6398 lines
1.5 MiB
6398 lines
1.5 MiB
{
|
||
"cells": [
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"**Chapter 2 – End-to-end Machine Learning project**\n",
|
||
"\n",
|
||
"*Welcome to Machine Learning Housing Corp.! Your task is to predict median house values in Californian districts, given a number of features from these districts.*\n",
|
||
"\n",
|
||
"*This notebook contains all the sample code and solutions to the exercices in chapter 2.*"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"**Note**: You may find little differences between the code outputs in the book and in these Jupyter notebooks: these slight differences are mostly due to the random nature of many training algorithms: although I have tried to make these notebooks' outputs as constant as possible, it is impossible to guarantee that they will produce the exact same output on every platform. Also, some data structures (such as dictionaries) do not preserve the item order. Finally, I fixed a few minor bugs (I added notes next to the concerned cells) which lead to slightly different results, without changing the ideas presented in the book."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"# Setup"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"First, let's make sure this notebook works well in both python 2 and 3, import a few common modules, ensure MatplotLib plots figures inline and prepare a function to save the figures:"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 1,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"# To support both python 2 and python 3\n",
|
||
"from __future__ import division, print_function, unicode_literals\n",
|
||
"\n",
|
||
"# Common imports\n",
|
||
"import numpy as np\n",
|
||
"import os\n",
|
||
"\n",
|
||
"# to make this notebook's output stable across runs\n",
|
||
"np.random.seed(42)\n",
|
||
"\n",
|
||
"# To plot pretty figures\n",
|
||
"%matplotlib inline\n",
|
||
"import matplotlib\n",
|
||
"import matplotlib.pyplot as plt\n",
|
||
"plt.rcParams['axes.labelsize'] = 14\n",
|
||
"plt.rcParams['xtick.labelsize'] = 12\n",
|
||
"plt.rcParams['ytick.labelsize'] = 12\n",
|
||
"\n",
|
||
"# Where to save the figures\n",
|
||
"PROJECT_ROOT_DIR = \".\"\n",
|
||
"CHAPTER_ID = \"end_to_end_project\"\n",
|
||
"\n",
|
||
"def save_fig(fig_id, tight_layout=True):\n",
|
||
" path = os.path.join(PROJECT_ROOT_DIR, \"images\", CHAPTER_ID, fig_id + \".png\")\n",
|
||
" print(\"Saving figure\", fig_id)\n",
|
||
" if tight_layout:\n",
|
||
" plt.tight_layout()\n",
|
||
" plt.savefig(path, format='png', dpi=300)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"# Get the data"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 2,
|
||
"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()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 3,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"fetch_housing_data()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 4,
|
||
"metadata": {
|
||
"collapsed": true
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"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": 5,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/html": [
|
||
"<div>\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": 5,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"housing = load_housing_data()\n",
|
||
"housing.head()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 6,
|
||
"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": 7,
|
||
"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": 7,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"housing[\"ocean_proximity\"].value_counts()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 8,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/html": [
|
||
"<div>\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": 8,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"housing.describe()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 9,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Saving figure attribute_histogram_plots\n"
|
||
]
|
||
},
|
||
{
|
||
"data": {
|
||
"image/png": 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ZwH7ArVQLyi/IzGvq512qryRJkiRJkiSpxZYskdEGlsiQJK0lT9Vt8lRdSRod\nS2TIeXdlLJEhSSvTlhIZkiRJkiRJkiT1cIG5ZUqqL1NKrKXECeXEWkqcUFas0iDuB73MSZP5aDIf\nvcyJNArz4x7AVPD9ajjM43CYx8nhArMkSZIkSZIkaVWswWxtJkkqnrUgm6wFKUmjYw1mOe+ujDWY\nJWllrMEsSZIkSZIkSWotF5hbpqT6MqXEWkqcUE6spcQJZcUqDeJ+0MucNJmPJvPRy5xIozA/7gFM\nBd+vhsM8Dod5nBwuMEuSJEmSJEmSVsUazNZmkqTiWQuyyVqQkjQ61mCW8+7KWINZklbGGsySJEmS\nJEmSpNZygbllSqovU0qspcQJ5cRaSpxQVqzSIO4HvcxJk/loMh+9zIk0CvPjHsBU8P1qOMzjcJjH\nyeECsyRJkiRJkiRpVazBbG0mSSqetSCbrAUpSaNjDWY5766MNZglaWWswSxJkiRJkiRJai0XmFum\npPoypcRaSpxQTqylxAllxSoN4n7Qy5w0mY8m89HLnEijMD/uAUwF36+GwzwOh3mcHC4wS5IkSZIk\nSZJWxRrM1maSpOJZC7LJWpCSNDrWYJbz7spYg1mSVsYazJIkSZIkSZKk1nKBuWVKqi9TSqylxAnl\nxFpKnFBWrG0REWdGxGci4rsRcVFX23ERcU1E7IqIyyPi0I62fSLioojYGRE3RcRLl9tXi3M/6GVO\nmsxHk/noZU7aq54//yoittZz6Oci4hfqtsMi4gcRcUdE3Fn/e1ZXX+fe1pgf9wCmgu9Xw2Eeh8M8\nTg4XmCVJapcbgfOAd3TeGREHAx8AzgIOAj4HvK/jIVuAw4GHAMcCr4iI45fZV5KkUq0DbgCemJkH\nAmcD7+9YDE7gwMw8IDM3ZOZrOvo690qShDWYF22bhNxIkvZcG2tBRsR5wIMz8/R6+wzgtMx8Qr29\nH7AD2JiZX42Ib9Ttl9ft5wIPy8xTlurb57WtBSlJI2IN5vaJiKuBVwH/BlwP3Ccz/7PP44Yy9zrv\nrow1mCVpZazBLEmSFhwNXL2wkZl3A9cBR0fEDPAg4PMdj7+67rNo3zUesyRJEyUiDgGOBL5Y35XA\n1oi4oS6HcXD9OOdeSZJqLjC3TEn1ZUqJtZQ4oZxYS4kTyop1AqwHdnbdtxM4oG7LrvaFtqX6agnu\nB73MSZP5aDIfvczJZIiIdcC7gIsz81qqI44fAxwG/BTVvPnu+uHOva0zP+4BTAXfr4bDPA6HeZwc\n68Y9AEkCRo2mAAAgAElEQVSStCy7gA1d920A7qzbot7e0dW2VN++Nm/ezNzcHAAzMzNs3LiRTZs2\nAbs/6JWyfdVVV7VqPG3Yvuqqq1o1nnFvmw/zsdT2gqXady+QtWv8w4h/fn6erVu30lZR1V14F3AP\n8GKAzLyLqkwGwG0R8SLg5ohYTzW3wpDmXufd5W9X5lnYT/ovLPdrb8f4277t557hbC9oy3gmddvf\nx9VtL/w8ynnXGszWZpKk4rWxFuQyajDvD9xKVcvx2oi4ETi1ow7kFuCIAXUgF/o+yhrMkjRe1mBu\nh4i4CDgUeGpm3jvgMYcANwEzmXlnnxrMq5p7nXdXxhrMkrQy1mCWJKkwEbF3RNwX2BtYFxH7RsTe\nwAep6i0/KyL2pbrK/dX1KbwAlwCvjIiZiHg4cAZwcd02qG/P4rIkSaWJiLcBDwee2bm4HBGPjYgj\no3Iw8EbgysxcOAr5nTj3SpLkAnPbdJ9OMc1KibWUOKGcWEuJE8qKtUVeCdwN/B7wnPrnszJzB3Ai\ncD5wO1VNyJM6+p0DfA3YBlwJvD4zLwNYRl8twv2glzlpMh9N5qOXOWmviDgUeD6wEdgeEXdGxB0R\ncTLwUODjwB1UF/P7LnBKR3fn3laZH/cApoLvV8NhHofDPE4OazBLktQimbkF2DKg7QrgqAFt9wLP\nq28r6itJUqky8wYWP/Dqbxfp69wrSRLWYF60bRJyI0nac22sBTlO1oKUpNGxBrOcd1fGGsyStDLW\nYJYkSZIkSZIktZYLzC1TUn2ZUmItJU4oJ9ZS4oSyYpUGcT/oZU6azEeT+ehlTqRRmB/3AKaC71fD\nYR6HwzxODheYJUmSJEmSJEmrYg1mazNJUvGsBdlkLUhJGh1rMMt5d2WswSxJK2MNZkmSJEmSJElS\na7nA3DIl1ZcpJdZS4oRyYi0lTigrVmkQ94Ne5qTJfDSZj17mRBqF+XEPYCr4fjUc5nE4zOPkcIFZ\nkiRJkiRJkrQq1mC2NpMkFc9akE3WgpSk0bEGs5x3V8YazJK0MtZgliRJkiRJkiS1lgvMLVNSfZlS\nYi0lTign1lLihLJilQZxP+hlTprMR5P56GVOpFGYH/cApoLvV8NhHofDPE4OF5glSZIkSZIkSati\nDWZrM0lS8awF2WQtSEkaHWswy3l3ZazBLEkrYw1mSZIkSZIkSVJrucDcMiXVlykl1lLihHJiLSVO\nKCtWaRD3g17mpMl8NJmPXuZEGoX5cQ9gKvh+NRzmcTjM4+RwgVmSJEmSJEmStCrWYLY2kyQVz1qQ\nTdaClKTRsQaznHdXxhrMkrQy1mCWJEmSJEmSJLWWC8wtU1J9mVJiLSVOKCfWUuKEsmKVBnE/6GVO\nmsxHk/noZU6kUZgf9wCmgu9Xw2Eeh8M8Tg4XmCVJkiRJkiRJq2INZmszSVLxrAXZZC1ISRodazDL\neXdlrMEsSStjDWZJkiRJkiRJUmu5wNwyJdWXKSXWUuKEcmItJU4oK1ZpEPeDXuakyXw0mY9e5kQa\nhflxD2Aq+H41HOZxOMzj5HCBWZIkSZIkSZK0KtZgtjaTJBXPWpBN1oKUpNGxBrOcd1fGGsyStDLW\nYJYkSZIkSZIktdayFpgj4p0RcVNE7IyIr0TE8zrajouIayJiV0RcHhGHdrTtExEX1f1uioiXdj3v\nwL6lKqm+TCmxlhInlBNrKXFCWbFKg7gf9DInTW3Ox+zsHBHR9zY7O7cmr9nmfIyLOZFGYX7cA5gK\nvl8Nh3kcDvM4OZZ7BPP5wGGZeSDwTODVEfGoiDgY+ABwFnAQ8DngfR39tgCHAw8BjgVeERHHAyyj\nryRJkqQ9sH37NqpTxXtvVZskSZK0Z1ZcgzkifgK4Evgt4H7AaZn5hLptP2AHsDEzvxoR36jbL6/b\nzwUelpmnRMQZi/Xtek1rMEuS1oy1IJusBSlNj6U+C7uvj581mOW8uzLWYJaklWlVDeaIeHNE3AVc\nA9wE/ANwNHD1wmMy827gOuDoiJgBHgR8vuNprq77sFjfVUUiSZIkSZIkSRqpZS8wZ+aZwHrgCcCl\nwL319s6uh+4EDqjbsqt9oY0l+harpPoypcRaSpxQTqylxAllxSoN4n7Qy5w0mY8m89HLnEijMD/u\nAUwF36+GwzwOh3mcHOtW8uD6vJ1PRcRzgRcCu4ANXQ/bANxZt0W9vaOrjSX69ti8eTNzc3MAzMzM\nsHHjRjZt2gTs/oUbtL17ouneZlntSz2/26vbXtCW8azV9lVXXdWq8bi959tXXXVVq8azltvT+vu7\n8PPWrVuRJEmSJEmrt+IazAARcSHVAvGXgM0ddZT3B26lqqN8bUTcCJzaUYN5C3DEgBrMC30fZQ1m\nSdIoWQuyyVqQ0vSwBnP7WYNZzrsrYw1mSVqZVtRgjogHRMSvRMT+EbFXRPw8cBJwOfAhqnrLz4qI\nfYGzgasz89q6+yXAKyNiJiIeDpwBXFy3fXBA38bisiRJkiRJkiSpnZZTgzmpymF8Hbgd+CPgtzPz\no5m5AzgROL9uewzV4vOCc4CvAduAK4HXZ+ZlAMvoW6TO07enXSmxlhInlBNrKXFCWbFKg7gf9DIn\nTePOx+zsHBHR9zYO485HG5kTaRTmxz2AqeD71XCYx+Ewj5NjyQXmzNyRmZsy86DMnMnMYzLzoo72\nKzLzqMzcPzOPzcwbOtruzcznZeaBmfnAzHxj13MP7CtJknpFxGER8bGIuD0iboqIv4iIveq2jRHx\n2Yi4KyI+ExHHdPV9fUTsiIjbIuL144lA0rBt376N6piQfjdJi4mIfSLiryJia0TsjIjPRcQvdLQf\nFxHXRMSuiLg8Ig7t6ntR3e+miHhp13MP7CtJ0jRZVQ3mUbMGsyRpLU1SLciI+BiwHfgN4H7A/wT+\nEng7cC3wp8BbgRcAvwM8LDO/HxG/AbwEOLZ+qv8JvDEz/7LPa1gLUpogft6dbNZgHq+I2A94OXBx\nZn49Ip4GvBf4SeAu4DrgdOCjwKuBJ2bm4+q+rwUeDzwDeBDVWbunZeYnIuLgxfp2jcF5dwWswSxJ\nKzOKedcFZiceSSpem77oLiUivgy8LDM/Xm//EXAAcClwUWY+pOOx24Az6i+6n6T68vxXddvpwK9n\n5uP7vIZfdKUJ4ufdyeYCc/tExNXAq4D707ww/X7ADqqL2n81Ir5Rty9c1P5cqj/s9ruofaNv1+s5\n766AC8yStDKtuMifRquk+jKlxFpKnFBOrKXECWXFOkEuAE6OiB+JiAcDvwh8HDga+HzXYz9f30/9\n79UdbVd3tGkR7ge9zEmT+WgyH73MyeSIiEOAI4Av0TV3ZubdVEclHx0RM1RHLXfOvZ1z68C+azn+\nss2PewBTwfer4TCPw2EeJ4cLzJIkTZb/RfXl9A7gBuAzmfn3wHpgZ9djd1Id3Uyf9p31fZIkCYiI\ndcC7gL+ujzJebG5dT3WYbPfcOmje7W6XJGlqrBv3ANS0adOmcQ9hZEqJtZQ4oZxYS4kTyop1EkR1\nTug/UtVYfhzVl9eL6wv23Qxs6OqyAbiz/nlXV/uG+r6+Nm/ezNzcHAAzMzNs3Ljxh78PC0cSlLK9\ncF9bxtOW7QVtGc+4txeM+/V3H8G3qeu+TX3bpzUfk7a928J2u8Y3jPjm5+fZunUrbVXPse8C7gFe\nXN/dPXfC7rl1F1Uthg1UpS8625bq28N5d2Xz8mLva0u1j3v8bd9euK8t43G77O2F+9oynknZXvh5\nlPOuNZitzSRJxWt7LcgF9QWDbgVmMvPO+r5fAs4DXkZVY7mzBvNWqhrMl9U1mC/KzHfUbadjDWZp\nKvh5d7JZg7kdIuIi4FDgqZl5b31fdx3l/anm4Y2ZeW1E3Aic2lGDeQtwxIAazAt9H2UN5j1jDWZJ\nWhlrMBeo868N066UWEuJE8qJtZQ4oaxYJ0FmfhO4HnhhROxd1388DbgK+Cfg+xHx4ojYJyJeRPXt\n6sq6+yXAyyLiQRHxIOoF6dFHMXncD3qZkybz0WQ+epmTdouItwEPB565sLhc+yBVveVnRcS+wNnA\n1Zl5bd1+CfDKiJiJiIcDZ7B7bh3Ut7G4rGGaH/cApoLvV8NhHofDPE4OF5glSZosJ1Bd2O824KvA\n94CXZeb3gP9GteD8LWAz8EuZ+X2AzHw78BHgC1QXJPpIZl448tFLktQiEXEo8HxgI7A9Iu6MiDsi\n4uTM3AGcCJwP3A48Bjipo/s5wNeAbVR/0H19Zl4GsIy+kiRNjakokTE7O8f27dsWeQZPGZQkDdbG\nU3XHyVN1pcliiYzJZokMOe+ujCUyJGllRjHvTsVF/qrF5cU+VEuSJEmSJEmShs0SGS1TUn2ZUmIt\nJU4oJ9ZS4oSyYpUGaeN+MDs7R0T0vc3Ozq3567cxJ+NkPprMRy9zIo3C/LgHMBV8vxoO8zgc5nFy\nuMA80L5j/eImSZI0yO6zt3pvi5cNkyRJkqThmooazHtSd856dZIka0E2WQuy/Zb67OP/X1mswTzZ\nrMEs592VsQazJK3MKOZdj2CWJEmSJEmSJK2KC8wtU1J9mVJiLSVOKCfWUuKEsmKVBnE/6GVOmsxH\nk/noZU6kUZgf9wCmgu9Xw2Eeh8M8Tg4XmCVJkiRJkiRJq2INZuvVSVLxrAXZZC3I9rMGszpZg3my\nWYNZzrsrYw1mSVoZazBLkiRJkiRJklrLBeaWKam+TCmxlhInlBNrKXFCWbFKg7gf9DInTeajyXz0\nMifSKMyPewBTwfer4TCPw2EeJ4cLzJIkSZIkSZKkVbEGs/XqJKl41oJsshZk+1mDWZ2swTzZrMEs\n592VsQazJK2MNZglSZIkSZIkSa3lAnPLlFRfppRYS4kTyom1lDihrFilQdwPepmTJvPRZD56mRNp\nFObHPYCp4PvVcJjH4TCPk8MFZkmSJEmSJEnSqliD2Xp1klQ8a0E2WQuy/azBrE7WYJ5s1mCW827T\n7Owc27dvW+JR1mCWpOUaxby7bi2fXJIkSZIkabmqxeWlFpAlSW1iiYyWKam+TCmxlhInlBNrKXFC\nWbFKg7gf9DInTeajyXz0MifSKMyPewBTwfer4TCPw2EeJ4cLzJIkSVNlXyKi7212dm7cg5MkSZI0\nZazBbL06SSqetSCbrAXZftbcVSd/HyabNZjlvNu0+HsaLP6+tlT79O5XkjTIKOZdj2CWJEmSWm52\ndm7gkemSJEnSOLnA3DIl1ZcpJdZS4oRyYi0lTigrVmkQ94Ne5qRpFPnYfdGrfrd28fejlzmRRmF+\n3AOYCr5fDYd5HA7zODlcYJYkSZIkSZIkrYo1mK1XJ0nFsxZkk7Ug28+au+VZq8+7/j6MnzWY5bzb\nZA1mSRouazBLkiRJkiRJklrLBeaWKam+TCmxlhInlBNrKXFCWbFKg7gf9DInTeajyXz0MifSKMyP\newBTwfer4TCPw2EeJ4cLzJIkSZIkSZKkVbEGs/XqJKl41oJsshZk+1mDuTzWYJ5e1mCW826TNZgl\nabiswSxJkiRJkiRJai0XmFumpPoypcRaSpxQTqylxAllxSoN4n7Qy5w0mY8m89HLnEijMD/uAUwF\n36+GwzwOh3mcHC4wS5IkSZIkSZJWxRrM1quTpOJZC7LJWpDtZw3m8liDeXpZg1nOu03WYJak4bIG\nsyRJkiRJkiSptVxgbpmS6suUEmspcUI5sZYSJ5QVqzSI+0Evc9JkPprMRy9zIo3C/LgHMBV8v6rM\nzs4REQNvs7Nzi/Y3j8NhHifHunEPQJIkSZIkSWqL7du3sVgplu3brfIjdbIGs/XqJKl4k1YLMiJO\nAs4GDgVuBjZn5icj4jjgTcBDgE8Dv5aZN9R99gHeBpwI3AW8ITP/bMDzWwuy5azB3Gt2dq7+Mtjr\nkEMO45Zbto52QENmDebpZQ3m8YqIM4HNwCOA92Tm6fX9hwHXA7vYvSO9PjNfU7cvOq8uNif3GYPz\nbgdrMKsNlvN76O+SJoU1mCVJUkNEPAV4LXBaZq4Hfhb4WkQcDHwAOAs4CPgc8L6OrluAw6m+6B4L\nvCIijh/l2KW1tPtIo97boIXntlnsdFxJa+ZG4DzgHX3aEjgwMw/IzA0Li8u1gfPqMuZkSZKmigvM\nLVNSfZlSYi0lTign1lLihLJinSCvAs7NzM8AZObNmXkzcALwxcy8NDPvrR93TEQcWfd7bt3vjsz8\nCnAh1RFbWoL7QS9z0jSsfCy2SD5J/P3oZU7aKzM/lJkfBm7v0xwM/s682Ly61JysNTE/7gFMBd+v\nhsM8Dod5nBwuMEuSNCEiYi/g0cCPRsS1EXFDRPx5RNwXOBq4euGxmXk3cB1wdETMAA8CPt/xdFfX\nfSRJUn8JbK3n24vqI5NZxrw6cE4eyaglSRoxazBbr06SitemWpCLiYgHUp3K+1ng6cD3gQ9THbIz\nC9yamX/Q8fh/Bv4SuALYBvxIfSQVEfFk4C8z86F9XsdakC1nDeZeS+VkEuL2M22ZrMHcDhFxHvDg\njhrM+wM/AVwFHAy8BTggM38hIn6MRebViPgrBszJmXlJn9d23u1gDWa1gTWYNU1GMe+uW8snlyRJ\nQ/Wd+t8/z8xbASLiT4FXAv8EbOh6/AbgTnZfoGgDsKOrra/NmzczNzcHwMzMDBs3bmTTpk3A7lPV\n3B7v9m4L25u67tvUt70t4x9dPprtbRnvoO2lxr/y9oX7+j//uON1u9rebWG7XeMbRnzz8/Ns3bqV\nSZGZdwH/Vm/eFhEvAm6OiPVU8yoMnld3MXhO7st5t/N9EBZ739rT9nHH5/ZkbO+2sL2pa5tWjddt\ntzu3F34e5bzrEcwtO9pjfn7+h78Y066UWEuJE8qJtZQ4oZxY23gk1SARcQPwB5n5rnr7BKqLCL0V\n2JyZT6jv3x+4FdiYmddGxI3AqZl5ed2+BTgiM0/p8xoeSdWhjfvBuI9gnsScrOXv9LDyMS2fadv4\n+zFui+XEI5jbofsI5j7thwA3ATOZeWdEfIPqgrs982pEnFG3dc/Jj8rMr/Z5bufdDqs/gnmeahHQ\nI5j3hO/hlT09gtk8Dod5HI5RzLt7reWTS5KkobsYeHFEPCAi7ge8BPgI8CGqesvPioh9gbOBqzPz\n2rrfJcArI2ImIh4OnFE/lyRJxYqIvetrGewNrIuIfev7HhsRR0blYOCNwJWZuXAU8jsZPK9+kP5z\ncs/isiRJ08AjmFt0tIckaTzaeCTVIBGxjupL7ilUJTPeB/xeZt4bEccCbwYOBT5NdUTzDXW/faiO\ncv5l4G7gdZn5xgGv4ZFULTfuI5jbyBrM/j5MKo9gHq+IOAc4h+Z/whbgq8D5wAOAO4DLgFd0lKha\ndF5dbE7uMwbn3Q7WYFYbWINZ02QU8+6SC8z1xPkW4MnA/YD/AM7KzI/X7ccBbwIeQjVx/lrXl9m3\nAScCdwFvyMw/63jugX27xuACsyRpzbTpi24b+EW3/Vxg7uUCs78Pk8oFZjnvNrnArDZwgVnTpC0l\nMtYBNwBPzMwDqU7veX9EHFqfKvQBqtqPBwGfozqSasEW4HCqBeRjgVdExPEAy+hbpN6C8tOrlFhL\niRPKibWUOKGsWKVB3A96mZMm89FkPnqZE2kU5sc9gKng+9VwmMfhMI+TY8kF5sy8OzPPzcyv19sf\nA64Hfgo4AfhiZl6amfcCrwKOiYgj6+7PBc7NzDsy8yvAhcDmum2pvpIkSRqqfYmIntvs7Ny4ByZJ\nkiRpQq24BnN99dzrgY3AbwL3ycwzO9q/QHWU85XA7cAhmXlb3XYicHZmHhMRFwzqm5kf7HpNS2RI\nktaMp+o2eapu+w3/s89kfLaZnZ1j+/Ztizyi/Z/d9iQGP9NOJ0tkyHm3yRIZagNLZGiatKVExg/V\nFxZ6F/DX9RVw1wM7ux62Ezigbsuu9oU2lugrSZIkNVQLszngNhmmIQZJkiSp07rlPjCqP9+8C7gH\neHF99y5gQ9dDNwB31m1Rb+/oaluqb4/NmzczNzcHwMzMDBs3bmTTpk0dj5gHNnX8zDK2WWV7VQdm\n4fUXasIMY7uzvsxaPH+btrtjHvd41mr7ggsuaPy+jns8a7ldyu/vVVddxUte8pLWjGctt6f193fh\n561btyItZb5jzlfFnDSZjybz0cucSKMwT+d3dq2O71fDYR6HwzxOjmWXyIiIi4BDgafWNZOJiDOA\n0zLzCfX2/sCtwMbMvDYibgROzczL6/YtwBGZecoifR9VHx3d+drFlMgoaecpJdZS4oRyYi0lTign\nVk/VbfJU3aY27gfjLpExrpzsSdxr+Tu9knyM43PrqPPSxn1m3BbLiSUy5LzbtPoSGfNUC8yWyNgT\nvodX9rREhnkcDvM4HKOYd5e1wBwRbwMeCTw5M+/uuP/+wLXA6cA/AOcCT8zMx9ftrwV+GngWMAtc\nQbWofNlSfbtev5gFZknS6PlFt8kvuu037gXmcWnrAvNKlLDArJVxgVnOu03WYFYbWINZ06QVNZgj\n4lDg+VQX9dseEXdGxB0RcXJm7gBOBM6nuqDfY4CTOrqfA3wN2EZ10b/XZ+ZlAMvoK0mSJEmSJElq\nsSUXmDPzhszcKzP3y8wD6tuGzHxv3X5FZh6Vmftn5rGZeUNH33sz83mZeWBmPjAz39j13AP7lqqz\nPui0KyXWUuKEcmItJU4oK1ZpEPeDXuakyXw0mY9e5kQahflxD2Aq+H41HOZxOMzj5FhygVmSJEnS\nNNqXiOh7m52dG/fgJEmSNCGWfZG/cbIGsyRpLVkLsslakO1nDea+rYu2tSW+ttVgnoScTTtrMMt5\nt8kazGoDazBrmrSiBrMkSZIkSZIkSf24wNwyJdWXKSXWUuKEcmItJU4oK1ZpEPeDXuakyXw0mY9e\n5kQahflxD2Aq+H41HOZxOMzj5HCBWZIkSZIkSZK0KtZgtiadJBXPWpBN1oJsh9nZObZv37bII6zB\n3NW6aFtb4rMGs7pZg1nOu03WYFYbWINZ02QU8+66tXxySZIkrU61uLzYwqAkSZIkjZ8lMlqmpPoy\npcRaSpxQTqylxAllxSoN4n7Qy5w0mY8m89HLnEijMD/uAUwF36+GwzwOh3mcHC4wS5IkFW9fIqLv\nbXZ2btyDkyRJktRi1mC2Jp0kFc9akE3WgmyH0X6+mYzPPdZg9vPuNLIGs5x3m6zBrDawBrOmySjm\nXY9gliRJkiRJkiStigvMq7J2p5GWVF+mlFhLiRPKibWUOKGsWKVB3A96TV5O1rYEyOTlY22Zj17m\nRBqF+XEPYCr4fjUc5nE4zOPkWDfuAUymexh0qsT27Z7pJUmS1C5+dpMkSZLWijWYrVcnScWzFmST\ntSDbodQazLOzc2zfvm2RR7Tjs9ti4zzkkMO45Zat/V+tZZ9b2/L/XjJrMMt5t8kazGoDazBrmoxi\n3nWB2Q/jklQ8v+g2+UW3HUpdYB7957r7Uh3h3GtPFooH5axtn1vb8v9eMheY5bzb5AKz2sAFZk0T\nL/JXoJLqy5QSaylxQjmxlhInlBWrNIj7Qa/pyslC+Yze2+JHUneaX5uhTajp+v0YDnMijcL8uAcw\nFXy/Gg7zOBzmcXJYg1mSJEmL2Lc+iqfXYkf4SpIkSSqDJTI8nVCSiuepuk2eqtsObSqRMcrPPe36\nXDe4fEZl2P3aFLufaUfFEhly3m2yRIbawBIZmiajmHc9glmSJEnqa6F8Rj+LfUZfbT9JkiRp8liD\nuWVKqi9TSqylxAnlxFpKnFBWrNIg7gf9zI97AC0zP+4BtIr7TC9zIo3C/LgHMBV8vxoO8zgc5nFy\nuMAsSZKkVarqM/e7zc7ODew1Ozs3sJ8kSZKkyWINZuvVSVLxrAXZZC3IdpiUGsyr+UzUts9utvVv\n831gNKzBLOfdJmswqw2swaxpMop51yOYJUmSJEmSJEmr4gJzy5RUX6aUWEuJE8qJtZQ4oaxYpUHc\nD/qZH/cAWmZ+3ANoFfeZXuZEGoX5cQ9gKvh+NRzmcTjM4+RwgVmSJEmSJEmStCrWYLZenSQVz1qQ\nTdaCbAdrMNs27jbfB0bDGsxy3m2yBrPaYOnfw/sC9wxsPeSQw7jllq1DHpW0OtZgliRJfUXEERHx\nnYi4pOO+UyJia0TcGRGXRsRMR9v9IuKDEbErIq6PiJPHM3JJktojIs6MiM9ExHcj4qKutuMi4pp6\n7rw8Ig7taNsnIi6KiJ0RcVNEvHS5fSVNg3uoFqD737Zv3zbGsUmj5wJzy5RUX6aUWEuJE8qJtZQ4\noaxYJ9CbgH9d2IiIo4G3Ac8BDgG+A7y14/FvAb4LPAD4VeCtEXHUyEY7wdwP+pkf9wBaZn7cA2gV\n95le5qTVbgTOA97ReWdEHAx8ADgLOAj4HPC+jodsAQ4HHgIcC7wiIo5fZl+tiflxD2Aq+H6lNvH3\ncXK4wCxJ0oSJiJOAbwGXd9x9CvDhzPxkZt4N/CFwQkTsHxH7AScAr8zM72TmJ4EPA88d9djVNDs7\nR0T0vUmS1l5mfigzPwzc3tV0AvDFzLw0M+8FXgUcExFH1u3PBc7NzDsy8yvAhcDmZfaVJGmqWIPZ\nenWSVLxJqgUZERuAz1AdLfXrwOGZeWpEfAj4ZGa+oeOxdwI/SzVpfTIz9+9o+x3gZzPzl/q8hrUg\nR6Q9n2GswWxbb5vvA6NhDeZ2iIjzgAdn5un19gXAfTLzzI7HfAE4G7iSakH6kMy8rW47ETg7M49Z\nrG9mfrDPazvvdrAGs9pgGL+H/q6pLUYx765byyeXJElDdy5wYWbe2HWU63pgZ9djdwIHAD9YpK2v\nzZs3Mzc3B8DMzAwbN25k06ZNwO5T1dwezvbuU3q7t1ll+8J9Sz3/aJ5vUPxr9Xrjz9d0vd64949S\ntndb2G7X+IYR3/z8PFu3bmWCrP//2bv3uEnK8sD7v4sZDnIcEJ1HCDCLSoIkMuSN0cWoIxrjaowR\nckCNMsGQEzFRk5hsZEHA6JrDhuyqcV8VDQIedhUlxiS6Qmuiu676CgQkwagDKswgAsMZxLneP6oe\n6BllIkQAACAASURBVO6nu5+enu6u6qrf9/Opzzzdd1f3dd1T1Xf13dVXATf33bc8du5LMbO0fUDb\nausO5LjbPS7C6PetXWuvOj9vz+/20tKGkbWQ168/gve//z0D13/Y8u1NfbfHa69Tf3i7PbeX/57n\nuOsZzDU726PT6Ty0YTRdW3JtS57Qnlzbkie0J9c6nkk1SERsBC4ENmbmgxFxFr1nMP9TZv5Z1+Pv\nAJ5BMWj9U2bu29X2GuAZnsG8ulnuB/U5htnZdToUH6RGrTf66ur1yHtabR16JzbqGufOtU36PtCW\nsWNnjOoTz2CuhyFnMK/NzN/qesxVwFk8fAbzozPzlrLtROCsrjOYB67rGcyrm/zM0Q6rj03N3a+m\npUnv4eNsS5P92qpY1zOYZ69J22OV5jHu7jbLJ5ckSVP1DOAI4IaIuAn4PeCkiPgicDWwcfmBEXEk\nsAdwXbmsjYjHdj3XscA18wpcbTTq6uqqvz2H1gdfWtpQdXDSPFxD77i6D8VF/a7OzNuBmyjG0mXd\n4+qwdR13JUmN5BnMNTrbQ5JUjTqeSTVIROwF7N911+9TTDj/OrAEfA54PnAF8HZgt8x8abnuxRSD\n12nAccDHgOMz89oBr+OZVHNSn2OYusRh26K0+R4xPZ7BXK2IWAPsTlFb+QcoxskHgQOBrwKnAh+n\nKFH1tMw8vlzvTcBTgBdRjMGXAadk5icj4uBR6w6IwXG3izWYNS2ewSwVPINZkiQ9JDPvy8yblxfg\nLuC+zLw1M79CMdF8MbAV2Ac4vWv104G9KWpCXgT8+qDJZUmSWuYM4B7gD4CXln+/rix9cRLwRopy\nGE8CTu5a7yzg68D1FCUz3pyZnwQYY11JkhrFCeaaWVlQvrnakmtb8oT25NqWPKFduS6izDw7M1/e\ndfv9mXlEZu6XmSeWP+FdbrstM1+Umftm5obM/EA1US8e94NBOlUHUDOdqgOoFfeZleyT+irH0t0y\nc03Xck7ZdllmHp2Z+2TmCZl5Q9d6D2TmKzLzgMx8TGb+Zd/zDl1Xs9KpOoBG8P1KdeL2uDicYJYk\nSZIkSZIkTcQazNakk6TWq1MtyDqwFuT81OcYpi5x2LYYbXtRXMRxpfXrj2Dr1i1D1tMg1mCW424v\nazBrWqzBLBWswSxJkiSpZu6n+FC9ctm27fqJnnFpaQMRMXBZWtowrcAlSZI0A04w10yb6su0Jde2\n5AntybUteUK7cpWGcT8YpFN1ADXTqTqAWplknykmpqc7aV0nvo9I89CpOoBG8P1KdeL2uDicYJYk\nSZK0kDzzWZIkqXrWYLYGsyS1nrUge1kLcn7qcwxTlzhsa0LbJO8fo/eF4TWfC4t9XG4NZjnu9rIG\ns6bFGsxSYR7j7tpZPrkkSZIk7Zrlms+DOEcpSZJUNUtk1Eyb6su0Jde25AntybUteUK7cpWGcT8Y\npFN1ADXTqTqAWnGfWck+keahU3UAjeD7lerE7XFxOMEsSZIkSZIkSZqINZhrUndOklQda0H2shbk\n/NTnGKYucdjWhLbp12Bu9nG5NZjluNvLGsyaFmswS4V5jLuewSxJkiRJkiRJmogTzDXTpvoybcm1\nLXlCe3JtS57QrlylYdwPBulUHUDNdKoOoFbcZ1ayT1RHS0sbiIiBy9LShqrDm0Cn6gAawfcr1Ynb\n4+JYW3UAkiRJkiRpvrZtu55hP/Hfts0KJpKk8VmDuaW13iRJD7MWZC9rQc5PfY5h6hKHbU1oswbz\nzrEGs6oad1fb76ra/qzBrGmxBrNUsAazJEmSJEmSJKm2nGCumTbVl2lLrm3JE9qTa1vyhHblKg3j\nfjBIp+oAaqZTdQC14j6zkn0izUOn6gAawfcr1Ynb4+JwglmSJGmGRl1ESWqePXnmM5/ZoIuGSZIk\naTVj1WCOiNOBzcCPABdn5qldbc8C3gIcBnwe+OXMvKFs2wN4O3AScDfwp5n5F+Os2/f61mCWJM2M\ntSB7WYN5uup2nGINZtuqbJus1mWzj8utwSxrMPe9sjWYNSXWYJYKdarB/G3gXOBd3XdGxCOBDwGv\nAw4CvgR8oOshZwOPpZhAPgF4bUQ8Z8x1F9SeQ89S8qwNSZIktdfw4+R5v57H5ZIkSdMz1gRzZn4k\nMy8Fbu1rOhG4OjM/nJkPAK8Hjo2Io8r2lwHnZOYdmfkvwDsozoQeZ90FdT/Ft1grl23brl917TbV\nl2lLrm3JE9qTa1vyhHblKg3jfjBIp+oAaqZTdQA10xly//Dj5NnYtePyafJ9RJqHTtUBNILvV6oT\nt8fFsXYX1z8GuHL5RmbeExFfA46JiJuBQ4Cruh5/JfDC1dYFrut/ofe///0DAzjwwAN3MQVJkiRJ\nkiRJ0iR2dYJ5X+Dmvvu2A/uVbVne7m9bbd0VXvayP2S33fYtb+3OmjUHsnbto7nvvr8p7+sAm7r+\nZozbTNi+a6+3/A3Mpk2bVtzetGnTyHZvL97t5fvqEs8sb7dp+11Wl3jcfnfu9vLfW7ZsQVpN9/6g\nZZuqDqBmNlUdQM1sqjqA2vF9RJqHTVUH0Ai+X6lO3B4Xx1gX+XvowRHnAocuX+QvIs4D1mbmb3U9\n5irgLOByipIaj87MW8q2E4GzMvPYUetm5iV9r5vwfQZV9Nh33yO5665vUKeLl3hBAUlaLF5sqJcX\n+ZsuL/Jnm231bKvL+5wX+ZMX+et7ZS/ypynxIn9SoU4X+RvmGmDj8o2I2Ifion5XZ+btwE3AsV2P\nP7ZcZ9S619Bi/WdHNllbcm1LntCeXNuSJ7QrV2kY94NBOlUHUDOdqgOomU7VAdSO7yPSPHSqDqAR\nfL9Snbg9Lo6xJpgjYk1E7AWsAdZGxJ4RsQa4hKLe8osiYk/gTODKzPxqueoFwBkRsS4ifgg4DXh3\n2TZs3RX1lyVJkiRJkiRJ9TNWiYyIOIui7EX3g8/OzHMi4gTgrcDhwOeBzZl5Q7neHsBfAT8H3AP8\n58z8y67nHbpu3+tbIkOSNDP+VLdXm0tkLC1tYNu26we2rV9/BFu3btnp57REhm221bOtLu9zlsiQ\nJTL6XtkSGZoSS2RIhXmMuztVg7kqTjBLkmbJD7q92jzBPIsP204w22ZbPdvq8j7nBLOcYO57ZSeY\nNSVOMEuFRajBrClrU32ZtuTaljyhPbm2JU9oV67ScJ3y3z2JiIHL0tKGCuOrQqfqAGqmU3UANdOp\nOoDacTyV5qFTdQCN0K73q+HHdqqHdm2Pi21t1QFIkiQthvsZdqbKtm1+EJEkSVosw4/tijOUJY3L\nEhkt/SmeJOlh/lS317x/qjuLuseT2pVyFpP9xLIubXWJwzbb5tW2F8XEwkr1ed9p7mcHx91elsjo\ne+WZlsgYvu/D/Pd/zdaubUuWyFBzzGPc9QxmSZJUqWJy2TODJc2Tv0iQ2mnUGavu/5I0KWsw10yb\n6su0Jde25AntybUteUK7cpWG64zxmLbV8OtUHUDNdKoOoGY6VQdQO46n0jx0qg6gEXy/Up24PS4O\nJ5glSZJ22fIZUYMWSZIkaTqWljYMPbGhnRefVh1Yg9kazJLUetaC7DXvWpB1qgG5GPWSZ9FWlzhs\ns60ebfV432nuZwfH3V7WYO575ZnWYLZubps0tQbzOHm5HavbPMZdz2CWJGlBRMQeEfHOiNgSEdsj\n4ksR8dyu9mdFxLURcVdEfCoiDu9b9/xyvRsj4tXVZCFJ0uKIiE5E3BsRd0TEnRFxbVfbS8ox+c6I\n+HBErOtqOzAiLinH5G9ExIuryUCSpNlzgrlm2lRfpi25tiVPaE+ubckT2pXrglgL3AA8LTMPAM4E\nPhgRh0fEI4EPAa8DDgK+BHyga92zgccChwEnAK+NiOfMM/jF1ak6gBrqVB1AzXSqDqBmOlUHUDuO\npwstgd/MzP0zc7/MPBogIo4B3g68FFgP3Av8Vdd6bwPuAx4F/BLwVxFx9Fwjb51O1QE0gu9XqhO3\nx8WxtuoAJEnSeDLzHuCcrtt/GxHfAP4f4GDg6sz8MEBEvB64JSKOyszrgJcBp2TmHcAdEfEOYDPw\niflmIUnSwhn0s+KXAJdm5mcBIuI/AddGxD4Uk9InAk/IzHuBz0bEpRRj8R/NKWZJkubGGswNrucm\nSRrPotaCjIj1wDeAjcBvArtn5uld7f9McZbz5cCtwPrM/E7ZdhJwZmYeO+B5rcE8JJbmttUlDtts\nq0dbPd53mvvZYZHG3Yi4HHgCxUbzr8AZmfnpiPgI8NnM/NOux94JPJ3iP/SzmblPV9vvAk/PzBcO\neA1rMHe/sjWYNSXWYJYK1mCWJEkDRcRa4ELgPeUZyvsC2/seth3Yr2zLvvbltkYadXXtNWv2Gdom\nSVKf1wJHAocC7wAujYgjWX3cHdYmSVLjWCKjZjqdDps2bao6jLloS65tyRPak2tb8oR25bpIopgJ\nvRC4H3hlefddwP59D90fuLNsi/L2LX1tA23evJkNGzYAsG7dOjZu3PjQtrBcC21atwsdYFPX3zx0\ne5Ln37bteh4+s6P3+XbsCIqTuge9Xqx4PJxHcZI4Ax7ffXtY+/J9g15vkueb9+sNer4rgFdN+HyT\nvN6g9ev0eoP6Y5avtzPtVbzeeay+fUzz9TpjPP9krzet97vl+0a1D4pn2u+3Vd1e/nvLli0smsz8\nQtfNCyLiZOB5jB53c0TbQPMcd1fb7ma1H0zruGB4e38Ow9Yffbsu+01Vt88777y5bH/z25465b87\ne5uptE8S/4knnsxtt21jPNN//Trdbtr2OK/by3/Pc9y1REbNfm7X6XT6BtbmakuubckT2pNrW/KE\n9uS6SD/VBYiI84HDgedl5gPlfadR1Fj+ifL2PsDNwMbM/GpEfBt4eWZ+qmw/G3h8Zr5kwPMvfImM\n6Za66FActNfrmKLaEhkdmt8nO9PWoXfio65xzqut+wucqmOZpG0viu/vVlq//gi2bt0yZL3hRo2n\nlshYLBHxceDjwBKwITN/qbz/SOArwCMp/kNvBY7JzK+V7X8NfDszV9RgtkRG3ytPXNagw66NTUV7\nU/e7cTXp+H9RS2RMo0xMU7bjJm2PVZrHuOsEc80mmCVJ87dIH3Qj4u3AE4Fnlxf9W77/YOCrwKkU\nH3zPAZ6WmceX7W8CngK8iOJD8WUUE9KfHPAaU/+gu7S0oTyreJg6TzC3oa0ucdhmW/3bpv3+6ARz\nfUXEAcCTgU8DDwInA28HfhTYHfgc8HyKnzG8HdgtM19arnsxxX/sacBxwMeA4zPz2gGv4wRz9ytb\ng1lT4gSzVLAGsyRJekhEHA78KkW9hm0RcWdE3BERL87MW4CTgDdSnDX1JIoPwsvOAr4OXE9xeuGb\nB00uz8rDJSsGLZIk1dLuwBsofhH0HeB04IWZ+dXM/Arw68DFwFZgn7J92enA3uW6FwG/PmhyWZKk\nJnCCuWa666U0XVtybUue0J5c25IntCvXRZCZN2Tmbpm5d2buVy77Z+b7yvbLMvPozNwnM0/IzBu6\n1n0gM1+RmQdk5mMy8y+ry2TRdKoOoIY6VQdQM52qA6iZTtUB1I7j6WLKzFsy88fLsfOgzDw+My/r\nan9/Zh5RjscnZubtXW23ZeaLMnPfzNyQmR+oJos26VQdQCPM+/1q1IWZl5Y2zDWW6dq9oXnNl+Pn\n4vAif5IkSZI0lj3Lny6vNGl9Zklqs94LM/e31b6Szgjfo5l5SYNZg3nBLxgiSdp1i1ILcl5mUQty\nV2oiD4tlV+o621bXOGyzbbHbplszvrk1NB13e7WxBvPkYzhYg7lZZrkdVl2DedK8rMGsaZvHuOsZ\nzHN1P36DJUnSdIw646U48JYkSXXkGC5JzWIN5pppU32ZtuTaljyhPbm2JU9oV67ScJ2qA6ihTtUB\n1Eyn6gBqplN1ALXjeCrNQ6fqABrB9yvVidvj4nCCWZIk1dieQy+QIkmSBrvoootYs2b3ocvee+9f\ndYiSpAaxBnON2hbh/0KSmshakL3qVoPZtnm01SUO22xb7DZrMI/HcbfXLMbdc889lzPPvBc4e2D7\nAQc8ne3b/w9VfT6d/LhgV9utXVs31mAe0GoNZk2ZNZglSZIkSdIE1gC7D2nzx8ySpOlxVKmZNtWX\naUuubckT2pNrW/KEduUqDdepOoAa6lQdQM10qg6gZjpVB1A7jqfSPHSqDqARfL9Snbg9Lg4nmCVJ\nkiRJkiRJE7EGc43aFuH/QpKayFqQvazB3Ma2usRhm22L3WYN5vE47vaaXQ3mB4BzB7YfcMBT2b79\nc1iDeWV7U/e7urIG84BWazBryqzBLEmSJEkLYc9yUmCl3Xbbmx077plzPJKkeho+XkiLyhIZNdOm\n+jJtybUteUJ7cm1LntCuXKXhOlUHUEOdqgOomU7VAdRMp+oAKnI/xRllK5dicnlwm6Rp6VQdQCN4\n/D8Pw8cLx4Vebo+LwzOYa2P0N1jr1x/B1q1b5heOJEmSJEmSJK3CGswL0Va0L8L/lSQtImtB9rIG\ncxvb6hKHbbbZ1n1/U4//HXd7WYN55Wtbg7k92lqDeXbPXbS7HavbPMZdS2RIkiRJkiRJkibiBHPt\ndKoOYG7aUkunLXlCe3JtS57Qrlyl4TpVB1BDnaoDqJlO1QHUTKfqACS1UqfqABrB43/Vidvj4nCC\nWZIkSZIkSZI0EWswL0Rb0b4I/1eStIisBdnLGsxtbKtLHLbZZlv3/U09/nfc7WUN5pWvbQ3m9rAG\n82za3Y7VzRrMkiRJkiSpVpaWNhARQ5elpQ1VhzihPRuaV3VW21ZG8/9DWhROMNdOp+oA5qYttXTa\nkie0J9e25AntylUarlN1ADXUqTqAmulUHUDNdKoOQNKMbdt2PcUZlIOXon3eOlN4jvupX17zNe3j\n/9W2ldH8/2g7P48ujrVVByBJkiRJkupkzzHOLpUkqWAN5oVoA9iL4tu7ldavP4KtW7eMWFeSNIq1\nIHtZg7mNbXWJwzbbbOu+fxE+q03CcbdXXWswz7IGbJ1rMFvbdrpmXQd58u1s11+7ns9dtLudqts8\nxl3PYF4Yyz8NWWnbNo/NJEmSJEmSJM2fNZhrp1N1AHPTllo6bckT2pNrW/KEduUqDdepOoAa6lQd\nQM10qg6gZjpVByCplTpVB9AIHv+3W90u4On2uDicYJYkSZIkSaqxURN/8570q489R06GaqXVJpDr\neQFPLQJrMC9E2+rrLsL/oyTVlbUgew2rBXnLLbfw0Y9+dOh6J510EuvWrRv2nNRnXLWtvnHYZptt\n3fc39RjfcbeXNZin/9qLWIN5aWnDGJN39ZsT2LW4oepaxYu6HU66ne5aXerRz636sgazxjT8Cr9e\nAFCSNC1vectbeOMbP8ruu//oirYHH/wCr3rVH3LXXbdUEJkkSdJie/jM0WHq+Z3MosYtaboskVE7\nnQnWWb4A4GL9fKEttXTakie0J9e25AntylWr27Ej+d73fpZ77nnXiuX73/8P5eTysJ/ULbJO1QHU\nUKfqAGqmU3UANdOpOgBJrdSpOoBG8Pi/CYaXDlk0bo+LwwlmSZIkSZKkVY2u+dveWsiql+EnIUqz\n4gRz7WyqOoC52bRpU9UhzEVb8oT25NqWPKFduUrDbao6gBraVHUANbOp6gBqZlPVAUhqpU1zeI1R\nE3fJtm1bF37yedDx/6gLw0mz5OfRxeEEsyRJkiRJmqLRZ/o212KWr1zNw3WWPSNWkxv1RcWsv4Sp\n8rXbwgnm2ulM+fmGD+xr1uxT6c7Vllo6bckT2pNrW/KEduUqDdepOoAa6lQdQM10qg6gZjpVByCp\ncqPP9J2Nzoyet108/tdow+eYVptHGv1FxeAvYaa1PU7y2to5a6sOQLO2PLCvtGNHDG3btq3J3ypL\nkiRJkiRp5wyfY3Ieqd2cYK6dTVUHMDdtqaXTljyhPbm2JU9oV67ScJuqDqCGNlUdQM1sqjqAmtlU\ndQCSWmlT1QGsYs/algdZWtrgGZyqpfl9Hh29f65ffwRbt26ZUyyLyRIZkiRJkiRJMzXLsiGja17v\naukCqflWu4CnX8CspvIJ5og4MCIuiYi7IuIbEfHiqmOqVqfqAEqT19UZV1tqO7UlT2hPrm3JE9qV\naxs45k6qU3UANdSpOoCa6VQdQM10qg5AqgXH3XnrVB1AhaY5OdaZenRqi+lf3NPPo4uj8glm4G3A\nfcCjgF8C/ioijq42pCpdUXUApVFXv906lcnnK66oS66z1ZY8oT25tiVPaFeuLeGYOxH3g5Xsk172\nRy/7Qyo57s6V7z3TYT9qUtM/S7/78+jS0oahc1Fr1uwz9cntXrM/CXPRVVqDOSL2Bk4EnpCZ9wKf\njYhLgZcBf1RlbNW5veoAxjCqqPteQ3fe/po1t9++CLnuurbkCe3JtS15QrtybTrH3F3hfrCSfdLL\n/uhlf0iOu1XwvWe4nan/bD+qKoO301e/+tVdtwbPRe3YEUPbCrs6yezFDVdT9RnMRwEPZubXuu67\nEjimoni0y8Y/8/nss88e+xunUd8IjfoWy2+SJOkhjrmSJM2P465qZJb1n6VpGbSdnkWbt9NR813j\nnLk9zzmxSs9gBvYFtvfdtx3Yb+VDz2bQNw4PPHDbDMKq0paqA5ih/m98NgPveejWqG+cRp0ZXdj5\n9XbbbW927Lhnqm2Driy6ZcuWgY9torbk2pY8oV25tsBOjLmjXM6g99wdO/5pwrAWwZaqA6ihLVUH\nUDNbqg6gZrZUHYBUB1Mad3fFp4HXD2y5774b5hfG3GypOoCG2FJ1AFKXLVUHMIbRvxAYNYc0Tvuo\nyfXVztye59nVkVndtwARsRH4p8zct+u+1wDPyMwXdt3Xzq8qJElzk5mN/m3TuGNueb/jriRpphx3\nex7ruCtJmqlZj7tVn8F8HbA2Ih7b9dOhY4Fruh/U9IMPSZLmYKwxFxx3JUmaAsddSVJrVHoGM0BE\nXExxPvdpwHHAx4DjM/PaSgOTJKlhHHMlSZofx11JUltUfZE/gNOBvYGbgYuAX3fAlSRpJhxzJUma\nH8ddSVIrVH4GsyRJkiRJkiRpMdXhDGZJkiRJkiRJ0gKq9QRzRBwYEZdExF0R8Y2IeHHVMY0rIk6P\niC9ExH0RcX5f27Mi4toyr09FxOFdbXtExPkRsT0iboyIV4+7bhXKeN8ZEVvKmL8UEc8dJ95Fy7WM\n6b1lrNsj4l8i4hVdbY3KFSAiHh8R90bEBV33vaT8/74zIj4cEeu62kbus6PWrUpEdMoc7yjjurar\nrVG5AkTEyRHxlTLur0bEU8v7G7H9lv19R9f/54MR8ZfjxLpIec7Katt108WEY3dTxS6M8U016XFA\nG+zsMUNTTXpc0WSTHHs01bD3kIh4ckR8IiK+GxHbIuIDEbFUdbx1Neq9uOsxZ0XEjog4oYoYF8Eq\nY9ojIuJtEfGdiLgtIjoVhlprq/TjL5Tvf9sj4uqIeGGVsS4Cjyemo78fI+J5EfGP5f58Y0T894jY\nd6ovmpm1XYD3lcsjgKcCtwNHVx3XmLH/LPAzwFuB87vuf2SZx4nAHsCfAP+7q/1NwKeB/YEfAm4C\nnjPOuhXluTdwJnBYefv5wB3A4U3LtYzraGD38u+jypiPa2KuZWz/UMZ9QXn7mPL/96nl//1FwPu6\nHj90n11t3QpzvBz45QH3NzHXnwS+ATypvP2Ycmnq9rv38v/DOLEuap5T7rOFHXenlP9EY3dTF3Zh\njG/qwoTHAW1Y2MljhqYuTHhc0dRl0mOPpi597yE/2PUe8lzgJGBfYC/gXcDfVR1vXZdh78Vd7UcC\nVwHfAk6oOt66LqP6EbgQuBg4CIju/nUZrx+BQ4D7efjzxPOAu4GDq465zovHEzPrxxcDzynHmAOA\njwNvm+prVp30iM7Yu9wZH9t13wXAG6uObSfzOJfeD6mnAf/Ul+c9wFHl7W8Bz+pqPwe4eJx167IA\nVwIvanqu5UHhjcDPNTFX4GTg/RSTC8tvSn8MXNj1mCPL/XSf1fbZUetWnOflwKkD7m9irp9l8Ife\nxm2/ZSynAP/W9Dyn2F+NGHen1Bc7NXa3aWHMMb4NCztxHND0hZ08Zqg63hn3xU4fV1Qd84z7Y6Jj\njzYs3e8hA9qOA7ZXHeMiLIP6kWLi5LkUX244wbyT/UgxSXo7sG/VcS3a0tePPw5s7Wu/GXhy1XHW\ndfF4Ynb9OOAxLwKunObr1rlExlHAg5n5ta77rqT49mKRHUORBwCZeQ/wNeCY8jT/Qyi+bV3WnfPQ\ndWcc89giYj3weOAaGpprRLw1Iu4GrqUYPD5Ow3KNiP2Bs4HfpfjGell/rF8HHqDYX1fbZ0etW7U3\nRcTN5U9GnlHe16hcI2I34MeAR5c/T70hIv5rROxFw7bfLi+nmCBd1tQ8p6Wp4+40tOH/f1XjjvHV\nRDc/O3scUEmQczThMUPT7exxRSNNeuxRTbTzM+Q9pN8zKN5rNcSwfoyInwfuz8y/rzK+RTGkH58M\n3ACcU5bIuDIiTqwyzrob0o9fBK6NiBdExG4R8bPAffR+3lDJ44npGNGP/aY+ztR5gnlfYHvffduB\n/SqIZZpG5bUvkH3t3TnXuk8iYi3FT2nek5nX0dBcM/N0ivh+AvgwxZtb03I9B3hHZn677/7V8hyV\nRx3zBHgtxbeghwLvAC6NiCNpXq7rgd0pfn75VGAj8KPAGTRv+6Ws4/h04K+77m5cnlPWhhwn1fq+\n2ckxvtEmOA5oukmOGZpskuOKppr02KPRBryH3N/dHhFPBP4T8Hvzj25xDOrHiNiH4mzH36kytkUy\nZEz7AeCHgdsoStq8EvjriPjBquKsu0HbY2buAN5LUWrkforjqF/LzHsrC7TePJ6YjmH9+JCI+Eng\nZRRjzdTUeYL5Loo6mN32B+6sIJZpGpXXXRTfMOw/oG21dSsVEUHxhnk/xQAEDc0VIAufAw4DfoMG\n5RoRG4FnA+cNaF4tz1F51CrPZZn5hcy8OzO/l5kXUPyU83k0L9flA5n/mpk3Z+atwH+hyPVOCvio\nZQAAIABJREFUGrL9dnk5xU9vr++6rzH76Yy0IcdJtbpvJhjjG28njwMaaxeOGRprwuOKppr02KPx\nBryHABARj6M48/GVZbtG6OvH36Q4a++CzLyh2sgWy4Dt8V6KieY3ZOaDmfkZivI/z6kwzNrr78eI\neBZFffmnZ+buwCbgXeWXSOri8cR0rNKPy495CkUd65P6frm6y+o8wXwdsDYiHtt137Es/k+FrqH4\n9h6A8lvWxwJXZ+btFAXhj+16fHfOw9atQ5+8CzgYODEzv1/e19Rcu62lOEvlapqT6zOAI4AbIuIm\nirMnToqIL7IyzyMpLsxyHavvs9fQ1Qd969ZRo3Itt8NvDWqimfvqy4D39N3XxDynqanj7jS04f9/\nlJ0Z49vSJ8tWOw5oen9MeszQRq3rjwmPPZq+z/RbS5E3EXEE8Eng7My8uNKoFs/ye/EJwG9HxE3l\ne9JhwAcj4vcrjW5xLPfjcgmHUT+v13DL+/VG4NOZ+WWAzPwi8HmKCUD18nhiOgb148+V/UhEHAd8\nBNicmZ2pv/o0CzpPe6H4KcFFFBd9eCrFTzQW4mr2wBqKqzO+kaIG6J7lfQeXebyovO/NwOe61nsT\nxbeD64Afoqjf85Nl28h1K8z17cDngL377m9UrsCjgF+kuMjbbsBPUXxr9tNNyrXcbh/dtfwp8EGK\nKwg/geKCD08t++G9wEVd6w7dZ1dbt6JcD6D4Jn55/3xp+X/6+KblWsZ1NsVBzaOAA4HPAK9v0vZb\nxnR8+f+4T9/9jcpzRn23sOPulPKfaOxu8sKEY3wTF3bhOKCpC7twzNDEhV04rmjqMumxRxOXVd5D\nDgH+DfjdquOs+7JKPx7Y9550A3Bi/xjmsmo/rqWYvHtd+V72VIqSBK25COeU+vHpFBf1O7Z87HHA\nd4BnVx133RaPJ+bSjz8MbAV+fmavX3UHrNI5BwKXUJwSvwX4xapj2onYzwJ2AN/vWs4s206gKP5+\nN3AZcHjXentQnCm0neJsut/pe96h61aU5+FlnveUb6R3AncAL25grgcDHeDW8g3uSrquEt6kXAds\nyxd03T4ZuL78v/4wsK6rbeQ+O2rdCv9P/2/5/3IrxSTKCV3tjcm1jGkt8FaKD3Q3An8B7NG07Zdi\nQuw9Q9oak+eM+m5hx90p5T/R2N3UhV0Y45u4sAvHAW1Z2IljhiYu7MJ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D1SHUiv3Ry/5Y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FVW5Q5AY/RpbrYvZepTX6B//Vk0F5glSZIkSZIkSTOxBrOkzrIGs+bFWpBtq5B3rcE8XVvfzwNp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\nNZi1DF2pBbks1mDOHcO8j1dCD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sHpmTP02yQRsXNEfK4+i+pe4DPA02fZ/RnA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\nGocAAAAASUVORK5CYII=\n",
|
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"text/plain": [
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"<matplotlib.figure.Figure at 0x7ff688f1fa20>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"%matplotlib inline\n",
|
||
"import matplotlib.pyplot as plt\n",
|
||
"housing.hist(bins=50, figsize=(20,15))\n",
|
||
"save_fig(\"attribute_histogram_plots\")\n",
|
||
"plt.show()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 10,
|
||
"metadata": {
|
||
"collapsed": true
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"# to make this notebook's output identical at every run\n",
|
||
"np.random.seed(42)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 11,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"import numpy as np\n",
|
||
"\n",
|
||
"# For illustration only. Sklearn has train_test_split()\n",
|
||
"def split_train_test(data, test_ratio):\n",
|
||
" shuffled_indices = np.random.permutation(len(data))\n",
|
||
" test_set_size = int(len(data) * test_ratio)\n",
|
||
" test_indices = shuffled_indices[:test_set_size]\n",
|
||
" train_indices = shuffled_indices[test_set_size:]\n",
|
||
" return data.iloc[train_indices], data.iloc[test_indices]"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 12,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"16512 train + 4128 test\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"train_set, test_set = split_train_test(housing, 0.2)\n",
|
||
"print(len(train_set), \"train +\", len(test_set), \"test\")"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 13,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"import hashlib\n",
|
||
"\n",
|
||
"def test_set_check(identifier, test_ratio, hash):\n",
|
||
" return hash(np.int64(identifier)).digest()[-1] < 256 * test_ratio\n",
|
||
"\n",
|
||
"def split_train_test_by_id(data, test_ratio, id_column, hash=hashlib.md5):\n",
|
||
" ids = data[id_column]\n",
|
||
" in_test_set = ids.apply(lambda id_: test_set_check(id_, test_ratio, hash))\n",
|
||
" return data.loc[~in_test_set], data.loc[in_test_set]"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 14,
|
||
"metadata": {
|
||
"collapsed": true
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"# This version supports both Python 2 and Python 3, instead of just Python 3.\n",
|
||
"def test_set_check(identifier, test_ratio, hash):\n",
|
||
" return bytearray(hash(np.int64(identifier)).digest())[-1] < 256 * test_ratio"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 15,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"housing_with_id = housing.reset_index() # adds an `index` column\n",
|
||
"train_set, test_set = split_train_test_by_id(housing_with_id, 0.2, \"index\")"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 16,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"housing_with_id[\"id\"] = housing[\"longitude\"] * 1000 + housing[\"latitude\"]\n",
|
||
"train_set, test_set = split_train_test_by_id(housing_with_id, 0.2, \"id\")"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 17,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/html": [
|
||
"<div>\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>8</th>\n",
|
||
" <td>8</td>\n",
|
||
" <td>-122.26</td>\n",
|
||
" <td>37.84</td>\n",
|
||
" <td>42.0</td>\n",
|
||
" <td>2555.0</td>\n",
|
||
" <td>665.0</td>\n",
|
||
" <td>1206.0</td>\n",
|
||
" <td>595.0</td>\n",
|
||
" <td>2.0804</td>\n",
|
||
" <td>226700.0</td>\n",
|
||
" <td>NEAR BAY</td>\n",
|
||
" <td>-122222.16</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>10</th>\n",
|
||
" <td>10</td>\n",
|
||
" <td>-122.26</td>\n",
|
||
" <td>37.85</td>\n",
|
||
" <td>52.0</td>\n",
|
||
" <td>2202.0</td>\n",
|
||
" <td>434.0</td>\n",
|
||
" <td>910.0</td>\n",
|
||
" <td>402.0</td>\n",
|
||
" <td>3.2031</td>\n",
|
||
" <td>281500.0</td>\n",
|
||
" <td>NEAR BAY</td>\n",
|
||
" <td>-122222.15</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>11</th>\n",
|
||
" <td>11</td>\n",
|
||
" <td>-122.26</td>\n",
|
||
" <td>37.85</td>\n",
|
||
" <td>52.0</td>\n",
|
||
" <td>3503.0</td>\n",
|
||
" <td>752.0</td>\n",
|
||
" <td>1504.0</td>\n",
|
||
" <td>734.0</td>\n",
|
||
" <td>3.2705</td>\n",
|
||
" <td>241800.0</td>\n",
|
||
" <td>NEAR BAY</td>\n",
|
||
" <td>-122222.15</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>12</th>\n",
|
||
" <td>12</td>\n",
|
||
" <td>-122.26</td>\n",
|
||
" <td>37.85</td>\n",
|
||
" <td>52.0</td>\n",
|
||
" <td>2491.0</td>\n",
|
||
" <td>474.0</td>\n",
|
||
" <td>1098.0</td>\n",
|
||
" <td>468.0</td>\n",
|
||
" <td>3.0750</td>\n",
|
||
" <td>213500.0</td>\n",
|
||
" <td>NEAR BAY</td>\n",
|
||
" <td>-122222.15</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>13</th>\n",
|
||
" <td>13</td>\n",
|
||
" <td>-122.26</td>\n",
|
||
" <td>37.84</td>\n",
|
||
" <td>52.0</td>\n",
|
||
" <td>696.0</td>\n",
|
||
" <td>191.0</td>\n",
|
||
" <td>345.0</td>\n",
|
||
" <td>174.0</td>\n",
|
||
" <td>2.6736</td>\n",
|
||
" <td>191300.0</td>\n",
|
||
" <td>NEAR BAY</td>\n",
|
||
" <td>-122222.16</td>\n",
|
||
" </tr>\n",
|
||
" </tbody>\n",
|
||
"</table>\n",
|
||
"</div>"
|
||
],
|
||
"text/plain": [
|
||
" index longitude latitude housing_median_age total_rooms \\\n",
|
||
"8 8 -122.26 37.84 42.0 2555.0 \n",
|
||
"10 10 -122.26 37.85 52.0 2202.0 \n",
|
||
"11 11 -122.26 37.85 52.0 3503.0 \n",
|
||
"12 12 -122.26 37.85 52.0 2491.0 \n",
|
||
"13 13 -122.26 37.84 52.0 696.0 \n",
|
||
"\n",
|
||
" total_bedrooms population households median_income median_house_value \\\n",
|
||
"8 665.0 1206.0 595.0 2.0804 226700.0 \n",
|
||
"10 434.0 910.0 402.0 3.2031 281500.0 \n",
|
||
"11 752.0 1504.0 734.0 3.2705 241800.0 \n",
|
||
"12 474.0 1098.0 468.0 3.0750 213500.0 \n",
|
||
"13 191.0 345.0 174.0 2.6736 191300.0 \n",
|
||
"\n",
|
||
" ocean_proximity id \n",
|
||
"8 NEAR BAY -122222.16 \n",
|
||
"10 NEAR BAY -122222.15 \n",
|
||
"11 NEAR BAY -122222.15 \n",
|
||
"12 NEAR BAY -122222.15 \n",
|
||
"13 NEAR BAY -122222.16 "
|
||
]
|
||
},
|
||
"execution_count": 17,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"test_set.head()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 18,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"from sklearn.model_selection import train_test_split\n",
|
||
"\n",
|
||
"train_set, test_set = train_test_split(housing, test_size=0.2, random_state=42)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 19,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/html": [
|
||
"<div>\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>20046</th>\n",
|
||
" <td>-119.01</td>\n",
|
||
" <td>36.06</td>\n",
|
||
" <td>25.0</td>\n",
|
||
" <td>1505.0</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>1392.0</td>\n",
|
||
" <td>359.0</td>\n",
|
||
" <td>1.6812</td>\n",
|
||
" <td>47700.0</td>\n",
|
||
" <td>INLAND</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>3024</th>\n",
|
||
" <td>-119.46</td>\n",
|
||
" <td>35.14</td>\n",
|
||
" <td>30.0</td>\n",
|
||
" <td>2943.0</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>1565.0</td>\n",
|
||
" <td>584.0</td>\n",
|
||
" <td>2.5313</td>\n",
|
||
" <td>45800.0</td>\n",
|
||
" <td>INLAND</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>15663</th>\n",
|
||
" <td>-122.44</td>\n",
|
||
" <td>37.80</td>\n",
|
||
" <td>52.0</td>\n",
|
||
" <td>3830.0</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>1310.0</td>\n",
|
||
" <td>963.0</td>\n",
|
||
" <td>3.4801</td>\n",
|
||
" <td>500001.0</td>\n",
|
||
" <td>NEAR BAY</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>20484</th>\n",
|
||
" <td>-118.72</td>\n",
|
||
" <td>34.28</td>\n",
|
||
" <td>17.0</td>\n",
|
||
" <td>3051.0</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>1705.0</td>\n",
|
||
" <td>495.0</td>\n",
|
||
" <td>5.7376</td>\n",
|
||
" <td>218600.0</td>\n",
|
||
" <td><1H OCEAN</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>9814</th>\n",
|
||
" <td>-121.93</td>\n",
|
||
" <td>36.62</td>\n",
|
||
" <td>34.0</td>\n",
|
||
" <td>2351.0</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>1063.0</td>\n",
|
||
" <td>428.0</td>\n",
|
||
" <td>3.7250</td>\n",
|
||
" <td>278000.0</td>\n",
|
||
" <td>NEAR OCEAN</td>\n",
|
||
" </tr>\n",
|
||
" </tbody>\n",
|
||
"</table>\n",
|
||
"</div>"
|
||
],
|
||
"text/plain": [
|
||
" longitude latitude housing_median_age total_rooms total_bedrooms \\\n",
|
||
"20046 -119.01 36.06 25.0 1505.0 NaN \n",
|
||
"3024 -119.46 35.14 30.0 2943.0 NaN \n",
|
||
"15663 -122.44 37.80 52.0 3830.0 NaN \n",
|
||
"20484 -118.72 34.28 17.0 3051.0 NaN \n",
|
||
"9814 -121.93 36.62 34.0 2351.0 NaN \n",
|
||
"\n",
|
||
" population households median_income median_house_value \\\n",
|
||
"20046 1392.0 359.0 1.6812 47700.0 \n",
|
||
"3024 1565.0 584.0 2.5313 45800.0 \n",
|
||
"15663 1310.0 963.0 3.4801 500001.0 \n",
|
||
"20484 1705.0 495.0 5.7376 218600.0 \n",
|
||
"9814 1063.0 428.0 3.7250 278000.0 \n",
|
||
"\n",
|
||
" ocean_proximity \n",
|
||
"20046 INLAND \n",
|
||
"3024 INLAND \n",
|
||
"15663 NEAR BAY \n",
|
||
"20484 <1H OCEAN \n",
|
||
"9814 NEAR OCEAN "
|
||
]
|
||
},
|
||
"execution_count": 19,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"test_set.head()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 20,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"<matplotlib.axes._subplots.AxesSubplot at 0x7ff68892a7b8>"
|
||
]
|
||
},
|
||
"execution_count": 20,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
},
|
||
{
|
||
"data": {
|
||
"image/png": 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71/709PRQ5fH3c+fvT09PD1We1n5RFExNTQG88ny5o3rWHCR9Avgs5ZO2KF/x70ZZH/gh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\ncjAzsy6eHMzMrMv/B7uUn9J05VJWAAAAAElFTkSuQmCC\n",
|
||
"text/plain": [
|
||
"<matplotlib.figure.Figure at 0x7ff688c1f7f0>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"housing[\"median_income\"].hist()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 21,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"# Divide by 1.5 to limit the number of income categories\n",
|
||
"housing[\"income_cat\"] = np.ceil(housing[\"median_income\"] / 1.5)\n",
|
||
"# Label those above 5 as 5\n",
|
||
"housing[\"income_cat\"].where(housing[\"income_cat\"] < 5, 5.0, inplace=True)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 22,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"3.0 7236\n",
|
||
"2.0 6581\n",
|
||
"4.0 3639\n",
|
||
"5.0 2362\n",
|
||
"1.0 822\n",
|
||
"Name: income_cat, dtype: int64"
|
||
]
|
||
},
|
||
"execution_count": 22,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"housing[\"income_cat\"].value_counts()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 23,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"<matplotlib.axes._subplots.AxesSubplot at 0x7ff688a6aef0>"
|
||
]
|
||
},
|
||
"execution_count": 23,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
},
|
||
{
|
||
"data": {
|
||
"image/png": 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HLbQiPQhJE8CFwJ8CvXe2FtjcOYiIe4DngNdVl+c7m0NlczVn2FwzM0usbonpIuDKiHig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\nrkJggg==\n",
|
||
"text/plain": [
|
||
"<matplotlib.figure.Figure at 0x7ff69547c208>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"housing[\"income_cat\"].hist()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 24,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"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]"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 25,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"3.0 0.350581\n",
|
||
"2.0 0.318847\n",
|
||
"4.0 0.176308\n",
|
||
"5.0 0.114438\n",
|
||
"1.0 0.039826\n",
|
||
"Name: income_cat, dtype: float64"
|
||
]
|
||
},
|
||
"execution_count": 25,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"housing[\"income_cat\"].value_counts() / len(housing)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 26,
|
||
"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",
|
||
" \"Overall\": income_cat_proportions(housing),\n",
|
||
" \"Stratified\": income_cat_proportions(strat_test_set),\n",
|
||
" \"Random\": income_cat_proportions(test_set),\n",
|
||
"}).sort_index()\n",
|
||
"compare_props[\"Rand. %error\"] = 100 * compare_props[\"Random\"] / compare_props[\"Overall\"] - 100\n",
|
||
"compare_props[\"Strat. %error\"] = 100 * compare_props[\"Stratified\"] / compare_props[\"Overall\"] - 100"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 27,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/html": [
|
||
"<div>\n",
|
||
"<table border=\"1\" class=\"dataframe\">\n",
|
||
" <thead>\n",
|
||
" <tr style=\"text-align: right;\">\n",
|
||
" <th></th>\n",
|
||
" <th>Overall</th>\n",
|
||
" <th>Random</th>\n",
|
||
" <th>Stratified</th>\n",
|
||
" <th>Rand. %error</th>\n",
|
||
" <th>Strat. %error</th>\n",
|
||
" </tr>\n",
|
||
" </thead>\n",
|
||
" <tbody>\n",
|
||
" <tr>\n",
|
||
" <th>1.0</th>\n",
|
||
" <td>0.039826</td>\n",
|
||
" <td>0.040213</td>\n",
|
||
" <td>0.039729</td>\n",
|
||
" <td>0.973236</td>\n",
|
||
" <td>-0.243309</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>2.0</th>\n",
|
||
" <td>0.318847</td>\n",
|
||
" <td>0.324370</td>\n",
|
||
" <td>0.318798</td>\n",
|
||
" <td>1.732260</td>\n",
|
||
" <td>-0.015195</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>3.0</th>\n",
|
||
" <td>0.350581</td>\n",
|
||
" <td>0.358527</td>\n",
|
||
" <td>0.350533</td>\n",
|
||
" <td>2.266446</td>\n",
|
||
" <td>-0.013820</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>4.0</th>\n",
|
||
" <td>0.176308</td>\n",
|
||
" <td>0.167393</td>\n",
|
||
" <td>0.176357</td>\n",
|
||
" <td>-5.056334</td>\n",
|
||
" <td>0.027480</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>5.0</th>\n",
|
||
" <td>0.114438</td>\n",
|
||
" <td>0.109496</td>\n",
|
||
" <td>0.114583</td>\n",
|
||
" <td>-4.318374</td>\n",
|
||
" <td>0.127011</td>\n",
|
||
" </tr>\n",
|
||
" </tbody>\n",
|
||
"</table>\n",
|
||
"</div>"
|
||
],
|
||
"text/plain": [
|
||
" Overall Random Stratified Rand. %error Strat. %error\n",
|
||
"1.0 0.039826 0.040213 0.039729 0.973236 -0.243309\n",
|
||
"2.0 0.318847 0.324370 0.318798 1.732260 -0.015195\n",
|
||
"3.0 0.350581 0.358527 0.350533 2.266446 -0.013820\n",
|
||
"4.0 0.176308 0.167393 0.176357 -5.056334 0.027480\n",
|
||
"5.0 0.114438 0.109496 0.114583 -4.318374 0.127011"
|
||
]
|
||
},
|
||
"execution_count": 27,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"compare_props"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 28,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"for set_ in (strat_train_set, strat_test_set):\n",
|
||
" set_.drop(\"income_cat\", axis=1, inplace=True)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"# Discover and visualize the data to gain insights"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 29,
|
||
"metadata": {
|
||
"collapsed": true
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"housing = strat_train_set.copy()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 30,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Saving figure bad_visualization_plot\n"
|
||
]
|
||
},
|
||
{
|
||
"data": {
|
||
"image/png": 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2NdW493zipg14+rW1BMJm8X+6lFbUJ480Da6KpJSQQKUTywSGNDFlaXqubB2qQhXNqU09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\n5TiO49Qk/w8l7dvTHf88UAAAAABJRU5ErkJggg==\n",
|
||
"text/plain": [
|
||
"<matplotlib.figure.Figure at 0x7ff688b56b38>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"housing.plot(kind=\"scatter\", x=\"longitude\", y=\"latitude\")\n",
|
||
"save_fig(\"bad_visualization_plot\")"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 31,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Saving figure better_visualization_plot\n"
|
||
]
|
||
},
|
||
{
|
||
"data": {
|
||
"image/png": 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6lfsun8/zhS+8huNMEAhs8/jjE6iqR6dTQFH82HabdDowdAXC/q7HdDqA59Votdp3vO4l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imXx5HvbX5k9KQjI6F25LtdAGlaMKqf/cjopoKcK5oTE7nA9uz8VB4pPsHq15vIIUZLdX8DZw0h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\n",
|
||
"text/plain": [
|
||
"<matplotlib.figure.Figure at 0x7ff688818860>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"housing.plot(kind=\"scatter\", x=\"longitude\", y=\"latitude\", alpha=0.1)\n",
|
||
"save_fig(\"better_visualization_plot\")"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"The argument `sharex=False` fixes a display bug (the x-axis values and legend were not displayed). This is a temporary fix (see: https://github.com/pandas-dev/pandas/issues/10611). Thanks to Wilmer Arellano for pointing it out."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 32,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Saving figure housing_prices_scatterplot\n"
|
||
]
|
||
},
|
||
{
|
||
"data": {
|
||
"image/png": 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gLXBBhjdKMrxdJv/mCSGEEL0sLCyMsLAwdy9DXEFq3PDQmviKBLxCCCFEO+Lj4+Uhr34i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KP0ewmd7seUbEKrkwirROJTExHUEbQlWaioJmp/Eiy3z77p9hEIpQERmOkBfRT0WgjsBLEHKWkt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9wokTahSKSEKhEIFAHWq1lWDQjk7vQh85Aku06eL79zkUCgUENeSPuo0P2jZQWZ2E++0PWLjw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\n",
|
||
"text/plain": [
|
||
"<matplotlib.figure.Figure at 0x7ff688858b00>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"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)\n",
|
||
"plt.legend()\n",
|
||
"save_fig(\"housing_prices_scatterplot\")"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 33,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Saving figure california_housing_prices_plot\n"
|
||
]
|
||
},
|
||
{
|
||
"data": {
|
||
"image/png": 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HJQCsWjWaOXOmRPbFDVPBYJDTp89QVl6H1Wogf+KoiE27UCgiYc+e/bz33mFgPElJN5KQ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7+1mu7uXozGXIqKCsZZOW3btp0NByah2T8DhiPgPwjCBXIBJDLxDips27SHwqwArV0j2ArW4nR/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45kM1arcaqXhSPZjNM1Ivds/wTlE2/4caOzAngCeADYCLwJ/OZKttQvhiw8ZWQ+gSAImEwf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\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fH+XprQn8vmk02Ry11XauWBLhgQ+fhxTleUBVVfr6Bjl4aJBMJssVS6tpa2s5b8Xuo9EY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\nFEVRxoUKPBVFURRFUZRx8f8DBTtr6raGBzgAAAAASUVORK5CYII=\n",
|
||
"text/plain": [
|
||
"<matplotlib.figure.Figure at 0x7ff6886fe2b0>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"import matplotlib.image as mpimg\n",
|
||
"california_img=mpimg.imread(PROJECT_ROOT_DIR + '/images/end_to_end_project/california.png')\n",
|
||
"ax = housing.plot(kind=\"scatter\", x=\"longitude\", y=\"latitude\", figsize=(10,7),\n",
|
||
" s=housing['population']/100, label=\"Population\",\n",
|
||
" c=\"median_house_value\", cmap=plt.get_cmap(\"jet\"),\n",
|
||
" colorbar=False, alpha=0.4,\n",
|
||
" )\n",
|
||
"plt.imshow(california_img, extent=[-124.55, -113.80, 32.45, 42.05], alpha=0.5)\n",
|
||
"plt.ylabel(\"Latitude\", fontsize=14)\n",
|
||
"plt.xlabel(\"Longitude\", fontsize=14)\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()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 34,
|
||
"metadata": {
|
||
"collapsed": true
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"corr_matrix = housing.corr()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 35,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"median_house_value 1.000000\n",
|
||
"median_income 0.687160\n",
|
||
"total_rooms 0.135097\n",
|
||
"housing_median_age 0.114110\n",
|
||
"households 0.064506\n",
|
||
"total_bedrooms 0.047689\n",
|
||
"population -0.026920\n",
|
||
"longitude -0.047432\n",
|
||
"latitude -0.142724\n",
|
||
"Name: median_house_value, dtype: float64"
|
||
]
|
||
},
|
||
"execution_count": 35,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"corr_matrix[\"median_house_value\"].sort_values(ascending=False)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 36,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Saving figure scatter_matrix_plot\n"
|
||
]
|
||
},
|
||
{
|
||
"data": {
|
||
"image/png": 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YLOzda6VTp3Xs2zcBWbZit6/C5aps5Z1QXddV4FsfvKBV39h6zdc83nlnNhMn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Osa7pNgKVTIEug9nZF/HRR7s46aT/ZcOGYpqbmwkKCsFuN7DbHVRXh+FwPExpqYvGxkZGjUoh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SG6a6N/unUBsz5EWxkkZwkAOR5Lkhm85tpnzNhzkHdLkDFjxpix7YmA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BuQ8V2B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\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Tv/9cli27m5wcw/TpYUU5Pj7eziwoNJf9rrrqYi655KeEQiFeeeUDAoFv8fu/7yrtdrt3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yczfHjhqYIUJpOJe+4Zx4sv3gnoTJo0jVtuuZ9Jk/5KUdFDjBp1H6NHP8zYsb9i+vTP8HgyaGpy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\nhbsZdT50KMu4jsuAYhYtWvS59f6zxFwXjz56I0uXPsAjj9zIjBkXkEik4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rHZsunRwwU4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JYzLZ1OxIlFsjyfSnINF9B8l6JjPymWuM4SZZi2U2UM1FalOakAf6bpJMbGa9VgtiIO43xteN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\n/fV7mDdv2kGxc9OmTWz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cjijfVWoMpiOKbS2wTo3jHsSo3KyOtSBU+BHgFURBjmP//pfw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z11+fLX5SuBYBmGMfEjPhMMwxhlGEYagGVZyz6LydXx2rGipoZh0KtXCeefP4Hx489g+PDx\nSJRxLTJoS0Ui8HlIRHKY2laEsJdlIsjNAmRgp4VzFyAToYuRTlKTQTQigzqdpDwWGWjmIAPHqCon\nrsptQSZmc5FoZxm2gGsxkjtVhERHUxCmwdHIxGs40nnPV3UJIRHWLOBrap87kY6/Re2bpPadp8q9\nRf2+DMljyUMGx1Gkw29Boq8PqOOKkByZNuA2DKMXNpFBKuCnuTmJjo70zmPjcYnomqYPlyuKZaUR\ni6XR1JRKPH5E3b+fAoXEYpm0tw8gNTUXy4pz772/5yc/eZyOjlTc7tNobs7E5SrA4RhMSsqPCQTy\nee65f3DjjY/yl7/8m5qaOG73UrzefKLRPIYOPQfDyKO2NsiLL+6loqKCPXuOMHx4Do899j/MnTuV\nsWOHMWfOeLZv///YO+8wqcqz/3/OmV62994oixRFpK2AKKDGqGhio1lQQLGgUbEk1vgmAdRgQ+xo\nQGNMYsyrry0WEFiIgopIZ9kF3N2Z3WXb9HZ+f9xzdpaiYgHLj/u69pqZM3Oe88zO/Zznee7vfX+/\nbrZvT2HXrhgrV+6iuTmXNWta6N8/jREjBgMxNO3g2NP2jeKqqsqMGRO57rrT4v/XPwCFbN68+YC+\n/H3Yvn2QY35U1cG55/4Ci8WBpg2lvV0hEomRlzeVhL/fhfhFMuJPeh1JGeKvx5BgWmtDFqH9kcXm\nUYh/tyAIgI6u2uPnnIeMlxiCTlWQYNW8EUELdCHjU+LXPhOpaymNtxNAbrgK4j/XIT5YjKCuKuKv\nD5HQJjKQSKfVU1YrEIZPeW6xDKVPn1yKi3ugaU5UdRgyFrOIRr10dg4mGOxE0yoJhTTcbo2mpnbC\nYY3k5D6AAVW9jFCoAI8nmaSkq4hEcvnlL+dQUtIfTYOjj/4AMFJc3I9zz124F4KlLxhnznyAhQuX\n8NhjS5g58wEee+z5/RhTq6vrmDRp/JciBfrvP2/eZRiN2ZSUzD5sNTHfFcXwer3U1LRgt/eipqbl\nkPbZ5/MRjaYi/mJFEM08xJczEf+/EAkw5CA+p4sNn47c/+zxc69DfK8nCYZNU/zcXMTfyhEGzmJk\njjAgPt8z3v5kNG0IdvtA3G6FN95YQW2ti7a2FnbvLuXVV7eydasfu30AKSlpXH75a9TVtaFpGqNG\nDWHu3MswmXIYO/ZpiopyCIXs+P2V+HyT0LR07rxzIiZTDuPGPcrJJ5/Mp5/+jU8/XcyUKWfxwQdb\n8XoraG29gI6OMlau3E0gMJlotB9bt3YcsFbwh6yz+rH044gdsSP287EfOkXwfeC9+N/73V6/D6wG\n3Iqi/FNRFMfh7JTD4aCqqpS6unlUVZWiaRrLlm2jouIW1q9vpU+fZL74YgOSR38RCb2qaxB05hlg\nHbK5KAUeRSa9BuBNZAE3EZlIVyEbCD1f34Sk7RUgRBk3IBN2X2SiPhuZqCfH29aFjXUWvbeAT4Ct\n8bYuQCKcVyNse/cjUdH5wIdIhN+FLEz/Hf8ut8TPbwc+RiL1t8cfyxDq35L49Z6Nt/Gn+OOjgCB9\ncs0C4FxkYbAb2YC54995KIpiRVHuQ1H2IJvDFBIEBhcTDKbFv3cT8A8gi1jsYhRlIBaLg+bm3Uh6\n5IB4+79HSBP6EgqtIxbrxOu9kLa2ckymARiNMaLRpWRmTiUWC5KcvAWD4QEGDcpg3bpOYDSRyEQU\nxUJq6t+IRFrIyroUl6ua/v3tbN78JibTVDo7++DzlfPf/9ZRV1fH2rWN9Ox5G59+2oyqWrHbRxMO\ntzFwYDpbt75DZeVNfPKJi+XLa+jR43dUV0veeFVVKQ0N8xk6NIf6+gVUVZV2IQ66KYrSJXatv7bb\n9dq/P5OofTt0pvfB5/OxbNkW/P5K/P4prF69E6czmWi0HLCiqhNpbv43slnaBtyB/PY6s+aHiL+2\nIOjR58jiMoBsklIR/y8lUYOXg4yDLCR19RgEBXiTRIDCjfjbVmQsPoyMwV8hG7wPgLXAS8h4mIz4\nTG8kHTEZ2TR9jCBrHfG+tyHj9i7Ev5IQny6K93MqsnH6DCHciAG/IhrdjNFoIxodgKpmoGl6eu40\n4GgMhu0oig2L5ZT497qCaLQ/mlaIz3cqimLH6VxNOLwbpzOKpr1HUpKfPXv+wpgxRzF8eDH19ZMZ\nNCiL448vp77+EY47Li/uF/uSXGxh2bJtXYtHPQ1q+PCSrnuc0+ncy8cO9Pvn5ORQVVXWdc6+fnqo\nbF///+bn21DVk1AU29d/+DtaONyK+M9NwHYkUJWG+FIzIhmgIffw/kjqdiFQjfh2AXI//oCEIHyI\nRA2XCRG+bkLmjPMQf1bj7fwxfvwLFGU1sBa//1NMpkEEg7nAn/F67fh8WzCZzicQaMJsHkdyskpN\nzf8AVnr1up3q6p04nU6qqsrYufNexow5ijFj+mG3b8ZuX8LQoUWUlpZ2vX/CCT3p0aMHSUlJAIwc\n2ROHYxspKc+TnFzDkCG5RCJP43BIdoUeAd53rj1cPrWv/Vj6ccSO2BH7+dgPmiKoKMovkFXJ/yAb\nKpCin1uQGSmGrCBf0zTt6u/pmgcFVetRXrvdzmOPPc+zz76OplmpqLCjaem88soL+P1ZJMR/RSzY\nYHDHab8dJFLsdCSnGVkwOpCJMoAsFHUiAG/8T09jSo1/vh7ZcLiRxWYTMtk2IpucNBJilFZkgk4n\nQfmuC7PqQsZ2JC1LF3rVaYT1BWQqspnSad6V+PW+iPe7e//9mExGolEDkEIspgsJ68Xd+mtzvC2h\no1fVKLFYPkZjC2lpyezZ0x4vENc/nwZ4UBQLyclhfD4T0WgIs1lFVbOwWjspLS0jFutk8+YAgcBO\nFCWPjIxODAY7gUAypaUqJlMun3++Br/fjtHo4Ze/HIamxVi+/AscjijXXHMeEyacTnZ2NlOmXMu/\n//0RYKKyMpny8qOJRNw4HCUEg/Xs2NFKU1Mzqir/v+zsDMrLMzAYsonFmrrEWmVDvp2hQwu56qo1\nc1RDAAAgAElEQVSLeeSRZ1mzpuGABeNAl5/5fL6uFK6vs1AoRHLyUQSDGVgsLXR0bMBsNh/ylA49\n5erttz9D01TKy7OJxTxs2uQlEGhAUSwYjSGCQV0cWPdF3f8ciA/qQsDJ8d+7iUSKYBYJ3wywd4pr\nOgkq+EwSwtMBZIzoUgfEn5chi9No/HNFiB9nkxAY1gWS9esaSKBbrcj49pIgoPHE/zJJyDDo1O4i\nM2CxWOnbNweDIRW3u4lIxERj43ai0WRMpiB9+/ZEVYM0N5uJRr/A50uJ//9sZGbmxMkoUhk6tASz\n2cz772/mxBMrmTLlbBwOBwsXLubJJ/9Na2uI7GwnRUXJWK0FjBhRvg/F/74pgLrgtTyfNGn8N9q8\ndE87/CmkUOliyMuWbWPUqB5dBB2HKkVw0KBf8umnG0n4l060Uoz4v4b4YiYJeQwFuX/raaY66VEj\nCbIhb/wcXaQ7SCLdxRFvU6dnb0Y2XAZMphiVlUVs3dpKOOzDYsklI8NHc3OUSMSEpjUTi2VjsXRQ\nWdmT3r3zMZlyulJAgb3STDs7O3n66b+xdm0jVVVlTJ8+oeu+BewlDRAIBFi9eifDhpVw1VUX89BD\nz7BkyTIMhigXX3zaXjTwPwaf+rH048dkR1IED3c7R/ryU+jLwaYI/tAbrDXAbE3T3tnn+FhgjqZp\ngxRFOR14SNO0sgOcnwe8iuQPOTVNiymKcj+Se7ZG07TrDnDONxroHo+HmTMfoLj4BjZuvB1VNZCa\nej5PPnkVsAi4EEW5FU37ExkZL9DaegEmkxX4DdHoPeTlHU1z81CSk5ficq1FFms9EURqKRLlnAS8\ngkQjX0CK6j8ANiOR+8fi7y1FSC/+A5wF/AtJ83sXSeGbi2z4MpGF6/EI6nMcQnzxOoWFGXi9n9PR\nAdHoRCSqvx7Z496GFFBvAZYjaVbzkCjs6VgsD2OzBWhr64kUcH+Comxh9OhCbLZUNm0axs6di7FY\nTsHrfQFBKm4BmjEY5hKN3k1KytWYzY/R2RkkKWkxLS0XUFDQj127/Fgs5xIK/Q8FBcehqifgdi+g\nT59n2bLlUqZM+TP19U9hMlkpK/sNNTX3cdtt53PUUUexbNkyzjzzT6SlvURb23mcdFIfKiqu55VX\nbuSEE/7ISy/NwGS6g1jsfk44oRiDwcjmzSqKcjL9+r3PE0/Mxu/3k5GRQW1tLZqmcffdfyUr6yrc\n7oeZPXs8d9/9Alu32onFqqisfIcHH7waRVGYPfspSktnU1s7l3nzLiMnJwfYv97ly2phDtb2nfwb\nGxs59tgZWK2/IhD4J2vXPkZubu4hnxBlPMwnLe1yXn75Ms4+ez6vvHIDFsslNDS8SjS6A78/iKYN\nQCL0zyILxxOQoWpHIvCrEb97GKE3ByGg+C1yE/QjyOdSoBahpL4fSe/bghBb9EBSpGIkNIJOjrft\nANbEr7Ue2dTtiLehC3SvAhYgaIMPIc9Ygmz0TkLG1TbgYQyG2xk5Mp/167fi99+I1/sHhBjmLWAP\nBsP5xGL/Ijn5Cjo75zBgwFO0t/+ON9/8H6LRKFdddT/V1SMwm9dgsWzk9dfnUFZW1iUE7PP5mDPn\nXxQX38CuXfO4//4rcDqd3X+Prtcul4trr32E9esVdu/uQ17eRgKB9Zx33pM0NDzKggWzcDqdB/Q7\nTdO48soHyMu7loaG+SxYcO2XfvZwLjQP5eL2QG0finHS2NhIVdU1GI0z2br1RgT1FyFw8eUPkc2T\nG7nPrgX+C5yGZA/MADbFPzcB8cUzEJT3U8Rnf4Gkq+rp2wsQFHU14qtZwEmYzbWEw0u5+OLH8Xpf\nJBgMU1R0PTU1v8dksrBx40k0NCzE601GVU8B3qOsLI2+fVXmzZtBeXn5XrV8Ourp9Xq77nl1dfN4\n5JFr9vp/XHnlg5SU3Mj27X9A02L06PE76urmsWDBLDRNY/r0eVRU/JadO+89oJ9+m9++ezD0mwSp\njtjX25EN1uFu50hffgp9+alssPzAQE3TNu1zvA+wVtM0m6IoJcAmTdP2y+9QFMWMJK6/jOTrHA3M\n0DTtckVRFgBPaZq2Zp9zvtFA717gHYm4qKnZQ319K7t3f46kZLgBI4oSQFHy0LQWpKsaUsDsJFHU\nr4v5muPPk5AopZ4WkoJEKnVa3SgJIV2d5lwXqcxFov6xeHtJJNKudCHjlvi/x4CgBJ74+xEk2pka\n/4wab18nBNCFjHOQdJN0wILR2EokolPG66KtPoxGG3l5TlwuC5rWgKI4CIX0CH8zEm0tQFWbMBgy\nicUCRKOtQBFG4xcYDEmEQqCqNjTNRSyWgqIYsFqD2GxFlJcb6N17KMcfXwZoLFr0Htu2bQYMlJUl\n06vXcbzxxj/p6EinoCDI7NmXs3JlHcuXv0FLi5NQaDvhcC6q2kT//uW0tQVpbtawWEKUlxcDHlpa\nHF2imMOHl/L00y+yaVOQjAwPubl9cLlqASO5uUVdVNWwvzjwoZjYD1SALbTQfYjFClHV3Xi9G7Fa\nrYd8QtQRgUWL3sPl2k1WVjoVFZnU1Hj57LNP8ft18ex2xAd3I35tQ8ZAEgm69j2Ij5iRcaAjXa2I\nz+jEKJZ4W03x4ymIL+cjY0NHSm0kxpABQQWs8WNN3drU0ar2eH/akPGQT4KYJgmdQl101to4+uhS\nWloM7N5dF6/5E8RMUaIoSiYGQzsmUxbhcBNgY8CAXC688Bzuuuth2tutSBlpGlZrmMzMZHJySmls\n3IjLFcXpTOLkk/tiNGYTjTahqlnEYm62b/ehKAEuuugXzJiRIEUJh13U1LTgdnvJznZ20a1/nUCv\nLgK7erXrgBTaX0d4cSjshyAYOFQI1tCh4/n443pisRbEb3UylBLE71Tk/ptOQp4gB7kP6/feKDJ/\neEigqjYS0gK6oLU+r+gIa4BEtkIMRekkNbUXJ59cgaKovP32FjRtD8nJeWhagPb2ljiduwWj0YvR\n6MRmc9KjR24XwgTsIzZdGie4yKGqqhSd8h8CXHjhqaiqcgCkdF/h9AOJqX9XGv7DJ1L9/5Md2WAd\n7naO9OWn0JefBMkFsAH4raIoFv1A/Pmt8fcgsSLazzRNC2ma1t7t0HAE3iH+OOz76OSkSWfGqYxz\nOOGEhYABu70/MAmD4QwsliymTn2Z9HQbRuNQJDo5DJn4LiVBVV4J3EmCuvcPCJp1LrJhKUOimJnI\n5NyDhODvEGSy7Y1kUZ6H7CeLgJk4nWaMxoFIVL0AicaXIpmWx8a/yWgk538wUrs1Bbu9D2bzMGAC\nJtMEIExKSgYSOZ0BlGK334jZXEA0modETItIUMtXEYn0pLHRQO/eC8nJqWTkyMHMmlXNCSf0Ydy4\n0fTuPYXS0gsYM2YMWVmZOJ1/BgpxOKahqnkUFo6hR487GTKkJ6pagNH4GJpmIjf3NoYP78nLLy/s\nEh8dP34M2dlOwuGZKMooNmxox+E4j5SU3kyd+hTDh4/j7LPHcdddE1HVQnr0eJJIJJ/U1EtxOMbQ\n3GzAYhlCdvblOBy5DB58O5s2hcjMfJRNm4JkZk7j/fc30dxspk+fx2luttPRcR7JyaMZNGgg//jH\n3YwfPwZN0w66AP/b0v/q53k8nv0KsFeuXEksVgi8TCxWyMqVK79R29/WFEVhwoQzyMtL47zz/kFu\nbjYLFtzNkiW3kpqag8k0ClWdjaLkYTSOR3zlt0A5JlMRgkT1Rzb3Hchi8J+IP/VDqNUHIYvOG5FA\nQg+kZlAv3v9F/PO3kpAoKELQ3cnIQrU0fn4mUsfSC9kQReLtzEbGgwEowmAoJj9/CBbLrchYvQDo\ng6KUYrHkUFx8K263gVNOmU9KSnK8jbuBVNLSyrjoosc58cSTGDSoiKKiP1JWNpacnDJef/2/dHSk\nYTA8DuRQXHwdJtNQ2tvT2LPnVL74wojBcAodHZNpa1O5/fYLMBiyKSi4kupqF52d5+P19uKdd9bj\ncrlYubKW4uIb0bRUFi++i08+eY7//GchS5Y8sJ9A74HM5/NhMGRzzjmPdpFifHm91o6vpGT/KtN9\nt7v8wJeNg58LwYDf76ei4mjS0wchdYLlyP23EPGVHkjw4XbED69G/PdCxJ8KkMyG25CAQhYSbBuL\nSAmciGz+b0Lqc0E2cLrwej+EGEYDxmI29yI9fQJ+v4VQyEFW1gAikcGYTBdTWVlJVdUIysqup7x8\nOJmZ5RQUnEAkchkdHWUsW7YZj8dDY2Mj77yzkY6OX9HWlkln5wVoWgbz5l3G5Mnj+eCD7Xi9E/H5\nevPBB1u7kaVMYsaMicydeynTp0/oSsucPn3CXse6//YrVnxzfzsYoqAjdsSO2BH7IeyHRrCGAv+L\nbPTWIzNDfwSWOV3TtP8qinIhkKNp2ryvaOddZBa6GfhI07S3FEUZAwzXNO2efT570JGUA1MZ1xII\n1PPqq+/h94uopMUCsZiVcNiDLNgsyMJRp3wOINHz9Pj7OmW1HYmsZ5IQHtXpeS3sTdHbTCJvP0gi\nRz+ELBz1GhE9VaodmcSzkIhnOwkadEGoFCUXTduFREId8WvoSIEdsKMoLkS4t5EE5bz+XEcSvPHP\np2O1NqMo2USjPnJzMxg6tBfvvLMKrzeFvn1tgMLHH7cg++IQZnM+qtpCNCpgZCTSgKbloCgeUlKs\npKTkkZOTzEUXnYqiwLPPvk9j4y46OjoJBPwoigOr1Uh5eRKqmo3b/QXZ2RlMmXIKzz77EuvWedG0\neozGbLKzraSlGfnsszpisXRSU9s55ZRfsW3bWlpa7F0IVlVVKUuXrubttz8DwiQnZ5KdncSFF57C\nihUfsnp1QkDzq0SC9/WhbxKh3fu8/aPBPp8Pp7MS2UjUHlKa9u6WqMHaQkdHHZDL0Uc7Wb78H/Ts\neQK7dgmlvvhbKuIngnSK7yYhY0Ah4d8GpE4qDYnAt5GgWdcRLF08Oi3eXndpg/T4oy7Q7SHhq7vj\n5+4hUY+okKix0sediwQtu46A6SiWEYNBQ1FSiUbb0LTkeH91oeIGVLWIWKwh3gcXRiOkpPTCam2h\nvr4FyCM1tZ2KimNpaqqno6MZRckhFttNe7sRMGG1hjjjjOMZPXoYK1fWEY262LbNS1NTLdnZZVx8\n8Wg0DZ577g1cLg/Z2U4uvvgXXbUsB2MH8kc4UL1W7bdGBA5M6166nw9/mRTBTxXBCofDZGYeQ0dH\nCJnKTPFP2UmITich92m9frAdGcN1iJ/rtYv6/KGzxdoQv9ezF7wIQpsff4yQECI2x9vqxGRyUlBQ\nxLBhJWzb1sSGDRsIBJIxGNqw28Hvz8LhaKesLJ+6ugh+fxNWq4OTTx7AiBFD4r6mi3V7yckp45JL\nuiP4gmaDoKzd66oOBq3Sj31bCYAjCNahtUMxTurq6njhhRe+c99KSkqYOHEiPzd05Ehffvx9+Umk\nCALEGQInI9CMAmwElmiadtAhqG4brMsBt6Zpf1cU5WygQNO0h/f5rHbHHXd0vR49ejSjR48+YLt6\n/VVJyY3U1c3j4YevjlPxRhk27Co8nmvp6PgjBQXZ7Nq1GYlCGoEwijIaVf0v0Wg7CfKKmxCR1b7x\n7t6PTKKVyOT5GyR6eTQSiVyCREHPROqZzgdWIJPoJOBpZPNUiKBSer3VYIQ+Xi+A9mE2uwmFFGQD\nNgr4CIvlVwSDTyJomwFYicHwKNHo1VitVjIzpxCJvERb2ygCgXeQXP8/YTJdQzj8ID17/ommpttp\nb1fQtOOBSlR1EcnJ1+DxLKK8fBo9ey5l3bpa8vOfZffuyfTtW8yKFUMxm9fR3r6Kfv3+xtatEzAY\nyggGz0DTFmAynUt+/lZ8vg0kJ1+B2fwxPXt6icUUtm0bB6ygtLQFTVOorT0dWE7v3kHCYR/bt5+O\npr1Hz55+VNXMhg0BNO1U+vZ9lzlzpnH99Y/wxhsejMZzUZR5VFffT79+/WhqaiIrK6uL5joWizFt\n2lx69PgdW7b8ngcfvApFURgx4lry8hbR0HAxK1Y8QG5u7lf6ZvcavpqaP/LYY9fjdDq/tuZgX997\n5JFrUBSl65xVq1YxfPhvkDqM+Vx66SkUFhZy1113HdINVmNjI8cfP4uUlPl88sl55OS8SGfnRfz7\n37cwdeojdHRcQnv7EmKxOoSm+miE1XINsplJQxDVNGAZwup3NrLB6YfEVvyI/x+H1JsoSOzkT0ht\nymakJuUipFZR39BdgNRV7UDGxGmILIAHGZsGZDwMRmoMa5BN1lUI++U04EkUZS6adivCyCbMarAL\nh+NGvN5nEVT6QWTh+zsEkTgXQeIeBmaiKDH69FlMbe1UCgqGoCgjGDjwQ2655ddkZGRw/fWPk5t7\nObW1f6azs5X//ncYVut2nM5NVFcvICkpCbvdjsvl4je/eYyioutpbHyAP/1pKlde+SBbt45FVVfS\nt2+EJ56YvVe91r62b43LgWpe9q3Bamxs5LrrFtCjx+/2qpc5GNN9Ny/vCv7+9ys455yn2LXr3v1q\ncrq3d7gJBg7FwnHdunUcffQ0BImyIf7/CnKfvhCZ4oYjdYnTkfq9OuBFxK+bkMyCNOQevTzezipk\nYzYbyXqYivh1MuKfs4EYRuONRCILkQ1YFar6Mg5HMQUFv+aYY1Zx881nc/rpd+ByXYiivEEkspEB\nA15iz56ZDB5cSXb21bz66kzOOut+6usfJxaLsmmTkVhsKL17L2PBgt+QnJzcVTfldrvJzMykqakJ\nh8NBUlJS12+37/1rwQKps9z3mN6Wy+Vi9uwnKSmZvd97X+cXR2qwDp0dinFyyy2/Zc6cD1DVqu/U\nZjQ6J/7s57V4P9KXH39ffjIbrO/DFEV5D9mxDACma5p2haIojwDPaJr20T6f/Y4IVh1DhhTy9NMv\nsm6dl2h0N2ZzHoriIhg0d51rMjkIh70I2qQLEBciKJFe/2RCFpMgi0S9pstPIpqpIZHJBhKirB4S\nbGqtyMIxD4nw63Us7cjmTYu/n00i5z8fmczT4tczxdveTQI52IPBkIPTGcHvNxMK6ZpanUASitKJ\npqUAHSgKaJoVRTFjNHYQidhR1RB2ewZ2exCvVyMQUAAPJlMakYiPcDgEhFDVLAoKomiag5aWCJrm\nJhKxoqp27PYYDoeV7OwMevbMpba2FbdbGNyys0spL3dQUyO1B4WFTmpr26mpqSUYjJGRkUFmpsKW\nLT7AzymnHMOYMcfzzDPvsHHjJwSDTpzOILfdNo1p0yZ20QvrjGMjR1YAdNUXXHTRL5g27QImTrya\nNWv2MHRoDn/5y5/x+/17LVw9Hg9A1+LR4/GwePG/uto5//zRWCwWli+v2YvVTPe37ovcA7Gf6eb3\n+7HbK9EFnn2+TdhstsOCYE2ceA1vv72Rjo7daFoOeXkBRo36JVu2fEhNTRivt5Fg0BP3FyMJgexA\nvBUdUQrF+18Xf66zWeoi1UZk4xVBxkuMBILqRyL/LYhP6nT+OsJrJIFUuUlorBF/HkPGjo1E/WIG\nCabCViS4sQsZv7rYsV5DqbNrCrImVOytaJrO7qkABVgsLvr2PRaj0UEs5sbttnDccZkYDAZee+0z\nIEjv3pls3dqA32/iqKNSWbr0RQwGAzod/5Qp17F6tYshQ7IZPLg/L774flft1YQJJ3LNNVP3Q1K7\nLzofe+x5li3bzNChJV2f/SpSi+51dvsiEwdjX41g1TJoUB5XX33J16K/h9IOxTgJBoNkZR1DZ6cu\nXG1HfFO/r2skEKwcEvf6cqTWVc9G0JFdvRbWHz8eRu7pevvZ6DpxJpOFcDgXmQN07cAgNpsDq7WA\nE08s5skn5zBz5u387/9+hqb5MJv9hEK59Oql0qtXD/7zny3EYp04nSays8sIherZvr2daNREXp6T\n2bMnM2XK2V0+uWpVIxkZfiorhx5QIH3hwiUsW7aFUaN6davnSsynkycnGCwPFvE6kA8e7s35obAf\n63c4FOPk5pt/y5w5diR1/Du1Gn/8eS3ej/Tlx9+Xg91gGfc9cLhNUZQiYCQyW+w142qadv/XnGsE\nXkc2Vm8gRRlBRVGWAZ/su7n6Fn1jxoyJTJ6ss289SHHxDSxaNJna2jAGwySMxn+SlHQ+Pt8Sjjvu\ncjZvnk96+nR2756DLPTykNz7z5D6qDeRyHk/hFntA2TD1IJEwZ9DNjz1SAR/B7Kwy0cm6pEIu5kb\nYRG8AFmQDkMQgZHxx4p4uy5kETkdifxXI5H/hSQo4zOQaP//IZN/GQAGg0Y0eiyx2IcI09tzSJ2L\nAUXZgqZNxmD4BxbLeeTnv8TOnR2EQoKaRaMWOju/oLOzEH0hazBU4vefjMPxGuHwVmA4sVgdqamF\nqGqY9HQzmpaOy+XA7R5NLPYqweAXZGYWUFvbytixT7Np0x2oqoHeve9g5855vPzyVP7yl5dZuHAp\nVmsawaATozGC251LQ8MWVPUSbLZX+OijZtasWYzTeRO9ekUJBltobU3hjjte4K9/fZ+pU09n0qQz\nWbToPbzeidTUPM8///l7li3bSkXFb6munkcw+Cxmcz7Tp/fj+utn8MQTf+2a/KdPn9BF5w9WLrpo\nNKBQXV3HwIE5lJbm094+ngcfnEss1kJS0i3U1LzApEnjSUpK2m+BMX36BDQNNE1Eife1TZs2xX3i\nZuB/2LRpEwMHDvzmTv4NTVEUhg07hurqBo47biL19S/j8eTQ2lpKr14K48aZWLjwXZKTe+L312A0\nFtHWdhLwOSbTWsJhK/AkUjeyC9iM03ksHk8DcjuagSC3xUhQIYz4Tyrix39AhrkLmILw23gQn/0U\nqbXajYyPdGQDF0MCG/nx9iIIY+HdyIL3LIS9cwCyMLUjYzA7/q17IEjhHCyWCoLBHTidv8TjeQ+L\n5XKCwTeJxVZhNhcAzYRCw5EF8O8IBm8iO1tl/vybqKq6mlDoGF59dTkZGSFCoTQslmPYsGE5oZAf\nRclg06adDBhwDkajg+zsJCZMOBFVzeLXv76Dd9+9nFWrXFRWzqa09F8MHJjLmjWNPP74C1+acjdw\nYA5//et77N6dw9tvL0bTYNasqXGyjAOnn0pdSx1jxy6gpuaPTJ48/hst+rrfN7ujCpqmEQw+w0cf\nNezX55+6SeDhajo7jSSkOToQH4og9VMbSIjG25FkjXbkXq8gtbl6lkEbUne7BhkLVyKMml7Eh79A\n/H8tsI5wWMFq/TWx2BuEQiJ34HB4MBiMmEz9eOON5Zx88rVccskY7r33Znw+H3fcsQSXK4Pt299n\nxYpP8HohEulFZ+fnBIM+WlqC2O3FmEy/xuHYwjPPvMry5TUcc0w2q1Y1kpPzCOvXT2LEiJmsXPko\nkyd792G+BEVR0X9i3S8mTfKwZMkrXHnlg3ttnHSf0TcZe9eeztuvffhh0ku/b/s5fIcjdsSO2P72\ng5JcKIoyCeGWfQKpfr+6299VX3e+pmkRTdPGaZqWEX/8UNO0azVNG6Vp2jVfd/5B9rFLhLOqqpTt\n2yVlyGbLRFU/RFUbiEaXUFraG5freVJSFKzWDRgMNiRVZAISnfQhE+Q2ZJKsRzY5nyITbW38tRFJ\nMypC6KhvQNClycgG6S/IZkwX5q1HJuv+yM+5goQmyhwkNSqE/ItXItH1t5Ao/MnIYsAJvIakHQ5E\nUqJiKIoBi+VENM2B2bwaWdTWIClfNlS1GnCTlvYSSUk5SOpKPkKEoUf3n4pf4zSi0V3A01gsFQgi\ncArgIxRqJxTqQzB4CZpmIBDYjtH4v0Sju4nFjiYUupho1MTmzXdy0kl9qaoqY9u2P3Dssbk4nU4+\n/7yVnj2vZteuHRQVXUEwqBCLDQBCKMq/CQTa6dHjerxeA/AeRmMYiyUdvz+daHQqHk9Pli3bHC+M\nDqAoK9C0AJqmMWRIMXV18xg0KI81axooK7uJzz9vo7m5OU44cANLl27G5XKxbNkWfL7eeL0Tee+9\nTSxbtoWSkhtZu7aRY4/NY8uWefTseTIej5Fo9E00zf+lRBZut5vq6rouUeJ9i7ZFULYD+BvQ0SUw\ne6jN6/Wydm0jffveypYtr2GxZNCnz81s3vx/DBiQzqZN7ZhMFxIMZpKcnEks5kL87WM0zYvF4kYI\nWFqBDFS1iEhEZ1KLIQvKJgTVOgvZOBUhvtWOjAcrgmQ9hWyk/oD4pQEJNGjIwnY4ImKcF/98KZJF\nnI+k4n6BLHiXx6+5AiEnuAIhwGhAxu5aZCy5CAZ3YLVegs+3lMzMDqLRxxCphQI0bT6qmhP//HZg\nNnZ7Mhs3huNoVIhgcB0WyxQCgWRsttMJh1cAblS1N7HYPUSjObS3l7BnzwQ8nnI++GAr/fqlUlMz\nF0Vx0qPHSWzePJdBg/JZv76V/PyZrFixYy//6E4csHp1HZGIitc7GIuljNWr63C73d1ILbaxbNmW\nLj/2eDxdoqs7d97LqFE9Dzo1sLvp901VVbtQCp/Px9q1jZSWzt6PiODriGC+LVHM4TK3282qVbpe\nYSFCdNQHSTsF8Ycp8ffWIdTrk4AxyD26BAl8ZSD3+mzgVOQ+6QceQFIF1yE+H0JkQmqQ+WQw4fA/\nkaDFNuA8AoEoVqsfn28r0Whf2tvP5733NuN0OrHZbBQXG9i69V1KSmbh8ZiJRFKIRo/GbE6hpaWR\n5OTbCQZ3Yrc/i832OarqpLDwej77rIVBg9JoaJhJZaWZxsYHqaoq2UucV9+kV1TcysqVdV0SAfpv\nvnJl3X6kJrrP6JuLrxL//SoCoIOx78OfDkTk8m3s50LycsSO2BHb235okovtSAL6bZqmRQ/TNb81\nXah+Q128+F8sWvQ6jY1tZGQ4KCtLw2IpIBxupK4uQGPjdjo7VdradiKbI31C1NM+OpEFnzv+ficy\nieYiizqd0teHpI/sJpGapNPx5iKbMkv8L4sEFXVhvJ18JHIPEkW1I4vUQhLUwbVItF7IBxQlhMlk\nRlECxGLpGI0hTCYfHR0ATgyGFszmIsCNxZLNqFHlLFp0Ly+88Cpz5/6NXbs2EovlAA0oiiOxYe0A\nACAASURBVIaqFqEoDUQiZlQ1maSkDpKSetPU9BnBoA2DwUFenkZzs4lIZE9cQ8yG2RzE6bRgNNrJ\nySmkvNyBpqWhKK3U1HhpbNxOTk45l1xyEoqisGLFDoLBeszmfDZsWMWmTT4ikQayskoYPrwUm62Q\nSMSNpqUyalQvQOPpp19jx44mFCXGuHH9Wbx4Po8//gJLl24GWqmtlbS2Cy44kauvvuQgECt49tk3\n2BfBqqoqZdq0C3jooUV89FE9mzevpqnJyvDheYwcOYRVq3buR489ffqEbijD/lHNQCCAzVaJLOga\n8Ps3HUaadikqP/bYXCwWC9XVdRx7bC4mk4l585bQ0ODDYPBSWZmH292JyxXFYGjjqKPKUNVM1q1b\nSTicjAQJ7EhAQEU2RnbE95NJ+LuGoFGdJFJt9fRYB+L7eipfEgmylmYSC1RfvL1MZJzoaYGpJHS3\ngsjmLZUEKUcIm82O368ATiyWNsLhTGKxzvh/RCcaqMdkKiY3N0JnpxOrtZPUVBN+f07X77xo0bs0\nNGzFYEgDvGRkFHLuuUP46KP1/Pvfy4lEUrFa95CVlY+i2OLf3Ql4SE8vwGBopaXFwaBB6Tz//INc\ndNH1e9Gtd9ct6p6KpWkazzzzDqoa5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VsRP+kk4YPO+OscBD3NRFCnZuQe30hCxN1Mgt69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\nAVw1MYHSu1CPAAAAAElFTkSuQmCC\n",
|
||
"text/plain": [
|
||
"<matplotlib.figure.Figure at 0x7ff6886fe5f8>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"from pandas.tools.plotting import scatter_matrix\n",
|
||
"\n",
|
||
"attributes = [\"median_house_value\", \"median_income\", \"total_rooms\",\n",
|
||
" \"housing_median_age\"]\n",
|
||
"scatter_matrix(housing[attributes], figsize=(12, 8))\n",
|
||
"save_fig(\"scatter_matrix_plot\")"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 37,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Saving figure income_vs_house_value_scatterplot\n"
|
||
]
|
||
},
|
||
{
|
||
"data": {
|
||
"image/png": 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N04njxVyGoYamZVHVFOBgGGVct086LYjjDHEckSQWhpFiOg0wTZsgcBAiRZIo6LpOFDmA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\n",
|
||
"text/plain": [
|
||
"<matplotlib.figure.Figure at 0x7ff688bbe128>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"housing.plot(kind=\"scatter\", x=\"median_income\", y=\"median_house_value\",\n",
|
||
" alpha=0.1)\n",
|
||
"plt.axis([0, 16, 0, 550000])\n",
|
||
"save_fig(\"income_vs_house_value_scatterplot\")"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 38,
|
||
"metadata": {
|
||
"collapsed": true
|
||
},
|
||
"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": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"Note: there was a bug in the previous cell, in the definition of the `rooms_per_household` attribute. This explains why the correlation value below differs slightly from the value in the book (unless you are reading the latest version)."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 39,
|
||
"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": 39,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"corr_matrix = housing.corr()\n",
|
||
"corr_matrix[\"median_house_value\"].sort_values(ascending=False)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 40,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"image/png": 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ZBg6HQjw+uOz8m80s3co6pecppmRjY2Pz1GxHWv8Sj8umXCuZxOWq4uTJupIntlkjuZUY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\nMGwajCgZDAaDYdNgRMlgMBgMm4b/H/15MudSVItoAAAAAElFTkSuQmCC\n",
|
||
"text/plain": [
|
||
"<matplotlib.figure.Figure at 0x7ff6674baf98>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"housing.plot(kind=\"scatter\", x=\"rooms_per_household\", y=\"median_house_value\",\n",
|
||
" alpha=0.2)\n",
|
||
"plt.axis([0, 5, 0, 520000])\n",
|
||
"plt.show()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 41,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/html": [
|
||
"<div>\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>rooms_per_household</th>\n",
|
||
" <th>bedrooms_per_room</th>\n",
|
||
" <th>population_per_household</th>\n",
|
||
" </tr>\n",
|
||
" </thead>\n",
|
||
" <tbody>\n",
|
||
" <tr>\n",
|
||
" <th>count</th>\n",
|
||
" <td>16512.000000</td>\n",
|
||
" <td>16512.000000</td>\n",
|
||
" <td>16512.000000</td>\n",
|
||
" <td>16512.000000</td>\n",
|
||
" <td>16354.000000</td>\n",
|
||
" <td>16512.000000</td>\n",
|
||
" <td>16512.000000</td>\n",
|
||
" <td>16512.000000</td>\n",
|
||
" <td>16512.000000</td>\n",
|
||
" <td>16512.000000</td>\n",
|
||
" <td>16354.000000</td>\n",
|
||
" <td>16512.000000</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>mean</th>\n",
|
||
" <td>-119.575834</td>\n",
|
||
" <td>35.639577</td>\n",
|
||
" <td>28.653101</td>\n",
|
||
" <td>2622.728319</td>\n",
|
||
" <td>534.973890</td>\n",
|
||
" <td>1419.790819</td>\n",
|
||
" <td>497.060380</td>\n",
|
||
" <td>3.875589</td>\n",
|
||
" <td>206990.920724</td>\n",
|
||
" <td>5.440341</td>\n",
|
||
" <td>0.212878</td>\n",
|
||
" <td>3.096437</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>std</th>\n",
|
||
" <td>2.001860</td>\n",
|
||
" <td>2.138058</td>\n",
|
||
" <td>12.574726</td>\n",
|
||
" <td>2138.458419</td>\n",
|
||
" <td>412.699041</td>\n",
|
||
" <td>1115.686241</td>\n",
|
||
" <td>375.720845</td>\n",
|
||
" <td>1.904950</td>\n",
|
||
" <td>115703.014830</td>\n",
|
||
" <td>2.611712</td>\n",
|
||
" <td>0.057379</td>\n",
|
||
" <td>11.584826</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>6.000000</td>\n",
|
||
" <td>2.000000</td>\n",
|
||
" <td>3.000000</td>\n",
|
||
" <td>2.000000</td>\n",
|
||
" <td>0.499900</td>\n",
|
||
" <td>14999.000000</td>\n",
|
||
" <td>1.130435</td>\n",
|
||
" <td>0.100000</td>\n",
|
||
" <td>0.692308</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>25%</th>\n",
|
||
" <td>-121.800000</td>\n",
|
||
" <td>33.940000</td>\n",
|
||
" <td>18.000000</td>\n",
|
||
" <td>1443.000000</td>\n",
|
||
" <td>295.000000</td>\n",
|
||
" <td>784.000000</td>\n",
|
||
" <td>279.000000</td>\n",
|
||
" <td>2.566775</td>\n",
|
||
" <td>119800.000000</td>\n",
|
||
" <td>4.442040</td>\n",
|
||
" <td>0.175304</td>\n",
|
||
" <td>2.431287</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>50%</th>\n",
|
||
" <td>-118.510000</td>\n",
|
||
" <td>34.260000</td>\n",
|
||
" <td>29.000000</td>\n",
|
||
" <td>2119.500000</td>\n",
|
||
" <td>433.000000</td>\n",
|
||
" <td>1164.000000</td>\n",
|
||
" <td>408.000000</td>\n",
|
||
" <td>3.540900</td>\n",
|
||
" <td>179500.000000</td>\n",
|
||
" <td>5.232284</td>\n",
|
||
" <td>0.203031</td>\n",
|
||
" <td>2.817653</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>75%</th>\n",
|
||
" <td>-118.010000</td>\n",
|
||
" <td>37.720000</td>\n",
|
||
" <td>37.000000</td>\n",
|
||
" <td>3141.000000</td>\n",
|
||
" <td>644.000000</td>\n",
|
||
" <td>1719.250000</td>\n",
|
||
" <td>602.000000</td>\n",
|
||
" <td>4.744475</td>\n",
|
||
" <td>263900.000000</td>\n",
|
||
" <td>6.056361</td>\n",
|
||
" <td>0.239831</td>\n",
|
||
" <td>3.281420</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>6210.000000</td>\n",
|
||
" <td>35682.000000</td>\n",
|
||
" <td>5358.000000</td>\n",
|
||
" <td>15.000100</td>\n",
|
||
" <td>500001.000000</td>\n",
|
||
" <td>141.909091</td>\n",
|
||
" <td>1.000000</td>\n",
|
||
" <td>1243.333333</td>\n",
|
||
" </tr>\n",
|
||
" </tbody>\n",
|
||
"</table>\n",
|
||
"</div>"
|
||
],
|
||
"text/plain": [
|
||
" longitude latitude housing_median_age total_rooms \\\n",
|
||
"count 16512.000000 16512.000000 16512.000000 16512.000000 \n",
|
||
"mean -119.575834 35.639577 28.653101 2622.728319 \n",
|
||
"std 2.001860 2.138058 12.574726 2138.458419 \n",
|
||
"min -124.350000 32.540000 1.000000 6.000000 \n",
|
||
"25% -121.800000 33.940000 18.000000 1443.000000 \n",
|
||
"50% -118.510000 34.260000 29.000000 2119.500000 \n",
|
||
"75% -118.010000 37.720000 37.000000 3141.000000 \n",
|
||
"max -114.310000 41.950000 52.000000 39320.000000 \n",
|
||
"\n",
|
||
" total_bedrooms population households median_income \\\n",
|
||
"count 16354.000000 16512.000000 16512.000000 16512.000000 \n",
|
||
"mean 534.973890 1419.790819 497.060380 3.875589 \n",
|
||
"std 412.699041 1115.686241 375.720845 1.904950 \n",
|
||
"min 2.000000 3.000000 2.000000 0.499900 \n",
|
||
"25% 295.000000 784.000000 279.000000 2.566775 \n",
|
||
"50% 433.000000 1164.000000 408.000000 3.540900 \n",
|
||
"75% 644.000000 1719.250000 602.000000 4.744475 \n",
|
||
"max 6210.000000 35682.000000 5358.000000 15.000100 \n",
|
||
"\n",
|
||
" median_house_value rooms_per_household bedrooms_per_room \\\n",
|
||
"count 16512.000000 16512.000000 16354.000000 \n",
|
||
"mean 206990.920724 5.440341 0.212878 \n",
|
||
"std 115703.014830 2.611712 0.057379 \n",
|
||
"min 14999.000000 1.130435 0.100000 \n",
|
||
"25% 119800.000000 4.442040 0.175304 \n",
|
||
"50% 179500.000000 5.232284 0.203031 \n",
|
||
"75% 263900.000000 6.056361 0.239831 \n",
|
||
"max 500001.000000 141.909091 1.000000 \n",
|
||
"\n",
|
||
" population_per_household \n",
|
||
"count 16512.000000 \n",
|
||
"mean 3.096437 \n",
|
||
"std 11.584826 \n",
|
||
"min 0.692308 \n",
|
||
"25% 2.431287 \n",
|
||
"50% 2.817653 \n",
|
||
"75% 3.281420 \n",
|
||
"max 1243.333333 "
|
||
]
|
||
},
|
||
"execution_count": 41,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"housing.describe()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"# Prepare the data for Machine Learning algorithms"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 42,
|
||
"metadata": {
|
||
"collapsed": true
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"housing = strat_train_set.drop(\"median_house_value\", axis=1) # drop labels for training set\n",
|
||
"housing_labels = strat_train_set[\"median_house_value\"].copy()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 43,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/html": [
|
||
"<div>\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",
|
||
" </tr>\n",
|
||
" </thead>\n",
|
||
" <tbody>\n",
|
||
" <tr>\n",
|
||
" <th>4629</th>\n",
|
||
" <td>-118.30</td>\n",
|
||
" <td>34.07</td>\n",
|
||
" <td>18.0</td>\n",
|
||
" <td>3759.0</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>3296.0</td>\n",
|
||
" <td>1462.0</td>\n",
|
||
" <td>2.2708</td>\n",
|
||
" <td><1H OCEAN</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>6068</th>\n",
|
||
" <td>-117.86</td>\n",
|
||
" <td>34.01</td>\n",
|
||
" <td>16.0</td>\n",
|
||
" <td>4632.0</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>3038.0</td>\n",
|
||
" <td>727.0</td>\n",
|
||
" <td>5.1762</td>\n",
|
||
" <td><1H OCEAN</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>17923</th>\n",
|
||
" <td>-121.97</td>\n",
|
||
" <td>37.35</td>\n",
|
||
" <td>30.0</td>\n",
|
||
" <td>1955.0</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>999.0</td>\n",
|
||
" <td>386.0</td>\n",
|
||
" <td>4.6328</td>\n",
|
||
" <td><1H OCEAN</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>13656</th>\n",
|
||
" <td>-117.30</td>\n",
|
||
" <td>34.05</td>\n",
|
||
" <td>6.0</td>\n",
|
||
" <td>2155.0</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>1039.0</td>\n",
|
||
" <td>391.0</td>\n",
|
||
" <td>1.6675</td>\n",
|
||
" <td>INLAND</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>19252</th>\n",
|
||
" <td>-122.79</td>\n",
|
||
" <td>38.48</td>\n",
|
||
" <td>7.0</td>\n",
|
||
" <td>6837.0</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>3468.0</td>\n",
|
||
" <td>1405.0</td>\n",
|
||
" <td>3.1662</td>\n",
|
||
" <td><1H OCEAN</td>\n",
|
||
" </tr>\n",
|
||
" </tbody>\n",
|
||
"</table>\n",
|
||
"</div>"
|
||
],
|
||
"text/plain": [
|
||
" longitude latitude housing_median_age total_rooms total_bedrooms \\\n",
|
||
"4629 -118.30 34.07 18.0 3759.0 NaN \n",
|
||
"6068 -117.86 34.01 16.0 4632.0 NaN \n",
|
||
"17923 -121.97 37.35 30.0 1955.0 NaN \n",
|
||
"13656 -117.30 34.05 6.0 2155.0 NaN \n",
|
||
"19252 -122.79 38.48 7.0 6837.0 NaN \n",
|
||
"\n",
|
||
" population households median_income ocean_proximity \n",
|
||
"4629 3296.0 1462.0 2.2708 <1H OCEAN \n",
|
||
"6068 3038.0 727.0 5.1762 <1H OCEAN \n",
|
||
"17923 999.0 386.0 4.6328 <1H OCEAN \n",
|
||
"13656 1039.0 391.0 1.6675 INLAND \n",
|
||
"19252 3468.0 1405.0 3.1662 <1H OCEAN "
|
||
]
|
||
},
|
||
"execution_count": 43,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"sample_incomplete_rows = housing[housing.isnull().any(axis=1)].head()\n",
|
||
"sample_incomplete_rows"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 44,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/html": [
|
||
"<div>\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",
|
||
" </tr>\n",
|
||
" </thead>\n",
|
||
" <tbody>\n",
|
||
" </tbody>\n",
|
||
"</table>\n",
|
||
"</div>"
|
||
],
|
||
"text/plain": [
|
||
"Empty DataFrame\n",
|
||
"Columns: [longitude, latitude, housing_median_age, total_rooms, total_bedrooms, population, households, median_income, ocean_proximity]\n",
|
||
"Index: []"
|
||
]
|
||
},
|
||
"execution_count": 44,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"sample_incomplete_rows.dropna(subset=[\"total_bedrooms\"]) # option 1"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 45,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/html": [
|
||
"<div>\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>population</th>\n",
|
||
" <th>households</th>\n",
|
||
" <th>median_income</th>\n",
|
||
" <th>ocean_proximity</th>\n",
|
||
" </tr>\n",
|
||
" </thead>\n",
|
||
" <tbody>\n",
|
||
" <tr>\n",
|
||
" <th>4629</th>\n",
|
||
" <td>-118.30</td>\n",
|
||
" <td>34.07</td>\n",
|
||
" <td>18.0</td>\n",
|
||
" <td>3759.0</td>\n",
|
||
" <td>3296.0</td>\n",
|
||
" <td>1462.0</td>\n",
|
||
" <td>2.2708</td>\n",
|
||
" <td><1H OCEAN</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>6068</th>\n",
|
||
" <td>-117.86</td>\n",
|
||
" <td>34.01</td>\n",
|
||
" <td>16.0</td>\n",
|
||
" <td>4632.0</td>\n",
|
||
" <td>3038.0</td>\n",
|
||
" <td>727.0</td>\n",
|
||
" <td>5.1762</td>\n",
|
||
" <td><1H OCEAN</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>17923</th>\n",
|
||
" <td>-121.97</td>\n",
|
||
" <td>37.35</td>\n",
|
||
" <td>30.0</td>\n",
|
||
" <td>1955.0</td>\n",
|
||
" <td>999.0</td>\n",
|
||
" <td>386.0</td>\n",
|
||
" <td>4.6328</td>\n",
|
||
" <td><1H OCEAN</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>13656</th>\n",
|
||
" <td>-117.30</td>\n",
|
||
" <td>34.05</td>\n",
|
||
" <td>6.0</td>\n",
|
||
" <td>2155.0</td>\n",
|
||
" <td>1039.0</td>\n",
|
||
" <td>391.0</td>\n",
|
||
" <td>1.6675</td>\n",
|
||
" <td>INLAND</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>19252</th>\n",
|
||
" <td>-122.79</td>\n",
|
||
" <td>38.48</td>\n",
|
||
" <td>7.0</td>\n",
|
||
" <td>6837.0</td>\n",
|
||
" <td>3468.0</td>\n",
|
||
" <td>1405.0</td>\n",
|
||
" <td>3.1662</td>\n",
|
||
" <td><1H OCEAN</td>\n",
|
||
" </tr>\n",
|
||
" </tbody>\n",
|
||
"</table>\n",
|
||
"</div>"
|
||
],
|
||
"text/plain": [
|
||
" longitude latitude housing_median_age total_rooms population \\\n",
|
||
"4629 -118.30 34.07 18.0 3759.0 3296.0 \n",
|
||
"6068 -117.86 34.01 16.0 4632.0 3038.0 \n",
|
||
"17923 -121.97 37.35 30.0 1955.0 999.0 \n",
|
||
"13656 -117.30 34.05 6.0 2155.0 1039.0 \n",
|
||
"19252 -122.79 38.48 7.0 6837.0 3468.0 \n",
|
||
"\n",
|
||
" households median_income ocean_proximity \n",
|
||
"4629 1462.0 2.2708 <1H OCEAN \n",
|
||
"6068 727.0 5.1762 <1H OCEAN \n",
|
||
"17923 386.0 4.6328 <1H OCEAN \n",
|
||
"13656 391.0 1.6675 INLAND \n",
|
||
"19252 1405.0 3.1662 <1H OCEAN "
|
||
]
|
||
},
|
||
"execution_count": 45,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"sample_incomplete_rows.drop(\"total_bedrooms\", axis=1) # option 2"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 46,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/html": [
|
||
"<div>\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",
|
||
" </tr>\n",
|
||
" </thead>\n",
|
||
" <tbody>\n",
|
||
" <tr>\n",
|
||
" <th>4629</th>\n",
|
||
" <td>-118.30</td>\n",
|
||
" <td>34.07</td>\n",
|
||
" <td>18.0</td>\n",
|
||
" <td>3759.0</td>\n",
|
||
" <td>433.0</td>\n",
|
||
" <td>3296.0</td>\n",
|
||
" <td>1462.0</td>\n",
|
||
" <td>2.2708</td>\n",
|
||
" <td><1H OCEAN</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>6068</th>\n",
|
||
" <td>-117.86</td>\n",
|
||
" <td>34.01</td>\n",
|
||
" <td>16.0</td>\n",
|
||
" <td>4632.0</td>\n",
|
||
" <td>433.0</td>\n",
|
||
" <td>3038.0</td>\n",
|
||
" <td>727.0</td>\n",
|
||
" <td>5.1762</td>\n",
|
||
" <td><1H OCEAN</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>17923</th>\n",
|
||
" <td>-121.97</td>\n",
|
||
" <td>37.35</td>\n",
|
||
" <td>30.0</td>\n",
|
||
" <td>1955.0</td>\n",
|
||
" <td>433.0</td>\n",
|
||
" <td>999.0</td>\n",
|
||
" <td>386.0</td>\n",
|
||
" <td>4.6328</td>\n",
|
||
" <td><1H OCEAN</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>13656</th>\n",
|
||
" <td>-117.30</td>\n",
|
||
" <td>34.05</td>\n",
|
||
" <td>6.0</td>\n",
|
||
" <td>2155.0</td>\n",
|
||
" <td>433.0</td>\n",
|
||
" <td>1039.0</td>\n",
|
||
" <td>391.0</td>\n",
|
||
" <td>1.6675</td>\n",
|
||
" <td>INLAND</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>19252</th>\n",
|
||
" <td>-122.79</td>\n",
|
||
" <td>38.48</td>\n",
|
||
" <td>7.0</td>\n",
|
||
" <td>6837.0</td>\n",
|
||
" <td>433.0</td>\n",
|
||
" <td>3468.0</td>\n",
|
||
" <td>1405.0</td>\n",
|
||
" <td>3.1662</td>\n",
|
||
" <td><1H OCEAN</td>\n",
|
||
" </tr>\n",
|
||
" </tbody>\n",
|
||
"</table>\n",
|
||
"</div>"
|
||
],
|
||
"text/plain": [
|
||
" longitude latitude housing_median_age total_rooms total_bedrooms \\\n",
|
||
"4629 -118.30 34.07 18.0 3759.0 433.0 \n",
|
||
"6068 -117.86 34.01 16.0 4632.0 433.0 \n",
|
||
"17923 -121.97 37.35 30.0 1955.0 433.0 \n",
|
||
"13656 -117.30 34.05 6.0 2155.0 433.0 \n",
|
||
"19252 -122.79 38.48 7.0 6837.0 433.0 \n",
|
||
"\n",
|
||
" population households median_income ocean_proximity \n",
|
||
"4629 3296.0 1462.0 2.2708 <1H OCEAN \n",
|
||
"6068 3038.0 727.0 5.1762 <1H OCEAN \n",
|
||
"17923 999.0 386.0 4.6328 <1H OCEAN \n",
|
||
"13656 1039.0 391.0 1.6675 INLAND \n",
|
||
"19252 3468.0 1405.0 3.1662 <1H OCEAN "
|
||
]
|
||
},
|
||
"execution_count": 46,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"median = housing[\"total_bedrooms\"].median()\n",
|
||
"sample_incomplete_rows[\"total_bedrooms\"].fillna(median, inplace=True) # option 3\n",
|
||
"sample_incomplete_rows"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 47,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"from sklearn.preprocessing import Imputer\n",
|
||
"\n",
|
||
"imputer = Imputer(strategy=\"median\")"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"Remove the text attribute because median can only be calculated on numerical attributes:"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 48,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"housing_num = housing.drop(\"ocean_proximity\", axis=1)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 49,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"Imputer(axis=0, copy=True, missing_values='NaN', strategy='median', verbose=0)"
|
||
]
|
||
},
|
||
"execution_count": 49,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"imputer.fit(housing_num)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 50,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"array([ -118.51 , 34.26 , 29. , 2119.5 , 433. ,\n",
|
||
" 1164. , 408. , 3.5409])"
|
||
]
|
||
},
|
||
"execution_count": 50,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"imputer.statistics_"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"Check that this is the same as manually computing the median of each attribute:"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 51,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"array([ -118.51 , 34.26 , 29. , 2119.5 , 433. ,\n",
|
||
" 1164. , 408. , 3.5409])"
|
||
]
|
||
},
|
||
"execution_count": 51,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"housing_num.median().values"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"Transform the training set:"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 52,
|
||
"metadata": {
|
||
"collapsed": true
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"X = imputer.transform(housing_num)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 53,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"housing_tr = pd.DataFrame(X, columns=housing_num.columns,\n",
|
||
" index = list(housing.index.values))"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 54,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/html": [
|
||
"<div>\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",
|
||
" </tr>\n",
|
||
" </thead>\n",
|
||
" <tbody>\n",
|
||
" <tr>\n",
|
||
" <th>4629</th>\n",
|
||
" <td>-118.30</td>\n",
|
||
" <td>34.07</td>\n",
|
||
" <td>18.0</td>\n",
|
||
" <td>3759.0</td>\n",
|
||
" <td>433.0</td>\n",
|
||
" <td>3296.0</td>\n",
|
||
" <td>1462.0</td>\n",
|
||
" <td>2.2708</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>6068</th>\n",
|
||
" <td>-117.86</td>\n",
|
||
" <td>34.01</td>\n",
|
||
" <td>16.0</td>\n",
|
||
" <td>4632.0</td>\n",
|
||
" <td>433.0</td>\n",
|
||
" <td>3038.0</td>\n",
|
||
" <td>727.0</td>\n",
|
||
" <td>5.1762</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>17923</th>\n",
|
||
" <td>-121.97</td>\n",
|
||
" <td>37.35</td>\n",
|
||
" <td>30.0</td>\n",
|
||
" <td>1955.0</td>\n",
|
||
" <td>433.0</td>\n",
|
||
" <td>999.0</td>\n",
|
||
" <td>386.0</td>\n",
|
||
" <td>4.6328</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>13656</th>\n",
|
||
" <td>-117.30</td>\n",
|
||
" <td>34.05</td>\n",
|
||
" <td>6.0</td>\n",
|
||
" <td>2155.0</td>\n",
|
||
" <td>433.0</td>\n",
|
||
" <td>1039.0</td>\n",
|
||
" <td>391.0</td>\n",
|
||
" <td>1.6675</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>19252</th>\n",
|
||
" <td>-122.79</td>\n",
|
||
" <td>38.48</td>\n",
|
||
" <td>7.0</td>\n",
|
||
" <td>6837.0</td>\n",
|
||
" <td>433.0</td>\n",
|
||
" <td>3468.0</td>\n",
|
||
" <td>1405.0</td>\n",
|
||
" <td>3.1662</td>\n",
|
||
" </tr>\n",
|
||
" </tbody>\n",
|
||
"</table>\n",
|
||
"</div>"
|
||
],
|
||
"text/plain": [
|
||
" longitude latitude housing_median_age total_rooms total_bedrooms \\\n",
|
||
"4629 -118.30 34.07 18.0 3759.0 433.0 \n",
|
||
"6068 -117.86 34.01 16.0 4632.0 433.0 \n",
|
||
"17923 -121.97 37.35 30.0 1955.0 433.0 \n",
|
||
"13656 -117.30 34.05 6.0 2155.0 433.0 \n",
|
||
"19252 -122.79 38.48 7.0 6837.0 433.0 \n",
|
||
"\n",
|
||
" population households median_income \n",
|
||
"4629 3296.0 1462.0 2.2708 \n",
|
||
"6068 3038.0 727.0 5.1762 \n",
|
||
"17923 999.0 386.0 4.6328 \n",
|
||
"13656 1039.0 391.0 1.6675 \n",
|
||
"19252 3468.0 1405.0 3.1662 "
|
||
]
|
||
},
|
||
"execution_count": 54,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"housing_tr.loc[sample_incomplete_rows.index.values]"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 55,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"'median'"
|
||
]
|
||
},
|
||
"execution_count": 55,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"imputer.strategy"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 56,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/html": [
|
||
"<div>\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",
|
||
" </tr>\n",
|
||
" </thead>\n",
|
||
" <tbody>\n",
|
||
" <tr>\n",
|
||
" <th>0</th>\n",
|
||
" <td>-121.89</td>\n",
|
||
" <td>37.29</td>\n",
|
||
" <td>38.0</td>\n",
|
||
" <td>1568.0</td>\n",
|
||
" <td>351.0</td>\n",
|
||
" <td>710.0</td>\n",
|
||
" <td>339.0</td>\n",
|
||
" <td>2.7042</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>1</th>\n",
|
||
" <td>-121.93</td>\n",
|
||
" <td>37.05</td>\n",
|
||
" <td>14.0</td>\n",
|
||
" <td>679.0</td>\n",
|
||
" <td>108.0</td>\n",
|
||
" <td>306.0</td>\n",
|
||
" <td>113.0</td>\n",
|
||
" <td>6.4214</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>2</th>\n",
|
||
" <td>-117.20</td>\n",
|
||
" <td>32.77</td>\n",
|
||
" <td>31.0</td>\n",
|
||
" <td>1952.0</td>\n",
|
||
" <td>471.0</td>\n",
|
||
" <td>936.0</td>\n",
|
||
" <td>462.0</td>\n",
|
||
" <td>2.8621</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>3</th>\n",
|
||
" <td>-119.61</td>\n",
|
||
" <td>36.31</td>\n",
|
||
" <td>25.0</td>\n",
|
||
" <td>1847.0</td>\n",
|
||
" <td>371.0</td>\n",
|
||
" <td>1460.0</td>\n",
|
||
" <td>353.0</td>\n",
|
||
" <td>1.8839</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>4</th>\n",
|
||
" <td>-118.59</td>\n",
|
||
" <td>34.23</td>\n",
|
||
" <td>17.0</td>\n",
|
||
" <td>6592.0</td>\n",
|
||
" <td>1525.0</td>\n",
|
||
" <td>4459.0</td>\n",
|
||
" <td>1463.0</td>\n",
|
||
" <td>3.0347</td>\n",
|
||
" </tr>\n",
|
||
" </tbody>\n",
|
||
"</table>\n",
|
||
"</div>"
|
||
],
|
||
"text/plain": [
|
||
" longitude latitude housing_median_age total_rooms total_bedrooms \\\n",
|
||
"0 -121.89 37.29 38.0 1568.0 351.0 \n",
|
||
"1 -121.93 37.05 14.0 679.0 108.0 \n",
|
||
"2 -117.20 32.77 31.0 1952.0 471.0 \n",
|
||
"3 -119.61 36.31 25.0 1847.0 371.0 \n",
|
||
"4 -118.59 34.23 17.0 6592.0 1525.0 \n",
|
||
"\n",
|
||
" population households median_income \n",
|
||
"0 710.0 339.0 2.7042 \n",
|
||
"1 306.0 113.0 6.4214 \n",
|
||
"2 936.0 462.0 2.8621 \n",
|
||
"3 1460.0 353.0 1.8839 \n",
|
||
"4 4459.0 1463.0 3.0347 "
|
||
]
|
||
},
|
||
"execution_count": 56,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"housing_tr = pd.DataFrame(X, columns=housing_num.columns)\n",
|
||
"housing_tr.head()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 57,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"array([0, 0, 4, ..., 1, 0, 3])"
|
||
]
|
||
},
|
||
"execution_count": 57,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"from sklearn.preprocessing import LabelEncoder\n",
|
||
"\n",
|
||
"encoder = LabelEncoder()\n",
|
||
"housing_cat = housing[\"ocean_proximity\"]\n",
|
||
"housing_cat_encoded = encoder.fit_transform(housing_cat)\n",
|
||
"housing_cat_encoded"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 58,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"['<1H OCEAN' 'INLAND' 'ISLAND' 'NEAR BAY' 'NEAR OCEAN']\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"print(encoder.classes_)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 59,
|
||
"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": 59,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"from sklearn.preprocessing import OneHotEncoder\n",
|
||
"\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": 60,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"array([[ 1., 0., 0., 0., 0.],\n",
|
||
" [ 1., 0., 0., 0., 0.],\n",
|
||
" [ 0., 0., 0., 0., 1.],\n",
|
||
" ..., \n",
|
||
" [ 0., 1., 0., 0., 0.],\n",
|
||
" [ 1., 0., 0., 0., 0.],\n",
|
||
" [ 0., 0., 0., 1., 0.]])"
|
||
]
|
||
},
|
||
"execution_count": 60,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"housing_cat_1hot.toarray()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 61,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"array([[1, 0, 0, 0, 0],\n",
|
||
" [1, 0, 0, 0, 0],\n",
|
||
" [0, 0, 0, 0, 1],\n",
|
||
" ..., \n",
|
||
" [0, 1, 0, 0, 0],\n",
|
||
" [1, 0, 0, 0, 0],\n",
|
||
" [0, 0, 0, 1, 0]])"
|
||
]
|
||
},
|
||
"execution_count": 61,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"from sklearn.preprocessing import LabelBinarizer\n",
|
||
"\n",
|
||
"encoder = LabelBinarizer()\n",
|
||
"housing_cat_1hot = encoder.fit_transform(housing_cat)\n",
|
||
"housing_cat_1hot"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 62,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"from sklearn.base import BaseEstimator, TransformerMixin\n",
|
||
"\n",
|
||
"# column index\n",
|
||
"rooms_ix, bedrooms_ix, population_ix, household_ix = 3, 4, 5, 6\n",
|
||
"\n",
|
||
"class CombinedAttributesAdder(BaseEstimator, TransformerMixin):\n",
|
||
" def __init__(self, add_bedrooms_per_room = True): # no *args or **kargs\n",
|
||
" self.add_bedrooms_per_room = add_bedrooms_per_room\n",
|
||
" def fit(self, X, y=None):\n",
|
||
" return self # nothing else to do\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,\n",
|
||
" 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)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 63,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/html": [
|
||
"<div>\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>rooms_per_household</th>\n",
|
||
" <th>population_per_household</th>\n",
|
||
" </tr>\n",
|
||
" </thead>\n",
|
||
" <tbody>\n",
|
||
" <tr>\n",
|
||
" <th>0</th>\n",
|
||
" <td>-121.89</td>\n",
|
||
" <td>37.29</td>\n",
|
||
" <td>38</td>\n",
|
||
" <td>1568</td>\n",
|
||
" <td>351</td>\n",
|
||
" <td>710</td>\n",
|
||
" <td>339</td>\n",
|
||
" <td>2.7042</td>\n",
|
||
" <td><1H OCEAN</td>\n",
|
||
" <td>4.62537</td>\n",
|
||
" <td>2.0944</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>1</th>\n",
|
||
" <td>-121.93</td>\n",
|
||
" <td>37.05</td>\n",
|
||
" <td>14</td>\n",
|
||
" <td>679</td>\n",
|
||
" <td>108</td>\n",
|
||
" <td>306</td>\n",
|
||
" <td>113</td>\n",
|
||
" <td>6.4214</td>\n",
|
||
" <td><1H OCEAN</td>\n",
|
||
" <td>6.00885</td>\n",
|
||
" <td>2.70796</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>2</th>\n",
|
||
" <td>-117.2</td>\n",
|
||
" <td>32.77</td>\n",
|
||
" <td>31</td>\n",
|
||
" <td>1952</td>\n",
|
||
" <td>471</td>\n",
|
||
" <td>936</td>\n",
|
||
" <td>462</td>\n",
|
||
" <td>2.8621</td>\n",
|
||
" <td>NEAR OCEAN</td>\n",
|
||
" <td>4.22511</td>\n",
|
||
" <td>2.02597</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>3</th>\n",
|
||
" <td>-119.61</td>\n",
|
||
" <td>36.31</td>\n",
|
||
" <td>25</td>\n",
|
||
" <td>1847</td>\n",
|
||
" <td>371</td>\n",
|
||
" <td>1460</td>\n",
|
||
" <td>353</td>\n",
|
||
" <td>1.8839</td>\n",
|
||
" <td>INLAND</td>\n",
|
||
" <td>5.23229</td>\n",
|
||
" <td>4.13598</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>4</th>\n",
|
||
" <td>-118.59</td>\n",
|
||
" <td>34.23</td>\n",
|
||
" <td>17</td>\n",
|
||
" <td>6592</td>\n",
|
||
" <td>1525</td>\n",
|
||
" <td>4459</td>\n",
|
||
" <td>1463</td>\n",
|
||
" <td>3.0347</td>\n",
|
||
" <td><1H OCEAN</td>\n",
|
||
" <td>4.50581</td>\n",
|
||
" <td>3.04785</td>\n",
|
||
" </tr>\n",
|
||
" </tbody>\n",
|
||
"</table>\n",
|
||
"</div>"
|
||
],
|
||
"text/plain": [
|
||
" longitude latitude housing_median_age total_rooms total_bedrooms population \\\n",
|
||
"0 -121.89 37.29 38 1568 351 710 \n",
|
||
"1 -121.93 37.05 14 679 108 306 \n",
|
||
"2 -117.2 32.77 31 1952 471 936 \n",
|
||
"3 -119.61 36.31 25 1847 371 1460 \n",
|
||
"4 -118.59 34.23 17 6592 1525 4459 \n",
|
||
"\n",
|
||
" households median_income ocean_proximity rooms_per_household \\\n",
|
||
"0 339 2.7042 <1H OCEAN 4.62537 \n",
|
||
"1 113 6.4214 <1H OCEAN 6.00885 \n",
|
||
"2 462 2.8621 NEAR OCEAN 4.22511 \n",
|
||
"3 353 1.8839 INLAND 5.23229 \n",
|
||
"4 1463 3.0347 <1H OCEAN 4.50581 \n",
|
||
"\n",
|
||
" population_per_household \n",
|
||
"0 2.0944 \n",
|
||
"1 2.70796 \n",
|
||
"2 2.02597 \n",
|
||
"3 4.13598 \n",
|
||
"4 3.04785 "
|
||
]
|
||
},
|
||
"execution_count": 63,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"housing_extra_attribs = pd.DataFrame(housing_extra_attribs, columns=list(housing.columns)+[\"rooms_per_household\", \"population_per_household\"])\n",
|
||
"housing_extra_attribs.head()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 64,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"from sklearn.pipeline import Pipeline\n",
|
||
"from sklearn.preprocessing import StandardScaler\n",
|
||
"\n",
|
||
"num_pipeline = Pipeline([\n",
|
||
" ('imputer', Imputer(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": 65,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"array([[-1.15604281, 0.77194962, 0.74333089, ..., -0.31205452,\n",
|
||
" -0.08649871, 0.15531753],\n",
|
||
" [-1.17602483, 0.6596948 , -1.1653172 , ..., 0.21768338,\n",
|
||
" -0.03353391, -0.83628902],\n",
|
||
" [ 1.18684903, -1.34218285, 0.18664186, ..., -0.46531516,\n",
|
||
" -0.09240499, 0.4222004 ],\n",
|
||
" ..., \n",
|
||
" [ 1.58648943, -0.72478134, -1.56295222, ..., 0.3469342 ,\n",
|
||
" -0.03055414, -0.52177644],\n",
|
||
" [ 0.78221312, -0.85106801, 0.18664186, ..., 0.02499488,\n",
|
||
" 0.06150916, -0.30340741],\n",
|
||
" [-1.43579109, 0.99645926, 1.85670895, ..., -0.22852947,\n",
|
||
" -0.09586294, 0.10180567]])"
|
||
]
|
||
},
|
||
"execution_count": 65,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"housing_num_tr"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 66,
|
||
"metadata": {
|
||
"collapsed": true
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"from sklearn.base import BaseEstimator, TransformerMixin\n",
|
||
"\n",
|
||
"# Create a class to select numerical or categorical columns \n",
|
||
"# since Scikit-Learn doesn't handle DataFrames yet\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": 67,
|
||
"metadata": {
|
||
"collapsed": true
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"num_attribs = list(housing_num)\n",
|
||
"cat_attribs = [\"ocean_proximity\"]\n",
|
||
"\n",
|
||
"num_pipeline = Pipeline([\n",
|
||
" ('selector', DataFrameSelector(num_attribs)),\n",
|
||
" ('imputer', Imputer(strategy=\"median\")),\n",
|
||
" ('attribs_adder', CombinedAttributesAdder()),\n",
|
||
" ('std_scaler', StandardScaler()),\n",
|
||
" ])\n",
|
||
"\n",
|
||
"cat_pipeline = Pipeline([\n",
|
||
" ('selector', DataFrameSelector(cat_attribs)),\n",
|
||
" ('label_binarizer', LabelBinarizer()),\n",
|
||
" ])"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 68,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"from sklearn.pipeline import FeatureUnion\n",
|
||
"\n",
|
||
"full_pipeline = FeatureUnion(transformer_list=[\n",
|
||
" (\"num_pipeline\", num_pipeline),\n",
|
||
" (\"cat_pipeline\", cat_pipeline),\n",
|
||
" ])"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 69,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"array([[-1.15604281, 0.77194962, 0.74333089, ..., 0. ,\n",
|
||
" 0. , 0. ],\n",
|
||
" [-1.17602483, 0.6596948 , -1.1653172 , ..., 0. ,\n",
|
||
" 0. , 0. ],\n",
|
||
" [ 1.18684903, -1.34218285, 0.18664186, ..., 0. ,\n",
|
||
" 0. , 1. ],\n",
|
||
" ..., \n",
|
||
" [ 1.58648943, -0.72478134, -1.56295222, ..., 0. ,\n",
|
||
" 0. , 0. ],\n",
|
||
" [ 0.78221312, -0.85106801, 0.18664186, ..., 0. ,\n",
|
||
" 0. , 0. ],\n",
|
||
" [-1.43579109, 0.99645926, 1.85670895, ..., 0. ,\n",
|
||
" 1. , 0. ]])"
|
||
]
|
||
},
|
||
"execution_count": 69,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"housing_prepared = full_pipeline.fit_transform(housing)\n",
|
||
"housing_prepared"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 70,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"(16512, 16)"
|
||
]
|
||
},
|
||
"execution_count": 70,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"housing_prepared.shape"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"# Select and train a model "
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 71,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"LinearRegression(copy_X=True, fit_intercept=True, n_jobs=1, normalize=False)"
|
||
]
|
||
},
|
||
"execution_count": 71,
|
||
"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": 72,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Predictions: [ 210644.60459286 317768.80697211 210956.43331178 59218.98886849\n",
|
||
" 189747.55849879]\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"# let's try the full pipeline on a few training instances\n",
|
||
"some_data = housing.iloc[:5]\n",
|
||
"some_labels = housing_labels.iloc[:5]\n",
|
||
"some_data_prepared = full_pipeline.transform(some_data)\n",
|
||
"\n",
|
||
"print(\"Predictions:\", lin_reg.predict(some_data_prepared))"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"Compare against the actual values:"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 73,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Labels: [286600.0, 340600.0, 196900.0, 46300.0, 254500.0]\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"print(\"Labels:\", list(some_labels))"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 74,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"array([[-1.15604281, 0.77194962, 0.74333089, -0.49323393, -0.44543821,\n",
|
||
" -0.63621141, -0.42069842, -0.61493744, -0.31205452, -0.08649871,\n",
|
||
" 0.15531753, 1. , 0. , 0. , 0. ,\n",
|
||
" 0. ],\n",
|
||
" [-1.17602483, 0.6596948 , -1.1653172 , -0.90896655, -1.0369278 ,\n",
|
||
" -0.99833135, -1.02222705, 1.33645936, 0.21768338, -0.03353391,\n",
|
||
" -0.83628902, 1. , 0. , 0. , 0. ,\n",
|
||
" 0. ],\n",
|
||
" [ 1.18684903, -1.34218285, 0.18664186, -0.31365989, -0.15334458,\n",
|
||
" -0.43363936, -0.0933178 , -0.5320456 , -0.46531516, -0.09240499,\n",
|
||
" 0.4222004 , 0. , 0. , 0. , 0. ,\n",
|
||
" 1. ],\n",
|
||
" [-0.01706767, 0.31357576, -0.29052016, -0.36276217, -0.39675594,\n",
|
||
" 0.03604096, -0.38343559, -1.04556555, -0.07966124, 0.08973561,\n",
|
||
" -0.19645314, 0. , 1. , 0. , 0. ,\n",
|
||
" 0. ],\n",
|
||
" [ 0.49247384, -0.65929936, -0.92673619, 1.85619316, 2.41221109,\n",
|
||
" 2.72415407, 2.57097492, -0.44143679, -0.35783383, -0.00419445,\n",
|
||
" 0.2699277 , 1. , 0. , 0. , 0. ,\n",
|
||
" 0. ]])"
|
||
]
|
||
},
|
||
"execution_count": 74,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"some_data_prepared"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 75,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"68628.198198489219"
|
||
]
|
||
},
|
||
"execution_count": 75,
|
||
"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": 76,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"49439.895990018973"
|
||
]
|
||
},
|
||
"execution_count": 76,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"from sklearn.metrics import mean_absolute_error\n",
|
||
"\n",
|
||
"lin_mae = mean_absolute_error(housing_labels, housing_predictions)\n",
|
||
"lin_mae"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 77,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"DecisionTreeRegressor(criterion='mse', max_depth=None, max_features=None,\n",
|
||
" max_leaf_nodes=None, min_impurity_split=1e-07,\n",
|
||
" min_samples_leaf=1, min_samples_split=2,\n",
|
||
" min_weight_fraction_leaf=0.0, presort=False, random_state=42,\n",
|
||
" splitter='best')"
|
||
]
|
||
},
|
||
"execution_count": 77,
|
||
"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": 78,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"0.0"
|
||
]
|
||
},
|
||
"execution_count": 78,
|
||
"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": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"# Fine-tune your model"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 79,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"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)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 80,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Scores: [ 70232.0136482 66828.46839892 72444.08721003 70761.50186201\n",
|
||
" 71125.52697653 75581.29319857 70169.59286164 70055.37863456\n",
|
||
" 75370.49116773 71222.39081244]\n",
|
||
"Mean: 71379.0744771\n",
|
||
"Standard deviation: 2458.31882043\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": 81,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Scores: [ 66760.97371572 66962.61914244 70349.94853401 74757.02629506\n",
|
||
" 68031.13388938 71193.84183426 64968.13706527 68261.95557897\n",
|
||
" 71527.64217874 67665.10082067]\n",
|
||
"Mean: 69047.8379055\n",
|
||
"Standard deviation: 2735.51074287\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",
|
||
"display_scores(lin_rmse_scores)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 82,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"RandomForestRegressor(bootstrap=True, criterion='mse', max_depth=None,\n",
|
||
" max_features='auto', max_leaf_nodes=None,\n",
|
||
" min_impurity_split=1e-07, min_samples_leaf=1,\n",
|
||
" min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
|
||
" n_estimators=10, n_jobs=1, oob_score=False, random_state=42,\n",
|
||
" verbose=0, warm_start=False)"
|
||
]
|
||
},
|
||
"execution_count": 82,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"from sklearn.ensemble import RandomForestRegressor\n",
|
||
"\n",
|
||
"forest_reg = RandomForestRegressor(random_state=42)\n",
|
||
"forest_reg.fit(housing_prepared, housing_labels)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 83,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"21941.911027380233"
|
||
]
|
||
},
|
||
"execution_count": 83,
|
||
"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": 84,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Scores: [ 51650.94405471 48920.80645498 52979.16096752 54412.74042021\n",
|
||
" 50861.29381163 56488.55699727 51866.90120786 49752.24599537\n",
|
||
" 55399.50713191 53309.74548294]\n",
|
||
"Mean: 52564.1902524\n",
|
||
"Standard deviation: 2301.87380392\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": "code",
|
||
"execution_count": 85,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"count 10.000000\n",
|
||
"mean 69047.837905\n",
|
||
"std 2883.481504\n",
|
||
"min 64968.137065\n",
|
||
"25% 67138.239562\n",
|
||
"50% 68146.544734\n",
|
||
"75% 70982.868509\n",
|
||
"max 74757.026295\n",
|
||
"dtype: float64"
|
||
]
|
||
},
|
||
"execution_count": 85,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"scores = cross_val_score(lin_reg, housing_prepared, housing_labels, scoring=\"neg_mean_squared_error\", cv=10)\n",
|
||
"pd.Series(np.sqrt(-scores)).describe()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 86,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"111094.6308539982"
|
||
]
|
||
},
|
||
"execution_count": 86,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"from sklearn.svm import SVR\n",
|
||
"\n",
|
||
"svm_reg = SVR(kernel=\"linear\")\n",
|
||
"svm_reg.fit(housing_prepared, housing_labels)\n",
|
||
"housing_predictions = svm_reg.predict(housing_prepared)\n",
|
||
"svm_mse = mean_squared_error(housing_labels, housing_predictions)\n",
|
||
"svm_rmse = np.sqrt(svm_mse)\n",
|
||
"svm_rmse"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 87,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"GridSearchCV(cv=5, error_score='raise',\n",
|
||
" estimator=RandomForestRegressor(bootstrap=True, criterion='mse', max_depth=None,\n",
|
||
" max_features='auto', max_leaf_nodes=None,\n",
|
||
" min_impurity_split=1e-07, min_samples_leaf=1,\n",
|
||
" min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
|
||
" n_estimators=10, n_jobs=1, oob_score=False, random_state=42,\n",
|
||
" verbose=0, warm_start=False),\n",
|
||
" fit_params={}, iid=True, n_jobs=1,\n",
|
||
" param_grid=[{'n_estimators': [3, 10, 30], 'max_features': [2, 4, 6, 8]}, {'bootstrap': [False], 'n_estimators': [3, 10], 'max_features': [2, 3, 4]}],\n",
|
||
" pre_dispatch='2*n_jobs', refit=True, return_train_score=True,\n",
|
||
" scoring='neg_mean_squared_error', verbose=0)"
|
||
]
|
||
},
|
||
"execution_count": 87,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"from sklearn.model_selection import GridSearchCV\n",
|
||
"\n",
|
||
"param_grid = [\n",
|
||
" # try 12 (3×4) combinations of hyperparameters\n",
|
||
" {'n_estimators': [3, 10, 30], 'max_features': [2, 4, 6, 8]},\n",
|
||
" # then try 6 (2×3) combinations with bootstrap set as False\n",
|
||
" {'bootstrap': [False], 'n_estimators': [3, 10], 'max_features': [2, 3, 4]},\n",
|
||
" ]\n",
|
||
"\n",
|
||
"forest_reg = RandomForestRegressor(random_state=42)\n",
|
||
"# train across 5 folds, that's a total of (12+6)*5=90 rounds of training \n",
|
||
"grid_search = GridSearchCV(forest_reg, param_grid, cv=5,\n",
|
||
" scoring='neg_mean_squared_error')\n",
|
||
"grid_search.fit(housing_prepared, housing_labels)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"The best hyperparameter combination found:"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 88,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"{'max_features': 8, 'n_estimators': 30}"
|
||
]
|
||
},
|
||
"execution_count": 88,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"grid_search.best_params_"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 89,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"RandomForestRegressor(bootstrap=True, criterion='mse', max_depth=None,\n",
|
||
" max_features=8, max_leaf_nodes=None, min_impurity_split=1e-07,\n",
|
||
" min_samples_leaf=1, min_samples_split=2,\n",
|
||
" min_weight_fraction_leaf=0.0, n_estimators=30, n_jobs=1,\n",
|
||
" oob_score=False, random_state=42, verbose=0, warm_start=False)"
|
||
]
|
||
},
|
||
"execution_count": 89,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"grid_search.best_estimator_"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"Let's look at the score of each hyperparameter combination tested during the grid search:"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 90,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"63825.0479302 {'max_features': 2, 'n_estimators': 3}\n",
|
||
"55643.8429091 {'max_features': 2, 'n_estimators': 10}\n",
|
||
"53380.6566859 {'max_features': 2, 'n_estimators': 30}\n",
|
||
"60959.1388585 {'max_features': 4, 'n_estimators': 3}\n",
|
||
"52740.5841667 {'max_features': 4, 'n_estimators': 10}\n",
|
||
"50374.1421461 {'max_features': 4, 'n_estimators': 30}\n",
|
||
"58661.2866462 {'max_features': 6, 'n_estimators': 3}\n",
|
||
"52009.9739798 {'max_features': 6, 'n_estimators': 10}\n",
|
||
"50154.1177737 {'max_features': 6, 'n_estimators': 30}\n",
|
||
"57865.3616801 {'max_features': 8, 'n_estimators': 3}\n",
|
||
"51730.0755087 {'max_features': 8, 'n_estimators': 10}\n",
|
||
"49694.8514333 {'max_features': 8, 'n_estimators': 30}\n",
|
||
"62874.4073931 {'bootstrap': False, 'max_features': 2, 'n_estimators': 3}\n",
|
||
"54561.9398157 {'bootstrap': False, 'max_features': 2, 'n_estimators': 10}\n",
|
||
"59416.6463145 {'bootstrap': False, 'max_features': 3, 'n_estimators': 3}\n",
|
||
"52660.245911 {'bootstrap': False, 'max_features': 3, 'n_estimators': 10}\n",
|
||
"57490.0168279 {'bootstrap': False, 'max_features': 4, 'n_estimators': 3}\n",
|
||
"51093.9059428 {'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": 91,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/html": [
|
||
"<div>\n",
|
||
"<table border=\"1\" class=\"dataframe\">\n",
|
||
" <thead>\n",
|
||
" <tr style=\"text-align: right;\">\n",
|
||
" <th></th>\n",
|
||
" <th>mean_fit_time</th>\n",
|
||
" <th>mean_score_time</th>\n",
|
||
" <th>mean_test_score</th>\n",
|
||
" <th>mean_train_score</th>\n",
|
||
" <th>param_bootstrap</th>\n",
|
||
" <th>param_max_features</th>\n",
|
||
" <th>param_n_estimators</th>\n",
|
||
" <th>params</th>\n",
|
||
" <th>rank_test_score</th>\n",
|
||
" <th>split0_test_score</th>\n",
|
||
" <th>...</th>\n",
|
||
" <th>split2_test_score</th>\n",
|
||
" <th>split2_train_score</th>\n",
|
||
" <th>split3_test_score</th>\n",
|
||
" <th>split3_train_score</th>\n",
|
||
" <th>split4_test_score</th>\n",
|
||
" <th>split4_train_score</th>\n",
|
||
" <th>std_fit_time</th>\n",
|
||
" <th>std_score_time</th>\n",
|
||
" <th>std_test_score</th>\n",
|
||
" <th>std_train_score</th>\n",
|
||
" </tr>\n",
|
||
" </thead>\n",
|
||
" <tbody>\n",
|
||
" <tr>\n",
|
||
" <th>0</th>\n",
|
||
" <td>0.055782</td>\n",
|
||
" <td>0.003593</td>\n",
|
||
" <td>-4.073637e+09</td>\n",
|
||
" <td>-1.107354e+09</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>2</td>\n",
|
||
" <td>3</td>\n",
|
||
" <td>{'max_features': 2, 'n_estimators': 3}</td>\n",
|
||
" <td>18</td>\n",
|
||
" <td>-3.963584e+09</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>-4.194135e+09</td>\n",
|
||
" <td>-1.116843e+09</td>\n",
|
||
" <td>-3.906732e+09</td>\n",
|
||
" <td>-1.112813e+09</td>\n",
|
||
" <td>-4.169669e+09</td>\n",
|
||
" <td>-1.129842e+09</td>\n",
|
||
" <td>0.000631</td>\n",
|
||
" <td>0.000542</td>\n",
|
||
" <td>1.160694e+08</td>\n",
|
||
" <td>1.927217e+07</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>1</th>\n",
|
||
" <td>0.172177</td>\n",
|
||
" <td>0.008357</td>\n",
|
||
" <td>-3.096237e+09</td>\n",
|
||
" <td>-5.813707e+08</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>2</td>\n",
|
||
" <td>10</td>\n",
|
||
" <td>{'max_features': 2, 'n_estimators': 10}</td>\n",
|
||
" <td>11</td>\n",
|
||
" <td>-3.070368e+09</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>-3.124982e+09</td>\n",
|
||
" <td>-5.780873e+08</td>\n",
|
||
" <td>-2.865117e+09</td>\n",
|
||
" <td>-5.713421e+08</td>\n",
|
||
" <td>-3.169914e+09</td>\n",
|
||
" <td>-5.797944e+08</td>\n",
|
||
" <td>0.002452</td>\n",
|
||
" <td>0.000872</td>\n",
|
||
" <td>1.297819e+08</td>\n",
|
||
" <td>6.782553e+06</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>2</th>\n",
|
||
" <td>0.549370</td>\n",
|
||
" <td>0.024622</td>\n",
|
||
" <td>-2.849495e+09</td>\n",
|
||
" <td>-4.394633e+08</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>2</td>\n",
|
||
" <td>30</td>\n",
|
||
" <td>{'max_features': 2, 'n_estimators': 30}</td>\n",
|
||
" <td>9</td>\n",
|
||
" <td>-2.697829e+09</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>-2.943808e+09</td>\n",
|
||
" <td>-4.374429e+08</td>\n",
|
||
" <td>-2.619893e+09</td>\n",
|
||
" <td>-4.374715e+08</td>\n",
|
||
" <td>-2.968460e+09</td>\n",
|
||
" <td>-4.451903e+08</td>\n",
|
||
" <td>0.031675</td>\n",
|
||
" <td>0.001817</td>\n",
|
||
" <td>1.593649e+08</td>\n",
|
||
" <td>2.961109e+06</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>3</th>\n",
|
||
" <td>0.108659</td>\n",
|
||
" <td>0.003603</td>\n",
|
||
" <td>-3.716017e+09</td>\n",
|
||
" <td>-9.850011e+08</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>4</td>\n",
|
||
" <td>3</td>\n",
|
||
" <td>{'max_features': 4, 'n_estimators': 3}</td>\n",
|
||
" <td>16</td>\n",
|
||
" <td>-3.729600e+09</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>-3.736527e+09</td>\n",
|
||
" <td>-9.172986e+08</td>\n",
|
||
" <td>-3.404974e+09</td>\n",
|
||
" <td>-1.035901e+09</td>\n",
|
||
" <td>-3.914186e+09</td>\n",
|
||
" <td>-9.711998e+08</td>\n",
|
||
" <td>0.005413</td>\n",
|
||
" <td>0.000532</td>\n",
|
||
" <td>1.690029e+08</td>\n",
|
||
" <td>4.047487e+07</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>4</th>\n",
|
||
" <td>0.345844</td>\n",
|
||
" <td>0.010100</td>\n",
|
||
" <td>-2.781569e+09</td>\n",
|
||
" <td>-5.160154e+08</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>4</td>\n",
|
||
" <td>10</td>\n",
|
||
" <td>{'max_features': 4, 'n_estimators': 10}</td>\n",
|
||
" <td>8</td>\n",
|
||
" <td>-2.667093e+09</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>-2.891599e+09</td>\n",
|
||
" <td>-4.960301e+08</td>\n",
|
||
" <td>-2.613393e+09</td>\n",
|
||
" <td>-5.422542e+08</td>\n",
|
||
" <td>-2.949550e+09</td>\n",
|
||
" <td>-5.158794e+08</td>\n",
|
||
" <td>0.005767</td>\n",
|
||
" <td>0.001052</td>\n",
|
||
" <td>1.278498e+08</td>\n",
|
||
" <td>1.498960e+07</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>5</th>\n",
|
||
" <td>0.890049</td>\n",
|
||
" <td>0.025157</td>\n",
|
||
" <td>-2.537554e+09</td>\n",
|
||
" <td>-3.878685e+08</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>4</td>\n",
|
||
" <td>30</td>\n",
|
||
" <td>{'max_features': 4, 'n_estimators': 30}</td>\n",
|
||
" <td>3</td>\n",
|
||
" <td>-2.387199e+09</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>-2.663178e+09</td>\n",
|
||
" <td>-3.789712e+08</td>\n",
|
||
" <td>-2.397951e+09</td>\n",
|
||
" <td>-4.036920e+08</td>\n",
|
||
" <td>-2.649850e+09</td>\n",
|
||
" <td>-3.846171e+08</td>\n",
|
||
" <td>0.040411</td>\n",
|
||
" <td>0.001709</td>\n",
|
||
" <td>1.209935e+08</td>\n",
|
||
" <td>8.424973e+06</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>6</th>\n",
|
||
" <td>0.134878</td>\n",
|
||
" <td>0.003163</td>\n",
|
||
" <td>-3.441147e+09</td>\n",
|
||
" <td>-9.030212e+08</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>6</td>\n",
|
||
" <td>3</td>\n",
|
||
" <td>{'max_features': 6, 'n_estimators': 3}</td>\n",
|
||
" <td>14</td>\n",
|
||
" <td>-3.119576e+09</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>-3.587747e+09</td>\n",
|
||
" <td>-9.360639e+08</td>\n",
|
||
" <td>-3.331544e+09</td>\n",
|
||
" <td>-9.025026e+08</td>\n",
|
||
" <td>-3.577062e+09</td>\n",
|
||
" <td>-8.612945e+08</td>\n",
|
||
" <td>0.003447</td>\n",
|
||
" <td>0.000230</td>\n",
|
||
" <td>1.884229e+08</td>\n",
|
||
" <td>2.639683e+07</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>7</th>\n",
|
||
" <td>0.441237</td>\n",
|
||
" <td>0.009476</td>\n",
|
||
" <td>-2.705037e+09</td>\n",
|
||
" <td>-5.014210e+08</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>6</td>\n",
|
||
" <td>10</td>\n",
|
||
" <td>{'max_features': 6, 'n_estimators': 10}</td>\n",
|
||
" <td>6</td>\n",
|
||
" <td>-2.553481e+09</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>-2.762945e+09</td>\n",
|
||
" <td>-4.996537e+08</td>\n",
|
||
" <td>-2.519522e+09</td>\n",
|
||
" <td>-4.989516e+08</td>\n",
|
||
" <td>-2.906270e+09</td>\n",
|
||
" <td>-5.063617e+08</td>\n",
|
||
" <td>0.026262</td>\n",
|
||
" <td>0.001327</td>\n",
|
||
" <td>1.464963e+08</td>\n",
|
||
" <td>3.357661e+06</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>8</th>\n",
|
||
" <td>1.293729</td>\n",
|
||
" <td>0.025095</td>\n",
|
||
" <td>-2.515436e+09</td>\n",
|
||
" <td>-3.840197e+08</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>6</td>\n",
|
||
" <td>30</td>\n",
|
||
" <td>{'max_features': 6, 'n_estimators': 30}</td>\n",
|
||
" <td>2</td>\n",
|
||
" <td>-2.371924e+09</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>-2.607962e+09</td>\n",
|
||
" <td>-3.805596e+08</td>\n",
|
||
" <td>-2.351220e+09</td>\n",
|
||
" <td>-3.856159e+08</td>\n",
|
||
" <td>-2.662399e+09</td>\n",
|
||
" <td>-3.904866e+08</td>\n",
|
||
" <td>0.043359</td>\n",
|
||
" <td>0.000991</td>\n",
|
||
" <td>1.283580e+08</td>\n",
|
||
" <td>3.796810e+06</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>9</th>\n",
|
||
" <td>0.154177</td>\n",
|
||
" <td>0.003283</td>\n",
|
||
" <td>-3.348400e+09</td>\n",
|
||
" <td>-8.884890e+08</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>8</td>\n",
|
||
" <td>3</td>\n",
|
||
" <td>{'max_features': 8, 'n_estimators': 3}</td>\n",
|
||
" <td>13</td>\n",
|
||
" <td>-3.351347e+09</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>-3.396841e+09</td>\n",
|
||
" <td>-8.596460e+08</td>\n",
|
||
" <td>-3.131753e+09</td>\n",
|
||
" <td>-8.893698e+08</td>\n",
|
||
" <td>-3.509451e+09</td>\n",
|
||
" <td>-9.146734e+08</td>\n",
|
||
" <td>0.002721</td>\n",
|
||
" <td>0.000364</td>\n",
|
||
" <td>1.226683e+08</td>\n",
|
||
" <td>2.730057e+07</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>10</th>\n",
|
||
" <td>0.509834</td>\n",
|
||
" <td>0.008054</td>\n",
|
||
" <td>-2.676001e+09</td>\n",
|
||
" <td>-4.923247e+08</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>8</td>\n",
|
||
" <td>10</td>\n",
|
||
" <td>{'max_features': 8, 'n_estimators': 10}</td>\n",
|
||
" <td>5</td>\n",
|
||
" <td>-2.572358e+09</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>-2.844608e+09</td>\n",
|
||
" <td>-4.730979e+08</td>\n",
|
||
" <td>-2.462797e+09</td>\n",
|
||
" <td>-5.154156e+08</td>\n",
|
||
" <td>-2.777049e+09</td>\n",
|
||
" <td>-4.979127e+08</td>\n",
|
||
" <td>0.006725</td>\n",
|
||
" <td>0.000394</td>\n",
|
||
" <td>1.393253e+08</td>\n",
|
||
" <td>1.446900e+07</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>11</th>\n",
|
||
" <td>1.706564</td>\n",
|
||
" <td>0.024939</td>\n",
|
||
" <td>-2.469578e+09</td>\n",
|
||
" <td>-3.809175e+08</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>8</td>\n",
|
||
" <td>30</td>\n",
|
||
" <td>{'max_features': 8, 'n_estimators': 30}</td>\n",
|
||
" <td>1</td>\n",
|
||
" <td>-2.358884e+09</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>-2.591134e+09</td>\n",
|
||
" <td>-3.772512e+08</td>\n",
|
||
" <td>-2.319816e+09</td>\n",
|
||
" <td>-3.881153e+08</td>\n",
|
||
" <td>-2.528200e+09</td>\n",
|
||
" <td>-3.807496e+08</td>\n",
|
||
" <td>0.037252</td>\n",
|
||
" <td>0.000853</td>\n",
|
||
" <td>1.089395e+08</td>\n",
|
||
" <td>4.853344e+06</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>12</th>\n",
|
||
" <td>0.082789</td>\n",
|
||
" <td>0.003475</td>\n",
|
||
" <td>-3.953191e+09</td>\n",
|
||
" <td>0.000000e+00</td>\n",
|
||
" <td>False</td>\n",
|
||
" <td>2</td>\n",
|
||
" <td>3</td>\n",
|
||
" <td>{'bootstrap': False, 'max_features': 2, 'n_est...</td>\n",
|
||
" <td>17</td>\n",
|
||
" <td>-3.792367e+09</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>-4.050371e+09</td>\n",
|
||
" <td>-0.000000e+00</td>\n",
|
||
" <td>-3.668520e+09</td>\n",
|
||
" <td>-0.000000e+00</td>\n",
|
||
" <td>-4.087237e+09</td>\n",
|
||
" <td>-0.000000e+00</td>\n",
|
||
" <td>0.001605</td>\n",
|
||
" <td>0.000376</td>\n",
|
||
" <td>1.898516e+08</td>\n",
|
||
" <td>0.000000e+00</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>13</th>\n",
|
||
" <td>0.268172</td>\n",
|
||
" <td>0.009459</td>\n",
|
||
" <td>-2.977005e+09</td>\n",
|
||
" <td>0.000000e+00</td>\n",
|
||
" <td>False</td>\n",
|
||
" <td>2</td>\n",
|
||
" <td>10</td>\n",
|
||
" <td>{'bootstrap': False, 'max_features': 2, 'n_est...</td>\n",
|
||
" <td>10</td>\n",
|
||
" <td>-2.780448e+09</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>-3.125519e+09</td>\n",
|
||
" <td>-0.000000e+00</td>\n",
|
||
" <td>-2.788623e+09</td>\n",
|
||
" <td>-0.000000e+00</td>\n",
|
||
" <td>-3.083440e+09</td>\n",
|
||
" <td>-0.000000e+00</td>\n",
|
||
" <td>0.001888</td>\n",
|
||
" <td>0.000476</td>\n",
|
||
" <td>1.577414e+08</td>\n",
|
||
" <td>0.000000e+00</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>14</th>\n",
|
||
" <td>0.111243</td>\n",
|
||
" <td>0.003342</td>\n",
|
||
" <td>-3.530338e+09</td>\n",
|
||
" <td>0.000000e+00</td>\n",
|
||
" <td>False</td>\n",
|
||
" <td>3</td>\n",
|
||
" <td>3</td>\n",
|
||
" <td>{'bootstrap': False, 'max_features': 3, 'n_est...</td>\n",
|
||
" <td>15</td>\n",
|
||
" <td>-3.604830e+09</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>-3.552984e+09</td>\n",
|
||
" <td>-0.000000e+00</td>\n",
|
||
" <td>-3.610963e+09</td>\n",
|
||
" <td>-0.000000e+00</td>\n",
|
||
" <td>-3.443133e+09</td>\n",
|
||
" <td>-0.000000e+00</td>\n",
|
||
" <td>0.002322</td>\n",
|
||
" <td>0.000319</td>\n",
|
||
" <td>7.532297e+07</td>\n",
|
||
" <td>0.000000e+00</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>15</th>\n",
|
||
" <td>0.378554</td>\n",
|
||
" <td>0.010418</td>\n",
|
||
" <td>-2.773101e+09</td>\n",
|
||
" <td>0.000000e+00</td>\n",
|
||
" <td>False</td>\n",
|
||
" <td>3</td>\n",
|
||
" <td>10</td>\n",
|
||
" <td>{'bootstrap': False, 'max_features': 3, 'n_est...</td>\n",
|
||
" <td>7</td>\n",
|
||
" <td>-2.722794e+09</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>-2.831963e+09</td>\n",
|
||
" <td>-0.000000e+00</td>\n",
|
||
" <td>-2.672258e+09</td>\n",
|
||
" <td>-0.000000e+00</td>\n",
|
||
" <td>-2.787588e+09</td>\n",
|
||
" <td>-0.000000e+00</td>\n",
|
||
" <td>0.018147</td>\n",
|
||
" <td>0.001086</td>\n",
|
||
" <td>6.697276e+07</td>\n",
|
||
" <td>0.000000e+00</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>16</th>\n",
|
||
" <td>0.156352</td>\n",
|
||
" <td>0.003608</td>\n",
|
||
" <td>-3.305102e+09</td>\n",
|
||
" <td>0.000000e+00</td>\n",
|
||
" <td>False</td>\n",
|
||
" <td>4</td>\n",
|
||
" <td>3</td>\n",
|
||
" <td>{'bootstrap': False, 'max_features': 4, 'n_est...</td>\n",
|
||
" <td>12</td>\n",
|
||
" <td>-3.143457e+09</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>-3.440323e+09</td>\n",
|
||
" <td>-0.000000e+00</td>\n",
|
||
" <td>-3.047980e+09</td>\n",
|
||
" <td>-0.000000e+00</td>\n",
|
||
" <td>-3.337950e+09</td>\n",
|
||
" <td>-0.000000e+00</td>\n",
|
||
" <td>0.002871</td>\n",
|
||
" <td>0.000310</td>\n",
|
||
" <td>1.867866e+08</td>\n",
|
||
" <td>0.000000e+00</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>17</th>\n",
|
||
" <td>0.496684</td>\n",
|
||
" <td>0.009928</td>\n",
|
||
" <td>-2.610587e+09</td>\n",
|
||
" <td>0.000000e+00</td>\n",
|
||
" <td>False</td>\n",
|
||
" <td>4</td>\n",
|
||
" <td>10</td>\n",
|
||
" <td>{'bootstrap': False, 'max_features': 4, 'n_est...</td>\n",
|
||
" <td>4</td>\n",
|
||
" <td>-2.531436e+09</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>-2.606596e+09</td>\n",
|
||
" <td>-0.000000e+00</td>\n",
|
||
" <td>-2.437626e+09</td>\n",
|
||
" <td>-0.000000e+00</td>\n",
|
||
" <td>-2.726341e+09</td>\n",
|
||
" <td>-0.000000e+00</td>\n",
|
||
" <td>0.020199</td>\n",
|
||
" <td>0.000613</td>\n",
|
||
" <td>1.177181e+08</td>\n",
|
||
" <td>0.000000e+00</td>\n",
|
||
" </tr>\n",
|
||
" </tbody>\n",
|
||
"</table>\n",
|
||
"<p>18 rows × 23 columns</p>\n",
|
||
"</div>"
|
||
],
|
||
"text/plain": [
|
||
" mean_fit_time mean_score_time mean_test_score mean_train_score \\\n",
|
||
"0 0.055782 0.003593 -4.073637e+09 -1.107354e+09 \n",
|
||
"1 0.172177 0.008357 -3.096237e+09 -5.813707e+08 \n",
|
||
"2 0.549370 0.024622 -2.849495e+09 -4.394633e+08 \n",
|
||
"3 0.108659 0.003603 -3.716017e+09 -9.850011e+08 \n",
|
||
"4 0.345844 0.010100 -2.781569e+09 -5.160154e+08 \n",
|
||
"5 0.890049 0.025157 -2.537554e+09 -3.878685e+08 \n",
|
||
"6 0.134878 0.003163 -3.441147e+09 -9.030212e+08 \n",
|
||
"7 0.441237 0.009476 -2.705037e+09 -5.014210e+08 \n",
|
||
"8 1.293729 0.025095 -2.515436e+09 -3.840197e+08 \n",
|
||
"9 0.154177 0.003283 -3.348400e+09 -8.884890e+08 \n",
|
||
"10 0.509834 0.008054 -2.676001e+09 -4.923247e+08 \n",
|
||
"11 1.706564 0.024939 -2.469578e+09 -3.809175e+08 \n",
|
||
"12 0.082789 0.003475 -3.953191e+09 0.000000e+00 \n",
|
||
"13 0.268172 0.009459 -2.977005e+09 0.000000e+00 \n",
|
||
"14 0.111243 0.003342 -3.530338e+09 0.000000e+00 \n",
|
||
"15 0.378554 0.010418 -2.773101e+09 0.000000e+00 \n",
|
||
"16 0.156352 0.003608 -3.305102e+09 0.000000e+00 \n",
|
||
"17 0.496684 0.009928 -2.610587e+09 0.000000e+00 \n",
|
||
"\n",
|
||
" param_bootstrap param_max_features param_n_estimators \\\n",
|
||
"0 NaN 2 3 \n",
|
||
"1 NaN 2 10 \n",
|
||
"2 NaN 2 30 \n",
|
||
"3 NaN 4 3 \n",
|
||
"4 NaN 4 10 \n",
|
||
"5 NaN 4 30 \n",
|
||
"6 NaN 6 3 \n",
|
||
"7 NaN 6 10 \n",
|
||
"8 NaN 6 30 \n",
|
||
"9 NaN 8 3 \n",
|
||
"10 NaN 8 10 \n",
|
||
"11 NaN 8 30 \n",
|
||
"12 False 2 3 \n",
|
||
"13 False 2 10 \n",
|
||
"14 False 3 3 \n",
|
||
"15 False 3 10 \n",
|
||
"16 False 4 3 \n",
|
||
"17 False 4 10 \n",
|
||
"\n",
|
||
" params rank_test_score \\\n",
|
||
"0 {'max_features': 2, 'n_estimators': 3} 18 \n",
|
||
"1 {'max_features': 2, 'n_estimators': 10} 11 \n",
|
||
"2 {'max_features': 2, 'n_estimators': 30} 9 \n",
|
||
"3 {'max_features': 4, 'n_estimators': 3} 16 \n",
|
||
"4 {'max_features': 4, 'n_estimators': 10} 8 \n",
|
||
"5 {'max_features': 4, 'n_estimators': 30} 3 \n",
|
||
"6 {'max_features': 6, 'n_estimators': 3} 14 \n",
|
||
"7 {'max_features': 6, 'n_estimators': 10} 6 \n",
|
||
"8 {'max_features': 6, 'n_estimators': 30} 2 \n",
|
||
"9 {'max_features': 8, 'n_estimators': 3} 13 \n",
|
||
"10 {'max_features': 8, 'n_estimators': 10} 5 \n",
|
||
"11 {'max_features': 8, 'n_estimators': 30} 1 \n",
|
||
"12 {'bootstrap': False, 'max_features': 2, 'n_est... 17 \n",
|
||
"13 {'bootstrap': False, 'max_features': 2, 'n_est... 10 \n",
|
||
"14 {'bootstrap': False, 'max_features': 3, 'n_est... 15 \n",
|
||
"15 {'bootstrap': False, 'max_features': 3, 'n_est... 7 \n",
|
||
"16 {'bootstrap': False, 'max_features': 4, 'n_est... 12 \n",
|
||
"17 {'bootstrap': False, 'max_features': 4, 'n_est... 4 \n",
|
||
"\n",
|
||
" split0_test_score ... split2_test_score split2_train_score \\\n",
|
||
"0 -3.963584e+09 ... -4.194135e+09 -1.116843e+09 \n",
|
||
"1 -3.070368e+09 ... -3.124982e+09 -5.780873e+08 \n",
|
||
"2 -2.697829e+09 ... -2.943808e+09 -4.374429e+08 \n",
|
||
"3 -3.729600e+09 ... -3.736527e+09 -9.172986e+08 \n",
|
||
"4 -2.667093e+09 ... -2.891599e+09 -4.960301e+08 \n",
|
||
"5 -2.387199e+09 ... -2.663178e+09 -3.789712e+08 \n",
|
||
"6 -3.119576e+09 ... -3.587747e+09 -9.360639e+08 \n",
|
||
"7 -2.553481e+09 ... -2.762945e+09 -4.996537e+08 \n",
|
||
"8 -2.371924e+09 ... -2.607962e+09 -3.805596e+08 \n",
|
||
"9 -3.351347e+09 ... -3.396841e+09 -8.596460e+08 \n",
|
||
"10 -2.572358e+09 ... -2.844608e+09 -4.730979e+08 \n",
|
||
"11 -2.358884e+09 ... -2.591134e+09 -3.772512e+08 \n",
|
||
"12 -3.792367e+09 ... -4.050371e+09 -0.000000e+00 \n",
|
||
"13 -2.780448e+09 ... -3.125519e+09 -0.000000e+00 \n",
|
||
"14 -3.604830e+09 ... -3.552984e+09 -0.000000e+00 \n",
|
||
"15 -2.722794e+09 ... -2.831963e+09 -0.000000e+00 \n",
|
||
"16 -3.143457e+09 ... -3.440323e+09 -0.000000e+00 \n",
|
||
"17 -2.531436e+09 ... -2.606596e+09 -0.000000e+00 \n",
|
||
"\n",
|
||
" split3_test_score split3_train_score split4_test_score \\\n",
|
||
"0 -3.906732e+09 -1.112813e+09 -4.169669e+09 \n",
|
||
"1 -2.865117e+09 -5.713421e+08 -3.169914e+09 \n",
|
||
"2 -2.619893e+09 -4.374715e+08 -2.968460e+09 \n",
|
||
"3 -3.404974e+09 -1.035901e+09 -3.914186e+09 \n",
|
||
"4 -2.613393e+09 -5.422542e+08 -2.949550e+09 \n",
|
||
"5 -2.397951e+09 -4.036920e+08 -2.649850e+09 \n",
|
||
"6 -3.331544e+09 -9.025026e+08 -3.577062e+09 \n",
|
||
"7 -2.519522e+09 -4.989516e+08 -2.906270e+09 \n",
|
||
"8 -2.351220e+09 -3.856159e+08 -2.662399e+09 \n",
|
||
"9 -3.131753e+09 -8.893698e+08 -3.509451e+09 \n",
|
||
"10 -2.462797e+09 -5.154156e+08 -2.777049e+09 \n",
|
||
"11 -2.319816e+09 -3.881153e+08 -2.528200e+09 \n",
|
||
"12 -3.668520e+09 -0.000000e+00 -4.087237e+09 \n",
|
||
"13 -2.788623e+09 -0.000000e+00 -3.083440e+09 \n",
|
||
"14 -3.610963e+09 -0.000000e+00 -3.443133e+09 \n",
|
||
"15 -2.672258e+09 -0.000000e+00 -2.787588e+09 \n",
|
||
"16 -3.047980e+09 -0.000000e+00 -3.337950e+09 \n",
|
||
"17 -2.437626e+09 -0.000000e+00 -2.726341e+09 \n",
|
||
"\n",
|
||
" split4_train_score std_fit_time std_score_time std_test_score \\\n",
|
||
"0 -1.129842e+09 0.000631 0.000542 1.160694e+08 \n",
|
||
"1 -5.797944e+08 0.002452 0.000872 1.297819e+08 \n",
|
||
"2 -4.451903e+08 0.031675 0.001817 1.593649e+08 \n",
|
||
"3 -9.711998e+08 0.005413 0.000532 1.690029e+08 \n",
|
||
"4 -5.158794e+08 0.005767 0.001052 1.278498e+08 \n",
|
||
"5 -3.846171e+08 0.040411 0.001709 1.209935e+08 \n",
|
||
"6 -8.612945e+08 0.003447 0.000230 1.884229e+08 \n",
|
||
"7 -5.063617e+08 0.026262 0.001327 1.464963e+08 \n",
|
||
"8 -3.904866e+08 0.043359 0.000991 1.283580e+08 \n",
|
||
"9 -9.146734e+08 0.002721 0.000364 1.226683e+08 \n",
|
||
"10 -4.979127e+08 0.006725 0.000394 1.393253e+08 \n",
|
||
"11 -3.807496e+08 0.037252 0.000853 1.089395e+08 \n",
|
||
"12 -0.000000e+00 0.001605 0.000376 1.898516e+08 \n",
|
||
"13 -0.000000e+00 0.001888 0.000476 1.577414e+08 \n",
|
||
"14 -0.000000e+00 0.002322 0.000319 7.532297e+07 \n",
|
||
"15 -0.000000e+00 0.018147 0.001086 6.697276e+07 \n",
|
||
"16 -0.000000e+00 0.002871 0.000310 1.867866e+08 \n",
|
||
"17 -0.000000e+00 0.020199 0.000613 1.177181e+08 \n",
|
||
"\n",
|
||
" std_train_score \n",
|
||
"0 1.927217e+07 \n",
|
||
"1 6.782553e+06 \n",
|
||
"2 2.961109e+06 \n",
|
||
"3 4.047487e+07 \n",
|
||
"4 1.498960e+07 \n",
|
||
"5 8.424973e+06 \n",
|
||
"6 2.639683e+07 \n",
|
||
"7 3.357661e+06 \n",
|
||
"8 3.796810e+06 \n",
|
||
"9 2.730057e+07 \n",
|
||
"10 1.446900e+07 \n",
|
||
"11 4.853344e+06 \n",
|
||
"12 0.000000e+00 \n",
|
||
"13 0.000000e+00 \n",
|
||
"14 0.000000e+00 \n",
|
||
"15 0.000000e+00 \n",
|
||
"16 0.000000e+00 \n",
|
||
"17 0.000000e+00 \n",
|
||
"\n",
|
||
"[18 rows x 23 columns]"
|
||
]
|
||
},
|
||
"execution_count": 91,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"pd.DataFrame(grid_search.cv_results_)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 92,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"RandomizedSearchCV(cv=5, error_score='raise',\n",
|
||
" estimator=RandomForestRegressor(bootstrap=True, criterion='mse', max_depth=None,\n",
|
||
" max_features='auto', max_leaf_nodes=None,\n",
|
||
" min_impurity_split=1e-07, min_samples_leaf=1,\n",
|
||
" min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
|
||
" n_estimators=10, n_jobs=1, oob_score=False, random_state=42,\n",
|
||
" verbose=0, warm_start=False),\n",
|
||
" fit_params={}, iid=True, n_iter=10, n_jobs=1,\n",
|
||
" param_distributions={'n_estimators': <scipy.stats._distn_infrastructure.rv_frozen object at 0x7ff6886eb978>, 'max_features': <scipy.stats._distn_infrastructure.rv_frozen object at 0x7ff6886eb630>},\n",
|
||
" pre_dispatch='2*n_jobs', random_state=42, refit=True,\n",
|
||
" return_train_score=True, scoring='neg_mean_squared_error',\n",
|
||
" verbose=0)"
|
||
]
|
||
},
|
||
"execution_count": 92,
|
||
"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": 93,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"49147.1524172 {'max_features': 7, 'n_estimators': 180}\n",
|
||
"51396.8768969 {'max_features': 5, 'n_estimators': 15}\n",
|
||
"50798.3025423 {'max_features': 3, 'n_estimators': 72}\n",
|
||
"50840.744514 {'max_features': 5, 'n_estimators': 21}\n",
|
||
"49276.1753033 {'max_features': 7, 'n_estimators': 122}\n",
|
||
"50776.7360494 {'max_features': 3, 'n_estimators': 75}\n",
|
||
"50682.7075546 {'max_features': 3, 'n_estimators': 88}\n",
|
||
"49612.1525305 {'max_features': 5, 'n_estimators': 100}\n",
|
||
"50472.6107336 {'max_features': 3, 'n_estimators': 150}\n",
|
||
"64458.2538503 {'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": "code",
|
||
"execution_count": 94,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"array([ 7.33442355e-02, 6.29090705e-02, 4.11437985e-02,\n",
|
||
" 1.46726854e-02, 1.41064835e-02, 1.48742809e-02,\n",
|
||
" 1.42575993e-02, 3.66158981e-01, 5.64191792e-02,\n",
|
||
" 1.08792957e-01, 5.33510773e-02, 1.03114883e-02,\n",
|
||
" 1.64780994e-01, 6.02803867e-05, 1.96041560e-03,\n",
|
||
" 2.85647464e-03])"
|
||
]
|
||
},
|
||
"execution_count": 94,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"feature_importances = grid_search.best_estimator_.feature_importances_\n",
|
||
"feature_importances"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 95,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"[(0.36615898061813418, 'median_income'),\n",
|
||
" (0.16478099356159051, 'INLAND'),\n",
|
||
" (0.10879295677551573, 'pop_per_hhold'),\n",
|
||
" (0.073344235516012421, 'longitude'),\n",
|
||
" (0.062909070482620302, 'latitude'),\n",
|
||
" (0.056419179181954007, 'rooms_per_hhold'),\n",
|
||
" (0.053351077347675809, 'bedrooms_per_room'),\n",
|
||
" (0.041143798478729635, 'housing_median_age'),\n",
|
||
" (0.014874280890402767, 'population'),\n",
|
||
" (0.014672685420543237, 'total_rooms'),\n",
|
||
" (0.014257599323407807, 'households'),\n",
|
||
" (0.014106483453584102, 'total_bedrooms'),\n",
|
||
" (0.010311488326303787, '<1H OCEAN'),\n",
|
||
" (0.0028564746373201579, 'NEAR OCEAN'),\n",
|
||
" (0.0019604155994780701, 'NEAR BAY'),\n",
|
||
" (6.0280386727365991e-05, 'ISLAND')]"
|
||
]
|
||
},
|
||
"execution_count": 95,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"extra_attribs = [\"rooms_per_hhold\", \"pop_per_hhold\", \"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": "code",
|
||
"execution_count": 96,
|
||
"metadata": {
|
||
"collapsed": true
|
||
},
|
||
"outputs": [],
|
||
"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",
|
||
"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)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 97,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"47766.003966433083"
|
||
]
|
||
},
|
||
"execution_count": 97,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"final_rmse"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"# Extra material"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"## Label Binarizer hack\n",
|
||
"`LabelBinarizer`'s `fit_transform()` method only accepts one parameter `y` (because it was meant for labels, not predictors), so it does not work in a pipeline where the final estimator is a supervised estimator because in this case its `fit()` method takes two parameters `X` and `y`.\n",
|
||
"\n",
|
||
"This hack creates a supervision-friendly `LabelBinarizer`."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 98,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"array([ 210644.60459286, 317768.80697211, 210956.43331178,\n",
|
||
" 59218.98886849, 189747.55849879])"
|
||
]
|
||
},
|
||
"execution_count": 98,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"class SupervisionFriendlyLabelBinarizer(LabelBinarizer):\n",
|
||
" def fit_transform(self, X, y=None):\n",
|
||
" return super(SupervisionFriendlyLabelBinarizer, self).fit_transform(X)\n",
|
||
"\n",
|
||
"# Replace the Labelbinarizer with a SupervisionFriendlyLabelBinarizer\n",
|
||
"cat_pipeline.steps[1] = (\"label_binarizer\", SupervisionFriendlyLabelBinarizer())\n",
|
||
"\n",
|
||
"# Now you can create a full pipeline with a supervised predictor at the end.\n",
|
||
"full_pipeline_with_predictor = Pipeline([\n",
|
||
" (\"preparation\", full_pipeline),\n",
|
||
" (\"linear\", LinearRegression())\n",
|
||
" ])\n",
|
||
"\n",
|
||
"full_pipeline_with_predictor.fit(housing, housing_labels)\n",
|
||
"full_pipeline_with_predictor.predict(some_data)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"## Model persistence using joblib"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 99,
|
||
"metadata": {
|
||
"collapsed": true
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"my_model = full_pipeline_with_predictor"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 100,
|
||
"metadata": {
|
||
"collapsed": true
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"from sklearn.externals import joblib\n",
|
||
"joblib.dump(my_model, \"my_model.pkl\") # DIFF\n",
|
||
"#...\n",
|
||
"my_model_loaded = joblib.load(\"my_model.pkl\") # DIFF"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"## Example SciPy distributions for `RandomizedSearchCV`"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 101,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"image/png": 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hf2B2ZN4scEA3ViPjc2OL1UqSerLcYNgJrB2ZtxbY0Y1lZHxubLFaSVJPlnu66j3A+XMT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\nMEiSGgaDJKlhMEiSGv8fS780gVQE6A4AAAAASUVORK5CYII=\n",
|
||
"text/plain": [
|
||
"<matplotlib.figure.Figure at 0x7ff6677237b8>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
},
|
||
{
|
||
"data": {
|
||
"image/png": 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4BVgAPAlc1nf854GngP2A9wKXJXnlkHOSJG3B0BaQk5wIPAr8BDisbT4ZuK6qbmqP+Svg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\nr8+ZA5SNCb4AAAAASUVORK5CYII=\n",
|
||
"text/plain": [
|
||
"<matplotlib.figure.Figure at 0x7ff6677230f0>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"from scipy.stats import geom, expon\n",
|
||
"geom_distrib=geom(0.5).rvs(10000, random_state=42)\n",
|
||
"expon_distrib=expon(scale=1).rvs(10000, random_state=42)\n",
|
||
"plt.hist(geom_distrib, bins=50)\n",
|
||
"plt.show()\n",
|
||
"plt.hist(expon_distrib, bins=50)\n",
|
||
"plt.show()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"# Exercise solutions"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"## 1."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {
|
||
"collapsed": true
|
||
},
|
||
"source": [
|
||
"Question: Try a Support Vector Machine regressor (`sklearn.svm.SVR`), with various hyperparameters such as `kernel=\"linear\"` (with various values for the `C` hyperparameter) or `kernel=\"rbf\"` (with various values for the `C` and `gamma` hyperparameters). Don't worry about what these hyperparameters mean for now. How does the best `SVR` predictor perform?"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 102,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Fitting 5 folds for each of 50 candidates, totalling 250 fits\n",
|
||
"[CV] C=10.0, kernel=linear ...........................................\n",
|
||
"[CV] C=10.0, kernel=linear ...........................................\n",
|
||
"[CV] C=10.0, kernel=linear ...........................................\n",
|
||
"[CV] C=10.0, kernel=linear ...........................................\n",
|
||
"[CV] ............................ C=10.0, kernel=linear, total= 21.9s\n",
|
||
"[CV] C=10.0, kernel=linear ...........................................\n",
|
||
"[CV] ............................ C=10.0, kernel=linear, total= 22.3s\n",
|
||
"[CV] C=30.0, kernel=linear ...........................................\n",
|
||
"[CV] ............................ C=10.0, kernel=linear, total= 22.0s\n",
|
||
"[CV] C=30.0, kernel=linear ...........................................\n",
|
||
"[CV] ............................ C=10.0, kernel=linear, total= 23.4s\n",
|
||
"[CV] C=30.0, kernel=linear ...........................................\n",
|
||
"[CV] ............................ C=30.0, kernel=linear, total= 22.0s\n",
|
||
"[CV] C=30.0, kernel=linear ...........................................\n",
|
||
"[CV] ............................ C=30.0, kernel=linear, total= 22.2s\n",
|
||
"[CV] C=30.0, kernel=linear ...........................................\n",
|
||
"[CV] ............................ C=10.0, kernel=linear, total= 23.6s\n",
|
||
"[CV] C=100.0, kernel=linear ..........................................\n",
|
||
"[CV] ............................ C=30.0, kernel=linear, total= 21.8s\n",
|
||
"[CV] C=100.0, kernel=linear ..........................................\n",
|
||
"[CV] ............................ C=30.0, kernel=linear, total= 22.7s\n",
|
||
"[CV] C=100.0, kernel=linear ..........................................\n",
|
||
"[CV] ........................... C=100.0, kernel=linear, total= 21.5s\n",
|
||
"[CV] C=100.0, kernel=linear ..........................................\n",
|
||
"[CV] ............................ C=30.0, kernel=linear, total= 21.8s\n",
|
||
"[CV] C=100.0, kernel=linear ..........................................\n",
|
||
"[CV] ........................... C=100.0, kernel=linear, total= 21.7s\n",
|
||
"[CV] C=300.0, kernel=linear ..........................................\n",
|
||
"[CV] ........................... C=100.0, kernel=linear, total= 22.2s\n",
|
||
"[CV] C=300.0, kernel=linear ..........................................\n",
|
||
"[CV] ........................... C=100.0, kernel=linear, total= 22.0s\n",
|
||
"[CV] C=300.0, kernel=linear ..........................................\n",
|
||
"[CV] ........................... C=100.0, kernel=linear, total= 22.2s\n",
|
||
"[CV] C=300.0, kernel=linear ..........................................\n",
|
||
"[CV] ........................... C=300.0, kernel=linear, total= 23.2s\n",
|
||
"[CV] C=300.0, kernel=linear ..........................................\n",
|
||
"[CV] ........................... C=300.0, kernel=linear, total= 23.9s\n",
|
||
"[CV] C=1000.0, kernel=linear .........................................\n",
|
||
"[CV] ........................... C=300.0, kernel=linear, total= 22.8s\n",
|
||
"[CV] C=1000.0, kernel=linear .........................................\n",
|
||
"[CV] ........................... C=300.0, kernel=linear, total= 24.7s\n",
|
||
"[CV] C=1000.0, kernel=linear .........................................\n",
|
||
"[CV] ........................... C=300.0, kernel=linear, total= 23.4s\n",
|
||
"[CV] C=1000.0, kernel=linear .........................................\n",
|
||
"[CV] .......................... C=1000.0, kernel=linear, total= 24.9s\n",
|
||
"[CV] C=1000.0, kernel=linear .........................................\n",
|
||
"[CV] .......................... C=1000.0, kernel=linear, total= 24.4s\n",
|
||
"[CV] C=3000.0, kernel=linear .........................................\n",
|
||
"[CV] .......................... C=1000.0, kernel=linear, total= 24.9s\n",
|
||
"[CV] C=3000.0, kernel=linear .........................................\n",
|
||
"[CV] .......................... C=1000.0, kernel=linear, total= 24.2s\n",
|
||
"[CV] C=3000.0, kernel=linear .........................................\n",
|
||
"[CV] .......................... C=1000.0, kernel=linear, total= 24.4s\n",
|
||
"[CV] C=3000.0, kernel=linear .........................................\n",
|
||
"[CV] .......................... C=3000.0, kernel=linear, total= 26.6s\n",
|
||
"[CV] C=3000.0, kernel=linear .........................................\n",
|
||
"[CV] .......................... C=3000.0, kernel=linear, total= 26.0s\n",
|
||
"[CV] C=10000.0, kernel=linear ........................................\n",
|
||
"[CV] .......................... C=3000.0, kernel=linear, total= 25.3s\n",
|
||
"[CV] C=10000.0, kernel=linear ........................................\n",
|
||
"[CV] .......................... C=3000.0, kernel=linear, total= 27.7s\n",
|
||
"[CV] C=10000.0, kernel=linear ........................................\n",
|
||
"[CV] .......................... C=3000.0, kernel=linear, total= 26.5s\n",
|
||
"[CV] C=10000.0, kernel=linear ........................................\n",
|
||
"[CV] ......................... C=10000.0, kernel=linear, total= 33.9s\n",
|
||
"[CV] C=10000.0, kernel=linear ........................................\n",
|
||
"[CV] ......................... C=10000.0, kernel=linear, total= 35.0s\n",
|
||
"[CV] C=30000.0, kernel=linear ........................................\n",
|
||
"[CV] ......................... C=10000.0, kernel=linear, total= 34.1s\n",
|
||
"[CV] C=30000.0, kernel=linear ........................................\n"
|
||
]
|
||
},
|
||
{
|
||
"name": "stderr",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"[Parallel(n_jobs=4)]: Done 33 tasks | elapsed: 5.1min\n"
|
||
]
|
||
},
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"[CV] ......................... C=10000.0, kernel=linear, total= 32.7s\n",
|
||
"[CV] C=30000.0, kernel=linear ........................................\n",
|
||
"[CV] ......................... C=10000.0, kernel=linear, total= 30.7s\n",
|
||
"[CV] C=30000.0, kernel=linear ........................................\n",
|
||
"[CV] ......................... C=30000.0, kernel=linear, total= 50.2s\n",
|
||
"[CV] C=30000.0, kernel=linear ........................................\n",
|
||
"[CV] ......................... C=30000.0, kernel=linear, total= 57.0s\n",
|
||
"[CV] C=1.0, gamma=0.01, kernel=rbf ...................................\n",
|
||
"[CV] ......................... C=30000.0, kernel=linear, total= 59.7s\n",
|
||
"[CV] C=1.0, gamma=0.01, kernel=rbf ...................................\n",
|
||
"[CV] ......................... C=30000.0, kernel=linear, total= 57.7s\n",
|
||
"[CV] C=1.0, gamma=0.01, kernel=rbf ...................................\n",
|
||
"[CV] ......................... C=30000.0, kernel=linear, total= 53.7s\n",
|
||
"[CV] C=1.0, gamma=0.01, kernel=rbf ...................................\n",
|
||
"[CV] .................... C=1.0, gamma=0.01, kernel=rbf, total= 43.9s\n",
|
||
"[CV] C=1.0, gamma=0.01, kernel=rbf ...................................\n",
|
||
"[CV] .................... C=1.0, gamma=0.01, kernel=rbf, total= 43.7s\n",
|
||
"[CV] C=1.0, gamma=0.03, kernel=rbf ...................................\n",
|
||
"[CV] .................... C=1.0, gamma=0.01, kernel=rbf, total= 41.4s\n",
|
||
"[CV] C=1.0, gamma=0.03, kernel=rbf ...................................\n",
|
||
"[CV] .................... C=1.0, gamma=0.01, kernel=rbf, total= 39.2s\n",
|
||
"[CV] C=1.0, gamma=0.03, kernel=rbf ...................................\n",
|
||
"[CV] .................... C=1.0, gamma=0.01, kernel=rbf, total= 43.0s\n",
|
||
"[CV] C=1.0, gamma=0.03, kernel=rbf ...................................\n",
|
||
"[CV] .................... C=1.0, gamma=0.03, kernel=rbf, total= 43.8s\n",
|
||
"[CV] C=1.0, gamma=0.03, kernel=rbf ...................................\n",
|
||
"[CV] .................... C=1.0, gamma=0.03, kernel=rbf, total= 42.0s\n",
|
||
"[CV] C=1.0, gamma=0.1, kernel=rbf ....................................\n",
|
||
"[CV] .................... C=1.0, gamma=0.03, kernel=rbf, total= 43.4s\n",
|
||
"[CV] C=1.0, gamma=0.1, kernel=rbf ....................................\n",
|
||
"[CV] .................... C=1.0, gamma=0.03, kernel=rbf, total= 43.9s\n",
|
||
"[CV] C=1.0, gamma=0.1, kernel=rbf ....................................\n",
|
||
"[CV] .................... C=1.0, gamma=0.03, kernel=rbf, total= 39.4s\n",
|
||
"[CV] C=1.0, gamma=0.1, kernel=rbf ....................................\n",
|
||
"[CV] ..................... C=1.0, gamma=0.1, kernel=rbf, total= 39.6s\n",
|
||
"[CV] C=1.0, gamma=0.1, kernel=rbf ....................................\n",
|
||
"[CV] ..................... C=1.0, gamma=0.1, kernel=rbf, total= 43.2s\n",
|
||
"[CV] C=1.0, gamma=0.3, kernel=rbf ....................................\n",
|
||
"[CV] ..................... C=1.0, gamma=0.1, kernel=rbf, total= 42.2s\n",
|
||
"[CV] C=1.0, gamma=0.3, kernel=rbf ....................................\n",
|
||
"[CV] ..................... C=1.0, gamma=0.1, kernel=rbf, total= 41.6s\n",
|
||
"[CV] C=1.0, gamma=0.3, kernel=rbf ....................................\n",
|
||
"[CV] ..................... C=1.0, gamma=0.1, kernel=rbf, total= 42.5s\n",
|
||
"[CV] C=1.0, gamma=0.3, kernel=rbf ....................................\n",
|
||
"[CV] ..................... C=1.0, gamma=0.3, kernel=rbf, total= 40.1s\n",
|
||
"[CV] C=1.0, gamma=0.3, kernel=rbf ....................................\n",
|
||
"[CV] ..................... C=1.0, gamma=0.3, kernel=rbf, total= 42.1s\n",
|
||
"[CV] C=1.0, gamma=1.0, kernel=rbf ....................................\n",
|
||
"[CV] ..................... C=1.0, gamma=0.3, kernel=rbf, total= 38.9s\n",
|
||
"[CV] C=1.0, gamma=1.0, kernel=rbf ....................................\n",
|
||
"[CV] ..................... C=1.0, gamma=0.3, kernel=rbf, total= 41.9s\n",
|
||
"[CV] C=1.0, gamma=1.0, kernel=rbf ....................................\n",
|
||
"[CV] ..................... C=1.0, gamma=0.3, kernel=rbf, total= 39.0s\n",
|
||
"[CV] C=1.0, gamma=1.0, kernel=rbf ....................................\n",
|
||
"[CV] ..................... C=1.0, gamma=1.0, kernel=rbf, total= 36.4s\n",
|
||
"[CV] C=1.0, gamma=1.0, kernel=rbf ....................................\n",
|
||
"[CV] ..................... C=1.0, gamma=1.0, kernel=rbf, total= 34.9s\n",
|
||
"[CV] C=1.0, gamma=3.0, kernel=rbf ....................................\n",
|
||
"[CV] ..................... C=1.0, gamma=1.0, kernel=rbf, total= 37.6s\n",
|
||
"[CV] C=1.0, gamma=3.0, kernel=rbf ....................................\n",
|
||
"[CV] ..................... C=1.0, gamma=1.0, kernel=rbf, total= 38.2s\n",
|
||
"[CV] C=1.0, gamma=3.0, kernel=rbf ....................................\n",
|
||
"[CV] ..................... C=1.0, gamma=1.0, kernel=rbf, total= 38.0s\n",
|
||
"[CV] C=1.0, gamma=3.0, kernel=rbf ....................................\n",
|
||
"[CV] ..................... C=1.0, gamma=3.0, kernel=rbf, total= 37.9s\n",
|
||
"[CV] C=1.0, gamma=3.0, kernel=rbf ....................................\n",
|
||
"[CV] ..................... C=1.0, gamma=3.0, kernel=rbf, total= 39.5s\n",
|
||
"[CV] C=3.0, gamma=0.01, kernel=rbf ...................................\n",
|
||
"[CV] ..................... C=1.0, gamma=3.0, kernel=rbf, total= 40.0s\n",
|
||
"[CV] C=3.0, gamma=0.01, kernel=rbf ...................................\n",
|
||
"[CV] ..................... C=1.0, gamma=3.0, kernel=rbf, total= 40.4s\n",
|
||
"[CV] C=3.0, gamma=0.01, kernel=rbf ...................................\n",
|
||
"[CV] ..................... C=1.0, gamma=3.0, kernel=rbf, total= 40.0s\n",
|
||
"[CV] C=3.0, gamma=0.01, kernel=rbf ...................................\n",
|
||
"[CV] .................... C=3.0, gamma=0.01, kernel=rbf, total= 40.4s\n",
|
||
"[CV] C=3.0, gamma=0.01, kernel=rbf ...................................\n",
|
||
"[CV] .................... C=3.0, gamma=0.01, kernel=rbf, total= 44.0s\n",
|
||
"[CV] C=3.0, gamma=0.03, kernel=rbf ...................................\n",
|
||
"[CV] .................... C=3.0, gamma=0.01, kernel=rbf, total= 41.1s\n",
|
||
"[CV] C=3.0, gamma=0.03, kernel=rbf ...................................\n",
|
||
"[CV] .................... C=3.0, gamma=0.01, kernel=rbf, total= 41.4s\n",
|
||
"[CV] C=3.0, gamma=0.03, kernel=rbf ...................................\n",
|
||
"[CV] .................... C=3.0, gamma=0.01, kernel=rbf, total= 40.3s\n",
|
||
"[CV] C=3.0, gamma=0.03, kernel=rbf ...................................\n",
|
||
"[CV] .................... C=3.0, gamma=0.03, kernel=rbf, total= 41.0s\n",
|
||
"[CV] C=3.0, gamma=0.03, kernel=rbf ...................................\n",
|
||
"[CV] .................... C=3.0, gamma=0.03, kernel=rbf, total= 39.3s\n",
|
||
"[CV] C=3.0, gamma=0.1, kernel=rbf ....................................\n",
|
||
"[CV] .................... C=3.0, gamma=0.03, kernel=rbf, total= 37.3s\n",
|
||
"[CV] C=3.0, gamma=0.1, kernel=rbf ....................................\n",
|
||
"[CV] .................... C=3.0, gamma=0.03, kernel=rbf, total= 39.4s\n",
|
||
"[CV] C=3.0, gamma=0.1, kernel=rbf ....................................\n",
|
||
"[CV] .................... C=3.0, gamma=0.03, kernel=rbf, total= 42.3s\n",
|
||
"[CV] C=3.0, gamma=0.1, kernel=rbf ....................................\n",
|
||
"[CV] ..................... C=3.0, gamma=0.1, kernel=rbf, total= 42.1s\n",
|
||
"[CV] C=3.0, gamma=0.1, kernel=rbf ....................................\n",
|
||
"[CV] ..................... C=3.0, gamma=0.1, kernel=rbf, total= 40.8s\n",
|
||
"[CV] C=3.0, gamma=0.3, kernel=rbf ....................................\n",
|
||
"[CV] ..................... C=3.0, gamma=0.1, kernel=rbf, total= 41.4s\n",
|
||
"[CV] C=3.0, gamma=0.3, kernel=rbf ....................................\n",
|
||
"[CV] ..................... C=3.0, gamma=0.1, kernel=rbf, total= 44.7s\n",
|
||
"[CV] C=3.0, gamma=0.3, kernel=rbf ....................................\n",
|
||
"[CV] ..................... C=3.0, gamma=0.1, kernel=rbf, total= 46.9s\n",
|
||
"[CV] C=3.0, gamma=0.3, kernel=rbf ....................................\n",
|
||
"[CV] ..................... C=3.0, gamma=0.3, kernel=rbf, total= 44.2s\n",
|
||
"[CV] C=3.0, gamma=0.3, kernel=rbf ....................................\n",
|
||
"[CV] ..................... C=3.0, gamma=0.3, kernel=rbf, total= 42.8s\n",
|
||
"[CV] C=3.0, gamma=1.0, kernel=rbf ....................................\n",
|
||
"[CV] ..................... C=3.0, gamma=0.3, kernel=rbf, total= 41.0s\n",
|
||
"[CV] C=3.0, gamma=1.0, kernel=rbf ....................................\n",
|
||
"[CV] ..................... C=3.0, gamma=0.3, kernel=rbf, total= 42.3s\n",
|
||
"[CV] C=3.0, gamma=1.0, kernel=rbf ....................................\n",
|
||
"[CV] ..................... C=3.0, gamma=0.3, kernel=rbf, total= 41.1s\n",
|
||
"[CV] C=3.0, gamma=1.0, kernel=rbf ....................................\n",
|
||
"[CV] ..................... C=3.0, gamma=1.0, kernel=rbf, total= 40.4s\n",
|
||
"[CV] C=3.0, gamma=1.0, kernel=rbf ....................................\n",
|
||
"[CV] ..................... C=3.0, gamma=1.0, kernel=rbf, total= 40.1s\n",
|
||
"[CV] C=3.0, gamma=3.0, kernel=rbf ....................................\n",
|
||
"[CV] ..................... C=3.0, gamma=1.0, kernel=rbf, total= 42.1s\n",
|
||
"[CV] C=3.0, gamma=3.0, kernel=rbf ....................................\n",
|
||
"[CV] ..................... C=3.0, gamma=1.0, kernel=rbf, total= 40.2s\n",
|
||
"[CV] C=3.0, gamma=3.0, kernel=rbf ....................................\n",
|
||
"[CV] ..................... C=3.0, gamma=1.0, kernel=rbf, total= 42.8s\n",
|
||
"[CV] C=3.0, gamma=3.0, kernel=rbf ....................................\n",
|
||
"[CV] ..................... C=3.0, gamma=3.0, kernel=rbf, total= 40.1s\n",
|
||
"[CV] C=3.0, gamma=3.0, kernel=rbf ....................................\n",
|
||
"[CV] ..................... C=3.0, gamma=3.0, kernel=rbf, total= 41.0s\n",
|
||
"[CV] C=10.0, gamma=0.01, kernel=rbf ..................................\n",
|
||
"[CV] ..................... C=3.0, gamma=3.0, kernel=rbf, total= 41.6s\n",
|
||
"[CV] C=10.0, gamma=0.01, kernel=rbf ..................................\n",
|
||
"[CV] ..................... C=3.0, gamma=3.0, kernel=rbf, total= 39.5s\n",
|
||
"[CV] C=10.0, gamma=0.01, kernel=rbf ..................................\n",
|
||
"[CV] ..................... C=3.0, gamma=3.0, kernel=rbf, total= 42.5s\n",
|
||
"[CV] C=10.0, gamma=0.01, kernel=rbf ..................................\n",
|
||
"[CV] ................... C=10.0, gamma=0.01, kernel=rbf, total= 43.3s\n",
|
||
"[CV] C=10.0, gamma=0.01, kernel=rbf ..................................\n",
|
||
"[CV] ................... C=10.0, gamma=0.01, kernel=rbf, total= 42.1s\n",
|
||
"[CV] C=10.0, gamma=0.03, kernel=rbf ..................................\n",
|
||
"[CV] ................... C=10.0, gamma=0.01, kernel=rbf, total= 40.8s\n",
|
||
"[CV] C=10.0, gamma=0.03, kernel=rbf ..................................\n",
|
||
"[CV] ................... C=10.0, gamma=0.01, kernel=rbf, total= 41.6s\n",
|
||
"[CV] C=10.0, gamma=0.03, kernel=rbf ..................................\n",
|
||
"[CV] ................... C=10.0, gamma=0.03, kernel=rbf, total= 42.4s\n",
|
||
"[CV] C=10.0, gamma=0.03, kernel=rbf ..................................\n",
|
||
"[CV] ................... C=10.0, gamma=0.01, kernel=rbf, total= 45.3s\n",
|
||
"[CV] C=10.0, gamma=0.03, kernel=rbf ..................................\n",
|
||
"[CV] ................... C=10.0, gamma=0.03, kernel=rbf, total= 46.9s\n",
|
||
"[CV] C=10.0, gamma=0.1, kernel=rbf ...................................\n",
|
||
"[CV] ................... C=10.0, gamma=0.03, kernel=rbf, total= 43.2s\n",
|
||
"[CV] C=10.0, gamma=0.1, kernel=rbf ...................................\n",
|
||
"[CV] ................... C=10.0, gamma=0.03, kernel=rbf, total= 40.6s\n",
|
||
"[CV] C=10.0, gamma=0.1, kernel=rbf ...................................\n",
|
||
"[CV] ................... C=10.0, gamma=0.03, kernel=rbf, total= 41.4s\n",
|
||
"[CV] C=10.0, gamma=0.1, kernel=rbf ...................................\n",
|
||
"[CV] .................... C=10.0, gamma=0.1, kernel=rbf, total= 40.4s\n",
|
||
"[CV] C=10.0, gamma=0.1, kernel=rbf ...................................\n",
|
||
"[CV] .................... C=10.0, gamma=0.1, kernel=rbf, total= 42.4s\n",
|
||
"[CV] C=10.0, gamma=0.3, kernel=rbf ...................................\n",
|
||
"[CV] .................... C=10.0, gamma=0.1, kernel=rbf, total= 41.8s\n",
|
||
"[CV] C=10.0, gamma=0.3, kernel=rbf ...................................\n",
|
||
"[CV] .................... C=10.0, gamma=0.1, kernel=rbf, total= 41.5s\n",
|
||
"[CV] C=10.0, gamma=0.3, kernel=rbf ...................................\n",
|
||
"[CV] .................... C=10.0, gamma=0.1, kernel=rbf, total= 40.9s\n",
|
||
"[CV] C=10.0, gamma=0.3, kernel=rbf ...................................\n",
|
||
"[CV] .................... C=10.0, gamma=0.3, kernel=rbf, total= 40.5s\n",
|
||
"[CV] C=10.0, gamma=0.3, kernel=rbf ...................................\n",
|
||
"[CV] .................... C=10.0, gamma=0.3, kernel=rbf, total= 39.2s\n",
|
||
"[CV] C=10.0, gamma=1.0, kernel=rbf ...................................\n",
|
||
"[CV] .................... C=10.0, gamma=0.3, kernel=rbf, total= 41.0s\n",
|
||
"[CV] C=10.0, gamma=1.0, kernel=rbf ...................................\n",
|
||
"[CV] .................... C=10.0, gamma=0.3, kernel=rbf, total= 40.8s\n",
|
||
"[CV] C=10.0, gamma=1.0, kernel=rbf ...................................\n",
|
||
"[CV] .................... C=10.0, gamma=0.3, kernel=rbf, total= 40.0s\n",
|
||
"[CV] C=10.0, gamma=1.0, kernel=rbf ...................................\n",
|
||
"[CV] .................... C=10.0, gamma=1.0, kernel=rbf, total= 36.0s\n",
|
||
"[CV] C=10.0, gamma=1.0, kernel=rbf ...................................\n",
|
||
"[CV] .................... C=10.0, gamma=1.0, kernel=rbf, total= 37.8s\n",
|
||
"[CV] C=10.0, gamma=3.0, kernel=rbf ...................................\n",
|
||
"[CV] .................... C=10.0, gamma=1.0, kernel=rbf, total= 39.7s\n",
|
||
"[CV] C=10.0, gamma=3.0, kernel=rbf ...................................\n",
|
||
"[CV] .................... C=10.0, gamma=1.0, kernel=rbf, total= 39.0s\n",
|
||
"[CV] C=10.0, gamma=3.0, kernel=rbf ...................................\n",
|
||
"[CV] .................... C=10.0, gamma=1.0, kernel=rbf, total= 38.8s\n",
|
||
"[CV] C=10.0, gamma=3.0, kernel=rbf ...................................\n",
|
||
"[CV] .................... C=10.0, gamma=3.0, kernel=rbf, total= 40.2s\n",
|
||
"[CV] C=10.0, gamma=3.0, kernel=rbf ...................................\n",
|
||
"[CV] .................... C=10.0, gamma=3.0, kernel=rbf, total= 40.1s\n",
|
||
"[CV] C=30.0, gamma=0.01, kernel=rbf ..................................\n",
|
||
"[CV] .................... C=10.0, gamma=3.0, kernel=rbf, total= 41.1s\n",
|
||
"[CV] C=30.0, gamma=0.01, kernel=rbf ..................................\n",
|
||
"[CV] .................... C=10.0, gamma=3.0, kernel=rbf, total= 41.4s\n",
|
||
"[CV] C=30.0, gamma=0.01, kernel=rbf ..................................\n",
|
||
"[CV] .................... C=10.0, gamma=3.0, kernel=rbf, total= 42.1s\n",
|
||
"[CV] C=30.0, gamma=0.01, kernel=rbf ..................................\n",
|
||
"[CV] ................... C=30.0, gamma=0.01, kernel=rbf, total= 45.9s\n",
|
||
"[CV] C=30.0, gamma=0.01, kernel=rbf ..................................\n",
|
||
"[CV] ................... C=30.0, gamma=0.01, kernel=rbf, total= 46.2s\n",
|
||
"[CV] C=30.0, gamma=0.03, kernel=rbf ..................................\n",
|
||
"[CV] ................... C=30.0, gamma=0.01, kernel=rbf, total= 44.7s\n",
|
||
"[CV] C=30.0, gamma=0.03, kernel=rbf ..................................\n",
|
||
"[CV] ................... C=30.0, gamma=0.01, kernel=rbf, total= 45.8s\n",
|
||
"[CV] C=30.0, gamma=0.03, kernel=rbf ..................................\n",
|
||
"[CV] ................... C=30.0, gamma=0.01, kernel=rbf, total= 42.9s\n",
|
||
"[CV] C=30.0, gamma=0.03, kernel=rbf ..................................\n",
|
||
"[CV] ................... C=30.0, gamma=0.03, kernel=rbf, total= 44.1s\n",
|
||
"[CV] C=30.0, gamma=0.03, kernel=rbf ..................................\n",
|
||
"[CV] ................... C=30.0, gamma=0.03, kernel=rbf, total= 45.1s\n",
|
||
"[CV] C=30.0, gamma=0.1, kernel=rbf ...................................\n",
|
||
"[CV] ................... C=30.0, gamma=0.03, kernel=rbf, total= 42.5s\n",
|
||
"[CV] C=30.0, gamma=0.1, kernel=rbf ...................................\n",
|
||
"[CV] ................... C=30.0, gamma=0.03, kernel=rbf, total= 42.7s\n",
|
||
"[CV] C=30.0, gamma=0.1, kernel=rbf ...................................\n",
|
||
"[CV] ................... C=30.0, gamma=0.03, kernel=rbf, total= 40.1s\n",
|
||
"[CV] C=30.0, gamma=0.1, kernel=rbf ...................................\n",
|
||
"[CV] .................... C=30.0, gamma=0.1, kernel=rbf, total= 41.2s\n",
|
||
"[CV] C=30.0, gamma=0.1, kernel=rbf ...................................\n",
|
||
"[CV] .................... C=30.0, gamma=0.1, kernel=rbf, total= 41.9s\n",
|
||
"[CV] C=30.0, gamma=0.3, kernel=rbf ...................................\n",
|
||
"[CV] .................... C=30.0, gamma=0.1, kernel=rbf, total= 43.9s\n",
|
||
"[CV] C=30.0, gamma=0.3, kernel=rbf ...................................\n",
|
||
"[CV] .................... C=30.0, gamma=0.1, kernel=rbf, total= 43.9s\n",
|
||
"[CV] C=30.0, gamma=0.3, kernel=rbf ...................................\n",
|
||
"[CV] .................... C=30.0, gamma=0.1, kernel=rbf, total= 43.6s\n",
|
||
"[CV] C=30.0, gamma=0.3, kernel=rbf ...................................\n",
|
||
"[CV] .................... C=30.0, gamma=0.3, kernel=rbf, total= 42.2s\n",
|
||
"[CV] C=30.0, gamma=0.3, kernel=rbf ...................................\n",
|
||
"[CV] .................... C=30.0, gamma=0.3, kernel=rbf, total= 43.7s\n",
|
||
"[CV] C=30.0, gamma=1.0, kernel=rbf ...................................\n",
|
||
"[CV] .................... C=30.0, gamma=0.3, kernel=rbf, total= 35.6s\n",
|
||
"[CV] C=30.0, gamma=1.0, kernel=rbf ...................................\n",
|
||
"[CV] .................... C=30.0, gamma=0.3, kernel=rbf, total= 38.7s\n",
|
||
"[CV] C=30.0, gamma=1.0, kernel=rbf ...................................\n",
|
||
"[CV] .................... C=30.0, gamma=0.3, kernel=rbf, total= 36.5s\n",
|
||
"[CV] C=30.0, gamma=1.0, kernel=rbf ...................................\n",
|
||
"[CV] .................... C=30.0, gamma=1.0, kernel=rbf, total= 37.3s\n",
|
||
"[CV] C=30.0, gamma=1.0, kernel=rbf ...................................\n",
|
||
"[CV] .................... C=30.0, gamma=1.0, kernel=rbf, total= 40.7s\n",
|
||
"[CV] C=30.0, gamma=3.0, kernel=rbf ...................................\n",
|
||
"[CV] .................... C=30.0, gamma=1.0, kernel=rbf, total= 40.4s\n",
|
||
"[CV] C=30.0, gamma=3.0, kernel=rbf ...................................\n",
|
||
"[CV] .................... C=30.0, gamma=1.0, kernel=rbf, total= 42.2s\n",
|
||
"[CV] C=30.0, gamma=3.0, kernel=rbf ...................................\n"
|
||
]
|
||
},
|
||
{
|
||
"name": "stderr",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"[Parallel(n_jobs=4)]: Done 154 tasks | elapsed: 36.7min\n"
|
||
]
|
||
},
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"[CV] .................... C=30.0, gamma=1.0, kernel=rbf, total= 37.2s\n",
|
||
"[CV] C=30.0, gamma=3.0, kernel=rbf ...................................\n",
|
||
"[CV] .................... C=30.0, gamma=3.0, kernel=rbf, total= 39.7s\n",
|
||
"[CV] C=30.0, gamma=3.0, kernel=rbf ...................................\n",
|
||
"[CV] .................... C=30.0, gamma=3.0, kernel=rbf, total= 41.1s\n",
|
||
"[CV] C=100.0, gamma=0.01, kernel=rbf .................................\n",
|
||
"[CV] .................... C=30.0, gamma=3.0, kernel=rbf, total= 38.1s\n",
|
||
"[CV] C=100.0, gamma=0.01, kernel=rbf .................................\n",
|
||
"[CV] .................... C=30.0, gamma=3.0, kernel=rbf, total= 40.5s\n",
|
||
"[CV] C=100.0, gamma=0.01, kernel=rbf .................................\n",
|
||
"[CV] .................... C=30.0, gamma=3.0, kernel=rbf, total= 42.5s\n",
|
||
"[CV] C=100.0, gamma=0.01, kernel=rbf .................................\n",
|
||
"[CV] .................. C=100.0, gamma=0.01, kernel=rbf, total= 40.4s\n",
|
||
"[CV] C=100.0, gamma=0.01, kernel=rbf .................................\n",
|
||
"[CV] .................. C=100.0, gamma=0.01, kernel=rbf, total= 40.4s\n",
|
||
"[CV] C=100.0, gamma=0.03, kernel=rbf .................................\n",
|
||
"[CV] .................. C=100.0, gamma=0.01, kernel=rbf, total= 40.2s\n",
|
||
"[CV] C=100.0, gamma=0.03, kernel=rbf .................................\n",
|
||
"[CV] .................. C=100.0, gamma=0.01, kernel=rbf, total= 45.0s\n",
|
||
"[CV] C=100.0, gamma=0.03, kernel=rbf .................................\n",
|
||
"[CV] .................. C=100.0, gamma=0.01, kernel=rbf, total= 41.0s\n",
|
||
"[CV] C=100.0, gamma=0.03, kernel=rbf .................................\n",
|
||
"[CV] .................. C=100.0, gamma=0.03, kernel=rbf, total= 42.8s\n",
|
||
"[CV] C=100.0, gamma=0.03, kernel=rbf .................................\n",
|
||
"[CV] .................. C=100.0, gamma=0.03, kernel=rbf, total= 40.7s\n",
|
||
"[CV] C=100.0, gamma=0.1, kernel=rbf ..................................\n",
|
||
"[CV] .................. C=100.0, gamma=0.03, kernel=rbf, total= 44.8s\n",
|
||
"[CV] C=100.0, gamma=0.1, kernel=rbf ..................................\n",
|
||
"[CV] .................. C=100.0, gamma=0.03, kernel=rbf, total= 43.8s\n",
|
||
"[CV] C=100.0, gamma=0.1, kernel=rbf ..................................\n",
|
||
"[CV] .................. C=100.0, gamma=0.03, kernel=rbf, total= 41.0s\n",
|
||
"[CV] C=100.0, gamma=0.1, kernel=rbf ..................................\n",
|
||
"[CV] ................... C=100.0, gamma=0.1, kernel=rbf, total= 41.9s\n",
|
||
"[CV] C=100.0, gamma=0.1, kernel=rbf ..................................\n",
|
||
"[CV] ................... C=100.0, gamma=0.1, kernel=rbf, total= 38.6s\n",
|
||
"[CV] C=100.0, gamma=0.3, kernel=rbf ..................................\n",
|
||
"[CV] ................... C=100.0, gamma=0.1, kernel=rbf, total= 37.7s\n",
|
||
"[CV] C=100.0, gamma=0.3, kernel=rbf ..................................\n",
|
||
"[CV] ................... C=100.0, gamma=0.1, kernel=rbf, total= 37.8s\n",
|
||
"[CV] C=100.0, gamma=0.3, kernel=rbf ..................................\n",
|
||
"[CV] ................... C=100.0, gamma=0.1, kernel=rbf, total= 40.2s\n",
|
||
"[CV] C=100.0, gamma=0.3, kernel=rbf ..................................\n",
|
||
"[CV] ................... C=100.0, gamma=0.3, kernel=rbf, total= 39.1s\n",
|
||
"[CV] C=100.0, gamma=0.3, kernel=rbf ..................................\n",
|
||
"[CV] ................... C=100.0, gamma=0.3, kernel=rbf, total= 37.0s\n",
|
||
"[CV] C=100.0, gamma=1.0, kernel=rbf ..................................\n",
|
||
"[CV] ................... C=100.0, gamma=0.3, kernel=rbf, total= 38.4s\n",
|
||
"[CV] C=100.0, gamma=1.0, kernel=rbf ..................................\n",
|
||
"[CV] ................... C=100.0, gamma=0.3, kernel=rbf, total= 40.2s\n",
|
||
"[CV] C=100.0, gamma=1.0, kernel=rbf ..................................\n",
|
||
"[CV] ................... C=100.0, gamma=0.3, kernel=rbf, total= 39.5s\n",
|
||
"[CV] C=100.0, gamma=1.0, kernel=rbf ..................................\n",
|
||
"[CV] ................... C=100.0, gamma=1.0, kernel=rbf, total= 38.3s\n",
|
||
"[CV] C=100.0, gamma=1.0, kernel=rbf ..................................\n",
|
||
"[CV] ................... C=100.0, gamma=1.0, kernel=rbf, total= 37.1s\n",
|
||
"[CV] C=100.0, gamma=3.0, kernel=rbf ..................................\n",
|
||
"[CV] ................... C=100.0, gamma=1.0, kernel=rbf, total= 37.8s\n",
|
||
"[CV] C=100.0, gamma=3.0, kernel=rbf ..................................\n",
|
||
"[CV] ................... C=100.0, gamma=1.0, kernel=rbf, total= 37.8s\n",
|
||
"[CV] C=100.0, gamma=3.0, kernel=rbf ..................................\n",
|
||
"[CV] ................... C=100.0, gamma=1.0, kernel=rbf, total= 36.0s\n",
|
||
"[CV] C=100.0, gamma=3.0, kernel=rbf ..................................\n",
|
||
"[CV] ................... C=100.0, gamma=3.0, kernel=rbf, total= 38.4s\n",
|
||
"[CV] C=100.0, gamma=3.0, kernel=rbf ..................................\n",
|
||
"[CV] ................... C=100.0, gamma=3.0, kernel=rbf, total= 37.9s\n",
|
||
"[CV] C=300.0, gamma=0.01, kernel=rbf .................................\n",
|
||
"[CV] ................... C=100.0, gamma=3.0, kernel=rbf, total= 36.1s\n",
|
||
"[CV] C=300.0, gamma=0.01, kernel=rbf .................................\n",
|
||
"[CV] ................... C=100.0, gamma=3.0, kernel=rbf, total= 38.5s\n",
|
||
"[CV] C=300.0, gamma=0.01, kernel=rbf .................................\n",
|
||
"[CV] ................... C=100.0, gamma=3.0, kernel=rbf, total= 40.5s\n",
|
||
"[CV] C=300.0, gamma=0.01, kernel=rbf .................................\n",
|
||
"[CV] .................. C=300.0, gamma=0.01, kernel=rbf, total= 38.5s\n",
|
||
"[CV] C=300.0, gamma=0.01, kernel=rbf .................................\n",
|
||
"[CV] .................. C=300.0, gamma=0.01, kernel=rbf, total= 41.6s\n",
|
||
"[CV] C=300.0, gamma=0.03, kernel=rbf .................................\n",
|
||
"[CV] .................. C=300.0, gamma=0.01, kernel=rbf, total= 39.0s\n",
|
||
"[CV] C=300.0, gamma=0.03, kernel=rbf .................................\n",
|
||
"[CV] .................. C=300.0, gamma=0.01, kernel=rbf, total= 39.4s\n",
|
||
"[CV] C=300.0, gamma=0.03, kernel=rbf .................................\n",
|
||
"[CV] .................. C=300.0, gamma=0.01, kernel=rbf, total= 40.9s\n",
|
||
"[CV] C=300.0, gamma=0.03, kernel=rbf .................................\n",
|
||
"[CV] .................. C=300.0, gamma=0.03, kernel=rbf, total= 38.7s\n",
|
||
"[CV] C=300.0, gamma=0.03, kernel=rbf .................................\n",
|
||
"[CV] .................. C=300.0, gamma=0.03, kernel=rbf, total= 39.2s\n",
|
||
"[CV] C=300.0, gamma=0.1, kernel=rbf ..................................\n",
|
||
"[CV] .................. C=300.0, gamma=0.03, kernel=rbf, total= 39.5s\n",
|
||
"[CV] C=300.0, gamma=0.1, kernel=rbf ..................................\n",
|
||
"[CV] .................. C=300.0, gamma=0.03, kernel=rbf, total= 39.5s\n",
|
||
"[CV] C=300.0, gamma=0.1, kernel=rbf ..................................\n",
|
||
"[CV] .................. C=300.0, gamma=0.03, kernel=rbf, total= 37.3s\n",
|
||
"[CV] C=300.0, gamma=0.1, kernel=rbf ..................................\n",
|
||
"[CV] ................... C=300.0, gamma=0.1, kernel=rbf, total= 36.8s\n",
|
||
"[CV] C=300.0, gamma=0.1, kernel=rbf ..................................\n",
|
||
"[CV] ................... C=300.0, gamma=0.1, kernel=rbf, total= 37.5s\n",
|
||
"[CV] C=300.0, gamma=0.3, kernel=rbf ..................................\n",
|
||
"[CV] ................... C=300.0, gamma=0.1, kernel=rbf, total= 37.3s\n",
|
||
"[CV] C=300.0, gamma=0.3, kernel=rbf ..................................\n",
|
||
"[CV] ................... C=300.0, gamma=0.1, kernel=rbf, total= 39.2s\n",
|
||
"[CV] C=300.0, gamma=0.3, kernel=rbf ..................................\n",
|
||
"[CV] ................... C=300.0, gamma=0.1, kernel=rbf, total= 37.2s\n",
|
||
"[CV] C=300.0, gamma=0.3, kernel=rbf ..................................\n",
|
||
"[CV] ................... C=300.0, gamma=0.3, kernel=rbf, total= 35.2s\n",
|
||
"[CV] C=300.0, gamma=0.3, kernel=rbf ..................................\n",
|
||
"[CV] ................... C=300.0, gamma=0.3, kernel=rbf, total= 39.8s\n",
|
||
"[CV] C=300.0, gamma=1.0, kernel=rbf ..................................\n",
|
||
"[CV] ................... C=300.0, gamma=0.3, kernel=rbf, total= 34.5s\n",
|
||
"[CV] C=300.0, gamma=1.0, kernel=rbf ..................................\n",
|
||
"[CV] ................... C=300.0, gamma=0.3, kernel=rbf, total= 35.2s\n",
|
||
"[CV] C=300.0, gamma=1.0, kernel=rbf ..................................\n",
|
||
"[CV] ................... C=300.0, gamma=0.3, kernel=rbf, total= 37.8s\n",
|
||
"[CV] C=300.0, gamma=1.0, kernel=rbf ..................................\n",
|
||
"[CV] ................... C=300.0, gamma=1.0, kernel=rbf, total= 35.0s\n",
|
||
"[CV] C=300.0, gamma=1.0, kernel=rbf ..................................\n",
|
||
"[CV] ................... C=300.0, gamma=1.0, kernel=rbf, total= 35.5s\n",
|
||
"[CV] C=300.0, gamma=3.0, kernel=rbf ..................................\n",
|
||
"[CV] ................... C=300.0, gamma=1.0, kernel=rbf, total= 35.3s\n",
|
||
"[CV] C=300.0, gamma=3.0, kernel=rbf ..................................\n",
|
||
"[CV] ................... C=300.0, gamma=1.0, kernel=rbf, total= 35.3s\n",
|
||
"[CV] C=300.0, gamma=3.0, kernel=rbf ..................................\n",
|
||
"[CV] ................... C=300.0, gamma=1.0, kernel=rbf, total= 34.8s\n",
|
||
"[CV] C=300.0, gamma=3.0, kernel=rbf ..................................\n",
|
||
"[CV] ................... C=300.0, gamma=3.0, kernel=rbf, total= 36.7s\n",
|
||
"[CV] C=300.0, gamma=3.0, kernel=rbf ..................................\n",
|
||
"[CV] ................... C=300.0, gamma=3.0, kernel=rbf, total= 39.8s\n",
|
||
"[CV] C=1000.0, gamma=0.01, kernel=rbf ................................\n",
|
||
"[CV] ................... C=300.0, gamma=3.0, kernel=rbf, total= 37.1s\n",
|
||
"[CV] C=1000.0, gamma=0.01, kernel=rbf ................................\n",
|
||
"[CV] ................... C=300.0, gamma=3.0, kernel=rbf, total= 40.9s\n",
|
||
"[CV] C=1000.0, gamma=0.01, kernel=rbf ................................\n",
|
||
"[CV] ................... C=300.0, gamma=3.0, kernel=rbf, total= 37.8s\n",
|
||
"[CV] C=1000.0, gamma=0.01, kernel=rbf ................................\n",
|
||
"[CV] ................. C=1000.0, gamma=0.01, kernel=rbf, total= 39.4s\n",
|
||
"[CV] C=1000.0, gamma=0.01, kernel=rbf ................................\n",
|
||
"[CV] ................. C=1000.0, gamma=0.01, kernel=rbf, total= 36.6s\n",
|
||
"[CV] C=1000.0, gamma=0.03, kernel=rbf ................................\n",
|
||
"[CV] ................. C=1000.0, gamma=0.01, kernel=rbf, total= 35.8s\n",
|
||
"[CV] C=1000.0, gamma=0.03, kernel=rbf ................................\n",
|
||
"[CV] ................. C=1000.0, gamma=0.01, kernel=rbf, total= 37.5s\n",
|
||
"[CV] C=1000.0, gamma=0.03, kernel=rbf ................................\n",
|
||
"[CV] ................. C=1000.0, gamma=0.01, kernel=rbf, total= 38.6s\n",
|
||
"[CV] C=1000.0, gamma=0.03, kernel=rbf ................................\n",
|
||
"[CV] ................. C=1000.0, gamma=0.03, kernel=rbf, total= 37.5s\n",
|
||
"[CV] C=1000.0, gamma=0.03, kernel=rbf ................................\n",
|
||
"[CV] ................. C=1000.0, gamma=0.03, kernel=rbf, total= 38.8s\n",
|
||
"[CV] C=1000.0, gamma=0.1, kernel=rbf .................................\n",
|
||
"[CV] ................. C=1000.0, gamma=0.03, kernel=rbf, total= 35.0s\n",
|
||
"[CV] C=1000.0, gamma=0.1, kernel=rbf .................................\n",
|
||
"[CV] ................. C=1000.0, gamma=0.03, kernel=rbf, total= 37.2s\n",
|
||
"[CV] C=1000.0, gamma=0.1, kernel=rbf .................................\n",
|
||
"[CV] ................. C=1000.0, gamma=0.03, kernel=rbf, total= 37.3s\n",
|
||
"[CV] C=1000.0, gamma=0.1, kernel=rbf .................................\n",
|
||
"[CV] .................. C=1000.0, gamma=0.1, kernel=rbf, total= 36.1s\n",
|
||
"[CV] C=1000.0, gamma=0.1, kernel=rbf .................................\n",
|
||
"[CV] .................. C=1000.0, gamma=0.1, kernel=rbf, total= 34.5s\n",
|
||
"[CV] C=1000.0, gamma=0.3, kernel=rbf .................................\n",
|
||
"[CV] .................. C=1000.0, gamma=0.1, kernel=rbf, total= 37.8s\n",
|
||
"[CV] C=1000.0, gamma=0.3, kernel=rbf .................................\n",
|
||
"[CV] .................. C=1000.0, gamma=0.1, kernel=rbf, total= 35.3s\n",
|
||
"[CV] C=1000.0, gamma=0.3, kernel=rbf .................................\n",
|
||
"[CV] .................. C=1000.0, gamma=0.1, kernel=rbf, total= 37.1s\n",
|
||
"[CV] C=1000.0, gamma=0.3, kernel=rbf .................................\n",
|
||
"[CV] .................. C=1000.0, gamma=0.3, kernel=rbf, total= 35.6s\n",
|
||
"[CV] C=1000.0, gamma=0.3, kernel=rbf .................................\n",
|
||
"[CV] .................. C=1000.0, gamma=0.3, kernel=rbf, total= 35.6s\n",
|
||
"[CV] C=1000.0, gamma=1.0, kernel=rbf .................................\n",
|
||
"[CV] .................. C=1000.0, gamma=0.3, kernel=rbf, total= 35.4s\n",
|
||
"[CV] C=1000.0, gamma=1.0, kernel=rbf .................................\n",
|
||
"[CV] .................. C=1000.0, gamma=0.3, kernel=rbf, total= 35.4s\n",
|
||
"[CV] C=1000.0, gamma=1.0, kernel=rbf .................................\n",
|
||
"[CV] .................. C=1000.0, gamma=0.3, kernel=rbf, total= 37.9s\n",
|
||
"[CV] C=1000.0, gamma=1.0, kernel=rbf .................................\n",
|
||
"[CV] .................. C=1000.0, gamma=1.0, kernel=rbf, total= 36.5s\n",
|
||
"[CV] C=1000.0, gamma=1.0, kernel=rbf .................................\n",
|
||
"[CV] .................. C=1000.0, gamma=1.0, kernel=rbf, total= 34.7s\n",
|
||
"[CV] C=1000.0, gamma=3.0, kernel=rbf .................................\n",
|
||
"[CV] .................. C=1000.0, gamma=1.0, kernel=rbf, total= 35.2s\n",
|
||
"[CV] C=1000.0, gamma=3.0, kernel=rbf .................................\n",
|
||
"[CV] .................. C=1000.0, gamma=1.0, kernel=rbf, total= 34.9s\n",
|
||
"[CV] C=1000.0, gamma=3.0, kernel=rbf .................................\n",
|
||
"[CV] .................. C=1000.0, gamma=3.0, kernel=rbf, total= 35.0s\n",
|
||
"[CV] C=1000.0, gamma=3.0, kernel=rbf .................................\n",
|
||
"[CV] .................. C=1000.0, gamma=1.0, kernel=rbf, total= 36.1s\n",
|
||
"[CV] C=1000.0, gamma=3.0, kernel=rbf .................................\n",
|
||
"[CV] .................. C=1000.0, gamma=3.0, kernel=rbf, total= 38.4s\n",
|
||
"[CV] .................. C=1000.0, gamma=3.0, kernel=rbf, total= 37.6s\n",
|
||
"[CV] .................. C=1000.0, gamma=3.0, kernel=rbf, total= 32.6s\n",
|
||
"[CV] .................. C=1000.0, gamma=3.0, kernel=rbf, total= 33.0s\n"
|
||
]
|
||
},
|
||
{
|
||
"name": "stderr",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"[Parallel(n_jobs=4)]: Done 250 out of 250 | elapsed: 60.0min finished\n"
|
||
]
|
||
},
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"GridSearchCV(cv=5, error_score='raise',\n",
|
||
" estimator=SVR(C=1.0, cache_size=200, coef0=0.0, degree=3, epsilon=0.1, gamma='auto',\n",
|
||
" kernel='rbf', max_iter=-1, shrinking=True, tol=0.001, verbose=False),\n",
|
||
" fit_params={}, iid=True, n_jobs=4,\n",
|
||
" param_grid=[{'C': [10.0, 30.0, 100.0, 300.0, 1000.0, 3000.0, 10000.0, 30000.0], 'kernel': ['linear']}, {'gamma': [0.01, 0.03, 0.1, 0.3, 1.0, 3.0], 'C': [1.0, 3.0, 10.0, 30.0, 100.0, 300.0, 1000.0], 'kernel': ['rbf']}],\n",
|
||
" pre_dispatch='2*n_jobs', refit=True, return_train_score=True,\n",
|
||
" scoring='neg_mean_squared_error', verbose=2)"
|
||
]
|
||
},
|
||
"execution_count": 102,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"from sklearn.model_selection import GridSearchCV\n",
|
||
"\n",
|
||
"param_grid = [\n",
|
||
" {'kernel': ['linear'], 'C': [10., 30., 100., 300., 1000., 3000., 10000., 30000.0]},\n",
|
||
" {'kernel': ['rbf'], 'C': [1.0, 3.0, 10., 30., 100., 300., 1000.0],\n",
|
||
" 'gamma': [0.01, 0.03, 0.1, 0.3, 1.0, 3.0]},\n",
|
||
" ]\n",
|
||
"\n",
|
||
"svm_reg = SVR()\n",
|
||
"grid_search = GridSearchCV(svm_reg, param_grid, cv=5, scoring='neg_mean_squared_error', verbose=2, n_jobs=4)\n",
|
||
"grid_search.fit(housing_prepared, housing_labels)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"The best model achieves the following score (evaluated using 5-fold cross validation):"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 103,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"70363.903139641669"
|
||
]
|
||
},
|
||
"execution_count": 103,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"negative_mse = grid_search.best_score_\n",
|
||
"rmse = np.sqrt(-negative_mse)\n",
|
||
"rmse"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"That's much worse than the `RandomForestRegressor`. Let's check the best hyperparameters found:"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 104,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"{'C': 30000.0, 'kernel': 'linear'}"
|
||
]
|
||
},
|
||
"execution_count": 104,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"grid_search.best_params_"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"The linear kernel seems better than the RBF kernel. Notice that the value of `C` is the maximum tested value. When this happens you definitely want to launch the grid search again with higher values for `C` (removing the smallest values), because it is likely that higher values of `C` will be better."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"## 2."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"Question: Try replacing `GridSearchCV` with `RandomizedSearchCV`."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 105,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Fitting 5 folds for each of 50 candidates, totalling 250 fits\n",
|
||
"[CV] C=629.782329591, gamma=3.01012143092, kernel=linear .............\n",
|
||
"[CV] C=629.782329591, gamma=3.01012143092, kernel=linear .............\n",
|
||
"[CV] C=629.782329591, gamma=3.01012143092, kernel=linear .............\n",
|
||
"[CV] C=629.782329591, gamma=3.01012143092, kernel=linear .............\n",
|
||
"[CV] C=629.782329591, gamma=3.01012143092, kernel=linear, total= 22.6s\n",
|
||
"[CV] C=629.782329591, gamma=3.01012143092, kernel=linear .............\n",
|
||
"[CV] C=629.782329591, gamma=3.01012143092, kernel=linear, total= 23.2s\n",
|
||
"[CV] C=26290.2064643, gamma=0.908446969632, kernel=rbf ...............\n",
|
||
"[CV] C=629.782329591, gamma=3.01012143092, kernel=linear, total= 23.4s\n",
|
||
"[CV] C=26290.2064643, gamma=0.908446969632, kernel=rbf ...............\n",
|
||
"[CV] C=629.782329591, gamma=3.01012143092, kernel=linear, total= 25.1s\n",
|
||
"[CV] C=26290.2064643, gamma=0.908446969632, kernel=rbf ...............\n",
|
||
"[CV] C=629.782329591, gamma=3.01012143092, kernel=linear, total= 23.2s\n",
|
||
"[CV] C=26290.2064643, gamma=0.908446969632, kernel=rbf ...............\n",
|
||
"[CV] C=26290.2064643, gamma=0.908446969632, kernel=rbf, total= 43.7s\n",
|
||
"[CV] C=26290.2064643, gamma=0.908446969632, kernel=rbf ...............\n",
|
||
"[CV] C=26290.2064643, gamma=0.908446969632, kernel=rbf, total= 45.0s\n",
|
||
"[CV] C=84.1410790058, gamma=0.0598387686087, kernel=rbf ..............\n",
|
||
"[CV] C=26290.2064643, gamma=0.908446969632, kernel=rbf, total= 44.5s\n",
|
||
"[CV] C=84.1410790058, gamma=0.0598387686087, kernel=rbf ..............\n",
|
||
"[CV] C=26290.2064643, gamma=0.908446969632, kernel=rbf, total= 43.3s\n",
|
||
"[CV] C=84.1410790058, gamma=0.0598387686087, kernel=rbf ..............\n",
|
||
"[CV] C=84.1410790058, gamma=0.0598387686087, kernel=rbf, total= 36.0s\n",
|
||
"[CV] C=84.1410790058, gamma=0.0598387686087, kernel=rbf ..............\n",
|
||
"[CV] C=84.1410790058, gamma=0.0598387686087, kernel=rbf, total= 38.3s\n",
|
||
"[CV] C=84.1410790058, gamma=0.0598387686087, kernel=rbf ..............\n",
|
||
"[CV] C=26290.2064643, gamma=0.908446969632, kernel=rbf, total= 46.7s\n",
|
||
"[CV] C=432.378848131, gamma=0.154161967467, kernel=linear ............\n",
|
||
"[CV] C=84.1410790058, gamma=0.0598387686087, kernel=rbf, total= 38.2s\n",
|
||
"[CV] C=432.378848131, gamma=0.154161967467, kernel=linear ............\n",
|
||
"[CV] C=432.378848131, gamma=0.154161967467, kernel=linear, total= 22.1s\n",
|
||
"[CV] C=432.378848131, gamma=0.154161967467, kernel=linear ............\n",
|
||
"[CV] C=84.1410790058, gamma=0.0598387686087, kernel=rbf, total= 39.9s\n",
|
||
"[CV] C=432.378848131, gamma=0.154161967467, kernel=linear ............\n",
|
||
"[CV] C=84.1410790058, gamma=0.0598387686087, kernel=rbf, total= 40.8s\n",
|
||
"[CV] C=432.378848131, gamma=0.154161967467, kernel=linear ............\n",
|
||
"[CV] C=432.378848131, gamma=0.154161967467, kernel=linear, total= 22.9s\n",
|
||
"[CV] C=24.1750829461, gamma=3.50355747516, kernel=rbf ................\n",
|
||
"[CV] C=432.378848131, gamma=0.154161967467, kernel=linear, total= 21.7s\n",
|
||
"[CV] C=24.1750829461, gamma=3.50355747516, kernel=rbf ................\n",
|
||
"[CV] C=432.378848131, gamma=0.154161967467, kernel=linear, total= 23.5s\n",
|
||
"[CV] C=24.1750829461, gamma=3.50355747516, kernel=rbf ................\n",
|
||
"[CV] C=432.378848131, gamma=0.154161967467, kernel=linear, total= 23.4s\n",
|
||
"[CV] C=24.1750829461, gamma=3.50355747516, kernel=rbf ................\n",
|
||
"[CV] . C=24.1750829461, gamma=3.50355747516, kernel=rbf, total= 39.2s\n",
|
||
"[CV] C=24.1750829461, gamma=3.50355747516, kernel=rbf ................\n",
|
||
"[CV] . C=24.1750829461, gamma=3.50355747516, kernel=rbf, total= 39.7s\n",
|
||
"[CV] C=113564.039406, gamma=0.000779069236658, kernel=rbf ............\n",
|
||
"[CV] . C=24.1750829461, gamma=3.50355747516, kernel=rbf, total= 40.2s\n",
|
||
"[CV] C=113564.039406, gamma=0.000779069236658, kernel=rbf ............\n",
|
||
"[CV] . C=24.1750829461, gamma=3.50355747516, kernel=rbf, total= 39.7s\n",
|
||
"[CV] C=113564.039406, gamma=0.000779069236658, kernel=rbf ............\n",
|
||
"[CV] . C=24.1750829461, gamma=3.50355747516, kernel=rbf, total= 38.4s\n",
|
||
"[CV] C=113564.039406, gamma=0.000779069236658, kernel=rbf ............\n",
|
||
"[CV] C=113564.039406, gamma=0.000779069236658, kernel=rbf, total= 35.1s\n",
|
||
"[CV] C=113564.039406, gamma=0.000779069236658, kernel=rbf ............\n",
|
||
"[CV] C=113564.039406, gamma=0.000779069236658, kernel=rbf, total= 36.7s\n",
|
||
"[CV] C=108.304882388, gamma=0.36275372946, kernel=rbf ................\n",
|
||
"[CV] C=113564.039406, gamma=0.000779069236658, kernel=rbf, total= 38.2s\n",
|
||
"[CV] C=108.304882388, gamma=0.36275372946, kernel=rbf ................\n",
|
||
"[CV] C=113564.039406, gamma=0.000779069236658, kernel=rbf, total= 36.3s\n",
|
||
"[CV] C=108.304882388, gamma=0.36275372946, kernel=rbf ................\n",
|
||
"[CV] C=113564.039406, gamma=0.000779069236658, kernel=rbf, total= 37.0s\n",
|
||
"[CV] C=108.304882388, gamma=0.36275372946, kernel=rbf ................\n",
|
||
"[CV] . C=108.304882388, gamma=0.36275372946, kernel=rbf, total= 36.9s\n",
|
||
"[CV] C=108.304882388, gamma=0.36275372946, kernel=rbf ................\n",
|
||
"[CV] . C=108.304882388, gamma=0.36275372946, kernel=rbf, total= 37.2s\n",
|
||
"[CV] C=21.3449536726, gamma=0.0233325235983, kernel=linear ...........\n",
|
||
"[CV] . C=108.304882388, gamma=0.36275372946, kernel=rbf, total= 35.0s\n",
|
||
"[CV] C=21.3449536726, gamma=0.0233325235983, kernel=linear ...........\n"
|
||
]
|
||
},
|
||
{
|
||
"name": "stderr",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"[Parallel(n_jobs=4)]: Done 33 tasks | elapsed: 7.5min\n"
|
||
]
|
||
},
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"[CV] C=21.3449536726, gamma=0.0233325235983, kernel=linear, total= 22.8s\n",
|
||
"[CV] C=21.3449536726, gamma=0.0233325235983, kernel=linear ...........\n",
|
||
"[CV] . C=108.304882388, gamma=0.36275372946, kernel=rbf, total= 38.3s\n",
|
||
"[CV] C=21.3449536726, gamma=0.0233325235983, kernel=linear ...........\n",
|
||
"[CV] . C=108.304882388, gamma=0.36275372946, kernel=rbf, total= 38.4s\n",
|
||
"[CV] C=21.3449536726, gamma=0.0233325235983, kernel=linear ...........\n",
|
||
"[CV] C=21.3449536726, gamma=0.0233325235983, kernel=linear, total= 22.8s\n",
|
||
"[CV] C=5603.27031743, gamma=0.150234528727, kernel=rbf ...............\n",
|
||
"[CV] C=21.3449536726, gamma=0.0233325235983, kernel=linear, total= 22.8s\n",
|
||
"[CV] C=5603.27031743, gamma=0.150234528727, kernel=rbf ...............\n",
|
||
"[CV] C=21.3449536726, gamma=0.0233325235983, kernel=linear, total= 23.4s\n",
|
||
"[CV] C=5603.27031743, gamma=0.150234528727, kernel=rbf ...............\n",
|
||
"[CV] C=21.3449536726, gamma=0.0233325235983, kernel=linear, total= 21.9s\n",
|
||
"[CV] C=5603.27031743, gamma=0.150234528727, kernel=rbf ...............\n",
|
||
"[CV] C=5603.27031743, gamma=0.150234528727, kernel=rbf, total= 37.7s\n",
|
||
"[CV] C=5603.27031743, gamma=0.150234528727, kernel=rbf ...............\n",
|
||
"[CV] C=5603.27031743, gamma=0.150234528727, kernel=rbf, total= 37.2s\n",
|
||
"[CV] C=157055.109894, gamma=0.26497040005, kernel=rbf ................\n",
|
||
"[CV] C=5603.27031743, gamma=0.150234528727, kernel=rbf, total= 36.9s\n",
|
||
"[CV] C=157055.109894, gamma=0.26497040005, kernel=rbf ................\n",
|
||
"[CV] C=5603.27031743, gamma=0.150234528727, kernel=rbf, total= 37.0s\n",
|
||
"[CV] C=157055.109894, gamma=0.26497040005, kernel=rbf ................\n",
|
||
"[CV] C=5603.27031743, gamma=0.150234528727, kernel=rbf, total= 36.4s\n",
|
||
"[CV] C=157055.109894, gamma=0.26497040005, kernel=rbf ................\n",
|
||
"[CV] . C=157055.109894, gamma=0.26497040005, kernel=rbf, total= 1.4min\n",
|
||
"[CV] C=157055.109894, gamma=0.26497040005, kernel=rbf ................\n",
|
||
"[CV] . C=157055.109894, gamma=0.26497040005, kernel=rbf, total= 1.5min\n",
|
||
"[CV] C=27652.4643587, gamma=0.222735862129, kernel=linear ............\n",
|
||
"[CV] . C=157055.109894, gamma=0.26497040005, kernel=rbf, total= 1.6min\n",
|
||
"[CV] C=27652.4643587, gamma=0.222735862129, kernel=linear ............\n",
|
||
"[CV] . C=157055.109894, gamma=0.26497040005, kernel=rbf, total= 1.3min\n",
|
||
"[CV] C=27652.4643587, gamma=0.222735862129, kernel=linear ............\n",
|
||
"[CV] C=27652.4643587, gamma=0.222735862129, kernel=linear, total= 45.7s\n",
|
||
"[CV] C=27652.4643587, gamma=0.222735862129, kernel=linear ............\n",
|
||
"[CV] C=27652.4643587, gamma=0.222735862129, kernel=linear, total= 46.9s\n",
|
||
"[CV] C=27652.4643587, gamma=0.222735862129, kernel=linear ............\n",
|
||
"[CV] C=27652.4643587, gamma=0.222735862129, kernel=linear, total= 50.6s\n",
|
||
"[CV] C=171377.395704, gamma=0.628789100541, kernel=linear ............\n",
|
||
"[CV] C=27652.4643587, gamma=0.222735862129, kernel=linear, total= 45.6s\n",
|
||
"[CV] C=171377.395704, gamma=0.628789100541, kernel=linear ............\n",
|
||
"[CV] . C=157055.109894, gamma=0.26497040005, kernel=rbf, total= 1.5min\n",
|
||
"[CV] C=171377.395704, gamma=0.628789100541, kernel=linear ............\n",
|
||
"[CV] C=27652.4643587, gamma=0.222735862129, kernel=linear, total= 38.4s\n",
|
||
"[CV] C=171377.395704, gamma=0.628789100541, kernel=linear ............\n",
|
||
"[CV] C=171377.395704, gamma=0.628789100541, kernel=linear, total= 2.4min\n",
|
||
"[CV] C=171377.395704, gamma=0.628789100541, kernel=linear ............\n",
|
||
"[CV] C=171377.395704, gamma=0.628789100541, kernel=linear, total= 3.1min\n",
|
||
"[CV] C=5385.29382017, gamma=0.186961251977, kernel=linear ............\n",
|
||
"[CV] C=171377.395704, gamma=0.628789100541, kernel=linear, total= 2.6min\n",
|
||
"[CV] C=5385.29382017, gamma=0.186961251977, kernel=linear ............\n",
|
||
"[CV] C=171377.395704, gamma=0.628789100541, kernel=linear, total= 3.3min\n",
|
||
"[CV] C=5385.29382017, gamma=0.186961251977, kernel=linear ............\n",
|
||
"[CV] C=5385.29382017, gamma=0.186961251977, kernel=linear, total= 26.9s\n",
|
||
"[CV] C=5385.29382017, gamma=0.186961251977, kernel=linear ............\n",
|
||
"[CV] C=5385.29382017, gamma=0.186961251977, kernel=linear, total= 27.6s\n",
|
||
"[CV] C=5385.29382017, gamma=0.186961251977, kernel=linear ............\n",
|
||
"[CV] C=5385.29382017, gamma=0.186961251977, kernel=linear, total= 27.5s\n",
|
||
"[CV] C=22.5990321662, gamma=2.85079687894, kernel=rbf ................\n",
|
||
"[CV] C=5385.29382017, gamma=0.186961251977, kernel=linear, total= 26.5s\n",
|
||
"[CV] C=22.5990321662, gamma=2.85079687894, kernel=rbf ................\n",
|
||
"[CV] C=5385.29382017, gamma=0.186961251977, kernel=linear, total= 27.9s\n",
|
||
"[CV] C=22.5990321662, gamma=2.85079687894, kernel=rbf ................\n",
|
||
"[CV] C=171377.395704, gamma=0.628789100541, kernel=linear, total= 2.2min\n",
|
||
"[CV] C=22.5990321662, gamma=2.85079687894, kernel=rbf ................\n",
|
||
"[CV] . C=22.5990321662, gamma=2.85079687894, kernel=rbf, total= 37.5s\n",
|
||
"[CV] C=22.5990321662, gamma=2.85079687894, kernel=rbf ................\n",
|
||
"[CV] . C=22.5990321662, gamma=2.85079687894, kernel=rbf, total= 34.9s\n",
|
||
"[CV] C=34246.7519463, gamma=0.363287859969, kernel=linear ............\n",
|
||
"[CV] . C=22.5990321662, gamma=2.85079687894, kernel=rbf, total= 35.5s\n",
|
||
"[CV] C=34246.7519463, gamma=0.363287859969, kernel=linear ............\n",
|
||
"[CV] . C=22.5990321662, gamma=2.85079687894, kernel=rbf, total= 38.2s\n",
|
||
"[CV] C=34246.7519463, gamma=0.363287859969, kernel=linear ............\n",
|
||
"[CV] . C=22.5990321662, gamma=2.85079687894, kernel=rbf, total= 40.7s\n",
|
||
"[CV] C=34246.7519463, gamma=0.363287859969, kernel=linear ............\n",
|
||
"[CV] C=34246.7519463, gamma=0.363287859969, kernel=linear, total= 55.2s\n",
|
||
"[CV] C=34246.7519463, gamma=0.363287859969, kernel=linear ............\n",
|
||
"[CV] C=34246.7519463, gamma=0.363287859969, kernel=linear, total= 51.8s\n",
|
||
"[CV] C=167.727895608, gamma=0.275787054226, kernel=rbf ...............\n",
|
||
"[CV] C=34246.7519463, gamma=0.363287859969, kernel=linear, total= 57.7s\n",
|
||
"[CV] C=167.727895608, gamma=0.275787054226, kernel=rbf ...............\n",
|
||
"[CV] C=34246.7519463, gamma=0.363287859969, kernel=linear, total= 54.5s\n",
|
||
"[CV] C=167.727895608, gamma=0.275787054226, kernel=rbf ...............\n",
|
||
"[CV] C=34246.7519463, gamma=0.363287859969, kernel=linear, total= 48.8s\n",
|
||
"[CV] C=167.727895608, gamma=0.275787054226, kernel=rbf ...............\n",
|
||
"[CV] C=167.727895608, gamma=0.275787054226, kernel=rbf, total= 34.2s\n",
|
||
"[CV] C=167.727895608, gamma=0.275787054226, kernel=rbf ...............\n",
|
||
"[CV] C=167.727895608, gamma=0.275787054226, kernel=rbf, total= 35.5s\n",
|
||
"[CV] C=61.543605425, gamma=0.683547228134, kernel=linear .............\n",
|
||
"[CV] C=167.727895608, gamma=0.275787054226, kernel=rbf, total= 37.7s\n",
|
||
"[CV] C=61.543605425, gamma=0.683547228134, kernel=linear .............\n",
|
||
"[CV] C=167.727895608, gamma=0.275787054226, kernel=rbf, total= 36.9s\n",
|
||
"[CV] C=61.543605425, gamma=0.683547228134, kernel=linear .............\n",
|
||
"[CV] C=167.727895608, gamma=0.275787054226, kernel=rbf, total= 38.3s\n",
|
||
"[CV] C=61.543605425, gamma=0.683547228134, kernel=linear .............\n",
|
||
"[CV] C=61.543605425, gamma=0.683547228134, kernel=linear, total= 24.3s\n",
|
||
"[CV] C=61.543605425, gamma=0.683547228134, kernel=linear .............\n",
|
||
"[CV] C=61.543605425, gamma=0.683547228134, kernel=linear, total= 23.2s\n",
|
||
"[CV] C=98.7389738992, gamma=0.496036536049, kernel=rbf ...............\n",
|
||
"[CV] C=61.543605425, gamma=0.683547228134, kernel=linear, total= 20.2s\n",
|
||
"[CV] C=98.7389738992, gamma=0.496036536049, kernel=rbf ...............\n",
|
||
"[CV] C=61.543605425, gamma=0.683547228134, kernel=linear, total= 21.7s\n",
|
||
"[CV] C=98.7389738992, gamma=0.496036536049, kernel=rbf ...............\n",
|
||
"[CV] C=61.543605425, gamma=0.683547228134, kernel=linear, total= 20.7s\n",
|
||
"[CV] C=98.7389738992, gamma=0.496036536049, kernel=rbf ...............\n",
|
||
"[CV] C=98.7389738992, gamma=0.496036536049, kernel=rbf, total= 38.0s\n",
|
||
"[CV] C=98.7389738992, gamma=0.496036536049, kernel=rbf ...............\n",
|
||
"[CV] C=98.7389738992, gamma=0.496036536049, kernel=rbf, total= 36.4s\n",
|
||
"[CV] C=8935.50563595, gamma=0.373546581658, kernel=rbf ...............\n",
|
||
"[CV] C=98.7389738992, gamma=0.496036536049, kernel=rbf, total= 34.4s\n",
|
||
"[CV] C=8935.50563595, gamma=0.373546581658, kernel=rbf ...............\n",
|
||
"[CV] C=98.7389738992, gamma=0.496036536049, kernel=rbf, total= 36.6s\n",
|
||
"[CV] C=8935.50563595, gamma=0.373546581658, kernel=rbf ...............\n",
|
||
"[CV] C=98.7389738992, gamma=0.496036536049, kernel=rbf, total= 36.8s\n",
|
||
"[CV] C=8935.50563595, gamma=0.373546581658, kernel=rbf ...............\n",
|
||
"[CV] C=8935.50563595, gamma=0.373546581658, kernel=rbf, total= 37.6s\n",
|
||
"[CV] C=8935.50563595, gamma=0.373546581658, kernel=rbf ...............\n",
|
||
"[CV] C=8935.50563595, gamma=0.373546581658, kernel=rbf, total= 36.3s\n",
|
||
"[CV] C=135.767758248, gamma=0.838636245625, kernel=linear ............\n",
|
||
"[CV] C=8935.50563595, gamma=0.373546581658, kernel=rbf, total= 36.9s\n",
|
||
"[CV] C=135.767758248, gamma=0.838636245625, kernel=linear ............\n",
|
||
"[CV] C=135.767758248, gamma=0.838636245625, kernel=linear, total= 23.4s\n",
|
||
"[CV] C=135.767758248, gamma=0.838636245625, kernel=linear ............\n",
|
||
"[CV] C=135.767758248, gamma=0.838636245625, kernel=linear, total= 22.9s\n",
|
||
"[CV] C=135.767758248, gamma=0.838636245625, kernel=linear ............\n",
|
||
"[CV] C=8935.50563595, gamma=0.373546581658, kernel=rbf, total= 36.7s\n",
|
||
"[CV] C=135.767758248, gamma=0.838636245625, kernel=linear ............\n",
|
||
"[CV] C=8935.50563595, gamma=0.373546581658, kernel=rbf, total= 37.2s\n",
|
||
"[CV] C=151136.202825, gamma=1.49224537714, kernel=rbf ................\n",
|
||
"[CV] C=135.767758248, gamma=0.838636245625, kernel=linear, total= 22.4s\n",
|
||
"[CV] C=151136.202825, gamma=1.49224537714, kernel=rbf ................\n",
|
||
"[CV] C=135.767758248, gamma=0.838636245625, kernel=linear, total= 19.6s\n",
|
||
"[CV] C=151136.202825, gamma=1.49224537714, kernel=rbf ................\n",
|
||
"[CV] C=135.767758248, gamma=0.838636245625, kernel=linear, total= 22.0s\n",
|
||
"[CV] C=151136.202825, gamma=1.49224537714, kernel=rbf ................\n",
|
||
"[CV] . C=151136.202825, gamma=1.49224537714, kernel=rbf, total= 5.7min\n",
|
||
"[CV] C=151136.202825, gamma=1.49224537714, kernel=rbf ................\n",
|
||
"[CV] . C=151136.202825, gamma=1.49224537714, kernel=rbf, total= 6.1min\n",
|
||
"[CV] C=761.43167585, gamma=2.61263365142, kernel=linear ..............\n",
|
||
"[CV] C=761.43167585, gamma=2.61263365142, kernel=linear, total= 22.5s\n",
|
||
"[CV] C=761.43167585, gamma=2.61263365142, kernel=linear ..............\n",
|
||
"[CV] C=761.43167585, gamma=2.61263365142, kernel=linear, total= 23.3s\n",
|
||
"[CV] C=761.43167585, gamma=2.61263365142, kernel=linear ..............\n",
|
||
"[CV] . C=151136.202825, gamma=1.49224537714, kernel=rbf, total= 7.2min\n",
|
||
"[CV] C=761.43167585, gamma=2.61263365142, kernel=linear ..............\n",
|
||
"[CV] C=761.43167585, gamma=2.61263365142, kernel=linear, total= 21.6s\n",
|
||
"[CV] C=761.43167585, gamma=2.61263365142, kernel=linear ..............\n",
|
||
"[CV] C=761.43167585, gamma=2.61263365142, kernel=linear, total= 20.2s\n",
|
||
"[CV] C=97392.8188304, gamma=0.0926554589531, kernel=linear ...........\n",
|
||
"[CV] . C=151136.202825, gamma=1.49224537714, kernel=rbf, total= 8.2min\n",
|
||
"[CV] C=97392.8188304, gamma=0.0926554589531, kernel=linear ...........\n",
|
||
"[CV] C=761.43167585, gamma=2.61263365142, kernel=linear, total= 22.4s\n",
|
||
"[CV] C=97392.8188304, gamma=0.0926554589531, kernel=linear ...........\n",
|
||
"[CV] C=97392.8188304, gamma=0.0926554589531, kernel=linear, total= 1.7min\n",
|
||
"[CV] C=97392.8188304, gamma=0.0926554589531, kernel=linear ...........\n",
|
||
"[CV] C=97392.8188304, gamma=0.0926554589531, kernel=linear, total= 1.7min\n",
|
||
"[CV] C=97392.8188304, gamma=0.0926554589531, kernel=linear ...........\n",
|
||
"[CV] C=97392.8188304, gamma=0.0926554589531, kernel=linear, total= 2.8min\n",
|
||
"[CV] C=2423.07599849, gamma=3.24861427024, kernel=linear .............\n",
|
||
"[CV] C=97392.8188304, gamma=0.0926554589531, kernel=linear, total= 1.8min\n",
|
||
"[CV] C=2423.07599849, gamma=3.24861427024, kernel=linear .............\n",
|
||
"[CV] C=97392.8188304, gamma=0.0926554589531, kernel=linear, total= 1.5min\n",
|
||
"[CV] C=2423.07599849, gamma=3.24861427024, kernel=linear .............\n",
|
||
"[CV] C=2423.07599849, gamma=3.24861427024, kernel=linear, total= 25.3s\n",
|
||
"[CV] C=2423.07599849, gamma=3.24861427024, kernel=linear .............\n",
|
||
"[CV] C=2423.07599849, gamma=3.24861427024, kernel=linear, total= 26.4s\n",
|
||
"[CV] C=2423.07599849, gamma=3.24861427024, kernel=linear .............\n",
|
||
"[CV] C=2423.07599849, gamma=3.24861427024, kernel=linear, total= 25.6s\n",
|
||
"[CV] C=717.363299726, gamma=0.316560443209, kernel=linear ............\n",
|
||
"[CV] C=2423.07599849, gamma=3.24861427024, kernel=linear, total= 25.5s\n",
|
||
"[CV] C=717.363299726, gamma=0.316560443209, kernel=linear ............\n",
|
||
"[CV] C=2423.07599849, gamma=3.24861427024, kernel=linear, total= 24.6s\n",
|
||
"[CV] C=717.363299726, gamma=0.316560443209, kernel=linear ............\n",
|
||
"[CV] C=717.363299726, gamma=0.316560443209, kernel=linear, total= 23.1s\n",
|
||
"[CV] C=717.363299726, gamma=0.316560443209, kernel=linear ............\n",
|
||
"[CV] C=717.363299726, gamma=0.316560443209, kernel=linear, total= 23.3s\n",
|
||
"[CV] C=717.363299726, gamma=0.316560443209, kernel=linear ............\n",
|
||
"[CV] C=717.363299726, gamma=0.316560443209, kernel=linear, total= 21.0s\n",
|
||
"[CV] C=4446.66752118, gamma=3.35972844566, kernel=rbf ................\n",
|
||
"[CV] C=717.363299726, gamma=0.316560443209, kernel=linear, total= 23.2s\n",
|
||
"[CV] C=4446.66752118, gamma=3.35972844566, kernel=rbf ................\n",
|
||
"[CV] . C=151136.202825, gamma=1.49224537714, kernel=rbf, total= 7.2min\n",
|
||
"[CV] C=4446.66752118, gamma=3.35972844566, kernel=rbf ................\n",
|
||
"[CV] C=717.363299726, gamma=0.316560443209, kernel=linear, total= 22.6s\n",
|
||
"[CV] C=4446.66752118, gamma=3.35972844566, kernel=rbf ................\n",
|
||
"[CV] . C=4446.66752118, gamma=3.35972844566, kernel=rbf, total= 37.5s\n",
|
||
"[CV] C=4446.66752118, gamma=3.35972844566, kernel=rbf ................\n",
|
||
"[CV] . C=4446.66752118, gamma=3.35972844566, kernel=rbf, total= 35.9s\n",
|
||
"[CV] C=2963.56412121, gamma=0.151898147821, kernel=linear ............\n",
|
||
"[CV] . C=4446.66752118, gamma=3.35972844566, kernel=rbf, total= 34.8s\n",
|
||
"[CV] C=2963.56412121, gamma=0.151898147821, kernel=linear ............\n",
|
||
"[CV] . C=4446.66752118, gamma=3.35972844566, kernel=rbf, total= 34.9s\n",
|
||
"[CV] C=2963.56412121, gamma=0.151898147821, kernel=linear ............\n",
|
||
"[CV] C=2963.56412121, gamma=0.151898147821, kernel=linear, total= 23.6s\n",
|
||
"[CV] C=2963.56412121, gamma=0.151898147821, kernel=linear ............\n",
|
||
"[CV] C=2963.56412121, gamma=0.151898147821, kernel=linear, total= 24.8s\n",
|
||
"[CV] C=2963.56412121, gamma=0.151898147821, kernel=linear ............\n",
|
||
"[CV] C=2963.56412121, gamma=0.151898147821, kernel=linear, total= 26.5s\n",
|
||
"[CV] C=91.6426738169, gamma=0.0157599448359, kernel=linear ...........\n",
|
||
"[CV] . C=4446.66752118, gamma=3.35972844566, kernel=rbf, total= 37.8s\n",
|
||
"[CV] C=91.6426738169, gamma=0.0157599448359, kernel=linear ...........\n",
|
||
"[CV] C=2963.56412121, gamma=0.151898147821, kernel=linear, total= 24.1s\n",
|
||
"[CV] C=91.6426738169, gamma=0.0157599448359, kernel=linear ...........\n",
|
||
"[CV] C=91.6426738169, gamma=0.0157599448359, kernel=linear, total= 21.4s\n",
|
||
"[CV] C=91.6426738169, gamma=0.0157599448359, kernel=linear ...........\n",
|
||
"[CV] C=2963.56412121, gamma=0.151898147821, kernel=linear, total= 24.1s\n",
|
||
"[CV] C=91.6426738169, gamma=0.0157599448359, kernel=linear ...........\n",
|
||
"[CV] C=91.6426738169, gamma=0.0157599448359, kernel=linear, total= 22.4s\n",
|
||
"[CV] C=24547.6019757, gamma=0.221539440506, kernel=rbf ...............\n",
|
||
"[CV] C=91.6426738169, gamma=0.0157599448359, kernel=linear, total= 23.3s\n",
|
||
"[CV] C=24547.6019757, gamma=0.221539440506, kernel=rbf ...............\n",
|
||
"[CV] C=91.6426738169, gamma=0.0157599448359, kernel=linear, total= 20.4s\n",
|
||
"[CV] C=24547.6019757, gamma=0.221539440506, kernel=rbf ...............\n",
|
||
"[CV] C=91.6426738169, gamma=0.0157599448359, kernel=linear, total= 21.7s\n",
|
||
"[CV] C=24547.6019757, gamma=0.221539440506, kernel=rbf ...............\n",
|
||
"[CV] C=24547.6019757, gamma=0.221539440506, kernel=rbf, total= 37.3s\n",
|
||
"[CV] C=24547.6019757, gamma=0.221539440506, kernel=rbf ...............\n",
|
||
"[CV] C=24547.6019757, gamma=0.221539440506, kernel=rbf, total= 39.8s\n",
|
||
"[CV] C=22.7692794106, gamma=0.221697602314, kernel=rbf ...............\n",
|
||
"[CV] C=24547.6019757, gamma=0.221539440506, kernel=rbf, total= 37.9s\n",
|
||
"[CV] C=22.7692794106, gamma=0.221697602314, kernel=rbf ...............\n",
|
||
"[CV] C=24547.6019757, gamma=0.221539440506, kernel=rbf, total= 38.2s\n",
|
||
"[CV] C=22.7692794106, gamma=0.221697602314, kernel=rbf ...............\n",
|
||
"[CV] C=24547.6019757, gamma=0.221539440506, kernel=rbf, total= 36.0s\n",
|
||
"[CV] C=22.7692794106, gamma=0.221697602314, kernel=rbf ...............\n",
|
||
"[CV] C=22.7692794106, gamma=0.221697602314, kernel=rbf, total= 35.6s\n",
|
||
"[CV] C=22.7692794106, gamma=0.221697602314, kernel=rbf ...............\n",
|
||
"[CV] C=22.7692794106, gamma=0.221697602314, kernel=rbf, total= 37.6s\n",
|
||
"[CV] C=16483.8505298, gamma=1.47521452604, kernel=linear .............\n",
|
||
"[CV] C=22.7692794106, gamma=0.221697602314, kernel=rbf, total= 36.4s\n",
|
||
"[CV] C=16483.8505298, gamma=1.47521452604, kernel=linear .............\n",
|
||
"[CV] C=22.7692794106, gamma=0.221697602314, kernel=rbf, total= 36.3s\n",
|
||
"[CV] C=16483.8505298, gamma=1.47521452604, kernel=linear .............\n",
|
||
"[CV] C=16483.8505298, gamma=1.47521452604, kernel=linear, total= 35.7s\n",
|
||
"[CV] C=16483.8505298, gamma=1.47521452604, kernel=linear .............\n",
|
||
"[CV] C=16483.8505298, gamma=1.47521452604, kernel=linear, total= 38.3s\n",
|
||
"[CV] C=16483.8505298, gamma=1.47521452604, kernel=linear .............\n",
|
||
"[CV] C=22.7692794106, gamma=0.221697602314, kernel=rbf, total= 37.0s\n",
|
||
"[CV] C=101445.668813, gamma=1.05290408458, kernel=rbf ................\n",
|
||
"[CV] C=16483.8505298, gamma=1.47521452604, kernel=linear, total= 31.9s\n",
|
||
"[CV] C=101445.668813, gamma=1.05290408458, kernel=rbf ................\n",
|
||
"[CV] C=16483.8505298, gamma=1.47521452604, kernel=linear, total= 36.0s\n",
|
||
"[CV] C=101445.668813, gamma=1.05290408458, kernel=rbf ................\n"
|
||
]
|
||
},
|
||
{
|
||
"name": "stderr",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"[Parallel(n_jobs=4)]: Done 154 tasks | elapsed: 44.0min\n"
|
||
]
|
||
},
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"[CV] C=16483.8505298, gamma=1.47521452604, kernel=linear, total= 37.8s\n",
|
||
"[CV] C=101445.668813, gamma=1.05290408458, kernel=rbf ................\n",
|
||
"[CV] . C=101445.668813, gamma=1.05290408458, kernel=rbf, total= 3.2min\n",
|
||
"[CV] C=101445.668813, gamma=1.05290408458, kernel=rbf ................\n",
|
||
"[CV] . C=101445.668813, gamma=1.05290408458, kernel=rbf, total= 3.1min\n",
|
||
"[CV] C=56681.8085903, gamma=0.976301191712, kernel=rbf ...............\n",
|
||
"[CV] . C=101445.668813, gamma=1.05290408458, kernel=rbf, total= 3.7min\n",
|
||
"[CV] C=56681.8085903, gamma=0.976301191712, kernel=rbf ...............\n",
|
||
"[CV] . C=101445.668813, gamma=1.05290408458, kernel=rbf, total= 4.1min\n",
|
||
"[CV] C=56681.8085903, gamma=0.976301191712, kernel=rbf ...............\n",
|
||
"[CV] C=56681.8085903, gamma=0.976301191712, kernel=rbf, total= 1.2min\n",
|
||
"[CV] C=56681.8085903, gamma=0.976301191712, kernel=rbf ...............\n",
|
||
"[CV] C=56681.8085903, gamma=0.976301191712, kernel=rbf, total= 1.4min\n",
|
||
"[CV] C=56681.8085903, gamma=0.976301191712, kernel=rbf ...............\n",
|
||
"[CV] C=56681.8085903, gamma=0.976301191712, kernel=rbf, total= 1.3min\n",
|
||
"[CV] C=48.1582239093, gamma=0.463335116798, kernel=rbf ...............\n",
|
||
"[CV] . C=101445.668813, gamma=1.05290408458, kernel=rbf, total= 3.3min\n",
|
||
"[CV] C=48.1582239093, gamma=0.463335116798, kernel=rbf ...............\n",
|
||
"[CV] C=56681.8085903, gamma=0.976301191712, kernel=rbf, total= 1.3min\n",
|
||
"[CV] C=48.1582239093, gamma=0.463335116798, kernel=rbf ...............\n",
|
||
"[CV] C=48.1582239093, gamma=0.463335116798, kernel=rbf, total= 44.1s\n",
|
||
"[CV] C=48.1582239093, gamma=0.463335116798, kernel=rbf ...............\n",
|
||
"[CV] C=56681.8085903, gamma=0.976301191712, kernel=rbf, total= 1.4min\n",
|
||
"[CV] C=48.1582239093, gamma=0.463335116798, kernel=rbf ...............\n",
|
||
"[CV] C=48.1582239093, gamma=0.463335116798, kernel=rbf, total= 42.0s\n",
|
||
"[CV] C=399.726815571, gamma=1.30787578396, kernel=rbf ................\n",
|
||
"[CV] C=48.1582239093, gamma=0.463335116798, kernel=rbf, total= 40.3s\n",
|
||
"[CV] C=399.726815571, gamma=1.30787578396, kernel=rbf ................\n",
|
||
"[CV] C=48.1582239093, gamma=0.463335116798, kernel=rbf, total= 42.4s\n",
|
||
"[CV] C=399.726815571, gamma=1.30787578396, kernel=rbf ................\n",
|
||
"[CV] C=48.1582239093, gamma=0.463335116798, kernel=rbf, total= 36.3s\n",
|
||
"[CV] C=399.726815571, gamma=1.30787578396, kernel=rbf ................\n",
|
||
"[CV] . C=399.726815571, gamma=1.30787578396, kernel=rbf, total= 42.4s\n",
|
||
"[CV] C=399.726815571, gamma=1.30787578396, kernel=rbf ................\n",
|
||
"[CV] . C=399.726815571, gamma=1.30787578396, kernel=rbf, total= 41.0s\n",
|
||
"[CV] C=251.140738863, gamma=0.823810520491, kernel=linear ............\n",
|
||
"[CV] C=251.140738863, gamma=0.823810520491, kernel=linear, total= 26.1s\n",
|
||
"[CV] C=251.140738863, gamma=0.823810520491, kernel=linear ............\n",
|
||
"[CV] . C=399.726815571, gamma=1.30787578396, kernel=rbf, total= 37.1s\n",
|
||
"[CV] C=251.140738863, gamma=0.823810520491, kernel=linear ............\n",
|
||
"[CV] . C=399.726815571, gamma=1.30787578396, kernel=rbf, total= 41.8s\n",
|
||
"[CV] C=251.140738863, gamma=0.823810520491, kernel=linear ............\n",
|
||
"[CV] . C=399.726815571, gamma=1.30787578396, kernel=rbf, total= 42.3s\n",
|
||
"[CV] C=251.140738863, gamma=0.823810520491, kernel=linear ............\n",
|
||
"[CV] C=251.140738863, gamma=0.823810520491, kernel=linear, total= 23.9s\n",
|
||
"[CV] C=60.1737364289, gamma=1.24912634432, kernel=linear .............\n",
|
||
"[CV] C=251.140738863, gamma=0.823810520491, kernel=linear, total= 25.1s\n",
|
||
"[CV] C=60.1737364289, gamma=1.24912634432, kernel=linear .............\n",
|
||
"[CV] C=251.140738863, gamma=0.823810520491, kernel=linear, total= 24.9s\n",
|
||
"[CV] C=60.1737364289, gamma=1.24912634432, kernel=linear .............\n",
|
||
"[CV] C=251.140738863, gamma=0.823810520491, kernel=linear, total= 27.3s\n",
|
||
"[CV] C=60.1737364289, gamma=1.24912634432, kernel=linear .............\n",
|
||
"[CV] C=60.1737364289, gamma=1.24912634432, kernel=linear, total= 25.4s\n",
|
||
"[CV] C=60.1737364289, gamma=1.24912634432, kernel=linear .............\n",
|
||
"[CV] C=60.1737364289, gamma=1.24912634432, kernel=linear, total= 25.0s\n",
|
||
"[CV] C=15415.1615449, gamma=0.269167751462, kernel=rbf ...............\n",
|
||
"[CV] C=60.1737364289, gamma=1.24912634432, kernel=linear, total= 26.7s\n",
|
||
"[CV] C=15415.1615449, gamma=0.269167751462, kernel=rbf ...............\n",
|
||
"[CV] C=60.1737364289, gamma=1.24912634432, kernel=linear, total= 26.3s\n",
|
||
"[CV] C=15415.1615449, gamma=0.269167751462, kernel=rbf ...............\n",
|
||
"[CV] C=60.1737364289, gamma=1.24912634432, kernel=linear, total= 27.4s\n",
|
||
"[CV] C=15415.1615449, gamma=0.269167751462, kernel=rbf ...............\n",
|
||
"[CV] C=15415.1615449, gamma=0.269167751462, kernel=rbf, total= 48.9s\n",
|
||
"[CV] C=15415.1615449, gamma=0.269167751462, kernel=rbf ...............\n",
|
||
"[CV] C=15415.1615449, gamma=0.269167751462, kernel=rbf, total= 47.4s\n",
|
||
"[CV] C=1888.914851, gamma=0.739678838777, kernel=linear ..............\n",
|
||
"[CV] C=15415.1615449, gamma=0.269167751462, kernel=rbf, total= 43.4s\n",
|
||
"[CV] C=1888.914851, gamma=0.739678838777, kernel=linear ..............\n",
|
||
"[CV] C=15415.1615449, gamma=0.269167751462, kernel=rbf, total= 43.5s\n",
|
||
"[CV] C=1888.914851, gamma=0.739678838777, kernel=linear ..............\n",
|
||
"[CV] C=1888.914851, gamma=0.739678838777, kernel=linear, total= 27.0s\n",
|
||
"[CV] C=1888.914851, gamma=0.739678838777, kernel=linear ..............\n",
|
||
"[CV] C=1888.914851, gamma=0.739678838777, kernel=linear, total= 26.4s\n",
|
||
"[CV] C=1888.914851, gamma=0.739678838777, kernel=linear ..............\n",
|
||
"[CV] C=15415.1615449, gamma=0.269167751462, kernel=rbf, total= 44.1s\n",
|
||
"[CV] C=55.5383891123, gamma=0.578634378499, kernel=linear ............\n",
|
||
"[CV] C=1888.914851, gamma=0.739678838777, kernel=linear, total= 29.1s\n",
|
||
"[CV] C=55.5383891123, gamma=0.578634378499, kernel=linear ............\n",
|
||
"[CV] C=1888.914851, gamma=0.739678838777, kernel=linear, total= 29.7s\n",
|
||
"[CV] C=55.5383891123, gamma=0.578634378499, kernel=linear ............\n",
|
||
"[CV] C=55.5383891123, gamma=0.578634378499, kernel=linear, total= 23.6s\n",
|
||
"[CV] C=55.5383891123, gamma=0.578634378499, kernel=linear ............\n",
|
||
"[CV] C=1888.914851, gamma=0.739678838777, kernel=linear, total= 28.6s\n",
|
||
"[CV] C=55.5383891123, gamma=0.578634378499, kernel=linear ............\n",
|
||
"[CV] C=55.5383891123, gamma=0.578634378499, kernel=linear, total= 25.5s\n",
|
||
"[CV] C=26.7144808239, gamma=1.01172955093, kernel=rbf ................\n",
|
||
"[CV] C=55.5383891123, gamma=0.578634378499, kernel=linear, total= 26.6s\n",
|
||
"[CV] C=26.7144808239, gamma=1.01172955093, kernel=rbf ................\n",
|
||
"[CV] C=55.5383891123, gamma=0.578634378499, kernel=linear, total= 22.6s\n",
|
||
"[CV] C=26.7144808239, gamma=1.01172955093, kernel=rbf ................\n",
|
||
"[CV] C=55.5383891123, gamma=0.578634378499, kernel=linear, total= 27.5s\n",
|
||
"[CV] C=26.7144808239, gamma=1.01172955093, kernel=rbf ................\n",
|
||
"[CV] . C=26.7144808239, gamma=1.01172955093, kernel=rbf, total= 41.3s\n",
|
||
"[CV] C=26.7144808239, gamma=1.01172955093, kernel=rbf ................\n",
|
||
"[CV] . C=26.7144808239, gamma=1.01172955093, kernel=rbf, total= 42.4s\n",
|
||
"[CV] C=3582.05527805, gamma=1.18913702221, kernel=linear .............\n",
|
||
"[CV] . C=26.7144808239, gamma=1.01172955093, kernel=rbf, total= 44.8s\n",
|
||
"[CV] C=3582.05527805, gamma=1.18913702221, kernel=linear .............\n",
|
||
"[CV] . C=26.7144808239, gamma=1.01172955093, kernel=rbf, total= 40.1s\n",
|
||
"[CV] C=3582.05527805, gamma=1.18913702221, kernel=linear .............\n",
|
||
"[CV] C=3582.05527805, gamma=1.18913702221, kernel=linear, total= 28.4s\n",
|
||
"[CV] C=3582.05527805, gamma=1.18913702221, kernel=linear .............\n",
|
||
"[CV] . C=26.7144808239, gamma=1.01172955093, kernel=rbf, total= 43.6s\n",
|
||
"[CV] C=3582.05527805, gamma=1.18913702221, kernel=linear .............\n",
|
||
"[CV] C=3582.05527805, gamma=1.18913702221, kernel=linear, total= 26.9s\n",
|
||
"[CV] C=198.700478181, gamma=0.528281974883, kernel=linear ............\n",
|
||
"[CV] C=3582.05527805, gamma=1.18913702221, kernel=linear, total= 31.5s\n",
|
||
"[CV] C=198.700478181, gamma=0.528281974883, kernel=linear ............\n",
|
||
"[CV] C=3582.05527805, gamma=1.18913702221, kernel=linear, total= 27.5s\n",
|
||
"[CV] C=198.700478181, gamma=0.528281974883, kernel=linear ............\n",
|
||
"[CV] C=198.700478181, gamma=0.528281974883, kernel=linear, total= 27.7s\n",
|
||
"[CV] C=198.700478181, gamma=0.528281974883, kernel=linear ............\n",
|
||
"[CV] C=3582.05527805, gamma=1.18913702221, kernel=linear, total= 30.8s\n",
|
||
"[CV] C=198.700478181, gamma=0.528281974883, kernel=linear ............\n",
|
||
"[CV] C=198.700478181, gamma=0.528281974883, kernel=linear, total= 30.7s\n",
|
||
"[CV] C=129.800060414, gamma=2.86213836765, kernel=linear .............\n",
|
||
"[CV] C=198.700478181, gamma=0.528281974883, kernel=linear, total= 28.2s\n",
|
||
"[CV] C=129.800060414, gamma=2.86213836765, kernel=linear .............\n",
|
||
"[CV] C=198.700478181, gamma=0.528281974883, kernel=linear, total= 28.3s\n",
|
||
"[CV] C=129.800060414, gamma=2.86213836765, kernel=linear .............\n",
|
||
"[CV] C=198.700478181, gamma=0.528281974883, kernel=linear, total= 28.8s\n",
|
||
"[CV] C=129.800060414, gamma=2.86213836765, kernel=linear .............\n",
|
||
"[CV] C=129.800060414, gamma=2.86213836765, kernel=linear, total= 25.9s\n",
|
||
"[CV] C=129.800060414, gamma=2.86213836765, kernel=linear .............\n",
|
||
"[CV] C=129.800060414, gamma=2.86213836765, kernel=linear, total= 25.5s\n",
|
||
"[CV] C=288.426929959, gamma=0.1758083585, kernel=rbf .................\n",
|
||
"[CV] C=129.800060414, gamma=2.86213836765, kernel=linear, total= 24.9s\n",
|
||
"[CV] C=288.426929959, gamma=0.1758083585, kernel=rbf .................\n",
|
||
"[CV] C=129.800060414, gamma=2.86213836765, kernel=linear, total= 25.3s\n",
|
||
"[CV] C=288.426929959, gamma=0.1758083585, kernel=rbf .................\n",
|
||
"[CV] C=129.800060414, gamma=2.86213836765, kernel=linear, total= 27.1s\n",
|
||
"[CV] C=288.426929959, gamma=0.1758083585, kernel=rbf .................\n",
|
||
"[CV] .. C=288.426929959, gamma=0.1758083585, kernel=rbf, total= 40.3s\n",
|
||
"[CV] C=288.426929959, gamma=0.1758083585, kernel=rbf .................\n",
|
||
"[CV] .. C=288.426929959, gamma=0.1758083585, kernel=rbf, total= 38.7s\n",
|
||
"[CV] C=6287.03948943, gamma=0.350456725533, kernel=linear ............\n",
|
||
"[CV] .. C=288.426929959, gamma=0.1758083585, kernel=rbf, total= 40.5s\n",
|
||
"[CV] C=6287.03948943, gamma=0.350456725533, kernel=linear ............\n",
|
||
"[CV] .. C=288.426929959, gamma=0.1758083585, kernel=rbf, total= 39.5s\n",
|
||
"[CV] C=6287.03948943, gamma=0.350456725533, kernel=linear ............\n",
|
||
"[CV] C=6287.03948943, gamma=0.350456725533, kernel=linear, total= 30.6s\n",
|
||
"[CV] C=6287.03948943, gamma=0.350456725533, kernel=linear ............\n",
|
||
"[CV] C=6287.03948943, gamma=0.350456725533, kernel=linear, total= 28.8s\n",
|
||
"[CV] C=6287.03948943, gamma=0.350456725533, kernel=linear ............\n",
|
||
"[CV] .. C=288.426929959, gamma=0.1758083585, kernel=rbf, total= 38.5s\n",
|
||
"[CV] C=61217.0442134, gamma=1.62796894074, kernel=rbf ................\n",
|
||
"[CV] C=6287.03948943, gamma=0.350456725533, kernel=linear, total= 30.3s\n",
|
||
"[CV] C=61217.0442134, gamma=1.62796894074, kernel=rbf ................\n",
|
||
"[CV] C=6287.03948943, gamma=0.350456725533, kernel=linear, total= 31.1s\n",
|
||
"[CV] C=61217.0442134, gamma=1.62796894074, kernel=rbf ................\n",
|
||
"[CV] C=6287.03948943, gamma=0.350456725533, kernel=linear, total= 30.8s\n",
|
||
"[CV] C=61217.0442134, gamma=1.62796894074, kernel=rbf ................\n",
|
||
"[CV] . C=61217.0442134, gamma=1.62796894074, kernel=rbf, total= 2.2min\n",
|
||
"[CV] C=61217.0442134, gamma=1.62796894074, kernel=rbf ................\n",
|
||
"[CV] . C=61217.0442134, gamma=1.62796894074, kernel=rbf, total= 2.6min\n",
|
||
"[CV] C=926.97876841, gamma=2.14797959306, kernel=rbf .................\n",
|
||
"[CV] . C=61217.0442134, gamma=1.62796894074, kernel=rbf, total= 2.5min\n",
|
||
"[CV] C=926.97876841, gamma=2.14797959306, kernel=rbf .................\n",
|
||
"[CV] . C=61217.0442134, gamma=1.62796894074, kernel=rbf, total= 2.5min\n",
|
||
"[CV] C=926.97876841, gamma=2.14797959306, kernel=rbf .................\n",
|
||
"[CV] .. C=926.97876841, gamma=2.14797959306, kernel=rbf, total= 41.0s\n",
|
||
"[CV] C=926.97876841, gamma=2.14797959306, kernel=rbf .................\n",
|
||
"[CV] .. C=926.97876841, gamma=2.14797959306, kernel=rbf, total= 41.5s\n",
|
||
"[CV] C=926.97876841, gamma=2.14797959306, kernel=rbf .................\n",
|
||
"[CV] .. C=926.97876841, gamma=2.14797959306, kernel=rbf, total= 42.1s\n",
|
||
"[CV] C=33946.1570649, gamma=2.26424264929, kernel=linear .............\n",
|
||
"[CV] .. C=926.97876841, gamma=2.14797959306, kernel=rbf, total= 42.2s\n",
|
||
"[CV] C=33946.1570649, gamma=2.26424264929, kernel=linear .............\n",
|
||
"[CV] . C=61217.0442134, gamma=1.62796894074, kernel=rbf, total= 2.5min\n",
|
||
"[CV] C=33946.1570649, gamma=2.26424264929, kernel=linear .............\n",
|
||
"[CV] .. C=926.97876841, gamma=2.14797959306, kernel=rbf, total= 42.0s\n",
|
||
"[CV] C=33946.1570649, gamma=2.26424264929, kernel=linear .............\n",
|
||
"[CV] C=33946.1570649, gamma=2.26424264929, kernel=linear, total= 1.1min\n",
|
||
"[CV] C=33946.1570649, gamma=2.26424264929, kernel=linear .............\n",
|
||
"[CV] C=33946.1570649, gamma=2.26424264929, kernel=linear, total= 57.5s\n",
|
||
"[CV] C=84789.8294774, gamma=0.31763590853, kernel=linear .............\n",
|
||
"[CV] C=33946.1570649, gamma=2.26424264929, kernel=linear, total= 53.6s\n",
|
||
"[CV] C=84789.8294774, gamma=0.31763590853, kernel=linear .............\n",
|
||
"[CV] C=33946.1570649, gamma=2.26424264929, kernel=linear, total= 1.0min\n",
|
||
"[CV] C=84789.8294774, gamma=0.31763590853, kernel=linear .............\n",
|
||
"[CV] C=33946.1570649, gamma=2.26424264929, kernel=linear, total= 56.6s\n",
|
||
"[CV] C=84789.8294774, gamma=0.31763590853, kernel=linear .............\n",
|
||
"[CV] C=84789.8294774, gamma=0.31763590853, kernel=linear, total= 1.7min\n",
|
||
"[CV] C=84789.8294774, gamma=0.31763590853, kernel=linear .............\n",
|
||
"[CV] C=84789.8294774, gamma=0.31763590853, kernel=linear, total= 2.5min\n",
|
||
"[CV] C=84789.8294774, gamma=0.31763590853, kernel=linear, total= 1.9min\n",
|
||
"[CV] C=84789.8294774, gamma=0.31763590853, kernel=linear, total= 2.4min\n",
|
||
"[CV] C=84789.8294774, gamma=0.31763590853, kernel=linear, total= 1.1min\n"
|
||
]
|
||
},
|
||
{
|
||
"name": "stderr",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"[Parallel(n_jobs=4)]: Done 250 out of 250 | elapsed: 73.0min finished\n"
|
||
]
|
||
},
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"RandomizedSearchCV(cv=5, error_score='raise',\n",
|
||
" estimator=SVR(C=1.0, cache_size=200, coef0=0.0, degree=3, epsilon=0.1, gamma='auto',\n",
|
||
" kernel='rbf', max_iter=-1, shrinking=True, tol=0.001, verbose=False),\n",
|
||
" fit_params={}, iid=True, n_iter=50, n_jobs=4,\n",
|
||
" param_distributions={'gamma': <scipy.stats._distn_infrastructure.rv_frozen object at 0x7f865397d1d0>, 'C': <scipy.stats._distn_infrastructure.rv_frozen object at 0x7f865397d0f0>, 'kernel': ['linear', 'rbf']},\n",
|
||
" pre_dispatch='2*n_jobs', random_state=42, refit=True,\n",
|
||
" return_train_score=True, scoring='neg_mean_squared_error',\n",
|
||
" verbose=2)"
|
||
]
|
||
},
|
||
"execution_count": 105,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"from sklearn.model_selection import RandomizedSearchCV\n",
|
||
"from scipy.stats import expon, reciprocal\n",
|
||
"\n",
|
||
"# see https://docs.scipy.org/doc/scipy-0.19.0/reference/stats.html\n",
|
||
"# for `expon()` and `reciprocal()` documentation and more probability distribution functions.\n",
|
||
"\n",
|
||
"# Note: gamma is ignored when kernel is \"linear\"\n",
|
||
"param_distribs = {\n",
|
||
" 'kernel': ['linear', 'rbf'],\n",
|
||
" 'C': reciprocal(20, 200000),\n",
|
||
" 'gamma': expon(scale=1.0),\n",
|
||
" }\n",
|
||
"\n",
|
||
"svm_reg = SVR()\n",
|
||
"rnd_search = RandomizedSearchCV(svm_reg, param_distributions=param_distribs,\n",
|
||
" n_iter=50, cv=5, scoring='neg_mean_squared_error',\n",
|
||
" verbose=2, n_jobs=4, random_state=42)\n",
|
||
"rnd_search.fit(housing_prepared, housing_labels)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"The best model achieves the following score (evaluated using 5-fold cross validation):"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 106,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"54767.99053704409"
|
||
]
|
||
},
|
||
"execution_count": 106,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"negative_mse = rnd_search.best_score_\n",
|
||
"rmse = np.sqrt(-negative_mse)\n",
|
||
"rmse"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"Now this is much closer to the performance of the `RandomForestRegressor` (but not quite there yet). Let's check the best hyperparameters found:"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 107,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"{'C': 157055.10989448498, 'gamma': 0.26497040005002437, 'kernel': 'rbf'}"
|
||
]
|
||
},
|
||
"execution_count": 107,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"rnd_search.best_params_"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"This time the search found a good set of hyperparameters for the RBF kernel. Randomized search tends to find better hyperparameters than grid search in the same amount of time."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"Let's look at the exponential distribution we used, with `scale=1.0`. Note that some samples are much larger or smaller than 1.0, but when you look at the log of the distribution, you can see that most values are actually concentrated roughly in the range of exp(-2) to exp(+2), which is about 0.1 to 7.4."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 108,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"image/png": 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JLbwnkequ2xnaWtxP6q7ZlTRn2onaPO/baNvaFlCaR+wFuburMg/jf5RZJrNWaygQi4ir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\nzMysJA7EzMzMzEry/wFvVcUKvodwIQAAAABJRU5ErkJggg==\n",
|
||
"text/plain": [
|
||
"<matplotlib.figure.Figure at 0x7f86539758d0>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"expon_distrib = expon(scale=1.)\n",
|
||
"samples = expon_distrib.rvs(10000, random_state=42)\n",
|
||
"plt.figure(figsize=(10, 4))\n",
|
||
"plt.subplot(121)\n",
|
||
"plt.title(\"Exponential distribution (scale=1.0)\")\n",
|
||
"plt.hist(samples, bins=50)\n",
|
||
"plt.subplot(122)\n",
|
||
"plt.title(\"Log of this distribution\")\n",
|
||
"plt.hist(np.log(samples), bins=50)\n",
|
||
"plt.show()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"The distribution we used for `C` looks quite different: the scale of the samples is picked from a uniform distribution within a given range, which is why the right graph, which represents the log of the samples, looks roughly constant. This distribution is useful when you don't have a clue of what the target scale is:"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 109,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"image/png": 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tIqaTTjV+WNJcUk/OuaSx67uB6wtZ+0ndo/sCRwPnaMcaX+3ymlVSRPygF4MySCv/R8Rr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\nEAAAAABJRU5ErkJggg==\n",
|
||
"text/plain": [
|
||
"<matplotlib.figure.Figure at 0x7f863b0de7f0>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"reciprocal_distrib = reciprocal(20, 200000)\n",
|
||
"samples = reciprocal_distrib.rvs(10000, random_state=42)\n",
|
||
"plt.figure(figsize=(10, 4))\n",
|
||
"plt.subplot(121)\n",
|
||
"plt.title(\"Reciprocal distribution (scale=1.0)\")\n",
|
||
"plt.hist(samples, bins=50)\n",
|
||
"plt.subplot(122)\n",
|
||
"plt.title(\"Log of this distribution\")\n",
|
||
"plt.hist(np.log(samples), bins=50)\n",
|
||
"plt.show()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"The reciprocal distribution is useful when you have no idea what the scale of the hyperparameter should be (indeed, as you can see on the figure on the right, all scales are equally likely, within the given range), whereas the exponential distribution is best when you know (more or less) what the scale of the hyperparameter should be."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"## 3."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"Question: Try adding a transformer in the preparation pipeline to select only the most important attributes."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 110,
|
||
"metadata": {
|
||
"collapsed": true
|
||
},
|
||
"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": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"Note: this feature selector assumes that you have already computed the feature importances somehow (for example using a `RandomForestRegressor`). You may be tempted to compute them directly in the `TopFeatureSelector`'s `fit()` method, however this would likely slow down grid/randomized search since the feature importances would have to be computed for every hyperparameter combination (unless you implement some sort of cache)."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"Let's define the number of top features we want to keep:"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 111,
|
||
"metadata": {
|
||
"collapsed": true
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"k = 5"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"Now let's look for the indices of the top k features:"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 112,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"array([ 0, 1, 7, 9, 12])"
|
||
]
|
||
},
|
||
"execution_count": 112,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"top_k_feature_indices = indices_of_top_k(feature_importances, k)\n",
|
||
"top_k_feature_indices"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 113,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"array(['longitude', 'latitude', 'median_income', 'pop_per_hhold', 'INLAND'], \n",
|
||
" dtype='<U18')"
|
||
]
|
||
},
|
||
"execution_count": 113,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"np.array(attributes)[top_k_feature_indices]"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"Let's double check that these are indeed the top k features:"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 114,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"[(0.36615898061813418, 'median_income'),\n",
|
||
" (0.16478099356159051, 'INLAND'),\n",
|
||
" (0.10879295677551573, 'pop_per_hhold'),\n",
|
||
" (0.073344235516012421, 'longitude'),\n",
|
||
" (0.062909070482620302, 'latitude')]"
|
||
]
|
||
},
|
||
"execution_count": 114,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"sorted(zip(feature_importances, attributes), reverse=True)[:k]"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"Looking good... Now let's create a new pipeline that runs the previously defined preparation pipeline, and adds top k feature selection:"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 115,
|
||
"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": 116,
|
||
"metadata": {
|
||
"collapsed": true
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"housing_prepared_top_k_features = preparation_and_feature_selection_pipeline.fit_transform(housing)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"Let's look at the features of the first 3 instances:"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 117,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"array([[-1.15604281, 0.77194962, -0.61493744, -0.08649871, 0. ],\n",
|
||
" [-1.17602483, 0.6596948 , 1.33645936, -0.03353391, 0. ],\n",
|
||
" [ 1.18684903, -1.34218285, -0.5320456 , -0.09240499, 0. ]])"
|
||
]
|
||
},
|
||
"execution_count": 117,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"housing_prepared_top_k_features[0:3]"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"Now let's double check that these are indeed the top k features:"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 118,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"array([[-1.15604281, 0.77194962, -0.61493744, -0.08649871, 0. ],\n",
|
||
" [-1.17602483, 0.6596948 , 1.33645936, -0.03353391, 0. ],\n",
|
||
" [ 1.18684903, -1.34218285, -0.5320456 , -0.09240499, 0. ]])"
|
||
]
|
||
},
|
||
"execution_count": 118,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"housing_prepared[0:3, top_k_feature_indices]"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"Works great! :)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"## 4."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"Question: Try creating a single pipeline that does the full data preparation plus the final prediction."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 119,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"prepare_select_and_predict_pipeline = Pipeline([\n",
|
||
" ('preparation', full_pipeline),\n",
|
||
" ('feature_selection', TopFeatureSelector(feature_importances, k)),\n",
|
||
" ('svm_reg', SVR(**rnd_search.best_params_))\n",
|
||
"])"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 120,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"Pipeline(steps=[('preparation', FeatureUnion(n_jobs=1,\n",
|
||
" transformer_list=[('num_pipeline', Pipeline(steps=[('selector', DataFrameSelector(attribute_names=['longitude', 'latitude', 'housing_median_age', 'total_rooms', 'total_bedrooms', 'population', 'households', 'median_income'])), ('imputer', Imputer(... gamma=0.26497040005002437, kernel='rbf', max_iter=-1, shrinking=True,\n",
|
||
" tol=0.001, verbose=False))])"
|
||
]
|
||
},
|
||
"execution_count": 120,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"prepare_select_and_predict_pipeline.fit(housing, housing_labels)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"Let's try the full pipeline on a few instances:"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 121,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Predictions:\t [ 203214.28978849 371846.88152572 173295.65441612 47328.3970888 ]\n",
|
||
"Labels:\t\t [286600.0, 340600.0, 196900.0, 46300.0]\n"
|
||
]
|
||
}
|
||
],
|
||
"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": [
|
||
"Well, the full pipeline seems to work fine. Of course, the predictions are not fantastic: they would be better if we used the best `RandomForestRegressor` that we found earlier, rather than the best `SVR`."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"## 5."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"Question: Automatically explore some preparation options using `GridSearchCV`."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 122,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Fitting 5 folds for each of 15 candidates, totalling 75 fits\n",
|
||
"[CV] preparation__num_pipeline__imputer__strategy=mean, feature_selection__k=3 \n",
|
||
"[CV] preparation__num_pipeline__imputer__strategy=mean, feature_selection__k=3 \n",
|
||
"[CV] preparation__num_pipeline__imputer__strategy=mean, feature_selection__k=3 \n",
|
||
"[CV] preparation__num_pipeline__imputer__strategy=mean, feature_selection__k=3 \n",
|
||
"[CV] preparation__num_pipeline__imputer__strategy=mean, feature_selection__k=3, total= 34.4s\n",
|
||
"[CV] preparation__num_pipeline__imputer__strategy=mean, feature_selection__k=3 \n",
|
||
"[CV] preparation__num_pipeline__imputer__strategy=mean, feature_selection__k=3, total= 34.6s\n",
|
||
"[CV] preparation__num_pipeline__imputer__strategy=median, feature_selection__k=3 \n",
|
||
"[CV] preparation__num_pipeline__imputer__strategy=mean, feature_selection__k=3, total= 35.4s\n",
|
||
"[CV] preparation__num_pipeline__imputer__strategy=median, feature_selection__k=3 \n",
|
||
"[CV] preparation__num_pipeline__imputer__strategy=mean, feature_selection__k=3, total= 37.4s\n",
|
||
"[CV] preparation__num_pipeline__imputer__strategy=median, feature_selection__k=3 \n",
|
||
"[CV] preparation__num_pipeline__imputer__strategy=median, feature_selection__k=3, total= 32.9s\n",
|
||
"[CV] preparation__num_pipeline__imputer__strategy=median, feature_selection__k=3 \n",
|
||
"[CV] preparation__num_pipeline__imputer__strategy=median, feature_selection__k=3, total= 34.9s\n",
|
||
"[CV] preparation__num_pipeline__imputer__strategy=median, feature_selection__k=3 \n",
|
||
"[CV] preparation__num_pipeline__imputer__strategy=mean, feature_selection__k=3, total= 36.7s\n",
|
||
"[CV] preparation__num_pipeline__imputer__strategy=most_frequent, feature_selection__k=3 \n",
|
||
"[CV] preparation__num_pipeline__imputer__strategy=median, feature_selection__k=3, total= 36.2s\n",
|
||
"[CV] preparation__num_pipeline__imputer__strategy=most_frequent, feature_selection__k=3 \n",
|
||
"[CV] preparation__num_pipeline__imputer__strategy=median, feature_selection__k=3, total= 35.4s\n",
|
||
"[CV] preparation__num_pipeline__imputer__strategy=most_frequent, feature_selection__k=3 \n",
|
||
"[CV] preparation__num_pipeline__imputer__strategy=median, feature_selection__k=3, total= 35.3s\n",
|
||
"[CV] preparation__num_pipeline__imputer__strategy=most_frequent, feature_selection__k=3 \n",
|
||
"[CV] preparation__num_pipeline__imputer__strategy=most_frequent, feature_selection__k=3, total= 35.4s\n",
|
||
"[CV] preparation__num_pipeline__imputer__strategy=most_frequent, feature_selection__k=3 \n",
|
||
"[CV] preparation__num_pipeline__imputer__strategy=most_frequent, feature_selection__k=3, total= 38.9s\n",
|
||
"[CV] preparation__num_pipeline__imputer__strategy=mean, feature_selection__k=4 \n",
|
||
"[CV] preparation__num_pipeline__imputer__strategy=most_frequent, feature_selection__k=3, total= 34.3s\n",
|
||
"[CV] preparation__num_pipeline__imputer__strategy=mean, feature_selection__k=4 \n",
|
||
"[CV] preparation__num_pipeline__imputer__strategy=most_frequent, feature_selection__k=3, total= 35.6s\n",
|
||
"[CV] preparation__num_pipeline__imputer__strategy=mean, feature_selection__k=4 \n",
|
||
"[CV] preparation__num_pipeline__imputer__strategy=most_frequent, feature_selection__k=3, total= 35.7s\n",
|
||
"[CV] preparation__num_pipeline__imputer__strategy=mean, feature_selection__k=4 \n",
|
||
"[CV] preparation__num_pipeline__imputer__strategy=mean, feature_selection__k=4, total= 36.3s\n",
|
||
"[CV] preparation__num_pipeline__imputer__strategy=mean, feature_selection__k=4 \n",
|
||
"[CV] preparation__num_pipeline__imputer__strategy=mean, feature_selection__k=4, total= 41.0s\n",
|
||
"[CV] preparation__num_pipeline__imputer__strategy=median, feature_selection__k=4 \n",
|
||
"[CV] preparation__num_pipeline__imputer__strategy=mean, feature_selection__k=4, total= 45.0s\n",
|
||
"[CV] preparation__num_pipeline__imputer__strategy=median, feature_selection__k=4 \n",
|
||
"[CV] preparation__num_pipeline__imputer__strategy=mean, feature_selection__k=4, total= 46.0s\n",
|
||
"[CV] preparation__num_pipeline__imputer__strategy=median, feature_selection__k=4 \n",
|
||
"[CV] preparation__num_pipeline__imputer__strategy=mean, feature_selection__k=4, total= 45.7s\n",
|
||
"[CV] preparation__num_pipeline__imputer__strategy=median, feature_selection__k=4 \n",
|
||
"[CV] preparation__num_pipeline__imputer__strategy=median, feature_selection__k=4, total= 38.5s\n",
|
||
"[CV] preparation__num_pipeline__imputer__strategy=median, feature_selection__k=4 \n",
|
||
"[CV] preparation__num_pipeline__imputer__strategy=median, feature_selection__k=4, total= 37.4s\n",
|
||
"[CV] preparation__num_pipeline__imputer__strategy=most_frequent, feature_selection__k=4 \n",
|
||
"[CV] preparation__num_pipeline__imputer__strategy=median, feature_selection__k=4, total= 40.8s\n",
|
||
"[CV] preparation__num_pipeline__imputer__strategy=most_frequent, feature_selection__k=4 \n",
|
||
"[CV] preparation__num_pipeline__imputer__strategy=median, feature_selection__k=4, total= 39.7s\n",
|
||
"[CV] preparation__num_pipeline__imputer__strategy=most_frequent, feature_selection__k=4 \n",
|
||
"[CV] preparation__num_pipeline__imputer__strategy=median, feature_selection__k=4, total= 44.3s\n",
|
||
"[CV] preparation__num_pipeline__imputer__strategy=most_frequent, feature_selection__k=4 \n",
|
||
"[CV] preparation__num_pipeline__imputer__strategy=most_frequent, feature_selection__k=4, total= 44.7s\n",
|
||
"[CV] preparation__num_pipeline__imputer__strategy=most_frequent, feature_selection__k=4 \n",
|
||
"[CV] preparation__num_pipeline__imputer__strategy=most_frequent, feature_selection__k=4, total= 43.5s\n",
|
||
"[CV] preparation__num_pipeline__imputer__strategy=mean, feature_selection__k=5 \n",
|
||
"[CV] preparation__num_pipeline__imputer__strategy=most_frequent, feature_selection__k=4, total= 42.3s\n",
|
||
"[CV] preparation__num_pipeline__imputer__strategy=mean, feature_selection__k=5 \n",
|
||
"[CV] preparation__num_pipeline__imputer__strategy=most_frequent, feature_selection__k=4, total= 40.9s\n",
|
||
"[CV] preparation__num_pipeline__imputer__strategy=mean, feature_selection__k=5 \n",
|
||
"[CV] preparation__num_pipeline__imputer__strategy=most_frequent, feature_selection__k=4, total= 42.8s\n",
|
||
"[CV] preparation__num_pipeline__imputer__strategy=mean, feature_selection__k=5 \n",
|
||
"[CV] preparation__num_pipeline__imputer__strategy=mean, feature_selection__k=5, total= 40.3s\n",
|
||
"[CV] preparation__num_pipeline__imputer__strategy=mean, feature_selection__k=5 \n",
|
||
"[CV] preparation__num_pipeline__imputer__strategy=mean, feature_selection__k=5, total= 42.3s\n",
|
||
"[CV] preparation__num_pipeline__imputer__strategy=median, feature_selection__k=5 \n",
|
||
"[CV] preparation__num_pipeline__imputer__strategy=mean, feature_selection__k=5, total= 38.2s\n",
|
||
"[CV] preparation__num_pipeline__imputer__strategy=median, feature_selection__k=5 \n"
|
||
]
|
||
},
|
||
{
|
||
"name": "stderr",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"[Parallel(n_jobs=4)]: Done 33 tasks | elapsed: 8.3min\n"
|
||
]
|
||
},
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"[CV] preparation__num_pipeline__imputer__strategy=mean, feature_selection__k=5, total= 40.0s\n",
|
||
"[CV] preparation__num_pipeline__imputer__strategy=median, feature_selection__k=5 \n",
|
||
"[CV] preparation__num_pipeline__imputer__strategy=mean, feature_selection__k=5, total= 39.9s\n",
|
||
"[CV] preparation__num_pipeline__imputer__strategy=median, feature_selection__k=5 \n",
|
||
"[CV] preparation__num_pipeline__imputer__strategy=median, feature_selection__k=5, total= 39.5s\n",
|
||
"[CV] preparation__num_pipeline__imputer__strategy=median, feature_selection__k=5 \n",
|
||
"[CV] preparation__num_pipeline__imputer__strategy=median, feature_selection__k=5, total= 40.5s\n",
|
||
"[CV] preparation__num_pipeline__imputer__strategy=most_frequent, feature_selection__k=5 \n",
|
||
"[CV] preparation__num_pipeline__imputer__strategy=median, feature_selection__k=5, total= 42.2s\n",
|
||
"[CV] preparation__num_pipeline__imputer__strategy=most_frequent, feature_selection__k=5 \n",
|
||
"[CV] preparation__num_pipeline__imputer__strategy=median, feature_selection__k=5, total= 39.3s\n",
|
||
"[CV] preparation__num_pipeline__imputer__strategy=most_frequent, feature_selection__k=5 \n",
|
||
"[CV] preparation__num_pipeline__imputer__strategy=median, feature_selection__k=5, total= 40.1s\n",
|
||
"[CV] preparation__num_pipeline__imputer__strategy=most_frequent, feature_selection__k=5 \n",
|
||
"[CV] preparation__num_pipeline__imputer__strategy=most_frequent, feature_selection__k=5, total= 41.5s\n",
|
||
"[CV] preparation__num_pipeline__imputer__strategy=most_frequent, feature_selection__k=5 \n",
|
||
"[CV] preparation__num_pipeline__imputer__strategy=most_frequent, feature_selection__k=5, total= 42.3s\n",
|
||
"[CV] preparation__num_pipeline__imputer__strategy=mean, feature_selection__k=6 \n",
|
||
"[CV] preparation__num_pipeline__imputer__strategy=most_frequent, feature_selection__k=5, total= 45.1s\n",
|
||
"[CV] preparation__num_pipeline__imputer__strategy=mean, feature_selection__k=6 \n",
|
||
"[CV] preparation__num_pipeline__imputer__strategy=most_frequent, feature_selection__k=5, total= 43.8s\n",
|
||
"[CV] preparation__num_pipeline__imputer__strategy=mean, feature_selection__k=6 \n",
|
||
"[CV] preparation__num_pipeline__imputer__strategy=most_frequent, feature_selection__k=5, total= 43.4s\n",
|
||
"[CV] preparation__num_pipeline__imputer__strategy=mean, feature_selection__k=6 \n",
|
||
"[CV] preparation__num_pipeline__imputer__strategy=mean, feature_selection__k=6, total= 41.7s\n",
|
||
"[CV] preparation__num_pipeline__imputer__strategy=mean, feature_selection__k=6 \n",
|
||
"[CV] preparation__num_pipeline__imputer__strategy=mean, feature_selection__k=6, total= 43.8s\n",
|
||
"[CV] preparation__num_pipeline__imputer__strategy=median, feature_selection__k=6 \n",
|
||
"[CV] preparation__num_pipeline__imputer__strategy=mean, feature_selection__k=6, total= 40.8s\n",
|
||
"[CV] preparation__num_pipeline__imputer__strategy=median, feature_selection__k=6 \n",
|
||
"[CV] preparation__num_pipeline__imputer__strategy=mean, feature_selection__k=6, total= 41.4s\n",
|
||
"[CV] preparation__num_pipeline__imputer__strategy=median, feature_selection__k=6 \n",
|
||
"[CV] preparation__num_pipeline__imputer__strategy=mean, feature_selection__k=6, total= 43.2s\n",
|
||
"[CV] preparation__num_pipeline__imputer__strategy=median, feature_selection__k=6 \n",
|
||
"[CV] preparation__num_pipeline__imputer__strategy=median, feature_selection__k=6, total= 40.0s\n",
|
||
"[CV] preparation__num_pipeline__imputer__strategy=median, feature_selection__k=6 \n",
|
||
"[CV] preparation__num_pipeline__imputer__strategy=median, feature_selection__k=6, total= 38.9s\n",
|
||
"[CV] preparation__num_pipeline__imputer__strategy=most_frequent, feature_selection__k=6 \n",
|
||
"[CV] preparation__num_pipeline__imputer__strategy=median, feature_selection__k=6, total= 38.6s\n",
|
||
"[CV] preparation__num_pipeline__imputer__strategy=most_frequent, feature_selection__k=6 \n",
|
||
"[CV] preparation__num_pipeline__imputer__strategy=median, feature_selection__k=6, total= 35.4s\n",
|
||
"[CV] preparation__num_pipeline__imputer__strategy=most_frequent, feature_selection__k=6 \n",
|
||
"[CV] preparation__num_pipeline__imputer__strategy=median, feature_selection__k=6, total= 38.0s\n",
|
||
"[CV] preparation__num_pipeline__imputer__strategy=most_frequent, feature_selection__k=6 \n",
|
||
"[CV] preparation__num_pipeline__imputer__strategy=most_frequent, feature_selection__k=6, total= 39.4s\n",
|
||
"[CV] preparation__num_pipeline__imputer__strategy=most_frequent, feature_selection__k=6 \n",
|
||
"[CV] preparation__num_pipeline__imputer__strategy=most_frequent, feature_selection__k=6, total= 49.1s\n",
|
||
"[CV] preparation__num_pipeline__imputer__strategy=mean, feature_selection__k=7 \n",
|
||
"[CV] preparation__num_pipeline__imputer__strategy=most_frequent, feature_selection__k=6, total= 50.5s\n",
|
||
"[CV] preparation__num_pipeline__imputer__strategy=mean, feature_selection__k=7 \n",
|
||
"[CV] preparation__num_pipeline__imputer__strategy=most_frequent, feature_selection__k=6, total= 48.6s\n",
|
||
"[CV] preparation__num_pipeline__imputer__strategy=mean, feature_selection__k=7 \n",
|
||
"[CV] preparation__num_pipeline__imputer__strategy=most_frequent, feature_selection__k=6, total= 56.5s\n",
|
||
"[CV] preparation__num_pipeline__imputer__strategy=mean, feature_selection__k=7 \n",
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"[CV] preparation__num_pipeline__imputer__strategy=mean, feature_selection__k=7, total= 54.3s\n",
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"[CV] preparation__num_pipeline__imputer__strategy=mean, feature_selection__k=7 \n",
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"[CV] preparation__num_pipeline__imputer__strategy=mean, feature_selection__k=7, total= 50.6s\n",
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"[CV] preparation__num_pipeline__imputer__strategy=median, feature_selection__k=7 \n",
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"[CV] preparation__num_pipeline__imputer__strategy=mean, feature_selection__k=7, total= 54.2s\n",
|
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"[CV] preparation__num_pipeline__imputer__strategy=median, feature_selection__k=7 \n",
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"[CV] preparation__num_pipeline__imputer__strategy=mean, feature_selection__k=7, total= 53.5s\n",
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"[CV] preparation__num_pipeline__imputer__strategy=median, feature_selection__k=7 \n",
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||
"[CV] preparation__num_pipeline__imputer__strategy=mean, feature_selection__k=7, total= 44.6s\n",
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"[CV] preparation__num_pipeline__imputer__strategy=median, feature_selection__k=7 \n",
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"[CV] preparation__num_pipeline__imputer__strategy=median, feature_selection__k=7, total= 46.5s\n",
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"[CV] preparation__num_pipeline__imputer__strategy=median, feature_selection__k=7 \n",
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"[CV] preparation__num_pipeline__imputer__strategy=median, feature_selection__k=7, total= 43.1s\n",
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"[CV] preparation__num_pipeline__imputer__strategy=most_frequent, feature_selection__k=7 \n",
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"[CV] preparation__num_pipeline__imputer__strategy=median, feature_selection__k=7, total= 44.6s\n",
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"[CV] preparation__num_pipeline__imputer__strategy=most_frequent, feature_selection__k=7 \n",
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"[CV] preparation__num_pipeline__imputer__strategy=median, feature_selection__k=7, total= 42.4s\n",
|
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"[CV] preparation__num_pipeline__imputer__strategy=most_frequent, feature_selection__k=7 \n",
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||
"[CV] preparation__num_pipeline__imputer__strategy=median, feature_selection__k=7, total= 42.3s\n",
|
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"[CV] preparation__num_pipeline__imputer__strategy=most_frequent, feature_selection__k=7 \n",
|
||
"[CV] preparation__num_pipeline__imputer__strategy=most_frequent, feature_selection__k=7, total= 46.0s\n",
|
||
"[CV] preparation__num_pipeline__imputer__strategy=most_frequent, feature_selection__k=7 \n",
|
||
"[CV] preparation__num_pipeline__imputer__strategy=most_frequent, feature_selection__k=7, total= 44.5s\n",
|
||
"[CV] preparation__num_pipeline__imputer__strategy=most_frequent, feature_selection__k=7, total= 42.2s\n",
|
||
"[CV] preparation__num_pipeline__imputer__strategy=most_frequent, feature_selection__k=7, total= 34.3s\n",
|
||
"[CV] preparation__num_pipeline__imputer__strategy=most_frequent, feature_selection__k=7, total= 36.1s\n"
|
||
]
|
||
},
|
||
{
|
||
"name": "stderr",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"[Parallel(n_jobs=4)]: Done 75 out of 75 | elapsed: 18.7min finished\n"
|
||
]
|
||
},
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"GridSearchCV(cv=5, error_score='raise',\n",
|
||
" estimator=Pipeline(steps=[('preparation', FeatureUnion(n_jobs=1,\n",
|
||
" transformer_list=[('num_pipeline', Pipeline(steps=[('selector', DataFrameSelector(attribute_names=['longitude', 'latitude', 'housing_median_age', 'total_rooms', 'total_bedrooms', 'population', 'households', 'median_income'])), ('imputer', Imputer(... gamma=0.26497040005002437, kernel='rbf', max_iter=-1, shrinking=True,\n",
|
||
" tol=0.001, verbose=False))]),\n",
|
||
" fit_params={}, iid=True, n_jobs=4,\n",
|
||
" param_grid=[{'preparation__num_pipeline__imputer__strategy': ['mean', 'median', 'most_frequent'], 'feature_selection__k': [3, 4, 5, 6, 7]}],\n",
|
||
" pre_dispatch='2*n_jobs', refit=True, return_train_score=True,\n",
|
||
" scoring='neg_mean_squared_error', verbose=2)"
|
||
]
|
||
},
|
||
"execution_count": 122,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"param_grid = [\n",
|
||
" {'preparation__num_pipeline__imputer__strategy': ['mean', 'median', 'most_frequent'],\n",
|
||
" 'feature_selection__k': [3, 4, 5, 6, 7]}\n",
|
||
"]\n",
|
||
"\n",
|
||
"grid_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": 123,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"{'feature_selection__k': 7,\n",
|
||
" 'preparation__num_pipeline__imputer__strategy': 'median'}"
|
||
]
|
||
},
|
||
"execution_count": 123,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"grid_search_prep.best_params_"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"Great! It seems that we had the right imputer stragegy (mean), and apparently only the top 7 features are useful (out of 9), the last 2 seem to just add some noise."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 124,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"(16512, 9)"
|
||
]
|
||
},
|
||
"execution_count": 124,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"housing.shape"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"Congratulations! You already know quite a lot about Machine Learning. :)"
|
||
]
|
||
}
|
||
],
|
||
"metadata": {
|
||
"kernelspec": {
|
||
"display_name": "Python 3",
|
||
"language": "python",
|
||
"name": "python3"
|
||
},
|
||
"language_info": {
|
||
"codemirror_mode": {
|
||
"name": "ipython",
|
||
"version": 3
|
||
},
|
||
"file_extension": ".py",
|
||
"mimetype": "text/x-python",
|
||
"name": "python",
|
||
"nbconvert_exporter": "python",
|
||
"pygments_lexer": "ipython3",
|
||
"version": "3.5.3"
|
||
},
|
||
"nav_menu": {
|
||
"height": "279px",
|
||
"width": "309px"
|
||
},
|
||
"toc": {
|
||
"navigate_menu": true,
|
||
"number_sections": true,
|
||
"sideBar": true,
|
||
"threshold": 6,
|
||
"toc_cell": false,
|
||
"toc_section_display": "block",
|
||
"toc_window_display": false
|
||
}
|
||
},
|
||
"nbformat": 4,
|
||
"nbformat_minor": 1
|
||
}
|