Chapter 2. end-to-end machine learning project.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import tarfile\n",
    "from six.moves import urllib\n",
    "\n",
    "DOWNLOAD_ROOT = \"https://raw.githubusercontent.com/ageron/handson-ml/master/\"\n",
    "HOUSING_PATH = os.path.join(\"datasets\", \"housing\")\n",
    "HOUSING_URL = DOWNLOAD_ROOT + \"datasets/housing/housing.tgz\"\n",
    "\n",
    "def fetch_housing_data(housing_url=HOUSING_URL, housing_path=HOUSING_PATH):\n",
    "    if not os.path.isdir(housing_path):\n",
    "        os.makedirs(housing_path)\n",
    "    tgz_path = os.path.join(housing_path, \"housing.tgz\")\n",
    "    urllib.request.urlretrieve(housing_url, tgz_path)\n",
    "    housing_tgz = tarfile.open(tgz_path)\n",
    "    housing_tgz.extractall(path=housing_path)\n",
    "    housing_tgz.close()\n",
    "    \n",
    "import pandas as pd\n",
    "\n",
    "def load_housing_data(housing_path=HOUSING_PATH):\n",
    "    csv_path = os.path.join(housing_path, \"housing.csv\")\n",
    "    return pd.read_csv(csv_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fetch_housing_data()\n",
    "\n",
    "housing = load_housing_data()\n",
    "housing.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "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",
      "longitude             20640 non-null float64\n",
      "latitude              20640 non-null float64\n",
      "housing_median_age    20640 non-null float64\n",
      "total_rooms           20640 non-null float64\n",
      "total_bedrooms        20433 non-null float64\n",
      "population            20640 non-null float64\n",
      "households            20640 non-null float64\n",
      "median_income         20640 non-null float64\n",
      "median_house_value    20640 non-null float64\n",
      "ocean_proximity       20640 non-null object\n",
      "dtypes: float64(9), object(1)\n",
      "memory usage: 1.6+ MB\n"
     ]
    }
   ],
   "source": [
    "housing.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<1H OCEAN     9136\n",
       "INLAND        6551\n",
       "NEAR OCEAN    2658\n",
       "NEAR BAY      2290\n",
       "ISLAND           5\n",
       "Name: ocean_proximity, dtype: int64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing[\"ocean_proximity\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\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>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": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x1080 with 9 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "housing.hist(bins=50, figsize=(20,15))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f79da1f6390>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "housing[\"income_cat\"] = np.ceil(housing[\"median_income\"] / 1.5)\n",
    "housing[\"income_cat\"].where(housing[\"income_cat\"] < 5, 5.0, inplace=True)\n",
    "housing[\"income_cat\"].hist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3.0    0.350533\n",
       "2.0    0.318798\n",
       "4.0    0.176357\n",
       "5.0    0.114583\n",
       "1.0    0.039729\n",
       "Name: income_cat, dtype: float64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import StratifiedShuffleSplit\n",
    "\n",
    "split = StratifiedShuffleSplit(n_splits=1, test_size=0.2, random_state=42)\n",
    "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]\n",
    "    \n",
    "strat_test_set[\"income_cat\"].value_counts() / len(strat_test_set)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "for set_ in (strat_test_set, strat_train_set):\n",
    "    set_.drop(\"income_cat\", axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f7a0c2d6cf8>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x504 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "housing = strat_train_set.copy()\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",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "median_house_value          1.000000\n",
       "median_income               0.687160\n",
       "rooms_per_household         0.146285\n",
       "total_rooms                 0.135097\n",
       "housing_median_age          0.114110\n",
       "households                  0.064506\n",
       "total_bedrooms              0.047689\n",
       "population_per_household   -0.021985\n",
       "population                 -0.026920\n",
       "longitude                  -0.047432\n",
       "latitude                   -0.142724\n",
       "bedrooms_per_room          -0.259984\n",
       "Name: median_house_value, dtype: float64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "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\"]\n",
    "corr_matrix = housing.corr()\n",
    "corr_matrix[\"median_house_value\"].sort_values(ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-118.51  ,   34.26  ,   29.    , 2119.5   ,  433.    , 1164.    ,\n",
       "        408.    ,    3.5409])"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing = strat_train_set.drop(\"median_house_value\", axis=1)\n",
    "housing_labels = strat_train_set[\"median_house_value\"].copy()\n",
    "\n",
    "from sklearn.impute import SimpleImputer\n",
    "\n",
    "imputer = SimpleImputer(strategy=\"median\")\n",
    "housing_num = housing.drop(\"ocean_proximity\", axis=1)\n",
    "imputer.fit(housing_num)\n",
    "\n",
    "imputer.statistics_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "17606     <1H OCEAN\n",
       "18632     <1H OCEAN\n",
       "14650    NEAR OCEAN\n",
       "3230         INLAND\n",
       "3555      <1H OCEAN\n",
       "19480        INLAND\n",
       "8879      <1H OCEAN\n",
       "13685        INLAND\n",
       "4937      <1H OCEAN\n",
       "4861      <1H OCEAN\n",
       "Name: ocean_proximity, dtype: object"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing_tr = pd.DataFrame(imputer.transform(housing_num), columns=housing_num.columns)\n",
    "\n",
    "housing_cat = housing[\"ocean_proximity\"]\n",
    "housing_cat.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 1, 2, 0, 2, 0, 2, 0, 0])"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing_cat_encoded, housing_categories = housing_cat.factorize()\n",
    "housing_cat_encoded[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "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": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.preprocessing import OneHotEncoder\n",
    "encoder = OneHotEncoder()\n",
    "housing_cat_1hot = encoder.fit_transform(housing_cat_encoded.reshape(-1,1))\n",
    "housing_cat_1hot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.base import BaseEstimator, TransformerMixin\n",
    "\n",
    "rooms_ix, bedrooms_ix, population_ix, households_ix = 3, 4, 5, 6\n",
    "\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[:, households_ix]\n",
    "        population_per_household = X[:, population_ix] / X[:, households_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",
    "attr_adder = CombinedAttributesAdder(add_bedrooms_per_room=False)\n",
    "housing_extra_attribs = attr_adder.transform(housing.values)\n",
    "\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": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.preprocessing import OneHotEncoder\n",
    "from sklearn.pipeline import FeatureUnion\n",
    "\n",
    "num_attribs = list(housing_num)\n",
    "cat_attribs = [\"ocean_proximity\"]\n",
    "\n",
    "num_pipeline = Pipeline([\n",
    "    ('selector', DataFrameSelector(num_attribs)),\n",
    "    ('imputer', SimpleImputer(strategy='median')),\n",
    "    ('attribs_adder', CombinedAttributesAdder()),\n",
    "    ('std_scaler', StandardScaler()), \n",
    "])\n",
    "\n",
    "cat_pipeline = Pipeline([\n",
    "    ('selector', DataFrameSelector(cat_attribs)),\n",
    "    ('cat_encoder', OneHotEncoder(sparse=False))\n",
    "])\n",
    "\n",
    "full_pipeline = FeatureUnion(transformer_list=[\n",
    "    ('num_pipeline', num_pipeline),\n",
    "    ('cat_pipeline', cat_pipeline),\n",
    "])\n",
    "\n",
    "housing_prepared = full_pipeline.fit_transform(housing)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None, normalize=False)"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "\n",
    "lin_reg = LinearRegression()\n",
    "lin_reg.fit(housing_prepared, housing_labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Predictions [210644.60459286 317768.80697211 210956.43331178  59218.98886849\n",
      " 189747.55849879]\n",
      "Labels [286600.0, 340600.0, 196900.0, 46300.0, 254500.0]\n"
     ]
    }
   ],
   "source": [
    "some_data = housing.iloc[:5]\n",
    "some_labels = housing_labels.iloc[:5]\n",
    "some_data_prepared = full_pipeline.transform(some_data)\n",
    "print('Predictions', lin_reg.predict(some_data_prepared))\n",
    "print('Labels', list(some_labels))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "68628.19819848922"
      ]
     },
     "execution_count": 37,
     "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_rmse = np.sqrt(lin_mse)\n",
    "lin_rmse"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.tree import DecisionTreeRegressor\n",
    "\n",
    "tree_reg = DecisionTreeRegressor()\n",
    "tree_reg.fit(housing_prepared, housing_labels)\n",
    "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": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Scores [68930.49240245 67248.17075411 71402.66014814 69341.03600711\n",
      " 71199.05747116 75036.14116115 70844.6517123  70863.69991756\n",
      " 76846.1955798  70264.95631316]\n",
      "Mean 71197.70614669421\n",
      "Standard Deviation 2682.788959985409\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import cross_val_score\n",
    "\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",
    "\n",
    "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": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Scores [66755.35819855 66966.14573098 70347.95244419 74769.18698807\n",
      " 68031.13388938 71229.17716103 64959.86064183 68270.70198961\n",
      " 71552.91566558 67665.10082067]\n",
      "Mean 69054.75335298848\n",
      "Standard Deviation 2744.2187083829585\n"
     ]
    }
   ],
   "source": [
    "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",
    "\n",
    "display_scores(lin_rmse_scores)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "18709.17038180588"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestRegressor\n",
    "\n",
    "forest_reg = RandomForestRegressor()\n",
    "forest_reg.fit(housing_prepared, housing_labels)\n",
    "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": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Scores [49940.30901356 47570.2758794  49936.96436025 52460.26647791\n",
      " 49632.87926787 53389.14440973 48563.95262776 47862.49777009\n",
      " 53140.33218201 50103.8872714 ]\n",
      "Mean 50260.05092599859\n",
      "Standard Deviation 1985.2568143136732\n"
     ]
    }
   ],
   "source": [
    "for_scores = cross_val_score(forest_reg, housing_prepared, housing_labels,\n",
    "                             scoring=\"neg_mean_squared_error\", cv=10)\n",
    "for_rmse_scores = np.sqrt(-for_scores)\n",
    "\n",
    "display_scores(for_rmse_scores)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=5, error_score=nan,\n",
       "             estimator=RandomForestRegressor(bootstrap=True, ccp_alpha=0.0,\n",
       "                                             criterion='mse', max_depth=None,\n",
       "                                             max_features='auto',\n",
       "                                             max_leaf_nodes=None,\n",
       "                                             max_samples=None,\n",
       "                                             min_impurity_decrease=0.0,\n",
       "                                             min_impurity_split=None,\n",
       "                                             min_samples_leaf=1,\n",
       "                                             min_samples_split=2,\n",
       "                                             min_weight_fraction_leaf=0.0,\n",
       "                                             n_estimators=100, n_jobs=None,\n",
       "                                             oob_score=False, random_state=None,\n",
       "                                             verbose=0, warm_start=False),\n",
       "             iid='deprecated', n_jobs=None,\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",
       "             pre_dispatch='2*n_jobs', refit=True, return_train_score=False,\n",
       "             scoring='neg_mean_squared_error', verbose=0)"
      ]
     },
     "execution_count": 49,
     "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",
    "forest_reg = RandomForestRegressor()\n",
    "grid_search = GridSearchCV(forest_reg, param_grid, cv=5, scoring='neg_mean_squared_error')\n",
    "grid_search.fit(housing_prepared, housing_labels)"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "grid_search.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'max_features': 8, 'n_estimators': 30}"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid_search.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "63505.79283692254 {'n_estimators': 3, 'max_features': 2}\n",
      "54937.30031386825 {'n_estimators': 10, 'max_features': 2}\n",
      "53276.73887196043 {'n_estimators': 30, 'max_features': 2}\n",
      "60610.319223718805 {'n_estimators': 3, 'max_features': 4}\n",
      "52961.89488481148 {'n_estimators': 10, 'max_features': 4}\n",
      "50360.91927880732 {'n_estimators': 30, 'max_features': 4}\n",
      "59749.56192903286 {'n_estimators': 3, 'max_features': 6}\n",
      "51907.80856612273 {'n_estimators': 10, 'max_features': 6}\n",
      "50034.88274995427 {'n_estimators': 30, 'max_features': 6}\n",
      "59067.49058086846 {'n_estimators': 3, 'max_features': 8}\n",
      "52422.70928842598 {'n_estimators': 10, 'max_features': 8}\n",
      "49895.23672499947 {'n_estimators': 30, 'max_features': 8}\n",
      "62222.60745733775 {'n_estimators': 3, 'max_features': 2, 'bootstrap': False}\n",
      "54547.382224809975 {'n_estimators': 10, 'max_features': 2, 'bootstrap': False}\n",
      "59679.66816560158 {'n_estimators': 3, 'max_features': 3, 'bootstrap': False}\n",
      "53373.35208815622 {'n_estimators': 10, 'max_features': 3, 'bootstrap': False}\n",
      "58382.35015805944 {'n_estimators': 3, 'max_features': 4, 'bootstrap': False}\n",
      "52227.15172601112 {'n_estimators': 10, 'max_features': 4, 'bootstrap': False}\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": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(0.4002949544487396, 'median_income'),\n",
       " (0.15167115617127228, 'INLAND'),\n",
       " (0.11304491253468595, 'pop_per_hhold'),\n",
       " (0.06520886775361999, 'longitude'),\n",
       " (0.06223235628184297, 'latitude'),\n",
       " (0.05062923679125268, 'bedrooms_per_room'),\n",
       " (0.04448610447335984, 'housing_median_age'),\n",
       " (0.0355290311206964, 'rooms_per_hhold'),\n",
       " (0.01626215431854211, 'total_rooms'),\n",
       " (0.015649141428811814, 'population'),\n",
       " (0.015243655996752066, 'total_bedrooms'),\n",
       " (0.014351758369651248, 'households'),\n",
       " (0.009842983202537464, '<1H OCEAN'),\n",
       " (0.003080500344976323, 'NEAR OCEAN'),\n",
       " (0.0023864905224441025, 'NEAR BAY'),\n",
       " (8.669624081525359e-05, 'ISLAND')]"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "feature_importances = grid_search.best_estimator_.feature_importances_\n",
    "extra_attribs = ['rooms_per_hhold', 'pop_per_hhold', 'bedrooms_per_room']\n",
    "cat_encoder = cat_pipeline.named_steps['cat_encoder']\n",
    "cat_one_hot_attribs = list(cat_encoder.categories_[0])\n",
    "attributes = num_attribs + extra_attribs + cat_one_hot_attribs\n",
    "sorted(zip(feature_importances, attributes), reverse=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "47732.09952561662"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_model = grid_search.best_estimator_\n",
    "X_test = strat_test_set.drop('median_house_value', axis=1)\n",
    "y_test = strat_test_set['median_house_value'].copy()\n",
    "X_test_prepared = full_pipeline.transform(X_test)\n",
    "final_predictions = final_model.predict(X_test_prepared)\n",
    "final_mse = mean_squared_error(y_test, final_predictions)\n",
    "final_rmse = np.sqrt(final_mse)\n",
    "final_rmse"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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