simple_regression.ipynb

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   "source": [
    "%matplotlib inline\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "from bayes_opt import BayesianOptimization\n",
    "\n",
    "from sklearn.model_selection import cross_val_score, train_test_split\n",
    "from sklearn.linear_model import ElasticNet\n",
    "from sklearn.metrics import r2_score\n",
    "\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import warnings\n",
    "\n",
    "pd.options.display.max_columns=999\n",
    "warnings.filterwarnings('ignore')\n",
    "plt.style.use('ggplot')\n",
    "seed = 101 # Lucky seed"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Load and inspect House Sales in King County, USA dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>date</th>\n",
       "      <th>price</th>\n",
       "      <th>bedrooms</th>\n",
       "      <th>bathrooms</th>\n",
       "      <th>sqft_living</th>\n",
       "      <th>sqft_lot</th>\n",
       "      <th>floors</th>\n",
       "      <th>waterfront</th>\n",
       "      <th>view</th>\n",
       "      <th>condition</th>\n",
       "      <th>grade</th>\n",
       "      <th>sqft_above</th>\n",
       "      <th>sqft_basement</th>\n",
       "      <th>yr_built</th>\n",
       "      <th>yr_renovated</th>\n",
       "      <th>zipcode</th>\n",
       "      <th>lat</th>\n",
       "      <th>long</th>\n",
       "      <th>sqft_living15</th>\n",
       "      <th>sqft_lot15</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>7129300520</td>\n",
       "      <td>20141013T000000</td>\n",
       "      <td>221900.0</td>\n",
       "      <td>3</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1180</td>\n",
       "      <td>5650</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>1180</td>\n",
       "      <td>0</td>\n",
       "      <td>1955</td>\n",
       "      <td>0</td>\n",
       "      <td>98178</td>\n",
       "      <td>47.5112</td>\n",
       "      <td>-122.257</td>\n",
       "      <td>1340</td>\n",
       "      <td>5650</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>6414100192</td>\n",
       "      <td>20141209T000000</td>\n",
       "      <td>538000.0</td>\n",
       "      <td>3</td>\n",
       "      <td>2.25</td>\n",
       "      <td>2570</td>\n",
       "      <td>7242</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>2170</td>\n",
       "      <td>400</td>\n",
       "      <td>1951</td>\n",
       "      <td>1991</td>\n",
       "      <td>98125</td>\n",
       "      <td>47.7210</td>\n",
       "      <td>-122.319</td>\n",
       "      <td>1690</td>\n",
       "      <td>7639</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5631500400</td>\n",
       "      <td>20150225T000000</td>\n",
       "      <td>180000.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1.00</td>\n",
       "      <td>770</td>\n",
       "      <td>10000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>770</td>\n",
       "      <td>0</td>\n",
       "      <td>1933</td>\n",
       "      <td>0</td>\n",
       "      <td>98028</td>\n",
       "      <td>47.7379</td>\n",
       "      <td>-122.233</td>\n",
       "      <td>2720</td>\n",
       "      <td>8062</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2487200875</td>\n",
       "      <td>20141209T000000</td>\n",
       "      <td>604000.0</td>\n",
       "      <td>4</td>\n",
       "      <td>3.00</td>\n",
       "      <td>1960</td>\n",
       "      <td>5000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>7</td>\n",
       "      <td>1050</td>\n",
       "      <td>910</td>\n",
       "      <td>1965</td>\n",
       "      <td>0</td>\n",
       "      <td>98136</td>\n",
       "      <td>47.5208</td>\n",
       "      <td>-122.393</td>\n",
       "      <td>1360</td>\n",
       "      <td>5000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1954400510</td>\n",
       "      <td>20150218T000000</td>\n",
       "      <td>510000.0</td>\n",
       "      <td>3</td>\n",
       "      <td>2.00</td>\n",
       "      <td>1680</td>\n",
       "      <td>8080</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>8</td>\n",
       "      <td>1680</td>\n",
       "      <td>0</td>\n",
       "      <td>1987</td>\n",
       "      <td>0</td>\n",
       "      <td>98074</td>\n",
       "      <td>47.6168</td>\n",
       "      <td>-122.045</td>\n",
       "      <td>1800</td>\n",
       "      <td>7503</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           id             date     price  bedrooms  bathrooms  sqft_living  \\\n",
       "0  7129300520  20141013T000000  221900.0         3       1.00         1180   \n",
       "1  6414100192  20141209T000000  538000.0         3       2.25         2570   \n",
       "2  5631500400  20150225T000000  180000.0         2       1.00          770   \n",
       "3  2487200875  20141209T000000  604000.0         4       3.00         1960   \n",
       "4  1954400510  20150218T000000  510000.0         3       2.00         1680   \n",
       "\n",
       "   sqft_lot  floors  waterfront  view  condition  grade  sqft_above  \\\n",
       "0      5650     1.0           0     0          3      7        1180   \n",
       "1      7242     2.0           0     0          3      7        2170   \n",
       "2     10000     1.0           0     0          3      6         770   \n",
       "3      5000     1.0           0     0          5      7        1050   \n",
       "4      8080     1.0           0     0          3      8        1680   \n",
       "\n",
       "   sqft_basement  yr_built  yr_renovated  zipcode      lat     long  \\\n",
       "0              0      1955             0    98178  47.5112 -122.257   \n",
       "1            400      1951          1991    98125  47.7210 -122.319   \n",
       "2              0      1933             0    98028  47.7379 -122.233   \n",
       "3            910      1965             0    98136  47.5208 -122.393   \n",
       "4              0      1987             0    98074  47.6168 -122.045   \n",
       "\n",
       "   sqft_living15  sqft_lot15  \n",
       "0           1340        5650  \n",
       "1           1690        7639  \n",
       "2           2720        8062  \n",
       "3           1360        5000  \n",
       "4           1800        7503  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('./data/kc_house_data.csv')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Select features of interest and split into train/test sets."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
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     "elapsed": 294,
     "status": "ok",
     "timestamp": 1516397196267,
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   "outputs": [],
   "source": [
    "X = df[['bedrooms', 'bathrooms', 'sqft_living',\n",
    "        'sqft_lot', 'floors', 'waterfront',\n",
    "        'view', 'condition', 'grade',\n",
    "        'sqft_above', 'sqft_basement', 'lat', 'long']].values\n",
    "y = df['price'].values\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=seed)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Define target function and instantiate Bayesion Optimization object."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def target(**params):\n",
    "    l1_ratio = params['l1_ratio']\n",
    "    model = ElasticNet(alpha=0.1, l1_ratio=l1_ratio, random_state=seed)\n",
    "    scores = cross_val_score(model, X_train, y_train, scoring='r2', cv=5)\n",
    "    return scores.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
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      {
       "item_id": 11
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    "colab_type": "code",
    "collapsed": false,
    "executionInfo": {
     "elapsed": 104247,
     "status": "ok",
     "timestamp": 1516397599997,
     "user": {
      "displayName": "Jason King",
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    "id": "VzOKud0wBhxl",
    "outputId": "08177c35-43ae-4887-e77d-b1841089c217"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[31mInitialization\u001b[0m\n",
      "\u001b[94m------------------------------------------\u001b[0m\n",
      " Step |   Time |      Value |   l1_ratio | \n",
      "    1 | 00m00s | \u001b[35m   0.61983\u001b[0m | \u001b[32m    0.5164\u001b[0m | \n",
      "    2 | 00m00s | \u001b[35m   0.62267\u001b[0m | \u001b[32m    0.5707\u001b[0m | \n",
      "    3 | 00m00s |    0.60422 |     0.0285 | \n",
      "    4 | 00m00s |    0.60760 |     0.1715 | \n",
      "    5 | 00m00s | \u001b[35m   0.63014\u001b[0m | \u001b[32m    0.6853\u001b[0m | \n",
      "\u001b[31mBayesian Optimization\u001b[0m\n",
      "\u001b[94m------------------------------------------\u001b[0m\n",
      " Step |   Time |      Value |   l1_ratio | \n",
      "    6 | 00m05s | \u001b[35m   0.66678\u001b[0m | \u001b[32m    1.0000\u001b[0m | \n",
      "    7 | 00m04s | \u001b[35m   0.66678\u001b[0m | \u001b[32m    1.0000\u001b[0m | \n",
      "    8 | 00m05s | \u001b[35m   0.66678\u001b[0m | \u001b[32m    1.0000\u001b[0m | \n",
      "    9 | 00m05s | \u001b[35m   0.66678\u001b[0m | \u001b[32m    0.9997\u001b[0m | \n",
      "   10 | 00m05s |    0.66678 |     0.9999 | \n",
      "   11 | 00m05s | \u001b[35m   0.66678\u001b[0m | \u001b[32m    0.9995\u001b[0m | \n",
      "   12 | 00m05s |    0.66678 |     0.9996 | \n",
      "   13 | 00m04s |    0.66676 |     0.9960 | \n",
      "   14 | 00m05s |    0.66178 |     0.9535 | \n",
      "   15 | 00m05s | \u001b[35m   0.66679\u001b[0m | \u001b[32m    0.9992\u001b[0m | \n"
     ]
    }
   ],
   "source": [
    "params = {'l1_ratio':(0,1)}\n",
    "bo = BayesianOptimization(target, params, random_state=seed)\n",
    "bo.gp.set_params(alpha=1e-8)\n",
    "bo.maximize(init_points=5, n_iter=10, acq='ucb', kappa=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
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    "executionInfo": {
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     "status": "ok",
     "timestamp": 1516397602212,
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    },
    "id": "W38StyqEEBVM",
    "outputId": "49afce1f-4615-49a4-ce7e-4a14d25147b1"
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'max_params': {'l1_ratio': 0.99918671124538316},\n",
       " 'max_val': 0.66678564361771464}"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bo.res['max']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Generate predictions and evaluate performance on test set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "colab": {
     "autoexec": {
      "startup": false,
      "wait_interval": 0
     }
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    "colab_type": "code",
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    "id": "Ylb4LAWtF_xY"
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6823066522957334"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = ElasticNet(**bo.res['max']['max_params']).fit(X_train, y_train)\n",
    "y_pred = model.predict(X_test)\n",
    "\n",
    "r2_score(y_test, y_pred)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Inspect hyperparameter space."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
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    "executionInfo": {
     "elapsed": 724,
     "status": "ok",
     "timestamp": 1516397631592,
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    },
    "id": "JQTpgOW85KaR",
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   "outputs": [
    {
     "data": {
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Qfvac/t7XxdXNLHv7GA6XwnWJIfib9IwfZiUiyPNhR25pCSGEEKLbldY28+Q7\nx2j9Wti5fqh3wk5v5PWVloUQQghxecrqWlj21jFanArpiSH4++i5foiVQcESds6RwCOEEEL0YefC\nTrOjbWQnyM9AxhVhBPkZvF1aryKBRwghhOijympbWPrWUZodbY+e24JMZIwIw8eg83ZpvY4EHiGE\nEKIPslc389Q7bSM7afHBxIf5M2GYFb1Opud2RAKPEEII0cecKmvgmfdOusPOkAj/Abc3VldJ4BFC\nCCH6kMNFtTy/I899Gysy2IdxQyXsfBsZ9xJCCCH6iMOFtfzvB3k0f7URqMXfyKThoQNuI9BLISM8\nQgghRB9w6GwtL+w8Q2OLi7GJIQT5Ghg/zIqfSf5T3hkywiOEEEL0cocLa/nzv89Q3+IkLSGEQB8D\no2ODCAswebu0PkMCjxBCCNGLHS6s4y//PkNtk5PUuGBC/AwkhPsxxGb2dml9ioyDCSGEEL1UbmEN\n63cVUNXYFnZCA4yEB5pIjQv2dml9jgQeIYQQohc6UFDDP7LPUtngYExsEOGBRkL8DFw/xIpGnsjq\nMgk8QgghRC/zeV41G3KKKK9vJTk6kMhgH/yMOiZeEYZWnsi6JBJ4hBBCiF5CVVU+y6vi9X3FlNS2\ncOXgAGKsvui1GjJHhMnj55dBAo8QQgjRC6iqSs7pKt45UIK9uoUrBplJCPNDBaaMDMegl+eMLof0\nnhBCCNEL7DtTw+aDpRRUNjPE5s8Qmz8uRSVzRBi+RtkM9HJJ4BFCCCG87EB+DVsOlnCmoomEMD+u\nGGTG4VIZP8xKoK/B2+X1CxJ4hBBCCC/KPVvLli9KOFnWSIzVlysHB9DqVElLCCY80Mfb5fUbEniE\nEEIILzlSVMeWL0o4WtzA4BAfUqIDaXWqXBkVSFyov7fL61ck8AghhBBecKSojq0HSzhcVE9EoIkx\nsUE4XCrxYX6MHBzg7fL6HXlKSwghhPCwL87WsONIOQcL6wg1G0mND8alqEQEmbgmXlZR7gkSeIQQ\nQggP2p9fw57jFRwoqMXib+DahGAUVcVqNjJuqKyi3FMk8AghhBAeoKoqn52p5tPTVXx+poZAHz1j\nE0IACPTVM2FYKFoJOz1GAo8QQgjRw9oWFazmQH4Nn+XV4mvUcV2iBa1Wg49By+QrwtDJKso9SgKP\nEEII0cP259fyxdkaPs2rxqjXkp5kwaDXoNNqyBwZjkEnzxD1NOlhIYQQogflnq1lf0ENOadr0Gg0\npCeF4GPQoqqQOSIMk2wZ4RHSy0IIIUQPOVZcx74z1Xx6uhpFVUlPCsHfpMfhUpk4PBR/k9xo8RSv\n9/T+/ftZv349iqKQkZHBjBl3sR4pAAAgAElEQVQzzmuTnZ3Nxo0b0Wg0xMbGMnfuXABuv/12YmJi\nAAgNDeXXv/61R2sXQgghLuRUWQMfn6oiJ6+aVqdC+hALgb4GWp0uxiZasJqN3i5xQPFq4FEUhXXr\n1rFw4UKsVisLFiwgNTWVqKgodxu73c6mTZtYtmwZZrOZmpoa92dGo5FVq1Z5o3QhhBDigvIrGvno\nRCWf5dXQ2OLiuiQLIX4GWhwKV0UFEWP183aJA45Xb2mdOHGCiIgIbDYber2e9PR0cnJy2rXZvn07\n06ZNw2w2AxAUFOSNUoUQQohOOVPewJ5jlew7U0Ntk5O0+BBCzUZanQpxoX6MkFWUvcKrIzyVlZVY\nrVb3a6vVyvHjx9u1KSoqAmDRokUoisLMmTNJSUkBwOFw8Oijj6LT6bjllltIS0s77xxZWVlkZWUB\nsGLFCkJDQ3vkWvR6fY8dW7Qnfe050teeIf3sOT3d1yeLa/m8sIKDRfVUNThIHxZOTKgZh8tFdLgf\nU5MHD5iFBXvb79qrgUdV1fPe++YPQVEU7HY7S5YsobKyksWLF7N69Wr8/f15/vnnsVgslJSUsHTp\nUmJiYoiIiGj3/czMTDIzM92vy8vLe+RaQkNDe+zYoj3pa8+RvvYM6WfP6cm+zitvYO/JKg6eraW4\nuoWUmEBC/bTU1NUT4mdgdKSJioqKHjl3b+SJ33VkZGSn23r1lpbVam33D7+iooKQkJB2bSwWC9dc\ncw16vZ7w8HAiIyOx2+3uzwBsNhsjRowgLy/PY7ULIYQQ55wqaws7h4vqsVe3cOXgAGKtfjhdCoE+\neiZeEYZWFhb0Kq8GnsTEROx2O6WlpTidTrKzs0lNTW3XJi0tjdzcXABqa2ux2+3YbDbq6+txOBzu\n948ePdpusrMQQgjhCSdLG8g5VcWxkgYKKpsYFmEmMdwfl6Lia9QxeUQYegk7XufVW1o6nY45c+aw\nfPlyFEVh0qRJREdHs2HDBhITE0lNTSU5OZkDBw4wb948tFots2bNIiAggKNHj/LCCy+g1WpRFIUZ\nM2ZI4BFCCOFRx4vr2Jdfy6myRk6XNZIY5sewCH8UVcWg0zBFVlHuNTRqRxNp+rFzk6C7m9yD9xzp\na8+RvvYM6WfP6c6+PmKv42BBLQUVTRwqqiPW6ktydCAAqgo3jLLha9R1y7n6ot42h8frCw8KIYQQ\nfU3u2VoOF9VSVN3CoaI6Bgf7uMOO06Uy9crwAR12eiMZZxNCCCG64FBhHYeL6iipbeVgQS22QBNj\n4oLQaDQ4XCrXD7MS5GfwdpniGyTwCCGEEJ10qrSB3MJaKupb+fxMDaFmI9fEB6PVaGhxKqTGBxMR\n5OPtMkUH5JaWEEII0Qn26mZy8qqoaXTyaV41wX4G0hKC0Wnbws7IyAASwvy9Xaa4ABnhEUIIIb5F\nZUMru49VUNfk5JNTVZhNesYmhmDQadu2jLD6cmVUoLfLFBchgUcIIYS4iIYWJx8cKae+xcnHp6rx\nMeq4LikEo16Lw6UQGmAkLSHk2w8kvEoCjxBCCHEBLQ4X2w6VUdfs5KOTVRj1Wr6TZMHHoMOlqPgb\n9YwfFjpg9sfqyyTwCCGEEB1ocbjYmltKbZODvSer0Gk1pCeF4GvUoaoqWo2GjJGyinJfIYFHCCGE\n+IZzYaeuqW1kRwXSkyz4m/SoqopTUckYGYpJL/8Z7Svkn5QQQgjxNefCTkOzk70nq3ApKumJIQT4\ntIUdh6IyYZiVQB9Za6cvkcAjhBBCfOVc2GlsdvHRySqanQpjE0MI8jO0hR2XysRhVsIDZa2dvkbW\n4RFCCCFoCzvv5ZbS1OJi76kqmlpdjE2yYPE3ukd2Jl0RSliAyduliksggUcIIcSA5w47rS4+Pl1F\nfYuTaxNCCDW3hR2nS2XyiDBCzUZvlyoukdzSEkIIMaCdCzvNrS4+OV1NTaOTa+KDCQ80/WeCsoSd\nPk8CjxBCiAHLHXYcCjl5NVQ2ONz7YZ0b2cm4IgyLhJ0+75JuaZ0+fZp//etfHDlyhIaGBp566ikS\nEhJ4+eWXGTFiBCkpKd1dpxBCCNGtWpwK7+WW0uJQ+DSvmvL6Vq6ODSIyuG1CssOlMn6YVcJOP9Hl\nEZ4vv/yShQsXUlhYyHe+8x1UVf3PwbRa3n///W4tUAghhOhuDqfC+1+N7Hx6ppqyulZGxwQRZfEF\n2sLQ2CSL7Hzej3Q58Lz00kskJyfzzDPPMHv27HafxcfHc/r06W4rTgghhOhuTpfCtkNlNLe6+PxM\nDaW1raREBxJj/U/YSY0LJuar8CP6hy4HntOnTzN16lQ0Gs15e4cEBARQW1vbbcUJIYQQ3cnpUth+\nuIz6FgefF9RQXNvCqKhAYkP9AGhxKIyKCiQx3N/LlYru1uU5PAaDgZaWlg4/q66uxs/P77KLEkII\nIbqboqhs+fws1U0ODhbUYq9u4crBAcSHfRV2nArDI81cERng5UpFT+jyCM/w4cPZvHkziqK43zs3\n0rNjxw6uvPLK7qtOCCGE6AaKovLh0XLK65o4WFBH0Vdh59xITqtTYUi4P8nRQV6uVPSULgee22+/\nnVOnTvGrX/2K1157DYCdO3fyxBNPcPz4cWbOnNntRQohhBCXSlFV/n2snLK6Fj49VUFRdTMjI9uH\nnVirH6NjJez0Z10OPHFxcTzxxBMEBQXxxhtvALB161YAfvOb3xAZGdm9FQohhBCXSFFVdh+roLi2\nhS/O1pFf3sCIyACSbG1hx+FUGBziQ1pC8HnzUkX/cknr8CQkJLB48WJaW1upr6/H398fk0n2FhFC\nCNF7qKpK9vFK7NXNHCqso7CqmeRYC3GWtnV1HC4VW5CJ9CSLhJ0BoEsjPE6nk1WrVnH48GEAjEYj\nFotFwo4QQoheRVVVPjpRydmqJr44W8fZqmauGGRmRFQw0HYbKyLIyLihVgk7A0SXAo9er+eLL75o\nt9igEEII0ZuoqsreU1XkfxV2CqubGREZwNAIM9AWdqIsvowbYkUrYWfA6PIcnmHDhnH8+PGeqEUI\nIYS4LKqq8smpKs6UN3KwoLZtgvLgAIZ8NWenxakQa/XlusQQGdkZYLoceO6++2527NjB1q1bqaio\nQFGU8/4nhBBCeJqqquScruZ0WWO7dXaSws+FHRdJEQGkJUjYGYi6PGn5kUceAWD9+vWsX7/+vM81\nGg2vvPJKp4+3f/9+1q9fj6IoZGRkMGPGjPPaZGdns3HjRjQaDbGxscydO9f9WWNjI/PmzSMtLY17\n7723q5cjhBCiH1BVlc/yqjlZ2sCBglpKalu4KiqAhLCvwo5DYYjNzLhhNioqKrxcrfCGLgeeH/zg\nB92WjBVFYd26dSxcuBCr1cqCBQtITU0lKirK3cZut7Np0yaWLVuG2Wympqam3TE2bNjAiBEjuqUe\nIYQQfdPnZ2o4WlLPgfxayupaSY4OJC70PysoXxFpZlR0kIzsDGBdDjw//OEPu+3kJ06cICIiApvN\nBkB6ejo5OTntAs/27duZNm0aZnPbZLOgoP8sDHXq1ClqampISUnh5MmT3VaXEEKIvmN/fg1f2mv5\n/EwtFQ0ORscEtdsIdFRUoGwXIS5tHZ7uUllZidVqdb+2Wq3nTYguKioCYNGiRSiKwsyZM0lJSUFR\nFF588UUeeughcnNzL3iOrKwssrKyAFixYgWhoaE9cCVtT7D11LFFe9LXniN97RnSz5fu0xNlnKhw\nsK+gnsoGB+lDw4kNa/sLcovDxaQRYYyICnG3l772nN7W15cUeKqqqnj77bc5cuQI9fX1mM1mRo4c\nyY033khwcHCnj9PR4+3fHG5UFAW73c6SJUuorKxk8eLFrF69ml27djF69Ohv7czMzEwyMzPdr8vL\nyztdX1eEhob22LFFe9LXniN97RnSz5fmi7M17M+v5bO8amqbnaTGBxPmr6WxsZFWp0JqfDDhPq52\nfSt97Tme6Ouu7O7Q5cBTVFTEkiVLqK+vZ/jw4dhsNmpqati8eTM7d+5k6dKlDBo0qFPHslqt7SaP\nVVRUEBIS0q6NxWJh6NCh6PV6wsPDiYyMxG63c+zYMY4cOcL7779Pc3MzTqcTHx8f7rzzzq5ekhBC\niD4m92wt+/Jq+DSvmsZWF9fGh2ALMqGqKg5F5TtDLAwO8fV2maIX6XLgeemll/D19WX58uWEh4e7\n3y8rK+PJJ5/kpZdecj/J9W0SExOx2+2UlpZisVjIzs7mF7/4Rbs2aWlp7N69m4kTJ1JbW4vdbsdm\ns7Vr9+GHH3Ly5EkJO0IIMQAcKaoj53QVn+bV0OpUuC7JQqjZiKqqOBWVyVeEEWo2ertM0ct0OfAc\nOnSI++67r13YAQgLC2PmzJmsW7eu08fS6XTMmTOH5cuXoygKkyZNIjo6mg0bNpCYmEhqairJyckc\nOHCAefPmodVqmTVrFgEBMvlMCCEGokOFdew9WUHO6RoUVSV9iIUQPwPKV1Mkpl0ZTqCvwctVit6o\ny4HH6XTi69vxMKGvry9Op7NLxxszZgxjxoxp997tt9/u/rNGo2H27NnMnj37gseYOHEiEydO7NJ5\nhRBC9C25Z2vZc6KCz/Jq0Go0jBtiIdDXgFNRMeo0ZI4Mw8/o1WdxRC/W5ZWW4+Li2LJly3krKquq\nyvvvv09cXFx31SaEEEIAbROUdx4tJ+d0DQadluuHtoUdh0shwEfHDVfZJOyIi+ryr+O2225jxYoV\nzJs3j/T0dEJCQqiuruajjz6iuLiYRx99tCfqFEIIMUB9cbaGHYfLOXC2lgCTnuuSQvAx6Gh1KkQG\n+5CeZEGrlQUFxcV1OfCkpKTw6KOP8sorr/D666+7309ISODXv/41ycnJ3VqgEEKIgUlVVQ4U1LLt\nUCmHi+qx+hu4NiEEg15Lq0Nh2CAzo6IDZfVk0SmXNP6XkpJCSkoKLS0tNDQ04O/vj8lk6u7ahBBC\nDFDndj3POlzGidJGIgJNpMYHo9VAq1Ph6vhgEr/aFFSIzuhy4Nm0aROVlZXMmTMHk8nULuj89a9/\nJTQ0lJtvvrlbixRCCDFwKKrKnuMVvJ9bxtmqZqItvqTEBALgUmDicCvhgT5erlL0NV2etPzhhx8S\nExPT4WdxcXF88MEHl12UEEKIgUlRVLYfLuPt/SWcrWpmqM2f0TGBKIqKXqvhhlHhEnbEJenyCE95\nefkFV1K22WyyZLcQQohL4lRUthwoZvuRcmqanO4dzx1OBYvZyIRhVvS6Lv89XQjgEgKPyWSisrKy\nw88qKirQ6+WxQCGEEF3jcCq8sc/OzqPltDgUrk0IJiLIh1anQlyo31fzd2Rysrh0XY7Kw4cP5623\n3sLhcLR73+Fw8M4773DFFVd0W3FCCCH6v6ZWF/+XXUDW4TKcStvqybZAEy1OF6NjAklLCJGwIy5b\nl4djZs6cyaJFi5g7dy7XX389FouFyspKdu3aRV1dHT//+c97ok4hhBD9UG2zg/W78jlQUIu/UcfY\nxBB8jTpcisqk4aEyX0d0my4Hnri4OJYsWcL//d//8eabb6KqKhqNhuHDh/PLX/5SVloWQgjRKWV1\nLfz5wzMcL20g1Gzkmq8eOzfoNEweGYa/j+yJJbrPJU24SUpK4oknnqC1tZX6+nrMZjNGo+xMK4QQ\nonNOlNTx110F2GtaiLH4khwdiFNRsZiNjB8qk5NF97usGcZGoxGLxdJdtQghhOjnVFXlo5OVvPpJ\nETVNTq4YZGaIzZ9Wp8pwWTlZ9KDLfqSqsbERu91OcHAwVqu1O2oSQgjRDymKypuf23kvt21y8jXx\nwQwKMuFUVMYNsTDY4uvtEkU/1qnAs3//fg4dOsSdd97Z7v3XX3+d1157DZfLBUB6ejoPPfQQOp2u\n+ysVQgjRZzU7XLy4u4CPT1dh0mu5fogFP5MOg07LlJFWzDJfR/SwTgWebdu2nTfEePDgQTZs2EBM\nTAyTJ0+msLCQbdu2kZCQwE033dQjxQohhOh7iqqbWL+rgBOlDVj8DaTFB6PRaAgLMDFuqBW97HQu\nPKBTgScvL48f/OAH7d774IMPMBgMPP744wQHB7vf3717twQeIYQQbRuAnq7itRw75fWtxFh9SY4K\nxKEoXDHIzJVRMl9HeE6nAk9NTQ02m63dewcPHmT48OHtws6YMWP497//3b0VCiGE6HMcToU3P7ez\n40g5zU6F5OhAYiw+uFSVcUOsDA6R+TrCszoVeHx9fWlpaXG/ttvt1NfXM2TIkPPaKYrSvRUKIYTo\nU2qbHby4u4DP82sw6rWMG2Ih0EePQa9l6vBQma8jvKJTCx1ERkaSk5Pjfn3uz8nJye3alZaWEhQU\n1I3lCSGE6EsKyht4ZutJPjtTQ4ifgQnDrJhNeixmIzdcZZOwI7ymUyM83/ve91i9ejX19fUEBQWx\nc+dOYmJiGDZsWLt2n3zyCbGxsT1SqBBCiN5t78lKXvm4kJomJwlhfowcHIDDqZJkMzM6RubrCO/q\nVOBJS0tj9uzZvPPOO+5bWffdd1+7H29FRQWHDh1i1qxZPVasEEKI3kdRVF7NKWT74XLQ4F5fx+FS\nuTYxmLhQf2+XKETnFx787ne/y3e/+90Lfm61Wvnb3/7WHTUJIYToI2oaW3l+Rx7HShoI8tVzTXww\nRp0WnVbDpOGhBPvLtkOid7jslZaFEEIMTJ+fqeHve/KpaXISF+rLyMgAXIpKbKgfV8cFo5P1dUQv\nIoFHCCFElyiKyst7z/Lh0Qq0X93CCgswotdpuX5oCLYgH2+XKMR5JPAIIYTotNomB8++d5K8iias\n/gbGxAWj00JksA/XJoTILuei15LAI4QQolP251fz5535NLa6GBZhJjHcDy0arkkIIcYqCwmK3s3r\ngWf//v2sX78eRVHIyMhgxowZ57XJzs5m48aNaDQaYmNjmTt3LmVlZTz99NMoioLL5WL69OlMnTrV\nC1cghBD9jy4/n4CVK9EVF+Ow2fj75Lt4q9qEn1HH9UMs+Jt0hPgZuX6oBZNBNowWvZ9XA4+iKKxb\nt46FCxditVpZsGABqampREVFudvY7XY2bdrEsmXLMJvN1NTUABASEsKTTz6JwWCgubmZX/7yl6Sm\npmKxWLx1OUII0S/o8vOx/OhHGM6cAcAEfP/fezk792nChiSh1+m4MiqAYRFmWVtH9BkXvdlaWlrK\n0qVLeeihh/j73/9Oa2ur+7MFCxZc9slPnDhBREQENpsNvV5Penp6uxWdAbZv3860adMwm80A7pWc\n9Xo9BkPbip0Oh0O2tBBCiG4SsHKlO+ycE1lZzN3b/4+EcH9uGh3B8EEBEnZEn3LREZ6//OUvpKWl\nMXToULZs2cKyZct47LHH8PX1xeVyXfbJKysrsVqt7tdWq5Xjx4+3a1NUVATAokWLUBSFmTNnkpKS\nAkB5eTkrVqyguLiYWbNmyeiOEEJ0g5b8Qvw6eD/OUUtQgvz/rOibLhp4ampqmD59OgAPPvggb7zx\nBkuXLmXRokXdkuxVVT3vvW8eV1EU7HY7S5YsobKyksWLF7N69Wr8/f0JDQ3l6aefprKyklWrVjF2\n7Nh2u7cDZGVlkZWVBcCKFSsIDQ297Lo7otfre+zYoj3pa8+RvvaM3tLPDS0O/ufdw0xw+DK5g8+N\nsbG9os7L0Vv6eiDobX190cDjcDjavf7+97+PTqdj6dKlNDU1XfbJrVYrFRUV7tcVFRWEhIS0a2Ox\nWBg6dCh6vZ7w8HAiIyOx2+0kJSW1axMdHc2XX37J2LFj230/MzOTzMxM9+vy8vLLrrsjoaGhPXZs\n0Z70tedIX3uGt/tZUVV2HC7jrf3F1DW7aPr+TxhdcoKQkkJ3G0dsLJVz5+Lq478Hb/f1QOKJvo6M\njOx024vO4Rk0aBAHDx5s997NN9/MuHHjKCkpubTqviYxMRG73U5paSlOp5Ps7GxSU1PbtUlLSyM3\nNxeA2tpa7HY7NpuNiooK95yi+vp6jh492qULF0IIAXllDTz59jFe2luI06VybUIwkSnDOPi/62mY\nMYOW9HQav/99Kl95BVdMjLfLFeKSXXSE5+GHH+7w/RtvvJH09PTLPrlOp2POnDksX74cRVGYNGkS\n0dHRbNiwgcTERFJTU0lOTubAgQPMmzcPrVbLrFmzCAgI4ODBg7z44otoNBpUVeWmm24iRv5lFEKI\nTml2uHjl40L2nqyixamQGObHEJs/Oq2G7yRZGBQymJprn/N2mUJ0G43a0USafuzcJOjuJsOkniN9\n7TnS157hyX5WVZWPT1Xxr0+LKK93EOSrJyUmCF+jFqu/iXFDLfj043V15DftOb3tlpbXFx4UQgjh\nGeX1Lfz13/l8WVyPTqvhqqgAYqy+qCqkxASRFO4vj5qLfqvTgefLL79k37596HQ60tLSiI+PP69N\nXV0d+/btY8KECd1apBBCiEvncim8vs/OjiPlNDsUoi0+jIgMAA0E+hoYl2TBzyR//xX9W6d+4Xv2\n7OEPf/iD+zHyN954gzlz5jB16lSqq6vZvXs3OTk5HDt2DEVRJPAIIUQvsS+vipc/LqSi3kGgr55r\n4oIJ8NVj1GlJjg4kNtRPRnXEgNCpwLNp0yZiY2P56U9/SlBQEIcOHeKVV16htbWVf/7znzidTgID\nA5kwYQJpaWk9XbMQQohvUVbXwp93nuF4SQNGvYbk6ECiQnwADUMj/Bk5OBCdVoKOGDg6FXiKi4uZ\nN28eCQkJAIwfPx6j0cizzz5LaGgo9957L6NHj5a/JQghhJfVNzt46aNCcvKqUVSVpHA/99ycQcE+\npMYFy2afYkDqVOBpbW0lMDCw3XujRo0C4K677mLMmDHdX5kQQohOa3EqvJZTyK5jlbQ4FQYFmbgi\n0oxRr2NQkImrY4Pw9zF4u0whvOaSZ6n5+PgAEB4e3m3FCCGE6Jpmh4vNB0v48MsK6pqdhPgbSI0L\nwuxjwBZoZExcMEG+EnSE6HTg+c1vfsPgwYOJiooiOjra/ey73MYSQgjPq2po5f3cUvaeqqK60Ym/\nScc18cFY/Q2YffRckxBCeIDJ22UK0Wt0KvDcf//95OXlkZeXx6effsru3bvdny1fvpzExETi4+NJ\nTEwkMTFRdi0XQogeUt3QypYvSvnsTA0V9a34GNqethoUbMKo03FVVACJsp6OEOfpVODJyMho99pu\nt5OXl8fp06c5c+YMeXl57N+/3/35hg0burdKIYQY4FqdCu/llrL7eAWlta0YdRpGRgYQF+qLokJC\nuB8p0UHodRfdIlGIAeuS5vAMGjSIQYMGcd1117nfq6mp4fTp0+Tl5XVXbUIIMeApqsq/j1bwXm4p\nxTUt6HUahkWYSQz3Q1FVAnwMXJcUQqDM0xHiorptac2goCBSUlJISUnprkMKIcSA5VJUdh0rZ9uh\nMoqqW9BrNQyL8CcxzB+NVoMWGB0TJLevhOgkWUtcCCF6EYdTYfuRMnZ+WUFxbVvQGfpV0AEVnU5D\nVIgPKTHBGPVy+0qIzpLAI4QQvUBTq5P3ckvZc7yK8vpWDDoNwweZibf6ogD+Jh1JNjOJ4f7oZYVk\nIbpMAo8QQnhRTVMrr2cdZdcRO9WNDkx6LSMizcRZfVFUDSFmAynRQYQGGOXWlRCXQQKPEEJ4mKqq\nFNe08M6BYg4W1FLf4sLXqOWqqACiLb4oCgT7GxgdE0SorKUjRLeQwCOEEB6iKCqH7XVsyy3jWEk9\nzQ6FAB89Y4eEEeqnwelSCfaToCNET5DAI4QQPcylqOScrmL7kXLyyhpxKipWs4Hk6EDCzEYMPr4E\n6F2kxAYR4mf0drlC9EsSeIQQoocoisru4xVkHSrjbHUzqgqRwSaSwv0J9DXgUlRsQT5MTomjtbHW\n2+UK0a9J4BFCiG6gy88nYOVKdMXFOMJtvDPjXt6pNlFW14pOqyHO6kdCuB8+ei2qCoNDfEiODsLX\nqCPQz0h5o7evQIj+TQKPEEJcJl1+PpYf/QjDmTMAmIDxu/ayfc5TjBieQKy1bVVkg05LrNWXq6KD\nMMkaOkJ4lAQeIYS4DKqq4lj8pDvsnBNZWcz/+2QDW9OXEeSrJyHcn/hQP9nrSggvkcAjhBCXQFVV\nPjlVxdsHSvjF8XyiOmgTVl/FTaMj8DPqPF6fEKI9CTxCCNEFLU6FXUfL2XywlKpGBwadhtawcMg7\nv61f7GBaJewI0StI4BFCiE4oqGjkrf1tCwW2ulQCfPSkxAQyKMiHAyEPMqzgKEHFZ93tHbGx1M2f\n78WKhRBfJ4FHCCEuoNWlsC23lD3HK7HXtKABBgX7EBViwupvJMjPSFigiYQrR9OU8iqGVavQlZTg\nstmomz8fV0yMty9BCPEVCTxCCPENFXUt/OszOwcKamlsdWHSaxkW4U9ksIkgPyMjBgUQF+aH4WsT\nkJXYWKrXrvVi1UKIi5HAI4QQtE1CPlBQwzsHSjhd1oiigtVs4IpIM6H+RkxGHcMizAyPMKOV3cqF\n6HO8Hnj279/P+vXrURSFjIwMZsyYcV6b7OxsNm7ciEajITY2lrlz55KXl8ef//xnmpqa0Gq13Hrr\nraSnp3vhCoQQfVlDq5OtB0vZfbyC6kYneq2GGKsfcVZfjAYtfgYd8WF+jBgciF6CjhB9llcDj6Io\nrFu3joULF2K1WlmwYAGpqalERf3nAU+73c6mTZtYtmwZZrOZmpoaAIxGIw899BCDBg2isrKSRx99\nlOTkZPz9/b11OUKIXurrqyC7IiKo+dWv2K8N4v3cMo4VN+BUVAJ99e69rUwGLaEBJoYPMmMLNKHR\nSNARoq/zauA58f/bu/fYKM87X+Df9zb3i+eCbQzGEIOTkjTQxuE0tEqJwknU7mkOypaou8tWKqc6\nzSbtUqKGQhsgLUXhRCEnUi4KqlDU7FYtCdv26HSlTQotTVInZ6EpJBDacCs3j7Fnxp775b0854/X\nnmDsYBvMjD18PxKCGT/MPPOTY755rsePo7m5GU1NTQCApUuXYv/+/cMCz969e3HvvffC5/MBAILB\nIACgpaWl0iYcDiMYDMstAtQAACAASURBVCKdTjPwENEwl56CDADp37+Dn311C/oizZgVcmNO2A2v\nU0aDx4EbGr244ZL1OUQ0/dU08CSTSUQikcrjSCSCY8eODWvT3d0NANi4cSMsy8LKlSuxePHiYW2O\nHz8OwzAqwelie/bswZ49ewAA27ZtQzQaneyPAQBQVfWavTYNx1pXTz3UWnrkkRGnIDcnY1j7n6/g\nze89iYDbgdaoF4vmhBGo0U3l9VDn6YK1rp6pVuuaBh4hxIjnLh06tiwLsVgMmzdvRjKZxKZNm7B9\n+/bKSE5/fz+effZZPPzww5Dlkf9Htnz5cixfvrzyOB6PT/KnsEWj0Wv22jQca10907nWA7kyfvlu\nDPcdOIpPjvL1GdkEPtXiwbxGD2RJQjmfrtkFntO5ztMNa1091aj1xbM9Y6lp4IlEIkgkEpXHiUQC\noVBoWJtwOIyOjg6oqorGxka0tLQgFoth/vz5yOfz2LZtG77yla+go6Oj2t0noimmbJh468Mk3jyW\nwJlEAZYA7gxERm0buqEVchOnwImuFzWdpG5vb0csFkNvby8Mw0BXVxc6OzuHtVmyZAkOHz4MAEin\n04jFYmhqaoJhGHjqqadw55134o477qhF94loCjAtgffOpvDM6yew9mdH8C9vn8P5/iLmRDz4fEcY\nf/6f38JA0/CbrvS2NmS/+90a9ZiIaqGmIzyKomD16tXYunUrLMvCXXfdhdbWVuzatQvt7e3o7OzE\nokWLcOjQIaxduxayLGPVqlXw+/144403cPToUWQyGezbtw8A8PDDD2Pu3Lm1/EhEVAWWEDibyGPv\n0TiOdmcRz5YBAFGfA5+Y6UPYa++0ivg0tN10C7K7d8Hx9FM8BZnoOiaJ0RbS1LGhRdCTjfPC1cNa\nV89UqXVJN9GXKeN8fx4n+vL4S08O3f1FGJaA2yFjTtiN2SE3VEWGz6lgfqMX85t90+bcnKlS5+sB\na109XMNDRDQGyxLoy5RwojeHRE5HIlPC+YEiYqkSciUTimTfaTUn4kaDR4MlBCJeJ26Z7ee5OUQ0\nKgYeIqo5IQSyJRNnk3n0pEroz+kolA0ksjrO9RcrU1YRn4YFTV7MDDphCQkuTcbskAs3zwrA7VBq\n/CmIaCpj4CGiqqsEnEQeF9IlpPIGSoYFSAIDOQNnkwX0pIowBeB1Krhppg+zQy54HApKhn0q8ida\n/GgNuyFzNIeIxoGBh4iqIl82cSaeRyxVxEDBQEm3oMiAKkvoz9sjOd39RZQMC5oioTXiQWvYhZBH\ngyRJKOkmHKqM/3JDEC0hd60/DhFNMww8RHRNmJZAb6aEU705JHM6ciUTkgRoigRJkqCbFv4aL+Bc\nsohc2YQsAc1BJ2aH3GgKOCs3kpd0Cy6HhCU3hDA36uH6HCK6Igw8RDQpdNNCX6aMM4k8Unkd2aKJ\nsmnBqcmQJQlOTUahbOJ0ooDz/QWkCgYAeyv5gmYvWhpclfurhBAo6iaCbg23tgYwN+rh1BURXRUG\nHiKaMMMSSOXK6B4oIpnTkS0ZyJVMWJaAYzDgKIoEt6KgqJvoHijifL/dFgAaPBpumeVHS8gFt6YM\ne13LEmgMOPHJ2QFEfLW524qI6g8DDxGNybAE4pkSTifydsApGjBMAU2RoA6OyjjUjw5uLxsWYgPF\nYTus/C4VN830YVbIBZ9z+I8e3bAgyxLmhN24tTUAl8YdV0Q0uRh4iGiEQtlEX7qE9y/E0N2bRHow\n4AxNT2mKjEszSdmwEEvZC4/7MmUI2DusOpq8mBVyIeDWRrxP2bDgUGXcONOHT7T4K1NaRESTjYGH\n6DpnCYFM0d4K3pcuIV00UCxbsCAQCvhQ1K1RAw4AlAwLPZeEHI9Dwfwme01O0K2OWGQshEDJEPA5\nFdw8J4j2Rm9lgTIR0bXCwEN0nTEsgWSmhDPJAvrzOjIFAyXTgipLlREWp2b/PtpC4aJuIpYqITZQ\nRPyikNPeaI/kjBZyAHvXlmEKRHwa7pgd4InIRFRVDDxEdWxo9KYnVUJfuoRM0UC2ZEA3BZyqDEWW\nIMsS3PLl18zkyyZiA0XEBopIDC489jovP5ID2KM5ZUPAoUqYFXLh5ll++F0jp7aIiK41Bh6iOmIO\n3kH113ge/XkduaIJw7IG193Y59983PTUxcRgUDqV6MfpvkxlC3nApeLGZi9mNrgQcI0ecgD7Lqyy\naSHk0bB4jg9tEQ+nrYiophh4iKYxSwj058o4nSginikhXdBhWAIO1V5crCoSVGV8O56EEOjP6fZ0\nVaqIXMkEAIQ8Gha2+DGzwTlid9WldMOCJAHNQRcWtQbgH2WhMhFRLTDwEE0jQgikCzpOxQuIZ8pI\nFfTKTidFtreIqxPY0W0Mjgj1DJRwIV1CaTCwzPA50N7oxbymBsAsj9mnsing0gZ3W830Q1O524qI\nphYGHqIpzrAEelJF/LUvj0SujHzZhKbIUGUJiixN+Jbwom6iJ1XChVQJfZkSTAGoioSmgBPNQSea\nAs7K4mWPU0U+P3rgsXdbWQi4VXyqLYA5EV7kSURTFwMP0RRTMiz0pos4mywiVbAP+TMtwKnaa3Dc\nEzyUTwiBVMHAhVQJPekiBvL2ehy3Q8aciAfNQSeiPse419h8tNvKgaWt9m4rIqKpjoGHqMYsIZDI\nlHEynkM8U0auZMK07Cmi8S4yvtTQvVYX0vZITsmwAAAhr4ZPzPShOeiE/zKLji81NJrj1BTMDruw\nsIW7rYhoemHgIaqBsmHhdCKPs8kCBnL6YJiwFxo7rmD9ixAC2ZKJCyl7LU4iV4YYnKpq9NvTVE0B\nZ+V8nfHSTQtlw0LE58CNzT60hFyctiKiaYmBh6gKhBDIlUyc6MuhZ6CEVEGHEIBDHTwHZ4LrcAA7\njMQzZfRmSuhN22t7APvOqvYZXjQFHAj7HBMOKEOjOS5NwY0zg5jt591WRDT9MfAQXSO6YaF7oIjT\niTwG8gbyZQPK4GnGVzqKkyoY6BsMOImsfcqxIkuY4XdgQZMXjQEnPFcQngDAMC1Ywp72un2mfZN5\n44wZiMfjV/R6RERTCQMP0STKlwwc782hJ1VCKq/DFPaJxpIkXdEoSVE30ZcpozddQl+mXFmLE3Cp\naG8cHMXxjn/B8aWEECjqFnwuBXOjPtzY7Lui0SYioqmOgYfoKgghMJDXcbw3h97BqxuGRnE0VcZE\nl/UalkAiW0bfYMBJF+0dVQ7VXoszw+9EY8BxVVNMQ1NWmiJjht+BT8z0I+p38F4rIqprDDxEE2RZ\nAj3pEk725pDIlpHXTTgU++C/iQYRazAw9WXKiGfKSObKsAQgS0DY68AnZvrQGHB+7F1VE6GbFsTg\nlNXiRi9aIx6ovO6BiK4TDDxE41A2LJxN5nE6bt8wrptWZapqIufiDN1R1ZcpI561fxmmAAAE3Srm\nzfCg0e9E2OeYlDAyNJrjcSpYEPXhppk+LkAmousSAw/RxygZFk725nAmWUAqr0MIUbnCQRnjdvEh\nQ7uz4ll7BCee/WgdjsehYFaDCzP8DkR9E98yfrn3LJv2haFRnwMLZwXQyCkrIrrOMfAQXcQwLZyK\n5/HXPvu2cQBwqOPfVSWEQL48POAUdTvgOFV7zUzU78AMnwOeMS7inIihkRyHIiPs09AW9WJ2yFW5\nIoKI6HrHwEPXPSEELqRL+HMsi3imBNMScGrKuELO0AhOIvvRFNXFASfiswNO1OeAz6lM6iiLaQno\npgW3Q0HU50R7kxdNfucV79giIqpnNQ88Bw8exEsvvQTLsnD33XdjxYoVI9p0dXXh1VdfhSRJaGtr\nw5o1awAAW7duxbFjx3DTTTdh/fr11e46TXOpvI4PL2TR3V9EXjfhUuUxbxsfWoOTyOqVkDM0RVUJ\nOD4HIj4H/K7JDThDoziSBPicKloaXOho8iLo0ThdRUQ0hpoGHsuysHPnTjz22GOIRCLYsGEDOjs7\nMXv27EqbWCyGX/3qV9iyZQt8Ph9SqVTla/fddx9KpRL27NlTi+7TNGMf3Kfjw54cLqSLyJUsaMrg\njeMfs5B3aBdVMqsjkbMP+9MHFxm7NHuKKjIYcCZ7BAewd1aZloDXqSLo1tAa9qClwQknFx4TEU1I\nTQPP8ePH0dzcjKamJgDA0qVLsX///mGBZ+/evbj33nvh8/kAAMFgsPK1T37ykzhy5Eh1O03TTiqv\n49iFLGKpInIlE1plC/nIKSvdtNCfs8NNMqujP1fGYL6B16lgZtCFiE9DxOeAxzH5AWdoFEeWJDR4\nNDQHPZjf6J3U9T5ERNejmv4UTSaTiEQilceRSATHjh0b1qa7uxsAsHHjRliWhZUrV2Lx4sXjfo89\ne/ZURoC2bduGaDQ6CT0fSVXVa/baNNx4ap0vGXjvdBJnE1mkCzocqgynywOna3i7XMlAX7qIeLqI\nvkwRqZx9XYMEoMHrQHtzADMCLswIuOB2TP5/LvZJxyYkSYLXqaLB68DcGT7Ma/TDcbm5tSrh93V1\nsM7Vw1pXz1SrdU0DjxBixHOX/h+zZVmIxWLYvHkzkskkNm3ahO3bt8Pr9Y7rPZYvX47ly5dXHl+r\ne4Gi0SjvHKqSj6u1ZQmcThZw/EIWyWwZiixBHdylVDLsr6cKOpK5oV8fLTBWZAlhr4aOZi/CXgdC\nXm3YDidhlJE3ylfdd8O0oFsCqizB51Tgd2noiLows8F10fk4BtID/Vf9XpOB39fVwTpXD2tdPdWo\ndUtLy7jb1jTwRCIRJBKJyuNEIoFQKDSsTTgcRkdHB1RVRWNjI1paWhCLxTB//vxqd5emICEEkrky\n/hzLojddQtkQcKgSnJqCom6iN1NE/2C4GcjrsAYzttthLzAOezWEvQ4E3OqEbxUfT99KhgUJElwO\nGQGXiqjPgVkhF4IeBxTupiIiqpqaBp729nbEYjH09vYiHA6jq6sL//zP/zyszZIlS/DWW29h2bJl\nSKfTiMVilTU/dH0SQmAgV8afe+yQky+bUGQJmaKB/pxu/8rryJdNAPY1DUG3hnlRT2X0ZrIvyBwK\nNwCgKTK8TgU+p4pZIReagy5eyElEVGM1DTyKomD16tXYunUrLMvCXXfdhdbWVuzatQvt7e3o7OzE\nokWLcOjQIaxduxayLGPVqlXw+/0AgE2bNuH8+fMoFot48MEH8eCDD05ofQ9NH0IIJHJlHL+QQ+bP\nKZzpHUCuZCBVMJDM6UgXLhq90WSEvBrmzfAg7NUQdGuTNpoihIBhCRimqNyd5XEq8DkVNAftU5Ov\nxWJmIiK6OpIYbSFNHRtaBD3ZOC88+UxL4EKqiA+6MzjRm0dfpoRMyUAqb1RGUxRZQoNbRcirIeR1\nIOSZnNGboUP9xOBFnk5NhtuhwOtQEfY50OR3IODR6v4kY35fVwfrXD2sdfVwDQ8RAOXMGfiffBJK\nTw/M5mZk1q2D3tqKZE7Hu38dwF96sugZKKI/r6MwuLAYAPwuFbPCXvidEkIeDf4Jrr0Rwh6d0S0B\nWZIgy4AqS9AUGZpi/+5QJXidGkJeDQ1uDR6nAuc4r5YgIqKpiYGHqk45cwbhr3wF2unTleeyb/4/\nbP0fW3HSO6PynFuT0eDVMM+jIeTREBwcUfF4PMjn8wDsgwF104JhCQghIEGCJEmQJHtruapI9pZ0\nxb4Py6nJaHDbYcbrUuHWFC4eJiK6DjDwUFUYlkB3fwEn+vJY+NhGNF0UdgCgMd6NVXv+BT//xkY7\n3LhVqIoMwxSQAEiyBFmyT0X2OFVoQoOm2IcH+pwqfC4FLk21T05WJKiDW9JVhhkiIgIDD10lwxLI\nlwykCjpKugVL2KcV96XL6E4VcL6/hL5MCYlsubKo+Mme3lFfq7WUwidnBeB1qnBqMgJuFSGPfSeV\nW1OgKvboDefgiYhoohh46GMZpoV82US6aCBbNJArGSjqFgxToKhbKOom8mUTmaKOVMFApmgiXdCR\nKRqVcKPK9hUJbRE3Am4NUb8G95wW4NR7I94v1N6K/7a4ucqfkoiIrgcMPISSbqI/r6MnVUJq8Pya\nkmGibAhYloCAHVwEgOzgLqlUQcdAXke6aGBon5+qSGhwa7hhhgdBtwavU4HHoSDkdWBO2I15jV44\nVRnKrM3Qv3J02Boeva0N+fXfrcnnJyKi+sfAc53RDQvxbBnn+wsYKNgjNyXdhCkEnOpHC3h1w76G\nIVUwkMoPjeAYldfRFHvkpn2GFw0eFUGPBq9DqUxpBd0a2qJutM/wjrjZ25wzB8mf/9zepXXhAsym\nJmTWrYM5Z05Va0FERNcPBp46ZQmBTNFAPFNCX6aMXMmefsqXTQhLwKHKkGUJQghYANIFA6lCsRJu\nhk4pBgCXJiPo1tDS4ETQbe+Wcmty5XA9a+iUYUnCrAYnFrb4EXBrl+2fOWcOBp577lqWgIiIqIKB\np45YlkBPuoTjF7JIZMsoGhZkSHCo9mJfIQTKhmWP3AxOS6UKBsrGR+fceJ1KZc1Nw+BuqUtHaCxh\nr+FRFQkBl33o35yIB40B56TfR0VERDQZGHimsZJuYiCvI5YqYSBfRjKno2xYcKoyTEugUDaRKhhI\nDwacdNGAObiaWJKAgEtFc8CJoEdF0K0h4FY/9uRgY/CsG69DRVPQiXlRD6J+J7d9ExHRtMDAM40Y\nlsDZZAGnenNIFexwY1r2lFSuaCJV0AenpnRkSx9NSWmKZK+pibjtKSm3Cr9LhXyZsGIJgdLgKI7f\npaI56sH8Ri98Ln7LEBHR9MN/vaY4yxI431/A8d4c+rJlpPI6CmUTmaJ9cealU1Ieh4KA276le7T1\nNqO5+EJMVZHgc6kID05TzeAoDhER1QEGnhoY7R6pi3coWULgVF8O+08N4GyygIG8gWzJ3lE1dL6N\nLNn3Sg1NSQXcGoIuFdoYdz6Zlr2OR5IATZbhccrwOjVE/U40B52TerM4ERHRVMHAM0kM0xq7EUa/\nRwr7D+D/bnkB7ykh9KSKiGfLKF50YaZTtU8dbpzhRcCtIuhW4XONfWnmULhRZAkehwKvU0HQraEx\n4ETIq8HjUC478kNERFQvGHgmyf85cBr9qSx8LhUNbhWzQm54nQp0U0A3LBQNC6l8GTes/8GIe6Q8\n584i+r+fwh+//Ch8LhVRn8MesXGrCLhVuC7ZJQXY01Al3YSABE2R4HUq0GR7q7kiA4oswa0pmBVy\nIex1jDnyQ0REVM8YeCbJ0GhLtmgglSvj4JkUMiUDmYK93iY7eC3D/zp9ftS/P8/I4L8ujMLtUKGp\nEgAJsmTvppIk+/VlSJBk+88OVUZzwInGgBNeJ0dqiIiILoeBZxIcu5DFB+cG0J3MIl0wkCnoMAfX\n2kjARaM2KqSWZuCv7494DV/bLPz321rGXGBMREREE8fAMwl2/Wc3TvTm4FAkBNwa5kY9CLjthcR+\nlwpZAoq6BYcq49g31mD+6T/De/5s5e/rbW0Qm74Hj2Pk1BURERFdPQaeSfC1z7XicE8J+Xy+Mjoj\nBq9bkCUJEZ+G+Y1eNAVdUOQWZDtegcR7pIiIiKqGgWcSzAq5cbLfRKEwdH2DQMin4XOtQUT9jhFT\nVLxHioiIqLoYeCZR2bDg0hR8bkEQs8LuWneHiIiIBjHwTBKnpuCWWX7c1OLnBZpERERTDAPPJPni\np1oRj8dr3Q0iIiIaBU+jIyIiorrHwENERER1j4GHiIiI6h4DDxEREdW9mi9aPnjwIF566SVYloW7\n774bK1asGNGmq6sLr776KiRJQltbG9asWQMA2LdvH37xi18AAO6//34sW7asml0nIiKiaaKmgcey\nLOzcuROPPfYYIpEINmzYgM7OTsyePbvSJhaL4Ve/+hW2bNkCn8+HVCoFAMhms9i9eze2bdsGAFi/\nfj06Ozvh8/lq8lmIiIho6qrplNbx48fR3NyMpqYmqKqKpUuXYv/+/cPa7N27F/fee28lyASDQQD2\nyNCtt94Kn88Hn8+HW2+9FQcPHqz6ZyAiIqKpr6YjPMlkEpFIpPI4Eong2LFjw9p0d3cDADZu3AjL\nsrBy5UosXrx4xN8Nh8NIJpPV6TgRERFNKzUNPEKIEc9deu+UZVmIxWLYvHkzkskkNm3ahO3bt4/6\nepf+XQDYs2cP9uzZAwDYtm0botHoJPR8JFVVr9lr03CsdfWw1tXBOlcPa109U63WNQ08kUgEiUSi\n8jiRSCAUCg1rEw6H0dHRAVVV0djYiJaWFsRiMYTDYXzwwQeVdslkEgsXLhzxHsuXL8fy5csrj6/V\nacjRaJQnLVcJa109rHV1sM7Vw1pXTzVq3dLSMu62NV3D097ejlgsht7eXhiGga6uLnR2dg5rs2TJ\nEhw+fBgAkE6nEYvF0NTUhMWLF+PQoUPIZrPIZrM4dOgQFi9eXIuPQURERFNcTUd4FEXB6tWrsXXr\nVliWhbvuugutra3YtWsX2tvb0dnZiUWLFuHQoUNYu3YtZFnGqlWr4Pf7AQB/+7d/iw0bNgAAvvzl\nL3OHFhEREY1KEqMtpKljQ4ugJxuHSauHta4e1ro6WOfqYa2rh1NaRERERFXGwENERER1j4GHiIiI\n6h4DDxEREdU9Bh4iIiKqeww8REREVPcYeIiIiKjuMfAQERFR3WPgISIiorrHwENERER1j4GHiIiI\n6h4DDxEREdU9Bh4iIiKqeww8REREVPcYeIiIiKjuMfAQERFR3WPgISIiorrHwENERER1j4GHiIiI\n6h4DDxEREdU9Bh4iIiKqeww8REREVPcYeIiIiKjuMfAQERFR3WPgISIiorrHwENERER1j4GHiIiI\n6h4DDxEREdU9SQghat0JIiIiomuJIzyTZP369bXuwnWDta4e1ro6WOfqYa2rZ6rVmoGHiIiI6h4D\nDxEREdU95fHHH3+81p2oFzfccEOtu3DdYK2rh7WuDta5eljr6plKteaiZSIiIqp7nNIiIiKiuqfW\nugPTzcGDB/HSSy/BsizcfffdWLFixbCv67qO5557DidPnoTf78e3v/1tNDY21qi309dYdf71r3+N\nvXv3QlEUBAIB/NM//RNmzJhRo95Ob2PVesg777yDp59+Gk888QTa29ur3Mv6MJ5ad3V14dVXX4Uk\nSWhra8OaNWtq0NPpb6xax+NxPP/888jlcrAsC3//93+PT3/60zXq7fT1wgsv4N1330UwGMT27dtH\nfF0IgZdeegl/+tOf4HQ68dBDD9VumkvQuJmmKb75zW+Knp4eoeu6+M53viPOnj07rM1//Md/iB07\ndgghhHjrrbfE008/XYuuTmvjqfP7778visWiEEKI1157jXW+QuOptRBC5PN5sWnTJvG9731PHD9+\nvAY9nf7GU+vu7m7x6KOPikwmI4QQYmBgoBZdnfbGU+sXX3xRvPbaa0IIIc6ePSseeuihWnR12jty\n5Ig4ceKEeOSRR0b9+h//+EexdetWYVmW+Mtf/iI2bNhQ5R5+hFNaE3D8+HE0NzejqakJqqpi6dKl\n2L9//7A2Bw4cwLJlywAAn/nMZ3D48GEILpOakPHU+ZZbboHT6QQALFiwAMlkshZdnfbGU2sA2LVr\nF+677z5omlaDXtaH8dR67969uPfee+Hz+QAAwWCwFl2d9sZTa0mSkM/nAQD5fB6hUKgWXZ32Fi5c\nWPl+Hc2BAwdw5513QpIkdHR0IJfLob+/v4o9/AgDzwQkk0lEIpHK40gkMuIf2ovbKIoCj8eDTCZT\n1X5Od+Op88V++9vfYvHixdXoWt0ZT61PnTqFeDyO2267rdrdqyvjqXV3dzdisRg2btyI73//+zh4\n8GC1u1kXxlPrlStX4s0338SDDz6IJ554AqtXr652N68LyWQS0Wi08nisn+fXEgPPBIw2UiNJ0oTb\n0OVNpIZvvPEGTp48ifvuu+9ad6sujVVry7Lwk5/8BF/96ler2a26NJ7va8uyEIvFsHnzZqxZswYv\nvvgicrlctbpYN8ZT6z/84Q9YtmwZXnzxRWzYsAHPPvssLMuqVhevG1Pp30QGngmIRCJIJBKVx4lE\nYsQw6MVtTNNEPp+/7HAfjTSeOgPAe++9h1/+8pdYt24dp1qu0Fi1LhaLOHv2LH7wgx/g4YcfxrFj\nx/Dkk0/ixIkTtejutDae7+twOIzbb78dqqqisbERLS0tiMVi1e7qtDeeWv/2t7/FHXfcAQDo6OiA\nruscjb8GIpEI4vF45fHH/TyvBgaeCWhvb0csFkNvby8Mw0BXVxc6OzuHtbntttuwb98+APaulptv\nvpkjPBM0njqfOnUKP/7xj7Fu3Tquc7gKY9Xa4/Fg586deP755/H8889jwYIFWLduHXdpXYHxfF8v\nWbIEhw8fBgCk02nEYjE0NTXVorvT2nhqHY1GK7U+d+4cdF1HIBCoRXfrWmdnJ9544w0IIfDhhx/C\n4/HULPDw4MEJevfdd/GTn/wElmXhrrvuwv33349du3ahvb0dnZ2dKJfLeO6553Dq1Cn4fD58+9vf\n5g+sKzBWnbds2YIzZ86goaEBgP3D67vf/W6Nez09jVXriz3++OP4x3/8RwaeKzRWrYUQePnll3Hw\n4EHIsoz7778fn/3sZ2vd7WlprFqfO3cOO3bsQLFYBACsWrUKixYtqnGvp59nnnkGH3zwATKZDILB\nIB544AEYhgEAuOeeeyCEwM6dO3Ho0CE4HA489NBDNfv5wcBDREREdY9TWkRERFT3GHiIiIio7jHw\nEBERUd1j4CEiIqK6x8BDREREdY+3pRPRNffKK69g9+7deOWVVwAA+/btw4EDB3Dy5EnE43F8/vOf\nx8MPP1z1fuVyOfz7v/87Ojs7R9zg/Pjjjw/7nYimNwYeIqq6N998E+l0GrfeeivefvvtmvUjl8th\n9+7diEQiIwLP17/+9Rr1ioiuBQYeIqq673//+5Ble0Z9si/I1HV9Uq4amT179iT0hoimCgYeIqq6\nobBztZ5//nm8//77eOSRR/Dyyy/j1KlTWL58Ob72ta/hD3/4A/bs2YMzZ86gXC5j5syZ+OIXv4hl\ny5YBAHp7e/HNMxwNDQAAA3pJREFUb34TALBjxw7s2LEDAPDQQw9h2bJlo05pdXd346c//SmOHDkC\nXdcxd+5crFy5EosXL56Uz0NE1w4DDxFNa/l8Hs888wy+9KUv4e/+7u/gcDgAABcuXMBnPvMZrFix\nApIk4ejRo9ixYwfK5TLuuecehEIhfOc738FTTz2FFStWVK7RaG5uHvV9kskkNm7cCLfbjdWrV8Pj\n8eC1117DE088gfXr1+NTn/pU1T4zEU0cAw8RTWvFYhHf+ta3cPvttw97/v7776/82bIs3Hzzzejv\n78dvfvMb3HPPPdA0DXPnzgUANDU1oaOj47Lv8+tf/xq5XA5bt26thKJPf/rTWLt2LX7+858z8BBN\ncQw8RDStKYqC2267bcTzsVgMu3btwtGjRzEwMIChawOvdH3P0aNHsWDBgmEjQLIs47Of/Sz+7d/+\nDfl8Hh6P58o+BBFdcww8RDStBQKBEWuCisUifvSjH8HhcOAf/uEf0NTUBFVV8frrr+N3v/vdFb1P\nNpvFvHnzRjzf0NAAIQRyuRwDD9EUxsBDRNOaJEkjnvvwww/R19eHH/7wh7jpppsqz5umecXv4/P5\nMDAwMOL5gYEBSJIEn893xa9NRNceT1omorpTKpUA2NNdQ7LZLA4cODCs3dD0VrlcHvM1Fy5ciGPH\njqG3t7fynGVZePvttzF37ly43e7J6DoRXSMc4SGiqjt37hzOnTsHwA4b8Xgc77zzDgA7WAQCgat6\n/RtvvBFutxs7d+7EAw88gGKxiF/84hfw+/3I5/OVdsFgEH6/H11dXWhra4PT6URjYyP8fv+I1/yb\nv/kb7Nu3Dz/60Y/wwAMPwO124/XXX0d3dzc2bNhwVf0lomuPgYeIqq6rqwu7d++uPD5y5AiOHDkC\nANi8eTNuvvnmq3r9QCCARx99FC+//DK2b9+OcDiML3zhC8hms8PeV5ZlfOMb38DPfvYzbNmyBaZp\nVs7huVQ4HMaWLVvwr//6r/jxj38MwzAwd+5cbNiwgefwEE0DkhjaukBERERUp7iGh4iIiOoep7SI\naMoRQsCyrMu2uXhBMhHRWBh4iGjK+f3vf48XXnjhsm1eeeWVKvWGiOoB1/AQ0ZSTyWSGbf8eTXt7\ne5V6Q0T1gIGHiIiI6h4XLRMREVHdY+AhIiKiusfAQ0RERHWPgYeIiIjqHgMPERER1b3/D0V7HRf3\nKTKTAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x215dc7fa4a8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "X_all = np.linspace(0, 1, 100)\n",
    "mu, std = bo.gp.predict(X_all.reshape(-1,1), return_std=True)\n",
    "\n",
    "plt.figure(figsize=(9,6))\n",
    "plt.scatter(bo.X.ravel(), bo.Y.ravel(), color='red', label='Samples', zorder=20)\n",
    "plt.plot(X_all, mu, color='steelblue', label='Prediction')\n",
    "plt.fill_between(X_all, mu-1.96*std, mu+1.96*std, color='steelblue', alpha=0.5, label='95% Confidence Interval')\n",
    "plt.legend(loc='best')\n",
    "plt.xlabel('l1_ratio', fontsize=16)\n",
    "plt.ylabel('$R^2$ Score', fontsize=16)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "colab": {
   "collapsed_sections": [],
   "default_view": {},
   "name": "simple_regression.ipynb",
   "provenance": [],
   "version": "0.3.2",
   "views": {}
  },
  "kernelspec": {
   "display_name": "Python [conda env:bayes_opt]",
   "language": "python",
   "name": "conda-env-bayes_opt-py"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}