01- Housing Prices.ipynb

{
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>longitude</th>\n",
       "      <th>latitude</th>\n",
       "      <th>housing_median_age</th>\n",
       "      <th>total_rooms</th>\n",
       "      <th>total_bedrooms</th>\n",
       "      <th>population</th>\n",
       "      <th>households</th>\n",
       "      <th>median_income</th>\n",
       "      <th>median_house_value</th>\n",
       "      <th>ocean_proximity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-122.23</td>\n",
       "      <td>37.88</td>\n",
       "      <td>41.0</td>\n",
       "      <td>880.0</td>\n",
       "      <td>129.0</td>\n",
       "      <td>322.0</td>\n",
       "      <td>126.0</td>\n",
       "      <td>8.3252</td>\n",
       "      <td>452600.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-122.22</td>\n",
       "      <td>37.86</td>\n",
       "      <td>21.0</td>\n",
       "      <td>7099.0</td>\n",
       "      <td>1106.0</td>\n",
       "      <td>2401.0</td>\n",
       "      <td>1138.0</td>\n",
       "      <td>8.3014</td>\n",
       "      <td>358500.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-122.24</td>\n",
       "      <td>37.85</td>\n",
       "      <td>52.0</td>\n",
       "      <td>1467.0</td>\n",
       "      <td>190.0</td>\n",
       "      <td>496.0</td>\n",
       "      <td>177.0</td>\n",
       "      <td>7.2574</td>\n",
       "      <td>352100.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-122.25</td>\n",
       "      <td>37.85</td>\n",
       "      <td>52.0</td>\n",
       "      <td>1274.0</td>\n",
       "      <td>235.0</td>\n",
       "      <td>558.0</td>\n",
       "      <td>219.0</td>\n",
       "      <td>5.6431</td>\n",
       "      <td>341300.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-122.25</td>\n",
       "      <td>37.85</td>\n",
       "      <td>52.0</td>\n",
       "      <td>1627.0</td>\n",
       "      <td>280.0</td>\n",
       "      <td>565.0</td>\n",
       "      <td>259.0</td>\n",
       "      <td>3.8462</td>\n",
       "      <td>342200.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   longitude  latitude  housing_median_age  total_rooms  total_bedrooms  \\\n",
       "0    -122.23     37.88                41.0        880.0           129.0   \n",
       "1    -122.22     37.86                21.0       7099.0          1106.0   \n",
       "2    -122.24     37.85                52.0       1467.0           190.0   \n",
       "3    -122.25     37.85                52.0       1274.0           235.0   \n",
       "4    -122.25     37.85                52.0       1627.0           280.0   \n",
       "\n",
       "   population  households  median_income  median_house_value ocean_proximity  \n",
       "0       322.0       126.0         8.3252            452600.0        NEAR BAY  \n",
       "1      2401.0      1138.0         8.3014            358500.0        NEAR BAY  \n",
       "2       496.0       177.0         7.2574            352100.0        NEAR BAY  \n",
       "3       558.0       219.0         5.6431            341300.0        NEAR BAY  \n",
       "4       565.0       259.0         3.8462            342200.0        NEAR BAY  "
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "housing = pd.read_csv(\"housing.csv\")\n",
    "housing.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- **1. longitude:** A measure of how far west a house is; a higher value is farther west\n",
    "\n",
    "- **2. latitude:** A measure of how far north a house is; a higher value is farther north\n",
    "\n",
    "- **3. housingMedianAge:** Median age of a house within a block; a lower number is a newer building\n",
    "\n",
    "- **4. totalRooms:** Total number of rooms within a block\n",
    "\n",
    "- **5. totalBedrooms:** Total number of bedrooms within a block\n",
    "\n",
    "- **6. population:** Total number of people residing within a block\n",
    "\n",
    "- **7. households:** Total number of households, a group of people residing within a home unit, for a block\n",
    "\n",
    "- **8. medianIncome:** Median income for households within a block of houses (measured in tens of thousands of US Dollars)\n",
    "\n",
    "- **9. medianHouseValue:** Median house value for households within a block (measured in US Dollars)\n",
    "\n",
    "- **10. oceanProximity:** Location of the house w.r.t ocean/sea"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(20640, 10)"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 20640 entries, 0 to 20639\n",
      "Data columns (total 10 columns):\n",
      " #   Column              Non-Null Count  Dtype  \n",
      "---  ------              --------------  -----  \n",
      " 0   longitude           20640 non-null  float64\n",
      " 1   latitude            20640 non-null  float64\n",
      " 2   housing_median_age  20640 non-null  float64\n",
      " 3   total_rooms         20640 non-null  float64\n",
      " 4   total_bedrooms      20433 non-null  float64\n",
      " 5   population          20640 non-null  float64\n",
      " 6   households          20640 non-null  float64\n",
      " 7   median_income       20640 non-null  float64\n",
      " 8   median_house_value  20640 non-null  float64\n",
      " 9   ocean_proximity     20640 non-null  object \n",
      "dtypes: float64(9), object(1)\n",
      "memory usage: 1.6+ MB\n"
     ]
    }
   ],
   "source": [
    "housing.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "longitude               0\n",
       "latitude                0\n",
       "housing_median_age      0\n",
       "total_rooms             0\n",
       "total_bedrooms        207\n",
       "population              0\n",
       "households              0\n",
       "median_income           0\n",
       "median_house_value      0\n",
       "ocean_proximity         0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### You can find out what categories exist and how many districts belong to each category by using the value_counts() method:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing[\"ocean_proximity\"].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Let’s look at the other fields. The describe() method shows a summary of the numerical attributes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>longitude</th>\n",
       "      <th>latitude</th>\n",
       "      <th>housing_median_age</th>\n",
       "      <th>total_rooms</th>\n",
       "      <th>total_bedrooms</th>\n",
       "      <th>population</th>\n",
       "      <th>households</th>\n",
       "      <th>median_income</th>\n",
       "      <th>median_house_value</th>\n",
       "    </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": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Summary of each numerical attribute\n",
    "- The count, mean, min and max are self-explanatory.Note that the null values are ignored (so, for example, the count of total_bedrooms is 20,433, not 20,640).\n",
    "- The std row shows the standard deviation, which measures how dispersed the values are.\n",
    "- The 25%(first quartile), 50%(median), and 75%(third quartile) rows show the corresponding percentiles: a percentile indicates the value below which a given percentage of observations in a group of observations fall."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Another quick way to get a feel of the type of data you are dealing with is to plot a histogram for each numerical attribute.\n",
    "#### A histogram shows the number of instances (on the vertical axis) that have a given value range (on the horizontal axis).\n",
    "#### You can either plot this one attribute at a time, or you can call the hist() method on the whole dataset (as shown in the following code example), and it will plot a histogram for each numerical attribute."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1440x1080 with 9 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "housing.hist(bins=50, figsize=(20,15))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### There are a few things you might notice in these histograms:\n",
    "- **1.** First, the median income attribute does not look like it is expressed in US dollars (USD). After checking with the team that collected the data, you are told that the data has been scaled and capped at 15 (actually, 15.0001) for higher median incomes, and at 0.5 (actually, 0.4999) for lower median incomes. The numbers represent roughly tens of thousands of dollars (e.g., 3 actually means about 30,000 USD). Working with preprocessed attributes is common in Machine Learning, and it is not necessarily a problem, but you should try to understand how the data was computed.\n",
    "- **2.** The housing median age and the median house value were also capped.                                                 The latter may be a serious problem since it is your target attribute (your labels). Your Machine Learning algorithms may learn that prices never go beyond that limit.  You need to check with your client team (the team that will use your system’s output) to see if this is a problem or not. If they tell you that they need precise predictions even beyond 500,000 USD, then you have two options:\n",
    "    - **a.** Collect proper labels for the districts whose labels were capped.\n",
    "    - **b.** Remove those districts from the training set (and also from the test set, since your system should not be evaluated poorly if it predicts values beyond 500,000 USD).\n",
    "- **3.** These attributes have very different scales. We will discuss this later in this chapter, when we explore feature scaling.\n",
    "- **4.** Finally, many histograms are tail-heavy: they extend much farther to the right of the median than to the left. This may make it a bit harder for some Machine Learning algorithms to detect patterns. We will try transforming these attributes later on to have more bell-shaped distributions.\n",
    "\n",
    "### Wait! Before you look at the data any further, you need to create a test set, put it aside, and never look at it."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Create a Test Set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "def split_train_test(data, test_ratio):\n",
    "    shuffled_indices = np.random.permutation(len(data)) # Randomly permute a sequence, or return a permuted range.\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": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "16512"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_set, test_set = split_train_test(housing, 0.2)\n",
    "len(train_set)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4128"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(test_set)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- **Well, this works, but it is not perfect: if you run the program again, it will generate a different test set!** Over time, you (or your Machine Learning algorithms) will get to see the whole dataset, which is what you want to avoid.\n",
    "- One solution is to save the test set on the first run and then load it in subsequent runs. Another option is to set the random number generator’s seed (e.g., with **np.random.seed(42)) before calling np.random.permutation() so that it always generates the same shuffled indices.**\n",
    "- But both these solutions will break the next time you fetch an updated dataset. To have a stable train/test split even after updating the dataset, a common solution is to use each instance’s identifier to decide whether or not it should go in the test set (assuming instances have a unique and immutable identifier). For example, you could compute a hash of each instance’s identifier and put that instance in the test set if the hash is lower than or equal to 20% of the maximum hash value. This ensures that the test set will remain consistent across multiple runs, even if you refresh the dataset. The new test set will contain 20% of the new instances, but it will not contain any instance that was previously in the training set.\n",
    "#### Here is a possible implementation:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "from zlib import crc32\n",
    "\n",
    "def test_set_check(identifier, test_ratio):\n",
    "    return crc32(np.int64(identifier)) & 0xffffffff < test_ratio * 2**32\n",
    "def split_train_test_by_id(data, test_ratio, id_column):\n",
    "    ids = data[id_column]\n",
    "    in_test_set = ids.apply(lambda id_: test_set_check(id_, test_ratio))\n",
    "    return data.loc[~in_test_set], data.loc[in_test_set]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Unfortunately, the housing dataset does not have an identifier column. \n",
    "#### The simplest solution is to use the row index as the ID:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "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": "markdown",
   "metadata": {},
   "source": [
    "- If you use the row index as a unique identifier, you need to make sure that new data gets appended to the end of the dataset and that no row ever gets deleted. \n",
    "- If this is not possible, then you can try to use the most stable features to build a unique identifier.\n",
    "#### For example, a district’s latitude and longitude are guaranteed to be stable for a few million years, so you could combine them into an ID like so:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "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": "markdown",
   "metadata": {},
   "source": [
    "- Scikit-Learn provides a few functions to split datasets into multiple subsets in various ways. \n",
    "- The simplest function is **train_test_split()**, which does pretty much the same thing as the function **split_train_test()**, with a couple of additional features. \n",
    "- First, there is a **random_state parameter** that allows you to set the random generator seed. \n",
    "- Second, you can pass it multiple datasets with an identical number of rows, and it will split them on the same indices (this is very useful, for example, if you have a separate DataFrame for labels):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "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": "markdown",
   "metadata": {},
   "source": [
    "- You may want to ensure that the test set is representative of the various categories of incomes in the whole dataset. Since the median income is a continuous numerical attribute, **you first need to create an income category attribute**.\n",
    "- It is important to have a sufficient number of instances in your dataset for each stratum, or else the estimate of a stratum’s importance may be biased. This means that you should not have too many strata, and **each stratum should be large enough**.\n",
    "- The following code uses the **pd.cut() function** to create an income category attribute with five categories (labeled from 1 to 5): category 1 ranges from 0 to 1.5 (i.e., less than $15,000), category 2 from 1.5 to 3, and so on:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2586fd108c8>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "housing[\"income_cat\"] = pd.cut(housing[\"median_income\"],\n",
    "                              bins = [0., 1.5, 3.0, 4.5, 6., np.inf], #np.inf: floating point representation of (positive) infinity.\n",
    "                              labels = [1, 2, 3, 4, 5])\n",
    "housing[\"income_cat\"].hist()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Now you are ready to do **stratified sampling** based on the income category."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import StratifiedShuffleSplit # Stratified ShuffleSplit cross-validator"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- This cross-validation object is a merge of StratifiedKFold and ShuffleSplit, which returns stratified randomized folds.\n",
    "- The folds are made by preserving the percentage of samples for each class."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "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": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3    0.350533\n",
       "2    0.318798\n",
       "4    0.176357\n",
       "5    0.114583\n",
       "1    0.039729\n",
       "Name: income_cat, dtype: float64"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "strat_test_set[\"income_cat\"].value_counts() / len(strat_test_set)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### The test set generated using stratified sampling has income category proportions almost identical to those in the full dataset, whereas the test set generated using purely random sampling is skewed.\n",
    "### Now you should remove the income_cat attribute so the data is back to its original state:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "for set_ in (strat_train_set, strat_test_set):\n",
    "    set_.drop(\"income_cat\", axis = 1, inplace=True) \n",
    "# axis: Whether to drop labels from the index (0 or ‘index’) or columns (1 or ‘columns’)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Discover and Visualize the Data to Gain Insights\n",
    "- So far you have only taken a quick glance at the data to get a general understanding of the kind of data you are manipulating.Now the goal is to go into a little more depth.\n",
    "- First, make sure you have put the test set aside and you are only exploring the training set. \n",
    "- Also, if the training set is very large, you may want to sample an exploration set, to make manipulations easy and fast. In our case, the set is quite small, so you can just work directly on the full set. \n",
    "- Let’s create a copy so that you can play with it without harming the training set:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "housing = strat_train_set.copy()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Visualizing Geographical Data\n",
    "- Since there is geographical information (latitude and longitude), it is a good idea to create a **scatterplot** of all districts to visualize the data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2586f996f48>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "housing.plot(kind = \"scatter\", x = \"longitude\", y = \"latitude\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- This looks like California all right, but other than that it is hard to see any particular pattern. \n",
    "- Setting the **alpha** option **to 0.1** makes it much easier to visualize the places **where there is a high density of data points:**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2586faed5c8>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Xl6rc6FSxhKAbZjTGpaDT+Yc0U6zvjehGGduDkK//aI/dXoJnS77y3DzzDY9hnFP3Ha61q2eGbo6kKoRgs/egBPWFhRo/2h6yN0xwHMlPXG3w8lKTimcXCfcLhnMuE/q5TAnqUaLdHB9UVFJS8ng89Y5Ba8NWP2ax4Y6N9OkTgwN89cUOP/vSPNKy8B3rKNF7XjLYdyxutKtgwHMkrm2R5gqtDTbHjaHWhsW6hzEwjDKMgKWGz0Y/wpjCiaS5wiDQxuBZNp5tIaU4mgQHpxPbSmt2ByH//PV77HRjYgWBA3/0g4SffH6elWaF9YMR2sCLi/WZxnMyWvRkCWqqDD91s0OkFa6QBN6DctTLhHMu+trLlqCWJaslJU+Gp1pET2tDnCviLEcYiy/daFOd8YlXWzZz1SoHYY4Btvrxkdb/wwThbFuy2q6gNBwME+7sh2Ras9GPaVUcMmUYxBlRqrjWrrBQ846a5vZGKfNVj1bFQwroxzlSGOq+U0xjkw+SwpP+CgFHEhhJrsi14a3bB7y7F3OQQ2hgP4U7vZy/eG+HH271+HB3yK3dIdn4fbME+ZoVhyzTpKrooF5qBiSZ4l4/4nCUsTtKScfvn3AZFdaHvfayiqzlzIWSkifHU3timOwmU6V4d3uI70iWW3V+fC3iG7eHGMACah7kSOI0w7EEzy/UcC15So8ozRVSiJnJYN+xWG0F3D4Ycb1dwXWKk0U3zJivumwNYow23NodsNNPqLg2riOZCwT9OOcnb7TZ6EWESUY3KqqAdoYpyhSzo1sVh3vd6CinkCqNY0niNOf23oBvrx8wyxxuDjRroxRjBOv7I/JcF45vapfdqjh0w4xca4yEpm/TqLjkSrM/So99nq1ezGqryC9MQj2Xkag4L/QzKw8RZRlxrvDHJ6dpypLVkpInx1PpGKZ3k45l0644fLAX0qzYPHdljru9DKVyYlUYlDRXxKkucgSWPKVH1Ko4vH2/h9IGS4pjyeAJRhRlqpNcghSCOMvY6CksKTiIM9b3hxyMcr50vYUlBIdRRjNw8NxCcvv2wYhOzcd1ivxBojQrDZ+N/lQoy9YkuWa54bN+MCLVmiSf/RwUsNmPubU74KUrDZQx7J7IUbx9v8f1ToXAdVhtwUYvxpYSA3Rq7tHnsS1JP064vT8qnMEjhG7OC/2czEOM4oytXowwYFlFU5xjy1PJ//McdklJyaPxVDqG6d2kNobAs2kENsv1KqlSVF2LrV6C73oYFBLJe7t9Pt9tca1TBTgyMlobumHG9XYFIQVm/H3Dd445h2nDlmvDZjciGoejPFviWpKq6zCMFdu9iNW5KmGS0/DsYg4E5phjcR2LNNdESpHrk30NutBqMgaVg3uObR6mGWmSYlvjzzO1yxZSHHV6A1R9hyVguRXgSsm9bnSsH2N/mB5VWV225PRh3conpdC3ejGdmgMY4izjzfWQ5UaRl5ivF+WyVc/inc0+xnCmwy4pKbk8T6VjOLn7nK+43D+MkJbkC6ttelHK776VIITGILjWDmgELjXP4d5BxNW54Gg+wyQfEIzLMrE4SgafDFl0ai47vZiNXoxrC27MV9k4jNjuJ9ycL9RY56ouYaZ4b2eAQDBfKxrDXEseW/MwztjsF7vr7UGCpDDck5yDOx79admSl1dbbL/Xnfksohzu92J+aS7Ato/fw4xPQJPmuIlkxSR0M50w1sbQqbpHVVaXDd1MnLUURTls4aT0sfdP8hBxrtgbxPzlB/ukmaaf5Ly0WONKw2O3n/DB7rAoq9WaqmOx2PBxLTnTYZeUlFyep9IxnDRqtm3xsy8tsDdIkJbgFz+3Qn+UkSuBIqcZ+HiO5JXlBsrA1VaAN27amk74nlVqOR0iUcbQqtjM132kEKzMBWyO1VzrgUOa5EQjxWrL52q7ciyfMVlznBZOYaXpUx0buo1ezBIc9VbYtmSpGbBQ9XhxocluL+bWTkw69RwaDlQci9q4z0EYTlUHff5qk26YMUpOVwvNkh+/aMnpJNFtCYERRSd1nOZs9yIsS6KUphk4iBPJESkFUsN37nWpOA5BxWajG/OtDw5IlWap7tOLUhbqLmGiaAcu3ShjpRWQparMMZSUfAQ8lY4Bjhu1LNfjGQASI+DlK03+7Z9Y480PDjgIM1xL8oXrLVzbQhmKxO6UsZ9O+J4cOnMyRJLmip1BQivQuI6Fa0k+t9JAKc1+mGGkYK7icrVdoTI+hUx23tM7ZiOKEwJAzXdYNkWIZzoRW/cdvny9Tc236YYp87WQH2z2SBJAQtWVVH0bx3FwbUmOoeLYpxrDGr5zZu/AdHL5oiWnH+4O2erH5EojhKBVsRklmjDNSTJNvWIzCHPCVOO7ISut4FiuIjGaqmtjW4K9QYJvW6SiCM99uBcWJx2rTpYbNAatIc3UsfBf2fhWUvLoPLWOAR5UwdwfD4YJ3CIUszdKeXVljnbFoztKCNNinkCiNCvNADjeSDZJ+HaqbiF9MaX3MwnFTPIZUgjmKg6J0mTj+P31TpXtfkwtcHCk5O5hyE4/Zq1TPWoUm+y8pRT4toUt5bHduWXJU9U5UgrWOlV2BwlfXGtzZ8+m6tu8tzVgGGfU/SLMUnUE9w8i4jSnMpbGnt5VX7Sy6GHdxlobPtwd8u7WAMcSbPRjkizHsySdqkemDDc6FTb7CdfaAcqAJTiVqwgsi8C1cSTMVVwqrs2tnQFxqrFsSbvm0h3lVD2LJFNoI1AGVlr+pUUES0pKTvNUOwY4u6zRsSXPL9RR8zWSVLHVj4FixGen5haGZWyc5dj47w4TAsc6pvez2gqQQjCKMw6jjDQr4uZfXK3iuVaRWDYPZjNDsfNf3w8ZRNnRgJ+TBv+izWOOLVlpBdQDm43uiOcXGlQcm/0w4fb2kO1+xCBKsaTF7393g7/9uWUW6o+uK3SeE8mUZmtcQSUEbByGaA2+I7Eswe4gw7WLhLfWxTOxbUmSHc81uK7FT78wz796b49ulOJYgq8+N0+ic8JE0wgc9ocxNc+lU/VYbPo0vCLktn4QlnLcJSWPyVPvGM6TY5icKPZGKYH7wODvDRKSTLE7KCS1ldZUbIuKZx9rdEvyHCMK0bk31w+RgmIWdMVhb5Sy5lWO7jG9BlsKVscJ7rNGgl5UC2jys0GccaUZ4NmS+YbLn7+7Q5QpBnlON5IYMcR3bYQU/Pj1NmvtKhVv9q//cUIxNgKEYH8UY0mJMYo4VQwiRafmYoxhsxcjLcFiPeDeQUgrcE+JBHZqHr/8+WV6acZhmLLbTdgPDS8tVrAtyULNpeE7OHaRdO5H+ZFDL3sbSkoej6feMZy3+550Rp+UuY4ydTSpLVOa3UFM4BTlppYUx6qDLCHAliw3/KMTwrSMxWRM6Mk1LI8T3A9b+8MM2qR88+5BSN23GcY5jpTc3guRNlQth1wZ7h6MqLmCTt1lqxexN0z58trcKefwMJmJ85yGY0kWmz55N+R+qot5DcKmEVhEOTQ8m+VWBcexaQZFDiFTGj3Vnjfr/gs1n6utnHsHIXvDlEwXPSdSCvypE9zuIHlooUBJScnDeeKOQQhhAa8D940xvyKEuAn8DtAG3gR+zRiTnneNx2XW7ntigHJdhD+WRZHknYjbebbFtbbLBztDPNsizjWerbizH3KtUzmmvIoumrAEHJOxONkd/aTUQKuuzWq7wlLD4zDOWN8dYNuSBS+gHysEilQZdocR63sjXrveRhvNvcOQF6b0kx7Wa/AwpyGl4Pp8FdsqwmeqU1Rd7Y9SpBB84WoTaQlyDVdbPluDGIlgt5+wOpdTde0z71/3HV5eavDclCzH/W504gRX5Ir2h2kpx11S8hh8HFpJvwG8M/X9fwv8D8aYF4FD4O9/DGtASnEUtpk2gHXfYaXps9mLGUYZmTKstAKscUf0YVwon1Zcm1bFK0ZZNvxjej+TE0GmDKOkGPk5yyBNr+Gj/mwrrQDXselUPV5abvDSYg1LAsYwinOEgExJenHGn723y1YvKeZPpA/aps/ThZp+ZoFjAUWV0EltIt+xWG4GrDQDfMfGkpLnFqq8stxAWnLcu1H0lfSinMMw42CUcu8gPOoZOUuXSkqB51h4Y5HDSXgOHszOqLr2hfWbSkpKZvNETwxCiFXgl4H/BvhPhRAC+Hng3xu/5H8D/kvgnzzJdZzkZEJ6uuP3qLlLFvpCcaqxfYsrdRchBELMNu4f53yAWeGc4/ev8R/8tOaf/PH77PT72BYsVV3aNY+DMGM+zMb5EMHuIKE6rlQ6Lx8zeWa5ht3BA2nu+bpHfVxWO1nbziChWXGZq3okucIYuDZXOdJYGsQZb2/0qDgWliVpVV12Bgk3OtULy1w8LEE/CcGVpaslJZfnSYeS/jHwj4D6+PsO0DXGTLap94Crs94ohPh14NcB1tbWPtJFnTWkfroc1HcK/aJMafpRhtKG3OhCs+eM2cuXEZR7VM4L50zur7VhoV7hN37hJf7Z6+v82Xs7KCSjVFP1HCy7aDq7UvcBZuZCoiwDw1EHOBqMNtzthVQdu9BTMuaYY5lca9rperbFIM5QxuCNu6Z9x2Kp5iMtwTDO6Y4yorGy60V0qSY8zBmXstwlJY/GEwslCSF+Bdgxxrwx/c8zXjpTJ9kY81vGmNeMMa8tLCx8pGu7TOhnrVNloeHTChwW6j7X56uX3nnOkrl+FC4qNa2MIcpyBknO1jCjUfWp+w6+axHnivmqz3LTxxlLZEyawrKxNMfiuIFPUJTvxpkiVZphmvHh7ogP90cMk5yFmncs1APHnW6cKe7sj9juJ9zvRkdS5o4lmW947A0T8vEsisW6y94g4WCUcq0VcL1T4VoroBtmpz7fZK15rs/tqShluUtKHo0neWL4aeBXhRC/BPhAg+IE0RJC2ONTwyqw8QTXcCaX2W1aQnCl5R/bGZ/krJDFR7lrvajUtDDFjOU4VdT9opKqF2XUPIcsV9R8m1bgkinDQt0jzhTb/ZhBkhLGGcKSLNUCfL+YNHf/IOTu4YC/+GCXvV7CQs0nXWwgAQ2sNIOjU9TE6W52I+4dRri2YK1TwZbiWCJ7tV1hZ5DgWgLLklxp+CSZIklyktwcPa+qZx3JdGdKEyY524OYJFf0I8V83SVw7FPPNVOaNFd4tnPusyopKTnNE3MMxpjfBH4TQAjxc8A/NMb8XSHE/w38HYrKpL8H/PMntYaHcVboZ1Z1zv4wpdqe/bjOMv6zrrPRjbh6Tv/CrLVMHM5FR2QaATXPYmdQ7NIlkk7FxbUFeJKvfe4KFddhb5iwdRhxrxex1Qv54+9v8MH+AAv4N75wlV/8/DV8x+L3v3ef//XPb3FQ9ABiA199fo7/+OdeYKnus9GLuNGuYtuFc/Adq9AuUpp64Bypt04MM7qYM73aCrAsgWcXXeO5EByGGRXHouLaREnGbj/naiNgsx+zfjDih5t9hCw0o640fCwBtbZ9qnpqo1uIFx6MUpZbAfZYKrwsXS0peTifRB/Dfwb8jhDivwbeAv7nT2AN53LRnfkkpLHRjfBsearE8uR1cm24f1gI0U06ni87uvK8hOvEiRhlGCaK1VaFX3x1hd/9zj0GI8XVtseP32jz/Y0BgzjHsQTtisv7Wz3+j2+t88HBg5nYt/54ne/e6fETz3X4P79158gpAOTAt24d8vf/RkY3thklORhYnaoCcqxi1KnWpihRHTuxLNfcHyTjRLYhNwZtigbAxWYhaRFmim6YchimuJY8ah6MUlXIaGiIlSZMFTvDhNV25Siklaea2/tDAsdirVNhqxexvh+yOhewPMmXlJSUnMvH4hiMMX8C/Mn46w+An/w47vuoXGRnfjQhLlds95MiXGIddyLT15FSsNWLjkpktTHnyjWc11MwKwQ27US0MdR8i1xB4EiuzQX4V2xuLFSRRnJ7f8RK06fmO+yHCa/f3jrmFKAw/m/e7mG0IkxPz8lOge9t9Pip53xqvo3nPFCJhcK5LtY9dgbJkRObfH9Sg2pygoJiYl3FtdjMY67UPQzFCNP9QYolORIyNFqTZprANePnKxlEGW/f67HRj6h6Ni9fqbPWqTIYq68+rKGwpKSk4Kme+fyoPCw5fbIPokhsFqM3T0puTK4ziDLS3BztWu/JSZkAACAASURBVE/W6J/kvJ6Ck/0QJxOtniUZxopO1cG2BQv1gKVGhaptsz8quoMd20IgsJCkarbBVBriXBPYs/9M1Hhg0HzNK04HxjBKc9YPQu4ehOwMEhbr3lFPgWPLU59p8rynh/Vk2pDkhXDgYsMncG20MICg4kkSpfAcSa4UDd9GCMF81eUHm30CV9KpeVhC8O72gDRTuLZ1ZiVZSUnJaZ56SYxH5bzk9MkQ0XmieJPrZGPZbnv87w+Ta7hoPmHWelzHolNzidJCp6jmF1U5uTHkytAMinGnh1HOKM2Zr1i4wMn2c9eGqufwhVWfnR8ekE397CtrVW4uNKh7hbR4OpYW2e7HY6HBYnToJP8grdOaUWmm0ONZD9PP/Ua7WnSfO0U4qlMxDKIcpTVubNGuCK6MmwyvdapUXbuQNtEGz3doQKGfFGZEmeLGfK0MIZWUXILSMZyDlAKdG2KlcKU8Sq6eNNoPE8WTUuBJi+VWcCHF1Ml7LpJPOC8pnRnNxmHEwShFWoKFqsf1TsBSK2AQKQJbYtc9/t2vPk+kJL/39u5R7XDVgleuNXlhqUmcK376hRbfv9dlFEOzAnEOf/XhPr04Z7EW4LuS+ZpHN8xY61RIVc52Ly7Gm477IRxbHoWT+nHC/jClU3W5142O5VtsW7LarrDVi4sBQlLy1ec6ZEqzOT6Z2VKyOlc50npyZXEaubM/wrELp9OpujzXqR2NSy0pKbkYpWM4h4NRwl99uEcY59QqDl+81qZTLWr8O7Wi7j7J9TFRPK0NSabIx2EW37aOVes8bJ7B9M/Oev3DktJFDN6w0Y3Z7MZsDWK01uiO5pWVBp9fbhUDjJRm8zAi8G3+k194mc+t1PnunT3CTLHaqXOlVWOtFfDhwYgkzXnbQAiEIWyGI+7sj8hyBattnr9SoxU49KKUD3cGHMYZRoNtC5I85831Q5YbPpYlma+6bPX1ufOjT352KHoq6r5z5Px2BglrjnUUhupUXfpRRpwpBHCl4R89+5KSkotTOoYzGEYZv/3ND3nrdhetQWD44Y0eP//KMraUOGODs1D3jvobJg1dP9ru88H2kIpvc7NT5bWbHVoVFzi7RPaskteTr79IUlprw63dAXf2huTGcHO+yihW5Br2R2nRhexYKG3YHibIUYJrW/yNF6/QrlTIhca1LHKluH0Q0gszdkYJw+P5aboJfHv9oOhkbvjcJcRo+M69HgjoVD1qwuLd7SGLdQ/PtRDAVj9GwIPTjRToXJ+q+pr+7A90lGZXiiljqPkOr91sk+f6aM5DpjTSiFISo6TkEpSOYQZaG97b7vHGB4e0Ki6pMsRZxhu3u7ywVKdd8Y+mr036G7QuROVu7w35+o92OBimCCSbhxGJ0vz8y1fwxob+5MngYaqm05xXSutYspDEEEXcPjV6PGEOPMfCiEJKfHKq2R7PlZ4MGApjzSsrdTTwF7f26Y4S7h9GgOGwHx/LMUzYH2bc2Q9p1nq8stSk4Vt4lsSxBVcaRVXR7d0hSw3/gXEWijhT7O0nCCEwxpyayXCSs8JlYiyNLgxoY+iHCbkuusw1AqMNYtzDUEpilJRcjNIxzEAZQ5QrcgNhmtONcowptHyGkWKuUhjXaaMMEKU572x02ewm1AMbpQotoW9+sM9yw2eu6tEMisYyPdYOWm4Fx8aDwvlduidLYNOxzMS0UZVScK1dZS7w6A4VwyjDcSxavs18zWWrH5MpfVRmu9IK0NpwECbsD1J2BgnbvYh7hyHreyN6cUqaazSnkcBSw8e3LZTSbPUzOjUXx5ZkGpRWKGOoexYaQzpV+ppNTgu6cHbnMSvn0qo43OtGKKXpxynvbPX58/f22OiGuJZkrV3h515e4tVrrVOd1yUlJWdTOoYZWELQ9F08S7DZjzBakKicuuew2Q+xxxIOwLFKIW0M3TBHWCAQSGnYGyY4tqAbZeyFKff3R9QCF8+2aFVsUqV5fr5WqIpmCiGLXe7DVEXv7I3YGRSxncWGR6o0vnywG64FDr/4+WW+8f4ut3ZHCGFYG+s8SQFV38YNUza7EdfHp59emLNYd7nXG3F7b8i9wxDXsakbh4FJqVmGkeLIQcz58IW1Ob72hRX2RxnKAOOJd3me4zuSYZqz1PDZ6Ea8sX6Aa0lagU2cGZpVl0Gs6FQKddUrzZRW4J6Ze5kYezWuZLrXjVBasztK+NatPd5cPyTJcrSGUClu7Y5wrG1cu2i+i6KcVmDTmHGPkpKSB5SOYQaFlk+VV1ebfPOWIjcaWzu8tNRgsRbQDGzuHURcHVciTYzM1VaFmm8jtWGvH2HbklTlXJ2r0h0m/GC7z73DmJ+80abechjGCkzMjU71UqqiriVxbcm1dnAkJzFrN9yquLyy3OSlK3U82yLMcr6/MWBlTmE0VD1JL8wZxBlSCmqexSjTiLFCa5JrDAppSRYahXzFnCsBRZLD8lyNH7veZhgrtDIIq1hbYEveuL3Pnb2QgyhjpenjezbvbXTZHSZkSjNfr/CZ5TpffX6eg7Bwqt+712OtXSnkz8chn7NyL5nSKKU5jDKM1oxShZEwSjRYEqk1lhB8sDtkt59gMFiWzcvLdf7miwu8ujpXhpVKSs6gdAxn4DsWX7rWZrUVkAFRlGGE4Fq7wo35GqMkP+rYzVRhhOq+w+euNnEtyVYvIs71WC9I8o3b++wPYkZJxnbfx3Msar5ddCprQzfMuNYO0MqgMByOUhq+MzMnoYzBABW3+PVJxMzQ0yTEVfEdhIHhUKG05u7eiLvdiDRTXG1XeOFKjZbv8ma3GGF6c76K51kYo9GqOP30EwVasXK1xmfXFshyQZLD1WZAL1HkxrDUKlRov7fRZbOX4DiSlnD4cK/Ph9sJMQ+kdFMVovKMJFN85kqDV9dauJZACI51UJ+Ve7FEIR1eCOUVI1UtXYTzjCieqZSCYZjg2Db1qssL8w7DOOet9UOaFZcXFurlyaGkZAalYzgDx5IsjU8D+2HKMMppBS4rc4XBcu2iqmezFx7tZjs1lxudGu2axyjO0AbuH4z4ve9s8v7ekDTTWKLY1X4lNyzUfdae74xnRGdsD2J2ejHCgprrUPdtKq7NzlhbaLJjdseyEFGaH50YpuWzJ04kyzWb/RgpipBXmGZI4PZBhCMFoYbuKOVffuc+X705T6IUAoPB8K/dnCfPDN/f7NLvaiZSSevvDvnDd4e8tlbjF3/sGp5n8eWlOu/vDGlXHCSC/iglzhR1z2akFNuHCdGJ59tPwY8yNnoR2mgcR+K7Du2qV+QnVFGldHIe93SifaU56QvRrM1X6cYpu4OIjV5MnIHW4EuwbNDKsH4YjVVlNXFSKq2WlJxF6RjOYDK/eNKU9cJCFUtKjIFMmdO6P0qzN0jwHIuOJcAUGj939ga8v9tHaXAsyHJY3w253va5uVjlMMzphSlv3D7g2+v79KIctKZacdg4DLnaCri5UKMRuEfqrEsNnzTXx3IM1ztV0vGOepLIzZQ+qjpKMsVWNybXBg0M05xmxcESAJJ+kuEIwd4wYb7q0ag4rM0H3NvvsTM6/mxy4C/Xh9hiHedLa8SZIskN37nbp+ZJDkcZSaYJ04QwyRjOKmcCBhFYvYgbrYBMw7WawzubfV66UmOjG6GMOTWPezr3UvFsvrw2x0Y3KuS5tca3BG/c2SfJFTu9FEEh6+GmKVlm2BvGXJ2r4Ht2qbRaUnIGpWM4B9+xuNGpHmuymnw9u2xU06m6fO9+DymKuQhKF+9zLIlBYEkFAhYbVV5ZbpFkmm/fPeSdjS7f/PCAcByWafo2/TAjcB1eWKyxPBewWPOwhCRKc1oVl0bFIc0KxVFbCO51oyNHFac59/sJLyzWCDybPNcM4pxeXIjRxdqAgUwbar7E6EKLyCCwLIFrW9zeC+nHpwX0Jrx5d0S9ss3Ly3OstAKWmx5SSl5aqrPdS/jBVpcw0wQ2JDOcQ+CBY9ss1H0qnkQpwzDPybSmaUtsSyKBjV7MsgHLkqe6xSuezXMLxaQ9QRF6uj7fYBAmRIkhzNQ43AdYRXjp89earLUvP3CppORZoXQMD+Fkg9nR1yd0fya7WdeSzNdcKq6NUppW1cV3HHKd4dguQ6WouBatmlM0YlmCYZzynftdlIHAteiOcnYHORU35IXFOm+uH9LcHeBbgnrF5cWlOj/78hKSQowvyxSp1qf0kqA4tQSujTKGimsX+YTA5a31A3phSrNi4zuCg1FhuRcaPivNgDjPaQQOgWdBNNs5JBoSrdkfJLiOZJQpfNvCsQVfuN6i4VvsDmPuuPC9zZjpqwggcAS+LRkmOUGYo3JQRpOrB0J7s+Zxz/odedLi2lwVzxIMo4xYwXzdH0tkCK63fF65OseLS0U3d5Yp3AvOxCgpedYoHcMlOJkEnkwqGyU5jiWZq7ps9mN2BymOlbHU8Hl5pcXLy13e2xqQ5oZ64LLc8tFasDNKeL5TJTcGlRsC26IXpWS6CNfsDVM8O2R/lBI4hTKqFMXp5P3NPn/zlWUCuzD6S3X/mKPSugh3GQOjJMcYQ7vi4FqSF6/UEQI2uiHCCCxp0am7HAyzoiHMKnolbi5U2TkccrsbznwengV5pjEY0twwjBPaVYcF1yMHtNHEqWaQJKfe61DE/QPX4k43ZL4VUPMdllset/dCWr5DZSxPfnIe91k0Ky5feX6ezV7EVj8Zz752ma8HfPlGizBO+J1vfsC/eOMOS80Kv/zFNb7y/EJZnVRScoLSMVyQWWWT8KDKRhnDdj+m5tlHA2LuHkZcb1f4O69d5/7BkM1+MT95oe6z0vKpeRaHUc5r19v8s9oGO4NCIE5QGE5HwtYgwWjN4UiR6EkPgeZ3vrXB/jDm3/nK81xtBeyN0lPzD67PV3EtySjN2RskSEtwey8kzTV1z+Gnnl9gsxshLcFqq8LaHNw9jOgOE/aGGS9daaCUppdu8MOd47Ggug3LcwF138WyiiGfcZbTjwxpptgfJhxGOfe7Ids9w8kzhwJ8rzD411oBc77DjU4x6Gerl/Dh/gjPsY/yJxfZ2UspeOlKgx+71uYLGBJl2O9HHIYZ33pvj2+sDx68lhFvbwz5z3/1c/zUc4vlyaGkZIrSMVyAWZIV9w5DjDb4rkXVc4nTnM1uTGPRwXfk0YCY650qN+drrO/XuLM/pB8rbixUsYUgyTVNz2au6vN3v3qd/+uv7jCIUqzA4NmSdsVjs5fg2pCOnYIAnHH+4gebA/pxwvNujSTTOLY8JbqX55rtXoznSNqeh2dJ1g9CrrUrpErTT3KSVGFLyWLNY6Xlo7Thx642WT8McW2bH79xhX/z8xa9UcLG/oj9TOEJm+vzNWq+gzJgDPSilB9tx2AKJzFKFUmaEM6IRAmKHX677mGExVY/BdkDI2kGDi+vNIrSXVP0RlyUuu/w6mqLvWHM3jAlDTzuHvb5yymnAMWz/NFOxB9+7z5futqmGjiP8ydSUvJUUTqGCzBrROe9gxBlDK2KWwyqORHT19ocDYjxHMHNhRoIGCQ5jiwSq9vDmA93hywOYiqew3/4N5/jX751H2MUGgulFDkGWxp2xgp2guIkoQyM4ozvrfep2DZX56pHzmCSB4kzxb3DkPvdiJpvM1/zCFwbSxbzEw7CjHbFYV+DUpqNXsyrV5vsDRJyY/hgd1RIexi40vQIPI/nlubY7sXcmK8yV/HY7sf04wzXEbxxJyFOi7GcW92EuMizz5TSCGy42qxyc7HObi9me1y22vAd1uYrSASuazFKckZxRqI1NdvG98//k5WyeNaWFKTKYAn4FzvhzDUoIIlzUqOpPtqfRknJU0npGC7ArBGdri2LSiNdyF4s1r1jMf2T8xMcS+I7Nq4lOYwy9nsh375zyAsLdbSBwIE4ha+9usJbtw/Y6ieEmeZzK3MMooSDQcrGsAhcpQoqLniuQ6Jy3tsd0al5x9Y8OeV4lqTq2cfX2fBIck03TIumvJWiXyPLCvO52Yu51x3RC1OutitIIdjqJ3i2YKlZY6HmMYgUymjqgc0ryzW+u9HlcBCyvpswOEf2yAdsAXM1Gy3g1s4QKWC+aoPR7A4j1vcLIb7FusdWP+b3vtNDmSIf8bXPLXNjvnbu78t3LJ6bryGAW7sDmp6NBafCWTZwc6lG1S7/G5SUTFP+j7gA0wJuaZKT5oa1TtHotjtI6EcZVcdirVPFd6xT8xMmSetJDqDu2+wPE661K3TqHmGi2OolCFGI0r12s40x8P7OgEyBMRWWW1W+eWuXe4cxWkHNs/jqzQ5X21XmKw4HYUamNN5YLylTmjRX1H2HhbrH7iBhGOc0fYdOxWWrH2G0IUoyttQ4c6EhUjn9JOT97QHDOAYp+OxSgzjTNAMHRwrmKj6WnbI/ivnuhwfc3Tng+xsp+2dXth7h29CqSpbbVToVn4MwZf1gxPc3MrQuxATfvNvli8tNAs/i9n7IQs2jVfMJXIs/+P4mf+8rNx96crDtokFxfX/EQqPC1VbEejc/9pqffqHFL3x2Fde1ThUWlJQ8y5SO4YLMGtFpW5KWbxMmOZYQ7AySU9LOJ5PWi3UPBBhlGMQ5dw9GDOKcONNF57CEd7aGtKsOzYpH4EgSVeQyfublRRxb0Btm9NMMLQT39kP6I4fPOPaxe250I7b7CQejlOVWwJW6R1JxaAQ2f/qjXaBwWGmmcF3J2lwVheK/+39/xBsfdkl1EbJ6/soQ34KDMMdzJN/fHKC14tvre/zV+z16F3AG01QDkNJipxszCFMagUM3SkgShbQshIDBKOVub4QlLPaGKTXfxU1ytDFUXIthnuPq0w741L1cm2vzVX72M1foVB2+vX7IQRix0qrwtVevcr1dR0pJmOSnusvLSqWSZ5nSMVyCkyM6oyxja5BwdS446sydFrOblbTeGSSstgIcx6Lp2/SijF6UEiU5oyRlb5gCBqUMy00PIS2uzgVYUrIyV2UU59Q6Nnu3E/I8o+oXye69fozKNNqSRQjJlkfVUev7IatzAUt1n9fXD7CloFVx6YUJH+4VlVMHYczX393m9Q+7aKDuC6LU8P5myO35Q37mc6sIIXj9gz2+/sNNPujNitqfj6BoUlPKYLuCOFd09zPizKANVMZNgEoXTtMWRSnsVi+m6Tvs9GKeX6xhC8H6QfhQQy5lUW2100/4mZeX+ML1Dnv9mFbVY3WuwmLDJ8+LbvLAtR46C6Ok5FmhdAyPwOT0EOcKYQq5Bpg9VWzWnAUjislv9w8jXpiv0g9Tqq7Ft9f71DzBVq+Y5/DD7RFfvdnEloK6a9FPFMM45dbOkJ1BjCsl7cBlI9NYEv701i6v3WiPDabEkrA6V2GU5Ky0gkJ8zxTrjzJFL8kZJRk7o4idkeDdnSG5AVtCkheJAgPshDm2JRglij98+z4bo7OezPlUJbi2iyVgkBh8u8jf+LYgzgWeJemGCRo4HCoWm1Wuz1XYGWXcPhhRdW3+9iuLdKN8prAecOoUMS2bcUUXZbpXGh6tioc2hmxs+ycNdefNwoDTvSwlJU8jpWN4RKQU+LaFNe56nu5+nshnnDV1zBKCqmvTqTrc2umz3Y/YG8Vs9SOaFZdRmhNlOXvDlL1+xOpcwGs3WnTjnL94f5ea5xC4NpnS/NXtA64v+LSrAb0o5QcbfRZqLjv9+OjU0hyXYtoIjIFhnLPVj3j7Xpd+lBPFOdWKg2U0xnCqvPS9rT5//t4Od7YGj+wUAOaqkGuFEVB3bWq+TZIr5msOG4cpvSRFSEHds1hqVJirFuNQb3R8KrbLF282WZ6rsj9KTznbUZqzP0xnniIqns1KK2CjG7FQ99gfZSAEgWOz0gzYGSRn/g7hgTPIxvpUZcip5GmndAyPwaypYtOVSOf9fBhlvLvV50/e3ebWzghLaKI4Z78XMcrAAqSE94Y5P9yN+aMfHR7d92oj5dpcncNRylYvIcwUtSDFtiQ1z6ZVsZnYtSzXbPbiQpFVChqBzcEoYa8fE6Y5napDs+oxiFPmawHtIGR7Sgq1IqFe9fnh/QHr+8NHflZX6zbzzQClNZiihNWSgldWGlRdiysNjURj2za+Y6MpJD5GsWalFbDYcLnZrrLVj8bzqB8YckFRBODZEtuySHPFvYOQG50qtl10ge8MEgLXoh44NAKHJNOstoIiST2e7jbrdzjJEeW6OJksN/2ZYcOSkqeJ0jE8JpOw0lnhhVk/D5Ocv/xgj7duHzKKMhxbMIgUB8OM4dSEtFP1lWPu9zXDqEeagefCSstHG8F37xxwpeFxpe6y1KqglWFrEGNyg+MUpbWjRPFcK+A76/ss1D0OBjGBK4gyxVY/JjdF13UGNB1YmfNxbQdHKtL08s/ncwsWP/vZZa7PN0Fqdg4zrnd83tkYMEpzci2oeDaOVZS+JplhruYRJjkCTaciCDyLD3ZHHIaFlPnXPruEEOLIkM+Pq65sSxazpIdFBRaiCKWdHJ3q2haZMhhx/u9wOkfkWMXMh8Mwo+LaDw05lZR8mikdw0fASaG9836uteHDvSHb/YhulJFoAVpyMEiJ1exmsFn0xgoVTaeQzE4zwzDO+cZ7u/xw45Cleo2bSxUOhzlzdRdHSpoVh+1+zLe7Id+6tc9OL6GXQyCgUS3KVZuBjRQ5BxEMMhhEGY1A0PQdGnWL/iXKkH7mRp0v3lyg4duMkkK0bq7qkipoBC4LTZ+q6xCmORuHERLJypzH3b1CPuTanI8A3t0aYEyhx2S05g9+sMWvffUGwXhQ0SRkl+aFUzDaUPVsvHEifrUVnBnSO+93OJ0j0sbgOpIkK2ZYa3X2+NWSkk87pWP4mMmU5mCQUvMcfMci7kfsDIYM0yLRK3igv/QwKuOhP2kWkyrwXTDG8Mb7h+ymh2jguQa8sNQmfuH/Z+9NY+zMzvvO3znv/t699iqyinvvrW71ppZkyZYljeMttrzGdgaTmYmciQM4k/mSCWAgM4PM4gmQDwECDxRgAmdgI/Emx/EWWZEU2bKkVu+ieiG7m2Sx9qq733dfznx4b1UXySpWkc3qFlv3BxRI3rrve88t1j3Pebb/U2xoEyWTv7ywwevNt4XtPAXeoNA/mqpZ5EiiPMaLCqmLkmXwyQfnKF/aYr27xT7jFXbQgR+8p8HT90xTMjRafkwvSiBXOKbOZMVFSokQEMc5qcqZrFgITdAchDiOjtaPaPoxlpS0/YQozVAI5hoObS/h4nqPiYqDolC5rbsGm72Qjl9MvpuqFN3oXlQk+28W8tuP63NEDcdgJc6I4mxPCfARI94vjAzDe4EUTJRNNK0YB1p3LKI4oH+jCOm+lIC6C8u7k8ERvLg4YPdt3urBSr/FRj/k+EQZg5xnr/bZi34KQRQxWy/RKBlIBZ/9wTPkqeCBY3WqtkGzH/PNK71912UADRcSVSS+x8sm/ThjvmwRDCfYdfyMB2bLvLLW582tPramYxkaIs9o+RGWplN1Dbwoo+cHdP3CkOqapBcUIaJNL6JRsrAMjYEXs973KQ2H7yTDhr3dnoFh3KgjdRA35Iik5LGFBoYuR1VJI97XjAzDu4yhSaaqFmtdn7kxl7pj0HA0Kq7B4lafbqjw0oPv4wGeV/wHCoqcwGCfOFSoYK3tU7INzl/t7r82IEcQxhn1ksWnH5zj3qk6y50QoaDmmHz/fVM8cKzKpZUWSx2f1gCyDNAgzAtxP5Rgox9ycbXLdMViYdylYuk0BzFpluNaGnGaE6c5QhUqq2ma0/Njen5KliUoAZapUXEtJhNFJ0gJkxRTFzQck46XcmXL5+XlDsttn36YcP9sle+7Z5IsZ6d3Y3Y4nhUODvntxUE5pBEj3o+MDMO7jJSikJFWsNKO8DXB6akKF9a62LpOmMUsbnpsDRSHyfVKwDRAz/edpwMUhsMPfbo3MTpjDnz2Y2dYmCgRxDmaphFlOWcmS6x3A15f72PrGo/OT/Cxs1O8cLWNAL693OHVpTaRD64Brq2RZwkb/ZheFJMFCW/FGTNVm8mKDQK6YcpM1aLiGKy1A9Z6PvN1m1rZZKsfszWICuORZViGzoM1h8mqg2VKgiij5upc3Ozx2noPXQh0KWl6CS9dbfOpB2eJ4qKayboD5aS3Y1BGjLibGRmG9wDb0DgzXaHs6Hz14iaaJpipOTx6skF3ELPlhby60kUTgmffarIW7H+vGDAV5AfkhMMQ/Gj/1LYO/NzTp3hoYQwNgVIJipznL7eouwbdIMW1NI7VXYQAP8oYL9ssNBzOTJcLBdS1Aa0YWu3C+rQHLVxL8sGFBhvdmDjNQAjGSyZxmuPHGbapcXKyRNuLkYZktmRjaYLNfsCxWomqa+IYgkGUc26yTIqi2Y9BCC6s98kzhWXrWLpWzJmOcsIwxTKNnbDS6KQ/YsStMTIM7xFSCqarDk8sjKEoSh6XOwE9kdIoOfzYI1U6fsypyQq///XLrNwk/+ClYAKPHnPY9EKWOzemrz1gubW39dCBn3lsmk/eP11oPnUjXFvy1QstVA5r3YjJsokfp/hRghCC5XaAa+rYls4gTMmylPZ1Ls5aAN96a4upmo1j6ARxzlzdRihYbMUIivLZJCvGiFqaRnsQo4B7ZqrM1mwsQ8fWNdprXa62fTpBwljJJIwzLE3SznIMXVCyJMudkDQ1yASULI3FdjF5btSMNmLErXFkhkEIYQNfBazh6/yeUuqfCiE+CfxziijIAPg7Sqk3jmod381IKTg25rLWDcmyHCXggbkKV9oBWS4wdZ37Z2v82JPzfP5bV9ncwziUgJk6HGuUmaq63DNd5j8+v8leTsZe7WkW8Mi8Q+B5/O63LmFrKeudFKFLpsolZsfLtPshr651GSvbBElGnsFUzWaqaiGU4vnFFi8sh3u+x40Anru0xWPz4xiGjhcllCyTmmvghyllWyKEgSkFvTCl1jAIoowwybF0wb0zZc5f7WLrGpnK2exHxGmGY2oIoO1Hho5d/AAAIABJREFUdLyIJCvmYMzXLV652qXk+NRdg7m6iz5sYHsvm9FGUhoj7iaO0mOIgB9USg2EEAbwV0KIPwN+A/gJpdSrQohfAX4N+DtHuI7vanbrLmUU4nHTFZtukJJkGbqm8dGzU3T8mD9/cZ3eddEgD1jqQMnOsPSIly519zQK+xEBz1zdvuJa02HS5pGFKpYmUQKiNGeqbCKE5EPzdTQpeWu9x5+8vHTT/osrmyFCdHjoeAOlBJaEIM6Yrtp0goT6cBjQvXWHsbKFUBCmGVdbPl6QsN4PqNgmtqFRdRK6fsyzl5q8ttphoxeS55BkULKLSXB//UaLRxYqPHFqCgWcmiiTp/l71oy211jYkfcy4ruZIzMMSinF2zuNMfxSw6/q8PEasHJUa7hb2NZdkgjiJKfiGBiapGxpREkGw16C8bKkd71loNjcX14LuGky4jaIgW8t9pi0YaZe4qkTNRxDJ84LGQrb0GhHPpv+ze/jp+CYGpMVk41+wHo/ZK7qUHNNqo6BF6dMlyyEFKhcYRgapMW4UV3XEEqS5QpDl5hScqXpcanZp+/nSFHcPweEyBAiI4gS5FLOdLWMUjBdttCGncvvNnsp7L7X3suIEQdxqGG6Qoh7hBD/WQhxfvjvDwghfu0Q12lCiBeBDeAvlFLfBP4u8KdCiCXgvwb+r32u/WUhxLNCiGc3NzcP+37uWqQUOwqovSAmVzBeMkEIulHMajc+dFf0ncaPoRfEvLDU49XVDhu9iJYX4UUpa9cnFvYgoXgvrqmRZpCnhbpsphRJroiSnFQpslxxpeWz1Q9Z7YbM1W2mqw7nZis0BxGtQUScZUhdI8uKaiwpCuUQBQxiiHJIASHgtbUei80BV9pFWGm31EWS5eT5YVsJb5+3u6ffVm/NVRFWGjHiu5XDTln/18A/ofiMo5R6GfhbB12klMqUUo8Cx4GnhBAPAf8I+BGl1HHg3wD/Yp9rP6eUekIp9cTk5OQhl3l3sy0RPVW1aTgGhq5RsTXOX+2SpClCvDebSdmmGEfai2kFCVNVi36UMVu1uW/mcNOSV7oBG72IMC7mTlxt+0yUTcZdA00KyqZOvWRyrF7IYExXLEp2sZnfO1Plkfk6czWbubrDwphN1TbRBcS7ym8zQOWQpIo0V7i64P65Gmcmy3T8hDxXhEnGYsvnastnseUTJrc4aegWyHNFnisERbMdsKccx4gR320cNpTkKqWeEdf+Mh+iDatAKdURQnwF+GHgkaHnAPDvgT8/7H2+F3AtndMTZTKlEAoGQUzFMZmuOyxu9eFAQYo7y0xJYhg6cR7TKJtMV21KZiGX3QtjDMNg3IDmAcvq+ikdL6Jq6+QIvvHmFsttn/GyxSCJuLjeoVzSOVYuo4CxsokRp1i6hi4F56arTJUtFtseUije2ujjxTG6lmOlhVGwDdAE2IaOrZsIXWOqamEbOl6UkgzDOLca1rmdxHGYZKx2gh3PJB5O/jusHMeIEe8lhzUMW0KIMwxlfIQQPwOs3uwCIcQkkAyNggN8Cvh1oCaEuEcpdQH4NPDqba/+fcp2Q1WS5TimzomJMrM1m5Ih2fjaVfbvXX7nnK2A60i8OMfQNQzTIs+gauvMNhxsQ+NK0yPJcv7Lq+s8e7mJbnKgvWp1IuKZmEubiiApBPW6QcjSls+rax5pBgh4dL7G9983zfy4S9kyinGoVYsTw3nas8plvOxwYqxCHOcklRyJomSb1C2D9X5Ao2wxW3V5aL7GcidkrGQBhU5Vmuc45t6DlfbidhLHea640vSK3oztmRiuwbG6UxiHkVEY8V3OYQ3DPwA+B9wnhFgGLgF/+4BrZoHfFEJoFCGr31FK/bEQ4rPA7wshcqAN/He3t/T3P5oQGLrG2ckSb2x6jJcdfvSJWX772Zva5HfEWM3mkw/M0hwkXFgfIFCEGdw/W8W2DLwww4sKqfA/eXmF7iGluDs5fOm1NnkOATcKBTqykPZ48WoX21CofIpPPjCDoUkSpdCFIE1ztvoRD8xWqTo6S+1akbMArmx6JLlivGbx0LEx5mo2gyijEyR0BjG2pbPeDVnrhUigNJypIIVAKPZshLvdxHGS5Wz0Iiq2vqPmutWPOT3BbRuFUbnriHeTQxkGpdRbwKeEECVAKqX2VmG79pqXgQ/u8fjngc/f6kK/F3lbxA3un6lwcszhsfkaz761yoXW0bzmM0shzyxdwhZg6TA/7vDRsxOcna3RsA02BhF+mPLHLy3vGAWNfUdHXMN+Wk4AQQ62gFzBlpey6cVcaXrUShZBnJGkw4E8gxjX1BgvWQghWekETJRNHjlep+EavLExwDIkQaJI00K0T9MEZavYpGcFrHRDZgBdSuquwVIn2NMj2G80662WveZKMQgjVtseU2UH1zUOfS2Myl1HvPvc1DAIIf6nfR4HQCm1Z+J4xJ3jehG3jh8xU3W50DqgRvQdkipwNFjpBLy01OVYw2WqbGNqGh4JflRYBW34lVN4AeMmtOLDS4fvJhxe1Oz6LLU9XlrS+PCZcVxLpxcmKFXkENa6YfFaJYMHZiscq7u0g4RMwXjZ4rW1PoYUmIbG/ZMV2n5Ko2wNBfx0Zqs2s3UHU0oW2z6aKMpptz2EbY/gZqNZb4ahSaYqFp0gZhClnF9q88KVDl+016m7Fj/35Dz3ztZ2nn8zb2BU7jriveAgj6Ey/PNe4Engj4b//nGKruYR7wK7Rdw0BKWSAxytYdApTtRJlhPGKRlFr8VU1SLPMyp2kVjIeNsoVHT49MMz9MOYb73ZojtshnZ1aB+yVEEA4zWHXpDx3JU2J8ZLPDhXY6CKk3exSQuiNAcBmpR0gmQ41lOiiaIUdr7u4AwnrW32e1ze9DB0iVKKumNi6xpenLLcDrAMiSYFE2Vrp5RUIg4c3bofUgpOTJTQ2oKLa12+s9Ll+JhL1TXo+gl/8PwS//ATLq5rHOgN3CmvZcSIW+GmhkEp9b8CCCG+ADy2HUISQvwvwO8e+epG3ECqFHXXpEGRoDmy1wGSpIj9NFyLp0+OU3FNhComvf3MUyeRz1/l5Ss9cqDiwE8/dpzvOzdNqnJOTtWI4oxmP+Cbb24e2jAoYKkdcGpKw9YNXEviJRlZnpPlCkvXcC2dKMlZGHcJooxk6AlAYaTaXoKpCRzTKCbSyaKvQeWKRBWzpPNcsdWP0CSgFFmmWOn4TFedazyC25Xdtg2N+YbLetfDMXTqZROAkqXTC2J6SYKd6wd6A7frtYwY8U44bPJ5Aa5RgY6Bk3d8NSMOxNY1xkoOZ+Zdnr16NF6DoAgjGYbkgdkqv/Thk1RdE12TBHGKJiVPn5lgYcxlqT2g7cd8/MwEhm5imRpxmtEPin6BfphyfKzEiucd+LrbtEMYG8ScmC8RRsXI0snhxrrWDfHijPGyxUo7oGoXk/DSLEdKwUYvZKpiYRsaSZqz3AmYqliUTJ21XogmBGu9kMmKRZhkxGnOK5sDUMV0uRPjN/Zl3K7stqFJxso2hibp+wkluyibtQ2NqmEcyhu4Xa9lxIh3wmENw/8HPCOE+DzFoe4zwL89slWN2BfH0vnwmTFeXmwyZkNrb+26d8SMBT/x2CSPnp3jqfkJXMfY2ZhUrhgrGeiimCNxvOGSKZgqW7y41EGKYoMzNcGV1oCOl+BaNg9Pw7fXD28cVtsxn37IouLoiDwjSlMcyyBTULIkuiZQalul1majHxFHKXGqODFRwtQlea7w4hQURTWSgKafEsUZ317ukmQZQZyx0HAJ45T1XsgrK128MGWm7uDoGkpw25VAUgpOTVT45IPTfOH8Gi0vouaY/NRjx3FdgzxXe3oD11dJ7fZahAIlitzDyDiMOCoOW5X0vw8F8D42fOi/VUq9cHTLGrEfUgoemR/jhx6cJcogiGI6g5irgzvXwbsawf/z9U1+cqD4yMmpnY3Ji1M2+xFSCK60fMbLJo6hM1O12OhHzNVsml5MmGSkucLSJBrQqBYyWSeimIGXHNgMB8Xp48uvrPPsW03yLMO2dR6fH2esamOZeiGON1ZGahJDlxyvO4RphiYEuizCLzkKU9Oo2TpLbZ9elGJpkvlxl7YXsdoJiFKFZaSFJpWArX5E20uw13o4lsZs1RnOqS68jlvdjG1D4wfumebx+QZenFK3zJ2qpL28gf2qpKQU+EHKYqswro6pM1t3dvIRo3LWEXeSQxkGIcQCsMWuMlMhxIJSavGoFjZif0qmzpMnJ+iEKaYmsEyd9Y7Hnzy3yJXDH8oP5A+/vYVU3+LXPvMEdcekOYixdEnJ0inbRZz/eN0pTrBKoWkSBWhSYJs6xxsuXlSI4UlR5EbGKyZTYUKU5oRRyso+0bC6C2u9iEtbETnFL+r5pQHTFQ3X1KnbklPTDT750CwVU2O1F4AqvJVUqZ0u47pr0PaKGQ9ZmjNRd5CAF2WUTYOGK4GcC8Pwkp9kqCzhuY0Bxxouy22fYzWHK02NY3WH4w13J59xWKQU1EoWtWGj3W6u9waWOsGeOYdOEPOF76zR8RMMTbIw7hBnOeemKsTD543KWUfcKQ77G/4nvF2B6ACngNeBB49iUSNujpSCE1Nlnk4mePZyi36YMtco8z/+yAcIs5jPffE1LvXuzGv9wfkOJ6bf4Jc/fu818XBT10gytRNqEcBqJ8A2NExNIgScmCgRpbDW9UlyhURStzRUppCyMBRTdbi46uPtqm+ddqBWtmmuh8QMB3oY0Eug18rY7pr46uWAb7zV4t6ZKqauU3cN5sddHjpe48HZOpoQLHUCTF1yerLMS0GHpVbATNWi5uqYukaew0Y3IEwyLF1jECU0vZj1XogXJ2wNEu6ZLDFTL5HlORv9iMcWGnsah9s9te/udN8r5xAlGS8ttun58U7l1GonRJeShYbLej8albOOuKMcNpT08O5/CyEeA/7ekaxoxKGwDY3HFsa4f7pKrHIcqXGp5bE1iPiFj57jd795mYutO6Or9B+fX+ZnnzgBQBinmIa2Ex/f3gQnKhZL7QApczQpmKu71B2T43WX1W7Am2t9XrzaRklBmBahHj/OePzUOI+eaLDe9Vht+mx6CSXTRJOFTnsMGDqEe7wVBby1GZLlOY8cH8OLMy6u9+n6CSfqJaolc2ej1TXJI/P1omnOMWj5MFOxcSyd2YqFa2roumS1H3BxrVeU6UbFLOnVfkTZsUgyhW0oFpseZ6cq6PrbGpR3ogltvwokL05Z6YYMkpx8EDNeNklzRZblo3LWEUfCbc1jUEo9L4R48k4vZsStIaWg5BiUKDYmKUWhKmqa/OxTJ/nL11a5sDJg/ZCyFfvRHCScX2ozVS3RDYtY+FTF4sREaedUWjJ1jjUcNMGO4XBMnXNTFc5MVWi4BhuDkIubPtM1A6UEcZ4RRDn3nqgxUXY4O52BUmx5Ef0gZasbEvuQDuct7IUAslzhxRmaLgljRVNEvHC1zdOnJ67ZaE1dsjBe4ljd4WSu2OhHBHFGCsyNuWz2I4IwJcgUJbNoqpNakcSulw38KCVOc5JMoeuS4w0X29AO3YR2kEexV85hqmKx2g0omRp12yBMU5ZaPiVLY7JmYUo5Kmcdccc5bI5hdwe0BB4D3v9DEu4StjemsqVzdrKMLgWmXuZDpyd47kqL81dbfONSk5X+7U106CTwr750kYfnGnzo3CRnp8qYusTU3j4xb8+TWOuGBHG2c2rWdYkNBImiUbKp9BNsXSPJc+6pW9imxv2zVaqWyVTNRgrBWi9gqx9QL5m8eKXFSidETxR75dcNAboQBHFMP0rQhWCqWgWlWGr7HG+4bHnxzkY7V3ewhif545okTDPWeyHlkkXNNljp+PSDlDBN0aRAKEXFMcmznE6YcO9UBcvQkMDy8P55rojTDEvfX5zvsB7F9X0TmVIIIXhgrsZ3Vrps9nMicmZqDqbUWOmF1F2Djp+MyllH3DEO6zFUdv09pcg5/P6dX86I22F3OCGXatgAllF3TE6MuwzCFMfQ+Q8vru65uR5Ew4B+pHhhuc2GF/GJ+6Z5+HhjZ+PbPgmbmtyzGUwNu5HHKybZUoaXZkhdkGVQMgzm6g5SFNPbSo7BuakqkxUbL1I8NFOnG0dUdZ1vLTb5T+c38YbvoSThwfkKE2WbS02fME4YK1us9ULOr/YRmkTXJXM1B0OXO2vaLmPd7EekWc56L2JhvDjd1xyLB45VudoKaA5C/Djj4YUa426x2UopSNKclW7IWjek6UX4YUaS51Rsg9m6s1MVtX1qv1VZi2v6JnKQQlCydJ46Nc7Aj1nuhZyZKmMbOmmW0/GTnSKAUVXSiDvBYQ3DK0qpazqdhRA/y6j7+buC62PTDcfgSpDwxtaAlW5EnOcsdQIcUzAIbk3FqCyg4uhkKicMMy6u5bhaMQFtYcwlM7QDT8KaELimzvyYy0fvmeT5yy1yBJYuOD7m8LmvvskgyNA0yYdOj/OJ+6ZoezGupVOxNKLcQijBZ09N8Jknj3NhzcMPfO6dGUfqkrVWSD9cwyhb2LZO20/RmwOeOFFHU3Cl5XFmvIw05c6chKV2gKkLpqo2pi5Y7QTMj7nUHJ2NbsD9s1Wgim0K5uslTo+XWeoFbA0ibFNjvRcihOLbSz0mSyYpRXnuYtPneMNhtu7sbNDvJA+wO7yUK4VmaMzWHGxDv+ZeShQNdSNG3AkOaxj+CTcagb0eG/EecH1sWgjBTNXGNiS2KVncHNCPMoSQHE4HtfjFmCqDlAaxUsRhTDsARcZaJ2IwDA2dnS5TtY2bnoR3J6fPTVawdMls1UYJeOatJr0gY67ukmWK56+0cDSJa+t0vIRnLjcxNYGhSX768eN8+OQMH5hLeXmxw5YfY2SKfNhUN12x0HSNTIX4SUaa5Ty/1ObN1SZJJvnIvWMcr9cwdImhFyf6rUHEVNVmqRXgRSljrsn8mEPZLprphIJNL8a2Qholk/VeRJhkRFmOIYvqq7JjEMQphiFpuOY14Sp457IWe5W0xmkRrts2yIe516jXYcRhOUhd9YeBHwGOCSH+5a5vVbmFCW4jjp7dm0eeK5Y7Aa6lM1OBuXGX5xdb5OpwRqFhADlEMaRpQi6gP7zUEqBp8Opqj3OrLUxD8IFjDeDmJ+GSqTNRMdnsh7iWwSDO0SWEcYatSyxDIExJP4K1vs+McHnmUpM4ywhShS01vvCdNYQUgGCqZtEOE5RQBFHKeNkkF4KKqWFJDU1TfOOtJl96dZ0LW0V7+L/++hJPn6zyq5+6j61BjEARpTmOLpismNRdg64PjmkQJBldP8UYlt6SK3p+wmTVxBACIWCzF2FqhdCgMSx91aW84eR+J2QtdoeX6q7B+eUuWa7QpOChY7UD7zWS7h5xKxzkMawAzwJ/E3hu1+N9itnNI76L2N48cvG21IJr6dwzXeET98/wR8+8wVZw8H3a26WhQ2PQsMDIiqoD1wRDl/ixYr0TsVT2KBuSsbKFbepIIfc9vUoElq4xUZY0BxE5opCuUBlppoiTYgympemAYhCl1EsGbT/BsXX8KCcIE7b8hIUxl8mKiSElFdtAl4LX1/skWUajZDJVNfn6hdUdo6BTnGS+cblH7Suv8JH751nthIRxystXm5wYLxMkGTM1i7m6w5VmQC9MOT1ZZqxksjGISDLFmGuwFaa4liTJFdNVkzDNqVhFN/bcrhDSbm5XjO968lzR8RNOjLkIWeRlOn5CdTgfe79rRtLdI26Fg9RVXwJeEkL8llJq5CHcJVwTl05zJko233dukm9f2eBC99Y739pR8adNUTbqRTnksNbxaHkRf/TCEhXb4P6ZGr/w4ZN7bjaZUhjDctE8V5yaLOGFKecmy/zp+TWWWh5SSp48NcaZyTJtP6JR1jGEpGbrSAGZyljuRbyw2OG1tR5jrslE1UZDsjBRZqbukCmwpKTmGvzF+RVgODOiaHAmBS51I0pXWyRJStMLeO2KT08V3xPAuAUnJmxOTdd2PInVbshM1WaialMv5URJzqfvr7DRj1CqaPSbrtz8FH67YnzX/xzzoeDf9pvzopvnK0a9DiNulYNCSb+jlPo54AUhxA1ZS6XUB45sZSPeEdefUAH+m4+f4j9deOm27mdRSFcHUfHneFmj5Wd0gwAlYbMfs9GLUGT86icfoFoyr7l+O86e52onzm7qxRr/h0aJThxjCknVMYmznOeutDk1VuHN5oAkV6gk4+xUmbVuxFTZIkwzLm/0udry+ciZCU5OVHfUX6+2fLwk4eykwwsrfjEzIn+7dd9WGS8tbvFm68bQmgK2IthaDnllNWQQZNw3V0eXcP9sBSnETte3Y+mctg28OGWrH9H0Ytp+cqRhmtvJV4yku0fcKgeFkv7h8M8fO+qFjLjzXH9C/cjZ4/ziE+v89rNrNz4XqJjsO8PZMeDspE2KpGRKFBI/zlnrZTiGjmkUm+Wzi12+vdLhw2cmb0hAT1UsVjoBiEI/aTvObts6M/bbv4q6Lnn8RIO6ozPTsImSHEMrEq0rnRDX1klDxZVWQDpsVPv5Jxc4MVFG0yRRmtH0Ej5wYorXtyJeXh7sGIXjVUnJdXh58WBRqSCHZy5uYWiC++ZqnF/p8PiJcWxdu2ZjbQ5idCl2QjtHGaa5nXzFSLp7xK1yUChpe+r8ryil/vHu7wkhfh34xzdeNeK7mf/jZx7nxx5d5ve+cYlWv49hukzUS0yWXLpexFLL4z+/2b3hul4Cq62Qk9Mu56arXG1H9EKPTAESlBCYmkAXsDmISLIcS759ag6TjI1+hKA4lW/PTNgPKQVl2+CkXnT2zlRsVroBfpLR92O+s9TF0DQmqwYSwZde2eDnn7RYH0RUHINaySTLcv7Gg1MYKmXTi1Cxou5aXNo8vNJgK4Wvvr7JW1se8zWHnpfyobPjnBwvA0WYJkhSvCjbSeyWLI0ky5FKXNM7cacqgm4nX3Gnchwjvjc4bLnqp7nRCPzwHo+NuAv4yNljPHVyll6UsNoJ+OuLy7yy5jHpSsolkwkTBjEkvF3cmgPLIaxe8ekngtMTZSy9zCDM6Pgp0pU0HJMT4xXK5rW/VruTn45pkGaFGN3CUE76erafb+oS1yqauFpBwsJ4iSjL+MJ31ugGCSfGXWbrNpqUrPVCVvoBWVaI9wFcWO/ytTda2JbFB8frtLyItZ63r7zGfnQTSNZ9Nrs+mq7IVI4fJYyXHUqm5OJ6j7qtUy87xEnGaifElBIxbHTb7ky+kxVB13uD1xuevQzRnchxjPje4KAcw98HfgU4LYR4ede3KsDXjnJhI44WXZeM6Rb/95+e5989d21oyQQMCSq/seshB65uevz0E3MIpXN6usyLlzpITTBdc/jouUnmx0vXlGzeavJzv+cbuuThYw2myhZ+9BYVS8cydPIsw9IEZUOjm2dkWY5j6WRpjmNqlCwDS5PYloalCRxHh+DWailiBZsBfPk7Ld5c6/BXr7ncNzfGWj9kEKbYuuCDJ8e4b7aOUgpNEzimTpxknF/ucmLcxdH1AyuCbsezuL4U9SgM0YjvLQ7yGH4b+DPg/wT+512P95VSrSNb1Yh3hT97+dINRgEKRdNkV7L2BgTM1UtMlmygxvednmDLT5ip2IxX7Gu6fuHWk58HPX+i6vC3P3SCL7yyziBKCOOMT9w3zUyjRDlMWOmGTJRNdEPjxHiZtp8QZwo/TKi4NnOWRha3WRocvgt824z4wGvNnNeaA567POD0lM141abj57xwuUXFNJis2hiy6G9QFCJ/crj2mxnF2+k1uL4UNU6HhmjMxTEPNkQjRuzFQTmGLtAFfgFACDFFUbVYFkKUR4N67l7CMOW3v3Zl3+/fbMt0TIGhCS5ueMzULBzT4N4ZB4FgvuFeI0cN+6uGZkoVWkDXbVj7JUt3D6TRdI1f+NACYZrR9mLGyjYArqUzWTKZrTlYukbZ1PnSaxts9UJc0+CxU3VqjsW56TpffX2Jb6/dvvSsD1zcCGn6KZoO7cDgIT+kVjK50vLQNUmWFb0ZaV4EsPbrVL7dXoPrvSspBFmuho2Ao9LUEbfHYdVVfxz4F8AcsAGcAF5lNKjnrqWXJKj85puiBjRM2Nr1tLoFn3nsBK+sFHMNgkRh6jndIKViaXhJSgn9BuOwO/mZpEWO4WYn473KbRdb/jUbZydIi5GeiSLNctJcsdYNiFOFaWg0SiZCCD7zyDH6SYJUMNsoFXpJ9YBOEOLoPa5sDWiFRU6laK07rHAIRECYpNQMAy9K8OLCkEVxiq8UlqZRdjQWm8W4zv06lW+31+B672r7NVSuQGNUmjritjhs8vmfAU8DX1RKfVAI8QmGXsSIuxNHasyNjcHV9X2fc++EycJ0hThJaHZCHl5o8FOPLxAksNwNKNsmoGj7CXmW82aUMt0N0TXJQ8dq1N1rexmkFJDD8iEnju1Olu433UwJmKnZrHQCltvFWMyFcRddil2qo86OxhBA2TYQ0qcXpNimzsnJKo0oQeU5mibp9ALWgv1nQFxPGINjQd02WO9GXLEHtL2IOBNYOpyZqvLYiQamoaFyRcuLcQytGD86fM+322uwl3f10LEaHT/Bi96b0tSRJtPdz2ENQ6KUagohpBBCKqW+PCxXHXGXUnIMfuiROb55ucmV7rWJWAd44JjDsbEKtqFhuTYnx+t89NwEE9USi20P19JpuDrNQcKVZp+2l3L/bJmKYxAnKd98c5OnT09QccxrNoc7dTLevXEahuRY3SHNCunr7dfbNhyaEGQopioWG/2Inh/ywmKX2aqNo0s6XkKmUlIlMaRGdaqM2hiwOpQPsSikQHrxtZ7EtsxGqMDrJZAnXFiVvL7S58RUieONEn6S8Y1LTR4/0UCTAj/JWGoHOw1+297SO+k12KsUtWob78nmPNJken9wWMPQEUKUga8CvyWE2GAkondXI6XgqdOTfPYTZ/nCy6u0/AihMsbKDlMVh5lGCcfUEIWCHAuNMnPUqZT2AAAgAElEQVR1lyTNQQnOjJcYJBlxmlEvWUyUbOolizfW+yy1PFa7Ia+tD/jImXEePt7Y2Rzu5Ml498ZpaIV4XZQWs5u3N6YkzVneFbaq2jptPyLOMjSp0Y8y3toa4MUpVVsjMeDiasDuIFvNhdl6GSGgbkgutgb0vHxHWFBRJOK6AzC0gCTNMdqQpTl11yJKUrYGIfiS5ZYPCBbGXAxNXOMtvZNeg+tLUd+L0tSRJtP7h8Mahp8AQgrhvF8CasD/dlSLGvHuUDJ1PnJmmg8cq7PejciEQirJAzMVOmHC5iBGKcVk2eLMVOE9ZEoxW3eKZjUpaJRMPtBweHW1TxinvLLSRSCYKNuMlUxevtqh5pqcnazsDLq5kyfjbeIsJx7mLgCmqhbzDZeNXWGrOM14ZbXHTMkkzSHMMuquUVQsxRlhku3oQu2m58MvPT1GveRwtRlQcyV//EpnzzVe6RYBqG7gMwgVc/WcqmPQ9mIcS0dKyXTVouXHzNUd8jS/oRnubk0SjzSZ3j8cyjAopXa3iv7mEa1lxLvM2+M4BZrU2BxETFYsUgSnJyucm377NL7TJEUxG2HBKLp7LV3D1CX3zlZ47lKLXpgw5losTLi4ps5GnNHzI1q+SdU0ME3tjpyM87wYFrTtaax1Q8q2TtU1iJOMTIEmBXGWIZAg3q7Y0U2N+2fKfP3NLV5f75HmirprEMYpu+uxNIrQUQZcbUU0SmVsXdJL8gOT014OG72APE/56LlJZusuNdfENkIMTRaGLMlIspzlYe7jbg+9jDSZ3j8c1ODWZ+/KRQEopVT1SFY14l3DNjSO1x0ut3LOTJQxDe3tzuRdIYDrE4pSCiypMTuc86xLyaPH6yTD6iBL1/CihK4f8/W3mlzc8DF1yUfPTjBTc97RyXh3HDtIEnQgzhRTNbd4T6aOF6V0/JiXrnYgB8uSnJkoFcYiyVBIxkomlqYxXjYJ4wzdhnU/2Xmd7eSzDjRKGpYhmGrYPLd4uJolP4GVbsKlrQGDKGWsbDFVtVntBCSZIk7zQhrd1DD1uz/0MtJkev9wUB9D5WbfH/H+QA1P06axdwjgZgnF60//jYrFV17f4GrLoxckRGnO6ZLLRNkiyXK+9sYWP/7wHKZ5e6fi3XHsC+t9/uC5JaIkw9Aln/ngcR6eb5AOK5i+s9xDk+DFOb0wxgszPn7PBJc2fcqWhmPq1F2LNFcYUuBFgrqR0Bnahu0T0aMnKsyOlxkvW1za7HNh+XBaSwKIc3jurR5fnl7iRz+4gGMaTJYtqq5B24/ZGsSEac5EudCO2v1zvxure0aaTO8PDptjGPE+5mYhgMMkFHef/mdqDj/5gWNc2OyR5DnPXupQL1m0/ZjJikUvTAiyDJMbDcNhNsLtOHaYZnz+hSXKps5ExSZLc/7wxSWmqxYVx6LuGjzvRYyXTRquwItSrjQ9NnshmVIcqzs4hs4901X+6sIGq50Ax8p46mSDQRTRGngsNkO6ISx1In7vm0t85Ow4tqGhGxRND3tQkdDPh0Zh+JgP/NbXV2j7ET/52CnGazZb/YiypVOydFSuihGjFWvn5343V/fczXmSEQVHZhiEEDZFFZM1fJ3fU0r9U1GUufwz4Gcpwre/oZT6l/vfacRRc7MQwH79AzdLKGqGpGwXIzANvUeU5EgJg7AYleloN25wh90It43YVjckSRXlqkGuFI2aRT+Kqdg6C2MuUZKRZ4peGFHWTTp+gqFJaiWTKCtmJxhaMaP5Uw/MMAgTLFNjYbzEeifkldUW/+H5NWplcE2TMEn5ymtbfObRaQw5nPpzHTMOPHl6jGcvt1i7zqnwgD98qUnTU/zkk/NESc6TJ8aYrFhs9iMGYUrNNjg+VoTDRtU9+3M3elJ3G0fpMUTADyqlBkIIA/grIcSfAfcD88B9Sql8KLMx4j1mvxDArSQUtz+wQhWhKakJnjw5xjfeahKlGVpV8LFzkzeEkW6lzHHbiHlxjBTQ9WJmGw49v2hWm3ALg9YOYtb6ISuLPlIKJssWj58cwzZ0ZmqSxaZPxZZ0gyL2L6RkrmZTsg3yiiJclOTAVNXBMjSy3MDb7GMYJj/79Cl+62tvsrFrTGrZhCfPTPLRc5OMVx3+7deXb0jOJcD5lTaPbdWZrZdY6vicmawwXbGIXIOTYyV0Xd6WMf5e4W72pO4mjswwKKUUMBj+0xh+KeDvA7+olMqHz9s4qjWMuDX2CgEcNqG4n8JnydL5gXOTVEsGDdvcM7dwK2WOeV5IPtw7VeO//9gpfudbS1xteliGxi88tUC5ZBLHGV9/s8nJ8RJTVYtekNAahMwMQzW6FBxrOByrO2hCoAQ7Mh1elKLrGk+drvGXb2yQZQppC/phTMnU0aVirGTzt546yQtXNpFScLrh4ro2TT/ljU2P8ZLFibrkUudar0ICGYKul3D/tEGaKfphgqlrHN+lMTWq7tmbUZ/Eu8eR5hiEEBrwHHAW+FdKqW8KIc4APy+E+AywCfyqUuriHtf+MvDLAAsLC0e5zBEHcFBCca8P7NtyFBzo8h92I7ze+Dww1+DXfrhKJ4qpWybl4TjRICvKQMeqNuVcZ7Ji4ZgaKWpHJmKuXngC22yX4G6/x7m6w88/EfHvv7VIK4gpWwZ/7wfO0PUzNE1xz2wN0zDoBzGTdZO1TowSRW/IRNni7HSNzX6bwa4CJglYUpGrHKEVP5Ppqn1NtzaMqnv2Y9Qn8e5xpIZBKZUBjwoh6sDnhRAPUeQcQqXUE0KInwL+X+Bje1z7OeBzAE888cTh9ZFHHAk3Syju94FVgmvmMtzs3gdthDc7LZavmy/taBpKwWLTxzYkYVLIT5wdr6AZcl9Dtfs92lLjM4/N8/F7JmgPIqq2ySDNubTRx0tyKrZJo5zw6lqHi+t9wjxnzDYoTbpMVCzGyg73zYQsdkNaQ3nviglnp+o8vDBG1TFpuAZtP6FiGzfEzUfVPTcy8qTePd6VqiSlVEcI8RXgbwBLwO8Pv/V54N+8G2sYcXTc7gd292a4l5rqdgOblOKWTou6Ljk3XeLFKx2iNEMCDx6rYJp7T4zbDykFk1WX8bLDYsvH0iSNsk09L3o1am4xDKhWNjDDlAxYbgWcHCtz/2yFuqvxSKy42hpQdk3mGg4PzTWoly1mqza6LvGiFC9OaQ7iG+Lmuw3VKOE68qTeTY6yKmmSQnyvI4RwgE8Bvw78IfCDFJ7C9wMXjmoNI94dbucDu18Scb++CVOTO8ZHCkGUZgjY0/hkqsgD/NBDszvaSclwY72dkMO2UXJMfaeKyI9i4lhRdkwQsNyLCFPFm5sDTENyerJGybLopDHTjTIPHavz8HyNIMmJkgwlCklsAWz2IyxdXuMJbYfhhIIgzdjqRyi45udxq4ZiL+NytxmckSf17nCUHsMs8JvDPIMEfkcp9cdCiL+iEOL7RxTJ6b97hGsY8S5xKx/Y3WEhKQrhu9VOwInxYlbzfiGjmZrNlabHRm+oh1SxiLMcW16b0N72YKQUVByzMCZqbyNyGHZ7RLahMV2xKJsaSilqls7XL7cQ5GiApuW8stzjoWMNHpir0fZDogSeONWgbJt4YcJKnBHFGZommRgaGn0YctM1SS+IuNzySLKcrX5MnOaUrKLLXJeCK1sepi6vMRQHVebsZWy3f9Z3W4XPqE/i6DnKqqSXgQ/u8XgH+NGjet0R7x2H/cBun8DTHDb7xcYUJTkTlaL7N81zMqXwBwmmoSFEcTo2hwqqC2MOpqHtGJjrq1Jux4PZXWp7fcJ8r/sdG3NRAs7OVPnShQ3EsBmw6hj0w5R+kGAbOpZucGrCQlE02UkpeWyhgaHLHUPVFPFOGC5OM5pezHzdoRNlmBo0vYSKrbHaDZit2qx2A443nKK0dp+fwfXv7Xpju9IJUHkxm7rnhyx2fBabOk+fmr7trvQR7x9Gnc8j3hV2hyw0IRDAaicYnlAFSik2+xFTZYsXF9v89RtN2l4RYvnAfJ2fe2KB2jDJbJvFr63UxE6egZzbTt5un6aDOKXpxYyXTRxDv6n0B4DMYbZhUHMNJEVuI44zelFGnGc0XGMnrLXQcPet0NptdPJcMV420fRiGpvUJE0vIk4zen7MRsVmuReiCYFrp0yULXJ18zDZXvmZ1iBiy4t55q1NfvfZq8TDfM7PPH6cf/DJ+24YsjTie4uDS0ZGjHgH5Lmi68e8sdHnStNjseUTZ4V3kGSKOCu8g5maQ64Ul7f6nF/q4EcxZVsnzHK+s9zhi6+sEQxLTdOs6A/Yzjckac5iy+dqy2ex5RMmRY2olOIaZdj91rfWDdEk+EmGrUu8KEMTwzBL/nZB3O77XW4O+PNXV/mL72xBntH2M5JMYRo6905X6PhZIZznWszVHXRd7ruWbaMzP+ZycryEYxQyGQDr3ZCKpbHUDtgcJLy+PqDhGPSjFKFgrVt02d0sTLY7FAYQpxntIKHvB/zus1eJkhxLN0ApPv/CCl9+dYU0Pez8uu9OttV3d///jTg8I49hxJERJhmXNwecX+mhS5is2sxW7Z3k6rGGgybYCQsNgozLTY+2H+PHGXlUhFiM3KAfJ1xpeZwaL7PlxTshne2pbLfb9LR9mjaEJMsVrqnjxylCCvI03/Mk3ulH/LtnFrF0jTHXwNE1WvGAscka5yZrTFRsFiZKPHFijPJ1PQr7JXt3h+GmKhYr3QBbl/hxhlKSumMwNW2z0QtxTJ00zYnSjDSH8ZK54zXtV4Z7vVcyWbFoDUKCYSmvUgrXNukFMau9mDjP0e/Sc+OoO/qdMzIMI46EPFesdAK2vBjH1HAMjV6YYuoxDbfYyLYrfIJ4eMLXBKaUNAcRl5oeW90E5HAughRMVQrhu7m6sxOjf6dNT9un6VwVHdVhXBgclas9S27zXPH6Zo+OFzNRsTi/3GWll9BPBRfWAzRp8uiJcY7V3eJn0B3Q7oY4tk69ZNPxUxCgS7nnhhUm2c6wIcfQuHemTMePCWMDJRSWoaFQTNVspis2UZrT9GKaXnzTTXB3KGx7/vV4WUeXkKcZlm0SRzEoaLgSU96dRmHUHX1nGBmGEUdCplSRUB2GX6BQHI3ijMjIWOkEhbuf55SMQh8oiBOkLqnaGp1BQgLYgGnA4taAq22PD8zXrp0VkfOOmp52n6ZdQ9vJMWSKPZvs/DilO4ixdI2+H3F500NKxVzdoeEYrHd9NKk4v9zmN778Ot98Y4t2WMzBdYGPnDT4rx47wwOzDVaU4uR46ZrS0e1NzTEN0uFsCyEktgH9OGGqbNGLUqq2gRgataLUVR64CV6rgmsTxikfOTvFX7+xQT+M0QQ8fmKMH7h3bkee425j1B19ZxgZhhFHgiYEuiYRAmqOTnMQE6U5jqHtnNDX+iHPL7b4xsUtpFRkSvDgTGnYVWxQznIMIciFQEiBphQoiIeSF5bU7kjT0+7T9Lk9qpLg7fBEL4h5q+UzP+7wrUtbeElK1dI5VncIM0UwiPjD567y8mKTq4NrX8cHvng54YuXX+OphQq/+OGTTFYsKrYB7L2pWYbGI8frbPYjcmwkgpmqjWUW4bflTnBNqetBm+B2KMvUJGenKnz246d5fKFMc5DRcCRPnplmuurcwv/0dxej7ug7w8gwjDgStseGbovTlS2dk+MmxxouW/1C9nq57fGNN7Zo+RFtLyLJ4JWlDmcnXPp+QlhEknBMKBuF8ulKNyDPCy9ktu5gG+9sVOju9d5sM13tBCil8JOM6apNL0j42H2TrPdibEPgJYo0zRhzTV5carM82PNWOzyz2OfU+Ab3Tla5d66GlGLfTa1iG1Rs48bmNKFuaRPcK/b+wFydmmMSJRmWoXGscXeHXEbd0XeGkWEYcWTYhsa56QonJ4rGte2Q0uYgwo8TNnvFh7flxagMSqZBGEe81fQZJOzMVQ5iqKRpUa2jYL5R5Bh2h02OsunJi1OW2gG6hM1BzEzVRkjBmGvwmceO89yVFhv9kJpt8ODxKi9/pXeo+y53At7Y6jM3VsyD/v/be/PgyrL7vu9z7n7f/h52NBrobXr2nuFMkxwOKYqiqMWiSIqyFFmJZDmxLVtZKrYqsqQ4qZSXqsQlJ44luWRRdsrxUpIlJYokWpYoiqvIGZOzsad7tl7RDTR2vP29u5/8cR8wABroBtBoAM0+nyrUDN67971z8Pqd3zm/5fu706K2W+VbuL3v/Xh/7tuqkvh2G4X7rdL7oFCGQXFPWekNvZbRosv15TYxAikFQZjKTSQkCE2j1k1uaYMzWY+4slBltOSy2E67wd0pf38rdrI4JElaX2EZAlPTMDTBQstnIGcxXHAYK2f51JlRvnppkVIu7TUttpnpuVDvcHmhjWMu8cFTA2RsA0vXGOlVJd8p1Ra2X3G+mZuqG4Z4UYxj6NsSO7yf2GyjoLKVts+3178GxX1BxjZ430Qfjw0Ved/xMlIkNLsBXhzj6HKT3mgpb8+2WWwHxEnCzWon1RraYZq6F8ab1jxsRSzTNxguurT9kGqny4WbNZZaPjN1n0rWolJw+c5HhtCFQRDCkcqd91smEEqNth/w9myLKwtNOn7E9eUO07UuM3WPoJeH74cx7W5Iyws3rS+4Xb1GFCV0gggZy3W1DG0vZLbuMVPtbuvvsJb7sUZg7YkpaxuYurilTkXxLurEoDgQcq7J8w/1M1p2CGP45pUlEOBFkmKrQ30T69DxfKaqLTpBjK4Jco7BVK27bVG53aQyrvj9L841+K0Xr3FtqYMmBEGY8ImnjlDrhBQck1LG4n0TFS4tNPnp73qUf/3VS5yb6276mgUdTg5lMQ2TvGMRRjEz9S62oZOxDTRNIwhjri6kgYqrS22uLrTIWAZHyy5nj/dtqzJ5ueVzbqpGFMUYhs7poTxBLOmGqVEYKTrkHHNHKZ33665bZSvtDGUYFAdGxjY4OZjnU0+P8cGH+mn5MWES8dsv3uDVyQb+huvPz3Qp56scrYS8/3gflZwNkm2Lyu1mcdA0Qc7W+N2XrrPcCajkHGxD8K0bNfrzNh97ZHj1fqELHMvgvcf76M9avHBljj99bYrZZvpajgbHRmwGslmWvJhCNs2qcqy0f0QsJVEiWWx4hHHC9aUO5YzB5FKLpVbAAj4LTY8wSfieR0fWpZSunCxiKXEMHS+M+ePzM1TbAX4ckzENZuoeH38yvU9IyPWyoba7SN7PNQIqW2lnKMOgOFBMXSPvWpSyFpoQdPyQqYc8mp7Phbn1pqEbwUuXl5mqegwUHVzHZKToMt/0OVpxyVjGbRer3S4OXhhjCI2S64AmCaOEIEqYWmrTDMLV+1de39AED48UGe/L8n1PjBFGEXECQSK5vthhruGRa3QZL2fJ2CYFy+BIOYOpa8zWu6m/P5FIEhabPjdrPuWMiZRg6oJL8y0+eDKiYFir47s41+Ct2SZSQiVrYRqCyYUOUkvHFeoJ9W7AVLXDqYE8klQawzJ0PD/Cj2JkLNNqwi24n3fdKltpZyjDoDhQ1n5hQ5kGn08N5rk2VOD68gLNsHcdaZZSkEAcJ3zh/A1evjTD46MFTh0ZwDZywO0Xq90uDiXbwrV16n6EiBO6QYIklc9wjHdXUk0Tq3IWQoKuazw2Wlrn5oqOJ1S9gPl6l/lWgABGii7HB3KEccK1xQ7twE8XYE2nGaQxlbjnC9eExtppJYlkernD5fk2RcdM1VK7AZNLLTRNkkiBY2vUOyEDBRs/jrle6xAmCbNLHpYhuLHcpeCYLLUCnjhS3NJNtZlhFb0xJEJuS732ILOBVC+H7aMMg+LA2fiFLTkmF6aqZE1BK5RIWA1Id0J4Y95bvfeP324yUZ7j73/qKR4ZLd3xFLCbxSGXtfjJDxzj1798masLTXRd430n+vjUM0fJ2OaqEVorZyFJNY9WXForRsqydIYsl4Gcw+leIHglcGz0qpiztkXGMmn5AV4QUclY1DoBBdfCNgTjlRwZyyBJJF4U0w1jEKz2sE4L3nSOVCyuLLSptyUhkrGSS8tL6M9q5B0TR9P4xvVlJioZco6FF0Scn67z3PG+TSufNxrWME5Awo1qBySMllwsXWO549FsB+QzFpWsQyTlpo2XDmKB3iqteT8N12EwkndCGQbFoWDtF7a/4PCTz59goRPy9XfmmGvExKSZPBvjDgCT1Yjf+OJb/O2PP0Ffxr3jKWA3NQ9njlb4X38oy2vTVbKmzmAxdVWFcfoF30zOYr7pM25u3k50szReKaAvb9H2Y7woxjIMnh4v852uyVuzDaJE4poGTx4prnZ1i6VkuR3Q9UOQCY5pIBMYLNiMFhxMXbDQCii5JuOVLIahYfUMiNAFUoJjpsuAYxm0A/+2AnorhjWME6ZrXZCSajfED2Penm0wVW3zhTfnqHYCSq7F9z85wnuP91HJ2qtxiTvFhPZ74dzPgPr9ErxXhkFxKOnLO/zE+4/xyFCWN27WCaOES/NtLmyR6TNb6xAGMWOj7j3T+SnkbN53YiB1eyUSbY2eUhgnq/73pJfiutJwaMUIRVFCkCRYmrbpGHUhcE2DnGUgtFTIL5YwWsowXHAJkoQklsy3fKarXUxdMFJysUzB23Mtml6IrgmeO97HRx8ZohPEjBTTFqEjRZeslWZxrbiChExPK0EUY+gaXhCha+KOAnqaJtBkKmtS74ZEcULdC3nx0hwvXF5O4xVCo+21+e2XruPHEZ88M7567+1iQvu9cO5nQH2txLspNKIkYWq5w7G+7KHTplKGQXEo0YWg4Fo8f2KQONZY7gTEsKVhWGpLdE1D3sV3eTs71a1cUSv+97YXUu2GBGFqFEaLLqauUesEnJ+uEyepiutmvvy1rpokStbFQDRNoCWC640OugDb1LB0jZu1Dm/cbDBWdhkqlFIdqURScS0G87e6azbGWD54qp9L8y26LX91XNtZpHQhQJLWWAQxfhhS78S0w9Q9lrE0OgE0OiHnJus8f9xjuJwh6NVL2MatAWySrdu63quTw34G1GMp6QYRnTCmG8TUugG2roOAsXLmUJ0clGFQHEreXSThmWNlXp+qkciE8ewi19u3Xn980EWIrRvW3GnR38lOda0rau3rDuZtXrleRRNgGTqDGZP5ps+oEJyfruMYGo5l3NaX75g6YyV305NFGCcEUUzWMdB7c/CCtI2oZehM1do0OxGRTLhRLTJaymFvcGVtlN+WAiquRYTc8iSz1d9gtORys9plqenTCWOEJkmShCSOCQ0dZIKpaxRcg7mmR84xEZpgsJBWrWuIdTGhg8h62s80ViFhqR1g64JuGKdxqSjG1MShS/tVhkFxaHFMncG8TRQnfOBkP3PNLhfn6lx/p7buurwBR/tynOjP3/LFShJJO4hYbPq39WnvZqe60Zj05SxGesqnKwao7Ud045g4kastSW/ny9/KQHlhzEyty1zDx+qkPS2W2j5hlFDr+szX23z1zSrNXiHv589P80PPHucjjwxxeqiwbr6alhbobXyfnbozMrbB00dLzDU9Kq7B40dKzLci3p6p0uj6uKbB8f4cT41XmKhkGSm7OIZO0Pv73pIZdpcS6rthP9NYpYC+nEW9E9L2IzKWQcHSewZw86ZQB4UyDIpDS5JI5ps+rqWjaQbvzNe5On/rcWGi4vKJp8fI9Aq2ViQbOn7EjVqbycUOjqVxsj9/i/ge7M6dsJkxWWz6CC3tZ61pgiCKSRKJLbTVJkArJ4bNfPlbGaixksts3cMyNMb7MszUukwuddA0CGLJhetLfHN6vYvtSjXhc69P9fpX6xzvy63KicPeuWxcx+CxIwWa3YhK3iHvmJwfcJlv+FSyJmfGKzx9tIzdS+3VNIGjbe6OO6hag/1KY12JIWUKqftIBzRdWzXOh6nYThkGxaFl7YLt+RHfmlxmshbect3J4QwTlSyzdY/BvN3TQWrzzSuLTC51aHoBpYzDqeEcH39iFNPU1y36u3EnbG5MEgbyNkutgEbXX236M9vyOTWY49J8i3awtS9/5TU1kTYu0oUgkWnAeuW9NE1SyZhcXWzRnzP50wszvDG9edzlwpzPm1NV8o6JF8XYero7HcjbREmC2QuU38kQBkFMO4wwhMAy9VW3z8rfLmuZFOzUTTRRyfDMRIWCbbDUSX3opqnfssBvlRl2ULUG91Kdd+17rBi+opvWjfQ5OnFya1Oog0YZBsWhZe2CHSOpN7qr9Qwa79Y2LDe9dFGLE6aqHZbbPlcXm3zl4gJtP0YIkFLwxnTMQN7i+ZOD6xb93exUtzImWcvALelcW24zUclgmemO3AsT3jdR2dKXnyRyVdZivuGhaWkKbDlrYWnaamB7qRNwbaFFrRNSdvU0y2iLMUrgm5NVEqFRbQacHM4DgnrHp+0n6anF1Ci7Jpqmrf5N1sZN5pseX3xrnvl6l6YfcrQ/g0aaVZNxdcZLWYZKLq1u2ifb0DQm+nM4ps5QaX0sI0neLYK7XcxnPxbpe8WdYlnrmkINbN4U6jCgDIPi0LJ2wY4jST5jrz63VmNP6CZhIjGMdzWDXrtRSxc+Q8PUBfMtnzFLMN8IKWbM1YU3lhIZSxIkgxkLnwRDitWK3q0WsrVj64YhMpH05+206IvUV26Z611TQhdk9FsD2itxhThOmKl7mJrANDSiRBJGMZ0gImtrXJhuEcQxtW6EoQveWWgTR/Ftl9C5qs+1XIvldkQ7iujPuSy2fJ4/2UeUpFlFN4OYZ8bLq+miN3sprRLJy9eqBFGCFIIb1S5/8vo03SB9z1LWYDBnc2qkxFNjJU4M5Dk1+G48Y6tYBnBf5PLvlO0mMNwPhk8ZBsWhZu0O6y996Dh/eH6Ohc67ZsHV4JmJMvVOxPG+HPMtn26UoCWQs02aXlolHcUJltDpz5m4uk617XNxps6VpSbX5pvEmmB6bpmFRod2AGeOlvjwY+OcGStTcC2WO+EtX3jH1OnPWrxxs8aNagcvThjOZxgu2lpZzJ8AACAASURBVAjA1MTqiWGta2qtkQGYqXURAkxDwzIEYZLKiS+1PD5/oYZrgh9Ksq7JQN6h4hqYusa3ZroITSPngOfd8qdLfdgGBDHUOwGXZlu0iglJErPcCTjZl0NogjBKUkMUJVyeb1LrhmgI2kHIzXqXStbirZka5yaXmG2nlegAC17ExaWIc1NtXrtR533H+nj/iYDne70lNouZ3Kx1EYBlaFvGN3baL+MwVBHfzwKDm6EMg+LQs7LDOj1Y5u998kn+j8+9SbUb4BgaP/recT7x1FEkYFs6oyWXVjfAsQyKSUK9q6+2rTw9kqOcs3lrrs7/+/IUX3xzmptbtOB8Y7nGb32rxhODGc4eL/HURJmjlQzljLP6he/4Eb/zyjW++vYCYZhQKbg8eSSi4TlkLAND61J0THKuyVjPrVJr+FyvtbF1HccysE2N68sdXEsniBLm6x7zTY9uGPHZV6eYrYd4CWR0cB2dIyWHD50eIEHDROOp8TKPDuX4xuQSk0shIWmFOKSuJEMTZGydJAbD0Gj7IX0Fm4uzLWxdQ9c0ylmLMEq4Xm3z8rUqMZJKNpXIuFntUGt1eX2qRq37rlFYSyuGhUab126k77XSMjRVi10fy2j7qeMrY6dLz8b4xk7Shg9TFfHdptoeFgO3gjIMivuKH3h6jLPHyrx0o8ZQ3mK0lCORclWawjQ1nhgr8zc+ovNHr89SzlrEieQ9x8uc6MvT7Ea8fqPGi1cWWLhDX2aA8/Mdri50+P9eu0lf3ubZoxU+/p4j5Gyd33v1Bv/265MkUmIZGpqu8/XLy3z00UEaXsjNxSYLjYAjgxkeGSxh6hp/9uYctq3haBoPjxSIe4qpw4ZDoxvQDGLmG13+5MIM0413m+c0Y4jbMVNxm5ev6jx7rI+MY3C6lMcwNI4O5nn1yhKvTzeo+xE6kHc0JvrynB4s0vEDbMsgaxsM5GxaXohApPGXRDK51Gax4THT6GLqOo1OQCwhY+m0/JBOGBFs0UFJkp5KvCCm4UVM1To8NFQgXqn0Fe/GMgw9XSY3C/TvZNd92Hbod1MPcZgM3ArKMCjuOwZLWT6aXfHvx7f2RtYEDw0X+euFDO8sNMg6BhnLJIhiWn6LThTRCm7vm19LLCFraDiGzuvTNUKZsHi6w+ffmCOKA5CS+TrML3vkTIj8Fg0fZhseXgj6ZTg1kCfnmGQsk2YgkUnCjWqbJ4/ksU2NFy5cp+r7OIaN4+gsNG7tqNYBOh4w3aCSszA0QdbR6c+5PDxc4CMPDWJokrcWmphCkM+YLNRDgiRmqWkikVSyNqau88hohqN9GQxdo94JmKt7ZC2dvpzNbC0NwhfdVNbcCwzGyy7v+F26WxmHUJKzDYYLDq1OTBDGzDV9hgo2tU5IywtodkPOTlTQNMHNWhdEjKFpm8qKwO133YdNAny3qbaHzcCtoAyD4r5kO2mNmYzJI6Ol3pc1AQSjRZfpxVYqRcD22lkaBhi6jqFptKIYTUIUwHI7YKa5phWphGYAM9dSh79G2pxHhHD+epOBos5oKctcw6MTxXS6MV98Y5H14YEudxrZQgifPb9IWYdXLi8wWBScHR/gRz7wEOPlAuP9BSDVQfLCmKlah64fUe9GFDMGzW7MaNHBMtKFSBcCIQSGrjGYt1lu+XhhKm+uWyaaoXOkkuP6Ypc4gGCTMdmOxtH+LAXHwLQ0ri+3WWyn2k1NL+BmtUPbCwmTiNFSFkfXVxVoLT1NzxVy+wVuKzv0IIxXdaXuthbgbt05u0m1PWwGbgVlGBT3LdvJ7tj4ZQ16shJP3ajih4vMtW/f89cAbBPKGRNd07D1mEreRlgJc7Xulv2pIc2cWhMnp12Pma032CROvI7tdl+uxlBtw/W25KWb8/zzF+d5ehjee2yI5x8e5pmJQbKWwUQlC6SLqRQQRqnya9tPd7ZHyhkSoNoOKDgmiZSMlFzGKhlqbZ+3rrVYbPjoBhgRRMn6rLCKDcN5m2o74MJ0jcFChrytA4JzN2p848oyk0ttwjjG/ZbFh0/38Zfee4y8a3JjubNOabWUMal1wnW7bmDVcEjB6n8LrsEbNxvEPaNwejjtaWGyvv/1dhb8vXLn7DTj6HYuqIOMOyjDoPi2Z+2X1dF0njpa4X/8uMs3rywxU2sx1/K4sdDiKxffbSdqAR8+XeKxkSJfu7RMzQtJZMzDwwW++7FhvDDcQpj69tzJKNwtr83Ca7Nz/MaLc/QBp4/A40dKPHniCEVHY7ETUbENHh4uk3EtXF0nTiSCmEa7C7rg5ECWjKFjGoK5Xj+IatsjjtcbuhUGCwZeDPV2gCHg1EiBth9T7wZcXmjyzlwDQ5O4toVMEl67VuPUwBLf8fDgLUqrtU7IWMldNQDtIOL6UhsvjJlrehQsgyCBStak2gkZLthI0vauf/bmHKNFl5GSy0RfdlVK5E4L/kG6c7ZyQa3IhhxU3EEZBsUDh6YJRkoZfuCM09MrEtT9kC+9PcO5q0sgNR4/VuZjj42Qt01+6vmApa6Ho+sUMja1bsRcvU1/3qC2vFV52cGzBLwwDS9M1+Ab6/WlLOC5CRfDgqm5Lu803n1uxIajg1n6cyZz9Q4zCwEzIZtmJAG8tRBRtCJODLh0Ap1rC23mTB9NQL0VpLEQ10YTaYC6HURM1z0uzjcBcYvSqhQQJ5LL802+db1KKwiZXu6gaQI/hiePFJHSRdMFsw2fmXqH68seSZJg6Wm1uCEEE33ZbS34B+3O2XiqBbi+3DnQuIMyDIoHFsPQVkXsBiydTz89wfc9dgQgTTftVSfbRZe+ort6X8G1GMrb/GfvP8ZnvnSJxc0VKQ41AfCVyc0HPuPDzI1NJGxvQz2AV6e7nC6HCE0ylHMwTJ1c1sA0NbwwxNQ0JKmYYM7SsQ2NKOrVmGg6QRiT9AoOry62eHWyyrnpOpNLTeqdiPFKhqytM131sHWNoaLDdL1DrR3iGJBInZu1LtV2iERQzlnbWvD3U2F1K9aeancShL9n47lXLyyEcIQQ3xBCfEsIcUEI8fc2PP8rQohtJAwqFPuDYWgUMhaFjHVbpVFNE7i2waefmeC//OAJxt0tL33geKca8aWLdT53YY5zN6oYCB4eKZC1DBIpcUydjz46zEceGeTUYIHhooMfJyy3fSaXOnSDiCuLLa4ttJhpeEiZgABD06h3Q/xQUmv7xImk4JhEsQSZEKeXoWmpUJ+laSy3g9XUWHr/3WzBX3HnhLGk7UeEsTxQ7aK1hgq2Hve95F6eGHzgo1LKlhDCBP5cCPEfpZQvCiHOAqV7+N4KxT2nL2vzscdGQcCv/OmVTeMHD5fAti0uzQUEsKWu0RELpjdL97lPqQYQznXxgoSnx4v8xAfGMYVA03SGSxm8SNLuhpiGxljB5fJSCyEktU5A04u4ONeg7QdkbB1DaOh6uovOOjpBIsnZOhnL4OmxErMNn+WWz7WlNkIIRgs2Y5UMEujviRreKYX0oMT7VtgYaD4Ildm13DPDIKWUwMqJwOz9SCGEDvwS8J8Dn75X769Q3Gs0TTAxkOND8RCTyx3+8OVZVpwzGvCh40U++PAgIyWXeifky2/McGmhRbUV0YzTHa6jw0PDOR4bKXBlscUr1xqbpoPej7QSuLLkI5NFdE3DMgxODubpBBHdMObqQptHR/Jcq7a5vNCi7YdcnGtyca7FXL2LpQuKWYuspWMaJq5pUHJMzhwr8/ypQfKOSRAn6JqGEBAkCYMFm5MD+V4KrCRrGWQrxrYW/P3QMNos02irAPlBGioh5e3T9e7qxVMj8DJwCvhnUsqfF0L894AmpfwnQoiWlDK3xb0/Dfw0wPj4+LOTk5P3bJwKxd2QJJJ6J+DtuTqTiw3iOOHJ8TLHKkUS0nqHJJE0/YCljk8QJ4RJzM0ljzCJGchlqeQsri+0uTC1xOX5Bufmvl3MA9hAOQPDlSwDWYcjFZfBgkslY3NmrMh0vcPnXp+jG4Qstj2CMCFBYCEIiSm5NqeG8zw8XOT0YI5TQ4VVSQ14VzixHUTUOyFCEzvK5NmvtNDNDICla2sCzWl8I4zlngWahRAvSynP7vS+exp8llLGwNNCiBLwe0KIDwM/CnxkG/d+BvgMwNmzZ++d9VIo7hJNE5RzNu/LDPDssf5bFpi8Y/bkp7NM1bokSUK1G1I0XRbbPg8P5XEsnecm+rh2osJc0+flawv886/cOMBZ7R0J4IVQbfuEoUTTYCjv0g4j3pprpq1QdcHlWpfr1Q5+mOAYMJCzGSw6nBzIc2asxEODBcbKGUxDW6d8G8QJc02fREqEJhjI22QtY1sL617KUdzOwGyVEjtSdA480LwZ+5KVJKWsCSG+BHwX6enhkkgDKRkhxCUp5an9GIdCcS/ZyhWx9vG1jVqKjsmzE+V1rUBPW0XyTpcjRRcZS379a1P7PY1dM2hBI7i1ViMkNQxlAQ0/ZLoqqGRbjBZdTE3gmBphHFHveFQ7CTHQDGGh63Oz5uNaJs+b/QgBr03VGCzYIGGo4JC1jFsW3KVWQLZy56VtL+sX7mRgtkqJhf1vZ7od7plhEEIMAGHPKLjAx4B/JKUcXnNNSxkFxYPEnXzHjqkz0ZcllpK/8dFHONaf5xd//80DGu32EUAhq2M7CX4gWfDW1z14wHIzwrF0hoccjvXnmFzqstgOKGdtYgl1L7rFrDZC+NbkIoMFi/efHMBAcKHe4epik6yhc/ahATKmiZtLe3XsZMe9V/UL2zEwW6XEmrp24IHmzbiXJ4YR4P/uxRk04LellJ+9h++nUNwX3CnIufJ8JWvz3EN9PN6vc2Fxu0IZB4MFxAhs3WbB8zYthgtC6MsJShmLWiei6JgMF+20IVDTx0CQMSTN6F2jkgAzbfijc7N84/IihhBMVn2iBHQBZy4u8slnjvL8QwOr2k/b3XHvVf3CdgzM7TKNtuqBfZDcy6ykc8B77nDNpoFnhUKRUnEdRvtLXFhcOpD3N4HhLBQyOgvtmGondQ1tJAaW2xGmFm36PIAQUMnaPD5awA8TbEtnsOBSyhpML3Wotz0ml241KhJodRKWOsEt2lRfm2ww13yHvGvwyEhpRzvuvUoL3a6BWXtaFBK6QUSjG1CwTBzHOFRd3VTls0JxiMm7Fp965ghvz9S5Xt9f+Q0T+O7TBfIZl6V2AHqAEG3qHfA2rN4x0A1AmGDr0NnkgOM6UHFtgjhhoe0zpDvEEkAw1p8n4xj8yevTdBaDdcYlKyC8TfrJYivkC2/N8cGTA+Rda0cL+16khe7EwKy0O33p2hJffWeBqNfX+wfPjHKs//Dsk5VhUCgOMZom+Nijo4S+5P/62kVen73XMnwpDpBz4PvfM8HMcpeZ1iIZS6eUdZFJF9lNjYEEbAGRBEMDx4CsrtFprd/b60B/1uTkcI4nxgp0/QQ/ARKYqXkMFSyO97v05x3enq1zbnKZuYZHo5vKnpOAiFkVOVz7urah0fIi2mFE3rUIe1Li213kd1K/sFXm0XYNTJJIri21eOHSInnHwDYNWl7In5yf4aeeO47jHI4l+XCMQqFQbIlj6nzq2aO871SFz527yf/z0jXOL6x32IzaEPiwuEfvGQKOY5G3NK4lElfX6MqEJAHTMNH1EFOSLu4aGAkMFnSGiy6mbqHrdebqMT5p/OFoxeD9J4d4eKSIF4Jp6hgydcP4kWAg71ByTNAERddiuODyzmyDq0tNllshrSDG0CGvs06bqmCD65iYukBHcH25c0tm0F7VKdwp82g7BiaMExotn24Y92IRIWGcEEtJK4pwDsmSfDhGoVAotmRlYRspZvmx507wgdNDvDG9yIvvzJCzLL7j8VHee2yIREqWWl1em63yhy9e4QtXdq/uJ4Dnj1dY7MQ8f7JCtR2w1KkRxTGupWFbNnEcI0jww1S3aLiSYyjv0J+3OFqxcM20GY8XJui6xkDBYaziMtsI8Nox9W7IRDkDQrDU9On4MQJBN4wRusbjY2VODRZZaKfNi25UuzT9gFLTo9aJ0HVwTZvhgsv7Tw7Q8mMsQ8PQNWQvU2gwbzPfq3G4mzqFu01tTRJJO4iYb3hUg4iFlo8QqSBj209ACjLawbbzXIsyDArFIWazXerpoQInB/N88unjSMG6nXAhYzHRX+Cjp0b56uVZ/uXnX+fVuZ2/bwQ8PV5mMO8wUsry/EMD2KZGJ4zp+jEkITONLllL52Y1RNMFhpBMDGSJIskjoxWKjsmlxRYZO2GkmKEvbzLbCChnTK52QhIJzV6/B6GBrgtkLChnLEquQcGxoBewlkiGsjatIMSPYi7cbLDYCkEkHKvkGCw4dMKY5U6QBn81gWvqhFFMxjbuuk7hblJbvTBmptZlqtrFMgRHixmePFLk/HSdthdRzto8f6of01aGQaFQ3IGtdqljJRdEz3WxRYCzmLH4gceP8l2nRrhRa/Lvvv4mr16p4XlwaZuK2r9/boq/+h2nCKKYnGXwxJESUZJwcabBH51bpOmHhCH0Fy3GC1n6cmnhGUIyWnbI2Qaali6qAzkbIQSzNQ+noFN0jN6Pmcqf6xrDhTRg2/UjJGAZGrahk0hJECVkXJNir15B0w0EEkNPVXCDKOHKYou2l/ZzWGz6mLpgrJzh5GAeQ0/fo+MFNP2QrGncVkF3I7tNbV35DIUA29SwdI12GPPU0TLH+7MM5h1yloFu6Ade1LYWZRgUikPKZrvURtfn2nIbTdxZD0jTBFnX5BG3wi/+4PuZqqb+9z+7cJN/8dUrVDdGcjfQaAf0OybTNY/Zelqx5liCL729QDdIfeRBkDDfCCg5Gv05m5YXYuoagR9zsx3SiWNypsFoOUMUJ8zUPardgIVWQMExqPkRRWGsFnslUmIa+qoLqBvGhHGCTCSTS21MXWOk5DJacpmte4RxepLK2QaLzYCmFzC93MXQBUiJITRaXsxTR0t0w4g3Z5pU6l1MXeOJI0VKGWtbn8VuU1tXPkPX1NF6C7+UUHZNwliSs030XpHbYahfWEEZBoXikLJxlxpEMUvtgIlKBsvcmWskYxucGswTS8npoUf4C0+P8G++dJXPvnaThU2yYHVgMGejmRrHiw5HSi5Tyx1evjzPfK0DQLObxiJkAk0vZrHZRROSEwNFXrtRw+31fS4MmrS8kKxt8p0PDfDGTIPjfRlaQUzeNkgSKGZMumG8Xl3U1Anj9CRQ74ZoWtoHOYgTHhrMr6sJuLLYSuMLmkYsJfM1D8vU6fNCDF1wca7JUsfnaClDKWvjBRHnp+s8d7xv9eRwpyD1blJbVz7DREoG8jYztS5hLDFyOs+MlzEN7dAUta1FGQaF4pCycZeaJJK+nIVl7k7CYW3WzIn+Ej/3iSf59HMTvHRlnt994TJvVNPrDODUUIYfee9x8lb6ftP1Np/5s9f52o3NjxlLjRDb9DEMjfb0MiAYr2QpZU06fsR80+eYaWBbOqNlF7c3Bwn4QcxYz7itXSQ1TUAM8w2frKVj9TKM5hs+x/qy2KaOhlhNT+3LmFyaa9ANY6QQDBYcgihBAmEcE8WSTpjgRjGOZdAOfLwoxhYQRgnzTZ84TpACRovuOgXXzf6GO/0MEykZKjj070Dk76BQhkGhOMRsrJadqnX3THAtYxs8MVrm0ZESP3L2BN+cnOfNqTqubfL0RIVHh0vMN31eubbIz//7b9G4jSpHNQJ/zqNW9/Ak9OVN9FQnE1MTjBkZdF2w0PRXx5sWe8UILXUjbbZQemHMQtOnZWqYhk7RvXXJ0oVA1zXKGQspBboGAompCRIBSQIF2yRKWBXZK7kGcSKZq6d1ITMNj1LGoOXFREl6EntmvLypcdgpB91bYTcow6BQHHI2U2fdK8G1ldc2Mxbf9fARPnRqBGB1oc4FIf/iC2/c1iis0AE6vfq7JT+k6S9i6IMYhk4US0xdo+1HDORsbta7LDcDEgEDOYuOH61TmYXUtbPcDhjMW7SDNNYwW/c4PZTH1N8NHGuaYDBvc325zXhfqth6rE+nGyUMZq20S1xflqEk4e2ZJg3Px9BhIG/jWGlKbRhF/KcrdfqyNo5l4Bpp/+gTA7k9Wcj3ownQXqIMg0JxH3Evd5+aJrA35NJ7YUw32p0Ux0xTcn2xydFKlqYfcHG2QSQlM3WPIIrRNbA0nfmWx5uzTR4eyuFYxqobJ5YSCUz055hreMRxghclDBWdW97LNDRGSy5jJZfpapfFlk83inl0OI9jGViGRkY3OHOkSCeKGS9lmGv5JLGkHUXM1j0WmwGOadDyIzK2wUjZ2ZHS6n41/NkPlGFQKO4z9nP3WbItKq4Ju2w4WuuGzNe7vDnTYDBnMTGQ5fhAjnYQY+oa3SB1i0mZcGmhTX/OWnXjOL1MHkMTHC1naHQDFpoBy+2ARjdal5GlC4GhaZi64NRwnjHfJZSSE305IinXnbKO9+ewdI3luQavXq8RRjFvz9awdJ2MLSi6DkEYE0UJYpstwvay4c9hYPuJvAqF4oEjl7X4b77vSexd3l9rhXSjiChOuLzU4vOvz/GFN+e4ttjGC2ISKWl7IZ0gQRek6ZtCcLOWVm0PFx3CWNLxIxZaAaMlh3xPAmO27pEk6cq9EuQNY0k3iBGaxngli2Foq6eso5UM45UMjqkTRQmX5lsYGjS6IeemGnz57Xl+/5Vp/vziAjeqHcI4YarWxQtv70dbW2+StY1bxnY/ogyDQqG4LWePD/K/fPoRbnXg3Jl6ANeXOgRxAkIDTeD5MR0/YqbRZarWYbHl0/EjhEjrGCxTA5HWAKws6iNll+GiQ9YxgTQjK5Gp62aFzQzACtqGAHc3TmMWcRLz5bfmWGr6tIKERjfk/NQyS80upay1rUX+3XoTbcux3W8ow6BQKG6LLgRnRit835P9O743Aq7Ntrk+X2O52SWIImpdH0iF+U4N5HlouAACWl5ILCVl18TQtHXZS46hY2hpLUcUJwRRvGlG1kYDsBW20Gj5ETcW2tS6IQiwjNS4hLGk1glptYO0duIOi/zaehPg0LTnvBuUYVAoFLdF0wTH+nM8PdHHscLO76/GcKMFVxZ86l5ArR3y9nyDWsdP6zJ0wcPDBQRgaQJNu7USWNMEpYzJ5FKHS/MtJpc6lDLmroO8uqlxsj/DpYUmc62IRgBtDzw/JJYJbT/keq3D6zeqNLo+SSK3PDWsdWO1/YgwltvOFksSSRgnh87tpILPCoXijuRck+eO9/G7/8lk8x5udyYCri6HjBYkCJisdvmDV6d5ZqKMYRiMFB1c22Cs5N6iY5Qk6S5+opJBaALZ+73g7Nw4JIkkCGIW2wHVjr/aFS4E5j0YdME0DD7/5jxJkqALjU++J2K8L7dlUHmn2WIraquLTR8Jhy5grU4MCoXijkRRwrWlDu3W7ozCWspZA8s0iGPJXK1DKCUCiW5o6AjkhjU1SSReFBPHCZapY+paWgW9iYvnTjtwL4y5vtxhcrnNzHKDN+ZvzbZKJHhBSBQnuJaJFPDVi4vEK5lHtzk5bMeN1fEjLs03eWWyylzDQ9fEoQtYqxODQqG4I0GSECUJ1d23eFglSnqLb5hQzpj0ZUxKGYemF6zuuFdqAlakKqIkYbbhMSIg55ib+vE3powO5u11WkRrs4eKGYt6e3Mj5wUwXfco52zagSDvGDS9AC9JcKW2o9qGjXT8iFeuV0FK6t2QoYLNQtPnSMklkcldvfZeogyDQqG4I5am4egGtk7a03OX5AE9AUsXOKbGSDkDaFxdaBFL6M85NLyQWickjhNmGh6jxTRFVQNu1j1GZLo778+/m0S7UaK87YW8cr3KSMFZVS/Ve4HkFbXaE6N54NZmFe0E7G6qDFsyDPwwrbkwBHcVVE4Syc16F01AzrVoBjHL7YBy1sLfIph+UChXkkKhuCOGofHoWIHT49m7ep1TIy6j5QwnBnK890Qfjw0ViaVksGjz7LEyOcfg/HQdXYBt6WgCqt2QREqyjslw0aGcs5DAQtPn+nIHL4zXpYwmUlLthmi911hx0wjJavZQIiVHCjmGc7cuxP0uuLaBFqbSIF6Q8NTREpZp3JUEyYrelWWkYoCDeRs/jOkGMVJyqKS31YlBoVBsi+G8y/efOcLXr76zGrDdLjbwUx8c42h/nvGKi6mnGkUrzX2KGWt10Y4TiegprVqGThCmMYOE1EVU74TYveY+a5sXrU0ZDcIEy3hXe8mP0gY+K1pTgR/hWBY/9/2P8yuff5PJWowAJko65VwW05A8d3qA0VIGx9A4e6wvPbXcxcL9rtifSbUTEkQxfTmHM2PFu37tvUYZBoVCsS0MQ+MHn5rgynybf/XC9I6Mwy98/BQnBsqYhlgtBPPDhJGCS7UTkiQSTU9dPXov60gz00V0pu7RDWMMTWMgn/rk1xaTbVz0oyT11Q/20lnXxiNMU2O8kiGME0xd48RAlqMVh3/6+XfwwoiRYhZdE4QxjPdlON6XZ6yc2ROV1bUS3GXXRApzS3nvg0bI+6A67+zZs/Kll1466GEoFApWMpTqfO7Naf7tFye5eYdOcD/+VIWf+PAjVDsRI0WHjGXgR6n7ZKIvS9Db9a8EjUsZk1on3DSIDHB9udOLJaQnhjCWq82KNgatb6ddtDZYfW2+xRfenqPlh1iGwfc+PsT7T/Tfk74J+ym2J4R4WUp5dsf3KcOgUCh2Q5JI/vSdKf67f3VuS4m9kwX4jb/2HRzty912sd64WN5u8dyuYN12FuC113heRM0PyFoGedc6VK6d3bJbw3D4zjAKheK+QNME33lylL/1vR1++XOX8DY8/+Swzd/71NMc68+v5viPm/qmi/VGxdjbKchut5hsOyq0a6/JZEwyGXMbM//2RxkGhUKxaxxT529+5DSfeHqEF67M8eaNeYJA4+xDg5wZG+RIOXNbA7Bb7rfGN/cbyjAoFIq7QtMERysFjlYKcPahb6uGNQ8qyjAoFIo9Re3m739UgZtCwe8tegAABw5JREFUoVAo1qEMg0KhUCjWoQyDQqFQKNahDINCoVAo1qEMg0KhUCjWcV9UPgshFoDJfXirfmBxH97nsPCgzRcevDk/aPMFNee1TEgpB3b6YveFYdgvhBAv7aZ8/H7lQZsvPHhzftDmC2rOe4FyJSkUCoViHcowKBQKhWIdyjCs5zMHPYB95kGbLzx4c37Q5gtqzneNijEoFAqFYh3qxKBQKBSKdSjDoFAoFIp1PJCGQQjxo0KIC0KIRAhxds3j3yOEeFkI8Xrvvx/d5N4/EEKc398R3z07nbMQIiOE+A9CiLd69/1vBzf6nbObz1gI8Wzv8UtCiF8WQtxXEqG3mXOfEOKLQoiWEOJXN9zz4705nxNC/LEQon//R747djlfSwjxGSHEO71/239x/0e+e3Yz5zXXbHvteiANA3Ae+GHgKxseXwQ+IaV8Evgp4N+sfVII8cNAa19GuPfsZs7/WEr5CPAe4INCiL+wLyPdG3Yz318Dfhp4qPfz/fswzr1kqzl7wP8M/A9rHxRCGMA/Bb5LSnkGOAf8t/swzr1iR/Pt8XeBeSnlaeAx4Mv3dIR7z27mvOO164HsxyClfBNg44ZQSvnqml8vAI4QwpZS+kKIHPCzpAvHb+/XWPeKXcy5A3yxd00ghHgFGNun4d41O50vUAEKUsoXevf9a+CHgP+4LwPeA24z5zbw50KIUxtuEb2frBBiCSgAl/ZhqHvCLuYL8F8Bj/SuS7jPKqR3M+fdrF0P6olhO/xF4FUppd/7/R8A/zvQObgh3XM2zhkAIUQJ+ATwZwcyqnvH2vkeAabWPDfVe+zbFillCPwM8Dpwk3QH/S8PdFD3kN6/Y4B/IIR4RQjxO0KIoQMd1P6w47Xr2/bEIIT4PDC8yVN/V0r5+3e493HgHwHf2/v9aeCUlPJvCyGO7fFQ94y9nPOaxw3gN4FfllJe2aux7gV7PN/N4gmHLpf7bua8yWuZpIbhPcAV4FeAXwT+4d2Oc6/Yy/mSrndjwNeklD8rhPhZ4B8DP3mXw9xT9vgz3tXa9W1rGKSUH9vNfUKIMeD3gL8spbzce/gDwLNCiGukf7NBIcSXpJQf2Yux7hV7POcVPgNclFL+n3c7vr1mj+c7xXpX2RjpLvpQsds5b8HTvde8DCCE+G3gF/bw9e+aPZ7vEumu+fd6v/8O8Ff38PX3hD2e867WLuVKWkPvqPkfgF+UUn5t5XEp5a9JKUellMeADwHvHDajsFu2mnPvuX8IFIG/dRBjuxfc5jOeAZpCiOd62Uh/GdjpjvR+Yxp4TAixor75PcCbBziee4pMq3n/EPhI76HvBt44sAHtA7teu6SUD9wP8GnSHaIPzAF/0nv8fwLawGtrfgY33HsMOH/Qc7jXcybdMUvShWLl8b920PO4l58xcJY06+My8Kv0lAHul5+t5tx77hqwTJqZMgU81nv8b/Y+43Oki2bfQc/jHs93gjSj5xxpzGz8oOdxr+e85vltr11KEkOhUCgU61CuJIVCoVCsQxkGhUKhUKxDGQaFQqFQrEMZBoVCoVCsQxkGhUKhUKxDGQbFA4EQYs/FD4UQnxRC/ELv/39ICPHYLl7jS2tVMhWKw4AyDArFLpFS/oGUckWO/IdItYYUivseZRgUDxQi5ZeEEOd7fQh+rPf4R3q799/t6fT/u5V+DEKIH+g99ue9Pg2f7T3+V4QQvyqEeB74JPBLQojXhBAn154EhBD9PUkChBCuEOK3ev0P/j3grhnb9wohXlgj8Jbb37+OQpHybauVpFBswQ+TagQ9BfQD3xRCrGjbvwd4nFQj6WukPSheAn4d+LCU8qoQ4jc3vqCU8utCiD8APiul/F24VRZ5DT8DdKSUZ4QQZ4BXetf3k1Zlf0xK2RZC/DypVPLf34tJKxQ7QRkGxYPGh4DflFLGwJwQ4svAe4EG8A0p5RSAEOI1UgmBFnBFSnm1d/9vkura75YPA78MIKU8J4Q413v8OVJX1Nd6RsUCXriL91Eodo0yDIoHjdu161zbhyIm/X7str1nxLuuWmfDc5vp0AjgT6WUP77L91Mo9gwVY1A8aHwF+DEhhN5TFf0w8I3bXP8WcGKNlv2PbXFdE8iv+f0a8Gzv/39kw/v/FwBCiCeAM73HXyR1XZ3qPZcRQpzexnwUij1HGQbFg8bvkSprfgv4AvB3pJSzW10spewC/zXwx0KIPydVtKxvculvAT8nhHhVCHGStAHMzwghvk4ay1jh14Bcz4X0d+gZJSnlAvBXgN/sPfcivRaUCsV+o9RVFYo7IITISSlbvSylf0bauOifHPS4FIp7hToxKBR35q/3gtEXSBsX/foBj0ehuKeoE4NCoVAo1qFODAqFQqFYhzIMCoVCoViHMgwKhUKhWIcyDAqFQqFYhzIMCoVCoVjH/w/xvAHT3lDMDgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "housing.plot(kind=\"scatter\", x=\"longitude\", y=\"latitude\", alpha=0.1) # The alpha blending value, between 0 (transparent) and 1 (opaque)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Now that’s much better: you can clearly see the high-density areas, namely the Bay Area and around Los Angeles and San Diego, plus a long line of fairly high density in the Central Valley, in particular around Sacramento and Fresno.\n",
    "- Our brains are very good at spotting patterns in pictures, but you may need to play around with visualization parameters to make the patterns stand out."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Now let’s look at the housing prices.\n",
    "- The radius of each circle represents the district’s population (option s), and the color represents the price (option c). - We will use a predefined color map (option cmap) called jet, which ranges from blue (low values) to red (high prices):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x2586f93ac48>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x504 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "housing.plot(kind=\"scatter\", x=\"longitude\", y=\"latitude\", alpha=0.4,\n",
    "            s=housing[\"population\"]/100, label=\"population\", figsize=(10,7),\n",
    "            c=\"median_house_value\", cmap=plt.get_cmap(\"jet\"), colorbar=True)\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### California housing prices: \n",
    "- Red is expensive, blue is cheap \n",
    "- Larger circles indicate areas with a larger population"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### This image tells you that the housing prices are very much related to the location (e.g., close to the ocean) and to the population density, as you probably knew already. A clustering algorithm should be useful for detecting the main cluster and for adding new features that measure the proximity to the cluster centers. The ocean proximity attribute may be useful as well, although in Northern California the housing prices in coastal districts are not too high, so it is not a simple rule."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Looking for Correlations\n",
    "- Since the dataset is not too large, you can easily compute the standard correlation coefficient (also called Pearson’s r) between every pair of attributes using the corr() method:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "corr_matrix = housing.corr()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Now let’s look at how much each attribute correlates with the median house value:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "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": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corr_matrix[\"median_house_value\"].sort_values(ascending = False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- The correlation coefficient **ranges from –1 to 1**. When it is close to 1, it means that there is a strong positive correlation; for example, the median house value tends to go up when the median income goes up. \n",
    "- When the coefficient is close to –1, it means that there is a strong negative correlation; you can see a small negative correlation between the latitude and the median house value (i.e., prices have a slight tendency to go down when you go north). \n",
    "- Finally, coefficients close to 0 mean that there is no linear correlation."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Another way to check for correlation between attributes is to use the pandas scatter_matrix() function, which plots every numerical attribute against every other numerical attribute. Since there are now 11 numerical attributes, you would get 11 = 121 plots, which would not fit on a page—so let’s just focus on a few promising attributes that seem most correlated with the median housing value:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x000002586FE5E3C8>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x000002586FE021C8>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x000002586FCE7388>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x000002586FA36D48>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x000002586FC86748>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x000002586F7ED1C8>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x000002586F8C9248>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x000002586F73F348>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x000002586F732F08>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x000002586F790088>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x000002586F773608>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x000002586F7566C8>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x000002586F81D808>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x0000025872A2D908>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x0000025872A66A08>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x0000025872A9DB48>]],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 16 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from pandas.plotting import scatter_matrix\n",
    "\n",
    "attributes = [\"median_house_value\", \"median_income\", \"total_rooms\", \"housing_median_age\"]\n",
    "scatter_matrix(housing[attributes], figsize=(12, 8))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### This scatter matrix plots every numerical attribute against every other numerical attribute, plus a histogram of each numerical attribute"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### The most promising attribute to predict the median house value is the median income, so let’s zoom in on their correlation scatterplot:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x258741161c8>"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "housing.plot(kind=\"scatter\", x=\"median_income\", y=\"median_house_value\", alpha=0.1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### This plot reveals a few things.\n",
    "- First, the correlation is indeed very strong; you can clearly see the upward trend, and the points are not too dispersed.\n",
    "- Second, the price cap that we noticed earlier is clearly visible as a horizontal line at 500,000 USD.\n",
    "- But this plot reveals other less obvious straight lines: a horizontal line around 450,000 USD, another around \n",
    "  350,000 USD, perhaps one around 280,000 USD, and a few more below that. You may want to try removing the corresponding\n",
    "  districts to prevent your algorithms from learning to reproduce these data quirks."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Experimenting with Attribute Combinations\n",
    "#### One last thing you may want to do before preparing the data for MachineLearning algorithms is to try out various attribute combinations.\n",
    "- For example, the total number of rooms in a district is not very useful if you don’t know how many households there are. What you really want is the number of rooms per household.\n",
    "- Similarly, the total number of bedrooms by itself is not very useful: you probably want to compare it to the number of rooms.\n",
    "- And the population per household also seems like an interesting attribute combination to look at.\n",
    "- **Let’s create these new attributes:**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "housing[\"rooms_per_household\"] = housing[\"total_rooms\"] / housing[\"households\"]\n",
    "housing[\"bedrooms_per_household\"] = housing[\"total_bedrooms\"] / housing[\"total_rooms\"]\n",
    "housing[\"population_per_household\"] = housing[\"population\"] / housing[\"households\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### And now let’s look at the correlation matrix again:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "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_household     -0.259984\n",
       "Name: median_house_value, dtype: float64"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corr_matrix = housing.corr()\n",
    "corr_matrix[\"median_house_value\"].sort_values(ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Hey, not bad! The new bedrooms_per_room attribute is much more correlated with the median house value than the total number of rooms or bedrooms.\n",
    "- Apparently houses with a **lower bedroom/room ratio tend to be more expensive.**\n",
    "- The number of rooms per household is also more informative than the total number of rooms in a district—obviously **the larger the houses, the more expensive they are.**\n",
    "- This round of exploration does not have to be absolutely thorough; the point is to start off on the right foot and quickly gain insights that will help you get a first reasonably good prototype. But this is an iterative process: once you get a prototype up and running, you can analyze its output to gain more insights and come back to this exploration step."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Prepare the Data for Machine Learning Algorithms\n",
    "#### It’s time to prepare the data for your Machine Learning algorithms. Instead of doing this manually, you should write functions for this purpose, for several good reasons:\n",
    "- This will allow you to reproduce these transformations easily on any dataset (e.g., the next time you get a fresh dataset).\n",
    "- You will gradually build a library of transformation functions that you can reuse in future projects.\n",
    "- You can use these functions in your live system to transform the new data before feeding it to your algorithms.\n",
    "- This will make it possible for you to easily try various transformations and see which combination of transformations works best.\n",
    "\n",
    "#### But first let’s revert to a clean training set (by copying strat_train_set once again). Let’s also separate the predictors and the labels, since we don’t necessarily want to apply the same transformations to the predictors and the target values (note that drop() creates a copy of the data and does not affect strat_train_set):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "housing = strat_train_set.drop(\"median_house_value\", axis=1) # Predictor values\n",
    "housing_labels = strat_train_set[\"median_house_value\"].copy() # Target value"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data Cleaning\n",
    "#### Most Machine Learning algorithms cannot work with missing features, so let’s create a few functions to take care of them. We saw earlier that the total_bedrooms attribute has some missing values, so let’s fix this. You have three options:\n",
    "1. Get rid of the corresponding districts.\n",
    "2. Get rid of the whole attribute.\n",
    "3. Set the values to some value (zero, the mean, the median, etc.).\n",
    "\n",
    "#### You can accomplish these easily using DataFrame’s dropna(), drop(), and fillna() methods:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "housing.dropna(subset=[\"total_bedrooms\"])   #option 1\n",
    "housing.drop(\"total_bedrooms\", axis=1)      #option 2\n",
    "median = housing[\"total_bedrooms\"].median()\n",
    "housing[\"total_bedrooms\"].fillna(median, inplace=True) #option 3"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### If you choose option 3, you should compute the median value on the training set and use it to fill the missing values in the training set. Don’t forget to save the median value that you have computed. You will need it later to replace missing values in the test set when you want to evaluate your system, and also once the system goes live to replace missing values in new data.\n",
    "#### Scikit-Learn provides a handy class to take care of missing values: SimpleImputer.                                                                  Here is how to use it. First, you need to create a SimpleImputer instance, specifying that you want to replace each attribute’s missing values with the median of that attribute:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.impute import SimpleImputer\n",
    "imputer = SimpleImputer(strategy=\"median\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Since the median can only be computed on numerical attributes, you need to create a copy of the data without the text attribute ocean_proximity:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "housing_num = housing.drop(\"ocean_proximity\", axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Now you can fit the imputer instance to the training data using the fit() method:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SimpleImputer(strategy='median')"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "imputer.fit(housing_num)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- The **imputer** has simply computed the median of each attribute and **stored the result in its statistics_ instance variable.** Only the total_bedrooms attribute had missing values, but we cannot be sure that there won’t be any missing values in new data after the system goes live, so it is safer to apply the imputer to all the numerical attributes:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-118.51  ,   34.26  ,   29.    , 2119.5   ,  433.    , 1164.    ,\n",
       "        408.    ,    3.5409])"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "imputer.statistics_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-118.51  ,   34.26  ,   29.    , 2119.5   ,  433.    , 1164.    ,\n",
       "        408.    ,    3.5409])"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing_num.median().values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Now you can use this “trained” imputer to transform the training set by replacing missing values with the learned medians:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = imputer.transform(housing_num)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### The result is a plain NumPy array containing the transformed features. If you want to put it back into a pandas DataFrame, it’s simple:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "housing_tr = pd.DataFrame(X, columns=housing_num.columns, index=housing_num.index)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Handling Text and Categorical Attributes\n",
    "#### - So far we have only dealt with numerical attributes, but now let’s look at text attributes. In this dataset, there is just one: the ocean_proximity attribute. Let’s look at its value for the first 10 instances:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ocean_proximity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>17606</th>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18632</th>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14650</th>\n",
       "      <td>NEAR OCEAN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3230</th>\n",
       "      <td>INLAND</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3555</th>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19480</th>\n",
       "      <td>INLAND</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8879</th>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13685</th>\n",
       "      <td>INLAND</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4937</th>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4861</th>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      ocean_proximity\n",
       "17606       <1H OCEAN\n",
       "18632       <1H OCEAN\n",
       "14650      NEAR OCEAN\n",
       "3230           INLAND\n",
       "3555        <1H OCEAN\n",
       "19480          INLAND\n",
       "8879        <1H OCEAN\n",
       "13685          INLAND\n",
       "4937        <1H OCEAN\n",
       "4861        <1H OCEAN"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing_cat = housing[[\"ocean_proximity\"]]\n",
    "housing_cat.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It’s not arbitrary text: there are a limited number of possible values, each of which represents a category.              So this attribute is a categorical attribute. Most Machine Learning algorithms prefer to work with numbers, so let’s convert these categories from text to numbers. For this, we can use **Scikit-Learn’s OrdinalEncoder class:**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.],\n",
       "       [0.],\n",
       "       [4.],\n",
       "       [1.],\n",
       "       [0.],\n",
       "       [1.],\n",
       "       [0.],\n",
       "       [1.],\n",
       "       [0.],\n",
       "       [0.]])"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.preprocessing import OrdinalEncoder\n",
    "ordinal_encoder = OrdinalEncoder()\n",
    "housing_cat_encoded = ordinal_encoder.fit_transform(housing_cat)\n",
    "housing_cat_encoded[:10]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can get the list of categories using the **categories_** instance variable. \n",
    "It is a list containing a 1D array of categories for each categorical attribute (in this case, a list containing a single array since there is just one categorical attribute):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[array(['<1H OCEAN', 'INLAND', 'ISLAND', 'NEAR BAY', 'NEAR OCEAN'],\n",
       "       dtype=object)]"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ordinal_encoder.categories_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- One issue with this representation is that ML algorithms will assume that two nearby values are more similar than two distant values. This may be fine in some cases (e.g., for ordered categories such as “bad,” “average,” “good,” and “excellent”), but it is obviously not the case for the ocean_proximity column (for example, categories 0 and 4 are clearly more similar than categories 0 and 1).\n",
    "- To fix this issue, a common solution is to create one binary attribute per category: one attribute equal to 1 when the category is “<1H OCEAN” (and 0 otherwise), another attribute equal to 1 when the category is “INLAND” (and 0 otherwise), and so on. \n",
    "- **This is called one-hot encoding, because only one attribute will be equal to 1 (hot), while the others will be 0 (cold).**\n",
    "- **The new attributes are sometimes called dummy attributes.** Scikit-Learn provides a **OneHotEncoder class** to convert categorical values into one-hot vectors:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "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": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.preprocessing import OneHotEncoder\n",
    "cat_encoder = OneHotEncoder()\n",
    "housing_cat_1hot = cat_encoder.fit_transform(housing_cat)\n",
    "housing_cat_1hot"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Notice that the output is a SciPy sparse matrix, instead of a NumPy array. \n",
    "- This is very useful when you have categorical attributes with thousands of categories. After one-hot encoding, we get a matrix with thousands of columns, and the matrix is full of 0s except for a single 1 per row. Using up tons ofmemory mostly to store zeros would be very wasteful, so instead a sparse matrix only stores the location of the nonzero elements. You can use it mostly like a normal 2D array, but if you really want to convert it to a (dense) NumPy array, just call the toarray() method:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "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": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing_cat_1hot.toarray()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Once again, you can get the list of categories using the encoder’s categories_ instance variable:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[array(['<1H OCEAN', 'INLAND', 'ISLAND', 'NEAR BAY', 'NEAR OCEAN'],\n",
       "       dtype=object)]"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cat_encoder.categories_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Custom Transformers\n",
    "- Although Scikit-Learn provides many useful transformers, you will need to write your own for tasks such as custom cleanup operations or combining specific attributes. \n",
    "- You will want your transformer to work seamlessly with Scikit-Learn functionalities (such as pipelines), and since Scikit-Learn relies on duck typing (not inheritance), all you need to do is create a class and implement three methods: fit() (returning self), transform(), and fit_transform().\n",
    "- **You can get the last one for free by simply adding TransformerMixin as a base class. If you add BaseEstimator as a base class (and avoid  *args and kargs in your constructor), you will also get two extra methods (get_params() and set_params()) that will be useful for automatic hyperparameter tuning.**\n",
    "- For example, here is a small transformer class that adds the combined attributes we discussed earlier:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.base import BaseEstimator, TransformerMixin\n",
    "rooms_ix, bedrooms_ix, population_ix, households_ix = 3, 4, 5, 6\n",
    "\n",
    "class CombinedAttributesAdder(BaseEstimator, TransformerMixin):\n",
    "    def __init__(self, add_bedrooms_per_room = True):# 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[:, households_ix]\n",
    "        population_per_household = X[:, population_ix] / X[:, households_ix]\n",
    "        if self.add_bedrooms_per_room:\n",
    "            bedrooms_per_room = X[:, bedrooms_ix] / X[:, rooms_ix]\n",
    "            return np.c_[X, rooms_per_household, population_per_household, bedrooms_per_room]\n",
    "        else:\n",
    "            return np.c_[X, rooms_per_household, population_per_household]\n",
    "\n",
    "attr_adder = CombinedAttributesAdder(add_bedrooms_per_room = False)\n",
    "housing_extra_attribs = attr_adder.transform(housing.values)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- In this example the transformer has one hyperparameter, add_bedrooms_per_room, set to True by default (it is often helpful to provide sensible defaults). \n",
    "- This hyperparameter will allow you to easily find out whether adding this attribute helps the Machine Learning algorithms or not.\n",
    "- More generally, you can add a hyperparameter to gate any data preparation step that you are not 100% sure about. \n",
    "- The more you automate these data preparation steps, the more combinations you can automatically try out, making it much more likely that you will find a great combination (and saving you a lot of time)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Feature Scaling\n",
    "- One of the most important transformations you need to apply to your data is feature scaling. With few exceptions, Machine Learning algorithms don’t perform well when the input numerical attributes have very different scales.\n",
    "- This is the case for the housing data: the total number of rooms ranges from about 6 to 39,320, while the median incomes only range from 0 to 15. Note that scaling the target values is generally not required.\n",
    "- **There are two common ways to get all attributes to have the same scale: min-max scaling and standardization.**\n",
    "- **Min-max scaling (many people call this normalization)** is the simplest: values are shifted and rescaled so that they end up ranging from 0 to 1. We do this by subtracting the min value and dividing by the max minus the min. Scikit-Learn provides a transformer called MinMaxScaler for this. It has a feature_range hyperparameter that lets you change the range if, for some reason, you don’t want 0–1.\n",
    "- **Standardization** is different: first it subtracts the mean value (so standardized values always have a zero mean), and then it divides by the standard deviation so that the resulting distribution has unit variance. \n",
    "    - Unlike min-max scaling, standardization does not bound values to a specific range, which may be a problem for some algorithms (e.g., neural networks often expect an input value ranging from 0 to 1). However, standardization is much less affected by outliers. \n",
    "    - For example, suppose a district had a median income equal to 100 (by mistake). Min-max scaling would then crush all the other values from 0–15 down to 0–0.15, whereas standardization would not be much affected. Scikit-Learn provides a transformer called StandardScaler for standardization.\n",
    "### WARNING\n",
    "- **As with all the transformations, it is important to fit the scalers to the training data only**, not to the full dataset (including the test set). Only then can you use them to transform the training set and the test set (and new data)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Transformation Pipelines\n",
    "\n",
    "As you can see, there are many data transformation steps that need to be executed in the right order. Fortunately, Scikit-Learn provides the Pipeline class to help with such sequences of transformations. Here is a small pipeline for the numerical attributes:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "num_pipeline = Pipeline([\n",
    "    (\"imputer\", SimpleImputer(strategy = \"median\")),\n",
    "    (\"attribs_adder\", CombinedAttributesAdder()),\n",
    "    (\"std_scaler\", StandardScaler()),\n",
    "])\n",
    "\n",
    "housing_num_tr = num_pipeline.fit_transform(housing_num)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- The Pipeline constructor takes a list of name/estimator pairs defining a sequence of steps. \n",
    "- All but the last estimator must be transformers (i.e., they must have a fit_transform() method). The names can be anything you like (as long as they are unique and don’t contain double underscores, __); they will come in handy later for hyperparameter tuning.\n",
    "- When you call the pipeline’s fit() method, it calls fit_transform() sequentially on all transformers, passing the output of each call as the parameter to the next call until it reaches the final estimator, for which it calls the fit() method.\n",
    "- The pipeline exposes the same methods as the final estimator.\n",
    "    - In this example, the last estimator is a StandardScaler, which is a transformer, so the pipeline has a transform() method that applies all the transforms to the data in sequence (and of course also a fit_transform() method, which is the one we used).\n",
    "- So far, we have handled the categorical columns and the numerical columns separately. \n",
    "- It would be more convenient to have a single transformer able to handle all columns, applying the appropriate transformations to each column. \n",
    "- In version 0.20, Scikit-Learn introduced the **ColumnTransformer** for this purpose, and the good news is that it works great with pandas DataFrames. Let’s use it to apply all the transformations to the housing data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.compose import ColumnTransformer\n",
    "\n",
    "num_attribs = list(housing_num)\n",
    "cat_attribs = [\"ocean_proximity\"]\n",
    "\n",
    "full_pipeline = ColumnTransformer([\n",
    "    (\"num\", num_pipeline, num_attribs),\n",
    "    (\"cat\", OneHotEncoder(), cat_attribs),\n",
    "])\n",
    "\n",
    "housing_prepared = full_pipeline.fit_transform(housing)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- First we import the ColumnTransformer class, next we get the list of numerical column names and the list of categorical column names, and then we construct a ColumnTransformer. \n",
    "- **The constructor requires a list of tuples, where each tuple contains a name, a transformer, and a list of names (or indices) of columns that the transformer should be applied to.**\n",
    "- In this example, we specify that the **numerical columns should be transformed using the num_pipeline that we defined earlier, and the categorical columns should be transformed using a OneHotEncoder.**\n",
    "    - Finally, we apply this ColumnTransformer to the housing data: it applies each transformer to the appropriate columns and concatenates the outputs along the second axis (the transformers must return the same number of rows).\n",
    "- Note that the OneHotEncoder returns a sparse matrix, while the num_pipeline returns a dense matrix. \n",
    "- When there is such a mix of sparse and dense matrices, the ColumnTransformer estimates the density of the final matrix (i.e., the ratio of nonzero cells), and it returns a sparse matrix if the density is lower than a given threshold (by default, sparse_threshold=0.3). In this example, it returns a dense matrix. \n",
    "- And that’s it! We have a preprocessing pipeline that takes the full housing data and applies the appropriate transformations to each column."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Select and Train a Model\n",
    "- At last! You framed the problem, you got the data and explored it, you sampled a training set and a test set, and you wrote transformation pipelines to clean up and prepare your data for Machine Learning algorithms automatically. You are now ready to select and train a Machine Learning model.\n",
    "\n",
    "### Training and Evaluating on the Training Set\n",
    "- The good news is that thanks to all these previous steps, things are now going to be much simpler than you might think. Let’s first train a Linear Regression model, like we did in the previous chapter:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 49,
     "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) # housing_prepared -> Predictor Values , housing_labels -> Target value"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Done! You now have a working Linear Regression model. Let’s try it out on a few instances from the training set:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Predictions:  [210644.60459286 317768.80697211 210956.43331178  59218.98886849\n",
      " 189747.55849879]\n"
     ]
    }
   ],
   "source": [
    "some_data = housing.iloc[:5]\n",
    "some_labels = housing_labels.iloc[:5]\n",
    "some_data_prepared = full_pipeline.transform(some_data)\n",
    "print(\"Predictions: \", lin_reg.predict(some_data_prepared))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "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": "markdown",
   "metadata": {},
   "source": [
    "#### It works, although the predictions are not exactly accurate (e.g., the first prediction is off by close to 40%!). Let’s measure this regression model’s RMSE on the whole training set using Scikit-Learn’s mean_squared_error() function:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "68628.19819848923"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import mean_squared_error\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": "markdown",
   "metadata": {},
   "source": [
    "- This is better than nothing, but clearly not a great score: most districts’ median_housing_values range between 120,000 USD and 265,000 USD, so a typical prediction error of 68,628 USD is not very satisfying.**This is an example of a model underfitting the training data.**\n",
    "- When this happens it can mean that the features do not provide enough information to make good predictions, or that the model is not powerful enough. \n",
    "- As we saw in the previous chapter, the main ways to fix underfitting are to select a more powerful model, to feed the training algorithm with better features, or to reduce the constraints on the model. This model is not regularized, which rules out the last option. You could try to add more features (e.g., the log of the population), **but first let’s try a more complex model to see how it does.**\n",
    "- Let’s train a **DecisionTreeRegressor**. This is a powerful model, capable of finding complex nonlinear relationships in the data. The code should look familiar by now:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeRegressor()"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.tree import DecisionTreeRegressor\n",
    "\n",
    "tree_reg = DecisionTreeRegressor()\n",
    "tree_reg.fit(housing_prepared, housing_labels)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Now that the model is trained, let’s evaluate it on the training set:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0"
      ]
     },
     "execution_count": 54,
     "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": [
    "- Wait, what!? No error at all? Could this model really be absolutely perfect? Of course, it is much more likely that the model has badly overfit the data. \n",
    "- How can you be sure? As we saw earlier, you don’t want to touch the test set until you are ready to launch a model you are confident about, so you need to use part of the training set for training and part of it for model validation."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Better Evaluation Using Cross-Validation\n",
    "- One way to evaluate the Decision Tree model would be to use the train_test_split() function to split the training set into a smaller training set and a validation set, then train your models against the smaller training set and evaluate them against the validation set. \n",
    "- It’s a bit of work, but nothing too difficult, and it would work fairly well.\n",
    "- A great alternative is to use Scikit-Learn’s **K-fold cross-validation** feature. The following code randomly splits the training set into 10 distinct subsets called folds, then it trains and evaluates the Decision Tree model 10 times, picking a different fold for evaluation every time and training on the other 9 folds. The result is an array containing the 10 evaluation scores:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "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": "markdown",
   "metadata": {},
   "source": [
    "### WARNING\n",
    "#### Scikit-Learn’s cross-validation features expect a utility function (greater is better) rather than a cost function (lower is better), so the scoring function is actually the opposite of the MSE (i.e., a negative value), which is why the preceding code computes -scores before calculating the square root."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Let’s look at the results:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Scores [68856.26154909 67139.97107285 71484.09869247 69067.98076598\n",
      " 69829.43041927 75536.30661637 71519.35382772 71776.93892218\n",
      " 76392.80770447 69361.6788985 ]\n",
      "Mean 71096.4828468902\n",
      "Standard Deviation 2792.0877967728193\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": "markdown",
   "metadata": {},
   "source": [
    "- Now the Decision Tree doesn’t look as good as it did earlier. In fact, it seems to perform worse than the Linear Regression model! Notice that cross-validation allows you to get not only an estimate of the performance of your model, but also a measure of how precise this estimate is (i.e., its standard deviation). \n",
    "- The Decision Tree has a score of approximately 71,407, generally ±2,439. You would not have this information if you just used one validation set. But crossvalidation comes at the cost of training the model several times, so it is not always possible.\n",
    "- Let’s compute the same scores for the Linear Regression model just to be sure:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Scores [66782.73843989 66960.118071   70347.95244419 74739.57052552\n",
      " 68031.13388938 71193.84183426 64969.63056405 68281.61137997\n",
      " 71552.91566558 67665.10082067]\n",
      "Mean 69052.46136345084\n",
      "Standard Deviation 2731.6740017983466\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": "markdown",
   "metadata": {},
   "source": [
    "- That’s right: **the Decision Tree model is overfitting so badly that it performs worse than the Linear Regression model.**\n",
    "- Let’s try one last model now: the **RandomForestRegressor.** \n",
    "- **Random Forests work by training many Decision Trees on random subsets of the features, then averaging out their predictions.**\n",
    "- **Building a model on top of many other models is called Ensemble Learning**, and it is often a great way to push ML algorithms even further. We will skip most of the code since it is essentially the same as for the other models:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Scores [49238.18614811 47394.21633383 49876.44483577 52279.65550339\n",
      " 49277.13954134 53558.42940579 48689.63500497 47831.91816016\n",
      " 53161.59060987 50308.87057668]\n",
      "Mean 50161.60861199144\n",
      "Standard Deviation 2049.756792854564\n"
     ]
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestRegressor\n",
    "forest_reg = RandomForestRegressor()\n",
    "forest_reg.fit(housing_prepared, housing_labels)\n",
    "\n",
    "forest_scores = cross_val_score(forest_reg, housing_prepared, housing_labels,\n",
    "                            scoring = \"neg_mean_squared_error\", cv = 10)\n",
    "forest_rmse_scores = np.sqrt(-forest_scores)\n",
    "display_scores(forest_rmse_scores)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Wow, this is much better: Random Forests look very promising. However, note that the score on the training set is still much lower than on the validation sets, meaning that the model is still overfitting the training set. \n",
    "- Possible solutions for overfitting are to simplify the model, constrain it (i.e., regularize it), or get a lot more training data. \n",
    "- Before you dive much deeper into Random Forests, however, you should try out many other models from various categories of Machine Learning algorithms (e.g., several Support Vector Machines with different kernels, and possibly a neural network), without spending too much time tweaking the hyperparameters. The goal is to shortlist a few (two to five) promising models."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## TIP\n",
    "- You should save every model you experiment with so that you can come back easily to any model you want. Make sure you save both the hyperparameters and the trained parameters, as well as the cross-validation scores and perhaps the actual predictions as well. \n",
    "- This will allow you to easily compare scores across model types, and compare the types of errors they make.\n",
    "- You can easily save Scikit-Learn models by using **Python’s pickle module** or by using the **joblib library**, which is more efficient at serializing large NumPy arrays.  \n",
    "For example:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "import joblib\n",
    "\n",
    "joblib.dump(my_model, \"my_model.pkl\")\n",
    "#and later ...\n",
    "my_model_loaded = joblib.load(\"my_model.pkl\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Fine-Tune Your Model\n",
    "- Let’s assume that you now have a shortlist of promising models. You now need to fine-tune them. Let’s look at a few ways you can do that.\n",
    "### Grid Search\n",
    "\n",
    "- One option would be to fiddle with the hyperparameters manually, until you find a great combination of hyperparameter values. This would be very tedious work, and you may not have time to explore many combinations.\n",
    "- Instead, you should get Scikit-Learn’s **GridSearchCV** to search for you. All you need to do is tell it which hyperparameters you want it to experiment with and what values to try out, and it will use cross-validation to evaluate all the possible combinations of hyperparameter values. \n",
    "- For example, the following code searches for the best combination of hyperparameter values for the RandomForestRegressor:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=5, estimator=RandomForestRegressor(),\n",
       "             param_grid=[{'max_features': [2, 4, 6, 8],\n",
       "                          'n_estimators': [3, 10, 30]},\n",
       "                         {'bootstrap': [False], 'max_features': [2, 3, 4],\n",
       "                          'n_estimators': [3, 10]}],\n",
       "             return_train_score=True, scoring='neg_mean_squared_error')"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import GridSearchCV\n",
    "\n",
    "param_grid = [\n",
    "    {\"n_estimators\" : [3, 10, 30], \"max_features\" : [2, 4, 6, 8]},\n",
    "    {\"bootstrap\": [False], \"n_estimators\": [3, 10], \"max_features\": [2, 3, 4]},\n",
    "    ]\n",
    "\n",
    "forest_reg = RandomForestRegressor()\n",
    "\n",
    "grid_search = GridSearchCV(forest_reg, param_grid, cv=5,\n",
    "                          scoring = \"neg_mean_squared_error\",\n",
    "                          return_train_score = True)\n",
    "grid_search.fit(housing_prepared, housing_labels)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### TIP\n",
    "When you have no idea what value a hyperparameter should have, a simple approach is to try out consecutive powers of 10 (or a smaller number if you want a more fine-grained search, as shown in this example with the n_estimators hyperparameter)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- This param_grid tells Scikit-Learn to first evaluate all 3 × 4 = 12 combinations of n_estimators and max_features hyperparameter values specified in the first dict, then try all 2 × 3 = 6 combinations of hyperparameter values in the second dict, but this time with the bootstrap hyperparameter set to False instead of True (which is the default value for this hyperparameter).\n",
    "- The grid search will explore 12 + 6 = 18 combinations of RandomForestRegressor hyperparameter values, and it will train each model 5 times (since we are using five-fold cross validation). In other words, all in all, there will be 18 × 5 = 90 rounds of training! It may take quite a long time, but when it is done you can get the best combination of parameters like this:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'max_features': 6, 'n_estimators': 30}"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid_search.best_params_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### TIP\n",
    "Since 8 and 30 are the maximum values that were evaluated, you should probably try searching again with higher values; the score may continue to improve.\n",
    "\n",
    "- You can also get the best estimator directly:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestRegressor(max_features=6, n_estimators=30)"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid_search.best_estimator_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### NOTE\n",
    "If GridSearchCV is initialized with refit=True (which is the default), then once it finds the best estimator using cross-validation, it retrains it on the whole training set. This is usually a good idea, since feeding it more data will likely improve its performance."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- And of course the evaluation scores are also available:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "63999.516835209506 {'max_features': 2, 'n_estimators': 3}\n",
      "55873.2437551273 {'max_features': 2, 'n_estimators': 10}\n",
      "53129.58574965188 {'max_features': 2, 'n_estimators': 30}\n",
      "60917.40976148984 {'max_features': 4, 'n_estimators': 3}\n",
      "52436.8126274115 {'max_features': 4, 'n_estimators': 10}\n",
      "50469.01668673122 {'max_features': 4, 'n_estimators': 30}\n",
      "58390.09594566094 {'max_features': 6, 'n_estimators': 3}\n",
      "52419.71471250895 {'max_features': 6, 'n_estimators': 10}\n",
      "50119.0666457023 {'max_features': 6, 'n_estimators': 30}\n",
      "58384.9455519426 {'max_features': 8, 'n_estimators': 3}\n",
      "52030.72889423511 {'max_features': 8, 'n_estimators': 10}\n",
      "50156.79807252813 {'max_features': 8, 'n_estimators': 30}\n",
      "62102.8845280004 {'bootstrap': False, 'max_features': 2, 'n_estimators': 3}\n",
      "54283.11790020912 {'bootstrap': False, 'max_features': 2, 'n_estimators': 10}\n",
      "60010.27014619279 {'bootstrap': False, 'max_features': 3, 'n_estimators': 3}\n",
      "52592.12643919421 {'bootstrap': False, 'max_features': 3, 'n_estimators': 10}\n",
      "59155.839963737686 {'bootstrap': False, 'max_features': 4, 'n_estimators': 3}\n",
      "52170.66783796158 {'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": "markdown",
   "metadata": {},
   "source": [
    "- In this example, we obtain the best solution by setting the max_features hyperparameter to 8 and the n_estimators hyperparameter to 30. \n",
    "- The RMSE score for this combination is 49,682, which is **slightly better than the score you got earlier** using the default hyperparameter values (which was 50,182).\n",
    "- **Congratulations, you have successfully fine-tuned your best model!**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### TIP\n",
    "Don’t forget that you can treat some of the data preparation steps as hyperparameters. For example, the grid search will automatically find out whether or not to add a feature you were not sure about (e.g., using the add_bedrooms_per_room hyperparameter of your CombinedAttributesAdder transformer). It may similarly be used to automatically find the best way to handle outliers, missing features, feature selection, and more."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Randomized Search\n",
    "- The grid search approach is fine when you are exploring relatively few combinations, like in the previous example, but when the hyperparameter search space is large, it is often preferable to use RandomizedSearchCV instead.\n",
    "- This class can be used in much the same way as the GridSearchCV class, but instead of trying out all possible combinations, it evaluates a given number of random combinations by selecting a random value for each hyperparameter at every iteration. \n",
    "- This approach has two main benefits:\n",
    "    - If you let the randomized search run for, say, 1,000 iterations, this approach will explore 1,000 different values for each hyperparameter (instead of just a few values per hyperparameter with the grid search approach).\n",
    "    - Simply by setting the number of iterations, you have more control over the computing budget you want to allocate to hyperparameter search."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Ensemble Methods\n",
    "- **Another way to fine-tune your system is to try to combine the models that perform best.**\n",
    "- The group (or “ensemble”) will often perform better than the best individual model (just like Random Forests perform better than the individual Decision Trees they rely on), especially if the individual models make very different types of errors."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Analyze the Best Models and Their Errors\n",
    "- You will often gain good insights on the problem by inspecting the best models.\n",
    "- For example, the RandomForestRegressor can indicate the relative importance of each attribute for making accurate predictions:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([7.94101273e-02, 7.54447123e-02, 4.20310747e-02, 1.73150850e-02,\n",
       "       1.57880393e-02, 1.74061725e-02, 1.63254656e-02, 3.08267481e-01,\n",
       "       5.65361458e-02, 1.01629834e-01, 8.44827703e-02, 1.37926813e-02,\n",
       "       1.62550119e-01, 4.75782152e-05, 3.34840384e-03, 5.62430932e-03])"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "feature_importances = grid_search.best_estimator_.feature_importances_\n",
    "feature_importances"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Let’s display these importance scores next to their corresponding attribute names:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(0.308267480603752, 'median_income'),\n",
       " (0.16255011946036763, 'INLAND'),\n",
       " (0.10162983434603712, 'pop_per_hhold'),\n",
       " (0.08448277031040431, 'bedrooms_per_room'),\n",
       " (0.07941012726085359, 'longitude'),\n",
       " (0.07544471234084818, 'latitude'),\n",
       " (0.05653614580390223, 'rooms_per_hhold'),\n",
       " (0.0420310747097884, 'housing_median_age'),\n",
       " (0.017406172504155288, 'population'),\n",
       " (0.01731508504723768, 'total_rooms'),\n",
       " (0.016325465648137874, 'households'),\n",
       " (0.015788039334797235, 'total_bedrooms'),\n",
       " (0.013792681258465557, '<1H OCEAN'),\n",
       " (0.005624309318244571, 'NEAR OCEAN'),\n",
       " (0.0033484038378399864, 'NEAR BAY'),\n",
       " (4.757821516844184e-05, 'ISLAND')]"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "extra_attribs = [\"rooms_per_hhold\", \"pop_per_hhold\", \"bedrooms_per_room\"]\n",
    "cat_encoder = full_pipeline.named_transformers_[\"cat\"]\n",
    "cat_one_hot_attribs = list(cat_encoder.categories_[0])\n",
    "attributes = num_attribs + extra_attribs + cat_one_hot_attribs\n",
    "sorted(zip(feature_importances, attributes), reverse=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- With this information, you may want to try dropping some of the less useful features (e.g., apparently only one ocean_proximity category is really useful, so you could try dropping the others).\n",
    "- You should also look at the specific errors that your system makes, then try to understand why it makes them and what could fix the problem (adding extra features or getting rid of uninformative ones, cleaning up outliers, etc.)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Evaluate Your System on the Test Set\n",
    "- After tweaking your models for a while, you eventually have a system that performs sufficiently well. Now is the time to evaluate the final model on the test set. \n",
    "- There is nothing special about this process; just get the predictors and the labels from your test set, **run your full_pipeline to transform the data (call transform(), not fit_transform()—you do not want to fit the test set!**), and evaluate the final model on the test set:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RMSE: 47216.51\n"
     ]
    }
   ],
   "source": [
    "final_model = grid_search.best_estimator_\n",
    "X_test = strat_test_set.drop(\"median_house_value\", axis = 1)\n",
    "y_test = strat_test_set[\"median_house_value\"].copy()\n",
    "\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)\n",
    "print(\"RMSE: {:.2f}\".format(final_rmse))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- In some cases, such a point estimate of the generalization error will not be quite enough to convince you to launch: what if it is just 0.1% better than the model currently in production? You might want to have an idea of how precise this estimate is. For this, you can compute a 95% confidence interval for the generalization error using scipy.stats.t.interval():"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### We can compute a 95% confidence interval for the test RMSE:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([45237.86867038, 49115.50016748])"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from scipy import stats\n",
    "confidence = 0.95\n",
    "squared_errors = (final_predictions - y_test)**2\n",
    "np.sqrt(stats.t.interval(confidence, len(squared_errors) -1, \n",
    "                        loc = squared_errors.mean(),\n",
    "                        scale = stats.sem(squared_errors)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- **If you did a lot of hyperparameter tuning, the performance will usually be slightly worse than what you measured using cross-validation (because your system ends up fine-tuned to perform well on the validation data and will likely not perform as well on unknown datasets). It is not the case in this example, but when this happens you must resist the temptation to tweak the hyperparameters to make the numbers look good on the test set; the improvements would be unlikely to generalize to new data.**\n",
    "- **Now comes the project prelaunch phase:** you need to present your solution (highlighting what you have learned, what worked and what did not, what assumptions were made, and what your system’s limitations are), document everything, and create nice presentations with clear visualizations and easy-toremember statements (e.g., “the median income is the number one predictor of housing prices”). In this California housing example, the final performance of the system is not better than the experts’ price estimates, which were often off by about 20%, but it may still be a good idea to launch it, especially if this frees up some time for the experts so they can work on more interesting and productive tasks."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### And the final step is\n",
    "## Launch, Monitor, and Maintain Your System\n",
    "## -----------------------------------------------------------------------------------------------------------------------"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Extra material\n",
    "## A full pipeline with both preparation and prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([210644.60459286, 317768.80697211, 210956.43331178,  59218.98886849,\n",
       "       189747.55849879])"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "full_pipeline_with_predictor = Pipeline([(\"preperation\", full_pipeline),\n",
    "                                        (\"linear\", LinearRegression())])\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": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "my_model = full_pipeline_with_predictor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "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": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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xVfUt4MCzyquBLW15C3DpSJsaotnmV1XfqKpDbfUOpu9rOmKG/hAlmQTOAu4cbydD9Qngg8DPxt3IkP0KMAX8XTt19Zkkrxh3U8NSVf8B/BXwMPAo8ERVfWO8XQ3dqVX1KEwffAGnjLmfo+n3gX8cxo4M/SFJ8krgS8D7q+pH4+5nGJK8DdhfVXePu5ejYBHwZuDaqjoL+B+O7dMDz9DOb68GTgd+CXhFkt8db1d6PpL8KdOnka8fxv4M/SFI8iKmA//6qvryuPsZonOBtyfZA9wAnJfk78fb0tDsBfZW1dN/ld3E9JvA8eItwA+qaqqq/g/4MvAbY+5p2B5LchpAe9w/5n6GLsla4G3Au2pIN1UZ+kcoSZg+L7yzqj4+7n6GqaqurKqlVTXJ9IeAt1XVcXG0WFX/CTyS5HWtdD7H11d8PwysTPLy9jt6PsfRB9XNVmBtW14L3DzGXoau/YNTHwLeXlVPDmu/hv6ROxd4N9NHwd9pPxePuykN5A+B65PcB5wJ/MWY+xma9hfMTcA9wHeZ/n/9mP3KgiRfAP4ZeF2SvUnWARuBtybZBby1rR+T5pjfXwOvAra1XPnbobyWX8MgSf3wSF+SOmLoS1JHDH1J6oihL0kdMfQlqSOGviR1xNCXpI78PwXaPP4CIL5nAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "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": [
    "## Exercises\n",
    "The following exercises are all based on this chapter’s housing dataset:\n",
    "1. 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": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\"from sklearn.model_selection import GridSearchCV\\nfrom sklearn.svm import SVR\\nparam_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    ]\\nsvm_reg = SVR()\\ngrid_search = GridSearchCV(svm_reg, param_grid, cv=5, scoring='neg_mean_squared_error', verbose=2)\\ngrid_search.fit(housing_prepared, housing_labels)\""
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\"\"\"from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.svm import SVR\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",
    "svm_reg = SVR()\n",
    "grid_search = GridSearchCV(svm_reg, param_grid, cv=5, scoring='neg_mean_squared_error', verbose=2)\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": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'negative_mse = grid_search.best_score_\\nrmse = np.sqrt(-negative_mse)\\nrmse'"
      ]
     },
     "execution_count": 72,
     "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": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'grid_search.best_params_'"
      ]
     },
     "execution_count": 73,
     "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. Try replacing GridSearchCV with RandomizedSearchCV."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'\\n# Note: gamma is ignored when kernel is \"linear\"\\nparam_distribs = {\\n        \\'kernel\\': [\\'linear\\', \\'rbf\\'],\\n        \\'C\\': reciprocal(20, 200000),\\n        \\'gamma\\': expon(scale=1.0),\\n    }\\n\\nsvm_reg = SVR()\\nrnd_search = RandomizedSearchCV(svm_reg, param_distributions=param_distribs,\\n                                n_iter=50, cv=5, scoring=\\'neg_mean_squared_error\\',\\n                                verbose=2, random_state=42)\\nrnd_search.fit(housing_prepared, housing_labels)'"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import RandomizedSearchCV\n",
    "from scipy.stats import expon, reciprocal\n",
    "from sklearn.svm import SVR\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, random_state=42)\n",
    "rnd_search.fit(housing_prepared, housing_labels)\"\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'negative_mse = rnd_search.best_score_\\nrmse = np.sqrt(-negative_mse)\\nrmse'"
      ]
     },
     "execution_count": 76,
     "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": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'rnd_search.best_params_\\n'"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\"\"\"rnd_search.best_params_\n",
    "\"\"\""
   ]
  },
  {
   "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.\n",
    "\n",
    "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": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 720x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "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": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "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. Try adding a transformer in the preparation pipeline to select only the most important attributes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.base import BaseEstimator, TransformerMixin\n",
    "\n",
    "def indices_of_top_k(arr, k):\n",
    "    return np.sort(np.argpartition(np.array(arr), -k)[-k:])\n",
    "\n",
    "class TopFeatureSelector(BaseEstimator, TransformerMixin):\n",
    "    def __init__(self, feature_importances, k):\n",
    "        self.feature_importances = feature_importances\n",
    "        self.k = k\n",
    "    def fit(self, X, y=None):\n",
    "        self.feature_indices_ = indices_of_top_k(self.feature_importances, self.k)\n",
    "        return self\n",
    "    def transform(self, X):\n",
    "        return X[:, self.feature_indices_]"
   ]
  },
  {
   "cell_type": "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).\n",
    "\n",
    "Let's define the number of top features we want to keep:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "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": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0,  7,  9, 10, 12], dtype=int64)"
      ]
     },
     "execution_count": 85,
     "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": 86,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['longitude', 'median_income', 'pop_per_hhold', 'bedrooms_per_room',\n",
       "       'INLAND'], dtype='<U18')"
      ]
     },
     "execution_count": 86,
     "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": 87,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(0.308267480603752, 'median_income'),\n",
       " (0.16255011946036763, 'INLAND'),\n",
       " (0.10162983434603712, 'pop_per_hhold'),\n",
       " (0.08448277031040431, 'bedrooms_per_room'),\n",
       " (0.07941012726085359, 'longitude')]"
      ]
     },
     "execution_count": 87,
     "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": 88,
   "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": 90,
   "metadata": {},
   "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": 95,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-1.15604281, -0.61493744, -0.08649871,  0.15531753,  0.        ],\n",
       "       [-1.17602483,  1.33645936, -0.03353391, -0.83628902,  0.        ],\n",
       "       [ 1.18684903, -0.5320456 , -0.09240499,  0.4222004 ,  0.        ]])"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing_prepared_top_k_features[:3]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let's double check that these are indeed the top k features:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-1.15604281, -0.61493744, -0.08649871,  0.15531753,  0.        ],\n",
       "       [-1.17602483,  1.33645936, -0.03353391, -0.83628902,  0.        ],\n",
       "       [ 1.18684903, -0.5320456 , -0.09240499,  0.4222004 ,  0.        ]])"
      ]
     },
     "execution_count": 96,
     "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. Try creating a single pipeline that does the full data preparation plus the final prediction."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\"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])\""
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "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": 99,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'prepare_select_and_predict_pipeline.fit(housing, housing_labels)'"
      ]
     },
     "execution_count": 99,
     "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": 100,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'some_data = housing.iloc[:4]\\nsome_labels = housing_labels.iloc[:4]\\n\\nprint(\"Predictions:\\t\", prepare_select_and_predict_pipeline.predict(some_data))\\nprint(\"Labels:\\t\\t\", list(some_labels))'"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "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. Automatically explore some preparation options using GridSearchCV."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\"param_grid = [{\\n    'preparation__num__imputer__strategy': ['mean', 'median', 'most_frequent'],\\n    'feature_selection__k': list(range(1, len(feature_importances) + 1))\\n}]\\n\\ngrid_search_prep = GridSearchCV(prepare_select_and_predict_pipeline, param_grid, cv=5,\\n                                scoring='neg_mean_squared_error', verbose=2)\\ngrid_search_prep.fit(housing, housing_labels)\""
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\"\"\"param_grid = [{\n",
    "    'preparation__num__imputer__strategy': ['mean', 'median', 'most_frequent'],\n",
    "    'feature_selection__k': list(range(1, len(feature_importances) + 1))\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",
    "grid_search_prep.fit(housing, housing_labels)\"\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'grid_search_prep.best_params_'"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\"\"\"grid_search_prep.best_params_\"\"\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The best imputer strategy is most_frequent and apparently almost all features are useful (15 out of 16). The last one (ISLAND) seems to just add some noise.\n",
    "\n",
    "Congratulations! You already know quite a lot about Machine Learning. :)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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