14053    -117.13     32.75                24.0       1877.0           519.0   \n",
       "20496    -118.70     34.28                27.0       3536.0           646.0   \n",
       "1481     -122.04     37.96                28.0       1207.0           252.0   \n",
       "18125    -122.03     37.33                23.0       4221.0           671.0   \n",
       "5830     -118.31     34.20                36.0       1692.0           263.0   \n",
       "17989    -121.95     37.27                17.0       1330.0           271.0   \n",
       "4861     -118.28     34.02                29.0        515.0           229.0   \n",
       "20615    -121.54     39.08                23.0       1076.0           216.0   \n",
       "11493    -117.99     33.69                12.0       2480.0           858.0   \n",
       "12386    -116.39     33.69                10.0      11659.0          2007.0   \n",
       "2167     -119.83     36.78                30.0       3162.0           640.0   \n",
       "14562    -117.22     32.83                31.0       2558.0           512.0   \n",
       "17821    -121.88     37.37                14.0       6016.0          1404.0   \n",
       "7667     -118.08     33.93                39.0       1478.0           324.0   \n",
       "10779    -117.91     33.65                17.0       1328.0           377.0   \n",
       "9390     -122.53     37.90                44.0       2846.0           551.0   \n",
       "11063    -117.86     33.79                42.0       1024.0           191.0   \n",
       "\n",
       "       population  households  median_income  median_house_value  \\\n",
       "12655      2237.0       706.0         2.1736             72100.0   \n",
       "15502      2015.0       768.0         6.3373            279600.0   \n",
       "2908        667.0       300.0         2.8750             82700.0   \n",
       "14053       898.0       483.0         2.2264            112500.0   \n",
       "20496      1837.0       580.0         4.4964            238300.0   \n",
       "1481        724.0       252.0         3.6964            165700.0   \n",
       "18125      1782.0       641.0         7.4863            412300.0   \n",
       "5830        778.0       278.0         5.0865            349600.0   \n",
       "17989       408.0       258.0         1.7171            181300.0   \n",
       "4861       2690.0       217.0         0.4999            500001.0   \n",
       "20615       724.0       197.0         2.3598             57500.0   \n",
       "11493      1441.0       788.0         1.6705            350000.0   \n",
       "12386      2186.0      1083.0         6.9833            238800.0   \n",
       "2167       1660.0       639.0         2.8359             80300.0   \n",
       "14562      1164.0       492.0         3.4318            200400.0   \n",
       "17821      3258.0      1316.0         3.5745            333700.0   \n",
       "7667       1127.0       320.0         3.5250            158000.0   \n",
       "10779       762.0       344.0         2.2222            276800.0   \n",
       "9390       1232.0       537.0         3.8839            327200.0   \n",
       "11063       483.0       187.0         4.1050            194500.0   \n",
       "\n",
       "      ocean_proximity income_cat  \n",
       "12655          INLAND          2  \n",
       "15502      NEAR OCEAN          5  \n",
       "2908           INLAND          2  \n",
       "14053      NEAR OCEAN          2  \n",
       "20496       <1H OCEAN          3  \n",
       "1481         NEAR BAY          3  \n",
       "18125       <1H OCEAN          5  \n",
       "5830        <1H OCEAN          4  \n",
       "17989       <1H OCEAN          2  \n",
       "4861        <1H OCEAN          1  \n",
       "20615          INLAND          2  \n",
       "11493       <1H OCEAN          2  \n",
       "12386          INLAND          5  \n",
       "2167           INLAND          2  \n",
       "14562      NEAR OCEAN          3  \n",
       "17821       <1H OCEAN          3  \n",
       "7667        <1H OCEAN          3  \n",
       "10779       <1H OCEAN          2  \n",
       "9390         NEAR BAY          3  \n",
       "11063       <1H OCEAN          3  "
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "strat_train_set.head(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "income_cat\n",
       "3    0.350533\n",
       "2    0.318798\n",
       "4    0.176357\n",
       "5    0.114341\n",
       "1    0.039971\n",
       "Name: count, dtype: float64"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 查看所有住房数据根据收入类别比例分布，收入占类别百分比\n",
    "strat_test_set['income_cat'].value_counts()/len(strat_test_set)  # 分层抽样测试集合"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "income_cat\n",
       "3    0.350581\n",
       "2    0.318847\n",
       "4    0.176308\n",
       "5    0.114438\n",
       "1    0.039826\n",
       "Name: count, dtype: float64"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing['income_cat'].value_counts()/len(housing)  # 完整数据集合"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "def income_cat_proportions(data):\n",
    "    return data[\"income_cat\"].value_counts() / len(data)\n",
    "\n",
    "train_set, test_set = train_test_split(housing, test_size=0.2, random_state=42)\n",
    "\n",
    "compare_props = pd.DataFrame({\n",
    "    \"全部数据\": income_cat_proportions(housing),\n",
    "    \"分层抽样\": income_cat_proportions(strat_test_set),\n",
    "    \"随机抽样\": income_cat_proportions(test_set),\n",
    "}).sort_index()\n",
    "compare_props[\"随机. %error\"] = 100 * compare_props[\"随机抽样\"] / compare_props[\"全部数据\"] - 100\n",
    "compare_props[\"分层. %error\"] = 100 * compare_props[\"分层抽样\"] / compare_props[\"全部数据\"] - 100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "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>全部数据</th>\n",
       "      <th>分层抽样</th>\n",
       "      <th>随机抽样</th>\n",
       "      <th>随机. %error</th>\n",
       "      <th>分层. %error</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>income_cat</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.039826</td>\n",
       "      <td>0.039971</td>\n",
       "      <td>0.040213</td>\n",
       "      <td>0.973236</td>\n",
       "      <td>0.364964</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.318847</td>\n",
       "      <td>0.318798</td>\n",
       "      <td>0.324370</td>\n",
       "      <td>1.732260</td>\n",
       "      <td>-0.015195</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.350581</td>\n",
       "      <td>0.350533</td>\n",
       "      <td>0.358527</td>\n",
       "      <td>2.266446</td>\n",
       "      <td>-0.013820</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.176308</td>\n",
       "      <td>0.176357</td>\n",
       "      <td>0.167393</td>\n",
       "      <td>-5.056334</td>\n",
       "      <td>0.027480</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.114438</td>\n",
       "      <td>0.114341</td>\n",
       "      <td>0.109496</td>\n",
       "      <td>-4.318374</td>\n",
       "      <td>-0.084674</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                全部数据      分层抽样      随机抽样  随机. %error  分层. %error\n",
       "income_cat                                                      \n",
       "1           0.039826  0.039971  0.040213    0.973236    0.364964\n",
       "2           0.318847  0.318798  0.324370    1.732260   -0.015195\n",
       "3           0.350581  0.350533  0.358527    2.266446   -0.013820\n",
       "4           0.176308  0.176357  0.167393   -5.056334    0.027480\n",
       "5           0.114438  0.114341  0.109496   -4.318374   -0.084674"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "compare_props"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 把测试集已经处理完成，用分层抽样的训练集数据来进行分析\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 将地理数据可视化"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 建立一个各区域的分布图，以便于数据可视化"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='longitude', ylabel='latitude'>"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "housing.plot(kind='scatter', x='longitude', y = 'latitude')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='longitude', ylabel='latitude'>"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 将alpha 设置为 0.1 ， 可以看出高密度数据点的位置\n",
    "housing.plot(kind='scatter', x='longitude', y = 'latitude', alpha=0.1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='longitude', ylabel='latitude'>"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x700 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 我们的大脑非常善于从图片中发现模式，但是需要玩转可视化参数才能让这些模式凸显出来\n",
    "housing.plot(kind=\"scatter\", x=\"longitude\", y=\"latitude\", alpha=0.4,\n",
    "    s=housing[\"population\"]/100, label=\"population\", figsize=(10,7),\n",
    "    c=\"median_house_value\", cmap=plt.get_cmap(\"jet\"), colorbar=True,\n",
    "    sharex=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\20255\\AppData\\Local\\Temp\\ipykernel_1760\\698309238.py:18: UserWarning: FixedFormatter should only be used together with FixedLocator\n",
      "  cbar.ax.set_yticklabels([\"$%dk\"%(round(v/1000)) for v in tick_values], fontsize=14)\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x700 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.image as mpimg\n",
    "california_img = mpimg.imread('./california.png')\n",
    "\n",
    "housing.plot(kind=\"scatter\", x=\"longitude\", y=\"latitude\", alpha=0.4,\n",
    "    s=housing[\"population\"]/100, label=\"population\", figsize=(10,7),\n",
    "    c=\"median_house_value\", cmap=plt.get_cmap(\"jet\"), colorbar=False,\n",
    "    sharex=False)\n",
    "\n",
    "plt.imshow(california_img, extent=[-124.55, -113.80, 32.45, 42.05], alpha=0.5,\n",
    "           cmap=plt.get_cmap(\"jet\"))\n",
    "plt.ylabel(\"Latitude\", fontsize=14)\n",
    "plt.xlabel(\"Longitude\", fontsize=14)\n",
    "\n",
    "\n",
    "prices = housing[\"median_house_value\"]\n",
    "tick_values = np.linspace(prices.min(), prices.max(), 11)\n",
    "cbar = plt.colorbar()\n",
    "cbar.ax.set_yticklabels([\"$%dk\"%(round(v/1000)) for v in tick_values], fontsize=14)\n",
    "cbar.set_label('Median House Value', fontsize=16)\n",
    "\n",
    "plt.legend(fontsize=16)\n",
    "# save_fig(\"california_housing_prices_plot\")\n",
    "plt.show()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 从图片中告诉我们，房屋价格与地理位置靠海，和人口密度息息相关"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 寻找特征相关性"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "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>longitude</th>\n",
       "      <th>latitude</th>\n",
       "      <th>housing_median_age</th>\n",
       "      <th>total_rooms</th>\n",
       "      <th>total_bedrooms</th>\n",
       "      <th>population</th>\n",
       "      <th>households</th>\n",
       "      <th>median_income</th>\n",
       "      <th>median_house_value</th>\n",
       "      <th>income_cat</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>longitude</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.924664</td>\n",
       "      <td>-0.108197</td>\n",
       "      <td>0.044568</td>\n",
       "      <td>0.069608</td>\n",
       "      <td>0.099773</td>\n",
       "      <td>0.055310</td>\n",
       "      <td>-0.015176</td>\n",
       "      <td>-0.045967</td>\n",
       "      <td>-0.010690</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>latitude</th>\n",
       "      <td>-0.924664</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.011173</td>\n",
       "      <td>-0.036100</td>\n",
       "      <td>-0.066983</td>\n",
       "      <td>-0.108785</td>\n",
       "      <td>-0.071035</td>\n",
       "      <td>-0.079809</td>\n",
       "      <td>-0.144160</td>\n",
       "      <td>-0.085528</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>housing_median_age</th>\n",
       "      <td>-0.108197</td>\n",
       "      <td>0.011173</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.361262</td>\n",
       "      <td>-0.320451</td>\n",
       "      <td>-0.296244</td>\n",
       "      <td>-0.302916</td>\n",
       "      <td>-0.119034</td>\n",
       "      <td>0.105623</td>\n",
       "      <td>-0.146920</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>total_rooms</th>\n",
       "      <td>0.044568</td>\n",
       "      <td>-0.036100</td>\n",
       "      <td>-0.361262</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.930380</td>\n",
       "      <td>0.857126</td>\n",
       "      <td>0.918484</td>\n",
       "      <td>0.198050</td>\n",
       "      <td>0.134153</td>\n",
       "      <td>0.220528</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>total_bedrooms</th>\n",
       "      <td>0.069608</td>\n",
       "      <td>-0.066983</td>\n",
       "      <td>-0.320451</td>\n",
       "      <td>0.930380</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.877747</td>\n",
       "      <td>0.979728</td>\n",
       "      <td>-0.007723</td>\n",
       "      <td>0.049686</td>\n",
       "      <td>0.015662</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>population</th>\n",
       "      <td>0.099773</td>\n",
       "      <td>-0.108785</td>\n",
       "      <td>-0.296244</td>\n",
       "      <td>0.857126</td>\n",
       "      <td>0.877747</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.907222</td>\n",
       "      <td>0.004834</td>\n",
       "      <td>-0.024650</td>\n",
       "      <td>0.025809</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>households</th>\n",
       "      <td>0.055310</td>\n",
       "      <td>-0.071035</td>\n",
       "      <td>-0.302916</td>\n",
       "      <td>0.918484</td>\n",
       "      <td>0.979728</td>\n",
       "      <td>0.907222</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.013033</td>\n",
       "      <td>0.065843</td>\n",
       "      <td>0.038490</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>median_income</th>\n",
       "      <td>-0.015176</td>\n",
       "      <td>-0.079809</td>\n",
       "      <td>-0.119034</td>\n",
       "      <td>0.198050</td>\n",
       "      <td>-0.007723</td>\n",
       "      <td>0.004834</td>\n",
       "      <td>0.013033</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.688075</td>\n",
       "      <td>0.902750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>median_house_value</th>\n",
       "      <td>-0.045967</td>\n",
       "      <td>-0.144160</td>\n",
       "      <td>0.105623</td>\n",
       "      <td>0.134153</td>\n",
       "      <td>0.049686</td>\n",
       "      <td>-0.024650</td>\n",
       "      <td>0.065843</td>\n",
       "      <td>0.688075</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.643892</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>income_cat</th>\n",
       "      <td>-0.010690</td>\n",
       "      <td>-0.085528</td>\n",
       "      <td>-0.146920</td>\n",
       "      <td>0.220528</td>\n",
       "      <td>0.015662</td>\n",
       "      <td>0.025809</td>\n",
       "      <td>0.038490</td>\n",
       "      <td>0.902750</td>\n",
       "      <td>0.643892</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    longitude  latitude  housing_median_age  total_rooms  \\\n",
       "longitude            1.000000 -0.924664           -0.108197     0.044568   \n",
       "latitude            -0.924664  1.000000            0.011173    -0.036100   \n",
       "housing_median_age  -0.108197  0.011173            1.000000    -0.361262   \n",
       "total_rooms          0.044568 -0.036100           -0.361262     1.000000   \n",
       "total_bedrooms       0.069608 -0.066983           -0.320451     0.930380   \n",
       "population           0.099773 -0.108785           -0.296244     0.857126   \n",
       "households           0.055310 -0.071035           -0.302916     0.918484   \n",
       "median_income       -0.015176 -0.079809           -0.119034     0.198050   \n",
       "median_house_value  -0.045967 -0.144160            0.105623     0.134153   \n",
       "income_cat          -0.010690 -0.085528           -0.146920     0.220528   \n",
       "\n",
       "                    total_bedrooms  population  households  median_income  \\\n",
       "longitude                 0.069608    0.099773    0.055310      -0.015176   \n",
       "latitude                 -0.066983   -0.108785   -0.071035      -0.079809   \n",
       "housing_median_age       -0.320451   -0.296244   -0.302916      -0.119034   \n",
       "total_rooms               0.930380    0.857126    0.918484       0.198050   \n",
       "total_bedrooms            1.000000    0.877747    0.979728      -0.007723   \n",
       "population                0.877747    1.000000    0.907222       0.004834   \n",
       "households                0.979728    0.907222    1.000000       0.013033   \n",
       "median_income            -0.007723    0.004834    0.013033       1.000000   \n",
       "median_house_value        0.049686   -0.024650    0.065843       0.688075   \n",
       "income_cat                0.015662    0.025809    0.038490       0.902750   \n",
       "\n",
       "                    median_house_value  income_cat  \n",
       "longitude                    -0.045967   -0.010690  \n",
       "latitude                     -0.144160   -0.085528  \n",
       "housing_median_age            0.105623   -0.146920  \n",
       "total_rooms                   0.134153    0.220528  \n",
       "total_bedrooms                0.049686    0.015662  \n",
       "population                   -0.024650    0.025809  \n",
       "households                    0.065843    0.038490  \n",
       "median_income                 0.688075    0.902750  \n",
       "median_house_value            1.000000    0.643892  \n",
       "income_cat                    0.643892    1.000000  "
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# corr() 计算出每对特征之间的相关系数， 称为皮尔逊相关系数\n",
    "housing1 = housing.drop('ocean_proximity', axis = 1)\n",
    "corr_matrix =  housing1.corr()\n",
    "corr_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "median_house_value    1.000000\n",
       "median_income         0.688075\n",
       "income_cat            0.643892\n",
       "total_rooms           0.134153\n",
       "housing_median_age    0.105623\n",
       "households            0.065843\n",
       "total_bedrooms        0.049686\n",
       "population           -0.024650\n",
       "longitude            -0.045967\n",
       "latitude             -0.144160\n",
       "Name: median_house_value, dtype: float64"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corr_matrix['median_house_value'].sort_values(ascending = False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "   * 相关系数范围 从 -1 变化到 1，越接近1 ，表示越强的正相关，，比如，当收入中位数上升时，房价中位数也趋于上升，\n",
    "   * 当系数接近-1 ，则表示有强烈的负相关，纬度和房价中位数之间出现轻微的负相关，越往北走，房价倾向于下降，\n",
    "   * 系数靠近0 ，说明二者之间没有线性相关性，\n",
    "   * 相关系数仅检测 线性相关性 ，如果x上升，y上升或者下降，可能会彻底遗漏非线性相关性，例如 如果x接近于0，y上升"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<Axes: xlabel='median_house_value', ylabel='median_house_value'>,\n",
       "        <Axes: xlabel='median_income', ylabel='median_house_value'>,\n",
       "        <Axes: xlabel='total_rooms', ylabel='median_house_value'>,\n",
       "        <Axes: xlabel='housing_median_age', ylabel='median_house_value'>],\n",
       "       [<Axes: xlabel='median_house_value', ylabel='median_income'>,\n",
       "        <Axes: xlabel='median_income', ylabel='median_income'>,\n",
       "        <Axes: xlabel='total_rooms', ylabel='median_income'>,\n",
       "        <Axes: xlabel='housing_median_age', ylabel='median_income'>],\n",
       "       [<Axes: xlabel='median_house_value', ylabel='total_rooms'>,\n",
       "        <Axes: xlabel='median_income', ylabel='total_rooms'>,\n",
       "        <Axes: xlabel='total_rooms', ylabel='total_rooms'>,\n",
       "        <Axes: xlabel='housing_median_age', ylabel='total_rooms'>],\n",
       "       [<Axes: xlabel='median_house_value', ylabel='housing_median_age'>,\n",
       "        <Axes: xlabel='median_income', ylabel='housing_median_age'>,\n",
       "        <Axes: xlabel='total_rooms', ylabel='housing_median_age'>,\n",
       "        <Axes: xlabel='housing_median_age', ylabel='housing_median_age'>]],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x800 with 16 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 使用padans scatter_matrix 函数，可以绘制特征之间的相关性\n",
    "from pandas.plotting import scatter_matrix\n",
    "\n",
    "attributes = ['median_house_value', 'median_income', 'total_rooms', 'housing_median_age']\n",
    "scatter_matrix(housing[attributes], figsize = (12, 8) )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.0, 16.0, 0.0, 550000.0)"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "housing.plot(kind = 'scatter', x='median_income', y = 'median_house_value', alpha = 0.1)\n",
    "plt.axis([0, 16, 0, 550000])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 50万美元是一条清晰的线，被设置上限了， 45w，35w，28w，也有虚线， 可能是异常值，我可以在后期把数据删除掉"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "   1. 在给机器学习算法输入数据之前，我们识别出了一些异常数据，需要提前清理掉，也发现了不同属性之间的某些联系，跟目标属性相关的联系\n",
    "   2. 某些属性分布显示出了明显的“重尾”分布，对进行转换处理，计算其对数\n",
    "   3. 在给机器学习算法输入数据之前，最后一件事儿，尝试各种属性的组合\n",
    "   4. 每个项目历程都不一样，大致思路都相识"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 试验不同特征的组合"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "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>longitude</th>\n",
       "      <th>latitude</th>\n",
       "      <th>housing_median_age</th>\n",
       "      <th>total_rooms</th>\n",
       "      <th>total_bedrooms</th>\n",
       "      <th>population</th>\n",
       "      <th>households</th>\n",
       "      <th>median_income</th>\n",
       "      <th>median_house_value</th>\n",
       "      <th>ocean_proximity</th>\n",
       "      <th>income_cat</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-122.23</td>\n",
       "      <td>37.88</td>\n",
       "      <td>41.0</td>\n",
       "      <td>880.0</td>\n",
       "      <td>129.0</td>\n",
       "      <td>322.0</td>\n",
       "      <td>126.0</td>\n",
       "      <td>8.3252</td>\n",
       "      <td>452600.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-122.22</td>\n",
       "      <td>37.86</td>\n",
       "      <td>21.0</td>\n",
       "      <td>7099.0</td>\n",
       "      <td>1106.0</td>\n",
       "      <td>2401.0</td>\n",
       "      <td>1138.0</td>\n",
       "      <td>8.3014</td>\n",
       "      <td>358500.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-122.24</td>\n",
       "      <td>37.85</td>\n",
       "      <td>52.0</td>\n",
       "      <td>1467.0</td>\n",
       "      <td>190.0</td>\n",
       "      <td>496.0</td>\n",
       "      <td>177.0</td>\n",
       "      <td>7.2574</td>\n",
       "      <td>352100.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-122.25</td>\n",
       "      <td>37.85</td>\n",
       "      <td>52.0</td>\n",
       "      <td>1274.0</td>\n",
       "      <td>235.0</td>\n",
       "      <td>558.0</td>\n",
       "      <td>219.0</td>\n",
       "      <td>5.6431</td>\n",
       "      <td>341300.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-122.25</td>\n",
       "      <td>37.85</td>\n",
       "      <td>52.0</td>\n",
       "      <td>1627.0</td>\n",
       "      <td>280.0</td>\n",
       "      <td>565.0</td>\n",
       "      <td>259.0</td>\n",
       "      <td>3.8462</td>\n",
       "      <td>342200.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   longitude  latitude  housing_median_age  total_rooms  total_bedrooms  \\\n",
       "0    -122.23     37.88                41.0        880.0           129.0   \n",
       "1    -122.22     37.86                21.0       7099.0          1106.0   \n",
       "2    -122.24     37.85                52.0       1467.0           190.0   \n",
       "3    -122.25     37.85                52.0       1274.0           235.0   \n",
       "4    -122.25     37.85                52.0       1627.0           280.0   \n",
       "\n",
       "   population  households  median_income  median_house_value ocean_proximity  \\\n",
       "0       322.0       126.0         8.3252            452600.0        NEAR BAY   \n",
       "1      2401.0      1138.0         8.3014            358500.0        NEAR BAY   \n",
       "2       496.0       177.0         7.2574            352100.0        NEAR BAY   \n",
       "3       558.0       219.0         5.6431            341300.0        NEAR BAY   \n",
       "4       565.0       259.0         3.8462            342200.0        NEAR BAY   \n",
       "\n",
       "  income_cat  \n",
       "0          5  \n",
       "1          5  \n",
       "2          5  \n",
       "3          4  \n",
       "4          3  "
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "housing['rooms_per_household'] = housing['total_rooms']/housing['households']\n",
    "housing['bedrooms_per_room'] = housing['total_bedrooms']/housing['total_rooms']\n",
    "housing['population_per_household'] = housing['population']/housing['households']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "median_house_value          1.000000\n",
       "median_income               0.688075\n",
       "income_cat                  0.643892\n",
       "rooms_per_household         0.151948\n",
       "total_rooms                 0.134153\n",
       "housing_median_age          0.105623\n",
       "households                  0.065843\n",
       "total_bedrooms              0.049686\n",
       "population_per_household   -0.023737\n",
       "population                 -0.024650\n",
       "longitude                  -0.045967\n",
       "latitude                   -0.144160\n",
       "bedrooms_per_room          -0.255880\n",
       "Name: median_house_value, dtype: float64"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 关联矩阵\n",
    "housing1 = housing.drop('ocean_proximity', axis = 1)\n",
    "corr_matrix =  housing1.corr()\n",
    "corr_matrix['median_house_value'].sort_values(ascending = False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 机器学习算法的数据准备"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>longitude</th>\n",
       "      <th>latitude</th>\n",
       "      <th>housing_median_age</th>\n",
       "      <th>total_rooms</th>\n",
       "      <th>total_bedrooms</th>\n",
       "      <th>population</th>\n",
       "      <th>households</th>\n",
       "      <th>median_income</th>\n",
       "      <th>median_house_value</th>\n",
       "      <th>ocean_proximity</th>\n",
       "      <th>income_cat</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>12655</th>\n",
       "      <td>-121.46</td>\n",
       "      <td>38.52</td>\n",
       "      <td>29.0</td>\n",
       "      <td>3873.0</td>\n",
       "      <td>797.0</td>\n",
       "      <td>2237.0</td>\n",
       "      <td>706.0</td>\n",
       "      <td>2.1736</td>\n",
       "      <td>72100.0</td>\n",
       "      <td>INLAND</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15502</th>\n",
       "      <td>-117.23</td>\n",
       "      <td>33.09</td>\n",
       "      <td>7.0</td>\n",
       "      <td>5320.0</td>\n",
       "      <td>855.0</td>\n",
       "      <td>2015.0</td>\n",
       "      <td>768.0</td>\n",
       "      <td>6.3373</td>\n",
       "      <td>279600.0</td>\n",
       "      <td>NEAR OCEAN</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2908</th>\n",
       "      <td>-119.04</td>\n",
       "      <td>35.37</td>\n",
       "      <td>44.0</td>\n",
       "      <td>1618.0</td>\n",
       "      <td>310.0</td>\n",
       "      <td>667.0</td>\n",
       "      <td>300.0</td>\n",
       "      <td>2.8750</td>\n",
       "      <td>82700.0</td>\n",
       "      <td>INLAND</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14053</th>\n",
       "      <td>-117.13</td>\n",
       "      <td>32.75</td>\n",
       "      <td>24.0</td>\n",
       "      <td>1877.0</td>\n",
       "      <td>519.0</td>\n",
       "      <td>898.0</td>\n",
       "      <td>483.0</td>\n",
       "      <td>2.2264</td>\n",
       "      <td>112500.0</td>\n",
       "      <td>NEAR OCEAN</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20496</th>\n",
       "      <td>-118.70</td>\n",
       "      <td>34.28</td>\n",
       "      <td>27.0</td>\n",
       "      <td>3536.0</td>\n",
       "      <td>646.0</td>\n",
       "      <td>1837.0</td>\n",
       "      <td>580.0</td>\n",
       "      <td>4.4964</td>\n",
       "      <td>238300.0</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       longitude  latitude  housing_median_age  total_rooms  total_bedrooms  \\\n",
       "12655    -121.46     38.52                29.0       3873.0           797.0   \n",
       "15502    -117.23     33.09                 7.0       5320.0           855.0   \n",
       "2908     -119.04     35.37                44.0       1618.0           310.0   \n",
       "14053    -117.13     32.75                24.0       1877.0           519.0   \n",
       "20496    -118.70     34.28                27.0       3536.0           646.0   \n",
       "\n",
       "       population  households  median_income  median_house_value  \\\n",
       "12655      2237.0       706.0         2.1736             72100.0   \n",
       "15502      2015.0       768.0         6.3373            279600.0   \n",
       "2908        667.0       300.0         2.8750             82700.0   \n",
       "14053       898.0       483.0         2.2264            112500.0   \n",
       "20496      1837.0       580.0         4.4964            238300.0   \n",
       "\n",
       "      ocean_proximity income_cat  \n",
       "12655          INLAND          2  \n",
       "15502      NEAR OCEAN          5  \n",
       "2908           INLAND          2  \n",
       "14053      NEAR OCEAN          2  \n",
       "20496       <1H OCEAN          3  "
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 得到一个分层抽样代表全局数据集的  训练集和测试集\n",
    "strat_train_set.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 将数据集的数据和标签分开 \n",
    "housing_labels = strat_train_set['median_house_value'].copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "housing = strat_train_set.drop('median_house_value', axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>longitude</th>\n",
       "      <th>latitude</th>\n",
       "      <th>housing_median_age</th>\n",
       "      <th>total_rooms</th>\n",
       "      <th>total_bedrooms</th>\n",
       "      <th>population</th>\n",
       "      <th>households</th>\n",
       "      <th>median_income</th>\n",
       "      <th>ocean_proximity</th>\n",
       "      <th>income_cat</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>12655</th>\n",
       "      <td>-121.46</td>\n",
       "      <td>38.52</td>\n",
       "      <td>29.0</td>\n",
       "      <td>3873.0</td>\n",
       "      <td>797.0</td>\n",
       "      <td>2237.0</td>\n",
       "      <td>706.0</td>\n",
       "      <td>2.1736</td>\n",
       "      <td>INLAND</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15502</th>\n",
       "      <td>-117.23</td>\n",
       "      <td>33.09</td>\n",
       "      <td>7.0</td>\n",
       "      <td>5320.0</td>\n",
       "      <td>855.0</td>\n",
       "      <td>2015.0</td>\n",
       "      <td>768.0</td>\n",
       "      <td>6.3373</td>\n",
       "      <td>NEAR OCEAN</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2908</th>\n",
       "      <td>-119.04</td>\n",
       "      <td>35.37</td>\n",
       "      <td>44.0</td>\n",
       "      <td>1618.0</td>\n",
       "      <td>310.0</td>\n",
       "      <td>667.0</td>\n",
       "      <td>300.0</td>\n",
       "      <td>2.8750</td>\n",
       "      <td>INLAND</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14053</th>\n",
       "      <td>-117.13</td>\n",
       "      <td>32.75</td>\n",
       "      <td>24.0</td>\n",
       "      <td>1877.0</td>\n",
       "      <td>519.0</td>\n",
       "      <td>898.0</td>\n",
       "      <td>483.0</td>\n",
       "      <td>2.2264</td>\n",
       "      <td>NEAR OCEAN</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20496</th>\n",
       "      <td>-118.70</td>\n",
       "      <td>34.28</td>\n",
       "      <td>27.0</td>\n",
       "      <td>3536.0</td>\n",
       "      <td>646.0</td>\n",
       "      <td>1837.0</td>\n",
       "      <td>580.0</td>\n",
       "      <td>4.4964</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       longitude  latitude  housing_median_age  total_rooms  total_bedrooms  \\\n",
       "12655    -121.46     38.52                29.0       3873.0           797.0   \n",
       "15502    -117.23     33.09                 7.0       5320.0           855.0   \n",
       "2908     -119.04     35.37                44.0       1618.0           310.0   \n",
       "14053    -117.13     32.75                24.0       1877.0           519.0   \n",
       "20496    -118.70     34.28                27.0       3536.0           646.0   \n",
       "\n",
       "       population  households  median_income ocean_proximity income_cat  \n",
       "12655      2237.0       706.0         2.1736          INLAND          2  \n",
       "15502      2015.0       768.0         6.3373      NEAR OCEAN          5  \n",
       "2908        667.0       300.0         2.8750          INLAND          2  \n",
       "14053       898.0       483.0         2.2264      NEAR OCEAN          2  \n",
       "20496      1837.0       580.0         4.4964       <1H OCEAN          3  "
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Index: 16512 entries, 12655 to 19773\n",
      "Data columns (total 10 columns):\n",
      " #   Column              Non-Null Count  Dtype   \n",
      "---  ------              --------------  -----   \n",
      " 0   longitude           16512 non-null  float64 \n",
      " 1   latitude            16512 non-null  float64 \n",
      " 2   housing_median_age  16512 non-null  float64 \n",
      " 3   total_rooms         16512 non-null  float64 \n",
      " 4   total_bedrooms      16354 non-null  float64 \n",
      " 5   population          16512 non-null  float64 \n",
      " 6   households          16512 non-null  float64 \n",
      " 7   median_income       16512 non-null  float64 \n",
      " 8   ocean_proximity     16512 non-null  object  \n",
      " 9   income_cat          16512 non-null  category\n",
      "dtypes: category(1), float64(8), object(1)\n",
      "memory usage: 1.3+ MB\n"
     ]
    }
   ],
   "source": [
    "housing.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 数据清洗"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "   * 大多数机器学算法无法在缺失的特征上工作，创建一些函数辅助，total_bedrooms有缺失，\n",
    "   1. 放弃这些区域\n",
    "   2. 放弃这个属性\n",
    "   3. 将缺失值 设置为 某个值，（0， 平均数或者中位数都可以）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>longitude</th>\n",
       "      <th>latitude</th>\n",
       "      <th>housing_median_age</th>\n",
       "      <th>total_rooms</th>\n",
       "      <th>total_bedrooms</th>\n",
       "      <th>population</th>\n",
       "      <th>households</th>\n",
       "      <th>median_income</th>\n",
       "      <th>ocean_proximity</th>\n",
       "      <th>income_cat</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1606</th>\n",
       "      <td>-122.08</td>\n",
       "      <td>37.88</td>\n",
       "      <td>26.0</td>\n",
       "      <td>2947.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>825.0</td>\n",
       "      <td>626.0</td>\n",
       "      <td>2.9330</td>\n",
       "      <td>NEAR BAY</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10915</th>\n",
       "      <td>-117.87</td>\n",
       "      <td>33.73</td>\n",
       "      <td>45.0</td>\n",
       "      <td>2264.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1970.0</td>\n",
       "      <td>499.0</td>\n",
       "      <td>3.4193</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19150</th>\n",
       "      <td>-122.70</td>\n",
       "      <td>38.35</td>\n",
       "      <td>14.0</td>\n",
       "      <td>2313.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>954.0</td>\n",
       "      <td>397.0</td>\n",
       "      <td>3.7813</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4186</th>\n",
       "      <td>-118.23</td>\n",
       "      <td>34.13</td>\n",
       "      <td>48.0</td>\n",
       "      <td>1308.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>835.0</td>\n",
       "      <td>294.0</td>\n",
       "      <td>4.2891</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16885</th>\n",
       "      <td>-122.40</td>\n",
       "      <td>37.58</td>\n",
       "      <td>26.0</td>\n",
       "      <td>3281.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1145.0</td>\n",
       "      <td>480.0</td>\n",
       "      <td>6.3580</td>\n",
       "      <td>NEAR OCEAN</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       longitude  latitude  housing_median_age  total_rooms  total_bedrooms  \\\n",
       "1606     -122.08     37.88                26.0       2947.0             NaN   \n",
       "10915    -117.87     33.73                45.0       2264.0             NaN   \n",
       "19150    -122.70     38.35                14.0       2313.0             NaN   \n",
       "4186     -118.23     34.13                48.0       1308.0             NaN   \n",
       "16885    -122.40     37.58                26.0       3281.0             NaN   \n",
       "\n",
       "       population  households  median_income ocean_proximity income_cat  \n",
       "1606        825.0       626.0         2.9330        NEAR BAY          2  \n",
       "10915      1970.0       499.0         3.4193       <1H OCEAN          3  \n",
       "19150       954.0       397.0         3.7813       <1H OCEAN          3  \n",
       "4186        835.0       294.0         4.2891       <1H OCEAN          3  \n",
       "16885      1145.0       480.0         6.3580      NEAR OCEAN          5  "
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rows = housing[housing.isnull().any(axis = 1)]\n",
    "rows.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-2 {color: black;background-color: white;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>SimpleImputer(strategy=&#x27;median&#x27;)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SimpleImputer</label><div class=\"sk-toggleable__content\"><pre>SimpleImputer(strategy=&#x27;median&#x27;)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "SimpleImputer(strategy='median')"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# # 放弃这些区域\n",
    "# rows.dropna(subset = ['total_bedrooms'])\n",
    "# # 放弃这个属性\n",
    "# rows.drop('total_bedrooms', axis = 1)\n",
    "#  将缺失值 设置为 某个值，（0， 平均数或者中位数都可以）\n",
    "\n",
    "# 创建一个imputer实例，指定你要用属性中的中位数值替换该属性的缺失值 \n",
    "from sklearn.impute import SimpleImputer\n",
    "\n",
    "imputer = SimpleImputer(strategy = 'median')\n",
    "\n",
    "# 使用fit() 方法将 imputer实例适配到训练集\n",
    "housing_num = housing.drop('ocean_proximity', axis =1)\n",
    "\n",
    "imputer.fit(housing_num)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-118.51   ,   34.26   ,   29.     , 2119.     ,  433.     ,\n",
       "       1164.     ,  408.     ,    3.54155,    3.     ])"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "imputer.statistics_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [],
   "source": [
    "#housing_num.median().values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-121.46  ,   38.52  ,   29.    , ...,  706.    ,    2.1736,\n",
       "           2.    ],\n",
       "       [-117.23  ,   33.09  ,    7.    , ...,  768.    ,    6.3373,\n",
       "           5.    ],\n",
       "       [-119.04  ,   35.37  ,   44.    , ...,  300.    ,    2.875 ,\n",
       "           2.    ],\n",
       "       ...,\n",
       "       [-122.72  ,   38.44  ,   48.    , ...,  172.    ,    3.1797,\n",
       "           3.    ],\n",
       "       [-122.7   ,   38.31  ,   14.    , ...,  501.    ,    4.1964,\n",
       "           3.    ],\n",
       "       [-122.14  ,   39.97  ,   27.    , ...,  197.    ,    3.1319,\n",
       "           3.    ]])"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = imputer.transform(housing_num)\n",
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "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>longitude</th>\n",
       "      <th>latitude</th>\n",
       "      <th>housing_median_age</th>\n",
       "      <th>total_rooms</th>\n",
       "      <th>total_bedrooms</th>\n",
       "      <th>population</th>\n",
       "      <th>households</th>\n",
       "      <th>median_income</th>\n",
       "      <th>income_cat</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>12655</th>\n",
       "      <td>-121.46</td>\n",
       "      <td>38.52</td>\n",
       "      <td>29.0</td>\n",
       "      <td>3873.0</td>\n",
       "      <td>797.0</td>\n",
       "      <td>2237.0</td>\n",
       "      <td>706.0</td>\n",
       "      <td>2.1736</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15502</th>\n",
       "      <td>-117.23</td>\n",
       "      <td>33.09</td>\n",
       "      <td>7.0</td>\n",
       "      <td>5320.0</td>\n",
       "      <td>855.0</td>\n",
       "      <td>2015.0</td>\n",
       "      <td>768.0</td>\n",
       "      <td>6.3373</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2908</th>\n",
       "      <td>-119.04</td>\n",
       "      <td>35.37</td>\n",
       "      <td>44.0</td>\n",
       "      <td>1618.0</td>\n",
       "      <td>310.0</td>\n",
       "      <td>667.0</td>\n",
       "      <td>300.0</td>\n",
       "      <td>2.8750</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14053</th>\n",
       "      <td>-117.13</td>\n",
       "      <td>32.75</td>\n",
       "      <td>24.0</td>\n",
       "      <td>1877.0</td>\n",
       "      <td>519.0</td>\n",
       "      <td>898.0</td>\n",
       "      <td>483.0</td>\n",
       "      <td>2.2264</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20496</th>\n",
       "      <td>-118.70</td>\n",
       "      <td>34.28</td>\n",
       "      <td>27.0</td>\n",
       "      <td>3536.0</td>\n",
       "      <td>646.0</td>\n",
       "      <td>1837.0</td>\n",
       "      <td>580.0</td>\n",
       "      <td>4.4964</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       longitude  latitude  housing_median_age  total_rooms  total_bedrooms  \\\n",
       "12655    -121.46     38.52                29.0       3873.0           797.0   \n",
       "15502    -117.23     33.09                 7.0       5320.0           855.0   \n",
       "2908     -119.04     35.37                44.0       1618.0           310.0   \n",
       "14053    -117.13     32.75                24.0       1877.0           519.0   \n",
       "20496    -118.70     34.28                27.0       3536.0           646.0   \n",
       "\n",
       "       population  households  median_income  income_cat  \n",
       "12655      2237.0       706.0         2.1736         2.0  \n",
       "15502      2015.0       768.0         6.3373         5.0  \n",
       "2908        667.0       300.0         2.8750         2.0  \n",
       "14053       898.0       483.0         2.2264         2.0  \n",
       "20496      1837.0       580.0         4.4964         3.0  "
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing_tr = pd.DataFrame(X, columns = housing_num.columns, index = housing_num.index)\n",
    "housing_tr.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Index: 16512 entries, 12655 to 19773\n",
      "Data columns (total 9 columns):\n",
      " #   Column              Non-Null Count  Dtype  \n",
      "---  ------              --------------  -----  \n",
      " 0   longitude           16512 non-null  float64\n",
      " 1   latitude            16512 non-null  float64\n",
      " 2   housing_median_age  16512 non-null  float64\n",
      " 3   total_rooms         16512 non-null  float64\n",
      " 4   total_bedrooms      16512 non-null  float64\n",
      " 5   population          16512 non-null  float64\n",
      " 6   households          16512 non-null  float64\n",
      " 7   median_income       16512 non-null  float64\n",
      " 8   income_cat          16512 non-null  float64\n",
      "dtypes: float64(9)\n",
      "memory usage: 1.3 MB\n"
     ]
    }
   ],
   "source": [
    "housing_tr.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## scikit-Learn\n",
    "* 一致性\n",
    "   1. 估算器\n",
    "       比如：各种机器学习算法\n",
    "       fit()执行估算器\n",
    "\n",
    "   2. 转换器  \n",
    "       比如：LabelBinarizer\n",
    "       transform()  执行转换数据集\n",
    "       fit_transform()  先估算，再转换\n",
    "   3. 预测器\n",
    "       predict()  对给定的新数据集进行预测\n",
    "       score()    评估测试集的预测质量\n",
    "* 检查\n",
    "    1. imputer.strategy_学习参数通过公共实例变量访问"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 处理文本和分类属性"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "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>ocean_proximity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>12655</th>\n",
       "      <td>INLAND</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15502</th>\n",
       "      <td>NEAR OCEAN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2908</th>\n",
       "      <td>INLAND</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14053</th>\n",
       "      <td>NEAR OCEAN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20496</th>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1481</th>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18125</th>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5830</th>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17989</th>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4861</th>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      ocean_proximity\n",
       "12655          INLAND\n",
       "15502      NEAR OCEAN\n",
       "2908           INLAND\n",
       "14053      NEAR OCEAN\n",
       "20496       <1H OCEAN\n",
       "1481         NEAR BAY\n",
       "18125       <1H OCEAN\n",
       "5830        <1H OCEAN\n",
       "17989       <1H OCEAN\n",
       "4861        <1H OCEAN"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing_cat = housing[['ocean_proximity']]\n",
    "housing_cat.head(10)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ocean_proximity\n",
       "<1H OCEAN     7277\n",
       "INLAND        5262\n",
       "NEAR OCEAN    2124\n",
       "NEAR BAY      1847\n",
       "ISLAND           2\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing['ocean_proximity'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 4, 1, ..., 0, 0, 1])"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 将文本转化成对应的数字分类 ， 使用转换器\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "encoder = LabelEncoder()\n",
    "housing_cat = housing['ocean_proximity']\n",
    "housing_cat_encoder = encoder.fit_transform(housing_cat)\n",
    "housing_cat_encoder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['<1H OCEAN', 'INLAND', 'ISLAND', 'NEAR BAY', 'NEAR OCEAN'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "encoder.classes_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "   1. 机器学习算法会以为两个相近的数字比远的数字更相似，比如0，4 比  0，1相似度更高，\n",
    "   2. 创建独热编码，当 <1H ,第0个属性为1，其余都为0， 列别是InLand时候，另一个属性为1，其余为0，1为热，0为冷，独热编码\n",
    "   3. OneHotEncoder编码器, fit_trans需要二维数组， 转换housing_cat"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "from sklearn.preprocessing import OneHotEncoder\n",
    "encoder = OneHotEncoder()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 4, 1, ..., 0, 0, 1])"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing_cat_encoder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1],\n",
       "       [4],\n",
       "       [1],\n",
       "       ...,\n",
       "       [0],\n",
       "       [0],\n",
       "       [1]])"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing_cat_encoder.reshape(-1, 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<16512x5 sparse matrix of type '<class 'numpy.float64'>'\n",
       "\twith 16512 stored elements in Compressed Sparse Row format>"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing_cat_hot = encoder.fit_transform(housing_cat_encoder.reshape(-1, 1))\n",
    "housing_cat_hot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0., 1., 0., 0., 0.],\n",
       "       [0., 0., 0., 0., 1.],\n",
       "       [0., 1., 0., 0., 0.],\n",
       "       ...,\n",
       "       [1., 0., 0., 0., 0.],\n",
       "       [1., 0., 0., 0., 0.],\n",
       "       [0., 1., 0., 0., 0.]])"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 从稀疏矩阵 转换成 numpy\n",
    "housing_cat_hot.toarray()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0, 1, 0, 0, 0],\n",
       "       [0, 0, 0, 0, 1],\n",
       "       [0, 1, 0, 0, 0],\n",
       "       ...,\n",
       "       [1, 0, 0, 0, 0],\n",
       "       [1, 0, 0, 0, 0],\n",
       "       [0, 1, 0, 0, 0]])"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 一次完成两个转换 文本--整数类型--独热类型\n",
    "housing_cat\n",
    "from sklearn.preprocessing import LabelBinarizer\n",
    "encoder = LabelBinarizer()\n",
    "housing_cat_1hot = encoder.fit_transform(housing_cat)\n",
    "housing_cat_1hot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<16512x5 sparse matrix of type '<class 'numpy.int32'>'\n",
       "\twith 16512 stored elements in Compressed Sparse Row format>"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 输出稀疏矩阵\n",
    "\n",
    "from sklearn.preprocessing import LabelBinarizer\n",
    "encoder = LabelBinarizer(sparse_output = True)\n",
    "housing_cat_1hot = encoder.fit_transform(housing_cat)\n",
    "housing_cat_1hot"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 自定义转换器"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.base import BaseEstimator, TransformerMixin\n",
    "\n",
    "\n",
    "rooms_ix, bedrooms_ix, population_ix, household_ix = [list(housing.columns).index(col) for col in (\"total_rooms\", \"total_bedrooms\", \"population\", \"households\")]\n",
    "rooms_ix, bedrooms_ix, population_ix, household_ix \n",
    "class CombinedAttributesAdder(BaseEstimator, TransformerMixin):\n",
    "    def __init__(self, add_bedrooms_per_room = True):\n",
    "        self.add_bedrooms_per_room = add_bedrooms_per_room\n",
    "    def fit(self, X, y = None):\n",
    "        return self\n",
    "    def transform(self, X, y = None):\n",
    "        rooms_per_household = X[:, rooms_ix] / X[:, household_ix]\n",
    "        population_per_household = X[:, population_ix] / X[:, household_ix]\n",
    "        if self.add_bedrooms_per_room:\n",
    "            bedrooms_per_room = X[:, bedrooms_ix] / X[:, rooms_ix]\n",
    "            return np.c_[X, rooms_per_household,population_per_household, bedrooms_per_room]\n",
    "        else:\n",
    "            return np.c_[X, rooms_per_household,population_per_household]\n",
    "        \n",
    "        \n",
    "        \n",
    "attr_adder = CombinedAttributesAdder(add_bedrooms_per_room = False)\n",
    "housing_extra_attribs = attr_adder.transform(housing.values)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-121.46, 38.52, 29.0, ..., 2, 5.485835694050992,\n",
       "        3.168555240793201],\n",
       "       [-117.23, 33.09, 7.0, ..., 5, 6.927083333333333,\n",
       "        2.6236979166666665],\n",
       "       [-119.04, 35.37, 44.0, ..., 2, 5.3933333333333335,\n",
       "        2.223333333333333],\n",
       "       ...,\n",
       "       [-122.72, 38.44, 48.0, ..., 3, 4.1104651162790695,\n",
       "        2.6627906976744184],\n",
       "       [-122.7, 38.31, 14.0, ..., 3, 6.297405189620759,\n",
       "        2.411177644710579],\n",
       "       [-122.14, 39.97, 27.0, ..., 3, 5.477157360406092,\n",
       "        3.1725888324873095]], dtype=object)"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing_extra_attribs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>10</th>\n",
       "      <th>11</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-121.46</td>\n",
       "      <td>38.52</td>\n",
       "      <td>29.0</td>\n",
       "      <td>3873.0</td>\n",
       "      <td>797.0</td>\n",
       "      <td>2237.0</td>\n",
       "      <td>706.0</td>\n",
       "      <td>2.1736</td>\n",
       "      <td>INLAND</td>\n",
       "      <td>2</td>\n",
       "      <td>5.485836</td>\n",
       "      <td>3.168555</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-117.23</td>\n",
       "      <td>33.09</td>\n",
       "      <td>7.0</td>\n",
       "      <td>5320.0</td>\n",
       "      <td>855.0</td>\n",
       "      <td>2015.0</td>\n",
       "      <td>768.0</td>\n",
       "      <td>6.3373</td>\n",
       "      <td>NEAR OCEAN</td>\n",
       "      <td>5</td>\n",
       "      <td>6.927083</td>\n",
       "      <td>2.623698</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-119.04</td>\n",
       "      <td>35.37</td>\n",
       "      <td>44.0</td>\n",
       "      <td>1618.0</td>\n",
       "      <td>310.0</td>\n",
       "      <td>667.0</td>\n",
       "      <td>300.0</td>\n",
       "      <td>2.875</td>\n",
       "      <td>INLAND</td>\n",
       "      <td>2</td>\n",
       "      <td>5.393333</td>\n",
       "      <td>2.223333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-117.13</td>\n",
       "      <td>32.75</td>\n",
       "      <td>24.0</td>\n",
       "      <td>1877.0</td>\n",
       "      <td>519.0</td>\n",
       "      <td>898.0</td>\n",
       "      <td>483.0</td>\n",
       "      <td>2.2264</td>\n",
       "      <td>NEAR OCEAN</td>\n",
       "      <td>2</td>\n",
       "      <td>3.886128</td>\n",
       "      <td>1.859213</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-118.7</td>\n",
       "      <td>34.28</td>\n",
       "      <td>27.0</td>\n",
       "      <td>3536.0</td>\n",
       "      <td>646.0</td>\n",
       "      <td>1837.0</td>\n",
       "      <td>580.0</td>\n",
       "      <td>4.4964</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "      <td>3</td>\n",
       "      <td>6.096552</td>\n",
       "      <td>3.167241</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       0      1     2       3      4       5      6       7           8  9   \\\n",
       "0 -121.46  38.52  29.0  3873.0  797.0  2237.0  706.0  2.1736      INLAND  2   \n",
       "1 -117.23  33.09   7.0  5320.0  855.0  2015.0  768.0  6.3373  NEAR OCEAN  5   \n",
       "2 -119.04  35.37  44.0  1618.0  310.0   667.0  300.0   2.875      INLAND  2   \n",
       "3 -117.13  32.75  24.0  1877.0  519.0   898.0  483.0  2.2264  NEAR OCEAN  2   \n",
       "4  -118.7  34.28  27.0  3536.0  646.0  1837.0  580.0  4.4964   <1H OCEAN  3   \n",
       "\n",
       "         10        11  \n",
       "0  5.485836  3.168555  \n",
       "1  6.927083  2.623698  \n",
       "2  5.393333  2.223333  \n",
       "3  3.886128  1.859213  \n",
       "4  6.096552  3.167241  "
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing_tr = pd.DataFrame(housing_extra_attribs)\n",
    "housing_tr.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
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       "    }\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>longitude</th>\n",
       "      <th>latitude</th>\n",
       "      <th>housing_median_age</th>\n",
       "      <th>total_rooms</th>\n",
       "      <th>total_bedrooms</th>\n",
       "      <th>population</th>\n",
       "      <th>households</th>\n",
       "      <th>median_income</th>\n",
       "      <th>ocean_proximity</th>\n",
       "      <th>income_cat</th>\n",
       "      <th>rooms_per_household</th>\n",
       "      <th>population_per_household</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>12655</th>\n",
       "      <td>-121.46</td>\n",
       "      <td>38.52</td>\n",
       "      <td>29.0</td>\n",
       "      <td>3873.0</td>\n",
       "      <td>797.0</td>\n",
       "      <td>2237.0</td>\n",
       "      <td>706.0</td>\n",
       "      <td>2.1736</td>\n",
       "      <td>INLAND</td>\n",
       "      <td>2</td>\n",
       "      <td>5.485836</td>\n",
       "      <td>3.168555</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15502</th>\n",
       "      <td>-117.23</td>\n",
       "      <td>33.09</td>\n",
       "      <td>7.0</td>\n",
       "      <td>5320.0</td>\n",
       "      <td>855.0</td>\n",
       "      <td>2015.0</td>\n",
       "      <td>768.0</td>\n",
       "      <td>6.3373</td>\n",
       "      <td>NEAR OCEAN</td>\n",
       "      <td>5</td>\n",
       "      <td>6.927083</td>\n",
       "      <td>2.623698</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2908</th>\n",
       "      <td>-119.04</td>\n",
       "      <td>35.37</td>\n",
       "      <td>44.0</td>\n",
       "      <td>1618.0</td>\n",
       "      <td>310.0</td>\n",
       "      <td>667.0</td>\n",
       "      <td>300.0</td>\n",
       "      <td>2.875</td>\n",
       "      <td>INLAND</td>\n",
       "      <td>2</td>\n",
       "      <td>5.393333</td>\n",
       "      <td>2.223333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14053</th>\n",
       "      <td>-117.13</td>\n",
       "      <td>32.75</td>\n",
       "      <td>24.0</td>\n",
       "      <td>1877.0</td>\n",
       "      <td>519.0</td>\n",
       "      <td>898.0</td>\n",
       "      <td>483.0</td>\n",
       "      <td>2.2264</td>\n",
       "      <td>NEAR OCEAN</td>\n",
       "      <td>2</td>\n",
       "      <td>3.886128</td>\n",
       "      <td>1.859213</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20496</th>\n",
       "      <td>-118.7</td>\n",
       "      <td>34.28</td>\n",
       "      <td>27.0</td>\n",
       "      <td>3536.0</td>\n",
       "      <td>646.0</td>\n",
       "      <td>1837.0</td>\n",
       "      <td>580.0</td>\n",
       "      <td>4.4964</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "      <td>3</td>\n",
       "      <td>6.096552</td>\n",
       "      <td>3.167241</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      longitude latitude housing_median_age total_rooms total_bedrooms  \\\n",
       "12655   -121.46    38.52               29.0      3873.0          797.0   \n",
       "15502   -117.23    33.09                7.0      5320.0          855.0   \n",
       "2908    -119.04    35.37               44.0      1618.0          310.0   \n",
       "14053   -117.13    32.75               24.0      1877.0          519.0   \n",
       "20496    -118.7    34.28               27.0      3536.0          646.0   \n",
       "\n",
       "      population households median_income ocean_proximity income_cat  \\\n",
       "12655     2237.0      706.0        2.1736          INLAND          2   \n",
       "15502     2015.0      768.0        6.3373      NEAR OCEAN          5   \n",
       "2908       667.0      300.0         2.875          INLAND          2   \n",
       "14053      898.0      483.0        2.2264      NEAR OCEAN          2   \n",
       "20496     1837.0      580.0        4.4964       <1H OCEAN          3   \n",
       "\n",
       "      rooms_per_household population_per_household  \n",
       "12655            5.485836                 3.168555  \n",
       "15502            6.927083                 2.623698  \n",
       "2908             5.393333                 2.223333  \n",
       "14053            3.886128                 1.859213  \n",
       "20496            6.096552                 3.167241  "
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing_extra_attribs = pd.DataFrame(\n",
    "    housing_extra_attribs,\n",
    "    columns=list(housing.columns)+[\"rooms_per_household\", \"population_per_household\"],\n",
    "    index=housing.index)\n",
    "housing_extra_attribs.head()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>longitude</th>\n",
       "      <th>latitude</th>\n",
       "      <th>housing_median_age</th>\n",
       "      <th>total_rooms</th>\n",
       "      <th>total_bedrooms</th>\n",
       "      <th>population</th>\n",
       "      <th>households</th>\n",
       "      <th>median_income</th>\n",
       "      <th>ocean_proximity</th>\n",
       "      <th>income_cat</th>\n",
       "      <th>rooms_per_household</th>\n",
       "      <th>population_per_household</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4859</th>\n",
       "      <td>-118.3</td>\n",
       "      <td>34.02</td>\n",
       "      <td>34.0</td>\n",
       "      <td>3184.0</td>\n",
       "      <td>772.0</td>\n",
       "      <td>2474.0</td>\n",
       "      <td>705.0</td>\n",
       "      <td>1.631</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "      <td>2</td>\n",
       "      <td>4.516312</td>\n",
       "      <td>3.50922</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10780</th>\n",
       "      <td>-117.91</td>\n",
       "      <td>33.64</td>\n",
       "      <td>37.0</td>\n",
       "      <td>1998.0</td>\n",
       "      <td>472.0</td>\n",
       "      <td>1030.0</td>\n",
       "      <td>436.0</td>\n",
       "      <td>3.9306</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "      <td>3</td>\n",
       "      <td>4.582569</td>\n",
       "      <td>2.362385</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16568</th>\n",
       "      <td>-121.42</td>\n",
       "      <td>37.71</td>\n",
       "      <td>7.0</td>\n",
       "      <td>8297.0</td>\n",
       "      <td>1433.0</td>\n",
       "      <td>4189.0</td>\n",
       "      <td>1271.0</td>\n",
       "      <td>4.3696</td>\n",
       "      <td>INLAND</td>\n",
       "      <td>3</td>\n",
       "      <td>6.527931</td>\n",
       "      <td>3.29583</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16936</th>\n",
       "      <td>-122.32</td>\n",
       "      <td>37.56</td>\n",
       "      <td>49.0</td>\n",
       "      <td>2016.0</td>\n",
       "      <td>299.0</td>\n",
       "      <td>691.0</td>\n",
       "      <td>288.0</td>\n",
       "      <td>5.549</td>\n",
       "      <td>NEAR OCEAN</td>\n",
       "      <td>4</td>\n",
       "      <td>7.0</td>\n",
       "      <td>2.399306</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20619</th>\n",
       "      <td>-121.56</td>\n",
       "      <td>39.01</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1891.0</td>\n",
       "      <td>340.0</td>\n",
       "      <td>1023.0</td>\n",
       "      <td>296.0</td>\n",
       "      <td>2.7303</td>\n",
       "      <td>INLAND</td>\n",
       "      <td>2</td>\n",
       "      <td>6.388514</td>\n",
       "      <td>3.456081</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16675</th>\n",
       "      <td>-120.7</td>\n",
       "      <td>35.14</td>\n",
       "      <td>17.0</td>\n",
       "      <td>5805.0</td>\n",
       "      <td>1097.0</td>\n",
       "      <td>1919.0</td>\n",
       "      <td>932.0</td>\n",
       "      <td>3.5352</td>\n",
       "      <td>NEAR OCEAN</td>\n",
       "      <td>3</td>\n",
       "      <td>6.228541</td>\n",
       "      <td>2.059013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5900</th>\n",
       "      <td>-118.31</td>\n",
       "      <td>34.17</td>\n",
       "      <td>12.0</td>\n",
       "      <td>3188.0</td>\n",
       "      <td>931.0</td>\n",
       "      <td>2118.0</td>\n",
       "      <td>850.0</td>\n",
       "      <td>3.1823</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "      <td>3</td>\n",
       "      <td>3.750588</td>\n",
       "      <td>2.491765</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1648</th>\n",
       "      <td>-121.96</td>\n",
       "      <td>37.99</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3129.0</td>\n",
       "      <td>707.0</td>\n",
       "      <td>1606.0</td>\n",
       "      <td>698.0</td>\n",
       "      <td>2.9591</td>\n",
       "      <td>INLAND</td>\n",
       "      <td>2</td>\n",
       "      <td>4.482808</td>\n",
       "      <td>2.30086</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9453</th>\n",
       "      <td>-123.24</td>\n",
       "      <td>39.81</td>\n",
       "      <td>25.0</td>\n",
       "      <td>1435.0</td>\n",
       "      <td>304.0</td>\n",
       "      <td>746.0</td>\n",
       "      <td>259.0</td>\n",
       "      <td>1.7788</td>\n",
       "      <td>INLAND</td>\n",
       "      <td>2</td>\n",
       "      <td>5.540541</td>\n",
       "      <td>2.880309</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6707</th>\n",
       "      <td>-118.15</td>\n",
       "      <td>34.14</td>\n",
       "      <td>27.0</td>\n",
       "      <td>1499.0</td>\n",
       "      <td>426.0</td>\n",
       "      <td>755.0</td>\n",
       "      <td>414.0</td>\n",
       "      <td>3.875</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "      <td>3</td>\n",
       "      <td>3.620773</td>\n",
       "      <td>1.823671</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14962</th>\n",
       "      <td>-116.91</td>\n",
       "      <td>32.75</td>\n",
       "      <td>5.0</td>\n",
       "      <td>8710.0</td>\n",
       "      <td>1614.0</td>\n",
       "      <td>4372.0</td>\n",
       "      <td>1527.0</td>\n",
       "      <td>4.7813</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "      <td>4</td>\n",
       "      <td>5.703995</td>\n",
       "      <td>2.86313</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4201</th>\n",
       "      <td>-118.24</td>\n",
       "      <td>34.11</td>\n",
       "      <td>39.0</td>\n",
       "      <td>1148.0</td>\n",
       "      <td>348.0</td>\n",
       "      <td>1161.0</td>\n",
       "      <td>333.0</td>\n",
       "      <td>2.2167</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "      <td>2</td>\n",
       "      <td>3.447447</td>\n",
       "      <td>3.486486</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1209</th>\n",
       "      <td>-120.46</td>\n",
       "      <td>38.15</td>\n",
       "      <td>16.0</td>\n",
       "      <td>4221.0</td>\n",
       "      <td>781.0</td>\n",
       "      <td>1516.0</td>\n",
       "      <td>697.0</td>\n",
       "      <td>2.3816</td>\n",
       "      <td>INLAND</td>\n",
       "      <td>2</td>\n",
       "      <td>6.055954</td>\n",
       "      <td>2.175036</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5319</th>\n",
       "      <td>-118.42</td>\n",
       "      <td>34.06</td>\n",
       "      <td>52.0</td>\n",
       "      <td>1881.0</td>\n",
       "      <td>334.0</td>\n",
       "      <td>640.0</td>\n",
       "      <td>321.0</td>\n",
       "      <td>6.871</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "      <td>5</td>\n",
       "      <td>5.859813</td>\n",
       "      <td>1.993769</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7136</th>\n",
       "      <td>-118.1</td>\n",
       "      <td>34.02</td>\n",
       "      <td>37.0</td>\n",
       "      <td>1022.0</td>\n",
       "      <td>232.0</td>\n",
       "      <td>653.0</td>\n",
       "      <td>238.0</td>\n",
       "      <td>3.0625</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "      <td>3</td>\n",
       "      <td>4.294118</td>\n",
       "      <td>2.743697</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20325</th>\n",
       "      <td>-119.04</td>\n",
       "      <td>34.28</td>\n",
       "      <td>21.0</td>\n",
       "      <td>1856.0</td>\n",
       "      <td>276.0</td>\n",
       "      <td>863.0</td>\n",
       "      <td>255.0</td>\n",
       "      <td>4.5833</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "      <td>4</td>\n",
       "      <td>7.278431</td>\n",
       "      <td>3.384314</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3635</th>\n",
       "      <td>-118.43</td>\n",
       "      <td>34.23</td>\n",
       "      <td>37.0</td>\n",
       "      <td>1737.0</td>\n",
       "      <td>369.0</td>\n",
       "      <td>1061.0</td>\n",
       "      <td>356.0</td>\n",
       "      <td>3.9615</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "      <td>3</td>\n",
       "      <td>4.879213</td>\n",
       "      <td>2.980337</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16104</th>\n",
       "      <td>-122.5</td>\n",
       "      <td>37.75</td>\n",
       "      <td>45.0</td>\n",
       "      <td>1620.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>941.0</td>\n",
       "      <td>328.0</td>\n",
       "      <td>4.3859</td>\n",
       "      <td>NEAR OCEAN</td>\n",
       "      <td>3</td>\n",
       "      <td>4.939024</td>\n",
       "      <td>2.868902</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13585</th>\n",
       "      <td>-117.3</td>\n",
       "      <td>34.12</td>\n",
       "      <td>43.0</td>\n",
       "      <td>1018.0</td>\n",
       "      <td>261.0</td>\n",
       "      <td>736.0</td>\n",
       "      <td>215.0</td>\n",
       "      <td>2.6</td>\n",
       "      <td>INLAND</td>\n",
       "      <td>2</td>\n",
       "      <td>4.734884</td>\n",
       "      <td>3.423256</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9267</th>\n",
       "      <td>-120.13</td>\n",
       "      <td>36.87</td>\n",
       "      <td>32.0</td>\n",
       "      <td>2089.0</td>\n",
       "      <td>468.0</td>\n",
       "      <td>1765.0</td>\n",
       "      <td>427.0</td>\n",
       "      <td>2.234</td>\n",
       "      <td>INLAND</td>\n",
       "      <td>2</td>\n",
       "      <td>4.892272</td>\n",
       "      <td>4.133489</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      longitude latitude housing_median_age total_rooms total_bedrooms  \\\n",
       "4859     -118.3    34.02               34.0      3184.0          772.0   \n",
       "10780   -117.91    33.64               37.0      1998.0          472.0   \n",
       "16568   -121.42    37.71                7.0      8297.0         1433.0   \n",
       "16936   -122.32    37.56               49.0      2016.0          299.0   \n",
       "20619   -121.56    39.01               22.0      1891.0          340.0   \n",
       "16675    -120.7    35.14               17.0      5805.0         1097.0   \n",
       "5900    -118.31    34.17               12.0      3188.0          931.0   \n",
       "1648    -121.96    37.99                2.0      3129.0          707.0   \n",
       "9453    -123.24    39.81               25.0      1435.0          304.0   \n",
       "6707    -118.15    34.14               27.0      1499.0          426.0   \n",
       "14962   -116.91    32.75                5.0      8710.0         1614.0   \n",
       "4201    -118.24    34.11               39.0      1148.0          348.0   \n",
       "1209    -120.46    38.15               16.0      4221.0          781.0   \n",
       "5319    -118.42    34.06               52.0      1881.0          334.0   \n",
       "7136     -118.1    34.02               37.0      1022.0          232.0   \n",
       "20325   -119.04    34.28               21.0      1856.0          276.0   \n",
       "3635    -118.43    34.23               37.0      1737.0          369.0   \n",
       "16104    -122.5    37.75               45.0      1620.0            NaN   \n",
       "13585    -117.3    34.12               43.0      1018.0          261.0   \n",
       "9267    -120.13    36.87               32.0      2089.0          468.0   \n",
       "\n",
       "      population households median_income ocean_proximity income_cat  \\\n",
       "4859      2474.0      705.0         1.631       <1H OCEAN          2   \n",
       "10780     1030.0      436.0        3.9306       <1H OCEAN          3   \n",
       "16568     4189.0     1271.0        4.3696          INLAND          3   \n",
       "16936      691.0      288.0         5.549      NEAR OCEAN          4   \n",
       "20619     1023.0      296.0        2.7303          INLAND          2   \n",
       "16675     1919.0      932.0        3.5352      NEAR OCEAN          3   \n",
       "5900      2118.0      850.0        3.1823       <1H OCEAN          3   \n",
       "1648      1606.0      698.0        2.9591          INLAND          2   \n",
       "9453       746.0      259.0        1.7788          INLAND          2   \n",
       "6707       755.0      414.0         3.875       <1H OCEAN          3   \n",
       "14962     4372.0     1527.0        4.7813       <1H OCEAN          4   \n",
       "4201      1161.0      333.0        2.2167       <1H OCEAN          2   \n",
       "1209      1516.0      697.0        2.3816          INLAND          2   \n",
       "5319       640.0      321.0         6.871       <1H OCEAN          5   \n",
       "7136       653.0      238.0        3.0625       <1H OCEAN          3   \n",
       "20325      863.0      255.0        4.5833       <1H OCEAN          4   \n",
       "3635      1061.0      356.0        3.9615       <1H OCEAN          3   \n",
       "16104      941.0      328.0        4.3859      NEAR OCEAN          3   \n",
       "13585      736.0      215.0           2.6          INLAND          2   \n",
       "9267      1765.0      427.0         2.234          INLAND          2   \n",
       "\n",
       "      rooms_per_household population_per_household  \n",
       "4859             4.516312                  3.50922  \n",
       "10780            4.582569                 2.362385  \n",
       "16568            6.527931                  3.29583  \n",
       "16936                 7.0                 2.399306  \n",
       "20619            6.388514                 3.456081  \n",
       "16675            6.228541                 2.059013  \n",
       "5900             3.750588                 2.491765  \n",
       "1648             4.482808                  2.30086  \n",
       "9453             5.540541                 2.880309  \n",
       "6707             3.620773                 1.823671  \n",
       "14962            5.703995                  2.86313  \n",
       "4201             3.447447                 3.486486  \n",
       "1209             6.055954                 2.175036  \n",
       "5319             5.859813                 1.993769  \n",
       "7136             4.294118                 2.743697  \n",
       "20325            7.278431                 3.384314  \n",
       "3635             4.879213                 2.980337  \n",
       "16104            4.939024                 2.868902  \n",
       "13585            4.734884                 3.423256  \n",
       "9267             4.892272                 4.133489  "
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing_extra_attribs.sample(20)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 特征缩放"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 最重要也是最需要应用在数据上的转换器，就是特征缩放，输入数值属性有很大的比例差异，会导致机器学习算法性能表现不佳\n",
    "# 房间总数 范围6到39320，收入中位数范围0到15\n",
    "# 目标值通常不需要缩放\n",
    "# 同比缩放所有属性，2种方法 最小-最大缩放和标准化\n",
    "# 最小-最大缩放，又叫归一化，将值重新缩放到0到1之间，将值减去最小值并除以最大值和最小值差，如果你不希望是0到1，可以调整超参数feature_range\n",
    "# 标准化 减去平均值 ，所以标准化均值总是0，然后除以方差，结果的分布具备单位方差，不同于归一化，标准化不将值绑定到特定范围，受异常值影响小\n",
    "# 缩放器仅用来拟合训练集，不是完成的数据集\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 转换流水线"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 许多数据转换的步骤需要以正确的顺序来执行， PipeLine来支持这样的转换\n",
    "# pipline构造函数会通过一系列名称/估算器的配对来定义步骤的序列，必须是转换器，必须有fit_fransform()方法\n",
    "# 调用流水芡的fit方法时，会在所有转换器上按照顺序依次调用fit_transform()，将一个调用的输出作为参数传递给下一个调用方法，直到传递到最终\n",
    "# 估算器，只会调用fit方法\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "num_pipeline = Pipeline([\n",
    "        ('imputer', SimpleImputer(strategy=\"median\")),\n",
    "        ('attribs_adder', CombinedAttributesAdder()),\n",
    "        ('std_scaler', StandardScaler())\n",
    "    ])\n",
    "\n",
    "housing_num_tr = num_pipeline.fit_transform(housing_num)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Index: 16512 entries, 12655 to 19773\n",
      "Data columns (total 9 columns):\n",
      " #   Column              Non-Null Count  Dtype   \n",
      "---  ------              --------------  -----   \n",
      " 0   longitude           16512 non-null  float64 \n",
      " 1   latitude            16512 non-null  float64 \n",
      " 2   housing_median_age  16512 non-null  float64 \n",
      " 3   total_rooms         16512 non-null  float64 \n",
      " 4   total_bedrooms      16354 non-null  float64 \n",
      " 5   population          16512 non-null  float64 \n",
      " 6   households          16512 non-null  float64 \n",
      " 7   median_income       16512 non-null  float64 \n",
      " 8   income_cat          16512 non-null  category\n",
      "dtypes: category(1), float64(8)\n",
      "memory usage: 1.1 MB\n"
     ]
    }
   ],
   "source": [
    "housing_num.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [],
   "source": [
    "# dataFrame -> series -> ndarray\n",
    "class DataFrameSelector(BaseEstimator, TransformerMixin):\n",
    "    def __init__(self, attribute_names):\n",
    "        self.attribute_names = attribute_names\n",
    "    def fit(self, X, y=None):\n",
    "        return self\n",
    "    def transform(self, X):\n",
    "        return X[self.attribute_names].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['longitude',\n",
       " 'latitude',\n",
       " 'housing_median_age',\n",
       " 'total_rooms',\n",
       " 'total_bedrooms',\n",
       " 'population',\n",
       " 'households',\n",
       " 'median_income',\n",
       " 'income_cat']"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "num_attribs =list(housing_num) \n",
    "num_attribs\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "num_pipeline = Pipeline([\n",
    "        ('selector', DataFrameSelector(num_attribs)),\n",
    "        ('imputer', SimpleImputer(strategy=\"median\")),\n",
    "        ('attribs_adder', CombinedAttributesAdder()),\n",
    "        ('std_scaler', StandardScaler()),\n",
    "    ])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.base import TransformerMixin \n",
    "class MyLabelBinarizer(TransformerMixin):\n",
    "    def __init__(self, *args, **kwargs):\n",
    "        self.encoder = LabelBinarizer(*args, **kwargs)\n",
    "    def fit(self, x, y=0):\n",
    "        self.encoder.fit(x)\n",
    "        return self\n",
    "    def transform(self, x, y=0):\n",
    "        return self.encoder.transform(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [],
   "source": [
    "cat_attribs = ['ocean_proximity']\n",
    "from sklearn.preprocessing import LabelBinarizer\n",
    "\n",
    "cat_pipeline = Pipeline([\n",
    "        ('selector', DataFrameSelector(cat_attribs)),               \n",
    "        ('LabelBinarizer', MyLabelBinarizer()),\n",
    "    ])\n",
    "\n",
    "# housing.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.pipeline import FeatureUnion\n",
    "\n",
    "full_pipeline = FeatureUnion(transformer_list=[\n",
    "        (\"num_pipline\", num_pipeline,),\n",
    "        ('cat_pipline', cat_pipeline),\n",
    "    ])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-0.94135046,  1.34743822,  0.02756357, ...,  0.        ,\n",
       "         0.        ,  0.        ],\n",
       "       [ 1.17178212, -1.19243966, -1.72201763, ...,  0.        ,\n",
       "         0.        ,  1.        ],\n",
       "       [ 0.26758118, -0.1259716 ,  1.22045984, ...,  0.        ,\n",
       "         0.        ,  0.        ],\n",
       "       ...,\n",
       "       [-1.5707942 ,  1.31001828,  1.53856552, ...,  0.        ,\n",
       "         0.        ,  0.        ],\n",
       "       [-1.56080303,  1.2492109 , -1.1653327 , ...,  0.        ,\n",
       "         0.        ,  0.        ],\n",
       "       [-1.28105026,  2.02567448, -0.13148926, ...,  0.        ,\n",
       "         0.        ,  0.        ]])"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing_finished = full_pipeline.fit_transform(housing)\n",
    "housing_finished"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
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       "        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>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>10</th>\n",
       "      <th>11</th>\n",
       "      <th>12</th>\n",
       "      <th>13</th>\n",
       "      <th>14</th>\n",
       "      <th>15</th>\n",
       "      <th>16</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-0.941350</td>\n",
       "      <td>1.347438</td>\n",
       "      <td>0.027564</td>\n",
       "      <td>0.584777</td>\n",
       "      <td>0.640371</td>\n",
       "      <td>0.732602</td>\n",
       "      <td>0.556286</td>\n",
       "      <td>-0.893647</td>\n",
       "      <td>-0.954687</td>\n",
       "      <td>0.017395</td>\n",
       "      <td>0.006223</td>\n",
       "      <td>-0.121122</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.171782</td>\n",
       "      <td>-1.192440</td>\n",
       "      <td>-1.722018</td>\n",
       "      <td>1.261467</td>\n",
       "      <td>0.781561</td>\n",
       "      <td>0.533612</td>\n",
       "      <td>0.721318</td>\n",
       "      <td>1.292168</td>\n",
       "      <td>1.890078</td>\n",
       "      <td>0.569256</td>\n",
       "      <td>-0.040811</td>\n",
       "      <td>-0.810867</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.267581</td>\n",
       "      <td>-0.125972</td>\n",
       "      <td>1.220460</td>\n",
       "      <td>-0.469773</td>\n",
       "      <td>-0.545138</td>\n",
       "      <td>-0.674675</td>\n",
       "      <td>-0.524407</td>\n",
       "      <td>-0.525434</td>\n",
       "      <td>-0.954687</td>\n",
       "      <td>-0.018024</td>\n",
       "      <td>-0.075371</td>\n",
       "      <td>-0.338273</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.221738</td>\n",
       "      <td>-1.351474</td>\n",
       "      <td>-0.370069</td>\n",
       "      <td>-0.348652</td>\n",
       "      <td>-0.036367</td>\n",
       "      <td>-0.467617</td>\n",
       "      <td>-0.037297</td>\n",
       "      <td>-0.865929</td>\n",
       "      <td>-0.954687</td>\n",
       "      <td>-0.595140</td>\n",
       "      <td>-0.106803</td>\n",
       "      <td>0.961205</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.437431</td>\n",
       "      <td>-0.635818</td>\n",
       "      <td>-0.131489</td>\n",
       "      <td>0.427179</td>\n",
       "      <td>0.272790</td>\n",
       "      <td>0.374060</td>\n",
       "      <td>0.220898</td>\n",
       "      <td>0.325752</td>\n",
       "      <td>-0.006432</td>\n",
       "      <td>0.251241</td>\n",
       "      <td>0.006109</td>\n",
       "      <td>-0.474513</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         0         1         2         3         4         5         6   \\\n",
       "0 -0.941350  1.347438  0.027564  0.584777  0.640371  0.732602  0.556286   \n",
       "1  1.171782 -1.192440 -1.722018  1.261467  0.781561  0.533612  0.721318   \n",
       "2  0.267581 -0.125972  1.220460 -0.469773 -0.545138 -0.674675 -0.524407   \n",
       "3  1.221738 -1.351474 -0.370069 -0.348652 -0.036367 -0.467617 -0.037297   \n",
       "4  0.437431 -0.635818 -0.131489  0.427179  0.272790  0.374060  0.220898   \n",
       "\n",
       "         7         8         9         10        11   12   13   14   15   16  \n",
       "0 -0.893647 -0.954687  0.017395  0.006223 -0.121122  0.0  1.0  0.0  0.0  0.0  \n",
       "1  1.292168  1.890078  0.569256 -0.040811 -0.810867  0.0  0.0  0.0  0.0  1.0  \n",
       "2 -0.525434 -0.954687 -0.018024 -0.075371 -0.338273  0.0  1.0  0.0  0.0  0.0  \n",
       "3 -0.865929 -0.954687 -0.595140 -0.106803  0.961205  0.0  0.0  0.0  0.0  1.0  \n",
       "4  0.325752 -0.006432  0.251241  0.006109 -0.474513  1.0  0.0  0.0  0.0  0.0  "
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing_prepared = pd.DataFrame(housing_finished)\n",
    "housing_prepared.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 选择和训练模型\n",
    "* 获得了数据\n",
    "* 数据探索\n",
    "* 对训练集和测试集进行拆分\n",
    "* 编写了转换数据流水线\n",
    "* 自动清理和准备机器学习算法的数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "12655     72100.0\n",
       "15502    279600.0\n",
       "2908      82700.0\n",
       "14053    112500.0\n",
       "20496    238300.0\n",
       "           ...   \n",
       "15174    268500.0\n",
       "12661     90400.0\n",
       "19263    140400.0\n",
       "19140    258100.0\n",
       "19773     62700.0\n",
       "Name: median_house_value, Length: 16512, dtype: float64"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame(housing_prepared)\n",
    "df.head()\n",
    "housing_labels"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##  训练模型和评估训练集"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-3 {color: black;background-color: white;}#sk-container-id-3 pre{padding: 0;}#sk-container-id-3 div.sk-toggleable {background-color: white;}#sk-container-id-3 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-3 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-3 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-3 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-3 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-3 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-3 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-3 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-3 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-3 div.sk-item {position: relative;z-index: 1;}#sk-container-id-3 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-3 div.sk-item::before, #sk-container-id-3 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-3 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-3 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-3 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-3 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-3 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-3 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-3 div.sk-label-container {text-align: center;}#sk-container-id-3 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-3 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LinearRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" checked><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LinearRegression</label><div class=\"sk-toggleable__content\"><pre>LinearRegression()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "lin_reg = LinearRegression()\n",
    "lin_reg.fit(housing_prepared, housing_labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Predictions: [ 83811.08312039 314388.27147935 145641.85902864 183704.64921202\n",
      " 238729.66725745]\n"
     ]
    }
   ],
   "source": [
    "# 预测数据\n",
    "some_data = housing.iloc[:5]\n",
    "some_labels = housing_labels.iloc[:5]\n",
    "\n",
    "some_data_prepared = full_pipeline.transform(some_data)\n",
    "\n",
    "print(\"Predictions:\", lin_reg.predict(some_data_prepared))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "68376.51254853733"
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import mean_squared_error\n",
    "\n",
    "housing_predictions = lin_reg.predict(housing_prepared)\n",
    "lin_mse = mean_squared_error(housing_labels, housing_predictions)\n",
    "lin_mse\n",
    "lin_rmse = np.sqrt(lin_mse)\n",
    "lin_rmse\n",
    "\n",
    "# 大多数地区的房屋中位数 在120000到265000美元之间，预测误差高达 68628,这是一个典型的模型对训练数据拟合不足的案例，\n",
    "# 原因可能是特征无法提供足够的信息来做出更好的预测，或者模型本身不够强大，\n",
    "# 1. 选择强大的模型，2 为算法提供更好的特征，3.减少对模型的限制等方法，\n",
    "\n",
    "# 决策树可以找到复杂的非线性关系\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-4 {color: black;background-color: white;}#sk-container-id-4 pre{padding: 0;}#sk-container-id-4 div.sk-toggleable {background-color: white;}#sk-container-id-4 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-4 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-4 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-4 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-4 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-4 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-4 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-4 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-4 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-4 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-4 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-4 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-4 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-4 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-4 div.sk-item {position: relative;z-index: 1;}#sk-container-id-4 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-4 div.sk-item::before, #sk-container-id-4 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-4 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-4 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-4 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-4 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-4 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-4 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-4 div.sk-label-container {text-align: center;}#sk-container-id-4 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-4 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-4\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>DecisionTreeRegressor(random_state=42)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" checked><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">DecisionTreeRegressor</label><div class=\"sk-toggleable__content\"><pre>DecisionTreeRegressor(random_state=42)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "DecisionTreeRegressor(random_state=42)"
      ]
     },
     "execution_count": 114,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.tree import DecisionTreeRegressor\n",
    "\n",
    "tree_reg = DecisionTreeRegressor(random_state=42)\n",
    "tree_reg.fit(housing_prepared, housing_labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0"
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing_predictions = tree_reg.predict(housing_prepared)\n",
    "tree_mse = mean_squared_error(housing_labels, housing_predictions)\n",
    "tree_rmse = np.sqrt(tree_mse)\n",
    "tree_rmse"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [],
   "source": [
    " # 完美，也可能是这个模型对数据严重过度拟合了，如何确认？轻易不要启动测试集，拿训练集中的一部分用于训练，另一部分用于模型的验证"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 使用交叉验证来更好的进行评估"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([72237.45096271, 69208.53094621, 69286.11573206, 70612.13634405,\n",
       "       70577.30202088, 76840.97092314, 71823.83279699, 72710.22512908,\n",
       "       67780.77127665, 71678.17802175])"
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 使用train_test_split函数将训练集分为较小的训练集和验证集，然后根据这些较小的训练集来训练模型，并对其进行评估\n",
    "# sklearn的交叉验证，将训练集随机分割成10个不同的子集，每个子集称为一个折叠，对模型进行10次训练和评估，每次挑选1个折叠进行评估，另外9个进行训练\n",
    "from sklearn.model_selection import cross_val_score\n",
    "# neg_mean_squared_error‘ 也就是 均方差回归损失 该统计参数是预测数据和原始数据对应点误差的平方和的均值\n",
    "\n",
    "# 决策树交叉验证\n",
    "scores = cross_val_score(tree_reg, housing_prepared, housing_labels,\n",
    "                         scoring=\"neg_mean_squared_error\", cv=10)\n",
    "tree_rmse_scores = np.sqrt(-scores)\n",
    "tree_rmse_scores\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Scores: [72237.45096271 69208.53094621 69286.11573206 70612.13634405\n",
      " 70577.30202088 76840.97092314 71823.83279699 72710.22512908\n",
      " 67780.77127665 71678.17802175]\n",
      "Mean: 71275.55141535088\n",
      "Standard deviation: 2362.02756776275\n"
     ]
    }
   ],
   "source": [
    "def display_scores(scores):\n",
    "    print(\"Scores:\", scores)\n",
    "    print(\"Mean:\", scores.mean())\n",
    "    print(\"Standard deviation:\", scores.std())\n",
    "\n",
    "display_scores(tree_rmse_scores)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Scores: [71523.78333874 64044.46774989 67454.97869698 68514.10137273\n",
      " 66303.62531226 72166.63405138 74464.08841381 68570.11804395\n",
      " 66063.64175868 69870.86192291]\n",
      "Mean: 68897.63006613276\n",
      "Standard deviation: 3002.746127534865\n"
     ]
    }
   ],
   "source": [
    "# 线性 交叉验证\n",
    "lin_scores = cross_val_score(lin_reg, housing_prepared, housing_labels,\n",
    "                             scoring=\"neg_mean_squared_error\", cv=10)\n",
    "lin_rmse_scores = np.sqrt(-lin_scores)\n",
    "display_scores(lin_rmse_scores)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-5 {color: black;background-color: white;}#sk-container-id-5 pre{padding: 0;}#sk-container-id-5 div.sk-toggleable {background-color: white;}#sk-container-id-5 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-5 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-5 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-5 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-5 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-5 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-5 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-5 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-5 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-5 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-5 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-5 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-5 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-5 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-5 div.sk-item {position: relative;z-index: 1;}#sk-container-id-5 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-5 div.sk-item::before, #sk-container-id-5 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-5 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-5 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-5 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-5 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-5 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-5 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-5 div.sk-label-container {text-align: center;}#sk-container-id-5 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-5 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-5\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestRegressor(n_estimators=10, random_state=42)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-5\" type=\"checkbox\" checked><label for=\"sk-estimator-id-5\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestRegressor</label><div class=\"sk-toggleable__content\"><pre>RandomForestRegressor(n_estimators=10, random_state=42)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "RandomForestRegressor(n_estimators=10, random_state=42)"
      ]
     },
     "execution_count": 120,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "\n",
    "forest_reg = RandomForestRegressor(n_estimators=10, random_state=42)\n",
    "forest_reg.fit(housing_prepared, housing_labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "22367.815456232496"
      ]
     },
     "execution_count": 121,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing_predictions = forest_reg.predict(housing_prepared)\n",
    "forest_mse = mean_squared_error(housing_labels, housing_predictions)\n",
    "forest_rmse = np.sqrt(forest_mse)\n",
    "forest_rmse"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Scores: [51553.65292335 48797.89565614 47005.23947642 52046.73567245\n",
      " 47700.78025873 51824.08544879 52582.59165129 49949.79025967\n",
      " 48680.25622229 54019.67674791]\n",
      "Mean: 50416.070431704204\n",
      "Standard deviation: 2201.612779754884\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import cross_val_score\n",
    "\n",
    "forest_scores = cross_val_score(forest_reg, housing_prepared, housing_labels,\n",
    "                                scoring=\"neg_mean_squared_error\", cv=10)\n",
    "forest_rmse_scores = np.sqrt(-forest_scores)\n",
    "display_scores(forest_rmse_scores)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 模型调参和网格搜索\n",
    "   1. 手动调整超参数，找到很好的组合很困难\n",
    "   2. 使用GridSearchCV替你进行搜索，告诉它，进行试验的超参数是什么，和需要尝试的值，它会使用交叉验证评估所有超参数的可能组合"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-6 {color: black;background-color: white;}#sk-container-id-6 pre{padding: 0;}#sk-container-id-6 div.sk-toggleable {background-color: white;}#sk-container-id-6 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-6 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-6 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-6 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-6 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-6 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-6 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-6 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-6 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-6 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-6 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-6 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-6 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-6 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-6 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-6 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-6 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-6 div.sk-item {position: relative;z-index: 1;}#sk-container-id-6 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-6 div.sk-item::before, #sk-container-id-6 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-6 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-6 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-6 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-6 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-6 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-6 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-6 div.sk-label-container {text-align: center;}#sk-container-id-6 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-6 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-6\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GridSearchCV(cv=5, estimator=RandomForestRegressor(random_state=42),\n",
       "             param_grid=[{&#x27;max_features&#x27;: [2, 4, 6, 8],\n",
       "                          &#x27;n_estimators&#x27;: [3, 10, 30]},\n",
       "                         {&#x27;bootstrap&#x27;: [False], &#x27;max_features&#x27;: [2, 3, 4],\n",
       "                          &#x27;n_estimators&#x27;: [3, 10]}],\n",
       "             return_train_score=True, scoring=&#x27;neg_mean_squared_error&#x27;)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-6\" type=\"checkbox\" ><label for=\"sk-estimator-id-6\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GridSearchCV</label><div class=\"sk-toggleable__content\"><pre>GridSearchCV(cv=5, estimator=RandomForestRegressor(random_state=42),\n",
       "             param_grid=[{&#x27;max_features&#x27;: [2, 4, 6, 8],\n",
       "                          &#x27;n_estimators&#x27;: [3, 10, 30]},\n",
       "                         {&#x27;bootstrap&#x27;: [False], &#x27;max_features&#x27;: [2, 3, 4],\n",
       "                          &#x27;n_estimators&#x27;: [3, 10]}],\n",
       "             return_train_score=True, scoring=&#x27;neg_mean_squared_error&#x27;)</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-7\" type=\"checkbox\" ><label for=\"sk-estimator-id-7\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">estimator: RandomForestRegressor</label><div class=\"sk-toggleable__content\"><pre>RandomForestRegressor(random_state=42)</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-8\" type=\"checkbox\" ><label for=\"sk-estimator-id-8\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestRegressor</label><div class=\"sk-toggleable__content\"><pre>RandomForestRegressor(random_state=42)</pre></div></div></div></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "GridSearchCV(cv=5, estimator=RandomForestRegressor(random_state=42),\n",
       "             param_grid=[{'max_features': [2, 4, 6, 8],\n",
       "                          'n_estimators': [3, 10, 30]},\n",
       "                         {'bootstrap': [False], 'max_features': [2, 3, 4],\n",
       "                          'n_estimators': [3, 10]}],\n",
       "             return_train_score=True, scoring='neg_mean_squared_error')"
      ]
     },
     "execution_count": 123,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import GridSearchCV\n",
    "\n",
    "param_grid = [\n",
    "    {'n_estimators': [3, 10, 30], 'max_features': [2, 4, 6, 8]},\n",
    "    {'bootstrap': [False], 'n_estimators': [3, 10], 'max_features': [2, 3, 4]},\n",
    "  ]\n",
    "\n",
    "forest_reg = RandomForestRegressor(random_state=42)\n",
    "grid_search = GridSearchCV(forest_reg, param_grid, cv=5,\n",
    "                           scoring='neg_mean_squared_error', return_train_score=True)\n",
    "grid_search.fit(housing_prepared, housing_labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'max_features': 6, 'n_estimators': 30}"
      ]
     },
     "execution_count": 124,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid_search.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-7 {color: black;background-color: white;}#sk-container-id-7 pre{padding: 0;}#sk-container-id-7 div.sk-toggleable {background-color: white;}#sk-container-id-7 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-7 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-7 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-7 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-7 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-7 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-7 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-7 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-7 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-7 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-7 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-7 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-7 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-7 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-7 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-7 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-7 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-7 div.sk-item {position: relative;z-index: 1;}#sk-container-id-7 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-7 div.sk-item::before, #sk-container-id-7 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-7 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-7 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-7 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-7 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-7 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-7 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-7 div.sk-label-container {text-align: center;}#sk-container-id-7 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-7 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-7\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestRegressor(max_features=6, n_estimators=30, random_state=42)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-9\" type=\"checkbox\" checked><label for=\"sk-estimator-id-9\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestRegressor</label><div class=\"sk-toggleable__content\"><pre>RandomForestRegressor(max_features=6, n_estimators=30, random_state=42)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "RandomForestRegressor(max_features=6, n_estimators=30, random_state=42)"
      ]
     },
     "execution_count": 125,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid_search.best_estimator_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "64794.30269596007 {'max_features': 2, 'n_estimators': 3}\n",
      "56170.628706376316 {'max_features': 2, 'n_estimators': 10}\n",
      "53112.07892952066 {'max_features': 2, 'n_estimators': 30}\n",
      "61836.14128922096 {'max_features': 4, 'n_estimators': 3}\n",
      "54015.050600771814 {'max_features': 4, 'n_estimators': 10}\n",
      "51501.312925751794 {'max_features': 4, 'n_estimators': 30}\n",
      "59654.781331729675 {'max_features': 6, 'n_estimators': 3}\n",
      "52654.921131020776 {'max_features': 6, 'n_estimators': 10}\n",
      "50743.74265388978 {'max_features': 6, 'n_estimators': 30}\n",
      "60051.26348940765 {'max_features': 8, 'n_estimators': 3}\n",
      "52909.31100796091 {'max_features': 8, 'n_estimators': 10}\n",
      "50800.78516244517 {'max_features': 8, 'n_estimators': 30}\n",
      "63176.30025923348 {'bootstrap': False, 'max_features': 2, 'n_estimators': 3}\n",
      "54596.210174483705 {'bootstrap': False, 'max_features': 2, 'n_estimators': 10}\n",
      "60999.682580275425 {'bootstrap': False, 'max_features': 3, 'n_estimators': 3}\n",
      "53236.2602917408 {'bootstrap': False, 'max_features': 3, 'n_estimators': 10}\n",
      "60376.15876878502 {'bootstrap': False, 'max_features': 4, 'n_estimators': 3}\n",
      "53060.99451893582 {'bootstrap': False, 'max_features': 4, 'n_estimators': 10}\n"
     ]
    }
   ],
   "source": [
    "cvres = grid_search.cv_results_\n",
    "for mean_score, params in zip(cvres[\"mean_test_score\"], cvres[\"params\"]):\n",
    "    print(np.sqrt(-mean_score), params)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 数据准备步骤也可以当作超参数来处理，网格搜索会自动查找是否添加你不确定的特征，比如是否使用转换器 combinedAttre的超参数add_bedrooms_per_rom\n",
    "# 也可是使用它自动寻找处理问题的最佳方法，比如异常值，缺失特征，特征选择等"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 随机搜索"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-8 {color: black;background-color: white;}#sk-container-id-8 pre{padding: 0;}#sk-container-id-8 div.sk-toggleable {background-color: white;}#sk-container-id-8 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-8 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-8 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-8 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-8 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-8 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-8 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-8 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-8 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-8 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-8 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-8 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-8 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-8 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-8 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-8 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-8 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-8 div.sk-item {position: relative;z-index: 1;}#sk-container-id-8 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-8 div.sk-item::before, #sk-container-id-8 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-8 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-8 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-8 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-8 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-8 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-8 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-8 div.sk-label-container {text-align: center;}#sk-container-id-8 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-8 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-8\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomizedSearchCV(cv=5, estimator=RandomForestRegressor(random_state=42),\n",
       "                   param_distributions={&#x27;max_features&#x27;: &lt;scipy.stats._distn_infrastructure.rv_discrete_frozen object at 0x000002474F2A2790&gt;,\n",
       "                                        &#x27;n_estimators&#x27;: &lt;scipy.stats._distn_infrastructure.rv_discrete_frozen object at 0x000002474F2A27F0&gt;},\n",
       "                   random_state=42, scoring=&#x27;neg_mean_squared_error&#x27;)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-10\" type=\"checkbox\" ><label for=\"sk-estimator-id-10\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomizedSearchCV</label><div class=\"sk-toggleable__content\"><pre>RandomizedSearchCV(cv=5, estimator=RandomForestRegressor(random_state=42),\n",
       "                   param_distributions={&#x27;max_features&#x27;: &lt;scipy.stats._distn_infrastructure.rv_discrete_frozen object at 0x000002474F2A2790&gt;,\n",
       "                                        &#x27;n_estimators&#x27;: &lt;scipy.stats._distn_infrastructure.rv_discrete_frozen object at 0x000002474F2A27F0&gt;},\n",
       "                   random_state=42, scoring=&#x27;neg_mean_squared_error&#x27;)</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-11\" type=\"checkbox\" ><label for=\"sk-estimator-id-11\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">estimator: RandomForestRegressor</label><div class=\"sk-toggleable__content\"><pre>RandomForestRegressor(random_state=42)</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-12\" type=\"checkbox\" ><label for=\"sk-estimator-id-12\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestRegressor</label><div class=\"sk-toggleable__content\"><pre>RandomForestRegressor(random_state=42)</pre></div></div></div></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "RandomizedSearchCV(cv=5, estimator=RandomForestRegressor(random_state=42),\n",
       "                   param_distributions={'max_features': <scipy.stats._distn_infrastructure.rv_discrete_frozen object at 0x000002474F2A2790>,\n",
       "                                        'n_estimators': <scipy.stats._distn_infrastructure.rv_discrete_frozen object at 0x000002474F2A27F0>},\n",
       "                   random_state=42, scoring='neg_mean_squared_error')"
      ]
     },
     "execution_count": 128,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import RandomizedSearchCV\n",
    "from scipy.stats import randint\n",
    "\n",
    "param_distribs = {\n",
    "        'n_estimators': randint(low=1, high=200),\n",
    "        'max_features': randint(low=1, high=8),\n",
    "    }\n",
    "\n",
    "forest_reg = RandomForestRegressor(random_state=42)\n",
    "rnd_search = RandomizedSearchCV(forest_reg, param_distributions=param_distribs,\n",
    "                                n_iter=10, cv=5, scoring='neg_mean_squared_error', random_state=42)\n",
    "rnd_search.fit(housing_prepared, housing_labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "49799.635737761106 {'max_features': 7, 'n_estimators': 180}\n",
      "52293.114093913726 {'max_features': 5, 'n_estimators': 15}\n",
      "51327.353255586764 {'max_features': 3, 'n_estimators': 72}\n",
      "51528.97117998048 {'max_features': 5, 'n_estimators': 21}\n",
      "49958.42482333546 {'max_features': 7, 'n_estimators': 122}\n",
      "51270.531241462595 {'max_features': 3, 'n_estimators': 75}\n",
      "51172.437672640175 {'max_features': 3, 'n_estimators': 88}\n",
      "50255.14987044715 {'max_features': 5, 'n_estimators': 100}\n",
      "50894.38729795359 {'max_features': 3, 'n_estimators': 150}\n",
      "65022.070435017646 {'max_features': 5, 'n_estimators': 2}\n"
     ]
    }
   ],
   "source": [
    "cvres = rnd_search.cv_results_\n",
    "for mean_score, params in zip(cvres[\"mean_test_score\"], cvres[\"params\"]):\n",
    "    print(np.sqrt(-mean_score), params)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 分析最佳模型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([6.76445809e-02, 6.24744883e-02, 4.48349664e-02, 1.82692423e-02,\n",
       "       1.78441912e-02, 1.96046284e-02, 1.81345873e-02, 2.43728155e-01,\n",
       "       1.64552239e-01, 5.15312129e-02, 1.00601503e-01, 5.56925429e-02,\n",
       "       1.39077386e-02, 1.13027019e-01, 8.79969201e-05, 2.68765993e-03,\n",
       "       5.37724723e-03])"
      ]
     },
     "execution_count": 130,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "feature_importances = grid_search.best_estimator_.feature_importances_\n",
    "feature_importances"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(0.2437281550348665, 'median_income'),\n",
       " (0.164552239433468, 'income_cat'),\n",
       " (0.11302701921836558, 'INLAND'),\n",
       " (0.10060150315157557, 'population_per_household'),\n",
       " (0.0676445808532661, 'longitude'),\n",
       " (0.06247448828832202, 'latitude'),\n",
       " (0.055692542877701, 'bedrooms_per_room'),\n",
       " (0.0515312129253161, 'rooms_per_household'),\n",
       " (0.04483496644921272, 'housing_median_age'),\n",
       " (0.01960462836331055, 'population'),\n",
       " (0.01826924231375522, 'total_rooms'),\n",
       " (0.018134587277114464, 'households'),\n",
       " (0.017844191166034768, 'total_bedrooms'),\n",
       " (0.013907738567035366, '<1H OCEAN'),\n",
       " (0.0053772472307463915, 'NEAR OCEAN'),\n",
       " (0.0026876599298535932, 'NEAR BAY'),\n",
       " (8.799692005595e-05, 'ISLAND')]"
      ]
     },
     "execution_count": 131,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# num_attribs\n",
    "extra_attribs = [\"rooms_per_household\", \"population_per_household\", \"bedrooms_per_room\"]\n",
    "cat_one_hot_attribs = list(encoder.classes_)\n",
    "attributes = num_attribs + extra_attribs + cat_one_hot_attribs\n",
    "sorted(zip(feature_importances, attributes), reverse = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 通过测试集评估系统\n",
    "   1. 从测试集中获取预测器和标签\n",
    "   2. 运行full_pipline来转换数据\n",
    "   3. 在测试集上评估最终模型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "48557.33618531538"
      ]
     },
     "execution_count": 132,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_model = grid_search.best_estimator_\n",
    "\n",
    "X_test = strat_test_set.drop('median_house_value', axis = 1)\n",
    "y_test = strat_test_set['median_house_value'].copy()\n",
    "\n",
    "X_test_prepared = full_pipeline.transform(X_test)\n",
    "# X_test_prepared\n",
    "\n",
    "# df = pd.DataFrame(X_test_prepared)\n",
    "# df.head()\n",
    "\n",
    "final_predictions = final_model.predict(X_test_prepared)\n",
    "\n",
    "final_mse = mean_squared_error(y_test, final_predictions)\n",
    "final_rmse = np.sqrt(final_mse)\n",
    "final_rmse"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 项目启动阶段\n",
    "   1. 展示解决方案 学习了什么\n",
    "   2. 什么有用\n",
    "   3. 什么没有用\n",
    "   4. 基于什么假设\n",
    "   5. 以及系统的限制有哪些\n",
    "   6. 制作漂亮的演示文稿，例如收入中位数是预测房价的首要指标"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 启动，监控和维护系统\n",
    "   1. 为生产环境做好准备，将生产数据源接入系统\n",
    "   2. 编写监控代码，定期检查系统的实时性能，性能下降时触发警报，系统崩溃和性能退化\n",
    "   3. 时间推移，模型会渐渐腐坏，定期使用新数据训练模型\n",
    "\n",
    "## 评估系统性能 \n",
    "   1. 需要对系统的预测结果进行抽样评估，需要人工分析，分析师可能是专家，平台工作人员，都需要将人工评估的流水线接入你的系统\n",
    "   2. 评估输入系统的数据质量\n",
    "   3. 使用新鲜数据定期训练你的模型，最多6个月"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 总结\n",
    "    本周主要学习 数据准备，构建监控工作，建立人工评估流水线，自动化定期训练模型上，熟悉整个机器学习流程，\n",
    "## 建议\n",
    "    选择一个数据集，尝试从A到Z的整个过程，从竞赛网站上，选择一个数据集，一个明确目标，以及可以一起分享经验的同伴\n",
    "    https://www.kaggle.com/"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 第二题"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1 7 6 0 3 2 5 4]\n",
      "[2. 0. 1. 3. 4. 5. 6. 7.]\n",
      "[2. 0. 1. 3.]\n"
     ]
    }
   ],
   "source": [
    "dists= np.array([ 3.0, 2.0, 5.0, 4.0, 7.0, 6.0, 1.0, 0.0])\n",
    "\n",
    "k = 5\n",
    "print(np.argpartition(dists, k))\n",
    "\n",
    "print(dists[np.argpartition(dists, k)])\n",
    "\n",
    "# 取X个比k名次还高的\n",
    "X = 4\n",
    "print(dists[np.argpartition(dists, k)[:X]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.base import BaseEstimator, TransformerMixin\n",
    "\n",
    "def indices_of_top_k(arr, k):\n",
    "    return np.sort(np.argpartition(np.array(arr), -k)[-k:])\n",
    "\n",
    "class TopFeatureSelector(BaseEstimator, TransformerMixin):\n",
    "    def __init__(self, feature_importances, k):\n",
    "        self.feature_importances = feature_importances\n",
    "        self.k = k\n",
    "    def fit(self, X, y=None):\n",
    "        self.feature_indices_ = indices_of_top_k(self.feature_importances, self.k)\n",
    "        return self\n",
    "    def transform(self, X):\n",
    "        return X[:, self.feature_indices_]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0,  7,  8, 10, 13], dtype=int64)"
      ]
     },
     "execution_count": 135,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "k = 5\n",
    "top_k_feature_indices = indices_of_top_k(feature_importances, k)\n",
    "top_k_feature_indices"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['longitude', 'median_income', 'income_cat',\n",
       "       'population_per_household', 'INLAND'], dtype='<U24')"
      ]
     },
     "execution_count": 136,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.array(attributes)[top_k_feature_indices]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['median_income', 'income_cat', 'INLAND',\n",
       "       'population_per_household', 'longitude'], dtype='<U32')"
      ]
     },
     "execution_count": 146,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bba = sorted(zip(feature_importances, attributes), reverse=True)[:k]\n",
    "bba_np = np.array(bba)\n",
    "bba_np\n",
    "bba_np[:,1]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {},
   "outputs": [],
   "source": [
    "preparation_and_feature_selection_pipeline = Pipeline([\n",
    "    ('preparation', full_pipeline),\n",
    "    ('feature_selection', TopFeatureSelector(feature_importances, k))\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-0.94135046, -0.8936472 , -0.95468705,  0.00622264,  1.        ],\n",
       "       [ 1.17178212,  1.292168  ,  1.89007821, -0.04081077,  0.        ],\n",
       "       [ 0.26758118, -0.52543365, -0.95468705, -0.07537122,  1.        ]])"
      ]
     },
     "execution_count": 139,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing_prepared_top_k_features = preparation_and_feature_selection_pipeline.fit_transform(housing)\n",
    "housing_prepared_top_k_features[0:3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "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>median_income</th>\n",
       "      <th>income_cat</th>\n",
       "      <th>INLAND</th>\n",
       "      <th>population_per_household</th>\n",
       "      <th>longitude</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-0.941350</td>\n",
       "      <td>-0.893647</td>\n",
       "      <td>-0.954687</td>\n",
       "      <td>0.006223</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.171782</td>\n",
       "      <td>1.292168</td>\n",
       "      <td>1.890078</td>\n",
       "      <td>-0.040811</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.267581</td>\n",
       "      <td>-0.525434</td>\n",
       "      <td>-0.954687</td>\n",
       "      <td>-0.075371</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.221738</td>\n",
       "      <td>-0.865929</td>\n",
       "      <td>-0.954687</td>\n",
       "      <td>-0.106803</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.437431</td>\n",
       "      <td>0.325752</td>\n",
       "      <td>-0.006432</td>\n",
       "      <td>0.006109</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   median_income  income_cat    INLAND  population_per_household  longitude\n",
       "0      -0.941350   -0.893647 -0.954687                  0.006223        1.0\n",
       "1       1.171782    1.292168  1.890078                 -0.040811        0.0\n",
       "2       0.267581   -0.525434 -0.954687                 -0.075371        1.0\n",
       "3       1.221738   -0.865929 -0.954687                 -0.106803        0.0\n",
       "4       0.437431    0.325752 -0.006432                  0.006109        0.0"
      ]
     },
     "execution_count": 140,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing_prepared = pd.DataFrame(housing_prepared_top_k_features, columns = bba_np[:,1])\n",
    "housing_prepared.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 第三题"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {},
   "outputs": [],
   "source": [
    "prepare_select_and_predict_pipeline = Pipeline([\n",
    "    ('preparation', full_pipeline),\n",
    "    ('final_model', RandomForestRegressor(**grid_search.best_params_))\n",
    "\n",
    "])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-9 {color: black;background-color: white;}#sk-container-id-9 pre{padding: 0;}#sk-container-id-9 div.sk-toggleable {background-color: white;}#sk-container-id-9 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-9 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-9 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-9 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-9 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-9 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-9 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-9 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-9 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-9 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-9 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-9 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-9 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-9 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-9 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-9 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-9 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-9 div.sk-item {position: relative;z-index: 1;}#sk-container-id-9 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-9 div.sk-item::before, #sk-container-id-9 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-9 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-9 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-9 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-9 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-9 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-9 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-9 div.sk-label-container {text-align: center;}#sk-container-id-9 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-9 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-9\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>Pipeline(steps=[(&#x27;preparation&#x27;,\n",
       "                 FeatureUnion(transformer_list=[(&#x27;num_pipline&#x27;,\n",
       "                                                 Pipeline(steps=[(&#x27;selector&#x27;,\n",
       "                                                                  DataFrameSelector(attribute_names=[&#x27;longitude&#x27;,\n",
       "                                                                                                     &#x27;latitude&#x27;,\n",
       "                                                                                                     &#x27;housing_median_age&#x27;,\n",
       "                                                                                                     &#x27;total_rooms&#x27;,\n",
       "                                                                                                     &#x27;total_bedrooms&#x27;,\n",
       "                                                                                                     &#x27;population&#x27;,\n",
       "                                                                                                     &#x27;households&#x27;,\n",
       "                                                                                                     &#x27;median_income&#x27;,\n",
       "                                                                                                     &#x27;income_cat&#x27;])),\n",
       "                                                                 (&#x27;imputer&#x27;,\n",
       "                                                                  SimpleImputer(strategy=&#x27;median&#x27;)),\n",
       "                                                                 (&#x27;attribs_adder&#x27;,\n",
       "                                                                  CombinedAttributesAdder()),\n",
       "                                                                 (&#x27;std_scaler&#x27;,\n",
       "                                                                  StandardScaler())])),\n",
       "                                                (&#x27;cat_pipline&#x27;,\n",
       "                                                 Pipeline(steps=[(&#x27;selector&#x27;,\n",
       "                                                                  DataFrameSelector(attribute_names=[&#x27;ocean_proximity&#x27;])),\n",
       "                                                                 (&#x27;LabelBinarizer&#x27;,\n",
       "                                                                  &lt;__main__.MyLabelBinarizer object at 0x000002474EAEFC70&gt;)]))])),\n",
       "                (&#x27;final_model&#x27;,\n",
       "                 RandomForestRegressor(max_features=6, n_estimators=30))])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-13\" type=\"checkbox\" ><label for=\"sk-estimator-id-13\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">Pipeline</label><div class=\"sk-toggleable__content\"><pre>Pipeline(steps=[(&#x27;preparation&#x27;,\n",
       "                 FeatureUnion(transformer_list=[(&#x27;num_pipline&#x27;,\n",
       "                                                 Pipeline(steps=[(&#x27;selector&#x27;,\n",
       "                                                                  DataFrameSelector(attribute_names=[&#x27;longitude&#x27;,\n",
       "                                                                                                     &#x27;latitude&#x27;,\n",
       "                                                                                                     &#x27;housing_median_age&#x27;,\n",
       "                                                                                                     &#x27;total_rooms&#x27;,\n",
       "                                                                                                     &#x27;total_bedrooms&#x27;,\n",
       "                                                                                                     &#x27;population&#x27;,\n",
       "                                                                                                     &#x27;households&#x27;,\n",
       "                                                                                                     &#x27;median_income&#x27;,\n",
       "                                                                                                     &#x27;income_cat&#x27;])),\n",
       "                                                                 (&#x27;imputer&#x27;,\n",
       "                                                                  SimpleImputer(strategy=&#x27;median&#x27;)),\n",
       "                                                                 (&#x27;attribs_adder&#x27;,\n",
       "                                                                  CombinedAttributesAdder()),\n",
       "                                                                 (&#x27;std_scaler&#x27;,\n",
       "                                                                  StandardScaler())])),\n",
       "                                                (&#x27;cat_pipline&#x27;,\n",
       "                                                 Pipeline(steps=[(&#x27;selector&#x27;,\n",
       "                                                                  DataFrameSelector(attribute_names=[&#x27;ocean_proximity&#x27;])),\n",
       "                                                                 (&#x27;LabelBinarizer&#x27;,\n",
       "                                                                  &lt;__main__.MyLabelBinarizer object at 0x000002474EAEFC70&gt;)]))])),\n",
       "                (&#x27;final_model&#x27;,\n",
       "                 RandomForestRegressor(max_features=6, n_estimators=30))])</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-14\" type=\"checkbox\" ><label for=\"sk-estimator-id-14\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">preparation: FeatureUnion</label><div class=\"sk-toggleable__content\"><pre>FeatureUnion(transformer_list=[(&#x27;num_pipline&#x27;,\n",
       "                                Pipeline(steps=[(&#x27;selector&#x27;,\n",
       "                                                 DataFrameSelector(attribute_names=[&#x27;longitude&#x27;,\n",
       "                                                                                    &#x27;latitude&#x27;,\n",
       "                                                                                    &#x27;housing_median_age&#x27;,\n",
       "                                                                                    &#x27;total_rooms&#x27;,\n",
       "                                                                                    &#x27;total_bedrooms&#x27;,\n",
       "                                                                                    &#x27;population&#x27;,\n",
       "                                                                                    &#x27;households&#x27;,\n",
       "                                                                                    &#x27;median_income&#x27;,\n",
       "                                                                                    &#x27;income_cat&#x27;])),\n",
       "                                                (&#x27;imputer&#x27;,\n",
       "                                                 SimpleImputer(strategy=&#x27;median&#x27;)),\n",
       "                                                (&#x27;attribs_adder&#x27;,\n",
       "                                                 CombinedAttributesAdder()),\n",
       "                                                (&#x27;std_scaler&#x27;,\n",
       "                                                 StandardScaler())])),\n",
       "                               (&#x27;cat_pipline&#x27;,\n",
       "                                Pipeline(steps=[(&#x27;selector&#x27;,\n",
       "                                                 DataFrameSelector(attribute_names=[&#x27;ocean_proximity&#x27;])),\n",
       "                                                (&#x27;LabelBinarizer&#x27;,\n",
       "                                                 &lt;__main__.MyLabelBinarizer object at 0x000002474EAEFC70&gt;)]))])</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><label>num_pipline</label></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-15\" type=\"checkbox\" ><label for=\"sk-estimator-id-15\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">DataFrameSelector</label><div class=\"sk-toggleable__content\"><pre>DataFrameSelector(attribute_names=[&#x27;longitude&#x27;, &#x27;latitude&#x27;,\n",
       "                                   &#x27;housing_median_age&#x27;, &#x27;total_rooms&#x27;,\n",
       "                                   &#x27;total_bedrooms&#x27;, &#x27;population&#x27;, &#x27;households&#x27;,\n",
       "                                   &#x27;median_income&#x27;, &#x27;income_cat&#x27;])</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-16\" type=\"checkbox\" ><label for=\"sk-estimator-id-16\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SimpleImputer</label><div class=\"sk-toggleable__content\"><pre>SimpleImputer(strategy=&#x27;median&#x27;)</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-17\" type=\"checkbox\" ><label for=\"sk-estimator-id-17\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">CombinedAttributesAdder</label><div class=\"sk-toggleable__content\"><pre>CombinedAttributesAdder()</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-18\" type=\"checkbox\" ><label for=\"sk-estimator-id-18\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">StandardScaler</label><div class=\"sk-toggleable__content\"><pre>StandardScaler()</pre></div></div></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><label>cat_pipline</label></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-19\" type=\"checkbox\" ><label for=\"sk-estimator-id-19\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">DataFrameSelector</label><div class=\"sk-toggleable__content\"><pre>DataFrameSelector(attribute_names=[&#x27;ocean_proximity&#x27;])</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-20\" type=\"checkbox\" ><label for=\"sk-estimator-id-20\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">MyLabelBinarizer</label><div class=\"sk-toggleable__content\"><pre>&lt;__main__.MyLabelBinarizer object at 0x000002474EAEFC70&gt;</pre></div></div></div></div></div></div></div></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-21\" type=\"checkbox\" ><label for=\"sk-estimator-id-21\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestRegressor</label><div class=\"sk-toggleable__content\"><pre>RandomForestRegressor(max_features=6, n_estimators=30)</pre></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "Pipeline(steps=[('preparation',\n",
       "                 FeatureUnion(transformer_list=[('num_pipline',\n",
       "                                                 Pipeline(steps=[('selector',\n",
       "                                                                  DataFrameSelector(attribute_names=['longitude',\n",
       "                                                                                                     'latitude',\n",
       "                                                                                                     'housing_median_age',\n",
       "                                                                                                     'total_rooms',\n",
       "                                                                                                     'total_bedrooms',\n",
       "                                                                                                     'population',\n",
       "                                                                                                     'households',\n",
       "                                                                                                     'median_income',\n",
       "                                                                                                     'income_cat'])),\n",
       "                                                                 ('imputer',\n",
       "                                                                  SimpleImputer(strategy='median')),\n",
       "                                                                 ('attribs_adder',\n",
       "                                                                  CombinedAttributesAdder()),\n",
       "                                                                 ('std_scaler',\n",
       "                                                                  StandardScaler())])),\n",
       "                                                ('cat_pipline',\n",
       "                                                 Pipeline(steps=[('selector',\n",
       "                                                                  DataFrameSelector(attribute_names=['ocean_proximity'])),\n",
       "                                                                 ('LabelBinarizer',\n",
       "                                                                  <__main__.MyLabelBinarizer object at 0x000002474EAEFC70>)]))])),\n",
       "                ('final_model',\n",
       "                 RandomForestRegressor(max_features=6, n_estimators=30))])"
      ]
     },
     "execution_count": 142,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prepare_select_and_predict_pipeline.fit(housing, housing_labels)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "some_data = housing.iloc[:4]\n",
    "some_labels = housing_labels.iloc[:4]\n",
    "\n",
    "print(\"Predictions:\\t\", prepare_select_and_predict_pipeline.predict(some_data))\n",
    "print(\"Labels:\\t\\t\", list(some_labels))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 第四题"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'grid_search_prep' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[144], line 8\u001b[0m\n\u001b[0;32m      1\u001b[0m param_grid \u001b[38;5;241m=\u001b[39m[{\n\u001b[0;32m      2\u001b[0m     \n\u001b[0;32m      3\u001b[0m     \u001b[38;5;124m'\u001b[39m\u001b[38;5;124madd_bedrooms_per_room\u001b[39m\u001b[38;5;124m'\u001b[39m:[\u001b[38;5;28;01mTrue\u001b[39;00m, \u001b[38;5;28;01mFalse\u001b[39;00m]\n\u001b[0;32m      4\u001b[0m }]\n\u001b[0;32m      6\u001b[0m d_search_prep \u001b[38;5;241m=\u001b[39m GridSearchCV(prepare_select_and_predict_pipeline, param_grid, cv\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m5\u001b[39m,\n\u001b[0;32m      7\u001b[0m                                 scoring\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mneg_mean_squared_error\u001b[39m\u001b[38;5;124m'\u001b[39m, verbose\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m2\u001b[39m, n_jobs\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m4\u001b[39m)\n\u001b[1;32m----> 8\u001b[0m \u001b[43mgrid_search_prep\u001b[49m\u001b[38;5;241m.\u001b[39mfit(housing, housing_labels)\n",
      "\u001b[1;31mNameError\u001b[0m: name 'grid_search_prep' is not defined"
     ]
    }
   ],
   "source": [
    "param_grid =[{\n",
    "    \n",
    "    'add_bedrooms_per_room':[True, False]\n",
    "}]\n",
    "\n",
    "d_search_prep = GridSearchCV(prepare_select_and_predict_pipeline, param_grid, cv=5,\n",
    "                                scoring='neg_mean_squared_error', verbose=2, n_jobs=4)\n",
    "grid_search_prep.fit(housing, housing_labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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